vendor/libonnxruntime.so in onnxruntime-0.6.1 vs vendor/libonnxruntime.so in onnxruntime-0.6.2
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-Ht$HH[USHHH' H H H H H HtH HtHs`H{P Hs0H{ HCH- HxH9t0H= HWt PJHЅ
-Ht$HCHxH9t0H= HWt PJHЅ
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-Ht$sHH[]USHHH' H H _ H H L H Ht[H HtJHs`H{P} Hs0H{ p HCH-E HxH9t0H= HWt PJHЅ
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H H
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-Ht$H+H[]USHHH' H H H H H HtH HtHs`H{P Hs0H{ HCH- HxH9t0H=L HWt PJHЅ
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-Ht$诽HHxH9t0H= HWt PJHЅ
-Ht$sH諾H[]SHHHtIHGHxH;= t0H=6 HWt PJHЅ
-Ht$HNH[USHHH' H H H H H HtH HtHs`H{P! Hs0H{ HCH- HxH9t0H=p HWt PJHЅ
-Ht$PHCHxH9t0H=3 HWt PJHЅ
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- HWt PJHЅ
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-Ht$$HCHxH9t0H= HWt PJHЅ
-Ht$HHxH9t0H= HWt PJHЅ
-Ht$諹HH[]SHHHtIHGHxH;= t0H=n HWt PJHЅ
-Ht$NH膺H[USHHH' H H ; H H ( H Ht7H Ht&Hs`H{PY Hs0H{ L HCH-! HxH9t0H= HWt PJHЅ
-Ht$舸HCHxH9t0H=k HWt PJHЅ
-Ht$KHHxH9t0H=/ HWt PJHЅ
-Ht$HGH[]SHHHtIHGHxH;=D t0H= HWt PJHЅ
-Ht$買HH[USHHH' H H H H H Ht蛸H Ht芸Hs`H{P Hs0H{ HCH- HxH9t0H= HWt PJHЅ
-Ht$HCHxH9t0H= HWt PJHЅ
-Ht$诶HHxH9t0H= HWt PJHЅ
-Ht$sH諷H[]SHHHtIHGHxH;= t0H=6 HWt PJHЅ
-Ht$HNH[USHHH' H H H H H HtH HtHs`H{P! Hs0H{ HCH- HxH9t0H=p HWt PJHЅ
-Ht$PHCHxH9t0H=3 HWt PJHЅ
-Ht$HHxH9t0H= HWt PJHЅ
-Ht$״HH[]USHHH' H H H H H Ht迵H Ht讵Hs`H{P Hs0H{ HCH- HxH9t0H=0 HWt PJHЅ
-Ht$HCHxH9t0H= HWt PJHЅ
-Ht$ӳHHxH9t0H= HWt PJHЅ
-Ht$藳HϴH[]USHHH' H H H H p H HtH HtnHs`H{P Hs0H{ HCH-i߹ HxH9t0H= HWt PJHЅ
-Ht$вHCHxH9t0H= HWt PJHЅ
-Ht$蓲HHxH9t0H=w HWt PJHЅ
-Ht$WH菳H[]USHHH' H H C H H 0 H Ht?H Ht.Hs`H{Pa Hs0H{ T HCH-) HxH9t0H=߹ HWt PJHЅ
-Ht$萱HCHxH9t0H=s߹ HWt PJHЅ
-Ht$SHHxH9t0H=7߹ HWt PJHЅ
-Ht$HOH[]USHHH' H H H H H HtH HtHs`H{P! Hs0H{ HCH-ܹ HxH9t0H=p HWt PJHЅ
-Ht$PHCHxH9t0H=3 HWt PJHЅ
-Ht$HHxH9t0H=ݹ HWt PJHЅ
-Ht$ׯHH[]USHHH' H H H H H Ht述H Ht记Hs`H{P Hs0H{ HCH-۹ HxH9t0H=0ݹ HWt PJHЅ
-Ht$HCHxH9t0H=ܹ HWt PJHЅ
-Ht$ӮHHxH9t0H=ܹ HWt PJHЅ
-Ht$藮HϯH[]USHHH' H H H H p H HtH HtnHs`H{P Hs0H{ HCH-iڹ HxH9t0H=۹ HWt PJHЅ
-Ht$ЭHCHxH9t0H=۹ HWt PJHЅ
-Ht$蓭HHxH9t0H=w۹ HWt PJHЅ
-Ht$WH菮H[]USHHH' H H C H H 0 H Ht?H Ht.Hs`H{Pa Hs0H{ T HCH-)ٹ HxH9t0H=ڹ HWt PJHЅ
-Ht$萬HCHxH9t0H=sڹ HWt PJHЅ
-Ht$SHHxH9t0H=7ڹ HWt PJHЅ
-Ht$HOH[]USHHH' H H H H H HtH HtHs`H{P! Hs0H{ HCH- HxH9t0H=pٹ HWt PJHЅ
-Ht$PHCHxH9t0H=3ٹ HWt PJHЅ
-Ht$HHxH9t0H=ع HWt PJHЅ
-Ht$תHH[]USHHH' H H H H H Ht迫H Ht讫Hs`H{P Hs0H{ HCH-ֹ HxH9t0H=0ع HWt PJHЅ
-Ht$HCHxH9t0H= HWt PJHЅ
-Ht$өHHxH9t0H= HWt PJHЅ
-Ht$藩HϪH[]SHHHtIHGHxH;=չ t0H=Z HWt PJHЅ
-Ht$:HrH[USHHH' H H ' H H H Ht#H HtHs`H{PE Hs0H{ 8 HCH-
չ HxH9t0H=ֹ HWt PJHЅ
-Ht$tHCHxH9t0H=Wֹ HWt PJHЅ
-Ht$7HHxH9t0H=ֹ HWt PJHЅ
-Ht$H3H[]SHHHtIHGHxH;=0Թ t0H=չ HWt PJHЅ
-Ht$螧H֨H[USHHH' H H H H x H Ht臨H HtvHs`H{P Hs0H{ HCH-qӹ HxH9t0H=Թ HWt PJHЅ
-Ht$ئHCHxH9t0H=Թ HWt PJHЅ
-Ht$蛦HHxH9t0H=Թ HWt PJHЅ
-Ht$_H藧H[]SHHHtIHGHxH;=ҹ t0H="Թ HWt PJHЅ
-Ht$H:H[USHHH' H H H H H HtH HtڦHs`H{P
Hs0H{ HCH-ѹ HxH9t0H=\ӹ HWt PJHЅ
-Ht$<HCHxH9t0H= ӹ HWt PJHЅ
-Ht$HHxH9t0H=ҹ HWt PJHЅ
-Ht$äHH[]USHHH' H H H H H Ht諥H Ht蚥Hs`H{P Hs0H{ HCH-й HxH9t0H= ҹ HWt PJHЅ
-Ht$HCHxH9t0H=ѹ HWt PJHЅ
-Ht$迣HHxH9t0H=ѹ HWt PJHЅ
-Ht$胣H軤H[]SHHHtIHGHxH;=Ϲ t0H=Fѹ HWt PJHЅ
-Ht$&H^H[USHHH' H H H H H HtH HtHs`H{P1 Hs0H{ $ HCH-ι HxH9t0H=й HWt PJHЅ
-Ht$`HCHxH9t0H=Cй HWt PJHЅ
-Ht$#HHxH9t0H=й HWt PJHЅ
-Ht$H H[]SHHHtIHGHxH;= ι t0H=Ϲ HWt PJHЅ
-Ht$芡H¢H[USHHH' H H w H H d H HtsH HtbHs`H{P Hs0H{ HCH-] HxH9t0H=ι HWt PJHЅ
-Ht$ĠHCHxH9t0H=ι HWt PJHЅ
-Ht$臠HHxH9t0H=kι HWt PJHЅ
-Ht$KH胡H[]SHHHtIHGHxH;=̹ t0H=ι HWt PJHЅ
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-Ht$ҞHCHxH9t0H=̹ HWt PJHЅ
-Ht$蕞HHxH9t0H=y̹ HWt PJHЅ
-Ht$YH葟H[]USHHH' H H E H H 2 H HtAH Ht0Hs`H{Pc Hs0H{ V HCH-+ʹ HxH9t0H=˹ HWt PJHЅ
-Ht$蒝HCHxH9t0H=u˹ HWt PJHЅ
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-Ht$HQH[]H5S| P]ZÐSHHHtIHGHxH;=6ɹ t0H=ʹ HWt PJHЅ
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-Ht$萛HȜH[USHHH' H H } H H j H HtyH HthHs`H{P Hs0H{ HCH-cǹ HxH9t0H=ȹ HWt PJHЅ
-Ht$ʚHCHxH9t0H=ȹ HWt PJHЅ
-Ht$荚HHxH9t0H=qȹ HWt PJHЅ
-Ht$QH艛H[]SHHHtIHGHxH;=ƹ t0H=ȹ HWt PJHЅ
-Ht$H,H[USHHH' H H H H H HtݚH Ht̚Hs`H{P Hs0H{ HCH-Ź HxH9t0H=Nǹ HWt PJHЅ
-Ht$.HCHxH9t0H=ǹ HWt PJHЅ
-Ht$HHxH9t0H=ƹ HWt PJHЅ
-Ht$赘HH[]USHHH' H H H H H Ht蝙H Ht茙Hs`H{P Hs0H{ HCH-Ĺ HxH9t0H=ƹ HWt PJHЅ
-Ht$HCHxH9t0H=Ź HWt PJHЅ
-Ht$豗HHxH9t0H=Ź HWt PJHЅ
-Ht$uH識H[]USHHH' H H a H H N H Ht]H HtLHs`H{P Hs0H{ r HCH-Gù HxH9t0H=Ĺ HWt PJHЅ
-Ht$讖HCHxH9t0H=Ĺ HWt PJHЅ
-Ht$qHHxH9t0H=UĹ HWt PJHЅ
-Ht$5HmH[]USHHH' H H ! H H H Ht H Ht Hs`H{P? Hs0H{ 2 HCH-¹ HxH9t0H=ù HWt PJHЅ
-Ht$nHCHxH9t0H=Qù HWt PJHЅ
-Ht$1HHxH9t0H=ù HWt PJHЅ
-Ht$H-H[]USHHH' H H H H H HtݕH Ht̕Hs`H{P Hs0H{ HCH- HxH9t0H=N¹ HWt PJHЅ
-Ht$.HCHxH9t0H=¹ HWt PJHЅ
-Ht$HHxH9t0H= HWt PJHЅ
-Ht$赓HH[]USHHH' H H H H H Ht蝔H Ht茔Hs`H{P Hs0H{ HCH- HxH9t0H= HWt PJHЅ
-Ht$HCHxH9t0H= HWt PJHЅ
-Ht$豒HHxH9t0H= HWt PJHЅ
-Ht$uH譓H[]USHHH' H H a H H N H Ht]H HtLHs`H{P Hs0H{ r HCH-G HxH9t0H=ο HWt PJHЅ
-Ht$讑HCHxH9t0H= HWt PJHЅ
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-Ht$5HmH[]USHHH' H H ! H H H Ht H Ht Hs`H{P? Hs0H{ 2 HCH- HxH9t0H= HWt PJHЅ
-Ht$nHCHxH9t0H=Q HWt PJHЅ
-Ht$1HHxH9t0H= HWt PJHЅ
-Ht$H-H[]USHHH' H H H H H HtݐH Ht̐Hs`H{P Hs0H{ HCH-ǻ HxH9t0H=N HWt PJHЅ
-Ht$.HCHxH9t0H= HWt PJHЅ
-Ht$HHxH9t0H=ռ HWt PJHЅ
-Ht$赎HH[]USHHH' H H H H H Ht蝏H Ht茏Hs`H{P Hs0H{ HCH- HxH9t0H= HWt PJHЅ
-Ht$HCHxH9t0H=ѻ HWt PJHЅ
-Ht$豍HHxH9t0H= HWt PJHЅ
-Ht$uH譎H[]USHHH' H H a H H N H Ht]H HtLHs`H{P Hs0H{ r HCH-G HxH9t0H=κ HWt PJHЅ
-Ht$讌HCHxH9t0H= HWt PJHЅ
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-Ht$5HmH[]USHHH' H H ! H H H Ht H Ht Hs`H{P? Hs0H{ 2 HCH- HxH9t0H= HWt PJHЅ
-Ht$nHCHxH9t0H=Q HWt PJHЅ
-Ht$1HHxH9t0H= HWt PJHЅ
-Ht$H-H[]USHHH' H H H H H HtH Ht̋Hs`H{P Hs0H{ HCH-Ƕ HxH9t0H=N HWt PJHЅ
-Ht$.HCHxH9t0H= HWt PJHЅ
-Ht$HHxH9t0H=շ HWt PJHЅ
-Ht$赉HH[]USHHH' H H H H H Ht蝊H Ht茊Hs`H{P Hs0H{ HCH- HxH9t0H= HWt PJHЅ
-Ht$HCHxH9t0H=Ѷ HWt PJHЅ
-Ht$豈HHxH9t0H= HWt PJHЅ
-Ht$uH證H[]USHHH' H H a H H N H Ht]H HtLHs`H{P Hs0H{ r HCH-G HxH9t0H=ε HWt PJHЅ
-Ht$讇HCHxH9t0H= HWt PJHЅ
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-Ht$5HmH[]USHHH' H H ! H H H Ht H Ht Hs`H{P? Hs0H{ 2 HCH- HxH9t0H= HWt PJHЅ
-Ht$nHCHxH9t0H=Q HWt PJHЅ
-Ht$1HHxH9t0H= HWt PJHЅ
-Ht$H-H[]USHHH' H H H H H Ht݆H Ht̆Hs`H{P Hs0H{ HCH-DZ HxH9t0H=N HWt PJHЅ
-Ht$.HCHxH9t0H= HWt PJHЅ
-Ht$HHxH9t0H=ղ HWt PJHЅ
-Ht$资HH[]SHHHtIHGHxH;=갹 t0H=x HWt PJHЅ
-Ht$XH萅H[USHHH' H H E H H 2 H HtAH Ht0Hs`H{Pc Hs0H{ V HCH-+ HxH9t0H= HWt PJHЅ
-Ht$蒃HCHxH9t0H=u HWt PJHЅ
-Ht$UHHxH9t0H=9 HWt PJHЅ
-Ht$HQH[]SHHHtHGHt$HxˀH#H[ÐSHHHtIHGHxH;= t0H= HWt PJHЅ
-Ht$莂HƃH[USHHH' H H { H H h H HtwH HtfHs`H{P Hs0H{ HCH-a HxH9t0H=诹 HWt PJHЅ
-Ht$ȁHCHxH9t0H= HWt PJHЅ
-Ht$苁HHxH9t0H=o HWt PJHЅ
-Ht$OH臂H[]SHHHtIHGHxH;= t0H= HWt PJHЅ
-Ht$H*H[USHHH' H H H H H HtہH HtʁHs`H{P Hs0H{ HCH-Ŭ HxH9t0H=L HWt PJHЅ
-Ht$,HCHxH9t0H= HWt PJHЅ
-Ht$HHxH9t0H=ӭ HWt PJHЅ
-Ht$HH[]SHHHtIHGHxH;=諹 t0H=v HWt PJHЅ
-Ht$VH莀H[USHHH' H H C H H 0 H Ht?H Ht.Hs`H{Pa Hs0H{ T HCH-) HxH9t0H= HWt PJHЅ
-Ht$~HCHxH9t0H=s HWt PJHЅ
-Ht$S~HHxH9t0H=7 HWt PJHЅ
-Ht$~HOH[]USHHH' H H H H H Ht~H Ht~Hs`H{P! Hs0H{ HCH-驹 HxH9t0H=p HWt PJHЅ
-Ht$P}HCHxH9t0H=3 HWt PJHЅ
-Ht$}HHxH9t0H= HWt PJHЅ
-Ht$|H~H[]SHHHtIHGHxH;= t0H= HWt PJHЅ
-Ht$z|H}H[USHHH' H H g H H T H Htc}H HtR}Hs`H{P Hs0H{ x HCH-M HxH9t0H=ԩ HWt PJHЅ
-Ht${HCHxH9t0H= HWt PJHЅ
-Ht$w{HHxH9t0H=[ HWt PJHЅ
-Ht$;{Hs|H[]USHHH' H H ' H H H Ht#|H Ht|Hs`H{PE Hs0H{ 8 HCH-
HxH9t0H= HWt PJHЅ
-Ht$tzHCHxH9t0H=W HWt PJHЅ
-Ht$7zHHxH9t0H= HWt PJHЅ
-Ht$yH3{H[]USHHH' H H H H H HtzH HtzHs`H{P Hs0H{ HCH-ͥ HxH9t0H=T HWt PJHЅ
-Ht$4yHCHxH9t0H= HWt PJHЅ
-Ht$xHHxH9t0H=ۦ HWt PJHЅ
-Ht$xHyH[]USHHH' H H H H H HtyH HtyHs`H{Pž Hs0H{ 踾 HCH- HxH9t0H= HWt PJHЅ
-Ht$wHCHxH9t0H=ץ HWt PJHЅ
-Ht$wHHxH9t0H= HWt PJHЅ
-Ht${wHxH[]USHHH' H H g H H T H HtcxH HtRxHs`H{P腽 Hs0H{ x HCH-M HxH9t0H=Ԥ HWt PJHЅ
-Ht$vHCHxH9t0H= HWt PJHЅ
-Ht$wvHHxH9t0H=[ HWt PJHЅ
-Ht$;vHswH[]USHHH' H H ' H H H Ht#wH HtwHs`H{PE Hs0H{ 8 HCH-
HxH9t0H= HWt PJHЅ
-Ht$tuHCHxH9t0H=W HWt PJHЅ
-Ht$7uHHxH9t0H= HWt PJHЅ
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!HCHHx HHHx HF$H[]ÐUSHHH H H n H H n H Ht#H Ht#Hs`H{PHl$i Hs0H{ i HCHHxU HCHHxE HHHx6 H#H[]ÐUSHHH H H An H H .n H Ht=#H Ht,#Hs`H{PHl$Zh Hs0H{ Mh HCHHx HCHHx HHHx~ H"H[]ÐSHHHtIHGHxH;=M t0H=`O HWt PJHЅ
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N H5 L LHŃ LH蒾 L
+ L2# HD$(HxH;= tIH H5| H HH|$0HtoH HH|$0HtQH LH L" HD$(HxH;=* tH H H LH@ Lh" HD$(HxH;= tL F H HʿX Ld$@L|$ Ll$`LLIHT$LD$LH H+ HHD$Hr HD$@Hq HD$`d L7 M& LH
q H5>r Y LHmr LL L\ L! HD$ HxH;= tH H5 { L7H|$`HtHP LH L)! HD$ HxH;= t@LzH H HǿX Lt$Ld$@LLH L9
+ Ll$`MH
2 H5ց LV LHl LH LY L HD$HxH;= tH H5 z H4 HH|$ Ht HF LH
L HD$HxH;= t6Hp H HHHxH;tHHxH;tH`Ht) H@Ht H Ht H HtHJ HrLhMtI|$謡 @ L/ H`HzH;t:H=S HJY I~^ @ Lᦚ H@HxH;cYHHxH;tHHxH;HHxH;tHHxH;H HH`H|H@HxH;6@ L楚 HHxH;tHHxH;
+ X L L@LI賩 L L`MH
{ H5~ L LH` LL衸 L LA H HxH;= tVH H5v L LH
+ L H HxH;=q tLJH H HŋBHJHL=, HxL9tHxHxL9tHHxL9tHHxL9tHHxL9tpHHtHHtH H HH1H HHDž HDž H; tHH0 t'HxL`HXI9 H0Hp HX LLLLHxIH LLH+ LHHHk HH k H ^ Lx M& LH
.k H5k LHk LL% L L HHxH;=; tHE H5^t Lv HHxL9@6 HLHIL@HH) H LH L HHxH;= t4Ln HH8IeLH-HHxL9yHHxL9e[X LLLLLIH H+ HHLHHj HH-i HA\ L M& LH
Oi H5i LHi LLF L L HHxH;=\ tHf H5r LX LLLLLIH H+ HHLHH-i HHGh H[[ L M& LH
ih H5h LHh LL` L L HHxH;=v tH H5q L { i HHmH0HDž0 HDž8 vHH! HHHxL9EHHxL9'HHxL9 Ht(UHGHHxH;=F t H ]陞 HtHLHD$0HxH;=
+ t HH<$ t H<$HkHD$@HxH;= t HKHD$@HxH;= t H,HD$@HxH;= te H
HD$@HxH;=u tF HHD$@HxH;=V t' HHEHxL9t HH}HtL% HEHxL9t HHpL% HxL9t HhHxL9t HxHxL9t HEHxL9t} H}HtH}Hs}iH}HtjL%k HEHxL9t/ HHEHxH;=? t HHEHxH;=! t HEHxH;= t HEHxH;= t HEHxH;= t H}HtHEHxH;= xxHEHxH;= ^^HX L HD$0HxH;=p tA HHX H蹛 HD$(HxH;=A uԐH|$0Ht%HHD$0HxH;= t HD$ HxH;<$t HxHD$ HxH;= uHHxH;= t HhHxH;= t HHtHHHxH;= tP HHxH;=d t5 HpHxH;=I t HHt)HxHxH;= QQHEHxH;= t H~HpHxH;= t HhHxH;= t HxHxH;= t HEHxH;= tm H}HtH}HtqHEHxH;=j t; H`HxH;=Q HHHEHxH;=7 uHEHxH;=$ uHEHxL9t HHEHxL9t HEHxL9uHEHxL9t ݐH HxH;= t HHxH;= tv H H t HtL HHHxH;=Z t+ HHxH;=A u*H H uH襸 H HHHxH;= t HHxH;= t HHxH;= uH HxH;= tu HHxH;= t\ HHxH;=p tA 뀐HHxH9t) LHHxH9t HxHxH9t HHxH9t HHxH9t HHtHHtHHxH9t L>HH HxH9tr HHxH9t] LH HHD$HHxH;<$t 2 HD$HHxH;=H t H|$PHt*HHD$@HxH;<$t HD$HHxH;<$t HD$@HxH;<$t HD$HHxH;= t 뎐HHxH;= uHh Hp
+ H ugL L H Hx HHHxH;=V t' HHxH;== t wH HHxH;= t HHtHHxH;= t HHxH;= u
HHxH;= ? 5HHxH;= td HHxH;=x tI HHxH;=] t. HHxH;=D HHxH;=" n d HH|$@a H|$ HvX L$ L$ LH$ LHI I+ L$ HL$ LH$ H\ H$ H[ H$ N LS M& LH
\ H5Z\ u LH\ LH Lx L H$ HxH;= t H H58e H$ HHD$CH$ Ht{H$ HD$ LH L( H$ HxH;= tn HvH$ HD$r H HH$ Hqq HHH$ !HDŽ$ HDŽ$ vH= t
+G GPWÐHt(UHGHHxH;= tH ] H<$ t H<$HHpL% HxL9tvHhHxL9taHxHxL9tLHEHxL9t:H}Ht"H}HtHEHxL9t HHEHxL9uH}HtL%ߺ H}HtL%ߺ HEHxL9tH}Ht뙐H}Ht
+( HzH|$˿ H H*( L轐 HD$HHxH;=Eߺ t@HH} @ H舐 HD$PHxH;=ߺ uH@ Hb H% HD$HxH;= tHWHD$HxH;= tH8HD$HxL9tH HD$ HxH;= uHEHxL9t+p$HEHxL9t\HEHxL9tJHEHxL9t8HHEHxL9uHEHxL9tÐHD$ HxH;=ݺ tHsHpHxH;=ݺ tHhHxH;=ݺ tHxHxH;=ݺ tHEHxH;=ݺ tH}HtsH}HteHEHxH;=^ݺ tYH`HxH;=Eݺ t@HHEHxH;='ݺ t"H}Ht
+L` LHI~Έ @ LQ HEHxH;=ܺ {qHH@ [HEHxH;=ܺ ZZHEHxL9tYRHEHxL9ttHHEHxL9t+Z$HEHxL9tFHEHxL9t4HEHxL9u뱐HD$XHxL9tH|$m H|$ t
+H|$HH|$8n HfHD$XL=ۺ HxL9tHD$PHxL9t렐 HH|$H4Z H|$J HX Lt$xL$ [LHT$hLHI LD$pI+ HL$ HD$pHzU LH$ HT H$ G L M& LH
T H5U LH1U LH訟 L LH HD$xHxH;=ں tH H5] H$ HHD$H$ HtH$ HD$ LH L HD$xHxH;=Kں tFH$H$ HD$ H H{H$ Htt HHLH$H HDŽ$H HDŽ$P R y$H% % % % L HH= t
+G GPWDÐX H$ L$ HT$`HLII LD$hI+ HLl$pHD$hH:S LH$ HPR H$ cE H I& LH
qR H5R LHR LLh L H HD$pHxH;=غ tH H5[ LMtLNH|$pHt?H LHx HH HD$pHxH;=غ tLH H HǿX HT$XHLII LD$`I+ HLl$hHD$`HQ LH$ HQ H$ D Hm I& LH
#Q H5tQ LHQ LL L H HD$hHxH;=2 tH< H5UZ LmX HT$PHLII LD$`I+ HLl$hHD$`HQ LH$ H+P H$ >C H I& LH
LP H5P LHP LLC L H HD$hHxH;=[ֺ tHe H5~Y L H HHH HD$hHxH;=ֺ tL H LH, H=p t
+G GPWÐHt(UHGHHxH;=պ tH ]㆚ ÿX Lt$8L$ KLHT$(LIH LD$0H+ HHl$`HD$0HmO HHD$`HN H$ A H I& LH
N H5N LH'O LL螙 L H> HD$8HxH;=Ժ tH H5W L HH|$ HH$ HtfH LH H HD$8HxH;=?Ժ thL H HͿX HT$pH5nr HHD$HHT$ HHD$ Ld$PL H|$ MH
Hq I H5q HL$Ht$ Hr Hn H|$ L HD$HHxH;=Ӻ tH H5V HX EHT$pH5ip HHD$HHT$ HHD$ Ld$PL H|$ MH
p E H5p 2 HL$Ht$ H)q H蹗 H|$ / LW HD$HHxH;=Һ tH H5U H
+@ L
HҺ H$HD$HHxH;<$tHD$@HxH;<$tHH|$ H H|$ H L HD$HHxH;=4Һ t]H
H H H HHl$XHtH}} @ HJ HT$PHzH;<$5H=o HJ
+Is} @ L HѺ H$L;$$L H|$ H L HD$HHxH;=>Ѻ tgHH H H H붋BHJHX L|$HLHT$8LII$ LD$@I+$ HLd$PHD$@HJ LHD$PHJ HD$p&= L~ M& LH
4J H5J LHJ LL+ L L HD$HHxH;=Cк tlHM H5fS L~X L|$HLl$pLHT$0LII$ LD$@I+$ HLd$PHD$@HJ LHD$PH3I HD$pI< L M& LH
WI H5I LHI LLN L L HD$HHxH;=fϺ tHp H5R L HH|$M H LHi L HD$HHxH;= Ϻ t2L H H X Lt$XH\$0LLIH H+ HT$HHLD$PHHD$PHH HD$`HG H$ : HS LD$0& LH
H H5XH s LHH LL Lv H|$0 HD$XHxH;=κ t=H H57Q LOX Lt$XL$ LHT$@LIH LD$PH+ HHD$PHG HD$`H G H$ HD$`HHHD$0: Hf LD$0& LH
G H5kG LHG LL L H|$0 HD$XHxH;='ͺ tPH1 H5JP Lb HH|$ K Hy LH* H|$0P HD$XHxH;=̺ tL H H 鐿X L|$HLl$pfLHT$8LII$ LD$@I+$ HLd$PHD$@HF LHD$PHE HD$p8 L
߀ M& LH
E H5F / LHCF LL躐 L2 LZ HD$HHxH;=˺ tH H5N L
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C H5C HL$HHD LL臎 L H|$@% H$ HxH;=ɺ tK& H H5L LX L$ SHL$LH$ IH$ H|$HL$ H H+ HH$ HbC H$ H{B H$ H$ HHHD$@{5 Hۀ LD$@& LH
B H5B HL$HHC LL| L H|$@ H$ HxH;=Ⱥ t@% H H5K L LH H|$@ H$ HxH;=FȺ t$ L W HK H HH$ H|# H= t
+G GPWÐHt(UHGHHxH;=Ǻ tH ]x ÿX Hl$pL$ gHT$XHLII LD$`I+ HLl$hHD$`HA LHD$pH@ H$ 3 H
ڀ I& LH
@ H5A / LHCA LL躋 L2 HZ HD$hHxH;=ƺ tHܾ H5I L
H|$pH H|$H5E H$ H$ 6 H$ H H$ H$ 1H H$ HDŽ$ HDŽ$ H;|$@t$H HqH$ H`>V LH6 H^ HD$hHxH;=ź tL H HH/H X Hl$pL$ qHT$PHLII LD$`I+ HLl$hHD$`H? LHD$pH> H$ 1 H I& LH
> H5 ? 9 LHM? LLĉ L< Hd HD$hHxH;=ĺ tH H5G L L$ H H|$ H
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+G GPWÐX Lt$ L|$lLHT$LIH H+ LHLD$HD$H= HD$ H< HD$@/ Lր M& LH
< H5!= < LHP= LLLJ L? Lg HD$HxH;=º tH H5F LLB H: LH L HD$HxH;=º tLdH H HH= t
+G GPW4ÐfD$` % X H\$HT$0HII LD$8I+ HHHD$8H)< HD$`HE; H$ HD$@HHD$(K. HԀ LD$& HH
W; H5; HL$(H; HLL H H|$ HD$@HxH;|$tHn H5D LH$ HxH;|$tL HH$ HxH;|$tHD$`HxH;|$u H% X Ll$8LHT$0IHD$ D$0Hai HD$`HD$`HHD$ HD$@HHD$(pӀ LD$(H|$H
Wi M H5j H\$1LLH H H|$( HD$8HxH;=/ tH9 H5RC Lj HHH舑 HHx H|$`HtHD$@HxH;|$ H|$(H/ HD$8HxH;= t>L H HH|$ HD$@HxH;|$tLG+ HH$ HxH;=> HHl H뙐Ht(UHGHHxH;= tG H ]Yp H<$HtHbH$0 HtH|$ V H$ HtH$ HtL* HH$0 HxH9t輐 HH$p HtUHH$p Ht;H$ Ht'HH$ Ht
HH$ H 5H$ H$H$ 賘 H$ 覘 H螘 L6H$P HtH HD$HHxH9tɏ H$ HtgH$ HtUHH|$pHtCL۞ HH$ Ht!HH$@ H$ H LH$ HtHeHD$HHxH9J @HHxH9 LH0HtHHu?H` HH H@ HH0HtG@H}% L H}HuHpHuL HpHtz= DH}ϖ Lǖ fH}HYOHpHu>X HLHxLHILLH H+ HHHH5 HH4 H' H Iغ& LH
4 H5&5 A LHU5 LL LD Hl HHxL9t' H H5 > L!HHtHHtH' HHh IHL HHxL9t諌 LC H IL MHXH HpH`H`H@H`Hฺ HLHHʾHP. L& !HHxL9- #HHxH;= HhuJ 륐H$ eJ L H|$88 HH|$Pq HEH$p HLp HD$HH|$@HH$ H|$Xe H$ HtLI 뀿X L$ L$ ˾HL$LLIHT$xL$ H H+ HH$ H2 H$ H 2 H$ H$ HHHD$ % HXˀ LD$& LH
2 H5]2 x LH2 LL} L{ H|$ H$ HxH;= tW H H59; LQH$ HnH|$P HD$`HL$hH$p H@Hp HD$HH|$@HH$ xH|$X HH$ HxH;=r 诉 H$ HxH9u LH H|$ H$ HxH;= ta L H HH=Һ t
+G GPWÐHt(UHGHHxH;= tH ]
h H|$ HtHHw H6H|$8 H迻HD$0H IL蚻H}Ht HEHxL9tH H뉐X L8L`ƻLH(LII I+ L@HL0LH0H/ H@H. H` " LcȀ M& LH
/ H5j/ LH/ LLz L L H8HxH;=& t%H0 H5I8 LaH`Ht&H3 Hl H LH L< H8HxH;= tL苼 H HH@Hu뉐H;=y t&H=к t
+G GPWIHD$8HxH|$0L H|$@HtHHt(UHGHHxH;= t H ]Se H 㳺 Ld$(ID$IHxH9t I9uLBh t
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* & H5* HHH+ Lu H H HHxH;= tp H H53 LʿHHE HH H HHxH;=' t L H HH`݊ Hъ dHHxL98 .H`袊 H薊 HHHxL9x HHH=˺ t
+G GPWHÐH$ HxH;=; t<$ tHD$8HxH;= tH|$`Ht H|$@HtHyLq Lg HD$8HxL9tTH$ Ht谸D$$yL- 댐H=Bʺ t
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+P PJH4H ]^ ÐHL$(H9L$t
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_ H5^ L1LLMp L L HD$hHxH;|$tHq H5. L袺 HHD$PHxH;|$tH论 LH HD$hHxH;|$t`L HH|$pHtdH$ HtH$ HyX 蚰H|$`I LH8H LHD$hq M LH
^ H5] HL$hHߚ LL o L L HD$hHxH;|$tH@ H5Y- Lq HH|$`H LH3 L[ HD$hHxH;|$t,HL諱 H H HH|$pHtH$ HtH$ H]H$ HH$ HX %H|$`I LH8HLHD$h M LH
] H53\ HL$hHUޚ LLm L LG HD$hHxH;|$tHˠ H5+ LH$ HH$ Hj } HHHxH; tHPHxH; tH`Ht
H}HtHXHxH; tLH@HxH; t3L# H8HxH; tH2H@ Y HEHxH; tH`HxH; uHuL1 lHuL1 [LI~S @ LX \HEHxH; 8BH$ Ht~H$@ HtH$0 HH|$pHtPH$ L0 H$ H$ 0 H' HI}eR @ LW L=y H$ HxL9tH$ HtH$@ HcH$0 HLX L$ HIb LH8HH$ HD$xHθ IQ LH
Z H5Y HL$xH'ۚ LLyj L H HD$xHxH;= tH H5( L̴ LH譼 H HD$xHxH;=M tL&^ H H HL~ה H|$_ @ L]V L= pL: H`H= LPH$ H$p Ld$Pv< H$P H$@ a< H$ H$ ; H$ H$ ; H$ H$ ; H$ H$ m; Ht$`L; H踳H|$PHtH衳H|$HHtH芳H= t
+G GPWԭÐHD$XHxH9tH|$`Ht 谭H$ 4 L HD$PHxH9tHH|$0Ք H|$( tH|$(b H$H$ HtBH$ HxH9t2H$ HxH9vlHD$`HxH;= llH H$0 HHzH$ H9 H H|$HH$ IGHt$L$ H Hm LHH$8 YH$ 輹 HD$`HxH9V趧L;t$u,گH=⽺ HJt<SIHD$H HxHD$H9tHD$몋BHJHtHثL`% % LBs H:H% % L s HHEH HxH9t^ Hb HILHIH HxHH9t& M9uL蹰LI^ H 말Ht(UHGHHxH;= te H ]WR H;Ht٪HaH;HtêHK H}I>Ht蠪H(H}Ht芪HEHxH;<$t HߥH;l$uH\$HF{ HH0HD$H}Ht:HEHxH;<$t H菥H9l$u賭H\$Hz HH0HD$ H輮I>HtߩHgL HEHkPHl$LtPH LHH 肤L H"H$ u0 LͶ HH|$[0 L賶 HH|$(q HEH$ L HD$0LHHH$( H|$ g H|$/ LS H苮H|$/ L9 HqH|$/ L HWHD$PHxH;= t H8HD$PHxH;= t HH|$( HEH$ L HD$0LHHH$( /H|$ 蕵 H$ #/ L{ H賭H|$(9 ID$HL$8L$ H HD$0LHHH$( âH|$ ) HD$PHxH;=ϝ t0 HHH$ . H L+LI. Hش LLh. HH轴 H|$0Ht^HHD$PHxH;=N t HǬHD$PHxH;=/ t H訬L0 HCH$ L MHD$LHH$( H<$' 'L HD$HL$H$ H@H HD$LIHH$( pH<$׳
H$ h- H LLP- HH襳 H|$0HtFHΫLI#- H{ L賫H$ HtngH$ HtH腫H$ HtHkH$ v H|$v HLH|$, H|$( H|$`Ht虥H|$/} HH$ HxH;=| t H|$`Ht^HH|$0l HEH$ HL HD$8LHH$x H|$(b LZ H$ HtH$ HxH9tP H$ HxH9\4 RH$ + H|$0 HEH$ HL HD$8LHH$x VH|$(輱 L贱 H$ B+ L蚱 HҩH|$0X HD$@Ht$HH$ H@H HD$8LHHH$x ޞH|$(D H$ * tH$ HxH;=ՙ t6 HNL* L* HH H$ Ht茣H|$`Ht}HL]* HH貰 LH* H|$0l HD$@HL$HH$ H@H HD$8LIHH$x H|$(X HP L舨H|$0 IFL$ L HD$8LHH$x 衝H|$(
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+ ~ytoje`[VQLGB=83.)$ I$ H QL | s j a X O F = 4 + " w n e \ S J A 8 / & { r i ` W N E < 3 * ! ATUSHH0 dH%( H$( 1HH谅w H{ HHSHMH!x H{ HHSHMH7} H{ t_HHSHMH4} H{ t:HHSHpMH$( dH3%( uH0 []A\ I HML
f. ATUSHH0 dH%( H$( 1HH8z H{ t0HHSHLH$( dH3%( uH0 []A\T IHLLx ATUSHH0 dH%( H$( 1HHP} H{ t0HHSH(LH$( dH3%( uH0 []A\ IHKL ATUSHH0 dH%( H$( 1HH蠷| H{ t0HHSHKH$( dH3%( uH0 []A\4 IH`KLX ATUSHH0 dH%( H$( 1HH谪u H{ ` HHSHKHu H{ HHSHJHH:x H{ HHSHJH| H{ HHSHJHPu H{ \ HHSHtJHlEv H{ 3 HHSHPJHHu H{
+ HHSH,JH2x H{ HHSHJHs H{ HHSHIHu H{ HHSHIH訮u H{ f HHSHIHD-v H{ = HHSHxIH s H{ HHSHTIH܇w H{ HHSH0IHs H{ HHSH IHu H{ HHSHHH5v H{ p HHSHHH:v H{ G HHSHHH(7v H{ HHSH|HHtu H{ HHSHXHHХu H{ HHSH4HHv H{ HHSHHH(u H{ z HHSHGHDu H{ Q HHSHGHps H{ ( HHSHGHu H{ HHSHGHu H{ HHSH\GHTu H{ HHSH8GH7v H{ HHSHGH輸u H{ t_HHSHFH| H{ t:HHSHFH$( dH3%( uH0 []A\p;fa\WRMHC>94/*% I$ HEL | s j a X O F = 4 + " ATUSHH0 dH%( H$( 1HHs H{ HHSHDHlu H{ 3 HHSHDHzr H{
+ HHSHDHs H{ HHSHhDHP t H{ HHSHDDHl
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+ HHSH=H8} H{ HHSH<H x H{ HHSH<H`Gr H{ HHSH<H|u H{ f HHSH<H } H{ = HHSH\<Hs H{ HHSH8<H%v H{ HHSH<H<3x H{ HHSH;Hx
} H{ HHSH;Hdt H{ p HHSH;Ht H{ G HHSH;Ht H{ HHSH`;Hht H{ HHSH<;Ht H{ HHSH;Hw H{ HHSH:H| H{ z HHSH:Hu H{ Q HHSH:H$q H{ ( HHSH:H u H{ HHSHd:H\| H{ HHSH@:H| H{ HHSH :H$| H{ HHSH9H蠔w H{ t_HHSH9H @x H{ t:HHSH9H$( dH3%( uH0 []A\T JE@;61,'" I H8L | s j a X O F = 4 + " ATUSHH0 dH%( H$( 1HH{ H{ HHSH7H| H{ HHSH7Hhw H{ HHSH|7H/v H{ HHSHX7Hx H{ HHSH47H̐u H{ p HHSH7Hxw H{ G HHSH6H} H{ HHSH6Hps H{ HHSH6H#x H{ HHSH6Ht H{ HHSH\6Hd t H{ z HHSH86H gw H{ Q HHSH6H bw H{ ( HHSH5Hx H{ HHSH5H4y H{ HHSH5H | H{ HHSH5H講x H{ HHSH`5HH| H{ t_HHSH@5H8x H{ t:HHSH 5H$( dH3%( uH0 []A\{vqlgb]X I Hu4Lm | s ATUSHH0 dH%( H$( 1HHq H{ HHSH3H蜎r H{ HHSH3Hh| H{ HHSHl3Hu H{ | HHSHH3Ht H{ S HHSH$3H|s H{ * HHSH 3H| H{ HHSH2Hs H{ HHSH2HEr H{ HHSH2H,x H{ HHSHp2H診| H{ ] HHSHL2H4| H{ 4 HHSH(2H{ H{ HHSH2H /v H{ HHSH1H| H{ HHSH1Hdq H{ HHSH1H0t H{ g HHSHt1H茰| H{ > HHSHP1H } H{ HHSH,1HT| H{ HHSH1H | H{ HHSH0H| H{ HHSH0H| H{ q HHSH0Ht| H{ H HHSHx0Hw H{ HHSHT0H y H{ HHSH00Hxy H{ HHSH 0HTy H{ HHSH/H0y H{ { HHSH/H y H{ R HHSH/Hy H{ ) HHSH|/HDy H{ HHSHX/H蠜y H{ HHSH4/Hy H{ HHSH/HXy H{ HHSH.H4y H{ \ HHSH.Hy H{ 3 HHSH.H{ H{
+ HHSH.HH| H{ HHSH\.Hu H{ HHSH8.HЅu H{ HHSH.H謄u H{ f HHSH-Hp H{ = HHSH-H$| H{ HHSH-HPw H{ HHSH-Hw H{ HHSH`-HSw H{ HHSH<-HDVw H{ p HHSH-H Xw H{ G HHSH,Haw H{ HHSH,H9{ H{ HHSH,H$B{ H{ HHSH,HpL{ H{ HHSHd,HlW{ H{ z HHSH@,H(b{ H{ Q HHSH ,Hth{ H{ ( HHSH+Hq{ H{ HHSH+H,|{ H{ HHSH+H| H{ HHSH+HDu H{ HHSHh+Hq H{ t_HHSHH+Hx H{ t:HHSH(+H$( dH3%( uH0 []A\~ytoje`[VQLGB=83.)$ I H)L | s j a X O F = 4 + " w n e \ S J A 8 / & ATUSHH0 dH%( H$( 1HH`y H{ HHSHt'H輠y H{ HHSHP'Hu H{ HHSH,'Ht H{ p HHSH'HKw H{ G HHSH&H,y H{ HHSH&H舎y H{ HHSH&H| H{ HHSHx&H`u H{ HHSHT&H|w H{ z HHSH0&H8#r H{ Q HHSH &Hԧs H{ ( HHSH%Hu H{ HHSH%Hu H{ HHSH%H蘉r H{ HHSH|%HDr H{ HHSHX%Hu H{ t_HHSH8%H谘u H{ t:HHSH%H$( dH3%( uH0 []A\}xsnid_Z If H#$Lf. ATUSHH0 dH%( H$( 1HH@q H{ HHSH#H|er H{ HHSH#Hxmr H{ ^ HHSH|#Htur H{ 5 HHSHX#Hqu H{ HHSH4#Hru H{ HHSH#Hsu H{ HHSH"Htu H{ HHSH"H s H{ h HHSH"H\s H{ ? HHSH"Hs H{ HHSH\"H蔇s H{
+ HHSH8"Hs H{
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+ HHSH!H,s H{ HHSH`!H舍u H{ HHSH<!H4s H{ HHSH!Hs H{ | HHSH H謨s H{ S HHSH Hhs H{ * HHSH Hts H{ HHSH HPs H{ HHSHd H̵s H{ HHSH@ HHs H{ HHSH H脹s H{ ] HHSH Hs H{ 4 HHSH Hs H{ HHSH Hs H{ HHSH HDs H{ HHSHh Hs H{ HHSHD H s H{ g HHSH Hht H{ > HHSH Hdt H{ HHSH Haw H{ HHSH H\mw H{ HHSH Hvw H{ HHSHl H$w H{ q HHSHH H y H{ H HHSH$ H y H{ HHSH Hx#y H{ HHSH H&y H{ HHSH H@*y H{ HHSH H-y H{ { HHSHp H0y H{ R HHSHL HT4y H{ ) HHSH( H7y H{ HHSH H ;y H{ HHSH Hh>y H{ HHSH HAy H{ HHSH H萢{ H{ \ HHSHt H輅| H{ 3 HHSHP Hx{ H{
+ HHSH, H{ H{ HHSH Hp{ H{ HHSHHܯ{ H{ HHSHH舲{ H{ f HHSHHԵ{ H{ = HHSHxH耻{ H{ HHSHTH,{ H{ HHSH0HH{ H{ HHSH H4{ H{ HHSHH { H{ p HHSHH{ H{ G HHSHH{ H{ HHSH|H{ H{ HHSHXHp{ H{ HHSH4H<{ H{ HHSHH8| H{ z HHSHH{ H{ Q HHSHH@{ H{ ( HHSHH{ H{ HHSHHh | H{ HHSH\Hx H{ HHSH8Hx H{ HHSHH p H{ t_HHSHH蜡p H{ t:HHSHH$( dH3%( uH0 []A\p;fa\WRMHC>94/*% ߸ڸոи˸Ƹ輸跸貸譸訸裸螸虸蔸菸芸腸耸{vqlgb]XSNID?:50+&!
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~ H{ Q HHSHHD~ H{ ( HHSHH~ H{ HHSHH< ~ H{ HHSHpHX~ H{ HHSHLHt"~ H{ HHSH(H'~ H{ t_HHSHH`+~ H{ t:HHSHH$( dH3%( uH0 []A\脯Ozupkfa\WRMHC>94/* If H
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To HPH$p HH H5|v H$ HH$` H$x HH$ NH$ HX H5lo DŽ$ DŽ$ HHD$xN HDŽ$ HDŽ$ HDŽ$ MH
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it 1 H5et H=ct fH|$0 HDŽ$` HL$@H9HH|$H sHL$PLd$XL9sMI<HI<I;<NI L9 H| H$H HHyH HL9rH$ LUct 1H
Dht H$@ H5dt H= ct <GH$ EtH$@ L ct H
gt 1 H5~dt H=bt D H$ 1H$ EtH$@ Lbt H
gt 1 H5'dt H=pbt 裡认H$ Lbt 1H
ngt H$@ H5ct H=3bt fqH$ LBbt 1H
1gt H$@ H5ct H=at )4H=ft Ө H HH$ Ht葧H$ HtLWH HX Ll$pHl$hBHHL$`LIHxt H$ HD$`Hxt MG0HHD$pHxt H$ ]LZ M躁 HH
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LLHxHL$PHHtH|$@ HILM HLQHD$Ht$HxNHfIĐHH @ ATUSLLNH7H_@ HHHH9HOH)HH?HHHH I@LVH9@M9 IAH9M9AD @ H I11IO AHA fADfAL fXffDHI9wL9tCCXBH9}7H< 1M 9M8HfB HfBfXf)HH9H9 L HH)J OMICLbH9@M9 IBH9M9AD @tlHvfHHIMtB11AHA fADfALfXffDHH9wLL9tAAX[]A\AAXHH9u @ H17f D 1f D AAXHH9uf HGHL@ HLL9IOH)HH?HHHH~&HWL
-HH21 AHH9uH9H ~. HwIHLH7H6fAf)HH9L9}6HWH
-HH HL9u L1\f D f. @ AWAVAUATIUSH H<$H~蟙5 H@`( LLh1LHLU5 HxI7 LI辐HD$ L|$( L莅LxLIL蜫L L\7 LI1DLIF7 HIDL$ H$ L$ HD$`H|$ L$ LL$ A HHD$ f. DLHsHxIHP HK H)H)HHH9 X Hl$ H\$@WH5c HHIL|$LPC H
#c H5 c MA L臀H c HLL蒊LL"HD$@LHx!HZH5 L HLI7 LBLD$ LL$(A L1 D HH H HPI9IP@H9@ I11IO HA fDfALfXfAfADHL9rL9tKBXH9H} fHfAXfA)HH9I9 H MI)LLIRH9HQ@I9@ I MIMMt@11AH fADfLfXffDHI9wLM9tIXAA9H$H H []A\A]A^A_ @ AXAHL9uLLHL9IOH)HH?HH2f. 1f D AXAHH9uL7 HH@HD$@HD$ H$ Hl$PHD$`HD$(HD$pHD$`H$ HD$@H$ HD$ H$ HD$ H$ gX Ld$ H\$@H5c LHHHD$`HIHD$MC L$ H
U c H5c M3 L }Hc HLH)L聆L蹅HD$@LHxHH5 H"LHGH|$}HD$@HHx|LHLLHH|$GHD$@LHxFH^HHHHHHH뜿X Hl$ H\$@H5
-c HHIoHD$`HIHD$ZLC L$ H
c H5dc M< L{Hc HLLXx D f. Tf. If. AWAVAUATUHSHHH HΒ HHGq5 Hܟ HHCX HC` Hl$@HHHܟ HHCP跒5 H@ H/HH$ .H|$@.H<$ uHĨ []A\A]A^A_H
<c Hc AT H1LC L|$X HHLI*Ld$ LJC Ll$pH
;c H5c MT Lzz1LLL芄LLHD$LHxHRH5 LHH<$HtHH֟ H
q HHHSPHQHILM誂HD$HLHxLIH @ f. @ UHh SH
HHHHH[]HHH"fUHh SHHHHHH[]HHjHfAWAVAUATUHSHHH Hn HHG5 HZ۟ HHCX HC` Hl$@HHHڟ HHCPW5 H@ H,HH$ +H|$@H<$ uHĨ []A\A]A^A_H
9c Hc AT H1IC L|$X HLIʉLd$ LHC Ll$pH
8c H5Jc MT Lx1LLL*L肁L躀HD$LHxHH5 L#HH<$Ht肸HHٟ H
HHHSPHQHJILMJHD$HLHxFL^IH @ f. @ UHh SHHHHHH[]HHJHfUHh SHmHHHHH[]HH
-HfAWAVAUATUHSHHH H HHG豍5 Hٟ HHCX HC` Hl$@HHHTٟ HHCP5 H@ HH*HH$ H)H|$@nH<$ uHĨ []A\A]A^A_H
n6c H7c AT H1WGC L|$X HLIjLd$ L-FC Ll$pH
5c H5c MT Lu1LLLL"LZ~HD$LHxYHH5; LHH<$Ht"HHן H
HHHSPHQHILM}HD$HLHxLIH @ f. @ UHh SHMHHHHH[]HHHbfUHh SH
HHHHH[]HHH"fAWAVAUATUHSHHH H HHGQ5 H؟ HHCX HC` Hl$@HHHן HHCP藋5 H@ H'HH$ &H|$@H<$ uHĨ []A\A]A^A_H
N3c H c AT H1DC L|$X (HLI
-Ld$ LCC Ll$pH
q2c H5 c MT LZs1LLLj}L|L{HD$LHxH2H5ۅ LcHH<$Ht³HHX֟ H
Q HHHSPHQHILM{HD$HLHxLIH @ f. @ UHh SHHHHHH[]HHHfUHh SHHHHHH[]HHJHfAWAVAUATUHSHHH HN HHG5 Hz֟ HHCX HC` Hl$@HHH֟ HHCP75 H@ H%HH$ $H|$@H<$ uHĨ []A\A]A^A_H
.0c Hwb AT H1BC L|$X HLI誂Ld$ LmAC Ll$pH
Q/c H5*b MT Lp1LLL
-{LbzLyHD$LHxHҊH5{ LHH<$HtbHHԟ H
HHHSPHQH*ILM*yHD$HLHx&L>IH @ f. @ UHh SHHHHHH[]HH*HfUHh SHMHHHHH[]HHHbfAWAVAUATUHSHHH H HHG葆5 Hԟ HHCX HC` Hl$@HHHtԟ HHCP׆5 H@ H(#HH$ ("H|$@NH<$ uHĨ []A\A]A^A_H
-c Hb AT H17@C L|$X hHLIJLd$ L
?C Ll$pH
1,c H5b MT Ln1LLLxLxL:wHD$LHx9HrH5 LHH<$HtHHӟ H
HHHSPHQHILMvHD$HLHxLIH @ f. @ UHh SH-HHHHH[]HHHBfUHh SHHHHHH[]HHHfAWAVAUATUHSHHH H HHG15 H:ӟ HHCX HC` Hl$@HHHҟ HHCPw5 H@ H HH$ H|$@H<$ uHĨ []A\A]A^A_H
)c Hb AT H1=C L|$X HLI}Ld$ L<C Ll$pH
)c H5jb MT L:l1LLLJvLuLtHD$LHxHH5~ LCHH<$Ht袬HHxџ H
1 HHHSPHQHjILMjtHD$HLHxfL~IH @ f. @ UHh SHHHHHH[]HHjHfUHh SHHHHHH[]HH*HfAWAVAUATUHSHHH H. HHGс5 Hџ HHCX HC` Hl$@HHH4џ HHCP5 H@ Hh HH$ h H|$@H<$ uHĨ []A\A]A^A_H
&c HWb AT H1w;C L|$X HLI{Ld$ LM:C Ll$pH
%c H5
-b MT Li1LLLsLBsLzrHD$LHxyHH5[| LHH<$HtBHHϟ H
р HHHSPHQH
-ILM
-rHD$HLHxL IH @ f. @ UHh SHmHHHHH[]HH
-HfUHh SH-HHHHH[]HHHBfAWAVAUATUHSHHH H HHGq5 Hϟ HHCX HC` Hl$@HHHϟ HHCP5 H@ H HH$ H|$@.H<$ uHĨ []A\A]A^A_H
#c Hb AT H19C L|$X HHLI*yLd$ L7C Ll$pH
"c H5b MT Lzg1LLLqLpLpHD$LHxHRH5y LHH<$HtHH8Ο H
q~ HHHSPHQHILMoHD$HLHxLIH @ f. @ UHh SH
HHHHH[]HHH"fUHh SHHHHHH[]HHjHfAWAVAUATUHSHHH Hn} HHG}5 HZΟ HHCX HC` Hl$@HHH͟ HHCPW}5 H@ HHH$ H|$@H<$ uHĨ []A\A]A^A_H
c Hb AT H16C L|$X HLIvLd$ L5C Ll$pH
c H5Jb MT Le1LLL*oLnLmHD$LHxH~H5w L#HH<$Ht肥HH̟ H
| HHHSPHQHJILMJmHD$HLHxFL^IH @ f. @ UHh SHHHHHH[]HHJHfUHh SHmHHHHH[]HH
-HfAWAVAUATUHSHHH H{ HHGz5 Hz̟ HHCX HC` Hl$@HHH̟ HHCPz5 H@ HHHH$ HH|$@nH<$ uHĨ []A\A]A^A_H
n c H7b AT H1W4C L|$X HLIjtLd$ L-3C Ll$pH
c H5b MT Lb1LLLlL"lLZkHD$LHxYH|H5;u LHH<$Ht"HH8 H
y HHHSPHQHILMjHD$HLHxLIH @ f. @ UHh SHMHHHHH[]HHHbfUHh SH
HHHHH[]HHH"fAWAVAUATUHSHHH Hx HHGQx5 Hʟ HHCX HC` Hl$@HHH4ʟ HHCPx5 H@ HHH$ H|$@H<$ uHĨ []A\A]A^A_H
Nc Hb AT H11C L|$X (HLI
-rLd$ L0C Ll$pH
qc H5b MT LZ`1LLLjjLiLhHD$LHxH2zH5r LcHH<$Ht HH H
Qw HHHSPHQHILMhHD$HLHxLIH @ f. @ UHh SHHHHHH[]HHHfUHh SHHHHHH[]HHJHfAWAVAUATUHSHHH HNv HHGu5 Hȟ HHCX HC` Hl$@HHHTȟ HHCP7v5 H@ HHH$ H|$@H<$ uHĨ []A\A]A^A_H
.c Hwb AT H1/C L|$X HLIoLd$ Lm.C Ll$pH
Qc H5*b MT L]1LLL
-hLbgLfHD$LHxHwH5{p LHH<$HtbHH8 H
t HHHSPHQH*ILM*fHD$HLHx&L>IH @ f. @ UHh SHHHHHH[]HH*HfUHh SHMHHHHH[]HHHbfAWAVAUATUHSHHH Hs HHGs5 HƟ HHCX HC` Hl$@HHHtƟ HHCPs5 H@ H(HH$ (H|$@NH<$ uHĨ []A\A]A^A_H
c Hb AT H17-C L|$X hHLIJmLd$ L
,C Ll$pH
1c H5b MT L[1LLLeLeL:dHD$LHx9HruH5n LHH<$HtHHX H
r HHHSPHQHILMcHD$HLHxLIH @ f. @ UHh SH-HHHHH[]HHHBfUHh SHHHHHH[]HHHfAWAVAUATUHSHHH Hq HHG1q5 Hğ HHCX HC` Hl$@HHHğ HHCPwq5 H@ H
HH$ H|$@H<$ uHĨ []A\A]A^A_H
c Hb AT H1*C L|$X HLIjLd$ L)C Ll$pH
c H5jb MT L:Y1LLLJcLbLaHD$LHxHsH5k LCHH<$Ht袙HH H
1p HHHSPHQHjILMjaHD$HLHxfL~IH @ f. @ UHh SHHHHHH[]HHjHfUHh SHHHHHH[]HH*HfAWAVAUATUHSHHH H.o HHGn5 Hß HHCX HC` Hl$@HHH HHCPo5 H@ Hh HH$ h
-H|$@H<$ uHĨ []A\A]A^A_H
c HWb AT H1w(C L|$X HLIhLd$ LM'C Ll$pH
c H5
-b MT LV1LLL`LB`Lz_HD$LHxyHpH5[i LHH<$HtBHHX H
m HHHSPHQH
-ILM
-_HD$HLHxL IH @ f. @ UHh SHmHHHHH[]HH
-HfUHh SH-HHHHH[]HHHBfAWAVAUATUHSHHH Hl HHGql5 H: HHCX HC` Hl$@HHH HHCPl5 H@ H HH$ H|$@.H<$ uHĨ []A\A]A^A_H
-c Hb AT H1&C L|$X HHLI*fLd$ L$C Ll$pH
c H5b MT LzT1LLL^L]L]HD$LHxHRnH5f LHH<$HtHH H
qk HHHSPHQHILM\HD$HLHxLIH @ f. @ UHh SH
HHHHH[]HHH"fUHh SHHHHHH[]HHjHfAWAVAUATUHSHHH Hnj HHGj5 HZ HHCX HC` Hl$@HHH HHCPWj5 H@ HHH$ H|$@H<$ uHĨ []A\A]A^A_H
c Hb AT H1#C L|$X HLIcLd$ L"C Ll$pH
c H5Jb MT LR1LLL*\L[LZHD$LHxHkH5d L#HH<$Ht肒HH H
i HHHSPHQHJILMJZHD$HLHxFL^IH @ f. @ UHh SHHHHHH[]HHJHfUHh SHmHHHHH[]HH
-HfAWAVAUATUHSHHH Hh HHGg5 Hz HHCX HC` Hl$@HHH HHCPg5 H@ HHHH$ HH|$@nH<$ uHĨ []A\A]A^A_H
nc H7b AT H1W!C L|$X HLIjaLd$ L- C Ll$pH
c H5b MT LO1LLLYL"YLZXHD$LHxYHiH5;b LHH<$Ht"HH8 H
f HHHSPHQHILMWHD$HLHxLIH @ f. @ UHh SHMHHHHH[]HHHbfUHh SH
HHHHH[]HHH"fAWAVAUATUHSHHH He HHGQe5 H HHCX HC` Hl$@HHH4 HHCPe5 H@ HHH$ H|$@H<$ uHĨ []A\A]A^A_H
Nc Hb AT H1 C L|$X (HLI
-_Ld$ L C Ll$pH
q c H5b MT LZM1LLLjWLVLUHD$LHxH2gH5_ LcHH<$HtHHة H
Qd HHHSPHQHILMUHD$HLHxLIH @ f. @ UHh SHHHHHH[]HHHfUHh SHHHHHH[]HHJHfAWAVAUATUHSHHH HNc HHGb5 H HHCX HC` Hl$@HHHT HHCP7c5 H@ HHH$ H|$@H<$ uHĨ []A\A]A^A_H
.b Hwb AT H1 C L|$X HLI\Ld$ LmC Ll$pH
Qb H5*b MT LJ1LLL
-ULbTLSHD$LHxHdH5{] LHH<$HtbHHx H
a HHHSPHQH*ILM*SHD$HLHx&L>IH @ f. @ UHh SHHHHHH[]HH*HfUHh SHMHHHHH[]HHHbfAWAVAUATUHSHHH H` HHG`5 H HHCX HC` Hl$@HHHt HHCP`5 H@ H(HH$ (H|$@NH<$ uHĨ []A\A]A^A_H
b Hb AT H17C L|$X hHLIJZLd$ L
C Ll$pH
1b H5b MT LH1LLLRLRL:QHD$LHx9HrbH5[ LHH<$HtHH H
_ HHHSPHQHILMPHD$HLHxƿLIH @ f. @ UHh SH-HHHHH[]HHHBfUHh SHHHHHH[]HHHfAWAVAUATUHSHHH H^ HHG1^5 H HHCX HC` Hl$@HHHT HHCPw^5 H@ HHH$ H|$@H<$ uHĨ []A\A]A^A_H
b Hb AT H1C L|$X HLIWLd$ LC Ll$pH
b H5jb MT L:F1LLLJPLOLNHD$LHxٽH`H5X LCHH<$Ht袆HH H
1] HHHSPHQHjILMjNHD$HLHxfL~IH @ f. @ UHh SHHHHHH[]HHjHfUHh SHHHHHH[]HH*HfAWAVAUATUHSHHH H.\ HHG[5 Hڳ HHCX HC` Hl$@HHHt HHCP\5 H@ HhHH$ hH|$@莿H<$ uHĨ []A\A]A^A_H
b HWb AT H1wC L|$X 診HLIULd$ LMC Ll$pH
b H5
-b MT LC1LLLMLBMLzLHD$LHxyH]H5[V LHH<$HtBHHX H
Z HHHSPHQH
-ILM
-LHD$HLHxL IH @ f. @ UHh SHmHHHHH[]HH
-HfUHh SH-HHHHH[]HHʽHBfAWAVAUATUHSHHH HY HHGqY5 H HHCX HC` Hl$@HHH HHCPY5 H@ HHH$ H|$@.H<$ uHĨ []A\A]A^A_H
b Hb AT H1C L|$X HHLI*SLd$ LC Ll$pH
b H5b MT LzA1LLLKLJLJHD$LHxHR[H5S LHH<$HtHH H
qX HHHSPHQHILMIHD$HLHx覸L边IH @ f. @ UHh SH
HHHHH[]HH誻H"fUHh SHͻHHHHH[]HHjHfHH8HH fA HH ÐHH8HH fUSHHtb~@tuH?H1[] D H. 3HU HHH[1]ÅuH HH1[]f HHH1[]ÐUSHHtb~@tuH?莺H1[] D H. 賺HU HHH[1]ÅuHź HH1[]f HHH1[]ÐATH7 UHSHHH HtHP H Hǃ HtH{xHtHPH Hǃ Ht L%} HEMtcЃt$H{`Ht螹HU HH[]A\ HE HPMHE t'ЃuHE HP뭋EPU띋E PU ATH UHSHHH HtHP H Hǃ HtH{xHtHPH Hǃ Ht L% HEMtcЃt$H{`Ht许HT HH[]A\ HE HPMHE t'ЃuHE HP뭋EPU띋E PU ATHW UHSHHH HtHP H Hǃ HtH{xHtHPH Hǃ Ht L% HEMtkЃt,H{`Ht辷HS HHH[]A\餷 @ HE HPMHE t'ЃuHE HP륋EPU땋E PU f. ATH UHSHHH HtHP H Hǃ HtH{xHtHPH Hǃ Ht L% HEMtkЃt,H{`Ht辶HR HHH[]A\餶 @ HE HPMHE t'ЃuHE HP륋EPU땋E PU f. AWWɸ AVAUATIUSH .D M HI IHHL蝙7 H IU IEH)HH H: HzE I@ H1HH H | IT$H9HU@I9@] IWI11J< (HAADD DHI9wH9t6HWAD H9~!HAH9D ~ AD H9I}f D H)D HH9H9 H4 II)I4HHFH9HB@H9@ I MIN Mt1W11(HDDHI9wLM9t8HAAD H9~#HAH9D ~AD fHĘ []A\A]A^A_f D M~HH IT$H1L H|$H$HT$HK<MPMHWLD$HO,O 1I H Mt/11(HAADDHH9t$wI9t=Ht$AL9H1D ~$JAE L9D ~J AD HH$HL9H~HPI9@H;D$@P1f D AHH9uf. AD HH9uf D HHHHH9HOH)HJHHIHfMWHLL< HHE1L H<$1L\$MkMc H Mt+1I IHH94$ BBDwL9t9Ht$ L9H 7D ~!J / L9D ~ J'D IHHM9LHILH~LHH9AL9@AO HAH9u @ I@A$\ H1HH HHƃHHكH9HFHg HE HEy HE` HE G HE. E H9teII)MIN Mt'(H| 1 HH)OI9wLM9t$HqD H9~HD H9~D H9}( @ )L HH9H9Ht HH)HHHH9HGHHFHtuHHz8 HD Hz$ HD Hz HD Hz HD Hz D HH9uH)IIJ< Ht*(H41 HH)NI9wHH94HBD H9!HD H9D 1f D AD HH9uf D HHHHH9HOH)HJHHHH{f. HH 11 X Ll$/Lt$0H5c LLIkHl$@H^C H\$`H
bc H5c Io H2Hc LHL<HN<H;HD$0HHx腪HLH5gE LHIHL <HA;HD$0LHx@LXHHHHAVH9AUATIUSHtDHNL.H?HCHL)H)HHH9wqLCLH)HH9w H H{H[]A\A]A^ D HIt H)H<HHtH LZHH<H{ D H H H9 L4 LHI H;Ht蓬K<4L#H{H{V HLHٱHH;H{1f. H L谱LCH;IL$LH)H<HI4$H)HH D HLLjH;HVVf E1E15 D Kf. AWAVAUATIUSHH( HHFHG H)HHpH Hl$@X ֦HIK]L$ L{ C L$0 H
c H5%
c M
L0HV
c HLL:Lh9L8HD$@H$ Hx蚧HIH5|B L @ I4$I|$H)HHI M H HH EHC H. HkH9 L=ՠ L$ H
kb I 1LL6Ho c 1LH-[ՠ HL$ LCLLHUHL$H
b H 1g6H" c 1L衣HHUH$ LHHL$r7 L$ I|$X耣HԠ Ƅ$ LHDŽ$ Ƅ$ 1HDŽ$ HDŽ$ HHDŽ$ HDŽ$ H$ HԠ HHH@HyH$ HL$HD$ H H0ՠ I|$@HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHDŽ$ H$ HԠ H@H$ HEԠ HH$ H
ՠ It$I|$XDŽ$ HH$ HEH$ [H5 c L臩H$ LHVsHD$LHPH_HD$LHPHKH$ H}H|$pH L$ L$ L9 L9o M L)H$` 1HInIIHH HLLLD$(蚦LD$(HI9 L$ Lt$pH$ LL虭H$ HxH9 HUӠ HH$ HBӠ H@H$ HӠ HH$ H$ HxH9 HҠ I|$@HH$ yHD$HT$ I|$XH$ H@H HѠ HH$ ޠH|$`L A LsHD$`HD$` MHCt<IFHxH9> LcLt$`MtIFHxH9 L@HD$pHxH9r H$ HxH9/ HD$HxH9 H$ HxH9t HD$HxH92 H$ HxH9 HkIEI+E L#H H$ H
c A LcH$ HDŽ$ MHCtHID$H-Р HxH9 LRL$ MtID$HxH9` L+H; H{ H{ H( []A\A]A^A_f Hn1T D H H H! LX7 HtIU IEH)HH HHI9C HH9j?f. M9 M @ Z M)H$` 1LIIHHt`HLLLD$(LD$(HI9 L$ Lt$pH$ LLv AA@9 AA@f D HHHAf HAHHH9*WfHt HHH{HtHC HǩH=Ϡ HW H$ ١ @ Lt$pIt$PL^f MH=la 苠L$ HHD$pHt$?Hx]L襪H$ LHxAH|$LH0H$ LHx H|$LH H$ LHxfH=1la @ HJHH9L$ X rLISH$ HB L$ H
c H5c I, L&Hc LLL0L0H</H$ H$ Hx3Hl@H59 L蝧Lt$PX ΜLICSH$ HsB L$` H
c H5 c I L%H)c LLL0L`/H.HD$PH$ Hx蒝H?H5t8 LH=͠ HW~ LsH=^͠ HG` ЅLCH=.͠ HWC kL^H= ͠ HGF Ѕ&LH=̠ HW L$ L诞H=̠ HW L聞H=l̠ HW mLS`H=>̠ HW3 H$0 H= ̠ HW LH=ˠ HW LĝA@ M8CD8 A@ M8CD8 JH=ˠ HWt\H$ iqLH,L!,HD$@H$ HxL3 HPJHHL$ PJHyHL$APQPJHHHD$pLL$ Hx蝚CHL$APQHH$ H$0 HxiPJHILM+HL++HD$PH$ Hx%L=IHHPJHRPJHxPJHPJHHHHL$ hHHȠ I|$XL$ HH$ 'HL$ PHL$ ,LI+HL6*H$ H$ Hx-
IH PJH;I|$HHD$HL$ H$ H@H 3OPJH @ AWAVAUATIUSHHH H.8 L$ Hl$ HHG75 H H58b HC` HCh HCp LHCx Hǃ HHHǃ Hǃ H補H|$HHLI?5 L|$M IGH-tƠ HxH9O L?HD$ HxH9 M% H<$L|$PH5|b LLpCP H|$@LLL ?5 Lt$@MtIFHxH9@ LʚHD$PHxH9 M H<$L$ H57g LL{XLpCT褛H|$pLLL=5 Lt$pMtIFHxH9 LNH$ HxH9f M L$ H5g LL{\L2H$ LLL =5 L$ MtID$HxH9~ LՙH$ HxH9 Mu'H []A\A]A^A_f H-Ġ UL$ X LIWKL$0 LB H$ H
hb H5b M H Hb LHL (Ht'L&H$ HHx訕H7H50 LHH HtHP H{xHǃ I$H{`Ht軘H4 HHH%H=UŠ HW* H$ 7L$ X LI5JH$ HeB H
Nb H5b I L HHb LLL'LZ&H%H$ H$ Hx艔H6H5k/ LH=Ġ HW8 yH$ egLt$`X LIfIH$ HB L$ H
wb H5b I L HAb LLL+&L%H$HD$`H$ Hx赓H5H5. L Lt$0X PLIHH$ HB L$P H
b H5Gb I LHpb LLL%L$H$HD$0H$ HxHM5H5- L~H=à HW H$ H= HW RH$ 轔@H= HW H$ 芔~H=u HW NH$ W<H=B HWc H$ $H= HWtmeH$ SILMr#HL"HD$0H$ Hx衑L蹒HŐHIPJHILM#HLN"H$ H$ HxEL]IHHPJHPJH$PJHPJH7IHL"L!H$ LHx运Lב#HHHILME"HLz!HD$`H$ HxtIH D HH$ H$ Hx?HHD$ H$ Hx!HHD$PH$ HxoHH$ H$ HxNPJHNPJHqPJHf. UH SH-HHHHH[]HHʒHBfUH SHHHHHH[]HH芒HfUH SH譒HHH?HH[]HHJHfUH SHmHHHHH[]HH
-H肘fAWAVAUATIUSHHH H. L$ Hl$ HHG-5 H͒ H5b HC` HCh HCp LHCx Hǃ HHHǃ Hǃ H胒H|$HHLI}55 L|$M IGH-T HxH9O L HD$ HxH9 M% H<$L|$PH5\b LLpCP H|$@LLL45 Lt$@MtIFHxH9@ L誐HD$PHxH9 M H<$L$ H5g LL{XLpCT脑H|$pLLLa35 Lt$pMtIFHxH9 L.H$ HxH9f M L$ H5g LL{\LH$ LLL25 L$ MtID$HxH9~ L赏H$ HxH9 Mu'H []A\A]A^A_f H- UL$ X LI7AL$0 LgB H$ H
b H5b M HHrb LHL HT L H$ HHx舋H-H5j& LHH HtHP H{xHǃ )H{`Ht蛎H* HHHH=5 HW* H$ L$ X 蠉LI@H$ HEB H
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yq Hq $ H=q H
Yq Hbq $ H=q M9: LPLPLLHHL)HHH)H9 HH9HBHI| H HDž M1M9tFLHLxLHLLxLHLHHH<H HDH9HCH9 H
+ HH1HHfoHH9uHHHHH HHH9tHHH HHOHM9t8LLHLLeHLLHN,MtLH%HHfHnfInflH)|HpHpH Lh(H`HH:HHgHpHpH Lh(H`HHHf D H@1H;Hu;u f HHHFHHHHH)HH9? HH HH@HH)HH9 HHH;uHjH@듐HDžP HDž HDž HDž HDž @ HHxH+8Hf II+?HHH
q Hq $ H=.q H
q Hq $ H=q H`LHHHHH`HH`1HII9F HHHDž` IFI+HHH9rH`11H;hu7 D HHHFHHhHH)HH9` IIGH4H)HH9: HHH;uH蘵H`H`LHHHHH`HH9HGHHHHLHLLMHHL)M)IHpHLHQHyhHLhH HLEMDH H(HqH@HH0舖HHu H] L H LL0(LH\HL HDž AZA[HtHH HtєH`HtHHHtDLrH
q Hq $ H=!q H1HHHIT HHH)HHHHH7IFI+HH H(fHDž HHH+@HDž0 H)H) H) HHH< HDž` HM Ht5L H(1L)HIHHTH9 MH9uH@HHH)Ht@H H(IHIH)NHIHItH9E L H9uI6IFH)Ht@H H(IHIH)NHIHItH9 L H9uH`HhH)Ht=LkH H(HH)HLIHLDI9 N H9uHpH LhHXHIHH@oa))HtH= @LLP HDžp HfInfInflHE)Ef)`
HMfouHPH`fInǿ LuHHH0H)EfHHpHEHhHDžP H)@k
HMfo}HPH@HHH@HPL`HHHL8HHHHLHEHH@SHATAUH0(H@H Ht H`Ht HHt@H@HHH HIHLH`Ht5 < S H@H0H HpH ҾH Ht H Ht HHt HHt HHt HHt H`Ht H@Htz H Hti HHtH%NJ X H踲q HHtHNJ X H藲q HEdH3%( H@He[A\A]A^A_]H
Gq HPq $ H=q H1IH @ HHHHH)HLH HHzH(H HHvHHH@HHYHMfM.HM9tAHM)LH)HHH9+ LLLLLHhH`HH17HL H+HL$LHLLӛ' HH@LHL0HLH`HRO HH
q H r H=q @3H`H 1ͦH H(H)HHHH+ HHH)H9 H9HBHH H% H9HGHHHIHL HIM)IO 7I)LIO!KHM9< LLLhHxLLL LLLLHxLLLhI9L HLLLHLHM fInLL) HL E1IHM)O'MLL9tGLL1L LLLHHLE1HLfE1LLLLxHLLHH9LLLxHHHH
q H< r H=`q C H
q H r H=Aq $ HpHH HpLh(H诋H@H0HtyQILLHH0AU-H@XH@ZHDž@ H9HHt"H@HtHhH9H
q H@s Z H=q X HHeH=dr H
q HK@s Z H=q H
q Hr H=q
+H=r J H X L H5]s LIH`HHH@HH0
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.Zs H5"Vs m HLHYs LHhH0"H HxH;= tӇH H5%| L= H Hz HzH`HtHHtHHuk H> HK H HH`HtEH@H8HtqH Ht H HtHHtHH>> H HHhH 6H`HtHHk5a H H@Htr H HH`HtOHHH*z5 H HhHN5X H HMLIH@Ws L`H`HWs H H@H#Ys H H@IHLH0蔪L| X L0HH
WWs v H5KSs HHHVs L H
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Ss e H5Os ;HHHTs L·H6
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!q H*q $ H=q yLL.2H`L HD9 7HPHHpHHH)HHI9 HXH8H@H H)HI9X NN M M M^MQKDm H`LAWL LLrXHEHE ZMtHLH 9J LX #q LuH Mt Lk9J X Lq H H}H H IjL;(H I9H
q Hq $ H= q H8HH;F H0@DMH8HH;Cz L0E1Aƅ7 IIL9(^IH@MH H`L HUH8H8H@H H)HI9H NBH9 H8HH;F LI*H
vq HOs H=q H
Wq HNs $ H=̰q H
8q HYOs H=q H
q H:Os H=q qH
q HMs $ H=oq RH
ۮq H s $ H=Pq 3H
q H s $ H=1q H
q H s $ H=q H
~q H?Ms $ H=q H
_q H` s $ H=ԯq H H H7E1H}HtH}HtMMtLwH0H}Ht' HH}Hti UHAWMAVAULjATISH EH@LHpLuHHHL<dH%( HE1J LH܆J H9 HL`LJ LH赆J H9u! HL`HXH+HXHHHIGI+H9@ H fHDž0 HH) vH@fHDžP HHx)@vH`fHDžp HHh)`^vHDž IFI+HHHH H`1HDž HDžP HDž HDž HDž HDž HHHH@HH)HH9> L$IGIH)HH9C L,IHI!H9[ M9! HHH`0LLHH;MIvI+6HHH(HHLfH+ LH+@HDž HHHDž H0HhH+`)H)HH˃H(H LHOHHH@LHOIFI LH9 H)HHHHHL)HHH9 H~wHCL)H
H r
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iq Hrq $ H=ޤq H
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q Hq $ H=q H
zq Hq $ H=q H`LHHHH`HH`LHeHHfH`HIE1H`I9F D HHHDž` VLIFI+HHIH9rH`11H;hu5c HHHFHHhHH)HH9( IIGH4H)HH9 HHH;uHHH`H9HGHHHHLHLLMHHL)M)IHpHLHQHyhHLhH H0LEMDH H(HqH@HHvhHHu H]
h0L H LL(LHʢHL HDž AZA[HtHfH HtfH`HtnHHHtLpH
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6q H?q $ H=q H1IH HHHHH)HLH HHHHLH(H HH\uHHH@HH?uMfM.HIM9t=HM)LH)HHH9# LLLLMHhH`LHt1wHL H+HLLHLLm H@LHH IHLH`HUAKH
q Hq H=q @=H`H 1xH H(H)HHHH+ HHH)H9 H9HBHH H H9HGHHHL HLIL)IL M)HO!KHM9) LLLhHxLLLLLLLHxLLLhI9X LLLLHLIM fInLL) L IHE1M)O'MLL9tCLL1LLLLSHLE1IL'qE1LLLLxHLLHLLLxI9HIH"HpHH HpLh(I^H@H0H QHMIAUHL膻H@XH@ZHDž@ H9HHt{\H@Htj\HhH)U H
Yq Hq H=Γq H
:q Hq H=q H
q Hlq H=q sH=r H
œq Hys Z H=Ŕq HHIJH=Xr H
q H:s Z H=q H- X H`L HH5/s LIH@HHW L HH
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J X Lqq HEdH3%( H@He[A\A]A^A_]H
q Hq $ H=q LLm2H`Lm HD9 7HPHHpHHH)HHI9 HXH8H@H H)HI9X NN M M M^MQKDm H`LAWL LLXHEHE ZMtHLH J LX pq LuH Mt L J X L~pq H H}H H IL;(H I9H
q H q $ H=q oH8HH;F H0@DMH8HH;Cz L0E1Aƅ7 IIL9(^IH@MH H`LkHUH8H8H@H H)HI9H NBH9 H8HH;F LI*H
q H"s H=kq NH
ׁq H s $ H=Lq /H
q H!s H=-q H
q H!s H=q H
zq H; s $ H=q H
[q H\r $ H=Ђq H
<q H=r $ H=q H
q H r $ H=q uH
q H s $ H=sq VH
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uq Hq $ H=^wq AH
uq H҂q $ H=>wq !M9: LPLPLLHHL)HHH)H9 HH9HBHI| H HDž M1M9tFLHLxLHLSLxLHLHHH<H HDH9HCH9 H
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oq H%|q $ H=pq tH1HHHIT HHH)HHHHH7IFI+HH H(fHDž HHH+@HDž0 H)H) H) HHH< HDž` HM Ht5L H(1L)HIHHTH9 MH9uH@HHH)Ht@H H(IHIH)NHIHItH9E L H9uI6IFH)Ht@H H(IHIH)NHIHItH9 L H9uH`HhH)Ht=LkH H(HH)HLIHLDI9 N H9uHpH LhHXHIHH@oa))HtH=đ @LLP HDžp HfInfInflHE)Ef)`THMfouHPH`fInǿ LuHHH0H)EfHHpHEHhHDžP H)@۱HMfo}HPH@HHH@HPL`HHHL8HHHHLHEHH@SHATAUH0˲H@H HtH`HtHHt$H@HHH HIaHLfH`Ht襰< S H@H0H HpH "yH HtaH HtPHHt?HHt.HHt HHt H`HtH@HtH HtٯHHtHI X H(Wq HHtHtI X HWq HEdH3%( H@He[A\A]A^A_]H
iq Hvq $ H=,kq H1IH @ HHHHH)HLH HHS H(H HHGHHH@HHGHMfM.HM9tAHM)LH)HHH9+ LL#LLLHhH`HHQG17HL H+HL蔐LHLLC@' HH@LHL0HL脊H`HR迭HH
hq HTq H=xiq [@3H`H 1=KH H(H)HHHH+ HHH)H9 H9HBHH H% H9HGHHHBIHL HIM)IO 7I)LIO!KHM9< LLLhHxLLL.LLLL艫HxLLLhI9L HLLLKHLHM fInLL) HL E1IHM)O'MLL9tGLL1LVLLL踪HLE1HL舫fE1LLLLxHLLfHH9LLLxHHHH
[eq Hq H=fq 賰H
<eq Hq H=fq 蔰HpHH HpLh(H 0H@H0HtyQILLHH0AU蝍H@XH@ZHDž@ H9HHt.H@Ht.HhHlH
Efq Hr Z H=Egq ȯHHeH=Xr 9H
fq Hr Z H=gq 芯H
dq Hdq H=eq kH=Xr 躪 H X L mH5fs LIH`HH*H@HH04W L0HH
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s H5r HLH r LdbHشH0H HxH;=r tC,H|H5 L譬 H Hz HzH`HtHHt
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s v H5r HHH/r L_HH0%H HxH;= tl)HH5 L֩ HOX PLIE8J H Hr L LH`Hr H`LH@H@L HHHH0RH߬W L0HH
s h H5r HLHr L^HH0H HxH;= t_(HH5 Lɨ H H H蛰H0H HxH;=5 t(LH覨 H H H H<H6H0ZH HxH;=И uL讠HF' H H HHDž HDž H H2 H HX HI 6J HH-r H LH`H,r L`HH@Hvr H H H@LIHLH0 GL葪W L0HH
r e H5`r 諽HHHbr L2\H覮H0H HxH;=@ t&HJH5c L{H`H H H H8H0\H HxH;=Җ t%L諞HC H H H'HhH
H HHDž HDž HDž HDž f. D UHAWAVAUATISH H@H~ dH%( HE1I|$ HI|$ HXI|$ H8I|$ HPHhII$HxG fLHHHLhPHE HE HHHMHH`)E)E~L}HhLk1Hh HE Lu1qHuH;u HHHuHEHHEH;h}BH HSH)HH9s H< tHuH;u} HHHuf L+HHEIDHH9Ey ID$ML$MD$hAoL$I$ )MHtH= @HPHH;P HXHH;Gq H0Hc APHxLEQH`x`H}YLx^HtAM H}Ht蹝H}Ht諝A AEƅ7 A HH(HhfLHE )E
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Vq Hcq $ H=Xq LL?2H`L? HD9 7HPHHpHHH)HHI9 HXH8H@H H)HI9X NN M M M^MQKDm H`LAWL LLrXHEHE ZMtHLH I LX Bq LuH Mt LI X LnBq H H}H H IښL;(H I9H
Uq Hbq $ H=|Vq _H8HH;F H0@DMH8HH;Cz L0E1Aƅ7 IIL9(^IH@MH H`L|=HUH8H8H@H H)HI9H NBH9 H8HH;F LI*H
Sq Hr H=[Uq >H
Sq Hr $ H=<Uq H
Sq Hr H= Uq H
Sq Hr H=Tq H
jSq H+r $ H=Tq H
KSq HLr $ H=Tq 裞H
,Sq H-r $ H=Tq 脞H
Sq Hr $ H=Tq eH
Rq Hr $ H=cTq FH
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1 HI~rLcPE1f D CH{X1L,LHIIHt+HHqf D I9tcH HtHq1HHI9tH IM9uIE H|$ HtHĸ L[]A\A]A^A_ LH HAD ?H I9 ILHt$ H|$(HH)HL< \H H9P L< LIt$MD$ HI)IN4 H
] LJ<< H H|$ t?Lt$(X Lt$;MD$0H
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J] H5[ I躗 HSH [ LHL^H越HHD$HHxH&H5ϓ LWHH|$ Ht%HH|$@HHt @ {IHL=HuHD$LHxtLHHfHf AWAVAUIHATUHSH1H <IH@HD$ IT$HD$( HD$ID$ HD$0 H)HHD E1HE1E11J<9MHL$ HL$(H|$0tHL H|$0HJ1H9HD$([ HH tHLpLt$(HT$ HD$@ HD$H HD$P I)IMq 1HE1E11IHHL$@HL$HLt$PtHLHLL|$@1HHL$HL謈/ H|$@IHtZLI~H1 H~H 1H ID$0x H|$M4$Mt$P1 HI~rLcPE1f D CH{X1L,LHIIHt+HHqf D I9tcH HtHq1HHI9tH IM9uIE H|$ Ht|Hĸ L[]A\A]A^A_ LH HAD ?H I9 ILhHt$ H|$(HH)HL< \H H9P L< L$It$MD$ HI)IN4 H
D] L7< H H|$ ;Lt$(X Lt$MD$0H
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*] H5[ I躗 H|H[ LHLHVH莅HD$HHxHƖH5o LHH|$ HtH=H|$@HHt$ @ IHL݅HHD$LHxL,HHfHf AWAVAUIHATUHSH1H IH@HD$ IT$HD$( HD$ID$ HD$0 H)HH< E1HE1E11J<9MHL$ HL$(H|$0tHL*H|$0HJ1H9HD$(S HH tHLpLt$(HT$ HD$@ HD$H HD$P I)IMi 1HE1E11IHHL$@HL$HLt$PtHLHLL|$@1HHL$HLL/ H|$@IHtL肈I~H1 H~H 1HID$0x H|$M4$Mt$PM1 HI~jLcPE1f D OH{X1LHIIHt*HHq D I9tcH HtHq1HHI9tH IM9uIE H|$ Ht$Hĸ L[]A\A]A^A_ LH HAD ?H I9 ILHt$ H|$(HH)HL< dH H9Q L< LIt$MD$ HI)IN4 H
] L3< H H|$ 6Lt$(X Lt$MD$0H
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R] H5+[ I躗 HxHT[ LHL覂HH6HD$HHx5HnH5 LHH|$ HtmHH|$@HHtSՐIHL舁HHD$LHxLHfHH @ AWIiAVIAUATUSHH>HwIn1IHIHIHt.HHKf I9tkHHtHK1HHI9t HHt#HxH LHC HC HLLLH(f D I?HsHWH;VuZwHCH[]A\A]A^A_HH
-HHjf. UHSHHHu @ HH HuHEH} 1H HE HE H[]f. AWIAVAUIATI̹ UHH SHHWHwLw H HH96 L< L^1HLIHu1HE LUHuU D IHIH2Ht?HHF1HHMMMuHEHHuMH> HI4HuH} 1LLu HH]IIK>HHt8HI$H L HEHL[]A\A]A^A_Lu IK>HHuHUI$LeI$HtHB1HuLM$HE HUHf HsHLu(HzHLH]H5 D AWAVAUATIHUSHH H HHGa/ H* I|$pID$X ID$` ID$h
- AD$p ?ID$x HI$ID$PHD$ H ID$XH9q H, HD1HHII$ I$ Ml$PIDŽ$ IDŽ$
- IDŽ$ ADŽ$ ?IDŽ$ HD$4H I$ H9 H, HH1HIHl$PDH$` H5] M$ HHH$NH$ H$ HHC/ H$ H H}H$ HHD$HHHt$ I$ HD$PHl$`HxXH$@ H5] IDŽ$ HHDŽ$ HDŽ$ HHDŽ$ HD$(H$ H$ HHHDŽ$ HDŽ$( HHDŽ$0 HD$0ң/ 1E1H$ R H, L$ ID- 1HD$Mt IFHxH;= LH$ HtHD$`H4$HxLHtHH$ Hl$pH5] HDŽ$ HDŽ$ HHHDŽ$ HD$8wL|$(H$ HHHDŽ$@ HDŽ$H HDŽ$P L訣/ H$ LHDŽ$ HE\$HDŽ$( HDŽ$0 LsHLH)HH 1E1LI9L$ L$( H$0 HLf Ht HHHHI9uH$( H$ HtHCH4$HxHEH$H H$@ H9` H= L5= HHxL9 HH9uH$@ HtHD$pH4$HxsH|$YxL;l$y H$ H9$( X H$ HT$OH5] HHL$ L?= L<$H
t] H5] M8 L oH6] HLH(yLxLwH$ Ht$HxHH5 H @ L|$M)IMM$ E E1 K\ I|$X1HHID$PHHHt/HHqf D H9 H HtHq1HHH9t HtKT H H@ HPH|$ HHHHHL1IM9`I$ H H|$0vMtLH []A\A]A^A_f D HH H9N HH ILsHH$( H+$ HH H H9 H< H$ H$( IH)HHHKHLH?H$ H$( H)HHI$ 1f H$ H|$H4HHH9uH$ H$ H|$HHLIIHH$ HtHD$`H4$Hx-H|$I$ vH|$ I|$PbHH= HWHI$HHD$PH$ HxHHH|$(tHD$pH4$HxH|$tMiL\HHH$ Ht$HxkHH|$0ItHf+H- 7H$ X HHYLt$8L;= L|$H
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- AD$p ?ID$x HI$ID$PHD$ <H ID$XH9q H, H1HHITI$ I$ Ml$PIDŽ$ IDŽ$
- IDŽ$ ADŽ$ ?IDŽ$ HD$H I$ H9 H, HH1HIHl$PH$` H5"] M$ HHH$H$ H$ HH賆/ H$ H H}H$ HHD$HHMHt$ I$ HD$PHl$`HxH$@ H5P] IDŽ$ HHDŽ$ HDŽ$ HHDŽ$ HD$(H$ H$ HHHDŽ$ HDŽ$( HHDŽ$0 HD$0B/ 1E1H$ R H, L$ ID- 1HD$Mt IFHxH;=n L@H$ Ht.HD$`H4$HxHtHH$ Hl$pH5%] HDŽ$ HDŽ$ HHHDŽ$ HD$8L|$(H$ HHHDŽ$@ HDŽ$H HDŽ$P L/ H$ LHDŽ$ HE\$HDŽ$( HDŽ$0 LsHLH)HH 1E1LI9L$ L$( H$0 HLf Ht HH0HHI9uH$( H$ HtHCH4$Hx]HH$H H$@ H9` H=L L5
- HHxL9 HH9uH$@ HtUHD$pH4$HxH|$lL;l$y H$ H9$( X H$ fHT$OH5"] HHL$ L3= L<$H
d] H5u] M8 LcH] HLHmLlL(lH$ Ht$Hx"H[}H5v H @ L|$M)IMM$ E E1 K\ I|$X1HHID$PHHHt/HHqf D H9 H HtHq1HHH9t $HtKT H H@ HPH|$ HHH4HHL1IM9`I$ H H|$0kMtLxH []A\A]A^A_f D HH H9N HH{ILsHH$( H+$ HH H H9 H< /H$ H$( IH)HHHKHLH*?H$ H$( H)HHI$ 1f H$ H|$H4jHHH9uH$ H$ H|$HHLIIHH$ HtHD$`H4$HxH|$胶I$ H|$ I|$PHH=w HWHI$8HHD$PH$ Hx?HHH|$(iHD$pH4$HxH|$hMiL^\HHH$ Ht$HxHH|$0hHfH- 7H$ X THHɌLt$8L/= L|$H
`] H5q] MD L_H%] HLHiLhL$hH$ LHx HYyH5r HH/H|$HhH|$8gH$ H4$HxHHHHHH<$H[hLgHtHI9tIH4$IHxsL9uPGH4$n9HHxL9uHH9u(PJ҉HH4$9HH$ Ht]H$ H4H= HWt# H4$H$ PJHHH$ HH24[f. UH SHHHHHH[]HHHfAWAVAUATIHUSHH Ht HHGAt/ H I|$pID$X ID$` ID$h
- AD$p ?ID$x HI$ID$PHD$ H ID$XH92
- H, H$1HHII$ I$ Ml$PIDŽ$ IDŽ$
- IDŽ$ ADŽ$ ?IDŽ$ HD$H I$ H9 H, HH1HIHl$P$H$` H5] M$ HHHD$-H$ H$ HH"{/ H$ H H}H$ HH$HeHH4$ I$ HD$PHl$`Hx9H$@ H5~] IDŽ$ HHDŽ$ HDŽ$ HHDŽ$ HD$0jH$ H$ HHHDŽ$ HDŽ$( HHDŽ$0 HD$(賌/ 1E1H$ _ H, L$ ID- 1HD$Mt IFHxH;= LH$ HtHD$`Ht$Hx,HtHH$ Hl$pH5}] HDŽ$ HDŽ$ HHHDŽ$ HD$8WL|$0H$ HHHDŽ$@ HDŽ$H HDŽ$P L舌/ H$ LHDŽ$ HE $HDŽ$( HDŽ$0 LsHLH)HH 1E1LI9L$ L$( H$0 HLf. Ht HHHHI9uH$( H$ H HCH-O HxH9Y HL$H H$@ I9T H= HHxH9 HI9uH$@ HtHD$pHxH9$ H<$:aL;l$H$ ~ H9$( X H$ HT$OH5] HHL$ Lp(= L|$H
Զ] H5] M8 LWH] HLHbL`aL`H$ H4$HxHqH5uj H D L|$M)IMM$ E1 K\ I|$X1HHID$PHHHt/HHqf D H9 H HtHq1HHH9t HtKT H H@ HPH|$ HHHHHL1IM9`I$ HD H|$(_MtLH []A\A]A^A_f D HH H9 HHILsHH- vH$( H+$ HH H H9 H< H$ H$( IH)HHH>HLH2H$( H)HHI$ > 1
D H$ H|$H4HHH9uH$ H$ H<$HLLJIH|$H^L^H$ H4$Hx H#H|$(]MtLLH|$ҪI$ 5H|$ KI|$P!HH=Gl HWHI$HwHwHH|$0z]HD$pHt$HxwH<$^]pH=y HW Ht$\uH$H=? HWtAHt$&PoHt$`PJHHHxH9u HI9uBPJH[PJ҉HHt$HH$ HtHD$`Ht$HxhwHHD$PH$ HxJYHH=M HWt$
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] H5'] MD L?RHۭ] HLHJ\L[LZH$ LHxHlH5d H@H<$Hd[H|$8ZH$ Ht$HxHHHHHHmH$ HfUH SHHHHHH[]HHzHfAWAVAUATIHUSHH H~h HHG!h/ Hj I|$pID$X ID$` ID$h
- AD$p ?ID$x HI$ID$PHD$ H ID$XH9q H, H1HHII$ I$ Ml$PIDŽ$ IDŽ$
- IDŽ$ ADŽ$ ?IDŽ$ HD$H I$ H9 H, HiH1HIHl$PH$` H5r] M$ HHH$H$ H$ HHo/ H$ H H}H$ HHD$HEHHt$ I$ HD$PHl$`HxH$@ H5r] IDŽ$ HHDŽ$ HDŽ$ HHDŽ$ HD$(IH$ H$ HHHDŽ$ HDŽ$( HHDŽ$0 HD$0蒀/ 1E1H$ R H, L$ ID- 1HD$Mt IFHxH;= LH$ Ht~HD$`H4$Hx HtH_H$ Hl$pH5uq] HDŽ$ HDŽ$ HHHDŽ$ HD$87L|$(H$ HHHDŽ$@ HDŽ$H HDŽ$P Lh/ H$ LHDŽ$ HE\$HDŽ$( HDŽ$0 LsHLH)HH 1E1LI9L$ L$( H$0 HLf Ht HHHHI9uH$( H$ HtHCH4$HxHH$H H$@ H9` H= L5 HHxL9 HH9uH$@ HtHD$pH4$Hx3H|$UL;l$y H$ H9$( X H$ HT$OH5r] HH_L$ LO = L<$H
4] H5ŧ] M8 LKH] HLHUL@ULxTH$ Ht$HxrHeH5T^ H @ L|$M)IMM$ E E1 K\ I|$X1HHID$PHHHt/HHqf D H9 H HtHq1HHH9t tHtKT H H@ HPH|$ HHHHHL1IM9`I$ H H|$0eSMtLH []A\A]A^A_f D HH H9N HHILsHH$( H+$ HH H H9 H< H$ H$( IH)HHHKHLHz?H$ H$( H)HHI$ 1f H$ H|$H4HHH9uH$ H$ H|$HHLIIHH$ Ht_HD$`H4$HxH|$ӞI$ 6H|$ LI|$P"HH=H` HWHI$HHD$PH$ HxHHH|$(kQHD$pH4$HxiH|$OQMiL\HHH$ Ht$Hx+HCH|$0 QHfH- ۽7H$ X 褾HHuLt$8LL= L|$H
0] H5] MD LGHu] HLHQL<QLtPH$ LHxpHaH5RZ HH/H|$HPH|$8+PH$ H4$Hx&HH;HHHH<$HPLOHļH I9tIH4$IHxþL9u)PGH4$9HHxL9uHH9u(PJ҉HH4$H_H$ HtH$ H H=B HWt# H4$.H$ PJHHH$ HH 諻f. UH SHMHHHHH[]HHHbfHHHHyH9HAHI*HOh\HOPYf. fHHHHH@*HBh\ HBPY HHHHyH9HAHIH*HOh\HOPYf. HHHHH@H*HBh\ HBPY f D HHHHyH9HAHILGhHOPA Z\ ZYffZ D HHHHH@HzhHBPZ\ZYffZÐHHHHyH9LAHAHOhA\HOPYf. HHHxHHHBh\ HBPY H י SHHHHhHtH{PHtҾHZ HH[fH֙ SHHHHhHt蠾H{PHt蒾HZ HH[fH ֙ SHHHHhHt`H{PHtRH{Z HH[fHՙ SHHHHhHt H{PHtH;Z HH[fUSHH~ ~\t @ uH?ֽH1[] D H. HU HHUHPHUHPHUHPHH[1]ÅuHә HH1[]f HHH1[]ÐUSHHtr~PtuH?>H1[] D H. cHU HHUHPHUHPHH[1]ÅuH5ә HH1[]f HHH1[]ÐUSHH~ ~\t @ uH?覼H1[] D H. ˼HU HHUHPHUHPHUHPHH[1]ÅuHҙ HH1[]f HHH1[]ÐUSHHtr~PtuH?H1[] D H. 3HU HHUHPHUHPHH[1]ÅuH%ҙ HH1[]f HHH1[]ÐUSHH~ ~\t @ uH?vH1[] D H. 蛻HU HHUHPHUHPHUHPHH[1]ÅuHљ HH1[]f HHH1[]ÐUSHHtr~PtuH?H1[] D H. HU HHUHPHUHPHH[1]ÅuHљ HH1[]f HHH1[]ÐUSHH~ ~\t @ uH?FH1[] D H. kHU HHUHPHUHPHUHPHH[1]ÅuHЙ HH1[]f HHH1[]ÐUSHHtr~PtuH?讹H1[] D H. ӹHU HHUHPHUHPHH[1]ÅuHЙ HH1[]f HHH1[]ÐUSHHtr~PtuH? H1[] D H. CHU HHUHPHUHPHH[1]ÅuHϙ HH1[]f HHH1[]ÐH9Й SHHHHhHt萸H{PHt肸HT HHH[kf. Hϙ SHHHHhHt@H{PHt2H[T HHH[f. HЙ SHHHHhHtH{PHtH T HHH[˷f. Hϙ SHHHHhHt蠷H{PHt蒷HS HHH[{f. ATUSH/HEHM H HH9H9}<HXHLdI9~ fHUHz t-HHHRL9u[]A\f D HHHL$賱 SHIHHHHHHHrH;pvH;rv)L衷HHH HHH[ H1LSАAWIAVAUATUSHHH HR L$ Ll$ HHGDR/ H͙ H5_b LLHDŽ$` HDŽ$h HDŽ$p HH@H$ H$ LLHDŽ$ HDŽ$ HHDŽ$ k/ H$ HCP HCX 1HC` 1E1E11IHHKPHKXLk`tHLHH$ LHKXH HPL5 HzL9 HQH$ Ht?HD$ HxL9 H$` HtLl$0H5] HHDŽ$` HDŽ$h LHDŽ$p H$ LLLHDŽ$ HDŽ$ HDŽ$ vj/ H$ HCh HCp 1HCx 1E1E11IHHKhHKpLkxtHLĹHLL$ HKpMtIGHxL9y L$H$ HtHD$0HxL9 H$` HtHSXHCPH9 H)HCpH+ChHHH9 HĘ []A\A]A^A_ L5ޚ K @ H$ L$ HCh HCp HCx I)IMLH?I9 IL~H$ H$ HH)HL< H$ L$ HCP HCX HC` I)IML"H?I9 ILH$ H$ HH)HL4 H=?ߚ HWd AL$0 L ,H= ߚ HW 8L$0 L#X L$ qLCpL+ChH
[ H5sݚ H$ L1ID>LCXL+CPLl$pH5Hݚ H
[ L1I>HX] 1LHL$0 H5J] LH$0 IVH|HHt$`HLH$ IVHHLIQH56e LBHHt$PH$ IVHHHHHT$XL$ L(= L$` H
] H5] MG L5HL$H<$Hږ] L?L?LJ>HD$PLHxIH$ LHx5HD$`LHx$H$ LHxH$0 LHxHD$pLHxH$ LHxHOH5G H<$@HH{hHtH{PHtHH5'L HHVHhX Ld$@9HT$ H5L] LHL$ L= L$0 H
3] H5d] ME L\4H ] LLHg>L=L<HD$@H$` HxH*NH5F H[H=ۚ HO L$0 H$LƭH$xH$ HHt H$ HtѮHD$ H$0 Hx[HH=cۚ HW fL$0 LBQH$ HHtK H$ HtQHD$0H$0 Hx۪'Jqr#PJHwL$ IH$ LMHx菪HD$`HHx~H$ HLHxgH$0 LHxSHD$pLHxBH$ LHx.H<$EcIIHILM:HD$PL$ LHx?ILHD$;HD$I뻋PJHPJHHHHHL$0 .HKHL$0 'LI;LF:HD$@H$` Hx@HLUsIIIUH SH譬HHHHH[]HHJH²fAWIAVAUATUSHHH HNH L$ Ll$ HHGG/ HÙ H5Ub LLHDŽ$` HDŽ$h HDŽ$p HHH$ H$ LLHDŽ$ HDŽ$ HHDŽ$ Na/ H$ HCP HCX 1HC` 1E1E11IHHKPHKXLk`tHL蜰HH$ LHKXH HPL5&֚ HzL9 HH$ HtߪHD$ HxL9 H$` Ht軪Ll$0H5'] HHDŽ$` HDŽ$h LHDŽ$p 蠫H$ LLLHDŽ$ HDŽ$ HDŽ$ `/ H$ HCh HCp 1HCx 1E1E11IHHKhHKpLkxtHLdHLL$ HKpMtIGHxL9y LĩH$ Ht販HD$0HxL9 H$` Ht莩HSXHCPH9 H)HCpH+ChHHH9 HĘ []A\A]A^A_ L5aԚ K @ H$ L$ HCh HCp HCx I)IMLH?I9 IL H$ H$ HH)HL< H$ L$ HCP HCX HC` I)IML"H?I9 IL薨H$ H$ HH)HL4 H=Ԛ HWd AL$0 L辦,H=Ԛ HW 8L$0 L舦#X L$ LCpL+ChH
u[ H5Ӛ H$ L1I3LCXL+CPLl$pH5Қ H
u[ L1I3H] 1L襭HL$0 H5] LH$0 IVH HHt$`HLH$ IVHHLIH5e LHHt$PH$ IVHHHHHT$L$ L< L$` H
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s] H5] ME L)H] LLH4L_3L2HD$@H$` Hx葡HCH5s< HH=њ HO L$0 H$LfH$H$ HHtkH$ HtqHD$ H$0 HxHH=њ HW fL$0 LQ蘞H$ HHt H$ HtHD$0H$0 Hx{'Jqr#PJHwL$ IH$ LMHx/HD$`HHx H$ HLHxH$0 LHxHD$pLHxH$ LHxΟH<$cIIHILM0HD$PL$ LHx芟?ILHD$#1HD$I뻋PJHPJHHHHHL$0 .HKHL$0 'LI0L/HD$@H$` HxHLsIIIUH SHMHHHHH[]HHHbfAWIAVAUATUSHHH H= L$ Ll$ HHG=/ Hm H51Kb LLHDŽ$` HDŽ$h HDŽ$p HH耢H$ H$ LLHDŽ$ HDŽ$ HHDŽ$ V/ H$ HCP HCX 1HC` 1E1E11IHHKPHKXLk`tHL<HH$ LHKXH HPL5˚ HzL9 H葠H$ HtHD$ HxL9 H$` Ht[Ll$0H5Dž] HHDŽ$` HDŽ$h LHDŽ$p @H$ LLLHDŽ$ HDŽ$ HDŽ$ U/ H$ HCh HCp 1HCx 1E1E11IHHKhHKpLkxtHLHLL$ HKpMtIGHxL9y LdH$ HtRHD$0HxL9 H$` Ht.HSXHCPH9 H)HCpH+ChHHH9 HĘ []A\A]A^A_ L5ʚ K @ H$ L$ HCh HCp HCx I)IMLH?I9 IL辞H$ H$ HH)HL< H$ L$ HCP HCX HC` I)IML"H?I9 IL6H$ H$ HH)HL4 H=ʚ HWd AL$0 L^,H=Iʚ HW 8L$0 L(#X L$ 豘LCpL+ChH
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:wS H5S Mq HcH$S LHLnHƶLHD$0HHx$H6H5߿ Lg. HD$@HLt$ Hx$H$ H5S LLCh+)H$ H$ LL # L$ M IFHHxo$LE1'HD$ HHxS$DsiLt$H5oS LL(H$ H$ LL# L$ MtpID$HHx#LE1T'HD$HHx#Hlh DcjHHH( H[]A\A]A^A_ @ L|$pH$ f D H$ A띐H$ Af D H+CPHH H H9H H$ H<$#HH{PHCXIH)HH ItM8Ht HHLD$+H{PLD$HHMxtHL$J&HL$H$HKPL{XHHC` @ H$nHH{PHt &H2 HHH%Hk,H96fIHD$HLHxg"IHD$ HLHxN"IHD$@HLHx5"yHHD$`H$ Hx"[HL'#KHHD$0H$ Hx!HL̲IHLH'fAWAVAUATIp USH( B%HH( LH{HH# L$ Hl$`H5T HCP HCX HC` LH%L$ HL$pHLL# L$ Mz IGH$ HHx
-!LE1_$HD$`HHx HCXH;C`x HtL8HHCXL|$@H5R LHDŽ$ L-%H|$PLLL*# Lt$PM IFHLd$0Hxw L#HD$@HHx^ X LILx1 H
*rS H5R Mq HSH S LHL^H趱LHD$0HHx H&H5Ϻ LW) HD$@HLt$ Hx H$ H5R LLCh$H$ H$ LL# L$ M IFHHx_ LE1"HD$ HHxC DsiLt$H5_R LL#H$ H$ LL# L$ MtpID$HHx LE1D"HD$HHx Hb DcjHHH( H[]A\A]A^A_ @ L|$pH$ f D H$ A띐H$ Af D H+CPHH H H9H H$ H<$HH{PHCXIH)HH ItM8Ht HHLD$&H{PLD$HHMxtHL$:!HL$H$HKPL{XHHC` @ H$nHH{PHt H" HHH H['H96fIHD$HLHxW IHD$ HLHx> IHD$@HLHx% yHHD$`H$ Hx [HL KHHD$0H$ Hx HL輭IHoLH'fAWAVAUATIp USH( 2 HH LH{HH赻# L$ Hl$`H5T HCP HCX HC` LH L$ HL$pHLL# L$ Mz IGH$ HHxLE1O HD$`HHxHCXH;C`x HtL8HHCXL|$@H5R LHDŽ$ L H|$PLLL# Lt$PM IFHLd$0HxgL HD$@HHxNX LIyLs1 H
mS H5R Mq HCHR LHLNH覬LޫHD$0HHxHH5 LG$ HD$@HLt$ HxH$ H5R LLCh H$ H$ LL # L$ M IFHHxOLE1 HD$ HHx3DsiLt$H5OR LL H$ H$ LL# L$ MtpID$HHxLE14 HD$HHxH ^ DcjHHH( H[]A\A]A^A_ @ L|$pH$ f D H$ A띐H$ Af D H+CPHH H H9H H$ H<$HH{PHCXIH)HH ItM8Ht HHLD$!H{PLD$HHMxtHL$* HL$H$HKPL{XHHC` @ H$nHH{PHtH HHHHK"H96fIHD$HLHxGIHD$ HLHx.IHD$@HLHxyHHD$`H$ Hx[HLKHHD$0H$ HxHL謨IH_LH'fAWAVAUATIp USH( "HH LH{HH襶# L$ Hl$`H5ЂT HCP HCX HC` LHL$ HL$pHLL裾# L$ Mz IGH$ HHxLE1?HD$`HHxHCXH;C`x HtL8HHCXL|$@H5R LHDŽ$ L
H|$PLLL
-# Lt$PM IFHLd$0HxWLHD$@HHx>X LIiLn1 H
-hS H5R Mq H3HR LHL>H薧LΦHD$0HHxHH5 L7 HD$@HLt$ HxH$ H5R LLChH$ H$ LL# L$ M IFHHx?LE1HD$ HHx#DsiLt$H5?R LLH$ H$ LL}# L$ MtpID$HHxLE1$HD$HHxH|X DcjHHH( H[]A\A]A^A_ @ L|$pH$ f D H$ A띐H$ Af D H+CPHH H H9H H$ H<$HH{PHCXIH)HH ItM8Ht HHLD$ H{PLD$HHMxtHL$HL$H$HKPL{XHHC` @ H$nHH{PHtH HHHH; H96fIHD$HLHx7IHD$ HLHx IHD$@HLHxyHHD$`H$ Hx[HLKHHD$0H$ HxHL蜣IHOLH'fAWAVAUATIp USH( HH LH{HH蕱# L$ Hl$`H5}T HCP HCX HC` LHL$ HL$pHLL蓹# L$ Mz IGH$ HHxLE1/HD$`HHxHCXH;C`x HtL8HHCXL|$@H5R LHDŽ$ LH|$PLLL# Lt$PM IFHLd$0HxGLHD$@HHx.X LIYLi1 H
bS H5R Mq H#HR LHL.H膢L辡HD$0HHxHH5 L' HD$@HLt$ HxH$ H5R LLChH$ H$ LL# L$ M IFHHx/LE1HD$ HHxDsiLt$H5/R LLxH$ H$ LLm# L$ MtpID$HHxLE1HD$HHxHlT DcjHHH( H[]A\A]A^A_ @ L|$pH$ f D H$ A띐H$ Af D H+CPHH H H9H H$ H<$HH{PHCXIH)HH ItM8Ht HHLD$H{PLD$HHMxtHL$
-HL$H$HKPL{XHHC` @ H$nHH{PHtH HHHH+H96fIHD$HLHx'IHD$ HLHxIHD$@HLHx
yHHD$`H$ Hx
[HLKHHD$0H$ Hx
HL茞IH?LH'fAWAVAUATIp USH( HH謍 LH{HH腬# L$ Hl$`H5xT HCP HCX HC` LHL$ HL$pHLL胴# L$ Mz IGH$ HHx LE1 HD$`HHx HCXH;C`x HtL8HHCXL|$@H5R LHDŽ$ LH|$PLLL# Lt$PM IFHLd$0Hx7 LHD$@HHx X
-LIILd1 H
]S H5R Mq HHR LHL HvL讜HD$0HHx HH5 L HD$@HLt$ Hxz H$ H5R LLChH$ H$ LLв# L$ M IFHHx LE1tHD$ HHx DsiLt$H5 R LLhH$ H$ LL]# L$ MtpID$HHx
-LE1HD$HHx
-H\N DcjHHH( H[]A\A]A^A_ @ L|$pH$ f D H$ A띐H$ Af D H+CPHH H H9H H$ H<$ӸHH{PHCXIH)HH ItM8Ht HHLD$H{PLD$HHMxtHL$ HL$H$HKPL{XHHC` @ H$nHH{PHt H⨍ HHH HH96fIHD$HLHx IHD$ HLHxIHD$@HLHxyHHD$`H$ Hx[HL KHHD$0H$ HxHL|IH/LH'fAWAVAUATIp USH( HHا LH{HHu# L$ Hl$`H5sT HCP HCX HC` LH~ L$ HL$pHLLs# L$ Mz IGH$ HHxLE1 HD$`HHxHCXH;C`x HtL8HHCXL|$@H5R LHDŽ$ L H|$PLLLڮ# Lt$PM IFHLd$0Hx'L
-HD$@HHxX LI9Lq_1 H
XS H5R Mq HHR LHLHfL螗HD$0HHxH֨H5 L HD$@HLt$ HxjH$ H5qR LLCh
-H$ H$ LL# L$ M IFHHxLE1d HD$ HHxDsiLt$H5R LLX
-H$ H$ LLM# L$ MtpID$HHxLE1HD$HHxH J DcjHHH( H[]A\A]A^A_ @ L|$pH$ f D H$ A띐H$ Af D H+CPHH H H9H H$ H<$óHH{PHCXIH)HH ItM8Ht HHLD$y
H{PLD$HHMxtHL$HL$H$HKPL{XHHC` @ H$nHH{PHtHң HHHH H96fIHD$HLHxIHD$ HLHxIHD$@HLHxyHHD$`H$ Hx[HLKHHD$0H$ HxHLlIH LH'fAWAVAUATIp USH( HHȢ LH{HHe# L$ Hl$`H5nT HCP HCX HC` LHnL$ HL$pHLLc# L$ Mz IGH$ HHxLE1HD$`HHxHCXH;C`x HtL8HHCXL|$@H5rR LHDŽ$ LH|$PLLLʩ# Lt$PM IFHLd$0HxLoHD$@HHxX LI)LaZ1 H
SS H5{R Mq HHR LHLHVL莒HD$0HHxHƣH5o L
- HD$@HLt$ HxZH$ H5aR LLChH$ H$ LL谨# L$ M IFHHx LE1THD$ HHx DsiLt$H5R LLHH$ H$ LL=# L$ MtpID$HHx LE1HD$HHxs H|D DcjHHH( H[]A\A]A^A_ @ L|$pH$ f D H$ A띐H$ Af D H+CPHH H H9H H$ H<$賮HH{PHCXIH)HH ItM8Ht HHLD$iH{PLD$HHMxtHL$HL$H$HKPL{XHHC` @ H$nHH{PHtH HHHHH96fIHD$HLHxIHD$ HLHxIHD$@HLHxyHHD$`H$ Hx[HLKHHD$0H$ HxyHL\IHLH'fAWAVAUATIp USH( HH LH{HHU# L$ Hl$`H5iT HCP HCX HC` LH^L$ HL$pHLLS# L$ Mz IGH$ HHxLE1 HD$`HHx~HCXH;C`x HtL8HHCXL|$@H5bR LHDŽ$ LH|$PLLL躤# Lt$PM IFHLd$0HxL_ HD$@HHxX LILQU1 H
NS H5kR Mq HHR LHLHFL~HD$0HHx}HH5_ L HD$@HLt$ HxJH$ H5QR LLCh H$ H$ LL蠣# L$ M IFHHxLE1DHD$ HHxDsiLt$H5R LL8 H$ H$ LL-# L$ MtpID$HHxLE1HD$HHxcH? DcjHHH( H[]A\A]A^A_ @ L|$pH$ f D H$ A띐H$ Af D H+CPHH H H9H H$ H<$裩HH{PHCXIH)HH ItM8Ht HHLD$YH{PLD$HHMxtHL$HL$H$HKPL{XHHC` @ H$nHH{PHtH HHHsHH96fIHD$HLHxIHD$ HLHxIHD$@HLHxyHHD$`H$ Hx[HLKHHD$0H$ HxiHLLIHLH'fAWAVAUATIp USH( HH LH{HHE# L$ Hl$`H5pdT HCP HCX HC` LHNL$ HL$pHLLC# L$ Mz IGH$ HHxLE1HD$`HHxnHCXH;C`x HtL8HHCXL|$@H5RR LHDŽ$ LH|$PLLL誟# Lt$PM IFHLd$0HxLOHD$@HHxX LI LAP1 H
IS H5[R Mq HHR LHLމH6LnHD$0HHxmHH5O L HD$@HLt$ Hx:H$ H5AR LLChH$ H$ LL萞# L$ M IFHHxLE14HD$ HHxDsiLt$H5R LL(H$ H$ LL # L$ MtpID$HHxoLE1HD$HHxSH\: DcjHHH( H[]A\A]A^A_ @ L|$pH$ f D H$ A띐H$ Af D H+CPHH H H9H H$ H<$蓤HH{PHCXIH)HH ItM8Ht HHLD$IH{PLD$HHMxtHL$HL$H$HKPL{XHHC` @ H$nHH{PHtyH HHHcHH96fIHD$HLHxIHD$ HLHxIHD$@HLHxyHHD$`H$ Hx[HLKHHD$0H$ HxYHL<IHLH'fAWAVAUATIp USH( HH LH{HH5# L$ Hl$`H5`_T HCP HCX HC` LH>L$ HL$pHLL3# L$ Mz IGH$ HHxzLE1HD$`HHx^HCXH;C`x HtL8HHCXL|$@H5BR LHDŽ$ LH|$PLLL蚚# Lt$PM IFHLd$0HxL?HD$@HHxX LIL1K1 H
DS H5KR Mq HzHR LHL΄H&L^HD$0HHx]HH5? L HD$@HLt$ Hx*H$ H51R LLChH$ H$ LL耙# L$ M IFHHxLE1$HD$ HHxDsiLt$H5R LLH$ H$ LL
# L$ MtpID$HHx_LE1HD$HHxCH 6 DcjHHH( H[]A\A]A^A_ @ L|$pH$ f D H$ A띐H$ Af D H+CPHH H H9H H$ H<$胟HH{PHCXIH)HH ItM8Ht HHLD$9H{PLD$HHMxtHL$HL$H$HKPL{XHHC` @ H$nHH{PHtiH HHHSHH96fIHD$HLHxIHD$ HLHxIHD$@HLHxyHHD$`H$ Hxw[HLKHHD$0H$ HxIHL,IH߀LH'fAWAVAUATUHp SHX HH HH{HH%# L$ Ll$H5U HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0
-L$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ [# H|$p H$ H$ HHD$ID;L|$pL$ MtIGLHxLZH$ HtHH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ tH$ HtHD$LHxoH$ HtL|$0H5_R LHDŽ$ LH|$ LLH跔# L|$ L$ M IGLHl$@HxLTHD$0LHxX HILFE1 H
o?S H5`R Mq LtHR HLL~L;~Ls}HD$@LHxrHH5T L @ HD$0LL|$PHxBH$ H5IR LLChH$ H$ LH蘓# L$ M IGLHxLE1<HD$PLHxD{iL|$`H5R LL0H$ LHL*# H$ HtUHELHx}H1HD$`LHxbH* @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHt]H HHHGHHHD$`LHxHHD$PLHxHH$ HtHD$H$ HxH$ HlbHHL}MHHD$@Ht$HxBHHD$0H$ Hx'H+HLzLH{fH|$pHH=h3 AWAVAUATUHp SHX bHHH HH{HH# L$ Ll$H5
U HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 L$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ # H|$p H$ H$ HHD$ID6L|$pL$ MtIGLHxLH$ HtH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ tH$ HtHD$LHx/H$ Ht}L|$0H5 R LHDŽ$ LzH|$ LLHw# L|$ L$ M IGLHl$@HxLHD$0LHxX YHIΜL@1 H
/:S H5 R Mq LoHYR HLLyLxL3xHD$@LHx2HkH5 L @ HD$0LL|$PHxH$ H5 R LLChcH$ H$ LHX# L$ M IGLHxLE1HD$PLHxD{iL|$`H5R LLH$ LHL# H$ HtUHELHx=H1HD$`LHx"H# @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHt HF HHHHHHD$`LHxHHD$PLHxuHH$ HtHD$H$ HxHH$ HlbHHL=MHHD$@Ht$HxHHD$0H$ HxH+HLuLHrvfH|$pHH=c3 AWAVAUATUHp SHX "HH HH{HH襃# L$ Ll$H5EU HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 L$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ # H|$p H$ H$ HHD$IDd1L|$pL$ MtIGLHxLH$ HtH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ trH$ Ht`HD$LHxH$ Ht=L|$0H5R LHDŽ$ L:H|$ LLH7# L|$ L$ M IGLHl$@Hx|LHD$0LHxcX HI莗L:1 H
4S H5R Mq LXjHR HLLctLsLrHD$@LHxH+H5| L\ @ HD$0LL|$PHxH$ H5R LLCh#H$ H$ LH# L$ M IGLHxgLE1HD$PLHxKD{iL|$`H5gR LLH$ LHL誈# H$ HtUHELHxH1SHD$`LHxHk @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHtH HHHH?HHD$`LHxKHHD$PLHx5HH$ Ht~HD$H$ HxH$ HlRbHHLMHHD$@Ht$HxHHD$0H$ HxH+HLpLH2qfH|$pHH=~^3 AWAVAUATUHp SHX HH~ HH{HHe~# L$ Ll$H5U HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 JL$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ 蛘# H|$p H$ H$ HHD$ID$,L|$pL$ MtIGLHxBLH$ HtH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ t2H$ Ht HD$LHxH$ HtL|$0H5R LHDŽ$ LH|$ LLH# L|$ L$ M IGLHl$@Hx<LHD$0LHx#X HINL51 H
/S H5R Mq LeHټR HLL#oL{nLmHD$@LHxH~H5w L @ HD$0LL|$PHxH$ H5R LLChH$ H$ LH# L$ M IGLHx'LE1|HD$PLHx D{iL|$`H5'R LLpH$ LHLj# H$ HtUHELHxH1HD$`LHxH @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHtHz HHHHHHD$`LHx HHD$PLHxHH$ Ht>HD$H$ HxH$ HlbHHLMHHD$@Ht$HxHHD$0H$ HxgH+HL?kLHkfH|$pHH=>Y3 AWAVAUATUHp SHX HHy HH{HH%y# L$ Ll$H5T HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0
-L$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ [# H|$p H$ H$ HHD$ID&L|$pL$ MtIGLHxLZH$ HtHH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ tH$ HtHD$LHxoH$ HtL|$0H5_R LHDŽ$ LH|$ LLH# L|$ L$ M IGLHl$@HxLTHD$0LHxX HILF01 H
o*S H5`R Mq L_HR HLLiL;iLshHD$@LHxrHyH5Tr L @ HD$0LL|$PHxBH$ H5IR LLChH$ H$ LH~# L$ M IGLHxLE1<HD$PLHxD{iL|$`H5R LL0H$ LHL*~# H$ HtUHELHx}H1HD$`LHxbH+ @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHt]Hu HHHGHHHD$`LHxHHD$PLHxHH$ HtHD$H$ HxH$ HlbHHL}MHHD$@Ht$HxBHHD$0H$ Hx'H+HLeLHffH|$pHH=S3 AWAVAUATUHp SHX bHHHt HH{HHs# L$ Ll$H5T HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 L$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ # H|$p H$ H$ HHD$ID!L|$pL$ MtIGLHxLH$ HtH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ tH$ HtHD$LHx/H$ Ht}L|$0H5 R LHDŽ$ LzH|$ LLHwz# L|$ L$ M IGLHl$@HxLHD$0LHxX YHI·L+1 H
/%S H5 R Mq LZHYR HLLdLcL3cHD$@LHx2HktH5m L @ HD$0LL|$PHxH$ H5 R LLChcH$ H$ LHXy# L$ M IGLHxLE1HD$PLHxD{iL|$`H5R LLH$ LHLx# H$ HtUHELHx=H1HD$`LHx"H+ @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHt HFp HHHHHHD$`LHxHHD$PLHxuHH$ HtHD$H$ HxHH$ HlbHHL=MHHD$@Ht$HxHHD$0H$ HxH+HL`LHrafH|$pHH=N3 AWAVAUATUHp SHX "HHo HH{HHn# L$ Ll$H5ET HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 L$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ ۈ# H|$p H$ H$ HHD$IDd L|$pL$ MtIGLHxLH$ HtH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ trH$ Ht`HD$LHxH$ Ht=L|$0H5߬R LHDŽ$ L:H|$ LLH7u# L|$ L$ M IGLHl$@Hx|LHD$0LHxcX HI莂L%1 H
S H5R Mq LXUHR HLLc_L^L]HD$@LHxH+oH5g L\ @ HD$0LL|$PHxH$ H5ɫR LLCh#H$ H$ LHt# L$ M IGLHxgLE1HD$PLHxKD{iL|$`H5gR LLH$ LHLs# H$ HtUHELHxH1SHD$`LHxHk @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHtHk HHHH?HHD$`LHxKHHD$PLHx5HH$ Ht~HD$H$ HxH$ HlRbHHLMHHD$@Ht$HxHHD$0H$ HxH+HL[LH2\fH|$pHH=~I3 AWAVAUATUHp SHX HHi HH{HHei# L$ Ll$H5T HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 JL$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ 蛃# H|$p H$ H$ HHD$ID$L|$pL$ MtIGLHxBLH$ HtH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ t2H$ Ht HD$LHxH$ HtL|$0H5R LHDŽ$ LH|$ LLHo# L|$ L$ M IGLHl$@Hx<LHD$0LHx#X HIN}L 1 H
S H5R Mq LPH٧R HLL#ZL{YLXHD$@LHxHiH5b L @ HD$0LL|$PHxH$ H5R LLChH$ H$ LHn# L$ M IGLHx'LE1|HD$PLHx D{iL|$`H5'R LLpH$ LHLjn# H$ HtUHELHxH1HD$`LHxH @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHtHe HHHHHHD$`LHx HHD$PLHxHH$ Ht>HD$H$ HxH$ HlbHHLMHHD$@Ht$HxHHD$0H$ HxgH+HL?VLHVfH|$pHH=>D3 AWAVAUATUHp SHX HHd HH{HH%d# L$ Ll$H5T HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0
-L$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ [~# H|$p H$ H$ HHD$IDL|$pL$ MtIGLHxLZH$ HtHH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ tH$ HtHD$LHxoH$ HtL|$0H5_R LHDŽ$ LH|$ LLHj# L|$ L$ M IGLHl$@HxLTHD$0LHxX HIxLF1 H
oS H5`R Mq LJHR HLLTL;TLsSHD$@LHxrHdH5T] L @ HD$0LL|$PHxBH$ H5IR LLChH$ H$ LHi# L$ M IGLHxLE1<HD$PLHxD{iL|$`H5R LL0H$ LHL*i# H$ HtUHELHx}H1HD$`LHxbH+ @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHt]H` HHHGHHHD$`LHxHHD$PLHxHH$ HtHD$H$ HxH$ HlbHHL}MHHD$@Ht$HxBHHD$0H$ Hx'H+HLPLHQfH|$pHH=>3 AWAVAUATUHp SHX bHHH_ HH{HH^# L$ Ll$H5T HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 L$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ y# H|$p H$ H$ HHD$ID L|$pL$ MtIGLHx¾LH$ HtH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ tH$ HtHD$LHx/H$ Ht}L|$0H5 R LHDŽ$ LzH|$ LLHwe# L|$ L$ M IGLHl$@Hx輽LHD$0LHx製X YHIrL1 H
/S H5 R Mq LEHYR HLLOLNL3NHD$@LHx2Hk_H5X L @ HD$0LL|$PHxH$ H5 R LLChcH$ H$ LHXd# L$ M IGLHx觼LE1HD$PLHx苼D{iL|$`H5R LLH$ LHLc# H$ HtUHELHx=H1蓿HD$`LHx"H @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHt HF[ HHHHHHD$`LHx苻HHD$PLHxuHH$ Ht达HD$H$ HxHH$ Hl蒾bHHL=MHHD$@Ht$HxHHD$0H$ HxH+HLKLHrLfH|$pHH=93 AWAVAUATUHp SHX "HHZ HH{HHY# L$ Ll$H5ET HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 芾L$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ s# H|$p H$ H$ HHD$IDdL|$pL$ MtIGLHx肹LڼH$ HtȼH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ trH$ Ht`HD$LHxH$ Ht=L|$0H5ߗR LHDŽ$ L:H|$ LLH7`# L|$ L$ M IGLHl$@Hx|LԻHD$0LHxcX HImL1 H
-S H5R Mq LX@HR HLLcJLILHHD$@LHxH+ZH5R L\ @ HD$0LL|$PHx·H$ H5ɖR LLCh#H$ H$ LH_# L$ M IGLHxgLE1輺HD$PLHxKD{iL|$`H5gR LL谻H$ LHL^# H$ HtUHELHxH1SHD$`LHxH @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHtݹHV HHHǹH?HHD$`LHxKHHD$PLHx5HH$ Ht~HD$H$ HxH$ HlRbHHLMHHD$@Ht$HxµHHD$0H$ Hx觵H+HLFLH2GfH|$pHH=~43 AWAVAUATUHp SHX HHT HH{HHeT# L$ Ll$H5T HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 JL$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ n# H|$p H$ H$ HHD$ID$L|$pL$ MtIGLHxBL蚷H$ Ht舷H$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ t2H$ Ht HD$LHx诳H$ HtL|$0H5R LHDŽ$ LH|$ LLHZ# L|$ L$ M IGLHl$@Hx<L蔶HD$0LHx#X ٱHINhL 1 H
S H5R Mq L;HْR HLL#EL{DLCHD$@LHx貲HTH5M L @ HD$0LL|$PHx育H$ H5R LLChH$ H$ LHY# L$ M IGLHx'LE1|HD$PLHx D{iL|$`H5'R LLpH$ LHLjY# H$ HtUHELHx轱H1HD$`LHx袱H @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHt蝴HP HHH致HHHD$`LHx HHD$PLHxHH$ Ht>HD$H$ HxȰH$ HlbHHL轱MHHD$@Ht$Hx肰HHD$0H$ HxgH+HL?ALHAfH|$pHH=>/3 AWAVAUATUHp SHX 袳HHO HH{HH%O# L$ Ll$H5T HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0
-L$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ [i# H|$p H$ H$ HHD$IDL|$pL$ MtIGLHxLZH$ HtHH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ tH$ HtHD$LHxoH$ Ht轱L|$0H5_R LHDŽ$ L躲H|$ LLHU# L|$ L$ M IGLHl$@HxLTHD$0LHxX 虬HIcLF1 H
o S H5`R Mq L5HR HLL?L;?Ls>HD$@LHxrHOH5TH Lܶ @ HD$0LL|$PHxBH$ H5IR LLCh裱H$ H$ LHT# L$ M IGLHxLE1<HD$PLHxˬD{iL|$`H5R LL0H$ LHL*T# H$ HtUHELHx}H1ӯHD$`LHxbHk @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHt]HK HHHGH迵HHD$`LHx˫HHD$PLHx赫HH$ HtHD$H$ Hx舫H$ HlҮbHHL}MHHD$@Ht$HxBHHD$0H$ Hx'H+HL;LH<fH|$pHH=)3 AWAVAUATUHp SHX bHHHJ HH{HHI# L$ Ll$H5T HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 ʮL$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ d# H|$p H$ H$ HHD$IDL|$pL$ MtIGLHx©LH$ HtH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ t責H$ Ht蠬HD$LHx/H$ Ht}L|$0H5 R LHDŽ$ LzH|$ LLHwP# L|$ L$ M IGLHl$@Hx輨LHD$0LHx裨X YHI]L1 H
/R H5 R Mq L0HYR HLL:L9L39HD$@LHx2HkJH5C L蜱 @ HD$0LL|$PHxH$ H5 R LLChcH$ H$ LHXO# L$ M IGLHx觧LE1HD$PLHx苧D{iL|$`H5R LLH$ LHLN# H$ HtUHELHx=H1蓪HD$`LHx"H @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHt HFF HHHHHHD$`LHx苦HHD$PLHxuHH$ Ht辩HD$H$ HxHH$ Hl蒩bHHL=MHHD$@Ht$HxHHD$0H$ HxH+HL6LHr7fH|$pHH=$3 AWAVAUATUHp SHX "HHE HH{HHD# L$ Ll$H5ET HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 芩L$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ ^# H|$p H$ H$ HHD$IDdL|$pL$ MtIGLHx肤LڧH$ HtȧH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ trH$ Ht`HD$LHxH$ Ht=L|$0H5߂R LHDŽ$ L:H|$ LLH7K# L|$ L$ M IGLHl$@Hx|LԦHD$0LHxcX HIXL0 H
R H5R Mq LX+HR HLLc5L4L3HD$@LHxH+EH5= L\ @ HD$0LL|$PHx¢H$ H5ɁR LLCh#H$ H$ LHJ# L$ M IGLHxgLE1輥HD$PLHxKD{iL|$`H5gR LL谦H$ LHLI# H$ HtUHELHxH1SHD$`LHxH+ @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHtݤHA HHHǤH?HHD$`LHxKHHD$PLHx5HH$ Ht~HD$H$ HxH$ HlRbHHLMHHD$@Ht$Hx HHD$0H$ Hx觠H+HL1LH22fH|$pHH=~ 3 AWAVAUATUHp SHX HH? HH{HHe?# L$ Ll$H5T HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 JL$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ Y# H|$p H$ H$ HHD$ID$L|$pL$ MtIGLHxBL蚢H$ Ht舢H$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ t2H$ Ht HD$LHx诞H$ HtL|$0H5}R LHDŽ$ LH|$ LLHE# L|$ L$ M IGLHl$@Hx<L蔡HD$0LHx#X ٜHINSL0 H
R H5}R Mq L&H}R HLL#0L{/L.HD$@LHx貝H?H58 L @ HD$0LL|$PHx肝H$ H5|R LLChH$ H$ LHD# L$ M IGLHx'LE1|HD$PLHx D{iL|$`H5'|R LLpH$ LHLjD# H$ HtUHELHx轜H1HD$`LHx袜H+ٍ @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHt蝟H; HHH臟HHHD$`LHx HHD$PLHxHH$ Ht>HD$H$ HxțH$ HlbHHL轜MHHD$@Ht$Hx肛HHD$0H$ HxgH+HL?,LH,fH|$pHH=>3 AWAVAUATUHp SHX 袞HH: HH{HH%:# L$ Ll$H5žT HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0
-L$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ [T# H|$p H$ H$ HHD$IDL|$pL$ MtIGLHxLZH$ HtHH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ tH$ HtHD$LHxoH$ Ht轜L|$0H5_xR LHDŽ$ L躝H|$ LLH@# L|$ L$ M IGLHl$@HxLTHD$0LHxX 虗HINLF0 H
oR H5`xR Mq L HxR HLL*L;*Ls)HD$@LHxrH:H5T3 Lܡ @ HD$0LL|$PHxBH$ H5IwR LLCh補H$ H$ LH?# L$ M IGLHxLE1<HD$PLHx˗D{iL|$`H5vR LL0H$ LHL*?# H$ HtUHELHx}H1ӚHD$`LHxbHԍ @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHt]H6 HHHGH迠HHD$`LHx˖HHD$PLHx赖HH$ HtHD$H$ Hx舖H$ HlҙbHHL}MHHD$@Ht$HxBHHD$0H$ Hx'H+HL&LH'fH|$pHH=3 AWAVAUATUHp SHX bHHH5 HH{HH4# L$ Ll$H5T HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 ʙL$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ O# H|$p H$ H$ HHD$IDL|$pL$ MtIGLHxLH$ HtH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ t貗H$ Ht蠗HD$LHx/H$ Ht}L|$0H5 sR LHDŽ$ LzH|$ LLHw;# L|$ L$ M IGLHl$@Hx輓LHD$0LHx裓X YHIHL0 H
/R H5 sR Mq LHYsR HLL%L$L3$HD$@LHx2Hk5H5. L蜜 @ HD$0LL|$PHxH$ H5 rR LLChcH$ H$ LHX:# L$ M IGLHx角LE1HD$PLHx苒D{iL|$`H5qR LLH$ LHL9# H$ HtUHELHx=H1蓕HD$`LHx"H+Ս @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHt HF1 HHHHHHD$`LHx苑HHD$PLHxuHH$ Ht辔HD$H$ HxHH$ Hl蒔bHHL=MHHD$@Ht$HxHHD$0H$ HxH+HL!LHr"fH|$pHH=3 AWAVAUATUHp SHX "HH0 HH{HH/# L$ Ll$H5ET HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 芔L$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ I# H|$p H$ H$ HHD$IDdL|$pL$ MtIGLHx肏LڒH$ HtȒH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ trH$ Ht`HD$LHxH$ Ht=L|$0H5mR LHDŽ$ L:H|$ LLH76# L|$ L$ M IGLHl$@Hx|LԑHD$0LHxcX HICL0 H
R H5mR Mq LXHnR HLLc L L HD$@LHxH+0H5( L\ @ HD$0LL|$PHxH$ H5lR LLCh#H$ H$ LH5# L$ M IGLHxgLE1輐HD$PLHxKD{iL|$`H5glR LL谑H$ LHL4# H$ HtUHELHxH1SHD$`LHxHk͍ @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHtݏH, HHHǏH?HHD$`LHxKHHD$PLHx5HH$ Ht~HD$H$ HxH$ HlRbHHLMHHD$@Ht$HxHHD$0H$ Hx觋H+HL LH2 fH|$pHH=~
-3 AWAVAUATUHp SHX HH* HH{HHe*# L$ Ll$H5T HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 JL$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ D# H|$p H$ H$ HHD$ID$L|$pL$ MtIGLHxBL蚍H$ Ht舍H$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ t2H$ Ht HD$LHx诉H$ HtL|$0H5hR LHDŽ$ LH|$ LLH0# L|$ L$ M IGLHl$@Hx<L蔌HD$0LHx#X هHIN>L0 H
R H5hR Mq LHhR HLL#L{LHD$@LHx貈H*H5# L @ HD$0LL|$PHx肈H$ H5gR LLChH$ H$ LH/# L$ M IGLHx'LE1|HD$PLHx D{iL|$`H5'gR LLpH$ LHLj/# H$ HtUHELHx轇H1HD$`LHx袇H+ȍ @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHt蝊H& HHH臊HHHD$`LHx HHD$PLHxHH$ Ht>HD$H$ HxȆH$ HlbHHL轇MHHD$@Ht$Hx肆HHD$0H$ HxgH+HL?LHfH|$pHH=>3 AWAVAUATUHp SHX 袉HH% HH{HH%%# L$ Ll$H5ũT HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0
-L$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ [?# H|$p H$ H$ HHD$IDL|$pL$ MtIGLHxLZH$ HtHH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ tH$ HtHD$LHxoH$ Ht轇L|$0H5_cR LHDŽ$ L躈H|$ LLH+# L|$ L$ M IGLHl$@HxLTHD$0LHxX 虂HI9LF0 H
oR H5`cR Mq L HcR HLLL;LsHD$@LHxrH%H5T L܌ @ HD$0LL|$PHxBH$ H5IbR LLCh裇H$ H$ LH*# L$ M IGLHxLE1<HD$PLHx˂D{iL|$`H5aR LL0H$ LHL**# H$ HtUHELHx}H1ӅHD$`LHxbH @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHt]H! HHHGH迋HHD$`LHxˁHHD$PLHx赁HH$ HtHD$H$ Hx舁H$ Hl҄bHHL}MHHD$@Ht$HxBHHD$0H$ Hx'H+HLLHfH|$pHH=3 AWAVAUATUHp SHX bHHH HH{HH # L$ Ll$H5T HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 ʄL$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ :# H|$p H$ H$ HHD$IDL|$pL$ MtIGLHxLH$ HtH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ t貂H$ Ht蠂HD$LHx/H$ Ht}L|$0H5 ^R LHDŽ$ LzH|$ LLHw&# L|$ L$ M IGLHl$@Hx~LHD$0LHx~X Y}HI3L0 H
/R H5 ^R Mq LHY^R HLLLL3HD$@LHx2~Hk H5 L蜇 @ HD$0LL|$PHx~H$ H5 ]R LLChcH$ H$ LHX%# L$ M IGLHx}LE1HD$PLHx}D{iL|$`H5\R LLH$ LHL$# H$ HtUHELHx=}H1蓀HD$`LHx"}H @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHt HF HHHHHHD$`LHx|HHD$PLHxu|HH$ HtHD$H$ HxH|H$ HlbHHL=}MHHD$@Ht$Hx|HHD$0H$ Hx{H+HL LHr
fH|$pHH=3 AWAVAUATUHp SHX "HH HH{HH# L$ Ll$H5ET HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 L$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ 4# H|$p H$ H$ HHD$IDdL|$pL$ MtIGLHxzL}H$ Ht}H$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ tr}H$ Ht`}HD$LHxyH$ Ht=}L|$0H5XR LHDŽ$ L:~H|$ LLH7!# L|$ L$ M IGLHl$@Hx|yL|HD$0LHxcyX xHI.L0 H
R H5XR Mq LXHYR HLLc L
-L HD$@LHxxH+H5 L\ @ HD$0LL|$PHxxH$ H5WR LLCh#}H$ H$ LH # L$ M IGLHxgxLE1{HD$PLHxKxD{iL|$`H5gWR LL|H$ LHL # H$ HtUHELHxwH1S{HD$`LHxwH븍 @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHtzH HHHzH?HHD$`LHxKwHHD$PLHx5wHH$ Ht~zHD$H$ HxwH$ HlRzbHHLwMHHD$@Ht$HxvHHD$0H$ HxvH+HLLH2fH|$pHH=~3 AWAVAUATUHp SHX yHH HH{HHe# L$ Ll$H5T HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 JzL$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ /# H|$p H$ H$ HHD$ID$L|$pL$ MtIGLHxBuLxH$ HtxH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ t2xH$ Ht xHD$LHxtH$ HtwL|$0H5SR LHDŽ$ LxH|$ LLH# L|$ L$ M IGLHl$@Hx<tLwHD$0LHx#tX rHIN)L0 H
R H5SR Mq LHSR HLL#L{LHD$@LHxsHH5 L } @ HD$0LL|$PHxsH$ H5RR LLChwH$ H$ LH# L$ M IGLHx'sLE1|vHD$PLHx sD{iL|$`H5'RR LLpwH$ LHLj# H$ HtUHELHxrH1vHD$`LHxrH+ @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHtuH HHHuH{HHD$`LHx rHHD$PLHxqHH$ Ht>uHD$H$ HxqH$ HlubHHLrMHHD$@Ht$HxqHHD$0H$ HxgqH+HL?LHfH|$pHH=>3 AWAVAUATUHp SHX tHH HH{HH%# L$ Ll$H5ŔT HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0
-uL$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ [*# H|$p H$ H$ HHD$IDL|$pL$ MtIGLHxpLZsH$ HtHsH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ trH$ HtrHD$LHxooH$ HtrL|$0H5_NR LHDŽ$ LsH|$ LLH# L|$ L$ M IGLHl$@HxnLTrHD$0LHxnX mHI$LF0 H
oR H5`NR Mq LHNR HLL L; LsHD$@LHxrnHH5T Lw @ HD$0LL|$PHxBnH$ H5IMR LLChrH$ H$ LH# L$ M IGLHxmLE1<qHD$PLHxmD{iL|$`H5LR LL0rH$ LHL*# H$ HtUHELHx}mH1pHD$`LHxbmH+ @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHt]pH HHHGpHvHHD$`LHxlHHD$PLHxlHH$ HtoHD$H$ HxlH$ HlobHHL}mMHHD$@Ht$HxBlHHD$0H$ Hx'lH+HLLHfH|$pHH=3 AWAVAUATUHp SHX boHHH HH{HH
-# L$ Ll$H5T HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 oL$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ %# H|$p H$ H$ HHD$ID褸L|$pL$ MtIGLHxjLnH$ HtnH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ tmH$ HtmHD$LHx/jH$ Ht}mL|$0H5 IR LHDŽ$ LznH|$ LLHw# L|$ L$ M IGLHl$@HxiLmHD$0LHxiX YhHI L0 H
/R H5 IR Mq LHYIR HLLLL3HD$@LHx2iHk H5 Lr @ HD$0LL|$PHxiH$ H5 HR LLChcmH$ H$ LHX# L$ M IGLHxhLE1kHD$PLHxhD{iL|$`H5GR LLlH$ LHL# H$ HtUHELHx=hH1kHD$`LHx"hH맍 @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHt kHF HHHkHqHHD$`LHxgHHD$PLHxugHH$ HtjHD$H$ HxHgH$ HljbHHL=hMHHD$@Ht$HxgHHD$0H$ HxfH+HLLHrfH|$pHH=3 AWAVAUATUHp SHX "jHH HH{HH# L$ Ll$H5ET HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 jL$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ # H|$p H$ H$ HHD$IDdL|$pL$ MtIGLHxeLhH$ HthH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ trhH$ Ht`hHD$LHxdH$ Ht=hL|$0H5CR LHDŽ$ L:iH|$ LLH7 # L|$ L$ M IGLHl$@Hx|dLgHD$0LHxcdX cHILƼ0 H
R H5CR Mq LXHDR HLLcLLHD$@LHxcH+H5 L\m @ HD$0LL|$PHxcH$ H5BR LLCh#hH$ H$ LH # L$ M IGLHxgcLE1fHD$PLHxKcD{iL|$`H5gBR LLgH$ LHL
-# H$ HtUHELHxbH1SfHD$`LHxbH @kjHHHX H[]A\A]A^A_ H$ @f H$ A2HH{PHteH HHHeH?lHHD$`LHxKbHHD$PLHx5bHH$ Ht~eHD$H$ HxbH$ HlRebHHLbMHHD$@Ht$HxaHHD$0H$ HxaH+HLLH2fH|$pHH=~3 AWAVAUATUHp SHX dHH HH{HHe # L$ Ll$H5T HCP HCX HC` LLHDŽ$ HDŽ$( HDŽ$0 JeL$ H|$pLHHDŽ$ HDŽ$ LHDŽ$ # H|$p H$ H$ HHD$ID$L|$pL$ MtIGLHxB`LcH$ HtcH$ H{PHCPH$ HHDŽ$ HCXH$ HDŽ$ HC`HDŽ$ t2cH$ Ht cHD$LHx_H$ HtbL|$0H5>R LHDŽ$ LcH|$ LLH# L|$ L$ M IGLHl$@Hx<_LbHD$0LHx#_X ]HINL膷0 H
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ymn Hjrn H=nn Ѹ SHH0HtH{HtH;Ht [f D [f D SHH Ht跲H Ht覲H{pHt蘲H{XHt芲H{0Ht|H{HtnH;Ht[`[f. @ AVIAUATIUHH(dH%( HD$1 A oE IHE)$HtH=Î tt@X 5HLLHHF I,$Hl$Ht#L-|Î MtGEtF HD$dH3%( ukH(L]A\A]A^Ð@f. EPUuHE HPMt#E uHE HP E PU P I IľX HXn H|$HtDL茶f. f AVIAUATIUHH(dH%( HD$1#!A oE IHE)$HtH=V tt@X հHLLHH1F I,$Hl$Ht#L- MtGEtF HD$dH3%( ukH(L]A\A]A^Ð@f. EPUuHE HPMt#E uHE HP E PU IJ IJ AVIAUATIUHH(dH%( HD$1A oE IHE)$HtH= tt@X 蕯HLLHHF I,$Hl$Ht#L- MtGEtF HD$dH3%( ukH(L]A\A]A^Ð@f. EPUuHE HPMt#E uHE HP E PU 谯 I IľX HUn H|$HtLf. f AVIAUATIUHH(dH%( HD$1 A oE IHE)$HtH= tt@X 5HLLHHF I,$Hl$Ht#L-| MtGEtF HD$dH3%( ukH(L]A\A]A^Ð@f. EPUuHE HPMt#E uHE HP E PU P I IľX HTn H|$HtDL茲f. f AVIAUATIUHH(dH%( HD$1A oE IHE)$HtH=V tt@X լHLLHH1F I,$Hl$Ht#L- MtGEtF HD$dH3%( ukH(L]A\A]A^Ð@f. EPUuHE HPMt#E uHE HP E PU I IľX H#Sn H|$HtL,f. f AVIAUATIUHH(dH%( HD$1A oE IHE)$HtH= tt@X uHLLHHF I,$Hl$Ht#L- MtGEtF HD$dH3%( ukH(L]A\A]A^Ð@f. EPUuHE HPMt#E uHE HP E PU 萫 IE IlE AVIAUATIUHH(dH%( HD$1A oE IHE)$HtH= tt@X 5HLLHHF I,$Hl$Ht#L-| MtGEtF HD$dH3%( ukH(L]A\A]A^Ð@f. EPUuHE HPMt#E uHE HP E PU P I IľX HPn H|$HtDL茮f. f AVIAUATIUHH(dH%( HD$1#A oE IHE)$HtH=V tt@X ըHLLHH1F I,$Hl$Ht#L- MtGEtF HD$dH3%( ukH(L]A\A]A^Ð@f. EPUuHE HPMt#E uHE HP E PU I C IB AVIAUATIUHH(dH%( HD$13A oE IHE)$HtH= tt@X 蕧HLLHHF I,$Hl$Ht#L-ܸ MtGEtF HD$dH3%( ukH(L]A\A]A^Ð@f. EPUuHE HPMt#E uHE HP E PU 谧 I IľX HMn H|$HtLf. f AVIAUATIUHH(dH%( HD$1CA oE IHE)$HtH= tt@X 5HLLHHF I,$Hl$Ht#L-| MtGEtF HD$dH3%( ukH(L]A\A]A^Ð@f. EPUuHE HPMt#E uHE HP E PU P I IľX HLn H|$HtDL茪f. f AVIAUATIUHH(dH%( HD$1A oE IHE)$HtH=V tt@X դHLLHH1F I,$Hl$Ht#L- MtGEtF HD$dH3%( ukH(L]A\A]A^Ð@f. EPUuHE HPMt#E uHE HP E PU I IľX H#Kn H|$HtL,f. f AVIAUATIUHH(dH%( HD$1
+A oE IHE)$HtH= tt@X uHLLHHF I,$Hl$Ht#L- MtGEtF HD$dH3%( ukH(L]A\A]A^Ð@f. EPUuHE HPMt#E uHE HP E PU 萣 I IľX HIn H|$HtĻf. f AVIAUATIUHH(dH%( HD$1A oE IHE)$HtH= tt@X HLLHHqF I,$Hl$Ht#L-\ MtGEtF HD$dH3%( ukH(L]A\A]A^Ð@f. EPUuHE HPMt#E uHE HP E PU 0 I IľX HcHn H|$Ht$Llf. f AVIAUATIUHH(dH%( HD$1#A oE IHE)$HtH=6 tt@X 赠HLLHHF I,$Hl$Ht#L- MtGEtF HD$dH3%( ukH(L]A\A]A^Ð@f. EPUuHE HPMt#E uHE HP E PU Р I IľX HGn H|$HtL f. f AVIAUATIUHH(dH%( HD$1A oE IHE)$HtH=ְ tt@X UHLLHHF I,$Hl$Ht#L- MtGEtF HD$dH3%( ukH(L]A\A]A^Ð@f. EPUuHE HPMt#E uHE HP E PU p I IľX HEn H|$HtdL謣f. f AVIAUATIUHH(dH%( HD$1 A oE IHE)$HtH=v tt@X HLLHHQF I,$Hl$Ht#L-< MtGEtF HD$dH3%( ukH(L]A\A]A^Ð@f. EPUuHE HPMt#E uHE HP E PU I IľX HCDn H|$HtLLf. f AVIAUATIUHH(dH%( HD$1C A oE IHE)$HtH= tt@X 蕜HLLHHF I,$Hl$Ht#L-ܭ MtGEtF HD$dH3%( ukH(L]A\A]A^Ð@f. EPUuHE HPMt#E uHE HP E PU 谜 I IľX HBn H|$HtLf. fH7 AVAUATL( UHSH8 Ht HsLHH[HuH L Ht HsL轠HH[HuH Ht˚H Ht躚 L% H]`LuPHtMH=A HsLILI}(H[HtjIE HxL9) LQHuH]0Lu HtMH=竎 HsLII}(H[HtIE HxL9 LHuHEHxL9q HEHxL90 HE HxL9 [Hp ]A\A]A^An f. HsLIaI}(H[HtIE HxL9^ LfHu @ HsLII}(H[Ht7IE HxL96 L Hu" @ H L HDH= L%ꎎ tHHsLI衝I}(H[Ht还IE HxL9 L覘Hu @ HsLIYI}(H[HtwIE HxL9v L^Hu @ P?95 @ P @ PJH D PJHڗ D H=x P諗f D H=H tfPf. H= t&PsOif. PJH D PJH D PJHZfPPF @ PJHyږo D ATIp USH ~Y dH%( H$ 1H 蕎 HD$P)D$`HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B/A HD$pr;A HD$x9A H$ ;>A H$ ^.A H$ <A H$ T8A H$ 6A H$ 3A H$ M5A H$ P?A fX H$ HD$@ )D$0蜕foL$pHPXH$ HD$0fo$ HT$@fo$ HHPfo$ fo$ PX `0h@HT$8.A HD$pq:A HD$x8A H$ :=A H$ ]-A H$ ;A H$ S7A H$ 5A H$ 2A H$ L4A H$ O>A fX H$ HD$ )D$蛔fot$pHPXfo$ H|$H$ HT$ H5mp fo$ 0fo$ HHPHL$0xfo$ HT$HT$p x0H@HD$NC HH5o (C HH5ɣu +C HH H
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uÎ HBHB(H5kp HB8HB@HBXHBhHBpH H H H0 H@ HH BH]Î hLC HH5v I&C HH5u )C HH H5Tn @
,C H8H]C HH ID$ I$HD$ HtuIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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+HD$ HtH|$ HHD$8dH3%( u$H@L[]A\ ID$( H|$Hu֏ H* H+ H+f ATIp USH@~ dH%( HD$81Hͼ HD$)D$ 臎HHE . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B*A HH|$H5fgp MC HH5RDo O"C HH5u %C HH H5Pn @
(C H8HYC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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&C H8HWC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu H' H' H'f ATIp USH@~ dH%( HD$81H HD$)D$ 臊HHE . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B"2A HH|$H5fcp IC HH5o O C HH5u !C HH H5Ln @
$C H8HUC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu։ H% H& H&f ATIp USH@~ dH%( HD$81H HD$)D$ 臈HHE~ . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B!A HH|$H5fap GC HH5u O C HH5u C HH H5Jn @
"C H8HSC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Huև HS$ Ho$ Hr$f ATIp USH@~ dH%( HD$81Hm HD$)D$ 臆HHE| . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B A HH|$H5f_p EC HH5o OC HH5u C HH H5Hn @ C H8HQC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Huօ H" H" H"f ATIp USH@~ dH%( HD$81H} HD$)D$ 臄HHEz . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B A HH|$H5f]p CC HH5o OC HH5u C HH H5Fn @ C H8HOC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Huփ H! H ! H"!f ATIp USH@~ dH%( HD$81H HD$)D$ 臂HHEx . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BA HH|$H5f[p AC HH5o OC HH5u C HH H5Dn @ C H8HMC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Huց H[ Hw Hz f ATIp USH@~ dH%( HD$81H HD$)D$ 臀HHEv . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BA HH|$H5fYp ?C HH5o OC HH5u C HH H5Bn @ C H8HKC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu H H H f ATIp USH@~ dH%( HD$81H} HD$)D$ ~HHEt . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BA HH|$H5fWp =C HH5mo OC HH5u C HH H5@n @
C H8HIC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu} H H' H* f ATIp USH@~ dH%( HD$81HM{ HD$)D$ |HHEr . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BB!A HH|$H5fUp ;C HH5u OC HH5u C HH H5>n @
C H8HGC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu{ Hc H Hf ATIp USH@~ ~ dH%( HD$81H-y HD$)D$ zHHEp . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BB A HH|$H5fSp 9C HH5 o OC HH5u C HH H5<n @ C H8HEC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Huy H H Hf ATIp USH@~ | dH%( HD$81Hݢw HD$)D$ xHHEn . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BA HH|$H5fQp 7C HH5o O C HH5u C HH H5:n @ C H8HCC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Huw H H/ H2f ATIp USH@~ z dH%( HD$81H u HD$)D$ vHHEl . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BA HH|$H5fOp 5C HH5u O
+C HH5u
C HH H58n @
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+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Huu Hk H Hf ATIp USH@~ x dH%( HD$81H s HD$)D$ tHHEj . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BBA HH|$H5fMp 3C HH5tn OC HH5u C HH H56n @
C H8H?C HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hus H H Hf ATIp USH@~ v dH%( HD$81H q HD$)D$ rHHEh . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BA HH|$H5fKp 1C HH5o OC HH5u C HH H54n @
C H8H=C HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Huq H H7 H:f ATIp USH@~ t dH%( HD$81H o HD$)D$ pHHEf . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B A HH|$H5fIp /C HH5pn OC HH5u C HH H52n @
+C H8H;C HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Huo Hs H Hf ATIp USH@~ r dH%( HD$81H=m HD$)D$ nHHEd . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BRA HH|$H5fGp -C HH5n OC HH5}u C HH H50n @ C H8H9C HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hum H H Hf ATIp USH@~ p dH%( HD$81H k HD$)D$ lHHEb . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B A HH|$H5fEp +C HH5o O C HH5{u C HH H5.n @
C H8H7C HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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C H8H5C HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hui H{ H H f ATIp USH@~ l dH%( HD$81H g HD$)D$ hHHE^ . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BA HH|$H5fAp 'C HH5o OB HH5wu B HH H5*n @
C H8H3C HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hug H H H f ATIp USH@~ j dH%( HD$81He HD$)D$ fHHE\ . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B A HH|$H5f?p %C HH5n OB HH5uu B HH H5(n @ C H8H1C HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hue H+ HG HJf ATIp USH@~ h dH%( HD$81H c HD$)D$ dHHEZ . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR@ HH|$H5f=p #C HH5o OB HH5su B HH H5&n @
B H8H/C HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Huc H H Hf ATIp USH@~ f dH%( HD$81Ha HD$)D$ bHHEX . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BA HH|$H5f;p !C HH5pn OB HH5qu B HH H5$n @ B H8H-C HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hua H H Hf ATIp USH@~ d dH%( HD$81H=t _ HD$)D$ `HHEV . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B"A HH|$H5f9p C HH5ʿo OB HH5ou B HH H5"n @ B H8H+C HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu_ H3 HO HRf ATIp USH@~ b dH%( HD$81H] HD$)D$ ^HHET . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5f7p C HH5ӽo OB HH5mu B HH H5 n @ B H8H)C HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu] H H Hf ATIp USH@~ ` dH%( HD$81H [ HD$)D$ \HHER . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5f5p C HH5tn OB HH5ku B HH H5 n @
B H8H'C HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+辿HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu[ H H H f ATIp USH@~ ^ dH%( HD$81H Y HD$)D$ ZHHEP . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5f3p C HH5o OB HH5iu B HH H5 n @
B H8H%C HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+辽HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$HuY H; HW HZf ATIp USH@~ \ dH%( HD$81H W HD$)D$ XHHEN . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5f1p C HH5ݷo OB HH5gu B HH H5n @ B H8H#C HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+辻HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$HuW H H Hf ATIp USH@~ Z dH%( HD$81HdU HD$)D$ VHHEL . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR@ HH|$H5f/p C HH5n OB HH5eu B HH H5n @ B H8H!C HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+边HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$HuU H H H
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+辷HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$HuS HC H_ Hbf ATIp USH@~ V dH%( HD$81H}Q HD$)D$ RHHEH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5f+p C HH5o OB HH5au B HH H5n @ B H8H C HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+辵HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$HuQ H H Hf ATIp USH@~ T dH%( HD$81H O HD$)D$ PHHEF . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BB@ HH|$H5f)p C HH5o OB HH5_u B HH H5n @
B H8HC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+辳HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$HuO H H Hf ATIp USH@~ R dH%( HD$81H]tM HD$)D$ NHHED . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5f'p
C HH5\t OB HH5]u B HH H5n @
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+辱HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$HuM HK Hg Hjf ATIp USH@~ P dH%( HD$81H[K HD$)D$ LHHEB . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5f%p C HH5u OB HH5[u B HH H5n @ B H8HC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+辯HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$HuK H H Hf ATIp USH@~ N dH%( HD$81H \I HD$)D$ JHHE@ . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5f#p C HH5n OB HH5Yu B HH H5 n @ B H8HC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+辭HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$HuI H H Hf ATIp USH@~ L dH%( HD$81HqG HD$)D$ HHHE> . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5f!p C HH5o OB HH5Wu B HH H5
+n @ B H8HC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+辫HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$HuG HS Ho Hrf ATIp USH@~ J dH%( HD$81HWE HD$)D$ FHHE< . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5f p C HH5ۺu OB HH5Uu B HH H5n @ B H8HC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+辩HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$HuE H H Hf ATIp USH@~ H dH%( HD$81H]C HD$)D$ DHHE: . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5f p C HH5n OB HH5Su B HH H5n @ B H8HC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+辧HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$HuC H H H"f ATIp USH@~ F dH%( HD$81H A HD$)D$ BHHE8 . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5fp C HH5o OB HH5Qu B HH H5n @
B H8H
C HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+辥HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$HuA H[ Hw Hzf ATIp USH@~ D dH%( HD$81H ? HD$)D$ @HHE6 . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR@ HH|$H5fp B HH5o OB HH5Ou B HH H5n @
B H8H C HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+辣HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu? H H Hf ATIp USH@~ B dH%( HD$81HmN= HD$)D$ >HHE4 . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BB@ HH|$H5fp B HH5۲u OB HH5Mu B HH H5 n @ B H8H C HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+辡HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu= H H' H*f ATIp USH@~ @ dH%( HD$81H ; HD$)D$ <HHE2 . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5fp B HH5ƛo OB HH5Ku B HH H5m @
B H8HC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+辟HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu; Hc H Hf ATIp USH@~ > dH%( HD$81H 9 HD$)D$ :HHE0 . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5fp B HH5o OB HH5Iu B HH H5m @
B H8HC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+辝HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu9 H H Hf ATIp USH@~ < dH%( HD$81HB7 HD$)D$ 8HHE. . HHT$Hp1HfHnH2H|$H flH
i HBHB(H5p HB8HB@HBXHBhHBpH H H H0 H@ HH BH}i hB HH5Wv IB HH5Gu B HH H H5m HHB H8HC HH ID$ I$HD$ HtvIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+豛HD$ HtH|$ HHD$8dH3%( u%H@L[]A\ @ ID$( H|$Hu7 H H' H*f. ATIp USH@~ : dH%( HD$81H 5 HD$)D$ w6HH5, . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B2@ HH|$H5Vp B HH5ݕo ?B HH5Eu B HH H5m @
B H8HC HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+讙HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu5 H[ Hw Hzf ATIp USH@~ 8 dH%( HD$81Hs 3 HD$)D$ w4HH5* . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5V
p B HH5o ?B HH5Cu B HH H5m @
B H8HB HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+讗HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu3 H H Hf ATIp USH@~ 6 dH%( HD$81H 1 HD$)D$ w2HH5( . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR@ HH|$H5V p B HH5o ?B HH5Au B HH H5m @
B H8HB HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+讕HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu1 H H' H*f ATIp USH@~ 4 dH%( HD$81H
w / HD$)D$ w0HH5& . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5V p B HH5o ?B HH5?u B HH H5m @
B H8HB HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+讓HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu/ Hc H Hf ATIp USH@~ 2 dH%( HD$81H
- HD$)D$ w.HH5$ . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B2@ HH|$H5Vp B HH5Ѣu ?B HH5=u B HH H5m @
B H8HB HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+讑HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu- H H Hf ATIp USH@~ 0 dH%( HD$81H U+ HD$)D$ w,HH5" . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5Vp B HH5o ?B HH5;u B HH H5m @ B H8HB HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+讏HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu+ H H/ H2f ATIp USH@~ . dH%( HD$81HM) HD$)D$ w*HH5 . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B2@ HH|$H5Vp B HH5`n ?B HH59u B HH H5m @ B H8HB HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+讍HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu) Hk H Hf ATIp USH@~ , dH%( HD$81H ' HD$)D$ w(HH5 . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5Vp B HH5o ?B HH57u 耿B HH H5m @
B H8HB HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+讋HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu' H H Hf ATIp USH@~ * dH%( HD$81H/% HD$)D$ w&HH5 . HHT$Hp1HfHnH2H|$H flH
eX HBHB(H5o HB8HB@HBXHBhHBpH H H H0 H@ HH BH-X XB HH5cn 9B HH5}5u zB HH H H5m HHB H8HB HH ID$ I$HD$ HtvIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+衉HD$ HtH|$ HHD$8dH3%( u%H@L[]A\ @ ID$( H|$Hu% H H/ H2f. ATIp USH@~' dH%( HD$81H6# HD$)D$ g$HH% . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B"@ HH|$H5Fo B HH5n /B HH5s3u pB HH H5m @ 觾B H8HB HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+螇HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu# Hc H Hf ATIp USH@~% dH%( HD$81H ! HD$)D$ g"HH% . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B袻@ HH|$H5Fo B HH5܁o /B HH5s1u pB HH H5m @
觼B H8HB HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+螅HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu! H H Hf ATIp USH@~# dH%( HD$81H-J HD$)D$ g HH% . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B袹@ HH|$H5Fo B HH5o /B HH5s/u pB HH H5m @ 觺B H8HB HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+螃HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu H H/ H2f ATIp USH@~! dH%( HD$81H) HD$)D$ g HH% . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bb@ HH|$H5Fo B HH5}o /B HH5s-u pB HH H5m @ 觸B H8HB HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+螁HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu Hk H Hf ATIp USH@~ dH%( HD$81H+ HD$)D$ g HH% . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5Fo B HH5{o /B HH5s+u pB HH H5m @ 觶B H8HB HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu H H Hf ATIp USH@~ dH%( HD$81H~ HD$)D$ gHH% . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B袳@ HH|$H5Fo B HH5u /B HH5s)u pB HH H5m @
觴B H8HB HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+}HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu H H7 H:f ATIp USH@~ dH%( HD$81H+ HD$)D$ gHH% . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B費@ HH|$H5Fo B HH5ާn /B HH5s'u pB HH H5m @ 觲B H8HB HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+{HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu Hs H Hf ATIp USH@~ dH%( HD$81H& HD$)D$ gHH% . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B2@ HH|$H5Fo B HH5u /B HH5s%u pB HH H5m @ 觰B H8HB HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+yHD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu H H Hf ATIp USH@~ dH%( HD$81H HD$)D$ gHH%
+ . HHT$Hp1HfHnH2H|$H flH
UE HBHB(H5o HB8HB@HBXHBhHBpH H H H0 H@ HH BH=E HB HH52v )B HH5m#u jB HH H H5m HH蚮B H8HB HH ID$ I$HD$ HtvIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+wHD$ HtH|$ HHD$8dH3%( u%H@L[]A\ @ ID$( H|$Hu H H7 H:f. ATIp USH@~ dH%( HD$81H
@~ HD$)D$ WHH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B蒫@ HH|$H56o B HH5qo B HH5c!u `B HH H5m @ 藬B H8HB HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+uHD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu Hk H Hf ATIp USH@~ dH%( HD$81H
~ HD$)D$ WHH . HHT$Hp1HfHnH2H|$H flH
@ HBHB(H5yo HB8HB@HBXHBhHBpH H H H0 H@ HH BH@ 8B HH5˖u B HH5] u ZB HH H H5m HH芪B H8HB HH ID$ I$HD$ HtvIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+sHD$ HtH|$ HHD$8dH3%( u%H@L[]A\ @ ID$( H|$Hu H H Hf. ATIp USH@~ dH%( HD$81H]q n
HD$)D$ GHH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5&o ~B HH5u B HH5S u PB HH H5m @
臨B H8H|B HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+~qHD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu
H H' H*f ATIp USH@~ dH%( HD$81H3n HD$)D$ G HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5&o ~B HH5qko B HH5Su PB HH H5m @ 臦B H8H|B HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+~oHD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu Hc H H邽f ATIp USH@~
dH%( HD$81HMn HD$)D$ G
+HH . HHT$Hp1HfHnH2H|$H flH
: HBHB(H5io HB8HB@HBXHBhHBpH H H H0 H@ HH BH: (B HH5u B HH5Mu JB HH H H5m HHzB H8HoB HH ID$ I$HD$ HtvIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+qmHD$ HtH|$ HHD$8dH3%( u%H@L[]A\ @ ID$( H|$Hu H鳻 Hϻ Hһf. ATIp USH@~ dH%( HD$81HMV ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Br@ HH|$H5o nB HH5vgo B HH5Cu @B HH H H5m HHpB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gkHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H H H"f ATIp USH@~ dH%( HD$81H͂ ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Br@ HH|$H5o nB HH5${n B HH5Cu @B HH H H5m HHpB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+giHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H[ Hw Hzf ATIp USH@~ dH%( HD$81HMs ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B肧@ HH|$H5o nB HH5Xco B HH5Cu @B HH H H5m HHpB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+ggHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H鳶 H϶ HҶf ATIp USH@~ dH%( HD$81H q^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Br@ HH|$H5o nB HH5ao B HH5Cu @B HH H H5m HHpB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+geHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H H' H*f ATIp USH@~ dH%( HD$81H=p^ HD$)D$ 7 HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR@ HH|$H5o nB HH5n B HH5Cu @B HH H H5m HHpB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gcHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu Hc H H邳f ATIp USH@~ dH%( HD$81HMy ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Br@ HH|$H5o nB HH5]o B HH5C
u @B HH H H5m HHpB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gaHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H黱 Hױ Hڱf ATIp USH@~ dH%( HD$81Hl ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bң@ HH|$H5o nB HH5X[o B HH5C u @B HH H H5m HHpB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g_HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H H/ H2f ATIp USH@~ dH%( HD$81HM< ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5o nB HH5XYo B HH5C u @B HH H H5m HHpB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g]HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu Hk H釮 H銮f ATIp USH@~ dH%( HD$81HM^ ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bb@ HH|$H5o nB HH5XWo B HH5Cu @B HH H H5m HHpB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g[HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu Hì H߬ Hf ATIp USH@~ dH%( HD$81H- ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5o nB HH5 kn B HH5Cu @B HH H
H5m HHpB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gYHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H H7 H:f ATIp USH@~ dH%( HD$81Hb^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Br@ HH|$H5o nB HH5n B HH5Cu @B HH H H5m HHpB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gWHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu Hs H鏩 H钩f ATIp USH@~ dH%( HD$81H
^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Br@ HH|$H5o nB HH5 gn B HH5Cu @B HH H H5m HHpB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gUHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H˧ H Hf ATIp USH@~ dH%( HD$81Hi ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5o nB HH5vOo B HH5Ct @B HH H H5m HHpB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gSHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H# H? HBf ATIp USH@~ dH%( HD$81H}^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Br@ HH|$H5o nB HH5 \t B HH5Ct @B HH H H5m HHpB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gQHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H{ H闤 H隤f ATIp USH@~ dH%( HD$81HML ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5o nB HH5`u B HH5Ct @B HH H H5m HHpB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gOHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu HӢ H Hf ATIp USH@~ dH%( HD$81H* ^ HD$)D$ 7HHߍ . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5o nB HH5XIo }B HH5Ct @B HH H H5m HHpB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gMHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H+ HG HJf ATIp USH@~ dH%( HD$81H^ HD$)D$ 7HHݍ . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5o nB HH5 ]n {B HH5Ct @B HH H H5m HHpB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gKHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H郟 H韟 H颟f ATIp USH@~ dH%( HD$81H^ HD$)D$ 7HHۍ . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5o nB HH5Zu yB HH5Ct @}B HH H H5m HHpB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gIHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H H Hf ATIp USH@~ dH%( HD$81H-^ HD$)D$ 7HHٍ . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B|@ HH|$H5o nB HH5 Yn wB HH5Ct @{B HH H H5m HHp~B H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gGHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H3 HO HRf ATIp USH@~ dH%( HD$81H]^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Br{@ HH|$H5o nB HH5qn uB HH5Ct @yB HH H H5m HHp|B H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gEHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H鋚 H駚 H骚f ATIp USH@~ dH%( HD$81HM^ߍ HD$)D$ 7HHՍ . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B肃@ HH|$H5o nB HH5on sB HH5Ct @wB HH H
H5m HHpzB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gCHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H H Hf ATIp USH@~ dH%( HD$81H< ^ݍ HD$)D$ 7HHӍ . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Brw@ HH|$H5o nB HH5Ru qB HH5Ct @uB HH H H5m HHpxB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gAHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H; HW HZf ATIp USH@~ߍ dH%( HD$81H
^ۍ HD$)D$ 7HHэ . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B@ HH|$H5o nB HH5kn oB HH5Ct @sB HH H H5m HHpvB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g?HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H铕 H鯕 H鲕f ATIp USH@~ݍ dH%( HD$81H^ٍ HD$)D$ 7HHύ . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B~@ HH|$H5o nB HH5in mB HH5Ct @qB HH H H5m HHptB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g=HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H H H
+f ATIp USH@~ۍ dH%( HD$81HMY ^ HD$)D$ 7HH͍ . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Brq@ HH|$H5o nB HH5Lu kB HH5Ct @oB HH H H5m HHprB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g;HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu HC H_ Hbf ATIp USH@~ٍ dH%( HD$81HM] ^Ս HD$)D$ 7HHˍ . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bz@ HH|$H5o nB HH5$Kn iB HH5Ct @mB HH H H5m HHppB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g9HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H雐 H鷐 H麐f ATIp USH@~ dH%( HD$81H^Ӎ HD$)D$ 7HHɍ . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bl@ HH|$H5o nB HH5cn gB HH5Ct @kB HH H
H5m HHpnB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g7HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H H Hf ATIp USH@~Ս dH%( HD$81H͉^э HD$)D$ 7HHǍ . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bu@ HH|$H5o nB HH5Fu eB HH5Ct @iB HH H
H5m HHplB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g5HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu HK Hg Hjf ATIp USH@~Ӎ dH%( HD$81H^ύ HD$)D$ 7HHō . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bs@ HH|$H5o nB HH5Du cB HH5Ct @gB HH H H5m HHpjB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g3HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H飋 H鿋 Hf ATIp USH@~э dH%( HD$81HM^͍ HD$)D$ 7HHÍ . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Brg@ HH|$H5o nB HH5Bu aB HH5Ct @eB HH H H5m HHphB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g1HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H H Hf ATIp USH@~ύ dH%( HD$81H^ˍ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bd@ HH|$H5o nB HH5@u _B HH5Ct @cB HH H H5m HHpfB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g/HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu HS Ho Hrf ATIp USH@~͍ dH%( HD$81Hm7^ɍ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Brc@ HH|$H5o nB HH5>u ]B HH5Ct @aB HH H H5m HHpdB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g-HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H髆 Hdž Hʆf ATIp USH@~ˍ dH%( HD$81H~^Ǎ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bl@ HH|$H5o nB HH5<u [B HH5Ct @_B HH H
H5m HHpbB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H H H"f ATIp USH@~ɍ dH%( HD$81H9 ^ō HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B^@ HH|$H5o nB HH5X%o YB HH5Ct @]B HH H H5m HHp`B H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g)HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H[ Hw Hzf ATIp USH@~Ǎ dH%( HD$81H^Í HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bh@ HH|$H5o nB HH5 9n WB HH5Ct @[B HH H
H5m HHp^B H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g'HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H鳁 Hρ Hҁf ATIp USH@~ō dH%( HD$81HM ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Br[@ HH|$H5o nB HH5!o UB HH5Ct @YB HH H H5m HHp\B H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g%HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu H H' H*f ATIp USH@~Í dH%( HD$81Hr^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BrY@ HH|$H5o nB HH5 5n SB HH5Ct @WB HH H
H5m HHpZB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g#HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膿 Hc~ H~ H~f ATIp USH@~ dH%( HD$81H ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BrW@ HH|$H5o n}B HH5Du QB HH5Ct @UB HH H H5m HHpXB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g!HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膽 H| H| H|f ATIp USH@~̿ dH%( HD$81Ho^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BrU@ HH|$H5o n{B HH50u OB HH5Ct @SB HH H H5~m HHpVB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膻 H{ H/{ H2{f ATIp USH@~̽ dH%( HD$81HM2 ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B^@ HH|$H5o nyB HH5o MB HH5Ct @QB HH H H5|m HHpTB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膹 Hky Hy Hyf ATIp USH@~̻ dH%( HD$81H n^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BrQ@ HH|$H5o nwB HH5,u KB HH5Ct @OB HH H
H5zm HHpRB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膷 Hw Hw Hwf ATIp USH@~̹ dH%( HD$81HM( ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BrO@ HH|$H5o nuB HH5o IB HH5Ct @MB HH H H5xm HHpPB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膵 Hv H7v H:vf ATIp USH@~̷ dH%( HD$81HM) ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BrM@ HH|$H5o nsB HH5Xo GB HH5Ct @KB HH H H5vm HHpNB H8HeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膳 Hst Ht Htf ATIp USH@~̵ dH%( HD$81H^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BrK@ HH|$H5o nqB HH5u EB HH5Ct @IB HH H H5tm HHpLB H8He}B HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膱 Hr Hr Hrf ATIp USH@~̳ dH%( HD$81H-k^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BT@ HH|$H5o noB HH5?n CB HH5Ct @GB HH H
H5rm HHpJB H8He{B HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膯 H#q H?q HBqf ATIp USH@~̱ dH%( HD$81H^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR@ HH|$H5o nmB HH5"u AB HH5Ct @EB HH H H5pm HHpHB H8HeyB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膭 H{o Ho Hof ATIp USH@~̯ dH%( HD$81H[^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BO@ HH|$H5o nkB HH5 !n ?B HH5Ct @CB HH H
H5nm HHpFB H8HewB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膫 Hm Hm Hmf ATIp USH@~̭ dH%( HD$81HM ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BB@ HH|$H5o niB HH5v o =B HH5Ct @AB HH H H5lm HHpDB H8HeuB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g
HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膩 H+l HGl HJlf ATIp USH@~̫ dH%( HD$81H* ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BH@ HH|$H5o ngB HH5Xo ;B HH5Ct @?B HH H H5jm HHpBB H8HesB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膧 Hj Hj Hjf ATIp USH@~̩ dH%( HD$81H ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BH@ HH|$H5o neB HH5Xo 9B HH5Ct @=B HH H H5hm HHp@B H8HeqB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+g HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膥 Hh Hh Hhf ATIp USH@~̧ dH%( HD$81H^^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Br=@ HH|$H5}o ncB HH53n 7B HH5Ct @;B HH H
H5fm HHp>B H8HeoB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膣 H3g HOg HRgf ATIp USH@~̥ dH%( HD$81Hl^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BF@ HH|$H5{o naB HH51n 5B HH5Ct @9B HH H
H5dm HHp<B H8HemB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膡 He He Hef ATIp USH@~̣ dH%( HD$81H=W^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B8@ HH|$H5yo n_B HH5u 3B HH5Ct @7B HH H
H5bm HHp:B H8HekB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膟 Hc Hc Hdf ATIp USH@~̡ dH%( HD$81HmR^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BB@ HH|$H5wo n]B HH5u 1B HH5Ct @5B HH H H5`m HHp8B H8HeiB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膝 H;b HWb HZbf ATIp USH@~̟ dH%( HD$81HM ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B?@ HH|$H5uo n[B HH5vn /B HH5Ct @3B HH H H5^m HHp6B H8HegB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膛 H` H` H`f ATIp USH@~̝ dH%( HD$81HG^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B=@ HH|$H5so nYB HH5)n -B HH5Ct @1B HH H H5\m HHp4B H8HeeB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膙 H^ H_ H
+_f ATIp USH@~̛ dH%( HD$81H=F^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Br1@ HH|$H5qo nWB HH5'n +B HH5Ct @/B HH H
H5Zm HHp2B H8HecB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膗 HC] H_] Hb]f ATIp USH@~̙ dH%( HD$81HM ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B:@ HH|$H5oo nUB HH5
+u )B HH5Ct @-B HH H H5Xm HHp0B H8HeaB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膕 H[ H[ H[f ATIp USH@~̗ dH%( HD$81H A^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B,@ HH|$H5mo nSB HH5#n 'B HH5Ct @+B HH H H5Vm HHp.B H8He_B HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膓 HY HZ HZf ATIp USH@~̕ dH%( HD$81H^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B5@ HH|$H5ko nQB HH5!n %B HH5Ct @)B HH H
H5Tm HHp,B H8He]B HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膑 HKX HgX HjXf ATIp USH@~̓ dH%( HD$81H ^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B-@ HH|$H5io nOB HH5vn #B HH5Ct @'B HH H H5Rm HHp*B H8He[B HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膏 HV HV HVf ATIp USH@~̑ dH%( HD$81H}G^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B&@ HH|$H5go nMB HH5 n !B HH5Ct @%B HH H H5Pm HHp(B H8HeYB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膍 HT HU HUf ATIp USH@~̏ dH%( HD$81H^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B/@ HH|$H5eo nKB HH5 n B HH5Ct @#B HH H H5Nm HHp&B H8HeWB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膋 HSS HoS HrSf ATIp USH@~̍ dH%( HD$81H^ HD$)D$ 7HH . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Br#@ HH|$H5co nIB HH5 s B HH5Ct @!B HH H H5Lm HHp$B H8HeUB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膉 HQ HQ HQf ATIp USH@~̋ dH%( HD$81H]R^ HD$)D$ 7HH} . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B,@ HH|$H5ao nGB HH5n B HH5Ct @ B HH H H5Jm HHp"B H8HeSB HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu膇 HP H P H"Pf ATIp USH@~̉ dH%( HD$81H}Q^ HD$)D$ 7HH{ . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B @ HH|$H5_o nEB HH5n B HH5Ct @ B HH H
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@n v H5<n LLHLAn LLLLHD$HxH;='ό tCH1H5JR LbX L|$Ll$LI胄LR Lt$0MH
4@n u H5X<n LLH@n LLs_ H H HLLHD$HxH;=kΌ t<CLDH f. AWAVAUATUHpLO8Hw(dH%( HD$h1HHHH(H HW LH H@0HW0HO@HHGHP N LOHH@HI 0NtcI9t.LN1I @ E<AHD<HL9uHHI9 I9uSHD$hdH3%( u>Hp]A\A]A^A_ @ I9tLN1I E4AHD4HL9u?X L|$Ll$LI蠂LR Lt$0MH
?n v H5u:n LLH>n LL萑LL0HD$HxH;=̌ tyAHH5O LX L|$Ll$_LILLR Lt$0MH
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0=m 1H"m L-Q HD$xdH3%( uHĈ L[]A\A]A^A_膺 H顶 H鸶 X D AWAVAUATUH( SH dH%( HD$x1NHIH: I|$HI$'A H HID$ Hl$@ID$ Ll$HI$HR HID$3A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`WHH$ %]H<$ t.IAT H
;m 1He!m LQ 齶 HD$xdH3%( uHĈ L[]A\A]A^A_ HG H^ D AWAVAUATUH( SH dH%( HD$x1ηHIH 9 I|$HI$_&A H HID$ Hl$@ID$ Ll$HI$HJ HID$|2A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`VHH$ [H<$ t.IAT H
0:m 1H m L-Q c HD$xdH3%( uHĈ L[]A\A]A^A_膷 H H 餶 D AWAVAUATUH( SH dH%( HD$x1NHIH7 I|$HI$$A H HID$ Hl$@ID$ Ll$HI$HB HID$0A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`THH$ %ZH<$ t.IAT H
8m 1He m L譾Q HD$xdH3%( uHĈ L[]A\A]A^A_ H铵 H骵 J D AWAVAUATUH( SH dH%( HD$x1δHIH 6 I|$HI$_#A H HID$ Hl$@ID$ Ll$HI$H: HID$|/A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`SHH$ XH<$ t.IAT H
07m 1H m L-Q 鯵 HD$xdH3%( uHĈ L[]A\A]A^A_膴 H9 HP D AWAVAUATUH( SH dH%( HD$x1NHIH4 I|$HI$!A H HID$ Hl$@ID$ Ll$HI$H2 HID$-A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`QHH$ %WH<$ t.IAT H
5m 1Hem L譻Q U HD$xdH3%( uHĈ L[]A\A]A^A_ Hߴ H 閵 D AWAVAUATUH( SH dH%( HD$x1αHIH 3 I|$HI$_ A H HID$ Hl$@ID$ Ll$HI$H* HID$|,A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`PHH$ UH<$ t.IAT H
04m 1Hm L-Q HD$xdH3%( uHĈ L[]A\A]A^A_膱 H酴 H霴 < D AWAVAUATUH( SH dH%( HD$x1NHIH1 I|$HI$ A H~ HID$ Hl$@ID$ Ll$HI$H"~ HID$*A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`NHH$ %TH<$ t.IAT H
2m 1Hem L譸Q 顴 HD$xdH3%( uHĈ L[]A\A]A^A_ H+ HB D AWAVAUATUH( SH dH%( HD$x1ήHIH 0 I|$HI$_ A H} HID$ Hl$@ID$ Ll$HI$H} HID$|)A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`MHH$ RH<$ t.IAT H
01m 1Hm L-Q G HD$xdH3%( uHĈ L[]A\A]A^A_膮 Hѳ H 鈴 D AWAVAUATUH( SH dH%( HD$x1NHIH. I|$HI$A Hx| HID$ Hl$@ID$ Ll$HI$H| HID$'A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`KHH$ %QH<$ t.IAT H
/m 1Hem L譵Q HD$xdH3%( uHĈ L[]A\A]A^A_ Hw H鎳 . D AWAVAUATUH( SH dH%( HD$x1ΫHIH - I|$HI$_A Hp{ HID$ Hl$@ID$ Ll$HI$H
+{ HID$|&A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`JHH$ OH<$ t.IAT H
0.m 1Hm L-Q 铳 HD$xdH3%( uHĈ L[]A\A]A^A_膫 H H4 Գ D AWAVAUATUH( SH dH%( HD$x1NHIH+ I|$HI$A Hhz HID$ Hl$@ID$ Ll$HI$Hz HID$$A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`HHH$ %NH<$ t.IAT H
,m 1Hem L譲Q 9 HD$xdH3%( uHĈ L[]A\A]A^A_ Hò Hڲ z D AWAVAUATUH( SH dH%( HD$x1ΨHIH * I|$HI$_A H`y HID$ Hl$@ID$ Ll$HI$Hx HID$|#A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`GHH$ LH<$ t.IAT H
0+m 1Hm L-Q ߲ HD$xdH3%( uHĈ L[]A\A]A^A_膨 Hi H進 D AWAVAUATUH( SH dH%( HD$x1NHIH( I|$HI$A HXx HID$ Hl$@ID$ Ll$HI$Hw HID$!A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`EHH$ %KH<$ t.IAT H
)m 1Hem L譯Q 酲 HD$xdH3%( uHĈ L[]A\A]A^A_ H H& Ʋ D AWAVAUATUH( SH dH%( HD$x1ΥHIH ' I|$HI$_A HPw HID$ Hl$@ID$ Ll$HI$Hv HID$| A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`DHH$ IH<$ t.IAT H
0(m 1H
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&m 1He m L譬Q ѱ HD$xdH3%( uHĈ L[]A\A]A^A_ H[ Hr D AWAVAUATUH( SH dH%( HD$x1HIH $ I|$HI$_A H@u HID$ Hl$@ID$ Ll$HI$Ht HID$| A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`AHH$ FH<$ t.IAT H
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#m 1He m L譩Q HD$xdH3%( uHĈ L[]A\A]A^A_ H駰 H龰 ^ D AWAVAUATUH( SH dH%( HD$x1ΟHIH ! I|$HI$_A Hr HID$ Hl$@ID$ Ll$HI$Hr HID$|A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`>HH$ CH<$ t.IAT H
0"m 1Hm L-Q ð HD$xdH3%( uHĈ L[]A\A]A^A_膟 HM Hd D AWAVAUATUH( SH dH%( HD$x1NHIH I|$HI$ A Hq HID$ Hl$@ID$ Ll$HI$Hq HID$A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`<HH$ %BH<$ t.IAT H
m 1Hem L警Q i HD$xdH3%( uHĈ L[]A\A]A^A_ H H
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0 m 1Hm L-Q HD$xdH3%( uHĈ L[]A\A]A^A_膜 H陯 H鰯 P D AWAVAUATUH( SH dH%( HD$x1NHIH I|$HI$ A H n HID$ Hl$@ID$ Ll$HI$H_ HID$A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`9HH$ %?H<$ t.IAT H
m 1Hem L譣Q 鵯 HD$xdH3%( uHĈ L[]A\A]A^A_ H? HV D AWAVAUATUH( SH dH%( HD$x1ΙHIH I|$HI$_A Hf HID$ Hl$@ID$ Ll$HI$H:f HID$|A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`8HH$ =H<$ t.IAT H
0 m 1Hm L-Q [ HD$xdH3%( uHĈ L[]A\A]A^A_膙 H H 霯 D AWAVAUATUH( SH dH%( HD$x1NHIH I|$HI$A HPb HID$ Hl$@ID$ Ll$HI$Ha HID$A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`6HH$ %<H<$ t.IAT H
m 1He m L譠Q HD$xdH3%( uHĈ L[]A\A]A^A_ H鋮 H颮 B D AWAVAUATUH( SH dH%( HD$x1ΖHIH I|$HI$_A HHa HID$ Hl$@ID$ Ll$HI$H` HID$|A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`5HH$ :H<$ t.IAT H
0m 1Hl L-Q 駮 HD$xdH3%( uHĈ L[]A\A]A^A_膖 H1 HH D AWAVAUATUH( SH dH%( HD$x1NHIH I|$HI$A H^ HID$ Hl$@ID$ Ll$HI$Hr^ HID$A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`3HH$ %9H<$ t.IAT H
m 1Hel L譝Q M HD$xdH3%( uHĈ L[]A\A]A^A_ H H 鎮 D AWAVAUATUH( SH dH%( HD$x1ΓHIH I|$HI$_A Hb HID$ Hl$@ID$ Ll$HI$Hb HID$|A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`2HH$ 7H<$ t.IAT H
0m 1Hl L-Q HD$xdH3%( uHĈ L[]A\A]A^A_膓 H} H锭 4 D AWAVAUATUH( SH dH%( HD$x1NHIH I|$HI$ A H0^ HID$ Hl$@ID$ Ll$HI$H] HID$ A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`0HH$ %6H<$ t.IAT H
m 1Hel L譚Q 陭 HD$xdH3%( uHĈ L[]A\A]A^A_ H# H: ڭ D AWAVAUATUH( SH dH%( HD$x1ΐHIH I|$HI$_@ H8\ HID$ Hl$@ID$ Ll$HI$H[ HID$| A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`/HH$ 4H<$ t.IAT H
0m 1Hl L-Q ? HD$xdH3%( uHĈ L[]A\A]A^A_膐 Hɬ H 逭 D AWAVAUATUH( SH dH%( HD$x1NHIH I|$HI$@ H\ HID$ Hl$@ID$ Ll$HI$H2\ HID$ A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`-HH$ %3H<$ t.IAT H
m 1Hel L譗Q HD$xdH3%( uHĈ L[]A\A]A^A_ Ho H醬 & D AWAVAUATUH( SH dH%( HD$x1HIH I|$HI$_@ H^ HID$ Hl$@ID$ Ll$HI$Hr^ HID$|A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`,HH$ 1H<$ t.IAT H
0m 1Hl L-Q 鋬 HD$xdH3%( uHĈ L[]A\A]A^A_膍 H H, ̬ D AWAVAUATUH( SH dH%( HD$x1NHIH
I|$HI$@ H@W HID$ Hl$@ID$ Ll$HI$HV HID$A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`*HH$ %0H<$ t.IAT H
m 1Hel L譔Q 1 HD$xdH3%( uHĈ L[]A\A]A^A_ H黫 Hҫ r D AWAVAUATUH( SH dH%( HD$x1ΊHIH I|$HI$_@ HS HID$ Hl$@ID$ Ll$HI$HzS HID$|A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`)HH$ .H<$ t.IAT H
0
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m 1Hel L譑Q } HD$xdH3%( uHĈ L[]A\A]A^A_ H H 龫 D AWAVAUATUH( SH dH%( HD$x1·HIH I|$HI$_@ HPY HID$ Hl$@ID$ Ll$HI$HX HID$|A HHD$@ HH o` H@ HD$P HT$HHX HD$p HT$XLHl$)D$`&HH$ +H<$ t.IAT H
0
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+(\(LuIHM9uH []A\A]A^ D M9INLL)HHHTI9IT @H9@ H H HffHfo4sb LoHoPfDoHohopH@ffDhD`fgoPfffDpffgohffgoPffffgohfffgpffghffgfohftAftfdfof`fhfDefofDofAifAaTfofefDafiPATAՈTPrjAֈTJRH9HIHx HHI9x @ HHBI9x @ HHBI9x @ HH B I9mx$ @ HH(BI9Lx, @(ufHH0BI9+x4 @0ufHH8BI9
+x< @8ufHH@B I9xD @@ufHHHB I9xL @HufHHPB$I9xT @PufHHXB(I9x\ @XufHH`B,I9exd @`ufHHhB0I9Dxl @hufHHpB4I9#xt @pufHHxB8I9 M9
+
#a Lf x u(_H(I9uD$ Wob M\$\$L$ D$( (YD$AEIAEM9tkA} tAE /#b w
= b /v\\$IT$L$ qT$AEAEL$ \$XM9u A|$ A$ (AD$I^AD$M9uH} HuH9HNHH)IIJTH9HTAH9A H I HffHfomnb Hf. oHoPfDoHohopH@ffDxDpfgoPfffDXffgohffgoPffffgohfffgpffghffgfohftAftfdfof`fhfDefofDofAifAaTfofefDafiPATATPrjAӈTJRH9IJJx HHH9 x @ HHBH9x @ HHBH9x @ HH B H9x$ @ 9 HH(BH9|x, @(ufHH0BH9[x4 @0ufHH8BH9:x< @8ufHH@B H9xD @@ufHHHB H9xL @HufHHPB$H9xT @PufHHXB(H9x\ @XufHH`B,H9xd @`ufHHhB0H9txl @hufHHpB4H9Sxt @pufHHxB8H92x| @x f D HHH9 x uH HH9uf HHI9x uH HI9uf HHH9x uH HH9u^f M9 L;w A$AD$IAFAFLwM9| A|$ A $#c t
+W
$jb (AD$M)A$IIH
z` Hb H=~{` a\\$IL$T$ ѾL$AEAEM9T$ \$X7f^AD$IAD$M9:d D f f3 ((f; (0 f (1 f f (" f f f ( f Le HEI9 A|$ A$$ ` (-Ghb d$ TWF
` d$ - hb H` X(^(f/v
+(\(HELe HL)H9 AD$ AD$I9a A|$ A$$ T\$(d$ W譼
M` d$ X(^(\$/v\(Le HEI9 L)AD$HA$I5H
Lw` Hb H=x` @ M9 A|$ A $ %%a f(d$ \$u
a \$YX
Wb YSb (Y\(Q.I \.(Q D$ HE YYa H;EM @ ( X ` (\(\XYf/ T$ 6f D M9W L;w A$AD$IAFAFLwLL)HM9 A|$ A $` t\AD$A$H*H
u` H*b H=&w` f M9 A|$ A$uf! @ 8la [=H
Ou` Hb H=v` H
0u` Hb H=v` LLLe Lu+LLLe LuLLmLe LuvH
t` HBb H=>v` !H
t` H#b H= v` L)HH a AD$ \$A\$AD$A $H
Jt` Hb H=u` 袿H
+t` Hb H=u` 胿H
t` Hb H=u` dH
s` Hfb H=bu` EH
s` HGb H=Cu` &H
s` H(b H=$u` H
s` H b H=u` H
qs` Hb H=t` ɾ(\$HIL$ݾD$ L$\$˯` YYa CL9e\$ d$L$茾` \$ d$L$ H
r` H?b H=;t` ¥a \$5%a d$ (L$L$\$T$L$\$T$L$ AWAVAUMATIUHSHHxH6HUdH%( HD$h1HD$ HGHH)LHxH1L)IIH~TH9i | u"DL9 A L9 AXHI9HHfH9 x t Ht/v
H(H)HHH9uHC(HH@H)HH9 HʹIE HH)IIHu HOHHIHH)HtRH H9 F H9 VLD$1fLH\jf. I ~ l HH9J XF (W7`b NVLD$HLf D IE L$sLH
+HEH+E H7 HD$hdH3%( k Hx[]A\A]A^A_ D D$ I F NH9cH
o` Hb $ H=q` fH
yo` Hb $ H=p` ѺH
Yo` Hb H=p` 豺H9 H9 XI F tpN FX@FH9 H9 Xf D H
n` HҦb $ H=6p` H9 HHHUH)HHX L|$Ll$
+LI_LF Lt$0MH
b \ H5b LLHzb LLnLL?HD$HxH;= t(0HH5, LfH
m` HRb $ H=No` 1 HH
m` H!b H= o` H
m` Hz` $ H=n` H
jm` H{b $ H=n` ¸H
Km` Hb H=n` 裸H
,m` Hb H=n` 脸H
m` Hb H=n` eH
l` Hb $ H=cn` FH
l` HHb H=Dn` 'H
l` H)b H=%n` H
` HRb H=n` DX L|$Ll$ LI]LF Lt$0MH
b H5b L
+LH0b LLl H H HLLHD$HxH;= t-LfH f. f AWAVAUATUSH H/H dH%( HD$x1HE0Hc}HHH9 HPHHDHD$H9\$ HHELH@L)HHHX LE0H[A M,I4O$@HvIM H9 H9 IM IUMM{H)M)L9~ HIt81 D HL9t&A| tAXDHL9uILMI9uLE H}M HE HHE(HH f HHHHD$H9\$HD$xdH3%( HĈ []A\A]A^A_H
i` Hb H=nk` QH
i` Hb H=Nk` 1LE H}MtMHU H
+HU(HHH9v/H[HI4AH9\$PHc}+ D H
ai` Hzb H=j` 蹴X L|$Ll$ ЪLIuZL轷F Lt$@MH
6b H5b LLHb LLeiLݻLHD$HxH;=} t*HH5' L踳 H" H HL腻LHD$HxH;=% t*LH薳f D HAWAVAUATUSH L'H dH%( H$ 1I$H H9J fH, HD$P )D$@H HͭH (HD$@IHL$P @ H@ H9uID$0Ict$HL$HHHH9 HHHHD$H9\$ HD$@HD$(LL9t @ H@ H9uM<$MOHMoPM)ILl$ IT$8L)ID$E1HHHL$H,K4HLLL$tLL$HH@0LV8L9u* I4HXFI9 H0H;t$ @rH
f` H b H= h` HPHHDHD$H9\$MtLH$ dH3%( H []A\A]A^A_ D IL;l$ MD$(II|$Ht$(IMLEHID$ HLt$@HH9\$nHL$H[ E11 H=?j` 貧} H X Lt$8Ld$`-LHVLF L$ MH
`b H5b L4LHb LHeL7L_HD$8HxH;=נ tH'HH5# HLL"HD$8HxH;= t 'HsH|$@HttH H H H믐 AWAVAUIATUHHSH Hr LD$L $IdH%( H$ 1HBI)I~ I H9 HHL0HL$ HB0x HZPHſH|$ HD$tH|$莝HD$LE Hu H3 IA HcUxL9 LehHcI9Ժ g LH9, D9@ HDŽ$ AMLcvfHnHL$D`$H$ fHnD$HH(@fInD$ @0~顀 글 )$ M1 H$ LLHPF H$ Ht
HHH$ dH3%( I H []A\A]A^A_H9 H A HD$ @ I HcUxL9 M~HD$ Lt$@HD$( HHD$0H$ HD$8L}hƄ$ DŽ$ Mt_LuHLmPfE1M)IM9
K4HHD$ +oD$ HP0H;P8
XBI$ M9uH|$ L|$(HL$Ht$J J4HT$8H<$LH\$0HHD$(H9D$@8f D LuhHcUtI9 LH9 D9DLHUUUUUUUMcI9 fO<d1HD$p )D$`IM LIT$J48HD$`Ht$pHV LLfHHHHHH @ H0BBH9uLLHH<RHI9t3fH@ Ht H@( @Ht H@@ @0( Ht$hHDŽ$ \fHnL|$`Dp$Lx ~ HX H$ )$ M H$ LLHMF H$ Ht
HHLT$hHt$`HA LLNH)HHH I9
LHVI9IAL)H)H9` H 1| HH9 H9uH
k_` Hb $ H=`` ê LH9 D9DLHUUUUUUULcL$0MMI9 fKd1HDŽ$ )$ HM HHT$(ͤHT$(H$ H<IT$H$ H MLfHIHLHH H0BBH9uLLHLRJI9t3fH@ Ht H@( @Ht H@@ @0H$ 8 HDŽ$ fInH$ HX fHnD$ HL$( $@(~! HHL$0 H$ H) $)$ M H$ LLHyKF H$ Ht
HHп8 HDŽ$ Y~D$HL$(Lp0fo$H$ D$HHL$0@ ~| H )$ M HLLJF H$ Ht
HHH$ H$ H9t,f H} H nHH9uH$ HHLLuhHcEtD$d D$` I9 M~\L}HLmPfE1M)IM9<
+ K4HHD$ PiD$ HP0H9P82
+ XBID$`M9uHL$Ht$HT$`H<$P HH91f D AXADA@ @ H$ L|$`HLHD$(sWHD$ HD$0 HHD$8M Ld$`HL$hLI9t H@ H9uLUHLmPL)E1HM)IQ Lt$@MLl$ ILL|$HIHI4LL!hHH@0Hr8H9u& @ IHXBH9 HH9 @rH
Z` H:b H=6\` LehHcI9 LH9 D9H$ DLH$ HHD$(McLI%V8 HDŽ$ /fHnHL$(LL$D`H$ fInHD$ HH@(~/ HX Է H$ )$ O*H$ Ht
HHп0 HDŽ$ 蟟H $D`HL~D$Lp(LHHL$(D$H$ @~ۯ HH )$ )H$ Ht
HHH$ H2H
$Y` H-f` $ H=Z` |HH;l$ LLt$@L|$HLHL$HD$0LH<$LHɹ LDHE HL$HtHD$0HD$0H\$8I9H|$`HHH9 fL$ HD$p )D$`H L:J HD$`IHT$pf H@ H9uLuhHT$hM HMHLePL)Lt$(HII)IHILd$ E1L9d$ V Jt HLdHH@0Hr8H9u)e IHXBH9A HL9 @rH
qW` Hb H=X` ɢHMx 1L$ / D H$ LH$ HH$ I9H$ Hu @ HqM1 1L$ . @ H$ LH$ HH$ I9H$ Hu蜗 @ III9LD$HT$H<$/LRiH+MW1L$ (f D LH$ HH$ I9 H$ Hu @ H!M4 1L$ . @ H$ LH$ HH$ I9-H$ Hu謖 @ HD$ fHnź$H$ HD$)$ fHnH$ D$L)$ H9 Lt$`HDŽ$ M M9L*E HT$`HH9HOH$ H^ HDŽ$ HT$`H$ LH$ fHnfHnH$ H$ flHPH$ H~d 夀 )$ pBF H$ H HHtE11JH
\T` H-b $ H=U` 负H
=T` HFa` $ H=U` 蕟H
T` Hb $ H=U` vH
S` Ha` $ H=tU` WH$ L$ LDŽ$ Ld$XHLHD$(LƄ$ NH$ H$ H$ HEhL$ HH$ H9 HD$PHD$X M H H L9E HT$PHH9HOHD$XH( HDŽ$ HT$PHL$(LfInfHnH$ Ht$XH$ flHHH ~ )$ @F H$ Ht
HHH$ H$ D$`HH9t X HD$`H9uH<2IL;d$(\LD$HT$Ht$`H<$~H
R` Hb $ H=S` jH
Q` Hb $ H=hS` KH
Q` Hb H=IS` ,LRLH1H$ HHH誦H9\$`H
wQ` HHb $ H=R` Ϝ1H$ MSHHH]H9\$`8HL$L|$(J41OI9H
Q` H%b H=R` dHU1 XBH H9l$P5L$ HL$ I|$H^KLLH0]H$ HH
LHS0H;S8uH
|P` Hmb $ H=Q` ԛE1HI, XBI L9d$PH$ L$ H}HLJLHH0 ]H$ LHzKHS0H;S8uH
O` Hb $ H=^Q` AH$ H$ +H$ H|$(1^`dH=T` 舑 Hy H0H|$(lL蔴HD$XHxH;= LLKHvH|$(,LTHD$XHxH;=̊ - H覒H|$`Ht觔H/ H X Ld$XL$ lLH@LYF H$ MH
b H5b HHD$(nHt$(LHOb HNH|$(mL蕳HD$XHxH;=
t~HH50
HHH$ HtH$ HH$ Ht跓H? H H HX Ld$XL$ dLH ?LQF H|$(MH
b H5b qHt$(LHRb HMH|$(pL蘲HD$XHxH;= tHH53 HKX Ld$XL$ ĎLIi>L豛F H$ MH
'b H5sb HHD$(ƮHt$(LHb LOMH|$(şLHD$XHxH;=e tHoH5 L蠗 H H H h k n| H H/H$ Ht
HHHH|$(^HHFH$ Ht HH H? HH$ HtH$ HH$ HD$(H$ HyH$ HbZ H H0H$ Ht
HHH$ H33H$ Htݺ HH H H H$ Ht HHH$ HtH$ H| I H"H$ Ht
HHLȕH$ HpH$ HY H;X HT$L|$`L$ ؋HT$LH$ IH{a` HL$XHt$(HD$XHha` LB0LH$ HUa` H$ FL}F H|$(MH
b` H5a` 蝫Ht$(LHa` L&JH|$(蜜LĮHD$`HxH;=< t HF}H5_ Lw H H(H$ H=H$ H&H$ HH$ H H H HH|$(LHD$`HxH;= t
+LYH H H(H$ HHH$ H1H$ HtH$ HL|$`. HH$ Ht
HHH AWAVAUMATIUHSHH H $dH%( H$ 1H H9 \ HBHR A HH)H L|$(fHT$0HL)D$05 H|$( A H
b L1Hêb 讖F X Ld$@LLL$ H)>LF HD$PMH
b H5vb HHD$H\$1LHHGHL$HD$@HxH;= t
HzH5 Hב H9 L0 foL$0HD$8)L$@HtH=띀 @f HD$` )D$PTL0HPHD$PHT$`HT$X2 HL$ HD$PHL$@HLHD$8 H|$PHtŋH|$HHt#Hd H3 W8 M HEPL fH+EHLMHHU u|L$`HM}}HD$PHEHD$hLH)HT$XIIH @$ LL$L9LD$pHsfob H $LD$xM@$ HD$t$ LlLI+dIM 17 Hɀ H9 IL HyH9 ID HI9\ H H I H)HH9 HI9D tƋPHxHHyHt$1.HM L9 f D M HEPL fH+EHHuH$ HMHU H$ HEH$ H|H)H$ DE}IIH/ $ HsH $L9focb L$ LL$ D$ M@$ H$ $ H$ dH3%( $ H []A\A]A^A_ @ L 8 Hl$8HtHϚ H UuHU H$HRH$Hv E tHE HPef. H
C` H2C` $ H=~D` a@g H9> f D WJOf. HHD$H|$RHD$H|$H G HP UJMf. H= t^Q>H$ۇH$,fH
B` H"O` $ H=C` qH=X t' D Qrq롋G PW L@E PU H
A` Hyb $ H=&C` H
A` Hyb $ H=C` E H/ H@ H H H6LmHD$@HxH;=| u;HÄH|$(Ht|(HL8 H|$8HtH0 H xH|$PHtxH|$HHt詺 AWAVAUIHATUHSH1HHdH%( HD$81;IH@ IT$H9 H)A HtL:HL$ HT$1HHL$HL$H$L|$ HD$ 5 HL$H$HIHC L|$ HL$HD$(HD$ 5 H{MLHHIE HD$8dH3%( u-HHL[]A\A]A^A_H
b LgE ` AWAVAUMATIUHSHHxH6HUdH%( HD$h1HD$ HGHH)LHxH1L)IIH~TH9i | u"DL9 A L9 AXHI9HHfH9 x t Ht/v
H(H)HHH9uHC(HH@H)HH9 HʹIE HH)IIHu HOHHIHH)HtRH H9 F H9 VLD$1fLHLzjf. I ~ l HH9J XF (W-b NVLD$HLyf D IE L$sLHZHEH+E H7 HD$hdH3%( k Hx[]A\A]A^A_ D D$ I F NH9cH
<` Hcb $ H=_>` BfH
<` Htb $ H=>>` !H
<` H"b H= >` H9 H9 XI F tpN FX@FH9 H9 Xf D H
<` H"tb $ H==` iH9 HHHUH)HHX L|$Ll$Z}LI,LGF Lt$0MH
b \ H5 b LdLHʻb LL;LgL菠HD$HxH;=w txHoH5*~ LBfH
);` Hb $ H=<` 聆 H4zH
:` Hqb H=m<` PH
:` HG` $ H=N<` 1H
:` Hrb $ H=/<` H
:` Hb H=<` H
|:` Hb H=;` ԅH
]:` H֭b H=;` 赅H
>:` HOrb $ H=;` 薅H
:` Hb H=;` wH
:` Hyb H=u;` XH
` Hb H=V;` 9蔀X L|$Ll$P{LI*L=F Lt$0MH
b H5b LZLHb LL9 H H HL=LeHD$HxH;=t tNL|HN f. f AWAVAUATUSH H/H dH%( HD$x1HE0Hc}HHH9 HPHHDHD$H9\$ HHELH@L)HHHX LE0H[A M,I4O$@HvIM H9 H9 IM IUMM{H)M)L9~ HIt81 D HL9t&A| tAXDHL9uILMI9uLE H}M HE HHE(HH f HHHHD$H9\$HD$xdH3%( HĈ []A\A]A^A_H
I7` Hbb H=8` 衂H
)7` HBb H=8` 聂LE H}MtMHU H
+HU(HHH9v/H[HI4AH9\$PHc}+ D H
6` Hʭb H=&8` d}X L|$Ll$ xLI'L
F Lt$@MH
~b H5Ұb L*LH`b LL6L-LUHD$HxH;=q t>HiH5~ L H" H HLՈLHD$HxH;=uq tLNyHf D HAWAVAUATUSH L'H dH%( H$ 1I$H H9J fH, HD$P )D$@H H {H (HD$@IHL$P @ H@ H9uID$0Ict$HL$HHHH9 HHHHD$H9\$ HD$@HD$(LL9t @ H@ H9uM<$MOHMoPM)ILl$ IT$8L)ID$E1HHHL$H,K4HLLL$9LL$HH@0LV8L9u* I4HXFI9 H0H;t$ @rH
3` Hpb H=l5` O HPHHDHD$H9\$MtL^yH$ dH3%( H []A\A]A^A_ D IL;l$ MD$(II|$Ht$(IMLEHID$ HLt$@HH9\$nHL$H[ E11 H=7` uy H X Lt$8Ld$`}tLH"$LjF L$ MH
b H5,b L脔LHjb LH3L臅L诗HD$8HxH;='n tH1fH5J~ Hb}LJLrHD$8HxH;=m t[HuH|$@HtwHL} H H H믐 AWAVAUIATUHHSH Hr LD$L $IdH%( H$ 1HBI)I~ I H9 HHL0HL$ HB0x HZPHH|$ HD$tH|$jHD$LAE Hu H3 IA HcUxL9 LehHcI9Ժ g LH9, D9@ HDŽ$ AMLcvfHnHL$D`$H$ fHnD$HH(@fInD$ @0~) 2n )$ M1 H$ LLHC F H$ Ht
HHH$ dH3%( I H []A\A]A^A_H9 H A HD$ @ I HcUxL9 M~HD$ Lt$@HD$( HHD$0H$ HD$8L}hƄ$ DŽ$ Mt_LuHLmPfE1M)IM9
K4HHD$ +4D$ HP0H;P8
XBI$ M9uH|$ L|$(HL$Ht$J J4HT$8H<$oLH\$0HHD$(H9D$@8f D LuhHcUtI9 LH9 D9DLHUUUUUUUMcI9 fO<d1HD$p )D$`IM LgtIT$J48HD$`Ht$pHV LLfHHHHHH @ H0BBH9uLLHH<RHI9t3fH@ Ht H@( @Ht H@@ @0( Ht$hHDŽ$ sfHnL|$`Dp$Lx ~C HX H$ )$ M H$ LLHAF H$ Ht
HHLT$hHt$`HA LLNH)HHH I9
LHVI9IAL)H)H9` H 1| HH9 H9uH
,` H4b $ H=0.` x LH9 D9DLHUUUUUUULcL$0MMI9 fKd1HDŽ$ )$ HM HHT$( rHT$(H$ H<IT$H$ H MLfHIHLHH H0BBH9uLLHLRJI9t3fH@ Ht H@( @Ht H@@ @0H$ 8 HDŽ$ QqfInH$ HX fHnD$ HL$( $@(~I HHL$0B H$ H) $)$ M H$ LLHF H$ Ht
HHп8 HDŽ$ p~D$HL$(Lp0fo$H$ D$HHL$0@ ~m H )$ M HLL8F H$ Ht
HHH$ H$ H9t,f H} H oHH9uH$ HHoLuhHcEtD$d D$` I9 M~\L}HLmPfE1M)IM9<
+ K4HHD$ P.D$ HP0H9P82
+ XBID$`M9uHL$Ht$HT$`H<$谁 HH91f D AXADA@ @ H$ L|$`HLHD$($HD$ HD$0 HHD$8M Ld$`HL$hLI9t H@ H9uLUHLmPL)E1HM)IQ Lt$@MLl$ ILL|$HIHI4LL!-HH@0Hr8H9u& @ IHXBH9 HH9 @rH
(` Hb H=)` isLehHcI9 LH9 D9H$ DLH$ HHD$(McLIu#8 HDŽ$ mfHnHL$(LL$D`H$ fInHD$ HH@(~q HX L H$ )$ H$ Ht
HHп0 HDŽ$ lH $D`HL~D$Lp(LHHL$(D$H$ @~u HH )$ H$ Ht
HHH$ H2H
t&` H}3` $ H='` qHH;l$ LLt$@L|$HLHL$HD$0LH<$LHɹ LDHE HL$HtHD$0HD$0H\$8I9H|$`HHH9 fL$ HD$p )D$`H LkJ HD$`IHT$pf H@ H9uLuhHT$hM HMHLePL)Lt$(HII)IHILd$ E1L9d$ V Jt HL)HH@0Hr8H9u)e IHXBH9A HL9 @rH
$` H:b H=6&` pH"Mx 1L$ / D H$ LH$ HH$ I9H$ HuLe @ HM1 1L$ . @ H$ LH$ HH$ I9H$ Hud @ III9LD$HT$H<$/L iH{MW1L$ (f D LH$ HH$ I9 H$ Hu\d @ HqM4 1L$ . @ H$ LH$ HH$ I9-H$ Huc @ HD$ fHnź$H$ HD$)$ fHnH$ D$L)$ H9 Lt$`HDŽ$ M M9LzE HT$`HH9HOH$ H^ HDŽ$ 4hHT$`H$ LH$ fHnfHnH$ H$ flHPH$ H~p ^ )$ F H$ H HHtE11JH
!` H}b $ H=!#` mH
!` H.` $ H=#` lH
n!` H_b $ H="` lH
O!` HX.` $ H="` lH$ L$ LDŽ$ Ld$XHLHD$(LƄ$ H$ H$ H$ HEhL$ HH$ H9 HD$PHD$X M H H L艩E HT$PHH9HOHD$XH( HDŽ$ FfHT$PHL$(LfInfHnH$ Ht$XH$ flHHH ~du k )$
F H$ Ht
HHH$ H$ D$`HH9t X HD$`H9uH<Oe2IL;d$(\LD$HT$Ht$`H<$~H
b ` H3b $ H= ` jH
C ` H4b $ H= ` jH
$ ` H=b H= ` |jLRLH1H$ HHH
+}H9\$`H
` Hb $ H=< ` j1H$ MSHHH|H9\$`8HL$L|$(J41OI9H
\ ` Hub H= ` iHU1 XBH H9l$P5L$ HL$ I|$HLLH0"H$ HH]HS0H;S8uH
` Hb $ H=A ` $iE1HI, XBI L9d$PH$ L$ H}HLLHH0 "H$ LHHS0H;S8uH
9 ` H*b $ H= ` hH$ H$ +H$ H|$(1%cH=e!` ^ Hy H0H|$(oLHD$XHxH;=\X L6`LHgH|$(|oL褁HD$XHxH;= X - H_H|$`HtaHg H X Ld$XL$ ]LHa
LjF H$ MH
b H5kb HHD$(}Ht$(LHb HG H|$(nLHD$XHxH;=]W tHgOH5~ HfH$ HtH$ HH$ HtaHf H H HX Ld$XL$ \LHY LiF H|$(MH
b H5fb |Ht$(LHb HJH|$(mLHD$XHxH;=`V tHjNH5~ HeX Ld$XL$ \LI LiF H$ MH
ob H5Ôb HHD$(|Ht$(LHGb LH|$(mL=HD$XHxH;=U t&HMH5~ Ld H H H h k n H H/H$ Ht
HHHH|$(HdH$ Ht HH H?\ HH$ HtH$ HH$ HD$(H$ HyH$ Hb誇 H H0H$ Ht
HHH$ H33H$ Htݺ HH H H H$ Ht HHH$ HtH$ H| I H"H$ Ht
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+ f. fC fA$IfAfA,(f]LD$f]f]f]f(M9LD$L9~(IMO<ffA IL|$f]f(L9u)HIH9PH9~\HMHIH H~&H4 D HH]f(H9uHHL9uIL9,$H([]A\A]A^A_fA 3 D H~HHH
iFV HrSV $ H=GV H
IFV HRSV $ H=GV 衑 AWAVAUATUSH(HL6L/H$L9 IUIEH)HHH HzH HJIU IE Iu(ILHIE0IL<o LIHHH9HOI)IQIIHHH~MML$ E1 EH~"K " f D D;DLHLH9uG III9|H9c LWHM IHH, MJH\$HLL$L HLL$H fH AoM +A Ir fAo4Ao IAo<AoffAo4Ao!Ao,foffAo9LL$ffffoffffofffoffofffffofffM9hLL$L9~@IMO$ Ao4$foAo<$ILd$fffofffL9uA)HIH9H9~PHM HIL0H~&L A f D ED9ALIIL9uH2HI9uIL94$]H([]A\A]A^A_f D A " D H~HHH
)CV H2PV $ H=DV 聎H
CV HPV $ H=~DV a AWAVAUATUSH(HL6L/H$L9 IUIEH)HHH HzH HJIU IE Iu(ILHIE0IL<o LIHHH9HOI)IQIIHHH~MML$ E1 EH~"K " f D D;DOHLH9uG III9|H9_ LWHM IHH, MJH\$HLL$L HLL$H fH AoM +A Ir fAo4Ao IAo<AoffAo4Ao!Ao,foffAo<LL$ffffoffffofffoffffofffofffM9hLL$L9~<IMO$ Ao $Ao4$ILd$fffofffL9uA)HIH9H9~THM HIL @ 0H~&L A f D ED9AOIIL9uH2HI9uIL94$]H([]A\A]A^A_f D A " D H~HHH
?V HLV $ H=^AV AH
?V HLV $ H=>AV ! AWAVAUATUSH(HL.L7H$L9. IVIFH)HHHL HzH^ HJIV IIv(IL HIF0IH HIHHH9HOI)IPIIHHH~UML$ E1 @ AH~%K,!A M IL_(L9uBIIL9H9 LOIMHIIHL|$MyL$ HL|$L< L|$ @ fHtAO"A I A A$IAA,(_LD$___(M9LD$L9~&IMO< A IL|$_(L9u)HIH9cH9~VHMHIH H~%H4 D HH_(H9uHHL9uIL9,$H([]A\A]A^A_ A : D H~HH& H
=V H"JV $ H=>V qH
<V HJV $ H=n>V Q AWAVAUATUSH(HL.L7H$L9. IVIFH)HHHL HzH^ HJIV IIv(IL HIF0IH HIHHH9HOI)IPIIHHH~UML$ E1 @ AH~%K,!A M IL](L9uBIIL9H9 LOIMHIIHL|$MyL$ HL|$L< L|$ @ fHtAO"A I A A$IAA,(]LD$]]](M9LD$L9~&IMO< A IL|$](L9u)HIH9cH9~VHMHIH H~%H4 D HH](H9uHHL9uIL9,$H([]A\A]A^A_ A : D H~HH& H
I:V HRGV $ H=;V 衅H
):V H2GV $ H=;V 聅 AWAVAUATUSH8L HHIsH$HLvpHLnXH~`IL)HH|$H9 H^xIH\$ LMD H9 $ H^0McLl$(H\$H^8H\$f. H|$H9|$ I{LT$fIHIHIM ILH~NL~PI H M O|
A IXAIXM9uIH9t ICXIL9T$uI{ HI9RH1H;D$|UHH9 $7H8[]A\A]A^A_ E1 I E1ɐBXML9LD$ f sNH\$(L H
Y8V HbEV $ H=9V 豃H
98V HBEV $ H=9V 葃H
8V H"EV $ H=9V q AWAVAUATUSH(HH.LH$HIXHH{pHsXHK`H)HH9 HKxHH IH9,$ If @ H{0Ls8MPL9 Ip(HNIHL$HIHHHL$ @ LMH L[PI H|$ L\$K IHYX(UX(X(XI9uLI9tHN C LYYXL9~-MHC YXH9~LA YXfHI9;.IH (QL$
+ A $HH;Sp|kHsXHK`H1H)HH9|qHH9,$tUM 1IO 1f. A LYXH9|e D LkxHH9,$uH([]A\A]A^A_ sNL, H
5V HBV $ H=>7V !H
5V HBV $ H= 7V H
5V HBV $ H=6V HD$HT$L$HD$HT$fL$ AWAVAUATUSHHH/HLLMH$IApMqXII`HHD$LHL)HHHL$(H9 IYxHH\$0HI IL9$H Iq0IY8Lt$8L]Ht$H\$Ht$H9t$ HuH\$H~HIHH|$ IHI1 LMH MyPI H|$ K<fM<>oHfL9ufofsffofsff~LI9tBN<C LL9~1MC LL9~!MHC H9~LA f. HH9\$AHu HB H9T$nH1H;D$(|qIL9$HH[]A\A]A^A_ @ 1gf O1f. A LH9HH9\$ Ll$0f sNHt$8L, H
3V H@V $ H=~4V a~H
2V H?V $ H=^4V A~H
2V H?V $ H=>4V !~ AWIAVAUATUSH(HH>MH$HIsHH~pL~XHN`L)HHL$H9d LFxLMH9<$ H^8MkL|$Lf0H\$ L;d$ I $IkM1LAD 1 @ MMH~7LvPIu{I)Lt
OT f D A /rH(HI9uIL9L$uIK HH H;Vp|iH1H;D$|lHH9<$^H([]A\A]A^A_ O| E1C /rH(MHL9IL9L$<fLFxf. sNH\$L H
1V H">V $ H=2V q|H
0V H>V $ H=n2V Q|H
0V H=V $ H=N2V 1| AWIAVAUATUSH8HH>IH$HHsHH~pHNXHL$IHN`L)HHL$(H9l LFxLMH9<$ HN0L{HL$HN8HL$Lt$L9t$ LL$Ls1II LE1f D MMM~-H^PHusI)I O$E E9HEHH9uIL9L$uIM LHH,H;Vp|xH1H;D$(|{HH9<$]H8[]A\A]A^A_f D O L\$ E1 @ Ld$ G$E9HEIHM9IL9L$9w @ LFxf. sNHL$LuH
/V H"<V $ H=0V qzH
.V H<V $ H=n0V QzH
.V H;V $ H=N0V 1z AWIAVAUATUSH(HH>MH$HIsHH~pL~XHN`L)HHL$H9d LFxLMH9<$ H^8MkL|$Lf0H\$ L;d$ I $IkM1LAD 1 @ MMH~9LvPIu{I)Lt
OT f D A f/rHf(HI9uIL9L$uIK HH H;Vp|gH1H;D$|jHH9<$\H([]A\A]A^A_ D O| E1C f/rHf(MHL9IL9L$:LFxf. sNH\$L H
)-V H2:V $ H=.V xH
-V H:V $ H=~.V axH
,V H9V $ H=^.V Ax AWIAVAUATUSH(HH>IH$HHsHH~pL~XHN`L)HHL$H9d LFxLMH9<$ HN8LkL|$Lf0HL$ L;d$ I $HkME1LAD 1 MMH~:LvPIu{I)Lt
O\ f D A /_LGH(I9uIL9T$uHK HL H;Vp|nH1H;D$|iHH9<$[H([]A\A]A^A_ @ O| E1C /_LGM(HL9IL9T$9 LFxfsNHL$L H
9+V HB8V $ H=,V vH
+V H"8V $ H=,V qvH
*V H8V $ H=n,V Qv AWAVAUATUSH(L HHIsH$HH^pHLnXH~`HL)HH|$H9T H~xH|$HI| H9 $ Lv8MSLf0Lt$ L;d$ M$IkICMMIH~*LvPIukO4IO AI](M9uIL9D$uMC HAH9mH1H;D$|pHH9 $rH([]A\A]A^A_ O<E1f C M](L9IL9D$Lf. H|$f sNI| H
Y)V Hb6V $ H=*V tH
9)V HB6V $ H=*V tH
)V H"6V $ H=*V qt AWIAVAUATUSH(HH>IH$HHsHH~pL~XHN`L)HHL$H9 LFxLMH9<$ HN8LkL|$Lf0HL$ L;d$% I $HkME1LAD 1 MMH~<LvPI I)Lt
O\ fA f/_LGHf(I9uIL9T$uHK HL H;Vp H1H;D$ HH9<$QH([]A\A]A^A_fO| E1 C f/_LGMf(HL9IL9T$'tf. LFxf. sNHL$LqH
I'V HR4V $ H=(V rH
)'V H24V $ H=(V rH
'V H4V $ H=~(V ar AWIAVAUATUSH(HH>IH$HHsHH~pL~XHN`L)HHL$H9d LFxLMH9<$ HN8LkL|$Lf0HL$ L;d$ I $HkME1LAD 1 MMH~:LvPIu{I)Lt
O\ f D A /]LGH(I9uIL9T$uHK HL H;Vp|nH1H;D$|iHH9<$[H([]A\A]A^A_ @ O| E1C /]LGM(HL9IL9T$9 LFxfsNHL$L H
Y%V Hb2V $ H=&V pH
9%V HB2V $ H=&V pH
%V H"2V $ H=&V qp AWAVAUATUSHHHL6HHD$LHYHH{pIIHC`HSXH)HI9 LkxMN,L9t$= H|$LMMMIf. Hk0Hs8LaH9+ HE LA H W f(fT{V fH~f..V HFHfHnHAf D HLH~KLKPI M HI @ Af(fT V f.V w
+f.z_IL9uHH9uL|$fMLl$ MIfM4$MH~X1fJD5 LD$8AD T$0L$(\kHD$HkPT$0L$(HLD$8XHAH9Hs8IL9tSLi D M1fAf(fTNV f.V w
+f.z_LH9HH9'HA ML|$f(Ll$ L$(LD$ J,GcL$(IL;kpLD$ XM |9HSXHC`IE1H)HI9|>IL9|$t"HD$Hf D LsxIL9|$uHH[]A\A]A^A_fsNN4 H
9"V HB/V $ H=#V mH
"V H"/V $ H=#V qmH
!V H/V $ H=n#V Qm AWAVAUATUSH(L H6L*IKHHHypLaXHy`L)HH|$H9; HyxHI<I9 HY0IkLd$Ly8H\$L9|$ I[LL$A f D MIH~+LaPIukNd INT f D M$IM9uIM9uMK HMH;Qp|_H1H;D$|bHI9uH([]A\A]A^A_f D Nt E1 OML9IM9V @ Hyxf. sNH\$H< H
y V H-V $ H=!V kH
Y V Hb-V $ H=!V kH
9 V HB-V $ H=!V k AWAVAUATUSHHHHH|$HD$ HHiHHD$H}pHT$IHUXHE`H)HI9 HD$HExJHT$IH9T$ Le0HU8LiI9 I$LA\ HAHpIHt$(HIHHHt$0LLMH LMPI H|$(g fnH\$0OL fp J< AoIfofffffofI9ufoLfsfoffffffofofsfoffffff~I9t@N GL D9ALLOL9~'MGL D9ALHH9~LA| 9LfHH9L|$(1ALMDM M} IH~TE1f K<fH|$8At$0)*0fLePt$0,H|$8HD$HHAL9HU8IL9tOLqf 1f O\ 1f ED9ALLH9|( fL|$(*ƉHA HL$IL$]HD$,HD$A $H;Ep|EHUXHE`IHD$ H)HI9|,HD$HD$H9D$ tJHD$He D Luxfs^N4 H
V H)V $ H=^ V AhHH[]A\A]A^A_ÐH
V H)V $ H=. V hH
V H)V $ H= V g AWAVAUATUSHXH HH6H{HD$HGpLXHHD$ HHHIHW`L)HHT$8H98 HOxHHL$@IIIH9t$ HO0LCL|$HHL$HO8HL$ HT$H9T$ HKLL$HQHHT$(HHHHHT$01 D MMH LoPI+ H|$( L|$0OfO,AoIfoffs ffpfpfbfM9ufofsffofsffA~DIH9t[O,G,EDMjL9~CMG,EDMjL9~,MIG,EDL9~MGED IL9L$HK IL9d$ oHE1H;D$8|iHH9t$HX[]A\A]A^A_ E1MO<E1f G MEDL9IL9L$ @ Lt$@또sNHL$HL4 H
V H'V $ H=~V aeH
V H&V $ H=^V AeH
V H&V $ H=>V !e AWIAVAUATUSH8HH>IH$HHsHH~pHNXHL$IHN`L)HHL$(H9l LFxLMH9<$ HN0L{HL$HN8HL$Lt$L9t$ LL$Ls1II LE1f D MMM~-H^PHusI)I O$E E9~HEHH9uIL9L$uIM LHH,H;Vp|xH1H;D$(|{HH9<$]H8[]A\A]A^A_f D O L\$ E1 @ Ld$ G$E9~HEIHM9IL9L$9w @ LFxf. sNHL$LuH
V H%V $ H=~V acH
V H$V $ H=^V AcH
V H$V $ H=>V !c AWAVAUATUSH8L HHIsH$HLvpHLnXH~`IL)HH|$H9 H^xIH\$ LMD H9 $ H^0McLl$(H\$H^8H\$f. H|$H9|$0 I{LT$fIHIHIM ILH~XL~PI H M O|
fAIfYXfXM9uIH9tIC YXIL9T$uI{ HI9hH1H;D$|kHH9 $-H8[]A\A]A^A_f D E1 I E1f B MYXL9 LD$ f sNH\$(L H
V H"V $ H=NV 1aH
V H"V $ H=.V aH
V H"V $ H=V ` AWAVAUATUSH8HL6HH$LHYHH{pIIHC`HSXH)HI9v LkxMN,L94$ H|$LMMMI @ Hk0Hs8LaH9 HE fLA (TV . W vf(HAHHLH~GLKPI M HI @ A(T V . Z W w .z_IL9uHH9uL|$MLl$MIfM4$MH~X1fJD5 LD$(AD T$$L$ \YHD$HkPT$$L$ HLD$(XHAH9Hs8IL9tSLi D M1fA(T V .
