vendor/libonnxruntime.so in onnxruntime-0.2.3 vs vendor/libonnxruntime.so in onnxruntime-0.3.0
- old
+ new
@@ -1,751 +1,852 @@
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_ZNSt9bad_allocD1Ev malloc omp_get_thread_num omp_get_num_threads omp_get_max_threads GOMP_parallel_start GOMP_parallel_end powf pow floorf atanhf acoshf asinhf atanf acosf asinf fmodf fmod sin roundf rint strtof _ZSt24__throw_invalid_argumentPKc tanh _ZNSs6assignEPKc _ZNSbIwSt11char_traitsIwESaIwEE4_Rep20_S_empty_rep_storageE _ZNSbIwSt11char_traitsIwESaIwEE4_Rep10_M_destroyERKS1_ iconv_open iconv _ZNSbIwSt11char_traitsIwESaIwEE6assignEPKwm iconv_close _ZNSbIwSt11char_traitsIwESaIwEE6assignERKS2_ _ZNSbIwSt11char_traitsIwESaIwEEC1ERKS2_ _ZNSbIwSt11char_traitsIwESaIwEE4_Rep10_M_disposeERKS1_ wmemcmp _ZNSt6localeC1EPKc _ZNSbIwSt11char_traitsIwESaIwEE12_M_leak_hardEv _ZSt9use_facetISt5ctypeIwEERKT_RKSt6locale _ZNSbIwSt11char_traitsIwESaIwEEC1EPKwRKS1_ _ZNSbIwSt11char_traitsIwESaIwEED1Ev _ZNKSt9exception4whatEv _ZNSt15__exception_ptr13exception_ptraSERKS0_ _ZNSt18condition_variable10notify_allEv _ZTVSt11logic_error _ZSt17current_exceptionv _ZNSt15__exception_ptr13exception_ptr4swapERS0_ 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pthread_rwlock_destroy pthread_rwlock_rdlock memrchr isalpha libdl.so.2 librt.so.1 libstdc++.so.6 get_nprocs usleep _ZNSt8__detail12__prime_listE floor _ZTISt14error_category _ZNKSt14error_category23default_error_conditionEi _ZNKSt14error_category10equivalentEiRKSt15error_condition _ZNKSt14error_category10equivalentERKSt10error_codei _ZTVSt14error_category libm.so.6 libgomp.so.1 libgcc_s.so.1 libpthread.so.0 libc.so.6 ld-linux-x86-64.so.2 libonnxruntime.so.1.1.1 $ORIGIN VERS_1.1.1 GLIBC_2.3 GLIBC_2.2.5 GCC_3.0 GOMP_1.0 GLIBC_2.3.2 GLIBCXX_3.4.10 GLIBCXX_3.4.9 GLIBCXX_3.4.11 CXXABI_1.3.3 CXXABI_1.3 GLIBCXX_3.4
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-Ht$ƃLބH[]A\A]A^ÐSHHHtIHGHxH;= t0H= HWt PJHЅ
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-Ht$ID$HxL9t0H= HWt PJHЅ
-Ht$فI$HxL9t0H=L HWt PJHЅ
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-Ht$IID$HxL9t0H= HWt PJHЅ
-Ht$ I$HxL9t0H=~ HWt PJHЅ
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ID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H=+ߔ HWt PJHЅ
-Ht${~ID$HxL9t0H=ޔ HWt PJHЅ
-Ht$=~I$HxL9t0H=ޔ HWt PJHЅ
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-Ht$|ID$HxL9t0H= ݔ HWt PJHЅ
-Ht$o|I$HxL9t0H=ܔ HWt PJHЅ
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-ܔ HWt PJHЅL\{HHq|ID$(I|$ H 1ID$8 ID$0 I|$ =|ID$HxL9t0H=۔ HWt PJHЅ
-Ht$zID$HxL9t0H=Q۔ HWt PJHЅ
-Ht$zI$HxL9t0H=۔ HWt PJHЅ
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-Ht$yID$HxL9t0H=ٔ HWt PJHЅ
-Ht$xI$HxL9t0H=Fٔ HWt PJHЅ
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-Ht$CwID$HxL9t0H=ה HWt PJHЅ
-Ht$wI$HxL9t0H=xה HWt PJHЅ
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-Ht$uuID$HxL9t0H=Ք HWt PJHЅ
-Ht$7uI$HxL9t0H=Ք HWt PJHЅ
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-Ht$sID$HxL9t0H=Ԕ HWt PJHЅ
-Ht$isI$HxL9t0H=Ӕ HWt PJHЅ
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-Ht$qID$HxL9t0H=KҔ HWt PJHЅ
-Ht$qI$HxL9t0H=Ҕ HWt PJHЅ
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-rI\$0L-ϔ Lt$HtYH{H+HtqHCHxL9t.H=6є HWt PJHЅLpHHqID$(I|$ H 1ID$8 ID$0 I|$ iqID$HxL9t0H=Д HWt PJHЅ
-Ht$ pID$HxL9t0H=}Д HWt PJHЅ
-Ht$oI$HxL9t0H=@Д HWt PJHЅ
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- I|$hHtnI|$PHtnI\$0L-p̔ Lt$HtYH{H+HtunHCHxL9t.H=͔ HWt PJHЅLmHH/nID$(I|$ H 1ID$8 ID$0 I|$ mID$HxL9t0H=M͔ HWt PJHЅ
-Ht$lID$HxL9t0H=͔ HWt PJHЅ
-Ht$_lI$HxL9t0H=̔ HWt PJHЅ
-Ht$"lL:mH[]A\A]A^ÐSHHHtIHGHxH;=ʔ t0H=n̔ HWt PJHЅ
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˔ HWt PJHЅH_jI$HHxOhLgkH[]A\A]A^AUATIUSHH# H H I$ I$ l I|$hHt
kI|$PHtjI\$0Ll$Ht]H{H+HtjHSHzH;=Ȕ t.H=-ʔ HOt JqrʅLiHHjID$(I|$ H\$H 1ID$8 ID$0 I|$ [jID$HHx*gID$HHxgI$HHx gL!jH[]A\A]AVAUATIUSHH H H ; I$ I$ & I|$hHtiI|$PHtiI\$0L-ǔ Lt$HtYH{H+HtiHCHxL9t.H=Ȕ HWt PJHЅL6hHHKiID$(I|$ H 1ID$8 ID$0 I|$ iID$HxL9t0H=iȔ HWt PJHЅ
-Ht$gID$HxL9t0H=+Ȕ HWt PJHЅ
-Ht${gI$HxL9t0H=ǔ HWt PJHЅ
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-Ht$eID$HxL9t0H=]Ɣ HWt PJHЅ
-Ht$eI$HxL9t0H= Ɣ HWt PJHЅ
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-Ht$ dID$HxL9t0H=Ĕ HWt PJHЅ
-Ht$cI$HxL9t0H=RĔ HWt PJHЅ
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-Ht$ObID$HxL9t0H= HWt PJHЅ
-Ht$bI$HxL9t0H= HWt PJHЅ
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-Ht$`ID$HxL9t0H= HWt PJHЅ
-Ht$C`I$HxL9t0H= HWt PJHЅ
-Ht$`L aH[]A\A]A^ÐSHHHtIHGHxH;=Ծ t0H=R HWt PJHЅ
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-Ht$W^ID$HxL9t0H=ɾ HWt PJHЅ
-Ht$^I$HxL9t0H= HWt PJHЅ
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-Ht$\ID$HxL9t0H= HWt PJHЅ
-Ht$K\I$HxL9t0H= HWt PJHЅ
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I$ I$ (
I|$hHt\I|$PHt\I\$0L- Lt$HtYH{H+Ht\HCHxL9t.H=滔 HWt PJHЅL8[HHM\ID$(I|$ H 1ID$8 ID$0 I|$ \ID$HxL9t0H=k HWt PJHЅ
-Ht$ZID$HxL9t0H=- HWt PJHЅ
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-Ht$XID$HxL9t0H=_ HWt PJHЅ
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-Ht$QUID$HxL9t0H=õ HWt PJHЅ
-Ht$UI$HxL9t0H= HWt PJHЅ
-Ht$TLUH[]A\A]A^P vQH H5X HHHHr [SHHHtIHGHxH;=v t0H= HWt PJHЅ
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-Ht$RID$HxL9t0H=k HWt PJHЅ
-Ht$RI$HxL9t0H=. HWt PJHЅ
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-Ht$+QID$HxL9t0H= HWt PJHЅ
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HWt PJHЅ
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-Ht$ OI$HxL9t0H= HWt PJHЅ
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-Ht$MID$HxL9t0H= HWt PJHЅ
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-Ht$eKID$HxL9t0H= HWt PJHЅ
-Ht$'KI$HxL9t0H= HWt PJHЅ
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-Ht$;IID$HxL9t0H= HWt PJHЅ
-Ht$HI$HxL9t0H=p HWt PJHЅ
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-Ht$GID$HxL9t0H= HWt PJHЅ
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-Ht$CEID$HxL9t0H= HWt PJHЅ
-Ht$EI$HxL9t0H=x HWt PJHЅ
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-Ht$uCID$HxL9t0H=磔 HWt PJHЅ
-Ht$7CI$HxL9t0H= HWt PJHЅ
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-Ht$AID$HxL9t0H= HWt PJHЅ
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-Ht$9>ID$HxL9t0H= HWt PJHЅ
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- HWt PJHЅ
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-Ht$<ID$HxL9t0H= HWt PJHЅ
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-Ht$;L<H[]A\A]A^ÐAVAUATIUSHH H H I$ I$ I|$hHtO<I|$PHt@<I\$0L- Lt$HtYH{H+Ht<HCHxL9t.H=l HWt PJHЅL:HH;ID$(I|$ H 1ID$8 ID$0 I|$ ;ID$HxL9t0H= HWt PJHЅ
-Ht$A:ID$HxL9t0H= HWt PJHЅ
-Ht$:I$HxL9t0H=v HWt PJHЅ
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-Ht$58I$HxL9t0H= HWt PJHЅ
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-Ht$6ID$HxL9t0H= HWt PJHЅ
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-Ht$4I$HxL9t0H= HWt PJHЅ
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-Ht$ 3ID$HxL9t0H={ HWt PJHЅ
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-Ht$;1ID$HxL9t0H= HWt PJHЅ
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-Ht$S/ID$HxL9t0H=ŏ HWt PJHЅ
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-Ht$-ID$HxL9t0H= HWt PJHЅ
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-Ht$[+ID$HxL9t0H=͋ HWt PJHЅ
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HL$ H|$L1X H|$ H \H9\$tH|$Hs h AA0 #HHt8Ht$Hx H@ H@ H@ H$ HE(HL$AHH IF(jHnHvaHHH$ Ht$8Hx AHt$Hx H@ H@ H@ $ E(`HHHuHH$ Ht$8Hx AHt$Hx H@ H@ H@ $ E(HfHnYHHH$ Ht$8HxH []A\A]A^A_ÐSHHHtHGHt$HxHH[ÐHSHt%H HtH{HtH[[HSHt H[[ÐAUATIUSHHt|H_HLl$Ht HCH+LHxSHHxID$@I|$8H 1ID$P ID$H I|$8DI|$j I|$0L(H[]A\A]ÐSHHHtIHGHxH;=Ь t0H=N HWt PJHЅ
+Ht$HH[SHHHtIHGHxH;=t t0H= HWt PJHЅ
+Ht$BHjH[USHHH H HtAH Ht0H{hHt"H{PHtH{8HtHC0Hl$HHxH{HtHCHHxHH[]SHHHtIHGHxH;=x t0H= HWt PJHЅ
+Ht$FHnH[ATUSHLgHH_@L9t6H;H- HD H5q H=b H$ = H9u1H[]A\SHHHtIHGHxH;=ƪ t0H=D HWt PJHЅ
+Ht$HH[SHHHtIHGHxH;=j t0H=諘 HWt PJHЅ
+Ht$8H`H[SHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$HH[SHHHtIHGHxH;= t0H=0 HWt PJHЅ
+Ht$HH[SHHHtIHGHxH;=V t0H=Ԫ HWt PJHЅ
+Ht$$HLH[SHHHtIHGHxH;= t0H=x HWt PJHЅ
+Ht$HH[USHHH H HtH HtH{hHtH{PHtH{8HtHC0H-Q HxH9t0H=ȩ HWt PJHЅ
+Ht$H{Ht:HCHxH9t0H=} HWt PJHЅ
+Ht$HH[]ATIUHSH HF@HXH C HH0E t Ht$HL$$Hd$HD$ Y H []A\ATIUHSH HF@HXHC HHE t$Ht$HL$$Ld$Hd$HD$ X H []A\AVIAUIATMUHSHF@LHxaC HHH|D ttH
+t t1]IE@Hx+C LHH|D tI$; uIF@Hx C HT$0HH|D tHD$0H ; []A\A]A^USHHH H Ht_H HtNH{hHt@H{PHt2H{8Ht$HC0Hl$HHxH{HtHCHHxHH[]ATUHSHH HG HG L`HH HLc Lc[]A\H;HHtHSHHHtIHGHxH;=. t0H= HWt PJHЅ
+Ht$H$H[AWIHAVAUEATIULSHH H|$ H|$@ H|$PL H|$`Hr H$ HHD$7(X H5C` HH` L$ Ld$pEHDLLH$ Ht$ LH$ HHD$e*D $ H$ H;=~ u2 H$ HH H$ HH$
+Ht$pH$ HHxHD$pHt$HxEu HD$O$HL$ Hx'>Z HD$LL$ Hx >Z ƃ$ EIElj$ H|$@LLd$ H|$PH H|$`I IH?H9D$HDŽ$ HDŽ$ HDŽ$ v H=t$` H|$ tLHD$H, H9H$ HtHD$HD$H$ H$ HH$ E J HD$ HD$(H$ HD$L9|$|#HT$H$ H$ HQ 1H$ H;$ t Ht HH$ H+$ I?HHEHrL9LFE1MtJ< 9IH$ H$ MH)HH ItAHtLLL$0#LL$0IH$ IHtLL$8LD$0LL$8LD$0KL$ L$ H$ HL9M1H$ H;$ t HAE t HH$
H|$L HII9M1H$ H;$ t HAE t HH$
H|$LK HII9HD$(HD$HIIHL$MLLHq_ wV H|$PHjV H|$`H]V H|$ IHDŽ$ HDŽ$ HDŽ$ y H=!` "H|$ tJHD$IILWH$ HtHD$( HD$(H$ H$ LH$ Et0O4?E1M9}:HL$MLHH` ILLMHL$MLHH` HD$H$ H|$HHX H$ HtfH$ H|$ HaF HHf%X H|$`HtH|$PHD$` HtH|$@HD$P H HH$ HHxHHD$pHHHx H$ HHt
+HH$ $X HH|$`Ht
HD$` HH|$PHtHD$P HH|$@HtHH H[]A\A]A^A_SHHHtIHGHxH;=의 t0H=j HWt PJHЅ
+Ht$HH[SHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$^HH[SHHHtIHGHxH;=4 t0H= HWt PJHЅ
+Ht$H*H[USHHH H HtH HtH{hHtH{PHtH{8HtHC0H- HxH9t0H= HWt PJHЅ
+Ht$RH{HttHCHxH9t0H= HWt PJHЅ
+Ht$H/H[]USHHH H HtH HtH{hHtH{PHtH{8HtHC0H- HxH9t0H= HWt PJHЅ
+Ht$VH{HtxHCHxH9t0H= HWt PJHЅ
+Ht$ H3H[]SHHHtIHGHxH;= t0H=^ HWt PJHЅ
+Ht$HH[ATUHSHH C Htx u
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xe H$ HL M "I~`HK I~0K LK J I~`H$ qK I~0hK LLt$`[K H|$MLHھ cE @uA|$ HD$`H8NC H HD$`Hx2C H m HD$`HxC H Q HD$`HxC HC H<$H6$ HHD$`HxC HmC H<$H$ HHD$TC H;D$02 A|$ 1H#N H|$`H HHHM HH|$`HtYLM HH$ H
j` H5` | HF H Lt$IE11LHL蔐 I~PH5` UH$ H
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LG L=? =H|$HG HL ? ?A|$ .HD$8HL$(H@@H9 HD$H@@H9 L$` H
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J HHQH$ H$ LH$ HxH;= t.H= HWt PJHЅL$p H$ H5g LDŽ$< HDŽ$0
$p IH$ Ht$^Hx芼_ HLLD H$ LHxcHt$LC HX H X HD$6 L. IHD$pHHx IHD$`HLHx IH$ LMHxIH$ H$ HxʻIH$ H$ Hx謻IH$ H$ Hx莻IH$ Ht
+觾IH$ Ht
+萾IH$ HLHxD,IH$ LHx+IHLX HHW HHH []A\A]A^A_SHHHtIHGHxH;=؇ t0H=V HWt PJHЅ
+Ht$覼HνH[SHHHtIHGHxH;=| t0H= HWt PJHЅ
+Ht$JHrH[SHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$HH[ATUSHLgHH_@L9t6H;H- HC H5 H= H$ n H9u1H[]A\SHHHtIHGHxH;=n t0H=쇘 HWt PJHЅ
+Ht$<HdH[SHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$HH[SHHHtIHGHxH;= t0H=4 HWt PJHЅ
+Ht$脺H謻H[AVAUATIUSHH H H [ I$ I$ F I|$hHtWI|$PHtHI\$0L- Lt$HtYH{H+Ht!HCHxL9t.H=d HWt PJHЅL趹HHۺID$(I|$ H 1ID$8 ID$0 I|$ 觺ID$HxL9t0H=酘 HWt PJHЅ
+Ht$9ID$HxL9t0H= HWt PJHЅ
+Ht$I$HxL9t0H=n HWt PJHЅ
+Ht$辸LH[]A\A]A^ÐAUATIUSHH# H H 菎 I$ I$ z I|$hHt苹I|$PHt|I\$0Ll$Ht]H{H+Ht\HSHzH;= t.H= HOt JqrʅLHHID$(I|$ H\$H 1ID$8 ID$0 I|$ ٸID$HHx蘵ID$HHx臵I$HHxwL蟸H[]A\A]AVAUATIUSHH H H I I$ I$ 4 I|$hHtEI|$PHt6I\$0L- Lt$HtYH{H+HtHCHxL9t.H=R HWt PJHЅL褶HHɷID$(I|$ H 1ID$8 ID$0 I|$ 蕷ID$HxL9t0H=ׂ HWt PJHЅ
+Ht$'ID$HxL9t0H= HWt PJHЅ
+Ht$I$HxL9t0H=\ HWt PJHЅ
+Ht$謵LԶH[]A\A]A^ÐAUATIUSHH# H H } I$ I$ h I|$hHtyI|$PHtjI\$0Ll$Ht]H{H+HtJHSHzH;= t.H= HOt JqrʅL۴HH ID$(I|$ H\$H 1ID$8 ID$0 I|$ ǵID$HHx膲ID$HHxuI$HHxeL荵H[]A\A]SHHHtIHGHxH;=6 t0H= HWt PJHЅ
+Ht$H,H[SHHHtIHGHxH;=~ t0H=X HWt PJHЅ
+Ht$訳HдH[AVAUATIUSHH H H I$ I$ j I|$hHt{I|$PHtlI\$0L-0~ Lt$HtYH{H+HtEHCHxL9t.H= HWt PJHЅLڲHHID$(I|$ H 1ID$8 ID$0 I|$ ˳ID$HxL9t0H=
HWt PJHЅ
+Ht$]ID$HxL9t0H=~ HWt PJHЅ
+Ht$ I$HxL9t0H=~ HWt PJHЅ
+Ht$L
+H[]A\A]A^ÐAVAUATIUSHH H H 豇 I$ I$ 蜇 I|$hHt譲I|$PHt螲I\$0L-b| Lt$HtYH{H+HtwHCHxL9t.H=} HWt PJHЅL HH1ID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H=?} HWt PJHЅ
+Ht$菰ID$HxL9t0H=} HWt PJHЅ
+Ht$QI$HxL9t0H=| HWt PJHЅ
+Ht$L<H[]A\A]A^ÐSHHHtIHGHxH;=z t0H=`| HWt PJHЅ
+Ht$谯HذH[SHHHtIHGHxH;=z t0H=| HWt PJHЅ
+Ht$TH|H[AUATIUSHH# H H - I$ I$ I|$hHt)I|$PHtI\$0Ll$Ht]H{H+HtHSHzH;=y t.H=9{ HOt JqrʅL苮HH谯ID$(I|$ H\$H 1ID$8 ID$0 I|$ wID$HHx6ID$HHx%I$HHxL=H[]A\A]AVAUATIUSHH| H H I$ I$ ҃ I|$hHtI|$PHtԮI\$0L-x Lt$HtYH{H+Ht譮HCHxL9t.H=y HWt PJHЅLBHHgID$(I|$ H\$H 1ID$8 ID$0 I|$ .ID$HxL9t.H=py HWt PJHЅH¬ID$HxL9t.H=4y HWt PJHЅH膬I$HHxvL螭H[]A\A]A^ÐAVAUATIUSHH H H E I$ I$ 0 I|$hHtAI|$PHt2I\$0L-v Lt$HtYH{H+Ht HCHxL9t.H=Nx HWt PJHЅL蠫HHŬID$(I|$ H 1ID$8 ID$0 I|$ 葬ID$HxL9t0H=w HWt PJHЅ
+Ht$#ID$HxL9t0H=w HWt PJHЅ
+Ht$I$HxL9t0H=Xw HWt PJHЅ
+Ht$訪LЫH[]A\A]A^ÐAVAUATIUSHH H H w I$ I$ b I|$hHtsI|$PHtdI\$0L-(u Lt$HtYH{H+Ht=HCHxL9t.H=v HWt PJHЅLҩHHID$(I|$ H 1ID$8 ID$0 I|$ êID$HxL9t0H=v HWt PJHЅ
+Ht$UID$HxL9t0H=u HWt PJHЅ
+Ht$I$HxL9t0H=u HWt PJHЅ
+Ht$ڨLH[]A\A]A^ÐAVAUATIUSHH H H ~ I$ I$ ~ I|$hHt襩I|$PHt薩I\$0L-Zs Lt$HtYH{H+HtoHCHxL9t.H=t HWt PJHЅLHH)ID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H=7t HWt PJHЅ
+Ht$臧ID$HxL9t0H=s HWt PJHЅ
+Ht$II$HxL9t0H=s HWt PJHЅ
+Ht$ L4H[]A\A]A^ÐAVAUATIUSHH H H | I$ I$ | I|$hHtקI|$PHtȧI\$0L-q Lt$HtYH{H+Ht衧HCHxL9t.H=r HWt PJHЅL6HH[ID$(I|$ H 1ID$8 ID$0 I|$ 'ID$HxL9t0H=ir HWt PJHЅ
+Ht$蹥ID$HxL9t0H=+r HWt PJHЅ
+Ht${I$HxL9t0H=q HWt PJHЅ
+Ht$>LfH[]A\A]A^ÐAVAUATIUSHH H H
{ I$ I$ z I|$hHt I|$PHtI\$0L-o Lt$HtYH{H+HtӥHCHxL9t.H=q HWt PJHЅLhHH荥ID$(I|$ H 1ID$8 ID$0 I|$ YID$HxL9t0H=p HWt PJHЅ
+Ht$ID$HxL9t0H=]p HWt PJHЅ
+Ht$譣I$HxL9t0H= p HWt PJHЅ
+Ht$pL蘤H[]A\A]A^ÐAVAUATIUSHH H H ?y I$ I$ *y I|$hHt;I|$PHt,I\$0L-m Lt$HtYH{H+HtHCHxL9t.H=Ho HWt PJHЅL蚢HH迣ID$(I|$ H 1ID$8 ID$0 I|$ 苣ID$HxL9t0H=n HWt PJHЅ
+Ht$ ID$HxL9t0H=n HWt PJHЅ
+Ht$ߡI$HxL9t0H=Rn HWt PJHЅ
+Ht$袡LʢH[]A\A]A^ÐAVAUATIUSHHQ H H qw I$ I$ \w I|$hHtmI|$PHt^I\$0L-"l Lt$HtYH{H+Ht7HCHxL9t.H=zm HWt PJHЅL̠HHID$(I|$ H\$H 1ID$8 ID$0 I|$ 踡ID$HHxwID$HxL9t.H=l HWt PJHЅH;I$HHx+LSH[]A\A]A^AVAUATIUSHH H H u I$ I$ u I|$hHtI|$PHtI\$0L-j Lt$HtYH{H+HtHCHxL9t.H=l HWt PJHЅLVHH{ID$(I|$ H 1ID$8 ID$0 I|$ GID$HxL9t0H=k HWt PJHЅ
+Ht$ٞID$HxL9t0H=Kk HWt PJHЅ
+Ht$蛞I$HxL9t0H=k HWt PJHЅ
+Ht$^L膟H[]A\A]A^ÐSHHHtIHGHxH;=,i t0H=j HWt PJHЅ
+Ht$H"H[AVAUATIUSHH H H s I$ I$ s I|$hHt͞I|$PHt辞I\$0L-h Lt$HtYH{H+Ht藞HCHxL9t.H=i HWt PJHЅL,HHQID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H=_i HWt PJHЅ
+Ht$诜ID$HxL9t0H=!i HWt PJHЅ
+Ht$qI$HxL9t0H=h HWt PJHЅ
+Ht$4L\H[]A\A]A^ÐSHHHtIHGHxH;=g t0H=h HWt PJHЅ
+Ht$ЛHH[AUATIUSHH# H H q I$ I$ q I|$hHt襜I|$PHt薜I\$0Ll$Ht]H{H+HtvHSHzH;=7f t.H=g HOt JqrʅLHH,ID$(I|$ H\$H 1ID$8 ID$0 I|$ ID$HHx貘ID$HHx衘I$HHx葘L蹛H[]A\A]SHHHtIHGHxH;=be t0H=f HWt PJHЅ
+Ht$0HXH[AVAUATIUSHH H H p I$ I$ o I|$hHtI|$PHtI\$0L-d Lt$HtYH{H+Ht͚HCHxL9t.H=f HWt PJHЅLbHH臚ID$(I|$ H 1ID$8 ID$0 I|$ SID$HxL9t0H=e HWt PJHЅ
+Ht$ID$HxL9t0H=We HWt PJHЅ
+Ht$觘I$HxL9t0H=e HWt PJHЅ
+Ht$jL蒙H[]A\A]A^ÐAVAUATIUSHH H H 9n I$ I$ $n I|$hHt5I|$PHt&I\$0L-b Lt$HtYH{H+HtHCHxL9t.H=Bd HWt PJHЅL蔗HH蹘ID$(I|$ H 1ID$8 ID$0 I|$ 腘ID$HxL9t0H=c HWt PJHЅ
+Ht$ID$HxL9t0H=c HWt PJHЅ
+Ht$ٖI$HxL9t0H=Lc HWt PJHЅ
+Ht$蜖LėH[]A\A]A^ÐSHHHtIHGHxH;=ja t0H=b HWt PJHЅ
+Ht$8H`H[AVAUATIUSHH H H l I$ I$ k I|$hHt I|$PHtI\$0L-` Lt$HtYH{H+HtՖHCHxL9t.H=b HWt PJHЅLjHH菖ID$(I|$ H 1ID$8 ID$0 I|$ [ID$HxL9t0H=a HWt PJHЅ
+Ht$ID$HxL9t0H=_a HWt PJHЅ
+Ht$诔I$HxL9t0H="a HWt PJHЅ
+Ht$rL蚕H[]A\A]A^ÐSHHHtIHGHxH;=@_ t0H=` HWt PJHЅ
+Ht$H6H[AVAUATIUSHHQ H H i I$ I$ i I|$hHtI|$PHtҔI\$0L-^ Lt$HtYH{H+Ht諔HCHxL9t.H=_ HWt PJHЅL@HHeID$(I|$ H\$H 1ID$8 ID$0 I|$ ,ID$HxL9t.H=n_ HWt PJHЅHID$HHx诐I$HHx蟐LǓH[]A\A]A^SHHHtIHGHxH;=n] t0H=^ HWt PJHЅ
+Ht$<HdH[AVAUATIUSHH H H h I$ I$ g I|$hHtI|$PHt I\$0L-\ Lt$HtYH{H+HtْHCHxL9t.H= ^ HWt PJHЅLnHH蓒ID$(I|$ H 1ID$8 ID$0 I|$ _ID$HxL9t0H=] HWt PJHЅ
+Ht$ID$HxL9t0H=c] HWt PJHЅ
+Ht$賐I$HxL9t0H=&] HWt PJHЅ
+Ht$vL螑H[]A\A]A^H5a P褊ZSHHHtIHGHxH;=.[ t0H=\ HWt PJHЅ
+Ht$H$H[SHHHtIHGHxH;=Z t0H=P\ HWt PJHЅ
+Ht$蠏HȐH[AVAUATIUSHH H H we I$ I$ be I|$hHtsI|$PHtdI\$0L-(Z Lt$HtYH{H+Ht=HCHxL9t.H=[ HWt PJHЅLҎHHID$(I|$ H 1ID$8 ID$0 I|$ ÏID$HxL9t0H=[ HWt PJHЅ
+Ht$UID$HxL9t0H=Z HWt PJHЅ
+Ht$I$HxL9t0H=Z HWt PJHЅ
+Ht$ڍLH[]A\A]A^ÐAVAUATIUSHH H H c I$ I$ c I|$hHt襎I|$PHt薎I\$0L-ZX Lt$HtYH{H+HtoHCHxL9t.H=Y HWt PJHЅLHH)ID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H=7Y HWt PJHЅ
+Ht$臌ID$HxL9t0H=X HWt PJHЅ
+Ht$II$HxL9t0H=X HWt PJHЅ
+Ht$ L4H[]A\A]A^ÐAUATIUSHH# H H a I$ I$ a I|$hHtٌI|$PHtʌI\$0Ll$Ht]H{H+Ht誌HSHzH;=kV t.H=W HOt JqrʅL;HH`ID$(I|$ H\$H 1ID$8 ID$0 I|$ 'ID$HHxID$HHxՈI$HHxňLH[]A\A]AVAUATIUSHH| H H ` I$ I$ ` I|$hHt蓋I|$PHt脋I\$0L-HU Lt$HtYH{H+Ht]HCHxL9t.H=V HWt PJHЅLHHID$(I|$ H\$H 1ID$8 ID$0 I|$ ފID$HxL9t.H= V HWt PJHЅHrID$HHxaI$HxL9t.H=U HWt PJHЅH&LNH[]A\A]A^ÐAUATIUSHH# H H ^ I$ I$ ^ I|$hHtI|$PHtI\$0Ll$Ht]H{H+HtĉHSHzH;=S t.H=U HOt JqrʅLUHHzID$(I|$ H\$H 1ID$8 ID$0 I|$ AID$HHx ID$HHxI$HHx߅LH[]A\A]AUATIUSHH# H H ] I$ I$ ] I|$hHt诈I|$PHt蠈I\$0Ll$Ht]H{H+Ht耈HSHzH;=AR t.H=S HOt JqrʅLHH6ID$(I|$ H\$H 1ID$8 ID$0 I|$ ID$HHx輄ID$HHx諄I$HHx蛄LÇH[]A\A]AVAUATIUSHH H H m\ I$ I$ X\ I|$hHtiI|$PHtZI\$0L- Q Lt$HtYH{H+Ht3HCHxL9t.H=vR HWt PJHЅLȅHHID$(I|$ H 1ID$8 ID$0 I|$ 蹆ID$HxL9t0H=Q HWt PJHЅ
+Ht$KID$HxL9t0H=Q HWt PJHЅ
+Ht$
I$HxL9t0H=Q HWt PJHЅ
+Ht$ЄLH[]A\A]A^ÐAVAUATIUSHH H H Z I$ I$ Z I|$hHt蛅I|$PHt茅I\$0L-PO Lt$HtYH{H+HteHCHxL9t.H=P HWt PJHЅLHH ID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H=-P HWt PJHЅ
+Ht$}ID$HxL9t0H=O HWt PJHЅ
+Ht$?I$HxL9t0H=O HWt PJHЅ
+Ht$L*H[]A\A]A^ÐAVAUATIUSHH H H X I$ I$ X I|$hHt̓I|$PHt较I\$0L-M Lt$HtYH{H+Ht藃HCHxL9t.H=N HWt PJHЅL,HHQID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H=_N HWt PJHЅ
+Ht$证ID$HxL9t0H=!N HWt PJHЅ
+Ht$qI$HxL9t0H=M HWt PJHЅ
+Ht$4L\H[]A\A]A^ÐAVAUATIUSHH H H W I$ I$ V I|$hHtI|$PHtI\$0L-K Lt$HtYH{H+HtɁHCHxL9t.H= M HWt PJHЅL^HH胁ID$(I|$ H 1ID$8 ID$0 I|$ OID$HxL9t0H=L HWt PJHЅ
+Ht$ID$HxL9t0H=SL HWt PJHЅ
+Ht$I$HxL9t0H=L HWt PJHЅ
+Ht$fL莀H[]A\A]A^ÐAUATIUSHH# H H 7U I$ I$ "U I|$hHt3I|$PHt$I\$0Ll$Ht]H{H+HtHSHzH;=I t.H=CK HOt JqrʅL~HHID$(I|$ H\$H 1ID$8 ID$0 I|$ ID$HHx@|ID$HHx/|I$HHx |LGH[]A\A]AVAUATIUSHH H H S I$ I$ S I|$hHt~I|$PHt~I\$0L-H Lt$HtYH{H+Ht~HCHxL9t.H=I HWt PJHЅLL}HHq~ID$(I|$ H 1ID$8 ID$0 I|$ =~ID$HxL9t0H=I HWt PJHЅ
+Ht$|ID$HxL9t0H=AI HWt PJHЅ
+Ht$|I$HxL9t0H=I HWt PJHЅ
+Ht$T|L|}H[]A\A]A^ÐAUATIUSHH# H H %R I$ I$ R I|$hHt!}I|$PHt}I\$0Ll$Ht]H{H+Ht|HSHzH;=F t.H=1H HOt JqrʅL{HH|ID$(I|$ H\$H 1ID$8 ID$0 I|$ o|ID$HHx.yID$HHx yI$HHx
yL5|H[]A\A]AUATIUSHH# H H P I$ I$ P I|$hHt{I|$PHt{I\$0Ll$Ht]H{H+Ht{HSHzH;=oE t.H=F HOt JqrʅL?zHHd{ID$(I|$ H\$H 1ID$8 ID$0 I|$ +{ID$HHxwID$HHxwI$HHxwLzH[]A\A]AUATIUSHH# H H O I$ I$ O I|$hHtzI|$PHtzI\$0Ll$Ht]H{H+HtjzHSHzH;=+D t.H=E HOt JqrʅLxHH zID$(I|$ H\$H 1ID$8 ID$0 I|$ yID$HHxvID$HHxvI$HHxvLyH[]A\A]ATIUSH? t,HGHH_(HhH9vH;Hxy[]I<$A\iy[]A\SHHHtIHGHxH;=C t0H=D HWt PJHЅ
+Ht$wHyH[AVAUATIUSHH H H M I$ I$ M I|$hHtxI|$PHtxI\$0L-nB Lt$HtYH{H+HtxHCHxL9t.H=C HWt PJHЅLwHH=xID$(I|$ H 1ID$8 ID$0 I|$ xID$HxL9t0H=KC HWt PJHЅ
+Ht$vID$HxL9t0H=
C HWt PJHЅ
+Ht$]vI$HxL9t0H=B HWt PJHЅ
+Ht$ vLHwH[]A\A]A^ÐSHHHtIHGHxH;=@ t0H=lB HWt PJHЅ
+Ht$uHvH[AVAUATIUSHHQ H H K I$ I$ ~K I|$hHtvI|$PHtvI\$0L-D@ Lt$HtYH{H+HtYvHCHxL9t.H=A HWt PJHЅLtHHvID$(I|$ H\$H 1ID$8 ID$0 I|$ uID$HHxrID$HxL9t.H= A HWt PJHЅH]tI$HHxMrLuuH[]A\A]A^SHHHtIHGHxH;= ? t0H=@ HWt PJHЅ
+Ht$sHuH[P qpH*r H5q HHHHr! {P BpHq H5q HHHHC! zAUATIUSHH# H H eI I$ I$ PI I|$hHtatI|$PHtRtI\$0Ll$Ht]H{H+Ht2tHSHzH;== t.H=q? HOt JqrʅLrHHsID$(I|$ H\$H 1ID$8 ID$0 I|$ sID$HHxnpID$HHx]pI$HHxMpLusH[]A\A]AVAUATIUSHH H H H I$ I$
+H I|$hHtsI|$PHt sI\$0L-< Lt$HtYH{H+HtrHCHxL9t.H=(> HWt PJHЅLzqHHrID$(I|$ H 1ID$8 ID$0 I|$ krID$HxL9t0H== HWt PJHЅ
+Ht$pID$HxL9t0H=o= HWt PJHЅ
+Ht$pI$HxL9t0H=2= HWt PJHЅ
+Ht$pLqH[]A\A]A^ÐAVAUATIUSHH H H QF I$ I$ <F I|$hHtMqI|$PHt>qI\$0L-; Lt$HtYH{H+HtqHCHxL9t.H=Z< HWt PJHЅLoHHpID$(I|$ H 1ID$8 ID$0 I|$ pID$HxL9t0H=; HWt PJHЅ
+Ht$/oID$HxL9t0H=; HWt PJHЅ
+Ht$nI$HxL9t0H=d; HWt PJHЅ
+Ht$nLoH[]A\A]A^ÐSHHHtIHGHxH;=9 t0H= ; HWt PJHЅ
+Ht$PnHxoH[AVAUATIUSHH H H 'D I$ I$ D I|$hHt#oI|$PHtoI\$0L-8 Lt$HtYH{H+HtnHCHxL9t.H=0: HWt PJHЅLmHHnID$(I|$ H 1ID$8 ID$0 I|$ snID$HxL9t0H=9 HWt PJHЅ
+Ht$mID$HxL9t0H=w9 HWt PJHЅ
+Ht$lI$HxL9t0H=:9 HWt PJHЅ
+Ht$lLmH[]A\A]A^ÐAVAUATIUSHH H H YB I$ I$ DB I|$hHtUmI|$PHtFmI\$0L-
+7 Lt$HtYH{H+Ht mHCHxL9t.H=b8 HWt PJHЅLkHHlID$(I|$ H 1ID$8 ID$0 I|$ lID$HxL9t0H=7 HWt PJHЅ
+Ht$7kID$HxL9t0H=7 HWt PJHЅ
+Ht$jI$HxL9t0H=l7 HWt PJHЅ
+Ht$jLkH[]A\A]A^ÐAVAUATIUSHH H H @ I$ I$ v@ I|$hHtkI|$PHtxkI\$0L-<5 Lt$HtYH{H+HtQkHCHxL9t.H=6 HWt PJHЅLiHH kID$(I|$ H 1ID$8 ID$0 I|$ jID$HxL9t0H=6 HWt PJHЅ
+Ht$iiID$HxL9t0H=5 HWt PJHЅ
+Ht$+iI$HxL9t0H=5 HWt PJHЅ
+Ht$hLjH[]A\A]A^ÐAVAUATIUSHH H H > I$ I$ > I|$hHtiI|$PHtiI\$0L-n3 Lt$HtYH{H+HtiHCHxL9t.H=4 HWt PJHЅLhHH=iID$(I|$ H 1ID$8 ID$0 I|$ iID$HxL9t0H=K4 HWt PJHЅ
+Ht$gID$HxL9t0H=
4 HWt PJHЅ
+Ht$]gI$HxL9t0H=3 HWt PJHЅ
+Ht$ gLHhH[]A\A]A^ÐAVAUATIUSHH H H < I$ I$ < I|$hHtgI|$PHtgI\$0L-1 Lt$HtYH{H+HtgHCHxL9t.H=2 HWt PJHЅLJfHHogID$(I|$ H 1ID$8 ID$0 I|$ ;gID$HxL9t0H=}2 HWt PJHЅ
+Ht$eID$HxL9t0H=?2 HWt PJHЅ
+Ht$eI$HxL9t0H=2 HWt PJHЅ
+Ht$ReLzfH[]A\A]A^ÐAVAUATIUSHH H H !; I$ I$ ; I|$hHt fI|$PHtfI\$0L-/ Lt$HtYH{H+HteHCHxL9t.H=*1 HWt PJHЅL|dHHeID$(I|$ H 1ID$8 ID$0 I|$ meID$HxL9t0H=0 HWt PJHЅ
+Ht$cID$HxL9t0H=q0 HWt PJHЅ
+Ht$cI$HxL9t0H=40 HWt PJHЅ
+Ht$cLdH[]A\A]A^ÐAVAUATIUSHH H H S9 I$ I$ >9 I|$hHtOdI|$PHt@dI\$0L-. Lt$HtYH{H+HtdHCHxL9t.H=\/ HWt PJHЅLbHHcID$(I|$ H 1ID$8 ID$0 I|$ cID$HxL9t0H=. HWt PJHЅ
+Ht$1bID$HxL9t0H=. HWt PJHЅ
+Ht$aI$HxL9t0H=f. HWt PJHЅ
+Ht$aLbH[]A\A]A^ÐAVAUATIUSHH H H 7 I$ I$ p7 I|$hHtbI|$PHtrbI\$0L-6, Lt$HtYH{H+HtKbHCHxL9t.H=- HWt PJHЅL`HHbID$(I|$ H 1ID$8 ID$0 I|$ aID$HxL9t0H=- HWt PJHЅ
+Ht$c`ID$HxL9t0H=, HWt PJHЅ
+Ht$%`I$HxL9t0H=, HWt PJHЅ
+Ht$_LaH[]A\A]A^ÐAVAUATIUSHH H H 5 I$ I$ 5 I|$hHt`I|$PHt`I\$0L-h* Lt$HtYH{H+Ht}`HCHxL9t.H=+ HWt PJHЅL_HH7`ID$(I|$ H 1ID$8 ID$0 I|$ `ID$HxL9t0H=E+ HWt PJHЅ
+Ht$^ID$HxL9t0H=+ HWt PJHЅ
+Ht$W^I$HxL9t0H=* HWt PJHЅ
+Ht$^LB_H[]A\A]A^ÐAVAUATIUSHH H H 3 I$ I$ 3 I|$hHt^I|$PHt^I\$0L-( Lt$HtYH{H+Ht^HCHxL9t.H=) HWt PJHЅLD]HHi^ID$(I|$ H 1ID$8 ID$0 I|$ 5^ID$HxL9t0H=w) HWt PJHЅ
+Ht$\ID$HxL9t0H=9) HWt PJHЅ
+Ht$\I$HxL9t0H=( HWt PJHЅ
+Ht$L\Lt]H[]A\A]A^ÐAVAUATIUSHH H H 2 I$ I$ 2 I|$hHt]I|$PHt]I\$0L-& Lt$HtYH{H+Ht\HCHxL9t.H=$( HWt PJHЅLv[HH\ID$(I|$ H 1ID$8 ID$0 I|$ g\ID$HxL9t0H=' HWt PJHЅ
+Ht$ZID$HxL9t0H=k' HWt PJHЅ
+Ht$ZI$HxL9t0H=.' HWt PJHЅ
+Ht$~ZL[H[]A\A]A^ÐSHHHtIHGHxH;=L% t0H=& HWt PJHЅ
+Ht$ZHB[H[AVAUATIUSHHQ H H / I$ I$ / I|$hHtZI|$PHtZI\$0L-$ Lt$HtYH{H+HtZHCHxL9t.H=% HWt PJHЅLLYHHqZID$(I|$ H\$H 1ID$8 ID$0 I|$ 8ZID$HxL9t.H=z% HWt PJHЅHXID$HHxVI$HHxVLYH[]A\A]A^AVAUATIUSHH H H {. I$ I$ f. I|$hHtwYI|$PHthYI\$0L-,# Lt$HtYH{H+HtAYHCHxL9t.H=$ HWt PJHЅLWHHXID$(I|$ H 1ID$8 ID$0 I|$ XID$HxL9t0H= $ HWt PJHЅ
+Ht$YWID$HxL9t0H=# HWt PJHЅ
+Ht$WI$HxL9t0H=# HWt PJHЅ
+Ht$VLXH[]A\A]A^ÐSHHHtIHGHxH;=! t0H=*# HWt PJHЅ
+Ht$zVHWH[AUATIUSHH# H H S, I$ I$ >, I|$hHtOWI|$PHt@WI\$0Ll$Ht]H{H+Ht WHSHzH;= t.H=_" HOt JqrʅLUHHVID$(I|$ H\$H 1ID$8 ID$0 I|$ VID$HHx\SID$HHxKSI$HHx;SLcVH[]A\A]AUATIUSHH# H H + I$ I$ * I|$hHt VI|$PHtUI\$0Ll$Ht]H{H+HtUHSHzH;= t.H=! HOt JqrʅLmTHHUID$(I|$ H\$H 1ID$8 ID$0 I|$ YUID$HHxRID$HHxRI$HHxQL UH[]A\A]AVAUATIUSHH H H ) I$ I$ ) I|$hHtTI|$PHtTI\$0L-z Lt$HtYH{H+HtTHCHxL9t.H= HWt PJHЅL$SHHITID$(I|$ H 1ID$8 ID$0 I|$ TID$HxL9t0H=W HWt PJHЅ
+Ht$RID$HxL9t0H= HWt PJHЅ
+Ht$iRI$HxL9t0H= HWt PJHЅ
+Ht$,RLTSH[]A\A]A^ÐAVAUATIUSHH H H ' I$ I$ ' I|$hHtRI|$PHtRI\$0L- Lt$HtYH{H+HtRHCHxL9t.H= HWt PJHЅLVQHH{RID$(I|$ H 1ID$8 ID$0 I|$ GRID$HxL9t0H= HWt PJHЅ
+Ht$PID$HxL9t0H=K HWt PJHЅ
+Ht$PI$HxL9t0H= HWt PJHЅ
+Ht$^PLQH[]A\A]A^ÐAVAUATIUSHH H H -& I$ I$ & I|$hHt)QI|$PHtQI\$0L- Lt$HtYH{H+HtPHCHxL9t.H=6 HWt PJHЅLOHHPID$(I|$ H 1ID$8 ID$0 I|$ yPID$HxL9t0H= HWt PJHЅ
+Ht$ OID$HxL9t0H=} HWt PJHЅ
+Ht$NI$HxL9t0H=@ HWt PJHЅ
+Ht$NLOH[]A\A]A^ÐAVAUATIUSHH H H _$ I$ I$ J$ I|$hHt[OI|$PHtLOI\$0L- Lt$HtYH{H+Ht%OHCHxL9t.H=h HWt PJHЅLMHHNID$(I|$ H 1ID$8 ID$0 I|$ NID$HxL9t0H= HWt PJHЅ
+Ht$=MID$HxL9t0H= HWt PJHЅ
+Ht$LI$HxL9t0H=r HWt PJHЅ
+Ht$LLMH[]A\A]A^ÐAVAUATIUSHH H H " I$ I$ |" I|$hHtMI|$PHt~MI\$0L-B Lt$HtYH{H+HtWMHCHxL9t.H= HWt PJHЅLKHHMID$(I|$ H 1ID$8 ID$0 I|$ LID$HxL9t0H= HWt PJHЅ
+Ht$oKID$HxL9t0H= HWt PJHЅ
+Ht$1KI$HxL9t0H= HWt PJHЅ
+Ht$JL LH[]A\A]A^ÐSHHHtIHGHxH;= t0H=@ HWt PJHЅ
+Ht$JHKH[AVAUATIUSHH H H g I$ I$ R I|$hHtcKI|$PHtTKI\$0L- Lt$HtYH{H+Ht-KHCHxL9t.H=p HWt PJHЅLIHHJID$(I|$ H 1ID$8 ID$0 I|$ JID$HxL9t0H= HWt PJHЅ
+Ht$EIID$HxL9t0H= HWt PJHЅ
+Ht$II$HxL9t0H=z HWt PJHЅ
+Ht$HLIH[]A\A]A^ÐAVAUATIUSHH H H I$ I$ I|$hHtII|$PHtII\$0L-J Lt$HtYH{H+Ht_IHCHxL9t.H= HWt PJHЅLGHHIID$(I|$ H 1ID$8 ID$0 I|$ HID$HxL9t0H=' HWt PJHЅ
+Ht$wGID$HxL9t0H= HWt PJHЅ
+Ht$9GI$HxL9t0H= HWt PJHЅ
+Ht$FL$HH[]A\A]A^ÐAVAUATIUSHH H H I$ I$ I|$hHtGI|$PHtGI\$0L-| Lt$HtYH{H+HtGHCHxL9t.H= HWt PJHЅL&FHHKGID$(I|$ H 1ID$8 ID$0 I|$ GID$HxL9t0H=Y HWt PJHЅ
+Ht$EID$HxL9t0H= HWt PJHЅ
+Ht$kEI$HxL9t0H= HWt PJHЅ
+Ht$.ELVFH[]A\A]A^ÐAVAUATIUSHH H H I$ I$ I|$hHtEI|$PHtEI\$0L- Lt$HtYH{H+HtEHCHxL9t.H= HWt PJHЅLXDHH}EID$(I|$ H 1ID$8 ID$0 I|$ IEID$HxL9t0H= HWt PJHЅ
+Ht$CID$HxL9t0H=M HWt PJHЅ
+Ht$CI$HxL9t0H= HWt PJHЅ
+Ht$`CLDH[]A\A]A^ÐAUATIUSHH# H H 1 I$ I$ I|$hHt-DI|$PHt DI\$0Ll$Ht]H{H+HtCHSHzH;=
t.H== HOt JqrʅLBHHCID$(I|$ H\$H 1ID$8 ID$0 I|$ {CID$HHx:@ID$HHx)@I$HHx@LACH[]A\A]SHHHtHGHt$Hx?HCH[ÐSHHHtIHGHxH;= t0H=: HWt PJHЅ
+Ht$AHBH[AVAUATIUSHH H H a I$ I$ L I|$hHt]BI|$PHtNBI\$0L- Lt$HtYH{H+Ht'BHCHxL9t.H=j
HWt PJHЅL@HHAID$(I|$ H 1ID$8 ID$0 I|$ AID$HxL9t0H= HWt PJHЅ
+Ht$?@ID$HxL9t0H= HWt PJHЅ
+Ht$@I$HxL9t0H=t HWt PJHЅ
+Ht$?L@H[]A\A]A^P D<H= H5= HHHHE FSHHHtIHGHxH;=d
+ t0H= HWt PJHЅ
+Ht$2?HZ@H[AVAUATIUSHH H H I$ I$ I|$hHt@I|$PHt?I\$0L- Lt$HtYH{H+Ht?HCHxL9t.H= HWt PJHЅLd>HH?ID$(I|$ H 1ID$8 ID$0 I|$ U?ID$HxL9t0H=
+ HWt PJHЅ
+Ht$=ID$HxL9t0H=Y
+ HWt PJHЅ
+Ht$=I$HxL9t0H=
+ HWt PJHЅ
+Ht$l=L>H[]A\A]A^ÐAVAUATIUSHH H H ; I$ I$ & I|$hHt7>I|$PHt(>I\$0L- Lt$HtYH{H+Ht>HCHxL9t.H=D HWt PJHЅL<HH=ID$(I|$ H 1ID$8 ID$0 I|$ =ID$HxL9t0H= HWt PJHЅ
+Ht$<ID$HxL9t0H= HWt PJHЅ
+Ht$;I$HxL9t0H=N HWt PJHЅ
+Ht$;L<H[]A\A]A^ÐAVAUATIUSHH H H m I$ I$ X I|$hHti<I|$PHtZ<I\$0L- Lt$HtYH{H+Ht3<HCHxL9t.H=v HWt PJHЅL:HH;ID$(I|$ H 1ID$8 ID$0 I|$ ;ID$HxL9t0H= HWt PJHЅ
+Ht$K:ID$HxL9t0H= HWt PJHЅ
+Ht$
:I$HxL9t0H= HWt PJHЅ
+Ht$9L:H[]A\A]A^ÐAVAUATIUSHH H H I$ I$ I|$hHt:I|$PHt:I\$0L-P Lt$HtYH{H+Hte:HCHxL9t.H= HWt PJHЅL8HH :ID$(I|$ H 1ID$8 ID$0 I|$ 9ID$HxL9t0H=- HWt PJHЅ
+Ht$}8ID$HxL9t0H= HWt PJHЅ
+Ht$?8I$HxL9t0H= HWt PJHЅ
+Ht$8L*9H[]A\A]A^ÐSHHHtIHGHxH;= t0H=N HWt PJHЅ
+Ht$7H8H[AVAUATIUSHH H H u
I$ I$ `
I|$hHtq8I|$PHtb8I\$0L-& Lt$HtYH{H+Ht;8HCHxL9t.H=~ HWt PJHЅL6HH7ID$(I|$ H 1ID$8 ID$0 I|$ 7ID$HxL9t0H= HWt PJHЅ
+Ht$S6ID$HxL9t0H= HWt PJHЅ
+Ht$6I$HxL9t0H= HWt PJHЅ
+Ht$5L 7H[]A\A]A^ÐSHHHtIHGHxH;= t0H=$ HWt PJHЅ
+Ht$t5H6H[AVAUATIUSHH H H K I$ I$ 6 I|$hHtG6I|$PHt86I\$0L- Lt$HtYH{H+Ht6HCHxL9t.H=T HWt PJHЅL4HH5ID$(I|$ H 1ID$8 ID$0 I|$ 5ID$HxL9t0H= HWt PJHЅ
+Ht$)4ID$HxL9t0H= HWt PJHЅ
+Ht$3I$HxL9t0H=^ HWt PJHЅ
+Ht$3L4H[]A\A]A^ÐSHHHtIHGHxH;=| t0H= HWt PJHЅ
+Ht$J3Hr4H[AVAUATIUSHH H H ! I$ I$ I|$hHt 4I|$PHt4I\$0L- Lt$HtYH{H+Ht3HCHxL9t.H=* HWt PJHЅL|2HH3ID$(I|$ H 1ID$8 ID$0 I|$ m3ID$HxL9t0H= HWt PJHЅ
+Ht$1ID$HxL9t0H=q HWt PJHЅ
+Ht$1I$HxL9t0H=4 HWt PJHЅ
+Ht$1L2H[]A\A]A^ÐAVAUATIUSHH H H S I$ I$ > I|$hHtO2I|$PHt@2I\$0L- Lt$HtYH{H+Ht2HCHxL9t.H=\ HWt PJHЅL0HH1ID$(I|$ H 1ID$8 ID$0 I|$ 1ID$HxL9t0H= HWt PJHЅ
+Ht$10ID$HxL9t0H= HWt PJHЅ
+Ht$/I$HxL9t0H=f HWt PJHЅ
+Ht$/L0H[]A\A]A^ÐAVAUATIUSHH H H I$ I$ p I|$hHt0I|$PHtr0I\$0L-6 Lt$HtYH{H+HtK0HCHxL9t.H= HWt PJHЅL.HH0ID$(I|$ H 1ID$8 ID$0 I|$ /ID$HxL9t0H= HWt PJHЅ
+Ht$c.ID$HxL9t0H= HWt PJHЅ
+Ht$%.I$HxL9t0H= HWt PJHЅ
+Ht$-L/H[]A\A]A^ÐAVAUATIUSHH H H I$ I$ I|$hHt.I|$PHt.I\$0L-h Lt$HtYH{H+Ht}.HCHxL9t.H= HWt PJHЅL-HH7.ID$(I|$ H 1ID$8 ID$0 I|$ .ID$HxL9t0H=E HWt PJHЅ
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+Ht$,LB-H[]A\A]A^ÐSHHHtIHGHxH;= t0H=f HWt PJHЅ
+Ht$+H,H[AVAUATIUSHH| H H I$ I$ x I|$hHt,I|$PHtz,I\$0L-> Lt$HtYH{H+HtS,HCHxL9t.H= HWt PJHЅL*HH
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+Ht$'H)H[AVAUATIUSHH H H I$ I$ I|$hHt(I|$PHt(I\$0L-r Lt$HtYH{H+Ht(HCHxL9t.H= HWt PJHЅL 'HHA(ID$(I|$ H 1ID$8 ID$0 I|$
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+Ht$&ID$HxL9t0H= HWt PJHЅ
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+Ht$$&LL'H[]A\A]A^ÐAVAUATIUSHH H H I$ I$ I|$hHt&I|$PHt&I\$0L- Lt$HtYH{H+Ht&HCHxL9t.H= HWt PJHЅLN%HHs&ID$(I|$ H 1ID$8 ID$0 I|$ ?&ID$HxL9t0H= HWt PJHЅ
+Ht$$ID$HxL9t0H=C HWt PJHЅ
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+Ht$V$L~%H[]A\A]A^ÐAVAUATIUSHH H H % I$ I$ I|$hHt!%I|$PHt%I\$0L- Lt$HtYH{H+Ht$HCHxL9t.H=. HWt PJHЅL#HH$ID$(I|$ H 1ID$8 ID$0 I|$ q$ID$HxL9t0H= HWt PJHЅ
+Ht$#ID$HxL9t0H=u HWt PJHЅ
+Ht$"I$HxL9t0H=8 HWt PJHЅ
+Ht$"L#H[]A\A]A^ÐAVAUATIUSHH H H W I$ I$ B I|$hHtS#I|$PHtD#I\$0L- Lt$HtYH{H+Ht #HCHxL9t.H=` HWt PJHЅL!HH"ID$(I|$ H 1ID$8 ID$0 I|$ "ID$HxL9t0H= HWt PJHЅ
+Ht$5!ID$HxL9t0H= HWt PJHЅ
+Ht$ I$HxL9t0H=j HWt PJHЅ
+Ht$ L!H[]A\A]A^ÐAVAUATIUSHH H H I$ I$ t I|$hHt!I|$PHtv!I\$0L-: Lt$HtYH{H+HtO!HCHxL9t.H= HWt PJHЅL HH !ID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H= HWt PJHЅ
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+Ht$ L H[]A\A]A^ÐAVAUATIUSHH H H I$ I$ I|$hHt I|$PHt I\$0L-l Lt$HtYH{H+Ht HCHxL9t.H= HWt PJHЅL HH; ID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H=I HWt PJHЅ
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U H5U HSHi" H$P Ht$ IE11HHVH}PH5U Ha.= H)HÐf D H$@ H PH$P H :H$x H $H$X H HD$hH H$ H5ЧX HHFH$p HÈD$0H$ Ht$(Hxŵ, HD$ DXEH$ H
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LU H5YU HdHH H$ IHt$ E11HHJH}PH5LU }H"= HTH$P H889 H$ H$P Hx 9 H$ H$P Hx9 H$ H$P Hx9 H$ H$P Hx 9 HD$XH$P Hx(9 HD$`H$P Hx09 H@9 H|$@H39 H9 HD$ xH$@ H
U H5 U ' H'GH H$ IHt$ E11HHpIH}PH5U @H!= HHH$ H$ H!= HHKHH$ HDŽ$8 H$0 HDŽ$@ H|$8HDŽ$0 H@@Hp t H$0 H$8 H)HHtjHD$ D@E: D$0 H$0 HQ@GHH$0 H "H$ H = "*H$ H9BuH$8 H;BvH*$8 Q.{(舲H$ H|$81H@@Hp蜸D$0' HD$ p5H$ H
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KU H5XU N Hc?H
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-PJ҉HL茰f. AVAUATUHSHH*} LP HH HI9H H=~ L-=~ Lt$ fHHxL9 HI9uHH Ht&H0 HtH HtH HtH HtH HtѰH HtH Ht诰H Ht螰H| HHE H[]A\A]A^LJH(L@HHxL9uHI9u
-PJ҉HL f. AVAUATUHSHHJ)} LP HH HI9H H=L~ L-
~ Lt$ fHHxL9 HI9uHH Ht覯H0 Ht蕯H Ht脯H HtsH HtbH HtQH Ht@H Ht/H Ht HW| HHHE H[]A\A]A^LBH L踭HHxL9uHI9uPJ҉HL脭fAVAUATUHSHH
-'} LP HH HI9H H=
~ L-= ~ Lt$ fHHxL9 HI9uHH Ht&H0 HtH HtH HtH HtH HtѭH HtH Ht语H Ht螭H| HHHE 臭H[]A\A]A^LBH L8HHxL9uHI9uPJ҉HLfAVAUATUHSHH
-&} LP HH HI9H H=L ~ L-
-~ Lt$ fHHxL9 HI9uHH Ht覬H0 Ht蕬H Ht脬H HtsH HtbH HtQH Ht@H Ht/H Ht HW| HHHE H[]A\A]A^LBH L踪HHxL9uHI9uPJ҉HL脪fAVAUATUHSHHJ$} LP HH HI9H H=
-~ L-= ~ Lt$ fHHxL9 HI9uHH Ht&H0 HtH HtH HtH HtH HtѪH HtH Ht诪H Ht螪H| HHHE 自H[]A\A]A^LBH L8HHxL9uHI9uPJ҉HLfAV1IAUATE1UH, HSHHH HG HG u5N, HHLktLLHLk[]A\A]A^f. H H9wL$ Lѩ:H;HHtzHүfAVAUATIUSHH H H YI$ I$ YI|$hHt!I|$PHtI\$0Lt$HtMH=c~ L-~ u
D HH{H+HtԨHCHxL9 H軨HuID$(I|$ 1H ͤI|$ ID$8 ID$0 聨ID$LHxPID$LHx?I$LHx/LGH[]A\A]A^úPeL XH{H+Ht
-HCHxL9uHH6H̋PJ҉HL貦AVHg9 AUATUHSH`HD$@H.HD$XH.HD$P8$ HD$-$ HD$HD$ HD$( HD$0 褧HXHt$ HHD$ H\$0脬H\$(Z.$ ImHù 1H߾
- HH~ H{@C C C@ ?HHH HKHK/F H HC(H9 L, LL1HIݢH H5F Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $;% HT$ H5HF H臘% H5G HH% H5ԔG H% HH55F HB 蠂% HH HD$PHE HU Ht HT$XH]HEHt$@HHU H<$HE( HtH|$ Ht+HD$PHtH|$@ HH`H[]A\A]A^譟IHCHHxʡHCHHx躡HHHx諡HLH|$ Ht豤HD$PHtH|$@ HHHHHMIHt
HHH} HtHE LH<$HtHf. AVHj AUATUHSH`HD$@H.+HD$XH2+HD$P5$ HD$*$ HD$HD$ HD$( HD$0 HXHt$ HHD$ H\$0H\$(4$ IͣHù 1H߾
- HHQ~ H{@C C C@ ?HHH HKHK,F H HC(H9 L, L]L1HI=H H5uF Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $蛑% HT$ H5F H% H5G H}% H54G HY~% HH5X2F HB % HH HD$PHE HU Ht HT$XH]HEHt$@HHU H<$HE( Ht*H|$ Ht苡HD$PHtH|$@ HH`H[]A\A]A^
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- HH} H{@C C C@ ?HHH HKHKd(F H HC(H9 L, L轟L1HI蝛H H5F Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $% HT$ H5F HG% H5?G Hz% H5G Hz% HH5.F HB `{% HH HD$PHE HU Ht HT$XH]HEHt$@HHU H<$HE( HtH|$ HtHD$PHtH|$@ HH`H[]A\A]A^mIHCHHx芚HCHHxzHHHxkHL耝H|$ HtqHD$PHtH|$@ HH谣HHHMIHt
HHH} HtHE LH<$HtHf. AVH? AUATUHSH`HD$@H#HD$XH#HD$P-$ HD$"$ HD$HD$ HD$( HD$0 ĜHXHt$ HHD$ H\$0褡H\$(z$$ I荜Hù 1H߾
- HH} H{@C C C@ ?HHH HKHK$F H HC(H9 L, L L1HIH H55F Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $[% HT$ H5hF H觍% H5G Hhv% H5G Hw% HH5+F HB w% HH HD$PHE HU Ht HT$XH]HEHt$@HHU H<$HE( HtH|$ HtKHD$PHtH|$@ HH`H[]A\A]A^͔IHCHHxHCHHxږHHHx˖HLH|$ HtљHD$PHtH|$@ HHHHHMIHt
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JG H5G M HH,G LHLHL HD$@HHx=HH5ϡ| LW H5G Hq H5G HU҃ HD$0HHxϏH|$PH9|$Xt#AD$GAD$GAD$ Ht趒H []A\A]A^A_HH|$PHt蒒HHHD$0HHHxSHHD$0HHx=HD$@H\$ HHx'HD$PHHxH莘H$ HHtHD$pHt$ HxHHD$@HHxЎLȏBHHHH\$ IHL1LiHfAWAVAUATIUHSH Lt$PH4$HT$ H5QG L|$@L角HT$ H5?G L蓒H<} H$ Ll$pLHLHHHD$pe4& H$ Hl$0LHIE訍H$ Ht$HHHt$ HD$HyԍHD$HHD$pHHx趍H5G H1҅= HD$0A$HHx舍HD$@HHxwHD$PHHxfH5hG LHHD$P HD$X HD$` 艑H4$H|$ LH4& Lt$ M IFHHxL HD$0HHxX 褋LIYL0/ H
G H5;G M HSHlG LHLH&L^HD$@HHx}HH5| L藕 H5YG H豈 H5BG H蕈҃ HD$0HHxH|$PH9|$Xt#AD$GAD$GAD$ HtH []A\A]A^A_HH|$PHtҎH*HHD$0HHHx蓋HHD$0HHx}HD$@H\$ HHxgHD$PHHxVHΔH$ HHtHD$pHt$ Hx&HHD$@HHxLBHHHH\$ IHLqLHfAWAVAUATIUHSH Lt$PH4$HT$ H5G L|$@LHT$ H5G LӎH|} H$ Ll$pLHLHHHD$p0& H$ Hl$0LHIEH$ Ht$HHHt$ HD$HyHD$H'HD$pHHxH5G HG1҅= HD$0A$HHxȉHD$@HHx跉HD$PHHx覉H5G LHHD$P HD$X HD$` ɍH4$H|$ LH51& Lt$ M IFHHxGL_HD$0HHx.X LIL!-/ H
-G H5{G M HHG LHLHfLHD$@HHx轈HFH5O| Lב H5G H H5G HՄ҃ HD$0HHxOH|$PH9|$Xt#AD$GAD$GAD$ Ht6H []A\A]A^A_HH|$PHtHjHHD$0HHHxӇHHD$0HHx轇HD$@H\$ HHx觇HD$PHHx薇HH$ HHtHD$pHt$ HxfHHD$@HHxPLHBHHHH\$ IHLLHfAWAVAUATIUHSH Lt$PH4$HT$ H5G L|$@L'HT$ H5G LH} H$ Ll$pLHLHHHD$p,& H$ Hl$0LHIE(H$ Ht$HHHt$ HD$HyTHD$HgHD$pHHx6H5 G H臂1҅= HD$0A$HHxHD$@HHxHD$PHHxH5G LHHD$P HD$X HD$` H4$H|$ LHu-& Lt$ M IFHHx臅L蟈HD$0HHxnX $LILa)/ H
jG H5G M HHG LHLNHLHD$@HHxHH5| L H5G H1 H5G H҃ HD$0HHx菄H|$PH9|$Xt#AD$GAD$GAD$ HtvH []A\A]A^A_HH|$PHtRH認HHD$0HHHxHHD$0HHxHD$@H\$ HHxHD$PHHxփHNH$ HHtYHD$pHt$ Hx覃HHD$@HHx萃L舄BHHHH\$ IHLL)HfAWAVAUATILUSHH MHt$HT$hHDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$
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EG ^D^A HD$O,LLI O$ YXHWAYAD HH9uMC~OHD$O,O 1M$1 ADYAXDADADHAHL9uIIIHHI9BH$ L4 5<F $ HD$pd WI>D(LoDF HD$xIFHD$ HHHD$HH HD$HD$PH$ HHL$HHHHHD$HHL$`HH$ H$ HHHL$@HD$XH HD$hA(L1H f D (1
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G H5MG M LH~G LLLXLLH$ HHxxHH5| L f D H5ǣG L9t H5G L t ~L(oH$ HL$ HxwH H5G LLH${H $H$ LL & H$ H H$ HL$ Hx wX uLIL$ L / L$ H
jG H5G M LsHG LLLLFL~H$ HHxvH#H5,| L @ H$ HHxlvH H H+ H+ HHH9$ L$ HH H5+G LLHD$izHL$H$ LL & H$ b L$0 H0 HT$ H5G LH$zH $H$ LLd & L$ MtL?H$0 HHxuH$ HtH$ HHxeuM H HCx HCh HCp HH+ H HCxL 1E1H$? HtHHSxHH H HJH)IHHI9HSx},H H; uH4$LVHSxH HP H+H ƃ HH ƃ HKpHHsxH H H+ 1HH Hɉd HCh H8 H+0 HH L$P X rLIL$ L/ H
mG H5G MB LvHG LLLLILH$P HHxsH&H5/| L| H H H+ H+ HHH9u H H H)HH C`~%W1.v .wrHH9uHĸ []A\A]A^A_ H8 H+0 HHtiHsxHCpHHZH H+ 1CP HH1QC` f. H$ HHx|r HCp HHH HD$HH0 HtVuH HtEuH Ht4uH Ht#uH HtuH HtuH HttH HttHH
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_G H5ظG M# LhH9G LLLL;LsH$ HHxpHH5!| LyILH|$ L$@ X oLIL$ LL/ L$ H
G H5&G M' LHG LLL1LLH$@ HHxoHfH5o| LxHH|$nILM<LMqH$@ HLHxoLpH|$,IIHHL^pH|$IH$P HLHx:oIHD$`HHx$oHD$pHHxoH$ kIH$ VLIĐkLH$ HLHxnBIIHH$ HooHHD$@LHxnH$ HggHHHD$0LHxQnH$ HIIHHHD$ H$P Hx nH$ HHHHf?ILLIpL$` X lLIAL$ L/ H
*G H5G MC L3HG LLLLL>H$` HHxZmHH5~| LtvL$p X kLIL$@ L'/ H
G H5 G MD H虾H=G LHLHlLH$p HHxlHIH5R~| LuILM,LMaH$` HLHxzlIIIH$ HtH$ HHx=lIH$0 HHx!lIILMLMH$ HLHxkLlHH HD$H}IIHIIGHHD$PLHxkIHLLFH$p LHxbkHH D f UHh SHnHHHHH[]HH*nHtfAWAVAUATIUSHHH H>| HHG!& H{PLuH^| L$ Ll$ H5G HDŽ$ HDŽ$ LLHDŽ$ HHnH$ H LLH'HD$ L$P LHx>jH$ HtLmL|$0H5IG Hǃ Hǃ Hǃ LHǃ Hǃ LHǃ HDŽ$ HDŽ$ HDŽ$ mH HLLHD$0LHxiH$ HtlL|$@H5G LHDŽ$ HDŽ$ LHDŽ$ mH HLLWHD$@LHxiH$ Ht$lL|$PH5/G Hǃ Hǃ Hǃ LLHDŽ$ HDŽ$ HDŽ$ lH HLLHD$PLHxhH$ HtkH$ H53G Hǃ0 Hǃ8 Hǃ@ LHǃH HǃP HHǃX HD$_lHt$pLHH5ԓG H<$DlL|$`HL$H$LLkH5G L,d1҅J HD$`` HL$ HxgHD$pHHxgH$ HHx~gH H5G LLH$kH $H$ LL& H$ H ԳH$ HL$ HxgX eLIL$ L / L$` H
G H5ݮG M LmHG LLLL@LxH$ HHxfH H5&x| Lof D H5WG Lb H5DG Lb ~L踲oH$ HL$ HxfH H5-G LLH$GjH $H$ LL
& H$ H jH$ HL$ HxeX ddLIL$ L / L$ H
G H5sG M LH$G LLL~LLH$ HHx*eHH5v| LDn @ H$ HHxdH H H+ H+ HHH9$ L$ HH H5G LLHD$hHL$H$ LL!& H$ b L$0 H0 HT$ H5}G LH$hH $H$ LL & L$ MtLϰH$0 HHxdH$ Ht詰H$ HHxcM H HCx HCh HCp HH+ H HCxL 1E1H$? HtHHSxHH H HJH)IHHI9HSx},H H; uH4$LEHSxH HP H+H ƃ HH ƃ HKpHHsxH H H+ 1HH Hɉd HCh H8 H+0 HH L$P X _aLIL$ L/ H
G H5vG MB LHG LLLLLH$P HHx-bHH5s| LGk H H H+ H+ HHH9u H H H)HH C`~%W1.v .wrHH9uHĸ []A\A]A^A_ H8 H+0 HHtiHsxHCpHHZH H+ 1CP HH1QC` f. H$ HHx a HCp HHH HD$HH0 HtcH HtcH HtcH HtcH HtcH HtcH HtcH HtocHH
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G H5hG M# LHɧG LLLsLLH$ HHx _HH5p| L9hILH|$ L$@ X ]LI\L$ L/ L$ H
ݬG H5G M' LFH7G LLLLLQH$@ HHxm^HH5o| LgHH|$nILMLMH$@ HLHx^L_H|$,IIHHL^H|$IH$P HLHx]IHD$`HHx]HD$pHHx]H$ kIH$ VLIĐL3H$ HLHxL]BIIHH$ HooHHD$@LHx]H$ HggHHHD$0LHx\H$ HIIHHHD$ H$P Hx\H$ HHHHf?ILPLI L$` X [LIL$ LQ / H
ZG H53G MC LíHG LLL>LLοH$` HHx[HsH5|m| LeL$p X ZLI7L$@ L. H
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G H5mG M LHG LLLxLйLH$ HHx$UHH5f| L>^f D H5G LYQ H5ԀG L=Q ~LHoH$ HL$ HxTH H5G LLH$XH $H$ LL@% H$ H H$ HL$ Hx>TX RLIL$ L). L$ H
JG H5G M L蓥HG LLLLfL螷H$ HHxSHCH5Le| L\ @ H$ HHxSH H H+ H+ HHH9$ L$ HH H5KG LLHD$WHL$H$ LL% H$ b L$0 H0 HT$ H5
G LH$;WH $H$ LL% L$ MtL_H$0 HHxRH$ Ht9H$ HHxRM H HCx HCh HCp HH+ H HCxL 1E1H$? HtHHSxHH H HJH)IHHI9HSx},H H; uH4$L4HSxH HP H+H ƃ HH ƃ HKpHHsxH H H+ 1HH Hɉd HCh H8 H+0 HH L$P X OLIL$ L$. H
MG H5G MB L薢H G LLLLiL衴H$P HHxPHFH5Ob| LY H H H+ H+ HHH9u H H H)HH C`~%W1.v .wrHH9uHĸ []A\A]A^A_ H8 H+0 HHtiHsxHCpHHZH H+ 1CP HH1QC` f. H$ HHxO HCp HHH HD$H7H0 HtvRH HteRH HtTRH HtCRH Ht2RH Ht!RH HtRH HtQHH
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dG H5G M L荕H.G LLLL`L蘧H$ HHxCH=H5FU| LLf D H5woG L? H5doG L? ~L؏oH$ HL$ Hx4CH H5MoG LLH$gGH $H$ LL% H$ H 芏H$ HL$ HxBX ALI9L$ L. L$ H
G H5G M L#HDuG LLL螧LL.H$ HHxJBHӳH5S| LdK @ H$ HHx BH H H+ H+ HHH9$ L$ HH H5mG LLHD$FHL$H$ LLA% H$ b L$0 H0 HT$ H5mG LH$EH $H$ LL% L$ MtLH$0 HHx;AH$ HtɍH$ HHxAM H HCx HCh HCp HH+ H HCxL 1E1H$? HtHHSxHH H HJH)IHHI9HSx},H H; uH4$L"HSxH HP H+H ƃ HH ƃ HKpHHsxH H H+ 1HH Hɉd HCh H8 H+0 HH L$P X >LI4L$ L. H
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-H HtH HtH{hHtځH{PHt́Hz HH[f. fHz SHHHH Ht荁H 1 H uH HtdH HtSHp HtBHX Ht1H@ Ht H( HtH HtH HtH Ht܀H HtˀH Ht躀H Ht詀H Ht蘀H{hHt芀H{PHt|Hz HHH[e D Hz SHHHH Ht=H H %H HtH HtHp HtHX HtH@ HtH( HtH HtH HtH HtH Ht{H HtjH HtYH HtHH{hHt:H{PHt,Hez HHH[ D LLG1LIHHIHt(HHq I9t#H HtHq1HII9t1 Hf. fLFHBI9 AELItjL99 AE| M9S HG LOHAHQHG HWHAHQHGHWHA M9 AEuVL9 @@ HOHG
-HJHJHGHOHJHBHGHOHB HGLGHFHVHGHWHFHVHGHWHF LZL9^ LYL9^AEEf LYL9ZAE6f D HG HAH9F HqH9r LOLGHwHG4f. t7HWHP HWHPHWHH HPH9uL;@uHwLGLOÐATIUHSHH9 H_ H9 @ LCL;E t{H{ LKLSH)HHHH~,H HHH@ HHHHHHHHHHuM LELULML9HH[]A\ H$ H{ f D HEH9CY UH SHH{HH LH LPHCHGLM1LIHE HHHt*HLC D M9t;HHtLC1LIH9tHLH4H [] {HH1[] L% sf. LFHBI9 AELItjL99 AE| M9S HG LOHAHQHG HWHAHQHGHWHA M9 AEuVL9 @@ HOHG
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M9 M6 M)HT$?1L2nIHL@ AAH9a L|$PLt$@LD$PLLl]I[INLt$@HsPLqhiMH= B dffH= B dMH= B c>f. H= B cLLLHL$(lHL$(I:LLLHL$(kHL$(I|IIuLt$@HsPLgMH= B YcH=
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B H5K@ MW HIHD$X5lH @ HLLwL8wLpvH$ LHxHUH5Nuw Lf D X I|$Ii H$ H$ H5J@ H$ HL$P LBr( H$ H
3B H5J@ MW HIHD$XdkH;@ HLLwLgvLuH$ LHxHH5}tw L D X 6I}I蚸 H$ H$ H5J@ H$ HCL$P Lsq( H$ H
dB H5
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LLH$0 H$HH tH\$pHhHCHxH;=x z HF D LLLD$ LD$ AHHO X L$ H{IH4 H@@Hx$ H5A HLH$ H5l( H$ H
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+ff. ff. Tff. ff. ff. HI D HIIHL}fHeH$ Ht$XHx4HHf0f AWI1AVAUATUSHH( H<$H; HH
+ HxH HP0 Hm H HIt HxH. HP0 M$$ H HIt HxH HP0 Mm HELt$pLLHLH@HD$IEH HD$ID$H@HD$KH|$p HDŽ$ 3 H$ H$ N' H$ L>' L$ L$ E1IVI;P% H
Vx I~H9 IxH9 L$ Mt#IVHzH9
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qB H5|7@ MW HIHD$XXH@ HLLcLcL?bH$ LHx[H$pH5 aw L D X I}I: H$ H$ H56@ H$ HsL$P L^( H$ H
qB H56@ MW HIHD$X5WH @ HLLbL8bLpaH$ LHxHUoH5N`w Lf D HD$ HDŽ$0 HDŽ$8 HDŽ$@ H$ HD$H$ IGXH$ IG`H$ H$ Lp HH$0 HL$@ HL$XH$0 1HL$8 IIE0H[x IF0IU IUPAPx ID$0IINPx HuI$ID$PMGhAGpLNLVHu0~ Hu H$ D$hHD$H$ L$ L$ L$ H$ H$0 H$ HuPHۉ|$lHPHH$ H$ HPH@HL$H$ H$( H$ H$ H$( H$ H$ H$ H$ H$ H$ H$ H$ H$ HD$hH$ H$ H$ H$ H$ H$ H$ H$ H$ HD$lH$ H$ H$ \ Hݏ' H$ H$ H$ H$ L$ L$ L$ L$ H|$h Hl$@D$<L$ L$ L$ Ht$PHT$ L\$0LT$(LD$HLL$H$ L$ HDŽ$ Ht$PHT$ H|$L\$0LT$(LD$HHpHt$@LL$HP HHx(L``L$ HpPH5ζL0LhLxHLX0LP8L@@LHHHhXL$<H$ t$H$ LH$ ԑ' H$ Ht
LLH$0 H$HH tH\$pHhHCHxH;=Px z HrF D LLLD$ LD$ AHHO &X L$ H{IH H@@HxW$ H5,@ HLH$ HX( H$ H
lB H5 A IZ HQ1LHL]H]H?\H$ HHx[H$jH5 [w LHH| SX L$ H{IH H@@H8$ H5Z@ HLH$ HX( H$ H
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ajB H5@ IZ HZP1LHL
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fB H5-@ Iغ LMH#.@ LLH5YLXHWH$ Ht$XHxHeH5Vw H9X L$ WLE0H
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+ff. ff. Tff. ff. ff. HI D HIIHL SHURH$ Ht$XHxo4HHf0f Hw SHHHHPHt`HUw HH[HYw SHHHHPHt0HyUw HHH[f Hw SHHHHPHtH9Uw HH[Hw SHHHHPHtH Uw HHH[f Hw SHHHHPHtHTw HH[Hyw SHHHHPHtPHTw HHH[9f H9w SHHHHPHtHYTw HH[H w SHHHHPHtH)Tw HHH[f Hw SHHHHPHtHSw HH[Hw SHHHHPHtpHSw HHH[Yf HYw SHHHHPHt0HySw HH[H)w SHHHHPHt HISw HHH[f Hw SHHHHPHtH Sw HH[Hw SHHHHPHtHRw HHH[yf Hyw SHHHHPHtPHRw HH[HIw SHHHHPHt HiRw HHH[ f H w SHHHHPHtH)Rw HH[Hw SHHHHPHtHQw HHH[f Hw SHHHHPHtpHQw HH[Hiw SHHHHPHt@HQw HHH[)f H)w SHHHHPHt HIQw HH[Hw SHHHHPHtHQw HHH[f Hw SHHHHPHtHPw HH[Hw SHHHHPHt`HPw HHH[If HIw SHHHHPHt HiPw HH[Hw SHHHHPHtH9Pw HHH[f Hw SHHHHPHtHOw HH[Hw SHHHHPHtHOw HHH[if Hiw SHHHHPHt@HOw HH[H9w SHHHHPHtHYOw HHH[f Hw SHHHHPHtHOw HH[Hw SHHHHPHtHNw HHH[f Hw SHHHHPHt`HNw HH[HYw SHHHHPHt0HyNw HHH[f Hw SHHHHPHtH9Nw HH[Hw SHHHHPHtH Nw HHH[f Hw SHHHHPHtHMw HH[Hyw SHHHHPHtPHMw HHH[9f H9w SHHHHPHtHYMw HH[H w SHHHHPHtH)Mw HHH[f Hw SHHHHPHtHLw HH[Hw SHHHHPHtpHLw HHH[Yf HYw SHHHHPHt0HyLw HH[H)w SHHHHPHt HILw HHH[f Hw SHHHHPHtH Lw HH[Hw SHHHHPHtHKw HHH[yf Hyw SHHHHPHtPHKw HH[HIw SHHHHPHt HiKw HHH[ f H w SHHHHPHtH)Kw HH[Hw SHHHHPHtHJw HHH[f Hw SHHHHPHtpHJw HH[Hiw SHHHHPHt@HJw HHH[)f Hiw SHHHHPHt HIJw HH[H9w SHHHHPHtHJw HHH[f Hw SHHHHPHtHIw HH[Hw SHHHHPHt`HIw HHH[If Hw SHHHHPHt HiIw HH[HYw SHHHHPHtH9Iw HHH[f Hw SHHHHPHtHHw HH[Hw SHHHHPHtHHw HHH[if Hw SHHHHPHt@HHw HH[Hyw SHHHHPHtHYHw HHH[f AUATUSHHHL HcHcLHHH9}H1HHL L9* L[LSH[f. I21H HHHH H HyHH<H?H9HGHIH HAH HAH HAH HA H HA(H HA0H HA8 H9tTH)IIIMMt<fH|1HfHI9wfoLfsffD$HD$M9tHIHL9H[]A\A]ÐMum1 ~ hf D Xf D Hf D 8f D (f D Lf D AWAVAUATUHSHHL HcHcLHHH9}H1HHHH9 L H]L]LefW f HIAH9 I3N f(HtHHI<@ HIHH9HOI)ILH?LHHtXN< HJ )fB? f(IfXuXH9] D XHH9uE D H2HOH4f. XHH9uf D H[]A\A]A^A_f IjMfBT?LHImIO4*I9~-NT? fAHI fXfAZI9fXI9fXfBfX @ AWAVAUATUSHHHL HcLcLHHI9}H1LIM M9 LcHkN H[f D HM LO HH4@ HIHHH9HOI)IIEMIIHI J IJ0H< (MX(X~H D XHH9uH9~f D XHH9uH*IIM9^A+H[]A\A]A^A_ @ H~HFHf D X HH9uf M}MDMHIO|5 MnM9~(HT f IH XRM9XI9XBXf. AWAVAUATUSHHHL Hc HcLHHH9}H1HHHH9H$o LcHkL H[ f D HHHH9HOIHL)HxHHIHI N IJ<7Bo
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HHH@H[]A\HHD$0HtH|$ HH0+H|$HHt^H<$HtPHf ATHgK USHH@HD$ HHD$8HHD$0茬 HIAaH5 @ LH H5@ H H5C H HH5> HB B
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HHH@H[]A\HHD$0HtH|$ HH(H|$HHtH<$HtH @ f. @ ATHI USHH@HD$ HHD$8HHD$0 HI^H5V@ LH H5 @ H0 H5cC H HH5> HB HHl$ Ht$H HHT$HkH|$HtH<$HtHD$0Ht
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> Hn> A L1\$ H$h H$ H$ HHtH8HH0H(H> oHH> aHRHOH*HBHOHOH$ HHtoH$h HtmH|$ps|H$ HtH$` HtHwX L$0 5H$ H5ܢ> LIH$ HZ$ H$` H
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T/< HP/< H5L/< LH`H$@ H0Y$ Lt$XH
> H52< Iغ L]RH2< LLH^L`]H\H$P LHxH}jH5v[s HHLC H|$XI]HLF\H$P H$ Hx]HUIHHHH$ Ht_H$ HHtHH$ HHA}7X L$@ H$ H5'> LH,H$ HW$ L$ H
> H5~> Iغ LQH> LLH\L \HA[H$@ LHx]H&iH5 Zs HH~HZLI[HLZH$@ H$ HxH :IH Hf AWAVAUATUHiSH8 HFPMH$ H$ LH$ H$ HDŽ$ H$0 DŽ$ DŽ$ DŽ$ Ƅ$ DŽ$ HDŽ$p HDŽ$x HDŽ$ I@ I+@HH H$p HL$x H$p LH)HHHH
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Z= HV= A H1
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U= HP= A 1i$ H$x H$ H$ HHtH9 :HH9 ,H H8HH0H(@X L$@ ^H$ H5O= LIcH$ H4$ H$p H
UT= H5&P= Iغ H螰HO= LHLHqHH$@ H$ Hx^HIH5Rpq LgHH|$pH$ HtaH$p HtaHgX L$P ]H$ H5Q= LHbH$ HE$ H$ H
fS= H57O= Iغ HIHD$H觯HQ= LLH"LzHH$P LHx]HWH5`oq HfH$ HHyH|$HI HLRH$P H$ Hxi]Ha^IH @ HIHLHH H$@ H$ Hx]L^HHfHHWsHHbHMH$0 HHt_H$ Ht_H|$H H$x HHH$ HHtwHWHW HquH뛿X L$` ZHHD$HH
: H: H5: LL@0H$P H $ L|$hH
P= H5N: Iغ L~Hw: LLHLQH艿H$` LHx[H.H57mq HdH$ HHt^H$ HdHH|$hIݿHLH$` H$0 Hx)[H!\kIH @ Hf AWAVAUI1ATUHSHHH , IH H IT$0IDJA A A AY H|$0LHLH+HD$0HD$0 HHtBHEL$ LHxMZHe]Hl$0HtHELHx+ZHC]H H[]A\A]A^A_f D HLHLH;H$H$ HHtKuH<$Ht=u H|$LHLH;HD$HD$ HHuuH|$Hua @ H|$ LHLH;HD$ HD$ HH-tH|$ HkH$ X WHIMd$0sH57M= * HL|$@]]LH袯 H5I= H >]HuLZHr Ll$pLd$PLHPH@H$ Hr H$ HH$ H$ HxXHr H}@HH$ ^Hr H}XHPH@H$ HRH H(r HH$ UL/# H
R= H5)I= Ml L衩1LLL!LyL豻HD$@HHxWHYH5biq L`HH;HtsH HaILM]HD$@HLHxyWLqXIIH?bLH D Hmq HHÐHmq HHÐHmq HHÐHymq HHÐHimq HHÐHYmq HHÐHImq HHÐH9mq HHÐH)mq HHÐHmq HHÐH mq HHÐHlq HHÐHlq HHÐHlq HHYf. Hlq HHmYf. Hlq HHMYf. Hylq HH-Yf. HYlq HH
Yf. H9lq HHXf. Hlq HHXf. Hkq HHXf. Hkq HHXf. Hkq HHmXf. Hkq HHMXf. Hykq HH-Xf. HYkq HH
Xf. AWAVAUATLgXUSHLHQHr ƃ8 HHǃ0 ƃ9 1Hǃ@ HǃH HHǃP HǃX HCXHr HhLhH}H+L/ ZH%r H{@HC HC HC LsHC( HC0 HPH@HC8 HCXHrr HHHC_H#r CH LLHHCHǴr HHCPzYH[]A\A]A^A_HHr LHHCXPH ]LI讗HUH+LL,f. @ SHHHtHGHxH;=Dr uHZVH[H=r HWtHt$U͋PJHfAWAVAUIATUHSH Heq HT$HHHGHHD$uREHuI}AEaRHE H+E1E1IE IE( IE0 HH7 LM} M}(I]0Lu LLeM9t Ht LH RIHM9uH<r I](H\$PIE8 IE@ IEH HHIEP+L$ LELd$@HLD$QHt$@LHV3VH5@ HI Vt$L[H5d> HIUHuLHVUHD$@HxH;=yr L|$0IvLRH;r LHH$ H%r H@H$ Hrr HH$ H$ HxQHr I~@HH$ IWHr I~XLxH@HHD$IGL$ H Hr HH$ NHt$0HHVUHD$0LHxPH5D> HTHD$HH0HVTH5J&@ HTHEH9E t`H5 : HTHEHm L`L)HH~4f D I4$HHVhTH5%@ HTTIHuHl$ HsH QI}PH YHD$ LHxOHr H{HHD$PHr H@H$ IGHL$H{XL|$PHLPHbr HH$ LH []A\A]A^A_ H H9wH HRILL$ HIELHxOIEHt$PHxNLYH_XHHHHD$ LHxNHYIEPHHHxNI}8mH|$#HHD$0LHx}NH=r HWtFL|$0LOPHHD$@Ht$0Hx4NL YfPJHHHLHLHL$ 5LI9tILIHxMI9uTHKVI} HP @ AWAVAUIX ATUSH PHHcq LH{HH Hq Ld$pHl$@H5"A LHHHQH|$ HL$HLf HD$@HxH;=&r u%H|$ uFHD$CPHĨ H[]A\A]A^A_H=r HWW Ht$PN벿X L|$0KHT$H5M= LI QLl$PL# H
\r= H5]N= M躪 L%H; LLL蠱LL0HD$0LHxOLHؽH5]q LiUHH|$ HtHH}bq HHHH+OHUILM腰L软HD$0HLHxKLLIIHfHHD$@Ht$PHxKH|wH냋PJHf D AWAVAUIX ATUSH NHHaq LH{HH H q Ld$pHl$@H5A LHHHkOH|$ HL$HL6 HD$@Ll$PLHxJH|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0|IHT$H5K= LINL# H
= H5WL= M躪 L H ; LLL蚯LL*HD$0LHxIJHһH5[q LcSHH|$ HtHHw`q HHHH%MH}SILML跭HD$0HLHxILJIIĐHHHD$@Ht$PHxIH{H @ AWAVAUIX ATUSH LHH_q LH{HH Hq Ld$pHl$@H5A LHHH{MH|$ HL$HLF HD$@HxH;=r u%H|$ uFHD$CPHĨ H[]A\A]A^A_H=_r HWW Ht$PJ벿X L|$0gGHT$H5I= LILLl$PL# H
= H5=J= M躪 LH; LLL耭LجLHD$0LHx/HHH5Yq LIQHH|$ HtHH]^q HHHH KHcQILMeL蝫HD$0HLHxGLHIIHfHHD$@Ht$PHxGH|wH냋PJHf D AWAVAUIX ATUSH JHH]q LH{HHu Hq Ld$pHl$@H5A LHHHKKH|$ HL$HL HD$@Ll$PLHxFH|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0\EHT$H5G= LIJL# H
S= H57H= M躪 LH; LLLzLҪL
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Fm= H5GF= M躪 LH; LLL芩LLHD$0LHx9DHµH5Uq LSMHH|$ HtHHgZq HHHHGHmMILMoL觧HD$0HLHxCLDIIĐHHHD$@Ht$PHxCH{H @ AWAVAUIX ATUSH FHHYq LH{HH H q Ld$pHl$@H5A LHHHkGH|$ HL$HL6 HD$@Ll$PLHxBH|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0|AHT$H5C= LIFL# H
Vn= H5WD= M躪 L H ; LLL蚧LL*HD$0LHxIBHҳH5Sq LcKHH|$ HtHHwXq HHHH%EH}KILML跥HD$0HLHxALBIIĐHHHD$@Ht$PHxAH{H @ AWAVAUIX ATUSH DHHWq LH{HH H.q Ld$pHl$@H5A LHHH{EH|$ HL$HLF HD$@Ll$PLHx AH|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0?HT$H5B= LIEL# H
f`= H5gB= M躪 L/H; LLL誥LL:HD$0LHxY@HH5Qq LsIHH|$ HtHHVq HHHH5CHIILM菤LǣHD$0HLHx?L@IIĐHHHD$@Ht$PHx?H{H @ AWAVAUIX ATUSH BHHUq LH{HH Hq Ld$pHl$@H5A LHHHCH|$ HL$HLV HD$@Ll$PLHx?H|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0=HT$H5@= LICL# H
p= H5w@= M躪 L?H ; LLL躣LLJHD$0LHxi>HH5Oq LGHH|$ HtHHTq HHHHEAHGILM蟢LסHD$0HLHx=L>IIĐHHHD$@Ht$PHx=H{H @ AWAVAUIX ATUSH @HHSq LH{HH Hq Ld$pHl$@H5"A LHHHAH|$ HL$HLf HD$@Ll$PLHx =H|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0;HT$H5!>= LI%AL# H
W= H5>= M躪 LOH0; LLLʡL"LZHD$0LHxy<HH5 Nq LEHH|$ HtHHRq HHHHU?HEILM诠LHD$0HLHx<L<IIĐHHHD$@Ht$PHx;H{H @ AWAVAUIX ATUSH ?HHQq LH{HH H q Ld$pHl$@H52A LHHH?H|$ HL$HLv HD$@Ll$PLHx0;H|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$09HT$H51<= LI5?L# H
i= H5<= M躪 L_H@; LLLڟL2LjHD$0LHx:HH5Lq LCHH|$ HtHHPq HHHHe=HCILM连LHD$0HLHx:L ;IIĐHHHD$@Ht$PHx9H{H @ AWAVAUIX ATUSH =HHPq LH{HH H.q Ld$pHl$@H5BA LHHH=H|$ HL$HL HD$@Ll$PLHx@9H|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$07HT$H5A:= LIE=L# H
[= H5:= M躪 LoHP; LLLLBLzHD$0LHx8H"H5+Jq LAHH|$ HtHHNq HHHHu;HAILMϜLHD$0HLHx#8L9IIĐHHHD$@Ht$PHx7H{H @ AWAVAUIX ATUSH ";HHNq LH{HH Hq Ld$pHl$@H5RA LHHH;H|$ HL$HL HD$@Ll$PLHxP7H|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$05HT$H5Q8= LIU;L
# H
k= H58= M躪 LH`; LLLLRL芚HD$0LHx6H2H5;Hq L?HH|$ Ht!HHLq HHHH9H?ILMߚLHD$0HLHx36L+7IIĐHHHD$@Ht$PHx6H{H @ AWAVAUIX ATUSH 29HH(Lq LH{HH Hq Ld$pHl$@H5bA LHHH9H|$ HL$HL HD$@Ll$PLHx`5H|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$03HT$H5a6= LIe9L # H
R= H56= M躪 L菆Hp; LLL
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_: H52= LIHl$0H# H\$PH
@= H5: I躩 HH1= LHL莕HH HD$ HHx=0HơH5Aq LW9HL 1H9IHL薔HΓHD$ Ht$ Hx/HHÐf. @ AUATUSHH H9w0Ht$uHHĈ []A\A]ÿX Ll$ U.HT$LC0H
/: H50= LIsHl$0Hv# H\$PH
?= H5: I躤 HH0= LHL^H趓HHD$ HHx
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HHH} HtnHE LH<$HtUHf. @ AVHWAUATUHSH0HD$HnHD$(HrHD$ X Ik*Hù 1H߾
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HHH} HtGHE LH<$Ht.H AVHAUATUHSH0HD$HNHD$(HRHD$ 8 IK'Hù 1H߾
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HHH} Ht'HE LH<$HtH AVHWAUATUHSH0HD$H.HD$(H2HD$ I+$Hù 1H߾
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HHH} HtHE LH<$HtH AVHgAUATUHSH0HD$HHD$(HHD$ I !Hù 1H߾
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HHH} HtHE LH<$HtH AVHWAUATUHSH0HD$HHD$(HHD$ ؤ I Hù 1H߾
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HHH} HtHE LH<$HtH AVHAUATUHSH0HD$HHD$(HHD$ 蘪 IHù 1H߾
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> H5> M躪 L/H`< LLL:;L:L9HD$0LHxHGH58s LCHH|$ HtqHH7=s HHHHHmILM :LW9HD$0HLHxsLkIIĐHHHD$@Ht$PHxCH{H @ AWAVAUIX ATUSH HH<s LH{HHu HNs Ld$pHl$@H5"C LHHH+H|$ HL$HL HD$@Ll$PLHx蠼H|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0 HT$H5a> LILm4$ H
> H5Ǎ> M躪 L-Hp< LLLJ9L8L7HD$0LHxHEH56s LSHH|$ HtHHG;s HHHHH}ILM/8Lg7HD$0HLHx胻L{IIĐHHHD$@Ht$PHxSH{H @ AWAVAUIX ATUSH 蒾HH:s LH{HH H^s Ld$pHl$@H52C LHHH;H|$ HL$HL& HD$@Ll$PLHx谺H|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0 HT$H5q> LIžL}2$ H
> H5> M躪 L+H< LLLZ7L6L5HD$0LHx HCH54s LcHH|$ HtHHW9s HHHHHILM?6Lw5HD$0HLHx蓹L苺IIĐHHHD$@Ht$PHxcH{H @ AWAVAUIX ATUSH 袼HH8s LH{HH Hs Ld$pHl$@H5BC LHHHKH|$ HL$HL6 HD$@Ll$PLHxH|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0,HT$H5> LIռL0$ H
> H5> M躪 L)H< LLLj5L4L3HD$0LHxHAH52s LsHH|$ HtHHg7s HHHHHILMO4L3HD$0HLHx裷L蛸IIĐHHHD$@Ht$PHxsH{H @ AWAVAUIX ATUSH 貺HH6s LH{HH H>s Ld$pHl$@H5RC LHHH[H|$ HL$HLF HD$@Ll$PLHxжH|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0<HT$H5> LIL.$ H
> H5> M躪 L'H< LLLz3L2L
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&> H5> M躪 L%H< LLL1L0L0HD$0LHx9H>H5.s L蓽HH|$ HtHH3s HHHH%H载ILMo0L/HD$0HLHxóL軴IIĐHHHD$@Ht$PHx蓳H{H @ AWAVAUIX ATUSH ҶHH2s LH{HH Hs Ld$pHl$@H5rC LHHH{H|$ HL$HLf HD$@HxH;=t u%H|$ uFHD$CPHĨ H[]A\A]A^A_H=Ot HWW Ht$P訴벿X L|$07HT$H5> LILl$PL*$ H
> H5> M躪 L#H< LLLp/L.L .HD$0LHx H;H5,s LyHH|$ HtHHm1s HHHH H裻ILMU.L-HD$0HLHx話L衲IIHfHHD$@Ht$PHxxH|wH냋PJHf D AWAVAUIX ATUSH 袴HH0s LH{HH Hns Ld$pHl$@H5B
C LHHHKH|$ HL$HL6 HD$@Ll$PLHxH|$ u HD$CPHĨ H[]A\A]A^A_ÿX L|$0,HT$H5> LIմL($ H
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+ H5< H H57QA HE HH5; HB HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( Ht&HD$ HtH|$ HH0H[]A\A]A^IHCHHx5HCHHx%HHHxHL;HD$ HtH|$ HH躨HHMIHt
HHH} HtnHE LH<$HtUHf. @ AVH'AUATUHSH0HD$HNHD$(HRHD$ H I軡Hù 1H߾
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HHH} HtGHE LH<$Ht.H AVH7AUATUHSH0HD$H.HD$(H2HD$ ( I蛞Hù 1H߾
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HHH} HtHE LH<$HtH AVHAUATUHSH0HD$HHD$(HHD$ 舔 IHù 1H߾
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HHH} HtHE LH<$HtnH AVHWAUATUHSH0HD$HnHD$(HrHD$ h IۋHù 1H߾
+ HHOUt H{@C C C@ ?HHH HKHK; H HC(H9 L, LkL1HI+H H53< Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $ Hft H5d< H H5< H H578A HE HH5; HB B HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( Ht HD$ HtH|$ HH0H[]A\A]A^IHCHHx.HCHHx HHHxHL4HD$ HtH|$ HH賏HHMIHt
HHH} HtgHE LH<$HtNH AVHAUATUHSH0HD$HNHD$(HRHD$ H I軈Hù 1H߾
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HHH;HtAH LH<$Ht)H @ f. @ AVHAUATUSHH0HD$H^HD$(HbHD$ X IP IH HI wH58 LH H58 LH H5l)9 LH H5 : H H5K;= HY HH57 HB B
+ HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\A]A^HHD$ HtH|$ HHHKIHt
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+ 9 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht4HD$ HtH|$ HH0H[]A\A]A^HHD$ HtH|$ HHGHKIHt