+W w .z_LH9HH9/ @ HA ML|$(Ll$L$ LD$J,_L$ IL;kpLD$XM |:HSXHC`IE1H)HI9|?IL9<$t#HD$H LsxIL9<$uH8[]A\A]A^A_ sNN4 H
V H V $ H=NV 1^H
V H V $ H=.V ^H
V H V $ H=V ] AWAVAUATUSH(L HHIsH$HH^pHLnXH~`HL)HH|$H9T H~xH|$HI| H9 $ Lv8MSLf0Lt$ L;d$ M$IkICMMIH~+LvPIukO4IO AI]f(M9uIL9D$uMC HAH9lH1H;D$|oHH9 $qH([]A\A]A^A_fO<E1f C M]f(L9IL9D$Kf H|$f sNI| H
V H V $ H=nV Q\H
V H V $ H=NV 1\H
V H V $ H=.V \ AWAVAUATUSH(LHL*IrHHL^pLfXH~`IL)HH|$H9H H^xHH\$HI<I9 H^0IjLd$L~8H $f D L9<$ IZL$LV f D MIH~,LfPIukNd INL f D AY$IM9uIM9uMB HAI9]H1H;D$|`HI9sH([]A\A]A^A_ @ Nt E1 CYML9IM9U H|$f sNH\$H< H
)V H2 V $ H=V ZH
V H V $ H=~V aZH
V HV $ H=^V AZ AWIAVAUATUSH8HH>IH$HHsHH~pHNXHL$IHN`L)HHL$(H9l LFxLMH9<$ HN0L{HL$HN8HL$Lt$L9t$ LL$Ls1II LE1f D MMM~-H^PHusI)I O$E E9|HEHH9uIL9L$uIM LHH,H;Vp|xH1H;D$(|{HH9<$]H8[]A\A]A^A_f D O L\$ E1 @ Ld$ G$E9|HEIHM9IL9L$9w @ LFxf. sNHL$LuH
)
V H2V $ H=V XH
V HV $ H=~V aXH
V HV $ H=^V AX AWAVAUATUSH8HL6LH|$(HD$LIXHH{pHKXIHS`H)HH9c HSxIHIL9t$ f D Hs0L[8IxL9 IHHQHT$HHHHT$ HHHT$1LMH HkPHS H|$? Hl$ N fLAoIfoffs ffpfpfbfI9ufofsffofsffA~DLL$I9tRK,,IiH9~=LՋ,IiH9~)LI,L9~MF ED HI9 fIH *J,f.f(Q( ,IU L;cp HKXHS`HE1H)HH9 IL9t$tiHt$(L] D E1&N,E1f GT IEDI9|HI9_Of. L{xIL9t$uH8[]A\A]A^A_fsNL<p D H
9
+V HBV $ H= V UH
+V H"V $ H= V qUH
V HV $ H=n V QUHD$L$1NHD$L$ff. AWIAVAUATUSH(HH>MH$HIsHH~pL~XHN`L)HHL$H9d LFxLMH9<$ H^8MkL|$Lf0H\$ L;d$ I $IkM1LAD 1 @ MMH~9LvPIu{I)Lt
OT f D A f/rHf(HI9uIL9L$uIK HH H;Vp|gH1H;D$|jHH9<$\H([]A\A]A^A_ D O| E1C f/rHf(MHL9IL9L$:LFxf. sNH\$L H
V H"V $ H= V qSH
V HV $ H=n V QSH
V HV $ H=N V 1S AWIAVAUATUSH(HH>IH$HHsHH~pL~XHN`L)HHL$H9 LFxLMH9<$ HN8LkL|$Lf0HL$ L;d$% I $HkME1LAD 1 MMH~<LvPI I)Lt
O\ fA f/]LGHf(I9uIL9T$uHK HL H;Vp H1H;D$ HH9<$QH([]A\A]A^A_fO| E1 C f/]LGMf(HL9IL9T$'tf. LFxf. sNHL$LqH
V HV $ H=~V aQH
V HV $ H=^V AQH
V HV $ H=>V !Q AWAVAUATUSH(L HHIsH$HH^pHLnXH~`HL)HH|$H9T H~xH|$HI| H9 $ Lv8MSLf0Lt$ L;d$ M$IkICMMIH~+LvPIukO4IO AI_f(M9uIL9D$uMC HAH9lH1H;D$|oHH9 $qH([]A\A]A^A_fO<E1f C M_f(L9IL9D$Kf H|$f sNI| H
)V H2V $ H=V OH
V HV $ H=~V aOH
V HV $ H=^V AO AWIAVAUATUSH8HH>IH$HHsHH~pHNXHL$IHN`L)HHL$(H9l LFxLMH9<$ HN0L{HL$HN8HL$Lt$L9t$ LL$Ls1II LE1f D MMM~-H^PHusI)I O$E E9}HEHH9uIL9L$uIM LHH,H;Vp|xH1H;D$(|{HH9<$]H8[]A\A]A^A_f D O L\$ E1 @ Ld$ G$E9}HEIHM9IL9L$9w @ LFxf. sNHL$LuH
)V H2V $ H=V MH
V HV $ H=~V aMH
V HV $ H=^V AM AWIAVAUATUSHXM HH>MD$H$I@pIHXHHD$ HHHL$HIIP`H)HHT$8H9@ IXxHH\$@IHIH9<$ IH0It$LD$HHL$IH8HL$ H\$H9\$ IL$LT$HYHIHH\$(IHHT$01 @ ILH LL$HIiPH. H|$( L\$0L fMAo Ifofr fffM9ufofsffofsffA~DMI9teI,D D,A E1DD)MYL9~AIIB,F, A1EA)DL9~ IF B A D1AE)D IL9T$IL$ IL9t$ vHE1H;D$8|pHH9<$HX[]A\A]A^A_ E1C H E1f F F,IA E1E)DL9nf D L|$@두sNH\$L< H
IU HR V $ H= V JH
)U H2 V $ H= V JH
U H V $ H=~ V aJ AWIAVAUATUSH(HH>IH$HHKHHypLqXHq`L)HHt$H9D LAxLMH9<$ Hq8L[Lt$La0Ht$ L;d$ I4$HkMLI4 MMH~)LiPIukO,IO4MU L9ILIM9uIL9L$uLK HI4H;Qp|gH1H;D$|bHH9<$tH([]A\A]A^A_ D O<E1f O4L9ILMI9|IL9L$N @ LAxfsNHt$L H
yU H
+V $ H=U HH
YU Hb
+V $ H=U HH
9U HB
+V $ H=U H AWAVAUATUSH8HLL'H$LIt$HH~pLvXHN`L)HHL$ H9 HNxHI IL9 $* H~0I\$Lt$(H|$H~8H|$HL$H9L$p I|$Hl$HOIHL$HIHHI1 LU MH L^PI H|$ NfO AoIfM9ufofsffI~LMI9t+O J MXL9~MIJ L9~
+MJ HH9l$aI|$ HJ H;Vp|nH1H;D$ |qIL9 $H8[]A\A]A^A_ @ E1} NE1f K ML9HH9l$ L~xf. sNH|$(L< H
U H"V $ H=U qFH
U HV $ H=nU QFH
U HV $ H=NU 1F AWAVAUATUSHXL HH6I{HD$HGpHOXHHD$ HHHL$HIHW`H)HHT$8H93 H_xHH\$@IHIH9t$ HO8MCH|$HHo0HL$ H;l$ IKHU HIHYLIIH\$(AIILL$0 H LH LL$HMaPI& H|$(
+ LL$0fnIfp Io
+o"HfffffI9ufoMfsfoffffffofsfoffffff~I9t>M$G$D9AOMaI9}&IG$D9AOIL9~IG D9AOѐHH9|$ IK IL9t$ oHE1H;D$8|qHH9t$HX[]A\A]A^A_ E1j M,E1f C\ 9OMI9|yf L|$@f sNH|$L< H
IU HRV $ H=U CH
)U H2V $ H=U CH
U HV $ H=~U aC AWAVAUATUSHXL HH6I{HD$HGpHOXHHD$ HHHL$HIHW`H)HHT$8H9C H_xHH\$@IHIH9t$ HO8MCH|$HHo0HL$ H;l$ IKHU HIHYLIIH\$(AIILL$0 H LH LL$HMaPI6 H|$( LL$0fnIfp IoHfofffffofI9ufoMfsfofffffoffofsfoffffff~I9tFM$G$D9ALMaI9}.IG$D9ALIL9~IG D9ALf HH9|$IK IL9t$ oHE1H;D$8|qHH9t$HX[]A\A]A^A_ E1b M,E1f C\ 9LMI9|yf L|$@f sNH|$L< H
iU HrV $ H=U @H
IU HRV $ H=U @H
)U H2V $ H=U @ AWAVAUATUSH(HL&LH|$H$LIYHH{pHIHC`HSXH)HH9 HCxIHIL9$$ M D Hs0L[8IyL9 IQHBIHD$HIHHHD$1 D LMH L{PI H|$ L|$J fIoHfL9ufofsffofsff~LI9t>N<BLyL9~-MBLyL9~ MHBH9~
L HI9CfIQ I*N<
+4,AL;kp|mHSXHC`HE1H)HH9|rIL9$$tVHD$L 1SN1f. ALH9HI9ofLsxIL9$$uH([]A\A]A^A_ sNL4 H
U HU $ H=NU 1>H
U HU $ H=.U >H
U HU $ H=U = AWIAVAUATUSH(HH>IH$HHKHHypLqXHq`L)HHt$H9D LAxLMH9<$ Hq8L[Lt$La0Ht$ L;d$ I4$HkMLI4 MMH~)LiPIukO,IO4MU L9IOIM9uIL9L$uLK HI4H;Qp|gH1H;D$|bHH9<$tH([]A\A]A^A_ D O<E1f O4L9IOMI9|IL9L$N @ LAxfsNHt$L H
U HU $ H=~U a<H
U HU $ H=^U A<H
U HU $ H=>U !< AWIAVAUATUSHHI(HH>LEHD$HMppHMhXIH`IL)HHL$(H9@ IXxIH\$0LML H9|$ Ip0IX8Ll$8%W L]D(D((Ht$(=W H\$ Ht$H9t$ HufH\$f(HNIHL$ HIHHIf D LMH MxPI H|$ K N<)(HD(DWTAUVX(UX(XXI9uLL9tYN<C /wAWLyXL9~9MC /wAWHXH9~LA /wWX D HH9\$HM HI9gH1H;D$(|jHH9|$HH[]A\A]A^A_ @ 1PO1f. A /wWLXH9yLL$0f sNHt$8L H
9U HBU $ H=U 9H
U H"U $ H=U q9H
U HU $ H=nU Q9 AWAVAUATUSH(L HHIsH$HH^pHLnXH~`HL)HH|$H9T H~xH|$HI| H9 $ Lv8MSLf0Lt$ L;d$ M$IkICMMIH~*LvPIukO4IO AI_(M9uIL9D$uMC HAH9mH1H;D$|pHH9 $rH([]A\A]A^A_ O<E1f C M_(L9IL9D$Lf. H|$f sNI| H
YU HbU $ H=U 7H
9U HBU $ H=U 7H
U H"U $ H=U q7 AWIAVAUATUSHXMHH>MCHD$I@pM`XHHD$ HHHHI@`L)HHD$8H9 I@xIHD$@HIIH9|$U I@0MSLd$HHD$I@8HD$ H\$H9\$ ISM#H\$HBHHD$(HHHHHD$01fL MH MxPI H|$( L|$0K4fIoHfL9ufofsffofsff~HH9t>N<CL~I9}-MCL~L9~ MHCH9~
LAf D HH9\$AIs IAL9t$ kHE1H;L$8|mHH9|$HX[]A\A]A^A_ 1cO 1f. ALH9|HH9\$ Ll$@f sNHD$HL, H
U HU $ H=.U 5H
U HU $ H=U 4H
yU HU $ H=U 4 AWIAVAUATUSHHM HH>MD$HD$HMxpHMpXIH`IL)HHL$(H9 IXxIH\$0LM H9|$N Ip0I\$Lt$8Ht$Ip8Ht$ @ HL$H9L$ It$Hl$fHNIHL$ HIHHI @ LU MH MXPI H|$ J N 1HYX(UX(XXI9uLI9tFN B LYYXL9~+MHB YXH9~L YXHH9l$AIL$ HI9nH1H;D$(|qHH9|$HH[]A\A]A^A_ 1cf N1f. A LYXH9u D LL$0f sNHt$8L H
)U H2U $ H=U 2H
U HU $ H=~U a2H
U HU $ H=^U A2 AWAVAUATUSH(HL&H|$H?HD$LH_HH{pHIHC`HSXH)HH9 HCxIHIL9d$ f D HK0L[8LGL9 HWfHILrHIHHD$ H1LH LKPI I LL$IIHX(UX(XXI9uLI9t8L 0CXLHL9~%IHCXH9~HAXf. HI9SHG IN41AL;kp|oHSXHC`HE1H)HH9||IL9d$tXHD$H8 @ 1af I41f. XLH9nf D L{xIL9d$uH([]A\A]A^A_f. sNL<y D H
U HU $ H= U 0H
U HU $ H=U /H
iU HrU $ H=U / AWIAVAUATUSH(HH>MH$HIsHH~pL~XHN`L)HHL$H9d LFxLMH9<$ H^8MkL|$Lf0H\$ L;d$ I $IkM1LAD 1 @ MMH~7LvPIu{I)Lt
OT f D A /rH(HI9uIL9L$uIK HH H;Vp|iH1H;D$|lHH9<$^H([]A\A]A^A_ O| E1C /rH(MHL9IL9L$<fLFxf. sNH\$L H
U HU $ H=.U .H
U HU $ H=U -H
yU HU $ H=U - AWIAVAUATUSHHM HH>MD$HD$HMxpHMpXIH`IL)HHL$(H9 IXxIH\$0LM H9|$6 Ip0I\$Lt$8Ht$Ip8Ht$ @ HL$H9L$` It$Hl$fHNIHL$ HIHHI @ LU MH MXPI H|$ J N 1HX(UX(XXI9uLI9t1N BXLYL9~ MHBXH9~ LX @ HH9l$YIL$ HI9VH1H;D$(|YHH9|$HH[]A\A]A^A_ 1xN1f. AXLH9LL$0f sNHt$8L H
YU HbU $ H=U +H
9U HBU $ H=U +H
U H"U $ H=U q+ AWIAVAUATUSHHIHH>LCH$I@pMpXIH`HHD$HHL)HHHL$(H9 IpxIHt$0LM H9<$v Ip0IH8Lt$8Lkfo "W Ht$HL$ HL$H9L$ HsL\$HNIHL$ HIHHIι @ I+LH MxPI H|$ MT foO<AoAo
+Iffs fs ffpfpfbM9ufofofsfs ffs ffpfpfbfofsffA~AMI9tIl BL IL9IL9\$"Hs H H9T$`H1H;D$(|cHH9<$HH[]A\A]A^A_f D E1 Il E1BL ML9IL9\$돐LL$0f sNHt$8L H
U HU $ H= U )H
U HU $ H=U (H
iU HrU $ H=U ( AWAVAUATUSHHLHHIrHD$HLvpHH^XH~`IH)HH|$(H9 H~xIH|$0LLH9L$ H~0MjH\$8H|$H~8H|$f H|$H9|$0 IzIfL\$IIIIILL$ f. I+LH~SL~PI H L|$ ML MA IXAIXM9uMI9t
+ICXD IL9\$ufIz HH*^I9`H1H;D$(|[HH9L$ HH[]A\A]A^A_ D E1 Il E1 BXD MI9|z @ LD$0릐sNH\$8L H
yU HU $ H=U &H
YU HbU $ H=U &H
9U HBU $ H=U & AWIAVAUATUSHHMHH>MCH$HM`pHMpXIH`IL)HHL$(H9 IXxHH\$0HI IH9<$I Ip0I[Lt$8Ht$Ip8Ht$ D HL$H9L$ IsM3fHl$HNIHL$HIHHHL$ LM MH MPPI H|$ LT$ J IHX(UX(XXI9uLI9t5N BXLQL9~"MHBXH9~LX HH9l$QfIK HI*^I9jH1H;D$(|eHH9<$HH[]A\A]A^A_ 1df N 1f. AXLH9|m D L|$0뜐sNHt$8L< H
U HU $ H=nU Q$H
U HU $ H=NU 1$H
U HU $ H=.U $ AWAVAUATUSHHHLLHD$LIJHHypIHHA`HQXH)HI9 HAxHJIL9L$J H|$(f D LA0Ly8MZM9K IzK#I HwHt$HHHt$I D ILH1 HiPH H|$w fnHt$If`faHfp oHfofdfffofH9ufofsfofdffffofsfofdffffofsfofdffffofsfofdfff)T$0D$0I9N LI,3l @8LHnH9/ Ll2@8LHnH9 l2@8LHnH9 l2@8LHnH9 l2@8LHnH9 l2@8LHnH9 l2@8LHnH9 l2@8LHnH9 l2@8LHn H9~pl2 @8LHn
+H9~\l2
+@8LHn H9~Hl2 @8LHn H9~4l2 @8LHn
H9~ l2
@8LHH9~ 28L IM9IR HB
+H;Yp HQXHA`I1H)HI9 IL9L$tsHD$(L' 1Nf HL$ 1Lf D 2:2LHH9|IHL$ M9 d LaxIL9L$uHH[]A\A]A^A_fsNN$f D H
U HU $ H=>U ! H
U HU $ H= U H
U HU $ H=U AWAVAUATUSHHHLLHD$LIJHHypIHHA`HQXH)HI9U HAxHJIL9L$ H|$(f D LA0Ly8MZM9 IzK#I HwHt$HHHt$I D ILH HiPHC H|$' fnHt$If`faHfp oHfH9ufofsffofsffofsffofsf)D$0D$0I9R LI,3l @8BHnH93 Ll2@8BHnH9 l2@8BHnH9 l2@8BHnH9 l2@8BHnH9 l2@8BHnH9 l2@8BHnH9 l2@8BHnH9 l2@8BHn H9~tl2 @8BHn
+H9~`l2
+@8BHn H9~Ll2 @8BHn H9~8l2 @8BHn
H9~$l2
@8BHH9~28B IM9IR HB
+H;Yp HQXHA`I1H)HI9 IL9L$tsHD$(Lw 1Jf HL$ 1Lf D 2:2BHH9|IHL$ M9ld LaxIL9L$uHH[]A\A]A^A_fsNN$f D H
U HU $ H=^U A H
U HU $ H=>U ! H
U HU $ H= U AWAVAUATUSHHHLLHD$LIJHHypIHHA`HQXH)HI9U HAxHJIL9L$ H|$(f D LA0Ly8MZM9 IzK#I HwHt$HHHt$I D ILH HiPHC H|$' fnHt$If`faHfp oHfH9ufofsffofsffofsffofsf)D$0D$0I9R LI,3l @8GHnH93 Ll2@8GHnH9 l2@8GHnH9 l2@8GHnH9 l2@8GHnH9 l2@8GHnH9 l2@8GHnH9 l2@8GHnH9 l2@8GHn H9~tl2 @8GHn
+H9~`l2
+@8GHn H9~Ll2 @8GHn H9~8l2 @8GHn
H9~$l2
@8GHH9~28G IM9IR HB
+H;Yp HQXHA`I1H)HI9 IL9L$tsHD$(Lw 1Jf HL$ 1Lf D 2:2GHH9|IHL$ M9ld LaxIL9L$uHH[]A\A]A^A_fsNN$f D H
U HU $ H=~U aH
U HU $ H=^U AH
U HU $ H=>U ! AWAVAUATUSHHHLLHD$LIJHHypIHHA`HQXH)HI9 HAxHJIL9L$J H|$(f D LA0Ly8MZM9K IzK#I HwHt$HHHt$I D ILH1 HiPH H|$w fnHt$If`faHfp ooHfdfffH9ufofsfofdffffofsfofdffffofsfofdffffofsfofdfff)L$0D$0I9R LI,3l @8OHnH93 Ll2@8OHnH9 l2@8OHnH9 l2@8OHnH9 l2@8OHnH9 l2@8OHnH9 l2@8OHnH9 l2@8OHnH9 l2@8OHn H9~tl2 @8OHn
+H9~`l2
+@8OHn H9~Ll2 @8OHn H9~8l2 @8OHn
H9~$l2
@8OHH9~28O IM9IR HB
+H;Yp HQXHA`I1H)HI9 IL9L$tsHD$(L' 1Jf HL$ 1Lf D 2:2OHH9|IHL$ M9 d LaxIL9L$uHH[]A\A]A^A_fsNN$f D H
U HU $ H=NU 1H
U HU $ H=.U H
U HU $ H=U @ D @8 D @8 D @ D LGHIE1HHNMI)H)I9uBHOH I1MIII)I)M9u%L9u&f D HHI9tLL9tE1D @ HHH9u# HHH9tH
+H9tf A HfMcHHHH*LfH*GfI*Gf. f SHHXHt H{0Ht H{Ht H;Ht[ [f. @ H7 AVAUATL( UHSH8 Ht HsL=HH[a HuH L Ht HsL
HH[1 HuH Ht H Ht
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+Hu @ HsLIiI}(H[Ht
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+ H5|U HHP* H8HE* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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W* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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W* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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W* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Huf H H Hf ATIp USH@~v dH%( HD$81H_ >u HD$)D$ HHu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR( HH|$H5W N* HH5 X ߓ* HH5#] * HH H5U @
W* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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] * HH H5U @
W* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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W* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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W* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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W* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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W* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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W* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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W* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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W* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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W* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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W* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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W* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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W* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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W* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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W~* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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W|* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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Wz* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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Wx* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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Wv* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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Wt* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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Wr* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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Wp* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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Wn* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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Wl* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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Wj* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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Wh* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Huf Hο Hο Hοf ATIp USH@~u dH%( HD$81HMp >u HD$)D$ ̹HHu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bk( HH|$H5W N* HH5_@] _* HH5#\ c* HH H5U @
Wf* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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Wd* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hufɹ H˿ H˿ H˿f ATIp USH@~u dH%( HD$81HW >u HD$)D$ ȹHHսu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BRa( HH|$H5W N* HH5 X [* HH5#\ _* HH H5U @
Wb* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hufǹ Hɿ Hɿ Hɿf ATIp USH@~u dH%( HD$81HS >u HD$)D$ ƹHHջu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bj( HH|$H5W N* HH5 X Y* HH5#\ ]* HH H5U @
W`* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$HufŹ H5ȿ HQȿ HTȿf ATIp USH@~u dH%( HD$81Hh >u HD$)D$ ĹHHչu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B[( HH|$H5W N* HH5'X W* HH5#\ [* HH H5U @
W^* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hufù Hƿ Hƿ Hƿf ATIp USH@~u dH%( HD$81H=d >u HD$)D$ ¹HHշu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bf( HH|$H5W N* HH5w6] U* HH5#\ Y* HH H5U @
W\* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Huf HĿ Hſ Hſf ATIp USH@~u dH%( HD$81HL >u HD$)D$ HHյu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BW( HH|$H5W N* HH5w4] S* HH5#\ W* HH H5U @
WZ* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Huf H=ÿ HYÿ H\ÿf ATIp USH@~u dH%( HD$81H0 >u HD$)D$ HHճu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BU( HH|$H5W N}* HH5X Q* HH5#\ U* HH H5U @
WX* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Huf H H Hf ATIp USH@~u dH%( HD$81H=i >u HD$)D$ HHձu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B`( HH|$H5W N{* HH5BX O* HH5#\ S* HH H5~U @
WV* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Huf H H H f ATIp USH@~u dH%( HD$81HD >u HD$)D$ HHկu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bb]( HH|$H5W Ny* HH5'X M* HH5#\ Q* HH H5|U @
WT* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Huf HE Ha Hdf ATIp USH@~u dH%( HD$81H-7 >u HD$)D$ HHխu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BRQ( HH|$H5W Nw* HH5w,] K* HH5#\ O* HH H5zU @
WR* H8HL* HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Huf H靼 H鹼 H鼼f ATIp USH@~u dH%( HD$81H=M >u HD$)D$ HHիu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BRO( HH|$H5W Nu* HH5_*] I* HH5#\ M* HH H
+ H5|xU HHPP* H8HE* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf H H Hf ATIp USH@~u dH%( HD$81H}H >u HD$)D$ HHթu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BRM( HH|$H5W Ns* HH5_(] G* HH5#\ K* HH H H5|vU HHPN* H8HE* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf HM Hi Hlf ATIp USH@~u dH%( HD$81HU >u HD$)D$ HHէu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BV( HH|$H5W Nq* HH52X E* HH5#\ I* HH H H5|tU HHPL* H8HE}* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf H饷 H Hķf ATIp USH@~u dH%( HD$81HM> >u HD$)D$ HHեu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BbS( HH|$H5W No* HH5 X C* HH5#\ G* HH H H5|rU HHPJ* H8HE{* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf H H H f ATIp USH@~u dH%( HD$81H
) >u HD$)D$ HHգu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BRG( HH|$H5W Nm* HH52X A* HH5#\ E* HH H
+ H5|pU HHPH* H8HEy* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf HU Hq Htf ATIp USH@~u dH%( HD$81H
>u HD$)D$ HHաu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BC( HH|$H5W Nk* HH5X ?* HH5#\ C* HH H
+ H5|nU HHPF* H8HEw* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf H魲 Hɲ H̲f ATIp USH@~u dH%( HD$81H] >u HD$)D$ HH՟u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BRC( HH|$H5W Ni* HH5 X =* HH5#\ A* HH H
+ H5|lU HHPD* H8HEu* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf H H! H$f ATIp USH@~u dH%( HD$81HQ >u HD$)D$ HH՝u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BE( HH|$H5W Ng* HH5_ ] ;* HH5#\ ?* HH H H5|jU HHPB* H8HEs* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf H] Hy H|f ATIp USH@~u dH%( HD$81H >u HD$)D$ HH՛u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR?( HH|$H5~W Ne* HH5X 9* HH5#\ =* HH H
+ H5|hU HHP@* H8HEq* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf H鵭 Hѭ Hԭf ATIp USH@~u dH%( HD$81H- >u HD$)D$ HHՙu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B;( HH|$H5|W Nc* HH5 X 7* HH5#\ ;* HH H
+ H5|fU HHP>* H8HEo* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf H
H) H,f ATIp USH@~u dH%( HD$81H( >u HD$)D$ HHu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR;( HH|$H5zW Na* HH5X 5* HH5#\ 9* HH H H5|dU HHP<* H8HEm* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf He H遪 H鄪f ATIp USH@~u dH%( HD$81HL >u HD$)D$ HHՕu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BD( HH|$H5xW N_* HH5BX 3* HH5#\ 7* HH H H5|bU HHP:* H8HEk* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf H齨 H٨ Hܨf ATIp USH@~u dH%( HD$81H R >u HD$)D$ HHՓu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BB( HH|$H5vW N]* HH5w] 1* HH5#\ 5* HH H H5|`U HHP8* H8HEi* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf H H1 H4f ATIp USH@~u dH%( HD$81H E >u HD$)D$ HHՑu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bb?( HH|$H5tW N[* HH5_] /* HH5#\ 3* HH H H5|^U HHP6* H8HEg* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf Hm H鉥 H錥f ATIp USH@~u dH%( HD$81H >u HD$)D$ HHՏu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR3( HH|$H5rW NY* HH5X -* HH5#\ 1* HH H H5|\U HHP4* H8HEe* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf Hţ H H䣿f ATIp USH@~u dH%( HD$81H >u HD$)D$ HHՍu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B<( HH|$H5pW NW* HH5X +* HH5#\ /* HH H H5|ZU HHP2* H8HEc* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf H H9 H<f ATIp USH@~u dH%( HD$81HM/ >u HD$)D$ HHՋu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR/( HH|$H5nW NU* HH5BX )* HH5#\ -* HH H
+ H5|XU HHP0* H8HEa* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf Hu H鑠 H锠f ATIp USH@~u dH%( HD$81H >u HD$)D$ HHՉu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B+( HH|$H5lW NS* HH5 X '* HH5#\ +* HH H H5|VU HHP.* H8HE_* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf H͞ H鞿 H잿f ATIp USH@~u dH%( HD$81H >u HD$)D$ HHՇu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR+( HH|$H5jW NQ* HH5 X %* HH5#\ )* HH H H5|TU HHP,* H8HE]* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf H% HA HDf ATIp USH@~u dH%( HD$81H >u HD$)D$ HHՅu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR)( HH|$H5hW NO* HH5X #* HH5#\ '* HH H
+ H5|RU HHP** H8HE[* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf H} H陛 H霛f ATIp USH@~u dH%( HD$81H-. >u HD$)D$ HHՃu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bb1( HH|$H5fW NM* HH5w] !* HH5#\ %* HH H
+ H5|PU HHP(* H8HEY* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf Hՙ H Hf ATIp USH@~u dH%( HD$81H: >u HD$)D$ HHՁu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B#( HH|$H5dW NK* HH5w ] * HH5#\ #* HH H
+ H5|NU HHP&* H8HEW* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+}HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf H- HI HLf ATIp USH@~u dH%( HD$81H >u HD$)D$ HHu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B!( HH|$H5bW NI* HH5B{X * HH5#\ !* HH H
+ H5|LU HHP$* H8HEU* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+{HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf H酖 H顖 H餖f ATIp USH@~u dH%( HD$81H >u HD$)D$ HH}u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR!( HH|$H5`W NG* HH5[ * HH5#\ * HH H
+ H5|JU HHP"* H8HES* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+yHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf Hݔ H Hf ATIp USH@~u dH%( HD$81H
>u HD$)D$ HH{u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B*( HH|$H5^W NE* HH5 wX * HH5#\ * HH H H5|HU HHP * H8HEQ* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+wHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf H5 HQ HTf ATIp USH@~u dH%( HD$81H}# >u HD$)D$ HHyu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B( HH|$H5\W NC* HH5w\ * HH5#\ * HH H H5|FU HHP * H8HEO* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+uHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf H鍑 H驑 H鬑f ATIp USH@~u dH%( HD$81H
4 >u HD$)D$ HHwu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR( HH|$H5ZW NA* HH5w\ * HH5#\ * HH H H5|DU HHP * H8HEM* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+sHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf H叿 H Hf ATIp USH@~u dH%( HD$81H >u HD$)D$ HHuu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bb#( HH|$H5XW N?* HH5 qX * HH5#\ * HH H H5|BU HHP* H8HEK* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+qHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf H= HY H\f ATIp USH@~u dH%( HD$81H=
+ >}u HD$)D$ ~HHsu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bb!( HH|$H5VW N=* HH5w\ * HH5#\ * HH H H5|@U HHP* H8HEI* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+oHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf} H镌 H鱌 H鴌f ATIp USH@~u dH%( HD$81H >{u HD$)D$ |HHqu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B( HH|$H5TW N;* HH5'mX * HH5#\ * HH H
+ H5|>U HHP* H8HEG* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+mHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf{ H튿 H H f ATIp USH@~}u dH%( HD$81H >yu HD$)D$ zHHou . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B ( HH|$H5RW N9* HH5 kX
* HH5#\ * HH H H5|<U HHP* H8HEE* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+kHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hufy HE Ha Hdf ATIp USH@~{u dH%( HD$81H >wu HD$)D$ xHHmu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bb( HH|$H5PW N7* HH5'iX * HH5#\ * HH H
+ H5|:U HHP* H8HEC* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+iHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hufw H靇 H鹇 H鼇f ATIp USH@~yu dH%( HD$81H >uu HD$)D$ vHHku . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR( HH|$H5NW N5* HH5 gX * HH5#\
* HH H
+ H5|8U HHP* H8HEA* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+gHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hufu H H Hf ATIp USH@~wu dH%( HD$81Hm >su HD$)D$ tHHiu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B ( HH|$H5LW N3* HH5[ * HH5#\ * HH H H5|6U HHP* H8HE?* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+eHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hufs HM Hi Hlf ATIp USH@~uu dH%( HD$81H >qu HD$)D$ rHHgu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B ( HH|$H5JW N1* HH5 cX * HH5#\ * HH H H5|4U HHP * H8HE=* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+cHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hufq H饂 H HĂf ATIp USH@~su dH%( HD$81H >ou HD$)D$ pHHeu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR ( HH|$H5HW N/* HH5 aX * HH5#\ * HH H H5|2U HHP
+* H8HE;* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+aHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hufo H H H f ATIp USH@~qu dH%( HD$81H >mu HD$)D$ nHHcu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR( HH|$H5FW N-* HH5[ * HH5#}\ * HH H H5|0U HHP* H8HE9* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+_HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hufm HU Hq Htf ATIp USH@~ou dH%( HD$81Hm >ku HD$)D$ lHHau . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B( HH|$H5DW N+* HH5 ]X ) HH5#{\ * HH H
+ H5|.U HHP* H8HE7* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+]HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hufk H} H} H}f ATIp USH@~mu dH%( HD$81H- >iu HD$)D$ jHH_u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bb
( HH|$H5BW N)* HH5'[X ) HH5#y\ * HH H H5|,U HHP* H8HE5* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+[HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hufi H| H!| H$|f ATIp USH@~ku dH%( HD$81H >gu HD$)D$ hHH]u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B ( HH|$H5@W N'* HH5w\ ) HH5#w\ ) HH H
+ H5|*U HHP* H8HE3* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+YHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hufg H]z Hyz H|zf ATIp USH@~iu dH%( HD$81H} >eu HD$)D$ fHH[u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR' HH|$H5>W N%* HH5'WX ) HH5#u\ ) HH H
+ H5|(U HHP * H8HE1* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+WHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hufe Hx Hx Hxf ATIp USH@~gu dH%( HD$81H] >cu HD$)D$ dHHYu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H5<W N#* HH5[ ) HH5#s\ ) HH H
+ H5|&U HHP) H8HE/* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+UHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hufc H
w H)w H,wf ATIp USH@~eu dH%( HD$81H >au HD$)D$ bHHWu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR' HH|$H5:W N!* HH5BSX ) HH5#q\ ) HH H H5|$U HHP) H8HE-* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+SHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hufa Heu Hu Huf ATIp USH@~cu dH%( HD$81H= >_u HD$)D$ `HHUu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H58W N * HH5QX ) HH5#o\ ) HH H H5|"U HHP) H8HE+* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+QHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf_ Hs Hs Hsf ATIp USH@~au dH%( HD$81H >]u HD$)D$ ^HHSu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H56W N * HH5'OX ) HH5#m\ ) HH H H5| U HHP) H8HE)* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+OHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf] Hr H1r H4rf ATIp USH@~_u dH%( HD$81H- >[u HD$)D$ \HHQu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B ( HH|$H54W N* HH5BMX ) HH5#k\ ) HH H
+ H5| U HHP) H8HE'* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+MHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf[ Hmp Hp Hpf ATIp USH@~]u dH%( HD$81H >Yu HD$)D$ ZHHOu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H52W N* HH5[ ) HH5#i\ ) HH H
+ H5| U HHP) H8HE%* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+KHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$HufY Hn Hn Hnf ATIp USH@~[u dH%( HD$81H] >Wu HD$)D$ XHHMu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H50W N* HH52IX ) HH5#g\ ) HH H
+ H5|U HHP) H8HE#* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+IHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$HufW H m H9m H<mf ATIp USH@~Yu dH%( HD$81H >Uu HD$)D$ VHHKu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bb' HH|$H5.W N* HH5_\ ) HH5#e\ ) HH H H5|U HHP) H8HE!* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+GHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$HufU Huk Hk Hkf ATIp USH@~Wu dH%( HD$81H >Su HD$)D$ THHIu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H5,W N* HH5_\ ) HH5#c\ ) HH H H5|U HHP) H8HE * HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+EHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$HufS Hi Hi Hif ATIp USH@~Uu dH%( HD$81H
>Qu HD$)D$ RHHGu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H5*W N* HH5_\ ) HH5#a\ ) HH H H5|U HHP) H8HE * HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+CHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$HufQ H%h HAh HDhf ATIp USH@~Su dH%( HD$81H >Ou HD$)D$ PHHEu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H5(W N* HH5[ ) HH5#_\ ) HH H H5|U HHP) H8HE* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+AHD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$HufO H}f Hf Hff ATIp USH@~Qu dH%( HD$81H >Mu HD$)D$ NHHCu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR' HH|$H5&W N
* HH5?X ) HH5#]\ ) HH H
+ H5|U HHP) H8HE* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+?HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$HufM Hd Hd Hdf ATIp USH@~Ou dH%( HD$81H >Ku HD$)D$ LHHAu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR' HH|$H5$W N * HH5w\ ) HH5#[\ ) HH H
+ H5|U HHP) H8HE* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+=HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$HufK H-c HIc HLcf ATIp USH@~Mu dH%( HD$81H] >Iu HD$)D$ JHH?u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H5"W N * HH52;X ) HH5#Y\ ) HH H H5| U HHP) H8HE* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+;HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$HufI Ha Ha Haf ATIp USH@~Ku dH%( HD$81H >Gu HD$)D$ HHH=u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H5 W N* HH5_\ ) HH5#W\ ) HH H H5|
+U HHP) H8HE* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+9HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$HufG H_ H_ H_f ATIp USH@~Iu dH%( HD$81H >Eu HD$)D$ FHH;u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR' HH|$H5 W N* HH56X ) HH5#U\ ) HH H
+ H5|U HHP) H8HE* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+7HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$HufE H5^ HQ^ HT^f ATIp USH@~Gu dH%( HD$81H] >Cu HD$)D$ DHH9u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR' HH|$H5 W N* HH5 5X ) HH5#S\ ) HH H H5|U HHP) H8HE* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+5HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$HufC H\ H\ H\f ATIp USH@~Eu dH%( HD$81H] >Au HD$)D$ BHH7u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H5W N* HH52X ) HH5#Q\ ) HH H H5|U HHP) H8HE
* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+3HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$HufA HZ H[ H[f ATIp USH@~Cu dH%( HD$81H} >?u HD$)D$ @HH5u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H5W N) HH5 1X ) HH5#O\ ) HH H
+ H5|U HHP) H8HE * HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+1HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf? H=Y HYY H\Yf ATIp USH@~Au dH%( HD$81HM >=u HD$)D$ >HH3u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH Bb' HH|$H5W N) HH5 /X ) HH5#M\ ) HH H
+ H5| U HHP) H8HE * HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+/HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf= HW HW HWf ATIp USH@~?u dH%( HD$81H >;u HD$)D$ <HH1u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR' HH|$H5W N) HH5_\ ) HH5#K\ ) HH H H5|T HHP) H8HE* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+-HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf; HU H V H Vf ATIp USH@~=u dH%( HD$81H >9u HD$)D$ :HH/u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H5W N) HH5B+X ) HH5#I\ ) HH H H5|T HHP) H8HE* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
++HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf9 HET HaT HdTf ATIp USH@~;u dH%( HD$81H >7u HD$)D$ 8HH-u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H5W N) HH5)X ) HH5#G\ ) HH H
+ H5|T HHP) H8HE* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+)HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf7 HR HR HRf ATIp USH@~9u dH%( HD$81H >5u HD$)D$ 6HH+u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H5W N) HH5'X ) HH5#E\ ) HH H
+ H5|T HHP) H8HE* HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+'HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf5 HP HQ HQf ATIp USH@~7u dH%( HD$81H >3u HD$)D$ 4HH)u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H5 W N) HH5$X ) HH5#C\ ) HH H H5|T HHP) H8HE) HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+%HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf3 HMO HiO HlOf ATIp USH@~5u dH%( HD$81H >1u HD$)D$ 2HH'u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H5
+W N) HH5_\ ) HH5#A\ ) HH H
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+!HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf/ HK HL H Lf ATIp USH@~1u dH%( HD$81Hm >-u HD$)D$ .HH#u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR' HH|$H5W N) HH5 X ) HH5#=\ ) HH H H5|T HHP) H8HE) HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+ HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf- HUJ HqJ HtJf ATIp USH@~/u dH%( HD$81H >+u HD$)D$ ,HH!u . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H5W N) HH5 X ߿) HH5#;\ ) HH H H5|T HHP) H8HE) HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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+ H5|T HHP) H8HE) HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf' H]E HyE H|Ef ATIp USH@~)u dH%( HD$81H= >%u HD$)D$ &HHu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H5V N) HH5 X ߹) HH5#5\ ) HH H H5|T HHP) H8HE) HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf% HC HC HCf ATIp USH@~'u dH%( HD$81HM >#u HD$)D$ $HHu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR' HH|$H5V N) HH5'X ߷) HH5#3\ ) HH H H5|T HHP) H8HE) HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf# H
B H)B H,Bf ATIp USH@~%u dH%( HD$81H >!u HD$)D$ "HHu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H5V N) HH5_\ ߵ) HH5#1\ ) HH H H5|T HHP) H8HE) HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Huf! He@ H@ H@f ATIp USH@~#u dH%( HD$81H > u HD$)D$ HHu . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B' HH|$H5V N) HH5 X ߳) HH5#/\ ) HH H H5|T HHP) H8HE) HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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S HzS H=S @<<76 H騄f. UfHAWAVAUDATI SH H8 HdH%( HE1)HDž HDž HDž )) O,Ǿ C,5eMt LLH5BJt MDL5{Dt LHPH PAVHH0tZH Ht :HHt9HHt9HEdH3%( @ He[A\A]A^A_] D 1HJL1HIְ) Is+t/Mt$Lr, H HL9J @ H fLHE HH[HE I)@)P)`)pEE)EL@ILLHML<H Ht8L+H H LLHNfX z9 HL@# H HH Ht\8Ld+H HtC8HHt28HHt!8H=f ATIN)HV(HLI$ A\ D UfHAWAVAUDATI SH H8 HdH%( HE1)HDž HDž HDž )) /)Ǿ #)5Lt LLH5:Ft MDL55t LHPH PAVHH0tZH Ht6HHt6HHt6HEdH3%( h He[A\A]A^A_] D 1HGL1HI趭) IS(t/Mt$LR, H HL6J @ H fLHE HH[HE I)@)P)`)pEE)EL@ILLHMLOH Ht5H}Ht5HpHt5HXHt5H@H~s5tf D LPTLHMRg6 HL@# H HH Ht5L (H Ht4HHt4HHt4Ha: ATIN)HV(HLI$ A\ D UfHAWAVAUDATI SH H8 HdH%( HE1)HDž HDž HDž )) %Ǿ %5@t LLH5j1t MDL5sGt LHPH PAVHH0tZH Ht3HHt3HHt3HEdH3%( @ He[A\A]A^A_] D 1HfDL1HIv) I%t/Mt$L, H HLy3J @ H fLHE HH[HE I)@)P)`)pEE)EKL@ILLHML`H Htx2L%H H LLHTfX zO3 HL@# H HH Ht1L%H Ht1HHt1HHt1HI7f ATIN)HV(HLI$ A\ D UfHAWAVAUDATI SH H8 HdH%( HE1)HDž HDž HDž )) "Ǿ "58t LLH5-t MDL5*t LHPH PAVHH0tZH Ht0HHtx0HHtg0HEdH3%( : He[A\A]A^A_] D 1HFAL1HIV) I!t/Mt$L, H HLY0J @ H fLHE HH[HE I)@)P)`)pEE)E+
L@ILLHMLwH HtX/L`"H H L LH#HH50 HL@# H HH Ht.L!H Ht.HHt.HHt.H/4f. D ATIN)HV(HLI$ A\ D AWAVAUATIUSH dH%( H$ 1HG0x HB0LoPHL/x LbPHFHL"HH)HA LzIHD$p .L(Ll$0 L~;t Lx A L`L4t HD$`)D$pS HE H;E Ld$`H0LLL9 HD$pHt
LLHC0x HuHE HKPH HH)HH9 H0H<H9t HvFp HAH9uH$ dH3%( HĘ []A\A]A^A_f D H
S H"S $ H=S q2H
S HS $ H=nS Q2H
S HS $ H=NS 12H
S HS $ H=.S 2l-X Hl$I*(LHt$LIHS LC0HHD$0HS HD$HS HD$|H4: I躁 LH
bT H5S HLH,S LLL 9H1KH$HxH;=!t tYHH5ͤr L0 H X Hl$IW'Ll$0LHIHt$LC0LHS HD$0HS HD$HS HD$H 4: I躁 LH
T H5#S .GLHTS LL# H HL8HH?JH$HxH;= t t YL(H)0X Hl$Iw&Ll$0LHIHt$MD$0LHS HD$0H S HD$H S HD$H+3: I躕 LH
iT H5BS MFLHsS LLLP7HxIH$HxH;= t tBXHH5r L,/ H H HD$pHH|$` H HD$pHH|$` H H H HL6HHHH$HxH;=& t twWL&H. AWAVAUATIUSH dH%( H$ 1HG0x HB0LoPHL/x ; LbPHFHL"HH)H LzIHD$p (L(Ll$0 L~$t Lx A L`L#t HD$`)D$pHE H;EU Ld$`H0LLL_9 HD$pHt
LLHC0x H}HU HCPHHH)HHH9 H
+H H9t{H fH)H*JHQHHHHtDHf(1HfHf. ff^HH9uHHHH9t ^ H$ dH3%( HĘ []A\A]A^A_ D H
YS HbS $ H=S ,H
9S HBS $ H=S ,H
S H"S $ H=S q,H
S HS $ H=nS Q,' H H X Hl$IR"Ll$0LHIHt$LC0LHS HD$0HS HD$HS HD$H/: I躁 LH
uS H5 S )BLHOS LLL,3HTEH$HxH;=t t THH5r L+X Hl$I!Ll$0LHIHt$MD$0LH!S HD$0HS HD$HS HD$H:.: I躕 LH
S H5QS \ALHS LLL_2HDH$HxH;= t tQSH
+H5#r L;*X Hl$I LHt$LIHaS LC0HHD$0HQS HD$HHS HD$ Hs-: I躁 LH
S H5S @LHS LL g H HL1HHCH$HxH;= t tpRL!H)HD$pHtH|$` H H HD$pHtH|$` H H H HL0HHCH$HxH;=zt tQLS!H(f. AWAVAUATIUSH dH%( H$ 1HG0xk HB0LoPHL/x, LbPHFHL"HH)H LzIHD$p #L(Ll$0 L~*t Lx A L`L2,t HD$`)D$pCHE H;E Ld$`H0LLL9 HD$pHt
LLHC0x3 HuHE HSPHHH)HH9H H0H<H9 H fH*PHH)HHHHHv=H(1H H D ^HH9uHHHH9t:HB^H9t%BHB^BH9tB^BH$ dH3%( HĘ []A\A]A^A_f H
yS HS $ H=S &H
YS HbS $ H=S &H
9S HBS $ H=S &H
S H"S $ H=S q&!X Hl$I Ll$0LHIHt$MD$0LH%S HD$0H S HD$HS HD$H>): I躕 LH
S H5US `<LHS LLLc-H?H$HxH;=t tUNHH5'r L?% He X Hl$ILl$0LHIHt$LC0LHMS HD$0HDS HD$H;S HD$Hf(: I躁 LH
S H5}S ;LHS LLL,H>H$HxH;=,t t}MH6
H5Or Lg$X Hl$ILHt$LIHS LC0HHD$0H}S HD$HtS HD$7H': I躁 LH
S H5S :LHS LLL4 HiHD$pHtxH|$` Hg H HL+HH=H$HxH;='t txLL " HHD$pHtH|$` HHt# H HL+HH?=H$HxH;=t t LLH)# H 2 2 AWAVIAUATUSHH HVdH%( H$ 1HHH)H HO0LxIy HK0LgPL'y~ LkPL+H9 HLHLHT$J HDŽ$ fInHT$fInLd$@flA L fHnfInǾ flHD$p@~t Y#t )$ Ll$pLLLL9 H$ Ht
LLHC0x HUHE HKPH HH)H HpH<H9tH9tE0 HAH9uH$ dH3%( HĨ []A\A]A^A_f D H
S HS $ H=fS I!f H
S HS $ H=>S !!H
S HS $ H= S !H
S HS $ H=S < X Hl$ Lt$Ld$@LHIHt$ME0LHS HD$@HS HD$ HS HD$EH#: I躕 LH
S H5S 6LHS LLZL'H9HD$HxH;=rt tHH|H5r L X Hl$ Lt$)LHt$LIHS LC0HHD$@HS HD$ HS HD${H": I躁 LH
aS H5S 6LH+S LLL'H09HD$HxH;=t tGHH5˒r L X Hl$ Lt$_Ld$@LHIHt$LC0LHS HD$@HS HD$ HS HD$H": I躁 LH
S H5+S 65LH\S LL, H HL"&HHG8HD$HxH;=t tGLH0 H$ H H> H- H7H$ HtH|$p HH L%HH7HD$HxH;=4t tFL
HH$ HtH|$p H c l Zf. AWAVIAUATUSHH HVdH%( H$ 1HHH)H HO0LxIy HK0LgPL'y LkPL+H9 HLHLHT$J C HDŽ$ ]fInHT$fInLd$@flA L fHnfInǾ flHD$p@~pt t )$ | Ll$pLLLL9 H$ Ht
LLHC0x R HuHU HCPHHH)H HJH H9ttH9 fH)H*
+HQHHHHt?Hf(1HfH D ff^HH9uHHHH9t ^ H$ dH3%( HĨ []A\A]A^A_ D H
S HS $ H=nS QH
S HS $ H=NS 1H
S HS $ H=.S H
S HS $ H=S LX Hl$ Lt$LHt$LIHS LC0HHD$@HS HD$ HS HD$ZH : I躁 LH
0S H5S 0LH
+S LLoL!H4HD$HxH;=
+t tBHH5r L Ho X Hl$ Lt$2Ld$@LHIHt$ME0LHS HD$@HS HD$ HS HD$H : I躕 LH
S H5S 0LH/S LLL !H43HD$HxH;= t tAHH5όr LX Hl$ Lt$cLd$@LHIHt$LC0LHS HD$@HS HD$ HS HD$H : I躁 LH
S H5/S :/LH`S LLQ HmH$ Ht|H|$p Hk H HL HH$2HD$HxH;=t t@Lu% HH$ HtH|$p HH H H HLq HH1HD$HxH;=t t_@LHf. D AWAVIAUATUSHH HVdH%( H$ 1HHH)H+ HO0LxIyU HK0LgPL'ye LkPL+H9 HLHLHT$J S HDŽ$ mfInHT$fInLd$@flA L fHnfInǾ flHD$p@~0#t t )$ Ll$pLLLL9 H$ Ht
LLHC0x, HMHE HSPHHH)H? HpH<H9 H9 fH*HH)HHHHHv=H(1H H D ^HH9uHHHH9t:HB^H9t%BHB^BH9tB^BH$ dH3%( HĨ []A\A]A^A_ÐH
S HS $ H=NS 1H
S HS $ H=.S H
S HS $ H=S H
yS HS $ H=S ,X Hl$ Lt$
+Ld$@LHIHt$LC0LHS HD$@H{S HD$ HrS HD$5H: I躁 LH
S H5S *LHS LLJLH-HD$HxH;=bt t<HlH5r L Hj X Hl$ Lt$
+Ld$@LHIHt$ME0LHS HD$@HS HD$ HS HD$ZH: I躕 LH
8S H5S )LH
+S LLoLH-HD$HxH;=t t;HH5r LX Hl$ Lt$> LHt$LIHS LC0HHD$@HS HD$ HS HD$H: I躁 LH
.S H5S )LH@S LLV HmH$ Ht|H|$p Hk H HLHH,HD$HxH;=|t t:LU
+% HH$ HtH|$p HH H H HLQHHv+HD$HxH;=t t?:L H_f. D AWAVAUATUHSH dH%( H$ 1HG0x HB0LgPL'x& HjPHFHH*HH)H LrIHDŽ$ w L Ld$@ L~O
t Lp A HhLJ#t HD$p)$ HH;CtwHl$pH0LLH!9 H$ Ht
HHH$ dH3%( uSHĨ []A\A]A^A_fH
S HS $ H=NS 1H
S HS $ H=.S l X HT$Ll$ L|$#HT$Ld$@LIHS HL$LHD$HS LB0LHD$ HS HD$@kL: M躁 LH
IS H5S %LHS LLLL )HD$HxH;=s t7HH5r LX Ll$ L|$OLd$@LLIHL$LE0LHS HD$HS HD$ HS HD$@蜿L: M躕 LH
S H5S &%LHLS LL, H H HLL%(HD$HxH;=s t6LvH H H$H$ HtH|$p HH
H$ HtH|$p H g AWAVAUATUHSH dH%( H$ 1HG0x HB0LgPL'x& HjPHFHH*HH)H LrIHDŽ$ L Ld$@ L~t Lp A HhLjs HD$p)$ HH;CtwHl$pH0LLH聲9 H$ Ht
HHH$ dH3%( uSHĨ []A\A]A^A_fH
9S HBS $ H=S H
S H"S $ H=S q X HT$Ll$ L|$HT$Ld$@LIH)S HL$LHD$HS LB0LHD$ HS HD$@˼L3: M躁 LH
iS H5JS U"LH{S LLLXL%HD$HxH;=s tI4HH5r L3 X Ll$ L|$Ld$@LLIHL$LE0LHKS HD$HBS HD$ H9S HD$@Ld: M躕 LH
2S H5{S !LHS LL, H H HL]L$HD$HxH;=s tN3LHn
+ H H$H$ HtH|$p HH8
+H$ HtH|$p H g AWAVAUATUHSH dH%( H$ 1HG0x HB0LgPL'x& HjPHFHH*HH)H LrIHDŽ$ 7L Ld$@ L~t Lp A HhL"t HD$p)$ pHH;CtwHl$pH0LLH9 H$ Ht
HHH$ dH3%( uSHĨ []A\A]A^A_fH
S HS $ H=S H
yS HS $ H=S ,X HT$Ll$ L|$HT$Ld$@LIHS HL$LHD$HxS LB0LHD$ HhS HD$@+L : M躁 LH
S H5S LHS LL@LL!HD$HxH;=Xs t0HbH5{{r LX Ll$ L|$Ld$@LLIHL$LE0LHS HD$HS HD$ HS HD$@\L
+: M躕 LH
S H5S LH S LLq, H H HLL HD$HxH;=]s t/L6H H H$H$ HtH|$p HHH$ HtH|$p H g AWAVAUATUHSH dH%( H$ 1HG0x HB0LgPL'x& HjPHFHH*HH)H LrIHDŽ$ L Ld$@ L~t Lp A HhL:t HD$p)$ HH;CtwHl$pH0LLHA9 H$ Ht
HHH$ dH3%( uSHĨ []A\A]A^A_fH
S HS $ H=nS QH
ٹS HS $ H=NS 1 X HT$Ll$ L|$CHT$Ld$@LIHS HL$LHD$HS LB0LHD$ HS HD$@苵L: M躁 LH
iS H5
+S LH;S LL蠹L L@ HD$HxH;=s t -HH5wr LX Ll$ L|$oLd$@LLIHL$LE0LH S HD$HS HD$ HS HD$@輴L$: M躕 LH
:S H5;S FLHlS LLѸ, H H HL LE HD$HxH;=s t,LH. H H$H$ HtH|$p HHH$ HtH|$p H g AWAVAUATUHSH dH%( H$ 1HG0x HB0LgPL'x & HjPHFHH*HH)H LrIHDŽ$ L Ld$@ L~s Lp A HhLs HD$p)$ 0HH;CtwHl$pH0LLH衧9 H$ Ht
HHH$ dH3%( uSHĨ []A\A]A^A_fH
YS HbS $ H=ηS H
9S HBS $ H=S X HT$Ll$ L|$HT$Ld$@LIHIS Ht$LHD$@H8S LB0LHD$ H(S HD$LS: M躁 LH
S H5jS uLHS LL LxLHD$HxH;=s ti)H"H5;tr LS X Ll$ L|$Ld$@LLIHt$LE0LHkS HD$@HbS HD$ HYS HD$ L: M躕 LH
:S H5S LHS LL1, H H HL}LHD$HxH;= s tn(LH H H$H$ HtH|$p HHXH$ HtH|$p H g AWAVAUATUHSH dH%( H$ 1HG0x HB0LgPL'x & HjPHFHH*HH)H LrIHDŽ$ WL Ld$@ L~s Lp A HhL"t HD$p)$ HH;CtwHl$pH0LLH9 H$ Ht
HHH$ dH3%( uSHĨ []A\A]A^A_fH
S H¿S $ H=.S H
S HS $ H=S LX HT$Ll$ L|$HT$Ld$@LIHS Ht$LHD$@HS LB0LHD$ HS HD$KL : M躁 LH
!S H5S LHS LL`LL HD$HxH;=xs t%HH5pr LX Ll$ L|$/Ld$@LLIHt$LE0LHS HD$@HS HD$ HS HD$|L9 M躕 LH
S H5S LH,S LL葱, H H HLLHD$HxH;=}s t$LVH H H$H$ HtH|$p HHH$ HtH|$p H g AWAVAUATUHSH dH%( H$ 1HG0x HB0LgPL'x & HjPHFHH*HH)H LrIHDŽ$ L Ld$@ L~s Lp A HhLZt HD$p)$ HH;CtwHl$pH0LLHa9 H$ Ht
HHH$ dH3%( uSHĨ []A\A]A^A_fH
S H"S $ H=S qH
S HS $ H=nS QX HT$Ll$ L|$cHT$Ld$@LIH S Ht$LHD$@HS LB0LHD$ HS HD$諪L9 M躁 LH
S H5*S 5LH[S LLL8L`HD$HxH;=s t)"HH5lr LX Ll$ L|$Ld$@LLIHt$LE0LH+S HD$@H"S HD$ HS HD$ܩLD9 M躕 LH
S H5[S fLHS LL, H H HL= LeHD$HxH;=s t.!LHN H H$H$ HtH|$p HHH$ HtH|$p H g AWAVAUATUHSHHxdH%( HD$h13HMHE HH)H HS0LPz H;HSPpH9tiLIM~>LLE1HאLI9t HAH9uIMLM9uHD$hdH3%( uMHx[]A\A]A^A_H
ګS HS $ H=OS 2H
S HĸS $ H=0S nX Ll$I,Lt$0LLIHt$LC0LHS HD$0HS HD$HS HD$yL9 M躁 LH
_S H5S
LH)S LL莫LL.H$HxH;=s t HH5ir L H" H HLLH$HxH;=Ps t L)H AWAVAUATUHSHHxdH%( HD$h1"HUHE HH)H HK0L@y
fH3HKPH*HH9 H(IH1H| MHf(IfIMMIIIJ D HHLH9t3It!I f Hf^@H9uHM9t ^HH9uHD$hdH3%( uMHx[]A\A]A^A_H
XS HaS $ H=ͪS H
9S HBS $ H=S X Ll$ILt$0LLIHt$LC0LHFS HD$0H=S HD$H4S HD$L_9 M躁 LH
S H5vS
+LHS LL LL
H$HxH;=%s tv H/H5Hgr L` H" H HL-LU
H$HxH;=s t LH?f. D AWAVAUATUHSH dH%( H$ 1HG0x HB0LgPL'x& HjPHFHH*HH)H LrIHDŽ$ WL Ld$@ L~s Lp A HhLs HD$p)$ HH;CtwHl$pH0LLH9 H$ Ht
HHH$ dH3%( uSHĨ []A\A]A^A_fH
S H³S $ H=.S H
S HS $ H=S LX HT$Ll$ L|$HT$Ld$@LIHS Ht$LHD$@HS LB0LHD$ HS HD$KL9 M躁 LH
1S H5ʽS LHS LL`LL HD$HxH;=xs tHH5dr LX Ll$ L|$/Ld$@LLIHt$LE0LH˼S HD$@H¼S HD$ HS HD$|L9 M躕 LH
"S H5S LH,S LL葥, H H HLL
+HD$HxH;=}s tLVH H H$H$ HtH|$p HHH$ HtH|$p H g AWAVAUATUHSH dH%( H$ 1HG0x HB0LgPL'x& HjPHFHH*HH)H LrIHDŽ$ L Ld$@ L~s Lp A HhLs HD$p)$ HH;CtwHl$pH0LLHa9 H$ Ht
HHH$ dH3%( uSHĨ []A\A]A^A_fH
S H"S $ H=S qH
S HS $ H=nS QX HT$Ll$ L|$cHT$Ld$@LIH S Ht$LHD$@HS LB0LHD$ HS HD$諞L9 M躁 LH
S H5*S 5LH[S LLL8L`HD$HxH;=s t)HH5`r LX Ll$ L|$Ld$@LLIHt$LE0LH+S HD$@H"S HD$ HS HD$ܝLD9 M躕 LH
S H5[S fLHS LL, H H HL=LeHD$HxH;=s t.LHN H H$H$ HtH|$p HHH$ HtH|$p H g AWAVAUATUHSH dH%( H$ 1HG0x HB0LgPL'x& HjPHFHH*HH)H LrIHDŽ$ L Ld$@ L~ s Lp A HhLs HD$p)$ PHH;CtwHl$pH0LLH9 H$ Ht
HHH$ dH3%( uSHĨ []A\A]A^A_fH
yS HS $ H=S H
YS HbS $ H=ΠS X HT$Ll$ L|$HT$Ld$@LIHiS Ht$LHD$@HXS LB0LHD$ HHS HD$ Ls9 M躁 LH
S H5S LHS LL LLHD$HxH;=8s tHBH5[]r LsX Ll$ L|$߷Ld$@LLIHt$LE0LHS HD$@HS HD$ HyS HD$<L9 M躕 LH
S H5S LHS LLQ, H H HLLHD$HxH;==s tLH H H$H$ HtH|$p HHxH$ HtH|$p H g AWAVAUATUHSH dH%( H$ 1HG0x HB0LgPL'x& HjPHFHH*HH)H LrIHDŽ$ wL Ld$@ L~s Lp A HhLs HD$p)$ HH;CtwHl$pH0LLH!9 H$ Ht
HHH$ dH3%( uSHĨ []A\A]A^A_fH
ٛS HS $ H=NS 1H
S H¨S $ H=.S lX HT$Ll$ L|$#ݷHT$Ld$@LIHɲS Ht$LHD$@HS LB0LHD$ HS HD$kL9 M躁 LH
S H5S LHS LL耛LL HD$HxH;=s tHH5Yr LX Ll$ L|$OܷLd$@LLIHt$LE0LHS HD$@HS HD$ HٱS HD$蜖L9 M躕 LH
zS H5S &LHLS LL豚, H H HLL%HD$HxH;=s t
LvݷH H H$H$ HtH|$p HHH$ HtH|$p H g AWAVAUATUHSH dH%( H$ 1HG0x HB0LgPL'x& HjPHFHH*HH)H LrIHDŽ$ L Ld$@ L~?s Lp A HhLs HD$p)$ HH;CtwHl$pH0LLH聉9 H$ Ht
HHH$ dH3%( uSHĨ []A\A]A^A_fH
9S HBS $ H=S H
S H"S $ H=S qX HT$Ll$ L|$ٷHT$Ld$@LIH)S Ht$LHD$@HS LB0LHD$ HS HD$˓L39 M躁 LH
iS H5JS ULH{S LLLXLHD$HxH;=s tI HH5Vr L3X Ll$ L|$طLd$@LLIHt$LE0LHKS HD$@HBS HD$ H9S HD$Ld9 M躕 LH
گS H5{S LHS LL, H H HL]LHD$HxH;=s tN
+LٷHn H H$H$ HtH|$p HH8H$ HtH|$p H g AWAVAUATUHSH dH%( H$ 1HG0x HB0LgPL'x& HjPHFHH*HH)H LrIHDŽ$ 7۷L Ld$@ L~s Lp A HhLs HD$p)$ pHH;CtwHl$pH0LLH9 H$ Ht
HHH$ dH3%( uSHĨ []A\A]A^A_fH
S HS $ H=S H
yS HS $ H=S ,۷X HT$Ll$ L|$շHT$Ld$@LIHS Ht$LHD$@HxS LB0LHD$ HhS HD$+L9 M躁 LH
ɲS H5S LH۫S LL@LLHD$HxH;=Xs tHbH5{Rr LX Ll$ L|$շLd$@LLIHt$LE0LHS HD$@HS HD$ HS HD$\L9 M躕 LH
:S H5۪S LH S LLq, H H HLLHD$HxH;=]s tL6ַHݷ H H$H$ HtH|$p HHݷH$ HtH|$p H g AWAVAUATUHSH dH%( H$ 1HG0x HB0LgPL'x& HjPHFHH*HH)H LrIHDŽ$ L Ld$@ L~s Lp A HhLs HD$p)$ HH;CtwHl$pH0LLHA9 H$ Ht
HHH$ dH3%( uSHĨ []A\A]A^A_fH
S HS $ H=nS QH
ِS HS $ H=NS 1X HT$Ll$ L|$CҷHT$Ld$@LIHS Ht$LHD$@HاS LB0LHD$ HȧS HD$苌L9 M躁 LH
1S H5
+S LH;S LL蠐LL@HD$HxH;=s t HH5Nr LڷX Ll$ L|$oѷLd$@LLIHt$LE0LH S HD$@HS HD$ HS HD$輋L$9 M躕 LH
S H5;S FLHlS LLя, H H HL LEHD$HxH;=s tLҷH.ڷ H H$H$ HtH|$p HHٷH$ HtH|$p H g AWAVAUATUHSH dH%( H$ 1HG0x HB0LgPL'x& HjPHFHH*HH)H LrIHDŽ$ ӷL Ld$@ L~ s Lp A HhLzs HD$p)$ 0HH;CtwHl$pH0LLH~9 H$ Ht
HHH$ dH3%( uSHĨ []A\A]A^A_fH
YS HbS $ H=ΎS H
9S HBS $ H=S ӷX HT$Ll$ L|$ηHT$Ld$@LIHIS Ht$LHD$@H8S LB0LHD$ H(S HD$LS9 M躁 LH
S H5jS uLHS LL LxLHD$HxH;=s ti H"H5;Kr LSX Ll$ L|$ͷLd$@LLIHt$LE0LHkS HD$@HbS HD$ HYS HD$ L9 M躕 LH
zS H5S LḤS LL1, H H HL}LHD$HxH;= s tnLηHַ H H$H$ HtH|$p HHXַH$ HtH|$p H g AWAVAUATUHSH dH%( H$ 1HG0x HB0LgPL'x& HjPHFHH*HH)H LrIHDŽ$ WзL Ld$@ L~s Lp A HhLRs HD$p)$ HH;CtwHl$pH0LLH{9 H$ Ht
HHH$ dH3%( uSHĨ []A\A]A^A_fH
S HS $ H=.S H
S HS $ H=S LзX HT$Ll$ L|$˷HT$Ld$@LIHS Ht$LHD$@HS LB0LHD$ HS HD$KL9 M躁 LH
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HH1[]f HHH1[]ÐUSHH ~lt @ uH?vyH1[] D H.( yHU HHUHPHUHPHUHPHU HP HH[1] uH HH1[]f HHH1[]ÐUHSH tuH?xH1[] D H H xHHHSHPHSHPHSHPHS HP HS(HP(HS0HP0HS8HP8HS@HP@HE H[1] uH5 HH1[]f HHH1[]ÐUSHH ~lt @ uH?wH1[] D H.( xHU HHUHPHUHPHUHPHU HP HH[1] uH HH1[]f HHH1[]ÐUHSH tuH?FwH1[] D H H kwHHHSHPHSHPHSHPHS HP HS(HP(HS0HP0HS8HP8HS@HP@HE H[1] uHտ HH1[]f HHH1[]ÐUSHH ~lt @ uH?vvH1[] D H.( vHU HHUHPHUHPHUHPHU HP HH[1] uH5 HH1[]f HHH1[]ÐUHSH tuH?uH1[] D H H uHHHSHPHSHPHSHPHS HP HS(HP(HS0HP0HS8HP8HS@HP@HE H[1] uHu HH1[]f HHH1[]ÐUSHH ~lt @ uH?tH1[] D H.( uHU HHUHPHUHPHUHPHU HP HH[1] uHս HH1[]f HHH1[]ÐUHSH tuH?FtH1[] D H H ktHHHSHPHSHPHSHPHS HP HS(HP(HS0HP0HS8HP8HS@HP@HE H[1] uH HH1[]f HHH1[]ÐUSHH ~lt @ uH?vsH1[] D H.( sHU HHUHPHUHPHUHPHU HP HH[1] uHu HH1[]f HHH1[]ÐUHSH tuH?rH1[] D H H rHHHSHPHSHPHSHPHS HP HS(HP(HS0HP0HS8HP8HS@HP@HE H[1] uH HH1[]f HHH1[]ÐUSHH ~lt @ uH?qH1[] D H.( rHU HHUHPHUHPHUHPHU HP HH[1] uH HH1[]f HHH1[]ÐUHSH tuH?FqH1[] D H H kqHHHSHPHSHPHSHPHS HP HS(HP(HS0HP0HS8HP8HS@HP@HE H[1] uHU HH1[]f HHH1[]ÐUSHH ~lt @ uH?vpH1[] D H.( pHU HHUHPHUHPHUHPHU HP HH[1] uH HH1[]f HHH1[]ÐUHSH tuH?oH1[] D H H oHHHSHPHSHPHSHPHS HP HS(HP(HS0HP0HS8HP8HS@HP@HE H[1] uH HH1[]f HHH1[]ÐUSHH ~lt @ uH?nH1[] D H.( oHU HHUHPHUHPHUHPHU HP HH[1] uHU HH1[]f HHH1[]ÐUHSH tuH?FnH1[] D H H knHHHSHPHSHPHSHPHS HP HS(HP(HS0HP0HS8HP8HS@HP@HE H[1] uH HH1[]f HHH1[]ÐUSHH ~lt @ uH?vmH1[] D H.( mHU HHUHPHUHPHUHPHU HP HH[1] uH HH1[]f HHH1[]ÐUHSH tuH?lH1[] D H H lHHHSHPHSHPHSHPHS HP HS(HP(HS0HP0HS8HP8HS@HP@HE H[1] uH5 HH1[]f HHH1[]ÐUSHH ~lt @ uH?kH1[] D H.( lHU HHUHPHUHPHUHPHU HP HH[1] uH HH1[]f HHH1[]ÐUHSH tuH?FkH1[] D H H kkHHHSHPHSHPHSHPHS HP HS(HP(HS0HP0HS8HP8HS@HP@HE H[1] uHմ HH1[]f HHH1[]ÐUSHH ~lt @ uH?vjH1[] D H.( jHU HHUHPHUHPHUHPHU HP HH[1] uH5 HH1[]f HHH1[]ÐAVH9L4 AUIATUHSL' f D I$LH0ID$HID$L HL9}|ID$1H8ID$ LH HH<IHHL9HuHID$(IT$0IH8ID$8H2HJhID$@H J 0II9i[]A\A]A^11fAVH9L4 AUIATUHSL' f D I$LH0ID$HID$L HL9}|ID$1H8ID$ LH HH<IHHL9HuHID$(IT$0IH8ID$8H2HzgID$@H J 0II9i[]A\A]A^11fAVH9L4 AUIATUHSL' f D I$LH0ID$HID$L HL9}|ID$1H8ID$ LH HH<IHHL9HuHID$(IT$0IH8ID$8H2HfID$@H J 0II9i[]A\A]A^11fAVH9L4 AUIATUHSL' f D I$MLID$HID$L HL9 ID$1H8ID$ H0L HH<HHHL9HuH< ID$(IT$0IH8HID$8J4HeID$@H J 0II9`[]A\A]A^11f AVH9L4 AUIATUHSL' f D I$MLID$HID$L HL9 ID$1H8ID$ H0L HH<HHHL9HuH< ID$(IT$0IH8HID$8J4HdID$@H J 0II9`[]A\A]A^11f AVH9L4 AUIATUHSL' f D I$MLID$HID$L HL9 ID$1H8ID$ H0L HH<HHHL9HuH< ID$(IT$0IH8HID$8J4HdID$@H J 0II9`[]A\A]A^11f AVH9L4 AUIATUHSL' f D I$MLID$HID$L HL9} ID$1H8ID$ H0L HH<HHHL9HuH<ID$(IT$0IH8HID$8J4JH5cID$@H J 0II9d[]A\A]A^11f. AVH9L4 AUIATUHSL' f D I$MLID$HID$L HL9} ID$1H8ID$ H0L HH<HHHL9HuH<ID$(IT$0IH8HID$8J4JHUbID$@H J 0II9d[]A\A]A^11f. AVH9L4 AUIATUHSL' f D I$MLID$HID$L HL9 ID$1H8ID$ H0L HH<HHHL9HuH< ID$(IT$0IH8HID$8J4HqaID$@H J 0II9`[]A\A]A^11f AVH9L4 AUIATUHSL' f D I$MLID$HID$L HL9 ID$1H8ID$ H0L HH<HHHL9HuH< ID$(IT$0IH8HID$8J4H`ID$@H J 0II9`[]A\A]A^11f AVH9L4 AUIATUHSL' f D I$MLID$HID$L HL9} ID$1H8ID$ H0L HH<HHHL9HuH<ID$(IT$0IH8HID$8J4JH_ID$@H J 0II9d[]A\A]A^11f. AVH9L4 AUIATUHSL' f D I$MLID$HID$L HL9 ID$1H8ID$ H0L HH<HHHL9HuH< ID$(IT$0IH8HID$8J4H^ID$@H J 0II9`[]A\A]A^11f AWH AVAUATUSH(H9L'Ht$HT$HL$ I$HL$IT$H HH,ID$H H H HHHHuxID$H0HHH<IIID$ H(J|- HN</L9s f. LILMH]K/MH9sL9vO/L9s:IIL9wHD$HD$HD$H9D$3H([]A\A]A^A_ @ LLHL$v]L$MI AWH AVAUATUSH(H9L'Ht$HT$HL$ I$HL$IT$H HH,ID$H H H HHHHuxID$H0HHH<IIID$ H(J|- HN</L9s f. LILMH\K/MH9sL9vO/L9s:IIL9wHD$HD$HD$H9D$3H([]A\A]A^A_ @ LLHL$6\L$MI AWH AVAUATUSH(H9L'Ht$HT$HL$ I$HL$IT$H HH,ID$H H H HHHHuxID$H0HHH<IIID$ H(J|- HN</L9s f. LILMH\[K/MH9sL9vO/L9s:IIL9wHD$HD$HD$H9D$3H([]A\A]A^A_ @ LLHL$ZL$MI AWH AVAUATUSH(H9L'Ht$HT$HL$ I$HL$IT$H HH4ID$H H H< HHHH| ID$HHHH<;L4 IID$ H H,J|5 H\ N<7L9s LILMHZKMH9sL9vOL9s:IIL9wHD$HD$HD$H9D$+H([]A\A]A^A_ @ LLHL$YL$MIAWH AVAUATUSH(H9L'Ht$HT$HL$ I$HL$IT$H HH4ID$H H H< HHHHuxID$HHHH<;L4 IID$ H H,pJ|5 H\M N<7L9s LILMHXKoMH9sL9vOoL9s:IIL9wHD$HD$HD$H9D$3H([]A\A]A^A_ @ LLHL$vXL$MI AWH AVAUATUSH(H9L'Ht$HT$HL$ I$HL$IT$H HH4ID$H H H< HHHH| ID$HHHH<;L4 IID$ H H,J|5 H\ N<7L9s LILMHWKMH9sL9vOL9s:IIL9wHD$HD$HD$H9D$+H([]A\A]A^A_ @ LLHL$.WL$MIAWH AVAUATUSH(H9L'Ht$HT$HL$ I$HL$IT$H HH4ID$H H H< HHHH| ID$HHHH<;L4 IID$ H H,J|5 H\ N<7L9s LILMHTVKMH9sL9vOL9s:IIL9wHD$HD$HD$H9D$+H([]A\A]A^A_ @ LLHL$UL$MIAWH AVAUATUSH(H9L'Ht$HT$HL$ I$HL$IT$H HH4ID$H H H< HHHH| ID$HHHH<;L4 IID$ H H,J|5 H\ N<7L9s LILMHUKMH9sL9vOL9s:IIL9wHD$HD$HD$H9D$+H([]A\A]A^A_ @ LLHL$TL$MIAWH AVAUATUSH(H9L'Ht$HT$HL$ I$HL$IT$H HH4ID$H H H< HHHH| ID$HHHH<;L4 IID$ H H,J|5 H\ N<7L9s LILMHSKMH9sL9vOL9s:IIL9wHD$HD$HD$H9D$+H([]A\A]A^A_ @ LLHL$nSL$MIAWH AVAUATUSH(H9L'Ht$HT$HL$ I$HL$IT$H HH4ID$H H H< HHHHuxID$HHHH<;L4 IID$ H H,pJ|5 H\M N<7L9s LILMHRKoMH9sL9vOoL9s:IIL9wHD$HD$HD$H9D$3H([]A\A]A^A_ @ LLHL$6RL$MI AWH AVAUATUSH(H9L'Ht$HT$HL$ I$HL$IT$H HH4ID$H H H< HHHH| ID$HHHH<;L4 IID$ H H,J|5 H\ N<7L9s LILMHTQKMH9sL9vOL9s:IIL9wHD$HD$HD$H9D$+H([]A\A]A^A_ @ LLHL$PL$MIAWH AVAUATUSH(H9L'Ht$HT$HL$ I$HL$IT$H HH4ID$H H H< HHHHuxID$HHHH<;L4 IID$ H H,pJ|5 H\M N<7L9s LILMH PKoMH9sL9vOoL9s:IIL9wHD$HD$HD$H9D$3H([]A\A]A^A_ @ LLHL$OL$MI USHHH H H [H H HH HtWPH HtFPHs`H{PyHs0H{ lHCH-A{ HxH9u(HCHxH9ueHHxH9u4HOH[]H=| HWtdHt$NH={| HWtLHt$hNH=V| HWt4Ht$CNsPJH떋PJH뮋PJH ATHUSHH0HD$H"KHD$(H&KHD$ q 8 HOOHƹ' 1HHHHz F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H s: H5G H$ H5|N H$ HH5'F HB
l% H8H! HE HE HC HHD$ Ht`HkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;HtH L ATHwUSHH0HD$H"IHD$(H&IHD$ x 8 HOMHƹ' 1HHHHx F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H s8 H5G H" H5zN H" HH5'F HB
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HHH;HtH L ATHUSHH0HD$H"GHD$(H&GHD$ n 8 HOKHƹ' 1HHHHv F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H s6 H5G H H5xN H HH5'F HB
l! H8H HE HE HC HHD$ Ht`HkHt$ HfoD$ HD$ CH<$HC( Ht
-GHD$ HtH|$ HH0H[]A\f1HH<$Ht HD$ HtH|$ HH
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HHH;HtH L ATH7USHH0HD$H"EHD$(H&EHD$ j 8 HOIHƹ' 1HHHHt F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H s4 H5G H H5vN H HH5'F HB
l H8H HE HE HC HHD$ Ht`HkHt$ HfoD$ HD$ CH<$HC( Ht
-GHD$ HtH|$ HH0H[]A\f1HH<$Ht HD$ HtH|$ HH
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HHH;HtH L ATH7USHH0HD$H"CHD$(H&CHD$ p 8 HOGHƹ' 1HHHHr F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H s2 H5G H H5tN H HH5'F HB
l H8H HE HE HC HHD$ Ht`HkHt$ HfoD$ HD$ CH<$HC( Ht
-GHD$ HtH|$ HH0H[]A\f1HH<$Ht HD$ HtH|$ HH
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HHH;HtH L ATHUSHH0HD$H"AHD$(H&AHD$ m 8 HOEHƹ' 1HHHHp F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H s0 H5G H H5rN H HH5'F HB
l H8H HE HE HC HHD$ Ht`HkHt$ HfoD$ HD$ CH<$HC( Ht
-GHD$ HtH|$ HH0H[]A\f1HH<$Ht HD$ HtH|$ HH
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HHH;HtH L ATH7USHH0HD$H"?HD$(H&?HD$ g 8 HOCHƹ' 1HHHHn F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H s. H5G H H5pN H HH5'F HB
l H8H HE HE HC HHD$ Ht`HkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;HtH L ATHUSHH0HD$H"=HD$(H&=HD$ h 8 HOAHƹ' 1HHHHl F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H s, H5G H H5nN H HH5'F HB
l H8H HE HE HC HHD$ Ht`HkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;HtH L ATHUSHH0HD$H";HD$(H&;HD$ ^ 8 HO?Hƹ' 1HHHHj F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H s* H5G H H5lN H HH5'F HB
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HHH;HtH L ATHwUSHH0HD$H"9HD$(H&9HD$ g 8 HO=Hƹ' 1HHHHh F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H s( H5G H H5jN H HH5'F HB
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HHH;HtH L ATHUSHH0HD$H"7HD$(H&7HD$ [ 8 HO;Hƹ' 1HHHHf F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H s& H5G H H5hN H HH5'F HB
l H8H
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HHH;HtH L ATHwUSHH0HD$H"5HD$(H&5HD$ ^ 8 HO9Hƹ' 1HHHHd F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H s$ H5G H H5fN H HH5'F HB
l H8H HE HE HC HHD$ Ht`HkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;HtH L ATHUSHH0HD$H"3HD$(H&3HD$ a 8 HO7Hƹ' 1HHHHb F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H s" H5G H H5dN H HH5'F HB B e
H8H HE HE HC HHD$ HtaHkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;HtH Lf. ATHGUSHH0HD$H1HD$(H1HD$ U 8 H?5Hƹ' 1HHHH` F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H c H5G H
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HHH;HtH Lf. ATH7USHH0HD$H/HD$(H/HD$ R 8 H/3Hƹ' 1HHHH] F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H S H5هG H H5``N H HH5F HB B E H8H HE HE HC HHD$ HtaHkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;HtH Lf. ATHUSHH0HD$H,HD$(H,HD$ V 8 H 1Hƹ' 1HHHH[ F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H C H5ɅG H H5P^N H HH5F HB B 5 H8Hz HE HE HC HHD$ HtaHkHt$ HfoD$ HD$ CH<$HC( Ht
-HD$ HtH|$ HH0H[]A\ 1HH<$HtHD$ HtH|$ HH5HHKIHt
HHH;HtH Lf. ATHWUSHH0HD$H*HD$(H*HD$ |W 8 H/Hƹ' 1HHHHY F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H 3 H5G H H5@\N H HH5F HB B % H8Hj HE HE HC HHD$ HtaHkHt$ HfoD$ HD$ CH<$HC( Ht
- HD$ HtH|$ HH0H[]A\ 1HH<$HtHD$ HtH|$ HH3HHKIHt
HHH;HtwH Lf. ATHUSHH0HD$H(HD$(H(HD$ lO 8 H,Hƹ' 1HHHHW F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H # H5G H H50ZN Hu HH5F HB B H8HZ HE HE HC HHD$ HtaHkHt$ HfoD$ HD$ CH<$HC( Ht
-HD$ HtH|$ HH0H[]A\ 1HH<$HtHD$ HtH|$ HH1HHKIHt
HHH;HtgH Lf. ATHwUSHH0HD$H&HD$(H&HD$ \O 8 H*Hƹ' 1HHHHU F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H H5G H H5 XN He HH5F HB B H8HJ HE HE HC HHD$ HtaHkHt$ HfoD$ HD$ CH<$HC( Ht
-HD$ HtH|$ HH0H[]A\ 1HH<$HtHD$ HtH|$ HH/HHKIHt
HHH;HtWH Lf. ATH'USHH0HD$H$HD$(H$HD$ LP 8 H(Hƹ' 1HHHHS F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H H5}G H H5VN HU HH5F HB B H8H: HE HE HC HHD$ HtaHkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;HtGH Lf. ATHWUSHH0HD$H"HD$(H"HD$ <P 8 H&Hƹ' 1HHHHQ F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H H5y{G H H5 TN HE HH5F HB B H8H* HE HE HC HHD$ HtaHkHt$ HfoD$ HD$ CH<$HC( Ht
-HD$ HtH|$ HH0H[]A\ 1HH<$HtHD$ HtH|$ HH+HHKIHt
HHH;Ht7H Lf. ATHUSHH0HD$H HD$(H HD$ ,H 8 H$Hƹ' 1HHHHO F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5H H5iyG H H5QN H5 HH5F HB B H8H HE HE HC HHD$ HtaHkHt$ HfoD$ HD$ CH<$HC( Ht
-HD$ HtH|$ HH0H[]A\ 1HH<$HtsHD$ HtH|$ HHr)HHKIHt
HHH;Ht'H Lf. ATH7USHH0HD$H HD$(H HD$ D 8 H"Hƹ' 1HHHHuM F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5tH
H5YwG Ht H5ON H% HH5F HB B H8H
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-HD$ HtH|$ HH0H[]A\ 1HH<$HtcHD$ HtH|$ HHb'HHKIHt
HHH;HtH Lf. ATHgUSHH0HD$Hr HD$(Hv HD$ L 8 H Hƹ' 1HHHHeK F HF HHHF(HF8HF@HFXHHNHNHFhH HFpH H4$H H H H H5dH H5IuG Hd H5MN H HH5wF HB B H8H HE HE HC HHD$ HtaHkHt$ HfoD$ HD$ CH<$HC( Ht
-HD$ HtH|$ HH0H[]A\ 1HH<$HtSHD$ HtH|$ HHR%HHKIHt
HHH;HtH Lf. AW1AVAUATUSHX H|$(HHT$HH@0x
-
- HHCP H|$LcLs HD$pHHLcH}I H, HDŽ$ HDŽ$( HDŽ$0 HH/ E11J (HH$ H$0 t LLHHL$&#H$ HL$Hs H{II)H$( IHH)HL9= HHH9tQL$ I9u BfH HH9t-HI9t$HpHJH9tHuHHrH9u @ H$@ H$ HhgH|$H19 HH@0x
-M HH$( M)HSPH$ IH)HH$ I9HIMHH$ H H HH9HFkH$ H$ H HH H9HF6H$ H$ H HH H9HFH$ HIHH$ MFH$ L$ H$ A HtJHIH M xKHHHH Hz HuH~H$ HIHH$ I H$ Ht HHHHIH$ IH$ M HkH$ H$ IH$ Ht$0HI|H$` H$ HDŽ$ HHT$8HHL$ HHD$H0jH$ H$ HDH$ HH$ HD$Hxz}' HcHHHHHD$ 1H|$ Ld$ H$ H$ IL$ HHH9 H$ L$ L1 HH<IHHH9HuHH$ H$ HHD$pJ4HH$` H HL9cH$ HxH$ H$ H9V H$ HL$@H$ HL$HH$ HL$PH$ HL$X H|$ E1H|$ D H$` H NH$ H4LHHH H$ HHH< L< HH$ N4@K<>M,vJ ?I9s D HHLHLHkMI9sL9Hr-f D HII9v H oI9rLLqHI9wH$ IL;d$"H$ HH9H$ H$` HD$(HH tH$ HD$p fHD$(H H$@ HtH$ HtHD$(HX []A\A]A^A_H H9" L, LH$ HT$oH56J HH|$(Hٺ T' H$ H$ Hx^H$ H$ L)HHDŽ$ wH$( H{HC HD$(LH Ht:H$ HDŽ$ Ht H$ HDŽ$ HtHDŽ$ D ( HDŽ$ HL$8HHL$@HHHL$0HHHL$HHHHL$PHH H$ HH$ HH$ H$ Hx|H*HD$HL$ $ HT$XHt$HDŽ$ HxHDŽ$ ' H$ HtH|$ HH$ H$ eHHHH H*XoH HDŽ$ H$ HL$8HH$ HH@HPH$ HPH$ HPH$ HP H$ HP(HT$pHP0H$ HP8H$ HH$ HH$ H$ HxxH*HD$H$ Ht$H$ HDŽ$ HDŽ$ H$ Hx蓁' H$ H HH11}HHH H*XsHL$ 9H$ HHt
HHH$` HtH$ HtLHDŽ$ H$ Ht.HDŽ$ H$ HtHDŽ$ H$@ HtH$ HtHH
X H$ LC0H
F HF H5F HIH$ HIHD$ Ph( H$ H
qJ H5jF M HHHD$0җHF HHLݡH5LmH$ HHxiHH5K LH H|$0HH|$ $H$ H$ HxL3HHHHHH$ H$ HxHaH$ HHH$ HH$ HHH|$ HH$ HHH|$ HHHHX L$ LC0H
F HF H5F LHH$ Huf( H$ H
6J H5F Iغ HIHD$0HF LLHLZH蒞H$ LHx
HǯH5p HHHH|$0I HL@H$ H$ Hx7
IHf. AW1AVAUATUSHX H|$0HHT$ HH@0x
- HE HEP H|$LeLm HD$p֘HH;UH{IH$ H$ ILHHL$(;HE H$( H}H$ IHI)H)IHI9 H9tQL$ I9u BfHu HH9t-HI9t$HpHJH9tHuHHrH9u @ H$@ HHYH|$H1~ HH@0x B
- HH$ M)HSPH$( IH)HH$ I9HIMHH$ s H H HH9HFH$ H$ H HH H9HF{H$ H$ H HH H9HFFH$ IHIH$ H$ x L$ L$ @ HtKHIH M xKHHT HHa HX HuH~H$ HIHH$ I H$ I4HHHHHH$ IH$ I<HkH$ H$ IH$` HHt$8HI|HT$(H$ HDŽ$ HL$ HHD$H\H$ H$ HDH$ H$ HD$Hxo' HcHHHHHD$ 1H|$ Ld$ H$ H$ IL$ HHH9 H$ L$ L1 D HH<IHHH9HuHH$ LHt$pH$ H# H$` H HL9gH$ HHH$ H$ H9H H$ Ht$@H$ Ht$HH$ Ht$PH$ Ht$XH|$ E1H|$ D H$` H4 N4H$ L,LHIH H$ LL$ HH<1MI<HIH,I9s HHLHL
-HD MI9sL9Hr, D HII9v H, I9rLL HI9wH$ IL;d$*H$ HH9H$ H$` HD$0HH t
-H$ HD$p HD$0H H$@ HtJ
-H$ Ht8
-HD$0HX []A\A]A^A_H$ HT$oH5J H% H|$0Hٺ ^G' H$ HxH;=4 tH=6 HW lHt$(l] LHHDŽ$ H)HL$(jH$( H}HE HD$0LH Ht H$ HDŽ$ Ht H$ HDŽ$ Htg HDŽ$ f D ( HDŽ$ * HL$8HHL$@HHHL$(HHHL$HHHHL$PHH H$ HH$ H H$ H$ Hx|H*HD$HL$ $ HT$XHt$HDŽ$ HxHDŽ$ Hu' H$ HtH|$ HH$ H$ eHHHH H*XoH HDŽ$ H$ HL$8HH$ HH@HPH$ HPH$ HPH$ HP H$ HP(HT$pHP0H$ HP8H$ HKH$ HH$ H$ HxxH*HD$H$ Ht$H$ HDŽ$ HDŽ$ H$ Hxs' H$ H HH11HHH H*XsHL$ YH$ HHtH|$ HH$` HttH$ HtHDŽ$ H$ HtHDŽ$ H$ HtvHDŽ$ H$@ HtH$ HtHn H$ HHTH|$ H@H8HBX H$ LE0H
ƝF HF H5F HI H$ HIHD$ Z( H$ H
J H5F M HIHD$(
-HâF HLLLmL襒H$ LHxHڣH5 L X L$ 9 LC0H
F HF H5F LH H$ HY( Lt$(H
J H5F Iغ LRH F LLH]L赒HH$ LHx H"H5˛ HS
-H|$(HvH|$ 謑H$ H$ Hx LHs
-HHHH|$(I'HL\H$ H$ HxS HkIHHH$ HHH$ HH$ HHt
HHHHHH$ Ht$(HxHHHPJH;f AW1AVAUATUSHX H|$(HHT${HH@0x
- HHCP H|$LcLs HD$pGHHGH}I H, HDŽ$ HDŽ$( HDŽ$0 HH/ E11J (HH$ H$0 t LLHHL$H$ HL$Hs H{II)H$( IHH)HL9= HHH9tQL$ I9u BfH HH9t-HI9t$HpHJH9tHuHHrH9u @ H$@ H$ HKH|$H1虏 HH@0x
M HH$( M)HSPH$ IH)HH$ I9HIMHH$ H H HH9HFH$ H$ H HH H9HFH$ H$ H HH H9HFaH$ HIHH$ MFH$ L$ H$ A HtJHIH M xKHHHH Hz HuH~H$ HIHH$ I H$ Ht HHHHIH$ IH$ M HkH$ H$ IH$ Ht$0HI|H$` H$ HDŽ$ HHT$8HHL$ HHD$HNH$ H$ HDH$ HH$ HD$Hxa' HcHHHHHD$ 1H|$ Ld$ H$ H$ IL$ HHH9 H$ L$ L1f D HH<IHHH9HuH H$ H$ HHD$pJ4%H$` H HL9`H$ HxH$ H$ H9R H$ HL$@H$ HL$HH$ HL$PH$ HL$X H|$ E1H|$ D H$` H NH$ H4LHHH H$ HHH< L< HH$ N4K<>M,J ?I9s HHLHL HMI9sL9Hr-f D HII9v H I9rLLHI9wH$ IL;d$"H$ HH9H$ H$` HD$(HH twH$ HD$pH HHD$(H H$@ HtAH$ Ht/HD$(HX []A\A]A^A_H H9" L, L5H$ HT$oH5J HH|$(Hٺ -9' H$ H$ HxD^H$ H$ L)HHDŽ$ [H$( H{HC HD$(LH HtH$ HDŽ$ Ht|H$ HDŽ$ Ht^HDŽ$ D ( HDŽ$ "HL$8HHL$@HHHL$0HHHL$HHHHL$PHH H$ HH$ HxH$ H$ Hx|H*HD$HL$ $ HT$XHt$HDŽ$ HxHDŽ$ @g' H$ HtH|$ HH$ H$ eHHHH H*XoH HDŽ$ H$ HL$8HH$ HH@HPH$ HPH$ HPH$ HP H$ HP(HT$pHP0H$ HP8H$ HH$ HdH$ H$ HxxH*HD$H$ Ht$H$ HDŽ$ HDŽ$ H$ Hxe' H$ H HH11HHH H*XsHL$ 9H$ HHt
HHH$` HtnH$ HtHDŽ$ H$ HtHDŽ$ H$ HtpHDŽ$ H$@ HtH$ HtHhH^X H$ ,LC0H
F HF H5F HIH$ HIHD$ L( H$ H
J H5ʔF M HHHD$02|HF HHL=H蕅L̈́H$ HHxHH5 L3H H|$0HNH|$ 脄H$ H$ Hx{LHKHHHHH$ H$ Hx@HaH$ HHH$ HH$ HHH|$ HH$ HHH|$ HHHHX L$ ILC0H
F HF H5F LHH$ HJ( H$ H
VJ H5F Iغ HIHD$0WzHF LLHbL躃HH$ LHxH'H5Ќ HXHHH|$0IkHL蠂H$ H$ HxIHf. AW1AVAUATUSHX H|$(HHT$k}HH@0x
- HHCP H|$LcLs HD$p7}HH9H}I H, HDŽ$ HDŽ$( HDŽ$0 HH- E11J (HH$ H$0 t LLHHL$vH$ HL$Hs H{II)H$( IHH)HL9; HHH9tQL$ I9u BfH HH9t-HI9t$HpHJH9tHuHHrH9u @ H$@ H$ H=H|$H1艁 HH@0x M HH$( M)HSPH$ IH)HH$ I9HIMHH$ H H HH9HFH$ H$ H HH H9HFH$ H$ H HH H9HFQH$ HIHH$ MFH$ L$ H$ A HtJHIH M xKHHHH Hx HuH~H$ HIHH$ I H$ Ht HHHHIH$ IH$ M HkH$ H$ IH$ Ht$0HI|H$` H$ HDŽ$ HHT$8HHL$ HHD$H@H$ H$ HDH$ HH$ HD$HxS' HcHHHHHD$ 1H|$ Ld$ H$ H$ IL$ HHH9 H$ L$ L1f D HH<IHHH9HuH H$ H$ HHD$pJ4H$` H HL9`H$ HxH$ H$ H9R H$ HL$@H$ HL$HH$ HL$PH$ HL$X H|$ E1H|$ D H$` H NH$ H4LHHH H$ HHH< L< HH$ N4K<>M,J ?I9s HHLHLHMI9sL9Hr-f D HII9v H I9rLLHI9wH$ IL;d$"H$ HH9H$ H$` HD$(HH tgH$ HD$p HD$(H H$@ Ht3H$ Ht!HD$(HX []A\A]A^A_H H9$ L, L'H$ HT$oH5J HH|$(Hٺ +' H$ H$ Hx6^H$ H$ L)HHDŽ$ MH$( H{HC HD$(LH HtH$ HDŽ$ HtnH$ HDŽ$ HtPHDŽ$ ( HDŽ$ HL$8HHL$@HHHL$0HHHL$HHHHL$PHH H$ HH$ HuH$ H$ Hx|H*HD$HL$ $ HT$XHt$HDŽ$ HxHDŽ$ 0Y' H$ HtH|$ HH$ H$ eHHHH H*XoH HDŽ$ H$ HL$8HH$ HH@HPH$ HPH$ HPH$ HP H$ HP(HT$pHP0H$ HP8H$ HH$ HsH$ H$ HxxH*HD$H$ Ht$H$ HDŽ$ HDŽ$ H$ HxW' H$ H HH11HHH H*XsHL$ 9H$ HHt
HHH$` Ht^H$ HtHDŽ$ H$ Ht~HDŽ$ H$ Ht`HDŽ$ H$@ HtH$ HtHXHNX H$ LC0H
ށF HځF H5ցF HIH$ HIHD$ >( H$ H
J H5F M HHHD$0"nHۆF HHL-xHwLvH$ HHxHH5 L#H H|$0H>wH|$ tvH$ H$ HxkLH;HHHHH$ H$ Hx0HaH$ HHH$ HH$ HHH|$ HH$ HHH|$ HHHHX L$ 9LC0H
F HF H5F LHH$ H<( H$ H
J H5߄F Iغ HIHD$0GlH F LLHRvLuHtH$ LHxHH5~ HHHHH|$0I[uHLtH$ H$ HxIHf. AW1AVAUATUSHX H|$(HHT$[oHH@0x
- HHCP H|$LcLs HD$p'oHH+H}I H, HDŽ$ HDŽ$( HDŽ$0 HH/ E11J (HH$ H$0 t LLHHL$fH$ HL$Hs H{II)H$( IHH)HL9= HHH9tQL$ I9u BfH HH9t-HI9t$HpHJH9tHuHHrH9u @ H$@ H$ H/H|$H1ys HH@0xM HH$( M)HSPH$ IH)HH$ I9HIMHH$ H H HH9HFH$ H$ H HH H9HFvH$ H$ H HH H9HFAH$ HIHH$ MFH$ L$ H$ A HtJHIH M xKHHHH Hz HuH~H$ HIHH$ I H$ Ht HHHHIH$ IH$ M HkH$ H$ IH$ Ht$0HI|H$` H$ HDŽ$ HHT$8HHL$ HHD$Hp2H$ H$ HDH$ HH$ HD$HxE' HcHHHHHD$ 1H|$ Ld$ H$ H$ IL$ HHH9 H$ L$ L1 HH<IHHH9HuHH$ H$ HHD$pJ4H H$` H HL9cH$ HxH$ H$ H9V H$ HL$@H$ HL$HH$ HL$PH$ HL$X H|$ E1H|$ D H$` H NH$ H4LHHH H$ HHH< L< HH$ N4@K<>M,vJ ?I9s D HHLHLHkMI9sL9Hr-f D HII9v H oI9rLLHI9wH$ IL;d$"H$ HH9H$ H$` HD$(HH tWH$ HD$p fHD$(H H$@ Ht!H$ HtHD$(HX []A\A]A^A_H H9" L, LH$ HT$oH5vJ HH|$(Hٺ
' H$ H$ Hx$^H$ H$ L)HHDŽ$ ?H$( H{HC HD$(LH HtzH$ HDŽ$ Ht\H$ HDŽ$ Ht>HDŽ$ D ( HDŽ$ HL$8HHL$@HHHL$0HHHL$HHHHL$PHH H$ HH$ HXiH$ H$ Hx|H*HD$HL$ $ HT$XHt$HDŽ$ HxHDŽ$ K' H$ HtH|$ HH$ H$ eHHHH H*XoH HDŽ$ H$ HL$8HH$ HH@HPH$ HPH$ HPH$ HP H$ HP(HT$pHP0H$ HP8H$ H{H$ HDgH$ H$ HxxH*HD$H$ Ht$H$ HDŽ$ HDŽ$ H$ HxI' H$ H HH11}HHH H*XsHL$ 9H$ HHt
HHH$` HtNH$ HtHDŽ$ H$ HtnHDŽ$ H$ HtPHDŽ$ H$@ HtH$ HtHHH>X H$ LC0H
sF HsF H5sF HIH$ HIHD$ 0( H$ H
J H5xF M HHHD$0`HxF HHL jHuiLhH$ HHxHyH5r LH H|$0H.iH|$ dhH$ H$ Hx[LsH+HHHHH$ H$ Hx HaH$ HHH$ HH$ HHH|$ HH$ HHH|$ HHHHX L$ )LC0H
qF HqF H5qF LHH$ H.( H$ H
֠J H5vF Iغ HIHD$07^HvF LLHBhLgHfH$ LHxHxH5p H8HHH|$0IKgHLfH$ H$ HxwIHf. AW1AVAUATUSHX H|$0HHT$KaHH@0xz HE HEP H|$LeLm HD$paHH{ H{HD$H$ Ht$H$ HHL$(HwaHE H$( H}H$ IHI)H)IHI9 H9tUL$ I9u$Ff D H} HH9t-HI9t$HpHJH9tHuHHrH9u @ H$@ HH!H|$H1e HH@0xJ HH$ M)HSPH$( IH)HH$ I9HIMHH$ { H H HH9HFH$ H$ H HH H9HFH$ H$ H HH H9HF~H$ IHIH$ H$ H L$ L$ @ HtKHIH M xKHHT HH3 H* HuH~H$ HIHH$ I H$ I4HHHHHH$ IH$ I<HkH$ H$ IH$` HHt$8HI|HT$(H$ HDŽ$ HL$ HHD$H$H$ H$ HDH$ H$ HD$Hx8' HcHHHHHD$ 1H|$ Ld$ H$ H$ IL$ HHH9r H$ L$ L1 D HH<IHHH9HuHH$ LHt$pH$ H[H$` H HL9gH$ HHH$ H$ H9P H$ Ht$@H$ Ht$HH$ Ht$PH$ Ht$Xf H|$l E1H|$ D H$` H4 N4H$ L,LHIH H$ LL$ HH<1MI<HIH,I9s HHLHLDHD MI9sL9Hr, D HII9v H, I9rLL HI9wH$ IL;d$*H$ HH9H$ H$` HD$0HH tH$ HD$p HD$0H H$@ HtzH$ HthHD$0HX []A\A]A^A_H$ HT$oH5J HUH|$0Hٺ ' H$ Ht$(HxLHHDŽ$ H)HL$(g2H$( H}HE HD$0LH Ht H$ HDŽ$ HtH$ HDŽ$ HtHDŽ$ @ ( HDŽ$ HL$8HHL$@HHHL$(HHHL$HHHHL$PHH H$ HrH$ Hh]H$ H$ Hx|H*HD$HL$ $ HT$XHt$HDŽ$ HxHDŽ$ =' H$ HtH|$ HH$ H$ H@HHH H*XoH HDŽ$ H$ HL$8HH$ HH@HPH$ HPH$ HPH$ HP H$ HP(HT$pHP0H$ HP8H$ HgH$ HT[H$ H$ HxxH*HD$H$ Ht$H$ HDŽ$ HDŽ$ H$ Hxc<' H$ H HH11HHH H*XsHL$ H$ HHt
HHHH$` HtH$ HtHDŽ$ H$ HtHDŽ$ H$ HtHDŽ$ H$@ HtoH$ Ht]HH$ HHTH|$ H@H8Hf@X L$ iLC0H
+fF H'fF H5#fF LH%H$ H"( Lt$(H
yJ H5kF Iغ LRH;kF LLH\L[H [H$ LHxHRlH5d HH$ HHbH$ HKH$ HH7H|$ H#X H$ ^LE0H
eF H eF H5eF HIH$ HIHD$ !( H$ H
J H5iF M HIHD$(dQH jF HLLo[LZLYH$ LHxH4kH5c LeHH$ Ht$(HxH|$(HjZH|$ YH$ H$ HxLHgHHHHOH)HHIH|$(IYHL0YH$ H$ Hx'H?IHfHf AW1IAVAUE1ATUSHX H|$(HHT$ SHHJ LHD$pLcLs SHH%H}I虮 H, 1HDŽ$ HDŽ$( HDŽ$0 HH6 J (HH$ H$0 t LLHHL$H$ HL$Hs H{II)H$( IHH)HL9I HHH9tUL$ I9u$Ff D H HH9t-HI9t$HpHJH9tHuHHrH9u @ H$@ H$ H@H|$ H1X HH@0x HH$( M)HSPH$ IH)HH$ I9HIMHH$ H H HH9HFCH$ H$ H HH H9HFH$ H$ H HH H9HFH$ HIHH$ MFH$ L$ H$ A HtJHIH M xKHHHH H HuH~H$ HIHH$ I H$ Ht HHHHIH$ IH$ M HkH$ H$ IH$ Ht$0HI|H$` H$ HDŽ$ HHT$8HHL$HHD$HH$ H$ HDH$ HH$ HD$ HxQ*' HcHHHHHD$ 1H|$ Ld$ H$ H$ IL$ HHH9 H$ L$ L1f D HH<IHHH9HuH H$ H$ HHD$pJ4H$` H HL9`H$ HxH$ H$ H9Z H$ HL$@H$ HL$HH$ HL$PH$ HL$X H|$ E1H|$ D H$` H NH$ H4LHHH H$ HHH< L< HH$ N4K<>M,J ?I9s f HHLHL|HMI9sL9Hr-f D HII9v H I9rLLAHI9wH$ IL;d$H$ HH9H$ H$` HD$(HH tH$ HD$pH HHD$(H H$@ HtH$ HtHD$(HX []A\A]A^A_H H9z L, LH$ HT$oH5J HdH|$(Hٺ ' H$ H$ Hx^H$ H$ L)HHDŽ$ h$H$( H{HC HD$(LH Ht
-H$ HDŽ$ HtH$ HDŽ$ HtHDŽ$ D ( HDŽ$ HL$8HHL$@HHHL$0HHHL$HHHHL$PHH H$ HjH$ HPH$ H$ Hx|H*HD$ HL$$ HT$XHt$HDŽ$ HxHDŽ$ /' H$ HtH|$ HH$ H$ eHHHH H*XoH HDŽ$ H$ HL$8HH$ H$ HH@HPH$ HPH$ HPH$ HP H$ HP(HT$pHP0H$ HP8H^H$ HNH$ H$ HxxH*HD$ H$ Ht$H$ HDŽ$ HDŽ$ H$ Hxc.' H$ H HH11yHHH H*XsHL$ 1H$ HHtH|$ HH$` HtH$ HtHDŽ$ H$ HtHDŽ$ H$ HtHDŽ$ H$@ HtpH$ Ht^HH$ HHTH|$ H@H8H ?H릿X L$ dLC0H
&XF H"XF H5 XF LH H$ H( H$ H
1J H5
-]F Iغ HIHD$0rDH+]F LLH}NLMH
MH$ LHx HB^H5V HsH$ HHQH$ H:H$ HHt
HHHH=vHlHFHH$ H$ HxePH|$0IMHL8LH$ H$ Hx/HG IHH AW1AVAUATUSHX H|$(HHT$FHH@0x
- HHCP H|$LcLs HD$pFHH,H}I蠡 H, HDŽ$ HDŽ$( HDŽ$0 HH- E11J (HH$ H$0 t LLHHL$H$ HL$Hs H{II)H$( IHH)HL9; HHH9tQL$ I9u BfH HH9t-HI9t$HpHJH9tHuHHrH9u @ H$@ H$ HHH|$H1K HH@0xM HH$( M)HSPH$ IH)HH$ I9HIMHH$ H H HH9HFKH$ H$ H HH H9HFH$ H$ H HH H9HFH$ HIHH$ MFH$ L$ H$ A HtJHIH M xKHHHH Hx HuH~H$ HIHH$ I H$ Ht HHHHIH$ IH$ M HkH$ H$ IH$ Ht$0HI|H$` H$ HDŽ$ HHT$8HHL$ HHD$H
-H$ H$ HDH$ HH$ HD$HxY ' HcHHHHHD$ 1H|$ Ld$ H$ H$ IL$ HHH9 H$ L$ L1f D HH<IHHH9HuH H$ H$ HHD$pJ4襸H$` H HL9`H$ HxH$ H$ H9R H$ HL$@H$ HL$HH$ HL$PH$ HL$X H|$ E1H|$ D H$` H NH$ H4LHHH H$ HHH< L< HH$ N4K<>M,J ?I9s HHLHL茷HMI9sL9Hr-f D HII9v H I9rLLQHI9wH$ IL;d$"H$ HH9H$ H$` HD$(HH tH$ HD$p HD$(H H$@ Ht÷H$ Ht豷HD$(HX []A\A]A^A_H H9$ L, L跷H$ HT$oH5{J HvH|$(Hٺ & H$ H$ HxƳ^H$ H$ L)HHDŽ$ zH$( H{HC HD$(LH Ht H$ HDŽ$ HtH$ HDŽ$ HtHDŽ$ ( HDŽ$ 袶HL$8HHL$@HHHL$0HHHL$HHHHL$PHH H$ HcH$ HxEH$ H$ Hx|H*HD$HL$ $ HT$XHt$HDŽ$ HxHDŽ$ "' H$ HtH|$ HH$ H$ eHHHH H*XoH HDŽ$ 藵H$ HL$8HH$ HH@HPH$ HPH$ HPH$ HP H$ HP(HT$pHP0H$ HP8H$ H#OH$ HdCH$ H$ HxxH*HD$H$ Ht$H$ HDŽ$ HDŽ$ H$ Hxs!' H$ H HH11HHH H*XsHL$ 9H$ HHt
HHH$` HtH$ Ht,HDŽ$ H$ HtHDŽ$ H$ HtHDŽ$ H$@ Ht肳H$ HtpHHޭX H$ 謮LC0H
nKF HjKF H5fKF HIhH$ HIHD$ 0( H$ H
1yJ H5JPF M HHHD$07HkPF HHLAHALM@H$ HHxIHQH5+J L賸H H|$0H@H|$ @H$ H$ HxLH˸HHHHH$ H$ HxHaH$ HHH$ HH$ HHH|$ HH$ HHH|$ HHHHX L$ ɬLC0H
IF HIF H5IF LH腸H$ HU( H$ H
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'+! H8H \! HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu6 H( HD HGf ATIp USH@~|l dH%( HD$81Hl HD$)D$ 现HHl . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B"( HH|$H5gN N! HH5DM "! HH5S %! HH H5VQL @
')! H8H Z! HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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''! H8H X! HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
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'%! H8H V! HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu6 H0| HL| HO|f ATIp USH@~|l dH%( HD$81H l HD$)D$ 爰HH~l . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B- HH|$H5aN H! HH5>M ! HH5S ! HH H5VKL @
'#! H8H T! HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu6 Hz Hz Hzf ATIp USH@~|l dH%( HD$81Hl HD$)D$ 熰HH|l . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B/ HH|$H5_N F! HH5<M ! HH5S ! HH H5VIL @
'!! H8H R! HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu6 Hx Hx Hxf ATIp USH@~|l dH%( HD$81H}l HD$)D$ 焰HHzl . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR% HH|$H5]N D! HH5:M ! HH5S ! HH H5VGL @
' ! H8H P! HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu6 H8w HTw HWwf ATIp USH@~|l dH%( HD$81Hl HD$)D$ 炰HHxl . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B* HH|$H5[N B! HH58M ! HH5S ! HH H5VEL @
' ! H8H N! HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu6 Hu Hu Huf ATIp USH@~|l dH%( HD$81H=l HD$)D$ 瀰HHvl . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B" HH|$H5YN @! HH56M ! HH5S ! HH H5VCL @
'! H8H L! HH ID$ I$HD$ Ht{Il$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u*H@L[]A\f ID$( H|$Hu6 Hs Ht Htf ATIp USH@~|l dH%( HD$81H~l HD$)D$ ~HHtl . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B HH|$H5WN >! HH54M ! HH5S ! HH H H5LAL HH ! H8HJ! HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu6~ H@r H\r H_rf ATIp USH@~|l dH%( HD$81H=|l HD$)D$ |HHrl . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B! HH|$H5UN <! HH52M ! HH5S ! HH H H5L?L HH ! H8HH! HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu6| Hp Hp Hpf ATIp USH@~|~l dH%( HD$81HMzl HD$)D$ zHHpl . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B" HH|$H5SN :! HH50M ! HH5S ! HH H H5L=L HH ! H8HF! HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu6z Hn H o Hof ATIp USH@~||l dH%( HD$81H}xl HD$)D$ xHHnl . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B2 HH|$H5QN 8! HH5.M ! HH5S ! HH H H5L;L HH ! H8HD! HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu6x HHm Hdm Hgmf ATIp USH@~|zl dH%( HD$81Hvl HD$)D$ vHHll . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B HH|$H5ON 6! HH5,M
+! HH5S
! HH H H5L9L HH ! H8HB! HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu6v Hk Hk Hkf ATIp USH@~|xl dH%( HD$81H tl HD$)D$ tHHjl . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B HH|$H5MN 4! HH5*M ! HH5S ! HH H H5L7L HH ! H8H@! HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu6t Hi Hj Hjf ATIp USH@~|vl dH%( HD$81Hmrl HD$)D$ rHHhl . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B
HH|$H5KN 2! HH5(M ! HH5S ! HH H H5L5L HH
! H8H>! HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu6r HPh Hlh Hohf ATIp USH@~|tl dH%( HD$81Hpl HD$)D$ pHHfl . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B HH|$H5IN 0! HH5&M ! HH5S ! HH H H5L3L HH ! H8H<! HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu6p Hf Hf Hff ATIp USH@~|rl dH%( HD$81Hnl HD$)D$ nHHdl . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH BR HH|$H5GN .! HH5$M ! HH5}S ! HH H H5L1L HH ! H8H:! HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu6n H e H e H ef ATIp USH@~|pl dH%( HD$81Hll HD$)D$ lHHbl . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B HH|$H5EN ,! HH5"M ! HH5{S ! HH H H5L/L HH ! H8H8! HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu6l HXc Htc Hwcf ATIp USH@~|nl dH%( HD$81H]jl HD$)D$ jHH`l . HHT$Hp1HfHnH2H HBHB(flHB8HB@HBXHBhHBpH H H H0 H@ HH B HH|$H5CN *! HH5 M HH5yS ! HH H H5L-L HH ! H8H6! HH ID$ I$HD$ HttIl$Ht$ HfoL$ H|$ID$( HD$ AL$Ht
+HD$ HtH|$ HHD$8dH3%( u#H@L[]A\fID$( H|$Hu6j Ha Ha Haf AWAVAUATIp USHH~vll dH%( HD$81Hhl HD$)D$ hH¹. 1H ^l HHT$HHsH HBHB(fHnHB8flHB@HBXHBhHBpH H H H0 H2H@ HH B HH|$H5AN (! HH5 M HH5wS HH H5P+L @
!! H8H4! HE HE ID$ I$HD$ H Il$Ht$ HfoL$ Hl$ID$( HD$ AL$H L8 L( Mt IuLmLMm8gMuL L Mt IuLlLMmgMuH HtfH Htf Lm`L}PMtMH=oxl IuLMzkI~(MmHtfIF HxH9G LfMuLm0L} MtMH=xl IuLM kI~(MmHt>fIF HxH9
L%fMuHEHxH9 HEHxH9 HE HxH9- p HP
L HD$ HtH|$ HHD$8dH3%( HHL[]A\A]A^A_ÐIuLMajI~(MmHteIF HxH9 LfeMu @ IuLMjI~(MmHt7eIF HxH9& L eMu @ ID$( Hl$H- @ L L MH=vl tGIuLMiI~(MmHtdIF HxH9m LdMu IuLMIiI~(MmHtgdIF HxH9F LNdMup @ P)d @ P d @ PJHc D PJHacW D H=hul Pcf D H=8ul PakcWf D H=ul tvPF?c<f. Pcw @ PJHb D PJH:fPJH D PJHJc H[ H[ H[f AW1AVAUIATUSHX H|$HdH%( H$H 1fsHH@0x HCPH LHD$@HkHC H)H$HI&sIH@0xk I$I|$HD$ID$PHD$$ fHDŽ$ L$ )$ HMII9w MM LaIK L$ H$ Mt"Ht$LHt$LHL$0w`HL$0IH$ HC LKHL)HL9 I9tGHfHf HHI9t)L9t$HpHzH9tHuHHrHI9uHfHDŽ$ L))$ HHu HH9
+ H`H$ L$ HIM)fHnHflH$ )$ L9tLLeHLH$ 1H$ L! HH@0x < HSPH$ LHH$ HT$HH)IIH9$ILH|$PH HH9-
H YH|$PHD$XHH9
HXH|$PHD$`HH9 HXHD$hIHD$PLL|$XHHD$pI LT$` M MS A f D Hx HD HJHH$ H HHuMu5HD$pHHD$pI4HD$pI HHHHIHT$pLIIHIuHD$pM~ IH\$PHHI<HD$HH9D$p HD$fHDŽ$ )$ H, HC H|^H (HH1H$ HD$PH$ L|$XH $HXiZH $H$ M II}HD$xH$ *1 HcHD$HHHD$0H H HDŽ$0 ]H\$xHT$pH$ fHnfHnHT$XflH\$PfHn fHnHT$HH\$`flfHnH$ @fHnH\$@fHnflH$ @ fHnflHP@H$ @0~\l _l )$0 H
+ fH*H$ I}fHt$H$ H)$ $ 2 H$0 Ht
HHHD$PHT$pHH$ H9$ H$ HL$`H$ fHnfHnH\$8fl)T$ H|$0 ( HDŽ$0 s\HT$h~D$8fo\$ H$ fHnHT$HflHP H$ @~^l *bl )$0 H fH*H$ I}fHt$H$ H)$ $ 2 H$0 Ht
HHHT$pH$ HH$ H9 HD$H$ H Ht@[LD$hMN H|$`HtS]H|$XHt D]fHD$@ HD$H H$ HtZH$ HtZH$H dH3%( HD$HX []A\A]A^A_ H$ L)H$ LHDŽ$ )LKH$ HC L$ @ E1I1HtQHAI@A~ Mu HI Hx Ht HN IHu HD$H L \H|$`Hf L$ H$ H5O LqoH|$L js1 H$ HxH;=Ol H=kl Pi_ @ H|$ H$ fH$H$H9D$H$ H$ H)HH9$ H$H HD$`H$ H L$HH HIHuHt$hHA LH\$HHIH<H<IHL<M9v
D IHHMHWK7LI9s f D IHM9!O7M9rHHLLT$EWLT$M9M1H|$ s @ HD$pLL$xH|$PLHHH9& Ht$XLD$`HI M! L1 HH<IHHHH9uHHt$@H$ H|$HLVH$ H$ H)HH9v7H HH9l$RH
L HL H=L \H
L HxL H=L \HЃfHH H*X1HDŽ$ HЃfHH H*XIp11PJHZH
L HO e H=L T\H
L HO e H=L 5\H
L HO e H=L \H
tL HO e H=tL [H=L {RFQAWH
?L HcO e H=?L [H=L FR Hx Ht HN IHHIAH@AuMu(HH
L HO e H=L HD$pB[HI뇺 MS Hx Ht HN IHVHMIAH@AuMu,HH
=L e HD$pH=?L IHLO ZHIH
L H/O e H= L Z H H H$0 Ht
HHH$ HtTHH|$hHtVH|$`HtVH|$XHtVH$ HtKTH$ Ht9THYH$0 HtH$ Hn H HnX L$ L$ OLLC0LIHw%L L$ H$ Hc%L H$ LH$ HL%L H$
+Lt\2 M LH
r,L H5%L oLH%L LL!L`LrH$ HxH;=6Il t}H@AH5Yj LqX H HLF`LnrH$ HxH;=Hl td}LPj H Hje H3TX L$ H$ pNHMD$0LIH$L L$ H$ H$L H$ LH$ H#L H$ L[2 M LH
&L H5,$L 7nHH]$L LL L:_LbqH$ HxH;=Gl tX|H?H5j LWX L$ H$ MHLC0LIH1#L L$ H$ H #L H$ LH$ H#L H$ L.Z2 M LH
(L H5E#L PmHHv#L LL LS^L{pH$ HxH;=Fl tq{H>H5j L+V H H HL]L pH$ HxH;=Fl t{LnNHV H H HL]LoH$ HxH;=@Fl tzLNHU H H#H$ HxH;=Fl zH$0 H< HH* H HH$0 HH$ H H5 f. AW1AVAUIATUSHX H|$HdH%( H$H 1F`HH@0x HCPH LHD$@HkHC H)H$HI`IH@0xk I$I|$HD$ID$PHD$$ fHDŽ$ L$ )$ HMII9w MM LNIK L$ H$ Mt"Ht$LHt$LHL$0WMHL$0IH$ HC LKHL)HL9 I9tGHfHf HHI9t)L9t$HpHzH9tHuHHrHI9uHfHDŽ$ L))$ HHu HH9
+ HMH$ L$ HIM)fHnHflH$ )$ L9tLLQHLH$ 1H$ L! HH@0x< HSPH$ LHH$ HT$HH)IIH9$ILH|$PH HH9-
HEH|$PHD$XHH9
HEH|$PHD$`HH9 HEHD$hIHD$PLL|$XHHD$pI LT$` M MS A f D Hx HD HJHH$ H HHuMu5HD$pHHD$pI4HD$pI HHHHIHT$pLIIHIuHD$pM~ IH\$PHHI<HD$HH9D$p HD$fHDŽ$ )$ H, HC H\KH (HH1H$ HD$PH$ L|$XH $HXIGH $H$ M II}HD$xH$
+1 HcHD$HHHD$0H H HDŽ$0 JH\$xHT$pH$ fHnfHnHT$XflH\$PfHn fHnHT$HH\$`flfHnH$ @fHnH\$@fHnflH$ @ fHnflHP@H$ @0~Ol \l )$0 H
+ fH*H$ I}fHt$H$ H)$ $ 1 H$0 Ht
HHHD$PHT$pHH$ H9$ H$ HL$`H$ fHnfHnH\$8fl)T$ H|$0 ( HDŽ$0 SIHT$h~D$8fo\$ H$ fHnHT$HflHP H$ @~DAl Ql )$0 H fH*H$ I}fHt$H$ H)$ $ 1 H$0 Ht
HHHT$pH$ HH$ H9 HD$H$ H Ht HLD$hMN H|$`Ht3JH|$XHt $JfHD$@ HD$H H$ HtGH$ HtGH$H dH3%( HD$HX []A\A]A^A_ H$ L)H$ LHDŽ$ {LKH$ HC L$ @ E1I1HtQHAI@A~ Mu HI Hx Ht HN IHu HD$H LHH|$`Hf L$ H$ H5O LQ\H|$L J`1 H$ HxH;=_<l H=Wl Pi_ @ H|$ H$ fH$H$H9D$H$ H$ H)HH9$ H$H HD$`H$ H L$HH HIHuHt$hHA LH\$HHIH<H<IHL<M9v
D IHHMHdDK7LI9s f D IHM9!O7M9rHHLLT$%DLT$M9M1H|$ s @ HD$pLL$xH|$PLHHH9& Ht$XLD$`HI M! L1 HH<IHHHH9uHHt$@H$ H|$HLCH$ H$ H)HH9v7H HH9l$RH
K HwL H=K IH
gK HXL H=K IHЃfHH H*X1HDŽ$ HЃfHH H*XIp11PJHZH
K HO e H= L 4IH
K HO e H= L IH
sK HO e H=s L HH
TK HxO e H=T L HH=L [?&>!DH
K HCO e H= L HH=L &? Hx Ht HN IHHIAH@AuMu(HH
K HO e H=K HD$p"HHI뇺 MS Hx Ht HN IHVHMIAH@AuMu,HH
K e HD$pH= K IH,O GHIH
K HO e H=K nG H H H$0 Ht
HHH$ HtmAHH|$hHtCH|$`Ht|CH|$XHtmCH$ Ht+AH$ HtAHFH$0 HtH$ Hn H HnX L$ L$ <LLC0LIHWL L$ H$ HCL H$ LH$ H,L H$ LTI2 M LH
L H5kL v\LHL LLLyML_H$ HxH;=6l tjH .H59j LQE H HL&MLN_H$ HxH;=5l tDjL=j H HJE H34X L$ H$ P;HMD$0LIHL L$ H$ HL H$ LH$ HL H$ LG2 M LH
L H5 L [HH=L LLLLLB^H$ HxH;=4l t8iH,H5ڷj LCX L$ H$ h:HLC0LIHL L$ H$ HL H$ LH$ HL H$ LG2 M LH
L H5%L 0ZHHVL LLL3KL[]H$ HxH;=3l tQhH+H5j L C H H HLJL ]H$ HxH;=u3l tgLN;HB H H HLJL\H$ HxH;= 3l tgL:HB H H#H$ HxH;=2l agH$0 H< HH* H HH$0 HH$ H H5 f. AW1AVAUIATUSHX H|$HdH%( H$H 1&MHH@0x HCPH LHD$@HkHC H)H$HILIH@0xk I$I|$HD$ID$PHD$$ fHDŽ$ L$ )$ HMII9w MM L;IK L$ H$ Mt"Ht$LHt$LHL$07:HL$0IH$ HC LKHL)HL9 I9tGHfHf HHI9t)L9t$HpHzH9tHuHHrHI9uHfHDŽ$ L))$ HHu HH9
+ H:H$ L$ HIM)fHnHflH$ )$ L9tLL>HLH$ 1H$ L走! HH@0x< HSPH$ LHH$ HT$HH)IIH9$ILH|$PH HH9-
H2H|$PHD$XHH9
H2H|$PHD$`HH9 H}2HD$hIHD$PLL|$XHHD$pI LT$` M MS A f D Hx HD HJHH$ H HHuMu5HD$pHHD$pI4HD$pI HHHHIHT$pLIIHIuHD$pM~ IH\$PHHI<HD$HH9D$p HD$fHDŽ$ )$ H, HC H<8H (HH1H$ HD$PH$ L|$XH $HX)4H $H$ M II}HD$xH$ z1 HcHD$HHHD$0H H HDŽ$0 7H\$xHT$pH$ fHnfHnHT$XflH\$PfHn fHnHT$HH\$`flfHnH$ @fHnH\$@fHnflH$ @ fHnflHP@H$ @0~Z6l K=l )$0 H
+ fH*H$ I}fHt$H$ H)$ $ 1 H$0 Ht
HHHD$PHT$pHH$ H9$ H$ HL$`H$ fHnfHnH\$8fl)T$ H|$0 ( HDŽ$0 36HT$h~D$8fo\$ H$ fHnHT$HflHP H$ @~4(l rBl )$0 H fH*H$ I}fHt$H$ H)$ $ 1 H$0 Ht
HHHT$pH$ HH$ H9 HD$H$ H Ht 5LD$hMN H|$`Ht7H|$XHt 7fHD$@ HD$H H$ Ht4H$ Ht4H$H dH3%( HD$HX []A\A]A^A_ H$ L)H$ LHDŽ$ gLKH$ HC L$ @ E1I1HtQHAI@A~ Mu HI Hx Ht HN IHu HD$H L5H|$`Hf L$ H$ H5O L1IH|$L *M1 H$ HxH;=?)l H=Dl Pi_ @ H|$ H$ fH$H$H9D$H$ H$ H)HH9$ H$H HD$`H$ H L$HH HIHuHt$hHA LH\$HHIH<H<IHL<M9v
D IHHMHD1K7LI9s f D IHM9!O7M9rHHLLT$1LT$M9M1H|$ s @ HD$pLL$xH|$PLHHH9& Ht$XLD$`HI M! L1 HH<IHHHH9uHHt$@H$ H|$HLa0H$ H$ H)HH9v7H HH9l$RH
fK HWK H=K 6H
GK H8K H=K 6HЃfHH H*X1HDŽ$ HЃfHH H*XIp11PJHZH
K HO e H=K 6H
rK HO e H=rK 5H
SK HwO e H=SK 5H
4K HXO e H=4K 5H=K ;,+1H
K H#O e H=K 5H=K , Hx Ht HN IHHIAH@AuMu(HH
K HO e H=K HD$p5HI뇺 MS Hx Ht HN IHVHMIAH@AuMu,HH
K e HD$pH=K IH O w4HIH
K HO e H=K N4 H H H$0 Ht
HHH$ HtM.HH|$hHtk0H|$`Ht\0H|$XHtM0H$ Ht .H$ Ht-H3H$0 HtH$ Hn H HnX L$ L$ )LLC0LIH7K L$ H$ H#K H$ LH$ H K H$ L462 M LH
L H5KK VILH|K LLLY:LLH$ HxH;="l twWH H5j L12 H HL:L.LH$ HxH;="l t$WL|*j H H*{%{ H3{X L$ H$ 0(HMD$0LIHK L$ H$ HK H$ LH$ HK H$ mL42 M LH
K H5K GHH K LLL8L"KH$ HxH;=!l tVHH5j L0X L$ H$ H'HLC0LIHK L$ H$ HK H$ LH$ HK H$ L32 M LH
L H5K GHH6K LLL8L;JH$ HxH;= l t1UHH5ӣj L/ H H HL7LIH$ HxH;=U l tTL.(H/ H H HLc7LIH$ HxH;= l tTL'Hq/ H H#H$ HxH;= l ATH$0 H< HH* H HH$0 HH$ H H5 f. AW1AVAUIATUSHX H|$HdH%( H$H 1:HH@0x HCPH LHD$@HkHC H)H$HI9IH@0xs I$I|$HD$ID$PHD$x$ fHDŽ$ L$ )$ HMII9 MU L}(IK L$ H$ Mt"Ht$LHt$LHL$0'HL$0IH$ HC LKHL)HL9 L9tGHfHv HHI9t)L9t$HpHzH9tHuHHrHI9uHfHDŽ$ L))$ HH HH9
+ H'H$ L$ HIM)fHnHflH$ )$ L9tLL+HLH$ 1H$ L蕝! HH@0xD HSPH$ LHH$ HT$HH)IIH9$ILH|$PH HH95
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K H5K LHK LL uLLH$ HxH;=2k tH<H5U3j Lm H HLBLjH$ HxH;=߯k t`L踷j H Hfa H3PX L$ H$ lHMD$0LIHK L$ H$ H K H$ LH$ HK H$ oL1 M LH
K H5(K 3HHYK LLsL6L^H$ HxH;=Ӯk tTHݦH51j LX L$ H$ 脴HLC0LIH-K L$ H$ HK H$ LH$ HK H$ nL*1 M LH
K H5AK LHHrK LLrLOLwH$ HxH;=k tmHH51j L' H H HLL H$ HxH;=k tLjH H H HLLH$ HxH;=<k tLH譼 H H#H$ HxH;= k }H$0 H< HH* H HH$0 HH$ H H5 f D AW1AVAUIATUSHX H|$HdH%( H$H 1FHH@0x HCPH LHD$@HkHC H)H$HIIH@0xs I$I|$HD$ID$PHD$I$ fHDŽ$ L$ )$ HMII9 MU L轵IK L$ H$ Mt"Ht$LHt$LHL$0WHL$0IH$ HC LKHL)HL9 L9tGHfHv HHI9t)L9t$HpHzH9tHuHHrHI9uHfHDŽ$ L))$ HH HH9
+ HʴH$ L$ HIM)fHnHflH$ )$ L9tLL帯HLH$ 1H$ L*! HH@0x D HSPH$ LHH$ HT$HH)IIH9$ILH|$PH HH95
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HHHT$pH$ HH$ H9 HD$H$ H Ht LD$hMZ H|$`Ht/H|$XHt& f D HD$@ HD$H H$ Ht轮H$ Ht諮H$H dH3%( HD$HX []A\A]A^A_ D H$ L)H$ LHDŽ$ H$ HC L$ LK @ E1 I1HtQHAI@Av Mm HI Hx Ht HN IHu HD$H LܯH|$`Hf L$ H$ H5-O LAH|$L :0 H$ HxH;=Ok z H|$ H$ f. H$H$H9D$H$ H$ H)HH9$ H$H<HD$`H$ H H4HH HHHuH\$hHI HHH< H IHD$HH,H| Ld L< M9v IHHMH\KLI9sM9'OM9sIHM9OM9rHHLLT$LT$M9M1H|$ efHD$pLL$xH|$PLHHH9, Ht$XLD$`HA M L1 HH<IHHHH9uH< HD$@H$ H|$HJ4kH$ H$ H)HH9v7H HH9l$LH
peK HajK H=fK ȰH
QeK HBjK H=fK 詰HЃfHH H*X HDŽ$ HЃfHH H*XIT11H
fK H)O e H=gK ,H
fK H)O e H=gK
H
kfK H)O e H=kgK H
LfK Hp)O e H=LgK ϯH=hK S H
fK H;)O e H=gK 蚯H=hK Hx Ht HN IHHIAH@AuMu(HH
eK H(O e H=fK HD$pHI뇺 M[ Hx Ht HN IH^HUIAH@AuMu,HH
eK e HD$pH=fK IH$(O 菮HIH
dK H(O e H=eK f H H H$0 Ht
HHH$ HteHH|$hHt胪H|$`HttH|$XHteH$ Ht#H$ HtH虭H$0 HtH$ Hn H HnX L$ L$ 覣LLC0LIHOyK L$ H$ H;yK H$ LH$ H$yK H$ ]LL1 M LH
K H5cyK nLHyK LLaLqLH$ HxH;=k tHH51 j LI H HL LFH$ HxH;=k t<L蔤j H HB= H3,X L$ H$ HHMD$0LIHwK L$ H$ HwK H$ LH$ HwK H$ \L1 M LH
yK H5xK HH5xK LL`LL:H$ HxH;=k t0HH5 j LꪯX L$ H$ `HLC0LIH wK L$ H$ HvK H$ LH$ HvK H$ [L1 M LH
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+ H誡H$ L$ HIM)fHnHflH$ )$ L9tLLťHLH$ 1H$ L! HH@0x
D HSPH$ LHH$ HT$HH)IIH9$ILH|$PH HH95
HəH|$PHD$XHH9
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PRK HAWK H=SK 訝H
1RK H"WK H=SK 艝HЃfHH H*X HDŽ$ HЃfHH H*XIT11H
SK HO e H=TK H
jSK HO e H=jTK H
KSK HoO e H=KTK ΜH
,SK HPO e H=,TK 诜H=UK 3H
RK HO e H=SK zH=UK Hx Ht HN IHHIAH@AuMu(HH
|RK HO e H=|SK HD$pHI뇺 M[ Hx Ht HN IH^HUIAH@AuMu,HH
QK e HD$pH=RK IHO oHIH
QK HO e H=RK F H H H$0 Ht
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pK H5CfK NLHtfK LLNLQLyH$ HxH;=k toHH5
j L) H HLL&H$ HxH;=k t Ltj H H" H3 X L$ H$ (HMD$0LIHdK L$ H$ HdK H$ LH$ HdK H$ eIL͛1 M LH
fK H5dK HHeK LLzMLLH$ HxH;=k tHH5 j LʗX L$ H$ @HLC0LIHcK L$ H$ HcK H$ LH$ HcK H$ ~HL1 M LH
dnK H5cK HH.dK LLLL L3H$ HxH;=k t)HH5
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0?K H!DK H=@K 舊H
?K HDK H=@K iHЃfHH H*X HDŽ$ HЃfHH H*XIT11H
i@K HO e H=iAK H
J@K HnO e H=JAK ͉H
+@K HOO e H=+AK 讉H
@K H0O e H= AK 菉H=BK ~لH
?K HO e H=@K ZH=kBK Hx Ht HN IHHIAH@AuMu(HH
\?K HO e H=\@K HD$pڈHI뇺 M[ Hx Ht HN IH^HUIAH@AuMu,HH
>K e HD$pH=?K IHO OHIH
>K HO e H=?K & H H H$0 Ht
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J]K H5#SK .LHTSK LL;L1LYH$ HxH;=vk tOHnH5i L H HLލLH$ HxH;={vk tLT~j H H H3X L$ H$ |HMD$0LIHQK L$ H$ HQK H$ LH$ HQK H$ E6L譈1 M LH
SK H5QK ϛHHQK LLZ:LҌLH$ HxH;=ouk tHymH5i L誄X L$ H$ {HLC0LIHPK L$ H$ HPK H$ LH$ HPK H$ ^5LƇ1 M LH
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pA 1 DD$DLW(HF8HohHD$8MI L_ I A 1H@HH H LW(HF8HohHD$8MI M\$ I@ M\$IuH *H$ E1 HD$ HD$ 1 ^ǹ A SMT$(HF8Il$hHD$8MIR ] A Nc!11 A E111 1 A 륹 A E1O@IJ J 1H@HH H +E1E111 O1 Ƹ 5LcM E1lE1A |E11 E1E1A _E1E11 lH$ E1E1A 4HD$ E1E1A H$ E1E1E11 H$ H$` N J4 L$ H$ E1H$H$ H\$`H$ HD$H$ H\$hH$ HD$H$ H\$pH$ HD$(H$ H\$xH$ HD$0H$( L$8 L$ HD$@H$0 HD$HH$P HD$PH$X HD$XH$h H$ H$ Ld$ IH$ H\$8M~1H$ H HD$ H1 HLHI9uE IL;$ H+\$H+l$A HH;|$( H+\$0H+l$@A IL;L$HZ H+\$PH+l$XAtbIL;d$`_ H+\$hH+l$pAt@IL;|$x+ H+$ E1E11H+$ E1E1ML9t$}E1E11 H@HH H 1߸ ظ Ѹ 1H@HH H E1 H$ LME1HIH\$hH$ H HD$HH$ H\$pH$( HD$H$ H\$xH$8 HD$PH$ H\$H$0 HD$XH$ H$ H$P HD$`H$ H$ H$X H$ H$h H\$ H$` H$ H$ H$ H$ H$ H$ LL$0H\$(H$ Ld$@IH$ H$ H$ H$ H$ H\$8Ht$8H$Ht$81H@0L HD$@LH@`HM B 2:L(HI9}Ht$8HT$HI9 HL$0H4 H$ HrHAI9~UHL$0H4 AD0HrI9~3HL$0H4 HAD0I9~HL$0AD0 E HD$HD$H;D$P H+\$XH+l$`A IL;d$h H+\$pH+l$xA HD$HD$H;$ V H+$ H+$ A HD$ HD$ H;$ | H+$ H+$ AtcHD$(HD$(H;$ i HD$ H+$ E1HD$ H+$ HD$( HD$ ML9t$HD$ HD$ E11<1E1 ME1E11E1 8E11E1 & 1A @LW(HF8HohHD$8MI j 1A HD$ E1 H@HH H HD$ ӿ 1A 1HD$ E1 HD$ HD$ E1 HD$ HD$ t4bS 1A Af D AWAVAUATUSH Ht$@dH%( H$ 1HH$8 HDŽ$@ HohHDŽ$H opox(H$ HGH$ $ Hoh$ $( opox(H$ )$ )$ )$ H$ L`8Ƅ$p J D$t H$@ JDŽ M Lx@$p J D$x H$H H|$JDŽ t
+I LpH$p J D$| H$P H|$ JDŽ t
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A H HLAI9~ HHH yL$,f D HAII9|HqII9zHHD$ H9D$ HHD$HH thH$ HtVH$ HtDH$ H.|H$ H$ HHt$@HLHD$PE1M0 @ LsHIEH|$0LvIM9 HIcHKdHIL$ HD$IMI} HH)HH9vH)HHT$HtDIUH)HH9w<HH1HHL$HT$ HHL$LD$ LLIM\LTH H)H9T$H HT$HHD$(H9HBH! H|$(7IM IUHD$ H)HHHL t H|$ LHLD$`.IM LD$`LD$ LL$H1HL$hILLLL$`LD$H{LD$HLL$`HL$hKHHD$HtHfHD$ Ht$HIE HD$(IuHt$IEH$ VHT$8H|$PLM H$ KvE1HHHL!H9uH HH$ MH$ KvML M BD% AHD$XHxKL+L! H HL!HHt"HHNLcL+$HHK;T }L$IM9uHD$HH tFH|$@D$,kHE1H$@ H8 * d$,C/HIL9( H'H$ I*HH*HD$jH "I$H$ H*Hx HP|$ t)W.wI$H HH*.v H$@ HHD$5}0> H .0H$( @Hx H@PI$H$@ HH9HJHHNHHHD HD$H IH$ H$ IHILLH0觩HHT$8LHp蓩LLl$01LA LL1HH$ HH~hL$ H$ E1HJ1HO~=1M L
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H$P HHtH$0 HtH$ HtH$p HtH$ HtH$ HtH H$P HHttH$0 HuHHmHwHH HD$ nH$P HHt!H$0 HtH$ HtH$ H HHH {HHH$P HHtH|$@\E1H=*{= SHf. @ AWAVAUATUSHH HD$@ $H D$HT$PH|$($8 D$Dl$tH|$P u?H
OB HOB H|$(HDȺ 8! HD$(H []A\A]A^A_LyI@IL+9I+ LIHI9tH
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OB H|$(Ҿ HD H$ H$ LMHHD$ <HT$ H$ LH|$8"H$ HL$ LƄ$ HH|$0H$p H$ IGHD$XH LHx"I<$pHTHcHHHHuI/ HD$8IL$ L$ H$ L$ D$ H$ H$0 H$ H$P H$ H$X H$ X I I |$ R IN> . > 8 .@. D # @.>? @ .=? I$HpHPHxH L46HH$ HHH|$`Lt$HD$] IWHL< HD$x H$ HHHD$pH HIHHT$hHL, HD$ HOt- LLl$HHHLt$@MuHD$HH$ O 6HD$0H$ L\$8H|$` LL$PH$ HD$X H|$ 9 LE11 H MtA1E1(ID( PLPDPDPHL9\$ wI9toHD$HLD HD$@LL9AAD~GKD HD$8LH;t$AAD~!LT$HD$0LBD AADHLD$HH;|$tYIIH LJ 8JL J!H9AH9A1f D DHH9uHD$XHl$hLL$pHD$`H9D$XH$ H$ H$ HD$PHD$xH$ H9D$xCHD$(H H$p Ht!H$ HtH$ H @ J 8HE L$P L$ LLH0襏HE H|$0LHp萏HE LHpH$0 HIrHE H$ LHpZL|$ 1LLHt$0 L LL LLL} H$P IH H$0 IE1Ld$PHT$H$ HH|$@H$P HHT$0HD$8H$ HHD$HHHD$ HD$0H|$@ H HD$ MwHD$Ll$HJ,L\$M] M~TIwLL$ @ LIH~3O1 HH D$xAHH9uIII9uIL;l$ uHD$0HD$8H9D$0RHD$(HH tH$0 HtH$ HtH$ HH$0 L$P LLHHD$H`E1M/ LsHIEH|$ LIM9 HE IcHKdHIL$0 HD$IMI} HH)HH9vH)HHT$@tDIUH)HH9w<HH1HHL$HT$HL$LD$LLIM[LSH H)H9T$@ HT$@HD$8H9HBH H|$8IM IUHD$H)HHHL t H|$LHLD$`IM LD$`LD$LL$@1HL$hILLLL$`LD$@LD$@LL$`HL$hKHHD$@tHHD$H|$@Ht$IE HD$8I}IEH$0 UHT$0LL訋M H$0 KE1HHHL!H9uH5 HH$P LL$PKMH$0 L MD$xBHD$XAHxFLm LfH HL!HHt"HHNLcL+$HHK;T }L$IM9uHD$(HH tH|$HH H9$H HHHHD$8 HD$ HE H$p H8 I $HE1I*HHH$0 I6H*H*HH$P j"|$ t)W.wI$H HH*.v H$p HI5 > .0H$X @Hx H@PI $H$p D$HHrH9HHNHփu HHHHD$PBHE IL9(HD$(H LHE L|$0L$P LH0L8HE LHpH$0 HIHE H$ LHpLL|$ 1L LL LLLm H$P IE H H$ MuE1L$0 H$P HD O HD$MLe ~PIuMLIH~5HD$PN1 @ HH D$xAHH9uIIM9uHH;l$uHD$(HH t0H$0 Ht H$ H@ HE L$P H$0 LIH0辇HE HT$0LHp詇LL|$ 1LW LLGHU H$P HH~]L$0 HrE1H$P M HI~1HD$PN1HH D$xAHH9uIIM9uHD$(HH t H$0 HBH$P HHtH$0 HtH$p HtH$ HtH$ HtH%HHH$ HuH$P HHtyH$0 HtgHfH$P HHtIH$0 Ht7H$ Ht%H$ H,,H HD$8]HH
HH!HHÐH$P HHtH|$HoE1H==k= fHؐAUATA USHHHWH+HHu}LHt$H HHCHt$IH)HH ItAE HHtHHH;IHt H+LLkHkH[]A\A] HH9v InH?HIH9LFMf. HGH;GtHt HHGf. UHSHHHu @ HH ]HuHEH} 1H 1HE HE H[]f. AUIATUSHHoHtlf. H} Le HuHH HuHEH}1H H}HE( HE HMtLIEI} 1H wIE IE H[]A\A]f. fAWWIAVAUATIUSH8.D$ 1Il$1HI$HIHt=HA.C H Ll$ Ht A.D$ zP D uI1I9tH81[]A\A]A^A_f D HA.C{gH HtAIl$.D$ {iǾ LL$ =1L$ HH|$iǾ L$ L$ uH8H[]A\A]A^A_/f. D AWAVAUATUSH $ H|$ D$Ht$hLHT$`HL$XL$D$DH$0 LD$PLL$HT$@HL$ L$ L$ HDŽ$0 HDŽ$8 HDŽ$@ HD$PL$P Ht$HHDŽ$P HDŽ$X HDŽ$` LH$ H\$PHDŽ$ H\$HH蜦H$ H$ HH$ HDŽ$ P1H$ HH\$PHDŽ$ H\$HH$AH$ HH$ HDŽ$ HH|$(H$ 0H$ H4$H$ H?HH@ H$ HHH H=| HW` H$ H$p H$x 踠H$ HT$PHD$pH HH4H$ HD$HH$ H HH$ HH H$ HHH$ H$ H H$ IOI+HHLAHHH~ J -> 1,$HD$0H HD$8 f. H*H$8 H;$@ $ f Ht
$ HH$8 HD$`WHH*]. H,H9$ HOH*HJH9HNH HH$ H$ H9\T
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> H*$ \T > tjHD$HHH9~v\$. $I|$ IH*\$XH|$Ht$0H*H*,:$2A L$LAT$ D HD$HH ? ?H9H|$ HDŽ$ HD$hL|$PH$HD$`HD$HD$XHD$IfHLLR/ H$ Ht
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HHH$p QHH$P Ht
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| HW H$ H$ H$ ƖH$ HT$HHD$pH$X H HH<H$ H$P H$ H HH$ HH H$ HH$ HH H$ HH$ HH H$ HHH$ H$ H H$ HMH+M HHLqHHLn V= IIE1$! * I*H$x H;$ $ n Ht
$ HH$x HD$`WHPH*]." H,H9$ HOH*HAH9HNJ HH$ H$ H9\T
> H*$ \T> HD$XHD$PHHL$pJHD$XHD$PHH$ JHT$HIL9 t$ .4$H{ HE H*\$`H*I*B,0B$( L$ HS ? ?Z 1Wf D H|$L$ HMH+M H$P HHHHLnLq Z= IIE1 $ D I*H$ H;$ $ f Ht
$ HH$ HD$XWHH*]. H,H9$ HOH*HJH9HNJ HH$ H$ H9\T
> H*$ \T> HT$PH$ JH$ HD$PJH$P IL9 t$.4$H{ HU H*\$XH*I*B,2B$* L$HS ? ?b 1Wf D H|$ L$ HMH+M HHHH$X HHH b= L4 L, E1$ D I*H$ H;$ $ V Ht
$ HH$ HD$PWHH*].
- H,H$ H9$ HNJHH|$PHHOH9HOH$ JHJ HH$ H$ H*H9\T
> H*$ \T> tbH$X IL9 d$.$$H{ HU H*\$PH*I*B,2B$* L$HS ? ?f1Wf D H|$(L$ H|$0 HDŽ$` 5 HD$PLd$hLl$`Lt$XH$O H$ LH # H$ Ht
LLH$` HH9D$0H$` HDŽ$ .H$p H|$8H$P L`LhHH$x HX@HxH<$HP0HT$HH\$ Lp HP8H$X Hx(H|$HX`HPHHT$DHPPHT$@HxXH|$(HPpHT$pHPxH$ HxhH H$ H H$ H H$ H H$ H H$ H H$ H H$ H H$ H H$ H H$ H H$ H |H$ H$ HHl$hH$ 4H~?Ht21f D H$ HLHH9H$ u HH$ EH$ HtH$ HtH$p HtH []A\A]A^A_H$ HDŽ$ H$ H$ HHt
LLH$ EHH$ Ht
H$H$H$ HHtfH$p HtTH̿G H @ f. H$ HHe LLSN 蛳Hf D AWAVAUATUSH $ H|$ D$Ht$hLHT$`HL$XL$D$DH$0 LD$PLL$HT$@HL$ L$ L$ HDŽ$0 HDŽ$8 HDŽ$@ HD$L$P Ht$HHDŽ$P HDŽ$X HDŽ$` LH$ H\$PHDŽ$ H\$HH\H$ H$ HH$ HDŽ$ H$ HH\$PHDŽ$ H\$HH$H$ HH$ HDŽ$ HH|$(H$ H$ H4$H$ H?HH@ H$ HHH H=| HW` H$ H$p H$x xH$ HT$PHD$pH HH4H$ HD$HH$ H HH$ HH H$ HHH$ H$ H H$ IOI+HHLAHHH~ J -H= 1,$HD$0H HD$8 f. H*H$8 H;$@ $ f Ht
$ HH$8 HD$`WHH*]. H,H9$ HOH*HJH9HNH HH$ H$ H9\T
> H*$ \T > HT$XHL$pHH$ HD$XHHT$PHH9 t$.4$IH|$0H*\$`I|$ H*H*,8H|$8$8 L$LAT$ ? ?b 1Wf D H|$H$ IOI+HD$HHHHHHH H< >= 1$H|$H< H|$0 H*H$X H;$` $ F Ht
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> H*$ \T> tjHD$HHH9~v\$. $I|$ IH*\$XH|$Ht$0H*H*,:$2A L$LAT$ D HD$HH ? ?H9H|$ HDŽ$ HD$hL|$PH$HD$`HD$HD$XHD$IfHLL H$ Ht
HHH$ HH9D$ H$ HDŽ$ #H$ H|$(Ht$Lx0LpPMHH$ Ld$hHxH<$HP(HT$HHpHHP8HT$DHxH|$HP@HT$@HPXHT$pHxH|$HP`H$ HPhH$ Hx HPpH$ HPxH$ H H$ H H$ H H$ H H$ HHFvH$ H$ M~dHt.1
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HHH$p a;HH$P Ht
H$ȯH$H$0 HHt误H'DMG @ H$ HHt HHHtf{f mf. AWAVAUATUSH $h H|$0D$ Ht$hLHT$`HL$XL$D$DH$p LD$PLL$HT$HH$` \$@L$ H$ HDŽ$p HDŽ$x HDŽ$ HD$jH$ H$P HDŽ$ HDŽ$ HDŽ$ HHD$ )H$ H$X HDŽ$ HDŽ$ HDŽ$ HHD$(L$ Ld$H L$P HDŽ$ LL$X bH$@ H$0 LH$0 HDŽ$@ Ld$H L$P LL$@ L$X HDŽ$ H$P H$` H$P LHDŽ$` HHD$8I} LH$` HH@x
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$ HH$x HD$`WHPH*]." H,H9$ HOH*HAH9HNJ HH$ H$ H9\T
= H*$ \T= HD$XHD$PHHL$pJHD$XHD$PHH$ JHT$HIL9 t$ .4$H{ HE H*\$`H*I*B,0B$( L$ HS ? ?Z 1Wf D H|$L$ HMH+M H$P HHHHLnLq = IIE1 $ D I*H$ H;$ $ f Ht
$ HH$ HD$XWHH*]. H,H9$ HOH*HJH9HNJ HH$ H$ H9\T
= H*$ \T= HT$PH$ JH$ HD$PJH$P IL9 t$.4$H{ HU H*\$XH*I*B,2B$* L$HS ? ?b 1Wf D H|$ L$ HMH+M HHHH$X HHH "= L4 L, E1$ D I*H$ H;$ $ V Ht
$ HH$ HD$PWHH*].
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LLH$` HH9D$0H$` HDŽ$ H$p H|$8H$P L`LhHH$x HX@HxH<$HP0HT$HH\$ Lp HP8H$X Hx(H|$HX`HPHHT$DHPPHT$@HxXH|$(HPpHT$pHPxH$ HxhH H$ H H$ H H$ H H$ H H$ H H$ H H$ H H$ H H$ H H$ H H$ HkH$ H$ HwwHl$hH$ 4H~?Ht21f D H$ HLHH9H$ u賞 HH$ k/H$ Ht٣H$ HtǣH$p Ht赣H []A\A]A^A_H$ HDŽ$ H$ H$ HHt
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$ HH$8 HD$`WHH*]. H,H9$ HOH*HJH9HNH HH$ H$ H9\T
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u= H*$ \TX= tjHD$HHH9~v\$. $I|$ IH*\$XH|$Ht$0H*H*,:$2A L$LAT$ D HD$HH ? ?H9H|$ HDŽ$ HD$hL|$PH$HD$`HD$HD$XHD$IfHLL H$ Ht
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|= H*$ \T_= HD$XHD$PHHL$pJHD$XHD$PHH$ JHT$HIL9 t$ .4$H{ HE H*\$`H*I*B,0B$( L$ HS ? ?Z 1Wf D H|$Lk$ HMH+M H$P HHHHLnLq = IIE1 $ D I*H$ H;$ $ f Ht
$ HH$ HD$XWHH*]. H,H9$ HOH*HJH9HNJ HH$ H$ H9\T
|= H*$ \T_= HT$PH$ JH$ HD$PJH$P IL9 t$.4$H{ HU H*\$XH*I*B,2B$* L$HS ? ?b 1Wf D H|$ Ls$ HMH+M HHHH$X HHH = L4 L, E1$ D I*H$ H;$ $ V Ht
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w) H5Yv) dLHv) LL^LgLáHD$HxH;=I thHH5* H LB H" H HLL7áHD$HxH;=I tL舡H AWAVAUATUHĀdH%( HD$x1HF0x
u*HFPHHT$xdH3%( uH]A\A]A^A_[X Ht$Ll$ L|$Ht$LLIHt) Lt$@HD$Ht) LF0LHD$ HL$Ht) HD$@ZYL« M躕 LH
@) H5t) 価LH
+u) LLo]L篡L¡HD$HxH;=I tHH5H L§ H" H HL華LHD$HxH;=/I t萶LH蠧 AWAVAUATUHĀdH%( HD$x1HF0xu*HFPHHT$xdH3%( uH]A\A]A^A_ۢX Ht$Ll$ L|$蒝Ht$LLIH:s) Lt$@HD$H,s) LF0LHD$ HL$Hs) HD$@WLB M躕 LH
8u) H5Ys) dLHs) LL[LgLHD$HxH;=I thHH5*H LB H" H HLL7HD$HxH;=I tL舞H AWAVAUATUHĀdH%( HD$x1HF0x u*HFPHHT$xdH3%( uH]A\A]A^A_[X Ht$Ll$ L|$Ht$LLIHq) Lt$@HD$Hq) LF0LHD$ HL$Hq) HD$@ZVL¨ M躕 LH
x{) H5q) 仡LH
+r) LLoZL笡LHD$HxH;=I tHH5H L¤ H" H HL菬L跾HD$HxH;=/I t萳LH蠤 AWAVAUATUHĀdH%( HD$x1HF0xu*HFPHHT$xdH3%( uH]A\A]A^A_۟X Ht$Ll$ L|$蒚Ht$LLIH:p) Lt$@HD$H,p) LF0LHD$ HL$Hp) HD$@TLB M躕 LH
y) H5Yp) dLHp) LLXLgL菽HD$HxH;=I thHH5*H LB H" H HLL7HD$HxH;=I tL舛H AWAVAUATUHĀdH%( HD$x1HF0xu*HFPHHT$xdH3%( uH]A\A]A^A_[X Ht$Ll$ L|$Ht$LLIHn) Lt$@HD$Hn) LF0LHD$ HL$Hn) HD$@ZSL¥ M躕 LH
`u) H5n) 両LH
+o) LLoWL穡LHD$HxH;=I tHH5H L¡ H" H HL菩L跻HD$HxH;=/I t萰LH蠡 AWAVAUATUHĀdH%( HD$x1HF0xu*HFPHHT$xdH3%( uH]A\A]A^A_ۜX Ht$Ll$ L|$蒗Ht$LLIH:m) Lt$@HD$H,m) LF0LHD$ HL$Hm) HD$@QLB M躕 LH
s) H5Ym) dLHm) LLULgL菺HD$HxH;=I thHH5*H LB H" H HLL7HD$HxH;=I tL舘H AWAVAUATUHĀdH%( HD$x1HF0xu*HFPHHT$xdH3%( uH]A\A]A^A_[X Ht$Ll$ L|$Ht$LLIHk) Lt$@HD$Hk) LF0LHD$ HL$Hk) HD$@ZPL¢ M躕 LH
q) H5k) 䵡LH
+l) LLoTL禡LHD$HxH;=I tHH5H L H" H HL菦L跸HD$HxH;=/I t萭LH蠞 AWAVAUATUHĀdH%( HD$x1HF0xu*HFPHHT$xdH3%( uH]A\A]A^A_ۙX Ht$Ll$ L|$蒔Ht$LLIH:j) Lt$@HD$H,j) LF0LHD$ HL$Hj) HD$@NLB M躕 LH
p) H5Yj) dLHj) LLRLgL菷HD$HxH;=I thHH5*H LB H" H HLL7HD$HxH;=I tL舕H AWAVAUATUHĀdH%( HD$x1HF0x u*HFPHHT$xdH3%( uH]A\A]A^A_[X Ht$Ll$ L|$Ht$LLIHh) Lt$@HD$Hh) LF0LHD$ HL$Hh) HD$@ZML M躕 LH
Pn) H5h) 䲡LH
+i) LLoQL磡LHD$HxH;=I tHH5H L H" H HL菣L践HD$HxH;=/I t萪LH蠛 AWAVAUATUHĀdH%( HD$x1HF0x
+u*HFPHHT$xdH3%( uH]A\A]A^A_ۖX Ht$Ll$ L|$蒑Ht$LLIH:g) Lt$@HD$H,g) LF0LHD$ HL$Hg) HD$@KLB M躁 LH
n) H5Yg) dLHg) LLOLgL菴HD$HxH;=I thHH5*H LB H" H HLL7HD$HxH;=I tL舒H AWAVAUATUHĀdH%( HD$x1HF0xu*HFPHHT$xdH3%( uH]A\A]A^A_[X Ht$Ll$ L|$Ht$LLIHe) Lt$@HD$He) LF0LHD$ HL$He) HD$@ZJL M躁 LH
m) H5e) 䯡LH
+f) LLoNL砡LHD$HxH;=I tHH5 H L H" H HL菠L跲HD$HxH;=/I t萧LH蠘 AWAVAUATUHĀdH%( HD$x1HF0x u*HFPHHT$xdH3%( uH]A\A]A^A_ۓX Ht$Ll$ L|$蒎Ht$LLIH:d) Lt$@HD$H,d) LF0LHD$ HL$Hd) HD$@HLB M躁 LH
k) H5Yd) dLHd) LLLLgL菱HD$HxH;=I thHH5* H LB H" H HLL7HD$HxH;=I tL舏H AWAVAUATUHĀdH%( HD$x1HF0xu*HFPHHT$xdH3%( uH]A\A]A^A_[X Ht$Ll$ L|$Ht$LLIHb) Lt$@HD$Hb) LF0LHD$ HL$Hb) HD$@ZGL M躁 LH
i) H5b) 䬡LH
+c) LLoKL睡LHD$HxH;=I tH~H5 H L H" H HL菝L路HD$HxH;=/I t萤LH蠕 AWAVAUATUHĀdH%( HD$x1HF0x
u*HFPHHT$xdH3%( uH]A\A]A^A_ېX Ht$Ll$ L|$蒋Ht$LLIH:a) Lt$@HD$H,a) LF0LHD$ HL$Ha) HD$@ELB M躁 LH
l) H5Ya) dLHa) LLILgL菮HD$HxH;=I thH}H5*H LB H" H HLL7HD$HxH;=I tL舌H AWAVAUATUHĀdH%( HD$x1HF0xu*HFPHHT$xdH3%( uH]A\A]A^A_[X Ht$Ll$ L|$Ht$LLIH_) Lt$@HD$H_) LF0LHD$ HL$H_) HD$@ZDL M躁 LH
j) H5_) 䩡LH
+`) LLoHL皡LHD$HxH;=I tH{H5H L H" H HL菚L跬HD$HxH;=/I t萡LH蠒 AWAVAUATUHĀdH%( HD$x1HF0x u*HFPHHT$xdH3%( uH]A\A]A^A_ۍX Ht$Ll$ L|$蒈Ht$LLIH:^) Lt$@HD$H,^) LF0LHD$ HL$H^) HD$@BLB M躁 LH
@h) H5Y^) dLH^) LLFLgL菫HD$HxH;=I thHzH5*H LB H" H HLL7HD$HxH;=I tL舉H AWAVAUATUHĀdH%( HD$x1HF0xu*HFPHHT$xdH3%( uH]A\A]A^A_[X Ht$Ll$ L|$Ht$LLIH\) Lt$@HD$H\) LF0LHD$ HL$H\) HD$@ZAL M躁 LH
@f) H5\) 䦡LH
+]) LLoEL痡LHD$HxH;=I tHxH5H L H" H HL菗L跩HD$HxH;=/I t萞LH蠏 AWAVAUATUHĀdH%( HD$x1HF0xu*HFPHHT$xdH3%( uH]A\A]A^A_ۊX Ht$Ll$ L|$蒅Ht$LLIH:[) Lt$@HD$H,[) LF0LHD$ HL$H[) HD$@?LB M躁 LH
8d) H5Y[) dLH[) LLCLgL菨HD$HxH;=I thHwH5*H LB H" H HLL7HD$HxH;=~I tL舆H AWAVAUATUHĀdH%( HD$x1HF0xu*HFPHHT$xdH3%( uH]A\A]A^A_[X Ht$Ll$ L|$Ht$LLIHY) Lt$@HD$HY) LF0LHD$ HL$HY) HD$@Z>L M躁 LH
xb) H5Y) 䣡LH
+Z) LLoBL甡LHD$HxH;=}I tHuH5 H L H" H HL菔L跦HD$HxH;=/}I t萛LH蠌 AWAVAUATUHĀdH%( HD$x1HF0xu*HFPHHT$xdH3%( uH]A\A]A^A_ۇX Ht$Ll$ L|$蒂Ht$LLIH:X) Lt$@HD$H,X) LF0LHD$ HL$HX) HD$@<LB M躁 LH
`) H5YX) dLHX) LL@LgL菥HD$HxH;=|I thHtH5*G LB H" H HLL7HD$HxH;={I tL舃H AWAVAUATUHĀdH%( HD$x1HF0xu*HFPHHT$xdH3%( uH]A\A]A^A_[X Ht$Ll$ L|$Ht$LLIHV) Lt$@HD$HV) LF0LHD$ HL$HV) HD$@Z;L M躁 LH
^) H5V) 䠡LH
+W) LLo?L瑡LHD$HxH;=zI tHrH5G L H" H HL菑L跣HD$HxH;=/zI t萘LH蠉 AWAVAUATUHĀdH%( HD$x1HF0x u*HFPHHT$xdH3%( uH]A\A]A^A_ۄX Ht$Ll$ L|$Ht$LLIH:U) Lt$@HD$H,U) LF0LHD$ HL$HU) HD$@9LB M躁 LH
@\) H5YU) dLHU) LL=LgL菢HD$HxH;=yI thHqH5*G LB H" H HLL7HD$HxH;=xI tL舀H AWAVAUATUHĀdH%( HD$x1HT$H;V0u*HFPHHL$xdH3 %( uH]A\A]A^A_ZX Ht$Ll$ L|$~Ht$LLILt$@HS) HD$ LF0LH - HT$HD$@uQL͊ M躩 LH
. H5S) LH - LLz<LLHD$HxH;=wI tHoH5G L͆ H" H HL蚎L HD$HxH;=:wI t蛕LH諆f. AWAVAUATUHĀdH%( HD$x1HT$H;V0u*HFPHHL$xdH3 %( uH]A\A]A^A_ځX Ht$Ll$ L|$|Ht$LLILt$@H4R) HD$ LF0LHo - HT$HD$@OLM M躤 LH
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+ 2H=B$ V HH$ L5~D H$ HQ H4裍I} HtňH,$HEHHxL9t{H;l$uH=D 踅H L9d$u HD$H HxHD$L9t4HD$ Hx AUATUSHHHH HLh0Hc@(L$ H9wo1Ht LCHJ,!MtHLLHH;fHnfHnHkflHtćH []A\A] @ H1[]A\A]H=tF$ 烈 HAWAVAUATUSHLgLLL)HH9 IHHIL)H IH4 H9 LHT$L$ZL$HT$HIIM)L|HK'H$H/MbMu?fHnLu$E H[]A\A]A^A_ D LHLD$PMLD$ L螆 @ HuJE11 @ LLLLD$腅LD$Mtf A *H=?$ 虂H9HFII f D AWAVAUATIUSH Ht$L$ L|$PHT$LdH%( H$ 1L|$@qHzD H +D 1HDŽ$ fHHk)$ H$ 1f$ HC)$ HHD$HEHl$PHLPHD$X H}L(Lk HC(L|$`1I}Ll$`HD$ LH H{D Hs0HH@HCH$ Ht$0fHnHHL$8HD$(H@HtPH{D HHD$PH{D HhH$ HD HfHnfl)D$`f)D$p)$ )$ 蛋HDD LDŽ$ HHD$hHzD HH$ HD$hHHD$H(H|$ Ht$LH~H|$Ht$LHdH5zD HI$H$ HD HzD L$ H$ HL9 I)1L,HzD ~D$8HHD$PHzD HhH$ HTD HfHnH$ flHxH;=yD )D$` HRD HHHD$hHD$(H\$0LHT$ H@H\PIELl$`H\$HT`HEHl$PH\PHxD HD$X HH$ }H$ dH3%( uSH L[]A\A]A^A_ @ H)1LI+ H$ L %Ƀ H> IG I I HI<$HH;=qxD t2H|$@hHH|$HfHD$(HL$0H@HLPIEHL$ Ll$`HL`HEHL$Hl$PHLPHD$X HvD LHH$ |Lqf. D AWAVAUATIUSH Ht$L$ L|$PHT$LdH%( H$ 1L|$@|HZvD H ˒D 1HDŽ$ fHHk)$ H$ 1f$ HC)$ HHD$HEHl$PHLPHD$X H}LȃLk HC(L|$`1I}Ll$`HD$ LH蠃HwD Hs0HH@HCH$ Ht$0fHnHHL$8HD$(H@HtPH^wD HHD$PHNwD HhH$ HK~D HfHnfl)D$`f)D$p)$ )$ ;HD LDŽ$ HHD$hH.vD HH$ HD$hHHD$HȂH|${Ht$LH H|${Ht$LHHuD HI$H$ HD HuD L$ H$ HL9 I)1LT(H=vD ~D$8HHD$PH'vD HhH$ HD HfHnH$ flHxH;=<uD )D$` H|D HHHD$haHD$(H\$0LHT$ H@H\PIELl$`H\$HT`HEHl$PH\PHsD HD$X HH$ yH$ dH3%( uSH L[]A\A]A^A_ @ H)1LIH' H$ L #%i H> IG I I HI<$HH;=tD tH|$@H考H|$HHD$(HL$0H@HLPIEHL$ Ll$`HL`HEHL$Hl$PHLPHD$X H}rD LHH$ ixLf. D AWAVAUIATUSH Ht$L$ Lt$`HL$HLHT$LD$dH%( H$ 1Lt$PwHqD H aD 1HDŽ$ fHHk)$ H$ 1f$ HC)$ HHD$ HEHl$`HL`HD$h H}L^Ls HC(Ld$p1I~Lt$pHD$(LH6H/sD HH@HCH[0fHnHL$@HD$0H@H\$8H\`HrD H$ HHHD$`HrD HhH$ HyD HfHnfl)D$pf)$ )$ )$ HwD LDŽ$ HHD$xHqD HH$ HD$xHHD$X[~H|$QwHt$LH|H|$7wHt$LH|HD$HLH0HV|H|$ wHt$LHi|H:qD HIE H$ HA HqD L$ H$ HL9 I)1L#HqD ~D$@HHD$`HqD HhH$ HYD HfHnH$ flHxH;=pD )D$p HWxD HHHD$x}HD$0Ht$8LHT$(H@Ht`IFLt$pHt$ HTpHEHl$`Ht`HoD HD$h HH$ tH$ dH3%( uPH L[]A\A]A^A_ÐH)1LI" H$ L (z H> IG I I HI} HH;=yoD t:H|$Pp}H~H|$XnHD$0HL$8H@HL`IFHL$(Lt$pHLpHEHL$ Hl$`HL`HD$h HmD LHH$ sLy~ AWAVAUATIUSH Ht$L$ L|$PHT$LdH%( H$ 1L|$@!sHjmD H ۉD 1HDŽ$ fHHk)$ H$ 1f$ HC)$ HHD$HEHl$PHLPHD$X H}LzLk HC(L|$`1I}Ll$`HD$ LHzHnD Hs0HH@HCH$ Ht$0fHnHHL$8HD$(H@HtPHnnD HHD$PH^nD HhH$ H[uD HfHnfl)D$`f)D$p)$ )$ K~HD LDŽ$ HHD$hH>mD HH$ HD$hHHD$HyH|$rHt$LH.xH|$rHt$LHxHlD HI$H$ HD HlD L$ H$ HL9 I)1Ld HMmD ~D$8HHD$PH7mD HhH$ HD HfHnH$ flHxH;=LlD )D$` HtD HHHD$hqyHD$(H\$0LHT$ H@H\PIELl$`H\$HT`HEHl$PH\PHjD HD$X HH$ pH$ dH3%( uSH L[]A\A]A^A_ @ H)1LIX H$ L 3%yv H> IG I I HI<$HH;=!kD tH|$@yHzH|$HHD$(HL$0H@HLPIEHL$ Ll$`HL`HEHL$Hl$PHLPHD$X HiD LHH$ yoL!zf. D AWAVAUATIUSH Ht$L$ L|$PHT$LdH%( H$ 1L|$@nH
+iD H {D 1HDŽ$ fHHk)$ H$ 1f$ HC)$ HHD$HEHl$PHLPHD$X H}LxvLk HC(L|$`1I}Ll$`HD$ LHPvHIjD Hs0HH@HCH$ Ht$0fHnHHL$8HD$(H@HtPHjD HHD$PHiD HhH$ HpD HfHnfl)D$`f)D$p)$ )$ yHD LDŽ$ HHD$hHhD HH$ HD$hHHD$HxuH|$nnHt$LHsH|$TnHt$LHsHhD HI$H$ HD HehD L$ H$ HL9 I)1LHhD ~D$8HHD$PHhD HhH$ HD HfHnH$ flHxH;=gD )D$` HoD HHHD$huHD$(H\$0LHT$ H@H\PIELl$`H\$HT`HEHl$PH\PH^fD HD$X HH$ DlH$ dH3%( uSH L[]A\A]A^A_ @ H)1LI H$ L`
+ %r H> IG I I HI<$HH;=fD tH|$@tH0vH|$H趔HD$(HL$0H@HLPIEHL$ Ll$`HL`HEHL$Hl$PHLPHD$X H-eD LHH$ kLuf. D AWAVAUATIUSH Ht$L$ L|$PHT$LdH%( H$ 1L|$@ajHdD H D 1HDŽ$ fHHk)$ H$ 1f$ HC)$ HHD$HEHl$PHLPHD$X H}LrLk HC(L|$`1I}Ll$`HD$ LHqHeD Hs0HH@HCH$ Ht$0fHnHHL$8HD$(H@HtPHeD HHD$PHeD HhH$ HlD HfHnfl)D$`f)D$p)$ )$ uH4}D LDŽ$ HHD$hH~dD HH$ HD$hHHD$HqH|$jHt$LHnoH|$iHt$LHToH%dD HI$H$ HD HdD L$ H$ HL9 I)1LHdD ~D$8HHD$PHwdD HhH$ HD|D HfHnH$ flHxH;=cD )D$` HBkD HHHD$hpHD$(H\$0LHT$ H@H\PIELl$`H\$HT`HEHl$PH\PHaD HD$X HH$ gH$ dH3%( uSH L[]A\A]A^A_ @ H)1LI H$ L s%m H> IG I I HI<$HH;=abD t"H|$@XpHqH|$HVHD$(HL$0H@HLPIEHL$ Ll$`HL`HEHL$Hl$PHLPHD$X H`D LHH$ fLaqf. D AWAVAUATIUSH Ht$L$ L|$PHT$LdH%( H$ 1L|$@fHJ`D H |D 1HDŽ$ fHHk)$ H$ 1f$ HC)$ HHD$HEHl$PHLPHD$X H}LmLk HC(L|$`1I}Ll$`HD$ LHmHaD Hs0HH@HCH$ Ht$0fHnHHL$8HD$(H@HtPHNaD HHD$PH>aD HhH$ H;hD HfHnfl)D$`f)D$p)$ )$ +qHxD LDŽ$ HHD$hH `D HH$ HD$hHHD$HlH|$eHt$LHkH|$eHt$LHjH_D HI$H$ HD H_D L$ H$ HL9 I)1LDH-`D ~D$8HHD$PH`D HhH$ HwD HfHnH$ flHxH;=,_D )D$` HfD HHHD$hQlHD$(H\$0LHT$ H@H\PIELl$`H\$HT`HEHl$PH\PH]D HD$X HH$ cH$ dH3%( uSH L[]A\A]A^A_ @ H)1LI8 H$ L %Yi H> IG I I HI<$HH;=^D tH|$@kHpmH|$HHD$(HL$0H@HLPIEHL$ Ll$`HL`HEHL$Hl$PHLPHD$X Hm\D LHH$ YbLmf. D AWAVAUATIUSH Ht$L$ L|$PHT$LdH%( H$ 1L|$@aH[D H [xD 1HDŽ$ fHHk)$ H$ 1f$ HC)$ HHD$HEHl$PHLPHD$X H}LXiLk HC(L|$`1I}Ll$`HD$ LH0iH)]D Hs0HH@HCH$ Ht$0fHnHHL$8HD$(H@HtPH\D HHD$PH\D HhH$ HcD HfHnfl)D$`f)D$p)$ )$ lHttD LDŽ$ HHD$hH[D HH$ HD$hHHD$HXhH|$NaHt$LHfH|$4aHt$LHfHe[D HI$H$ HD HE[D L$ H$ HL9 I)1L
H[D ~D$8HHD$PH[D HhH$ HsD HfHnH$ flHxH;=ZD )D$` HbD HHHD$hgHD$(H\$0LHT$ H@H\PIELl$`H\$HT`HEHl$PH\PH>YD HD$X HH$ $_H$ dH3%( uSH L[]A\A]A^A_ @ H)1LI H$ L@ %d H> IG I I HI<$HH;=YD tbH|$@gHiH|$H薇HD$(HL$0H@HLPIEHL$ Ll$`HL`HEHL$Hl$PHLPHD$X H
XD LHH$ ]Lhf. D AWAVAUATIUSH Ht$L$ L|$PHT$LdH%( H$ 1L|$@A]HWD H sD 1HDŽ$ fHHk)$ H$ 1f$ HC)$ HHD$HEHl$PHLPHD$X H}LdLk HC(L|$`1I}Ll$`HD$ LHdHXD Hs0HH@HCH$ Ht$0fHnHHL$8HD$(H@HtPHXD HHD$PH~XD HhH$ H{_D HfHnfl)D$`f)D$p)$ )$ khHpD LDŽ$ HHD$hH^WD HH$ HD$hHHD$HcH|$\Ht$LHNbH|$\Ht$LH4bHWD HI$H$ HD HVD L$ H$ HL9 I)1L HmWD ~D$8HHD$PHWWD HhH$ H$oD HfHnH$ flHxH;=lVD )D$` H"^D HHHD$hcHD$(H\$0LHT$ H@H\PIELl$`H\$HT`HEHl$PH\PHTD HD$X HH$ ZH$ dH3%( uSH L[]A\A]A^A_ @ H)1LIx H$ L S%` H> IG I I HI<$HH;=AUD tH|$@8cHdH|$H6HD$(HL$0H@HLPIEHL$ Ll$`HL`HEHL$Hl$PHLPHD$X HSD LHH$ YLAdf. D AWAVAUATIUSH Ht$L$ L|$PHT$LdH%( H$ 1L|$@XH*SD H oD 1HDŽ$ fHHk)$ H$ 1f$ HC)$ HHD$HEHl$PHLPHD$X H}L`Lk HC(L|$`1I}Ll$`HD$ LHp`HiTD Hs0HH@HCH$ Ht$0fHnHHL$8HD$(H@HtPH.TD HHD$PH TD HhH$ H[D HfHnfl)D$`f)D$p)$ )$ dHkD LDŽ$ HHD$hHRD HH$ HD$hHHD$H_H|$XHt$LH]H|$tXHt$LH]HRD HI$H$ HD HRD L$ H$ HL9 I)1L$H
SD ~D$8HHD$PHRD HhH$ HjD HfHnH$ flHxH;= RD )D$` HYD HHHD$h1_HD$(H\$0LHT$ H@H\PIELl$`H\$HT`HEHl$PH\PH~PD HD$X HH$ dVH$ dH3%( uSH L[]A\A]A^A_ @ H)1LI H$ L %9\ H> IG I I HI<$HH;=PD t袾H|$@^HP`H|$H~HD$(HL$0H@HLPIEHL$ Ll$`HL`HEHL$Hl$PHLPHD$X HMOD LHH$ 9UL_f. D AWAVAUATIUSH Ht$L$ L|$PHT$LdH%( H$ 1L|$@THND H ;kD 1HDŽ$ fHHk)$ H$ 1f$ HC)$ HHD$HEHl$PHLPHD$X H}L8\Lk HC(L|$`1I}Ll$`HD$ LH\H PD Hs0HH@HCH$ Ht$0fHnHHL$8HD$(H@HtPHOD HHD$PHOD HhH$ HVD HfHnfl)D$`f)D$p)$ )$ _HTgD LDŽ$ HHD$hHND HH$ HD$hHHD$H8[H|$.THt$LHYH|$THt$LHtYHEND HI$H$ HD H%ND L$ H$ HL9 I)1L HND ~D$8HHD$PHND HhH$ HdfD HfHnH$ flHxH;=MD )D$` HbUD HHHD$hZHD$(H\$0LHT$ H@H\PIELl$`H\$HT`HEHl$PH\PH LD HD$X HH$ RH$ dH3%( uSH L[]A\A]A^A_ @ H)1LI H$ L 蓺%W H> IG I I HI<$HH;=LD tBH|$@xZH[H|$HvzHD$(HL$0H@HLPIEHL$ Ll$`HL`HEHL$Hl$PHLPHD$X HJD LHH$ PL[f. D AWAVAUATIUSH Ht$L$ L|$PHT$LdH%( H$ 1L|$@!PHjJD H fD 1HDŽ$ fHHk)$ H$ 1f$ HC)$ HHD$HEHl$PHLPHD$X H}LWLk HC(L|$`1I}Ll$`HD$ LHWHKD Hs0HH@HCH$ Ht$0fHnHHL$8HD$(H@HtPHnKD HHD$PH^KD HhH$ H[RD HfHnfl)D$`f)D$p)$ )$ K[HbD LDŽ$ HHD$hH>JD HH$ HD$hHHD$HVH|$OHt$LH.UH|$OHt$LHUHID HI$H$ HD HID L$ H$ HL9 I)1LdHMJD ~D$8HHD$PH7JD HhH$ HbD HfHnH$ flHxH;=LID )D$` HQD HHHD$hqVHD$(H\$0LHT$ H@H\PIELl$`H\$HT`HEHl$PH\PHGD HD$X HH$ MH$ dH3%( uSH L[]A\A]A^A_ @ H)1LIX H$ L 3%yS H> IG I I HI<$HH;=!HD tH|$@VHWH|$HvHD$(HL$0H@HLPIEHL$ Ll$`HL`HEHL$Hl$PHLPHD$X HFD LHH$ yLL!Wf. D AWAVAUATIUSH Ht$L$ L|$PHT$LdH%( H$ 1L|$@KH
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5D LHH$ :LEf. D AWAVAUATIUSH Ht$L$ L|$PHT$LdH%( H$ 1L|$@A:H4D H PD 1HDŽ$ fHHk)$ H$ 1f$ HC)$ HHD$HEHl$PHLPHD$X H}LALk HC(L|$`1I}Ll$`HD$ LHAH5D Hs0HH@HCH$ Ht$0fHnHHL$8HD$(H@HtPH5D HHD$PH~5D HhH$ H{<D HfHnfl)D$`f)D$p)$ )$ kEHMD LDŽ$ HHD$hH^4D HH$ HD$hHHD$H@H|$9Ht$LHN?H|$9Ht$LH4?H4D HI$H$ HD H3D L$ H$ HL9 I)1LHm4D ~D$8HHD$PHW4D HhH$ H$LD HfHnH$ flHxH;=l3D )D$` H";D HHHD$h@HD$(H\$0LHT$ H@H\PIELl$`H\$HT`HEHl$PH\PH1D HD$X HH$ 7H$ dH3%( uSH L[]A\A]A^A_ @ H)1LIx H$ L՝ S%= H> IG I I HI<$HH;=A2D tH|$@8@HAH|$H6`HD$(HL$0H@HLPIEHL$ Ll$`HL`HEHL$Hl$PHLPHD$X H0D LHH$ 6LAAf. D AWAVAUATIUSH Ht$L$ L|$PHT$LdH%( H$ 1L|$@5H*0D H LD 1HDŽ$ fHHk)$ H$ 1f$ HC)$ HHD$HEHl$PHLPHD$X H}L=Lk HC(L|$`1I}Ll$`HD$ LHp=Hi1D Hs0HH@HCH$ Ht$0fHnHHL$8HD$(H@HtPH.1D HHD$PH 1D HhH$ H8D HfHnfl)D$`f)D$p)$ )$ AHHD LDŽ$ HHD$hH/D HH$ HD$hHHD$H<H|$5Ht$LH:H|$t5Ht$LH:H/D HI$H$ HD H/D L$ H$ HL9 I)1L$H
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+ L{|
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+ , @ H@ H@H H/ @ IG HxHoH=p/ c HC| HHHM<
+ A(HCH f H=/ tP~UPJH D msf H5I/ @ uHH0{HCu HHHuX8
+ HuHCHuHH8ns Hb D AW1AVAUATUH1SH1H8dH%( HD$(1]OHE HPH9vf D HE HHPHtgHE HHPHB(t'uJHB @t@HPH/ HtB(uHB @t H HE HPH9wHE HPHK HE HPH3 HE HPHB(= HB @ HPHS/ Hu @ HE Ld$ Lt$H5" LLHՁLHHL$ L=a/ HyL9tHD$HD$f E1Ht AHE H5 LLHsLHHL$ HyL9tHD$HD$f D Ht
1ۅHE 1HPx( H/ H@H H@ A1HNlHE P(x(IA Lp MfANM+ IT$ H IcD$; HH|AL$L{ HE HPx(z H/ H@Hw H@ 1HLdHE P(x(H Lh ImAMH HU HtEHcE;}=HH|ML fHD$(dH3%( H8[]A\A]A^A_ D H}?4
+ H}H Hf D I|$ 4
+ I|$H Hf H
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+ H
+ HCC( = HH13
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+ HCC( uIHH/
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+ HCC( HH+
+ HC I D H@ HXHH / H@ H@H Hp/ ID$uBHIL*
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+ HLo Hf. L踿 L谿 H@ H@ L5~/ @ L=~/ HD$RHD$tHD$>HD$?]s HSރ H_ރ Hrރf. H @ H @ H @ AW1AVAUATUH1SH1H dH%( H$ 1= HE HPH9w1H$ dH3%( HĘ []A\A]A^A_ HE HHPHtHE HHPHB(t'uHB @tHPH |/ HtB(uHB @pHt QHE 1HPx(Z H|/ L`MW HE HPx(M H|/ LhMJ AMAD$L$݃D$ 9D$ރ1Lt$ L|$PV @ IT$ CHHtLY IE HcLHtE |$Hu|$xt/L L 9\$~.uID$ Hp HD$pH9D$@tsރ HE HPH HE 1HP(x(H Lp MnANM HE Lt$HT$H5 LH-oLHH H5Ӯ H莤HD$HxH;=4O/ ~ I]< HH|DuID$ AMtPIcHt D;t$ nDIU HtIcE;|I}Du"
+ HHs HID$ uHpN @ H HCC( HH#
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HǺB H5 q HLvHD$Ht
HHL觰H$H dH3%( uHP L]A\A]Ws Iۃ Iۃ AUATIUHP dH%( H$H 1Ll$ HL5H5g H+HL HH5 HH5ee 2 HǾ
HǺG H5 q HLvHD$Ht
HHL觯H$H dH3%( uHP L]A\A]Vs Iۃ Iۃ AUATIUHP dH%( H$H 1Ll$ HL4H5/ H+HL HH5 HH5ed 2 HǾ
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HHL觮H$H dH3%( uHP L]A\A]Us I-ڃ I8ڃ AUATIUHP dH%( H$H 1Ll$ HL3H5g H+HL HH5B HH5ec 2 HǾ
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HHL觭H$H dH3%( uHP L]A\A]Ts ITك I_ك AUATIUHP dH%( H$H 1Ll$ HL2H5& H[HL HH5 HH5eb 2 HǾ HǺu H5 q HLvHD$Ht
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M jH j[ HHE1ɾ j LD H
jHZ jz[ HHE1ɾ j L H
jHt; jK[ HHE1ɾ j L H
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jH jYS HH L H H ۴ fHnfHnH flLH H5l Hp)`覐 HH HfHnfHnLflL)9 HH5K@
A HH5V #B HǾ v9 HǺ H5 bA HLgHHt
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LLH HEdH3%( uHeL[A\A]]臯p Iu I邶f. D UHAWAVAUATISH dH%( HE1HHHH8
HE11j L HH
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jHk jO LPH IHGH5 LQÄ L`HLښHHHFH5 H ÄLLHL HLX HDžX HS H5˰ HH
uH
fHnfHnLflL)*5 HH5<c k< HH5 = HǾ 4 HǺ/ H5 < HLHHt
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H|$*HT$H|$H5z *~D$HT$H-
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HHL#mH$h dH3%( Hp L]A\A] @ H=$+ t^PGo fH=#+ t&P of. PJH D PJHo ILl$@` I= H-+ IP I I9 AUATIUHP dH%( H$H 1Ll$ HLz H5C; HƖHL˜ HH5 ܠ HH50! HǾ P HǺk H5= < HLAMHD$Ht
HHLrkH$H dH3%( uHP L]A\A]o IY Idf AUATIUHP dH%( H$H 1Ll$ HLz H5K: HƕHL˛ HH5 ܟ HH50 HǾ P HǺp H5< < HLALHD$Ht
HHLrjH$H dH3%( uHP L]A\A]o I造 I鋠f AUATIUHP dH%( H$H 1Ll$ HLz H5w HƔHL˚ HH5l ܞ HH50 HǾ P HǺu H5; < HLAKHD$Ht
HHLriH$H dH3%( uHP L]A\A]o I駟 I鲟f AUH5: ATIUHp dH%( H$h 1HT$
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HH5a . HǾ 联 HǺz H58 m HLrHHD$0Ht
HHLfH$h dH3%( Hp L]A\A] @ H= + t^P o fH=h + t&P of. PJH D PJH o ILl$@閜 Is H-P+ I醝 I. Io AUH5 ATIUHp dH%( H$h 1HT$
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蒘 HH5 賙 HǾ HǺ H5_5 HLDHD$0Ht
HHL(cH$h dH3%( Hp L]A\A]ÐH=+ tfP Oof. H=+ t&P of. PJH D PJH o ILl$@W I4 H-* IG I I0 AUH5: ATIUHp dH%( H$h 1HT$
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HH5f 3 HǾ 膍 HǺ H51 r HLwAHD$0Ht
HHL_H$h dH3%( Hp L]A\A]ÐH=+ tfP of. H=h+ t&Pof. PJH D PJHo ILl$@ I H-P* I I鰘 I AUH5 ATIUHp dH%( H$h 1HT$
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蒑 HH5 賒 HǾ HǺ H5_. HL=HD$0Ht
HHL(\H$h dH3%( Hp L]A\A]ÐH=+ tfP Oof. H=+ t&P of. PJH D PJHo ILl$@ٕ I鶕 H-* Iɖ Iq I鲕 AUH5: ATIUHp dH%( H$h 1HT$
H|$4HT$H|$H5 ~D$HT$H-D* H|$XH5. HED$HD$HD$HD$P)D$@fHD$h1D$h $ H$ $ $ $ f)$ ~* HD$x
+ H$ H$ $H ~;+ HD$p + HDŽ$ Ƅ$ HDŽ$ H$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$ HDŽ$ DŽ$ Ƅ$ DŽ$0 Ƅ$4 HDŽ$ $h 5x HD$HDŽ$X HxH9 HD$HxH9 Hl$ H5% HaLl$@HL HH5L
HH5f 3 HǾ 膆 HǺ H5* r HLw:HD$0Ht
HHLXH$h dH3%( Hp L]A\A]ÐH=+ tfP nf. H=h+ t&Pnf. PJH D PJHn ILl$@隓 Iw H-P* I銔 I2 Is ttt1 H71fHa* H1 H% H
HfHnfHnflG ATUHH5I HXdH%( HD$H1Ld$HT$LYM HH[ H5o_ HD$HxH;=c* u ~E* M H, H58I HHD$ |* )$ E1 HH[ H5H E1 HH[ H5H E1 HH\ H5H E1 HH] H5zH HE11jLD HH
?_ jH jw HHA jLf H
b jHj jE HHA jL H
b jHcj j A 1HHL H
f jH
jjD HHA jL H
/g jHj j H
+ HH
+ fHnfHnLd$@ flH
+ L)L$@L H5 HD$PH ^ HL H
+ H5J Lfg HD$ 4 fo$LHH )D$0HD$ c HD$0Ht
LLHD$HdH3%( uGHX]A\ @ H= + t&Ponef. PJHn H! H4 AW1AVAUATUHSHX dH%( H$H 1H$ HHD$Z H$ 1HHD$@Z H$ 1HHD$Z H$P 1HHD$ Z H$ L$ HH5E LHD$pH$ L$ HH5[ LHD$h{HE LHHt
+@p L$ AWIGIO H u* HD$HH $H9v H$ HxH;<$ H$ HxH;<$
I_H HP LH5D L
HE LHH$ HzH @ H H;<$ H~'$x tH|$ qZ DŽ$x H$p LH5D L\
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+ H5bB L?nuKHCH=?H= |7u3$ tH|$W DŽ$ HDŽ$ H
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+ HHH$@ HD$0 IE HLkKMv IEH\$(HD$xH$@ HD$0,f HH|AMH [ H0H;\$0% IU HtIcE;|I}c H|$xHo H뽐HbidirectI9AionaA lH
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+fPJHfBHJCfAGPAW HABIW D I] I] HC HIL] HCIHC HIL5 HCIf Lc I] I] Lc Z HC HIH$@ LHD$0踲 HCIV @ HCf HIH$@ LHD$0耲 HCI# @ ID$ HHHT ID$H ID$ HHH$ ID$H HD$HHD &f D 1HH8f1iHH8]1ZHH8N1HH81HH81HH8HL(HL(
HH(HL(HH(9HL(-UnH=
+ ynH=
+ mn H H HH* H$ H~ H 䀀 H݀ H H H H?~ H@~ H ~ 釀 HG Hg H& Z Q H阃 H鸃 Hw H颃 H頃 H鞃 H颃 H H Hu H鴃 H׃ H҃ UH5
+ HAWAVH?LHAUATSH H dH%( HE1HPHHHH>H5
+ LHHL%* H~HH5 ID$PHHHPHH@HH )ufHH1(f)~b* H$* HHp~* Dž * ƅ HDž HDž HDž8 H@HDžH HDžP HDžX Dž` ?HDžh HDžp Džx ƅ| Dž ƅ HDž [ HHHE HxL9 HPHxL9 LpH<H5 L H=HxH5 f HDž` )P6nHIHPH@H`3 L9 HII IH(I9 I] SHCHKyH?HCHH(HCH9 H9 H H9HCH(HHR9H & H9 Hȁ H?H H)H9HFH(HyBnHHSH(A HyHAHt'HN HH(nHSH(HL9@ H H~f. HH5R LLXCH HH5- H&LLLH L HHE1ɾ jLй H
jHl j HHE1ɾ jL H
jH` jׇ HHA jLo H
u jHg j襇 H(H H57 IHHLj HH56 o HH5b /p HǾ g HǺ H5 no H HoHHt
HHH@HxL9 HHHxL9 HPHXHH9tAH=* I IHxL9 LHIH9uHPHHtnHxHxL9 HpHxL9 L8HEdH3%( t H He[A\A]A^A_] @ H=* tnV D H(H "nfIIHxL9u LHIH9<IHxL9tPJHn D C Pn @ AHSL9 D HD H=* " P;nf D H=* P" nf D H=* Pnf D H=x* POnEf D H=H* P3{n)f D H=* tfPOnf. PJHfPJHfPJHZfPJHfPJH D PJHZH=f
+ &nn HLX{ H"{ Hz HLc{ Hg{ H{| L%x* HK| H| H{ HLzf. UH5
+ HAVAULH~ATILSHp dH%( HE1H}HH5}
+ ~H * HHH5 HCHHH)QfHH1(8f)~>* H * HH~* Dž * ƅ HDž HDž HDžH HPHDžX HDž` HDžh Džp ?HDžx HDž Dž ƅ Dž ƅ HDž R HHE HxH9 HHxH9 LE1 H H5% L HM HDž H H5e% HHE1ɾ jLv H
jH j謀 HHE1ɾ jLG H
jH j} HHA jL H
jHt` jK HH H5' IH>HLc HH5 g HH5 h HǾ (` HǺu H5y
h HLHHt
HHLH2HEdH3%( HeL[A\A]A^] D H=8* tfP7on-f. H=* t&P?nf. PJH D PJH0n Isx INx IH* Hdy Iy IIxf. UH5[
+ HAWAVLxHnAUATILSHpHx dH%( HE1BHmH5
+ H,~pL-U* HoHH56 IExHpHxH)fHH1(f)~* H* HHp~F* Dž %* ƅ HDž HDž HDž8 H@HDžH HDžP HDžX Dž` ?HDžh HDžp Džx ƅ| Dž ƅ HDž DN HpHE HxL9x HxHxL9 LE1 H67 H5<! L HIع HDžp H H5l! F HM HDžx H7 H5?( HHE1ɾ jLح H
jHt j| HHE1ɾ jL H
jHh j{ HHA jLw H
m9 jH[ j{ HHA jLE H
k; jHB j{{ HHA jL H
; jHG jI{ HH H54 IHI9HL^ HHA jL H
? jHTB jLt HH H5M4 yb HH5 c HǾ Z HǺ H5> b HLHHt
HHL
-HEdH3%( HeL[A\A]A^A_] H=* tfPl/nbf. H=* t&PPnFf. PJH D PJHn H|t HWt HH* Imu H
u HRtf. ttt1 H71fHѣ* H1 ttt1 H71fH* H1 ttt1 H71fH* H1 H H
^HfHnfHnflG H5 H
^HfHnfHnflG HE H
^HfHnfHnflG ATUHH5 SH0dH%( HD$(1H\$HT$HIع HHv/ H53 HD$HxH;=* $ E1 HH/ H5t E1 HH H5n E1 HH H5a Iع HHh H5 HD$ S E1 HHI1 H5, U HE11j L HH
2 jHr H (
+ j"w HHA j L H
5 jH2> jv HHA j L H
+6 jH> jv H1Hj A LY H
jH; jo HE1Hj L/ H
: jH= jo Hh
+ HfHnfHnLd$0 flHT
+ L)D$0L H5ԧ HD$@H L HH
+ L"; H5 HD$ HD$(dH3%( uDH0[]A\H=x* t&Pnf. PJHn Hq @ ATUHH5R HXdH%( HD$H1Ld$HT$LM HH, H5/0 HD$HxH;=#* u ~* E1 H, H5 HE* )$ E1 HH, H5| E1 HH- H5o M HHv H5 HD$ a E1 HHW. H5: c HE11j L HH
/ jH j7t HHA j L& H
2 jHG; jt HHA j L H
@3 jH#; js A 1HHLp H
j HR jjm HHA j LA H
7 jH: jl Hz
+ HH
`
+ fHnfHnLd$@ flH_
+ L)L$@L H5ߤ HD$PH HL H
+ H5
+ L&8 HD$ fo$LHH0 )D$0HD$ #S HD$0Ht
LLHD$HdH3%( uGHX]A\ @ H=H* t&Ponef. PJHn Hn Hn ATUHH5 HXdH%( HD$H1Ld$HT$L߂M HHW) H5, HD$HxH;=* M ~* E1 H) H5P H* )$ E1 HH) H5< E1 HH* H5/ h E1 HH>+ H5! J HE11j L HH
, jHg j q HHA j L
H
/ jH.8 jp HHA j L H
'0 jH
+8 jp A 1HHLW H
u4 j H9 jji HHA j L( H
4 jH7 ji Ha
+ HH
G
+ fHnfHnLd$@ flHF
+ L)L$@L H5ơ HD$PH HL H
+ H5 L
5 HD$ ۦ fo$LHHI< )D$0HD$
+P HD$0Ht
LLHD$HdH3%( u>HX]A\ H=0* t PgnfPJHpn Hk Hk UH5
+ HAVAUH~ATIHSHp dH%( HE1܂H}HH5
+ ܂~H ͼ* HHH5 HCHHH)T܂fHH1(8f)~A* H* HH~* Dž * ƅ HDž HDž HDžH HPHDžX HDž` HDžh Džp ?HDžx HDž Dž ƅ Dž ƅ HDž > HHE HxH9 HHxH9 LE1 H H5 L HHE1ɾ j L H
0 jH> jl HHE1ɾ j Ls H
jH2 jl HHA j LA H
? jHL jwl HH H5S IHjHLO HH53 S HH54 U HǾ TL HǺ H5 @T HLE HHt
HHLt HEdH3%( HeL[A\A]A^]ÐH=h* tfPdnZf. H=8* t&PHon>f. PJH D PJH`n Ig Ig IH"* Hh Ih Igf. UH5
+ HAWAVH?LHAUATSH H dH%( HE1HPHHH`H>H5)
+ LHH@L%q* H~HH5Q ID$PHHHPHH@HH )ׂfHH1(f)~ҽ* H* HHp~O* Dž .* ƅ HDž HDž HDž8 H@HDžH HDžP HDžX Dž` ?HDžh HDžp Džx ƅ| Dž ƅ HDž M: HHHE HxL9 HPHxL9 LpH<H5 LpւH=HxH5s Vւf HDž` )PnHIHPH@H`3 L9 HII IH(I9 I] SHCHKyH?HCHH(HCH9 H9 H H9HCH(HHR9H & H9 Hȁ H?H H)H9HFH(Hy貿nHHSH(A HyHAHt'HN HH(GnHSH(HL9@ H H~f. HH5 LLXԂH HH5s HԂLLLH L腻 HHE1ɾ j L@ H
jH܌ jvf HHE1ɾ j L H
jH jGf HHA j Lߗ H
jH>F jf H(H H5t IH8HLmI HH5T ~M HH5 N HǾ E HǺ H5 M H HHHt
HHH@HxL9 HHHxL9 HPHXHH9tAH=* I IHxL9 LHIH9uHPHHtnHxHxL9 HpHxL9 L]HEdH3%( t H He[A\A]A^A_] @ H=H* tnV D H(H 蒷nfIIHxL9u LHIH9<IHxL9tPJH/n D C Pn @ AHSL9 D HD H=x* " P諻nf D H=H* P"{nf D H=* PKnf D H=* POnEf D H=* P3n)f D H=* tfP迺nf. PJHfPJHfPJHZfPJHfPJH D PJHZH=s
+ 薶nan HLh` H2` H ` HLs` Hw` Ha L%* H[a Ha H` HL_f. UH5;
+ HAVAULH~ATILSHp dH%( HE1+ςH}HH5
+ ς~H :* HHH5 HCHHH)fHH1(8f)~* Hp* HH~+* Dž
+* ƅ HDž HDž HDžH HPHDžX HDž` HDžh Džp ?HDžx HDž Dž ƅ Dž ƅ HDž )1 HHE HxH9 HHxH9 LE1 HS H5! Lt HM HDž H H5 + HHE1ɾ j L H
t jH j _ HHE1ɾ j L H
jHv j^ HHA j L H
jH> j^ HH H5
IHHLB HH5w
$F HH5x EG HǾ > HǺ
+ H51 F HLHHt
HHLHEdH3%( HeL[A\A]A^] D H=* tfP7ߵn-f. H=x* t&P诵nf. PJH D PJH蠶n I] I^] IHb* Ht^ I^ IY]f. UH5˹
+ HAVAULH~ATILSHp dH%( HE1ʂH}HH5}
+ ʂ~H ʪ* HHH5 HCHHH)QʂfHH1(8f)~>* H * HH~* Dž * ƅ HDž HDž HDžH HPHDžX HDž` HDžh Džp ?HDžx HDž Dž ƅ Dž ƅ HDž , HHE HxH9E HHxH9Y LE1 H H5
L HM HDž HO H5 軣 HHE1ɾ j Lv H
4 jH jZ HHE1ɾ j LG H
jH j}Z HHA j L H
jHt: jKZ HHA j L H
jHs! jZ HHA j L H
_ jH jY HH H5D IH
+HL?= HHA j LW H
e jH jR HH H5 A HH5n ;B HǾ 9 HǺ H5' zA HLHHt
HHL HEdH3%( HeL[A\A]A^] H=* t^PװnfH=x* t&P诰nf. PJH D PJH蠱n IY IY IHb* HZ I]Z IYf. UH5˴
+ HAWAVLPAUATSH H LdH%( HE1H8HHłHHH7H5q
+ HHHłL%* H~HH5 ID$PHHHPHH@HH ))łfHH1(f)~* Hؼ* HHp~* Dž r* ƅ HDž HDž HDž8 H@HDžH HDžP HDžX Dž` ?HDžh HDžp Džx ƅ| Dž ƅ HDž ' HHHE HxL9 HPHxL9 LH5q HÂ] H 1H艽IT$HLpHH5 LH4HzÂH5HxH5} `Âf HDž` )P谭nHIHPH@H`-fL9 HII IH(I9 I] SHCHKyH?HCHH(HCH9; H9 H H9HCH(HHR9H N H9E Hȁ H?H H)H9HFH(H~¬nHHSH(A HyHAHt'H~ HH(WnHSH(HL9p H H~f. HHH5 LXH H6H5
HLM HHL葨 HHE1ɾ j LL H
" jHy jS HHE1ɾ j L H
jHܿ jSS HHA j L H
jHJ3 j!S H(H H5 IHtHLy6 HH5` : HH5 ; HǾ 2 HǺ H5 : H HHHt
HHH@HxL9 HHHxL9 HPHXHH9t=H=* I IHxL98 LHIH9uHPHHt
nHxHxL9 HpHxL9 HHxL9 H8HxL9
LEHEdH3%( H He[A\A]A^A_] @ H=0* tv. D H(Hznf. IIHxL9u LHIH9 IHxL9tPJHn D C Pn @ AHSL9 D HD H=X* P苨nf D H=(* P[nf D H=* " P/+n%f D H=ȹ* Pnf D H=* P˧nf D H=h* H C蘧n H=8* Pknf D H=* tFP/?n%f. PJHfPJHfPJH D PJHzf@PS=fPJHfPJHZfPJHH=6`
+ nn L%* HR H6R HLR HNR HP HQ HLR HL%R HQ HLMR HLOR HhR UH5{
+ HAWAVHnAULpATISH dH%( HE1HxHI_HmH5(
+ LI~pH r* HoHH5S HCxHpHxH)fHH1(f)~* H* HHp~c* Dž B* ƅ HDž HDž HDž8 H@HDžH HDžP HDžX Dž` ?HDžh HDžp Džx ƅ| Dž ƅ HDž a HpHE HxH9 HxHxH91 LH5f L菹R H+ 1LYHSL(E1 HLHY H5'
Lz HM HDžx H H5
1 HHE1ɾ j L| H
z jHq j"K HHE1ɾ j L| H
jH| jJ HHA j L| H
jH* jJ IHH H5
HHX
HXL. HH5r
2 HH5s @3 HǾ * HǺ H5, 2 HLބHHtHX HI}H9 HpHxH9 LHEdH3%( HeL[A\A]A^A_]f H=x* P諡nf D H=H* P{nf D H=* AE5Jn+ D H=* tFP nf. PJHzfPJH:fPJH D AEPAUyn ILM HLM IgM IM I4N H ~* IuN IMf UH5
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f. 1Ҩ0HH$f. 1ҨHHf. g H9t'ATIUHHElHLH]A\3 f. D H9t'ATIUHH`HLH]A\c f. D IHL>f. @ AWAVAUAATIUHSHA9}5AIcL?D)H HLt LHxHCL9uE~ A1Ht I<rHHL9uH[]A\A]A^A_ AVAUATUHSHvH@tH( HH{z u? u( Hu8H{8= DeXE DepE- D E DeEtcCA' AZ A A A A ! A@K AĀtH H D CA t2A t A t D c[]A\A]A^ D H L DLLh, DLLHH D D+ H D D D; D HExLshDLLh, DLLHHCxD D+CpHCxDcpDcpD; D HE`LsPDLLhI, DLLHHC`D D+CX=HC`DcXDcXD; .D &Dc(H{(D/ E(C(IcHC0HcU(Hu0H<H hf DcH{DP2 HS EIcCHu H<HcUHެhf H H CHCH HH CAH H CHCH/ HH裏 CAsH H CHCH HHf CA@H H CHCH HH) CA
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gHULL$ HL$DD$H5# I9I IHxH9Y I HCDI.HEH9v II HMHEEAȀLyI9H @ HH$pPH HHHf1H$ dH3 %( HĘ []A\A]A^A_ HHL~IoH;nwF~ HL
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MEIEEAɀMxI9L D HH}gf D HHf E H5# AAHUI9 @ IAD IHxH9 H=#
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I. AF I^AD M7f D H$H@HHET @ AE H5# ABIUI9. IAD HE HxH9"f D H=# K PHt$ LD$DL$ gHt$ LD$DL$ IHl$(HLDLvMnL;n LLT${T$IHHIHPH9@ LhH$BD2 P\fAE M}CD Lm ^ D T$0HH @ Fp Pjh"fPrpfPDRDPpPrpfPDJDH`H # @ LpH$BD: P\ Q PDJDHHPDJDHH\$HHl$(HHHHE{H$P\Yf H] H] Mw, I^ I] H]( InBPDRDPH # QH ܠ# @ HhBD8 g@ HhBD8 I]H # @ HPBD0
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+ LHHL$HD$ѦgIWLL$HL$H5# I9 IHxH9S I HCD,M>IGH9h II
+ IOIGEÀHiH9Hf D H$Ml$L0MI$D$ M>HD$A Ll$ 9 AG M>IGH9 AGp M>I_IGH?H9HCH9 H92 H H9HBHS9H H9 H H)H9HGH{HL$蠦gIWHL$HXIHXH@ t-H LHHL$HD$1gIWLL$HL$H5# I9 IHxH9c I HCD,M>IGH9 II IOIGEÀHiH9Hf D A$[ ID$HD$D$ H$L8M D, M7Af Hl$(9 AFT M7IFI9 AF8 M7I^IFH?L9IBH9O H9 H H9HBHS9H H9 H H)H9HGH{HL$gIVHL$HXIHXH@ t-H LHHL$HD$聣gIVLL$HL$H5d# I9# IHxH9 I HC@,M7IFH9H IIb INIFD̀LaI9H H$L8 D$ D$MV I7Aƃ Ld$(ILHL$0E f. AE Lm IEI9 AEg Lm I]IEH?I9ICH9 H9( H H9HBHS9H H9 H H)H9HGH{LD$>gIULD$HXIHXH@ t-H LHLD$HD$ϡgIULT$LD$H5# I9 HE HxH9 H] HCD$Lm IEH9 II MEIEEÀMxI9LH$L8MD4 I7A Hl$ILLl$ S f D AD$ Le ID$I9 AD$ Le I\$ID$H?I9ICH9 H9c H H9HBHS9H H9 H H?H)H9HGH{LD$菡gIT$LD$HXIHXH@ t.H4 LHLD$HD$ gIT$LT$LD$H5# I9 HE HxH9 H] HCD,Le ID$H9 II MD$ID$EÀMxI9L D H{u H{ I H5f# if I H5N# H{ H] H5-# EH{ H] H5
# tH{= I H5# ILd$(Hl$0HLHNLiL;nwF~LLHL${HL$IHPD4IHPH9t@ LhD @ mXE\H $HLHm\IŃEXEPEP ;D$ML;M\$ M'kQ Ll$A AD$3 M'ID$I9 AD$ M'I\$ID$H?L9IBH9 H9 H H9HBHS9H y H9p H H)H9HGH{H $蜞gIT$H $HXIHXH@ t,H
+ LHH $HD$.gIT$LL$H $H5# I9p IHxH9 I HCD,M'ID$H9D HH~
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Ht$HL$AgHt$HL$fIAD fH=ȣ# 2 PHL$DL$gHL$DL$IAD fH=x# B PHt$H $袑gHt$H $ @ IAD fPDJDHHPDJDHH\$0Ll$HLHI{PDRDPXPrpfPDRDPHI_ I_* PDJDHPrpfI\$ f D Mg I]< Mg I^j PDJDHH q# H e# P@ HhD @ HhD hI\$\H %# S@ L`D H # *H # ;@ L`D h@ L`BD8 nH # tH=8I gÐg IH L gf. f HdH%( HD$1H4$HZHT$dH3%( uH`g AWIAVIHAUATUHnSHXD&dH%( HD$H1E MnLH)D$D9 D L9 ] l uބf HAHA;W Iw AL9rMnL)D+D$HcՃ Dl$AE)A~ LƲH MnHcHLH)D$A9HMcII9vQ D u @ ]ބ= HE7LE;w IWB AI9wI9t 1HD$HdH3%( . HXH[]A\A]A^A_ÃLHT$ƉD$
IwHT$D$ AL9] fH HhHHHl @ HZfAǶHAH; E7@HLE;wEnLD
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+ IGJ E/I9HHHH @ 1fHHD$0 fL$8HL$ )D$ AoM H L$HL$)L$ I9sO A4$@ynEl$DAAD AIAHA;GtSIOL,AL9wMCL9:ML+D$MFL(f D ADIHA;GuhLHD$r IWHD$L,A/L9I LȯIIHV诈gf. D IHL f. @ AWIAVIHAUATUHnSHXD&dH%( HD$H1E MnLH)D$D9 D L9 ] l uބf HAHA;W IwH AL9rMnL)D+D$HcՃ Dl$AE)A~ LH MnHcHLH)D$A9GMcII9vP @ u @ ]ބ= HE7LE;w IWJ AI9wI9t 1HD$HdH3%( . HXH[]A\A]A^A_ÃLHT$ƉD$ IwHT$D$H AL9] H H舭HHHl @ HZfAHAH; E7@HLE;wEnLD IGJ E/I9HHHH @ 1fHHD$0 fL$8HL$ )D$ AoM H L$HL$)L$ I9sO A4$@ynEl$DAAD AIAHA;GtSIOL,AL9wMCL9:ML+D$MFL(f D ADIHA;GuhLHD$ IWHD$L,A/L9I LثIIHV迄gf. D IHL f. @ AWAVAUIATUSHHL%Py# H=# HZLtM|$u" HxL9 L} HL9t6H+HE @HE HPL9tH@ H@ L9uAE H[]A\A]A^A_ÐHxL9ugL} HL9tH+HE @HE HPL9t@ H@ PCYg9 @ PJH>gf. f AUATUSHHcGLgo ID9~] L/H9LLHcH< MtDH1L[HCk MtIc$3 MtIHcSHDH[]A\A] @ ہgHC D HHxIt$莀gHCA$MuLggHC멐 G9| @ )f ATISHHHGHt&;W t HcSJKLdHL[A\þ HHCf. LGM ED9 H9AAAAtnC
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- HH 1.7.0 out of index internal error not enough space: expected , got RegisterCustomOps was false. Stacktrace:
- Integer overflow opaque( ml_type != nullptr non_tensor_base != nullptr invalid location range IsTensorSequence() vector::reserve lengths allocation failed elem_type_ != nullptr input name cannot be empty CPUExecutionProvider output name cannot be empty onnxruntime_profile_ ORT_LOAD_CONFIG_FROM_MODEL != T Tensor type mismatch. output buffer is too small input array is too short shape is invalid ++index < c.size() vector::_M_default_append i < tensors_.size() vector::_M_range_check Encountered nullptr. value_type != nullptr in[idx]->IsTensor() Unsupported input type FlatBuffers 1.12.0 CUDA execution provider is not enabled. The given version [%u] is not supported, only version 1 to %u is supported in this build.