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: H<$~H,Ht$H<$LL9LNLGLNL9LNLO HNH9HNLIHO(LVHL9}.HHIHDHIH9{L9LO IOHG(IHNL9~,HIIHDHII9tSH9HNLHIHO LGHI9#HIHHDHHI9H9HNIHOLGf. LV0HIHULHG0 HOPISH^HHI)I9INLFHVHLH_[M)LV8LHHIHV ILM)I9LOHVL^PLLLGL)LL^(IH)L9IOHFXIHWHW(MLO HHHFH]HG8HFPHG@HFXHGHHf. ATUSHH0HHHHHHOHHHOH@HW HGHFHG0 HG8 HG@ HW(HPHG` Gh Gl HGp Gx H HPG| HLJ LJ LJ H HPH HP H HP(H HP0H HP8H H HPHpHHHGX HHt$Hw0HL$HO8HW@HWPuHGH H0[]A\ÐHA? H?A)DHH9t A@ A)AL$@11@@@HHHH?Ho HAt$EHCpAN1AO։KxS|HSPHS8HHSHeHA? H?A)DHH9t A@ A)AL$@11@@@HHHH?H<oAt$H EHC`AN1AM։KhSlH0[]A\f. AWHHHAVAUATUSHh H$ H$ HD$p H$ HD$x HD$h H$ HH$ HHHIHH$ @ HD$hLL$pLT$xHD$0 H LXHD$HH\$`HXH\$8HXH\$@$ H$ M$ D$ H$ H$ D$($ T$$L$ LL$0 H$ D$LHIHT$HHL)HDIILHMH)HHIH?HHLHL$H)HL$$HHHII)MJ9a H$H H$H H!L$(H LH$LHH ILHIHT$8HL)HDIILHMH)HHIH?HHLHL$H)HL$$HHHII)MJ9 H$H H$H L$(H LHH$LH ILHIHT$@HL)HDIILHMH)HHIH?HHLHL$H)HL$$HHHII)MJ9 H$H HD$H H!H LHD$LHH HHHLH<L$(H)HDMHHHHLI)LH?ILHHL$H)HL$$HHLM)MJ9F H$H HT$H H\$0H HD$`H$ IHT$I HD$0HD$HIfo$HD$@HD$8fH|$0@HD$hHD$pHD$hHT$x H9$ `H$ HHHHIIIL9$ H$ Ll$HIHH H?MH\$(IYIH\$0IYH\$8IYH\$@b H$H HD$PH Lt$@H!L$$H LHD$PLHH ILHLIL)HDIILHMI)LLH?HHLH $H)HL$HHHII)MJ9 H$H HT$PH Lt$8L$$H LHHT$PLH ILHLIL)HDIILHMI)LLH?HHLH $H)HL$HHHII)MJ9? H$H HD$XH H!H LHD$XLHH HHHH<HT$0L$$HH)HDIHHHHII)LH?ILHH $H)HL$HHLM)LH9 H$H HT$XH IH\$(H IH$ I HD$(HD$0HT$XHD$8HD$@L9\$HfoL$Pf Q MMދ$ H$ $ D$ H$ H$ D$$$ T$L$ LL$0 H$ $LHLIL)HDIILHMI)LLH?HHLH $H)HL$HHHII)MJ9H$8 fH$8 H$8 H$8 H$8 8H$8 vH$8 H$8 Ll$HL;$ % H$ H$ LH?L$ L$0 H$ 1L$ HD$LH$ HHH$HHT$0 H$H C IH4$H|$L;$ L\$$ MM)I苌$ MMILIMI)LLMH?HHIIʋ$ M)I鋌$ MIHL$II)MN9L VH$8 Q Hh []A\A]A^A_f D ATUSHH0HHHHHHOHHHOH@HW HGHFHG0 HG8 HG@ HW(HPHG` Gh Gl HGp Gx H HPG| HLJ LJ LJ H HPH HP H HP(H HP0H HP8H H HPHpHHHGX HHt$Hw0HL$HO8HW@HWPuHGH H0[]A\ÐHA? H?A)DHH9t A@ A)AL$@11@@@HHHH?Hc HAt$EHCpAN1AO։KxS|HSPHS8HHSHeHA? H?A)DHH9t A@ A)AL$@11@@@HHHH?HLcAt$H EHC`AN1AM։KhSlH0[]A\f. AWHHHAVAUATUSH H$ H$ H$ H$ HH$ HHHIHH$ B H$ H$0 L$p L$` H$ H$@ H$ $8 D$L H$ Ht$PHD$X AË$< HDŽ$ HDŽ$ HDŽ$ D$@$H D$HȱHH$ HH$ HHD$`HHT$hHH$ HH$ HHD$pHT$xf D H$ L$ L$ HD$ HDML)IHL$@IILHMI)LLH?HHHL$LH)HDHHH*HHI)MC. H$ D$HH$T$HH LvH!ȉDH HT$XH$HD$PLLLH)IHL$@IILHMI)LLH?HHHL$LH)HDHHH*HHI)MC.v H$ D$HH$D$HLvDH H H$ H$H$ LLLH)IHL$@IILHMI)LLH?HHHL$LH)HDHHH*HHI)MC. H$ D$HHD$T$HH LvH!ȉDH HT$xHD$HD$pLLLH)IHL$@IILHMI)LLH?HHHL$LH)HDHHH*HHI)MC. H$ HT$D$HD$HHD$ H H H$ HT$( $LL$`LT$h) HH|$ H$ H$ H$ H$ H$ H$ H9$ H$ HxHHIHH9$ H$ L$0 H$ L$ L$p H|$HH$ $8 LL$ L$` HD$( H$@ $$< D$H D$L D$@HH$ IHHH?HD$PHHT$XIHLIMHT$HL$ HD$HȱILT$HD$`LHT$hHLL$HD$pHT$xr f H$ D$HD$0T$H LwH!ȉ $H HT$(HD$0HD$ LLLH)IHL$@IILHMI)LLH?HHLHDH)HDHHH*HHI)MC. H$ D$HT$0D$Lw $H H HD$`HT$0HT$hLLLH)IHL$@IILHMI)LLH?HHLHDH)HDHHH*HHI)MC./ H$ D$HD$8T$H LwH!ȉ $H HT$xHD$8HD$pLLLH)IHL$@IILHMI)LLH?HHLHDH)HDHHH*HHI)MC.Z H$ HT$8D$D$H H H$ HT$8(T$0)HLL$PLT$XH9|$Hg $HML)IHL$@IILHMI)LLH?HHLHDH)HDHHH*HHI)MC.% H$x f D EH$x @vH$x qf H$x f H$x f H$x f H$x f H$x f H|$HH;$ e H$p L$0 HH?E1IL$ L$` HD$@$8 LIH$ L$@ H$ D$$< D$ $H D$0$L D$HHLIHH$HT$6f H$ CIH4$H|$L;$ Ll$L$MMM)IL$ MILHLl$@I)LLMH?IIIIL$0M)IL$HMII*LHI)MC.UOH$x Jf D HĨ []A\A]A^A_f D ATUSHH0HHHHHHOHHHOH@HW HGHFHG0 HG8 HG@ HW(HPHG` Gh Gl HGp Gx H HPG| HLJ LJ LJ H HPH HP H HP(H HP0H HP8H H HPHpHHHGX HHt$Hw0HL$HO8HW@HWPuHGH H0[]A\ÐHA? H?A)DHH9t A@ A)AL$@11@@@HHHH?H]V HAt$EHCpAN1AO։KxS|HSPHS8HHSHeHA? H?A)DHH9t A@ A)AL$@11@@@HHHH?HUAt$H EHC`AN1AM։KhSlH0[]A\f. AWHHHAVAUATUSH H$ H$ H$ H$ HH$ HHHIHH$ 2 H$ H$0 L$p L$` H$ H$@ H$ $8 D$L H$ Ht$PHD$X AË$< HDŽ$ HDŽ$ HDŽ$ D$$H D$DHȱHH$ HH$ HHD$`HHT$hHH$ HH$ HHD$pHT$xf D H$ L$ L$ HD$ HDML)IHL$IILHMI)LLH?HHLHL$DH)HDHHHHI)MKcH9 H$ H$D$HT$HH LvH!ȉDH HT$XH$HD$PLLLH)IHL$IILHMI)LLH?HHLHL$DH)HDHHHHI)MKcH96 H$ H$D$HD$HLvDH H H$ H$H$ LLLH)IHL$IILHMI)LLH?HHLHL$DH)HDHHHHI)MKcH9X H$ HD$D$HT$HH LvH!ȉDH HT$xHD$HD$pLLLH)IHL$IILHMI)LLH?HHLHL$DH)HDHHHHI)MKcH9 H$ HT$D$HD$HHD$ H H H$ HT$( $LL$`LT$h) HH|$ %H$ H$ H$ H$ H$ H$ H9$ H$ HxHHIHH9$ } H$ H$0 L$ L$p H|$HL$` H$ $8 Hl$ Ll$ HD$( L$ $$< H$@ D$H $L D$HH$ HHHD$PHHT$XH?IHIMIIMA LAHHD$`HHT$hILMHD$pHT$x` H$ HD$0D$DT$DH LgH!ȉ $H HT$(HD$0HD$ LLLH)IHL$IILIMI)LLH?HHLHDH)HHHHHI)MKcH9 H$ HT$0D$DD$DLg $H H HD$`HT$0HT$hLLLH)IHL$IILIMI)LLH?HHLHDH)HHHHHI)MKcH9 H$ HD$8D$DT$DH LgH!ȉ $H HT$xHD$8HD$pLLLH)IHL$IILIMI)LLH?HHLHDH)HHHHHI)MKcH96 H$ HT$8D$DD$DH H H$ HT$8(T$0)HLl$PLt$XH9|$H+ $HML)IHL$IILIMI)LLH?HHLHDH)HHHHHI)MKcH95H$x 0 @ H$x n H$x H$x , H$x H$x H$x H$x < H|$HH;$ U H$p L$0 HH?E1IL$ L$` HD$$8 LIH$ L$@ H$ D$D$< D$ $H D$0$L D$HHLIHH$HT$.H$ CIH4$H|$L;$ Ll$L$DMMM)IL$ MILHLl$I)LLMH?IIIIL$0M)IL$HMILHI)MKc L9XH$x S HĨ []A\A]A^A_f. @ ATUSHH0HHHHHHOHHHOH@HW HGHFHG0 HG8 HG@ HW(HPHG` Gh Gl HGp Gx H HPG| HLJ LJ LJ H HPH HP H HP(H HP0H HP8H H HPHpHHHGX HHt$Hw0HL$HO8HW@HWPuHGH H0[]A\ÐHA? H?A)DHH9t A@ A)AL$@11@@@HHHH?HMI HAt$EHCpAN1AO։KxS|HSPHS8HHSHeHA? H?A)DHH9t A@ A)AL$@11@@@HHHH?HHAt$H EHC`AN1AM։KhSlH0[]A\f. SH0HHHFHGHFHGLLGLNLO HFHG(LLHFHFH HG HG A HG( A LHLHwHOHG@ HGX HHOPHILG(IHFHIIIDMLHHOIHOHLHG8HLW`Hw0H0[ @ HA HINLW LW(L9dtaWHNH9HNHHO(LNINHD HHL9LNMLO LMOIDHIL9LNLG D H*Ht$
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b: H : A H1A H$ I$H( L[]A\A]A^A_ LX H,HHD$0 IGHL$`Mo M+oHHHD$HL${*H $IIEHIPHD$H9H $ HH9 H $HD$HT$0Ll$HHHILHH$HD$`H4XL`LHHD$ *H1H H$ HD$Ht
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9 Lg fI$ f D H<$LT H|$L$ Ll$0Q"HL$H$ Ht$ H$ H|$PH$ L$ L$ H$ H$ HL$H$ H$ HD$PHl$XHQH$ H$ "H$ I$ HHuJ11 y o e [ Q GHH$ HHtH|$`HttH H$ HHuHH$ HHtHHD$@H$ HxHMHf D AWAVAUATUHHSHH( H4$1L$ # HI IHLHLIRH$ tGH
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+ HH ao H{@C C C@ ?HHH HKHKb6 H HC(H9 L, L;L1HIH H5Y8 Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $Y H59 Hz H5 D< H+ HH56 HB HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( Ht<HD$ HtH|$ HH0H[]A\A]A^ޏIHCHHxHCHHx HHHxHL!HD$ HtH|$ HH蠛HHMIHt
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9 H59 M" L1HL$1LLL7LoHD$`HHx{HWH5Pm L*ILM5HD$`HLHxQ{I ATUH S~L%m HHH{ID$H葎 H{PHHn HHH[]A\HH~H謄IT$HHf. D ATUH S ~L%(m HHH{ID$H H{PHuHn HHH[]A\HH}H,IT$HHf. D ATUH S}L%m HHH{ID$H葍 H{PHH~n HHH[]A\HH}H謃IT$HHf. D ATUH S }L%(m HHH{ID$H H{PHuH>n HHH[]A\HH|H,IT$HHf. D ATUH S|L%m HHH{ID$H葌 H{PHHn HHH[]A\HH|H謂IT$HHf. D ATUH S |L%(m HHH{ID$H H{PHuH>n HHH[]A\HH{H,IT$HHf. D UHSHHHHG HG HG H$ HG(HVHW0H)1HH H}HU HCHH)HHHH
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9 Hv9 A L16 H$ H\$(HH$p Ht4qH$ Ht"qH$ HtqH$ HtpH$ HtpH$p HtpH$P HtpH$ HtpHD$(H []A\A]A^A_H$ Ht$xHHD$UH$ H$ H$ L$ H)HI)HIIH$H$ HH$ trHt$H$ H$ I}8H$ H$ L$ HDŽ$ HDŽ$ HDŽ$ H$ L$ I)HDŽ$ HDŽ$ LHDŽ$ HH HDŽ$ E1L$ H$ H$L$ L)H)HHH9< L$ X jLIH$ H H$ H
9 H57 Iغ H>Hg7 LHLHAHyH$ H$@ HxkHYH5Rm Ltf. H H9 HHnL<(H1HH$ L$ pjH H9 HH|nH$ H$ IH)HH, <L$ H$ L)HH9HLNt7H$ Lȹ H HI HHHuH$ KHH$ uJ H$0 Ht$pH$ HJH|$hH1;u HH$ H$P 1HM H$p H$P LHH<H?I9IFI H* H)Hv?HBHAHH v(HBHAHH vHBHAHŸ I9 MI)MIMMt~fHL 2L<111Ao 7HfAo3HfoL9fofoffs ffs fffs ffrfoLfsff $H,$M9t HHHH$ H$ H$ H)H$ HHH$ H$@ HH$cHD$X R H$P HD$(HH tkH$P HtkH$0 HtpkH$ Ht^kH$ HtLkH$ HH|$hH1r HT$X- HD$(H 1H<I<H<I<H<HH9T$ uHLhH$ HtjH$ HtjH$ HtjH$ HtjH$ Ht|jH$ HtjjH$ HtXjH$p HtFjH$P Ht4jH$ Ht"jHpX L$ xeH$ H59 LI kH$ H H$ H
9 H5ء9 Iغ HH9 LHLHH3H$ H$ HxJfHH5 m LoX L$ dH$ H59 LIhjH$0 H H$ H
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+HfN1H @ L|$@Lt$ ~D$ flMJ L<$HD$$@ H LfD$0H HL$ HH$H$p HT$HL$ foD$0H<HT$HH,H$H4 H)H H)HHH<H?H9HHHFH HHrL2~ HHrLrl HHrLrZ HHr LrH HHr(Lr 6 Lr(H0H9t8II)MIMMt!H41HHfFL9rM9JtL2H HH<H?H9HFHHH HHHL0t@HHHLpt2HHHLpt$HHH LptHHH(Lp uHH0Lp(H9t8II)MIMMt!H41HHfFL9rM9J tL1H Lf$sfo$H$H H$P LH$ I4LH H$p L LH4HHH)HH4 HHH H)HHH<H?H9HFHHHn HHxL0U HHxLpC HHxLp1 HHx Lp HHx(Lp
Lp(H0H9t8II)LHIMt!H41HHfFH9wM9JtL0LIH HH<H?H9HHHFH HH{L3t@HH{Lst2HH{Lst$HH{ LstHH{(Ls uH{0Ls(H9t8II)MIMMt!H41HHfFI9wM9J<tL7H HHH 1H 1HH1HHvH f1HH> D HD$@Ll$ L,$LHHD$$@ L< H|$ N4;LgH$p Ht$MH<2Ht$HH,2LH H)H I)LHH<H?H9HHHFH HHrL HHrLB{ HHrLBi HHr LBW HHr(LB E LB(H0H9tEII)MIMMt.L$H41~$flHHfFL9rM9JtLHHp HH<H?H9HFHIH HHHH0t@HHHHpt2HHHHpt$HHH HptHHH(Hp uHH0Hp(H9tFII)MIMMt/H4$L1~$flHIfA@L9rM9J tH1H D LH$H H$P H$ HHIH4IHHHH)HH$p LLL HMLJ H)MtPN L 1 Ht&1H<$ D H<H<HH9uH<$LLHLI9uL HL)Mt9IHE1 @ Ht1H H HH9uLLILM9uILH H1HHGf1MHcL f. HD$@Ll$ L,$LHHD$$@ H H|$ HH$p Ht$L2Ht$HH,2H H<H)1HHtH7HHHH9Hqu1MHHtH1HH4HI9uJ f LZH$H H$P H$ HHIH4IO<HHHH)H$p LLL MLJ H H)MtNL I1f Ht&1H<$ D H<H<HH9uH<$LLHLI9uHHHH)MtJL IHE1 D Ht"1f H H HH9uLLILM9uILH Hu?111FH$P HHt\H$0 Ht\LHHHS1HHfWH WH$ HHt H$p HHH}IHLHHH$ H$@ HxX>HH @ f. @ AWIAVIAUIATUSH 1H' LHXL` &HH@0x? HAHQ H)HH H X H\$PVHHD$0H5 9 HHHD$\Ll$pL; L$ H
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9 H9 A L1 H$ H\$`HH$p HtPH$ HtPH$ HtPH$ HtoPH$ Ht]PH$p HtKPH$P Ht9PH$ Ht'PHD$`H []A\A]A^A_L$ Ht$xLˮH$ H$ L$ L$ H$ I)I)IIHIIH$ SH$ HD$H$ H$ LH$ L$ HDŽ$ Hx8HDŽ$ HDŽ$ H$ L$ BI)HDŽ$ HDŽ$ LHDŽ$ HH HDŽ$ E1L$ H$ I)IL$ L)HL98 L$ X +JLI@H$ H H$ H
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B
- 6 HH HD$0IE IU Ht HT$8I]IEHt$ HIU Ld$IE( M I$ I$ I$ I$ I|$hHtI|$PHtI\$0L|$HtKH=4l u f. HH{H+HtLHCHxL9 H3HuID$(I|$ 1H EI|$ ID$8 ID$0 ID$LHxID$LHxI$LHxLHD$0HtH|$ HHHL[]A\A]A^A_úPGLZ: D IHCHl$HHx3HCHHx#HHHxHL)HD$0HtH|$ HHhIMHHt
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HHH@H[]A\HHD$0HtH|$ HHyH|$HHtH<$HtH @ f. @ ATHUSHHpHD$PH2HD$hH6HD$`,s HD$ "y HD$(HD$0 HD$8 HD$@ xyHhHt$ HHD$0Hl$@~Hl$8| HIbH5;5 LH1 HT$0H5=6 H} H57 H> H5&9 H HH5o3 HB
B
- O HHl$PHt$H HHT$H{LH|$HtH<$HtH|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHt&H<$HtH|$0HtyH @ H ATHUSHHpHD$PHbHD$hHfHD$`L HD$ B HD$(HD$0 HD$8 HD$@ 8HhHt$ HHD$0Hl$@Hl$8 HI=H5X3 LH HT$0H5-H5 H] H5S6 H H5Q7 H HH5ΐ2 HB
+ HHl$PHt$H HHT$HoH|$HtH<$HtH|$0HtOxHD$`Ht
HHHpH[]A\HHD$`HtH|$P HH~H|$HHt&H<$HtH|$0HtwH @ H ATHWUSHHpHD$PHRHD$hHVHD$`Lq HD$ Bw HD$(HD$0 HD$8 HD$@ wHhHt$ HHD$0Hl$@|Hl$8s HI`H595 LHQ HT$0H5]6 H H5י7 H^ H5%9 H HH53 HB
B
- o HHl$PHt$H HHT$HJH|$HtH<$HtH|$0Ht HD$`Ht
HHHpH[]A\HHD$`HtH|$P HH(H|$HHtFH<$Ht8H|$0HtH @ H ATHwUSHHpHD$PHHD$hHHD$`l HD$ b HD$(HD$0 HD$8 HD$@ XHhHt$ HHD$0Hl$@8Hl$8 HI;H5x3 LH1 HT$0H5MF5 H} H5Q6 H> H5q7 H HH52 HB
+ HHl$PHt$H HHT$HmH|$HtH<$HtH|$0HtovHD$`Ht
HHHpH[]A\HHD$`HtH|$P HH|H|$HHtFH<$Ht8H|$0HtuH @ H ATHUSHHpHD$PHrHD$hHvHD$`lo HD$ bu HD$(HD$0 HD$8 HD$@ uHhHt$ HHD$0Hl$@zHl$8q HI^H585 LHq HT$0H5}6 H H57 H~ H5!#9 H/ HH53 HB
B
- HHl$PHt$H HHT$HHH|$HtH<$HtH|$0Ht/HD$`Ht
HHHpH[]A\HHD$`HtH|$P HHHH|$HHtfH<$HtXH|$0HtH @ H ATHGUSHHpHD$PHHD$hHHD$`茄 HD$ 肎 HD$(HD$0 HD$8 HD$@ xHhHt$ HHD$0Hl$@XHl$8. HI9H53 LHQ HT$0H5mD5 H H5P6 H^ H57 H HH52 HB
+ HHl$PHt$H HHT$HkH|$HtH<$HtH|$0HttHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHzH|$HHtfH<$HtXH|$0HttH @ H ATHGUSHHpHD$PHHD$hHHD$`m HD$ s HD$(HD$0 HD$8 HD$@ sHhHt$ HHD$0Hl$@xHl$8.u HI]H5(65 LH HT$0H56 H H57 H H5A!9 HO HH5Ͻ3 HB
B
- HHl$PHt$H HHT$HFH|$HtH<$HtH|$0HtOHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHhH|$HHtH<$HtxH|$0HtH @ H ATHgUSHHpHD$PHHD$hHƂHD$`謂 HD$ 袌 HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@x Hl$8N HI8H53 LHq HT$0H5B5 H H5'N6 H~ H57 H/ HH5.2 HB
+ HHl$PHt$H HHT$HiH|$HtH<$HtH|$0HtrHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHyH|$HHtH<$HtxH|$0Ht9rH @ H ATHUSHHpHD$PHHD$hHHD$`k HD$ q HD$(HD$0 HD$8 HD$@ qHhHt$ HHD$0Hl$@wHl$8No HI#[H5H45 LH HT$0H56 H H577 H H5a 9 Ho HH53 HB
B
- HHl$PHt$H HHT$HDH|$Ht H<$HtH|$0HtoHD$`Ht
HHHpH[]A\HHD$`HtH|$P HH H|$HHtH<$HtH|$0HtH @ H ATHUSHHpHD$PHHD$hHHD$`̀ HD$ HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@Hl$8n HI#6H53 LH HT$0H5@5 H H5GL6 H H57 HO HH5N2 HB
+ HHl$PHt$H HHT$HhH|$Ht H<$HtH|$0HtpHD$`Ht
HHHpH[]A\HHD$`HtH|$P HH(wH|$HHtH<$HtH|$0HtYpH @ H ATHgUSHHpHD$PHҚHD$hH֚HD$`i HD$ o HD$(HD$0 HD$8 HD$@ pHhHt$ HHD$0Hl$@(uHl$8nr HICYH5h25 LH HT$0H5݆6 H H5W7 H H5 9 H HH53 HB
B
- HHl$PHt$H HHT$HCH|$Ht<H<$Ht.H|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHtH<$HtH|$0HtH @ H ATHUSHHpHD$PHHD$hHHD$`~ HD$ HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@Hl$8~ HIC4H53 LH HT$0H5>5 H H5gJ6 H H57 Ho HH5n2 HB
+ / HHl$PHt$H HHT$H;fH|$Ht<H<$Ht.H|$0HtnHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHHuH|$HHtH<$HtH|$0HtynH @ H ATHUSHHpHD$PHHD$hHHD$`g HD$ m HD$(HD$0 HD$8 HD$@ 8nHhHt$ HHD$0Hl$@HsHl$8g HIcWH505 LH HT$0H56 H= H5w7 H H59 H课 HH5/3 HB
B
- HHl$PHt$H HHT$H;AH|$Ht\H<$HtNH|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHtH<$HtH|$0Ht9H @ H ATHUSHHpHD$PH"}HD$hH&}HD$` } HD$ HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@Hl$8讆 HIc2H53 LH HT$0H5<5 H H5H6 H H57 H HH52 HB
+ O HHl$PHt$H HHT$H[dH|$Ht\H<$HtNH|$0HtmHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHhsH|$HHtH<$HtH|$0HtlH @ H ATHUSHHpHD$PHHD$hHHD$` f HD$ l HD$(HD$0 HD$8 HD$@ XlHhHt$ HHD$0Hl$@hqHl$8k HIUH5.5 LH HT$0H5 6 H] H57 H H59 Hϼ HH5O3 HB
B
- / HHl$PHt$H HHT$H[?H|$Ht|H<$HtnH|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHtH<$HtH|$0HtYH @ H ATHGUSHHpHD$PHB{HD$hHF{HD$`,{ HD$ " HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@Hl$8{ HI0H583 LH HT$0H5
;5 H= H5F6 H H517 H HH52 HB
+ o HHl$PHt$H HHT$H{bH|$Ht|H<$HtnH|$0Ht/kHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHqH|$HHtH<$HtH|$0HtjH @ H ATH'USHHpHD$PH2HD$hH6HD$`,d HD$ "j HD$(HD$0 HD$8 HD$@ xjHhHt$ HHD$0Hl$@oHl$8d HISH5,5 LH1 HT$0H5=6 H} H57 H> H59 H HH5o3 HB
B
- O HHl$PHt$H HHT$H{=H|$HtH<$HtH|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHt&H<$HtH|$0HtyH @ H ATHUSHHpHD$PHbyHD$hHfyHD$`Ly HD$ B HD$(HD$0 HD$8 HD$@ 8HhHt$ HHD$0Hl$@Hl$8 HI.H5X3 LH HT$0H5-95 H] H5D6 H H5Q7 H HH52 HB
+ 菻 HHl$PHt$H HHT$H`H|$HtH<$HtH|$0HtOiHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHoH|$HHt&H<$HtH|$0HthH @ H ATHUSHHpHD$PHRHD$hHVHD$`Lb HD$ Bh HD$(HD$0 HD$8 HD$@ hHhHt$ HHD$0Hl$@mHl$8m HIQH5*5 LHQ HT$0H5]6 H H57 H^ H59 H HH53 HB
B
- o HHl$PHt$H HHT$H;H|$HtH<$HtH|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HH(H|$HHtFH<$Ht8H|$0HtH @ H ATHGUSHHpHD$PHwHD$hHwHD$`lw HD$ b HD$(HD$0 HD$8 HD$@ XHhHt$ HHD$0Hl$@8Hl$8 HI,H5x3 LH1 HT$0H5M75 H} H5B6 H> H5q7 H HH52 HB
+ 诹 HHl$PHt$H HHT$H^H|$HtH<$HtH|$0HtogHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHmH|$HHtFH<$Ht8H|$0HtfH @ H ATHWUSHHpHD$PHrHD$hHvHD$`l` HD$ bf HD$(HD$0 HD$8 HD$@ fHhHt$ HHD$0Hl$@kHl$8e HIOH5)5 LHq HT$0H5}}6 H H57 H~ H5!9 H/ HH53 HB
B
- HHl$PHt$H HHT$H9H|$HtH<$HtH|$0Ht/HD$`Ht
HHHpH[]A\HHD$`HtH|$P HHHH|$HHtfH<$HtXH|$0HtH @ H ATH'USHHpHD$PHuHD$hHuHD$`u HD$ HD$(HD$0 HD$8 HD$@ xHhHt$ HHD$0Hl$@XHl$8.x HI*H53 LHQ HT$0H5m55 H H5A6 H^ H57 H HH5~2 HB
+ Ϸ HHl$PHt$H HHT$H\H|$HtH<$HtH|$0HteHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHkH|$HHtfH<$HtXH|$0HteH @ H ATHUSHHpHD$PHHD$hHHD$`^ HD$ d HD$(HD$0 HD$8 HD$@ dHhHt$ HHD$0Hl$@iHl$8.] HINH5('5 LH HT$0H5{6 H H57 H螴 H5A9 HO HH5Ϯ3 HB
B
- HHl$PHt$H HHT$H7H|$HtH<$HtH|$0HtOHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHhH|$HHtH<$HtxH|$0HtH @ H ATH7USHHpHD$PHsHD$hHsHD$`s HD$ } HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@xHl$8Nr HI)H53 LHq HT$0H535 H H5'?6 H~ H57 H/ HH5.|2 HB
+ HHl$PHt$H HHT$HZH|$HtH<$HtH|$0HtcHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHjH|$HHtH<$HtxH|$0Ht9cH @ H ATHUSHHpHD$PHHD$hHHD$`\ HD$ b HD$(HD$0 HD$8 HD$@ bHhHt$ HHD$0Hl$@hHl$8Nc HI#LH5H%5 LH HT$0H5y6 H H577 H農 H5a9 Ho HH53 HB
B
- HHl$PHt$H HHT$H5H|$Ht H<$HtH|$0HtoHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHtH<$HtH|$0HtH @ H ATH'USHHpHD$PHqHD$hHqHD$`q HD$ { HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@Hl$8nv HI#'H53 LH HT$0H515 H H5G=6 H H5Ѽ7 HO HH5Nz2 HB HHl$PHt$H HHT$H"4H|$HtCH<$Ht5H|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHtH<$HtH|$0Ht HH @ f. @ ATHUSHHpHD$PHpHD$hHpHD$`o HD$ y HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@Hl$8z HIC%H53 LH HT$0H5/5 H H5g;6 H H57 Ho HH5nx2 HB HHl$PHt$H HHT$HB2H|$HtcH<$HtUH|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHtH<$HtH|$0Ht@HH @ f. @ ATH7USHHpHD$PH"nHD$hH&nHD$` n HD$ x HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@Hl$8y HIc#H53 LH HT$0H5-5 H H596 H H57 H HH5v2 HB 6 HHl$PHt$H HHT$Hb0H|$HtH<$HtuH|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHt
H<$HtH|$0Ht`HH @ f. @ ATHUSHHpHD$PHBlHD$hHFlHD$`,l HD$ "v HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@Hl$8m HI!H583 LH HT$0H5
,5 H= H576 H H517 H HH5t2 HB V HHl$PHt$H HHT$H.H|$HtH<$HtH|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHt-H<$Ht H|$0HtHH @ f. @ ATHUSHHpHD$PHbjHD$hHfjHD$`Lj HD$ Bt HD$(HD$0 HD$8 HD$@ 8HhHt$ HHD$0Hl$@Hl$8q HI H5X3 LH HT$0H5-*5 H] H556 H H5Q7 HϾ HH5r2 HB v HHl$PHt$H HHT$H,H|$HtH<$HtH|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HH/H|$HHtMH<$Ht?H|$0HtHH @ f. @ ATHUSHHpHD$PHhHD$hHhHD$`lh HD$ br HD$(HD$0 HD$8 HD$@ XHhHt$ HHD$0Hl$@8Hl$8l HI H5x3 LH1 HT$0H5M(5 H} H536 H> H5q7 H HH5p2 HB 薽 HHl$PHt$H HHT$H*H|$HtH<$HtH|$0Ht6HD$`Ht
HHHpH[]A\HHD$`HtH|$P HHOH|$HHtmH<$Ht_H|$0HtHH @ f. @ ATHUSHHpHD$PHfHD$hHfHD$`f HD$ p HD$(HD$0 HD$8 HD$@ xHhHt$ HHD$0Hl$@XHl$8.f HIH53 LHQ HT$0H5m&5 H H526 H^ H57 H HH5o2 HB 趻 HHl$PHt$H HHT$H(H|$HtH<$HtH|$0HtVHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHoH|$HHtH<$HtH|$0HtHH @ f. @ ATHWUSHHpHD$PHdHD$hHdHD$`d HD$ n HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@xHl$8Nn HIH53 LHq HT$0H5$5 H H5'06 H~ H57 H/ HH5.m2 HB ֹ HHl$PHt$H HHT$H'H|$Ht#H<$HtH|$0HtvHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHtH<$HtH|$0Ht HH @ f. @ ATHUSHHpHD$PHbHD$hHbHD$`b HD$ l HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@Hl$8nc HI#H53 LH HT$0H5"5 H H5G.6 H螶 H5ѭ7 HO HH5Nk2 HB HHl$PHt$H HHT$H"%H|$HtCH<$Ht5H|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHtH<$HtH|$0Ht HH @ f. @ ATH״USHHpHD$PHaHD$hHaHD$`` HD$ j HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@Hl$8g HICH53 LH HT$0H5 5 H H5g,6 H辴 H57 Ho HH5ni2 HB HHl$PHt$H HHT$HB#H|$HtcH<$HtUH|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHtH<$HtH|$0Ht@HH @ f. @ ATHUSHHpHD$PH"_HD$hH&_HD$` _ HD$ i HD$(HD$0 HD$8 HD$@ HhHt$ HHD$0Hl$@Hl$8g HIcH53 LH HT$0H5 5 H H5*6 H H57 H菳 HH5g2 HB 6 HHl$PHt$H HHT$Hb!H|$HtH<$HtuH|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHt
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5 H= H5(6 H H517 H诱 HH5e2 HB V HHl$PHt$H HHT$H H|$Ht裿H<$Ht蕿H|$0HtHD$`Ht
HHHpH[]A\HHD$`HtH|$P HHH|$HHt-H<$Ht H|$0HtHH @ f. @ ATHUSHHpHD$PHb[HD$hHf[HD$`L[ HD$ Be HD$(HD$0 HD$8 HD$@ 8HhHt$ HHD$0Hl$@Hl$8Y HIH5X3 LH HT$0H5-5 H] H5&6 H H5Q7 Hϯ HH5c2 HB v HHl$PHt$H HHT$H H|$HtýH<$Ht赽H|$0HtHD$`Ht
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5 H5w5 M L Hw5 HLL(3L2L1HD$0HxLH`?H5ih LMtIvIF H)HHtvX Hl$ H\$0RH5;u5 HHILt$PLq L$ H
A5 H5v5 M L HKw5 HLLe28 LtX Hl$ H\$0H5tx5 HHIFLt$PLp L$ H
5 H5Sv5 M Lc H w5 HLL1f M It$ID$ H)HH| X Hl$ H\$0(H5w5 HHILt$PLVp L$ H
5 H5u5 M L Hv5 HLL;1f D LoH HuHE H)HHt}X Hl$ H\$0yH5bw5 HHILt$PLo L$ H
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HH []A\A]A^A_E1MI~#f AIIIAEM9M9Lt$N$O,Lt$MN,Ld$O4MIM9MnAL9l$AEP H|$ D H|$ tPE1HD$E1LLd$(HC,CD,C.CD.IH;D$rLt$L9t$HD$L4$L;D$8B1BD5 Lt$@L;D$0B1BD5 Lt$(B1BD5 LcȽ L)MYL,NDVN9tO4E1M)IN$ N "IIK\(L9DH\IM9I.KD L9D8 M̐BD IM9uM @ BBD IM9uHD$ IA E1ND E1A 'A A A AW? A@ AVAUIATUSH8 HWHHG8H|$@HDŽ$ LDHWPHDMLHWXHD$H$ HG@L$ MTHW`L$ LL$0HDLI\L$ LT$8H$ IIH$ H$ MML$ LD$(ML$ Ht$ IHD$LH$ Ht$IL$ H?)A)HI9LDD11AOL$HAO@@@@HHLH?HX HEANLL$A$8 OL$HH$0 A@ IH?$< ? A))HI9LDD11AOL$HAO@@@@HHLH?HqWA HEENAH$@ D$H A DOT$HIĺ A@ H?A)D$L A? A)DHI9LED11AO@EW@DT$HM@@I?HHLHVA HEENLD$(AD$X A DOT$HH$P A@ IH?D$\ A? A)A)DHI9LED11AO@EW@DT$HM@@I?HHLH4VA HEENH$` AA DOD$HHH?D$h A? A)º A@ DA)HD$l H9HED11AO@EG@DD$HI@@I?HHLHUA HEENA ADOD$H 11 D$x H$p D$| LUL\$@Iu HH$ M DŽ$ DŽ$ I HA H?IHHHI)LHA HI HIMChI L)III?LHHHA I)LHA HHHICpI H)I IHII?LHHHA I)LHA HHHICxI H)I IHII?LHHHA I)LHA HHHI I H)I IHII?LHHHA I)LHA HHHI I H)I HHH$ H$ HD$ LL$H|$LT$8HL$H$ I( LD$(L$ LL$0H$ H$ H$ I0 foI64 L$ L$ MH$ I8 H$ H$ HOhL$ H$ H$ I@ L$ L$ HDŽ$ L$( H$ IH H$ IP H$ f$ M` HDŽ$ fo54 HDŽ$( L\$f$ wH$ H$ H$ H$ H$ H$ H$ H?HH H"XHHL\$H9t"HU1HH)HH SL\$1H$ M HD$p HD$x MV fH؉HL| HI?H$ HH$ L$0 HD$pIHt$xL$@ I⋄$8 IljD$$< H։D$ HH)HHH$ HHD$(HI)LLI䋄$H H?ỈljD$0$L HʉAD$8LH)HDL$P HIS8HHIt8IS@I|8HH$ HH|$@I)LLI$X H?IȉljD$L$\ HʉD$LH)HL$HISHHHI|8HH$ HH|$PH$` I)LLH?HH狄$h D$`Hʉ$l D$HLH)HL$HHICPHHIL8HHH$p H$ H$ HHD$XI)LLH?HH拄$x D$dHʉ$| D$LH)HL$HHH$ HHL$hIK`HI)ISXMt8ID8H$ IL9 A (KDM I? HL$LHH T LILMIL)HL$ IHD$(III)LLH?IIHL$0LH)HL$8HIS8M|8IS@HHHD$@IL8I)ILLH?IIHL$LLH)HL$HISHHHIL8IHD$PI)LLH?HHHL$`LH)HL$HHISPHHHD$XIL8I)ILLH?HHLHL$dH)HL$HHT$hHIK`HI)ISXMt8ID8IML9fHغ HAHH T HHD$pHT$x H;$ HtH SH8 []A\A]A^A_ D H$ H $ I AWAVAUATUSHH H` HHD$ LH LW`LH?HILL L)H苏 IILHGhL I)H LH?HILHʋ H)H苏 HHHHGpH I)H LIH?HILHʋ H)H苏 HHHHGxH II)H LH?HILHʋ H)H苏 HHHH H I)H LIH?HILHʋ H)H苏 HHHH H I)H L LHD$LI; HX% HP HHB H@ H8EAAHF0A H HOX1 f. LHJ| D9uA E1McM9~*A LL)H LK8JD8H9 H( fIHD$JD8fD$PfD$`fD$pf$ f$ f$ f$ f$ f$ f$ f$ f$ f$ f$ f$0 HD$ Ho8 Ld$PL)E1LL @ IH LSIt@K[MH[8I)IN/( HLLP IHXL(MLLhLPIIIuL\$HFHFHFHF HF(HF0HHD$ HD$L|$LIHWLIHL$H< H IOJ4 N H|$ HH@IHHD$HLIIHHD$HL)IHHD$IJ LD$HD$LL L$L@HHD$@HHD$(H LD$8IHD$HHH|$H|$PLD$0IH|$H|$HLGHD$1f. H|$H|$Lt$H<I,r H|$ & H|$ E1MI~F Ifp (A$A 4(C CMM9Hl$I9M} @ IE HI9uH|$ t]H|$xHH9|$pH|$xhH|$E1Ho0% Lu(HIH0MnLmL;o |CH+WH+GM9HG( uLt$LHL9t$ H|$H|$f HHG H|$ E1HE IE HHI9u H|$I= E1MI~Df D AII(A$A 4(C CMM9Hl$I9MBIE HI9uE1MII~nHL$Ht$HT$HL$ HT$Ht$H A IA$$IA 4A I(AFM9HL$Ht$HT$H|$I9M HBD II9uE1MI~$f IIfp AEM9I9Lt$N$O$Lt$O,4L9LoAM9AEs H|$ g H|$ t8E1MIIL;l$ C4CD4rLt$L9t$,H;L$8L4$BD5 H;L$@Lt$0BD5 H|$(D= HH []A\A]A^A_E1MI~#f AIIIAEM9I9Lt$N$
-O,Lt$MN,Ld$O4MIM9MnAL9l$AEp H|$ d H|$ tULd$HD$E1HD$ 1(HC,CD,C.CD.IH;D$rLt$L9t$HD$L4$H;L$8B7BD5 Lt$0H;L$@B7BD5 Lt$(B7BD5 HH []A\A]A^A_Lc L)MhLT8JL(H9( MH( M)MM)N II;C)JDIM9HLH)LL8JD8H9M D BD II9uM @ BBD II9uHD$ A A A JL E1tHA E1,A AWHAVAUIHATIUSH H$ HH$ L$@ H- h L$H L$P L$X L$` L$h } 0 L5h I HL9 } HDŽ$ HDŽ$ HDŽ$ HDŽ$` HOH$h H$` H$ LH$ H$ H9 H$@ L$H L$P L$X L$` H$h HH$ L$ IL$ L$ L$ H$ H$@ L$H L$P L$X IL$` H$h H$p L$x L$ L$ L$ H$ IIHHX fo0 A A f$p L$x H$p f$ f$ L$ H$ H$x H$ L$h HDŽ$ HDŽ$ H$ HH$@ L$ HHHIH$ H$H HDHHH$ IH$ HH$P HHHH$ IH$ HH$X HHHH$ HH$( HH$` HHHH$ HH$0 JDHHHH$8 LIH$ IHHHHH$ LH$` H$ HH$ H$ H$X IH$ IH$ H$P H$ H$ H$H H$ HIIIIHH HHT$EHT$H$ H H$ HH$ m H$` LLMMIH$ H$0 HHDŽ$ H$ H$ H$ H$ H$ D H$ HHH?HHHH$ H<$ H)H苌$ HHHH$ H$8 H)H$ HH?HHHHʋ$ H)H苌$ HHHH$ H$@ H)H$ HHH?HHHHʋ$ H)H苌$ HHHH$ H$H HH)H$ HH?HHHHʋ$ H)H苌$ HHHH$ H$P H)H$ HHH?HHHHʋ$ H)H苌$$ HHSxHHh IL$ H$X L)LL$pH$` HHH$ H$ H$ H9$ H$ L$p L$x L$ H$ H$ H$ H$ ML$@ L$H L$P HDŽ$ HHLM)M9MOL$ HH$ L$h I)LHHLHI)M9MOL$ H$ HL$p I)MML$ LHILHI)M9MOMH$ IL$ L$x I)LM HL$X IHHI)I9INIH$ IL$ H$ I)LIHIH$` LL)H9HOIL$ H$ L$ I)H$h LHMH)H9HOHH$ HH$ H$ H$ J HMHH$` IH$ MML$ L$ L$ H$ L$ L$ L$ H$ H$ L$ HL$ L$ L$ H$ H$ L$ "
L$P L$ L| L$ H$ L|$pL$ L$ L$ MH$ H$( H$0 L$8 L$@ L$H H$X H$` L$h L$p L$x H$ H$ L$ H$x A? H H$ H$ H HH$ H$ H HH$ H$ H HH$ H$ H HH$ H$ H IHD$HIH$ Hº H$ HHD$HHHD$ HH?A)DHH9t A@ A)AL$@HT$ 11@@@HHHH?H6HE H$ AN1AD$(AD$A? LHt$D$0HH?A)DHH9t A@ A)AL$@HT$11@@@HHHH?H6ELp AN1AD$8AD$A? LH$ D$@HH?A)DHH9t A@ A)AL$@H$ 11A? @@@HHHH?H5ELh AN1AD$HAD$ LH$ D$PHH?A)DHH9t A@ A)AO@H$ 11@@@HHHH?H5EL` AN1AD$XAGA? LHt$D$`HH?A)DHH9t A@ A)AO@HD$11@@@HHHHH?H~4E HhANAGt$h1ALHމ$ H$ H?HHHHHH<$ H)H苌$ HHHH$ H$8 H)H$ HH?HHHHHʋ$ H)H苌$ HHHH$ H$@ H)H$ HHH?HHHHHʋ$ H)H苌$ HHHH$ H$H HH)H$ HH?HHHHHʋ$ H)H苌$ HHHH$ H$P H)H$ HHH?HH$ HHHH$ Hʋ$ H)H苌$$ HHHH$ H$X H)H$` HHH$ H$ H$ HD$ L|$H$ L$ L$ H$ HD$H$ L$ L$ L$ E1H$ H$ L$ HDŽ$ HDŽ$ H$ H$ H$ H$h H$ H$p H$ H$x H$ H$ H$ H$ H$( H$ H$0 HD$pH$8 HDŽ$ IH$ H$ H$ HDŽ$ HDŽ$( HDŽ$0 HDŽ$8 jHH$ H$ H$ H$ ILx?H$ IIL#7IHI9t H@1HI)IJ 2H$ H$ ' H$ L$ 1L$ HD$ A HD$p HD$x H$ D HHHHH$ LHH
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+ HH!_b H{@C C C@ ?HHH HKHKd) H HC(H9 L, L=L1HIH H5W+ Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $[ HT$ H5g, H H5ڶ- Hh H5 B/ H HH5) HB HH HD$PHE HU Ht HT$XH]HEHt$@HHU H<$HE( HtjH|$ HtkHD$PHtH|$@ HH`H[]A\A]A^轍IHCHHxHCHHxHHHxۏHL H|$ HtHD$PHtH|$@ HHpHHHMIHt
HHH} HtHE LH<$HttHf. AWHAVIAUATUSHxHD$PHHD$hH HD$` HD$ HD$(HD$0 HD$8 HD$@ BHXHt$ HHD$0H\$@RH\$8蘕 H Hù 1HL-[b
+ HH{@C C IEC@ ?HHCHC) H HC(H9 L$ L蛑L1HI[H H|$H5T+ L{ HCP HH H H HCX HC` HCh HCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H\$ HT$0H5§, H H55- H H5f>/ Ht HH5) HB HH HD$`IF IHt HT$hI^IFHt$PHIV Ld$IF( M
+ I$ I$ dI$ I$ dI|$hHt蝏I|$PHt莏I\$0HtFH=Zb L|$u HH{H+Ht\HCHxL9 HCHuID$(I|$ 1H 5I|$ ID$8 ID$0 ID$HxL9
+ ID$HxL9X I$HxL9 L̎H|$0Ht轎HD$`HtH|$P HHxL[]A\A]A^A_úP5LH( IHCHl$HHx#HCHHxHHHxHL)H|$0HtHD$`HtH|$P HH虔HHH==Yb HWt~Ht$薌H{H+Ht谍HCHxL9uRH蛍HTHH=Xb HWtEHt$6|PJHyPJ҉HL 뗋PJHH=}Xb HWtk<Ht$-HH|$HINHHt
HHI>HtI H붋PJH AVH'AUATUHSH`HD$@H.HD$XH2HD$P( HD$ HD$HD$ HD$( HD$0 tHXHt$ HHD$ H\$0脑H\$(ʈ I=Hù 1H߾
+ HHUb H{@C C C@ ?HHH HKHK) H HC(H9 L, L͋L1HI荇H H5;N+ Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $ HT$ H5, H7 H5j- H H58/ H HH5)) HB P HH HD$PHE HU Ht HT$XH]HEHt$@HHU H<$HE( HtH|$ HtHD$PHtH|$@ HH`H[]A\A]A^MIHCHHx芆HCHHxzHHHxkHL萉H|$ Ht聉HD$PHtH|$@ HH HHHMIHt
HHH} Ht HE LH<$HtHf. AWHwAVIAUATUSHxHD$PHHD$hHHD$`膂 HD$ | HD$(HD$0 HD$8 HD$@ ҈HXHt$ HHD$0H\$@H\$8( H蛈Hù 1HL-Rb
+ HH{@C C IEC@ ?HHCHCR) H HC(H9 L$ L+L1HIH H|$H5J+ L{ HCP HH H H HCX HC` HCh HCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H\$F HT$0H5R, H H5ũ- HS H54/ H HH5) HB HH HD$`IF IHt HT$hI^IFHt$PHIV Ld$IF( M
+ I$ I$ 1[I$ I$ [I|$hHt-I|$PHt I\$0HtFH=dQb L|$u HH{H+HtHCHxL9 HӅHuID$(I|$ 1H ŁI|$ ID$8 ID$0 虅ID$HxL9
+ ID$HxL9X I$HxL9 L\H|$0HtMHD$`HtH|$P HHxL[]A\A]A^A_úP5L( {IHCHl$HHx賁HCHHx裁HHHx蔁HL蹄H|$0Ht誄HD$`HtH|$P HH)HHH=Ob HWt~Ht$&H{H+Ht@HCHxL9uRH+HTHH=mOb HWtEHt$Ƃ|PJHyPJ҉HL蜂뗋PJHH=
Ob HWtk<Ht$f-HH|$HkINHHt
HHI>Ht@I H붋PJH AWHAVIAUATUSHxHD$PHHD$hHHD$`| HD$ 謂 HD$(HD$0 HD$8 HD$@ HXHt$ HHD$0H\$@H\$8X H˂Hù 1HL-GLb
+ HH{@C C IEC@ ?HHCHC) H HC(H9 L$ L[L1HI~H H|$H5D+ L{ HCP HH H H HCX HC` HCh HCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H\$v HT$0H5, H H5- H H5&// H4 HH5) HB HH HD$`IF IHt HT$hI^IFHt$PHIV Ld$IF( M
+ I$ I$ aUI$ I$ LUI|$hHt]I|$PHtNI\$0HtFH=Kb L|$u HH{H+Ht HCHxL9 HHuID$(I|$ 1H {I|$ ID$8 ID$0 ID$HxL9
+ ID$HxL9X I$HxL9 LH|$0Ht}HD$`HtH|$P HHxL[]A\A]A^A_úP5L~( yIHCHl$HHx{HCHHx{HHHx{HL~H|$0Ht~HD$`HtH|$P HHYHHH=Ib HWt~Ht$V}H{H+Htp~HCHxL9uRH[~HTHH=Ib HWtEHt$||PJHyPJ҉HL|뗋PJHH==Ib HWtk<Ht$|-HH|$HINHHt
HHI>HtpI H붋PJH AVHAUATUHSH`HD$@HHD$XHHD$Pv HD$| HD$HD$ HD$( HD$0 4}HXHt$ HHD$ H\$0DH\$(u I|Hù 1H߾
+ HHqFb H{@C C C@ ?HHH HKHK贿) H HC(H9 L, L|L1HIMxH H5>+ Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $ HT$ H5, H H5*- H H5[)/ Hi HH5) HB HH HD$PHE HU Ht HT$XH]HEHt$@HHU H<$HE( HtH|$ HtzHD$PHtH|$@ HH`H[]A\A]A^
uIHCHHxJwHCHHx:wHHHx+wHLPzH|$ HtAzHD$PHtH|$@ HHHHHMIHt
HHH} HtHE LH<$HtHf. AVHAUATUHSH`HD$@HNHD$XHRHD$PHs HD$>y HD$HD$ HD$( HD$0 yHXHt$ HHD$ H\$0~H\$(~ I]yHù 1H߾
+ HHBb H{@C C C@ ?HHH HKHK) H HC(H9 L, LxL1HItH H5[;+ Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $ HT$ H5, HW H5- H H5%/ H HH5I) HB p HH HD$PHE HU Ht HT$XH]HEHt$@HHU H<$HE( HtH|$ HtwHD$PHtH|$@ HH`H[]A\A]A^mqIHCHHxsHCHHxsHHHxsHLvH|$ HtvHD$PHtH|$@ HH }HHHMIHt
HHH} Ht=HE LH<$Ht$Hf. AVHAUATUHSH`HD$@HHD$XHHD$Po HD$u HD$HD$ HD$( HD$0 uHXHt$ HHD$ H\$0{H\$(Ju IuHù 1H߾
+ HH1?b H{@C C C@ ?HHH HKHKt) H HC(H9 L, LMuL1HI
qH H57+ Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $k HT$ H5w, H H5- Hx H5"/ H) HH5) HB HH HD$PHE HU Ht HT$XH]HEHt$@HHU H<$HE( HtzH|$ Ht{sHD$PHtH|$@ HH`H[]A\A]A^mIHCHHx
+pHCHHxoHHHxoHLsH|$ HtsHD$PHtH|$@ HHyHHHMIHt
HHH} HtHE LH<$HtHf. AVHAUATUHSH`HD$@HHD$XHHD$Pl HD$q HD$HD$ HD$( HD$0 TrHXHt$ HHD$ H\$0dwH\$(k I rHù 1H߾
+ HH;b H{@C C C@ ?HHH HKHKԴ) H HC(H9 L, LqL1HImmH H54+ Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $ HT$ H5ׇ, H H5J- H H5{ / H HH5 ) HB 0 HH HD$PHE HU Ht HT$XH]HEHt$@HHU H<$HE( HtH|$ HtoHD$PHtH|$@ HH`H[]A\A]A^-jIHCHHxjlHCHHxZlHHHxKlHLpoH|$ HtaoHD$PHtH|$@ HHuHHHMIHt
HHH} HtHE LH<$HtHf. H)` HHÐH` HHÐH ` HHnf. H` HHnf. AWAVAUATUHX SH nHH` HH{HH~ HNa Ll$0Ld$H5$. L{PLLHHGoLt$ LLHL Hl$ HtOHEL%7b HxL9 HmHD$HxL9 Hu!HĈ H[]A\A]A^A_ L%q7b ȿX iHILr Hl$PH
&- H5_- M HH- HHLJHLH$HHxiHH5` LTsH
]` HHQHHlHsH=C8b HW
Ht$PkH=8b HWtTHt$PlkIHLL!H$LHxAiL9jHMPJHHHD$Ht$PHxiH"PJHLHHHH f. D AWAVAUATUHX SH "lHH(` HH{HH| Ha L$ Ld$@H5(. LsPLLHHlH|$PLLH葈 Lt$PM IFL%85b HxL9b LSkHD$@HxL9 Mp Lt$H5. LL{TLMlH|$ LLH Hl$ HtHEHxL9% HjHD$HxL9 Hu$H H[]A\A]A^A_ L%4b YX fHI'Hl$`Hz H
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e` HHQHHjHpH=K5b HWN H$ hL|$0X )eLI>L$ L H$ H
- H5x- M HHɌ- LHLcHLHD$0HHxfHH5` LloH=|4b HW H$ gH=I4b HWt`H$ goH=4b HWt>H$ pgPJHPJH뚋PJHILMHLH$H$ HxeL fHPJHHHD$@H$ HxdHHHD$H$ HxdHIvHyHHIHLLNHD$0LHxmdLeeHqHHHf. @ AWHAVIAUATUSHHHD$ HHD$8H HD$0a HigHù 1HL-0b
+ HH{@C C IEC@ ?HHCHC ) H HC(H9 L$ LfL1HIbH H|$H5b)+ L{ HCP HH H H HCX HC` HCh HCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H\$ H5- H5 H5/ H HH5f) HB 荷 HH HD$0IF IHt HT$8I^IFHt$ HIV Ld$IF( M I$ I$ :I$ I$ 9I|$hHteI|$PHt eI\$0HtHH=F0b L|$u
+s fHH{H+HtdHCHxL9 HdHuID$(I|$ 1H `I|$ ID$8 ID$0 ydID$HxL9` ID$HxL9~ I$HxL9
L<dHD$0HtH|$ HHHL[]A\A]A^A_úPDLb7fk^IHCHl$HHx`HCHHx`HHHx`HLcHD$0HtH|$ HH(jHHH|$Ht0H{H+Ht^cHCHxL9 HEcHHH=.b HW Ht$aH=W.b HW Ht$apH='.b HW aHt$|aRINHHt
HHI>Ht/I HPJ҉HL)aPJHVPJHPJHf @ AWHAVIAUATUSHHHD$ HHD$8HHD$0\ HbHù 1HL-+b
+ HH{@C C IEC@ ?HHCHCФ) H HC(H9 L$ LaL1HIi]H H|$H5$+ L{ HCP HH H H HCX HC` HCh HCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H\$ H5@, H H5/ H薱 HH5) HB = HH HD$0IF IHt HT$8I^IFHt$ HIV Ld$IF( M I$ I$ 4I$ I$ 4I|$hHt_I|$PHt_I\$0HtHH=*b L|$u
+s fHH{H+Ht|_HCHxL9 Hc_HuID$(I|$ 1H U[I|$ ID$8 ID$0 )_ID$HxL9` ID$HxL9~ I$HxL9
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~- H5) MW LHL* HLHVLLHD$0LHxVHH5` H__ YXAHH9u LHHHڃH9HNH)HpHHIHf1f D YXAHH9uw1HIHD$0Ht$@HxGUH?VHLH JSX SI|$ILd$ Hh H@@H8~x
H5Ss* HL?Hl$@H H$ H
|- H5t* IZ H)1LHLH1HiHD$ HHxTHQH5J` L]HI X Ll$`RLC0H
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z- H5) Iغ L.HǤ) LLHILHM#LMX)f ff. HI D H
f D AWAVAUI1ATIUHHSH U HH HxHs HR0e HLs1LL\ LI HC0xm I|$AUPL3L{PT$ 軠 HID$0K >T$ xs M$M\$POA H1HH H I@H9HCI9 (IIW J 1E1(IDYA AD HM9wH9t?HAYH9A~'HYH9A~YAH9}+(H HYA)HH9H9 H II)ILLLHBH9HAAH9Av Il MIN Mt<(1WE1 (IDYDHM9wLM9t;HFYH9A~#HYH9A~ YAHE H H[]A\A]A^A_ @ X &NH{H H\$0H$ H5) H$ H6Ll$`Li L$ H
v- H5) MW LHjE* HLH>LLHD$0LHxNHH5` HGX YAHH9u LHHHڃH9HNH)HpHHIHf1f D YAHH9u1bHIHD$0Ht$@Hx;NH3OHLH >LX LI|$ILd$ Ha H@@H8rq
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rg- H5) MW L諮H3* HLLVL讹LHD$@LHx=HH5Ƿ` L_F H:f D HC0x HHCP1HHcHD$ `@ HH HxH HP0 HH HsHC LkHD$P HD$X HD$` H)HH E1E1E11IMHL$PHL$XL|$`tHLDHLLHL$X HT$ HD$0H9 HH9 HHDŽ$ HD$PH|$PH4H$ 'Ht$PHD$XHDŽ$ HDŽ$ HDŽ$ H)HH 1E1E11IHH$ H$ L$ tHLCHH$ L1HH$ $F H$ HHt?>Le H+I9t@H{0HPhHt
+x L H{0IHPIHLHCDH|$PI Ht=H L[]A\A]A^A_ X &9H{I H\$@H$ H5) H$ H6Ll$pLi L$ H
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8^* L HH 6X F7H}IHl$0HL H@@H8\
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AVSHHD_H HcT$8LcT$0HD$ H?Dڋ F4McMDB I8D$(DHH~AHHcL$HX= 1҅IЋD$XHcL$PCHHH[A^ AWIAVIAUATUSH Hr ZH:L$TH$ AnDVv HWHT(L$ $ $ $ Av D$ H$ HQL$ H$ H$8 $ H$ H$0 H$P H$X H$p H$x H$ H$ H$ H$ H$ HD$p E1MHD$@H$ HD$HH$ HD$XH$ H$ H$x H$ LAHfA]Ht$@DH|$HD'yAr E1Hl$`LE D EHt$@H|$HEuDD} DȇD$THL$@EHT$`L$ LD$XH$ D$H$ H|$H\$8D|$0\$(D|$ Dd$Dl$H$lMQD9uIHl$`AAWA9 L$ DAމLMI @ D$TALHL$@L$ LD$XH|$HD$H$ ډT$0T$ H\$Dt$8Dt$(Dd$H$AOQ9MDAAE9W HcӉ$ Dd$hH IcH4 HD$xC$ C$ CHLEI։$ AD$$ AD$$ AD$I$ H$ H$ D $ DT$hH$ H$ D} DEAMcMcMFD$ D$ DMcMFD$ D$ D$ McMFHcLD$ I H$ $ HL$pH$ oH$ oB'IH$ L$TD$`H$ fn\$`fp fn(L$`H$p fnd$`HD$xffp Hffp f8@foffk$ {MLfgf: HcA :$ M{LAAHcE :$ M{LAAHcE :$ {L;HcA ?MD9DDd$hIAHAU AHD$pBD9 HDDMI9A} HcMH HD$xH$ HD$@H$ HD$HH$ HD$`H$P H$ H$8 H$ LAH A]Ht$@DH|$HD uA E1Hl$XLE D EHt$@H|$HEuDD} DЃD$THL$@EHT$XL$ LD$`H$ D$H$ H|$H\$8D|$0\$(D|$ Dd$Dl$H$蔎UJD9uIHl$XAAWD9 IcHcDd$pH L MIADH$ M T$pH$ $ LT$hHt$xH$ E:A߃HcLIo LIH$ H$ oH$ D$XD$Tfnt$X2H$ fp H$ f8@fD$XH$0 fn|$XH fp fn 0ffp ffk}IcHU LT$hfgAf: Hc:}AWAHcHU AAHcD :}AWAAHcHU AAMcHcD :UL} AHcAARB9Dd$pAADME9| LL$ ADIƐD$TALL$ LD$`HL$@H|$HD$H$ ډT$0T$ H\$Dl$8Dl$(Dd$H$AV9MAAHD$xE9e H []A\A]A^A_E1E111Mf. 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HFHHL(HxHvPHʈ$ H{HS HH$ jWD;$ D$G $ V H$ Dl$$D$ D$ D$@ HD$H$ HD$(H$ HD$`H$ HD$h t$@H\$x$ H|$8D$ S)D9DNHt$D$ $ HHH$ 謬D$$ $ 1 fHD$HH|$0Ht$8H$ HD$XH$ H|$H$ DH$ HD$(H$ boU $ |$@Ht$D$ D$ $ A$ H$ LJLL($` H$@ LBLD(H$ $ H|$`HT$($ HL$HL$hH|$H|$PD$ D$ H $D$ 9\$$~dT$$)9NЀ|$G AHD$pHcHt$H|$0D$ D$ HH$ H$ f. D$ Dd$@D$@9$ H$ H@PH@ H@ H []A\A]A^A_Ht$pH|$0聞Uf. fAWAVAUATUSLH( B HL$hAHH|$0dHt$8HT$@L$AH LL$HD$,L$duLH$h LL$HIHL$hHT$@Ht$8H|$0HD$H$` H$zH( []A\A]A^A_Dl$,D|$͉DD?LAD$tLt$0H$ DIH|$PINhAF\AF`HL$pANX$AɉL$DZH$ LLHHD$xeNL}S H$ $ D$( HL$ H$ HL$X @ |$(T$dHt$h)9NV1D$`HcHH|$xHt$ $ $ H$ D$,$ ÜT$D$HDŽ$ HDŽ$ HDŽ$ D$ ҉D$ fD$ DHL$@D$|$A DqP D$D<RAA9DOEEA)McL!;Ht$0H \ H@ L` Dh(Dp0Hpt$,HHH|$8p,H$ HxHP8H$ HP@H$ HPHH$ HPPH$ HPXH$ HP`H$ HPhH$ HPpH$ HPxH$ H HH S|$(t$`H$` H$ S S H|$H Ht$P H H$h H Ht$XD H H H|$ _D$D$ D$9D$JHt$ H|$pkUH$ Ht9$ D$(L$(9L$dxHD$0 H@PH@ H@ H$ HHty9H?f. @ AWAVIAUATUSH8 H Hwo,D$$ ÉD$\H$ HHD$(CII I~Q H$ QHw LGPH$ L$ HcH ? 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+H9L$0 M Ht$PD$8E1LL$xfHD$(HIt Hfn<nIIfn.fn fbfn>Ht$fAoQfbfs flfofs ffpfAqfpL9T$fbfwfoD$8Mfsffofsff~\$8D$8L;\$@tIMC4MB4IpI9~.MILA4B4M9~LIGDDHHH;|$ H$ HL$ H$ H9$ j D A A A ~A sA hA ]A RA GH$ H$ H$` H$ H$ H$` H$x HDŽ$ H$p H$ HDŽ$ HDŽ$ HDŽ$ L$ L$ L$ HDŽ$ L$ HDŽ$ HDŽ$ HDŽ$ L$ L$ H$ H$` H$ $ A ILLL$` HDŽ$h L$ L$ =H$ HH$ H}f. AWAVIAUATUSH HH|$0HL$L$ LL$(H$ HBHD$ HH= } HD$Hl HwHHH$ HwH$ x HL$ LH|$HHLO$HD$XHMHHt$`HD$ HLt$HD$PH$ HD$HHD$HHD$HHD$HHH$ H$ H$ H HD$hHHHD$xKIHHD$pf H\$HR HD$IH(HHHH9HFH HD$xH\$E1HT$Lt$E1Ll$E1E1H11H\$H\$1Ld$Lt$IHH\$HHH\$LH1H\$1 Ld$T E4Ld$DDE4Ld$DEEt DEAE4Ld$EE4Ld$DEE4Ld$DDE4DDLt$AHL9wH9D$Ld$Ll$ HT$L)HT$@HHT$HHHT$8 Lt$IH$ fEJT= D$ EEMH$ H$ Lt$Lt$EfEoAfEoˉMfEoLt$Lt$1fAofAoMfAoLt$Lt$fAoML4$Lt$MLt$Lt$MLt$Lt$ML|$Lt$E1HL$Ifoo HL$fofs ffofofpfs ffpfbo HL$ffofs ffpffpfbo H $ffofs ffpffpfbo HL$ffofs ffpffpfbo HL$ffofs ffpffpfbo HL$fDfofs ffpffpfbo fDfofs ffpffpfbfDAoHL;t$ffs fpffpfbfDxfAoˉDEfsfDfAoEEHT$8fsfDfAofD~\$L$H$ fsfDfAoL|$@fsfDfAofD~T$t$D$ fsfDfAoHfsfDfAofD~L$|$fsfDfAofsfDfofD~D$DD$fsffofsff~|$foDL$fsffofsffof~t$DT$fsffofsff~l$foD\$fsffofsff~d$\$L9 Ld$Ll$Ld$Ll$L|$Hl$Hl$HT$Dt Hl$DDDt Hl$DEDt Hl$DEDt Hl$DEDt DEEt DDE4DAHDH;D$vLd$Ll$$ D$ D$ D$ D$ $ $ $ HD$(A] E $HD$ET E HD$`ED A<HD$pAt A HD$XIIHD$PHD$HD$HD$HD$HD$hH9D$uH$ Lt$L$ H HD$pHI9Hn Ll$(HHT$ IHD$H$ IH$ Lt$LLL$xH)L$xHHl$xHD$LLL$xHNLH HLL$@HHt$HHL ILD$8ML2L N<HHHLT$H4
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+ IHxH9] IL9uJ<9HtIDzIE HxH9 I(L9l$uH$X HtDzH$H HHD$H$@ H9HD$ Hp@HhLpIHt$(Hp8Ht$ ML+l$HD$(ID HxH9 HD$ ID HxH9b K|5 Hu f D LL?}ƲMuHE H}1H q²HE KD5 H}GƲI$HxH9| IPHPL9d$SH$@ HtƲH$0 HHD$H$( H9HD$ Hp@HhLpIHt$(Hp8Ht$ f D ML+l$HD$(ID HxH9e HD$ ID HxH9 K|5 Hu f D LL?uŲMuHE H}1H iHE KD5 H}?ŲI$HxH9 IPHPL9d$SH$( Ht ŲHD$0H$ Hx H$ HxH9 H$ HxH9 H$ HxH9 H$ HxH9G H$ HxH9 HD$`HxH9 HD$pHxH9P H$ HtH|$H HHD$8H []A\A]A^A_H1H豽VHHǐHpHt$@HL$ HT$²HL$ HT$nH\$\IH$ HLHxHD$`HHxnHD$pHHx]H$ HtH$ HHʲHHH$ HHtH$ HH=K HWtVHt$@pH=ZK HWt5:Ht$@+H6PJH릋PJHH=
+K HWtNeHt$@aVH=܍K HWt-Ht$@3PJH뮋PJHH=K HWtN|Ht$@mH=fK HWt-Ht$0PJH뮋PJHH= K HWtN7Ht$0u(H=K HWt-Ht$0GPJH뮋PJHH=K HWtNHt$@H=zK HWt-WHt$@ѿHPJH뮋PJHH=2K HWtLHt$@艿H=K HWt+L]PJH밋PJHHH$ HtHt$0 H0 HH$ HtHD$0 H HH$ HtHt$0 H HHD$0IH nH$p ῲH\$0H `H{pwH{XnH$ H{ 譢H$ LHxiH$ LH\$\HxPH$ LHx<H$ LHx(H$ HHxH$ Ht$HxcH$ HHHD$0 H HH$ HHHD$0 H HFI IIH\$\bH=K HWtoOHt$@M@H=ȉK HWtNHt$@HL$HL$H|$0IH\$\^cPJH덋PrpH=_K HWtL:L踼-H=3K HWt-Ht$@芼PJH밋PJHH=K HWt:NHt$@B?IHxH9uIL9uPJHpDFD@Ht$@HL$ HT$HL$ HT$ AWH5 AVAUATUSH HD$pH|$(HHD$0ѤHD$`HT$NH5g> HHD$8Ll$MLt$PH5I> LLHD$PH kK Ld$OHT$`H5j H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ xLI
+ H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X H H$8 H9 H, HcHH1I# L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ 㺲H$ HDx HDŽ$ H$ HzH$ 觺H$ HxHDŽ$ H$ HzH$ HD$ HP 4 HD$PHxH9 HD$`HxH9= Ht$0H|$ Lڠ HI H5| H_ L HI H5 Hm_ H54 H H5g H HR H5 F H H|$(HLt$ IP P H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H [H$0 θH$ H$ H9H$T LeHE IMILH)J!HxH9p JT9N49L9 H=ǃK f IHxH9
IL9uJ<9Ht9IE HxH9 I(L9,$zH$ HtH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?mMuHE H}1H aHE KD5 H}7I$HxH9 IPHPL9$$TH$ HtH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?mMuHE H}1H aHE KD5 H}7I$HxH9 IPHPL9$$TH$ HtH|$ H$ H 옵H$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$HL$HT$HH$ LHxDZH$ LHx賱H$ LHx蟱HD$PHt$LHx茱HD$`LHx{H$ HtH|$0 HH'HHH=K HW Ht$8 H=K HW Ht$8H=iK HW mHt$8HL$跲HL$TIHxH9u^IL9u|H=K HW LhPJH!PJHCpDFD@Ht$8HL$HT$ HL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H GH$0 躲Lt$ I 9~I~pP|I~XG|H$ I~ 膕H$ LHxBbH$ HHtHD$ H Hj转H{H=}K HWtNiHt$8ܰZH=W}K HWt-&Ht$8记PJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=|K HW sHt$8dH=|K HWu L௲H=[|K HW GHt$8讯8H=)|K HW ,Ht$8| H={K HW Ht$8JH={K HW ,Ht$8 H={K HW Ht$8殲H|$ H4TH=O{K HWt0HHt$8覮9PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWAVAUATUSHHH H1HD$@ HD$P PHHt
+x( L$ kHL$@L0 H0 H50 LHLH苳H.J H$ HHH~xK HHCH$ HxeHH5W-J H迴 H@ @ HHt$PHP(P(IăvP L$ 让HL$PL/ H 0 H5/ LHLHβ>f }(" HoK h L;
+ AD$( 1
+ ID$ Hh 1HHPH9wH []A\A]A^A_HHHPHtHHHPP(t'uH@ @tH@HuHɧK P(uH@ @tHHuH1HP(x(I L}
+ AD$( 1
+ HID$ HEMHHD$ H HPx(< HPK HhH9 H1HPx(C H'K H@H# L$ H\$pHD$p HD$x HDŽ$ LHH$ L_HD$xH;$ H$ H H(Ht$xHHt$xHL$pH)HH 11HR 9LHH9u H$ HD$ HD$HD$HHD$( D L|$ H|$E
+ H|$xHT$pHH)HH E1HD$ 1A) f D HHH)HH9 H ډA )D9HQ(DLHHt3{ $ Etf$ H$ tH
K tpHK H8H1HWH;VtIALm
+ H|$xHT$p^ID$ HE H|$Lۉ A u D H$ f D H$ HtH|$H9H HD$kE H|$ HD$AH@( H HL$I 9U HL$I$9 H\$H{
+ HcS HK(rs HDH|$]
+ HD$ ;l$ H|$pH躩HD$ @ HD$H@(H HL$HcI 9p HL$I$9 H\$H{
+ HcS HK(rs HDHÃ{(tH
+ C( HD$HC @HU H|$HcO 9
O$9? H\$H{}
+ HcS HK(HǍrs HDHt$ HD$H Hl$`H~ H5 HH6HHEH'J Ht$?HHH;rK HHCHD$`Hx%HܵH5'J H HD$H$H|$(qS
HD$H@(H|$QH\W QW H|HD$H$H|$(q
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HD$H@(Hh H@ HhHH-3K 1GH K H@ HH|$pHtPH譲1a HD$HEDLHZHt$xHH|$
+ H?HH$ H$ Hx軣H賤H{H L$ 4HL$@LN( HP( H5`( LHLHT rHHD$`Ht$?Hx3HH(L
+ 0HH fffff. AWAVAUATUSHHH H1HD$@ P(HHt[@( uKHH
+ E( 1
+ HE H@ 1HHPH9 H []A\A]A^A_ÿ L$ HL$@LS' Hq' H5/' LHLH#H$J H$ HHHoK HHCH$ HxHٵH5#J HW HHHPHDHHHPP(t/'H@ @H@HuH0K P(uH@ @HHH1HP(x(Ih L
+ AD$( 1L
+ HID$ HEMHHD$ H HPx(, HK HhH) H1HPx(3 HK H@H L$ H\$`HD$` HD$h HD$p LHH$ VHD$hH;D$pH$ H H(Ht$hHHt$hHL$`H)HH_ 11HR 9LHH9u> H$ HD$ HD$HD$HHD$(fL|$ H|$E
+ H|$hHT$`HH)HH E1HD$ 1A) f D HHH)HH9 H ډA )D9HQ(DLHHtr $ EtV$ H$ tH
K t`HK H8H1HWH;Vt9AL
+ H|$hHT$`^f D H|$LS A su D H$ f D H$ HtH|$H9tH HD$ @ E H|$ HD$AH@( H HL$I 9U HL$I$9 H\$H{
+ HcS HK(rs HDH|$
+ HD$ ;l$ H|$`H2vHD$ @ HD$H@(H HL$HcI 9p HL$I$9 H\$H{h
+ HcS HK(rs HDHÃ{(tH蔢
+ C( HD$HC @HU H|$HcO 9
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+ HcS HK(HǍrs HDHt$e HD$H Hl$P葛H H5W HHH.HH轤HF J Ht$?HHHiK HHCHD$PHx蝜H6ԵH5 J H HD$H$H|$(q HD$H@(H|$QH\W QW H|HD$H$H|$(q HD$H@(VHD$H$H|$(q\ HD$H@(Hh HE 4H@ HhHH-K 1QHK H@ LHQHt$h0HH|$`Ht語HBHH$ H$ HxVHNHHHD$PHt$?Hx0HH%L
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+ HH1P
+ HD$HEHHHAT1USHH0HHD$ P(HHt\@( uLH4
+ E( 1
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1HHH襙HD$ Ht$HxHfK H5gK H @ HE
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+ E( 1M
+ HHE H{KHt9LQ H0[]A\ HX H@ @ L%ٖK ;
+ HHCHHטH蟡HHD$ Ht$Hx蹗H@ f. USHHtb~@tuH?讚H1[] D H. ӚHU HHH[1]ÅuHU5K HH1[]f HHH1[]ÐUSHHtb~@tuH?.H1[] D H. SHU HHH[1]ÅuH4K HH1[]f HHH1[]ÐSHtO~5tuH?賙1[ ޙH1[ uHU4K H1[f D HH1[fffff. AUATUSHH5 HXL/Ld$ Hl$0LHdIU H5 L LH膕HH HD$0L%bK HxL9H H H5 I HHD$0 Q H\ H5W E1 HMN Lr[ H
g H D$ $ E11H|I LA[ H
f H D$ $ E1ɾ HHI Lg H
C H D$ $ E11HD H@ HD$@ Ht HD$0H˵HD$HH˵HD$@HH HD$@Ht
HHHD$ HxL9u>HX[]A\A]H=bK HW Ll$ LLH=bK HWt}H&HHD$0Ll$ LHx HD$ LHxHHL$@HHt
HHLl$ ƋPJH`HPJHzfff. AVAUATUSHH5| HpL/Hl$ Ld$PHLҗIU H5\ H_ HT$ H|$@H5| 詗HT$@H5f H5 HD$@L-)`K HxL90 Lt$0HL謒LH HD$0HxL9X LX H
| H[ D$ $ E11HF LX H
| H D$ $ E1ɾ HF Lٰ H
H D$ $ E11HYB HD$` 趕HD$PHzv LHHD$hHHD$`> HD$`Ht
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HHHff. UHSHHHG 蒓H(HHuHCHHCHH[]HKHHt
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+ DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( H H$ H9 L, LpHL1I0 L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` H$P HQN HDŽ$ H$h HPH$` 贐H$p H%NHP HDŽ$ H$ HPH$ F
+ HD$ HxH;=YK HD$0HxH;=YK HH L HI H5R H5 Lv HI H5 H{5 H5 H H5= H H` H5~ H, HL}HsHD$PHt
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HHHH=YK HW PHt$ AH=YK HW+
+LH$ IHtH HH$` HtH HH RaH$ ōH H D HPJH1IHqLIH$ HtH0 HH$ HRH H<H$` IHFH H0PJHfffff. AWH5y AVAUATUSH HD$pH|$(HHD$0QHD$`HT$NH5G HHD$8ӍLl$MLt$PH5) LL跍HD$PH KVK Ld$OHT$`H5: H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ XLI
+ H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X n H H$8 H9 H, HCHH1I L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ ÊH$ H$H HDŽ$ H$ HtJH$ 臊H$ HGHDŽ$ H$ HJH$ HD$ HP
+ HD$PHxH9 HD$`HxH9= Ht$0H|$ Lx HI H5L Ho/ Lp HѹI H5 HM/ H5 H H57 H H2 H5x H H|$(HwLt$ IP 0
+ H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H ;\H$0 讈H$ H$ H9H$T LeHE IMILH)J!HxH9p JT9N49L9 H=SK f IHxH9
IL9uJ<9HtIE HxH9 I(L9,$zH$ Ht臲H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?MMuHE H}1H AHE KD5 H}I$HxH9 IPHPL9$$TH$ Ht↲H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?MMuHE H}1H AHE KD5 H}I$HxH9 IPHPL9$$TH$ HtⅲH|$ H$ H hH$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$̓HL$HT$HH$ LHx见H$ LHx蓁H$ LHxHD$PHt$LHxlHD$`LHx[H$ HtH|$0 HHHHH=OK HW Ht$8 H={OK HW Ht$8H=IOK HW mHt$8HL$藂HL$TIHxH9u^IL9u|H=NK HW LHPJH!PJHCpDFD@Ht$8HL$HT$ HL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H 'VH$0 蚂Lt$ I NI~p0LI~X'LH$ I~ feH$ LHx"bH$ HHtHD$ H Hj|H{H=eMK HWtNiHt$8輀ZH=7MK HWt-&Ht$8莀PJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=LK HW sHt$8dH=kLK HWu LH=;LK HW GHt$88H= LK HW ,Ht$8\ H=KK HW Ht$8*H=KK HW ,Ht$8~ H=sKK HW Ht$8~H|$ HdTH=/KK HWt0HHt$8~9PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5Hk AVAUATUSH HD$pH|$(HHD$0!HD$`HT$NH5 HHD$8Ll$MLt$PH5i LLHD$PH HK Ld$OHT$`H5, H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ LI
+ H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X H H$8 H9 H, H}HH1ICy L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ }H$ Hd: HDŽ$ H$ H<H$ |H$ H8:HDŽ$ H$ H<H$ HD$ HP T
+ HD$PHxH9 HD$`HxH9= Ht$0H|$ L2k HI H5> H! L c HI H5 H! H55 H H5) H Hr H5j } H> H|$(HiLt$ IP p
+ H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H {NH$0 zH$ H$ H9H$T LeHE IMILH)J!HxH9p JT9N49L9 H=EK f IHxH9
IL9uJ<9HtYzIE HxH9 I(L9,$zH$ Ht(zH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?yMuHE H}1H uHE KD5 H}WyI$HxH9 IPHPL9$$TH$ Ht"yH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?xMuHE H}1H tHE KD5 H}WxI$HxH9 IPHPL9$$TH$ Ht"xH|$ H$ H [H$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$
vHL$HT$HH$ LHxsH$ LHxsH$ LHxsHD$PHt$LHxsHD$`LHxsH$ HtH|$0 HHG}HHH=AK HW Ht$8@uH=AK HW Ht$8uH=AK HW mHt$8HL$tHL$TIHxH9u^IL9u|H=3AK HW LtPJH!PJHCpDFD@Ht$8HL$HT$@tHL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H gHH$0 tLt$ I Y@I~pp>I~Xg>H$ I~ WH$ LHxbqbH$ HHtHD$ H HjnH{H=?K HWtNiHt$8rZH=w?K HWt-&Ht$8rPJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=>K HW sHt$80rdH=>K HWu L rH={>K HW GHt$8q8H=I>K HW ,Ht$8q H=>K HW Ht$8jqH==K HW ,Ht$88q H==K HW Ht$8qH|$ HTVTH=o=K HWt0HHt$8p9PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5 AVAUATUSH HD$pH|$(HHD$0aHD$`HT$NH5 HHD$8SrLl$MLt$PH5 LL7rHD$PH :K Ld$OHT$`H5 H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ qLI
+ H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X H H$8 H9 H, HoHH1Ik L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ CoH$ H, HDŽ$ H$ H.H$ oH$ Hx,HDŽ$ H$ H-/H$ HD$ HP
+ HD$PHxH9 HD$`HxH9= Ht$0H|$ A L:U HSI H51 H L@U H)I H5@ H H5 H H5 H? H H5+] a H~ H|$(H!\Lt$ IP
+ H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H @H$0 .mH$ H$ H9H$T LeHE IMILH)J!HxH9p JT9N49L9 H='8K f IHxH9
IL9uJ<9HtlIE HxH9 I(L9,$zH$ HthlH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?kMuHE H}1H gHE KD5 H}kI$HxH9 IPHPL9$$TH$ HtbkH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?jMuHE H}1H fHE KD5 H}jI$HxH9 IPHPL9$$TH$ HtbjH|$ H$ H LMH$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$MhHL$HT$HH$ LHx'fH$ LHxfH$ LHxeHD$PHt$LHxeHD$`LHxeH$ HtH|$0 HHoHHH=-4K HW Ht$8gH=3K HW Ht$8NgH=3K HW mHt$8HL$gHL$TIHxH9u^IL9u|H=s3K HW LfPJH!PJHCpDFD@Ht$8HL$HT$fHL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H :H$0 gLt$ I 2I~p0I~X0H$ I~ IH$ LHxcbH$ HHtHD$ H Hj aH{H=1K HWtNiHt$8<eZH=1K HWt-&Ht$8ePJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH= 1K HW sHt$8pddH=0K HWu L@dH=0K HW GHt$8d8H=0K HW ,Ht$8c H=W0K HW Ht$8cH=%0K HW ,Ht$8xc H=/K HW Ht$8FcH|$ HHTH=/K HWt0HHt$8c9PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5O AVAUATUSH HD$pH|$(HHD$0HD$`HT$NH5 HHD$8dLl$MLt$PH5 LLwdHD$PH -K Ld$OHT$`H5D H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ dLI
+ H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X . H H$8 H9 H, HbHH1I] L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ aH$ H HDŽ$ H$ H4!H$ GaH$ H HDŽ$ H$ Hm!H$ HD$ HP Է
+ HD$PHxH9 HD$`HxH9= Ht$0H|$ LzG HI H5H# H/ LG HYI H5{ H
H5ק HN H5Q H H H5kO o H H|$(HaNLt$ IP
+ H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H 2H$0 n_H$ H$ H9H$T LeHE IMILH)J!HxH9p JT9N49L9 H=g*K f IHxH9
IL9uJ<9Ht^IE HxH9 I(L9,$zH$ Ht^H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?
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+ H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X n H H$8 H9 H, HCTHH1IP L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ SH$ H$ HDŽ$ H$ HtH$ SH$ HHDŽ$ H$ HH$ HD$ HP
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HHHX L[]A\A]A^A_'IH H{pH{XH$ H{ LHD$xLHx)HD$pLHx)HD$hLHx)HD$`LHx)HD$ Ht$ Hx)HD$0H4$Hxn)HD$PHt
HHH 3H=F HW Ht$$+H=F HW' NL*AH$ IHtH HH$` HtH HH VH$ +H HHÐPJH5IHLIH$ HtH0 HH$ HVH H@H$` IHJH H4PJHf D AWH5<g AVAUATIUSHX Hl$@HhHT$ H|$0H5^ +HD$ HL$ H5H HHH$HL$+HD$ HT$0H\$`Lt$ H5 H{HD$`HBF HT$hLHHD$ HD$0HD$p+HC(H
+ DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( l H H$ H9 L, Lp)HL1I0% L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` (H$P HQ HDŽ$ H$h HH$` (H$p H%HP HDŽ$ H$ HH$ F HD$ HxH;=F HD$0HxH;=F K HH H5o H H5 H1 H褊 H5 V Hp HLH HD$PHt
HHHX L[]A\A]A^A_!IH H{pH{X H$ H{ LG
+HD$xLHx$HD$pLHx#HD$hLHx#HD$`LHx#HD$ Ht$ Hx#HD$0H4$Hx#HD$PHt
HHH_-H=F HW Ht$d%H=F HW' NL6%AH$ IHtH HH$` HtH HH H$ &H HHÐPJH5IH
+LIH$ HtH0 HH$ HVH H@H$` IHJH H4PJHf D AWH5L AVAUATIUSHX Hl$@HHT$ H|$0H5 2&HD$ HL$ H5 HHH$HL$
&HD$ HT$0H\$`Lt$ H5 H{HD$`HF HT$hLHHD$ HD$0HD$p%HC(H
+ DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( f H H$ H9 L, L#HL1Ip L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` 0#H$P H HDŽ$ H$h HH$` "H$p HeHP HDŽ$ H$ HH$ y HD$ HxH;=6F HD$0HxH;= F K HH/ H5 H@ H5C Hq H H5 ` H谆 HLUH HD$PHt
HHHX L[]A\A]A^A_< IH =H{pTH{XKH$ H{ LHD$xLHxF HD$pLHx5 HD$hLHx$ HD$`LHx HD$ Ht$ Hx HD$0H4$Hx HD$PHt
HHH'H=OF HW Ht$ H= F HW' NLv AH$ IHtH HH$` HtH HH H$ I H HHÐPJH5IH@LIH$ HtH0 HH$ HVH H@H$` IHJH H4PJHf D AWH5t AVAUATIUSHX Hl$@HHT$ H|$0H5ޠ r HD$ HL$ H5Ƞ HHH$HL$M HD$ HT$0H\$`Lt$ H5 H{HD$`HF HT$hLHHD$ HD$0HD$p HC(H
+ DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( a H H$ H9 L, L HL1I L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` p H$P Hڰ HDŽ$ H$h H!ݰH$` 4 H$p HڰHP HDŽ$ H$ HSݰH$ s HD$ HxH;=vF HD$0HxH;=`F K HHo H5h H耀 H5 H豁 H$ H5 j H HL
+H] HD$PHt
HHHX L[]A\A]A^A_|IH }H{pH{XH$ H{ LHD$xLHxHD$pLHxuHD$hLHxdHD$`LHxSHD$ Ht$ Hx@HD$0H4$Hx.HD$PHt
HHH!H=F HW Ht$H=_F HW' NLAH$ IHtH HH$` HtH HH H$ H HHÐPJH5IHLIH$ HtH0 HH$ HVH H@H$` IHJH H4PJHf D AWH5 AVAUATUSH HD$pH|$(HHD$0!HD$`HT$NH5 HHD$8Ll$MLt$PH5 LLHD$PH F Ld$OHT$`H5T H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ (LI
+ H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X >[ H H$8 H9 H, HHH1I L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ H$ H HDŽ$ H$ HDװH$ WH$ HHDŽ$ H$ H}װH$ HD$ HP m HD$PHxH9 HD$`HxH9 Ht$0H|$ | H50 Hz H5 H{ HFy H5? e H{ H|$(HLt$ IP Do H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H OH$0 H$ H$ H9H$X LeHE IMI @ LH)J!HxH9p JT9N49L9 H=F f IHxH9
IL9uJ<9Ht)IE HxH9 I(L9,$zH$ HtH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?]MuHE H}1H QHE KD5 H}'I$HxH9 IPHPL9$$TH$ HtH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?]MuHE H}1H QHE KD5 H}'I$HxH9 IPHPL9$$TH$ HtH|$ H$ H H$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$HL$HT$HH$ LHxH$ LHxH$ LHxHD$PHt$LHx|HD$`LHxkH$ HtH|$0 HHHHH=F HW Ht$8H=F HW Ht$8H=YF HW mHt$8HL$HL$TIHxH9u^IL9u|H=F HW LXPJH!PJHCpDFD@Ht$8HL$HT$HL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H 7H$0 Lt$ I )۰I~p@ٰI~X7ٰH$ I~ vH$ LHx2 bH$ HHtHD$ H Hj H{H=uF HWtNiHt$8
ZH=GF HWt-&Ht$8
PJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=F HW sHt$8
dH={F HWu FL 9H=KF HW GHt$8 8H=F HW ,Ht$8l H=F HW Ht$8: H=F HW ,Ht$8 H=F HW Ht$8 H|$ H$TH=?F HWt0HHt$8 9PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5t AVAUATUSH HD$pH|$(HHD$0衮HD$`HT$NH5 HHD$8#
Ll$MLt$PH5y LL
HD$PH F Ld$OHT$`H5Թ H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ LI
+ H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X M H H$8 H9 H, H
+HH1IS L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$
+H$ Htǰ HDŽ$ H$ HɰH$ H$ HHǰHDŽ$ H$ HɰH$ HD$ HP d` HD$PHxH9 HD$`HxH9 Ht$0H|$ o H5 H"m H5% HSn Hk H5 o Hm H|$(H5Lt$ IP a H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H ۰H$0 BH$ H$ H9H$X LeHE IMI @ LH)J!HxH9p JT9N49L9 H=7F f IHxH9
IL9uJ<9HtIE HxH9 I(L9,$zH$ HtxH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?MuHE H}1H HE KD5 H}I$HxH9 IPHPL9$$TH$ HtrH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?MuHE H}1H HE KD5 H}I$HxH9 IPHPL9$$TH$ HtrH|$ H$ H \H$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$]HL$HT$HH$ LHx7H$ LHx#H$ LHxHD$PHt$LHx HD$`LHx H$ HtH|$0 HH
+HHH==F HW Ht$8H= F HW Ht$8^H=F HW mHt$8HL$'HL$TIHxH9u^IL9u|H=F HW LPJH!PJHCpDFD@Ht$8HL$HT$HL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H հH$0 *Lt$ I ͰI~p˰I~X˰H$ I~ H$ LHxbH$ HHtHD$ H Hj-H{H=F HWtNiHt$8L ZH=F HWt-&Ht$8 PJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=-F HW sHt$8dH=F HWu FLP9H=F HW GHt$8 8H=F HW ,Ht$8 H=gF HW Ht$8H=5F HW ,Ht$8 H=F HW Ht$8VH|$ HTH=F HWt0HHt$89PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5 AVAUATUSH HD$pH|$(HHD$0!HD$`HT$NH5 HHD$8Ll$MLt$PH5 LLHD$PH F Ld$OHT$`H5T H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ (LI
+ H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X >@ H H$8 H9 H, HHH1I L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ H$ H HDŽ$ H$ HDH$ WH$ HȹHDŽ$ H$ H}H$ HD$ HP R HD$PHxH9 HD$`HxH9 Ht$0H|$ a H5 H_ H5 H` HF^ H5? H` H|$(HLt$ IP DT H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H OΰH$0 H$ H$ H9H$X LeHE IMI @ LH)J!HxH9p JT9N49L9 H=F f IHxH9
IL9uJ<9Ht)IE HxH9 I(L9,$zH$ HtH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?]MuHE H}1H QHE KD5 H}'I$HxH9 IPHPL9$$TH$ HtH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?]MuHE H}1H QHE KD5 H}'I$HxH9 IPHPL9$$TH$ HtH|$ H$ H ڰH$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$HL$HT$HH$ LHxH$ LHxH$ LHxHD$PHt$LHx|HD$`LHxkH$ HtH|$0 HHHHH=F HW Ht$8H=F HW Ht$8H=YF HW mHt$8HL$HL$TIHxH9u^IL9u|H=F HW LXPJH!PJHCpDFD@Ht$8HL$HT$HL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H 7ȰH$0 Lt$ I )I~p@I~X7H$ I~ vװH$ LHx2bH$ HHtHD$ H HjH{H=uF HWtNiHt$8ZH=GF HWt-&Ht$8PJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=F HW sHt$8 dH={F HWu FL9H=KF HW GHt$88H=F HW ,Ht$8l H=F HW Ht$8:H=F HW ,Ht$8 H=F HW Ht$8H|$ H$ְTH=?F HWt0HHt$89PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWIAVIAUATUSH H\$PH<$HL$H{hH˺F H-LF HƄ$ HDŽ$ 1Ƅ$ HDŽ$ HLeHDŽ$ H$ HEHDŽ$ HDŽ$ Ld$PHHD$ID$HLPHD$X I|$5Lm 1IELl$`H|HE(HHD$HEHU0H{PHD$ H@HT$(HTPHF HD$p HD$x HDŽ$ HDŽ$ HHDŽ$ HDŽ$ HD$PHF HhH$ HF H@HD$`HF HHD$h'HF HsH{hDŽ$ HHD$hHMF HH$ ,M LH{HLM LkH{HLlHD$H{H0+HF HxH$H8H$ H L$ H$ L9o H9F H H)HT$?1HIIIHH HLHHL;=_F Hl$@H<$HL$@HHD$@HxH;=3F H.F HHD$PH F HhH$ H F H@HD$`H3F HHD$hH$ HxH;=F H:F H{PHHD$hHHD$ HL$(H{hHt$H@HLPIEHL$Ll$`HL`ID$Ld$PHtPHcF HD$X HH$ H$H []A\A]A^A_ I9 H I)HT$?1L,IIHHtOHLHHL;=F y Hl$@H<$HL$@Hkf D E AG] E AGf D HD$`H@H|w M{f D HD$`H@H|w nf. H HW H<$Hs`f D MH=(g Hl$@IH$HH8HHL H=f H=TF HWtzHt$@H=(F HW ZHt$>}KAG M7CD7 qAG M7CD7 PJHIHD$@Ht$>Hx)I$IT$HL$HLd$PHLPHD$X HF H{hHH$ 0HH{IDHuHLHf D lPJH f. AWIAVIAUATUSH H\$PH|$H{h6HOF H-еF HƄ$ HDŽ$ 1Ƅ$ HDŽ$ HLeHDŽ$ H$ HEHDŽ$ HDŽ$ Ld$PHHD$ID$HLPHD$X I|$Lm 1IELl$`H|HE(HHD$HEHU0H{PHD$ H@HT$(HTPHF HD$p HD$x HDŽ$ HDŽ$ HHDŽ$ HDŽ$ HD$PH4F HhH$ H!F H@HD$`HyF HHD$hH4F HsH{hDŽ$ HHD$hHѲF HH$ Mw LH{HLM LH{HLHyF HxHD$H8H$ H L$ H$ L9s H9J H H)HT$?1HI,IIHH HLH}HL;=F Hl$@H|$HL$@HfHD$@HxH;=ƱF HF HHD$PHF HhH$ HF H@HD$`HƲF HHD$hH$ HxH;=jF HͱF H{PHHD$hHD$ HL$(H{hHt$H@HLPIEHL$Ll$`HL`ID$Ld$PHtPHF HD$X HH$ <HD$H []A\A]A^A_ D I9 H I)HT$?1LIIHHtOHLHHL;=F y Hl$@H|$HL$@H D E AGY E AGf D HD$`H@H|w WMf D HD$`H@H|w /zf. H HW H|$Hs`" D MH=` Hl$@IHD$HH8HHLG H=y` TH=F HWtzHt$@=H=F HW WHt$>
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+x( Ll$`HL$@LNF HeF H5*F LHLH ɭHCE Ht$0HHHF HHCHD$`HxHH5BE HXʭ H@ @ I$Ht$PLP(HË@(vG Ll$`HHL$PLE HE H5tE LHSLHhȭE }(tTHF j tQH C( 1U HC Hh LI$PHu' HĈ []A\A]A^A_HU j uHC I$1LPHtI$1LPP(t'uH@ @tH@HuH`F P(uH@ @tI$Ll$`L|$/H5 LLHíLLHT$`HzH;=sF H\$0H\$H{ H I$1LPx(F HF LpM> IcF 1LHD$I$P(x(H H C( 1 HHC HZJH I$HD$` HT$.HD$h HD$p H5 H|$H HD$Ht$LHD$IHD$0HxH;=zF MtIcD$(It$0L$ H5LL$hH|$`1LH)HHu, HH9tHHyHT$HHH9uH|$ L|$HCE11McHD$II9, D LH)HHHHs H;x H;Wh H;W H;Wg H-fH;V V H;P\ H;Pj H;Px HHF HIuLL)HH Hr Hz HS(H HcK 9= K$9i H{ HcS HK(HǍrs HDIF(Jt( H|$`M9IT$g LL$hIMcI9HuHC(H% HcK 9 K$9 H{7 HcS HK(Iōrs HDA}(tLb AE( IE eIf. M9vfLF(M9b LF0M9Jf D LF8M92 AH|C I;tII;tII;tHS(HK$H|$q HS(QLlS K$H|$q HC(H-耾#ILGM9LGM9r LGM9Zf D HP
H@ L5ĹF 1HT$ HT$HHBH=8F HOtPH\$0H\$,HHD$肼HD$HHD$`Ht$0HxbH*ĭJqrHHD$`Ht$0Hx7H/HíH Ll$`賸HL$@L> H> H5> LH LHf. |H=IF HWtN6L袻)HHD$0LHx艹H|$`Ht誼HBíHPJHAWH5 AVAUATIUSHX Hl$@HHT$ H|$0H5= 肽HD$ HL$ H5= HHH$HL$]HD$ HT$0H\$`Lt$ H5,j H{HD$`H҅F HT$hLHHD$ HD$0HD$pHC(H
+ DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( +
H H$ H9 L, L HL1I L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` 耺H$P Hw HDŽ$ H$h H1zH$` DH$p HwHP HDŽ$ H$ HczH$ HD$ HxH;=F HD$0HxH;=pF K HH H55E H H5g H H4 H5=x H HL襧HmHD$PHt
HHHX L[]A\A]A^A_茳IH 荄H{p褂H{X蛂H$ H{ LכHD$xLHx薵HD$pLHx腵HD$hLHxtHD$`LHxcHD$ Ht$ HxPHD$0H4$Hx>HD$PHt
HHHﾭH=F HW Ht$H=oF HW' NLƶAH$ IHtH HH$` HtH HH &H$ 虷H HHÐPJH5IH萛LIH$ HtH0 HH$ HVH H@H$` IHJH H4PJHf D AWH5 AVAUATIUSHX Hl$@HHT$ H|$0H5.8 ·HD$ HL$ H58 HHH$HL$蝷HD$ HT$0H\$`Lt$ H5ld H{HD$`HF HT$hLHHD$ HD$0HD$pRHC(H
+ DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( k
H H$ H9 L, L@HL1I L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` H$P H!r HDŽ$ H$h HqtH$` 脴H$p HqHP HDŽ$ H$ HtH$ HD$ HxH;=}F HD$0HxH;=}F K HH H5n? H H5a H Ht H5}r H@ HL塰H譗HD$PHt
HHHX L[]A\A]A^A_̭IH ~H{p|H{X|H$ H{ LHD$xLHx֯HD$pLHxůHD$hLHx贯HD$`LHx裯HD$ Ht$ Hx萯HD$0H4$Hx~HD$PHt
HHH/H=}F HW Ht$4H=}F HW' NLAH$ IHtH HH$` HtH HH fH$ ٱH HHÐPJH5IHЕLIH$ HtH0 HH$ HVH H@H$` IHJH H4PJHf D AWH5 m AVAUATIUSHX Hl$@HHT$ H|$0H5n2 HD$ HL$ H5X2 HHH$HL$ݱHD$ HT$0H\$`Lt$ H5^ H{HD$`HRzF HT$hLHHD$ HD$0HD$p蒱HC(H
+ DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$(
H H$ H9 L, L耯HL1I@ L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` H$P Hal HDŽ$ H$h HnH$` ĮH$p H5lHP HDŽ$ H$ HnH$ V HD$ HxH;=xF HD$0HxH;=wF K HH H5o9 H H5\ HA H H5l u H HL%H푰HD$PHt