- tried creating tensor with negative value in shape RegisterCustomOpsLibrary: Failed to load library RegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in library /onnxruntime_src/onnxruntime/core/common/safeint.h Specified domain and type names combination does not refer to a registered opaque type /onnxruntime_src/onnxruntime/core/session/onnxruntime_c_api.cc Opaque type is not a non_tensor type!!! Trying to get a Tensor, but got: /onnxruntime_src/include/onnxruntime/core/framework/ml_value.h this API does not support strings location dimensions do not match shape size Trying to get a TensorSeq, but got: is not supported in this function string buffer allocation failed Output buffer allocation failed Tensor sequence must contain only primitive types /onnxruntime_src/onnxruntime/core/framework/TensorSeq.h /onnxruntime_src/include/onnxruntime/core/framework/tensor.h utils::IsPrimitiveDataType<T>(dtype_) input array doesn't equal tensor size offsets buffer is not equal to tensor size element index is out of bounds buffer size is too small for string Sequence is missing type entry for its element /onnxruntime_src/include/onnxruntime/core/framework/data_types_internal.h Map is missing type entry for its value onnxruntime::DataTypeImpl::GetType<T>() == type_ Input is not of one of the supported sequence types. Input is not of type sequence or map. Unsupported tensor element type in the input: Invalid index requested for map type. Input is not of one of the supported map types. Tensor must always contain primitive types. Found: len >= 0 && static_cast<uint64_t>(len) < std::numeric_limits<size_t>::max() Value type is not supported yet: Number of values should be at least 1. For map type num_values MUST be 2 Key and value tensors have unequal number of elements. Key type is not supported yet. Each element of the sequence should be either tensor or map. At least one element in the sequence is of a type different from others. 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. Either the key tensor or the value tensor has NumDimensions > 1 0@P`p 848<8L8\8l8|88888888887 OrtStatus* OrtApis::GetOpaqueValue(const char*, const char*, const OrtValue*, void*, size_t) OrtStatus* OrtApis::CreateOpaqueValue(const char*, const char*, const void*, size_t, OrtValue**) void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType) T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = int] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] T* onnxruntime::Tensor::MutableData() [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] T* onnxruntime::Tensor::MutableData() [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] T* onnxruntime::Tensor::MutableData() [with T = bool] OrtStatus* OrtCreateValueImplSeqHelper(const OrtValue* const*, size_t, OrtValue**) OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = double; OrtStatus = OrtStatus; OrtValue = OrtValue] OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = float; OrtStatus = OrtStatus; OrtValue = OrtValue] OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue] OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = std::basic_string<char>; OrtStatus = OrtStatus; OrtValue = OrtValue] OrtStatus* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue] OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string<char>; ValueType = double; OrtStatus = OrtStatus; OrtValue = OrtValue] OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string<char>; ValueType = float; OrtStatus = OrtStatus; OrtValue = OrtValue] OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string<char>; ValueType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue] OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string<char>; ValueType = std::basic_string<char>; OrtStatus = OrtStatus; OrtValue = OrtValue] OrtStatus* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string<char>; OrtStatus = OrtStatus; OrtValue = OrtValue] const T& OrtValue::Get() const [with T = std::map<long int, double>] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = double; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] const T& OrtValue::Get() const [with T = std::map<long int, float>] const T& OrtValue::Get() const [with T = std::map<long int, long int>] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = long int; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = std::basic_string<char>; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, double>] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = std::basic_string<char>; V = double; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, float>] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, long int>] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, std::basic_string<char> >] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = std::basic_string<char>; V = std::basic_string<char>; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = unsigned int] const T* onnxruntime::Tensor::Data() const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = short unsigned int] const T* onnxruntime::Tensor::Data() const [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = float] const onnxruntime::Tensor& onnxruntime::TensorSeq::Get(size_t) const const T& OrtValue::Get() const [with T = std::vector<std::map<long int, float> >] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::vector<_RealType> >::check(const Cont&, size_t) [with T = std::map<long int, float>; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] const T& OrtValue::Get() const [with T = std::vector<std::map<std::basic_string<char>, float> >] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = std::basic_string<char>; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::vector<_RealType> >::check(const Cont&, size_t) [with T = std::map<std::basic_string<char>, float>; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] static void SafeIntExceptionHandler<onnxruntime::OnnxRuntimeException>::SafeIntOnOverflow() const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] St11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EE PFvPvE *ZN7OrtApis19GetBoundOutputNamesEPK12OrtIoBindingP12OrtAllocatorPPcPPmS7_EUlS7_E_ *ZN7OrtApis19GetBoundOutputNamesEPK12OrtIoBindingP12OrtAllocatorPPcPPmS7_EUlS5_E0_ *ZN7OrtApis20GetBoundOutputValuesEPK12OrtIoBindingP12OrtAllocatorPPP8OrtValuePmEUlS7_E_ St16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EE N11onnxruntime23NotImplementedExceptionE N11onnxruntime20OnnxRuntimeExceptionE N11onnxruntime10IAllocatorE N11onnxruntime16AllocatorWrapperE St19_Sp_counted_deleterIPvPFvS0_ESaIiELN9__gnu_cxx12_Lock_policyE2EE St19_Sp_make_shared_tag St23_Sp_counted_ptr_inplaceIN11onnxruntime16AllocatorWrapperESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE /\ basic_string::substr env_ptr == p_instance_ basic_string::_S_construct null not valid /onnxruntime_src/onnxruntime/core/session/ort_env.cc static void OrtEnv::Release(OrtEnv*) N11onnxruntime7logging5ISinkE 14LoggingWrapper N11onnxruntime7logging8CLogSinkE /onnxruntime_src/onnxruntime/core/session/IOBinding.cc BindInput execution_mode is not valid graph_optimization_level is not valid /onnxruntime_src/onnxruntime/core/session/abi_session_options.cc Result buffer is not large enough /onnxruntime_src/onnxruntime/core/session/custom_ops.cc custom op registered at runtime There must be one (and only one) dynamic typed input to the custom op. Its type info at runtime will be used to infer the type info of this dynamic typed output which is required for the success of the model loading step. More than one dynamic typed inputs are currently not supported as differing types at runtime means the output type cannot be inferred without which model loading cannot proceed. %u Unsupported version ' ' in custom op ' is not implemented %lu all types type_id_counter == 1 T0 onnxruntime::CustomOpKernel::CustomOpKernel(const onnxruntime::OpKernelInfo&, const OrtCustomOp&) CreateCustomRegistry onnxruntime::common::Status onnxruntime::CreateCustomRegistry(const std::vector<OrtCustomOpDomain*>&, std::shared_ptr<onnxruntime::CustomRegistry>&) ComputeAsync *ZN11onnxruntime20CreateCustomRegistryERKSt6vectorIP17OrtCustomOpDomainSaIS2_EERSt10shared_ptrINS_14CustomRegistryEEEUlRKNS_12OpKernelInfoEE_ N4onnx15ISchemaRegistryE N11onnxruntime8OpKernelE N11onnxruntime30IOnnxRuntimeOpSchemaCollectionE N11onnxruntime14CustomOpKernelE N4onnx8OpSchema19num_inputs_allowed_MUliE_E N4onnx8OpSchema20num_outputs_allowed_MUliE_E St23_Sp_counted_ptr_inplaceIN11onnxruntime14KernelRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime27OnnxRuntimeOpSchemaRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime14CustomRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE 12OrtAllocator N3Ort9ExceptionE 16OrtAllocatorImpl 19OrtDefaultAllocator No requested allocator available Env is null OrtMemoryInfo is null Cpu tensor(uint8) tensor(uint16) tensor(uint32) tensor(uint64) tensor(int8) tensor(int16) tensor(int32) tensor(int64) tensor(bfloat16) tensor(float16) tensor(float) tensor(double) tensor(string) tensor(bool) tensor(complex64) tensor(complex128) unknown Element type of input [TypeInferenceError] expected to have tensor type Input Output com.microsoft com.microsoft.experimental com.microsoft.nchwc com.microsoft.mlfeaturizers (domain: ) 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 in onnx/defs/schema.h). Schema error: intra-op inter-op MemcpyFromHost MemcpyToHost Exception caught: [ShapeInferenceError] Allocator with this OrtMemoryInfo is already registered. Only CPU devices are supported for now. Received invalid value for arena extend strategy. Valid values can be either 0, 1 or -1. Trying to register schema with name , but it is already registered from file bumped the operator version but forgot to update the version range in DomainToVersionRange Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type. /onnxruntime_src/onnxruntime/core/session/environment.cc expected to have sequence type expected to have type but instead is null FvRN4onnx16InferenceContextEE FvON4onnx8OpSchemaEE *ZN11onnxruntime11Environment26CreateAndRegisterAllocatorERK13OrtMemoryInfoPK11OrtArenaCfgEUliE_ *ZN11onnxruntime11Environment26CreateAndRegisterAllocatorERK13OrtMemoryInfoPK11OrtArenaCfgEUliE0_ N4onnx14InferenceErrorE N4onnx11SchemaErrorE PFvRN4onnx16InferenceContextEE PFvON4onnx8OpSchemaEE Load model from failed. Only bytes were able to be read. )) , expected: ( other_error [json.exception. off Model was not loaded Model was not loaded. Profiler is disabled. %Y-%m-%d_%H-%M-%S .json Session was not initialized Session not initialized. Invalid rank for input: Expected: for the following indices
- Compute_ Create_State_ Release_State_ Output vector pointer is NULL p_fetches->size(): Invalid Output Name: elements, but feeds has elements. Invalid Feed Input Name: Input with name: IsSparseTensor() sparse_tensor seq Running with tag: model_run Exception during loading: Unknown exception in Load() model_loading_uri model_loading_proto model_loading_istream Provider has already been registered. DmlExecutionProvider CUDAExecutionProvider TensorrtExecutionProvider Node placements ]. Provider: [ MemcpyTransformer are '0' and '1'. is_model_proto_parsed status.IsOK() session.set_denormal_as_zero CastFloat16Transformer st.IsOK() Error during EndProfiling(): ORTM 1.4.0 session.load_model_format ORT model_loading_array Initializing session. session.use_env_allocators session.save_model_format session_initialization /onnxruntime_src/onnxruntime/core/session/inference_session.cc Unexpected input data type. Actual: ( Flush-to-zero and denormal-as-zero are Received nullptr for graph transformer Graph transformers must be registered before the session is initialized. Attempt to use DefaultLogger but none has been registered. /onnxruntime_src/include/onnxruntime/core/common/logging/logging.h Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast<int>(logging::Severity::kFATAL) Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast<int>(logging::Severity::kFATAL) Could not write a profile because no model was loaded. /onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.h timeout_in_ms >0 is not supported session->NewIOBinding(&binding_).IsOK() Please fix either the inputs or the model. Got invalid dimensions for input: Received nullptr for custom registry Exceeded max transformer level. Current level is set to graph_optimization_level <= TransformerLevel::MaxLevel At least one output should be requested. Output vector incorrectly sized: output_names.size(): Size mismatch: feed_names has is not expected to be of type tensor. is not expected to be of type sparse tensor. Trying to get a SparseTensor, but got: is not expected to be of type tensor sequence. Encountered unknown exception in Run() This session already contains a loaded model. Encountered unknown exception in Load() 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. model_loading_from_saved_proto /onnxruntime_src/onnxruntime/core/framework/execution_providers.h Received nullptr for exec provider Execution providers must be registered before the session is initialized. Execution providers must be registered before the session is initialized. Having memory pattern enabled is not supported while using the DML Execution Provider. So disabling it for this session since it uses the DML Execution Provider. Parallel execution mode does not support the DML Execution Provider. So making the execution mode sequential for this session since it uses the DML Execution Provider. Parallel execution mode does not support the CUDA Execution Provider. So making the execution mode sequential for this session since it uses the CUDA Execution Provider. Could not find an implementation for the node All nodes have been placed on [ The only supported values for the environment variable The environment variable contained the value: Reading the provided model for the ORT config ModelProto needs to be parsed to check for ORT config within it Could not finalize session options while constructing the inference session. Error Message: graph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK() Creating and using per session threadpools since use_per_session_threads_ is true Failed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIAL Using global/env threadpools since use_per_session_threads_ is false When the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API. session_env.EnvCreatedWithGlobalThreadPools() Given model could not be parsed while creating inference session. Error message: Could not parse model successfully while constructing the inference session Unknown error during EndProfiling() Failed to load model because protobuf parsing failed. This session has already been initialized. ORT model verification failed. InferenceSession is null. Invalid ORT format model. Serialized version info is null. Invalid ORT format model. The ORT format model version [ ] is not supported this build Missing Model. Invalid ORT format model. SessionState is null. Invalid ORT format model. Session has already been initialized. Adding default CPU execution provider. This session will use the allocator registered with the environment. Unable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes. Serializing optimized 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: Encountered unknown exception in Initialize() virtual void onnxruntime::InferenceSession::AddPredefinedTransformers(onnxruntime::GraphTransformerManager&, onnxruntime::TransformerLevel, const std::vector<std::basic_string<char> >&) void onnxruntime::InferenceSession::InitLogger(onnxruntime::logging::LoggingManager*) const onnxruntime::logging::Logger& onnxruntime::InferenceSession::CreateLoggerForRun(const RunOptions&, std::unique_ptr<onnxruntime::logging::Logger>&) EndProfiling NewIOBinding GetModelOutputs GetOverridableInitializers GetModelInputs GetModelMetadata onnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector<std::basic_string<char> >&, const std::vector<std::basic_string<char> >&, const onnxruntime::OrtValueNameIdxMap&) FeedsFetchesInfo Run const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor] ValidateInputs operator() operator() Initialize LoadOrtModel LoadOrtModelBytes operator() PartitionOrtFormatModel TransformGraph Load SaveToOrtFormat AddCustomOpDomains Add RegisterExecutionProvider operator() ~InferenceSession onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, const void*, int) onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, const string&) operator() static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() onnxruntime::common::Status onnxruntime::FinalizeSessionOptions(const onnxruntime::SessionOptions&, const onnx::ModelProto&, bool, onnxruntime::SessionOptions&) FinalizeSessionOptions ConstructorCommon void onnxruntime::InferenceSession::ConstructorCommon(const onnxruntime::SessionOptions&, const onnxruntime::Environment&) St14default_deleteIN11onnxruntime5ModelEE *ZN11onnxruntime16InferenceSession4LoadEPKviEUlRSt10shared_ptrINS_5ModelEEE_ *ZN11onnxruntime16InferenceSession4LoadEvEUlRSt10shared_ptrINS_5ModelEEE_ *ZN11onnxruntime16InferenceSession12LoadOrtModelERKSsEUlvE_ *ZN11onnxruntime16InferenceSession12LoadOrtModelEPKviEUlvE_ N11onnxruntime16GraphTransformerE N11onnxruntime16InferenceSessionE N8nlohmann6detail9exceptionE N8nlohmann6detail11other_errorE ZN11onnxruntime20CPUExecutionProviderC1ERKNS_24CPUExecutionProviderInfoEEUliE_ ZN11onnxruntime16InferenceSession4LoadIcEENS_6common6StatusERKSbIT_St11char_traitsIS4_ESaIS4_EEEUlRSt10shared_ptrINS_5ModelEEE_ St19_Sp_counted_deleterIPN11onnxruntime5ModelESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceISt13unordered_mapISsN11onnxruntime11FuncManager8FuncInfoESt4hashISsESt8equal_toISsESaISt4pairIKSsS3_EEESaISC_ELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime16AllocatorManagerESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE _ _ C at line , column parse_error parse error invalid_iterator type_error out_of_range object boolean discarded number cannot use at() with key ' ' not found map::at <U+%.4X> <uninitialized> true literal false literal null literal string literal number literal '[' '{' ']' '}' ':' ',' unknown token end of input '[', '{', or a literal <parse error> syntax error while parsing - ; last read: ' unexpected ; expected cannot get value type must be number, but is Sequential mode Parallel mode true session_options intra_op_num_threads inter_op_num_threads execution_mode Setting execution_mode to graph_optimization_level enable_profiling Setting enable_profiling to vector::_M_emplace_back_aux vector<bool>::_M_insert_aux iterator out of range cannot use erase() with invalid literal object key object separator number overflow parsing ' ort_config Parsing RunOptions from ModelProto is not supported yet 961c151d2e87f2686a955a9be24d316f1362bf21 3.7.1 The Model Proto hasn't been checked for the ORT config json. Did not find session options in the model file to be used while running the model cannot compare iterators of different containers intra_op_num_threads option in the model file must be an integer Unsupported value for intra_op_num_threads: Setting intra_op_num_threads to /onnxruntime_src/onnxruntime/core/session/inference_session_utils.cc inter_op_num_threads option in the model file must be an integer Unsupported value for inter_op_num_threads: Setting inter_op_num_threads to execution_mode option in the model file must be an integer Unsupported execution_mode value in ORT config: graph_optimization_level option in the model file must be an integer Setting graph_optimization_level to ORT_DISABLE_ALL Setting graph_optimization_level to ORT_ENABLE_BASIC Setting graph_optimization_level to ORT_ENABLE_EXTENDED Setting graph_optimization_level to ORT_ENABLE_ALL Unsupported graph_optimization_level value in ORT config: enable_profiling option in the model file must be an integer Unsupported value for enable_profiling option: Ignoring unsupported session option in ORT config: invalid string: ill-formed UTF-8 byte invalid string: control character U+000A (LF) must be escaped to \u000A or \n invalid string: missing closing quote invalid string: '\u' must be followed by 4 hex digits invalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFF invalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFF invalid string: forbidden character after backslash invalid string: control character U+0000 (NUL) must be escaped to \u0000 invalid string: control character U+0001 (SOH) must be escaped to \u0001 invalid string: control character U+0002 (STX) must be escaped to \u0002 invalid string: control character U+0003 (ETX) must be escaped to \u0003 invalid string: control character U+0004 (EOT) must be escaped to \u0004 invalid string: control character U+0005 (ENQ) must be escaped to \u0005 invalid string: control character U+0006 (ACK) must be escaped to \u0006 invalid string: control character U+0007 (BEL) must be escaped to \u0007 invalid string: control character U+0008 (BS) must be escaped to \u0008 or \b invalid string: control character U+0009 (HT) must be escaped to \u0009 or \t invalid string: control character U+000B (VT) must be escaped to \u000B invalid string: control character U+000C (FF) must be escaped to \u000C or \f invalid string: control character U+000D (CR) must be escaped to \u000D or \r invalid string: control character U+000E (SO) must be escaped to \u000E invalid string: control character U+000F (SI) must be escaped to \u000F invalid string: control character U+0010 (DLE) must be escaped to \u0010 invalid string: control character U+0011 (DC1) must be escaped to \u0011 invalid string: control character U+0012 (DC2) must be escaped to \u0012 invalid string: control character U+0013 (DC3) must be escaped to \u0013 invalid string: control character U+0014 (DC4) must be escaped to \u0014 invalid string: control character U+0015 (NAK) must be escaped to \u0015 invalid string: control character U+0016 (SYN) must be escaped to \u0016 invalid string: control character U+0017 (ETB) must be escaped to \u0017 invalid string: control character U+0018 (CAN) must be escaped to \u0018 invalid string: control character U+0019 (EM) must be escaped to \u0019 invalid string: control character U+001A (SUB) must be escaped to \u001A invalid string: control character U+001B (ESC) must be escaped to \u001B invalid string: control character U+001C (FS) must be escaped to \u001C invalid string: control character U+001D (GS) must be escaped to \u001D invalid string: control character U+001E (RS) must be escaped to \u001E invalid string: control character U+001F (US) must be escaped to \u001F iterator does not fit current value invalid number; expected digit after '-' invalid number; expected digit after '.' invalid number; expected '+', '-', or digit after exponent invalid number; expected digit after exponent sign invalid BOM; must be 0xEF 0xBB 0xBF if given The Model Proto has already been checked for the ORT config json. Found session/run/environment configuration in the model file to be used while running the model ORT config json from the model: Json stored in the `ort_config` key cannot be parsed. Error message: ~r \|l\\\ DL<, |l\X 0@P`p\$LL t\86666h6P6888x82P888 8877777x7`75555h5P585 554448444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444444222222222222222222222222222222222222222222222222222222222222222222(6(6(6(6(6(6(6(6(6(6(6(6(6(6(6(6(6(6(6(6(6(6(6(6(6(6(6(6(6(666666666666665667666h85555555555555555555555555555555555555555555555555555555555555555m5555[55555555I5555755%5Y0:9:`:p:::::CCEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEELDEEEEEEEEEBD2DEE2D2D2D2D2D2D2D2D2D2D(DEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEEDE\DEEEEEEEEfDEEEEEEEDEEEEEDEEEEEE DEDdihiixh(hgfecddddXbJ}}4~$}~\}\}|}4$썋ČD,D 4444̅SetEnableProfiling SetGraphOptimizationLevel SetExecutionMode SetInterOpNumThreads SetIntraOpNumThreads ParseSessionOptionsFromModelProto operator() ParseOrtConfigJsonInModelProto N8nlohmann6detail11parse_errorE N8nlohmann6detail16invalid_iteratorE N8nlohmann6detail10type_errorE N8nlohmann6detail12out_of_rangeE N8nlohmann6detail22input_adapter_protocolE N8nlohmann6detail20input_buffer_adapterE St23_Sp_counted_ptr_inplaceIN8nlohmann6detail20input_buffer_adapterESaIS2_ELN9__gnu_cxx12_Lock_policyE2EE /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer.cc Apply /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_mgr.cc This transformer is already registered ApplyTransformers Level _RuleBasedTransformer NchwcTransformer NhwcTransformer MatMulScaleFusion EliminateIdentity EliminateSlice UnsqueezeElimination EliminateDropout ExpandElimination CastElimination FuseReluClip ShapeToInitializer ConvAddFusion ConvMulFusion ConvBNFusion Unsupported level session.enable_quant_qdq MatMulAddFusion ReshapeFusion GemmActivationFusion MatMulIntegerToFloatFusion DynamicQuantizeMatMulFusion ACLExecutionProvider ArmNNExecutionProvider ConvActivationFusion SimplifiedLayerNormFusion AttentionFusion EmbedLayerNormFusion BiasGeluFusion BiasSoftmaxFusion SkipLayerNormFusion FastGeluFusion Unsupported level GeluApproximation /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_utils.cc CommonSubexpressionElimination std::vector<std::unique_ptr<onnxruntime::GraphTransformer> > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, const onnxruntime::SessionOptions&, const onnxruntime::IExecutionProvider&, const std::vector<std::basic_string<char> >&) std::vector<std::unique_ptr<onnxruntime::RewriteRule> > onnxruntime::optimizer_utils::GenerateRewriteRules(onnxruntime::TransformerLevel, const std::vector<std::basic_string<char> >&) N11onnxruntime11RewriteRuleE N11onnxruntime17EliminateIdentityE Max: node_index < nodes_.size() Inserted_Cast dtype Validating no unexpected access using an invalid node_index. Got: /onnxruntime_src/include/onnxruntime/core/graph/graph.h RemoveDuplicateCastTransformer /onnxruntime_src/include/onnxruntime/core/optimizer/graph_transformer.h /onnxruntime_src/onnxruntime/core/optimizer/insert_cast_transformer.cc cast node to cast from float16 to float32 on cpu InsertCastTransformer works on the assumption that `dtype` attribute holds an integer. dtype_attribute->second.has_i() virtual onnxruntime::common::Status onnxruntime::InsertCastTransformer::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) const ApplyImpl Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime21InsertCastTransformerE N11onnxruntime30RemoveDuplicateCastTransformerE _Map_base::at size is different size_ == size unsupported data type: ai.onnx Sqrt Pow fused LayerNorm subgraphs epsilon SimplifiedLayerNormalization /onnxruntime_src/onnxruntime/core/optimizer/initializer.h ReadExternalRawData() failed: /onnxruntime_src/onnxruntime/core/optimizer/layer_norm_fusion.cc q!qqqqqnoqqppqqqqpApplyImpl Initializer onnxruntime::Initializer::Initializer(const onnx::TensorProto&, const onnxruntime::Path&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl '7
N11onnxruntime15LayerNormFusionE N11onnxruntime25SimplifiedLayerNormFusionE fused Matmul and Add gemm /onnxruntime_src/onnxruntime/core/optimizer/matmul_add_fusion.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
N11onnxruntime15MatMulAddFusionE MatMulInteger MatMulIntegerToFloat /onnxruntime_src/onnxruntime/core/optimizer/matmul_integer_to_float.cc onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl
N11onnxruntime26MatMulIntegerToFloatFusionE bad optional access Unsupported data type: div_inputs.size() == 2 mul_inputs.size() == 2 FusedMatMul TransposeMatMul Fused MatMul and Scale _FusedMatMulAndScale /onnxruntime_src/onnxruntime/core/optimizer/matmul_scale_fusion.cc Constant initializer NodeArg shape should not be null. NodeArg: input_node.InputDefs().size() == 2 && scale_and_index.value().second < 2 output_node.OutputDefs().size() == 1 std::vector<onnxruntime::{anonymous}::ScaleMergeInfo> onnxruntime::{anonymous}::GetOutputNodeMerges(onnxruntime::Graph&, onnxruntime::Node&, const std::unordered_set<std::basic_string<char> >&) void onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<Ret>::operator()(int32_t, Ret&) const [with Ret = onnxruntime::common::Status; int32_t = int] operator() operator() operator() operator() operator() operator() operator() operator() GetScalarConstantInitializer nonstd::optional_lite::optional<float> onnxruntime::{anonymous}::GetScalarConstantInitializer(const onnxruntime::Graph&, const onnxruntime::NodeArg&) nonstd::optional_lite::optional<std::pair<float, int> > onnxruntime::{anonymous}::GetScaleFromNode(const onnxruntime::Graph&, const onnxruntime::Node&, const std::unordered_set<std::basic_string<char> >&) std::vector<onnxruntime::{anonymous}::ScaleMergeInfo> onnxruntime::{anonymous}::GetInputNodeMerges(onnxruntime::Graph&, onnxruntime::Node&, const std::unordered_set<std::basic_string<char> >&) Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
N11onnxruntime17MatMulScaleFusionE N6nonstd13optional_lite19bad_optional_accessE ?%%%%%%%%%%%|%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%&%&&&&%%&& &%&&&& &$&%$&$&$&$&% &$&$& &%$&$&$&$& &@&&@&@&@&@&&(&@&@&8&&@&@&@&@&8&\&,&\&\&\&\&4&D&\&\&T&$&\&\&\&\&T&x&H&x&x&x&x&P&`&x&x&p&@&x&x&x&x&p&&d&&&&&l&|&&&&\&&&&&&&&&&&&&&&&&x&&&&&&.-....</0..01.....h43h4h4h4h445h4h46(7h4h4h4h44:9::::;d;::;,<:::::strides dilations kernel_shape auto_pad SAME_UPPER SAME_LOWER NOTSET VALID ReorderOutput channels_last perm reorder ReorderInput activation asymmetric Upsample scales nearest nearest_mode spatial bn_scale bn_B _bn_nchwc FusedConv Concat BatchNormalization GlobalMaxPool coordinate_transformation_mode /onnxruntime_src/onnxruntime/core/optimizer/nchwc_transformer.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
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- N11onnxruntime16NchwcTransformerE _nhwc NhwcMaxPool storage_order DequantizeLinear QLinearConv nhwc_permutated_pads QLinearAdd QLinearMul QLinearLeakyRelu QLinearSigmoid QLinearGlobalAveragePool Pad /onnxruntime_src/onnxruntime/core/optimizer/nhwc_transformer.cc WӍGӍWӍWӍWӍWӍ7Ӎ'ӍWӍWӍӍӍWӍWӍWӍWӍӍRecurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
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N11onnxruntime15NhwcTransformerE data type is not supported. min FuseReluClip_ _min_zero_constant data type is not supported Unexpected data type for Clip 'min' input of /onnxruntime_src/onnxruntime/core/optimizer/relu_clip_fusion.cc (@p<
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- - - - - - - - - - - - onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ToProto onnxruntime::Initializer::Initializer(onnx::TensorProto_DataType, const string&, const std::vector<long int>&) virtual onnxruntime::common::Status onnxruntime::FuseReluClip::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const
N11onnxruntime12FuseReluClipE Unsqueeze Gather Reshape Squeeze starts ends Fused reshape node: Cannot replace concat node with initializer: /onnxruntime_src/onnxruntime/core/optimizer/reshape_fusion.cc Total fused reshape node count: Fuse_Subgraph Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
N11onnxruntime13ReshapeFusionE /onnxruntime_src/onnxruntime/core/optimizer/rule_based_graph_transformer.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl ApplyRulesOnNode N11onnxruntime25RuleBasedGraphTransformerE
N11onnxruntime18ShapeToInitializerE SkipLayerNormalization /onnxruntime_src/onnxruntime/core/optimizer/skip_layer_norm_fusion.cc fused SkipLayerNorm subgraphs Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl ̼+
N11onnxruntime19SkipLayerNormFusionE Ɏ)Ɏ)Ɏ)Ɏ)Ɏ1ɎɎ)Ɏ)ɎȎȎ)Ɏ)Ɏ)Ɏ)ɎȎYŎfŎfŎfŎfŎLŎ?ŎfŎfŎzŎmŎfŎfŎfŎfŎzŎƎƎƎƎƎƎƎƎƎƎvƎƎƎƎƎƎƎƎƎƎƎƎƎƎƎƎ~ƎƎƎƎƎƎĎŎŎŎŎĎĎŎŎĎĎŎŎŎŎĎqǎ~ǎ~ǎ~ǎ~ǎdǎWǎ~ǎ~ǎJǎ=ǎ~ǎ~ǎ~ǎ~ǎJǎ
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N11onnxruntime14EliminateSliceE VitisAIExecutionProvider ' doesn't support memcpy Execution type ' Memcpy Copy from/to host memory /onnxruntime_src/onnxruntime/core/optimizer/transformer_memcpy.cc dup_replacements.find(&arg) == dup_replacements.end() onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&)::__lambda12 bool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&) ForEachWithIndex void onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node&, const onnxruntime::KernelRegistryManager&, onnxruntime::InitializedTensorSet&) Recurse ApplyImpl *ZN11onnxruntime21TransformerMemcpyImpl11ProcessDefsERNS_4NodeERKNS_21KernelRegistryManagerERSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSA_EEEEUlRKNS_7NodeArgEmE_ *ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE_ *ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE0_ N11onnxruntime17MemcpyTransformerE UnsqueezeElimination_ UnsqueezeElimination cannot remove node /onnxruntime_src/onnxruntime/core/optimizer/unsqueeze_elimination.cc Apply N11onnxruntime20UnsqueezeEliminationE 0) )0)0)0)0)))0)0)()(0)0)0)0)()L) )L)L)L)L)$)4)L)L)D))L)L)L)L)D)vector::_M_range_insert RandomUniform RandomNormal RandomUniformLike RandomNormalLike Multinomial 77777777777777777%88%8%8%8%877%8%877%8%8%8%87c8V8c8c8c8c8I8<8c8c8/8"8c8c8c8c8/8 '7w+2̄̈́̈́̈́̈́ ̈́̈́̈́̈́̄̄̈́̈́̈́̈́̄̄ ̈́ ̈́ ̈́ ̈́̄̈́ ̈́ ̈́̄̄ ̈́ ̈́ ̈́ ̈́̄Start ValidateGemmInitializer Pass ValidateGemmInitializer Expected value: Where Expand Failed to find mask path where const not matched. Failed to find shape path equal const not matched. gather indices not matched. Start CheckNodesInPathQ q_reshape const not matched qk_div const not matched. Pass CheckNodesInPathQ Start CheckNodesInPathK k_reshape const not matched Pass CheckNodesInPathK Start MatchInputMaskSubgraph Failed to find Softmax node Failed to find path for mask Pass MatchInputMaskSubgraph Start CheckNodesInPathV Pass CheckNodesInPathV unidir mask is not constant Start MatchGemmSubgraph Faild to match gemm path Gemm does not have 3 inputs Gemm bias is not constant Gemm bias shape not expected Pass MatchGemmSubgraph Start MatchPastSubgraph Failed to match v_concat past_v_gather indices != 1 past_k_gather indices != 0 Pass MatchPastSubgraph Start MatchUnidirMaskSubgraph Pass MatchUnidirMaskSubgraph Mask_Int32 Cast mask from int64 to int32 MaskCast Start FuseGptAttention Faild to find path v to Split Failed to find path for q Failed to find path for k Fused Attention subgraphs num_heads unidirectional qkv_weights qkv_bias k root is not layer norm Faild to find path v Fused an attention node. Gemm bias is not constant initializer Gemm bias shape is not expected Gemm weight is not constant initializer Gemm weight shape is not expected /onnxruntime_src/onnxruntime/core/optimizer/attention_fusion_helper.h input_indices.size() == expected_values.size() && input_indices.size() > 0 Slice does not have enough number of inputs Slice ends is less than INT_MAX Slice parameter is not expected. Input index: Start MatchInputMaskSubgraphDistilBert Output edge count not expected for mask nodes Softmax attribute axis is expected to be 3 Failed to find reshape shape path 1 Failed to find reshape shape path 2 Pass MatchInputMaskSubgraphDistilBert q_transpose perm attribute not matched Output edge count not expected for Softmax mask_unsqueeze_1 axes not matched. Expect: 1 mask_unsqueeze_2 axes not matched. Expect: 2 mask_sub const input not matched mask_mul const input not matched Output edge count not expected for nodes in path v Failed in match Transpose attribute perm. Expected: 0, 2, 1, 3 Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3 hidden_size != num_heads * head_size v_reshape initializer value is not expected reshape initializer value is not expected unidir mask shape not expected This optimizer does not support external data for unidirectional mask right now Mask is neither unidirectional nor all ones Expect mask data type is uint8 or float Input of reshape_before_gemm is not the input of subgraph Output edge count not expected for nodes in gemm path CheckSliceParameters return false concat first input value is not -1 Faild to match concat node for Gather paths concat_after_gather does not have expected number of inputs or output edges concat_after_gather input 2 does not have expected value Faild to match gemm gather path Output edge count not expected for nodes in gemm gather path unsqueeze_after_gather axes value not expected gather axis value not expected gather input 1 value is not expected Failed to find path for past_k Failed to find path for present_k Failed to find path for present_v and past_v past_k_transpose perm attribute not matched present_k_transpose perm attribute not matched present_k_unsqueeze axes value not expected present_v_unsqueeze axes value not expected past_v_gather and past_k_gather does not have same past input Output edge count not expected for nodes in past subgraph Faild to match the path (Div-->Where-->Add) for unidirectional mask Faild to match path 1 for unidirectional mask Output edge count not expected for nodes in path 1 of unidirectional mask Div and Shape1 does not have edge CheckSliceParameters returns false for last_slice CheckSliceParameters returns false for mask_slice ValidateUnidirMask returns false for mask_slice CheckSliceParameters returns false for slice1 Faild to match path 2 for unidirectional mask Output edge count not expected for unsqueeze2 of unidirectional mask Faild to match path 3 for unidirectional mask Output edge count not expected for unsqueeze3 of unidirectional mask Faild to match path 4 for unidirectional mask Div and Shape does not have edge Output edge count not expected for squeeze_2/slices2/shape2 of unidirectional mask CheckSliceParameters return false for slice2 Faild to find path to qkv_matmul CheckNodesInPathV return false MatchInputMaskSubgraph returns false MatchUnidirMaskSubgraph returns NULL q and v are not from same Split node CheckNodesInPathQ returns false k and v are not from same Split node CheckNodesInPathK returns false MatchPastSubgraph returns false Fused an attention node for GPT. q root should be layer normalization q_matmul and q_add shape not matched k_matmul and k_add shape not matched Failed 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. Mask shape is unknown or not 2D, or data type unknown Mask data type is not int32 or int64 or float32 Failed to convert mask to int32 /onnxruntime_src/onnxruntime/core/optimizer/attention_fusion.cc Output edge count not expected for Add or MatMul in path v Failed in match v_matmul and v_add input shape Failed in match input mask subgraph shape of layer norm bias tensor not expected Total fused Attention node count: FuseSubGraphQKDistilBert ConvertMaskToInt32 FuseSubGraphQKImpl FuseSubGraphQK FuseSubGraph Recurse ApplyImpl FuseGptAttention CheckNodesInPathK CheckNodesInPathQ CheckNodesInPathV CheckDistilBertReshapeShape onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const MatchPastSubgraph MatchInputMaskSubgraph MatchInputMaskSubgraph MatchUnidirMaskSubgraph ValidateUnidirMask ValidateGemmInitializer MatchGemmSubgraph CheckSliceParameters bool onnxruntime::AttentionFusionHelper::CheckSliceParameters(const onnxruntime::Graph&, const onnxruntime::Node&, const std::vector<int>&, const std::vector<long int>&, const onnxruntime::logging::Logger&)
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N11onnxruntime15AttentionFusionE @ FastGelu BiasGelu fused Add and Gelu /onnxruntime_src/onnxruntime/core/optimizer/bias_gelu_fusion.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
N11onnxruntime14BiasGeluFusionE ROCMExecutionProvider is not in valid range [- axis fused and into softmax(input + bias) BiasSoftmax softmax_axis broadcast_axis /onnxruntime_src/onnxruntime/core/optimizer/bias_softmax_fusion.cc /onnxruntime_src/onnxruntime/core/providers/common.h axis >= -tensor_rank && axis <= tensor_rank - 1 int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime17BiasSoftmaxFusionE N11onnxruntime15CastEliminationE n Lnn욐<Not eliminating output of node /onnxruntime_src/onnxruntime/core/optimizer/common_subexpression_elimination.cc representative.output_index != kInvalidOutputIndex ] because it's the graph's output. fߡffponnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse virtual onnxruntime::common::Status onnxruntime::CommonSubexpressionElimination::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) const ApplyImpl N11onnxruntime30CommonSubexpressionEliminationE ConstantFolding can't constant fold Unsupported output type of . Can't constant fold /onnxruntime_src/onnxruntime/core/optimizer/constant_folding.cc Could not find OrtValue with name ' Could not find a CPU kernel and hence fetches.size() == node->OutputDefs().size() const T& OrtValue::Get() const [with T = onnxruntime::Tensor] Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const virtual onnxruntime::common::Status onnxruntime::ConstantFolding::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) const ApplyImpl N11onnxruntime15OpKernelContextE N11onnxruntime15ConstantFoldingE fused Conv with activation activation_params Unexpected data type for Clip input of /onnxruntime_src/onnxruntime/core/optimizer/conv_activation_fusion.cc 5(5555555555fYffffL?ff2%ffff2 onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const onnxruntime::{anonymous}::GetClipConstantMinMax(const onnxruntime::Graph&, const onnxruntime::Node&, float&, float&)::__lambda10 Recurse ApplyImpl
N11onnxruntime20ConvActivationFusionE conv_W_tensor_proto add_B_tensor_proto conv_B_tensor_proto ConvAddFusion_B_ ConvAddFusion_Add_B_ /onnxruntime_src/onnxruntime/core/optimizer/conv_add_fusion.cc .P.....-n-..-,....-545555T6 7557,85555588888888888888888888888888888888889 9999988998899998&9 9&9&9&9&999&9&988&9&9&9&989999999999999999999999999999999999ToProto onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const virtual onnxruntime::common::Status onnxruntime::ConvAddFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const
N11onnxruntime13ConvAddFusionE 8GG8G8G8G8GG G8G8G0G G8G8G8G8G0GTG$GTGTGTGTG,G<GTGTGLG GTGTGTGTGLGpG@GpGpGpGpGHGXGpGpGhG8GpGpGpGpGhGG\GGGGGdGtGGGGTGGGGGGGxGGGGGGGGGGpGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGGHGHHHHG HHHHGHHHHH4HH4H4H4H4H H H4H4H,HG4H4H4H4H,Hbn_scale_tensor_proto bn_B_tensor_proto bn_mean_tensor_proto bn_var_tensor_proto ConvBnFusion_W_ ConvBnFusion_BN_B_ /onnxruntime_src/onnxruntime/core/optimizer/conv_bn_fusion.cc VUVVVV_UVVUVTVVVVS_\____3[T^__XZ-Y____UX onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const virtual onnxruntime::common::Status onnxruntime::ConvBNFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const N11onnxruntime12ConvBNFusionE qqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq rq r r r rqq r rqq r r r rq rq r r r rqr r rrq r r r rr8rr8r8r8r8rr r8r8r0r r8r8r8r8r0rTr$rTrTrTrTr,r<rTrTrLr rTrTrTrTrLrpr@rprprprprHrXrprprhr8rprprprprhrr\rrrrrdrtrrrrTrrrrrrrxrrrrrrrrrrprrrrrrrrrrrrrrrrrrrrrrrmul_B_tensor_proto ConvMulFusion_W_ ConvMulFusion_Mul_B_ /onnxruntime_src/onnxruntime/core/optimizer/conv_mul_fusion.cc xu[vxuxuxuxuw+yxuxuXz{xuxuxuxuudkdddd폑 dd܍ddddT pnȋƉȋToProto onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const virtual onnxruntime::common::Status onnxruntime::ConvMulFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const
N11onnxruntime13ConvMulFusionE
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N11onnxruntime16EliminateDropoutE DynamicQuantizeMatMul /onnxruntime_src/onnxruntime/core/optimizer/dynamic_quantize_matmul_fusion.cc onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl N11onnxruntime27DynamicQuantizeMatMulFusionE Input shape is unknown or not 2D, or data type unknown Input data type is not int32 or int64 /onnxruntime_src/onnxruntime/core/optimizer/embed_layer_norm_fusion.cc Failed 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. two paths share the same shape Output 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. 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. Cast Input from int64 to int32 fused EmbedLayerNorm subgraphs Word 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_ids and segment id should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). NonZero ConstantOfShape Failed to match Shape node. mask_index EmbedLayerNormalization position_embeddings Input id is not valid. Segment id is not valid. SFSSSS9,SSSSSSFuseSubGraph CheckInput MatchInputToConcatSubgraph MatchPositionEmbeddingSubgraphsFromGather FuseSubGraphDistilBert Recurse ApplyImpl ̼+
N11onnxruntime20EmbedLayerNormFusionE N11onnxruntime17ExpandEliminationE fast_gelu_output fused GPT2Gelu subgraphs GPT2Gelu /onnxruntime_src/onnxruntime/core/optimizer/fast_gelu_fusion.cc Recurse ApplyImpl onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const
N11onnxruntime14FastGeluFusionE '7=*BL? @@ ?FreeDimensionOverrideTransformer Invalid free dimension override. /onnxruntime_src/onnxruntime/core/optimizer/free_dim_override_transformer.cc which does not equal the specified override of Conflicting free dimension overrides. The model has input ' ' with a fixed dimension size ApplyImpl onnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformer(gsl::span<const onnxruntime::FreeDimensionOverride>) N11onnxruntime32FreeDimensionOverrideTransformerE Gelu approximation /onnxruntime_src/onnxruntime/core/optimizer/gelu_approximation.cc Total Gelu Approximation (FastGelu) node count: Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
N11onnxruntime17GeluApproximationE Erf fused Gelu subgraphs /onnxruntime_src/onnxruntime/core/optimizer/gelu_fusion.cc onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl
N11onnxruntime10GeluFusionE ??Elu HardSigmoid Selu Softsign ScaledTanh ParametricSoftplus ThresholdedRelu fused Gemm FusedGemm activation_ /onnxruntime_src/onnxruntime/core/optimizer/gemm_activation_fusion.cc onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl
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N11onnxruntime20GemmActivationFusionE Computed size: , external_data.length: External data type must not be UNDEFINED or STRING. /onnxruntime_src/onnxruntime/core/optimizer/initializer.cc model_path must not be empty. Ensure that a path is provided when the model is created or loaded. TensorProto external data size mismatch. ReadExternalRawData /onnxruntime_src/onnxruntime/core/optimizer/optimizer_execution_frame.cc Tried to allocate without valid type information, ort_value index= Failed to get allocator for optimizer allocator_ptr_ void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType) ForEachWithIndex operator() Info onnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector<const onnxruntime::Node*>&, const InitializedTensorSet&, const onnxruntime::Path&, const onnxruntime::IExecutionProvider&) *ZN11onnxruntime23OptimizerExecutionFrame4InfoC1ERKSt6vectorIPKNS_4NodeESaIS5_EERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSE_EEERKNS_4PathERKNS_18IExecutionProviderEEUlRKNS_7NodeArgEmE_ N11onnxruntime23OptimizerExecutionFrameE MIGraphXExecutionProvider OpenVINOExecutionProvider DnnlExecutionProvider NupharExecutionProvider NnapiExecutionProvider RknpuExecutionProvider /onnxruntime_src/onnxruntime/core/providers/cpu/cpu_execution_provider.cc RegisterCPUKernels virtual std::shared_ptr<onnxruntime::KernelRegistry> onnxruntime::CPUExecutionProvider::GetKernelRegistry() const GetKernelRegistry RegisterOnnxMLOperatorKernels RegisterOnnxOperatorKernels N11onnxruntime20CPUExecutionProviderE N11onnxruntime25IExecutionProviderFactoryE N11onnxruntime18CpuProviderFactoryE St23_Sp_counted_ptr_inplaceIN11onnxruntime18CpuProviderFactoryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE T1 T2 utils::HasDataType(t_proto) Missing Input: Must have a single dimension t_proto.dims_size() == 1 t_proto.dims()[0] == 1 Unsupported value attribute datatype with size: /onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape_base.h ONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type()) !utils::HasExternalData(t_proto) Unsupported value attribute datatype: Tensor proto with external data for value attribute is not supported. /onnxruntime_src/include/onnxruntime/core/framework/op_kernel.h Must have a valid input shape. /onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape.cc Unsupported output datatype with size: Must have a single dimension of 1 bmlkj j$i<hbggf|f,f void onnxruntime::ConstantOfShapeBase<EnabledOutputTypeList>::SetValue(size_t, void*) [with EnabledOutputTypeList = onnxruntime::TypeList<onnxruntime::MLFloat16, float, double, signed char, short int, int, long int, unsigned char, short unsigned int, unsigned int, long unsigned int, bool>; size_t = long unsigned int] SetValueFromTensorProto void onnxruntime::ConstantOfShapeBase<EnabledOutputTypeList>::SetValueFromTensorProto(const onnx::TensorProto&) [with EnabledOutputTypeList = onnxruntime::TypeList<onnxruntime::MLFloat16, float, double, signed char, short int, int, long int, unsigned char, short unsigned int, unsigned int, long unsigned int, bool>] onnxruntime::ConstantOfShapeBase<EnabledOutputTypeList>::ConstantOfShapeBase(const onnxruntime::OpKernelInfo&) [with EnabledOutputTypeList = onnxruntime::TypeList<onnxruntime::MLFloat16, float, double, signed char, short int, int, long int, unsigned char, short unsigned int, unsigned int, long unsigned int, bool>] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] PrepareCompute Compute N11onnxruntime19ConstantOfShapeBaseINS_8TypeListIINS_9MLFloat16EfdasilhtjmbEEEEE FPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEE *N11onnxruntime12_GLOBAL__N_115ConstantOfShapeE PFPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEE seed Invalid dtype of high sample_size FLOAT16 is not supported Invalid data type of input count mismatch batch_size is < 1 num_classes is < 1 num_samples is < 1 /onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.h info.GetAttr<float>("mean", &mean_).IsOK() info.GetAttr<float>("scale", &scale_).IsOK() info.GetAttr<int64_t>("dtype", &dtype).IsOK() ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINED info.GetAttrs<int64_t>("shape", shape).IsOK() info.GetAttr<float>("high", &high_).IsOK() info.GetAttr<float>("low", &low_).IsOK() info.GetAttr<int64_t>("sample_size", &num_samples_).IsOK() ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINED /onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.cc Could not infer data type from input tensor with data type Empty dimensions for input tensor T* onnxruntime::Tensor::MutableData() [with T = long int] MultinomialCompute T* onnxruntime::Tensor::MutableData() [with T = int] MultinomialCompute const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::RandomUniformCompute(float, float, std::default_random_engine&, onnx::TensorProto::DataType, onnxruntime::Tensor&) onnxruntime::common::Status onnxruntime::RandomNormalCompute(float, float, std::default_random_engine&, onnx::TensorProto::DataType, onnxruntime::Tensor&) onnxruntime::Multinomial::Multinomial(const onnxruntime::OpKernelInfo&) onnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo&) onnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo&) onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo&) onnxruntime::RandomNormal::RandomNormal(const onnxruntime::OpKernelInfo&) N11onnxruntime12RandomNormalE N11onnxruntime16RandomNormalLikeE N11onnxruntime13RandomUniformE N11onnxruntime17RandomUniformLikeE N11onnxruntime11MultinomialE 0 A C ? "~+@"~+@W6}+W6}++eG?+eG? ? ? ? ? @.? @.?ʫy>ʫy>K͉ ?K͉ ?~, ?~, ?_6.>_6.>9d?9d?t ?t ? @ @ delta in Range operator can not be zero! start in Range operator should be scalar like tensor, yet got shape: limit in Range operator should be scalar like tensor, yet got shape: delta in Range operator should be scalar like tensor, yet got shape: T* onnxruntime::Tensor::MutableData() [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] N11onnxruntime5RangeE min_ <= max_ min should be a scalar. max should be a scalar. called_ == 1 /onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.h /onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.cc min->Shape().NumDimensions() == 0 max->Shape().NumDimensions() == 0 void onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnce() T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::clip_internal::Clip_6Base<T>::Clip_6Base(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime13clip_internal10Clip_6BaseIfEE N11onnxruntime4ClipE N11onnxruntime6Clip_6IfEE exclusive reverse CumSum starts.size()= dims.size()= dims.size() == starts.size() extents.size()= steps.size()= dims.size() == steps.size() /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/utils.h dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size() dims.size() == extents.size() && dims.size() >= steps.size() Axis tensor must be provided to the CumSum op Axis tensor should be 0D or 1D Axis tensor should be of type `int32_t` or `int64_t` dims.size() == extents_.size() Cannot apply CumSum operator on a scalar /onnxruntime_src/onnxruntime/core/providers/cpu/math/cumsum.cc void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::common::Status onnxruntime::CumSum<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] Compute void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] onnxruntime::common::Status onnxruntime::CumSum<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] Compute void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int] T* onnxruntime::Tensor::MutableData() [with T = int] onnxruntime::common::Status onnxruntime::CumSum<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] Compute void onnxruntime::SliceIteratorBase::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int] onnxruntime::SliceSkips::SliceSkips(const onnxruntime::TensorShape&, gsl::span<const long int>, gsl::span<const long int>) T* onnxruntime::Tensor::MutableData() [with T = long int] onnxruntime::common::Status onnxruntime::CumSum<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] Compute int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) N11onnxruntime6CumSumIfEE N11onnxruntime6CumSumIdEE N11onnxruntime6CumSumIiEE N11onnxruntime6CumSumIlEE d('''('''''''''''('''''''(('t('''$(''D('''D('''''''''('((''l)'('(T($(4((),)4(<)L)\)'''''''''''''''''''''''d('D('''''''''''''''('('(T(('''('(T('(t($(4(''(Det X != nullptr /onnxruntime_src/onnxruntime/core/providers/cpu/math/det.cc Input tensor should have a rank of at least 2 Matrix dimensions are not equal. Square matrix is expected const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::Det<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] N11onnxruntime3DetIfEE is not supported yet Einsum equation Missing 'equation' attribute -> /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum.cc Einsum op: An implementation for the input type /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum.h info.GetAttr<std::string>("equation", &equation_).IsOK() Einsum op: There must be atleast one input There was a problem acquiring temporary memory allocator in Einsum op DeviceCompute const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::Einsum::Einsum(const onnxruntime::OpKernelInfo&) FN11onnxruntime6common6StatusERKNS_6TensorERS2_PvE FN11onnxruntime6common6StatusEPKiS3_PimmmmmmmPNS_11concurrency10ThreadPoolEPvE FN11onnxruntime6common6StatusEPKdS3_PdmmmmmmmPNS_11concurrency10ThreadPoolEPvE FN11onnxruntime6common6StatusEPKlS3_PlmmmmmmmPNS_11concurrency10ThreadPoolEPvE FSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_llSt10shared_ptrINS0_10IAllocatorEEPvE FN11onnxruntime6common6StatusERKSt6vectorImSaImEERKNS_6TensorERS7_PKNS_11TensorShapeEPvE FN11onnxruntime6common6StatusEPKfS3_PfmmmmmmmPNS_11concurrency10ThreadPoolEPvE FN11onnxruntime6TensorERKS0_RKSt6vectorIlSaIlEEbSt10shared_ptrINS_10IAllocatorEEPKNS_11TensorShapeEPNS_11concurrency10ThreadPoolEPvE N11onnxruntime6EinsumE PFSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_llSt10shared_ptrINS0_10IAllocatorEEPvE PFN11onnxruntime6common6StatusERKSt6vectorImSaImEERKNS_6TensorERS7_PKNS_11TensorShapeEPvE PFN11onnxruntime6common6StatusEPKfS3_PfmmmmmmmPNS_11concurrency10ThreadPoolEPvE PFN11onnxruntime6TensorERKS0_RKSt6vectorIlSaIlEEbSt10shared_ptrINS_10IAllocatorEEPKNS_11TensorShapeEPNS_11concurrency10ThreadPoolEPvE PFN11onnxruntime6common6StatusERKNS_6TensorERS2_PvE PFN11onnxruntime6common6StatusEPKiS3_PimmmmmmmPNS_11concurrency10ThreadPoolEPvE PFN11onnxruntime6common6StatusEPKdS3_PdmmmmmmmPNS_11concurrency10ThreadPoolEPvE PFN11onnxruntime6common6StatusEPKlS3_PlmmmmmmmPNS_11concurrency10ThreadPoolEPvE Einsum op: Transpose failed: %ld Tensor size ( ) != new size ( for input shape Einsum op: The candidate output does not match the actual output's shape /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_auxiliary_ops.cc output.SizeInBytes() == input.SizeInBytes() The rank of the input must match permutation size for Transpose input_rank == permutation.size() Length of permutation must match the rank of the input to be permutated The innermost dims should have the same dim value to parse the diagonal elements input_dims[rank - 2] == input_dims[rank - 1] Einsum op: Unsupported data type for Diagonal rank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2] shape_.Size() == new_shape.Size() Cannot parse the diagonal elements along dims Data types of the inputs must match for MatMul input_1.DataType() == input_2.DataType() input_shape_1_override.size() == 3 && input_shape_2_override.size() == 3 Batch dimension should match for MatMul; input_shape_1_override[0] == input_shape_2_override[0] Incompatible matrix dimensions for matMul input_shape_1_override[2] == input_shape_2_override[1] Einsum op: Exception during MatMul operation: Only 1 batch dimension is allowed for MatMul T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector<long int>&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul<T>&) [with T = long int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>; onnxruntime::EinsumOp::DeviceHelpers::MatMul<T> = std::function<onnxruntime::common::Status(const long int*, const long int*, long int*, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, onnxruntime::concurrency::ThreadPool*, void*)>] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector<long int>&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul<T>&) [with T = double; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>; onnxruntime::EinsumOp::DeviceHelpers::MatMul<T> = std::function<onnxruntime::common::Status(const double*, const double*, double*, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, onnxruntime::concurrency::ThreadPool*, void*)>] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector<long int>&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul<T>&) [with T = int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>; onnxruntime::EinsumOp::DeviceHelpers::MatMul<T> = std::function<onnxruntime::common::Status(const int*, const int*, int*, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, onnxruntime::concurrency::ThreadPool*, void*)>] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector<long int>&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul<T>&) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>; onnxruntime::EinsumOp::DeviceHelpers::MatMul<T> = std::function<onnxruntime::common::Status(const float*, const float*, float*, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, onnxruntime::concurrency::ThreadPool*, void*)>] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::Transpose(const onnxruntime::Tensor&, const std::vector<long int>&, const std::vector<long unsigned int>&, onnxruntime::AllocatorPtr, void*, const Transpose&) bool onnxruntime::EinsumOp::IsTransposeRequired(size_t, const std::vector<long unsigned int>&) void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&) std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDims(const onnxruntime::Tensor&, bool, onnxruntime::AllocatorPtr) std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonal(const onnxruntime::Tensor&, int64_t, int64_t, onnxruntime::AllocatorPtr, void*) onnxruntime::common::Status onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopy(const onnxruntime::Tensor&, onnxruntime::Tensor&, void*) . The shape is: in the same dimension vector::_M_fill_insert dim_iter == rank Inputs have ellipses in them but the provided output subscript does not contain an ellipsis Number of subscripts in the input equation does not match number of input tensors Found a '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the input Ellipsis must indicate a fixed number of dimensions across all inputs Found '.' 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 Another operand has a dim value of Einsum 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 The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z) Found a '.' not part of an ellipsis in the output subscript provided Found '.' not part of an ellipsis in the output subscript provided Output subscript contains repeated letters Output subscript contains letters not seen in the inputs Rank of the input must match number of subscript labels corresponding to the input /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_compute_preprocessor.cc num_broadcasted_indices < num_of_ellipsis_dims_ The broadcasted dimensions of the inputs are incompatible void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&) onnxruntime::common::Status onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDims() Run Left shape override: Left shape: Right shape override: Right shape: left_rank == right_rank left_dim == right_dim Einsum op: The candidate output cannot be reshaped into the op's output /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_typed_compute_processor.cc candidate_output.Shape().Size() == output_shape.Size() Not all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: candidate_output_dims[iter] == 1 Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: The override dims are not compatible with given tensor's shape. left.Shape().Size() == left_shape_override.Size() right.Shape().Size() == right_shape_override.Size() Ranks of pair-wise operands must be equal. Einsum op: Input dimensions must be equal along an axis to be reduced across all inputs Einsum op: Input shapes do not align void onnxruntime::EinsumTypedComputeProcessor<T>::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&) [with T = long int] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumTypedComputeProcessor<T>::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, bool) [with T = long int] void onnxruntime::EinsumTypedComputeProcessor<T>::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&) [with T = double] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumTypedComputeProcessor<T>::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, bool) [with T = double] void onnxruntime::EinsumTypedComputeProcessor<T>::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&) [with T = int] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumTypedComputeProcessor<T>::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, bool) [with T = int] void onnxruntime::EinsumTypedComputeProcessor<T>::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&) [with T = float] void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&) std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumTypedComputeProcessor<T>::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, bool) [with T = float] offset % span_size_ == 0 fmod (fmod == 0) || (fmod == 1) ) for tensor of length: Invalid start/ending offset [ Broadcast Output range [ cur1 == end1 cur_out == end_out Abs Neg Floor Ceil Reciprocal Less Greater BitShift Not And Or Xor Sin Cos Tan Asin Acos Atan Sinh Cosh Asinh Acosh Atanh PRelu Mod axis == 1 || axis == largest direction LEFT RIGHT Invalid direction value of ' Can broadcast 0 by 0 or 1. largest <= 1 !helper.HaveTwoTensorInputs() fmod_ p_ml_value Unsupported Y type: Unsupported X type: Must have 1 or more inputs inputCount >= 1 data_n.Shape() == shape input_count >= 1 data_1.Shape() == shape InputBroadcaster can only start at span boundary! /onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.h fmod must have value either 0 or 1 /onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.cc start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= len ) are not at boundary of span with size: start_offset % span_size == 0 && real_end % span_size == 0 BroadcastLooper requires two tensors as input. Attempting to broadcast an axis by a dimension other than 1. '. Valid values are 'LEFT' or 'RIGHT'. /onnxruntime_src/onnxruntime/core/providers/cpu/element_wise_ranged_transform.h input_size < std::numeric_limits<std::ptrdiff_t>::max() Tensor with shape information must be 1 dimensional. shape_data_tensor.Shape().GetDims().size() == 1 ExpandBroadcastLooper should only have a shape for the second input. fmod attribute must be true for float, float16 and double types Please fetch output tensor with specified shape. All inputs must have the same shape Invalid usage. Input 1 is a shape with no data. onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<signed char>] T* onnxruntime::Tensor::MutableData() [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = short int] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<short int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<long int>] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<unsigned char>] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] const T* onnxruntime::Tensor::Data() const [with T = short unsigned int] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<short unsigned int>] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] const T* onnxruntime::Tensor::Data() const [with T = unsigned int] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<unsigned int>] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<long unsigned int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg<signed char>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg<int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg<long int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Floor<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Ceil<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Reciprocal<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Reciprocal<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sqrt<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sqrt<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Exp<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Exp<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Log<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Log<double>] onnxruntime::common::Status onnxruntime::Sum_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::common::Status onnxruntime::Sum_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda219 onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda219 onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda219 void onnxruntime::ExpandBroadcastLooper(onnxruntime::BroadcastHelper&, const onnxruntime::ProcessBroadcastSpanFuncs&) void onnxruntime::UntypedExpand(onnxruntime::OpKernelContext&, const onnxruntime::ProcessBroadcastSpanFuncs&) onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>]::__lambda238 void onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnce() virtual onnxruntime::common::Status onnxruntime::Mod::Compute(onnxruntime::OpKernelContext*) const onnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo&) T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] T* onnxruntime::OpKernelContext::Output(int) [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::Mean_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = bool] onnxruntime::common::Status onnxruntime::Max_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] void onnxruntime::InputBroadcaster::AdvanceBy(size_t) void onnxruntime::BroadcastLooper(TBroadcastHelper&, const onnxruntime::ProcessBroadcastSpanFuncs&) [with TBroadcastHelper = onnxruntime::BroadcastHelper] onnxruntime::OutputBroadcaster::OutputBroadcaster(size_t, onnxruntime::Tensor&, ptrdiff_t, ptrdiff_t) void onnxruntime::BroadcastIterator::Append(ptrdiff_t, ptrdiff_t) void onnxruntime::BroadcastIterator::Init(ptrdiff_t, ptrdiff_t) onnxruntime::Broadcaster::Broadcaster(const std::vector<long int>&, const std::vector<long int>&) onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext&) onnxruntime::common::Status onnxruntime::Min_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = long unsigned int] onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned int] onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned char] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Log<double>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Log<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Exp<double>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Exp<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Sqrt<double>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Sqrt<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Reciprocal<double>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Reciprocal<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Ceil<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Floor<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg<long int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg<int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg<signed char>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg<double>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<long unsigned int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<unsigned int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<short unsigned int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<unsigned char>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<long int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<short int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<signed char>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<double>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<float>] ElementWiseKernel ( ( `( `(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfO *ZN11onnxruntime19UntypedBroadcastTwoERNS_15OpKernelContextERKNS_25ProcessBroadcastSpanFuncsEdPvEUlllE_ *ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE_ *ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE0_ *ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE1_ N11onnxruntime3PowE N11onnxruntime5Min_8E N11onnxruntime5Max_8E N11onnxruntime3NotE N11onnxruntime3AndE N11onnxruntime2OrE N11onnxruntime3XorE N11onnxruntime8functors26ElementWiseRangedTransformIfEE N11onnxruntime8functors3ExpIfEE N11onnxruntime3AddIfEE N11onnxruntime3AddIdEE N11onnxruntime3AddIiEE N11onnxruntime3AddIlEE N11onnxruntime3SubIfEE N11onnxruntime3SubIdEE N11onnxruntime3SubIiEE N11onnxruntime3SubIlEE N11onnxruntime3MulIfEE N11onnxruntime3MulIdEE N11onnxruntime3MulIiEE N11onnxruntime3MulIlEE N11onnxruntime3DivIfEE N11onnxruntime3DivIdEE N11onnxruntime3DivIiEE N11onnxruntime3DivIlEE N11onnxruntime8functors3AbsIfEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIfEEEE N11onnxruntime8functors26ElementWiseRangedTransformIdEE N11onnxruntime8functors3AbsIdEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIdEEEE N11onnxruntime8functors26ElementWiseRangedTransformIaEE N11onnxruntime8functors3AbsIaEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIaEEEE N11onnxruntime8functors26ElementWiseRangedTransformIsEE N11onnxruntime8functors3AbsIsEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIsEEEE N11onnxruntime8functors26ElementWiseRangedTransformIiEE N11onnxruntime8functors3AbsIiEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIiEEEE N11onnxruntime8functors26ElementWiseRangedTransformIlEE N11onnxruntime8functors3AbsIlEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIlEEEE N11onnxruntime8functors26ElementWiseRangedTransformIhEE N11onnxruntime8functors3AbsIhEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIhEEEE N11onnxruntime8functors26ElementWiseRangedTransformItEE N11onnxruntime8functors3AbsItEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsItEEEE N11onnxruntime8functors26ElementWiseRangedTransformIjEE N11onnxruntime8functors3AbsIjEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIjEEEE N11onnxruntime8functors26ElementWiseRangedTransformImEE N11onnxruntime8functors3AbsImEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsImEEEE N11onnxruntime8functors3NegIfEE N11onnxruntime17ElementWiseKernelINS_8functors3NegIfEEEE N11onnxruntime8functors3NegIdEE N11onnxruntime17ElementWiseKernelINS_8functors3NegIdEEEE N11onnxruntime8functors3NegIaEE N11onnxruntime17ElementWiseKernelINS_8functors3NegIaEEEE N11onnxruntime8functors3NegIiEE N11onnxruntime17ElementWiseKernelINS_8functors3NegIiEEEE N11onnxruntime8functors3NegIlEE N11onnxruntime17ElementWiseKernelINS_8functors3NegIlEEEE N11onnxruntime8functors5FloorIfEE N11onnxruntime17ElementWiseKernelINS_8functors5FloorIfEEEE N11onnxruntime8functors4CeilIfEE N11onnxruntime17ElementWiseKernelINS_8functors4CeilIfEEEE N11onnxruntime8functors10ReciprocalIfEE N11onnxruntime17ElementWiseKernelINS_8functors10ReciprocalIfEEEE N11onnxruntime8functors10ReciprocalIdEE N11onnxruntime17ElementWiseKernelINS_8functors10ReciprocalIdEEEE N11onnxruntime8functors4SqrtIfEE N11onnxruntime17ElementWiseKernelINS_8functors4SqrtIfEEEE N11onnxruntime8functors4SqrtIdEE N11onnxruntime17ElementWiseKernelINS_8functors4SqrtIdEEEE N11onnxruntime17ElementWiseKernelINS_8functors3ExpIfEEEE N11onnxruntime8functors3ExpIdEE N11onnxruntime17ElementWiseKernelINS_8functors3ExpIdEEEE N11onnxruntime8functors3LogIfEE N11onnxruntime17ElementWiseKernelINS_8functors3LogIfEEEE N11onnxruntime8functors3LogIdEE N11onnxruntime17ElementWiseKernelINS_8functors3LogIdEEEE N11onnxruntime5Sum_6IfEE N11onnxruntime5Sum_6IdEE N11onnxruntime5Sum_8IfEE N11onnxruntime5Sum_8IdEE N11onnxruntime5Max_6IfEE N11onnxruntime5Min_6IfEE N11onnxruntime4LessIfEE N11onnxruntime4LessIdEE N11onnxruntime4LessIiEE N11onnxruntime4LessIlEE N11onnxruntime7GreaterIfEE N11onnxruntime7GreaterIdEE N11onnxruntime7GreaterIiEE N11onnxruntime7GreaterIlEE N11onnxruntime5EqualIbEE N11onnxruntime5EqualIiEE N11onnxruntime5EqualIlEE N11onnxruntime5EqualIfEE N11onnxruntime5EqualIdEE N11onnxruntime6Mean_6IfEE N11onnxruntime6Mean_8IfEE N11onnxruntime8BitShiftIhEE N11onnxruntime8BitShiftIjEE N11onnxruntime8BitShiftImEE N11onnxruntime3ErfIfEE N11onnxruntime3SinIfEE N11onnxruntime3SinIdEE N11onnxruntime3CosIfEE N11onnxruntime3TanIfEE N11onnxruntime4AsinIfEE N11onnxruntime4AcosIfEE N11onnxruntime4AtanIfEE N11onnxruntime4SinhIfEE N11onnxruntime4CoshIfEE N11onnxruntime5AsinhIfEE N11onnxruntime5AcoshIfEE N11onnxruntime5AtanhIfEE N11onnxruntime5PReluIfEE N11onnxruntime8Expand_8ISsEE N11onnxruntime3ModE pA @ 8 ,F F ? ? ? ? ? ? ? ? ? ? ? ? B B B B5?5?5?5?!=!=!=!=뽸뽸뽸O]O]O]O]>>>>L>L>L>L>%=%=%=%=P*P*P*P*>>>>^^^^ 1? 1? 1? 1? "?"?"?"? @K @K @K @K ɿ ɿ ɿ ɿ .4 .4 .4 .4t.t.t.t. ,F ,F ,F ,F s7 s7 s7 s7nnnn*=*=*=*= pMpMpMpMӅ<Ӆ<Ӆ<Ӆ<**** F F F F @ @ @ .@-DT!< @c_shape != nullptr right.NumDimensions() == 2 N: K: GEMM: Dimension mismatch, W: M_ >= 0 && K_ > 0 && N_ >= 0 transA transB c_shape is required if c_data is provided /onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm.cc /onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm_helper.h left.NumDimensions() == 2 || left.NumDimensions() == 1 Gemm: Invalid bias shape for broadcast /onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm.h info.GetAttr<int64_t>("transA", &temp).IsOK() info.GetAttr<int64_t>("transB", &temp).IsOK() info.GetAttr<float>("alpha", &alpha_).IsOK() info.GetAttr<float>("beta", &beta_).IsOK() void onnxruntime::GemmBroadcastBias(int64_t, int64_t, float, const T*, const onnxruntime::TensorShape*, T*) [with T = double; int64_t = long int] void onnxruntime::GemmBroadcastBias(int64_t, int64_t, float, const T*, const onnxruntime::TensorShape*, T*) [with T = float; int64_t = long int] onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&) const T* onnxruntime::Tensor::Data() const [with T = float] onnxruntime::Gemm<T>::Gemm(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::Gemm<T>::Gemm(const onnxruntime::OpKernelInfo&) [with T = float] ZNK11onnxruntime4GemmIdE17ComputeActivationEPdmPNS_11concurrency10ThreadPoolEEUlllE_ N11onnxruntime4GemmIfEE N11onnxruntime4GemmIdEE ZNK11onnxruntime4GemmIfE17ComputeActivationEPfmPNS_11concurrency10ThreadPoolEEUlllE_ Hardmax . N= , D= /onnxruntime_src/onnxruntime/core/providers/cpu/math/hardmax.cc Hardmax inputs N, D and N * D must be < T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime7HardmaxIfEE MatMul dimension mismatch M_ == 1 && N_ == 1 was false /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_helper.h left_num_dims and right_num_dims must be >= 1 left operand cannot broadcast on dim right operand cannot broadcast on dim num_dims_with_pad != num_output_dims num_dims_with_pad - 1 != num_output_dims num_dims_with_pad - 2 != num_output_dims /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul.cc Compute Compute Compute T* onnxruntime::Tensor::MutableData() [with T = float] Compute Compute N11onnxruntime6MatMulIfEE N11onnxruntime6MatMulIdEE N11onnxruntime6MatMulIiEE N11onnxruntime6MatMulIlEE T3 Input B should not be null. /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_integer.cc IsScalarOr1ElementVector(a_zero_point) IsScalarOr1ElementVector(b_zero_point) MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1 MatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1 T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext*) const Compute N11onnxruntime17MatMulIntegerBaseE N11onnxruntime13MatMulIntegerE QLinearMatMul /onnxruntime_src/onnxruntime/core/providers/cpu/math/quantize_linear_matmul.cc IsScalarOr1ElementVector(a_offset) IsScalarOr1ElementVector(b_offset) IsScalarOr1ElementVector(y_offset) IsScalarOr1ElementVector(a_scale) IsScalarOr1ElementVector(b_scale) IsScalarOr1ElementVector(y_scale) QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1 QLinearMatmul : weight zero point must be a scalar or 1D tensor of size 1 QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1 QLinearMatmul : input scale must be a scalar or 1D tensor of size 1 QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1 QLinearMatmul : result scale must be a scalar or 1D tensor of size 1 T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] virtual onnxruntime::common::Status onnxruntime::QLinearMatMul::Compute(onnxruntime::OpKernelContext*) const Compute N11onnxruntime13QLinearMatMulE Round T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime5RoundINS_9MLFloat16EEE N11onnxruntime5RoundIfEE N11onnxruntime5RoundIdEE Sign void onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnce() T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] const T* onnxruntime::Tensor::Data() const [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] const T* onnxruntime::Tensor::Data() const [with T = short unsigned int] T* onnxruntime::Tensor::MutableData() [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] N11onnxruntime4SignE LogSoftmax /onnxruntime_src/onnxruntime/core/providers/cpu/math/softmax.cc ComputeImplOpset13 const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] ComputeImplOpset13 const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7SoftmaxIfEE N11onnxruntime7SoftmaxIdEE SoftmaxCPU inputs N, D and N * D must be < ;k_temp > 0 sorted I TopK k argument [ /onnxruntime_src/onnxruntime/core/providers/cpu/math/top_k.cc op_kernel_info.GetAttr<int64_t>("k", &k_temp).IsOK() op_kernel_info.GetAttr<int64_t>("axis", &axis_temp).IsOK() op_kernel_info.GetAttr<int64_t>("largest", &largest_temp).IsOK() op_kernel_info.GetAttr<int64_t>("sorted", &sorted_temp).IsOK() ] should not be greater than specified axis dim value [ output count mismatch, expected 2 outputs to be present for TopK operator input count mismatch, expected 1 input - the tensor to be processed input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k value value of k must not be negative k tensor should be a 1D tensor of size 1 void onnxruntime::TopkOpset11ConstructorCommon(const onnxruntime::OpKernelInfo&, int&, bool&, bool&) const T* onnxruntime::Tensor::Data() const [with T = long int] void onnxruntime::TopkOpset10ConstructorCommon(const onnxruntime::OpKernelInfo&, int&) T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) void onnxruntime::TopkOpset9ConstructorCommon(const onnxruntime::OpKernelInfo&, int&, unsigned int&) *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ N11onnxruntime4TopKILi9EfEE N11onnxruntime4TopKILi9EdEE N11onnxruntime4TopKILi10EfEE N11onnxruntime4TopKILi10EdEE N11onnxruntime4TopKILi11EfEE N11onnxruntime4TopKILi11EdEE N11onnxruntime4TopKILi11ElEE 333333? ArrayFeatureExtractor ai.onnx.ml ) >= Invalid argument: X input has empty dimensions. Invalid Y argument: num_indices = 0 Invalid Y argument: index is out of range: Y[ T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] N11onnxruntime2ml23ArrayFeatureExtractorOpIfEE N11onnxruntime2ml23ArrayFeatureExtractorOpIdEE N11onnxruntime2ml23ArrayFeatureExtractorOpIiEE N11onnxruntime2ml23ArrayFeatureExtractorOpIlEE N11onnxruntime2ml23ArrayFeatureExtractorOpISsEE Binarizer Input data with index: is NaN T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime2ml11BinarizerOpIfEE /onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.h info.GetAttr<std::string>("cast_to", &attr).IsOK() Expected TO_FLOAT, TO_STRING or TO_INT64 /onnxruntime_src/onnxruntime/core/providers/cpu/ml/ml_common.h info.GetAttr<std::string>("map_form", &attr).IsOK() info.GetAttr<int64_t>("max_map", &max_map_).IsOK() max_map must be > 0 if map_form is SPARSE map_form_ != PACK_MAP::SPARSE || max_map_ > 0 /onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.cc cur_input == end_input || cur_input->first >= 0 Negative index values are not permitted. First entry in map has index value of Expected std::map<int64_t, float> or std::map<int64_t, std::string> CastMap cast_to TO_FLOAT TO_STRING TO_INT64 Invalid CAST_TO value of map_form DENSE Expected DENSE or SPARSE Invalid PACK_MAP value of max_map %f stof stoll Invalid input type of value: Unexpected CAST_TO value of onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = std::basic_string<char>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = long int] const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, std::basic_string<char> >] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = std::map<long int, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, float>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = float] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] onnxruntime::ml::PACK_MAP onnxruntime::ml::MakePack(const string&) onnxruntime::ml::CAST_TO onnxruntime::ml::MakeCast(const string&) onnxruntime::ml::CastMap::CastMap(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml7CastMapE CategoryMapper cats_strings cats_int64s default_string default_int64 Input of string must have output of int64 Input of int64 must have output of string /onnxruntime_src/onnxruntime/core/providers/cpu/ml/category_mapper.h info.GetAttrs<std::string>("cats_strings", string_categories).IsOK() info.GetAttrs<int64_t>("cats_int64s", int_categories).IsOK() info.GetAttr<std::string>("default_string", &default_string_).IsOK() info.GetAttr<int64_t>("default_int64", &default_int_).IsOK() num_entries == int_categories.size() T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::CategoryMapper::CategoryMapper(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml14CategoryMapperE DictVectorizer string_vocabulary int64_vocabulary /onnxruntime_src/onnxruntime/core/providers/cpu/ml/dictvectorizer.h info.GetAttrs(std::is_same<AttrType, std::string>::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK() T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, long int>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, long int>] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, float>] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, double>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, double>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, std::basic_string<char> >] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = std::map<long int, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, float>] T* onnxruntime::Tensor::MutableData() [with T = double] const T& OrtValue::Get() const [with T = std::map<long int, double>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, double>] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = double] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = float] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = std::basic_string<char>] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = double] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = float] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = long int] N11onnxruntime2ml16DictVectorizerOpISslEE N11onnxruntime2ml16DictVectorizerOpISsfEE N11onnxruntime2ml16DictVectorizerOpISsdEE N11onnxruntime2ml16DictVectorizerOpIlSsEE N11onnxruntime2ml16DictVectorizerOpIlfEE N11onnxruntime2ml16DictVectorizerOpIldEE inputdimensions FeatureVectorizer input_tensor_ptr != nullptr Invalid input type: Number of inputs ( /onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.h status.IsOK() && !input_dimensions_.empty() inputdimensions attribute must be provided /onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.cc input_count >= 0 && static_cast<size_t>(input_count) == input_dimensions_.size() ) does not match number of inputdimensions values ( const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = float] virtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml17FeatureVectorizerE imputed_value_floats imputed_value_int64s replaced_value_float replaced_value_int64 Imputer Empty input dimensions. Invalid type Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specified /onnxruntime_src/onnxruntime/core/providers/cpu/ml/imputer.cc Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specified Must provide imputed_values_float_ or imputed_values_int64_ but not both. imputed_values_float_.empty() ^ imputed_values_int64_.empty() Empty value of imputed values. T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml9ImputerOpE keys_floats values_strings _Unused keys_strings values_floats default_float keys_int64s values_int64s LabelEncoder attribtues in LabelEncoder The values is and the number of ) must have the same length. num_keys == num_values classes_strings /onnxruntime_src/onnxruntime/core/providers/cpu/ml/label_encoder.h info.GetAttrs<TKey>(_key_field_name, keys).IsOK() info.GetAttrs<TValue>(_value_field_name, values).IsOK() However, the number of key is Input of tensor(string) must have output of tensor(int64) Input of tensor(int64) must have output of tensor(string) info.GetAttrs<std::string>("classes_strings", string_classes).IsOK() gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = std::basic_string<char>] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = std::basic_string<char>] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string<char>; TValue = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = std::basic_string<char>] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = float] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string<char>; TValue = float] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = long int] onnxruntime::ml::LabelEncoder::LabelEncoder(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml12LabelEncoderE N11onnxruntime2ml14LabelEncoder_2IfSsEE N11onnxruntime2ml14LabelEncoder_2ISsfEE N11onnxruntime2ml14LabelEncoder_2IlfEE N11onnxruntime2ml14LabelEncoder_2IflEE N11onnxruntime2ml14LabelEncoder_2IlSsEE N11onnxruntime2ml14LabelEncoder_2ISslEE N11onnxruntime2ml14LabelEncoder_2IllEE multi_class NONE post_transform LOGISTIC SOFTMAX SOFTMAX_ZERO intercepts classlabels_strings classlabels_ints coefficients !coefficients_.empty() LinearClassifier shape_size == out.length() /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearclassifier.cc scores.size() == static_cast<size_t>(expected_num_scores) Unexpected value for 'add_second_class' of Scores output is incorrect size. Expected: scores_output_data.length() >= scores_output_size Input shape needs to be at least a single dimension. Unsupported input element type of const T* onnxruntime::Tensor::Data() const [with T = double] void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span<float>&) [with SrcType = double] const T* onnxruntime::Tensor::Data() const [with T = long int] void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span<float>&) [with SrcType = long int] const T* onnxruntime::Tensor::Data() const [with T = int] void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span<float>&) [with SrcType = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] void onnxruntime::ml::batched_update_scores_inplace(gsl::span<T>, int64_t, int64_t, onnxruntime::ml::POST_EVAL_TRANSFORM, int, bool, onnxruntime::concurrency::ThreadPool*) [with T = float; int64_t = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] void onnxruntime::ml::LinearClassifier::ComputeImpl(gsl::span<const float>, int64_t, int64_t, int64_t, const std::vector<float>&, const std::vector<float>&, onnxruntime::Tensor&, onnxruntime::Tensor&, onnxruntime::ml::POST_EVAL_TRANSFORM, bool, onnxruntime::concurrency::ThreadPool*) const onnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml16LinearClassifierE ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_EEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE_ ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_EEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE0_ ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_EEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE1_ @@3ֳLinearRegressor Unsupported data type of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearregressor.cc info.GetAttr<int64_t>("targets", &num_targets_).IsOK() info.GetAttrs<float>("coefficients", coefficients_).IsOK() Input shape had more than 2 dimension. Dims= T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::LinearRegressor::LinearRegressor(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml15LinearRegressorE /onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.h info.GetAttr<std::string>("norm", &norm).IsOK() Rank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of Invalid normalize value of const T* onnxruntime::Tensor::Data() const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::NORMALIZE onnxruntime::ml::MakeNormalize(const string&) onnxruntime::ml::Normalizer::Normalizer(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml10NormalizerE OneHotEncoder zeros num_categories_ > 0 Unknown Category and zeros = 0. /onnxruntime_src/onnxruntime/core/providers/cpu/ml/onehotencoder.cc tmp_cats_int64s.empty() || tmp_cats_strings.empty() One and only one of the 'cats_*' attributes must be defined const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = float] const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] onnxruntime::ml::OneHotEncoderOp<T>::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = std::basic_string<char>] onnxruntime::ml::OneHotEncoderOp<T>::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::OneHotEncoderOp<T>::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = float] onnxruntime::ml::OneHotEncoderOp<T>::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = long int] N11onnxruntime2ml15OneHotEncoderOpIlEE N11onnxruntime2ml15OneHotEncoderOpIfEE N11onnxruntime2ml15OneHotEncoderOpIdEE N11onnxruntime2ml15OneHotEncoderOpISsEE offset Empty scale in attributes !scale_.empty() Scale size: ( ) != ( ) or 1 /onnxruntime_src/onnxruntime/core/providers/cpu/ml/scaler.cc scale_.size() == offset_.size() Invalid argument: input has empty dimensions. Either both scale and offset can be of feature size ( const T* onnxruntime::Tensor::Data() const [with T = float] const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = int] onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = long int] onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = float] ZNK11onnxruntime2ml8ScalerOpIiE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime2ml8ScalerOpIiE7ComputeEPNS_15OpKernelContextEEUllE1_ ZNK11onnxruntime2ml8ScalerOpIlE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime2ml8ScalerOpIlE7ComputeEPNS_15OpKernelContextEEUllE1_ ZNK11onnxruntime2ml8ScalerOpIdE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime2ml8ScalerOpIdE7ComputeEPNS_15OpKernelContextEEUllE1_ ZNK11onnxruntime2ml8ScalerOpIfE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime2ml8ScalerOpIfE7ComputeEPNS_15OpKernelContextEEUllE1_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRSt8functionIFvlEEEEvPS1_lOT_lEUllE_ N11onnxruntime2ml8ScalerOpIfEE N11onnxruntime2ml8ScalerOpIdEE N11onnxruntime2ml8ScalerOpIlEE N11onnxruntime2ml8ScalerOpIiEE SVMClassifier LINEAR kernel_type POLY RBF kernel_params vectors_per_class prob_a prob_b support_vectors rho coefficients_.size() > 0 /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.h info.GetAttrs<float>("kernel_params", kernel_params).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.cc info.GetAttrs<float>("rho", rho_).IsOK() proba_.size() == probb_.size() info.GetAttrs<std::string>("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs<int64_t>("classlabels_ints", classlabels_ints_).IsOK() classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0 T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo&) onnxruntime::ml::SVMClassifier::SVMClassifier(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml9SVMCommonE *ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZNKS_2ml13SVMClassifier11ComputeImplERNS_15OpKernelContextEN3gsl4spanIKfEERKNS_11TensorShapeEEUllE_EEvPS1_lOT_lEUllE_ N11onnxruntime2ml13SVMClassifierE ? 4
-ף;n_supports one_class SVMRegressor Unexpected mode: /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmregressor.cc num_features == feature_count_ gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::common::Status onnxruntime::ml::SVMRegressor<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo&) onnxruntime::ml::SVMRegressor<T>::SVMRegressor(const onnxruntime::OpKernelInfo&) [with T = float] ZN11onnxruntime10IAllocator13MakeUniquePtrIfEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPfE_ N11onnxruntime2ml12SVMRegressorIfEE BRANCH_EQ TreeEnsembleClassifier n_targets_or_classes > 0 AVERAGE SUM MIN BRANCH_LEQ LEAF BRANCH_LT BRANCH_GTE BRANCH_GT is already there. in tree Node (truenode). Unable to find node (falsenode). (weights). classlabels_int64s class_weights class_treeids class_nodeids class_ids nodes_values nodes_truenodeids nodes_treeids nodes_nodeids nodes_modes nodes_hitrates nodes_featureids nodes_falsenodeids base_values aggregate_function predictions.size() == 2 X dims is empty. /onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_common.h nodes_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() One falsenode is pointing either to itself, either to another tree. nodes_missing_value_tracks_true /onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_aggregator.h it->i < (int64_t)predictions.size() predictions.size() == predictions2.size() classes.size() == 2 || classes.size() == 1
-lblkkk kljjjj\jjyzly yxjzxtxdxLx xxJڄg 4|̃ɐ-i l\L쎤,| int64_t onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::_set_score_binary(int&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = float; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = float; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = float; OTYPE = float] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::ml::detail::TreeEnsembleCommonClassifier<ITYPE, OTYPE>::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = float; OTYPE = float] compute int64_t onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::_set_score_binary(int&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = double; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = double; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = double; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = double; OTYPE = float] const T* onnxruntime::Tensor::Data() const [with T = double] void onnxruntime::ml::detail::TreeEnsembleCommonClassifier<ITYPE, OTYPE>::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = double; OTYPE = float] compute int64_t onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::_set_score_binary(int&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = long int; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = long int; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = long int; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = long int; OTYPE = float] void onnxruntime::ml::detail::TreeEnsembleCommonClassifier<ITYPE, OTYPE>::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = long int; OTYPE = float] compute int64_t onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::_set_score_binary(int&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = int; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = int; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = int; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = int; OTYPE = float] const T* onnxruntime::Tensor::Data() const [with T = int] void onnxruntime::ml::detail::TreeEnsembleCommonClassifier<ITYPE, OTYPE>::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = int; OTYPE = float] compute const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::TreeEnsembleCommon(int, int, const string&, const std::vector<IT>&, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const std::vector<long int>&, const std::vector<std::basic_string<char> >&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const string&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&) [with ITYPE = int; OTYPE = float; std::string = std::basic_string<char>; int64_t = long int] onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::TreeEnsembleCommon(int, int, const string&, const std::vector<IT>&, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const std::vector<long int>&, const std::vector<std::basic_string<char> >&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const string&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&) [with ITYPE = long int; OTYPE = float; std::string = std::basic_string<char>; int64_t = long int] onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::TreeEnsembleCommon(int, int, const string&, const std::vector<IT>&, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const std::vector<long int>&, const std::vector<std::basic_string<char> >&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const string&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&) [with ITYPE = double; OTYPE = float; std::string = std::basic_string<char>; int64_t = long int] onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::TreeEnsembleCommon(int, int, const string&, const std::vector<IT>&, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const std::vector<long int>&, const std::vector<std::basic_string<char> >&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const string&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&) [with ITYPE = float; OTYPE = float; std::string = std::basic_string<char>; int64_t = long int] ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS4_24TreeAggregatorClassifierIifEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS4_24TreeAggregatorClassifierIifEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS4_24TreeAggregatorClassifierIlfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS4_24TreeAggregatorClassifierIlfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_24TreeAggregatorClassifierIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_24TreeAggregatorClassifierIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_24TreeAggregatorClassifierIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_24TreeAggregatorClassifierIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOT_lEUllE_ N11onnxruntime2ml22TreeEnsembleClassifierIfEE N11onnxruntime2ml22TreeEnsembleClassifierIdEE N11onnxruntime2ml22TreeEnsembleClassifierIlEE N11onnxruntime2ml22TreeEnsembleClassifierIiEE TreeEnsembleRegressor target_weights target_treeids target_nodeids target_ids n_targets this->base_values_.size() == predictions.size() predictions.size() == (size_t)n_targets_or_classes_ Unknown aggregation function in TreeEnsemble. void onnxruntime::ml::detail::TreeAggregatorMax<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeAggregator<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = float; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorMin<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorAverage<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = float; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorMax<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = double; OTYPE = float] void onnxruntime::ml::detail::TreeAggregator<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = double; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorMin<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = double; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorAverage<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = double; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = double; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = double; OTYPE = float] void onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = double; OTYPE = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_21TreeAggregatorAverageIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_21TreeAggregatorAverageIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_17TreeAggregatorSumIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_17TreeAggregatorSumIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_17TreeAggregatorMinIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_17TreeAggregatorMinIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_17TreeAggregatorMaxIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_17TreeAggregatorMaxIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_21TreeAggregatorAverageIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_21TreeAggregatorAverageIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_17TreeAggregatorSumIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_17TreeAggregatorSumIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_17TreeAggregatorMinIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_17TreeAggregatorMinIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_17TreeAggregatorMaxIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_17TreeAggregatorMaxIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOT_lEUllE_ N11onnxruntime2ml21TreeEnsembleRegressorIfEE N11onnxruntime2ml21TreeEnsembleRegressorIdEE ZipMap Input features_per_batch[ ] != number of classlabels[ Must provide classlabels_strings or classlabels_int64s but not both. /onnxruntime_src/onnxruntime/core/providers/cpu/ml/zipmap.cc classlabels_strings_.empty() ^ classlabels_int64s_.empty() Zipmap does not support empty dim count Zipmap only supports 1D or 2D input tensors T* OrtValue::GetMutable() [with T = std::vector<std::map<long int, float> >] T* OrtValue::GetMutable() [with T = std::vector<std::map<std::basic_string<char>, float> >] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::ZipMapOp::ZipMapOp(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml8ZipMapOpE MaxUnpool Unsupported pooling size. inferred output shape: No kernel shape is set. kernel_shape_[dim] > 0 /onnxruntime_src/onnxruntime/core/providers/cpu/nn/Unpool.cc Input dimension cannot be less than 3. Index tensor shape should be same as that of the input data tensor to unpool. Shape must be 1 dimensional as it's tensor data of a shape output_shape is smaller than minimum required. output_shape: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/unpool.h info.GetAttrs<int64_t>("kernel_shape", kernel_shape_).IsOK() pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim] strides_.size() == kernel_shape_.size() Pad should be smaller than kernel. T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = float] Compute onnxruntime::MaxUnpool::MaxUnpool(const onnxruntime::OpKernelInfo&) N11onnxruntime9MaxUnpoolE Invalid input scale: dimension != Invalid input B: Invalid input mean: Invalid input var: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/batch_norm.h 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 B: NumDimensions() != Invalid input B: 0th dimension != Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input var: NumDimensions() != Invalid input var: 0th dimension != T* onnxruntime::Tensor::MutableData() [with T = float] Compute T* onnxruntime::Tensor::MutableData() [with T = double] Compute onnxruntime::BatchNorm<T>::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::BatchNorm<T>::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime9BatchNormIfEE N11onnxruntime9BatchNormIdEE kernel_shape: Invalid input shape: X: C: kernel channels: group: M: Unknown AutoPadType String kernel_shape num_dims is not compatible with W num_dims. kernel_shape is not compatible with W shape. Not enough elements in strides. Expected: Not enough elements in kernel shape. Expected: Not enough elements in dilations. Expected: Not enough elements in pads. Expected: Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER. ComputePad: pad type not supported. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_attributes.h X num_dims does not match W num_dims. Input channels C is not equal to kernel channels * group. Output channels M is not divisible by group. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv.cc A Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributes auto_pad == AutoPadType::NOTSET onnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&) onnxruntime::ConvAttributes::ConvAttributes(const onnxruntime::OpNodeProtoHelper<onnxruntime::ProtoHelperNodeContext>&) const T* onnxruntime::Tensor::Data() const [with T = float] ComputePadAndOutputShape InferOutputShape Compute N11onnxruntime4ConvIfEE ConvInteger /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_integer.cc IsScalarOr1ElementVector(X_Zero_Point) IsScalarOr1ElementVector(W_Zero_Point) Must be a scalar or 1D tensor or size 1. Non per-tensor quantization is not supported now. Compute virtual onnxruntime::common::Status onnxruntime::ConvInteger::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime11ConvIntegerE output_padding output_shape ConvTranspose group count is <= 0 filter_number: num_input_channels: *out_size >= 0 dim_size > 0 filter number not equal to input channel number. Input channels is not divisible by group. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.h /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose.cc void onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape(int64_t, int64_t, int64_t, int64_t, int64_t, onnxruntime::AutoPadType, int64_t*, int64_t*, int64_t*) const void onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(onnxruntime::TensorShape, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, int64_t, std::vector<long int>*, std::vector<long int>*) const const T* onnxruntime::Tensor::Data() const [with T = long int] PrepareForCompute DoConvTranspose N11onnxruntime13ConvTransposeIfEE ratio input should have a single value. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/dropout_op.h ratio_tensor->Shape().Size() == 1 ratio must be in the range [0, 1) 0.0f <= ratio_value && ratio_value < 1.0f !mask || mask->Shape() == X_shape X and mask should have the same shape onnxruntime::common::Status onnxruntime::Dropout<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = float; T2 = float] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] onnxruntime::common::Status onnxruntime::Dropout<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = float; T2 = double] const T* onnxruntime::Tensor::Data() const [with T = float] float onnxruntime::{anonymous}::GetRatioOrDefault(const onnxruntime::Tensor*) [with T2 = float] onnxruntime::common::Status onnxruntime::Dropout<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = double; T2 = float] const T* onnxruntime::Tensor::Data() const [with T = bool] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = bool] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] float onnxruntime::{anonymous}::GetRatioOrDefault(const onnxruntime::Tensor*) [with T2 = double] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double] onnxruntime::common::Status onnxruntime::Dropout<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = double; T2 = double] N11onnxruntime7DropoutIffEE N11onnxruntime7DropoutIfdEE N11onnxruntime7DropoutIdfEE N11onnxruntime7DropoutIddEE Flatten /onnxruntime_src/onnxruntime/core/providers/cpu/nn/flatten.h info.GetAttr<int64_t>("axis", &axis_).IsOK() The rank of input tensor must be >= axis gsl::narrow_cast<int64_t>(X_shape.NumDimensions()) >= axis onnxruntime::Flatten::Flatten(const onnxruntime::OpKernelInfo&) int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Flatten::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7FlattenE 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 Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count /onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.h op_kernel_info.GetAttr<float>("epsilon", &epsilon_).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.cc vs. InstanceNormalization const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::InstanceNorm<T>::InstanceNorm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime12InstanceNormIfEE LpNormalization p_ == 1 || p_ == 2 /onnxruntime_src/onnxruntime/core/providers/cpu/nn/lp_norm.h op_kernel_info.GetAttr<int64_t>("axis", &axis_).IsOK() op_kernel_info.GetAttr<int64_t>("p", &p_).IsOK() const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::LpNorm<T>::LpNorm(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::LpNorm<T>::LpNorm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime6LpNormIfEE N11onnxruntime6LpNormIdEE size_ > 0 size_ % 2 == 1 alpha_ > 0.0f beta_ > 0.0f LRN /onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.h info.GetAttr<int64_t>("size", &size).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.cc X->Shape().NumDimensions() == 4 onnxruntime::LRN<T>::LRN(const onnxruntime::OpKernelInfo&) [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::common::Status onnxruntime::LRN<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] N11onnxruntime3LRNIfEE N11onnxruntime8functors4PowxIfEE t@GlobalLpPool ceil_mode count_include_pad kernel_shape[dim] > 0 input_dims.size() >= 2 Unsupported AutoPad Type. Unsupported pooling size : /onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_base.h info.GetAttr<int64_t>("p", &p_).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_attributes.h info.GetAttrs<int64_t>("kernel_shape", kernel_shape).IsOK() info.GetAttr<std::string>("auto_pad", &auto_padding).IsOK() info.GetAttr<int64_t>("count_include_pad", &temp).IsOK() info.GetAttr("storage_order", &storage_order).IsOK() pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim] strides.size() == kernel_shape.size() Dilations dimensions should match kernel shape dilations.size() == kernel_shape.size() Invalid input shape. Only N can be zero. Got: input_shape.Size() > 0 || input_shape[0] == 0 /onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool.cc kernel_shape num_dims is not compatible with X num_dims. Compute void onnxruntime::PoolProcessContext::init(const onnxruntime::OpKernelInfo&) onnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&) onnxruntime::PoolAttributes::PoolAttributes(const onnxruntime::OpNodeProtoHelper<onnxruntime::ProtoHelperNodeContext>&, const string&, int) T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] ComputeImpl T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] ComputeImpl T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] ComputeImpl ComputeImpl void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) const void onnxruntime::PoolAttributes::InferOutputSize(const std::vector<long int>&, std::vector<long int>*, std::vector<long int>*) const std::vector<long int> onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, std::vector<long int>*) const Compute N11onnxruntime8PoolBaseE N11onnxruntime10Pool1DTaskIfNS_6LpPoolEEE N11onnxruntime10Pool2DTaskIfNS_6LpPoolEEE N11onnxruntime10Pool3DTaskIfNS_6LpPoolEEE N11onnxruntime9MaxPoolV8E N11onnxruntime4PoolIfNS_7MaxPoolILi1EEEEE N11onnxruntime4PoolIfNS_11AveragePoolEEE N11onnxruntime4PoolIfNS_6LpPoolEEE N11onnxruntime13MaxPool1DTaskIfEE N11onnxruntime13MaxPool2DTaskIfEE N11onnxruntime13MaxPool3DTaskIfEE N11onnxruntime13MaxPool1DTaskIdEE N11onnxruntime13MaxPool2DTaskIdEE N11onnxruntime13MaxPool3DTaskIdEE N11onnxruntime13MaxPool1DTaskIaEE N11onnxruntime13MaxPool2DTaskIaEE N11onnxruntime13MaxPool3DTaskIaEE N11onnxruntime13MaxPool1DTaskIhEE N11onnxruntime13MaxPool2DTaskIhEE N11onnxruntime13MaxPool3DTaskIhEE /onnxruntime_src/onnxruntime/core/providers/cpu/nn/qlinearconv.cc IsScalarOr1ElementVector(X_zero_point) IsScalarOr1ElementVector(Y_zero_point) QLinearConv : filter zero point must be constant IsScalarOr1ElementVector(X_scale) IsScalarOr1ElementVector(Y_scale) QLinearConv : input zero point must be a scalar or 1D tensor of size 1 QLinearConv : result zero point must be a scalar or 1D tensor of size 1 QLinearConv : filter zero point shape invalid QLinearConv : input scale must be a scalar or 1D tensor of size 1 QLinearConv : result scale must be a scalar or 1D tensor of size 1 QLinearConv : filter scale shape invalid T4 const T* onnxruntime::Tensor::Data() const [with T = unsigned char] Compute virtual onnxruntime::common::Status onnxruntime::QLinearConv::Compute(onnxruntime::OpKernelContext*) const *ZNK11onnxruntime11QLinearConv7ComputeEPNS_15OpKernelContextEEUllE_ N11onnxruntime11QLinearConvE PA 0A >pooled_shape pooled_shape.size() == 2 pooled_height_ > 0 pooled_width_ > 0 spatial_scale spatial_scale_ > 0 MaxRoiPool R->Shape()[1] == 5 roi_batch_id >= 0 roi_batch_id < batch_size /onnxruntime_src/onnxruntime/core/providers/cpu/nn/roi_pool.h info.GetAttrs<int64_t>("pooled_shape", pooled_shape).IsOK() info.GetAttr<float>("spatial_scale", &spatial_scale_).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/nn/roi_pool.cc T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::RoiPool<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::RoiPool<T>::RoiPool(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7RoiPoolIfEE lambd Shrink /onnxruntime_src/onnxruntime/core/providers/cpu/nn/shrink.h op_kernel_info.GetAttr<float>("bias", &bias_temp).IsOK() op_kernel_info.GetAttr<float>("lambd", &lambd_temp).IsOK() T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] const T* onnxruntime::Tensor::Data() const [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] const T* onnxruntime::Tensor::Data() const [with T = short unsigned int] T* onnxruntime::Tensor::MutableData() [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] onnxruntime::Shrink::Shrink(const onnxruntime::OpKernelInfo&) N11onnxruntime6ShrinkE WCHAR_T StringNormalizer is_case_sensitive case_change_action locale stopwords Empty stopwords not allowed !sw.empty() p.second wstr != wconv_error Conversion Error en_US.UTF-8 Failed to construct locale with name: :Please, install necessary language-pack-XX and configure locales /onnxruntime_src/onnxruntime/core/providers/cpu/nn/string_normalizer.cc attribute is_case_sensitive is not set attribute case_change_action is not set attribute case_change_action has invalid value Duplicate stopwords not allowed Stopword contains invalid utf8 chars Input contains invalid utf8 chars at: Single dimension value must be greater than 0 Input dimensions are either[C > 0] or [1][C > 0] allowed C o n v e r s i o n E r r o r const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::string_normalizer::Locale::Locale(const string&)::__lambda11 onnxruntime::StringNormalizer::StringNormalizer(const onnxruntime::OpKernelInfo&) N11onnxruntime16StringNormalizerE TfIdfVectorizer id: p.first->second->id_ == 0 mode is required TF TFIDF mode: min_gram_length min_gram_length is required impl_->min_gram_length_ > 0 max_gram_length >= max_skip_count max_skip_count is required impl_->max_skip_count_ >= 0 ngram_counts <= ngram_indexes must be of equal size but ngram_indexes size: Got weights of size: pool_strings !pool_strings.empty() pool_int64s -grams (items % ngram_size == 0) Input shape must have either [C] or [B,C] dimensions with B > 0. Duplicate ngram detected, size: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/tfidfvectorizer.cc is unrecognized, acceptable values are TF,IDF,TFIDF impl_->weighting_criteria_ != kNone Required min_gram_length must be positive: min_gram_length >= max_gram_length required: impl_->max_gram_length_ >= impl_->min_gram_length_ max_skip_count must be non-negative: status.IsOK() && !impl_->ngram_counts_.empty() min_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size() max_gram_length must be inbounds of ngram_counts: size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size() status.IsOK() && !impl_->ngram_indexes_.empty() Negative ngram_indexes values are not allowed std::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; }) impl_->weights_.size() == impl_->ngram_indexes_.size() pool_strings must not be empty if specified status.IsOK() && !pool_int64s.empty() n-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_items Number of items must compose whole Non-empty ngram_counts is required Non-empty ngram_indexes is required non-empty pool_int64s is required if pool_strings not provided const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = float] size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map&) [with K = std::basic_string<char>; ForwardIter = __gnu_cxx::__normal_iterator<std::reference_wrapper<const std::basic_string<char> >*, std::vector<std::reference_wrapper<const std::basic_string<char> > > >; Map = std::unordered_map<std::reference_wrapper<const std::basic_string<char> >, std::unique_ptr<onnxruntime::ngram_details::NgramPart<std::basic_string<char> > >, std::hash<std::basic_string<char> >, std::equal_to<std::basic_string<char> > >; size_t = long unsigned int] size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map&) [with K = long int; ForwardIter = const long int*; Map = std::unordered_map<long int, std::unique_ptr<onnxruntime::ngram_details::NgramPart<long int> > >; size_t = long unsigned int] onnxruntime::TfIdfVectorizer::TfIdfVectorizer(const onnxruntime::OpKernelInfo&) *ZNK11onnxruntime15TfIdfVectorizer7ComputeEPNS_15OpKernelContextEEUllE_ N11onnxruntime15TfIdfVectorizerE ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForISt8functionIFvlEEEEvPS1_lOT_lEUllE_ G G G Gcenter_point_box NonMaxSuppression boxes_tensor scores_tensor boxes must be a 3D tensor. scores must be a 3D tensor. output != nullptr /onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.cc iou_threshold must be in range [0, 1]. center_point_box only support 0 or 1 /onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.h 0 == center_point_box_ || 1 == center_point_box_ boxes and scores should have same num_batches. boxes and scores should have same spatial_dimension. The most inner dimension in boxes must have 4 data. T* onnxruntime::Tensor::MutableData() [with T = long int] virtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const Compute GetThresholdsFromInputs const T* onnxruntime::Tensor::Data() const [with T = float] const T* onnxruntime::Tensor::Data() const [with T = long int] PrepareCompute static onnxruntime::common::Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::PrepareContext&) onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase(const onnxruntime::OpKernelInfo&) N11onnxruntime21NonMaxSuppressionBaseE N11onnxruntime17NonMaxSuppressionE Null input X ptr Null rois_ptr Null batch_indices_ptr RoiAlign avg Invalid mode of value output_height output_width sampling_ratio sampling_ratio_ >= 0 Number of dimensions for batch indices should be exactly 1 Number 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 match specified. It should be either avg or max /onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/roialign.h Sampling ratio should be >=0, but it was const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::RoiAlignBase::RoiAlignBase(const onnxruntime::OpKernelInfo&) *ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIdEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUlllE_ *ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIfEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUlllE_ N11onnxruntime12RoiAlignBaseE N11onnxruntime8RoiAlignIfEE N11onnxruntime8RoiAlignIdEE ReduceL1 ReduceL2 ReduceLogSum ReduceLogSumExp ReduceMin ReduceProd ReduceSumSquare ArgMax ArgMin keepdims noop_with_empty_axes select_last_index count == 1 Input to be reduced is null Axes input is null axes_tensor != nullptr keepdims_ keep_dims /onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.h info.GetAttr("keepdims", &keepdims).IsOK() Reduction on all axes, output size should be 1. /onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.cc last_results.last_loop_red_size > 0 last_results.last_loop_size > 0 last_results.projected_index.size() > 0 The input shape override's size does not match the input tensor's shape size input_tensor_ptr->Shape().Size() == input_shape_override->Size() An axes tensor must be a vector tensor. axes_tensor->Shape().NumDimensions() == 1 Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape: void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorL1<float, float>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorL1<float, float>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorL1<int, int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorL1<int, int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorL2<float, float>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorL2<float, float>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorL2<int, int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorL2<int, int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorLogSum<float, float>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorLogSum<float, float>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorLogSum<int, int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorLogSum<int, int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorLogSumExp<float, float>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorLogSumExp<float, float>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorLogSumExp<int, int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorLogSumExp<int, int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = double; AGG = onnxruntime::ReduceAggregatorLogSumExp<double, double>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = double; AGG = onnxruntime::ReduceAggregatorLogSumExp<double, double>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorMax<float, float>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorMax<float, float>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorMax<int, int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorMax<int, int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = long int; AGG = onnxruntime::ReduceAggregatorMax<long int, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = long int; AGG = onnxruntime::ReduceAggregatorMax<long int, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = double; AGG = onnxruntime::ReduceAggregatorMax<double, double>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = double; AGG = onnxruntime::ReduceAggregatorMax<double, double>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = signed char; AGG = onnxruntime::ReduceAggregatorMax<signed char, signed char>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = signed char; AGG = onnxruntime::ReduceAggregatorMax<signed char, signed char>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = unsigned char; AGG = onnxruntime::ReduceAggregatorMax<unsigned char, unsigned char>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = unsigned char; AGG = onnxruntime::ReduceAggregatorMax<unsigned char, unsigned char>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorMean<float, float>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorMean<float, float>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorMean<int, int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorMean<int, int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = double; AGG = onnxruntime::ReduceAggregatorMean<double, double>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = double; AGG = onnxruntime::ReduceAggregatorMean<double, double>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorMin<float, float>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorMin<float, float>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorMin<int, int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorMin<int, int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = long int; AGG = onnxruntime::ReduceAggregatorMin<long int, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = long int; AGG = onnxruntime::ReduceAggregatorMin<long int, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = double; AGG = onnxruntime::ReduceAggregatorMin<double, double>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = double; AGG = onnxruntime::ReduceAggregatorMin<double, double>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = signed char; AGG = onnxruntime::ReduceAggregatorMin<signed char, signed char>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = signed char; AGG = onnxruntime::ReduceAggregatorMin<signed char, signed char>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = unsigned char; AGG = onnxruntime::ReduceAggregatorMin<unsigned char, unsigned char>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = unsigned char; AGG = onnxruntime::ReduceAggregatorMin<unsigned char, unsigned char>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorProd<float, float>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorProd<float, float>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorProd<int, int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorProd<int, int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = long int; AGG = onnxruntime::ReduceAggregatorProd<long int, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = long int; AGG = onnxruntime::ReduceAggregatorProd<long int, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorSumSquare<float, float>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorSumSquare<float, float>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorSumSquare<int, int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorSumSquare<int, int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = double; AGG = onnxruntime::ReduceAggregatorSumSquare<double, double>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = double; AGG = onnxruntime::ReduceAggregatorSumSquare<double, double>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorArgMax<float, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorArgMax<float, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex<float, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex<float, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorArgMax<int, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorArgMax<int, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex<int, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex<int, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = double; AGG = onnxruntime::ReduceAggregatorArgMax<double, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = double; AGG = onnxruntime::ReduceAggregatorArgMax<double, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = double; AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex<double, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = double; AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex<double, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorArgMin<float, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorArgMin<float, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorArgMinLastIndex<float, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorArgMinLastIndex<float, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorArgMin<int, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorArgMin<int, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorArgMinLastIndex<int, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorArgMinLastIndex<int, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = double; AGG = onnxruntime::ReduceAggregatorArgMin<double, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = double; AGG = onnxruntime::ReduceAggregatorArgMin<double, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = double; AGG = onnxruntime::ReduceAggregatorArgMinLastIndex<double, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = double; AGG = onnxruntime::ReduceAggregatorArgMinLastIndex<double, long int>; int64_t = long int] static onnxruntime::Tensor onnxruntime::ReduceSum<T>::Impl(const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, bool, const onnxruntime::TensorShape*) [with T = long int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = long int; AGG = onnxruntime::ReduceAggregatorSum<long int, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = long int; AGG = onnxruntime::ReduceAggregatorSum<long int, long int>; int64_t = long int] static onnxruntime::Tensor onnxruntime::ReduceSum<T>::Impl(const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, bool, const onnxruntime::TensorShape*) [with T = double; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = double; AGG = onnxruntime::ReduceAggregatorSum<double, double>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = double; AGG = onnxruntime::ReduceAggregatorSum<double, double>; int64_t = long int] static onnxruntime::Tensor onnxruntime::ReduceSum<T>::Impl(const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, bool, const onnxruntime::TensorShape*) [with T = int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorSum<int, int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorSum<int, int>; int64_t = long int] static onnxruntime::Tensor onnxruntime::ReduceSum<T>::Impl(const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, bool, const onnxruntime::TensorShape*) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorSum<float, float>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorSum<float, float>; int64_t = long int] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) bool onnxruntime::SetupForReduce(const onnxruntime::Tensor*, const std::vector<long int>&, std::vector<long int>&, onnxruntime::TensorShape&, std::vector<long int>&, bool&, const onnxruntime::TensorShape*) onnxruntime::ReduceKernelBase<allow_multi_axes>::ReduceKernelBase(const onnxruntime::OpKernelInfo&, nonstd::optional_lite::optional<long int>) [with bool allow_multi_axes = false] onnxruntime::ReduceKernelBase<allow_multi_axes>::ReduceKernelBase(const onnxruntime::OpKernelInfo&, nonstd::optional_lite::optional<long int>) [with bool allow_multi_axes = true] N11onnxruntime16ReduceKernelBaseILb1EEE N11onnxruntime16ReduceKernelBaseILb0EEE ZN11onnxruntime17NoTransposeReduceIdNS_31ReduceAggregatorArgMinLastIndexIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIdNS_22ReduceAggregatorArgMinIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_31ReduceAggregatorArgMinLastIndexIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_22ReduceAggregatorArgMinIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_31ReduceAggregatorArgMinLastIndexIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_22ReduceAggregatorArgMinIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIdNS_31ReduceAggregatorArgMaxLastIndexIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIdNS_22ReduceAggregatorArgMaxIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_31ReduceAggregatorArgMaxLastIndexIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_22ReduceAggregatorArgMaxIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_31ReduceAggregatorArgMaxLastIndexIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_22ReduceAggregatorArgMaxIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIdNS_25ReduceAggregatorSumSquareIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_25ReduceAggregatorSumSquareIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_25ReduceAggregatorSumSquareIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIlNS_20ReduceAggregatorProdIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_20ReduceAggregatorProdIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_20ReduceAggregatorProdIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIhNS_19ReduceAggregatorMinIhhEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIaNS_19ReduceAggregatorMinIaaEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIdNS_19ReduceAggregatorMinIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIlNS_19ReduceAggregatorMinIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_19ReduceAggregatorMinIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_19ReduceAggregatorMinIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIdNS_20ReduceAggregatorMeanIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_20ReduceAggregatorMeanIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_20ReduceAggregatorMeanIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIhNS_19ReduceAggregatorMaxIhhEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIaNS_19ReduceAggregatorMaxIaaEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIdNS_19ReduceAggregatorMaxIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIlNS_19ReduceAggregatorMaxIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_19ReduceAggregatorMaxIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_19ReduceAggregatorMaxIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIdNS_25ReduceAggregatorLogSumExpIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime17NoTransposeReduceIiNS_25ReduceAggregatorLogSumExpIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime17NoTransposeReduceIfNS_25ReduceAggregatorLogSumExpIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime17NoTransposeReduceIiNS_22ReduceAggregatorLogSumIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_22ReduceAggregatorLogSumIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_18ReduceAggregatorL2IiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_18ReduceAggregatorL2IffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_18ReduceAggregatorL1IiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_18ReduceAggregatorL1IffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ N11onnxruntime12ReduceKernelILb1EEE N11onnxruntime12ReduceKernelILb0EEE N11onnxruntime8ReduceL1IfEE N11onnxruntime8ReduceL1IiEE N11onnxruntime8ReduceL2IfEE N11onnxruntime8ReduceL2IiEE N11onnxruntime12ReduceLogSumIfEE N11onnxruntime12ReduceLogSumIiEE N11onnxruntime15ReduceLogSumExpIfEE N11onnxruntime15ReduceLogSumExpIiEE N11onnxruntime15ReduceLogSumExpIdEE N11onnxruntime9ReduceMaxIfEE N11onnxruntime9ReduceMaxIiEE N11onnxruntime9ReduceMaxIlEE N11onnxruntime9ReduceMaxIdEE N11onnxruntime9ReduceMaxIaEE N11onnxruntime9ReduceMaxIhEE N11onnxruntime10ReduceMeanIfEE N11onnxruntime10ReduceMeanIiEE N11onnxruntime10ReduceMeanIdEE N11onnxruntime9ReduceMinIfEE N11onnxruntime9ReduceMinIiEE N11onnxruntime9ReduceMinIlEE N11onnxruntime9ReduceMinIdEE N11onnxruntime9ReduceMinIaEE N11onnxruntime9ReduceMinIhEE N11onnxruntime10ReduceProdIfEE N11onnxruntime10ReduceProdIiEE N11onnxruntime10ReduceProdIlEE N11onnxruntime9ReduceSumIfEE N11onnxruntime9ReduceSumIiEE N11onnxruntime9ReduceSumIlEE N11onnxruntime9ReduceSumIdEE N11onnxruntime15ReduceSumSquareIfEE N11onnxruntime15ReduceSumSquareIiEE N11onnxruntime15ReduceSumSquareIdEE N11onnxruntime6ArgMaxIfEE N11onnxruntime6ArgMaxIiEE N11onnxruntime6ArgMaxIdEE N11onnxruntime6ArgMinIfEE N11onnxruntime6ArgMinIiEE N11onnxruntime6ArgMinIdEE ZN11onnxruntime17NoTransposeReduceIfNS_19ReduceAggregatorSumIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_19ReduceAggregatorSumIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIdNS_19ReduceAggregatorSumIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIlNS_19ReduceAggregatorSumIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ GRU hidden_size activations activation_alpha activation_beta clip clip_ > 0.f forward bidirectional linear_before_reset cur + size <= end /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.h info.GetAttr("direction", &direction).IsOK() info.GetAttr("linear_before_reset", &int64_value).IsOK() info.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0 '. Must be one of 'forward', 'reverse', or 'bidirectional'. Invalid 'direction' argument of ' /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.h activation_func_names.size() == static_cast<size_t>(num_directions_) * 2 lda >= K && ldb >= K && ldc >= N A + (M * lda - (lda - K)) <= A_end B + (N * ldb - (ldb - K)) <= B_end C + (M * ldc - (ldc - N)) <= C_end offset + size <= size_t(span.size()) /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.cc GRU operator does not support double yet Invalid data type for GRU operator of T* onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span<T>::iterator, typename gsl::span<T>::iterator, size_t) [with T = float; typename gsl::span<T>::iterator = float*; size_t = long unsigned int] const T* onnxruntime::rnn::detail::SafeRawConstPointer(typename gsl::span<T>::const_iterator, typename gsl::span<T>::const_iterator, size_t) [with T = float; typename gsl::span<T>::const_iterator = const float*; size_t = long unsigned int] T* onnxruntime::rnn::detail::SafeRawPointer(gsl::span<T>, size_t, size_t) [with T = float; size_t = long unsigned int] void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = float*; TSpanBIter = const float*; TSpanCIter = float*] void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = const float*; TSpanBIter = const float*; TSpanCIter = float*] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] ComputeImpl virtual onnxruntime::common::Status onnxruntime::DeepCpuGruOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&) onnxruntime::DeepCpuGruOp::DeepCpuGruOp(const onnxruntime::OpKernelInfo&) N11onnxruntime12DeepCpuGruOpE ZN11onnxruntime10IAllocator13MakeUniquePtrIiEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPiE_ input_forget /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/lstm_base.h activation_func_names.size() == static_cast<size_t>(num_directions_) * 3 LSTM operator does not support double yet Invalid data type for LSTM operator of /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.cc const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::DeepCpuLstmOp::Compute(onnxruntime::OpKernelContext*) const const T* onnxruntime::Tensor::Data() const [with T = float] onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&) onnxruntime::LSTMBase::LSTMBase(const onnxruntime::OpKernelInfo&) N11onnxruntime8LSTMBaseE N11onnxruntime13DeepCpuLstmOpE Input B must have shape { }. Actual: Input P must have shape { Input X must have 3 dimensions only. Actual: 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 { Input initial_c must have shape { /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/lstm_base.cc ComputeImpl const T* onnxruntime::Tensor::Data() const [with T = int] ComputeImpl gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] RNN Affine /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.cc /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.h 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<size_t>(num_directions) RNN op: Invalid activation attribute - allowed_activations.find(activations_[direction]) != allowed_activations.end() const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = float] Compute onnxruntime::RNN<T>::RNN(const onnxruntime::OpKernelInfo&) [with T = float] FffffE N11onnxruntime3RNNIfEE PFffffE Dump matrix: Input W must have shape { Input R must have shape { A + (M * K) <= A_end weights.quant_para_ affine leakyrelu thresholdedrelu scaledtanh hardsigmoid softsign softplus Expecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.cc Quantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0f alpha == 1.0f && (beta == 0.0f || beta == 1.0f) Invalid activation function of Invalid LSTM merge activation function of Invalid GRU reset gate activation function: Invalid GRU hidden gate activation function: void (* onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByName(const string&))(float*, const float*, const float*, float*, int, float, float) void (* onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByName(const string&))(const float*, float*, float*, int, float, float) void (* onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByName(const string&))(const float*, float*, const float*, float*, int, float, float) void (* onnxruntime::rnn::detail::deepcpu::ActivationFuncByName(const string&))(float*, int, float, float) void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, const float*, const float*, const onnxruntime::rnn::detail::GemmWeights<unsigned char>&, float, float*, float*, int, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*) void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, const float*, const float*, const onnxruntime::rnn::detail::GemmWeights<float>&, float, float*, float*, int, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*) std::string onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCount(const string&, std::vector<float>::const_iterator&, const const_iterator&, std::vector<float>::const_iterator&, const const_iterator&, float&, float&) gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] 27MLAS_QGEMM_OUTPUT_PROCESSOR %%%%~3a*~3a*~3a*~3a*7.7.7.7.A \3A \3A \3A \3JCy7JCy7JCy7JCy7
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':Y;Ӡ5֕8;Y; A C rows[ const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span<T>, size_t, size_t) [with T = float; size_t = long unsigned int] const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span<T>, size_t, size_t) [with T = const float; size_t = long unsigned int] *ZN11onnxruntime4lstmL23ExecuteLambdaInParallelIZNS0_18UniDirectionalLstmIfE7ComputeIfEEvRKN3gsl4spanIKfEERKNS6_IKiEEiRKNS_3rnn6detail11GemmWeightsIT_EESL_RNS6_IfEESN_SN_EUliPNS_11concurrency10ThreadPoolEE_EEvT_iidSQ_EUlllE_ *ZN11onnxruntime4lstmL23ExecuteLambdaInParallelIZNS0_18UniDirectionalLstmIfE7ComputeIhEEvRKN3gsl4spanIKfEERKNS6_IKiEEiRKNS_3rnn6detail11GemmWeightsIT_EESL_RNS6_IfEESN_SN_EUliPNS_11concurrency10ThreadPoolEE_EEvT_iidSQ_EUlllE_ new_axis ConcatFromSequence Must have valid 'axis' attribute /onnxruntime_src/onnxruntime/core/providers/cpu//tensor/concat.h Got nullptr for sequence input. /onnxruntime_src/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.cc const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq] virtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo&, bool) N11onnxruntime10ConcatBaseE N11onnxruntime18ConcatFromSequenceE Y != nullptr Unsupported 'dtype' value: SequenceConstruct SequenceLength SequenceErase SequenceInsert SequenceAt SplitToSequence SequenceEmpty I != nullptr Invalid sequence index ( split_size_sum ( ) != split_dim_size ( S != nullptr Got nullptr for input tensor. ), input tensor data type ( num_inputs >= 1 Split should be > 0 split_scalar > 0 SequenceEmpty: Got nullptr for output sequence /onnxruntime_src/onnxruntime/core/providers/cpu/sequence/sequence_ops.cc Got nullptr input for index tensor ) specified for sequence of size ( SequenceAt: Got nullptr for output tensor SequenceLength: Got nullptr for output tensor Invalid data type for split tensor Data type of the input tensor MUST be same as that of the input sequence. Sequence data type ( SequenceInsert: Got nullptr for output sequence SequenceErase: Got nullptr for output sequence SequenceConstruct: Got nullptr for output sequence Violation of the requirment that all input tensors must have the same data type. Invalid value in 'split' input. All values must be >= 0 std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; }) SplitToSequence operator does not support <, 츲̸ܸ|l<<4 int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = std::basic_string<char>] ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = int] ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = double] ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] void onnxruntime::GetSplitSizesInput(const onnxruntime::Tensor&, std::vector<long int>&) int64_t onnxruntime::GetScalarSplitInput(const onnxruntime::Tensor&) ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = float] virtual onnxruntime::common::Status onnxruntime::SplitToSequence::Compute(onnxruntime::OpKernelContext*) const virtual onnxruntime::common::Status onnxruntime::SequenceConstruct::Compute(onnxruntime::OpKernelContext*) const virtual onnxruntime::common::Status onnxruntime::SequenceErase::Compute(onnxruntime::OpKernelContext*) const virtual onnxruntime::common::Status onnxruntime::SequenceInsert::Compute(onnxruntime::OpKernelContext*) const CreateCopyAndAppendCpuTensor void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType) T* OrtValue::GetMutable() [with T = onnxruntime::TensorSeq] virtual onnxruntime::common::Status onnxruntime::SequenceEmpty::Compute(onnxruntime::OpKernelContext*) const const onnxruntime::Tensor& onnxruntime::TensorSeq::Get(size_t) const int64_t onnxruntime::GetSeqIdx(const onnxruntime::Tensor&) virtual onnxruntime::common::Status onnxruntime::SequenceAt::Compute(onnxruntime::OpKernelContext*) const T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq] virtual onnxruntime::common::Status onnxruntime::SequenceLength::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime14SequenceLengthE N11onnxruntime10SequenceAtE N11onnxruntime13SequenceEmptyE N11onnxruntime14SequenceInsertE N11onnxruntime13SequenceEraseE N11onnxruntime17SequenceConstructE N11onnxruntime15SplitToSequenceE Attribute to is not set. stoull stod -INF %.8g snprintf_result > 0 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/cast_op.cc snprintf() failed with return value: snprintf_result > 0 && gsl::narrow_cast<size_t>(snprintf_result) == buffer_span.size() - 1 Failed to write value with snprintf(). void onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnce() typename std::enable_if<std::is_floating_point<_Tp>::value, void>::type onnxruntime::{anonymous}::CastToString(const SrcType&, std::string&) [with SrcType = double; typename std::enable_if<std::is_floating_point<_Tp>::value, void>::type = void; std::string = std::basic_string<char>] typename std::enable_if<std::is_floating_point<_Tp>::value, void>::type onnxruntime::{anonymous}::CastToString(const SrcType&, std::string&) [with SrcType = float; typename std::enable_if<std::is_floating_point<_Tp>::value, void>::type = void; std::string = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::{anonymous}::Cast::Cast(const onnxruntime::OpKernelInfo&) *N11onnxruntime12_GLOBAL__N_14CastE A A O O O O A A Compress /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/compress.cc axes_right_stride >= 0 && static_cast<uint64_t>(axes_right_stride) < std::numeric_limits<size_t>::max() const T* onnxruntime::Tensor::Data() const [with T = bool] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) virtual onnxruntime::common::Status onnxruntime::Compress::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime8CompressE Cannot concatenate scalars got: Data type mismatch /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.h /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.cc Ranks of input data are different, cannot concatenate them. expected rank: inputs_n_rank == inputs_0_rank Non concat axis dimensions must match: Axis has mismatched dimensions of const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) onnxruntime::common::Status onnxruntime::ConcatBase::PrepareForCompute(onnxruntime::OpKernelContext*, const std::vector<const onnxruntime::Tensor*>&, onnxruntime::Prepare&) const PrepareForCompute onnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo&, bool) N11onnxruntime6ConcatE DynamicQuantizeLinear x_ptr != nullptr /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/dynamicquantizelinear.cc T* onnxruntime::Tensor::MutableData() [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::DynamicQuantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] N11onnxruntime21DynamicQuantizeLinearIhEE invalid expand shape T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] const T* onnxruntime::Tensor::Data() const [with T = short unsigned int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] const T* onnxruntime::Tensor::Data() const [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = bool] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] ZNK11onnxruntime6ExpandINS_9MLFloat16EE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandINS_9MLFloat16EE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandIbE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandIbE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandImE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandImE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandIjE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandIjE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandItE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandItE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandIhE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandIhE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandIlE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandIlE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandIiE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandIiE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandIsE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandIsE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandIaE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandIaE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandIdE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandIdE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandIfE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandIfE7ComputeEPNS_15OpKernelContextEEUlllE0_ N11onnxruntime6ExpandIfEE N11onnxruntime6ExpandIdEE N11onnxruntime6ExpandIaEE N11onnxruntime6ExpandIsEE N11onnxruntime6ExpandIiEE N11onnxruntime6ExpandIlEE N11onnxruntime6ExpandIhEE N11onnxruntime6ExpandItEE N11onnxruntime6ExpandIjEE N11onnxruntime6ExpandImEE N11onnxruntime6ExpandIbEE N11onnxruntime6ExpandINS_9MLFloat16EEE EyeLike T1 != nullptr EyeLike : Input tensor dimension is not 2 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/eye_like.cc ɵɵɵɵɵɵɵɵɵɵɵɵɵdɵ T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = int] T* onnxruntime::Tensor::MutableData() [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::EyeLike::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7EyeLikeE Tind Missing/Invalid 'axis' attribute value /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather.h indices element out of data bounds, idx= must be within the inclusive range [ /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather.cc Gather Tind type not supported in this build. const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = int] Compute int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::GatherBase::GatherBase(const onnxruntime::OpKernelInfo&) N11onnxruntime10GatherBaseE N11onnxruntime6GatherE ZN11onnxruntime14GatherCopyDataIiEENS_6common6StatusEPKNS_6TensorEPKhPhbmlllllRKNS_11TensorShapeElPNS_11concurrency10ThreadPoolEEUlllE0_ ZN11onnxruntime14GatherCopyDataIlEENS_6common6StatusEPKNS_6TensorEPKhPhbmlllllRKNS_11TensorShapeElPNS_11concurrency10ThreadPoolEEUlllE0_ GatherElements ]. Actual value is , /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.h GatherElements op: Cannot operate on scalar input GatherElements 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: Value in indices must be within bounds [ /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.cc GatherElements op: Data type of input 'data' should match the data type of the output onnxruntime::core_impl(const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, int64_t, onnxruntime::concurrency::ThreadPool*) [with bool is_string = false; T = signed char; Tin = long int; int64_t = long int]::__lambda12 onnxruntime::core_impl(const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, int64_t, onnxruntime::concurrency::ThreadPool*) [with bool is_string = false; T = signed char; Tin = int; int64_t = long int]::__lambda12 onnxruntime::core_impl(const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, int64_t, onnxruntime::concurrency::ThreadPool*) [with bool is_string = true; T = std::basic_string<char>; Tin = long int; int64_t = long int]::__lambda12 onnxruntime::core_impl(const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, int64_t, onnxruntime::concurrency::ThreadPool*) [with bool is_string = true; T = std::basic_string<char>; Tin = int; int64_t = long int]::__lambda12 int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::GatherElements::GatherElements(const onnxruntime::OpKernelInfo&) *ZN11onnxruntimeL9core_implILb1ESsiEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL9core_implILb1ESsiEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE3_ *ZN11onnxruntimeL9core_implILb1ESslEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL9core_implILb1ESslEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE3_ *ZN11onnxruntimeL9core_implILb0EaiEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE2_ *ZN11onnxruntimeL9core_implILb0EaiEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE4_ *ZN11onnxruntimeL9core_implILb0EalEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE2_ *ZN11onnxruntimeL9core_implILb0EalEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE4_ N11onnxruntime14GatherElementsE batch_dims GatherND invalid index found, index = indices tensor must has rank larger than 0 GatherNDBase PrepareForCompute: Input count mismatch /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_nd.cc input_tensor != nullptr && indices_tensor != nullptr last dimension of indices must not be larger than rank of input tensor indices tensor data type not supported const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::GatherND::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime12GatherNDBaseE *ZNK11onnxruntime8GatherND12GatherNumberERKNS_12GatherNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ *ZNK11onnxruntime8GatherND12GatherStringERKNS_12GatherNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ N11onnxruntime8GatherNDE ZNK11onnxruntime12GatherNDBase17PrepareForComputeIiEENS_6common6StatusERKNS_11TensorShapeEPKNS_6TensorElRNS0_7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ ZNK11onnxruntime12GatherNDBase17PrepareForComputeIlEENS_6common6StatusERKNS_11TensorShapeEPKNS_6TensorElRNS0_7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ Tensor type mismatch. type == dtype_ /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/identity_op.h onnxruntime::common::Status onnxruntime::IdentityOp<is_dropout>::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = true] void* onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::IdentityOp<is_dropout>::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = false] N11onnxruntime10IdentityOpILb1EEE N11onnxruntime10IdentityOpILb0EEE detect_positive detect_negative IsInf Failed to obtain detect_positive /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/isinf.cc Failed to obtain detect_negative void onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnce() const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = bool] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::IsInf::IsInf(const onnxruntime::OpKernelInfo&) N11onnxruntime5IsInfE IsNaN Null input ptr const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] T* onnxruntime::Tensor::MutableData() [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime5IsNaNIfEE N11onnxruntime5IsNaNINS_9MLFloat16EEE | | | | MeanVarianceNormalization Input is expected to have four dimensions corresponding to [N,C,H,W] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime27MeanVarianceNormalization_0IfEE N11onnxruntime27MeanVarianceNormalization_1IfEE X input is required! failed to get first output! /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/nonzero_op.cc const T* onnxruntime::Tensor::Data() const [with T = bool] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = int] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = long int] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = float] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] N11onnxruntime7NonZeroIbEE N11onnxruntime7NonZeroIhEE N11onnxruntime7NonZeroIiEE N11onnxruntime7NonZeroIlEE N11onnxruntime7NonZeroIfEE OneHot Depth is negative. Invalid argument for values; either it's rank is more than 1 or it has more than 2 elements Invalid argument for depth; it's not a scalar. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/onehot.cc Compute Compute Compute Compute Compute Compute Compute Compute Compute Compute Compute int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) N11onnxruntime8OneHotOpIlllEE N11onnxruntime8OneHotOpIfllEE N11onnxruntime8OneHotOpIlSslEE N11onnxruntime8OneHotOpIfSslEE N11onnxruntime8OneHotOpIlflEE N11onnxruntime8OneHotOpIifiEE N11onnxruntime8OneHotOpIiffEE N11onnxruntime8OneHotOpIfffEE N11onnxruntime8OneHotOpIlifEE N11onnxruntime8OneHotOpIlffEE N11onnxruntime8OneHotOpIlfiEE >Unexpected mode of reflect edge data_rank > 0 data_rank * 2 == pads.size() pads_size == 2 * data_rank Cannot use 'reflect' mode to pad dimension with a value of 0. Input shape: Invalid 'mode' attribute value /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/pad.h Invalid 'pads' attribute value Input tensor has no dimensions /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/pad.cc 'pads' has wrong number of values Pads tensor should be an INT64 tensor pads_tensor.IsDataType<int64_t>() pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1) Pads tensor size should be equal to twice the input dimension count value_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1 Unsupported input data type of Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank] Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensor PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = unsigned char] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = long unsigned int] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = unsigned int] onnxruntime::PadValue onnxruntime::PadValueFromFloat(float, onnxruntime::MLDataType) virtual onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext*) const onnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo&) N11onnxruntime7PadBaseE N11onnxruntime3PadE /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/quantize_linear.cc zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr) scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dim zero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim) x_zero_point must be null or a scalar or 1D tensor or size 1. scale must be 1D tensor with size x_zero_point must be null or 1D tensor with size zero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](int32_t zp) { return zp == 0; }) DequantizeLinear with type int32 should have no zero point or all zero points should be 0 const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::common::Status onnxruntime::DequantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) void onnxruntime::PrepareForQDQ(const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::Tensor*, int64_t, int64_t&, int64_t&, int64_t&) N11onnxruntime16DequantizeLinearIaEE N11onnxruntime16DequantizeLinearIhEE N11onnxruntime16DequantizeLinearIiEE N11onnxruntime14QuantizeLinearIaEE N11onnxruntime14QuantizeLinearIhEE Attribute shape is not set. requested_shape[i] >= -1 unknown_dim == -1 , requested shape: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape.h A dimension cannot be less than -1, got /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape_helper.h At most one dimension can be -1. The dimension with value zero exceeds the dimension size of the input tensor. i < input_shape.NumDimensions() The input tensor cannot be reshaped to the requested shape. Input shape: size != 0 && (input_shape.Size() % size) == 0 gsl::narrow_cast<int64_t>(input_shape.Size()) == size A shape tensor must be a vector tensor. shapeTensor->Shape().NumDimensions() == 1 onnxruntime::Reshape_1::Reshape_1(const onnxruntime::OpKernelInfo&) onnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape&, std::vector<long int>&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = long int] virtual onnxruntime::common::Status onnxruntime::Reshape::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7ReshapeE N11onnxruntime9Reshape_1E Resize operator Upsample operator scale >= 1 scale > 0 cubic mode attribute is . It can only be (default) or extrapolation_value pytorch_half_pixel tf_half_pixel_for_nn align_corners tf_crop_and_resize coordinate_transform_mode:[ ] is not supportted! yet this opset round_prefer_floor round_prefer_ceil nearest_mode:[ ] is not supported! cubic_coeff_a exclude_outside scales_size > 0 Scale value should be greater than or equal to 1. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.h Scale value should be greater than 0. 'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) || scales.size() == 3 || (scales.size() == 5 && scales[0] == 1 && scales[1] == 1) 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) info.GetAttr<std::string>("mode", &mode).IsOK() info.GetAttrs<float>("scales", scales_).IsOK() `tf_half_pixel_for_nn` is deprecated since opset 13, model uses the deprecated attribute exclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to scales size should be greater than 0. (p const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor*, std::vector<float>&) const onnxruntime::ResizeNearestMode onnxruntime::UpsampleBase::StringToNearestMode(const string&) onnxruntime::ResizeCoordinateTransformationMode onnxruntime::UpsampleBase::StringToCoordinateTransformationMode(const string&) void onnxruntime::UpsampleBase::ScalesValidation(const std::vector<float>&, onnxruntime::UpsampleMode) const onnxruntime::UpsampleMode onnxruntime::UpsampleBase::StringToUpsampleMode(const string&) UpsampleBase onnxruntime::UpsampleBase::UpsampleBase(onnxruntime::OpKernelInfo) N11onnxruntime12UpsampleBaseE N11onnxruntime8UpsampleIfEE N11onnxruntime6ResizeIfEE N11onnxruntime8UpsampleIiEE N11onnxruntime6ResizeIiEE N11onnxruntime8UpsampleIhEE N11onnxruntime6ResizeIhEE ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE0_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE1_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE2_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE3_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE4_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE0_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE1_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE2_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE3_ @ ?batch_axis . Must be 0 or 1 Invalid batch_axis of batch_axis < 2 Invalid time_axis of time_axis < 2 batch_axis != time_axis ReverseSequence Unknown tensor type of . batch_size= /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.h info.GetAttr<int64_t>("batch_axis", &batch_axis).IsOK() info.GetAttr<int64_t>("time_axis", &time_axis).IsOK() time_axis and batch_axis must have different values but both are /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.cc sequence_lens shape must be {batch_size}. Got: XVa!XX~߹ gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::BFloat16] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::BFloat16] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::MLFloat16] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::MLFloat16] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = short unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = short int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned char] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = std::basic_string<char>] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = std::basic_string<char>] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = double] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = bool] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = bool] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] virtual onnxruntime::common::Status onnxruntime::ReverseSequenceOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ReverseSequenceOp::ReverseSequenceOp(const onnxruntime::OpKernelInfo&) N11onnxruntime17ReverseSequenceOpE /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter.cc data type is different from updates type Indices and updates must have the same rank Indices vs updates dimensions differs at position= Indices must have the same rank as Input. Indices rank= Expecting indices to be either int32_t or int64_t ScatterElements Scatter vs . Input rank= Indices dim= at pos= is greater than input dim= |P$̨tH ħrCmA髺e9
᪺]1 int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) virtual onnxruntime::common::Status onnxruntime::Scatter::Compute(onnxruntime::OpKernelContext*) const onnxruntime::Scatter::Scatter(const onnxruntime::OpKernelInfo&) N11onnxruntime7ScatterE input tensor and indices tensor must has rank larger than 0. updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter_nd.cc invalid indice found, indice = , indices shape: updates shape: , data shape: ScatterND Compute const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] PrepareForCompute N11onnxruntime13ScatterNDBaseE *ZNK11onnxruntime9ScatterND13ScatterNumberERKNS_13ScatterNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ *ZNK11onnxruntime9ScatterND13ScatterStringERKNS_13ScatterNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ N11onnxruntime9ScatterNDE T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime5ShapeE Size T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime4SizeE Unexpected element size of output == output_end 'axes' has duplicates 'step' value cannot be 0 Starts must be a 1-D array Ends must be a 1-D array Cannot slice scalars /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.h has_starts && has_ends && attr_starts_.size() == attr_ends_.size() !has_axes || attr_axes_.size() == attr_starts_.size() Missing or invalid starts and ends attribute Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributes /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.cc 'axes' has an axis outside of the tensor dimension count Starts and ends shape mismatch Starts and axes shape mismatch Starts and steps shape mismatch Data type for starts and ends inputs' is not supported in this build. Got onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&) [with T = unsigned char]::__lambda14 onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&) [with T = short unsigned int]::__lambda14 onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&) [with T = long unsigned int]::__lambda14 onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&) [with T = unsigned int]::__lambda14 void* onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep(void*) onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&) [with T = std::basic_string<char>]::__lambda14 Compute FillVectorsFromInput int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) onnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo&, bool) N11onnxruntime9SliceBaseE N11onnxruntime6Slice1E N11onnxruntime7Slice10E blocksize CRD DCR SpaceToDepth DepthToSpace Attribute blocksize is not set. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.h info.GetAttr("blocksize", &blocksize_).IsOK() DepthToSpace op: only 'DCR' and 'CRD' modes are supported /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.cc input.Shape().NumDimensions() == 4 input_depth % (blocksize_ * blocksize_) == 0 input_shape.NumDimensions() == 4 input_height % this->blocksize_ == 0 input_width % this->blocksize_ == 0 onnxruntime::common::Status onnxruntime::DepthToSpace<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::SpaceToDepth<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::DepthToSpace<T>::DepthToSpace(const onnxruntime::OpKernelInfo&) [with T = float] onnxruntime::SpaceDepthBase::SpaceDepthBase(const onnxruntime::OpKernelInfo&) N11onnxruntime14SpaceDepthBaseE N11onnxruntime12SpaceToDepthIfEE N11onnxruntime12DepthToSpaceIfEE e@*> Axis= NumOutputs= Input shape= Invalid value in 'split' attribute. All values must be > 0 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.h std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; }) Input cannot be split evenly on selected axis. Input shape= Cannot split using values in 'split' attribute. Axis= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was An split tensor must be a vector tensor. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.cc split_tensor->Shape().NumDimensions() == 1 Split operator does not support T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] ComputeImpl onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&) const [with T = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] ComputeImpl onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&) const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = long int] ComputeImpl onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&) const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] ComputeImpl onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&) const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] ComputeImpl onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&) const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Split::Compute(onnxruntime::OpKernelContext*) const int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) onnxruntime::SplitBase::SplitBase(const onnxruntime::OpKernelInfo&) N11onnxruntime9SplitBaseE N11onnxruntime5SplitE . shape= must be 1 instead of Dimension of input input_shape[i] == 1 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/squeeze.h int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) static std::vector<long int> onnxruntime::SqueezeBase::ComputeOutputShape(const onnxruntime::TensorShape&, const std::vector<long int>&) const T* onnxruntime::Tensor::Data() const [with T = long int] virtual onnxruntime::common::Status onnxruntime::Squeeze::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime11SqueezeBaseE N11onnxruntime7SqueezeE Tile Input count of Tile OP mismatch, the first one is empty Input count of Tile OP mismatch, the second one is empty the 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 Tile doesn't support string type yet /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/tile.cc !input_tensor.IsDataType<std::string>() Tile doesn't have an implementation yet for the type: const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Tile::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime4TileE is outside range. is repeated. naxes > 0 num_axes > 0 [ perm: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.h v >= 0 && static_cast<uint64_t>(v) <= std::numeric_limits<size_t>::max() Attribute perm of Transpose has an invalid value. Value Method IncrementIndexAndComputeOffset assumes this value is strictly positive. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.cc (local_source >= source) && (local_source < source + sizeof(T) * num_blocks) Transpose of element size not supported in this build. Size= Transpose not implemented for empty tensors. (local_source >= source) && (local_source < source + num_blocks) (local_source >= source) && (local_source < source + num_blocks * num_elts_in_block) (local_source >= source) && (local_source < source + num_blocks * blocksize) Mismatched data types between input and output Tensors. does not align with rank of input data: onnxruntime::TransposeBase::TransposeBase(const onnxruntime::OpKernelInfo&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext*) const void onnxruntime::DoTransposeImpl(int64_t, const std::vector<long int>&, size_t, size_t, const std::vector<long unsigned int>&, const uint8_t*, uint8_t*, size_t) void onnxruntime::DoTransposeImpl(int64_t, const std::vector<long int>&, size_t, size_t, const std::vector<long unsigned int>&, const string*, std::string*) void onnxruntime::DoTransposeEltWise(int64_t, const std::vector<long int>&, size_t, const std::vector<long unsigned int>&, const string*, std::string*) T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector<long int>&, size_t, const std::vector<long unsigned int>&, const uint8_t*, uint8_t*) [with T = unsigned char; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char] bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector<long int>&, size_t, const std::vector<long unsigned int>&, const uint8_t*, uint8_t*) [with T = short unsigned int; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char] bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector<long int>&, size_t, const std::vector<long unsigned int>&, const uint8_t*, uint8_t*) [with T = unsigned int; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char] void onnxruntime::IncrementIndexAndComputeOffsetSetup(onnxruntime::MultiIndex&, size_t, const std::vector<long int>&, const std::vector<long unsigned int>&, size_t) bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector<long int>&, size_t, const std::vector<long unsigned int>&, const uint8_t*, uint8_t*) [with T = long unsigned int; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char] N11onnxruntime13TransposeBaseE N11onnxruntime9TransposeE Unique Unsupported tensor type of gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) N11onnxruntime6UniqueE 'axes' has a duplicate axis nullptr != p.output_tensor Missing/Invalid 'axes' attribute value /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.h info.GetAttrs("axes", axes_).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.cc axes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1 An axes tensor must be a scalar or a 1-D tensor. 'axes' has an out of range axis Compute int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::UnsqueezeBase::Prepare&) const onnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo&) N11onnxruntime13UnsqueezeBaseE N11onnxruntime9UnsqueezeE Dimension: Input dim value: Output dim value: output_dims[i] == 0 Resize: unexpected mode Upsample: unexpected mode Invalid roi input index. roi_input_idx_ > 0 sizes == nullptr Input dim is zero but required output dim is non-zero. Cannot scale 0 by any factor to generate a non-zero value. Resize: input/output value is nullptr Upsample: input/output value is nullptr Resize: input/output value's dimension mismatch Upsample: input/output value's dimension mismatch Resize: input shape needs to be at least a single dimension Upsample: input shape needs to be at least a single dimension. Resize: input tensor's dimension does not match the scales. Upsample: input tensor's dimension does not match the scales. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.cc Rank of input and output tensor should be same. output_dims.size() == dims.size() Resize: size of roi array should be 2 * N where N is the rank of input tensor X. Only one of scales or sizes must be provided as input. sizes != nullptr && sizes->Shape().Size() != 0 Resize: input tensor's rank does not match the output tensor's rank. X->Shape().GetDims().size() == output_dims.size() Either scales or sizes MUST be provided as input. BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = float] onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = int] onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = unsigned char] void onnxruntime::UpsampleBase::ParseScalesDataFromOutputSize(const std::vector<long int>&, const std::vector<long int>&, std::vector<float>&) const onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor*, std::vector<float>&) const ZN11onnxruntime16UpsampleBilinearIhEEvllllllffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEESt8functionIFfffffffEEPNS_11concurrency10ThreadPoolEEUllE_ ZN11onnxruntime17UpsampleTrilinearIhEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEESt8functionIFfffffffEEPNS_11concurrency10ThreadPoolEEUllE_ ZN11onnxruntime16UpsampleBilinearIiEEvllllllffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEESt8functionIFfffffffEEPNS_11concurrency10ThreadPoolEEUllE_ ZN11onnxruntime17UpsampleTrilinearIiEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEESt8functionIFfffffffEEPNS_11concurrency10ThreadPoolEEUllE_ ZN11onnxruntime16UpsampleBilinearIfEEvllllllffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEESt8functionIFfffffffEEPNS_11concurrency10ThreadPoolEEUllE_ ZN11onnxruntime17UpsampleTrilinearIfEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEESt8functionIFfffffffEEPNS_11concurrency10ThreadPoolEEUllE_ @ A @ N11onnxruntime5WhereIhEE N11onnxruntime5WhereIiEE N11onnxruntime5WhereIlEE N11onnxruntime5WhereIfEE N11onnxruntime5WhereIdEE N11onnxruntime5WhereISsEE /onnxruntime_src/onnxruntime/contrib_ops/cpu/cpu_contrib_kernels.cc RegisterCpuContribKernels RegisterQuantizationKernels RegisterNchwcKernels border input dimensions instead Input's height ( ) + bottomBorder ( Input's width ( ) + rightBorder ( ) + scale_[0] ( ) + scale_[1] ( Crop Attribute border needs to be specified with four border elements, got Input is expected to have four dimensions corresponding to [N,C,H,W], got ) needs to be greater than or equal to the topBorder ( ) needs to be greater than or equal to the leftBorder ( /onnxruntime_src/onnxruntime/contrib_ops/cpu/crop.h T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime7contrib8CropBaseE N11onnxruntime7contrib4CropIfEE bilinear CropAndResize Null crop_size_ptr /onnxruntime_src/onnxruntime/contrib_ops/cpu/crop_and_resize.h specified. It should be either bilinear or nearest Number of dimensions for crop size should be exactly 1 const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::CropAndResize<T>::CropAndResize(const onnxruntime::OpKernelInfo&) [with T = float] ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib20CropAndResizeForwardIfEEvRKNS_11TensorShapeEPKT_fllSA_lPS8_RKSsPKiPS1_EUllE_EEvSG_lOS8_lEUllE_ N11onnxruntime7contrib13CropAndResizeIfEE DynamicSlice /onnxruntime_src/onnxruntime/contrib_ops/cpu/element_wise_ops.h info.GetAttr("scale", &scale_).IsOK() info.GetAttr("alpha", &alpha_).IsOK() info.GetAttr("beta", &beta_).IsOK() onnxruntime::contrib::Scale<T>::Scale(const onnxruntime::OpKernelInfo&) [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::Affine<T>::Affine(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib6AffineIfEE N11onnxruntime7contrib5ScaleIfEE ExpandDims Axis must be within range [ Axis is /onnxruntime_src/onnxruntime/contrib_ops/cpu/expand_dims.h axis_tensor->Shape().IsScalar() axis <= X_NumDims && axis >= -X_NumDims An axis tensor must be a scalar tensor. const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = int] virtual onnxruntime::common::Status onnxruntime::contrib::ExpandDims::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7contrib10ExpandDimsE /onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_conv.cc GetFusedActivationAttr(info, activation_).IsOK() onnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib14FusedConvFloatE /onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_gemm.cc onnxruntime::contrib::FusedGemm<T>::FusedGemm(const onnxruntime::OpKernelInfo&) [with T = float] FusedGemm N11onnxruntime7contrib9FusedGemmIfEE ImageScaler Bias size ( /onnxruntime_src/onnxruntime/contrib_ops/cpu/image_scaler.h info.GetAttrs<float>("bias", bias_).IsOK() ) does not match the number of channels ( T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::ImageScaler<T>::ImageScaler(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib11ImageScalerIfEE Inverse void onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnce() T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] *ZNK11onnxruntime7contrib7Inverse7ComputeEPNS_15OpKernelContextEEUllE_ N11onnxruntime7contrib7InverseE /onnxruntime_src/onnxruntime/contrib_ops/cpu/layer_norm.cc op_kernel_info.GetAttr("axis", &axis_).IsOK() Compute T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] Compute Compute T* onnxruntime::Tensor::MutableData() [with T = double] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T* onnxruntime::Tensor::Data() const [with T = double] Compute onnxruntime::contrib::LayerNorm<T, simplified>::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = double; bool simplified = true] onnxruntime::contrib::LayerNorm<T, simplified>::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = double; bool simplified = false] onnxruntime::contrib::LayerNorm<T, simplified>::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = float; bool simplified = true] onnxruntime::contrib::LayerNorm<T, simplified>::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = float; bool simplified = false] ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib9LayerNormIdLb1EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib9LayerNormIdLb0EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib9LayerNormIfLb1EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib9LayerNormIfLb0EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ N11onnxruntime7contrib9LayerNormIfLb0EEE N11onnxruntime7contrib9LayerNormIfLb1EEE N11onnxruntime7contrib9LayerNormIdLb0EEE N11onnxruntime7contrib9LayerNormIdLb1EEE MatMulInteger16 A != nullptr && B != nullptr /onnxruntime_src/onnxruntime/contrib_ops/cpu/matmul_integer16.cc const T* onnxruntime::Tensor::Data() const [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = int] Compute onnxruntime::common::Status onnxruntime::contrib::MatMulInteger16<T1, T2, T3>::Compute(onnxruntime::OpKernelContext*) const [with T1 = short int; T2 = short int; T3 = int] N11onnxruntime7contrib15MatMulInteger16IssiEE MaxpoolWithMask /onnxruntime_src/onnxruntime/contrib_ops/cpu/maxpool_with_mask.h T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) const void onnxruntime::PoolAttributes::InferOutputSize(const std::vector<long int>&, std::vector<long int>*, std::vector<long int>*) const std::vector<long int> onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, std::vector<long int>*) const Compute N11onnxruntime7contrib15MaxpoolWithMaskE N11onnxruntime7contrib21MaxpoolWithMask1DTaskIfEE N11onnxruntime7contrib21MaxpoolWithMask2DTaskIfEE N11onnxruntime7contrib21MaxpoolWithMask3DTaskIfEE across_channels normalize_variance /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/mean_variance_normalization.h info.GetAttr<int64_t>("across_channels", &across_channels_).IsOK() info.GetAttr<int64_t>("normalize_variance", &normalize_variance_).IsOK() onnxruntime::MeanVarianceNormalization_0<T>::MeanVarianceNormalization_0(const onnxruntime::OpKernelInfo&, bool) [with T = float] MurmurHash3 keys input_num_bytes % 4 == 0 /onnxruntime_src/onnxruntime/contrib_ops/cpu/murmur_hash3.cc Invalid assumption of output element size sizeof(uint32_t) == output_element_bytes const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::contrib::MurmurHash3::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7contrib11MurmurHash3E invalid channel count channels_ > 0 scales_.size() == 4 X_shape.NumDimensions() == 4 X_rank == 4 channels_ <= X_shape[1] Unsupported convolution size. /onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.h info.GetAttr<int64_t>("channels", &channels_).IsOK() info.GetAttr<int64_t>("channels_last", &channels_last_).IsOK() info.GetAttrs<int64_t>("scales", scales_).IsOK() scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1 pool_attrs_.kernel_shape.size() == 2 /onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.cc (X_shape[1] % MlasNchwcGetBlockSize()) == 0 (static_cast<size_t>(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0) output and sum shape must match virtual onnxruntime::common::Status onnxruntime::contrib::NchwcUpsample::Compute(onnxruntime::OpKernelContext*) const void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) const void onnxruntime::PoolAttributes::InferOutputSize(const std::vector<long int>&, std::vector<long int>*, std::vector<long int>*) const std::vector<long int> onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, std::vector<long int>*) const onnxruntime::common::Status onnxruntime::contrib::NchwcPoolBase::NchwcPool(onnxruntime::OpKernelContext*, MLAS_POOLING_KIND) const virtual onnxruntime::common::Status onnxruntime::contrib::NchwcConv::Compute(onnxruntime::OpKernelContext*) const Compute virtual onnxruntime::common::Status onnxruntime::contrib::ReorderOutput::Compute(onnxruntime::OpKernelContext*) const virtual onnxruntime::common::Status onnxruntime::contrib::ReorderInput::Compute(onnxruntime::OpKernelContext*) const onnxruntime::contrib::NchwcUpsample::NchwcUpsample(const onnxruntime::OpKernelInfo&) onnxruntime::contrib::NchwcPoolBase::NchwcPoolBase(const onnxruntime::OpKernelInfo&) onnxruntime::contrib::NchwcConv::NchwcConv(const onnxruntime::OpKernelInfo&) onnxruntime::contrib::ReorderOutput::ReorderOutput(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib13NchwcPoolBaseE N11onnxruntime7contrib12ReorderInputE N11onnxruntime7contrib13ReorderOutputE N11onnxruntime7contrib9NchwcConvE N11onnxruntime7contrib12NchwcMaxPoolE N11onnxruntime7contrib16NchwcAveragePoolE N11onnxruntime7contrib13NchwcUpsampleE /onnxruntime_src/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc IsScalarOr1ElementVector(tensor_a_scale) tensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point) IsScalarOr1ElementVector(tensor_b_scale) tensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point) IsScalarOr1ElementVector(tensor_c_scale) tensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point) MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1 MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if given MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1 MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if given MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1 MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if given const T* onnxruntime::Tensor::Data() const [with T = signed char] void onnxruntime::contrib::{anonymous}::QLinearImpl(onnxruntime::OpKernelContext&, double, const onnxruntime::ProcessBroadcastSpanFuncs&) [with T = signed char] void onnxruntime::InputBroadcaster::AdvanceBy(size_t) void onnxruntime::BroadcastLooper(TBroadcastHelper&, const onnxruntime::ProcessBroadcastSpanFuncs&) [with TBroadcastHelper = onnxruntime::contrib::{anonymous}::QLinearBroadcastHelper] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] void onnxruntime::contrib::{anonymous}::QLinearImpl(onnxruntime::OpKernelContext&, double, const onnxruntime::ProcessBroadcastSpanFuncs&) [with T = unsigned char] *ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE_ *ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE0_ *ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE1_ N11onnxruntime7contrib10QLinearAddIaEE N11onnxruntime7contrib10QLinearAddIhEE N11onnxruntime7contrib10QLinearMulIaEE N11onnxruntime7contrib10QLinearMulIhEE /onnxruntime_src/onnxruntime/contrib_ops/cpu/qlinear_global_average_pool.cc IsScalarOr1ElementVector(tensor_x_scale) IsScalarOr1ElementVector(tensor_x_zero_point) IsScalarOr1ElementVector(tensor_y_scale) IsScalarOr1ElementVector(tensor_y_zero_point) Input x_scale must be a scalar or 1D tensor of size 1 input x_zero_point must be a scalar or 1D tensor of size 1 if given input y_scale must be a scalar or 1D tensor of size 1 input y_zero_point must be a scalar or 1D tensor of size 1 if given const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute virtual onnxruntime::common::Status onnxruntime::contrib::QLinearGlobalAveragePool::Compute(onnxruntime::OpKernelContext*) const *ZN11onnxruntime7contrib18ComputeAveragePoolEPKhfhPhfhlllbPNS_11concurrency10ThreadPoolEEUlllE_ *ZN11onnxruntime7contrib18ComputeAveragePoolEPKhfhPhfhlllbPNS_11concurrency10ThreadPoolEEUlllE0_ *ZN11onnxruntime7contrib18ComputeAveragePoolEPKhfhPhfhlllbPNS_11concurrency10ThreadPoolEEUlllE1_ N11onnxruntime7contrib24QLinearGlobalAveragePoolE P@/onnxruntime_src/onnxruntime/contrib_ops/cpu/qlinear_lookup_table.cc tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point) tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point) QLinearLeakyRelu : input X_scale must be a scalar or 1D tensor of size 1 QLinearLeakyRelu : input X_zero_point must be a scalar or 1D tensor of size 1 QLinearLeakyRelu : input Y_scale must be a scalar or 1D tensor of size 1 QLinearLeakyRelu : input Y_zero_point must be a scalar or 1D tensor of size 1 T* onnxruntime::Tensor::MutableData() [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] void onnxruntime::contrib::QlinearBuildLookupTable(uint8_t*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const LookupTableArrayTransformer&) [with T = unsigned char; uint8_t = unsigned char; onnxruntime::contrib::LookupTableArrayTransformer = std::function<void(const float*, float*, long unsigned int)>] const T* onnxruntime::Tensor::Data() const [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::contrib::QlinearBuildLookupTable(uint8_t*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const LookupTableArrayTransformer&) [with T = signed char; uint8_t = unsigned char; onnxruntime::contrib::LookupTableArrayTransformer = std::function<void(const float*, float*, long unsigned int)>] ZNK11onnxruntime7contrib14QLinearSigmoidIhE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_ ZNK11onnxruntime7contrib17QLinearLookupBaseIhE11ComputeBaseIZNKS0_14QLinearSigmoidIhE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_EENS_6common6StatusES7_T_EUlllE_ ZNK11onnxruntime7contrib14QLinearSigmoidIaE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_ ZNK11onnxruntime7contrib17QLinearLookupBaseIaE11ComputeBaseIZNKS0_14QLinearSigmoidIaE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_EENS_6common6StatusES7_T_EUlllE_ ZNK11onnxruntime7contrib16QLinearLeakyReluIhE7ComputeEPNS_15OpKernelContextEEUlfE_ ZNK11onnxruntime7contrib17QLinearLookupBaseIhE11ComputeBaseIZNKS0_16QLinearLeakyReluIhE7ComputeEPNS_15OpKernelContextEEUlfE_EENS_6common6StatusES7_T_EUlllE_ ZNK11onnxruntime7contrib16QLinearLeakyReluIaE7ComputeEPNS_15OpKernelContextEEUlfE_ ZNK11onnxruntime7contrib17QLinearLookupBaseIaE11ComputeBaseIZNKS0_16QLinearLeakyReluIaE7ComputeEPNS_15OpKernelContextEEUlfE_EENS_6common6StatusES7_T_EUlllE_ *ZN11onnxruntime7contribL23QlinearBuildLookupTableIaEEvPhPKNS_6TensorES5_S5_S5_RKSt8functionIFffEEEUlPKfPfmE_ *ZN11onnxruntime7contribL23QlinearBuildLookupTableIhEEvPhPKNS_6TensorES5_S5_S5_RKSt8functionIFffEEEUlPKfPfmE_ N11onnxruntime7contrib17QLinearLookupBaseIaEE N11onnxruntime7contrib16QLinearLeakyReluIaEE N11onnxruntime7contrib17QLinearLookupBaseIhEE N11onnxruntime7contrib16QLinearLeakyReluIhEE N11onnxruntime7contrib14QLinearSigmoidIaEE N11onnxruntime7contrib14QLinearSigmoidIhEE ZN11onnxruntime7contrib16QLinearLeakyReluIaEC1ERKNS_12OpKernelInfoEEUlfE_ ZN11onnxruntime7contrib16QLinearLeakyReluIhEC1ERKNS_12OpKernelInfoEEUlfE_ ZN11onnxruntime7contrib14QLinearSigmoidIaEC1ERKNS_12OpKernelInfoEEUlPKfPfmE_ ZN11onnxruntime7contrib14QLinearSigmoidIhEC1ERKNS_12OpKernelInfoEEUlPKfPfmE_
-#< QAttention /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention_cpu_base.h /onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/attention_quant.cc weight must be a scalar or 1D tensor of size 1 input zero point must be a scalar or 1D tensor of size 1. weight zero point must be a scalar or 1D tensor of size 1. T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] ApplyAttention const T* onnxruntime::Tensor::Data() const [with T = float] Compute N11onnxruntime7contrib13AttentionBaseE N11onnxruntime7contrib16AttentionCPUBaseE ZNK11onnxruntime7contrib10QAttentionIfE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime7contrib16AttentionCPUBase21ComputeAttentionProbsIfEEvPT_PKS3_S6_PKiPKSt6vectorIlSaIlEES4_iiiiS6_S4_PNS_11concurrency10ThreadPoolEEUlllE_ ZNK11onnxruntime7contrib16AttentionCPUBase23ComputeVxAttentionScoreIfEEvPT_S4_PKS3_S6_iiiiiS6_S4_PNS_11concurrency10ThreadPoolEEUlllE_ N11onnxruntime7contrib10QAttentionIfEE @ F @ @ @ @ @ F @ F @ F @ FDynamicQuantizeLSTM DynamicQuantizeLSTM : Weight point must be constant Recurrent W_zero_point , 4* W_scale Weight zero point must be zero } for per-tensor/layer quantization or shape { } for per-channel quantization. Actual: const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7contrib19DynamicQuantizeLSTME /onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_matmul.cc IsScalarOr1ElementVector(a_scale_tensor) IsScalarOr1ElementVector(b_scale_tensor) IsScalarOr1ElementVector(a_zero_point_tensor) IsScalarOr1ElementVector(b_zero_point_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. 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. DynamicQuantizeMatMul : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet. DynamicQuantizeMatMul : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet. const T* onnxruntime::Tensor::Data() const [with T = unsigned char] virtual onnxruntime::common::Status onnxruntime::contrib::MatMulIntegerToFloat::Compute(onnxruntime::OpKernelContext*) const Compute virtual onnxruntime::common::Status onnxruntime::contrib::DynamicQuantizeMatMul::Compute(onnxruntime::OpKernelContext*) const const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] ComputeCommon N11onnxruntime7contrib24MatMulIntegerToFloatBaseE N11onnxruntime7contrib21DynamicQuantizeMatMulE N11onnxruntime7contrib20MatMulIntegerToFloatE /onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/nhwc_max_pool.cc input_shape.Size() > 0 || N == 0 void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) const const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::contrib::NhwcMaxPool::Compute(onnxruntime::OpKernelContext*) const Compute N11onnxruntime7contrib11NhwcMaxPoolE SampleOp T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7contrib8SampleOpIfEE epsilon_ >= 0 /onnxruntime_src/onnxruntime/contrib_ops/cpu/skip_layer_norm.cc input is expected to have 3 dimensions, got gamma is expected to have 1 dimension, got Last dimension of gamma and input does not match beta is expected to have 1 dimension, got Last dimension of beta and input does not match bias is expected to have 1 dimension, got Last dimension of bias and input does not match skip is expected to have same shape as input const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::SkipLayerNorm<T>::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::contrib::SkipLayerNorm<T>::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = float] ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib13SkipLayerNormIdE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib13SkipLayerNormIfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ N11onnxruntime7contrib13SkipLayerNormIfEE N11onnxruntime7contrib13SkipLayerNormIdEE Tokenizer mark attribute mark is not set pad_value mincharnum mincharnum_ > 0 separators tokenexp !tokenexp.empty() separators must not be empty !separators.empty() Can not digest separators: Can not digest tokenexp: /onnxruntime_src/onnxruntime/contrib_ops/cpu/tokenizer.cc attribute pad_value is not set attribute mincharnum is not set attribute mincharnum must have a positive value Either one of the separators OR tokenexp attributes required but none is set Expecting a non-empty tokenexp mincharnum is too big for char level tokenezation !char_tokenezation_ || mincharnum_ < 2 Input string contains invalid utf8 chars: Match contains invalid utf8 chars: tensor(string) expected as input Input dimensions are either [C] or [N][C] allowed const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::Tokenizer::Tokenizer(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib9TokenizerE upper Trilu IsScalarOr1ElementVector(k) /onnxruntime_src/onnxruntime/contrib_ops/cpu/trilu.h info.GetAttr<int64_t>("upper", &temp).IsOK() /onnxruntime_src/onnxruntime/contrib_ops/cpu/trilu.cc k should be a 1-D or 0-D tensor. const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::contrib::Trilu::Compute(onnxruntime::OpKernelContext*) const onnxruntime::contrib::Trilu::Trilu(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib5TriluE Input tensor to Unique op should be 1D T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7contrib6UniqueIfEE conv_window_size char_embedding_size WordConvEmbedding embedding_size attribute: conv filter size: conv_window_size attribute: conv kernal size 1: Char embedding size: Conv kernal size 2 : Conv filter size does not match embedding_size attribute. Conv kernal size 1 does not match conv_window_size attribute . Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size does not match conv kernal size 2. /onnxruntime_src/onnxruntime/contrib_ops/cpu/word_conv_embedding.cc const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime7contrib17WordConvEmbeddingE hӥhӥhӥh'is defined. No attribute with name:' unknown kernel type Attribute name and type don't match for ' /onnxruntime_src/onnxruntime/core/providers/cpu/activation/activations.h /onnxruntime_src/onnxruntime/contrib_ops/cpu/activations.h onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Elu<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::HardSigmoid<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::LeakyRelu<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Selu<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sigmoid<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sigmoid<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Softplus<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Softsign<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Tanh<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Tanh<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ThresholdedRelu<float>] Init Init onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ThresholdedRelu<float>] ElementWiseKernel Init onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Selu<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::LeakyRelu<float>] ElementWiseKernel Init onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::HardSigmoid<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Elu<float>] ElementWiseKernel N11onnxruntime8functors7SigmoidIfEE N11onnxruntime8functors4TanhIfEE N11onnxruntime8functors3EluIfEE N11onnxruntime17ElementWiseKernelINS_8functors3EluIfEEEE N11onnxruntime8functors11HardSigmoidIfEE N11onnxruntime17ElementWiseKernelINS_8functors11HardSigmoidIfEEEE N11onnxruntime8functors9LeakyReluIfEE N11onnxruntime17ElementWiseKernelINS_8functors9LeakyReluIfEEEE N11onnxruntime8functors4ReluIfEE N11onnxruntime17ElementWiseKernelINS_8functors4ReluIfEEEE N11onnxruntime8functors4ReluIdEE N11onnxruntime17ElementWiseKernelINS_8functors4ReluIdEEEE N11onnxruntime8functors4SeluIfEE N11onnxruntime17ElementWiseKernelINS_8functors4SeluIfEEEE N11onnxruntime17ElementWiseKernelINS_8functors7SigmoidIfEEEE N11onnxruntime8functors7SigmoidIdEE N11onnxruntime17ElementWiseKernelINS_8functors7SigmoidIdEEEE N11onnxruntime8functors8SoftplusIfEE N11onnxruntime17ElementWiseKernelINS_8functors8SoftplusIfEEEE N11onnxruntime8functors8SoftsignIfEE N11onnxruntime17ElementWiseKernelINS_8functors8SoftsignIfEEEE N11onnxruntime17ElementWiseKernelINS_8functors4TanhIfEEEE N11onnxruntime8functors4TanhIdEE N11onnxruntime17ElementWiseKernelINS_8functors4TanhIdEEEE N11onnxruntime8functors15ThresholdedReluIfEE N11onnxruntime17ElementWiseKernelINS_8functors15ThresholdedReluIfEEEE N11onnxruntime8functors18ParametricSoftplusIfEE N11onnxruntime8functors10ScaledTanhIfEE A AOO@9BBBB¥¥¥;?;?;?;?giP9giP9giP9giP9C:C:C:C:<<<<*=*=*=*=*>*>*>*> B B B BO@O@O@O@OOOO99997777 9@ >@If 'If' node has outputs. then_branch info == nullptr else_branch ' attribute. session_state Failed to create output tensor for If output outputs which doesn't match the subgraph's /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/if.cc num_subgraph_outputs == static_cast<size_t>(num_outputs) SetupSubgraphExecutionInfo should only be called once for each subgraph. Failed to create output tensor for Only tensors or sequence of tensors are suppported then_feeds_fetches_manager_ && else_feeds_fetches_manager_ Subgraph SessionState was not found for ' CreateFeedsFetchesManager must be called prior to execution of graph. info.GetAttr<ONNX_NAMESPACE::GraphProto>("then_branch", &proto).IsOK() info.GetAttr<ONNX_NAMESPACE::GraphProto>("else_branch", &proto).IsOK() Execute T* OrtValue::GetMutable() [with T = onnxruntime::TensorSeq] Initialize const T* onnxruntime::Tensor::Data() const [with T = bool] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute virtual onnxruntime::common::Status onnxruntime::If::Compute(onnxruntime::OpKernelContext*) const onnxruntime::If::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&) SetupSubgraphExecutionInfo virtual onnxruntime::common::Status onnxruntime::If::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) onnxruntime::If::If(const onnxruntime::OpKernelInfo&) *ZN11onnxruntime6IfImpl7ExecuteERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK13OrtMemoryInfoR8OrtValueRbE_ N11onnxruntime11controlflow18IControlFlowKernelE N11onnxruntime2IfE Expected: Loop inputs. Found: 'Loop' node has but has Unable to get an allocator body feeds_fetches_manager_ Inconsistent shape in loop output for output. Graph in 'body' attribute of Loop should have /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/loop.cc static_cast<size_t>(num_subgraph_inputs) == subgraph_inputs.size() outputs so the subgraph requires num_subgraph_outputs - 1 == num_outputs 'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' input 'cond' should be a scalar tensor. Got shape of last_outputs[j + 1].IsTensor() All scan outputs MUST be tensors Loop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}. Subgraph SessionState was not found for 'body' attribute. info.GetAttr<ONNX_NAMESPACE::GraphProto>("body", &proto).IsOK() T* OrtValue::GetMutable() [with T = onnxruntime::TensorSeq] const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] onnxruntime::LoopImpl::Execute(const onnxruntime::FeedsFetchesManager&)::__lambda13 Execute ConcatenateLoopOutput void onnxruntime::LoopImpl::SaveOutputsAndUpdateFeeds(const std::vector<OrtValue>&, std::vector<OrtValue>&) T* onnxruntime::Tensor::MutableData() [with T = bool] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = long int] Compute virtual onnxruntime::common::Status onnxruntime::Loop::Compute(onnxruntime::OpKernelContext*) const onnxruntime::Loop::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&) SetupSubgraphExecutionInfo virtual onnxruntime::common::Status onnxruntime::Loop::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) onnxruntime::Loop::Loop(const onnxruntime::OpKernelInfo&) FN11onnxruntime6common6StatusEPvRSt6vectorI8OrtValueSaIS4_EES2_mE N11onnxruntime4LoopE PFN11onnxruntime6common6StatusEPvRSt6vectorI8OrtValueSaIS4_EES2_mE Scan<8> spec does not support transpose of output. This should never be called. /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_8.cc dimensions or more but input had shape of Scan inputs have inconsistent batch size. Previous value was Scan inputs have inconsistent sequence lengths. Previous value was Invalid entries in sequence_lens. Max sequence length was Output OrtValue has not been created for loop state variable output /onnxruntime_src/onnxruntime/core/framework/ort_value_tensor_slicer.h position_ >= 0 && position_ < sequence_length_ info.GetAttr<int64_t>("num_scan_inputs", &num_scan_inputs_).IsOK() feeds_fetches_manager_ && info_ Scan Invalid scan input: has batch size of has length of sequence_lens length of did not match batch size of p_mlvalue num_scan_inputs Subgraph in 'body' produces outputs but Scan expects T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] Execute typename std::enable_if<(! std::is_const<T&>::value), T&>::type onnxruntime::OrtValueTensorSlicer<T>::Iterator::operator*() [with T = const OrtValue; typename std::enable_if<(! std::is_const<T&>::value), T&>::type = const OrtValue&] CreateLoopStateVariables onnxruntime::common::Status onnxruntime::Scan8Impl::CreateLoopStateVariables(std::vector<std::vector<onnxruntime::scan::detail::LoopStateVariable> >&) AllocateOutputTensors gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int] ValidateInput Initialize const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::common::Status onnxruntime::Scan<OpSet>::Compute(onnxruntime::OpKernelContext*) const [with int OpSet = 8] onnxruntime::common::Status onnxruntime::Scan<OpSet>::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) [with int OpSet = 8; std::string = std::basic_string<char>] onnxruntime::Scan<OpSet>::Scan(const onnxruntime::OpKernelInfo&) [with int OpSet = 8] FN11onnxruntime20OrtValueTensorSlicerI8OrtValueEERS1_llE FN11onnxruntime20OrtValueTensorSlicerIK8OrtValueEERS2_llE *ZN11onnxruntime4ScanILi8EEC1ERKNS_12OpKernelInfoEEUlRKSt6vectorImSaImEERKNS_6TensorERSA_E_ *ZN11onnxruntime4ScanILi8EEC1ERKNS_12OpKernelInfoEEUlPvmE0_ N11onnxruntime4ScanILi8EEE N11onnxruntime20OrtValueTensorSlicerIK8OrtValueE8IteratorE PFN11onnxruntime20OrtValueTensorSlicerIK8OrtValueEERS2_llE PFN11onnxruntime20OrtValueTensorSlicerI8OrtValueEERS1_llE final_output_mlvalue_ . Output tensor rank was but input ' ' dimension . Input tensor rank was output_mlvalue scan_input_directions scan_output_directions scan_input_axes but expected scan_output_axes /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_9.cc Attempt to retrieve final output before it was set. /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.h Invalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null. Invalid value in scan_input_axes for input Number of entries in 'scan_input_axes' was gsl::narrow_cast<int64_t>(input_axes_.size()) == num_scan_inputs_ Number of entries in 'scan_output_axes' was gsl::narrow_cast<int64_t>(output_axes_.size()) == num_scan_outputs const OrtValue& onnxruntime::scan::detail::OutputIterator::GetOutput() const TransposeOutput onnxruntime::common::Status onnxruntime::ScanImpl::TransposeOutput() Execute onnxruntime::common::Status onnxruntime::ScanImpl::CreateLoopStateVariables(std::vector<onnxruntime::scan::detail::LoopStateVariable>&) CreateLoopStateVariables AllocateOutputTensors T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] SetupInputs ValidateInput Initialize Compute onnxruntime::common::Status onnxruntime::Scan<OpSet>::Compute(onnxruntime::OpKernelContext*) const [with int OpSet = 9] onnxruntime::common::Status onnxruntime::Scan<OpSet>::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) [with int OpSet = 9; std::string = std::basic_string<char>] onnxruntime::Scan<OpSet>::Scan(const onnxruntime::OpKernelInfo&) [with int OpSet = 9] *ZN11onnxruntime4ScanILi9EEC1ERKNS_12OpKernelInfoEEUlRKSt6vectorImSaImEERKNS_6TensorERSA_E_ *ZN11onnxruntime4ScanILi9EEC1ERKNS_12OpKernelInfoEEUlPvmE0_ N11onnxruntime4ScanILi9EEE Number of entries in ' ' was Invalid values in ' is not compatible with !is_concrete_shape_ did not. Misuse of LoopStateVariable. Attempt to move beyond end of sequence /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.cc iteration_num_ < sequence_len_ Expected AllocateFinalOutput to have been called to before we increment the iterator directions.size() == num_entries '. 0 == forward. 1 == reverse. cur_iteration_ < num_iterations_ Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator. Mismatch between expected shape and shape from first output The subgraph in 'body' requires inputs but Scan was only given num_variadic_inputs == num_subgraph_inputs Failed to create output tensor for output # If shape was concrete we shouldn't be using a custom allocator Subgraph must have the shape set for all outputs but onnxruntime::scan::detail::OutputIterator& onnxruntime::scan::detail::OutputIterator::operator++() typename std::enable_if<(! std::is_const<T&>::value), T&>::type onnxruntime::OrtValueTensorSlicer<T>::Iterator::operator*() [with T = OrtValue; typename std::enable_if<(! std::is_const<T&>::value), T&>::type = OrtValue&] OrtValue& onnxruntime::scan::detail::OutputIterator::operator*() AllocateFinalOutput onnxruntime::common::Status onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput(const onnxruntime::TensorShape&) AllocateFinalBuffer const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Initialize void onnxruntime::scan::detail::LoopStateVariable::Next() const T& OrtValue::Get() const [with T = onnxruntime::Tensor] operator() typename std::enable_if<(! std::is_const<T&>::value), T&>::type onnxruntime::OrtValueTensorSlicer<T>::Iterator::operator*() [with T = const OrtValue; typename std::enable_if<(! std::is_const<T&>::value), T&>::type = const OrtValue&] IterateSequence CreateFeedsFetchesManager void onnxruntime::scan::detail::ReadDirections(const onnxruntime::OpKernelInfo&, const string&, std::vector<long int>&, size_t) onnxruntime::scan::detail::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&, int, bool) *ZN11onnxruntime4scan6detail15IterateSequenceERNS_23OpKernelContextInternalERKNS_12SessionStateERSt6vectorINS1_17LoopStateVariableESaIS8_EERS7_INS_20OrtValueTensorSlicerIK8OrtValueE8IteratorESaISG_EEliiiRKS7_IPSE_SaISK_EERS7_ISt10unique_ptrINS1_14OutputIteratorESt14default_deleteISQ_EESaIST_EERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK13OrtMemoryInfoRSD_RbE_ N11onnxruntime20OrtValueTensorSlicerI8OrtValueE8IteratorE onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ParametricSoftplus<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ScaledTanh<float>] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ThresholdedRelu<float>] ElementWiseKernel Init onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ScaledTanh<float>] ElementWiseKernel Init onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ParametricSoftplus<float>] ElementWiseKernel ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib4GeluIfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ N11onnxruntime17ElementWiseKernelINS_8functors18ParametricSoftplusIfEEEE N11onnxruntime17ElementWiseKernelINS_8functors10ScaledTanhIfEEEE N11onnxruntime7contrib4GeluIfEE 5? @AttnLSTM }, actural: ], while found! , am_attn_size}, Got: }, Got: }. Got: , aw_attn_size}. Got: /onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.h static_cast<int>(activation_func_names.size()) == num_directions_ * 3 Attention mechanism memory shape error! Expected: { Attention mechanism memory sequence lengths must have shape { Attention mechanism memory sequence lengths value must in (0, Attention memory layer weight shape error! Expected:{ Attention query layer weight shape error! Expected:{ Attention v weight shape error! Expected:{ Attention layer weight shape error! Expected: { /onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.cc ValidateInputs gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] ComputeImpl virtual onnxruntime::common::Status onnxruntime::contrib::DeepCpuAttnLstmOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&) onnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib17DeepCpuAttnLstmOpE N11onnxruntime7contrib16AttentionWrapperIfEE row[ [seqno= Expect to have present state output when past state input is given /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention.cc info.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0 Input '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 0 Input 'weights' dimension 1 should be 3 times of dimension 0 Input '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 0 shall have length of 2 Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0 Inputs 'past' dimension 2 shall have length of num_heads Inputs 'past' dimension 2 shall have length of Input 'mask_index' is expected to have 1, 2 or 3 dimensions, got Inputs 'mask_index' dimension 0 shall have length of batch_size or 2 * batch_size Inputs 'mask_index' with raw attention mask shall have shape batch_size x (past_sequence_length + sequence_length) Inputs 'mask_index' of 3d shall have shape batch_size x sequence_length x (past_sequence_length + sequence_length) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::Tensor* onnxruntime::contrib::AttentionBase::GetPresent(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, int, int, int, int&) const onnxruntime::contrib::AttentionBase::AttentionBase(const onnxruntime::OpKernelInfo&) ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUlllE_ N11onnxruntime7contrib9AttentionIfEE use_approximation /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/bias_gelu.cc Compute const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::common::Status onnxruntime::contrib::BiasGelu<T, use_approximation>::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool use_approximation = false] Compute N11onnxruntime7contrib8BiasGeluIfLb0EEE N11onnxruntime7contrib8BiasGeluIfLb1EEE ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib8BiasGeluIfLb0EE7ComputeEPNS_15OpKernelContextEEUllE0_EEvPS1_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib8BiasGeluIfLb1EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib8BiasGeluIfLb1EE7ComputeEPNS_15OpKernelContextEEUllE0_EEvPS1_lOT_lEUllE_ y"=y"=y"=y"=*BL?*BL?*BL?*BL?y"= Input 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 0 const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] input index out of range /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/embed_layer_norm.cc T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::contrib::EmbedLayerNorm<T>::EmbedLayerNorm(const onnxruntime::OpKernelInfo&) [with T = float] ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib14EmbedLayerNormIfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ N11onnxruntime7contrib14EmbedLayerNormIfEE 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 segment_embedding shall have same dimension 1 word_embedding and position_embedding shall have same dimension 1 beta 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 Input 0 and 1 shall have same shape Input 0 and 7 (mask) shall have same shape const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] CDist sqeuclidean /onnxruntime_src/onnxruntime/contrib_ops/cpu/cdist.h info.GetAttr<std::string>("metric", &metric).IsOK() The second input of CDist kernel has wrong shape: Input shape dimensions mismatch: The first input of CDist kernel has wrong shape: T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::CDist<T>::CDist(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::contrib::CDist<T>::CDist(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib5CDistIfEE N11onnxruntime7contrib5CDistIdEE ConvTransposeWithDynamicPads N11onnxruntime7contrib28ConvTransposeWithDynamicPadsIfEE unimplemented activation: activation_params count mismatch Real memory steps is not in (0, not support normalize yet. !normalize_ /onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/bahdanau_attention.cc mem_steps <= max_memory_steps_ && mem_steps > 0 void onnxruntime::contrib::BahdanauAttention<T>::PrepareMemory(const gsl::span<const T>&, const gsl::span<const int>&) [with T = float] onnxruntime::contrib::BahdanauAttention<T>::BahdanauAttention(onnxruntime::AllocatorPtr, const onnxruntime::logging::Logger&, int, int, int, int, int, bool, onnxruntime::concurrency::ThreadPool*) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] N11onnxruntime7contrib19IAttentionMechanismIfEE N11onnxruntime7contrib17BahdanauAttentionIfEE /onnxruntime_src/onnxruntime/core/util/math_cpu.cc Gemv found an unexpected CBLAS_TRANSPOSE input of dims[d_i] < d_max bool onnxruntime::math::NextPosition(int64_t, const int64_t*, int64_t*) void onnxruntime::math::Gemv(CBLAS_TRANSPOSE, int, int, float, const T*, const T*, float, T*, Provider*) [with T = double; Provider = onnxruntime::CPUMathUtil] Received null OrtThreadingOptions Received invalid value for allow_spinning. Valid values are 0 or 1 Device:[ DeviceType: MemoryType: DeviceId: OrtMemoryInfo:[ name: id: OrtMemType: OrtAllocatorType: Cuda CudaPinned Specified device is not supported. /onnxruntime_src/onnxruntime/core/framework/allocator.cc operator() N11onnxruntime12CPUAllocatorE duplicated allocator Received invalid value of arena_extend_strategy /onnxruntime_src/onnxruntime/core/framework/allocatormgr.cc void onnxruntime::AllocatorManager::InsertAllocator(onnxruntime::AllocatorPtr) static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() CreateAllocator St14default_deleteIN11onnxruntime10IAllocatorEE St14default_deleteIN11onnxruntime8BFCArenaEE St19_Sp_counted_deleterIPN11onnxruntime8BFCArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE St19_Sp_counted_deleterIPN11onnxruntime10IAllocatorESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE cudaMalloc hipMalloc Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: MaxAllocSize: Could not find Region for h < chunks_.size() Size: | Requested Size: | in_use: , prev: , next: p_int >= base_int c2->prev == h1 h != kInvalidChunkHandle c->bin_num == bin_num Creating BFCArena for max_dead_bytes_per_chunk: memory limit: arena_extend_strategy Creating bins of max chunk size !chunk->in_use() Available memory of Extended allocation by bytes. Total allocated bytes: Allocated memory at Could not find chunk in bin Chunk Free Allocator: : Chunks . Bytes Requested Bin for bytes has max bytes of , Chunk State: Overall chunks summary: chunks of size . Total Sum Total of in-use chunks: Stats:
- tried to allocate 0 bytes Extending BFCArena for . bin_num: rounded_bytes: /onnxruntime_src/onnxruntime/core/framework/bfc_arena.cc /onnxruntime_src/onnxruntime/core/framework/bfc_arena.h p_int < base_int + memory_size_ !c->in_use() && (c->bin_num != kInvalidBinNum) !c->in_use() && (c->bin_num == kInvalidBinNum) !c1->in_use() && !c2->in_use() bin->free_chunks.count(h) == 1 with following configs: initial_chunk_size_bytes: BinForSize(bin_size) == BinFromIndex(b) BinForSize(bin_size + 255) == BinFromIndex(b) BinForSize(bin_size * 2 - 1) == BinFromIndex(b) BinForSize(bin_size * 2) != BinFromIndex(b) is smaller than requested bytes of Incorrect arena extend strategy. 0 == memory_size % kMinAllocationSize Failed to allocate memory for requested buffer of size BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0 c->in_use() && (c->bin_num == kInvalidBinNum) Bin size: Chunks in_use/total (if not zero). Allocated bytes in_use/total. Requested bytes. b->free_chunks.size() == bin_info.total_chunks_in_bin - bin_info.total_chunks_in_use Diff between in-use and requested bytes is Summary of in-use chunks by size: 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. reserved_chunks_.find(ptr) == reserved_chunks_.end() void onnxruntime::BFCArena::DumpMemoryLog(size_t) DumpMemoryLog std::array<onnxruntime::BFCArena::BinDebugInfo, 21ul> onnxruntime::BFCArena::get_bin_debug_info() void onnxruntime::BFCArena::FreeAndMaybeCoalesce(onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::RemoveFreeChunkFromBin(onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::RemoveFreeChunkIterFromBin(onnxruntime::BFCArena::Bin::FreeChunkSet*, const iterator&) void onnxruntime::BFCArena::InsertFreeChunkIntoBin(onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::Merge(onnxruntime::BFCArena::ChunkHandle, onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::DeallocateRawInternal(void*) void onnxruntime::BFCArena::SplitChunk(onnxruntime::BFCArena::ChunkHandle, size_t) void* onnxruntime::BFCArena::FindChunkPtr(onnxruntime::BFCArena::BinNum, size_t, size_t) void* onnxruntime::BFCArena::AllocateRawInternal(size_t, bool) AllocateRawInternal virtual void* onnxruntime::BFCArena::Reserve(size_t) int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void*) const RegionFor onnxruntime::BFCArena::AllocationRegion::AllocationRegion(void*, size_t) onnxruntime::BFCArena::Extend(size_t)::__lambda3 Extend onnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::ChunkFromHandle(onnxruntime::BFCArena::ChunkHandle) static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() onnxruntime::BFCArena::BFCArena(std::unique_ptr<onnxruntime::IAllocator>, size_t, onnxruntime::ArenaExtendStrategy, int, int) BFCArena N11onnxruntime15IArenaAllocatorE N11onnxruntime8BFCArenaE fff?/onnxruntime_src/onnxruntime/core/framework/data_transfer.cc src.SizeInBytes() == dst.SizeInBytes() virtual onnxruntime::common::Status onnxruntime::CPUDataTransfer::CopyTensor(const onnxruntime::Tensor&, onnxruntime::Tensor&, int) const CopyTensors N11onnxruntime13IDataTransferE N11onnxruntime15CPUDataTransferE Tensor size mismatch There's no data transfer registered for copying tensors from /onnxruntime_src/onnxruntime/core/framework/data_transfer_manager.cc data_transfer registered is nullptr. CopyTensors (null) double uint8 uint16 uint32 uint64 bfloat16 Not implemented sparse tensor type elem_proto != nullptr value_proto != nullptr MLDataType for: Ln onnnnnnnpnn@o0o oLnLno /onnxruntime_src/onnxruntime/core/framework/data_types.cc thisProto->value_case() == TypeProto::ValueCase::kTensorType utils::HasElemType(thisProto->tensor_type()) thisProto->value_case() == TypeProto::ValueCase::kSparseTensorType utils::HasElemType(thisProto->sparse_tensor_type()) thisProto->value_case() == TypeProto::ValueCase::kSequenceType utils::HasElemType(thisProto->sequence_type()) thisProto->value_case() == TypeProto::ValueCase::kMapType utils::HasKeyType(thisProto->map_type()) thisProto->value_case() == TypeProto::ValueCase::kOpaqueType Invalid DataTypeImpl TypeProto definition Only ONNX MLDataType can be registered We do not expect duplicate registration of types for: expected to be a registered ONNX type /onnxruntime_src/include/onnxruntime/core/framework/data_types.h is not currently registered or supported }L}}}}|}t|,|HȀ8\\l|̶,D\\4 onnxruntime::utils::ContainerChecker::ContainerChecker(onnxruntime::MLDataType) void onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(onnxruntime::MLDataType) static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::map<long int, float>] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::map<std::basic_string<char>, float>] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = onnxruntime::BFloat16] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = onnxruntime::MLFloat16] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::basic_string<char>] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = bool] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = long unsigned int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = long int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = unsigned int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = short unsigned int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = short int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = unsigned char] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = signed char] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = double] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = float] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = double] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = float] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = long int] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = std::basic_string<char>] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = double] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = float] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = long int] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = std::basic_string<char>] virtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue&, size_t, void*) const virtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void*, size_t, OrtValue&) const bool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto&) const bool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto&) const virtual bool onnxruntime::SequenceTensorTypeBase::IsCompatible(const onnx::TypeProto&) const virtual bool onnxruntime::SparseTensorTypeBase::IsCompatible(const onnx::TypeProto&) const virtual bool onnxruntime::TensorTypeBase::IsCompatible(const onnx::TypeProto&) const bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Sequence&, const onnx::TypeProto_Sequence&) bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Map&, const onnx::TypeProto_Map&) GetElementType GetElementType GetElementType N11onnxruntime12DataTypeImplE N11onnxruntime14TensorTypeBaseE N11onnxruntime20SparseTensorTypeBaseE N11onnxruntime17NonTensorTypeBaseE N11onnxruntime22SequenceTensorTypeBaseE N11onnxruntime21PrimitiveDataTypeBaseE N11onnxruntime10TensorTypeIiEE N11onnxruntime10TensorTypeIfEE N11onnxruntime10TensorTypeIbEE N11onnxruntime10TensorTypeISsEE N11onnxruntime10TensorTypeIaEE N11onnxruntime10TensorTypeIhEE N11onnxruntime10TensorTypeItEE N11onnxruntime10TensorTypeIsEE N11onnxruntime10TensorTypeIlEE N11onnxruntime10TensorTypeIdEE N11onnxruntime10TensorTypeIjEE N11onnxruntime10TensorTypeImEE N11onnxruntime10TensorTypeINS_9MLFloat16EEE N11onnxruntime10TensorTypeINS_8BFloat16EEE N11onnxruntime16SparseTensorTypeIiEE N11onnxruntime16SparseTensorTypeIfEE N11onnxruntime16SparseTensorTypeIbEE N11onnxruntime16SparseTensorTypeIaEE N11onnxruntime16SparseTensorTypeIhEE N11onnxruntime16SparseTensorTypeItEE N11onnxruntime16SparseTensorTypeIsEE N11onnxruntime16SparseTensorTypeIlEE N11onnxruntime16SparseTensorTypeIdEE N11onnxruntime16SparseTensorTypeIjEE N11onnxruntime16SparseTensorTypeImEE N11onnxruntime16SparseTensorTypeINS_9MLFloat16EEE N11onnxruntime16SparseTensorTypeINS_8BFloat16EEE N11onnxruntime13NonTensorTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEE N11onnxruntime7MapTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEE N11onnxruntime13NonTensorTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEE N11onnxruntime7MapTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEE N11onnxruntime13NonTensorTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEE N11onnxruntime7MapTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEE N11onnxruntime13NonTensorTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEE N11onnxruntime7MapTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEE N11onnxruntime13NonTensorTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEE N11onnxruntime7MapTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEE N11onnxruntime13NonTensorTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEE N11onnxruntime7MapTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEE N11onnxruntime13NonTensorTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEE N11onnxruntime7MapTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEE N11onnxruntime13NonTensorTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEE N11onnxruntime7MapTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEE N11onnxruntime18SequenceTensorTypeIfEE N11onnxruntime18SequenceTensorTypeIdEE N11onnxruntime18SequenceTensorTypeIaEE N11onnxruntime18SequenceTensorTypeIhEE N11onnxruntime18SequenceTensorTypeIsEE N11onnxruntime18SequenceTensorTypeItEE N11onnxruntime18SequenceTensorTypeIiEE N11onnxruntime18SequenceTensorTypeIjEE N11onnxruntime18SequenceTensorTypeIlEE N11onnxruntime18SequenceTensorTypeImEE N11onnxruntime18SequenceTensorTypeIbEE N11onnxruntime18SequenceTensorTypeISsEE N11onnxruntime18SequenceTensorTypeINS_9MLFloat16EEE N11onnxruntime18SequenceTensorTypeINS_8BFloat16EEE N11onnxruntime13NonTensorTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEE N11onnxruntime12SequenceTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEE N11onnxruntime13NonTensorTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEE N11onnxruntime12SequenceTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEE N11onnxruntime17PrimitiveDataTypeIiEE N11onnxruntime17PrimitiveDataTypeIfEE N11onnxruntime17PrimitiveDataTypeIbEE N11onnxruntime17PrimitiveDataTypeISsEE N11onnxruntime17PrimitiveDataTypeIaEE N11onnxruntime17PrimitiveDataTypeIhEE N11onnxruntime17PrimitiveDataTypeItEE N11onnxruntime17PrimitiveDataTypeIsEE N11onnxruntime17PrimitiveDataTypeIlEE N11onnxruntime17PrimitiveDataTypeIdEE N11onnxruntime17PrimitiveDataTypeIjEE N11onnxruntime17PrimitiveDataTypeImEE N11onnxruntime17PrimitiveDataTypeINS_9MLFloat16EEE N11onnxruntime17PrimitiveDataTypeINS_8BFloat16EEE ZN11onnxruntime19data_types_internal16DataTypeRegistryC1EvEUlPKNS_12DataTypeImplEE_ Ss N11onnxruntime9MLFloat16E N11onnxruntime8BFloat16E St3mapISsfSt4lessISsESaISt4pairIKSsfEEE St3mapIlfSt4lessIlESaISt4pairIKlfEEE Unloading DSO Failed to unload DSO: A dso with name has already been loaded. Caught exception while destructing CustomOpsLoader with message: /onnxruntime_src/onnxruntime/core/framework/ex_lib_loader.cc Caught exception while loading custom ops with message: LoadExternalLib operator() static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() ~ExLibLoader N11onnxruntime11ExLibLoaderE Requested shape: size= TraceFree for ort_value_idx= invalid index ort_value.Fence() == nullptr For ort_value with index: but the actually size is: Invalid allocation kind: mlvalue.Fence() == nullptr Memory type should only appear once. returned nullptr failed. Error: Memory pattern planner is not enabled on this execution framework. /onnxruntime_src/onnxruntime/core/framework/execution_frame.h ort_value_index >= 0 && static_cast<size_t>(ort_value_index) < all_values_size_ /onnxruntime_src/onnxruntime/core/framework/node_index_info.h offset >= 0 && static_cast<size_t>(offset) < node_values_size_ OrtValue shape verification failed. Current shape: /onnxruntime_src/onnxruntime/core/framework/execution_frame.cc shape && tensor.Shape() == *shape ort_value_idx >= 0 && static_cast<size_t>(ort_value_idx) < alloc_plan.size() TraceAllocation for ort_value_idx= node_index_info and ort_value_idx_map are out of sync and cannot be used node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx() Shape 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. feeds.size() == feed_mlvalue_idxs.size() fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() entries which doesn't match the number of fetches the frame was initialized with of Fetches vector passed to GetOutputs contains Trying to allocate memory for unused optional inputs/outputs Tensor shape cannot contain any negative value , block in memory pattern size is: , fall back to default allocation behavior ort_value_index >= 0 && static_cast<size_t>(ort_value_index) < alloc_plan.size() We don't expect custom allocators for non-tensor types, so a shape is mandatory here. Allocation of tensor types requires a shape. buffers_.find(location) == buffers_.end() static_activation_memory_sizes_in_byte_.find(location.name) == static_activation_memory_sizes_in_byte_.end() Allocation of memory pattern buffer for TraceFree void onnxruntime::ExecutionFrame::TraceFree(int) TraceAllocate const onnxruntime::AllocPlanPerValue& onnxruntime::ExecutionFrame::GetAllocationPlan(int) ReleaseMLValueImpl onnxruntime::common::Status onnxruntime::AllocateSparseTensor(OrtValue&, const onnxruntime::DataTypeImpl&, onnxruntime::AllocatorPtr, const onnxruntime::TensorShape&, size_t, bool, const onnxruntime::SessionState&) AllocateAsPerAllocationPlan onnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan(OrtValue&, int, const onnxruntime::TensorShape*, size_t) AllocateMLValueTensorPreAllocateBuffer AllocateMLValueTensorSelfOwnBufferHelper onnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue&, int, onnxruntime::MLDataType, const OrtMemoryInfo&, const onnxruntime::TensorShape&, bool) operator() ExecutionFrame onnxruntime::ExecutionFrame::ExecutionFrame(const std::vector<int>&, const std::vector<OrtValue>&, const std::vector<int>&, const std::vector<OrtValue>&, const std::unordered_map<long unsigned int, std::function<onnxruntime::common::Status(const onnxruntime::TensorShape&, const OrtMemoryInfo&, OrtValue&, bool&)> >&, const onnxruntime::SessionState&) T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] Init void onnxruntime::IExecutionFrame::Init(const std::vector<int>&, const std::vector<OrtValue>&, const std::unordered_map<int, OrtValue>&, const std::vector<OrtValue>&) int onnxruntime::NodeIndexInfo::GetMLValueIndex(int) const const OrtValue& onnxruntime::IExecutionFrame::GetMLValue(int) const onnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(int, const onnxruntime::TensorShape*, OrtValue*&, size_t) onnxruntime::IExecutionFrame::IExecutionFrame(const onnxruntime::OrtValueNameIdxMap&, const onnxruntime::NodeIndexInfo&, const std::vector<int>&) N11onnxruntime15IExecutionFrameE N11onnxruntime14ExecutionFrameE IExecutionProvider::Compile with fused Node and dll path is not implemented by IExecutionProvider::Compile with FusedNodeAndGraph is not implemented by IExecutionProvider::Compile with fused Node is not implemented by IExecutionProvider constructor must be called with true for use_metadef_id_creator /onnxruntime_src/onnxruntime/core/framework/execution_provider.cc metadef_id_generator_ duplicated allocator: virtual int onnxruntime::IExecutionProvider::GenerateMetaDefId(const onnxruntime::GraphViewer&, uint64_t&) const static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() TryInsertAllocator void onnxruntime::IExecutionProvider::InsertAllocator(onnxruntime::AllocatorPtr) N11onnxruntime18IExecutionProviderE Error mapping feeds: Error mapping output names: /onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.cc input_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknown void onnxruntime::FeedsFetchesManager::SetDeviceCopyChecks(onnxruntime::DeviceCopyCheck, onnxruntime::DeviceCopyCheck) onnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector<std::basic_string<char> >&, const std::vector<std::basic_string<char> >&, const onnxruntime::OrtValueNameIdxMap&) FeedsFetchesInfo MapNamesToMLValueIdxs EP: has Compile error: 1 == capability.nodes.size() No provider specified. /onnxruntime_src/onnxruntime/core/framework/func_kernel.h compute_info_->create_state_func(&context, &func_state_) == 0 /onnxruntime_src/onnxruntime/core/framework/graph_partitioner.cc single_node_compute_func should have 1 elements Existing entry in compiled kernel hashes for . Execution Provider must generate unique names across the entire model. Must use Function based fusion when exporting compiled nodes to dll. fusion_style == IExecutionProvider::FusionStyle::Function did not return correct number of compiled functions Compiled kernel hashes must be provided compiled_kernel_hashes != nullptr onnxruntime::common::Status onnxruntime::GraphPartitioner::Partition(onnxruntime::Graph&, bool, onnxruntime::FuncManager&, onnxruntime::GraphPartitioner::Mode, std::unordered_map<std::basic_string<char>, long unsigned int>*) const Partition onnxruntime::common::Status onnxruntime::PartitionOrtFormatModelImpl(onnxruntime::Graph&, onnxruntime::FuncManager&, onnxruntime::KernelRegistryManager&, onnxruntime::KernelRegistry&, onnxruntime::IExecutionProvider&, std::unordered_map<std::basic_string<char>, long unsigned int>&, int&) PartitionOrtFormatModelImpl PartitionOrtFormatModel InlineNodes onnxruntime::FunctionKernel::FunctionKernel(const onnxruntime::OpKernelInfo&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const onnxruntime::Node* onnxruntime::PlaceNode(onnxruntime::Graph&, const onnxruntime::IndexedSubGraph&, const onnxruntime::KernelRegistryManager&, const string&, onnxruntime::IExecutionProvider::FusionStyle, onnxruntime::GraphPartitioner::Mode, int&) onnxruntime::common::Status onnxruntime::PartitionOnnxFormatModelImpl(onnxruntime::Graph&, bool, onnxruntime::FuncManager&, onnxruntime::KernelRegistryManager&, onnxruntime::KernelRegistry&, onnxruntime::IExecutionProvider&, onnxruntime::GraphPartitioner::Mode, int&) PartitionOnnxFormatModelImpl PartitionOnnxFormatModel N11onnxruntime14FunctionKernelE kernel def can't be NULL Failed to add kernel for and type ( Version mismatch. node_version: kernel start version: kernel_end_version: (node_version: ), Op with name ( kernel not found in No matching hash for kernel is not supported in Kernel not found : Conflicting with a registered kernel with op versions. /onnxruntime_src/onnxruntime/core/framework/kernel_registry.cc len <= op_schema.inputs().size() Found kernel for Op with name ( in the supported version range 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 ( Encountered following errors: ( TryCreateKernel void onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::__lambda12; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::__lambda13] void onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::__lambda10; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::__lambda11] The node is not placed on any Execution Provider. (node ). Failed to find kernel for found duplicated provider in KernelRegistryManager nullptr != type_proto /onnxruntime_src/onnxruntime/core/framework/mldata_type_utils.cc const onnxruntime::DataTypeImpl* onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg&) _fmUu|$HوO/onnxruntime_src/onnxruntime/core/framework/node_index_info.cc onnxruntime::NodeIndexInfo::Init(const TValidNodes&, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap&) [with TValidNodes = onnxruntime::ValidNodes<const std::vector<const onnxruntime::Node*> >; onnxruntime::NodeIndex = long unsigned int]::__lambda12 onnxruntime::NodeIndexInfo::Init(const TValidNodes&, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap&) [with TValidNodes = onnxruntime::ConstGraphNodes; onnxruntime::NodeIndex = long unsigned int]::__lambda12 ZN11onnxruntime13NodeIndexInfo4InitINS_15ConstGraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ ZN11onnxruntime13NodeIndexInfo4InitINS_15ConstGraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ type_proto is not of type map!
-
type_proto is not of type sequence! :tensor type \̩\ OrtValue is TensorSequence type but has no element Tensor DataType. Tensor types should have been handled already ` Ը Ը const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] TempSpace allocator not found Execution frame was null frame != nullptr OpKernel was null kernel != nullptr . Num args is Invalid arg_num of arg_num < arg_counts.size() /onnxruntime_src/onnxruntime/core/framework/op_kernel.cc node_offsets_index < node_offsets_size_ OrtValue* onnxruntime::OpKernelContext::GetOrCreateOutputMLValue(int) int onnxruntime::OpKernelContext::NumVariadicInputs(size_t) const OrtValue* onnxruntime::OpKernelContext::OutputMLValue(int, const onnxruntime::TensorShape&, size_t) T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] int onnxruntime::NodeIndexInfo::GetNodeOffset(onnxruntime::NodeIndex) const onnxruntime::OpKernelContext::OpKernelContext(onnxruntime::IExecutionFrame*, const onnxruntime::OpKernel*, onnxruntime::concurrency::ThreadPool*, const onnxruntime::logging::Logger&) cannot find allocator /onnxruntime_src/onnxruntime/core/framework/op_kernel_info.cc const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const OrtMemoryInfo& onnxruntime::OpKernelInfo::GetMemoryInfo(int, OrtMemType) const is defined. No attribute with name: but is of type: expected to be of type: Attribute: attr != nullptr is expected to have type: Requested attribute: Attibute name and type don't match No attribute with this name is defined. /onnxruntime_src/onnxruntime/core/framework/op_node_proto_helper.cc values.size() == static_cast<size_t>(attr->floats_size()) values.size() == static_cast<size_t>(attr->ints_size()) values.size() == static_cast<size_t>(attr->strings_size()) /onnxruntime_src/include/onnxruntime/core/framework/op_node_proto_helper.h values.size() == static_cast<size_t>(attr->tensors_size()) values.size() == static_cast<size_t>(attr->graphs_size()) !using_counters_ current <= buffer_size_ /onnxruntime_src/onnxruntime/core/framework/mem_pattern_planner.h void onnxruntime::MemPatternPlanner::TraceAllocation(int, size_t) ort_value.IsTensor() ort_value.IsAllocated() . Shape: . Dimension 0 is Invalid dim0_offset of dim0_offset < dim0_size /onnxruntime_src/onnxruntime/core/framework/ort_value_tensor_slicer.cc Insufficient dimensions to slice on gsl::narrow_cast<int64_t>(tensor_shape.NumDimensions()) >= slice_dimension Can't slice a non-tensor OrtValue. Type was OrtValue has not been allocated so can't be sliced. onnxruntime::OrtValueTensorSlicer<T>::Iterator::Iterator(T&, size_t, size_t, int64_t, onnxruntime::OrtValueTensorSlicer<T>::Iterator::Direction) [with T = const OrtValue; size_t = long unsigned int; int64_t = long int] static onnxruntime::OrtValueTensorSlicer<T> onnxruntime::OrtValueTensorSlicer<T>::Create(T&, int64_t, int64_t) [with T = const OrtValue; int64_t = long int] onnxruntime::OrtValueTensorSlicer<T>::Iterator::Iterator(T&, size_t, size_t, int64_t, onnxruntime::OrtValueTensorSlicer<T>::Iterator::Direction) [with T = OrtValue; size_t = long unsigned int; int64_t = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] static onnxruntime::OrtValueTensorSlicer<T> onnxruntime::OrtValueTensorSlicer<T>::Create(T&, int64_t, int64_t) [with T = OrtValue; int64_t = long int] Provider_SetHost GetProvider libonnxruntime_providers_shared.so /onnxruntime_src/onnxruntime/core/framework/provider_bridge_ort.cc OrtSessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared library SessionOptionsAppendExecutionProvider_Tensorrt: Failed to load shared library SessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared library OrtSessionOptionsAppendExecutionProvider_Dnnl: Failed to load shared library OrtSessionOptionsAppendExecutionProvider_Tensorrt: Failed to load shared library libonnxruntime_providers_dnnl.so libonnxruntime_providers_openvino.so libonnxruntime_providers_tensorrt.so Ensure Get const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&) static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() N11onnxruntime23NodeAttributes_IteratorE N11onnxruntime44Provider_TensorShapeProto_Dimension_IteratorE N11onnxruntime18Node__NodeIteratorE N11onnxruntime18Node__EdgeIteratorE N11onnxruntime12ProviderHostE N11onnxruntime49Provider_TensorShapeProto_Dimension_Iterator_ImplE N11onnxruntime28NodeAttributes_Iterator_ImplE N11onnxruntime23Node__NodeIterator_ImplE N11onnxruntime23Node__EdgeIterator_ImplE N11onnxruntime19OpKernel_TranslatorE N11onnxruntime16ProviderHostImplE ZN11onnxruntime16ProviderHostImpl24KernelRegistry__RegisterEPNS_14KernelRegistryEONS_25Provider_KernelCreateInfoEEUlRKNS_12OpKernelInfoEE_ Session Config with key [ ] already exists with value [ ]. It will be overwritten Received nullptr for name. Config value is longer than maximum length 1024 Config key is empty or longer than maximum length 128 /onnxruntime_src/onnxruntime/core/framework/session_options.cc Received nullptr for OrtValue. Buffer containing the initializer must be owned by the user. An OrtValue for this name has already been added. Received OrtValue is not a tensor. Only tensors are supported. const T& OrtValue::Get() const [with T = onnxruntime::Tensor] static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() AddConfigEntry /onnxruntime_src/onnxruntime/core/framework/session_state.cc SetGraphAndCreateKernels must be called prior to GetExecutionInfo. entry != kernel_create_info_map_.cend() . Do you have duplicated calls to SessionState::AddInitializedTensor function? Done saving OrtValue mappings. Allocator already registered for Kernel create info is null. Invalid ORT format model. Kernel create info node indices are null. Invalid ORT format model. Kernel create info hashes are null. Invalid ORT format model. Size mismatch for kernel create info node indexes and hashes. Invalid ORT format model. Unable to find compiled kernel hash for node ' SessionState for subgraphs is null. Invalid ORT format model. Subgraph SessionState entry for Failed to find output name in the mapping: Failed to find input name in the mapping: Missing session state for subgraph. Node:' entry != node_to_subgraph_ss.second.cend() Using an input in multiple nodes on different devices is not supported currently. Input: Only one node should produce an output. Existing entry for output_names_to_nodeinfo.empty() existing_entries.find(attribute_name) == existing_entries.cend() Main Graph instance should have populated all subgraphs when being resolved. Could not find OrtValue with idx ' node_index_info_ duplicated ort_value index: SaveMLValueNameIndexMapping . Ignoring allocator from Can't find node with index . Invalid ORT format model. '. Subgraph SessionState for session.disable_prepacking ' OpType: Attribute: p_op_kernel is used by node ) and node Entry exists in node for attribute onnxruntime::common::Status onnxruntime::SessionState::FinalizeSessionStateImpl(const std::basic_string<char>&, onnxruntime::KernelRegistryManager&, const onnxruntime::Node*, const onnxruntime::SessionOptions&, bool, std::unordered_map<std::basic_string<char>, long unsigned int>&) FinalizeSessionStateImpl FinalizeSessionState operator() LoadFromOrtFormat CreateSubgraphSessionState onnxruntime::common::Status onnxruntime::SessionState::CreateSubgraphSessionState() GetSubGraphSessionStatesOrtFormat SaveToOrtFormat void onnxruntime::SessionState::UpdateToBeExecutedNodes(const std::vector<int>&) const onnxruntime::NodeIndexInfo& onnxruntime::SessionState::GetNodeIndexInfo() const void onnxruntime::SessionState::AddSubgraphSessionState(onnxruntime::NodeIndex, const string&, std::unique_ptr<onnxruntime::SessionState>) void onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const string&, const onnxruntime::SessionState::NodeInfo&) PrepackConstantInitializedTensors const onnxruntime::KernelCreateInfo& onnxruntime::SessionState::GetNodeKernelCreateInfo(onnxruntime::NodeIndex) const PopulateKernelCreateInfo CreateGraphInfo SetupAllocators *ZN11onnxruntime12SessionState23UpdateToBeExecutedNodesERKSt6vectorIiSaIiEEEUlPKNS_4NodeEE_ *ZN11onnxruntime12SessionState24FinalizeSessionStateImplERKSsRNS_21KernelRegistryManagerEPKNS_4NodeERKNS_14SessionOptionsEbRSt13unordered_mapISsmSt4hashISsESt8equal_toISsESaISt4pairIS1_mEEEEUliRK8OrtValueRKNS_11OrtCallbackEbE0_ *ZN11onnxruntime12SessionState15SetupAllocatorsEvEUli10OrtMemTypeE_ N11onnxruntime25ISequentialPlannerContextE N11onnxruntime24SequentialPlannerContextE Graph input with name is not used by any node. Saving initialized tensors. bytes for , Got Failed to copy tensor to Deserialize tensor /onnxruntime_src/onnxruntime/core/framework/session_state_utils.cc OrtValue indexes should have been populated. ort_value_name_idx_map.MaxIdx() > -1 Cannot use user supplied initializer with name: ( ) because the ORT planned memory location device ) is different from what is supplied ( entry != initialized_tensors_to_allocate.end() [Memory] SessionStateInitializer statically allocates Using user supplied initializer with name ( string tensor is not supported for copying between allocators Internal error. The preallocated buffer is too small. Requires Done saving initialized tensors ForEachWithIndex operator() operator() SaveInputOutputNamesToNodeMapping DeserializeTensorProto operator() onnxruntime::common::Status onnxruntime::session_state_utils::SaveInitializedTensors(const onnxruntime::Env&, const std::basic_string<char>&, const onnxruntime::GraphViewer&, const OrtMemoryInfo&, const onnxruntime::OrtValueNameIdxMap&, const std::vector<int>&, onnxruntime::ITensorAllocator&, const std::function<onnxruntime::common::Status(int, const OrtValue&, const onnxruntime::OrtCallback&, bool)>&, const onnxruntime::logging::Logger&, const onnxruntime::DataTransferManager&, const onnxruntime::ExecutionPlanBase&, const onnxruntime::SessionOptions&) SaveInitializedTensors *ZN11onnxruntime19session_state_utils33SaveInputOutputNamesToNodeMappingERKNS_11GraphViewerERNS_12SessionStateERKSt6vectorIPKNS_7NodeArgESaIS9_EEEUlRS8_mE_ *ZN11onnxruntime19session_state_utils33SaveInputOutputNamesToNodeMappingERKNS_11GraphViewerERNS_12SessionStateERKSt6vectorIPKNS_7NodeArgESaIS9_EEEUlRS8_mE0_ tensor size overflow shape.Size() must >=0 dtype_ != nullptr p_type != nullptr /onnxruntime_src/onnxruntime/core/framework/tensor.cc Tensor is expected to contain one of the primitive data types. Got: tensor failed memory size calculation void onnxruntime::Tensor::Init(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, onnxruntime::AllocatorPtr, ptrdiff_t) size_t onnxruntime::Tensor::SizeInBytes() const onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, std::shared_ptr<onnxruntime::IAllocator>) onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, const OrtMemoryInfo&, ptrdiff_t) Internal error. Mem pattern for initializer ' is not found ' failed duplicated location /onnxruntime_src/onnxruntime/core/framework/tensor_allocator_with_mem_pattern.h Weight buffer for initializer ' Get preallocated buffer for initializer ' Failed to get allocator for location: Trace FinalizePlan N11onnxruntime16ITensorAllocatorE N11onnxruntime29TensorAllocatorWithMemPatternE model format error! Need a key for the external data info model format error! Need a value for the external data info model format error! Missing 'location' checksum model format error! Invalid dimension of dimension <= num_dims /onnxruntime_src/onnxruntime/core/framework/tensor_shape.cc dimstart <= dimend && dimend <= size() Invalid tensor shape slice argument. for SizeFromDimension. Tensor has onnxruntime::TensorShape onnxruntime::TensorShape::Slice(size_t, size_t) const int64_t onnxruntime::TensorShape::SizeFromDimension(size_t) const int64_t onnxruntime::TensorShape::SizeToDimension(size_t) const OrtValue is not a Tensor Argument is not a tensor /onnxruntime_src/onnxruntime/core/framework/tensor_type_and_shape.cc
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const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] OrtStatus* OrtApis::GetTensorTypeAndShape(const OrtValue*, OrtTensorTypeAndShapeInfo**) data overflow Invalid TensorProto nullptr == p_data ) in proto Unsupported type: Indicies expected to be INT64 in 'Constant' node ' Must have a valid data type HasDataType(dense_proto) UnpackTensor: the pre-allocated size does not match the raw data size, expected External data type cannot be STRING. UnpackTensor: the pre-allocate size does not match the size in proto Tensor does not have external data to read from. /onnxruntime_src/onnxruntime/core/framework/tensorprotoutils.cc External data type cannot be UNDEFINED or STRING. TensorProto external data size mismatch. Computed size: ) does not match the data size( corrupted protobuf data: tensor shape size( string tensor can not have raw data tensor can't contain negative dims The buffer planner is not consistent with tensor buffer size, expected initialize preallocated buffer failed Initialized tensor with unexpected type: Unsupported sparse tensor data type of Sparse Indicies raw data size does not match expected. Invalid SparseTensor indices. Should either have raw or int64 data rank == dims.size() && rank > 0 cur_index == &*indices_data.cend() Invalid SparseTensor indices. Should be rank 0 or 1. Got: BUG! Report to onnxruntime team. Unsupported attribute value type of j
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UnpackTensorWithExternalDataImpl ReadExternalDataForTensor UnpackInitializerData DenseTensorToSparseTensorProto onnxruntime::common::Status onnxruntime::utils::DenseTensorToSparseTensorProto(const onnx::TensorProto&, const onnxruntime::Path&, onnx::SparseTensorProto&) onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto&, gsl::span<const long int>, std::function<void(long unsigned int, long unsigned int)>) [with T = unsigned int; size_t = long unsigned int] CopySparseData onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto&, gsl::span<const long int>, std::function<void(long unsigned int, long unsigned int)>) [with T = unsigned char; size_t = long unsigned int] CopySparseData onnxruntime::common::Status onnxruntime::utils::SparseTensorProtoToDenseTensorProto(const onnx::SparseTensorProto&, const onnxruntime::Path&, onnx::TensorProto&) SparseTensorProtoToDenseTensorProto onnxruntime::common::Status onnxruntime::utils::ConstantNodeProtoToTensorProto(const onnx::NodeProto&, const onnxruntime::Path&, onnx::TensorProto&) ConstantNodeProtoToTensorProto GetFileContent GetExternalDataInfo TensorProtoToMLValue *ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE_ *ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE0_ CoreMLExecutionProvider exec_plan_ptr allocator != nullptr copy_info.size() == num_feeds /onnxruntime_src/onnxruntime/core/framework/utils.cc InternalTestingExecutionProvider feed_locations.size() == copy_info.size() fetch_alloc_info.size() == copy_info.size() Failed to find allocator for device Only one thread was configured for parallel execution. Hence will use sequential execution. CopyOutputsAcrossDevices CopyInputsAcrossDevices onnxruntime::common::Status onnxruntime::utils::CopyInputsAcrossDevices(const onnxruntime::SessionState&, const std::vector<OrtValue>&, std::vector<OrtValue>&, const std::vector<onnxruntime::MLValueCopyInfo>&) ExecuteGraphImpl ExecuteGraph T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] BatchOrCopyMLValue onnxruntime::common::Status onnxruntime::utils::BatchOrCopyMLValue(const onnxruntime::SessionState&, const onnxruntime::MLValueCopyInfo&, const OrtValue&, OrtValue&, std::vector<onnxruntime::IDataTransfer::SrcDstPair>*) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] CopyOneInputAcrossDevices bool onnxruntime::utils::FinalizeCopyInfoForFetches(const std::vector<const OrtMemoryInfo*>&, std::vector<onnxruntime::MLValueCopyInfo>&) bool onnxruntime::utils::FinalizeCopyInfoForFeeds(const std::vector<OrtDevice>&, std::vector<onnxruntime::MLValueCopyInfo>&) CalculateStaticCopyInfoForFeed CalculateStaticCopyInfoForFeeds InitializeFeedFetchCopyInfo const OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::OrtValueNameIdxMap&, const onnxruntime::SequentialExecutionPlan&, const string&) FindMemoryInfoForValue const OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::SessionState&, const string&) N11onnxruntime9IExecutorE Allocate AllocateStatically PreExisting Reuse AllocateOutput Share NotSet nullptr != tensor_type_base reused != reused_for Can not find the node No matching 'start' entry. end >= starts_.back() p_provider Allocation Plan:
-
-Execution Plan:
- ) , use fence when async Index out-of-range! nullptr != node Free ml-values: INVALID INDEX vector<bool>::_M_fill_insert p_kernel_def /onnxruntime_src/onnxruntime/core/framework/allocation_planner.cc Invalid program_counter entries at index entry.program_counter.HasValidEntries() SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex: entry != kernel_create_info_map.cend() id >= 0 && static_cast<size_t>(id) < ort_value_info_.size() n >= 0 && static_cast<size_t>(n) < ort_value_info_.size() n >= 0 && static_cast<size_t>(n) < plan_.allocation_plan.size() AllocPlan(ml_value_idx).program_counter.Ends().back() == program_counter Previous entry was not terminated. /onnxruntime_src/onnxruntime/core/framework/sequential_execution_plan.h starts_.size() == ends_.size() starts_.empty() || start > ends_.back() Invalid 'start'. Value is smaller than previous 'end'. starts_.size() == ends_.size() + 1 Invalid 'end'. Value is larger than 'start'. (ort_value_idx) output_name : <allocation plan>
- Should not have entry in kernel create info with nullptr for kernel_def Can not find the execution provider void onnxruntime::PlannerImpl::VerifyMemoryTimeSchedule() void onnxruntime::PlannerImpl::GenerateDeallocationPlan() void onnxruntime::AllocPlanPerValue::ProgramCounter::AddEnd(size_t) onnxruntime::OrtValueIndex& onnxruntime::PlannerImpl::Buffer(onnxruntime::OrtValueIndex) void onnxruntime::AllocPlanPerValue::ProgramCounter::AddStart(size_t) void onnxruntime::PlannerImpl::Reuse(onnxruntime::OrtValueIndex, onnxruntime::OrtValueIndex, onnxruntime::AllocKind) static size_t onnxruntime::PlannerImpl::GetElementSize(const string* const&) OrtMemoryInfo onnxruntime::PlannerImpl::GetLocationForNodeInput(size_t, const onnxruntime::Node&) GeneratePlanForWeights ComputeReusePlan onnxruntime::AllocPlanPerValue& onnxruntime::PlannerImpl::AllocPlan(onnxruntime::OrtValueIndex) int& onnxruntime::PlannerImpl::UseCount(onnxruntime::OrtValueIndex) void onnxruntime::PlannerImpl::ProcessDef(onnxruntime::OrtValueIndex, const onnxruntime::NodeArg*) onnxruntime::OrtValueIndex onnxruntime::PlannerImpl::Index(const OrtValueName&) const onnxruntime::KernelCreateInfo& onnxruntime::GetKernelCreateInfo(const std::unordered_map<long unsigned int, gsl::not_null<const onnxruntime::KernelCreateInfo*> >&, onnxruntime::NodeIndex) ComputeUseCounts onnxruntime::common::Status onnxruntime::PlannerImpl::ComputeUseCounts() CreatePlan N11onnxruntime17ExecutionPlanBaseE N11onnxruntime23SequentialExecutionPlanE ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEmE_ ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEbE0_ ZN11onnxruntime11PlannerImpl22GeneratePlanForWeightsEvEUlRKNS_7NodeArgEmE_ /onnxruntime_src/onnxruntime/core/framework/endian_utils.cc source and destination buffer size mismatch CopyLittleEndian Divide by zero static void SafeIntExceptionHandler<onnxruntime::OnnxRuntimeException>::SafeIntOnDivZero() func info for node: already exist. Can't use func with null ptr /onnxruntime_src/onnxruntime/core/framework/fuse_nodes_funcs.cc GetFuncs *ZNK11onnxruntime11FuncManager8GetFuncsERKSsRPNS_15NodeComputeInfoEEUlPvPK6OrtApiP16OrtKernelContextE_ *ZNK11onnxruntime11FuncManager8GetFuncsERKSsRPNS_15NodeComputeInfoEEUlPNS_14ComputeContextEPPvE0_ *ZNK11onnxruntime11FuncManager8GetFuncsERKSsRPNS_15NodeComputeInfoEEUlPvE1_ Begin execution does not. entry != nullptr op_name _fence_before node. Name:' ' Status Message: _kernel_time _fence_after Multiple errors were found. ParallelExecutor::Execute Unknown exception was caught by catch-all handler. Exception running nodes starting at Exiting due to terminate flag being set to true. /onnxruntime_src/onnxruntime/core/framework/parallel_executor.cc Got nullptr from GetKernel for node: All implicit inputs should have OrtValue instances by now. /onnxruntime_src/onnxruntime/core/framework/op_kernel_context_internal.h Non-zero status code returned while running onnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState&, onnxruntime::IExecutionFrame&, const onnxruntime::OpKernel&, const onnxruntime::logging::Logger&, const bool&) onnxruntime::common::Status onnxruntime::ParallelExecutor::RunNodeAsync(size_t, const onnxruntime::SessionState&, const onnxruntime::logging::Logger&) RunNodeAsync const T& OrtValue::Get() const [with T = onnxruntime::Tensor] Execute *ZN11onnxruntime16ParallelExecutor11EnqueueNodeEmRKNS_12SessionStateERKNS_7logging6LoggerEEUlvE_ N11onnxruntime16ParallelExecutorE N11onnxruntime23OpKernelContextInternalE graph_index exec_plan_index activation_size parameter_size output_size SequentialExecutor::Execute /onnxruntime_src/onnxruntime/core/framework/sequential_executor.cc [Memory] ExecutionFrame statically allocates [Memory] ExecutionFrame dynamically allocates ReleaseNodeMLValues onnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState&, onnxruntime::IExecutionFrame&, const onnxruntime::OpKernel&, const onnxruntime::logging::Logger&, const bool&) Execute N11onnxruntime18SequentialExecutorE invalid ort_value_index: ', location: /onnxruntime_src/onnxruntime/core/framework/simple_tensor_allocator.cc Failed to get allocator for initializer ' GetPreallocatedBuffer N11onnxruntime21SimpleTensorAllocatorE Number of attention heads past present input_scale weight_scale input_zero_point weight_zero_point LongformerAttention window global_weight global_bias global Constrain to integer types input_ids segment_ids 2D with shape (,hidden_size) word_embedding 2D with shape (, hidden_size) position_embedding segment_embedding bias tensor skip inv_std_var Value of alpha Value of beta 1D input tensor 1D output tensor The scale to apply. Threshold value Input tensor Output tensor Sliced data tensor. GivenTensorFill The shape of filled tensor The filled tensor input_as_shape extra_shape Input data to be scaled Output data after scaling GRUUnit drop_states hidden_prev gates seq_lengths hidden Scaling value Rfft signal_ndim normalized onesided Irfft ComplexMul input_0 input_1 ComplexMulConj activation_gamma Strings to tokenize Tokenized strings N-dimensional matrix A N-dimensional matrix B Matrix multiply results An input tensor to hash. 32-bit hash value. Tensor of rank r >= 1. Tensor of rank q >= 1. Specify weights of conv Specify bias of conv Constrain to tensor(int32). Constrain to tensor(float). Input tensor. Tensor after padding. idx 2D matrix with shape (M,N) 2D matrix with shape (K,N) rois batch_indices crop_size stash_type Scale tensor. Bias tensor. Output data tensor. The input data as Tensor. The output. The normal input data. The bias (or mask) as Tensor. Attribute should specify a shape Input's shape must be 4-D ) + bottom_border ( ) + right_border ( ) + scale[0] ( ) + scale[1] ( Input axis is invalid: Input rank must be >= 2. != mat_h: Incompatible dimensions inputs are expected to have tensor type and output type should not be null. /onnxruntime_src/onnxruntime/core/graph/contrib_ops/contrib_defs.cc Whether 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_size 2D input tensor with shape (hidden_size, 3 * hidden_size) 1D input tensor with shape (3 * hidden_size) Attention mask with shape (batch_size, past_sequence_length + sequence_length) or (batch_size, sequence_length, past_sequence_length + sequence_length), or index with shape (batch_size) or (2 * batch_size). past 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) present 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 types scale of quantized input tensor. It's a scalar, which means a per-tensor/layer quantization. scale of weight scale. It's a scalar, which means a per-tensor/layer quantization. Attention mask index with shape (batch_size) zero point of quantized input tensor.It's a scalar, which means a per-tensor/layer quantization. zero 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. One sided attention windows length W, or half of total window length Attention mask with shape (batch_size, sequence_length) Global attention flags with shape (batch_size, sequence_length) The epsilon value to use to avoid division by zero. 2D words IDs with shape (batch_size, sequence_length) 2D segment IDs with shape (batch_size, sequence_length) 1D gamma tensor for layer normalization with shape (hidden_size) 1D beta tensor for layer normalization with shape (hidden_size) 2D attention mask with shape (batch_size, sequence_length) 1D mask_index tensor with shape (batch_size) Constrain input and output integer tensors types Constrain input and output float tensors types. Constrain input and output types to float or half tensors. 3D input tensor with shape (batch_size, sequence_length, hidden_size) 3D skip tensor with shape (batch_size, sequence_length, hidden_size) 1D input tensor with shape (hidden_size) 1D skip tensor with shape (hidden_size 1D bias tensor with shape (hidden_size Saved mean used during training to speed up gradient computation Saved inverse standard variance used during training to speed up gradient computation. Constrain mean and inv_std_var to float tensors. Bias applied to each channel, same size as C. Input tensor of shape [N,C,H,W] Result, has same shape and type as input A 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. Constrain input and output types to all tensor types. Tensor of data to extract slices from. 1-D tensor of starting indices of corresponding axis in `axes` 1-D tensor of ending indices (exclusive) of corresponding axis in axes 1-D tensor of axes that `starts` and `ends` apply to. Constrain indices to integer types Bool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length. The previous GRU hidden state. Unactivated gate outputs from forget, update, and output gates, pre-activation. Array of sequence lengths. len(seq_lengths) should equal batch size N. The timestep for this operation. The 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. The scaled hyperbolic tangent values of the input tensor computed element-wise Constrain input0 and output types to float tensors Constrain input and output types to float tensors Input 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 transposed Whether B should be transposed Scalar multiplier for the product of input tensors A * B. Scalar multiplier for input tensor C. Specified axis to insert a dimension Input/Output is a string tensor Boolean 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. An optional string. Token's regular expression in basic POSIX format (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 ignored Matrix multiply results from A * B Constrain input A data types as 16-bit integer tensor Constrain input B data types as 16-bit integer tensor Constrain 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). Scalar multiplier for the product of the input tensors. Whether A should be transposed on the last two dimensions before doing multiplication Whether B should be transposed on the last two dimensions before doing multiplication 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. Seed for the hashing algorithm, unsigned 32-bit integer, default to 0. If value is 1, output type is uint32_t, else int32_t. Default value is 1. Constrain input and output types to any tensor type. Tensor of rank q-1+r-indices[-1]. Constrain indice type to int32 or int64 Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weight This 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. Specify batchs of sequence words to embedding Specify embedding vector of char Three 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 array 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). Input can be of any tensor type. 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' A 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques' A 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x' 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". 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. RoIs (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. 1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch. 1-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). type used for stash mean/inv_std_var Input data tensor from the previous layer. 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 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. Boolean. Indicates whether upper or lower part of matrix is retained. Default is true. Input tensor of rank 2 or higher. A 0-D tensor containing a single value corresponding to the number diagonals above or the main diagonal to exclude or include.Default value is 0 if it's not specified. Constrain input and output types to all numeric tensors and bool tensors. apply softmax to elements for dimensions softmax_axis or higher broadcast bias across input for dimensions broadcast_axis to softmax_axis-1 Negative values are not allowed in a shape specification 'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64 Pads has incorrect number of values Inputs 0 shall be 3 dimensions Inputs 4 shall be 5 dimensions Inputs 0 shall be 2 dimensions word_embedding should have 2 dimensions and dimension size is known. '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 ( ) needs to be greater than or equal to the left_border ( First input does not have rank 2 Second input does not have rank 2 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 int64 first input tensor has wrong dimension rois input tensor has wrong dimension batch_indices shape input tensor has wrong dimension crop_size shape input tensor has wrong dimension The inner-most 2 dimensions must have the same size (mat_w: Input tensors of wrong rank (0). Incompatible dimensions for matrix multiplication ̼+̼+ *ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE1_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE10_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE11_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE12_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE13_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE14_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE15_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE16_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE17_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE18_ tensor rank too small invalid scales dimension invalid scales value /onnxruntime_src/onnxruntime/core/graph/contrib_ops/nchwc_schema_defs.cc FvRN4onnx8OpSchemaEE *ZN11onnxruntime7contrib26NchwcPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ *ZN11onnxruntime7contrib32NchwcGlobalPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ *ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE1_ PFvRN4onnx8OpSchemaEE /onnxruntime_src/onnxruntime/core/graph/contrib_ops/nhwc_schema_defs.cc Input data tensor from the previous operator; According to channels_last, dimensions for image case are (N x C x H x W), or (N x H x W x C) 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), or (N x D1 X D2 ... Dn x C) where N is the batch size. Scale of quantized input 'X'. It must be a scalar. Zero point tensor for input 'X'. It must be a scalar. Scale of quantized output 'Y'. It must be a scalar. Zero point tensor for output 'Y'. It must be a scalar. Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. with the N and C value keep it value, while the otherdimensions are all 1. Constrain input and output types to singed/unsigned int8 tensors. Input tensor must have at least 2 dimensions Output tensor must have at least 2 dimensions inputs are expected to have tensor type. input and zero_point pair is expected to have be same type. weight and zero_point pair is expected to have same type. x_scale x_zero_point w_scale w_zero_point y_scale y_zero_point *ZN11onnxruntime7contrib19RegisterNhwcSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib19RegisterNhwcSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib19RegisterNhwcSchemasEvEUlRN4onnx16InferenceContextEE1_ N4onnx16InferenceContextE N11onnxruntime7contrib20NhwcInferenceContextE
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+ basic_string::_S_create vector::_M_realloc_insert Output buffer allocation failed lengths allocation failed string buffer allocation failed %d %s:%d: %s: Assertion '%s' failed.
+ __builtin_expect(__n < this->size(), true) std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = std::basic_string<char>; _Alloc = std::allocator<std::basic_string<char> >; std::vector<_Tp, _Alloc>::const_reference = const std::basic_string<char>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] /opt/rh/devtoolset-9/root/usr/include/c++/9/bits/stl_vector.h internal error get() != pointer() typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::IOBinding; _Dp = std::default_delete<onnxruntime::IOBinding>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::IOBinding&] /opt/rh/devtoolset-9/root/usr/include/c++/9/bits/unique_ptr.h out of index basic_string::_S_construct null not valid RegisterCustomOpsLibrary: Failed to load library RegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in library RegisterCustomOps max_mem arena_extend_strategy initial_chunk_size_bytes max_dead_bytes_per_chunk Invalid key found: initial_growth_chunk_size_bytes cannot create std::vector larger than max_size() tried creating tensor with negative value in shape std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = long int; _Alloc = std::allocator<long int>; std::vector<_Tp, _Alloc>::reference = long int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] not enough space: expected , got std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::utils::data_types_internal::TypeNode; _Alloc = std::allocator<onnxruntime::utils::data_types_internal::TypeNode>; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::utils::data_types_internal::TypeNode&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] is not supported in this function mxmxmxmxmxmxmx nx nx,nx<nxLnx\nxlnxmxmxlxvector::reserve vx8wxHwxXwxhwxxwxwxwxwxwxwxwxwxwxvxvx@vxbasic_string::append ORT_LOAD_CONFIG_FROM_MODEL onnxruntime_profile_ vector::_M_default_append basic_string::assign input name cannot be empty CPUExecutionProvider output name cannot be empty std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::basic_string<char>; _Alloc = std::allocator<std::basic_string<char> >; std::vector<_Tp, _Alloc>::reference = std::basic_string<char>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = OrtValue; _Alloc = std::allocator<OrtValue>; std::vector<_Tp, _Alloc>::reference = OrtValue&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] basic_string::replace %s: __pos (which is %zu) > this->size() (which is %zu) was false. Stacktrace:
+ Trying to get a Tensor, but got: T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] /onnxruntime_src/include/onnxruntime/core/framework/ml_value.h this API does not support strings location dimensions do not match shape size std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = long int; _Alloc = std::allocator<long int>; std::vector<_Tp, _Alloc>::const_reference = const long int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] invalid location range const T& OrtValue::Get() const [with T = onnxruntime::Tensor] Integer overflow static void SafeIntExceptionHandler<onnxruntime::OnnxRuntimeException>::SafeIntOnOverflow() /onnxruntime_src/onnxruntime/core/common/safeint.h Map is missing type entry for its value static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = std::basic_string<char>; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] /onnxruntime_src/include/onnxruntime/core/framework/data_types_internal.h ++index < c.size() Sequence is missing type entry for its element static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::vector<T> >::check(const Cont&, size_t) [with T = std::map<std::basic_string<char>, float>; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::vector<T> >::check(const Cont&, size_t) [with T = std::map<long int, float>; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = double; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] opaque( ml_type != nullptr non_tensor_base != nullptr Specified domain and type names combination does not refer to a registered opaque type OrtStatus* OrtApis::CreateOpaqueValue(const char*, const char*, const void*, size_t, OrtValue**) /onnxruntime_src/onnxruntime/core/session/onnxruntime_c_api.cc Opaque type is not a non_tensor type!!! OrtStatus* OrtApis::GetOpaqueValue(const char*, const char*, const OrtValue*, void*, size_t) != T Tensor type mismatch. T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] /onnxruntime_src/include/onnxruntime/core/framework/tensor.h utils::IsPrimitiveDataType<T>(dtype_) const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] shape is invalid offsets buffer is not equal to tensor size output buffer is too small element index is out of bounds buffer size is too small for string const T* onnxruntime::Tensor::Data() const [with T = float] const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = std::vector<std::map<std::basic_string<char>, float> >] onnxruntime::DataTypeImpl::GetType<T>() == type_ const T& OrtValue::Get() const [with T = std::vector<std::map<long int, float> >] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, float>] const T& OrtValue::Get() const [with T = std::map<long int, float>] Trying to get a TensorSeq, but got: const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] IsTensorSequence() Input is not of one of the supported sequence types. Input is not of type sequence or map. input array is too short input array doesn't equal tensor size const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16] const T* onnxruntime::Tensor::Data() const [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = short unsigned int] Encountered nullptr. T* onnxruntime::Tensor::MutableData() [with T = bool] T* onnxruntime::Tensor::MutableData() [with T = float] T* onnxruntime::Tensor::MutableData() [with T = double] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16] T* onnxruntime::Tensor::MutableData() [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] Unsupported tensor element type in the input: const onnxruntime::Tensor& onnxruntime::TensorSeq::Get(size_t) const /onnxruntime_src/onnxruntime/core/framework/TensorSeq.h i < tensors_.size() Tensor must always contain primitive types. Found: OrtStatus* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string<char>; OrtStatus = OrtStatus; OrtValue = OrtValue] OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string<char>; ValueType = std::basic_string<char>; OrtStatus = OrtStatus; OrtValue = OrtValue] len >= 0 && static_cast<uint64_t>(len) < std::numeric_limits<size_t>::max() OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string<char>; ValueType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue] OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string<char>; ValueType = float; OrtStatus = OrtStatus; OrtValue = OrtValue] OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = std::basic_string<char>; ValueType = double; OrtStatus = OrtStatus; OrtValue = OrtValue] Value type is not supported yet: value_type != nullptr vector::_M_range_check: __n (which is %zu) >= this->size() (which is %zu) static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = std::basic_string<char>; V = std::basic_string<char>; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, std::basic_string<char> >] Invalid index requested for map type. static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = std::basic_string<char>; V = long int; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, long int>] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = std::basic_string<char>; V = double; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, double>] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = std::basic_string<char>; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = long int; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] const T& OrtValue::Get() const [with T = std::map<long int, long int>] const T& OrtValue::Get() const [with T = std::map<long int, double>] Input is not of one of the supported map types. Number of values should be at least 1. For map type num_values MUST be 2 Either the key tensor or the value tensor has NumDimensions > 1 Key and value tensors have unequal number of elements. OrtStatus* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue] OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = std::basic_string<char>; OrtStatus = OrtStatus; OrtValue = OrtValue] OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = long int; OrtStatus = OrtStatus; OrtValue = OrtValue] OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = float; OrtStatus = OrtStatus; OrtValue = OrtValue] OrtStatus* OrtCreateMapMLValue(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; ValueType = double; OrtStatus = OrtStatus; OrtValue = OrtValue] Key type is not supported yet. Each element of the sequence should be either tensor or map. At least one element in the sequence is of a type different from others. Expecting all elements to be tensors. Got: OrtStatus* OrtCreateValueImplSeqHelper(const OrtValue* const*, size_t, OrtValue**) Sequences must have tensors of the same data type. There was at least one tensor in the input that was different. std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::Tensor; _Alloc = std::allocator<onnxruntime::Tensor>; std::vector<_Tp, _Alloc>::reference = onnxruntime::Tensor&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] Tensor sequence must contain only primitive types void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType) in[idx]->IsTensor() elem_type_ != nullptr Unsupported input type St11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EE St19_Sp_make_shared_tag PFvPvE St16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EE N11onnxruntime23NotImplementedExceptionE N11onnxruntime20OnnxRuntimeExceptionE N11onnxruntime10IAllocatorE N11onnxruntime16AllocatorWrapperE *ZN7OrtApis19GetBoundOutputNamesEPK12OrtIoBindingP12OrtAllocatorPPcPPmS7_EUlS7_E_ *ZN7OrtApis19GetBoundOutputNamesEPK12OrtIoBindingP12OrtAllocatorPPcPPmS7_EUlS5_E0_ *ZN7OrtApis20GetBoundOutputValuesEPK12OrtIoBindingP12OrtAllocatorPPP8OrtValuePmEUlS7_E_ St19_Sp_counted_deleterIPvPFvS0_ESaIvELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime16AllocatorWrapperESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE ?/\ basic_string::substr static void OrtEnv::Release(OrtEnv*) /onnxruntime_src/onnxruntime/core/session/ort_env.cc env_ptr == p_instance_ N11onnxruntime7logging5ISinkE 14LoggingWrapper N11onnxruntime7logging8CLogSinkE std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = std::unique_ptr<onnxruntime::IExecutionProvider>; _Alloc = std::allocator<std::unique_ptr<onnxruntime::IExecutionProvider> >; std::vector<_Tp, _Alloc>::const_reference = const std::unique_ptr<onnxruntime::IExecutionProvider>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] /onnxruntime_src/onnxruntime/core/session/IOBinding.cc BindInput std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = OrtDevice; _Alloc = std::allocator<OrtDevice>; std::vector<_Tp, _Alloc>::reference = OrtDevice&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] execution_mode is not valid graph_optimization_level is not valid onnxruntime_profResult buffer is not large enough basic_string::insert is not implemented ComputeAsync %u Unsupported version ' ' in custom op ' onnxruntime::CustomOpKernel::CustomOpKernel(const onnxruntime::OpKernelInfo&, const OrtCustomOp&) /onnxruntime_src/onnxruntime/core/session/custom_ops.cc %lu type_id_counter == 1 CreateCustomRegistry There must be one (and only one) dynamic typed input to the custom op. Its type info at runtime will be used to infer the type info of this dynamic typed output which is required for the success of the model loading step. More than one dynamic typed inputs are currently not supported as differing types at runtime means the output type cannot be inferred without which model loading cannot proceed. onnxruntime::common::Status onnxruntime::CreateCustomRegistry(const std::vector<OrtCustomOpDomain*>&, std::shared_ptr<onnxruntime::CustomRegistry>&) N11onnxruntime8OpKernelE N11onnxruntime14CustomOpKernelE N4onnx8OpSchema19num_inputs_allowed_MUliE_E N4onnx8OpSchema20num_outputs_allowed_MUliE_E *ZN11onnxruntime20CreateCustomRegistryERKSt6vectorIP17OrtCustomOpDomainSaIS2_EERSt10shared_ptrINS_14CustomRegistryEEEUlRKNS_12OpKernelInfoEE_ St23_Sp_counted_ptr_inplaceIN11onnxruntime14KernelRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime27OnnxRuntimeOpSchemaRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime14CustomRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE custom op regist12OrtAllocator N3Ort9ExceptionE 16OrtAllocatorImpl 19OrtDefaultAllocator Env is null OrtMemoryInfo is null No requested allocator available Cpu Schema error: tensor(uint8) tensor(uint16) tensor(uint32) tensor(uint64) tensor(int8) tensor(int16) tensor(int32) tensor(int64) tensor(bfloat16) tensor(float16) tensor(float) tensor(double) tensor(string) tensor(bool) tensor(complex64) tensor(complex128) Allocator with this OrtMemoryInfo is already registered. Only CPU devices are supported for now. Received invalid value for arena extend strategy. Valid values can be either 0, 1 or -1. Target= [ShapeInferenceError] Mismatch between source and target type. Source= [TypeInferenceError] Input unknown Element type of input Output expected to have sequence type expected to have type but instead is null expected to have tensor type com.microsoft com.microsoft.experimental com.microsoft.nchwc com.microsoft.mlfeaturizers Trying to register schema with name , but it is already registered from file bumped the operator version but forgot to update the version range in DomainToVersionRange (domain: ) from file , but its domain is not known by the checker. , but its version is not in the inclusive range [ ] (usually, this means you in onnx/defs/schema.h). MemcpyFromHost MemcpyToHost Exception caught: /onnxruntime_src/onnxruntime/core/session/environment.cc Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type. FvRN4onnx16InferenceContextEE N4onnx14InferenceErrorE N4onnx11SchemaErrorE PFvRN4onnx16InferenceContextEE *ZN11onnxruntime11Environment26CreateAndRegisterAllocatorERK13OrtMemoryInfoPK11OrtArenaCfgEUliE_ *ZN11onnxruntime11Environment26CreateAndRegisterAllocatorERK13OrtMemoryInfoPK11OrtArenaCfgEUliE0_ ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver1EEEviEUlONS_8OpSchemaEE_ ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver2EEEviEUlONS_8OpSchemaEE_ ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver3EEEviEUlONS_8OpSchemaEE_ ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver4EEEviEUlONS_8OpSchemaEE_ ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver5EEEviEUlONS_8OpSchemaEE_ ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver6EEEviEUlONS_8OpSchemaEE_ ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver7EEEviEUlONS_8OpSchemaEE_ ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver8EEEviEUlONS_8OpSchemaEE_ ZN4onnx19RegisterOpSetSchemaINS_15OpSet_Onnx_ver9EEEviEUlONS_8OpSchemaEE_ ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver10EEEviEUlONS_8OpSchemaEE_ ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver11EEEviEUlONS_8OpSchemaEE_ ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver12EEEviEUlONS_8OpSchemaEE_ ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver13EEEviEUlONS_8OpSchemaEE_ ZN4onnx19RegisterOpSetSchemaINS_16OpSet_Onnx_ver14EEEviEUlONS_8OpSchemaEE_ ZN4onnx19RegisterOpSetSchemaINS_17OpSet_OnnxML_ver1EEEviEUlONS_8OpSchemaEE_ ZN4onnx19RegisterOpSetSchemaINS_17OpSet_OnnxML_ver2EEEviEUlONS_8OpSchemaEE_ typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnx::ModelProto; _Dp = std::default_delete<onnx::ModelProto>; typename std::add_lvalue_reference<_Tp>::type = onnx::ModelProto&] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::unique_ptr<onnxruntime::IExecutionProvider>; _Alloc = std::allocator<std::unique_ptr<onnxruntime::IExecutionProvider> >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr<onnxruntime::IExecutionProvider>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] The provided PrePackedWeightsContainer instance to be added to the session is null The session already has a PrePackedWeightsContainer instance EndProfiling Profiler is disabled. /onnxruntime_src/onnxruntime/core/session/inference_session.cc Could not write a profile because no model was loaded. GetModelMetadata Model was not loaded Model was not loaded. GetModelInputs GetOverridableInitializers GetModelOutputs NewIOBinding Session was not initialized Session not initialized. typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::SessionState; _Dp = std::default_delete<onnxruntime::SessionState>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::SessionState&] off operator() Flush-to-zero and denormal-as-zero are Unexpected input data type. Actual: ( )) , expected: ( PartitionOrtFormatModel %Y-%m-%d_%H-%M-%S .json Output vector pointer is NULL p_fetches->size(): Invalid Output Name: At least one output should be requested. Output vector incorrectly sized: output_names.size(): Invalid rank for input: Expected: for the following indices
+ Please fix either the inputs or the model. Got invalid dimensions for input: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = long unsigned int; _Alloc = std::allocator<long unsigned int>; std::vector<_Tp, _Alloc>::reference = long unsigned int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] Compute_ Create_State_ Release_State_ Received nullptr for custom registry AddCustomOpDomains LoadOrtModelBytes Load model from failed. Only bytes were able to be read. C|D|HD|pD|D|C|C| D|cpu gpu Unsupported device specified in the memory arena shrink list: Unsupported device id in the memory arena shrink list: Did not find an arena based allocator registered for device-id combination in the memory arena shrink list: The registered allocator for device-id combination is not an arena based allocator: Load _M_get() != nullptr Exception during loading: Unknown exception in Load() This session already contains a loaded model. std::__shared_ptr_access<_Tp, _Lp, <anonymous>, <anonymous> >::element_type& std::__shared_ptr_access<_Tp, _Lp, <anonymous>, <anonymous> >::operator*() const [with _Tp = onnxruntime::Model; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool <anonymous> = false; bool <anonymous> = false; std::__shared_ptr_access<_Tp, _Lp, <anonymous>, <anonymous> >::element_type = onnxruntime::Model] /opt/rh/devtoolset-9/root/usr/include/c++/9/bits/shared_ptr_base.h Encountered unknown exception in Load() model_loading_uri 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. model_loading_from_saved_proto ShrinkMemoryArenas Unable to shrink arena: OrtMemoryInfo:[ name: id: OrtMemType: OrtAllocatorType: Device:[ DeviceType: MemoryType: DeviceId: error message: Attempt to use DefaultLogger but none has been registered. static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() /onnxruntime_src/include/onnxruntime/core/common/logging/logging.h Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: const onnxruntime::logging::Logger& onnxruntime::InferenceSession::CreateLoggerForRun(const RunOptions&, std::unique_ptr<onnxruntime::logging::Logger>&) run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast<int>(logging::Severity::kFATAL) Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: void onnxruntime::InferenceSession::InitLogger(onnxruntime::logging::LoggingManager*) session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast<int>(logging::Severity::kFATAL) Size mismatch: feed_names has is not expected to be of type tensor. is not expected to be of type sparse tensor. Trying to get a SparseTensor, but got: const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor] is not expected to be of type tensor sequence. elements, but feeds has elements. Invalid Feed Input Name: Input with name: ValidateInputs sparse_tensor IsSparseTensor() seq Run FeedsFetchesInfo Running with tag: model_run memory.enable_memory_arena_shrinkage /onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.h onnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector<std::basic_string<char> >&, const std::vector<std::basic_string<char> >&, const onnxruntime::OrtValueNameIdxMap&) std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = OrtDevice; _Alloc = std::allocator<OrtDevice>; std::vector<_Tp, _Alloc>::const_reference = const OrtDevice&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::MLValueCopyInfo; _Alloc = std::allocator<onnxruntime::MLValueCopyInfo>; std::vector<_Tp, _Alloc>::reference = onnxruntime::MLValueCopyInfo&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] Encountered unknown exception in Run() Provider has already been registered. /onnxruntime_src/onnxruntime/core/framework/execution_providers.h Received nullptr for exec provider Execution providers must be registered before the session is initialized. Execution providers must be registered before the session is initialized. Having memory pattern enabled is not supported while using the DML Execution Provider. So disabling it for this session since it uses the DML Execution Provider. Parallel execution mode does not support the DML Execution Provider. So making the execution mode sequential for this session since it uses the DML Execution Provider. Parallel execution mode does not support the CUDA Execution Provider. So making the execution mode sequential for this session since it uses the CUDA Execution Provider. RegisterExecutionProvider DmlExecutionProvider CUDAExecutionProvider TensorrtExecutionProvider are '0' and '1'. FinalizeSessionOptions is_model_proto_parsed status.IsOK() session.set_denormal_as_zero ConstructorCommon session- -intra-op -inter-op The only supported values for the environment variable The environment variable contained the value: Reading the provided model for the ORT config ModelProto needs to be parsed to check for ORT config within it onnxruntime::common::Status onnxruntime::FinalizeSessionOptions(const onnxruntime::SessionOptions&, const onnx::ModelProto&, bool, onnxruntime::SessionOptions&) Could not finalize session options while constructing the inference session. Error Message: void onnxruntime::InferenceSession::ConstructorCommon(const onnxruntime::SessionOptions&, const onnxruntime::Environment&) graph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK() Creating and using per session threadpools since use_per_session_threads_ is true session.intra_op.allow_spinning session.inter_op.allow_spinning Failed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIAL Using global/env threadpools since use_per_session_threads_ is false When the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API. session_env.EnvCreatedWithGlobalThreadPools() TransformGraph Node placements ]. Provider: [ MemcpyTransformer typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Node; _Dp = std::default_delete<onnxruntime::Node>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Node&] Could not find an implementation for the node All nodes have been placed on [ CastFloat16Transformer Given model could not be parsed while creating inference session. Error message: onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, const string&) Could not parse model successfully while constructing the inference session onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, std::istream&) onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, const void*, int) Error during EndProfiling(): ~InferenceSession Unknown error during EndProfiling() Failed to load model because protobuf parsing failed. LoadOrtModel ORTM This session has already been initialized. ORT model verification failed. Serialized version info is null. Invalid ORT format model. The ORT format model version [ ] is not supported this build Missing Model. Invalid ORT format model. typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Model; _Dp = std::default_delete<onnxruntime::Model>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Model&] SessionState is null. Invalid ORT format model. session.load_model_format ORT model_loading_array SaveToOrtFormat Initialize Initializing session. session.use_env_allocators session.save_model_format NchwcTransformer session_initialization Session has already been initialized. Adding default CPU execution provider. This session will use the allocator registered with the environment. Unable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes. Serializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED and the NchwcTransformer enabled. The generated model may contain hardware specific optimizations, and should only be used in the same environment the model was optimized in. Session successfully initialized. Exception during initialization: Encountered unknown exception in Initialize() St14default_deleteIN11onnxruntime5ModelEE N11onnxruntime16GraphTransformerE N11onnxruntime16InferenceSessionE N8nlohmann6detail9exceptionE N8nlohmann6detail11other_errorE ZN11onnxruntime20CPUExecutionProviderC4ERKNS_24CPUExecutionProviderInfoEEUliE_ *ZN11onnxruntime16InferenceSession4LoadEPKviEUlRSt10shared_ptrINS_5ModelEEE_ *ZN11onnxruntime16InferenceSession4LoadEvEUlRSt10shared_ptrINS_5ModelEEE_ *ZN11onnxruntime16InferenceSession12LoadOrtModelERKSsEUlvE_ *ZN11onnxruntime16InferenceSession12LoadOrtModelEPKviEUlvE_ ZN11onnxruntime16InferenceSession4LoadIcEENS_6common6StatusERKSbIT_St11char_traitsIS4_ESaIS4_EEEUlRSt10shared_ptrINS_5ModelEEE_ St19_Sp_counted_deleterIPN11onnxruntime5ModelESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceISt13unordered_mapISsN11onnxruntime11FuncManager8FuncInfoESt4hashISsESt8equal_toISsESaISt4pairIKSsS3_EEESaISC_ELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime16AllocatorManagerESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE K _ _ @ @B 0C ? C[json.exception. out_of_range invalid_iterator type_error at line , column parse_error parse error number boolean discarded object map::at cannot use at() with key ' ' not found ~}~}7} }+}6~}6~}6~}~}<U+%.4X> '[', '{', or a literal <uninitialized> false literal null literal string literal number literal '[' '{' ']' '}' ':' ',' unknown token end of input true literal <parse error> syntax error while parsing - ; last read: ' unexpected ; expected z}}2}&}}}}}}}}ޅ}҅}ƅ}>}}}}҃}b}}~}r}r}r}f}Z}N}B}6}*} }}}} }}}Ɋ}O}}}}}ٌ}_}cannot get value type must be number, but is ޒ}Ғ}}p}[}̑}̑}̑}O}Sequential mode Parallel mode true session_options intra_op_num_threads SetIntraOpNumThreads inter_op_num_threads SetInterOpNumThreads execution_mode SetExecutionMode Setting execution_mode to graph_optimization_level SetGraphOptimizationLevel enable_profiling SetEnableProfiling Setting enable_profiling to The Model Proto hasn't been checked for the ORT config json. /onnxruntime_src/onnxruntime/core/session/inference_session_utils.cc ParseSessionOptionsFromModelProto Did not find session options in the model file to be used while running the model cannot compare iterators of different containers intra_op_num_threads option in the model file must be an integer Unsupported value for intra_op_num_threads: Setting intra_op_num_threads to inter_op_num_threads option in the model file must be an integer Unsupported value for inter_op_num_threads: Setting inter_op_num_threads to execution_mode option in the model file must be an integer Unsupported execution_mode value in ORT config: graph_optimization_level option in the model file must be an integer Setting graph_optimization_level to ORT_DISABLE_ALL Setting graph_optimization_level to ORT_ENABLE_BASIC Setting graph_optimization_level to ORT_ENABLE_EXTENDED Setting graph_optimization_level to ORT_ENABLE_ALL Unsupported graph_optimization_level value in ORT config: enable_profiling option in the model file must be an integer Unsupported value for enable_profiling option: Ignoring unsupported session option in ORT config: vector<bool>::_M_insert_aux iterator does not fit current value std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::iterator std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::erase(std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::iterator) [with _Key = std::basic_string<char>; _Val = std::pair<const std::basic_string<char>, nlohmann::basic_json<> >; _KeyOfValue = std::_Select1st<std::pair<const std::basic_string<char>, nlohmann::basic_json<> > >; _Compare = std::less<void>; _Alloc = std::allocator<std::pair<const std::basic_string<char>, nlohmann::basic_json<> > >; std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::iterator = std::_Rb_tree_iterator<std::pair<const std::basic_string<char>, nlohmann::basic_json<> > >] /opt/rh/devtoolset-9/root/usr/include/c++/9/bits/stl_tree.h iterator out of range __position != end() cannot use erase() with S}}}}}O}O}O}ҹ} __builtin_expect(!this->empty(), true) std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = nlohmann::basic_json<>*; _Alloc = std::allocator<nlohmann::basic_json<>*>; std::vector<_Tp, _Alloc>::reference = nlohmann::basic_json<>*&] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = nlohmann::basic_json<>; _Alloc = std::allocator<nlohmann::basic_json<> >; std::vector<_Tp, _Alloc>::reference = nlohmann::basic_json<>&] invalid string: ill-formed UTF-8 byte invalid string: control character U+000A (LF) must be escaped to \u000A or \n invalid string: missing closing quote invalid string: '\u' must be followed by 4 hex digits invalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFF invalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFF invalid string: forbidden character after backslash invalid string: control character U+0000 (NUL) must be escaped to \u0000 invalid string: control character U+0001 (SOH) must be escaped to \u0001 invalid string: control character U+0002 (STX) must be escaped to \u0002 invalid string: control character U+0003 (ETX) must be escaped to \u0003 invalid string: control character U+0004 (EOT) must be escaped to \u0004 invalid string: control character U+0005 (ENQ) must be escaped to \u0005 invalid string: control character U+0006 (ACK) must be escaped to \u0006 invalid string: control character U+0007 (BEL) must be escaped to \u0007 invalid string: control character U+0008 (BS) must be escaped to \u0008 or \b invalid string: control character U+0009 (HT) must be escaped to \u0009 or \t invalid string: control character U+000B (VT) must be escaped to \u000B invalid string: control character U+000C (FF) must be escaped to \u000C or \f invalid string: control character U+000D (CR) must be escaped to \u000D or \r invalid string: control character U+000E (SO) must be escaped to \u000E invalid string: control character U+000F (SI) must be escaped to \u000F invalid string: control character U+0010 (DLE) must be escaped to \u0010 invalid string: control character U+0011 (DC1) must be escaped to \u0011 invalid string: control character U+0012 (DC2) must be escaped to \u0012 invalid string: control character U+0013 (DC3) must be escaped to \u0013 invalid string: control character U+0014 (DC4) must be escaped to \u0014 invalid string: control character U+0015 (NAK) must be escaped to \u0015 invalid string: control character U+0016 (SYN) must be escaped to \u0016 invalid string: control character U+0017 (ETB) must be escaped to \u0017 invalid string: control character U+0018 (CAN) must be escaped to \u0018 invalid string: control character U+0019 (EM) must be escaped to \u0019 invalid string: control character U+001A (SUB) must be escaped to \u001A invalid string: control character U+001B (ESC) must be escaped to \u001B invalid string: control character U+001C (FS) must be escaped to \u001C invalid string: control character U+001D (GS) must be escaped to \u001D invalid string: control character U+001E (RS) must be escaped to \u001E invalid string: control character U+001F (US) must be escaped to \u001F (}}{}f}Q}<}'}}}}}}}~}i}Y}D}/}}}}}}}}}r}]}H}3} } }}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}l}S}S}S}S}S}S}S}S}S}S}S}S}:}S}S}}}}}}}}}}}}{}}}}}}}}}}}}}}}}}}a}}invalid number; expected digit after '-' invalid number; expected digit after '.' invalid number; expected '+', '-', or digit after exponent invalid number; expected digit after exponent sign void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = char; _Alloc = std::allocator<char>] invalid BOM; must be 0xEF 0xBB 0xBF if given invalid literal }}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}~}}}}}}}}}}}}}}\}}}}}}}}}}}}}}}}}}}}z}}}}}}}}}}}}}}}} void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = nlohmann::basic_json<>*; _Alloc = std::allocator<nlohmann::basic_json<>*>] object key object separator number overflow parsing ' }}p}}0}}}8}}p}}}}}}l~~~~~d~d~l~\ ~ ~4
~ ~
~ ~ ~ ~ void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = nlohmann::basic_json<>; _Alloc = std::allocator<nlohmann::basic_json<> >] ~~h~~ ~ ~ ~ ~ ~!~~~~~"~ The Model Proto has already been checked for the ORT config json. ParseOrtConfigJsonInModelProto Found session/run/environment configuration in the model file to be used while running the model ORT config json from the model: Json stored in the `ort_config` key cannot be parsed. Error message: ort_config N8nlohmann6detail11parse_errorE N8nlohmann6detail16invalid_iteratorE N8nlohmann6detail10type_errorE N8nlohmann6detail12out_of_rangeE N8nlohmann6detail22input_adapter_protocolE N8nlohmann6detail20input_buffer_adapterE St23_Sp_counted_ptr_inplaceIN8nlohmann6detail20input_buffer_adapterESaIS2_ELN9__gnu_cxx12_Lock_policyE2EE Parsing RunOptions from ModelProto is not suppor Apply /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer.cc ApplyTransformers /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_mgr.cc This transformer is already registered _RuleBasedTransformer EliminateIdentity EliminateSlice UnsqueezeElimination EliminateDropout ExpandElimination CastElimination NoopElimination DivMulFusion FuseReluClip GemmTransposeFusion NotWhereFusion ShapeToInitializer ConvAddFusion ConvMulFusion ConvBNFusion ReluQuantRewrite Unsupported level std::vector<std::unique_ptr<onnxruntime::RewriteRule> > onnxruntime::optimizer_utils::GenerateRewriteRules(onnxruntime::TransformerLevel, const std::unordered_set<std::basic_string<char> >&) /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_utils.cc session.disable_quant_qdq MatMulAddFusion ReshapeFusion ROCMExecutionProvider ACLExecutionProvider ArmNNExecutionProvider QDQS8ToU8Transformer QDQPropagationTransformer QDQTransformer GemmActivationFusion MatMulIntegerToFloatFusion DynamicQuantizeMatMulFusion ConvActivationFusion SimplifiedLayerNormFusion AttentionFusion EmbedLayerNormFusion BiasDropoutFusion MatmulTransposeFusion BiasGeluFusion BiasSoftmaxFusion SkipLayerNormFusion FastGeluFusion MatMulScaleFusion GeluApproximation NhwcTransformer Unsupported level optimization.enable_gelu_approximation CommonSubexpressionElimination std::vector<std::unique_ptr<onnxruntime::GraphTransformer> > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, const onnxruntime::SessionOptions&, const onnxruntime::IExecutionProvider&, const std::unordered_set<std::basic_string<char> >&) N11onnxruntime11RewriteRuleE YieldOp std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::NodeArg*; _Alloc = std::allocator<onnxruntime::NodeArg*>; std::vector<_Tp, _Alloc>::const_reference = onnxruntime::NodeArg* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::NodeArg*; _Alloc = std::allocator<onnxruntime::NodeArg*>; std::vector<_Tp, _Alloc>::reference = onnxruntime::NodeArg*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] Validating no unexpected access using an invalid node_index. Got: onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const /onnxruntime_src/include/onnxruntime/core/graph/graph.h Max: node_index < nodes_.size() N11onnxruntime17EliminateIdentityE /onnxruntime_src/include/onnxruntime/core/optimizer/graph_transformer.h /onnxruntime_src/onnxruntime/core/optimizer/insert_cast_transformer.cc Recurse ApplyImpl Inserted_Cast typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::NodeArg; _Dp = std::default_delete<onnxruntime::NodeArg>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::NodeArg&] cast node to cast from float16 to float32 on cpu dtype InsertCastTransformer works on the assumption that `dtype` attribute holds an integer. virtual onnxruntime::common::Status onnxruntime::InsertCastTransformer::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) const dtype_attribute->second.has_i() RemoveDuplicateCastTransformer N11onnxruntime21InsertCastTransformerE N11onnxruntime30RemoveDuplicateCastTransformerE <MLMTM\M<M<MlM|M<M<MDMM<M<M<M<MDM_Map_base::at size is different size_ == size unsupported data type: onnxruntime::Initializer::Initializer(const onnx::TensorProto&, const onnxruntime::Path&) /onnxruntime_src/onnxruntime/core/optimizer/initializer.h ReadExternalRawData() failed: w!tsDswwrerwwqqwwwwqai.onnx Div Sqrt Pow fused LayerNorm subgraphs epsilon /onnxruntime_src/onnxruntime/core/optimizer/layer_norm_fusion.cc std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::reference_wrapper<onnxruntime::Node>; _Alloc = std::allocator<std::reference_wrapper<onnxruntime::Node> >; std::vector<_Tp, _Alloc>::reference = std::reference_wrapper<onnxruntime::Node>&] Cast_Scale cast scale of layer norm SimplifiedLayerNormalization layer_norm_out cast output of layer norm N11onnxruntime15LayerNormFusionE N11onnxruntime25SimplifiedLayerNormFusionE '7fused Matmul and Add gemm /onnxruntime_src/onnxruntime/core/optimizer/matmul_add_fusion.cc std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::NodeArg*; _Alloc = std::allocator<onnxruntime::NodeArg*>; std::vector<_Tp, _Alloc>::reference = onnxruntime::NodeArg*&] N11onnxruntime15MatMulAddFusionE MatMulIntegerToFloat /onnxruntime_src/onnxruntime/core/optimizer/matmul_integer_to_float.cc N11onnxruntime26MatMulIntegerToFloatFusionE Unsupported data type: void onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<Ret>::operator()(int32_t, Ret&) const [with Ret = onnxruntime::common::Status; int32_t = int] /onnxruntime_src/onnxruntime/core/optimizer/matmul_scale_fusion.cc Constant initializer NodeArg shape should not be null. NodeArg: nonstd::optional_lite::optional<float> onnxruntime::{anonymous}::GetScalarConstantInitializer(const onnxruntime::Graph&, const onnxruntime::NodeArg&) GetScalarConstantInitializer div_inputs.size() == 2 mul_inputs.size() == 2 nonstd::optional_lite::optional<std::pair<float, int> > onnxruntime::{anonymous}::GetScaleFromNode(const onnxruntime::Graph&, const onnxruntime::Node&, const std::unordered_set<std::basic_string<char> >&) _FusedMatMulAndScale FusedMatMul Fused MatMul and Scale std::vector<onnxruntime::{anonymous}::ScaleMergeInfo> onnxruntime::{anonymous}::GetInputNodeMerges(onnxruntime::Graph&, onnxruntime::Node&, const std::unordered_set<std::basic_string<char> >&) input_node.InputDefs().size() == 2 && scale_and_index.value().second < 2 std::vector<onnxruntime::{anonymous}::ScaleMergeInfo> onnxruntime::{anonymous}::GetOutputNodeMerges(onnxruntime::Graph&, onnxruntime::Node&, const std::unordered_set<std::basic_string<char> >&) output_node.OutputDefs().size() == 1 N11onnxruntime17MatMulScaleFusionE cannot create std::deque larger than max_size() !node_consumers.empty() size_t onnxruntime::UpdateConsumerCount(onnxruntime::Graph&, onnxruntime::NodeArg*, std::unordered_map<onnxruntime::NodeArg*, long unsigned int>&) /onnxruntime_src/onnxruntime/core/optimizer/matmul_transpose_fusion.cc perm bool onnxruntime::GetTransposePerms(const onnxruntime::Node&, std::vector<long int>&) transpose_node.InputDefs().size() == 1 _transformed cast != nullptr onnxruntime::Node* onnxruntime::ReorderCastAndTranspose(onnxruntime::Graph&, onnxruntime::Node*, std::unordered_map<onnxruntime::NodeArg*, long unsigned int>&, std::deque<long unsigned int>&) Created a new Cast node to interchange Cast and Transpose nodes Created a new Transpose node to interchange Cast and Transpose nodes fused MatMul and Transpose MatMul_With_Transpose transA transB N11onnxruntime21MatmulTransposeFusionE 8HPX88hx88@8888@ttttt|tttt| Dll<؆! ؆؆؆؆߆膀؆؆؆؆߆MA5) 0yma00UI007@00007\\\l\\cx\\\\cчŇ净هćЇ(P 팀ጀ̌Ì،Ì茀1%茀茀
茀茀茀茀茀茀]QE9$0@~r@@fZ@@HQ@@@@Hlll|llsllllsፀՍɍ쎀Td䏀Б
ББ鑀ББבББББבE9-! (qeY((MA((/8((((/TTTydTT[pTTTT[ɒ钀ݒђȒstrides dilations kernel_shape auto_pad NOTSET SAME_UPPER SAME_LOWER VALID ReorderOutput channels_last reorder std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::NchwcTransformerImpl::NchwcArgument*; _Alloc = std::allocator<onnxruntime::NchwcTransformerImpl::NchwcArgument*>; std::vector<_Tp, _Alloc>::reference = onnxruntime::NchwcTransformerImpl::NchwcArgument*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] activation typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::NchwcTransformerImpl::NchwcArgument; _Dp = std::default_delete<onnxruntime::NchwcTransformerImpl::NchwcArgument>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::NchwcTransformerImpl::NchwcArgument&] nearest asymmetric align_corners nearest_mode scales coordinate_transformation_mode ҀҀҀҀҀҀҀҀҀҀҀҀҀҀҀҀҀReorderInput Reshape std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = int; _Alloc = std::allocator<int>; std::vector<_Tp, _Alloc>::reference = int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] reshape _bn_nchwc bn_scale bn_B spatial FusedConv BatchNormalization GlobalMaxPool /onnxruntime_src/onnxruntime/core/optimizer/nchwc_transformer.cc N11onnxruntime16NchwcTransformerE
+
+ _nhwc NhwcMaxPool storage_order DequantizeLinear QLinearConv nhwc_permutated_pads 7|||s|7|7|f|L|7|7|?|Y|7|7|7|7|?|QLinearAdd QLinearMul QLinearLeakyRelu QLinearSigmoid QLinearGlobalAveragePool QLinearAveragePool QLinearConcat Pad /onnxruntime_src/onnxruntime/core/optimizer/nhwc_transformer.cc N11onnxruntime15NhwcTransformerE ؍؍؍؍؍؍؍ydtgddZMddCddddCdddyddoddddodِ̐dddd㐁dddd<d됁<d<dސѐ<d<dǐ<d<d<d<dǐ>1$
+ N11onnxruntime15NoopEliminationE Where Not N11onnxruntime14NotWhereFusionE up_node should have only one Edge that points to down_node and its output is not graph output void onnxruntime::SwapAdjacentNodes(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::Node&) /onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/qdq_propagation.cc optimizer_utils::CheckOutputEdges(graph, up_node, 1) up_node should be parent of down_node and NodeArg slots of the edge between up_node and down_node should be (0, 0). edge_it->GetDstArgIndex() == 0 && edge_it->GetSrcArgIndex() == 0 && edge_it->GetNode().Index() == down_node.Index() SwapAdjacentNodes N11onnxruntime25QDQPropagationTransformerE ļ̼Լ企qdq_s8_to_u8_zp_conversion qdq_s8_to_u8_quant /onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/qdq_s8_to_u8.cc N11onnxruntime20QDQS8ToU8TransformerE /onnxruntime_src/onnxruntime/core/optimizer/qdq_transformer/qdq_transformer.cc N11onnxruntime14QDQTransformerE DequantizeLinear $, <L \ N11onnxruntime15ReluQuantFusionE std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = short unsigned int; _Alloc = std::allocator<short unsigned int>; std::vector<_Tp, _Alloc>::reference = short unsigned int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = float; _Alloc = std::allocator<float>; std::vector<_Tp, _Alloc>::reference = float&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = double; _Alloc = std::allocator<double>; std::vector<_Tp, _Alloc>::reference = double&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = signed char; _Alloc = std::allocator<signed char>; std::vector<_Tp, _Alloc>::reference = signed char&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = unsigned char; _Alloc = std::allocator<unsigned char>; std::vector<_Tp, _Alloc>::reference = unsigned char&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] data type is not supported ToProto ;mbonnxruntime::Initializer::Initializer(onnx::TensorProto_DataType, const string&, const std::vector<long int>&) : U y min FuseReluClip_ _min_zero_constant Unexpected data type for Clip 'min' input of virtual onnxruntime::common::Status onnxruntime::FuseReluClip::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const /onnxruntime_src/onnxruntime/core/optimizer/relu_clip_fusion.cc %D&7&*&%% &&%%%&%%%%%dt&g&Z&ddM&@&dd3&&dddd3&Vd&&&VdVd&}&VdVdp&&VdVdVdVdp& N11onnxruntime12FuseReluClipE
Unsqueeze Gather std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = const onnxruntime::Node::EdgeEnd*; _Alloc = std::allocator<const onnxruntime::Node::EdgeEnd*>; std::vector<_Tp, _Alloc>::reference = const onnxruntime::Node::EdgeEnd*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] Squeeze starts ends std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::front() const [with _Tp = int; _Alloc = std::allocator<int>; std::vector<_Tp, _Alloc>::const_reference = const int&] Fuse_Subgraph /onnxruntime_src/onnxruntime/core/optimizer/reshape_fusion.cc Cannot replace concat node with initializer: allowzero Fused reshape node: Total fused reshape node count: N11onnxruntime13ReshapeFusionE
ApplyRulesOnNode /onnxruntime_src/onnxruntime/core/optimizer/rule_based_graph_transformer.cc typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::RewriteRule; _Dp = std::default_delete<onnxruntime::RewriteRule>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::RewriteRule&] N11onnxruntime25RuleBasedGraphTransformerE N11onnxruntime18ShapeToInitializerE SkipLayerNormalization /onnxruntime_src/onnxruntime/core/optimizer/skip_layer_norm_fusion.cc fused SkipLayerNorm subgraphs N11onnxruntime19SkipLayerNormFusionE ̼+ ׂbׂUׂHׂ ׂ ׂ;ׂ.ׂ ׂ ׂׂ!ׂ ׂ ׂ ׂ ׂׂMׂׂׂׂMׂMׂׂkׂMׂMׂZׂ|ׂMׂMׂMׂMׂZׂxgVE4##ttttttttt܂܂܂܂܂܂܂܂܂܂|܂܂܂܂܂܂܂܂܂܂܂܂܂܂܂܂܂܂܂܂܂܂ N11onnxruntime14EliminateSliceE
+
onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&)::<lambda(const onnxruntime::NodeArg&, size_t)> /onnxruntime_src/onnxruntime/core/optimizer/transformer_memcpy.cc dup_replacements.find(&arg) == dup_replacements.end() ForEachWithIndex VitisAIExecutionProvider ' doesn't support memcpy Execution type ' void onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node&, const onnxruntime::KernelRegistryManager&, onnxruntime::InitializedTensorSet&) Memcpy Copy from/to host memory bool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&) N11onnxruntime17MemcpyTransformerE *ZN11onnxruntime21TransformerMemcpyImpl11ProcessDefsERNS_4NodeERKNS_21KernelRegistryManagerERSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSA_EEEEUlRKNS_7NodeArgEmE_ *ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE_ *ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE0_ /onnxruntime_src/onnxruntime/core/optimizer/unsqueeze_elimination.cc UnsqueezeElimination cannot remove node UnsqueezeElimination_ N11onnxruntime20UnsqueezeEliminationE vector::_M_range_insert ;dddd;d;dd}d;d;dpdd;d;d;d;dpdd8e#eedddddddJeddddd@eee{e@e@eneTe@e@eGeae@e@e@e@eGeddhOh:hdd%hhddghddddgPdqh\hGhPdPd2hhPdPdghPdPdPdPdg,kvkik\k,k,kOk8k,k,k1kBk,k,k,k,k1k`kkkk`k`kkk`k`khkrk`k`k`k`khkRandomUniform RandomNormal RandomUniformLike RandomNormalLike Multinomial '7w+2 /onnxruntime_src/onnxruntime/core/optimizer/attention_fusion_helper.h Gemm bias is not constant initializer Gemm bias shape is not expected Gemm weight is not constant initializer Gemm weight shape is not expected Start ValidateGemmInitializer Pass ValidateGemmInitializer ValidateUnidirMask unidir mask is not constant unidir mask shape not expected This optimizer does not support external data for unidirectional mask right now Mask is neither unidirectional nor all ones Expect mask data type is uint8 or float CheckDistilBertReshapeShape Failed to find shape path Start CheckNodesInPathK k_reshape const not matched Pass CheckNodesInPathK Start CheckNodesInPathQ q_reshape const not matched qk_div const not matched. Pass CheckNodesInPathQ q_transpose perm attribute not matched Start MatchInputMaskSubgraph Failed to find Softmax node Failed to find path for mask Pass MatchInputMaskSubgraph Output edge count not expected for Softmax Output edge count not expected for mask nodes Softmax attribute axis is expected to be 3 mask_unsqueeze_1 axes not matched. Expect: 1 mask_unsqueeze_2 axes not matched. Expect: 2 mask_sub const input not matched mask_mul const input not matched Start MatchInputMaskSubgraphDistilBert Failed to find reshape shape path 1 Failed to find reshape shape path 2 Pass MatchInputMaskSubgraphDistilBert Expand Failed to find mask path where const not matched. equal const not matched. gather indices not matched. Start CheckNodesInPathV Pass CheckNodesInPathV Output edge count not expected for nodes in path v Failed in match Transpose attribute perm. Expected: 0, 2, 1, 3 Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3 hidden_size != num_heads * head_size v_reshape initializer value is not expected reshape initializer value is not expected Start MatchPastSubgraph Failed to match v_concat past_v_gather indices != 1 past_k_gather indices != 0 Pass MatchPastSubgraph Failed to find path for past_k Failed to find path for present_k Failed to find path for present_v and past_v past_k_transpose perm attribute not matched present_k_transpose perm attribute not matched present_k_unsqueeze axes value not expected present_v_unsqueeze axes value not expected past_v_gather and past_k_gather does not have same past input Output edge count not expected for nodes in past subgraph std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = int; _Alloc = std::allocator<int>; std::vector<_Tp, _Alloc>::const_reference = const int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] bool onnxruntime::AttentionFusionHelper::CheckSliceParameters(const onnxruntime::Graph&, const onnxruntime::Node&, const std::vector<int>&, const std::vector<long int>&, const onnxruntime::logging::Logger&) input_indices.size() == expected_values.size() && input_indices.size() > 0 Slice does not have enough number of inputs Slice ends is less than INT_MAX Slice parameter is not expected. Input index: CheckSliceParameters Expected value: Start MatchUnidirMaskSubgraph Pass MatchUnidirMaskSubgraph Faild to match the path (Div-->Where-->Add) for unidirectional mask Faild to match path 1 for unidirectional mask Output edge count not expected for nodes in path 1 of unidirectional mask Div and Shape1 does not have edge CheckSliceParameters returns false for last_slice CheckSliceParameters returns false for mask_slice ValidateUnidirMask returns false for mask_slice CheckSliceParameters returns false for slice1 Faild to match path 2 for unidirectional mask Output edge count not expected for unsqueeze2 of unidirectional mask Faild to match path 3 for unidirectional mask Output edge count not expected for unsqueeze3 of unidirectional mask Faild to match path 4 for unidirectional mask Div and Shape does not have edge Output edge count not expected for squeeze_2/slices2/shape2 of unidirectional mask CheckSliceParameters return false for slice2 Start MatchGemmSubgraph Faild to match gemm path Gemm does not have 3 inputs Gemm bias is not constant Gemm bias shape not expected Pass MatchGemmSubgraph Input of reshape_before_gemm is not the input of subgraph Output edge count not expected for nodes in gemm path CheckSliceParameters return false concat first input value is not -1 Faild to match concat node for Gather paths concat_after_gather does not have expected number of inputs or output edges concat_after_gather input 2 does not have expected value Faild to match gemm gather path Output edge count not expected for nodes in gemm gather path unsqueeze_after_gather axes value not expected gather axis value not expected gather input 1 value is not expected Mask_Int32 Cast mask from int64 to int32 MaskCast Start FuseGptAttention Faild to find path v to Split Failed to find path for q Failed to find path for k Fused Attention subgraphs num_heads unidirectional Faild to find path to qkv_matmul CheckNodesInPathV return false MatchInputMaskSubgraph returns false MatchUnidirMaskSubgraph returns NULL q and v are not from same Split node CheckNodesInPathQ returns false k and v are not from same Split node CheckNodesInPathK returns false MatchPastSubgraph returns false Fused an attention node for GPT. qkv_weights qkv_bias Өƨ§§\[N\\A4\\q槄\\\\q(֧((ɧ(( 㧄(((( Ԧ䨄רԦԦbUԦԦ HԦԦԦԦ 8REl_̦̦4秄44B544{4444{FuseSubGraphQKImpl k root is not layer norm ConvertMaskToInt32 /onnxruntime_src/onnxruntime/core/optimizer/attention_fusion.cc q root should be layer normalization std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::reference_wrapper<const onnxruntime::Node>; _Alloc = std::allocator<std::reference_wrapper<const onnxruntime::Node> >; std::vector<_Tp, _Alloc>::reference = std::reference_wrapper<const onnxruntime::Node>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] q_matmul and q_add shape not matched k_matmul and k_add shape not matched Failed 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. Mask shape is unknown or not 2D, or data type unknown Mask data type is not int32 or int64 or float32 Failed to convert mask to int32 FuseSubGraph Faild to find path v FuseSubGraphQK Fused an attention node. FuseSubGraphQKDistilBert Output edge count not expected for Add or MatMul in path v Failed in match v_matmul and v_add input shape Failed in match input mask subgraph shape of layer norm bias tensor not expected Total fused Attention node count: N11onnxruntime15AttentionFusionE @ BiasDropout fused Add and Dropout seed /onnxruntime_src/onnxruntime/core/optimizer/bias_dropout_fusion.cc N11onnxruntime17BiasDropoutFusionE FastGelu BiasGelu fused Add and Gelu /onnxruntime_src/onnxruntime/core/optimizer/bias_gelu_fusion.cc N11onnxruntime14BiasGeluFusionE is not in valid range [- axis fused and into softmax(input + bias) BiasSoftmax softmax_axis broadcast_axis /onnxruntime_src/onnxruntime/core/optimizer/bias_softmax_fusion.cc int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) /onnxruntime_src/onnxruntime/core/providers/common.h axis >= -tensor_rank && axis <= tensor_rank - 1 N11onnxruntime17BiasSoftmaxFusionE N11onnxruntime15CastEliminationE LKMM L LDMLL#PyTTT#P#P6TT
+SNot eliminating output of node /onnxruntime_src/onnxruntime/core/optimizer/common_subexpression_elimination.cc std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector<const onnxruntime::{anonymous}::EquivalenceClass*>; _Alloc = std::allocator<std::vector<const onnxruntime::{anonymous}::EquivalenceClass*> >; std::vector<_Tp, _Alloc>::reference = std::vector<const onnxruntime::{anonymous}::EquivalenceClass*>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] virtual onnxruntime::common::Status onnxruntime::CommonSubexpressionElimination::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) const representative.output_index != kInvalidOutputIndex ] because it's the graph's output. N11onnxruntime30CommonSubexpressionEliminationE /onnxruntime_src/onnxruntime/core/optimizer/common_subexpressioncan't constant fold Unsupported output type of . Can't constant fold /onnxruntime_src/onnxruntime/core/optimizer/constant_folding.cc Could not find OrtValue with name ' Could not find a CPU kernel and hence virtual onnxruntime::common::Status onnxruntime::ConstantFolding::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) const fetches.size() == node->OutputDefs().size() N11onnxruntime15ConstantFoldingE Unexpected data type for Clip input of onnxruntime::{anonymous}::GetClipConstantMinMax(const onnxruntime::Graph&, const onnxruntime::Node&, float&, float&)::<lambda(const onnxruntime::Node&, size_t, float&)> /onnxruntime_src/onnxruntime/core/optimizer/conv_activation_fusion.cc d ddʼddddddP(PP缅PPڼPPPPڼfused Conv with activation activation_params N11onnxruntime20ConvActivationFusionE
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E9-! (qeY((M8((/D((((/conv_W_tensor_proto add_B_tensor_proto conv_B_tensor_proto ConvAddFusion_B_ ConvAddFusion_Add_B_ virtual onnxruntime::common::Status onnxruntime::ConvAddFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const /onnxruntime_src/onnxruntime/core/optimizer/conv_add_fusion.cc N11onnxruntime13ConvAddFusionE 5 3<0$ eYMA,#8#DuDDi]DDKTDDDDKlll}lltllllt1%
bn_scale_tensor_proto bn_B_tensor_proto bn_mean_tensor_proto bn_var_tensor_proto ConvBnFusion_W_ ConvBnFusion_BN_B_ virtual onnxruntime::common::Status onnxruntime::ConvBNFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const /onnxruntime_src/onnxruntime/core/optimizer/conv_bn_fusion.cc N11onnxruntime12ConvBNFusionE mul_B_tensor_proto ConvMulFusion_W_ ConvMulFusion_Mul_B_ virtual onnxruntime::common::Status onnxruntime::ConvMulFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const /onnxruntime_src/onnxruntime/core/optimizer/conv_mul_fusion.cc N11onnxruntime13ConvMulFusionE R)+R)R)R)R)9)*R)R)**//.dd..ddm-/ddddm-/~/q/SdSd..SdSd[-.SdSdSdSd[-.$//
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+ DynamicQuantizeLinear DynamicQuantizeMatMul /onnxruntime_src/onnxruntime/core/optimizer/dynamic_quantize_matmul_fusion.cc N11onnxruntime27DynamicQuantizeMatMulFusionE /onnxruntime_src/onnxruntime/core/optimizer/embed_layer_norm_fusion.cc Input shape is unknown or not 2D, or data type unknown Input data type is not int32 or int64 CheckInput MatchInputToConcatSubgraph Failed 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. two paths share the same shape NonZero ConstantOfShape Failed to match Shape node. MatchPositionEmbeddingSubgraphsFromGather Output 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. 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. Cast Input from int64 to int32 mask_index EmbedLayerNormalization fused EmbedLayerNorm subgraphs position_embeddings Input id is not valid. Segment id is not valid. Word 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_ids and segment id should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). znbn궆ҶƶҶFuseSubGraphDistilBert N11onnxruntime20EmbedLayerNormFusionE ̼+ N11onnxruntime17ExpandEliminationE fast_gelu_output fused GPT2Gelu subgraphs GPT2Gelu /onnxruntime_src/onnxruntime/core/optimizer/fast_gelu_fusion.cc N11onnxruntime14FastGeluFusionE '7=*BL? @@ ?FreeDimensionOverrideTransformer Invalid free dimension override. onnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformer(gsl::span<const onnxruntime::FreeDimensionOverride>) /onnxruntime_src/onnxruntime/core/optimizer/free_dim_override_transformer.cc Conflicting free dimension overrides. which does not equal the specified override of The model has input ' ' with a fixed dimension size N11onnxruntime32FreeDimensionOverrideTransformerE Gelu approximation /onnxruntime_src/onnxruntime/core/optimizer/gelu_approximation.cc Total Gelu Approximation (FastGelu) node count: N11onnxruntime17GeluApproximationE Erf fused Gelu subgraphs /onnxruntime_src/onnxruntime/core/optimizer/gelu_fusion.cc N11onnxruntime10GeluFusionE ??Elu HardSigmoid Selu Softsign ScaledTanh ParametricSoftplus ThresholdedRelu fused Gemm FusedGemm activation_ /onnxruntime_src/onnxruntime/core/optimizer/gemm_activation_fusion.cc N11onnxruntime20GemmActivationFusionE
Fused Gemm with Transpose N11onnxruntime19GemmTransposeFusionE External data type must not be UNDEFINED or STRING. model_path must not be empty. Ensure that a path is provided when the model is created or loaded. /onnxruntime_src/onnxruntime/core/optimizer/initializer.cc TensorProto external data size mismatch. ReadExternalRawData Computed size: , external_data.length: e/optimizer/initReadExternalRawDtypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::NodeIndexInfo; _Dp = std::default_delete<onnxruntime::NodeIndexInfo>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::NodeIndexInfo&] /onnxruntime_src/onnxruntime/core/optimizer/optimizer_execution_frame.cc Tried to allocate without valid type information, ort_value index= Failed to get allocator for optimizer onnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector<const onnxruntime::Node*>&, const InitializedTensorSet&, const onnxruntime::Path&, const onnxruntime::IExecutionProvider&) allocator_ptr_ N11onnxruntime23OptimizerExecutionFrameE *ZN11onnxruntime23OptimizerExecutionFrame4InfoC4ERKSt6vectorIPKNS_4NodeESaIS5_EERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSE_EEERKNS_4PathERKNS_18IExecutionProviderEEUlRKNS_7NodeArgEmE_ *ZN11onnxruntime23OptimizerExecutionFrame4InfoC4ERKSt6vectorIPKNS_4NodeESaIS5_EERKSt13unordered_mapISs8OrtValueSt4hashISsESt8equal_toISsESaISt4pairIKSsSB_EEERKNS_4PathERKNS_18IExecutionProviderEEUlRKNS_7NodeArgEmE_ std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator<const onnxruntime::Node*>; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::Node* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] N11onnxruntime25QDQAveragePoolTransformerE *ZN11onnxruntime50Register_AveragePool_qdq_QDQAveragePoolTransformerEvEUlRNS_4NodeERNS_5GraphEE_ QLinear N11onnxruntime22QDQBinaryOpTransformerE *ZN11onnxruntime39Register_Add_qdq_QDQBinaryOpTransformerEvEUlRNS_4NodeERNS_5GraphEE_ *ZN11onnxruntime39Register_Mul_qdq_QDQBinaryOpTransformerEvEUlRNS_4NodeERNS_5GraphEE_ N11onnxruntime20QDQConcatTransformerE *ZN11onnxruntime40Register_Concat_qdq_QDQConcatTransformerEvEUlRNS_4NodeERNS_5GraphEE_ N11onnxruntime18QDQConvTransformerE *ZN11onnxruntime36Register_Conv_qdq_QDQConvTransformerEvEUlRNS_4NodeERNS_5GraphEE_ QLinearMatMul N11onnxruntime20QDQMatMulTransformerE *ZN11onnxruntime40Register_MatMul_qdq_QDQMatMulTransformerEvEUlRNS_4NodeERNS_5GraphEE_ 855dd0fa-cd7b-4b10-ae5a-df64cabfe1f8 35b188f7-c464-43e3-8692-97ac832bb14a N11onnxruntime22QDQOperatorTransformerE N11onnxruntime20QDQSimpleTransformerE N11onnxruntime21QDQMaxPoolTransformerE *ZN11onnxruntime42Register_MaxPool_qdq_QDQMaxPoolTransformerEvEUlRNS_4NodeERNS_5GraphEE_ *ZN11onnxruntime41Register_Reshape_qdq_QDQSimpleTransformerEvEUlRNS_4NodeERNS_5GraphEE_ *ZN11onnxruntime40Register_Gather_qdq_QDQSimpleTransformerEvEUlRNS_4NodeERNS_5GraphEE_ *ZN11onnxruntime43Register_Transpose_qdq_QDQSimpleTransformerEvEUlRNS_4NodeERNS_5GraphEE_ MIGraphXExecutionProvider OpenVINOExecutionProvider DnnlExecutionProvider NupharExecutionProvider NnapiExecutionProvider RknpuExecutionProvider RegisterOnnxOperatorKernels /onnxruntime_src/onnxruntime/core/providers/cpu/cpu_execution_provider.cc RegisterOnnxMLOperatorKernels RegisterCPUKernels virtual std::shared_ptr<onnxruntime::KernelRegistry> onnxruntime::CPUExecutionProvider::GetKernelRegistry() const GetKernelRegistry N11onnxruntime20CPUExecutionProviderE N11onnxruntime25IExecutionProviderFactoryE N11onnxruntime18CpuProviderFactoryE St23_Sp_counted_ptr_inplaceIN11onnxruntime18CpuProviderFactoryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE T1 T2 void onnxruntime::ConstantOfShapeBase<EnabledOutputTypeList>::SetValueFromTensorProto(const onnx::TensorProto&) [with EnabledOutputTypeList = onnxruntime::TypeList<onnxruntime::MLFloat16, float, double, signed char, short int, int, long int, unsigned char, short unsigned int, unsigned int, long unsigned int, bool>] /onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape_base.h ONNX_NAMESPACE::TensorProto::DataType_IsValid(t_proto.data_type()) Tensor proto with external data for value attribute is not supported. !utils::HasExternalData(t_proto) Unsupported value attribute datatype: utils::HasDataType(t_proto) SetValueFromTensorProto [z,D\t[z~}||,{ const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] /onnxruntime_src/include/onnxruntime/core/framework/op_kernel_context.h Must have a valid input shape. gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int] Missing Input: PrepareCompute /onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape.cc Unsupported output datatype with size: Must have a single dimension t_proto_p->dims_size() == 1 t_proto_p->dims()[0] == 1 onnxruntime::ConstantOfShapeBase<EnabledOutputTypeList>::ConstantOfShapeBase(const onnxruntime::OpKernelInfo&) [with EnabledOutputTypeList = onnxruntime::TypeList<onnxruntime::MLFloat16, float, double, signed char, short int, int, long int, unsigned char, short unsigned int, unsigned int, long unsigned int, bool>] Must have a single dimension of 1 N11onnxruntime19ConstantOfShapeBaseINS_8TypeListIJNS_9MLFloat16EfdasilhtjmbEEEEE FPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEE *N11onnxruntime12_GLOBAL__N_115ConstantOfShapeE PFPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEE Invalid dtype of onnxruntime::RandomNormal::RandomNormal(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.h info.GetAttr<float>("mean", &mean_).IsOK() info.GetAttr<float>("scale", &scale_).IsOK() info.GetAttr<int64_t>("dtype", &dtype).IsOK() ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINED info.GetAttrs<int64_t>("shape", shape).IsOK() high onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo&) info.GetAttr<float>("high", &high_).IsOK() info.GetAttr<float>("low", &low_).IsOK() onnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo&) onnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo&) sample_size onnxruntime::Multinomial::Multinomial(const onnxruntime::OpKernelInfo&) info.GetAttr<int64_t>("sample_size", &num_samples_).IsOK() ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINED Output type not supported in this build: std::uniform_real_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = float] /opt/rh/devtoolset-9/root/usr/include/c++/9/bits/random.h std::uniform_real_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = double] _M_a <= _M_b Could not infer data type from input tensor with data type input count mismatch _M_stddev > _RealType(0) std::normal_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = float] std::normal_distribution<_RealType>::param_type::param_type(_RealType, _RealType) [with _RealType = double] Empty dimensions for input tensor /onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.cc batch_size is < 1 num_classes is < 1 num_samples is < 1 MultinomialCompute Invalid data type of N11onnxruntime12RandomNormalE N11onnxruntime16RandomNormalLikeE N11onnxruntime13RandomUniformE N11onnxruntime17RandomUniformLikeE N11onnxruntime11MultinomialE 0??? A C??? "~+@"~+@W6}+W6}++eG?+eG? ? ? ? ? @.? @.?ʫy>ʫy>K͉ ?K͉ ?~, ?~, ?_6.>_6.>9d?9d?t ?t ? @ @ const T& onnxruntime::OpKernelContext::RequiredInput(int) const [with T = onnxruntime::Tensor] start in Range operator should be scalar like tensor, yet got shape: limit in Range operator should be scalar like tensor, yet got shape: delta in Range operator should be scalar like tensor, yet got shape: delta in Range operator can not be zero! is not present. Required input at index input_ptr N11onnxruntime5RangeE min_ <= max_ onnxruntime::clip_internal::Clip_6Base<T>::Clip_6Base(const onnxruntime::OpKernelInfo&) [with T = float] /onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.h void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = unsigned char] /onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.cc min should be a scalar. min->Shape().IsScalar() max should be a scalar. max->Shape().IsScalar() void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = signed char] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = long unsigned int] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = double] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = long int] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = float] called_ == 1 void onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableHelper::CheckCalledOnce() N11onnxruntime13clip_internal10Clip_6BaseIfEE N11onnxruntime4ClipE N11onnxruntime6Clip_6IfEE CumSum onnxruntime::SliceSkips::SliceSkips(const onnxruntime::TensorShape&, gsl::span<const long int>, gsl::span<const long int>) /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/utils.h dims.size() == extents.size() && dims.size() >= steps.size() void onnxruntime::SliceIteratorBase::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size() Axis tensor must be provided to the CumSum op Axis tensor should be 0D or 1D Axis tensor should be of type `int32_t` or `int64_t` starts.size()= dims.size()= dims.size() == starts.size() extents.size()= steps.size()= dims.size() == steps.size() void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float] dims.size() == extents_.size() Cannot apply CumSum operator on a scalar /onnxruntime_src/onnxruntime/core/providers/cpu/math/cumsum.cc onnxruntime::common::Status onnxruntime::CumSum<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double] onnxruntime::common::Status onnxruntime::CumSum<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int] onnxruntime::common::Status onnxruntime::CumSum<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int] onnxruntime::common::Status onnxruntime::CumSum<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] N11onnxruntime6CumSumIfEE N11onnxruntime6CumSumIdEE N11onnxruntime6CumSumIiEE N11onnxruntime6CumSumIlEE $LdL4LdLLLLdLLLLÊLLLLLLLÊtÊLLLLLLLLLLLLLLLLL 4Ê $ÊL $D,tTLLLLLLLLLLLLLLLLLdLLLLL$dLLLLLLLLLLLLLLLD $D$ $DDLLTÊDet onnxruntime::common::Status onnxruntime::Det<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] /onnxruntime_src/onnxruntime/core/providers/cpu/math/det.cc Input tensor should have a rank of at least 2 Matrix dimensions are not equal. Square matrix is expected X != nullptr N11onnxruntime3DetIfEE basic_string::basic_string Einsum DeviceCompute is not supported yet typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::EinsumEquationPreprocessor; _Dp = std::default_delete<onnxruntime::EinsumEquationPreprocessor>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::EinsumEquationPreprocessor&] /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum.cc std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = const onnxruntime::Tensor*; _Alloc = std::allocator<const onnxruntime::Tensor*>; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::Tensor* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] Einsum op: An implementation for the input type Einsum op: There must be atleast one input There was a problem acquiring temporary memory allocator in Einsum op Missing 'equation' attribute onnxruntime::Einsum::Einsum(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum.h info.GetAttr<std::string>("equation", &equation_).IsOK() FN11onnxruntime6common6StatusEPKfS3_PfmmmmmmmPNS_11concurrency10ThreadPoolEPvE FSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_RKSt6vectorIlSaIlEEbSt10shared_ptrINS0_10IAllocatorEEPKNS0_11TensorShapeEPNS0_11concurrency10ThreadPoolEPvE FN11onnxruntime6common6StatusERKNS_6TensorERS2_PvE FN11onnxruntime6common6StatusEPKiS3_PimmmmmmmPNS_11concurrency10ThreadPoolEPvE FN11onnxruntime6common6StatusEPKdS3_PdmmmmmmmPNS_11concurrency10ThreadPoolEPvE FN11onnxruntime6common6StatusEPKlS3_PlmmmmmmmPNS_11concurrency10ThreadPoolEPvE FSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_llSt10shared_ptrINS0_10IAllocatorEEPvE FN11onnxruntime6common6StatusERKSt6vectorImSaImEERKNS_6TensorERS7_PKNS_11TensorShapeEPvE N11onnxruntime6EinsumE PFSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_llSt10shared_ptrINS0_10IAllocatorEEPvE PFN11onnxruntime6common6StatusERKSt6vectorImSaImEERKNS_6TensorERS7_PKNS_11TensorShapeEPvE PFN11onnxruntime6common6StatusEPKfS3_PfmmmmmmmPNS_11concurrency10ThreadPoolEPvE PFSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_RKSt6vectorIlSaIlEEbSt10shared_ptrINS0_10IAllocatorEEPKNS0_11TensorShapeEPNS0_11concurrency10ThreadPoolEPvE PFN11onnxruntime6common6StatusERKNS_6TensorERS2_PvE PFN11onnxruntime6common6StatusEPKiS3_PimmmmmmmPNS_11concurrency10ThreadPoolEPvE PFN11onnxruntime6common6StatusEPKdS3_PdmmmmmmmPNS_11concurrency10ThreadPoolEPvE PFN11onnxruntime6common6StatusEPKlS3_PlmmmmmmmPNS_11concurrency10ThreadPoolEPvE Einsum op: The candidate output does not match the actual output's shape onnxruntime::common::Status onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopy(const onnxruntime::Tensor&, onnxruntime::Tensor&, void*) /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_auxiliary_ops.cc output.SizeInBytes() == input.SizeInBytes() The innermost dims should have the same dim value to parse the diagonal elements std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDims(const onnxruntime::Tensor&, bool, onnxruntime::AllocatorPtr) input_dims[rank - 2] == input_dims[rank - 1] Einsum op: Unsupported data type for Diagonal %ld Tensor size ( ) != new size ( void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&) shape_.Size() == new_shape.Size() The rank of the input must match permutation size for Transpose bool onnxruntime::EinsumOp::IsTransposeRequired(size_t, const std::vector<long unsigned int>&) input_rank == permutation.size() std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = long unsigned int; _Alloc = std::allocator<long unsigned int>; std::vector<_Tp, _Alloc>::const_reference = const long unsigned int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] Length of permutation must match the rank of the input to be permutated std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::Transpose(const onnxruntime::Tensor&, const std::vector<long int>&, const std::vector<long unsigned int>&, onnxruntime::AllocatorPtr, void*, const Transpose&) typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Tensor; _Dp = std::default_delete<onnxruntime::Tensor>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Tensor&] Einsum op: Transpose failed: for input shape Cannot parse the diagonal elements along dims std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonal(const onnxruntime::Tensor&, int64_t, int64_t, onnxruntime::AllocatorPtr, void*) rank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2] Data types of the inputs must match for MatMul std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector<long int>&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul<T>&) [with T = long int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>; onnxruntime::EinsumOp::DeviceHelpers::MatMul<T> = std::function<onnxruntime::common::Status(const long int*, const long int*, long int*, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, onnxruntime::concurrency::ThreadPool*, void*)>] input_1.DataType() == input_2.DataType() Only 1 batch dimension is allowed for MatMul input_shape_1_override.size() == 3 && input_shape_2_override.size() == 3 Batch dimension should match for MatMul; input_shape_1_override[0] == input_shape_2_override[0] Incompatible matrix dimensions for matMul input_shape_1_override[2] == input_shape_2_override[1] Einsum op: Exception during MatMul operation: std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector<long int>&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul<T>&) [with T = double; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>; onnxruntime::EinsumOp::DeviceHelpers::MatMul<T> = std::function<onnxruntime::common::Status(const double*, const double*, double*, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, onnxruntime::concurrency::ThreadPool*, void*)>] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector<long int>&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul<T>&) [with T = int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>; onnxruntime::EinsumOp::DeviceHelpers::MatMul<T> = std::function<onnxruntime::common::Status(const int*, const int*, int*, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, onnxruntime::concurrency::ThreadPool*, void*)>] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector<long int>&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul<T>&) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>; onnxruntime::EinsumOp::DeviceHelpers::MatMul<T> = std::function<onnxruntime::common::Status(const float*, const float*, float*, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, onnxruntime::concurrency::ThreadPool*, void*)>] vector::_M_fill_insert Found a '.' not part of an ellipsis in the output subscript provided Found '.' not part of an ellipsis in the output subscript provided The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z) Output subscript contains repeated letters Output subscript contains letters not seen in the inputs onnxruntime::common::Status onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDims() /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_compute_preprocessor.cc num_broadcasted_indices < num_of_ellipsis_dims_ The broadcasted dimensions of the inputs are incompatible dim_iter == rank std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector<long int>; _Alloc = std::allocator<std::vector<long int> >; std::vector<_Tp, _Alloc>::reference = std::vector<long int>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] Rank of the input must match number of subscript labels corresponding to the input Inputs have ellipses in them but the provided output subscript does not contain an ellipsis Number of subscripts in the input equation does not match number of input tensors Found a '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the input Ellipsis must indicate a fixed number of dimensions across all inputs Found '.' 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 Another operand has a dim value of Einsum 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 . The shape is: in the same dimension Einsum op: The candidate output cannot be reshaped into the op's output void onnxruntime::EinsumTypedComputeProcessor<T>::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&) [with T = float] /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_typed_compute_processor.cc candidate_output.Shape().Size() == output_shape.Size() Not all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: candidate_output_dims[iter] == 1 Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: void onnxruntime::EinsumTypedComputeProcessor<T>::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&) [with T = int] void onnxruntime::EinsumTypedComputeProcessor<T>::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&) [with T = double] void onnxruntime::EinsumTypedComputeProcessor<T>::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&) [with T = long int] Left shape override: Left shape: Right shape override: Right shape: left_rank == right_rank left_dim == right_dim The override dims are not compatible with given tensor's shape. std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumTypedComputeProcessor<T>::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, bool) [with T = float] left.Shape().Size() == left_shape_override.Size() right.Shape().Size() == right_shape_override.Size() Ranks of pair-wise operands must be equal. Einsum op: Input dimensions must be equal along an axis to be reduced across all inputs Einsum op: Input shapes do not align std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::TensorShape; _Alloc = std::allocator<onnxruntime::TensorShape>; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::TensorShape&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::unique_ptr<onnxruntime::Tensor>; _Alloc = std::allocator<std::unique_ptr<onnxruntime::Tensor> >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr<onnxruntime::Tensor>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumTypedComputeProcessor<T>::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, bool) [with T = int] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumTypedComputeProcessor<T>::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, bool) [with T = double] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumTypedComputeProcessor<T>::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, bool) [with T = long int] std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::front() const [with _Tp = long int; _Alloc = std::allocator<long int>; std::vector<_Tp, _Alloc>::const_reference = const long int&] Mod Abs Sinh Sin Asinh Atanh Atan Neg BitShift Cos Floor Acosh Cosh Asin Reciprocal Tan Ceil Acos PRelu LessOrEqual Less GreaterOrEqual Xor And Greater Or axis == 1 || axis == largest Attempting to broadcast an axis by a dimension other than 1. void onnxruntime::BroadcastIterator::Init(ptrdiff_t, ptrdiff_t) /onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.h void onnxruntime::BroadcastIterator::Append(ptrdiff_t, ptrdiff_t) std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = long int; _Alloc = std::allocator<long int>; std::vector<_Tp, _Alloc>::reference = long int&] InputBroadcaster can only start at span boundary! void onnxruntime::InputBroadcaster::AdvanceBy(size_t) offset % span_size_ == 0 direction RIGHT Invalid direction value of ' LEFT onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned char] /onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.cc '. Valid values are 'LEFT' or 'RIGHT'. onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned int] onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = long unsigned int] fmod (fmod == 0) || (fmod == 1) fmod must have value either 0 or 1 onnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo&) ) for tensor of length: Invalid start/ending offset [ Broadcast Output range [ onnxruntime::OutputBroadcaster::OutputBroadcaster(size_t, onnxruntime::Tensor&, ptrdiff_t, ptrdiff_t) start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= len ) are not at boundary of span with size: start_offset % span_size == 0 && real_end % span_size == 0 Can broadcast 0 by 0 or 1. largest <= 1 onnxruntime::Broadcaster::Broadcaster(const std::vector<long int>&, const std::vector<long int>&) onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]::<lambda(onnxruntime::BroadcastHelper&)> cur1 == end1 cur_out == end_out onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]::<lambda(onnxruntime::BroadcastHelper&)> onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Log<float>] /onnxruntime_src/onnxruntime/core/providers/cpu/element_wise_ranged_transform.h input_size < std::numeric_limits<std::ptrdiff_t>::max() onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Exp<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sqrt<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Reciprocal<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Ceil<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Floor<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<long unsigned int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<unsigned int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<short unsigned int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<unsigned char>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<long int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<short int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<signed char>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<float>] onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::<lambda(onnxruntime::BroadcastHelper&)> onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg<signed char>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg<int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<int>] onnxruntime::common::Status onnxruntime::Min_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] All inputs must have the same shape Must have 1 or more inputs inputCount >= 1 data_n.Shape() == shape onnxruntime::common::Status onnxruntime::Max_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] Tensor with shape information must be 1 dimensional. void onnxruntime::UntypedExpand(onnxruntime::OpKernelContext&, const onnxruntime::ProcessBroadcastSpanFuncs&) shape_data_tensor.Shape().GetDims().size() == 1 ExpandBroadcastLooper should only have a shape for the second input. void onnxruntime::ExpandBroadcastLooper(onnxruntime::BroadcastHelper&, const onnxruntime::ProcessBroadcastSpanFuncs&) !helper.HaveTwoTensorInputs() onnxruntime::common::Status onnxruntime::Sum_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] input_count >= 1 data_1.Shape() == shape BroadcastLooper requires two tensors as input. void onnxruntime::BroadcastLooper(TBroadcastHelper&, const onnxruntime::ProcessBroadcastSpanFuncs&) [with TBroadcastHelper = onnxruntime::BroadcastHelper] fmod attribute must be true for floating point types void onnxruntime::mod_internal::CallModImpl<T, typename std::enable_if<std::is_floating_point<_Tp>::value, void>::type>::operator()(bool, onnxruntime::OpKernelContext*) const [with T = float; typename std::enable_if<std::is_floating_point<_Tp>::value, void>::type = void] void onnxruntime::mod_internal::CallModImpl<T, typename std::enable_if<std::is_floating_point<_Tp>::value, void>::type>::operator()(bool, onnxruntime::OpKernelContext*) const [with T = double; typename std::enable_if<std::is_floating_point<_Tp>::value, void>::type = void] void onnxruntime::mod_internal::CallModImpl<onnxruntime::MLFloat16>::operator()(bool, onnxruntime::OpKernelContext*) const Unsupported Y type: Unsupported X type: onnxruntime::common::Status onnxruntime::Mean_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::TensorAllocator::TensorAllocator(onnxruntime::OpKernelContext&) Please fetch output tensor with specified shape. T* onnxruntime::OpKernelContext::Output(int) [with T = onnxruntime::Tensor] p_ml_value onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg<long int>] Invalid usage. Input 1 is a shape with no data. onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>]::<lambda(onnxruntime::BroadcastHelper&)> onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Log<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Exp<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sqrt<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Reciprocal<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<double>] onnxruntime::common::Status onnxruntime::Sum_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<float>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<double>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<signed char>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<short int>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<int>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<long int>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<unsigned char>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<short unsigned int>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<unsigned int>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<long unsigned int>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg<float>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg<double>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg<signed char>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg<int>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg<long int>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Floor<float>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Ceil<float>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Reciprocal<float>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Reciprocal<double>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Sqrt<float>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Sqrt<double>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Exp<float>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Exp<double>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Log<float>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Log<double>] N11onnxruntime3PowE N11onnxruntime5Min_8E N11onnxruntime5Max_8E N11onnxruntime3NotE N11onnxruntime3AndE N11onnxruntime2OrE N11onnxruntime3XorE N11onnxruntime8functors26ElementWiseRangedTransformIfEE N11onnxruntime8functors3ExpIfEE N11onnxruntime3AddIfEE N11onnxruntime3AddIdEE N11onnxruntime3AddIiEE N11onnxruntime3AddIlEE N11onnxruntime3SubIfEE N11onnxruntime3SubIdEE N11onnxruntime3SubIiEE N11onnxruntime3SubIlEE N11onnxruntime3MulIfEE N11onnxruntime3MulIdEE N11onnxruntime3MulIiEE N11onnxruntime3MulIlEE N11onnxruntime3DivIfEE N11onnxruntime3DivIdEE N11onnxruntime3DivIiEE N11onnxruntime3DivIlEE N11onnxruntime8functors3AbsIfEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIfEEEE N11onnxruntime8functors26ElementWiseRangedTransformIdEE N11onnxruntime8functors3AbsIdEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIdEEEE N11onnxruntime8functors26ElementWiseRangedTransformIaEE N11onnxruntime8functors3AbsIaEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIaEEEE N11onnxruntime8functors26ElementWiseRangedTransformIsEE N11onnxruntime8functors3AbsIsEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIsEEEE N11onnxruntime8functors26ElementWiseRangedTransformIiEE N11onnxruntime8functors3AbsIiEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIiEEEE N11onnxruntime8functors26ElementWiseRangedTransformIlEE N11onnxruntime8functors3AbsIlEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIlEEEE N11onnxruntime8functors26ElementWiseRangedTransformIhEE N11onnxruntime8functors3AbsIhEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIhEEEE N11onnxruntime8functors26ElementWiseRangedTransformItEE N11onnxruntime8functors3AbsItEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsItEEEE N11onnxruntime8functors26ElementWiseRangedTransformIjEE N11onnxruntime8functors3AbsIjEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIjEEEE N11onnxruntime8functors26ElementWiseRangedTransformImEE N11onnxruntime8functors3AbsImEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsImEEEE N11onnxruntime8functors3NegIfEE N11onnxruntime17ElementWiseKernelINS_8functors3NegIfEEEE N11onnxruntime8functors3NegIdEE N11onnxruntime17ElementWiseKernelINS_8functors3NegIdEEEE N11onnxruntime8functors3NegIaEE N11onnxruntime17ElementWiseKernelINS_8functors3NegIaEEEE N11onnxruntime8functors3NegIiEE N11onnxruntime17ElementWiseKernelINS_8functors3NegIiEEEE N11onnxruntime8functors3NegIlEE N11onnxruntime17ElementWiseKernelINS_8functors3NegIlEEEE N11onnxruntime8functors5FloorIfEE N11onnxruntime17ElementWiseKernelINS_8functors5FloorIfEEEE N11onnxruntime8functors4CeilIfEE N11onnxruntime17ElementWiseKernelINS_8functors4CeilIfEEEE N11onnxruntime8functors10ReciprocalIfEE N11onnxruntime17ElementWiseKernelINS_8functors10ReciprocalIfEEEE N11onnxruntime8functors10ReciprocalIdEE N11onnxruntime17ElementWiseKernelINS_8functors10ReciprocalIdEEEE N11onnxruntime8functors4SqrtIfEE N11onnxruntime17ElementWiseKernelINS_8functors4SqrtIfEEEE N11onnxruntime8functors4SqrtIdEE N11onnxruntime17ElementWiseKernelINS_8functors4SqrtIdEEEE N11onnxruntime17ElementWiseKernelINS_8functors3ExpIfEEEE N11onnxruntime8functors3ExpIdEE N11onnxruntime17ElementWiseKernelINS_8functors3ExpIdEEEE N11onnxruntime8functors3LogIfEE N11onnxruntime17ElementWiseKernelINS_8functors3LogIfEEEE N11onnxruntime8functors3LogIdEE N11onnxruntime17ElementWiseKernelINS_8functors3LogIdEEEE N11onnxruntime5Sum_6IfEE N11onnxruntime5Sum_6IdEE N11onnxruntime5Sum_8IfEE N11onnxruntime5Sum_8IdEE N11onnxruntime5Max_6IfEE N11onnxruntime5Min_6IfEE N11onnxruntime4LessIfEE N11onnxruntime4LessIdEE N11onnxruntime4LessIiEE N11onnxruntime4LessIlEE N11onnxruntime7GreaterIfEE N11onnxruntime7GreaterIdEE N11onnxruntime7GreaterIiEE N11onnxruntime7GreaterIlEE N11onnxruntime5EqualIbEE N11onnxruntime5EqualIiEE N11onnxruntime5EqualIlEE N11onnxruntime5EqualIfEE N11onnxruntime5EqualIdEE N11onnxruntime11LessOrEqualIfEE N11onnxruntime11LessOrEqualIdEE N11onnxruntime11LessOrEqualIiEE N11onnxruntime11LessOrEqualIlEE N11onnxruntime14GreaterOrEqualIfEE N11onnxruntime14GreaterOrEqualIdEE N11onnxruntime14GreaterOrEqualIiEE N11onnxruntime14GreaterOrEqualIlEE N11onnxruntime6Mean_6IfEE N11onnxruntime6Mean_8IfEE N11onnxruntime8BitShiftIhEE N11onnxruntime8BitShiftIjEE N11onnxruntime8BitShiftImEE N11onnxruntime3ErfIfEE N11onnxruntime3SinIfEE N11onnxruntime3SinIdEE N11onnxruntime3CosIfEE N11onnxruntime3TanIfEE N11onnxruntime4AsinIfEE N11onnxruntime4AcosIfEE N11onnxruntime4AtanIfEE N11onnxruntime4SinhIfEE N11onnxruntime4CoshIfEE N11onnxruntime5AsinhIfEE N11onnxruntime5AcoshIfEE N11onnxruntime5AtanhIfEE N11onnxruntime5PReluIfEE N11onnxruntime8Expand_8ISsEE N11onnxruntime3ModE *ZN11onnxruntime19UntypedBroadcastTwoERNS_15OpKernelContextERKNS_25ProcessBroadcastSpanFuncsEdPvEUlllE_ *ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE_ *ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE0_ *ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE1_ ( ( `( `(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfO ? ? ? ? ? ? ? ? ? ? ? ?5?5?5?5? !=!=!=!=뽸뽸뽸%=%=%=%=O]O]O]O]>>>>P*P*P*P*L>L>L>L>>>>>^^^^ 1? 1? 1? 1? @ @ @ @"?"?"?"? @K @K @K @K @ @ @ @ ɿ ɿ ɿ ɿ .4 .4 .4 .4t.t.t.t. ,F ,F ,F ,FpMpMpMpMӅ<Ӆ<Ӆ<Ӆ<**** s7 s7 s7 s7nnnn*=*=*=*= F F F F pA @ 8 ,F F @ @ .@-DT!<std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::unique_ptr<void, onnxruntime::BufferDeleter>; _Alloc = std::allocator<std::unique_ptr<void, onnxruntime::BufferDeleter> >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr<void, onnxruntime::BufferDeleter>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] onnxruntime::Gemm<T>::Gemm(const onnxruntime::OpKernelInfo&) [with T = float] /onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm.h info.GetAttr<int64_t>("transA", &temp).IsOK() info.GetAttr<int64_t>("transB", &temp).IsOK() info.GetAttr<float>("alpha", &alpha_).IsOK() info.GetAttr<float>("beta", &beta_).IsOK() onnxruntime::Gemm<T>::Gemm(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&) /onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm_helper.h left.NumDimensions() == 2 || left.NumDimensions() == 1 Gemm: Invalid bias shape for broadcast right.NumDimensions() == 2 N: K: GEMM: Dimension mismatch, W: M_ >= 0 && K_ > 0 && N_ >= 0 c_shape is required if c_data is provided void onnxruntime::GemmBroadcastBias(int64_t, int64_t, float, const T*, const onnxruntime::TensorShape*, T*) [with T = float; int64_t = long int] /onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm.cc c_shape != nullptr void onnxruntime::GemmBroadcastBias(int64_t, int64_t, float, const T*, const onnxruntime::TensorShape*, T*) [with T = double; int64_t = long int] ZNK11onnxruntime4GemmIdE17ComputeActivationEPdmPNS_11concurrency10ThreadPoolEEUlllE_ N11onnxruntime4GemmIfEE N11onnxruntime4GemmIdEE ZNK11onnxruntime4GemmIfE17ComputeActivationEPfmPNS_11concurrency10ThreadPoolEEUlllE_ Hardmax /onnxruntime_src/onnxruntime/core/providers/cpu/math/hardmax.cc Hardmax inputs N, D and N * D must be < . N= , D= N11onnxruntime7HardmaxIfEE /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_helper.h left_num_dims and right_num_dims must be >= 1 left operand cannot broadcast on dim right operand cannot broadcast on dim num_dims_with_pad != num_output_dims num_dims_with_pad - 1 != num_output_dims num_dims_with_pad - 2 != num_output_dims MatMul dimension mismatch M_ == 1 && N_ == 1 was false /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul.cc std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = MLAS_SGEMM_DATA_PARAMS; _Alloc = std::allocator<MLAS_SGEMM_DATA_PARAMS>; std::vector<_Tp, _Alloc>::reference = MLAS_SGEMM_DATA_PARAMS&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] N11onnxruntime6MatMulIfEE N11onnxruntime6MatMulIdEE N11onnxruntime6MatMulIiEE N11onnxruntime6MatMulIlEE T3 MatMulInteger Per-column quantization parameter of batched matrix should have same dimension as the matrix,and its size by K should be equal to the matrix's size. MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1 virtual onnxruntime::common::Status onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext*) const /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_integer.cc IsScalarOr1ElementVector(a_zero_point) MatmulInteger : B zero point is not valid IsBQuantParamSupported(b_zero_point->Shape(), b ? b->Shape() : b_shape_) std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = MLAS_GEMM_U8X8_DATA_PARAMS; _Alloc = std::allocator<MLAS_GEMM_U8X8_DATA_PARAMS>; std::vector<_Tp, _Alloc>::reference = MLAS_GEMM_U8X8_DATA_PARAMS&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] N11onnxruntime17MatMulIntegerBaseE N11onnxruntime13MatMulIntegerE e/providers/cpu/math/matmul_helpQLinearMatmul : input zero point must be a scalar or 1D tensor of size 1 virtual onnxruntime::common::Status onnxruntime::QLinearMatMul::Compute(onnxruntime::OpKernelContext*) const /onnxruntime_src/onnxruntime/core/providers/cpu/math/quantize_linear_matmul.cc IsScalarOr1ElementVector(a_offset) QLinearMatmul : weight zero point must be a scalar, 1D tensor of size 1, or last to second dimension is 1 IsBQuantParamSupported(b_offset->Shape(), b ? b->Shape() : b_shape_) QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1 IsScalarOr1ElementVector(y_offset) QLinearMatmul : input scale must be a scalar or 1D tensor of size 1 IsScalarOr1ElementVector(a_scale) QLinearMatmul : weight scale must be a scalar, 1D tensor of size 1, or last to second dimension is 1 IsBQuantParamSupported(b_scale->Shape(), b ? b->Shape() : b_shape_) QLinearMatmul : result scale must be a scalar or 1D tensor of size 1 IsScalarOr1ElementVector(y_scale) N11onnxruntime13QLinearMatMulE Round N11onnxruntime5RoundINS_9MLFloat16EEE N11onnxruntime5RoundIfEE N11onnxruntime5RoundIdEE Sign N11onnxruntime4SignE LogSoftmax /onnxruntime_src/onnxruntime/core/providers/cpu/math/softmax.cc ComputeImplOpset13 N11onnxruntime7SoftmaxIfEE N11onnxruntime7SoftmaxIdEE SoftmaxCPU inputs N, D and N * D must be < ;I TopK k_temp > 0 void onnxruntime::TopkOpset9ConstructorCommon(const onnxruntime::OpKernelInfo&, int&, unsigned int&) /onnxruntime_src/onnxruntime/core/providers/cpu/math/top_k.cc op_kernel_info.GetAttr<int64_t>("k", &k_temp).IsOK() op_kernel_info.GetAttr<int64_t>("axis", &axis_temp).IsOK() void onnxruntime::TopkOpset10ConstructorCommon(const onnxruntime::OpKernelInfo&, int&) void onnxruntime::TopkOpset11ConstructorCommon(const onnxruntime::OpKernelInfo&, int&, bool&, bool&) op_kernel_info.GetAttr<int64_t>("largest", &largest_temp).IsOK() op_kernel_info.GetAttr<int64_t>("sorted", &sorted_temp).IsOK() sorted ] should not be greater than specified axis dim value [ output count mismatch, expected 2 outputs to be present for TopK operator k argument [ input count mismatch, expected 1 input - the tensor to be processed input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k value k tensor should be a 1D tensor of size 1 value of k must not be negative N11onnxruntime4TopKILi9EfEE N11onnxruntime4TopKILi9EdEE N11onnxruntime4TopKILi10EfEE N11onnxruntime4TopKILi10EdEE N11onnxruntime4TopKILi11EfEE N11onnxruntime4TopKILi11EdEE N11onnxruntime4TopKILi11EiEE N11onnxruntime4TopKILi11ElEE *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIdEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIiEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ 333333?ArrayFeatureExtractor ai.onnx.ml Invalid argument: X input has empty dimensions. Invalid Y argument: num_indices = 0 Invalid Y argument: index is out of range: Y[ ) >= N11onnxruntime2ml23ArrayFeatureExtractorOpIfEE N11onnxruntime2ml23ArrayFeatureExtractorOpIdEE N11onnxruntime2ml23ArrayFeatureExtractorOpIiEE N11onnxruntime2ml23ArrayFeatureExtractorOpIlEE N11onnxruntime2ml23ArrayFeatureExtractorOpISsEE Binarizer Input data with index: is NaN N11onnxruntime2ml11BinarizerOpIfEE CastMap cast_to TO_STRING TO_INT64 Invalid CAST_TO value of map_form DENSE Expected DENSE or SPARSE Invalid PACK_MAP value of max_map TO_FLOAT onnxruntime::ml::CastMap::CastMap(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.h info.GetAttr<std::string>("cast_to", &attr).IsOK() Expected TO_FLOAT, TO_STRING or TO_INT64 onnxruntime::ml::CAST_TO onnxruntime::ml::MakeCast(const string&) /onnxruntime_src/onnxruntime/core/providers/cpu/ml/ml_common.h info.GetAttr<std::string>("map_form", &attr).IsOK() onnxruntime::ml::PACK_MAP onnxruntime::ml::MakePack(const string&) info.GetAttr<int64_t>("max_map", &max_map_).IsOK() max_map must be > 0 if map_form is SPARSE map_form_ != PACK_MAP::SPARSE || max_map_ > 0 %f const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, float>] Negative index values are not permitted. First entry in map has index value of onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = std::basic_string<char>] /onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.cc cur_input == end_input || cur_input->first >= 0 const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, std::basic_string<char> >] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = std::basic_string<char>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = long int] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = float] stof onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = float] stoll onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = long int] Expected std::map<int64_t, float> or std::map<int64_t, std::string> Invalid input type of value: Unexpected CAST_TO value of N11onnxruntime2ml7CastMapE CategoryMapper Input of string must have output of int64 Input of int64 must have output of string cats_strings cats_int64s default_string default_int64 onnxruntime::ml::CategoryMapper::CategoryMapper(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/ml/category_mapper.h info.GetAttrs<std::string>("cats_strings", string_categories).IsOK() info.GetAttrs<int64_t>("cats_int64s", int_categories).IsOK() info.GetAttr<std::string>("default_string", &default_string_).IsOK() info.GetAttr<int64_t>("default_int64", &default_int_).IsOK() num_entries == int_categories.size() N11onnxruntime2ml14CategoryMapperE DictVectorizer onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = long int] /onnxruntime_src/onnxruntime/core/providers/cpu/ml/dictvectorizer.h info.GetAttrs(std::is_same<AttrType, std::string>::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK() onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = float] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = double] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = std::basic_string<char>] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = float] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = double] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, long int>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, double>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, double>] N11onnxruntime2ml16DictVectorizerOpISslEE N11onnxruntime2ml16DictVectorizerOpISsfEE N11onnxruntime2ml16DictVectorizerOpISsdEE N11onnxruntime2ml16DictVectorizerOpIlSsEE N11onnxruntime2ml16DictVectorizerOpIlfEE N11onnxruntime2ml16DictVectorizerOpIldEE string_vocabularint64_vocabularyFeatureVectorizer inputdimensions attribute must be provided onnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.h status.IsOK() && !input_dimensions_.empty() Number of inputs ( input_tensor_ptr != nullptr Invalid input type: ) does not match number of inputdimensions values ( virtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext*) const /onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.cc input_count >= 0 && static_cast<size_t>(input_count) == input_dimensions_.size() N11onnxruntime2ml17FeatureVectorizerE Imputer Empty value of imputed values. Empty input dimensions. imputed_value_floats imputed_value_int64s replaced_value_float replaced_value_int64 Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specified onnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/ml/imputer.cc Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specified Must provide imputed_values_float_ or imputed_values_int64_ but not both. imputed_values_float_.empty() ^ imputed_values_int64_.empty() std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = float; _Alloc = std::allocator<float>; std::vector<_Tp, _Alloc>::const_reference = const float&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] virtual onnxruntime::common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext*) const Invalid type N11onnxruntime2ml9ImputerOpE LabelEncoder keys_int64s values_floats default_float keys_floats values_int64s keys_strings values_strings _Unused Input of tensor(string) must have output of tensor(int64) Input of tensor(int64) must have output of tensor(string) classes_strings onnxruntime::ml::LabelEncoder::LabelEncoder(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/ml/label_encoder.h info.GetAttrs<std::string>("classes_strings", string_classes).IsOK() onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = std::basic_string<char>] info.GetAttrs<TKey>(_key_field_name, keys).IsOK() info.GetAttrs<TValue>(_value_field_name, values).IsOK() However, the number of key is attribtues in LabelEncoder The values is and the number of ) must have the same length. num_keys == num_values onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string<char>; TValue = long int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = std::basic_string<char>] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = std::basic_string<char>] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = std::basic_string<char>] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string<char>; TValue = float] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = float] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = long int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long int] N11onnxruntime2ml12LabelEncoderE N11onnxruntime2ml14LabelEncoder_2IfSsEE N11onnxruntime2ml14LabelEncoder_2ISsfEE N11onnxruntime2ml14LabelEncoder_2IlfEE N11onnxruntime2ml14LabelEncoder_2IflEE N11onnxruntime2ml14LabelEncoder_2IlSsEE N11onnxruntime2ml14LabelEncoder_2ISslEE N11onnxruntime2ml14LabelEncoder_2IllEE LinearClassifier void onnxruntime::ml::batched_update_scores_inplace(gsl::span<T>, int64_t, int64_t, onnxruntime::ml::POST_EVAL_TRANSFORM, int, bool, onnxruntime::concurrency::ThreadPool*) [with T = float; int64_t = long int] scores.size() == static_cast<size_t>(expected_num_scores) Unexpected value for 'add_second_class' of multi_class NONE post_transform SOFTMAX SOFTMAX_ZERO intercepts classlabels_strings classlabels_ints coefficients !coefficients_.empty() LOGISTIC onnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearclassifier.cc Scores output is incorrect size. Expected: void onnxruntime::ml::LinearClassifier::ComputeImpl(gsl::span<const float>, int64_t, int64_t, int64_t, const std::vector<float>&, const std::vector<float>&, onnxruntime::Tensor&, onnxruntime::Tensor&, onnxruntime::ml::POST_EVAL_TRANSFORM, bool, onnxruntime::concurrency::ThreadPool*) const scores_output_data.length() >= scores_output_size Input shape needs to be at least a single dimension. void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span<float>&) [with SrcType = int] void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span<float>&) [with SrcType = long int] void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span<float>&) [with SrcType = double] Unsupported input element type of shape_size == out.length() N11onnxruntime2ml16LinearClassifierE ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_EEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE_ ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_EEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE0_ ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_EEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE1_ @@3ֳLinearRegressor onnxruntime::ml::LinearRegressor::LinearRegressor(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearregressor.cc info.GetAttr<int64_t>("targets", &num_targets_).IsOK() info.GetAttrs<float>("coefficients", coefficients_).IsOK() Input shape had more than 2 dimension. Dims= Unsupported data type of N11onnxruntime2ml15LinearRegressorE onnxruntime::ml::Normalizer::Normalizer(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.h info.GetAttr<std::string>("norm", &norm).IsOK() onnxruntime::ml::NORMALIZE onnxruntime::ml::MakeNormalize(const string&) Invalid normalize value of Rank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of N11onnxruntime2ml10NormalizerE OneHotEncoder Unknown Category and zeros = 0. One and only one of the 'cats_*' attributes must be defined onnxruntime::ml::OneHotEncoderOp<T>::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = long int] /onnxruntime_src/onnxruntime/core/providers/cpu/ml/onehotencoder.cc tmp_cats_int64s.empty() || tmp_cats_strings.empty() num_categories_ > 0 onnxruntime::ml::OneHotEncoderOp<T>::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = float] onnxruntime::ml::OneHotEncoderOp<T>::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::OneHotEncoderOp<T>::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = std::basic_string<char>] N11onnxruntime2ml15OneHotEncoderOpIlEE N11onnxruntime2ml15OneHotEncoderOpIfEE N11onnxruntime2ml15OneHotEncoderOpIdEE N11onnxruntime2ml15OneHotEncoderOpISsEE Empty scale in attributes !scale_.empty() Scale size: ( ) != ( onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = float] /onnxruntime_src/onnxruntime/core/providers/cpu/ml/scaler.cc scale_.size() == offset_.size() onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = long int] onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = int] Invalid argument: input has empty dimensions. Either both scale and offset can be of feature size ( ) or 1 ZNK11onnxruntime2ml8ScalerOpIiE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime2ml8ScalerOpIiE7ComputeEPNS_15OpKernelContextEEUllE1_ ZNK11onnxruntime2ml8ScalerOpIlE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime2ml8ScalerOpIlE7ComputeEPNS_15OpKernelContextEEUllE1_ ZNK11onnxruntime2ml8ScalerOpIdE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime2ml8ScalerOpIdE7ComputeEPNS_15OpKernelContextEEUllE1_ ZNK11onnxruntime2ml8ScalerOpIfE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime2ml8ScalerOpIfE7ComputeEPNS_15OpKernelContextEEUllE1_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRSt8functionIFvlEEEEvPS1_lOT_lEUllE_ N11onnxruntime2ml8ScalerOpIfEE N11onnxruntime2ml8ScalerOpIdEE N11onnxruntime2ml8ScalerOpIlEE N11onnxruntime2ml8ScalerOpIiEE SVMClassifier LINEAR kernel_type RBF kernel_params POLY onnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.h info.GetAttrs<float>("kernel_params", kernel_params).IsOK() vectors_per_class prob_a prob_b support_vectors rho coefficients_.size() > 0 onnxruntime::ml::SVMClassifier::SVMClassifier(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.cc info.GetAttrs<float>("rho", rho_).IsOK() proba_.size() == probb_.size() info.GetAttrs<std::string>("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs<int64_t>("classlabels_ints", classlabels_ints_).IsOK() classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0 N11onnxruntime2ml9SVMCommonE N11onnxruntime2ml13SVMClassifierE *ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIRZNKS_2ml13SVMClassifier11ComputeImplERNS_15OpKernelContextEN3gsl4spanIKfEERKNS_11TensorShapeEEUllE_EEvPS1_lOT_lEUllE_ 4
+ף;SVMRegressor n_supports one_class onnxruntime::ml::SVMRegressor<T>::SVMRegressor(const onnxruntime::OpKernelInfo&) [with T = float] /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmregressor.cc onnxruntime::common::Status onnxruntime::ml::SVMRegressor<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] num_features == feature_count_ Unexpected mode: ZN11onnxruntime10IAllocator13MakeUniquePtrIfEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPfE_ N11onnxruntime2ml12SVMRegressorIfEE TreeEnsembleClassifier onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::TreeEnsembleCommon(int, int, const string&, const std::vector<IT>&, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const std::vector<long int>&, const std::vector<std::basic_string<char> >&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const string&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&) [with ITYPE = long int; OTYPE = float; std::string = std::basic_string<char>; int64_t = long int] /onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_common.h nodes_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() std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::ml::NODE_MODE; _Alloc = std::allocator<onnxruntime::ml::NODE_MODE>; std::vector<_Tp, _Alloc>::reference = onnxruntime::ml::NODE_MODE&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::ml::detail::TreeNodeElement<float>; _Alloc = std::allocator<onnxruntime::ml::detail::TreeNodeElement<float> >; std::vector<_Tp, _Alloc>::reference = onnxruntime::ml::detail::TreeNodeElement<float>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] One falsenode is pointing either to itself, either to another tree. n_targets_or_classes > 0 AVERAGE SUM MIN BRANCH_LEQ LEAF BRANCH_LT BRANCH_GTE BRANCH_GT BRANCH_EQ (truenode). Unable to find node (falsenode). (weights). is already there. in tree Node classlabels_int64s class_weights class_treeids class_nodeids class_ids nodes_values nodes_truenodeids nodes_treeids nodes_nodeids nodes_modes nodes_hitrates nodes_featureids nodes_falsenodeids base_values aggregate_function nodes_missing_value_tracks_true onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::TreeEnsembleCommon(int, int, const string&, const std::vector<IT>&, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const std::vector<long int>&, const std::vector<std::basic_string<char> >&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const string&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&) [with ITYPE = int; OTYPE = float; std::string = std::basic_string<char>; int64_t = long int] onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::TreeEnsembleCommon(int, int, const string&, const std::vector<IT>&, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const std::vector<long int>&, const std::vector<std::basic_string<char> >&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const string&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&) [with ITYPE = float; OTYPE = float; std::string = std::basic_string<char>; int64_t = long int] onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::TreeEnsembleCommon(int, int, const string&, const std::vector<IT>&, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const std::vector<long int>&, const std::vector<std::basic_string<char> >&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const string&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&) [with ITYPE = double; OTYPE = float; std::string = std::basic_string<char>; int64_t = long int] |\ׂSց std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::ml::detail::TreeNodeElement<float>*; _Alloc = std::allocator<onnxruntime::ml::detail::TreeNodeElement<float>*>; std::vector<_Tp, _Alloc>::const_reference = onnxruntime::ml::detail::TreeNodeElement<float>* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::ml::detail::ScoreValue<float>; _Alloc = std::allocator<onnxruntime::ml::detail::ScoreValue<float> >; std::vector<_Tp, _Alloc>::reference = onnxruntime::ml::detail::ScoreValue<float>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::ml::detail::SparseValue<float>; _Alloc = std::allocator<onnxruntime::ml::detail::SparseValue<float> >; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::ml::detail::SparseValue<float>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] ԆĆ䆜 |>HdL l Ύ\ĖdԖz4F std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector<onnxruntime::ml::detail::ScoreValue<float>, std::allocator<onnxruntime::ml::detail::ScoreValue<float> > >; _Alloc = std::allocator<std::vector<onnxruntime::ml::detail::ScoreValue<float>, std::allocator<onnxruntime::ml::detail::ScoreValue<float> > > >; std::vector<_Tp, _Alloc>::reference = std::vector<onnxruntime::ml::detail::ScoreValue<float>, std::allocator<onnxruntime::ml::detail::ScoreValue<float> > >&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = long int; OTYPE = float] /onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_aggregator.h it->i < (int64_t)predictions.size() void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = double; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = int; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = float; OTYPE = float; int64_t = long int] void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = onnxruntime::ml::detail::ScoreValue<float>; _Alloc = std::allocator<onnxruntime::ml::detail::ScoreValue<float> >] int64_t onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::_set_score_binary(int&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = float; OTYPE = float; int64_t = long int] classes.size() == 2 || classes.size() == 1 std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::ml::detail::ScoreValue<float>; _Alloc = std::allocator<onnxruntime::ml::detail::ScoreValue<float> >; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::ml::detail::ScoreValue<float>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] predictions.size() == 2 void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = float; OTYPE = float] predictions.size() == predictions2.size() compute void onnxruntime::ml::detail::TreeEnsembleCommonClassifier<ITYPE, OTYPE>::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = float; OTYPE = float] X dims is empty. void onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = double; OTYPE = float; int64_t = long int] int64_t onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::_set_score_binary(int&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = double; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = double; OTYPE = float] void onnxruntime::ml::detail::TreeEnsembleCommonClassifier<ITYPE, OTYPE>::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = double; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = long int; OTYPE = float; int64_t = long int] int64_t onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::_set_score_binary(int&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = long int; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = long int; OTYPE = float] void onnxruntime::ml::detail::TreeEnsembleCommonClassifier<ITYPE, OTYPE>::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = long int; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = int; OTYPE = float; int64_t = long int] int64_t onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::_set_score_binary(int&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = int; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = int; OTYPE = float] void onnxruntime::ml::detail::TreeEnsembleCommonClassifier<ITYPE, OTYPE>::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = int; OTYPE = float] ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS4_24TreeAggregatorClassifierIifEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS4_24TreeAggregatorClassifierIifEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS4_24TreeAggregatorClassifierIlfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS4_24TreeAggregatorClassifierIlfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_24TreeAggregatorClassifierIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_24TreeAggregatorClassifierIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_24TreeAggregatorClassifierIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_24TreeAggregatorClassifierIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ N11onnxruntime2ml22TreeEnsembleClassifierIfEE N11onnxruntime2ml22TreeEnsembleClassifierIdEE N11onnxruntime2ml22TreeEnsembleClassifierIlEE N11onnxruntime2ml22TreeEnsembleClassifierIiEE TreeEnsembleRegressor target_weights target_treeids target_nodeids target_ids n_targets void onnxruntime::ml::detail::TreeAggregatorAverage<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = float; OTYPE = float; int64_t = long int] this->base_values_.size() == predictions.size() void onnxruntime::ml::detail::TreeAggregatorAverage<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = double; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregator<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = float; OTYPE = float; int64_t = long int] predictions.size() == (size_t)n_targets_or_classes_ void onnxruntime::ml::detail::TreeAggregatorMax<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorMin<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeAggregator<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = double; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorMax<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = double; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorMin<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = double; OTYPE = float] Unknown aggregation function in TreeEnsemble. void onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = double; OTYPE = float] void onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = float; OTYPE = float] ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_21TreeAggregatorAverageIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_21TreeAggregatorAverageIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_17TreeAggregatorSumIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_17TreeAggregatorSumIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_17TreeAggregatorMinIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_17TreeAggregatorMinIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_17TreeAggregatorMaxIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS4_17TreeAggregatorMaxIdfEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_21TreeAggregatorAverageIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_21TreeAggregatorAverageIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_17TreeAggregatorSumIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_17TreeAggregatorSumIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_17TreeAggregatorMinIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_17TreeAggregatorMinIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_17TreeAggregatorMaxIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE_EEvSA_lOSF_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS4_17TreeAggregatorMaxIffEEEEvPS1_PKNS_6TensorEPSB_SE_RKT_EUllE2_EEvSA_lOSF_lEUllE_ N11onnxruntime2ml21TreeEnsembleRegressorIfEE N11onnxruntime2ml21TreeEnsembleRegressorIdEE ZipMap Must provide classlabels_strings or classlabels_int64s but not both. onnxruntime::ml::ZipMapOp::ZipMapOp(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/ml/zipmap.cc classlabels_strings_.empty() ^ classlabels_int64s_.empty() Zipmap does not support empty dim count Zipmap only supports 1D or 2D input tensors T* OrtValue::GetMutable() [with T = std::vector<std::map<std::basic_string<char>, float> >] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::map<std::basic_string<char>, float>; _Alloc = std::allocator<std::map<std::basic_string<char>, float> >; std::vector<_Tp, _Alloc>::reference = std::map<std::basic_string<char>, float>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] T* OrtValue::GetMutable() [with T = std::vector<std::map<long int, float> >] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::map<long int, float>; _Alloc = std::allocator<std::map<long int, float> >; std::vector<_Tp, _Alloc>::reference = std::map<long int, float>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] Input features_per_batch[ ] != number of classlabels[ N11onnxruntime2ml8ZipMapOpE MaxUnpool No kernel shape is set. kernel_shape_[dim] > 0 onnxruntime::MaxUnpool::MaxUnpool(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/nn/unpool.h info.GetAttrs<int64_t>("kernel_shape", kernel_shape_).IsOK() Pad should be smaller than kernel. pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim] strides_.size() == kernel_shape_.size() /onnxruntime_src/onnxruntime/core/providers/cpu/nn/Unpool.cc Input dimension cannot be less than 3. Index tensor shape should be same as that of the input data tensor to unpool. Shape must be 1 dimensional as it's tensor data of a shape output_shape is smaller than minimum required. output_shape: Unsupported pooling size. inferred output shape: N11onnxruntime9MaxUnpoolE st.IsOK() !is_train_ onnxruntime::BatchNorm<T>::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = float] /onnxruntime_src/onnxruntime/core/providers/cpu/nn/batch_norm.h Training mode is not supported in this build. onnxruntime::BatchNorm<T>::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = double] 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 B: NumDimensions() != Invalid input B: 0th dimension != Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input var: NumDimensions() != Invalid input var: 0th dimension != Invalid input scale: dimension != Invalid input B: Invalid input mean: Invalid input var: N11onnxruntime9BatchNormIfEE N11onnxruntime9BatchNormIdEE Unknown AutoPadType String onnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&) A Conv/ConvTranspose node has both 'auto_pad' and 'pads' attributes onnxruntime::ConvAttributes::ConvAttributes(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_attributes.h auto_pad == AutoPadType::NOTSET X num_dims does not match W num_dims. std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::back() const [with _Tp = long int; _Alloc = std::allocator<long int>; std::vector<_Tp, _Alloc>::const_reference = const long int&] Input channels C is not equal to kernel channels * group. Output channels M is not divisible by group. X: C: kernel channels: group: M: Not enough elements in strides. Expected: Not enough elements in kernel shape. Expected: Not enough elements in dilations. Expected: Not enough elements in pads. Expected: Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER. ComputePad: pad type not supported. ComputePadAndOutputShape InferOutputShape Invalid input shape: kernel_shape num_dims is not compatible with W num_dims. kernel_shape is not compatible with W shape. kernel_shape: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv.cc N11onnxruntime4ConvIfEE ConvInteger Must be a scalar or 1D tensor or size 1. virtual onnxruntime::common::Status onnxruntime::ConvInteger::Compute(onnxruntime::OpKernelContext*) const /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_integer.cc IsScalarOr1ElementVector(X_Zero_Point) Non per-tensor quantization is not supported now. IsScalarOr1ElementVector(W_Zero_Point) N11onnxruntime11ConvIntegerE output_padding output_shape void onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape(int64_t, int64_t, int64_t, int64_t, int64_t, onnxruntime::AutoPadType, int64_t*, int64_t*, int64_t*) const /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.h void onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(onnxruntime::TensorShape, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, int64_t, std::vector<long int>*, std::vector<long int>*) const *out_size >= 0 dim_size > 0 filter number not equal to input channel number. Input channels is not divisible by group. group count is <= 0 filter_number: num_input_channels: PrepareForCompute DoConvTranspose /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose.cc N11onnxruntime13ConvTransposeIfEE ratio input should have a single value. float onnxruntime::{anonymous}::GetRatioOrDefault(const onnxruntime::Tensor*) [with T2 = float] /onnxruntime_src/onnxruntime/core/providers/cpu/nn/dropout_op.h ratio_tensor->Shape().Size() == 1 ratio must be in the range [0, 1) 0.0f <= ratio_value && ratio_value < 1.0f float onnxruntime::{anonymous}::GetRatioOrDefault(const onnxruntime::Tensor*) [with T2 = double] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = double] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = bool] X and mask should have the same shape onnxruntime::common::Status onnxruntime::Dropout<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = double; T2 = double] !mask || mask->Shape() == X_shape onnxruntime::common::Status onnxruntime::Dropout<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = float; T2 = double] onnxruntime::common::Status onnxruntime::Dropout<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = double; T2 = float] onnxruntime::common::Status onnxruntime::Dropout<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = float; T2 = float] N11onnxruntime7DropoutIffEE N11onnxruntime7DropoutIfdEE N11onnxruntime7DropoutIdfEE N11onnxruntime7DropoutIddEE Flatten onnxruntime::Flatten::Flatten(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/nn/flatten.h info.GetAttr<int64_t>("axis", &axis_).IsOK() The rank of input tensor must be >= axis virtual onnxruntime::common::Status onnxruntime::Flatten::Compute(onnxruntime::OpKernelContext*) const gsl::narrow_cast<int64_t>(X_shape.NumDimensions()) >= axis N11onnxruntime7FlattenE InstanceNormalization onnxruntime::InstanceNorm<T>::InstanceNorm(const onnxruntime::OpKernelInfo&) [with T = float] /onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.h op_kernel_info.GetAttr<float>("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 Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count vs. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.cc N11onnxruntime12InstanceNormIfEE LpNormalization p_ == 1 || p_ == 2 onnxruntime::LpNorm<T>::LpNorm(const onnxruntime::OpKernelInfo&) [with T = float] /onnxruntime_src/onnxruntime/core/providers/cpu/nn/lp_norm.h op_kernel_info.GetAttr<int64_t>("axis", &axis_).IsOK() op_kernel_info.GetAttr<int64_t>("p", &p_).IsOK() onnxruntime::LpNorm<T>::LpNorm(const onnxruntime::OpKernelInfo&) [with T = double] N11onnxruntime6LpNormIfEE N11onnxruntime6LpNormIdEE LRN size_ > 0 size_ % 2 == 1 alpha_ > 0.0f beta_ > 0.0f onnxruntime::LRN<T>::LRN(const onnxruntime::OpKernelInfo&) [with T = float] /onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.h info.GetAttr<int64_t>("size", &size).IsOK() onnxruntime::common::Status onnxruntime::LRN<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] /onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.cc X->Shape().NumDimensions() == 4 N11onnxruntime3LRNIfEE N11onnxruntime8functors4PowxIfEE t@GlobalLpPool void onnxruntime::PoolProcessContext::init(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_base.h info.GetAttr<int64_t>("p", &p_).IsOK() void onnxruntime::PoolAttributes::InferOutputSize(const std::vector<long int>&, std::vector<long int>*, std::vector<long int>*) const /onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool_attributes.h void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) const input_dims.size() >= 2 Unsupported AutoPad Type. Invalid input shape. Only N can be zero. Got: std::vector<long int> onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, std::vector<long int>*) const input_shape.Size() > 0 || input_shape[0] == 0 /onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool.cc kernel_shape num_dims is not compatible with X num_dims. Unsupported pooling size : ceil_mode count_include_pad kernel_shape[dim] > 0 onnxruntime::PoolAttributes::PoolAttributes(const onnxruntime::OpNodeProtoHelper<onnxruntime::ProtoHelperNodeContext>&, const string&, int) info.GetAttrs<int64_t>("kernel_shape", kernel_shape).IsOK() info.GetAttr<std::string>("auto_pad", &auto_padding).IsOK() info.GetAttr<int64_t>("count_include_pad", &temp).IsOK() info.GetAttr("storage_order", &storage_order).IsOK() pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim] strides.size() == kernel_shape.size() Dilations dimensions should match kernel shape dilations.size() == kernel_shape.size() ComputeImpl N11onnxruntime8PoolBaseE N11onnxruntime10Pool1DTaskIfNS_6LpPoolEEE N11onnxruntime10Pool2DTaskIfNS_6LpPoolEEE N11onnxruntime10Pool3DTaskIfNS_6LpPoolEEE N11onnxruntime9MaxPoolV8E N11onnxruntime4PoolIfNS_7MaxPoolILi1EEEEE N11onnxruntime4PoolIfNS_11AveragePoolEEE N11onnxruntime4PoolIfNS_6LpPoolEEE N11onnxruntime13MaxPool1DTaskIfEE N11onnxruntime13MaxPool2DTaskIfEE N11onnxruntime13MaxPool3DTaskIfEE N11onnxruntime13MaxPool1DTaskIdEE N11onnxruntime13MaxPool2DTaskIdEE N11onnxruntime13MaxPool3DTaskIdEE N11onnxruntime13MaxPool1DTaskIaEE N11onnxruntime13MaxPool2DTaskIaEE N11onnxruntime13MaxPool3DTaskIaEE N11onnxruntime13MaxPool1DTaskIhEE N11onnxruntime13MaxPool2DTaskIhEE N11onnxruntime13MaxPool3DTaskIhEE T4 virtual onnxruntime::common::Status onnxruntime::QLinearConv::UseSharedPrePackedBuffers(std::vector<std::unique_ptr<void, onnxruntime::BufferDeleter> >&, int, bool&) /onnxruntime_src/onnxruntime/core/providers/cpu/nn/qlinearconv.cc prepacked_buffers[0].get() == nullptr QLinearConv : input zero point must be a scalar or 1D tensor of size 1 virtual onnxruntime::common::Status onnxruntime::QLinearConv::Compute(onnxruntime::OpKernelContext*) const IsScalarOr1ElementVector(X_zero_point) QLinearConv : result zero point must be a scalar or 1D tensor of size 1 IsScalarOr1ElementVector(Y_zero_point) QLinearConv : filter zero point shape invalid QLinearConv : filter zero point must be constant QLinearConv : input scale must be a scalar or 1D tensor of size 1 IsScalarOr1ElementVector(X_scale) QLinearConv : result scale must be a scalar or 1D tensor of size 1 IsScalarOr1ElementVector(Y_scale) QLinearConv : filter scale shape invalid N11onnxruntime11QLinearConvE *ZNK11onnxruntime11QLinearConv7ComputeEPNS_15OpKernelContextEEUllE_ @ >MaxRoiPool pooled_shape pooled_shape.size() == 2 pooled_height_ > 0 pooled_width_ > 0 spatial_scale spatial_scale_ > 0 onnxruntime::RoiPool<T>::RoiPool(const onnxruntime::OpKernelInfo&) [with T = float] /onnxruntime_src/onnxruntime/core/providers/cpu/nn/roi_pool.h info.GetAttrs<int64_t>("pooled_shape", pooled_shape).IsOK() info.GetAttr<float>("spatial_scale", &spatial_scale_).IsOK() onnxruntime::common::Status onnxruntime::RoiPool<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] /onnxruntime_src/onnxruntime/core/providers/cpu/nn/roi_pool.cc R->Shape()[1] == 5 roi_batch_id >= 0 roi_batch_id < batch_size N11onnxruntime7RoiPoolIfEE Shrink onnxruntime::Shrink::Shrink(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/nn/shrink.h op_kernel_info.GetAttr<float>("bias", &bias_temp).IsOK() op_kernel_info.GetAttr<float>("lambd", &lambd_temp).IsOK() N11onnxruntime6ShrinkE StringNormalizer :Please, install necessary language-pack-XX and configure locales Failed to construct locale with name: onnxruntime::string_normalizer::Locale::Locale(const string&)::<lambda()> /onnxruntime_src/onnxruntime/core/providers/cpu/nn/string_normalizer.cc WCHAR_T Input contains invalid utf8 chars at: Single dimension value must be greater than 0 Input dimensions are either[C > 0] or [1][C > 0] allowed is_case_sensitive case_change_action locale stopwords Empty stopwords not allowed !sw.empty() p.second wstr != wconv_error attribute is_case_sensitive is not set onnxruntime::StringNormalizer::StringNormalizer(const onnxruntime::OpKernelInfo&) attribute case_change_action is not set attribute case_change_action has invalid value Duplicate stopwords not allowed Stopword contains invalid utf8 chars Conversion Error en_US.UTF-8 C o n v e r s i o n E r r o r N11onnxruntime16StringNormalizerE TfIdfVectorizer typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::TfIdfVectorizer::Impl; _Dp = std::default_delete<onnxruntime::TfIdfVectorizer::Impl>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::TfIdfVectorizer::Impl&] Input shape must have either [C] or [B,C] dimensions with B > 0. id: p.first->second->id_ == 0 Duplicate ngram detected, size: size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map&) [with K = std::basic_string<char>; ForwardIter = __gnu_cxx::__normal_iterator<std::reference_wrapper<const std::basic_string<char> >*, std::vector<std::reference_wrapper<const std::basic_string<char> > > >; Map = std::unordered_map<std::reference_wrapper<const std::basic_string<char> >, std::unique_ptr<onnxruntime::ngram_details::NgramPart<std::basic_string<char> > >, std::hash<std::basic_string<char> >, std::equal_to<std::basic_string<char> > >; size_t = long unsigned int] /onnxruntime_src/onnxruntime/core/providers/cpu/nn/tfidfvectorizer.cc size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map&) [with K = long int; ForwardIter = const long int*; Map = std::unordered_map<long int, std::unique_ptr<onnxruntime::ngram_details::NgramPart<long int> > >; size_t = long unsigned int] mode is required TFIDF mode: min_gram_length min_gram_length is required impl_->min_gram_length_ > 0 max_gram_length >= max_skip_count max_skip_count is required impl_->max_skip_count_ >= 0 ngram_counts <= ngram_indexes must be of equal size but ngram_indexes size: Got weights of size: pool_strings !pool_strings.empty() pool_int64s -grams (items % ngram_size == 0) TF onnxruntime::TfIdfVectorizer::TfIdfVectorizer(const onnxruntime::OpKernelInfo&) is unrecognized, acceptable values are TF,IDF,TFIDF impl_->weighting_criteria_ != kNone Required min_gram_length must be positive: min_gram_length >= max_gram_length required: impl_->max_gram_length_ >= impl_->min_gram_length_ max_skip_count must be non-negative: Non-empty ngram_counts is required status.IsOK() && !impl_->ngram_counts_.empty() min_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size() max_gram_length must be inbounds of ngram_counts: size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size() Non-empty ngram_indexes is required status.IsOK() && !impl_->ngram_indexes_.empty() Negative ngram_indexes values are not allowed std::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; }) impl_->weights_.size() == impl_->ngram_indexes_.size() pool_strings must not be empty if specified non-empty pool_int64s is required if pool_strings not provided status.IsOK() && !pool_int64s.empty() n-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_items Number of items must compose whole std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = unsigned int; _Alloc = std::allocator<unsigned int>; std::vector<_Tp, _Alloc>::reference = unsigned int&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] N11onnxruntime15TfIdfVectorizerE *ZNK11onnxruntime15TfIdfVectorizer7ComputeEPNS_15OpKernelContextEEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForISt8functionIFvlEEEEvPS1_lOT_lEUllE_ G G G GNonMaxSuppression /onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.cc iou_threshold must be in range [0, 1]. GetThresholdsFromInputs center_point_box only support 0 or 1 onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.h 0 == center_point_box_ || 1 == center_point_box_ static onnxruntime::common::Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::PrepareContext&) boxes and scores should have same num_batches. boxes and scores should have same spatial_dimension. The most inner dimension in boxes must have 4 data. boxes_tensor scores_tensor boxes must be a 3D tensor. scores must be a 3D tensor. output != nullptr void std::priority_queue<_Tp, _Sequence, _Compare>::pop() [with _Tp = onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const::BoxInfoPtr; _Sequence = std::vector<onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const::BoxInfoPtr>; _Compare = std::less<onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const::BoxInfoPtr>] /opt/rh/devtoolset-9/root/usr/include/c++/9/bits/stl_queue.h virtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime21NonMaxSuppressionBaseE N11onnxruntime17NonMaxSuppressionE center_point_boxobject_detection/non_max_suppresstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::{anonymous}::PreCalc<double>; _Alloc = std::allocator<onnxruntime::{anonymous}::PreCalc<double> >; std::vector<_Tp, _Alloc>::reference = onnxruntime::{anonymous}::PreCalc<double>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::{anonymous}::PreCalc<float>; _Alloc = std::allocator<onnxruntime::{anonymous}::PreCalc<float> >; std::vector<_Tp, _Alloc>::reference = onnxruntime::{anonymous}::PreCalc<float>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] RoiAlign Null input X ptr Null rois_ptr Null batch_indices_ptr Number of dimensions for batch indices should be exactly 1 Number 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 match avg Invalid mode of value output_height output_width sampling_ratio sampling_ratio_ >= 0 specified. It should be either avg or max onnxruntime::RoiAlignBase::RoiAlignBase(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/roialign.h Sampling ratio should be >=0, but it was N11onnxruntime12RoiAlignBaseE *ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIdEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUlllE_ *ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIfEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUlllE_ N11onnxruntime8RoiAlignIfEE N11onnxruntime8RoiAlignIdEE ReduceL2 ReduceL1 ArgMax ArgMin ReduceLogSum ReduceMin ReduceProd ReduceLogSumExp ReduceSumSquare Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape: void onnxruntime::ValidateKeepDims(const onnxruntime::TensorShape&, int64_t) /onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.cc keepdims void onnxruntime::ResultsNoTransposePrepareForReduce::ValidateNotEmpty() last_loop_red_size > 0 last_loop_size > 0 projected_index.size() > 0 must be overloaded. void onnxruntime::ValidateMustBeOverloaded() Reduction on all axes, output size should be 1. void onnxruntime::ValidateNoTransposeReduce(int64_t) count == 1 Axes input is null axes_tensor != nullptr void onnxruntime::ValidateCommonFastReduce(const onnxruntime::Tensor*) An axes tensor must be a vector tensor. axes_tensor->Shape().NumDimensions() == 1 Only works on matrices with two dimensions. void onnxruntime::ValidateFastReduceKR(const std::vector<long int>&, const onnxruntime::Tensor&) fast_shape[0] == output.Shape().Size() fast_shape.size() == 2 Output size mismatch. void onnxruntime::ValidateFastReduceRK(const std::vector<long int>&, const onnxruntime::Tensor&) fast_shape[1] == output.Shape().Size() void onnxruntime::ValidateFastReduceKRK(const std::vector<long int>&, const onnxruntime::Tensor&) fast_shape[0] * fast_shape[2] == output.Shape().Size() fast_shape.size() == 3 noop_with_empty_axes select_last_index onnxruntime::ReduceKernelBase<allow_multi_axes>::ReduceKernelBase(const onnxruntime::OpKernelInfo&, nonstd::optional_lite::optional<long int>) [with bool allow_multi_axes = false] /onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.h info.GetAttr("keepdims", &keepdims).IsOK() onnxruntime::ReduceKernelBase<allow_multi_axes>::ReduceKernelBase(const onnxruntime::OpKernelInfo&, nonstd::optional_lite::optional<long int>) [with bool allow_multi_axes = true] N11onnxruntime16ReduceKernelBaseILb0EEE N11onnxruntime16ReduceKernelBaseILb1EEE ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMinLastIndexIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMinIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMinLastIndexIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMinIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMinLastIndexIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMinIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_31ReduceAggregatorArgMaxLastIndexIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorArgMaxIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_25ReduceAggregatorSumSquareIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_25ReduceAggregatorSumSquareIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_25ReduceAggregatorSumSquareIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorProdIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorProdIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorProdIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIhhEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIaaEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMinIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorMeanIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorMeanIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_20ReduceAggregatorMeanIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIhhEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIaaEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorMaxIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime23NoTransposeReduce2LoopsINS_25ReduceAggregatorLogSumExpIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime23NoTransposeReduce2LoopsINS_25ReduceAggregatorLogSumExpIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime23NoTransposeReduce2LoopsINS_25ReduceAggregatorLogSumExpIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorLogSumIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_22ReduceAggregatorLogSumIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL2IiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL2IffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL1IiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_18ReduceAggregatorL1IffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime19ReduceAggregatorMinIhhE12FastReduceKRERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMinIhhE12FastReduceRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMinIhhE13FastReduceKRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMinIaaE12FastReduceKRERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMinIaaE12FastReduceRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMinIaaE13FastReduceKRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMinIddE12FastReduceKRERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMinIddE12FastReduceRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMinIddE13FastReduceKRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMinIllE12FastReduceKRERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMinIllE12FastReduceRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMinIllE13FastReduceKRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMinIiiE12FastReduceKRERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMinIiiE12FastReduceRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMinIiiE13FastReduceKRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMinIffE12FastReduceKRERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMinIffE12FastReduceRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMinIffE13FastReduceKRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMaxIhhE12FastReduceKRERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMaxIhhE12FastReduceRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMaxIhhE13FastReduceKRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMaxIaaE12FastReduceKRERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMaxIaaE12FastReduceRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMaxIaaE13FastReduceKRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMaxIddE12FastReduceKRERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMaxIddE12FastReduceRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMaxIddE13FastReduceKRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMaxIllE12FastReduceKRERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMaxIllE12FastReduceRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMaxIllE13FastReduceKRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMaxIiiE12FastReduceKRERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMaxIiiE12FastReduceRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMaxIiiE13FastReduceKRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMaxIffE12FastReduceKRERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMaxIffE12FastReduceRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorMaxIffE13FastReduceKRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ N11onnxruntime12ReduceKernelILb1EEE N11onnxruntime12ReduceKernelILb0EEE N11onnxruntime8ReduceL1IfEE N11onnxruntime8ReduceL1IiEE N11onnxruntime8ReduceL2IfEE N11onnxruntime8ReduceL2IiEE N11onnxruntime12ReduceLogSumIfEE N11onnxruntime12ReduceLogSumIiEE N11onnxruntime15ReduceLogSumExpIfEE N11onnxruntime15ReduceLogSumExpIiEE N11onnxruntime15ReduceLogSumExpIdEE N11onnxruntime9ReduceMaxIfEE N11onnxruntime9ReduceMaxIiEE N11onnxruntime9ReduceMaxIlEE N11onnxruntime9ReduceMaxIdEE N11onnxruntime9ReduceMaxIaEE N11onnxruntime9ReduceMaxIhEE N11onnxruntime10ReduceMeanIfEE N11onnxruntime10ReduceMeanIiEE N11onnxruntime10ReduceMeanIdEE N11onnxruntime9ReduceMinIfEE N11onnxruntime9ReduceMinIiEE N11onnxruntime9ReduceMinIlEE N11onnxruntime9ReduceMinIdEE N11onnxruntime9ReduceMinIaEE N11onnxruntime9ReduceMinIhEE N11onnxruntime10ReduceProdIfEE N11onnxruntime10ReduceProdIiEE N11onnxruntime10ReduceProdIlEE N11onnxruntime9ReduceSumIfEE N11onnxruntime9ReduceSumIiEE N11onnxruntime9ReduceSumIlEE N11onnxruntime9ReduceSumIdEE N11onnxruntime15ReduceSumSquareIfEE N11onnxruntime15ReduceSumSquareIiEE N11onnxruntime15ReduceSumSquareIdEE N11onnxruntime6ArgMaxIfEE N11onnxruntime6ArgMaxIiEE N11onnxruntime6ArgMaxIdEE N11onnxruntime6ArgMinIfEE N11onnxruntime6ArgMinIiEE N11onnxruntime6ArgMinIdEE ZN11onnxruntime19ReduceAggregatorSumIffE12FastReduceKRERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorSumIffE12FastReduceRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorSumIffE13FastReduceKRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorSumIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime19ReduceAggregatorSumIiiE12FastReduceKRERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorSumIiiE12FastReduceRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorSumIiiE13FastReduceKRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorSumIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime19ReduceAggregatorSumIddE12FastReduceKRERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorSumIddE12FastReduceRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorSumIddE13FastReduceKRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorSumIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime19ReduceAggregatorSumIllE12FastReduceKRERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorSumIllE12FastReduceRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime19ReduceAggregatorSumIllE13FastReduceKRKERKNS_6TensorERKSt6vectorIlSaIlEERS2_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime22NoTransposeReduce1LoopINS_19ReduceAggregatorSumIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ GRU void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = const float*; TSpanBIter = const float*; TSpanCIter = float*] /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.h lda >= K && ldb >= K && ldc >= N A + (M * lda - (lda - K)) <= A_end B + (N * ldb - (ldb - K)) <= B_end C + (M * ldc - (ldc - N)) <= C_end void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = float*; TSpanBIter = const float*; TSpanCIter = float*] T* onnxruntime::rnn::detail::SafeRawPointer(gsl::span<T>, size_t, size_t) [with T = float; size_t = long unsigned int] offset + size <= size_t(span.size()) const T* onnxruntime::rnn::detail::SafeRawConstPointer(typename gsl::span<T>::const_iterator, typename gsl::span<T>::const_iterator, size_t) [with T = float; typename gsl::span<T>::const_iterator = const float*; size_t = long unsigned int] cur + size <= end T* onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span<T>::iterator, typename gsl::span<T>::iterator, size_t) [with T = float; typename gsl::span<T>::iterator = float*; size_t = long unsigned int] linear_before_reset hidden_size activations activation_alpha activation_beta clip clip_ > 0.f forward reverse bidirectional layout layout_ == 0 onnxruntime::DeepCpuGruOp::DeepCpuGruOp(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.h info.GetAttr("direction", &direction).IsOK() info.GetAttr("linear_before_reset", &int64_value).IsOK() info.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0 '. Must be one of 'forward', 'reverse', or 'bidirectional'. Invalid 'direction' argument of ' onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&) activation_func_names.size() == static_cast<size_t>(num_directions_) * 2 Batchwise recurrent operations (layout == 1) are not supported. If you need support create a github issue with justification. /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.cc gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::rnn::detail::ActivationFuncs::Entry; _Alloc = std::allocator<onnxruntime::rnn::detail::ActivationFuncs::Entry>; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::rnn::detail::ActivationFuncs::Entry&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] GRU operator does not support double yet Invalid data type for GRU operator of virtual onnxruntime::common::Status onnxruntime::DeepCpuGruOp::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime12DeepCpuGruOpE ZN11onnxruntime10IAllocator13MakeUniquePtrIiEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPiE_ PrePack /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.cc input_forget onnxruntime::LSTMBase::LSTMBase(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/lstm_base.h activation_func_names.size() == static_cast<size_t>(num_directions_) * 3 LSTM operator does not support double yet Invalid data type for LSTM operator of virtual onnxruntime::common::Status onnxruntime::DeepCpuLstmOp::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime8LSTMBaseE N11onnxruntime13DeepCpuLstmOpE Input X must have 3 dimensions only. Actual: 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 { Input initial_c must have shape { Input B must have shape { }. Actual: Input P must have shape { /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/lstm_base.cc RNN Affine /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.cc onnxruntime::RNN<T>::RNN(const onnxruntime::OpKernelInfo&) [with T = float] /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.h 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<size_t>(num_directions) RNN op: Invalid activation attribute - allowed_activations.find(activations_[direction]) != allowed_activations.end() FffffE N11onnxruntime3RNNIfEE PFffffE affine leakyrelu thresholdedrelu scaledtanh hardsigmoid softplus softsign Invalid activation function of void (* onnxruntime::rnn::detail::deepcpu::ActivationFuncByName(const string&))(float*, int, float, float) /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.cc Invalid LSTM merge activation function of void (* onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByName(const string&))(const float*, float*, const float*, float*, int, float, float) Invalid GRU reset gate activation function: void (* onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByName(const string&))(const float*, float*, float*, int, float, float) Invalid GRU hidden gate activation function: void (* onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByName(const string&))(float*, const float*, const float*, float*, int, float, float) void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, const float*, const float*, const onnxruntime::rnn::detail::GemmWeights<float>&, float, float*, float*, int, uint8_t*, int32_t*, onnxruntime::concurrency::ThreadPool*) A + (M * K) <= A_end Expecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got std::string onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCount(const string&, std::vector<float>::const_iterator&, const const_iterator&, std::vector<float>::const_iterator&, const const_iterator&, float&, float&) void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, const float*, const float*, const onnxruntime::rnn::detail::GemmWeights<unsigned char>&, float, float*, float*, int, uint8_t*, int32_t*, onnxruntime::concurrency::ThreadPool*) Quantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0f alpha == 1.0f && (beta == 0.0f || beta == 1.0f) weights.quant_para_ Input W must have shape { Input R must have shape { ZN11onnxruntime24GetQuantizationParameterIhLb0ELb0ELi0EEEvPKflRfRT_PNS_11concurrency10ThreadPoolEEUlllE_ ZN11onnxruntime17ParQuantizeLinearIhEEvPKfPT_mfS3_PNS_11concurrency10ThreadPoolEEUlllE_ %%%%~3a*~3a*~3a*~3a*7777A \3A \3A \3A \3JCy7JCy7JCy7JCy7
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':Y;Ӡ5֕8;Y; A C p@const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span<T>, size_t, size_t) [with T = const float; size_t = long unsigned int] const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span<T>, size_t, size_t) [with T = float; size_t = long unsigned int] rows[ *ZN11onnxruntime4lstmL23ExecuteLambdaInParallelIZNS0_18UniDirectionalLstmIfE7ComputeIfEEvRKN3gsl4spanIKfEERKNS6_IKiEEiRKNS_3rnn6detail11GemmWeightsIT_EESL_RNS6_IfEESN_SN_EUliPNS_11concurrency10ThreadPoolEE_EEvSI_iidSQ_EUlllE_ *ZN11onnxruntime4lstmL23ExecuteLambdaInParallelIZNS0_18UniDirectionalLstmIfE7ComputeIhEEvRKN3gsl4spanIKfEERKNS6_IKiEEiRKNS_3rnn6detail11GemmWeightsIT_EESL_RNS6_IfEESN_SN_EUliPNS_11concurrency10ThreadPoolEE_EEvSI_iidSQ_EUlllE_ ZN11onnxruntime10IAllocator13MakeUniquePtrIhEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPhE_ ConcatFromSequence Must have valid 'axis' attribute onnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo&, bool) /onnxruntime_src/onnxruntime/core/providers/cpu//tensor/concatbase.h const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq] Got nullptr for sequence input. virtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext*) const /onnxruntime_src/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.cc N11onnxruntime10ConcatBaseE N11onnxruntime18ConcatFromSequenceE SplitToSequence SequenceEmpty SequenceConstruct SequenceLength SequenceInsert SequenceErase SequenceAt CreateCopyAndAppendCpuTensor /onnxruntime_src/onnxruntime/core/providers/cpu/sequence/sequence_ops.cc T* OrtValue::GetMutable() [with T = onnxruntime::TensorSeq] virtual onnxruntime::common::Status onnxruntime::SequenceLength::Compute(onnxruntime::OpKernelContext*) const SequenceLength: Got nullptr for output tensor Y != nullptr int64_t onnxruntime::GetSeqIdx(const onnxruntime::Tensor&) Invalid data type for split tensor int64_t onnxruntime::GetScalarSplitInput(const onnxruntime::Tensor&) virtual onnxruntime::common::Status onnxruntime::SequenceAt::Compute(onnxruntime::OpKernelContext*) const Got nullptr input for index tensor ) specified for sequence of size ( SequenceAt: Got nullptr for output tensor I != nullptr Invalid sequence index ( split_size_sum ( ) != split_dim_size ( void onnxruntime::GetSplitSizesInput(const onnxruntime::Tensor&, std::vector<long int>&) virtual onnxruntime::common::Status onnxruntime::SequenceInsert::Compute(onnxruntime::OpKernelContext*) const Data type of the input tensor MUST be same as that of the input sequence. Sequence data type ( SequenceInsert: Got nullptr for output sequence S != nullptr Got nullptr for input tensor. ), input tensor data type ( Split should be > 0 split_scalar > 0 onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = std::basic_string<char>] Invalid value in 'split' input. All values must be >= 0 std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; }) onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = int] onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = double] onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = float] onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = long int] SplitToSequence operator does not support SequenceEmpty: Got nullptr for output sequence virtual onnxruntime::common::Status onnxruntime::SequenceEmpty::Compute(onnxruntime::OpKernelContext*) const Unsupported 'dtype' value: @gt$4@g@g$virtual onnxruntime::common::Status onnxruntime::SequenceConstruct::Compute(onnxruntime::OpKernelContext*) const SequenceConstruct: Got nullptr for output sequence Violation of the requirment that all input tensors must have the same data type. num_inputs >= 1 virtual onnxruntime::common::Status onnxruntime::SequenceErase::Compute(onnxruntime::OpKernelContext*) const SequenceErase: Got nullptr for output sequence N11onnxruntime14SequenceLengthE N11onnxruntime10SequenceAtE N11onnxruntime13SequenceEmptyE N11onnxruntime14SequenceInsertE N11onnxruntime13SequenceEraseE N11onnxruntime17SequenceConstructE N11onnxruntime15SplitToSequenceE Attribute to is not set. onnxruntime::{anonymous}::Cast::Cast(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/cast_op.cc -INF %.8g stoull stod *N11onnxruntime12_GLOBAL__N_14CastE O O O O A A A A Compress virtual onnxruntime::common::Status onnxruntime::Compress::Compute(onnxruntime::OpKernelContext*) const /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/compress.cc axes_right_stride >= 0 && static_cast<uint64_t>(axes_right_stride) < std::numeric_limits<size_t>::max() N11onnxruntime8CompressE std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::Prepare::InputInfo; _Alloc = std::allocator<onnxruntime::Prepare::InputInfo>; std::vector<_Tp, _Alloc>::reference = onnxruntime::Prepare::InputInfo&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concatbase.h /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.cc Ranks of input data are different, cannot concatenate them. expected rank: onnxruntime::common::Status onnxruntime::ConcatBase::PrepareForCompute(onnxruntime::OpKernelContext*, const std::vector<const onnxruntime::Tensor*>&, onnxruntime::Prepare&) const inputs_n_rank == inputs_0_rank Non concat axis dimensions must match: Axis has mismatched dimensions of Cannot concatenate scalars got: Data type mismatch N11onnxruntime6ConcatE PrepareForComputonnxruntime::common::Status onnxruntime::DynamicQuantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/dynamicquantizelinear.cc x_ptr != nullptr N11onnxruntime21DynamicQuantizeLinearIhEE typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = long int; _Dp = std::default_delete<long int []>; typename std::add_lvalue_reference<_Tp>::type = long int&; std::size_t = long unsigned int] invalid expand shape ZNK11onnxruntime6ExpandINS_9MLFloat16EE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandINS_9MLFloat16EE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandIbE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandIbE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandImE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandImE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandIjE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandIjE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandItE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandItE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandIhE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandIhE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandIlE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandIlE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandIiE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandIiE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandIsE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandIsE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandIaE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandIaE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandIdE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandIdE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime6ExpandIfE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime6ExpandIfE7ComputeEPNS_15OpKernelContextEEUlllE0_ N11onnxruntime6ExpandIfEE N11onnxruntime6ExpandIdEE N11onnxruntime6ExpandIaEE N11onnxruntime6ExpandIsEE N11onnxruntime6ExpandIiEE N11onnxruntime6ExpandIlEE N11onnxruntime6ExpandIhEE N11onnxruntime6ExpandItEE N11onnxruntime6ExpandIjEE N11onnxruntime6ExpandImEE N11onnxruntime6ExpandIbEE N11onnxruntime6ExpandINS_9MLFloat16EEE EyeLike onnxruntime::Tensor& onnxruntime::OpKernelContext::RequiredOutput(int, const onnxruntime::TensorShape&) Required output at index output_ptr N11onnxruntime7EyeLikeE EyeLike : Input tensor dimensionTind Missing/Invalid 'axis' attribute value onnxruntime::GatherBase::GatherBase(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gatherbase.h indices element out of data bounds, idx= must be within the inclusive range [ /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather.cc Gather Tind type not supported in this build. N11onnxruntime10GatherBaseE N11onnxruntime6GatherE ZN11onnxruntime14GatherCopyDataIiEENS_6common6StatusEPKNS_6TensorEPKhPhbmlllllRKNS_11TensorShapeElPNS_11concurrency10ThreadPoolEEUlllE0_ ZN11onnxruntime14GatherCopyDataIlEENS_6common6StatusEPKNS_6TensorEPKhPhbmlllllRKNS_11TensorShapeElPNS_11concurrency10ThreadPoolEEUlllE0_ GatherElements GatherElements op: Cannot operate on scalar input GatherElements 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: onnxruntime::GatherElements::GatherElements(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.h GatherElements op: Value in indices must be within bounds [ onnxruntime::core_impl(const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, int64_t, onnxruntime::concurrency::ThreadPool*) [with bool is_string = false; T = signed char; Tin = int; int64_t = long int]::<lambda(ptrdiff_t)> /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.cc ]. Actual value is , onnxruntime::core_impl(const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, int64_t, onnxruntime::concurrency::ThreadPool*) [with bool is_string = false; T = signed char; Tin = long int; int64_t = long int]::<lambda(ptrdiff_t)> onnxruntime::core_impl(const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, int64_t, onnxruntime::concurrency::ThreadPool*) [with bool is_string = true; T = std::basic_string<char>; Tin = long int; int64_t = long int]::<lambda(ptrdiff_t)> onnxruntime::core_impl(const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, int64_t, onnxruntime::concurrency::ThreadPool*) [with bool is_string = true; T = std::basic_string<char>; Tin = int; int64_t = long int]::<lambda(ptrdiff_t)> GatherElements op: Data type of input 'data' should match the data type of the output N11onnxruntime14GatherElementsE *ZN11onnxruntimeL9core_implILb1ESsiEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL9core_implILb1ESsiEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE3_ *ZN11onnxruntimeL9core_implILb1ESslEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL9core_implILb1ESslEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE3_ *ZN11onnxruntimeL9core_implILb0EaiEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE2_ *ZN11onnxruntimeL9core_implILb0EaiEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE4_ *ZN11onnxruntimeL9core_implILb0EalEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE2_ *ZN11onnxruntimeL9core_implILb0EalEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE4_ GatherND indices tensor must has rank larger than 0 invalid index found, index = GatherNDBase PrepareForCompute: Input count mismatch virtual onnxruntime::common::Status onnxruntime::GatherND::Compute(onnxruntime::OpKernelContext*) const /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_nd.cc input_tensor != nullptr && indices_tensor != nullptr last dimension of indices must not be larger than rank of input tensor indices tensor data type not supported N11onnxruntime12GatherNDBaseE N11onnxruntime8GatherNDE *ZNK11onnxruntime8GatherND12GatherNumberERKNS_12GatherNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ *ZNK11onnxruntime8GatherND12GatherStringERKNS_12GatherNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ ZNK11onnxruntime12GatherNDBase17PrepareForComputeIiEENS_6common6StatusERKNS_11TensorShapeEPKNS_6TensorElRNS0_7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ ZNK11onnxruntime12GatherNDBase17PrepareForComputeIlEENS_6common6StatusERKNS_11TensorShapeEPKNS_6TensorElRNS0_7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ onnxruntime::common::Status onnxruntime::IdentityOp<is_dropout>::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = true] /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/identity_op.h void* onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType) Tensor type mismatch. type == dtype_ Unable to get an allocator onnxruntime::common::Status onnxruntime::IdentityOp<is_dropout>::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = false] N11onnxruntime10IdentityOpILb1EEE N11onnxruntime10IdentityOpILb0EEE IsInf detect_positive detect_negative Failed to obtain detect_positive onnxruntime::IsInf::IsInf(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/isinf.cc Failed to obtain detect_negative N11onnxruntime5IsInfE IsNaN Null input ptr N11onnxruntime5IsNaNIfEE N11onnxruntime5IsNaNINS_9MLFloat16EEE | | | | | | | |MeanVarianceNormalization Input is expected to have four dimensions corresponding to [N,C,H,W] N11onnxruntime27MeanVarianceNormalization_0IfEE N11onnxruntime27MeanVarianceNormalization_1IfEE onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/nonzero_op.cc X input is required! failed to get first output! onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] N11onnxruntime7NonZeroIbEE N11onnxruntime7NonZeroIhEE N11onnxruntime7NonZeroIiEE N11onnxruntime7NonZeroIlEE N11onnxruntime7NonZeroIfEE Invalid 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 OneHot Depth is negative. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/onehot.cc std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = Eigen::internal::TensorBlockScratchAllocator<Eigen::DefaultDevice>::Allocation; _Alloc = std::allocator<Eigen::internal::TensorBlockScratchAllocator<Eigen::DefaultDevice>::Allocation>; std::vector<_Tp, _Alloc>::reference = Eigen::internal::TensorBlockScratchAllocator<Eigen::DefaultDevice>::Allocation&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] N11onnxruntime8OneHotOpIlllEE N11onnxruntime8OneHotOpIfllEE N11onnxruntime8OneHotOpIlSslEE N11onnxruntime8OneHotOpIfSslEE N11onnxruntime8OneHotOpIlflEE N11onnxruntime8OneHotOpIifiEE N11onnxruntime8OneHotOpIiffEE N11onnxruntime8OneHotOpIfffEE N11onnxruntime8OneHotOpIlifEE N11onnxruntime8OneHotOpIlffEE N11onnxruntime8OneHotOpIlfiEE reflect edge Invalid 'mode' attribute value onnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/padbase.h Invalid 'pads' attribute value Cannot use 'reflect' mode to pad dimension with a value of 0. Input shape: Unexpected mode of Input tensor has no dimensions onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = unsigned int] /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/pad.cc 'pads' has wrong number of values data_rank > 0 data_rank * 2 == pads.size() PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = long unsigned int] Pads tensor should be an INT64 tensor virtual onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext*) const pads_tensor.IsDataType<int64_t>() Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank] pads_tensor_dims.size() == 1 || (pads_tensor_dims.size() == 2 && pads_tensor_dims[0] == 1) Pads tensor size should be equal to twice the input dimension count Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensor value_tensor->DataType() == data_type && value_tensor->Shape().Size() == 1 Unsupported input data type of onnxruntime::PadValue onnxruntime::PadValueFromFloat(float, onnxruntime::MLDataType) onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = unsigned char] pads_size == 2 * data_rank N11onnxruntime7PadBaseE N11onnxruntime3PadE x_zero_point must be null or a scalar or 1D tensor or size 1. void onnxruntime::PrepareForQDQ(const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::Tensor*, int64_t, int64_t&, int64_t&, int64_t&) /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/quantize_linear.cc zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr) scale must be 1D tensor with size scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dim x_zero_point must be null or 1D tensor with size zero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim) DequantizeLinear with type int32 should have no zero point or all zero points should be 0 onnxruntime::common::Status onnxruntime::DequantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] zero_point == nullptr || std::all_of(zero_point, zero_point + x_zero_point->Shape().Size(), [](int32_t zp) { return zp == 0; }) ZN11onnxruntime17ParQuantizeLinearIaEEvPKfPT_mfS3_PNS_11concurrency10ThreadPoolEEUlllE_ N11onnxruntime16DequantizeLinearIaEE N11onnxruntime16DequantizeLinearIhEE N11onnxruntime16DequantizeLinearIiEE N11onnxruntime14QuantizeLinearIaEE N11onnxruntime14QuantizeLinearIhEE Attribute shape is not set. onnxruntime::Reshape_1::Reshape_1(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape.h A dimension cannot be less than -1, got onnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape&, std::vector<long int>&, bool) /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape_helper.h The input tensor cannot be reshaped to the requested shape. Input shape: At most one dimension can be -1. The dimension with value zero exceeds the dimension size of the input tensor. i < input_shape.NumDimensions() size != 0 && (input_shape.Size() % size) == 0 gsl::narrow_cast<int64_t>(input_shape.Size()) == size requested_shape[i] >= -1 , requested shape: !allow_zero unknown_dim == -1 A shape tensor must be a vector tensor. virtual onnxruntime::common::Status onnxruntime::Reshape::Compute(onnxruntime::OpKernelContext*) const shapeTensor->Shape().NumDimensions() == 1 N11onnxruntime7ReshapeE N11onnxruntime9Reshape_1E cubic mode attribute is . It can only be (default) or onnxruntime::UpsampleMode onnxruntime::UpsampleBase::StringToUpsampleMode(const string&) /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.h Resize operator Upsample operator scale >= 1 scale > 0 Scale value should be greater than or equal to 1. void onnxruntime::UpsampleBase::ScalesValidation(const std::vector<float>&, onnxruntime::UpsampleMode) const Scale value should be greater than 0. 'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) || scales.size() == 3 || (scales.size() == 5 && scales[0] == 1 && scales[1] == 1) 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) extrapolation_value pytorch_half_pixel tf_half_pixel_for_nn tf_crop_and_resize coordinate_transform_mode:[ ] is not supportted! UpsampleBase yet this opset round_prefer_floor round_prefer_ceil nearest_mode:[ ] is not supported! cubic_coeff_a exclude_outside scales_size > 0 onnxruntime::UpsampleBase::UpsampleBase(const onnxruntime::OpKernelInfo&) info.GetAttr<std::string>("mode", &mode).IsOK() info.GetAttrs<float>("scales", scales_).IsOK() onnxruntime::ResizeCoordinateTransformationMode onnxruntime::UpsampleBase::StringToCoordinateTransformationMode(const string&) `tf_half_pixel_for_nn` is deprecated since opset 13, model uses the deprecated attribute onnxruntime::ResizeNearestMode onnxruntime::UpsampleBase::StringToNearestMode(const string&) exclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to scales size should be greater than 0. void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor*, std::vector<float>&) const :DDD_D3 N11onnxruntime12UpsampleBaseE N11onnxruntime8UpsampleIfEE N11onnxruntime6ResizeIfEE N11onnxruntime8UpsampleIiEE N11onnxruntime6ResizeIiEE N11onnxruntime8UpsampleIhEE N11onnxruntime6ResizeIhEE ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE0_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE1_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE2_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE3_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE4_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE0_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE1_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE2_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE3_ @ ?ReverseSequence batch_axis . Must be 0 or 1 Invalid batch_axis of batch_axis < 2 Invalid time_axis of time_axis < 2 batch_axis != time_axis onnxruntime::ReverseSequenceOp::ReverseSequenceOp(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.h info.GetAttr<int64_t>("batch_axis", &batch_axis).IsOK() info.GetAttr<int64_t>("time_axis", &time_axis).IsOK() time_axis and batch_axis must have different values but both are Invalid sequence length: . Value must be in range [0, gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned char] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = bool] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = short unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = short int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::MLFloat16] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::MLFloat16] sequence_lens shape must be {batch_size}. Got: gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::BFloat16] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::BFloat16] virtual onnxruntime::common::Status onnxruntime::ReverseSequenceOp::Compute(onnxruntime::OpKernelContext*) const /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.cc . batch_size= Unknown tensor type of 6he!-Y<6h6h N11onnxruntime17ReverseSequenceOpE ScatterElements Scatter onnxruntime::Scatter<EnabledDataTypes>::Scatter(const onnxruntime::OpKernelInfo&) [with EnabledDataTypes = onnxruntime::TypeList<float, double, long int, long unsigned int, int, unsigned int, short int, short unsigned int, signed char, unsigned char, onnxruntime::MLFloat16, onnxruntime::BFloat16, bool, std::basic_string<char, std::char_traits<char>, std::allocator<char> > >] /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter.cc data type is different from updates type Indices and updates must have the same rank Indices vs updates dimensions differs at position= Indices must have the same rank as Input. Indices rank= Indices type is not supported. vs . Input rank= Indices dim= at pos= is greater than input dim= N11onnxruntime7ScatterINS_8TypeListIJfdlmijstahNS_9MLFloat16ENS_8BFloat16EbSsEEEEE ScatterND input tensor and indices tensor must has rank larger than 0. updates tensor should have shape equal to indices.shape[:-1] + data.shape[indices.shape[-1]:]. , indices shape: updates shape: , data shape: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter_nd.cc invalid indice found, indice = N11onnxruntime13ScatterNDBaseE N11onnxruntime9ScatterNDE *ZNK11onnxruntime9ScatterND13ScatterNumberERKNS_13ScatterNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ *ZNK11onnxruntime9ScatterND13ScatterStringERKNS_13ScatterNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ last dimension of indices must not be larger than rank of input N11onnxruntime5ShapeE Size N11onnxruntime4SizeE Missing or invalid starts and ends attribute onnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo&, bool) /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.h has_starts && has_ends && attr_starts_.size() == attr_ends_.size() Invalid axes attribute, axes attribute (if present) should have the same size as starts/ends attributes !has_axes || attr_axes_.size() == attr_starts_.size() Unexpected element size of void* onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep(void*) onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&) [with T = std::basic_string<char>]::<lambda(onnxruntime::SliceIterator<std::basic_string<char> >&)> /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/slice.cc output == output_end onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&) [with T = unsigned int]::<lambda(onnxruntime::SliceIterator<unsigned int>&)> onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&) [with T = long unsigned int]::<lambda(onnxruntime::SliceIterator<long unsigned int>&)> onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&) [with T = short unsigned int]::<lambda(onnxruntime::SliceIterator<short unsigned int>&)> onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&) [with T = unsigned char]::<lambda(onnxruntime::SliceIterator<unsigned char>&)> FillVectorsFromInput Starts must be a 1-D array Ends must be a 1-D array Starts and ends shape mismatch Starts and axes shape mismatch Starts and steps shape mismatch Data type for starts and ends inputs' is not supported in this build. Got 'axes' has duplicates 'axes' has an axis outside of the tensor dimension count 'step' value cannot be 0 Cannot slice scalars N11onnxruntime9SliceBaseE N11onnxruntime6Slice1E N11onnxruntime7Slice10E FillVectorsFromIDepthToSpace SpaceToDepth Attribute blocksize is not set. onnxruntime::SpaceDepthBase::SpaceDepthBase(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.h info.GetAttr("blocksize", &blocksize_).IsOK() DepthToSpace op: only 'DCR' and 'CRD' modes are supported onnxruntime::DepthToSpace<T>::DepthToSpace(const onnxruntime::OpKernelInfo&) [with T = float] CRD DCR onnxruntime::common::Status onnxruntime::SpaceToDepth<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.cc input_shape.NumDimensions() == 4 input_height % this->blocksize_ == 0 input_width % this->blocksize_ == 0 onnxruntime::common::Status onnxruntime::DepthToSpace<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] input.Shape().NumDimensions() == 4 input_depth % (blocksize_ * blocksize_) == 0 N11onnxruntime14SpaceDepthBaseE N11onnxruntime12SpaceToDepthIfEE N11onnxruntime12DepthToSpaceIfEE e@ *> Invalid value in 'split' attribute. All values must be > 0 onnxruntime::SplitBase::SplitBase(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.h std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; }) Axis= NumOutputs= Input shape= Input cannot be split evenly on selected axis. Input shape= Cannot split using values in 'split' attribute. Axis= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was An split tensor must be a vector tensor. onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&) const [with T = float] /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.cc split_tensor->Shape().NumDimensions() == 1 onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&) const [with T = int] onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&) const [with T = long int] onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&) const [with T = unsigned char] onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&) const [with T = std::basic_string<char>] Split operator does not support virtual onnxruntime::common::Status onnxruntime::Split::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime9SplitBaseE N11onnxruntime5SplitE static std::vector<long int> onnxruntime::SqueezeBase::ComputeOutputShape(const onnxruntime::TensorShape&, const std::vector<long int>&) /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/squeeze.h . shape= must be 1 instead of Dimension of input input_shape[i] == 1 virtual onnxruntime::common::Status onnxruntime::Squeeze::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime11SqueezeBaseE N11onnxruntime7SqueezeE Tile Input count of Tile OP mismatch, the first one is empty Input count of Tile OP mismatch, the second one is empty the 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 Tile doesn't support string type yet virtual onnxruntime::common::Status onnxruntime::Tile::Compute(onnxruntime::OpKernelContext*) const /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/tile.cc !input_tensor.IsDataType<std::string>() Tile doesn't have an implementation yet for the type: N11onnxruntime4TileE is outside range. is repeated. onnxruntime::TransposeBase::TransposeBase(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.h v >= 0 && static_cast<uint64_t>(v) <= std::numeric_limits<size_t>::max() Attribute perm of Transpose has an invalid value. Value Method IncrementIndexAndComputeOffset assumes this value is strictly positive. void onnxruntime::IncrementIndexAndComputeOffsetSetup(onnxruntime::MultiIndex&, size_t, const std::vector<long int>&, const std::vector<long unsigned int>&, size_t) /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.cc naxes > 0 bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector<long int>&, size_t, const std::vector<long unsigned int>&, const uint8_t*, uint8_t*) [with T = long unsigned int; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char] (local_source >= source) && (local_source < source + sizeof(T) * num_blocks) bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector<long int>&, size_t, const std::vector<long unsigned int>&, const uint8_t*, uint8_t*) [with T = unsigned int; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char] bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector<long int>&, size_t, const std::vector<long unsigned int>&, const uint8_t*, uint8_t*) [with T = short unsigned int; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char] bool onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector<long int>&, size_t, const std::vector<long unsigned int>&, const uint8_t*, uint8_t*) [with T = unsigned char; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char] Transpose of element size not supported in this build. Size= Transpose not implemented for empty tensors. void onnxruntime::DoTransposeEltWise(int64_t, const std::vector<long int>&, size_t, const std::vector<long unsigned int>&, const string*, std::string*) (local_source >= source) && (local_source < source + num_blocks) void onnxruntime::DoTransposeImpl(int64_t, const std::vector<long int>&, size_t, size_t, const std::vector<long unsigned int>&, const string*, std::string*) (local_source >= source) && (local_source < source + num_blocks * num_elts_in_block) void onnxruntime::DoTransposeImpl(int64_t, const std::vector<long int>&, size_t, size_t, const std::vector<long unsigned int>&, const uint8_t*, uint8_t*, size_t) (local_source >= source) && (local_source < source + num_blocks * blocksize) num_axes > 0 Mismatched data types between input and output Tensors. [ perm: does not align with rank of input data: virtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime13TransposeBaseE N11onnxruntime9TransposeE Trilu onnxruntime::Trilu::Trilu(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/trilu.h info.GetAttr<int64_t>("upper", &temp).IsOK() k should be a 1-D or 0-D tensor. virtual onnxruntime::common::Status onnxruntime::Trilu::Compute(onnxruntime::OpKernelContext*) const /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/trilu.cc IsScalarOr1ElementVector(k) N11onnxruntime5TriluE Unique std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = std::vector<long int>; _Alloc = std::allocator<std::vector<long int> >; std::vector<_Tp, _Alloc>::const_reference = const std::vector<long int>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] Unsupported tensor type of N11onnxruntime6UniqueE Missing/Invalid 'axes' attribute value onnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.h info.GetAttrs("axes", axes_).IsOK() onnxruntime::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::UnsqueezeBase::Prepare&) const /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.cc An axes tensor must be a scalar or a 1-D tensor. axes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1 'axes' has an out of range axis 'axes' has a duplicate axis nullptr != p.output_tensor N11onnxruntime13UnsqueezeBaseE N11onnxruntime9UnsqueezeE Output dim value: Input dim value: Dimension: output_dims[i] == 0 Cannot scale 0 by any factor to generate a non-zero value. Input dim is zero but required output dim is non-zero. void onnxruntime::UpsampleBase::ParseScalesDataFromOutputSize(const std::vector<long int>&, const std::vector<long int>&, std::vector<float>&) const Resize: input/output value is nullptr Upsample: input/output value is nullptr Resize: input/output value's dimension mismatch Upsample: input/output value's dimension mismatch Resize: input shape needs to be at least a single dimension Upsample: input shape needs to be at least a single dimension. Resize: input tensor's dimension does not match the scales. Upsample: input tensor's dimension does not match the scales. onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = float] /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.cc Rank of input and output tensor should be same. output_dims.size() == dims.size() Resize: size of roi array should be 2 * N where N is the rank of input tensor X. Resize: unexpected mode Upsample: unexpected mode BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] Only one of scales or sizes must be provided as input. Either scales or sizes MUST be provided as input. sizes != nullptr && sizes->Shape().Size() != 0 Resize: input tensor's rank does not match the output tensor's rank. X->Shape().GetDims().size() == output_dims.size() Invalid roi input index. roi_input_idx_ > 0 sizes == nullptr onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = int] onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = unsigned char] onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] ZN11onnxruntime16UpsampleBilinearIhEEvllllllffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEESt8functionIFfffffffEEPNS_11concurrency10ThreadPoolEEUllE_ ZN11onnxruntime17UpsampleTrilinearIhEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEESt8functionIFfffffffEEPNS_11concurrency10ThreadPoolEEUllE_ ZN11onnxruntime16UpsampleBilinearIiEEvllllllffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEESt8functionIFfffffffEEPNS_11concurrency10ThreadPoolEEUllE_ ZN11onnxruntime17UpsampleTrilinearIiEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEESt8functionIFfffffffEEPNS_11concurrency10ThreadPoolEEUllE_ ZN11onnxruntime16UpsampleBilinearIfEEvllllllffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEESt8functionIFfffffffEEPNS_11concurrency10ThreadPoolEEUllE_ ZN11onnxruntime17UpsampleTrilinearIfEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEESt8functionIFfffffffEEPNS_11concurrency10ThreadPoolEEUllE_ @ A @ N11onnxruntime5WhereIhEE N11onnxruntime5WhereIiEE N11onnxruntime5WhereIlEE N11onnxruntime5WhereIfEE N11onnxruntime5WhereIdEE N11onnxruntime5WhereISsEE RegisterQuantizationKernels /onnxruntime_src/onnxruntime/contrib_ops/cpu/cpu_contrib_kernels.cc RegisterNchwcKernels RegisterCpuContribKernels Crop Attribute border needs to be specified with four border elements, got Input is expected to have four dimensions corresponding to [N,C,H,W], got ) needs to be greater than or equal to the topBorder ( ) needs to be greater than or equal to the leftBorder ( input dimensions instead Input's height ( ) + bottomBorder ( Input's width ( ) + rightBorder ( ) + scale_[0] ( ) + scale_[1] ( /onnxruntime_src/onnxruntime/contrib_ops/cpu/crop.h N11onnxruntime7contrib8CropBaseE N11onnxruntime7contrib4CropIfEE CropAndResize bilinear specified. It should be either bilinear or nearest onnxruntime::contrib::CropAndResize<T>::CropAndResize(const onnxruntime::OpKernelInfo&) [with T = float] /onnxruntime_src/onnxruntime/contrib_ops/cpu/crop_and_resize.h Number of dimensions for crop size should be exactly 1 Null crop_size_ptr ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNS_7contrib20CropAndResizeForwardIfEEvRKNS_11TensorShapeEPKT_fllSA_lPS8_RKSsPKiPS1_EUllE_EEvSG_lOS8_lEUllE_ N11onnxruntime7contrib13CropAndResizeIfEE DynamicSlice onnxruntime::contrib::Affine<T>::Affine(const onnxruntime::OpKernelInfo&) [with T = float] /onnxruntime_src/onnxruntime/contrib_ops/cpu/element_wise_ops.h info.GetAttr("alpha", &alpha_).IsOK() info.GetAttr("beta", &beta_).IsOK() onnxruntime::contrib::Scale<T>::Scale(const onnxruntime::OpKernelInfo&) [with T = float] info.GetAttr("scale", &scale_).IsOK() N11onnxruntime7contrib6AffineIfEE N11onnxruntime7contrib5ScaleIfEE ExpandDims An axis tensor must be a scalar tensor. virtual onnxruntime::common::Status onnxruntime::contrib::ExpandDims::Compute(onnxruntime::OpKernelContext*) const /onnxruntime_src/onnxruntime/contrib_ops/cpu/expand_dims.h axis_tensor->Shape().IsScalar() axis <= X_NumDims && axis >= -X_NumDims Axis is Axis must be within range [ N11onnxruntime7contrib10ExpandDimsE onnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_conv.cc GetFusedActivationAttr(info, activation_).IsOK() N11onnxruntime7contrib14FusedConvFloatE /onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_gemm.cc onnxruntime::contrib::FusedGemm<T>::FusedGemm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib9FusedGemmIfEE TransposeMatMul ImageScaler onnxruntime::contrib::ImageScaler<T>::ImageScaler(const onnxruntime::OpKernelInfo&) [with T = float] /onnxruntime_src/onnxruntime/contrib_ops/cpu/image_scaler.h info.GetAttrs<float>("bias", bias_).IsOK() ) does not match the number of channels ( Bias size ( N11onnxruntime7contrib11ImageScalerIfEE Inverse N11onnxruntime7contrib7InverseE *ZNK11onnxruntime7contrib7Inverse7ComputeEPNS_15OpKernelContextEEUllE_ onnxruntime::contrib::LayerNorm<T, simplified>::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = float; bool simplified = false] /onnxruntime_src/onnxruntime/contrib_ops/cpu/layer_norm.cc op_kernel_info.GetAttr("axis", &axis_).IsOK() onnxruntime::contrib::LayerNorm<T, simplified>::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = float; bool simplified = true] onnxruntime::contrib::LayerNorm<T, simplified>::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = double; bool simplified = false] onnxruntime::contrib::LayerNorm<T, simplified>::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = double; bool simplified = true] ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib9LayerNormIdLb1EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib9LayerNormIdLb0EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib9LayerNormIfLb1EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib9LayerNormIfLb0EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ N11onnxruntime7contrib9LayerNormIfLb0EEE N11onnxruntime7contrib9LayerNormIfLb1EEE N11onnxruntime7contrib9LayerNormIdLb0EEE N11onnxruntime7contrib9LayerNormIdLb1EEE MatMulInteger16 onnxruntime::common::Status onnxruntime::contrib::MatMulInteger16<T1, T2, T3>::Compute(onnxruntime::OpKernelContext*) const [with T1 = short int; T2 = short int; T3 = int] /onnxruntime_src/onnxruntime/contrib_ops/cpu/matmul_integer16.cc A != nullptr && B != nullptr N11onnxruntime7contrib15MatMulInteger16IssiEE MaxpoolWithMask /onnxruntime_src/onnxruntime/contrib_ops/cpu/maxpool_with_mask.h N11onnxruntime7contrib15MaxpoolWithMaskE N11onnxruntime7contrib21MaxpoolWithMask1DTaskIfEE N11onnxruntime7contrib21MaxpoolWithMask2DTaskIfEE N11onnxruntime7contrib21MaxpoolWithMask3DTaskIfEE across_channels normalize_variance onnxruntime::MeanVarianceNormalization_0<T>::MeanVarianceNormalization_0(const onnxruntime::OpKernelInfo&, bool) [with T = float] /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/mean_variance_normalization.h info.GetAttr<int64_t>("across_channels", &across_channels_).IsOK() info.GetAttr<int64_t>("normalize_variance", &normalize_variance_).IsOK() MurmurHash3 virtual onnxruntime::common::Status onnxruntime::contrib::MurmurHash3::Compute(onnxruntime::OpKernelContext*) const /onnxruntime_src/onnxruntime/contrib_ops/cpu/murmur_hash3.cc Invalid assumption of output element size sizeof(uint32_t) == output_element_bytes keys input_num_bytes % 4 == 0 N11onnxruntime7contrib11MurmurHash3E onnxruntime::contrib::ReorderInput::ReorderInput(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.h info.GetAttr<int64_t>("channels_last", &channels_last_).IsOK() invalid channel count channels_ > 0 onnxruntime::contrib::ReorderOutput::ReorderOutput(const onnxruntime::OpKernelInfo&) info.GetAttr<int64_t>("channels", &channels_).IsOK() scales_.size() == 4 ' for NCHWc Upsample Unsupported mode ' onnxruntime::contrib::NchwcUpsample::NchwcUpsample(const onnxruntime::OpKernelInfo&) info.GetAttrs<int64_t>("scales", scales_).IsOK() scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1 info.GetAttr<std::string>("coordinate_transformation_mode", &transformation_mode).IsOK() Unsupported transformation mode ' transformation_mode_ == TransformationMode::ASYMMETRIC virtual onnxruntime::common::Status onnxruntime::contrib::ReorderInput::Compute(onnxruntime::OpKernelContext*) const /onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.cc X_rank == 4 (channels % 4) == 0 virtual onnxruntime::common::Status onnxruntime::contrib::ReorderOutput::Compute(onnxruntime::OpKernelContext*) const channels_ <= X_shape[1] virtual onnxruntime::common::Status onnxruntime::contrib::NchwcUpsample::Compute(onnxruntime::OpKernelContext*) const (X_shape[1] % MlasNchwcGetBlockSize()) == 0 X_shape.size() == 4 onnxruntime::contrib::NchwcConv::NchwcConv(const onnxruntime::OpKernelInfo&) onnxruntime::contrib::NchwcPoolBase::NchwcPoolBase(const onnxruntime::OpKernelInfo&) pool_attrs_.kernel_shape.size() == 2 X_shape.NumDimensions() == 4 Unsupported convolution size. virtual onnxruntime::common::Status onnxruntime::contrib::NchwcConv::Compute(onnxruntime::OpKernelContext*) const (static_cast<size_t>(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0) output and sum shape must match onnxruntime::common::Status onnxruntime::contrib::NchwcPoolBase::NchwcPool(onnxruntime::OpKernelContext*, MLAS_POOLING_KIND) const N11onnxruntime7contrib13NchwcPoolBaseE N11onnxruntime7contrib12ReorderInputE N11onnxruntime7contrib13ReorderOutputE N11onnxruntime7contrib9NchwcConvE N11onnxruntime7contrib12NchwcMaxPoolE N11onnxruntime7contrib16NchwcAveragePoolE N11onnxruntime7contrib13NchwcUpsampleE *ZNK11onnxruntime7contrib12ReorderInput7ComputeEPNS_15OpKernelContextEEUllE_ *ZNK11onnxruntime7contrib13NchwcUpsample7ComputeEPNS_15OpKernelContextEEUllE_ void onnxruntime::BroadcastLooper(TBroadcastHelper&, const onnxruntime::ProcessBroadcastSpanFuncs&) [with TBroadcastHelper = onnxruntime::contrib::{anonymous}::QLinearBroadcastHelper] MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1 void onnxruntime::contrib::{anonymous}::QLinearImpl(onnxruntime::OpKernelContext&, double, const onnxruntime::ProcessBroadcastSpanFuncs&) [with T = signed char] /onnxruntime_src/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc IsScalarOr1ElementVector(tensor_a_scale) MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if given tensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point) MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1 IsScalarOr1ElementVector(tensor_b_scale) MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if given tensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point) MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1 IsScalarOr1ElementVector(tensor_c_scale) MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if given tensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point) void onnxruntime::contrib::{anonymous}::QLinearImpl(onnxruntime::OpKernelContext&, double, const onnxruntime::ProcessBroadcastSpanFuncs&) [with T = unsigned char] *ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE_ *ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE0_ *ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE1_ N11onnxruntime7contrib10QLinearAddIaEE N11onnxruntime7contrib10QLinearAddIhEE N11onnxruntime7contrib10QLinearMulIaEE N11onnxruntime7contrib10QLinearMulIhEE At least two inputs are needed, and each input must be (tensor, scale, zero_point) tuple! onnxruntime::contrib::QLinearConcat::QLinearConcat(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/contrib_ops/cpu/qlinear_concat.cc input_def_count >= 8 && (input_def_count - 2) % 3 == 0 Input scale is not float for input def @ tensor_x_scale->IsDataType<float>() Wrong input type encountered for zero point input def @ tensor_x_zero_point->GetElementType() == tensor_y_zero_point->GetElementType() std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector<unsigned char>; _Alloc = std::allocator<std::vector<unsigned char> >; std::vector<_Tp, _Alloc>::reference = std::vector<unsigned char>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] virtual onnxruntime::common::Status onnxruntime::contrib::QLinearConcat::Compute(onnxruntime::OpKernelContext*) const input_count_x3 >= 6 && input_count_x3 % 3 == 0 std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = const onnxruntime::Tensor*; _Alloc = std::allocator<const onnxruntime::Tensor*>; std::vector<_Tp, _Alloc>::reference = const onnxruntime::Tensor*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] Input scale is not float for quantized input @ Wrong input type encountered for zero point of quantized input @ std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = std::vector<unsigned char>; _Alloc = std::allocator<std::vector<unsigned char> >; std::vector<_Tp, _Alloc>::const_reference = const std::vector<unsigned char>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] N11onnxruntime7contrib13QLinearConcatE *ZN11onnxruntime7contrib13QLinearConcatC4ERKNS_12OpKernelInfoEEUlfE_ *ZNK11onnxruntime7contrib13QLinearConcat7ComputeEPNS_15OpKernelContextEEUlfE_ Input x_scale must be a scalar or 1D tensor of size 1 virtual onnxruntime::common::Status onnxruntime::contrib::QLinearGlobalAveragePool::Compute(onnxruntime::OpKernelContext*) const /onnxruntime_src/onnxruntime/contrib_ops/cpu/qlinear_global_average_pool.cc IsScalarOr1ElementVector(tensor_x_scale) input x_zero_point must be a scalar or 1D tensor of size 1 if given IsScalarOr1ElementVector(tensor_x_zero_point) input y_scale must be a scalar or 1D tensor of size 1 IsScalarOr1ElementVector(tensor_y_scale) input y_zero_point must be a scalar or 1D tensor of size 1 if given IsScalarOr1ElementVector(tensor_y_zero_point) N11onnxruntime7contrib24QLinearGlobalAveragePoolE *ZN11onnxruntime7contrib18ComputeAveragePoolEPKhfhPhfhlllbPNS_11concurrency10ThreadPoolEEUlllE_ *ZN11onnxruntime7contrib18ComputeAveragePoolEPKhfhPhfhlllbPNS_11concurrency10ThreadPoolEEUlllE0_ *ZN11onnxruntime7contrib18ComputeAveragePoolEPKhfhPhfhlllbPNS_11concurrency10ThreadPoolEEUlllE1_ @ P@QlinearBuildLookupTable : input X_scale must be a scalar or 1D tensor of size 1 void onnxruntime::contrib::QlinearBuildLookupTable(uint8_t*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const LookupTableArrayTransformer&) [with T = unsigned char; uint8_t = unsigned char; onnxruntime::contrib::LookupTableArrayTransformer = std::function<void(const float*, float*, long unsigned int)>] /onnxruntime_src/onnxruntime/contrib_ops/cpu/qlinear_lookup_table.cc QlinearBuildLookupTable : input X_zero_point must be a scalar or 1D tensor of size 1 tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point) QlinearBuildLookupTable : input Y_scale must be a scalar or 1D tensor of size 1 QlinearBuildLookupTable : input Y_zero_point must be a scalar or 1D tensor of size 1 tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point) void onnxruntime::contrib::QlinearBuildLookupTable(uint8_t*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const LookupTableArrayTransformer&) [with T = signed char; uint8_t = unsigned char; onnxruntime::contrib::LookupTableArrayTransformer = std::function<void(const float*, float*, long unsigned int)>] ZNK11onnxruntime7contrib14QLinearSigmoidIhE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_ ZNK11onnxruntime7contrib17QLinearLookupBaseIhE11ComputeBaseIZNKS0_14QLinearSigmoidIhE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_EENS_6common6StatusES7_T_EUlllE_ ZNK11onnxruntime7contrib14QLinearSigmoidIaE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_ ZNK11onnxruntime7contrib17QLinearLookupBaseIaE11ComputeBaseIZNKS0_14QLinearSigmoidIaE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_EENS_6common6StatusES7_T_EUlllE_ ZNK11onnxruntime7contrib16QLinearLeakyReluIhE7ComputeEPNS_15OpKernelContextEEUlfE_ ZNK11onnxruntime7contrib17QLinearLookupBaseIhE11ComputeBaseIZNKS0_16QLinearLeakyReluIhE7ComputeEPNS_15OpKernelContextEEUlfE_EENS_6common6StatusES7_T_EUlllE_ ZNK11onnxruntime7contrib16QLinearLeakyReluIaE7ComputeEPNS_15OpKernelContextEEUlfE_ ZNK11onnxruntime7contrib17QLinearLookupBaseIaE11ComputeBaseIZNKS0_16QLinearLeakyReluIaE7ComputeEPNS_15OpKernelContextEEUlfE_EENS_6common6StatusES7_T_EUlllE_ N11onnxruntime7contrib17QLinearLookupBaseIaEE N11onnxruntime7contrib16QLinearLeakyReluIaEE N11onnxruntime7contrib17QLinearLookupBaseIhEE N11onnxruntime7contrib16QLinearLeakyReluIhEE N11onnxruntime7contrib14QLinearSigmoidIaEE N11onnxruntime7contrib14QLinearSigmoidIhEE ZN11onnxruntime7contrib23QlinearBuildLookupTableIhEEvPhPKNS_6TensorES5_S5_S5_RKSt8functionIFffEEEUlPKfPfmE_ ZN11onnxruntime7contrib23QlinearBuildLookupTableIaEEvPhPKNS_6TensorES5_S5_S5_RKSt8functionIFffEEEUlPKfPfmE_ ZN11onnxruntime7contrib16QLinearLeakyReluIaEC4ERKNS_12OpKernelInfoEEUlfE_ ZN11onnxruntime7contrib16QLinearLeakyReluIhEC4ERKNS_12OpKernelInfoEEUlfE_ ZN11onnxruntime7contrib14QLinearSigmoidIaEC4ERKNS_12OpKernelInfoEEUlPKfPfmE_ ZN11onnxruntime7contrib14QLinearSigmoidIhEC4ERKNS_12OpKernelInfoEEUlPKfPfmE_
+#< void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = long int; _Alloc = std::allocator<long int>] virtual onnxruntime::common::Status onnxruntime::contrib::QLinearAveragePool::Compute(onnxruntime::OpKernelContext*) const /onnxruntime_src/onnxruntime/contrib_ops/cpu/qlinear_pool.cc Unsupported 'dtype' in QLinear Pooling: QLinear Pooling unsupported pooling size! N11onnxruntime7contrib18QLinearAveragePoolE *ZNK11onnxruntime7contrib18QLinearAveragePool7ComputeEPNS_15OpKernelContextEEUlllE_ N11onnxruntime7contrib21QLinearPoolNhwc1DTaskIhNS_11AveragePoolEEE N11onnxruntime7contrib17QLinearPool1DTaskIhNS_11AveragePoolEEE N11onnxruntime7contrib21QLinearPoolNhwc2DTaskIhNS_11AveragePoolEEE N11onnxruntime7contrib17QLinearPool2DTaskIhNS_11AveragePoolEEE N11onnxruntime7contrib21QLinearPoolNhwc3DTaskIhNS_11AveragePoolEEE N11onnxruntime7contrib17QLinearPool3DTaskIhNS_11AveragePoolEEE QAttention onnxruntime::contrib::AttentionBase::AttentionBase(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention_base.h info.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0 4D mask in attention cpu kernel is not supported ApplyAttention /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention_cpu_base.h /onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/attention_quant.cc input zero point must be a scalar or 1D tensor of size 1. std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR; _Alloc = std::allocator<MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR>; std::vector<_Tp, _Alloc>::reference = MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] N11onnxruntime7contrib13AttentionBaseE N11onnxruntime7contrib16AttentionCPUBaseE ZNK11onnxruntime7contrib16AttentionCPUBase21ComputeAttentionProbsIfEEvPT_PKS3_S6_PKiPKSt6vectorIlSaIlEES4_iiiiS6_S4_PNS_11concurrency10ThreadPoolEEUlllE_ ZNK11onnxruntime7contrib16AttentionCPUBase23ComputeVxAttentionScoreIfEEvPT_S4_PKS3_S6_iiiiiS6_S4_PNS_11concurrency10ThreadPoolEEUlllE_ N11onnxruntime7contrib10QAttentionIfEE @ F @ @ @ @ DynamicQuantizeLSTM /onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_lstm.cc } for per-tensor/layer quantization or shape { } for per-channel quantization. Actual: Weight zero point must be zero W_zero_point , 4* R_zero_point W_scale R_scale DynamicQuantizeLSTM : Weight point must be constant Recurrent N11onnxruntime7contrib19DynamicQuantizeLSTME ComputeCommon /onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_matmul.cc MatmulInteger : b zero point is not valid onnxruntime::common::Status onnxruntime::contrib::MatMulIntegerToFloatBase::ComputeCommon(onnxruntime::OpKernelContext*, const uint8_t*, const onnxruntime::TensorShape&, float, uint8_t, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*) const IsBQuantParamSupported(b_zp_tensor->Shape(), b_tensor ? b_tensor->Shape() : b_shape_) MatMulIntegerToFloat : input a 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*) const IsScalarOr1ElementVector(a_zero_point_tensor) N11onnxruntime7contrib24MatMulIntegerToFloatBaseE N11onnxruntime7contrib21DynamicQuantizeMatMulE N11onnxruntime7contrib20MatMulIntegerToFloatE /onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/nhwc_max_pool.cc virtual onnxruntime::common::Status onnxruntime::contrib::NhwcMaxPool::Compute(onnxruntime::OpKernelContext*) const input_shape.Size() > 0 || N == 0 N11onnxruntime7contrib11NhwcMaxPoolE SampleOp N11onnxruntime7contrib8SampleOpIfEE onnxruntime::contrib::SkipLayerNorm<T>::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = float] /onnxruntime_src/onnxruntime/contrib_ops/cpu/skip_layer_norm.cc epsilon_ >= 0 onnxruntime::contrib::SkipLayerNorm<T>::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = double] input is expected to have 3 dimensions, got skip is expected to have same shape as input gamma is expected to have 1 dimension, got Last dimension of gamma and input does not match beta is expected to have 1 dimension, got Last dimension of beta and input does not match bias is expected to have 1 dimension, got Last dimension of bias and input does not match ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib13SkipLayerNormIdE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib13SkipLayerNormIfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ N11onnxruntime7contrib13SkipLayerNormIfEE N11onnxruntime7contrib13SkipLayerNormIdEE Tokenizer Input string contains invalid utf8 chars: Match contains invalid utf8 chars: mark attribute mark is not set pad_value mincharnum mincharnum_ > 0 separators tokenexp !tokenexp.empty() separators must not be empty !separators.empty() Can not digest separators: Can not digest tokenexp: onnxruntime::contrib::Tokenizer::Tokenizer(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/contrib_ops/cpu/tokenizer.cc attribute pad_value is not set attribute mincharnum is not set attribute mincharnum must have a positive value Either one of the separators OR tokenexp attributes required but none is set Expecting a non-empty tokenexp mincharnum is too big for char level tokenezation !char_tokenezation_ || mincharnum_ < 2 __pos <= size() const value_type& std::basic_string<_CharT, _Traits, _Alloc>::operator[](std::basic_string<_CharT, _Traits, _Alloc>::size_type) const [with _CharT = char; _Traits = std::char_traits<char>; _Alloc = std::allocator<char>; std::basic_string<_CharT, _Traits, _Alloc>::const_reference = const char&; std::basic_string<_CharT, _Traits, _Alloc>::value_type = char; std::basic_string<_CharT, _Traits, _Alloc>::size_type = long unsigned int] /opt/rh/devtoolset-9/root/usr/include/c++/9/bits/basic_string.h std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::vector<re2::StringPiece>; _Alloc = std::allocator<std::vector<re2::StringPiece> >; std::vector<_Tp, _Alloc>::reference = std::vector<re2::StringPiece>&] tensor(string) expected as input Input dimensions are either [C] or [N][C] allowed N11onnxruntime7contrib9TokenizerE N11onnxruntime7contrib6UniqueIfEE Input tensor to Unique op shouldtypename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::OpKernelInfo; _Dp = std::default_delete<onnxruntime::OpKernelInfo>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::OpKernelInfo&] WordConvEmbedding Conv filter size does not match embedding_size attribute. Conv kernal size 1 does not match conv_window_size attribute . Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size does not match conv kernal size 2. embedding_size attribute: conv filter size: conv_window_size attribute: conv kernal size 1: Char embedding size: Conv kernal size 2 : /onnxruntime_src/onnxruntime/contrib_ops/cpu/word_conv_embedding.cc N11onnxruntime7contrib17WordConvEmbeddingE conv_window_sizechar_embedding_shӥhӥhӥhCelu No attribute with name:' 'is defined. Attribute name and type don't match for ' Init /onnxruntime_src/onnxruntime/core/providers/cpu/activation/activations.h unknown kernel type /onnxruntime_src/onnxruntime/contrib_ops/cpu/activations.h onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Elu<float>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::LeakyRelu<float>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Celu<float>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ThresholdedRelu<float>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::HardSigmoid<float>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Selu<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Elu<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::HardSigmoid<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::LeakyRelu<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Selu<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sigmoid<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Softplus<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Softsign<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Celu<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ThresholdedRelu<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Tanh<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu<signed char>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu<int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sigmoid<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Tanh<float>] N11onnxruntime8functors7SigmoidIfEE N11onnxruntime8functors4TanhIfEE N11onnxruntime8functors3EluIfEE N11onnxruntime17ElementWiseKernelINS_8functors3EluIfEEEE N11onnxruntime8functors11HardSigmoidIfEE N11onnxruntime17ElementWiseKernelINS_8functors11HardSigmoidIfEEEE N11onnxruntime8functors9LeakyReluIfEE N11onnxruntime17ElementWiseKernelINS_8functors9LeakyReluIfEEEE N11onnxruntime8functors4ReluIfEE N11onnxruntime17ElementWiseKernelINS_8functors4ReluIfEEEE N11onnxruntime8functors4ReluIdEE N11onnxruntime17ElementWiseKernelINS_8functors4ReluIdEEEE N11onnxruntime8functors4ReluIaEE N11onnxruntime17ElementWiseKernelINS_8functors4ReluIaEEEE N11onnxruntime8functors4ReluIiEE N11onnxruntime17ElementWiseKernelINS_8functors4ReluIiEEEE N11onnxruntime8functors4SeluIfEE N11onnxruntime17ElementWiseKernelINS_8functors4SeluIfEEEE N11onnxruntime17ElementWiseKernelINS_8functors7SigmoidIfEEEE N11onnxruntime8functors7SigmoidIdEE N11onnxruntime17ElementWiseKernelINS_8functors7SigmoidIdEEEE N11onnxruntime8functors8SoftplusIfEE N11onnxruntime17ElementWiseKernelINS_8functors8SoftplusIfEEEE N11onnxruntime8functors8SoftsignIfEE N11onnxruntime17ElementWiseKernelINS_8functors8SoftsignIfEEEE N11onnxruntime17ElementWiseKernelINS_8functors4TanhIfEEEE N11onnxruntime8functors4TanhIdEE N11onnxruntime17ElementWiseKernelINS_8functors4TanhIdEEEE N11onnxruntime8functors4CeluIfEE N11onnxruntime17ElementWiseKernelINS_8functors4CeluIfEEEE N11onnxruntime8functors15ThresholdedReluIfEE N11onnxruntime17ElementWiseKernelINS_8functors15ThresholdedReluIfEEEE N11onnxruntime8functors18ParametricSoftplusIfEE N11onnxruntime8functors10ScaledTanhIfEE A AO@O9 BBBB¥¥¥;?;?;?;? B B B BgiP9giP9giP9giP9C:C:C:C:<<<<*=*=*=*=*>*>*>*>O@O@O@O@OOOO9999 >@ 9@ @If std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = const OrtValue*; _Alloc = std::allocator<const OrtValue*>; std::vector<_Tp, _Alloc>::const_reference = const OrtValue* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::pair<onnxruntime::IfImpl::AllocationType, OrtValue>; _Alloc = std::allocator<std::pair<onnxruntime::IfImpl::AllocationType, OrtValue> >; std::vector<_Tp, _Alloc>::reference = std::pair<onnxruntime::IfImpl::AllocationType, OrtValue>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/if.cc Failed to create output tensor for If output outputs. 'If' node has outputs which doesn't match the subgraph's onnxruntime::If::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&) num_subgraph_outputs == static_cast<size_t>(num_outputs) std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = const onnxruntime::NodeArg*; _Alloc = std::allocator<const onnxruntime::NodeArg*>; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::NodeArg* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] then_branch info == nullptr SetupSubgraphExecutionInfo SetupSubgraphExecutionInfo should only be called once for each subgraph. virtual onnxruntime::common::Status onnxruntime::If::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::FeedsFetchesManager; _Dp = std::default_delete<onnxruntime::FeedsFetchesManager>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::FeedsFetchesManager&] Failed to create output tensor for Only tensors or sequence of tensors are suppported else_branch ' attribute. session_state CreateFeedsFetchesManager must be called prior to execution of graph. virtual onnxruntime::common::Status onnxruntime::If::Compute(onnxruntime::OpKernelContext*) const then_feeds_fetches_manager_ && else_feeds_fetches_manager_ Subgraph SessionState was not found for ' typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::If::Info; _Dp = std::default_delete<onnxruntime::If::Info>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::If::Info&] void onnxruntime::If::Init(const onnxruntime::OpKernelInfo&) info.GetAttr<ONNX_NAMESPACE::GraphProto>("then_branch", &proto).IsOK() info.GetAttr<ONNX_NAMESPACE::GraphProto>("else_branch", &proto).IsOK() N11onnxruntime11controlflow18IControlFlowKernelE N11onnxruntime2IfE *ZN11onnxruntime6IfImpl7ExecuteERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK13OrtMemoryInfoR8OrtValueRbE_ Loop std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::front() [with _Tp = OrtValue; _Alloc = std::allocator<OrtValue>; std::vector<_Tp, _Alloc>::reference = OrtValue&] /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/loop.cc ConcatenateLoopOutput inputs. Found: but has 'Loop' node has Graph in 'body' attribute of Loop should have onnxruntime::Loop::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&) static_cast<size_t>(num_subgraph_inputs) == subgraph_inputs.size() outputs so the subgraph requires num_subgraph_outputs - 1 == num_outputs virtual onnxruntime::common::Status onnxruntime::Loop::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = OrtValue; _Alloc = std::allocator<OrtValue>; std::vector<_Tp, _Alloc>::const_reference = const OrtValue&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector<OrtValue>; _Alloc = std::allocator<std::vector<OrtValue> >; std::vector<_Tp, _Alloc>::reference = std::vector<OrtValue>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] All scan outputs MUST be tensors void onnxruntime::LoopImpl::SaveOutputsAndUpdateFeeds(const std::vector<OrtValue>&, std::vector<OrtValue>&) last_outputs[j + 1].IsTensor() onnxruntime::LoopImpl::Execute(const onnxruntime::FeedsFetchesManager&)::<lambda(const OrtValue&, int)> Loop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}. 'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' input 'cond' should be a scalar tensor. Got shape of Loop subgraph input 0 has unknown shape: Loop subgraph input 1 has unknown shape: body feeds_fetches_manager_ Subgraph SessionState was not found for 'body' attribute. virtual onnxruntime::common::Status onnxruntime::Loop::Compute(onnxruntime::OpKernelContext*) const typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Loop::Info; _Dp = std::default_delete<onnxruntime::Loop::Info>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Loop::Info&] Expected: Inconsistent shape in loop output for output. void onnxruntime::Loop::Init(const onnxruntime::OpKernelInfo&) info.GetAttr<ONNX_NAMESPACE::GraphProto>("body", &proto).IsOK() FN11onnxruntime6common6StatusEPvRSt6vectorI8OrtValueSaIS4_EES2_mE N11onnxruntime4LoopE PFN11onnxruntime6common6StatusEPvRSt6vectorI8OrtValueSaIS4_EES2_mE Scan<8> spec does not support transpose of output. This should never be called. Scan onnxruntime::common::Status onnxruntime::Scan<OpSet>::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) [with int OpSet = 8; std::string = std::basic_string<char>] /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_8.cc typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Scan<8>::Info; _Dp = std::default_delete<onnxruntime::Scan<8>::Info>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Scan<8>::Info&] dimensions or more but input had shape of Scan inputs have inconsistent batch size. Previous value was Scan inputs have inconsistent sequence lengths. Previous value was Invalid scan input: has batch size of has length of ValidateInput sequence_lens length of did not match batch size of Invalid entries in sequence_lens. Max sequence length was Output OrtValue has not been created for loop state variable output onnxruntime::common::Status onnxruntime::Scan8Impl::CreateLoopStateVariables(std::vector<std::vector<onnxruntime::scan::detail::LoopStateVariable> >&) std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector<onnxruntime::scan::detail::LoopStateVariable>; _Alloc = std::allocator<std::vector<onnxruntime::scan::detail::LoopStateVariable> >; std::vector<_Tp, _Alloc>::reference = std::vector<onnxruntime::scan::detail::LoopStateVariable>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::OrtValueTensorSlicer<const OrtValue>::Iterator; _Alloc = std::allocator<onnxruntime::OrtValueTensorSlicer<const OrtValue>::Iterator>; std::vector<_Tp, _Alloc>::reference = onnxruntime::OrtValueTensorSlicer<const OrtValue>::Iterator&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::unique_ptr<onnxruntime::scan::detail::OutputIterator>; _Alloc = std::allocator<std::unique_ptr<onnxruntime::scan::detail::OutputIterator> >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr<onnxruntime::scan::detail::OutputIterator>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::scan::detail::OutputIterator; _Dp = std::default_delete<onnxruntime::scan::detail::OutputIterator>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::scan::detail::OutputIterator&] typename std::enable_if<(! std::is_const<T&>::value), T&>::type onnxruntime::OrtValueTensorSlicer<T>::Iterator::operator*() [with T = const OrtValue; typename std::enable_if<(! std::is_const<T&>::value), T&>::type = const OrtValue&] /onnxruntime_src/onnxruntime/core/framework/ort_value_tensor_slicer.h position_ >= 0 && position_ < sequence_length_ p_mlvalue CreateLoopStateVariables std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnxruntime::OrtValueTensorSlicer<const OrtValue>::Iterator; _Alloc = std::allocator<onnxruntime::OrtValueTensorSlicer<const OrtValue>::Iterator>; std::vector<_Tp, _Alloc>::reference = onnxruntime::OrtValueTensorSlicer<const OrtValue>::Iterator&] num_scan_inputs void onnxruntime::Scan<OpSet>::Init(const onnxruntime::OpKernelInfo&) [with int OpSet = 8] info.GetAttr<int64_t>("num_scan_inputs", &num_scan_inputs_).IsOK() Subgraph in 'body' produces outputs but Scan expects AllocateOutputTensors onnxruntime::common::Status onnxruntime::Scan<OpSet>::Compute(onnxruntime::OpKernelContext*) const [with int OpSet = 8] feeds_fetches_manager_ && info_ FN11onnxruntime20OrtValueTensorSlicerIK8OrtValueEERS2_llE FN11onnxruntime20OrtValueTensorSlicerI8OrtValueEERS1_llE N11onnxruntime4ScanILi8EEE N11onnxruntime20OrtValueTensorSlicerIK8OrtValueE8IteratorE PFN11onnxruntime20OrtValueTensorSlicerIK8OrtValueEERS2_llE PFN11onnxruntime20OrtValueTensorSlicerI8OrtValueEERS1_llE *ZN11onnxruntime4ScanILi8EE4InitERKNS_12OpKernelInfoEEUlRKSt6vectorImSaImEERKNS_6TensorERSA_E_ *ZN11onnxruntime4ScanILi8EE4InitERKNS_12OpKernelInfoEEUlPvmE0_ N11onnxruntime20OrtValueTensorSlicerI8OrtValueE8IteratorE onnxruntime::common::Status onnxruntime::Scan<OpSet>::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) [with int OpSet = 9; std::string = std::basic_string<char>] /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_9.cc typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Scan<9>::Info; _Dp = std::default_delete<onnxruntime::Scan<9>::Info>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Scan<9>::Info&] SetupInputs output_mlvalue onnxruntime::common::Status onnxruntime::ScanImpl::CreateLoopStateVariables(std::vector<onnxruntime::scan::detail::LoopStateVariable>&) Attempt to retrieve final output before it was set. const OrtValue& onnxruntime::scan::detail::OutputIterator::GetOutput() const /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.h Invalid value in scan_output_axes for output Outputs from Scan are not optional and should never be null. onnxruntime::common::Status onnxruntime::ScanImpl::TransposeOutput() final_output_mlvalue_ . Output tensor rank was TransposeOutput but input ' ' dimension Invalid value in scan_input_axes for input . Input tensor rank was scan_input_directions scan_output_directions scan_input_axes but expected scan_output_axes void onnxruntime::Scan<OpSet>::Init(const onnxruntime::OpKernelInfo&) [with int OpSet = 9] Number of entries in 'scan_input_axes' was gsl::narrow_cast<int64_t>(input_axes_.size()) == num_scan_inputs_ Number of entries in 'scan_output_axes' was gsl::narrow_cast<int64_t>(output_axes_.size()) == num_scan_outputs onnxruntime::common::Status onnxruntime::Scan<OpSet>::Compute(onnxruntime::OpKernelContext*) const [with int OpSet = 9] N11onnxruntime4ScanILi9EEE *ZN11onnxruntime4ScanILi9EE4InitERKNS_12OpKernelInfoEEUlRKSt6vectorImSaImEERKNS_6TensorERSA_E_ *ZN11onnxruntime4ScanILi9EE4InitERKNS_12OpKernelInfoEEUlPvmE0_ /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.cc CreateFeedsFetchesManager Mismatch between expected shape and shape from first output is not compatible with Misuse of LoopStateVariable. Attempt to move beyond end of sequence void onnxruntime::scan::detail::LoopStateVariable::Next() iteration_num_ < sequence_len_ OrtValue& onnxruntime::scan::detail::OutputIterator::operator*() cur_iteration_ < num_iterations_ Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator. typename std::enable_if<(! std::is_const<T&>::value), T&>::type onnxruntime::OrtValueTensorSlicer<T>::Iterator::operator*() [with T = OrtValue; typename std::enable_if<(! std::is_const<T&>::value), T&>::type = OrtValue&] Expected AllocateFinalOutput to have been called to before we increment the iterator onnxruntime::scan::detail::OutputIterator& onnxruntime::scan::detail::OutputIterator::operator++() std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::scan::detail::LoopStateVariable; _Alloc = std::allocator<onnxruntime::scan::detail::LoopStateVariable>; std::vector<_Tp, _Alloc>::reference = onnxruntime::scan::detail::LoopStateVariable&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] IterateSequence ' was Number of entries in ' Invalid values in ' void onnxruntime::scan::detail::ReadDirections(const onnxruntime::OpKernelInfo&, const string&, std::vector<long int>&, size_t) directions.size() == num_entries '. 0 == forward. 1 == reverse. inputs but Scan was only given The subgraph in 'body' requires onnxruntime::scan::detail::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&, int, bool) num_variadic_inputs == num_subgraph_inputs Failed to create output tensor for output # AllocateFinalBuffer If shape was concrete we shouldn't be using a custom allocator onnxruntime::common::Status onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput(const onnxruntime::TensorShape&) !is_concrete_shape_ AllocateFinalOutput Subgraph must have the shape set for all outputs but did not. *ZN11onnxruntime4scan6detail15IterateSequenceERNS_23OpKernelContextInternalERKNS_12SessionStateERSt6vectorINS1_17LoopStateVariableESaIS8_EERS7_INS_20OrtValueTensorSlicerIK8OrtValueE8IteratorESaISG_EEliiiRKS7_IPSE_SaISK_EERS7_ISt10unique_ptrINS1_14OutputIteratorESt14default_deleteISQ_EESaIST_EERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK13OrtMemoryInfoRSD_RbE_ onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ParametricSoftplus<float>] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ScaledTanh<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ScaledTanh<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ParametricSoftplus<float>] ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib4GeluIfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ N11onnxruntime17ElementWiseKernelINS_8functors18ParametricSoftplusIfEEEE N11onnxruntime17ElementWiseKernelINS_8functors10ScaledTanhIfEEEE N11onnxruntime7contrib4GeluIfEE 5? @AttnLSTM onnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.h static_cast<int>(activation_func_names.size()) == num_directions_ * 3 Attention mechanism memory shape error! Expected: { Attention mechanism memory sequence lengths must have shape { Attention mechanism memory sequence lengths value must in (0, Attention memory layer weight shape error! Expected:{ Attention query layer weight shape error! Expected:{ Attention v weight shape error! Expected:{ Attention layer weight shape error! Expected: { /onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.cc }, actural: ], while found! , am_attn_size}, Got: }, Got: }. Got: , aw_attn_size}. Got: virtual onnxruntime::common::Status onnxruntime::contrib::DeepCpuAttnLstmOp::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7contrib17DeepCpuAttnLstmOpE N11onnxruntime7contrib16AttentionWrapperIfEE row[ [seqno= Expect to have present state output when past state input is given onnxruntime::Tensor* onnxruntime::contrib::AttentionBase::GetPresent(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, int, int, int, int&) const /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention.cc Input 'input' is expected to have 3 dimensions, got Input 'weights' is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0 Input 1 dimension 1 should be 3 times of hidden dimension hidden_size should be divisiable by num_heads. Input '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 0 shall have length of 2 Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0 Inputs 'past' dimension 2 shall have length of num_heads Inputs 'past' dimension 2 shall have length of Inputs 'mask_index' with 1D data shall have length of batch_size or 2 * batch_size Inputs 'mask_index' with 2D data shall have shape batch_size x (past_sequence_length + sequence_length) Inputs 'mask_index' with 3D data shall have shape batch_size x sequence_length x (past_sequence_length + sequence_length) Inputs 'mask_index' with 4D data shall have shape batch_size x 1 x max_sequence_length x max_sequence_length) Inputs 'mask_index' with 4D data shall have is_unidirectional_ set to false Input 'mask_index' is expected to have 1, 2, 3 or 4 dimensions, got ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUlllE_ N11onnxruntime7contrib9AttentionIfEE /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/bias_gelu.cc onnxruntime::common::Status onnxruntime::contrib::BiasGelu<T, use_approximation>::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool use_approximation = false] use_approximation N11onnxruntime7contrib8BiasGeluIfLb0EEE N11onnxruntime7contrib8BiasGeluIfLb1EEE ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib8BiasGeluIfLb0EE7ComputeEPNS_15OpKernelContextEEUllE0_EEvPS1_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib8BiasGeluIfLb1EE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib8BiasGeluIfLb1EE7ComputeEPNS_15OpKernelContextEEUllE0_EEvPS1_lOT_lEUllE_ y"=y"=y"=y"=*BL?*BL?*BL?*BL?Integer overflowy"= Input 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 dimensioonnxruntime::contrib::EmbedLayerNorm<T>::EmbedLayerNorm(const onnxruntime::OpKernelInfo&) [with T = float] /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/embed_layer_norm.cc input index out of range ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZNKS_7contrib14EmbedLayerNormIfE7ComputeEPNS_15OpKernelContextEEUllE_EEvPS1_lOT_lEUllE_ N11onnxruntime7contrib14EmbedLayerNormIfEE Input 0 and 1 shall have same shape Input 0 and 7 (mask) shall have same shape 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 segment_embedding shall have same dimension 1 word_embedding and position_embedding shall have same dimension 1 beta 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 CDist onnxruntime::contrib::CDist<T>::CDist(const onnxruntime::OpKernelInfo&) [with T = float] /onnxruntime_src/onnxruntime/contrib_ops/cpu/cdist.h info.GetAttr<std::string>("metric", &metric).IsOK() sqeuclidean onnxruntime::contrib::CDist<T>::CDist(const onnxruntime::OpKernelInfo&) [with T = double] The first input of CDist kernel has wrong shape: The second input of CDist kernel has wrong shape: Input shape dimensions mismatch: N11onnxruntime7contrib5CDistIfEE N11onnxruntime7contrib5CDistIdEE ConvTransposeWithDynamicPads N11onnxruntime7contrib28ConvTransposeWithDynamicPadsIfEE unimplemented activation: activation_params count mismatch activation_paramnot support normalize yet. !normalize_ onnxruntime::contrib::BahdanauAttention<T>::BahdanauAttention(onnxruntime::AllocatorPtr, const onnxruntime::logging::Logger&, int, int, int, int, int, bool, onnxruntime::concurrency::ThreadPool*) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] /onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/bahdanau_attention.cc is not in (0, Real memory steps void onnxruntime::contrib::BahdanauAttention<T>::PrepareMemory(const gsl::span<const T>&, const gsl::span<const int>&) [with T = float] mem_steps <= max_memory_steps_ && mem_steps > 0 N11onnxruntime7contrib19IAttentionMechanismIfEE N11onnxruntime7contrib17BahdanauAttentionIfEE bool onnxruntime::math::NextPosition(int64_t, const int64_t*, int64_t*) /onnxruntime_src/onnxruntime/core/util/math_cpu.cc dims[d_i] < d_max Gemv found an unexpected CBLAS_TRANSPOSE input of void onnxruntime::math::Gemv(CBLAS_TRANSPOSE, int, int, float, const T*, const T*, float, T*, Provider*) [with T = float; Provider = onnxruntime::CPUMathUtil] void onnxruntime::math::Gemv(CBLAS_TRANSPOSE, int, int, float, const T*, const T*, float, T*, Provider*) [with T = double; Provider = onnxruntime::CPUMathUtil] ? Received null OrtThreadingOptions Received invalid value for allow_spinning. Valid values are 0 or 1 Cuda CudaPinned Specified device is not supported. /onnxruntime_src/onnxruntime/core/framework/allocator.cc N11onnxruntime12CPUAllocatorE /onnxruntime_src/onnxruntime/core/framework/allocatormgr.cc Received invalid value of arena_extend_strategy CreateAllocator duplicated allocator void onnxruntime::AllocatorManager::InsertAllocator(onnxruntime::AllocatorPtr) St14default_deleteIN11onnxruntime10IAllocatorEE St14default_deleteIN11onnxruntime8BFCArenaEE St19_Sp_counted_deleterIPN11onnxruntime8BFCArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE St19_Sp_counted_deleterIPN11onnxruntime10IAllocatorESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE cudaMalloc hipMalloc onnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::ChunkFromHandle(onnxruntime::BFCArena::ChunkHandle) /onnxruntime_src/onnxruntime/core/framework/bfc_arena.cc h < chunks_.size() Size: | Requested Size: | in_use: , prev: , next: int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void*) const /onnxruntime_src/onnxruntime/core/framework/bfc_arena.h p_int < base_int + memory_size_ p_int >= base_int std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::iterator std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::erase(std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::const_iterator) [with _Key = long unsigned int; _Val = long unsigned int; _KeyOfValue = std::_Identity<long unsigned int>; _Compare = onnxruntime::BFCArena::Bin::ChunkComparator; _Alloc = std::allocator<long unsigned int>; std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::iterator = std::_Rb_tree_iterator<long unsigned int>; std::_Rb_tree<_Key, _Val, _KeyOfValue, _Compare, _Alloc>::const_iterator = std::_Rb_tree_const_iterator<long unsigned int>] void onnxruntime::BFCArena::RemoveFreeChunkIterFromBin(onnxruntime::BFCArena::Bin::FreeChunkSet*, const iterator&) !c->in_use() && (c->bin_num != kInvalidBinNum) void onnxruntime::BFCArena::RemoveFreeChunkFromBin(onnxruntime::BFCArena::ChunkHandle) BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0 Could not find chunk in bin void onnxruntime::BFCArena::InsertFreeChunkIntoBin(onnxruntime::BFCArena::ChunkHandle) !c->in_use() && (c->bin_num == kInvalidBinNum) RegionFor Could not find Region for c->bin_num == bin_num std::array<onnxruntime::BFCArena::BinDebugInfo, 21> onnxruntime::BFCArena::get_bin_debug_info() bin->free_chunks.count(h) == 1 BFCArena Creating BFCArena for max_dead_bytes_per_chunk: memory limit: arena_extend_strategy: Creating bins of max chunk size with following configs: initial_chunk_size_bytes: initial_growth_chunk_size_bytes: onnxruntime::BFCArena::BFCArena(std::unique_ptr<onnxruntime::IAllocator>, size_t, onnxruntime::ArenaExtendStrategy, int, int, int) BinForSize(bin_size) == BinFromIndex(b) BinForSize(bin_size + 255) == BinFromIndex(b) BinForSize(bin_size * 2 - 1) == BinFromIndex(b) BinForSize(bin_size * 2) != BinFromIndex(b) void onnxruntime::BFCArena::Merge(onnxruntime::BFCArena::ChunkHandle, onnxruntime::BFCArena::ChunkHandle) !c1->in_use() && !c2->in_use() c2->prev == h1 void onnxruntime::BFCArena::FreeAndMaybeCoalesce(onnxruntime::BFCArena::ChunkHandle) c->in_use() && (c->bin_num == kInvalidBinNum) BFC Arena shrunk by bytes. Could not find Region for: entry != regions_.end() The total allocated bytes is now void onnxruntime::BFCArena::RegionManager::RemoveAllocationRegion(void*) void onnxruntime::BFCArena::DeallocateRawInternal(void*) h != kInvalidChunkHandle void onnxruntime::BFCArena::SplitChunk(onnxruntime::BFCArena::ChunkHandle, size_t) void* onnxruntime::BFCArena::FindChunkPtr(onnxruntime::BFCArena::BinNum, size_t, size_t) !chunk->in_use() Chunk Free DumpMemoryLog Allocator: : Chunks . Bytes Requested Bin for bytes has max bytes of , Chunk State: Overall chunks summary: chunks of size . Total Sum Total of in-use chunks: Stats:
+ Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: NumReserves: NumArenaExtensions: NumArenaShrinkages: MaxAllocSize: 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 Diff between in-use and requested bytes is Summary of in-use chunks by size: AllocateRawInternal tried to allocate 0 bytes Extending BFCArena for . bin_num: (requested) num_bytes: (actual) 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) Available memory of Extend Extended allocation by bytes. Total allocated bytes: Allocated memory at is smaller than requested bytes of Incorrect arena extend strategy. onnxruntime::BFCArena::Extend(size_t)::<lambda(size_t)> Failed to allocate memory for requested buffer of size onnxruntime::BFCArena::AllocationRegion::AllocationRegion(void*, size_t, int64_t) 0 == memory_size % kMinAllocationSize Reserve Reserving memory in BFCArena for virtual void* onnxruntime::BFCArena::Reserve(size_t) reserved_chunks_.find(ptr) == reserved_chunks_.end() N11onnxruntime15IArenaAllocatorE N11onnxruntime8BFCArenaE e/framework/bfc_fff? Config key is empty or longer than maximum length 128 Config value is longer than maximum length 1024 /onnxruntime_src/onnxruntime/core/framework/config_options.cc AddConfigEntry Config with key [ ] already exists with value [ ]. It will be overwritten CopyTensors /onnxruntime_src/onnxruntime/core/framework/data_transfer.cc virtual onnxruntime::common::Status onnxruntime::CPUDataTransfer::CopyTensor(const onnxruntime::Tensor&, onnxruntime::Tensor&, int) const src.SizeInBytes() == dst.SizeInBytes() N11onnxruntime13IDataTransferE N11onnxruntime15CPUDataTransferE data_transfer registered is nullptr. Tensor size mismatch There's no data transfer registered for copying tensors from /onnxruntime_src/onnxruntime/core/framework/data_transfer_manager.cc (null) double uint8 uint16 uint32 uint64 bfloat16 `````x`h```(a``aa``X`GetElementType bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Sequence&, const onnx::TypeProto_Sequence&) /onnxruntime_src/onnxruntime/core/framework/data_types.cc k{kk.{H{kb{zbool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Map&, const onnx::TypeProto_Map&) k|kk;|!|kU|{virtual bool onnxruntime::TensorTypeBase::IsCompatible(const onnx::TypeProto&) const thisProto->value_case() == TypeProto::ValueCase::kTensorType utils::HasElemType(thisProto->tensor_type()) virtual bool onnxruntime::SparseTensorTypeBase::IsCompatible(const onnx::TypeProto&) const thisProto->value_case() == TypeProto::ValueCase::kSparseTensorType utils::HasElemType(thisProto->sparse_tensor_type()) virtual bool onnxruntime::SequenceTensorTypeBase::IsCompatible(const onnx::TypeProto&) const thisProto->value_case() == TypeProto::ValueCase::kSequenceType utils::HasElemType(thisProto->sequence_type()) bool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto&) const thisProto->value_case() == TypeProto::ValueCase::kMapType utils::HasKeyType(thisProto->map_type()) bool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto&) const Not implemented virtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void*, size_t, OrtValue&) const virtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue&, size_t, void*) const Invalid DataTypeImpl TypeProto definition onnxruntime::utils::ContainerChecker::ContainerChecker(onnxruntime::MLDataType) Only ONNX MLDataType can be registered void onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(onnxruntime::MLDataType) We do not expect duplicate registration of types for: tensor type f kdtĠܠ $<Tf kf k4 expected to be a registered ONNX type static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = float] /onnxruntime_src/include/onnxruntime/core/framework/data_types.h elem_proto != nullptr static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = double] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = signed char] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = unsigned char] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = short int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = short unsigned int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = unsigned int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = long int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = long unsigned int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = bool] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::basic_string<char>] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = onnxruntime::MLFloat16] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = onnxruntime::BFloat16] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = std::basic_string<char>] value_proto != nullptr static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = long int] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = float] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = double] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = std::basic_string<char>] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = long int] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = float] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = double] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::map<std::basic_string<char>, float>] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::map<long int, float>] is not currently registered or supported MLDataType for: N11onnxruntime12DataTypeImplE N11onnxruntime14TensorTypeBaseE N11onnxruntime20SparseTensorTypeBaseE N11onnxruntime17NonTensorTypeBaseE N11onnxruntime22SequenceTensorTypeBaseE N11onnxruntime21PrimitiveDataTypeBaseE N11onnxruntime10TensorTypeIiEE N11onnxruntime10TensorTypeIfEE N11onnxruntime10TensorTypeIbEE N11onnxruntime10TensorTypeISsEE N11onnxruntime10TensorTypeIaEE N11onnxruntime10TensorTypeIhEE N11onnxruntime10TensorTypeItEE N11onnxruntime10TensorTypeIsEE N11onnxruntime10TensorTypeIlEE N11onnxruntime10TensorTypeIdEE N11onnxruntime10TensorTypeIjEE N11onnxruntime10TensorTypeImEE N11onnxruntime10TensorTypeINS_9MLFloat16EEE N11onnxruntime10TensorTypeINS_8BFloat16EEE N11onnxruntime16SparseTensorTypeIiEE N11onnxruntime16SparseTensorTypeIfEE N11onnxruntime16SparseTensorTypeIbEE N11onnxruntime16SparseTensorTypeIaEE N11onnxruntime16SparseTensorTypeIhEE N11onnxruntime16SparseTensorTypeItEE N11onnxruntime16SparseTensorTypeIsEE N11onnxruntime16SparseTensorTypeIlEE N11onnxruntime16SparseTensorTypeIdEE N11onnxruntime16SparseTensorTypeIjEE N11onnxruntime16SparseTensorTypeImEE N11onnxruntime16SparseTensorTypeINS_9MLFloat16EEE N11onnxruntime16SparseTensorTypeINS_8BFloat16EEE N11onnxruntime13NonTensorTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEE N11onnxruntime7MapTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEE N11onnxruntime13NonTensorTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEE N11onnxruntime7MapTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEE N11onnxruntime13NonTensorTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEE N11onnxruntime7MapTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEE N11onnxruntime13NonTensorTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEE N11onnxruntime7MapTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEE N11onnxruntime13NonTensorTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEE N11onnxruntime7MapTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEE N11onnxruntime13NonTensorTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEE N11onnxruntime7MapTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEE N11onnxruntime13NonTensorTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEE N11onnxruntime7MapTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEE N11onnxruntime13NonTensorTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEE N11onnxruntime7MapTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEE N11onnxruntime18SequenceTensorTypeIfEE N11onnxruntime18SequenceTensorTypeIdEE N11onnxruntime18SequenceTensorTypeIaEE N11onnxruntime18SequenceTensorTypeIhEE N11onnxruntime18SequenceTensorTypeIsEE N11onnxruntime18SequenceTensorTypeItEE N11onnxruntime18SequenceTensorTypeIiEE N11onnxruntime18SequenceTensorTypeIjEE N11onnxruntime18SequenceTensorTypeIlEE N11onnxruntime18SequenceTensorTypeImEE N11onnxruntime18SequenceTensorTypeIbEE N11onnxruntime18SequenceTensorTypeISsEE N11onnxruntime18SequenceTensorTypeINS_9MLFloat16EEE N11onnxruntime18SequenceTensorTypeINS_8BFloat16EEE N11onnxruntime13NonTensorTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEE N11onnxruntime12SequenceTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEE N11onnxruntime13NonTensorTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEE N11onnxruntime12SequenceTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEE N11onnxruntime17PrimitiveDataTypeIiEE N11onnxruntime17PrimitiveDataTypeIfEE N11onnxruntime17PrimitiveDataTypeIbEE N11onnxruntime17PrimitiveDataTypeISsEE N11onnxruntime17PrimitiveDataTypeIaEE N11onnxruntime17PrimitiveDataTypeIhEE N11onnxruntime17PrimitiveDataTypeItEE N11onnxruntime17PrimitiveDataTypeIsEE N11onnxruntime17PrimitiveDataTypeIlEE N11onnxruntime17PrimitiveDataTypeIdEE N11onnxruntime17PrimitiveDataTypeIjEE N11onnxruntime17PrimitiveDataTypeImEE N11onnxruntime17PrimitiveDataTypeINS_9MLFloat16EEE N11onnxruntime17PrimitiveDataTypeINS_8BFloat16EEE ZN11onnxruntime19data_types_internal16DataTypeRegistryC4EvEUlPKNS_12DataTypeImplEE_ Ss N11onnxruntime9MLFloat16E N11onnxruntime8BFloat16E St3mapISsfSt4lessISsESaISt4pairIKSsfEEE St3mapIlfSt4lessIlESaISt4pairIKlfEEE A dso with name has already been loaded. LoadExternalLib /onnxruntime_src/onnxruntime/core/framework/ex_lib_loader.cc Caught exception while loading custom ops with message: Unloading DSO Failed to unload DSO: Caught exception while destructing CustomOpsLoader with message: N11onnxruntime11ExLibLoaderE e/framework/ex_lMemory pattern planner is not enabled on this execution framework. /onnxruntime_src/onnxruntime/core/framework/execution_frame.cc does not match actual shape of VerifyOutputSizes Expected shape from model of int onnxruntime::NodeIndexInfo::GetMLValueIndex(int) const /onnxruntime_src/onnxruntime/core/framework/node_index_info.h offset >= 0 && static_cast<size_t>(offset) < node_values_size_ const onnxruntime::AllocPlanPerValue& onnxruntime::ExecutionFrame::GetAllocationPlan(int) ort_value_idx >= 0 && static_cast<size_t>(ort_value_idx) < alloc_plan.size() TraceAllocate size= TraceAllocation for ort_value_idx= void onnxruntime::ExecutionFrame::TraceFree(int) TraceFree for ort_value_idx= Trying to allocate memory for unused optional inputs/outputs Tensor shape cannot contain any negative value onnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue&, int, onnxruntime::MLDataType, const OrtMemoryInfo&, const onnxruntime::TensorShape&, bool) std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::MemoryPattern; _Alloc = std::allocator<onnxruntime::MemoryPattern>; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::MemoryPattern&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] , block in memory pattern size is: , fall back to default allocation behavior ort_value.Fence() == nullptr For ort_value with index: but the actually size is: node_index_info and ort_value_idx_map are out of sync and cannot be used onnxruntime::IExecutionFrame::IExecutionFrame(const onnxruntime::OrtValueNameIdxMap&, const onnxruntime::NodeIndexInfo&, const std::vector<int>&) node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx() void onnxruntime::IExecutionFrame::Init(const std::vector<int>&, const std::vector<OrtValue>&, const std::unordered_map<int, OrtValue>&, const std::vector<OrtValue>&) feeds.size() == feed_mlvalue_idxs.size() fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() should only appear once. Memory type returned nullptr failed. Error: onnxruntime::ExecutionFrame::ExecutionFrame(const std::vector<int>&, const std::vector<OrtValue>&, const std::vector<int>&, const std::vector<OrtValue>&, const std::unordered_map<long unsigned int, std::function<onnxruntime::common::Status(const onnxruntime::TensorShape&, const OrtMemoryInfo&, OrtValue&, bool&)> >&, const onnxruntime::SessionState&) buffers_.find(location) == buffers_.end() static_activation_memory_sizes_in_byte_.find(location.name) == static_activation_memory_sizes_in_byte_.end() Allocation of memory pattern buffer for invalid index const OrtValue& onnxruntime::IExecutionFrame::GetMLValue(int) const /onnxruntime_src/onnxruntime/core/framework/execution_frame.h ort_value_index >= 0 && static_cast<size_t>(ort_value_index) < all_values_size_ ReleaseMLValueImpl Fetches vector passed to GetOutputs contains entries which doesn't match the number of fetches the frame was initialized with of Shape 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. AllocateMLValueTensorPreAllocateBuffer onnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan(OrtValue&, int, const onnxruntime::TensorShape*, size_t) ort_value_index >= 0 && static_cast<size_t>(ort_value_index) < alloc_plan.size() We don't expect custom allocators for non-tensor types, so a shape is mandatory here. Allocation of tensor types requires a shape. onnxruntime::common::Status onnxruntime::AllocateSparseTensor(OrtValue&, const onnxruntime::DataTypeImpl&, onnxruntime::AllocatorPtr, const onnxruntime::TensorShape&, size_t, bool, const onnxruntime::SessionState&) AllocateAsPerAllocationPlan Invalid allocation kind: mlvalue.Fence() == nullptr Requested shape: OrtValue shape verification failed. Current shape: onnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(int, int, const onnxruntime::TensorShape*, OrtValue*&, const onnxruntime::Node&, size_t) shape && tensor.Shape() == *shape N11onnxruntime15IExecutionFrameE N11onnxruntime14ExecutionFrameE AllocateMLValueTensorSelfOwnBuffIExecutionProvider::Compile with fused Node is not implemented by IExecutionProvider::Compile with fused Node and dll path is not implemented by IExecutionProvider::Compile with FusedNodeAndGraph is not implemented by IExecutionProvider constructor must be called with true for use_metadef_id_creator virtual int onnxruntime::IExecutionProvider::GenerateMetaDefId(const onnxruntime::GraphViewer&, uint64_t&) const /onnxruntime_src/onnxruntime/core/framework/execution_provider.cc metadef_id_generator_ void onnxruntime::IExecutionProvider::InsertAllocator(onnxruntime::AllocatorPtr) TryInsertAllocator duplicated allocator: N11onnxruntime18IExecutionProviderE MapNamesToMLValueIdxs /onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.cc Error mapping feeds: Error mapping output names: void onnxruntime::FeedsFetchesManager::SetDeviceCopyChecks(onnxruntime::DeviceCopyCheck, onnxruntime::DeviceCopyCheck) input_copy_needed != DeviceCopyCheck::Unknown && output_copy_needed != DeviceCopyCheck::Unknown InlineNodes /onnxruntime_src/onnxruntime/core/framework/graph_partitioner.cc onnxruntime::FunctionKernel::FunctionKernel(const onnxruntime::OpKernelInfo&) /onnxruntime_src/onnxruntime/core/framework/func_kernel.h compute_info_->create_state_func(&context, &func_state_) == 0 PartitionOnnxFormatModelImpl 1 == capability.nodes.size() onnxruntime::Node* onnxruntime::PlaceNode(onnxruntime::Graph&, const onnxruntime::IndexedSubGraph&, const onnxruntime::KernelRegistryManager&, const string&, onnxruntime::IExecutionProvider::FusionStyle, onnxruntime::GraphPartitioner::Mode, int&) Must use Function based fusion when exporting compiled nodes to dll. onnxruntime::common::Status onnxruntime::PartitionOnnxFormatModelImpl(onnxruntime::Graph&, bool, onnxruntime::FuncManager&, onnxruntime::KernelRegistryManager&, onnxruntime::KernelRegistry&, onnxruntime::IExecutionProvider&, onnxruntime::GraphPartitioner::Mode, int&) fusion_style == IExecutionProvider::FusionStyle::Function did not return correct number of compiled functions std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::NodeComputeInfo; _Alloc = std::allocator<onnxruntime::NodeComputeInfo>; std::vector<_Tp, _Alloc>::reference = onnxruntime::NodeComputeInfo&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::Node*; _Alloc = std::allocator<onnxruntime::Node*>; std::vector<_Tp, _Alloc>::reference = onnxruntime::Node*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::unique_ptr<onnxruntime::ComputeCapability>; _Alloc = std::allocator<std::unique_ptr<onnxruntime::ComputeCapability> >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr<onnxruntime::ComputeCapability>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::ComputeCapability; _Dp = std::default_delete<onnxruntime::ComputeCapability>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::ComputeCapability&] typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::IndexedSubGraph; _Dp = std::default_delete<onnxruntime::IndexedSubGraph>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::IndexedSubGraph&] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr<onnxruntime::GraphViewer>; _Alloc = std::allocator<std::unique_ptr<onnxruntime::GraphViewer> >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr<onnxruntime::GraphViewer>&] typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::GraphViewer; _Dp = std::default_delete<onnxruntime::GraphViewer>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::GraphViewer&] typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::IExecutionProvider; _Dp = std::default_delete<onnxruntime::IExecutionProvider>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::IExecutionProvider&] PartitionOnnxFormatModel PartitionOrtFormatModelImpl EP: has Compile error: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::IExecutionProvider::FusedNodeAndGraph; _Alloc = std::allocator<onnxruntime::IExecutionProvider::FusedNodeAndGraph>; std::vector<_Tp, _Alloc>::reference = onnxruntime::IExecutionProvider::FusedNodeAndGraph&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] single_node_compute_func should have 1 elements . Execution Provider must generate unique names across the entire model. Existing entry in compiled kernel hashes for onnxruntime::common::Status onnxruntime::PartitionOrtFormatModelImpl(onnxruntime::Graph&, onnxruntime::FuncManager&, onnxruntime::KernelRegistryManager&, onnxruntime::KernelRegistry&, onnxruntime::IExecutionProvider&, std::unordered_map<std::basic_string<char>, long unsigned int>&, int&) No provider specified. Partition Compiled kernel hashes must be provided onnxruntime::common::Status onnxruntime::GraphPartitioner::Partition(onnxruntime::Graph&, bool, onnxruntime::FuncManager&, onnxruntime::GraphPartitioner::Mode, std::unordered_map<std::basic_string<char>, long unsigned int>*) const compiled_kernel_hashes != nullptr N11onnxruntime14FunctionKernelE onnxruntime::KernelDefBuilder& onnxruntime::KernelDefBuilder::VariadicAlias(int, int) /onnxruntime_src/onnxruntime/core/framework/kernel_def_builder.cc input_offset >= 0 && output_offset >= 0 kernel def can't be NULL Failed to add kernel for typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::KernelDef; _Dp = std::default_delete<onnxruntime::KernelDef>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::KernelDef&] : Conflicting with a registered kernel with op versions. Op with name ( and type ( Version mismatch. node_version: kernel start version: kernel_end_version: (node_version: ), void onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::<lambda(const onnx::OpSchema::FormalParameter&)>; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::<lambda(const onnx::OpSchema::FormalParameter&, const onnx::TypeProto*)>] /onnxruntime_src/onnxruntime/core/framework/kernel_registry.cc len <= op_schema.inputs().size() std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnx::OpSchema::FormalParameter; _Alloc = std::allocator<onnx::OpSchema::FormalParameter>; std::vector<_Tp, _Alloc>::const_reference = const onnx::OpSchema::FormalParameter&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] void onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::<lambda(const onnx::OpSchema::FormalParameter&)>; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::<lambda(const onnx::OpSchema::FormalParameter&, const onnx::TypeProto*)>] Found kernel for Op with name ( in the supported version range 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 not found in No matching hash for kernel is not supported in Kernel not found Encountered following errors: ( TryCreateKernel (node ). found duplicated provider in KernelRegistryManager The node is not placed on any Execution ProviderFailed to find kconst onnxruntime::DataTypeImpl* onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg&) /onnxruntime_src/onnxruntime/core/framework/mldata_type_utils.cc nullptr != type_proto lt|G
SZonnxruntime::NodeIndexInfo::Init(const TValidNodes&, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap&) [with TValidNodes = onnxruntime::ConstGraphNodes; onnxruntime::NodeIndex = long unsigned int]::<lambda(const onnxruntime::NodeArg&, bool)> /onnxruntime_src/onnxruntime/core/framework/node_index_info.cc onnxruntime::NodeIndexInfo::Init(const TValidNodes&, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap&) [with TValidNodes = onnxruntime::ValidNodes<const std::vector<const onnxruntime::Node*> >; onnxruntime::NodeIndex = long unsigned int]::<lambda(const onnxruntime::NodeArg&, bool)> ZN11onnxruntime13NodeIndexInfo4InitINS_15ConstGraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ ZN11onnxruntime13NodeIndexInfo4InitINS_15ConstGraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ type_proto is not of type map! type_proto is not of type sequence! \ t LtOrtValue is TensorSequence type but has no element Tensor DataType. Tensor types should have been handled already XxXTempSpace allocator not found Execution frame was null frame != nullptr OpKernel was null kernel != nullptr onnxruntime::OpKernelContext::OpKernelContext(onnxruntime::IExecutionFrame*, const onnxruntime::OpKernel*, onnxruntime::concurrency::ThreadPool*, const onnxruntime::logging::Logger&) /onnxruntime_src/onnxruntime/core/framework/op_kernel.cc int onnxruntime::NodeIndexInfo::GetNodeOffset(onnxruntime::NodeIndex) const node_offsets_index < node_offsets_size_ OrtValue* onnxruntime::OpKernelContext::OutputMLValue(int, const onnxruntime::TensorShape&, size_t) . Num args is Invalid arg_num of arg_num < arg_counts.size() int onnxruntime::OpKernelContext::NumVariadicInputs(size_t) const OrtValue* onnxruntime::OpKernelContext::GetOrCreateOutputMLValue(int) N11onnxruntime15OpKernelContextE cannot find allocator const OrtMemoryInfo& onnxruntime::OpKernelInfo::GetMemoryInfo(int, OrtMemType) const /onnxruntime_src/onnxruntime/core/framework/op_kernel_info.cc No attribute with this name is defined. Attibute name and type don't match No attribute with name: is defined. Attribute: expected to be of type: but is of type: Requested attribute: is expected to have type: std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::AllocPlanPerValue; _Alloc = std::allocator<onnxruntime::AllocPlanPerValue>; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::AllocPlanPerValue&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::MemPatternPlanner::OrtValueAllocationBlock; _Alloc = std::allocator<onnxruntime::MemPatternPlanner::OrtValueAllocationBlock>; std::vector<_Tp, _Alloc>::reference = onnxruntime::MemPatternPlanner::OrtValueAllocationBlock&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] void onnxruntime::MemPatternPlanner::TraceAllocation(int, size_t) /onnxruntime_src/onnxruntime/core/framework/mem_pattern_planner.h !using_counters_ current <= buffer_size_ onnxruntime::OrtValueTensorSlicer<T>::Iterator::Iterator(T&, size_t, size_t, int64_t, onnxruntime::OrtValueTensorSlicer<T>::Iterator::Direction) [with T = const OrtValue; size_t = long unsigned int; int64_t = long int] /onnxruntime_src/onnxruntime/core/framework/ort_value_tensor_slicer.cc Can't slice a non-tensor OrtValue. Type was static onnxruntime::OrtValueTensorSlicer<T> onnxruntime::OrtValueTensorSlicer<T>::Create(T&, int64_t, int64_t) [with T = const OrtValue; int64_t = long int] OrtValue has not been allocated so can't be sliced. Insufficient dimensions to slice on gsl::narrow_cast<int64_t>(tensor_shape.NumDimensions()) >= slice_dimension ort_value.IsTensor() ort_value.IsAllocated() . Shape: . Dimension 0 is Invalid dim0_offset of dim0_offset < dim0_size static onnxruntime::OrtValueTensorSlicer<T> onnxruntime::OrtValueTensorSlicer<T>::Create(T&, int64_t, int64_t) [with T = OrtValue; int64_t = long int] onnxruntime::OrtValueTensorSlicer<T>::Iterator::Iterator(T&, size_t, size_t, int64_t, onnxruntime::OrtValueTensorSlicer<T>::Iterator::Direction) [with T = OrtValue; size_t = long unsigned int; int64_t = long int] libonnxruntime_providers_shared.so /onnxruntime_src/onnxruntime/core/framework/provider_bridge_ort.cc Ensure Provider_SetHost Get GetProvider OrtSessionOptionsAppendExecutionProvider_Cuda: Failed to load shared library SessionOptionsAppendExecutionProvider_Tensorrt: Failed to load shared library SessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared library GetProviderInfo_CUDA GetProviderInfo_CUDA called, returning nullptr CUDA Provider not available, can't get interface for it ProviderInfo_CUDA* onnxruntime::GetProviderInfo_CUDA() CUDA execution provider is not enabled. const void* onnxruntime::Tensor::DataRaw(onnxruntime::MLDataType) const libonnxruntime_providers_cuda.so libonnxruntime_providers_dnnl.so libonnxruntime_providers_openvino.so libonnxruntime_providers_tensorrt.so N11onnxruntime23NodeAttributes_IteratorE N11onnxruntime35TensorShapeProto_Dimension_IteratorE N11onnxruntime18Node__NodeIteratorE N11onnxruntime18Node__EdgeIteratorE N11onnxruntime12ProviderHostE N11onnxruntime40TensorShapeProto_Dimension_Iterator_ImplE N11onnxruntime28NodeAttributes_Iterator_ImplE N11onnxruntime23Node__NodeIterator_ImplE N11onnxruntime23Node__EdgeIterator_ImplE N11onnxruntime16ProviderHostImplE Received nullptr for name. Received nullptr for OrtValue. Received OrtValue is not a tensor. Only tensors are supported. Buffer containing the initializer must be owned by the user. An OrtValue for this name has already been added. /onnxruntime_src/onnxruntime/core/framework/session_state.cc SetGraphAndCreateKernels must be called prior to GetExecutionInfo. const onnxruntime::NodeIndexInfo& onnxruntime::SessionState::GetNodeIndexInfo() const node_index_info_ KernelUseSharedPrePackedBuffers The kernel corresponding to the node doesn't have an implementation that can consume provided pre-packed weights const onnxruntime::KernelCreateInfo& onnxruntime::SessionState::GetNodeKernelCreateInfo(onnxruntime::NodeIndex) const entry != kernel_create_info_map_.cend() std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::OpKernel*; _Alloc = std::allocator<onnxruntime::OpKernel*>; std::vector<_Tp, _Alloc>::reference = onnxruntime::OpKernel*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] Failed to find input name in the mapping: CreateGraphInfo SaveMLValueNameIndexMapping Done saving OrtValue mappings. Allocator already registered for . Ignoring allocator from duplicated ort_value index: . Do you have duplicated calls to SessionState::AddInitializedTensor function? Only one node should produce an output. Existing entry for void onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const string&, const onnxruntime::SessionState::NodeInfo&) output_names_to_nodeinfo.empty() Using an input in multiple nodes on different devices is not supported currently. Input: is used by node ) and node onnxruntime::SessionState::PrepackConstantInitializedTensors(std::unordered_map<std::basic_string<char>, long unsigned int>&, const std::unordered_map<std::basic_string<char>, const OrtValue*>&)::<lambda(bool)> allocator_for_caching.get() != nullptr doesn't have an implementation that can cache computed pre-packed weights weights_to_be_filled_in.buffers_.size() > 0 The op type of a node cannot be empty Using cached version of pre-packed weight for constant initializer: Unable to write the provided PrePackedWeights instance into the container !op_type.empty() + used in the node: which is of op type: FinalizeSessionStateImpl session.disable_prepacking Attribute: ' OpType: p_op_kernel typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::SequentialExecutionPlan; _Dp = std::default_delete<onnxruntime::SequentialExecutionPlan>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::SequentialExecutionPlan&] typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::ITensorAllocator; _Dp = std::default_delete<onnxruntime::ITensorAllocator>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::ITensorAllocator&] Missing session state for subgraph. Node:' onnxruntime::common::Status onnxruntime::SessionState::FinalizeSessionStateImpl(const std::basic_string<char>&, onnxruntime::KernelRegistryManager&, const onnxruntime::Node*, const onnxruntime::SessionOptions&, bool, std::unordered_map<std::basic_string<char>, long unsigned int>&) entry != node_to_subgraph_ss.second.cend() PopulateKernelCreateInfo LoadFromOrtFormat Can't find node with index . Invalid ORT format model. '. Subgraph SessionState for Kernel create info is null. Invalid ORT format model. Kernel create info node indices are null. Invalid ORT format model. Kernel create info hashes are null. Invalid ORT format model. Size mismatch for kernel create info node indexes and hashes. Invalid ORT format model. Unable to find compiled kernel hash for node ' SessionState for subgraphs is null. Invalid ORT format model. is missing. Invalid ORT format model. Subgraph SessionState entry for is null. Invalid ORT format model. Entry exists in node for attribute void onnxruntime::SessionState::AddSubgraphSessionState(onnxruntime::NodeIndex, const string&, std::unique_ptr<onnxruntime::SessionState>) existing_entries.find(attribute_name) == existing_entries.cend() CreateSubgraphSessionState Main Graph instance should have populated all subgraphs when being resolved. onnxruntime::common::Status onnxruntime::SessionState::CreateSubgraphSessionState() FinalizeSessionState Could not find OrtValue with idx ' void onnxruntime::SessionState::UpdateToBeExecutedNodes(const std::vector<int>&) GetSubGraphSessionStatesOrtFormat N11onnxruntime25ISequentialPlannerContextE N11onnxruntime24SequentialPlannerContextE *ZN11onnxruntime12SessionState23UpdateToBeExecutedNodesERKSt6vectorIiSaIiEEEUlPKNS_4NodeEE_ *ZN11onnxruntime12SessionState24FinalizeSessionStateImplERKSsRNS_21KernelRegistryManagerEPKNS_4NodeERKNS_14SessionOptionsEbRSt13unordered_mapISsmSt4hashISsESt8equal_toISsESaISt4pairIS1_mEEEEUliRK8OrtValueRKNS_11OrtCallbackEbE0_ *ZN11onnxruntime12SessionState15SetupAllocatorsEvEUli10OrtMemTypeE_ e/framework/sessshape.Size() must >=0 Failed memory size calculation input with name is not used by any node. SaveInputOutputNamesToNodeMapping /onnxruntime_src/onnxruntime/core/framework/session_state_utils.cc DeserializeTensorProto() takes either pre-allocated buffer or an allocator! Internal error. The preallocated buffer is too small. Requires string tensor is not supported for copying between allocators , Got DeserializeTensorProto Failed to copy tensor to SaveInitializedTensors Saving initialized tensors. bytes for Deserialize tensor OrtValue indexes should have been populated. onnxruntime::common::Status onnxruntime::session_state_utils::SaveInitializedTensors(const onnxruntime::Env&, const std::basic_string<char>&, const onnxruntime::GraphViewer&, const AllocatorPtr&, const onnxruntime::OrtValueNameIdxMap&, const std::vector<int>&, onnxruntime::ITensorAllocator&, const std::function<onnxruntime::common::Status(int, const OrtValue&, const onnxruntime::OrtCallback&, bool)>&, const onnxruntime::logging::Logger&, const onnxruntime::DataTransferManager&, const onnxruntime::ExecutionPlanBase&, const onnxruntime::SessionOptions&) ort_value_name_idx_map.MaxIdx() > -1 Cannot use user supplied initializer with name: ( ) because the ORT planned memory location device ) is different from what is supplied ( entry != initialized_tensors_to_allocate.end() && entry->second->data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING [Memory] SessionStateInitializer statically allocates Using user supplied initializer with name ( session.use_device_allocator_for_initializers Done saving initialized tensors *ZN11onnxruntime19session_state_utils33SaveInputOutputNamesToNodeMappingERKNS_11GraphViewerERNS_12SessionStateERKSt6vectorIPKNS_7NodeArgESaIS9_EEEUlRS8_mE_ *ZN11onnxruntime19session_state_utils33SaveInputOutputNamesToNodeMappingERKNS_11GraphViewerERNS_12SessionStateERKSt6vectorIPKNS_7NodeArgESaIS9_EEEUlRS8_mE0_ ion_state_utils.tensor size overflow size_t onnxruntime::Tensor::SizeInBytes() const /onnxruntime_src/onnxruntime/core/framework/tensor.cc dtype_ != nullptr void onnxruntime::Tensor::Init(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, onnxruntime::AllocatorPtr, ptrdiff_t) Tensor is expected to contain one of the primitive data types. Got: onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, const OrtMemoryInfo&, ptrdiff_t) p_type != nullptr onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, std::shared_ptr<onnxruntime::IAllocator>) tensor failed memory size calculation onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, std::shared_ptr<onnxruntime::IAllocator>, ptrdiff_t) Trace /onnxruntime_src/onnxruntime/core/framework/tensor_allocator_with_mem_pattern.h Weight buffer for initializer ' Get preallocated buffer for initializer ' Mem pattern for initializer ' is not found ' failed std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = OrtMemoryInfo; _Alloc = std::allocator<OrtMemoryInfo>; std::vector<_Tp, _Alloc>::reference = OrtMemoryInfo&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] Failed to get allocator for location: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::MemoryPattern; _Alloc = std::allocator<onnxruntime::MemoryPattern>; std::vector<_Tp, _Alloc>::reference = onnxruntime::MemoryPattern&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] duplicated location FinalizePlan N11onnxruntime16ITensorAllocatorE N11onnxruntime29TensorAllocatorWithMemPatternE model format error! Need a key for the external data info model format error! Need a value for the external data info model format error! Missing 'location' offset checksum model format error! Invalid tensor shape slice argument. onnxruntime::TensorShape onnxruntime::TensorShape::Slice(size_t, size_t) const /onnxruntime_src/onnxruntime/core/framework/tensor_shape.cc dimstart <= dimend && dimend <= size() Invalid dimension of dimension <= num_dims for SizeFromDimension. Tensor has int64_t onnxruntime::TensorShape::SizeToDimension(size_t) const int64_t onnxruntime::TensorShape::SizeFromDimension(size_t) const OrtValue is not a Tensor Argument is not a tensor OrtStatus* OrtApis::GetTensorTypeAndShape(const OrtValue*, OrtTensorTypeAndShapeInfo**) /onnxruntime_src/onnxruntime/core/framework/tensor_type_and_shape.cc
+
UnpackTensor: the pre-allocate size does not match the size in proto Invalid TensorProto I---IIbasic_string::resize Unsupported sparse tensor data type of /onnxruntime_src/onnxruntime/core/framework/tensorprotoutils.cc Tensor does not have external data to read from. External data type cannot be UNDEFINED or STRING. TensorProto external data size mismatch. Computed size: GetExternalDataInfo ReadExternalDataForTensor UnpackTensor: the pre-allocated size does not match the raw data size, expected data overflow ) in proto ) does not match the data size( corrupted protobuf data: tensor shape size( UnpackInitializerData Unsupported type: XMM8NNNXOO8QXMQQXRxPPXMXMP TensorProtoToTensor() tensor shape mismatch! can not be writen into Tensor type string tensor can not have raw data tensor can't contain negative dims Initialized tensor with unexpected type: TensorProto type TensorProtoToTensor GetFileContent ^Z;Z[G[[Z^H]]\J^\]^^F\ TensorProtoToMLValue() must take a pre-allocated MemBuffer! string tensor can not use pre-allocated buffer UnpackTensorWithExternalDataImpl nullptr == p_data CopySparseData Indicies expected to be INT64 Sparse Indicies raw data size does not match expected. Invalid SparseTensor indices. Should either have raw or int64 data onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto&, gsl::span<const long int>, std::function<void(long unsigned int, long unsigned int)>) [with T = short unsigned int; size_t = long unsigned int] rank == dims.size() && rank > 0 cur_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<const long int>, std::function<void(long unsigned int, long unsigned int)>) [with T = unsigned char; size_t = long unsigned int] SparseTensorProtoToDenseTensorProto onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto&, gsl::span<const long int>, std::function<void(long unsigned int, long unsigned int)>) [with T = unsigned int; size_t = long unsigned int] onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto&, gsl::span<const long int>, std::function<void(long unsigned int, long unsigned int)>) [with T = long unsigned int; size_t = long unsigned int] BUG! Report to onnxruntime team. element_size of: is not supported. ConstantNodeProtoToTensorProto Unsupported attribute value type of onnxruntime::common::Status onnxruntime::utils::ConstantNodeProtoToTensorProto(const onnx::NodeProto&, const onnxruntime::Path&, onnx::TensorProto&) in 'Constant' node ' ZkkT<ܳZkkt̴ZkkZkk̲Must have a valid data type HasDataType(dense_proto) data_type: onnxruntime::common::Status onnxruntime::utils::DenseTensorToSparseTensorProto(const onnx::TensorProto&, const onnxruntime::Path&, onnx::SparseTensorProto&) DenseTensorToSparseTensorProto *ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE_ *ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE0_ *ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE1_ *ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERKNS_4PathERNS1_11TensorProtoEEUlmmE2_ External data type cannot be STRCalculateStaticCopyInfoForFeed /onnxruntime_src/onnxruntime/core/framework/utils.cc std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::front() [with _Tp = onnxruntime::SessionState::NodeInfo; _Alloc = std::allocator<onnxruntime::SessionState::NodeInfo>; std::vector<_Tp, _Alloc>::reference = onnxruntime::SessionState::NodeInfo&] CoreMLExecutionProvider InternalTestingExecutionProvider bool onnxruntime::utils::FinalizeCopyInfoForFeeds(const std::vector<OrtDevice>&, std::vector<onnxruntime::MLValueCopyInfo>&) feed_locations.size() == copy_info.size() bool onnxruntime::utils::FinalizeCopyInfoForFetches(const std::vector<const OrtMemoryInfo*>&, std::vector<onnxruntime::MLValueCopyInfo>&) fetch_alloc_info.size() == copy_info.size() const OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::SessionState&, const string&) const OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::OrtValueNameIdxMap&, const onnxruntime::SequentialExecutionPlan&, const string&) exec_plan_ptr FindMemoryInfoForValue CalculateStaticCopyInfoForFeeds InitializeFeedFetchCopyInfo Failed to find allocator for device onnxruntime::common::Status onnxruntime::utils::BatchOrCopyMLValue(const onnxruntime::SessionState&, const onnxruntime::MLValueCopyInfo&, const OrtValue&, OrtValue&, std::vector<onnxruntime::IDataTransfer::SrcDstPair>*) allocator != nullptr BatchOrCopyMLValue CopyOneInputAcrossDevices ExecuteGraphImpl copy_info.size() == num_feeds CopyInputsAcrossDevices CopyOutputsAcrossDevices Only one thread was configured for parallel execution. Hence will use sequential execution. onnxruntime::common::Status onnxruntime::utils::CopyInputsAcrossDevices(const onnxruntime::SessionState&, const std::vector<OrtValue>&, std::vector<OrtValue>&, const std::vector<onnxruntime::MLValueCopyInfo>&) std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnxruntime::MLValueCopyInfo; _Alloc = std::allocator<onnxruntime::MLValueCopyInfo>; std::vector<_Tp, _Alloc>::const_reference = const onnxruntime::MLValueCopyInfo&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] ExecuteGraph std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = const OrtMemoryInfo*; _Alloc = std::allocator<const OrtMemoryInfo*>; std::vector<_Tp, _Alloc>::reference = const OrtMemoryInfo*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::AllocPlanPerValue; _Alloc = std::allocator<onnxruntime::AllocPlanPerValue>; std::vector<_Tp, _Alloc>::reference = onnxruntime::AllocPlanPerValue&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector<OrtMemoryInfo>; _Alloc = std::allocator<std::vector<OrtMemoryInfo> >; std::vector<_Tp, _Alloc>::reference = std::vector<OrtMemoryInfo>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = OrtMemoryInfo; _Alloc = std::allocator<OrtMemoryInfo>; std::vector<_Tp, _Alloc>::const_reference = const OrtMemoryInfo&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] /onnxruntime_src/onnxruntime/core/framework/allocation_planner.cc GeneratePlanForWeights vector<bool>::_M_fill_insert int& onnxruntime::PlannerImpl::UseCount(onnxruntime::OrtValueIndex) n >= 0 && static_cast<size_t>(n) < ort_value_info_.size() onnxruntime::AllocPlanPerValue& onnxruntime::PlannerImpl::AllocPlan(onnxruntime::OrtValueIndex) n >= 0 && static_cast<size_t>(n) < plan_.allocation_plan.size() onnxruntime::OrtValueIndex& onnxruntime::PlannerImpl::Buffer(onnxruntime::OrtValueIndex) Previous entry was not terminated. void onnxruntime::AllocPlanPerValue::ProgramCounter::AddStart(size_t) /onnxruntime_src/onnxruntime/core/framework/sequential_execution_plan.h starts_.size() == ends_.size() std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = long unsigned int; _Alloc = std::allocator<long unsigned int>; std::vector<_Tp, _Alloc>::reference = long unsigned int&] Invalid 'start'. Value is smaller than previous 'end'. starts_.empty() || start > ends_.back() No matching 'start' entry. end >= starts_.back() void onnxruntime::AllocPlanPerValue::ProgramCounter::AddEnd(size_t) starts_.size() == ends_.size() + 1 Invalid 'end'. Value is larger than 'start'. SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex: const onnxruntime::KernelCreateInfo& onnxruntime::GetKernelCreateInfo(const std::unordered_map<long unsigned int, gsl::not_null<const onnxruntime::KernelCreateInfo*> >&, onnxruntime::NodeIndex) entry != kernel_create_info_map.cend() OrtMemoryInfo onnxruntime::PlannerImpl::GetLocationForNodeInput(size_t, const onnxruntime::Node&) p_provider static size_t onnxruntime::PlannerImpl::GetElementSize(const string* const&) nullptr != tensor_type_base std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::SequentialExecutionPlan::NodeExecutionPlan; _Alloc = std::allocator<onnxruntime::SequentialExecutionPlan::NodeExecutionPlan>; std::vector<_Tp, _Alloc>::reference = onnxruntime::SequentialExecutionPlan::NodeExecutionPlan&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::back() const [with _Tp = long unsigned int; _Alloc = std::allocator<long unsigned int>; std::vector<_Tp, _Alloc>::const_reference = const long unsigned int&] void onnxruntime::PlannerImpl::GenerateDeallocationPlan() AllocPlan(ml_value_idx).program_counter.Ends().back() == program_counter void onnxruntime::PlannerImpl::Reuse(onnxruntime::OrtValueIndex, onnxruntime::OrtValueIndex, onnxruntime::AllocKind) reused != reused_for onnxruntime::OrtValueIndex onnxruntime::PlannerImpl::Index(const OrtValueName&) ComputeReusePlan !IsNonTensor(*node_output) Only tensors are supported for external outputs for now. onnxruntime::common::Status onnxruntime::PlannerImpl::ComputeReusePlan() void onnxruntime::PlannerImpl::ProcessDef(onnxruntime::OrtValueIndex, const onnxruntime::NodeArg*) id >= 0 && static_cast<size_t>(id) < ort_value_info_.size() Should not have entry in kernel create info with nullptr for kernel_def onnxruntime::common::Status onnxruntime::PlannerImpl::ComputeUseCounts() Can not find the execution provider Can not find the node p_kernel_def ComputeUseCounts CreatePlan Invalid program_counter entries at index void onnxruntime::PlannerImpl::VerifyMemoryTimeSchedule() entry.program_counter.HasValidEntries() N11onnxruntime17ExecutionPlanBaseE N11onnxruntime23SequentialExecutionPlanE ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEmE_ ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEbE0_ ZN11onnxruntime11PlannerImpl22GeneratePlanForWeightsEvEUlRKNS_7NodeArgEmE_ source and destination buffer size mismatch e/framework/endiCopyLittleEndian/onnxruntime_src/onnxruntime/core/framework/fallback_cpu_capability.cc Candidate for fallback CPU execution: std::unordered_set<long unsigned int> onnxruntime::GetCpuPreferredNodes(const onnxruntime::GraphViewer&, const string&, const std::vector<const onnxruntime::KernelRegistry*>&, const std::vector<long unsigned int>&) void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = long unsigned int; _Alloc = std::allocator<long unsigned int>] ORT optimization- Force fallback to CPU execution for node: because the CPU execution path is deemed faster than overhead involved with execution on other EPs capable of executing this node kernel_info != nullptr GetCpuPreferredNodes *ZN11onnxruntime20GetCpuPreferredNodesERKNS_11GraphViewerERKSsRKSt6vectorIPKNS_14KernelRegistryESaIS8_EERKS5_ImSaImEEEUlRKNS_7NodeArgEmE0_ Divide by zero static void SafeIntExceptionHandler<onnxruntime::OnnxRuntimeException>::SafeIntOnDivZero() func info for node: not found. GetFuncs /onnxruntime_src/onnxruntime/core/framework/fuse_nodes_funcs.cc already exist. std::__shared_ptr_access<_Tp, _Lp, <anonymous>, <anonymous> >::element_type& std::__shared_ptr_access<_Tp, _Lp, <anonymous>, <anonymous> >::operator*() const [with _Tp = std::unordered_map<std::basic_string<char>, onnxruntime::FuncManager::FuncInfo>; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; bool <anonymous> = false; bool <anonymous> = false; std::__shared_ptr_access<_Tp, _Lp, <anonymous>, <anonymous> >::element_type = std::unordered_map<std::basic_string<char>, onnxruntime::FuncManager::FuncInfo>] Can't use func with null ptr *ZNK11onnxruntime11FuncManager8GetFuncsERKSsRPNS_15NodeComputeInfoEEUlPvPK6OrtApiP16OrtKernelContextE_ *ZNK11onnxruntime11FuncManager8GetFuncsERKSsRPNS_15NodeComputeInfoEEUlPNS_14ComputeContextEPPvE0_ *ZNK11onnxruntime11FuncManager8GetFuncsERKSsRPNS_15NodeComputeInfoEEUlPvE1_ Unknown exception was caught by catch-all handler. Exception running nodes starting at _fence_before _kernel_time _fence_after RunNodeAsync Begin execution does not. entry != nullptr op_name node. Name:' ' Status Message: /onnxruntime_src/onnxruntime/core/framework/parallel_executor.cc Exiting due to terminate flag being set to true. onnxruntime::common::Status onnxruntime::ParallelExecutor::RunNodeAsync(size_t, const onnxruntime::SessionState&, const onnxruntime::logging::Logger&) Got nullptr from GetKernel for node: typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::ExecutionFrame; _Dp = std::default_delete<onnxruntime::ExecutionFrame>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::ExecutionFrame&] All implicit inputs should have OrtValue instances by now. onnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState&, onnxruntime::IExecutionFrame&, const onnxruntime::OpKernel&, const onnxruntime::logging::Logger&, const bool&) /onnxruntime_src/onnxruntime/core/framework/op_kernel_context_internal.h Non-zero status code returned while running Multiple errors were found. ParallelExecutor::Execute N11onnxruntime9IExecutorE N11onnxruntime16ParallelExecutorE N11onnxruntime23OpKernelContextInternalE *ZN11onnxruntime16ParallelExecutor11EnqueueNodeEmRKNS_12SessionStateERKNS_7logging6LoggerEEUlvE_ CPUExecutionProvthread_schedulinuint64_t onnxruntime::PrePackedWeights::GetHash() const /onnxruntime_src/onnxruntime/core/framework/prepacked_weights.cc buffers_.size() == buffer_sizes_.size() Unsupported device allocator in the context of pre-packed weights caching: onnxruntime::AllocatorPtr onnxruntime::PrepackedWeightsContainer::GetOrCreateAllocator(const string&) /onnxruntime_src/onnxruntime/core/framework/prepacked_weights_container.cc *ZN11onnxruntime25PrepackedWeightsContainer20GetOrCreateAllocatorERKSsEUliE_ graph_index exec_plan_index activation_size parameter_size output_size thread_scheduling_stats ReleaseNodeMLValues SequentialExecutor::Execute /onnxruntime_src/onnxruntime/core/framework/sequential_executor.cc [Memory] ExecutionFrame statically allocates [Memory] ExecutionFrame dynamically allocates N11onnxruntime18SequentialExecutorE e/framework/sequential_executor. N11onnxruntime21SimpleTensorAllocatorE inputs are expected to have tensor type and output type should not be null. isinf_only isnan_only !(isinf_only && isnan_only) Both attributes isinf_only and isnan_only cannot be set. Unset both to check for both conditions. /onnxruntime_src/onnxruntime/core/graph/contrib_ops/contrib_defs.cc onnxruntime::contrib::RegisterContribSchemas()::<lambda(onnx::InferenceContext&)> Incompatible dimensions Inputs 0 shall be 3 dimensions Inputs 4 shall be 5 dimensions Invalid bias shape 'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64 Pads has incorrect number of values should specify a shape Attribute input_as_shape extra_shape Negative values are not allowed in a shape specification Input's shape must be 4-D border ) + bottom_border ( ) + right_border ( ) + scale[0] ( ) + scale[1] ( '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 ( ) needs to be greater than or equal to the left_border ( First input does not have rank 2 Second input does not have rank 2 Input axis is invalid: 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 int64 first input tensor has wrong dimension rois input tensor has wrong dimension batch_indices shape input tensor has wrong dimension crop_size shape input tensor has wrong dimension Input rank must be >= 2. != mat_h: The inner-most 2 dimensions must have the same size (mat_w: expected to have rank > but has rank Dimension mismatch in unification between stash_type Inputs 0 shall be 2 dimensions word_embedding should have 2 dimensions and dimension size is known. Input tensors of wrong rank (0). Incompatible dimensions for matrix multiplication Constant XShape Rank Zero1D Axis1D PrefixShape NumReducedAxes SuffixShape ReducedShape X2D XU Mean2D MeanOfSquare SquareOfMean Var VarPlusEpsilon Deviation Normalized NormalizedT Biased InvStdDev2D InvStdDev Half One ERFCX ERFCXPlus1 PhiX X_bias T5 T6 T7 Number of attention heads past present input_scale weight_scale input_zero_point weight_zero_point LongformerAttention window global_weight global_bias global Constrain to integer types input_ids segment_ids 2D with shape (,hidden_size) word_embedding 2D with shape (, hidden_size) position_embedding segment_embedding bias tensor skip inv_std_var Whether every token can only attend to previous tokens. Default value is 0. 3D input tensor with shape (batch_size, sequence_length, input_hidden_size) 2D input tensor with shape (input_hidden_size, 3 * hidden_size), where hidden_size = num_heads * head_size 1D input tensor with shape (3 * hidden_size) Attention mask with shape (batch_size, 1, max_sequence_length, max_sequence_length), (batch_size, past_sequence_length + sequence_length)or (batch_size, sequence_length, past_sequence_length + sequence_length), or index with shape (batch_size) or (2 * batch_size). past 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) present 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 types 2D input tensor with shape (input_hidden_size, 3 * hidden_size), hidden_size = num_heads * head_size scale of quantized input tensor. It's a scalar, which means a per-tensor/layer quantization. scale of weight scale. It's a scalar or a 1D tensor, which means a per-tensor/per-column quantization.Its size should be 3 * hidden_size if it is per-column quantization Attention mask index with shape (batch_size) zero point of quantized input tensor.It's a scalar, which means a per-tensor/layer quantization. zero point of quantized weight tensor. It's a scalar or a 1D tensor, which means a per-tensor/per-column quantization.Its size should be 3 * hidden_size if it is per-column quantization 3D output tensor with shape (batch_size, sequence_length, hidden_size) Constrain input and output types to int8 tensors. One sided attention windows length W, or half of total window length 3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_size 2D input tensor with shape (hidden_size, 3 * hidden_size) Attention mask with shape (batch_size, sequence_length) Global attention flags with shape (batch_size, sequence_length) The epsilon value to use to avoid division by zero. 2D words IDs with shape (batch_size, sequence_length) 2D segment IDs with shape (batch_size, sequence_length) 1D gamma tensor for layer normalization with shape (hidden_size) 1D beta tensor for layer normalization with shape (hidden_size) 2D attention mask with shape (batch_size, sequence_length) 1D mask_index tensor with shape (batch_size) Constrain input and output integer tensors types Constrain input and output float tensors types. Constrain input and output types to float or half tensors. 3D input tensor with shape (batch_size, sequence_length, hidden_size) 3D skip tensor with shape (batch_size, sequence_length, hidden_size) 1D input tensor with shape (hidden_size) 1D skip tensor with shape (hidden_size 1D bias tensor with shape (hidden_size Saved mean used during training to speed up gradient computation Saved inverse standard variance used during training to speed up gradient computation. Constrain mean and inv_std_var to float tensors. Value of alpha Value of beta 1D input tensor 1D output tensor The scale to apply. Threshold value Input tensor Output tensor Sliced data tensor. GivenTensorFill The shape of filled tensor The filled tensor Input data to be scaled Output data after scaling GRUUnit drop_states hidden_prev gates seq_lengths hidden Scaling value Rfft signal_ndim normalized onesided Irfft ComplexMul input_0 input_1 ComplexMulConj Z activation_gamma Strings to tokenize Tokenized strings N-dimensional matrix A N-dimensional matrix B Matrix multiply results An input tensor to hash. 32-bit hash value. Tensor of rank r >= 1. Tensor of rank q >= 1. conv_window_size char_embedding_size Specify weights of conv Specify bias of conv Constrain to tensor(int32). Constrain to tensor(float). Input tensor. Tensor after padding. 2D matrix with shape (M,N) 2D matrix with shape (K,N) rois batch_indices crop_size Scale tensor. Bias tensor. Output data tensor. The input data as Tensor. The output. The normal input data. TorchEmbedding padding_idx scale_grad_by_freq upper The bias (or mask) as Tensor. residual training_mode The output mask of dropout. IsAllFinite If true, check only for NaN. Input tensors to check. Bias applied to each channel, same size as C. Input tensor of shape [N,C,H,W] Result, has same shape and type as input A 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. Tensor of data to extract slices from. 1-D tensor of starting indices of corresponding axis in `axes` 1-D tensor of ending indices (exclusive) of corresponding axis in axes 1-D tensor of axes that `starts` and `ends` apply to. Constrain input and output types to all tensor types. Constrain indices to integer types Bool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length. The previous GRU hidden state. Unactivated gate outputs from forget, update, and output gates, pre-activation. Array of sequence lengths. len(seq_lengths) should equal batch size N. The timestep for this operation. The 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. The scaled hyperbolic tangent values of the input tensor computed element-wise Constrain input0 and output types to float tensors Constrain input and output types to float tensors Input 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 transposed Whether B should be transposed Scalar multiplier for the product of input tensors A * B. Scalar multiplier for input tensor C. Specified axis to insert a dimension Input/Output is a string tensor Boolean 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. An optional string. Token's regular expression in basic POSIX format (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 ignored Matrix multiply results from A * B Constrain input A data types as 16-bit integer tensor Constrain input B data types as 16-bit integer tensor Constrain 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). Scalar multiplier for the product of the input tensors. Whether A should be transposed on the last two dimensions before doing multiplication Whether B should be transposed on the last two dimensions before doing multiplication 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. Seed for the hashing algorithm, unsigned 32-bit integer, default to 0. If value is 1, output type is uint32_t, else int32_t. Default value is 1. Tensor of rank q-1+r-indices[-1]. Constrain input and output types to any tensor type. Constrain indice type to int32 or int64 Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weight This 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. Specify batchs of sequence words to embedding Specify embedding vector of char Three 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 array 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). 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' A 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques' A 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". 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. RoIs (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. 1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch. 1-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). type used for stash mean/inv_std_var Input data tensor from the previous layer. Saved inverse standard deviation used during training to speed up gradient computation. Constrain input types and output Y type to float tensors. Type of Mean and InvStdDev tensors. 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 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. The embedding matrix of size N x M. 'N' is equal to the maximum possible index + 1, and 'M' is equal to the embedding size Long tensor containing the indices to extract from embedding matrix. A 0-D scalar tensor. If specified, the entries at `padding_idx` do not contribute to the gradient; therefore, the embedding vector at `padding_idx` is not updated during training, i.e. it remains as a fixed pad. A 0-D bool tensor. If given, this will scale gradients by the inverse of frequency of the indices (words) in the mini-batch. Default is ``False`` Output tensor of the same type as the input tensor. Shape of the output is * x M, where '*' is the shape of input indices, and 'M' is the embedding size. Constrain input and output types to all numeric tensors. Boolean. Indicates whether upper or lower part of matrix is retained. Default is true. Input tensor of rank 2 or higher. A 0-D tensor containing a single value corresponding to the number diagonals above or the main diagonal to exclude or include.Default value is 0 if it's not specified. Constrain input and output types to all numeric tensors and bool tensors. apply softmax to elements for dimensions softmax_axis or higher broadcast bias across input for dimensions broadcast_axis to softmax_axis-1 (Optional) Seed to the random generator, if not specified we will auto generate one. The bias input, a vector with the same shape as last dim of data The residual input, must have the same shape as data The 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 input, which is typically the case during training. If 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. Constrain input 'ratio' types to float tensors. Constrain output 'mask' types to boolean tensors. If true, check only for Inf, -Inf. Constrain the output to a boolean tensor. The output scalar. Its value is true if all input tensors are finite. Otherwise, the output value would be false. *ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE1_ *ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRKN4onnx24FunctionBodyBuildContextERKNS1_8OpSchemaERNS1_13FunctionProtoEE2_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE10_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE11_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE12_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE13_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE14_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE15_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRKN4onnx24FunctionBodyBuildContextERKNS1_8OpSchemaERNS1_13FunctionProtoEE16_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE17_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRKN4onnx24FunctionBodyBuildContextERKNS1_8OpSchemaERNS1_13FunctionProtoEE18_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE19_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE20_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE21_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE22_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE23_ ̼+̼+ >;f? F? AD?tensor rank too small invalid scales dimension invalid scales value /onnxruntime_src/onnxruntime/core/graph/contrib_ops/nchwc_schema_defs.cc FvRN4onnx8OpSchemaEE *ZN11onnxruntime7contrib26NchwcPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ *ZN11onnxruntime7contrib32NchwcGlobalPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ *ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE1_ PFvRN4onnx8OpSchemaEE *ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE2_ Input tensor must have at least 2 dimensions Output tensor must have at least 2 dimensions inputs are expected to have tensor type. input and zero_point pair is expected to have be same type. weight and zero_point pair is expected to have same type. /onnxruntime_src/onnxruntime/core/graph/contrib_ops/nhwc_schema_defs.cc Input data tensor from the previous operator; According to channels_last, dimensions for image case are (N x C x H x W), or (N x H x W x C) 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), or (N x D1 X D2 ... Dn x C) where N is the batch size. Scale of quantized input 'X'. It must be a scalar. Zero point tensor for input 'X'. It must be a scalar. Scale of quantized output 'Y'. It must be a scalar. Zero point tensor for output 'Y'. It must be a scalar. Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. with the N and C value keep it value, while the otherdimensions are all 1. Constrain input and output types to singed/unsigned int8 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. 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. 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. Whether to use ceil or floor (default) to compute the output shape. Works on NHWC layout or not? Default not. 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 ...]. Input scale. It's a scalar, which means a per-tensor/layer quantization. Input zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Output scale. It's a scalar, which means a per-tensor/layer quantization. Output zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used Constrain input and output types to 8 bit tensors. x_scale x_zero_point w_scale w_zero_point y_scale y_zero_point N4onnx16InferenceContextE N11onnxruntime7contrib20NhwcInferenceContextE *ZN11onnxruntime7contrib19RegisterNhwcSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib19RegisterNhwcSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib19RegisterNhwcSchemasEvEUlRN4onnx16InferenceContextEE1_ *ZN11onnxruntime7contrib19RegisterNhwcSchemasEvEUlRN4onnx16InferenceContextEE2_
Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support).
{additionalDocumentation}
- Input A's scale. It's a scalar, which means a per-tensor/layer quantization. Input A zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Input B's scale. It's a scalar, which means a per-tensor/layer quantization. Input B zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Output scale. It's a scalar, which means a per-tensor/layer quantization. Output zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Result, has same element type as two inputs Constrain input and output types to 8 bit signed and unsigned tensors. /onnxruntime_src/onnxruntime/core/graph/contrib_ops/quantization_defs.cc The axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors. N-D full precision Input tensor to be quantized. Scale for doing quantization to get 'y'. It could be a scalar or a 1-D tensor,which means a per-tensor or per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. Zero point for doing quantization to get 'y'. It could be a scalar or a 1-D tensor, which means a per-tensoror per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. N-D quantized output tensor. It has same shape as input 'x'. Constrain 'x', 'y_scale' to float tensors. Constrain 'y_zero_point' and 'y' to 8-bit integer tensors. N-D quantized Input tensor to be de-quantized. Scale for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. Zero point for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. N-D full precision output tensor. It has same shape as input 'x'. Constrain 'x' and 'x_zero_point' to 8-bit integer tensors. Constrain 'y', 'x_scale' to float tensors. 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. Keep the reduced dimension or not, default 1 mean keep reduced dimension. Constrain output to 32 bit tensor Constrain input types to 8 bit signed and unsigned tensors. Constrain output types to 32 bit tensors. Scale 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'. Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'. 1D input tensor, whose dimension is same as B's last dimension Constrain input A, b_scale and output Y data type as float tensor. Constrain input B data type to 8-bit integer tensor. Scale 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'. 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-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. C = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_point C = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_point Input scale. It's a scalar, which means a per-tensor/layer quantization. Input zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. 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. 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. 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. 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 used Constrain input and output types to 8 bit tensors. Input X's scale. It's a scalar, which means a per-tensor/layer quantization. Input X's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Output Y's scale. It's a scalar, which means a per-tensor/layer quantization. Output Y's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional. Number of neurons in the hidden layer 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. Cell 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. 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. The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`. The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, input_size, 4*hidden_size]`. The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, hidden_size, 4*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. 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]`. Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`. Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`. The 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. W's scale. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size. W's zero point. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size. R's scale. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size. R's zero point. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size. 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]`. The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`. Constrain seq_lens to integer tensor. Constrain weights types to 8 bit tensors. Input data type does not match the expected data type Input data type does not match the expected data type. Current data type is Scale and Zero-point must be a scalar Scale and Zero-point must be of rank 1 Scale 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. axis must be in [-rank, rank-1]. input rank was {name} {additionalDocumentation} First operand. A_scale A_zero_point Second operand. B_scale B_zero_point C_scale C_zero_point ReduceSumInteger An input tensor. Reduced output tensor. reduced b_scale b_zero_point addition multiplication QLinearReduceMean data_scale data_zero_point reduced_scale reduced_zero_point QLinearAveragePool Coefficient of leakage. X_scale X_zero_point Y_scale Y_zero_point sequence_lens initial_h initial_c R_scale R_zero_point Y_h Y_c *ZZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_ENKUlRN4onnx8OpSchemaEE_clES5_EUlRNS3_16InferenceContextEE_ *ZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_EUlRN4onnx8OpSchemaEE_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE1_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE2_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE3_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE4_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE5_ start limit delta 1-D Tensor of the range. Unsupported type: Constrain 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. Can not get shape initializer data! Unsupported non-raw-data data type! *ZN11onnxruntime7contrib21RegisterRangeOpSchemaEON4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ index < data_.size() Inputs:
+ Input A's scale. It's a scalar, which means a per-tensor/layer quantization. Input A zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Input B's scale. It's a scalar, which means a per-tensor/layer quantization. Input B zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Result, has same element type as two inputs Constrain input and output types to 8 bit signed and unsigned tensors. {name} {additionalDocumentation} First operand. A_scale A_zero_point Second operand. B_scale B_zero_point C_scale C_zero_point Input data type does not match the expected data type Input data type does not match the expected data type. Current data type is Scale and Zero-point must be a scalar Scale and Zero-point must be of rank 1 Scale 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. axis must be in [-rank, rank-1]. input rank was axis must be in [-rank, rank) Dimension= Required attribute axis is missing All inputs to Concat must have same rank Can't merge shape info. Both source and target dimension have values but they differ. Source= /onnxruntime_src/onnxruntime/core/graph/contrib_ops/quantization_defs.cc The axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors. N-D full precision Input tensor to be quantized. Scale for doing quantization to get 'y'. It could be a scalar or a 1-D tensor,which means a per-tensor or per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. Zero point for doing quantization to get 'y'. It could be a scalar or a 1-D tensor, which means a per-tensoror per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. N-D quantized output tensor. It has same shape as input 'x'. Constrain 'x', 'y_scale' to float tensors. Constrain 'y_zero_point' and 'y' to 8-bit integer tensors. N-D quantized Input tensor to be de-quantized. Scale for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. Zero point for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. N-D full precision output tensor. It has same shape as input 'x'. Constrain 'x' and 'x_zero_point' to 8-bit integer tensors. Constrain 'y', 'x_scale' to float tensors. 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. Keep the reduced dimension or not, default 1 mean keep reduced dimension. Constrain output to 32 bit tensor Constrain input types to 8 bit signed and unsigned tensors. Constrain output types to 32 bit tensors. Scale 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'. Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'. 1D input tensor, whose dimension is same as B's last dimension Constrain input A, b_scale and output Y data type as float tensor. Constrain input B data type to 8-bit integer tensor. Scale 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'. 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-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. C = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_point C = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_point Input X's scale. It's a scalar, which means a per-tensor/layer quantization. Input X's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Output Y's scale. It's a scalar, which means a per-tensor/layer quantization. Output Y's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional. Number of neurons in the hidden layer 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. Cell 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. 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. The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`. The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, input_size, 4*hidden_size]`. The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, hidden_size, 4*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. 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]`. Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`. Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`. The 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. W's scale. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size. W's zero point. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size. R's scale. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size. R's zero point. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size. 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]`. The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`. Constrain seq_lens to integer tensor. Constrain weights types to 8 bit tensors. List of tensors/scale/zero_point for concatenation Constrain scale types to any float tensor type. Sequence of (Tensor, Scale, ZeroPoint) tuples. The type is sequence of (T8, TF, T8). ReduceSumInteger An input tensor. Reduced output tensor. reduced b_scale b_zero_point addition multiplication QLinearReduceMean data_scale data_zero_point reduced_scale reduced_zero_point Coefficient of leakage. X_scale X_zero_point Y_scale Y_zero_point sequence_lens initial_h initial_c Y_h Y_c Which axis to concat on Y's scale. T8 Y's zero point. TV Concatenated tensor *ZZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_ENKUlRN4onnx8OpSchemaEE_clES5_EUlRNS3_16InferenceContextEE_ *ZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_EUlRN4onnx8OpSchemaEE_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE1_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE2_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE3_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE4_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE5_ start limit delta 1-D Tensor of the range. Constrain 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. Can not get shape initializer data! Unsupported non-raw-data data type! Unsupported type: *ZN11onnxruntime7contrib21RegisterRangeOpSchemaEON4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnx::TypeProto; _Alloc = std::allocator<onnx::TypeProto>; std::vector<_Tp, _Alloc>::reference = onnx::TypeProto&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] 2222@2 22X22ForThisAndAllSubgraphs /onnxruntime_src/onnxruntime/core/graph/graph.cc Inputs:
Nodes:
) -> Outputs:
- input index: input edges output edges ai.onnx.training ai.onnx.preview.training node_arg_name cannot be null ] op_type [ ], could not find NodeArg _token_ Node [ node_arg Input to set must exist. input->Exists() Constant !found Removing initializer ' fbs_attr cannot be null Can't remove node nullptr != func_meta_def Target= Dimension= ' source: target: Output: Type mismatch. Current= Input= ' has element type does not match. but different TensorProto. existing->second == &tensor graph_proto cannot be null graph_proto != nullptr ' Model is invalid. utils::HasName(sparse_tensor) . Please, fix your model. Initializer NoOp ) input arg ( ' of input parameter ( ) of operator ( ) in node ( ) is invalid. Type Error: Type parameter ( ) bound to different types ( Node ( ) output arg ( ) type inference failed Type Error: Type ( ) of output arg ( ) of node ( Node: ) Op ( Node index is out of range Outer scope node arg name ' Fatal error: ) attribute ( inputs but subgraph has inputs and requires Subgraph input missing type. /onnxruntime_src/onnxruntime/core/graph/graph.cc /onnxruntime_src/include/onnxruntime/core/common/const_pointer_container.h Graph attribute inferencing failed: info->Name() != new_value_info->Name() Error: trying to add an existing value info. Invalid source node arg slot specified when adding edge. Invalid destination node arg slot specified when adding edge. Argument type mismatch when adding edge. Invalid node indexes specified when adding edge. Node::LoadEdgesFromOrtFormat, edge is missing for is not the same as this node's index: This is an invalid model. The sum of input arg count is not equal to size of input defs in node ( Invalid source node arg slot specified when removing edge. Invalid destination node arg slot specified when removing edge. Argument mismatch when removing edge. Invalid node indexes specified when removing edge. Some nodes are not included in the topological sort, graph have a cycle. fbs_node_arg_names cannot be null LoadNodeArgsFromOrtFormat: Node [ NodeArg Name is missing. Invalid ORT format model. Error: Duplicate definition-site for ( No Graph instance was found for attribute Serialization of fused function body is not currently supported, does not have the graph for key graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty() Graph ctor should have created NodeArg for initializer. Missing: This is an invalid model. Graph output ( ) does not exist in the graph. Graph state to be loaded into must be empty. This is an invalid model. Error: two nodes with same node name ( This is an invalid model. Error: Duplicate definition of name ( Failed to find existing initializer with name Replacement tensor's dimensions do not match. Replacement tensor's data type does not match. graph_proto_ is not in sync with name_to_initial_tensor_ existing_entry != mutable_initializers.pointer_end() This is an invalid model. Error: the graph is not acyclic. sparse_tensor_names_ not in sync with name_to_initial_tensor_ sparse_tensor_names_.count(tensor_name) == 0 graph_proto_ is not in sync with name_to_initial_tensor_. must be either specified in graph inputs or graph initializers. '. It is not used by any node and should be removed from the model. '. It is no longer used by any node. Serialization error. Graph attribute was serialized without Graph instance Node::LoadFromOrtFormat, input_arg_counts is missing as it still has output edges. node->GetOutputEdgesCount() == 0 This 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= dst_implicit_input_idx < (int)node->ImplicitInputDefs().size() nodes_.size() < static_cast<unsigned int>(std::numeric_limits<int>::max()) Mismatch between number of source and target dimensions. Source= Can't merge shape info. Both source and target dimension have values but they differ. Source= Error merging shape info for output. ' . Falling back to lenient merge. Tensor element type mismatch. SparseTensor element type mismatch. This is an invalid model. Model input ( ) does not have type information. Type Error: Data in initializer ' but usage of initializer in graph expects Type Error: Shape of initializer AddInitializedTensor already has tensor with name ALLOW_RELEASED_ONNX_OPSET_ONLY are '0' and '1'. The environment variable contained the value: /onnxruntime_src/onnxruntime/core/graph/model_load_utils.h Duplicate constant node sparse initializer name: ' Sparse initializer must have a name. This model is invalid Duplicate sparse_tensor_initializer: ' Duplicate initializer (dense, sparse or ConstantNode): ' ' the model will use the latest encountered initializer This is an invalid model. Tensor does not have type information. 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. This is an invalid model. Node ( ) does not have type information set by parent node. This is an invalid model. Type Error: Type ' ) does not match expected type ( UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphs node.GetAttributeNameToMutableSubgraphMap().empty() Initializer tensor is missing. Invalid ORT format model. Duplicate initializer (dense or ConstantNode): ' Sparse Initializer tensor is missing. Invalid ORT format model. NodeArg is missing. Invalid ORT format model. Node is missing. Invalid ORT format model. NodeEdge is missing. Invalid ORT format model. 'was added but does not exist. is not a registered function/op ) is required but not specified. This is an invalid model. Error in Node: Size mismatch validating subgraph inputs. Got inputs. Either provide all subgraph inputs, or just the required inputs. Shouldn't be possible to have NodeArgs that haven't been handled already. outer_scope_node_args_consumed.empty() 's number of inputs is different from function body graph's number of input. 's number of outputs is different from function body graph's number of outputs. node.MutableOutputDefs().size() == subgraph.GetOutputs().size() Failed to convert dense initializer to sparse p8` operator() LoadFromOrtFormat LoadFromOrtFormat void onnxruntime::Graph::SetInputs(const std::vector<const onnxruntime::NodeArg*>&) InlineFunction onnxruntime::common::Status onnxruntime::Graph::InlineFunction(onnxruntime::Node&) void onnxruntime::Graph::FinalizeFuseSubGraph(const onnxruntime::IndexedSubGraph&, onnxruntime::Node&) onnxruntime::Node& onnxruntime::Graph::CreateFusedSubGraphNode(const onnxruntime::IndexedSubGraph&, const string&) gsl::not_null<onnxruntime::Node*> onnxruntime::Graph::AllocateNode() CleanUnusedInitializers void onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto&) const onnx::GraphProto onnxruntime::Graph::ToGraphProto() const bool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex) SaveToOrtFormat void onnxruntime::Graph::RemoveInitializedTensor(const string&) void onnxruntime::Graph::AddInitializedTensor(const onnx::TensorProto&) onnxruntime::common::Status onnxruntime::Graph::Resolve(const onnxruntime::Graph::ResolveOptions&) Resolve ForThisAndAllSubgraphs PerformTypeAndShapeInferencing InitInputsInitializersOutputs VerifyNodeAndOpMatch InferAndVerifyTypeMatch onnxruntime::common::Status onnxruntime::Graph::UpdateShapeInference(onnxruntime::Node&) InferAndVerifySubgraphTypes const T* onnxruntime::ConstPointerContainer<Container>::at(size_t) const [with Container = std::vector<onnxruntime::NodeArg*>; onnxruntime::ConstPointerContainer<Container>::T = onnxruntime::NodeArg; size_t = long unsigned int] void onnxruntime::Graph::KahnsTopologicalSort(const std::function<void(const onnxruntime::Node*)>&, const std::function<bool(const onnxruntime::Node*, const onnxruntime::Node*)>&) const BuildConnections void onnxruntime::Graph::RemoveEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int) void onnxruntime::Graph::AddEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int) SetOuterScopeNodeArgs void onnxruntime::Graph::InitializeStateFromModelFileGraphProto() bool onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet() Graph onnxruntime::Graph::Graph(const onnxruntime::Model&, onnx::GraphProto*, const std::unordered_map<std::basic_string<char>, int>&, onnxruntime::Version, onnxruntime::IOnnxRuntimeOpSchemaCollectionPtr, onnxruntime::Graph*, const onnxruntime::Node*, const onnxruntime::logging::Logger&) operator() LoadEdgesFromOrtFormat operator() LoadFromOrtFormat SaveToOrtFormat MergeShapeInfo UpdateTypeAndShape FN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_RKNS5_14ResolveOptionsEE *ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E0_ *ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E1_ *ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E2_ N4onnx15GraphInferencerE N4onnx24FunctionBodyBuildContextE N4onnx28FunctionBodyBuildContextImplE N11onnxruntime5GraphE N11onnxruntime19GraphInferencerImplE N11onnxruntime20InferenceContextImplE PFN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_RKNS5_14ResolveOptionsEE UNDEFINED INT FLOATS INTS STRINGS GRAPHS SPARSE_TENSOR SPARSE_TENSORS /onnxruntime_src/onnxruntime/core/graph/graph_flatbuffers_utils.cc Graph attribute value was null. Invalid ORT format model. SaveAttributeOrtFormat: Unsupported attribute type: Missing dimensions for initializer. Invalid ORT format model. Missing string data for initializer. Invalid ORT format model. Missing raw data for initializer. Invalid ORT format model. Missing values for sparse initializer. Invalid ORT format model. Missing name for SparseTensor initializer. Invalid ORT format model. Missing indicies for sparse initializer: Missing dims for sparse initializer: Null string attribute. Invalid ORT format model. Null tensor attribute. Invalid ORT format model. Null graph attribute. Invalid ORT format model. Empty graph proto from deserialization of ORT format model Null floats attribute. Invalid ORT format model. Null ints attribute. Invalid ORT format model. Null strings attribute. Invalid ORT format model. Null string in strings attribute. Invalid ORT format model. Null tensors attribute. Invalid ORT format model. Null tensor in tensors attribute. Invalid ORT format model. 4DDD$,dt $ LoadAttributeOrtFormat LoadSparseInitializerOrtFormat LoadInitializerOrtFormat SaveAttributeOrtFormat SaveSparseInitializerOrtFormat SaveInitializerOrtFormat itr != node.InputDefs().end() ExplicitInputs: . Index: Invalid input index for node ImplicitInputs: Implicit input name cannot be safely updated to in one of the subgraphs. Attempting to get index for an input which does not exist. /onnxruntime_src/onnxruntime/core/graph/graph_utils.cc index >= 0 && static_cast<size_t>(index) < inputs.size() Attempting to get an input that does not exist. index >= 0 && static_cast<size_t>(index) < outputs.size() Attempting to get an output that does not exist. Failed since multiple edges matched: Can only add a new input at the end of the current ones. num_explicit_inputs == static_cast<size_t>(target_input_idx) std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0 Node must only have one used output std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; }) Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here. Initializer with same name exists. Name: !graph.GetInitializedTensor(new_initializer.name(), existing) FindPath void onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&) void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&) onnxruntime::NodeArg& onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph&, const onnx::TensorProto&) void onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph(onnxruntime::Node&, const string&, const string&) bool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(onnxruntime::Graph&, onnxruntime::Node&) bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph&, onnxruntime::Node&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const CanUpdateImplicitInputNameInSubgraphs const string& onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node&, int) const string& onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node&, int) int onnxruntime::graph_utils::GetNodeInputIndexFromInputName(const onnxruntime::Node&, const string&) Invalid ExecutionOrder filter_info_ == nullptr nodearg /onnxruntime_src/onnxruntime/core/graph/graph_viewer.cc Not supported with filtered graph. IndexedSubGraph contains values not present in the Graph graph_->GetNode(idx) != nullptr Mismatch between Graph and IndexedSubGraph. Input not found: Mismatch between Graph and IndexedSubGraph. Output not found: Mismatch between Graph and IndexedSubGraph. Node not found: const std::vector<long unsigned int>& onnxruntime::GraphViewer::GetRootNodes() const const std::vector<long unsigned int>& onnxruntime::GraphViewer::GetNodesInTopologicalOrder(onnxruntime::ExecutionOrder) const onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const onnxruntime::GraphViewer::GraphViewer(const onnxruntime::Graph&, const onnxruntime::IndexedSubGraph*) *ZN11onnxruntime11GraphViewerC1ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlmE_ *ZN11onnxruntime11GraphViewerC1ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlPKNS_4NodeEE0_ *ZN11onnxruntime11GraphViewerC1ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlPKNS_4NodeEE1_ N11onnxruntime11NodeCompareE N11onnxruntime19PriorityNodeCompareE Protobuf parsing failed. is till opset <p_fd> is less than 0. Invalid istream object. Null model_proto ptr. <p_fd> less than 0. failed. File doesn't exist Load model system error number /onnxruntime_src/onnxruntime/core/graph/model.cc ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain ModelProto does not have a graph. Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported. ONNX 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. Unknown model file format version. No graph was found in the protobuf. Failed to load model with error: Graph is null. Invalid ORT format model. Protobuf serialization failed. LoadFromOrtFormat SaveToOrtFormat Save Load SaveModel Load Model onnxruntime::Model::Model(onnx::ModelProto&&, const PathString&, const IOnnxRuntimeOpSchemaRegistryList*, const onnxruntime::logging::Logger&) ValidateOpsetForDomain void onnxruntime::model_load_utils::ValidateOpsetForDomain(const std::unordered_map<std::basic_string<char>, int>&, const onnxruntime::logging::Logger&, bool, const string&, int) bool onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet() N6google8protobuf2io19ZeroCopyInputStreamE St15_Sp_counted_ptrIPN11onnxruntime5ModelELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime21SchemaRegistryManagerESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime5ModelESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE Domain already set in registry /onnxruntime_src/onnxruntime/core/graph/schema_registry.cc , but it its version is higher than the operator set version static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() RegisterOpSchemaInternal RegisterOpSet N11onnxruntime27OnnxRuntimeOpSchemaRegistryE N11onnxruntime21SchemaRegistryManagerE QW MW memory_seq_lens AW Couple the input and forget gates if 1, default 0. Number 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]` 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]`. 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]` The 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]` The 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]` The sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` The 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]`. Not supported input_arg->Type() != nullptr fused_function_subgraph for domain for the same domain result.second Resolve subgraph failed: /onnxruntime_src/onnxruntime/core/graph/function_impl.h A node with a function body within a subgraph within another function body is currently not supported in ORT /onnxruntime_src/onnxruntime/core/graph/function.cc ONNX model does not support multiple opset versions for a domain. Model imports opset version and function is trying to import opset version void onnxruntime::update_subgraphs_within_function_body(onnx::GraphProto&, const onnxruntime::Graph&, const onnx::NodeProto&, const std::unordered_map<std::basic_string<char>, int>&, const std::unordered_map<std::basic_string<char>, int>&) std::unordered_map<std::basic_string<char>, int> onnxruntime::CreateOpsetImportsForFunction(const onnx::FunctionProto&, const std::unordered_map<std::basic_string<char>, int>&) onnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph&, const NodeIndex&, const onnx::FunctionProto&, const onnxruntime::logging::Logger&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const std::unique_ptr<onnx::OpSchema> onnxruntime::CreateSchema(const onnxruntime::Graph&, const onnxruntime::IndexedSubGraph&) onnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph&, const onnxruntime::IndexedSubGraph&, const onnxruntime::logging::Logger&) virtual const onnxruntime::Graph& onnxruntime::ViewerFunctionImpl::Body() const *ZN11onnxruntime12FunctionImplC1ERKNS_5GraphERKmRKN4onnx13FunctionProtoERKNS_7logging6LoggerEEUlRNS6_16InferenceContextEE_ N11onnxruntime8FunctionE N11onnxruntime12FunctionImplE N11onnxruntime18ViewerFunctionImplE Default logger already set. ISink must be provided. System onnxruntime /onnxruntime_src/onnxruntime/core/common/logging/logging.cc default_logger_id must be provided if instance_type is InstanceType::Default Only one instance of LoggingManager created with InstanceType::Default can exist at any point in time. void onnxruntime::logging::LoggingManager::CreateDefaultLogger(const string&) onnxruntime::logging::LoggingManager::LoggingManager(std::unique_ptr<onnxruntime::logging::ISink>, onnxruntime::logging::Severity, bool, onnxruntime::logging::LoggingManager::InstanceType, const string*, int) N@VIWEF is not a valid year is not a valid day is not a valid date N11onnxruntime7logging11OStreamSinkE Failed to parse path root: src root: , dst root: /onnxruntime_src/onnxruntime/core/common/path.cc Cannot concatenate with a string containing a path separator. String: first_separator == value.end() Paths must have the same root to compute a relative path. static onnxruntime::Path onnxruntime::Path::Parse(const PathString&) ParsePathRoot Parse Writing profiler data to file /onnxruntime_src/onnxruntime/core/common/profiler.cc Maximum number of events reached, could not record profile event. [
+ ALLOW_RELEASED_ONNX_OPSET_ONLY are '0' and '1'. The environment variable contained the value: bool onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet() /onnxruntime_src/onnxruntime/core/graph/model_load_utils.h std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::unique_ptr<onnxruntime::Node>; _Alloc = std::allocator<std::unique_ptr<onnxruntime::Node> >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr<onnxruntime::Node>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] Serialization of fused function body is not currently supported, does not have the graph for key Node [ ] op_type [ ], could not find NodeArg fbs_node_arg_names cannot be null LoadNodeArgsFromOrtFormat: Node [ This is an invalid model. The sum of input arg count is not equal to size of input defs in node ( Input to set must exist. input->Exists() void onnxruntime::Graph::SetInputs(const std::vector<const onnxruntime::NodeArg*>&) LoadEdgesFromOrtFormat input index: input edges output edges is not the same as this node's index: Invalid node indexes specified when adding edge. Invalid source node arg slot specified when adding edge. void onnxruntime::Graph::AddEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int) Invalid destination node arg slot specified when adding edge. Argument type mismatch when adding edge. Invalid node indexes specified when removing edge. void onnxruntime::Graph::RemoveEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int) Invalid source node arg slot specified when removing edge. Invalid destination node arg slot specified when removing edge. Argument mismatch when removing edge. ai.onnx.training ai.onnx.preview.training This is an invalid model. Error: two nodes with same node name ( This is an invalid model. Error: Duplicate definition of name ( std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::pair<const onnxruntime::Node*, bool>; _Alloc = std::allocator<std::pair<const onnxruntime::Node*, bool> >; std::vector<_Tp, _Alloc>::reference = std::pair<const onnxruntime::Node*, bool>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] __builtin_expect(__first != __last, true) void std::pop_heap(_RandomAccessIterator, _RandomAccessIterator, _Compare) [with _RandomAccessIterator = __gnu_cxx::__normal_iterator<const onnxruntime::Node**, std::vector<const onnxruntime::Node*> >; _Compare = std::function<bool(const onnxruntime::Node*, const onnxruntime::Node*)>] /opt/rh/devtoolset-9/root/usr/include/c++/9/bits/stl_heap.h void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator<const onnxruntime::Node*>] Some nodes are not included in the topological sort, graph have a cycle. void onnxruntime::Graph::KahnsTopologicalSort(const std::function<void(const onnxruntime::Node*)>&, const std::function<bool(const onnxruntime::Node*, const onnxruntime::Node*)>&) const const T* onnxruntime::ConstPointerContainer<Container>::at(size_t) const [with Container = std::vector<onnxruntime::NodeArg*>; onnxruntime::ConstPointerContainer<Container>::T = onnxruntime::NodeArg; size_t = long unsigned int] /onnxruntime_src/include/onnxruntime/core/common/const_pointer_container.h index < data_.size() Graph attribute inferencing failed: No Graph instance was found for attribute Error: Duplicate definition-site for ( _token_ SetOuterScopeNodeArgs Graph state to be loaded into must be empty. void onnxruntime::Graph::InitializeStateFromModelFileGraphProto() graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty() Graph ctor should have created NodeArg for initializer. Missing: ) does not exist in the graph. This is an invalid model. Graph output ( node_arg This is an invalid model. Error: the graph is not acyclic. must be either specified in graph inputs or graph initializers. std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = const onnxruntime::NodeArg*; _Alloc = std::allocator<const onnxruntime::NodeArg*>; std::vector<_Tp, _Alloc>::reference = const onnxruntime::NodeArg*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] InitInputsInitializersOutputs MergeShapeInfo ' source: target: Output: Mismatch between number of source and target dimensions. Source= Error merging shape info for output. ' . Falling back to lenient merge. Type mismatch. Current= Input= UpdateTypeAndShape Tensor element type mismatch. SparseTensor element type mismatch. This is an invalid model. Model input ( ) does not have type information. Type Error: Data in initializer ' but usage of initializer in graph expects Type Error: Shape of initializer ' has element type does not match. but different TensorProto. existing->second == &tensor AddInitializedTensor already has tensor with name void onnxruntime::Graph::AddInitializedTensor(const onnx::TensorProto&) as it still has output edges. bool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex) node->GetOutputEdgesCount() == 0 Can't remove node BuildConnections Invalid model. Node input ' Graph may not conform to the ONNX spec and contain initializers that are not graph inputs. This is an invalid model. At top level graph without matching NodeArg that subgraph consumes. Name= This is an invalid model. Failed to find NodeArg in all parent graphs. Name= ' is not a graph input, initializer, or output of a previous node. void onnxruntime::Graph::FinalizeFuseSubGraph(const onnxruntime::IndexedSubGraph&, onnxruntime::Node&) dst_implicit_input_idx < (int)node->ImplicitInputDefs().size() nullptr != func_meta_def gsl::not_null<onnxruntime::Node*> onnxruntime::Graph::AllocateNode() nodes_.size() < static_cast<unsigned int>(std::numeric_limits<int>::max()) Serialization error. Graph attribute was serialized without Graph instance Node::LoadFromOrtFormat, input_arg_counts is missing graph_proto cannot be null graph_proto != nullptr ' Model is invalid. utils::HasName(sparse_tensor) . Please, fix your model. Initializer onnxruntime::Graph::Graph(const onnxruntime::Model&, onnx::GraphProto*, const std::unordered_map<std::basic_string<char>, int>&, onnxruntime::Version, onnxruntime::IOnnxRuntimeOpSchemaCollectionPtr, onnxruntime::Graph*, const onnxruntime::Node*, const onnxruntime::logging::Logger&) Duplicate constant node sparse initializer name: ' Sparse initializer must have a name. This model is invalid Duplicate sparse_tensor_initializer: ' Duplicate initializer (dense, sparse or ConstantNode): ' ' the model will use the latest encountered initializer This is an invalid model. Tensor does not have type information. 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. NoOp onnxruntime::Node& onnxruntime::Graph::CreateFusedSubGraphNode(const onnxruntime::IndexedSubGraph&, const string&) std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::unique_ptr<onnxruntime::Function>; _Alloc = std::allocator<std::unique_ptr<onnxruntime::Function> >; std::vector<_Tp, _Alloc>::reference = std::unique_ptr<onnxruntime::Function>&] typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Function; _Dp = std::default_delete<onnxruntime::Function>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Function&] This is an invalid model. Node ( ) does not have type information set by parent node. This is an invalid model. Type Error: Type ' std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = onnx::TypeProto; _Alloc = std::allocator<onnx::TypeProto>; std::vector<_Tp, _Alloc>::const_reference = const onnx::TypeProto&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] ) does not match expected type ( ) input arg ( ' of input parameter ( ) of operator ( ) in node ( ) is invalid. Type Error: Type parameter ( ) of Optype ( ) bound to different types ( InferAndVerifyTypeMatch Node ( ) output arg ( ) type inference failed Type Error: Type ( ) of output arg ( ) of node ( Node: ) Op ( ) UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphs onnxruntime::common::Status onnxruntime::Graph::UpdateShapeInference(onnxruntime::Node&) node.GetAttributeNameToMutableSubgraphMap().empty() sparse_tensor_names_ not in sync with name_to_initial_tensor_ void onnxruntime::Graph::RemoveInitializedTensor(const string&) sparse_tensor_names_.count(tensor_name) == 0 graph_proto_ is not in sync with name_to_initial_tensor_. !found Duplicate initializer (dense or ConstantNode): ' Node index is out of range Outer scope node arg name ' 'was added but does not exist. void onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto&) const VerifyNodeAndOpMatch Fatal error: ) attribute ( is not a registered function/op ) is required but not specified. This is an invalid model. Error in Node: PerformTypeAndShapeInferencing Size mismatch validating subgraph inputs. Got inputs. Either provide all subgraph inputs, or just the required inputs. std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = const onnx::TypeProto*; _Alloc = std::allocator<const onnx::TypeProto*>; std::vector<_Tp, _Alloc>::const_reference = const onnx::TypeProto* const&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] inputs but subgraph has inputs and requires Subgraph input missing type. InferAndVerifySubgraphTypes Resolve Shouldn't be possible to have NodeArgs that haven't been handled already. onnxruntime::common::Status onnxruntime::Graph::Resolve(const onnxruntime::Graph::ResolveOptions&) outer_scope_node_args_consumed.empty() InlineFunction 's number of inputs is different from function body graph's number of input. 's number of outputs is different from function body graph's number of outputs. onnxruntime::common::Status onnxruntime::Graph::InlineFunction(onnxruntime::Node&) node.MutableOutputDefs().size() == subgraph.GetOutputs().size() Failed to convert dense initializer to sparse onnx::GraphProto onnxruntime::Graph::ToGraphProto() const CleanUnusedInitializers Removing initializer ' '. It is not used by any node and should be removed from the model. '. It is no longer used by any node. FN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_RKNS5_14ResolveOptionsEE N4onnx15GraphInferencerE N4onnx24FunctionBodyBuildContextE N4onnx28FunctionBodyBuildContextImplE N11onnxruntime5GraphE N11onnxruntime19GraphInferencerImplE N11onnxruntime20InferenceContextImplE PFN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_RKNS5_14ResolveOptionsEE *ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E0_ *ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E1_ *ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E2_ std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = flatbuffers::Offset<flatbuffers::String>; _Alloc = std::allocator<flatbuffers::Offset<flatbuffers::String> >; std::vector<_Tp, _Alloc>::reference = flatbuffers::Offset<flatbuffers::String>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] SaveInitializerOrtFormat /onnxruntime_src/onnxruntime/core/graph/graph_flatbuffers_utils.cc SaveSparseInitializerOrtFormat SaveAttributeOrtFormat Graph attribute value was null. Invalid ORT format model. SaveAttributeOrtFormat: Unsupported attribute type: Oqdfgign@jlbMissing dimensions for initializer. Invalid ORT format model. Missing string data for initializer. Invalid ORT format model. Missing raw data for initializer. Invalid ORT format model. Missing values for sparse initializer. Invalid ORT format model. LoadSparseInitializerOrtFormat Missing name for SparseTensor initializer. Invalid ORT format model. Missing indicies for sparse initializer: Missing dims for sparse initializer: LoadAttributeOrtFormat Null string attribute. Invalid ORT format model. Null tensor attribute. Invalid ORT format model. Null graph attribute. Invalid ORT format model. Null floats attribute. Invalid ORT format model. Null ints attribute. Invalid ORT format model. Null strings attribute. Invalid ORT format model. Null tensors attribute. Invalid ORT format model. $tTUNDEFINED INT FLOATS INTS STRINGS GRAPHS SPARSE_TENSOR SPARSE_TENSORS FlatBuffers 1.12.0 e/graph/graph_flatbuffers_utils.LoadInitializerOLoadSparseInitiaEmpty graph proto from deserialization of ORT fostd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = const onnxruntime::Node*; _Alloc = std::allocator<const onnxruntime::Node*>; std::vector<_Tp, _Alloc>::reference = const onnxruntime::Node*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] FindPath -> /onnxruntime_src/onnxruntime/core/graph/graph_utils.cc Failed since multiple edges matched: std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::front() [with _Tp = int; _Alloc = std::allocator<int>; std::vector<_Tp, _Alloc>::reference = int&] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector<const onnxruntime::Node*>; _Alloc = std::allocator<std::vector<const onnxruntime::Node*> >; std::vector<_Tp, _Alloc>::reference = std::vector<const onnxruntime::Node*>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] itr != node_args.end() Attempting to get index by a name which does not exist: int onnxruntime::graph_utils::GetIndexFromName(const onnxruntime::Node&, const string&, bool) Attempting to get an input that does not exist. const string& onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node&, int) index >= 0 && static_cast<size_t>(index) < inputs.size() Attempting to get an output that does not exist. const string& onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node&, int) index >= 0 && static_cast<size_t>(index) < outputs.size() std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::front() const [with _Tp = std::reference_wrapper<onnxruntime::Node>; _Alloc = std::allocator<std::reference_wrapper<onnxruntime::Node> >; std::vector<_Tp, _Alloc>::const_reference = const std::reference_wrapper<onnxruntime::Node>&] std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::back() const [with _Tp = std::reference_wrapper<onnxruntime::Node>; _Alloc = std::allocator<std::reference_wrapper<onnxruntime::Node> >; std::vector<_Tp, _Alloc>::const_reference = const std::reference_wrapper<onnxruntime::Node>&] void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&) ImplicitInputs: ExplicitInputs: . Index: Invalid input index for node Can only add a new input at the end of the current ones. void onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&) num_explicit_inputs == static_cast<size_t>(target_input_idx) CanUpdateImplicitInputNameInSubgraphs Implicit input name cannot be safely updated to in one of the subgraphs. void onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph(onnxruntime::Node&, const string&, const string&) std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0 Node must only have one used output 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; }) Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here. bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph&, onnxruntime::Node&) Initializer with same name exists. Name: onnxruntime::NodeArg& onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph&, const onnx::TensorProto&) !graph.GetInitializedTensor(new_initializer.name(), existing) Invalid ExecutionOrder const std::vector<long unsigned int>& onnxruntime::GraphViewer::GetNodesInTopologicalOrder(onnxruntime::ExecutionOrder) const /onnxruntime_src/onnxruntime/core/graph/graph_viewer.cc Not supported with filtered graph. const std::vector<long unsigned int>& onnxruntime::GraphViewer::GetRootNodes() const filter_info_ == nullptr nodearg IndexedSubGraph contains values not present in the Graph onnxruntime::GraphViewer::GraphViewer(const onnxruntime::Graph&, const onnxruntime::IndexedSubGraph*) graph_->GetNode(idx) != nullptr Mismatch between Graph and IndexedSubGraph. Input not found: Mismatch between Graph and IndexedSubGraph. Output not found: Mismatch between Graph and IndexedSubGraph. Node not found: *ZN11onnxruntime11GraphViewerC4ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlmE_ *ZN11onnxruntime11GraphViewerC4ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlPKNS_4NodeEE0_ N11onnxruntime11NodeCompareE *ZN11onnxruntime11GraphViewerC4ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlPKNS_4NodeEE1_ N11onnxruntime19PriorityNodeCompareE typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::Graph; _Dp = std::default_delete<onnxruntime::Graph>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::Graph&] Protobuf parsing failed. /onnxruntime_src/onnxruntime/core/graph/model.cc Failed to load model with error: is till opset ValidateOpsetForDomain is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset void onnxruntime::model_load_utils::ValidateOpsetForDomain(const std::unordered_map<std::basic_string<char>, int>&, const onnxruntime::logging::Logger&, bool, const string&, int) is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain ModelProto does not have a graph. onnxruntime::Model::Model(onnx::ModelProto&&, const PathString&, const IOnnxRuntimeOpSchemaRegistryList*, const onnxruntime::logging::Logger&) 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. ONNX 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. No graph was found in the protobuf. std::__shared_ptr<_Tp, _Lp>::_SafeConv<_Yp> std::__shared_ptr<_Tp, _Lp>::reset(_Yp*) [with _Yp = onnxruntime::Model; _Tp = onnxruntime::Model; __gnu_cxx::_Lock_policy _Lp = __gnu_cxx::_S_atomic; std::__shared_ptr<_Tp, _Lp>::_SafeConv<_Yp> = void] __p == 0 || __p != _M_ptr Graph is null. Invalid ORT format model. <p_fd> less than 0. failed. File doesn't exist Load model system error number <p_fd> is less than 0. Save Protobuf serialization failed. SaveModel St15_Sp_counted_ptrIPN11onnxruntime5ModelELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime21SchemaRegistryManagerESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime5ModelESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE Domain already set in registry std::deque<_Tp, _Alloc>::const_reference std::deque<_Tp, _Alloc>::operator[](std::deque<_Tp, _Alloc>::size_type) const [with _Tp = std::shared_ptr<onnxruntime::IOnnxRuntimeOpSchemaCollection>; _Alloc = std::allocator<std::shared_ptr<onnxruntime::IOnnxRuntimeOpSchemaCollection> >; std::deque<_Tp, _Alloc>::const_reference = const std::shared_ptr<onnxruntime::IOnnxRuntimeOpSchemaCollection>&; std::deque<_Tp, _Alloc>::size_type = long unsigned int] /opt/rh/devtoolset-9/root/usr/include/c++/9/bits/stl_deque.h /onnxruntime_src/onnxruntime/core/graph/schema_registry.cc , but it its version is higher than the operator set version RegisterOpSchemaInternal , but it its domain is not RegisterOpSet N4onnx15ISchemaRegistryE N11onnxruntime30IOnnxRuntimeOpSchemaCollectionE N11onnxruntime27OnnxRuntimeOpSchemaRegistryE N11onnxruntime21SchemaRegistryManagerE QW MW memory_seq_lens AW Couple the input and forget gates if 1, default 0. Number 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]` 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]`. 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]` The 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]` The 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]` The sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` The 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]`. typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnx::OpSchema; _Dp = std::default_delete<onnx::OpSchema>; typename std::add_lvalue_reference<_Tp>::type = onnx::OpSchema&] Not supported virtual const onnxruntime::Graph& onnxruntime::ViewerFunctionImpl::Body() const /onnxruntime_src/onnxruntime/core/graph/function_impl.h A node with a function body within a subgraph within another function body is currently not supported in ORT void onnxruntime::update_subgraphs_within_function_body(onnx::GraphProto&, const onnxruntime::Graph&, const onnx::NodeProto&, const std::unordered_map<std::basic_string<char>, int>&, const std::unordered_map<std::basic_string<char>, int>&) /onnxruntime_src/onnxruntime/core/graph/function.cc for the same domain for domain and function is trying to import opset version ONNX model does not support multiple opset versions for a domain. Model imports opset version std::unordered_map<std::basic_string<char>, int> onnxruntime::CreateOpsetImportsForFunction(const onnx::FunctionProto&, const std::unordered_map<std::basic_string<char>, int>&) (result.first->second == opset_version) input_arg->Type() != nullptr std::unique_ptr<onnx::OpSchema> onnxruntime::CreateSchema(const onnxruntime::Graph&, const onnxruntime::IndexedSubGraph&) std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::pair<std::basic_string<char>, std::basic_string<char> >; _Alloc = std::allocator<std::pair<std::basic_string<char>, std::basic_string<char> > >; std::vector<_Tp, _Alloc>::reference = std::pair<std::basic_string<char>, std::basic_string<char> >&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] fused_function_subgraph onnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph&, const onnxruntime::IndexedSubGraph&, const onnxruntime::logging::Logger&) _dummy Resolve subgraph failed: onnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph&, const NodeIndex&, const onnx::FunctionProto&, const onnxruntime::logging::Logger&) N11onnxruntime8FunctionE N11onnxruntime12FunctionImplE N11onnxruntime18ViewerFunctionImplE *ZN11onnxruntime12FunctionImplC4ERKNS_5GraphERKmRKN4onnx13FunctionProtoERKNS_7logging6LoggerEEUlRNS6_16InferenceContextEE_ Default logger already set. void onnxruntime::logging::LoggingManager::CreateDefaultLogger(const string&) /onnxruntime_src/onnxruntime/core/common/logging/logging.cc ISink must be provided. onnxruntime::logging::LoggingManager::LoggingManager(std::unique_ptr<onnxruntime::logging::ISink>, onnxruntime::logging::Severity, bool, onnxruntime::logging::LoggingManager::InstanceType, const string*, int) default_logger_id must be provided if instance_type is InstanceType::Default Only one instance of LoggingManager created with InstanceType::Default can exist at any point in time. System onnxruntime N@ is not a valid year is not a valid day is not a valid date VIWEF N11onnxruntime7logging11OStreamSinkE /onnxruntime_src/onnxruntime/core/common/path.cc ParsePathRoot Failed to parse path root: Parse static onnxruntime::Path onnxruntime::Path::Parse(const PathString&) /\ /onnxruntime_src/onnxruntime/core/common/profiler.cc Writing profiler data to file [
]
- {"cat" : " "pid" : "tid" : "dur" : "ts" : "ph" : "X", "name" :" "args" : { }
+ {"cat" : " "pid" : "tid" : "dur" : "ts" : "ph" : "X", "name" :" "args" : { " : }
},
- enabled_ session_logger != nullptr custom_logger != nullptr Session Node EndProfiling EndTimeAndRecordEvent void onnxruntime::profiling::Profiler::Initialize(const onnxruntime::logging::Logger*) onnxruntime::TimePoint onnxruntime::profiling::Profiler::StartTime() const SUCCESS INVALID_ARGUMENT NO_SUCHFILE NO_MODEL ENGINE_ERROR RUNTIME_EXCEPTION INVALID_PROTOBUF MODEL_LOADED NOT_IMPLEMENTED INVALID_GRAPH EP_FAIL GENERAL ERROR OK SystemError [ONNXRuntimeError] *B* ++******** /onnxruntime_src/onnxruntime/core/common/status.cc code != static_cast<int>(common::OK) onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const char*) onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const string&) !current_parallel_section n >= 0 Nested parallelism not supported /onnxruntime_src/onnxruntime/core/common/threadpool.cc void onnxruntime::concurrency::ThreadPool::ParallelFor(std::ptrdiff_t, const onnxruntime::TensorOpCost&, const std::function<void(long int, long int)>&) onnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSection(onnxruntime::concurrency::ThreadPool*) ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE20RunInParallelSectionERNS0_25ThreadPoolParallelSectionESt8functionIFvjEEjEUljE_ ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE13SummonWorkersERNS3_9PerThreadERNS0_25ThreadPoolParallelSectionEjRKSt8functionIFvjEEEUlvE_ *ZN11onnxruntime11concurrency10ThreadPool35ParallelForFixedBlockSizeSchedulingEllRKSt8functionIFvllEEEUljE_ *ZN11onnxruntime11concurrency10ThreadPool17SimpleParallelForElRKSt8functionIFvlEEEUlllE_ N5Eigen19ThreadPoolInterfaceE N11onnxruntime11concurrency27ExtendedThreadPoolInterfaceE N11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEEE ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE10WorkerLoopEiEUlvE_ ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE10WorkerLoopEiEUlvE0_ ? j@? A{Gz? @Failed to get symbol with error: lib .so Got null library handle file " " failed: realpath close open \/ Invalid fd was supplied: fstat Failed to load library pthread_attr_init failed pthread_create failed pthread_setaffinity_np failed remove() failed. Error code: , path: munmap failed. error code: - error code: file_path == nullptr offset < 0 mmap length > buffer.size() lseek read File: , offset: , length: Failed to unload library with error: Received negative size from stat call /onnxruntime_src/onnxruntime/core/platform/posix/env.cc DeleteFolder(): nftw() failed with error: pthread_attr_setstacksize failed Failed to close file descriptor ReadFileIntoBuffer - unexpected end of file. onnxruntime::{anonymous}::PosixThread::PosixThread(const char*, int, unsigned int (*)(int, Eigen::ThreadPoolInterface*), Eigen::ThreadPoolInterface*, const onnxruntime::ThreadOptions&) ReadFileIntoBuffer CleanUp UnmapFile MapFileIntoMemory static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() nftw_remove DeleteFolder *N11onnxruntime12_GLOBAL__N_111PosixThreadE *N11onnxruntime12_GLOBAL__N_18PosixEnvE N11onnxruntime9EnvThreadE N11onnxruntime3EnvE N11onnxruntime9TelemetryE *N11onnxruntime12_GLOBAL__N_112PosixEnvTimeE N11onnxruntime7EnvTimeE ? ? packing unavailable
- bad dimensions *ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZ8MlasPool17MLAS_POOLING_KINDmPKlS5_S5_S5_S5_PKfPfPS1_EUllE_EEvS9_lOT_lEUllE_ ? @ @@ @ @ @ @H"#" h! AQ5@.N3;A8X} <2?~>++{16?:>=4?~? ? AQ5@.N3;A8X} <2?~>++{16?:>=4?~? A A A AQ5@.Q5@.Q5@.Q5@.N3N3N3N3;A8;A8;A8;A8X} <X} <X} <X} <2?~>2?~>2?~>2?~>++++++++{1{1{1{16666?:?:?:?:>=>=>=>=4?~?4?~?4?~?4?~? A%~3a*7A \3JCy7
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=ppppu>u>u>u> l? l? l? l?7777fȹfȹfȹfȹ~;~;~;~;ƼƼƼƼX=X=X=X=P"?P"?P"?P"?>>>> r1 r1 r1 r1mL:mL:mL:mL:e9 <e9 <e9 <e9 <*=*=*=*=*>*>*>*>>>>> rB¥B @K;? r1 :n/ <*=(*>> ? ?µµµrBrBrBrB : : : :n/ <n/ <n/ <n/ <*=*=*=*=(*>(*>(*>(*> 38MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR ElementSize must be power of 2 and less or equal than 16! QLinearGlobalAveragePool ImageSize too large! QLinearGlobalAveragePool parameter out of computation range! looooo
-l / C *Z19MlasExecuteThreadedPFvPviES_iPN11onnxruntime11concurrency10ThreadPoolEEUllE_ We do not support type [ ] for now is missing type info. is not supported currently Null type info for sequence_type map_type /onnxruntime_src/onnxruntime/core/flatbuffers/flatbuffers_utils.cc SaveValueInfoOrtFormat: value_info_proto for Model must have opset imports. Invalid ORT format model. opset id is null. Invalid ORT format model. opset import domain is null. Invalid ORT format model. Null tensor type info. Invalid ORT format model. Null entry in dimensions. Invalid ORT format model. dim_param value with no name. Invalid ORT format model. Null sequence type info. Invalid ORT format model. Null value type info in fbs::SequenceType. Invalid ORT format model. Null map type info. Invalid ORT format model. Null value type info in fbs::MapType. Invalid ORT format model. LoadOpsetImportOrtFormat LoadMapTypeOrtFormat LoadSequenceTypeOrtFormat LoadTensorDimensionOrtFormat LoadTensorShapeOrtFormat LoadTensorTypeAndShapeOrtFormat LoadTypeInfoOrtFormat LoadValueInfoOrtFormat SaveMapTypeOrtFormat SaveSequenceTypeOrtFormat SaveTensorTypeAndShapeOrtFormat SaveTypeInfoOrtFormat SaveValueInfoOrtFormat Sparse tensor indices ( ) has values, but NNZ is Sparse tensor ( ) index value at position [ ] out of range [0, ] not in sorted order. is required to be non-empty. value_info ' of Field ' is required but missing. elem_type key_type value_type data_type ) to UNDEFINED is not allowed float_data int32_data string_data raw_data double_data uint64_data TensorProto ( tensor name: ) should be stored in TensorProto (tensor name: STRING data (tensor name: ' instead of ' ' should be stored in field ' values of data_type ' ] out of range. sparse_tensor_proto ) must have rank 1. Sparse tensor values ( ) must have a dense-rank > 0 ) must have INT64 type. ) must have rank 1 or 2. ) has no index values. , elem_type: Sequence ( Structure name: Map (name: init ' of node:
- Bad node spec:
+ onnxruntime::TimePoint onnxruntime::profiling::Profiler::StartTime() const enabled_ void onnxruntime::profiling::Profiler::Initialize(const onnxruntime::logging::Logger*) session_logger != nullptr EndTimeAndRecordEvent Maximum number of events reached, could not record profile event. SystemError [ONNXRuntimeError] SUCCESS GENERAL ERROR EP_FAIL INVALID_GRAPH NOT_IMPLEMENTED MODEL_LOADED INVALID_PROTOBUF RUNTIME_EXCEPTION ENGINE_ERROR NO_SUCHFILE INVALID_ARGUMENT NO_MODEL OK 000o0Y000C0-0/.0 onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const string&) /onnxruntime_src/onnxruntime/core/common/status.cc code != static_cast<int>(common::OK) onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const char*) typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp, _Dp>::operator*() const [with _Tp = onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat; _Dp = std::default_delete<onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat>; typename std::add_lvalue_reference<_Tp>::type = onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat&] UnknownEvent Distribution Wait WaitRevoke DistributionEnqueue TTTTTstd::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnxruntime::concurrency::ThreadPoolProfiler::ChildThreadStat; _Alloc = std::allocator<onnxruntime::concurrency::ThreadPoolProfiler::ChildThreadStat>; std::vector<_Tp, _Alloc>::reference = onnxruntime::concurrency::ThreadPoolProfiler::ChildThreadStat&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] unnamed_thread_pool "num_run": thread::id of a non-executing thread LogStart must pair with LogEnd void onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEnd(onnxruntime::concurrency::ThreadPoolProfiler::ThreadPoolEvent) /onnxruntime_src/onnxruntime/core/common/threadpool.cc std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = std::chrono::time_point<std::chrono::_V2::system_clock, std::chrono::duration<long int, std::ratio<1, 1000000000> > >; _Alloc = std::allocator<std::chrono::time_point<std::chrono::_V2::system_clock, std::chrono::duration<long int, std::ratio<1, 1000000000> > > >; std::vector<_Tp, _Alloc>::reference = std::chrono::time_point<std::chrono::_V2::system_clock, std::chrono::duration<long int, std::ratio<1, 1000000000> > >&] !points_.empty() void onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::LogEndAndStart(onnxruntime::concurrency::ThreadPoolProfiler::ThreadPoolEvent) Nested parallelism not supported onnxruntime::concurrency::ThreadPool::ParallelSection::ParallelSection(onnxruntime::concurrency::ThreadPool*) !current_parallel_section std::string onnxruntime::concurrency::ThreadPoolProfiler::MainThreadStat::Reset() "thread_id": " ", "block_size": [ ], "core": Profiler not started yet {"main_thread": { "thread_pool_name": " ", }, "sub_threads": { }} std::string onnxruntime::concurrency::ThreadPoolProfiler::Stop() void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = std::pair<int, unsigned int>; _Alloc = std::allocator<std::pair<int, unsigned int> >] void onnxruntime::concurrency::ThreadPool::ParallelFor(std::ptrdiff_t, const onnxruntime::TensorOpCost&, const std::function<void(long int, long int)>&) n >= 0 ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE20RunInParallelSectionERNS0_25ThreadPoolParallelSectionESt8functionIFvjEEjlEUljE_ ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE21RunInParallelInternalERNS3_9PerThreadERNS0_25ThreadPoolParallelSectionEjbSt8functionIFvjEEEUlvE_ ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE26ScheduleOnPreferredWorkersERNS3_9PerThreadERNS0_25ThreadPoolParallelSectionERSt6vectorIiSaIiEEjjSt8functionIFvjEEEUlvE_ N5Eigen19ThreadPoolInterfaceE N11onnxruntime11concurrency27ExtendedThreadPoolInterfaceE N11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEEE *ZN11onnxruntime11concurrency10ThreadPool35ParallelForFixedBlockSizeSchedulingEllRKSt8functionIFvllEEEUljE_ *ZN11onnxruntime11concurrency10ThreadPool17SimpleParallelForElRKSt8functionIFvlEEEUlllE_ ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE10WorkerLoopEiEUlvE_ ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE10WorkerLoopEiEUlvE0_ unnamed_thread_p ? j@? A @{Gz?\/ Failed to get symbol with error: lib .so Failed to load library Got null library handle Failed to unload library with error: file " " failed: realpath close open Invalid fd was supplied: fstat Received negative size from stat call /onnxruntime_src/onnxruntime/core/platform/posix/env.cc DeleteFolder(): nftw() failed with error: DeleteFolder pthread_attr_init failed pthread_create failed pthread_setaffinity_np failed onnxruntime::{anonymous}::PosixThread::PosixThread(const char*, int, unsigned int (*)(int, Eigen::ThreadPoolInterface*), Eigen::ThreadPoolInterface*, const onnxruntime::ThreadOptions&) pthread_attr_setstacksize failed Failed to close file descriptor CleanUp - error code: MapFileIntoMemory file_path == nullptr offset < 0 mmap ReadFileIntoBuffer length > buffer.size() lseek read File: , offset: , length: ReadFileIntoBuffer - unexpected end of file. UnmapFile munmap failed. error code: nftw_remove remove() failed. Error code: , path: N11onnxruntime9EnvThreadE N11onnxruntime3EnvE *N11onnxruntime12_GLOBAL__N_111PosixThreadE *N11onnxruntime12_GLOBAL__N_18PosixEnvE N11onnxruntime9TelemetryE N11onnxruntime7EnvTimeE *N11onnxruntime12_GLOBAL__N_112PosixEnvTimeE ? ? *Z13MlasGemmBatch15CBLAS_TRANSPOSES_mmmPK22MLAS_SGEMM_DATA_PARAMSmPN11onnxruntime11concurrency10ThreadPoolEEUllE_ *Z13MlasGemmBatchRK27MLAS_GEMM_U8X8_SHAPE_PARAMSPK26MLAS_GEMM_U8X8_DATA_PARAMSmPN11onnxruntime11concurrency10ThreadPoolEEUllE_
+ 0Abad dimensions *ZN11onnxruntime11concurrency10ThreadPool19TryBatchParallelForIZ8MlasPool17MLAS_POOLING_KINDmPKlS5_S5_S5_S5_PKfPfPS1_EUllE_EEvS9_lOT_lEUllE_ ? @ @@ @ @ @ @H9: ;<X7 8 AQ5@.N3;A8X} <2?~>++{16?:>=4?~? ? AQ5@.N3;A8X} <2?~>++{16?:>=4?~? A A A AQ5@.Q5@.Q5@.Q5@.N3N3N3N3;A8;A8;A8;A8X} <X} <X} <X} <2?~>2?~>2?~>2?~>++++++++{1{1{1{16666?:?:?:?:>=>=>=>=4?~?4?~?4?~?4?~? A%~3a*7A \3JCy7
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=ppppu>u>u>u>7777fȹfȹfȹfȹ~;~;~;~;ƼƼƼƼX=X=X=X=P"?P"?P"?P"?>>>> r1 r1 r1 r1 mL:mL:mL:mL:e9 <e9 <e9 <e9 <*=*=*=*=*>*>*>*>>>>> rB¥B @K;? r1 :n/ <*=(*>> ? ?µµµrBrBrBrB : : : :n/ <n/ <n/ <n/ <*=*=*=*=(*>(*>(*>(*>27MLAS_QGEMM_OUTPUT_PROCESSOR 38MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR ElementSize must be power of 2 and less or equal than 16! QLinearGlobalAveragePool ImageSize too large! QLinearGlobalAveragePool parameter out of computation range! =u / C *Z13MlasGemmBatch15CBLAS_TRANSPOSES_mmmPK22MLAS_DGEMM_DATA_PARAMSmPN11onnxruntime11concurrency10ThreadPoolEEUllE_ *Z19MlasExecuteThreadedPFvPvlES_lPN11onnxruntime11concurrency10ThreadPoolEEUllE_ Model must have opset imports. Invalid ORT format model. opset import domain is null. Invalid ORT format model. SaveTensorTypeAndShapeOrtFormat /onnxruntime_src/onnxruntime/core/flatbuffers/flatbuffers_utils.cc SaveTypeInfoOrtFormat SaveSequenceTypeOrtFormat SaveMapTypeOrtFormat We do not support type [ ] for now SaveValueInfoOrtFormat is missing type info. SaveValueInfoOrtFormat: value_info_proto for dim_param value with no name. Invalid ORT format model. LoadTensorShapeOrtFormat LoadTypeInfoOrtFormat LoadSequenceTypeOrtFormat LoadMapTypeOrtFormat is not supported currently Null tensor type info. Invalid ORT format model. LoadTensorTypeAndShapeOrtFormat Null sequence type info. Invalid ORT format model. Null value type info in fbs::SequenceType. Invalid ORT format model. Null map type info. Invalid ORT format model. Null value type info in fbs::MapType. Invalid ORT format model. Null type info for LoadValueInfoOrtFormat e/flatbuffers/flLoadOpsetImportOLoadTensorDimensLoadTypeInfoOrtFLoadSequenceTypeLoadMapTypeOrtFoLoadValueInfoOrtSparse tensor indices ( Sparse tensor ( ) index value at position [ ] out of range. ) first dimension size does not equal NNZ. ) second dimension size does not match rank of tensor. ] not in lexicographic sorted order. ) has values, but NNZ is ] out of range [0, ] not in sorted order. Field ' ' of value_info is required to be non-empty. is required but missing. elem_type key_type value_type Unrecognized type value case (value_info name: lFllFPFlpFFdata_type ) to UNDEFINED is not allowed float_data int32_data string_data raw_data double_data uint64_data ) should be stored in TensorProto ( tensor name: TensorProto (tensor name: STRING data (tensor name: ' instead of ' ' should be stored in field ' values of data_type ' setting data_type field (tensor name: Data of TensorProto ( tensor name: ) is stored externally and should not have data field. , but it doesn't exist or is not accessible. ) is stored externally but doesn't have a location. ) is 0-element but contains data! ) should contain one and only one value field. ) should not be stored in raw_data field Unrecognized data_type (tensor name: lK L L L L LL(L L L6LKKK6L Lsparse_tensor_proto Sparse tensor values ( ) must have rank 1. ) must have a dense-rank > 0 ) must have INT64 type. ) must have rank 1 or 2. ) has no index values. ) dimensions are not positive. ATen ConstantFill op_type NodeProto (name: , type: No opset import for domain ' No Op registered for with domain_version of ) has zero input and zero output. Warning: Checker does not support models with experimental ops: is deprecated in domain_version of init ' of node:
+ OpType:
-==> Context: attr Attribute (name: op_type NodeProto (name: , type: No opset import for domain ' No Op registered for with domain_version of since_version imports opset version function ( ATen ConstantFill Unable to open model file: Unrecognized type value case (value_info name: setting data_type field (tensor name: ) is stored externally and should not have data field. Data of TensorProto ( tensor name: ) is stored externally but doesn't have a location. , but it doesn't exist or is not accessible. ) is 0-element but contains data! ) should contain one and only one value field. ) should not be stored in raw_data field Unrecognized data_type (tensor name: ) first dimension size does not equal NNZ. ) second dimension size does not match rank of tensor. ] not in lexicographic sorted order. ) dimensions are not positive. ) is not have a valid element type. setting key_type field (map name: Length of map keys and map values are not the same (map name: ) to invalid TensorProto key_type ) should not contain more than one keys field. ' has been used as graph input names multiple times. Graph must be in single static assignment (SSA) form, however ' Tensor initializers must have a non-empty name initializer name is not unique in initializer but not in graph input Sparse tensor initializers must have a non-empty name sparse initializer name is not unique across initializers and sparse_initializers Nodes in a graph must be topologically sorted, however input '
- is not output of any previous nodes. ' has been used as output names multiple times. ) should not contain more than one value field. type field and data field mismatch in attribute ) should refer to attribute in parent node. ) has zero input and zero output. Warning: Checker does not support models with experimental ops: is deprecated in domain_version of ONNX models do not support multiple opset version imports for a domain. Function where as the model imports opset version ' has been used multiple times. ) 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 ' The model does not have an ir_version set properly. Your model ir_version is higher than the checker's. Your model has duplicate keys in metadata_props. model with IR version >= 3 must specify opset_import for ONNX model with IR version < 3 cannot have opset_import specified . Please check if it is a valid file. . Please check if it is a valid protobuf file of model. Unable to parse model from file: <<<DĬ<IиииииXииhIhи N4onnx7checker15ValidationErrorE seq(tensor(uint8)) seq(tensor(uint16)) seq(tensor(uint32)) seq(tensor(uint64)) seq(tensor(int8)) seq(tensor(int16)) seq(tensor(int32)) seq(tensor(int64)) seq(tensor(float16)) seq(tensor(float)) seq(tensor(double)) seq(tensor(string)) seq(tensor(bool)) seq(tensor(complex64)) seq(tensor(complex128)) Input type was null Input element type of axis value All Tensor and Sequence types Condition for the if Only bool v_initial v_final_and_scan_outputs All Tensor types initial_state_and_scan_inputs final_state_and_scan_outputs Int64 tensor Scan input was not a tensor. outputs. Expected was not target= source= Mismatched type: If node has but subgraphs produce Input was expected to have tensor type. Got Element type of input was unknown does not match existing output type of Output was expected to have tensor type. Got is invalid for a tensor of rank Values 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. 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. /onnxruntime_src/cmake/external/onnx/onnx/defs/controlflow/defs.cc A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip. A boolean termination condition. Optional. Pass empty string to skip. The initial values of any loop-carried dependencies (values that change across loop iterations) Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors. The 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. Initial values of the loop's N state variables followed by M scan_inputs Final values of the loop's N state variables followed by K scan_outputs The 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. 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. 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]. Input was expected to have sequence type. Got Input was expected to have either tensor or sequence type. Got ) is not equal to number of scan inputs ( Number of scan input axes specified ( ) is not equal to number of scan outputs ( Number of scan output axes specified ( Graph attribute inferencing returned type information for Scan 'body' subgraph outputs should all be tensors but output Mismatched tensor element type: source sequence type missing element type. target sequence type missing element type. then_branch and else_branch produce different number of outputs. Loop 'body' subgraph outputs should all be tensors or sequences but output Loop 'body' subgraph scan outputs should all be tensors but output then= Mismatched type for output else= Mismatched tensor element type for output Optional 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). /onnxruntime_src/cmake/external/onnx/onnx/defs/controlflow/old.cc Final N loop carried dependency values then K scan_outputs 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. Values 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. Loop 'body' subgraph outputs should all be tensors but output complex64 complex128 seq( map( sparse_tensor( opaque $C>$C$ClA@$C\B= not specified Value of attribute Invalid shape value: sparse_value value_int value_ints value_string value_strings Constrain input types. Number of times to sample. Input tensor must have rank 2 loop_body_attribute cond_out sub_result sub_result_casted delta_casted div_result ceil_result ceil_result_relu ceil_result_relu_int ceil_result_relu_bool variadic_output Scalar. Value to step by. Attribute expected to have tensor type Attribute expected to have a one-dim tensor should be of integer type and specify a type. does not specify a valid type. Input to 'Range' op should be scalars (Tensor with only one element and shape empty) Shape input must be a one-dimensional tensor. The value for the elements of the output tensor. The value for the elements of the output tensor in sparse format. The value for the sole element for the scalar, int64, output tensor. The values for the elements for the 1D, int64, output tensor. The value for the sole element for the scalar, float32, output tensor. The values for the elements for the 1D, float32, output tensor. The value for the sole element for the scalar, UTF-8 string, output tensor. The values for the elements for the 1D, UTF-8 string, output tensor. Output tensor containing the same value of the provided tensor. /onnxruntime_src/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 float32 1D 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 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. Upper boundary of the output values. (Optional) Seed to the random generator, if not specified we will auto generate one. The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT. The shape of the output tensor. Output tensor of random values drawn from uniform distribution Constrain output types to float tensors. The 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. (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 input types to float tensors. Constrain output types to integral 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. Input tensor must be 2-dimensional All inputs to 'Range' op must be of the same type Output type must be int32 or int64 Scalar. First entry for the range of output values. Scalar. Exclusive upper limit for the range of output values. A 1-D tensor with same type as the inputs containing generated range of values. Constrain input types to common numeric type tensors. *ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25ConstantOfShape_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17EyeLike_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_23RandomUniform_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22RandomNormal_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27RandomUniformLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26RandomNormalLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_21Multinomial_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Range_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Attribute '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. /onnxruntime_src/cmake/external/onnx/onnx/defs/generator/old.cc *ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Result tensor. xor greater less Z O1 O2 greater_equal GreaterOrEqual less_equal LessOrEqual First input operand for the logical operator. Second input operand for the logical operator. Constrains input to boolean tensor. Constrains output to boolean tensor. /onnxruntime_src/cmake/external/onnx/onnx/defs/logical/defs.cc Constrains input types to all numeric tensors. Constrains input/output to boolean tensors. First operand, input to be shifted. Second operand, amounts of shift. Constrain input and output types to integer tensors. Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift). *ZZN4onnx23BinaryLogicDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx23BinaryLogicDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_19BitShift_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Enable broadcasting broadcast If set, defines the broadcast dimensions. Left input tensor for the logical operator. Right input tensor for the logical operator. Constrains input to float tensors. /onnxruntime_src/cmake/external/onnx/onnx/defs/logical/old.cc Constrains input to integral tensors. *ZZN4onnx31BinaryLogicDocGenerator_opset12EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx31BinaryLogicDocGenerator_opset12EPKcEUlRNS_8OpSchemaEE_ *ZN4onnx30BinaryLogicDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx30BinaryLogicDocGenerator_opset7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx30BinaryLogicDocGenerator_opset7EPKcEUlRNS_8OpSchemaEE_ List of tensors for data_0 Output tensor. Rank of input basic_string::at subtraction Dividend tensor Divisor tensor Remainder tensor division Coefficient of ELU. Celu slope Value of alpha. Value of beta. normalized exponential log of softmax hardmax Values Indices scale of quantized input a scale of quantized input b scale of quantized output y loss reduction NegativeLogLikelihoodLoss Einsum expression string. Operands Inputs scores labels log_prob SoftmaxCrossEntropyLoss 'axis' must be in [ ]. Its actual value is: Weight rank must be 1. none X_alpha Elu_Result X_ReduceMax X_Sub X_Exp X_ReduceSum X_Log const_zero const_one expanded_target input_gather_element loss_NCdd loss_N1dd loss_Ndd weight_gather loss_unweighted loss_sum weight_gather_sum const_ignore_index const_zero_target_typed expanded_target_int64 transform_targets const_zero_float squeeze_mask const_one_float weight_gather_temp weight_gather_temp_1 X_Max X_RS X_Div The 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). Constrain input and output types to high-precision numeric tensors. Number of input tensors does not match the operands in the equation. Ellipsis represents incompatible dimensions. does not match the equation indices. /onnxruntime_src/cmake/external/onnx/onnx/defs/math/defs.cc Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment Constrain 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). 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. The exponential of the input tensor computed element-wise The natural log of the input tensor computed element-wise The hyperbolic tangent values of the input tensor computed element-wise First operand, base of the exponent. Second operand, power of the exponent. Constrain input X and output types to float/int tensors. Constrain input Y types to float/int tensors. Slope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to X Output tensor (same size as X) Constrain input and output types to numeric tensors. Input tensor whose elements to be clipped Minimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape). Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape). Output tensor with clipped input elements Softmax(input, axis) = Exp(input) / ReduceSum(Exp(input), axis=axis, keepdims=1) LogSoftmax(input, axis) = Log(Softmax(input, axis=axis)) Hardmax(element in input, axis) = 1 if the element is the first maximum value along the specified axis, 0 otherwise The softsign (x/(1+|x|)) values of the input tensor computed element-wise Optional 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). Tensor of shape [a_1, a_2, ..., a_n, r] A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieve Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensor Tensor 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 int64 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-wise The cosine of the input tensor computed element-wise The tangent of the input tensor computed element-wise The arcsine of the input tensor computed element-wise The arccosine of the input tensor computed element-wise The arctangent of the input tensor computed element-wise Constrain input and output types to all tensors. A 1-D tensor indicates the shape you want to expand to, following the broadcast rule The hyperbolic sine values of the input tensor computed element-wise The hyperbolic cosine values of the input tensor computed element-wise The hyperbolic arcsine values of the input tensor computed element-wise The hyperbolic arccosine values of the input tensor computed element-wise The hyperbolic arctangent values of the input tensor computed element-wise The 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. N-dimensional quantized matrix a zero point of quantized input a N-dimensional quantized matrix b zero point of quantized input b zero point of quantized output y Quantized matrix multiply results from a * b Constrain 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. 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 elements axis tensor can be int32 or int64 only Constrain input and output types to floating-point tensors. Input 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 loss Type 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 types Constrain input and output types to all numerical tensor types. The 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). Target rank must be 1 less than the input rank. Input and target dimension value mismatch. input_gather_element_transform Invalid value for attribute axis K 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. 'shape' input must be 1D tensor of type INT64 input and zero_point pair is expected to have same type. Type 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. FbRKN4onnx24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE *ZZN4onnx16MathDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx16MathDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_S1_EUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx30ElementwiseMultiOpDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx30ElementwiseMultiOpDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_18Softmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_ *ZN4onnx11GetOpSchemaINS_21LogSoftmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_ *ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17MatMul_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Expand_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24QLinearMatMul_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24MatMulInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Det_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Einsum_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ PFbRKN4onnx24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE Pass 1 to enable broadcasting consumed_inputs logsoftmax 1-D input tensor Value of alpha default to 0.2 Value of beta default to 0.5 List of tensors for Max. List of tensors for Min List of tensors for Sum. List of tensors for Mean. Input tensor A Input tensor B Input tensor C Invalid value for attribute k If set, defines the broadcast dimensions. See doc for details. 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 A Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). legacy optimization attribute. /onnxruntime_src/cmake/external/onnx/onnx/defs/math/old.cc 1 for the first maximum value, and 0 for all others
+==> Context: Graph must be in single static assignment (SSA) form, however ' ' has been used as graph input names multiple times. Tensor initializers must have a non-empty name initializer name is not unique in initializer but not in graph input Sparse tensor initializers must have a non-empty name sparse initializer name is not unique across initializers and sparse_initializers Nodes in a graph must be topologically sorted, however input '
+ is not output of any previous nodes. ' has been used as output names multiple times. Bad node spec for node. Name: attr Attribute (name: type field and data field mismatch in attribute ) should not contain more than one value field. ) should refer to attribute in parent node. N4onnx7checker15ValidationErrorE Input type was null Input element type of Input was expected to have tensor type. Got Element type of input was unknown does not match existing output type of Output was expected to have tensor type. Got is invalid for a tensor of rank axis value Input was expected to have sequence type. Got Input was expected to have either tensor or sequence type. Got Scan input was not a tensor. outputs. Expected was not 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 ( std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = onnx::TypeProto; _Alloc = std::allocator<onnx::TypeProto>; std::vector<_Tp, _Alloc>::reference = onnx::TypeProto&] Graph attribute inferencing returned type information for std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = const onnx::TypeProto*; _Alloc = std::allocator<const onnx::TypeProto*>; std::vector<_Tp, _Alloc>::reference = const onnx::TypeProto*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] Scan 'body' subgraph outputs should all be tensors but output Mismatched type: source= target= Mismatched tensor element type: source sequence type missing element type. target sequence type missing element type. If node has but subgraphs produce then_branch and else_branch produce different number of outputs. Loop 'body' subgraph outputs should all be tensors or sequences but output Loop 'body' subgraph scan outputs should all be tensors but output Condition for the if All Tensor and Sequence types seq(tensor(uint8)) seq(tensor(uint16)) seq(tensor(uint32)) seq(tensor(uint64)) seq(tensor(int8)) seq(tensor(int16)) seq(tensor(int32)) seq(tensor(int64)) seq(tensor(float16)) seq(tensor(float)) seq(tensor(double)) seq(tensor(string)) seq(tensor(bool)) seq(tensor(complex64)) seq(tensor(complex128)) Only bool Values 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. 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. /onnxruntime_src/cmake/external/onnx/onnx/defs/controlflow/defs.cc v_initial v_final_and_scan_outputs A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip. A boolean termination condition. Optional. Pass empty string to skip. The initial values of any loop-carried dependencies (values that change across loop iterations) Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors. The 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. Initial values of the loop's N state variables followed by M scan_inputs Final values of the loop's N state variables followed by K scan_outputs The 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. 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. 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]. initial_state_and_scan_inputs final_state_and_scan_outputs Int64 tensor All Tensor types then= Mismatched type for output else= Mismatched tensor element type for output Optional 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). /onnxruntime_src/cmake/external/onnx/onnx/defs/controlflow/old.cc Final N loop carried dependency values then K scan_outputs 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. Values 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. Loop 'body' subgraph outputs should all be tensors but output Invalid data type complex64 complex128 Invalid tensor data type seq( map( sparse_tensor( Unsuported type proto value case. xl\Kxlxl|J,IxlHGDataTypeUtils::FromDataTypeString - Received invalid data type string opaque not specified Value of attribute should be of integer type and specify a type. does not specify a valid type. Input to 'Range' op should be scalars (Tensor with only one element and shape empty) std::vector<_Tp, _Alloc>::const_reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) const [with _Tp = double; _Alloc = std::allocator<double>; std::vector<_Tp, _Alloc>::const_reference = const double&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] Invalid shape value: Attribute expected to have tensor type Attribute expected to have a one-dim tensor Shape input must be a one-dimensional tensor. sparse_value value_int value_ints value_string value_strings 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. The value for the elements of the output tensor. The value for the elements of the output tensor in sparse format. The value for the sole element for the scalar, int64, output tensor. The values for the elements for the 1D, int64, output tensor. The value for the sole element for the scalar, float32, output tensor. The values for the elements for the 1D, float32, output tensor. The value for the sole element for the scalar, UTF-8 string, output tensor. The values for the elements for the 1D, UTF-8 string, output tensor. Output tensor containing the same value of the provided tensor. /onnxruntime_src/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 float32 1D 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 output types to be numerics. Constrain input types. (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. Upper 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 distribution Constrain output types to float tensors. The 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 input types to float tensors. Constrain output types to integral tensors. Input tensor must be 2-dimensional All inputs to 'Range' op must be of the same type Output type must be int32 or int64 Input tensor must have rank 2 loop_body_attribute cond_out sub_result sub_result_casted delta_casted div_result ceil_result ceil_result_relu ceil_result_relu_int ceil_result_relu_bool variadic_output Scalar. First entry for the range of output values. Scalar. Exclusive upper limit for the range of output values. A 1-D tensor with same type as the inputs containing generated range of values. Constrain input types to common numeric type tensors. Scalar. Value to step by. *ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25ConstantOfShape_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17EyeLike_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_23RandomUniform_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22RandomNormal_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27RandomUniformLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26RandomNormalLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_21Multinomial_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Range_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Attribute '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. /onnxruntime_src/cmake/external/onnx/onnx/defs/generator/old.cc *ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Result tensor. First input operand for the logical operator. Second input operand for the logical operator. Constrains input to boolean tensor. Constrains output to boolean tensor. /onnxruntime_src/cmake/external/onnx/onnx/defs/logical/defs.cc xor greater Constrains input types to all numeric tensors. less Constrains input/output to boolean tensors. First operand, input to be shifted. Second operand, amounts of shift. Constrain input and output types to integer tensors. Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift). less_equal O1 O2 greater_equal *ZZN4onnx23BinaryLogicDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx23BinaryLogicDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_19BitShift_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Enable broadcasting broadcast If set, defines the broadcast dimensions. Left input tensor for the logical operator. Right input tensor for the logical operator. /onnxruntime_src/cmake/external/onnx/onnx/defs/logical/old.cc Constrains input to float tensors. Constrains input to integral tensors. *ZZN4onnx31BinaryLogicDocGenerator_opset12EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx31BinaryLogicDocGenerator_opset12EPKcEUlRNS_8OpSchemaEE_ *ZN4onnx30BinaryLogicDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx30BinaryLogicDocGenerator_opset7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx30BinaryLogicDocGenerator_opset7EPKcEUlRNS_8OpSchemaEE_ The 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). List of tensors for data_0 Output tensor. Rank of input Number of input tensors does not match the operands in the equation. Ellipsis represents incompatible dimensions. does not match the equation indices. basic_string::at: __n (which is %zu) >= this->size() (which is %zu) 'axis' must be in [ ]. Its actual value is: equation Target rank must be 1 less than the input rank. Input and target dimension value mismatch. Weight rank must be 1. reduction none /onnxruntime_src/cmake/external/onnx/onnx/defs/math/defs.cc subtraction Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment Constrain input and output types to high-precision numeric tensors. Dividend tensor Divisor tensor Remainder tensor division Constrain input and output types to signed 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. The exponential of the input tensor computed element-wise The natural log of the input tensor computed element-wise The hyperbolic tangent values of the input tensor computed element-wise First operand, base of the exponent. Second operand, power of the exponent. Constrain input X and output types to float/int tensors. Constrain input Y types to float/int tensors. Slope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to X Output tensor (same size as X) slope Value of alpha. Value of beta. Constrain input and output types to numeric tensors. Input tensor whose elements to be clipped Minimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape). Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape). Output tensor with clipped input elements Softmax(input, axis) = Exp(input) / ReduceSum(Exp(input), axis=axis, keepdims=1) normalized exponential LogSoftmax(input, axis) = Log(Softmax(input, axis=axis)) log of softmax Hardmax(element in input, axis) = 1 if the element is the first maximum value along the specified axis, 0 otherwise hardmax The softsign (x/(1+|x|)) values of the input tensor computed element-wise Optional 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). Tensor of shape [a_1, a_2, ..., a_n, r] A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieve Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensor Tensor 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 int64 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. Indices The sine of the input tensor computed element-wise The cosine of the input tensor computed element-wise The tangent of the input tensor computed element-wise The arcsine of the input tensor computed element-wise The arccosine of the input tensor computed element-wise The arctangent of the input tensor computed element-wise A 1-D tensor indicates the shape you want to expand to, following the broadcast rule Constrain input and output types to all tensors. The hyperbolic sine values of the input tensor computed element-wise The hyperbolic cosine values of the input tensor computed element-wise The hyperbolic arcsine values of the input tensor computed element-wise The hyperbolic arccosine values of the input tensor computed element-wise The hyperbolic arctangent values of the input tensor computed element-wise The 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. N-dimensional quantized matrix a zero point of quantized input a N-dimensional quantized matrix b zero point of quantized input b zero point of quantized output y Quantized matrix multiply results from a * b Constrain 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. scale of quantized input a scale of quantized input b scale of quantized output y 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. 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 elements axis tensor can be int32 or int64 only exclusive Constrain input and output types to floating-point tensors. Input 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 loss Type 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 types loss NegativeLogLikelihoodLoss Einsum expression string. Operands Constrain input and output types to all numerical tensor types. The 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). scores labels log_prob SoftmaxCrossEntropyLoss const_zero_float const_zero_casted const_one_float const_one_casted const_zero const_one expanded_target input_gather_element loss_NCdd loss_N1dd loss_Ndd weight_gather loss_unweighted loss_sum weight_gather_sum const_ignore_index const_zero_target_typed expanded_target_int64 transform_targets squeeze_mask weight_gather_temp weight_gather_temp_1 input_gather_element_transform HS_X HardSwish X_alpha Elu_Result X_ReduceMax X_Sub X_Exp X_ReduceSum X_Log Shape3D X_NCD X_NDC X_LogSM X_LogSM_NCD X_shape Invalid value for attribute axis K 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. 'shape' input must be 1D tensor of type INT64 input and zero_point pair is expected to have same type. FbRKN4onnx24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE *ZZN4onnx16MathDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx16MathDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_S1_EUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ PFbRKN4onnx24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE *ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx30ElementwiseMultiOpDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx30ElementwiseMultiOpDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_18Softmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_ *ZN4onnx11GetOpSchemaINS_21LogSoftmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_ *ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17MatMul_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Expand_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24QLinearMatMul_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24MatMulInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Det_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Einsum_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Type 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. Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size Pass 1 to enable broadcasting consumed_inputs legacy optimization attribute. If set, defines the broadcast dimensions. See doc for details. 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 A /onnxruntime_src/cmake/external/onnx/onnx/defs/math/old.cc logsoftmax 1 for the first maximum value, and 0 for all others
Pow takes input data (Tensor<T>) and exponent Tensor, and
produces one output data (Tensor<T>) where the function `f(x) = x^exponent`,
is applied to the data tensor elementwise.