HHHX L[]A\A]A^A_ IH
yH{p$wH{XwH$ H{ LWHD$xLHxHD$pLHxHD$hLHxHD$`LHx㩭HD$ Ht$ HxЩHD$0H4$Hx辩HD$PHt
HHHoH= xF HW Ht$tH=wF HW' NLFAH$ IHtH HH$` HtH HH H$ H HHÐPJH5IHLIH$ HtH0 HH$ HVH H@H$` IHJH H4PJHf D AWH5g AVAUATIUSHX Hl$@H(HT$ H|$0H5, BHD$ HL$ H5, HHH$HL$ HD$ HT$0H\$`Lt$ H5X H{HD$`HtF HT$hLHHD$ HD$0HD$pҫHC(H
+ DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$(
H H$ H9 L, LHL1I耥 L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` @H$P Hf HDŽ$ H$h HhH$` H$p HufHP HDŽ$ H$ H#iH$ HD$ HxH;=FrF HD$0HxH;=0rF K HH? H53 HP H5SV H
H
+ H5f k H HLeH-HD$PHt
HHHX L[]A\A]A^A_LIH MsH{pdqH{X[qH$ H{ L藊HD$xLHxVHD$pLHxEHD$hLHx4HD$`LHx#HD$ Ht$ HxHD$0H4$HxHD$PHt
HHH语H=_rF HW Ht$败H=/rF HW' NL膥AH$ IHtH HH$` HtH HH yH$ YH HHÐPJH5IHPLIH$ HtH0 HH$ HVH H@H$` IHJH H4PJHf D AWH5t AVAUATIUSHX Hl$@HhHT$ H|$0H5& 肦HD$ HL$ H5& HHH$HL$]HD$ HT$0H\$`Lt$ H5,S H{HD$`HnF HT$hLHHD$ HD$0HD$pHC(H
+ DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( +
H H$ H9 L, L HL1I L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` 耣H$P H` HDŽ$ H$h H1cH$` DH$p H`HP HDŽ$ H$ HccH$ HD$ HxH;=lF HD$0HxH;=plF K HH H5) H H5P H H4 H5=a f H HL襐HmHD$PHt
HHHX L[]A\A]A^A_茜IH mH{pkH{XkH$ H{ LׄHD$xLHx薞HD$pLHx腞HD$hLHxtHD$`LHxcHD$ Ht$ HxPHD$0H4$Hx>HD$PHt
HHH吝H=lF HW Ht$H=olF HW' NLƟAH$ IHtH HH$` HtH HH &tH$ 虠H HHÐPJH5IH萄LIH$ HtH0 HH$ HVH H@H$` IHJH H4PJHf D AWH5 \ AVAUATIUSHX Hl$@HHT$ H|$0H5.! HD$ HL$ H5! HHH$HL$蝠HD$ HT$0H\$`Lt$ H5lM H{HD$`HiF HT$hLHHD$ HD$0HD$pRHC(H
+ DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( k
H H$ H9 L, L@HL1I L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` H$P H![ HDŽ$ H$h Hq]H$` 脝H$p HZHP HDŽ$ H$ H]H$ HD$ HxH;=fF HD$0HxH;=fF K HH H5( H H5J H Ht H5}[ H@ HL劰H譀HD$PHt
HHHX L[]A\A]A^A_̖IH gH{peH{XeH$ H{ LHD$xLHx֘HD$pLHxŘHD$hLHx贘HD$`LHx裘HD$ Ht$ Hx萘HD$0H4$Hx~HD$PHt
HHH/H=fF HW Ht$4H=fF HW' NLAH$ IHtH HH$` HtH HH fnH$ ٚH HHÐPJH5IH~LIH$ HtH0 HH$ HVH H@H$` IHJH H4PJHf D AWH51V AVAUATUSH HD$pH|$(HHD$0HD$`HT$NH5g HHD$8Ll$MLt$PH5I LLךHD$PH kcF Ld$OHT$`H5G H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ xLI
+ H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X
H H$8 H9 H, HcHH1I# L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ 㗭H$ HDU HDŽ$ H$ HWH$ 觗H$ HUHDŽ$ H$ HWH$ HD$ HP 4 HD$PHxH9 HD$`HxH9 Ht$0H|$ H52" H H5D H# H H5U z Hb H|$(HLt$ IP H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H iH$0 H$ H$ H9H$X LeHE IMI @ LH)J!HxH9p JT9N49L9 H=aF f IHxH9
IL9uJ<9HtyIE HxH9 I(L9,$zH$ HtHH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?譔MuHE H}1H 衐HE KD5 H}wI$HxH9 IPHPL9$$TH$ HtBH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?譓MuHE H}1H 衏HE KD5 H}wI$HxH9 IPHPL9$$TH$ HtBH|$ H$ H ,vH$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$-HL$HT$HH$ LHxH$ LHxH$ LHxߎHD$PHt$LHx̎HD$`LHx軎H$ HtH|$0 HHgHHH=
]F HW Ht$8`H=\F HW Ht$8.H=\F HW mHt$8HL$HL$TIHxH9u^IL9u|H=S\F HW L訏PJH!PJHCpDFD@Ht$8HL$HT$`HL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H cH$0 Lt$ I y[I~pYI~XYH$ I~ rH$ LHx肌bH$ HHtHD$ H HjH{H=ZF HWtNiHt$8 ZH=ZF HWt-&Ht$8PJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=YF HW sHt$8PdH=YF HWu FL 9H=YF HW GHt$88H=iYF HW ,Ht$8輌 H=7YF HW Ht$8芌H=YF HW ,Ht$8X H=XF HW Ht$8&H|$ HtqTH=XF HWt0HHt$8拭9PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5H AVAUATUSH HD$pH|$(HHD$0aHD$`HT$NH5
HHD$8sLl$MLt$PH5
LLWHD$PH UF Ld$OHT$`H5$: H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ LI
+ H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X
H H$8 H9 H, H㊭HH1I裆 L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ cH$ HG HDŽ$ H$ HJH$ 'H$ HGHDŽ$ H$ HMJH$ HD$ HP HD$PHxH9 HD$`HxH9 Ht$0H|$ a H5;y Hr H5u7 H H H5 H \ H H|$(HwLt$ IP H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H \H$0 蒈H$ H$ H9H$X LeHE IMI @ LH)J!HxH9p JT9N49L9 H=SF f IHxH9
IL9uJ<9HtIE HxH9 I(L9,$zH$ HtȇH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?-MuHE H}1H !HE KD5 H}I$HxH9 IPHPL9$$TH$ HtH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?-MuHE H}1H !HE KD5 H}I$HxH9 IPHPL9$$TH$ Ht
H|$ H$ H hH$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$譃HL$HT$HH$ LHx臁H$ LHxsH$ LHx_HD$PHt$LHxLHD$`LHx;H$ HtH|$0 HH犭HHH=OF HW Ht$8H=[OF HW Ht$8讂H=)OF HW mHt$8HL$wHL$TIHxH9u^IL9u|H=NF HW L(PJH!PJHCpDFD@Ht$8HL$HT$HL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H VH$0 zLt$ I MI~pLI~XLH$ I~ FeH$ LHxbH$ HHtHD$ H Hj}|H{H=EMF HWtNiHt$8蜀ZH=MF HWt-&Ht$8nPJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=}LF HW sHt$8dH=KLF HWu FL9H=LF HW GHt$8n8H=KF HW ,Ht$8< H=KF HW Ht$8
+H=KF HW ,Ht$8~ H=SKF HW Ht$8~H|$ HcTH=KF HWt0HHt$8f~9PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5L AVAUATUSH HD$pH|$(HHD$0HD$`HT$NH5g HHD$8Ll$MLt$PH5I LLHD$PH kHF Ld$OHT$`H5, H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ xLI
+ H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X
H H$8 H9 H, Hc}HH1I#y L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ |H$ HD: HDŽ$ H$ H<H$ |H$ H:HDŽ$ H$ H<H$ HD$ HP 4 HD$PHxH9 HD$`HxH9 Ht$0H|$ H5a H H5) H# H H5: R Hb H|$(HjLt$ IP H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H NH$0 {H$ H$ H9H$X LeHE IMI @ LH)J!HxH9p JT9N49L9 H=FF f IHxH9
IL9uJ<9HtyzIE HxH9 I(L9,$zH$ HtHzH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?yMuHE H}1H uHE KD5 H}wyI$HxH9 IPHPL9$$TH$ HtByH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?xMuHE H}1H tHE KD5 H}wxI$HxH9 IPHPL9$$TH$ HtBxH|$ H$ H ,[H$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$-vHL$HT$HH$ LHxtH$ LHxsH$ LHxsHD$PHt$LHxsHD$`LHxsH$ HtH|$0 HHg}HHH=
BF HW Ht$8`uH=AF HW Ht$8.uH=AF HW mHt$8HL$tHL$TIHxH9u^IL9u|H=SAF HW LtPJH!PJHCpDFD@Ht$8HL$HT$`tHL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H HH$0 tLt$ I y@I~p>I~X>H$ I~ WH$ LHxqbH$ HHtHD$ H HjnH{H=?F HWtNiHt$8 sZH=?F HWt-&Ht$8rPJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=>F HW sHt$8PrdH=>F HWu FL r9H=>F HW GHt$8q8H=i>F HW ,Ht$8q H=7>F HW Ht$8qH=>F HW ,Ht$8Xq H==F HW Ht$8&qH|$ HtVTH==F HWt0HHt$8p9PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5I AVAUATUSH HD$pH|$(HHD$0aHD$`HT$NH5
HHD$8srLl$MLt$PH5
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H H$8 H9 H, HoHH1Ik L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ coH$ H, HDŽ$ H$ H/H$ 'oH$ H,HDŽ$ H$ HM/H$ HD$ HP HD$PHxH9 HD$`HxH9 Ht$0H|$ a H5 Hr H5u H H H5 - W H H|$(H\Lt$ IP H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H AH$0 mH$ H$ H9H$X LeHE IMI @ LH)J!HxH9p JT9N49L9 H=8F f IHxH9
IL9uJ<9HtlIE HxH9 I(L9,$zH$ HtlH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?-lMuHE H}1H !hHE KD5 H}kI$HxH9 IPHPL9$$TH$ HtkH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?-kMuHE H}1H !gHE KD5 H}jI$HxH9 IPHPL9$$TH$ HtjH|$ H$ H MH$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(HĈ []A\A]A^A_HHCHf?H7pHt$8HL$HT$hHL$HT$HH$ LHxfH$ LHxsfH$ LHx_fHD$PHt$LHxLfHD$`LHx;fH$ HtH|$0 HHoHHH=4F HW Ht$8gH=[4F HW Ht$8gH=)4F HW mHt$8HL$wgHL$TIHxH9u^IL9u|H=3F HW L(gPJH!PJHCpDFD@Ht$8HL$HT$fHL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H ;H$0 zgLt$ I 2I~p1I~X1H$ I~ FJH$ LHxdbH$ HHtHD$ H Hj}aH{H=E2F HWtNiHt$8eZH=2F HWt-&Ht$8nePJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=}1F HW sHt$8ddH=K1F HWu FLd9H=1F HW GHt$8nd8H=0F HW ,Ht$8<d H=0F HW Ht$8
+dH=0F HW ,Ht$8c H=S0F HW Ht$8cH|$ HHTH=0F HWt0HHt$8fc9PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH59 AVAUATUSH HD$pH|$(HHD$0HD$`HT$NH5g
HHD$8dLl$MLt$PH5I
LLdHD$PH k-F Ld$OHT$`H5 H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ xdLI
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dD H{PHHD$hHD$ HL$(H{hHt$H@HLPIEHL$Ll$`HL`ID$Ld$PHtPH6cD HD$X HH$ |HD$H []A\A]A^A_ D I9 H I)HT$?1LIIHHtOHLHQHL;=bD y Hl$@H|$HL$@H: D E AGY E AGf D HD$`H@H|w 藞Mf D HD$`H@H|w ozf. H HW H|$Hs`b D MH= ӕHl$@IHD$HH8HǔHOL臞 H= 蔕H=$cD HWtzHt$@}H=bD HW WHt$>MHAG M7CD7 qAG M7CD7 PJHIHD$@Ht$>HxI%IT$HL$HLd$PHLPHD$X H`D H{hHH$ HxH{IHuHL荏Hf D lPJH f. AWAVIAUMATUSH H\$`H<$Ht$HL$LL$ H{hH`D H-aD HƄ$ HDŽ$ 1Ƅ$ HDŽ$ HLeHDŽ$ H$ HEHDŽ$ HDŽ$ Ld$`HHD$ID$HL`HD$h I|${L} 1IGL|$pH|HE(HHD$(VHEHU0H{PHD$0H@HT$8HT`HH_D HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHDŽ$ HDŽ$ HD$`H^D HhH$ H^D H@HD$pH5_D HHD$xgH_D HsH{hDŽ$ HHD$xH^D HH$ lH|$ Hl$HÏH{HHĕM L裏H{HL褕HD$H{0ĚM LsH{HLtHD$ H{0蔚H]D HxH$H8H$ H L$ H$ L9p H9 H& H)HT$O1HI衛IIHH HLHHL;5h]D 4 Hl$PH<$HL$PHܙHD$PHt$NHx9H:]D HHD$`H*]D HhH$ H]D H@HD$pH?^D HHD$xH$ HxH;=\D i HF]D H{PHHD$xTHD$0HL$8H{hH@HL`IGHL$(L|$pHLpID$HL$Ld$`HL`Ho\D HD$h HH$ 赌H$H []A\A]A^A_ I9O Hn I)HT$O1L4IIHH HLH腘HL;5[D Hl$PH<$HL$PHoHD$PHxH;=[D H=I]D HW( oHt$N螐`f E AF E AFq HD$pH@H|w 臗Mf D HD$pH@H|w _f. HD$pH@H|w 7fH H H<$Hs`3f D MH= 裎Hl$PIH$HH8H蘍H LX H= dH=[D HW vHt$PIgAF M.CD. 9AF M.CD. IHD$PHt$NHxRH{IHuHL∫HYD H{hHH$ H莖I IT$HL$HLd$`HL`HD$h H뭋PJHbPJHf. AWAVIAUMATUSH H\$`H<$Ht$HL$LL$ H{hHYD H-ZD HƄ$ HDŽ$ 1Ƅ$ HDŽ$ HLeHDŽ$ H$ HEHDŽ$ HDŽ$ Ld$`HHD$ID$HL`HD$h I|${L} 1IGL|$pH|HE(HHD$(VHEHU0H{PHD$0H@HT$8HT`HHXD HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHDŽ$ HDŽ$ HD$`HWD HhH$ HWD H@HD$pH5XD HHD$xgHXD HsH{hDŽ$ HHD$xHWD HH$ lH|$ Hl$HÈH{HHĎM L裈H{HL褎HD$H{0ēM LsH{HLtHD$ H{0蔓HVD HxH$H8H$ H L$ H$ L9p H9_ H H)HT$O1HI衔IIHH HLHHL;5hVD Hl$PH<$HL$PHܒHD$PHt$NHx9H:VD HHD$`H*VD HhH$ HVD H@HD$pH?WD HHD$xH$ HxH;=UD HFVD H{PHHD$xTHD$0HL$8H{hH@HL`IGHL$(L|$pHLpID$HL$Ld$`HL`HoUD HD$h HH$ 赅H$H []A\A]A^A_ I9 H I)HT$O1L4IIHHtOHLH艑HL;5TD e Hl$PH<$HL$PHsf D E AF\ E AFf D HD$pH@H|w ϐMKf D HD$pH@H|w 觐>fHD$pH@H|w 臐NfH H? H<$Hs`背f D MH= Hl$PIH$HH8H膫HpL訐 H= 贇H=DUD HW Ht$P虈AF M.CD. AF M.CD. IHD$PHt$NHxRRH{I1HuHL2HSD H{hHH$ fHޏI IT$HL$HLd$`HL`HD$h HrPJH f. AWIAVIAUATUSH H\$PH|$H{hfHRD H- TD HƄ$ HDŽ$ 1Ƅ$ HDŽ$ HLeHDŽ$ H$ HEHDŽ$ HDŽ$ Ld$PHHD$ID$HLPHD$X I|$銫Lm 1IELl$`H|HE(HHD$ĊHEHU0H{PHD$ H@HT$(HTPHQD HD$p HD$x HDŽ$ HDŽ$ HHDŽ$ HDŽ$ HD$PHdQD HhH$ HQQD H@HD$`HQD HHD$hۏHdRD HsH{hDŽ$ HHD$hHQD HH$ Mw L?H{HL@M L H{HL HPD HxHD$H8H$ H L$ H$ L9s H9J H H)HT$?1HI\IIHH HLH譌HL;=#PD Hl$@H|$HL$@H薌HD$@HxH;=OD HOD HHD$PHOD HhH$ HOD H@HD$`HPD HHD$hH$ HxH;=OD HOD H{PHHD$h HD$ HL$(H{hHt$H@HLPIEHL$Ll$`HL`ID$Ld$PHtPH&OD HD$X HH$ lHD$H []A\A]A^A_ D I9 H I)HT$?1L쌫IIHHtOHLHAHL;=ND y Hl$@H|$HL$@H* D E AGY E AGf D HD$`H@H|w 臊Mf D HD$`H@H|w _zf. H HW H|$Hs`R D MH= ÁHl$@IHD$HH8H跀H?Lw H= 脁H=OD HWtzHt$@mH=ND HW WHt$>=HAG M7CD7 qAG M7CD7 PJHIHD$@Ht$>HxI%IT$HL$HLd$PHLPHD$X HLD H{hHH$ |HhH{I|ܬHuHL}{Hf D lPJH f. AWIAVIAUATUSH H\$PH|$H{h{HLD H-MD HƄ$ HDŽ$ 1Ƅ$ HDŽ$ HLeHDŽ$ H$ HEHDŽ$ HDŽ$ Ld$PHHD$ID$HLPHD$X I|$yLm 1IELl$`H|HE(HHD$THEHU0H{PHD$ H@HT$(HTPHFKD HD$p HD$x HDŽ$ HDŽ$ HHDŽ$ HDŽ$ HD$PHJD HhH$ HJD H@HD$`H9KD HHD$hkHKD HsH{hDŽ$ HHD$hHJD HH$ pMw L{H{HLЁM L{H{HL谁H9JD HxHD$H8H$ H L$ H$ L9s H9J H H)HT$?1HI쇫IIHH HLH=HL;=ID Hl$@H|$HL$@H&HD$@HxH;=ID HID HHD$PHqID HhH$ H^ID H@HD$`HJD HHD$hH$ HxH;=*ID HID H{PHHD$h蛂HD$ HL$(H{hHt$H@HLPIEHL$Ll$`HL`ID$Ld$PHtPHHD HD$X HH$ xHD$H []A\A]A^A_ D I9 H I)HT$?1L|IIHHtOHLHфHL;=GHD y Hl$@H|$HL$@H躄 D E AGY E AGf D HD$`H@H|w Mf D HD$`H@H|w zf. H HW H|$Hs` D MH=x S{Hl$@IHD$HH8HGzHρL H=9 {H=HD HWtzHt$@{H=xHD HW WHt$>{HAG M7CD7 qAG M7CD7 PJHIHD$@Ht$>HxyyI%IT$HL$HLd$PHLPHD$X H5FD H{hHH$ vHH{I ֬HuHL
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ҍTIHըt"IExH HPHlDIHʼnLHH[]A\A]Hlfff. USHHHGuHHX? CH Hht H{HlDIHʼnHHH[]H빐H= > uH? PH H? Zfff. S(Hw%W(H,: HcH H HtHPC( [ UHSHHHt C( Hk H[] UHSHHHt C( Hk H[] UHSHHOHt C( Hk H[] UHSHH Ht C( Hk H[] UHSHHHt C( Hk H[] UHSHH_H; > t HtHHxH;=G> u H]E(tHH[]H=> HWtHt$뽋PJH @ USHHH > HHdHCu H> HC HHH[]HHx HuHHzH;=> u
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-HIH=> HOtHt$~ыJqr @ SHWH[~fffff. wfff. HGSucH? H HXGtBt"HWHHJH\ ҍTIHӨtHG HHDIH\H[Hfffff. H= > uH, ? PUD H ? Zfff. fffff. H9? HLJ LJ HGxH ATIUSHH_xH-? H9t HtHHxH;=> u;H}I$ H9t HtHHxH;=x> u6H}H[]A\H=> HWt<Ht$B|H=> HWt$Ht$ |륋PJH뾋PJHwfff. AUATIUSHHG H? A|$ H Hp~7MD$(1 ITHHHJʃҍTIHH9A|$8H~2MD$@1fIDHH H@ɍLIHH9A|$PH~2MD$X1fIDHH H@ɍLIHH9El$hDHEt71 D ID$pHcӃH|HTIHHA9uAD$ t#IT$xHHJHl
ҍTIHըt&I$ HHJHl
ҍTIHըt I$ HHҍTIHlt#A$
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ҍDIHlLHH[]A\A]H*f. AUATUHSHHG H? Dm H EL`Et31 HE(HcӃH|ҍTIHIA9uDm8DIEt91f HE@HcӃH|ҍTIHIA9uDmPDEM$Dt11HEXHcӃH|,HTIHIA9uыE t"HU`HHJMd ҍTIIԨt"HUhHHJMd ҍTIIԨt"HUpHHJMd ҍTIIԨt"HUxHHJMd ҍTIIԨuZ tH HHҍTIMd@tH HHDIMdDHHL[]A\A] H $MdDIIċE t{H H=> uH ? P> H? Zfff. USHHH> HG G G HHH=> uHH[]r> fH{H" H+> HHH~fff. SH`/ 1- 1- H ? HZH=; H [ @ USHHHt:Hp uRHD H߾0 4 H1Ht HHH[] 0 wHHHH[]H5r> 0 (6 HHkwH}fUSHHH0> HG G G HHH=\> uHH[]"= fH{H H> HHHL}fff. SH. 1- 1- H B ? HZH=c: H [e @ USHHHt:Hp uRH$ H߾( G3 H1Ht HHH[] ( vHHHH[]H5> ( 4 HHvHs|fUSHHH> HG G G HHH=> uH H[]; fH{H H> HHH{fff. SH, 1- 1- y H ? HZH=9 HK [ @ USHHHt:Hp uRH H߾( 1 H1Ht HHH[] ( >uHHHH[]H5> ( 3 HHtH#{fUSHHHP> HG G G HHH=|> uHH[]: fH{H2 H;> HHHzfff. SHp+ 1- 1- ) H ? HZH=7 H [e @ USHHHt:Hp uRH H߾0 0 H1Ht HHH[] 0 sHHHH[]H5> 0 82 HH{sHyfUSHHH@> HG G G HHH=> uHH[]29 fH{H H> HHH\yfff. SH * 1- 1- H ? HZH=s6 H [5 @ USHHHt:Hp uRH H߾( W/ H1Ht HHH[] ( rHHHH[]H5> ( 0 HH+rHxfUSHHH> HG G G HHH= > uHnH[]7 fH{H H> HHH xfff. USHHHt:Hp uRH H߾ G. H1Ht HEHH[] qHH#HH[]H5> / HHqHswfUSHHH > HG G G HHH=> uHH[]6 fH{H H> HHHvfff. USHHHt:Hp uRH H߾( 7- H1Ht HEHH[] ( ~pHH#HH[]H5> ( . HH pHcvfUSHHH> HG G G HHH= > uHH[]5 fH{Hr H{> HHHufff. SH& 1- 1- i H > HZH=3 H; [u @ USHHHt:Hp uRHt H߾( + H1Ht HHH[] ( .oHHHH[]H5> ( x- HHnHufUSHHH > HG G G HHH=L> uHH[]r4 fH{H" H+> HHHtfff. SH`% 1- 1- H > HZH=1 H [ @ USHHHt:Hp uRH4 H߾( * H1Ht HHH[] ( mHHHH[]H5R> ( (, HHkmHsfUSHHHP> HG G G HHH=L> uHH[]"3 fH{H Hۼ> HHHLsfff. UH$ 1- 1- SH H > HHH [0 H H-|> HHHy H-> HHH_ 5H[]f. USHHHt:Hp uRH H߾0 ( H1Ht HHH[] 0 >lHHHH[]H5ҫ> 0 * HHkH#rfUSHHHP> HG G G HHH=<> uH>H[]1 fH{H2 H;> HHHqfff. SHp" 1- 1- ) H > HZH=. H [ @ USHHHt:Hp uRHd
H߾( ' H1Ht HHH[] ( jHHHH[]H5> ( 8) HH{jHpfAUATUSHHH> HH H H Ht H H{xHǃ HChHt H H{`HCx HCPHt H H{8HsHHC` pS,~H{0Ht
-H? C ~H{ Ht
-H? H{s H|> HHH[]A\A]f. D'LoE~$1fI| HtHPHA9H Kif D D'LoE~!1fI| HtHPHA9H{xif D'LoE~!1fI| HtHPHA9H{`hf hf D hf D SH'H[hfffff. USHHH> HG G G HG G HG$ HG( HHG0 G8 G< HG@ HGH GP GT HGX H=r> uH$H[]- H{HHsXHOH{0Hs@IH{Hs(<H{s H|> HHHmfff. USHHHt:Hp uRH H߾ '$ H1Ht HHH[] ngHHHH[]H5"> % HHfHSmfAUATUSHHH|> HH0H{XHCHHtHtNH{0Hs@HCX 9H{Hs(,H{c
Hl> HHH[]A\A]f. D'LoE~!1fI| HtHPHA9H{X>f{ SH7H[ ffffff. USHHH> HG G G HG G HG$ HG( HH=> uHH[]+ H{Hs(HH{Y Hb> HHHk SH 1- 1- Y H > H:H=( H+ [e @ USHHHt:Hp uRH H߾8 ! H1Ht HHH[] 8 eHHHH[]H52> 8 h# HHdHkfUSHHHЦ> HG G G HG G HG$ HG( HHG0 G8 G< HG@ HGH GP GT HGX H=2> uHtH[]* H{HHsXHLH{0Hs@ H{Hs(TLH{
- H> HHH
jffff. SH 1- 1- H 2> HH=#' H[ [鵳 @ USHHHt:Hp uRH H߾ H1Ht HHH[] NcHHcHH[]H5"> ! HHbH3ifAUATUSHHH> HH`H{XHCHHtHtVH{0Hs@HCX 蹐H{(HCHtHtwH{HC( 6 H?> HHH[]A\A] D D'LoE~)1f. I| HtHPHA9H{Xbkf D'LoE~!1fI| HtHPHA9H{(aR SHH[afffff. USHHHЭ> HG G G HG G HG$ HG( HHG0 G8 G< HG@ HGH GP GT HGX HG` Gh Gl HGp H=> uHH[]& H{`HspHhH{HHsXKH{0Hs@>H{Hs(1H{h Hq> HHHffSH 1- 1- i H > HH=$ H; [ @ USHHHt:Hp uRH H߾ H1Ht HeHH[] .`HHCHH[]H5> x HH_HffAUATUSHHHܫ> HHH{pHC`HtHtVH{HHsXHCp H{0Hs@H{Hs(ߠH{ H > HHH[]A\A] D D'LoE~)1f. I| HtHPHA9H{p^k SH'H[^fffff. USHHHТ> HG G G HG G HG$ HG( HHG0 G8 G< HG@ HGH GP GT HGX HG` Gh Gl HGp HGx LJ LJ HLJ HLJ LJ LJ HLJ HLJ LJ LJ HLJ H=> uH苵H[]# H H H'H H FH{xH TH{`HspGH{HHsX:H{0Hs@DH{Hs(谋H{ H> HHHQcUSHHHt:Hp uRH H߾ H1Ht HHH[] \HHHH[]H5> ( HHk\HbfAUATUSHHHl> HH`H H H H Ht H H{xH Hǃ H{`HspH{HHsXH{@HC0HtHtzH{Hs(HC@ UH{| H> HHH[]A\A] D'LoE~$1fI| HtHPHA9H [[Ef D D'LoE~!1fI| HtHPHA9H{@ [O SHH[Zfffff. USHHH > HG G G HG G HG$ HG( HH=.> uHH[] H{Hs(HCH{9 HB> HHH` SH 1- 1- 9 H > H:H= H [e @ USHHHt:Hp uRH H߾0 H1Ht HHH[] 0 YHHHH[]H5Ҙ> 0 H HHYH_fUSHHHp> HG G G G G HHG HH=> uH8H[], H{H H{ Hި> HHHO_ffffff. SH 1- 1- H > H:H=c H [镺 @ USHHHt:Hp uRHt H߾8 G H1Ht HHH[] 8 XHHHH[]H5"> 8 HHXHs^fUSHHH`> HG G G G G HHG G( HG, HG0 HG8 G@ GD HGH HGP GX G\ HG` HGh Gp Gt HGx HLJ LJ LJ HLJ H=> uHnH[]" fH H H踄H{hHsx>H{PHs`j>H{8HsHaH{(x H{? H{ H> HHH ]UH
1- 1- SH H > HeHH HS H-> HHH9 H-> HzHH :UH[]
-f. USHHHt:Hp uRHT H߾ H1Ht HHH[] UHHHH[]H5r> H HHUH[fUSHHHК> HG G G G G HHG G( HG, HG0 G@ GD HGH HGX G` Gd HGh Gp Gt HGx LJ LJ HLJ LJ LJ HLJ HLJ LJ LJ HLJ H=N> uHpH[]d H H HlH H t H{p H{XHsh螕H{@% H{(| H{ H{ Hã> HHH4Z @ SH 1- 1- H > H*H=S H [酮 @ USHHHt:Hp uRHT H߾ 7 H1Ht HHH[] ~SHHHH[]H5> HH SHcYfSHHHG HV HW VHF GWV FG W HV0F HG0HW0V(HF0G(W(V,F(G,W,HVHF,HGHHWHV@HFHG@W@F@VDGDWDHVhFDHGhHWhV`HFhG`W`VdF`GdWdHVxFdHGxHWxVpHFxGpWpVtFpGtWtH FtH 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 V GWHFHF
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H[ tHHƃu%HH[VH~) H{HƉH{Ht$
Ht$HǐfH9t &f D fffff. AWAVAUATIUSHHwHHHLt$Ih Ht$L[ Hl$ 1LH Mt$M|$ B D HH u
-@ ft@HH HH;C H| ƍ@~vu Ha H:> LHHD$ HH[]A\A]A^A_ @
-rAL$It$H;54> t5H b D AL$It$ H;5> uL0It$ L0It$1^1<HH赇 HH> HHHT$Lg HTH AVAUATIUSHHwH@HLt$I Ht$L識 Hl$ 1LH; Df HH u
-@ tCHH- H HSH9 HӃ ƍ@~vu H踆 H> LHHD$p H@[]A\A]A^Ð@zH HSAL$H9sFx?HH ID$n @ H HSAL$H9s+x$HH ID$ @HF} ua1[H3} tH HSf D 1 Hҙ> HHHT$L HSHH˅ HHH HSO @ AVAUATIUSHHwH@HLt$I Ht$Lݖ Hl$ 1LHk HHKMt$J D HH tCHHR V HHKH9_ H ƍ@~vu H݄ H> LHHD$ H@[]A\A]A^f D @uLHHKH9 HHID$ AD$( Vf @%AL$It$H;5d> H @A|$(t1LvH> I|$ AD$( ID$ H,It$ It$ H> H9uI|$ D Hz uX1Le,It$m1H}> HHHT$LY HPHHHq HHHKAWAVAUATIUSHHwHHHLt$I Ht$L苔 Hl$ 1LH Mt$M|$ B D HH u
-@ ft@HH HH;C H ƍ@~vu H葂 Hj> LHHD$I HH[]A\A]A^A_ @
-rAL$It$H;5d> t5H b D AL$It$ H;5>> uL*It$ L*It$1^1<HH HH> HHHT$L HNH AVAUATIUSHHwH@HLt$I Ht$Lݒ Hl$ 1LHk HHKMt$I D HH u
-@ ftCHHS HHKH9 H} ƍ@~vu Hހ H> LHHD$ H@[]A\A]A^ @
-rAL$It$H;5> tOH b D HHKAL$H9sxHHID$ BHnw uU1]L(It$1,HH" HH> HHHT$L H<MHHHKfff. AWAVAUATIUSHHwHXLl$Lt$ L IuL
- Hl$01LH蘂 ID$Mt$0H$? HHt_u
-@ t@HH HH;C~ H*| ƍ@~vu f@
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-tQt@HH" HH;CE Hy ƍ@~vu xf D ID$(H IcL$ 9 AL$$9 I|$WIcT$ IL$(IƍrAt$ HDHH;Ss~DHw y?1H6| H> LHHD$ HX[]A\A]A^A_ D HHHgm IH xHL1tDHbm D QLtAT$ YAL$$H|$q ID$(1Hj> HHHT$ LF HHHHH^{ HȐ AWAVAUATIUSHHwHHHLt$I Ht$L{ Hl$ 1LH ~ : HH t_u
-@ t?HH tvH HSH9; Hw ƍ@~vu Af D @uH HSAL$H9 1 Hps H 1HMz H&> LHHD$ HH[]A\A]A^A_ @ Mt$AL$MtzHH;Ss~UHt xHYk IH xHLctDHPk HH At$ HH뮉H HS1_IID$r1HT> HHHT$L0 HF1HHHAy Hfff. AWAVAUATIUSHHwHHHLt$Ih Ht$L[ Hl$ 1LH{ : HH t_u
-@ t?HH tvH HSH9; Hu ƍ@~vu Af D @uH HSAL$H9 1 HPq H 1H-x H> LHHD$ HH[]A\A]A^A_ @ Mt$AL$MtzHH;Ss~UHr xH9i IH xHLCtDH0i HH At$ HH뮉H HS1?IID$r1H4> HHHT$L HxD1HHH!w Hfff. AVAUATIUSHHwH@HLt$IJ Ht$L= Hl$ 1LHy <f HH t_u
-@ t?HH tnH HSH9 Hks ƍ@~vu 9f D @uH HSAL$H9 1vaH4o Hye1Hv H> LHHD$ H@[]A\A]A^f D It$AL$Ht)H :HH At$ 4H HS18HID$1$HL\ HBH]> HHHT$1<HH]u H AWAVAUATIUSHHwH Ll$PLt$`L IuLw Hl$p1LHx HD$@Lt$0L|$ H$ID$HD$ HH;C HHw$H1
HcH @ @B f D HH uf 1Hft H?> LHHD$` HĘ []A\A]A^A_f. @"uA|$( L6AD$( 1FHD$ID$ HH;Ss Hn g f@
-6A|$(q LҷAD$( 1HD$ID$ LH t$ Hd HD$H H|$H ; f D @2AL$It$H;5T> R H @:A|$( L"AD$( 1HD$ID$ LH et$0H9d HD$H HH|$HK f D @*A|$(# L袶AD$( 1HD$ID$ HH;Ss k H m Hc HD$H H|$H t$Hc 1Hn ƍ@~,Z jA|$( LAD$( 1THD$ID$ H4$H &t$@Hb HD$H H|$H<LID$ HD$ID$ HD$aID$ HD$ID$ HD$/ID$ HD$rHHH{b HD$H H|$HM HHH|$It$H> HHHT$`L} H=HHHp HAWAVAUATIUSHHwHHHLt$I Ht$L軁 Hl$ 1LHIs Mt$ M|$C D HH t@HH2 HH;C Hl ƍ@~vu Ho H> LHHD$x HH[]A\A]A^A_ @
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H y1,HID$11HLL H:HHim HH?> HHHT$fffff. SHHHVHGHWHV HFHG HW VHF GWHFHFu
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H[ tHHƃu%HH[9H~y H{HƉH{Ht$] Ht$HǐfH9t Vf D fffff. SHHHVHGHWVHFGWHFHFu
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H[ tHHƃu%HH[29H~ H{HƉH{Ht$ Ht$HǐfH9t ff D fffff. SHHHVHGHWHV HFHG HW VHF GWHFHFu
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H[ tHHƃu%HH[r8H~ H{HƉH{Ht$ Ht$HǐfH9t Vf D fffff. SHHHVHGHWHV HFHG HW VHF GWHFHFu
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H[ tHHƃu%HH[7H~I H{HƉH{Ht$- Ht$HǐfH9t Vf D fffff. SHHHVHGHWV HFG W VF GWHFHFuuH[ tHHƃu%HH[6H~ H{HƉH{Ht$m Ht$HǐfH9t Vf D fffff. SHHHVHGHWHV HFHG HW HV(HF HG(HW(VHF(GWHFHFu
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H[ tHHƃu%HH["6H~ H{HƉH{Ht$ Ht$HǐfH9t Ff D fffff. SHHHVHGHWV HFG W VF GWHFHFuuH[ tHHƃu%HH[b5H~ H{HƉH{Ht$ Ht$HǐfH9t Vf D fffff. SHHHVHGHWV HFG W VF GWHFHFuuH[ tHHƃu%HH[4H~9 H{HƉH{Ht$ Ht$HǐfH9t Vf D fffff. SHHHVHGHWHV HFHG HW VHF GWHFHFu
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H[ tHHƃu%HH[3H~y H{HƉH{Ht$] Ht$HǐfH9t Vf D fffff. SHHHVHGHWHV HFHG HW V(HF G(W(VF(GWHFHFuuH[ tHHƃu%HH[3H~ H{HƉH{Ht$ Ht$HǐfH9t Ff D fffff. SHHHVHGHWHV HFHG HW V(HF G(W(VF(GWHFHFuuH[ tHHƃu%HH[B2H~ H{HƉH{Ht$ Ht$HǐfH9t Ff D fffff. SHHHV(HG(HW(V HF(G W V$F G$W$VF$GWHFHFu
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H[ tHHƃu%HH[z1H~ H{HƉH{Ht$ Ht$Hǐf. H9t Ff D fffff. SHHHV(HG(HW(V HF(G W V$F G$W$HV0F$HG0HW0VHF0GWHFHFu
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H[ tHHƃu%HH[0H~1 H{HƉH{Ht$ Ht$Hǐf. H9t 6f D fffff. SHHHV HG HW VHF GWV FG W HV(F HG(HW(HV0HF(HG0HW0VHF0GWHFHFu
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H[ tHHƃu%HH[/H~A H{HƉH{Ht$% Ht$Hǐf. H9t &f D fffff. SHHHG(HV(HW(HF(V G W F V$G$W$F$HV@HG@HW@HF@V8G8W8F8V<G<W<F<HVXHGXHWXHFXVPGPWPFPVTGTWTHV`FTHG`HW`HVhHF`HGhHWhHVpHFhHGpHWpHVxHFpHGxHWxVHFxGWHFHF uH[ tHHƃu%HH[B.H~ H{HƉH{Ht$ Ht$HǐfH9t f D fffff. SHHHG(HV(HW(HF(V G W F V$G$W$F$HV@HG@HW@HF@V8G8W8F8V<G<W<F<HVXHGXHWXHFXVPGPWPFPVTGTWTHV`FTHG`HW`HVhHF`HGhHWhHVpHFhHGpHWpHVxHFpHGxHWxH HFxH H H H H H H H H H VH GWHFHF
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H[ tHHƃu%HH[,H~) H{HƉH{Ht$
Ht$HǐfH9t ff D fffff. AUATUHSHHHj> HG HHFG GHFt%LgHIL LH& HEHCHE HC H[]A\A]LHZ Hcu> HHH+ @ SHHHG(HV(HW(HF(V G W F V$G$W$F$HV@HG@HW@HF@V8G8W8F8V<G<W<F<HVXHGXHWXHFXVPGPWPFPVTGTWTHVpFTHGpHWpVhHFpGhWhVlFhGlWlHVxFlHGxHWxH HFxH H H H H H H V GWHFHFu
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H[ tHHƃu%HH[R*H~ H{HƉH{Ht$ Ht$HǐfH9t vf D fffff. UHSHHHv@u4CttHSHUu EH[]f HS HU HGHH@t H;$HHt$, Ht$Hf{f. SHHHG(HV(HW(HF(V G W F V$G$W$F$HV@HG@HW@HF@V8G8W8F8V<G<W<F<HVXHGXHWXHFXVPGPWPFPVTGTWTHVpFTHGpHWpVhHFpGhWhVlFhGlWlH FlH H H H H H H H H H H H H H H H H H VH GWHFHF
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+0LH0^HH$ H$ Hx/H$ HHx/HH$ HHH$ L8H|$L Hu_LL|$(Ht$LLL蒠 H$ HxH;=_@ uHCL9HuPJ҉HHt$051H|$LLI; LHp4Hjf D ,HL$ ,HL$ ,HL$ Hf AWIiAVAUATUSHHH>Hw{r Hk1IHHHIH| HHttHKE1! MuKHHtCHK1HHI9u2I9uI?HsHWH;Vu,uHIHu HL[]A\A]A^A_f D H1[]A\A]A^A_f. D AWIiAVAUATUSHHH>Hwq Hk1IHHHIH HHt|H E1) MuKHHtCH 1HHI9u/I9uI?HsHWH;Vu+uHIHu @ HL[]A\A]A^A_f D H1[]A\A]A^A_ f. f. f. f. f. f. f. f. f. f. f. f. f. f. f. f. f. f. f. f. SH5ж HHHT$/HH[fffff. SH5 HHHT$/HH[fffff. SH5 HHHT$/HH[fffff. SH5w HHHT$g/HH[fffff. SH5W HHHT$7/HH[fffff. SH5C HHHT$/HH[fffff. SH5) HHHT$.HH[fffff. SH5 HHHT$.HH[fffff. SH5 HHHT$w.HH[fffff. SH5ӵ HHHT$G.HH[fffff. SH5 HHHT$.HH[fffff. SH5 HHHT$-HH[fffff. SH5p HHHT$-HH[fffff. SH5V HHHT$-HH[fffff. SH5> HHHT$W-HH[fffff. SH5! HHHT$'-HH[fffff. SH5
HHHT$,HH[fffff. SH5 HHHT$,HH[fffff. SH5Ҵ HHHT$,HH[fffff. SH5 HHHT$g,HH[fffff. AUATUHSHHHtHtHE H[]A\A] D LhE~!1fI| HtHPHA9HE H*볐 AUATUHSHHHtHtHE H[]A\A] D LhE~!1fI| HtHPHA9HE HK*볐 AUATUHSHHHtHtHE H[]A\A] D LhE~!1fI| HtHPHA9HE H)볐 AUATUHSHHHtHtHE H[]A\A] D LhE~!1fI| HtHPHA9HE Hk)볐 AUATUHSHHHtHtHE H[]A\A] D LhE~!1fI| HtHPHA9HE H(볐 UH SH )H5f0A HH;%H] H[]HH(HM/fff. AVHIAULoATLg UHoSHH I;AV\; 8vcHQ׃ HHt> uB@u<KH{ H;=M@ LHa Hu1 Hf. LHHm HK1z @
+uKH{H;=@ uLHD$H{HD$of D HY % LHD$kH{ HD$6DGEFPH[]A\A]A^AHIcN I+vH9tHL\ ufff. AVHIAULoATLg UHoSHH I;AV\; 8vcHQ׃ HHt> uB@u<KH{ H;=@ LHL` Hu1 Hf. LHH,l HK1z @
+uKH{H;=3@ uLHD$H{HD$of D HfW % LHD$H{ HD$6DGEFPH[]A\A]A^AHIcN I+vH9tHLE[ ufff. USHHH`@ HHHGu H@ HC HHH[]HHx HuHHzH;=@ u
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+H#H=<@ HWt$Ht$"ыPJH떋PJHfff. SHH[#fffff. USHHH@ HH;=@ HtHHtHPHCu H@ HC HHH[]HHx HuHHzH;=@ u
+H#H=T@ HOtHt$!ыJqrfSHGH["fffff. USHHHp@ HH;=]@ HtHHtHPHCu H@ HC HHH[]HHx HuHHzH;=@ u
+H6"H=@ HOtHt$ ыJqrfSHGH[!fffff. USHHH@ HH;=@ HtHHtHPHCu H@ HC HHH[]HHx HuHHzH;=@@ u
+Hf!H=@ HOtHt$ ыJqrfSHGH[ !fffff. USHHH0@ HH;=@ HtHHtHPHCu H"@ HC HHH[]HHx HuHHzH;=p@ u
+H H=@ HOtHt$A ыJqrfSHGH[N fffff. AUHATIUSHHo@u$utHK H[]A\A] HWH
+LiI/I $H)HI9} HN
+DnH2LH&J(H L tHS H
+HiH:I $H)HH9}* HH@hH2HH%H(I H L` ,HLH1HVX[]A\A]O_ fffff. ATIUSoH@tUH;2 HCH HʀHVHV HHɀJHwHr D tSI;4$ HC Hv\HʀHVHVvVHHɀJHwHrf. HCud[]HA\ÈFH FH FH FH\L H[H HHLHH []A\PH] AUHATIUSHHo@u$utHK H[]A\A] HWH
+LiI/I $H)HI9} HN
+DnH2LH#J(H LP tHS H
+HiH:I $H)HH9}* HH@hH2HHQ#H(I H L ,HLH1HVX[]A\A]\ fffff. AUHATIUSHHo@uttWI;$ HS H HɀHHHH HHȀAHwHAf D HK H[]A\A]HWH
+LiI7I $H)HI9}'HN
+DnH2LH6"J(I D H L / PH| @ PHlHL) HLH1HVX[]A\A][ ATHUHSO(Ht+ Cu|HS []A\f. H;2 HS H HɀHHHH HHȀAHwCHAtf. HSH H [ k HW H
+LaIGHM H)HI9}7HNDfH2LH J D PH
+ @ H HX H {(
+ 1҈PHHHH[H2]A\V'Z f D AUHIATUHSHDg Etv1>HpʀPv|HȀFwHH7A9t4H9E vUIU(HcH|
+OwHpHHA9uIUu+H[]A\A]f. HHpHH HHHH2VH[]A\A];Y f. AUHIATUHSHG Ee Et|1Df D HpʀPv|HȀFwHHgA9t4H9E vuIU(HcH| OwHpHH3A9uIUuNH[]A\A]f. HHp HW0HH |X 4HH {HHHH2VH[]A\A]X HH;@ t H @ Y f =y A SuH=m A u4H5 H=f A ٺ
:o HcH A Ht=[ H
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+ A A O D Sy H A [H= A HH SH
HHH= A IHJm t$H[fffff. f =)A SuH= A u4H5ͤ H=&A ٺ :n HcHA Ht=[ H
A H5 H=A n H=A A O D Sx H A [H=A HH SH HHH=A IHJl t$H[fffff. f =A SuH=A u4H5 H=A ٺ :m HcHA Ht=[ H
A H5r H=A m H=ZA A O D Sw H A [H=3A HH SH HHH=)A IHJk t$H[fffff. f =A SuH=A u4H5 H=A ٺ :l HcHNA Ht=[ H
9A H5¡ H=kA l H=:A A O D Sv HA [H=A HH SH HHH= A IHJj t$H[fffff. f =IA SuH==A u4H5ՠ H=FA ٺ :k HcH^A Ht=[ H
IA H5 H= A k H=A A O D Su HA [H=A HH SH HHH=A IHJi t$H[fffff. H)A H2A H HdA H HA H fffff. H H H wfff. H=Y@ uHA Pw HA Zfff. H A HBA HP(Ðfff. HG(Ðf. wfff. H=I@ uHA Pew HA Zfff. fffff. wfff. H=@ uH,A Pw H A Zfff. H A HRA H f. H wfff. H=@ uH A Pv HA Zfff. fffff. wfff. GS1ۨt't HWHHJʃҍTIH\uHGu3H[ HG H HPH\DIHHH HXf D H=@ uH A Pu HA Zfff. fffff. wfff. AUIATUS1HHG(Hco HPHHEL$L9t, D H;HHTIHHI9uAEt"IE0H HPHlDIHIEuL^HH[]A\A]HH HhېfH=@ uH
A Pt H
A Zfff. fffff. wfff. H= @ uH A Pt H A Zfff. fffff. Hɺ@ HG G G HG HG HHff. SH - - ُ H A HH=cp H+/ [u @ wfff. GS1ۨt tHWHHҍ\IHuHGu)H[ HG HHDIH\HH HXʐ H=I@ uH A PEs H A Zfff. H
+A H A H f. H AWIAVAUATIUSHHG~1L, B HʀHQHQf HHɀJHwLzHL9t.L9; LID$ H(HwHALyL9uAD$D$t2L9;L7 Ic$ M HH+ Lx@(D$t@L;; MLLHſ yM }HnM HHH IIcD$(~<L;; LH "H3 It$0HHL IAl$P~}L;; L *HHp @)IcD$@LyIl$HLl $ HȀH I9QLyv%L;; LHHcUHwLyI9wMl$XL%x A~ 1Ef D HL)HI9}GEGA2MOH0LLD$LσoLD$D9N< t5Lw HLBI~LH¾ H߃ D9IuA$ ~~L9; L :HHT @)IcD$pLyIl$xLl % @ HȀH I9QLyv%L;; LHHUHwLyI9wD$ D$ Ic$ ~?L;;g LH RH I$ HHJ IA$ L;; L ZHH @)Ic$ LyI$ Ll ( HȀH; I9QLyv%L;; LHHUHwLyI9wD$ A$ ' E1Tf. HqʀQ HʀVwHHAD9I L9; LI$ IcH|jGwAHqf D ALy @ HHHȀAHwLy HHHȀAHwLy HHHȀAHwLy AHq @ D$ t2L9;L Ic$ p I HH Lx@(ID$ HL[]A\A]A^A_I$ L HG IQI$ L HG IDI$ L HG IHɀHHHvaHHȀAHwLyf. HɀHHHv-HHȀAHwLy PLxZPLxLHW HLHD HLH1 H&LH HLH HH HHȀAxLH dH@qH H HHLH0VH[]A\A]A^A_ F LHp H@qLHE HIc$ HȀALH HIcD$(KLH IHȀALH rf D wfff. AVAUATUHHS ' UL$U(1HHt!҉й
+ xDIMd E8H}@I% 1Ht!¹
+ xҍTIщMd LmXIĉUPLMq LI@q A~71 L3q H H@ҍTIHID9uH}pI& 1Ht!¹
+ xҍTIщMd I1HHt!҉й
+ xDIMd H I& 1Ht!¹
+ xҍTIщMd Hc 1HIH HHHHEL,L9t0f H;HHTIHII9uً]? t%H H HPMdDIIt%H H HPMdDIIt%H H HPMdDIIurt!