- Input tensor of any shape, base of the exponent. Input tensor of any shape broadcastable to X shape, the exponent component. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). 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. Slope tensor. If `Slope` is of size 1, the value is sharedacross different channels Output tensor. Same dimension as inputs. Minimum value, under which element is replaced by min Maximum value, above which element is replaced by max Input tensor C, can be inplace. Whether C should be broadcasted Scalar 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. Number of top elements to retrieve Dimension on which to do the sort.
+ Input tensor of any shape, base of the exponent. Input tensor of any shape broadcastable to X shape, the exponent component. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). 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. 1-D input tensor Slope tensor. If `Slope` is of size 1, the value is sharedacross different channels Value of alpha default to 0.2 Value of beta default to 0.5 List of tensors for Max. Output tensor. Same dimension as inputs. List of tensors for Min List of tensors for Sum. List of tensors for Mean. Minimum value, under which element is replaced by min Maximum value, above which element is replaced by max Input tensor A Input tensor B Input tensor C, can be inplace. Whether C should be broadcasted Scalar 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 C Number of top elements to retrieve Dimension on which to do the sort. Invalid value for attribute k *ZZN4onnx24MathDocGenerator_opset13EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx24MathDocGenerator_opset13EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx24MathDocGenerator_opset_7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx24MathDocGenerator_opset_7EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_EUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx32SoftmaxFamilyDocGenerator_opset1EPKcS1_EUlRNS_8OpSchemaEE_ *ZN4onnx20MathDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx27MathDocGenerator_old_opset6EPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_13Pow_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14TopK_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_
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
@@ -8127,26 +11774,280 @@
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.
- *ZZN4onnx24MathDocGenerator_opset_7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx24MathDocGenerator_opset_7EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_EUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx32SoftmaxFamilyDocGenerator_opset1EPKcS1_EUlRNS_8OpSchemaEE_ *ZN4onnx20MathDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx27MathDocGenerator_old_opset6EPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_13Pow_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14TopK_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_ p value of the Lp norm used to pool over the input data. 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. 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]). 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). 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 ...]. 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. Optional 1D bias to be added to the convolution, has size of M. Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths. 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. 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 pads Additional 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. dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis. 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. Constrain input and output types to float and 8 bit tensors. The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero). /onnxruntime_src/cmake/external/onnx/onnx/defs/nn/defs.cc The output of each pooling window is maximum number of elements exclude pad. The storage order of the tensor. 0 is row major, and 1 is column major. Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis. Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn). 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. 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. Scale 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). Zero 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 = 0 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'. 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. 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.
+ 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]). 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. 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. 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. 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). 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 ...]. 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. Optional 1D bias to be added to the convolution, has size of M. Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths. 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. 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 pads Additional 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. dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis. Constrain input and output types to float and 8 bit tensors. Input tensor must have atleast 2 dimensions Attribute dilations has incorrect size Attribute strides has incorrect size Attribute kernel_shape has incorrect size Attribute kernel_shape must be specified Attribute pads has incorrect size Second input tensor has wrong dimension ngram_indexes must be non-empty with no negative values Input tensor must have rank 1 or 2 Input shape must have either [C] or [1,C] dimensions where C > 0 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. RoIs tensor must have 2 dimensions Attribute pooled_shape has incorrect length Attribute pooled_shape must be specified Ratio of Dropout must be a scalar. training_mode of Dropout must be a scalar. The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero). /onnxruntime_src/cmake/external/onnx/onnx/defs/nn/defs.cc average The output of each pooling window is maximum number of elements exclude pad. The storage order of the tensor. 0 is row major, and 1 is column major. Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis. Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn). 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. a filter 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. Scale 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). Zero 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 = 0 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'. 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. lp pool 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.
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 five required inputs 'X', 'scale', 'B', 'input_mean' and
+'input_var'.
+Note that 'input_mean' and 'input_var' are expected to be the estimated
+statistics in inference mode (training_mode=False, default),
+and the running statistics in training mode (training_mode=True).
+There are multiple cases for the number of outputs, which we list below:
+
+Output case #1: Y, running_mean, running_var (training_mode=True)
+Output case #2: Y (training_mode=False)
+
+When training_mode=False, extra outputs are invalid.
+The outputs are updated as follows when training_mode=True:
+```
+running_mean = input_mean * momentum + current_mean * (1 - momentum)
+running_var = input_var * momentum + current_var * (1 - momentum)
+
+Y = (X - current_mean) / sqrt(current_var + epsilon) * scale + B
+
+where:
+
+current_mean = ReduceMean(X, axis=all_except_channel_index)
+current_var = ReduceVar(X, axis=all_except_channel_index)
+
+Notice that ReduceVar refers to the population variance, and it equals to
+sum(sqrd(x_i - x_avg)) / N
+where N is the population size (this formula does not use sample size N - 1).
+
+```
+
+When training_mode=False:
+```
+Y = (X - input_mean) / sqrt(input_var + epsilon) * scale + B
+```
+
+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.
+ Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum). If set to true, it indicates BatchNormalization is being used for training, and outputs 1, 2, 3, and 4 would be populated. 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 1 running (training) or estimated (testing) mean tensor of shape (C). running (training) or estimated (testing) variance tensor of shape (C). The output tensor of the same shape as X The running mean after the BatchNormalization operator. The running variance after the BatchNormalization operator. This op uses the population size (N) for calculating variance, and not the sample size N-1. Constrain mean and variance types to float tensors. It allows all float type for U. momentum Scale tensor of shape (C). Bias tensor of shape (C). input_mean input_var running_mean running_var 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 matrix Matrix after normalization The axis on which to apply normalization, -1 mean last axis. The order of the normalization, only 1 or 2 are supported. The 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. The output mask. The lambd value for the Shrink formulation. Default is 0.5. The bias value added to output. Default is 0. lambd 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. Constrain input and output to all tensor types. 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). The number of channels to sum over Output tensor, which has the shape and type as input tensor Constrain input and output types to float tensors. Scaling parameter. The exponent. Input for n-gram extraction Ngram results 1-D tensor of floats Input is ether string UTF-8 or int32/int64 Maximum 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=1 List 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 normalize UTF-8 Normalized strings string 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 false List 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. The pads attribute cannot be used simultaneously with auto_pad attribute This number of op outputs should be 3 when Training_mode = True, but it is not. This number of op outputs should be 1 when Training_mode = False, but it is not. Invalid value( ) for attribute 'axis' Exponent X_RM EX_squared E_Xsquared Variance X_variance Processed_STD 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. *ZZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_20MaxUnpool_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx23LpPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx23LpPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx24RoiPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx24RoiPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx21ConvOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx21ConvOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_22QLinearConv_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22ConvInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx30ConvTransposeOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx30ConvTransposeOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_ *ZZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_29BatchNormalization_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_31InstanceNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25LpNormalization_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25TfIdfVectorizer_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27StringNormalizer_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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 `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER. 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 `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER. Stride along each spatial axis. 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. /onnxruntime_src/cmake/external/onnx/onnx/defs/nn/old.cc The output of each pooling window is divided by the number of elements exclude pad. The output of each pooling window is maximum number of elements exclude pad. Dilation value along each spatial axis of filter. 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 1 If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1. If 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. is_test saved_mean saved_var
+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.
- Factor 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 1 running (training) or estimated (testing) mean tensor of shape (C). running (training) or estimated (testing) variance tensor of shape (C). The output tensor of the same shape as X The running mean after the BatchNormalization operator. The running variance after the BatchNormalization operator. Saved mean used during training to speed up gradient computation. Saved variance used during training to speed up gradient computation. 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. The axis on which to apply normalization, -1 mean last axis. The order of the normalization, only 1 or 2 are supported. The 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. If 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. 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. Constrain input and output to all tensor types. 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). The number of channels to sum over Output tensor, which has the shape and type as input tensor Constrain input and output types to float tensors. Input is ether string UTF-8 or int32/int64 Maximum 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=1 List 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) Boolean. Whether the identification of stop words in X is case-sensitive. Default is false string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE" List 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. Input tensor must have atleast 2 dimensions Attribute dilations has incorrect size Attribute strides has incorrect size Attribute kernel_shape has incorrect size Attribute kernel_shape must be specified Attribute pads has incorrect size Second input tensor has wrong dimension ngram_indexes must be non-empty with no negative values Input tensor must have rank 1 or 2 Input shape must have either [C] or [1,C] dimensions where C > 0 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. RoIs tensor must have 2 dimensions Attribute pooled_shape has incorrect length Attribute pooled_shape must be specified Ratio of Dropout must be a scalar. training_mode of Dropout must be a scalar. The pads attribute cannot be used simultaneously with auto_pad attribute 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. 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 `output_shape[i] = input_shape[i] * strides[i]` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER. 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 `output_shape[i] = ceil(input_shape[i] / strides[i])` for each axis `i`. The padding is split between the two sides equally or almost equally (depending on whether it is even or odd). In case the padding is an odd number, the extra padding is added at the end for SAME_UPPER and at the beginning for SAME_LOWER. average a filter lp pool momentum Scale tensor of shape (C). Bias tensor of shape (C). saved_mean saved_var Input matrix Matrix after normalization training_mode The output mask. A tensor of rank >= axis. Scaling parameter. The exponent. Input for n-gram extraction Ngram results 1-D tensor of floats UTF-8 strings to normalize UTF-8 Normalized strings Invalid value( ) for attribute 'axis' Exponent Epsilon X_RM EX_squared E_Xsquared Variance X_variance Processed_STD *ZZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_20MaxUnpool_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx23LpPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx23LpPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx24RoiPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx24RoiPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx21ConvOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx21ConvOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_22QLinearConv_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22ConvInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx30ConvTransposeOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx30ConvTransposeOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_ *ZZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_31InstanceNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25LpNormalization_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25TfIdfVectorizer_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27StringNormalizer_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Stride along each spatial axis. 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. /onnxruntime_src/cmake/external/onnx/onnx/defs/nn/old.cc The output of each pooling window is divided by the number of elements exclude pad. The output of each pooling window is maximum number of elements exclude pad. Dilation value along each spatial axis of filter. 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 1 If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1. If 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. Constrain 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). 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. 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. Stride along each axis. is_test The ratio of random dropout *ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_ *ZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx26LpPoolOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx26LpPoolOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx24ConvOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx24ConvOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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]. /onnxruntime_src/cmake/external/onnx/onnx/defs/object_detection/defs.cc An 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] Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output. Float representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0. Float representing the threshold for deciding when to remove boxes based on score. It is a scalar. selected 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. Dimension mismatch in unification between boxes max_output_boxes_per_class iou_threshold score_threshold selected_indices expected to have rank but has rank expected to have rank > *ZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ /onnxruntime_src/cmake/external/onnx/onnx/defs/object_detection/old.cc *ZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ N-D quantized input tensor to be de-quantized. Scale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization. Zero point for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization. It's optional. 0 is the default value when it's not specified. (Optional) The axis of the dequantizing dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input) Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor. /onnxruntime_src/cmake/external/onnx/onnx/defs/quantization/defs.cc Scale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D Tensor for per-axis quantization. Zero point for doing quantization to get 'y'. It can be a scalar, which means a per-tensor/layer quantization, or a 1-D tensor for per-axis quantization. Default value is uint8 typed 0 if it's not specified. (Optional) The axis of the quantization dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input) Constrain 'x' to float or int32 tensor. Constrain 'y_zero_point' and 'y' to 8-bit integer tensor. Output zero point. It's a scalar, which means a per-tensor/layer quantization. Constrain 'x' to float tensor. Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor. Q_Min Q_Max X_Min X_Min_Adjusted X_Max_Adjusted X_Range Min_Scaled Initial_ZeroPoint_FP Clipped_ZeroPoint_FP Rounded_ZeroPoint_FP Zeropoint Quantized output tensor *ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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. /onnxruntime_src/cmake/external/onnx/onnx/defs/quantization/old.cc Scale 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. *ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data). Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index). Reduced output tensor with integer data type. 'axis' must be in [-rank(indices), rank(indices)-1] Constrain input and output types to high-precision and 8 bit numeric tensors. Defines behaviour if 'axes' is empty. Default behaviour with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor. Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data). A 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). /onnxruntime_src/cmake/external/onnx/onnx/defs/reduction/defs.cc axes as an input and attribute cannot be specified at the same time. product log sum log sum exponent L2 norm sum square L1 norm *ZZN4onnx18ReduceDocGeneratorEPKcbbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx18ReduceDocGeneratorEPKcbbEUlRNS_8OpSchemaEE_ *ZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ The axis in which to compute the arg indices.
+ The running variance after the BatchNormalization operator. Saved mean used during training to speed up gradient computation. Saved variance used during training to speed up gradient computation. 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 dropout Constrain 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). *ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_ *ZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx26LpPoolOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx26LpPoolOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx24ConvOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx24ConvOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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. 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. expected to have rank 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]. /onnxruntime_src/cmake/external/onnx/onnx/defs/object_detection/defs.cc An 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] Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output. Float representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0. Float representing the threshold for deciding when to remove boxes based on score. It is a scalar. selected 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. boxes max_output_boxes_per_class iou_threshold score_threshold selected_indices center_point_box *ZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ /onnxruntime_src/cmake/external/onnx/onnx/defs/object_detection/old.cc *ZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Scale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D Tensor for per-axis quantization. Zero point for doing quantization to get 'y'. It can be a scalar, which means a per-tensor/layer quantization, or a 1-D tensor for per-axis quantization. Default value is uint8 typed 0 if it's not specified. (Optional) The axis of the quantization dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input) Constrain 'x' to float or int32 tensor. Constrain 'y_zero_point' and 'y' to 8-bit integer tensor. /onnxruntime_src/cmake/external/onnx/onnx/defs/quantization/defs.cc N-D quantized input tensor to be de-quantized. Scale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization. Zero point for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization. It's optional. 0 is the default value when it's not specified. (Optional) The axis of the dequantizing dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input) Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor. Quantized output tensor Q_Min Q_Max X_Min X_Min_Adjusted X_Max X_Max_Adjusted X_Range Min_Scaled Initial_ZeroPoint_FP Clipped_ZeroPoint_FP Rounded_ZeroPoint_FP Zeropoint Output zero point. It's a scalar, which means a per-tensor/layer quantization. Constrain 'x' to float tensor. Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor. *ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Scale 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. /onnxruntime_src/cmake/external/onnx/onnx/defs/quantization/old.cc 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. *ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data). Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index). Reduced output tensor with integer data type. Constrain input and output types to high-precision and 8 bit numeric tensors. Defines behaviour if 'axes' is empty. Default behaviour with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor. Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data). A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data). 'axis' must be in [-rank(indices), rank(indices)-1] axes as an input and attribute cannot be specified at the same time. /onnxruntime_src/cmake/external/onnx/onnx/defs/reduction/defs.cc sum square product log sum log sum exponent L1 norm L2 norm *ZZN4onnx18ReduceDocGeneratorEPKcbbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx18ReduceDocGeneratorEPKcbbEUlRNS_8OpSchemaEE_ *ZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ The axis in which to compute the arg indices.
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. /onnxruntime_src/cmake/external/onnx/onnx/defs/reduction/old.cc *ZZN4onnx26ReduceDocGenerator_opset12EPKcbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx26ReduceDocGenerator_opset12EPKcbEUlRNS_8OpSchemaEE_ *ZZN4onnx29ArgReduceDocGenerator_opset12EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx29ArgReduceDocGenerator_opset12EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx25ReduceDocGenerator_opset1EPKciENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx25ReduceDocGenerator_opset1EPKciEUlRNS_8OpSchemaEE_ *ZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx29ArgReduceDocGenerator_opset11EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx29ArgReduceDocGenerator_opset11EPKcEUlRNS_8OpSchemaEE_ /onnxruntime_src/cmake/external/onnx/onnx/defs/rnn/defs.cc 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. When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate. 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]`. The 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 One (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. First input tensor must have rank 3 *ZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_ foward output_sequence 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. The sequence output for the hidden is optional if 0. Default 0. 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. /onnxruntime_src/cmake/external/onnx/onnx/defs/rnn/old.cc *ZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_ Attributes: (no type) (no doc) (unnamed) Outputs: Defined at (no documentation yet) Operator ' ) has input size not in range [min= , max= not in allowed input sizes. ) has output size not in allowed output sizes. ) than declared ( ) has more inputs ( ) in op definition. ) has more outputs ( ' appeared multiple times. Attribute ' has unknown expected type Required attribute ' ' is missing. )'s input )'s output for operator Unrecognized attribute: ONNX Schema (inputs_.size() - 1) == i (outputs_.size() - 1) == i !(it.GetName().empty()) , has unsupported type: typestr: has inconsistent type has unsupported type (no explicit description available) ' has been deprecated since version 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' is marked single but has an empty string in the graph : failed validating the check: Duplicate type constraint name Attribute specification type mismatch. P:11199x86w16p531 *ZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_ *ZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_ N4onnx16OpSchemaRegistryE ], Value= sum of split values= Empty sequence. Tensors. Constrain to any tensor type. Input sequence. input_sequence position The tensor to split Concatenated tensor concat_result Tensor= Attribute dtype should be of integer type and specify a type. Input type for input at index 0 is null. Type info is expected. Required attribute axis is missing new_axis must be either 0 or 1 Invalid value of attribute 'axis'. Accepted range=[ Invalid value of attribute 'axis'. Rank= Only supports `int32_t` or `int64_t` inputs for split Input 'split' can not be empty. Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size= Constrain output types to any tensor type. (Optional) The data type of the tensors in the output sequence. The default type is 'float'. /onnxruntime_src/cmake/external/onnx/onnx/defs/sequence/defs.cc Constrain input types to any tensor type. Sequence enclosing the input tensors. Input tensor to be inserted into the input sequence. Position 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). Constrain output types to all tensor types. Constrain input types to all tensor types. 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 splitting Constrain 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]`. Insert and concatenate on a new axis or not, default 0 means do not insert new axis. Sequence of tensors for concatenation Input Sequence and Tensor are expected to have type info. Current type is null. Input Sequence and Tensor are expected to have the same elem type. Sequence= SequenceConstruct is expected to have at least 1 input. Input type for input at index is null. Type info is expected. Element type of inputs are expected to be the same. *ZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Input tensor to be cast. Specified shape for output. Reshaped data. reshaped Shape of the input tensor steps Transposed output. transposed updates Tensor of rank q + (r - 1). squeezed Original tensor expanded Input tensor of any shape. repeats N-D tensor N-D tensor after resizing roi sizes Tensor to copy input into. condition depth Constrain to boolean tensors. Tensor of rank r >= 2. inverse_indices constant_value Invalid position of 0 Invalid dimension value: Invalid attribute perm { }, input shape = { axis must be in [-r, r-1] 'input' must have rank >= 2 ) and outputs ( Input axes has invalid data 'step' cannot be 0 Blocksize must be positive Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto Output tensor with the same shape as input with type specified by the 'to' argument Constrain input types. Casting from complex is not supported. Constrain output types. Casting to complex is not supported. /onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/defs.cc Input tensor can be of arbitrary type. Constrain output to int64 tensor. Total number of elements of the input tensor Constrain 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).. List of tensors for concatenation Optional length of each output. Values should be >= 0.Sum of the values must be equal to the dim value at 'axis' specified. One or more outputs forming list of tensors after splitting Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input). 1-D tensor of ending indices (exclusive) of corresponding axis in `axes` 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. A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. Input and output types can be of any tensor type. Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Tensor of rank r >=1 (same rank and shape as indices) Tensor of rank r >= 1 (same rank as input). Tensor of rank q + r - indices_shape[-1] - 1. Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Tensor of the same shape as indices. Tensors with at least max(dims) dimensions. 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). Reshaped tensor with same data as input. 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). 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]. DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order. Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize]. 1D 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. Constrain input 'X' and output 'Y' to all tensor types. Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc) 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'. 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.
+The type of the output tensor is integer. /onnxruntime_src/cmake/external/onnx/onnx/defs/reduction/old.cc *ZZN4onnx26ReduceDocGenerator_opset12EPKcbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx26ReduceDocGenerator_opset12EPKcbEUlRNS_8OpSchemaEE_ *ZZN4onnx29ArgReduceDocGenerator_opset12EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx29ArgReduceDocGenerator_opset12EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx25ReduceDocGenerator_opset1EPKciENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx25ReduceDocGenerator_opset1EPKciEUlRNS_8OpSchemaEE_ *ZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx29ArgReduceDocGenerator_opset11EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx29ArgReduceDocGenerator_opset11EPKcEUlRNS_8OpSchemaEE_ The shape format of inputs X, initial_h and outputs Y, Y_h. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = [batch_size, num_directions, hidden_size]. First input tensor must have rank 3 One (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. /onnxruntime_src/cmake/external/onnx/onnx/defs/rnn/defs.cc 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. When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate. 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]`. The 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 The shape format of inputs X, initial_h, initial_c and outputs Y, Y_h, Y_c. If 0, the following shapes are expected: X.shape = [seq_length, batch_size, input_size], Y.shape = [seq_length, num_directions, batch_size, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. If 1, the following shapes are expected: X.shape = [batch_size, seq_length, input_size], Y.shape = [batch_size, seq_length, num_directions, hidden_size], initial_h.shape = Y_h.shape = initial_c.shape = Y_c.shape = [num_directions, batch_size, hidden_size]. *ZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_ foward output_sequence 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. The sequence output for the hidden is optional if 0. Default 0. 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. /onnxruntime_src/cmake/external/onnx/onnx/defs/rnn/old.cc
+Computes an one-layer simple RNN. This operator is usually supported
+via some custom implementation such as CuDNN.
+
+Notations:
+
+`X` - input tensor
+
+`i` - input gate
+
+`t` - time step (t-1 means previous time step)
+
+`Wi` - W parameter weight matrix for input gate
+
+`Ri` - R recurrence weight matrix for input gate
+
+`Wbi` - W parameter bias vector for input gate
+
+`Rbi` - R parameter bias vector for input gate
+
+`WBi` - W parameter weight matrix for backward input gate
+
+`RBi` - R recurrence weight matrix for backward input gate
+
+`WBbi` - WR bias vectors for backward input gate
+
+`RBbi` - RR bias vectors for backward input gate
+
+`H` - Hidden state
+
+`num_directions` - 2 if direction == bidirectional else 1
+
+Activation functions:
+
+ Relu(x) - max(0, x)
+
+ Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
+
+ Sigmoid(x) - 1/(1 + e^{-x})
+
+ (NOTE: Below are optional)
+
+ Affine(x) - alpha*x + beta
+
+ LeakyRelu(x) - x if x >= 0 else alpha * x
+
+ ThresholdedRelu(x) - x if x >= alpha else 0
+
+ ScaledTanh(x) - alpha*Tanh(beta*x)
+
+ HardSigmoid(x) - min(max(alpha*x + beta, 0), 1)
+
+ Elu(x) - x if x >= 0 else alpha*(e^x - 1)
+
+ Softsign(x) - x/(1 + |x|)
+
+ Softplus(x) - log(1 + e^x)
+
+Equations (Default: f=Tanh):
+
+ - Ht = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Wbi + Rbi)
+
+Computes an one-layer GRU. This operator is usually supported via some custom
+implementation such as CuDNN.
+
+Notations:
+
+`X` - input tensor
+
+`z` - update gate
+
+`r` - reset gate
+
+`h` - hidden gate
+
+`t` - time step (t-1 means previous time step)
+
+`W[zrh]` - W parameter weight matrix for update, reset, and hidden gates
+
+`R[zrh]` - R recurrence weight matrix for update, reset, and hidden gates
+
+`Wb[zrh]` - W bias vectors for update, reset, and hidden gates
+
+`Rb[zrh]` - R bias vectors for update, reset, and hidden gates
+
+`WB[zrh]` - W parameter weight matrix for backward update, reset, and hidden gates
+
+`RB[zrh]` - R recurrence weight matrix for backward update, reset, and hidden gates
+
+`WBb[zrh]` - W bias vectors for backward update, reset, and hidden gates
+
+`RBb[zrh]` - R bias vectors for backward update, reset, and hidden gates
+
+`H` - Hidden state
+
+`num_directions` - 2 if direction == bidirectional else 1
+
+Activation functions:
+
+ Relu(x) - max(0, x)
+
+ Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
+
+ Sigmoid(x) - 1/(1 + e^{-x})
+
+ (NOTE: Below are optional)
+
+ Affine(x) - alpha*x + beta
+
+ LeakyRelu(x) - x if x >= 0 else alpha * x
+
+ ThresholdedRelu(x) - x if x >= alpha else 0
+
+ ScaledTanh(x) - alpha*Tanh(beta*x)
+
+ HardSigmoid(x) - min(max(alpha*x + beta, 0), 1)
+
+ Elu(x) - x if x >= 0 else alpha*(e^x - 1)
+
+ Softsign(x) - x/(1 + |x|)
+
+ Softplus(x) - log(1 + e^x)
+
+Equations (Default: f=Sigmoid, g=Tanh):
+
+ - zt = f(Xt*(Wz^T) + Ht-1*(Rz^T) + Wbz + Rbz)
+
+ - rt = f(Xt*(Wr^T) + Ht-1*(Rr^T) + Wbr + Rbr)
+
+ - ht = g(Xt*(Wh^T) + (rt (.) Ht-1)*(Rh^T) + Rbh + Wbh) # default, when linear_before_reset = 0
+
+ - ht = g(Xt*(Wh^T) + (rt (.) (Ht-1*(Rh^T) + Rbh)) + Wbh) # when linear_before_reset != 0
+
+ - Ht = (1 - zt) (.) ht + zt (.) Ht-1
+
+Computes an one-layer LSTM. This operator is usually supported via some
+custom implementation such as CuDNN.
+
+Notations:
+
+`X` - input tensor
+
+`i` - input gate
+
+`o` - output gate
+
+`f` - forget gate
+
+`c` - cell gate
+
+`t` - time step (t-1 means previous time step)
+
+`W[iofc]` - W parameter weight matrix for input, output, forget, and cell gates
+
+`R[iofc]` - R recurrence weight matrix for input, output, forget, and cell gates
+
+`Wb[iofc]` - W bias vectors for input, output, forget, and cell gates
+
+`Rb[iofc]` - R bias vectors for input, output, forget, and cell gates
+
+`P[iof]` - P peephole weight vector for input, output, and forget gates
+
+`WB[iofc]` - W parameter weight matrix for backward input, output, forget, and cell gates
+
+`RB[iofc]` - R recurrence weight matrix for backward input, output, forget, and cell gates
+
+`WBb[iofc]` - W bias vectors for backward input, output, forget, and cell gates
+
+`RBb[iofc]` - R bias vectors for backward input, output, forget, and cell gates
+
+`PB[iof]` - P peephole weight vector for backward input, output, and forget gates
+
+`H` - Hidden state
+
+`num_directions` - 2 if direction == bidirectional else 1
+
+Activation functions:
+
+ Relu(x) - max(0, x)
+
+ Tanh(x) - (1 - e^{-2x})/(1 + e^{-2x})
+
+ Sigmoid(x) - 1/(1 + e^{-x})
+
+ (NOTE: Below are optional)
+
+ Affine(x) - alpha*x + beta
+
+ LeakyRelu(x) - x if x >= 0 else alpha * x
+
+ ThresholdedRelu(x) - x if x >= alpha else 0
+
+ ScaledTanh(x) - alpha*Tanh(beta*x)
+
+ HardSigmoid(x) - min(max(alpha*x + beta, 0), 1)
+
+ Elu(x) - x if x >= 0 else alpha*(e^x - 1)
+
+ Softsign(x) - x/(1 + |x|)
+
+ Softplus(x) - log(1 + e^x)
+
+Equations (Default: f=Sigmoid, g=Tanh, h=Tanh):
+
+ - it = f(Xt*(Wi^T) + Ht-1*(Ri^T) + Pi (.) Ct-1 + Wbi + Rbi)
+
+ - ft = f(Xt*(Wf^T) + Ht-1*(Rf^T) + Pf (.) Ct-1 + Wbf + Rbf)
+
+ - ct = g(Xt*(Wc^T) + Ht-1*(Rc^T) + Wbc + Rbc)
+
+ - Ct = ft (.) Ct-1 + it (.) ct
+
+ - ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo)
+
+ - Ht = ot (.) h(Ct)
+ *ZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_ *ZN4onnx16RNNDocGenerator2EPKcEUlRNS_8OpSchemaEE_ Attributes: (unnamed) (no doc) (no type) Outputs: (no documentation yet) Defined at std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnx::OpSchema::FormalParameter; _Alloc = std::allocator<onnx::OpSchema::FormalParameter>; std::vector<_Tp, _Alloc>::reference = onnx::OpSchema::FormalParameter&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] typestr: , has unsupported type: has inconsistent type has unsupported type Operator ' ) has input size not in range [min= , max= not in allowed input sizes. ) has output size not in allowed output sizes. ) has more inputs ( ) than declared ( ) in op definition. )'s input ) has more outputs ( )'s output ' appeared multiple times. Attribute ' Unrecognized attribute: for operator has unknown expected type Required attribute ' ' is missing. ' has been deprecated since version is marked single but has an empty string in the graph 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' lmAoAoAoqqqqqq|qlqAo Duplicate type constraint name Attribute specification type mismatch. ONNX Schema (inputs_.size() - 1) == i (outputs_.size() - 1) == i !(it.GetName().empty()) : failed validating the check: N4onnx16OpSchemaRegistryE *ZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_ Attribute dtype should be of integer type and specify a type. Input type for input at index 0 is null. Type info is expected. new_axis ], Value= new_axis must be either 0 or 1 Invalid value of attribute 'axis'. Accepted range=[ sum of split values= Invalid value of attribute 'axis'. Rank= Only supports `int32_t` or `int64_t` inputs for split Input 'split' can not be empty. Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size= (Optional) The data type of the tensors in the output sequence. The default type is 'float'. Constrain output types to any tensor type. /onnxruntime_src/cmake/external/onnx/onnx/defs/sequence/defs.cc Empty sequence. Tensors. Sequence enclosing the input tensors. Constrain input types to any tensor type. Input sequence. input_sequence position Constrain to any tensor type. Input tensor to be inserted into the input sequence. Position 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). The tensor to split 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 splitting Constrain input types to all tensor types. Constrain split size to integral tensor. Constrain output types to all tensor types. 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]`. Insert and concatenate on a new axis or not, default 0 means do not insert new axis. Sequence of tensors for concatenation concat_result Input Sequence and Tensor are expected to have type info. Current type is null. Input Sequence and Tensor are expected to have the same elem type. Sequence= Tensor= SequenceConstruct is expected to have at least 1 input. is null. Type info is expected. Input type for input at index Element type of inputs are expected to be the same. *ZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto Output tensor with the same shape as input with type specified by the 'to' argument Constrain input types. Casting from complex is not supported. Constrain output types. Casting to complex is not supported. /onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/defs.cc Input tensor to be cast. (Optional) By default, when any value in the 'shape' input is equal to zero the corresponding dimension value is copied from the input tensor dynamically. allowzero=1 indicates that if any value in the 'shape' input is set to zero, the zero value is honored, similar to NumPy. Specified shape for output. Reshaped data. reshaped Shape of the input tensor Input tensor can be of arbitrary type. Constrain output to int64 tensor. Total number of elements of the input tensor Constrain 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).. List of tensors for concatenation Optional length of each output. Values should be >= 0.Sum of the values must be equal to the dim value at 'axis' specified. One or more outputs forming list of tensors after splitting Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input). 1-D tensor of ending indices (exclusive) of corresponding axis in `axes` 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. steps A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. Transposed output. transposed Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Tensor of rank r >=1 (same rank and shape as indices) Tensor of rank r >= 1 (same rank as input). Input and output types can be of any tensor type. updates Tensor of rank q + r - indices_shape[-1] - 1. Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Tensor of rank q + (r - 1). 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. Tensors with at least max(dims) dimensions. 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). Reshaped tensor with same data as input. squeezed Original tensor expanded 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). 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]. blocksize DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order. Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize]. Input tensor of any shape. repeats 1D 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. N-D tensor N-D tensor after resizing Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc) 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'. Constrain input 'X' and output 'Y' to all tensor types. 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.
This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. <br/>
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, <br/>
@@ -8161,11 +12062,11 @@
if coordinate_transformation_mode is "asymmetric", <br/>
x_original = x_resized / scale, <br/>
if coordinate_transformation_mode is "tf_crop_and_resize", <br/>
-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). 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. 1-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'. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list. The 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. (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. Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1. Constrains to boolean tensors. (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. 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. 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 Y values selected at indices where condition is True values selected at indices where condition is False Tensor of shape equal to the broadcasted shape of condition, X, and Y. Constrain to all tensor types. (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 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. A 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:] 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. Constrains input and output to only numeric types. 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`. (Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0). 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 Target shape may not have multiple -1 dimensions Invalid Target shape product of 0 Dimension could not be inferred: incompatible shapes axis must be in [-rank, rank-1]. All inputs to Concat must have same rank data tensor must have rank >= 1 values in 'axes' are beyond the bounds of the computed output shape 'axes' attribute must not contain any duplicates '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. 'sequence_lens' must have rank of 1 Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0. Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensor 'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64 Mismatch between number of splits ( Mismatch between the sum of 'split' ( ) and the split dimension of the input ( The input is not evenly splittable Slice op must have either three, four or five inputs. Incorrect or missing input value for starts and ends Input axes has incorrect length Input steps has incorrect length Input tensor must be 4-dimensional *ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20Transpose_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_23SpaceToDepth_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Tile_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16IsNaN_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18NonZero_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Which axis to concat on outputs... Which axis to split on length of each output paddings New shape tiles Axis along which to repeat. width_scale height_scale 4-D tensor, [N,C,H,W] Which axis to split on. Ranks inferred ( /onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/old.cc length of each output. Values should be >= 0.
+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). 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. 1-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'. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list. The 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. roi sizes Tensor to copy input into. Constrain input and output types to all tensor and sequence types. (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. Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1. Constrains to boolean tensors. condition (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. 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. depth 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. Constrain to boolean tensors. When True (nonzero), yield X, otherwise yield Y values selected at indices where condition is True values selected at indices where condition is False Tensor of shape equal to the broadcasted shape of condition, X, and Y. Constrain to all tensor types. (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 specifying lengths of the sequences in a batch. It has shape `[batch_size]`. Tensor with same shape of input. Tensor of rank r >= 2. (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. A 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' inverse_indices The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:] 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. batch_dims constant_value 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`. (Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0, empty string or False). A 0-D tensor containing a single value corresponding to the number diagonals above or below the main diagonal to exclude or include. Default value is 0 if it's not specified. Target shape may not have multiple -1 dimensions. Invalid Target shape product of 0. Product cannot be 0 in combination with -1 Dimension could not be inferred: incompatible shapes Invalid position of 0. Invalid dimension value: axis must be in [-rank, rank-1]. Invalid attribute perm { }, input shape = { data tensor must have rank >= 1 axis must be in [-r, r-1] 'axes' attribute must not contain any duplicates values 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. Indices tensor must have rank >= 1 'input' must have rank >= 2 'sequence_lens' must have rank of 1 Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0. Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensor 'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64 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. Mismatch between number of splits ( Mismatch between the sum of 'split' ( ) and the split dimension of the input ( The input is not evenly splittable ) and outputs ( Slice op must have either three, four or five inputs. Incorrect or missing input value for starts and ends Input axes has incorrect length Input steps has incorrect length Input axes has invalid data 'step' cannot be 0 Blocksize must be positive Input tensor must be 4-dimensional *ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20Transpose_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_23SpaceToDepth_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Tile_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16IsNaN_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18NonZero_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Trilu_Onnx_ver14EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ /onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/old.cc length of each output. Values should be >= 0.
This attribute describes how to transform the coordinate in the resized tensor to the coordinate in the original tensor. <br/>
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, <br/>
@@ -8183,32 +12084,22 @@
if coordinate_transformation_mode is "tf_half_pixel_for_nn", <br/>
x_original = (x_resized + 0.5) / scale, <br/>
if coordinate_transformation_mode is "tf_crop_and_resize", <br/>
-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). 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. Constrain input types. Casting from strings and complex are not supported. Constrain output types. Casting to strings and complex are not supported. Which axis to concat on. Default value is 1. Optional list of output lengths (see also arg 'split') List 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, edge One float, indicates the value to be filled, default is 0 Number of repeated copies to make of the input tensor. Output tensor of same shape and type as input. Constrain tiles and axis's type to int64 tensors. The scale along width dimension. It takes value greater than or equal to 1. The scale along height dimension. It takes value greater than or equal to 1. Two interpolation modes: nearest(default), bilinear 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 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 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). 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. List of non-negative integers, indicate the dimensions to squeeze. List of non-negative integers, indicate the dimensions to be inserted (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. 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 [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use. (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Rank 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. 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. 'axis' must be in [-rank(indices)-1, rank(indices)] rank must be greater than axis ) 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 ends Attribute axes has incorrect length Attribute value for pads is required Attribute pads has incorrect length *ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ ) 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. Dimension value inferred ( Data to be selected Data to be binarized Binarized output data Input data A dictionary. Data to be processed. Imputed output data Value(s) to change to A value that needs replacing. Value(s) to change to. Output data. A list of ints. A list of floats. A string. An integer. A float. Data to be classified. A collection of intercepts. Data to be regressed. Weights of the model(s). Encoded output data One of 'MAX,' 'L1,' 'L2' Data to be encoded. Data to be scaled. Scaled output data. Chosen support vectors Support vector coefficients. Input of shape [N,F] N, Top class for each point Tree id for each node. Feature id for each node. N classes The node id of each weight The weight for each target The total number of targets. The input values The output map seq(map(string, float)) seq(map(int64, float)) map(string, int64) map(int64, string) map(int64, float) map(int64, double) map(string, float) map(string, double) 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 set Input type is not int64 tensor but keys_int64s is set Input type is not float tensor but keys_floats is set Only one of values_*'s can be set in label encoder. The indices, based on 0 as the first index of any dimension. Selected output data as an array The input must be a tensor of a numeric type or string. The output will be of the same tensor type. /onnxruntime_src/cmake/external/onnx/onnx/defs/traditionalml/defs.cc The 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 tensor A tensor representing the same data as the input map, ordered by their keys 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'. Indicates 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).<br>One of 'DENSE', 'SPARSE'. If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor. Output 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' sequence The 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.<br>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.<br>One and only one of the 'default_*' attributes must be defined. A 1-D tensor holding values from the input dictionary. 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.<br>One and only one of the vocabularies must be defined. An integer vocabulary array.<br>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 list The 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. Input data. It can be either tensor or scalar. 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 strings. One and only one of 'value_*'s should be set. Classification outputs (one class per example). Classification scores ([N,E] - one score for each class and example The 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). 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.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' 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.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' 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 data, having one more dimension than X. List of categories, ints.<br>One and only one of the 'cats_*' attributes must be defined. List of categories, strings.<br>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. First, offset by this.<br>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.<br>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.<br>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.<br>If these are provided then output Z are probability estimates, otherwise they are raw scores. Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' Class labels if using string labels.<br>One and only one of the 'classlabels_*' attributes must be defined. Class labels if using integer labels.<br>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]. Flag indicating whether the regression is a one-class SVM or not. The number of support vectors. Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.' The 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. Node id for each node. Ids may restart at zero for each tree, but it not required to. 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.<br>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.<br>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. <br> 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) Node id for each node. Node ids must restart at zero for each tree and increase sequentially. Child node if expression is true Child node if expression is false For 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.<br>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 index of the target that each weight is for Defines how to aggregate leaf values within a target. <br>One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.' The output will be a sequence of string or integer maps to float. The keys when using string keys.<br>One and only one of the 'classlabels_*' attributes must be defined. The keys when using int keys.<br>One and only one of the 'classlabels_*' attributes must be defined. Input's shape should be 1D or 2D *ZN4onnx11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26CategoryMapper_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26DictVectorizer_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28LinearClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25SVMClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Input data. A list of labels. 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. /onnxruntime_src/cmake/external/onnx/onnx/defs/traditionalml/old.cc *ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ , node name: (op_type: is out of bounds opaque_type sparse_tensor_type NOT_SET ) vs ( type case mismatch. existing= inferred= does not contain a graph. Shape inference error(s): inputs but Graph has were provided |n|||||||| Inferred elem type differs from existing elem type: ( Inferred shape and existing shape differ in rank: ( Inferred shape and existing shape differ in dimension type case unsupported. existing= GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. Warning: Shape inference does not support models with experimental operators: Warning: Unsupported operator . No schema registered for this operator. ONNX models don't support multiple opset version imports for a domain. Function Unable to save inferred model to the target path: N4onnx15shape_inference19GraphInferencerImplE N4onnx15shape_inference20InferenceContextImplE Func_ Input for function node Output for function node onnx.AttributeProto onnx.ValueInfoProto onnx.NodeProto onnx.TrainingInfoProto onnx.ModelProto onnx.StringStringEntryProto onnx.TensorAnnotation onnx.GraphProto onnx.TensorProto.Segment onnx.TensorProto onnx.SparseTensorProto onnx.TensorShapeProto onnx.TypeProto.Tensor onnx.TypeProto.Sequence onnx.TypeProto.Map onnx.TypeProto.SparseTensor onnx.TypeProto.Opaque onnx.TypeProto onnx.OperatorSetIdProto onnx.TensorShapeProto.Dimension /build/Release/external/onnx/onnx/onnx-ml.pb.cc `C CCChBAA@0@OON,NMMtMDMMLLTL LLKJ8[ [ZZXZZYXYXXWHW8[W8[8[8[8[8[8[hV VVHU(`_x_@__]]h]hffhhdhhhhhdeXec`ba`r(rqqqHqqpo`r`r`r`r`rn`r`r`r`r`rmT TT|ĹT4P IR_VERSIONIR_VERSION_2017_10_10IR_VERSION_2017_10_30IR_VERSION_2017_11_3IR_VERSION_2019_1_22IR_VERSION_2019_3_18IR_VERSION_2019_9_19_START_VERSION DEFAULTEXTERNAL
-
BFLOAT16BOOLCOMPLEX128COMPLEX64DOUBLEFLOATFLOAT16INT16INT32INT64INT8STRINGUINT16UINT32UINT64UINT8UNDEFINED
- FLOATFLOATSGRAPHGRAPHSINTINTSSPARSE_TENSORSPARSE_TENSORSSTRINGSTRINGSTENSORTENSORSUNDEFINED
-
- N4onnx14AttributeProtoE N4onnx14ValueInfoProtoE N4onnx9NodeProtoE N4onnx17TrainingInfoProtoE N4onnx10ModelProtoE N4onnx22StringStringEntryProtoE N4onnx16TensorAnnotationE N4onnx10GraphProtoE N4onnx19TensorProto_SegmentE N4onnx11TensorProtoE N4onnx17SparseTensorProtoE N4onnx26TensorShapeProto_DimensionE N4onnx16TensorShapeProtoE N4onnx16TypeProto_TensorE N4onnx18TypeProto_SequenceE N4onnx13TypeProto_MapE N4onnx22TypeProto_SparseTensorE N4onnx16TypeProto_OpaqueE N4onnx9TypeProtoE N4onnx18OperatorSetIdProtoE N6google8protobuf8internal29InternalMetadataWithArenaBaseISsNS1_29InternalMetadataWithArenaLiteEE9ContainerE onnx.FunctionProto onnx.OperatorProto onnx.OperatorSetProto /build/Release/external/onnx/onnx/onnx-operators-ml.pb.cc >>@>==(=<;x;0:@EEDxD@DCCxCXBA EXPERIMENTALSTABLE N4onnx13FunctionProtoE N4onnx13OperatorProtoE N4onnx16OperatorSetProtoE onnx.SequenceProto onnx.MapProto /build/Release/external/onnx/onnx/onnx-data.pb.cc ww@wvtPts{ {z\{dz$z MAPSEQUENCESPARSE_TENSORTENSORUNDEFINED N4onnx13SequenceProtoE N4onnx8MapProtoE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/arena.cc CHECK failed: (min_bytes) <= (std::numeric_limits<size_t>::max() - kBlockHeaderSize): /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/generated_message_util.cc CHECK failed: (scc->visit_status.load(std::memory_order_relaxed)) == (SCCInfoBase::kRunning): Not implemented field number with type L,dԣL,lԥ <lĦ$TDtԨ4 t4$4TԻddtD4<dĬLĭ 4Ld|ܮı4lܲL|Դ D|ĵ4t,T\LltL <t |L |T|lL4dl\ lD l,| ,\4<l\, | l< /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/coded_stream.cc A 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): /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl.cc Can't BackUp() after failed Next(). CHECK failed: !is_closed_: close() failed: N6google8protobuf2io19CopyingOutputStreamE N6google8protobuf2io15FileInputStream22CopyingFileInputStreamE N6google8protobuf2io15FileInputStreamE N6google8protobuf2io16FileOutputStream23CopyingFileOutputStreamE N6google8protobuf2io16FileOutputStreamE N6google8protobuf2io18IstreamInputStream25CopyingIstreamInputStreamE N6google8protobuf2io18IstreamInputStreamE N6google8protobuf2io19OstreamOutputStream26CopyingOstreamOutputStreamE N6google8protobuf2io19OstreamOutputStreamE N6google8protobuf2io24ConcatenatingInputStreamE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl_lite.cc CHECK 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: CHECK failed: (buffer_used_) == (buffer_size_): 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(). CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: Parameter to BackUp() can't be negative. CHECK failed: (count) <= (target_->size()): Cannot allocate buffer larger than kint32max for CHECK failed: (backup_bytes_) == (0): StringOutputStream. N6google8protobuf2io16ArrayInputStreamE N6google8protobuf2io17ArrayOutputStreamE N6google8protobuf2io18StringOutputStreamE N6google8protobuf2io18CopyingInputStreamE N6google8protobuf2io25CopyingInputStreamAdaptorE N6google8protobuf2io26CopyingOutputStreamAdaptorE N6google8protobuf2io19LimitingInputStreamE (cannot determine missing fields for lite message) " because it is missing required fields: /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/message_lite.cc exceeded maximum protobuf size of 2GB: 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. MessageLite at 0x Can't parse message of type " N6google8protobuf11MessageLiteE N6google8protobuf24ZeroCopyCodedInputStreamE ]]]}]]]parsing Can't happen /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/parse_context.h Hq`sq`toXp[libprotobuf %s %s:%d] %s
- %d.%d.%d %c %g %p %lld %llu ".) INFO WARNING FATAL /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/common.cc This 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 " N6google8protobuf14FatalExceptionE N6google8protobuf7ClosureE N6google8protobuf8internal16FunctionClosure0E , lo= /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/int128.cc Division or mod by zero: dividend.hi= CANCELLED UNKNOWN DEADLINE_EXCEEDED NOT_FOUND ALREADY_EXISTS PERMISSION_DENIED UNAUTHENTICATED RESOURCE_EXHAUSTED FAILED_PRECONDITION ABORTED OUT_OF_RANGE UNIMPLEMENTED INTERNAL UNAVAILABLE DATA_LOSS ػȼػ(HhȽ(H$i$/E[q=S basic_string::erase CHECK failed: dest: \x%02x \%03o CHECK failed: i >= 0: 0123456789abcdef nullptr output boolean given. %.*g CHECK failed: s != nullptr: Logic problem? szsrc = %.1f CHECK failed: (size) <= (6): /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/strutil.cc CHECK failed: result != nullptr: FastHexToBuffer() wants non-negative integers, not CHECK failed: value != nullptr: This can't happen; base64 decoder state = CHECK failed: (temp[0]) == ('1'): CHECK failed: (temp[size - 1]) == ('5'): ֽppppppppppppppppppppppppppppppppp`ppppHpppppppp ppppppppppppppppppppppppppppppppppppppp`pppPppppppp@ppp0p p p 0123456789abcdef ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/ >456789:;<=
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- /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/wire_format_lite.cc CHECK failed: (value.size()) <= (kint32max): buffer. Use the 'bytes' type if you intend to send raw '%s' String field contains invalid UTF-8 data when a protocol bytes. serializing N6google8protobuf8internal12FieldSkipperE N6google8protobuf8internal29CodedOutputStreamFieldSkipperE Multiple extension registrations for type " /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/extension_set.cc CHECK 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: Non-primitive types can't be packed. 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). Invalid message set extension. /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/extension_set_inl.h ", field number can't reach here. Can't get here. Extension not found. :,:,:,:,:,:,:,:,:t::$?8<;== >8<;>>>>>2?L=;8<>Q>|;;;L<L<,<;;<l<<<l<<,<;;<<>;;IAAA;;i?|??@j@@ A;; ?>BBBBBBBBBBBxCCDD DHDpDDCE@CBZ<Z<Z<Z<Z<Z<Z<Z<ZLZ\Z[\H\`\p\\\\\[ \Te{%u{nizx9po=fpuDgynvowxkej hYhhuii.jjbdq[r
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-+61Td|t(H0hh( X N6google8protobuf8internal20RepeatedPtrFieldBaseE N6google8protobuf8internal15ExtensionFinderE N6google8protobuf8internal24GeneratedExtensionFinderE N6google8protobuf8internal12ExtensionSet8KeyValueE N6google8protobuf13RepeatedFieldIiEE N6google8protobuf13RepeatedFieldIlEE N6google8protobuf13RepeatedFieldIjEE N6google8protobuf13RepeatedFieldImEE N6google8protobuf13RepeatedFieldIfEE N6google8protobuf13RepeatedFieldIdEE N6google8protobuf13RepeatedFieldIbEE N6google8protobuf16RepeatedPtrFieldISsEE N6google8protobuf16RepeatedPtrFieldINS0_11MessageLiteEEE St3mapIiN6google8protobuf8internal12ExtensionSet9ExtensionESt4lessIiESaISt4pairIKiS4_EEE N6google8protobuf8internal19ImplicitWeakMessageE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream.cc This ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug. N6google8protobuf2io20ZeroCopyOutputStreamE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/stringprintf.cc CHECK failed: (v.size()) <= (kStringPrintfVectorMaxArgs): StringPrintfVector currently only supports up to Feel free to add support for more if you need it. arguments. Error reverse compiling ' Unknown encoding Error parsing ' ': Error compiling ' \x00 Invalid RE2: startpos: endpos: text size: DFA out of memory: size bytemap range list count SearchDFA inconsistency SearchOnePass inconsistency SearchBitState inconsistency SearchNFA inconsistency requested group in regexp invalid rewrite pattern: /onnxruntime_src/cmake/external/re2/re2/re2.cc pattern too large - reverse compile failed pattern too large - compile failed RE2: invalid startpos, endpos pair. [ Rewrite 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.
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Regexp not destroyed. Stack not empty. Bad reference count Walk NULL no error unexpected error invalid escape sequence invalid character class invalid character class range missing ] missing ) trailing \ invalid repetition size bad repetition operator invalid perl operator invalid UTF-8 invalid named capture group /onnxruntime_src/cmake/external/re2/re2/regexp.cc Unexpected op in Regexp::Equal: NumCapturesWalker::ShortVisit called NamedCapturesWalker::ShortVisit called CaptureNamesWalker::ShortVisit called /onnxruntime_src/cmake/external/re2/re2/walker-inl.h no argument for repetition operator l\\ĊĊĊ̉܊ Ĩt\\DDDܧ N3re26Regexp6WalkerIiEE N3re217NumCapturesWalkerE N3re219NamedCapturesWalkerE N3re218CaptureNamesWalkerE GrowStack() failed: njob_ = job_.size() = Unexpected opcode: arg Bad arg in kInstAltMatch: Bad arg in kInstCapture: /onnxruntime_src/cmake/external/re2/re2/bitstate.cc XVU$SRRQdQCompiler::Copy called! should never happen No ranges in char class Missing case in Compiler: hello, world /onnxruntime_src/cmake/external/re2/re2/compile.cc 4TܒdēdDĔ\D,4 N3re26Regexp6WalkerINS_4FragEEE N3re28CompilerE /onnxruntime_src/cmake/external/re2/re2/dfa.cc StateSaver failed to restore state. Unexpected special state in RunStateOnByte RunStateOnByteUnlocked failed after ResetCache RunStateOnByteUnlocked failed after Reset Failed to analyze start state. %s%d (%p) flag=%#x || unhandled opcode: DeadState in RunStateOnByte NULL state in RunStateOnByte context does not contain text A8h8unhandled in AddToThreadq Unhandled in step (?,?) (%d,?) (%d,%d) Bad args: nsubmatch= in ComputeFirstByte in Prog::Fanout() /onnxruntime_src/cmake/external/re2/re2/nfa.cc Unexpected opcode in short circuit: XAF@D8EDpDD Dqo@pppppp%p{ z8{zzzziz/onnxruntime_src/cmake/external/re2/re2/onepass.cc Cannot use SearchOnePass for unanchored matches. !ܗ 0 0 0 0 Bad hex digit Concat of Any RE2: unexpected op: unknown round: /onnxruntime_src/cmake/external/re2/re2/parse.cc RepetitionWalker::ShortVisit called AddFoldedRange recurses too much. Bad call to ParseState::ParsePerlFlags we \L t d , Y#$$$$$$$#########################################$####~$#######v$###$#$#`$# $ N3re216RepetitionWalkerE 0 9 A F a f 0 9 A Z _ _ a z A Z
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0 9 [: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:] \d \D \s \S \w \W /i alt -> %d | %d altmatch -> %d | %d byte%s [%02x-%02x] -> %d capture %d -> %d emptywidth %#x -> %d match! %d nop -> %d fail %d. %s
+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). 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. (Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0). Constrains input and output to only numeric types. Constrain input types. Casting from strings and complex are not supported. Constrain output types. Casting to strings and complex are not supported. Which axis to concat on. Default value is 1. Optional list of output lengths (see also arg 'split') outputs... Which axis to split on length of each output List 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, edge One float, indicates the value to be filled, default is 0 paddings New shape Number of repeated copies to make of the input tensor. Output tensor of same shape and type as input. Constrain tiles and axis's type to int64 tensors. tiles Axis along which to repeat. The scale along width dimension. It takes value greater than or equal to 1. The scale along height dimension. It takes value greater than or equal to 1. Two interpolation modes: nearest(default), bilinear 4-D tensor after resizing, [N,C,H,W] Constrain output types to bool, int32, int64, float16, float, double tensors. width_scale height_scale 4-D tensor, [N,C,H,W] 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'. 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 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). 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. List of non-negative integers, indicate the dimensions to squeeze. List of non-negative integers, indicate the dimensions to be inserted (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. 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 [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use. (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Rank 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. 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. Target shape may not have multiple -1 dimensions Invalid Target shape product of 0 Invalid position of 0 rank must be greater than axis Ranks 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 ends Attribute axes has incorrect length Attribute value for pads is required Attribute pads has incorrect length 'axis' must be in [-rank(indices)-1, rank(indices)] *ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ ) is not equal to the existing dim value ( Dimension value inferred ( 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. 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 set Input type is not int64 tensor but keys_int64s is set Input type is not float tensor but keys_floats is set Only one of values_*'s can be set in label encoder. Data to be selected The indices, based on 0 as the first index of any dimension. Selected output data as an array The input must be a tensor of a numeric type or string. The output will be of the same tensor type. /onnxruntime_src/cmake/external/onnx/onnx/defs/traditionalml/defs.cc Data to be binarized Binarized output data The 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. map(int64, string) map(int64, float) The input map that is to be cast to a tensor A tensor representing the same data as the input map, ordered by their keys 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'. Indicates 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).<br>One of 'DENSE', 'SPARSE'. If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor. Input data Output 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' sequence The 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.<br>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.<br>One and only one of the 'default_*' attributes must be defined. A dictionary. map(string, int64) map(int64, double) map(string, float) map(string, double) string_vocabulary int64_vocabulary A 1-D tensor holding values from the input dictionary. 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.<br>One and only one of the vocabularies must be defined. An integer vocabulary array.<br>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 list inputdimensions Data to be processed. Imputed output data Value(s) to change to A value that needs replacing. Value(s) to change to. The 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. Input data. It can be either tensor or scalar. 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 strings. One and only one of 'value_*'s should be set. Output data. A list of ints. A list of floats. A string. An integer. A float. Data to be classified. A collection of intercepts. Classification outputs (one class per example). Classification scores ([N,E] - one score for each class and example The 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). 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.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' Data to be regressed. Weights of the model(s). 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.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' 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 data One of 'MAX,' 'L1,' 'L2' Data to be encoded. zeros Encoded output data, having one more dimension than X. List of categories, ints.<br>One and only one of the 'cats_*' attributes must be defined. List of categories, strings.<br>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.<br>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.<br>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.<br>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.<br>If these are provided then output Z are probability estimates, otherwise they are raw scores. Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' Class labels if using string labels.<br>One and only one of the 'classlabels_*' attributes must be defined. Class labels if using integer labels.<br>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]. Flag indicating whether the regression is a one-class SVM or not. The number of support vectors. Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.' Chosen support vectors Support vector coefficients. Input of shape [N,F] N, Top class for each point Tree id for each node. Feature id for each node. The 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. Node id for each node. Ids may restart at zero for each tree, but it not required to. 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.<br>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.<br>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. <br> 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 classes The node id of each weight The weight for each target The total number of targets. Node id for each node. Node ids must restart at zero for each tree and increase sequentially. Child node if expression is true Child node if expression is false For 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.<br>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 index of the target that each weight is for Defines how to aggregate leaf values within a target. <br>One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.' The input values The output map seq(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.<br>One and only one of the 'classlabels_*' attributes must be defined. The keys when using int keys.<br>One and only one of the 'classlabels_*' attributes must be defined. Input's shape should be 1D or 2D *ZN4onnx11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26CategoryMapper_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26DictVectorizer_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28LinearClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25SVMClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Input data. A list of labels. 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. /onnxruntime_src/cmake/external/onnx/onnx/defs/traditionalml/old.cc *ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ sequence_type map_type opaque_type sparse_tensor_type NOT_SET AmmTmTmmmTmm.m, node name: (op_type: is out of bounds Inferred elem type differs from existing elem type: ( Inferred shape and existing shape differ in rank: ( Inferred shape and existing shape differ in dimension ) vs ( type case mismatch. existing= inferred= type case unsupported. existing= GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. does not contain a graph. Warning: Shape inference does not support models with experimental operators: Warning: Unsupported operator . No schema registered for this operator. ONNX models don't support multiple opset version imports for a domain. Function where as the model imports opset version imports opset version Shape inference error(s): inputs but Graph has were provided N4onnx15shape_inference19GraphInferencerImplE N4onnx15shape_inference20InferenceContextImplE std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = onnx::NodeProto; _Alloc = std::allocator<onnx::NodeProto>; std::vector<_Tp, _Alloc>::reference = onnx::NodeProto&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] SSSȀS0_<___l8P h`( DDD$T@p`x x 8P$ @x@ pP(` P pr**r*r**|*r***iXYiXiXYxXiXXhY N4onnx14AttributeProtoE N4onnx14ValueInfoProtoE N4onnx9NodeProtoE N4onnx17TrainingInfoProtoE N4onnx10ModelProtoE N4onnx22StringStringEntryProtoE N4onnx16TensorAnnotationE N4onnx10GraphProtoE N4onnx19TensorProto_SegmentE N4onnx11TensorProtoE N4onnx17SparseTensorProtoE N4onnx26TensorShapeProto_DimensionE N4onnx16TensorShapeProtoE N4onnx16TypeProto_TensorE N4onnx18TypeProto_SequenceE N4onnx13TypeProto_MapE N4onnx22TypeProto_SparseTensorE N4onnx16TypeProto_OpaqueE N4onnx9TypeProtoE N4onnx18OperatorSetIdProtoE IR_VERSIONIR_VERSION_2017_10_10IR_VERSION_2017_10_30IR_VERSION_2017_11_3IR_VERSION_2019_1_22IR_VERSION_2019_3_18IR_VERSION_2019_9_19_START_VERSION DEFAULTEXTERNAL BFLOAT16BOOLCOMPLEX128COMPLEX64DOUBLEFLOATFLOAT16INT16INT32INT64INT8STRINGUINT16UINT32UINT64UINT8UNDEFINED
+ FLOATFLOATSGRAPHGRAPHSINTINTSSPARSE_TENSORSPARSE_TENSORSSTRINGSTRINGSTENSORTENSORSUNDEFINED onnx.TensorProtoonnx.TensorShapeonnx.SparseTensoonnx.StringStrinonnx.OperatorSetonnx.TypeProto.Sonnx.TensorAnnotonnx.TypeProto.Tonnx.TypeProto.Oonnx.AttributePronnx.TrainingInfonnx.TypeProto.Monnx.ValueInfoPrMPp~P~@}X0x8hPN4onnx13FunctionProtoE N4onnx13OperatorProtoE N4onnx16OperatorSetProtoE EXPERIMENTALSTABLE onnx.OperatorProonnx.FunctionPro Ш dd N4onnx13SequenceProtoE N4onnx8MapProtoE MAPSEQUENCESPARSE_TENSORTENSORUNDEFINED onnx.SequencePro/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/arena.cc CHECK failed: (min_bytes) <= (std::numeric_limits<size_t>::max() - SerialArena::kBlockHeaderSize): N6google8protobuf8internal19ImplicitWeakMessageE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl.cc Can't BackUp() after failed Next(). CHECK failed: !is_closed_: close() failed: N6google8protobuf2io19ZeroCopyInputStreamE N6google8protobuf2io19CopyingOutputStreamE N6google8protobuf2io15FileInputStream22CopyingFileInputStreamE N6google8protobuf2io15FileInputStreamE N6google8protobuf2io16FileOutputStream23CopyingFileOutputStreamE N6google8protobuf2io16FileOutputStreamE N6google8protobuf2io19OstreamOutputStream26CopyingOstreamOutputStreamE N6google8protobuf2io19OstreamOutputStreamE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl_lite.cc CHECK 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: (buffer_used_) == (buffer_size_): 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(). CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: Parameter to BackUp() can't be negative. CHECK failed: (backup_bytes_) == (0): N6google8protobuf2io18CopyingInputStreamE N6google8protobuf2io25CopyingInputStreamAdaptorE N6google8protobuf2io26CopyingOutputStreamAdaptorE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/message_lite.cc exceeded maximum protobuf size of 2GB: 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. Can't parse message of type " " because it is missing required fields: N6google8protobuf11MessageLiteE (cannot determine missing fields for lite messagp`8/onnxruntime_src/cmake/external/protobuf/src/google/protobuf/parse_context.h Can't happen X l/$23t5<00[libprotobuf %s %s:%d] %s
+ N6google8protobuf14FatalExceptionE INFO WARNING FATAL /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream.cc This ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug. N6google8protobuf2io20ZeroCopyOutputStreamE /onnxruntime_src/cmake/external/re2/re2/re2.cc Unknown encoding Invalid RE2: startpos: endpos: text size: DFA out of memory: size bytemap range list count SearchDFA inconsistency SearchOnePass inconsistency SearchBitState inconsistency SearchNFA inconsistency RE2: invalid startpos, endpos pair. [ Error reverse compiling ' Error parsing ' ': Error compiling ' pattern too large - compile failed pattern too large - reverse compunexpected error std::basic_string<_CharT, _Traits, _Alloc>::value_type& std::basic_string<_CharT, _Traits, _Alloc>::operator[](std::basic_string<_CharT, _Traits, _Alloc>::size_type) [with _CharT = char; _Traits = std::char_traits<char>; _Alloc = std::allocator<char>; std::basic_string<_CharT, _Traits, _Alloc>::reference = char&; std::basic_string<_CharT, _Traits, _Alloc>::value_type = char; std::basic_string<_CharT, _Traits, _Alloc>::size_type = long unsigned int] /onnxruntime_src/cmake/external/re2/re2/regexp.cc NumCapturesWalker::ShortVisit called Regexp not destroyed. Bad reference count /onnxruntime_src/cmake/external/re2/re2/walker-inl.h std::stack<_Tp, _Sequence>::reference std::stack<_Tp, _Sequence>::top() [with _Tp = re2::WalkState<int>; _Sequence = std::deque<re2::WalkState<int>, std::allocator<re2::WalkState<int> > >; std::stack<_Tp, _Sequence>::reference = re2::WalkState<int>&] /opt/rh/devtoolset-9/root/usr/include/c++/9/bits/stl_stack.h std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = re2::WalkState<int>; _Alloc = std::allocator<re2::WalkState<int> >; std::deque<_Tp, _Alloc>::reference = re2::WalkState<int>&] void std::stack<_Tp, _Sequence>::pop() [with _Tp = re2::WalkState<int>; _Sequence = std::deque<re2::WalkState<int>, std::allocator<re2::WalkState<int> > >] Stack not empty. Unexpected op in Regexp::Equal: *"@ std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = re2::Regexp*; _Alloc = std::allocator<re2::Regexp*>; std::vector<_Tp, _Alloc>::reference = re2::Regexp*&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] Walk NULL N3re26Regexp6WalkerIiEE N3re217NumCapturesWalkerE no error invalid escape sequence invalid character class invalid character class range missing ] missing ) trailing \ invalid repetition size bad repetition operator invalid perl operator invalid UTF-8 invalid named capture group no argument for repetition operator /onnxruntime_src/cmake/external/re2/re2/bitstate.cc typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = re2::Prog::Inst; _Dp = re2::PODArray<re2::Prog::Inst>::Deleter; typename std::add_lvalue_reference<_Tp>::type = re2::Prog::Inst&; std::size_t = long unsigned int] typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = unsigned int; _Dp = re2::PODArray<unsigned int>::Deleter; typename std::add_lvalue_reference<_Tp>::type = unsigned int&; std::size_t = long unsigned int] typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = re2::Job; _Dp = re2::PODArray<re2::Job>::Deleter; typename std::add_lvalue_reference<_Tp>::type = re2::Job&; std::size_t = long unsigned int] GrowStack() failed: njob_ = job_.size() = Unexpected opcode: arg Bad arg in kInstAltMatch: Bad arg in kInstCapture: typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = const char*; _Dp = re2::PODArray<const char*>::Deleter; typename std::add_lvalue_reference<_Tp>::type = const char*&; std::size_t = long unsigned int] 6 ` /onnxruntime_src/cmake/external/re2/re2/compile.cc Compiler::Copy called! std::stack<_Tp, _Sequence>::reference std::stack<_Tp, _Sequence>::top() [with _Tp = re2::WalkState<re2::Frag>; _Sequence = std::deque<re2::WalkState<re2::Frag>, std::allocator<re2::WalkState<re2::Frag> > >; std::stack<_Tp, _Sequence>::reference = re2::WalkState<re2::Frag>&] std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = re2::WalkState<re2::Frag>; _Alloc = std::allocator<re2::WalkState<re2::Frag> >; std::deque<_Tp, _Alloc>::reference = re2::WalkState<re2::Frag>&] void std::stack<_Tp, _Sequence>::pop() [with _Tp = re2::WalkState<re2::Frag>; _Sequence = std::deque<re2::WalkState<re2::Frag>, std::allocator<re2::WalkState<re2::Frag> > >] should never happen No ranges in char class Missing case in Compiler: UPRRRRpT0TTSS USSS`S@ST0STTPS N3re26Regexp6WalkerINS_4FragEEE N3re28CompilerE
+ unhandled opcode: typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = int; _Dp = re2::PODArray<int>::Deleter; typename std::add_lvalue_reference<_Tp>::type = int&; std::size_t = long unsigned int] /onnxruntime_src/cmake/external/re2/re2/dfa.cc srrrXsrr{\z {\z\zz\z\z StateSaver failed to restore state. DeadState in RunStateOnByte NULL state in RunStateOnByte Unexpected special state in RunStateOnByte RunStateOnByteUnlocked failed after ResetCache RunStateOnByteUnlocked failed after Reset context does not contain text Failed to analyze start state. unhandled in Prog::Fanout() /onnxruntime_src/cmake/external/re2/re2/nfa.cc in ComputeFirstByte 4444 in AddToThreadq typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = re2::SparseArray<re2::NFA::Thread*>::IndexValue; _Dp = re2::PODArray<re2::SparseArray<re2::NFA::Thread*>::IndexValue>::Deleter; typename std::add_lvalue_reference<_Tp>::type = re2::SparseArray<re2::NFA::Thread*>::IndexValue&; std::size_t = long unsigned int] >$lUnhandled in step Bad args: nsubmatch= Unexpected opcode in short circuit: /onnxruntime_src/cmake/external/re2/re2/onepass.cc Cannot use SearchOnePass for unanchored matches. L
+
+8
+
/onnxruntime_src/cmake/external/re2/re2/parse.cc RepetitionWalker::ShortVisit called Bad call to ParseState::ParsePerlFlags AddFoldedRange recurses too much. Any RE2: unexpected op: Bad hex digit _$^$^$^$^$^$^$^]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]_]]]]_]]]]]]]_]]]_]i^]V^]|^Concat of std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::Frame; _Alloc = std::allocator<re2::Frame>; std::vector<_Tp, _Alloc>::reference = re2::Frame&] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = re2::Splice; _Alloc = std::allocator<re2::Splice>; std::vector<_Tp, _Alloc>::reference = re2::Splice&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] void std::vector<_Tp, _Alloc>::pop_back() [with _Tp = re2::Frame; _Alloc = std::allocator<re2::Frame>] unknown round: XȄȄȄ(ȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄhPȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȄȅ N3re216RepetitionWalkerE [: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:] \d \D \s \S \w \W 0 9 A F a f 0 9 A Z _ _ a z A Z
! / : @ [ ` { ~ ~ a z ! ~ 0 9 A Z a z 0 9 A Z a z 0 9 A Z _ _ a z
+
0 9 %d. %s
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- [%02x-%02x] -> %d
- /onnxruntime_src/cmake/external/re2/re2/prog.cc Unexpected opcode in IsMatch: x<<<<=8=X<X=X`X`___h_ah_cPcXdXdXdcdc Malformed repeat Simplify case not handled: Simplify failed on /onnxruntime_src/cmake/external/re2/re2/simplify.cc CoalesceWalker::ShortVisit called SimplifyWalker::ShortVisit called Case not handled in ComputeSimple: DoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is (((p x؏88~8xx888888x0 `` Ш H N3re26Regexp6WalkerIPS0_EE N3re214CoalesceWalkerE N3re214SimplifyWalkerE []^-\ \r \t \n \f \x{%x} (){}[]*+?|.^$\ [^\x00-\x{10ffff}] (?:) Bad final char: ? + {%d,} {%d} {%d,%d} \C ^ $ (?-m:^) (?-m:$) \z \b \B (?HaveMatch:%d) (?: kRegexpCapture cap() == 0 ?P< [truncated] /onnxruntime_src/cmake/external/re2/re2/tostring.cc T, 6\\4lL, tllD N3re214ToStringWalkerE A Z . @2 6 @9 G @J v @x x y } @ @ O @ @ @ @ @ @ ~" 2 @: : +* ; ; = = ]> > (* A A C C =D D E E E G F N @E E t p r @v v t & % @ ? @ ~ P / ` @ @ @ . @1 V 0 ` ` ` É @ @ @ A @ ( / 8 ? H M Y Y [ [ ] ] _ _ h o &! &! *! *! A+! +! 2! 2! `! o! ! ! $ $ , ., 0 `, `, b, b, c, c, d, d, g, k, @m, m, n, n, o, o, p, p, r, r, u, u, ~, , , , @, , @, , @ l @ @" . @2 n @y { @} } u~ @ Z @ @ Z Z Z Z Z Z Z Z @p 0h! : ' ( ( @ @n _n ! " ] A Z a j k k l r s s t z F y / 2 7 9 H J w x x y ~ O a 8 ~" 3 : : +* ; < = = ]> > (* ? @ ?* A B C C =D D E E E G F O P P * Q Q * R R * S S .T T 2V W 3Y Y 6[ [ 5\ \ O ` ` 3a a K c c 1e e ( f f D h h /i i -j j D k k ) l l A o o -q q ) r r +u u *} } ) & & * & ' % E E T p s v w { } t & % @ ? @ 6 0 ] # ~ P / 0 1 2 2 N 3 3 4 4 M 5 = > > D ? @ A B B C I J J < K O P _ ` b c c $ d / 1 V 0 a ` ` ` З @ @y y } } ` a a : b A ' ( / 0 7 8 ? @ E H M Q Q S S U U W W Y Y [ [ ] ] _ _ ` g h o p q J r u V v w d x y z { p | } ~ &! &! *! *! !+! +! 2! 2! N! N! `! o! p! ! ! ! $ $ $ $ , ., 0 0, ^, `, a, b, b, c, c, d, d, e, e, f, f, g, l, m, m, n, n, o, o, p, p, r, s, u, v, ~, , , , , , , , - %- '- '- -- -- @ J K K =vL m " / 2 o y | } } u~ Z Z Z Z Z Z Z Z Z S S `p 0h! : A Z ' ( ( O ( @ @n _n `n n ! " " C 68<>>@ACDFO 5 9 ? P ` h 8j ` ~ ! " $ $ ' ' ) 2 4 7 9 9 ; ; B B G G I I K K M O Q R T T W W Y Y [ [ ] ] _ _ a b d d g j l r t w y | ~ ~
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+ typename std::add_lvalue_reference<_Tp>::type std::unique_ptr<_Tp [], _Dp>::operator[](std::size_t) const [with _Tp = re2::SparseArray<int>::IndexValue; _Dp = re2::PODArray<re2::SparseArray<int>::IndexValue>::Deleter; typename std::add_lvalue_reference<_Tp>::type = re2::SparseArray<int>::IndexValue&; std::size_t = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = std::vector<int>; _Alloc = std::allocator<std::vector<int> >; std::vector<_Tp, _Alloc>::reference = std::vector<int>&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::back() [with _Tp = re2::Prog::Inst; _Alloc = std::allocator<re2::Prog::Inst>; std::vector<_Tp, _Alloc>::reference = re2::Prog::Inst&] (ܭ$$$\ std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](std::vector<_Tp, _Alloc>::size_type) [with _Tp = re2::Prog::Inst; _Alloc = std::allocator<re2::Prog::Inst>; std::vector<_Tp, _Alloc>::reference = re2::Prog::Inst&; std::vector<_Tp, _Alloc>::size_type = long unsigned int] /onnxruntime_src/cmake/external/re2/re2/simplify.cc SimplifyWalker::ShortVisit called CoalesceWalker::ShortVisit called std::stack<_Tp, _Sequence>::reference std::stack<_Tp, _Sequence>::top() [with _Tp = re2::WalkState<re2::Regexp*>; _Sequence = std::deque<re2::WalkState<re2::Regexp*>, std::allocator<re2::WalkState<re2::Regexp*> > >; std::stack<_Tp, _Sequence>::reference = re2::WalkState<re2::Regexp*>&] std::deque<_Tp, _Alloc>::reference std::deque<_Tp, _Alloc>::back() [with _Tp = re2::WalkState<re2::Regexp*>; _Alloc = std::allocator<re2::WalkState<re2::Regexp*> >; std::deque<_Tp, _Alloc>::reference = re2::WalkState<re2::Regexp*>&] void std::stack<_Tp, _Sequence>::pop() [with _Tp = re2::WalkState<re2::Regexp*>; _Sequence = std::deque<re2::WalkState<re2::Regexp*>, std::allocator<re2::WalkState<re2::Regexp*> > >] Case not handled in ComputeSimple: \\,,, DoCoalesce failed: r1->op() is DoCoalesce failed: r2->op() is Malformed repeat Simplify case not handled: .XX8xN3re26Regexp6WalkerIPS0_EE N3re214CoalesceWalkerE N3re214SimplifyWalkerE []^-\ \r \t \n \f \x%02x \x{%x} (){}[]*+?|.^$\ basic_string::_M_replace_aux (?: kRegexpCapture cap() == 0 ?P< /onnxruntime_src/cmake/external/re2/re2/tostring.cc [^\x00-\x{10ffff}] (?-m:^) (?-m:$) (?:) basic_string::erase Bad final char: ? {%d,} {%d} {%d,%d} \C ^ $ \z \b \B (?HaveMatch:%d) | 0 0 00 H [truncated] N3re214ToStringWalkerE ] A Z a j k k l r s s t z F y / 2 7 9 H J w x x y ~ O a 8 ~" 3 : : +* ; < = = ]> > (* ? @ ?* A B C C =D D E E E G F O P P * Q Q * R R * S S .T T 2V W 3Y Y 6[ [ 5\ \ O ` ` 3a a K c c 1e e ( f f D h h /i i -j j D k k ) l l A o o -q q ) r r +u u *} } ) & & * & ' % E E T p s v w { } t & % @ ? @ 6 0 ] # ~ P / 0 1 2 2 N 3 3 4 4 M 5 = > > D ? @ A B B C I J J < K O P _ ` b c c $ d / 1 V 0 a ` ` ` З @ @y y } } ` a a : b A ' ( / 0 7 8 ? @ E H M Q Q S S U U W W Y Y [ [ ] ] _ _ ` g h o p q J r u V v w d x y z { p | } ~ &! &! *! *! !+! +! 2! 2! N! N! `! o! p! ! ! ! $ $ $ $ , ., 0 0, ^, `, a, b, b, c, c, d, d, e, e, f, f, g, l, m, m, n, n, o, o, p, p, r, s, u, v, ~, , , , , , , , - %- '- '- -- -- @ J K K =vL m " / 2 o y | } } u~ Z Z Z Z Z Z Z Z Z S S `p 0h! : A Z ' ( ( O ( @ @n _n `n n ! " " C Adlam Ahom Anatolian_Hieroglyphs Arabic Armenian Avestan Balinese Bamum Bassa_Vah Batak Bengali Bhaiksuki Bopomofo Brahmi Braille Buginese Buhid Canadian_Aboriginal Carian Caucasian_Albanian Cc Cf Chakma Cham Cherokee Co Coptic Cs Cuneiform Cypriot Cyrillic Deseret Devanagari Dogra Duployan Egyptian_Hieroglyphs Elbasan Ethiopic Georgian Glagolitic Gothic Grantha Greek Gujarati Gunjala_Gondi Gurmukhi Han Hangul Hanifi_Rohingya Hanunoo Hatran Hebrew Hiragana Imperial_Aramaic Inherited Inscriptional_Pahlavi Inscriptional_Parthian Javanese Kaithi Kannada Katakana Kayah_Li Kharoshthi Khmer Khojki Khudawadi Lao Latin Lepcha Limbu Linear_A Linear_B Lisu Ll Lm Lo Lt Lu Lycian Lydian Mahajani Makasar Malayalam Mandaic Manichaean Marchen Masaram_Gondi Mc Me Medefaidrin Meetei_Mayek Mende_Kikakui Meroitic_Cursive Meroitic_Hieroglyphs Miao Mn Modi Mongolian Mro Multani Myanmar Nabataean Nd New_Tai_Lue Newa Nko Nl No Nushu Ogham Ol_Chiki Old_Hungarian Old_Italic Old_North_Arabian Old_Permic Old_Persian Old_Sogdian Old_South_Arabian Old_Turkic Oriya Osage Osmanya Pahawh_Hmong Palmyrene Pau_Cin_Hau Pc Pd Pe Pf Phags_Pa Phoenician Pi Po Ps Psalter_Pahlavi Rejang Runic Samaritan Saurashtra Sc Sharada Shavian Siddham SignWriting Sinhala Sk Sm So Sora_Sompeng Soyombo Sundanese Syloti_Nagri Syriac Tagalog Tagbanwa Tai_Le Tai_Tham Tai_Viet Takri Tamil Tangut Telugu Thaana Thai Tibetan Tifinagh Tirhuta Ugaritic Vai Warang_Citi Yi Zanabazar_Square Zl Zp Zs 68<>>@ACDFO 5 9 ? P ` h 8j ` ~ ! " $ $ ' ' ) 2 4 7 9 9 ; ; B B G G I I K K M O Q R T T W W Y Y [ [ ] ] _ _ a b d d g j l r t w y | ~ ~
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+0 0 0000000000000 0 0??557799;;==??AACCGGYY[[]];;[[__bb mu 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 some mode
- ɚ; panic: nsync_mu not held in some mode when calling nsync_mu_wait_with_deadline()
+ ɚ; panic: nsync_mu not held in some mode when calling nsync_mu_wait_with_deadline()
nsync_mu_wait woke but condition not true
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-(A ABBA < 0 {" ԏ BBA A(GU
-(A ABBA < L0 p{" BBA A(GU
-(A ABBA < 0 `{" BBA A(GU
-(A ABBA < 0 P{" q BBA A(GU
-(A ABBA < 1 @{" P BBA A(GU
-(A ABBA < L1 0{" / BBA A(GU
-(A ABBA < 1 {" BBA A(GU
-(A ABBA < 1 {" BBA A(GU
-(A ABBA < 2 {" ̎ BBA A(GU
-(A ABBA < L2 {" BBA A(GU
-(A ABBA L 2 { BBB B(D0D8Dp
-8D0A(B BBBG L 2 p{3 W BBH E(D0D8DPj
-8D0A(B BBBD L 3 `{9 BBB B(A0D8D 8A0A(B BBB L 44 P{ BBB B(A0D8Dm8A0A(B BBB L 4 { BBB B(A0D8Dm8A0A(B BBB L 4 +&y ? BBB B(A0D8J8A0A(B BBB L $5 { BBB B(A0D8Dm8A0A(B BBB L t5 { BBB B(A0A8Gp8A0A(B BBB L 5 @{ BBB B(A0A8Gp8A0A(B BBB L \5 {. BBB B(A0A8J q
-8A0A(B BBBA d6 zT KH |6 { 6 { 6 x{ 6 p{ 6 h{ 6 p{ 7 x{ L $7 6y BEE A(A0
-(A BMBE A(A BBB T 6 0{. C BBE B(D0A8Gc
-8D0A(B BBBI T 7 || ; BFG B(D0A8G
-8A0A(B BBBE $8 0| <8 8| L 7 0| BBB E(A0A8DP
-8A0A(B BBBA L 7 |
- BBE E(D0A8Gx
-8D0A(B BBBA 8 | AO D \8 |] | BBB A(A0Gy
-0A(A BBBH \9 (zT KH t9 5y\ AG RA 9 || < 9 H| BDA \
-ABJ i
-ABL L 9 |g BEB B(A0A8Dp\
-8A0A(B BBBI L <: 8| BEB B(A0A8Dpw
-8A0A(B BBBF : | : | : x| : p| : h| L ; `| BBB B(A0A8D
-8A0A(B BBBA | T; | BBE B(G0L8DPu
-8A0A(B BBBG S
-8E0A(B BBBG W
-8E0A(B BBBK L ; p |~ BOB B(A0A8Dp~
-8A0A(B BBBE T l; "|! BEB E(A0D8G;
-8D0A(B BBBF < |< x+| BBG A(G@
-(A ABBB L < 8,|j BBE B(D0D8G`,
-8D0A(B BBBE = X/| AO 4 t< X/|[ BDA GP
- DABD d= z. K^ |= h0|% , < 0|, ` AGG XCA , = 0|0 4 ADG _CA <= 0|$ D_ > 0| ,> 0| D> 0| \> x0| , = p0|, AGG XCA > p0| > h0| > `0|r > 0| ? 0| ? 0|
- $ |> 0| Ї AM@F
-AA $ > (1| AM@N
-AC L > 1| BBB I(H0A8DP
-8A0A(B BBBH 4 ? 6| BID D0]
- AABA D T? 6|, w BBB I(A0DP
-0A(A BBBA L T@ 9| BBB B(D0A8DPl
-8A0A(B BBBA @ >|" DN
-N A L @ >| BRB E(A0A8D`
-8A0A(B BBBE 4 \@ ?| BAD GPo
- CABE 4 @ x@| s BAD GPo
- CABE A
z. K^ A A| A A|/ D j A (A| ,A A|- ߅ Hd B 0A| A\ $B 0A| <B (A| TB A| lB A| B A| B A| B A| B @| ,B A|4 Hg C A| C (A| 4C 0A| LC 8A| dC @A| |C HA| C PA| C XA| C `A| HS C `A|- L C xA| AAG ]
-CAF [
-ACA R
-CAJ JCA , LD A|b D\
-H P
-H Q
-G I , |D A|b D\
-H P
-H Q
-G I D (B|- D @B|- < D XB| BAA a
-ABH i
-ABL $ E (C|^ AG b
-AA , DE `C|0 AF
FK
- D < D `D| W BED A(Dpz
-(A ABBH < D 0E| BED A(Dpz
-(A ABBH E F|' Ae F F|* Ah |E F| AN < E F|P BEJ D(DPn(C ABB L E F| G BEE E(A0A8D
-8A0A(B BBBG L ,F G| BBB B(D0A8Gn
-8D0A(B BBBA L 4G `J| BBB B(A0A8D
-8A0A(B BBBA L G K| BBB B(A0A8D
-8A0A(B BBBA , G M|\ R ADG l
-AAA $ H M|V ADD JAAL ,H N|W BBB E(A0A8DP
-8A0A(B BBBA L |H O| BBB B(A0A8D
-8A0A(B BBBA L H hT| BBB B(A0A8Gp8A0A(B BBB , I U|I AAG0
-AAA 4 H W| ~ BAD D0
- AABA L I X| BBB B(D0A8D`
-8A0A(B BBBA L I Y| b~ BJB A(A0W
-(A BBDH ^(A BBBL lI Y| ~ BEB E(I0H8DP
-8D0A(B BBBD L I [| } BEB E(I0H8DP
-8D0A(B BBBD d J ]| } BFE E(D0A8Dp/
-8A0A(B BBBE w
-8A0A(B BBBA L tJ x_| } BEB E(I0H8D@
-8D0A(B BBBA , |K a| BFA D0 AABL K a| BHB E(A0A8D`
-8A0A(B BBBE 4 K c| BDA w
-AEA {
-AFI T |K c| | BBE E(D0A8Gv
-8D0A(B BBBC $ K 8g| | AFD@ 4 L g|@ BAD D0
- AABA D L h|B BBE A(A0D@m
-0A(A BBBA , |L i|\ | ADG l
-AAA < L i|^ { BBA D(G0
-(A ABBA 4 L k| { BFD D0I
- DABA L $M k| { BOE B(A0C8GP
-8A0A(B BBBD D tM l| { BBB D(A0G@:
-0A(A BBBA L M n| { BBB B(A0A8GPC
-8A0A(B BBBA N p|
N h z. K^ D N p|" BBB A(A0G@m
-0A(A BBBA T N q| z BBB B(A0D8D
-8A0A(B BBBA 4 O pt|J BDA o
-ABG A
-ABA L O t| BBB B(A0A8Gp8A0A(B BBB L P Xv| BBE B(A0A8D`
-8A0A(B BBBA D O w| y BBJ A(D0G@q
-0D(A BBBA T O 0z| y BEB B(A0A8G
-8A0A(B BBBG L TP Ȃ|- 'z BLB B(D0A8DP
-8A0A(B BBBF T P |w z BBB B(D0A8G
-S
-8A0A(B BBBA Q П| Q ȟ| Q | Q ȟ| R П|+ Hb 4R |2 He TR | lR | $ R |g AG k
-AA $ R 8|o AG s
-AA 4 R | Cz BAA G@I
- AABA S | $S | <S | , R |" y AAG0i
-AAD S yT KH S ء|
S С|
S ȡ| S | S | T | < tS | y BEA A(MP^
-(A ABBF $ S 8| x AD@t
-AF 4 T | BDA r
-ABL y
-ABA T yT KH T P| T H|F U | ,U x|F L DU | AAG ]
-CAF k
-ACA R
-CAJ JCA U y- AG cA , U Ф|b D\
-H P
-H Q
-G I $ U |Z AID FAD V H|U AS $ ,V |g AG k
-AA $ TV Х|g AG k
-AA $ |V |o AG s
-AA $ V `|o AG s
-AA L V |T v BBB B(A0A8GZ
-8D0A(B BBBH T dV | ov BBB B(A0A8G
-8A0A(B BBBF T V | 9v BBB B(A0A8G
-8A0A(B BBBF T W H| v BBB B(A0A8G
-8A0A(B BBBF < lW | u AAGPQ
-AAD O
-AAG T W ж| u BBB B(A0A8G
-8A0A(B BBBF T X | u BBB B(A0A8G
-8A0A(B BBBG Y | N D ,Y | BBE A(A0D@l
-0A(A BBBA < X | t BBD A(G0T
-(A ABBA d Y P|j BBB B(A0D8D`^
-8A0A(B BBBE
-8A0A(B BBBA L Z X|n BEB E(A0A8DP
-8D0A(B BBBH T Y x| Kt BBB B(A0A8Ja
-8D0A(B BBBF d Z @| ;t BBF E(D0A8D`
-8D0A(B BBBE v
-8A0A(B BBBA 4 tZ | t BFD D0
- DABA , d[ `| ADD Z
-AAA [ yT KH [ | [ | 4 $[ |5 s ADG
+
-AAA , \[ | qw ADGv
-AAA , [ P|G nw ADGe
-AAA , [ p|G Nw ADGe
-AAA , [ |G .w ADGe
-AAA 4 \ |u w ADGO
-AAA 4 T\ | ,x ADG
-AAA , \ | y ADG
-AAA 4 \ `|5 ;y ADG4
-AAA 4 \ h| Nz ADG&
-AAA 4 ,] | z ADG
-AAA 4 d] H|f f} ADG
-AAA 4 ] | } ADG_
-AAA , ] }d # ADG@
-AAA 4 ^ }f ADG
-AAA , <^ P
-}G ADGe
-AAA < l^ p
-}; z BIA A(D`
-(A ABBA < d_ p} BEE A(D@M
-(A ABBA L ^ } BEB E(I0H8D@
-8D0A(B BBBD L <_ P}: s BJE B(A0A8D@
-8A0A(B BBBA L D` @} BBE B(A0A8D`
-8A0A(B BBBA L _ } BEB E(I0H8D@
-8D0A(B BBBD L ,` } Ӏ BJE B(A0A8D`8A0A(B BBB D |` }n BBB A(C0K@
-0A(A BBBA L ` H} s BBH E(D0D8D`
-8D0A(B BBBF l a }N K BBB E(D0I8DD
-8D0A(B BBBC
-8D0A(B BBBF T a x%} ' BEB B(A0A8Jq
-8A0A(B BBBA T a .} _ BBE B(D0D8J
-8D0A(B BBBJ T 4b ;}g ߀ BBB E(D0I8D
-8D0A(B BBBA T b =}
- BBB B(D0A8J
-8A0A(B BBBA D b HH}7 BCA GpG
- AABH
- AABE c @K} c 8K} d 0K} ,d (K} Dd K} \d (K} td 0K} d 8K} d @K} d HK} d PK}0 An d `K}> A| L e K} AAG ]
-CAF k
-ACA R
-CAJ JCA L de K} AAG ]
-CAF k
-ACA R
-CAJ JCA e L}" DN
-N A e L}- AG cA e L}- AG cA L f 0L} AAG ]
-CAF [
-ACA R
-CAJ JCA L df `L} AAG ]
-CAF c
-CAI R
-CAJ JCA , f L}b D\
-H P
-H Q
-G I L f L} AAG ]
-CAF [
-ACA R
-CAJ JCA 4g M}- Lg (M}[ L f pM}r (} BBB B(E0A8J@
-8A0A(B BBBA < f N} | BBA A(D@(A ABB g @O}- h XO}U AS ,h O}- L g O} a| BEE E(A0D8G
-8D0A(B BBBC < g PU} i| AAG0M
-DAE K
-DAA L h U} :| BBE D(D0G
-0D(A BBBC < lh @W}p | BED A(G@|
-(A ABBA D h pW} { BAA G0c
- DABK k
- DABA 4 i W} BEA A(D@(A ABBL ,i X}f e{ BBB B(A0D8GP^
-8A0A(B BBBD L |i Y}2 ){ BBB B(A0A8GP
-8A0A(B BBBB L i Z} z BBB B(A0A8GPd
-8A0A(B BBBA j \} j x\} k ]} k ]}s L 4k ]} BBB B(A0D8MPD
-8A0A(B BBBH 4 j `}
- z BFA K0
- AABA k a} k a} AS k a}- AG cA T \k a}l ~y BBL B(A0D8G
-8D0A(B BBBC L k d} gy BFB E(A0A8GP
-8A0A(B BBBH l f}2 Al $ l (f}_ AAG LDAD Ll `f} y BEE A(C0G@n
-0A(A BBBC T l (g} x BBA D(G`
-(A ABBJ 8
-(A ABBA m h} DW L m h} x BBB H(D0M2
-0D(A BBBD L Tm j} x BBB H(D0M2
-0D(A BBBD L m hl}
- rx BBB H(D0M2
-0D(A BBBD L m (n}
- ]x BBB H(D0M2
-0D(A BBBD 4 Dn o} Hx SGB A(A0D \ |n p}9 (x BBB A(A0J
-0A(A BBBE s
-0A(A BBBE D n s} w BBB A(A0J
-0A(A BBBA < $o u} w BBA D(Jz
-(D ABBJ < do x}% w BEA D(G
-(D ABBA L o xy} w BEE E(A0C8J
-8A0A(B BBBB < o {}Z w BDA z
-ABL A
-ABA L 4p |} w BEB B(A0A8GP
-8A0A(B HBBH <q ~}B Tq }B L lq 8}> BBB B(A0D8MPd
-8A0A(B BBBA q (}B D q `}; BDA G0
- AABF N
- AABF r X}c < 4r } BBB D(G0
-(A EBBE d tr } BBB B(D0A8DP
-8A0A(B BBBH 8F0A(B BBB L r (} BBB B(A0D8MP
-8A0A(B BBBH < ,s } BBB A(G0e
-(A BBBA < ls 8} BBB A(G05
-(A BBBA L s } BBB B(D0A8MP|
-8A0A(B BBBH < s } BBA G(J0
-(A ABBD L s ؞}. ct BBB B(D0A8D`
-8D0A(B BBBE d t } BEB B(A0A8D@{
-8A0A(B BBBJ D
-8A0A(B BBBA 4 t P}Z BDA y
-ABM A
-ABA , ,u x}Y BDA U
-AEF L t }
s BEE A(A0G
-0A(A BBBA u h} < u `} BEB D(A0M
-(A BBBF 4 v }Z BDA y
-ABM A
-ABA 4 <v }u BEA A(D@a(A ABB$ tv P}v AAK0fAAD u } pr BGB A(A0K@
-0A(A BBBA $ v @}i AAG0]AA4 w } BFD A(D@(A ABBT v } q BEB A(A0[
-(A BBBK A
-(A BBBA d w X}; BLB B(A0A8D`q
-8A0A(B BBBE S
-8A0A(B BBBK L Lw 0} ,q BBE B(H0D8M
-8D0A(B BBBH , Tx }h BDA ]AB L x 0} BBB B(A0A8Gp8A0A(B BBB D x }n BBE A(A0D@Q
-0A(A BBBA L y د} BBB B(A0A8D@
-8A0A(B BBBE D ly } BBB D(A0D@g
-0A(A BBBA 4 y }u BEA A(D@a(A ABB y 8} AL z 8}A AK sA ,z h}I AK {A 4 Lz }u BEA A(D@a(A ABB4 z } BBH A(D@(A ABBT z }K n BEE B(D0A8G
-8D0A(B BBBE { } < ,{ (} BBG A(G@
-(A ABBG L z ȶ} n BEE E(D0A8G
-8A0A(B BBBF $ { H}V ADD JAA, ,{ }T m BGA FAB , | }v BDA kAB $ D| }V ADD JAAL l| 8}) BEB B(A0A8D`
-8A0A(B BBBA < | } BHB D(A0(A BBB 4 | } BEA A(D@o(A ABB< 4} } BEE A(D0(A BBB 4 t} p} BEA D(D0(A ABB, | }e l AID l
-DAA D $} (} k BBB D(A0&
-(A BBBA D l} p} k BJB A(A0Gp
-0A(A BBBA < l~ H} BEB A(A0
-(A BBBA 4 ~ } BEA A(D@|(A ABB, ~ }> BDA sAB L } BBB B(A0A8Gp8A0A(B BBB , ~ }z j AAG c
-AAB 4 }u BEA A(D@a(A ABB4 8} BEA A(D@o(A ABBL } BBB B(A0D8D`O
-8A0A(B BBBF T } i BBD D(D@%
-(D ABBB ]
-(D ABBJ < }z i BBG A(G`k
-(D ABBI , }v BAA i
-ABA 4 H}~ BEA A(D@j(A ABBL T } BEB A(D0
-(A BBBB O
-(A BBBJ , P} ADD ]
-DAE < ԁ } BBD I(D0m
-(D ABBE T \ }~ Fh BBE J(A0A8G9
-8D0A(B BBBF L H} +h BEB E(I0H8DP
-8D0A(B BBBD 4 } h BFD D0
- DABA < } BBG A(G@
-(A ABBG \ 4 `} BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 ܂ }Y Sg AIG t
-AAI DCA T } g BBB B(A0A8G
-8A0A(B BBBA T l 0} g BGB D(A0z
-(A BBDE H
-(A BBGA L ă }a f BBB B(A0D8Kp
-8A0A(B BBBA $ ̄ }V ADD JAAD < }( nf BBB A(D0D@
-0A(A BBBE < }" DN
-N A D \ } BBB B(A0A8D`8A0A(B BBBL }|
e BBE E(A0I8Dp
-8D0A(B BBBJ \ } BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBT P}} e BBE B(D0A8G
-8D0A(B BBBH \ x} BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBL T } De BBB B(D0A8G
-8A0A(B BBBK L }s e BBB E(D0D8J
-
-8D0A(B BBBI D ~ BBB B(A0G8G`c8D0A(B BBB< < ~ 'f BLD D(G0
-(A ABBE 4 | ~= f BFD D0
- DABF < ~ e BED I(K@`
-(D ABBH T x~ e BBB B(A0D8J
-8A0A(B BBBG < L
-~ e BLD D(G0
-(A ABBE < ` ~ e BED I(KPc
-(D ABBE L ̈ ~ ge BGD A(G0z
-(C ABBI `(F ABB T ` ~ e BBB B(D0A8G
-8A0A(B BBBF , ~ AL T ~= d BEB E(A0D8J 6
-8D0A(B BBBH T ~ ;e BHE E(A0A8G
-8D0A(B BBBB T D ~w 'e BBD A(GpO
-(D ABBH
-(D ABBA T 0~1 AK cA L P~ d BEB E(I0H8D@
-8D0A(B BBBD L ~ d BJE B(A0A8D`8A0A(B BBB T \ ` ~ hd BEF E(D0D8G
-8D0A(B BBBJ T ~ Rd BBB B(D0D8G
-8D0A(B BBBC D Č %~ BBE A(A0D@`
-0A(A BBBA L T &~ +d BEB E(I0H8D@
-8D0A(B BBBD L '~* d BJE B(A0A8D@
-8A0A(B BBBA L x(~'
c BBE B(I0D8Gw
-8A0A(B BBBH 4 X5~h BEA A(G@Q(A ABB\ 4 5~ BBD A(D@V
-(A ABBG Y
-(A ABBA a
-(A ABBI T ܍ 5~ c BIB B(A0D8J
-
-8A0A(B BBBK y. K^ PO~ HO~% HX < XO~- H` \ hO~% HX | xO~- H` T O~
- +d BBB J(D0A8K
-8A0A(B BBBA T < Z~ rd BJB B(D0D8N(
-8A0A(B BBBA T e~
- d BBB B(A0D8N"
-8A0A(B BBBA T pp~ +e BBB B(A0D8G&
-8A0A(B BBBD t~ AL < d t~ d BEA D(G
-(D ABBD \ u~y AG oAL Đ u~ d BBE B(H0G8J
-8D0A(B BBBG 4 X~ e BDD L@]
- DABA L L ~ ue BBE L(D0A8D
-8D0A(B BBBI 4 @~ Ie BDA LPz
- DABA L ԑ ~ $e BEB L(D0A8G
-8D0A(B BBBG ܒ 8~z AD sAT D ~
- d BEB B(D0A8G
-8D0A(B BBBE L ~ d BBB B(A0D8J)
-8D0A(B BBBD ~ ~ ԓ ~ د~
Я~ د~ 4 ~ L ~ d ~ | ~% HX ~- H` ~% HX ܔ (~- H` 8~% HX H~- H` < X~% HX \ h~- H` | x~- , ~0 AF
FK
- D $ ĕ ~^ AG b
-AA L 4 ȱ~ 7d BVB D(A0D3
-0D(A BBBG T ~ d BGB B(A0D8G
-8D0A(B BBBH L ܕ ~` d BLB E(A0A8Gp
-8D0A(B BBBA L , ~` c BLB E(A0A8Gp
-8D0A(B BBBA L | ~` c BLB E(A0A8Gp
-8D0A(B BBBA , ~ ADD0e
-AAA < ~ cc BBD D(J@F
-(D ABBA , л~ AAG0M
-AAH 4 l P~ b BAA G0c
- AABF T ؼ~ b BBE E(A0A8G
-8D0A(B BBBA L ~E b BBB B(A0A8G
-8E0A(B BBBF , L ~\ b ADG l
-AAA T | @~ cb AAG@r
-AAC G
-AAG y
-AAE ]
-AAA L Ԙ ~= *b BBB B(D0A8DP
-8D0A(B BBBA T $ x~w a BBE E(K0A8G
-8D0A(B BBBF < | ~5 a NAG JH
-CAA < ~5 a KAG MH
-CAA t ~ a BBB A(A0De
-0A(A BBBG H
-0A(A BBBH (
-0A(A BBFA , ~ L ~ a BBB B(A0A8Gi
-8A0A(B BBBD L ܚ `~ a BBB B(D0A8GA{
-8A0A(B BBBG < ~7 BBA A(G0\
-(A ABBA d $ ~ BBB E(D0D8Gp
-8A0A(B BBBD f
-8A0A(B BBBA X~y \ \d ~T BIB I(D0H8G
-8A0A(B BBBJ Q
-8A0A(B BBGH d ~ BBE F(A0H8GP
-8C0A(B BBBD
-8F0A(B BBBE d p~@ a BIB B(A0E8DP
-8A0A(B BBBJ \
-8F0A(B BBBE < ܝ H BBG A(G@
-(A ABBG $ | b 0a AY
-F A L HE a BBB B(A0A8D
-8D0A(B BBBF < H BBA A(G@d
-(A ABBI 4
` BFB A(E0G@
-0F(A BBBH y
-0A(A BBBG
-0F(A BBBD
-0F(A BBBD d | BBB A(A0
-(A BBBA
-(A BBBE N
-(A BBBE , , C _ AID \
-DAA L \ _ BBB B(A0D8DP
-8A0A(B BBBI < d P BBG A(G@
-(A ABBG L = BBB H(A0D8DP
-8A0A(B BBBE L = BBB H(A0D8DP
-8A0A(B BBBE , D AG ]
-AJ D
-AK L t @ BBB H(A0D8D@
-8A0A(B BBBJ L ġ = BBB H(A0D8DP
-8A0A(B BBBE L \ ] BBE B(A0D8D@
-8A0A(B BBBI L d @ = BBB H(A0D8DP
-8A0A(B BBBE L 0 M BBB H(A0D8DP
-8A0A(B BBBE 0 5 D d X \ BAC GPF
- EABE
- AABJ < \ AAGPF
-EAK
-AAB D # \ BAA GPI
- ECBB X
- CABB D 4 x% c\ BAC GPF
- EABE
- AABG d 4 (E BBB B(A0A8DP
-8A0A(B BBBE \
-8F0A(B BBBA l ) [ BBB E(I0D8D0
-8D0A(B BBBG s
-8D0A(B BBBH L (,' BFB E(A0A8Dp
-8F0A(B BBBH T -5 ;[ BBB B(A0D8J
-8C0A(B BBBH , E \ AID0H
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+0D(A BBBH X S@ D & l S@ FBJ A(J0J`^
+0D(A BBBH l X '@ H ' lZ PBB B(A0A8DP
+8A0A(B BBBF H ,( lZ PBB B(A0A8DP
+8A0A(B BBBC H x( lZ PBB B(A0A8DP
+8A0A(B BBBC L ( l OBA D(G0C
+(A ABBJ pH0 , ) ^X~ GAD aIB ( D) lA JDG aAAH ( p) XF GAD iIBL ( l k> FBB B(D0A8GP
+8D0A(B BBBB P H) l /> FBB B(D0A8G
+8D0A(B BBBB P ) dl = FBB B(D0A8Gv
+8D0A(B BBBE P ) l = FBB B(D0A8Gp
+8D0A(B BBBK P D* |lE = FBB B(D0A8G
+8D0A(B BBBD P * xlA O= FEB B(D0A8GM
+8A0A(B BBBF D * tlq = FBB A(A0Gz
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+8F0A(B BBBF
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+ < FGB B(A0A8D L , l _< FEE B(A0A8DP
+8A0A(B BBBB T& OX '< D H- (l '< FBB B(A0Dy
+0A(B BBBE D - lm < FBB A(A0Dw
+0A(A BBBA D - l ; FBB B(A0Dy
+0A(B BBBE D . lm ; FBB A(A0Dw
+0A(A BBBA D h. Hl ; FBB B(A0Dy
+0A(B BBBE P . l o; FBB E(D0K8GC
+8A0A(B BBBF , zPLR xA 4 Xq ; P L/ Ĺlt K; FBB E(D0K8G
+8A0A(B BBBE Xn ; D / غls O; FBB A(A0Gu
+0A(A BBBA D 0 ls "; FBB A(A0Gu
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+ MABL $ zPLR x 0 , &Xo : L 0 Plv : FBD D(D@e
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2
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l @| [ BBB K(F0A8DZ
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-0A(A BBBD D @ BBB A(A0G@m
-0A(A BBBD D 4 @ BBB A(A0G@m
-0A(A BBBD D | @ BBB A(A0G@m
-0A(A BBBD D İ @ BBB A(A0G@m
-0A(A BBBD D @ BBB A(A0G@m
-0A(A BBBD D T @ BBB A(A0G@m
-0A(A BBBD D @ BBB A(A0G@m
-0A(A BBBD D @ BBB A(A0G@m
-0A(A BBBD D , ؤ@ BBB A(A0G@m
-0A(A BBBD D t Х@ BBB A(A0G@m
-0A(A BBBD D Ȧ@ BBB A(A0G@m
-0A(A BBBD D @ BBB A(A0G@m
-0A(A BBBD D L @ BBB A(A0G@m
-0A(A BBBD D @ BBB A(A0G@m
-0A(A BBBD D ܳ @ BBB A(A0G@m
-0A(A BBBD D $ @ BBB A(A0G@m
-0A(A BBBD D l @ BBB A(A0G@m
-0A(A BBBD D @ BBB A(A0G@m
-0A(A BBBD D @ BBB A(A0G@m
-0A(A BBBD D D @ BBB A(A0G@m
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-0A(A BBBA D C BBE A(A0D@
-0A(A BBBA D \ C BBE A(A0D@
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-0A(A BBBA D 7 BBE A(A0D@
-0A(A BBBA D \ C BBE A(A0D@
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-(A ABBH | T BBE B(G0L8DPu
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-(A ABBG < # BBG A(G@x
-(A ABBG < $ @$ BBG A(G@
-(A ABBD < d $ BBG A(G@
-(A ABBD < % BBG A(G@
-(A ABBH T , & 3H BEB B(D0A8Jm
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-8A0A(B BBBA T 8R I BEE B(A0D8J
-8A0A(B BBBA < L y SJ BIA A(D`)
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-0A(A BBBA D xiC BBE A(A0D@
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-8A0A(B BBBA , HS < L F BBA A(G@q
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-8A0A(B BBBE \
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-8D0A(B BBBG t d BBD A(DP
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-8D0A(B BBBA d T | h T L GA BEB E(I0H8D@
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j A BBB E(D0A8GP
-8A0A(B BBBK D
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-8A0A(B BBBI H1 Dl 8{y. K^ $ P" DR
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-8A0A(B BBBE k
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-8A0A(B BBBA T L g
-? BBE E(D0A8G
-8A0A(B BBBA l > BBB L(A0D8DM
-8A0A(B BBBA
-8A0A(B BBBA d # > BBB B(H0D8Dp^
-8A0A(B BBBH
-8A0A(B BBBH L | @% ]> BBB B(A0H8J
-8A0A(B BBBG L , > BBB B(A0A8JX
-8A0A(B BBBJ T `/ f> BBE B(A0A8J
-8A0A(B BBBH L t 3 H> BBB B(A0D8Jd
-8A0A(B BBBK L X>7
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-8A0A(B BBBJ T HH
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-8A0A(B BBBA | x\ AG RAD S7 BBE A(A0D@
-0A(A BBBA D TC BBE A(A0D@
-0A(A BBBA T t UY k> BEB B(A0A8GR
-8A0A(B BBBH , ^ BDA AG T _ 2> BBE B(D0A8G
-8D0A(B BBBA L T b = BEB E(I0H8D@
-8D0A(B BBBA \ uy. K^ t c" DR
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-8A0A(B BBBH T 4 m = BBH E(A0A8J
-8A0A(B BBBA D D q\ BEE E(D0A8F@u8A0A(B BBB \ qA BBE A(D0r
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-A \l T hr < BDB B(A0D8GL
-8A0A(B BBBD
-8A0A(B BBBG L t < BBB B(A0A8J[
-8A0A(B BBBG \ Hw ~< BDB A(D0GpN
-0A(A BBBF }
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-8A0A(B BBBG L ~ =< BDB B(A0D8JT
-8A0A(B BBBI d ( C< BPD A(QP_
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-8A0A(B BBBG dx\ AG RAD $ 7 BBE A(A0D@
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-8C0A(B BBBG T x k; BEE B(A0D8J
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-
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-8D0A(B BBBJ d H BBB F(D0D8D@r
-8F0A(B BBBG z
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-8A0A(B BBBB L L 8 BBB B(D0A8D@d
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-8D0A(B BBBD d hI BBE E(A0A8D@
-8F0A(B BBBC S
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-8A0A(B BBBJ T C2 BBH B(D0A8DY
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-8A0A(B BBBA , h ADD ]
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-(A ABBH P
-(D ABBA T ൃn _+ BBE E(D0D8GAL
-8C0A(B BBBK T ex , BEE E(D0D8JG
-8A0A(B BBBA L \ ԃ , BBB B(A0D8I:S
-8C0A(B BBBK T M. BBB E(A0A8G*X
-8D0A(B BBBG T x
- / BEB B(A0G8G #
-8A0A(B BBBA kx\ AG RAD 4 7 BBE A(A0D@
-0A(A BBBA D | C BBE A(A0D@
-0A(A BBBA T . BEE B(A0G8G
-8A0A(B BBBA Hy. K^ 4 <jx\ AG RAD T 0!7 BBE A(A0D@
-0A(A BBBA D ("C BBE A(A0D@
-0A(A BBBA T , 0#1 _. BEE E(D0A8GY
-8D0A(B BBBE T )K E. BBH B(A0A8G
-8A0A(B BBBJ Fy. K^ $ D0 AGJ XDA EY } N $ HEg AG k
-AA 4 \ E . BDA MPO
- AABI 4 xF {. BHA G@h
- DABA Fy. K^ 4 GJ BDA q
-ABE A
-ABA < G BBG A(G@
-(A ABBG L \ G - BEB E(I0H8D@
-8D0A(B BBBD L `I - BEB E(I0H8D@
-8D0A(B BBBD gx\ AG RAL Jv BBE B(A0A8Dp
-8A0A(B BBBA , $ gx BDA D0 AABL L , BBB B(D0A8Dp
-8A0A(B BBBI D O7 BBE A(A0D@
-0A(A BBBA D xPC BBE A(A0D@
-0A(A BBBA d 4 Q' BEB B(A0D8D
-8C0A(B BBBH M
-8D0A(B BBBA < HV + BLD D(G0
-(A ABBE D W BKM D(A0D@
-0A(A BBBA T l W/ ?+ BEB B(A0A8G
-8A0A(B BBBA | HBy. K^ ` X P $ X^ AG b
-AA D BBE A(A0D@
-0A(A BBBA D L 8 BBE A(A0D@
-0A(A BBBA D e [+ BBB A(D0D@
-0A(A BBBG L $ + BEE E(K0A8G`p
-8A0A(B BBBA D , BBE A(A0D@l
-0A(A BBBA t @y. K^ T ( ( s* BHB B(A0A8G
-8A0A(B BBBA cx\ AG RAD 7 BBE A(A0D@
-0A(A BBBA D L C BBE A(A0D@
-0A(A BBBA L M _+ BBB B(A0D8Gz
-8C0A(B BBBF T , Ȅ" + BEE B(A0G8G
-8A0A(B BBBA < >y. K^ T ߄Y } N $ l ߄g AG k
-AA 4 + BHA G@h
- DABA T
- + BEB B(A0D8G<
-8A0A(B BBBC t $ pY BBB A(D0DP
-0D(A BBBA q
-0D(A BBBD
-0D(A BBBE L Xl W+ BPB H(A0K8Do
-8A0A(B BBBK h=y. K^ L L `
- + BBB E(A0D8J
-8D0A(B BBBC T [ l [ `[ [ [ 8 [ [ [ [ , X[ D Y } N $ \ g AG k
-AA 4 0 W* BHA G@h
- DABA L ;* BPB H(A0K8Dp
-8D0A(B BBBG L BEB A(D0l
-(A BBBG
-(A BBBA \ (;y. K^ T PE% ) BEB B(A0A8GV
-8A0A(B BBBD H,[ ,[ ,[ ! -[ ,! h-[ D! -[ \! -[ t! @.[ ! .[ ! .[ ! /Y } N $ ! `/g AG k
-AA 4 D! / [) BHA G@h
- DABA L 4" P0l BEE B(A0A8Dp
-8A0A(B BBBB L ! p9l ( BPB H(A0K8Do
-8A0A(B BBBK " 8y. K^ L 4" x< ( BEB B(D0D8G
-
-8D0A(B BBBE $ <# Y0 AGJ XDA D d# Y BRK J0]
- AABF Z
- AABB # ZY } N $ # PZg AG k
-AA 4 4# Z ( BHA G@h
- DABA $$ 7y. K^ <$ Zx\ AG RAD \$ [7 BBE A(A0D@
-0A(A BBBA D $ \C BBE A(A0D@
-0A(A BBBA $ (7y. K^ T L$ \ ' BEE B(A0D8J,
-8A0A(B BBBA 4 $ xig ( BAD D
-ABA 4 $ ig ' BAD D
-ABA D % i
- BBE A(A0D@
-0A(A BBBA D & j BBE A(A0D@
-0A(A BBBA \& ZXx\ AG RA |& hk% AQ
-F L & xk. Aa
-F E 4 & k & BAD Jd
- AABG $ & pm D
-A f
-B $ ' Hn D
-A a
-J D' om A~
-A l , & po [& AID0|
-DAA T & oq O& BBB E(A0A8G%
-8C0A(B BBBI T 4' } & BBB B(A0A8G6
-8D0A(B BBBF T ' @d ' BBB B(A0A8Q7
-8C0A(B BBBB ( 3y. K^ L ' ^Vxz ' BBB H(D0A8GU8A0A(B BBB T L( [x ' BBB E(D0D8G
-8A0A(B BBBA T ( ' BBB B(D0A8G9M
-8A0A(B BBBE T ( 0 ( BEE B(A0G8G!
-8A0A(B BBBH $ * ȅ0 AGJ XDA 4* ȅY } N $ L* ȅg AG k
-AA 4 ) Ʌ S) BHA G@h
- DABA * 1y. K^ L * Ʌ ) BBB H(A0J8DQ
-8C0A(B BBBF + 6_x\ AG RAD 4+ P̅7 BBE A(A0D@
-0A(A BBBA D |+ HͅC BBE A(A0D@
-0A(A BBBA D + P΅ ;( BEE E(J0J@X
-0A(A BBBK L T+ υZ ( BEB L(D0D8G\
-8A0A(B BBBF L + (҅ ' BSL E(D0A8JE
-8D0A(B BBBI L + ۅJ ( BIL L(A0D8JI
-8D0A(B BBBH t , KHE A(GP(A ABBEP
-(A ABBM 4
-(A ABBF D , 0/y ' BIB A(A0D`*
-0A(A BBBA T - z ' BEE B(A0G8G
-8A0A(B BBBA D . i BBB H(A0D@
-0A(A BBBA D \. 8q BBB H(A0D@
-0A(A BBBA L . p BBF B(A0A8D@
-8D0A(B BBBB d . E BBB B(A0A8DP
-8A0A(B BBBE \
-8F0A(B BBBA T . c' BBE B(A0A8O
-8A0A(B BBBG / .y. K^ T / X ( BBB B(A0A8G
-8A0A(B BBBJ 4 $0 *{ BAA Y
-ADF JAG \0 JZx\ AG RAD |0 *7 BBE A(A0D@
-0A(A BBBA D 0 +C BBE A(A0D@
-0A(A BBBA T T0 , ;' BEE B(D0A8M
-
-8A0A(B BBBH d1 -y. K^ |1 Yx\ AG RAD 1 X=7 BBE A(A0D@
-0A(A BBBA D 1 P>C BBE A(A0D@
-0A(A BBBA < t1 X? & BGD D(K0l
-(A ABBB , l2 XxV BAA D0I AABT 1 ?< z& BEE B(A0G8G
-8A0A(B BBBA < <2 +y W' BIA A(D`)
-(A ABBA 4 43 XxL BDA v
-ABE AAD l3 Xx\ AG RAD 3 V7 BBE A(A0D@
-0A(A BBBA D 3 WC BBE A(A0D@
-0A(A BBBA 4 h,y. K^ T |3 W2 ?& BEE B(A0G8G
-8A0A(B BBBA 4 Wx\ AG RAD 4 BBE A(A0D@e
-0A(A BBBA T <4 P 2 [' BBB B(A0A8J
-8A0A(B BBBD L5 h+y. K^ d5 ໆ A^ 5 ໆ 5 軆 HS 5 軆 AT L 5 軆 AAG ]
-CAF k
-ACA R
-CAJ JCA $ ,6 (^ AG b
-AA T 5 ` ( BEB B(A0H8G
-8A0A(B BBBC T 5 ' BBE B(A0D8G
-8D0A(B BBBH L L6 Ɔ ' BEB E(I0H8DP
-8D0A(B BBBA L 6 dž ' BEB E(I0H8D@
-8D0A(B BBBA L 6 `Ɇ s' BEB E(I0H8D@
-8D0A(B BBBA , 7 ʆ BCD D0 AABL l7 ˆ ' BEB E(I0H8D@
-8D0A(B BBBA L 7 0͆ & BEB E(I0H8D@
-8D0A(B BBBA d 8 Ά, & BGE E(A0A8D@
-8F0A(B BBBJ g
-8F0A(B BBBA T t8 xφ & BBE B(A0A8G
-8A0A(B BBBF T 8 ׆r & BHE B(D0A8L
-8D0A(B BBBA T $9 & BBE B(A0A8J
-8A0A(B BBBA 4: &y. K^ L 9 m' ZIB A(A0DPM
-0H(A BBBI : : : : : ; ,; D; \; t; ; ; $ Ab , ; ~ BFA l
-ABA ; Px\ AG RA, < AAG0W
-AAF L ; % BBB B(D0A8DPz
-8A0A(B BBBA L ; J% BBB B(D0A8DP
-8A0A(B BBBA T ,< $ BBB I(A0A8K
-8A0A(B BBBG T < $ BBB I(A0A8K
-8A0A(B BBBG L < J $ BBB A(D0DF
-0D(A BBBH = 0#y. K^ = h > ` ,> X D> P \> H t> P > X > ` > h- 4 > BGI _
-ABD NAB , T> E i# BDC rAH T > =# BBB B(I0A8G^
-8D0A(B BBBD L ? / BEB B(A0A8D`u
-8A0A(B BBBH , ,? p
" AAG0
-CAH @ 0!y. K^ ,@ D@ F \@ H t@ @ @ Lx\ AG RAT ? ( " BBE B(D0A8G
-8D0A(B BBBG , A lLx@ AAG04AA L |@ ! BIB A(D0G
-0D(A BBBA L @ X ! BBB B(A0A8Ji
-8A0A(B BBBI T A ! BBB B(D0D8G
-8A0A(B BBBB T tA 5 # BBB B(D0A8L
-8A0A(B BBBE \ A G9 ?$ BBH A(A0GM
-0D(A BBBC D
-0D(A BBBA B y. K^ L DB xL # BBE B(D0A8G
-8A0A(B BBBA , B Oc # AID |
-DAA |C P C P C O C O C O C P D P0 Hg ,D P7 Hj LD 8P0 Hg lD HP7 Hj $ D hP^ AG b
-AA T C P " BBE B(D0A8G
-8D0A(B BBBC T TD 8U |" BBB B(D0A8R
-8A0A(B BBBA , D ^> # AID W
-DAA T D ^ " BBB B(D0A8R
-8A0A(B BBBA , 4E h> n# AID W
-DAA T dE hB N# BBB B(D0A8R
-8A0A(B BBBJ , E n> # AID W
-DAA T E nB g# BBB B(D0A8R
-8A0A(B BBBJ , DF t> # AID W
-DAA T tF tg # BBB B(A0I8G
-8D0A(B BBBI T F y # BBE B(D0A8G
-8D0A(B BBBB T $G h~ # BBE B(D0A8G
-8D0A(B BBBC , 4H |Fx@ AAG04AA < G Ђ ;# BHD Dv
- DABA < G # BHD Dv
- DABA < ,H , # BHD D
- DABA < lH , " BHD D
- DABA < H p, " BHD D
- DABA L H ` " BBB B(G0D8G
-8A0A(B BBBA < <I " AAG L
-AAI p
-AAB L |I Pq " BBB A(A0G
-0D(A BBBF < I r T" BBA K(M0x
-(D ABBA T J '" BBB G(D0G8G
-8D0A(B BBBH D dJ x C" BBA A(G
-(D ABBE < J r " BBA K(M0x
-(D ABBA T J ` ! BBB G(D0G8G
-8D0A(B BBBH < K g BBA A(DV(A ABB T K HS ! BBB E(A0A8L
-8A0A(B BBBA L y. K^ L 8 L 0 , $L 8[ ! AID t
-DAA , TL h[ ! AID t
-DAA , <M Bx@ AAG04AA < L h X! BHD G
- DABA < L >! BHD G
- DABA T 4M $! BBE B(D0A8G
-8D0A(B BBBB L M Ƈ/
-! BDB B(A0G8J
-8D0A(B BBBH N @y. K^ N N N N , TN [ # AID t
-DAA , N H[ # AID t
-DAA , N x[ # AID t
-DAA T N # BBE B(D0A8G
-8D0A(B BBBH , O <Ax@ AAG04AA 4 lO @e A# BHD D`"
- DABE D O x ## BIA D(Dw
-(D ABBA D O # BIA D(G
-(D ABBA D 4P " BIA D(G
-(D ABBA T |P " BBB B(A0I8G(
-8D0A(B BBBB \ P u " BEE A(A0De
-0A(A BBBA j
-0A(A BBBF \ 4Q E " BEE A(A0D=
-0A(A BBBI j
-0A(A BBBF D Q =# BEE A(A0D
-0A(A BBBD D Q `" # BEE A(A0D
-0A(A BBBD L $R '5 $ BBB A(D0G
-0A(A BBBB L tR / b$ BBB A(D0G[
-0A(A BBBK T R 88s
- $ BDB B(A0G8J
-8D0A(B BBBF T S `B _% BGB B(D0G8G
-8D0A(B BBBB ,T
y. K^ DT G \T xG tT pG T hG T `G T hG T pG T xG U G- AG cA , $U G BGD l
-ABD T T Hf "$ BBB B(A0A8G
-8A0A(B BBBA L U (Lq BBA D(G@n
-(A ABBD
-(A ABBH T DU XM # BBE E(D0A8GE
-8A0A(B BBBA L U S0 s# BBE B(D0D8G`
-8A0A(B BBBA T U S <# BBB E(A0A8KP
-8A0A(B BBBF , DV U> # AID W
-DAA T tV U " BBB E(A0A8KP
-8A0A(B BBBF , V PW> " AID W
-DAA T V `W " BBB E(A0A8KP
-8A0A(B BBBF , TW Y> ]" AID W
-DAA T W Y =" BBB E(A0A8KP
-8A0A(B BBBF , W Z> " AID W
-DAA $ X Z AAG0AA< 4X h[ ! BHA G
- DABA < tX ] ! BHA G
- DABA < X ` ! BHA G
- DABA < X Xb o! BHA G
- DABA D 4Y d T! BBB A(A0J`
-0D(A BBBC 4Z g* A[
-L A TZ h* A[
-L A tZ h* A[
-L A Z h* A[
-L A Z 0h* A[
-L A T Z @h BEJ E(D0D8G(
-8D0A(B BBBF T tZ i m BEJ E(D0D8G(
-8D0A(B BBBF T Z Pk ( BEJ E(D0D8G(
-8D0A(B BBBF T $[ l BEB B(A0A8J
-8A0A(B BBBH L |[ `r( BBB B(A0A8J
-8A0A(B BBBA T [ @tN BEB E(A0D8G
-8A0A(B BBBF T $\ 8v BBB B(D0D8G
-8A0A(B BBBH T |\ y ? BEE B(A0A8JN
-8A0A(B BBBF L \ 8
- BBB B(A0D8G
-8A0A(B BBBG T $] Y BEB B(A0A8J
-8A0A(B BBBH L |] ( \ BBB B(A0A8J
-8A0A(B BBBA T ] pN > BEB E(A0D8G
-8A0A(B BBBF T $^ h BEB E(A0A8G
-8A0A(B BBBF T |^ 0 BEE B(A0A8JN
-8A0A(B BBBF L ^ x
- w BBB B(A0D8G
-8A0A(B BBBG T $_ H BEB B(A0A8J
-8A0A(B BBBH L |_ Ь BBB B(A0A8J
-8A0A(B BBBH T _ N BEB E(A0D8G
-8A0A(B BBBF T $` t BBB B(D0D8G
-8A0A(B BBBH T |` д 0 BEE B(A0A8JN
-8A0A(B BBBF L `
- BBB B(A0D8G
-8A0A(B BBBG T $a Ĉ J BEB B(A0A8J
-8A0A(B BBBH L |a pʈ( M BBB B(A0A8J
-8A0A(B BBBA T a P̈N / BEB E(A0D8G
-8A0A(B BBBF T $b HΈ BBB B(D0D8G
-8A0A(B BBBH T |b ҈ BEE B(A0A8JN
-8A0A(B BBBF L b H؈
- h BBB B(A0D8G
-8A0A(B BBBG c Xx. K^ c d , lc [ x AID t
-DAA D Td 0 BBB B(A0A8s
-0A(B BBBA L d BBB B(A0A8J@
-8A0A(B BBBA d 82 I < e ` AM
-AF
-AH
-EH , De ,-x@ AAG04AA 4 d 8 BHD D`
- DABG < e BBD D(G0b
-(A ABBE , e x ADT
-AF
-AA D f 8H BBB B(A0A8
-0A(B BBBE L df @] BBB B(D0A8G
-8A0A(B BBBA d f Pc BBB E(D0D8D
-8A0A(B BBBD
-8A0A(B BBBK L g XY BEB B(A0A8DX/
-8A0A(B BBBA D lg h BBE B(A0A8
-0A(B BBBA D g 0E BNB B(A0A8
-0A(B BBBA L g 8
-d BEE E(D0A8G)
-8A0A(B BBBA L Lh X] BIE E(D0A8J
-8A0A(B BBBA 4 g h' * AC
P
- A \ h 0 BBE B(A0A8
-0A(B BBBA 0A(B BBB L 4i 4% BBE B(A0A8D
-8A0A(B BBBA , i @$ AM
DL
- E d i Ac BBB E(D0D8D
-8A0A(B BBBD
-8A0A(B BBBK d j EH BEE B(E0A8DX
-8A0A(B BBBA I
-8A0A(B BBBA 4 i J Q AC
FMl
- E d j O ( BBA A(JW
-(A ABBC 3
-(A ABBG T
-(A ABBF T lj Q BEB B(D0A8JR
-8A0A(B BBBA L |k HX BBB B(A0A8G
-8A0A(B BBBB L k ] ; BBB B(D0A8GP
-8A0A(B BBBB T dk (a $ BBA A(DU
-(A ABBK
-(A ABEC L k c%
- BGB B(A0A8G
-8A0A(B BBBH l x. K^ l XmF l m A] m mF ,m mu ASP\D Lm (nF dm `nF |m n AU m nF m nu ASP\D m 0oF m hou ASP\D n oF $n pu ASP\D Dn `pF \n T%x\ AG RAL |n xpN BBE B(A0A8DP
-8A0A(B BBBF L n xr\ BBB E(A0A8DP
-8A0A(B BBBF D dn t BBB D(A0D@
-0A(A BBBI < n x AAG
-AAF p
-AAB < n x k AAG
-AAF p
-AAB < ,o `y / AAG
-AAF p
-AAB < lo z AAG
-AAF p
-AAB dp zX 4 o | BAA P0`
- DABE 4 o | u BAA M@z
- DABF T 4p x}) K BEE E(A0A8G
-8D0A(B BBBF , Dq "x@ AAG04AA < p % BHD D
- DABA T p |
-
- BBB B(A0A8O6
-8A0A(B BBBA < r 螉 BBG A(G@
-(A ABBG L q BEE B(A0D8Gu
-8D0A(B BBBD r x. K^ r AL r x"x DN
-E A r r"x\ AG RAT \r BEE E(D0A8G'
-8D0A(B BBBG T r 8T BEB E(D0A8G
-8D0A(B BBBJ L s @ BBB B(A0A8D8A0A(B BBB L t BBB B(A0A8D8A0A(B BBB L dt BBB B(A0A8D8A0A(B BBB L t BBB B(A0A8D8A0A(B BBB 4 Lt W BHA G@n
- DABI 4 t - BHA G@n
- DABI 4 t P BHA G@n
- DABI 4 t BHA G@n
- DABI D ,u ඉ BBE A(D0G`
-0D(A BBBE T tu } BEE E(A0D8Jg
-8D0A(B BBBD T u ຉ E BEE E(A0D8Jg
-8D0A(B BBBD T $v (
BEE E(A0D8Jg
-8D0A(B BBBD T |v p BEE E(A0D8Jg
-8D0A(B BBBD T v T BEB E(D0A8G
-8D0A(B BBBJ D ,w ƉR BBB D(G0GA
-0D(A BBBA 4 tw ljH ` SGB A(A0D T w ȉ I BEB E(A0D8G
-8D0A(B BBBD T x X͉ H BEB B(D0A8G
-8D0A(B BBBA T \x Ӊ E BFB B(D0A8G
-8D0A(B BBBE T x e
- BBB E(A0A8J<
-8D0A(B BBBH T y e
- T BBB E(A0A8J<
-8D0A(B BBBH T dy e
- BBB E(A0A8J<
-8D0A(B BBBH T y e
- BBB E(A0A8J<
-8D0A(B BBBH z x. K^ $ z ^ AG b
-AA { ${ <{ T{ l{ { { < z [ BBD D(GPC
-(A ABBD L <{ P
/ BBE E(A0A8G_
-8D0A(B BBBE L { S ? BBE B(D0A8Dp
-8A0A(B BBBD d | g BLB B(A0A8D@q
-8A0A(B BBBE
-8A0A(B BBBF T D| BBE B(D0A8G
-8D0A(B BBBG d T} < BLB B(A0A8DPc
-8A0A(B BBBK
-8A0A(B BBBF d } h g BLB B(A0A8D@q
-8A0A(B BBBE
-8A0A(B BBBF L l} p BLE B(A0D8D@
-8A0A(B BBBI L t~ " BLB H(A0D8DPV
-8A0A(B BBBG L ~ 0$ S BEB B(A0A8J
-8A0A(B BBBF d 3 BBB B(D0A8DP
-8A0A(B BBBF
-8A0A(B BBBH T ~ (9 4 BBB E(A0A8J
-8D0A(B BBBJ L =m BBB H(A0D8DP
-8A0A(B BBBA T l >
BBB B(A0H8G
-8A0A(B BBBG T 8P' 8 BBB E(A0A8G
-8A0A(B BBBA 4 Wb A BDA G0x
- DABC x. K^ $ x DN
-E A D X" DN
-N A d X Y 4 Ā (Xg
BAD D
-ABA L `X BLA A(D0]
-(A ABBI X
-(A ABBB L X BLA A(D0]
-(A ABBI X
-(A ABBB < T @Y BBA A(G@b
-(A ABBK $ Z^ AG b
-AA D 8Z BBB D(E0Ml
-0A(A BBBH L L ^# BEB E(D0D8G`B
-8A0A(B BBBG L a# ~ BEB E(D0D8G`B
-8A0A(B BBBG L d# N BEB E(D0D8G`B
-8A0A(B BBBG L < g# BEB E(D0D8G`B
-8A0A(B BBBG D pj < \ xj BBA A(G@a
-(A ABBD T 8kZ BEE E(D0A8Gi
-8D0A(B BBBE T < @n Y BBE E(D0A8G,
-8D0A(B BBBE T pZ BEE E(D0A8Gf
-8D0A(B BBBH d sg BBB B(A0A8GP_
-8A0A(B BBBF <
-8A0A(B BBBJ L w; BBB B(A0D8DPd
-8A0A(B BBBA d \ zg BBB B(A0A8GP_
-8A0A(B BBBF <
-8A0A(B BBBJ | Ć ~ BBB B(D0A8DP
-8A0A(B BBBB U
-8G0A(B BBBK
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- BHA G`
- DABA 4 @D
- BHA G`
- DABA 4 XD
- BHA G`
- DABA 4 , pK
- BHA G`
- DABA 4 d K
- BHA G`
- DABA 4 K
- BHA G`
- DABA 4 K
- BHA G`
- DABA 4 ЇD e
- BHA G`
- DABA 4 D 舍D F
- BHA G`
- DABA 4 | K '
- BHA G`
- DABA , J
- AHG`
-DAA , 8J
- AHG`
-DAA , XC ڔ
- AHG`
-DAA 4 D xK Ô
- BHA G`
- DABA , | J
- AHG`
-DAA , J
- AHG`
-DAA , БC v
- AHG`
-DAA < i _
- BIA A(Gp
-(D ABBA < L i 8
- BIA A(Gp
-(D ABBA < Pi
- BIA A(Gp
-(D ABBA < i
- BIA A(Gp
-(D ABBA < i Ó
- BIA A(Gp
-(D ABBA < L i
- BIA A(Gp
-(D ABBA < b u
- BIA A(Gp
-(D ABBA < @b N
- BIA A(Gp
-(D ABBA < pb '
- BIA A(Gp
-(D ABBA < L b
- BIA A(Gp
-(D ABBA < Оi ْ
- BIA A(Gp
-(D ABBA < i
- BIA A(Gp
-(D ABBA < 0i
- BIA A(Gp
-(D ABBA < L `i d
- BIA A(Gp
-(D ABBA < i =
- BIA A(Gp
-(D ABBA < i
- BIA A(Gp
-(D ABBA < b
- BIA A(Gp
-(D ABBA < L b ȑ
- BIA A(Gp
-(D ABBA < Pb
- BIA A(Gp
-(D ABBA < b z
- BIA A(Gp
-(D ABBA < i S
- BIA A(Gp
-(D ABBA < L ્i ,
- BIA A(Gp
-(D ABBA < i
- BIA A(Gp
-(D ABBA < @i ސ
- BIA A(Gp
-(D ABBA < pi
- BIA A(Gp
-(D ABBA < L i
- BIA A(Gp
-(D ABBA < бi i
- BIA A(Gp
-(D ABBA < i B
- BIA A(Gp
-(D ABBA < 0i
- BIA A(Gp
-(D ABBA < L `i
- BIA A(Gp
-(D ABBA < b ͏
- BIA A(Gp
-(D ABBA < b
- BIA A(Gp
-(D ABBA < b
- BIA A(Gp
-(D ABBA < L b X
- BIA A(Gp
-(D ABBA < Pb 1
- BIA A(Gp
-(D ABBA 4 K
-
- BHA G`
- DABA 4 ! K
- BHA G`
- DABA 4 <! D ̎
- BHA G`
- DABA 4 t! ȿD
- BHA G`
- DABA 4 ! D
- BHA G`
- DABA 4 ! D o
- BHA G`
- DABA 4 " ÍK P
- BHA G`
- DABA 4 T" (čD 1
- BHA G`
- DABA < " @ōb
- BIA A(Gp
-(D ABBA < " pƍb
- BIA A(Gp
-(D ABBA < # Ǎb č
- BIA A(Gp
-(D ABBA < L# ȍb
- BIA A(Gp
-(D ABBA 4 # ʍD v
- BHA G`
- DABA 4 # ˍD W
- BHA G`
- DABA 4 # 0̍D 8
- BHA G`
- DABA 4 4$ H͍D
- BHA G`
- DABA 4 l$ `D
- BHA G`
- DABA 4 $ xύD ی
- BHA G`
- DABA 4 $ ЍD
- BHA G`
- DABA 4 % эD
- BHA G`
- DABA 4 L% ҍD ~
- BHA G`
- DABA 4 % ӍD _
- BHA G`
- DABA 4 % ԍD @
- BHA G`
- DABA 4 % ֍D !
- BHA G`
- DABA 4 ,& K
- BHA G`
- DABA 4 d& 8؍D
- BHA G`
- DABA 4 & PٍK ċ
- BHA G`
- DABA 4 & hڍD
- BHA G`
- DABA 4 ' ۍ9
- AHJ
-DAA 4 D' ݍ4 t
- AHJ
-DAA < 4( ߍ BBA A(G@a
-(A ABBD < ' @ "
- BBA A(J]
-(A ABBE < '
- BBA A(JE
-(A ABBE d ( @B BBB B(A0A8G@x
-8A0A(B BBBE
-8A0A(B BBBF , ( (~
- AAG0W
-DAA , ( x d
- AAG@k
-DAA , ) E
- AAG`
-DAA D 4) ho '
- BEE D(D0G
-0D(A BBBB L |) 7
- BBB B(D0A8N
-8A0A(B BBBG , ) >
- AID W
-DAA L ) 7 Չ
- BBB B(D0A8N
-8A0A(B BBBG , L* > ى
- AID W
-DAA L |* 7
- BBB B(D0A8N
-8A0A(B BBBG , * >
- AID W
-DAA L *
- BDB B(A0D8G
-8A0A(B BBBA , , BAA D0w AABT 4, p BAA D0
- AABE
-
- AABJ
- AABH 4 , BBA A(D0q(A ABB, , P BAA D0x AABT , BAA D0
- AABE
-
- AABJ
- AABH 4 L- 8 BBA A(D0q(A ABB, - BAA D0z AABT - BAA D0!
- AABC
- AABG AAB 4 . BBA A(D0t(A ABB, D. BAA D0w AABT t. @ BAA D0"
- AABB
- AABF AAB 4 . BBA A(D0q(A ABB, / 0 BAA D0y AABT 4/ BAA D0
- AABJ
- AABD
- AABA 4 / BBA A(D0t(A ABB, / BAA D0z AABT / p BAA D0
- AABJ
- AABD
- AABA 4 L0 BBA A(D0t(A ABB, 0 BAA D0| AABL 0 P BAA D0
- AABH
- AABI AAB4 1 BBA A(D0w(A ABB, <1 BAA D0y AABL l1 h BAA D0
- AABG
- AABH AAB4 1
- BBA A(D0t(A ABB, 1 BAA D0 AABT $2 BAA D0
- AABD
- AABH
- AABG 4 |2 BBA A(D0z(A ABB, 2 p BAA D0 AABT 2 BAA D0
- AABD
- AABH
- AABG 4 <3 h BBA A(D0z(A ABB, t3 BAA D0u AABT 3 BAA D0(
- AABD }
- AABG
- AABF 4 3 BBA A(D0p(A ABB, 44 BAA D0 AABT d4 ` BAA D0)
- AABK H
- AABD %
- AABG 4 4 BBA A(D0z(A ABB, 4 P BAA D0u AABT $5 B BAA D0
- AABG
- AABF
- AABJ 4 |5 BBA A(D0o(A ABB, 5 BAA D0v AABT 5 PB BAA D0
- AABG
- AABF
- AABJ 4 <6 H BBA A(D0o(A ABB, t6 BAA D0x AABT 6 R BAA D0
- AABI E
- AABG
- AABH 4 6 BBA A(D0r(A ABB, 47 P BAA D0u AABT d7 R BAA D0
- AABB }
- AABG
- AABH 4 7 " BBA A(D0p(A ABB< <7 #
}
- BEA A(G@z
-(D ABBE , 48 #: BDD G0a DAB , d8 #: BDD G0a DAB , 8 $: BDD G0a DAB , 8 $: BDD G0a DAB , 8 $: BDD G0a DAB , $9 0$: BDD G0a DAB , T9 @$: BDD G0a DAB , 9 P$: BDD G0a DAB , 9 `$: BDD G0a DAB , 9 p$: BDD G0a DAB , : $: BDD G0a DAB , D: $: BDD G0a DAB , t: $: BDD G0a DAB , : $: BDD G0a DAB , : $: BDD G0a DAB , ; $: BDD G0a DAB , 4; $: BDD G0a DAB < d; $l BBE A(A0Y(A BBB < ; % BHB A(A0~(A BBL < ; % BHB A(A0~(A BBL < $< % BHB A(A0~(A BBL T ; @&G Vy
- BBB B(A0G8G
-8D0A(B BBBA T < 8(G 4y
- BBB B(A0G8G
-8D0A(B BBBA T \< 0*G y
- BBB B(A0G8G
-8D0A(B BBBA T < (,G x
- BBB B(A0G8G
-8D0A(B BBBA T = .G x
- BBB B(A0G8G
-8D0A(B BBBA T d= 0G x
- BBB B(A0G8G
-8D0A(B BBBA T = 2G x
- BBB B(A0G8G
-8D0A(B BBBA T > 4G hx
- BBB B(A0G8G
-8D0A(B BBBA < $? 6 BHB A(A0n(A BBL < d? P6 BHB A(A0n(A BBL < ? 6 BHB A(A0n(A BBL < ? 6 BHB A(A0n(A BBL < $@ @7 BHB A(A0n(A BBL < d@ 7 BHB A(A0n(A BBL L @ 7 BBB B(A0A8G
-8A0A(B BBBD L @ `= BBB B(A0A8G
-8A0A(B BBBD D @ B &v
- BBE A(G0D
-0D(A BBBA T @ (Ev !v
- BBB B(A0G8G
-8D0A(B BBBA T ,A PHv 4v
- BBB B(A0G8G
-8D0A(B BBBA T A xKv Gv
- BBB B(A0G8G
-8D0A(B BBBA T A Nv Zv
- BBB B(A0G8G
-8D0A(B BBBA T 4B Qv mv
- BBB B(A0G8G
-8D0A(B BBBA T B TE v
- BEE B(D0A8G
-8A0A(B BBBD T B ] v
- BBB E(A0A8O
-8A0A(B BBBG C PcF AJ0vD T \C c v
- BBB E(I0A8J
-8A0A(B BBBF lD e6 K0j D C ff _v
- BBB D(D0J
-0D(A BBBF T D Hn v
- BBB B(A0F8G
-8A0A(B BBBA L lD r uv
- BIB A(D0N0
-0D(A BBBE $ tE wa ADK@KDA$ E 8xa ADK@KDAT E x v
- BBB B(A0D8G
-8A0A(B BBBD d dE |3 u
- AAMv
-DAF
-DAD D
-DAG
-DAG
-DAA d E ~3 u
- AAMv
-DAF
-DAD D
-DAG
-DAG
-DAA d 4F x3 Nu
- AAMv
-DAF
-DAD D
-DAG
-DAG
-DAA d F P3 u
- AAMv
-DAF
-DAD D
-DAG
-DAG
-DAA \ G (S t
- BAD J
- DABE
- DABJ D
- DABA H (P AS0wD <H XP AS0wD \H P AS0wD |H P AS0wD H 膎R AJ0BD H (R AJ0BD H hR AJ0BD H P AL0~D I ؇P AL0~D <I P AL0~D \I 8P AL0~D |I hP AL0~D I P AL0~D I ȈP AL0~D I P AL0~D I (P AL0~D J XP AL0~D <J P AL0~D \J P AL0~D |J 艎P AL0~D J P AL0~D J HP AL0~D J xP AL0~D J P AL0~D K ؊P AL0~D <K P AL0~D \K 8P AL0~D |K hP AL0~D K P AL0~D K ȋP AL0~D K P AL0~D K (P AL0~D L XP AL0~D <L P AL0~D \L P AL0~D |L 茎P AL0~D , L : BDD G0a DAB 4 L ( BBA A(C
- ABBB 4 M BBA A(C
- ABBB D <M BBB B(A0A8
-0A(B BBBE , M : BDD G0a DAB 4 M D BBA A(
- ABBD 4 M D BBA A(
- ABBD D $N BBB B(A0A8
-0A(B BBBD , lN h: BDD G0a DAB < N x BBB A(A0i
-(A BBBH < N BBB A(A0i
-(A BBBH D O BBB B(A0A8
-0A(B BBBG , dO : BDD G0a DAB O B O ؤB O Z , O X: BDD G0a DAB P h[ $P [ <P d , TP P: BDD G0a DAB P `[ P [ P d T P H k
- BDD M
- DABE b
- DABG N
- DABK T lP k
- BDD M
- DABE b
- DABG N
- DABK , |Q 8: BDD G0a DAB 4 Q H BBA A(C
- ABBB 4 Q BBA A(C
- ABBB D R 8 BBB B(A0A8
-0A(B BBBE , dR : BDD G0a DAB 4 R D BBA A(
- ABBD 4 R (D BBA A(
- ABBD D S @ BBB B(A0A8
-0A(B BBBD , LS : BDD G0a DAB < |S BBB A(A0e
-(A BBBD < S (Î BBB A(A0e
-(A BBBD D S Ŏ BBB B(A0A8
-0A(B BBBG , DT Ȏ: BDD G0a DAB tT Ȏ: T Ɏ: T ʎZ , T 8̎: BDD G0a DAB T H̎[ U ̎[ U ̎d , 4U 0͎: BDD G0a DAB dU @͎[ |U ͎[ U ͎d , U (Ύ BAA D0r AAB, U xΎ BAA D0r AAB4 V Ύ BBA A(D0p(A ABB DV ώY \V hώ[ tV ώa , V Ў BAA D0r AAB, V XЎ BAA D0r AAB4 V Ў BBA A(D0p(A ABB $W ю~ <W hю TW ю A , tW PҎ BAA D0{ AAB, W Ҏ BAA D0{ AAB4 W ӎ BBA A(D0x(A ABB X hӎb $X ӎc <X Ԏo , TX pԎ BAA D0{ AAB, X Ԏ BAA D0{ AAB4 X 0Վ BBA A(D0x(A ABB X Վ Y ֎ Y x֎ , 4Y BAA D0{ AAB, dY ` BAA D0{ AAB4 Y BBA A(D0x(A ABB Y ؎` Y `؎` Y ؎l , Z َ BAA D0w AAB, DZ `َ BAA D0w AAB4 tZ َ BBA A(D0q(A ABB Z ڎ Z ڎ Z ێ < Z ێ BBA A(D@
-(A ABBD < 4[ p BBA A(D@
-(A ABBD < t[ Pݎ BEL A(A0(A BBB [ ގc [ xގc [ ގo , [ (ߎ BAA D0z AAB, ,\ ߎ BAA D0z AAB4 \\ ߎ BBA A(D0t(A ABB \ @ \ \ 0 $ \ ) KA|A T L\ _
- BBB B(D0A8Gm
-8A0A(B BBBA T \ h _
- BBB B(D0A8Gm
-8A0A(B BBBA $ ] $ A]A ] D`l
-H X ] D`s
-I X T ^ BBB A(A0D
-0A(A BBBI u0A(A BBB T t^ BBB A(A0D
-0A(A BBBI u0A(A BBB ^ f D`i
-C 4 ^ BBA A(^
- ABBG T l^ =^
- BBB B(D0A8G.
-8A0A(B BBBD T ^ H A^
- BBB B(D0A8G
-8A0A(B BBBA L _ Y E^
- BBB B(A0A8D
-8A0A(B BBBA 4 l_ ^
- BGB A(A0D \` G L _ ]
- BBB B(A0A8D`~
-8A0A(B BBBA ` ` < <` `R r]
- BBA A(D
-(A ABBJ 4a La ( < ` D @]
- BBA A(D
-(A ABBD a a h < a " ]
- BBA A(D
-(A ABBG b ,b Db p db R |b H R b b !S b !S b "W b `" c $ $c ' <c 8) Tc @+ lc H-r c /S c /S c @0W c 0 c @3 c 5 d 7: ,d : Dd 0> dd @ |d XC
- d PF d 8HS d HS d HW d I e K $e `N <e P Te S: le V e 8Y
- e 0\ e ^ , e `: BDD G0a DAB 4 f ` BBA A(
- ABBE 4 <f b BBA A(
- ABBE D tf d BBB B(A0A8
-0A(B BBBD , f f: BDD G0a DAB 4 f f BBA A(*
- ABBK 4 $g `i BBA A(*
- ABBK D \g k BBB B(A0A8
-0A(B BBBE g nX g nX g oa g xoU h oU h p^ 4 4h Pp$ BBA A(
- ABBE 4 lh Hr BBA A(
- ABBA D h 0t BBB B(A0A8
-0A(B BBBD 4 h xv BBA A(.
- ABBG 4 $i x BBA A(.
- ABBG D \i H{ BBB B(A0A8
-0A(B BBBE i ~ i ~ i < i BBB A(A0s
-(A BBBF < ,j x BBB A(A0s
-(A BBBF D lj BBB B(A0A8
-0A(B BBBG 4 j BBA A(
- ABBE 4 j ؊ BBA A(
- ABBE D $k BBB B(A0A8
-0A(B BBBD 4 lk BBA A(*
- ABBK 4 k p BBA A(*
- ABBK D k ؓ BBB B(A0A8
-0A(B BBBE $l <l X Tl < ll ؘy BBB A(A0
-(A BBBG < l q BBB A(A0
-(A BBBF D l X BBB B(A0A8T
-0A(B BBBI 4 4m П$ BBA A(
- ABBE 4 lm ȡ BBA A(
- ABBA D m BBB B(A0A8
-0A(B BBBD 4 m BBA A(.
- ABBG 4 $n ` BBA A(.
- ABBG D \n Ȫ BBB B(A0A8
-0A(B BBBE n n H n < n ȯq BBB A(A0
-(A BBBG < ,o q BBB A(A0
-(A BBBG D lo H BBB B(A0A8T
-0A(B BBBI 4 o BBA A(
- ABBE 4 o BBA A(
- ABBE D $p BBB B(A0A8
-0A(B BBBD 4 lp ؼ BBA A(*
- ABBK 4 p @ BBA A(*
- ABBK D p BBB B(A0A8
-0A(B BBBE T lp pďG M
- BBB B(A0G8G
-8D0A(B BBBA T p hƏG M
- BBB B(A0G8G
-8D0A(B BBBA T q `ȏG M
- BBB B(A0G8G
-8D0A(B BBBA T tq XʏG M
- BBB B(A0G8G
-8D0A(B BBBA T q P̏G tM
- BBB B(A0G8G
-8D0A(B BBBA T $r HΏG RM
- BBB B(A0G8G
-8D0A(B BBBA < 4s @Џ BHB A(A0n(A BBL T r ЏG L
- BBB B(A0G8G
-8D0A(B BBBA T s ҏG L
- BBB B(A0G8G
-8D0A(B BBBA T ls ԏG L
- BBB B(A0G8G
-8D0A(B BBBA T s x֏G L
- BBB B(A0G8G
-8D0A(B BBBA L t p؏< hL
- BBB B(A0G8G
-8D0A(B BBBA L lt `ڏ< NL
- BBB B(A0G8G
-8D0A(B BBBA , tu P7 BAA
-ABA u `ޏ T u 8ߏ K
- BBB B(D0A8G
-8A0A(B BBBE 4 v @w; BOA K0 AAB L u H K
- BBB B(A0D8N
-8A0A(B BBBA , u > K
- AID W
-DAA , v > xK
- AID W
-DAA L Dv XK
- BBB B(A0D8N
-8A0A(B BBBA , v > 8K
- AID W
-DAA , v > K
- AID W
-DAA L v J
- BBB B(A0D8N
-8A0A(B BBBA , Dw 8> J
- AID W
-DAA , tw H> J
- AID W
-DAA L w X J
- BBB B(A0D8N
-8A0A(B BBBA , w > xJ
- AID W
-DAA , $x > XJ
- AID W
-DAA L Tx 8J
- BBB B(A0D8N
-8A0A(B BBBA , x > J
- AID W
-DAA , x > I
- AID W
-DAA L y I
- BBB B(A0D8N
-8A0A(B BBBA , Ty H> I
- AID W
-DAA , y X> I
- AID W
-DAA L y h xI
- BBB B(A0D8N
-8A0A(B BBBA , z > XI
- AID W
-DAA , 4z > 8I
- AID W
-DAA L dz I
- BBB B(A0D8N
-8A0A(B BBBA , z > H
- AID W
-DAA , z > H
- AID W
-DAA L { H
- BBB B(A0D8N
-8A0A(B BBBA , d{ X> H
- AID W
-DAA , { h> xH
- AID W
-DAA L { x XH
- BBB B(A0D8N
-8A0A(B BBBA , | > 8H
- AID W
-DAA , D| > H
- AID W
-DAA L t| ( G
- BBB B(A0D8N
-8A0A(B BBBA , | > G
- AID W
-DAA , | > G
- AID W
-DAA L $} G
- BBB B(A0D8N
-8A0A(B BBBA , t} h> xG
- AID W
-DAA , } x> XG
- AID W
-DAA L } 8G
- BBB B(A0D8N
-8A0A(B BBBA , $~ > G
- AID W
-DAA , T~ (> F
- AID W
-DAA L ~ 8 F
- BBB B(A0D8N
-8A0A(B BBBA , ~ > F
- AID W
-DAA , > F
- AID W
-DAA L 4 xF
- BBB B(A0D8N
-8A0A(B BBBA , x> XF
- AID W
-DAA , > 8F
- AID W
-DAA L F
- BBB B(A0D8N
-8A0A(B BBBA , 4 ( > E
- AID W
-DAA , d 8 > E
- AID W
-DAA L H E
- BBB B(A0D8N
-8A0A(B BBBA , > E
- AID W
-DAA , > xE
- AID W
-DAA L D XE
- BBB B(A0D8N
-8A0A(B BBBA , > 8E
- AID W
-DAA , ā > E
- AID W
-DAA L D
- BBB B(A0D8N
-8A0A(B BBBA , D 8> D
- AID W
-DAA , t H> D
- AID W
-DAA L X D
- BBB B(A0D8N
-8A0A(B BBBA , > xD
- AID W
-DAA , $ > XD
- AID W
-DAA L T 8D
- BBB B(A0D8N
-8A0A(B BBBA , > D
- AID W
-DAA , ԃ > C
- AID W
-DAA L C
- BBB B(A0D8N
-8A0A(B BBBA , T H
-> C
- AID W
-DAA , X
-> C
- AID W
-DAA L h
- xC
- BBB B(A0D8N
-8A0A(B BBBA , > XC
- AID W
-DAA , 4 > 8C
- AID W
-DAA L d C
- BBB B(A0D8N
-8A0A(B BBBA ,
> B
- AID W
-DAA ,
> B
- AID W
-DAA L
B
- BBB B(A0D8N
-8A0A(B BBBA , d X> B
- AID W
-DAA , h> xB
- AID W
-DAA | x p h L ć ` AAG ]
-CAF [
-ACA R
-CAJ JCA L AAG ]
-CAF [
-ACA R
-CAJ JCA 4 pA
- BHE
-ABD 4 x <A
- BHE
-ABD 4 0 A
- BHE
-ABI 4 T @
- BHE
-ABI D w\ AG RAT @
- BJB B(D0A8G
-8A0A(B BBBG < BEB D(A0M
-(A BBBF T D
@
- BBB B(D0A8J
-8A0A(B BBBJ T + h@
- BBB B(D0A8NQ
-8A0A(B BBBJ , 4> @
- AID W
-DAA , $ 4> @
- AID W
-DAA , T 4> @
- AID W
-DAA , 4> f@
- AID W
-DAA T 4 F@
- BBB B(D0A8NQ
-8A0A(B BBBJ , => @
- AID W
-DAA , < => @
- AID W
-DAA , l => d@
- AID W
-DAA , => D@
- AID W
-DAA d = BBB B(A0D8Gpk
-8A0A(B BBBG }
-8D0A(B BKBM L 4 >1 ?
- RED D(D(A ABBB , < w@ AAG04AA 4 H?U Y?
- BHD D`
- DABH 4 pBU ;?
- BHD D`
- DABH 4 $ EU ?
- BHD D`
- DABA 4 \ HU >
- BHD D`
- DABA 4 KU >
- BHD D`
- DABA 4 ̍ OU >
- BHD D`
- DABA 4 8RU >
- BHD D`
- DABA 4 < `UU >
- BHD D`
- DABA T t X+ i>
- BEH B(D0A8L
-8D0A(B BBBC L ̎ `] S>
- BBB B(A0A8J[
-8A0A(B BBBG , ԏ `U ADG T
-DAK L L `1
->
- RED D(D(A ABBB T a =
- BBG H(A0A8J
-8D0A(B BBBE w. K^ Đ Pk ܐ Hk w\ AG RAT \ 0k l=
- BBA I(Dp
-(D ABBA L
-(D ABBA T hl 0=
- BBA I(Dp
-(D ABBA L
-(D ABBA T m <
- BBA I(Dp
-(D ABBA L
-(D ABBA , w@ AAG04AA 4 nU <
- BHD D`
- DABH 4 ̑ qU j<
- BHD D`
- DABA 4 tU L<
- BHD D`
- DABA T < xP" /<
- BBB B(A0D8L
-D
-8A0A(B BBBI L xw. K^ d | 虐 ē ܓ , Y ADG T
-DAK , l 0[ <
- AID t
-DAA , `[ <
- AID t
-DAA , ̓ [ <
- AID t
-DAA , [ _<
- AID t
-DAA , , [ ?<
- AID t
-DAA , \ [ <
- AID t
-DAA , P[ ;
- AID t
-DAA 4 ;
- BHE l
-ABB 4 ;
- BHE o
-ABO 0w\ AG RAL >
BEB B(A0A8G
8A0A(B BBB T A
-J AT :
- BBB B(A0I8D
-8D0A(B BBBA T 謐 :
- BBB B(A0I8D
-8D0A(B BBBA T l :
- BBB B(A0I8D
-8D0A(B BBBA T Ė x :
- BBE E(D0A8G#
-8D0A(B BBBF T @ D:
- BBE E(D0A8G#
-8D0A(B BBBF , , I AAG0
-AAA 4 (l 9
- BHA Gp
- DABA 4 ܗ `t 9
- BHA Gp
- DABA 4 l 9
- BHA Gp
- DABA 4 L ࿐t 9
- BHA Gp
- DABA 4 ( 9
- BHA GPt
- DABC 4 Ð m9
- BHA GP{
- DABD 4 Ő O9
- BHD D`
- DABH 4 , PȐ 19
- BHD D`
- DABH 4 d ʐ 9
- BHD D`
- DABH 4 `͐ 8
- BHD D`
- DABG d ϐ?
- BBB B(A0A8GP
-8G0A(B BBBP ^
-8A0A(B BBBH T < ِ o8
- BEB B(A0A8G
-8A0A(B BBBJ T H p9
- BBB B(D0A8L;
-8A0A(B BBBB T ' V9
- BGB B(A0A8J
-8A0A(B BBBG T D ' 9
- BGB B(A0A8J
-8A0A(B BBBG T ' 8
- BGB B(A0A8J
-8A0A(B BBBG Hw. K^ , P 8
- AID k
-DAA 4 < w8
- BHE r
-ABD 4 t X C8
- BHE r
-ABD 4 8
- BHE u
-ABI 4 7
- BHE u
-ABI L @ 7
- BBB B(A0D8GT
-8D0A(B BBBK , $ Lv@ AAG04AA D `
- @7
- BIA D(D
-(D ABBA T 9 7
- BBE B(G0A8L
-8D0A(B BBBE 0w. K^ $ , [ 7
- AID t
-DAA l Dv\ AG RA, v@ AAG04AA D +7
- BIA D(D
-(D ABBA T L m 7
- BDB B(A0D8J
-8A0A(B BBBJ \ w. K^ t 5 x5 p5 h5 Ԡ p5 x5 , L 5[ z7
- AID t
-DAA , | 5[ Z7
- AID t
-DAA , 5[ :7
- AID t
-DAA , v@ AAG04AA 4 5U 6
- BHD D`
- DABH 4 D 9U 6
- BHD D`
- DABH 4 | 0<U 6
- BHD D`
- DABH T X? 6
- BDH B(A0D8Gl
-8D0A(B BBBA T C 6
- BDH B(A0D8Gd
-8D0A(B BBBA T d hH 7
- BDH B(A0D8Gd
-8D0A(B BBBA t w. K^ L L , L[ 6
- AID t
-DAA , 4 M[ 6
- AID t
-DAA , pv@ AAG04AA < MB 6
- BHD G
- DABA < ԣ QI v6
- BHD G
- DABA D (U ]6
- BBE A(H0D
-0A(A BBBA < \ Wb Y6
- BBA D(K
-(A ABBA D Y [6
- BBB A(F0Gu
-0A(A BBBG T Xw #8
- BDB B(D0A8M5
-8D0A(B BBBB w. K^ xz $ pz < hz T pz $ l xz^ AG b
-AA D ܥ z 7
- BBB A(I0Dp
-0D(A BBBH D $ 8| ]7
- BBB A(I0Dp
-0D(A BBBH D l } 77
- BBB A(I0Dp
-0D(A BBBH D H 7
- BBB A(I0Dp
-0D(A BBBH D Ѐ 6
- BBB A(I0Dp
-0D(A BBBH D D X 6
- BBB A(I0Dp
-0D(A BBBH D 6
- BBB A(I0Dp
-0D(A BBBH D ԧ h y6
- BBB A(I0Dp
-0D(A BBBH D S6
- BBB A(I0Dp
-0D(A BBBH D d x -6
- BBB A(I0Dp
-0D(A BBBH D 6
- BBB A(I0Dp
-0D(A BBBH D 5
- BBB A(I0Dp
-0D(A BBBH , I AAG0
-AAA 4 l 0 5
- BHA GP
- DABH 4 m5
- BHA GP
- DABH 4 ܩ O5
- BHA GP
- DABH 4 蓑e 15
- BHD D`"
- DABE 4 L U 5
- BHD D`
- DABH 4 HU 4
- BHD D`
- DABH 4 pU 4
- BHD D`
- DABA 4 U 4
- BHD D`
- DABA 4 , U 4
- BHD D`
- DABA 4 d 覑U }4
- BHD D`
- DABA 4 b _4
- BHD D`
- DABD 4 ԫ Hb A4
- BHD D`
- DABD T #4
- BGB B(D0D8G\
-8D0A(B BBBK T d Ñ 4
- BBB G(A0D8M+
-8D0A(B BBBI T Ǒ 4
- BGB B(D0D8G\
-8D0A(B BBBK T Hڑ k5
- BBB G(A0D8M+
-8D0A(B BBBI $ w. K^ < ޑ- Ak T ޑ K5
- BBE B(A0H8G
-8A0A(B BBBD `w. K^ D ̮ 8 BBB B(A0A8
-0A(B BBBA D 8 BBB B(A0A8
-0A(B BBBA D \ : BBB B(A0A8
-0A(B BBBA D : BBB B(A0A8
-0A(B BBBA D B BBB B(A0A8
-0A(B BBBA D 4 B BBB B(A0A8
-0A(B BBBA | İ ܰ x p h $ ` < h T p l x L ̱ AAG e
-CAF S
-ACA R
-CAJ JCA L ADD e
-CAF [
-ACI R
-CAJ JCA L l ADD e
-CAF [
-ACI R
-CAJ JCA L AAG e
-CAF S
-ACA R
-CAJ JCA L x ADD e
-CAF [
-ACI R
-CAJ JCA L \ ADD e
-CAF [
-ACI R
-CAJ JCA L x AAG e
-CAF S
-ACA R
-CAJ JCA L ADD e
-CAF [
-ACI R
-CAJ JCA L L h ADD e
-CAF [
-ACI R
-CAJ JCA L AAG e
-CAF S
-ACA R
-CAJ JCA L X ADD e
-CAF [
-ACI R
-CAJ JCA L < ADD e
-CAF [
-ACI R
-CAJ JCA L X AAG e
-CAF S
-ACA R
-CAJ JCA L ܵ ADD e
-CAF [
-ACI R
-CAJ JCA L , H ADD e
-CAF [
-ACI R
-CAJ JCA L | AAG e
-CAF S
-ACA R
-CAJ JCA L ̶ 8 ADD e
-CAF [
-ACI R
-CAJ JCA L ADD e
-CAF [
-ACI R
-CAJ JCA 4 l 8 BEA A(X
- ABBJ 4 BEA A(X
- ABBJ , ܷ BDA
-ABD , BDA
-ABD , < BDA
-ABD , l BDA z
-ABD X N T `z \+
- BEE B(A0A8G
-8A0A(B BBBG , T V t+
- BHI o
-ABA , > T+
- AID W
-DAA , V 4+
- BHI o
-ABA , > +
- AID W
-DAA L *
- BLB B(A0A8J}
-8A0A(B BBBK , d R *
- BHI k
-ABA , > *
- AID W
-DAA , Ĺ R *
- BHI k
-ABA , H> o*
- AID W
-DAA T $ X O*
- BLB E(A0A8G
-8A0A(B BBBA , | Z r*
- BHI s
-ABA , > R*
- AID W
-DAA , ܺ Z 2*
- BHI s
-ABA ,
-> *
- AID W
-DAA , <
-Z )
- BHI s
-ABA , l P
-> )
- AID W
-DAA , T xv@ AAG04AA 4 ̻ 0
- )
- BHD D`2
- DABE 4 d)
- BHD D`2
- DABE 4 < ` F)
- BHD D`2
- DABE 4 t ()
- BHD D`9
- DABF 4
-)
- BHD D`9
- DABF 4 ( (
- BHD D`9
- DABF 4 (
- BHD D`9
- DABF T T X Z (
- BEE E(D0A8Gk
-8D0A(B BBBK T ` s(
- BEB E(A0D8Jr
-8D0A(B BBBD T (1 )
- BBA D(J`y
-(D ABBK F
-(D ABBA L \ 1 (
- BEE E(A0F8M
-8D0A(B BBBJ d 3" A` 4 A^ T 4 l(
- BBE B(D0A8Mm
-8A0A(B BBBD T D x9 L(
- BBE B(D0A8Mm
-8A0A(B BBBD L > ,(
- BBB B(A0D8M
-8D0A(B BBBG < D BDA k
-ABK @
-ABA L DQ BBB B(A0A8D
-8A0A(B BBBH L 4 HQ BBB B(A0A8D
-8A0A(B BBBH L MQ BBB B(A0A8D
-8A0A(B BBBH L QQ BBB B(A0A8D
-8A0A(B BBBH L $ Uf BBB B(A0A8GM
-8A0A(B BBBH L t @Yf BBB B(A0A8GM
-8A0A(B BBBH T `]3 '&
- BEB E(A0D8JR
-8D0A(B BBBD T d Ho &
- BBA D(J`y
-(D ABBK F
-(D ABBA L o u&
- BEE E(A0F8M
-8D0A(B BBBJ r" A` r A^ D r BHB B(A0A8DP8A0A(B BBBD L r BHB B(A0A8DP8A0A(B BBBD s BHB B(A0A8DP8A0A(B BBBD Xt BHB B(A0A8DP8A0A(B BBBD $ u BEE B(A0A8DP8A0A(B BBBD l u BEE B(A0A8DP8A0A(B BBB, v EG
-AE o
-AH D w BHE E(D0A8DP8A0A(B BBBL , x= BHE E(A0A8D@8A0A(B BBB L | z BBB B(A0A8G
-8A0A(B BBBH , x EG
-AE o
-AH D h BHE E(D0A8DP8A0A(B BBBL D M BHE E(A0A8D@(8A0A(B BBB L BBB B(A0A8G
-8A0A(B BBBH , ` EG
-AE o
-AH D P BHE E(D0A8DP8A0A(B BBBL \ ȋ? BHE E(A0A8D@8A0A(B BBB L BBB B(A0A8G
-8A0A(B BBBH , 8 EG
-AE o
-AH D , ( BHE E(D0A8DP8A0A(B BBBL t S BHE E(A0A8D@.8A0A(B BBB L BBB B(A0A8G
-8A0A(B BBBH < FDK
-ABB w
-ABF L T BHE E(D0A8DP
-8A0A(B BBBK L @ BHE E(A0A8D@
-8A0A(B BBBA L p BBB B(A0A8G
-8A0A(B BBBA < D FDK
-ABB w
-ABF L BHE E(D0A8DP
-8A0A(B BBBK L `@ BHE E(A0A8D@
-8A0A(B BBBA L $ P BBB B(A0A8G
-8A0A(B BBBA t Кw. K^ ȱ 4 L d , ȱ[
- AID t
-DAA , [
- AID t
-DAA , $ ([ n
- AID t
-DAA , T X[ N
- AID t
-DAA , [ .
- AID t
-DAA , l v@ AAG04AA 4 U
- BHD D`
- DABH 4 U
- BHD D`
- DABH 4 T ظU
- BHD D`
- DABH 4 U
- BHD D`
- DABH 4 (U f
- BHD D`
- DABH T P H
- BEE E(D0A8G*
-8D0A(B BBBD T T ǒ .
- BDB B(A0D8G&
-8A0A(B BBBA T P͒ ;
- BDE H(A0A8G$
-8D0A(B BBBA T ֒
- BDE H(A0A8G,
-8D0A(B BBBA T \ `ݒ
- BDE H(A0A8G,
-8D0A(B BBBA T (
- BDE H(A0A8G,
-8D0A(B BBBA w. K^ D T b
- BBB A(D0Kp
-0A(A BBBJ , >
- AID W
-DAA , v@ AAG04AA 4 U h
- BHD D`
- DABH L 4 K
- BBE B(F0G8G
-8D0A(B BBBI < hw. K^ T l x , 0 AF
FK
- D T | /
- BBE E(D0A8G
-8D0A(B BBBE T T |
- BBE E(D0A8G
-8D0A(B BBBE , d (v@ AAG04AA <
- BHD G
- DABA T
- BBE B(D0A8G
-8D0A(B BBBG T t
-E
-
- BBB E(A0A8N
-8A0A(B BBBA , > (
- AID W
-DAA L
- BBB B(D0F8Gb
-8A0A(B BBBK T L @k
- BBB B(A0F8J`
-8A0A(B BBBE T X u '
- BBB B(A0F8Jw
-8A0A(B BBBF T #
- BBH E(F0A8G?
-8D0A(B BBBE T T ()
- BBB E(F0A8J>
-8A0A(B BBBD T .
- BBB B(F0D8Gv
-8A0A(B BBBG T 4 ;
- BBB B(D0F8Jy
-8A0A(B BBBA T \ 0; w
- BBB G(A0D8M
-8D0A(B BBBB l hw. K^ D B BBE A(A0D@
-0A(A BBBA D 8E BBE A(A0D@
-0A(A BBBA , v@ AAG04AA < G
- BHD G
- DABA T K
- BBB E(A0G8L@
-8D0A(B BBBA < $ T #
- BLD D(G0
-(A ABBE < d hU*
- BED I(KPc
-(D ABBE < XV
- BLD D(G0
-(A ABBE < W*
- BED I(KP_
-(D ABBI T $ X-
- BBM B(A0D8G
-8A0A(B BBBD , | j> g
- AID W
-DAA d w. K^ | j0 Hg j7 Hj k0 Hg k7 Hj 8k0 Hg Hk7 Hj D < hk BBE A(A0D@g
-0A(A BBBA D k BBE A(A0D@g
-0A(A BBBA D xl BBE A(A0D@g
-0A(A BBBA D m BBE A(A0D@o
-0A(A BBBA D \ m BBE A(A0D@o
-0A(A BBBA D 0n BBE A(A0D@o
-0A(A BBBA , v@ AAG04AA < d nu
- BIA D(Dp)
-(D ABBJ < qu i
- BIA D(Dp)
-(D ABBJ < uu C
- BIA D(Dp)
-(D ABBJ < $ Xxu
- BIA D(Dp)
-(D ABBJ < d {u
- BIA D(Dp)
-(D ABBJ < ~u
- BIA D(Dp)
-(D ABBJ L
- BBJ B(A0A8G
-8D0A(B BBBD L 4 x
- BBJ B(A0A8G
-8D0A(B BBBD L ؆
- BBJ B(A0A8G
-8D0A(B BBBD L 8 l
- BBB B(A0I8G
-8D0A(B BBBD L $ W
- BBB B(A0I8G
-8D0A(B BBBD L t ؍ B
- BBB B(A0I8G
-8D0A(B BBBD T ( /
- BBE B(A0F8M9
-8D0A(B BBBA T S
- BBE B(A0F8M0
-8D0A(B BBBA T t q w
- BBG B(A0A8J
-8A0A(B BBBA T 0v
- BBB E(A0A8L
-8A0A(B BBBK T $ Xm
- BBB E(A0A8L
-8A0A(B BBBD T | p}
- BBB B(D0A8L
-8A0A(B BBBG w. K^ 0 Hg 7 Hj T ,
- BBB J(A0A8G?
-8D0A(B BBBK T
- BEE E(D0A8G
-8D0A(B BBBH T
- BBE B(D0A8G
-8D0A(B BBBG , `v@ AAG04AA < d 8 :
- BHD D
- DABA T ؾc
- BBB B(F0A8G
-8A0A(B BBBE w. K^ ړ> Hu ړE Hx v\ AG RAT t ۓ
-
- BBB B(D0A8N
-8A0A(B BBBD , > Z
- AID W
-DAA , 4v@ AAG04AA < ,
-
- BHD Dm
- DABA T l
- BBE E(A0A8GV
-8D0A(B BBBF T x /
- BBE B(D0A8GH
-8D0A(B BBBD T o
- BBB G(D0A8M
-8D0A(B BBBE , hw. K^ $ D p ADD0AA$ l ADD0AA$ ADD0AA$ ADD0AA$ ADD0AA$ ADD0AAD 4 1 BBE A(A0D@
-0A(A BBBA D | > BBB D(A0D@
-0A(A BBBA D F BBB D(A0D@
-0A(A BBBA D N BBB D(A0D@
-0A(A BBBA $ T ^ AG b
-AA L 2 7
- BGB B(A0K8U
-8A0A(B BBBA <
- BQA A(N`
-(A ABBA < T h
- BQA A(N`
-(A ABBA <
- BQA A(N`
-(A ABBA L 2 u
- BGB B(A0K8U
-8A0A(B BBBA < $ =
- BQA A(N`
-(A ABBA < d 8
- BQA A(N`
-(A ABBA $ \ AAG0AA< p
- BHA JU
- DABA < P
- BHA JU
- DABA < L 0
- BHA JU
- DABA <
- BHA JU
- DABA < }
- BHA JU
- DABA < n
- BHA JU
- DABA < L _
- BHA JU
- DABA < B P
- BHA J
- DABA D BBE A(A0D@l
-0A(A BBBA $ 8 i AAG0]AA$ q AAG0eAAD BBE A(A0D@l
-0A(A BBBA $ d p | AAG0pAA i AG _A q AG gA$ x AAG0xAAT <
- BEJ E(D0D8G
-8D0A(B BBBG T $M
- BBB I(A0A8JN
-8A0A(B BBBA , +>
- AID W
-DAA \ + BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBL | , 3
- BBB E(A0G8I[
-8D0A(B BBBA < / /
- BGB D(A0~
-(A BBBK T p0M
-
- BBB M(A0D8Gg
-8A0A(B BBBA , d h:> W
- AID W
-DAA T x: 7
- BBB E(A0H8K
-8A0A(B BBBA , @> ]
- AID W
-DAA T @ ?
- BBB E(I0A8K}
-8D0A(B BBBF T t hC
- BBB B(A0H8J
-8A0A(B BBBA , L> >
- AID W
-DAA L L
- BEB E(I0H8DP
-8D0A(B BBBD L L N
- BEB E(I0H8D@
-8D0A(B BBBA T P'
- BBB I(A0A8J
-8A0A(B BBBA , V>
- AID W
-DAA T $ V
- BBB E(I0A8K}
-8D0A(B BBBF T | 0Y=
- BBB B(A0D8N
-8A0A(B BBBA , a>
- AID W
-DAA L (a
- BEB E(I0H8D@
-8D0A(B BBBA T T bH
- BBI B(A0A8J.
-8A0A(B BBBA , i>
- AID W
-DAA \ i BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBT < j 7
- BBB M(A0A8J
-8A0A(B BBBA , q>
- AID W
-DAA < qH _
- BBA A(Gp
-(D ABBA < rH C
- BBA A(Gp
-(D ABBA T s8 BEB F(G0DP
-0D(A BBBA l0D(A BBB < tH
- BBA A(Gp
-(D ABBA d u/
- BBB E(A0F8Dp
-8A0A(B BBBF d8A0A(B BBB < D xvH X
- BBA A(Gp
-(D ABBA T < w9 BEB G(A0IP
-0D(A BBBE m0D(A BBB < pxH
- BBA A(Gp
-(D ABBA d y
- BGB B(A0D8D
-8A0A(B BBBA @8A0A(B BBB d ({# n
- BBB B(D0F8D
-8A0A(B BBBA E8A0A(B BBB < |H
- BBA A(Gp
-(D ABBA T ~; BEB F(G0DP
-0D(A BBBE o0D(A BBB L ~
- BBH E(A0F8D`
-8D0A(B BBBH 8mw. K^ , b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , 4 0b D\
-H P
-H Q
-G I , d p AJ@
-AG MA v\ AG RAD BBE A(A0D@
-0A(A BBBA D BBE A(A0D@
-0A(A BBBA T
- BEB B(H0A8G\
-8A0A(B BBBG , X>
- AID W
-DAA T h
- BEE E(D0A8G
-8D0A(B BBBE , $ `v@ AAG04AA < `
- BHD G'
- DABA $
- AL0p
-DA $ H
- AL0p
-DA $ , Н f
- AL0p
-DA d T X L
- KGB B(A0A8R8A0A(B BBBG T O
- BBB B(A0A8G
-8A0A(B BBBG T
- BBH B(F0A8J
-8D0A(B BBBG $ hw. K^ < Ɣ> Hu \ ƔE Hx | Hv\ AG RAT Ɣ
-
- BBB B(D0A8N
-8A0A(B BBBJ , < pє> a
- AID W
-DAA , $ v@ AAG04AA 4 PєU
- BHD D`
- DABH T xԔI
- BBB B(A0D8L
-8A0A(B BBBK @gw. K^ X P T t X, S
- BBE B(D0A8G
-8D0A(B BBBK , ܩv@ AAG04AA <
- BHD D
- DABA L <
- BBB J(A0A8G
-8D0A(B BBBH T p
- BBE B(D0A8G
-8D0A(B BBBG T 8
- BBE B(D0A8G
-8D0A(B BBBH \ ^ KLY H(D0I8J8A0A(B BBBG \ T KEJ E(J0I8J8A0A(B BBBG \ KLQ I(G0I8M8A0A(B BBBI L \ @
- BHB E(A0F8G
-8D0A(B BBBD L f
- BEB G(A0G8G
-8D0A(B BBBD L
- BEB G(A0A8M
-8D0A(B BBBD L L P
- BEB G(A0A8J
-8D0A(B BBBG L $
- BBB G(D0G8G
-8D0A(B BBBA bw. K^ v\ AG RAD -} BBB D(A0D@
-0A(A BBBA D $ @.} BBB D(A0D@
-0A(A BBBA D l x/} BBB D(A0D@
-0A(A BBBA D 0} BBB D(A0D@
-0A(A BBBA D 1 BBB D(A0D@
-0A(A BBBA D D 03 BBB D(A0D@
-0A(A BBBA D x4 BBB D(A0D@
-0A(A BBBA D 5 BBB D(A0D@
-0A(A BBBA , v@ AAG04AA 4 6U 7
+8F0A(B BBBE * Xo 9 X 1 l 9 FBE D(D0D@J
+0A(A BBBE N
+0A(A BBBA L. Xo 9 D T2 ܽls 9 FBB A(A0Gu
+0A(A BBBA D 2 ls n9 FBB A(A0Gu
+0A(A BBBA D 2 Lls A9 FBB A(A0Gu
+0A(A BBBA D ,3 ll 9 FBB A(A0Gr
+0A(A BBBA D t3 ll 8 FBB A(A0Gr
+0A(A BBBA D 3 ll 8 FBB A(A0Gr
+0A(A BBBA D 4 ll 8 FBB A(A0Gr
+0A(A BBBA D L4 $l 8 FBB A(A0Gc
+0C(A BBBA ( zPLR x 0
X 8 D 4 (l 8 FBB E(A0G`z
+0D(B BBBC ( zPLR x% ` 0 bXv 7 P d5 ,l 7 FEE B(A0A8G
+8A0A(B BBBA lX 7 D 5 `l 7 FBB D(D0Dd
+0A(A BBBF ( zPLR x- 0 X 37 L \6 l G7 FEB B(D0A8Gr
+8A0A(B BBBI <X 7 D 6 ls '7 FBB A(A0Gu
+0A(A BBBA D 7 Tls 6 FBB A(A0Gu
+0A(A BBBA D T7 ls 6 FBB A(A0Gu
+0A(A BBBA D 7 ls 6 FBB A(A0Gu
+0A(A BBBA D 7 ls s6 FBB A(A0Gu
+0A(A BBBA D ,8 4ls F6 FBB A(A0Gu
+0A(A BBBA D t8 lls 6 FBB A(A0Gu
+0A(A BBBA P 8 l" 5 FBB B(A0D8J
+8D0A(B BBBA D 9 ls 67 FBB A(A0Gu
+0A(A BBBA D X9 Xls 7 FBB A(A0Gu
+0A(A BBBA D 9 ls 6 FBB A(A0Gu
+0A(A BBBA L 9 l 6 FBB E(D0A8Gp
+8A0A(B BBBF D 8: Hm 6 FBB A(D0D
+0A(A BBBA l
X A6 L <; m, OEA A(G0U
+(A ABBH @H0 L ; m OBA D(G0C
+(A ABBJ pH0 L ; (m OBA D(G0C
+(A ABBJ pH0 P ; mr o5 BBE B(D0A8G
- BHD D`
- DABH 4 :U
+8A0A(B BBBH , zPLR xi 4 XH 76 L < !m OBA D(G0C
+(A ABBJ pH0 ( = ,"mA JDG aAAH ( D= P"mA JDG aAAH L < t"m9 G6 FBB B(A0D8Gp8
+8D0A(B BBBC P = d%m 76 FEB B(A0A8Q
+8A0A(B BBBB L p= )m* 6 FEJ B(A0D8G@
+8D0A(B BBBA D = *m 5 BEB E(D0JS
+0A(B BBBH ( zPLR x= 0 X! 5 L L> .m 6 FBE E(D0C8G@
+8A0A(B BBBF x! `XD 6 L > Bm ;8 FBO B(A0A8Dp
+8A0A(B BBBG L ? <FmS 8 FBB B(A0A8Gb
+8D0A(B BBBD , zPLR x 4 !X 8 @@ kZ Hv
+A \@ ^m p@ ^m @ ^m @ ^m @ ^m @ 0X' < @ ,0X KBB D(A0(D GBB A ^m (A ^m L @ ^m 8 FBA D(D0N
+(I ABBK D(A ABB A _mD FG l
+EA < A `m~ 8 EC
HDn
+ F .C. H. & 0X 8 8 B ,im FBD A(D0@
+(D ABBF P A jm G8 FEB B(A0C8G
+8D0A(B BBBJ D A \qm "8 FFG A(D0GP
+0A(A BBBF 0 *0XF 7 L TB rm 7 FBB B(A0A8Gh
+8A0A(B BBBI , zPLR x 4 /X k7 C kZ Hv
+A C rm C L1X' C `1X. Fb C trm H D rm FBE B(A0A8D@
+8A0A(B BBBF H PD tm FBB E(A0A8D@
+8A0A(B BBBH D xvm D tvm D pvm D lvm D hvm L E dvm OBA D(G0C
+(A ABBJ pH0 H PE vm? PBB B(A0A8QpN
+8A0A(B BBBG H E {m PBB B(A0A8DP
+8A0A(B BBBA E ,}m FEE B(D0D8R@s
+8A0A(B BBBG X
+8E0A(B BBBJ \
+8E0A(B BBBF \
+8E0A(B BBBF P E ~m 4 FEB E(D0A8G:
+8D0A(B BBBK ,* .X! g4 L DF mB W4 FBE E(D0A8G`
+8D0A(B BBBD 8G mD FJ i
+EA 4 F 8ml 3 FKA DP
+ DABI $ zPLR xU P , -X 3 G k2 HW G -X' L H m FBA A(G0\
+(A ABBE 8
+(A ABBJ < G Ԕm> 3 FBI A(D@
+(A ABBC +-X 2 H m! JQ H Жm% P 4H m 2 FEA A(J@
+(C ABBH
+(C ABBE P H 8m o2 FEA A(G@
+(C ABBH
+(C ABBE ( I m EAG o
+CAH I m I m I m I m P XI m 1 FEA A(J@
+(C ABBH
+(C ABBE PJ 4m
+ dJ 0m
+ xJ ,mr J m
+ J m
+ J m , $J m 0 FNG0a
+DBF , TJ m 0 FJD0j
+DBD L J m 0 FBB B(A0D8Gp
+8A0A(B BBBD 2 *X 0 4 J mV 0 FIA G0\
+ AABA +X 0 H K m PBB B(A0A8QPB
+8A0A(B BBBK L K xm ;0 FBB B(A0A8D`
+8C0A(B BBBE L K (m / FBB B(A0A8D`
+8C0A(B BBBE L (k2 HW L m L ̭m M حm% F^ (M m
+ <M m- PM m
+ dM m- xM m5 M Hm M Dm M @m M Lm M Hm M Dm N @m tM <mJ W. Lr
+J A <N hm PN dm dN `m $ xN \mG FAG rDB $ N mG FAG rDB $ N mG FAG rDB N Ԯm O m) LX O m2 N mQ ?- L@ 8 TO `m FAA M
+ABH q
+DBI , O $m AF
HK
+ C ( O mR BAA \
+DBJ O (XO FC dO m) o, Ec O m, S, Ef O ,m 7, EN < O ,m{ , FBJ D(GPP
+(C ABBA < P lm + FBE D(DP
+(D BBBG < DP Lm + FBE D(DP
+(D BBBG L P ,m g+ FBB E(D0D8Dp
+8D0A(B BBBE L P m + FFB E(D0D8D
+8D0A(B BBBF $5 u&X * L <Q m * FFB E(D0D8D
+8D0A(B BBBF 5 %&X {* 0 HR Lmw EAG [
+AAF DAAL |R m FBB A(A0
+(D BBBJ (A BBB ` R m FBB B(A0A8D@
+8D0A(B BBBM 8A0A(B BBB \ 0S dmW FBB B(A0A8G@
+8A0A(B BBBJ d8A0A(B BBBH S dm FBE H(A0A8G@c
+8D0A(B BBBB < S 8mk FBB A(A0
+(A BBBJ T hm" EV
+E A X <T xm OBD A(G0P
+(A ABBE (A ABBCH0 ( T mA JDG aAAH X T @m{ KBB H(D0w
+(I BBBO H0 T |T dm ' FBB A(D0
+(I BBBL
+(A BBBH H xU m6 FBB B(D0A8D`d
+8D0A(B BBBJ H U ma PBE B(A0A8G`
+8A0A(B BBBJ < lU 4m> & FBA D(G0
+(A ABBJ < U 4m> & FBA D(G0
+(A ABBJ L U 4mc [& FGB E(A0D8D`q
+8A0A(B BBBH < <V Tm> ?& FBA D(G0
+(A ABBJ D |V Tm & FDE A(A0G@z
+0A(A BBBF V !X % FFD@ L V m % FBB E(A0A8O
+8D0A(B BBBA , zPLR x
4 Xi o% 8 $X mS FHA
+ABF }
+ABH L `X m[ FBA A(G0f
+(A ABBC
+(A ABBJ < X m % FEA D(G0
+(A ABBK D LX tm $ BBB A(A0L@
+0D(A BBBF < X mK $ FEA D(G0
+(A ABBD L X m $ FBB B(A0A8DP8
+8A0A(B BBBD Y m Y Xk2 HW L Y m6 OEA A(G0[
+(A ABBB H0 L Y m& # FEB B(A0A8G`
+8D0A(B BBBA P Y mJ S$ FBB E(A0A8DX
+8A0A(B BBBI h DZ n s% FBB B(A0A8G@
+8A0A(B BBBE
+8A0A(B BBBJ P Z n % FBB B(A0A8T
- BHD D`
- DABH 4 (=U
- BHD D`
- DABH 4 < P@U
- BHD D`
- DABH T t xC
- BBB B(A0D8L7
-8A0A(B BBBF T GW
- BBB H(A0D8L
-8D0A(B BBBH T $
- KW
- BBB H(A0D8L
-8D0A(B BBBH T |
- OL
- BBB H(A0D8L
-8D0A(B BBBH L
- S
- BJE B(A0A8D@
-8A0A(B BBBA $ TV ADD JAAL L T O
- BEB E(I0H8D@
-8D0A(B BBBA T PVE '
- BBB B(H0A8J
-8A0A(B BBBG , Ha>
- AID W
-DAA T $ XaE g
- BBB B(H0A8J
-8A0A(B BBBG , | Pl>
- AID W
-DAA T `l
- BBB B(H0A8J
-8A0A(B BBBG ,
w>
+8A0A(B BBBB 4 [ <%nU FAA v
+DBL AAB T <[ d%n>( $ EC
DLF. r. -. . . . . l.
+ A A : X^ % T\ 0Mn h\ ,Mn |\ 8Mn \ DMn- L\ \ XMn \ TMn \ PMn[ L_
+E \ Mnk Ll
+H 4 h\ Mn $ FAA D@m
+ AABA Q #X? $ 4 \ hNn $ FAA G0k
+ AABB d, {#XI $ ] kZ Hv
+A ] Nn
] Nn" ] Nn" , d] On 3$ FAD0
+DBA 4 8^ On KAD gIBV , ] tPnN # EJGPx
+AAH zPLR xI P ( "XO # ^ kZ Hv
+A ^ 8Qn _ 4Qn
$_ 0Qn9 8_ \Qn- L_ xQn- `_ Qn- t_ Qn- _ Qn- _ Qn- _ Rn- _ Rn- _ <Rn- _ XRn- ` tRn- ` Rn- (` Rn- <` Rn- P` Rn- d` Sn- x` Sn- ` 8SnP FI $ ` lSnH FID nDB H ` Sn EAG U
+CAJ [
+CAA d
+CAH NCA a Sn2 0a x X. Fb ( La X BAD AB xa Snk Ll
+H a Tnk Ll
+H a tTn[ L_
+E a Tn[ L_
+E ( a Tn FPD j
+AEF L pa Unw
+ W FBB B(A0A8GF
+8D0A(B BBBH H db _n FBA A(G0x
+(A ABBI d(A ABB L b `n* FBB E(A0A8G`
+8A0A(B BBBJ H c en PBB B(A0A8Dp
+8A0A(B BBBD H Lc hn FEE E(A0A8DPg
+8D0A(B BBBA H c Ljn FEB B(D0A8DP
+8D0A(B BBBJ L @c lns FBB B(D0A8G
+8D0A(B BBBE lF X p Ld qn FFE D(D0L
+(A BBBD
+(A BBBH A
+(D DBBC A
+(C BBBF < d sn> k FBA D(G0
+(A ABBJ < \d tn> G FBA D(G0
+(A ABBJ P d un^ # FBB B(D0A8Go
+8D0A(B BBBD 4 d {nU FAA GPs
+ AABJ P (e ~nO FBB B(D0A8G^
+8D0A(B BBBE P |e nO FBB B(D0A8G^
+8D0A(B BBBE P e nO FBB B(D0A8G^
+8D0A(B BBBE P $f nO U FBB B(D0A8G^
+8D0A(B BBBE g jZ Hv
+A 8g n Lg n D f nI FBB B(D0J
+0A(B BBBJ 4 g nE FCD DPg
+ AABD 0 <g $n'
+ FAH 0
+ABA , pg nm BDGK
+ABA $ zPLR x , DX h n h n h n h n h n h n h n i n $i n 8i n Li n `i n ti n i n i n i n 8 i n I FAA J
+ AABA 4 \i Tn a! FAA J
+ AABA 4 i <n q! FAA JP
+ AABA 4 i n J! FAA JP
+ AABA 4 j n #! FAA JP
+ AABA 8 <j Dn/ FAA J
+ AABA 8 xj 8ng " FAA J
+ AABA 8 j ln h$ FAA J$
+ AABA 8 j n $ FAA J`
+ AABA 8 ,k n n& FAA J
+ AABA 8 hk hn= ' FAA J
+ AABA 8 k ln r+ FAA J
+ AABA 8 k n m, FAA JD
+ AABA 8 l 4n u1 FAA J
+ AABA 8 Xl n ,2 FAA J
+ AABA 4 l \n '3 FAA JP
+ AABA P l n9 3 FBB B(A0A8G
+8A0A(B BBBB $ m X4 3 A < Hm xoT W4 FED A(LP
+(A ABBD , ,n
+oI KDA mABH D m
+o 4 FBJ A(A0G@
+0A(A BBBH ` n oS FBB B(A0A8GP
+8A0A(B BBBD T
+8A0A(B BBBJ L dn o w3 FGB E(A0A8G`
+8A0A(B BBBH L n o [3 FGB E(A0D8D`
+8A0A(B BBBH P o <o~ ?3 FEE B(A0A8Ii
+8A0A(B BBBH P Xo h)o 3 EC
DGD.0. B.0. y
+ D . M. . M. U XI '4 L o 8oG O4 FBB E(I0D8Gp
+8D0A(B BBBB X X'
4 p :oF p :o p :o q :o $q :o 8q :o Lq :o `q :o- tq :o5 q :o5 q (;o5 q T;o q P;o q L;o q H;o r D;o r @;o (r <;oP FI Dr p;o Xr l;o lr h;o $ r d;oG FAG rDB $ r ;oG FAG rDB $ r ;o> FAG iDB r ;o s ;o s ;oa FG I
+EA Ds <ok FJ P
+EA hs X AQ H s P<o EAG U
+CAJ [
+CAA d
+CAH NCA H s <o EAG U
+CAJ [
+CAA d
+CAH NCA t <oO H 0t 4=o FEE E(A0D8GPg
+8D0A(B BBBK |t =o2 t =o? F]U t =o? F]U t >oe t p>o H t >o FBA A(G0x
+(A ABBI d(A ABB L t ?ok / FBA A(G0
+(A ABBO t(A ABB u @oO ( u AoR BAA \
+DBJ 4 0u @Bo . BAA L0a
+ DABA v rXD A~ < u Bo . FBD A(D@
+(D ABBE L u Co . FGB E(A0A8G@X
+8D0A(B BBBF $ v ,Dof Y. EC
+H A
+G H v tDo( FBB B(A0A8GP
+8A0A(B BBBH ,w XFos @w Fo Tw @Go{ H hw Go FBB B(A0D8M`g
+8D0A(B BBBF H w Io FBB B(A0D8M@g
+8D0A(B BBBF 8 x Lo6 FBB A(G0g
+(D BBBE H <x No5 FBB B(A0D8MPd
+8D0A(B BBBA 8 x Wo FBB A(G0g
+(D BBBE 8 x \oJ FBB A(G0g
+(D BBBE H y $`o FBB B(A0D8M@1
+8D0A(B BBBD H Ly hco FBB B(A0D8MPi
+8D0A(B BBBD d y go+ FBB B(A0A8G@
+8D0A(B BBBG
+8A0A(B BBBB L z jo* FBA A(G0
+(A ABBC
+(A ABBI L Pz lo FBA A(G0
+(D ABBM
+(A ABBF L z no FBA A(G0
+(D ABBL
+(A ABBF z oo { ooH FG p
+EA ({ oo[ Eb
+I Y
+G < z oo ) FBD D(G@T
+(A ABBA , z Xpo ) FBG@s
+EBG zPLR x- @ ( X> ^) { po- H | qo FBA A(G0x
+(A ABBI d(A ABB L { qo ( BBB B(A0D8Gp
+8A0A(B BBBG | XX ( Ap< (| huo ( FBD A(Je
+(D ABBK ( zPLR xݴ 0 X0 ( H | vo z( FBB D(H0M
+0D(A BBBG ( zPLR xM 0 &X9 '( H <} wo ( FBB D(H0M
+0D(A BBBG X9 ' H } yo ' FBB D(H0M
+0D(A BBBG X9 ' H ~ \zo ' FBB D(H0M
+0D(A BBBG \ X9 ' L h~ {o r' FBB E(H0G8J
+8D0A(B BBBC , zPLR x 4 JX8 !' L o ' FFB B(D0D8D`
+8A0A(B BBBE @ P o & BBA D(Jf
+(A ABBA ( zPLR x 0 Xt & | Xo+ HZ H po ~& BBB D(A0G
+0D(A BBBH c X' L& H o FBA A(D0
+(D ABBJ d(A ABB H D o FBA A(G0x
+(A ABBI d(A ABB 8 Do2 % EQ
DMFDr.y.
+ H f X! x% H 0o FBA A(G0x
+(A ABBI d(A ABB L 0 o FBB A(A0
+(A BBBE q(A BBB L ܁ 4o $ FBB A(A0
+(D BBBL Q(A BBBH Ђ o+ FBA A(G0
+(A ABBJ t(A ABB H ȍo FBA A(G0x
+(A ABBI d(A ABB h ok Lf
+F Y
+O ؎ok Ln
+F H ,o FBB A(A0
+(A BBBH A(A BBBH Џo FBA A(G0x
+(A ABBI d(A ABB L o9 # FBB B(D0A8Dpa
+8D0A(B BBBE L oI FBB A(A0
+(A BBBE Q(A BBB H o FBA A(D0
+(D ABBJ d(A ABB H , Ho+ FBA A(G0
+(A ABBJ t(A ABB 4 x ,oe FDA w
+DBH QAB ( doa FDA X
+ABJ ` ܅ o FBB B(A0A8G@
+8A0A(B BBBJ 8A0A(B BBB P o [! FBB B(D0A8G
+8A0A(B BBBE o H oF FBB B(D0A8D@m
+8D0A(B BBBA 8 o FBD A(D0X
+(D ABBF P $oJ FBB B(A0A8GJ
+8A0A(B BBBG , zPLR xe 4 X] ; P ( ؠou + FBB B(A0A8DJ
+8A0A(B BBBJ q
+ X H 8 oc FBA A(G0k
+(A ABBN (A ABBH o FBB K(D0G
+0A(A BBBE L , o W FGE A(A0P
+(C BBBC d(A BBBL | ԧoX FBB L(D0A8D
+8A0A(B BBBG , zPLR xy 4 XJ 8 o FEA A(D0j
+(A ABBG H PoZ PBB B(A0A8DP
+8A0A(B BBBF P do FBE B(D0A8G
+8D0A(B BBBD , zPLR xU 4 X_ 0 8 oi KDD KABG P $o) OEB B(A0A8GP
+8A0A(B BBBH X< d o OBE A(D0(A BBBKT oa OBE B(A0A8GP-
+8A0A(B BBBH < ȳo OBD D(D0(A ABBI d ho G FBB B(A0A8GP
+8A0A(B BBBJ 8A0A(B BBB HX AQ ` o FBB B(A0A8GPj
+8A0A(B BBBG D8A0A(B BBB P o
+ c FBE E(I0A8Dr
+8D0A(B BBBA L Ԍ ,o C FBB E(A0A8G`
+8A0A(B BBBF 4 ȍ lol FBD A(D0T(A ABB8 o FBA A(D0
+(A ABBK ( < hoA JDG aAAH 8 h o FBA A(D0
+(A ABBK H Po PBB B(A0A8Dp
+8A0A(B BBBD D L o FBB D(A0DP>
+0D(A BBBD 4} X, < do{ FFD A(Jp
+(D ABBK $ zPLR xY p , xXC t Џ do! YBB E(A0A8J@q
+8A0A(B BBBG a8A0A(B BBBEH@ L o BBE B(D0D8J
+8D0A(B BBBE , zPLR xQ 4 X , < 4o FDD0n
+DBF ~ X ( ( o FDD K
+DBA L o t FGE A(A0P
+(C BBBC d(A BBB4 `o, ( FAA G0P
+ AABE L 8 Xor FBB B(D0A8Gp
+8A0A(B BBBF P ob FBE B(A0D8GD
+8A0A(B BBBG L ܑ o FBB E(A0A8Gp
+8A0A(B BBBF P , $o FEE B(A0A8G-
+8A0A(B BBBF L po FBB E(A0A8Gpx
+8A0A(B BBBF P В @o_ FEE B(A0A8G-
+8A0A(B BBBF H ȓ LoZ PBB B(A0A8DP
+8A0A(B BBBF L `o OBD A(D0p
+(D ABBE `X0 P o ' FBB B(D0A8G
+8D0A(B BBBB P o
FEB B(A0A8J
+Y
+8A0A(B BBBB , zPLR xݜ
+ 4 W < p> FBA D(G0
+(A ABBJ L p FBB B(D0A8Lp*
+8A0A(B BBBG < @ p> FBA D(G0
+(A ABBJ < p> FBA D(G0
+(A ABBJ ( d
pa RIG wAAE < D
p> / FBA D(G0
+(A ABBJ P , Dp7
+ BBB B(D0A8G
+8A0A(B BBBD $ Wy A < p> FBA D(G0
+(A ABBJ L p BGB E(A0D8D`R
+8A0A(B BBBK ( zPLR x
` 0 WL w L | Dp { FBB B(D0A8Gp
+8A0A(B BBBI Wp _ pA FG i
+EA P !pA + FBB E(D0A8J
+
+8D0A(B BBBK $ \ bW G A P $p c FEB E(A0A8Je
+8D0A(B BBBH < ؘ X(p7 E FBB D(Dpu
+(D BBBG $ zPLR x- p , zWU P X )p FBB B(D0A8GG
+8D0A(B BBBD P ,p FBB B(D0A8G
+8D0A(B BBBJ P @0p { FBB B(D0A8G]
+8D0A(B BBBF P T 3pb G FBB B(A0A8G !
+8A0A(B BBBH , zPLR x 4 8W( { P Ap { FBB B(D0A8GX
+8D0A(B BBBC P D Dp D FBB B(D0A8G
+8D0A(B BBBJ D Hp<
FBB A(A0Ds
+0A(A BBBA P Ip FBB B(A0A8JR
+8A0A(B BBBD 4m W L L 4Kp? FBB B(A0A8G
+8A0A(B BBBE , zPLR x 4 W ) P KpG ) FEB B(D0A8GX
+8A0A(B BBBC , zPLR x
4 Wv 6 < Tp o EC
DEED0
+ F q
+. X. 4 X] T | cp FBE E(G0D8D`VhIpIhA`T
+8D0A(B BBBA L Ԟ cp FBA A(G0
+(A ABBG
+(A ABBJ L epS FGB E(A0A8G`
+8A0A(B BBBD P О gp k FFB H(D0G8G
+8D0A(B BBBA L $ Llp J FBB B(D0A8G
+8D0A(B BBBD , zPLR xё 4 X O ` ` rpu OBB B(D0A8G@N
+8A0A(B BBBG H@p Ġ sp FBA A(G0Z
+(A ABBG F
+(A ABBL S
+(A ABBG W
+(D ABBH D ltp g EC
FIH .0. A
+ G .0. P X* L ,p FBA A(G0V
+(A ABBC
+(A ABBJ L D p2 FGB E(A0D8D`m
+8A0A(B BBBD \ ܋p FBB B(D0A8O
+
+8A0A(B BBBF \
+^
+G
+B
+, zPLR xQ
+ 4 Xl j2 HW ܗp
ؗp) LX ( p6 Le D p) LX ` $p6 Le L | Hp FBA A(D0E
+(I ABBO
+(A ABBF P ( p FFB E(D0A8G4
+8A0A(B BBBC XE P pu
+ FIB B(D0A8J
+8A0A(B BBBD $
+Xs A P p<
+ FFB B(D0A8J
+8A0A(B BBBF `
Xr P | p FBB B(A0A8J
+8A0A(B BBBC , zPLR xu 4 vX[ _ p EQ < 4 p8 G FED A(G
+(D ABBC ( zPLR xы 0 1X# L p
FBE B(D0H8M
+8D0A(B BBBE , zPLR x= 4 X E , P p 6 FNOPd
+DBA zPLR xŊ P ( X P $p FBB E(D0K8D
+8D0A(B BBBC t XM 4 ( p FBI G`
+ DBBA $ zPLR x ` , X@ @ P p 4 FBB B(D0K8D
+8D0A(B BBBH , fX P Dpu FBE B(A0A8J
+8D0A(B BBBB , zPLR x 4 ^X P (p EC
I
+ D .0. O. x. .n. .0u. p X/ p Щ p p& p p p $ 4 pG FAG rDB \ p) LX x p6 Le p) LX p6 Le ̪ (p) LX <p6 Le `p) LX tp6 Le < pP D T p< h p% | p2 , Dp AF
HK
+ C ( qR BAA \
+DBJ XO FC 4 ,q EAG e
+AAD D
+AAJ L q FBB B(A0D8G`
+8A0A(B BBBJ 4 tq EAG K
+AAF {
+AAC 0 $ , q FAD d
+ABF L X
+q { FBB B(G0D8G@
+8D0A(B BBBA D q : FOG D(A0D`O
+0D(A BBBA L pqi FBB E(D0J8Dp
+8D0A(B BBBJ L @ qi
FBB E(D0J8Dp
+8D0A(B BBBJ L qi
FBB E(D0J8Dp
+8D0A(B BBBJ P qY
FBB B(D0D8D
+8D0A(B BBBG H خ q FBE H(A0A8GP
+8D0A(B BBBK L ` q C
FBB B(A0A8Gp
+8A0A(B BBBG L Ю "q 7
FBB B(D0A8D`@
+8D0A(B BBBF P p%q FBE B(D0C8D
+8D0A(B BBBA P t +q
FBA D(D0G
+(A ABBB y
+(A ABBA 8 ȯ (-q, SAG MC D
+CBA 8 /q$ PAG HC D
+CBA 4 @ 1qL FBA D@}
+ ABBA H (2qF FBE B(A0D8DP
+8A0A(B BBBK < İ ,4q h FBA A(GPw
+(A ABBB P 5q% D FBB B(D0A8G
+8D0A(B BBBD , zPLR x 4 X D Zq< Ev | ` Zq FBB B(D0D8Gp
+8A0A(B BBBH [
+8A0A(B BBBC
+8A0A(B BBBG 4^qm H ^qZ PBB B(A0A8DP
+8A0A(B BBBF ` @ _q FBB B(D0A8DPh
+8A0A(B BBBI
+8A0A(B BBBG L `q FBB B(A0A8D
+8D0A(B BBBG P P eq. FBB A(A0a
+(A BBBL A
+(A BBBH ` H fq FBB E(A0D8DPo
+8A0A(B BBBG
+8A0A(B BBBH < (hq. FBA C(Gpo
+(F ABBC 4 H kqb FAA G`s
+ FABE L Pnq FBB B(D0C8Go
+8F0A(B BBBH < д sq FBA A(L0k
+(D ABBF L `tq S FBB B(A0A8QP
+8A0A(B BBBI H uqZ PBB B(A0A8DP
+8A0A(B BBBF L wq
+ FBO B(A0A8D`R
+8A0A(B BBBE H Dzq? PEB B(A0A8DP
+8A0A(B BBBE 8{q EG q
+AB H {q FEB F(D0D8G`o
+8A0A(B BBBE ( \ h}q EAG0
+AAD L ܀qf FBB B(E0A8Gp
+8A0A(B BBBG L 4 q FBB B(E0A8Gpu
+8D0A(B BBBE 4 |q _ FAD D@
+ AABA x ` 4qt FBB B(A0A8DP
+8F0A(B BBBI D
+8F0A(B BBBE L8F0A(B BBB ܸ 8qz FFB B(D0D8DP
+8A0A(B BBBD
+8D0A(B BBBE R
+8F0A(B BBBG D
+8G0A(B BBBL d и q # FBB E(I0A8GP
+8D0A(B BBBK
+8D0A(B BBBJ P 8 ȟq+ FBB H(H0D8J
+8A0A(B BBBJ P q FBB B(D0A8Gq
+8D0A(B BBBJ L q S FBB B(A0A8DP
+8A0A(B BBBF , 0 pq C EADP
+FAG P ` `q ! FBB B(A0D8L
+8F0A(B BBBA , q9 EADp
+FAH P q FLB I(A0D8J|
+8A0A(B BBBF P 8 qn
+ FEB B(A0D8JZ
+8A0A(B BBBF P q /
+ FBB B(D0A8G
+8D0A(B BBBD + `
X* G
+ 8j2 HW q[ A]
+J , м Tq PAA b
+DBF < \ $q #
+ FHD D(G`
+(D ABBC X7
+ X j2 HW t `q2 q P q FBB H(F0A8D
+8A0A(B BBBA L VX6 i H <q FBB E(A0A8Dp
+8A0A(B BBBE < r> FBA D(G0
+(A ABBJ L rz FGB E(A0D8D`m
+8A0A(B BBBD < @ @r> FBA D(G0
+(A ABBJ D @r FDE A(A0G@
+0A(A BBBH P Ⱦ r FEB E(A0A8G
+8D0A(B BBBG X. N ؿ |j2 HW r2 ( X BAD AB ( 4 r AC
FKA
+ J 4 `
+re FDA w
+DBH QAB 4 <
+re FDA w
+DBH QAB ` t
+r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` 4 r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD `
r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` Xr FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` ` r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` ( <r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` T r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` lr FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` Pr FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` H r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` 4 r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` t r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` !r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` < #r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` d$r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` %r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` h &r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` H(r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` 0 )r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` *r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` ,,r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD ` \ x-r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD D .r V FBB A(D0G@
+0A(A BBBG L d 0r FGE A(A0P
+(C BBBC d(A BBB` X 0r FBB B(D0D8DPd
+8A0A(B BBBJ
+8A0A(B BBBD D 82r n FQB D(A0DP
+0D(A BBBD X. : H 3r FBB E(A0A8Dp
+8A0A(B BBBE L 5r FFB B(A0D8Gp
+8A0A(B BBBC P ;r* FIE B(A0A8G%
+8D0A(B BBBG X U P erV l FEB B(D0A8GZ
+8D0A(B BBBF Ա mXh > P jr2 . EG
DIEH?
- AID W
-DAA T 4
wE
- BBB B(H0A8J
-8A0A(B BBBG ,
> G
+ K E. x. . N. s. g. . . iX j2 HW X' ( rS < rk Ld
+H L <r FBB B(I0A8DP
+8D0A(B BBBF H rZ PBB B(A0A8DP
+8A0A(B BBBG r L d Lr FBB B(A0D8Pt
+8D0A(B BBBF X P r FBB B(D0A8G
+8D0A(B BBBA P rp s FFB E(D0D8G
+8D0A(B BBBA p &X? 3 0 j2 HW L 2 X' ` F X. Fb L | Hr[ FBA A(G0
+(A ABBK
+(A ABBJ L Xr[ FBA A(G0
+(A ABBK
+(A ABBJ 8 hr[ FBA D(D0
+(I ABBL 8 X r[ FBA D(D0
+(I ABBL 0 r^ EGG k
+CAF SAA < ܭr FBB A(A0y
+(D BBBA < d |r ] FBA A(G0
+(A ABBI L LrD ! FBB B(A0A8DG
+8A0A(B BBBE \6 <X 0 4rw EAG [
+AAF DAA< @ r FGA A(G0s
+(D ABBF ( $ rA JDG aAAH ( P $rA JDG aAAH ` | Hr FBB E(D0A8P@
+8D0A(B BBBI [
+8D0A(B BBBH ` r FBB E(D0A8P@
+8D0A(B BBBI [
+8D0A(B BBBH L r w FGE A(A0V
+(C BBBE d(A BBBH r PBB B(A0A8D`C
+8A0A(B BBBG < < $r> FBA D(G0
+(A ABBJ < | $r> FBA D(G0
+(A ABBJ L $r FBE F(D0A8G`
+8A0A(B BBBB < rY s EC
BIK@. >. %
+ A $j2 HW L d r FBB B(D0A8L`0
+8A0A(B BBBI < <r EC
BLEED.Y.