+ x
ҍDIMd u HEulDH<[]LA\A]A^Ë
+ x
ҍDIMd D H MdDIItfHH L`fffff. H=@ uHL@ Pug H>@ Zfff. H@ H"@ HP(HP0Ð HG(Ðf. HG0Ðf. AVHAUATIUHS_t>H;2/ IHLIſ
+ cB A}HWB LHLt>I;$ IHLHÿ B {HB LHHU~q1L, ;HHpʀHPvbHHȀFHwHFHL9t'I9$v@HM H HwHHHL9uHUuF[]A\A]A^ @ HHHL HL IH I[]HLHA\H2A]A^V"@ fwfff. ATUSHH! SDcAH,t AuQAu#HCukH[H]A\f H{0wHlDIHf D H{(OHlDIHAtHH Hh눐fH=@ uH\@ Pd HN@ Zfff. fffff. wfff. S1Gt HGH HPЃDIH\G(tHtHGuUH[ HG H HPH\DIH @ HG HHDIH\HH HX랐H=@ uH,@ Pc H @ Zfff. USHH(tC( H[] Ho H;-%@ tHtHE HxH;=@ uHC( H[]H=@ HWtHt$tŋPJH @ fffff. wfff. AUIATUS1HHG(Hco HPHHEL$L9t, D H;HDHTIHHI9uIEuLHH[]A\A]HH Hhfff. H=@ uH|@ Peb Hn@ Zfff. HY@ H"@ HPÐfff. HGÐf. ATHIUSoH@t!H;2 HcS H PHt^I;$ HHE PHǃ HuȀEvxHȀFwHLHS []A\ @ HɀHHHHvJHHȀAHwHAPf UHu UHu PHH HL H
HLH[H2]A\VK; f. wfff. ATU1SDgHAt
AuGAu!HCu]H[H]A\f D S
+ x
ҍDIHlH7ҍTIAHltHH Hhfffff. H=@ uH@ P_ H@ Zfff. H@ H@ HPÐfff. HGÐf. wfff. H=@ uHL@ P_ H>@ Zfff. H9@ H@ HPÐfff. HGÐf. wfff. H=Y@ uH@ P_ H@ Zfff. H @ HR@ HPÐfff. HGÐf. ATHIUSoH@t!H;2 HcS H PHt^I;$ HHE PHǃ HuȀEvxHȀFwHLHS []A\ @ HɀHHHHvJHHȀAHwHAPf UHu UHu PHH HL H
HLH[H2]A\V7 f. wfff. ATU1SDgHAt
AuGAu!HCu]H[H]A\f D S
+ x
ҍDIHlHҍTIAHltHH Hhfffff. H=@ uH @ P\ H@ Zfff. fffff. wfff. GS1ۨt't HWHHJʃҍTIH\uHGu3H[ HG H HPH\DIHHH HXf D H=@ uH @ P[ H@ Zfff. fffff. HG Ðf. HG Ðf. HG Ðf. HG Ðf. HG Ðf. AUHATUHSHHW( x C S( ufH;E I IHA$BPHǃ It$ȀAD$ HȀFwHHHS H[]A\A]f D H;u IHAE
+xIuI5 HHL9C*HSH H4 S(H;E w IHA$:PHǃ It$ȀAD$ HȀFwHHY @ H;u IHLIĿ* 5 A|$H5 HHL L H;u IHAE "xIuI4 HHL
@ AT$It$Xf AT$It$[AT$It$KAT$It$ H I^H I H IVHH IHH IyHHHH2VX[]A\A]2 fffff. AUHATIUSHHo@ @t`I;$ IHAE PHǃ IuȀAEv|HȀFwHLu5HSu|H[]A\A]ÐHWHL l2 g HS H LL2 f. AUIuf D AUIuHLX I#HHLH2VX[]A\A]1 @ AVHAUATUHSDo HE E1D D HpɀHvTHȀFwHHA E9t9H9 HU(IcH|
+WwPHpƈPHp DmA De8E E1?HpɀHv\HȀFwHHAFE9tAH9 HU@IcH| *WwPHp PHp A& DmPE E1C @ HpɀHv\HȀFwHHA6E9tAH9 HUXIcH| ZWwPHp PHp DmhE E1Ef D HpɀHvTHȀFwHHAE9t9H9l HUpIcH| bWwPHpƈPHp D E E1B HpɀHv\HȀFwHHAE9tAH9 H IcH| jWwPHp D PHp D E E1B HpɀHv\HȀFwHHA6E9tAH9t H IcH| rWwPHp D PHp D E E1B HpɀHv\HȀFwHHA.E9tAH; H IcH| zWwPHp D PHp HU []A\A]A^fH H H- B @ H H
+ Hi- HH HH \HHy HHi HHY $HHI |HH9 HHH[]A\H2A]A^V, f D AWIAVAUATUHSHHGD$J D$ t$H;] H A R- HXD$teH;] Hv M '- IM DIHXʀI DIHȀCIwD#H D D$ D$t@H;] % L HIĿ* , A|$H, HHLHD$ t@H;] LHIĿ2 e, A|$HX, HHLHAG~5E1L, H9] IG HB IC=CM9uAG(~|E1L, AfHʀHSHSv HHɀJHwHZIM9t+H9] S IG0J @HwICHM9uMo8LW A E1If. HU H)HH9}GKJLKH0HHL$LA_HL$E9H t7DLV HHJH~HH¾ HA E9HuEgXE E1J HsʀSvlHʀVwHHAE9HtNH9] i IG`IcH|RGwCHs @ CH @ CHs EgpE E1Hf D HsʀSv\HʀVwHHA.E9Ht>H9] u IGxIcH|ZGwCHs @ CHs D$ D$ t1H;] H Ic u) HH HXD$ D$@t@H;] > LHIĿ .) A|$H!) HHLsHE E E1E HsʀSvdHʀVwHHA E9HtFH9] ~ I IcH|CGwCHs D CHs IG HH[]A\A]A^A_fI Hپ Hi' HH HH I' HI Hپ
H)' HTI Hپ H ' HD HX @ HHXɀHv8HHȀCHwH(f. D`HXPHXHH HxHH HHH HHH HoHH HHHm HH] H_HHHH0VH[]A\A]A^A_% H* HzHH H"HH HH Hfff. AWAVLwAUILATUHSHHQ A~|E1@HU H)HH9}GK
+LCH0HHL$LA?HL$E9H t7DLQ HHJH~HH¾ HA E9HuMu0L^Q A~}E1AfHU H)HH9}GKLCH0HHL$LAHL$E9H t7DLQ HHJH~HH¾ HA2 E9HuEuA A EePE E1BHsʀSv\HʀVwHHA6E9Ht>H;] IEXIcH|*GwCHs @ CHs A Au`IE HH[]A\A]A^A_fIU`Hپ H|# AH IUhHپ H[# H IUxHپ H<# H IUpHپ H # H^HHI H
HHHH0VH[]A\A]A^A_" fffff. AWAVAUIATIUSHHGD$ D$ tcL;+L I$ U# H+ HHHʀHb HHȀAHwLi@) D D$ t2L;+LY Ic$ " HH Lh@(Mt$LjN A~~1E HL)HI9}FEEAE "MMH0LL$LσL$D9N, t5L N HLBI~LH¾ H߃= D9IuMt$0LM A~|1C D HL)HI9}FEEAE *MMH0LL$Lσ'L$D9N, t5LM HLBI~LH¾ H߃ D9IuMt$HLHM A~|1C D HL)HI9}FEEAE 2MMH0LL$LσL$D9N, t5L M HLBI~LH¾ H߃ D9IuAl$h E1E HqʀQv\HʀVwHHAfD9It>L9+ LID$pIcH|:GwAHq뿐AHq D$ uIID$ HL[]A\A]A^A_f. HWxHH߾ | I @ I$ L HX I @(Lh @ HɀHHHv8HHȀAHwLif. @hLhPLhLH HH HH H HHLH0VH[]A\A]A^A_a AUHATIUSHHo@ tdH;Z M$ ) I DIʀIHP DIHȀBIwHBD* @ @ @ @@tdH;H M$ ( I DIʀIHP DIHȀBIwHBD* @ @ t=H;- ILLHſ: % }H HHHLEl$ E 1Lf. HpɀH HȀFwHHڃA9t8H9r IT$(HcH| BWwHpPH諿A9uEl$8E 1F @ HpɀH( HȀFwHHڃûA9t8H; IT$@HcH| rWwHpPH苻A9uEl$PE 1F @ HpɀH HȀFwHHڃA9t<H9b IT$XHcH| @WwHpPHA9uIT$D H[]A\A]f PHpf @ PHp @ PHpf @ IT$`H H @IT$hH H @ IT$pH H IT$xH H 8f D D(H @ D(H @ DhH DhHcHH訾 HH蘾 ~HH舾 HHx IHHHH2VH[]A\A] HI HH9 HATHIUSGHtYH;2} HHYE
+PHǃvZHuȀEv6HȀFwHLtHSu4[]A\f D UHu UHuH菽 HvHLH[H2]A\V fffff. ATHIUSoH@t!H;2 HcS H PHt^I;$ HHE PHǃ HuȀEvxHȀFwHLvHS []A\ @ HɀHHHHvJHHȀAHwHAPf UHu UHu PHH< HL, H
HLH[H2]A\V f. wfff. U1SHHGt HGH HPЃDIHl{(w:S(H` HcH H{ 7HlDIHHCuSHnHH[] @ H{ D H{ D H{ D H{ HH Hh ATU1SDgHAt
AuGAu!HCu]H[H]A\f D S
+ x
ҍDIHlHҍTIAHltHH Hhfffff. U1SHHGu HCu6HHH[] HGҍTIHlHH Hh @ U1SHHGtMt HWHHJʃҍTIHlt"HS HHJHl
ҍTIHըu!HCu;H HH[] D H{(HlDIHHH Hh븐H=@ uH,@ PU: H @ Zfff. S(Hw%W(H^ HcH H HtHPC( [ UHSHHHt C( Hk H[] UHSHHHt C( Hk H[] UHSHHOHt C( Hk H[] UHSHH Ht C( Hk H[] UHSHHHt C( Hk H[] fffff. wfff. GS1ۨt't HWHHJʃҍTIH\uHGu)H[ HG HHDIH\HH HXʐH=@ uH@ Pe8 H@ Zfff. fffff. wfff. H=y@ uH @ P8 H@ Zfff. USHHHt:Hp uRH H߾( % H1Ht HeHH[] ( NҧHHCHH[]H5w@ ( ( HHѧHsاfUSHHHz@ HG G G HHH= @ u H @ HC( HCHC H[]7 H{H H@ HHHקffffff. SHZ - - R H R@ HJH=#3 H [ @ USHHHt:Hp uRHD H߾0 $ H1Ht HHH[] 0 ЧHHHH[]H5u@ 0 & HH{ЧHקfUSHHHz@ HG G G HHH=@ uH@ HCHC H[]5 H{H HV@ HHH֧ SHY - - IQ H @ HZH=1 H [ž @ USHHHt:Hp uRH$
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- HDŽ$ HDŽ$ HDŽ$ Hx DŽ$ ?HDŽ$ HD$? H H$ H9_ H, HUHH1ItQ@ L$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ TH$ H$ HHH,THE H$ Hs(HU H$ H|$H H$ H$ HE H$ $ H H$ H$ H$ H$ D$`HD$`HHD$ K
+u A @ A { DHHELcIIILzu(Hs8H芷DLHHYA^ I$A HC(AD$C0 H}H5. KH$HHH$ HL$@H H$ L$ H9d L9t_MV L)HT$?1HHD$H|$IHHq AE A@L;$t HD$A@ I AD HD$PH|$@HL$PHHD$HD$PHxH;<$t H|$@H-: HWHDHD$@D$`HxH;<$ Ml$ID$8 D A 1Vf D Hl$`D$` H}`mۡL5: L%: 1Ƅ$ HDŽ$ Ƅ$ HDŽ$ IFHDŽ$ HDŽ$ HDŽ$ H$ ID$HD$hHD$H@H|ID$HHD$ L=ܫ: H}HHD$x HDŽ$ HDŽ$ HDŽ$ HDŽ$ IGHDŽ$ HD$hIG@H$ H: HD$HHD$p<Hū: HuH}`DŽ$ HD$HHD$pH]: H$HH$ 8H}H5ͺ . H}H5| H} H5/ HlH}H5i W|$` HT$ HD$ IOHL$hIO@H$ HL$HHL$pH$ HyH;<$` HD$HD$H}HH$HHHL$p HD$H$H}`HD$hH@HThIFH$ ١C@8 @ L9t[M L)1HHT$HT$?H(H|$IHH AE A@L;$tHD$A@ I AD HD$PH|$@HL$PHHD$T H}H5 H$HHH$ HL$@HQ H$ H9 H$ H9Ht$tNH|$ V H+D$HT$?1HHD$ MH|$ IHHd HD$ A@L;${ HD$PH|$@HL$PHHD$HD$PHxH;<$ H|$@H: HWHHD$@D$`HxH;<$ ID$IT$H$ H9HD$tQH|$ b H+T$1HHT$ HT$?HwH|$ IHH HD$ A@L;$ HD$PH|$@HL$PHHD$%HT$HLLD$ LD$ HHT$HLLD$ fLD$ HvHuXH|$@+ߡHT$ Ht$HLD$(/LD$(HHT$ Ht$HLD$( LD$(H6HCH=~W YڡHD$PHD$HD$@Ht$HxL١ա H=IW $ڡHuXH|$@ޡ<HH=!W ١f. H= W ١H=t: HOt>{Ht$PH$ڡH$dHD$PHt$>HxءIJqrHD$ A@ I AD jH}Hb5IEHL$Ll$hHLhIFH}`H$ աHHHHRYHH}H~HH}H4HD$HL$ HD$hH@HLhH=n: HWt6Ht$P١HD$@Ht$?Hxס^PJHH= : HWt$oHt$>v١`PJH *HD$ A@ I AD H=: Hw Ht$PHD$H$١HD$H$^H=w: HWt^Ht$>ءH=K: HWtQHt$Pءf. PJH뜋qDFDALPJH멐fffff. AWAVAUATUSHH H$ H t'H$ 1HĈ []A\A]A^A_ H$ Hl$IHwhAEHEHT$ 裬HT$I4$I|$D|$@L|$ HD$H D$PHHRHt$ H|$(LHHl$XD$` HD$0H$ HT$8HD$h Dl$ADt$BHD$ptNHT$HH HtaLH覭|$` t7H$ 1fHD$TD$ H$ HT$hH$ Hf D E8I$tIT$H$ H D 1HHHyޡ AWAVAUMATUHSHHHHHrL$ HHD$0Ht$8A$ { SA @t Hu H9t$0t1HH[]A\A]A^A_ @ @ t2 AA : A^ V E1I AMD$ DD @ DHuH$ DKHT$0L$$EHHHL$HL$ AHL$ED7A<$ ?7|$ t { HE H;D$ @ M C HU HD$ H)IU IE lj H|$0Hu H|$8HuH9At:E1A D E1H$ D$ A A A E1D$ A HE HUHD$0HT$8f HE HU HUHL$ H)IM IUfAWAVE1AUIATUSH HT$L$tH D[]A\A]A^A_ D HD$@H|$`I
+ HD$H HD$P HD$X D$` ?HD$h HD$ H HD$HH9 H, Hԡ1HHHСH\$@H\$0IuhHH\$%Ht$pLHD$p HD$x HDŽ$ HDŽ$ Ƅ$ Ƅ$ HDŽ$ DŽ$ H$ Ƅ$ HDŽ$ HDŽ$ Ƅ$ 'A L$ I I` I11LHP֡IEHHD$(LҡX DD$EV D$ H|$H1LHHD$@HHHt2HHqf L9 H HtHq1HHH9t tӡHtH L`@ H|$ HLH HH H5 1LԡH|$H5 1}ԡ D H|$ަH|$PHufHH ҡHuHD$HH|$@1H ΡH|$@HD$X HD$P {ҡ<f D E1 H9 H|$H1LHHD$@HHHt*HHq D L9t[H HtHq1HHH9t HҡHtH L`@ H|$ HLH HH H LLYHtSHt Hth@
ub1(f D H v p t6LL HHuE1H|$(֡tg͡ H|$PHu @ HH %ѡHuHD$HH|$@1H ͡H|$11HD$X HD$P L$ HHPӡL$ E1H|$H1LHHD$@HHHt-HHq @ L9R H HtHq1HHH9t СHtH L`@ H|$ HLH*
HH. H|$H1LHHD$@HHHt0HHq L9 H HtHq1HHH9t $СHtH L`@ H|$ HLH HH @ Htov@f/LL HHu Ӿ L̡D$ D$9D$
IHH}H|$Ӿ ̡AD;|$tIEH\$HA HHx AH|$(HH|$H|$ O H|$@ΡH]ա8ɡHHfffff. ATAUHSH HHD[]A\H @ AWAVIAUATUS1Hh Ht$8tHh []A\A]A^A_HD$pHwhHHHD$@¡H$8 H$ LHDŽ$ HDŽ$ HHDŽ$ HDŽ$ Ƅ$ Ƅ$! Ƅ$" HDŽ$( DŽ$0 Ƅ$@ HDŽ$H HDŽ$P f H$( HU HK H$
+ HDŽ$ HDŽ$ HDŽ$ Hx DŽ$ ?HDŽ$ HD$O H H$ H9_ H, H$͡HH1Iȡ@ L$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ ̡H$ H$ HHH,`̡HE H$ Hs(HU H$ H|$H H$ H$ HE H$ $ H H$ H$ H$ H$ D$`HD$`HHD$ K
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:T A= ~H|$0HcFE1H 4 HD$PHD$(H$ HD$HH$ H9$ H8H|$H$ HWH9 HH$ HD$H9D$0M Ld$0 Mc,$Ht$LD HHHD$`l H L$ 1IH$ LIHM I:LHwH9t6f. HH| Hq1HII9g H9HHuH8 R A I2 BD(xMM,t-I
-Hq @ H9 H HtHq1HIH9t $RHtH HT$`@ HPH|$HHH% HHAE IL9d$1HD$8Hx t.HD$H9 P HD$8۾ IDHP M$ tLCQH|$0Ht4QH$ Ht8H$ H$ L`I9vH} HQI9wH$ PH$ HuHH/PHuH$ H$ 1H LH$ HDŽ$ HDŽ$ P1H|$@ H?H9 H, HPH<(1HHD$0H|$HkLI6 Hw A I BD(xAS D Hc@ H$ HT$(Ht$ H|$$ D$P H$ H$ HWH9 HH\$`tHH$ H\$`HL$ H$ 1HIH$ HL# D Hc@E H$ .OH$ HPH$ H@H H$ H$ H$ H$ H$ HH+$ HH)H NHCH$ H\$`HtHH\$`H$ HPH$ H@H H$ H$ HL= uHD$0 HH:QH|$H1Ҿ H$ TH?H9 H?NH 1HIJMHtLMH|$0HtMH$ Ht7H$ H$ L`I9vH;HMI9wH$ MHH|$ H$ hMH|$@HSHHiHQHHRHOHH$ "MHDŽ$ HDŽ$ PGHHSRoH$ H$ HH+$ HH)Hvb LHCH$ H$( HtHH$ HPH$ H@H H$ H$ H$ H$ 1Ҿ H$ HxHaFSH7HH[;f. H7JuH9Hf UHSHHHu @ HH KHuHEH} 1H GHE HE H[]f. AWIAVAUIATI̹ UHH SHHWHwLwl H HH96 L< LnK1HLINGHu1HE LUHuU D IHIH2Ht?HHF1HHMMMuHEHHuMH> HI4HuH} J1LLu HH]IIK>HHt8HI$H L HEHL[]A\A]A^A_Lu IK>HHuHUI$LeI$HtHB1HuLM$HE HUHf HDHELu(MHJOHELIMH-OH%P D ATIUH SHIHH H LLPA$GLM1LIHE HHHt*HLC D M9t;HHtLC1LIH9tHLH[] A\f. IH1[]A\ÐL% { AWIAVAUIATI̹ UHH SHHWHwLw H HH9. L< LH1HLIDHu1HE LUHuU D IHIH2Ht?HHF1HHMMMuHEHHuMH> HI4HuH} HL1H]HLu IIMl$K>HHt/HI$H L HEHL[]A\A]A^A_ Lu fHUI$LeI$HtHB1HuLM$HE HUH D H +BHBLu(*KHLHzBLRG
KHLHMf. AWAVAUATUHSHHHOHHw(HH)HL`HGM<,K<?H9vUL)H1HHEH,H+H9 HH)HH H HIK f D H9HHCLlH I9 J< T$ HFT$ LIL)1Hs(HHEH,HCHLHPH)HHt HHfKH; FL3LkIHk(HU JD%HCHHSH HS H HC8H HC@H[]A\A]A^A_ HIH)HHtH J|% H)JP@HBSIL HH?HHHH9|
+Hq @ H9 H HtHq1HIH9t ɡHtH HT$`@ HPH|$HHH% HHAE IL9d$1HD$8Hx t.HD$H9 P HD$8۾ IDHP M$ tLȡH|$0HtȡH$ Ht8H$ H$ L`I9vH} HȡI9wH$ ȡH$ HuHH/eȡHuH$ H$ 1H QġH$ HDŽ$ HDŽ$ ȡ1H|$@ H?H9 H, H&ȡH<(1HHD$0H|$HáI6 Hw A I BD(xAS D Hc@ H$ HT$(Ht$ H|$$ D$P H$ H$ HWH9 HH\$`tHH$ H\$`HL$ H$ 1HIH$ HL# D Hc@E H$ ơH$ HPH$ H@H H$ H$ H$ H$ H$ HH+$ HH)H ơHCH$ H\$`HtHH\$`H$ HPH$ H@H H$ H$ HL= uHD$0 HH:QH|$H1Ҿ H$ TH?H9 HšH 1HIMHtLešH|$0HtVšH$ Ht7H$ H$ L`I9vH;H$šI9wH$ šHH|$ H$ ġH|$@HˡaHɿȡHHWʡH诿H$ ġHDŽ$ HDŽ$ ȡHHʡoH$ H$ HH+$ HH)Hvb ġHCH$ H$( HtHH$ HPH$ H@H H$ H$ H$ H$ 1Ҿ H$ HxHaASH7HH[;f. HuH詿f UHSHHHu @ HH ]áHuHEH} 1H QHE HE H[]f. AWIAVAUIATI̹ UHH SHHWHwLw| H HH96 L< L¡1HLI达Hu1HE LUHuU D IHIH2Ht?HHF1HHMMMuHEHHuMH> HI4HuH} 7¡1LLu HH]IIK>HHt8HI$H L HEHL[]A\A]A^A_Lu IK>HHuHUI$LeI$HtHB1HuLM$HE HUHf HH{Lu(ršH
+ǡHbLjUšHơHǡ D ATIUH SHyHH H LLPA$GLM1LIHE HHHt*HLC D M9t;HHtLC1LIH9tHLH[] A\f. H1[]A\ÐL% { AWIAVAUIATI̹ UHH SHHWHwLw H HH9. L< Ln1HLI.Hu1HE LUHuU D IHIH2Ht?HHF1HHMMMuHEHHuMH> HI4HuH} 觿L1H]HLu IIMl$K>HHt/HI$H L HEHL[]A\A]A^A_ Lu fHUI$LeI$HtHB1HuLM$HE HUH D H 苹HLu(¡HġHڹL⾡¡HeġHmšf. AWAVAUATUHSHHHOHHw(HH)HL`HGM<,K<?H9vUL)H1HHEH,H+H9 HH)HH H HIá f D H9HHCLlH I9 J< T$ H9T$ LIL)1Hs(HHEH,HCHLHPH)HHt HH&áH;讽L3LkIHk(HU JD%HCHHSH HS H HC8H HC@H[]A\A]A^A_ HIH)HHtH J|% H)¡谷HBSIL HH?HHHH9|
6 D IMZKIN EF;D } HL EH9F|uHII?LHH9t\HPII?IIH9~;FD90 DD9~ LIDI@HH?HHL9|N A [f D HD THAL f. AUHH)ATIUSHHCr HI HoHAL$II~9 9 9 E $A<$DNED$A $HIf. A9L} HDA9|HA9~ HDA9H9sD LSD DHDCA $LH$HL)HC MtJH49|%9UA $A$
-DNED$A $NE$A $ED$DN8HHHhIHHA LHLHuHA$ H1LL)HHHH[]A\A]f D AWDIAVAUEATUSH DD$$LF(H6H|$T$!L$#HI_ItIHHD$H H$HxA@
HD$ D$" E1I9 HD$@L$HD$8 HD$(HD$pHD$0 f|$! ELD$ Aw0LHH)UCHHLD$B +HH|$HcHI H@ HF A
t3HCLsELDIGPHt Ht$~tm|$# D$"I9\ IL;$GEzkHtAw0L)LHFHLD$} HXf. qxH9HcL qLX MdJ|f. HWC;Ps)L@LcODMcE9sC;t
ELc C u|$#
MwH $ H|$LƉLD$HHLD$=9 t,HD$8Ht"HH)HD$H HHH9 H$Ht$H|$(LD$nLD$Ht$H|$0LWIw8H|$H|$(HH$> H|$0H+ H|$H@HHo H|$0HL$蕌H|$(苌HL$H\$8H H MgH H []A\A]A^A_ IGPHtH|$ ID$" I7|$$ tdIwIGIGH9 .H|$HcHDxHHHa H H[MwHD$"VI7LLI9w nf. I_H %AxEMHcL A*Lp M OLf D IAQ;Ps)LPLcODIc*D9sA;t
ELcC uHD$ H Hc@f D @
t+IWPD$"HtH|$ I_HD$"JLI7LLAG@H|$0腊H|$({1H|$HΉH $HH $Ht$Ll$pHL蟉Iw8H|$ALIH H|$H衾H> LH$H$ H/H0HcH< LR MtqHt>f. HFt6;Bs)HzLcJ<DLcE9sC;t
DHc:AuD$"LGAG@1HD>
-HxtуuH$ H{H{H54 <H1AG@oHH|$0H|$(HBHH1HHH$ HE{H{H54 <Hm1AG@L萈1,HL~H&BHH;1Hf D ATIUSH Ht=I$ Hu*f HH{H+Htl=Hd;HuI$ HtM;I|$pIDŽ$ Ht2;I|$`ID$p Ht;I|$HID$` Ht;I|$8ID$H Ht:[]ID$8 A\fff. AWAVAUATUSH ҉L$0LD$@LL$8H$@ AHH D$XH$ ID$ HD$H$ Ht$HF HD$ H$ HD$PHD$pHD$H l$HcD$HHLbM Ht1V҉ L$0L=P4 I h HS HcH ;CZ L LH{LcD; sIB;
+DNED$A $NE$A $ED$DN8HHHhIHHA LHLHuHA$ H1LL)HHHH[]A\A]f D AWDIAVAUEATUSH DD$$LF(H6H|$T$!L$#HI_ItIHHD$H H$HxA@
HD$ D$" E1I9 HD$@L$HD$8 HD$(HD$pHD$0 f|$! ELD$ Aw0LHH)庡HHLD$B +HH|$HcHI H@ HF A
t3HCLsELDIGPHt Ht$~tm|$# D$"I9\ IL;$GEzkHtAw0L)LH~HLD$} HXf. qxH9HcL qLX MdJ|f. HWC;Ps)L@LcODMcE9sC;t
ELc C u|$#
MwH $ H|$LƉLD$HHLD$=Ԑ: t,HD$8Ht"HH)HD$H HHH9 H$Ht$H|$(LD$nLD$Ht$H|$0LWIw8H|$H|$(HH$> H|$0H+ H|$H@HHo H|$0HL$蕌H|$(苌HL$H\$8H H MgH H []A\A]A^A_ IGPHtH|$ ID$" I7|$$ tdIwIGIGH9 .H|$HcHDxHHHa H H[MwHD$"VI7LLI9w nf. I_H %AxEMHcL A*Lp M OLf D IAQ;Ps)LPLcODIc*D9sA;t
ELcC uHD$ H Hc@f D @
t+IWPD$"HtH|$ I_HD$"JLI7LLAG@H|$0腊H|$({1H|$HΉH $HH $Ht$Ll$pHL蟉Iw8H|$ALIH H|$H衾H> LH$H$ H/H0HcH< LR MtqHt>f. HFt6;Bs)HzLcJ<DLcE9sC;t
DHc:AuD$"LGAG@1HD>
+HxtуuH$ H{H{H5ݎ H1AG@oHH|$0H|$(H_HH1HHH$ HE{H{H5 襳Hm1AG@L萈1,HL~HHH;1Hf D ATIUSH HtHI$ Hu*f HH{H+Ht HHuI$ HtݲI|$pIDŽ$ Ht²I|$`ID$p Ht課I|$HID$` Ht蒲I|$8ID$H Htz[]ID$8 A\fff. AWAVAUATUSH ҉L$0LD$@LL$8H$@ AHH D$XH$ ID$ HD$H$ Ht$HF HD$ H$ HD$PHD$pHD$H l$HcD$HHLbM Ht1V҉ L$0L=p I h HS HcH ;CZ L LH{LcD; sIB;
* D DDMcAIB
-DHc?HHD: LH{LK HcI} HPHLL4A6Ic<Lf D H|$ Ƅ$ 2H9 Ƅ$ 1HL$HDŽ$ Ƅ$ HDŽ$ HHDŽ$ HDŽ$ H$ HՖ9 HDŽ$ LpIFL$ H|H9 LxL?A;HZ9 HL$HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHyHHDŽ$ H$ H9 HDŽ$ H@H$ H{9 HH$ "@H+9 HL$H|$ DŽ$ HHqH$ H9 HH$ n:HD$H51 . Hx8HD$H5a Hx8HD$ Hx=H5 H8HD$H50
- Hx8HD$Hx=H50 Hu8$ H9 HH$ H͕9 H@H$ H9 HH$ H$ HxH;=9 H&9 HH$ HD$HxH9IFL$ H|$ L Hd9 HH$ 0D$ LHt$*f. A~ ENEV|$0t|$XFA9D9f A$LbA6u$ u&t$HcHHT$HB D$Ht$8H|$@AnL$4LD$( Ѕ<ALD$(L$4 @u)|$HcHHT$HB A6D$| u&t$HcHHT$HB D$McNAU A9 HcD$DPHHD$L`I H HE I HuE I|$~0LZ1IfHHHDHDHL9uHuHD$8DT$IJA D A$Lb H{IH< I HI ?f. HSE1H<HD$H5 HxV5H9 HhH$ Hl$pH
L$ L$ L9 L9 M L)HT$o1HI%;IHHH AEH;-v9 Ht$PH|$HH$ I9H$ HxH;=C9 H|$pHY9 HWH:HD$pƄ$ HxH;=9 H9 LpLxf D M9 M& M)HT$o1LT:IHHHu_AEH;-9 U Ht$PH|$HH$ I8. @ H []A\A]A^A_HLL8HHLL8H땿 L$\DT$4DL$(LD$2H IcE LD$H< L9HHG;*AU HEHLD$LcL$(DT$4L$\H-HXHD$H|$HHpX3sMH=P /H=D /L$ H}L{.6+I̐H$ Ht$nHxU.Hl$pE L} BD= Hl$pH=9 HWt%Ht$n0 듋PJHE L} BD= kH|$Hl&HD7H|$HH8H+7HHD$HxqIFL$ L Ho9 H|$HH`H$ *H6HH=؏9 HWt87Ht$P//(HD$pHt$oHx-PJHH=9 HWt%1Ht$P."PJHא @ AWAVAUATUSH H^ Ht$Hc6L$4HT$(LD$8LL$ HHH* LL$ IHHHT$@IW`HT$HT$pHT$H' HL$Hc1HA HHHHH9 HkHtAt$@t(A$ t I$ HUHH9 D M $HcIyPLAAAA A A H|$Ƅ$ (H9 Ƅ$ 1HDŽ$ Ƅ$ HDŽ$ HDŽ$ HHDŽ$ HDŽ$ H$ HW9 LpIFL$ I|H;9 H@HHD$0H9 IHHDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHDŽ$ H$ H9 H@H$ H 9 HH$ 5H9 H|$IwDŽ$ HH$ HR9 HH$ 0IH5N' . .IH5 w.I] i3H5 HU.IH5&
- @.ID43H5& H .$ Ll$ H9 HH$ Hs9 H@H$ H9 HH$ H$ HxH;=a9 H̊9 IHHH$ /IFL$ H|$L H9 HH$ n&E E I$ Ht$HE I$ HF Hc6 H9uHcҋЃt @tAD$ HuI$ ~.HP1HL L LLLLHH9uM $AƄ$ E E HD$HHt$HP H
+DHc?HHD: LH{LK HcI} HPHLL4A6Ic<Lf D H|$ Ƅ$ fHz: Ƅ$ 1HL$HDŽ$ Ƅ$ HDŽ$ HHDŽ$ HDŽ$ H$ HMz: HDŽ$ LpIFL$ H|H%z: LxL?Hz: HL$HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHyHHDŽ$ H$ Hzz: HDŽ$ H@H$ Hy: HH$ "Hz: HL$H|$ DŽ$ HHqH$ H?y: HH$ HD$H5ڋ . Hx脰HD$H5aK HxjHD$ Hx臵H5 HCHD$H5
+ Hx)HD$HxIH5 H$ HXy: HH$ HEy: H@H$ Hy: HH$ H$ HxH;=;x: Hx: HH$ HD$HxH褱IFL$ H|$ L Hw: HH$ 3D$ LHt$*f. A~ ENEV|$0t|$XFA9D9f A$LbA6u$ u&t$HcHHT$HB D$Ht$8H|$@AnL$4LD$( Ѕ<ALD$(L$4 @u)|$HcHHT$HB A6D$| u&t$HcHHT$HB D$McNAU A9 HcD$DPHHD$L`I H HE I HuE I|$~0LZ1IfHHHDHDHL9uHuHD$8DT$IJA D A$Lb H{IH< I HI ?f. HSE1H<HD$H5Y Hx次Hou: HhH$ Hl$pH
L$ L$ L9 L9 M L)HT$o1HI%IHHH AEH;-t: Ht$PH|$HH$ IfH$ HxH;=t: H|$pHu: HWHHD$pƄ$ HxH;=t: Ht: LpLxf D M9 M& M)HT$o1LTIHHHu_AEH;-)t: U Ht$PH|$HH$ I虰. @ H []A\A]A^A_HLL`HHLLJH땿 L$\DT$4DL$(LD$H IcE LD$H< L9HHG蛡AU HEHLD$LcL$(DT$4L$\H-HXHD$H|$HHpX蕫sMH=0$ H=$$ L$ H}L覢I̐H$ Ht$nHxեHl$pE L} BD= Hl$pH=1t: HWt%Ht$n舧 듋PJHE L} BD= kH|$Hl&HH|$HH蓰HHHD$Hx
+IFL$ L Hq: H|$HH`H$ 5H譮HH=Xs: HWt87Ht$P详(HD$pHt$oHx藤PJHH=s: HWt%1Ht$P\"PJHא @ AWAVAUATUSH H^ Ht$Hc6L$4HT$(LD$8LL$ HHH* LL$ IHHHT$@IW`HT$HT$pHT$H' HL$Hc1HA HHHHH9 HkHtAt$@t(A$ t I$ HUHH9 D M $HcIyPLAAAA A A H|$Ƅ$ Hp: Ƅ$ 1HDŽ$ Ƅ$ HDŽ$ HDŽ$ HHDŽ$ HDŽ$ H$ Ho: LpIFL$ I|Ho: H@HHD$zH[p: IHHDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHDŽ$ H$ Ho: H@H$ Ho: HH$ 谭H9p: H|$IwDŽ$ HH$ Hn: HH$ 豧IH5n . IH5@ I] )H58 H奡IH5
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H<sHH[]fH5 sHH[]H;Ht$HHQnHwf HHfH7 HHHGl Ha7 HHÐHQ7 HHÐHj H7 HHwf. SHH[p HH 1.1.1 out of index internal error /\ basic_string::substr not enough space: expected , got RegisterCustomOps was false. Stacktrace:
- opaque( ml_type != nullptr non_tensor_base != nullptr env_ptr == p_instance_ IsTensorSequence() elem_type_ != nullptr input name cannot be empty CPUExecutionProvider output name cannot be empty != T Tensor type mismatch. input array is too short space is not enough shape is invalid ++index < c.size() vector::reserve vector::_M_default_append DmlExecutionProvider onnxruntime_profile_ i < tensors_.size() vector::_M_range_check Encountered nullptr. value_type != nullptr in[idx]->IsTensor() Unsupported input type RegisterCustomOpsLibrary: Failed to load library RegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in library 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 Trying to get a TensorSeq, but got: is not supported in this function 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_) 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 Mem pattern should be disabled when using DML execution provider. Sequential execution should be enabled when using DML execution provider. onnxruntime::DataTypeImpl::GetType<T>() == type_ Input is not of one of the supported sequence types. Input is not of type sequence or map. Check for duplicate types in MLTypeCallDispatcherRet Unsupported tensor element type in the input: Invalid index requested for map type. 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 Key and value tensors have unequal number of elements. Tensor must always contain primitive types. Found: Value type is not supported yet: 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 00,dl| ,, static void OrtEnv::Release(OrtEnv*) 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* OrtCreateValueImplMapHelper(const onnxruntime::Tensor&, const onnxruntime::Tensor&, OrtValue**) [with KeyType = long int; 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] Ret onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper<Ret, UnsupportedPolicy>::Get() [with Ret = OrtStatus*; UnsupportedPolicy = c_api_internal::UnsupportedReturnFailStatus] 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] 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 St16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EE N11onnxruntime23NotImplementedExceptionE N11onnxruntime20OnnxRuntimeExceptionE N11onnxruntime10IAllocatorE N11onnxruntime16AllocatorWrapperE N11onnxruntime7logging5ISinkE N11onnxruntime7logging8CLogSinkE 14LoggingWrapper St19_Sp_counted_deleterIPvPFvS0_ESaIiELN9__gnu_cxx12_Lock_policyE2EE St19_Sp_make_shared_tag St23_Sp_counted_ptr_inplaceIN11onnxruntime16AllocatorWrapperESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE execution_mode is not valid graph_optimization_level is not valid Result buffer is not large enough basic_string::_S_construct null not valid /onnxruntime_src/onnxruntime/core/session/custom_ops.cc %u Unsupported version ' ' in custom op ' is not implemented Description CreateCustomRegistry ComputeAsync *ZN11onnxruntime20CreateCustomRegistryERKSt6vectorIP17OrtCustomOpDomainSaIS2_EERSt10shared_ptrINS_14CustomRegistryEEEUlRKNS_12OpKernelInfoEE_ N4onnx15ISchemaRegistryE N11onnxruntime30IOnnxRuntimeOpSchemaCollectionE N11onnxruntime8OpKernelE 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 tensor(uint8) tensor(uint16) tensor(uint32) tensor(uint64) tensor(int8) tensor(int16) tensor(int32) tensor(int64) tensor(float16) tensor(float) tensor(double) tensor(string) tensor(bool) tensor(complex64) tensor(complex128) unknown Element type of input [TypeInferenceError] expected to have tensor type Input Output com.microsoft com.microsoft.nchwc com.microsoft.automl (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: MemcpyFromHost MemcpyToHost Exception caught: 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 FvON4onnx8OpSchemaEE FvRN4onnx16InferenceContextEE N4onnx14InferenceErrorE N4onnx11SchemaErrorE PFvRN4onnx16InferenceContextEE PFvON4onnx8OpSchemaEE Cpu Model was not loaded Model was not loaded. Profiler is disabled. CUDAExecutionProvider subgraph_session_state p_op_kernel ) , expected: ( other_error [json.exception. %Y-%m-%d_%H-%M-%S .json Session was not initialized Session not initialized. Compute_ Create_State_ Release_State_ Error during EndProfiling(): Invalid rank for input: Expected: for the following indices
- Output vector pointer is NULL p_fetches->size(): Invalid Output Name: Provider has already been registered. elements, but feeds has elements. Invalid Feed Input Name: Input with name: IsSparseTensor() Running with tag: model_run Exception during loading: Unknown exception in Load() model_loading_uri Load model from model_loading_proto model_loading_istream model_loading_array Node placements ]. Provider: [ MemcpyTransformer Initializing session. session_initialization Environment::IsInitialized() are '0' and '1'. model_proto intra_op_thread_pool inter_op_thread_pool CastFloat16Transformer ort_config session_options ORT_LOAD_CONFIG_FROM_MODEL Attempt to use DefaultLogger but none has been registered. Received nullptr for graph transformer /onnxruntime_src/onnxruntime/core/session/inference_session.cc Could not write a profile because no model was loaded. 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) CreateSubgraphSessionState should have created an entry earlier. /onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.h Unexpected input data type. Actual: ( Exceeded max transformer level. Current level is set to graph_optimization_level <= TransformerLevel::MaxLevel timeout_in_ms >0 is not supported Main Graph instance should have populated all subgraphs when being resolved. Unknown error during EndProfiling() Received nullptr for custom registry Please fix either the inputs or the model. Got invalid dimensions for input: At least one output should be requested. Output vector incorrectly sized: output_names.size(): /onnxruntime_src/onnxruntime/core/framework/execution_providers.h allocator already registered. Received nullptr for exec provider Memory pattern must be disabled before registering DMLExecutionProvider Sequential execution must be enabled before registering DMLExecutionProvider 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 Could not find an implementation for the node All nodes have been placed on [ Session has already been initialized. Adding default CPU execution provider. Parallel execution is currently not supported for the registered CUDA Execution Provider. Serializing Optimized ONNX model with Graph Optimization level greater than ORT_ENABLE_EXTENDED. The generated model may contain hardware and execution provider specific optimizations, and should only be used in the same environment the model was optimized for. Session successfully initialized. Exception during initialization: Encountered unknown exception in Initialize() Environment must be initialized before creating an InferenceSession. The only supported values for the environment variable The environment variable contained the value: Reading the provided model for the ORT config Model needs to be provided to check for ORT config within it Could not finalize session options while constructing the inference session. Error Message: Given model could not be parsed while creating inference session. Error message: Could not parse model successfully while constructing the inference session Failed to load model because protobuf parsing failed. 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 GetModelOutputs GetOverridableInitializers GetModelInputs 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 Initialize InitializeSubgraphSessions onnxruntime::common::Status onnxruntime::InferenceSession::InitializeSubgraphSessions(onnxruntime::Graph&, onnxruntime::SessionState&) CreateSubgraphSessionState onnxruntime::common::Status onnxruntime::InferenceSession::CreateSubgraphSessionState(onnxruntime::Graph&, onnxruntime::SessionState&) TransformGraph Load AddCustomOpDomains Add ~InferenceSession onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const void*, int, onnxruntime::logging::LoggingManager*) onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const string&, onnxruntime::logging::LoggingManager*) onnxruntime::common::Status onnxruntime::FinalizeSessionOptions(const onnxruntime::SessionOptions&, const onnx::ModelProto*, onnxruntime::SessionOptions&) FinalizeSessionOptions void onnxruntime::InferenceSession::ConstructorCommon(const onnxruntime::SessionOptions&, onnxruntime::logging::LoggingManager*) Max Used St14default_deleteIN11onnxruntime10DummyArenaEE *ZN11onnxruntime16InferenceSession4LoadEvEUlRSt10shared_ptrINS_5ModelEEE_ N11onnxruntime16IDeviceAllocatorE N6google8protobuf2io19ZeroCopyInputStreamE N5Eigen19ThreadPoolInterfaceE N5Eigen15ThreadPoolTemplINS_20StlThreadEnvironmentEEE N11onnxruntime16GraphTransformerE N11onnxruntime16InferenceSessionE N11onnxruntime15IArenaAllocatorE N11onnxruntime10DummyArenaE N11onnxruntime11controlflow18IControlFlowKernelE N8nlohmann6detail9exceptionE N8nlohmann6detail11other_errorE ZN11onnxruntime20CPUExecutionProviderC1ERKNS_24CPUExecutionProviderInfoEEUliE_ St19_Sp_counted_deleterIPN11onnxruntime10DummyArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceISt13unordered_mapISsN11onnxruntime11FuncManager8FuncInfoESt4hashISsESt8equal_toISsESaISt4pairIKSsS3_EEESaISC_ELN9__gnu_cxx12_Lock_policyE2EE _ _ C%lu 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 false true 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 ' 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: 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+000A (LF) must be escaped to \u000A or \n 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: ææææ|ææææ¦ĦĦ$ŦTŦĦĦĦĦtʦɦʦʦʦdʦdʦdʦTʦDʦʦʦʦʦL̦ʦ4ʦ̦8˦ʦH˦X˦h˦h˦h˦x˦˦˦˦˦˦˦˦˦ϦͦLΦΦ ϦtͦҦѦtѦѦDҦЦd֦զ֦֦֦֦֦֦t֦x`H0 pX@(hP8 xxhxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx(
@ 000 '''''''''''''''''''''''''''''''''d'''''''''ZJ''JJJJJJJJJJ@'''''''''''''''''''''''''''''''''t''''''''~''''''''''''''''''6',9(>>= =<<@;:899997BPP,Q PQTPTPtP [_4_^^^^],\Y [ [ [ [X SetEnableProfiling SetGraphOptimizationLevel SetExecutionMode SetInterOpNumThreads SetIntraOpNumThreads ParseSessionOptionsFromModelProto 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 EliminateIdentity EliminateSlice UnsqueezeElimination EliminateDropout FuseReluClip ShapeToInitializer ConvAddFusion ConvMulFusion ConvBNFusion Unsupported level ConstantFolding RandomUniform RandomNormal RandomUniformLike RandomNormalLike Multinomial MatMulAddFusion ReshapeFusion GemmActivationFusion ConvActivationFusion GeluFusion AttentionFusion EmbedLayerNormFusion BiasGelu SkipLayerNormFusion GeluApproximation Unsupported level /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_utils.cc std::vector<std::unique_ptr<onnxruntime::GraphTransformer> > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, gsl::span<const onnxruntime::FreeDimensionOverride>, 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() CastDef_%d Validating no unexpected access using an invalid node_index. Got: /onnxruntime_src/include/onnxruntime/core/graph/graph.h /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 RemoveDuplicateCastTransformer ApplyImpl Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime21InsertCastTransformerE N11onnxruntime30RemoveDuplicateCastTransformerE size is different size_ == size data type is not supported ai.onnx ReduceMean Sub Div Sqrt Add Pow fused LayerNorm subgraphs epsilon /onnxruntime_src/onnxruntime/core/optimizer/initializer.h /onnxruntime_src/onnxruntime/core/optimizer/layer_norm_fusion.cc #3#####T!!##M""PPPPPPPPPPyPkPInitializer onnxruntime::Initializer::Initializer(const onnx::TensorProto&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl N11onnxruntime15LayerNormFusionE 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 pads strides dilations kernel_shape auto_pad SAME_UPPER SAME_LOWER NOTSET VALID ReorderOutput reorder ReorderInput activation _nchwc FusedConv GlobalMaxPool GlobalAveragePool Concat /onnxruntime_src/onnxruntime/core/optimizer/nchwc_transformer.cc ĂĂĂĂĂĂĂtRecurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
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H̸HH[]fH5<3 诸HH[]H;Ht$HHH艽f HHfH}8 HHHGX Hɀ8 HHÐH8 HHÐH3 Hi}8 HH齽f. SHH[N HH 1.2.0 out of index internal error not enough space: expected , got RegisterCustomOps was false. Stacktrace:
+ opaque( ml_type != nullptr non_tensor_base != nullptr IsTensorSequence() elem_type_ != nullptr input name cannot be empty CPUExecutionProvider output name cannot be empty != T Tensor type mismatch. input array is too short shape is invalid space is not enough ++index < c.size() vector::reserve vector::_M_default_append DmlExecutionProvider onnxruntime_profile_ i < tensors_.size() vector::_M_range_check Encountered nullptr. value_type != nullptr in[idx]->IsTensor() Unsupported input type RegisterCustomOpsLibrary: Failed to load library RegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in library 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 Trying to get a TensorSeq, but got: is not supported in this function 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_) 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 Mem pattern should be disabled when using DML execution provider. Sequential execution should be enabled when using DML execution provider. onnxruntime::DataTypeImpl::GetType<T>() == type_ Input is not of one of the supported sequence types. Input is not of type sequence or map. Check for duplicate types in MLTypeCallDispatcherRet 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 DPC`CpCCCCCCCCC DD D DBT4T<TLT\TlT|TTTTTTTTTTS 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] Ret onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper<Ret, UnsupportedPolicy>::Get() [with Ret = OrtStatus*; UnsupportedPolicy = c_api_internal::UnsupportedReturnFailStatus] 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] 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 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 execution_mode is not valid graph_optimization_level is not valid Result buffer is not large enough /onnxruntime_src/onnxruntime/core/session/custom_ops.cc %u Unsupported version ' ' in custom op ' is not implemented Description CreateCustomRegistry ComputeAsync *ZN11onnxruntime20CreateCustomRegistryERKSt6vectorIP17OrtCustomOpDomainSaIS2_EERSt10shared_ptrINS_14CustomRegistryEEEUlRKNS_12OpKernelInfoEE_ N4onnx15ISchemaRegistryE N11onnxruntime30IOnnxRuntimeOpSchemaCollectionE N11onnxruntime8OpKernelE 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 tensor(uint8) tensor(uint16) tensor(uint32) tensor(uint64) tensor(int8) tensor(int16) tensor(int32) tensor(int64) tensor(float16) tensor(float) tensor(double) tensor(string) tensor(bool) tensor(complex64) tensor(complex128) unknown Element type of input [TypeInferenceError] expected to have tensor type Input Output com.microsoft 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: MemcpyFromHost MemcpyToHost Exception caught: 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 FvRN4onnx16InferenceContextEE FvON4onnx8OpSchemaEE N4onnx14InferenceErrorE N4onnx11SchemaErrorE PFvRN4onnx16InferenceContextEE PFvON4onnx8OpSchemaEE Cpu Model was not loaded Model was not loaded. Profiler is disabled. CUDAExecutionProvider subgraph_session_state p_op_kernel ) , expected: ( other_error [json.exception. %Y-%m-%d_%H-%M-%S .json Session was not initialized Session not initialized. Compute_ Create_State_ Release_State_ Error during EndProfiling(): Invalid rank for input: Expected: for the following indices
+ Output vector pointer is NULL p_fetches->size(): Invalid Output Name: Provider has already been registered. elements, but feeds has elements. Invalid Feed Input Name: Input with name: IsSparseTensor() Running with tag: model_run model_proto_ == nullptr Exception during loading: Unknown exception in Load() model_loading_uri Load model from model_loading_proto model_loading_istream model_loading_array Node placements ]. Provider: [ MemcpyTransformer Initializing session. session_initialization ORT_LOAD_CONFIG_FROM_MODEL are '0' and '1'. model_proto intra_op_thread_pool inter_op_thread_pool CastFloat16Transformer Failed to load model because protobuf parsing failed. Attempt to use DefaultLogger but none has been registered. Received nullptr for graph transformer /onnxruntime_src/onnxruntime/core/session/inference_session.cc Could not write a profile because no model was loaded. 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) CreateSubgraphSessionState should have created an entry earlier. /onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.h Unexpected input data type. Actual: ( Exceeded max transformer level. Current level is set to graph_optimization_level <= TransformerLevel::MaxLevel timeout_in_ms >0 is not supported Main Graph instance should have populated all subgraphs when being resolved. Unknown error during EndProfiling() Received nullptr for custom registry Please fix either the inputs or the model. Got invalid dimensions for input: At least one output should be requested. Output vector incorrectly sized: output_names.size(): allocator already registered. /onnxruntime_src/onnxruntime/core/framework/execution_providers.h Received nullptr for exec provider Memory pattern must be disabled before registering DMLExecutionProvider Sequential execution must be enabled before registering DMLExecutionProvider 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. Failed to clear up model_proto_ in Inference Session 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 Could not find an implementation for the node All nodes have been placed on [ Session has already been initialized. Adding default CPU execution provider. Parallel execution is currently not supported for the registered CUDA Execution Provider. Serializing Optimized ONNX model with Graph Optimization level greater than ORT_ENABLE_EXTENDED. The generated model may contain hardware and execution provider specific optimizations, and should only be used in the same environment the model was optimized for. Session successfully initialized. Exception during initialization: Encountered unknown exception in Initialize() The only supported values for the environment variable The environment variable contained the value: Reading the provided model for the ORT config Model needs to be provided to check for ORT config within it Could not finalize session options while constructing the inference session. Error Message: Given model could not be parsed while creating inference session. Error message: Could not parse model successfully while constructing the inference session 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 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 Initialize InitializeSubgraphSessions onnxruntime::common::Status onnxruntime::InferenceSession::InitializeSubgraphSessions(onnxruntime::Graph&, onnxruntime::SessionState&) CreateSubgraphSessionState onnxruntime::common::Status onnxruntime::InferenceSession::CreateSubgraphSessionState(onnxruntime::Graph&, onnxruntime::SessionState&) TransformGraph onnxruntime::common::Status onnxruntime::InferenceSession::Load(std::function<onnxruntime::common::Status(std::shared_ptr<onnxruntime::Model>&)>, const string&) Load AddCustomOpDomains Add ~InferenceSession onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const void*, int, onnxruntime::logging::LoggingManager*) onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const string&, onnxruntime::logging::LoggingManager*) onnxruntime::common::Status onnxruntime::FinalizeSessionOptions(const onnxruntime::SessionOptions&, const onnx::ModelProto*, onnxruntime::SessionOptions&) FinalizeSessionOptions void onnxruntime::InferenceSession::ConstructorCommon(const onnxruntime::SessionOptions&, onnxruntime::logging::LoggingManager*) St14default_deleteIN11onnxruntime16IDeviceAllocatorEE *ZN11onnxruntime16InferenceSession4LoadEvEUlRSt10shared_ptrINS_5ModelEEE_ N6google8protobuf2io19ZeroCopyInputStreamE N11onnxruntime16IDeviceAllocatorE N5Eigen19ThreadPoolInterfaceE N5Eigen15ThreadPoolTemplINS_20StlThreadEnvironmentEEE N11onnxruntime16GraphTransformerE N11onnxruntime16InferenceSessionE N11onnxruntime11controlflow18IControlFlowKernelE N8nlohmann6detail9exceptionE N8nlohmann6detail11other_errorE ZN11onnxruntime20CPUExecutionProviderC1ERKNS_24CPUExecutionProviderInfoEEUliE_ St19_Sp_counted_deleterIPN11onnxruntime16IDeviceAllocatorESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceISt13unordered_mapISsN11onnxruntime11FuncManager8FuncInfoESt4hashISsESt8equal_toISsESaISt4pairIKSsS3_EEESaISC_ELN9__gnu_cxx12_Lock_policyE2EE _ _ C%lu 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 false 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: "22
+211.2.2.2 123T3332228788888888t8$999|:d88H::89999999999(:h9x9:=L<<$==; At??L@@ ?DCEED$E$E$EDvptXt@t(ttshvPv8v v0puuuuuuhuPu8u uupsXs@s(ssrrrrrhrhrvhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrxrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhrhr0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0p0pssssssssssssssssssssssssssssssssssssssssssssssttttvKs]s]s]s]s]s]s]s]s]s]s]s]s9s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s]s's]s]s]s]s]ss]s]s]ss]s]s]s]s]s]s]sr]s]s]sr]srnwvwPw`wwwwpw@@Ԁʀ#䀤_-p襤Хत@ ppppT츤ܷ4¤ȤȤDǤȤlǤȤĤ䤤¤¤t SetEnableProfiling SetGraphOptimizationLevel SetExecutionMode SetInterOpNumThreads SetIntraOpNumThreads ParseSessionOptionsFromModelProto 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 EliminateIdentity EliminateSlice UnsqueezeElimination EliminateDropout FuseReluClip ShapeToInitializer ConvAddFusion ConvMulFusion ConvBNFusion Unsupported level ConstantFolding RandomUniform RandomNormal RandomUniformLike RandomNormalLike Multinomial MatMulAddFusion ReshapeFusion GemmActivationFusion ConvActivationFusion AttentionFusion EmbedLayerNormFusion BiasGelu SkipLayerNormFusion GeluApproximation FastGeluFusion Unsupported level /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_utils.cc std::vector<std::unique_ptr<onnxruntime::GraphTransformer> > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, gsl::span<const onnxruntime::FreeDimensionOverride>, 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() CastDef_%d Validating no unexpected access using an invalid node_index. Got: /onnxruntime_src/include/onnxruntime/core/graph/graph.h /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 RemoveDuplicateCastTransformer ApplyImpl Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime21InsertCastTransformerE N11onnxruntime30RemoveDuplicateCastTransformerE size is different size_ == size data type is not supported ai.onnx Sub Div Sqrt Pow fused LayerNorm subgraphs epsilon saved_mean saved_inv_std_var /onnxruntime_src/onnxruntime/core/optimizer/initializer.h /onnxruntime_src/onnxruntime/core/optimizer/layer_norm_fusion.cc |ʥʥʥʥʥʥʥzʥʥʥlʥ^ʥInitializer onnxruntime::Initializer::Initializer(const onnx::TensorProto&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl N11onnxruntime15LayerNormFusionE 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 \< rfZQHXXXXXXX
x
l`
WN{ocZQ l L$VUVVVVI=VV4+wneD xxxxxxxl`llllTHll?6 f ZN E<& i& & & & ]Q& & H?pads strides dilations kernel_shape auto_pad SAME_UPPER SAME_LOWER NOTSET VALID ReorderOutput perm channels_last reorder ReorderInput activation bn_scale bn_B _bn_nchwc spatial FusedConv GlobalMaxPool GlobalAveragePool Concat BatchNormalization /onnxruntime_src/onnxruntime/core/optimizer/nchwc_transformer.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
- N11onnxruntime16NchwcTransformerE data type is not supported. min FuseReluClip_ _min_zero_constant Unexpected data type for Clip 'min' input of /onnxruntime_src/onnxruntime/core/optimizer/relu_clip_fusion.cc ߨxXߨQp Xx x x x x x x 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 Gather Unsqueeze Reshape Fused reshape node: Cannot replace concat node with initializer: /onnxruntime_src/onnxruntime/core/optimizer/reshape_fusion.cc Total fused reshape node count: Fuse_Subgraph1 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 starts ends aTG:-
tg))))))(mmmmmm,9999 99 }pc
+ N11onnxruntime16NchwcTransformerE data type is not supported. min FuseReluClip_ _min_zero_constant Unexpected data type for Clip 'min' input of /onnxruntime_src/onnxruntime/core/optimizer/relu_clip_fusion.cc wuwwww(vvwwvhu~ޓ?xkȘ.ȘȘȘȘ ȘȘߔ 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 Gather Unsqueeze Reshape Fused reshape node: Cannot replace concat node with initializer: /onnxruntime_src/onnxruntime/core/optimizer/reshape_fusion.cc Total fused reshape node count: Fuse_Subgraph1 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 starts ends q!!!!dW!!J= ! ! ! ! ! !w9 9 9 9 9 9 8 } } } } + } } < I I I I / " I I """"""""""s"
- N11onnxruntime14EliminateSliceE /onnxruntime_src/onnxruntime/core/optimizer/transformer_memcpy.cc dup_replacements.find(&arg) == dup_replacements.end() TensorrtExecutionProvider NGRAPHExecutionProvider ' doesn't support memcpy Execution type ' Memcpy Copy from/to host memory DnnlExecutionProvider NupharExecutionProvider OpenVINOExecutionProvider ACLExecutionProvider onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&)::__lambda8 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 vector::_M_range_insert {
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- 4 <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 v_reshape initializer value is not expected reshape initializer value is not expected Failed in match v_matmul and v_add input shape 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 q root should be layer normalization q_transpose perm attribute not matched q_matmul and q_add shape not matched k_transpose perm attribute 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 /onnxruntime_src/onnxruntime/core/optimizer/attention_fusion.cc shape of layer norm bias tensor not expected Attention subgraph expects 1 Add and 3 MatMul as children of LayerNormalization. Total fused Attention node count: qkv_weights qkv_bias Faild to find path v Failed to find path for mask Failed to find path for q q_reshape const not matched qk_div const not matched. perm Failed to find path for k k root is not layer norm k_reshape const not matched Mask_Int32 Cast mask from int64 to int32 MaskCast MaskIndex_Int32 Count number of words ReduceSum MaskIndex keepdims Failed to create mask index Fused Attention subgraphs Attention num_heads Fused an attention node. xhXH8ProcessMask FuseSubGraph Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime15AttentionFusionE @ ?fused Add and Gelu /onnxruntime_src/onnxruntime/core/optimizer/bias_gelu_fusion.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime8BiasGeluE Unsupported output type of . Can't constant fold node ' ort_value.IsTensor() /onnxruntime_src/onnxruntime/core/optimizer/constant_folding.cc Could not find OrtValue with name ' 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 LeakyRelu fused Conv with activation fused activation_params /onnxruntime_src/onnxruntime/core/optimizer/conv_activation_fusion.cc Unexpected data type for Clip input of
-n\rBĪ5ĪBĪBĪBĪBĪ ĪĪBĪBĪĪê onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const onnxruntime::{anonymous}::GetClipConstantMinMax(const onnxruntime::Graph&, const onnxruntime::Node&, float&, float&)::__lambda15 Recurse ApplyImpl N11onnxruntime20ConvActivationFusionE conv_W_tensor_proto add_B_tensor_proto conv_B_tensor_proto ConvAddFusion_B_ ConvAddFusion_Add_B_ ̪(̪̪̪̪ͪp̪̪ͪͪΪ̪(ҪѪ(Ҫ(Ҫ(Ҫ(ҪΪѪ(Ҫ(ҪѪѪѪFѪѪѪѪѪ˪:ѪѪѪ1Ѫ(ѪѪЪѪѪѪѪ<ϪЪѪѪЪЪѪ4ѪѪѪѪѪͪ(ѪѪѪ ѪѪ8Ѫ+Ѫ8Ѫ8Ѫ8Ѫ8ѪVͪ Ѫ8Ѫ8ѪѪ
Ѫ/onnxruntime_src/onnxruntime/core/optimizer/conv_add_fusion.cc ժHӪժժժժӪ(ԪժժԪժToProto 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 BatchNormalization bn_scale_tensor_proto bn_B_tensor_proto bn_mean_tensor_proto bn_var_tensor_proto ConvBnFusion_W_ ConvBnFusion_BN_B_ 88888(88xPXXXXXXXK?3*!IIIIIIIHHHHH8HH`ypg."
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AnbVMD@ @@@@@@ ~XG6(/onnxruntime_src/onnxruntime/core/optimizer/conv_bn_fusion.cc (((((((I}qA)AAAA8/AA#'t''''kb''\%FUL@ k 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 mul_B_tensor_proto ConvMulFusion_W_ ConvMulFusion_Mul_B_ /onnxruntime_src/onnxruntime/core/optimizer/conv_mul_fusion.cc .-....W-,..,B,u3@2u3u3u3u3/2 2u3u30U0S31S3S3S3S311S3S31/11311313131300131304/2222222222212/22220o0220-#6}5#6#6#6#64;4#6#632ToProto 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
- N11onnxruntime16EliminateDropoutE 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. Cast Input from int64 to int32 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. Output edge count not expected for nodes in path1. Second input of Gather should be a constant with value 1. The parent of shape nodes are expected to be input_ids. Failed to match position subgraph. Output edge count not expected for nodes in path 1. The first input of Range should be a constant with value 0. The third input of Range should be a constant with value 1. 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. Input_ids and mask should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). fused EmbedLayerNorm subgraphs _Cast position_embeddings Expand Squeeze NonZero ConstantOfShape Failed to match Shape node. Failed to find path 1. Input id is not valid. Segment id is not valid. Mask is not valid. EmbedLayerNormalization 6}-}6}6}6}6}!}}6}6}|||||||||||||| onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const CheckInput MatchPositionEmbeddingSubgraph2 MatchPositionSubgraph MatchPositionEmbeddingSubgraph1 Recurse ApplyImpl N11onnxruntime20EmbedLayerNormFusionE FreeDimensionOverrideTransformer /onnxruntime_src/onnxruntime/core/optimizer/free_dim_override_transformer.cc with a fixed dimension denotation ' but the size of this dimension does not equal the specified override of Invalid free dimension override. The model has input ' ' ApplyImpl N11onnxruntime32FreeDimensionOverrideTransformerE Gelu approximation FastGelu /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 ? ?fused Gemm FusedGemm leaky_relu_ /onnxruntime_src/onnxruntime/core/optimizer/gemm_activation_fusion.cc onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl N11onnxruntime20GemmActivationFusionE _Map_base::at allocator_ptr_ != nullptr Tried to allocate without valid type information, ort_value index= Failed to get allocator for optimizer /onnxruntime_src/onnxruntime/core/optimizer/optimizer_execution_frame.cc void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType) ForEachWithIndex operator() Info onnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector<const onnxruntime::Node*>&, const InitializedTensorSet&) *ZN11onnxruntime23OptimizerExecutionFrame4InfoC1ERKSt6vectorIPKNS_4NodeESaIS5_EERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSE_EEEEUlRKNS_7NodeArgEmE_ N11onnxruntime23OptimizerExecutionFrameE /onnxruntime_src/onnxruntime/core/providers/cpu/cpu_execution_provider.cc virtual std::shared_ptr<onnxruntime::KernelRegistry> onnxruntime::CPUExecutionProvider::GetKernelRegistry() const GetKernelRegistry RegisterCPUKernels RegisterOnnxMLOperatorKernels RegisterOnnxOperatorKernels N11onnxruntime20CPUExecutionProviderE N11onnxruntime25IExecutionProviderFactoryE N11onnxruntime18CpuProviderFactoryE St23_Sp_counted_ptr_inplaceIN11onnxruntime18CpuProviderFactoryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE utils::HasDataType(t_proto) unpack_status.IsOK() T1 T2 Missing Input: Must have a single dimension t_proto.dims_size() == 1 t_proto.dims()[0] == 1 Unsupported value attribute datatype with sizeof=: /onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape.h /onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape.cc TensorProto::DataType_IsValid(t_proto.data_type()) Value attribute unpacking failed: Unsupported value attribute datatype: /onnxruntime_src/include/onnxruntime/core/framework/op_kernel.h Must have a valid input shape. input_shape.NumDimensions() > 0 Must have a single dimension of 1 DLLlL$LKKLKDKJlJ,JI virtual onnxruntime::common::Status onnxruntime::ConstantOfShape::Compute(onnxruntime::OpKernelContext*) const Compute 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::ConstantOfShapeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::Tensor**) const onnxruntime::ConstantOfShapeBase::ConstantOfShapeBase(const onnxruntime::OpKernelInfo&) void onnxruntime::ConstantOfShapeBase::SetValue(size_t, void*) void onnxruntime::ConstantOfShapeBase::SetValueFromTensorProto(const onnx::TensorProto&) N11onnxruntime19ConstantOfShapeBaseE FPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEE N11onnxruntime15ConstantOfShapeE PFPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEE seed dtype 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&) T* onnxruntime::Tensor::MutableData() [with T = float] 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 ? @ @ Unsupported data type: 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: Ret onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<Ret>::operator()(int32_t) const [with Ret = onnxruntime::common::Status; int32_t = 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 = 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] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime5RangeE min should be a scalar. max should be a scalar. /onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.h info.GetAttr<T>("max", &max_).IsOK() info.GetAttr<T>("min", &min_).IsOK() min->Shape().NumDimensions() == 0 max->Shape().NumDimensions() == 0 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::Clip<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::Clip_6<T>::Clip_6(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime6Clip_6IfEE N11onnxruntime4ClipIfEE exclusive reverse CumSum starts.size()= dims.size()= dims.size() == starts.size() extents.size()= steps.size()= dims.size() == steps.size() ) but got: Axis should be in the range [ /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/utils.h dims.size() == extents.size() && dims.size() >= steps.size() dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size() dims.size() == extents_.size() Axis tensor should be 0D or 1D void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float] const T* onnxruntime::Tensor::Data() 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 = float] T* onnxruntime::Tensor::MutableData() [with T = float] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double] const T* onnxruntime::Tensor::Data() 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 = double] T* onnxruntime::Tensor::MutableData() [with T = double] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [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 = int] T* onnxruntime::Tensor::MutableData() [with T = int] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = 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] N11onnxruntime6CumSumIfEE N11onnxruntime6CumSumIdEE N11onnxruntime6CumSumIiEE N11onnxruntime6CumSumIlEE Ttt$$4$4DTd$4\dl|tD$4D4$4DDTdDet 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 j@fmod (fmod == 0) || (fmod == 1) Abs Neg Floor Ceil Reciprocal Log Less Greater Equal 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 ' X_ptr != nullptr vector::_M_fill_insert Can broadcast 0 by 0 or 1. largest <= 1 failed to get first output! Must have 1 or more inputs inputCount >= 1 data_n.Shape() == shape input_count >= 1 data_1.Shape() == shape p_ml_value helper.called_ < 2 helper.called_ == 1 vs fmod_ cur1 == end1 cur_out == end_out fmod must have value either 0 or 1 /onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.cc Attempting to broadcast an axis by a dimension other than 1. /onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.h '. Valid values are 'LEFT' or 'RIGHT'. All inputs must have the same shape Please fetch output tensor with specified shape. Shape must be 1 dimensional as it's tensor data is a shape tensor_shape.Shape().GetDims().size() == 1 Check for duplicate types in MLTypeCallDispatcher X and Y input types do not match: fmod attribute must be true for float, float16 and double types onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda100; Input1Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda101; General = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda102] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = double] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = double; TOutput = double; Input0Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda100; Input1Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda101; General = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda102] onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda126 onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda126 onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda126 onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long unsigned int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = int] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] const T* onnxruntime::Tensor::Data() const [with T = unsigned int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = short int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short unsigned int] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] const T* onnxruntime::Tensor::Data() const [with T = short unsigned int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short unsigned int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = signed char] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned char] void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {const bool&, const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*&}; Fn = onnxruntime::mod_internal::CallModImpl; Types = {unsigned char, signed char, short unsigned int, short int, unsigned int, int, long unsigned int, long int}] T* onnxruntime::Tensor::MutableData() [with T = double] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = double] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = float] virtual onnxruntime::common::Status onnxruntime::Mod::Compute(onnxruntime::OpKernelContext*) const T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] void onnxruntime::mod_internal::BroadCastMFloat16FMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) onnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo&) onnxruntime::common::Status onnxruntime::Erf<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] T* onnxruntime::OpKernelContext::Output(int) [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda121; Input1Scalar = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda122; General = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda123] 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] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Min_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda97; Input1Scalar = onnxruntime::Min_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda98; General = onnxruntime::Min_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda99] onnxruntime::common::Status onnxruntime::Min_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] void onnxruntime::BroadcastIterator::Append(int64_t, int64_t) void onnxruntime::BroadcastIterator::Init(int64_t, int64_t) onnxruntime::Broadcaster::Broadcaster(const std::vector<long int>&, const std::vector<long int>&) onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = float] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda94; Input1Scalar = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda95; General = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda96] onnxruntime::common::Status onnxruntime::Sum_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] ( ( `( `(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfO ZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE_ ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE_ ZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE0_ ZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE1_ ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE0_ ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE1_ N11onnxruntime3NotE N11onnxruntime3AndE N11onnxruntime2OrE N11onnxruntime3XorE N11onnxruntime3AddIfEE N11onnxruntime3AddIdEE N11onnxruntime3AddIiEE N11onnxruntime3AddIlEE N11onnxruntime3SubIfEE N11onnxruntime3SubIdEE N11onnxruntime3SubIiEE N11onnxruntime3SubIlEE N11onnxruntime3MulIfEE N11onnxruntime3MulIdEE N11onnxruntime3MulIiEE N11onnxruntime3MulIlEE N11onnxruntime3DivIfEE N11onnxruntime3DivIdEE N11onnxruntime3DivIiEE N11onnxruntime3DivIlEE N11onnxruntime3AbsIfEE N11onnxruntime3AbsIdEE N11onnxruntime3AbsIaEE N11onnxruntime3AbsIsEE N11onnxruntime3AbsIiEE N11onnxruntime3AbsIlEE N11onnxruntime3AbsIhEE N11onnxruntime3AbsItEE N11onnxruntime3AbsIjEE N11onnxruntime3AbsImEE N11onnxruntime3NegIfEE N11onnxruntime3NegIdEE N11onnxruntime3NegIaEE N11onnxruntime3NegIiEE N11onnxruntime5FloorIfEE N11onnxruntime4CeilIfEE N11onnxruntime10ReciprocalIfEE N11onnxruntime4SqrtIfEE N11onnxruntime4SqrtIdEE N11onnxruntime3PowIfEE N11onnxruntime3PowIdEE N11onnxruntime3ExpIfEE N11onnxruntime3ExpIdEE N11onnxruntime3LogIfEE N11onnxruntime5Sum_6IfEE N11onnxruntime5Sum_8IfEE N11onnxruntime5Min_6IfEE N11onnxruntime5Min_8IfEE N11onnxruntime5Max_6IfEE N11onnxruntime5Max_8IfEE N11onnxruntime5Max_8IdEE N11onnxruntime4LessIfEE N11onnxruntime4LessIdEE N11onnxruntime4LessIiEE N11onnxruntime4LessIlEE N11onnxruntime7GreaterIfEE N11onnxruntime7GreaterIiEE N11onnxruntime7GreaterIlEE N11onnxruntime5EqualIbEE N11onnxruntime5EqualIiEE N11onnxruntime5EqualIlEE N11onnxruntime5EqualIfEE N11onnxruntime6Mean_6IfEE N11onnxruntime6Mean_8IfEE N11onnxruntime8BitShiftIhEE N11onnxruntime8BitShiftIjEE N11onnxruntime8BitShiftImEE N11onnxruntime3ErfIfEE N11onnxruntime3SinIfEE N11onnxruntime3SinIdEE N11onnxruntime3CosIfEE N11onnxruntime3TanIfEE N11onnxruntime4AsinIfEE N11onnxruntime4AcosIfEE N11onnxruntime4AtanIfEE N11onnxruntime4SinhIfEE N11onnxruntime4CoshIfEE N11onnxruntime5AsinhIfEE N11onnxruntime5AcoshIfEE N11onnxruntime5AtanhIfEE N11onnxruntime5PReluIfEE N11onnxruntime8Expand_8IfEE N11onnxruntime8Expand_8IdEE N11onnxruntime8Expand_8IaEE N11onnxruntime8Expand_8IsEE N11onnxruntime8Expand_8IiEE N11onnxruntime8Expand_8IlEE N11onnxruntime8Expand_8IhEE N11onnxruntime8Expand_8ItEE N11onnxruntime8Expand_8IjEE N11onnxruntime8Expand_8ImEE N11onnxruntime8Expand_8IbEE N11onnxruntime8Expand_8INS_9MLFloat16EEE N11onnxruntime3ModE ,F F @ @@ ? ? ? ? B B B B ? ? ? ?5?5?5?5?!=!=!=!=뽸뽸뽸O]O]O]O]>>>>L>L>L>L>%=%=%=%=P*P*P*P*>>>>^^^^ 1? 1? 1? 1? BBBB¥¥¥;?;?;?;?giP9giP9giP9giP9C:C:C:C:<<<<*=*=*=*=*>*>*>*> B B B B ? ? ? ? "?"?"?"? @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!< @ @right.NumDimensions() == 2 %ld N: K: GEMM: Dimension mismatch, W: M_ >= 0 && K_ > 0 && N_ >= 0 transA transB /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() Not implemented fused activation: onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::Gemm<T>::Gemm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime4GemmIfEE A %~3a*7.A \3JCy7
':Y;Ӡ5֕8;Y; A A A A %%%%~3a*~3a*~3a*~3a*7777A \3A \3A \3A \3JCy7JCy7JCy7JCy7
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':Y;Y;Y;Y;Ӡ5Ӡ5Ӡ5Ӡ5֕8֕8֕8֕8;;;;Y;Y;Y;Y;Hardmax is not in valid range [- axis . N= , D= /onnxruntime_src/onnxruntime/core/providers/common.h axis >= -tensor_rank && axis <= tensor_rank - 1 Hardmax inputs N, D and N * D must be < T* onnxruntime::Tensor::MutableData() [with T = float] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) 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] N11onnxruntime7HardmaxIfEE Right: MatMul dimension mismatch /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_helper.h Not satsified: left_num_dims >= 1 && right_num_dims >= 1
- MatMul dimension mismatch, Left: Not satsified: K_ == right_shape[right_num_dims - 2]
- left operand cannot broadcast on dim Not satsified: left_padded_dims_[idx_dim] == 1
- right operand cannot broadcast on dim Not satsified: right_padded_dims_[idx_dim] == 1
- Not satsified: num_dims_with_pad == num_output_dims
- Not satsified: M_ == 1 && N_ == 1
- Not satsified: num_dims_with_pad - 1 == num_output_dims
- Not satsified: num_dims_with_pad - 2 == num_output_dims
- Not satsified: K_ == right_shape[0]
- /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul.cc const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] Compute const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] Compute const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = int] Compute const T* onnxruntime::Tensor::Data() const [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] Compute const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] Compute const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] Compute Compute N11onnxruntime6MatMulIfEE N11onnxruntime6MatMulIdEE N11onnxruntime6MatMulIiEE N11onnxruntime6MatMulIjEE N11onnxruntime6MatMulIlEE N11onnxruntime6MatMulImEE T3 MatMulInteger a != nullptr && b != nullptr /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_integer.cc t->Shape().NumDimensions() <= 1 && t->Shape().Size() == 1 t->IsDataType<int8_t>() || t->IsDataType<uint8_t>() Currently only scalar zero_point is supported. TODO: add per channel zero point support. 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 MatMulInteger: Unsupported input types with zero point const T* onnxruntime::Tensor::Data() const [with T = signed char] onnxruntime::MatMulInteger<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = signed char]::__lambda9 Compute onnxruntime::common::Status onnxruntime::MatMulInteger<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = signed char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = int] Compute onnxruntime::common::Status onnxruntime::MatMulInteger<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = unsigned char] N11onnxruntime13MatMulIntegerIhhEE N11onnxruntime13MatMulIntegerIhaEE 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 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] Compute onnxruntime::common::Status onnxruntime::QLinearMatMul<T1, T2, T3>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = unsigned char; T3 = unsigned char] N11onnxruntime13QLinearMatMulIhhhEE ORound 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 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] void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor*&, onnxruntime::Tensor*&}; Fn = onnxruntime::sign_internal::CallSignImpl; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int}] 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 int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) N11onnxruntime7SoftmaxIfLb0EEE N11onnxruntime7SoftmaxIfLb1EEE 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 onnxruntime::TopK<OpSet, T>::TopK(const onnxruntime::OpKernelInfo&) [with int OpSet = 11; T = float] const T* onnxruntime::Tensor::Data() const [with T = long int] onnxruntime::TopK<OpSet, T>::TopK(const onnxruntime::OpKernelInfo&) [with int OpSet = 10; 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] onnxruntime::TopK<OpSet, T>::TopK(const onnxruntime::OpKernelInfo&) [with int OpSet = 9; T = float] N11onnxruntime4TopKILi9EfEE N11onnxruntime4TopKILi10EfEE N11onnxruntime4TopKILi11EfEE 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 string_vocabulary int64_vocabulary DictVectorizer /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 and 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 = long int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = std::basic_string<char>] 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 SOFTMAX_ZERO LinearClassifier multi_class NONE post_transform LOGISTIC SOFTMAX intercepts classlabels_strings classlabels_ints coefficients !coefficients_.empty() length overflow /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearclassifier.cc Input shape needs to be at least a single dimension. 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] void onnxruntime::ml::write_scores(std::vector<_RealType>&, onnxruntime::ml::POST_EVAL_TRANSFORM, int64_t, onnxruntime::Tensor*, int) [with T = float; int64_t = long int] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = int] onnxruntime::ml::LinearClassifier<T>::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = int] onnxruntime::ml::LinearClassifier<T>::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = long int] onnxruntime::ml::LinearClassifier<T>::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::LinearClassifier<T>::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml16LinearClassifierIfEE N11onnxruntime2ml16LinearClassifierIdEE N11onnxruntime2ml16LinearClassifierIlEE N11onnxruntime2ml16LinearClassifierIiEE @@3ֳLinearRegressor /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearregressor.cc info.GetAttr<int64_t>("targets", &targets_).IsOK() info.GetAttrs<float>("coefficients", coefficients_).IsOK() T* onnxruntime::Tensor::MutableData() [with T = float] void onnxruntime::ml::write_scores(std::vector<_RealType>&, onnxruntime::ml::POST_EVAL_TRANSFORM, int64_t, onnxruntime::Tensor*, int) [with T = float; int64_t = long 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] onnxruntime::ml::LinearRegressor<T>::LinearRegressor(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml15LinearRegressorIfEE Invalid normalize value of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.h info.GetAttr<std::string>("norm", &norm).IsOK() Unexpected NORMALIZE value of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.cc const T* onnxruntime::Tensor::Data() const [with T = int] void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = long int] void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = double] void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = float] void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::ml::Normalizer* const, onnxruntime::OpKernelContext*&}; Fn = onnxruntime::ml::Normalizer::CallNormalizerImpl; Types = {float, double, long int, 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::ml::Normalizer::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ml::NORMALIZE onnxruntime::ml::MakeNormalize(const string&) onnxruntime::ml::Normalizer::Normalizer(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml10NormalizerE 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 OneHotEncoder zeros num_categories_ > 0 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] 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 No support vectors. /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 = float] void onnxruntime::ml::write_scores(std::vector<_RealType>&, onnxruntime::ml::POST_EVAL_TRANSFORM, int64_t, onnxruntime::Tensor*, int) [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>] onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = int] onnxruntime::ml::SVMClassifier<T>::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = int] onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = long int] onnxruntime::ml::SVMClassifier<T>::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = long int] onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::SVMClassifier<T>::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = float] onnxruntime::ml::SVMClassifier<T>::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml9SVMCommonIfEE N11onnxruntime2ml9SVMCommonIdEE N11onnxruntime2ml9SVMCommonIlEE N11onnxruntime2ml9SVMCommonIiEE N11onnxruntime2ml13SVMClassifierIfEE N11onnxruntime2ml13SVMClassifierIdEE N11onnxruntime2ml13SVMClassifierIlEE N11onnxruntime2ml13SVMClassifierIiEE
-ף;?n_supports one_class SVMRegressor /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmregressor.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::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = float] onnxruntime::ml::SVMRegressor<T>::SVMRegressor(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml12SVMRegressorIfEE BRANCH_EQ TreeEnsembleClassifier BRANCH_LEQ LEAF BRANCH_LT BRANCH_GTE BRANCH_GT it != parents.end() nodes_treeids nodes_nodeids nodes_featureids nodes_values nodes_hitrates nodes_modes nodes_truenodeids nodes_falsenodeids class_nodeids class_treeids class_ids class_weights base_values classlabels_int64s !nodes_treeids_.empty() Invalid mode of value: treeindex >= 0 X dims is empty. /onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_classifier.cc base_values_.empty() || base_values_.size() == static_cast<size_t>(class_count_) || base_values_.size() == weights_classes_.size() nodes_missing_value_tracks_true class_nodeids_.size() == class_ids_.size() class_nodeids_.size() == class_weights_.size() nodes_nodeids_.size() == nodes_featureids_.size() nodes_nodeids_.size() == nodes_modes_names_.size() nodes_nodeids_.size() == nodes_values_.size() nodes_nodeids_.size() == nodes_truenodeids_.size() nodes_nodeids_.size() == nodes_falsenodeids_.size() (nodes_nodeids_.size() == nodes_hitrates_.size()) || (nodes_hitrates_.empty()) Must provide classlabels_strings or classlabels_int64s but not both. classlabels_strings_.empty() ^ classlabels_int64s_.empty() std::all_of( std::begin(missing_tracks_true_), std::end(missing_tracks_true_), [](int64_t elem) { return elem >= 0; }) عععع0 @ݹݹHx 0onnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = float; int64_t = long int] const T* onnxruntime::Tensor::Data() const [with T = float] Compute onnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = double; int64_t = long int] const T* onnxruntime::Tensor::Data() const [with T = double] Compute onnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = long int; int64_t = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] Compute onnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = int; int64_t = long int] const T* onnxruntime::Tensor::Data() const [with T = int] Compute void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = int] onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = int] void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = long int] onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = long int] void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = double] onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = double] void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = float] onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml22TreeEnsembleClassifierIfEE N11onnxruntime2ml22TreeEnsembleClassifierIdEE N11onnxruntime2ml22TreeEnsembleClassifierIlEE N11onnxruntime2ml22TreeEnsembleClassifierIiEE TreeEnsembleRegressor target_nodeids target_treeids target_ids target_weights SUM aggregate_function AVERAGE MIN n_targets it != indices.end() /onnxruntime_src/onnxruntime/core/providers/cpu/ml/treeregressor.cc info.GetAttr<int64_t>("n_targets", &n_targets_).IsOK() target_nodeids_.size() == target_ids_.size() target_nodeids_.size() == target_weights_.size() nodes_id_size == nodes_featureids_.size() nodes_id_size == nodes_values_.size() nodes_id_size == nodes_modes_.size() nodes_id_size == nodes_truenodeids_.size() nodes_id_size == nodes_falsenodeids_.size() (nodes_id_size == nodes_hitrates_.size()) || (nodes_hitrates_.empty()) base_values_.empty() || base_values_.size() == static_cast<size_t>(n_targets_) treeindex evaluated to a negative value, which should not happen. Compute Compute onnxruntime::ml::TreeEnsembleRegressor<T>::TreeEnsembleRegressor(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::TreeEnsembleRegressor<T>::TreeEnsembleRegressor(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml21TreeEnsembleRegressorIfEE N11onnxruntime2ml21TreeEnsembleRegressorIdEE ZipMap Input features_per_batch[ ] != number of classlabels[ /onnxruntime_src/onnxruntime/core/providers/cpu/ml/zipmap.cc 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 Input dimension cannot be less than 3. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/Unpool.cc Not satsified: X_shape.NumDimensions() >= 3
- Not satsified: I_shape == X_shape
- Shape must be 1 dimensional as it's tensor data of a shape Not satsified: tensor_shape->Shape().GetDims().size() == 1
- output_shape is smaller than minimum required. output_shape: Not satsified: given_shape.Size() >= shape.Size()
- Index tensor shape should be same as that of the input data tensor to unpool. /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 st.IsOK() spatial 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 != /onnxruntime_src/onnxruntime/core/providers/cpu/nn/batch_norm.cc T* onnxruntime::Tensor::MutableData() [with T = float] Compute onnxruntime::BatchNorm<T>::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime9BatchNormIfEE kernel_shape num_dims is not compatible with W num_dims. kernel_shape is not compatible with W shape. 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/autopad_type.h Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_attributes.h /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv.cc /onnxruntime_src/onnxruntime/core/util/math.h kernel_shape: X: C: kernel channels: group: M: Unknown AutoPadType String dilation == 1 pad type not supported. Invalid input shape: Out of bound access to array d_iter[d_i] < d_max onnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&) void onnxruntime::math::Im2colNd<T, 2>::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, bool, T) [with T = float; int64_t = long int] onnxruntime::common::Status onnxruntime::ComputePadAndOutputShape(int64_t, int64_t, int64_t, int64_t, onnxruntime::AutoPadType, int64_t*, int64_t*, int64_t*) [with bool ForceSymmetricAutoPadding = false; int64_t = long int] InferOutputShape Compute N11onnxruntime4ConvIfEE /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. ConvInteger void onnxruntime::math::Im2colNd<T, 2>::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, bool, T) [with T = unsigned char; int64_t = long int] Compute virtual onnxruntime::common::Status onnxruntime::ConvInteger::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime11ConvIntegerE ConvTranspose *out_size >= 0 pad type not supported dim_size > 0 output_padding output_shape group count is <= 0 filter_number: num_input_channels: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.h Only 1D and 2D ConvTranspose is supported. filter number not equal to input channel number. Input channels is not divisible by group. /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 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 p_ == 1 || p_ == 2 LpNormalization /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] 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 = float] N11onnxruntime6LpNormIfEE 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 GlobalLpPool ceil_mode count_include_pad storage_order 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() 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 Not satsified: input_dims >= 3
- kernel_shape num_dims is not compatible with X num_dims. Not satsified: pooling_dims == pool_attrs_.kernel_shape.size()
- Not satsified: x_shape.NumDimensions() >= 3
- 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 = long int] Compute 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 N11onnxruntime4PoolIfNS_7MaxPoolILi1EEEEE N11onnxruntime4PoolIfNS_11AveragePoolEEE N11onnxruntime4PoolIfNS_7MaxPoolILi8EEEEE N11onnxruntime4PoolIfNS_6LpPoolEEE /onnxruntime_src/onnxruntime/core/providers/cpu/nn/qlinearconv.cc IsScalarOr1ElementVector(input_offset) IsScalarOr1ElementVector(filter_offset) IsScalarOr1ElementVector(result_offset) IsScalarOr1ElementVector(input_scale) IsScalarOr1ElementVector(filter_scale) IsScalarOr1ElementVector(result_scale) QLinearConv : input zero point must be a scalar or 1D tensor of size 1 QLinearConv : filter 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 : input scale must be a scalar or 1D tensor of size 1 QLinearConv : filter scale must be a scalar or 1D tensor of size 1 QLinearConv : result scale must be a scalar or 1D tensor of size 1 T4 QLinearConv const T* onnxruntime::Tensor::Data() const [with T = int] void onnxruntime::math::Im2colNd<T, 2>::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, bool, T) [with T = unsigned char; int64_t = long int] Compute virtual onnxruntime::common::Status onnxruntime::QLinearConv::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime11QLinearConvE 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() Ret onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper<Ret, UnsupportedPolicy>::Get() [with Ret = onnxruntime::common::Status; UnsupportedPolicy = onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<onnxruntime::common::Status>] 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 :Please, install necessary language-pack-XX and configure locales Failed to construct locale with name: /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&) onnxruntime::StringNormalizer::StringNormalizer(const onnxruntime::OpKernelInfo&) N11onnxruntime16StringNormalizerE TfIdfVectorizer 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 Got weights of size: but ngram_indexes size: must be of equal size pool_strings !impl_->pool_strings_.empty() pool_int64s -grams (items % ngram_size == 0) -grams detected pool_int64s duplicate poll_strings duplicate Input shape must have either [C] or [B,C] dimensions with B > 0. Invalid type of the input argument /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 (before_insert + ngrams) == impl_->int64_set_.size() (before_insert + ngrams) == impl_->str_set_.size() 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* 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] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::TfIdfVectorizer::TfIdfVectorizer(const onnxruntime::OpKernelInfo&) N11onnxruntime15TfIdfVectorizerE G G G Gcenter_point_box NonMaxSuppression boxes_tensor scores_tensor boxes must be a 3D tensor. scores must be a 3D tensor. Not satsified: ret.IsOK()
- output != nullptr iou_threshold must be in range [0, 1]. /onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.cc Not satsified: (iou_threshold >= 0 && iou_threshold <= 1.f)
- 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_ Not satsified: boxes_shape.NumDimensions() == 3
- Not satsified: scores_shape.NumDimensions() == 3
- boxes and scores should have same num_batches. Not satsified: boxes_dims[0] == scores_dims[0]
- boxes and scores should have same spatial_dimension. Not satsified: boxes_dims[1] == scores_dims[2]
- The most inner dimension in boxes must have 4 data. Not satsified: boxes_dims[2] == 4
- virtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const Compute GetThresholdsFromInputs 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] 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 output_height output_width sampling_ratio sampling_ratio_ >= 0 Invalid mode of value 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 /onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/roialign.h Sampling ratio should be >=0, but it was specified. It should be either avg or max 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<T>::RoiAlignBase(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::RoiAlignBase<T>::RoiAlignBase(const onnxruntime::OpKernelInfo&) [with T = float] *ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIdEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUliE_ *ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIfEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUliE_ N11onnxruntime12RoiAlignBaseIfEE N11onnxruntime12RoiAlignBaseIdEE N11onnxruntime8RoiAlignIfEE N11onnxruntime8RoiAlignIdEE ReduceL1 ReduceL2 ReduceLogSum ReduceLogSumExp ReduceMax ReduceMin ReduceProd ReduceSumSquare ArgMax ArgMin in_dim != 0 /onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.h info.GetAttr("keepdims", &keepdims).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.cc Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape: bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = long int; int64_t = long int] bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = double; int64_t = long int] bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = float; int64_t = long int] bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, std::vector<_RealType>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = int; int64_t = long int] onnxruntime::ReduceKernelBase<allow_multi_axes>::ReduceKernelBase(const onnxruntime::OpKernelInfo&) [with bool allow_multi_axes = false] onnxruntime::ReduceKernelBase<allow_multi_axes>::ReduceKernelBase(const onnxruntime::OpKernelInfo&) [with bool allow_multi_axes = true] N11onnxruntime16ReduceKernelBaseILb1EEE N11onnxruntime16ReduceKernelBaseILb0EEE N11onnxruntime12ReduceKernelILb1EEE N11onnxruntime12ReduceKernelILb0EEE N11onnxruntime8ReduceL1IfEE N11onnxruntime8ReduceL1IiEE N11onnxruntime8ReduceL2IfEE N11onnxruntime8ReduceL2IiEE N11onnxruntime12ReduceLogSumIfEE N11onnxruntime12ReduceLogSumIiEE N11onnxruntime15ReduceLogSumExpIfEE N11onnxruntime15ReduceLogSumExpIiEE N11onnxruntime9ReduceMaxIfEE N11onnxruntime9ReduceMaxIiEE N11onnxruntime9ReduceMaxIlEE N11onnxruntime10ReduceMeanIfEE N11onnxruntime10ReduceMeanIiEE N11onnxruntime9ReduceMinIfEE N11onnxruntime9ReduceMinIiEE N11onnxruntime9ReduceMinIlEE N11onnxruntime10ReduceProdIfEE N11onnxruntime10ReduceProdIiEE N11onnxruntime9ReduceSumIfEE N11onnxruntime9ReduceSumIiEE N11onnxruntime9ReduceSumIlEE N11onnxruntime9ReduceSumIdEE N11onnxruntime15ReduceSumSquareIfEE N11onnxruntime15ReduceSumSquareIiEE N11onnxruntime15ReduceSumSquareIdEE N11onnxruntime6ArgMaxIfEE N11onnxruntime6ArgMaxIiEE N11onnxruntime6ArgMinIfEE N11onnxruntime6ArgMinIiEE 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 Invalid 'direction' argument of ' '. Must be one of 'forward', 'reverse', or 'bidirectional'. /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] 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&) N3gsl15narrowing_errorE N11onnxruntime12DeepCpuGruOpE ZN11onnxruntime10IAllocator13MakeUniquePtrIfEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPfE_ ZN11onnxruntime10IAllocator13MakeUniquePtrIiEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPiE_ }. Actual: Input P must have shape { std::future_error DEEPCPU_LSTM input_forget rows[ Processing batch Input initial_c must have shape { /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.cc /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.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 ValidateInputs 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] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] ComputeImpl virtual onnxruntime::common::Status onnxruntime::DeepCpuLstmOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&) onnxruntime::DeepCpuLstmOp::DeepCpuLstmOp(const onnxruntime::OpKernelInfo&) NSt13__future_base7_ResultIvEE N11onnxruntime13DeepCpuLstmOpE NSt13__future_base11_State_base7_SetterIvNS0_19__exception_ptr_tagEEE NSt13__future_base11_State_base7_SetterIvvEE St23_Sp_counted_ptr_inplaceINSt13__future_base11_State_baseESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE ZN11onnxruntime3rnn6detail23ExecuteLambdaInParallelIZNS_6detail18UniDirectionalLstmIfE7ComputeERKN3gsl4spanIKfEERKNS7_IKiEEiSB_SB_RNS7_IfEESH_SH_EUliE_EEvRKSsT_iiRNS_11concurrency10ThreadPoolERKNS_7logging6LoggerEEUlvE_ St23_Sp_counted_ptr_inplaceISt7promiseIvESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE RNN Affine ThresholdedRelu ScaledTanh HardSigmoid Elu Softsign /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) 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: affine leakyrelu thresholdedrelu scaledtanh hardsigmoid softsign softplus * Input W must have shape { Input R must have shape { Input B must have shape { 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 Invalid activation function of Invalid LSTM merge activation function of Invalid GRU reset gate activation function: Invalid GRU hidden gate activation function: Input X must have 3 dimensions only. Actual: Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length= Input sequence_lens must have shape { Input initial_h must have shape { 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) 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] 7.7.7.7. A Anew_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 Tensor size ( ) != new size ( Y != nullptr Unsupported 'dtype' value: SequenceEmpty SequenceLength SequenceConstruct SequenceErase SequenceInsert SequenceAt SplitToSequence 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 yet shape_.Size() == new_shape.Size() SequenceEmpty: Got nullptr for output sequence /onnxruntime_src/onnxruntime/core/providers/cpu/sequence/sequence_ops.cc SequenceLength: Got nullptr for output tensor Got nullptr input for index tensor ) specified for sequence of size ( SequenceAt: 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 $55544444444t4d4T4$5$5 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>] 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] void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&) 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. Tensor type mismatch. type == dtype_ allocator != nullptr len > 0 buffer stol stoul stoi -INF stoull stod /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/cast_op.cc Casting from 'float16' to 'string' is not supported yet. Cast op must have 'to' argument of type DataType Unexpected 'to' argument value: Input is missing. The operator Cast expects one and only one input BBBtCCtDDTEE|FF\G AAdbceTgVXZ\^ i__tRtTܟ|Dě<̧̪<LLD,ԾD4||<l\ LldtD<L|$d
\, $ <$t l&4'(|* !"T$d,$$ $ =l><?\A|789l;<<|Cl<<56DQQ4RTTNOdPPdV|VV<WLMT^^D_$`a<bbb c4cc4d] ^lp qqtlDn4o p,wDwzT{j,l\l|d void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = signed char] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = double] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = float] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = long unsigned int] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = unsigned int] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = short unsigned int] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = unsigned char] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = long int] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = int] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = short int] onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = std::basic_string<char>] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = signed char; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = double; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void* onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType) const void* onnxruntime::Tensor::DataRaw(onnxruntime::MLDataType) const void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = long unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = short unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = unsigned char; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = long int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = short int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = bool; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = onnxruntime::MLFloat16] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = double] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = double; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = double] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = float] onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = bool] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = bool] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = bool; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = bool] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = long int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = long int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = long int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = short int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = short int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = short int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = signed char] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = signed char; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = signed char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = long unsigned int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = long unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = long unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = unsigned int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = short unsigned int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = short unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = short unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned char] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = unsigned char; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = unsigned char] N11onnxruntime4CastIhEE N11onnxruntime4CastItEE N11onnxruntime4CastIjEE N11onnxruntime4CastImEE N11onnxruntime4CastIaEE N11onnxruntime4CastIsEE N11onnxruntime4CastIiEE N11onnxruntime4CastIlEE N11onnxruntime4CastIbEE N11onnxruntime4CastIfEE N11onnxruntime4CastIdEE N11onnxruntime4CastINS_9MLFloat16EEE N11onnxruntime4CastISsEE 8 O O O O A A A ACompress /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 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.cc Not satsified: input_count >= 1
- Not satsified: inputs_0_rank > 0
- 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 Not satsified: dim_value == inputs_0_dims[axis_index]
- Not satsified: data_n.DataType() == p.output_tensor->DataType()
- /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.h 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 CEyeLike T1 != nullptr EyeLike : Input tensor dimension is not 2 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/eye_like.cc 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 Type for Tind not supported yet in Gather. 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 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: Data type for 'indices' tensor must be 'int32_t' and 'int64_t' /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.cc GatherElements op: Value in indices must be within bounds [ GatherElements op: Data type of input 'data' should match the data type of the output std::vector<long int> onnxruntime::parse_and_validate_indices_tensor(const onnxruntime::Tensor*, int64_t, const onnxruntime::TensorShape&) T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] 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&) N11onnxruntime14GatherElementsE GatherND invalid index found, index = GatherND op: Input count mismatch /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_nd.cc input_tensor != nullptr && indices_tensor != nullptr indices tensor must has rank larger than 0 last dimension of indices must not be larger than rank of input tensor const T* onnxruntime::Tensor::Data() const [with T = long int] onnxruntime::common::Status onnxruntime::GatherNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::GatherNDBase::Prepare&) const [with Tind = long int] const T* onnxruntime::Tensor::Data() const [with T = int] onnxruntime::common::Status onnxruntime::GatherNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::GatherNDBase::Prepare&) const [with Tind = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime12GatherNDBaseE N11onnxruntime8GatherNDE /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 Data type X must be float or double, but instead got 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] virtual onnxruntime::common::Status onnxruntime::IsInf::Compute(onnxruntime::OpKernelContext*) const 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! /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 'axis' attribute must have a value in the range [ Compute Compute Compute Compute Compute Compute Compute Compute Compute Compute Compute N11onnxruntime8OneHotOpIlllEE N11onnxruntime8OneHotOpIfllEE N11onnxruntime8OneHotOpIlSslEE N11onnxruntime8OneHotOpIfSslEE N11onnxruntime8OneHotOpIlflEE N11onnxruntime8OneHotOpIifiEE N11onnxruntime8OneHotOpIiffEE N11onnxruntime8OneHotOpIfffEE N11onnxruntime8OneHotOpIlifEE N11onnxruntime8OneHotOpIlffEE N11onnxruntime8OneHotOpIlfiEE >Unexpected mode of Pad 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->IsDataType<T>() && value_tensor->Shape().Size() == 1 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 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = float] onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = double] onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = int] onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = long int] onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] onnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo&) N11onnxruntime7PadBaseE N11onnxruntime3PadIfEE N11onnxruntime3PadIdEE N11onnxruntime3PadIiEE N11onnxruntime3PadIlEE y_zero_point x_scale x_zero_point DequantizeLinear /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/quantize_linear.cc IsScalarOr1ElementVector(&y_scale) IsScalarOr1ElementVector(&y_zero_point) y_scale.Shape().NumDimensions() == 1 && y_scale.Shape().Size() == broadcastDim y_zero_point.Shape().NumDimensions() == 1 && y_zero_point.Shape().Size() == broadcastDim x_scale must be a scalar or 1D tensor or size 1. x_zero_point must be a scalar or 1D tensor or size 1. x_scale must be 1D tensor with size x_zero_point must be 1D tensor with size x_scale.Shape().NumDimensions() == 1 && x_scale.Shape().Size() == broadcastDim x_zero_point.Shape().NumDimensions() == 1 && x_zero_point.Shape().Size() == broadcastDim IsScalarOr1ElementVector(&x_scale) IsScalarOr1ElementVector(&x_zero_point) onnxruntime::common::Status onnxruntime::DequantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::common::Status onnxruntime::DequantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] onnxruntime::common::Status onnxruntime::QuantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = signed char] onnxruntime::common::Status onnxruntime::QuantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char] N11onnxruntime16DequantizeLinearIhEE N11onnxruntime16DequantizeLinearIaEE N11onnxruntime14QuantizeLinearIhEE N11onnxruntime14QuantizeLinearIaEE BAttribute shape is not set. requested_shape[i] >= -1 unknown_dim == -1 , requested shape: Reshape_1 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape.h A dimension cannot be less than -1. /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: (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 nearest cubic mode attribute is . It can only be (default) or scales extrapolation_value asymmetric pytorch_half_pixel tf_half_pixel_for_nn align_corners tf_crop_and_resize coordinate_transform_mode:[ ] is not supportted! round_prefer_floor nearest_mode round_prefer_ceil nearst_mode:[ 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 and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the 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() coordinate_transformation_mode exclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to scales size should be greater than 0. const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor*, std::vector<float>&) const onnxruntime::ResizeNearestMode onnxruntime::UpsampleBase::StringToNearstMode(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&) 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 Invalid batch_axis of . Must be 0 or 1 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: `#O! o7'
_ ) `#`# 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 = 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] 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::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 . Input rank= Indices dim= at pos= is greater than input dim= tO*qL'2
yT/
-vQ22, 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 ScatterND input_tensor != nullptr indice_tensor != nullptr update_tensor != nullptr , indices shape: updates shape: , data shape: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter_nd.cc 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]:]. invalid indice found, indice = T* onnxruntime::Tensor::MutableData() [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::common::Status onnxruntime::ScatterNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::ScatterNDBase::Prepare&) const [with Tind = long int] Compute N11onnxruntime13ScatterNDBaseE 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 Starts must be a 1-D array Ends must be a 1-D array 'axes' has duplicates 'step' value cannot be 0 output == output_end 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 nullptr != start_tensor && start_tensor->Shape().NumDimensions() == 1 nullptr != ends_tensor && ends_tensor->Shape().NumDimensions() == 1 start_tensor->Shape() == ends_tensor->Shape() nullptr == axes_tensor || start_tensor->Shape() == axes_tensor->Shape() nullptr == steps_tensor || start_tensor->Shape() == steps_tensor->Shape() Data type for starts and ends inputs' need to be int32_t or int64_t, but instead got Starts and ends shape mismatch Starts and axes shape mismatch Starts and steps shape mismatch 'axes' has an axis outside of the tensor dimension count Missing input tensor to be processed Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = double; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = bool; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>; bool dynamic = false] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = unsigned char]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = short unsigned int]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = short unsigned int] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = unsigned int]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = long unsigned int]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = signed char]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = signed char] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = short int]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = short int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short int; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = int]::__lambda46 void onnxruntime::SliceIterator<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] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = int; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = long int]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long int; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = float]::__lambda46 void onnxruntime::SliceIterator<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] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = double]::__lambda46 void onnxruntime::SliceIterator<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] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = double; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = onnxruntime::MLFloat16]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = onnxruntime::MLFloat16] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = bool]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = bool] T* onnxruntime::Tensor::MutableData() [with T = bool] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = bool; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = std::basic_string<char>]::__lambda46 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>; bool dynamic = true] void onnxruntime::SliceBase::FillVectorsFromInput(const onnxruntime::OpKernelContext*, std::vector<long int>&, std::vector<long int>&, std::vector<long int>&, std::vector<long int>&) const int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) onnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo&, bool) N11onnxruntime9SliceBaseE N11onnxruntime5SliceIhLb0EEE N11onnxruntime5SliceItLb0EEE N11onnxruntime5SliceIjLb0EEE N11onnxruntime5SliceImLb0EEE N11onnxruntime5SliceIaLb0EEE N11onnxruntime5SliceIsLb0EEE N11onnxruntime5SliceIiLb0EEE N11onnxruntime5SliceIlLb0EEE N11onnxruntime5SliceIfLb0EEE N11onnxruntime5SliceIdLb0EEE N11onnxruntime5SliceINS_9MLFloat16ELb0EEE N11onnxruntime5SliceIbLb0EEE N11onnxruntime5SliceISsLb0EEE N11onnxruntime5SliceIhLb1EEE N11onnxruntime5SliceItLb1EEE N11onnxruntime5SliceIjLb1EEE N11onnxruntime5SliceImLb1EEE N11onnxruntime5SliceIaLb1EEE N11onnxruntime5SliceIsLb1EEE N11onnxruntime5SliceIiLb1EEE N11onnxruntime5SliceIlLb1EEE N11onnxruntime5SliceIfLb1EEE N11onnxruntime5SliceIdLb1EEE N11onnxruntime5SliceINS_9MLFloat16ELb1EEE N11onnxruntime5SliceIbLb1EEE N11onnxruntime5SliceISsLb1EEE blocksize CRD DCR DepthToSpace SpaceToDepth 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_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 *> Axis= NumOutputs= Input shape= Split 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 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.cc 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 T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] ComputeImpl T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] ComputeImpl 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 onnxruntime::TensorShape&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] 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 have an implementation yet for the type: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/tile.cc 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 Mismatched data types between input and output Tensors. /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 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.cc does not align with rank of input data: is outside range. is repeated. [ perm: 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 T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<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] N11onnxruntime6UniqueE 'axes' has a duplicate axis 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' has an out of range axis 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::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::UnsqueezeBase::Prepare&) const onnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo&) N11onnxruntime13UnsqueezeBaseE N11onnxruntime9UnsqueezeE Upsample Resize: unexpected mode Upsample: unexpected mode Invalid roi input index. roi_input_idx_ > 0 scales != nullptr sizes == nullptr 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. 'Linear' mode only support 2-D inputs ('Bilinear') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the 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] 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 void onnxruntime::UpsampleBase::ScalesValidation(const std::vector<float>&, onnxruntime::UpsampleMode) const @ A @Where condition && X && Y condition, X, and Y inputs are required! /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/where_op.cc T* onnxruntime::Tensor::MutableData() [with T = unsigned char] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = unsigned char] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = int] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = int] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = long int] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = long int] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = float] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = std::basic_string<char>] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>] N11onnxruntime5WhereIhEE N11onnxruntime5WhereIiEE N11onnxruntime5WhereIlEE N11onnxruntime5WhereIfEE N11onnxruntime5WhereISsEE /onnxruntime_src/onnxruntime/contrib_ops/cpu_contrib_kernels.cc RegisterCpuContribKernels RegisterNchwcKernels /onnxruntime_src/onnxruntime/automl_ops/cpu_automl_kernels.cc RegisterCpuAutoMLKernels gamma Selu 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] N11onnxruntime7SigmoidIfEE N11onnxruntime4TanhIfEE N11onnxruntime3EluIfEE N11onnxruntime11HardSigmoidIfEE N11onnxruntime9LeakyReluIfEE N11onnxruntime4ReluIfEE N11onnxruntime4SeluIfEE N11onnxruntime18ParametricSoftplusIfEE N11onnxruntime8SoftsignIfEE N11onnxruntime15ThresholdedReluIfEE