+ K h [XD L r FBE B(A0A8G
+8A0A(B BBBA , zPLR xY 4 X L ru ` Tr2 t X. Fb L dr[ FBA A(G0
+(A ABBK
+(A ABBJ L tr[ FBA A(G0
+(A ABBK
+(A ABBJ 8 0 r[ FBA D(D0
+(I ABBL 8 l r[ FBA D(D0
+(I ABBL r E
+D Y
+O s= nN @ @ s FFB G(D0M
+(A BBBC L s [ FFB B(D0A8ZPj
+8A0A(B BBBE \sC 3 d< s FFS D(K0F
+(A ABBF ` s FBB B(A0A8GP]
+8A0A(B BBBD $
+8A0A(B BBBB L s FGE A(A0V
+(C BBBE d(A BBB< s = FGE A(A0(A BBB H PsZ PBB B(A0A8DP
+8A0A(B BBBF ds- H $ sZ PBB B(A0A8DP
+8A0A(B BBBF \ p s- FEA D(J0K
+(A ABBE o
+(A ABBK U
+(A ABBE H d sa FBB O(A0A8DP
+8A0A(B BBBG H
+sZ PBB B(A0A8DP
+8A0A(B BBBC H h sZ PBB B(A0A8DP
+8A0A(B BBBC H s? PEB B(A0A8DP
+8A0A(B BBBE H
s? PEB B(A0A8DP
+8A0A(B BBBE H L sZ PBB B(A0A8DP
+8A0A(B BBBG s FEE B(D0D8R@s
+8A0A(B BBBG X
+8E0A(B BBBJ \
+8E0A(B BBBF \
+8E0A(B BBBF H , sp BBA A(D0J
+(C ABBD D(F ABB P <s Q FBB B(D0A8G
+8D0A(B BBBE D ( s FBB A(A0DG
+0A(A BBBA P p s FJB F(D0A8Jg
+8A0A(B BBBH 8 #s9 EC
BGF
+ E .. t W l < j. FIA A(D`
+(A ABBA x X0 L p pCs FJB E(D0A8Dp'
+8A0A(B BBBG L Fsg# n EC
BIDl
+ E .. 7.. D.o. XX is2 HX. Fb L is[ FBA A(G0
+(A ABBK
+(A ABBJ 8 L ks[ FBA D(D0
+(I ABBL j2 HW 4 lsU FAA t
+DBN AAB 8 4 <ls EC
BEO
+ E .. B
X ? , X' @ X. Fb L \ Hs[ FBA A(G0
+(A ABBK
+(A ABBJ 8 Xs[ FBA D(D0
+(I ABBL ܂j2 HW < \ ds s EC
BEEME.O.
+ E *X \ X' ` p Ԑs BBB E(A0D8DPf
+8A0A(B BBBD
+8A0A(B BBBG L sS FBA A(G0*
+(A ABBG
+(A ABBJ 8 $ s[ FBA D(D0)
+(I ABBH ` XO FC ` | sS OEB B(A0A8G@
+8A0A(B BBBC H@H sK FBB B(A0A8D@
+8A0A(B BBBC < s> W FBA D(G0
+(A ABBB 0 X
3 FGB A(A0D P s FBB E(D0F8G
+
+8D0A(B BBBI D P sY B BBE B(G0G
+0D(B BBBG ( zPLR xJ 0 X P ts BBB L(D0K8M'
+8D0A(B BBBF X X x اst FBB B(A0A8DP
+8F0A(B BBBI D
+8F0A(B BBBE L8F0A(B BBB h ܨsz FFB B(D0D8DP
+8A0A(B BBBD
+8D0A(B BBBE R
+8F0A(B BBBG D
+8G0A(B BBBL P \ Īs BBB B(A0A8G6
+8A0A(B BBBG $ XZ { A | }j2 HW L @sO { FGB B(D0D8D`e
+8D0A(B BBBA L @ @s g FBB E(A0A8G`/
+8A0A(B BBBG H 4 s# FBA A(G0
+(A ABBO l(A ABB 8 ts + EC
BJ
+ E .. X !X' !X. Fb L s[ FBA A(G0
+(A ABBK
+(A ABBJ 8 T s[ FBA D(D0
+(I ABBL t s) UBB B(A0D8J@C8A0A(B BBBIH@n
+8A0A(B BBBH H |s FBB B(A0D8GP
+8A0A(B BBBK H T PsZ PBB B(A0A8DP
+8A0A(B BBBF < ds> FBA D(G0
+(A ABBJ < < ds s FEG D(K@x
+(D ABBF H 4s FDA D(G0L
+(E ABBD {(A EBB P s BBB B(D0G8GK
+8D0A(B BBBF ( XJ L 4 s BBB B(A0A8M
+8A0A(B BBBF
+!XF @ yj2 HW @ s[ AC
P.|. .~.
+ F l "X: 8
tt EC
I.. ^
+ E %X + H $tX BBD A(G0[
+(A ABBG Y(A ABB X $tu l X%tu %tu 0&t2 *X. Fb L @&t[ FBA A(G0
+(A ABBK
+(A ABBJ 8 P't[ FBA D(D0
+(I ABBL P t(t0 8 d (t FBD A(DP
+(D ABBG 8 0t FBD A(DP
+(D ABBG 8 x7t FBD A(DP
+(D ABBG L t =t g FBB B(A0D8D`
+8D0A(B BBBD Y T)X , 4 t@tu FAA _
+ABF AAB L Ct# FBB B(A0D8Nf
+8A0A(B BBBA (X7 L Gt PBB A(A0`
+(A BBBC E
+(A BBBD L Gt M FBB B(D0D8D@
+8D0A(B BBBD L DJt " FBE E(A0A8DP
+8A0A(B BBBG L l Kt FEB B(K0A8D
+8A0A(B BBBE 'X t x Pt) UBB B(A0D8J@C8A0A(B BBBIH@n
+8A0A(B BBBH t Qt) UBB B(A0D8J@C8A0A(B BBBIH@n
+8A0A(B BBBH 8 \Rt, EC
P..
+ I t 'X 8 8Xt EC
P
+ H .. 'X H \tZ PBB B(A0A8DP
+8A0A(B BBBF < ]t> FBA D(G0
+(A ABBJ D ^t w FDE A(A0G@z
+0A(A BBBF L @ @`tP S FBB B(D0D8G`
+8A0A(B BBBC \^ "'X < (at> FBA D(G0
+(A ABBJ < (bt FEG D(K@x
+(D ABBF 4 bt FDI v
+ABD XFB < ` ct> FBA D(G0
+(A ABBJ D dt c FDE A(A0G@z
+0A(A BBBF oj2 HW P etT ' FEB E(D0A8GG
+8A0A(B BBBA t%XC L l gt FBB B(D0K8Gu
+8A0A(B BBBG P%X0 L jt FBB B(A0A8Dp
+8A0A(B BBBD %X0 q 8 < Dnt
a EC
Pq
+ G -.\. $X 8 {tn EC
P
+ J &.. @ h%X 4 t EC
PH
+ H .h. &XO 8 4 ,t EC
P.K. <
+ G &Xi R 8 ؙt B EC
BEH .. }
+ D 8 'X b P 4t S FFB B(A0D8G
+8A0A(B BBBA 'X d 8 H t T EC
P^ .c. X
+ G 'X % H @ Tt FSB B(A0G8D
+8A0A(B BBBD P t FIB E(A0D8G
- AID W
-DAA t [w. K^ 肕4 ) (5 P* hK ? ȃ5 4 ) L > Hu l (> Hu H> Hu h> Hu L AAG e
-CAF s
-ACA R
-CAJ JCA L AAG ]
-CAF k
-ACA R
-CAJ JCA L l AAG e
-CAF s
-ACA R
-CAJ JCA L h AAG ]
-CAF k
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L \ AAG ]
-CAF k
-ACA R
-CAJ JCA L 8 AAG e
-CAF s
-ACA R
-CAJ JCA L AAG ]
-CAF k
-ACA R
-CAJ JCA L L Ȇ AAG ]
-CAF k
-ACA R
-CAJ JCA E Hx 8E Hx hE Hx E Hx , ȇm BAA J
-ABG L v\ AG RA l 臕_ AF
-I T (
-
- BEB B(A0A8N
-8A0A(B BBBH , , > T
- AID W
-DAA T \
- 4
- BEB B(A0A8N
-8A0A(B BBBH , ț> y
- AID W
-DAA T ؛
- Y
- BEB B(A0A8N
-8A0A(B BBBH , < >
- AID W
-DAA T l
- ~
- BEB B(A0A8N
-8A0A(B BBBH , x>
- AID W
-DAA , v@ AAG04AA 4 $ Xb s
- BHD D`
- DABD 4 \ b U
- BHD D`
- DABD 4 ȵb 7
- BHD D`
- DABD 4 b
- BHD D`
- DABD D 8
- ADGP
-AAG _
-AAG o
-AAG T L
- BBB G(D0G8G
-8D0A(B BBBJ T h˕
- BBB G(D0G8G
-8D0A(B BBBB T 0ؕ
/
- BBB G(D0G8G
-8D0A(B BBBJ T T
- BBB G(D0G8G
-8D0A(B BBBJ d PQw. K^ | h L p AAG ]
-CAF k
-ACA R
-CAJ JCA v\ AG RAD v BBB A(D0D@
-0A(A BBBA D L ~ BBB A(D0D@
-0A(A BBBA vV ATL BBB B(A0D8DPd
-8A0A(B BBBA l D X
- BBE E(A0A8G
-8A0A(B BBBA
-8A0A(B BBBA L l K BBB B(A0A8G@d
-8A0A(B BBBA , Ζv@ AAG04AA < 4 x
- BHD G'
- DABA T , O BAA G05
- AABD
- AABD e
- AABO T !
- BBB B(A0D8N E
-8A0A(B BBBF , $ )> B
- AID W
-DAA T T ) "
- BOB B(A0A8G]
-8A0A(B BBBA , d P7` BAA B
-ABG T 7*
- BBE E(A0A8J6
-8A0A(B BBBA T 4 XEu [
- BBB B(D0A8M
-8D0A(B BBBC D Lw. K^ \ hP t `PR HI PY HL $ P^ AG b
-AA T Q- BDA ]
-ADG w
-ADD Q
-ABD I
-ABD T | Q
- BBB B(D0A8N
-8A0A(B BBBD , Xf>
- AID W
-DAA , v@ AAG04AA 4 4 8fU 7
- BHD D`
- DABH $ l `iG
- As
-L A
-A T i
- BBG B(A0D8G
-8A0A(B BBBH pJw. K^ d D L
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-CAF K
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-CAF C
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-CAF {
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-CAF K
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-CAF K
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-CAJ JCA L ! AAG e
-CAF S
-ACA R
-CAJ JCA L " P AAG e
-CAF K
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-CAF C
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-CAF {
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-CAF K
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-CAF K
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-CAF S
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-CAJ JCA L # Ј AAG e
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-CAF C
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-CAF K
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-CAF C
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-CAF k
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-CAF k
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-CAF k
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-CAF k
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-CAF k
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-CAF k
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-CAF k
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BBB B(A0D8G
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-8D0A(B BBBI L O Dk [ BBB A(A0Dg
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-CAF K
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-CAF C
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-CAF {
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-CAF C
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-CAF {
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-CAF C
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-CAF k
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-CAF k
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-CAF k
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-CAF k
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-CAJ JCA L c ~ AAG ]
-CAF k
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-CAF k
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-CAF k
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-CAF k
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-CAF k
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-CAF k
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-CAF k
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-CAF k
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-CAF k
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-CAF k
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-CAF k
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-CAF k
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-0A(A BBBA D \j X< BBB A(A0D@
-0A(A BBBA D j P< BBB A(A0D@
-0A(A BBBA D j H< BBB A(A0D@
-0A(A BBBA D 4k @< BBB A(A0D@
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-8A0A(B BBBA D | BEB A(E0H@
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-8A0A(B BBBA L L} x BBB B(A0A8D@s
-8A0A(B BBBE L } BBB B(A0A8D@s
-8A0A(B BBBE < } BBB A(A0
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-8A0A(B BBBA T s O BBB B(A0A8Dk
-8A0A(B BBBA T 8 BBB B(D0A8Dd
-8A0A(B BBBA T \ H BBB B(A0D8G
-8A0A(B BBBH D BBB A(D0D`Q
-0A(A BBBA L | BBB A(A0D
-0A(A BBBA L L P R BBB A(A0D
-0A(A BBBA T ( BEB B(A0D8J6
-8A0A(B BBBA T h BBB E(G0A8L
-8D0A(B BBBC T L ř BEB B(A0D8J6
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-8D0A(B BBBC v. K^ ̍ )v\ AG RAD ۙ BBE A(A0D@u
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-8A0A(B BBBA , > o AID W
-DAA , *(v@ AAG04AA < | BHD Dm
- DABA \ t ` KLE B(A0A8DPi8A0A(B BBBIP \ ԏ KLE B(A0A8DPi8A0A(B BBBIP l 4 y BBF D(A0G@I
-0A(A BBBI w
-0A(A BBBA D
-0F(A BBBG d E BBB B(A0A8DP
-8A0A(B BBBE \
-8F0A(B BBBA l T B o BBB J(D0D8D4
-8D0A(B BBBC i
-8A0A(B BBBE L Đ # BBB B(A0A8J
-8A0A(B BBBA ̑ v. K^ `O HF V HI , $ J&v@ AAG04AA < u o BIA D(Dp)
-(D ABBJ < ܑ
-u I BIA D(Dp0
-(D ABBC T F # BHG E(A0A8G~
-8D0A(B BBBF T t +# BBF B(A0D8N{
-8A0A(B BBBA , ̒ 3> AID W
-DAA , 4> AID W
-DAA v. K^ 3 3 , 3 D 3 L 3r & BBB B(E0A8J@
-8A0A(B BBBA $ (5^ AG b
-AA 4 Ԕ `5l ADG b
-AAH l
-AAA 4 5l ADG b
-AAH l
-AAA T 5 S BBB B(A0I8G
-8D0A(B BBBE T 8 B BBB B(A0I8G
-8D0A(B BBBE T < < 1 BBB B(A0I8G
-8D0A(B BBBE T H? BBB B(A0I8G
-8D0A(B BBBE T pB BBE B(D0A8G
-8D0A(B BBBH T D 8G BBE B(D0A8G
-8D0A(B BBBG T LB ۽ BBB B(D0A8G
-8D0A(B BBBD , "v@ AAG04AA 4 $ ` K BHD D`
- DABG 4 \ @c - BHD D`
- DABG 4 e BHD D`
- DABH 4 ̗ @h BHD D`
- DABH L j ӽ BBB B(A0D8G
-8A0A(B BBBA L T Xy ٽ BGB B(A0D8G
-8A0A(B BBBA \ v. K^ t DV L Ԙ r BBB B(E0A8J@
-8A0A(B BBBA $ ܙ H^ AG b
-AA $ AG u
-AA $ , AG }
-AA T BBB B(D0D8J
-8D0A(B BBBC L ; BBB B(A0D8DPd
-8A0A(B BBBA $ r DS
-I H
-E , $ v@ AAG04AA 4 b BHD D`
- DABG 4 Ԛ D BHD D`
- DABH T W
- & BBB E(A0A8G
-8A0A(B BBBG T d ( = BEE B(A0A8G6
-8A0A(B BBBI T BBB L(A0A8J(
-8A0A(B BBBH 4 hΚx ? BAI H
-ABA 4 L Κx BAI H
-ABA < v. K^ L Κr BBB B(E0A8J@
-8A0A(B BBBA $ К^ AG b
-AA $ ̝ HК AG u
-AA $ К AG }
-AA xњT , 4 v@ AAG04AA < Ӛ BIA D(DpL
-(D ABBG T @֚ BBE B(A0A8G
-8A0A(B BBBH 4 D q BAI A
-ABA 4 0v. K^ L r J BBB B(E0A8J@
-8A0A(B BBBA $ ^ AG b
-AA $ ğ @ AG
-AA $ AG
-AA $ AG M
-AA T o BBB B(A0I8G
-8D0A(B BBBG T ܟ e Y BBB B(A0I8G
-8D0A(B BBBF T 4 R C BBE B(D0A8G
-8D0A(B BBBF , D * v@ AAG04AA 4 BHD D`
- DABG 4 ۺ BHD D`
- DABH \ m BBD D(J@
-(A ABBD T
-(A ABBF _
-(A ABBK T ] BBE B(D0C8L
-8A0A(B BBBE 8 Z AK
-L AT x x
BBB B(D0D8J
-8A0A(B BBBH * AN 4 `v. K^ L x*8 Ho l *? Hr *8 Ho *? Hr ̣ *8 Ho +? Hr 8+8 Ho , X+? Hr D x+ ݹ BBB A(I0Dp
-0D(A BBBF D ܣ - BBB A(I0Dp
-0D(A BBBF D $ . BBB A(I0Dp
-0D(A BBBF D l 0 n BBB A(I0Dp
-0D(A BBBF D 1 I BBB A(I0Dp
-0D(A BBBF D 3 $ BBB A(I0Dp
-0D(A BBBF D D 4 BBB A(I0Dp
-0D(A BBBF D 06 ڸ BBB A(I0Dp
-0D(A BBBF , "v@ AAG04AA D 7 BIB A(D0DpC
-0D(A BBBD D L : W BIB A(D0DpC
-0D(A BBBD D 8> ) BIB A(D0DpC
-0D(A BBBD D ܦ A BIB A(D0DpC
-0D(A BBBD D $ D ͷ BIB A(D0DpJ
-0D(A BBBE D l @H BIB A(D0DpJ
-0D(A BBBE D K q BIB A(D0DpJ
-0D(A BBBE D N C BIB A(D0DpJ
-0D(A BBBE \ D HR BBB A(A0GP
-0A(A BBBI L
-0A(A BBBA \ T BBB A(A0GL
-0A(A BBBE L
-0A(A BBBA T hW7 BBE B(A0A8G
-8A0A(B BBBG T \ P`7 Z BBE B(A0A8G
-8A0A(B BBBG T 8iL
- BBE B(A0A8G
-8A0A(B BBBG T 0s\
- BBE B(A0A8G
-8A0A(B BBBG v. K^ 4 } L } L }q BBB B(A0I8G
-8D0A(B BBBD L Pq BBB B(A0I8G
-8D0A(B BBBD L L q BBB B(A0I8G
-8D0A(B BBBD L q BBB B(A0I8G
-8D0A(B BBBD T ÷ BBH B(D0F8G
-8D0A(B BBBA , v@ AAG04AA 4 t b BHD D`
- DABD 4 0e BHD D`"
- DABE 4 he BHD D`"
- DABE 4 e } BHD D`"
- DABE v. K^ $ < T BBE E(A0A8G
-8D0A(B BBBE , v@ AAG04AA 4 $ (U ķ BHD D`
- DABH L \ Pq BBB B(A0I8G
-8D0A(B BBBD T BBB B(A0A8J"
-8A0A(B BBBH hv. K^ ԯ ț ț ț ț , 4 :v@ AAG04AA 4 țU BHD D`
- DABH 4 ˛U ŷ BHD D`
- DABH T ϛj BBB B(A0D8R
-8A0A(B BBBD , t (ԛ> ̷ AID W
-DAA T 8ԛj BBB B(A0D8R
-8A0A(B BBBD , Pٛ> ѷ AID W
-DAA , `ٛ JTAC , @ڛ
JVAC , D ۛ J\AK , t ܛ
JUAL L ܛ BBE G(A0G8G
-8D0A(B BBBD L < BBE G(A0G8G
-8D0A(B BBBD D v. K^ \ ( t < (t BBB I(M0F@I0A(A BBBL ̳ h AAG e
-CAF s
-ACA R
-CAJ JCA T d x 3 BBB B(D0A8N9
-8A0A(B BBBB , > AID W
-DAA , > ɷ AID W
-DAA , Դ v@ AAG04AA 4 L U y BHD D`
- DABH 4 U [ BHD D`
- DABA T ? BBB B(A0A8G
-8A0A(B BBBA ̵ v. K^ D `+ BEB B(A0D8
-0A(B BBBA D , Ht BEB B(A0A85
-0A(B BBBE L t BBB B(A0A8Dp3
-8A0A(B BBBE D Ķ . BEB B(A0D8
-0A(B BBBA D } BEB B(A0A8>
-0A(B BBBD L T BBB B(A0A8DpC
-8A0A(B BBBE < 0 BHB D(A0
-(A BBBA D 5 BEB B(A0A8
-0A(B BBBG L , ` BBB B(A0A8DPG8A0A(B BBB < | ! BHB D(A0
-(A BBBA D !2 BEB B(A0A8
-0A(B BBBG L #] BBB B(A0A8DPD8A0A(B BBB L T & ADD e
-CAF
-ACA V
-CAF JCA L ' ADD e
-CAF
-ACA V
-CAF JCA L @( ADD e
-CAF
-ACA V
-CAF JCA L D ( ADD e
-CAF
-ACA V
-CAF JCA L ) AAG ]
-CAF b
-CAJ R
-CAJ JCA L )= BBB B(A0A8D`$8A0A(B BBB L 4 * BBB B(A0A8D
-8A0A(B BBBH L , BBB B(A0A8D8A0A(B BBB L Ի / ADD e
-CAF k
-ACI V
-CAF JCA L $ / AAG ]
-CAF b
-CAJ R
-CAJ JCA L t / ADD e
-CAF k
-ACI V
-CAF JCA L ļ p0 AAG ]
-CAF b
-CAJ R
-CAJ JCA L 0 ADD e
-CAF k
-ACI V
-CAF JCA L d @1 AAG ]
-CAF b
-CAJ R
-CAJ JCA L 1 ADD e
-CAF k
-ACI V
-CAF JCA L 2 AAG ]
-CAF b
-CAJ R
-CAJ JCA L T P2 ADD e
-CAF k
-ACI V
-CAF JCA L 2 ADD e
-CAF
-ACA V
-CAF JCA $ 3^ AG b
-AA $ 3 AG r
-AA $ D @4 AG r
-AA $ l 4 AG r
-AA $ P5 AG r
-AA $ 5 AG z
-AA $ p6 AG z
-AA $ 7 AG z
-AA $ 4 7 AG z
-AA $ \ 88 AG L
-AA D ̿ 8 BBB D(A0Np
-0A(A BBBG L h:r BEB L(D0A8M
-8A0A(B BBBD < d L BBA I(D@e
-(D ABBA < M BBA I(D@e
-(D ABBA \ M$ ֭ BBB A(I0D@
-0D(A BBBG W
-0D(A BBBA \ D hN$ BBB A(I0D@
-0D(A BBBG W
-0D(A BBBA \ 8O$ X BBB A(I0D@
-0D(A BBBG W
-0D(A BBBA \ P$ BBB A(I0D@
-0D(A BBBG W
-0D(A BBBA \ d P$ ڬ BBB A(I0D@
-0D(A BBBG W
-0D(A BBBA \ Q$ BBB A(I0D@
-0D(A BBBG W
-0D(A BBBA \ $ xR$ \ BBB A(I0D@
-0D(A BBBG W
-0D(A BBBA \ HS$ BBB A(I0D@
-0D(A BBBG W
-0D(A BBBA \ T$ ޫ BBB A(I0D@
-0D(A BBBG W
-0D(A BBBA T D T BBE E(D0A8G
-8D0A(B BBBI $ T W AAG0AA4 X : BHA GPt
- DABC 4 Y BHA GPt
- DABC 4 4 [ BHA GPt
- DABC 4 l p] BHA GP{
- DABD 4 H_ ª BHA GP{
- DABD 4 a BHA GP{
- DABD 4 b BHA GP{
- DABD < L d h BHA G
- DABA < 0g M BHA G
- DABA 4 i 2 BHA GPt
- DABC 4 hk BHA GPt
- DABC T 0mU BAA G0
- AABA
- AABK f
- AABF T 8q BEE B(G0D8G
-8D0A(B BBBD T |! ĩ BBE G(A0G8G
-8D0A(B BBBC AR AY T i BBB E(D0C8JZ
-8D0A(B BBBE T G
BBB B(D0G8L
-8D0A(B BBBF T 4 蚜G
BBB B(D0G8L
-8D0A(B BBBF T ড়G
[ BBB B(D0G8L
-8D0A(B BBBF \ ش] BBB F(G0G
-0D(A BBBK L
-0D(A BBBI T D ط
- o BBB E(G0A8L
-8D0A(B BBBF T `v. K^ L l Hk BBB B(A0A8G
-8A0A(B BBBJ , hǜl AAG
-AAA L ǜ AAG ]
-CAF b
-CAJ R
-CAJ JCA L ǜr BBB B(E0A8J@
-8A0A(B BBBA $ ɜ^ AG b
-AA 4 Pɜ\ C BAA G0F
- AABK 4 4 x˜\ BAA G0N
- AABC T l ͜{ ۨ BBB B(A0D8J
-8D0A(B BBBB 4 Ҝ Ȩ BAD v
-ABH L `ӜL BBB B(A0A8GP_
-8A0A(B BBBF L L `Ԝ D BBB B(A0A8Gk
-8A0A(B BBBA , T u@ AAG04AA D ۜg BIA D(D
-(D ABBA D (ޜg BIA D(D
-(D ABBA d PB BBB B(A0A8G@x
-8A0A(B BBBE
-8A0A(B BBBF T 8 2 BBB B(A0I8D#
-8D0A(B BBBJ T BBB B(A0I8D#
-8D0A(B BBBJ $ , DL
-A V
-I T $ BBE H(A0A8J6
-8D0A(B BBBH 8v. K^
-
- T <
- BBB B(A0D8N
-8A0A(B BBBC , > . AID W
-DAA , | u@ AAG04AA 4 U ާ BHD D`
- DABH T , d ç BBB B(A0A8J
-8A0A(B BBBA < ؚv. K^ T 6 l 5 T 6y BBB B(A0I8G>
-8A0A(B BBBG , u@ AAG04AA 4 T 9b BHD D`
- DABD D 0=d } BEI A(D0D
-0D(A BBBA < X?r x BBH D(J
-(D ABBA D A z BBE H(D0D
-0D(A BBBF D \ @D u BBE H(D0D
-0D(A BBBF D G p BBE D(H0D
-0D(A BBBA D I k BBE D(H0D
-0D(A BBBA D 4 XL f BBE D(H0D
-0D(A BBBA D | O a BBE D(H0D
-0D(A BBBA D Q \ BBE D(H0D
-0D(A BBBA D PT W BBE D(H0D
-0D(A BBBA D T V R BBE D(H0D
-0D(A BBBA L YV M BBE D(H0D
-0D(A BBBI T \ C BBB E(G0A8LK
-8D0A(B BBBI Hv. K^ $ n^ AG b
-AA D < o BBB D(A0D@
-0A(A BBBA D q BBB D(A0D@%
-0A(A BBBA D Xt BBB D(A0Gn
-0D(A BBBE D \ u ] BBB D(A0Gn
-0D(A BBBE d \ Hw BLB B(A0A8D@c
-8A0A(B BBBK s
-8A0A(B BBBC , u@ AAG04AA 4 < wU BHD D`
- DABH d t z y BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB < @{ BBG A(G@
-(A ABBF L { ץ BEB E(I0H8DP
-8D0A(B BBBD 4 l }^ BBB A(A0O T ~5 BBE B(A0A8M M
-8A0A(B BBBA , > ߦ AID W
-DAA T , Д BBE B(D0A8GO
-8A0A(B BBBH T X Ѧ BBE B(D0A8GO
-8A0A(B BBBH T ढ़ BBB B(D0A8J_
-8A0A(B BBBH L 4
BBB B(A0A8G
-8A0A(B BBBA $ < 8v AHD0AAL d AAG e
-CAF s
-ACA R
-CAJ JCA L ` AAG ]
-CAF k
-ACA R
-CAJ JCA , m BAA J
-ABG T | ༝| BBE E(D0A8G
-8D0A(B BBBE T , m BBE B(D0A8G
-8D0A(B BBBK T , ƝG S BEE E(D0A8G
-8D0A(B BBBH T ˝, 9 BBE B(D0A8G
-8D0A(B BBBK T Н BEE E(D0A8G
-8D0A(B BBBH T 4 ՝ BEE E(D0A8G
-8D0A(B BBBJ T 0| BBE E(D0A8G
-8D0A(B BBBE , Ru@ AAG04AA < (7 BHD D
- DABA L ( BBB B(A0A8D`8A0A(B BBB 4 \ BEA A(D0|(A ABB$ p ADD dAA ( AL T $ ( BEE E(D0A8G
-8D0A(B BBBE L | P BEB B(A0D8O8A0A(B BBB T ( < BEB E(A0A8G=
-8A0A(B BBBB d BBB B(A0D8DPJ
-8A0A(B BBBK
-8A0A(B BBBD T BBB G(D0A8M
-8D0A(B BBBC L BEB E(I0H8D@
-8D0A(B BBBA T L J ۣ BBE B(A0A8G
-8A0A(B BBBA L ǣ BEB E(I0H8D@
-8D0A(B BBBD T H
- BBE B(A0A8GP
-8A0A(B BBBA T L
', BBB I(A0A8Jt
-8A0A(B BBBA , 9> 0 AID W
-DAA v. K^ 9 x9 $ 9 EDf
-AH < h; BBA A(GP
-(A ABBE T H> ` BBB B(D0A8G-
-8D0A(B BBBB L F \ BBB B(I0A8G
-8D0A(B BBBE L , H F BBB B(I0A8G
-8D0A(B BBBE , 4 u@ AAG04AA 4 K5 BHI D`
- DABB 4 NE BHI D`
- DABK < Q1 BBG A(GP
-(A ABBF T \ R BGE H(A0A8G
-8D0A(B BBBF L g
BIE B(A0A8Ms
-8A0A(B BBBA v. K^ t xt pt xt L 4 t BGB B(A0A8G
-8A0A(B BBBC L }~ BFB B(A0A8G
-8A0A(B BBBD L @ ADD e
-CAF {
-ACI V
-CAF JCA L $ ADD e
-CAF {
-ACI V
-CAF JCA t u\ AG RAD ` BAA GP
- DABK
- DABG T $ | գ BBE E(D0A8G
-8D0A(B BBBE T | @ BBE B(D0A8G
-8D0A(B BBBG , >u@ AAG04AA < ؒu q BIA D(Dp)
-(D ABBJ < D u K BIA D(Dp)
-(D ABBJ T X % BBB L(A0D8G
-8A0A(B BBBB 4 q M BAI A
-ABA 4 q ) BAI A
-ABA L L P: BGB B(A0G8J
-8D0A(B BBBJ L @: BGB B(A0G8J
-8D0A(B BBBJ v. K^ D Þ) BEB B(A0A80A(B BBB D Ğ& BBB E(A0A80A(B BBB D L Ğ* BEB B(A0A80A(B BBB D Ş BBB B(D0A8
-0A(B BBBA D ƞS BBE B(A0A88
-0A(B BBBA D $ ǞB BBE B(A0A8'
-0A(B BBBA D l Ȟ^ BBB B(A0A8F
-0A(B BBBA D ɞ^ BBB B(A0A8F
-0A(B BBBA D ʞK BBE B(A0A80
-0A(B BBBA D D ̞> BBE B(A0A8#
-0A(B BBBA D ̞[ BBE B(A0A8@
-0A(B BBBA D ΞJ BBE B(A0A8/
-0A(B BBBA D Ϟ^ BBB B(A0A8F
-0A(B BBBA D d 0О^ BBB B(A0A8F
-0A(B BBBA D HўS BBE B(A0A88
-0A(B BBBA D `ҞF BBE B(A0A8+
-0A(B BBBA D < hӞ2 BEB B(A0A80A(B BBB D `Ԟ5 BBB B(D0A8 0A(B BBB D X՞1 BEB B(A0A80A(B BBB D P֞ BBB B(D0A8
-0A(B BBBA D \ (מ2 BBB E(A0A80A(B BBB D ؞1 BEB B(A0A80A(B BBB D ٞ BBE E(A0A80A(B BBB D 4 ٞ BBE E(A0A80A(B BBB D | ڞ4 BEB B(A0A8 0A(B BBB D ۞( BBE B(A0A80A(B BBB D hܞA BBB B(D0A8*0A(B BBB D T pݞ3 BEB B(A0A8 0A(B BBB D hޞ6 BEB B(A0A8 0A(B BBB D `ߞ3 BBB B(A0D8 0A(B BBB D , X5 BEB B(A0A8 0A(B BBB D t P BBE E(A0A80A(B BBB D BBE E(A0A80A(B BBB D 4 BEB B(A0A8 0A(B BBB D L ( BBE B(A0A80A(B BBB D A BBB B(D0A8*0A(B BBB D 3 BEB B(A0A8 0A(B BBB D $ @ BEB B(A0A8)0A(B BBB D l P BEB B(A0A890A(B BBB 0 Hg 7 Hj 0 Hg 7 Hj 4 0 Hg T 7 Hj t 00 Hg @7 Hj `0 Hg p7 Hj 0 Hg 7 Hj 4 0 Hg T 7 Hj t 0 Hg 7 Hj 0 Hg 07 Hj P0 Hg `7 Hj 4 0 Hg T 7 Hj t 0 Hg 7 Hj 0 Hg 7 Hj 0 Hg 7 Hj 4 @0 Hg T P7 Hj t p0 Hg 7 Hj 0 Hg 7 Hj 0 Hg 7 Hj 4 0 Hg T 7 Hj t 00 Hg @7 Hj `0 Hg p7 Hj 0 Hg 7 Hj 4 0 Hg T 7 Hj t 0 Hg 7 Hj 0 Hg 07 Hj P0 Hg `7 Hj 4 0 Hg T 7 Hj t 0 Hg 7 Hj 0 Hg 7 Hj 0 Hg 7 Hj 4 @0 Hg T P7 Hj t p0 Hg 7 Hj 0 Hg 7 Hj 0 Hg 7 Hj 4 0 Hg T 7 Hj t 00 Hg @7 Hj `0 Hg p7 Hj 0 Hg 7 Hj 4 0 Hg T 7 Hj t 0 Hg 7 Hj 0 Hg 07 Hj P0 Hg `7 Hj D 4 BBB B(A0A8JP8A0A(B BBBL | X3 BEB B(A0A8D8A0A(B BBB L H BEB B(A0A8D8A0A(B BBB L BEB B(A0A8Dp8A0A(B BBB D l BHB B(A0A8GP8A0A(B BBBL ; BBB B(A0A8G`
-8A0A(B BBBA L L BBE B(A0A8G`
-8A0A(B BBBA T u- AG cA t ` Y L h AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L , AAG e
-CAF s
-ACA R
-CAJ JCA L | X AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L l H AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L \ 8 AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L L ( AAG e
-CAF s
-ACA R
-CAJ JCA L x AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L < AAG e
-CAF s
-ACA R
-CAJ JCA L h AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L ,
- AAG e
-CAF s
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-CAJ JCA L |
- X AAG e
-CAF s
-ACA R
-CAJ JCA L
- AAG e
-CAF s
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-CAJ JCA L AAG e
-CAF s
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-CAJ JCA L l H AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L \ 8 AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
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-CAJ JCA L L
( AAG e
-CAF s
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-CAJ JCA L
x AAG e
-CAF s
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AAG e
-CAF s
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-CAJ JCA L < AAG e
-CAF s
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-CAJ JCA L h AAG e
-CAF s
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-CAJ JCA L AAG e
-CAF s
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-CAJ JCA L , AAG e
-CAF s
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-CAJ JCA L | X AAG e
-CAF s
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-CAJ JCA L AAG e
-CAF s
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-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L l H AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L \ 8 AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L L ( AAG e
-CAF s
-ACA R
-CAJ JCA L x AAG e
-CAF s
-ACA R
-CAJ JCA , qfIn
-B C xB Av
-I A $ < AAG0AA4 @ K o~ BHA G`
- DABA 4 X
-K P~ BHA G`
- DABA 4 p K 1~ BHA G`
- DABA 4 T K ~ BHA G`
- DABA 4
D } BHA G`
- DABA 4 D } BHA G`
- DABA 4 K } BHA G`
- DABA 4 4 K } BHA G`
- DABA 4 l K w} BHA G`
- DABA 4 K X} BHA G`
- DABA 4 0D 9} BHA G`
- DABA 4 HD } BHA G`
- DABA 4 L `K | BHA G`
- DABA 4 xK | BHA G`
- DABA 4 K | BHA G`
- DABA 4 K | BHA G`
- DABA 4 , D | BHA G`
- DABA 4 d D `| BHA G`
- DABA 4 K A| BHA G`
- DABA 4 K "| BHA G`
- DABA 4 K | BHA G`
- DABA 4 D 8 K { BHA G`
- DABA 4 | P!D { BHA G`
- DABA 4 h"D { BHA G`
- DABA 4 #K { BHA G`
- DABA 4 $ $K h{ BHA G`
- DABA 4 \ %D I{ BHA G`
- DABA 4 &K *{ BHA G`
- DABA 4 'K { BHA G`
- DABA 4 (K z BHA G`
- DABA 4 < *K z BHA G`
- DABA 4 t (+K z BHA G`
- DABA 4 @,K z BHA G`
- DABA 4 X-K pz BHA G`
- DABA 4 p.K Qz BHA G`
- DABA 4 T /K 2z BHA G`
- DABA 4 0K z BHA G`
- DABA 4 1K y BHA G`
- DABA 4 2K y BHA G`
- DABA 4 4 3K y BHA G`
- DABA 4 l 5K y BHA G`
- DABA 4 6D xy BHA G`
- DABA 4 07D Yy BHA G`
- DABA 4 H8D :y BHA G`
- DABA 4 L `9D y BHA G`
- DABA 4 x:D x BHA G`
- DABA 4 ;D x BHA G`
- DABA 4 <K x BHA G`
- DABA 4 , =K x BHA G`
- DABA 4 d >K x BHA G`
- DABA 4 ?K ax BHA G`
- DABA 4 AD Bx BHA G`
- DABA 4 BD #x BHA G`
- DABA 4 D 8CK x BHA G`
- DABA 4 | PDK w BHA G`
- DABA 4 hED w BHA G`
- DABA 4 FK w BHA G`
- DABA 4 $ GK w BHA G`
- DABA 4 \ HK iw BHA G`
- DABA 4 IK Jw BHA G`
- DABA 4 JK +w BHA G`
- DABA 4 KK w BHA G`
- DABA 4 < MK v BHA G`
- DABA 4 t (NK v BHA G`
- DABA 4 @OK v BHA G`
- DABA 4 XPK v BHA G`
- DABA 4 ! pQK qv BHA G`
- DABA 4 T! RK Rv BHA G`
- DABA 4 ! SK 3v BHA G`
- DABA 4 ! TK v BHA G`
- DABA 4 ! UD u BHA G`
- DABA 4 4" VD u BHA G`
- DABA 4 l" XD u BHA G`
- DABA 4 " YD u BHA G`
- DABA 4 " 0ZD yu BHA G`
- DABA 4 # H[D Zu BHA G`
- DABA 4 L# `\K ;u BHA G`
- DABA 4 # x]K u BHA G`
- DABA 4 # ^K t BHA G`
- DABA 4 # _K t BHA G`
- DABA 4 ,$ `K t BHA G`
- DABA 4 d$ aK t BHA G`
- DABA 4 $ bD t BHA G`
- DABA 4 $ dD bt BHA G`
- DABA 4 % eD Ct BHA G`
- DABA 4 D% 8fK $t BHA G`
- DABA 4 |% PgK t BHA G`
- DABA 4 % hhK s BHA G`
- DABA 4 % iK s BHA G`
- DABA 4 $& jK s BHA G`
- DABA 4 \& kK s BHA G`
- DABA 4 & lK js BHA G`
- DABA 4 & mK Ks BHA G`
- DABA 4 ' nD ,s BHA G`
- DABA 4 <' pD
s BHA G`
- DABA 4 t' (qD r BHA G`
- DABA 4 ' @rD r BHA G`
- DABA 4 ' XsK r BHA G`
- DABA 4 ( ptK r BHA G`
- DABA 4 T( uK rr BHA G`
- DABA 4 ( vK Sr BHA G`
- DABA 4 ( wK 4r BHA G`
- DABA 4 ( xK r BHA G`
- DABA 4 4) yD q BHA G`
- DABA 4 l) {D q BHA G`
- DABA 4 ) |D q BHA G`
- DABA 4 ) 0}K q BHA G`
- DABA 4 * H~K zq BHA G`
- DABA 4 L* `K [q BHA G`
- DABA 4 * xK <q BHA G`
- DABA 4 * K q BHA G`
- DABA 4 * D p BHA G`
- DABA 4 ,+ D p BHA G`
- DABA 4 d+ D p BHA G`
- DABA 4 + K p BHA G`
- DABA 4 + K p BHA G`
- DABA 4 , K cp BHA G`
- DABA 4 D, 8K Dp BHA G`
- DABA 4 |, PK %p BHA G`
- DABA 4 , hD p BHA G`
- DABA 4 , D o BHA G`
- DABA 4 $- D o BHA G`
- DABA , \- o AGJ@|
-AAH T - P o BBE E(A0A8G
-8D0A(B BBBC < . BED A(D0Y
-(A ABBA T $. v o BEB B(A0D8M!
-8A0A(B BBBA T |. ) n BBB L(A0A8J
-8A0A(B BBBD 4 . n BAI D0Q
- DABA 4 / P n BAI D0Q
- DABA 4 D/ n BAI D0Q
- DABA 4 |/ n BAI D0Q
- DABA 4 / X in BAI D0Q
- DABA 4 / En BAI D0Q
- DABA 4 $0 !n BAI D0Q
- DABA 4 \0 ` m BAI D0Q
- DABA 4 0 m BAI D0Q
- DABA 4 0 m BAI D0Q
- DABA 4 1 h m BAI D0Q
- DABA 4 <1 mm BAI D0Q
- DABA 4 t1 Im BAI D0Q
- DABA 4 1 p %m BAI D0Q
- DABA 4 1 Ȥ m BAI D0Q
- DABA 4 2 l BAI D0Q
- DABA 4 T2 x l BAI D0Q
- DABA 4 2 Х l BAI D0Q
- DABA 4 2 ( ql BAI D0Q
- DABA 4 2 Ml BAI D0Q
- DABA 4 43 ئ )l BAI D0Q
- DABA 4 l3 0 l BAI D0Q
- DABA 4 3 k BAI D0Q
- DABA 4 3 য় k BAI D0Q
- DABA 4 4 8 k BAI D0Q
- DABA 4 L4 uk BAI D0Q
- DABA 4 4 訟 Qk BAI D0Q
- DABA 4 4 @ -k BAI D0Q
- DABA 4 4 k BAI D0Q
- DABA 4 ,5 j BAI D0Q
- DABA 4 d5 H j BAI D0Q
- DABA 4 5 j BAI D0Q
- DABA 4 5 yj BAI D0Q
- DABA 4 6 P Uj BAI D0Q
- DABA 4 D6 1j BAI D0Q
- DABA 4 |6
j BAI D0Q
- DABA 4 6 X i BAI D0Q
- DABA 4 6 i BAI D0Q
- DABA 4 $7 i BAI D0Q
- DABA 4 \7 ` }i BAI D0Q
- DABA 4 7 Yi BAI D0Q
- DABA 4 7 5i BAI D0Q
- DABA 4 8 h i BAI D0Q
- DABA 4 <8 h BAI D0Q
- DABA 4 t8 h BAI D0Q
- DABA 4 8 p h BAI D0Q
- DABA 4 8 ȯ h BAI D0Q
- DABA 4 9 ]h BAI D0Q
- DABA 4 T9 x 9h BAI D0Q
- DABA 4 9 а h BAI D0Q
- DABA 4 9 ( g BAI D0Q
- DABA 4 9 g BAI D0Q
- DABA 4 4: ر g BAI D0Q
- DABA 4 l: 0 g BAI D0Q
- DABA 4 : ag BAI D0Q
- DABA 4 : ಟ =g BAI D0Q
- DABA 4 ; 8 g BAI D0Q
- DABA 4 L; f BAI D0Q
- DABA 4 ; 賟 f BAI D0Q
- DABA 4 ; @ f BAI D0Q
- DABA 4 ; f BAI D0Q
- DABA 4 ,< ef BAI D0Q
- DABA 4 d< H Af BAI D0Q
- DABA 4 < f BAI D0Q
- DABA 4 < e BAI D0Q
- DABA 4 = P e BAI D0Q
- DABA 4 D= e BAI D0Q
- DABA 4 |= e BAI D0Q
- DABA 4 = X ie BAI D0Q
- DABA 4 = Ee BAI D0Q
- DABA T $> !e BEE B(H0D8J
-8A0A(B BBBG T |> п ge BEE B(H0D8J
-8A0A(B BBBE T > hǟ e BEE B(H0D8J
-8A0A(B BBBD T ,? Ο e BBB B(I0A8G
-8D0A(B BBBE T ? ӟ e BBB B(I0A8G
-8D0A(B BBBE T ? `؟ f BBB B(I0A8G
-8D0A(B BBBE T 4@ ݟ f BBB B(I0A8G
-8D0A(B BBBE T @ f BBB B(I0A8G
-8D0A(B BBBE T @ f BBB B(I0A8G
-8D0A(B BBBE T <A @ f BBB B(I0A8G
-8D0A(B BBBE T A $f BBB B(I0A8G
-8D0A(B BBBE T A +f BBB B(I0A8G
-8D0A(B BBBE T DB h 2f BBB B(I0A8G
-8D0A(B BBBE T B 9f BBB B(I0A8G
-8D0A(B BBBE T B @f BBB B(I0A8G
-8D0A(B BBBE T LC Gf BBB B(I0A8G
-8D0A(B BBBE T C H Nf BBB B(I0A8G
-8D0A(B BBBE T C Uf BBB B(I0A8G
-8D0A(B BBBE T TD \f BBB B(I0A8G
-8D0A(B BBBE T D p= cf BBB B(A0I8G
-8D0A(B BBBD T E X = kf BBB B(A0I8G
-8D0A(B BBBD T \E @$= sf BBB B(A0I8G
-8D0A(B BBBD T E ()= {f BBB B(A0I8G
-8D0A(B BBBD T F .= f BBB B(A0I8G
-8D0A(B BBBD T dF 2= f BBB B(A0I8G
-8D0A(B BBBD T F 7= f BBB B(A0I8G
-8D0A(B BBBD T G <= f BBB B(A0I8G
-8D0A(B BBBD T lG A= f BBB B(A0I8G
-8D0A(B BBBD T G F= f BBB B(A0I8G
-8D0A(B BBBD T H K= f BBB B(A0I8G
-8D0A(B BBBD T tH hP= f BBB B(A0I8G
-8D0A(B BBBD T H PU= f BBB B(A0I8G
-8D0A(B BBBD T $I 8Z= f BBB B(A0I8G
-8D0A(B BBBD T |I _= f BBB B(A0I8G
-8D0A(B BBBD T I d= f BBB B(A0I8G
-8D0A(B BBBD T ,J h= f BBB B(A0I8G
-8D0A(B BBBD T J m= f BBB B(A0I8G
-8D0A(B BBBD T J r= f BBB B(A0I8G
-8D0A(B BBBD T 4K w= f BBB B(A0I8G
-8D0A(B BBBD T K |= g BBB B(A0I8G
-8D0A(B BBBD T K x= g BBB B(A0I8G
-8D0A(B BBBD T <L `= g BBB B(A0I8G
-8D0A(B BBBD T L H= g BBB B(A0I8G
-8D0A(B BBBD T L 0= #g BBB B(A0I8G
-8D0A(B BBBD T DM = +g BBB B(A0I8G
-8D0A(B BBBD T M = 3g BBB B(A0I8G
-8D0A(B BBBD T M 螠= ;g BBB B(A0I8G
-8D0A(B BBBD T LN У= Cg BBB B(A0I8G
-8D0A(B BBBD T N = Kg BBB B(A0I8G
-8D0A(B BBBD T N = Sg BBB B(A0I8G
-8D0A(B BBBD T TO = [g BBB B(A0I8G
-8D0A(B BBBD T O p= cg BBB B(A0I8G
-8D0A(B BBBD T P X= kg BBB B(A0I8G
-8D0A(B BBBD T \P @= sg BBB B(A0I8G
-8D0A(B BBBD T P (Ơ= {g BBB B(A0I8G
-8D0A(B BBBD Q ˠ E
-K < Q ˠ BKD A(G0(A ABB L lQ p͠ #g BBB B(A0A8Gq
-8A0A(B BBBD T Q @֠f ?g BEB B(D0A8Jq
-8A0A(B BBBK L R Xݠ? zg BFB B(D0A8J
-8D0A(B BBBA T dR Hޠ ?g BBE B(D0D8N
-8D0A(B BBBF T R n [g BEB B(D0A8Jq
-8A0A(B BBBK L S ? g BFB B(D0A8J
-8D0A(B BBBA T dS [g BBE B(D0D8N
-8D0A(B BBBF T S n wg BEB B(D0A8Jq
-8A0A(B BBBK L T 8? g BFB B(D0A8J
-8D0A(B BBBA T dT ( wg BBE B(D0D8N
-8D0A(B BBBF tU G U L U R BBD A(DP
-(A ABBF *
-(A ABBH T <U g BEE B(H0D8J
-8A0A(B BBBH T U hf Yg BEB B(D0A8Jq
-8A0A(B BBBK L U ? g BFB B(D0A8J
-8D0A(B BBBA T <V p Yg BBE B(D0D8N
-8D0A(B BBBF T V ug BEE B(D0H8JK
-8A0A(B BBBG T V @&f g BEB B(D0A8Jq
-8A0A(B BBBK T DW X- g BEE B(H0D8J
-8A0A(B BBBF T W 3f <h BEB B(D0A8Jq
-8A0A(B BBBK D W ; wh BBB D(A0J
-0D(A BBBD T <X P< Ph BEE B(D0H8J6
-8A0A(B BBBD T X Bf h BEB B(D0A8Jq
-8A0A(B BBBK T X I h BEE B(H0D8J
-8A0A(B BBBG T DY XPf i BEB B(D0A8Jq
-8A0A(B BBBK D Y pW Qi BBB D(A0J
-0D(A BBBD T Y X *i BEE B(D0H8JD
-8A0A(B BBBF T <Z 0_f pi BEB B(D0A8Jq
-8A0A(B BBBK T Z Hf i BEE B(H0D8J
-8A0A(B BBBF T Z lf i BEB B(D0A8Jq
-8A0A(B BBBK D D[ s ,j BBB D(A0J
-0D(A BBBD T [ @u j BEE B(D0H8JK
-8A0A(B BBBG T [ {f Kj BEB B(D0A8Jq
-8A0A(B BBBK T <\ Ђ j BEE B(H0D8J
-8A0A(B BBBF T \ hf j BEB B(D0A8Jq
-8A0A(B BBBK D \ k BBB D(A0J
-0D(A BBBD T 4] ȑ j BEE B(D0H8J6
-8A0A(B BBBD T ] 0f %k BEB B(D0A8Jq
-8A0A(B BBBK T ] H `k BEE B(H0D8J
-8A0A(B BBBG T <^ Хf k BEB B(D0A8Jq
-8A0A(B BBBK D ^ 謡 k BBB D(A0J
-0D(A BBBD T ^ 0 k BEE B(D0H8JD
-8A0A(B BBBF T 4_ f l BEB B(D0A8Jq
-8A0A(B BBBK T _ ;l BEE B(H0D8J
-8A0A(B BBBF T _ X¡f l BEB B(D0A8Jq
-8A0A(B BBBK D <` pɡ l BBB D(A0J
-0D(A BBBD T ` ʡ l BEE B(H0D8J
-8A0A(B BBBD T ` ҡv l BEB B(D0A8Jq
-8A0A(B BBBK D 4a 8١6 m BFE A(A0J
-0D(A BBBA T |a 0ڡ l BEE B(H0D8J
-8A0A(B BBBE T a v )m BEB B(D0A8Jq
-8A0A(B BBBK D ,b 6 dm BFE A(A0J
-0D(A BBBA T tb 1m BEE B(H0D8J
-8A0A(B BBBD T b f wm BEB B(D0A8Jq
-8A0A(B BBBK D $c 6 m BFE A(A0J
-0D(A BBBA T lc < m BEE B(H0D8J
-8A0A(B BBBE T c f m BEB B(D0A8Jq
-8A0A(B BBBK D d
6 n BFE A(A0J
-0D(A BBBA T dd
m BEE B(H0D8J
-
-8A0A(B BBBH T d n n BEB B(D0A8Jq
-8A0A(B BBBK D e 6 Nn BFE A(A0J
-0D(A BBBA T \e n BEE B(D0H8J0
-8A0A(B BBBB T e H&f an BEB B(D0A8Jq
-8A0A(B BBBK D f `-6 n BFE A(A0J
-0D(A BBBA T Tf X.X in BEE B(D0H8J
-8A0A(B BBBJ T f `7f n BEB B(D0A8Jq
-8A0A(B BBBK D g x>6 n BFE A(A0J
-0D(A BBBA T Lg p? n BEE B(H0D8J
-8A0A(B BBBJ T g Fn n BEB B(D0A8Jq
-8A0A(B BBBK D g N6 8o BFE A(A0J
-0D(A BBBA T Dh O o BEE B(D0H8J+
-8A0A(B BBBG T h `Uf Jo BEB B(D0A8Jq
-8A0A(B BBBK D h x\6 o BFE A(A0J
-0D(A BBBA T <i p]w Ro BEE B(H0D8J
-8A0A(B BBBI T i if o BEB B(D0A8Jq
-8A0A(B BBBK D i p6 o BFE A(A0J
-0D(A BBBA T 4j q o BEE B(H0D8J
-8A0A(B BBBK T j @yn o BEB B(D0A8Jq
-8A0A(B BBBK D j X6 !p BFE A(A0J
-0D(A BBBA T ,k P o BEE B(H0D8J
-8A0A(B BBBA T k n 4p BEB B(D0A8Jq
-8A0A(B BBBK D k Џ6 op BFE A(A0J
-0D(A BBBA T $l Ȑ <p BEE B(H0D8J@
-8A0A(B BBBB T |l Pf p BEB B(D0A8Jq
-8A0A(B BBBK D l h6 p BFE A(A0J
-0D(A BBBA T m ` p BEE B(H0D8J
-8A0A(B BBBA T tm 覢n p BEB B(D0A8Jq
-8A0A(B BBBK D m 6 q BFE A(A0J
-0D(A BBBA T n p BEE B(D0H8J0
-8A0A(B BBBB T ln f q BEB B(D0A8Jq
-8A0A(B BBBK D n 6 Yq BFE A(A0J
-0D(A BBBA T o h &q BEE B(D0H8J
-8A0A(B BBBB T do Ȣf lq BEB B(D0A8Jq
-8A0A(B BBBK D o Ϣ6 q BFE A(A0J
-0D(A BBBA T p Т tq BEE B(H0D8J
-8A0A(B BBBJ T \p `آn q BEB B(D0A8Jq
-8A0A(B BBBK D p xߢ6 q BFE A(A0J
-0D(A BBBA T p p q BEE B(D0H8J+
-8A0A(B BBBG T Tq f r BEB B(D0A8Jq
-8A0A(B BBBK D q 6 Br BFE A(A0J
-0D(A BBBA T q r BEE B(H0D8J
-8A0A(B BBBK T Lr pn Ur BEB B(D0A8Jq
-8A0A(B BBBK D r 6 r BFE A(A0J
-0D(A BBBA T r ]r BEE B(H0D8J
-8A0A(B BBBE T Ds v r BEB B(D0A8Jq
-8A0A(B BBBK D s @ 6 r BFE A(A0J
-0D(A BBBA T s 8
r BEE B(H0D8J
-8A0A(B BBBH T <t v r BEB B(D0A8Jq
-8A0A(B BBBK D t 6 ,s BFE A(A0J
-0D(A BBBA T t r BEE B(H0D8J
-8A0A(B BBBE T 4u X# ?s BEB B(D0A8Jq
-8A0A(B BBBK D u *6 zs BFE A(A0J
-0D(A BBBA T u + Gs BEE B(H0D8J
-8A0A(B BBBG T ,v 3v s BEB B(D0A8Jq
-8A0A(B BBBK D v :6 s BFE A(A0J
-0D(A BBBA T v ; s BEE B(H0D8J
-8A0A(B BBBE T $w hC s BEB B(D0A8Jq
-8A0A(B BBBK D |w J6 t BFE A(A0J
-0D(A BBBA |x KN T w L s BEE B(D0H8J
-8A0A(B BBBH T 4x S t BEB B(D0A8Jq
-8A0A(B BBBK D x [6 Lt BFE A(A0J
-0D(A BBBA L y \ BBD A(D@
-(A ABBF
-(A ABBE T $y a s BEE B(H0D8J8
-8A0A(B BBBJ T |y Phf t BEB B(D0A8Jq
-8A0A(B BBBK D y ho6 Jt BFE A(A0J
-0D(A BBBA T z `p t BEE B(D0H8J
-8A0A(B BBBB T tz wf ]t BEB B(D0A8Jq
-8A0A(B BBBK D z 0~6 t BFE A(A0J
-0D(A BBBA { (c T ,{ Mt BEE B(D0H8J
-8A0A(B BBBH T { H t BEB B(D0A8Jq
-8A0A(B BBBK D { Ў6 t BFE A(A0J
-0D(A BBBA | ȏ T <| Џ t BEE B(H0D8J
-8A0A(B BBBK T | X t BEB B(D0A8Jq
-8A0A(B BBBK D | 6 u BFE A(A0J
-0D(A BBBA T 4} t BEE B(H0D8J
-8A0A(B BBBG T } f u BEB B(D0A8Jq
-8A0A(B BBBK D } 6 Ru BFE A(A0J
-0D(A BBBA ~ u. K^ ~ D BBE A(A0D@g
-0A(A BBBA D \ x BBE A(A0D@o
-0A(A BBBA Nu\ AG RAT - BDA ]
-ADG w
-ADD Q
-ABD I
-ABD T d ȸ s BBE E(D0A8G
-8D0A(B BBBH D t BBE A(A0D@P
-0A(A BBBA $ x s A
-G A
-A , Mu@ AAG04AA < \ , 1s BHD D
- DABA L ģ4 s BBB B(A0D8D`
-8A0A(B BBBD L ƣ4 r BBB B(A0D8D`
-8A0A(B BBBD T < ȣ{ r BEB I(A0A8G
+8A0A(B BBBE ( < (X A *X' *X. Fb L < t[ FBA A(G0
+(A ABBK
+(A ABBJ 8 t[ FBA D(D0
+(I ABBL L $ t FBB E(A0D8G`
+8A0A(B BBBH @e ,*X 4 0 tt- FAA
+ABL AAB ` h lt+ FBB B(A0A8GP]
+8A0A(B BBBD
+8A0A(B BBBG 8 ( 8tl EC
P..
+ I T)X ' < | Tt> FBA D(G0
+(A ABBJ D Tt FDE A(A0G@z
+0A(A BBBF ij2 HW 4 t{ EAG s
+AAF \
+AAJ P T t FBH B(D0A8G
+8A0A(B BBBB b (X { D d Ptd FBB B(A0A8D@G8A0A(B BBBP xt / FBB A(A0X
+(A BBBE
+(A BBBJ $ \ 4t Eu
+F A
+G L tD FBB B(D0D8D`h
+8A0A(B BBBF h |(XH L tk FBB B(A0A8DpW
+8A0A(B BBBE 8 \(X/ R 8 T t B EC
M.}.
+ K 8(Xu a D ht Q FBB A(A0D`
+0A(A BBBC 8 N(X $ 8 u3 EC
P
+ J i.. (XR Z 8 \ u: K EC
M.z. Z
+ G )Xu ~ P u n FBB B(A0A8G
+8A0A(B BBBH , zPLR xA/ 4 (Xs 6 H u3 FIB B(D0K8G_
+8A0A(B BBBF P u FEB E(A0D8G$
+8A0A(B BBBD ( (X A $ ((u6 FJG XDB @(uS (uk Ld
+H L p (u o FIB E(A0A8D`
+8D0A(B BBBI P +u ? FBB B(A0A8G
+8D0A(B BBBE , zPLR x1- 4 X)X9 cj2 HW L x 0u FBB E(D0A8Dk
+8A0A(B BBBC l )X' 1uS 02uk Ld
+H L 2u FBB B(I0A8DP
+8D0A(B BBBG H 4uZ PBB B(A0A8DP
+8A0A(B BBBF P 5u FDB E(A0A8G
+8A0A(B BBBG , zPLR xI+ 4 'Xz Y \aj2 HW `:u AU`S
+AF ( *X' < +X. Fb L X ;u[ FBA A(G0
+(A ABBK
+(A ABBJ 8 <u[ FBA D(D0
+(I ABBL L @=u FBA A(D0E
+(D ABBL D
+(A ABBF L =u ? BBB H(A0D8M}
+8D0A(B BBBF )X^ _j2 HW P Au BBE E(A0A8O
+8A0A(B BBBK @ *X P | DKu FBB B(D0A8G
+8A0A(B BBBE z-X* P Pu FBB B(A0D8G
+8A0A(B BBBE pd 8/XA P T ,Vu FEE B(D0G8J
+8A0A(B BBBE 1X] d
+ Yuu x
+ ,Zu2
+ ,2X. Fb L
+ <Zu[ FBA A(G0
+(A ABBK
+(A ABBJ 8
+ L[u[ FBA D(D0
+(I ABBL 4 ]j2 HW P
+ X\u FEE B(A0G8G
+8A0A(B BBBE , zPLR xI& 4 1X hu 4X' 4X. Fb L 0 Thu[ FBA A(G0
+(A ABBK
+(A ABBJ 8 diu[ FBA D(D0
+(I ABBL L ju FGE A(A0P
+(C BBBC d(A BBB(
juA JDG aAAH ` 8
ju FBF E(A0A8G@
+8D0F(B BBBC [
+8F0A(B BBBF < ku> ' FBA D(G0
+(A ABBJ D 8
lu FDE A(A0G@z
+0A(A BBBF P
nu?
+ FJB B(A0A8G`
+8A0A(B BBBI P
wu^ FBB E(A0G8G
+8A0A(B BBBD , zPLR x # 4 Z1X { Yj2 HW , }uu @ ~uu L p~u# [ FBB B(A0A8GY
+8D0A(B BBBE , zPLR xE" 4 t3X" XYj2 HW L ` uC FBA A(G0
+(A ABBH (A ABB $ uk Ek
+H Y
+O $ 8uk _ Ek
+H Y
+O L uC ; FBA A(G0
+(A ABBH (A ABB P P uq FBB B(A0D8G
+8A0A(B BBBG 4 <Xj AJ
HO[.
+ A P 1X ^j 4 Hu FHK J`[
+ AABF H2X' uS Huk Ld
+H L u k FIB E(A0A8D`
+8D0A(B BBBI L ܐu ; FBE B(D0D8J
+8D0A(B BBBI , zPLR x! 4 \1X6 t]j2 HW L uF FBB L(K0D8Gpl
+8A0A(B BBBF | 2X' uS Puk Ld
+H L u + FIB E(A0A8D`
+8D0A(B BBBI L l u FGE A(A0P
+(C BBBC d(A BBB` ` 4u FBD D(G0
+(A ABBJ
+(A ABBE
+(A ABBJ ` u FBD D(G0-
+(A ABBF
+(A ABBA
+(A ABBG ` ( ,ua FBD D(G0
+(A ABBF a
+(A ABBI
+(A ABBK \ 8u% FBD D(G0|
+(A ABBG \
+(A ABBF E
+(A ABBM \ u% FBD D(G0|
+(A ABBG \
+(A ABBF E
+(A ABBM ` L ؠu FBD D(G0*
+(A ABBI
+(A ABBD
+(A ABBL ` dua FBD D(G0
+(A ABBF `
+(A ABBJ
+(A ABBL P p pud FBB B(D0A8G
+8D0A(B BBBG L u FEB B(H0G8Dp
+8A0A(B BBBK P Lu FEB E(D0A8G
+8A0A(B BBBA Xj2 HW P u T FBE E(D0A8G<
+8D0A(B BBBI :-X c u2 &0X. Fb L u[ FBA A(G0
+(A ABBK
+(A ABBJ 8 u[ FBA D(D0
+(I ABBL L u FBB B(A0D8G`
+8A0A(B BBBF 4 4u EAG s
+AAF D
+AAJ , 4 u EDD@
+AAB .X ` L | Du S FFE B(A0A8Z`
+8A0A(B BBBF L p uK FBA A(D0h
+(D ABBI \
+(A ABBF L u FBA A(G0Q
+(D ABBE q
+(G DBBA P l u FBB B(A0A8Q
+8D0A(B BBBG u -X, P u FBB B(D0A8Q
+8D0A(B BBBJ \ n.X 8 D u EC
HDq
+ K .. /X < Tj2 HW 4 u EC
P
+ G .y. \ /X' ' P pv / FBE B(A0G8GK
+8A0A(B BBBE X
j1X I 3X' ( 3X. Fb 8 D v FBA D(D0
+(I ABBG L dv FBA A(G0
+(A ABBG
+(A ABBJ L , v! FBB B(A0A8Dp
+8A0A(B BBBA x (3X P H 8 v PBB B(A0A8D`C
+8A0A(B BBBG P pv FBB B(A0A8GD
+8A0A(B BBBE 2X @ L v EC
DEMy.k.
+ E p.n. 2Xu L Qj2 HW d 4X0 4 "vo FLK J@W
+ ABBE 4$vS $vk Ld
+H L < $v [ FBB B(I0A8DP
+8D0A(B BBBG 0 TQj2 HW P 'vb FBB B(A0A8G
+8A0A(B BBBD , zPLR xM 4 2X7 *v2 2X. Fb L ! +v[ FBA A(G0
+(A ABBK
+(A ABBJ 8 d! ,v[ FBA D(D0
+(I ABBL L 4-v FFB B(D0A8D`
+8D0A(B BBBK < L! /v BBB A(D0W
+(A BBBG L ! T1v y BBB B(D0A8G@Z
+8A0A(B BBBH < ! DOj. 2 FIA A(D`
+(A ABBA < B1X0 8 4" |2v) EC
BID|
+ E .. 1XD $ ,# [v6 FJG XDB T# "5X' h# [vS |# 8\vk Ld
+H L " \v FIB E(A0A8D`
+8D0A(B BBBI L D# ^v W FBL B(K0D8Jt
+8D0A(B BBBH , zPLR x
4 64X H $ tNj2 HW $ (sv5 $ Tsv5 H $ sv EAG U
+CAJ [
+CAA e
+CAG NCA % :4X' ( $% N4X BAD AB ( P% svp ADG0\
+AAA L |% sv# FBA A(G0
+(A ABBG
+(A ABBJ 8 % uv+ FBA D(D0
+(I ABBO & h4XO FC H $& wv FBA A(G0x
+(A ABBI d(A ABB ( p& TxvA JDG aAAH ( & xxvA JDG aAAH ( & xvA JDG aAAH < & xvq KBE A(D0M(A BBBEH 4' yv FBB B(A0A8G@
+8A0A(B BBBE H ' zv% FBB B(A0E8D@
+8D0A(B BBBI ` ' {v5 FBF E(A0A8G@m
+8F0A(B BBBH R
+8D0F(B BBBD ` 0( |v7 FBF B(D0D8DP[
+8I0A(B BBBG F
+8F0A(B BBBK ` ( `~v
FBE B(D0A8LP
+8D0A(B BBBE d
+8D0A(B BBBG H ( v FBB F(A0D8D@Y
+8D0A(B BBBA D) @v FG }
+EA x h) ܂v FBB L(G0D8DP
+8F0A(B BBBA D
+8F0A(B BBBE D
+8F0A(B BBBE ` ) v FBB E(A0D8DPK
+8I0A(B BBBK D
+8C0A(B BBBH ` H* v FBB E(A0D8DPK
+8I0A(B BBBK D
+8C0A(B BBBH * hv P * vi
+ FBB B(A0A8G
+8A0A(B BBBH < /X < * v> FBA D(G0
+(A ABBJ L * vc FGB E(A0D8D`q
+8A0A(B BBBH H + 8vG FBB B(G0A8Gp
+8D0A(B BBBK p , <v FFE D(D0L
+(A BBBD
+(A BBBI A
+(D DBBC A
+(C BBBF L + hv% FEJ B(A0D8G@
+8D0A(B BBBA D (, Hv FEB B(A0G
+0A(B BBBH ( zPLR x 0 -X P , ܙvE FBB B(A0D8J
+8A0A(B BBBA ' 8.X - pEj2 HW 8 8- v 6 EC
BQP.6.
+ J |1XK P - Dv FBB B(A0A8J
+8D0A(B BBBI , zPLR xe 4 ,2X# P (. hvY FBB E(A0D8G
+8D0A(B BBBH | 3X U P . \v E FEB B(A0A8J
+8A0A(B BBBA , zPLR x] 4 X3X / 4X' D/ v] E^
+M 0 vS 0 Hvk Ld
+H L / v FBB B(I0A8DP
+8D0A(B BBBH 0 Bj2 HW P / vD K FBB B(A0A8G
+8A0A(B BBBE , zPLR x 4 2X C <1 v2 P1 v d1 vc H^
+J o 1 v+ Ee 1 4vf 1 v[ ED
+G GH 1 v FBB B(A0A8D@
+8D0A(B BBBI h |1 Dv [ FBB B(D0A8Dp
+8A0A(B BBBF
+8A0A(B BBBB s3X L 2 vC FBA A(G0
+(A ABBD
+(A ABBJ | P2 vt FGE E(A0A8D@W
+8C0A(B BBBH
+8A0A(B BBBJ D8F0A(B BBB ` t3 v FBB B(A0A8G@
+8A0A(B BBBK 8A0A(B BBB 3 v 3 v OBB E(G0G8K`_
+8G0A(B BBBH
+8A0A(B BBBG H` H p4 $vp FBA A(D0@
+(C ABBJ D(F ABB P 4 Hv3 FBB B(A0D8G_
+8A0A(B BBBG 8 l4 4v FGD I
+ DABH $ zPLR x , 0X& < 5 vm FEG D(I
+(D ABBF zR x ( J0X L `5 v FBB E(D0D8Gy
+8D0A(B BBBD , zPLR x 4 /X < 5 =j EJ
DPJ. Z. .
+ A /Xr l D 6 pv FBB A(A0Gp
+0A(A BBBK <7 v2 P7 /X. Fb , 6 (vN AHG o
+AAA / b/X , 7 0vT AHG u
+AAA \/ 0/X L 7 Hv[ FBA A(G0
+(A ABBK
+(A ABBJ 8 L8 Xv[ FBA D(D0
+(I ABBL 4 7 |v BAA G@X
+ AABI 4 8 v BAA GPX
+ AABI @ T8 vN BBB D(A0R
+(A BBBD L 8 v O BFE B(A0A8Z`
+8A0A(B BBBF 8 -Xr G A`L 9 w: 7 BFE B(D0A8D`
+8A0A(B BBBI ( -Xq # L t9 w\ BHB F(A0A8RP
+8A0A(B BBBA 2 -XD P 9 w FBB E(A0D8G
+8D0A(B BBBB 08 ~-X # P H: w FBH E(A0D8J-
+8D0A(B BBBB 8 .X g < : , w X FFS D(K0F
+(A ABBF L : w & FEE B(A0I8J]
+8D0A(B BBBH @ /X P \; $w FBB B(A0D8G
+8D0A(B BBBJ 0X = ` l< h1w FEE B(I0D8D`
+8A0A(B BBBB
+8D0A(B BBBF < ,< 3w FFS D(K0F
+(A ABBF t = D4w REH E(H0D8G`
+8A0A(E BBBE 8A0A(B BBBDH` = 6w RHH E(D0G8HP
+8A0A(E BBBD ;8A0A(B BBBCHPD
+8A0A(B BBBJ x= ;wC dP = P;w x FEB B(A0A8G
+8D0A(B BBBC /X P = Gw FBE B(A0D8G
+8D0A(B BBBG ` 0Xn P h> H^w2 FBE B(A0A8G
+8D0A(B BBBG , zPLR x 4 2X z P ? xw+ k FEB B(A0A8G
+8D0A(B BBBA , zPLR x 4 4X ( D@ wZ H(]0F8D@DHDPI T
+A ` p@ w+ FBB B(A0A8GP]
+8A0A(B BBBD
+8A0A(B BBBG H @ w> FBB E(A0D8G`M
+8A0A(B BBBF 0 A tw FDD G0
+ AABA x TA wF FBE E(G0H8G@n
+8A0A(B BBBH {
+8A0A(B BBBC
+8A0A(B BBBA H A w FBB B(E0A8DP
+8D0A(B BBBA ` B w FBB B(D0A8L`/
+8F0A(B BBBE l
+8D0F(B BBBB L B w OBA D(G0C
+(A ABBJ pH0 ` B tw FBB B(A0A8GP]
+8A0A(B BBBD $
+8A0A(B BBBB H 4C wZ PBB B(A0A8DP
+8A0A(B BBBF T C wY OEE E(D0D8G@
+8A0A(B BBBE ` H C w7 FBB E(A0A8D`
+8A0A(B BBBE T $D w9 OBF I(D0D8G@
+8A0A(B BBBH P P C wb FEB B(A0A8G
+8D0A(B BBBG , zPLR x 4 1X P tD w FEE E(D0D8G
+8A0A(B BBBD | c4Xv P D @w! FBB E(D0A8G
+8D0A(B BBBG , zPLR x 4 =6X P |E dw>! FBB B(A0A8Iu
+8D0A(B BBBG , zPLR xu 4 s8X e F /j2 HW 8 0F w
> EC
DKN.z. ,
+ F + ;Xt D F ,xR FEH D(D0DPE
+0D(A BBBA l5 <X T H F ,
+x! D EC
DH;
+ A = . w. #.|. u.. , j<X P HG *x, BEE E(D0D8J
+8D0A(B BBBK H ?X P G ;x$ AG
P
+ I U.{. .w. .u. .. |- @X ) H H pZxP& EC
P/
-8A0A(B BBBE , ݣ> s AID W
-DAA T ā ݣ s BBE E(A0D8G
-8D0A(B BBBD $ pG Hs As
-L A
-A T D $s BFA A(GP
-(A ABBF k
-(A ABBA l P s BBB B(A0A8G
-8A0A(B BBBD
-8A0A(B BBBF T r BBE E(A0D8G
-8D0A(B BBBF D 8 BEB B(A0A80A(B BBB < B *r BDA A(G
-(A ABBA , ]r PGA A(D , Jr ]GA A(D , L ` 7r PGA A(D < | 0B $r BDA A(G
-(A ABBA T @ Wr BBB B(D0D8J
-8A0A(B BBBA T / r BHB B(A0F8J
-8D0A(B BBBB T l & r BBB H(F0D8G6
-8D0A(B BBBA | u. K^ @Iu\ AG RAD . kr BBB A(D0D@[
-0A(A BBBF D D 0 'r BBB A(D0D@c
-0A(A BBBF T 3
- q BBB B(D0D8G
-8A0A(B BBBI , Hu@ AAG04AA D < q BIA D(D
-(D ABBA T \ @@\ q BBE B(A0A8J
-8A0A(B BBBB 4 HW r BAI
-ABA L X r BBB E(A0A8Jh
-8D0A(B BBBD [k D \
-H T \ [ Wr BBB H(D0F8Gs
-8D0A(B BBBA l u. K^ Gu\ AG RAT plA r BEE E(D0A8Gg
-8D0A(B BBBG < D hr_ r AAG
-AAI p
-AAB T w r BEB B(A0A8G]
-8A0A(B BBBE T ܉ 0 r BHB B(F0A8J
-8D0A(B BBBD T 4 s BHB B(F0A8J
-8D0A(B BBBD D u. K^ \ ȼ t м ȼ ȼ ԋ ȼ F ) G] 4 J W c L H D k
-Q z l H G
-A 8 H0W
-I $ ؾ^ AG b
-AA D r BBE A(A0D@
-0A(A BBBA $ d 迤g ]r AG k
-AA , 0\ 9r ADG l
-AAA , t XDu@ AAG04AA < 0u q BIA D(Dp)
-(D ABBJ D pä| BBB A(D0D@
-0A(A BBBA D , Ĥ BBB A(D0D@
-0A(A BBBA T Ť, /q BBE B(D0A8G
-8D0A(B BBBK T ʤe q BEB B(A0A8GL
-8A0A(B BBBA T l ̤ p BBB B(A0A8G
-8A0A(B BBBG L | Ф BBF B(A0A8D@
-8D0A(B BBBB D Ф p BBE A(A0D@
-0A(A BBBA L \ pѤ ;p BEB E(I0H8D@
-8D0A(B BBBD L Ҥw p BHI E(A0A8Gp
-8A0A(B BBBF L դ p BJE B(A0A8D@
-8A0A(B BBBA L L դ^ o BBB B(A0D8J
-8D0A(B BBBE T ܤO o BBB B(A0D8J
-8A0A(B BBBA d E BBB B(A0A8DP
-8A0A(B BBBE \
-8F0A(B BBBA T \ o BBB B(A0A8N
-8A0A(B BBBA , > p AID W
-DAA u. K^ ̒ x
r J e p , 7 D 0 Z \ x# t P(B ) p. X0J D ԓ 5 BBE A(A0D@u
-0A(A BBBA D (6 BBE A(A0D@u
-0A(A BBBA L 6r n BBB B(E0A8J@
-8A0A(B BBBA 7J ̔ (8J L `8 BBB B(A0A8D`
-8A0A(B BBBA , | 8 m AAD@W
-AAA , 09 m AAD@W
-AAA , ܔ 9 m AAD@W
-AAA , 9 m AAD@W
-AAA , < P: lm AAD@W
-AAA , l : Hm AAD@W
-AAA , ; $m AAD@W
-AAA , ̕ p; m AAD@W
-AAA $ ; RMIp L ܖ H= BBB B(A0A8D`
-8A0A(B BBBA , t = dl AAD@W
-AAA , > @l AAD@W
-AAA , Ԗ x> l AAD@W
-AAA , > k AAD@W
-AAA , 4 8? k AAD@W
-AAA , d ? k AAD@W
-AAA , ? k AAD@W
-AAA , ė X@ hk AAD@W
-AAA L @ BBB B(A0A8Dp
-8A0A(B BBBA , D xA j AAD@W
-AAA , t A j AAD@W
-AAA , 8B j AAD@W
-AAA , Ԙ B j AAD@W
-AAA , B dj AAD@W
-AAA , 4 XC @j AAD@W
-AAA , d C j AAD@W
-AAA , D i AAD@W
-AAA < | xD BBA A(D@i
-(A ABBA , D i AAD@W
-AAA , 4 E pi AAD@W
-AAA , d xE Li AAD@W
-AAA , E (i AAD@W
-AAA , Ě 8F i AAD@W
-AAA , F h AAD@W
-AAA , $ F h AAD@W
-AAA , T XG h AAD@W
-AAA T G th BBB B(D0D8G
-8D0A(B BBBJ T ܛ Lc Gh BEE E(D0A8G
-8D0A(B BBBD T 4 P
-h BBB B(D0D8G
-8D0A(B BBBH T `F Zh BLA H(G
-(A ABBB
-(A ABBA T xb Eh BIE E(D0A8G
-8A0A(B BBBG T < @p, h BBE B(D0A8G
-8D0A(B BBBK T uJ sh BIA A(GT
-(A ABBB ^
-(A ABBD T w ;h BBE B(D0A8G
-8D0A(B BBBF T D {J !h BIA A(GT
-(A ABBB ^
-(A ABBD T }D g BBE B(D0A8G
-8D0A(B BBBK T J g BIA A(GT
-(A ABBB ^
-(A ABBD T L , g BBE B(D0A8G
-8D0A(B BBBK T hJ }g BIA A(GT
-(A ABBB ^
-(A ABBD L ` Gg BBB B(D0A8G`8
-8A0A(B BBBJ T L 'g BBB B(D0A8G
-8A0A(B BBBA L ( f BEB E(I0H8DP
-8D0A(B BBBD L 葥 f BHI E(A0A8Gps
-8A0A(B BBBB L D f BEB E(I0H8DP
-8D0A(B BBBD L X f BHI E(A0A8Gpr
-8A0A(B BBBC T (u {f BIB B(A0A8GS
-8A0A(B BBBA L AAG ]
-CAF c
-CAI R
-CAJ JCA L D AAG ]
-CAF c
-CAI R
-CAJ JCA , 00 AC
FK
- E ģ 0? A} < P ADG v
-GAF
-AAF $ S L < 蜥+ ULB A(A0@(A BBBD0 D ԣ ȡe d BFB A(A0GP
-0A(A BBBC d i d BBB B(A0A8G@*
-8A0A(B BBBK _
-8A0A(B BBBG < , Bd ZGA A(D , ̤ /d ZGA A(D T p
d BBB B(A0A8G<
-8A0A(B BBBI T T ¥ c BEB B(A0A8J
-8A0A(B BBBA T ҥ c BEB B(A0A8J
-8A0A(B BBBA T c BBB B(A0A8Gl
-8A0A(B BBBA T \ ^c BBB B(A0A8Gl
-8A0A(B BBBA l Hu. K^ T b BBB J(A0A8G
-8D0A(B BBBD , +u@ AAG04AA 4 U b BHD D`
- DABH T ob BEB B(G0A8G
-8D0A(B BBBJ ̨ u. K^ x p , h D ` \ X t P H P X ԩ ` h p x 4 DV , [ /a AID t
-DAA , ĩ [ a AID t
-DAA , [ ` AID t
-DAA , $ [ ` AID t
-DAA , T H[ ` AID t
-DAA 4 < x BAA s
-AEK AAB L r W` BBB B(E0A8J@
-8A0A(B BBBA $ ī ^ AG b
-AA 8 d L @ _ BBB B(A0D8Op
-8A0A(B BBBH U
-8A0A(B BBBA , > _ AID W
-DAA T k_ BBB B(D0A8K#
-8A0A(B BBBK , < p> 7_ AID W
-DAA D l _ BBB A(A0JR
-0A(A BBBA L X{ ^ BBB D(D0J
-0D(A BBBG , ^ qGA A(D T 4 x ^ BBA A(In
-(C ABBK
-(A ABBA T ^ BEE E(D0D8G(
-8D0A(B BBBK d 8 = BLB B(A0A8DPq
-8A0A(B BBBE
-8A0A(B BBBE < L g
-^ BED D(G
-(D ABBD $ D @
AAG0AA4
] BHA GP
- DABI 4 ] BHA GP
- DABI < $ x] BHA G
- DABA < d ]] BHA G
- DABA < H B] BHA G
+ I `.0C. ) .0e. .0t. - BXM P H \x FEE B(A0G8J
+8A0A(B BBBE
+ KX& I x2 I NX. Fb L I x[ FBA A(G0
+(A ABBK
+(A ABBJ 8 J 0x[ FBA D(D0
+(I ABBL PJ ,j2 HW P I <x
+ FBE E(H0A8G1
+8A0A(B BBBC , zPLR x- 4 ~MX 8 `J x EC
BEHV.. 1
+ E 0 ,OX \K اx2 pK RX. Fb L K x[ FBA A(G0
+(A ABBK
+(A ABBJ 8 K x[ FBA D(D0
+(I ABBL 8 tK x
o EC
BQW.l.
+ E $1 RXH lL *j2 HW L 4TX' L HTX. Fb L L x[ FBA A(G0
+(A ABBK
+(A ABBJ 8 M x[ FBA D(D0
+(I ABBL L x Dv
+F \
+D P L pxD FBB B(D0A8G.
+8D0A(B BBBE @ M lx { EC
HD
+ E . S. i .f. $ XM SXy A $N 0)j2 HW $ <N x6 FJG XDB dN $XX0 xN xS N xk Ld
+H L N \x FBB B(I0A8DP
+8D0A(B BBBE 4 TN x FDG G@s
+ AABA lC WX Y HO L(j2 HW 4 dO jWX= BAA l
+ABE AAB H O x BBB B(A0A8DP
+8D0A(B BBBD O $WX' O 8WX. Fb H P xb LBB B(A0A8DP
+8A0A(B BBBJ L O x - BGB B(A0D8D`
+8D0A(B BBBA L P `x BEE B(I0A8K@
+8D0A(B BBBH H$ ^VXC H Q x] BBB B(A0A8Dp
+8D0A(B BBBF h P xU g BBB E(D0A8L@q
+8F0A(B BBBD Y
+8I0A(B BBBE % UXC L Q x[ FBA A(G0
+(A ABBK
+(A ABBJ 8 <R x[ FBA D(D0
+(I ABBL L xR x LBB B(A0D8D
+8A0A(B BBBE , R "UX KAD IB P TR |x& FEB B(G0A8G
+
+8A0A(B BBBA , zPLR x
+ 4 ,UX9 S @$j2 HW S hy2 L S yc FBA A(G02
+(A ABBG
+(A ABBJ 8 T y[ FBA D(D01
+(I ABBH LT ]XO FC P hT y9 FBB A(A0
+(A BBBG
+(D BFBE L T yW FBE E(D0A8Lpc
+8A0A(B BBBA d< &]X $U "j2 HW P T yM* FEE B(A0A8M
+8A0A(B BBBE , zPLR xY 4 \X i U 8Jy2 U VcX. Fb L V HJy[ FBA A(G0
+(A ABBK
+(A ABBJ 8 \V XKy[ FBA D(D0
+(I ABBL , V |Ly FHD o
+EBH V Ly- L 8V Myb BBB B(A0A8JK
+8D0A(B BBBD , zPLR x 4 bX 2 L V Oy-" 3 EC
BID
+ E .. h.]. 3.\. < bX| j W x j2 HW W qy2 X qyS X ryk Ld
+H L W \ryz G FIB B(D0D8Nh
+8A0A(B BBBE ; ziX 8 X tvy FBD A(DP
+(D ABBG L 8X }y FIB E(A0A8D`
+8D0A(B BBBI ,Y h j2 HW P X y FBB E(D0D8G
+8D0A(B BBBK hX t Y y2 Y ̅yS Y yk Ld
+H L TY lys y FIB B(D0D8Nh
+8A0A(B BBBE = vlX H 0 `Z y FDA D@
+ DABA @ Z y FBB D(A0DP
+0D(A BBBE L Z \y FBB B(A0D8D
+8A0A(B BBBA L Z y s FIB E(A0A8D`
+8D0A(B BBBI ` x[ Ly= FBB B(A0D8G@l
+8A0A(B BBBB B
+8A0A(B BBBD [ j2 HW P P[ y
+ Ǿ FEE B(A0D8GV
+8D0A(B BBBG , zPLR x 4 \jX3 \ y2 \ <yS \ yk Ld
+H L 4\ ܧyz FIB B(D0D8Nh
+8A0A(B BBBE @ qX ˾ L \ y FIB E(A0A8D`
+8D0A(B BBBI ] j2 HW P ] y s FBB E(D0D8G
+8D0A(B BBBK "qX7 v ^ BvX' (^ LyS <^ yk Ld
+H L ] y FIB E(A0A8D`
+8D0A(B BBBI L ^ ,yt S FBE B(D0D8J
+8D0A(B BBBH T/ uX& L l^ Dy FIL E(D0A8Qw
+8A0A(B BBBI , zPLR x 4 vXF _ j2 HW $ _ `y6 FJG XDB 0 _ xy FLK JP`
+ ABBD ` yS 4` Pyk Ld
+H L _ y FBB B(I0A8DP
+8D0A(B BBBE ` j2 HW ` .uX' ` BuX. Fb L ` y[ FBA A(G0
+(A ABBK
+(A ABBJ 8 <a y[ FBA D(D0
+(I ABBL xa \j2 HW < ` y
+ ϻ EC
BEEM"
+ E .O. F ptXY a Xy2 b wX. Fb , xa hyT ѻ AHG u
+AAA Y |wX , a yN AHG o
+AAA Z JwX q ( b yR BAA \
+DBJ L b y[ FBA A(G0
+(A ABBK
+(A ABBJ 8 (c y[ FBA D(D0
+(I ABBL L b y BFE B(A0A8Z`
+8A0A(B BBBF c @vXr A`@ 4c ,y\ BBD D(GU
+(A ABBB JvXO y P c 0y i BBB B(K0A8Gn
+8D0A(B BBBJ -vXr P c y) BIB B(A0A8G ?
+8D0A(B BBBD XY 3wX P hd 8z
Լ BFB E(A0K8G
+8D0A(B BBBF ( zXG xe z H}
+K J
+F e xj2 HW 8 e z h AC
HN].. ,
+ J J xzX < de ,,zI
+ AC
BEGK..
+ F K {Xt H 8 e $6z 9 AC
DL
+ G .]. lK |X l 8 f PzN o EC
BGMM
+ E X.d. K t~X W $ g ez6 FJG XDB 4g ƂX' Hg ezS \g 8fzk Ld
+H L f fz FIB E(A0A8D`
+8D0A(B BBBI g j2 HW L <g hz5 FBB E(D0G8Gt
+8D0A(B BBBF K X | 0 Lh kz ADD Z
+KAI gKA h lz2 h DX. Fb L h lz[ FBA A(G0
+(A ABBK
+(A ABBJ 8 i $mz[ FBA D(D0
+(I ABBL L h Hnz y BBE E(D0D8N
+8D0A(B BBBK LS zX3 C i qz OEH H(H0D8DP
+8G0A(B BBBH 6
+8A0A(B BBBH HP L i Huz9 FPB L(D0D8G
+8D0A(B BBBD X L i }z FFB E(D0K8Q
+8D0A(B BBBC , VX ʼ D Xj j FIB A(A0D`N
+0A(A BBBA K XK 8 j z EC
DEM> ..
+ E hP X l k |X' < k z MBB E(D0E
+(I BBBJ P l lz# MBB E(A0I
+(A BBBI
+(A BBBL H \l Hz FBB B(E0A8DP
+8D0A(B BBBA H l z BBB E(D0A8D@q
+8D0A(B BBBF x l @zt FBB B(A0A8DP
+8F0A(B BBBI D
+8F0A(B BBBE L8F0A(B BBB d l Dz / FBB E(I0A8GPu
+8D0F(B BBBI }
+8F0A(B BBBD P 4m ̨z FBB B(A0D8Q
+8A0A(B BBBH dP ,X ӻ Dn j2 HW P m ذz FBB B(A0A8G
+8A0A(B BBBG 8X n X' n ЌX. Fb L n z[ FBA A(G0
+(A ABBK
+(A ABBJ ` Lo z BBB B(A0A8DP
+8A0A(B BBBA Y
+8F0A(B BBBG 8 o ļz[ FBA D(D0
+(I ABBL < Ho zM { EC
DEED)
+ E .. T X Dp @ j2 HW \p z2 pp X. Fb L p z[ FBA A(G0
+(A ABBK
+(A ABBJ 8 p z[ FBA D(D0
+(I ABBL < tp z FFA D(a0R
+(A ABBF H Xq zp BBA A(D0J
+(C ABBD D(F ABB 8 q z k EC
BEOE
+ A
+.. V X ӹ < Tq
+j. $ FIA A(D`
+(A ABBA K \X0 Tr pX' hr X. Fb 4 r zX BBD U
+DBE gBB L r z[ FBA A(G0
+(A ABBK
+(A ABBJ 8 s z[ FBA D(D0
+(I ABBL Hs
+j2 HW 8 r $z;$ Ҹ EC
DO
+ E
+.. lX ~X0 1 s X' s {S s H {k Ld
+H L Ts { FIB E(A0A8D`
+8D0A(B BBBI L s { ߹ FLE B(A0A8G
+8D0A(B BBBG ڤX? ֹ 8 t t{n ƹ EE
FFH.x.
+ G Y ƤX ) u 0 j2 HW u x!{2 0u X. Fb L Lu !{ FBA A(G0V
+(A ABBC
+(A ABBJ 8 t H"{# ˹ EC
FMt
+ A .. Z X u j2 HW v @{ v @{ 4v @{S FG {
+EA Xv <@{ lv H@{) LX v \@{I FG q
+EA H v @{ EAG U
+CAJ [
+CAA d
+CAH NCA H v @{ EAG U
+CAJ [
+CAA d
+CAH NCA Dw X. Ah `w X' tw ΪXO FC 8 w @{k FAA j
+ABK }
+ABH , w B{ EL
HKr
+ J 0 w C{w EAG [
+AAF DAA4 0x 4D{ FAA {
+ABJ AAB 0 hx D{w EAG [
+AAF DAA< w D{: FBA A(Dp
+(A ABBD P X& ڷ 8 Px F{" ʷ EC
FED. (
+ B . ^ X L x K{" FBB E(A0A8Lp<
+8A0A(B BBBE < x O{> FBA D(G0
+(A ABBJ D 4y P{ s FDB D(A0G@
+0A(A BBBF < |y Q{> O FBA D(G0
+(A ABBJ D y R{ + FDB D(A0G@
+0A(A BBBF < z LT{> FBA D(G0
+(A ABBJ D Dz LU{ FDB D(A0G@
+0A(A BBBF < z V{> FBA D(G0
+(A ABBJ D z W{ FDB D(A0G@
+0A(A BBBF P { Y{8 w FBB E(G0A8G)
+8D0A(B BBBD 4$ /X} h P { c{ [ FBB B(D0D8L
+8D0A(B BBBK L @Xu P { m{ FFB B(A0A8J
+8A0A(B BBBH F JX7 | j2 HW } Pu{ (} Lu{- ( <} hu{= FID _EE h} |u{ |} X' , | du{? S EDG
+AAE } tv{ Fo
+K 4 <} v{ EC
I?.\.
+ J ~ j2 HW 4~ 0{ H~ ,{- \~ H{- ( p~ d{= FID _EE ~ x{ ~ ޫX' ( ~ `{= FID _EE , L~ t{ m EDG
+AAA {} Fa
+I @ t{} Fa
+I ` Ԃ{ OBB E(G0G8K`_
+8G0A(B BBBH
+8A0A(B BBBG H` 4 @ {] EC
P.\.
+ F i2 HW 4 { H {- ( \ 8{= FID _EE L{ X' < 4{ A FBA D(G0
+(A ABBD ԑ{} Fd
+F 8 l 4{
+ EG
FM.\.
+ A L (i2 HW d { x {- ( {= FID _EE ,{ ́ X' H < { FAD
+ABG D
+AEF x
+EEF , {u F[
+G 4 { % EC
PQ.\.
+ A 0i2 HW { {- ( Ă {= FID _EE Ԫ{ X' , t {n EDD @
+AAA H {} Fd
+F 4 Ă \{ EC
P._.
+ A 4 ĵ{J EC
P.b.
+ J ܼ{- i2 HW {M {' 4 i BBD G@V
+ ABBA xq ʦX P { FFA A(G0A
+(A ABBD K
+(A ABBA D , i ; FIB B(A0Dp
+0A(B BBBA ( zPLR xѬ p 0 X> P {f ۱ FBB B(A0A8G
+8A0A(B BBBC | ܥX 8 ȅ { FLD n
+DBF M
+BFK 0{
+ ,{ , ({ @ ${- T @{- h \{- | x{- ( {= FID _EE ( {= FID _EE { { BX' ( $ {= FID _EE ( P {= FID _EE < ؆ {
ZAD N
+CBF {
+ABJ 4 H{# կ FAA D@G
+ AABI @{} Fh
+B {} Fh
+B 4 {} Fd
+F T `{} Ff
+D P Ї {F % FBB B(D0A8J0
+8F0A(B BBBF Ȉ i2 HW {; < T { FBA A(DPs
+(H ABBB $ zPLR x P , X. | H{
+ D{ @{ <{ ̉ H{+ Fd \{ X' XO FC 4 , 8{ EAG S
+AAF {
+AAK P { FBA A(G0
+(A ABBH
+(A ABBJ @ { ? MBB E(D0
+(I BBBJ P X X{/ FBB B(A0A8K
+8A0A(B BBBF ϢX P Ċ {7 FBB B(A0A8K
+8A0A(B BBBF X m 4 0 {& ] FBD G0`
+ DBBE $ zPLR xݥ 0 , )XB P {: FBB B(D0A8D
+8D0A(B BBBG $ X A Ȍ ,i2 HW P{ L{ H{ D{ 4 @{ $ H <{G FAG rDB p d{% $ {c FIF JAB, {C FDH kCB , 8 {p FID0E
+DBA { X H $ { PBB B(A0A8D`
+8A0A(B BBBE 4 ̍ d{ = FAA G@
+ CABF i2 HW Ď 0{
؎ ,{9 X{) t{7 Lf X' 0 XO FC < L РX KBB A(D0(I BBBP ({ % FBB B(I0A8G
+8D0A(B BBBG O X P T { FBB B(D0A8GY
+8D0A(B BBBB P H{ FBB B(D0A8GX
+8D0A(B BBBC H | FBB A(A0G
+0A(A BBBH L H | FBB E(D0F8J
+8D0A(B BBBJ L #| FBB B(D0A8M{
+8D0A(B BBBJ
+ ҡX g i2 HW L (| ? FBB G(A0D8G
+8A0A(B BBBG , h (,| H FAL0_
+DBA X $ T p,|) h ,|7 Lf ,|) ,|7 Lf ,|) Ȓ -|7 Lf 0-|R Lu
+O A p-|R Lu
+O A $ ڠX' 8 -|T LC T -|T LC ( p X BAD AB XO FC < X KBB A(D0(I BBBP T -| p FBB B(I0A8G
+8D0A(B BBBI d ԣX I P P4| 9 FBB B(I0A8G
+8D0A(B BBBI e X P , ;|' FBB B(I0A8G
+8D0A(B BBBG e &X ۧ P A|' ˧ FBB B(I0A8G
+8D0A(B BBBG e OX P H|' FBB B(I0A8G
+8D0A(B BBBG Xf xX m 8 p PO| ] EAG G
+AAJ {
+AAC P O|- % FBB B(D0A8N
+8A0A(B BBBK , [|= FAL W
+DBA zPLR x ( X @ P l t[|- 0 FBB B(D0A8N
+8A0A(B BBBK , Pg|= FAL W
+DBA X o P Hg| _ FBB B(D0A8N
+8A0A(B BBBJ , \ o|= FAL W
+DBA ` X f P o| V FBB B(D0A8N
+8A0A(B BBBJ , v|= FAL W
+DBA |X ] P @ v|z M FBB B(D0A8N
+8A0A(B BBBJ , ||= Z FAL W
+DBA X 6 L ܘ ||: & BBB B(D0A8J
+8D0A(B BBBG A X < D ̀| FBK A(M@X
+(D ABBA hh LX ˦ P |^ FBB B(A0A8G
+8A0A(B BBBG P | FEF B(A0A8G
+8A0A(B BBBD P D |s S FBB G(G0D8GD
+8D0A(B BBBD HXV ? 4 | EC
HK3. }
+ E i. \ NX 8 | w EC
FGNr
+ K >. c. X P T L|! FBG E(A0A8G
+8A0A(B BBBH , zPLR x 4 X( Ϧ i2 HW H|) d|7 Lf , 8 | ; FAL0W
+DBA X $ н|2 , | FAL0W
+DBA =X Ϧ ( :X BAD AB XO FC < ȝ @X KBB A(D0(I BBBP d | 7 FBB B(I0A8G_
+8D0A(B BBBG XX P Н | FBB B(I0A8G_
+8D0A(B BBBG X ٥ P < D|c ˥ FDB B(D0A8G
+8A0A(B BBBC Dn X L i2 HW d 0|) x L|7 Lf p|) |7 Lf , | FAL0W
+DBA | qX ߧ nX' , | | FAL0W
+DBA ؍ QX , ğ ,| FAL0W
+DBA X c ( X BAD AB < ܠ X KBB A(D0(I BBBP x |g FBB B(I0A8D=
+8D0A(B BBBD 0 XX P | FBB B(I0A8G
+8D0A(B BBBD X q P P p| a FBB B(I0A8G
+8D0A(B BBBE X : P | * FBB B(I0A8G
+8D0A(B BBBK s DX P ( } FBB B(A0I8Gl
+8D0A(B BBBJ _ mX å P } ϥ FGB B(G0D8G
+8D0A(B BBBG P } FBB E(D0A8G0
+8A0A(B BBBK P < }
+ I FBB E(D0A8GP
+8A0A(B BBBK P $} FBB B(A0D8J
+8A0A(B BBBK P L+}( Ц FBB B(A0D8J
+8A0A(B BBBK P 8 (4}
FBB B(A0D8J
+8A0A(B BBBK P :} N FBB B(A0D8JA
+8A0A(B BBBJ P D}< FJE B(A0A8G
+8D0A(B BBBB ȋ X` c Di2 HW E}) E}7 Lf < E}) P F}7 Lf l 8F}) TF}7 Lf xF}) F}7 Lf ̦ F}2 F}? F]U 8 G} LBA A(D0
+(A ABBA @ G} LBB A(A0f
+(A BBBA , H} FHD o
+EBH tH}Q | d $ H}Q h dL I} KBB A(D0(I BBBM0 @ DK}j FBI A(D
+(D ABBA ( zPLR xu 0 4Xu @ ,M}c FBI A(D
+(D ABBA MXu @ p @O}c r FBI A(D
+(D ABBA fXu O @ ̨ TQ}c ? FBI A(D
+(D ABBA D Xu @ ( hS}c FBI A(D
+(D ABBA Xu @ |U}j ٤ FBI A(D
+(D ABBA Xu @ W}j FBI A(D
+(D ABBA X Xu @ < Y}j s FBI A(D
+(D ABBA Xu P < [} Of
+A P (\} FBB B(A0D8Ks
+8A0A(B BBBG , ^}= FAL W
+DBA X ̣ , T |^}= FAL W
+DBA X iX L t^} FBB B(A0D8Kp
+8A0A(B BBBE , `}= \ FAL W
+DBA X 8 , 4 `}= ( FAL W
+DBA 8 X L | `} FBB B(A0D8Kpx
+8A0A(B BBBB , ̬ Dc}= Ȣ FAL W
+DBA 0X , <c}= FAL W
+DBA X p P \ 4c} ` FBB B(A0D8Ks
+8A0A(B BBBG , e}= 0 FAL W
+DBA vX , e}= FAL W
+DBA CX ء ( e} AAG X
+DAJ P l f}Z FBB B(A0A8GQ
+8A0A(B BBBH P l}T FEB E(A0D8J
+8A0A(B BBBE < ,q} { FBB D(G
+(D BBBH ( zPLR x $ 0 X 3 lFPD L xr}* E[
+H A L ȯ r}t FBB B(A0D8LK
+8D0A(B BBBA P u} FEE E(D0A8Jw
+8D0A(B BBBH P l w}O FEB B(A0A8G
+8A0A(B BBBC P |} FBB E(D0A8G
+8A0A(B BBBJ 4 ~}P AC
BEIF}.
+ B P L } p BBB E(D0A8J2
+8A0A(B BBBJ ` } Y BHB B(D0D8JfFyE
+8A0A(B BBBJ T }
2 EC
BGP
+ H Q.s. F.. e.. F.s. c. P \ }O x FEB B(A0A8G
+8A0A(B BBBC P } m FBB E(D0A8G
+8A0A(B BBBJ 4 }P L AC
BEIF}.
+ B P < } - BBB E(D0A8J3
+8A0A(B BBBI ` Я} BHB B(D0D8JfFyE
+8A0A(B BBBJ T }
EC
BGP
+ H Q.s. F.. e.. F.s. c. P L Ŀ}O 5 FEB B(A0A8G
+8A0A(B BBBC P } * FBB E(D0A8G
+8A0A(B BBBJ 4 }P AC
BEIF}.
+ H P , 4} BBB E(D0A8J2
+8A0A(B BBBJ ` } ӟ BHB B(D0D8JfFyE
+8A0A(B BBBB T }
EC
BGP
+ H Q.s. F.. e.. F.s. c. P < } FEB B(A0A8J
+8A0A(B BBBB P } FBB E(D0A8G
+8A0A(B BBBJ 8 l}
+ Ɵ AC
BEIEJa.
+ D 4 @}P AC
BEIF}.
+ E P X X} ~ BBB E(D0A8J3
+8A0A(B BBBI T }
g EC
BGP
+ H Q.s. F.. e.. F.s. c. i2 HW ĸ ~) ظ ~7 Lf , P ~ a FAL0W
+DBA X = < X' H P $~K BBE B(A0A8DXj
+8A0A(B BBBK D ( ~ BBE E(A0A8
+0A(B BBBA D
~ BBB B(A0A8x
+0A(B BBBA H , H~ BBE B(A0A8DH?
+8A0A(B BBBF D x ~ BEB B(A0G8
+0A(B BBBD h ~ BEE E(D0A8Gt
+BDBGGiH
+8A0A(B BBBF h , )~j BEE E(D0A8GZ
+BGBBBr-
+8A0A(B BBBA qX5 EAF ( 8~\ EGG@
+AAD < ^X KBB A(D0(I BBBP | >~W I FBB B(I0A8D'
+8D0A(B BBBB 4 vXX
+ 0 D~ EC
P(
+ H D $P~ FBB B(A0E8
+0A(B BBBG L R~ FBB B(A0A8G
+8D0A(B BBBG H X Y~J FBB E(D0D8GpJ
+8A0A(B BBBF X ^~ FBB B(A0A8
+0A(B BBBD 0A(B BBB L a~N FFB B(A0A8G
+8A0A(B BBBH , P u~v EC
BN
+ F H $x~J FBB E(D0D8GpJ
+8A0A(B BBBF D ̾ (}~ FMM G(A0D8
+0A(B BBBI 0 p ~ EC
DO
+ C 8 ~N l ET
P
+ G .0. T Й~$ FBB B(A0A8GP}XO`SXAP
+8A0A(B BBBF L 8 ~
FFB B(A0D8GP
+8A0A(B BBBA L h~H ט FJB B(A0D8G
+8D0A(B BBBD | 8i2 HW P~9 |~\ FV u
+EA ~9 @ ~ FKM8Z@GHGPGXG`GhGpY0T
+EBA $ @~9 8 l~9 L ~K FP j
+EA p ĺ~9 @ ~ FKM8Z@GHGPGXG`GhGpY0T
+EBA L~9 @ x~ FKM8Z@GHGPGXG`GhGpY0T
+EBA Ի~9 @ 4 ~ FKM8Z@GHGPGXG`GhGpY0T
+EBA x \~9 X' < t~~ MBB H(A0
+(D GBBI XO FC L ~~ MBB E(A0
+(F BBBK
+(A BBBH P ~ FBA A(G0
+(A ABBJ D
+(A ABBF 8 d~ _ EAG
+AAG {
+AAC 8 8 ~ ' EAG
+AAG {
+AAC 8 t ~ EAG
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+0A(B BBBI ` TU d FBB A(A0=
+(A BBBH
+(A BBBB S
+(A BBBF ( U q GQ
+AG h, U FAA
+ABE , V FAA
+ABG DV ( XV Y LAA GAB V Pd
+I X V 2 $ V Ȩ FgI$ V @ FgI W W B $ 4W а FgI \W H2 pW t E
+H W $ $ W EA
+AJ $ W EA
+AJ $ W EA
+AJ $ X EA
+AJ < DX Â/ IBE A(D0
+(A BBBF ` X płd FBB A(A0=
+(A BBBH
+(A BBBB S
+(A BBBF ` X |Ȃd FBB A(A0=
+(A BBBH
+(A BBBB S
+(A BBBF ` LY ˂d FBB A(A0=
+(A BBBH
+(A BBBB S
+(A BBBF H Y @ FBB B(A0A8D@
+8A0A(B BBBH 8 Y Ђd FAA
+ABO
+ABG 8 8Z Ԃ4 FDA
+ABF T
+ABI 8 tZ 4 FDA
+ABF T
+ABI 8 Z ܂d FAA
+ABO
+ABG $ Z E_
+D
+F D [ ` FBB B(A0A8
+0A(B BBBJ D \[ ( FBB B(A0A8
+0A(B BBBJ D [ FBB B(A0A8
+0A(B BBBJ L H[ x FBB B(A0A8D`
- DABA < b '] BHA J
- DABA 4 $ ] BHD D`
- DABG L \ ` \ BBB B(G0D8G
-8A0A(B BBBA \ " \ BBB A(A0G
-0A(A BBBI O
-0A(A BBBA D "D \ BDH D(A0G
-0D(A BBBD D T & \ BDB D(A0J~
-0D(A BBBA < )0 \ BBA A(Jc
-(A ABBG T ܱ p*a \ BEE B(D0A8G
-8D0A(B BBBI L 2B BLH B(D0A8DP
-8A0A(B BBBB < 39 Af
-I D L 3, M\ BBB E(A0A8Mn
-8D0A(B BBBK T 5% #\ BDB E(A0A8G*
-8A0A(B BBBF T L `=l w\ BDB B(D0A8G6
-8A0A(B BBBJ T xB \ BBB B(A0D8G
-8A0A(B BBBA < E \ BBH A(Jx
-(A ABBK T < GE
l\ BBB B(A0E8G
-8A0A(B BBBA T xTE
\ BBB B(A0E8G
-8A0A(B BBBA T paE
0] BBB B(A0E8G
-8A0A(B BBBA T D hnE
] BBB B(A0E8G
-8A0A(B BBBA T `{"
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-8A0A(B BBBA L 8 W^ BBB G(A0D8M
-8D0A(B BBBE u. K^ Ћ , ȋ D u\ AG RA, d 0 AF
FK
- D L ܶ " ] BBB J(A0A8G
-8D0A(B BBBA L , " ] BBB J(A0A8G
-8D0A(B BBBA < | p u] BBA A(GK
-(A ABBA , t 4 u@ AAG04AA 4 #] BHI D`?
- DABC 4 $ ] BHI D`F
- DABD , \ ` \ AAG
-DAA L 08 \ BBB B(D0D8G
-8A0A(B BBBH L ܸ \ BBB B(D0D8G
-8A0A(B BBBH L , h\ BBB B(D0D8G
-8A0A(B BBBH L | Ѧ 9\ BBB B(D0D8G
-8A0A(B BBBH L ̹
-\ BBB B(D0D8G
-8A0A(B BBBH L [ BBB B(D0D8G
-8A0A(B BBBH L l 8 [ BBB B(D0D8G
-8A0A(B BBBH L 'Z }[ BBB B(D0D8G
-8A0A(B BBBH L 9O I[ BBB B(D0D8G
-8A0A(B BBBH L G{ BBE E(A0A8G
-8A0A(B BBBH T P Z BBE B(D0A8G
-8D0A(B BBBC L xUW Z BBB B(A0A8JB
-8A0A(B BBBH D X5 BBB D(D0GP
-0A(A BBBD D T k!
BBB D(D0GP
-0A(A BBBD D hx BBB D(D0G`
-0A(A BBBD L , Ѐg Y BFB B(A0A8JF
-8A0A(B BBBH D 4 BBB D(D0GP
-0A(A BBBD T Ľ x Y BBG B(D0G8G
-8D0A(B BBBG , Ծ pu BOA K0 AAB L | m 3Y BBB B(A0I8Dp
-8D0A(B BBBF L ̾ m X BBB B(A0I8Dp
-8D0A(B BBBF , Կ u@ AAG04AA < L u X BIA D(Dp)
-(D ABBJ < Pu uX BIA D(Dp0
-(D ABBC T ̿ OX BBB E(A0A8G
-8A0A(B BBBA Hu. K^ L $ BLB B(A0A8D
-8A0A(B BBBE L 0 {X BBB B(I0A8G
-8D0A(B BBBF L eX BBB B(I0A8G
-8D0A(B BBBF L \ OX BBB B(I0A8G
-8D0A(B BBBF T ç| 9X BBE E(D0A8G
-8D0A(B BBBE T Hȧ X BEE E(D0A8G
-8D0A(B BBBE L pͧ BOB A(A0k
-(A BBBI b
-(A BBBG T Χ| W BBE E(D0A8G
-8D0A(B BBBE , lu@ AAG04AA 4 4 ҧU kW BHD D`
- DABH 4 l ֧U MW BHD D`
- DABA 4 H٧U /W BHD D`
- DABA L pܧ BBB E(D0D8G@L
-8D0A(B BBBE < ݧ BLH D(A0
-(A BBBF T l ާ V BBB B(A0A8G
-
-8A0A(B BBBK T +W BBB I(D0A8GU
-8D0A(B BBBC u. K^ x p , d x[ V AID t
-DAA , L u@ AAG04AA 4 xU V BHD D`
- DABH T V BDB H(A0A8J
-8D0A(B BBBB xu. K^ $ < T l , D \ t 4 L , [ T AID t
-DAA , 0[ T AID t
-DAA , `[ T AID t
-DAA , < [ wT AID t
-DAA , l [ WT AID t
-DAA , [ 7T AID t
-DAA , [ T AID t
-DAA , P[ S AID t
-DAA , , [ S AID t
-DAA , \ [ S AID t
-DAA , [ S AID t
-DAA , [ wS AID t
-DAA , @[ WS AID t
-DAA , p[ 7S AID t
-DAA , L [ S AID t
-DAA , | [ R AID t
-DAA , [ R AID t
-DAA , 0[ R AID t
-DAA , `[ R AID t
-DAA , < [ wR AID t
-DAA , l [ WR AID t
-DAA , [ 7R AID t
-DAA , [ R AID t
-DAA , P[ Q AID t
-DAA L ADD e
-CAF [
-ACI R
-CAJ JCA L 4 AAG e
-CAF {
-ACI R
-CAJ JCA L ` ADD e
-CAF [
-ACI R
-CAJ JCA L AAG e
-CAF {
-ACI R
-CAJ JCA L $ @
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-CAF [
-ACI R
-CAJ JCA L t
- AAG e
-CAF {
-ACI R
-CAJ JCA L ADD e
-CAF [
-ACI R
-CAJ JCA L AAG e
-CAF {
-ACI R
-CAJ JCA L d ADD e
-CAF [
-ACI R
-CAJ JCA L AAG e
-CAF {
-ACI R
-CAJ JCA L ADD e
-CAF [
-ACI R
-CAJ JCA L T `
AAG e
-CAF {
-ACI R
-CAJ JCA L
ADD e
-CAF [
-ACI R
-CAJ JCA L @ AAG e
-CAF {
-ACI R
-CAJ JCA L D ADD e
-CAF [
-ACI R
-CAJ JCA L AAG e
-CAF {
-ACI R
-CAJ JCA L ADD e
-CAF [
-ACI R
-CAJ JCA L 4 AAG e
-CAF {
-ACI R
-CAJ JCA L ` ADD e
-CAF [
-ACI R
-CAJ JCA L AAG e
-CAF {
-ACI R
-CAJ JCA L $ @ ADD e
-CAF [
-ACI R
-CAJ JCA L t AAG e
-CAF {
-ACI R
-CAJ JCA L ADD e
-CAF [
-ACI R
-CAJ JCA L AAG e
-CAF {
-ACI R
-CAJ JCA < d BME A(D0
-(A BBBA < BME A(D0
-(A BBBA < BME A(D0
-(A BBBA < $ BME A(D0
-(A BBBA < d P BME A(D0
-(A BBBA < BME A(D0
-(A BBBA < BME A(D0
-(A BBBA < $ 0 BME A(D0
-(A BBBA < d BME A(D0
-(A BBBA < p BME A(D0
-(A BBBA < BME A(D0
-(A BBBA < $ BME A(D0
-(A BBBA L d P8 BJB B(A0A8D`
-8A0A(B BBBE L @8 BJB B(A0A8D`
-8A0A(B BBBE L 0 8 BJB B(A0A8D`
-8A0A(B BBBE L T @ BJB B(A0A8D`
-8A0A(B BBBE L 8 BJB B(A0A8D`
-8A0A(B BBBE L @ BJB B(A0A8D`
-8A0A(B BBBE L D @ BJB B(A0A8D`
-8A0A(B BBBE L @ BJB B(A0A8D`
-8A0A(B BBBE L !@ BJB B(A0A8D`
-8A0A(B BBBE L 4 "8 BJB B(A0A8D`
-8A0A(B BBBE L #@ BJB B(A0A8D`
-8A0A(B BBBE L $8 BJB B(A0A8D`
-8A0A(B BBBE , $ %I AAG0
-AAA 4 & gC BHA GPt
- DABC 4 x( IC BHA GPt
- DABC 4 @* +C BHA GPt
- DABC 4 D ,
C BHA GPt
- DABC 4 | - B BHA GPt
- DABC 4 / B BHA GPt
- DABC 4 `1 B BHA GPt
- DABC 4 $ (3 B BHA GPt
- DABC 4 \ 4 wB BHA GPt
- DABC 4 6 YB BHA GPt
- DABC 4 8 ;B BHA GPt
- DABC 4 H: B BHA GPt
- DABC 4 < < A BHA GP{
- DABD 4 t = A BHA GP{
- DABD 4 ? A BHA GP{
- DABD 4 A A BHA GP{
- DABD 4 pC A BHA GP{
- DABD 4 T HE iA BHA GP{
- DABD 4 G KA BHA GP{
- DABD 4 H -A BHA GP{
- DABD 4 J A BHA GP{
- DABD 4 4 L @ BHA GP{
- DABD 4 l N @ BHA GP{
- DABD 4 XP @ BHA GP{
- DABD T 0R @ BDB B(A0A8G
-8A0A(B BBBA T 4 _
@ BDB B(A0A8GA
-8A0A(B BBBA T m (A BDB B(A0A8G
-8A0A(B BBBA T z tA BDB B(A0A8G
-8A0A(B BBBA T < A BDB B(A0A8G
-8A0A(B BBBA T HW
B BDB B(A0A8GQ
-8A0A(B BBBA T P LB BGB E(A0A8G
-8A0A(B BBBA T D 诨 {B BDB B(A0A8G
-8A0A(B BBBA T B BDB B(A0A8G
-8A0A(B BBBA T X˨W
C BDB B(A0A8GQ
-8A0A(B BBBA T L `ب SC BDB B(A0A8G
-8A0A(B BBBA T C BDB B(A0A8G
-8A0A(B BBBA u. K^ T D C BBB B(I0A8D'
-8D0A(B BBBF , T Tt@ AAG04AA < CC BHD GU
- DABA L p6 5C BBB B(A0D8Jo
-8D0A(B BBBE L \ `g
-C BBB B(A0D8Jo
-8D0A(B BBBE L s B BBB B(A0D8Go
-8D0A(B BBBH L V B BBB B(A0D8Jo
-8D0A(B BBBE L L V B BBB B(A0D8Jo
-8D0A(B BBBE T _B BBB G(D0G8GN
-8D0A(B BBBF 8u. K^ p h < p BBD A(D0
-(A ABBF < 4 0 BBD A(D0
-(A ABBD L t AAG ]
+8A0A(B BBBJ h [ K FBB B(A0A8Dp
+8A0A(B BBBD
+8A0A(B BBBI \ k F]U $\ 4 Hp
+C D\ <4 Hp
+C d\ \4 Hp
+D 0 \ |/ FAA
+DBA \] x: Eh
+K A |] r Ef
+E A< \ T FEB D(GP
+(D BBBB < 8] 8 FEB D(GP
+(D BBBB TG NX$ < ] T FEB D(GP
+(D BBBB < ] T FEB D(GP
+(D BBBB < ^ @T Y FEB D(GP
+(D BBBB < P^ `8 0 FEB D(GP
+(D BBBB lH MX$ < ^ HT FEB D(GP
+(D BBBB < ^ h 8 FEB D(GP
+(D BBBB I PMX$ < @_ P
+T FEB D(GP
+(D BBBB < _ p T b FEB D(GP
+(D BBBB < _ T 9 FEB D(GP
+(D BBBB < `
T FEB D(GP
+(D BBBB < @` T FEB D(GP
+(D BBBB < ` T FEB D(GP
+(D BBBB < ` T FEB D(GP
+(D BBBB < a 0T l FEB D(GP
+(D BBBB < @a PT C FEB D(GP
+(D BBBB X $b p{ KBB H(D0w
+(I BBBO H0 8 a @ FIA G
+ DABA $ zPLR x- , \JX 4 Xb X y FIA D`
+ DABD $ zPLR x ` , yJXX . 4 b FIA D`
+ DABD | JXX 4 c FIA D`
+ DABJ JXX 4 pc @! FIA D`
+ DABJ JXX 4 c " FIA D`
+ DABJ l JXX b 4 d $ R FIA D`
+ DABJ JXX / 4 `d P& FIA D`
+ DABJ JXX 4 d ( FIA D`
+ DABJ \ JXX 4 e ) FIA D`
+ DABJ JXX 4 Pe `+ FIA D`
+ DABJ JXX c 4 e - S FIA D`
+ DABJ L JXX 0 4 e . FIA D`
+ DABJ JXX 4 @f p0 FIA D`
+ DABJ JXX 4 f 2 FIA D`
+ DABJ < JXX 4 f 3 FIA D`
+ DABJ JXX d 4 0g 5 T FIA D`
+ DABJ JXX 1 4 g 07 ! FIA D`
+ DABJ , JXX 4 g 8 FIA D`
+ DABJ | KXX 4 h : FIA D`
+ DABJ KXX 4 ph @< FIA D`
+ DABJ KXX e 4 h = U FIA D`
+ DABJ l KXX 2 4 i ? " FIA D`
+ DABJ !KXX 4 `i PA FIA D`
+ DABJ )KXX 4 i C FIA D`
+ DABJ \ 1KXX 4 j D FIA D`
+ DABJ 9KXX f 4 Pj `F V FIA D`
+ DABJ AKXX 3 4 j H # FIA D`
+ DABJ L IKXX 4 j I FIA D`
+ DABJ QKXX 4 @k pK FIA D`
+ DABJ YKXX 4 k M FIA D`
+ DABJ < aKXX g 4 k N W FIA D`
+ DABJ iKXX 4 4 0l P $ FIA D`
+ DABJ qKXX 4 l 0R FIA D`
+ DABJ ,
+ yKXX 4 l S FIA D`
+ DABJ |
+ KXX 4 m U FIA D`
+ DABJ
+ KXX h 4 pm @W X FIA D`
+ DABJ KXX 5 4 m X % FIA D`
+ DABJ l KXX 4 n Z FIA D`
+ DABJ KXX 4 `n P\ FIA D`
+ DABJ KXX 4 n ^ FIA D`
+ DABJ \ KXX i 4 o _ Y FIA D`
+ DABJ KXX 6 4 Po `a & FIA D`
+ DABJ KXX 4 o c FIA D`
+ DABJ L
KXX 4 o d FIA D`
+ DABJ
KXX 4 @p pf FIA D`
+ DABJ
KXX j 4 p h Z FIA D`
+ DABE < KXX 7 4 p i ' FIA D`
+ DABJ KXX 4 0q k FIA D`
+ DABJ KXX 4 q @m FIA D`
+ DABJ , KXX 4 q n FIA D`
+ DABJ | LXX k 4 r p [ FIA D`
+ DABJ LXX 8 4 pr Pr ( FIA D`
+ DABJ LXX 4 r t FIA D`
+ DABJ l LXX 4 s u FIA D`
+ DABJ !LXX 4 `s `w FIA D`
+ DABE )LXX l 4 s y \ FIA D`
+ DABJ \ 1LXX 9 4 t z ) FIA D`
+ DABJ 9LXX 4 Pt | FIA D`
+ DABJ ALXX 4 t 0~ FIA D`
+ DABJ L ILXX 4 t FIA D`
+ DABJ QLXX m 4 @u ] FIA D`
+ DABJ YLXX : 4 u @ * FIA D`
+ DABJ < aLXX 4 u
FIA D`
+ DABJ iLXX
4 0v
FIA D`
+ DABE qLXX
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+ DABJ , yLXX n
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+ DABE | LXX ;
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+ DABJ LXX
4 pw FIA D`
+ DABJ LXX 4 w 0 FIA D`
+ DABE l LXX 4 x FIA D`
+ DABC LXX o 4 `x _ FIA D`
+ DABC LXX < 4 x P , FIA D`
+ DABC \ LXX 4 y FIA D`
+ DABC LXX 4 Py FIA D`
+ DABC LXX 4 y ` FIA D`
+ DABC L LXX p 4 y ` FIA D`
+ DABC LXX = 4 @z - FIA D`
+ DABC LXX
+ 4 z p
+ FIA D`
+ DABC < LXX
+ 4 z
+ FIA D`
+ DABC LXX
+ 4 0{ С
+ FIA D`
+ DABC LXX q
+ 4 { a
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+ DABC , LXX >
+ 4 { 0 .
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+ DABC | MXX
+ 4 | ঃ FIA D`
+ DABC MXX 4 p| FIA D`
+ DABC MXX 4 | @ FIA D`
+ DABC l MXX r 4 } b FIA D`
+ DABC !MXX ? 4 `} / FIA D`
+ DABC )MXX 4 } P FIA D`
+ DABC \ 1MXX 4 ~ FIA D`
+ DABC 9MXX 4 P~ FIA D`
+ DABC AMXX s 4 ~ ` c FIA D`
+ DABC L IMXX @ 4 ~ 0 FIA D`
+ DABC QMXX
4 @ FIA D`
+ DABC YMXX 4 p FIA D`
+ DABC < aMXX 4 FIA D`
+ DABC iMXX t 4 0 м d FIA D`
+ DABC qMXX A 4 1 FIA D`
+ DABC , yMXX 4 Ѐ 0 FIA D`
+ DABC | MXX 4 FIA D`
+ DABC MXX 4 p à FIA D`
+ DABC MXX u 4 @Ń e FIA D`
+ DABC l MXX B 4 ƃ 2 FIA D`
+ DABC MXX 4 ` ȃ FIA D`
+ DABC MXX 4 Pʃ FIA D`
+ DABC \ MXX 4 ̃ FIA D`
+ DABC MXX v 4 P ̓ f FIA D`
+ DABC MXX C 4 `σ 3 FIA D`
+ DABC L! MXX 4 у FIA D`
+ DABC ! MXX 4 @ ҃ FIA D`
+ DABC ! MXX 4 pԃ FIA D`
+ DABC <" MXX w 4 փ g FIA D`
+ DABC " MXX D 4 0 ׃ 4 FIA D`
+ DABC " MXX 4 ك FIA D`
+ DABC ,# MXX 4 Ѕ 0ۃ FIA D`
+ DABC |# NXX 4 ܃ FIA D`
+ DABC # NXX x 4 p ރ h FIA D`
+ DABC $ NXX E 4 @ 5 FIA D`
+ DABC l$ NXX 4 FIA D`
+ DABC $ !NXX 4 ` FIA D`
+ DABC % )NXX 4 P FIA D`
+ DABC \% 1NXX y 4 i FIA D`
+ DABC % 9NXX F 4 P 6 FIA D`
+ DABC % ANXX 4 ` FIA D`
+ DABC L& INXX 4 FIA D`
+ DABC & QNXX 4 @ FIA D`
+ DABC & YNXX z 4 p j FIA D`
+ DABC <' aNXX G 4 7 FIA D`
+ DABC ' iNXX 4 0 FIA D`
+ DABC ' qNXX 4 FIA D`
+ DABC ,( yNXX 4 Њ 0 FIA D`
+ DABC |( NXX { 4 k FIA D`
+ DABC ( NXX H 4 p 8 FIA D`
+ DABC ) NXX 4 @ FIA D`
+ DABC l) NXX
+ 4
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+ DABC ) NXX
+ 4 `
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+ DABC * NXX |
+ 4 P l
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+ DABC \* NXX I
+ 4 9
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+ DABC * NXX
+ 4 P
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+ DABG * NXX
+ 4 &
+ FIA D`
+ DABH L+ NXX
+ 4 `
+ FIA D`
+ DABH + NXX }
+ 4 @ 0 m
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+ DABH + NXX J
+ 4 :
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+ DABH <, NXX
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+ DABH , NXX
+ 4 0
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+ DABH , NXX
+ 4 p
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+ DABH ,- NXX ~
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+ 4 ;
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+ DABH - OXX
+ 4 p
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+ DABH . OXX
+ 4
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+ DABH l. OXX
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+ DABH . !OXX
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+ DABH / )OXX L
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+ DABH \/ 1OXX
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+ DABH / 9OXX
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+ DABH / AOXX
+ 4 "
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+ DABH L0 IOXX
+ 4 `$ p
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+ DABH 0 QOXX M
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+ DABH 0 YOXX
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+ FIA D`
+ DABH <1 aOXX
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+ DABH 1 iOXX
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+ FIA D`
+ DABH 1 qOXX
+ 4 p- q
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+ DABH ,2 yOXX N
+ 4 Д @/ >
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+ DABH |2 OXX
+ 4 1
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+ DABH 2 OXX
+ 4 p 2
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+ DABH 3 OXX
+ 4 4
+ FIA D`
+ DABI l3 OXX
+ 4 6 r
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+ DABI 3 OXX O
+ 4 ` P8 ?
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+ DABI 4 OXX
+ 4 :
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+ DABI \4 OXX
+ 4 ;
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+ DABI 4 OXX
+ 4 P =
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+ DABI 4 OXX
+ 4 ? s
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+ DABI L5 OXX P
+ 4 `A @
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+ DABI 5 OXX
+ 4 @ 0C
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+ DABI 5 OXX
+ 4 E
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+ DABI <6 OXX
+ 4 F
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+ DABI 6 OXX
+ 4 0 H t
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+ DABI 6 OXX Q
+ 4 pJ A
+ FIA D`
+ DABI ,7 OXX
+ 4 Й @L
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+ DABI |7 PXX
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+ DABI 7 PXX
+ 4 p O
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+ DABI 8 PXX
+ 4 Q u
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+ DABI l8 PXX R
+ 4 S B
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+ DABI 8 !PXX
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+ DABI 9 )PXX
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+ DABI \9 1PXX
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+ DABI 9 9PXX
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+ F 8 80
+ FBB B(G0D8G
+8D0A(B BBBG KIAQJNA!FPE `
}h2 HW |
X'
d77 Fp @
75
+ EC
DGO
+ G . T. y. .
T}h2 HW D hC FEB B(A0A8,
+0A(B BBBJ D T D FEB B(A0A8,
+0A(B BBBJ D F FEB B(A0A8.
+0A(B BBBH D pG FEB B(A0A8.
+0A(B BBBH D , H FBB B(A0A89
+0A(B BBBH D t 0J FBB B(A0A89
+0A(B BBBH D K FBB B(A0A8&
+0A(B BBBC D L FBB B(A0A8&
+0A(B BBBC L HN) ` dN7 Lf | N) N7 Lf N) N7 Lf O) $O7 Lf HO) dO7 Lf < O) P O7 Lf l O) O7 Lf P) $P7 Lf H HP EAG U
+CAJ [
+CAA w
+CAE NCA H P EAG Y
CAF [
-ACA R
-CAJ JCA L AAG ]
+CAA
+CAE NCA H d Q EAG Y
CAF [
-ACA R
-CAJ JCA L \ P A BBB B(I0A8G
-8D0A(B BBBF L mA BBB B(I0A8G
-8D0A(B BBBF L WA BBB B(I0A8G
-8D0A(B BBBF , t@ AAG04AA 4 | u A BHD D`,
- DABK 4 (!u @ BHD D`3
- DABD 4 p$u @ BHD D`3
- DABD , $ '~ @ AAG0W
-DAA T T ( @ BBH E(A0A8L
-8D0A(B BBBE T 1P A BBB B(A0A8G
-8D0A(B BBBE T :P %A BBB B(A0A8G
-8D0A(B BBBG L \ C ;A BFB B(D0A8JS
-8D0A(B BBBE , d u BOA K0i AAB t4 tJ E E E* A[
-L A E* A[
-L A 4 F* A[
-L A T F* A[
-L A t FU hFL F] FT 0GM hGE G] GQ t | 0H :? BBD A(D0C
-(C ABBH
-(C ABBG R
-(C ABBF J
-(C ABBF t xI > BBD A(D0C
-(C ABBH
-(C ABBE R
-(C ABBF J
-(C ABBF t l J p> BBD A(D0C
-(C ABBH
-(C ABBF R
-(C ABBF J
-(C ABBF t L > BBD A(D0C
-(C ABBH
-(C ABBD R
-(C ABBF J
-(C ABBF t \ PM{ = BBD A(D0I
-(C ABBB )
-(A ABDG V
-(C ABBJ J
-(C ABBF t XOk S= BBD A(D0I
-(C ABBB (
-(A ABBB V
-(C ABBB J
-(C ABBF t L PQk = BBD A(D0I
-(C ABBB '
-(A ABDA V
-(C ABBB J
-(C ABBF t HS{ < BBD A(D0I
-(C ABBB *
-(A ABBH V
-(C ABBJ J
-(C ABBF < PU BBB D(A0&
-(A BBBH L | V < BBB B(A0I8G
-8D0A(B BBBD L X < BBB B(A0I8G
-8D0A(B BBBD T [D ; BBE B(D0A8G
-8D0A(B BBBG d t ` ; BAA G@u
- DABI k
- DABF K
- DABF F
- DABA , t@ AAG04AA < pa, d; BHD D
- DABA < L `d7 I; BHD D
- DABA T `g .; BEE E(D0A8G&
-8D0A(B BBBH 4 JtE BDD f
-AJE AAB T l : BEE E(D0A8G
-8D0A(B BBBD L t t
- : BBE E(A0D8J
-8A0A(B BBB T o : BLE B(E0D8GG
-8A0A(B BBBJ T ~p ; BEE B(E0D8JC
-8A0A(B BBBB T t ؍K ~; BBE F(D0D8G>
-8A0A(B BBBJ L М ; BBE B(G0F8G
-8D0A(B BBBH ~u. K^ T Z MGB B(A0H8)0A(B BBBB8T D MGB B(A0A8`0A(B BBBB8 X P 4 Xs BBA A(D0b(A ABBD BBB B(A0A8DP8A0A(B BBBL L AAG ]
+CAA
+CAE NCA H tQ EAG U
+CAJ [
+CAA w
+CAE NCA H Q EAG Y
CAF [
-ACA R
-CAJ JCA L H AAG ]
+CAA
+CAE NCA H H <R EAG Y
CAF [
-ACA R
-CAJ JCA L x AAG ]
+CAA
+CAE NCA H R EAG U
+CAJ [
+CAA w
+CAE NCA H S EAG Y
CAF [
-ACA R
-CAJ JCA L < AAG ]
+CAA
+CAE NCA H , hS EAG Y
CAF [
-ACA R
-CAJ JCA L ثx 9 BBB B(A0I8Dp
-8D0A(B BBBJ L $ x 9 BBB B(A0I8Dp
-8D0A(B BBBJ L t 8x K9 BBB B(A0I8Dp
-8D0A(B BBBJ L hx 9 BBB B(A0I8Dp
-8D0A(B BBBJ 4 8 BDI Gp
- DABG 4 L 8 BDI Gp
- DABG , < t@ AAG04AA < 8u u8 BIA D(Dp)
-(D ABBJ < xu O8 BIA D(Dp0
-(D ABBC < 4 u )8 BIA D(Dp0
-(D ABBC < t 8 BHD D
- DABA T 7 BBE B(D0A8G
-8D0A(B BBBG T `ĩU BAA G0
- AABA
- AABK f
- AABF L d hȩ" v7 BBE B(D0A8G|
-8A0A(B BBBC L HΩ" ~7 BBE B(D0A8G|
-8A0A(B BBBC | (ԩ BBB B(D0A8DP
-8A0A(B BBBB U
-8G0A(B BBBK
-8A0A(B BBBI T ֩ 7 BBB H(D0A8G
-8D0A(B BBBH @wu. K^ , T [ +7 AID t
-DAA , [ 7 AID t
-DAA , H[ 6 AID t
-DAA , x[ 6 AID t
-DAA T 6 BEE E(D0A8G
-8D0A(B BBBE , $ 6t@ AAG04AA 4 b a6 BHD D`
- DABD 4 e C6 BHD D`"
- DABE D %6 BIA D(G
-(D ABBA D T 6 BIA D(G
-(D ABBA L 5 BDE H(A0A8G
-8D0A(B BBBK L l #6 BDB H(D0A8G
-8D0A(B BBBB tu. K^ $ T 6 BBB B(A0D8R:
-8A0A(B BBBA , > &6 AID W
-DAA , t@ AAG04AA L < k 5 BIB A(D0D
-0D(A BBBA L U 5 BBB E(F0A8M
-8D0A(B BBBD ru. K^ & & & & , T &[ k6 AID t
-DAA , &[ K6 AID t
-DAA , '[ +6 AID t
-DAA , 8'[ 6 AID t
-DAA , t@ AAG04AA < D 8'u 5 BIA D(Dp)
-(D ABBJ < x*u 5 BIA D(Dp)
-(D ABBJ < -u o5 BIA D(Dp0
-(D ABBC < 0u I5 BIA D(Dp0
-(D ABBC T D 84
- #5 BDB E(A0G8G
-8D0A(B BBBF T ; c5 BDB B(D0G8G
-8D0A(B BBBI ou. K^ C C C C , $ t@ AAG04AA 4 BU 4 BHD D`
- DABH 4 FU 4 BHD D`
- DABA \ @Ij 4 BBB A(I0DW
-0D(A BBBJ n
-0D(A BBBG \ l PMj 4 BBB A(I0DW
-0D(A BBBJ n
-0D(A BBBG `Q L R? /4 BBB E(A0A8G
-8A0A(B BBBE mu. K^
- o
- o 4
- o L
- o d
- o |
- o
- o
- o
- o
- o , <
- o[ 3 AID t
-DAA , l
- p[ w3 AID t
-DAA ,
- @p[ W3 AID t
-DAA ,
- pp[ 73 AID t
-DAA ,
- p[ 3 AID t
-DAA , , p[ 2 AID t
-DAA , \ q[ 2 AID t
-DAA , 0q[ 2 AID t
-DAA , `q[ 2 AID t
-DAA , q[ w2 AID t
-DAA , t@ AAG04AA 4 L qU '2 BHD D`
- DABH 4 tU 2 BHD D`
- DABH 4 wU 1 BHD D`
- DABH 4 {U 1 BHD D`
- DABH 4 ,
0~U 1 BHD D`
- DABH 4 d
XU 1 BHD D`
- DABA 4
U s1 BHD D`
- DABA 4
U U1 BHD D`
- DABA 4 ЊU 71 BHD D`
- DABA 4 D U 1 BHD D`
- DABA T | 0 BDB B(A0A8G
-8A0A(B BBBA T 41 BDB B(A0A8G
-8A0A(B BBBA T , 0 h1 BDB B(A0A8G
-8A0A(B BBBA T 1 BDB B(A0A8G
-8A0A(B BBBA T ಪ 1 BDB B(A0A8G
-8A0A(B BBBA fu. K^ x 4 p L h d ` | X P H @ 8 0 ( $ 0 < 8 T @ l H P X ` h p x Y \ t , / BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ X, / BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ 4 (, </ BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ , . BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ Ⱦ, . BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ T , p. BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ h, ,. BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ 8, - BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ t ª, - BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ ª, `- BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ 4 ê, - BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ xĪ, , BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ HŪ, , BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ T ƪ, P, BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ ƪ, , BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ Ǫ, + BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ t Ȫ, + BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ Xɪ, @+ BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ 4 (ʪ, * BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ ʪ, * BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ ˪, t* BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA \ T ̪, 0* BBB A(I0Dp
-0D(A BBBG ^
-0D(A BBBA $ l hͪ^ AG b
-AA D ͪ BEE D(A0D`n
-0A(A BBBE 8ΪV $ Ϊ Az
-E
-A $ Ϊ AAG0AAD Ϫ ) BIB A(A0G`,
-0D(A BBBC D Ѫ ( BIB A(A0G`,
-0D(A BBBC D Ҫ ( BIB A(A0G`,
-0D(A BBBC D d Ӫ ( BIB A(A0G`,
-0D(A BBBC D pժ X( BIB A(A0G`,
-0D(A BBBC D ֪ )( BIB A(A0G`,
-0D(A BBBC D < `ت ' BIB A(A0G`,
-0D(A BBBC D ٪ ' BIB A(A0G`,
-0D(A BBBC D P۪ ' BIB A(A0G`,
-0D(A BBBC D ܪ m' BIB A(A0G`,
-0D(A BBBC D \ @ު >' BIB A(A0G`,
-0D(A BBBC D ߪ ' BIB A(A0G`3
-0D(A BBBD D @ & BIB A(A0G`3
-0D(A BBBD D 4 & BIB A(A0G`3
-0D(A BBBD D | P & BIB A(A0G`3
-0D(A BBBD D S& BIB A(A0G`3
-0D(A BBBD D ` $& BIB A(A0G`3
-0D(A BBBD D T % BIB A(A0G`3
-0D(A BBBD D p % BIB A(A0G`3
-0D(A BBBD D % BIB A(A0G`3
-0D(A BBBD D ,! h% BIB A(A0G`3
-0D(A BBBD D t! 9% BIB A(A0G`3
-0D(A BBBD L t" BBB B(D0D8G@H
-8A0A(B BBBG L " BBB B(D0D8G@H
-8A0A(B BBBG T \" 0V j$ BBE E(D0E8J$
-8D0A(B BBBF L l# 8 BOB A(A0]
-(A BBBG Y
-(A BBBH < # GDPE
-AAE
-AAG 4 # g ADD H
-AAM DAA , 4$ BTA gAB T # G# BBB B(A0A8G
-8A0A(B BBBE L $ h* # BGB B(A0G8J|
-8D0A(B BBBH L T$ H " BBH B(F0A8J{
-8D0A(B BBBI L \% BBB B(A0A8Dxi
-8A0A(B BBBG L % 8 BBB B(A0A8DxN
-8A0A(B BBBJ < % BEB A(F0z
-(A BBBG L <& 8 BBB B(D0A8GP
-8A0A(B BBBH D & 8 ADG m
-AAE V
-DAE UAA L & O BEB B(A0A8Q
-8A0A(B BBBA T l& , 7! BBB B(D0A8Gn
-8A0A(B BBBD L & X * BGB B(A0G8J|
-8D0A(B BBBH L ' 8$* BGB B(A0G8J|
-8D0A(B BBBH L d' )* BGB B(A0G8J|
-8D0A(B BBBH L l( -: BEB B(A0A8Q
-8A0A(B BBBA T ( 1, BBB B(D0A8Gn
-8A0A(B BBBD L \( 7Q = BGB B(A0G8J|
-8D0A(B BBBH L d) ;b BEB B(A0A8Q
-8A0A(B BBBA T ( ?, BBB B(D0A8Gn
-8A0A(B BBBD L T) E/ BGB B(A0G8J|
-8D0A(B BBBH L ) IB BGB B(A0G8J|
-8D0A(B BBBH L * M BBB B(A0H8J
-8A0A(B BBEA T D* Q, < BBB B(D0A8Gn
-8A0A(B BBBD L * W* BGB B(A0G8J|
-8D0A(B BBBH D + \8 ADG m
-AAE V
-DAE UAA L + ] BEB B(A0A8Q5
-8A0A(B BBBA T + `X S BBB B(A0D8G
-8A0A(B BBBA L + hQ
- BGB B(A0G8J|
-8D0A(B BBBH L , m BEB B(A0A8QN
-8A0A(B BBBA T |, PpX BBB B(A0D8G
-8A0A(B BBBA L , Xx* i BGB B(A0G8J|
-8D0A(B BBBH - Ju. K^ - }: . H~ $. P> Hu D. pE Hx D d. BEB E(D0D8PP~8A0A(B BBB$ . ^ AG b
-AA d . @q BEB B(A0A8G@
-8A0A(B BBBC
-8A0A(B BBBF L . X BBB E(D0D8Gp
-8A0A(B BBBC < . / BEI D(G
-(D ABBG < / / BEI D(G
-(D ABBF L T/ k n AAGPa
-DAA A
-DAJ }
-DAA < / / ; BEI D(G
-(D ABBF $ 0 AAG0AA< 0 0 BHA J
- DABA < L0 BHA J"
- DABA 4 0 @ BHD D`
- DABG < 0 BHD D}
- DABA < 1 [ BBE D(A0
-(A BBBF T D1 8 BBB B(K0A8Jo
-8A0A(B BBBI 4 1 0q > BAI A
-ABA 4 1 xq BAI A
-ABA 4 2 q BAI A
-ABA 4 D2 q BAI A
-ABA , |2 P ADG0
-AAB T 2 К! BBB B(A0D8G
-8A0A(B BBBA T 3 X*F BEE B(A0A8G
-8A0A(B BBBA 4 Du. K^ ,4 D4 \4 t4 4 4 4 4 4 5 L d4 x BBB B(A0I8Dp
-8D0A(B BBBE L 4 Hx BBB B(A0I8Dp
-8D0A(B BBBE L 5 xx BBB B(A0I8Dp
-8D0A(B BBBE L T5 x R BBB B(A0I8Dp
-8D0A(B BBBE L 5 x BBB B(A0I8Dp
-8D0A(B BBBE L 5 x BBB B(A0I8Dp
-8D0A(B BBBE L D6 8 x BBB B(A0I8Dp
-8D0A(B BBBE L 6 h
-x BBB B(A0I8Dp
-8D0A(B BBBE L 6 x N BBB B(A0I8Dp
-8D0A(B BBBE L 47 x BBB B(A0I8Dp
-8D0A(B BBBE T 7
BBE B(D0A8G
-8D0A(B BBBG T 7 BBE B(D0A8G
-8D0A(B BBBG T 48 BEE B(D0D8Jw
-8A0A(B BBBG , D9 Vt@ AAG04AA 4 8 U BHD D`
- DABH 4 8 U BHD D`
- DABH 4 ,9 0"U l BHD D`
- DABH 4 d9 X%U N BHD D`
- DABA 4 9 (U 0 BHD D`
- DABA 4 9 +U BHD D`
- DABA < : .u BIA D(Dp)
-(D ABBJ < L: 2u BIA D(Dp)
-(D ABBJ < : P5u BIA D(Dp0
-(D ABBC < : 8u BIA D(Dp0
-(D ABBC T ; ;y \ BBG B(A0A8J(
-8A0A(B BBBE T d; =y * BBG B(A0A8J(
-8A0A(B BBBE T ; @ BBG B(A0A8Jx
-8A0A(B BBBA T < F BBB B(A0D8G|
-8A0A(B BBBF T l< M BBB B(A0D8Gd
-8A0A(B BBBF |= :u. K^ = T = S = T0 Hg = T7 Hj , L= 0T[ c AID t
-DAA , |= `T[ C AID t
-DAA L = T # BBB J(A0A8G
-8D0A(B BBBA T = V| BBE E(D0A8G
-8D0A(B BBBF L T> \ BBE A(D0G
-0A(A BBBA , \? ~t@ AAG04AA 4 > hae
BHD D`"
- DABE < ? d BIA D(Dp5
-(D ABBF < L? g BIA D(Dp<
-(D ABBG T ? @kL BBE B(A0D8J
-8D0A(B BBBF T ? 8p+
- BGH B(A0D8G
-8D0A(B BBBH @ 7u. K^ A y $A y$ <A z
TA z1 lA (zi A z A z AL A xz DI A z DI A z DI B z AL $B z AL DB pz# TI \B zA I[
-L K , |B zb D\
-H P
-H Q
-G I , B zb D\
-H P
-H Q
-G I , B 8{b D\
-H P
-H Q
-G I , C x{b D\
-H P
-H Q
-G I , <C {b D\
-H P
-H Q
-G I , lC {b D\
-H P
-H Q
-G I , C 8|b D\
-H P
-H Q
-G I , C x|b D\
-H P
-H Q
-G I , C |b D\
-H P
-H Q
-G I , ,D |b D\
-H P
-H Q
-G I , \D 8}b D\
-H P
-H Q
-G I $ C x}r H\
-L A $ C }r H\
-L A $ $D (~r H\
-L A $ LD ~r k H\
-L A $ tD ~r G H\
-L A $ D 0r # H\
-L A D q Hd D q Hd E Hq Hd $E q Hd DE q Hd dE hq s Hd <F ެt\ AG RAT E t 7 BBE B(D0A8G
-8D0A(B BBBH T E Іt BBE B(D0A8G
-8D0A(B BBBH D TF BBB A(A0Gb
-0A(A BBBG $ F R ) AI
-F A , |G t@ AAG04AA 4 F U BHD D`
- DABH 4 ,G ГU BHD D`
- DABH 4 dG U BHD D`
- DABH 4 G U { BHD D`
- DABA 4 G HU ] BHD D`
- DABA 4 H pU ? BHD D`
- DABA 4 DH U ! BHD D`
- DABA 4 |H U BHD D`
- DABA 4 H 詬U BHD D`
- DABA T H % BBB E(A0A8JN
-8A0A(B BBBA < DI xҬ BBI A(Ga
-(D ABBA < I Ҭ BBI A(Ga
-(D ABBA < I 8Ӭ BBI A(Ga
-(D ABBA < J Ӭ h BBI A(Ga
-(D ABBA < DJ Ӭ ? BBI A(Ga
-(D ABBA < J XԬ BBI A(Ga
-(D ABBA < J Ԭ BBI A(Ga
-(D ABBA < K լ BBI A(Ga
-(D ABBA < DK xլ BBI A(Ga
-(D ABBA <L x,u. K^ TL լ
lL լ
L լ L լ L լ L լ T ,L լ| BBE E(D0A8G
-8D0A(B BBBI T L ڬ| BBE E(D0A8G
-8D0A(B BBBI T L BBE B(D0A8G
-8D0A(B BBBG T 4M | BBB B(A0D8NH
-8A0A(B BBBC , M > AID W
-DAA , tN Bt@ AAG04AA 4 M U BHD D`
- DABH T $N (^ p BBB B(A0D8G3
-8A0A(B BBBA T |N 0E \ BBB B(A0D8G2
-8A0A(B BBBA T N ( H BBB B(A0D8Gs
-8A0A(B BBBA T ,O p 4 BBB B(A0D8Gs
-8A0A(B BBBA T O 7 BBB B(F0A8J
-8A0A(B BBBJ P P(u. K^ P h< P `< L $P h<r BBB B(E0A8J@
-8A0A(B BBBA L tP =X \ BEE D(D0G
-0D(A BBBA T P > BEB E(A0A8G;
-8D0A(B BBBA L Q A) BBB B(A0I8G
-8D0A(B BBBA L lQ B) BBB B(A0I8G
-8D0A(B BBBA L Q D) BBB B(A0I8G
-8D0A(B BBBA T R F BBE E(A0A8G
-8D0A(B BBBH $ S ڢt AAG0AA< R H8 ; BHD D
- DABA < R KB BHD D
- DABA < S NB BHD D
- DABA < T QQ BEH H(A0n(A BBE T S R BEB B(A0D8J
-8A0A(B BBBA T S HV } BEB B(A0D8J
-8A0A(B BBBA T <T Z P BEB B(A0D8J
-8A0A(B BBBA T T ^ # BEB B(A0D8J
-8A0A(B BBBA T T c
BEB B(A0D8J
-8A0A(B BBBA T DU hg
BEB B(A0D8J
-8A0A(B BBBA T U k
BEB B(A0D8J
-8A0A(B BBBA T U o o
BEB B(A0D8J
-8A0A(B BBBA T LV @t B
BEB B(A0D8J
-8A0A(B BBBA T V x
BEB B(A0D8J
-8A0A(B BBBA T V | BEB B(A0D8J
-8A0A(B BBBA T TW BEB B(A0D8J
-8A0A(B BBBA T W P BEB B(A0D8J
-8A0A(B BBBA T X a BEB B(A0D8J
-8A0A(B BBBA T \X : BEB B(A0D8J
-8A0A(B BBBA T X X
BEB B(A0D8J
-8A0A(B BBBA T Y BEB B(A0D8J
-8A0A(B BBBA T dY BEB B(A0D8J
-8A0A(B BBBA T Y P BEB B(A0D8J
-8A0A(B BBBA T Z Y BEB B(A0D8J
-8A0A(B BBBA T lZ , BEB B(A0D8J
-8A0A(B BBBA T Z 8
- BEB B(A0D8J
-8A0A(B BBBA T [
- BEB B(A0D8J
-8A0A(B BBBA T t[ ȴ
- BEB B(A0D8J
-8A0A(B BBBA T [ x
- BEB B(A0D8J
-8A0A(B BBBA T $\ x K
- BEB B(A0D8J
-8A0A(B BBBA T |\
- BEB B(A0D8J
-8A0A(B BBBA T \ (ƭ BEB B(A0D8J
-8A0A(B BBBA L ,] ʭ
BHE B(A0F8G
-8D0A(B BBBA 4^ u. K^ L^ d^ 4 |^ v BBA A(D0e(A ABBD ^ ح BBB B(A0A8DP8A0A(B BBB, D^ ح[ k AID t
-DAA , t^ ح[ K AID t
-DAA L \_ ح AAG ]
+CAA
+CAE NCA H x S EAG U
+CAJ [
+CAA w
+CAE NCA H 0T EAG Y
CAF [
-ACA R
-CAJ JCA L _ (٭ AAG ]
+CAA
+CAE NCA H T EAG Y
CAF [
-ACA R
-CAJ JCA _ t\ AG RAT d_ 8٭
k BBE E(D0D8GD
-8D0A(B BBBB 4 _ p BDI Gp
- DABG 4 _ X BDI Gp
- DABG , ` &t@ AAG04AA 4 \` U B BHD D`
- DABH 4 ` 8U $ BHD D`
- DABA L ` ` BDB E(A0A8J
-8A0A(B BBBA D a $ @ BBB D(A0G
-0D(A BBBF b (u. K^ 4b Lb , a [ AID t
-DAA , a [ AID t
-DAA , b t@ AAG04AA < <b u Z BIA D(Dp)
-(D ABBJ < |b u 4 BIA D(Dp0
-(D ABBC T b P BDB E(G0A8Gw
-8D0A(B BBBA c u. K^ c p c h , \c p[ AID t
-DAA , c [ AID t
-DAA , td t@ AAG04AA D c [ o BIA D(G
-(D ABBA D 4d d N BIA D(G
-(D ABBA L |d / BDB B(D0A8M
-8D0A(B BBBA e u. K^ e (O HF e XO HF e V HI e V HI L de r BBB B(E0A8J@
-8A0A(B BBBA $ lf 8^ AG b
-AA f pB Aw
-H A L e T @ BBB B(A0A8JU
-8A0A(B BBBE 4 Lf s Y BAI C
-ABA 4 f v 5 BAI F
-ABA 4 f @v BAI F
-ABA 4 f v BAI F
-ABA L ,g BBB A(A0M
-0D(A BBBF < |g ` f BBD D(DG
-(A ABBA < g f BBD D(DG
-(A ABBA < g f j BBD D(DG
-(A ABBA < <h !f O BBD D(DG
-(A ABBA < |h #f 4 BBD D(DG
-(A ABBA d ti P$ BEB B(A0D8DPi
-8A0A(B BBBI
-8G0A(B BBBJ D $i &o BEE D(D0G
-0D(A BBBB $ $j ' AAG0AA4 i X( S BHI D`
- DABE 4 i * 5 BHA LP
- DABC 4 j , BHI D`:
- DABH 4 <j p/% BHA LP
- DABD $ ,k h1V ADD JAAL j 1 BEB E(I0H8D@
-8D0A(B BBBA < k 3 BFA D0}
- FABE D FAB T ,k 35 K BEB B(D0A8G~
-8A0A(B BBBA T k x; = BEH B(A0D8G
-8A0A(B BBBA l B1 Dl T k BE BBB B(D0A8J
-8D0A(B BBBI T Tl N' : BGE B(G0A8G p
-8D0A(B BBBD dm i AP m i AP , m 0 u BOA K0 AAB m i m i n i n i 4n i Ln i L m i 5 BBB B(A0I8G
-8D0A(B BBBF L m l BBB B(A0I8G
-8D0A(B BBBF T Ln `ng BBB B(I0A8Gc
-8D0A(B BBBG T n xsg BBB B(I0A8Gc
-8D0A(B BBBG T n xg ! BBB B(I0A8Gc
-8D0A(B BBBG , p t@ AAG04AA 4 o x}U BHD D`
- DABH 4 o U BHD D`
- DABH 4 o ȃU BHD D`
- DABA 4 ,p U BHD D`
- DABA 4 dp U BHD D`
- DABA L Tq @ BBB B(A0A8G&
-8A0A(B BBBA L q BEB E(D0A8Gp
-8A0A(B BBBD T <q BJB B(A0A8G
-
-8A0A(B BBBE T q E BBB E(F0G8GO
-8D0A(B BBBE T q Ǯ BGB B(D0G8G;
-8D0A(B BBBI r u. K^ s Ю0 Hg 4s Ю7 Hj $ Ts Ю^ AG b
-AA L r Ю8 BBB B(A0I8G
-8D0A(B BBBH L s ֮8 BBB B(A0I8G
-8D0A(B BBBH L ds ܮ8 BBB B(A0I8G
-8D0A(B BBBH T s \ BBE B(A0D8G
-8D0A(B BBBI T t |
- BBE E(D0A8G
-8D0A(B BBBI , u t@ AAG04AA < t BHD G
- DABA < t h BHD G
- DABA < u BHD G
- DABA \ v m BBD D(J@
-(A ABBD T
-(A ABBF _
-(A ABBK T u BEF B(D0D8G^
-8A0A(B BBBA T v B BEF B(D0D8G^
-8A0A(B BBBA T dv H r BEF E(A0D8G^
-8A0A(B BBBA T v BEF B(D0D8G^
-8A0A(B BBBA T w x M BEF B(D0D8Gl
-8A0A(B BBBA L lw p'} BBB G(D0A8M
-8D0A(B BBBF tx pu. K^ x .0 Hg x .7 Hj x ~t\ AG RA x ._ , y ._ ADG k
-DAD T |x / S BEE E(D0A8G'
-8D0A(B BBBG , y t@ AAG04AA 4 y x4b BHD D`
- DABD 4 <y 7e BHD D`"
- DABE 4 ty :e BHD D`"
- DABE L dz > BBB B(D0A8G@
-8A0A(B BBBA D y ? _ BBB A(I0D
-0D(A BBBE D Dz A : BBB A(I0D
-0D(A BBBE D z C BBB A(I0D
-0D(A BBBE T z HE' BBE B(A0A8LV
-8A0A(B BBBG { 0t. K^ { S | S , t{ S[ 1 AID t
-DAA , { 8S[ AID t
-DAA L | hS BEB E(A0A8GN
-8A0A(B BBBA d | U
- BBB B(A0A8GPE
-8A0A(B BBBH 8F0A(B BBB T | U 9 BBE B(D0A8G
-8D0A(B BBBG , } Jt@ AAG04AA D } HZR BIA D(G
-(D ABBA D \} `^Y BIA D(G
-(D ABBA L } xb BDB B(D0A8M
-8D0A(B BBBC ~ t. K^ ~ u0 Hg ~ u7 Hj 0u BEE F(D0A8D1
-8A0A(B BBBA
-8A0A(B BBBA D
-8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB 0x BEF E(D0D8D.
-8A0A(B BBBA
-8A0A(B BBBA D
-8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB , d 0{ A|
-C K
-E R
-A $ |^ AG b
-AA 8|2 Ai
-F A ܀ X|K T < || o BBE E(D0A8G
-8D0A(B BBBI T | U BBE E(D0A8G
-8D0A(B BBBI L ; BBB B(K0A8J
-8A0A(B BBBF 4 < Pq Y BAI A
-ABA 4 t q 5 BAI A
-ABA T BBE B(D0A8G
-8D0A(B BBBG , j}t@ AAG04AA 4 4 xU BHI D`
- DABE 4 l b BHI D`
- DABF d \ ؙB BBB B(A0A8G@x
-8A0A(B BBBE
-8A0A(B BBBF D " # BEE D(D0G
-0A(A BBBE T T F BBB B(G0D8G
-8A0A(B BBBA T = O BEF B(A0A8G
-8A0A(B BBBA T * BBB B(D0D8J
-8D0A(B BBBD T \ `3 BBB B(A0A8L
-
-8A0A(B BBBC , l t BOA K0i AAB ӯ ӯ 4 ӯg + BAD D
-ABA T L Pӯ BBB B(I0A8D%
-8D0A(B BBBH $ \ ԯ^ AG b
-AA L ̅ կ BHB B(A0D8J^
-8A0A(B BBBA , Ԇ zt@ AAG04AA 4 L `د U BHD D`
- DABG 4 < گJ BDA q
-ABE A
-ABA < گF BBA A(Gq
-(A ABBA L ܯ BBB B(A0A8Gp8A0A(B BBB L ݯM BBB B(A0D8Dp18A0A(B BBB L T ߯ BBB B(A0A8Gp8A0A(B BBB L M BBB B(A0D8Dp18A0A(B BBB L BBB B(A0A8Gp8A0A(B BBB < F S BBA A(Gq
-(A ABBA L M BBB B(A0D8Dp18A0A(B BBB D ԉ BBB A(A0G@R
-0A(A BBBA L BBB E(D0A8D`
-8A0A(B BBBA l m , ADD X
-AAA d BHD G0W
- AABH F
- AABF g
- AABE D
- FABK , H ADD ]
-AAA d L BBB E(I0D8DP
-8D0A(B BBBC |
-8D0A(B BBBA , ADD [
-AAA t ` BIA D(G0C
-(A ABBH o
-(A ABBC g
-(A ABBC D
-(F ABBA d \ E BBB B(A0A8DP
-8A0A(B BBBE \
-8F0A(B BBBA l BBB E(I0D8D
-8A0A(B BBBG
-8D0A(B BBBE 4 pu L L BEB B(A0A8DP
-8A0A(B BBBH l h' ? BBB E(D0I8D`M
-8D0A(B BBBJ
-8D0A(B BBBB T T (M BBB B(A0A8J
-8A0A(B BBBA d
-^ L | h
- BEB B(A0A8DP
-8A0A(B BBBH l
-' BBB E(D0I8D`M
-8D0A(B BBBJ
-8D0A(B BBBB T
_ BBB B(A0A8J
-8A0A(B BBBA `#V L # BEB B(A0A8DP
-8A0A(B BBBH l D 8$ # BBB E(D0I8D`:
-8D0A(B BBBE
-8D0A(B BBBJ T &x BBB B(A0A8J
-8A0A(B BBBA d Đ >! BHF K(H0A8D`}
-8A0A(B BBBA d8C0A(B BBB L , ? BEB B(A0A8DP
-8A0A(B BBBH | | 8@f BIB B(A0D8GPz
-8A0A(B BBBA v
-8A0A(B BBBH D
-8F0A(B BBBE d D (A BBJ B(A0A8G`
-8D0A(B BBBE N
-8A0A(B BBBH T B BBB B(A0A8J4
-8A0A(B BBBA D Y BGA D(M
-(D ABBF t. K^ ]0 Hg < ]7 Hj L ^i > BBB B(A0I8G]
-8D0A(B BBBE L 8`i ) BBB B(A0I8G]
-8D0A(B BBBE L D Xbi BBB B(A0I8G]
-8D0A(B BBBE , L Znt@ AAG04AA 4 ē Hdb BHD D`
- DABD 4 ge BHD D`"
- DABE 4 4 je BHD D`"
- DABE T l m w BEB B(D0G8L
-8D0A(B BBBI \ Ĕ z c BFE A(A0D`W
-0D(A BBBK f
-0D(A BBBG ܕ Xt. K^ D {/ BBB B(A0A80A(B BBB D < } BBB B(A0A80A(B BBB D 7 BBB B(A0A8#0A(B BBB D ̖ BBB B(A0A80A(B BBB D BBB B(A0A8 0A(B BBB D \ BBB B(A0A80A(B BBB L AAG ]
-CAF b
-CAJ R
-CAJ JCA L ` AAG ]
-CAF b
-CAJ R
-CAJ JCA L D AAG ]
-CAF b
-CAJ R
-CAJ JCA L AAG ]
-CAF b
-CAJ R
-CAJ JCA L AAG ]
-CAF b
-CAJ R
-CAJ JCA L 4 ` AAG ]
-CAF b
-CAJ R
-CAJ JCA L BLA A(D0]
-(A ABBI X
-(A ABBB $ ԙ ^ AG b
-AA D D HZ BBE A(A0G@
-0A(A BBBC D `$ \ xH Gj
-G F
-E L ę BBB B(A0A8G
-8A0A(B BBBA , ̚ it@ AAG04AA 4 D U BHD D`
- DABH 4 | @ 7 BHD D`
- DABH 4 ș BHD D`
- DABH 4 P BHD D`
- DABH 4 $ ؞ BHD D`
- DABH 4 \ ` BHD D`
- DABH L 裰6 BBB B(A0A8Gb
-8A0A(B BBBC < ئ BEA A(Gc
-(D ABBA < $ 8 i BEA A(Gc
-(D ABBA < d ? BEA A(Gc
-(D ABBA < BEA A(Gc
-(D ABBA < X BEA A(Gc
-(D ABBA < $ BEA A(Gc
-(D ABBA , d \ ADG l
-AAA , H\ s ADG l
-AAA L | x BBB E(D0D8L`Y
-8A0A(B BBBF L ̞ ( BBB E(D0D8L`Y
-8A0A(B BBBF L ج BBB E(D0D8L`Y
-8A0A(B BBBF l N < BDD k
-ABH [
-ABA D BBF K(H0G@Q
-0A(A BBBC L T h7 BEB B(A0D8Gm
-8A0A(B BBBA L X" BEB B(A0A8Jm
-8A0A(B BBBA L 8ϰ 1 BBB B(A0A8Jp
-8A0A(B BBBA < ް BBG A(G@
-(A ABBD < h߰% $ T ߰V ADD JAA4 | ߰ BEA A(D0(A ABBd @1 BHB B(D0A8Dpv
-8C0A(B BBBG {
-8D0A(B BBBA T d G BBB B(A0A8G
-8A0A(B BBBA T ` G BBB B(A0A8G
-8A0A(B BBBA T G BBB B(A0A8G
-8A0A(B BBBA T l G BBB B(A0A8G
-8A0A(B BBBA T Ģ 8 G BBB B(A0A8G
-8A0A(B BBBA T G BBB B(A0A8G
-8A0A(B BBBA T t ! G BBB E(I0A8K}
-8D0A(B BBBF L ̣ $ BEB B(D0A8Gpu
-8A0A(B BBBA L P% BEB B(D0A8Gpu
-8A0A(B BBBA T l & BBB E(I0A8K}
-8D0A(B BBBF L Ĥ ()* k BEB B(D0A8Gp
-8A0A(B BBBA $ ̥ +V ADD JAA< < @+ BLD D(G0
-(A ABBE D 4 , BHI H(D0D@
-0A(A BBBA T ĥ , BBB B(A0A8J3
-8A0A(B BBBA L =,
BHE E(D0A8G
-8D0A(B BBBA T l JW BBB H(D0A8L
-8D0A(B BBBJ L Ħ X,
BHE E(D0A8G
-8D0A(B BBBA T dW BBB H(D0A8L
-8D0A(B BBBJ L l r,
BHE E(D0A8G
-8D0A(B BBBA T W BBB H(D0A8L
-8D0A(B BBBJ ̨ t. K^ , D \ t ԩ , L [ h AID t
-DAA , | [ H AID t
-DAA , [ ( AID t
-DAA , ܩ P[ AID t
-DAA , [ AID t
-DAA , < [ AID t
-DAA $ Zt DN
-E A 4 BAA D@
- AABA | 2 r c ī p2 ܫ z $ " < " T | l z j ؝i 0 ̬ o , D p \ 袱 t ࢱ" 4 BAA i
-ABH rAB ĭ pz T ܭ ئ BBB A(A0
-(V BBBI
-(A BBBG L 4 BBB A(A0
-(A BBBM
-(A BBBD T BBB A(A0
-(V BBBG
-(A BBBF $ ܮ > I
-K
-D g DPb g DPb4 4 p HA
-AA d
-AK l 8g DPb g DPb4 趱 AC
-AF
-AA ԯ pe DP` Ȼe DP`4 BAA i
-ABH rAB L BBB B(A0A8Dp
-8A0A(B BBBA L ԯ ؽ h BBB B(A0A8Dp
-8A0A(B BBBA L $ ([ 2 BBB B(A0A8D`
-8A0A(B BBBA $ , 8 AAG0AA4 BHA GPt
- DABC 4 ± BHA GPt
- DABC 4 `ı BHD D`
- DABG 4 D Ʊ z BHD D`
- DABG 4 | Pɱ \ BHD D`
- DABG 4 ˱ > BHD D`
- DABG @α A
-H L α= BBB D(D0Gk
-0A(A BBBH T \ ѱ BBE F(D0F8M
-8D0A(B BBBF < 8Աi BEA D(G
-(D ABBA T hձ p BBE F(D0F8M
-8D0A(B BBBF < L ױi 8 BEA D(G
-(D ABBA T ٱ BBE F(D0F8M
-8D0A(B BBBF < ۱i BEA D(G
-(D ABBA T $ ܱ BBE F(D0F8M
-8D0A(B BBBF < | 0߱i BEA D(G
-(D ABBA T ` _ BBE F(D0F8M
-8D0A(B BBBF < i ' BEA D(G
-(D ABBA T T BBE F(D0F8M
-8D0A(B BBBF < i BEA D(G
-(D ABBA t. K^ Զ lNt\ AG RAT < Y BBB I(A0A8K_
-8A0A(B BBBK T ( BBB I(A0A8K_
-8A0A(B BBBK T BBB I(A0A8K
-8A0A(B BBBG t. K^ @2 Hb
-F A 4 `7 Hj T HMt\ AG RAT `f / BBB B(I0A8G
-8D0A(B BBBF T xU BBE B(A0A8G{
-8D0A(B BBBD , $ Lt@ AAG04AA 4 PU L BHD D`
- DABH L Ը x\
- / BBB E(F0A8G
-8A0A(B BBBA ܹ t. K^ L p AAG ]
-CAF k
-ACA R
-CAJ JCA $ D m AG q
-AA $ l u AG y
-AA Lt\ AG RAL 0 I BBB B(A0A8G
-8A0A(B BBBA , Lt@ AAG04AA < | $u / BIA D(Dp)
-(D ABBJ T @'| BBE E(D0A8G
-8D0A(B BBBE T h,T BBB B(I0A8G
-8D0A(B BBBH L $ p1Y BEB B(A0D8G
-
-8A0A(B BBBA T 8& BGB B(A0A8J
-8A0A(B BBBA ̼ Xt. K^ , , ^ AID
-AAA , Kt@ AAG04AA < 0_ BHD D
- DABA пt. K^ b b ̽ b b , Lt@ AAG04AA 4 t bU BHD D`
- DABH 4 eU BHD D`
- DABH L hq BBB B(A0I8G
-8D0A(B BBBD T 4 k BBB B(A0I8G
-8D0A(B BBBD T @o BBB G(D0G8G
-8D0A(B BBBD T vJ BBB G(D0G8G
-8D0A(B BBBE t. K^ x} $ p} , x}[ AID t
-DAA T }A BEE E(D0A8GY
-8D0A(B BBBE , Jt@ AAG04AA < < p q BIA D(Dp5
-(D ABBF T | K BGH B(A0D8G
-8D0A(B BBBI (t. K^ $ ` AG u
-AA $ AG }
-AA , Jt@ AAG04AA 4 l `U w BHD D`
- DABH T 8 Y BBB J(A0A8G>
-8D0A(B BBBA 0t. K^ 4 X # BHE
-ABD 4 L BHE
-ABI < Jt\ AG RA, \ Jt@ AAG04AA 4 U k BHD D`
- DABH Pt. K^ T $ 7 BBE B(A0A8J
-8A0A(B BBBA , | > _ AID W
-DAA T 0 ? BBB B(I0A8D
-8D0A(B BBBH T ة BBB B(I0A8D
-8D0A(B BBBH , \Jt@ AAG04AA 4 PU BHD D`
- DABH 4 xU BHD D`
- DABH t. K^ 0 Hg 7 Hj , Jt@ AAG04AA 4 U BHD D`
- DABH T BBB B(A0I8G6
-8D0A(B BBBD T BBB E(A0A8G
-
-8A0A(B BBBD $ Pt. K^ < (ϲ T ϲ L l (ϲ AAG e
-CAF {
-ACI R
-CAJ JCA , ϲ[ ' AID t
-DAA L ϲ BBB B(A0A8J@
-8A0A(B BBBA D < hղ BEB B(A0A8
-0A(B BBBA L 0۲
BIE E(D0A8J
-8A0A(B BBBA % % % (% 4 @ AN
-A T @ L l a BBB B(D0D8GP
-8A0A(B BBBG , Ht@ AAG04AA < L 0" BHD D
- DABA T m BDB B(G0D8G
-8D0A(B BBBG 8 AY < 8y BDD H
-ABK A
-ABL x AY x AY < < x BBD D(G0b
-(A ABBE | e aC < ( BBD D(G0b
-(A ABBE D BBB B(A0A80A(B BBB L $ BIB B(A0D8G
-8D0A(B BBBD d t S BBB E(D0D8D
-8A0A(B BBBH
-8A0A(B BBBG D x BBB B(A0A8
-0A(B BBBA D $ 0c BEB B(A0A8H
-0A(B BBBA D l X. BBE B(A0A8
-0A(B BBBA , @ AET
-AE
-AA L BBB B(A0A8G
-8A0A(B BBBD L L 1 BEB B(A0A8G
-8A0A(B BBBA 4 8ID AC
P\
- A L PU BBI B(A0A8G
-8A0A(B BBBA , $ c AC
BEH
- D d T PfS BBB E(D0D8D
-8A0A(B BBBI
-8A0A(B BBBG L Hj BEB B(A0A8J
-8A0A(B BBBA < y BBB A(A0
-(A BBBA 4 z f AC
DGK
- G t P = BBB A(A0JW
-0A(A BBBG <
-0A(A BBBD
-0A(A BBBK T D BEB B(A0A8Jp
-8A0A(B BBBA D T 0 BBB A(A0G`
-0A(A BBBD D ؕ| I BBB D(A0@
-(A BBBF 4 , 9 AC
PC
- I D BEB B(A0A8
-0A(B BBBF L d u BBB B(D0A8G
-8A0A(B BBBA d 3 BBB E(D0D8D
-8A0A(B BBBB
-8A0A(B BBBK D BBB B(A0A8
-0A(B BBBA D d w BEB B(A0A8Y
-0A(B BBBA D BBB B(A0A8
-0A(B BBBA D з BKB B(G0A8t
-0A(B BBBA L < X% BEE E(D0A8G
-8A0A(B BBBJ 4 8ϳP AC
P\
- A \ Pس BBB B(A0A8
-0A(B BBBA 0A(B BBB L $ ۳T
- BBB B(A0A8D
-8A0A(B BBBA , t $ AM
DL
- E d S BBB E(D0D8D
-8A0A(B BBBB
-8A0A(B BBBC d BEE B(E0A8DX7
-8A0A(B BBBA S
-8A0A(B BBBA 4 @
- AC
P
- A D BBB B(A0A8j
-0A(B BBBA 4 < P V AC
DGK
- H d t 8 - BBA A(JW
-(A ABBC 3
-(A ABBG T
-(A ABBF T ` BEB B(D0A8Jx
-8A0A(B BBBA T 4 BBB E(A0A8Gp
-8A0A(B BBBB 4 0
- AC
P^
- A T Z BBB B(A0A8J
-8A0A(B BBBA L $ K BDB B(A0A8G@
-8A0A(B BBBA $ t. K^ < 6 T 6 l 6 6 5 6 6 6 L 6 AAG ]
-CAF k
-ACA R
-CAJ JCA L L X6 AAG ]
-CAF k
-ACA R
-CAJ JCA L 6 AAG ]
-CAF k
-ACA R
-CAJ JCA L 6 AAG ]
-CAF k
-ACA R
-CAJ JCA < 7t\ AG RAL \ 6x BBB B(A0A8D`
-8A0A(B BBBJ L (9 BBB B(A0A8Dp
-8A0A(B BBBG L ; BBB B(A0A8D`v
-8A0A(B BBBB L L (>3 BBB B(A0A8Dpm
-8A0A(B BBBK , 6t@ AAG04AA 4 @U ~ BHD D`
- DABH 4 L DU ` BHD D`
- DABH 4 8GU B BHD D`
- DABH 4 `JU $ BHD D`
- DABH T Mi BBB B(A0D8R
-8A0A(B BBBF , L Q>
AID W
-DAA T | Qi BBB B(A0D8R
-8A0A(B BBBF , U> AID W
-DAA T Ui BBB B(A0D8R
-8A0A(B BBBF , \ Y> AID W
-DAA T Zi BBB B(A0D8R
-8A0A(B BBBF , ^> AID W
-DAA , (^d AAD@
-AAG , h` AAD@
-AAG < , b BBG A(G@
-(A ABBG T c BHB E(A0A8G
-8D0A(B BBBA T ug BHE E(A0A8G
-8D0A(B BBBA T d 8 BHB E(A0A8G
-8D0A(B BBBA T BHB E(A0A8G
-8D0A(B BBBA t. K^ , \ [ c AID t
-DAA < D @ BBB A(A0
-(A BBBA L BBB B(A0A8JHv
-8A0A(B BBBA L p1 BIB B(A0A8G
-8A0A(B BBBA , $ 1t@ AAG04AA < 0 3 BIA D(DpL
-(D ABBG < ྴ BBD D(G0b
-(A ABBE L PK BBB B(A0A8D@28A0A(B BBB d $ PǴS BBB E(D0D8D
-8A0A(B BBBD
-8A0A(B BBBG 4 H˴ AC
FM
- G l д BBB B(D0A8Gc
-8A0A(B BBBG p
-8A0A(B BBBN T | P BEB B(D0D8Ju
-8A0A(B BBBD T r BDH B(A0A8J
-8D0A(B BBBI t. K^ D 8 BEB E(A0A8e
-0A(B BBBA L D BBB B(A0A8D
-8A0A(B BBBH L BBB B(A0A8J
-8A0A(B BBBI L AAG ]
-CAF b
-CAJ R
-CAJ JCA L 4 0 ADD e
-CAF k
-ACI V
-CAF JCA L ADD e
-CAF
-ACA V
-CAF JCA $ ` AG r
-AA $ AG z
-AA < l r BBA I(D@e
-(D ABBA T N BBE B(D0A8G
-8D0A(B BBBB , P-t@ AAG04AA 4 4 U BHD D`
- DABH T l BBE B(A0A8L
-
-8A0A(B BBBE | t. K^ , -t@ AAG04AA 4 U BHD D`
- DABA T D $ BBB B(A0I8G
-8D0A(B BBBD T t. K^ l 8( 0( T 8( ] BBB B(I0A8D
-8D0A(B BBBE * *" L $ * BEE B(D0A8G@i
-8A0A(B BBBC , t -t@ AAG04AA < + ~ BHD GN
- DABA L , /U o BGB B(A0D8G
-8A0A(B BBBC 4 `t. K^ L x6 d p6 | h6 p6 x60 Hg 67 Hj , 4 6[ / AID t
-DAA 6- AG cA L 6r BBB B(E0A8J@
-8A0A(B BBBA 8} AG sA x8} AG sA 8 AG {A H9 AG {A 9 AG vA , : AG ~AT :C BBB B(A0D8Ra
-8A0A(B BBBA , >> AID W
-DAA T > BBB B(A0D8R9
-8A0A(B BBBA , t 8B>
AID W
-DAA L HB BBB B(I0A8D}
-8D0A(B BBBA L C BBB B(I0A8D}
-8D0A(B BBBA L D hE BBB B(I0A8D}
-8D0A(B BBBA L F BBB B(I0A8D}
-8D0A(B BBBA T H BBB J(A0A8G&
-8D0A(B BBBA $ J AAG0AA4 d J O BHA GPt
- DABC 4 `L 1 BHA GPt
- DABC 4 (N BHA GPt
- DABC 4 O BHA GPt
- DABC 4 D Q BHA GPt
- DABC 4 | S BHA GPt
- DABC 4 HU BHA GPt
- DABC 4 W
} BHA GP
- DABD T $ X _ BBB E(D0A8G
-8A0A(B BBBC D | pa @ BDB D(G0Dy
-0D(A BBBA D (c : BEG A(I
-(D ABBA L e : BBG D(G0D
-0D(A BBBA T \ hB 8 BBB E(A0A8G
-8A0A(B BBBH T u ̿ BBB E(G0F8G
-8D0A(B BBBJ { AR { AY t. K^ { 4 { L { d { | x{ { { { , $ {[ ߾ AID t
-DAA , T {[ AID t
-DAA , {[ AID t
-DAA , (|[ AID t
-DAA L X| AAG ]
-CAF c
-CAI R
-CAJ JCA L | AAG ]
-CAF c
-CAI R
-CAJ JCA L < | AAG ]
-CAF c
-CAI R
-CAJ JCA } D } D ~ D Xj D e j D e j D e 4 `j D e L s D0n d s D0n | s D0n s D0n Xs D0n s D0n (s D0n s D0nt T BBB A(A0G'
-0A(A BBBB
-0A(A BBBB
-0A(A BBBB , H t@ AAG04AA 4 @ BHD D`
- DABG 4 4 ƻ BHD D`
- DABG 4 l 0 BHD D`
- DABG 4 BHD D`
- DABG s D0n s D0n j D e Hs D0n s D0n j D e $ ps D0n < ؞s D0n T @j D e l s D0n s D0n hj D eT 5
L BBB B(A0A8J
-8A0A(B BBBH F AJ0vD , حF AJ0vD T 5
Ѻ BBB B(A0A8J
-8A0A(B BBBH F AJ0vD F AJ0vD t. K^ 8 0 L , 8 AAG e
-CAF {
-ACI R
-CAJ JCA L | AAG e
-CAF S
-ACA R
-CAJ JCA L AAG e
-CAF C
-ACA R
-CAJ JCA d d h BBE E(D0G8GPO
-8D0A(B BBBL |
-8A0A(B BBBA d ~ ȹ BHB B(D0A8Dp
-8D0A(B BBBM T
-8A0A(B BBBA d 4 BBE E(D0A8D`
-8D0A(B BBBK d
-8A0A(B BBBA D 0q > BBB A(I0Dp
-0D(A BBBC T h BEE E(D0D8G
-8D0A(B BBBG T < ĵ BBE B(D0A8G
-8D0A(B BBBH , L t@ AAG04AA 4 ȵ5 BHI D`
- DABB T ˵+ BBB B(A0A8J H
-8A0A(B BBBJ t. K^ $ P < X , T ` BGA
-ABA 0 Hg 7 Hj @0 Hg P7 Hj 4 p0 Hg T 7 Hj t 0 Hg 7 Hj 0 Hg 7 Hj 0 Hg 7 Hj L 4 0 BBB B(D0A8D@b
-8A0A(B BBBA p h ` X L P AAG ]
+CAA
+CAE NCA H \ T EAG U
+CAJ [
+CAA w
+CAE NCA H \U EAG Y
CAF [
-ACA R
-CAJ JCA L 4 AAG ]
+CAA
+CAE NCA H U EAG Y
CAF [
-ACA R
-CAJ JCA L AAG ]
+CAA
+CAE NCA H @ $V EAG U
+CAJ [
+CAA w
+CAE NCA H V EAG Y
CAF [
-ACA R
-CAJ JCA L AAG ]
+CAA
+CAE NCA H V EAG Y
CAF [
-ACA R
-CAJ JCA , $ b D\
-H P
-H Q
-G I , T Pb D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I L AAG e
-CAF s
-ACA R
-CAJ JCA , b D\
-H P
-H Q
-G I L 4 ` AAG e
-CAF s
-ACA R
-CAJ JCA L AAG ]
+CAA
+CAE NCA H $ PW EAG U
+CAJ [
+CAA w
+CAE NCA H p W EAG Y
CAF [
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L $ 0 AAG ]
+CAA
+CAE NCA H X EAG Y
CAF [
-ACA R
-CAJ JCA L t ` AAG e
-CAF s
-ACA R
-CAJ JCA L BBB B(D0A8D@b
-8A0A(B BBBA , BGA
-ABA , D BGA
-ABA , t 0 BGA
-ABA , t@ AAG04AA 4 U { BHD D`
- DABH 4 T U ] BHD D`
- DABH 4 U ? BHD D`
- DABH 4 U ! BHD D`
- DABH L @
-g BBE D(D0JU
-0A(A BBBH \ L ` BBB G(D0O
-0A(A BBBB
-0A(A BBBC 4 S BAI D0\
- DABA L [ 1 BBE B(G0D8R
-8A0A(B BBBG L 4
- ( BBB B(A0D8K
-8A0A(B BBBG ,
- > Ҳ AID W
-DAA L
- 7 BBE D(D0J2
-0A(A BBBA \ " ? BBB G(D0O
-0A(A BBBB
-0A(A BBBC 4 d $ BAI D0\
- DABA L @%[ BBE B(G0D8R
-8A0A(B BBBG L P( BBB B(A0D8K
-8A0A(B BBBG , < )> AID W
-DAA T l )d a BBH G(A0A8J
-8D0A(B BBBI |
- AG MD T -d M BBH G(A0A8J
-8D0A(B BBBI
1 AG MD T \
1L 9 BBH G(D0A8J
-8D0A(B BBBJ l 6 AO T
6L 1 BBH G(D0A8J
-8D0A(B BBBJ ; AO Pqt. K^ L ;* BBB B(A0D8G 8A0A(B BBB L l = ADD e
-CAF s
-ACA V
-CAF JCA L 8> ADD e
-CAF
-ACA V
-CAF JCA L > ADD e
-CAF
-ACA V
-CAF JCA 4 x? Ѱ BAI w
-ABA L ? BBB B(A0D8Dp8A0A(B BBB $ PA^ AG b
-AA 4 T A 8 BHE l
-ABB 4 B BHE o
-ABO | B\ DX
-D L C BBE B(A0A8D`
-8A0A(B BBBA , t@ AAG04AA L d 8D 0 BIB A(D0D
-0D(A BBBA L H BBB B(D0A8GT
-8D0A(B BBBK L HL BBB E(A0A8G
-8A0A(B BBBA L T W BBB B(D0D8J
-8D0A(B BBBF T nA = BHG B(D0A8J
-8D0A(B BBBF lt. K^ L xr BBB B(E0A8J@
-8A0A(B BBBA D d } BBB A(D0D@[
-0A(A BBBF D 9 BBB A(D0D@c
-0A(A BBBF T H BEE E(D0A8G
-8D0A(B BBBG T L А ۮ BEE E(D0A8G
-8D0A(B BBBG L X BBB B(D0A8Gf
-8D0A(B BBBI o D \
-H , t@ AAG04AA D D ( < BIA D(D
-(D ABBA 4 BAI
-ABA T BBE B(D0D8J
-8D0A(B BBBG it. K^ , 4 8 AID k
-DAA , d خ AID k
-DAA 4 BHE r
-ABD 4 BHE r
-ABD 4 H Q BHE u
-ABI 4 < BHE u
-ABI , t\ AG RA, L t@ AAG04AA < H, BHD D
- DABA < 8G ~ BHD D
- DABA T D H c BBB E(G0D8J
-8A0A(B BBBA T ¶5 E BBE G(G0A8J
-8D0A(B BBBG T ʶ խ BGB B(G0A8J
-8D0A(B BBBJ ft. K^ 8Ӷc HZ < Ӷj H] $ \ Ӷ^ AG b
-AA D Զ BBB A(I0DP}
-0D(A BBBA , t@ AAG04AA 4 D ԶU > BHD D`
- DABH T | # BBB B(F0A8G6
-8A0A(B BBBJ et. K^ L x BBB B(A0I8Dp
-8D0A(B BBBE L < x BBB B(A0I8Dp
-8D0A(B BBBE L x S BBB B(A0I8Dp
-8D0A(B BBBE L x BBB B(A0I8Dp
-8D0A(B BBBE , t@ AAG04AA < \ u BIA D(Dp)
-(D ABBJ < Pu BIA D(Dp)
-(D ABBJ < u o BIA D(Dp)
-(D ABBJ < u I BIA D(Dp)
-(D ABBJ 0ct. K^ , D , [ ۫ AID t
-DAA , t@ AAG04AA 4 b BHD D`
- DABD T < 0B o BDB E(G0A8G3
-8D0A(B BBBD L (bt. K^ d | L AAG ]
-CAF k
-ACA R
-CAJ JCA L P AAG ]
-CAF k
-ACA R
-CAJ JCA D d BBB A(A0DP:
-0A(A BBBB D h BBB A(A0D@
-0A(A BBBH , tt@ AAG04AA 4 l U ϩ BHD D`
- DABH 4 U BHD D`
- DABH L h BBB B(D0A8R
-8A0A(B BBBK , ,! > AID W
-DAA L \! h v BBB B(D0A8R
-8A0A(B BBBK , ! 0> y AID W
-DAA , " @ AD
-AE a
-AF , " 0 AD
-AJ a
-AF T <" BBE B(D0A8G
-8D0A(B BBBG T " # ߨ BBE B(D0A8G
-8D0A(B BBBG T " P( Ũ BBE B(D0A8G
-8D0A(B BBBG 4 D# - BAD G`
- AABA 4 |# 4 BAD Gp
- AABA T # X=
- o BGB B(A0G8J%
-8D0A(B BBBG T $ G
- BBB B(A0G8J(
-8D0A(B BBBA % \t. K^ 4% tt DN
-E A $ T% Q^ AG b
-AA < |% R BEA A(D@
-(A ABBA < % R BEA A(D@
-(A ABBA % xS L & Up BEA A(D0H
-(A ABBE D
-(A ABBA T % U W BEE E(D0A8G
-8D0A(B BBBE T & Z, = BBE B(D0A8G
-8D0A(B BBBK 4 ' _J BDA q
-ABE A
-ABA d L' _g BLB B(A0A8D@q
-8A0A(B BBBE
-8A0A(B BBBF , ' s@ AAG04AA 4 ,' `U S BHD D`
- DABH < ( c BBG A(G@
-(A ABBF L \( d~ BLB H(A0D8D@
-8A0A(B BBBG < ( e BBG A(G@
-(A ABBF L ( xfm BBB H(A0D8DP
-8A0A(B BBBA T ( g BBB B(H0A8J
-"
-8A0A(B BBBA , ( @}> K AID W
-DAA L ) P}d + BBB E(D0D8J
-8A0A(B BBBA T \) p 0 BBE B(A0H8J
-8A0A(B BBBB L l* BLB H(A0D8DPV
-8A0A(B BBBG T *
- ڦ BEE B(A0A8N
-8A0A(B BBBB T \* B BBB G(D0A8M
-8D0A(B BBBC l+ hVt. K^ + + L * ˦ BBB J(A0A8G
-8A0A(B BBBG , , s@ AAG04AA D |+ BIA D(D
-(D ABBA T + HH c BBB G(A0A8J
-8A0A(B BBBA , 0Ut. K^ , ( - , d, ([ AID t
-DAA , L- s@ AAG04AA 4 , (U BHD D`
- DABH $ - PV ADD JAAd $- 1 m BHB B(D0A8Dpv
-8C0A(B BBBG {
-8D0A(B BBBA T - ` BBB E(I0A8K}
-8D0A(B BBBF d - BEB B(D0A8Gp
-8E0A(B BBBC
-8E0A(B BBBA T L. · BDB B(A0A8Gp
-8A0A(B BBBA \/ Rt. K^ t/ ѷ / ѷ / s\ AG RAT / з BBB B(A0I8G
-8D0A(B BBBE D 0 Է BBB B(A0A8D`8A0A(B BBB d0 շo T / XշX k BBA D(G`
-(A ABBK T
-(D ABBA T 0 `ַ ' BBB B(D0A8G
-8A0A(B BBBA , ,1 s@ AAG04AA < 0 u BIA D(Dp)
-(D ABBJ T 0 (E BBE B(D0A8G5
-8D0A(B BBBG T <1 , BBB B(A0G8J?
-8D0A(B BBBJ L2 Pt. K^ d2 |2 2 2 2 2 2 3 $3 <3 T3 l3 3 3 x 3 p 3 h 3 p 3 x 4 ,4 D4 \4 t4 4 4 4 4 4 5 5 45 L5 d5 |5 5 5 5 5 5 6 x $6 p <6 h T6 ` l6 X 6 ` " 6 x 6 " 6 6 " 6 7 " ,7 D7 " \7 t7
" 7
7
" 7 8
7 @
" 7 X
8 `
" 8 x
48
" L8
d8
|8
8
" 8
8
" 8
8
" 9 B A@ ,9 8B A@ L9 h8 Av l9 . Al 9 . Al 9 . Al 9 . Al 9 . Al : . Al ,: B A@ L: . Al l: (. Al : 88 Av : XB A@ : 8 Av : ; ; 4; 4 L; V BJD G(F0r(A ABB L ; AAG k
-CAH |
-CAH R
-CAB JCA L ; 0 AAG k
-CAH r
-CAJ R
-CAJ JCA L $< AAG k
-CAH r
-CAJ R
-CAJ JCA L t< AAG k
-CAH r
-CAJ R
-CAJ JCA L < P AAG k
-CAH r
-CAJ R
-CAJ JCA L = AAG k
-CAH r
-CAJ R
-CAJ JCA L d= AAG k
-CAH r
-CAJ R
-CAJ JCA L = p AAG k
-CAH F
-CAF R
-CAJ JCA L > AAG k
-CAH r
-CAJ R
-CAJ JCA L T> @ AAG k
-CAH r
-CAJ R
-CAJ JCA L > AAG k
-CAH |
-CAH R
-CAB JCA L > AAG k
-CAH F
-CAF R
-CAJ JCA L D? p AAG k
-CAH |
-CAH R
-CAB JCA ? R ? ? " 4 ? (t BJD G(D@R(A ABB @ p 4 ,@ h BLD G AAB d@ @ , ? 8 o AID [
-DAA , ? O AID [
-DAA , $@ / AID [
-DAA , T@ X AID [
-DAA , @ AID [
-DAA , @ ϗ AID [
-DAA , @ x AID [
-DAA , A AID [
-DAA , DA 8 o AID [
-DAA , tA O AID [
-DAA , A / AID [
-DAA , A X AID [
-DAA , B AID [
-DAA , 4B ϖ AID [
-DAA 4 C x | BLD I\ AAB 4 TC BJD G(D@(A ABB C x L C p BHB J(D0A8DPe
-8A0A(B BBBB C < D BLA
-ABD r
-ABC < LD " BLA "
-ABD q
-ABD < D $ BLA
-ABG N
-ABG D h( D `(2 D ) 4 E )V BJD G(F0r(A ABB LE )" < dE * BLA
-ABD r
-ABC < E , BLA "
-ABD q
-ABD $ E /^ AG b
-AA F 8/ D $F 0E BBJ G(D0DP
-0A(A BBBD D E 3w _ BEJ D(D0G
-0D(A BBBE D E P4w * BEJ D(D0G
-0D(A BBBE L DF 5~ BBB E(D0A8Lp
-8D0A(B BBBH $ LG 6 AAG0AA4 F P7
BHA GP
- DABG 4 F (9
~ BHA GP
- DABG 4 ,G ;
` BHA GP
- DABG 4 dG <
B BHA GP
- DABG 4 G >
$ BHA GP
- DABG 4 G @
BHA GP
- DABG 4 H `B
BHA GP
- DABG 4 DH 8D
ʑ BHA GP
- DABG 4 |H F
BHA GP
- DABG 4 H G
BHA GP
- DABG 4 H I
p BHA GP
- DABG 4 $I K
R BHA GP
- DABG 4 \I pM
4 BHA GP
- DABG 4 I HO BHA GP
- DABH 4 I 0Q BHA GP
- DABH 4 J S ڐ BHA GP
- DABH 4 <J U BHA GP
- DABH 4 tJ V BHA GP
- DABH 4 J X BHA GP
- DABH L J Z8 b BBB B(A0I8G
-8D0A(B BBBH L 4K ]' f BBB B(A0I8G
-8D0A(B BBBA L K _' R BBB B(A0I8G
-8D0A(B BBBA L K ha' > BBB B(A0I8G
-8D0A(B BBBA L $L Hcl * BBL E(A0A8Gy
-8D0A(B BBBD L tL hd BBB B(A0I8G~
-8D0A(B BBBA L L el BBL E(A0A8Gy
-8D0A(B BBBD L M gl BBL E(A0A8Gy
-8D0A(B BBBD L dM (hl BBL E(A0A8Gy
-8D0A(B BBBD D M Hi t BBE D(K0J`
-0D(A BBBC T M ng X BBG B(A0G8J"
-8D0A(B BBBA T TN pG 6 BDB B(A0G8G
-8D0A(B BBBA T N rG BDB B(A0G8G
-8D0A(B BBBA T O tG BDB B(A0G8G
-8D0A(B BBBA T \O vG Ў BDB B(A0G8G
-8D0A(B BBBA T O xy BBB G(A0G8J1
-8D0A(B BBBA T P zG BDB B(A0G8G
-8D0A(B BBBA T dP |g j BBG B(A0G8J"
-8D0A(B BBBA T P ~ H BBB G(A0G8J9
-8D0A(B BBBA T Q (g & BBG B(A0G8J"
-8D0A(B BBBA T lQ @G BDB B(A0G8G
-8D0A(B BBBA T Q 8G BDB B(A0G8G
-8D0A(B BBBA T R 0G BDB B(A0G8G
-8D0A(B BBBA ,S h/t. K^ L DS AAG ]
-CAF k
-ACA R
-CAJ JCA $ S P^ AG b
-AA D S BBE A(A0D@n
-0A(A BBBA D T BBB D(A0D@
-0A(A BBBA LT , L dT BBB B(D0A8DP
-8A0A(B BBBA T P AL d T P BBB B(A0A8D`
-8A0A(B BBBE d
-8A0A(B BBBA T T ȑ BBE B(D0A8G
-8D0A(B BBBF T T `D t BBE B(D0A8G
-8D0A(B BBBH L 4U Xi Z BBE E(D0A8Dp2
-8D0A(B BBBJ , <V s@ AAG04AA 4 U H BHD D`2
- DABE 4 U ࡸ Њ BHD D`9
- DABF 4 $V x BHD D`9
- DABF T \V BBB B(D0A8J
-8A0A(B BBBA , V Hq { BDA fAB < W BBG A(G@
-(A ABBG T $W XP BBB E(A0A8JK
-8A0A(B BBBD L 4X P BOB B(A0A8Dp
-8A0A(B BBBJ X й L W X[ Ή BBB B(A0A8J
-8A0A(B BBBH D 4X h¸& BDA D@
- DABE L
- DABA L |X Pø BEB E(I0H8D@
-8D0A(B BBBA T X ĸ BBB B(A0D8G\
-8A0A(B BBBA T $Y h˸ BBB B(A0D8G
-8A0A(B BBBA 4Z (t. K^ T Y ڸ BBB E(A0A8N@
-8A0A(B BBBK , Y `߸> - AID W
-DAA , Z p߸>
AID W
-DAA , LZ ߸> AID W
-DAA 4[ ߸ AX T[ ߸F L Z ߸r BBB B(E0A8J@
-8A0A(B BBBA $ [ ^ AG b
-AA d ,[ 0 2 BJE B(A0A8D@Y
-8C0A(B BBBB i8A0A(B BBB 4 L\ w AAG Y
-DAI DAA , [ BHE AB \ AL L \ J BEE B(A0D8G
-8A0A(B BBBA < l\ 6 BBA A(J
-(A ABBA < \ 6 BBA A(J
-(A ABBA d ] " BBB B(A0A8DpO
-8A0A(B BBBI j
-8A0A(B BBBA d ^ r BBB B(A0A8Dp
-8A0A(B BBBI j
-8A0A(B BBBA $ t^ s AAG0AA< ] چ BIA D(DpL
-(D ABBG < $^ X BIA D(DpS
-(D ABBH < d^ BIA D(DpS
-(D ABBH T ^ " h BBB B(A0A8J
-8A0A(B BBBA D ^ H y BFB A(D0J
-0A(A BBBK < D_
| BEA A(G@z
-(D ABBE L _ h O BBE D(C0JU
-0D(A BBBF L _ O BBE D(C0JU
-0D(A BBBF d ` i BBB B(A0A8Dm
-8A0A(B BBBA
-8A0A(B BBBG L ` BEB B(A0A8L
-8A0A(B BBBI T ` ) Ӆ BBB E(A0D8G
-8A0A(B BBBA , a X BDA n
-ABH b 5 T |a 2 T BBB B(A0A8J
-8A0A(B BBBE L a u BBE B(D0D8G_
-8A0A(B BBBE T $b x t BBE E(D0A8D
-8D0A(B BBBF T |b "> G BBB B(A0A8J
-8A0A(B BBBA T b - BBB B(D0K8G
-8A0A(B BBBH c t. K^ T Dc (3 BBE B(A0A8N
-8A0A(B BBBA , c 5> AID W
-DAA , c 5> n AID W
-DAA , c 5> N AID W
-DAA d 6 AU e 6F e 86 AU <e 86F Te p6 A[ $ d p6 BFA D@ , e 6b D\
-H P
-H Q
-G I , e 7b D\
-H P
-H Q
-G I < e X7( BHE m
-ABI A
-ABD , <f H8( BHE u
-ABI L e H9r BBB B(E0A8J@
-8A0A(B BBBA $ f x:^ AG b
-AA < ,f :^ O BDA n
-EBD C
-EBA $ lf :b A]
-B A L f ; BBB B(A0A8J
-8D0A(B BBBA , f <z ڂ BHE gAB g > AL 4 g >J BDA o
-ABG A
-ABA d $h 0> BLB B(A0A8Dq
-8A0A(B BBBE
-8A0A(B BBBJ $ h s AAG0AA4 g F Ɓ BHD D`9
- DABF L 4h XI BBE B(D0D8Gp
-8A0A(B BBBD D h O BBB D(A0J
-
-0D(A BBBJ L h pWP # BEB E(A0D8DP
-8D0A(B BBBA d i pY9 BLB B(D0A8D@q
-8A0A(B BBBJ
-8A0A(B BBBA L i HZ
- BLB B(D0D8D@
-8A0A(B BBBG L j ] BBD D(D0O
-(A ABBK \
-(A ABBE < j ^ BAD
-ABE
-ABJ T dj 8c BBB B(A0A8D
-8A0A(B BBBJ d tk l BBB B(A0A8D7
-8A0A(B BBBI l
-8A0A(B BBBA 4 k rJ BDA l
-ABJ A
-ABA \ l r% GLE B(A0A8DPV8A0A(B BBBHP T k s BBB B(A0D8G
-8A0A(B BBBA T l 8|| BBB E(A0A8G+
-8A0A(B BBBA $m t. K^ T l H BBB B(A0D8N
-8A0A(B BBBA , l > AID W
-DAA d m BEB B(A0A8D@
-8A0A(B BBBE
-8A0A(B BBBA T tm R g BFB B(A0D8JT
-8A0A(B BBBA d n BEB B(A0A8D@
-8A0A(B BBBF
-8A0A(B BBBA L n 8 BBE B(A0A8D`
-8A0A(B BBBC L n ~ BBE B(A0A8J
-8A0A(B BBBC < n 8 ~ BEA D(DP
-(D ABBC T o ~ BBB B(A0A8J
-8D0A(B BBBG $p AX Dp A[ , dp b D\
-H P
-H Q
-G I , p `b D\
-H P
-H Q
-G I L p r ~ BBB B(E0A8J@
-8A0A(B BBBA $ q С^ AG b
-AA L p } BBB B(A0A8J
-8D0A(B BBBA , p z }} BHE gAB q AL T $q 1} BBB E(A0A8GQ
-8A0A(B BBBA L |q Ъr g} BBE E(D0D8I{
-8D0A(B BBBI $ r Js AAG0AA4 q ج } BHD D`2
- DABE 4 ,r p | BHD D`9
- DABF T dr | BBB B(D0A8JW
-8D0A(B BBBE T r @ | BBB B(A0A8J
-8A0A(B BBBA s L s ཹ BLB B(A0A8D
-8A0A(B BBBA L |s P¹ b| BEB B(D0D8G
-8A0A(B BBBE T s ȹE \| BBB B(A0D8G
-8A0A(B BBBF t xt. K^ T <t Ϲ 0| BBB B(A0D8N
-8A0A(B BBBG , t > | AID W
-DAA , t > | AID W
-DAA T t ( ~| BBF A(A0
-(A BBBG W
-(A BBBJ d v `߹ BBB B(A0A8DPB
-8A0A(B BBBE L
-8A0A(B BBBA L u h { BBE B(A0A8J
-8A0A(B BBBC D v $ { BBE A(D0D`
-0D(A BBBD T Lv % { BBB B(A0A8J
-8A0A(B BBBK L \w h AAG ]
-CAF k
-ACA R
-CAJ JCA $ w ^ AG b
-AA < w ( BHE m
-ABI A
-ABD , x ( BHE u
-ABI D Dx BBB D(A0D@
-0A(A BBBA 4 x H BDA D0
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-8A0A(B BBBE \ x z BBB A(A0Do
-0A(A BBBE i
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-(A BBBG Y
-(A BBBH T y # y BBE E(D0A8G,
-8D0A(B BBBE < z h y ACG@s
-DAE J
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-ABG A
-ABA 4{ @ AU
-A < z @ x BEA D(G@&
-(D ABBF \ z x BBB D(D0J
-0A(A BBBF
-0A(A BBBG T <{ | x BBB B(A0A8J
-8A0A(B BBBA \ L| 8 KOB B(A0A8Dpk8A0A(B BBBGp d | 3 BOB B(A0A8D`E
-8A0A(B BBBF |
-8D0A(B BBBE L } BLB E(A0A8DpC
-8A0A(B BBBH T | 07 w BEB F(D0D8J
-8D0A(B BBBC T } % yw BBB B(A0A8Ga
-8A0A(B BBBD L ~ *F BEE B(D0D8D`
-8A0A(B BBBF L d~ + BEE B(G0D8G`C
-8A0A(B BBBC L } - #w BLB B(A0D8D`
-8A0A(B BBBD L 2 BBD D(D0O
-(A ABBK \
-(A ABBE T ~ p4 v BBB B(A0D8G
-8A0A(B BBBF D ~ = v BBB A(D0J
-0D(A BBBD D < 0A w BBB D(A0D
-0D(A BBBD L D w BEE E(A0D8D`
-8D0A(B BBBK t. K^ | 0G} v BFE E(D0D8Gp
-8A0A(B BBBF T
-8A0A(B BBBB |
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yv BBE E(G0A8GJ
-8D0A(B BBBD D | Q GBA A(G@H(A ABBH@ L ā 0S BEE E(D0A8G@p
-8D0A(B BBBF t. K^ , S D S \ S t S S" S" , S[ hu AID t
-DAA L S AAG k
-CAH F
-CAF R
-CAJ JCA L < hT AAG k
-CAH F
-CAF R
-CAJ JCA L T AAG ]
-CAF k
-ACA R
-CAJ JCA ܃ U U5 zs\ AG RAL , Yo BBB B(A0A8D`
-8A0A(B BBBJ , | fs@ AAG04AA 4 \U s BHD D`
- DABH 4 , 0_b js BHD D`
- DABD 4 d hbb Ls BHD D`
- DABD 4 ee .s BHD D`"
- DABE T Ԅ h s BBB B(A0I8G6
-8D0A(B BBBD T , @n s BBB B(A0I8G6
-8D0A(B BBBD T s^ ,s BBB B(A0A8O
-8D0A(B BBBG T ܅ w "s BDB B(D0A8G
-8A0A(B BBBI T 4 's BBB G(A0G8J
-8D0A(B BBBE T ` s BBB G(A0G8J
-8D0A(B BBBE Hs. K^ D BBE A(A0D@g
-0A(A BBBA D ( BBE A(A0D@o
-0A(A BBBA L r Gs BBB B(E0A8J@
-8A0A(B BBBA $ ^ AG b
-AA $ (G r A4
+CAA
+CAE NCA H |X EAG U
+CAJ [
+CAA w
+CAE NCA H T X EAG Y
+CAF [
+CAA
+CAE NCA H DY EAG Y
+CAF [
+CAA
+CAE NCA Y2 H Y BDB A(A0
+(A BBBI _(A BBBH L xZ BDB A(A0
+(A BBBI _(A BBBH [ BDB A(A0
+(A BBBJ _(A BBBH [ BDB A(A0
+(A BBBJ _(A BBB@ 0 d\/ BGA A(
+ ABBD i ABB @ t P]/ BGA A(
+ ABBD i ABB @ <^. BGA A(
+ ABBF p ABB @ (_. BGA A(
+ ABBF p ABB L @ `p FBB B(A0A8DK
+8A0A(B BBBI L 4d( FBB B(A0A8D
+8A0A(B BBBE L hh FBB B(A0A8D
+8A0A(B BBBE L 0 4lp FBB B(A0A8DK
+8A0A(B BBBI L Tph FBB B(A0A8D
+8A0A(B BBBE L ttx FBB B(A0A8DK
+8A0A(B BBBI L x( FBB B(A0A8D
+8A0A(B BBBE L p |x FBB B(A0A8DK
+8A0A(B BBBI X { KBB H(D0w
+(I BBBO H0 4 x
+ FIA D`
+ DABI $ ϲXX |
+ 4 l
+ FIA D`
+ DABI t ײXX I
+ 4 x 9
+ FIA D`
+ DABI Ļ ߲XX
+ P h Hw
+ FBB B(I0A8DA
+8D0A(B BBBH ˲XX ǰ
+ P \w
+ FBB B(I0A8DA
+8D0A(B BBBH XX x
+ P @ pw h
+ FBB B(I0A8DA
+8D0A(B BBBH XX )
+ P w
+ FBB B(I0A8DA
+8D0A(B BBBH d
+ XX گ
+ P w ʯ
+ FBB B(I0A8DH
+8D0A(B BBBA
+ {XX
+ P {
+ BBB B(D0A8J
+8A0A(B BBBG gX ?
+ 4 n
+ FAD K0W
+ AABA ܴX# B
+ , @! P= 2
+ FAL W
+DBA D X
+ 4 ! H
+ FAD K0W
+ AABA 4 |X# Ү
+ , ! = ®
+ FAL W
+DBA ܋ WX
+ P "
+ FBE B(A0A8K
+8A0A(B BBBE ܅ X M
+ , " 䬈= S
+ FAL W
+DBA ԴX /
+ P " ܬe
+ BEB B(D0A8G_
+8A0A(B BBBH $ }X
+ 4 @# ை
+ FAD K0[
+ AABA X#
+ , # =
+ FAL W
+DBA X
+ 4 #
+ FAD K0[
+ AABA X#
+ , ($ X= q
+ FAL W
+DBA , X M
+ 4 p$ P =
+ FAD K0[
+ AABA PX#
+ , $ =
+ FAL W
+DBA Ď +X ݬ
+ 4 % ͬ
+ FAD K0[
+ AABA X#
+ , X% Ȱ=
+ FAL W
+DBA \ ˵X m
+ X % ]
+ FEE E(A0GM
+0A(B BBBA uFKD P % 4
+ FBE B(A0A8K
+8A0A(B BBBE X ۫
+ , h& =
+ FAL W
+DBA l X
+ P &
+ BBB E(D0D8J
+8A0A(B BBBE , zPLR xA
+ $ 4 eXM ʫ
+ FKD H \' ň W
+ FBG D(DPgXN`OXAPX
+(D ABBC >X
+ X ' ň8
+ FEB B(G0A8D`
+8D0A(B BBBH LhKpLhA` X ҫ
+ L 4( |Lj «
+ FBB B(G0A8J
+8D0A(B BBBI , zPLR x 4 rX
+ ` ( ڈ8
+ FBB B(G0D8D
+8D0A(B BBBE RNLA X: I
+ P H) X܈ O
+ BBB E(D0D8J
+8A0A(B BBBE $ XM
+ FKD H ) )
+ FBG D(DPgXN`OXAPX
+(D ABBC X
+ X (* 8 ۬
+ FEB B(G0A8D`
+8D0A(B BBBH LhKpLhA` P \X
+ ` * t8
+ FBB B(G0D8D
+8D0A(B BBBE RNLA h X: T
+ L + 8 Z
+ FBB B(G0A8J
+8D0A(B BBBI X Ь
+ P $, OEB B(A0D8H@
+8A0A(B BBBG PP x, , OEB B(A0D8H@
+8A0A(B BBBG PP , OEB B(A0D8H@
+8A0A(B BBBC PP - OEB B(A0D8H@
+8A0A(B BBBC PL t- 0 OEB B(A0D8H@8A0A(B BBBE L - OEB B(A0D8H@8A0A(B BBBE L . OEB B(A0D8H@8A0A(B BBBE L d. OEB B(A0D8H@8A0A(B BBBE $ . K
+D B
+F D $ .
+ K
+D B
+F D $ / ` K
+F F
+B D $ ,/ ( K
+F F
+B D 0 T/ KGz
+DH _
+DE DD 0 / L
KGz
+DH _
+DE DD 0 /
KGz
+DH _
+DE DD 0 / KGz
+DH _
+DE DD D $0 `m JBE F(G0G8
+0A(B BBBJ D l0 FEE H(H0D8G@8A0A(B BBBL 0 XEB B(D0D8DP_8A0A(B BBBJh `0 p q
+ FBB B(A0A8D
+8A0A(B BBBF
+8D0A(B BBBO D p1 m JBE F(G0G8
+0A(B BBBJ D 1 FEE H(H0D8G@8A0A(B BBBL 2 T XEB B(D0D8DP_8A0A(B BBBJh 1 2
+ FBB B(A0A8D
+8A0A(B BBBF
+8D0A(B BBBO D 2 m JBE F(G0G8
+0A(B BBBF D 3 FEE H(H0D8G@8A0A(B BBBL L3 XEB B(D0D8DP_8A0A(B BBBJh 2 "
+ FBB B(A0A8D
+8A0A(B BBBF
+8D0A(B BBBO D 4 L'm JBE F(G0G8
+0A(B BBBF D P4 t( FEE H(H0D8G@8A0A(B BBBL 4 ) XEB B(D0D8DP_8A0A(B BBBJh D4 l*
+ FBB B(A0A8D
+8A0A(B BBBF
+8D0A(B BBBO D T5 /5 JBB E(L0D8
+0A(B BBBK H 5 0 FEE H(H0D8G@D
+8A0A(B BBBG L 5 1q XEE F(D0D8DP08A0A(B BBBBh 5 L2/ q
+ FBB B(A0A8D\
+8A0A(B BBBH t
+8D0A(B BBBO D 6 75 JBB E(L0D8
+0A(B BBBK H 6 8 FEE H(H0D8G@D
+8A0A(B BBBG L 87 |8q XEE F(D0D8DP08A0A(B BBBBh 6 9/ .
+ FBB B(A0A8D\
+8A0A(B BBBH t
+8D0A(B BBBO D 7 p>5 JBB E(L0D8
+0A(B BBBG H <8 h? FEE H(H0D8G@D
+8A0A(B BBBG L 8 ?q XEE F(D0D8DP08A0A(B BBBBh 48 A
+ FBB B(A0A8DA
+8A0A(B BBBC d
+8D0A(B BBBO D D9 E5 JBB E(L0D8
+0A(B BBBG H 9 F FEE H(H0D8G@D
+8A0A(B BBBG L 9 Gq XEE F(D0D8DP08A0A(B BBBBh 9 <H
+ FBB B(A0A8DA
+8A0A(B BBBC d
+8D0A(B BBBO : Ph2 HW : L) : L7 Lf : L) : M7 Lf ; 8M) ; TM7 Lf <; xM) P; M7 Lf l; M) ; M7 Lf , : M A
+ FAL0W
+DBA T) X
+ , @; @N
+ FAL0W
+DBA ) X
+ , ; N ٝ
+ FAL0W
+DBA ) X
+ , ; N
+ FAL0W
+DBA ,* bX
+ , < O q
+ FAL0W
+DBA t* /X M
+ = ,X' ( = @X BAD AB < D= X KBB A(D0(I BBBP < NW
+ FBB B(I0A8D'
+8D0A(B BBBB ' *XX ~
+ P L= TW n
+ FBB B(I0A8D'
+8D0A(B BBBB ( XX /
+ P = ZW
+ FBB B(I0A8D'
+8D0A(B BBBB p( XX
+ P $> `W Л
+ FBB B(I0A8D'
+8D0A(B BBBB ( XX
+ P > fW
+ FBB B(I0A8D'
+8D0A(B BBBB H) XX B
+ L > l 3
+ FDB E(A0A8G
+8A0A(B BBBD L L? 4
+ FGB B(A0A8G
+8A0A(B BBBB L ? ߜ
+ FGB B(A0A8G
+8A0A(B BBBB L ? $
+ FGB B(A0A8G
+8A0A(B BBBB L <@
+ FGB B(A0A8G
+8A0A(B BBBB 0A Jh2 HW LA ʼn) `A Ɖ7 Lf |A >X' < @ $Ɖ
+ FBA I(DP
+(D ABBD XG Ǟ
+ < A @X KBB A(D0(I BBBP A \ljW w
+ FBB B(I0A8D'
+8D0A(B BBBB <, XXX 8
+ L A P͉ +
+ FBB G(G0D8G
+8D0A(B BBBG B Ih2 HW B Չ) C Չ7 Lf ,C X' , @C Չ AF
HK
+ C ( pC X BAD AB < C X KBB A(D0(I BBBP 8C HG
+ FBB B(I0A8G
+
+8D0A(B BBBD : X X
+ P C ,މa H
+ FBB B(D0A8GD
+8D0A(B BBBG P C HI
+ FBB B(D0A8NO
+8A0A(B BBBH , LD D= n
+ FAL W
+DBA P vX J
+ P D <
+ ;
+ FBB E(D0A8L
+8A0A(B BBBG P D , w
+ FBB E(D0A8L
+8A0A(B BBBG P <E
+ FBE B(D0A8LK
+8A0A(B BBBK P E 0
+ FHE B(F0A8GN
+8D0A(B BBBB P E l #
+ FEB B(F0A8G
+8A0A(B BBBG P 8F g
+ FBE G(A0D8G
+8A0A(B BBBG P F
+ FBG E(G0A8Go
+8D0A(B BBBA (X
+ G Dh2 HW G X' 8 G ${ FBA D(D0B
+(I ABBO L H P&{ FBA A(G08
+(F ABBL d
+(A ABBF H TH ( FBA A(G0x
+(A ABBI d(A ABB H H D) FBA A(G0x
+(A ABBI d(A ABB < H X KBB A(D0(I BBBP H )' 4
+ FBB B(I0A8G
+8D0A(B BBBG X
+ < H 0>
+ FBA D(G0
+(A ABBJ < 4I 1> ۜ
+ FBA D(G0
+(A ABBJ L tI 2
+ FBB E(G0F8G
+8D0A(B BBBB X~
+ P I 7H
+ FBB B(A0D8N
+8A0A(B BBBE , 0J N= Ý
+ FAL W
+DBA 4 vX
+ K hAh2 HW 4K MR Lu
+O A TK 0NR Lu
+O A tK pNR Lu
+O A K X' ( K X BAD AB K pNT LC K NT LC L NT LC 8 (L <O FBD A(D0
+(I ABBO 8 dL P FBD A(D0
+(I ABBO 8 L P FBD A(D0
+(I ABBO L L Q FBA A(G0u
+(F ABBO D
+(A ABBF L ,M xR FBA A(G0u
+(F ABBO D
+(A ABBF L |M 8S FBA A(G0u
+(F ABBO D
+(A ABBF < M X KBB A(D0(I BBBP hM Sw
+ FBB B(I0A8DA
+8D0A(B BBBH 8 XX `
+ P M Yw P
+ FBB B(I0A8DA
+8D0A(B BBBH 8 XX
+ P @N _w
+ FBB B(I0A8DA
+8D0A(B BBBH 8 XX
+ P N ew
+ FBB B(I0A8DA
+8D0A(B BBBH d9 XX s
+ P O lw c
+ FBB B(I0A8DA
+8D0A(B BBBH 9 XX $
+ P O rw
+ FBB B(I0A8DA
+8D0A(B BBBH <: XX
+ P O 0x Ř
+ FJB B(A0A8GD
+8D0A(B BBBB D! XD
+ P \P y
+ FJB B(A0A8GD
+8D0A(B BBBB ! bXD T
+ P P x{ [
+ FJB B(A0A8GD
+8D0A(B BBBB " :XD
+ P 4Q } &
+ FBB B(A0I8D8
+8D0A(B BBBA X=
+ P Q ~
+ FBB B(A0I8D8
+8D0A(B BBBA \ X=
+ P R D
+ FBB B(A0I8D8
+8D0A(B BBBA ȵ X=
+ P xR
+ FBB B(D0F8G
+8A0A(B BBBI P R
+ FBB B(D0F8G
+8A0A(B BBBF P S p Ǘ
+ FBB B(D0F8G
+8A0A(B BBBF P tS <8
+ FBB E(G0A8L
+8D0A(B BBBA P S ('
+ FBB E(G0F8G$
+8D0A(B BBBA P T ) /
+ FBB E(G0F8G&
+8D0A(B BBBA U 7h2 HW ,U ȤR Lu
+O A LU X' `U T LC ( |U X BAD AB < U tX KBB A(D0(I BBBP DU ̤ {
+ FBB B(I0A8G
+8D0A(B BBBG & Xu H
+ P U 8
+ FBB B(D0A8G
+8D0A(B BBBH P V D
+ FBB J(A0A8D
+8D0A(B BBBJ AX} Ŗ
+ P pV w Ӗ
+ FBB G(A0A8Gj
+8A0A(B BBBB T RX
+ W 5h2 HW W xZ LC
+I A W X' W b LQ ( W X BAD AB X XO FC < 4X X KBB A(D0(I BBBP W p
+ FBB B(I0A8Dp
+8D0A(B BBBI \ 2Xu
+ P <X
+ FBB E(A0A8G
+8D0A(B BBBG P X ʊn
+ FBB B(A0D8N
+8A0A(B BBBI O X/ ݖ
+ , X ъ=
+ FAL W
+DBA X
+ P DY ъ
+ FBB E(A0A8G
+8D0A(B BBBH L Y xي
+ FBB G(G0D8G
+8D0A(B BBBA H (X
+ Z 2h2 HW Z ي2 4 Z $ڊ EAG
+FAL D
+AAB L [ ڊ FBA A(G0
+(F ABBL t
+(A ABBF L X[ <܊ FBA A(G0
+(F ABBL t
+(A ABBF 8 [ ݊ FBA D(D0
+(I ABBO ( [ @ߊK EDD
+IAG 8 \ d+ FBA D(D0
+(I ABBO 8 L\ X{ FBA D(D0B
+(I ABBO 8 \ FBA D(D0
+(I ABBO ( \ @K EDD
+IAG ( \ dK EDD
+IAG 8 ] + FBA D(D0
+(I ABBO L X] |{ FBA A(G08
+(F ABBL d
+(A ABBF L ] FBA A(G0
+(F ABBL t
+(A ABBF 4 ] <K EAG
+FAL D
+AAB 4 0^ T EAG
+FAL D
+AAB L h^ < FBA A(G0
+(F ABBL t
+(A ABBF X ^ KBB H(D0w
+(I BBBO H0 @ p^ ` ǒ
+ FBI A(Go
+(D ABBA ( zPLR x 0 X
+ 8 ^ t
+ FIA D<
+ DABA $ zPLR x , X =
+ 8 t_ -
+ FIA D<
+ DABA X
+ 8 _ x
+ FIA D<
+ DABA RX
+ 8 `
+ FIA D<
+ DABA ( X
+ 8 p` P Б
+ FIA D<
+ DABA | X
+ 8 `
+ FIA D<
+ DABA X
+ 8 a (
+ FIA D<
+ DABA $ VX
+ L la s
+ FGE A(A0P
+(C BBBC d(A BBBH `b FBA A(G0x
+(A ABBI d(A ABB H b FBA A(G0x
+(A ABBI d(A ABB < Tb l
+k
+ FIA D(KP+
+(A ABBH X ^
+ < b k N
+ FIA D(KP,
+(A ABBG \ pX
+ < c k
+ FIA D(KP,
+(A ABBG 4X
+ < \c k ̏
+ FIA D(KP%
+(A ABBF X
+ < c k
+ FIA D(KP&
+(A ABBE d X Z
+ D d J
+ FPB B(A0G`b
+0A(B BBBC 8/ xX.
+ D ld T
+ FPB B(A0G`b
+0A(B BBBC / FX. Ԏ
+ L d c ǎ
+ FGB E(A0D8D`q
+8A0A(B BBBH < e
+ FEG D(K@x
+(D ABBF P \e
+ FBE E(A0A8J
+8D0A(B BBBH L e p" N
+ FBB E(G0A8L
+8D0A(B BBBG 7 (X
+ P f (% O
+ FBB E(A0A8Rc
+8A0A(B BBBH , lf d0= i
+ FAL W
+DBA p X E
+ | f \0_ 5
+ FBB B(A0D8N
+8A0A(B BBBG SMHHFHHDHI , 4g <8=
+ FAL W
+DBA 8 lX
+ | |g 48b
+ FBB B(A0D8R
+8A0A(B BBBJ jSMAHMAHHHN , g $>= ˍ
+ FAL W
+DBA X
+ P Dh >
+ FBB E(A0F8G2
+8A0A(B BBBG P h AS
+ FBB B(D0F8G
+8A0A(B BBBI L h F o
+ FBB B(A0D8G`2
+8A0A(B BBBD D <i TH_ C
+ FBE G(D0KP
+0D(A BBBB | i lI
+ FBB B(A0D8Rd
+8A0A(B BBBG SMAHMAHHHN , j Q=
+ FAL W
+DBA X
+ < Lj Q>
+ FBA D(G0
+(A ABBJ | j R
+ FBB B(A0D8N
+8A0A(B BBBD SMAHMDHHHI , k \= Ì
+ FAL W
+DBA X
+ < Tk \>
+ FBA D(G0
+(A ABBJ | k ] k
+ FBB B(A0D8N
+8A0A(B BBBH SMAHMDHHHI , l d= g
+ FAL W
+DBA X C
+ L \l d 3
+ FBB B(A0D8G`2
+8A0A(B BBBD D l fH
+ FBE G(D0KP
+0D(A BBBB | l g ߋ
+ FBB B(A0D8N
+8A0A(B BBBK eSMAHMDHHHI , tm n= Ƌ
+ FAL W
+DBA x X
+ < m n>
+ FBA D(G0
+(A ABBJ | m o o
+ FBB B(A0D8N
+8A0A(B BBBC SMAHMDHHHI , |n $w= k
+ FAL W
+DBA X G
+ D n w 7
+ FBB A(A0GC
+0D(A BBBA P o dx
+ FGH E(A0A8GP
+8D0A(B BBBH L `o |~ ڊ
+ FGE H(A0A8D
+8D0A(B BBBE D o P~
+ FBB A(A0GC
+0D(A BBBA P o
+ FBB E(A0F8G
+8A0A(B BBBJ H p 4# FGE H(A0A8D@
+8D0A(B BBBH P p M
+ FBB G(A0D8G
+8A0A(B BBBA q h2 HW q ! q - q 0- q L q X- r t ED A 0r X' 8 Dr , FBA D(D0
+(I ABBE L r 䇋4 FBA A(G0
+(F ABBK D
+(A ABBF r `XO FC < r X KBB A(D0(I BBBP r xW ?
+ FBB B(I0A8G
+8D0A(B BBBA i X
+ P r l*
+ FBB B(A0D8G
+8A0A(B BBBH P Hs H
+ FBB B(A0D8N
+8A0A(B BBBD ) X O
+ , s 쭋= g
+ FAL W
+DBA X C
+ < s 䭋
3
+ EC
PP.0
+ K .. Y X ]
+ 8 Tt
+
+ EC
BLK.@[. i
+ D Z dX
+
+ Lu xh2 HW du ċZ LC
+I A u X' u ċb LQ u XO FC < u X KBB A(D0(I BBBP lu ċW
+ FBB B(I0A8D'
+8D0A(B BBBB $` XX ̈
+ P u ʋ
+ FBB E(A0A8NQ
+8A0A(B BBBF (m Xh
+ , Dv ,=
+ FAL W
+DBA H X
+ 8 v $8
+
+ EC
FI.0R
+ E r. <\ X
+ w h2 HW w ߋ) w 7 Lf w Y' ( w ,Y BAD AB < x Y KBB A(D0(I BBBP w ߋ s
+ FBB B(I0A8G
+8D0A(B BBBH H Yu @
+ P x 0
+ FEB B(A0A8N
+8A0A(B BBBA , hx h=
+ FAL W
+DBA l Y
+ d Ty ` OLM N(K0K8\U
+8A0A(B BBBI A d y OMQ P(O0L8S
+8A0A(B BBBG A ` $z OIB J(J0D8Upu
+8A0A(B BBBJ kApd z OLM P(D0H8Y
+8A0A(B BBBC A P Lz ] O
+ FBB B(G0D8L
+8D0A(B BBBE P z P φ
+ FBB B(A0D8L
+8A0A(B BBBC P z S
+ FBB B(A0D8L
+8A0A(B BBBK P H{ H+ ׇ
+ FBB B(A0D8L
+8A0A(B BBBK P { > [
+ FBB E(G0F8D
+8D0A(B BBBF X YX '
+ | h2 HW | Y' 8 | lC FBA D(D0
+(I ABBK 8 } D FBA D(D0
+(I ABBK 8 P} 4F FBA D(D0
+(I ABBK 8 } G FBA D(D0
+(I ABBK L } H FBA A(G0
+(F ABBH l
+(A ABBF L ~ LJ FBA A(G0
+(F ABBH l
+(A ABBF L h~ K FBA A(G0
+(F ABBH l
+(A ABBF L ~ L FBA A(G0
+(F ABBH l
+(A ABBF ( Y BAD AB 4 YO FC < P Y KBB A(D0(I BBBP ~ MW
+ FBB B(I0A8D'
+8D0A(B BBBB i YX D
+ P X SW 4
+ FBB B(I0A8D'
+8D0A(B BBBB j YX
+ P YW
+ FBB B(I0A8D'
+8D0A(B BBBB |j YX
+ P 0 _W
+ FBB B(I0A8D'
+8D0A(B BBBB j YX W
+ 0 @ ew EAG [
+AAF DAAL Ѐ ec
+ FGB E(A0D8D`q
+8A0A(B BBBH P h
+ FBB B(G0D8L
+8D0A(B BBBB P t q O
+ FBE B(F0A8G
+8A0A(B BBBG d Y /
+ P u
+ FBB B(G0D8L
+8D0A(B BBBB P 4 }
+ FBB B(G0D8L
+8D0A(B BBBB < H> K
+ FBA D(G0
+(A ABBJ D Ȃ H '
+ FDE A(A0G@z
+0A(A BBBF P
+ FIB B(A0D8G
+8A0A(B BBBJ , d L= [
+ FAL W
+DBA h Y 7
+ P D '
+ FIB B(A0D8G
+8A0A(B BBBJ , =
+ FAL W
+DBA k Y [
+ P H K
+ FIB B(A0D8G
+8A0A(B BBBJ , =
+ FAL W
+DBA
+Y
+ P o
+ FIB B(A0D8G
+8A0A(B BBBJ , 8 hÌ= Dž
+ FAL W
+DBA < ]
+Y
+ $ `h2 HW < HÌZ LC
+I A \ ÌZ LC
+I A | ÌZ LC
+I A ČZ LC
+I A8 HČ FBA A(D@x
+(A ABBD Y' H Č EAG U
+CAJ [
+CAA e
+CAG NCA H X Č EAG U
+CAJ [
+CAA d
+CAH NCA H PŌ EAG U
+CAJ [
+CAA e
+CAG NCA H Ō EAG U
+CAJ [
+CAA d
+CAH NCA H < Ō EAG U
+CAJ [
+CAA e
+CAG NCA H Lƌ EAG U
+CAJ [
+CAA d
+CAH NCA H Ԉ ƌ EAG U
+CAJ [
+CAA e
+CAG NCA H ƌ EAG U
+CAJ [
+CAA d
+CAH NCA H l Hnj EAG U
+CAJ [
+CAA d
+CAH NCA njb LQ ԉ njb LQ DȌb LQ Ȍb LQ ( ( ~Y BAD AB Ȍ c
+ H{
+E Љ 0Ɍ G
+ HS
+E Ɍ +
+ H@
+H ʌ
+ H
+I 0 ʌ
+ H\
+D P ˌ ׀
+ Ha
+G p ˌ
+ Ht
+D P̌
+ H`
+H < T PY KBB A(D0(I BBBP ̌g C
+ FBB B(I0A8D3
+8D0A(B BBBF u hYX
+ P \ Ҍg
+ FBB B(I0A8D3
+8D0A(B BBBF v TYX
+ P ȋ ،g
+ FBB B(I0A8D3
+8D0A(B BBBF v @YX f
+ P 4 ތg V
+ FBB B(I0A8D3
+8D0A(B BBBF v ,YX
+ P
+
+ FBB B(A0D8NW
+8A0A(B BBBH , l= P
+ FAL W
+DBA Y ,
+ P < d
+
+ FBB B(A0D8NW
+8A0A(B BBBH , = e
+ FAL W
+DBA aY A
+ P ؍
+ 1
+ FBB B(A0D8NW
+8A0A(B BBBH , , = z
+ FAL W
+DBA 0 Y V
+ P t
+ F
+ FBB B(A0D8NW
+8A0A(B BBBH , Ȏ =
+ FAL W
+DBA SY k
+ P 0 [
+ FHB B(A0D8L
+8D0A(B BBBF P d \ 0 /
+ FHB B(A0D8L
+8D0A(B BBBF P 8-0
+ FHB B(A0D8L
+8D0A(B BBBF P >0 ׁ
+ FHB B(A0D8L
+8D0A(B BBBF g2 HW N H 0 N EAG U
+CAJ [
+CAA d
+CAH NCA | Y' 8 $O FBA D(D0
+(I ABBO L ̑ P FBA A(G0
+(F ABBM D
+(A ABBF YO FC l Q w
+ FEB B(A0A8D=
+8A0A(B BBBD ONBADY < Y KBB A(D0(I BBBP D YW ۀ
+ FBB B(I0A8G
+8D0A(B BBBA Y
+ | T `o OEE E(A0D8GP&HP
+8A0A(B BBBJ 1
+8A0A(B BBBE t ԓ c) UBB B(A0D8J@C8A0A(B BBBIH@n
+8A0A(B BBBH P dC
+ FBB B(D0A8G
+8A0A(B BBBH P m
+ FBB B(A0D8N
+8A0A(B BBBA xX Yr
+ , h =
+ FAL W
+DBA l Y
+ ` Ѕ
~
+ BGB B(A0A8G,GPA^
+8A0A(B BBBF <Y Y F
+ , Е dm FAA O
+ABF D \ e
+ EC
BIK9.0.
+ A .0. { Y% E
+ P c
+ FBB H(D0A8G
+8D0A(B BBBE Y g
+ ̖ 8g2 HW j LQ
+K A Y' , Ȩp L_ P H : FAA ]
+CBF s
+CBH v
+ABG M
+ABH YO FC < Y KBB A(D0(I BBBP T W
+ FBB B(I0A8D'
+8D0A(B BBBB YX |
+ P U l
+ FBB B(A0D8N
+8A0A(B BBBB , =
+ FAL W
+DBA Y
+ 8 \
+ EC
HM
+ D .0. < g2 HW H T EAG U
+CAJ [
+CAA v
+CAF NCA H t EAG U
+CAJ [
+CAA n
+CAF NCA H EAG U
+CAJ [
+CAA m
+CAG NCA H 8 EAG U
+CAJ [
+CAA v
+CAF NCA H EAG U
+CAJ [
+CAA v
+CAF NCA H К EAG U
+CAJ [
+CAA w
+CAE NCA H H EAG U
+CAJ [
+CAA v
+CAF NCA H h EAG U
+CAJ [
+CAA n
+CAF NCA H EAG U
+CAJ [
+CAA m
+CAG NCA H T EAG U
+CAJ [
+CAA v
+CAF NCA H L EAG U
+CAJ [
+CAA v
+CAF NCA H EAG U
+CAJ [
+CAA w
+CAE NCA H EAG U
+CAJ [
+CAA v
+CAF NCA H 0 EAG U
+CAJ [
+CAA n
+CAF NCA H | 8 EAG U
+CAJ [
+CAA m
+CAG NCA H ȝ EAG U
+CAJ [
+CAA v
+CAF NCA H EAG U
+CAJ [
+CAA v
+CAF NCA H ` T EAG U
+CAJ [
+CAA w
+CAE NCA H EAG U
+CAJ [
+CAA v
+CAF NCA H EAG U
+CAJ [
+CAA n
+CAF NCA H D p EAG U
+CAJ [
+CAA m
+CAG NCA H EAG U
+CAJ [
+CAA v
+CAF NCA H ܟ ( EAG U
+CAJ [
+CAA v
+CAF NCA H ( EAG U
+CAJ [
+CAA w
+CAE NCA H t EAG U
+CAJ [
+CAA d
+CAH NCA H D EAG U
+CAJ [
+CAA d
+CAH NCA H EAG U
+CAJ [
+CAA d
+CAH NCA H X EAG U
+CAJ [
+CAA d
+CAH NCA H @ EAG U
+CAJ [
+CAA d
+CAH NCA H EAG U
+CAJ [
+CAA d
+CAH NCA H < EAG U
+CAJ [
+CAA d
+CAH NCA H < EAG U
+CAJ [
+CAA d
+CAH NCA Ԣ 2 8 FBA D(D0
+(I ABBI 8 $ @ FBA D(D0
+(I ABBI 8 ` FBA D(D0
+(I ABBI 8 H FBA D(D0
+(I ABBI L أ FBA A(G0
+(F ABBH d
+(A ABBF L ( < FBA A(G0
+(F ABBH d
+(A ABBF L x FBA A(G0
+(F ABBH d
+(A ABBF L Ȥ FBA A(G0
+(F ABBH d
+(A ABBF YO FC 8 4 p FBD D(D@n
+(A ABBH < p |Y KBB A(D0(I BBBP t
+ FBB B(I0A8D
+8D0A(B BBBG Yu s
+ P x s
+ FBB B(I0A8D
+8D0A(B BBBG Yu s
+ P }s
+ FBB B(I0A8D
+8D0A(B BBBG p Yu Js
+ P P P
+ :s
+ FBB B(I0A8D
+8D0A(B BBBG Yu s
+ D r
+ FBB D(A0GP
+0D(A BBBB < r
+ FFD A(G`
+(D ABBF < D l r
+ FFD A(G`
+(D ABBF P O or
+ FFB B(D0A8G`
+8D0A(B BBBG 4 | e FDA v
+DBI QAB L r
+ FBB B(D0A8Dp
+8D0A(B BBBB H P"F FBB B(D0A8D@m
+8D0A(B BBBA ( P T#A JDG aAAH ` | x# FBF E(A0A8G@
+8D0F(B BBBC [
+8F0A(B BBBF t 4$ YBB B(D0N8D@r8A0A(B BBBGH@
+8A0A(B BBBA ( X %A JDG aAAH %n P ,& p
+ FBB B(D0A8G
+8D0A(B BBBI H *b PBB B(A0A8DP
+8A0A(B BBBC 4 8 + EDD S
+AAF G
+AAG ` p d,4 FBB E(I0A8GP
+8D0A(B BBBE h
+8D0A(B BBBK 4 ԫ @. FDA T
+FBI tCB H .Z PBB B(A0A8DP
+8A0A(B BBBF P /z n
+ FFB B(A0A8J
+8A0A(B BBBA 8 M p
+ EC
DGH.pK. D
+ F , D g= p
+ FAL W
+DBA H T Y p
+ P gz p
+ FFB B(A0A8J
+8A0A(B BBBA 8 Ѕ q
+ EC
DGH.pK. D
+ F , = r
+ FAL W
+DBA
+Y r
+ P d |z xr
+ FFB B(A0A8J
+8A0A(B BBBA 8 s
+ EC
DGH.pK. D
+ F , \= vt
+ FAL W
+DBA Y Rt
+ P < Tz Bt
+ FFB B(A0A8J
+8A0A(B BBBA 8 ru
+ EC
DGH.pK. D
+ F , ̮ 4= @v
+ FAL W
+DBA Y v
+ , 0 ( u
+ FAD h
+ABJ L \ u
+ FBB B(A0A8Dpx
+8A0A(B BBBD L DO |u
+ FBB B(A0A8DP
+8A0A(B BBBH DC 0u
+ d, |y PAA W
+ABA D C t
+ d( z OAD W
+AAA 4 , Xe FDA v
+DBI QAB d 0 < `t
+ FAD h
+ABJ L 0t
+ FBB B(A0A8Dpx
+8A0A(B BBBD L X O s
+ FBB B(A0A8DP
+8A0A(B BBBH L 0 " s
+ FAD h
+ABJ L P# Ts
+ FBB B(A0A8Dpx
+8A0A(B BBBD L @ %O s
+ FBB B(A0A8DP
+8A0A(B BBBH 4 & 0 ) r
+ FAD h
+ABJ L ز X* xr
+ FBB B(A0A8Dpx
+8A0A(B BBBD L ( ,O ,r
+ FBB B(A0A8DP
+8A0A(B BBBH , x -_ q
+ Hd
+D u
+K k
+E H
+H ` L H.] FBB B(A0D8G@l
+8A0A(B BBBB K
+8A0A(B BBBK D/ OEE E(A0D8GP8A0A(B BBBHHPv
+8A0A(B BBBH
+8A0A(B BBBG P 1 p
+ PBB B(A0A8G
+8A0A(B BBBI P 5 p
+ FBB B(A0A8G
+8A0A(B BBBD P D 8 Xp
+ FBB B(A0A8G
+8A0A(B BBBD P < $p
+ FBB B(A0A8G
+8A0A(B BBBD P d@ o
+ FBB B(A0A8G
+8A0A(B BBBD P @ 0D o
+ PBB B(A0A8G
+8A0A(B BBBI P lG o
+ PBB B(A0A8G
+8A0A(B BBBI P J To
+ PBB B(A0A8G
+8A0A(B BBBI D < Ms o
+ FBB A(A0Gu
+0A(A BBBA , O_ n
+ Hd
+D u
+K k
+E H
+H , LP_ n
+ Hd
+D u
+K k
+E H
+H , |Q_ n
+ Hd
+D u
+K k
+E H
+H t RA UBB B(D0G8D@O8A0A(B BBBEH@~
+8A0A(B BBBH t S
m
+ BBE A(A0G`
+0A(A BBBE
+0A(A BBBJ D
+0A(A BBBD < `> m
+ FID D(L`
+(A ABBE 0 D e `m
+ FAA
+ABH L x 8gT 5m
+ FBB B(D0A8Dp
+8A0A(B BBBH P ȹ Hh l
+ FBB B(A0A8DE
+8A0A(B BBBG < i> l
+ FID D(L`
+(A ABBI 0 \ m vl
+ FAA
+ABH L 0oT Kl
+ FBB B(D0A8Dp
+8A0A(B BBBH P @p l
+ FBB B(A0A8DE
+8A0A(B BBBG < 4 q> k
+ FID D(L`
+(A ABBI 0 t u k
+ FAA
+ABH L (wT ak
+ FBB B(D0A8Dp
+8A0A(B BBBH P 8x k
+ FBB B(A0A8DE
+8A0A(B BBBG < L y> j
+ FID D(L`
+(A ABBI 0 } j
+ FAA
+ABH L T wj
+ FBB B(D0A8Dp
+8A0A(B BBBH P 0 0j
+ FBB B(A0A8DE
+8A0A(B BBBG H b PBB B(A0A8DP
+8A0A(B BBBC \ i
+ BBB A(D0GP
+0A(A BBBG R
+0A(A BBBF P @ Gi
+ FBB E(D0D8GB
+8A0A(B BBBF P d J /i
+ FBB B(A0A8G
+8A0A(B BBBA P h
+ PBB B(A0A8G
+8A0A(B BBBF P 䧏 h
+ FBB E(A0G8G
+8A0A(B BBBA P ` ďi i
+ FBB E(D0D8Jh
+8A0A(B BBBE H X ,ˏ FBB H(A0D8I
+8D0A(B BBBA P ̏ ni
+ FBB E(D0D8GB
+8A0A(B BBBF P T LҏJ Vi
+ FBB B(A0A8G
+8A0A(B BBBA P HՏ "i
+ PBB B(A0A8G
+8A0A(B BBBF P ؏ h
+ FBB E(A0G8G
+8A0A(B BBBA P P i i
+ FBB E(D0D8Jh
+8A0A(B BBBE H H FBB H(A0D8I
+8D0A(B BBBA P i
+ FBB E(D0D8GB
+8A0A(B BBBF P D J }i
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+8A0A(B BBBE H ( ,\ FBB H(A0D8I
+8D0A(B BBBA t g2 HW \ H
+H ]2 ]? F]U H ] EAG U
+CAJ [
+CAA v
+CAF NCA H ( \^ EAG U
+CAJ [
+CAA n
+CAF NCA H t ^ EAG U
+CAJ [
+CAA m
+CAG NCA H _ EAG U
+CAJ [
+CAA v
+CAF NCA H h_ EAG U
+CAJ [
+CAA v
+CAF NCA H X _ EAG U
+CAJ [
+CAA w
+CAE NCA H 0` EAG U
+CAJ [
+CAA v
+CAF NCA H ` EAG U
+CAJ [
+CAA n
+CAF NCA H < ` EAG U
+CAJ [
+CAA m
+CAG NCA H <a EAG U
+CAJ [
+CAA v
+CAF NCA H a EAG U
+CAJ [
+CAA v
+CAF NCA H b EAG U
+CAJ [
+CAA w
+CAE NCA H l hb EAG U
+CAJ [
+CAA v
+CAF NCA H b EAG U
+CAJ [
+CAA n
+CAF NCA H c EAG U
+CAJ [
+CAA m
+CAG NCA H P tc EAG U
+CAJ [
+CAA v
+CAF NCA H c EAG U
+CAJ [
+CAA v
+CAF NCA H <d EAG U
+CAJ [
+CAA w
+CAE NCA H 4 d EAG U
+CAJ [
+CAA v
+CAF NCA H e EAG U
+CAJ [
+CAA n
+CAF NCA H Xe EAG U
+CAJ [
+CAA m
+CAG NCA H e EAG U
+CAJ [
+CAA v
+CAF NCA H d f EAG U
+CAJ [
+CAA v
+CAF NCA H tf EAG U
+CAJ [
+CAA w
+CAE NCA H f EAG U
+CAJ [
+CAA v
+CAF NCA H H <g EAG U
+CAJ [
+CAA n
+CAF NCA H g EAG U
+CAJ [
+CAA m
+CAG NCA H g EAG U
+CAJ [
+CAA v
+CAF NCA H , Hh EAG U
+CAJ [
+CAA v
+CAF NCA H x h EAG U
+CAJ [
+CAA w
+CAE NCA H i EAG U
+CAJ [
+CAA v
+CAF NCA H ti EAG U
+CAJ [
+CAA n
+CAF NCA H \ i EAG U
+CAJ [
+CAA m
+CAG NCA H j EAG U
+CAJ [
+CAA v
+CAF NCA H j EAG U
+CAJ [
+CAA v
+CAF NCA H @ j EAG U
+CAJ [
+CAA w
+CAE NCA H Hk EAG U
+CAJ [
+CAA v
+CAF NCA H k EAG U
+CAJ [
+CAA n
+CAF NCA H $ l EAG U
+CAJ [
+CAA m
+CAG NCA H p Tl EAG U
+CAJ [
+CAA v
+CAF NCA H l EAG U
+CAJ [
+CAA v
+CAF NCA H m EAG U
+CAJ [
+CAA w
+CAE NCA H T m EAG U
+CAJ [
+CAA v
+CAF NCA H m EAG U
+CAJ [
+CAA n
+CAF NCA H 8n EAG U
+CAJ [
+CAA m
+CAG NCA H 8 n EAG U
+CAJ [
+CAA v
+CAF NCA H n EAG U
+CAJ [
+CAA v
+CAF NCA H To EAG U
+CAJ [
+CAA w
+CAE NCA H o EAG U
+CAJ [
+CAA d
+CAH NCA H h p EAG U
+CAJ [
+CAA d
+CAH NCA H `p EAG U
+CAJ [
+CAA d
+CAH NCA H p EAG U
+CAJ [
+CAA d
+CAH NCA H L q EAG U
+CAJ [
+CAA d
+CAH NCA H \q EAG U
+CAJ [
+CAA d
+CAH NCA H q EAG U
+CAJ [
+CAA d
+CAH NCA H 0 r EAG U
+CAJ [
+CAA d
+CAH NCA H | Xr EAG U
+CAJ [
+CAA d
+CAH NCA H r EAG U
+CAJ [
+CAA d
+CAH NCA H s EAG U
+CAJ [
+CAA d
+CAH NCA H ` Ts EAG U
+CAJ [
+CAA d
+CAH NCA H s EAG U
+CAJ [
+CAA d
+CAH NCA H s EAG U
+CAJ [
+CAA d
+CAH NCA H D Pt EAG U
+CAJ [
+CAA d
+CAH NCA H t EAG U
+CAJ [
+CAA d
+CAH NCA ( tz KAD [
+AAA , Lu MAH |
+IBK , 8 u MAH |
+IBK 4 h lv MAE s
+FBJ QAB 4 v MAE s
+FBJ QAB 4 \w% aU
+ AA^ < T lw EU
+ FBA A(D@J
+(A ABBB < yt U
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+(A ABBB < <|t T
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+(A ABBB < |~ T
+ FBA A(D@
+(A ABBB < T 쀐 UT
+ FBA A(D@J
+(A ABBB < | T
+ FBA A(D@
+(A ABBB < 셐 S
+ FBA A(D@J
+(A ABBB < | S
+ FBA A(D@J
+(A ABBB T s eS
+ T p ps MS
+ T ԋs 5S
+ T 8s S
+ T < h XX( KBB A(D0(I BBBP \ R
+ FBB B(I0A8D
+8D0A(B BBBB XX R
+ P p vR
+ FBB B(I0A8D
+8D0A(B BBBB ( XX 7R
+ D Ĕ 'R
+ BBB B(A0D
+0A(B BBBA ( zPLR x!S 0 X Q
+ D h Q
+ BBB B(A0D
+0A(B BBBA 0X sQ
+ D h sQ
+ BBB B(A0D
+0A(B BBBA X /Q
+ D ( ؕ /Q
+ BBB B(A0D
+0A(B BBBA P HX P
+ 4 8 P
+ EC
BIH.p
+ H , = Q
+ FAL W
+DBA I X Q
+ 4 qQ
+ EC
BIH.p
+ H , @ = +R
+ FAL W
+DBA DJ IX R
+ L e Q
+ FBB B(D0A8D`
+8A0A(B BBBD L 8 Q
+ FBB B(D0A8D`
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+ FEB B(A0A8D`
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+ FEB B(A0A8D`
+8A0A(B BBBF 0 ȿ P
+ FAD h
+ABJ L t P
+ FBB B(A0A8Dpx
+8A0A(B BBBD 0 L 4 KP
+ FAD h
+ABJ L P
+ FBB B(A0A8Dpx
+8A0A(B BBBD < Đ O
+ FAD u
+ABE S
+ABB L Ő_ O
+ FBB B(A0A8Dp
+8A0A(B BBBB < ` ǐ GO
+ FAD u
+ABE S
+ABB L pȐ_ O
+ FBB B(A0A8Dp
+8A0A(B BBBB L ʐ N
+ FBB B(A0A8DP
+8A0A(B BBBC L @ ː sN
+ FBB B(A0A8DP
+8A0A(B BBBC L ͐O 'N
+ FBB B(A0A8DP
+8A0A(B BBBH L ΐO M
+ FBB B(A0A8DP
+8A0A(B BBBH L 0 ϐr M
+ FBB B(A0A8Dp
+8A0A(B BBBG L 0ѐ CM
+ FBB B(A0A8Dp
+8A0A(B BBBF L Ӑ L
+ FBB B(A0A8Dp(
+8A0A(B BBBD L P L
+ FBB B(A0A8Dp(
+8A0A(B BBBD , ِ PAA
+ABA ِS /L
+ tL (ڐe L
+ FBB B(D0A8D`
+8A0A(B BBBD L Hې K
+ FBB B(D0A8D`
+8A0A(B BBBB L X ܐ K
+ FEB B(A0A8D`
+8A0A(B BBBF L xݐ 7K
+ FEB B(A0A8D`
+8A0A(B BBBF 0 ސ J
+ FAD h
+ABJ L , ߐ J
+ FBB B(A0A8Dpx
+8A0A(B BBBD 0 | D oJ
+ FAD h
+ABJ L ?J
+ FBB B(A0A8Dpx
+8A0A(B BBBD < I
+ FAD u
+ABE S
+ABB L @ _ I
+ FBB B(A0A8Dp
+8A0A(B BBBB < kI
+ FAD u
+ABE S
+ABB L _ /I
+ FBB B(A0A8Dp
+8A0A(B BBBB L H
+ FBB B(A0A8DP
+8A0A(B BBBC L p H
+ FBB B(A0A8DP
+8A0A(B BBBC L O KH
+ FBB B(A0A8DP
+8A0A(B BBBH L O G
+ FBB B(A0A8DP
+8A0A(B BBBH L ` r G
+ FBB B(A0A8Dp
+8A0A(B BBBG L @ gG
+ FBB B(A0A8Dp
+8A0A(B BBBF L G
+ FBB B(A0A8Dp(
+8A0A(B BBBD L P ` F
+ FBB B(A0A8Dp(
+8A0A(B BBBD L F
+ PBB B(A0A8Dp
+8A0A(B BBBK P ` <F
+ FBB B(A0A8G
+8A0A(B BBBD P D , F
+ FBB B(A0A8G
+8A0A(B BBBD P E
+ FBB B(A0A8G
+8A0A(B BBBD P E
+ FBB B(A0A8G
+8A0A(B BBBD P @ lE
+ FBB B(A0A8D>
+8A0A(B BBBF P \ !E
+ FBB B(A0A8D>
+8A0A(B BBBF P ( D
+ FBB B(A0A8D>
+8A0A(B BBBF P < D
+ FBB B(A0A8D>
+8A0A(B BBBF P @D
+ PBB B(A0A8G
+8A0A(B BBBI P D
+ PBB B(A0A8G
+8A0A(B BBBI P 8 8 C
+ PBB B(A0A8G
+8A0A(B BBBI P t C
+ PBB B(A0A8G
+8A0A(B BBBI L ! pC
+ PBB B(A0A8Dp
+8A0A(B BBBK L 0 $ )C
+ PBB B(A0A8Dp
+8A0A(B BBBK L P& B
+ PBB B(A0A8Dp
+8A0A(B BBBK \ ( B
+ BBE A(A0G@
+0A(A BBBD R
+0A(A BBBF P 0 `> IB
+ FEB B(A0A8D
+8A0A(B BBBF P ,B B
+ FGE E(D0H8G
+8A0A(B BBBH P S B
+ FEB B(A0A8D
+8A0A(B BBBF P , dW NB
+ FGE E(D0H8G
+8A0A(B BBBH ( $ hh NAIXH j B
+ BHB B(A0D
+0A(B BBBA X LB
+ P `o LB
+ FEG E(D0H8G
+8A0A(B BBBH P d B
+ FEG E(D0H8G
+8A0A(B BBBH ( \ ؙh NAIXH C
+ BHB B(A0D
+0A(B BBBA X B
+ P H h B
+ FEG E(D0H8G
+8A0A(B BBBH P ZC
+ FEG E(D0H8G
+8A0A(B BBBH P ʑ C
+ PBB B(A0A8G
+8A0A(B BBBF P D lϑ C
+ PBB B(A0A8G
+8A0A(B BBBF P ӑ C
+ FBB B(A0A8D
+8A0A(B BBBJ P Tؑ ]C
+ FBB B(A0A8D
+8A0A(B BBBJ P @ ܑn )C
+ FEE E(D0D8K.
+8A0A(B BBBH H C
+ FBB G(D0I(
+0D(A BBBG P `n fC
+ FEE E(D0D8K.
+8A0A(B BBBH H 4 | C
+ FBB G(D0I(
+0D(A BBBG P
+ C
+ PBB B(A0A8Gy
+8A0A(B BBBF P | C
+ PBB B(A0A8Gy
+8A0A(B BBBF P ( [C
+ PBB B(A0A8Gy
+8A0A(B BBBF P | T# 7C
+ PBB B(A0A8Gy
+8A0A(B BBBF P + C
+ FBB B(A0A8Gr
+8A0A(B BBBG P $ L4 B
+ FBB B(A0A8Gr
+8A0A(B BBBG P x < B
+ FBB B(A0A8Gr
+8A0A(B BBBG P dE B
+ FBB B(A0A8Gr
+8A0A(B BBBG P M B
+ PBB B(A0A8G
+8A0A(B BBBF P t T B
+ PBB B(A0A8G
+8A0A(B BBBF P [ B
+ FBB B(A0A8Gp
+8A0A(B BBBA P a jB
+ FBB B(A0A8Gp
+8A0A(B BBBA g2 HW , X' 8 @ g FBD A(D0
+(I ABBH L | h FBA A(G0
+(F ABBI D
+(A ABBF XO FC < X KBB A(D0(I BBBP ,i 9A
+ FBB B(I0A8Dp
+8D0A(B BBBI u Xu A
+ t `o YLB B(D0D8Dp}8A0A(B BBBDHp)
+8A0A(B BBBA t qo YLB B(A0D8G`{8A0A(B BBBFH`
+8A0A(B BBBA p s FFE D(D0L
+(A BBBD
+(A BBBI A
+(D DBBC A
+(C BBBF x vt FBB B(A0A8DP
+8F0A(B BBBI D
+8F0A(B BBBE L8F0A(B BBB h wh ?
+ FBB J(D0A8Gp
+8A0A(E BBBC
+8D0A(B BBBG P < $} >
+ FBB B(A0D8N
+8A0A(B BBBB $ X >
+ A , = >
+ FAL W
+DBA j GX >
+ L = >
+ FBB E(G0A8L
+8D0A(B BBBF X >
+ g2 HW $ @j LQ
+K A D 2 X p L_ ( t X BAD AB < X KBB A(D0(I BBBP < w >
+ FBB B(I0A8DA
+8D0A(B BBBH XX \>
+ P w L>
+ FBB B(I0A8DH
+8D0A(B BBBA ` XX
>
+ P ̡s =
+ FBB B(D0A8N
+8A0A(B BBBC , h = S>
+ FAL W
+DBA lm X />
+ , = >
+ FAL W
+DBA m yX =
+ L 誒} =
+ FHB B(F0D8G
+8D0A(B BBBE &X
>
+ @g2 HW Ȓ) 0 ɒ7 Lf L (ɒ) ` Dɒ7 Lf | hɒ2 ( X BAD AB bXO FC < X KBB A(D0(I BBBP t ɒg >
+ FBB B(I0A8D.
+8D0A(B BBBK , XX >
+ P ϒg >
+ FBB B(I0A8D.
+8D0A(B BBBK XX Q>
+ P L Ւ A>
+ FBB B(I0A8DN
+8D0A(B BBBK XX >
+ P 8ے =
+ FBB B(I0A8DN
+8D0A(B BBBK p rXX =
+ P $ \ =
+ FBB B(I0A8DU
+8D0A(B BBBD ^XX d=
+ P T=
+ FBB B(I0A8DU
+8D0A(B BBBD H JXX =
+ P s =
+ FBB B(D0A8G
+8D0A(B BBBD P P <
+ FBB B(A0D8N{
+8A0A(B BBBD , = <
+ FAL W
+DBA r X <
+ , = <
+ FAL W
+DBA r X <
+ , 4 |= x<
+ FAL W
+DBA 8s LX T<
+ P | t D<
+ FBB B(A0D8N{
+8A0A(B BBBD , 0= V<
+ FAL W
+DBA s X 2<
+ ,
+ (= "<
+ FAL W
+DBA t X ;
+ , `
+ = ;
+ FAL W
+DBA dt _X ;
+ P
+ + ;
+ FBB E(A0A8G
+
+8D0A(B BBBC `
+ && =
+ FBB B(A0D8G9XXA]
+8A0A(B BBBG ` ` L~$ >
+ FBB B(A0D8G9XXA]
+8A0A(B BBBG h g2 HW p2 ( X BAD AB p L
+H q Lv
+N A
+G
XO FC < $
X KBB A(D0(I BBBP qW ?
+ FBB B(I0A8D'
+8D0A(B BBBB x XX @?
+ P ,
wg 0?
+ FBB B(I0A8D.
+8D0A(B BBBK XX >
+ P
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+ FJB L(A0A8J
+8A0A(B BBBI 4
?
+ FAI D0m
+ DABA X1 >
+ 4 < T >
+ FAI D0m
+ DABA kX1 >
+ P
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+ FEB H(A0D8GU
+8D0A(B BBBE P >
+ FEE B(A0A8G
+8A0A(B BBBC P 4 , H?
+ FBB B(D0D8JG
+8D0A(B BBBF X 蹓 ?
+ EC
BJEK
+ I R. r. .PS. .. .0z. l. $ ,X @
+ A g2 HW (ԓ2 ( X BAD AB (ԓ L
+K , ԓ Ls
+I A
+G P VXO FC < l X KBB A(D0(I BBBP Փ S@
+ FBB B(I0A8D[
+8D0A(B BBBF XX @
+ 4 t <ۓ @
+ FAI D0d
+ DABA X1 ?
+ H ۓI ?
+ EC
DH
+ D . y. .`. .@. xX: g@
+ 8~g2 HW Y' ( Y BAD AB 0 A
+ MAD
+IBG < A
+ MAA
+FBE I
+ABD YO FC , ? 'A
+ aAD0
+AAG 0 EH
K A
-A $ , PG r A<
-E , s@ AAG04AA < H, lr BHD D
- DABA T Ĉ 8Z Sr BEB B(H0A8G H
-8A0A(B BBBC , @> 3s AID W
-DAA $ L P7 s A
-O A
-A < t hH r BBA A(Gp
-(D ABBA T x r BEB B(A0D8G+y
-8D0A(B BBBC T κ s BBG B(A0A8J)
-8A0A(B BBBB T d h~ t BBB G(A0D8M
-8D0A(B BBBA t s. K^ , x0 AC
FK
- E $ x~ Al
-C V
-J < ADG v
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-AAF $ S L < h+ ULB A(A0@(A BBBD0 L ԋ H cs BBE D(A0G,
-0D(A BBBD D $ u (s BFB A(A0GP
-0A(A BBBD d l P
-9 <s BBB B(A0A8G@s
-8A0A(B BBBB _
-8A0A(B BBBG T Ԍ ( r BBB B(A0A8J
-8A0A(B BBBK T , p r BBB B(A0D8G~
-8A0A(B BBBA < s. K^ L T 1 BBB B(A0D8D
-8A0A(B BBBD L `3 AAG e
-CAF S
-ACA R
-CAJ JCA ns\ AG RA4 \ 3 q BHE l
-ABB 4 H4 q BHE o
-ABO L ̎ 4 hq BKB B(D0D8G
-8D0A(B BBBA L 6^ Iq BEL B(A0A8G
-8A0A(B BBBH 4 l 9 /q BAI
-ABA T H: q BBE B(D0A8G
-8D0A(B BBBG T ? p BBE B(D0A8G
-8D0A(B BBBH T T C p BBE B(D0A8G
-8D0A(B BBBH T H p BBE B(D0A8G
-8D0A(B BBBG T hM
p BBB E(A0A8G
-8D0A(B BBBD , s@ AAG04AA 4 ZU q BHD D`
- DABH L đ ^ p BBB B(D0A8GT
-8D0A(B BBBK T b# p BBB B(G0F8J
-8D0A(B BBBJ $ Ps. K^ < Hu T @u l 8u @u , Hu[ _q AID t
-DAA L ̓ xu AAG ]
-CAF k
-ACA R
-CAJ JCA L u AAG ]
-CAF k
-ACA R
-CAJ JCA L l u AAG ]
-CAF k
-ACA R
-CAJ JCA Bs\ AG RAL ܔ v BBB B(A0A8D`V
-8A0A(B BBBB \ , xy BEB D(A0C
-(A BBBH n
-(A BBBK Q(A BBB, (| BAA z
-ABG \ |X BBD A(G0
-(A ABBB q
-(A ABBI d(G ABB , BAA z
-ABG , L s@ AAG04AA 4 ĕ U n BHD D`
- DABH L sn BBB H(A0D8JK
-8D0A(B BBBK L L u co BBH B(A0D8JK
-8D0A(B BBBK T Ps. K^ T تu Gp BBB G(A0G8G
-8D0A(B BBBD ė s. K^ ܗ 軻 L 軻 AAG ]
-CAF k
-ACA R
-CAJ JCA L \ ( BBB B(A0A8DP
-8A0A(B BBBH , s@ AAG04AA 4 $ XU o BHD D`
- DABH L \ »h o BBB B(D0A8R
-8A0A(B BBBK , Ż> o AID W
-DAA L ܘ Ż1 o BEB B(A0D8G S
-8D0A(B BBBA s. K^ T D H s BEE E(D0A8G
-8D0A(B BBBE T p r BEE E(D0A8G
-8D0A(B BBBE T 90 r BBB B(D0A8OJ
-8D0A(B BBBE 0s. K^ , d h1[ u AID t
-DAA , L s@ AAG04AA 4 Ě h1U gu BHD D`
- DABH s. K^ ̛ x4 p4 h4 p4 , , s@ AAG04AA 4 H4U t BHD D`
- DABH 4 ܛ p7U t BHD D`
- DABH L : et BBB J(A0A8G
-8D0A(B BBBA L d = bt BBB J(A0A8G
-8D0A(B BBBA T 8A _t BBE B(D0A8G
-8D0A(B BBBB T F Et BBE B(D0A8G
-8D0A(B BBBB T d J| +t BBE E(D0A8G
-8D0A(B BBBF t O" Qr pR O_ L 0S s BBH B(A0D8G
-8D0A(B BBBE $ d TAA L xe wt BBH B(A0D8G
-8D0A(B BBBJ s. K^ u AQ ğ s\ AG RA$ u AG
-AA $ hv AG
-AA , 4 s@ AAG04AA 4 vU Wt BHD D`
- DABH T z 9t BBB B(A0I8G
-8D0A(B BBBK s. K^ $ ^ AG b
-AA L | s BBE B(A0D8G
-8D0A(B BBBF ps. K^ , o ADG`
-AAD ̡ H PY l D Ws BBB B(D0A8G
-8A0A(B BBBD Z
-8A0A(B BBBD l s. K^ ̢ L o BEE B(A0A8J
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-(A BBBH L 0\ ! BEB E(I0H8D@
-8D0A(B BBBA L , ] W! BEB E(I0H8D@
-8D0A(B BBBA T | 0_ /! BBB B(A0A8G
-8A0A(B BBBA D cb BBB A(A0GF
-0A(A BBBA T d BBE B(H0A8G$
-8A0A(B BBBG T t xhD BBB B(D0H8G
-8A0A(B BBBG Pls. K^ L h"sw BBB E(A0A8DP[8A0A(B BBB L } BBB B(A0A8DT
-8A0A(B BBBA L < h BBB B(A0A8DI
-8A0A(B BBBG T 膿M
- c BBB B(A0A8J
-8A0A(B BBBJ #s\ AG RAT L | BBE E(D0A8G
-8D0A(B BBBE T 蕿% BBA A(D^
-(A ABBB P
-(A ABBA T 4 BBB B(D0D8G
-8A0A(B BBBK D T BFE D(A0G
-0D(A BBBH D T GBA A(G@X(A ABBH@ X C BIB E(D0A8F`w
-8A0A(B BBBB
-8A0A(B BBBA V
-8A0A(B BBBA T l У BEG E(A0A8J
-8D0A(B BBBA | hs. K^ , @F BDD p
-DBA , `5 BAD fAB pY Af
-I 4 \ BHE Y
-ABE 4 8 g BHE \
-ABJ $ ^ AG b
-AA < , s BBA D(D@z(A ABB d 4 BBB B(A0A8DP
-8A0A(B BBBH D
-8A0A(B BBBA T k BBE B(D0A8G
-8D0A(B BBBF L H Q BBE B(A0A8D`
-8A0A(B BBBA l D! - BLB B(A0A8Dc
-8A0A(B BBBK 6
-8A0A(B BBBA T ! h BBB B(A0D8N
-8A0A(B BBBI , " > AID W
-DAA , <" 0> AID W
-DAA , l" @> ` AID W
-DAA , T# PI AAG0
-AAA , " pi ADG U
-AAA < # BBG A(G@
-(A ABBG < # p BBG A(G@
-(A ABBG L |# 0 d BFE E(E0A8G`
-8D0A(B BBBH T # ph ) BBE B(A0A8D
-8A0A(B BBBA < $ ¿ BBG A(G@
-(A ABBF < % Hÿ BBG A(G@
-(A ABBG T $ ĿV Y BIH B(D0D8G
-8D0A(B BBBG % bs. K^ d % ƿ BLB B(A0A8De
-8A0A(B BBBI
-8A0A(B BBBH T |% ˿ BLB B(A0A8J{
-8A0A(B BBBE d % 8 G BEB E(A0D8D
-8D0A(B BBBK P
-8A0A(B BBBA L <& BBB F(A0A8G S
-8A0A(B BBBF L & ` BEB B(A0D8G
-8A0A(B BBBG T & 0 BBB B(D0D8GH
-8D0A(B BBBD T 4' BHB B(A0A8GF
-8A0A(B BBBA D( AO , ' z AAG@s
-DAA ( AS , ' ; AAG@w
-DAA ( p AS , L( p AAG@w
-DAA < 4) BEB D(A0M
-(A BBBF t) ` L ) X BBB B(A0A8Dh
-8A0A(B BBBA < ) BBG A(G@
-(A ABBF * X
-` D {
-A <*
-` D {
-A d \*
-E BBB B(A0A8DP
-8A0A(B BBBE \
-8F0A(B BBBA l * s O BBB J(D0D8D
-8D0A(B BBBI |
-8A0A(B BBBJ T |* < BBE B(A0A8GN
-8A0A(B BBBD 4 * BDD DPv
- AABA L + 0 BBB B(D0A8D
-8D0A(B BBBE , 4 t+ 2 BDD DPx
- AABA < + BED D(D`y
-(A ABBA , [s. K^ D , X BBE A(A0D@
-0A(A BBBA , - bs@ AAG04AA d 4- BFB B(A0A8DP
-8A0A(B BBEK
-8D0A(B BBBH d - 8 BFB B(A0A8DP
-8A0A(B BBEK
-8D0A(B BBBH L L- BBB E(I0D8DP^
-8D0A(B BBBA T - O BBE B(D0A8D
-8D0A(B BBBK L - h L 2 BBE D(D0G
-0D(A BBBA L D. h! BEB E(I0H8DP
-8D0A(B BBBD d . (#{ BBB J(A0D8G`
-8F0A(B BBBF B
-8F0A(B BBBA T . @$3 BBE B(A0A8G
-8D0A(B BBBA T T/ (4k BLE B(A0A8NH
-8A0A(B BBBI d0 @D AR L / @D] BEE I(D0A8JP
-8D0A(B BBBG 0 Ws. K^ L 0 8E BEB H(A0D8G]
-8D0A(B BBBA 4 <1 xE BAD D0~
- AABC 4 t1 F} AAG R
-GAM D
-AAA L 0 XF BJB A(A0W
-(A BBDH ^(A BBBT D1 F BEB B(A0A8GI
-8A0A(B BBBI T2 X AQ T 1 X BFE E(E0D8G
-8C0A(B BBBC 4 2 8_ BAD D0~
- AABC L L2 _ G BEB E(I0H8D@
-8D0A(B BBBD d 2 Pa BBB B(I0A8G`
-8F0A(B BBBF r
-8F0A(B BBBG T 3 b BBB B(A0A8G
-8A0A(B BBBG 4 Ts. K^ < t3 8k BBA A(DV
-(A ABBA l4 Ts. K^ 4 k Fq
-A 4 n 4 n L 4 n AAG ]
+CAA H
+CAD NCAH D2 EAG Y
CAF [
-ACA R
-CAJ JCA L $5 n AAG ]
-CAF k
-ACA R
-CAJ JCA L t5 o AAG ]
+CAA [
+CAA NCAH 2 EAG Y
CAF [
-ACA R
-CAJ JCA L 5 Po AAG ]
-CAF k
-ACA R
-CAJ JCA 6
s\ AG RAL |5 po BBB B(A0A8Ji
-8A0A(B BBBI L 5 q BBB B(A0A8Ji
-8A0A(B BBBI T 6 t=
- BBB B(D0A8GS
-8A0A(B BBBG < t6 } } BBD D(G0g
-(A ABBA T 6 h~=
- K BBB B(D0A8GS
-8A0A(B BBBG 4 7 P A BDA GPv
- AABA 7 (Qs. K^ 8 Ј < ,8 ؈\ BBD D(D@`
-(D ABBA l8 7 ~ D M
-A $ 7 P2 D]
-O A 8 Ps. K^ T ,8 P K BBA A(Di
-(A ABBG e
-(A ABBE <9 4 T9 R BAD D0`
- DABA 8 ~ D M
-A $ 8 x* D\
-H A 9 Os. K^ D 49 h s BAA D`R
- AABB U
- AABG 4: * L: & d: & |: 8& $ : Pv AG z
-AA : : : ; ; 4; " DN
-N A \ T; Ћ BBA A(G@P
-(C ABBK \
-(D ABBK
-(A ABBE : \ BBB E(A0A8DJ
-8D0A(B BBBH e
-8A0A(B BBBA q
-8D0A(B BBBB n
-8C0A(B BBBF T ; t S BBE B(D0A8G
-8D0A(B BBBH T ; f 9 BBD D(G
-(A ABBK
-(C ABBF = Ls. K^ = s\ AG RA< <= ț BBD D(I@
-(D ABBF |= H = @$ A]
-B C 4 = PY ACG
-AAD DCA = x 4 > pa ACG E
-AAF DCA <> 4 T> Y ACG B
-AAA DCA > ȝ DT 4 > НS AAF h
-CAD RCA 4 > [ AAF n
-CAF RCA 4 ? [ AAF k
-CAI RCA T > H
-
- BBA A(Iu
-(C ABBD E
-(A ABBA < > W BBA A(J
-(A ABBA < ,? R BDA A(G
-(A ABBH < l? С BED A(G`
-(A ABBC L ? y BDB E(D0C8Dp
-8A0A(B BBBD D ? @ = BBB A(D0G
-0A(A BBBA T D@ P BEE E(D0A8G
-8D0A(B BBBE < @ @S 6 BBA A(Gx
-(A ABBA A PHs. K^ A H2 A p6 G n A AQ A B ,B DB A^ D A & G BLB A(A0GQ
-0A(A BBBF T A hk $ BBB B(A0A8G
-8A0A(B BBBA C Gs. K^ C h 4C ` L B h BJB A(A0b
-(A BBDE _(A BBBD B ȱ e BJD G@o
- DABC ]
- DABD D ,C 0 BJD G@l
- DABF ]
- DABD D tC x{ BJD G@s
- DABG @
- DABA T C BEE E(D0A8G6
-8D0A(B BBBH T D Xq BHD D(G`
-(D ABBI `
-(D ABBG T lD q | BHD D(G`
-(D ABBI `
-(D ABBG T D B BEE E(D0A8G6
-8D0A(B BBBH T E P BHE A(D0D@|
-0D(A BBBA m0D(A BBB T ,F BHE A(D0D@y
-0D(A BBBD m0D(A BBB L E ` x BHE B(A0D8D`
-8D0A(B BBBA L F _ B BHB B(D0D8G
-8D0A(B BBBE $G Cs. K^ D F ` BJD JT
- DABK `
- DABI D F ` BJD JT
- DABK `
- DABI $ G 8V ADD JAA< G p9 BLA F(G@
-(A ABBF d 4H p BBB J(A0D8GP
-8F0A(B BBBG
-8D0A(B BBGG T G BIB E(A0A8D/
-8A0A(B BBBA < H BFB A(A0S
-(A EBBG L |H X BBB D(C0y
-(A MBBH k(A EBBT H P BEB E(A0C8J
-8D0A(B BBBE < I 1 BLG A(G@
-(A ABBH L dI BEB E(I0H8D@
-8D0A(B BBBA L I 8 BLB B(D0A8Dp8
-8A0A(B BBBA l J o BBE B(A0A8DJ
-8A0A(B BBBA T
-8A0A(B BBBA ,K H?s. K^ T J `) BBB B(D0D8J
-8A0A(B BBBC T J 8) < BBB B(D0D8J
-8A0A(B BBBC T <K ( g BBB A(A0U
-(A BBBD h
-(H BBBJ T K ( C BBB A(A0U
-(A BBBD h
-(H BBBJ T K BBE B(D0A8G
-8D0A(B BBBG T DL BBE E(D0A8G
-8D0A(B BBBK T L BEE E(D0A8G
-8D0A(B BBBE D L BBB A(D0J
-0A(A BBBD D <M P BBB A(D0J
-0A(A BBBD <N h<s. K^ TN lN N N N N x N p N h O ` ,O X DO P \O H tO @ O 8 O 0 O ( AT O ( O P $P <P TP lP P P P P P P Q ,Q
DQ \Q tQ Q Q Q Q
Q
R R 4R LR
dR |R R R R R R S
- $S <S TS lS S S S S
S
S x T
,T x DT p \T x tT p T h T ` T X T P T H U @ U 8 4U 0 LU ( dU
- |U U U U U U V $V <V TV lV V V V V V V W
- ,W DW \W x tW p W h W ` W X W P W H X @ X 8 4X 09 Hp TX P lX X X ` X h X p X x X X Y @ Hs $ 4Y G AIF qAD $ \Y N ACL xDA , X < AAL hDA , X ? AKL aDA $ Y 9 AAL eDA Z 8/ Am ,Z H9 \ \ DZ p. T Y \Z . T Y tZ Z
Z Z Z AQ Z AO $ [ - ADG [DA <[ AL \[ x t[ p [ h [ p [ x [ AT [ AU \ AU 4\ AU T\ AU t\ \ x AO \ x \ p AO $ \ pS AD W
-AC qA ] " DQ
-K A ,] D] 6 Ad
-K E d] |] m A
-H d ] m A
-H d ] hm A~
-A l ] m A
-H d ] ^ ,^ D^ \^ AO |^ $ ^ , ADG ]AA ^ ^ AX ^ _ $_ <_ T_ $ l_ , ADG ZDA 4 ^ s BAA S
-ABA _ , ,_ H AAG0yDA , \_ H AHG0rDA , _ @H s AAG0yDA , _ `H G AHG0rDA ` ` x ` p ` h a ` a X. AJ0^D <a h Ta `- la x a p a h ` ` a X a P a H b @ ,b 8 Db 0 \b ( tb b b b b
- b Lb AO $c
<c AU \c AU |c b 3 DR c c AT c A
-E Q d X5 As , tc x@ BDA b
-ABA , c > AAL W
-DAA , c > _ AAL W
-DAA $ d Z AIG CAD< ,d BFD D(DPV
-(A ABBA , ld P AAL
-AAA Te m A~
-A l < d 0
- BBA I(D0~
-(A DBBA T d
- } BDA B
-ADB E
-ADF Q
-ABD I
-ABA D Te H 7 BBB D(A0D@
-0A(A BBBA $ Tf
^ AG b
-AA L e 8
BJE B(D0D8D`
-8D0A(B BBBE f / f / $ f AD0A L lf 0 [ BGB B(A0A8JM
-8A0A(B BBBH D f p| BBB A(A0GP
-0A(A BBBI D g d BBB A(I0D@
-0A(D BBBA 4 Lg ` . SGB A(A0D 4 g ( SGB A(A0D L g " BBB B(A0D8GP
-8D0A(B BBBG 4 h Q ADN \
-DAD SDA 4 h Q ADN \
-DAD SDA L |h BBB B(D0J8G
-8A0A(B BBBA 4 i J BDA o
-ABG A
-ABA , i [ ADD0Z
-AAK , 4i 9 ADD0Z
-AAK , di h! ADD0Z
-AAK , i " ADD0Z
-AAK $ |j " AAG0AA$ j r AAG0AA, j 8# BAD D0 AABD Dj # S BBD D(G-
-(D ABBG d Dk &/ BBE E(E0A8D@P
-8C0A(B BBBA
-8D0A(B BBGA k '% G ] k '% G ] T $k '_ BJB E(D0A8G
-8D0A(B BBBA 4l )" DU
-G A d k ) @ BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB l H* , l @*" TGA A(G , Ll @+" TGA A(G L 4m @,O BEB B(A0A8D`
-8A0A(B BBBE , l @.` R AAG@Z
-AAK , l p/` & AAG@Z
-AAK < ,m 0 BLD D(G0
-(A ABBE \ $n `1 BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB\ n 1 BBE B(A0D8D@r
-8D0A(B BBBE D8C0A(B BBB, ,n 2X ADG y
-EAA 4 \n 02t BDD Z
-ABI Lo s- A D n X2 BBB D(A0G@C
-0A(A BBBC L n 3 BBB E(D0D8GP
-8A0A(B BBBB p 3 DR p 3* DY
-K A <p 3 D Tp 3 BBE A(A0D@
-0A(A BBBA D p H5 BBB A(A0D@
-0A(A BBBA < ,p 6+ o BBA A(G`
-(A ABBH , $q 8 BAA G0 AAB< p @9 ' BBA A(L@Y
-(D ABBA 4 q 9 BBA A(G@(A ABB4 q h:D BEA A(D0p(A ABB 4 Lq :o BGA GC
- DABA D <r :4 BBB A(D0D@
-0A(A BBBA 4 q ;o BGA GC
- DABA T r ; BEH D(G`@
-(A ABBH
-(A ABBA $ \r =p YX
-G `
-A <s h= Ts s DP $ r H= g AL r
-AA s = s = , s =1 ' AAG cCA s = t = $t =
- <t = Tt = L s = BBB E(D0A8GPx
-8D0A(B BBBD L t (> 7 BEB E(I0H8D@
-8D0A(B BBBD L Tt ? BJE B(A0A8D@8A0A(B BBB T t X@e BEE E(A0G8G
-8D0A(B BBBG L t pK BEB E(I0H8DP
-8D0A(B BBBD d Lu M, BGE E(A0A8D@
-8F0A(B BBBI g
-8F0A(B BBBA L u M" g BBB E(A0A8Jy
-8D0A(B BBBC v hs. K^ v P v P w P AV $w PA Dz <w P Tw P- AG cA L tw P AAG ]
+CAA y
+CAC NCAH 2 EAG Y
CAF [
-ACA R
-CAJ JCA L w P AAG ]
+CAA H
+CAD NCAH (3 , EAG Y
CAF [
-ACA R
-CAJ JCA L x Q AAG ]
+CAA [
+CAA NCAH t3 EAG Y
CAF [
-ACA R
-CAJ JCA dx PQS |x QS L w Qr [ BBB B(E0A8J@
-8A0A(B BBBA < ,x Sw BLD A(G@|
-(A ABBA , $y PSD AKD i
-DAA Ty pSY Af
-I ty SY Af
-I , y S ADD \
-AAI L y PT BBB B(A0C8Gp
-8A0A(B BBBF < z V BEA H(G0
-(A ABBE Tz W lz W z W z W 4 z W[ BDA J
-ABD AB z W { W < dz W BVA A(G`
-(D ABBA 4 z XX PGB A(A0D < z 0YD BGA A(D
-(A ABBA T { @Z BEE E(D0D8G
-8D0A(B BBBH < ,| x[ BEB D(A0
-(A EBBD D { ]o BEE D(D0G
-0D(A BBBB L | 0^ BOB A(A0]
-(A BBBG Y
-(A BBBH L L| ^ BHI E(D0D8KP|
-8D0A(B BBBD < T} P_ BDA A(G0
-(A ABBF L } ` BFB B(A0A8D@f
-8D0A(B BBBK 4 ,} p` BKD G0|
- DABA d d} ` y BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB < ~ `a BBG A(G@
-(A ABBF ~ @b* A^
-I A 4 ,~ Pb0 BAD
-ABA D d~ Hc BBB D(A0D@;
-0A(A BBBA < d d BBG A(G@
-(A ABBG < pe BBG A(G@
-(A ABBG , , 0fi ADG U
-AAA d pf BBB B(D0A8DP
-8A0A(B BBBC
-8A0A(B BBBH D | k BCD D0
- FABE D
- FABA T l9 BEE E(A0D8I
-8D0A(B BBBG o AV 4 o BFD y
-ABH , t `ph BDA ]AB p" DN
-N A $ ā pB AAG0vAA L 4 pP BEB E(I0H8DP
-8D0A(B BBBA < < r BED A(D0U
-(A ABBE < | Hs BBG A(G@
-(A ABBH < t BBG A(G@
-(A ABBH , D ti 7 ADG U
-AAA \ , t BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 Ԃ 8uY AIG t
-AAI DCA T `u[ w BBB B(D0D8D
-8A0A(B BBBF T d hw_ 4 BIB B(D0A8D
-8A0A(B BBBA T py BBB B(A0A8G
-8A0A(B BBBA L ~ BEB E(I0H8D@
-8D0A(B BBBA , 8 BCA AB D d k BGB E(D0G`w
-0A(A BBBF T ܄ 6 BBB E(A0A8Gn
-8A0A(B BBBA L x BHB B(A0A8D`q
-8A0A(B BBBA < H BFB B(D0D8GPv
-8A0A(B BBBE S
-8A0A(B BBBC w
-8A0A(B BBBG D
-8F0A(B BBBE T 3 7 BED I(D@
-(D ABBK c
-(D ABBD T t f BBE B(A0A8GF
-8A0A(B BBBD L ̆ BEB E(I0H8D@
-8D0A(B BBBA T @ BFB E(A0A8G
-8A0A(B BBBF T t C BBB B(A0A8J
-8A0A(B BBBJ , ADD ]
-AAA l PD S BBB E(I0D8DP
-8D0A(B BBBG
-8D0A(B BBBA T l 0d BDB E(A0A8G
-8D0A(B BBBF , | H ADD ]
-AAA t BIA D(G0B
-(A ABBA o
-(A ABBK g
-(A ABBC D
-(F ABBA d $ 0 BBB E(D0A8L`x
-8D0A(B BBBG
-8D0A(B BBBF T ԉ 6 BBB B(D0D8G
-8D0A(B BBBA \ p BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBT + BED D(GPT
-(D ABBE C
-(D ABBA \ X BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 D Y AIG t
-AAI DCA 4 гz \Y
-A T 0 T (
BBB B(D0A8J
-8A0A(B BBBF Č - AG cA L , BEB E(I0H8D@
-8D0A(B BBBD < | p g BGB D(A0~
-(A BBBK T O BBB E(D0N8GU
-8D0A(B BBBJ D , BIB A(D0JF
-0A(A BBBF $ B AAG0vAA L BEB E(I0H8D@
-8D0A(B BBBD \ ԍ x BBB I(D0D@
-0F(A BBBD U0F(A BBB T 4 ( [ BBE B(F0D8Mc
-8A0A(B BBBA T g BLB B(D0A8G
+CAA y
+CAC NCAH 3 T EAG Y
+CAF [
+CAA H
+CAD NCAH 4 ȕ EAG Y
+CAF [
+CAA [
+CAA NCAH X4 L EAG Y
+CAF [
+CAA y
+CAC NCAH 4 EAG Y
+CAF [
+CAA H
+CAD NCAH 4 d EAG Y
+CAF [
+CAA [
+CAA NCAH <5 藕 EAG Y
+CAF [
+CAA y
+CAC NCAH 5 EAG Y
+CAF [
+CAA H
+CAD NCAH 5 EAG Y
+CAF [
+CAA R
+CAJ NCAH 6 EAG Y
+CAF [
+CAA i
+CAC NCA l6 L~
+F 6 L~
+F 6 X L~
+F 6 L~
+F 6 Lp
+L A
+G 7 4 Lp
+L A
+G 47 Н Lp
+L A
+G X7 l Lp
+L A
+G |7 k F]U 6 X3 4
+ T 7 C 3
+ dL (7 3
+ FEB B(A0A8D`;
+8A0A(B BBBF P x7 x 3
+ FBB B(A0A8G
+8A0A(B BBBI P 7 ?3
+ FBB B(A0A8Gm
+8A0A(B BBBA L 8 Ъ 2
+ FEB B(A0A8D`N
+8A0A(B BBBC P p8 2
+ FBB B(A0A8G
+8A0A(B BBBA P 8 < S2
+ FBB B(A0A8G}
+8A0A(B BBBA @ 9 2
+ IBB A(A0
+(A BBBE P \9 1
+ FBB B(A0A8DQ
+8A0A(B BBBC P 9 s1
+ FBB B(A0A8G
+8A0A(B BBBA @ : , #1
+ IBB A(A0
+(A BBBE P H: 0
+ FBB B(A0A8DI
+8A0A(B BBBK P : ĕ 0
+ FBB B(A0A8G
+8A0A(B BBBA L : 0ʕ C0
+ FBB B(A0A8D`j
+8A0A(B BBBJ P @; ˕n /
+ FBB B(A0A8D
+8A0A(B BBBB 8< Ε EL
+G Y
+O X \< hϕ{ KBB H(D0w
+(I BBBO H0 8 < ӕ '/
+ FIA Dc
+ DABA (Y /
+ 4 h< ֕ /
+ FIA D`
+ DABJ (YX .
+ 4 < ו .
+ FIA D`
+ DABJ d (YX .
+ 4 = xٕ .
+ FIA D`
+ DABJ (YX .
+ 4 X= (ە o.
+ FIA D`
+ DABJ (YX L.
+ 4 = ܕ <.
+ FIA D`
+ DABJ T )YX .
+ 4 = ޕ .
+ FIA D`
+ DABC )YX -
+ 4 H> 8 -
+ FIA D`
+ DABC )YX -
+ 4 > -
+ FIA D`
+ DABC D )YX -
+ 4 > p-
+ FIA D`
+ DABC )YX M-
+ 8 8? H7 =-
+ FIA G
+ DABA $ zPLR x , (Y -
+ P ? ,
+ FBB B(D0K8D7
+8A0A(B BBBH P @ ,
+ FBE E(A0A8G
+8A0A(B BBBH L \@ d ,
+ FBE E(G0D8G
+8D0A(B BBBG 4 @ ,
+ EC
FEN|
+ C . X& J(Y ,
+ A 4G FM i
+EA A `T FM v
+EA P DA . U,
+ FGB B(A0D8G4
+8A0A(B BBBE L A x i,
+ FBB E(K0F8M
+8A0A(B BBBC L A s-
+ FBB B(A0D8JPj
+8A0A(B BBBA L 8B 6-
+ FBB B(A0I8DP
+8D0A(B BBBH ; 'Y9 ,
+ L B P ,
+ FBB B(A0I8DP
+8D0A(B BBBH < u'Y9 ,
+ 4 C ! ,
+ FAI D0d
+ DABA ^'Y1 ,
+ L XC " p,
+ FBB B(A0I8DP
+8D0A(B BBBH < ''Y9 8,
+ 4 C # (,
+ FAI D0d
+ DABA l 'Y1 ,
+ L D # +
+ FBB B(A0I8DP
+8D0A(B BBBH = &Y9 +
+ L xD X% +
+ FBB B(A0I8DP
+8D0A(B BBBH = &Y9 r+
+ L D & b+
+ FBB B(A0I8DP
+8D0A(B BBBH T> {&Y9 *+
+ L HE H( +
+ FBB B(A0I8G
+8D0A(B BBBJ L&Y> *
+ L E * *
+ FBB B(A0I8G
+8D0A(B BBBJ "'Y> *
+ L F - *
+ FBB B(A0I8G
+8D0A(B BBBJ h 'Y> *
+ P F @0 *
+ FBE B(D0C8J
+8D0A(B BBBD P F |?G *
+ FHB B(F0D8G$
+8D0A(B BBBD P (G xQG *
+ FHB B(F0D8G$
+8D0A(B BBBD P |G tcG +
+ FHB B(F0D8G$
+8D0A(B BBBD D G pu; J+
+ FBE G(F0D`
+0D(A BBBC `) 'YC +
+ P 0H Py +
+ FEB B(A0A8DM
+8A0A(B BBBA ( (I }9 FAD jAB TI pIg2 HW H lI }C EAG Y
+CAF [
+CAA
+CAJ NCA I ~2 ( I &Y BAD AB 0 TI ~{ )
+ MAD J
+IBI < I 耖[ )
+ MAA >
+FBK
+ABD lJ 'YO FC I 삖 c)
+ FBB B(A0A8G
+\ABBDCBCBCBDCi ZABBBEBBBCHDBjHMFA^
+8A0A(B BBBH zHBBCJGGUrFBP K ܉ 0 K ȋ I(
+ FDA c
+ABG ` K T FBB B(A0D8DPd
+8A0A(B BBBE n
+8A0A(B BBBH < DL %Y KBB A(D0(I BBBP K @ u'
+ FBB B(I0A8D
+8D0A(B BBBK l 'Yu B'
+ P LL ē 2'
+ FBB B(I0A8D
+8D0A(B BBBK 'Yu &
+ \M H L L &
+ FBB B(A0A8Gl
+8A0A(B BBBA 'Y &
+ L 8M C &
+ FEB B(A0A8J}
+8A0A(B BBBF
+ V'Y f&
+ D M s i&
+ FBB A(A0Gu
+0A(A BBBA D M 8 <&
+ FBB A(I0D`
+0D(A BBBF x/ X(Y~ &
+ D HN ت &
+ FBB A(I0D`
+0D(A BBBF / v(Y~ %
+ @ N x %
+ EC
BL
+ I :. p. .@. `4 (Y[ &
+ O \Cg2 HW O Ȗ) O Ȗ7 Lf O BY' ( P BY BAD AB < 4P vCY KBB A(D0(I BBBP O ȖW '
+ FBB B(I0A8D'
+8D0A(B BBBB : EYX '
+ P <P Ζ '
+ FBB B(A0D8Nb
+8A0A(B BBBE , P P֖= '
+ FAL W
+DBA JEY '
+ P P H֖V '
+ FBB B(D0A8GL
+8A0A(B BBBA 81 DY {'
+ Q \Ag2 HW R ޖ) R <ޖ7 Lf 4R \QY' < HR pQY KBB A(D0(I BBBP Q ޖ '
+ FBB B(I0A8Gt
+8D0A(B BBBB SY '
+ P PR ` '
+ FBB B(A0D8K
+8A0A(B BBBA , R = '
+ FAL W
+DBA SY '
+ L R x'
+ FBE I(A0D8G
+8D0A(B BBBA <$ .SY> 0'
+ L TS & I'
+ FBB E(H0A8J
+8D0A(B BBBA $ UYD '
+ L S T '
+ FBB B(H0D8J
+8D0A(B BBBC L T '
+ FBB B(H0D8J
+8D0A(B BBBA L \T &
+ FBB B(H0D8J
+8D0A(B BBBA P T $Y &
+ FBB B(H0D8JM
+8D0A(B BBBD L U 0 &
+ FBB B(H0D8J
+8D0A(B BBBA L PU &
+ FBB B(H0D8J
+8D0A(B BBBA L U P z&
+ FBB B(H0D8J
+8D0A(B BBBA L U `&
+ FBB B(H0D8J
+8D0A(B BBBA L @V p F&
+ FBB B(H0D8J
+8D0A(B BBBA L V ,&
+ FBB B(H0D8J
+8D0A(B BBBA P V + &
+ FBE E(D0D8I.
+8D0A(B BBBB L 4W l %
+ FBB H(F0D8D
+8D0A(B BBBA hSY %
+ @X D;g2 HW \X (WY' pX <WY' X C 8 X { FBA D(D0C
+(I ABBN X C L X L FBA A(G09
+(F ABBK d
+(A ABBF 8Y VYO FC L TY p k FBN A(D@
+(D ABBG
+(D ABBE L Y FBN A(D@
+(D ABBH
+(D ABBF H Y FBA A(G0x
+(A ABBI d(A ABB L @Z !G PBA A(D0c
+(A ABBG
+(A ABBD < Z UY KBB A(D0(I BBBP ,Z d"W S#
+ FBB B(I0A8D'
+8D0A(B BBBB D WYX #
+ 8 <[ X( FBD A(D0@
+(D ABBF L Z ) "
+ FJB B(A0A8G@
+8D0A(B BBBI ` [ |* FBB B(A0A8GP]
+8A0A(B BBBD $
+8A0A(B BBBB L [ + "
+ FJB B(A0A8G@
+8D0A(B BBBA H |\ x, FFB E(A0D8JP
+8A0A(B BBBE P $\ . !
+ FBB B(D0A8G
+8D0A(B BBBB 0 x\ VY> J!
+ FBB A(A0L P \ T4 +!
+ FFB E(A0D8J
+8A0A(B BBBA P ] F !
+ FFB B(D0D8G
+8A0A(B BBBA P T] WM !
+ FFB E(D0D8G
+8A0A(B BBBA L ] g S"
+ EC
BT
+ C . . B. . . 7. lC UY "
+ L ^ `{ /#
+ FJE B(A0A8G`
+8A0A(B BBBC < `^ ~> #
+ FBA D(G0
+(A ABBJ L ^ "
+ FJE B(A0A8Gp
+8A0A(B BBBA P ^ "
+ FBB B(D0H8G
-8A0A(B BBBA L (7 G BBE E(D0A8Ga
-8D0A(B BBBH $ AAD AAD BBB E(A0A8HP8A0A(B BBBL \ Hg BBE B(A0A8Dp+
-8A0A(B BBBJ h E
-D < ̐ 8 BKD A(G0(A ABB ^ < $ BDB D(C0E
-(A EBBA L BBH E(D0D8DPU
-8D0A(B BBBA d 0 BBE B(A0A8DP
-8F0A(B BBBC [
-8F0A(B BBBF T d ( BBB B(D0A8J
-8A0A(B BBBH L t BLB I(D0A8D@
-8A0A(B BBBD L Ē 0 BHB E(D0D8Gpl8A0A(B BBB L BHB B(A0A8D
-8A0A(B BBBA T 0
] BLE B(A0A8G
-8A0A(B BBBA r. K^ L ԓ AAG e
-CAF K
-ACI R
-CAJ JCA L $ P AAG e
-CAF C
-ACA R
-CAJ JCA $ t ^ AG b
-AA L BBE A(D0
-(E BBED V
-(A BBEH L 4 x
- 1 BBE B(D0D8FP
-8D0A(B BBBA L 5 BBB E(D0A8Gp
-8D0A(B BBBG L Ԕ & BBB B(D0G8Dc
-8D0A(B BBBI L $
BBB B(D0G8Dc
-8D0A(B BBBI $ , xV ADD JAA< m ? BBA D(G0$
-(A ABBA L BBB B(A0A8Gp8A0A(B BBB d `/ BBE B(D0E8D@O
-8F0A(B BBBA
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- # BBB B(D0A8J
-8A0A(B BBBA L < O BEB E(I0H8D@
-8D0A(B BBBA , D ACG i
-ADG T % BBB B(D0A8G
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-8A0A(B BBBD ̘ r. K^ T , F BBE B(D0D8GW
-8A0A(B BBBE < xr. K^ 4 I F BAD `
-ABF D K BBB A(D0DPa
-0A(A BBBH < 0L AAG @
-AAE p
-AAB D \ L0 BBB A(A0Gz
-0A(A BBBA D xM d BEB A(D0?
-(D BBBI T POt BBE B(D0A8G
-8D0A(B BBBH L D xT BEE B(D0D8M
-8A0A(B BBBE D (W BBB A(A0G
-0A(A BBBC L ܚ X BBE D(D0J,
-0A(A BBBA r. K^ (] ] , ] D Ҩr\ AG RAT ] h BBA D(GPR
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-(D ABBG L 8^ 8 BEB B(A0A8G[
-8D0A(B BBBD d p BBB B(A0A8DP
-8A0A(B BBBE D
-8A0A(B BBBA t Pq AP L ܜ Pq , BEE B(D0D8D@
-8D0A(B BBBJ d s
BEE F(A0A8D@`
-8F0A(B BBBI
-8D0A(B BBGG L L t BBB B(A0A8Gp8A0A(B BBB L (v BLB B(A0A8DP
-8A0A(B BBBA L v BLB B(A0A8DP
-8A0A(B BBBE L w BLB E(A0A8D`
-8A0A(B BBBF L Ԟ y ` BBE B(A0D8Gj
-8A0A(B BBBE ܟ 8r. K^ T < `| K BBE E(D0A8G
-8D0A(B BBBE L r. K^ T pJ BBE B(D0A8G
-8A0A(B BBBJ , h0 AC
FK
- E d h BBB E(A0A8G@p
-8A0A(B BBBB i
-8A0A(B BBBA T Ў BBB A(D0o
-(A BBBG E
-(A BBBD L H F BGB E(A0K8Dp
-8D0A(B BBBA $ D Ȑ AG@q
-AA D l @2 BBB A(A0D
-0A(A BBBA l 8 AW < 8) ]SN
-AAD DHAO ̢ ( T , 0 c BEE E(D0A8G
-8D0A(B BBBG < < I BEA A(Gh
-(A ABBA < Ģ 7 - BBA A(Gf
-(A ABBA r. K^ $ AL b
-AA D BBE A(A0D@n
-0A(A BBBA D $ AG@c
-AA ` X `0 Dk ̤ xH , AR ' DR
-J F $ Ȝ AG0S
-AD P K IN T ̤ BEB B(A0A8D
-8A0A(B BBBD L $ BBB E(D0D8FS
-8A0A(B BBBJ T t ; g BBD A(J`
-(A ABBD U
-(A ABBA L ̥ N # BBB A(D0G
-0A(A BBBE \ _ BBB D(D0G`
-0A(A BBBG U
-0A(A BBBK D | BEF A(A0G`
-0A(A BBBI | (r. K^ x ħ p ܧ h ` h $ p L < x AAG ]
-CAF k
-ACA R
-CAJ JCA L AAG ]
-CAF k
-ACA R
-CAJ JCA L $ r z BBB B(E0A8J@
-8A0A(B BBBA T t (
- ? BGH I(D0J8JL
-8D0A(B BBBE $ ^ AG b
-AA L > BIE I(D0A8F`
-8A0A(B BBBF , k AAF Q
-CAC , H DI \ D P BBA D(D@h
-(D ABBB B
-(D ABBE O
-(D ABBH , F Dy
-C
-F f
-J , Ԫ Dy
-C W
-I f
-J , Dy
-C W
-I f
-J < 4 BBE D(D0_
-(A BBBI t ? T GBB A(D0D@G0A(A BBBB@ AV L 4 Q BEE E(A0A8DPz
-8D0A(B BBBB T ̫ BBB B(D0D8G
-8D0A(B BBBF D $ K BHE A(A0Gph
-0D(A BBBE D l I " BBB D(D0G}
-0D(A BBBC l AU AU AU ̭ AU AU AU , AU L AU T BEE E(D0D8G'
-8D0A(B BBBD T Ѻ AGG`m
-DAG b
-DAA i
-DAB
-DAF T d Xw AGG`m
-DAG b
-DAA l
-DAG z
-DAI T d G AGG`m
-DAG b
-DAA i
-DAB {
-DAH T d AGG`m
-DAG b
-DAA i
-DAB {
-DAH T l AGG`m
-DAG b
-DAA i
-DAB
-DAG T į x x AGG`m
-DAG b
-DAA i
-DAB
-DAG T @d 3 AGG`m
-DAG b
-DAA i
-DAB z
-DAA T t XT AGG`m
-DAG b
-DAA i
-DAB o
-DAD T ̰ `g AGG`m
-DAG b
-DAA l
-DAG w
-DAD T $ x d AGG`m
-DAG b
-DAA l
-DAG H
-DAK T | BBE B(D0D8J
-8D0A(B BBBD T Ա H BBE E(G0D8JA
-8D0A(B BBBG \ , c BBE D(D0Gr
-0D(A BBBK ]
-0D(A BBBH \ c ڸ BBE D(D0Gr
-0D(A BBBK ]
-0D(A BBBH \ c BBE D(D0Gr
-0D(A BBBK ]
-0D(A BBBH \ L c X BBE D(D0Gr
-0D(A BBBK ]
-0D(A BBBH \ c BBE D(D0Gr
-0D(A BBBK ]
-0D(A BBBH \ c ַ BBE D(D0Gr
-0D(A BBBK ]
-0D(A BBBH \ l c BBE D(D0Gr
-0D(A BBBK ]
-0D(A BBBH \ ̴ 0c T BBE D(D0Gr
-0D(A BBBK ]
-0D(A BBBH T , @ BBE E(A0A8G
-8D0A(B BBBA 4 x ֶ BAK D`
- AABA T P 3 BBA D(Dpd
-(D ABBF
-(D ABBA T 8 w BBE E(A0A8G
-8D0A(B BBBH L $ p] BEE E(A0A8DPz
-8D0A(B BBBB D o BEE D(D0G
-0D(A BBBB D o BEE D(D0G
-0D(A BBBB T L ~ BBE E(A0A8G
-8D0A(B BBBC T A BBE B(A0A8G
-
-8A0A(B BBBA D #Z ն BBE A(A0G
-0D(A BBBF r. K^ L \ ( BBE B(A0D8G`
-8D0A(B BBBA T * | BBB B(D0A8G
-8D0A(B BBBA X0 Թ P0 $ X0^ AG b
-AA , 00 AC
FK
- E 4 1 ! BDA GPg
- DABD T Ĺ (2 BBB B(A0I8Dr
-8A0A(B BBBA T 4+ ѵ BBB B(I0A8D
-8A0A(B BBBA , , h6f AG d
-AC D
-CA \ 6 t 6 AY
-F d ܺ p7V > KBA A(DX(A ABBGN
-(A ABBD T D h: BEB B(A0A8J
-8A0A(B BBBA T 0? / BEE B(D0A8G
-8D0A(B BBBC T G, O BBE B(D0A8G
-8D0A(B BBBK d L BLB B(A0A8D@q
-8A0A(B BBBE
-8A0A(B BBBA < l 8M BLG A(G@
-(A ABBH T N& BEE E(D0D8J^
-8D0A(B BBBB L L V ״ BBB B(D0D8J
-8D0A(B BBBE L T pZ~ BOB B(A0A8Dp~
-8A0A(B BBBE T ^3 h BBE B(A0A8J
-8A0A(B BBBE n9 AG0lD L d n BEB B(A0D8Jw
-8A0A(B BBBE l r. K^ 0x 8x$ Px ̿ Hx L Px AAG e
-CAF {
-ACI R
-CAJ JCA L 4 x AAG ]
+8A0A(B BBBF , zPLR x 4 ZY #
+ , _ = #
+ FAL W
+DBA cY #
+ H x` L3g FIB B(A0A8DP[
+8A0A(B BBBA 8 ` FBA A(D@x
+(A ABBD H a $ EAG U
+CAJ [
+CAA e
+CAG NCA La bY' H `a d EAG U
+CAJ [
+CAA d
+CAH NCA ( a bY BAD AB H a FBA A(G0
+(A ABBK D(A ABB H $b FBA A(G0
+(A ABBK D(A ABB L pb t MBB E(A0+
+(F BBBJ Q(A BBB b 䛗 EQ < b bY KBB A(D0(I BBBP xb !
+ FBB B(I0A8G
+8D0A(B BBBH 3 dYu
+ | c _ OEE E(A0D8GPHP
+8A0A(B BBBH *
+8A0A(B BBBD P dc 쥗p H
+ FBB B(D0A8G
+8D0A(B BBBC P c
+ FBB B(D0A8G
+8D0A(B BBBG P d X
+ FBB B(A0A8G
+8A0A(B BBBG % cYM
+ L xd
+ FBB G(G0D8G
+8D0A(B BBBD ( fdY
+ P d
+ FBB B(A0A8G
+8A0A(B BBBE P 4e Ls
+ FBB B(A0A8G
+8A0A(B BBBE P e xƗs
+ FBE B(H0A8G
+8A0A(B BBBD a fY
+ , e ٗ= "
+ FAL W
+DBA x|Y !
+ P <f ٗ !
+ FEB B(F0D8M
+8A0A(B BBBI ` "|Y !
+ Lg ޗ! `g d.g2 HW |g ޗ) g $ޗ7 Lf ( g Hޗe FAD VAB L g ߗ LBA A(D0c
+(A ABBC
+(A ABBA (h 2 H <h H BBB B(D0A8D`.
+8A0A(B BBBG ( h }Y BAD AB h ~YO FC < h ~Y KBB A(D0(I BBBP lh G
+ FBB B(I0A8D
+8D0A(B BBBI $S YX
+ P h G
+ FBB B(I0A8D
+8D0A(B BBBB S YX
+ P Di y
+ FBB B(D0A8G
+8D0A(B BBBK րY Y
+ L i 0 I
+ FBB B(A0I8D
+8D0A(B BBBA h Y?
+ L j
+ FBB B(A0I8D
+8D0A(B BBBA Y?
+ P j B
+ FBB B(F0A8J
+8A0A(B BBBD < ȂYE
+ L j d- 3!
+ FFE B(A0A8M
+8A0A(B BBBA , zPLR x 4 vYl
+ (l )g2 HW Dl ) Xl 7 Lf tl ) l < 7 Lf H l ` EAG Y
CAF [
-ACA R
-CAJ JCA L x AAG e
-CAF s
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-CAJ JCA 0y L y BEB B(A0A8DP
-8A0A(B BBBA L < hz BBB E(A0A8DP
-8A0A(B BBBE $ {^ AG b
-AA D @{ BGA
-ABD
-ABA A
-ALE $ }; F^
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-8A0A(B BBBA < $ K BBA A(D
-(A ABBA < , ) BBA A(D
-(E ABGA < l 3 BBA A(D
-(E AFEA d ' Ae < ư BBA A(D
-(E ABGA L Њ$ BBB H(D0D8J
-8A0A(B BBBJ L c BBE H(D0A8D`
-8C0A(B BBBF L d Џ BBB B(A0A8GPf
-8A0A(B BBBG D pf BBA A(G
-(A ABBE d BLB B(A0A8D@c
-8A0A(B BBBK |
-8A0A(B BBBJ T 0 BBE E(A0A8G
-8D0A(B BBBH l h \ BBB B(A0D8D;
-8A0A(B BBBJ W
-8A0A(B BBBA 4 , ؘJ BDA q
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-ABA L Z Ȯ BBB B(D0A8D
-8D0A(B BBBJ T BBB B(A0D8G
-8A0A(B BBBA T T x BGB B(A0A8K
-8A0A(B BBBA L F BBB A(A0D
-0A(A BBBI D 3 BBB A(A0DT
-0A(A BBBH l D h - BBB B(A0A8G
-8D0A(B BBBA
-8A0A(B BBBA d l BEE F(A0A8D@`
-8F0A(B BBBI
-8D0A(B BBGG L j BBE F(A0A8F`
-8D0A(B BBBH D $ GBA A(G@Q(A ABBG@ | l H; KLB B(D0A8D`8A0A(B BBBK`
-8A0A(B BBBK BBD G(R@
-(D ABBD Q
-(D ABBF Q
-(E ABBE Q
-(E ABBE Q
-(E ABBA L ` ٫ BBB H(A0A8Dp
-8D0A(B BBBF d BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB < , 1 BLG A(G@
-(A ABBH L BBB B(G0D8G[
-8D0A(B BBBF < h BBG A(G@
-(A ABBF d (f BBE B(A0D8Dp
-8A0A(B BBBB }
-8A0A(B BBBI T 0
- G BBB B(I0A8G
-8A0A(B BBBF T R BBB B(A0A8G
-8A0A(B BBBC L \ 4 r BEE E(D0D8L
-8D0A(B BBBA d пr. K^ T
BBB B(A0A8GL
-8A0A(B BBBI 01 AG0dD P 4 T H BGB A(A0D $ D Dw
-E c
-M l r. K^ A[ L AAG ]
+CAA Q
+CAK NCAH l EAG Y
CAF [
-ACA R
-CAJ JCA L $ AAG ]
+CAA Q
+CAK NCA <m Y' ( Pm ʗY BAD AB |m YO FC P l
+
+ FBB B(A0A8G
+8A0A(B BBBG P Hm )
+ b
+ FBB B(A0A8Gw
+8A0A(B BBBJ < @n (Y KBB A(D0(I BBBP m 3w
+ FBB B(I0A8DA
+8D0A(B BBBH X @YX
+ P Hn 9w
+ FBB B(I0A8DA
+8D0A(B BBBH Y ,YX N
+ 4 n @_ >
+ FDA D@?
+ DABK c 4YC 2
+ P o CD "
+ FBB L(A0D8G
+8A0A(B BBBF 4 Xo N =
+ FAI D0d
+ DABA ? әY1
+ 4 o hN
+ FAI D0d
+ DABA T? Y1
+ P o N
+ FGH E(A0A8G8
+8D0A(B BBBH P Lp Da
+ FGH E(A0A8G8
+8D0A(B BBBH Dq %g2 HW \q s5 pq s5 q t5 ( q <t WA
+AH P L q u1 xPI F(D0E8
+0A(B BBBF h r v5 D (r <v FBB B(A0A8
+0A(B BBBE D pr x FBB B(A0A8
+0A(B BBBB D r l{c FBB B(A0A8
+0A(B BBBJ s }5 s }5 (s }5 <s ~5 Ps D~5 ds p~5 xs ~5 s ~5 s ~5 s 5 s L5 s x5 s 5 t 5 t 5 ,t (5 @t T5 Tt 5 ht 5 |t 5 t 5 t 05 t \5 t 5 t 5 t 5 u 5 u 85 0u d5 Du 5 Xu 5 lu 肘5 u 5 u @5 u l5 u 5 u ă5 u 5 u 5 v H5 v t5 4v 5 Hv ̄Z \v Z D pv d FEB B(A0A8<
+0A(B BBBJ v e D v FBB B(A0A8
+0A(B BBBC D w FEB B(A0A8
+0A(B BBBJ \w Z pw Z D w @ FEB B(A0A8<
+0A(B BBBJ w e D w FBB B(A0A8
+0A(B BBBI D (x ̎ FEB B(A0A8
+0A(B BBBJ px x L x XBB B(A0A8
+0A(B BBBA L x ,! XBB B(A0A8,
+0A(B BBBK D 8y mMB A(E0o
+(A BBBH P L y qIJ G(D0M8
+0A(B BBBG L y $ qIJ G(D0M8
+0A(B BBBF D z mMB A(E0_
+(A BBBH P L hz 1 XBB B(A0A8
+0A(B BBBK `D z mMB A(E0o
+(A BBBH P D { mMB A(E0_
+(A BBBH P H{ "Y FQ H d{ EAG U
+CAJ [
+CAA d
+CAH NCA H { T EAG U
+CAJ [
+CAA e
+CAG NCA H { EAG U
+CAJ [
+CAA d
+CAH NCA H H| EAG U
+CAJ [
+CAA e
+CAG NCA H | P EAG U
+CAJ [
+CAA d
+CAH NCA H | EAG U
+CAJ [
+CAA e
+CAG NCA H ,} EAG U
+CAJ [
+CAA d
+CAH NCA H x} L EAG U
+CAJ [
+CAA e
+CAG NCA H } EAG U
+CAJ [
+CAA d
+CAH NCA H ~ EAG U
+CAJ [
+CAA e
+CAG NCA H \~ H EAG U
+CAJ [
+CAA d
+CAH NCA H ~ EAG U
+CAJ [
+CAA e
+CAG NCA H ~ EAG U
+CAJ [
+CAA d
+CAH NCA H @ D EAG U
+CAJ [
+CAA e
+CAG NCA H EAG U
+CAJ [
+CAA d
+CAH NCA H 쪘 EAG U
+CAJ [
+CAA e
+CAG NCA H $ @ EAG U
+CAJ [
+CAA d
+CAH NCA H p EAG U
+CAJ [
+CAA e
+CAG NCA H 諘 EAG U
+CAJ [
+CAA d
+CAH NCA H < EAG U
+CAJ [
+CAA e
+CAG NCA H T EAG U
+CAJ [
+CAA d
+CAH NCA H 䬘 EAG U
+CAJ [
+CAA e
+CAG NCA H 8 EAG U
+CAJ [
+CAA d
+CAH NCA H 8 EAG U
+CAJ [
+CAA e
+CAG NCA H EAG U
+CAJ [
+CAA d
+CAH NCA H Ђ 4 EAG U
+CAJ [
+CAA e
+CAG NCA H EAG U
+CAJ [
+CAA d
+CAH NCA H h ܮ EAG U
+CAJ [
+CAA e
+CAG NCA H 0 EAG U
+CAJ [
+CAA d
+CAH NCA H EAG U
+CAJ [
+CAA e
+CAG NCA H L د EAG U
+CAJ [
+CAA d
+CAH NCA H , EAG U
+CAJ [
+CAA e
+CAG NCA < |EP D(
+ ABBG P < $ в |EP D(
+ ABBG P < d yEP D(j
+ ABBG P < |EP D(_
+ ABBG P `2 R Lu
+O A ̺R Lu
+O A 8 R Lu
+O A X LR Lu
+O A x R Lu
+O A ̻R Lu
+O A T LC Ԇ PT LC T LC ؼT LC ( T LC D `T LC ` R Lu
+O A 佘R Lu
+O A $R Lu
+O A dR Lu
+O A R Lu
+O A 侘R Lu
+O A $R Lu
+O A @ dR Lu
+O A ` R Lu
+O A 俘R Lu
+O A $R Lu
+O A dR Lu
+O A R Lu
+O A R Lu
+O A $R Lu
+O A @ dR Lu
+O A ` R Lu
+O A R Lu
+O A $R Lu
+O A dR Lu
+O A R Lu
+O A R Lu
+O A $ØR Lu
+O A @ dØR Lu
+O A ` ØR Lu
+O A ØR Lu
+O A $ĘR Lu
+O A dĘR Lu
+O A ĘR Lu
+O A ĘR Lu
+O A $ŘR Lu
+O A @ dŘR Lu
+O A ` ŘR Lu
+O A ŘR Lu
+O A $ƘT LC hƘT LC ؋ ƘT LC ƘT LC 4ǘT LC , xǘT LC H ǘT LC d ȘT LC DȘT LC ȘT LC ȘT LC Ԍ ɘT LC TɘT LC ɘT LC ( ɘT LC D ʘT LC ` dʘT LC | ʘT LC ʘT LC 0˘T LC Ѝ t˘T LC ˘T LC ˘T LC $ @̘T LC @ ̘T LC \ ̘T LC x ͘T LC P͘T LC ͘T LC ̎ ͘T LC ΘT LC `ΘT LC ΘT LC < ΘT LC t ,Ϙ3
+ FBA A(G0U
+(C ABBB z
+(C ABBF N
+(C ABBB J
+(A ABBH t , И3 m
+ FBA A(G0U
+(C ABBB z
+(C ABBF N
+(C ABBB J
+(A ABBH t Ҙe
+ FBA A(G0U
+(C ABBB l
+(C ABBD N
+(C ABBB
+(A ABBH t Әe
+ FBA A(G0U
+(C ABBB l
+(C ABBD N
+(C ABBB
+(A ABBH t Ԙe S
+ FBA A(G0U
+(C ABBB l
+(C ABBD N
+(C ABBB
+(A ABBH t e FBA A(G0U
+(C ABBB l
+(C ABBD N
+(C ABBB
+(A ABBH t ֘e FBA A(G0U
+(C ABBB l
+(C ABBD N
+(C ABBB
+(A ABBH t טe ' FBA A(G0U
+(C ABBB l
+(C ABBD N
+(C ABBB
+(A ABBH t t ؘe FBA A(G0U
+(C ABBB l
+(C ABBD N
+(C ABBB
+(A ABBH t ٘e _ FBA A(G0U
+(C ABBB l
+(C ABBD N
+(C ABBB
+(A ABBH t d |ژe FBA A(G0U
+(C ABBB l
+(C ABBD N
+(C ABBB
+(A ABBH t ܓ tۘ3 FBA A(G0U
+(C ABBB z
+(C ABBF N
+(C ABBB J
+(A ABBH t T <ݘe E FBA A(G0U
+(C ABBB l
+(C ABBD N
+(C ABBB
+(A ABBH t ̔ 4ޘe FBA A(G0U
+(C ABBB l
+(C ABBD N
+(C ABBB
+(A ABBH t D ,ߘe } FBA A(G0U
+(C ABBB l
+(C ABBD N
+(C ABBB
+(A ABBH t $3 FBA A(G0U
+(C ABBB z
+(C ABBF N
+(C ABBB J
+(A ABBH 8 4 FAA \(N0K(A cAB 8 p ` FAA \(N0K(A cAB 8 _ FAA \(N0K(A cAB 8 H + FAA \(N0K(A cAB 4 $ ! [AA
+ABH H4 \ ! [AA
+ABH H4 ! [AA
+ABH H4 ̗ ! [ [AA
+ABH HL ' FBB B(A0A8D`a
+8A0A(B BBBC L T , FBB B(A0A8D`a
+8A0A(B BBBC L ? FBB B(A0A8D`
+8A0A(B BBBG L ? C FBB B(A0A8D`
+8A0A(B BBBG L D FBB B(A0A8D`H
+8A0A(B BBBD L FBB B(A0A8D`H
+8A0A(B BBBD L _ FBB B(A0A8DpK
+8A0A(B BBBI L 4 \ FBB B(A0A8D`
+8A0A(B BBBD P FBB B(A0A8D
+8A0A(B BBBE L ؚ w FEB B(A0A8D`
+8A0A(B BBBH L ( + FEB B(A0A8Dp
+8A0A(B BBBG L x X FEB B(A0A8D`
+8A0A(B BBBF L ț FEB B(A0A8D`
+8A0A(B BBBE L G FBB B(A0A8D`
+8A0A(B BBBD L h ( FEB B(A0A8D`
+8A0A(B BBBC L FEB B(A0A8D`
+8A0A(B BBBE P c FBB B(A0A8D
+8A0A(B BBBC L \ T FBB B(A0A8D`
+8A0A(B BBBG P
FBB B(A0A8DB
+8A0A(B BBBB P w FBB B(A0A8D
+8A0A(B BBBD L T ' FEB B(A0A8Dp
+8A0A(B BBBG L </ FBB B(A0A8DpU
+8A0A(B BBBG L FBB B(A0A8Dpp
+8A0A(B BBBD L D C FBB B(A0A8D`
+8A0A(B BBBC L \ FBB B(A0A8D`
+8A0A(B BBBE L FEB B(A0A8Dp
+8A0A(B BBBG L 4 _ FBB B(A0A8Dpa
+8A0A(B BBBC L \! FEB B(A0A8D`
+8A0A(B BBBF L Ԡ " FEB B(A0A8D`
+8A0A(B BBBC L $ $ { FBB B(A0A8D`
+8A0A(B BBBC L t L& / FEB B(A0A8Dp
+8A0A(B BBBG P ġ ' FEB B(A0A8D
+8A0A(B BBBD L * FEB B(A0A8D`
+8A0A(B BBBF L h ,_ G FBB B(A0A8Dp
+8A0A(B BBBE P . FBB B(A0A8D
+8A0A(B BBBD P 0 FBB B(A0A8D
+8A0A(B BBBD L ` 3 [ FBB B(A0A8D`
+8A0A(B BBBD L `5 FEB B(A0A8D`
+8A0A(B BBBF P 6 FEB B(A0A8D
+8A0A(B BBBE L T \9 s FBB B(A0A8D`
+8A0A(B BBBD P : ' FEB B(A0A8D
+8A0A(B BBBH P 8= FEB B(A0A8D
+8A0A(B BBBD L L t? FBB B(A0A8D`
+8A0A(B BBBK L A ; FEB B(A0A8D`
+8A0A(B BBBH P DC_ FEB B(A0A8D
+8A0A(B BBBD P @ PE FEB B(A0A8D
+8A0A(B BBBG P G/ O FBB B(A0A8Df
+8A0A(B BBBF P I FEB B(A0A8D
+8A0A(B BBBI P < K/ FBB B(A0A8D
+8A0A(B BBBC P O _ FBB B(A0A8DQ
+8A0A(B BBBC P S FBB B(A0A8DQ
+8A0A(B BBBC P 8 V/ FBB B(A0A8D
+8A0A(B BBBC 0 Z D Z X |Z l xZ tZ [D L[B Ev
+E A X ȩ |[{ KBB H(D0w
+(I BBBO H0 4 _ { FIA D`
+ DABC ,G +`YX X 4 Щ Pa H FIA D`
+ DABJ |G 3`YX % 4 c FIA D`
+ DABJ G ;`YX 4 p d FIA D`
+ DABJ H C`YX 4 `f FIA D`
+ DABJ lH K`YX 4 h | FIA D`
+ DABJ H S`YX Y 4 ` i I FIA D`
+ DABJ I [`YX & 4 pk FIA D`
+ DABJ \I c`YX 4 m FIA D`
+ DABJ I k`YX 4 P n FIA D`
+ DABJ I s`YX 4 p } FIA D`
+ DABJ LJ {`YX Z 4 0r J FIA D`
+ DABJ J `YX ' 4 @ s FIA D`
+ DABJ J `YX 4 u FIA D`
+ DABJ <K `YX 4 @w FIA D`
+ DABJ K `YX 4 0 x ~ FIA D`
+ DABJ K `YX [ 4 z K FIA D`
+ DABJ ,L `YX ( 4 Ю P| FIA D`
+ DABJ |L `YX 4 ~ FIA D`
+ DABJ L `YX 4 p FIA D`
+ DABJ M `YX 4 ` FIA D`
+ DABJ lM `YX \ 4 L FIA D`
+ DABJ M `YX ) 4 ` FIA D`
+ DABJ N `YX 4 p FIA D`
+ DABJ \N `YX 4 FIA D`
+ DABJ N `YX 4 P Љ FIA D`
+ DABJ N `YX ] 4 M FIA D`
+ DABJ LO `YX * 4 0 FIA D`
+ DABJ O aYX 4 @ FIA D`
+ DABJ O aYX 4 FIA D`
+ DABJ <P aYX 4 @ FIA D`
+ DABJ P aYX ^ 4 0 N FIA D`
+ DABJ P #aYX + 4 FIA D`
+ DABJ ,Q +aYX 4 г P FIA D`
+ DABJ |Q 3aYX 4 FIA D`
+ DABJ Q ;aYX 4 p FIA D`
+ DABJ R CaYX _ 4 ` O FIA D`
+ DABJ lR KaYX , 4 FIA D`
+ DABJ R SaYX 4 ` FIA D`
+ DABJ S [aYX 4 p FIA D`
+ DABJ \S caYX 4 FIA D`
+ DABJ S kaYX ` 4 P Ф P FIA D`
+ DABC S saYX - 4 FIA D`
+ DABC LT {aYX 4 0 FIA D`
+ DABC T aYX 4 @ ਖ਼ FIA D`
+ DABC T aYX 4 FIA D`
+ DABC <U aYX a 4 @ Q FIA D`
+ DABC U aYX . 4 0 FIA D`
+ DABC U aYX 4 FIA D`
+ DABC ,V aYX 4 и P FIA D`
+ DABC |V aYX 4 FIA D`
+ DABC V aYX b 4 p R FIA D`
+ DABC W aYX / 4 ` FIA D`
+ DABC lW aYX 4 FIA D`
+ DABC W aYX 4 ` FIA D`
+ DABC X aYX 4 p FIA D`
+ DABC \X aYX c 4 S FIA D`
+ DABC X aYX 0 4 P п FIA D`
+ DABC X aYX 4 FIA D`
+ DABC LY aYX 4 0Ù FIA D`
+ DABC Y bYX 4 @ ę FIA D`
+ DABC Y bYX d 4 ƙ T FIA D`
+ DABC <Z bYX 1 4 @ș ! FIA D`
+ DABC Z bYX 4 0 ə FIA D`
+ DABC Z #bYX 4 ˙ FIA D`
+ DABC ,[ +bYX 4 н P͙ FIA D`
+ DABC |[ 3bYX e 4 ϙ U FIA D`
+ DABC [ ;bYX 2 4 p Й " FIA D`
+ DABC \ CbYX 4 `ҙ FIA D`
+ DABC l\ KbYX 4 ԙ FIA D`
+ DABC \ SbYX 4 ` ՙ FIA D`
+ DABC ] [bYX f 4 pי V FIA D`
+ DABC \] cbYX 3 4 ٙ # FIA D`
+ DABC ] kbYX 4 P ڙ FIA D`
+ DABC ] sbYX 4 ܙ FIA D`
+ DABC L^ {bYX 4 0ޙ FIA D`
+ DABC ^ bYX g 4 @ ߙ W FIA D`
+ DABC ^ bYX 4 4 $ FIA D`
+ DABC <_ bYX 4 @ FIA D`
+ DABC _ bYX 4 0 FIA D`
+ DABC _ bYX 4 FIA D`
+ DABC ,` bYX h 4 P X FIA D`
+ DABC |` bYX 5 4 % FIA D`
+ DABC ` bYX 4 p FIA D`
+ DABC a bYX 4 ` FIA D`
+ DABC la bYX 4 FIA D`
+ DABC a bYX i 4 ` Y FIA D`
+ DABC b bYX 6 4 p & FIA D`
+ DABC \b bYX 4 FIA D`
+ DABC b bYX 4 P FIA D`
+ DABC b bYX 4 FIA D`
+ DABC Lc bYX j 4 0 Z FIA D`
+ DABC c cYX 7 4 @ ' FIA D`
+ DABC c cYX 4 FIA D`
+ DABC <d cYX 4 @ FIA D`
+ DABC d cYX 4 0 FIA D`
+ DABC d #cYX k 4 [ FIA D`
+ DABC ,e +cYX 8 4 P ( FIA D`
+ DABC |e 3cYX 4 FIA D`
+ DABC e ;cYX 4 p FIA D`
+ DABC f CcYX 4 ` FIA D`
+ DABC lf KcYX l 4
+ \ FIA D`
+ DABC f ScYX 9 4 ` ) FIA D`
+ DABC g [cYX 4 p
FIA D`
+ DABC \g ccYX 4 FIA D`
+ DABC g kcYX 4 P FIA D`
+ DABC g scYX m 4 ] FIA D`
+ DABC Lh {cYX : 4 0 * FIA D`
+ DABC h cYX 4 @ FIA D`
+ DABC h cYX 4 FIA D`
+ DABC <i cYX 4 @ FIA D`
+ DABC i cYX n 4 0 ^ FIA D`
+ DABC i cYX ; 4 + FIA D`
+ DABC ,j cYX 4 P FIA D`
+ DABC |j cYX 4 FIA D`
+ DABC j cYX 4 p ! FIA D`
+ DABC k cYX o 4 `# _ FIA D`
+ DABC lk cYX < 4 % , FIA D`
+ DABC k cYX 4 ` & FIA D`
+ DABC l cYX 4 p( FIA D`
+ DABC \l cYX 4 * FIA D`
+ DABC l cYX p 4 P + ` FIA D`
+ DABC l cYX = P - - FBB B(A0D8GP
+8A0A(B BBBA в cY/ @ ;i FBB D(A0
+(G BBBI @ P =i FBB D(A0
+(G BBBI @ <>i FBB D(A0
+(G BBBI @ h?i z FBB D(A0
+(G BBBI D @I K BBB B(A0Dm
+0A(B BBBA D bY D | @f FBB A(A0DH
+0A(A BBBA 4 @cY 4 eY BGB B(A0A8D fY
+ E fY
+ E fY
+ ED P @L FBB A(A0Dn
+0A(A BBBA tfY X D @] ] FBB A(A0D
+0A(A BBBA h gY D @ < BBB A(A0DU
+0A(A BBBG Ȟ hY D p P@ BBB A(A0D[
+0A(A BBBA ( OjY D @ BBB A(A0Dj
+0A(A BBBB kY P 0 A FIE E(D0D8G
+8A0A(B BBBH P Es FBB B(A0D8G}
+8A0A(B BBBA P J FBB B(D0A8D
+8A0A(B BBBG P , dL
+ X FEB B(A0D8G
+8A0A(B BBBE P O . FBB B(D0A8D
+8A0A(B BBBG P P
+ FEB B(A0D8G
+8A0A(B BBBE D ( hSs FBB A(A0Gu
+0A(A BBBA P p T( FIE E(D0D8G
+8A0A(B BBBB P |Ys g FBB B(A0D8G{
+8A0A(B BBBC P ^ J FBB B(D0A8D
+8A0A(B BBBG P l 4`
+ FEB B(A0D8G
+8A0A(B BBBE P b FBB B(D0A8D
+8A0A(B BBBG P |d
+ FEB B(A0D8G
+8A0A(B BBBE P h 8gH FIE E(D0D8G
+8A0A(B BBBI P 4kA K FBB B(A0D8GO
+8A0A(B BBBG P 0p . FBB B(D0A8D
+8A0A(B BBBG P d q
+ FEB B(A0D8G
+8A0A(B BBBE P xt FBB B(D0A8D
+8A0A(B BBBG P v
+ FEB B(A0D8G
+8A0A(B BBBE L ` x d FBB B(D0D8G@d
+8D0A(B BBBD P y1 FBB B(A0D8GL
+8A0A(B BBBB P l~p FFE E(D0D8J
+8A0A(B BBBC P X FBB B(D0A8D
+8A0A(B BBBG P
+ FEB B(A0D8G
+8A0A(B BBBE P Ѕ l FBB B(D0A8D
+8A0A(B BBBG P T \
+ 1 FEB B(A0D8G
+8A0A(B BBBE P FFB B(A0K8J
+8A0A(B BBBF 4 FAI D@t
+ DABA dY1 4 L FAI D@t
+ DABA d dY1 4 t FAI D@t
+ DABA dY1 q 4 ԏ a FAI D@t
+ DABA rdY1 ; 4 < 4 + FAI D@t
+ DABA T SdY1 4 FAI D@t
+ DABA 4dY1 4 FAI D@t
+ DABA dY1 4 , T FAI D@t
+ DABA D cY1 c 4 | S FAI D@t
+ DABA cY1 - 4 FAI D@t
+ DABA cY1 4 t FAI D@t
+ DABA 4 cY1 4 l Ԓ FAI D@t
+ DABA zcY1 4 4 { FAI D@t
+ DABA [cY1 U 4 E FAI D@t
+ DABA $ <cY1 4 \ FAI D@t
+ DABA t cY1 4 T FAI D@t
+ DABA bY1 P 4 FFB B(A0K8J(
+8A0A(B BBBH 4 P FAI D@t
+ DABA h bY1 4 u FAI D@t
+ DABA lbY1 O 4 ` ? FAI D@t
+ DABA MbY1 4 @ FAI D@t
+ DABA X .bY1 4 FAI D@t
+ DABA bY1 4 FAI D@t
+ DABA aY1 w 4 0 g FAI D@t
+ DABA H aY1 A 4 @ 1 FAI D@t
+ DABA aY1 4 FAI D@t
+ DABA aY1 4 FAI D@t
+ DABA 8 taY1 4 p ` FAI D@t
+ DABA UaY1 i 4 Y FAI D@t
+ DABA 6aY1 3 4 # FAI D@t
+ DABA ( aY1 4 ` FAI D@t
+ DABA x `Y1 4 ࠚ FAI D@t
+ DABA `Y1 4 @ FAI D@t
+ DABA `Y1 [ 4 P K FAI D@t
+ DABA h `Y1 % 4 FAI D@t
+ DABA |`Y1 4 ` FAI D@t
+ DABA ]`Y1 4 @ FAI D@t
+ DABA X >`Y1 4 s FAI D@t
+ DABA `Y1 M 4 = FAI D@t
+ DABA `Y1 4 0 ࣚ FAI D@t
+ DABA H _Y1 4 @ FAI D@t
+ DABA _Y1 4 FAI D@t
+ DABA _Y1 u 4 e FAI D@t
+ DABA 8 _Y1 ? 4 p ` / FAI D@t
+ DABA e_Y1 4 FAI D@t
+ DABA F_Y1 4 FAI D@t
+ DABA ( '_Y1 4 ` FAI D@t
+ DABA x _Y1 g 4 চ W FAI D@t
+ DABA ^Y1 1 4 @ ! FAI D@t
+ DABA ^Y1 4 P FAI D@t
+ DABA h ^Y1 ſ 4 FAI D@t
+ DABA ^Y1 4 ` FAI D@t
+ DABA m^Y1 Y 4 @ I FAI D@t
+ DABA X N^Y1 # 4 FAI D@t
+ DABA /^Y1 4 ݾ FAI D@t
+ DABA ^Y1 4 0 ਗ਼ FAI D@t
+ DABA H ]Y1 4 @ q FAI D@t
+ DABA ]Y1 K 4 ; FAI D@t
+ DABA ]Y1 4 FAI D@t
+ DABA 8 ]Y1 ߽ 4 p ` Ͻ FAI D@t
+ DABA u]Y1 4 FAI D@t
+ DABA V]Y1 s 4 c FAI D@t
+ DABA ( 7]Y1 = 4 ` - FAI D@t
+ DABA x ]Y1 4 ଚ FAI D@t
+ DABA \Y1 Ѽ 4 @ FAI D@t
+ DABA \Y1 4 P FAI D@t
+ DABA h \Y1 e 4 U FAI D@t
+ DABA \Y1 / 4 ` FAI D@t
+ DABA }\Y1 4 @ FAI D@t
+ DABA X ^\Y1 û 4 FAI D@t
+ DABA ?\Y1 4 } FAI D@t
+ DABA \Y1 W 4 0 G FAI D@t
+ DABA H \Y1 ! 4 @ FAI D@t
+ DABA [Y1 4 ۺ FAI D@t
+ DABA [Y1 4 FAI D@t
+ DABA 8 [Y1 4 p ` o FAI D@t
+ DABA [Y1 I 4 9 FAI D@t
+ DABA f[Y1 4 FAI D@t
+ DABA ( G[Y1 ݹ 4 ` FAI D@t
+ DABA x ([Y1 4 ಚ FAI D@t
+ DABA [Y1 q 4 @ a FAI D@t
+ DABA ZY1 ; 4 P + FAI D@t
+ DABA h ZY1 4 FAI D@t
+ DABA ZY1 ϸ 4 ` FAI D@t
+ DABA ZY1 4 @ FAI D@t
+ DABA X nZY1 c 4 S FAI D@t
+ DABA OZY1 - 4 FAI D@t
+ DABA 0ZY1 4 0 ൚ FAI D@t
+ DABA H ZY1 4 @ FAI D@t
+ DABA YY1 4 { FAI D@t
+ DABA YY1 U 4 E FAI D@t
+ DABA 8 YY1 4 p ` FAI D@t
+ DABA YY1 4 ٶ FAI D@t
+ DABA vYY1 4 FAI D@t
+ DABA ( WYY1 } 4 ` m FAI D@t
+ DABA x 8YY1 G 4 บ 7 FAI D@t
+ DABA YY1 4 @ FAI D@t
+ DABA XY1 ۵ 4 P ˵ FAI D@t
+ DABA h XY1 4 FAI D@t
+ DABA XY1 o 4 ` _ FAI D@t
+ DABA XY1 9 4 @ ) FAI D@t
+ DABA X ~XY1 4 FAI D@t
+ DABA _XY1 ʹ 4 FAI D@t
+ DABA @XY1 4 0 FAI D@t
+ DABA H !XY1 a 4 @ Q FAI D@t
+ DABA XY1 + 4 FAI D@t
+ DABA WY1 4 FAI D@t
+ DABA 8 WY1 4 p ` FAI D@t
+ DABA WY1 4 y FAI D@t
+ DABA WY1 S 4 C FAI D@t
+ DABA ( gWY1 4 `
FAI D@t
+ DABA x HWY1 4 ྚ ײ FAI D@t
+ DABA )WY1 4 @ FAI D@t
+ DABA
+WY1 { 4 P k FAI D@t
+ DABA h VY1 E 4 5 FAI D@t
+ DABA VY1 4 ` FAI D@t
+ DABA VY1 ٱ 4 @ ɱ FAI D@t
+ DABA X VY1 4 FAI D@t
+ DABA oVY1 m 4 ] FAI D@t
+ DABA PVY1 7 4 0 ' FAI D@t
+ DABA H 1VY1 4 @ FAI D@t
+ DABA VY1 ˰ 4 FAI D@t
+ DABA UY1 4 Ú FAI D@t
+ DABA 8 UY1 _ P p `ÚE O FBB E(A0A8G
+8D0A(B BBBA $ UY = tFPD 8 4Κ 6 EC
DEH^.R. I
+ F VY$ 8 @ Қ EG
FFI.0a
+ F E. Ԛ+ Fd 8 Ԛ ǯ EG
FFI.0a
+ F E. x ך+ Fd 8 ך EG
FFI.0a
+ F E. dښ+ Fd 8 H xښ Y EG
FFI.0a
+ F E. ( ,ݚ+ Fd P @ݚY " FBB B(D0A8Gg
+8A0A(B BBBG P L ( FBB B(D0A8G
+8A0A(B BBBF P H . FBB B(D0A8G
+8A0A(B BBBJ P 4 / FBE B(A0A8J
+8A0A(B BBBG P 5 FBE B(A0A8J
+8A0A(B BBBF P D 6 FBE B(A0A8J)
+8A0A(B BBBB P 7 FBB B(A0D8G
+8A0A(B BBBC P 4 & FBB B(A0D8G
+8A0A(B BBBC P @ FBB B(A0D8G
+8A0A(B BBBC P FBB B(A0D8G
+8A0A(B BBBC P FBB B(A0D8G
+8A0A(B BBBC P < d FBB B(A0D8G
+8A0A(B BBBC P Ѯ FBB B(A0D8G
+8A0A(B BBBC L / FBB B(A0D8G
+8A0A(B BBBA L 4
+ FBB B(A0D8G
+8A0A(B BBBA P
+ ^ FBB B(A0D8G
+8A0A(B BBBC P
+ h M FBB B(A0D8G
+8A0A(B BBBC P , < FBB B(A0D8G
+8A0A(B BBBC P # + FBB B(A0D8G
+8A0A(B BBBC P L& FBB B(A0D8G
+8A0A(B BBBC P ( ) FBB B(A0D8G
+8A0A(B BBBC P | , FBB B(A0D8G
+8A0A(B BBBC P 00 FBB B(A0D8G
+8A0A(B BBBC P $
|3 ֭ FBB B(A0D8G
+8A0A(B BBBC P x
6 ŭ FBB B(A0D8G<
+8A0A(B BBBA P
49 FBB B(D0A8G9
+8A0A(B BBBE 8 <H EG
DGD.. 4
+ H P \ E FBB B(D0A8G9
+8A0A(B BBBE 8 @IH EG
DGD.. 4
+ H P TR FBB B(D0A8G9
+8A0A(B BBBE 8 @ UH EG
DGD.. 4
+ H P | ^ FBB B(D0A8G9
+8A0A(B BBBE 8 `bH EG
DGD.. 4
+ H P tk ĭ FBB B(D0A8G8
+8A0A(B BBBF P ` o FBB B(D0A8G8
+8A0A(B BBBF P r FBB B(D0A8G8
+8A0A(B BBBF P Hv FFE E(D0D8J
+8A0A(B BBBC 8 \ x b EJ
DPLHn.0a
+ J E. P x{ H FFE E(D0D8J
+8A0A(B BBBC 8 4~ EJ
DPLHn.0a
+ J E. K Fk
+G R P H 耛 ߬ FFE E(D0D8J
+8A0A(B BBBC 8 EJ
DPLHn.0a
+ J E. P FFE E(D0D8J
+8A0A(B BBBC 8 , Ĉ g EJ
DPLHn.0a
+ J E. HK Fk
+G R P x - FFE E(D0D8J
+8A0A(B BBBC 8 $ EJ
DPLHn.0a
+ J E. P FFE E(D0D8J
+8A0A(B BBBC 8 l d EJ
DPLHn.0a
+ J E. L 蕛K Fk
+G R P { FFE E(D0D8J
+8A0A(B BBBC 8 Ę L EJ
DPLHn.0a
+ J E. P X H 2 FFE E(D0D8J
+8A0A(B BBBC 8 EJ
DPLHn.0a
+ J E. K Fk
+G R P ɪ FFE E(D0D8J
+8A0A(B BBBC 8 \ d EJ
DPLHn.0a
+ J E. P 襛 FFE E(D0D8J
+8A0A(B BBBC 8 Q EJ
DPLHn.0a
+ J E. K Fk
+G R P H H FFE E(D0D8J
+8A0A(B BBBC 8 EJ
DPLHn.0a
+ J E. P x Ω FFE E(D0D8J
+8A0A(B BBBC 8 , 4 EJ
DPLHn.0a
+ J E. K Fk
+G R P 赛] e FFE E(D0D8J
+8A0A(B BBBC 8 6 EJ
DPLHn.0a
+ J E. ) Fb P 4 FFE E(D0D8J
+8A0A(B BBBC 8 Ѩ EJ
DPLHn.0a
+ J E. h ě) Fb P ě FFE E(D0D8J
+8A0A(B BBBC 8 4 <ɛ l EJ
DPLHn.0a
+ J E. ˛) Fb P ˛ 6 FIE E(D0D8J
+8A0A(B BBBJ 8 Λ EJ
DPLHn.0a
+ J E. ћ) Fb P 8 (ћ ҧ FIE E(D0D8G
+8A0A(B BBBI 8 ֛ EJ
DPLHn.0a
+ J E. l Hٛ) Fb P \ٛ g FIE E(D0D8G'
+8A0A(B BBBG 8 8 ݛ 2 EJ
DPLHn.0a
+ J E. ߛ) Fb P ߛ
+ FEB B(A0D8G
+8A0A(B BBBE P \ Ҧ FBB B(D0G8G
+8A0A(B BBBH P 8
+ FEB B(A0D8G
+8A0A(B BBBE P FFE E(D0D8J
+8A0A(B BBBH 8 @ Z EG
FFI.0a
+ F E. ) Fb D 8 s # FBB A(A0Gu
+0A(A BBBA P 0
+ FEB B(A0D8G
+8A0A(B BBBE P
+ ̥ FEB B(A0D8G
+8A0A(B BBBE P ( FFE E(D0D8J
+8A0A(B BBBH 8 | t s EG
FFI.0a
+ F E. \ ) Fb P , < FIE E(D0D8G
+8A0A(B BBBC 8 (
EG
FFI.0a
+ F E. ! ) Fb P ֤ FFE E(D0D8J
+8A0A(B BBBC 8 < EG
FFI.0a
+ F E. ! ) Fb P ,! p FIE E(D0D8G
+8A0A(B BBBC 8 ! A EG
FFI.0a
+ F E. `" $) Fb P ! 8
+ FFE E(D0D8J
+8A0A(B BBBC 8 ," ۣ EG
FFI.0a
+ F E. # ) Fb P " m FIE E(D0D8G
+8A0A(B BBBI 8 " u EG
FFI.0a
+ F E. # ) Fb P 0# 5 > FIE E(D0D8G
+8A0A(B BBBC 8 # # EG
FFI.0a
+ F E. d$ @&) Fb P # T& آ FIE E(D0D8G
+8A0A(B BBBC 8 0$ + EG
FFI.0a
+ F E. % -) Fb P $ - r FFE E(D0D8J
+8A0A(B BBBH 8 $ d2 C EG
FFI.0a
+ F E. % 5) Fb P 4% 5 FFE E(D0D8J
+8A0A(B BBBH 8 % 9 ݡ EG
FFI.0a
+ F E. h& <) Fb P % < FIE E(D0D8G
+8A0A(B BBBC 8 4& L@ w EG
FFI.0a
+ F E. ' C) Fb P & C @ FFE E(D0D8J
+8A0A(B BBBC 8 & E EG
FFI.0a
+ F E. ' H) Fb P 8' H ڠ FIE E(D0D8G
+8A0A(B BBBC 8 ' 4N EG
FFI.0a
+ F E. l( P) Fb P ' P t FFE E(D0D8J
+8A0A(B BBBC 8 8( T E EG
FFI.0a
+ F E. ) lW) Fb P ( Wg FIE E(D0D8G
+8A0A(B BBBC 8 ( [' ߟ EJ
DPLHn.0a
+ J E. ) ^) Fb P <) ^e FIE E(D0D8G
+8A0A(B BBBC 8 ) d z EJ
DPLHn.0a
+ J E. p* Dg) Fb P ) Xg| D FIE E(D0D8G
+8A0A(B BBBC 8 <* k EJ
DPLHn.0a
+ J E. + hn) Fb P * |nX ߞ FFE E(D0D8J
+8A0A(B BBBC 8 * s EJ
DPLHn.0a
+ J E. + v) Fb P @+ 0v z FFE E(D0D8J
+8A0A(B BBBC 8 + z K EJ
DPLHn.0a
+ J E. t, }) Fb P + } FFE E(D0D8J
+8A0A(B BBBC 8 @, 0 EJ
DPLHn.0a
+ J E. - ) Fb P , FFE E(D0D8J
+8A0A(B BBBC 8 , EJ
DPLHn.0a
+ J E. - x) Fb P D- h K FIE E(D0D8G
+8A0A(B BBBF 8 - EJ
DPLHn.0a
+ J E. x. <) Fb P - P FIE E(D0D8G
+8A0A(B BBBD 8 D. EJ
DPLHn.0a
+ J E. $/ ) Fb H . Ԝ u OBB A(A0
+(A BBBE G / H . OBB A(A0
+(A BBBE G / 0 ff2 HW 0 x 00 v+Y' 8 D0 p FBD A(D0
+(I ABBO L 0 D FBA A(G0u
+(F ABBO D
+(A ABBF ( 0 *Y BAD AB 0 +YO FC P 1 : FAA ]
+CBF s
+CBH v
+ABG M
+ABH , 0 Q EHG0@
+AAJ < 1 +Y KBB A(D0(I BBBP 81 ( FBB B(I0A8D
+8D0A(B BBBG Ĩ -Yu P 1 l FBB B(I0A8D
+8D0A(B BBBH 0 -Yu o L 2 _ FBB B(A0A8Tp
+8A0A(B BBBB L `2 p ; FBB B(A0A8Tp
+8A0A(B BBBB $ 2 v EG ^
+AA P 2 x FBB E(D0A8G<
+8G0A(B BBBI D ,3 $ FFB A(A0GP
+0A(A BBBF P t3 ŜN FOG B(A0A8Gr
+8A0A(B BBBE P 3 М q FEE B(D0A8G(
+8G0A(B BBBJ h 4 Ԝ 2 FEB B(A0A8DJOAABLT
+8A0A(B BBBA h 4 ל ! FEB B(A0A8DJOAABLT
+8A0A(B BBBA D 4 ڜE FHF B(A0Dm
+0A(B BBBA D <5 ۜ> FFB B(A0Di
+0D(B BBBA D 5 ܜ> FFB B(A0Di
+0D(B BBBA @ 5 ݜ- EC
I.@. `.@y. p
+ A D 6 ^ FBB A(A0GC
+0D(A BBBA P X6 7 FEB B(A0A8JD
+8A0A(B BBBG , 6 T= 3 FAL W
+DBA 2)Y , 6 L= FAL W
+DBA (Y ۗ P <7 D3 ˗ EC
P7
+ A
+.@s.. J.@Y..@K.. L 7 I o FBB E(G0F8D
+8D0A(B BBBD H X(Yn 7 8 (^f2 HW 8 ,Y' , $8 J g FDA Y
+AJA < T8 \L ; FBA D(D0P
+(I ABBI P 8 N FBA A(G0I
+(F ABBK
+(A ABBF ( 9 +Y BAD AB 9 ,YO FC < 9 ,Y KBB A(D0(I BBBP p9 P ' FBB B(I0A8D
+8D0A(B BBBG /Yu P 9 V FBB B(I0A8D
+8D0A(B BBBH h
/Yu L H: (] FBB B(D0D8JM
+8D0A(B BBBH t /Y$ _ L : `a ` FBB B(D0A8DPC
+8D0A(B BBBK $4 /Y, / P ; c FEB E(A0A8G
+8A0A(B BBBG 4 l; 4 FAI D0
+ DABA ^/Y1 4 ; ā ٘ FAI D0
+ DABA h ?/Y1 ` < T= FBH E(A0A8LPPA^
+8D0A(B BBBF l3 .YQ c ,= Yf2 HW D= 2 ( X= ;Y BAD AB = ;YO FC , < 䅝
+ EHG0H
+AAB D ,= Č ˘ FBB A(A0GC
+0D(A BBBA P t= FBB E(A0A8J
+8A0A(B BBBF <;Y T D = 04 [ EC
PK
+ E l .`. %.`S. . D (> ݝ4 ˛ EC
PK
+ E l .`. %.`S. . ? 0Xf2 HW ,? h @? t T? p
h? l |? x ? t) ? ? 9 ? B T a ? ' HZ ? H i
+O | ( @ <Y BAD AB H@ <Y' \@ H L A
+C x@ L0U
+G L ? `C FBA A(G0
+(A ABBH (A ABB @ p<YO FC $ \@ Dk S Ek
+H Y
+O < (A |<Y KBB A(D0(I BBBP @ Lw FBB B(I0A8DA
+8D0A(B BBBH |+ >YX P 0A `w FBB B(I0A8DH
+8D0A(B BBBA + >YX a < @B t! MBF A(D0R
+(I BBBE P B T$+ MBF H(A0C
+(F BBBK
+(A BBBH L 0B 0'C FBA A(G0
+(A ABBH (A ABB H $C 0( FBB B(E0A8DP
+8D0A(B BBBA P B ( % FBB B(A0A8G
+8A0A(B BBBA < C +> FBA D(G0
+(A ABBJ < `C , ϙ FEG D(K@}
+(D ABBI d C -k FGB E(A0D8DP
+8A0E(B BBBE l
+8E0A(B BBBF L D /F k FEE F(E0D8GP
+8A0A(B BBBE 4 XD 3 W EC
DGK
+ B . 8 D @9
+" S EC
P
+ C . \. @* ;Y Ǚ x E Zt FBB B(A0A8DP
+8F0A(B BBBI D
+8F0A(B BBBE L8F0A(B BBB L `E \f FJB B(A0A8D`*
+8A0A(B BBBB P E _ Ӛ FBB B(A0A8R5
+8A0A(B BBBA , F z= FAL W
+DBA MY , LF z= FAL W
+DBA P MY i 8G LPf2 HW TG z hG lY |G 0 G Ԅx G @ G L- G h9 G G P0 H l H H 0H B DH P[ XH S lH 衞2 H H EAG U
+CAJ [
+CAA m
+CAG NCA H H h EAG U
+CAJ [
+CAA m
+CAG NCA H I FBA A(G0x
+(A ABBI d(A ABB H dI FBA A(G0x
+(A ABBI d(A ABB I DI I V H I ̤ FBB B(A0A8D
+8A0A(B BBBA , I p m FADPe
+ABA 4 JY E , I 5 FADPe
+ABA | ~JY
, J FADPe
+ABA ģ UJY , XJ H ŗ FADPe
+ABA ,JY , J FADPe
+ABA T JY e , J ئ U FADPe
+ABA IY - , 0K FADPe
+ABA IY , xK h FADPe
+ABA , IY dL c H xL FBB B(A0A8D
+8A0A(B BBBA , L M FADPe
+ABA ԥ HY % , hL FADPe
+ABA HY , L @ ݕ FADPe
+ABA d HY , L FADPe
+ABA HY } , @M Ъ m FADPe
+ABA [HY E , M 5 FADPe
+ABA < 2HY
, M ` FADPe
+ABA HY Ք , N Ŕ FADPe
+ABA ̧ GY H O FBB B(A0A8D
+8A0A(B BBBA , N A FADPe
+ABA ` kGY , N FADPe
+ABA BGY , <O D ѓ FADPe
+ABA GY , O FADPe
+ABA 8 FY q , O ԭ a FADPe
+ABA FY 9 , P ) FADPe
+ABA ȩ FY , \P d FADPe
+ABA uFY ɒ , P FADPe
+ABA X LFY 8 Q FBA A(D`
+(A ABBA , (Q x E FADPe
+ABA ܪ EY , pQ
FADPe
+ABA $ EY , Q Ց FADPe
+ABA l EY , R P FADPe
+ABA lEY u , HR e FADPe
+ABA CEY = , R ఞ - FADPe
+ABA D EY , R ( FADPe
+ABA DY ͐ , S p FADPe
+ABA Ԭ DY H hS FBB D(D0J
+0A(A BBBD 4 S <` R FBD O
+ ABBA H T d FBA A(G0x
+(A ABBI d(A ABB H T ( FBA A(G0x
+(A ABBI d(A ABB L T 츞 FFB B(A0D8Gp
+8A0A(B BBBC L T l FFB B(A0D8Gp
+8A0A(B BBBC P $U Þ FBB B(N0D8D
+8A0A(B BBBK P xU H˞` FBB B(D0A8G
+8D0A(B BBBD P U TОP m FBB B(D0A8G<
+8D0A(B BBBG P V P֞P 7 FBB B(D0A8G=
+8D0A(B BBBF D tV Lܞ; FBB D(A0D
+0A(A BBBH ," {AY D V ,; FBB D(A0D
+0A(A BBBH " BY t D 4W ; o FBB D(A0D
+0A(A BBBH " BY + D W ; & FBB D(A0D
+0A(A BBBH L# CY x W C ݍ FBB B(A0A8GBKAW
+8A0A(B BBBA AKLFAAL p) gCY j L X FBB B(A0A8JL
+8D0A(B BBBA P X Q FBB E(D0A8G
+8D0A(B BBBF T DY" 8 DY \x EG
Pa.a.
+ G > RFYk P Y FDB B(A0A8M#
+8A0A(B BBBF QFY nj | Z > FBB B(D0A8J0
+8A0A(B BBBK ADII[AII^ IY ! 8 Z <f
EJ
DLIH. C
+ A L@ VJYa [ 05 [ \5 [ [ dJY' ( [ AC
FKA
+ J P \ t: FAA ]
+CBF s
+CBH v
+ABG M
+ABH d\ `A E{ 4 \ EDG
+AAI ^
+CAN \ X \ \ 8 QJE B(A0D8
+0A(B BBBA 0A(B BBBJ$ \ v G EG ^
+AA < \ @ # FFA A(GPG
+(A ABBF L \ *( 3 FPB B(A0A8G`v
+8A0A(B BBBE D ] /2 FBB B(A0A8
+0A(B BBBF P ] 2 FEE B(D0A8G
+8G0A(B BBBJ D ] 5^ l FIB A(A0GP>
+0A(A BBBH D $^ 7D 8 FHE F(A0Di
+0A(B BBBA D l^ 8D
+ FHE F(A0Di
+0A(B BBBA P ^ 9I ܉ FBB B(A0A8G
+8A0A(B BBBE _ H ԉ FBB B(A0A8JUBFAFFU}
+8A0A(B BBBB FFAFAfwsBBBHGGGDnKWHABAn _ Z
+ ! FBB B(A0A8JVDFAFFZu
+8A0A(B BBBB FFAFAfwsBBBHDDGGq\WHABAn \a XkB FBB B(A0A8GGQAFATCeHBBCGGGDd
+8A0A(B BBBF a qB FBB B(A0A8GGQAFATCeHBBCGGGDd
+8A0A(B BBBF b H:f2 HW b v) b v7 Lf b CY' < b CY KBB A(D0(I BBBP b vW FBB B(I0A8D'
+8D0A(B BBBB <M EYX c P b | S FBB B(A0I8D
+8D0A(B BBBG EYT # P \c D / FBB H(D0A8G|
+8D0A(B BBBI | FYp ld 8f2 HW d ) d ́7 Lf d ) d 7 Lf d 0) d L7 Lf e p) (e 7 Lf De ) Xe ̂7 Lf te ) e 7 Lf e 0) e L7 Lf , 0e p FAL0W
+DBA S HJY ׄ , xe DŽ FAL0W
+DBA S JY , e FAL0W
+DBA T IY o , f H _ FAL0W
+DBA dT IY ; , Pf + FAL0W
+DBA T |IY <g 2 ( Pg fIY BAD AB |g k F]U g ( D g 43 FBB A(D0KP
+0A(A BBBG U IY9 M , lg = = FAL W
+DBA p IY P g FBB B(A0D8KF
+8A0A(B BBBD lR VIYV Ԃ , h = Ă FAL W
+DBA $ dIY 4 i U FAA s
+DBO AAB ` Di PBB B(A0A8DPq
+8A0A(B BBBI
+8A0A(B BBBA X i \{ KBB H(D0w
+(I BBBO H0 @ `i FBI A(G{
+(D ABBA ( zPLR x 0 GY a 4 i R FIA D`
+ DABJ aHYX / 4 8j FIA D`
+ DABH iHYX 4 j FIA D`
+ DABH 4 qHYX ɀ @ j Xc FBI A(D
+(D ABBA P mHYu @ 4k lc FBI A(D
+(D ABBA HYu c @ k } S FBI A(D
+(D ABBA HYu 0 H l PBB B(A0A8Tp
+8A0A(B BBBJ L 8l x FFB B(D0D8M
+8D0A(B BBBE 0 `HYP L l Р1 FBB B(A0D8LK
+8D0A(B BBBA D l PBB B(A0GQ
+0A(B BBBA D 8m n BBB B(A0Gt
+0A(B BBBC ` GY . L m H 3 FDB E(G0A8D
+8D0A(B BBBA P dHY ~ < n y ' BBB A(GA
+(A BBBJ ( zPLR x 0 JY ~ < n o ~ BBB A(GA
+(A BBBJ ( zPLR x 0 uKY Z~ P o j Z~ FDB B(G0D8G"
+8D0A(B BBBA LY ~ P to 9 [~ FBB B(A0A8Jm
+8D0A(B BBBF $ m NYw "~ zFPD L o `R ~ BBB B(A0H8J
+8A0A(B BBBH OY } D Xp Xg } BBB A(A0Gw
+0A(A BBBB < XPY } P p h } FJB B(D0A8G
+8D0A(B BBBE PYG e} 8 $q } EG
FM.@l. O
+ H 8 `q ͟ N~ EG
FN.@l. O
+ K 8 q dܟ: ~ EG
FN.@l. O
+ K 8 q hx P EG
FN.@l. O
+ K 8 r EG
FN.@l. O
+ K H Pr
R FBB E(G0L
+0D(B BBBF ( zPLR x 0 xRY) D r ` FBE D(D0D
+0D(A BBBC > BRY P @s ` FBB B(A0D8G
+8A0A(B BBBE ` UYX P s r FDE B(A0A8G|
+8A0A(B BBBH WYU t (f2 HW t ) t 7 Lf u 2 u [Y. Fb , 4u AF
HK
+ C < du [Y KBB A(D0(I BBBP u : FBB B(I0A8DV
+8D0A(B BBBC _ ]YX ~ P lu ~ FBB B(I0A8D]
+8D0A(B BBBD $` ]YX ~ 0 u &^ ~ FDG$
+DBA 0 v $(^ |~ FDG$
+DBA L @v P)d \~ FBB J(A0A8G
+8D0A(B BBBG G ]Y_ $~ ( Lw X* BAJk
+ABE ( xw ,+ BAJl
+ABD L w ,d } FBB J(A0A8G
+8D0A(B BBBG PH ]Y_ } D hw -s } FBB A(A0Gu
+0A(A BBBA D w @.s } FBB A(A0Gu
+0A(A BBBA D w x/s R} FBB A(A0Gu
+0A(A BBBA D @x 0s %} FBB A(A0Gu
+0A(A BBBA D x 1s | FBB A(A0Gu
+0A(A BBBA D x 3s | FBB A(A0Gu
+0A(A BBBA L y X4\
+ | FBB G(D0D8G
+8A0A(B BBBE D hy h>s o| FBB A(A0Gu
+0A(A BBBA L y ? B| FBB G(D0D8G
+8A0A(B BBBG L z PQ | FBB G(D0D8G
+8A0A(B BBBG L Pz _N { FBB G(D0D8G
+8A0A(B BBBG L z p { FBB G(D0D8G
+8A0A(B BBBE L z Pq { FBB G(D0D8G
+8A0A(B BBBG L @{ ! W{ FBB G(D0D8G
+8A0A(B BBBG L { ` ({ FBB G(D0D8G
+8A0A(B BBBG L { ! z FBB G(D0D8G
+8A0A(B BBBG L 0| ͠ z FBB G(D0D8G
+8A0A(B BBBE L | z FBB G(D0D8G
+8A0A(B BBBE L |
lz FBG E(D0A8G
+8A0A(B BBBG L } z FBB B(F0D8J
+8A0A(B BBBE L p} ,
z FBB B(F0D8J
+8A0A(B BBBE P } p: z FBB H(A0D8L5
+8D0A(B BBBK & HXY z ( ~ f FHD0
+ABA ~ @) @7 Lf , \Y' ( @ \Y BAD AB D ~ @ sz FBB A(I0DP
+0D(A BBBG m []YG Cz D ( (B 3z FBB A(I0DP
+0D(A BBBG n B]YG z < , r]Y KBB A(D0(I BBBP HCw y FBB B(I0A8DA
+8D0A(B BBBH j _YX ty P 4 \Iw dy FBB B(I0A8DH
+8D0A(B BBBA j v_YX %y P pO
+ y FEB G(A0A8G
+8A0A(B BBBK , zPLR xQ $ 4 2_Y
+ x IVD f2 HW 8Y) TY7 Lf < xY2 P Y Gy FBE B(A0K8G]
+8A0A(B BBBG < HiY KBB A(D0(I BBBP @ 0]W x FBB B(I0A8D'
+8D0A(B BBBB l `kYX x P $cg rx FBB B(I0A8D.
+8D0A(B BBBK dm LkYX 3x P (ig #x FBB B(I0A8D.
+8D0A(B BBBK m 8kYX w H ( ,o FBO B(A0A8D`
+8A0A(B BBBE P Ѓ pp w FBB B(A0I8D1
+8D0A(B BBBH jYG Lw P < r Ww FBB B(A0I8D1
+8D0A(B BBBH kYG w P s &w FBB B(A0I8D1
+8D0A(B BBBH d lYG v P ,u v FBB B(A0A8G
+8A0A(B BBBD $ h^ jmY w FIVD L @ w FBH E(D0A8G
+8D0A(B BBBD h rYn w 8f2 HW ) Ȇ 7 Lf , @ w FAL0W
+DBA t vY w , vY' < @ vY KBB A(D0(I BBBP ܆ ԖW w FBB B(I0A8D'
+8D0A(B BBBB q xYX v P H Ȝ v FDB B(A0A8J
+8A0A(B BBBA @ f2 HW \ ) p 47 Lf X) t7 Lf ) Ј 7 Lf ب) 7 Lf ) 0 47 Lf L X) ` t7 Lf | ) 7 Lf ة) 7 Lf ܉ ) 47 Lf X) t7 Lf < ) P 7 Lf l ت) 7 Lf , t FAL0W
+DBA Tx 6vY t , @ ` t FAL0W
+DBA x vY t , t FAL0W
+DBA x uY ot , Њ _t FAL0W
+DBA ,y uY ;t , 8 +t FAL0W
+DBA ty juY t , ` s FAL0W
+DBA y 7uY s , Ȭ s FAL0W
+DBA z uY s , s FAL0W
+DBA Lz tY ks , 8 X [s FAL0W
+DBA z tY 7s , 's FAL0W
+DBA z ktY s , Ȍ 譡 r FAL0W
+DBA ${ 8tY r , 0 r FAL0W
+DBA l{ tY r x2 H EAG Y
CAF [
-ACA R
-CAJ JCA L t AAG ]
+CAA
+CAE NCA H \ EAG U
+CAJ [
+CAA m
+CAG NCA H \ EAG Y
CAF [
-ACA R
-CAJ JCA $ ^ AG b
-AA $ 4 X U AG Y
-AA T \ F 1 BBE E(D0D8G
-8D0A(B BBBA , [ AID J
-DAA , ( G AAG0i
-AAA L BEB E(I0H8D@
-8D0A(B BBBD L d X BJE B(A0A8D`8A0A(B BBB T ) BBE E(A0A8J
-8D0A(B BBBA T _ z BBE E(A0D8J
-8D0A(B BBBA xr. K^ 4 L 0 Hg l 7 Hj L AAG e
-CAF s
-ACA R
-CAJ JCA d ( BBB B(A0A8DP
-8A0A(B BBBE D
-8A0A(B BBBA $ D ^ AG b
-AA , l ADD0e
-AAA \ X ɥ BBB A(D0Gpq
-0A(A BBBE
-0A(A BBBD L BOB A(A0]
-(A BBBG Y
-(A BBBH T | B BBE E(D0A8G
-8D0A(B BBBE D @ BBE A(A0D@d
-0A(A BBBA T 4 1 BEE E(D0A8GI
-8D0A(B BBBE 4 .or Ƥ BAD G`
- DABA L BBB B(A0D8D`b
-8A0A(B BBBC < BBG A(G@
-(A ABBG T T ; BEB B(A0A8G
-8A0A(B BBBA T $" O BBB B(A0A8D:
-8A0A(B BBBF r. K^ L &q ' BBB E(A0A8DP
-8A0A(B BBBA D $ )4 BBE A(A0D@
-0A(A BBBA T * BEE B(D0A8J
-8A0A(B BBBF D ;@ BBE A(A0D@
-0A(A BBBA < $ < d < < BBB B(A0A8DP
-8A0A(B BBBE D
-8A0A(B BBBA $ =^ AG b
-AA , X=0 AC
FK
- E T D X>< BBE B(D0A8G
-8D0A(B BBBF T r. K^ T (C- BBB B(A0D8J
-8A0A(B BBBF p xp DV L p, BEB B(A0A8DP8A0A(B BBB L D `q3 BBE B(A0A8DP8A0A(B BBB L Pr, BGB B(D0D8D
-8D0A(B BBBA < 0u BEH D(A0H@0A(A EBBT l u Ǥ BKB E(A0D8K
-8D0A(B BBBE | r. K^ , b D\
-H P
-H Q
-G I , @b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , $ b D\
-H P
-H Q
-G I , T b D\
-H P
-H Q
-G I , @b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , D @b D\
-H P
-H Q
-G I , t b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , @b D\
-H P
-H Q
-G I , 4 b D\
-H P
-H Q
-G I , d b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , @b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , $ b D\
-H P
-H Q
-G I , T b D\
-H P
-H Q
-G I , @b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I m A~
-A l L L BBB F(A0A8J@X
-8A0A(B BBBA 4 T ADG X
-CAH [AA L / BBB B(A0A8DW
-8D0A(B BBBF L $ 8> BBB B(A0A8GZ
-8D0A(B BBBH < t (B ߟ AADPU
-AAC
-AAA D 8 BAA G@
- AABH
- AABH 4 BAG G`}
- AABA D 4 H s BED D(G$
-(D ABBE L 4 BBB A(A0E
-(A BBGG I
-(A BBDA D BED D(G$
-(D ABBE D BED D(G$
-(D ABBE D \ BED D(G$
-(D ABBE D X O BED D(G$
-(D ABBE D BED D(G$
-(D ABBE < 4 (a BBA A(JP
-(A ABBI T t X BBB B(A0A8G
-8A0A(B BBBG D BED D(G$
-(D ABBE < BBG A(G@
-(A ABBG ا $ 0r. K^ L BAA GQ
- AABH n
- AABF T ] BBB B(A0A8G0
-8A0A(B BBBE T , Q c BEB B(A0A8J
-8A0A(B BBBA < 8U" F AFGPe
-AAK T
-AAJ L (VQ BDB B(A0C8J
-_
-8A0A(B BBBG L 8h BBB B(A0A8Lv
-8A0A(B BBBJ L d kE BBB H(A0A8GT
-8A0A(B BBBK L o= BBB B(A0I8G
-8A0A(B BBBG L u BBB B(A0C8O\
-8A0A(B BBBG L T C BBJ B(A0A8GZ
-8A0A(B BBBC l H BBB J(A0A8D^
-8A0A(B BBBB
-8A0A(B BBBD T Ȑ BBB B(A0A8L
-8A0A(B BBBJ T l @o t BBB B(A0A8O-
-8A0A(B BBBH L X R BAA LP
- AABH
- AABK L ) . BBB B(A0C8JY
-8A0A(B BBBG L d ȩ} [ BBB B(A0A8O^
-8A0A(B BBBG \ N BBB A(A0LP@
-0A(A BBBD
-0A(A BBBH l x BBB B(A0A8Lp
-8A0A(B BBBJ
-8A0A(B BBBA < ( AAJPH
-AAJ j
-AAD , ) AAJP~
-AAD , ) u AAJP~
-AAD < $ 2 ^ AAJPH
-AAJ
-AAI L d I BBB B(A0A8M
-8A0A(B BBBH L h c BBB B(A0A8M
-8A0A(B BBBF L } BGB B(A0A8G
-8A0A(B BBBA 8 $ 0 < ( T F , XT AHG`
-AAA , b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , D @b D\
-H P
-H Q
-G I , t b D\
-H P
-H Q
-G I , AHGp
-AAA T BBE E(A0A8G
-8A0A(B BBBE T t BBE E(A0A8G
-8A0A(B BBBE T e { BEE B(A0A8G
-8A0A(B BBBF T $ e s BEE B(A0A8G
-8A0A(B BBBF 4 Pr. K^ T S BBA A(Gk
-(A ABBB
-(A ABBC D P W BBB A(A0JH
-0A(A BBBF T 4 h7 U BBA D(Gi
-(A ABBA |
-(A ABBA D PG BBB A(A0JE
-0A(A BBBA L X BDB B(A0I8G\
-8A0A(B BBBG L $ ! BDB B(A0I8G\
-8A0A(B BBBG , ' D ' x'
פ t p' h'
`' X' `' h' , p'b D\
-H P
-H Q
-G I , 4 'b D\
-H P
-H Q
-G I , d 'b D\
-H P
-H Q
-G I T 0(e BEE B(A0A8G
-8A0A(B BBBF T 4 H.e BEE B(A0A8G
-8A0A(B BBBF T `4e BEE B(A0A8G
-8A0A(B BBBF T x:e BEE B(A0A8G
-8A0A(B BBBF T < @e BEE B(A0A8G
-8A0A(B BBBF L hr. K^ L F? g BBB B(A0A8Gj
-8A0A(B BBBK `5 Hl `< Ho T < `: BBB B(A0A8G2
-8A0A(B BBBC \ g BBB A(A0Jo
-0A(A BBBG Q
-0A(A BBBG D hpW BBB A(A0JH
-0A(A BBBF L < v a BBB B(A0A8M
-8A0A(B BBBG D 0W ̤ BBA A(N
-(A ABBA L H AAG ]
-CAF c
-CAI R
-CAJ JCA , b D\
-H P
-H Q
-G I , ȃb D\
-H P
-H Q
-G I , < b D\
-H P
-H Q
-G I , l Hb D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , Ȅb D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , , Hb D\
-H P
-H Q
-G I \ m A~
-A l , h BDD y
-ABA D l BAA JPV
- AABH
- AABC r. K^ L T (%- I BBB B(A0A8G
-8A0A(B BBBI T ģ BBE E(D0C8J
-
-8A0A(B BBBG D Pr ) BBB A(A0J@
-0A(A BBBF D D , A BCA OP_
- AABH
- AABC L pG
- BDB B(A0A8O
-8A0A(B BBBA , p ACL@F
-AAH D r BBB A(A0J}
-0A(A BBBI T T BDB E(A0C8GR
-8A0A(B BBBA T p4 BDA A(L`
-(A ABBF
-(A ABBB T X BBA A(Jpd
-(A ABBF
-(A ABBA , b D\
-H P
-H Q
-G I , @ AAL`
-DAA T e s BEE B(A0A8G
-8A0A(B BBBF $ k JFD@ $ < V GFD@ $ d A GFD@ $ Fr , BFA D@ $ H JFD@ T u BEE B(A0A8G
-8A0A(B BBBE T 4 H e BEE B(A0A8G
-8A0A(B BBBF D Џr. K^ L H ٢ BBB A(A0M
-0A(A BBBB S @ 8 $ @ < H
T @ l 8 A] 8F p x , 4 \ AAG o
-AAA , d b a ADG r
-AAA $ L A ACH pCA $ t A ACH pCA HY Af
-I - AG cA , b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I L < AAG ]
+CAA
+CAE NCA H EAG U
+CAJ [
+CAA m
+CAG NCA H @ EAG Y
CAF [
-ACA R
-CAJ JCA H Y PS m A~
-A l L $ r BBB B(E0A8J@
-8A0A(B BBBA < t w r BLD A(G@|
-(A ABBA , l X{ BFA i
-ABA , { BFA i
-ABA , { BFA i
-ABA HY Af
-I 4 d BKE
-AJA 4 ^ BKE
-AJE L * BBB B(A0A8GPd
-8A0A(B BBBA T $ BBB B(D0A8J]
-8D0A(B BBBA L | ` BJB A(A0W
-(A BBDH _(A BBBL h BBB B(A0A8GP
-8A0A(B BBBA < " BED A(G0
-(A ABBA # , #B Av
-I A L ($ d $, A^
-A K 0$, A^
-A K @$ 8$% P$G DB $2 D]
-G I 4 $M AAG0o
-AAF D
-AAJ $ D %V AD s
-AG 4 l %R ADJ Y
-DAK MDA & &
- & &
- &
- % AO < % T % l % D[ % % $ % AIK0AAd & BBE B(D0A8G@
-8A0A(B BBBG
-8A0A(B BBBC L
- 8( 4 ) BDA D@u
- AABD L )2 BBB B(A0D8D
-8D0A(B BBBH
- + + + |
- + L +: d + AV L
- + י BFB E(A0A8GP
-8A0A(B BBBH d - BBB B(D0A8D`H
-8A0A(B BBBE
-8A0A(B BBBK d x/ h BBB B(D0A8D`H
-8A0A(B BBBE
-8A0A(B BBBK D 1 BOE A(C0x
-(A BBBG 4 2J BDA l
-ABJ A
-ABA T l 29 BBA A(Dq
-(C ABBE L
-(A ABBA T 3 ~ BEE E(D0D8G
-8D0A(B BBBI L
5 BOB A(A0]
-(A BBBG Y
-(A BBBH < $ 5 BEB D(A0M
-(A BBBF D
7o BEE D(D0G
-0D(A BBBB 4 (8Z BDA y
-ABM A
-ABA P8Q 9 N , \ 9\ ADG l
-AAA T 9 BBB B(A0A8G
-8A0A(B BBBD T H=Z BEB B(A0A8G
+CAA
+CAE NCA H x EAG U
+CAJ [
+CAA m
+CAG NCA H ؏ ̰ EAG Y
+CAF [
+CAA
+CAE NCA H $ 0 EAG U
+CAJ [
+CAA m
+CAG NCA H p EAG Y
+CAF [
+CAA
+CAE NCA H 象 EAG U
+CAJ [
+CAA m
+CAG NCA H < EAG Y
+CAF [
+CAA
+CAE NCA H T EAG U
+CAJ [
+CAA m
+CAG NCA H EAG Y
+CAF [
+CAA
+CAE NCA H X EAG U
+CAJ [
+CAA m
+CAG NCA H 8 EAG Y
+CAF [
+CAA
+CAE NCA H EAG U
+CAJ [
+CAA m
+CAG NCA H В d EAG Y
+CAF [
+CAA
+CAE NCA H ȴ EAG U
+CAJ [
+CAA m
+CAG NCA H h EAG Y
+CAF [
+CAA
+CAE NCA H EAG U
+CAJ [
+CAA m
+CAG NCA H Ե EAG Y
+CAF [
+CAA
+CAE NCA H L 8 EAG U
+CAJ [
+CAA m
+CAG NCA H EAG Y
+CAF [
+CAA
+CAE NCA H EAG U
+CAJ [
+CAA m
+CAG NCA , D Wk FAL0W
+DBA lY 3k , Ԕ #k FAL0W
+DBA 0 klY j , Է j FAL0W
+DBA x 8lY j , d j FAL0W
+DBA lY j , d j FAL0W
+DBA kY cj , Sj FAL0W
+DBA P kY /j , < j FAL0W
+DBA lkY i , < i FAL0W
+DBA 9kY i , ̖ i FAL0W
+DBA ( kY i , ̹ i FAL0W
+DBA p jY _i , \ Oi FAL0W
+DBA jY +i , \ i FAL0W
+DBA mjY h < / h FBA A(D0
+(A ABBF < , ? h FBA A(D0
+(A ABBE < l ? yh FBA A(D0
+(A ABBE < ? Bh FBA A(D0
+(A ABBE < / h FBA A(D0
+(A ABBA < , / g FBA A(D0
+(A ABBF < l t? g FBA A(D0
+(A ABBE < t/ fg FBA A(D0
+(A ABBA < d¡/ /g FBA A(D0
+(A ABBA < , Tá/ f FBA A(D0
+(A ABBF < l Dġ? f FBA A(D0
+(A ABBE < Dš? f FBA A(D0
+(A ABBE L Dơ Sf FBB B(A0A8D`b
+8A0A(B BBBB L < ǡ f FBB B(A0A8D`b
+8A0A(B BBBB L ɡ e FBB B(A0A8D`b
+8A0A(B BBBB L ܛ dʡ ~e FBB B(A0A8D`r
+8A0A(B BBBB L , ˡ 7e FBB B(A0A8D`r
+8A0A(B BBBB L | D͡ d FBB B(A0A8D`b
+8A0A(B BBBB L ̜ Ρ d FBB B(A0A8D`r
+8A0A(B BBBB L С bd FBB B(A0A8D`r
+8A0A(B BBBB L l ѡ d FBB B(A0A8D`r
+8A0A(B BBBB L ҡ c FBB B(A0A8D`b
+8A0A(B BBBB L Tԡ c FBB B(A0A8D`r
+8A0A(B BBBB L \ ա Fc FBB B(A0A8D`b
+8A0A(B BBBB X P $ס{ KBB H(D0w
+(I BBBO H0 4 Hۡ b FIA D`
+ DABC < cYX b 4 X ܡ pb FIA D`
+ DABJ = cYX Mb 4 ޡ =b FIA D`
+ DABJ T= &cYX b 4 X
+b FIA D`
+ DABJ = .cYX a 4 H a FIA D`
+ DABJ = 6cYX a 4 a FIA D`
+ DABJ D> >cYX a 4 h qa FIA D`
+ DABJ > FcYX Na 4 8 >a FIA D`
+ DABJ > NcYX a 4 a FIA D`
+ DABJ 4? VcYX ` 4 ء x ` FIA D`
+ DABJ ? ^cYX ` 4 ( ( ` FIA D`
+ DABJ ? fcYX ` 4 x r` FIA D`
+ DABJ $@ ncYX O` 4 Ȣ ?` FIA D`
+ DABC t@ vcYX ` 4 8 ` FIA D`
+ DABC @ ~cYX _ 4 h _ FIA D`
+ DABC A cYX _ 4 _ FIA D`
+ DABC dA cYX _ 4 H s_ FIA D`
+ DABC A cYX P_ 4 X @_ FIA D`
+ DABC B cYX _ 4
_ FIA D`
+ DABC TB cYX ^ 4 X ^ FIA D`
+ DABC B cYX ^ 4 H ^ FIA D`
+ DABC B cYX ^ 4 t^ FIA D`
+ DABC DC cYX Q^ 4 h A^ FIA D`
+ DABC C cYX ^ P 8 W ^ FBB B(I0A8D'
+8D0A(B BBBB cYX ] P ] FDB E(A0A8G
+8A0A(B BBBI P :^ FDB E(A0A8G
+8A0A(B BBBI P L - ^ FDB E(A0A8G
+8A0A(B BBBI P p@ 0_ FDB E(A0A8G
+8A0A(B BBBF P <S8 _ FDB E(A0A8G
+8A0A(B BBBH P H (f &` FDB E(A0A8G
+8A0A(B BBBH P x ` FDB E(A0A8G
+8A0A(B BBBB P a FDB E(A0A8G
+8A0A(B BBBB P D l a FDB E(A0A8G
+8A0A(B BBBH P 8 b FDB E(A0A8G
+8A0A(B BBBF P Ģ b FDB E(A0A8G
+8A0A(B BBBH P @ ֢ c FDB E(A0A8G
+8A0A(B BBBH 8 e2 HW T ) h 7 Lf _Y' L #c FBB B(A0I8D`
+8D0A(B BBBI y_Yc b < _Y KBB A(D0(I BBBP $g b FBB B(I0A8G
+8D0A(B BBBF l aY b P ( b BBB B(A0A8G
+8A0A(B BBBH (U LbY 9b P t :b BBB B(A0A8G
+8A0A(B BBBH U cY a P a BBB B(A0A8G
+8A0A(B BBBF V cY a P L 4: a FBB G(G0D8G
+8D0A(B BBBG ~ hdYb oa \ 8e2 HW t 5 5 H) d7 Lf ̮ BmY' 4 < e a BDA D0i
+ DABA } mY la ( 0 mY BAD AB d \ TDE E(K0F8GP
+8A0A(B BBBH PAP d į P TDE E(K0F8GP
+8A0A(B BBBB PAP < , mY KBB A(D0(I BBBP ȯ Xw ` FBB B(I0A8DD
+8D0A(B BBBE 6oYX _ P 4 l w _ FBB B(I0A8DK
+8D0A(B BBBF "oYX _ P w _ FBB B(I0A8DK
+8D0A(B BBBF X oYX C_ P 3_ FBB B(A0I8D'
+8D0A(B BBBB nYG ^ P x _ FBE B(A0A8G
+8D0A(B BBBI P ̱ T" ^ FBB B(A0I8D'
+8D0A(B BBBB oYG ^ P 8 # ^ FBB B(A0I8D'
+8D0A(B BBBB \pYG ^ P \% ^ FEB E(A0F8G
+8A0A(B BBBA $ 8qY: s^ FPD P ( ^ FBB B(A0A8G
+8D0A(B BBBG t ,1S ^ FFB B(D0A8Gi
+8D0A(B BBBF !JAADBDDRLJAADBDDR H+ ZwYy o^ $ ̴ e EK x
+RE 2) 27 Lf $ dwY# A[
+E A D hwY# A[
+E A d lwY# A[
+E A pwY# A[
+E A twY' ( wY BAD AB t @ 02e G] FBA A(G0U
+(C ABBB l
+(C ABBD N
+(C ABBB
+(A ABBA $ zPLR x{ 0 , wY \ t 2e \ FBA A(G0U
+(C ABBB l
+(C ABBD N
+(C ABBB
+(C ABBA >wY =\ t 3 -\ FBA A(G0U
+(C ABBB z
+(C ABBF N
+(C ABBB F
+(A ABBD L vY# [ t X5 [ FBA A(G0U
+(C ABBB z
+(C ABBF N
+(C ABBB L
+(A ABBF VvY# o[ t 6e _[ FBA A(G0U
+(C ABBB l
+(C ABBD N
+(C ABBB
+(A ABBG l uY Z t 8 7e Z FBA A(G0U
+(C ABBB l
+(C ABBD N
+(C ABBB
+(C ABBG nuY }Z t ȸ 8 mZ FBA A(G0U
+(C ABBB z
+(C ABBF N
+(C ABBB H
+(C ABBH tY# Z t X 8: Z FBA A(G0U
+(C ABBB z
+(C ABBF N
+(C ABBB J
+(C ABBF tY# Y < ;; Y FBA A(D0o
+(A ABBE ( = nY H[
+M H x> RY d d > :Y HZ
+N ? Y d < @ Y FBA A(D0
+(A ABBB < `B X FBA A(D0
+(A ABBB < D; X FBA A(D0o
+(A ABBE < sY KBB A(D0(I BBBP pF 1X FBB B(I0A8D
+8D0A(B BBBG ,3 2uYu W P L W FBB B(I0A8D
+8D0A(B BBBH 3 ;uYu W , x S W FBG@
+EBE A huY3 W P T W FBB B(D0A8G
+8D0A(B BBBE P LY TW FBB B(A0I8D(
+8D0A(B BBBA tYG W P ZV #W BBB B(D0A8J!
+8A0A(B BBBA $ d uY[ W IVD P o^ W BBB B(D0A8J
+8A0A(B BBBA $ yYe aX IVD P x kX FBB B(A0I8D(
+8D0A(B BBBA 4" ~}YG /X P : :X BBB B(A0A8J
+8A0A(B BBBA $ \ Y~Y X IVD P ` Y BBB B(A0A8Ji
+8A0A(B BBBA $ D Y Y IVD L ܿ Y FBH B(F0A8J
+8D0A(B BBBD $ ( 0Yv Y FPD e2 HW @5 ( l5 < 5 P IJ5 d ) x 7 Lf Y' ( Y BAD AB D 0 cY FBB A(I0DP
+0D(A BBBD YG 3Y D ` #Y FBB A(I0DP
+0D(A BBBD x fYG X D е X FBB A(I0DP
+0D(A BBBD ذ MYG X D P @ X FBB A(I0DP
+0D(A BBBD 8 4YG sX 0 cX VDD ABD h , 3X TDH G(D0A8D`
+8A0A(B BBBK A` X P W TGE G(A0A8D`
+8A0A(B BBBA x X $ }W TGE G(A0A8D`
+8A0A(B BBBA x , %W FNI
+DBG $ zPLR x
m , Y V , x ( V FNI
+DBG t Y V < d Y KBB A(D0(I BBBP w QV FBB B(I0A8DA
+8D0A(B BBBH YX V P l ţw V FBB B(I0A8DH
+8D0A(B BBBA $ YX U P ˣw U FBB B(I0A8DH
+8D0A(B BBBA YX tU P D ѣ dU FBB B(I0A8D
+8D0A(B BBBG = pYu 1U ` T أa FBD D(G0
+(A ABBF `
+(A ABBJ
+(A ABBL ,ڣ OBB E(G0G8K`_
+8G0A(B BBBH
+8A0A(B BBBG H` P hܣ: 9T FBB B(A0D8GI
+8A0A(B BBBE P T: HT FBB B(A0D8GI
+8A0A(B BBBE @ @ @ WT EC
HN
+ E .R. N._. Y cT @ e2 HW \ X) p t7 Lf ) 7 Lf , T FAL0W
+DBA t Y gT , ` WT FAL0W
+DBA Y 3T L Y' , T T FAL0W
+DBA Y S , S FAL0W
+DBA ` ^Y S , L S FAL0W
+DBA +Y S ( 8 (Y BAD AB d YO FC < .Y KBB A(D0(I BBBP g R FBB B(I0A8D3
+8D0A(B BBBF Ե FYX R P g R FBB B(I0A8D:
+8D0A(B BBBG @ 2YX ]R P g MR FBB B(I0A8D:
+8D0A(B BBBG YX R P ` Q FBB B(I0A8G
+8D0A(B BBBI
+Yu Q P $
Q FBB B(I0A8G
+8D0A(B BBBI Yu Q P 8 p xQ FBB B(D0A8G
+8D0A(B BBBK P | CQ FDB E(A0A8G
+8A0A(B BBBI P ( ?R FDB E(A0A8G
+8A0A(B BBBI e2 HW p9) 97 Lf $ (Y' < 8 <Y KBB A(D0(I BBBP \9' R FBB B(I0A8G
+8D0A(B BBBG ( TY xR P @ @ hR FBB B(A0D8KZ
+8A0A(B BBBH 2 ~Y{ 3R , dB= \R FAL W
+DBA 9 Y 8R P \B +R FBB E(G0A8Ly
+8D0A(B BBBG D ZYO Q e2 HW N) 0 N7 Lf L O) ` $O7 Lf , HO R FAL0W
+DBA 4 Y R , O R FAL0W
+DBA | Y _R Y' , | O ;R FAL0W
+DBA ؿ Y R , P R FAL0W
+DBA dY Q ( bY BAD AB < 4Y KBB A(D0(I BBBP x Ow gQ FBB B(I0A8DA
+8D0A(B BBBH 0 LYX (Q P Uw Q FBB B(I0A8DA
+8D0A(B BBBH 8YX P P P \w P FBB B(I0A8DH
+8D0A(B BBBA $YX P P $bw zP FBB B(I0A8DH
+8D0A(B BBBA t YX ;P P ( 8h +P FDE B(G0A8Dw
+8D0A(B BBBG 7 Y O P k WP FDB B(G0D8D?
+8D0A(B BBBG P8 Y P pe2 HW n) n7 Lf o) $o7 Lf Y' < 0 *Y KBB A(D0(I BBBP nW O FBB B(I0A8D'
+8D0A(B BBBB BYX tO P 8 tg dO FBB B(I0A8D.
+8D0A(B BBBK .YX %O @ zT O FBA I(Dg
+(D ABBJ ( zPLR x]Z 0 YR N @ , ~T N FBA I(Dg
+(D ABBJ YR N P ȂH( N FEB B(A0A8G
+8A0A(B BBBH e2 HW ) Ī7 Lf 誤) 7 Lf () D7 Lf , h) @ 7 Lf \ ) p ī7 Lf , 諤 M FAL0W
+DBA D Y M , 0 0 M FAL0W
+DBA kY M , x x {M FAL0W
+DBA 8Y WM , GM FAL0W
+DBA Y #M , M FAL0W
+DBA d ҷY L зY' , d < L FAL0W
+DBA Y L , L FAL0W
+DBA Y sL , ̭ cL FAL0W
+DBA P MY ?L , < /L FAL0W
+DBA Y L , \ K FAL0W
+DBA Y K ( p Y BAD AB < Y KBB A(D0(I BBBP 8 8W [K FBB B(I0A8D'
+8D0A(B BBBB ιYX K P ,W K FBB B(I0A8D'
+8D0A(B BBBB \ YX J P W J FBB B(I0A8D'
+8D0A(B BBBB YX ~J P | W nJ FBB B(I0A8D'
+8D0A(B BBBB 4 YX /J P ƤW J FBB B(I0A8D'
+8D0A(B BBBB ~YX I P T ˤg I FBB B(I0A8D.
+8D0A(B BBBK jYX I P Ҥg I FBB B(I0A8D.
+8D0A(B BBBK x VYX BI P , ؤg 2I FBB B(I0A8D.
+8D0A(B BBBK BYX H P ޤg H FBB B(I0A8D.
+8D0A(B BBBK P .YX H P g H FBB B(I0A8D.
+8D0A(B BBBK YX UH OBB E(G0G8K`_
+8G0A(B BBBH
+8A0A(B BBBG H` P L G FDB B(D0A8G[
+8A0A(B BBBA P H WH FDB B(D0A8Gk
+8A0A(B BBBA P H FDB B(D0A8G[
+8A0A(B BBBA P {I FDB B(D0A8G[
+8A0A(B BBBA P D L J FDB B(D0A8G[
+8A0A(B BBBA < Xe2 HW T &) h '7 Lf 0') L'7 Lf p') '7 Lf ') '7 Lf ') ( (7 Lf D 0() X L(7 Lf t p() (7 Lf () (7 Lf () )7 Lf 0)) L)7 Lf 4 p)) H )7 Lf d )2 x Y. Fb )M At
+K < )> /H FBA I(D`
+(D ABBA l YH H ( *R BAA \
+DBJ < ,p G FBA I(D`
+(D ABBA pYH G < (-p G FBA I(D`
+(D ABBA H `YH kG < @ @.p [G FBA I(D`
+(D ABBA PYH 3G < X/p #G FBA I(D`
+(D ABBA @YH F < p0p F FBA I(D`
+(D ABBA P 0YH F < H 1p F FBA I(D`
+(D ABBA YH F < 2p {F FBA I(D`
+(D ABBA YH SF < 3p CF FBA I(D`
+(D ABBA X YH F < P 4p F FBA I(D`
+(D ABBA YH E < 5p E FBA I(D`
+(D ABBA YH E < 7p E FBA I(D`
+(D ABBA ` гYH sE < X 8p cE FBA I(D`
+(D ABBA YH ;E < 09p +E FBA I(D`
+(D ABBA YH E < H:p D FBA I(D`
+(D ABBA h YH D < ` `;p D FBA I(D`
+(D ABBA YH D < x<p D FBA I(D`
+(D ABBA YH [D < =p KD FBA I(D`
+(D ABBA p pYH #D < h >p D FBA I(D`
+(D ABBA `YH C < ?p C FBA I(D`
+(D ABBA PYH C < @p C FBA I(D`
+(D ABBA x @YH {C < p Ap kC FBA I(D`
+(D ABBA 0YH CC $ l C EAL0AA$ C C F{
+G |
+D X HDz KBB H(D0w
+(I BBBO H0 4 t lG B FDA D`\
+ DABF |YX lB 4 H \B FDA D`U
+ DABE p YX 9B 4 <J )B FDA D`U
+ DABE YX B 4 d K A FDA D`U
+ DABE YX A 4 L A FDA D`U
+ DABE ` YX A 4 \N A FDA D`U
+ DABE YX mA 4 T O ]A FDA D`U
+ DABE YX :A 4 Q *A FDA D`U
+ DABE P YX A 4 |R @ FDA D`U
+ DABE YX @ 4 D S @ FDA D`U
+ DABE IJYX @ 4 <U @ FDA D`U
+ DABE @ ̲YX n@ 4 V ^@ FDA D`U
+ DABE ԲYX ;@ 4 4 W +@ FDA D`\
+ DABF ܲYX @ 4 lY ? FDA D`\
+ DABF 0 YX ? 4 Z ? FDA D`\
+ DABF YX ? 4 $ L\ ? FDA D`\
+ DABF Џ YX o? 4 t ] _? FDA D`\
+ DABF YX <? 4 ,_ ,? FDA D`\
+ DABF p YX ? 4 ` > FDA D`\
+ DABF YX > 4 d b > FDA D`\
+ DABF YX > 4 |c > FDA D`\
+ DABF ` YX p> 4 d `> FDA D`\
+ DABF $YX => P T \f -> FEB F(D0D8J
+8D0A(B BBBK $ YM = nFPD L t g PBB A(A0`
+(A BBBC E
+(A BBBD 8 @h@ = EC
DNN
+ D _.j. 0 Dog EDD D
+AAM DAA8 o [= EC
DNN
+ D _.j. H p vb PBB B(A0A8DP
+8A0A(B BBBF P w = FGF B(A0A8G]
+8A0A(B BBBK 8 l 4} < EC
DNN
+ D _.j. P xg < FGF B(A0A8Gj
+8A0A(B BBBF 8 < EC
DNN
+ D f.j. 8 8 袥} < EC
DNN
+ D a.j. P t , < FGF B(A0A8G
+8A0A(B BBBF 8 x} m< EC
DNN
+ D _.j. 8 } s< EC
DNN
+ D a.j. 8 @ ¥} y< EC
DNN
+ D _.j. P | DƥG < FGF B(A0A8G
+8A0A(B BBBF 8 @إ A< EC
DNN
+ D a.j. P ݥ _< FGF B(A0A8G
+8A0A(B BBBF 8 ` 0 !< EC
DNN
+ D a.j. P '< FGF B(A0A8Gm
+8A0A(B BBBK 8 ; EC
DNN
+ D _.j. e2 HW 8 Z LC
+I A x 2 b LQ < k F]U L H
+/ c; BEE E(D0D8KP}
+8A0A(B BBBE L ( ; FBB E(A0D8DP,
+8F0A(B BBBE < Y KBB A(D0(I BBBP (W : FBB B(I0A8D'
+8D0A(B BBBB P ƯYX W: P G: FBB B(I0A8G
+8D0A(B BBBB X Y : P p G : FBB B(I0A8G
+8D0A(B BBBK ۯY 9 P " 9 FBB B(I0A8G
+8D0A(B BBBF X Y 9 L H h*A 9 BBB E(A0A8D@
+8A0A(B BBBA < h, YP t,7 G9 FBB E(D0A8J2
+8A0A(B BBBF P `1 79 FFB B(A0K8J
+8A0A(B BBBA 4 T 8 ?9 FAI D0d
+ DABA =Y1 9 4 L9 9 FAI D0d
+ DABA P Y1 8 4 9 8 FAI D0d
+ DABA Y1 8 4 D 9 8 FAI D0d
+ DABA Y1 w8 P <: g8 FBB E(D0A8G
+8D0A(B BBBE u Yl A8 8 `?* 18 AC
DH`
+ H 9. Z. Y }8 8 T i& 8 AC
BQG
+ B 9. Z. ]Y 8 8 88 #9 EC
BJ
+ A . Z. X YW 9 te2 HW Ȧ) Ȧ7 Lf Ȧ) Ȧ7 Lf ɦ) 0 $ɦ7 Lf L Hɦ) ` dɦ7 Lf | ɦ) ɦ7 Lf ɦS Y' H ʦ EAG U
+CAJ [
+CAA m
+CAG NCA ( غY BAD AB D (ʦ 8 FBB A(I0DP
+0D(A BBBH aYG 8 D ˦ p8 FBB A(I0DP
+0D(A BBBH HYG @8 D h Hͦ 08 FBB A(I0DP
+0D(A BBBH P /YG 8 D Φ 7 FBB A(I0DP
+0D(A BBBH YG 7 D ( hЦ 7 FBB A(I0DP
+0D(A BBBH YG 7 D Ѧ p7 FBB A(I0DP
+0D(A BBBH p YG @7 D Ӧ 07 FBB A(I0DP
+0D(A BBBH ˺YG 7 D H զ 6 FBB A(I0DP
+0D(A BBBH 0 YG 6 D ֦ 6 FBB A(I0DP
+0D(A BBBH YG 6 D 8ئ p6 FBB A(I0DP
+0D(A BBBH YG @6 < Y KBB A(D0(I BBBP ٦W 5 FBB B(I0A8D'
+8D0A(B BBBB ` ȼYX 5 P |ߦW 5 FBB B(I0A8D'
+8D0A(B BBBB YX b5 P pW R5 FBB B(I0A8D'
+8D0A(B BBBB 8 YX 5 P dg 5 FBB B(I0A8D.
+8D0A(B BBBK YX 4 P X hg 4 FBB B(I0A8D.
+8D0A(B BBBK xYX u4 P lg e4 FBB B(I0A8D.
+8D0A(B BBBK | dYX &4 P 0 pw 4 FBB B(I0A8DA
+8D0A(B BBBH PYX 3 P w 3 FBB B(I0A8DA
+8D0A(B BBBH T <YX 3 P
+ w x3 FBB B(I0A8DH
+8D0A(B BBBA (YX 93 P t
+ w )3 FBB B(I0A8DH
+8D0A(B BBBA , YX 2 P
+ r 2 BEB E(D0A8J=
+8A0A(B BBBD $ 0 YW 2 eFPD ` \ W 2 FBB B(F0D8GZT]A
+8A0A(B BBBA ` 2 FEB B(A0F8GVbA
+8A0A(B BBBI ` $ ,2 3 FEB B(A0F8GVbA
+8A0A(B BBBA p D, 4 FBB B(A0F8GVPAA
+8A0A(B BBBA WUPA p TT, _5 FBB B(A0F8GVPAA
+8A0A(B BBBA WUPA @e2 HW , c) @ d7 Lf \ 8dR Lu
+O A | Y' ddT LC ( Y BAD AB < 4 |d o5 FBA I(DP
+(D ABBB =YF G5 < e 75 FBA I(DP
+(D ABBB < +YF 5 < lg 4 FBA I(DP
+(D ABBB YF 4 < HY KBB A(D0(I BBBP | hg 4 FBB B(I0A8D=
+8D0A(B BBBD 4 `YX H4 P n 84 FBB B(I0A8DP
+8D0A(B BBBI LYX 3 P T t 3 FBB B(I0A8DW
+8D0A(B BBBB 8YX 3 P z 3 FBB B(I0A8DW
+8D0A(B BBBB x $YX ]3 P , ` M3 FBB B(D0A8G
+8D0A(B BBBA L d 3 FBB J(A0A8D
+8D0A(B BBBB 8u Y 2 L (Y 2 FBB B(D0D8G
+8A0A(B BBBJ P 8 8 G3 FFB E(D0D8G$
+8D0A(B BBBE P c3 FBJ B(G0D8G{
+8D0A(B BBBB e2 HW
- П5 - $- @< ( l- < q P - d x - 8- T- p- - - ġA FG i
+EA n ( LR < HM T TL l 8[ A FG i
+EA A FG i
+EA Y' 8 ȣx 1 Lk X (x 1 Lk x x w1 Lk 褧 [1 Ls X ?1 Ls ȥ #1 Ls 8 1 Ls 0 Ls 8 0 Ls X x 0 Lk x 觧x 0 Lk Hx {0 Lk ( \ Y BAD AB VYO FC `T 0 EN < jY KBB A(D0(I BBBP ` `W / FBB B(I0A8D'
+8D0A(B BBBB YX |/ P TW l/ FBB B(I0A8D'
+8D0A(B BBBB nYX -/ P 8 HW / FBB B(I0A8D'
+8D0A(B BBBB ZYX . P <g . FBB B(I0A8D.
+8D0A(B BBBK \ FYX . P @g . FBB B(I0A8D.
+8D0A(B BBBK 2YX @. P | DƧg 0. FBB B(I0A8D.
+8D0A(B BBBK 4 YX - P Ḩg - FBB B(I0A8D.
+8D0A(B BBBK
+YX - P T Lҧg - FBB B(I0A8D.
+8D0A(B BBBK YX S- P Pاg C- FBB B(I0A8D.
+8D0A(B BBBK x YX - H , TާR , FBB D(A0G
+0A(A BBBD D x hV , FBB A(A0Gq
+0A(A BBBD P + - FHB B(A0F8J
+8A0A(B BBBJ 4 , . FJA Dpu
+ DBBA $ zPLR x p , ڽY X. 4 d H. FJA Dpu
+ DBBA | Y ". 4 . FJA Dpu
+ DBBA tY - 4 , $ - FJA Dpu
+ DBBA AY - 4 | - FJA Dpu
+ DBBA l Y - 4 p- FJA Dpu
+ DBBA ۼY J- 4 D :- FJA Dpu
+ DBBA Y - 4 l - FJA Dpu
+ DBBA \ uY , 4 , FJA Dpu
+ DBBA BY , $e2 HW H T ` l x) 0 7 Lf L Y' ( ` Y BAD AB < Y KBB A(D0(I BBBP ( 8 |+ FBB B(I0A8G
+8D0A(B BBBC Y W+ P . G+ FBB B(A0D8N
+8A0A(B BBBH , &= + FAL W
+DBA Y f+ P 0! & V+ BBB B(A0A8G
+8A0A(B BBBA LY 4+ P ! $5
+ S+ BBB B(A0A8G\
+8A0A(B BBBI Y '+ P " ?
+ A+ BBB B(A0A8G\
+8A0A(B BBBI ;Y + P t" J
+ /+ BBB B(A0A8G\
+8A0A(B BBBI t Y + P " T + BBB B(A0A8G)
+8A0A(B BBBD QY * P L# \ + BBB B(A0A8GD
+8A0A(B BBBA | Y * P # e4 * BBB B(A0A8G
+8A0A(B BBBB gY * P $$ lt * BBB B(A0A8G
+8A0A(B BBBI t~ Y * P $ u * BBB B(A0A8G)
+8A0A(B BBBD }Y * P $ $~4 * BBB B(A0A8G
+8A0A(B BBBB Y * P h% t * BBB B(A0A8G
+8A0A(B BBBI Y * P % D * BBB B(A0A8G
+8A0A(B BBBH Y s* P @& } * BBB B(A0A8Gk
+8A0A(B BBBA ` Y a* ` & {* FHG B(A0D8G
+8D0A(B BBBA EDDVKEDDV~EDDVAEDDVAEDDVAEDDVAEDDVAEDDVAEDDVAEDDVAEDDVAEDDVAEDDV Y {) ( H{e2 HW ( `) ( |7 Lf ) >Y' ( () RY BAD AB < T) $Y KBB A(D0(I BBBP ( * FBB B(I0A8GL
+8D0A(B BBBJ <Y * P \) d * FBB B(I0A8GS
+8D0A(B BBBC Y * P ) u* FBB B(I0A8GS
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-(A ABBG L D xr BEB E(I0H8DP
-8D0A(B BBBD L 8t w BJE B(A0A8D@8A0A(B BBB 4 t / BFD D0
- DABA < u BLG A(G@
-(A ABBH T \ vB ߊ BLB E(A0A8DK
-8C0A(B BBBF L l ~] BBB H(A0D8DP
-8A0A(B BBBG | U BBB E(A0A8DP
-8A0A(B BBBA |
-8A0A(B BBBJ "
-8A0A(B BBBD , < x ADD v
-AAG T l X BEB A(D0D@
-0D(A BBGE D
-0C(A BBBJ T j BEB B(A0D8J
-8A0A(B BBBA T d ؉ m BBB B(A0A8G*
-8A0A(B BBBK T p < BEB E(D0A8Gr
-8D0A(B BBBG L 1 BBB B(D0A8Gp
-8D0A(B BBBG ! hV L 4! BBB B(D0A8D@m
-8A0A(B BBBH T 0b BEB E(A0A8G
-8A0A(B BBBF < ! H BBG A(G@
-(A ABBG L d! J BBB B(A0A8G
-8A0A(B BBBI L ! H BBK I(K0G8Mp
-8D0A(B BBBA L " m BBB B(A0A8Dp#
-8A0A(B BBBA l T" 8 BNB B(A0D8G
-8A0A(B BBBA n
-8A0A(B BBBA L |# BEE E(A0A8G@Q
-8A0A(B BBBK L # (Y - BLB E(H0G8D
-8A0A(B BBBH L d# 8 BEB B(D0A8D`c
-8A0A(B BBBA L # BEB E(I0H8D@
-8D0A(B BBBA , $ BFA D0 AABT 4$ 1 c BBB B(A0D8G#
-8A0A(B BBBA D $ , BJB K(A0
-(A BBBA D $ h g BBJ A(D0
-(A BBEA $ % @ AHG tAAL % ] BBB H(A0D8DP
-8A0A(B BBBG < L& BBG A(G@
-(A ABBG L % x BEB E(I0H8DP
-8D0A(B BBBD L $& 8 _ BJE B(A0A8D@
-8A0A(B BBBA < ,' BBG A(G@
-(A ABBG D l' Ȳ BBB H(G0G0A(A BBB < ' BBG A(G@
-(A ABBH < ' P BED A(D0Y
-(A ABBA L |' / BEB E(I0H8DP
-8D0A(B BBBD L ' BJE B(A0A8D@
-8A0A(B BBBA < ( P BBG A(G@
-(A ABBG 4 \( Y AIG t
-AAI DCA < ( 8 k BGD A(G0~
-(A ABBG L ( 3 BGB A(A0N
-0A(A BBBA < $) ( c BGB D(A0~
-(A BBBK 4 * ȽI ADG q
-AAA DCA T ) BBB B(D0A8G
-8A0A(B BBBC L ) 8 , BBB B(A0A8J
-8A0A(B BBBC < * BFA D0}
- FABE D FAB 4 * xY AIG t
-AAI DCA T * BEE I(A0A8D
-8D0A(B BBBA < + BED D(GPf
-(A ABBF T T+ 8 BEH B(A0A8G
-8A0A(B BBBA T + ہ BBB B(G0A8G
-8A0A(B BBBA T , 8 BHB B(A0A8J
-8A0A(B BBBE \ - BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 , Pa ' AIG
-AAF DCA D , BBB A(D0GP
-0D(A BBBK T <- ʀ BBB B(D0D8G
-8A0A(B BBBH L - h Ӏ BEB E(I0H8DP
-8D0A(B BBBD \ - ( BEH D(I0D@
-0F(A BBBF U
-0F(A BBBA 4 D. i o AID G
-AAI DCAL |. ; BEB E(I0H8DP
-8D0A(B BBBD d . , BGE E(A0A8D@
-8F0A(B BBBI g
-8F0A(B BBBA T 4/ D
BBB B(A0A8G
-8A0A(B BBBA L / ' BEB E(I0H8DP
-8D0A(B BBBD T / P BLB B(A0A8Nc
-8A0A(B BBBA L 40 BEB E(I0H8D@
-8D0A(B BBBA < <1 BFA D0}
- FABE D FAB T 0 BBE B(D0D8J
-8A0A(B BBBA L 1 w BHB B(A0D8J
-8A0A(B BBBI T l1 m BBB B(D0D8Gc
-8A0A(B BBBA , 1 ( Z AAD@o
-AAA L 2 X BBB B(A0D8G@j
-8A0A(B BBBH \ 2 BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 2 8Y ~ AIG t
-AAI DCA < 2 `m ~ BID D
-AFF AEF L 3 BEA D(FPf
-(A ABBB L
-(A ABBF $4 4 Af
-I C T 3 b } BBB B(A0K8J+
-8A0A(B BBBA < 3 } BLD D(G0
-(A ABBE \ $4 } BEH D(I0D@
-0F(A BBBF U
-0F(A BBBA \ 4 x S} BEH D(I0D@
-0F(A BBBF U
-0F(A BBBA \ 4 ( } BEH D(I0D@
-0F(A BBBF U
-0F(A BBBA < D5 | BGB D(A0~
-(A BBBK \ <6 x
- BBE B(A0D8D@m
-8D0A(B BBBB D8C0A(B BBB< 5
- c| BLD D(G0
-(A ABBE < $6 G| BED I(K@`
-(D ABBH T d6 8 '| BBB D(I0j
-(A BGBG R
-(A BBGB < 6 { BLD D(G0
-(A ABBE $ 7 `
q ADD0eAAL $7
{ BEB E(I0H8D@
-8D0A(B BBBD L t7 8 s{ BJE B(A0A8D`8A0A(B BBB < |8 BBG A(G@
-(A ABBF < 8 x z BLA D(G@
-(D ABBC 8 H T \8 @ z BBB E(A0A8G
-8A0A(B BBBE l9 x AO 9 x T 8 p z BBB B(K0A8G
-8A0A(B BBBH 9 ' E
-K < : ' BKD A(G0(A ABB L 9 x)~ z BBE B(A0A8Ga
-8A0A(B BBBA T 9 5 -z BBB B(A0K8K
-8A0A(B BBBG ; P8 AO $; P8 T : H8
- y BEE B(A0A8G m
-8A0A(B BBBA , ; BE BAG wAB ; B1 Dl ; `Tr. K^ D D; B Oy BGB D(A0G@~
-0A(A BBBC D< pC1 Ak T ; C y BHB B(D0A8J
-8A0A(B BBBI < < (M Yy BED D(G
-(A ABBH < D< M +y BED D(G
-(A ABBI D < hN x BEE A(D0G
-0A(A BBBI L < O, x BEE B(A0D8G
-8A0A(B BBBA L = P, x BEE B(A0D8G
-8A0A(B BBBA < $> P BBA D(D@
-(A ABBA L d> R& BBB B(A0A8D`
-8A0A(B BBBA L > `T: BEB B(A0A8Dp
-8A0A(B BBBA D ? PW8 BDE A(A0D@
-0A(A BBBF \ > HY- &w BBB D(D0G
-0A(A BBBA G
-0C(A BBBA T > \
v BBB B(A0A8G
-8A0A(B BBBA L @ h BBB A(D0Z
-(A BBBD )
-(G BBBJ T ? pj v BBE B(A0A8G
-8A0A(B BBBA \ @ (q GEB B(A0A8Dp|8A0A(B BBBDp L T@ t `v BBB A(D0D
-0D(A BBBA L @ { ]v BEE B(D0D8Dp
-8D0A(B BBBA < @ X1 7v BGA D(G@
-(A ABBB d 4A X u BBB F(D0D8J`g
-8A0A(B BBBA K
-8A0A(B BBBK < A u BDE C
-ABG H
-ABE B @" DR
-J A < B P BEB A(A0D@0A(A BBBT <B u BEE B(D0A8G/
-8D0A(B BBBA , LC AAG f
-AAG T B Ȇ t RHB B(A0A8G{
-8A0A(B BBBD L C
wt BEB B(A0A8G \
-8D0A(B BBBC L lC Pf t BEE B(A0D8JS
-8D0A(B BBBC 4 C p t BAA G@
- DABJ $ D (Y AK Y
-AA L D ` ot BEB E(I0H8D@
-8D0A(B BBBD L lD 2 Gt BJE B(A0A8DP
-8A0A(B BBBA L D О 't BEB B(A0A8G
-8A0A(B BBBD D E `$ ,t BGI A(D0JL
-0A(A BBBK 4 F H~ BEA A(D@j(A ABBL E s BBL B(A0A8D`M
-8A0A(B BBBA F Ь AL L E Ь rs BBB E(D0E8G`
-8A0A(B BBBA T LF @$ s BEE E(D0D8Ga
-8A0A(B BBBA , F du AADP~
-AAJ , F Bu AGDP
-AAG l G x #u BGB B(A0D8GF
-8A0A(B BBBG (
-8A0A(B BBBF T tG t BEE E(A0H8G;
-8A0A(B BBBJ T G ` t BBB B(A0A8J~
-8D0A(B BBBA T $H 7 t BEB B(D0A8T7
-8D0A(B BBBH T |H t BBB B(A0A8G
-8D0A(B BBBA < H t BBD A(G@
-(D ABBG T I 8 kt BFE B(A0A8G
-8A0A(B BBBA D lI p 5 u BBB A(A0Dr
-0D(A BBBA D lJ h
^ BBB D(A0D@
-0A(A BBBA D J i BBB D(A0D@
-0A(A BBBA < DJ t BBB A(K0(A BBB T J ({
t BLB B(A0D8G%
-8A0A(B BBBC T J P t BEB B(A0A8JP
-8A0A(B BBBG L 4K 8[ ?v BEE B(D0D8G`
-8D0A(B BBBH 4 <L 9D BEA A(D0p(A ABB T K :\ u BBB E(A0A8Jz
-8A0A(B BBBA L @# Aa L 4L (@ u BBB E(D0A8GPe
-8A0A(B BBBA T L BE Su BBB B(A0A8G
-8A0A(B BBBG M G* AM
-B Y T L G Ku BBB B(A0A8G
-8A0A(B BBBA < TM hS u BED A(G@x
-(D ABBD T M SI u BBB B(D0D8J?
-8A0A(B BBBE L M Z u BBB B(D0D8JX
-8D0A(B BBBA L <N paA u BEE B(A0D8D
-8D0A(B BBBK T N pc u BBB B(A0A8J@
-8A0A(B BBBA T N 8ob u BBB B(D0A8Gf
-8A0A(B BBBD D <O Pt u BBE A(A0G@
-0D(A BBBA L O tr u BBB B(E0A8J@
-8A0A(B BBBA $ P (v^ AG b
-AA L O `v Cu BBB B(A0A8GPd
-8A0A(B BBBA d LP w u BBB B(A0A8G`I
-8A0A(B BBBD j
-8A0A(B BBBL L P x t BBB E(A0C8G`x
-8A0A(B BBBH L Q Xz wt BEB B(A0A8GP
-8A0A(B BBBA d TQ ~ Bt BBB E(D0A8G`
-8A0A(B BBBD N
-8A0A(B BBBH T Q 0 s BBB B(D0A8J
-8A0A(B BBBE d R s BBB B(D0A8D`H
-8A0A(B BBBE
-8A0A(B BBBK d |R s BBB B(D0A8D`H
-8A0A(B BBBE
-8A0A(B BBBK d R X Ls BBB B(D0A8D`H
-8A0A(B BBBE
-8A0A(B BBBK L LS r BBB B(A0D8G@
-8C0A(B BBBI L S p_ r BEE E(A0D8G`
-8D0A(B BBBF L T BEA D(G0_
-(A ABBH K
-(A ABBG T <T <r BBE E(D0A8G
-8D0A(B BBBI T T q BBE E(D0A8G
-8D0A(B BBBI T T P q BBE E(D0A8G
-8D0A(B BBBI T DU " q BBB E(D0D8Jn
-8A0A(B BBBK TV :r. K^ L U q BBH E(A0A8J
-8D0A(B BBBD T V xZ Ur BEB B(A0D8J|
-8D0A(B BBBE T \V r BEH B(A0D8J
-8D0A(B BBBE L V s BBB B(D0A8Dp
-8C0A(B BBBA W ( W 4 W AEG @
-AAA HAA 4 $X `_ BAD U
-ADG oAB D W 5 r BHE D(A0Dp_
-0A(A BBBA L W r BJB A(A0b
-(A BBDE _(A BBB4 X T AAK q
-CAF DFA ,Y DY L \Y BBD D(F@v
-(A ABBB g(A ABB Y h DR Y p AW d ,Y p dq BEL B(A0A8D`
-8C0A(B BBBF L8A0A(B BBB d Y q BEL B(A0A8D`
-8C0A(B BBBF L8A0A(B BBB 4 Z `S AAK q
-CAF DEA Z $ DQ
-K D TZ r Vp BBE A(D0DU
-0A(A BBBA D Z ) /p BBB A(A0D
-0A(A BBBA D Z 2 p BBB A(A0D
-0A(A BBBA L [ BEB B(A0A8D`\
-8A0A(B BBBA T |[ @, o BBE B(D0A8G
-8D0A(B BBBK T [ A ro BEE E(D0A8Go
-8D0A(B BBBG T ,\ Xo BBA A(JK
-(A ABBG K
-(A ABBA L <] X BOB A(A0]
-(A BBBG Y
-(A BBBH D \ n BEB D(D0D@
-0D(A BBBA l ] p n BBB B(A0A8DX
-8A0A(B BBBA k
-8A0A(B BBBA T ] 7n BBB B(A0A8G+
-8C0A(B BBBA ^ (. [R < ^ @ BBG A(G@
-(A ABBG T <^ n BBB B(D0G8J
-8A0A(B BBBA L L_ hc BKB B(A0A8Dpa
-8A0A(B BBBF T ^ m BBB I(D0D8D)
-8D0A(B BBBA L <_
-d m BBB B(D0A8Dg
-8A0A(B BBBA T _ Nm BBB B(D0A8D
-8A0A(B BBBA L _ XV m BBB B(D0A8D
-8D0A(B BBBA L 4` hm l BBB B(D0H8Kp
-8A0A(B BBBA , <a 0 BDD ^AB T ` hl BBE E(A0A8G
-8A0A(B BBBA T a ,l BBE E(A0H8D9
-8D0A(B BBBG L da XV k BEB B(D0A8D
-8D0A(B BBBA D a hc k BBB A(D0GB
-0A(A BBBD L b BBB B(A0D8G@j
-8A0A(B BBBH Lb " ;k BBB E(A0D8D
-8A0A(B BBDA
-8F0A(B BBBE p
-8C0A(B BBBA T b j BBB E(A0F8G*
-8C0A(B BBBA c ,r. K^ c H&" DR
-J A T dc X&" zj BBB E(A0A8G
-8A0A(B BBBF T c 0,w dj BBB B(D0D8J
-8F0A(B BBBA T d X1e yj BEB E(D0D8G
-8A0A(B BBBA L ld p5 qj BBB B(A0D8Jr
-8A0A(B BBBE T d : j BEB B(A0A8G
-8D0A(B BBBA L e > AAG ]
+8A0A(B BBBJ < ,T H FEB D(GP
+(D BBBB < D LT FEB D(GP
+(D BBBB < lT FEB D(GP
+(D BBBB < Ĕ T FEB D(GP
+(D BBBB < T FEB D(GP
+(D BBBB < D ̢T { FEB D(GP
+(D BBBB X ( 죮{ KBB H(D0w
+(I BBBO H0 4 FIA D`
+ DABJ 3 RZX 4 0 FIA D`
+ DABJ 3 RZX 4 p FIA D`
+ DABJ ,4 RZX m 4 Ж ] FIA D`
+ DABJ |4 RZX : 4 Ю * FIA D`
+ DABJ 4 RZX P p W FBB B(I0A8D'
+8D0A(B BBBB ( RZX P ܗ t BEB B(D0F8JI
+8D0A(B BBBK 8 Tj RZ} 'IPEFPD P l BPI B(A0D8Go
+8A0A(B BBBF 4 ZZL JIPE FPDP FFB B(D0D8Ju
+8D0A(B BBBD P L $î FFB B(D0D8Ju
+8D0A(B BBBD P Ʈ FFB B(D0D8Ju
+8D0A(B BBBD P \ʮ FFB B(D0D8Ju
+8D0A(B BBBD P H ͮ FFB B(D0D8Ju
+8D0A(B BBBD P Ѯ FFB B(D0D8Ju
+8D0A(B BBBD e2 HW ծ $ծk F]U P < tծ # FBB B(A0A8K
+8A0A(B BBBF :ZZ P ( FBB B(A0A8K
+8A0A(B BBBF d YZ P FBB B(A0A8N
+8A0A(B BBBF hm YZ6 P $ e2 HW < 82 P dR Lu
+O A p S LB VYZO FC P X FBB B(A0I8DM
+8D0A(B BBBD 5YZ < YZ KBB A(D0(I BBBP W P FBB B(I0A8D'
+8D0A(B BBBB h [ZX P t FEB B(A0A8G
+8D0A(B BBBF L p 0b
FBB E(A0A8L
+8A0A(B BBBA d
+e2 HW | l[Z' H $& EAG U
+CAJ [
+CAA d
+CAH NCA ( ܟ 4[Z BAD AB L& LF
+N ( & Lx
+L A
+G L [ZO FC < h [Z KBB A(D0(I BBBP &w FBB B(I0A8DA
+8D0A(B BBBH ^ZX X P p ,) H FBB E(A0A8N
+8A0A(B BBBG , Ġ 5= 1 FAL W
+DBA
+ ]Z
L 5 FBE B(A0A8G
+8A0A(B BBBF , ?m FAA O
+ABF 8 T? } FAA G\
+ AABA 8 ȡ C [ EC
BTG
+ K . h. e2 HW < Ģ \Z KBB A(D0(I BBBP ` P[ o FBB B(I0A8D
+8D0A(B BBBG ^Zu < , ̢ a , FAL0
+DBA ( ^Z e2 HW ԣ a) a7 Lf b) <b7 Lf 4 z^Z' ( H ^Z BAD AB < t `_Z KBB A(D0(I BBBP aW FBB B(I0A8D'
+8D0A(B BBBB Ȏ xaZX P | gW FBB B(I0A8D'
+8D0A(B BBBB 4 daZX o P m _ FBB B(A0I8G
+8D0A(B BBBB <v PaZ / P T Lp M FBB B(A0I8D(
+8D0A(B BBBA bZ. P q FGB B(G0D8G
+8D0A(B BBBA w cZ9 P , tP K FGB B(G0D8G`
+8D0A(B BBBH w ZhZ9 < e2 HW T w) h w7 Lf , x 7 FAL0W
+DBA < mZ ̧ mZ' ( mZ BAD AB < mZ KBB A(D0(I BBBP w FBB B(I0A8DP
+8D0A(B BBBI ` oZX D P }D 4 FBB B(D0A8G.
+8D0A(B BBBE ` h 肯 FGB B(G0D8GT
+8D0A(B BBBD JKD p e2 HW joZ' Lv
+N A
+G ĩ L
+H < :oZ KBB A(D0(I BBBP 0W FBB B(I0A8D'
+8D0A(B BBBB 8 RqZX H $ FBB B(A0On
+0D(B BBBJ ( zPLR x
0 qZh T D e2 HW < NrZ' < D , MAE
+FBJ y
+ABD 0 MAH
+IBL ī qZO FC < "rZ KBB A(D0(I BBBP | W d FBB B(I0A8D'
+8D0A(B BBBB 4 :tZX % d2 HW P `M FBB B(A0D8J
+8A0A(B BBBF , T \= FAL W
+DBA X sZ D sZ' L <r FBB B(D0A8K`
+8A0A(B BBBF , l= FAL W
+DBA ksZ t , L d= d FAL W
+DBA P 8sZ @ < 8 6sZ KBB A(D0(I BBBP ԭ W FBB B(I0A8D'
+8D0A(B BBBB NuZX P @ W FBB B(I0A8D'
+8D0A(B BBBB :uZX b P d2 HW l 躯R Lu
+O A >uZ' T LC < 6uZ KBB A(D0(I BBBP X W FBB B(I0A8D'
+8D0A(B BBBB NwZX g P į W FBB B(A0D8N
+8A0A(B BBBI , ů= Q FAL W
+DBA
+wZ - P ` ů FGB B(A0D8G
+8A0A(B BBBH X d2 HW t د) د7 Lf , @د [ FAL0W
+DBA \ 7vZ 7 H د EAG U
+CAJ [
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+8A0A(B BBBF D ޯ BBE E(A0A8
+0A(B BBBA D BBB B(A0A8x
+0A(B BBBA H ( ! BBB B(A0A8DX
+8A0A(B BBBH L t BBB B(D0A8G;
+8A0A(B BBBG D ij BBE E(A0A8
+0A(B BBBA D H BBB B(A0G8
+0A(B BBBA D T ` BBE B(A0A8D
+0A(B BBBA D ( BBB E(A0A8
+0A(B BBBA D 9 BBB E(D0D8
+0A(B BBBA L , BEE E(D0A8D
+8A0A(B BBBI | ES
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+8A0A(B BBBI < rZ KBB A(D0(I BBBP
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+8D0A(B BBBG RsZu P ( p FDB B(G0D8G
+8D0A(B BBBG H_ \sZn S 8 # EY T 0# EY p D# EY @ XR FBB B(A0A8:0A(B BBB L з t FBB B(A0A8G
+8A0A(B BBBK H FBB E(F0D8G`@
+8A0A(B BBBF l $ YCH L % F FBB B(A0A8JE
+8A0A(B BBBA kN EG {
+AA kA FBB B(A0A8G$AJGGBGBGGGGGrZ
+8A0A(B BBBK 0 < EC
P
+ D L q. FBB B(A0A8KO&
+8A0A(B BBBF , EC
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+ G H < FBB E(F0D8G`@
+8A0A(B BBBF L ) FEB B(A0A8G
+8A0A(B BBBA 0 4 < EC
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+ H 0 8>l EC
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+ D 8 ܻ tNb EG
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+ B . H UZ FEE B(A0A8Dhl
+8A0A(B BBBB L [U FEE E(D0A8G
+8A0A(B BBBA D X p FBB B(A0A8
+0A(B BBBC L s FBB B(A0A8GH
+8D0A(B BBBF H dyu FBB E(F0D8G`8
+8A0A(B BBBF X < } FBB B(A0A8
+0A(B BBBH 0A(B BBB L ܀ FBB B(A0A8K!
+8A0A(B BBBD , \; EC
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+ I H lu FBB E(F0D8G`8
+8A0A(B BBBF D d FNN G(A0D8@
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+ G 8 < 4 ET
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I.
+ J . L ޱ FBB B(A0A8G
+8A0A(B BBBC x biZ p d2 HW 8) T7 Lf x) 7 Lf ) 0 7 Lf L ) ` 7 Lf H | 8 EAG U
+CAJ [
+CAA d
+CAH NCA H EAG U
+CAJ [
+CAA d
+CAH NCA H EAG U
+CAJ [
+CAA d
+CAH NCA H ` 4 EAG U
+CAJ [
+CAA d
+CAH NCA 2 H FBB B(A0A8D`
+8A0A(B BBBE H ( FBB B(A0A8Dp
+8A0A(B BBBI H X O FBB B(A0A8Dp+
+8A0A(B BBBI H FBB B(A0A8Dp
+8A0A(B BBBH ( mZ BAD AB nZO FC < 8 nZ KBB A(D0(I BBBP W ' FBB B(I0A8D'
+8D0A(B BBBB pZX P @ W FBB B(I0A8D'
+8D0A(B BBBB pZX P W FBB B(I0A8D'
+8D0A(B BBBB d pZX J P W : FBB B(I0A8D'
+8D0A(B BBBB а pZX P | FBB B(A0D8N
+8A0A(B BBBI , = FAL W
+DBA 0 bpZ P FBB B(A0D8N
+8A0A(B BBBI , t = FAL W
+DBA x1 oZ P w FBB B(A0D8N
+8A0A(B BBBI , = q FAL W
+DBA 2 ToZ M P X = FBB B(A0D8N
+8A0A(B BBBI , = 7 FAL W
+DBA 2 nZ @ FJB A(A0DPT
+0A(A BBBD @ H FFB A(A0DP
+0A(A BBBD ` + FBB B(A0A8GP]
+8A0A(B BBBD
+8A0A(B BBBG ` + FBB B(A0A8GP]
+8A0A(B BBBD
+8A0A(B BBBG ` D \! FBB E(A0A8G
+8A0A(B BBBA 0IVD ` < 3 FBB E(A0A8G
+8A0A(B BBBA IVD ` dY" FBB E(A0A8G
+8A0A(B BBBA IVD ` p { FBB E(A0A8G
+8A0A(B BBBB IVD x ,d2 HW ) 7 Lf , Л FAL0W
+DBA | nkZ 2 H D BBE B(A0A8DH$
+8A0A(B BBBA D l ء BBB B(A0G8
+0A(B BBBA L BFB B(A0A8Gs
+8A0A(B BBBF ( tjZ BAD AB < 0 FkZ KBB A(D0(I BBBP W FBB B(I0A8D[
+8D0A(B BBBF ^mZX H : FBB E(D0D8GpJ
+8A0A(B BBBF 0 EC
DO
+ F @ EC
Pq.@G
+ H A
+ G . P Ͳ w FDB B(A0D8G
+8A0A(B BBBB , zPLR xa
4 VlZI # < d2 HW T pxZ' H h ܲ EAG Y
CAF [
-ACA R
-CAJ JCA L f > AAG ]
+CAA H
+CAD NCAH ݲ EAG Y
CAF [
-ACA R
-CAJ JCA L lf ? AAG ]
+CAA R
+CAJ NCAH ݲ EAG Y
CAF [
-ACA R
-CAJ JCA , f 8?b D\
-H P
-H Q
-G I , f x?b D\
-H P
-H Q
-G I g ? 4g 0@
- Lg (@ dg @ d f (@ h BEB B(D0A8DPn
-8C0A(B BBBB g8D0A(B BBB g @ g @ h @ ,h @
- Dh @ \h @" th @ h @ h @ h @ DU T h @ g SGB B(A0A8GP 4 th E og NGB A(A0D d h E Ug BBA A(G
-(A ABBD m
-(A ABBE G
-(C ABBA T i G, g BBE B(D0A8G
-8D0A(B BBBK T li pL, f BBE B(D0A8G
-8D0A(B BBBK |j HQ, D\
-H j XQ> D k
-A j xQ> D k
-A T $j Q zf BBB E(A0D8G
-8A0A(B BBBB D |j X Of BKI D(A0G@L
-0A(A BBBA d j Y, /f BGE E(A0A8D@
-8F0A(B BBBI g
-8F0A(B BBBA T ,k pZ e BEB B(D0A8J
-8A0A(B BBBA <l t Tl t ll h$r. K^ l t l xt l pt l ht l `t l Xt m Pt ,m Xt Dm `t" \m xt tm t m t m t m t- m t- $ m t^ AG b
-AA L \m u e BBB B(A0A8GPd
-8A0A(B BBBA L m @v d BBB B(A0A8GPl
-8A0A(B BBBI n w n w< d W n w< d W n w< d W o x ,o x< d W Do @x\ D x
-D R
-A do x |o xx o px L n hxN c BBB A(A0G6
-0A(A BBBA D o h{= BDA G0
- AABC N
- AABF T o `| ,c BBE E(D0A8G
-8D0A(B BBBI T o ~ b BBE E(D0A8G
-8D0A(B BBBI T <p b BBE E(A0A8G
-8D0A(B BBBA < Lq ȃ BBG A(G@
-(A ABBH T p 5b BEB B(A0A8J
-8A0A(B BBBA L ,q " b BJE B(A0A8D@
-8A0A(B BBBA T |q d a BBE E(I0D8J
-8A0A(B BBBB < q b BAD '
-ABG O
-ABA r 8 r. K^ T ,r ^
- a BEE B(A0D8JU
-8A0A(B BBBD T r #b BBE E(D0A8J
-8A0A(B BBBI D s @ BBE A(A0D@
-0A(A BBBA T $s ] b BBB B(D0A8G
-8D0A(B BBBI 4 |s b BAA
-ABA T s X
- rb BBE B(D0D8G
-8A0A(B BBBH D t BBB A(A0D@
-0A(A BBBA D u Ƚ4 BBB A(A0D@
-0A(A BBBA 4 t Ob BDA JD
- DABD D t (N -b BBE A(A0Jo
-0D(A BBBA D u 0% b BDD Gp
- DABF ~
- DABC < du a BBD D(G
-(D ABBJ < u a BED A(J
-(D ABBE L u H a BEE E(A0D8Gg
-8D0A(B BBBG T 4v a BEE E(A0D8J
-8D0A(B BBBH l v ` a BEE E(D0H8D`i
-8A0A(B BBBD
-8A0A(B BBBB w 2q\ AG RAd w Ga BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d w ` BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB L w p ` BJB A(A0W
-(A BBDH ^(A BBBL x BBB B(A0D8G@
-8A0A(B BBBB Dy _ L x _ BBB I(I0A8DPv
-8D0A(B BBBH L x _ BEB E(I0H8DP
-8D0A(B BBBD L Dy H _ BJE B(A0A8D@
-8A0A(B BBBA L y 9 W_ BEB F(G0D8D`
-8D0A(B BBBA d y s _ BBB J(A0A8G`
-8F0A(B BBBF B
-8F0A(B BBBA T Lz ^ BBB B(A0A8J
-8A0A(B BBBC d z h] ^ BBB B(I0A8G`
-8F0A(B BBBF A
-8F0A(B BBBA T { `t 7^ BBJ B(A0H8Fa
-8D0A(B BBBC T d{ t ^ BBJ B(A0H8Fa
-8D0A(B BBBC | t| BBB B(D0A8DP
-8A0A(B BBBB U
-8G0A(B BBBK
-8A0A(B BBBI L | 0 BEB B(A0A8DP4
-8A0A(B BBBA , D} BAH D` AAB, t} BAH D` AAB } r. K^ L } h BBE B(A0A8D`
-8A0A(B BBBA L ~ BBE B(A0A8D`
-8A0A(B BBBA l } XY [ BBB J(D0A8DR
-
-8F0A(B BBBA b
-8D0A(B BBGA T ~ H [ BHB B(A0D8J
-8D0A(B BBBJ $ AL \ ~ \ BBE D(A0P@|
-0D(A BBBK d
-0D(A BBBA T ~ p~ \ BBE E(D0C8J>
-8A0A(B BBBI 4 D I\ BKA Dp
- AABA 4 0r. K^ L X& d P& | H& @& , H&b [ ADG r
-AAA , ܀ &> BDA aAB & L $ & AAG ]
+CAA i
+CAC NCA( L wZ BAD AB x ݲ L~
+F Lp
+L A
+G P (߲ FBB B(A0A8DY
+8A0A(B BBBK P l o _ FBB B(A0A8G
+8A0A(B BBBJ P FBB B(A0A8G
+8A0A(B BBBA < 2wZ KBB A(D0(I BBBP T W FBB B(I0A8D'
+8D0A(B BBBB JyZX @ L 0 FBB B(A0I8D`
+8D0A(B BBBD ; :yZN P ( %' FBG B(A0D8G
+8A0A(B BBBD d2 HW < yZ' < P yZ KBB A(D0(I BBBP Tg FBB B(I0A8D.
+8D0A(B BBBK ,{ZX P X X FBB B(A0I8G
+8D0A(B BBBB {Z h h d2 HW ) 7 Lf |Z' L $ & FBB B(A0I8Dp
+8D0A(B BBBH p ]|Zc 0 # D #& H X # FHB B(D0C8D@y
+8A0A(B BBBH < 4|Z KBB A(D0(I BBBP @ $ 6 FBB B(I0A8GR
+8D0A(B BBBD p L~Z P T+ FBG B(A0D8G
+8A0A(B BBBG v~Z d2 HW `,) |,7 Lf ,) ,7 Lf 4 ,R Lu
+O A L T - FBA A(D0
+(A ABBG z
+(A ABBH H p. EAG Y
CAF [
-ACA R
-CAJ JCA $ t &^ AG b
-AA , &0 AF
FK
- D 4 ' Z BGB A(A0D < ( BDD k
-ABH V
-ABA L D )6 BEB B(A0A8D`h
-8A0A(B BBBA < *4 BBB A(A0d
-(A BBBE T + Y BBE E(A0A8G
-8D0A(B BBBD , t 8-k kY AID r
-DAA D \ x- BBE A(A0D@l
-0A(A BBBA d . Y BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d T . X BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB L / GX BEB E(I0H8DP
-8D0A(B BBBD L 0 X BJE B(A0A8D@
-8A0A(B BBBA 15 4 t 1Y W AIG t
-AAI DCA T 1j W BBB E(A0A8IL
-8A0A(B BBBA r. K^ , ԅ 4 AAK0i
-AAA P5" DN
-N A $ $ `5M AAG AAA L 5 AL L l 5
BEE A(A0]
-(A BBBF
-(E BBBK T H6 V BBB B(A0A8Jg
-8A0A(B BBBA T \ F# %W BBB B(A0I8D
-8D0A(B BBBA T I W BEB B(A0A8G
-8A0A(B BBBA , N &W AAG0d
-AAA O" DR
-J A T \ 0O V BBA H(J`R
-(A ABBA
-(A ABBA T OR V BBB H(H0A8G
-8A0A(B BBBA < [T V BEE D(A0h
-(A BBBA D \x BBB A(K0D@
-0A(A BBBA L 8] AL T 8]# SV BBE B(D0A8G
-8A0A(B BBBA ĉ D
-D D D # X BBB A(D0D@
-0A(A BBBA 4 D ZHA G HAB 4 ĉ x W BAA G0_
- AABB L WW BEH B(D0D8Dp`
-8D0A(B BBBA AX D $ BBB D(D0G@T
-0A(A BBBG l DP DP T M oV BBB B(D0D8G
-8A0A(B BBBB D T V BBB A(A0Dl
-0A(A BBBA D V BBB A(A0J
-0A(A BBBA < h V BBA A(G@z
-(A ABBC T $ ؗ! V BBB B(A0D8G
-8A0A(B BBBA 4 L h d q\ AG RA< ̌ ] 3W AAG0O
-DAC [
-DAA D @ W BEB A(A0G@y
-0D(A BBBA Ⱥ $ к D Ⱥ V BEE A(C0G@
-0D(A BBBA L ̍ V BBB B(A0D8D`m
-8A0A(B BBBH T - SV BBB B(A0A8G
-8A0A(B BBBJ D t t V BEB A(A0GC
-0D(A BBBA @ V BBB E(A0A8D
-8A0A(B BBBI
-8A0A(B BBBG
-8A0A(B BBBI L xf BBB B(D0G8D`u
-8D0A(B BBBG T
- U BEB B(A0A8G
-8A0A(B BBBA < #V BBA A(DO
-(A ABBA , , V OGA A(D 4 \ U BJD K@g
- AABJ $ L hc AK c
-AA L U BBB B(A0A8Gp@
-8A0A(B BBBA T s SU BLB B(D0A8G
-8A0A(B BBBA T d 7U BHF E(D0A8G
-8A0A(B BBBD T p U BEL B(D0D8J
-8A0A(B BBBE ̒ q. K^ q\ AG RA p x 4 p cL L D q T BBB A(D0Gpt
-0D(A BBBG D T BBB D(D0D`
-0A(A BBBA D < T BBB D(D0De
-0A(A BBBA < ( T l ( Af Ԕ b A` h , $ `? BDD l
-ABA L T p AAG ]
-CAF c
-CAI R
-CAJ JCA L AAG ]
+CAA
+CAE NCA H . EAG Y
CAF [
-ACA R
-CAJ JCA L AAG ]
-CAF k
-ACA R
-CAJ JCA L D AAG ]
+CAA H
+CAD NCA < H/2 P t/T LC ( l Z BAD AB / L~
+F ,0 L~
+F 0 L
+K |1 Ls
+I A
+G 2 Lp
+L A
+G @ 2 Lp
+L A
+G d P3k F]U L 3 FBB B(A0A8DPV
+8A0A(B BBBF X 4{ KBB H(D0w
+(I BBBO H0 4 9 FIA D`
+ DABH 8x ZX 4 : FIA D`
+ DABJ x ZX 4 , t< FIA D`
+ DABJ x ZX k 4 | $> [ FIA D`
+ DABJ (y ZX 8 4 ? ( FIA D`
+ DABJ xy ZX 4 A FIA D`
+ DABJ y ǃZX 4 l 4C FIA D`
+ DABJ z σZX 4 D FIA D`
+ DABJ hz ׃ZX l D F \ FFE D(D0G@
+0D(A BBBE d ЃZ P l H FBB B(A0I8D
+8D0A(B BBBJ $ zZ. A P (J FBB B(A0D8NF
+8A0A(B BBBA , < M= FAL W
+DBA @H Z P M_ FBB B(A0D8N
+8A0A(B BBBE , Y= a FAL W
+DBA H uZ = P YE - FBB H(F0A8J
+8D0A(B BBBH ` Z/ P ] FBB H(D0A8IL
+8D0A(B BBBG HC Z P _ FBB B(G0F8J
+8D0A(B BBBD T fZS L d dd FBJ B(A0A8D
+8D0A(B BBBG D QZ L e FBB B(A0I8G
+8D0A(B BBBJ Z` @ 4 Li EC
DEFG
+ E v. p. .@[. Z 4 uz EC
FHK. T
+ H < *Z yG FM i
+EA ,yT FM v
+EA L ( hy FBB B(A0I8G
+8D0A(B BBBJ x Z` L | FBB B(A0I8G
+8D0A(B BBBJ Z` L 8 FBB B(A0I8G
+8D0A(B BBBJ H ~Z` d d2 HW 5 0 5 D 5 X ) l (7 Lf L) h7 Lf ) 7 Lf ̄) 脳7 Lf $ H _(C0N(A T
+A $ @ H _(C0N(A T
+A h c H|C [ $ L H _(C0N(A T
+A $ Ć H _(C0N(A T
+A <c H|C [ $ H _(C0N(A T
+A $ H _(C0N(A T
+A H |c H|C [ $ h ̈ H _(C0N(A T
+A $ D H _(C0N(A T
+A c H|C [ , 4 FAL0W
+DBA Z y , | T i FAL0W
+DBA Z E , 5 FAL0W
+DBA \Z , 䊳 FAL0W
+DBA h )Z &Z' ( :Z BAD AB 슳t H
+I Ht r H
+H t W H
+H < Z KBB A(D0(I BBBP @ W FBB B(I0A8D'
+8D0A(B BBBB ZX P W FBB B(I0A8D'
+8D0A(B BBBB d ZX n P W ^ FBB B(I0A8D'
+8D0A(B BBBB ZX P W FBB B(I0A8D'
+8D0A(B BBBB < |ZX D BBB A(A0G
+0A(A BBBB ( zPLR x
G
0 HZ_ g P | # { BBB B(A0A8J
+8A0A(B BBBK 8 ;Z&+ {
+FPDIQE Tn FJ0S
+EA n FJ0S
+EA P T 쳳s BBB B(A0D8G+
+8A0A(B BBBG 8 \ Z" IQEhFPD ܻn FJ0S
+EA (n FJ0S
+EA Td2 HW X T- p- ( Z' < x , P t MDE w
+DGH 4 MAE e
+FBH QAB ( JZ BAD AB 4 0e FDA v
+DBI QAB < Z KBB A(D0(I BBBP (G O FBB B(I0A8D
+8D0A(B BBBI p ZX t ó# YLB B(A0D8G@8A0A(B BBBDH@
+8A0A(B BBBE | @ ų_ OEE E(A0D8GPHP
+8A0A(B BBBH *
+8A0A(B BBBD P ɳr FBB B(D0A8N
+8A0A(B BBBJ ` Zm
+ , г= FAL W
+DBA Y Z P г) FGB B(D0A8G
+8A0A(B BBBJ l Z~ d2 HW X۳) t۳7 Lf H , ۳ EAG U
+CAJ [
+CAA m
+CAG NCA H x ۳ EAG U
+CAJ [
+CAA w
+CAE NCA H Pܳ EAG U
+CAJ [
+CAA n
+CAF NCA Z' 8 ܳ + EC
M.]
+ N N
+ A 0 Z < <ݳx EC
M. p
+ N N
+ B O. Z( < , dx EC
M. l
+ J N
+ B O. XZ" ( ( bZ BAD AB < `߳ c FBA I(DP
+(D ABBK `j ZF ; P + FBE E(D0D8GW
+8D0A(B BBBK j Z < vZ KBB A(D0(I BBBP G FBB B(I0A8D
+8D0A(B BBBI l ZX 8 a w EC
BQ. B
+ H v. Z d2 HW 4 5 H 5 \ H! p d5 ! 5 - - - ,- H! d5 $ ! 8 5 L ` t H FBB B(A0A8DPr
+8A0A(B BBBA [' H H EAG U
+CAJ [
+CAA e
+CAG NCA H H EAG U
+CAJ [
+CAA e
+CAG NCA H EAG U
+CAJ [
+CAA e
+CAG NCA H D EAG U
+CAJ [
+CAA e
+CAG NCA H , FBB B(A0A8DPr
+8A0A(B BBBA x R Lu
+O A ,R Lu
+O A lR Lu
+O A R Lu
+O A T LC 0T LC 0 tT LC L T LC ( h p[ BAD AB $ EA
+AD h @ @ @ @ R Lu
+O A , XT LC H R Lu
+O A h T LC < R[ KBB A(D0(I BBBP W w FBB B(I0A8D'
+8D0A(B BBBB j[X 8 P W ( FBB B(I0A8D'
+8D0A(B BBBB D V[X P
W FBB B(I0A8D'
+8D0A(B BBBB B[X P d W FBB B(I0A8D'
+8D0A(B BBBB .[X K P ; FBE E(D0D8J?
+8A0A(B BBBA , $ <" FADP^
+ABA L T " FBB E(D0I8G
+8A0A(B BBBA < $ X FBA I(DP
+(D ABBK Tt Z[4 7 D d& ' FBB D(I0G
+0A(A BBBH , D ( FAL0u
+DBA [ P )
+ FBE E(D0D8Jf
+8A0A(B BBBA , 2 A FADP^
+ABA L @3 FBB E(D0I8G
+8A0A(B BBBA < ` 5 FBA I(DP
+(D ABBK v [4 D 6 FBB D(I0G
+0A(A BBBH , @9 FAL0u
+DBA \ [ i P H 9 [ FBH B(D0F8Gn
+8D0A(B BBBB @ tKA FG i
+EA P K FBH B(D0F8Gn
+8D0A(B BBBB l]A FG i
+EA P 8 ]$ FEH B(D0F8Gn
+8D0A(B BBBG 0 toD FJ i
+EA P o$ FEH B(D0F8Gn
+8D0A(B BBBG |D FJ i
+EA d2 HW H EAG U
+CAJ [
+CAA d
+CAH NCA D @[' H X ܅ EAG Y
CAF [
-ACA R
-CAJ JCA L P AAG ]
+CAA H
+CAD NCAH P EAG Y
CAF [
-ACA R
-CAJ JCA $ ` AAh
-AE " DV
-F A < t xQ BBD D(DPT
-(A ABBA l 84 Am
-A 4 Ԗ X *Q BAA
-ABD < ė BFA A(D0
-(A ABBA , ACD M
-AAI t | , P BBA A(G0L
-(C ABBG V
-(C ABBJ R
-(C ABBF J
-(C ABBA T /P BBB E(A0A8Jv
-8A0A(B BBBI T L ` O BBD A(G
-(A ABBF l
-(A ABBF T ( O BBE B(A0A8DB
-8A0A(B BBBA T XO BBB E(A0A8D9
-8A0A(B BBBA d T h O BBA A(D
-(A ABBA U
-(A ABBA
-(C ABBA t 2 Ad
-K A < ܙ N AAD@y
-AAG Q
-AAA , 0 ]N AAG n
-AAG T L 1N BBD D(DP
-(A ABBC s
-(A ABBG \ 4 t BIA m
-ABD vAI 8 DO D @. M FC
BHnBBBA H 4 T ( lM BDI G@^
- AABA D 4 \ n AAD V
-CAH p
-CAD D ܛ L BBB A(D0G
-0A(A BBBC T $ x L BBA D(Gk
-(A ABBG
-(A ABBA 4 "+ f d 8" L BBB B(A0A8DP
-8A0A(B BBBE D8A0A(B BBB 4 "] BDA A
-ABM AAB L 4 " K BBE E(D0D8J`
-8A0A(B BBBA T 8( K BLB B(D0A8I
-8A0A(B BBBA 8 FP 8" DV
-F A < ̞ 8 BBG A(G@
-(A ABBF L T 9
- VK BBB B(A0C8J
-8A0A(B BBBA L HD JK BEB D(C0G$
-0A(A BBBA T Ea K BBB E(A0C8J(
-8A0A(B BBBE < L Iv J BEA A(G0D
-(D ABBA D J
\ I
t I I I I Ԡ I I" AN ND I $ I < I" $ T JG AG a
-DC RD | 8J , ܠ 0Jb cI AAG V
-CAA ġ pJ8 Gp4 ܡ J BAD D0
- AABA K $ t J4 H AN cA $ K< H AN kA L | (K BBA D(D@x
-(D ABBB
-(D ABBG \ Mh OH BEB D(D0Gp
-0D(A BBBK L
-0D(A BBBA D t O H BBE D(D0D
-0D(A BBBD D pS G BAD GPl
- DABG
- DABA T Ta G BEE I(A0A8G ^
-8A0A(B BBBJ L \ Z G BGD D(D0z
-(D ABBH V
-(D ABBA , 0[ G AAI@T
-DAD , [T BKI _
-ABH , Ĥ [D BHD ^
-ABA L < \ G BBB E(D0A8F
-8A0A(B BBBH L ] F BBB B(A0C8J
F
-8D0A(B BBBG D ܤ kC G BBB D(D0Gpn
-0D(A BBBB L $ m F BVB B(D0A8G^
-8D0A(B BBBE , t x{ @G YX
-G Z
-A L { G BLE E(A0D8J
-8A0A(B BBBE < Xd <G BJD D(D@o
-(D ABBA L 4 G BBE B(A0A8GS
-8D0A(B BBBD L 8r SG BBB B(D0A8G_
-8A0A(B BBBC L Ԧ h cG BBB B(D0A8GQ
-8A0A(B BBBA < $ ؖd G BGF A(J0i
-(D ABBA T d | kG BBE E(D0A8G
-8D0A(B BBBE T 0| QG BBE E(D0A8G
-8D0A(B BBBE T X' 7G BEE B(A0A8G
-8D0A(B BBBF T l 45 H BBB B(A0A8G{
-8A0A(B BBBA | ĩ ܩ $ < T l ̪ < J BEE D(D0\(Q BBB $ HT D # DT \ t # D ^ , oH YX
-G H
-A @ $ 8 H
-B i D \ q t D hh BFB B(A0A8H
-0A(B BBBA 4 Ԭ BFA A(
- ABBA L H BEB B(A0A8G
-8A0A(B BBBA < \ AC
T
- E f
- A
- A L 8 BBB B(A0A8Gb
-8A0A(B BBBA T BHB A(A0D`
-0A(A BBBB \0A(A BBB < D L BBE A(A0
-(A BBBN , AKFAA , AKFoAA - Ae L BBF F(D0A8D`}
-8A0A(B BBBH , T G4
-E g
-I `
-H P 0 , ! AC
T , ̯ ' AC
T L , BBB B(A0A8Dp
-8A0A(B BBBA L .B L d P. BBB B(A0A8J
-8A0A(B BBBJ \ 1Q BEB B(A0A8
-0A(B BBBB V
-0A(B BBBA < 3L BBE A(A0
-(A BBBN , T 4 AKJAA , 5 AKJAA $ 5 Cb
-K E
-K 4 $ 6Y uB AC
BEHS
- J , 09 AC
T
- G 4 D > AF
TL
- I c , | CE AF
T
- A He D IJ 0IS BFB B(G0A850A(B BBB D HKC BFB B(G0A8%0A(B BBB L T PM BDB B(A0D8G
-8A0A(B BBBB L N BME B(A0A8DX
-8A0A(B BBBG L R BNE B(A0G8DQ
-8A0A(B BBBB L D `V BEE B(A0A8G:
-8A0A(B BBBA XM G EL XX BBE B(A0A8J
-8A0A(B BBBH d _ BBB B(A0A8GPl
-8A0A(B BBBI
-8A0A(B BBBI D d `e BEB E(A0A80A(B BBB D e BEB B(D0A8
-0A(B BBBD L g BEB B(A0A8D
-8A0A(B BBBB D D j BBB K(A0A8
-0A(B BBBD L j2 BBB B(A0D8DD
-8A0A(B BBBD L ܶ lp BEB B(A0A8D
-8A0A(B BBBE , o D p L \ q BBB E(A0A8GBb
-8A0A(B BBBA L v@ BBB B(A0D8GA
-8A0A(B BBBE L z BBE B(A0A8GA
-8A0A(B BBBA L L x BBB B(A0A8G@6
-8A0A(B BBBG L AAG ]
-CAF k
-ACA R
-CAJ JCA , 8 BAA v
-ABC T d P; BBB B(A0A8GQ
-8A0A(B BBBD D t BEB E(A0A8
-0A(B BBBA L BBB E(A0A8DH
-8A0A(B BBBA L hd BBB B(A0A8G=
-8A0A(B BBBA L \ u BBB B(A0A8JG
-8A0A(B BBBA L , BBB B(A0A8G
-8A0A(B BBBG L BBB B(A0D8J8A0A(B BBB L L ( BBB B(A0D8G8A0A(B BBB L { BBE B(A0C8G3
-8A0A(B BBBE L ` BBB B(D0A8J
-8A0A(B BBBH L T
- BBB B(A0C8JO
-8A0A(B BBBA L BBB B(A0C8J
-8A0A(B BBBG L p BBB B(A0C8J
-8A0A(B BBBA , D ` BDA
-ABD < t Pt BBA A(G
-(A ABBH , { AAGoAA L T BBB B(A0D8G58A0A(B BBB 4 KIA D(G@(A ABBF@
-(A ABBO
-(G EBBH D(G EBB ľ ܾ 4 $ BDD G0 AAB \ x L t p` BDB B(A0A8Dp
-8A0A(B BBBD Ŀ O ܿ ; $ f < ( T , l q BDD GP[ AAB H D BEB B(A0A8_
-0A(B BBBK D ` \ X t ` h p D x BKB A(A0D`Q
-0A(A BBBB D BKB A(A0D`a
-0A(A BBBB D L xD BBB B(A0A8n
-0A(B BBBA $ 2 mFD@ T 1 BBE B(A0A8G
-8A0A(B BBBE T \ ` 1 BBB B(A0A8G
-8A0A(B BBBB l '~ D (X BIB B(A0A85
-0A(B BBBA 4 () BFA A(
- ABBA D +, BIE B(A0A8
-0A(B BBBA 4 L / AC
T1
- A
- A L 4< BHB B(A0A8Dp
-8A0A(B BBBF < p5L BBE A(A0
-(A BBBN , 6 AKFAA $ D AC
- I , l (p AG
FEO
- H , v AG
FEO
- H , } AC
I
- A , AC
I
- A , L D AAG ]
-CAF c
-CAI R
-CAJ JCA D @? w. BDA DP
- AABD M
- AABA L $ 8r M. BBB B(E0A8J@
-8A0A(B BBBA $ , h^ AG b
-AA L - BBB B(A0A8GPd
-8A0A(B BBBA < RBA A(G0
-(A ABBA , p2 FDH LGA d t
- &- BBB B(D0A8D`H
-8A0A(B BBBE V
-8A0A(B BBBH T 0 , BBE E(D0A8G#
-8D0A(B BBBF < BBG A(G@
-(A ABBH T t e V, BBB B(A0D8J^
-8A0A(B BBBA T 9 g, BEB B(A0A8Gn
-8A0A(B BBBD < $ x> 3, BLD D(G0
-(A ABBE T d 8?
- , BBB E(A0A8G
-8A0A(B BBBA t q. K^ T HV ?, BEB B(A0D8J
-8A0A(B BBBI D , ^ T- BBG D(J
-(D ABBA , D 6 AG lA L `c /- BBB F(A0A8J@X
-8A0A(B BBBA p> AG tA , d{ BFA i
-ABA L L e , BBE A(A0J
-0A(A BBBA , p , AHK s
-AAA \ j BBB B(A0A8D@
-8C0A(B BBEA D8C0A(B BBBL , G ?, BEE D(D0G
-0D(A BBBA T | q , BEE E(D0D8G0
-8D0A(B BBBA T q + BEE E(D0D8G0
-8D0A(B BBBA T , @q u+ BEE E(D0D8G0
-8D0A(B BBBA T hq /+ BEE E(D0D8G0
-8D0A(B BBBA T q * BEE E(D0D8G0
-8D0A(B BBBA T 4 q * BEE E(D0D8G0
-8D0A(B BBBA < h ]* BBA A(J
-(A ABBC T q * BEE E(D0D8G0
-8D0A(B BBBA L $ G n* BEE D(D0G
-0D(A BBBA T t Z 0* BEE E(D0D8G
-8D0A(B BBBA L G ) BEE D(D0G
-0D(A BBBA L G ) BEE D(D0G
-0D(A BBBA L l G n) BEE D(D0G
-0D(A BBBA L G 0) BEE D(D0G
-0D(A BBBA L ( BEE E(D0D8G
-8D0A(B BBBA L \ ` ( BEE E(D0D8G
-8D0A(B BBBA T Z v( BEE E(D0D8G
-8D0A(B BBBA T k 0( BEB B(A0A8G
-8A0A(B BBBK T \ q ( BEE E(D0D8G0
-8D0A(B BBBA L q( BEE E(D0D8Gp
-8D0A(B BBBA T 8q 3( BEE E(D0D8G0
-8D0A(B BBBA L \ `G ' BEE D(D0G
-0D(A BBBA T `v ' BEE E(D0D8G5
-8D0A(B BBBA L G i' BEE D(D0G
-0D(A BBBA T T q +' BEE E(D0D8G0
-8D0A(B BBBA L G & BEE D(D0G
-0D(A BBBA L G & BEE D(D0G
-0D(A BBBA L L G i& BEE D(D0G
-0D(A BBBA T v +& BEE E(D0D8G5
-8D0A(B BBBA L t % BBE A(D0G
-0A(A BBBA T D q % BEE E(D0D8G0
-8D0A(B BBBA L G % BEE D(D0G
-0D(A BBBA L G e% BEE D(D0G
-0D(A BBBA L < G '% BEE D(D0G
-0D(A BBBA L G $ BEE D(D0G
-0D(A BBBA d v $ BBA A(J
-(A ABBA U
-(A ABBA U
-(A ABBA T D H{ $ BDA DPm
- AABD x
- AABD
- AABA < } $ BBA A(G`
-(A ABBA T q $ BEE E(D0D8G0
-8D0A(B BBBA T 4 q k$ BEE E(D0D8G0
-8D0A(B BBBA L G %$ BEE D(D0G
-0D(A BBBA L G # BEE D(D0G
-0D(A BBBA L , G # BEE D(D0G
-0D(A BBBA T | q k# BEE E(D0D8G0
-8D0A(B BBBA T (q 1# BEE E(D0D8G0
-8D0A(B BBBA L , PG " BEE D(D0G
-0D(A BBBA T | Pq " BEE E(D0D8G0
-8D0A(B BBBA L xG g" BEE D(D0G
-0D(A BBBA T $ xq )" BEE E(D0D8G0
-8D0A(B BBBA T | q ! BEE E(D0D8G0
-8D0A(B BBBA L G ! BEE D(D0G
-0D(A BBBA L $ G _! BEE D(D0G
-0D(A BBBA L t G !! BEE D(D0G
-0D(A BBBA L G BEE D(D0G
-0D(A BBBA L G BEE D(D0G
-0D(A BBBA L d G g BEE D(D0G
-0D(A BBBA , \ s BEH m
-ABA 4 xi ADG S
-CAE ]
-FCB d L BJE B(A0A8D@
-8F0A(B BBBI l8F0A(B BBB T z S BBB B(A0A8Gh
-8A0A(B BBBA L @ G! BBB D(A0D
-0A(A BBBG T \ ! BBB B(A0D8G1
-8A0A(B BBBA T x G" BEB B(A0A8G
-
-8A0A(B BBBA T | " BBE B(A0A8G^
-8A0A(B BBBD < d 8" # BJA A(D
-(A ABBA T Xf " BEB E(A0C8J
-8A0A(B BBBA | p BIE B(A0A8GP
-8A0A(B BBBD B
-8D0A(B BBBA
-8A0A(B BBBG T | q O" BIB B(A0A8Go
-8A0A(B BBBA T 1 " BBB B(A0A8G
-8A0A(B BBBE T , 2 N" BLA D(JPx
-(D ABBB Z
-(D ABBA < q; D v T q. K^ 4 ! BDD H
-ABK 4 ! BDD H
-ABK T $ X ! BED A(G0Q
-(D ABBC k
-(D ABBA 4 | R! BDD Q
-ABB 4 (
- ,! BDD Q
-ABB <
- ! BBD D(G0
-(D ABBA < , BBD D(G0
-(D ABBA < l BEA D(G0m
-(D ABBA < ~ BBD D(G0
-(D ABBA <
Q BBD D(G0
-(D ABBA D ,
$ BEE D(A0Q
-(A BBBG , X AQ L Xm A~
-A l L BBB F(A0A8J@X
-8A0A(B BBBA L hD BBB B(A0A8GZ
-8D0A(B BBBH D T F BED D(G$
-(D ABBE D `H V BED D(G$
-(D ABBE < I5 ! BBA A(JW
-(A ABBC L $ O F BEE D(D0G0
-0D(A BBBJ T t 8Q BEE E(D0D8GU
-8D0A(B BBBA < R| AAD`
-AAH p
-AAF T S ] BEE E(D0D8GE
-8D0A(B BBBF L d U BBB B(D0A8Gi
-8A0A(B BBBI T W BEE E(D0D8GD
-8D0A(B BBBA T X BEE E(D0D8GD
-8D0A(B BBBA D d Y g BED D(G$
-(D ABBE T `[ 2 BEE E(D0D8GH
-8D0A(B BBBA 4 ( BCA GPc
- AABA T < `\ BEE E(D0D8GB
-8D0A(B BBBA T ] BEE E(D0D8GB
-8D0A(B BBBA d ^M ; BLB B(A0A8DPq
-8A0A(B BBBE
-8A0A(B BBBG L _ BOB A(A0]
-(A BBBG Y
-(A BBBH T (` BEE E(D0D8GE
-8D0A(B BBBF < a BBG A(G@
-(A ABBG L < @b ; BLB B(D0A8DP
-8A0A(B BBBJ L c^ BLH B(D0A8G@
-8A0A(B BBBB d5 < d BBG A(G@
-(A ABBG 4 e k BBB E(A0A8D
-8A0A(B BBBI n
-8A0A(B BBBH
-8A0A(B BBBI t 0q. K^ D i BIB A(A0G`o
-0A(A BBBA T % BIB B(D0A8GW
-8D0A(B BBBA T t u BIB B(D0A8G
-8D0A(B BBBA L @3 N BIB D(A0G
-0D(A BBBA < l BBA A(J
-(A ABBJ 4 \ @p BAA GP_
- AABB T BEB B(A0A8G
-p
-8A0A(B BBBA L @q' t BBB B(A0A8J]
-8A0A(B BBBE T < & x BIB B(D0A8G
-8A0A(B BBBA T X,{
BBB B(A0D8G
-8A0A(B BBBJ puZ um A~
-A l L $ v BBB F(A0A8J@X
-8A0A(B BBBA L t w y BBB B(A0A8Gv
-8A0A(B BBBG T V BEE E(D0D8G
-8D0A(B BBBA T V Y BEE E(D0D8G
-8D0A(B BBBA 4 t Xy % BCA GPc
- AABA T r S BEE E(D0D8G1
-8D0A(B BBBA T r
BEE E(D0D8G1
-8D0A(B BBBA T \ r BEE E(D0D8G1
-8D0A(B BBBA T ( BEE E(D0D8GU
-8D0A(B BBBA T p ; BEE E(D0D8GU
-8D0A(B BBBA T d hx BIB B(A0A8J$
-8A0A(B BBBG < `Q G BDD v
-ABE AAB q. K^ L Ȁ BIB D(A0Gq
-0D(A BBBA L d : BIB D(A0G
-0D(A BBBA L : v BIB D(A0G
-0D(A BBBA L x3 K BIB D(A0G
-0D(A BBBA L T h BIB D(A0G
-0D(A BBBA L ؎ BIB D(A0G
-0D(A BBBA 4 HD BEA A(D0p(A ABB L , Pj k BBB E(A0C8DP
-8A0A(B BBBA T | . BBB B(A0A8J
-8A0A(B BBBA T x$ BEB B(A0A8G
-
-8A0A(B BBBA T , PG A BEB B(A0A8G
-8A0A(B BBBA T HG L BEB B(A0A8G
-8A0A(B BBBA T @ W BIB B(A0D8G$
-8A0A(B BBBG D X BBE A(A0D@~
-0A(A BBBA D 4 BBE A(A0D@I
-0A(A BBBA < ( YHD M
-HAE
-HAA L BBB B(A0A8G@
-8D0A(B BBBG $ @ < H T @ l 8 0 ( 0 ( 0& PT H/ SZ ` L , he BEA A(D0s
-(C ABBH D(G ABB | ' AR
-E N L m BEA A(D0~
-(C ABBE D(G ABB ' AR
-E N $ $ ! AAG SCA L d $ | 1 ADJ [AA $ AAG`AA B AZ 4 L L 0 BJB A(A0W
-(A BBDH ^(A BBBT P BGB I(A0A8G
-8A0A(B BBBD $ T { YX
-G z
-A L 4 @ BBD D(D@v
-(D ABBA X(D ABB T '
- BBB E(A0D8D
-8D0A(B BBBG L $ [ BEB A(A0d
-(A BBDH [(A BBGL t / BBE B(A0A8DP
-8A0A(B BBBA | q. K^ T t BBB B(A0G8G
-8A0A(B BBBF T 4 BIB B(A0D8DH
-8D0A(B BBBC , U ADD`v
-AAA , t 0b D\
-H P
-H Q
-G I , pb D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , 4 0b D\
-H P
-H Q
-G I , d pb D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , 0b D\
-H P
-H Q
-G I 4 l p~ BAD D
-ABA L BBB F(A0A8J@X
-8A0A(B BBBA , hk AAG@B
-AAC , $ k { AAG@B
-AAC Hl , $ m ADG [
-AAG < m x ACDP
-AAA O
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-AAA O
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-(D ABBE D d p V BED D(G$
-(D ABBE < r ! AADP
-AAI O
-AAA T t S BEE E(D0D8GT
-8D0A(B BBBG T D u BEE E(D0D8GT
-8D0A(B BBBG T pw BEE E(D0D8GT
-8D0A(B BBBG < (y BED C(GP{
-(A ABBJ D 4 z BED D(G$
-(D ABBE 4 4 |J BDA l
-ABJ A
-ABA l |m A~
-A l h|m A~
-A l D | 1 BED D(G$
-(D ABBE 4 < ~ AAD
-DAA 4 t x AAD$
-DAA 4 Ѐ AAD
-DAA T H r BBB B(A0A8JB
-8A0A(B BBBA @q. K^ 4 T g BDD G~
- AABA 4 BAD G
-ABA 4 |
- J BDA o
-ABG A
-ABA T @ , BBB B(A0I8G
-8D0A(B BBBF < T
-
BAI G
- DABA <
- h" BAI G
- DABA <
- X8 BAI G
- DABA < X8 BAI G
- DABA T T XD q BBB B(I0A8Gw
-8D0A(B BBBA T PD D BBB B(I0A8Gw
-8D0A(B BBBA < HK BAI G
- DABA 4 xv BDA D0a
- AABA L 4
BBB B(A0A8Dp
-8A0A(B BBBA T h BIB B(A0A8J9
-8A0A(B BBBB
X/ < <
P/ u BBK A(DPq
-(A ABBA < |
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-(A ABBA D
p0W BBB A(A0GP
-0A(A BBBH \ 2n BBB A(A0GP
-0A(A BBBD j
-0A(A BBBA \ d 3n > BBB A(A0GP
-0A(A BBBD j
-0A(A BBBA L 4 BDB A(A0L
-0A(A BBBE T 88 BBB B(A0A8J.
-8A0A(B BBBD T l p> BBB B(A0A8G
-8A0A(B BBBA D \ w BBI A(G
-(D ABBA , }b D\
-H P
-H Q
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-H P
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-G I , $ }b D\
-H P
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-G I , T }b D\
-H P
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BBB B(A0A8G
-8A0A(B BBBJ T $ Xe m BEE B(A0A8G
-8A0A(B BBBF T | pe BEE B(A0A8G
-8A0A(B BBBF T e BEE B(A0A8G
-8A0A(B BBBF T , e BEE B(A0A8G
-8A0A(B BBBF T e BEE B(A0A8G
-8A0A(B BBBF T e BEE B(A0A8G
-8A0A(B BBBF T 4 V BBB B(A0A8Gk
-8A0A(B BBBJ T e BEE B(A0A8G
-8A0A(B BBBF T h BBB B(A0A8G
-8A0A(B BBBB pq. K^ T T ؊x BBB B(A0I8G
-8D0A(B BBBA T
BBJ B(A0A8G
-8D0A(B BBBD T 8 BBJ B(A0A8G
-8D0A(B BBBD T \ p BBB B(A0A8G
-8A0A(B BBBA T 3 BIB B(A0A8J
-8A0A(B BBBJ ` , hb D\
-H P
-H Q
-G I , b D\
-H P
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-G I , < b D\
-H P
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-G I 4 (~ BAD D
-ABA < p BBD A(G@a
-(A ABBA , , k AYLp
-AAA L \ P] BBB B(A0A8DW
-8D0A(B BBBF , _ c AAG o
-DAA D paQ BCA Gpg
- AABH
- AABA T $ de BEE B(A0A8G
-8A0A(B BBBF 4 `}q. K^ 4 & BKA G
- DABA 4 & BKA G
- DABA 4 x & x BKA G
- DABA T < p
- [ BIE B(A0A8G&
-8D0A(B BBBB T c BIE B(A0A8G&
-8D0A(B BBBB 4 & k BKA G
- DABA < $ K BAD GS
- DABA < d BAI G
- DABA T !U BIB B(A0A8G !
-8A0A(B BBBE T 4 BIB B(A0A8G
-
-8A0A(B BBBF L T xE O! BBB B(A0A8Mi
-8A0A(B BBBA L HM V! BBB B(A0A8MF
-8A0A(B BBBA , (gb D\
-H P
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-G I , hgb D\
-H P
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-H P
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-AAA , ik AYLp
-AAA , D j AMQ`:
-AAA , t Xl_ t AAG o
-DAA , l_ P AAG o
-DAA , l_ , AAG o
-DAA D Q BCA Gpg
- AABH
- AABA wq. K^ T d l
- " BIB B(D0A8G
-8D0A(B BBBA T @r
- " BIB B(D0A8G
-8D0A(B BBBA T w
- " BIB B(D0A8G
-8D0A(B BBBA T l }
- m" BIB B(D0A8G
-8D0A(B BBBA T h
- W" BIB B(D0A8G
-8D0A(B BBBA T
- A" BIB B(D0A8G
-8D0A(B BBBA T t ؎
- +" BIB B(D0A8G
-8D0A(B BBBA T
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-8D0A(B BBBA T $! H
! BIB B(A0A8G
-8A0A(B BBBA T |! ! BIB B(A0A8G"
-8A0A(B BBBA T ! 8
" BIB B(A0A8G
-8A0A(B BBBA T ," " BIB B(A0A8G"
-8A0A(B BBBA L " ( ?" BBB B(A0A8Mi
-8A0A(B BBBA L " F" BBB B(A0A8Mi
-8A0A(B BBBA # # F T T# " BBB B(K0A8G
-8A0A(B BBBA T # N " BBB B(A0D8G
-8A0A(B BBBA , $ b D\
-H P
-H Q
-G I , $ b D\
-H P
-H Q
-G I , % 8b D\
-H P
-H Q
-G I , L% xb D\
-H P
-H Q
-G I , |% b D\
-H P
-H Q
-G I , % b D\
-H P
-H Q
-G I L % 8 AAG ]
+CAA I
+CAC NCAH Ć EAG Y
CAF [
-ACA R
-CAJ JCA , ,& hb D\
-H P
-H Q
-G I , \& b D\
-H P
-H Q
-G I , & b D\
-H P
-H Q
-G I , & ( b D\
-H P
-H Q
-G I , & h b D\
-H P
-H Q
-G I , ' b D\
-H P
-H Q
-G I , L' b D\
-H P
-H Q
-G I , |' (b D\
-H P
-H Q
-G I , ' hb D\
-H P
-H Q
-G I , ' b D\
-H P
-H Q
-G I , ( b D\
-H P
-H Q
-G I , <( (b D\
-H P
-H Q
-G I , l( hb D\
-H P
-H Q
-G I ( m A~
-A l < ( BBD A(G@a
-(A ABBA 4 D( ~ BAD D
-ABA T |( BZB J(A0A8G)
-8A0A(B BBBD ) H L ( BBB B(A0A8DW
-8D0A(B BBBF , <) _ k AAG o
-DAA , l) P _ G AAG o
-DAA , ) e # ADG r
-DAA T ) BBB B(A0A8Gw
-8A0A(B BBBF 4 * HJ BDA l
-ABJ A
-ABA T \* ` 5 BBB B(A0A8G
-8A0A(B BBBG T * e BBB B(A0D8G
-8A0A(B BBBG + 0kq. K^ , $+ H6 G ADG T
-AAA L T+ X ' BLH B(D0D8D@
-8A0A(B BBBG 4 + H BKA G
- DABA 4 + ' BKA G
- DABA T , h BBB B(I0A8G)
-8D0A(B BBBA 4 l, BKA G
- DABA 4 , BKA G
- DABA < , BAD GS
- DABA T - h o BBE B(A0A8G>
-8D0A(B BBBA < t- " W BAD GS
- DABA < - X$ , BAD GS
- DABA < - % BAD GS
- DABA < 4. ' BAD GS
- DABA < t. x( BAD GS
- DABA < . ) BAD G{
- DABA < . h+ T BAD G{
- DABA < 4/ , ( BAD G
- DABA < t/ . BAD G{
- DABA D / 80' BBA D(G
-(D ABBA < / 2 BAD GS
- DABA < <0 3 r BAD GS
- DABA < |0 4 G BAD GS
- DABA < 0 @6 BAI G
- DABA < 0 8 BAD G
- DABA < <1 9 BAD GS
- DABA < |1 ; BAD G
- DABA T 1 < s BIE B(A0A8G
-8D0A(B BBBG T 2 H? _ BIE B(A0A8G
-8D0A(B BBBG 4 l2 A K BKA G
- DABA 4 2 B * BKA G
- DABA T 2 pC BBB E(A0A8G
-8D0A(B BBBI < 43 G BBI A(G
-(D ABBA < t3 hH BBI A(G
-(D ABBA 4 3 I BYA G
- DABA < 3 pJ BAD GS
- DABA < ,4 K a BAD GS
- DABA < l4 0M 6 BAI Gl
- DABA < 4 O BAI G
- DABA T 4 QR BBB J(A0A8G
-8D0A(B BBBB < D5 W BAD GS
- DABA < 5 X BAD GS
- DABA < 5 XZ BAD GS
- DABA < 6 [ b BAD GS
- DABA < D6 ] 7 BAD GS
- DABA < 6 x^ BAD GS
- DABA T 6 _ BBJ B(A0A8G
-8D0A(B BBBJ < 7 e BAD GS
- DABA < \7 f BAD GS
- DABA < 7 @h BAD GS
- DABA < 7 i V BAD GS
- DABA < 8 k + BAD GS
- DABA T \8 `lh BBE B(A0A8G>
-8D0A(B BBBA T 8 xoh BBE B(A0A8G>
-8D0A(B BBBA < 9 r BAI G4
- DABA < L9 v BAI GJ
- DABA T 9 x BBE B(A0A8G
-8D0A(B BBBH < 9 } w BAD GS
- DABA < $: 8 L BAI GL
- DABA D d: B % BBA I(G
-(D ABBA T : R BBB J(A0A8G
-8D0A(B BBBB D ; Ȉ BBA I(G
-(D ABBA 4 L; BAD G
-ABA L << BLB B(A0A8D
-8A0A(B BBBA < h l ; Y BDB B(A0A8L\
-8A0A(B BBBB
-8A0A(B BBBD L \< BDB J(A0A8Ib
-8A0A(B BBBG T < ` BDA I(Dp
-(A ABBA U
-(A ABBE L = BDB B(A0I8Dpk
-8A0A(B BBBC T T= xV BDA C(L`^
-(A ABBF
-(A ABBE T = V BDA C(L`^
-(A ABBF
-(A ABBE T > V P BDA C(L`^
-(A ABBF
-(A ABBE L \> BDB B(A0A8O\
-8A0A(B BBBG L > 0i BLB B(A0A8IZ
-8A0A(B BBBG , > P d AALP
-DAJ , ,? M AALP
-DAD , \? 6 AALP
-DAK T ? e BBB B(A0D8J
-8F0A(B BBBA T ? BBB B(A0A8J 3
-8F0A(B BBBA @ T T@ BEB B(A0A8J
-8F0A(B BBBA dA T @ xZ BBE B(A0A8Gsp
-8F0A(B BBBE T A S( # BBB B(A0A8J
-8F0A(B BBBI L tA kb
$ BDB B(A0A8O|
-8A0A(B BBBG L A x7 % BDJ B(A0A8Ir
-8A0A(B BBBG < B G $ BDA A(L
-(A ABBF L TB $ BBB B(D0G8JP
-8A0A(B BBBI \C AZ 4 B T % BAA J@
- AABB < B @a % BBA A(JP
-(A ABBI , C b D\
-H P
-H Q
-G I , $D `b D\
-H P
-H Q
-G I , TD b D\
-H P
-H Q
-G I , D b D\
-H P
-H Q
-G I , D b D\
-H P
-H Q
-G I , D `b D\
-H P
-H Q
-G I L E AAG ]
+CAA i
+CAC NCAH < X EAG Y
CAF [
-ACA R
-CAJ JCA , dE Шb D\
-H P
-H Q
-G I , E b D\
-H P
-H Q
-G I , E Pb D\
-H P
-H Q
-G I , E b D\
-H P
-H Q
-G I , $F Щb D\
-H P
-H Q
-G I , TF b D\
-H P
-H Q
-G I , F Pb D\
-H P
-H Q
-G I , F b D\
-H P
-H Q
-G I , F Ъb D\
-H P
-H Q
-G I , G b D\
-H P
-H Q
-G I , DG Pb D\
-H P
-H Q
-G I , tG b D\
-H P
-H Q
-G I , G Ыb D\
-H P
-H Q
-G I , G b D\
-H P
-H Q
-G I , H Pb D\
-H P
-H Q
-G I , 4H b D\
-H P
-H Q
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+CAA Z
+CAB NCAH ܇ EAG Y
CAF [
-ACA R
-CAJ JCA , H b D\
-H P
-H Q
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-ABA I xm A~
-A l T H ȭ BBB B(K0A8G
-8A0A(B BBBA T H > BBB B(K0A8G
-8A0A(B BBBA 4 4I x~ BAD D
-ABA < lI c BBD A(G@a
-(A ABBA T I p K BZB J(A0A8G)
-8A0A(B BBBD T J S BNO B(A0A8G|
-8A0A(B BBBH , \J Z W AMQ`
-AAA , J v < AMQp
-AAA , J b ' AMQP&
-AAA , J @_ / AAG o
-DAA , K p_ AAG o
-DAA , LK e ADG r
-DAA , |K _ AAG o
-DAA , K _ AAG o
-DAA , K @_ { AAG o
-DAA , L pe W ADG r
-DAA T <L @X BEE B(A0A8G
-8A0A(B BBBF LM Iq. K^ 4 L @ /! BAD G
-ABA 4 L " BKA G
- DABA 4 M P w" BKA G
- DABA 4 TM V" BKA G
- DABA 4 M 5" BKA G
- DABA 4 M x " BRA G
- DABA 4 M 0 ! BRA G
- DABA 4 4N ! BRA G
- DABA T lN ! BBB J(A0A8G
-8D0A(B BBBD < N h ! BAD GS
- DABA T O h w! BBE B(A0A8G>
-8D0A(B BBBA < \O _! BAD GS
- DABA < O @ 4! BAD GS
- DABA < O ! BAD GS
- DABA < P BAD GS
- DABA < \P ` BAD GS
- DABA < P BAD GS
- DABA < P ] BAD GS
- DABA < Q 2 BAD GS
- DABA < \Q BAD GS
- DABA < Q @ BAI G
- DABA < Q BAD GS
- DABA T R BIE B(A0A8G
-8D0A(B BBBG T tR w BIE B(A0A8G
-8D0A(B BBBG 4 R p c BKA G
- DABA 4 S H B BKA G
- DABA T <S # BBB E(A0A8G
-8D0A(B BBBI < S BAI Gl
- DABA < S h BAI G
- DABA T T ( BBB J(A0A8G
-8D0A(B BBBE T lT h BBE B(A0A8G>
-8D0A(B BBBA T T h BBE B(A0A8G>
-8D0A(B BBBA < U M BAD G
- DABA D \U B k BBA I(G
-(D ABBA D U C BBA I(G
-(D ABBA 4 U P BRA G
- DABA 4 $V BRA G
- DABA 4 \V BRA G
- DABA 4 V x BKA G
- DABA 4 V 0 BKA G
- DABA 4 W v BKA G
- DABA 4 <W U BKA G
- DABA 4 tW X
- 4 BKA G
- DABA 4 W BKA G
- DABA 4 W BKA G
- DABA 4 X BKA G
- DABA D TX 8
BBA D(G6
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-0D(A BBBB 4 X `= i ADG
-DAA 4 $Y h= D ADG
-DAA 4 \Y p= ADG
-DAA 4 Y x= ADG
-DAA 4 Y = ADG
-DAA 4 Z = ADG
-DAA 4 <Z = ADG
-DAA 4 tZ e f ADG@
-DAA 4 Z A ADGh
-DAA 4 Z ( e ADG@
-DAA 4 [ ` = ADG
-DAA 4 T[ h = ADG
-DAA 4 [ p = ADG
-DAA 4 [ x y ADGT
-DAA < [ ! c BAD G
- DABA < <\ `# 8 BAD G
- DABA 4 |\ %=
ADG
-DAA 4 \ & ADGh
-DAA 4 \ `'@ ADG
-DAA 4 $] h(@ ADG
-DAA 4 \] p)@ y ADG
-DAA 4 ] x*@ T ADG
-DAA 4 ] +y / ADGT
-DAA 4 ^ ,b
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-DAA < <^ / BAI G
- DABA < |^ 1 BAI Gi
- DABA < ^ 4 BAI Gi
- DABA < ^ 7 s BAD GV
- DABA < <_ 8 H BAD GV
- DABA < |_ 9 BAD GV
- DABA < _ @; BAD GV
- DABA < _ < BAI G
- DABA < <` `>a BAI G
- DABA < |` @| z BAI G
- DABA < ` B T BAD G
- DABA < ` D ( BAD G
- DABA 4 <a `F BKA G
- DABA 4 ta G BKA G
- DABA T a G BBB B(A0D8G
-8F0A(B BBBA L b Hax [ BBB B(F0A8Jp
-8A0A(B BBBE d Tb xqi BBB B(A0A8L^
-8A0A(B BBBB
-8A0A(B BBBI L b s( BBB B(A0C8O\
-8A0A(B BBBG c `wl BBB B(A0C8Lo
-8A0A(B BBBG
-8A0A(B BBBJ
-8A0A(B BBBE L c H}( f BBB B(A0C8O\
-8A0A(B BBBG L c (( d BBB B(A0C8O\
-8A0A(B BBBG < 4d (F BEA D(D`s
-(A ABBG D td Ȅ i BBB A(I0F`E
-0A(A BBBE D d 5 BBB A(I0F`E
-0A(A BBBE T e xV BBA C(L``
-(A ABBF
-(A ABBE T \e V BBA C(L``
-(A ABBF
-(A ABBE T e V BBA C(L``
-(A ABBF
-(A ABBE T f V V BBA C(L``
-(A ABBF
-(A ABBE L df BJB B(A0A8I\
-8A0A(B BBBG L f 8 w BBJ B(A0A8Il
-8A0A(B BBBG L g Z BBB B(A0A8L
-8A0A(B BBBF L Tg H
- R BBB B(A0A8Ov
-8A0A(B BBBG L g m BBB B(A0A8Ov
-8A0A(B BBBG L g
- BDB B(D0A8I_
-8A0A(B BBBG h L \h BBB B(D0D8MP
-8A0A(B BBBI di AX , h AALP
-DAJ , h AALP
-DAD , ,i @ AALP
-DAK T \i b BBE B(A0A8G#
-8F0A(B BBBA lj < i PAW S! BSA Q
- AABA , j pC :! ASQpe
-AAA < <j 0E %! BYA Lt
- AABA < |j HQ ! BYA L
- AABA , j K` AYLp
-AAA , j PL AMQp
-AAA L k M AAG ]
+CAA y
+CAC NCAH EAG Y
CAF [
-ACA R
-CAJ JCA L $l M AAG ]
-CAF Z
-ACB R
-CAJ JCA , tl Nb D\
-H P
-H Q
-G I , l PNb D\
-H P
-H Q
-G I , l Nb D\
-H P
-H Q
-G I , m Nb D\
-H P
-H Q
-G I , 4m Ob D\
-H P
-H Q
-G I , dm POb D\
-H P
-H Q
-G I , m Ob D\
-H P
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-G I , m Ob D\
-H P
-H Q
-G I , m Pb D\
-H P
-H Q
-G I , $n PPb D\
-H P
-H Q
-G I , Tn Pb D\
-H P
-H Q
-G I , n Pb D\
-H P
-H Q
-G I , n Qb D\
-H P
-H Q
-G I , n PQb D\
-H P
-H Q
-G I , o Qb D\
-H P
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-G I , Do Qb D\
-H P
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-G I , to Rb D\
-H P
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-G I , o PRb D\
-H P
-H Q
-G I , o Rb D\
-H P
-H Q
-G I , p Rb D\
-H P
-H Q
-G I , 4p Sb D\
-H P
-H Q
-G I , dp PSb D\
-H P
-H Q
-G I p Sm A~
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-(A ABBA 4 <p T~ BAD D
-ABA L tp T BBB F(A0A8J@X
-8A0A(B BBBA T p XV BBB E(A0A8D
-8D0A(B BBBA T q YK o BTB B(A0A8QK
-8A0A(B BBBA , tq \h P BDD y
-ABA , q X\_ , AAG o
-DAA , q \_ AAG o
-DAA , r \_ AAG o
-DAA , 4r \_ AAG o
-DAA , dr ]_ AAG o
-DAA , r H]_ x AAG o
-DAA D r BED D(G$
-(D ABBE D s BED D(G$
-(D ABBE , t x ADD Z
-AAA $ s #q A
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- DABA < lt ^ BAI GL
- DABA 4 t a t BKA G
- DABA 4 t a S BKA G
- DABA 4 u b 2 BKA G
- DABA D Tu Hc BBA I(G
-(D ABBA D u h BBA I(G9
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- DABA 4 v `l BRA G
- DABA 4 Tv m } BRA G
- DABA 4 v m \ BRA G
- DABA T v n
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-8A0A(B BBBG < w @tY BAI G
- DABA < \w `v BAI G
- DABA < w 0x BAI Gk
- DABA T w z BBE B(A0A8G
-8D0A(B BBBI T 4x ( BBB J(A0A8G
-8D0A(B BBBD < x 6 BAD G
- DABA < x | BAI G
- DABA T y U BBB B(A0I8G
-8D0A(B BBBA T dy ؏ 5 BEE B(D0D8L
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-8A0A(B BBBG l| | ( A[ 4 { d BAA J@
- AABJ L $| BFB B(A0A8Gp
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-8D0A(B BBBA T ~ BIB B(A0A8G
-8A0A(B BBBF L 8 BTB A(A0N
-0A(A BBBA < T x0 Z BSA Q
- AABA < hW A BSA Q
- AABA < ( BYA Lt
- AABA < XQ BYA L
- AABA , xb D\
-H P
-H Q
-G I , < b D\
-H P
-H Q
-G I , l b D\
-H P
-H Q
-G I , 8 b D\
-H P
-H Q
-G I L ́ x AAG ]
+CAA j
+CAB NCA( [ BAD AB L 舴 L~
+F l Lp
+L A
+G L $ g FBB B(A0A8D`
+8A0A(B BBBF P < FBB B(A0A8G
+8A0A(B BBBH P 0y FBB B(A0A8G
+8A0A(B BBBA h \ { FEB B(A0A8GS
+8A0A(B BBBK
+8D0A(B BBBE < [ KBB A(D0(I BBBP G FBB B(I0A8D
+8D0A(B BBBI H [X 4 d BAD D0
+ AABB h 4 쥴 d FKE B(A0A8M@
+8D0A(B BBBE B
+8A0A(B BBBA H D FEA A(D0t
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+8A0A(B BBBA I
+8D0A(B BBBE H FEA A(D0t
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+8D0A(B BBBF \ [ P ` FBB B(I0A8J~
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4 0[U W P ԥd2 HW T l HԴ FBB B(A0A8DP%Xq``hBpIPN8A0A(B BBBH մ EAG Y
CAF [
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-H P
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- DABA 4 p* BYA G
- DABA 4 D X+! B_D G
- DABA 4 | P, BYA G
- DABA < -| BYA G
- DABA < H. B_G G=
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- DABA < t (1 @ BAI GL
- DABA L 3 BIB A(D0G
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- DABA 4 < 06 BKA G
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- DABA 4 7G ~ ADG"
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-DAA < @- BAD G
- DABA < B BAD G
- DABA < , DB { BAI G
- DABA T l F T BBB B(A0A8G
-8A0A(B BBBD < ċ K BAI Gu
- DABA T M BBJ B(A0A8Gd
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-8A0A(B BBBF T 0 W BBE E(D0D8L
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-8A0A(B BBBG , , D b D\
-H P
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-8A0A(B BBBI T D H ! BBE E(A0A8G
-8A0A(B BBBK T
E BBE E(A0A8G6
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+CAA R
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+8A0A(B BBBA @ | J EG
DKG
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@ / FBA A(G0I
+(F ABBK
+(A ABBF ( \+[ BAD AB .,[O FC < , b,[ KBB A(D0(I BBBP B W FBB B(I0A8D
+8D0A(B BBBE T z.[u $ P 4 H FBB B(D0A8G
+8D0A(B BBBG P 8M FBB B(D0A8G
+8D0A(B BBBE L R FBB B(D0D8G`
+8D0A(B BBBH -[ d d D V~ T FBB B(D0D8GPG
+8D0A(B BBBA GXF`VXAPXI`VXAP f-[, 4 X FAI D0
+ DABA p B-[1 <Yr FEB B(D0A8G
+8A0A(B BBBJ nIEIBVlIEIBVlIEIBV4P[A ,v ,[+ d2 HW i
+ i
+ , ( i FAL0s
+DBA C@[ , p j FAL0s
+DBA @[ $ \ xj EA
+AJ $ l EA
+AJ D ho FBB B(A0A8
+0A(B BBBJ 0r2 b?[. Fb < @r MAA w
+FBJ y
+ABD < r } MAA w
+FBJ y
+ABD 0 s A MAD
+IBL 0 4 lt MAD
+IBL H h 8u FAD
+CBJ A
+FBG v
+DBD d vx FBB B(D0A8GP
+8D0A(B BBBO d
+8A0A(B BBBB @ =[' V < =[ KBB A(D0(I BBBP t Lx FBB B(I0A8DG
+8D0A(B BBBB @[u P | FBB B(I0A8Dg
+8D0A(B BBBB l @[u D L T FBB A(A0DK
+0A(A BBBA P y BNG B(E0A8J
+8A0A(B BBBH 4 y ?[< 5 UFPDIPE P ` 5 FBB B(E0A8G
+8A0A(B BBBF zA[ _ 8 EC
BGPc
+ D . p. < D[ d ` FBB H(F0D8GDGENR
+8D0A(B BBBE $ E[ K d2 HW r L_
+E A < G[' P 䢵~ Lm < l G[ KBB A(D0(I BBBP W ֿ FBB B(I0A8D'
+8D0A(B BBBB I[X 8 t Q EC
FM
+ A .@d. $ I[ A L T FBB B(A0I8DP
+8D0A(B BBBB L T[F @d2 HW T[' D l O FBB A(I0DP
+0D(A BBBH T T[G D Tõ FBB A(I0DP
+0D(A BBBH T[G ߿ D , ĵ Ͽ FBB A(I0DP
+0D(A BBBH
+ T[G D tƵ FBB A(I0DP
+0D(A BBBH t
+ tT[G _ < T[ KBB A(D0(I BBBP , ǵw FBB B(I0A8DA
+8D0A(B BBBH V[X о P ͵w FBB B(I0A8DA
+8D0A(B BBBH P V[X P ӵw q FBB B(I0A8DA
+8D0A(B BBBH V[X 2 P p ڵw " FBB B(I0A8DA
+8D0A(B BBBH ( V[X d2 HW ߵ) 7 Lf , ( 8 FAL0W
+DBA DV[ c BV[' < ( VV[ KBB A(D0(I BBBP ,g FBB B(I0A8D3
+8D0A(B BBBF | nX[X P 0 0 FDB B(G0D8Gf
+8D0A(B BBBE ( |d2 HW D `) X |7 Lf t ) 7 Lf 2 H EAG U
+CAJ [
+CAA d
+CAH NCA H ! ` EAG U
+CAJ [
+CAA d
+CAH NCA H P! FBB B(A0A8Dp
+8A0A(B BBBH H ! FBB B(A0A8Dp
+8A0A(B BBBG ( ! V[ BAD AB < " lW[ KBB A(D0(I BBBP ! `W FBB B(I0A8D'
+8D0A(B BBBB h Y[X | P " TW l FBB B(I0A8D'
+8D0A(B BBBB pY[X - P " H FBB B(A0D8K
+8A0A(B BBBF , "
+= FAL W
+DBA ,Y[ P $#
+ ݹ FBB B(A0D8K
+8A0A(B BBBF , x#
= ѹ FAL W
+DBA | X[ d d$
FJB B(A0A8D`b
+8A0A(B BBBJ
+8A0A(B BBBF | $ (E FFB B(A0A8D`>
+8A0A(B BBBB
+8A0A(B BBBC
+8A0A(B BBBE P $ 9 FHB B(A0D8G
+8D0A(B BBBD P $ R5 FHB B(A0D8G
+8D0A(B BBBD % d2 HW & V[ FQ (& V[' ( <& W[ BAD AB 8 h& FBA D(D0
+(I ABBK L & FBA A(G0
+(F ABBI \
+(A ABBF 4 & pe FDA v
+DBI QAB L ,' PBA A(D0q
+(A ABBA
+(A ABBH < |' V[ KBB A(D0(I BBBP ' ȊW FBB B(I0A8D'
+8D0A(B BBBB X[X h H (( FBB B(A0D8D`&
+8A0A(B BBBK H t( PLB B(A0A8DP&
+8A0A(B BBBJ H ( FBB B(A0A8QP
+8A0A(B BBBC H ) 蕶5 FEB B(A0N8Dp
+8A0A(B BBBK H X) ܗ PBB B(D0D8G`O
+8A0A(B BBBB 8 ) ߹ EG
DH
+ H .. $ <) nW[d A P d) ܫ s FBB B(A0D8N
+8A0A(B BBBD V][ , ) = FAL W
+DBA ԓ f[ _ P * 趶# O FBB E(D0D8JH
+8A0A(B BBBE t$ f[ O 8 * ¶ EG
BEH
+ D .. 4 k[ P * (Ѷ g FBB E(G0F8GA
+8D0A(B BBBG , q[ O + <d2 HW , Զ) , Զ7 Lf , + Զ ; FAL0W
+DBA s[ |, s[' 0 , նw EAG [
+AAF DAA< , s[ KBB A(D0(I BBBP `, նW FBB B(I0A8D'
+8D0A(B BBBB u[X @ L , ۶< 0 FFB B(D0D8DP
+8A0A(B BBBH L - ܶ FEB B(D0A8D`B
+8A0A(B BBBD D l- Tݶ] FBE G(D0K@v
+0D(A BBBD L - l| FFB B(D0D8D`
+8A0A(B BBBC P . ߶ ? FDE B(A0A8G
+8A0A(B BBBG d t[ + / Pd2 HW ,/ ) @/ 7 Lf \/ }[' ( p/ }[ BAD AB / ~[O FC L / / FBB B(A0I8D`k
+8D0A(B BBBF 0 ~[u L 0 ORF E(A0D8D`8A0A(B BBBA < p0 @ OPL D(
+ ABBA H 8 0 u EC
DO.0E.
+ A ~[ h < 1 ~[ KBB A(D0(I BBBP 0 w FBB B(I0A8DA
+8D0A(B BBBH X [X ٸ P 1 4 ɸ FBE B(A0A8J
+8D0A(B BBBK [
+ 8 x1
U EC
HK{
+ J . .@ ( Z[ ' p2 4d2 HW 2 2 2 2 2 2 3 3 ,3 @3 T3 h3 |3 3 3 3 | 3 x 3 t 3 p
4 l
4 h
04 d
D4 `
X4 \
l4 X
4 T
4 P
4 L
4 H
4 D
4 @
4 <
5 8
5 4
45 0
H5 ,
\5 ( p5 : Et 5 : Et 5 < Ev 5 < Ev 5 $2 El 5 H2 El 6 l2 El 46 2 El P6 2 El l6 2 El 6 : Et 6 2 El 6 D 2 El 6 h 2 El 6 2 El 7 < Ev 07 : Et L7 < Ev h7 |7 7 7 7 7 7 7 8 8 08 D8 X8 l8 8 8 8 8 8 ) 8 7 Lf 9 ) 9 0 7 Lf 09 T ) D9 p 7 Lf `9 ) t9 7 Lf 9 ) 9 7 Lf 9 ) 9 0 7 Lf 9 T ) : p 7 Lf : ) 4: 7 Lf P: ) d: 7 Lf : ) : 0 7 Lf : T ) : p 7 Lf : ) : 7 Lf ; ) $; 7 Lf @; ) T; 0 7 Lf p; T ) ; p 7 Lf ; ) ; 7 Lf ; ; #Q $ ; % E
+H
+A ( < d(P IEE yAB L< (" `< ( t< (" < ( < (2 , < ) s FAL0
+DBA h* [ P , T< l) @ FAL0
+DBA * f[ , < )
FAL0
+DBA * 3[ , < <* گ FAL0
+DBA @+ [ , ,= * FAL0
+DBA + ͉[ , t= + t FAL0
+DBA + [ Q , = t+ A FAL0
+DBA , g[ , > + FAL0
+DBA `, 4[ , L> D, ۮ FAL0
+DBA , [ , > , FAL0
+DBA , Έ[ H ? - EAG Y
+CAF W
+CAE |
+CAH NCA H ? x- EAG Y
+CAF W
+CAE |
+CAH NCA H @ - EAG U
+CAJ W
+CAE N
+CAF rCA H d@ 0. EAG U
+CAJ W
+CAE N
+CAF rCA H @ . EAG U
+CAJ W
+CAE N
+CAF rCA H @ . EAG U
+CAJ W
+CAE N
+CAF rCA H HA ,/ EAG U
+CAJ W
+CAE N
+CAF rCA H A / EAG U
+CAJ W
+CAE N
+CAF rCA H A / EAG U
+CAJ W
+CAE z
+CAB NCA H ,B 80 EAG U
+CAJ W
+CAE N
+CAF rCA H xB 0 EAG U
+CAJ W
+CAE N
+CAF rCA H B 0 EAG U
+CAJ W
+CAE N
+CAF rCA H C 41 EAG U
+CAJ W
+CAE N
+CAF rCA H \C 1 EAG Y
+CAF W
+CAE |
+CAH NCA H C 1 EAG U
+CAJ W
+CAE z
+CAB NCA H C P2 EAG Y
+CAF W
+CAE |
+CAH NCA @D 2_ TD 3 , hD 3 FHA D0i AAB D l3 ( D x3 IEE nAB D 3 D 3L , E $5 FHA D0 AAB( 0E 5 IEE AB \E h6 , D t6 FAL0
+DBA (3 [ b , E 6 R FAL0
+DBA p3 x[ / , \E D7 FAL0
+DBA 3 E[ , E 7 FAL0
+DBA 4 [ ɧ F 8 F < , F < FAL0
+DBA p4 [ n , \F $= ^ FAL0
+DBA 4 [ ; , F = + FAL0
+DBA 5 Q[ , F = FAL0
+DBA H5 [ զ ( G \> 'K(BF H A ` H A FIE B(A0A8DP
+8A0A(B BBBA _
+8A0A(B BBBG |H xD ( H DY LAA GAB H D ` H DG FIE B(A0A8DP
+8A0A(B BBBH Z
+8A0A(B BBBD 4I I HI I \I La $ pI O E
+F
+A I Q\ I RM Ax
+G I ,Sk F]U I |S @ I XT= FIB D(A0DP
+0A(A BBBE X @J TW{ KBB H(D0w
+(I BBBO H0 4 I x[ FIA D`
+ DABD ~[X ޣ 4 HJ 8] Σ FIA D`
+ DABK '~[X 4 J ^ FIA D`
+ DABK D /~[X x 4 J ` h FIA D`
+ DABK 7~[X E 4 8K xb 5 FIA D`
+ DABK ?~[X 4 K 8d FIA D`
+ DABK 4 G~[X ߢ 4 K e Ϣ FIA D`
+ DABK O~[X 4 (L g FIA D`
+ DABK W~[X y 4 xL xi i FIA D`
+ DABK $ _~[X F 4 L 8k 6 FIA D`
+ DABK t g~[X 4 M l FIA D`
+ DABK o~[X 4 hM n С FIA D`
+ DABK w~[X 4 M xp FIA D`
+ DABK d ~[X z 4 N 8r j FIA D`
+ DABK ~[X G 4 XN s 7 FIA D`
+ DABK ~[X 4 N u FIA D`
+ DABK T ~[X 4 N xw Ѡ FIA D`
+ DABK ~[X 4 HO 8y FIA D`
+ DABD ~[X { 4 O z k FIA D`
+ DABD D ~[X H 4 O | 8 FIA D`
+ DABD ~[X 4 8P x~ FIA D`
+ DABD ~[X 4 P 8 ҟ FIA D`
+ DABD 4 ~[X 4 P FIA D`
+ DABD ~[X | 4 (Q l FIA D`
+ DABD ~[X I L xQ x 9 FBB B(G0D8J]
+8D0A(B BBBE L Q 5 FBB E(D0A8G`
+8D0A(B BBBG L R 8 FBB E(D0A8G`
+8D0A(B BBBG L hR ȏ FBB E(D0K8Jp
+8D0A(B BBBD P R 8 FBB B(A0I8D"
+8D0A(B BBBG t }[. ޞ P $S ߞ FBB B(A0I8D"
+8D0A(B BBBG E~[. P S FBB B(A0I8D"
+8D0A(B BBBG L [. v P S 4 w FBB B(A0I8D"
+8D0A(B BBBG [. B P hT C FBB B(A0I8Dt
+8D0A(B BBBE $ [@ & P T ' FBB B(A0I8Dt
+8D0A(B BBBE _[@
+ P @U FBG B(G0A8J<
+8D0A(B BBBA P U L FBG B(G0A8J$
+8D0A(B BBBA P U Ý FBG B(G0A8J<
+8D0A(B BBBA P <V 䩷 FDB B(G0A8J
+8D0A(B BBBA P V { FBG B(G0A8J$
+8D0A(B BBBA P V \ W FDB B(G0A8J
+8D0A(B BBBA P 8W 3 FDB B(G0A8J
+8D0A(B BBBA P W Բ FDB B(G0A8J
+8D0A(B BBBA P W FBG B(G0A8J<
+8D0A(B BBBA P 4X l ǜ FBG B(G0A8J<
+8D0A(B BBBA P X ȹh FDB H(A0A8J1
+8D0A(B BBBA P X 佷h FDB H(A0A8J1
+8D0A(B BBBA P 0Y ·h FDB H(A0A8J1
+8D0A(B BBBA P Y Ʒh Ĝ FDB H(A0A8J1
+8D0A(B BBBA P Y 8ʷh Ϝ FDB H(A0A8J1
+8D0A(B BBBA P ,Z Tη ڜ FDB H(A0A8J1
+8D0A(B BBBA $[ Vd2 HW H <[ ҷ EAG U
+CAJ [
+CAA d
+CAH NCA [ |[' ( [ |[ BAD IB ( [ }[} BAD fIB < [ ҷ| MBB E(D0
+(I BBBH P 4\ | MBB H(A0
+(F BBBH
+(A BBBH \ }[O FC \ ܷ2 8 \ ݷ Q FAA
+ABI AAB L \ pݷq FBB A(A0
+(D BBBL i(A BBB ` D] PBB B(A0A8D`q
+8A0A(B BBBI
+8A0A(B BBBE < ] |[ KBB A(D0(I BBBP D] w % FBB B(I0A8DA
+8D0A(B BBBH G ~[X P ] w ֙ FBB B(I0A8DH
+8D0A(B BBBA hH ~[X P ^ w FBB B(I0A8DH
+8D0A(B BBBA H ~[X H H ,_ Z PBB B(A0A8DP
+8A0A(B BBBF H x_ PBB B(A0A8Qp
+8A0A(B BBBC _ @ < 4_ > FBA D(G0
+(A ABBJ 8 t_ k EG
DGK6. ^.
+ D $E }[ L _ 8 FEE E(D0A8Dp
+8D0A(B BBBG H -~[ = P 0` - FFB B(D0A8J
+8A0A(B BBBI P }[K P ` Tk FBB B(A0A8G
+8A0A(B BBBD R ~[' % P a X" 1 FBB B(D0H8G(
+8A0A(B BBBG 3 |[r U D ta 0 d FBB D(A0DP
+0D(A BBBE \P [8 6 P a 2= ' FBB B(A0A8G
+8D0A(B BBBC
+ Z[ + b @Od2 HW P Xb 9 FBB B(F0A8G
+8A0A(B BBBJ B V[ e 4 b ; FAI D0r
+ DABA p2 [m ] 4 c << M FAI D0r
+ DABA 2 ӊ[m ' 4 dc < FAI D0r
+ DABA 3 [m \d <T FS p
+EA d 4=9 d `=2 ( d [ BAD IB ( d ̋[ BAD IB @ \d 4= 9 MBF A(D0
+(I BBBF T d @ MBF D(A0
+(F BBBN
+(A BBBH e Ck F]U d e 8D FBB B(A0A8GP
+8A0A(B BBBF
+8A0A(B BBBG ` $f G FBB B(A0A8DPM
+8D0A(B BBBL L8A0A(B BBB < f ފ[ KBB A(D0(I BBBP $f H y FBB B(I0A8D[
+8D0A(B BBBF P [X ; P f N + FBB B(I0A8Db
+8D0A(B BBBG HQ [X P f TS ݔ FBB B(I0A8Db
+8D0A(B BBBG Q Ό[X t h X# YLB B(A0D8G@8A0A(B BBBDH@
+8A0A(B BBBE H h @[ FBB B(A0A8D`
+8A0A(B BBBC P ,h ` ˓ FBB B(D0A8G
+8D0A(B BBBB P h `dD FBB B(D0A8G
+8D0A(B BBBE P h \h< ] FBB B(A0A8G
+8A0A(B BBBH N[5 6 P @i 0lz & FLF B(A0D8G
+8A0A(B BBBG [` ܒ P i Dm FBB E(D0C8J
+8D0A(B BBBC P j qM FFB B(A0A8M
+8A0A(B BBBH $ Pa [< ǒ 2DKA P |j dvJ ֒ FBB B(A0A8Gz
+8A0A(B BBBG d x[ $ P j HB 1 FBE B(D0A8G
+8A0A(B BBBD [ L Tk ,t BBB E(D0D8GM
+8A0A(B BBBG T, |[P ϒ 8 k D} / EG
I. . k
+ G lQ x[
P l p FBB E(D0C8J
+8D0A(B BBBC P dl FBB B(D0A8L
+8D0A(B BBBD > ƚ[ P l t FEB L(D0A8Gy
+8D0A(B BBBE ,g [M ӓ P <m ¸` FBB B(A0A8Gz
+8A0A(B BBBG \ ߜ[M Ln Dd2 HW P m ø FEB B(A0A8Lx
+8A0A(B BBBA $ [ ~ 4 ,n Ÿ FAI D0u
+ DABA = 9[h a 4 |n Ÿ Q FAI D0u
+ DABA (> Q[h + 4 n DƸ FAI D0u
+ DABA x> i[h o ƸN FP m
+EA o Ƹ9 o ƸN FP m
+EA p $Ǹ9 4p PǸ- Hp lǸ- \p Ǹ$ F] xp [' , p Ǹ? MAD m
+IBF , p ȸ? MAD m
+IBF 8 p ɸ# MAA g
+ABG
+ABD 8 (q ʸ# MAA g
+ABG
+ABD ( dq >[ BAD AB @ p d˸< MBF A(D0
+(I BBBN q ̟[O FC T Lq Dϸ< MBF A(A0
+(F BBBH
+(A BBBH q [ e FFD@ ` lr Ӹ FBB B(A0A8D@
+8D0A(B BBBH 8A0A(B BBB d ,r ԸZ PBB B(A0A8D`t
+8A0A(B BBBF
+8A0A(B BBBA ` 8s |ظ' FBB B(A0A8DP
+8D0A(B BBBL l8A0A(B BBB r Hݸe O E_ < s Ҟ[ KBB A(D0(I BBBP Xs Xݸw FBB B(I0A8DH
+8D0A(B BBBA ^ [X t ht l# YLB B(A0D8G@8A0A(B BBBDH@
+8A0A(B BBBE L <t $K / FBB B(A0A8Gp
+8A0A(B BBBE 8 0u $~ FAA }
+ABH
+DBI P t h FBB B(A0A8D
+8A0A(B BBBF ` u : ˍ FBB B(A0A8GbMOA^
+8D0A(B BBBA L n[ { L u FBB E(D0A8I
+8A0A(B BBBA H [Z P v FBB B(D0A8J
+8D0A(B BBBH [` L w I FBB A(A0q
+(D BBBI (A BBB P v ! э FFB B(D0A8G
+8A0A(B BBBA W [o ύ 8 (w h3 EG
I>
+ A .0l. \ ȧ[ F x :d2 HW P w D` : FBB B(F0A8J
+8A0A(B BBBG [ L x H; - FBB B(A0I8DP
+8D0A(B BBBA tq c[ d y XI[ FBB B(A0A8GP
+8A0A(B BBBK L
+8A0A(B BBBB d ty PL` FBB B(A0A8D`
+8D0A(B BBBJ T
+8A0A(B BBBB L 8y HO FLF B(A0D8J
+8A0A(B BBBA J [ L Dz O FEB E(A0N8D
+8A0A(B BBBD < y pT< EC
I.0e.
+ J .0a. _ 8[< ڌ < Hz X_V ˌ FBD D(DP
+(D ABBJ [[ P z `b& FBB B(A0A8G=
+8D0A(B BBBA ? [R { i- { <i- { XiM FS i
+EA | i$ F] | i2 ( 0| ư[ BAD IB ( \| `[ BAD AB , | 2[ KAD IB @ | <i< MBF A(D0
+(I BBBN | 8mk F]U T x| m I MBF A(A0
+(F BBBG
+(A BBBH < t} R[ KBB A(D0(I BBBP } o FBB B(I0A8D
+8D0A(B BBBB g j[X v P |} $t f FBB B(I0A8D
+8D0A(B BBBB 4h V[X ' 4 } hx FAD D0h
+ AABE H ~ y PBB B(A0A8Dp
+8A0A(B BBBJ P l~ ĉM FBB B(D0A8G
+8D0A(B BBBK \ ~ F o FBB B(A0A8GJOAW
+8D0A(B BBBF P ^[ ! P 8 U & FFB B(A0C8J]
+8A0A(B BBBK |{ [ 5 P x @ FFB E(A0D8J
+8A0A(B BBBC ` [C P * FBB B(D0A8I
+8A0A(B BBBE l[ 8 | dK B EG
P7
+ E .0N. ,f [i S L Ѐ `3 C FBE B(A0A8G
+8D0A(B BBBF l [ P 8 8d O FBB E(D0A8G
+8D0A(B BBBF p[; H 0d2 HW P $ FBB B(F0A8J
+8A0A(B BBBD 4 ([ 4 ( Ź/ I FAI D0
+ DABA Q d[ $ 4 x ƹ/ FAI D0
+ DABA $R [ d l ǹ FBB B(D0A8DP
+8A0A(B BBBJ \
+8A0A(B BBBB h 0 ˹ y FZF B(A0A8GPw
+8A0A(B BBBF
+8A0A(B BBBJ | ^[( < ι EC
I.0^.
+ G .0Z. hi .[ < D FBB D(GP
+(D BBBH (n [Y d d ٹW FBB B(A0A8GVEEQw
+8D0A(B BBBG |E [A H h۹ EAG U
+CAJ [
+CAA d
+CAH NCA l[' [O FC ۹= ۹E ` 0 ۹ FBB B(A0A8D@
+8D0A(B BBBH 8A0A(B BBB D pݹu Æ FEE D(A0DPD
+0D(A BBBH t [ t ߹O YEB B(N0D8D`8A0A(B BBBHH`
+8A0A(B BBBG P Ȇ h
+ FIE B(A0D8G
+8A0A(B BBBG i [ = L 4 - FEE E(A0D8D`
+8A0A(B BBBJ L L FEE E(D0A8D`:
+8A0A(B BBBF L ԇ K FBB B(A0A8Gp
+8A0A(B BBBE L Ȉ FED A(D0G
+(A ABBG L
+(G ABBH < t < c BED A(DP
+(D ABBA $ {[ - P ̈ FBB B(D0A8G
+8D0A(B BBBC P @D FBB B(D0A8G
+8D0A(B BBBF D t <V FBB A(A0Dx
+0A(A BBBA ,U [ k L ԉ < p FBB B(A0A8Dy
+8A0A(B BBBK *[ ( D < ] FBB A(A0Da
+0D(A BBBD U [ 8 EG
Ig. ^.
-8A0A(B BBBA T d BBB B(A0A8GX
-8A0A(B BBBA T y BDB B(A0A8J
-8A0A(B BBBH ̓ ( < , " BBA A(JPx
-(A ABBB $ PQ Az
-E P , D b D\
-H P
-H Q
-G I t q. K^ T ԓ BBB B(A0A8GX
-8A0A(B BBBA , #b D\
-H P
-H Q
-G I , @#b D\
-H P
-H Q
-G I 4 #~ # BAD D
-ABA D Ĕ #l BAA JPV
- AABH
- AABC ĕ q. K^ T $ %D BBB B(A0I8G
-8D0A(B BBBA T | ,f BBB B(A0I8G
-8D0A(B BBBA \ ԕ 3 BBB A(A0MJ
-0A(A BBBI
-0A(A BBBD T 4 x;D BIB B(A0A8G*+
-8A0A(B BBBD , D b D\
-H P
-H Q
-G I , t b D\
-H P
-H Q
-G I D `l BAA JPV
- AABH
- AABC q. K^ T L p " BBB B(A0A8G
-8A0A(B BBBA T (' " BBB B(A0A8G
-8A0A(B BBBA \ s" BBB A(A0MJ
-0A(A BBBI
-0A(A BBBD L AAG ]
+ A Lp M[Z w L $a FBB B(D0A8J
+8A0A(B BBBH $ 4 ?[E L FKD P h x& T FFB B(A0A8M
+8A0A(B BBBG 4 [0 P ԋ *< FBB B(D0D8J5
+8A0A(B BBBC t
[k Ƀ P @ 1 փ FBB E(A0A8J
+8D0A(B BBBG d [
+ P A FBE E(A0A8G
+8D0A(B BBBJ `J F[W L Fb ; FBB B(A0D8D
+8D0A(B BBBE 6[W $ L KN / FBB E(D0D8Gp
+8D0A(B BBBH u %[8 $d2 HW P pQ ݃ FFB E(D0A8K
+8A0A(B BBBC x [B D l S< EC
FHK.0 . .0.
+ C (t [ ߃ x t pd YBE B(A0D8G@I8A0A(B BBBGH@
+8A0A(B BBBB L L ti] P FEE E(D0A8G@
+8D0A(B BBBE @ $#d2 HW \ hj) p j7 Lf j) j7 Lf j) А k7 Lf , H (k a FAL0W
+DBA ~ [ = 4
+[' H [. Fb H d @k EAG U
+CAJ W
+CAE z
+CAB NCA H k EAG U
+CAJ W
+CAE z
+CAB NCA H l EAG U
+CAJ [
+CAA d
+CAH NCA H \l H \ hl FBB B(A0A8DP^
+8A0A(B BBBF ( [ BAD AB @ Ԓ oM FBB A(A0DP
+0A(A BBBD < z[ KBB A(D0(I BBBP qW FBB B(I0A8D'
+8D0A(B BBBB l} [X P wg FBB B(I0A8D6
+8D0A(B BBBC } ~[X { P }g k FBB B(I0A8D6
+8D0A(B BBBC D~ j[X , P g FBB B(I0A8D=
+8D0A(B BBBD ~ V[X ~ P d ܉ ~ FBB B(A0I8G
+8D0A(B BBBF e B[| ~ P Д ~ FBB B(A0I8G
+8D0A(B BBBF $f R[| ~ P < D ~ FBB B(A0I8D"
+8D0A(B BBBG b[. b~ P 9 c~ FDE H(A0A8G
+8D0A(B BBBE P ~ FBB B(G0D8Gw
+8D0A(B BBBA P P FBB B(G0D8Gw
+8D0A(B BBBA H \ d2 HW d 2 8 x L FBD A(D0
+(I ABBO L FBA A(G0u
+(F ABBO D
+(A ABBF 4 ` ௺8 ~ FHD D0
+ AABK ( < [ BAD AB , ė $ C~ EHG0
+AAJ k F]U , $ } EHG0
+AAB 4 D H } FHD D0
+ AABK , | $ } EHG0
+AAJ < P r[ KBB A(D0(I BBBP D +} FBB B(I0A8D
+8D0A(B BBBG x [u | P X ƺ | FBB B(D0A8G
+8D0A(B BBBA P ˺P | FBB B(D0A8GC
+8D0A(B BBBH P к@ {| FEB I(A0A8G
+8A0A(B BBBG , T = G} FAL W
+DBA X [ #} * } FFB B(D0A8G
+
+8D0A(B BBBE
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+ F d
. a. U. Q. .`. a. H. X. .`i. . . d. .`f. . L < J* FBB E(G0F8G
+8D0A(B BBBA ,[ ~ H d2 HW d pL x lL
+ [' ( TL AC
FKA
+ J $ ̝ HN} Eh
+C V
+A V
+A 4 N EDG
+AAI ^
+CAN , PX \ @ DQ QJE B(A0D8
+0A(B BBBA 0A(B BBBJH U } BBB D(A0G
+0G(A BBBH < H 8Y } FFA A(GP
+(A ABBF L i } FPB B(A0A8G`G
+8A0A(B BBBD P ؞ xo |} FBB B(A0A8G
+8A0A(B BBBF < , ~ z} EC
DO.@.P.
+ E d2 HW x , H FBB B(A0A8DBKAc
+8A0A(B BBBG B`FFIBO H \ EAG Y
CAF [
-ACA R
-CAJ JCA , d b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , ę @b D\
-H P
-H Q
-G I T <
- ! BNO B(A0A8G
-8A0A(B BBBH , g ! BDC y
-ABA , ę (_ ! AAG o
-DAA T Xu P$ BEE B(A0A8G
-8A0A(B BBBD L L $ BBB B(A0G8Dh
-8A0A(B BBBJ T Po $ BBB B(A0A8J
-8D0A(B BBBK T h2 $ BNB B(K0A8GX
-8A0A(B BBBG \
-m BBD D(J@
-(A ABBD T
-(A ABBF _
-(A ABBK d Pp. K^ 4 ě $& BRA G
- DABA 4 & BOA G
- DABA 4 4 H % BOA G
- DABA 4 l % BOA G
- DABA 4 % BOA G
- DABA T ܜ p % BIB B(D0A8G[
-8D0A(B BBBA T 4 h% BIB B(D0A8G[
-8D0A(B BBBA T ` Q% BPB B(D0A8G[
-8D0A(B BBBA T :% BPB B(D0A8G[
-8D0A(B BBBA T < P #% BMB B(D0A8G[
-8D0A(B BBBA T % BMB B(D0A8G[
-8D0A(B BBBA T @ $ BMB B(D0A8G[
-8D0A(B BBBA L D E *% BBB B(A0A8MH
-8A0A(B BBBA L L AAG ]
-CAF Z
-ACB R
-CAJ JCA , b D\
-H P
-H Q
-G I , ̠ b D\
-H P
-H Q
-G I , Xb D\
-H P
-H Q
-G I L , AAG ]
-CAF Y
-ACC R
-CAJ JCA , | b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , ܡ H b D\
-H P
-H Q
-G I L AAG ]
-CAF [
-ACA R
-CAJ JCA , \ b D\
-H P
-H Q
-G I npZ AX T '# BNB L(A0A8G
-8A0A(B BBBH T L P C# BNB O(A0A8G
-8A0A(B BBBG T 7# BNB O(A0A8Ge
-8A0A(B BBBG T +# BLB B(A0A8G
-8A0A(B BBBC , T d ## ACG r
-DAA , _ " AAG o
-DAA , ( c " ACG q
-DAA , h _ " AAG o
-DAA , e " ADG r
-DAA L D _# BBB B(A0G8Dh
-8A0A(B BBBJ L ! $ BBB B(A0G8Dh
-8A0A(B BBBJ L % $ BBB B(A0G8Dh
-8A0A(B BBBJ \ 4 * $ BBE A(A0Da
-0D(A BBBE
-0D(A BBBH T - $ BQB B(K0A8G
-8A0A(B BBBA @p. K^ 4 (1 x& BPA G
- DABA 4 < 1 W& BPA G
- DABA 4 t 2 6& BMA G
- DABA 4 P3 & BMA G
- DABA 4 4 % BMA G
- DABA 4 4 % BMA G
- DABA 4 T x5 % BMA G
- DABA 4 06 % BMA G
- DABA 4 ħ 6 p% BMA G
- DABA 4 7 O% BMA G
- DABA 4 4 X8 .% BKA G
- DABA 4 l 9
% BKA G
- DABA 4 9 $ BPA G
- DABA 4 ܨ : $ BPA G
- DABA 4 8; $ BPA G
- DABA 4 L ; $ BPA G
- DABA 4 < h$ BPA G
- DABA 4 `= G$ BPA G
- DABA 4 > &$ BPA G
- DABA 4 , > $ BPA G
- DABA 4 d ? # BPA G
- DABA 4 @@ # BPA G
- DABA 4 Ԫ @ # BPA G
- DABA 4 A # BPA G
- DABA 4 D hB `# BKA G
- DABA 4 | C ?# BKA G
- DABA 4 C # BKA G
- DABA 4 D " BKA G
- DABA L $ HE u# BDB B(I0A8G\
-8A0A(B BBBA l t K d# BDB B(I0A8D\
-8A0A(B BBBA
-8A0A(B BBBA , XQb D\
-H P
-H Q
-G I , Q
- " AANp~
-AAA , D xT_ " AAG o
-DAA , p. K^ T T & BIB B(D0A8G-
-8D0A(B BBBA T [ & BIB B(A0A8G
-8A0A(B BBBA T < `jC & BIB B(A0A8G
-8A0A(B BBBA T X|W$ & BBB B(A0A8J
-8A0A(B BBBK , `b D\
-H P
-H Q
-G I , ԯ b D\
-H P
-H Q
-G I , L ( AAN`\
-AAA , | 1 e( AANp
-AAA , _ W( AAG o
-DAA , ܯ _ 3( AAG o
-DAA İ p. K^ T $ ئC + BIB B(A0A8G
-8A0A(B BBBA T | и + BIB B(A0A8GQ
-8A0A(B BBBA T + BIB B(A0A8G
-8A0A(B BBBA T , + BIB B(A0A8Ga
-8A0A(B BBBA T $ + BBB B(A0A8J n
-8A0A(B BBBD A H IJ @ ܲ S ` A $ < T l x p h ` ̳ X P ^ cz
- $ t 3, AG0D T
- l AL , Գ h + ADDP~
-AAA $ (
-$ ACK ODA 4 , 0
-r + BDD DPA
- AABA x
- AP , x
-h G+ ADDP~
-AAA l
- $ ̴
- + AG0D ( + 4 ĵ @ BIA A(G0(D ABB< BEI A(A0(A BBB < T AZ L t 4 BBB B(D0A8GP
-8A0A(B BBBA T ) BBB E(D0A8D
-8D0A(B BBBB < d ) YHD M
-HAE
-HAA \ D t BBE E(G0D8DP~8D0A(B BBB, h BAA
-ABA T 4 #) BEE J(D0D8G>
-8D0A(B BBBA L 0 ( BJE D(D0G%
-0D(A BBBH T ܷ p{ ( BJE E(D0D8G5
-8D0A(B BBBA T 4 { Z( BJE E(D0D8G5
-8D0A(B BBBA T ( BJE E(D0D8GZ
-8D0A(B BBBA T { ' BJE E(D0D8G5
-8D0A(B BBBA T < 0 ' BJE E(D0D8GZ
-8D0A(B BBBA T x{ B' BJE E(D0D8G5
-8D0A(B BBBA L & BJE D(D0G!
-0D(A BBBD T < c & BJE E(D0D8G
-8D0A(B BBBA L y& BEE I(D0G
-0D(A BBBE L 8 <& BJE D(D0G!
-0D(A BBBD L 4 x % BJE D(D0G!
-0D(A BBBD < 0 AG fA $ \ ȒF AAG0zAA 7 Dh
-D F D & BBJ A(D0
-(A BBEA T L % BDD u
-ADD
-ABE U
-ABH I
-ABA T % BDD u
-ADD
-ABE U
-ABH I
-ABA < m% BDA G
- DABG < < @% BDA G
- DABG L | 0 % BHE B(D0D8DP
-8D0A(B BBBB L BLB B(A0A8Dz
-8A0A(B BBBA $ !% YX
-G z
-A T D U W% BBB B(D0A8G
-8A0A(B BBBA \ T BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 Y q& AIG t
-AAI DCA \ BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 XY % AIG t
-AAI DCA L ̿ % BEB E(I0H8D@
-8D0A(B BBBA T 4D % BBB B(A0A8G
-8A0A(B BBBA L t 6 D% BLB A(A0G
-0A(A BBBA $ | XF AAG0zAA L G% BEB E(I0H8D@
-8D0A(B BBBD \ < ] % BBB I(D0D@
-0F(A BBBK U
-0F(A BBBA T @;! $ BLH B(A0A8G
-8D0A(B BBBA T @ $ BLB B(A0A8G
-8A0A(B BBBE \ L P= $ BEH D(I0D@
-0F(A BBBG U
-0F(A BBBA T 0C $ BEB B(A0A8O
-8A0A(B BBBF L ؚU $ BBB E(E0E8G`
-8A0A(B BBBA l T M $ BEB B(A0A8D
-8A0A(B BBBF Y
-8A0A(B BBBE T I $ BBB E(A0F8G-
-8D0A(B BBBE L 0M= k$ BBB F(A0D8D
-8A0A(B BBBA T l N_ 3$ BBB E(A0F8G
-8D0A(B BBBF L (Q= # BBB F(A0D8D
-8A0A(B BBBA p. K^ $ `M AAK0}AA D BEB B(A0A8DP8A0A(B BBBL T P
BEE A(A0]
-(A BBBF
-(E BBBJ @Q D Q T HQz w$ BBB B(A0A8J
-8A0A(B BBBG < \ pS <$ BBD A(G
-(D ABBA L @T $ BHF E(D0A8Dm
-8A0A(B BBBA T Tu # BBB B(A0A8GM
-8D0A(B BBBE L D V # BHE E(D0A8Dm
-8A0A(B BBBA T Wu o# BBB B(A0A8GM
-8D0A(B BBBE L Y A# BHE E(D0A8Dm
-8A0A(B BBBA T < @Z # BBB B(A0A8G]
-8D0A(B BBBE L \ " BHE E(D0A8Dm
-8A0A(B BBBA T ] " BBB B(A0A8GR
-8D0A(B BBBH T < `_ s" BBB B(A0A8GR
-8D0A(B BBBH T a E" BBB B(A0A8G
-8A0A(B BBBA T d " BBB B(A0A8G
-8A0A(B BBBA T D fo ! BBB B(A0A8GJ
-8A0A(B BBBA T h ! BBB B(A0A8G
-8A0A(B BBBA T Hk ! BBB B(A0A8G
-8A0A(B BBBA , b D\
-H P
-H Q
-G I , 4 b D\
-H P
-H Q
-G I , d 0b D\
-H P
-H Q
-G I , pb D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , $ 0b D\
-H P
-H Q
-G I , T pb D\
-H P
-H Q
-G I T # BEE B(A0A8G
-8A0A(B BBBI 4 $ X" BAA GP
- AABG T \ p# BEE B(A0A8G
-8A0A(B BBBI T H# BEE B(A0A8G
-8A0A(B BBBI T # BEE B(A0A8G
-8A0A(B BBBI T d v BBB B(A0A8G7
-8A0A(B BBBF T # BEE B(A0A8G
-8A0A(B BBBI ,
- AAJ@}
-AAE , D x } AAJ@j
-AAA T t # d BEE B(A0A8G
-8A0A(B BBBI L Мu
- BBB B(A0A8M
-8A0A(B BBBG | BBB B(A0D8DP
-8A0A(B BBBB
-8A0A(B BBBE "
-8A0A(B BBBD T @% A BEB B(A0A8G=
-8D0A(B BBBB L ( . BBB B(A0A8M
-8A0A(B BBBG p. K^ T \ ! BBJ B(A0A8Gi
-8D0A(B BBBA T X ! BBB B(A0I8G
-8D0A(B BBBA T { h! BBB B(A0I8G
-8D0A(B BBBA T d H; M! BBB B(A0I8Ge
-8D0A(B BBBA T 0 3! BBJ B(A0A8G
-8D0A(B BBBI T V '! BBJ B(A0A8G
-8D0A(B BBBA T l ! BBB B(A0I8G
-8D0A(B BBBA T A ! BBB B(A0I8Gh
-8D0A(B BBBA T BBB B(A0A8M
-8A0A(B BBBG 4 , ADD K
-AAJ ]DA T BEB B(A0A8J
-8A0A(B BBBG % %
, %b D\
-H P
-H Q
-G I , %b D\
-H P
-H Q
-G I , L &b D\
-H P
-H Q
-G I , | X&b D\
-H P
-H Q
-G I , &b D\
-H P
-H Q
-G I , &b D\
-H P
-H Q
-G I , 'b D\
-H P
-H Q
-G I , < X'b D\
-H P
-H Q
-G I , l 'b D\
-H P
-H Q
-G I , 'b D\
-H P
-H Q
-G I , (b D\
-H P
-H Q
-G I , X(b D\
-H P
-H Q
-G I , , (b D\
-H P
-H Q
-G I , \ (b D\
-H P
-H Q
-G I , )b D\
-H P
-H Q
-G I , X)b D\
-H P
-H Q
-G I , )b D\
-H P
-H Q
-G I , )b D\
-H P
-H Q
-G I , L *b D\
-H P
-H Q
-G I , | X*b D\
-H P
-H Q
-G I , *b D\
-H P
-H Q
-G I , *b D\
-H P
-H Q
-G I , +b D\
-H P
-H Q
-G I , < X+b D\
-H P
-H Q
-G I , l +b D\
-H P
-H Q
-G I , +b D\
-H P
-H Q
-G I , ,b D\
-H P
-H Q
-G I , X,b D\
-H P
-H Q
-G I , , ,b D\
-H P
-H Q
-G I , \ ,b D\
-H P
-H Q
-G I - Y -m A~
-A l 4 p- AAJ v
-AAD fAA L D - c BBB F(A0A8J@X
-8A0A(B BBBA < L x/] BAA m
-ABD
-ABD , 0! ADDP
-AAE , 1D O AAJPl
-AAF , 4 2D 8 AAJPl
-AAF 4 d 3 Z BAA N
- AABD D % BED D(G$
-(D ABBE D & BED D(G$
-(D ABBE D , `( BED D(G$
-(D ABBE D t ) \ BED D(G$
-(D ABBE D 0+ ' BED D(G$
-(D ABBE D , BED D(G$
-(D ABBE D L . BED D(G$
-(D ABBE L h/1 Dl L 85 { BBB B(A0A8Je
-8D0A(B BBBJ p. K^ < 8 BAI G
- DABA L \ `: q BBB I(A0G
-0D(A BBBA L < L BBB I(A0G
-0D(A BBBA L 0? ' BBB I(A0G
-0D(A BBBA L L A BBB I(A0G
-0D(A BBBA L C BBB I(A0G,
-0D(A BBBA L F BBB I(A0G
-0D(A BBBA L < I BBB I(A0G
-0D(A BBBA T @La n BBB B(I0A8G
-8D0A(B BBBA L XO A BBB I(A0G3
-0D(A BBBA T 4 RT BBB B(I0A8G
-8D0A(B BBBA T U BBB B(I0A8GK
-8D0A(B BBBA T W BBB B(I0A8GK
-8D0A(B BBBA L < Z BBB I(A0G
-0D(A BBBA L ] p BBB I(A0G
-0D(A BBBA L _ K BBB I(A0G
-0D(A BBBA T , a & BBB B(I0A8G*
-8D0A(B BBBA L d BBB I(A0G
-0D(A BBBA T 8g8 BBB B(I0A8GM
-8D0A(B BBBA T , js BBJ B(A0A8G.
-8D0A(B BBBA L Hob BBB D(A0G
-0D(A BBBA L hq n BBB I(A0G7
-0D(A BBBA T $ t I BBJ B(A0A8G
-8D0A(B BBBA < | @y @ BAI Gn
- DABA < z- BAI G
- DABA L | BBB I(A0GU
-0D(A BBBA L L BBB I(A0G
-0D(A BBBA T $ BBB B(I0A8GW
-8D0A(B BBBA T | BBB B(I0A8G
-8D0A(B BBBA T L O BBB B(I0A8G)
-8D0A(B BBBA T o " BBB B(I0A8G
-8D0A(B BBBA T S BEB B(A0A8JP
-8A0A(B BBBG < T 8" AFGPe
-AAK T
-AAJ T ( | BBB B(A0I8G
-8A0A(B BBBF L BBB J(A0A8GF
-8A0A(B BBBA T < BDB B(A0D8G
-8A0A(B BBBA L d A`
-G b P A`
-G b 2 Ac
-L A T y BBB B(A0I8D*
-8A0A(B BBBF 8 A`
-G mL ; 8 BBB B(I0A8GT
-8A0A(B BBBA T c
- BBB J(A0A8G
-8A0A(B BBBA T , BBB B(A0I8G
-8A0A(B BBBH < (
BBA A(LPW
-(A ABBA < ACL@\
-AAB
-AAE L BBB B(A0A8L`N
-8A0A(B BBBB T T X BBB B(A0A8L
-8A0A(B BBBH T BBB B(A0A8O
-8A0A(B BBBA T
BBB B(A0C8J
-8A0A(B BBBG L \ BBB B(A0A8J
-8A0A(B BBBA d , r AGD`
-AAH T m BDB B(A0D8G
-8A0A(B BBBH T d i BDB B(A0D8G
-8A0A(B BBBH t ` h
` X , P b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , 4 b D\
-H P
-H Q
-G I , d b D\
-H P
-H Q
-G I , P b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , $ b D\
-H P
-H Q
-G I , T P b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , b D\
-H P
-H Q
-G I , !b D\
-H P
-H Q
-G I , P!b D\
-H P
-H Q
-G I , D !b D\
-H P
-H Q
-G I , t !b D\
-H P
-H Q
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-H P
-H Q
-G I , P"b D\
-H P
-H Q
-G I , "b D\
-H P
-H Q
-G I , 4 "b D\
-H P
-H Q
-G I , d #b D\
-H P
-H Q
-G I , P#b D\
-H P
-H Q
-G I , #b D\
-H P
-H Q
-G I , #b D\
-H P
-H Q
-G I , $ $b D\
-H P
-H Q
-G I , T P$b D\
-H P
-H Q
-G I , $b D\
-H P
-H Q
-G I , $b D\
-H P
-H Q
-G I , %b D\
-H P
-H Q
-G I , P%b D\
-H P
-H Q
-G I , D %b D\
-H P
-H Q
-G I , t %b D\
-H P
-H Q
-G I , &b D\
-H P
-H Q
-G I , P&b D\
-H P
-H Q
-G I , &b D\
-H P
-H Q
-G I , 4 &b D\
-H P
-H Q
-G I , d 'b D\
-H P
-H Q
-G I , P'b D\
-H P
-H Q
-G I , 'b D\
-H P
-H Q
-G I , 'b D\
-H P
-H Q
-G I $ (m A~
-A l 4 D `( AAJ v
-AAD fAA L ( w BBB F(A0A8J@X
-8A0A(B BBBA < h* BAA M
-ABD
-ABD , T +! ADDP
-AAE < , ; AAJPh
-AAJ
-AAH 4 x. N BAA N
- AABD 4 1 u BAA N
- AABD T 4 H! M BEE E(D0D8Gn
-8D0A(B BBBE T q BEE E(D0D8G
-8D0A(B BBBD T Hq BEE E(D0D8G
-8D0A(B BBBE D < p ~ BED D(G$
-(D ABBE D ؒ I BED D(G$
-(D ABBE D @ : BED D(G$
-(D ABBE T ! BEE E(D0D8Go
-8D0A(B BBBD T l 03 BBB B(A0A8G:
-8D0A(B BBBH T h7 BBB B(A0A8G:
-8D0A(B BBBH 0p. K^ < 4 ; BAI G
- DABA L t H= BBB I(A0G
-0D(A BBBA L ? BBB I(A0G
-0D(A BBBA L B w BBB I(A0G
-0D(A BBBA L d xD R BBB I(A0G
-0D(A BBBA L F - BBB I(A0G
-0D(A BBBA L hI BBB I(A0G
-0D(A BBBA L T L BBB I(A0G
-0D(A BBBA L O BBB I(A0G3
-0D(A BBBA T QT BBB B(I0A8G
-8D0A(B BBBA T L T l BBB B(I0A8GK
-8D0A(B BBBA T W ? BBB B(I0A8GK
-8D0A(B BBBA T PZ2 BBB B(I0A8G
-8D0A(B BBBA L T
- 8] BBB I(A0G
-0D(A BBBA L
- _ BBB I(A0G
-0D(A BBBA T
- a BBB B(I0A8G*
-8D0A(B BBBA L L
- d t BBB I(A0G
-0D(A BBBA T
- Pgm O BBJ B(A0A8G(
-8D0A(B BBBA T
- hl 4 BBB B(D0A8G
+CAA
+CAE NCA 2 0 d 엻 j| MAH }
+IBJ < :| MAE u
+FBH y
+ABD ( | &[ BAD AB [O FC , ' { YAD0
+AAF < [ KBB A(D0(I BBBP W J{ FBB B(I0A8D'
+8D0A(B BBBB H [X { P ԟ) z FBB B(A0I8G
+8D0A(B BBBD # [+ z P h z FKB B(D0A8J5
+8D0A(B BBBJ [ z P Ԣ z FBB B(D0A8G4
+8D0A(B BBBG ` ^[ f{ 8 @ [{ EC
DQ
+ H ..@. L | dػ } FBB B(D0A8GJ
+8D0A(B BBBA p d2 HW ) 7 Lf 8) Ф T7 Lf , H x w| FAL0W
+DBA [ S| H 4 EAG U
+CAJ [
+CAA d
+CAH NCA H EAG U
+CAJ [
+CAA d
+CAH NCA H ̥ h EAG U
+CAJ [
+CAA d
+CAH NCA [' , [. Fb H H FBB B(A0A8DP
+8A0A(B BBBF ( [ BAD AB \ FBB D(D0
+(A BBBD
+(A BBBJ A(D EEB, $ FAA
+ABG \ P & FBB D(D0C
+(A BBBD
+(A BBBJ A(D EEB, t FAA
+ABG < B[ KBB A(D0(I BBBP | W Wy FBB B(I0A8D'
+8D0A(B BBBB 4 Z[X y L = y FBB B(D0G8Jv
+8D0A(B BBBD L 8 y FBB B(D0G8Jv
+8D0A(B BBBD , x d2 HW D [' ( X [ BAD AB L ; z FBG B(G0A8G
+8D0A(B BBBA w[ z
+d2 HW %) %7 Lf 4 [' H H % EAG U
+CAJ [
+CAA d
+CAH NCA ( [ BAD AB L % FBB B(A0A8D
+8A0A(B BBBH < "[ KBB A(D0(I BBBP $-W y FBB B(I0A8D'
+8D0A(B BBBB d :[X py P 3 `y FBB B(A0D8K
+8A0A(B BBBF , l t6= Ty FAL W
+DBA p [ 0y \ X l6 XBB B(A0A8Dp8A0A(B BBBH`pL ,=5 x FHB B(A0D8J
+8A0A(B BBBE d2 HW $ r2 ( 8 [ BAD AB P r z FBB B(A0A8Li
+8A0A(B BBBC 8 [ { Э d2 HW , H } FAL0W
+DBA [ } < 4 [ KBB A(D0(I BBBP Э W g} FBB B(I0A8D'
+8D0A(B BBBB [X (} d2 HW d2 HW ) ( 7 Lf D ) X ܑ7 Lf t [' ( [ BAD AB < f \ KBB A(D0(I BBBP P W | FBB B(I0A8D'
+8D0A(B BBBB ~\X { P tW { FBB B(I0A8D'
+8D0A(B BBBB t j\X v{ P ( hq f{ FBJ B(A0A8D6
+8D0A(B BBBC V\ *{ P |q 6{ FBJ B(A0A8D6
+8D0A(B BBBC P \ z t ) UBB B(A0D8J@C8A0A(B BBBIH@n
+8A0A(B BBBH < x H z EC
BHGMD
+ F . . < ļ(# { EC
BHGMD
+ F . . d2 HW F FL i
+EA ز \' < H | MAA
+FBE I
+ABD 0 X S| MAD
+IBG ` n\O FC < | \ KBB A(D0(I BBBP W { FBB B(I0A8D'
+8D0A(B BBBB Н \X { P x{ FBB B(A0I8G,
+8D0A(B BBBJ \ a{ d2 HW 2 P %{ FBB B(I0D8D
+8D0A(B BBBD \ z , xd2 HW T D T EDG@
+AAC _HIPFXA`ChOpI@ZH^PFXD`ChBpI@ ( 4Q P Vz FKB B(A0A8G
+8A0A(B BBBE d2 HW 4 H , \ 8 p 4 0n FFB B(A0A8GiAFACOP~BeLJBY
+8A0A(B BBBA \' P
y FHB B(A0A8J}
+8A0A(B BBBK h | x FBB B(D0D8JSFDCFI[
+8A0A(B BBBH 0c2 HW \' , BAA
+ABA H P d BBB B(A0A8DHs
+8A0A(B BBBE $ ( H`hhBpKhA`T
+A $ ĸ H`\hBpKhA`T
+A $ H`VhBpKhA`T
+A $ p H`QhBpKhA`T
+A $ < H`ehBpKhA`T
+A d P < x FEH D(A0
+(A BBBE D < ̹ TJE A(0
+ ABBG x < X" TJE A(
+ ABBF @ L # L ` D% FEB B(A0A8Jt
+8A0A(B BBBG L * FBE B(A0A8G
+8A0A(B BBBJ L d.
+ FIL E(A0D8Jf
+8A0A(B BBBA P 8+ d 9~ x l9 P 9a u FBB B(D0A8GC
+8D0A(B BBBH P < = t FBB E(A0A8G
+8A0A(B BBBA P BE t FBE B(A0A8G
+8A0A(B BBBA d H FFE E(D0G8GpPxFPGIIIpT
+8A0A(B BBBA P L 4IA /t FBE B(A0A8G
+8A0A(B BBBA P 0T< $t FBB B(A0A8G
+8A0A(B BBBC L [ FBB B(A0A8D
+8A0A(B BBBA L d9 FBB B(A0A8D
+8A0A(B BBBI H 8 m9 FBB E(D0D8GpJ
+8A0A(B BBBF L r3 FBB B(A0A8Gj
+8A0A(B BBBG , Ծ p EI
M
+ J 0 ` 0e r EC
I.@B
+ A L 8 l FOG F(A0A8JU
+8A0A(B BBBC L ̋ FFE B(A0A8G
+8A0A(B BBBD , ؿ EI
Mo
+ F L , FEB B(A0A8Jr
+8A0A(B BBBI 0 5 iq EC
DO
+ A 0 g Iq EC
I.@B
+ A H D FOG F(A0A8G
+8A0A(B BBBE ( EC
I
+ C P p FHE E(A0D8P
+8A0A(B BBBH c2 HW 9 [p Es ൽ 쵽! ! $A `) x ( |F FBE E(G0H8G@n
+8A0A(B BBBH {
+8A0A(B BBBC
+8A0A(B BBBA L P( _o FBB B(D0A8D
+8D0A(B BBBF % [@ 8o 4 H \ [' p 4 n FLJ J(D0R
+(H GBBI f
+(E DBBE `
+(E DBBC f(E DBB ̺
Ⱥ ĺ 4 H \ t g P Q m FBB B(A0A8J
+8A0A(B BBBG $ ý m EG0Q
+AB zPLR x
m 0 ( a[ m @ t \ýw m FHF A(G
+(A BBBD ( zPLR xl 0 [[ 7m c2 HW 8Ľ 4Ľ @Ľ <Ľ HĽ DĽ 4 @Ľ H <Ľ $ \ 8Ľ> FAG iDB $ PĽ> FAG iDB 0[' TĽ < Ľ FFB A(A0w
+(D BBBG ( `Ža RIG wAAE ( @ Ža RIG wAAE < Ž> k FBA D(G0
+(A ABBJ < ƽk _k FEG D(K@x
+(D ABBF 4 H Ƚ ?k EC
P .u.
+ G $ [ Pk A H L Ͻ FBE B(D0A8D@
+8D0A(B BBBE p |н JBB D(D0_
+(A BBBD
+(A BBBI A
+(G BBBJ F
+(A BBBK P h ҽ: j FBF B(D0A8M
+8A0A(B BBBF q [ _j | Խ Խ ԽI FG q
+EA Խ2 (ս 4 L $ս i BDC L0
+ AABI [ i ( @ սA JDG aAAH ` ս i MBB H(A0
+(I BBBK Q
+(A BBBA A(A BBB t EQ H x FBB B(A0A8DP
+8D0A(B BBBF 4 [ h BAD D@ AAB t p YBB B(D0N8D@8A0A(B BBBEH@
+8A0A(B BBBA ( LٽA JDG aAAH H pٽ PBB B(A0A8QP
+8A0A(B BBBA ( ` D۽ FCJ \
+DBK H ۽Z PBB B(A0A8DP
+8A0A(B BBBF p ܽ FFE D(D0L
+(A BBBD
+(A BBBI A
+(D DBBC A
+(C BBBF P ߽a f FBB B(D0A8GD
+8D0A(B BBBG D 4 f PBB B(A0DU
+0A(B BBBA $ N[ =f $ T. EDG [AA P \S f FBB B(D0A8J
+8D0A(B BBBI < | h FAG d
+DBH D
+DBF UDBH FBB E(E0D8DP
+8D0A(B BBBF ` < FBB F(D0D8DP
+8I0A(B BBBC F
+8F0A(B BBBK D e FBB B(A0DC
+0C(B BBBA L 0 d FBB B(D0D8Dn
+8A0A(B BBBA 2 [ d L x o d FBB B(A0A8J
+8A0A(B BBBG Ƞ [ ld D d FBB A(A0Gx
+0A(A BBBE .[ Id P @ Id FGB B(A0A8GE
+8A0A(B BBBA [ d P dc d FFB B(D0D8J
+8A0A(B BBBA [( d L hi e PBB B(A0D8Gz
+8D0A(B BBBG L h d FBB B(D0D8Dy
+8A0A(B BBBA 5 \ d L d FBE E(D0A8D
+8A0A(B BBBA 5 \ d L 8 ( 9 d FBB B(D0D8D
+8A0A(B BBBJ 5 b\ Wd P
Wd FBB B(A0A8J
+8A0A(B BBBA |7 \ sd L $
+e PBB B(A0D8G
+8A0A(B BBBE 3 D
\} d P t -e OBI E(A0(B BBBGH0 ( zPLR x}] 0 0 *\ d P d FBE B(D0A8JC
+8A0A(B BBBE 4 \z fd P x 8 d FBB B(A0A8GY
+8A0A(B BBBH ̥ \ d P :+ d FBB B(A0A8Gc"
+8A0A(B BBBF , zPLR x
\ 4 ,\# e P 0<> 7g FBB B(A0A8G
+8D0A(B BBBG )\9 Vg D P D4 gg FBB E(A0A8G
+8A0A(B BBBK , zPLR xZ 4 n-\2 g < V> ?h FBA D(G0
+(A ABBJ P Wc h FBB B(A0A8G
+8D0A(B BBBK 0 4\ h ~8\' L \ ^ Gh FEE E(A0A8GPx
+8D0A(B BBBE < T`> g FBA D(G0
+(A ABBJ L Ta g FGB E(A0D8D`]
+8A0A(B BBBD L < c g FBE B(D0G8J
+8D0A(B BBBH 2 b7\ qg L jD FG l
+EA p j jE FG m
+EA j (c2 HW jE FJ j
+EA j j j 4 \8\' D j ~f FBB D(A0D@
+0D(A BBBA (8\5 Df L Lk 4f FBB E(A0A8G
+8D0A(B BBBA 7\ e c2 HW , j9\' @ ~9\. Fb 8 \ HkU FBD D(D0f
+(A ABBH H lk FBB E(A0A8Dp
+8A0A(B BBBE @mg Fx
+B [
+E L d m
+ d FEI E(D0A8J
+8D0A(B BBBD H 8\q e p wD FJ i
+EA P w d FBB B(A0A8G
-8D0A(B BBBA < L
- o BAI Gn
- DABA T
- p BBB B(I0A8G
-8D0A(B BBBA T
- xs BBB B(I0A8G)
-8D0A(B BBBA T <
- vo BBB B(I0A8G
-8D0A(B BBBA 4
- 8yb Z ADG=
-DAA <
- pz 5 BAI G
- DABA 4
- 0|@ ADG
-DAA L D
- 8} BBB I(A0G
-0D(A BBBA 4
- ADGx
-DAA <
- BHD G
- DABA 4
- [ w ADG6
-DAA < D
- S R BAI G
- DABA <
- + BHD G
- DABA T
- x BBB B(I0A8G*
-8D0A(B BBBA T
- @0
BBB B(I0A8GE
-8D0A(B BBBA T t
- 0
BBB B(I0A8GE
-8D0A(B BBBA L
-
BBB I(A0G
-0D(A BBBA L
- i
BBB I(A0G
-0D(A BBBA T l
- T D
BBB B(I0A8G
-8D0A(B BBBA T
- :
BBB B(I0A8G%
-8D0A(B BBBA T
- Л BBB B(I0A8GK
-8D0A(B BBBA T t
- 2 BBB B(I0A8G
-8D0A(B BBBA L
- BBB I(A0G
-0D(A BBBA T
- k BBJ B(A0A8G
-8D0A(B BBBA L t
- b BIE A(A0G
-0D(A BBBA L
- 4 BBB I(A0G
-0D(A BBBA <
+8A0A(B BBBG v9\~ e dc2 HW 0 D X l 셾 腾
+ 䅾
+ ܅ ԅ
+ 4 Ѕ H ̅ \ ȅ p ą
+
+
+ $ 8 5 L ȅ ` ą t ( < P d
+ x
+
+ | x
+ t p
+ l , h
+ @ d T `
+ h \ | X
+ T P
+ L H
+ D @
+ < 8
+ 0 4 D 0
+ X , l (
+ $
+
+
+ 4 H
+ \ p
+ 섾 脾 䄾
+ ܄
+ Ԅ $ Є 8 ̄
+ L Ȅ ` Ą t
+
+ & H] & H] : Hq 4 : Hq L (: Hq d P: Hq | x: Hq : Hq ȅ: Hq : Hq $ 0 < , H @ T T ` h l | x 0 D X l |! JQ 2 ( A Jn ( D Y KDA tDG0 p $w EAG [
+AAF DAA pk J\R EL ć 8 FDA [
+DBL V
+ABO 8 0 D FDA [
+DBL V
+ABO l Ȉ Ĉ Q L , HT cX MAE q
+ABA $ zPLR x
H , -\ X 8 $ 4 W aX
+G z
+A zPLR xG ( v-\ W ` , \T W MAE q
+ABA .-\ gW t $ ` p CW aX
+G z
+A ,\ #W D , T V MAE q
+ABA ,\ V ԉ $ Љ V aX
+G z
+A j,\ V , d T sV MAE q
+ABA ` ",\ OV P 4 t d 0a QF
+I FZHH
+H H
+H H
+H H
+H H
+H H
+H H
+H H
+H H
+H H
+H H
+H H
+H H
+H H ( d[
+A M $ 8 L d[
+A M l 싾 苾 d[
+A M X T P L , H d[
+A M L ` t d[
+A M d[
+A M | , x @ t T p h l d[
+A M ܍ ؍ ԍ Ѝ ̍ d[
+A M < 8 4 4 0 H , d[
+A M h | d[
+A M ( 쎾 d[
+A M H \ \ X p T P L d[
+A M d[
+A M ( < P d x d[
+A M | x t p 0 ll FDD v
+DBF UDB 0 w EAG [
+AAF DAAH P FEE B(A0A8G`
+8A0A(B BBBE H X FEE B(A0A8G`
+8A0A(B BBBE L D I N FFB E(A0A8D`
+8D0A(B BBBG `\ 8$\& lN P OBB E(G0G8K`_
+8G0A(B BBBH
+8A0A(B BBBG H` P 0 a M FBB B(D0A8GD
+8D0A(B BBBG n#\ M FFD@ #\ M FFD@ ^$\ kM FFD@ D { PM FBB B(A0DF
+0A(B BBBH $\ M D P N M FBB B(A0Dp
+0A(B BBBF x %\ L L L FIB B(A0A8GZ
+8A0A(B BBBH hY %\ L L 螾 L FIB B(A0A8Ga
+8A0A(B BBBI Y '\ [L L 0 sL FIB B(A0A8Gj
+8A0A(B BBBH 8Z ~(\ +L D CL FBB B(A0DU
+0A(B BBBA )\ L ȟ ğ ( < P d x D {K FBB B(A0D^
+0A(B BBBH *\ ;K 蟾 䟾 4 p f,\ +K FGB B(A0A8D 4 N-\ K FGB B(A0A8D L p J FFB B(A0A8J
+8A0A(B BBBG \ -\ J < H ء> J FBA D(G0
+(A ABBJ P آ J FBB E(A0A8J
+8A0A(B BBBF D ( @Q JDD jIA 8 t KBD A(D0V(I ABB( ȦQ JDD jIA ( ( Q JDD jIA P T 0 OBA D(D0L
+(I ABBL pH0 P ̧ OBA D(D0L
+(I ABBL pH0 X h OBB B(D0A8D@w
+8I0A(B BBBI h P X , OBA D(D0L
+(I ABBL pH0 P ȩ OBA D(D0L
+(I ABBL pH0 P dK OBA D(D0`
+(I ABBH @H0 T Pc2 HW l H: La
+K A h EQ l x) LX $ 4 GG aX
+G p
+H zPLR x3 ( *\ G < 8 P 4 d @) LX $ T F aX
+G p
+H H*\ F $ ,) LX $ ` @ GF aX
+G p
+H 0 )\ -F D X l ) LX $ , E aX
+G p
+H )\ E ) LX $ h kE aX
+G p
+H 8 )\ QE L 课 ` 䯾 t ) LX $ D aX
+G p
+H (\ D а ܰ) LX $ p D aX
+G p
+H @ T(\ uD T h | ȱ) LX $ ܱ !D aX
+G p
+H '\ D ) LX $ x Ȳ C aX
+G p
+H H '\ C \ p ) LX $ EC aX
+G p
+H ('\ +C ) LX $ B aX
+G p
+H P &\ B d p x l x) LX $ iB aX
+G p
+H `&\ OB \ X d) LX $ x A aX
+G p
+H X %\ A l H $ D A ED0s
+HD ? %\% A ) LX $ L 4 dA aX
+G p
+H ~%\ JA 0 , x &A FCDP[
+ABE T^ B%\ @ $ 8 @ EDs
+HD zPLR x5, ( f%\% @ $ L t @ EDs
+HD zPLR x+ ( (%\% g@ T : La
+K A t ༾ EQ 伾 ) LX $ ? aX
+G p
+H $\ ? Խ н ( ܽ) LX $ }? aX
+G p
+H p >$\ c? Ⱦ) LX $ $ ܾ ? aX
+G p
+H #\ > 0 ) LX $ ȿ > aX
+G p
+H x
+ v#\ > ) LX $ , 3> aX
+G p
+H
+ #\ > $ 8 ) LX $ = aX
+G p
+H "\ = p¾ l¾ x¾) LX $ 4 ¾ W= aX
+G p
+H J"\ ==
+ \þ ,
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+G p
+H !\ <
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+ Dľ
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+G p
+H \ ; ( Ǿ < Ǿ P Ǿ) LX $ (Ǿ 1; aX
+G p
+H V \ ; Ǿ Ǿ Ⱦ) LX $ L Ⱦ : aX
+G p
+H \ : 0
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+0H(B BBBD 4/ 2 \@ 9 ɾ $ ɾ 8 ɾ) LX D
ɾ 9 FBB B(A0Ds
+0H(B BBBD / \@ j9 ʾ |ʾ ʾ) LX D T ʾ -9 FBB B(A0Ds
+0H(B BBBD |0 j \@ 8 X <˾ l 8˾ D˾) LX D X˾ 8 FBB B(A0Ds
+0H(B BBBD 1 !\@ ~8 ˾ ˾ $ ̾) LX D ̾ A8 FBB B(A0Ds
+0H(B BBBD 1 !\@ 8 ̾ ̾ ̾) LX D @ ̾ 7 FBB B(A0Ds
+0H(B BBBD h2 >"\@ 7 D p; X l; l x;) LX D ; U7 FBB B(A0Ds
+0H(B BBBD 3 "\@ 7 ,ξ (ξ 4ξ) LX D Hξ 6 FBB B(A0Ds
+0H(B BBBD 3 v#\@ 6 ξ ξ ξ) LX D , Ͼ i6 FBB B(A0Ds
+0H(B BBBD T4 $\@ 06 0 Ͼ D Ͼ X Ͼ) LX D Ͼ 5 FBB B(A0Ds
+0H(B BBBD 4 $\@ 5 `о \о hо) LX D t |о }5 FBB B(A0Ds
+0H(B BBBD 5 J%\@ D5 x Ѿ Ѿ $Ѿ) LX D 8Ѿ 5 FBB B(A0Ds
+0H(B BBBD @6 %\* 4 Ѿ 0 Ѿ D Ѿ) LX D Ѿ 4 FBB B(A0Ds
+0H(B BBBD 6 l&\* W4 Ҿ Ҿ Ҿ) LX D ` Ҿ 4 FBB B(A0Ds
+0H(B BBBD 7 &\* 3 d PӾ $ LӾ 3 EDs
+HD '\% 3 8 ԾT 3 aHD M
+HAE
+HAA l '\% 3 վ: La
+K A , վ @ վ) LX D ־U #3 FBB B(A0Ds
+0H(B BBBD 8 '\< 2 ) LX D \ $U 2 FBB B(A0Ds
+0H(B BBBD 9 '\R u2 ` $ؾ t ؾ ,ؾ) LX D @ؾU 82 FBB B(A0Ds
+0H(B BBBD (: V(\R 2 @پ <پ , Hپ) LX D \پU 1 FBB B(A0Ds
+0H(B BBBD : )\R 1 \ھ Xھ dھ) LX D H xھU N1 FBB B(A0Ds
+0H(B BBBD p; )\< 1 L x۾ ` t۾ t ۾) LX D ۾U 0 FBB B(A0Ds
+0H(B BBBD < J*\R 0 ܾ ܾ ܾ) LX D ܾU c0 FBB B(A0Ds
+0H(B BBBD < *\R +0 ݾ ݾ ݾ) LX D 4 ݾU / FBB B(A0Ds
+0H(B BBBD \= +\R / 8 L ` ) LX D y/ FBB B(A0Ds
+0H(B BBBD > T,\< @/ ߾ ߾ ߾) LX D | ߾ / FBB B(A0Ds
+0H(B BBBD > ,\< . D $ @j EJL
+AD < . FBB A(G
+(A BBBF ( zPLR x 0 h-\E :. $ zPLR x `< } . FBB A(G`
+(D BBBD l .-\/ - 8 i FEA D(D0}
+(D ABBF T P ( L 4 < H FAA f
+ABG DAE zR x $ q,\ h J,\' ^,\O FC L 4 OBA D(G0C
+(A ABBJ pH0 H @ FBB B(E0A8DP
+8D0A(B BBBA x xt FBB B(A0A8DP
+8F0A(B BBBI D
+8F0A(B BBBE L8F0A(B BBB h d |h + FBB J(D0A8Gq
+8A0A(E BBBB
+8D0A(B BBBG L m 7+ FBF H(D0E8J
+8D0A(B BBBC X *\I + P 8! ( #+ FBB B(A0A8G[
+8A0A(B BBBF $ ! +\ + A X" <
+ EQ t" @
+ " <
+L FG t
+EA " h
+E FG m
+EA " 6\ FQ " 6\' # 7\O FC L # H
+y MBF A(A0
+(D BBBH q(A BBB l# x a FN y
+EF # B FG j
+EA # L $# Y * FEE E(A0A8GP
+8A0A(B BBBG @6\C * 0$
m FG u
+EI P # * FEB B(D0A8GS
+8A0A(B BBBH 5\ Q* 8 $ A* FAA
+ABI AAB H X$ * OBE A(A0n
+(A BBBC P ` H% FBB B(A0A8GP]
+8A0A(B BBBD $
+8A0A(B BBBB < % FFB A(A0w
+(D BBBG L H% ) FGB E(A0D8D`e
+8A0A(B BBBD L <& H OEB B(A0A8G@l8A0A(B BBBI L % ( FBB B(A0D8DP]
+8D0A(B BBBI & ( EQ < T& , > ;( FBA D(G0
+(A ABBJ H 8' , FBE B(D0A8D@
+8D0A(B BBBE p ' JBB D(D0_
+(A BBBD
+(A BBBI A
+(G BBBJ F
+(A BBBK P T' | W' FBB B(D0A8G
+8D0A(B BBBH D ' # ' FBB B(A0DS
+0A(B BBBC I 2\ & $ ( H#g & Ev
+E C
+E ( # 0 ( # EDG s
+DAH DGAD x( # t& FBB A(A0Fi
+0A(A BBBA 0 2\ 4& L ( ($| 4& FBB B(A0A8GS
+8A0A(B BBBF \ 03\@ & L @) @' % FBB B(A0A8GI
+8A0A(B BBBH 3\, % 8 ) *, % EC
HK_
+ F Q .. X 3\ & , * 9M FG V(D0F(A T
+EA L ,* 9= % FFB B(D0A8G
+8A0A(B BBBG &5\ % P * :@ % FBE B(A0A8M
+8A0A(B BBBA ! 5\g % P + Ix & FEE B(D0D8J
+8A0A(B BBBJ U :\ W& P l+ ] & FBB B(D0C8G
+8D0A(B BBBC C\g & < + b & FBE D(G@\
+(D BBBB $ zPLR x- @ , C\ S& P X, c< C& FBE E(A0A8J
+8D0A(B BBBF C\g & ` , i
& FBB B(A0A8GsMUAc
+8A0A(B BBBH $ D\ & t @- @wF ;& FEE E(D0A8GA
+8D0A(B BBBA _GSAGKA F\ s& t. A FG i
+EA L - , & FBE B(D0F8J[
+8D0A(B BBBE fN\ & / c2 HW / x 0/ t D/ P\' X/ \ , . 芿 & FMG0U
+EBJ zPLR xM 0 ( tP\ A& , 4/ < 1& FMG0U
+EBJ p JP\ & , |/ % FMG0U
+EBJ P\ % < h0 , FBB A(D0
+(A BBBD < 0 |> % FBA D(G0
+(A ABBJ D D0 | g% FDE A(A0G@z
+0A(A BBBF < 0 ď> C% FBA D(G0
+(A ABBJ P 0 Đ, % FBB B(A0A8GR
+8A0A(B BBBG N\J $ D 81 $ FBB A(A0Dh
+0D(A BBBA N\ $ P 1 ȕJ $ FBF B(D0A8M
+8A0A(B BBBF \O\ $ P 2
$ FBB B(H0A8J
+8A0A(B BBBG , zPLR x 4 O\ $ D3 c2 HW @ 2 4S\D $ KBB D(A0(D GBBh 2 ^$ FBB B(A0A8D`,
+8D0A(B BBBE
+8A0A(B BBBJ P h3 H # FBB O(D0D8D
+8A0A(B BBBG P 3 # FLB B(A0A8D
+8A0A(B BBBK x T\ # 4 V\' 4 V\O FC x 4 8 YBE B(A0D8G@T8A0A(B BBBDH@
+8A0A(B BBBH h 4 " FJB B(A0D8G`p
+8A0A(B BBBF 6
+8A0A(B BBBH @5 2V\ " A`P d5 Ŀ " FBB B(A0A8D
+8A0A(B BBBC V\O " L 5 ʿ " FFB E(D0A8G`
+8D0A(B BBBK V\d " L 86 Ͽ o" FBB B(A0A8GT
+8A0A(B BBBE V\ #" P 6 п '" FFB E(A0A8Gn
+8A0A(B BBBD $ 6 W\ 3" A 7 c2 HW 8 7 ؿ FBD D(GPe
+(D ABBA 8 ؿ2 4 7 ٿ ! FAD D0
+ AABA 4 7 ٿ ! FAA G0
+ AABG 8 X\O FC 0 8 (Y\ K! KAD sIB 8 H8 hڿ ! FDA l
+DBK QAB d 8 ڿo FBB B(A0A8DP
+8D0A(B BBBM t8A0A(B BBB d 8 ܿ PBB B(A0A8Dpc
+8A0A(B BBBG -
+8A0A(B BBBI X 9 ߿{ KBB H(D0w
+(I BBBO H0 H T: Z PBB B(A0A8DP
+8A0A(B BBBF H : Z PBB B(A0A8DP
+8A0A(B BBBF P H: 0 FBB B(A0N8D
+8A0A(B BBBJ H @; PBB B(A0D8D`
+8A0A(B BBBI H ; Z PBB B(A0A8DP
+8A0A(B BBBF P 4; , Q BBB B(H0D8D
+8A0A(B BBBF HW\7 / P ; a FBB B(D0A8GH
+8D0A(B BBBC P ; k FBB B(D0A8N
+8A0A(B BBBI , H< = FAL W
+DBA L V\ , < = FAL W
+DBA \V\ , < = } FAL W
+DBA ܦ )V\ Y = {c2 HW d 8= 1 PBB B(A0A8Dpc
+8A0A(B BBBG
+8A0A(B BBBG d = P 1 BBB B(A0A8G v
+8A0A(B BBBG N Q D I >U\ d > .3 ; FBB E(D0D8GJNALP
+8A0A(B BBBD " \]\# ` > 0y/ BEB B(H0A8G
+1
+J
+O
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+8A0A(B BBBE ` ]\h ` ? _ FBB B(A0A8D`JTA
+8A0A(B BBBG ) b\ < ? ` t EC
HK.k. g
+ D e.k. L% b\> _ @ c\' < @ d FBB D(A0A
+(D BBBF < @ es FBB A(A0
+(D BBBK < (A f FBB D(A0A
+(D BBBF < hA |hs FBB A(A0
+(D BBBI < A i FBB D(A0A
+(D BBBF < A Lks FBB A(A0
+(D BBBI H (B lj FBB B(D0A8D@
+8D0A(B BBBF H tB oF FBB B(D0A8D@m
+8D0A(B BBBA H B pg FBE B(A0A8Q`
+8A0A(B BBBD C rc H w
+A b ,C (sc H w
+A b LC xs H `C Tt FBB B(A0A8Gpc
+8A0A(B BBBF H C y FBB B(A0A8D``
+8A0A(B BBBD x C ,|t FBB B(A0A8DP
+8F0A(B BBBI D
+8F0A(B BBBE L8F0A(B BBB h C 0}4 FBB J(D0A8Gk
+8D0A(B BBBH
+8D0A(B BBBG L D FEB E(D0A8DT
+8D0A(B BBBD 0E L D FBB B(A0A8D`
+8A0A(B BBBE ^\ L E FBB B(A0A8D`
+8A0A(B BBBG $ ^\ U P pE `v E FEB J(A0D8D
+8D0A(B BBBA ) b^\" L E t FBB B(A0A8D`
+8A0A(B BBBG ^\ L DF ̌ FBB B(A0A8G_
+8A0A(B BBBA ]\ ] d PG FFH F(D0D8Ki
+8A0A(B BBBH
+8A0A(B BBBE L G U BBB B(A0A8Dk
+8A0A(B BBBE P dG , FHB B(A0A8J
+8A0A(B BBBH T^\ c tH pqc2 HW H 6^\' ( H J^\ BAD IB < H ^\ KBB A(D0(I BBBH I (, FFB B(A0A8DP
+8A0A(B BBBA H XI , FFB B(A0A8DP
+8A0A(B BBBA L I F # FBB J(A0D8Gp
+8D0A(B BBBH P PI " FBE B(A0A8D
+8D0A(B BBBH 4 _\k P I FBB E(D0D8D
+8D0A(B BBBG H _\n < (J h> FBA D(G0
+(A ABBJ L hJ h" FBB E(D0A8Lp=
+8A0A(B BBBI P J H# s FBB E(A0A8J
+8D0A(B BBBI I `_\ P $K FBE B(A0A8GT
+8A0A(B BBBJ , zPLR x 4 a\ 9 dL G FM i
+EA \ K <` FIB B(D0D8J
+8D0A(B BBBA QMNA D b\Z M $mc2 HW M b\' X 0M FEB I(G0D8NuHVAi
+8D0A(B BBBA 8 M FAA y
+ABD Q
+ABL 8 M ,r KAG uDAS HDA L `M p FGE A(A0P
+(C BBBC d(A BBBP M 0 Y FFF E(D0A8Gd
+8D0A(B BBBD h8 ta\. P N FEB B(A0A8G
+8A0A(B BBBG 6a\H 4 ,O F FL i
+EA 8 PO 4 FAA y
+ABD Q
+ABL L N V FFB B(A0A8J
+8A0A(B BBBE a\ O pjc2 HW P ^b\' L |O h FBB B(A0A8GC
+8A0A(B BBBA ,
!b\ [ P jc2 HW $ P JD
+A P h P d5 P Q 5 Q c\' H 0Q EAG U
+CAJ [
+CAA d
+CAH NCA H |Q EAG U
+CAJ [
+CAA d
+CAH NCA ( Q b\ BAD AB Q lc\O FC P lQ d FBB B(A0A8G)
+8A0A(B BBBH , Q EHGP{
+AAA c\< P R FBB B(A0A8Gi
+8A0A(B BBBH < \R | j FID A(G0N
+(A ABBA b\# 2 L R N " FBB B(D0A8GR
+8A0A(B BBBD L S N FBB B(D0A8GR
+8A0A(B BBBD S fc2 HW T 8 (T FBA D(G@@
+(D ABBC dT @ S <" H M
+A R wa\o T 41 JX T Tfc2 HW D DT < [ FBB A(A0DpB
+0D(A BBBC ( zPLR x p 0 "a\ tU 0 U FAA G0@
+ DABJ U " H M
+A TS `\o PU 1 JX V Pec2 HW < U _ FBA A(DpB
+(D ABBG `\ - V `\' V . V * V * V * V k Ez
+A Y
+G W X W T 4W ` HW l \W x pW ! JQ 8 W FBA A(D@j
+(D ABBG P $W n
FBB B(A0A8D
+8D0A(B BBBG _\F
H W %^
FBB A(D0D
+0D(A BBBE ! ^\_ t
X cc2 HW X '
+ X ' X ' X a\' Y b\. Fb ( Y b\ BAD AB , X '@
FEI [
+BBA $ zPLR xm , b\ Y X'
Y T'
Y P'
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+(D ABBB 4Z (
+ ( HZ (- FDA ]AB tZ (# E] 0 Z (Y ECG ~
+AAA DCA Z (
+ 0 Z (i ECG D
+AAK DCA [ ()
+ ( [ $)- FDA ]AB 0 L[ ()a ECG A
+AAF DCA [ d) HP 4 [ l)c ICG n
+ACK DCAH4 [ )c ICG t
+ACE DCAH4 \ )c ICG q
+ACH DCAHL [ *B
+ FBB B(A0A8J
+8A0A(B BBBE L _\ \ \ *
+ FBB B(A0A8GoHSAh
+8A0A(B BBBF d- b\! < |\ t+q FDB A(DP
+(D BBBA c\ < \ +
FID D(DP
+(D ABBF vd\ i D ,] T, Y FFB A(A0DP
+0D(A BBBD L ,d\, & D ] ,s FBB A(A0DU
+0A(A BBBA ' c\% T ] - FBB D(A0GuGTAe
+0A(A BBBA 0( d\! _ \c2 HW _ e\' 0_ -6 D_ @-/ HOC T d_ P-F FG n
+EA _ |- _ x- _ t- _ p- _ l-P FJ u
+EA L X_ - FGE A(A0P
+(C BBBC d(A BBBD _ - J FIE A(A0Gu
+0A(A BBBF u Ld\:
+ D ` .l FBB A(A0DK
+0A(A BBBE + &e\3 a [c2 HW $a *f\' 8a l0 La x0 L ` 0 FGB A(D0f
+(D BBBD f(A EBB@ a 0 FHE A(D0D@
+0D(A BBBH @ a 1 FHE A(D0D@
+0D(A BBBD L a \2 FHE B(A0D8D`
+8D0A(B BBBD d\ s $ zPLR xI P< $b 4< ; FHE D(GPR
+(D BBBF l d\ < |b 5< FHE D(GPP
+(D BBBH Pd\ D b |6 FHE D(A0GP[
+0D(A BBBK Q d\ L 4c <9D s FHB B(D0D8Js
+8D0A(B BBBD J c\ t L c $@D e FHB B(D0D8Js
+8D0A(B BBBD J 6d\ f P d G`
+ W FHB E(A0D8J
+8D0A(B BBBH d hWc2 HW L pd Q N FHB E(D0A8J
+8D0A(B BBBD <d\s ) L d (U FHB E(D0A8J
+8D0A(B BBBD L Hd\s e d\' Te <Yi |0 f Yw EAG [
+AAF DAAL e Y FBF E(A0A8DP;
+8D0A(B BBBG H f \ FBB E(A0N8D`9
+8A0A(B BBBK P <f ^M FBB E(A0A8D
+8A0A(B BBBF |c\h < Lg c FFB A(A0w
+(D BBBG P f xd s FBB D(A0
+
+(D JBBE S
+(D BBBC H g e PBB B(A0A8D`
+8A0A(B BBBD H ,h f FBB B(A0A8Q`
+8A0A(B BBBD P g Lj FBB B(A0A8G<
+8A0A(B BBBE $_ Lb\h s P @h po' s FBB B(A0A8GD
+8A0A(B BBBE P h Lt m FED A(M0t
+(A ABBI V
+(D ABBA i Sc2 HW i a\' d i t FBB B(A0A8G@;
+8A0A(B BBBF
+8A0A(B BBBG d |i Tw FBB B(A0A8G@;
+8A0A(B BBBF
+8A0A(B BBBG P i yO FJB B(A0D8J
+8A0A(B BBBE L 8j FBB B(D0A8J
+8D0A(B BBBC L j FBB B(D0A8J
+8D0A(B BBBC P j xO FJB B(A0D8J
+8A0A(B BBBE k Qc2 HW k X l T l P (l L
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+EA w t! w (w <w Pw dw xw |A FG i
+EA $ w u FID0^AB w ( Fa , <w - FID
+ABA x [ FT $ ,x H FIG kDB $ Tx P FID vDB $ |x HH FID nDB ( x pC { FIN _DB ( ,x @ S FID fDB x 3 Fl $ y ̐= FID cDB @y l Fe \y 8) JT xy L y H HL y P HL y X y d y ` y \J FP i
+EA z J FP i
+EA @z J FP i
+EA dz J FP i
+EA z J FP i
+EA z 8J FP i
+EA z dJ FP i
+EA z { I FO i
+EA ,{ D FJ i
+EA P{ d{ x{ G FM i
+EA { ( { $( HX { <% HU { T { ` | \ $ x{ XD FJ i
+EA D| X| | l| x | | | | | x | t | p | l } x } t 4} p | l HL d} p x} l } x } t1 } } Y FJ0~
+EA } ؓ , \} ԓP / FEI [
+BBA , } P FID Z
+DBA `~ , } P FID Z
+DBA ~ 0 ~ , ~ ( ~ $ < P~ k FFD A(D`{
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+DBA , ~ $q FDD0W
+DBA , tq FKD0P
+DBA , < ĕq FDD0W
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+EA ԕG FM i
+EA $ 2 FDG ZDB < P d x ȃ ܃ J FP i
+EA ( LM FS i
+EA L x $ ` t0 EDG ]AA | x , tP FID Z
+DBA D FJ i
+EA , @ T
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+EA ؖ D FJ i
+EA D X l R FS n
+EA PR FS n
+EA Ȇ R FS n
+EA ȗR FS n
+EA R FS n
+EA 4 @R FS n
+EA X |R FS n
+EA | R FS n
+EA R FS n
+EA ć 0R FS n
+EA lR FS n
+EA R FS n
+EA 0 M FS i
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+EA 4 0 ( Ȉ <Z FEG0{
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+AD qA D N ]\
+G ` F E`
+K N F E`
+K N DE E`
+K M tF E`
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+8D0A(B BBBA 0 ԛ% D X l 2 ? F]U L 4 FBB E(A0A8G@
+8D0A(B BBBE L L FBE B(D0A8L@
+8D0A(B BBBG @ DE E`
+K M ` t? t ? ̝W IJ
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+G @ @ p FBE A(A0
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+DBJ @ ȡ BBJ A(A0B
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+AC L Ȍ ȥI FEE E(D0A8D`
+8A0A(B BBBB Ȭ Ѝ Ĭ , @ w EDD@w
+AAJ , p U EDD@w
+AAJ , 3 EDD@w
+AAJ < Ѝ `D FEE D(DP
+(D BBBB \ p OBB B(A0D8D@h8I0A(B BBBP@ X z OBB H(D0w
+(I BBBK H0 L p FBE A(A0
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+(D DBBD ԶK H }
+A @ FBB A(A0D@b
+0A(A BBBA DK H }
+A < x1 JX H X PBB B(A0A8Dp
+8A0A(B BBBD L 0 FBB F(D0D8J
+8A0A(B BBBH L P FBB B(A0A8G|
+8A0A(B BBBE L 0 FBB B(A0A8Gz
+8D0A(B BBBD ( FEN0I
+EBG 4 FAA GP
+ AABD hJ 9\ ( d FEN0I
+EBG 4 FAA GP
+ AABD J 9\ ( FEN0I
+EBG 4 \ [ FAA GP
+ AABD `K 9\ C D d L C FIB A(A0GL
+0A(A BBBB ( zPLR x 0 9\ , pJ EF X
+AD D
+ME :\o , 8 xR EG Z
+AI D
+ME :\o D k FJB B(A0Dr
+0A(B BBBA D ȓ
+ FGB A(A0DK
+0A(A BBBA D $ |h o FGB B(A0Dr
+0A(B BBBA < l F FBB A(GE
+(D BBBA D % FBB B(A0Du
+0A(B BBBA D < FBB B(A0Du
+0A(B BBBA D < t FBB B(A0Du
+0A(B BBBA D FBB B(A0Du
+0A(B BBBA D ̕ q FBB B(A0Du
+0A(B BBBA D D FBB B(A0Du
+0A(B BBBA D \ T FBB B(A0Du
+0A(B BBBA D FBB B(A0Du
+0A(B BBBA D FBB B(A0Du
+0A(B BBBA D 4 FBB B(A0Du
+0A(B BBBA D | 4 c FBB B(A0Du
+0A(B BBBA D ė l 6 FBB B(A0Du
+0A(B BBBA D FBB B(A0Du
+0A(B BBBA D T FBB B(A0Du
+0A(B BBBA D FBB B(A0Du
+0A(B BBBA D L FBB B(A0Du
+0A(B BBBA D , U FBB B(A0Du
+0A(B BBBA D t ( FBB B(A0Du
+0A(B BBBA D FBB B(A0Du
+0A(B BBBA D , FBB B(A0Du
+0A(B BBBA D L d FBB B(A0Du
+0A(B BBBA D t FBB B(A0Du
+0A(B BBBA D ܚ G FBB B(A0Du
+0A(B BBBA D $ FBB B(A0Du
+0A(B BBBA D l D FBB B(A0Du
+0A(B BBBA D | FBB B(A0Du
+0A(B BBBA D u FBB B(A0Dv
+0A(B BBBA D D u f FBB B(A0Dv
+0A(B BBBA 0 c% E 4 s EG
DFP. =
+ G L P% FEB E(D0A8Dp
+8A0A(B BBBK 4 0 0 FGA G
+ DABJ 4 h FGA G
+ DABJ P D OBA D(D0+
+(I ABBM H0 < V + FEG D(K@
+(D ABBF L 4 , FBB B(A0A8DP
+8A0A(B BBBE d FBB E(A0D8DP
+8E0A(B BBBE a
+8A0E(B BBBA 8 $ FBA D(D0u
+(A ABBD L ( ^ S FBB B(D0A8Gp
+8A0A(B BBBF P x W FBB B(A0A8DO
+8A0A(B BBBE P p , OBA D(D0
+(I ABBF pH0 P ` FBA A(G0o
+(A ABBJ i(A ABB , t } FBI Z
+BBA T l c OBA D(D0w
+(I ABBI H0 P 4* FBA A(D0
+(I ABBI
+(A ABBF X P OBB A(D0'
+(I BBBJ xH0` g OBB B(A0D8D@28I0A(B BBBV@ G FM i
+EA آ J FP i
+EA , HQ TAF gABK , x @ T h , آ ? EDD@w
+AAJ J FP i
+EA J FP i
+EA J FP i
+EA , J FP i
+EA P DI FO i
+EA $ У pm U aX
+G `
+A H *\ 5 Ȥ c HP $ @ EL `
+AA v*\o $
+ 8 P OHA D(D@
+(C ABBH hH@ ȥ ܥ , ` FKG0a
+DBA 4 )\' < \ > FBA D(G0
+(A ABBJ L \ FBB E(A0A8Lp=
+8A0A(B BBBD L 4 "[ FBB B(D0A8G
+8D0A(B BBBD w (\ ? @ 4c2 HW \ # p # # #5 $5 4$L FG t
+EA `$5 $E Hz $ $ $6 Ld
+A @ $2 T %? F]U 0 Ч 0% BAA
+AGC ( % AC
FKA
+ J ( Ԩ 'R BAA \
+DBJ H )! FBB B(A0C8GpN
+8A0A(B BBBA ( L + HD lAAA x \, X, T, P, L $ L, FBB A(A0
+(D BBBM Q(A BBB( ,Y RAD ~AB D 0- X ,- < ȩ (- 7 BBK D(G@T
+(A ABBA x v&\ @ `- FEA A(Jd
+(D ABBD D d .R FBB A(A0Dt
+0A(A BBBA v %\ ~ H h .c FBE B(D0A8G@
+8A0A(B BBBB < /u FBB D(A0l
+(D BBBK L P 01d FFB B(D0A8Dp
+8D0A(B BBBG L P2 FFB E(H0H8Gp
+8D0A(B BBBA P 3 _ BFB E(A0A8GM
+8A0A(B BBBA i W%\D 2 L \ d9A " BLB B(D0A8L`<
+8D0A(B BBBD x 3%\. L Ĭ L; FGE A(A0~
+(A BBBG A(C BBBD ; FDB B(A0Dk
+0A(B BBBA < $\ S H < FBB B(D0A8DP^
+8A0A(B BBBC 4 = FAA G0
+ AABA | = OEE E(A0D8GPHP
+8A0A(B BBBH
+8A0A(B BBBF P x XA L FBB B(A0D8D
+8A0A(B BBBA & #\ H Ez PBB B(A0A8D`
+8A0A(B BBBH L 0 G FGE A(A0P
+(C BBBC d(A BBB< pG
+ t FEN D(D@F
+(D ABBH ~ #\4 H L د (H 8 FGE A(A0P
+(C BBBC d(A BBB ̰ xH ZYM H H E
+D XH IZ PBB B(A0A8DP
+8A0A(B BBBF h J E
+D XP |K 0 FEE E(D0A8Jz
+8D0A(B BBBE H ܱ HMZ PBB B(A0A8DP
+8A0A(B BBBF P \N FEE E(D0A8J
+8D0A(B BBBH 4 ر P D FAA G0
+ AABA L @Q FDA A(G0
+(F ABBD D
+(C ABBD L ` R FEB E(A0A8GP
+8A0A(B BBBK L `U FEB B(A0D8G
+8A0A(B BBBK H VF FBD D(D0
+(D ABBA L(F DBB L W7 FBI A(G0
+(D FBBG
+(F ABBB H @ YZ PBB B(A0A8DP
+8A0A(B BBBG P Z BIB E(A0A8D3
+8A0A(B BBBA < \ h P T ] X FGB E(A0D8DC
+8A0A(B BBBF P _ FBB B(D0A8GR
+8A0A(B BBBD 0 \ ; H |j FBB E(D0A8D@{
+8D0A(B BBBH k FFB B(D0D8D@p
+8A0A(B BBBJ
+8F0A(B BBBC D
+8G0A(B BBBL D
+8F0A(B BBBE P |l>
O FBB B(A0A8G
+8A0A(B BBBF \ k < ` Py> o FBA D(G0
+(A ABBJ < Pz K FEG D(K@x
+(D ABBF 4 {9 FIA
+ABF xFB P (|< FBB E(D0C8J|
+8D0A(B BBBG t v \i ( J FG r
+EA ( L ( EDD V
+AAK l x FFD D(D0I
+(D ABBF X
+(G ABBL D
+(F DBBF D(J ABB L D < FBB B(A0D8F`E
+8D0A(B BBBA `! \ ( P EDD V
+AAK l | X FFD D(D0I
+(D ABBF X
+(G ABBL D
+(F DBBF D(J ABB ` FBB E(A0I8G@S
+8D0A(B BBBH
+8D0A(B BBBJ L FBB B(F0D8G`U
+8D0A(B BBBA " `\ v < |> g FBA D(G0
+(A ABBJ L T |I C FGB E(A0D8D`m
+8A0A(B BBBD L | + FIB B(D0A8Jb
+8A0A(B BBBB x\$ P ċ@ FBB E(N0A8J>
+8D0A(B BBBE 4 0\R < x > FBA D(G0
+(A ABBJ D \ OEB D(A0r
+(A BBBD H < s 7 FEA D(G0
+(A ABBK @ @ @P AC
Ph.. O
+ A .[. ^\ , 4 ݾ FNH@Y
+DBE zPLR xyt @ ( \ H h, EC
BJj. p. O
+ C .@Z. .W. Ȣ \ + < l 4> [ FBA D(G0
+(A ABBJ < 4^ 7 FDA A(D@~
+(A ABBD Ќ "$\& < <> FBA D(G0
+(A ABBJ < D < FEG D(K@x
+(D ABBF P L Ǿ FBB E(A0A8G
+8A0A(B BBBH g \#\f P FBB H(D0A8G%
+8D0A(B BBBH D V#\ 5 < \ > ' FBA D(G0
+(A ABBJ H @ = FBB B(A0A8Dp
+8A0A(B BBBA L Q FBB A(A0
+(A BBBL (A BBB P 8 g FBG E(D0D8G
+8A0A(B BBBG A $\ C P 2 _ FBB B(A0A8J*
- 8b BAI G
- DABA L T
+8A0A(B BBBD F&\ P 8 ] EC
BEFEG
+ I .. +\R J @ - JEL D(
+ ABBF O ABB @ L VHF A(A0g(A BBBD P 4z FFB B(D0A8Jo
+8A0A(B BBBH +\ H H FFB E(A0D8D`
+8D0A(B BBBK H H JEE F(D0G8Dpx
+8A0A(B BBBE H FBE E(A0D8G`
+8A0A(B BBBA L { FBB B(A0A8G<
+8A0A(B BBBE P D FFB B(A0A8G
+8A0A(B BBBA 4,\ b2 HW H EAG U
+CAJ [
+CAA v
+CAF NCA H D EAG U
+CAJ [
+CAA n
+CAF NCA L 2 < ` BBB D(D0
+(D BBBB k F]U D X BEE G(A0GPG
+0D(A BBBC 9+\> * 0 w EAG [
+AAF DAA( T ` A JDG aAAH ` FBF E(A0A8G@|
+8D0F(B BBBF Z
+8F0A(B BBBG < @ @ W FEA D(G0
+(A ABBE ( $ FCJ \
+DBK < D!> FBA D(G0
+(A ABBJ P D"- FBE B(A0A8J;
- h BBB I(A0G
-0D(A BBBA T
+8A0A(B BBBA )\ l L X 8y a FBB B(G0D8GR
+8D0A(B BBBC < *\ Z L > J FBB B(G0D8GR
+8D0A(B BBBC V+\ 2 P ( xC
# BBB B(A0A8G
+8A0A(B BBBE H+ {+\ 8 P3 EC
P
+ H . |. D E-\ # hb2 HW P _ FBB E(D0D8G6
+8A0A(B BBBB 5\_ - $b2 HW , 5\' 0 FAD N
+ABD H t P FBA D(D@b
+(A ABBG D
+(A ABBF D q EAG {
+AAF L
+DAG T
+DAG @ d |H - FBE A(A0
+(D BBBD D FBB A(A0G_
+0A(A BBBA ` 4\ L FEE B(D0D8M
+8A0A(B BBBD X "5\ e L p @8 FFB B(A0A8G
+8A0A(B BBBF (/ 6\" ; P A FFB B(A0A8J
+8A0A(B BBBC , p7\ L D h 2 FFB B(A0A8G
+8A0A(B BBBF / 9\" P $b2 HW l ~:\' :\. Fb < d FBD A(GP
+(D ABBA DE FG m
+EA P p p, W FBE D(A0i
+(t BBBK X
+(A BBBA |A 9\ ` 4 FBB B(I0F8GP
+8F0A(B BBBA C
+8D0F(B BBBK ( A JDG aAAH H FBB B(D0A8KPZ
+8I0A(B BBBH H \ h FBB B(H0A8G@A
+8A0A(B BBBI < FFB A(A0w
+(D BBBG P D , FBB B(D0A8G
+8D0A(B BBBH P h8 ۷ FBB E(A0D8G
+8A0A(B BBBA < T 7 FID A(K@\
+(D ABBD b2 HW ~7\' b2 HW P t ̹n ̷ FBE E(A0A8G~
+8A0A(B BBBE R &7\ 8\' ( AC
FKA
+ J @ PBB A(A0
+(A BBBF P d \ JFB A(D0L
+(A BBBF K
+(A BBBA pE h7\ > L . FGB B(D0K8D`D
+8D0A(B BBBK : 7\5 $ 8 h EG0Q
+AB , 7\ ɶ P x FBB B(D0A8G
+8D0A(B BBBG $ T Qo
+H sG zPLR xQ^ $ v6\ > < @ h FBB A(DI
+(D BBBA d 6\ < (L FG t
+EA D Tx õ FBB B(A0GU
+0A(B BBBA ( zPLR xA] 0 `6\% W D H Hs X FBB B(A0GP
+0A(B BBBA &7\% L b2 HW d 8\' $ <J EL k
+AA t 8\o ߴ P L OBD A(D0e
+(I ABBK @H0 $ | s EG@r
+AA zPLR x[ @ ( 7\o ? 0 , 84 Hk ` T ' HR
+F F $ p s EG0s
+AH zPLR xZ 0 ( %7\o { 4 w Q IN \7\o o P
+ k FBB B(A0A8D
+8A0A(B BBBD L ` $ { FBE E(A0C8Gpz
+8D0A(B BBBD 7\ : < = * FBD A(J`B
+(A ABBA ( 6\ D } FBE D(A0G`_
+0A(A BBBH 6\ D
FIF A(A0GP
+0D(A BBBA h D6\ u $ P2 g EK W
+AA 6\{ W D P W FBB A(A0D
+0A(A BBBA T 56\ / $ Pb2 HW < P d x
, H @ EAG U
+CAJ [
+CAA d
+CAH NCA H 8 EAG U
+CAJ [
+CAA d
+CAH NCA H EAG U
+CAJ [
+CAA d
+CAH NCA H $ EAG U
+CAJ [
+CAA d
+CAH NCA p 42 `k F]U 0 FDD Q
+DBK VDB HI H _ FBB B(D0A8DPh
+8D0A(B BBBF < ( PBA A(D0
+(A ABBA < P PBA A(D0
+(D ABBE H FBE E(D0A8DP
+8D0A(B BBBG H 0 u FBE E(D0A8DP
+8D0A(B BBBG L 8 ] FBB B(D0D8D@
+8D0A(B BBBE ( Q ' dH * BIE E(E0D8Fp
+8A0A(B BBBE L [ ǭ FBB B(D0A8D`z
+8D0A(B BBBA P % FBB B(D0A8G
+8D0A(B BBBF P 0 5 [ FBB B(D0A8G
+8D0A(B BBBJ P U $ BBB B(A0A8GI
+8A0A(B BBBD $a 3\ N 8 ? AF
BKFG
+ G T.e. <5\8 P D
BEK E(D0D8M
+8D0A(B BBBI Y 5\u T P ) FM g
+EA x \ FM g
+EA X
FM g
+EA , TM d FHG`m
+EBG zPLR xO ` ( 4\ , 8 FHG`m
+EBG p |4\ , ܫ FHG`m
+EBG S4\ , h FHG`m
+EBG *4\ , ` x FHG`m
+EBG H 4\ V , 8 F FHG`m
+EBG 3\ $ , FHG`m
+EBG 3\ , 8
+ FHG`m
+EBG 3\ , FHG`m
+EBG h ]3\ , ~ FHG`m
+EBG 43\ \ P
+ L FBB E(D0D8J
+8D0A(B BBBE 2\_ * 8 | T* EC
DGK
+ D O .p. , 2\` P 8C FBB B(D0A8G
+8D0A(B BBBD 4\o L < t< ߪ BBB B(D0A8G
+8D0A(B BBBE 4\ Ӫ H @J FP i
+EA L (A FBE B(D0D8G
+8D0A(B BBBF hBJ FP i
+EA L < B Q FBE B(D0D8G
+8D0A(B BBBF 0 CJ FP i
+EA L D FBE B(D0D8G
+8D0A(B BBBF @EJ FP i
+EA L $ lE FBE B(D0D8G
+8D0A(B BBBF FJ FP i
+EA L F d FBE B(D0D8G
+8D0A(B BBBF HJ FP i
+EA L DH FBE B(D0D8G
+8D0A(B BBBF IJ FP i
+EA L I ƨ FBE B(D0D8G
+8D0A(B BBBF t JJ FP i
+EA L K w FBE B(D0D8G
+8D0A(B BBBF P D \L L BEB E(D0D8J
+8A0A(B BBBF 1\x P W BEB E(D0D8J
+8A0A(B BBBF 4\x P $c% FEB B(A0A8Gq
+8A0A(B BBBE N 7\ L | FBB B(A0D8G
+8D0A(B BBBE FA\4 ֪ b2 HW L FBB B(D0D8D`
+8D0A(B BBBJ $T A\& P p `$
+ u EC
Pb
+ F .. L.M. .. Q.f. 8 A\ C\' ( AC
FKA
+ J xC\O FC 4 < B BHA GP}
+ DABJ sC\& 0 i FD K
+BI zR x AC\. < q FBB A(A0
+(A BBBC L 4 ` FBE B(A0I8Dp
+8A0A(B BBBC B\6 2 L 4^ " FBB B(A0I8Dp
+8A0A(B BBBE B\6 L , PBA A(D0q
+(A ABBA
+(A ABBA H ̥ PBB B(A0A8D`
+8A0A(B BBBD H D ? PBB B(A0A8QpN
+8A0A(B BBBG H < FBB B(A0DW
+0A(B BBBG oA\ L P ħ FBB B(D0A8J
+8D0A(B BBBI J B\9 ʧ P x] ۧ FBB B(D0A8G!
+8A0A(B BBBE D\ L $ l8 BEE E(D0D8J
+8D0A(B BBBK p ^E\ P D ? FBB E(D0A8J
+8D0A(B BBBA H\* @ x AC
BGM. n. f. n.
+ H I\ h 0 T FG R(D0D8E@I T
+EA < W
+ < EC
DEED
+ G . . < ?L\ 2 pb2 HW 5 H EAG U
+CAJ [
+CAA m
+CAG NCA H h EAG U
+CAJ [
+CAA e
+CAG NCA H 2 4 \ MAE
+DBK QAB , MAH
+IBL M\O FC < k L 4 X X FAA G@
+ AABK P q æ FBB B(A0A8J
+8A0A(B BBBA x , YBE B(A0D8G@ 8A0A(B BBBEH@k
+8A0A(B BBBK | YBE E(D0A8DP
+8A0A(B BBBH
+8A0A(B BBBE `AP H PBB B(A0A8D`
+8A0A(B BBBD H $ FBB B(A0A8QP
+8A0A(B BBBF d h FBB B(A0A8Dp
+8A0A(B BBBF K
+8A0A(B BBBK L
+2 FED G(R0
+(A ABBI V
+(E ABBH D 0 V FBB B(A0DH
+0A(B BBBA D x
j W FBB B(A0D\
+0A(B BBBA D V * FBB B(A0DH
+0A(B BBBA D S FBB A(A0Dy
+0A(A BBBG D P K FBB A(A0Dw
+0A(A BBBA D BDB B(A0Dw
+0A(B BBBA L I\ P FBB E(D0I8G{
+8D0A(B BBBE L L da FBH E(D0D8G
+8D0A(B BBBA P g FBB B(A0E8G
+8A0A(B BBBA < P! O FBB A(D
+(A BBBA P 0 $ w FGB B(A0A8J/
+8A0A(B BBBJ L + i FBB B(A0A8Js
+8A0A(B BBBA P \,0 8 FBB B(G0A8G@
+8D0A(B BBBH P ( 80 FBB H(A0A8DA
+8D0A(B BBBJ L | 2 ۢ FBB B(A0C8D`
+8D0A(B BBBD P 45 FBB B(D0A8G
+8A0A(B BBBA P Or FBB E(A0A8GT
+8A0A(B BBBJ P t iI
+ FBB B(D0D8GA
+8D0A(B BBBG E\; ٤ @ s ٤ FBI A(G`
+(D ABBA ( zPLR x!5 0 F\1 | (b2 HW $ F\' P t' @ FHB B(A0A8G7
+8A0A(B BBBD X SF\( H $ A FG i
+EA H EAG U
+CAJ [
+CAA e
+CAG NCA F\' , G\O FC , \DD ABA D D h BBB D(A0D@
+0A(A BBBE 0 w EAG [
+AAF DAA< P > FBA D(G0
+(A ABBJ < Ϣ FEG D(K@x
+(D ABBF D Ѓi BDE A(A0G@
+0A(A BBBE ( E\& < 0 { FEG D(K@x
+(D ABBF L p [ BBB E(D0D8G
+8D0A(B BBBG p) BE\ J | HG FG o
+EA P t b FBE B(D0A8J
+8A0A(B BBBA H\: hb2 HW $ N\' 8
+ 4 N\
+ FGB B(A0A8D H zR x 1O\
+ b2 HW Q FP p
+EA L5 x 0 5 D X 5 l آ2 pN\. Fb $ p EY
+B Y
+G L P L FGE A(A0P
+(C BBBC d(A BBB< p c FBD A(G@
+(D ABBF L Ф Ϡ FBE E(D0D8GY
+8D0A(B BBBI zM\ Ҡ < 8> à FBA D(G0
+(A ABBJ 8 X 8 EAG i
+AAH |
+AAJ L g FGB E(A0D8D`}
+8A0A(B BBBD < , K FED D(J
+(D ABBK :M\9 ) D < FEE D(A0J
+0D(A BBBG L M\? @ b2 HW \ ( p $! @2 La H d EAG U
+CAJ [
+CAA e
+CAG NCA L\' < L\ KBB D(D0{(I BBB @ M\O FC 4 \ H EAG d
+AAE D
+AAJ < FEE D(JD
+(A ABBH d^ L\p k L H _ FOB B(A0A8Dp
+8A0A(B BBBC H < XZ PBB B(A0A8DP
+8A0A(B BBBF L L\4 BBB B(H0D8G8A0A(B BBB P 4 1 ˝ FBE B(A0A8G
+8A0A(B BBBA , zPLR x) 4 P\Y / L 0 { FBB B(A0A8Dp
+8A0A(B BBBD W\ [ Ap Lb2 HW L \ G FFB B(D0A8D`
+8A0A(B BBBA xq NX\ L h <, FBA A(G0
+(A ABBJ |
+(A ABBF 8 EC
DK.. b
+ G FX\m <
+ , FBD A(D0
+(I ABBJ L
+ \\\' L 9 k FFB B(K0A8G
+8A0A(B BBBD X \\ $
+ h-
+ P FI
+ ]\' < l
+ ߞ FGE A(A0(A BBB P
+ 3 FBB A(A0
+(A BBBG i
+(A BBBH L W FGE A(A0Q
+(C BBBB a(F BBB< P @> FBA D(G0
+(A ABBJ < @> FBA D(G0
+(A ABBJ 8 t @ FAA y
+ABD Q
+ABL L n FGB E(A0D8D`y
+8A0A(B BBBH P \ k FBE E(D0A8G1
+8D0A(B BBBD ^[\ - P 3 ' FBB A(A0
+(A BBBG i
+(A BBBH L
ל FBB B(I0A8G`
+8A0A(B BBBF P l
!K FBB E(A0D8D
+8C0A(B BBBI $ [\ k | $ $ $2 ( $R BAA \
+DBJ D & KBD A(D0(I ABBT0 ( , & AC
FKA
+ J X (k F]U x b2 HW ` (T2 G FBB B(D0D8G
T
W
E
K
+8A0A(B BBBH , zPLR x!
4 pY\ 7 < Z P Z 8 d Z FBD A(D0
+(I ABBK H $[ FBA A(G0
+(A ABBI T(A ABB L [ FBB B(D0A8G
+8D0A(B BBBJ 4 (] W FDD G@
+ DABI t @b2 HW ^- ^- ^- _- (_- D_- `_- |_- , _- @ _- T _- h _- | `- $`- @`- \`- x`- `- `- `- `- 0 a- D a- X <a- l Xa- ta- a- a- a- a2 b= A`
+G M 0bt Ab
+M CL b x FGB D(D0W
+(D BBBH g(A EBB0 t cf FDK S
+ABE rAB L <c BFB B(A0D8G@O
+8A0A(B BBBG < v\\+ Ø L l d BFB B(D0A8GPQ
+8A0A(B BBBE
:\\M ( x <fR BAA \
+DBJ pg_ $ g C Fs
+G R
+F hO EN
+E e
+C 0 @h FDG g
+ABE DCBL h Ǘ FBB B(A0A8D}
+8D0A(B BBBD L l0 FBB B(A0A8GF
+8D0A(B BBBH L r FBA A(G0
+(F ABBE D
+(C ABBA H ( ,sZ PBB B(A0A8DP
+8A0A(B BBBF P @tQ FBB B(D0A8G
+8D0A(B BBBE P $ LxQ FBB B(D0A8G
+8D0A(B BBBE P x X| FBB E(A0A8GY
+8D0A(B BBBB P Q _ FBB B(D0A8G
+8D0A(B BBBE P Q 3 FBB B(D0A8G
+8D0A(B BBBE P t Q FBB B(D0A8G
+8D0A(B BBBE P Q ە FBB B(D0A8G
+8D0A(B BBBE P ĐQ FBB B(D0A8G
+8D0A(B BBBE P p ДQ FBB B(D0A8G
+8D0A(B BBBE P ܘQ W FBB B(D0A8G
+8D0A(B BBBE P Q + FBB B(D0A8G
+8D0A(B BBBE P l Q FBB B(D0A8G
+8D0A(B BBBE P Q Ӕ FBB B(D0A8G
+8D0A(B BBBE P Q FBB B(D0A8G
+8D0A(B BBBE P h Q { FBB B(D0A8G
+8D0A(B BBBE P $Q O FBB B(D0A8G
+8D0A(B BBBE P 0Q # FBB B(D0A8G
+8D0A(B BBBE P d <Q FBB B(D0A8G
+8D0A(B BBBE P HQ ˓ FBB B(D0A8G
+8D0A(B BBBE P TQ FBB B(D0A8G
+8D0A(B BBBE < ` ` s FBA D(G@
+(A ABBF S\ < 4 5 FAA D@
+ AABF 4 FAD DPb
+ AABK < S\ 4 @ d FAC D@q
+ AABE P x H FBB B(A0K8D(
+8A0A(B BBBB 4 DS\+ 4 b FAD D@
+ AABF 4 D @ FBB E(D0D8D@c8A0A(B BBB آb2 HW P pl 9 FBB B(A0A8Gh
+8A0A(B BBBA , O FDG@A
+ABK L ? FDB B(I0D8Gd
+8A0A(B BBBH 0 S\6 ͑ ! 4! , Ǒ BFG@I
+ABE d U\ L ! FDB B(A0D8II
+8A0A(B BBBA p| U\ P T! BDB B(A0D8G(
+8A0A(B BBBH vX\ d" P x" L L ! H FBB G(D0A8G
+8A0A(B BBBD Z\} " * E` # * E` L " 5 BDB B(A0D8IE
+8A0A(B BBBI r\ r # P # | p FBB B(F0D8G
+8A0A(B BBBK t
t\ , P p# , FBB B(A0F8G
+8A0A(B BBBH t\ L # FBB B(A0C8LpB
+8A0A(B BBBH (
+u\d L D$ FBB B(A0F8G~
+8A0A(B BBBF v\ v < $ p FBA F(G`
+(A ABBG wv\N 9 < % 2 W FBA D(F@|
+(A ABBK ( mx\ P \% M FBB G(D0A8G
+8A0A(B BBBE z\ P % x
+' 9 FBB B(A0A8O
+8A0A(B BBBF 5}\ 4 4& <b FAF GPR
+ AABF \ D & \ FBB A(F0G``
+0A(A BBBH \ Ȑ < & Ð FBA F(G`R
+(A ABBB D ;\ L <' FBB B(A0F8Gp
+8A0A(B BBBJ \> V P ' \ F FBB B(A0D8J
+8A0A(B BBBD w\= L ( & : FDB B(A0D8G
+8A0A(B BBBC \ L\8 m ) * 0) * 4 D) *U FAA s
+DBO AAB 4 ( * # BMA G@
+ DABK Ԅ\< < () X+ FEG A(J~
+(A BBBF L h) ,' Ï FFB B(A0D8`@y
+8A0A(B BBBH 4 \* ,U FAA v
+DBL AAB < ) - c FEG A(J~
+(A BBBF 4 0* - 5 FGA J
+ ABBH ` + .o FBB B(A0A8D@"
+8D0A(B BBBO 8A0A(B BBB H p+ 0 PBB B(A0A8Dp
+8A0A(B BBBF + h4 L + $5 FBA A(G0
+(D ABBE
+(A ABBF P |+ 6 FFB B(A0A8Z
+8A0A(B BBBD d t, @? FFE B(A0A8DP
+8D0A(B BBBJ \
+8A0A(B BBBB P 8, xD Í BBB B(D0C8GB
+8D0A(B BBBK 4U \ T , H/J BBB B(A0A8G L@I@q
+8D0A(B BBBD , zPLR xI @ 4 \ ; - , L X- 8R 3 BBB B(A0A8G n
+8D0A(B BBBD $ - \ A t. L - ,O BBB B(A0D8Gn
+8D0A(B BBBA $ 4. T\ 7 A / d p. O EC
N
+ F . . . . . i. . 2. B. . . . ( zPLR xm 0 D\ P / > T EC
P
+ B . e. r. . . . . . . . (. . Q. . . . . . . . . . . . . . W. . . . . . . . . . . . V. . . . `. P. . . i. q. . W. . e. . W. . . . . . . . . k. '. . . a. `. . . . _. . l. _. _. . l. _. . . . p. . z. . n. . . . . . 9. K. P. . . . . . ^. . ]. . . d. . . . . \ 1 m- 2 m- $2 m- 82 n- L2 (n- `2 Dn
t2 @n9 2 ln- H 1 n- FHGPfXG`ShIpIXG`ShIpaPm
+ABA ȋ \ p 3 To 3 x\' L 2 <oh 8 FHG`hGpSxIIhGpSxIb`m
+ABE % ;\> P 2 Dqv FBB B(D0A8G
+8D0A(B BBBE P @3 puv ə FBB B(D0A8G
+8D0A(B BBBE 84 b2 HW L 3 y5 FDB B(A0F8Gp|
+8A0A(B BBBF Q\ J L 4 \} C FDB B(A0F8G|
+8A0A(B BBBF \ L |4 FDB B(A0F8G|
+8A0A(B BBBF \D 4 4 FAD GPj
+ AABH 0 ݴ\ 4 45 FAD GPm
+ AABE \ P 5 L EC
N
+ H &. `. . . . `. . `. \$ 6
6 HN 6 46 HN 6 7 - 7 - 07 4- D7 P- X7 Z\' l7 X9 7 9 7 7 < P 7 Ȫv FBB B(D0A8G
+8D0A(B BBBE P l7 v FBB B(D0A8G
+8D0A(B BBBE P 7 v T FBB B(D0A8G
+8D0A(B BBBE 8 b2 HW 8 4* LY P H8 H FBB B(A0A8Jv
+8A0A(B BBBH
+ \ X9 |7 Lf < 8 FBA D(DP
+(A ABBG 8\ , (9 ( ʗ FAG@h
+ABG | \ 4 p9 FAD D@
+ AABE . Q\ P 9 ` FBB B(A0D8D^
+8A0A(B BBBC t\ T ,: EC
N
+ D *. 1. . 7. . u. . . \ Η H D; p FBB B(A0A8H`
+8A0A(B BBBE ( ; 3 FEG WGB ; b2 HW t 0; 9 FBD A(H@l
+(D ABBF
+(D ABBH t
+(D ABBC v
+(D ABBA
+ y\ ϖ d< - x< 5 < - < - < - < ,- < H- < d- = - = 2 ,= 9 4 < EC
BLIj. . p
+ C ( zPLR xq
+ 0 1\4 P = FBB B(D0A8G/
+8D0A(B BBBD P l= W FBB B(D0A8G4
+8D0A(B BBBG P = v + FBB B(D0A8G
+8D0A(B BBBE P > <v FBB B(D0A8G
+8D0A(B BBBE P h> hv Ӕ FBB B(D0A8G
+8D0A(B BBBE P > v FBB B(D0A8G
+8D0A(B BBBE P ? v { FBB B(D0A8G
+8D0A(B BBBE P d? v O FBB B(D0A8G
+8D0A(B BBBE P ?
+v # FBB B(D0A8G
+8D0A(B BBBE 4 @ DR FAD G@n
+ AABD $5 !\ ȓ P \@ T- Ó FBB E(A0A8G
+8D0A(B BBBG TA b2 HW P @ FEE E(D0D8D#
+8A0A(B BBBB % \` 5 4 4A L | FCI G@K
+ AABH L6 \ M L A H FDB B(A0F8G|
+8A0A(B BBBF % m\ * 0 A D 7 EE
I
+ G ' \ C.Q. 4 @B #p > FAD GPe
+ AABE \ 4 B $p > FAD GPb
+ AABH F\ < B %) > FBA D(G`
+(A ABBB @ \ < 8C )d FDA F(G`0
+(A ABBB W\ C -l( EC
L
+ A . . . . . . . . . u. . . . ". . . . 7. . 7. U. 7. ( zPLR x
+ 0 D\Q E PU- (E f\' l D XU FHDpxGSIIxLSGIxOSGIxGSImpy
+DBA zPLR x=
+ p ( \ 3 , DE V # BAD0F
+ABH 3 \ , E V| BAD0D
+ABB 3 "\ Ē , E W| BAD0E
+ABA 04 \ $ F \ BFA D0 , DF W u BAD0L
+ABJ 4 \ I 0G }b2 HW P F LW * FBB B(A0D8J
+8A0A(B BBBJ X \K G ] G ] G ] G ] H ]S FV l
+EA (H ]9 <H ^- PH $^- dH @^5 xH l^ H h^6 Ld
+A H ^2 H ^? F]U L 8H ^ FGB A(D0g
+(D BBBK f(A EBB$ ,I X_ Ag
+H h
+H V
+J L TI _ KBD A(D0p
+(D ABBA `P0 0 I p`f FDK S
+ABE rAB ( I `R BAA \
+DBJ J \D A~ |I a[ i |< 8J bH BBJ A(A0
+(A BBBK 8 xJ c BBA A(G0j
+(A ABBC J c
J |c J xc6 J cQ H]
+K ` K c7 Ha
+G F D 0K d FDA ^
+ABD `
+DBJ d
+ABI xK Leu ED s
+AC 4 K e} EDJ Y
+DAO _
+DAL K e K e K e L e $L e 8L e EO TL e hL e |L e$ H[ L e L e L L f ͍ FGB D(D0]
+(D BBBB g(A EBB( M tf EDK@
+DAE L L (gM U PFB B(D0A8DP
+8D0A(B BBBJ F R\ H M jQ FBB B(D0A8GP
+8A0A(B BBBH M $l L `M l FBB B(A0A8Dp
+8D0A(B BBBG 5 \[ lN n N o N o N o : HP L N o " FBJ E(A0A8G@
+8A0A(B BBBH O o= (O o EV < N o BBK D(G@T
+(A ABBA \ r L N o^ b FBB B(A0A8G
+8A0A(B BBBF 8 O r FBD D(G@N
+(A ABBE $ O to Ey
+J C
+E < PP t FEB A(A0
+(A DBBA D P u RBB A(C0F@
+0J(C DBBJ H P ,v FBA A(G0x
+(A ABBI d(A ABB H $Q v FBA A(G0
+(A ABBJ (A ABBL pQ Dy PBB A(A0`
+(A BBBC E
+(A BBBD H Q yF FBB B(D0A8D@m
+8D0A(B BBBA 0 R zw EAG [
+AAF DAA4 @R {e FDA w
+DBH QAB H xR <{ FBA A(G0
+(A ABBK t(A ABB H R | FBA A(G0x
+(A ABBI d(A ABB lR |C d8 R }^ WFD OABEA 8 R 0}^ և WFD OABEA H S T} FBA A(G0x
+(A ABBI d(A ABB H S ~ FBA A(G0x
+(A ABBI d(A ABB 8T ~) Ec d TT ~ FFB B(A0A8GE
+8D0F(B BBBH G
+8F0A(B BBBB L T FBB E(D0A8G
+8A0A(B BBBH P hT P : LBE B(A0A8G
+8D0A(B BBBF %
\ P T LBB B(A0A8G>
+8D0A(B BBBJ \ P @U Ж8 FBB B(A0D8G
+8A0A(B BBBI H% j\ ( PV A JDG aAAH 0 |V ȫ^ FDG v
+DBC GDB L V BBB B(A0A8D
+8A0A(B BBBE 8 W T FEA A(D0j
+(A ABBG P V BBB B(A0A8G
+8A0A(B BBBJ \ ` W <+ FBB B(A0A8GP[
+8A0A(B BBBF
+8A0A(B BBBH P hW FBB B(D0A8J'
+8D0A(B BBBA ,4 l\ ܃ L W l σ FBB E(A0A8G`(
+8A0A(B BBBF H X FBB B(A0D8D@
+8A0A(B BBBE P pX { FBB B(A0D8Qt
+8A0A(B BBBH \ 5 L X : FGE A(A0P
+(C BBBC d(A BBBH Y 4 FBB B(A0D8D`&
+8A0A(B BBBK L xY FGE A(A0P
+(C BBBC d(A BBBH lZ FBB E(A0A8Dp
+8A0A(B BBBE L Z
+ FGE A(A0~
+(A BBBG A(C BBBL dZ FJB B(A0A8G@
+8D0A(B BBBI X[ HV L Z Z FJB B(A0A8G@
+8D0A(B BBBK L [ $ FGE A(A0
+(A BBBA A(C BBBL l[ j À FBB E(A0A8G`(
+8A0A(B BBBF ` `\ + FBB B(A0A8GP]
+8A0A(B BBBD
+8A0A(B BBBG H \ p FBB E(A0A8Dp
+8A0A(B BBBE L ] D FDA A(D0
+(F ABBF D
+(C ABBD H `] PBB B(A0A8D`
+8A0A(B BBBG P ] HF
+ o PFB B(A0A8G
+8D0A(B BBBJ \. \& P \ ^ , UEB B(D0D8N`48A0A(B BBBFH` ( x^ EDD h
+DAF P ^ ~ BBB B(A0A8G:
+8A0A(B BBBK \f i~ P l^ l~ FBB B(D0G8J.
+8D0A(B BBBD \ T~ ` |_ FBB B(D0D8D@T
+8I0A(B BBBB D
+8C0A(B BBBH P <_ } FBB B(D0G8Ux
+8A0A(B BBBB \ \ } L` ^ H `` T FBB B(A0A8G@\
+8D0A(B BBBB P ` } FBB E(G0J8L
+8A0A(B BBBH \1 \ 6} H a , FBB E(A0A8Dp
+8A0A(B BBBE H da Z PBB B(A0A8DP
+8A0A(B BBBF H a Z PBB B(A0A8DP
+8A0A(B BBBF L Xa ( | FEB B(D0A8Dp
+8A0A(B BBBB , Lb FCD x
+ABH |b L a T | FGB E(A0D8D`e
+8A0A(B BBBD < <b I { FEG D(K@}
+(D ABBI ` |b { FGB E(A0D8D@
+8A0E(B BBBF l8E0A(B BBBD b k{ FIF A(D0DP
+0A(A BBBI < (c XI { FEG D(K@}
+(D ABBI P hc h
o{ FBB B(A0A8J
+8A0A(B BBBF l$ \6 { L c k { BBB B(A0I8G`
+8A0A(B BBBH \& c{ < <d > _{ FBA D(G0
+(A ABBJ P |d B ;{ FBB B(A0A8G
+8A0A(B BBBA P d { FBE E(D0A8G
+8A0A(B BBBG P $e z FEB B(A0D8D
+8A0A(B BBBH L xe 6 z FEB B(A0A8Dp
+8A0A(B BBBA P e
+ {z FBB B(A0A8G
+8A0A(B BBBJ P f z FBB B(A0A8J
+8A0A(B BBBA \, z , f !o z FADPO
+ABA < \ z < f 0!> {z FBA D(G0
+(A ABBJ < g 0" Wz FEG D(K@x
+(D ABBF H g # FDA D(G0L
+(E ABBD {(A EBB P g t#_ y FFB B(A0A8G@
+8A0A(B BBBA $ g \ z A L h X/% z FEJ B(A0D8G@
+8D0A(B BBBA < hh 80> y FBA D(G0
+(A ABBJ L h 81+ y FGB E(A0D8D`]
+8A0A(B BBBD < h 3I y FEG D(K@}
+(D ABBI L 8i (4 wy FBB E(A0I8G`
+8A0A(B BBBH L i h5 Ky FBB E(A0I8G`
+8A0A(B BBBH L i 6 y FBB E(A0I8G`
+8A0A(B BBBH D j 7 OEB D(A0v
+(A BBBH P < pj `8> x FBA D(G0
+(A ABBJ L j `9+ x FGB E(A0D8D`]
+8A0A(B BBBD < k @;> ox FBA D(G0
+(A ABBJ L @k @< Kx FBB E(A0I8D`
+8A0A(B BBBF D k =s #x FBB B(A0De
+0A(B BBBA P k ?$ w FBB B(D0A8G
+8D0A(B BBBH P ,l B w FBB B(D0A8G;
+8D0A(B BBBH 4 l @G w EJ
HHK.c.
+ A P l J w FBB E(A0A8G
+8D0A(B BBBJ D m N aw FBB D(A0G@
+0D(A BBBI P Tm LP 8w FBB E(A0A8G
+8D0A(B BBBD < m T w FEG D(K@}
+(D ABBI ` m U v BBB E(A0D8DP~
+8F0A(B BBBG k8F0A(B BBBL Ln DV v FBB B(D0H8Dp]
+8A0A(B BBBA V \. ]v P n W Mv FBE B(A0A8J
+8D0A(B BBBA $@ t\p +v P o @\ v FEB B(A0A8Jr
+8A0A(B BBBI x\u u L o b u FBB B(A0A8D`
+8A0A(B BBBG L o e u FBB B(A0D8Gn
+8A0A(B BBBH ,A 6\o u < Dp |h u FEG D(K@x
+(D ABBF H (q Li FGB A(D0K
+(D DBBE p(F BBB< p i> /u FBA D(G0
+(A ABBJ < q jI u FEG D(K@}
+(D ABBI L Pq k| t FBB E(A0I8G`
+8A0A(B BBBF L q l| t FBB B(A0I8G`
+8A0A(B BBBI < q n> t FBA D(G0
+(A ABBJ L 0r o ot BBB B(D0A8Lp*
+8A0A(B BBBC |Z R\ ?t P r p Ct FBB B(A0A8G
+8A0A(B BBBA h l\ *t < s \z> ct FBA D(G0
+(A ABBJ D Ds \{i ?t BDE A(A0G@
+0A(A BBBE o X\& t H Ht l|{ FEB B(D0A8DP4
+8A0A(B BBBJ , t } FAA
+ABH P t `~e s FBB B(A0D8G
- ز
! BEB B(A0A8JP
-8A0A(B BBBG <
+8A0A(B BBBA E \ s 0u dD FJ i
+EA 4 Tu EAG q
+AAH \
+AAJ u D u $ FBB E(D0D8D@c8A0A(B BBB@ u l- JEL D(
+ ABBH O ABB D ,v X XHF A(A0
+(A BBBK ` L u Gr FBB B(A0A8GK
+8A0A(B BBBA <G \ _r P 8v X/
+ Or FBB B(A0D8D
+8A0A(B BBBB \ +r Hw D FJ i
+EA lw Mb2 HW d v 0 q FBB F(D0F8J@v
+8A0A(B BBBD K
+8A0A(B BBBC 0K |\ q < `w uq FEF m
+ABG H
+ABE l 4\ Bq \x ,\ AU L xx \Q FBB A(A0
+(A BBBK (A BBB < $x li p FEG D(K@}
+(D ABBI P dx p FIE E(A0D8D,
+8D0A(B BBBE \ H\= zp 4 ty _ EAG ^
+AAC h
+AAF L y H 2p FBB B(A0A8Gr
+8A0A(B BBBG P Xy " o BBE B(A0A8G
+8D0A(B BBBE \ \ o L y ̺` gp FBE B(D0G8Iv
+8D0A(B BBBJ ( 0\ ^p , ,z Ŀ Op FDD0{
+DBA h ~\ (p L tz , p FGB B(D0D8D`e
+8D0A(B BBBA h{ l3 Ef < z o FJA F(J
+(A ABBF ( zPLR x%
+ 0 \ o < d{ o FJA F(J
+(A ABBE \\ Mo < { 3 =o FJA F(J
+(A ABBE \
+o P | g n FBG B(A0I8G
+8A0A(B BBBE Xx \ n P | g n FBG B(A0I8G
+8A0A(B BBBE x \\ ^n d | Nn FGE B(A0A8G`8
+8F0A(B BBBD D
+8C0A(B BBBH L T} <a m FGB B(D0D8D`]
+8D0A(B BBBI 4 H~ \U FAA s
+DBO AAB @ } m PFE D(A0
+(A BBBA P ~ @b m FBB B(A0A8G
+8A0A(B BBBC P \ rm L ~ Dz bm FBB B(H0G8JP
+8A0A(B BBBA u \% !m H \# FBA A(G0
+(A ABBO l(A ABB L @ @ l PBB B(A0A8D`4
+8A0A(B BBBF L l FBB B(A0A8T`6
+8A0A(B BBBF 8 l MAA
+DBO QAB 0 T Wl MAD
+IBJ x P 'l YLB E(A0A8JP<8A0A(B BBBBHP{
+8A0A(B BBBC @ ̀ 4N k MBF A(A0^
+(A BBBL @ EQ d Ё DK FBB B(A0A8D`E
+8D0A(B BBBL |
+8A0A(B BBBJ L 8 , FBB B(A0N8D
+8A0A(B BBBD H BBB B(A0D8D@
+8I0A(B BBBE 0 Ԃ @w FDD ^
+DBF xDB P d v Wj FBB E(D0A8D:
+8D0A(B BBBA B\# j P Ђ j FBB B(A0A8JF
+8A0A(B BBBH Tj \ Oj L FBA D(G0Z
+(A ABBD
+(G ABBE P j FBB B(A0A8G#
+8A0A(B BBBF \C i t YEB B(A0A8GP|8A0A(B BBBGHP
+8A0A(B BBBD P p PD |i FBB B(A0A8G
+8D0A(B BBBK U 4\ 6i H 4 > FEB B(A0A8Qp
+8A0A(B BBBI 8 ( ( h EC
BJ+
+ A {.N. j \ i H d) FGH E(D0A8Gp
+8D0A(B BBBB L ȅ +& h FBE F(D0D8Gp
+8A0A(B BBBE n \ |h P 0 1# oh FEB E(A0D8J
+8A0A(B BBBE $ \N 7i A , DT j EC
GB. N
+ A $ zPLR xi
+ , b\ i , T. i EI
G^. L
+ A t 0\ i L d US ui FGB E(D0A8Dp
+8A0A(B BBBF o \D Ki P ̇ W ;i FBE E(A0D8G
+8A0A(B BBBH l \ i 8 8 ^ i EG
BQ.o
+ C d. m 8\x i 8 dh i EC
BGLN}.
+ D Z. <n \\2 h 8 g h EC
Pl.m
+ G . n :\D h < 4 Lm7 h FBB D(G@
+(D BBBD `] &\ nh D 4nC ^h FBB D(A0G@
+0D(A BBBC \, 3h P $o% #h FBB E(D0A8G
+8A0A(B BBBH F \ i < X u i FBB A(DV
+(D BBBA \ Si H T S FBA A(G0
+(A ABBH d(A ABB 8 $T FBA D(D0
+(I ABBH P 8 H h FBB B(D0H8M
+8A0A(B BBBI \ h < | h FBA D(D0\
+(A ABBE P A mh FBB B(A0D8J
+8A0A(B BBBI $B 4\ h P P h FHB B(A0A8Gn
+8A0A(B BBBE \ \i 4 4 Mi EC
BEEFu.
+ J hr T\# )i P G i FBB E(A0A8J
+8A0A(B BBBF |^ \ h 1 YW L [ h FBB E(D0A8Op
+8A0A(B BBBA P h FEB B(A0A8G
+8A0A(B BBBA \B h p. VW P l h FBE B(A0A8G
+8A0A(B BBBA \ h < ؎ ( i FBE D(G@\
+(D BBBB c `\, h P 0 h FBB B(D0A8J
+8A0A(B BBBC v \ h L $ h FBB E(D0A8JB
+8D0A(B BBBC ܋ <\ h 8 L h EC
BEGHn.. q
+ D u \P h 8 X h EC
FK*
+ E .. v \ h P Tw h FBB E(A0D8G
+8D0A(B BBBD |: F\ h P h h FBB B(A0A8G
+8A0A(B BBBJ | \ h ( ( H FGD kAJ T 2 h :\. Fb H 0 BBB B(A0A8G`q
+8A0A(B BBBD P , Oh PBB B(A0A8D
+8D0A(B BBBG P 9. h FEB B(A0A8J
+8A0A(B BBBE Љ X\G ah L Thy Qh FEB E(D0D8G
+8D0A(B BBBF <w 7\& 6h H lsI RBE E(A0A8Gpr
+8A0A(B BBBE H D p FBE E(A0A8Gp
+8A0A(B BBBA P s! g FEB E(A0D8J
+8A0A(B BBBD t Y\ 4h /b2 HW P p h FBB B(A0D8J
+8A0A(B BBBD \h h L ܔ $ h FBE B(A0D8L
+8D0A(B BBBI d \ i P D 5 i FBB B(G0D8J
+8D0A(B BBBH d> \ i T 2\' h \ | ( ` FBB A(A0P
+(D BBBJ ^
+(D BBBH R
+(A EBBD 4 m FBD U
+DBI tBB L h FGB D(D0c
+(D BBBD f(A EBB0 | XY EAN j
+FAK DCA @ ė FBE A(D0G@D
+0D(A BBBH < HR D! ET L X g FBB E(D0H8D`
+8A0A(B BBBC L qg FBB E(D0H8D`
+8A0A(B BBBC L 8 .g FBB B(A0A8D`
+8A0A(B BBBF 0 , 8Q EAN j
+EAD DCA ` d HQ L x l FLE A(G0m
+(A ABBF y
+(A ABBI ( ș E FAA yAB L P 7 #f FBB E(D0G8Dp
+8A0A(B BBBF i\. e L e BBB B(A0A8D@
+8A0A(B BBBH p /\ e Ě `= _Z 8 $ {e FBB B(A0D8G
+8F0A(B BBBH
+8A0A(B BBBD
+8C0A(B BBBH \ ?e H x Z PBB B(A0A8DP
+8A0A(B BBBF L 0 d FBB E(D0D8G
+8A0A(B BBBG \ d , 5 ED jA P d BBB E(D0A8Dw
+8D0A(B BBBH \ ^d OBB E(G0G8K`_
+8G0A(B BBBH
+8A0A(B BBBG H` L H " c PBB B(A0A8DpB
+8D0A(B BBBE \G c H FBB B(A0A8QPA
+8A0A(B BBBF P L Kc FBB B(D0A8GC
+8D0A(B BBBH L c FBE F(D0A8D
+8A0A(B BBBF X \ b x t D 0 pn b FBB A(A0DP
+0A(A BBBA i u\ \b D q ab FBB A(A0DS
+0A(A BBBA Hj \ b 4 w FDD r
+ABE kAB D ( H a FMB D(A0D`
+0D(A BBBA |\' a D H a FMB D(A0D`
+0D(A BBBA D\' ja < Za FBA D(DP
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+8A0A(B BBBK \ ` L h L du ` FBB B(D0D8G`
+8A0A(B BBBD O\ ` L $ | g r` FFB I(D0A8Dp
+8D0A(B BBBG p \' 2` < % "` FEA D(DP
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+0A(A BBBG ( t w\F _ sFGFN D X _ FBB A(D0Dp
+0A(A BBBE n I\ E_ P 8 J_ BBE E(A0D8G
+8D0A(B BBBA z \ _ L $ \# _ FBB B(D0D8DE
+8A0A(B BBBI t 2\' ^ P % ^ FBB E(A0D8GD
+8D0A(B BBBD H \' ^ P ( ^ FFB B(D0A8O
+8D0A(B BBBG $ L \ s^ A b2 HW 0 \! FU P / o^ BBB B(A0A8G4
+8A0A(B BBBA t \ H^ P 6 O^ FBB E(D0A8JX
+8F0A(B BBBK d :\+ ^ P 7 )^ FEB E(D0A8JY
+8A0A(B BBBD \< ] L T9 ] FBB B(D0D8Gd
+8A0A(B BBBG 8 ]. ] P T ; ] FBE B(A0A8G
+8D0A(B BBBH j ]9 ] d @5 x ,@5 X@- t@5 @- ȧ L]' ܧ @ 4A 0A ,A ` 8A \ FEB B(D0A8GPh
+8C0A(B BBBA q8G0A(B BBB A" A" A" ̨ B B B* B (B" 0 DB D PB HU L \ XB PBB A(A0`
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MK
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+0A(A BBBD Xu ] Z D IQ Z FBB A(A0Ds
+0A(A BBBA u :] Z D ` I Z FBB A(A0D
+0D(A BBBA t ]+ IZ L @J KZ FBE F(D0A8G`
+8A0A(B BBBB L L 'Z BBB E(A0A8Lp=
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BHO. .
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+EC ܯ i L i1 W R d i1 V R i E P Hj,2 V FBB B(A0D8J:
+8A0A(B BBBA e ], V 4 ]3 V BAG DP AAB L < ԛ^ V FGB E(A0D8D`q
+8A0A(B BBBH P V FBB B(D0A8G'
+8D0A(B BBBD ` @} PV FJE E(A0A8J
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+8D0A(B BBBE $
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FM
+ B .C. ]f V 4 y V FDA J
+ DABD $ zPLR xm}
+ , ] V < V FBB D(D
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+8D0A(B BBBF x ]< U P Xl U BBB B(D0A8JP
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+EA D ( {U FBB B(D0G
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+A ط ]' H FBB B(A0D8Dp
+8I0A(B BBBH ` 8 FBB B(A0A8Gp
+8A0A(B BBBL 8A0A(B BBB L x 'T FEE E(D0H8D`c
+8A0A(B BBBF
]O FC ` FBB B(A0D8GPA
+8A0A(B BBBE
+8A0A(B BBBB L ȸ [S FGE A(A0P
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+8G0A(B BBBH
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+8D0A(B BBBF ~ ]/ SR APd P[ CR FBB B(A0I8GP
+8F0A(B BBBF K
+8F0A(B BBBF P x H{ Q FBB E(A0A8Gt
+8A0A(B BBBJ c ]0 Q P \ Q FBB H(G0D8D
+8D0A(B BBBG ]# yQ P P iQ FBB H(G0D8D
+8D0A(B BBBG J ]# +Q < > Q FBA D(G0
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+8D0A(B BBBA h% v
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+0A(A BBBH P l 2P FBB B(A0C8DZ
+8A0A(B BBBA ]) P P O FBB E(G0D8G
- P" w AFGPe
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- x BBB B(A0A8L
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8 BBB B(A0A8Oq
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n BBJ A(A0Gg
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+8A0A(B BBBH < T> L FBA D(G0
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+8A0A(B BBBD P d V L BBB E(A0A8M
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DS;. . b. o. m
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DKDZ. . . . . .
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+8D0A(B BBBD d2 ]% L T M FG u
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+0A(A BBBA ] L D D L FBB A(A0J
+0A(A BBBC L ] K J ]' < 4 K FBA A(G@u
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+8A0A(B BBBH @ t # K FBB D(A0
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+8D0A(B BBBE 6 ] K l FEA G(Q0O
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FBB B(G0A8GP
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+8A0A(B BBBE 0 ] I L $ I FIB B(D0A8DX
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+0A(B BBBA @ ] H 8 5C FGE D(K0
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+0A(B BBBA " ] 8H P 8* ;H FFB E(D0A8Dl
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+8A0A(B BBBJ - : ] DH a2 HW ]' ]O FC K K K d[
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+8A0A(B BBBG 8 ` ] G P \ W G FBB B(D0A8D
+
+8A0A(B BBBG 9 ] F l X X |X xX5 X5 X5 X X5 $Y| Fu , Y @ Y T Y
0 h Y FAA G0\
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+ABA H `d> C FBB D(A0G!
+0A(A BBBA < Th B FBA A(D@
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+J A
+G @ jT B MBF D(A0
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+DAE 4o EL ` 8o+ FBB B(A0A8GPY
+8A0A(B BBBH
+8A0A(B BBBH ` p FBB B(A0A8GPb
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+8A0A(B BBBG
+8A0A(B BBBB , h u ? FADP
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+ABI L l{ e> FBE A(A0
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+8A0A(B BBBK , 6] w= P o g= FBB B(A0A8G
+8A0A(B BBBD < KQ 0 D P @ 0= FBB B(A0D8Gt
+8A0A(B BBBJ T ] = D < FBB A(A0Dt
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+0A(A BBBA @ ] \< X# QJ P lZ E< FBB B(A0D8G
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+8A0A(B BBBE ] ; P Ħ ; FBB B(A0A8G
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+EH P t; FEB B(D0A8G
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-8D0A(B BBBA D | < {s 'J BBD D(G0U(A ABB < 8| I BBE D(D0`(L BFB L $ | I BEB E(D0D8G@8A0A(B BBB l t | oI BEE E(A0A8D
-8A0A(B BBBF D
-8C0A(B BBBA ~E ~
- \ ~ H BBB A(A0D`
-0F(A BBBG
-0D(A BBBE , 8 T 0 H BED I(G0
-(F ABBE X
-(C ABBH T ȅ EH BED I(G0
-(F ABBE X
-(C ABBH l < `h G JEA K(D`
-(A ABBH D
-(C ABBD XH` d ` | h L ` rG BEE B(E0H8G@\
-8F0A(B BBBK
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- < \ BDA A(G@X
-(C ABBA h L p BED A(D0
-(A ABBH }
-(G ABBG D L F BDA G@G
- AABG [
- AABA L h E BBB B(A0D8Gb
-8A0A(B BBBH 4 AK0{
-DE J
-DB v
-DF Ў+ AG@^D , e ADG0|
-AAF L l DE BBB B(D0D8Gp
-8A0A(B BBBB L ( E BBE E(D0A8Fp;
-8A0A(B BBBA
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-
- $ < T T < BDC D0
- AABH _
- AABE _
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-8F0A(B BBBA , L
XL AAN l
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L AAN l
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-AAA , ؟L AAN l
-AAA , < L AAN l
-AAA , l G ADG i
-AAA D 8. AHG0
-AAF ^
-AAH _
-AAA D . AHG0
-AAF ^
-AAH _
-AAA D , & AHF0
-AAD ^
-AAH _
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-AAF ^
-AAH _
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-AAD ^
-AAH _
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-AAF ^
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-AAE , | H ADK0[
-AAC , ئ ACK0Z
-AAE , h ADK0[
-AAC 4 BAH G0i
- AABA 4 D BAH G0j
- AABH , | ACK0[
-AAD D . AHG0
-AAF ^
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-(A ABBD T | 0 = BBD H(Gp
-(A ABBF `
-(A ABBJ < z= BBD H(Gp
-(A ABBD T U= BBD H(Gp
-(A ABBF `
-(A ABBJ \ $ pW BEA H(N0
-(A ABBI _
-(A ABBK `
-(A ABBA , p= BID
-ABF AU \ g BJH D(G0
-(A ABBK _
-(A ABBK `
-(A ABBA , 4 ) BDH
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-CAF K
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-N A 4 w ADD [
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-(A ABBH l t ;8 BBB E(A0D8D
-8A0A(B BBBH
-8A0A(B BBBA $ UL
-G p L 8 BBE E(D0D8Fc
-8A0A(B BBBG L \ о 7 BEE E(D0D8F
-8A0A(B BBBA L 7 BHE E(D0D8F
-8A0A(B BBBA ( 0 , 8s AAD y
-CAA < t 7 BED H(G`o
-(A ABBA l d 6 BBA A(G0q
-(A ABBD M
-(A ABBE w
-(A ABBK , m AAD x
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- DABE , T { BAA |
-DBA , 5 AADPs
-AAE \ BBA A(D@j
-(A ABBF j
-(A ABBH h
-(A ABBJ < ADD Z
-AAK Z
-AAD HAA4 T 0 ADD
-AAF D
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-AAE , AZ 4 X4 BAD D`[
- AABF A] 4 !4 BAD D`\
- AABE AZ 4 D 3 BAD D`[
- AABF 4 p A] 4 p 3 BAD D`\
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- AABD x) D d 4 < U3 BAD D`]
- AABD , A[ 4 3 BAD D`\
- AABE AQ
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- AABF A] 4 d 2 BAC D`[
- AABG $ T p5 AW
-H G
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- AABD N 8 % DQ
-K D < L 1 BBD D(Dp\
-(A ABBF < 0 1 BBD D(Dp\
-(A ABBF < 1 BBD D(Dp\
-(A ABBF < ! 1 BBD D(Dp\
-(A ABBF 4 L! n1 BAD D`i
- AABH 4 ! X1 BAD D`i
- AABH < ! B1 BBD D(Dp\
-(A ABBF , ! #1 AAD`T
-AAD < ," p 1 BBD D(Dp\
-(A ABBF 4 l" 0 BAC D`[
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- AABD , " 0 AAD`D
-AAD , # 0 AAD`C
-AAE T <# @ 0 BBD D(DpL
-(A ABBF Q
-(A ABBA d L$ 2 BBB B(A0D8D
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< BEG A(D@
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-0A(A BBBE
-0E(A BBBE 4 & x BDA D0F
- DABA , T& e XGH _ID , & { BAA |
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-E A & ^" DQ
-K A L & ^ BBB B(A0A8Gp8A0A(B BBB D & - BAD G
-ABG J
-ABE KAB , ' _= BDA rAB d ' _Q BBB E(D0E8D@O
-8F0A(B BBBA
-8D0A(B BBGA D $( w BDC D`
- AABA W
- AABA $ l( " ADG QAC $ ( 2 AFF0bAA D ( BHE D(D0D@
-0A(A BBBD $ ) 4 AFG0cAA D ,) BHE D(D0D@
-0A(A BBBD $ t) 2 AFF0bAA D ) BHE D(D0D@
-0A(A BBBD $ ) p4 AFG0cAA D * BHE D(D0D@
-0A(A BBBD T* @> AI0rA < t* ` BHD D(DP
-(A ABBF * > AI0rA < * @ BHD D(DP
-(A ABBF $ + 2 AFF0bAA D <+ BHE D(D0D@
-0A(A BBBD < * ) BEG D(D@
-(A ABBJ $ + 02 AFF0bAA D + H BHE D(D0D@
-0A(A BBBD 4 4, BGA G0f
- AABE 4 + ( BGD D0f
- AABE \ , BBG D(G@e
-(A ABBG r
-(A ABBH P(A ABB d - P 0 BBB B(D0C8GP}
-8A0A(B BBBC A
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ADG H
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-8A0A(B BBBF T . \? % BEE E(A0C8J0
-8D0A(B BBBD / 0, L D/ (,( % BBE E(D0D8GO
-8A0A(B BBBJ T L0 =} BHE D(A0Gpv
-0A(A BBBG T0A(A BBB 4 / 0=} % BAA DPT
- AABA 4 $0 x= $ BAA DPY
- AABA L \0 =r $ BBB E(A0C8G
-8A0A(B BBBG < d1 A BED D(G0K
-(A ABBI L 0 `A ;$ BEH E(A0A8G
-8A0A(B BBBA D <1 B
- # BBB D(D0G`
-0A(A BBBD L <2 hL BBB B(D0D8D@
-8A0A(B BBBK D 1 N # BAD G@P
- AABF t
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-CAA 5 qo. K^ L 5 ` BBE B(A0D8JW
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-DAA , t5 b / AAJDA \6 b G D 5 c6 BEA D(JF
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-A L
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-A L
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-DAE Y
-DAB L 6 + BBF B(D0E8GPr
-8A0A(B BBBH L 7 @ BBB B(E0A8Gp
-8A0A(B BBBK L l7 g BLB B(A0A8Jm
-8A0A(B BBBK t8 m# DZ 8 mI L 7 n BBB B(A0A8J
-8A0A(B BBBD T <8 pt BBB E(D0A8J
-8A0A(B BBBE 4 8 8 BAF GP
- AABH 4 8 BFA GP
- AABA 4 9 BFA G@
- AABA 4 <9 ` BAA G0x
- AABA $ ,: n AG Z
-AA T: @ l: H- KQJ $ : Xn AG Z
-AA $ : n AG Z
-AA L $: BBB B(D0D8DP
-8D0A(B BBBE l t: x, z BBB E(A0A8D_
-8A0A(B BBBF D
-8C0A(B BBBA L : 8Z* 3 BBB B(A0D8G[
-8D0A(B BBBD L 4; H^ BEB B(A0A8GZ
-8A0A(B BBBH << X T< ` l< h6 AU ^A < 3 AR ^A , < Dl
-H _
-A Z
-A Z < Hb L << d
BEB B(D0A8G
-8A0A(B BBBG T < d BBB E(G0D8G
-8C0A(B BBBC T < BBB E(A0A8J#
-8A0A(B BBBD T <= P BBE E(G0D8G
-8A0A(B BBBF L> d> . LW |> > & > 8& > P& L > hR BBB B(A0A8GK
-8A0A(B BBBJ L ,? xd BBB B(A0A8GK
-8A0A(B BBBJ $ |? > AG _
-AH LA$ ? > AG ^
-AI LA$ ? = AG ^
-AI KA$ ? = AG ]
-AJ KAL @ R BBB B(A0A8GK
-8A0A(B BBBJ L l@ d BBB B(A0A8GK
-8A0A(B BBBJ D @ ( BBB D(A0GN
-0A(A BBBH D A p BBB D(A0GN
-0A(A BBBH LA
- dA
- |A
- A A
- A
- A
- A B x
- $B p
- <B h
- TB ` lB X
- B P
- B H
- B @ B 8
- B 0
- B (
- C ,C
- DC
- \C
- tC C
- C
- C
- C C
- D
- D
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-8A0A(B BBBA L D 8 BBB B(A0A8D
-8A0A(B BBBA 4 4D BAA G0
- AABG L $E BBB B(A0A8Gp8A0A(B BBB l tE x BBF D(A0G@F
-0A(A BBBD v
-0A(A BBBJ D
-0F(A BBBG T ,E 0 BLB E(A0A8D
-8C0A(B BBBA <F h $ TF p7 HTTF |F k F k F k F k F 8^ AW
-A L DF x; BDB B(A0A8GQ
-8A0A(B BBBB T F j BEB B(A0A8G:
-8D0A(B BBBE T F 0p BEB B(A0A8G:
-8D0A(B BBBE T DG u BEB B(A0A8G:
-8D0A(B BBBE TH { DM
-A tH @; D H h BDA
-ABA O
-ABF ]AB , H D < BDH _
-ABA LH 0 BBB E(A0A8F
-8A0A(B BBBD
-8A0A(B BBBF
-8D0A(B BBBH W
-8A0A(B BBBG I I ( I 0 < 4I 8` i BEI C(D0l
-(D ABBA < tI Xc 9 BEJ D(C0p
-(A BBBA , I D BDH _
-ABA d I BED A(D0O
+
+ M A H } FBB B(A0A8D`
+8A0A(B BBBD L d P FBB B(A0A8GS
+8A0A(B BBBF L } FBB B(A0G8DP
+8F0A(B BBBI D n I
+ Iv MvXvxvp
vh v`avP
+
+ M D H L H FEG E(A0A8D@w
+8G0D(E BBBF ̙4 P * FBB B(D0D8G
+8A0A(B BBBE r ]@ * l $ , xZ EDJ ADAL T I
+ Iv GvxvpE
vhGvX v`HvPs
+
+ M C D `~ NLG F(J0E8{
+0A(B BBBA D n OJG F(J0E8|
+0A(B BBBA 4 Z L FDB B(A0D8GBBLJLbM
+8A0A(B BBBJ }
+8A0A(B BBBI L t FKG E(A0A8G
+8A0A(B BBBH L FKG E(A0A8G
+8A0A(B BBBA x p 4} FBB E(D0A8D<JSDJSBA`w
+JZ ~
+8A0A(B BBBA 8@ K]C T X FBE B(A0A8GKWBwBPGBQGv
+8A0A(B BBBE DJYBQK[A| h FJG F(A0A8GP
+8A0A(B BBBH I
+8A0A(B BBBE n
+8A0A(B BBBH D ,
+ x IBB B(A0A8
+0A(B BBBG D t
+ 0 FEE E(A0A8
+0A(B BBBA L
+ FEB B(A0A8D
+8A0A(B BBBA D
+ FEB B(A0A8
+0A(B BBBF L T
+ n FEE B(A0A8D
+8A0A(B BBBA L
+ FHB B(A0A8G\
+8A0A(B BBBA
+ P
+ , T
+ } FBB B(A0A8G L@LB
+8A0A(B BBBA T t
+ $ FBB B(A0A8G L@LA
+8A0A(B BBBJ T
+ FBB B(A0A8G L@LB
+8A0A(B BBBA T $
+ ` FBB B(A0A8G L@LA
+8A0A(B BBBG H |
+ ( EAG U
+CAJ [
+CAA d
+CAH NCA ,
+ | FAA
+ABD P T
+ " FBB B(A0A8G
+8A0A(B BBBG ~] " D d
+ p2 FBB B(A0A8
+0A(B BBBA \
+ h FEB B(A0D8*
+0A(B BBBI
+0A(B BBBA
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+ FBB B(A0A8Dh
+8A0A(B BBBD L l
+ h FEB B(A0A8GX
+8A0A(B BBBF L
+ FEE B(A0A8G
+8A0A(B BBBF L
+ H FBB B(A0A8G
+8A0A(B BBBJ H \
+ FBB B(D0A8Gp
+8A0A(B BBBG L
+
FBB B(A0D8J
+8A0A(B BBBA P
+ ( FBB B(A0A8G
+8A0A(B BBBD L
+ x `
+
+ FEE B(A0A8GJBDDDDDMGBGGGv_DKDDDDDADAGGG^SELDDDDAGBGGGvcGEDDDDDADAGGGfJBDDDDDMGBGGGvX
+8A0A(B BBBG n
+R p
+ FBB B(A0D8JGKGGLDAk
+8A0A(B BBBG x \
+ x FBB B(D0A8J{EGJGJGAGAmr
+8A0A(B BBBJ
+ # FBB E(D0A8G'eGGMGGGAJAGGGe
+8A0A(B BBBH d
+
+ *T FBB B(A0D8J8KJDDDyJBDDDK
+8A0A(B BBBE sKJDDDyEBLDDVnKJDDDy, @
+ 2 FAA
+ALP T p
+ 4 FBB A(A0G{IyAP
+0A(A BBBG 8
+ <6 FAGrZFDx
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+ 6 FBE E(A0A8G
+8A0A(B BBBI L T
+ 8 FEE E(D0H8GPR8A0A(B BBB
+ 9B OEA K(G0
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+(A ABBK o
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+ B H
+ F \
+ (F p
+ I 0
+ I FAD G0
+ AABC
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+ Mq FDB B(A0A8D9
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+ NO 0
+ P D
+ Pi X
+ lS l
+ xT H .
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+ \V
+ HW1 0
+ tX FNM J`z
+ AABA
+ X
+ Y
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+ [ FFA A(
+ ABBI l
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+ Pf JJO K(F0E8Hp
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+ l JJO K(F0E8Hpw
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+ Xs D
+ Ts
+ FMF J(E0D8a
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+ } ( X
+ }; FAD dDB Xz 4]I `
+ } FBB B(A0A8GBZA[
+8A0A(B BBBB ] h `
+ 8 d FBB B(A0A8GOBPAm
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+ ABBA L
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+ h I
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+
+ M A L
+ FGF A(D08K@QHBPAXE`S(A ABBG0
+ H Hh H(C0LP
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+ 8
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+ xz- FBB B(A0A8J
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+ Ժ L FBB B(A0A8J
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+ dI FBE B(D0D8J
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+ \ ' FBE E(D0A8J
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+ EC
CP
+ E _.M. P L0
+ d FBE E(D0A8J
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+ C H
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+ C
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+ B
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+ O v FBB B(A0A8D*
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+ 4] FEB B(A0A8G`:
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+ AABK P ?
+ v FBE B(D0D8Jy
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+ w N FBE B(D0D8Jy
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+ ,y FBE B(D0D8Jy
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+ z FEE D(G@a
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+ h| FBB E(D0JD
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+ e y FAD
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+ P B
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+ G
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+ $c FIB B(A0A8Gy
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+ 8- `V
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+ ( FBB B(D0A8G
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BJS
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+ B m ^[ M 4
+ x = FBD G
+ DBBA 2 ^' 4 X
+ xy FBD G
+ DBBA 2 ^' 4
+ (z FBD G
+ DBBA 43 ^' 4
+ z FBD G
+ DBBA 3 p^' u 4 H
+ { e FBD G
+ DBBA 3 G^' ? 4
+ 8| / FBD G
+ DBBA $4 ^' 4
+ | FBD G
+ DBBA t4 ^' 4 8
+ } FBD G
+ DBBA 4 ^' 4
+ H~ FBD G
+ DBBA 5 ^' g 4 ؞
+ ~ W FBD G
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+ X) EC
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+ A x ^ 0
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+ A ^ 0 4
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+ A X^ 0
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+ A \ ^ i 0 ̡
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+ A ^ 3 0
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+ A ^ 0 d
+ EC
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+ A @ h ^ 0
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+ A , ^ 0
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+ A $ ] % 0
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+ A p x] 0
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+ A v] 0 d
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+ A ] y 0
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+ A ] C 0 H
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+ A $ ]
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+ DBBA A ]' 4
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+ T]) K P @
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BED
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+ DBBA j B^' 4 |
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+ DBBA Xk B^' 0
+ m ~ EC
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+ A 4 `B^ X 8 h
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+ A A^) X 4
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+ D
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+(D ABBH ( zPLR x 0 F^: L FBB H(D0D8Hp
+8A0A(B BBBB
+ ^5 d 0 FBE B(A0A8D
+8A0A(B BBBO 8A0A(B BBBL & FBB E(K0D8Dp
+8A0A(B BBBI
+ ~^ D H v
+A -
+- (
+- D
+- `
+- |
+- ,
+- @
+- T ^' L
+ o FBB B(A0A8GF
+8D0A(B BBBH lf OKH E(E0G8GpP
+8A0A(B BBBD 8A0A(B BBBBHp
+8A0A(B BBBE P Hv FBB B(D0A8G
+8D0A(B BBBE P t v FBB B(D0A8G
+8D0A(B BBBE P P !v _ FBB B(D0A8G
+8D0A(B BBBE P %v 3 FBB B(D0A8G
+8D0A(B BBBE < )- FBA D(D`
+(A ABBE X
+ 9^ ڿ P P +v ӿ FBB B(D0A8G
+8D0A(B BBBE P /v FBB B(D0A8G
+8D0A(B BBBE 4 (4 { FAD G0b
+ AABH
+ ^ K , H x4i D FAJ0O
+ABA v^ P 4L FBB B(A0D8J
+8A0A(B BBBC 0
+ ^ P 9v 5 FBB B(D0A8G
+8D0A(B BBBE P P = FBB E(A0A8Gl
+8D0A(B BBBG P ,B ݾ FBB E(A0A8Gn
+8D0A(B BBBE P F FBB E(A0A8Gl
+8D0A(B BBBG P L $K FBB B(A0D8G
+8A0A(B BBBK
+ ^} q P XV FBB E(A0A8GY
+8D0A(B BBBB T`2 HW 8 $ Zw { EJ
DIQN. ]. s
+ E G^ 8 x bp
+ EC
PY. T.
+ G $ ^m Ǿ A 8 l EC
PV. . n
+ G $ ^m A 8 @ w
+ ߾ EC
PV. . `
+ G $ | ^m A 8 g EC
DSE. .
+ I T ^ 8 8 EJ
DIQ0. I.
+ E 0^ 8 L @ EC
PV. .
+ G $ ^m O A 8
+ C EC
P. T.
+ G $ ^m s A P ! ̥# g FBB B(A0A8M
+8A0A(B BBBB 4
+ ^ . 4 $" EDD m
+AAL L
+DAG P ! M BBB B(A0A8J
+8A0A(B BBBH ^' پ " " - " - # 0- # L- ,# h- @# - T# - h# - |# س- # - # - # ,- # H- # d- # - $ - $ - 0$ Դ- D$ - X$ - l$ (- $ D- $ `- $ |- $ - $ - $ е- $ - % - % $- 4% @- H% \ \% X p% d2 % = A`
+G M % t Ab
+M C4 % - EDG J
+AAD
+AAK 8 % FAD
+ABD
+ABH ( 8& _ EDG t
+DAG 0 d& кe FFL U
+ABH _DE 0 & e FFL U
+ABH _DE 4 (& H; FHA G@
+ ABBC H `& P ں FBB E(D0JD
+0D(B BBBC H & FBB E(D0JD
+0D(B BBBC H & f FBB E(D0JD
+0D(B BBBC D D' L , BFB D(A0Gx
+0D(A BBBG ,z 6^ D H( OEB D(A0v
+(A BBBH P H ' T FBB D(D0Jl
+0D(A BBBE D 8( FBB A(K0DP
+0A(A BBBH ;^
S H ( x w FBB E(D0Jt
+0D(B BBBC D ( L G FDE A(A0GP{
+0A(A BBBE ) t`2 HW 4 D) e EC
DKl. x. L
+ A X L^) 8 ) EC
HK|. . =
+ E D !^f 8 ) EC
HKO. P. m
+ I 3^f 8 <* d EC
HKO. P. m
+ I E^f s 8 * c EC
HKs. P. =
+ E @ W^f M 8 * = EC
HKO. . j
+ G i^f ' 8 8+ hf EC
HKO. .
+ B {^f 8 + EC
HKm. P. =
+ K < ^f ۷ 8 + 6 ˷ EC
HK. .
+ A ^f 8 4, EC
HKO. . =
+ K ^f 8 , (6 EC
HK|. .
+ I 8 ^f i 8 , Y EC
HK|. .
+ H ^f C 8 0- 3 EC
HK|. .
+ H ^f 8 -
EC
HKO. . =
+ G 4 ^f 8 - EC
HKO. . =
+ J ^f Ѷ 8 ,. $ EC
HKp. P. =
+ H ^f 8 . ` EC
HK. P. M
+ J 0 /^{ 8 . { EC
HKO. . m
+ J V^f e 8 (/ U EC
HK. . M
+ D h^{ E 8 |/ dj 5 EC
HD. .
+ D , ^ 0 8 / # EC
DSE. [.
+ H ^ 3 8 $0 3 EC
HKm. . m
+ D ^f 8 x0 x
+
EC
P. .
+ D ( ^ 4 0 4 EC
DKK. .
+ A 5^) 4 1 @ ص EC
DKK. ]. c
+ A 0 ^) 8 l1 EC
HKO. . ]
+ K ^f 8 1 f ~ EC
HKO. P. =
+ I p ^f g 8 2 W EC
HK. . =
+ H ^f A 8 h2 HF 1 EC
HK. . =
+ H ^f 8 2 D EC
HK|. . =
+ E l +^f 8 3 1 EC
HD. . M
+ E =^{ Դ 8 d3 " Ĵ EC
HK|. . =
+ J d^f , 3 % BFG@I
+ABE w ^ v P 4 `% q FBB B(A0F8J
+8A0A(B BBBD x
+ ^ J 0 l4 + EC
BQl
+ I _ A.Q. L 4 0 Ĵ BDB B(A0A8JH
+8A0A(B BBBH J_ 5 |7 0 5 x7 FAL U
+ABE xDEL l5 8k FBB B(A0F8IC
+8A0A(B BBBG __ V 0 x6 = FAL
+DEF DABL 6 = . FBB B(A0A8OI
+8A0A(B BBBH E _P P p6 0A! FBB B(A0A8O
+8A0A(B BBBA
+ ) _@ ҳ L 6 H BDB B(D0A8GH
+8A0A(B BBBH H
+ _ 7 |L < X7 xL2 FBA F(G`
+(A ABBK _ 4 7 `M FAF I0A
+ AABE \ _\ j L 8 N} u FBB B(A0F8Gp
+8A0A(B BBBA L _ : L h8 T 3 FBB B(A0C8LB
+8A0A(B BBBH # 4_ L 8 `X BDB B(D0A8GH
+8A0A(B BBBH <
+
+ N_ ɲ 9 [ , L9 [ ̲ FAL0
+ABC ' _ 0 8: <\ FAL U
+ABE xDE0 l: \ FAL U
+ABE xDEP 9 T]& 5 FEB B(A0D8Gk
+8A0A(B BBBH d
+ _
P h: c ۲ FBB B(A0D8It
+8A0A(B BBBH P! #_U P : |g BBB B(A0A8J*
+8A0A(B BBBH $ (; )_A A ; r < d; r C FBD F(Gp
+(D ABBE < ; s FBD F(Gp
+(D ABBE P ; lu BDB B(A0D8G_
+8A0A(B BBBA $ 8< 2_w ز A = x P t< x BDB B(A0D8G_
+8A0A(B BBBA $ < 3_w A = z 4 = z FAD GPu
+ AABE P
+ 4_ u < T= ~~ p FGA F(Gp
+(A ABBG
+ 4_ > T> 0- h> L- |> h- > - > - > - > - > - > - ? ,- ? H- 0? d- D? - X? - l? - ? Ԁ- ? - ? - ? (- ? D- ? `- ? |- @ - @ - 4@ Ё- H@ - \@ - p@ $- @ @- @ \- @ x- @ - @ - @ ̂- @ - A - $A - 8A <- LA X- `A t- tA A A 2 A ă= A`
+G M A t Ab
+M C A D 8 B @ FAD
+ABD
+ABH 4 @B Ԇ- EDG J
+AAD
+AAK D A ̇ BFB D(A0Gx
+0D(A BBBG 0_i ح P 4B \Y FBE E(D0A8J
+8D0A(B BBBD D B h FBB A(K0DP
+0A(A BBBH p1 1_
y D B ȋ FBB A(K0DP
+0A(A BBBH 1 r3_
h 4 HC ([ FAD G@
+ AABA `8 /5_ ] <D H`2 HW 4 C A @ EC
DKl. \. D
+ A 5_) 8 D EC
HKO. . =
+ J ) Z5_f 8 TD l EC
HKO. . =
+ J * l5_f Ь 8 D EC
HKO. P. m
+ I X* ~5_f 8 D EC
HKO. P. m
+ I * 5_f 8 PE P r EC
HKs. P. =
+ E + 5_f \ 8 E L EC
HKO. S.
+ K T+ 5_f 5 8 E f % EC
HKO. .
+ B + 5_f 8 LF EC
HKm. P. =
+ K + 5_f 8 F P ٫ EC
HKO. . =
+ K P, 5_f ë 8 F ̦6 EC
HK|. .
+ I , 5_f 8 HG EC
HK|. .
+ H , 6_f w 8 G t g EC
HK|. .
+ H L- 6_f Q 8 G 0 A EC
HKm. P. =
+ K - 26_f + 8 DH l EC
HKp. P. =
+ H - D6_f 8 H EC
HKp. P. =
+ H H. V6_f ߪ 8 H Ϫ EC
HK. P. M
+ J . h6_{ 8 @I EC
HKO. . m
+ J . 6_f 8 I B EC
HD. .
+ B D/ 6_ 8 I f u EC
HKO. P. >
+ H / 6_f ^ 8 <J 4f N EC
HKO. P. >
+ H / 7_f 7 4 J P ' EC
DKK. .
+ A 7_) 8 J f EC
HKO. P. =
+ I 0 6_f ܩ 8 4K ̩ EC
HK|. . =
+ E 0 7_f 8 K 1 EC
HD. . M
+ E 81 7_{ 0 K t EC
BDm. N.
+ A B7_ _ 4 (L , O EC
DKl. N. =
+ A < 7_) + 0 xL EC
BDm. . q
+ A T 6_ 8 L EC
HKs. P. =
+ E t2 6_f Ϩ 0 M X EC
BDL. .
+ A 6_ 4 dM $ EC
FK. .
+ E u6_) e 0 M U EC
BD. . q
+ A R6_ / 4 N R EC
DKK. .
+ A
+ 6_) 4 PN 0d EC
FK. .
+ K 5_) ǧ 8 N P EC
HK. P. M
+ J P4 5_{ 8 N EC
HD. . M
+ G 4 5_{ 8 HO v EC
HD. . M
+ G 4 6_{ e < O U EC
HKO. b. n. d. =
+ E P5 16_f ; 8 O f + EC
HKO. .
+ B 5 C6_f 8 HP 6 EC
HK|. .
+ I 5 U6_f 8 P ߦ EC
HKp. P. =
+ H L6 g6_f ɦ 8 P @ EC
HK|. .
+ H 6 y6_f 8 DQ EC
HKm. P. =
+ K 6 6_f } 8 Q 8 m EC
HKp. P. =
+ H H7 6_f W 8 Q t G EC
P. .
+ D 7 6_ U 8 @R 0 F EC
HKm. .
+ J 7 17_O * 8 R l EC
HKO. S.
+ K D8 ,7_f 4 R EC
FK. .
+ G L$ B7_B ե 8 8S ť EC
HKO. . =
+ J 8 07_f , S T BFG@I
+ABE N7_ w P S r BDB B(A0D8G)
+8A0A(B BBBG
+ 7_ ? T T 0 hT ; EC
DO~
+ G : =_ e A.Q. L T BDB B(A0A8JH
+8A0A(B BBBH ; @_ u U @ 0 U < FAL U
+ABE xDEL hU j R BDB B(A0D8FB
+8A0A(B BBBG @ B_ tV V 0 V FAL
+DEF DABL ,V d Ǥ FBB B(A0A8L~
+8A0A(B BBBF |: C_ P V FBB B(A0A8O
+8A0A(B BBBF
+ 7D_" d L W p( | BDB B(D0A8GH
+8A0A(B BBBH l(
+ F_ F X + L |W + O FBB B(A0C8LB
+8A0A(B BBBH 0B H_ L W <0 BDB B(D0A8GH
+8A0A(B BBBH P)
+ I_ X 3 , `X 3# EC
I
+ I > K_ ͣ C.Q. TY 7 L X 7 ߣ FEB E(A0A8GC
+8A0A(B BBBH 0*
+ M_} P ,Y $: * FBB B(A0A8O
+8A0A(B BBBK L
+ U_ L Y ? , FBB B(A0A8L~
+8A0A(B BBBF = X_ L Z Ca FBB B(A0A8L~
+8A0A(B BBBF D Y_. ã D hZ (Iv FBB A(A0OK
+0A(A BBBB SY_- L Z HN FBB B(A0C8LpB
+8A0A(B BBBH C \_d j 0 [ R FAL U
+ABE xDE0 \ S FAL U
+ABE xDEP [ S&
FEB B(A0D8Gk
+8A0A(B BBBH
+ \_w P \ \Ye ʣ BBB B(A0D8I
+8A0A(B BBBB t-
+ k_ ] `] (] \] P \ X] ݣ BBB B(A0A8J*
+8A0A(B BBBH $ \ s_N ѣ A ] g P (] g BDB B(A0D8G_
+8A0A(B BBBA $ |] x_w A H^ j P ] j BDB B(A0D8G_
+8A0A(B BBBA $ ^ y_w ֣ A ^ <m ^ 8m P \^ 4m
+ BBB B(A0A8J*
+8A0A(B BBBH E lz_h l_ w @ ^ wg FBA D(G
+(A ABBD < d_ £ _ _' ` L_ y VBE B(A0A8D@u8A0A(B BBBDH@ P _ zv M FBB B(D0A8G
+8D0A(B BBBE P ` ,~v ! FBB B(D0A8G
+8D0A(B BBBE P X` X
FBB E(A0A8G
+8D0A(B BBBD 0 ` ɢ EC
HH
+ C TF ~_ P.M. 0 ` d EC
HH
+ C F /_ j P.M. P La
a FBB E(A0A8G
+8D0A(B BBBF Db o`2 HW , a X EC
I
+ G \G ]_ Z.M. 0 b ؒ EE
BNo
+ G G _ } .M. c 2 c OBB E(G0G8K`_
+8G0A(B BBBH
+8A0A(B BBBG H` < b R FFD A(D0h
+(D ABBJ _& ( < Pc FFD A(D0h
+(D ABBJ ܭ j_& Ld X OBB E(G0G8K`_
+8G0A(B BBBH
+8A0A(B BBBG H` < ,d Z FFD A(D0h
+(D ABBJ _& 0 (e L OBB E(G0G8K`_
+8G0A(B BBBH
+8A0A(B BBBG H` < e FFD A(D0n
+(D ABBD _& r f l`2 HW 4 xe J FDA I0E
+ DABH $5 _ L e h FBB B(D0A8IP
+8D0A(B BBBA <_ d_ ͟ L 0f Ф FBB B(D0A8IP
+8D0A(B BBBA _ _ { L f ( k FBB B(D0A8IP
+8D0A(B BBBA ` _ ) g - g - g - g ԥ- g - h - h (- 0h D- Dh _' @ Xh L EDG C
+AAK O
+AAG {
+AAK 4 h EDG Z
+AAD r
+AAD 4 0h FAD D@
+ AABD H] k_ Ý P h Z FFL B(A0A8D
+8A0A(B BBBJ $ h _ A P h $ FFB L(A0A8D
+8A0A(B BBBB
+ &_ w P hi Z s FFL B(A0A8D
+8A0A(B BBBJ $ i Z_ W A P i v C FBB B(D0A8G
+8D0A(B BBBE P 8j ȿ BBE B(A0A8G
+8A0A(B BBBD a _ Hk \ \k f`2 HW 4 j @7 EJ
KR. . w
+ A 8. s_0 ם 8 k 0' ̝ EJ
KIR. .
+ G < O_0 8 tk EJ
D]%. u.
+ E $Q +_y 8 k EJ
KKK. |.
+ H xQ P_d 8 l $# u EJ
MR. . n
+ E = `_I _ 4 pl T EJ
KR
+. .
+ E / Y_0 < < l 1 EJ
DPIN. .
+ C tR 1_0 4 m EC
FKK. . Z
+ H |> _B 4 hm x EC
FKK. .
+ H > _B 8 m EJ
KKK. . 6
+ F hS _H 8 n EJ
DYN. .
+ A S _I p 8 `n w a EJ
KIR. .
+ D ? ڕ_0 E 4 n 7 : EJ
KR. .
+ B 2 _0 " 4 o w EC
FKK. .
+ I h@ _Z 4 To , { EC
FKK. .
+ J @ _A ӛ 4 o \ Û EC
FKK. . 1
+ H A _B 4 o k EC
FKK. .
+ J XA _A w 8 Dp s g EJ
MR. u.
+ C A t_I Q P p
G BMB B(A0D8G
+8A0A(B BBBD P
+ R_. q l) q h)- @ q ) EDG C
+AAK O
+AAG {
+AAK r *; (r \``2 HW 8 q 4*k ? EJ
DRN. |.
+ I LW _; / r P/ r \/% r x/ r t/ r p/2 L Tr / FGB D(D0
+(D BBBG D(A EBB( Hs 0R BAA \
+DBJ < ts @1 FBE A(A0
+(A BBBG s _ BG EH s 2 FBA A(G0x
+(A ABBI d(A ABB H t d3+ FBA A(G0
+(A ABBI t(A ABB H lt H4 FBA A(G0x
+(A ABBI d(A ABB L t 5{ FBB E(D0D8Dp
+8D0A(B BBBG `\ ,_o ژ L |t $7 ʘ FJB B(A0A8G@
+8D0A(B BBBI ` pu 7+ FBB B(A0A8GP]
+8A0A(B BBBD
+8A0A(B BBBG L 0u p8
FBB E(A0A8G`(
+8A0A(B BBBF L u 0< FGE A(A0~
+(A BBBG A(C BBBH tv <Z PBB B(A0A8DP
+8A0A(B BBBF < v = FBA A(Dp|
+(A ABBA < \v T? f FBA A(DpB
+(A ABBA < v A M PBA A(DpM
+(A ABBA w B5 < v C> # FBA D(G0
+(A ABBJ L 0w D( FGB E(A0D8D`
+8A0A(B BBBD < w E> FBA D(G0
+(A ABBJ < w F> Ö FBA D(G0
+(A ABBJ L x G( FGB E(A0D8D`
+8A0A(B BBBD P Px I FBB B(D0A8G
+8D0A(B BBBC P x N [ FBB B(A0D8J
+8A0A(B BBBH hU
+ _8 8 D y PQ w FBB B(A0Gf
+0A(B BBBE ( zPLR x 0 _ D @z Q FDD
+ABM z
+ABK A
+ABD P y S FBB B(D0A8G
+8D0A(B BBBE P 8z xW FBB E(A0A8Gq
+8D0A(B BBBJ P z 4\s FBB E(A0A8G
+8D0A(B BBBH < z `a> c FBA D(G0
+(A ABBJ L { `b ? FBB E(D0A8LpE
+8A0A(B BBBA L p{ 0fh ' FBB B(A0A8D`
+8D0A(B BBBF d| `V`2 HW $ || 8i= EAD pAA L | Pi FBA A(G0
+(A ABBK
+(A ABBF P P| l w FFF B(A0A8Jf
+8A0A(B BBBH 8 H} lu FBA D(D0
+(D ABBH P | x 7 FBE F(A0A8J
+
+8A0A(B BBBA $ 4} _ A
+ P \} D ˔ BBE E(D0D8G
+'
+8A0A(B BBBA , zPLR x
+ 4 ڛ_ _ 0 } Ȫ' S EC
BID
+ D c _ ? H.O. ~ T`2 HW L `~ p PBB B(A0A8Dp
+8A0A(B BBBA f _q l | x t 4 p H l \ h p d ` \ X T Ԁ P L H D
+ $ @ 8 < L 8 ` 4 t 0 , ( $ ā ( < P d x > Ȃ z AV
+I Z
+F 4 H xc { EAG A
+AAH DAA $ EL @ % T д EL 4 ̂ Դc EAG A
+AAH DAA EL F Lk
+I A < EL 4 L_M BBA A(D0|(A ABB < K 8 D # MAE w
+ABC t
+ABI EL 0 : ϐ MAL
+ABO ܄ EL 4 _M BBA A(D0|(A ABB 0 _ L~
+F J P Ķ EL 4 l _M BBA A(D0|(A ABB e FL
+F ą ] FI
+A } F`
+J U F~
+D ķe FN
+D @ m FY
+A ` dm FR
+H m FY
+A m FR
+H Tm FP
+J ] FI
+A ] FI
+A $m FT
+F @ t] FI
+A ` m FT
+F e FK
+G Te FN
+D m FP
+J ] FG
+C 4e FK
+G 4 FAA _
+ABF }
+ABH H X ] FED D(D0]
+(D ABBC (G ABB H 0] FED D(D0]
+(D ABBC (G ABB H Dh FED D(D0p
+(D ABBH (G ABB 4 < hg FDD
+DBF yGB L t J FHB D(A0p
+(A BBBD (D EBB 8 ĉ FDD m
+DBG Y
+GBE H d FHB D(A0`
+(A BBBD }(D EBBH L s FBA A(G0e
+(A ABBD D
+(E ABBJ H s FBA A(G0e
+(A ABBD D
+(E ABBJ 8 P FAA M
+ABH B
+ABK L $ FBA A(K0
+(A ABBG D
+(E ABBJ 4 p p EAG l
+AAE D
+EAF EP
+K q ̋ $ < FM
+E W
+A zPLR x (
_ b D X l , EDK
+AAA P v_ Lj = , 0 g MEH X
+ABA _ ? ȍ U ܍ H D @ , 5 8 @ MAH V
+ABA X
+ABM | Id
+C I
+G 4 FDA }
+ABE d
+ABI D 8 ( FBA D(D0d
+(D ABBB d x L H- ( dr EDG P
+AAF ̏ I}
+J y ( $r EDG ~
+AAH , x @ t ` T p FBB E(D0A8GPP
+8D0A(B BBBH =8G0A(B BBB , EDK
+AAA ` _# ܄ < FBB A(H0?
+(D BBBD T h L | FBE D(D0
+(D BBBD (D EBB ̑ 9 ( FAH u
+ABI d7 4 H FW
+C h <W EQ
+J `
+H xX @ Lr
+B S EL 4 ̒ MAE a
+ABI t
+ABI EL F Ll
+H A @ D EL \ HF Ll
+H A | x EL D |? MAH e
+ABB r
+DBH G
+ABF 4 t FAA _
+ABF }
+ABH 4 ] FAA @
+ABE EAB P 4B E^
+E U p dB E^
+E U @ V EDG {
+AAC l
+DAO B
+AAD Ԕ ( 1 KDG IGB 2 ( 8 < FBA D(D0^
+(D ABBH x < H 8] FBD D(G0
+(D ABBA l(G ABB ؕ L4 x , t FDD B
+ABE 0 D - X l 4 H ] FBD D(G0
+(D ABBA l(G ABB ,4 X , 0 T FDD B
+ABE ` 5 t Id
+C I
+G 4 x FDA }
+ABE d
+ABI 4 H H \ FBD D(G0
+(D ABBG (G ABBL L FEE D(A0e
+(D BBBD 1(D EBB ` FEB B(A0D8G@
+8A0A(B BBBE 8G0A(B BBB ` \ ( FBB B(D0D8GP
+8A0A(B BBBH 8G0A(B BBB ` & FBF B(D0D8JP
+8D0A(B BBBH I8G0A(B BBB ` $ ` A FBB E(D0D8D@R
+8A0A(B BBBI 8G0A(B BBB ` L! FBB B(D0D8G@X
+8A0A(B BBBC 8G0A(B BBB H FED A(G0G
+(D ABBI _(G ABB H 8 ] FBD D(G0
+(D ABBA l(G ABB 7 ( FDG e
+DBD ( ؛ FDG n
+DBK 8
+ FBA D(D0M
+(D ABBI < @ T
FBB A(H0
+(D BBBA < FBF A(G0%
+(D BBBK 8 N FBA D(D0
+(D ABBF ( k FDG ]
+DBD , ( FDD B
+ABE X |d EN
+E Ԝ X Xx Lr
+B S EL 4 { x EAG X
+AAI DAA T EL 4 _M BBA A(D0|(A ABB < w MAL
+ABA t
+ABI EL 8 MAL S
+ABH t
+ABI ܞ x EL < T | v MAL
+ABD t
+ABI 8 EL 0 v MAL
+ABN EL 0 @v MAL j
+ABI ؟ < EL @ F Ll
+H A p EL 0 t F Ll
+H A P EL 4 l FAA _
+ABF }
+ABH @ @ EW
+D [ Ġ ` B E^
+E U H FBA A(G0o
+(A ABBJ D
+(E ABBJ L 0 FBA A(G0
+(A ABBI D
+(E ABBJ L "J FBA A(K0&
+(A ABBG D
+(E ABBJ L С &: FBA A(K0
+(A ABBD D
+(E ABBJ 8 ) FAA
+ABB E
+ABH L \ 8* FBA A(G0
+(A ABBD D
+(E ABBJ -8 4 - EDG `
+AAF l
+AAJ |. x. Id
+C y
+G 8 0 . FBA D(D0T
+(D ABBB , l h1 FDA O
+ABC 1 ( 11 KDG IGB 8 8 2n r FAD f
+DBI [
+DBA H
+ ҉_ q 0 $2 D 2 8 2n q FAD f
+DBI [
+DBA
+ f_ tq 82 42 8 0 02n <q FAD f
+DBI [
+DBA @
+ _ q ( L2 < H2 8 D2n p FAD f
+DBI [
+DBA
+ _ p `2 \2 8 ( X2n lp FAD f
+DBI [
+DBA 8
+ "_ <p t2 4 p2 8 l2n p FAD f
+DBI [
+DBA
+ _ o 2 2 8 2n o FAD f
+DBI [
+DBA 0
+ J_ lo 2 , 2 8 2n 4o FAD f
+DBI [
+DBA
+ ކ_ o 2 2 8 2n n FAD f
+DBI [
+DBA (
+ r_ n 2 $ 2 8 2n dn FAD f
+DBI [
+DBA
+ _ 4n 2 2 8 2n m FAD f
+DBI [
+DBA
+ _ m 2 2 8 2n m FAD f
+DBI [
+DBA
+ ._ dm 3 2 8 2n ,m FAD f
+DBI [
+DBA
+ _ l 3 3 8 3n l FAD f
+DBI [
+DBA
+ V_ l | (3 $3 8 3n \l FAD f
+DBI [
+DBA
+ _ ,l <3 83 8 | 43n k FAD f
+DBI [
+DBA
+ ~_ k t P3 L3 8 H3n k FAD f
+DBI [
+DBA
+ _ \k d3 `3 8 t \3n $k FAD f
+DBI [
+DBA
+ _ j l x3 t3 8 p3n j FAD f
+DBI [
+DBA
+ :_ j 3 3 8 l 3n Tj FAD f
+DBI [
+DBA |
+ _ $j d 3 x 3 $ 3I Eg
+D I
+G H ( 3 EAG n
+AAC p t4 FBH E(A0
+(D BBBF o
+(A EBBG A
+(A BEIF k(A BBB @ T 5% FEB D(A0N@
+0D(A BBBC H 7 FBE F(A0D8G@
+8D0A(B BBBD p : FBH E(A0
+(D BBBF o
+(A EBBG A
+(A BEIF k(A BBB H X l;4 FBH F(D0A8G@
+8D0A(B BBBE , `= FED \
+EBF , ԯ = FED Y
+DBB , > FED Y
+DBB , 4 > FED
+ABD , d 0?C FED
+HBH , P@ FED S
+HBD , İ A FED P
+EBJ , B FED m
+HBJ , $ C FED
+HBJ H T D FBE B(A0A8R`F
+8D0A(B BBBJ H $Gr FFB E(D0H8Gp
+8D0A(B BBBA H XP FBB E(D0D8H`
+8D0A(B BBBI H 8 Ts FEB B(A0A8R`
+8D0A(B BBBE H @V FBE B(D0A8NP
+8D0A(B BBBD H в X FBE B(D0A8NP
+8D0A(B BBBD H H[ FBE B(D0A8NP
+8D0A(B BBBD @ h ]D FBB D(H0J@
+0D(A BBBB @ b FBE A(A0N@U
+0D(A BBBC H De| FIB E(A0H8G`
+8D0A(B BBBH ( < xm EGG0j
+AAA L h m FFE B(D0D8K}
+8D0A(B BBBH H <w FIB B(H0D8G`Q
+8D0A(B BBBD H z FLB B(A0C8Gp!
+8D0A(B BBBA L P 4: FBB H(H0A8K
+8D0A(B BBBH @ $/ FBE A(A0N@
+0D(A BBBB D FBB E(D0D8D@c8A0A(B BBBL @ d FBB A(D0w
+(A BBBC m
+(A BBBL < v FBB A(D0
+(A BBBA @ 8 <j FAD E
+ABE O
+ABF 4 p H l D \ x FBB E(D0D8D@c8A0A(B BBB( 1 KDG IGB L , ԍ _ FBB B(A0D8Gp
+8A0A(B BBBH x
+w_d _ P |I _ FBE D(G0}
+(A ABBE I
+(A ABBA 4 w_" e_ ` D l FBB E(D0D8D@c8A0A(B BBB D FBB E(D0D8D@c8A0A(B BBBL \ FBB A(D0m
+(A BBBE H
+(A BBBI h , d FDA N
+ABD ( 1 KDG IGB , 0 $ FDA N
+ABD ` ԕ t ЕM EDG z
+AAD V
+AAH V
+AAH V
+AAH V
+AAH R
+AAL O
+AAO C
+AAK C
+AAK C
+AAK
+AAO 8 xc FDA C
+ABG ^
+ABG X ( l 1 KDG IGB D ș FBB E(D0D8D@c8A0A(B BBBP FBB A(D0
+(A BBBD
+(A BBBK H L \ FBB A(D0\
+(A BBBF
+(A BBBJ ( $ D Լ 0 FBB E(D0D8D@c8A0A(B BBB< x FBB A(D0
+(A BBBA \ Ȣ ( p Ģ1 KDG IGB ( آ1 KDG IGB Ƚ D ܽ FBB E(D0D8D@c8A0A(B BBB< $ @f FBB A(D0
+(A BBBF d p ( x l1 KDG IGB D FBB E(D0D8D@c8A0A(B BBBL Ԩ FBB A(D0y
+(A BBBA t
+(A BBBE P ( d 1 KDG IGB D FBB E(D0D8D@c8A0A(B BBBd H
X FBB F(A0H8KP
+8A0A(B BBBD
+8A0A(B BBBF Ը \o_W W T ȿ W MBF A(D0
+(A BBBG
+(A BBBB 3 Do_ W L 8 ȱ W FBB B(A0D8GP
+8A0A(B BBBH o_ W @ W MBF A(D0
+(A BBBD 4 o_ iW 4 t EDG j
+AAD Z
+AAL , FDA N
+ABD 0 ( D 1 KDG IGB h p D EDG
+AAI C
+AAK C
+AAK C
+AAK C
+AAK
+AAO < 8 V MAH J
+ABE ^
+ABG D n_ U < U MAH P
+ABG d
+ABI n_ U ( X1 KDG IGB ( l1 KDG IGB @ @ @U MBB E(D0W
+(A BBBH <7 n_A
+U @ `G H> \ p
+ ܿz AV
+I Z
+F 4 h 8c .T EAG A
+AAH DAA D p EL ` t] FG
+C ] FI
+A ] FG
+C H 4 FBA A(G0o
+(A ABBJ D
+(E ABBJ H FED A(G0
+(D ABBG w(G ABB X < R FBE D(K0
+(A ABBA T k_ R ` \D
+ Ij
+E
+G , $ FAD b
+ABH T ` h \p | 8 n Q FAD f
+DBI [
+DBA 0
+ j_ Q 8 h n `Q FAD f
+DBI [
+DBA x1
+ Lj_ 0Q ` t 8 n P FAD f
+DBI [
+DBA 1
+ i_ P H @ FIB E(A0D8GPG
+8D0A(B BBBA , P ; FED
+GBH < / FBB A(D0
+(A BBBD D FBB E(D0D8D@c8A0A(B BBB 0 4 D D @ FBB E(D0D8D@c8A0A(B BBB< FBB A(D0
+(A BBBJ h ` HL LJ FBA A(K0
+(A ABBG D
+(E ABBJ ( H L1 KDG IGB L t `" FBA A(G0
+(A ABBD D
+(E ABBJ L @ M FBB F(A0H8KP*
+8A0A(B BBBH f_ M 0 * M MAL x
+ABK ` EL < * NM MAL
+ABC t
+ABI EL H N FFE E(A0H8Gp
+8D0A(B BBBI H $ 7 FEB H(E0C8KpG
+8D0A(B BBBD ` p ` FBB B(D0D8GP.
+8A0A(B BBBE 8G0A(B BBB L FEB D(D0
+(A BBBE (G BBB < $ FBF A(G0
+(D BBBH < d M FBA D(D0
+(D ABBE
+ ] FG
+C ( e FK
+G H z AV
+I Z
+F l x , p &J EAK P
+AAA P c_ I 8 ` n I FAD f
+DBI [
+DBA p9
+ 8c_ I X , l ( 8 $n rI FAD f
+DBI [
+DBA 9
+ b_ BI @ < 8 D D FBB E(D0D8D@c8A0A(B BBB< X / FBB A(D0
+(A BBBB | , xo FAD l
+ABF D FBB E(D0D8D@c8A0A(B BBB L `
+ H 8 h FAA
+ABF }
+ABH H P
+ FBA A(K0x
+(A ABBE D
+(E ABBJ < L G MAE s
+ABG t
+ABI 0 t EL xg F LO
+E p EL H k FEB E(A0H8Jp
+8D0A(B BBBA H FBE I(A0D8G`
+8D0A(B BBBA L $ P< FBB A(D0
+(D BBBG (G BBB ` t @ J FBB B(A0D8GP
+8D0A(B BBBB U8G0A(B BBB 8 , FBA D(D0y
+(D ABBE < [ FBI A(E0
+(D BBBD T C D < D FBB A(D0Gp
+0A(A BBBA ]_ ~D 4 lV FFD D(G0t(A ABB ( ; FDD iAB 8 , FEA E(G0J
+(A ABBH 0 h L EDG Y
+AAE xDA( EDG h
+AAF L | FED D(D0G
+(A ABBD I
+(J ABBH \ FED D(D0{
+(A ABBH I
+(J ABBH I
+(D AFBJ x L `2 HW 0C lk E]
+N A
+G d 8 ZB FBB B(D0A8DP
+8G0A(B BBBO
+8A0A(B BBBH d[_ B 4 \ EAG f
+AAK i
+AAE r EP
+K t
+D r Qb
+M A
+G p $ | _D ^AFp @ t`2 HW \ v \ p d OBE E(D0C8GPC8F0A(B BBBEFP $`2 HW
< Hs > Hl
+D A 8 C ( L EAG k
+AAF $ | l ? QQ
+F s
+E L Y_ ? L ? FBB B(D0D8D@2
+8A0A(B BBBL L X_ ? 8 " FBA A(D0_
+(A ABBE \ ` p `2 HW H"
+ D"
@" <"d EG
+D "S "[ L_
+E # H , (# FBB B(D0D8DPD
+8D0A(B BBBG x `2 HW @ p$r FBB D(E0GP
+0A(A BBBD %; Qi H %; FBB F(E0A8G`
+8D0A(B BBBB 8 @ &~ FBB A(D0g(A BBB X | ' FEB D(J0i
+(D BBBB s
+(D BBBC D(D BBB @ \' EGD0
+EAD D
+AAJ TAA @ 8( FGD D0
+ EABG l
+ DABM ` )9 t P_2 HW ) )
+ ) ) EL < @ ) ; FBD D(GpN
+(D ABBB O T_ ; 4 ) v; FBD F`Z
+ ABBG t7 zT_ X; L H*6 H; FEE B(D0A8D
+8A0A(B BBBA "T_ ; +
+
+
0 +* ET L (+' 4 D+ : FAA G`S
+ AABA h} vS_ x: 4 + h: FAA D`x
+ AABH , D l,H 4: MDI ^
+ABA @ S_ : 0 t, EL L x,# , ,L 9 FKG c
+BBA R_ 9 4 , 9 FAA G`S
+ AABA ~ ^R_ ~9 4 T <- n9 FAA D`x
+ AABH -- :9 L\ P - EL l - EL - , -H 8 MDI ^
+ABA Q_ 8 _2 HW -
-
$ -
8 -
( -> 68 EEG bEA x - EL -* LY -2 La D . FBB A(A0G I l
+0D(A BBBA p. ( l.
4 h. J7 FAC G`E
+ AABF D O_0 W7 4 0 G7 FAC G`J
+ AABA O_0 Z7 1B Li
+K A 1 EL 4 t 1 7 FAD F`X
+ AABC nO_ 6 h 2B | 22 QZ
+E A4 2 6 FAA G`H
+ AABE N_ 6 \ h3 FBA A(D0Q
+(D ABBH I
+(D ABBF i
+(A ABBI L 3A ` $42 QZ
+E A D4) ( `4s ML lAJH YA 4B 5 Ln
+F A 4 EL 4 4 4^ FDD r
+FBH JAB @ L 56 FBI D(D0GP
+0A(A BBBB _2 HW 2M_' 5} F_
+K D < 46H 4 FBB A(D0G
+0A(A BBBH p L_# [4 @ $8 ( T 8 EAD x
+AAD 8O Hx
+H P 8c 3 FSB B(A0D8Dl
+8A0A(B BBBD L :L_K 3 8 > FIA D(M
+(D ABBK ( D P@ EDG0G
+AAG 0 p A FDG D0|
+ LABH 0 A PEG F
+ DABA ( |B FDD x
+GGL 8 C FBA D(D0h
+(A ABBA L C 2 FBB B(A0D8J
+8A0A(B BBBD J_# P2 F ( F EDD0h
+DAF 8 D <G 2 FGD \
+ABH ]
+AIA L vJ_ 1 H < G FBB B(A0A8QP
+8A0A(B BBBC D H< q1 FEB A(A0GP
+0A(A BBBA I_% >1 _2 HW $ zPLR xE 4 I 0 FIA GX
+ AABE d zI_ 0 8 | I: FBA A(G@x
+(D ABBF 0 Kn ECG B
+CAK GEA L EJ0
+DC L $ Mu 8 \N 0 L N{ EHJ B
+AAE XAAH 4OV FBB B(D0E8D`
+8A0A(B BBBG H HS0 FBB E(G0E8DP
+8A0A(B BBBH H ,T0 FBB E(G0E8DP
+8A0A(B BBBH ` d U FBB J(A0D8GP#
+8A0A(B BBBC u8A0A(B BBB ` |V FEE E(D0D8DPc
+8C0A(B BBBH }
+8F0A(B BBBA d , X FEB E(D0D8D`
+8C0A(B BBBH e
+8F0A(B BBBD X `Z FHD E(D0_
+(J ABBG T
+(D ABBC U(D ABB P L ZT ;- FBB B(K0D8G
+
+8A0A(B BBBH L wt , FBB B(A0A8N
+8D0A(B BBBE Б@ H r
+A L FEH B(A0E8Ds
+8D0A(B BBBA t L FEH B(A0E8Ds
+8D0A(B BBBA d L x FEH B(A0E8Ds
+8D0A(B BBBA _2 HW ` 4 LEB E(A0A8D@
+8A0A(B BBBB HH@8 X \ FBA A(D0w
+(A ABBE ( U FDG h
+DBA 0 D^F ) \ + QBA A(G0(A ABBHH0D
+(D ABBK h ) \ | # QBA A(G0(A ABBEH0D
+(D ABBK ؠ) \ # QBA A(G0(A ABBEH0D
+(D ABBK P ġ) \ d + QBA A(G0(A ABBHH0D
+(D ABBK ) \ ̢# QBA A(G0(A ABBEH0D
+(D ABBK 8 L L FBA A(G0S
+(A ABBF S
+(A ABBG H
D @ L $ X E`
+K x
+H _ 4 Ф EDG r
+AAD iAA ` L K QEB B(A0A8G@
+8A0A(B BBBJ PH@ H FBE E(A0A8F`N
+8A0A(B BBBF 4 ` TZ FAA H
+ABE ~AB 4 |r FAA _
+ABF ~AB H Ĩ FBD A(D0`
+(D ABBF H(D ABB _2 HW 4 $ H Q ImB OAA p 4[ L_
+E x EL | K < ĩ FBB G(D0
+(D BBBA D L FON D(A0G
+0D(A BBBH 8 X FBD A(D0]
+(D ABBI 4 s # FAA G0E
+ AABH <_ # 0 L_2 HW p_2 HW , _: Hq D _2 HW \ Ȭ_ p O PO _2 HW _2 HW , ( X " FRD`U
+CBA ;_ y" _2 HW , _2 HW D p H \ 2 p $ EG@
+AA L ! FBF B(E0D8GP=
+8A0A(B BBBI L ` OBA D(G0C
+(A ABBJ pH0 L 4 OBA D(G0C
+(A ABBJ pH0 T ! FBB A(A0F
+(A BBBG 1
+(A BBBH 4 8 FAA G@
+ AABH L p B FBB B(A0A8J
+8A0A(B BBBJ 0
+ 89_z ~ n FBB B(A0A8JQ
+8D0A(B BBBJ RPAzNQB4TSBTTBMOB+TSANPANQB D^ 8_ < BBG A(DP
+(D ABBH |u
+ J;_ L $ t LBB B(A0A8JW
+8A0A(B BBBA t :_ h 0 d+ H^ P |p
A FBE E(A0A8J(
+8A0A(B BBBH ,Z $;_| 0 s FCH R
+ABA T<_= ] , @ T a ET zR x ;_ 2 HP
+A ' 4 FDA g
+ABK a
+ABD $ zPLR x0 L Y FBB B(D0A8Dp@
+8D0A(B BBBF :_Y < 0 b FEA I(F0t
+(A ABBA K :_ | < e l FEJ A(C0x
+(A BBBA u :_ 8 , E ( FEA h
+BBA P:_ ` ( FBD A(D0N
(D ABBH F
(C ABBJ a
-(D ABBA , LJ F BDI `
-ABA 4K 0 n AL@_A TK 4 AS [D \ J # = BBB A(D0G`u
-0D(A BBBF K
-0D(A BBBA K p K x8 L A L L |K w BBB B(A0A8G`
-8D0A(B BBBA L 2 Gj L ( L 0 A
-A L C < L s BBE E(A0X
-(A BBBA L ,M v BBB B(A0A8Gp8A0A(B BBB L L _ BBB B(A0A8Je
-8A0A(B BBBE M H
-! A_ < M w BBG A(G@
-(A ABBG L ,N x BEB A(D0l
-(A BBBG
-(A BBBA L M
- BBB E(D0A8Gd
-8A0A(B BBBK T N y BBB B(A0A8J
-8A0A(B BBBG < lN u BHE L
-AEO AAB < N u U BHE L
-AEO AAB < N 0| BEA A(D0a(D ABB < ,O p| BEA A(D0a(D ABB < lO BHE |
-AEO AAB < O e BBD A(D0(D ABB < O ) BHE |
-AEO AAB < ,P
BBD A(D0(D ABB d $Q p2 BBB E(D0E8D@P
-8F0A(B BBBA
-8D0A(B BBGA d Q X BBE B(A0A8FP[
-8C0A(B BBBA W
-8F0A(B BBBA , Q ; BDE lAB d $R 2 BBB E(D0E8D@P
-8F0A(B BBBA
-8D0A(B BBGA L R (? BBB B(A0C8DP
-8A0A(B BBBA l R 8y BBF D(A0G@I
-0A(A BBBI w
-0A(A BBBA D
-0F(A BBBG L R W BBB B(A0A8GPQ
-8C0A(B BBBB L R H BBB B(A0A8G`U
-8D0A(B BBBE d 4S 8
BEB B(F0D8Je
-8A0A(B BBBB S
-8C0A(B BBBA L TT @ BBB B(A0A8Gp8A0A(B BBB d S
k
- BBB B(A0A8GPZ
-8A0A(B BBBK D
-8A0A(B BBBJ T TT m
- BBB B(A0A8JS
-8A0A(B BBBA T T $
- BBD C(G0J
-(A ABBF d
-(D ABBA U % U % U x% l V `x BBF D(A0G@F
-0A(A BBBD v
-0A(A BBBJ D
-0F(A BBBG L tV p BBB B(A0C8D`I
-8C0A(B BBBK < V BBG A(G@
-(A ABBF T LV p$ BLB E(A0A8D
-8A0A(B BBBG D V h/ G BJB K(A0
-(A BBBA D V PM ' BFA Dp
- AABH O
- AABA d W XE BBB B(A0A8DP
-8A0A(B BBBE \
-8F0A(B BBBA l W @B BBB J(D0D8D4
-8D0A(B BBBC i
-8A0A(B BBBE L X N BBB B(A0A8DV
-8A0A(B BBBB L Y 2 BBB B(A0D8G@@
-8A0A(B BBBB T X $ BBB B(A0D8Jc
-8A0A(B BBBD < Y H3 BBD A(DP
-(D ABBA T DY 3x BBB B(D0A8J
-8A0A(B BBBD < Y E BBA D(DP
-(D ABBA T Y EQ BBB B(A0D8J+
-8A0A(B BBBD < 4Z U BBA D(DP
-(D ABBA ,[ H+ Ai L[ X < d[ ` ZBA A(%
- DBFA [ ) 4 [ bGHX
-AG [ ИX \ AS ,\ D\ \\ D0W t\ B , \ ؚK BAC G0w CAB L \ | BAC G0L
- AABC D
- CABF D DAB L ] ( BEE B(A0A8G
-8A0A(B BBBF L \] BBB E(A0A8GP
-8D0A(B BBBA , ] AAG j
-AAA , $] AAJ
-DAA , T] AAJAA <^ 8 G \^ s , t^ _ AAG N
-AAA L ] @ Y BBB E(A0D8GG
-8A0A(B BBBH T <^ S BDB B(A0D8Gx
+(D ABBA L 9_ , <F FEA i
+BBA ` 9_ s Dw EL@_
+AA L B FBB D(A0
+(D FBBC l
+(A MBBA 6 S , @ $6 Kj X L l H E
+D ]
+C V H @ FBB E(A0A8H@_
+8D0A(B BBBK 0 L $ MDE
+UBA L FBB B(D0A8G`v
+8D0A(B BBBE m 7_. 8 BOA A(D0[
+(A ABBH ( |A JDG aAAH ` FBB E(D0A8P@
+8D0A(B BBBJ [
+8D0A(B BBBH 4 FBD DPy
+ DBBG $ zPLR xY+ P , 6_ ( A JDG aAAH H FBB F(A0D8D@Y
+8D0A(B BBBA H H Z PBB B(A0A8DP
+8A0A(B BBBF ` FBF B(A0D8G@
+8D0F(B BBBA ]
+8F0A(B BBBD @ FBB A(A0D@
+0A(A BBBD ( < (C FAA wAB ` h L FBF B(A0D8G@
+8D0F(B BBBA ]
+8F0A(B BBBD 8 ^ FBA A(D@
+(A ABBA L d O FHE E(A0A8D
+8A0A(B BBBD 44_ H p
I PBE B(A0A8G`
+8A0A(B BBBJ P FFB B(A0A8D
+8A0A(B BBBI 3_ p P ` FBB B(A0A8G6
+8A0A(B BBBK L hx @ FBB B(A0A8JS
+8A0A(B BBBK
+ 2__
+ 7 T b P X
+ FBB B(A0D8GV
+8A0A(B BBBH Ѓ 2_~ ,
+ ! EDDP~
+AAC 3_ ` d# FEE A(G0O
+(D BBBG O
+(D BBBG \
+(D BBBA $ zPLR x% 0 , t2_* ! T # h # | # L $< FBB B(A0A8Gq
+8A0A(B BBBH 8 < )} MAE P
+IBJ AAB 8 x 8* FBA D(D0j(I ABBL * S BBB B(A0A8GU
+8A0A(B BBBH t
+ 21_z 2 L
/` " FBB B(A0D8Gp
+8D0A(B BBBD h D1_ ` ( 1; FBB B(A0D8G@n
+8A0A(B BBBH
+8A0A(B BBBH P
3 m FBE E(A0D8I
+8C0A(B BBBF < < < b FBD A(D`
+(D ABBA L0_e / < <# P < d = x > H?G H ?7 BJJ E(A0F8G
+8A0A(B BBBI 4 L t@ g FDA G
+ DABA p /_> 9 @ A K
+A \ /_' p Au , B\ FAA K
+ABA L @B8 FBB E(D0C8J
+8A0A(B BBBA
+ 4/_{ P x G FDB B(D0A8G)
+8A0A(B BBBC D/_ L W FBB A(D0
+(A BBBK
+
+(D BBBL 4 4 Z\ EF
FED.^
+ H j/_2 h , ([ @ $[ T [ h [ | 0/_' [% AA^ L [ BBB B(A0A8DP[
+8D0A(B BBBJ | ._ L p \ BBB B(A0A8DP[
+8D0A(B BBBJ ._ X | (^ $^ 0^L 8 l^s FGA A(D0U
+(A ABBB H ^0 FBB B(A0D8DP
+8D0A(B BBBE @ _ T _
h h _ NBA A(G0W
+(A ABBJ D(A ABBN0Y
+(C DBBD \ 0` FEA A(G0R
+(A ABBD J
+(N ABBC D
+(D ABBE L 4 ` FBE I(D0y
+(A FBEG Y
+(D BBBE @a" EY L Ta FEE I(D0{
+(A FBEJ A
+(D BBBE 4 ac EID n
+FAI D
+DAE \ ( a FBD A(D0H
+(A ABBI Z
+(G AFBN d
+(D ABBE 4 lbQ FID f
+ABD A
+DBE 4 bi FID s
+AFK A
+DBE ` b FBB B(D0A8D@
+8G0A(F BEBD D
+8D0A(B BBBE $ \ Xcu EAG eAA4 cc FKD f
+ABB _AB c dO J d @ d FBB A(D0DP{
+0A(A BBBJ D `e~ , X e OAG
+DAI P |f' EZ < f> # FBA D(G0
+(A ABBJ L @ g FEB B(A0D8KP
+8D0A(B BBBI (_& P (i FBB B(D0A8GA
+8A0A(B BBBE l
+ z(_y L lB FBB B(A0A8G
+8A0A(B BBBH 8 d r
MAA
+IBJ AAB D s EQ @ s 3 FIF A(A0e
+(A BBBA '_2 P u ' FBB B(A0A8G
+8A0A(B BBBF
+ (_y x ( \y FBB E(D0A8GP
+8D0A(B BBBA
+8C0A(B BBBH x
+8A0A(B BBBF @ p{ LDA H
+ABD HABEH {A aY @ | FFE A(A0Rp
+0A(A BBBA H H |K FBB B(A0A8D`
+8A0A(B BBBE P | FBB B(A0D8G
+8A0A(B BBBH t
+ D'_ ` [ FBB B(A0D8G@w
+8A0A(B BBBG
+8A0A(B BBBG P ! 2 FBE E(A0D8N
+8A0A(B BBBJ P ( ' FBB E(D0A8I
+8D0A(B BBBF X '_ 8 L d &_' h FBF B(D0A8Gp
+8A0A(B BBBJ B
+8A0A(B BBBA < &_ _ 0 X ؙb P FAA J
+ABA X5 '_ ( 0 X FAA @
+ABA 5 &_ < FAA S
+ABB a
+ABA 5 &_* $ X EDD zAA, p ~ EAG AA P&_ R \ ' K[ &&_ : Qh 8 %_ HT 8 %_ P EJ 0 ! ԛ EDG M
+DEJ \AA zR x \%_ d! ! x! A ! X EG@
+AI ! ) JT , ! UD
+AE DAK P X! FDI B(A0D8I
+8A0A(B BBBH
+ b$_ ~ D h" ]ED D(F0(A ABBAA08 " p FEE D(D0P(A BBB L H" FBE B(A0D8G
+8A0A(B BBBH , #_ < " > FBA D(G0
+(A ABBJ L " FBB E(A0D8GP
+8A0A(B BBBF H # D FDI E(D0C8G
+8A0A(B BBBE $ zR x , f#_ L # h FBB B(A0A8J{
+8D0A(B BBBH < #_ ( $ д! ID
+AJ OA@ % Եi REB E(A0=(A BBBE H D% 8 PBB B(A0D8Dp
+8D0A(B BBBK zR x p ( "_ P $%
FBB B(A0D8G
+8A0A(B BBBE ."_e " 4 % @ FED K0S
+ DBBA d
+ D#_ P % ] BEB B(A0A8GZ
+8A0A(B BBBH Ć "_ & t
' p ' l ,' h P & tM BEB B(A0A8GB
+8A0A(B BBBH B#_ ' X
' T ' P ' L D X' XV X FBB G(A0D@p
+0D(A BBBG # #_ P ' X
+
+ FBB B(A0A8J
+8D0A(B BBBE (
+ L#_ L $( | FBB B(A0D8Gl
+8D0A(B BBBG @ #_ 0) FBB B(D0D8Gpu
+8A0A(B BBBF lxPNBIpMxMNBIpxFFBIpZxENBIp ) 8#_' 4 ) D FDA
+ABL AAB * * E[
+H A L ) ^ FBB B(A0C8GP^
+8A0A(B BBBA < ) " y FJE A(G0
+(A ABBA pt d"_) C * * E[
+H A P \* i FBB B(A0A8G
+8A0A(B BBBA "_ L * { FBB B(A0A8GK
+8A0A(B BBBF ,"_ ` 0+ FBB B(D0A8KHG^A
+8A0A(B BBBC <"_ ` + d FBB B(A0A8GmL_An
+8D0A(B BBBG p D"_n 4 (, ; EC
BEIKDn.|
+ G b#_ - Z#_' 0- t b H- RBB B(D0D8GPL
+8D0A(B BBBH ^
+8A0A(B BBBH
+8A0A(B BBBD L (- "
+ FBE B(A0A8G
+8A0A(B BBBH
+ "_ L - , FBB B(A0A8J
+8D0A(B BBBE "_ . 88 . 9 . 9" . 92 8 . 9 BBA A(D@
+(A ABBG (/ :7 0 . : 0 FAA s
+ABB p/ ;m / ;c / <<B Me
+F E / l< / h<; H </ < FBA d
+BBG n
+BBF D
+EBE "_ W D0 =c ` X0 |=M FBB B(A0A8D@
+8A0A(B BBBD D
+8C0A(B BBBH < 0 h>- FDA I(G0Z
+(A ABBE z X!_* , p0 @?G FEI ^
+BBE :!_ y \1 H?" p1 d?" 0 1 ?C EDD K
+FAI XAA P 1 ?
FBA D(D0m
+(G ABBF D
+(F ABBE { _* , 1 ?Q FID d
+GBE
+ j _ L 1 @ FBB B(A0I8GP
+8G0A(B BBBL <+ _* e d 02 PA U FBB D(A0f
+(A BBBD b
+(G BBBI
+(D EBBI P _? D T3 `B FBB B(A0A8DP8D0A(B BBBL 3 B FBB B(A0D8G
+8A0A(B BBBH ` 3 H FBB B(A0D8D`m
+8A0A(B BBBD
+8A0A(B BBBH L P4 J FBB B(A0A8D
+8A0A(B BBBF H 4 L FBD A(D0V
+(D ABBH Y(D ABB P H4 (M w FBB A(D0
+(A BBBE ]
+(A BBBD T _ 6 d 4 M & FBE B(A0A8D`
+8A0A(B BBBK \
+8A0A(B BBBA _ , 45 \Ok FAG A
+IBE 8
+ \ _ ( 6 O EN h
+IL }
+AB L6 HP, Eb @ h6 \P FBA ~
+BBE O
+BBM RBB 6 PN Ed
+G ] H 6 Q7 FBB B(A0D8Q`
+8A0A(B BBBD P t6 Sa FBB B(A0A8G0
+8C0A(B BBBG 8 _w p P 6 W| ` FBB B(D0A8G{
+8A0A(B BBBC H _y P L L7 $_J @ BBB B(A0A8G
+8A0A(B BBBH + _w # @ X8 d FHM z
+IBH ^
+GBE N
+ABA D 7 df BBE D(D0D
+0A(A BBBG ( zPLR x 0 _ p L (9 lfo FBB B(A0A8D
+8A0A(B BBBA P 8 i% FBB B(A0A8G9
+8A0A(B BBBH (7 ._ 8 @9 Pq} MAE P
+IBJ AAB 8 |9 q FBA D(D0j(I ABBL 9 q` BBB B(A0A8GN
+8A0A(B BBBG x _y b H : u BBB B(D0D8D`W
+8D0A(B BBBH P ; { FBD W
+^BE M
+BBG Y
+BBC `
+BBD \ d; 0| FEE E(A0D8G@h
+8A0A(B BBBE P8C0A(B BBBL ; | R FBB B(D0A8Gl
+8A0A(B BBBB < _* " L ; H} FBB B(A0D8G
+8A0A(B BBBA _} L < ` FBB B(A0A8G
+8A0A(B BBBH P @< FFB B(A0A8G
+8D0A(B BBBA ` _ L < FEE E(A0D8FpM
+8D0A(B BBBF $ _{ } P = |
+ m FBB E(D0A8I
+8D0A(B BBBD d4 _ [ $> 8> , Ef P T> Ч KEB B(D0A8H@t
+8A0A(B BBBA P > < > ب > > > ? ? 4? H? W > XB Ep
+K A ? EG@
+AA ? ` < ? l FBA A(D`
+(A ABBA t _! @ 4* E[
+H A 0@ DW H0
+I @ ? E M FBE A(A0
+(A BBBA @ ? FEB D(D0e
+(A FBBA @ X H v
+B W P P@ @g FKB B(D0A8Go
+8A0A(B BBBF 7 _5 y H `A D PLB B(A0A8DP&
+8A0A(B BBBJ P A غ FHE E(D0D8D+
+8A0A(B BBBG L \A FBE E(A0D8Gp
+8A0A(B BBBF H PB : PBB B(A0A8DP
+8A0A(B BBBB L A K FBE E(D0D8G`u
+8A0A(B BBBH H B H FBB E(A0D8Dp&
+8A0A(B BBBH D 8C FBE E(D0D8GP8A0A(B BBBL C d TEE F(A0A8G@F8A0A(B BBBC4 ,C Y + EC
Pv
+ B .V. ( _ u $D 8D LD `D tD D _' 8 D SDG
+AAI D
+FAE p, 4D l} FED@]
+BBA P 0_ D |D } FBD V
+BBJ f
+EBC f
+EBA zPLR x ( _ ' 0 E $ MDE
+UBA P 4E FBB B(A0A8G=
+8A0A(B BBBD ! 8_w P E @ FBB B(A0A8G=
+8A0A(B BBBD d" D_w L F B FBB B(A0A8G
+8A0A(B BBBH 8 \F } MAE P
+IBJ AAB 8 F } h MAE P
+IBJ AAB 8 F <} 0 MAE P
+IBJ AAB 8 G FBA D(D0j(I ABB8 LG FBA D(D0j(I ABB8 G ( FBA D(D0j(I ABBL G | P FBB B(A0A8Jk
+8A0A(B BBBC $ _z . P ,H $ FBB E(A0A8JG
+8A0A(B BBBD _ L h H * = FBB B(D0A8D@
+8D0A(B BBBG D
+8D0A(B BBBG &_ P I D FBE B(D0D8GU
+8D0A(B BBBH _ L I % FBB B(A0D8G
+8D0A(B BBBF H& :_ ` J P [ FBB B(A0D8G@w
+8A0A(B BBBG
+8A0A(B BBBG P TJ L FBE E(A0D8J
+8A0A(B BBBE P J ~ FBB B(A0D8Do
+8D0A(B BBBG `5 _) = K x K t2 H K 2 LBB B(A0A8D@
+8A0A(B BBBE X 0L BGF r
+DBI j
+FBN i
+FBO D
+IIJ D
+IIJ D L X BDC j
+HBK M
+FBK W
+ABE 8 0L } MAE P
+IBJ AAB 8 lL 4 FBA D(D0j(I ABBL L FBB B(A0D8G
+8D0A(B BBBB _~ P M "9 FBB B(A0C8J
+8C0A(B BBBA x _ L |M = FBB E(D0A8Dps
+8D0A(B BBBH 5 >_ g N ? N ? H N ? BBB E(A0C8D`
+8A0A(B BBBA L O A FBE B(A0D8G
+8A0A(B BBBA h PO DE RNE B(A0A8DPr
+8A0A(B BBBG
+8F0A(B BBBE d O F! RNB B(G0C8G@
+8A0A(B BBBH D8G0A(B BBB $P G( HL S @P G TP G hP G
|P GB P G P H* FW
+C I P H! MOD P $H P 0H Q ,H Q 8H H 0Q 4H[ FEB E(A0D8GP&
+8A0A(B BBBB |Q HI? 4 Q tIn ZAG Y
+ABB cDB 4 Q In ZAG V
+ABE cDB 8 R I JDD cAAIH nIA8 <R 8J FBA A(G0Y
+(D ABBA H xR JM FBB B(D0A8Ipz
+8A0A(B BBBB R Nt R N| L R hO FBB B(D0D8Gh
+8D0A(B BBBH <S Q $ PS Ql E[
+H DL QAD xS <RB FBD D(D`H
+(D ABBK {hLpShA` ( S DS] HWN QAH
+I S xS0 T S HI T S' ,T Sh FMG LT _2 HW $ dT S] EF |
+DE IC4 T (TW FGD C(G0r(D ABB T PTs lF , T T FAA
+ABH U DVv cQ
+D } 4U V
L HU Vt BBB D(A0o
+(A BBBG
+(A BBBI 8 U W BBA E(J0m
+(D ABBF H U X FBB B(A0A8D@Y
+8G0A(B BBBE V DYA El
+G D DV pYy Oe \V Y EU |V Y L V Y FBB B(A0D8DY
+8A0A(B BBBA $ V p]@ HI MAH
+I K L hV ]j BBB B(E0A8J@
+8A0A(B BBBA L V ^ BBF B(A0A8J`g
+8A0A(B BBBA W x`% w D` , $W `\ _ BAK0}
+ABA E _ 7 X ` AW ,X ` AW , W `\ BAK0}
+ABA F _ X ` X _/ A X ` AC
BL $ zPLR x , _& F ,Y 8a AS
C $ LY a AW
G
+ A < tY bj BEB F(A0
+(A BBBG Y c Y c Y c AL }} }} }} }} }} | }} t h ) }} \ P }} H < ) }} 0 $ }} / }} }} "M }} }} }} }} # }} x l }} d X }} @ 4 }} , }} }} B ), }} } }} } %9 }} } t }} } h \
+/B }} } < 0 }} } $
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+/B }} } }} }
+/B }} } }} } | p
+/B }} } \ P }} } D 8 I }} } ( }} } Q }} } }} } O }} } }} } N }} } }} } t h
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+(9 }} } }} }
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+
+
+
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-8A0A(B BBBH T L_ BBB A(D0F
-(A BBBH :
-(E BBBH L ^ BEE B(D0D8G
-8C0A(B BBBF _ P ` ( $` <` L _ BBE B(A0A8D`s
-8D0A(B BBBG L _ BBE B(A0A8D`s
-8D0A(B BBBG ` ` a X $a `A < <a h BGD A(D0T
-(A ABBD < |a BBA D(D0
-(A ABBG a h a ` DM T 4a h
BEB B(A0A8GG
-8A0A(B BBBK \ Db BIH D(G0Q
-(A ABBK s
-(C ABEB I(C AEB L b BEA D(G0O
-(A ABBH E
-(G AFEH L b BEB I(D0^
-(A BFEF a(A BBB Dc ` A\ L dc `q BEE I(D0v
-(A BFEC I(A BBB 4 c _ AID i
-JDK LAA L c BBA D(G0^
-(A ABBD a
-(A ABBA 4 <d E BID a
-ABE IAB 4 td 0] BID n
-EEI IAB \ d X BBB B(A0D8G@p
-8G0A(B BFEE L8A0A(B BBB$ e l ADD `AA4 4e b BNA o
-ABE TCB le 8 e 5 T d W BBB E(D0D8J
-8A0A(B BBBC | e , BBB B(D0C8GP
-8A0A(B BBBH
-8C0A(B BBBD A
-8D0A(B BBBJ D tf BAA L
-ABE G
-ABF K
-ABJ f f B X^ 4 f 0 BPD A(K`Z(A ABB| ,g BBB A(A0GPU
-0C(A BBBB R
-0A(A BBBF o
-0D(A BBBF N0A(A BBB g n 4 g P AAG
-AAB tCA g $ Ab T dg H * BBB B(A0A8J+
-8A0A(B BBBG < g u BHE L
-AEO AAB < g @| BEA A(D0a(D ABB < <h 0 BHH
-AEG AAB 4i AL L h C BEB E(I0H8D@
-8D0A(B BBBA L i 4 BIF A(R0
-(A ABBA K(D ABB d i W BDB B(A0A8Dpq
-8A0A(B BBBE
-8A0A(B BBBA \j T i x K BBB B(A0A8J
-8A0A(B BBBG D j xU OMB L(E0
-(A BBBA D \j M c BFA Dp
- AABH O
- AABA 4 j 8 4 BAA
-ABA L k ` BBB B(A0D8G@
-8A0A(B BBBD T ,k
- BBB E(D0H8J
-8A0A(B BBBG L k W BBE D(C0G
-0A(A BBBG l
- l
L l c f BBE D(D0G
-0D(A BBBK m L ll BBB F(A0E8GPg
-8A0A(B BBBA tm y DM
-A l l BFB B(A0D8G`"
-8A0A(B BBBD V
-8A0A(B BBBH , Lm @ e AAD I
-AAA , |m [ AAD
-AAA < m AAD R
-AAF d
-AAA 4 m b BKD D0
- DABA < $n b 8 BEA D(DP
-(D ABBE $ o
ADD AA, n @+ ADD
-AAA to @% DW
-A $ o P5 AJ
-E ]
-A o h DP
-A o hK AI 4 o ADG Z
-AAP bAA 4p Lp < , dp 0( AG o
-AH V
-AI p 0" DQ
-K A p @ T p 8@ [ BBB H(A0A8Jr
-8A0A(B BBBJ < $q BEE D(D0e(A BBB T p pa BBB E(D0A8GP
-8A0A(B BBBG 4 q ! BED D(F0(A ABB$ q puV ADD JAAL dq u BEB E(I0H8D@
-8D0A(B BBBA < lr (w BDI ~
-AGK K
-ABB L q w 7 BEB E(I0H8D@
-8D0A(B BBBD d r !? BBB B(H0G8Fp0
-8D0A(B BBBF W
-8A0A(B BBBG L r #M BBB B(A0D8GQ
-8A0A(B BBBA L s px BBB B(A0D8GP>
-8A0A(B BBBD t y E
-G < $t z BHD A(D0(A ABB d dt 0) BBB B(K0D8Fpb
-8A0A(B BBBG j
-8A0A(B BBBA T t . BBB B(D0A8Jo
-8A0A(B BBBH < lt ?f BED D(H0m
-(D ABBA T t zZ BHB E(A0A8GP
-8A0A(B BBBD u h? T u p? BBB E(A0A8G
-8A0A(B BBBF ,v E Dv E \v E tv E T u E ) BBB E(A0A8G
-8A0A(B BBBF v K v K w K ,w K L v K BBB B(A0D8I@L
-8C0A(B BBBB T v LA
l BBB B(A0A8J
-8D0A(B BBBH L 4w Y BBB B(A0D8Gk
-8C0A(B BBBE L <x Zm BBB E(A0D8GR
-8A0A(B BBBE L w \ BBE E(A0A8GV
-8D0A(B BBBF , x hc, BDD WAB L Tx hc
- BBE B(A0A8IR
-8C0A(B BBBA \y m AO , |y (~ BDA AB T x ~
- BDB B(A0A8G$
-8A0A(B BBBA T Ly ` BBB B(A0A8G{
-8A0A(B BBBA < y y BED C(G@
-(D ABBC T y s G BBB B(A0A8J
-8A0A(B BBBE z `2 Ap < \z BBB A(C0Z
-(A BBBA < z p } BBA A(G0d
-(A ABBA L z X BEH E(D0D8Gr
-8A0A(B BBBA { 2 Ap T L{ BLE B(A0A8G
-8C0A(B BBBC T { (
- BLB E(A0A8G
-8A0A(B BBBA | j | h T ,| `[ BBB B(A0D8G
-8A0A(B BBBG d <} ' BBB B(A0A8GPe
-8D0A(B BBBM
-8A0A(B BBBB L | G BBB B(A0A8Jk
-8C0A(B BBBE } A , ~ BDD E
-ABF <~ (% L T~ @ BBD A(D@|
-(A ABBA D
-(F ABBA T } BBB B(A0A8G3
-8C0A(B BBBH L D~ H BEB B(A0A8I_
-8A0A(B BBBA L : < ~ BBA A(D@z
-(A ABBA u T H E BEB B(A0A8G<
-8A0A(B BBBF h , (; H^
-J F L H d @S L BDA }
-ABI \
-ABA G
-ABF ̀ (y L , x BBB B(A0D8I
-8A0A(B BBBG d 4 @@ BBB B(D0A8D@l
-8A0A(B BBBI
-8C0A(B BBBA < , BDA D(L0u
-(A ABGI , $ G BDI ]
-ABE ( $ 0 4 < 8B AAG L
-FAL WAA < P b BAA x
-ABI A
-ABJ < < BBD A(L@e
-(A ABBH , < P Q AID c
-DDE T l b BBD D(D0m
-(A ABBE
-(D ABBG D | BND `
-GBE o
-IBL _AB L ă BBB B(A0A8DPj
-8D0A(B BBBK L BEB B(A0A8Gp
-8C0A(B BBBH \ d ` BEB E(D0D8G@i
-8A0A(B BBBH T8C0A(B BBBT BEB B(A0D8G
-8A0A(B BBBG D d | BDE A(G0DP\0A(A BBB 4 d l AAD Y
-DAD v
-DAE d 1
- BBA D(G0
-(A ABGC `
-(A ABBJ S
-(F ABBB l L ; BEB B(A0A8D`
-8A0A(B BBBO C
-8A0A(B BBBA , k Y AAG @
-IAE $ Aw
-H h
-H ̆ x( Ab 4 ADD0|
-AAI }
-GAK $ N Ad
-K ] @ BBE D(A0Gp
-0A(A BBBH o
-0A(A BBBI
-0A(A BBBG d
-0A(A BBBA d ̇ H BGB B(A0A8FI
-8A0A(B BBBH Y
-8F0A(B BBBH L | BBB B(D0A8JG
-8A0A(B BBBH < ̇ \u BHE L
-AEO AAB < ]| BEA A(D0a(D ABB L P]$ BLB B(D0A8D`
-8A0A(B BBBE < T 0_1 BBG A(GP
-(A ABBF L 0!s BBB B(A0A8G
-8A0A(B BBBF L `#1 BBB E(A0C8I
-8A0A(B BBBA T | P%$
- 1 BBB B(A0A8G
-8A0A(B BBBA T ԉ (/+ A BBB B(A0A8G
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_ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceLogSum_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_63kCpuExecutionProvider_ReduceLogSumExp_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_65kCpuExecutionProvider_ReduceLogSumExp_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_64kCpuExecutionProvider_ReduceLogSumExp_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_66kCpuExecutionProvider_ReduceLogSumExp_kOnnxDomain_ver11_12_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ 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_ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver1_10_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver1_10_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver11_11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver11_11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver11_11_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver11_11_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver12_12_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver12_12_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver12_12_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ 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_ZZN11onnxruntime21BuildKernelCreateInfoINS_61kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver11_12_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver1_10_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver11_12_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver1_10_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver1_10_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver11_11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver11_11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver11_11_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver11_11_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver12_12_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver12_12_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver12_12_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver12_12_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver12_12_int8_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver12_12_uint8_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver13_int8_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver13_uint8_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver1_10_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_61kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver11_12_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_61kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver11_12_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver1_10_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver1_10_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_12_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_12_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_12_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_63kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_65kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_64kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver1_10_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_64kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_66kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver11_12_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_65kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver11_12_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_61kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_63kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_62kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ArgMax_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ArgMax_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ArgMax_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ArgMax_kOnnxDomain_ver11_12_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ArgMax_kOnnxDomain_ver11_12_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_ArgMax_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ArgMax_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_ArgMax_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ArgMin_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ArgMin_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ArgMin_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ArgMin_kOnnxDomain_ver11_12_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ArgMin_kOnnxDomain_ver11_12_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_ArgMin_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ArgMin_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_ArgMin_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime16ReduceKernelBaseILb1EEC1ERKNS_12OpKernelInfoEN6nonstd13optional_lite8optionalIlEEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIlNS_19ReduceAggregatorSumIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIfNS_19ReduceAggregatorSumIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIdNS_19ReduceAggregatorSumIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime16ReduceKernelBaseILb0EEC1ERKNS_12OpKernelInfoEN6nonstd13optional_lite8optionalIlEEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime14SetupForReduceEPKNS_6TensorERKSt6vectorIlSaIlEERS5_RNS_11TensorShapeES8_RbPKS9_E19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIlNS_19ReduceAggregatorSumIllEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime9ReduceSumIlE4ImplERKNS_6TensorERKSt6vectorIlSaIlEESt10shared_ptrINS_10IAllocatorEEPNS_11concurrency10ThreadPoolEbPKNS_11TensorShapeEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIfNS_19ReduceAggregatorSumIffEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime9ReduceSumIfE4ImplERKNS_6TensorERKSt6vectorIlSaIlEESt10shared_ptrINS_10IAllocatorEEPNS_11concurrency10ThreadPoolEbPKNS_11TensorShapeEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIdNS_19ReduceAggregatorSumIddEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime9ReduceSumIdE4ImplERKNS_6TensorERKSt6vectorIlSaIlEESt10shared_ptrINS_10IAllocatorEEPNS_11concurrency10ThreadPoolEbPKNS_11TensorShapeEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIiNS_19ReduceAggregatorSumIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIiNS_19ReduceAggregatorSumIiiEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime9ReduceSumIiE4ImplERKNS_6TensorERKSt6vectorIlSaIlEESt10shared_ptrINS_10IAllocatorEEPNS_11concurrency10ThreadPoolEbPKNS_11TensorShapeEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIdNS_31ReduceAggregatorArgMinLastIndexIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIdNS_31ReduceAggregatorArgMinLastIndexIdlEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIdNS_22ReduceAggregatorArgMinIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIdNS_22ReduceAggregatorArgMinIdlEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIiNS_31ReduceAggregatorArgMinLastIndexIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIiNS_31ReduceAggregatorArgMinLastIndexIilEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIiNS_22ReduceAggregatorArgMinIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIiNS_22ReduceAggregatorArgMinIilEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIfNS_31ReduceAggregatorArgMinLastIndexIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIfNS_31ReduceAggregatorArgMinLastIndexIflEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIfNS_22ReduceAggregatorArgMinIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIfNS_22ReduceAggregatorArgMinIflEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIdNS_31ReduceAggregatorArgMaxLastIndexIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIdNS_31ReduceAggregatorArgMaxLastIndexIdlEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIdNS_22ReduceAggregatorArgMaxIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIdNS_22ReduceAggregatorArgMaxIdlEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIiNS_31ReduceAggregatorArgMaxLastIndexIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIiNS_31ReduceAggregatorArgMaxLastIndexIilEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIiNS_22ReduceAggregatorArgMaxIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIiNS_22ReduceAggregatorArgMaxIilEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIfNS_31ReduceAggregatorArgMaxLastIndexIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIfNS_31ReduceAggregatorArgMaxLastIndexIflEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIfNS_22ReduceAggregatorArgMaxIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIfNS_22ReduceAggregatorArgMaxIflEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIdNS_25ReduceAggregatorSumSquareIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIdNS_25ReduceAggregatorSumSquareIddEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ 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_ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_LogSoftmax_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_LogSoftmax_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_LogSoftmax_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_Softmax_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Softmax_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_Softmax_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_Softmax_kOnnxDomain_ver1_10_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_LogSoftmax_kOnnxDomain_ver1_10_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_LogSoftmax_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_Softmax_kOnnxDomain_ver11_12_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_Softmax_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_LogSoftmax_kOnnxDomain_ver11_12_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZNK11onnxruntime7SoftmaxIfE18ComputeImplOpset13ERKNS_6TensorERS2_mPNS_11concurrency10ThreadPoolEPNS_15OpKernelContextEE12__FUNCTION__ _ZZNK11onnxruntime7SoftmaxIdE18ComputeImplOpset13ERKNS_6TensorERS2_mPNS_11concurrency10ThreadPoolEPNS_15OpKernelContextEE12__FUNCTION__ _GLOBAL__sub_I_softmax.cc _GLOBAL__sub_I_softmax_shared.cc _ZZN11onnxruntime2ml21BuildKernelCreateInfoINS0_62kCpuExecutionProvider_LabelEncoder_kMLDomain_ver2_float_stringEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES6_ _ZZN11onnxruntime2ml21BuildKernelCreateInfoINS0_61kCpuExecutionProvider_LabelEncoder_kMLDomain_ver2_int64_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES6_ _ZZN11onnxruntime2ml21BuildKernelCreateInfoINS0_62kCpuExecutionProvider_LabelEncoder_kMLDomain_ver2_string_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES6_ _ZZN11onnxruntime2ml21BuildKernelCreateInfoINS0_62kCpuExecutionProvider_LabelEncoder_kMLDomain_ver2_int64_stringEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES6_ _ZZN11onnxruntime2ml21BuildKernelCreateInfoINS0_61kCpuExecutionProvider_LabelEncoder_kMLDomain_ver2_float_int64EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES6_ _ZZN11onnxruntime2ml21BuildKernelCreateInfoINS0_62kCpuExecutionProvider_LabelEncoder_kMLDomain_ver2_string_int64EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES6_ _ZZN11onnxruntime2ml21BuildKernelCreateInfoINS0_61kCpuExecutionProvider_LabelEncoder_kMLDomain_ver2_int64_int64EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES6_ _ZZN11onnxruntime2ml21BuildKernelCreateInfoINS0_51kCpuExecutionProvider_LabelEncoder_kMLDomain_ver1_1EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES6_ _ZZN11onnxruntime2ml14LabelEncoder_2IlSsEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml12LabelEncoderC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml14LabelEncoder_2ISslEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml14LabelEncoder_2IfSsEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml14LabelEncoder_2IlfEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml14LabelEncoder_2IflEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml14LabelEncoder_2IllEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml14LabelEncoder_2ISsfEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _GLOBAL__sub_I_label_encoder.cc _ZZN11onnxruntime18PoolProcessContext4initERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime14PoolAttributesC1ERKNS_17OpNodeProtoHelperINS_22ProtoHelperNodeContextEEERKSsiE19__PRETTY_FUNCTION__ _ZZN11onnxruntime21BuildKernelCreateInfoINS_49kCpuExecutionProvider_MaxPool_kOnnxDomain_ver8_11EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_47kCpuExecutionProvider_MaxPool_kOnnxDomain_ver12EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_48kCpuExecutionProvider_MaxPool_kOnnxDomain_ver1_7EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_GlobalMaxPool_kOnnxDomain_ver1EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_46kCpuExecutionProvider_LpPool_kOnnxDomain_ver11EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_48kCpuExecutionProvider_LpPool_kOnnxDomain_ver2_10EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_GlobalLpPool_kOnnxDomain_ver2EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_GlobalAveragePool_kOnnxDomain_ver1EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_AveragePool_kOnnxDomain_ver11EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_AveragePool_kOnnxDomain_ver7_9EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_AveragePool_kOnnxDomain_ver10_10EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZNKSt14default_deleteIN11onnxruntime9KernelDefEEclEPS1_.isra.210 _ZZNK11onnxruntime8PoolBase7ComputeEPNS_15OpKernelContextE17MLAS_POOLING_KINDE12__FUNCTION__ _ZZNK11onnxruntime9MaxPoolV811ComputeImplIfEENS_6common6StatusEPNS_15OpKernelContextEE12__FUNCTION__ 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_ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E7_E9_M_invokeERKSt9_Any_dataS2_ _GLOBAL__sub_I_contrib_defs.cc _ZGVZN11onnxruntime7contrib19RegisterBertSchemasEvE33op_schema_register_onceAttention0 _ZGVZN11onnxruntime7contrib19RegisterBertSchemasEvE34op_schema_register_onceQAttention1 _ZGVZN11onnxruntime7contrib19RegisterBertSchemasEvE43op_schema_register_onceLongformerAttention2 _ZGVZN11onnxruntime7contrib19RegisterBertSchemasEvE47op_schema_register_onceEmbedLayerNormalization3 _ZGVZN11onnxruntime7contrib19RegisterBertSchemasEvE32op_schema_register_onceFastGelu4 _ZGVZN11onnxruntime7contrib19RegisterBertSchemasEvE46op_schema_register_onceSkipLayerNormalization5 _ZZN11onnxruntime7contrib19RegisterBertSchemasEvE46op_schema_register_onceSkipLayerNormalization5 _ZZN11onnxruntime7contrib19RegisterBertSchemasEvE32op_schema_register_onceFastGelu4 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib19RegisterBertSchemasEvEUlS2_E1_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib19RegisterBertSchemasEvE47op_schema_register_onceEmbedLayerNormalization3 _ZZN11onnxruntime7contrib19RegisterBertSchemasEvE43op_schema_register_onceLongformerAttention2 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib19RegisterBertSchemasEvEUlS2_E0_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib19RegisterBertSchemasEvE34op_schema_register_onceQAttention1 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib19RegisterBertSchemasEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib19RegisterBertSchemasEvE33op_schema_register_onceAttention0 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceAffine6 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceParametricSoftplus7 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceImageScaler8 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE28op_schema_register_onceCrop9 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceThresholdedRelu10 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE37op_schema_register_onceDynamicSlice11 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceGivenTensorFill12 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceScale13 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE32op_schema_register_onceGRUUnit14 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceGivenTensorFill15 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceScale16 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE32op_schema_register_onceGRUUnit17 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE50op_schema_register_onceMeanVarianceNormalization18 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceScaledTanh19 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE31op_schema_register_onceAffine20 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE43op_schema_register_onceParametricSoftplus21 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceImageScaler22 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE29op_schema_register_onceCrop23 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE37op_schema_register_onceDynamicSlice24 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceScaledTanh25 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceSampleOp26 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceMaxpoolWithMask27 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE29op_schema_register_onceRfft28 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceIrfft29 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceComplexMul30 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE39op_schema_register_onceComplexMulConj31 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE53op_schema_register_onceConvTransposeWithDynamicPads32 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE34op_schema_register_onceFusedConv33 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE34op_schema_register_onceFusedGemm34 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceExpandDims35 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceAttnLSTM36 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceRange37 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE34op_schema_register_onceTokenizer38 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceMatMulInteger1639 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceTransposeMatMul40 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceFusedMatMul41 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceMurmurHash342 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceGatherND43 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceWordConvEmbedding44 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE28op_schema_register_oncePad45 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE31op_schema_register_onceUnique46 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceCDist47 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE38op_schema_register_onceCropAndResize48 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE43op_schema_register_onceLayerNormalization49 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE53op_schema_register_onceSimplifiedLayerNormalization50 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE29op_schema_register_onceGelu51 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceBiasGelu52 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE32op_schema_register_onceInverse53 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceTrilu54 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceBiasSoftmax55 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceBiasSoftmax55 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E18_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceTrilu54 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE29op_schema_register_onceGelu51 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceBiasGelu52 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E17_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE32op_schema_register_onceInverse53 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E15_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE43op_schema_register_onceLayerNormalization49 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E14_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE38op_schema_register_onceCropAndResize48 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceCDist47 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E13_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE31op_schema_register_onceUnique46 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E12_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE28op_schema_register_oncePad45 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceWordConvEmbedding44 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E11_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceGatherND43 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceMurmurHash342 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E9_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceFusedMatMul41 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E8_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceTransposeMatMul40 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceMatMulInteger1639 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E6_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE34op_schema_register_onceTokenizer38 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceRange37 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceAttnLSTM36 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E5_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceExpandDims35 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E4_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE34op_schema_register_onceFusedGemm34 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E3_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE34op_schema_register_onceFusedConv33 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE53op_schema_register_onceConvTransposeWithDynamicPads32 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE39op_schema_register_onceComplexMulConj31 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceComplexMul30 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceIrfft29 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE29op_schema_register_onceRfft28 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E2_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceMaxpoolWithMask27 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceSampleOp26 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceScaledTanh25 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE37op_schema_register_onceDynamicSlice24 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E1_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE29op_schema_register_onceCrop23 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceImageScaler22 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE43op_schema_register_onceParametricSoftplus21 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE31op_schema_register_onceAffine20 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceScaledTanh19 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE50op_schema_register_onceMeanVarianceNormalization18 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE32op_schema_register_onceGRUUnit17 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceScale16 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E0_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceGivenTensorFill15 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE32op_schema_register_onceGRUUnit14 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceScale13 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceGivenTensorFill12 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE37op_schema_register_onceDynamicSlice11 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceThresholdedRelu10 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE28op_schema_register_onceCrop9 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceImageScaler8 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceParametricSoftplus7 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceAffine6 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E16_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE53op_schema_register_onceSimplifiedLayerNormalization50 _ZN4onnx34propagateElemTypeFromInputToOutputERNS_16InferenceContextEmm.constprop.482 _ZTSZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE_ _ZTSZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE0_ _ZTSZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE1_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE0_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE10_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE11_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE12_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE13_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE14_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE15_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE16_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE17_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE18_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib32NchwcGlobalPoolOpSchemaGeneratorERNS0_8OpSchemaEEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib32NchwcGlobalPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS3_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSA_St18_Manager_operation _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib26NchwcPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS3_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSA_St18_Manager_operation _ZTIZN11onnxruntime7contrib26NchwcPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ _ZTIZN11onnxruntime7contrib32NchwcGlobalPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE0_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE0_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE1_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE1_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib26NchwcPoolOpSchemaGeneratorERNS0_8OpSchemaEEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _GLOBAL__sub_I_nchwc_schema_defs.cc _ZGVZN11onnxruntime7contrib20RegisterNchwcSchemasEvE36op_schema_register_onceReorderInput0 _ZGVZN11onnxruntime7contrib20RegisterNchwcSchemasEvE37op_schema_register_onceReorderOutput1 _ZGVZN11onnxruntime7contrib20RegisterNchwcSchemasEvE28op_schema_register_onceConv2 _ZGVZN11onnxruntime7contrib20RegisterNchwcSchemasEvE31op_schema_register_onceMaxPool3 _ZGVZN11onnxruntime7contrib20RegisterNchwcSchemasEvE35op_schema_register_onceAveragePool4 _ZGVZN11onnxruntime7contrib20RegisterNchwcSchemasEvE37op_schema_register_onceGlobalMaxPool5 _ZGVZN11onnxruntime7contrib20RegisterNchwcSchemasEvE41op_schema_register_onceGlobalAveragePool6 _ZGVZN11onnxruntime7contrib20RegisterNchwcSchemasEvE32op_schema_register_onceUpsample7 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlS2_E1_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib20RegisterNchwcSchemasEvE32op_schema_register_onceUpsample7 _ZZN11onnxruntime7contrib20RegisterNchwcSchemasEvE41op_schema_register_onceGlobalAveragePool6 _ZZN11onnxruntime7contrib20RegisterNchwcSchemasEvE37op_schema_register_onceGlobalMaxPool5 _ZZN11onnxruntime7contrib20RegisterNchwcSchemasEvE35op_schema_register_onceAveragePool4 _ZZN11onnxruntime7contrib20RegisterNchwcSchemasEvE31op_schema_register_onceMaxPool3 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlS2_E0_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib20RegisterNchwcSchemasEvE28op_schema_register_onceConv2 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ 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_ZNSt14_Function_base13_Base_managerIZZN4onnx24RoiPoolOpSchemaGeneratorEPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_21ConvOpSchemaGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_21ConvOpSchemaGeneratorEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx21ConvOpSchemaGeneratorEPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_30ConvTransposeOpSchemaGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_30ConvTransposeOpSchemaGeneratorEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx30ConvTransposeOpSchemaGeneratorEPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_30GlobalPoolingOpSchemaGeneratorEPKcS5_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_30GlobalPoolingOpSchemaGeneratorEPKcS5_ENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS3_ENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_32GlobalLpPoolingOpSchemaGeneratorEPKcS5_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_32GlobalLpPoolingOpSchemaGeneratorEPKcS5_ENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS3_ENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt14_Function_base13_Base_managerIZN4onnx21PoolOpSchemaGeneratorEPKcS3_S3_bbEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx21PoolOpSchemaGeneratorEPKcS3_S3_bbENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_20MaxUnpool_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_20MaxUnpool_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx23LpPoolOpSchemaGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx23LpPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZTIZZN4onnx23LpPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx24RoiPoolOpSchemaGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx24RoiPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZTIZZN4onnx24RoiPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx21ConvOpSchemaGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx21ConvOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZTIZZN4onnx21ConvOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ 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_ZNSt14_Function_base13_Base_managerIZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS3_EUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_ _ZTIZZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS3_EUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_ _ZTIZZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28BatchNormalization_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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_ZTIZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx24PoolOpSchemaGenerator_10EPKcS3_S3_biEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biEUlRNS_8OpSchemaEE_ _ZTIZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19MaxUnpool_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx26LpPoolOpSchemaGenerator_10EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx26LpPoolOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTIZZN4onnx26LpPoolOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx24ConvOpSchemaGenerator_10EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx24ConvOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTIZZN4onnx24ConvOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTIZZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Dropout_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28BatchNormalization_Onnx_ver6EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Flatten_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Flatten_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28BatchNormalization_Onnx_ver7EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZN6google8protobuf8internal20RepeatedPtrFieldBase3AddINS0_16RepeatedPtrFieldIN4onnx26TensorShapeProto_DimensionEE11TypeHandlerEEEPNT_4TypeESB_.constprop.453 _ZNSt6vectorISsSaISsEEC2ESt16initializer_listISsERKS0_.constprop.452 _ZNSt6vectorISsSaISsEEC2ESt16initializer_listISsERKS0_.constprop.451 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Dropout_Onnx_ver12EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Flatten_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19MaxUnpool_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Dropout_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28BatchNormalization_Onnx_ver6EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Flatten_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Flatten_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28BatchNormalization_Onnx_ver7EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_ _ZTSZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biEUlRNS_8OpSchemaEE_ _ZTSZN4onnx11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZZN4onnx26LpPoolOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx26LpPoolOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx24ConvOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx24ConvOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTSZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19RoiAlign_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28NonMaxSuppression_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19RoiAlign_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28NonMaxSuppression_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.224 _ZTSZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28NonMaxSuppression_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28NonMaxSuppression_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25QuantizeLinear_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_27DequantizeLinear_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt6vectorISsSaISsEEC2ESt16initializer_listISsERKS0_.constprop.258 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_27DequantizeLinear_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25QuantizeLinear_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25QuantizeLinear_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_27DequantizeLinear_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_27DequantizeLinear_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25QuantizeLinear_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx18ReduceDocGeneratorEPKcbbEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx18ReduceDocGeneratorEPKcbbEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx18ReduceDocGeneratorEPKcbbENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx18ReduceDocGeneratorEPKcbbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_21ArgReduceDocGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_21ArgReduceDocGeneratorEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_18ReduceDocGeneratorEPKcbbEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_18ReduceDocGeneratorEPKcbbENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZZN4onnx18ReduceDocGeneratorEPKcbbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx18ReduceDocGeneratorEPKcbbEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx15RNNDocGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_15RNNDocGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx18RNNDocGeneratorOldEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx16RNNDocGenerator1EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_18RNNDocGeneratorOldEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_16RNNDocGenerator1EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_ _ZTSZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_ _ZNSt17_Function_handlerIFbiEZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_E9_M_invokeERKSt9_Any_datai _ZNKSt10_HashtableIPKSsS1_SaIS1_ENSt8__detail9_IdentityESt8equal_toIS1_ESt4hashIS1_ENS3_18_Mod_range_hashingENS3_20_Default_ranged_hashENS3_20_Prime_rehash_policyENS3_17_Hashtable_traitsILb0ELb1ELb1EEEE19_M_find_before_nodeEmRKS1_m.isra.371 _ZNSt17_Function_handlerIFbiEZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_E9_M_invokeERKSt9_Any_datai _ZGVZN4onnx16OpSchemaRegistry8InstanceEvE8instance _ZZN4onnx16OpSchemaRegistry8InstanceEvE8instance _ZGVZN4onnx16OpSchemaRegistry33GetMapWithoutEnsuringRegistrationEvE3map _ZZN4onnx16OpSchemaRegistry33GetMapWithoutEnsuringRegistrationEvE3map _ZNSt14_Function_base13_Base_managerIZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_E10_M_managerERSt9_Any_dataRKSA_St18_Manager_operation _ZTIZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_ _ZNSt14_Function_base13_Base_managerIZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_E10_M_managerERSt9_Any_dataRKSA_St18_Manager_operation _ZTIZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_ _ZGVZN4onnx16OpSchemaRegistry20DomainToVersionRange8InstanceEvE23domain_to_version_range _ZZN4onnx16OpSchemaRegistry20DomainToVersionRange8InstanceEvE23domain_to_version_range _GLOBAL__sub_I_schema.cc _ZTSZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_ _ZTSZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_24SequenceEmpty_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28SequenceConstruct_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25SequenceInsert_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_21SequenceAt_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_24SequenceErase_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25SequenceLength_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26SplitToSequence_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_29ConcatFromSequence_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_24SequenceEmpty_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_21SequenceAt_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_24SequenceErase_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_29ConcatFromSequence_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_26SplitToSequence_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28SequenceConstruct_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25SequenceInsert_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25SequenceLength_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19Upsample_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Resize_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Cast_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Reshape_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Shape_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Size_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Concat_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Split_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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_ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17OneHot_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16IsNaN_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16IsNaN_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16IsInf_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Where_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18NonZero_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18NonZero_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26ReverseSequence_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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