-#<L>}-?_}?V If 'If' node has outputs. then_branch info == nullptr did not. else_branch ' attribute. 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. Subgraph must have the shape set for all outputs but Failed to create output tensor for 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 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_ N11onnxruntime2IfE Expected: Loop inputs. Found: 'Loop' node has but has info_ == nullptr 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 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() Execute ConcatenateLoopOutput 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&) FN11onnxruntime6common6StatusERSt6vectorI8OrtValueSaIS3_EEPvmE N11onnxruntime4LoopE PFN11onnxruntime6common6StatusERSt6vectorI8OrtValueSaIS3_EEPvmE 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] FN11onnxruntime20OrtValueTensorSlicerIK8OrtValueEERS2_llE FN11onnxruntime20OrtValueTensorSlicerI8OrtValueEERS1_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] FN11onnxruntime6common6StatusERKSt6vectorImSaImEERKNS_6TensorERS7_E *ZN11onnxruntime4ScanILi9EEC1ERKNS_12OpKernelInfoEEUlPvmE_ N11onnxruntime4ScanILi9EEE PFN11onnxruntime6common6StatusERKSt6vectorImSaImEERKNS_6TensorERS7_E Number of entries in ' ' was Invalid values in ' is not compatible with !is_concrete_shape_ 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 num_entries < 0 || gsl::narrow_cast<int64_t>(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 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>&, int64_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 ParametricSoftplus 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] ZNK11onnxruntime7contrib4GeluIfE7ComputeEPNS_15OpKernelContextEEUliE_ N11onnxruntime7contrib10ScaledTanhIfEE N11onnxruntime7contrib4GeluIfEE 5?AttnLSTM DEEPCPU_ATTN_LSTM }, 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 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= /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention.cc info.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0 Input 0 is expected to have 3 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute. Input 1 is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0 Input 1 dimension 1 should be 3 times of dimension 0 Input 2 is expected to have 1 dimension, got Input 2 dimension 0 should have same length as dimension 1 of input 1 Input 3 is expected to have 1 dimension, got Inputs 3 and 0 shall have same length at dimension 0 T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] Compute const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::AttentionBase::AttentionBase(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib13AttentionBaseE ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUliE_ ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUliE0_ ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUliE1_ ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUliE2_ N11onnxruntime7contrib9AttentionIfEE @ @ @ @ Shape of Input 0 is expected to have at least 1 dimension, got Input 1 is expected to have 1 dimension, got dimension 0 of Input 1 should have same length as the last dimension of input 0 /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/bias_gelu_fusion.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] Compute ZNK11onnxruntime7contrib8BiasGeluIfE7ComputeEPNS_15OpKernelContextEEUliE_ N11onnxruntime7contrib8BiasGeluIfEE 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 ZNK11onnxruntime7contrib14EmbedLayerNormIfE7ComputeEPNS_15OpKernelContextEEUliE_ 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 position_embedding shall have same dimension 1 word_embedding and segment_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] /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: CDist sqeuclidean T* onnxruntime::Tensor::MutableData() [with T = float] 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 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] ZN11onnxruntime7contrib20CropAndResizeForwardIfEEvRKNS_11TensorShapeEPKT_fllS7_lPS5_RKSsPKiPNS_11concurrency10ThreadPoolEEUliE_ N11onnxruntime7contrib13CropAndResizeIfEE DynamicSlice Scale /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 leaky_relu_alpha onnxruntime::Gemm<T>::Gemm(const onnxruntime::OpKernelInfo&) [with T = float] 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 /onnxruntime_src/onnxruntime/contrib_ops/cpu/layer_norm.cc op_kernel_info.GetAttr("axis", &axis_).IsOK() T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] 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>::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::contrib::LayerNorm<T>::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = float] ZNK11onnxruntime7contrib9LayerNormIdE7ComputeEPNS_15OpKernelContextEEUliE_ ZNK11onnxruntime7contrib9LayerNormIfE7ComputeEPNS_15OpKernelContextEEUliE_ N11onnxruntime7contrib9LayerNormIfEE N11onnxruntime7contrib9LayerNormIdEE 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 const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime7contrib15MaxpoolWithMaskE 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 /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 X_shape.NumDimensions() == 4 channels_ <= Y_shape[1] Unsupported convolution size. /onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.h info.GetAttr<int64_t>("channels", &channels_).IsOK() 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) Not satsified: Y->Shape() == sum_shape
- output and sum shape must match onnxruntime::common::Status onnxruntime::contrib::ReorderInput<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::common::Status onnxruntime::contrib::ReorderOutput<T>::Compute(onnxruntime::OpKernelContext*) 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 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 onnxruntime::contrib::NchwcPoolBase::NchwcPoolBase(const onnxruntime::OpKernelInfo&) onnxruntime::contrib::NchwcConv::NchwcConv(const onnxruntime::OpKernelInfo&) onnxruntime::contrib::ReorderOutput<T>::ReorderOutput(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib13NchwcPoolBaseE N11onnxruntime7contrib9NchwcConvE N11onnxruntime7contrib12NchwcMaxPoolE N11onnxruntime7contrib16NchwcAveragePoolE N11onnxruntime7contrib12ReorderInputIfEE N11onnxruntime7contrib13ReorderOutputIfEE y_scale 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 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] ZNK11onnxruntime7contrib13SkipLayerNormIdE7ComputeEPNS_15OpKernelContextEEUliE_ ZNK11onnxruntime7contrib13SkipLayerNormIfE7ComputeEPNS_15OpKernelContextEEUliE_ N11onnxruntime7contrib13SkipLayerNormIfEE N11onnxruntime7contrib13SkipLayerNormIdEE q=̼+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 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 embedding_size attribute: conv filter size: conv_window_size attribute: conv kernal size 1: Char embedding size: Conv kernal size 2 : 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. /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 hӥhӥhӥhDateTimeTransformer const T* onnxruntime::Tensor::Data() const [with T = long int] T* OrtValue::GetMutable() [with T = Microsoft::Featurizer::DateTimeFeaturizer::TimePoint] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime6automl19DateTimeTransformerE 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 unimplemented activation: activation_params count mismatch Expecting an instance of ExternalTimePoint /onnxruntime_src/onnxruntime/automl_ops/automl_types.cc sizeof(DateTimeFeaturizerTimePointData) == data_size const T& OrtValue::Get() const [with T = Microsoft::Featurizer::DateTimeFeaturizer::TimePoint] static void onnxruntime::NonTensorTypeConverter<Microsoft::Featurizer::DateTimeFeaturizer::TimePoint>::ToContainer(const OrtValue&, size_t, void*) static void onnxruntime::NonTensorTypeConverter<Microsoft::Featurizer::DateTimeFeaturizer::TimePoint>::FromContainer(onnxruntime::MLDataType, const void*, size_t, OrtValue&) N11onnxruntime13NonTensorTypeIN9Microsoft10Featurizer18DateTimeFeaturizer9TimePointEEE N11onnxruntime10OpaqueTypeIN9Microsoft10Featurizer18DateTimeFeaturizer9TimePointEXadL_ZNS_15kMsAutoMLDomainEEEXadL_ZNS_14kTimepointNameEEEEE DateTimeFeaturizer_TimePoint com.microsoft.automl SSE, 12x4, depth 2 allocation failure gemmlowp error: %s
- N8gemmlowp10KernelBaseE N8gemmlowp24SSE4_64_Kernel12x4Depth2E N8gemmlowp17DefaultKernelImplILb0ELb0EEE N8gemmlowp4TaskE N8gemmlowp13DefaultKernelINS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEES3_EEEE N8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE1ELSD_1ELSD_1ENS_9VectorDupIKiLNS_11VectorShapeE0EEENSE_ISF_LSG_1EEESt5tupleIINS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEE N8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE1ELSD_1ELSD_1ENS_9VectorDupIKiLNS_11VectorShapeE0EEENSE_ISF_LSG_1EEESt5tupleIINS_23OutputStageBiasAdditionINS_9VectorMapISF_LSG_0EEEEENS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEE N8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE0ELSD_0ELSD_0ENS_9VectorDupIKiLNS_11VectorShapeE1EEENSE_ISF_LSG_0EEESt5tupleIINS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEE N8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE0ELSD_0ELSD_0ENS_9VectorDupIKiLNS_11VectorShapeE1EEENSE_ISF_LSG_0EEESt5tupleIINS_23OutputStageBiasAdditionINS_9VectorMapISF_LSG_1EEEEENS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEE @ @ Gemv float found an unexpected CBLAS_TRANSPOSE input of /onnxruntime_src/onnxruntime/core/util/math_cpu.cc void onnxruntime::math::Im2colNd<T, 2>::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, bool, T) [with T = float; int64_t = long int] /onnxruntime_src/onnxruntime/core/util/protobuf_parsing_utils.cc Can't BackUp() after failed Next(). CHECK failed: !is_closed_: close() failed: N6google8protobuf2io19CopyingOutputStreamE N6google8protobuf2io15FileInputStream22CopyingFileInputStreamE N6google8protobuf2io15FileInputStreamE N6google8protobuf2io16FileOutputStream23CopyingFileOutputStreamE N6google8protobuf2io16FileOutputStreamE OrtMemoryInfo: [ id: mem_type: Cuda CudaPinned Specified device is not supported. N11onnxruntime12CPUAllocatorE St14default_deleteIN11onnxruntime16IDeviceAllocatorEE St14default_deleteIN11onnxruntime8BFCArenaEE St19_Sp_counted_deleterIPN11onnxruntime8BFCArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE St19_Sp_counted_deleterIPN11onnxruntime16IDeviceAllocatorESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE /onnxruntime_src/onnxruntime/core/framework/bfc_arena.h /onnxruntime_src/onnxruntime/core/framework/bfc_arena.cc !c->in_use() && (c->bin_num != kInvalidBinNum) !c->in_use() && (c->bin_num == kInvalidBinNum) p_int < base_int + memory_size_ !c1->in_use() && !c2->in_use() bin->free_chunks.count(h) == 1 Creating bin of max chunk size BinForSize(bin_size) == BinFromIndex(b) BinForSize(bin_size + 255) == BinFromIndex(b) BinForSize(bin_size * 2 - 1) == BinFromIndex(b) BinForSize(bin_size * 2) != BinFromIndex(b) 0 == memory_size % kMinAllocationSize BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0 c->in_use() && (c->bin_num == kInvalidBinNum) b->free_chunks.size() == bin_info.total_chunks_in_bin - bin_info.total_chunks_in_use client-requested in use in bin. Summary of in-use Chunks by size: BFC Arena ran out of memory trying . Current allocation summary follows. reserved_chunks_.find(ptr) == reserved_chunks_.end() Could not find Region for h < chunks_.size() Size: | Requested Size: | in_use: p_int >= base_int c2->prev == h1 h != kInvalidChunkHandle c->bin_num == bin_num !chunk->in_use() Extended allocation by bytes. Total allocated bytes: Allocated memory at Could not find chunk in bin Chunk Free Bin ( ): Total Chunks: , Chunks in use: allocated for chunks. in use in bin. Bin for , Chunk State: , prev: , next: Chunks of size totalling Sum Total of in-use chunks: Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: MaxAllocSize: Stats:
- tried to allocate 0 bytes to allocate DumpMemoryLog void onnxruntime::BFCArena::DumpMemoryLog(size_t) 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) AllocateRawInternal virtual void* onnxruntime::BFCArena::Reserve(size_t) int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void*) const RegionFor onnxruntime::BFCArena::AllocationRegion::AllocationRegion(void*, size_t) Extend onnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::ChunkFromHandle(onnxruntime::BFCArena::ChunkHandle) onnxruntime::BFCArena::BFCArena(std::unique_ptr<onnxruntime::IDeviceAllocator>, size_t) BFCArena 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 N11onnxruntime13IDataTransferE N11onnxruntime15CPUDataTransferE Device: [ memory_type: device_id: Tensor size mismatch There's no data transfer registered for copying tensors from data_transfer registered is nullptr. (null) tensor(bfloat16) Not implemented sparse tensor type elem_proto != nullptr value_proto != nullptr MLDataType for: DؠȠhx8(DD /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 Dl$@ x0$<Tl 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::IsOpaqueCompatible(const onnx::TypeProto&) 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_ ZN11onnxruntime19data_types_internal16DataTypeRegistryC1EvEUlPKNS_12DataTypeImplEE0_ Ss N11onnxruntime9MLFloat16E N11onnxruntime8BFloat16E St3mapISsfSt4lessISsESaISt4pairIKSsfEEE St3mapIlfSt4lessIlESaISt4pairIKlfEEE Caught exception while destructing CustomOpsLoader with message: /onnxruntime_src/onnxruntime/core/framework/ex_lib_loader.cc Caught exception while loading custom ops with message: Unloading DSO Failed to unload DSO: A dso with name has already been loaded. LoadExternalLib ~ExLibLoader N11onnxruntime11ExLibLoaderE 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_ /onnxruntime_src/onnxruntime/core/framework/execution_frame.cc ort_value_idx >= 0 && static_cast<size_t>(ort_value_idx) < alloc_plan.size() TraceAllocation for ort_value_idx= OrtValue shape verification failed. Current shape: shape && tensor.Shape() == *shape 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. entries which doesn't match the number of fetches the frame was initialized with of Fetches vector passed to GetOutputs contains feeds.size() == feed_mlvalue_idxs.size() fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() 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() 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 , block not found in target location. 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(mem_patterns_->locations[i]) == buffers_.end() size= TraceFree for ort_value_idx= Requested shape: invalid index ort_value.Fence() == nullptr For ort_value with index: but the actually size is: Invalid allocation kind: mlvalue.Fence() == nullptr 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) T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] AllocateMLValueTensorPreAllocateBuffer AllocateMLValueTensorSelfOwnBufferHelper onnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue&, int, onnxruntime::MLDataType, const OrtMemoryInfo&, const onnxruntime::TensorShape&, bool) 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&) int onnxruntime::NodeIndexInfo::GetMLValueIndex(int) const const OrtValue& onnxruntime::IExecutionFrame::GetMLValue(int) const const T& OrtValue::Get() const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(int, const onnxruntime::TensorShape*, OrtValue*&, size_t) onnxruntime::IExecutionFrame::IExecutionFrame(const std::vector<int>&, const std::vector<OrtValue>&, const std::unordered_map<int, OrtValue>&, const std::vector<int>&, const std::vector<OrtValue>&, const OrtValueNameIdxMap&, const onnxruntime::NodeIndexInfo&) N11onnxruntime15IExecutionFrameE N11onnxruntime14ExecutionFrameE duplicated allocator /onnxruntime_src/onnxruntime/core/framework/execution_provider.cc 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 /onnxruntime_src/onnxruntime/core/framework/func_kernel.h create_func(&context, &func_state_) == 0 /onnxruntime_src/onnxruntime/core/framework/graph_partitioner.cc Provider doesn't return correct number of compiled functions node_compute_funcs.size() == nodes_need_compile.size() No provider specified. 1 == capability->nodes.size() onnxruntime::FunctionKernel::FunctionKernel(const onnxruntime::OpKernelInfo&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const onnxruntime::Node* onnxruntime::PlaceNode(onnxruntime::Graph&, std::unique_ptr<onnxruntime::IndexedSubGraph>, const onnxruntime::KernelRegistryManager&, const string&, int&) onnxruntime::common::Status onnxruntime::GraphPartitioner::Partition(onnxruntime::Graph&, bool, onnxruntime::FuncManager&) const Partition N11onnxruntime14FunctionKernelE Op: Incompatible types. kernel def can't be NULL Failed to add kernel for Version mismatch. node_version: kernel start version: kernel_end_version: kernel is not supported in Failed to find kernel for : Conflicting with a registered kernel with op versions. /onnxruntime_src/onnxruntime/core/framework/kernel_registry.cc len <= op_schema.inputs().size() Failed to create kernel for op: since it was ill-formed during registration Encountered following errors: ( TryFindKernel void onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::__lambda9; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::__lambda10] void onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::__lambda7; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::__lambda8] The node is not placed on any Execution Provider, therefore, can't find a suitable kernel for The node is not placed on any Execution Provider (node 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&) /onnxruntime_src/onnxruntime/core/framework/node_index_info.cc onnxruntime::NodeIndexInfo::Init(const TValidNodes&, onnxruntime::NodeIndex, const OrtValueNameIdxMap&) [with TValidNodes = onnxruntime::ValidNodes<const std::vector<const onnxruntime::Node*> >; onnxruntime::NodeIndex = long unsigned int; onnxruntime::OrtValueNameIdxMap = onnxruntime::OrtValueNameIdxMap]::__lambda8 onnxruntime::NodeIndexInfo::Init(const TValidNodes&, onnxruntime::NodeIndex, const OrtValueNameIdxMap&) [with TValidNodes = onnxruntime::GraphNodes; onnxruntime::NodeIndex = long unsigned int; onnxruntime::OrtValueNameIdxMap = onnxruntime::OrtValueNameIdxMap]::__lambda8 ZN11onnxruntime13NodeIndexInfo4InitINS_10GraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ ZN11onnxruntime13NodeIndexInfo4InitINS_10GraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ :tensor type Tensor types should have been handled already @{z@{@{zXz@{`{z,܇,,,܇ 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:' attr != nullptr 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()) Attibute name and type don't match /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()) 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. static onnxruntime::OrtValueTensorSlicer<T> onnxruntime::OrtValueTensorSlicer<T>::Create(T&, int64_t, int64_t) [with T = const OrtValue; 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] node_index_info_ with error: duplicated ort_value index: SaveMLValueNameIndexMapping is used by node ) and node Entry exists in node for attribute SetGraphAndCreateKernels must be called prior to GetExecutionInfo. /onnxruntime_src/onnxruntime/core/framework/session_state.cc Failed to find input name in the mapping: Failed to find output name in the mapping: Could not create kernel for node: as there's no execution provider allocated. Kernel creation failed for node: . Do you have duplicated calls to SessionState::AddInitializedTensor function? Done saving OrtValue mappings. 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() 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&) SetGraph /onnxruntime_src/onnxruntime/core/framework/session_state_initializer.cc OrtValue indexes should have been populated. ort_value_name_idx_map.MaxIdx() > 0 Invalid allocation info. Provider name = string tensor is not supported for copying between allocators Internal error. The preallocated buffer is too small. Requires Failed to copy tensor to execution provider: Done saving initialized tensors Execution plan was not found in SessionState. CreatePlan must be called first. Saving initialized tensors. , Got Deserialize tensor Subgraph Graph exec_plan_ptr input with name is not used by any node. ForEachWithIndex operator() operator() SaveInputOutputNamesToNodeMapping DeserializeTensorProto onnxruntime::common::Status onnxruntime::SaveInitializedTensors(const onnxruntime::Env&, const std::basic_string<char>&, const onnxruntime::Graph&, const onnxruntime::ExecutionProviders&, const OrtValueNameIdxMap&, onnxruntime::ITensorAllocator*, const T&, const onnxruntime::logging::Logger&, const onnxruntime::DataTransferManager&) [with T = onnxruntime::SessionStateInitializer::CreatePlan(const onnxruntime::Node*, const onnxruntime::ConstPointerContainer<std::vector<onnxruntime::NodeArg*> >*, ExecutionMode)::__lambda17; onnxruntime::OrtValueNameIdxMap = onnxruntime::OrtValueNameIdxMap] SaveInitializedTensors onnxruntime::common::Status onnxruntime::SessionStateInitializer::CreatePlan(const onnxruntime::Node*, const onnxruntime::ConstPointerContainer<std::vector<onnxruntime::NodeArg*> >*, ExecutionMode) CreatePlan *ZN11onnxruntimeL33SaveInputOutputNamesToNodeMappingERKNS_5GraphERKNS_21KernelRegistryManagerERNS_12SessionStateEPKNS_21ConstPointerContainerISt6vectorIPNS_7NodeArgESaISB_EEEEEUlRKSA_mE_ *ZN11onnxruntimeL33SaveInputOutputNamesToNodeMappingERKNS_5GraphERKNS_21KernelRegistryManagerERNS_12SessionStateEPKNS_21ConstPointerContainerISt6vectorIPNS_7NodeArgESaISB_EEEEEUlRKSA_mE0_ N11onnxruntime25ISequentialPlannerContextE N11onnxruntime24SequentialPlannerContextE 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, int64_t) size_t onnxruntime::Tensor::SizeInBytes() const onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, std::shared_ptr<onnxruntime::IAllocator>, int64_t) onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, const OrtMemoryInfo&, int64_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 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
-
const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] OrtStatus* OrtApis::GetTensorTypeAndShape(const OrtValue*, OrtTensorTypeAndShapeInfo**) Invalid TensorProto ) in proto data overflow UnpackTensor: the pre-allocate size does not match the size in proto source and destination buffer size mismatch /onnxruntime_src/onnxruntime/core/framework/endian_utils.h Not satsified: source_bytes.size_bytes() == destination.size_bytes()
- UnpackTensor: the pre-allocated size does not match the raw data size, expected /onnxruntime_src/onnxruntime/core/framework/tensorprotoutils.cc ) 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: CC$$XXC$XX$~~ee@@~e@@e\8\Z~[PY[
-Z![XgZYHXWX\\W
-
GetFileContent TensorProtoToMLValue ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData NnapiExecutionProvider invalid allocator copy_info.size() == num_feeds /onnxruntime_src/onnxruntime/core/framework/utils.cc feed_locations.size() == copy_info.size() fetch_alloc_info.size() == copy_info.size() Only one thread was configured for parallel execution. Hence will use sequential execution. CopyOutputsAcrossDevices CopyInputsAcrossDevices onnxruntime::common::Status onnxruntime::utils::CopyInputsAcrossDevices(const std::vector<OrtValue>&, std::vector<OrtValue>&, const std::vector<onnxruntime::MLValueCopyInfo>&, const onnxruntime::DataTransferManager&) ExecuteGraphImpl ExecuteGraph T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] CopyMLValue const T& OrtValue::Get() const [with T = onnxruntime::Tensor] bool onnxruntime::utils::FinalizeCopyInfoForFetches(const onnxruntime::SessionState&, 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 OrtValueNameIdxMap&, const onnxruntime::SequentialExecutionPlan&, const string&) FindMemoryInfoForValue const OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::SessionState&, const string&) N11onnxruntime9IExecutorE /onnxruntime_src/onnxruntime/core/framework/allocation_planner.cc 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() kernel_create_info != nullptr && kernel_create_info->kernel_def != nullptr (ort_value_idx) output_name : <allocation plan>
+ N11onnxruntime14EliminateSliceE /onnxruntime_src/onnxruntime/core/optimizer/transformer_memcpy.cc dup_replacements.find(&arg) == dup_replacements.end() TensorrtExecutionProvider NGRAPHExecutionProvider ' doesn't support memcpy Execution type ' Memcpy Copy from/to host memory DnnlExecutionProvider NupharExecutionProvider OpenVINOExecutionProvider ACLExecutionProvider onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&)::__lambda9 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 vector::_M_range_insert {k憧憧憧憧憧̆憧憧执ӉĉzjzzzzWHzz8/w㐧ݐ㐧㐧㐧㐧Ԑː㐧㐧Őw '7w+2 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 v_reshape initializer value is not expected reshape initializer value is not expected Failed in match v_matmul and v_add input shape 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 q root should be layer normalization q_transpose perm attribute not matched q_matmul and q_add shape not matched k_transpose perm attribute 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 /onnxruntime_src/onnxruntime/core/optimizer/attention_fusion.cc shape of layer norm bias tensor not expected Attention subgraph expects 1 Add and 3 MatMul as children of LayerNormalization. Total fused Attention node count: qkv_weights qkv_bias Faild to find path v Failed to find path for mask Failed to find path for q q_reshape const not matched qk_div const not matched. Failed to find path for k k root is not layer norm k_reshape const not matched Mask_Int32 Cast mask from int64 to int32 MaskCast MaskIndex_Int32 Count number of words ReduceSum MaskIndex keepdims Failed to create mask index Fused Attention subgraphs Attention num_heads Fused an attention node. , ,,,, ,,쏧ProcessMask FuseSubGraph Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime15AttentionFusionE @ ?FastGelu fused Add and Gelu /onnxruntime_src/onnxruntime/core/optimizer/bias_gelu_fusion.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime8BiasGeluE Unsupported output type of . Can't constant fold node ' ort_value.IsTensor() /onnxruntime_src/onnxruntime/core/optimizer/constant_folding.cc Could not find OrtValue with name ' 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 LeakyRelu fused Conv with activation fused activation_params /onnxruntime_src/onnxruntime/core/optimizer/conv_activation_fusion.cc Unexpected data type for Clip input of TTTTTT}TmTTTSS,U U,U,U,U,U
U U,U,UTT3W&W3W3W3W3WW W3W3WVV;`.`;`;`;`;`J` `;`;` `_ onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const onnxruntime::{anonymous}::GetClipConstantMinMax(const onnxruntime::Graph&, const onnxruntime::Node&, float&, float&)::__lambda16 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 k8ikkkkijkkjjhrrhrhrhrhrsXshrhrsrw=wwwwwt1www(w wwvwwww(qvwwvvHwvHwHwHwHwtvHwHwzvqvHwvHwHwHwHwsvHwHwvvvvvvvvrvvvvvToProto 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 bn_scale_tensor_proto bn_B_tensor_proto bn_mean_tensor_proto bn_var_tensor_proto ConvBnFusion_W_ ConvBnFusion_BN_B_ <<<<<勨(<<jg(u((((i]((TK_______蒨䍨䍨䍨䍨䍨䍨䍨TLTTTT@4TT+"뒨ڒ̒ /onnxruntime_src/onnxruntime/core/optimizer/conv_bn_fusion.cc ȠȠȠȠȠn'ȠȠ㞨令㦨ڦCݧ&ݧݧݧݧ;/ݧݧGulcT ssssssstUUUUU'UU3 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 mul_B_tensor_proto ConvMulFusion_W_ ConvMulFusion_Mul_B_ /onnxruntime_src/onnxruntime/core/optimizer/conv_mul_fusion.cc zzzzzmzz¿ͨt̨̨̨ͨͨͨͨͨͨf̨Y˨ͨͨͨͨͨͨfͨMͨͨͨ7ͨt̨ͨ4ɨͨͨͨͨ#ɨɨͨͨɨǨFͨʨFͨFͨFͨFͨʨʨFͨFͨʨɨ-ͨ@Ǩ-ͨ-ͨ-ͨ-ͨ_ǨNǨ-ͨ-ͨpǨŨĨèĨĨĨĨ1è¨ĨĨo¨¨Ԩ.ѨԨԨԨԨ Ѩ ѨԨԨШϨӨѨӨӨӨӨҨѨӨӨѨ ѨӨSϨӨӨӨӨrϨaϨӨӨϨ.ΨyӨkӨyӨyӨyӨyӨZӨIӨyӨyӨ;ӨѨrӨͨrӨrӨrӨrӨͨͨrӨrӨ̨ͨ֨ը֨֨֨֨\ըԨ֨֨=ԨӨhèhèhèhèhè9hèhèߨߨHyZyyyyyhyyXXXXXXX/Z////yh//ToProto 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
+ N11onnxruntime16EliminateDropoutE 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. Cast Input from int64 to int32 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. Output edge count not expected for nodes in path1. Second input of Gather should be a constant with value 1. The parent of shape nodes are expected to be input_ids. Failed to match position subgraph. Output edge count not expected for nodes in path 1. The first input of Range should be a constant with value 0. The third input of Range should be a constant with value 1. 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. Input_ids and mask should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). fused EmbedLayerNorm subgraphs _Cast position_embeddings Expand Squeeze NonZero ConstantOfShape Failed to match Shape node. Failed to find path 1. Input id is not valid. Segment id is not valid. Mask is not valid. EmbedLayerNormalization &z&&&&&n&b&&&Y&P& (' ( ( ( ('' ( (''CheckInput MatchPositionEmbeddingSubgraph2 MatchPositionSubgraph MatchPositionEmbeddingSubgraph1 Recurse ApplyImpl N11onnxruntime20EmbedLayerNormFusionE 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 /onnxruntime_src/onnxruntime/core/optimizer/free_dim_override_transformer.cc with a fixed dimension denotation ' but the size of this dimension does not equal the specified override of Invalid free dimension override. The model has input ' ' ApplyImpl 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 ??fused Gemm FusedGemm leaky_relu_ /onnxruntime_src/onnxruntime/core/optimizer/gemm_activation_fusion.cc onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl N11onnxruntime20GemmActivationFusionE _Map_base::at allocator_ptr_ != nullptr Tried to allocate without valid type information, ort_value index= Failed to get allocator for optimizer /onnxruntime_src/onnxruntime/core/optimizer/optimizer_execution_frame.cc void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType) ForEachWithIndex operator() Info onnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector<const onnxruntime::Node*>&, const InitializedTensorSet&) *ZN11onnxruntime23OptimizerExecutionFrame4InfoC1ERKSt6vectorIPKNS_4NodeESaIS5_EERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSE_EEEEUlRKNS_7NodeArgEmE_ N11onnxruntime23OptimizerExecutionFrameE /onnxruntime_src/onnxruntime/core/providers/cpu/cpu_execution_provider.cc virtual std::shared_ptr<onnxruntime::KernelRegistry> onnxruntime::CPUExecutionProvider::GetKernelRegistry() const GetKernelRegistry RegisterCPUKernels RegisterOnnxMLOperatorKernels RegisterOnnxOperatorKernels N11onnxruntime20CPUExecutionProviderE N11onnxruntime25IExecutionProviderFactoryE N11onnxruntime18CpuProviderFactoryE St23_Sp_counted_ptr_inplaceIN11onnxruntime18CpuProviderFactoryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE utils::HasDataType(t_proto) unpack_status.IsOK() T1 T2 Missing Input: Must have a single dimension t_proto.dims_size() == 1 t_proto.dims()[0] == 1 Unsupported value attribute datatype with sizeof=: /onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape.h /onnxruntime_src/onnxruntime/core/providers/cpu/generator/constant_of_shape.cc TensorProto::DataType_IsValid(t_proto.data_type()) Value attribute unpacking failed: Unsupported value attribute datatype: /onnxruntime_src/include/onnxruntime/core/framework/op_kernel.h Must have a valid input shape. input_shape.NumDimensions() > 0 Must have a single dimension of 1 !))L))(t(,(!''L' '& virtual onnxruntime::common::Status onnxruntime::ConstantOfShape::Compute(onnxruntime::OpKernelContext*) const Compute 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::ConstantOfShapeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::Tensor**) const onnxruntime::ConstantOfShapeBase::ConstantOfShapeBase(const onnxruntime::OpKernelInfo&) void onnxruntime::ConstantOfShapeBase::SetValue(size_t, void*) void onnxruntime::ConstantOfShapeBase::SetValueFromTensorProto(const onnx::TensorProto&) N11onnxruntime19ConstantOfShapeBaseE FPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEE N11onnxruntime15ConstantOfShapeE PFPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEE Integer overflow seed dtype 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/common/safeint.h /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 static void SafeIntExceptionHandler<onnxruntime::OnnxRuntimeException>::SafeIntOnOverflow() 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 ? @ @ Unsupported data type: 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: Ret onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<Ret>::operator()(int32_t) const [with Ret = onnxruntime::common::Status; int32_t = 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 = 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] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime5RangeE min should be a scalar. max should be a scalar. /onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.h info.GetAttr<T>("max", &max_).IsOK() info.GetAttr<T>("min", &min_).IsOK() min->Shape().NumDimensions() == 0 max->Shape().NumDimensions() == 0 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::Clip<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::Clip_6<T>::Clip_6(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime6Clip_6IfEE N11onnxruntime4ClipIfEE exclusive reverse CumSum axis is not in valid range [- 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() == 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` /onnxruntime_src/onnxruntime/core/providers/common.h axis >= -tensor_rank && axis <= tensor_rank - 1 dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size() dims.size() == extents_.size() Cannot apply CumSum operator on a scalar /onnxruntime_src/onnxruntime/core/providers/cpu/math/cumsum.cc void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float] const T* onnxruntime::Tensor::Data() 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 = float] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::common::Status onnxruntime::CumSum<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] Compute void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double] const T* onnxruntime::Tensor::Data() 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 = double] T* onnxruntime::Tensor::MutableData() [with T = double] onnxruntime::common::Status onnxruntime::CumSum<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] Compute void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [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 = int] T* onnxruntime::Tensor::MutableData() [with T = int] onnxruntime::common::Status onnxruntime::CumSum<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] Compute void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = 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) const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = int] N11onnxruntime6CumSumIfEE N11onnxruntime6CumSumIdEE N11onnxruntime6CumSumIiEE N11onnxruntime6CumSumIlEE ٫ث$٫ث٫ث$٫ثثثث$٫ثثثث٫ثثثثثثثګګث٫ثثثd٫ثث٫ثثث٫ثثثثثثثث4٫$ګ4٫D٫٫ثثګ4٫D٫ثT٫٫d٫t٫4ګDګlګt٫|ګګګثثثثثثثثثثثثثثثثث$٫ثثثثث٫$٫٫ثثثثثثثثثثثثثثثT٫4٫D٫ثT٫٫D٫ثث4٫D٫ثT٫٫ثT٫٫d٫t٫ثث٫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 j@fmod (fmod == 0) || (fmod == 1) Abs Neg Floor Ceil Reciprocal Log Less Greater Equal 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 ' X_ptr != nullptr vector::_M_fill_insert Can broadcast 0 by 0 or 1. largest <= 1 failed to get first output! Must have 1 or more inputs inputCount >= 1 data_n.Shape() == shape input_count >= 1 data_1.Shape() == shape p_ml_value helper.called_ < 2 helper.called_ == 1 vs fmod_ cur1 == end1 cur_out == end_out fmod must have value either 0 or 1 /onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.cc Attempting to broadcast an axis by a dimension other than 1. /onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.h '. Valid values are 'LEFT' or 'RIGHT'. All inputs must have the same shape Please fetch output tensor with specified shape. Shape must be 1 dimensional as it's tensor data is a shape tensor_shape.Shape().GetDims().size() == 1 Check for duplicate types in MLTypeCallDispatcher X and Y input types do not match: fmod attribute must be true for float, float16 and double types onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda102; Input1Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda103; General = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda104] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = double] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = double; TOutput = double; Input0Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda102; Input1Scalar = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda103; General = onnxruntime::Max_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda104] onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda128 onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda128 onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda128 onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool] onnxruntime::common::Status onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = long unsigned int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = int] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] const T* onnxruntime::Tensor::Data() const [with T = unsigned int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = short int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short unsigned int] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] const T* onnxruntime::Tensor::Data() const [with T = short unsigned int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short unsigned int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = signed char] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned char] void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {const bool&, const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*&}; Fn = onnxruntime::mod_internal::CallModImpl; Types = {unsigned char, signed char, short unsigned int, short int, unsigned int, int, long unsigned int, long int}] T* onnxruntime::Tensor::MutableData() [with T = double] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = double] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = float] virtual onnxruntime::common::Status onnxruntime::Mod::Compute(onnxruntime::OpKernelContext*) const T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] void onnxruntime::mod_internal::BroadCastMFloat16FMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) onnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo&) onnxruntime::common::Status onnxruntime::Erf<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] T* onnxruntime::OpKernelContext::Output(int) [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda123; Input1Scalar = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda124; General = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda125] 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] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Min_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda99; Input1Scalar = onnxruntime::Min_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda100; General = onnxruntime::Min_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda101] onnxruntime::common::Status onnxruntime::Min_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] void onnxruntime::BroadcastIterator::Append(int64_t, int64_t) void onnxruntime::BroadcastIterator::Init(int64_t, int64_t) onnxruntime::Broadcaster::Broadcaster(const std::vector<long int>&, const std::vector<long int>&) onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = float] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda96; Input1Scalar = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda97; General = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda98] onnxruntime::common::Status onnxruntime::Sum_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] ( ( `( `(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfO ZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE_ ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE_ ZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE0_ ZNK11onnxruntime3PowIdE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIdLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEdE1_ ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE0_ ZNK11onnxruntime3PowIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEENS5_IKS7_Li0ES9_EEfE1_ N11onnxruntime3NotE N11onnxruntime3AndE N11onnxruntime2OrE N11onnxruntime3XorE N11onnxruntime3AddIfEE N11onnxruntime3AddIdEE N11onnxruntime3AddIiEE N11onnxruntime3AddIlEE N11onnxruntime3SubIfEE N11onnxruntime3SubIdEE N11onnxruntime3SubIiEE N11onnxruntime3SubIlEE N11onnxruntime3MulIfEE N11onnxruntime3MulIdEE N11onnxruntime3MulIiEE N11onnxruntime3MulIlEE N11onnxruntime3DivIfEE N11onnxruntime3DivIdEE N11onnxruntime3DivIiEE N11onnxruntime3DivIlEE N11onnxruntime3AbsIfEE N11onnxruntime3AbsIdEE N11onnxruntime3AbsIaEE N11onnxruntime3AbsIsEE N11onnxruntime3AbsIiEE N11onnxruntime3AbsIlEE N11onnxruntime3AbsIhEE N11onnxruntime3AbsItEE N11onnxruntime3AbsIjEE N11onnxruntime3AbsImEE N11onnxruntime3NegIfEE N11onnxruntime3NegIdEE N11onnxruntime3NegIaEE N11onnxruntime3NegIiEE N11onnxruntime3NegIlEE N11onnxruntime5FloorIfEE N11onnxruntime4CeilIfEE N11onnxruntime10ReciprocalIfEE N11onnxruntime4SqrtIfEE N11onnxruntime4SqrtIdEE N11onnxruntime3PowIfEE N11onnxruntime3PowIdEE N11onnxruntime3ExpIfEE N11onnxruntime3ExpIdEE N11onnxruntime3LogIfEE N11onnxruntime5Sum_6IfEE N11onnxruntime5Sum_8IfEE N11onnxruntime5Min_6IfEE N11onnxruntime5Min_8IfEE N11onnxruntime5Max_6IfEE N11onnxruntime5Max_8IfEE N11onnxruntime5Max_8IdEE N11onnxruntime4LessIfEE N11onnxruntime4LessIdEE N11onnxruntime4LessIiEE N11onnxruntime4LessIlEE N11onnxruntime7GreaterIfEE N11onnxruntime7GreaterIiEE N11onnxruntime7GreaterIlEE N11onnxruntime5EqualIbEE N11onnxruntime5EqualIiEE N11onnxruntime5EqualIlEE N11onnxruntime5EqualIfEE N11onnxruntime6Mean_6IfEE N11onnxruntime6Mean_8IfEE N11onnxruntime8BitShiftIhEE N11onnxruntime8BitShiftIjEE N11onnxruntime8BitShiftImEE N11onnxruntime3ErfIfEE N11onnxruntime3SinIfEE N11onnxruntime3SinIdEE N11onnxruntime3CosIfEE N11onnxruntime3TanIfEE N11onnxruntime4AsinIfEE N11onnxruntime4AcosIfEE N11onnxruntime4AtanIfEE N11onnxruntime4SinhIfEE N11onnxruntime4CoshIfEE N11onnxruntime5AsinhIfEE N11onnxruntime5AcoshIfEE N11onnxruntime5AtanhIfEE N11onnxruntime5PReluIfEE N11onnxruntime8Expand_8IfEE N11onnxruntime8Expand_8IdEE N11onnxruntime8Expand_8IaEE N11onnxruntime8Expand_8IsEE N11onnxruntime8Expand_8IiEE N11onnxruntime8Expand_8IlEE N11onnxruntime8Expand_8IhEE N11onnxruntime8Expand_8ItEE N11onnxruntime8Expand_8IjEE N11onnxruntime8Expand_8ImEE N11onnxruntime8Expand_8IbEE N11onnxruntime8Expand_8INS_9MLFloat16EEE N11onnxruntime3ModE ,F F @ ? ? ? ? B B B B ? ? ? ?5?5?5?5?!=!=!=!=뽸뽸뽸O]O]O]O]>>>>L>L>L>L>%=%=%=%=P*P*P*P*>>>>^^^^ 1? 1? 1? 1? BBBB¥¥¥;?;?;?;?giP9giP9giP9giP9C:C:C:C:<<<<*=*=*=*=*>*>*>*> B B B B ? ? ? ? "?"?"?"? @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!< @ @/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_src/onnxruntime/core/providers/cpu/math/gemm_helper.h left.NumDimensions() == 2 || left.NumDimensions() == 1 Gemm: Invalid bias shape for broadcast Not implemented fused activation: transA transB right.NumDimensions() == 2 %ld N: K: GEMM: Dimension mismatch, W: M_ >= 0 && K_ > 0 && N_ >= 0 onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::Gemm<T>::Gemm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime4GemmIfEE A %~3a*7.A \3JCy7
':Y;Ӡ5֕8;Y; A A A A %%%%~3a*~3a*~3a*~3a*7777A \3A \3A \3A \3JCy7JCy7JCy7JCy7
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':Y;Y;Y;Y;Ӡ5Ӡ5Ӡ5Ӡ5֕8֕8֕8֕8;;;;Y;Y;Y;Y;Hardmax . N= , D= Hardmax inputs N, D and N * D must be < T* onnxruntime::Tensor::MutableData() [with T = float] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) 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] N11onnxruntime7HardmaxIfEE MatMul dimension mismatch /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_helper.h Not satisfied: left_num_dims >= 1 && right_num_dims >= 1
+ Not satisfied: K_ == right_shape[right_num_dims - 2] || transb && K_ == right_shape[right_num_dims - 1]
+ left operand cannot broadcast on dim Not satisfied: left_padded_dims_[idx_dim] == 1
+ right operand cannot broadcast on dim Not satisfied: right_padded_dims_[idx_dim] == 1
+ Not satisfied: K_ == right_shape[transb ? right_num_dims - 1 : right_num_dims - 2]
+ Not satisfied: num_dims_with_pad == num_output_dims
+ Not satisfied: M_ == 1 && N_ == 1
+ Not satisfied: num_dims_with_pad - 1 == num_output_dims
+ Not satisfied: num_dims_with_pad - 2 == num_output_dims
+ Not satisfied: K_ == right_shape[0]
+ /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul.cc const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] Compute const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] Compute const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = int] Compute const T* onnxruntime::Tensor::Data() const [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] Compute const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] Compute const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] Compute Compute N11onnxruntime6MatMulIfEE N11onnxruntime6MatMulIdEE N11onnxruntime6MatMulIiEE N11onnxruntime6MatMulIjEE N11onnxruntime6MatMulIlEE N11onnxruntime6MatMulImEE T3 MatMulInteger a != nullptr && b != nullptr /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_integer.cc t->Shape().NumDimensions() <= 1 && t->Shape().Size() == 1 t->IsDataType<int8_t>() || t->IsDataType<uint8_t>() Currently only scalar zero_point is supported. TODO: add per channel zero point support. 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 MatMulInteger: Unsupported input types with zero point const T* onnxruntime::Tensor::Data() const [with T = signed char] onnxruntime::MatMulInteger<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = signed char]::__lambda10 Compute onnxruntime::common::Status onnxruntime::MatMulInteger<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = signed char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = int] Compute onnxruntime::common::Status onnxruntime::MatMulInteger<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = unsigned char] N11onnxruntime13MatMulIntegerIhhEE N11onnxruntime13MatMulIntegerIhaEE 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 const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] Compute onnxruntime::common::Status onnxruntime::QLinearMatMul<T1, T2, T3>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = unsigned char; T3 = unsigned char] N11onnxruntime13QLinearMatMulIhhhEE ORound 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 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] void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor*&, onnxruntime::Tensor*&}; Fn = onnxruntime::sign_internal::CallSignImpl; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int}] 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 int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) N11onnxruntime7SoftmaxIfLb0EEE N11onnxruntime7SoftmaxIfLb1EEE N11onnxruntime7SoftmaxIdLb0EEE N11onnxruntime7SoftmaxIdLb1EEE 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&) onnxruntime::TopK<OpSet, T>::TopK(const onnxruntime::OpKernelInfo&) [with int OpSet = 10; T = float] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) onnxruntime::TopK<OpSet, T>::TopK(const onnxruntime::OpKernelInfo&) [with int OpSet = 9; T = float] N11onnxruntime4TopKILi9EfEE N11onnxruntime4TopKILi10EfEE N11onnxruntime4TopKILi11EfEE N11onnxruntime4TopKILi11ElEE 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 /onnxruntime_src/onnxruntime/core/providers/cpu/ml/dictvectorizer.h info.GetAttrs(std::is_same<AttrType, std::string>::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK() DictVectorizer string_vocabulary int64_vocabulary 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 and 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 SOFTMAX_ZERO LinearClassifier multi_class NONE post_transform LOGISTIC SOFTMAX intercepts classlabels_strings classlabels_ints coefficients !coefficients_.empty() length overflow /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearclassifier.cc Input shape needs to be at least a single dimension. 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] void onnxruntime::ml::write_scores(std::vector<_RealType>&, onnxruntime::ml::POST_EVAL_TRANSFORM, int64_t, onnxruntime::Tensor*, int) [with T = float; int64_t = long int] const T* onnxruntime::Tensor::Data() const [with T = int] onnxruntime::ml::LinearClassifier<T>::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = int] onnxruntime::ml::LinearClassifier<T>::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = long int] onnxruntime::ml::LinearClassifier<T>::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::LinearClassifier<T>::LinearClassifier(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml16LinearClassifierIfEE N11onnxruntime2ml16LinearClassifierIdEE N11onnxruntime2ml16LinearClassifierIlEE N11onnxruntime2ml16LinearClassifierIiEE @@3ֳLinearRegressor /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearregressor.cc info.GetAttr<int64_t>("targets", &targets_).IsOK() info.GetAttrs<float>("coefficients", coefficients_).IsOK() T* onnxruntime::Tensor::MutableData() [with T = float] void onnxruntime::ml::write_scores(std::vector<_RealType>&, onnxruntime::ml::POST_EVAL_TRANSFORM, int64_t, onnxruntime::Tensor*, int) [with T = float; int64_t = long 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] onnxruntime::ml::LinearRegressor<T>::LinearRegressor(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml15LinearRegressorIfEE Invalid normalize value of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.h info.GetAttr<std::string>("norm", &norm).IsOK() Unexpected NORMALIZE value of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.cc const T* onnxruntime::Tensor::Data() const [with T = int] void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = long int] void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = double] void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::ml::Normalizer::Normalize(onnxruntime::OpKernelContext*) const [with T = float] void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::ml::Normalizer* const, onnxruntime::OpKernelContext*&}; Fn = onnxruntime::ml::Normalizer::CallNormalizerImpl; Types = {float, double, long int, 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::ml::Normalizer::Compute(onnxruntime::OpKernelContext*) const 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>] T* onnxruntime::Tensor::MutableData() [with T = float] 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] 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 No support vectors. /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>] onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = int] onnxruntime::ml::SVMClassifier<T>::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = int] onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = long int] onnxruntime::ml::SVMClassifier<T>::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = long int] onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::SVMClassifier<T>::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = float] onnxruntime::ml::SVMClassifier<T>::SVMClassifier(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml9SVMCommonIfEE N11onnxruntime2ml9SVMCommonIdEE N11onnxruntime2ml9SVMCommonIlEE N11onnxruntime2ml9SVMCommonIiEE N11onnxruntime2ml13SVMClassifierIfEE N11onnxruntime2ml13SVMClassifierIdEE N11onnxruntime2ml13SVMClassifierIlEE N11onnxruntime2ml13SVMClassifierIiEE
+ף;? 4n_supports one_class SVMRegressor /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmregressor.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::ml::SVMCommon<T>::SVMCommon(const onnxruntime::OpKernelInfo&) [with T = float] onnxruntime::ml::SVMRegressor<T>::SVMRegressor(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml12SVMRegressorIfEE BRANCH_EQ TreeEnsembleClassifier BRANCH_LEQ LEAF BRANCH_LT BRANCH_GTE BRANCH_GT it != parents.end() nodes_treeids nodes_nodeids nodes_featureids nodes_values nodes_hitrates nodes_modes nodes_truenodeids nodes_falsenodeids class_nodeids class_treeids class_ids class_weights base_values classlabels_int64s !nodes_treeids_.empty() Invalid mode of value: treeindex >= 0 X dims is empty. /onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_classifier.cc base_values_.empty() || base_values_.size() == static_cast<size_t>(class_count_) || base_values_.size() == weights_classes_.size() nodes_missing_value_tracks_true class_nodeids_.size() == class_ids_.size() class_nodeids_.size() == class_weights_.size() nodes_nodeids_.size() == nodes_featureids_.size() nodes_nodeids_.size() == nodes_modes_names_.size() nodes_nodeids_.size() == nodes_values_.size() nodes_nodeids_.size() == nodes_truenodeids_.size() nodes_nodeids_.size() == nodes_falsenodeids_.size() (nodes_nodeids_.size() == nodes_hitrates_.size()) || (nodes_hitrates_.empty()) Must provide classlabels_strings or classlabels_int64s but not both. classlabels_strings_.empty() ^ classlabels_int64s_.empty() std::all_of( std::begin(missing_tracks_true_), std::end(missing_tracks_true_), [](int64_t elem) { return elem >= 0; }) NNNMMNPT8T T`S8ShTYYYXXY@_(__^(^P_onnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = float; int64_t = long int] const T* onnxruntime::Tensor::Data() const [with T = float] Compute onnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = double; int64_t = long int] const T* onnxruntime::Tensor::Data() const [with T = double] Compute onnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = long int; int64_t = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] Compute onnxruntime::common::Status onnxruntime::ml::TreeEnsembleClassifier<T>::ProcessTreeNode(std::map<long int, float>&, int64_t, const T*, int64_t) const [with T = int; int64_t = long int] const T* onnxruntime::Tensor::Data() const [with T = int] Compute void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = int] onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = int] void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = long int] onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = long int] void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = double] onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = double] void onnxruntime::ml::TreeEnsembleClassifier<T>::Initialize() [with T = float] onnxruntime::ml::TreeEnsembleClassifier<T>::TreeEnsembleClassifier(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml22TreeEnsembleClassifierIfEE N11onnxruntime2ml22TreeEnsembleClassifierIdEE N11onnxruntime2ml22TreeEnsembleClassifierIlEE N11onnxruntime2ml22TreeEnsembleClassifierIiEE TreeEnsembleRegressor target_nodeids target_treeids target_ids target_weights SUM aggregate_function AVERAGE MIN n_targets it != indices.end() /onnxruntime_src/onnxruntime/core/providers/cpu/ml/treeregressor.cc info.GetAttr<int64_t>("n_targets", &n_targets_).IsOK() target_nodeids_.size() == target_ids_.size() target_nodeids_.size() == target_weights_.size() nodes_id_size == nodes_featureids_.size() nodes_id_size == nodes_values_.size() nodes_id_size == nodes_modes_.size() nodes_id_size == nodes_truenodeids_.size() nodes_id_size == nodes_falsenodeids_.size() (nodes_id_size == nodes_hitrates_.size()) || (nodes_hitrates_.empty()) base_values_.empty() || base_values_.size() == static_cast<size_t>(n_targets_) treeindex evaluated to a negative value, which should not happen. Compute Compute onnxruntime::ml::TreeEnsembleRegressor<T>::TreeEnsembleRegressor(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::TreeEnsembleRegressor<T>::TreeEnsembleRegressor(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml21TreeEnsembleRegressorIfEE N11onnxruntime2ml21TreeEnsembleRegressorIdEE ZipMap Input features_per_batch[ ] != number of classlabels[ /onnxruntime_src/onnxruntime/core/providers/cpu/ml/zipmap.cc 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 No kernel shape is set. kernel_shape_[dim] > 0 Unsupported pooling size. inferred 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. Input dimension cannot be less than 3. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/Unpool.cc Not satisfied: X_shape.NumDimensions() >= 3
+ Not satisfied: I_shape == X_shape
+ Shape must be 1 dimensional as it's tensor data of a shape Not satisfied: tensor_shape->Shape().GetDims().size() == 1
+ output_shape is smaller than minimum required. output_shape: Not satisfied: given_shape.Size() >= shape.Size()
+ Index tensor shape should be same as that of the input data tensor to unpool. 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 st.IsOK() 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 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. kernel_shape num_dims is not compatible with W num_dims. kernel_shape is not compatible with W shape. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/autopad_type.h Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_attributes.h /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv.cc /onnxruntime_src/onnxruntime/core/util/math.h X: C: kernel channels: group: M: kernel_shape: Unknown AutoPadType String dilation == 1 pad type not supported. Invalid input shape: Out of bound access to array d_iter[d_i] < d_max onnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&) void onnxruntime::math::Im2colNd<T, 2>::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, bool, T) [with T = float; int64_t = long int] onnxruntime::common::Status onnxruntime::ComputePadAndOutputShape(int64_t, int64_t, int64_t, int64_t, onnxruntime::AutoPadType, int64_t*, int64_t*, int64_t*) [with bool ForceSymmetricAutoPadding = false; int64_t = long int] InferOutputShape Compute N11onnxruntime4ConvIfEE /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. ConvInteger void onnxruntime::math::Im2colNd<T, 2>::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, bool, T) [with T = unsigned char; int64_t = long int] Compute virtual onnxruntime::common::Status onnxruntime::ConvInteger::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime11ConvIntegerE ConvTranspose output_padding output_shape group count is <= 0 filter_number: num_input_channels: *out_size >= 0 pad type not supported dim_size > 0 Only 1D and 2D ConvTranspose is supported. 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 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 p_ == 1 || p_ == 2 LpNormalization /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] 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 = float] N11onnxruntime6LpNormIfEE 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 GlobalLpPool ceil_mode count_include_pad storage_order 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() 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() input_shape.Size() > 0 || input_shape[0] == 0 Invalid input shape. Only N can be zero. Got: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool.cc Not satisfied: input_dims >= 3
+ kernel_shape num_dims is not compatible with X num_dims. Not satisfied: pooling_dims == pool_attrs_.kernel_shape.size()
+ Not satisfied: x_shape.NumDimensions() >= 3
+ 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 = long int] Compute const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [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 N11onnxruntime8PoolBaseE N11onnxruntime4PoolIfNS_7MaxPoolILi1EEEEE N11onnxruntime4PoolIfNS_11AveragePoolEEE N11onnxruntime4PoolIfNS_7MaxPoolILi8EEEEE N11onnxruntime4PoolIfNS_6LpPoolEEE /onnxruntime_src/onnxruntime/core/providers/cpu/nn/qlinearconv.cc IsScalarOr1ElementVector(input_offset) IsScalarOr1ElementVector(filter_offset) IsScalarOr1ElementVector(result_offset) IsScalarOr1ElementVector(input_scale) IsScalarOr1ElementVector(filter_scale) IsScalarOr1ElementVector(result_scale) QLinearConv : input zero point must be a scalar or 1D tensor of size 1 QLinearConv : filter 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 : input scale must be a scalar or 1D tensor of size 1 QLinearConv : filter scale must be a scalar or 1D tensor of size 1 QLinearConv : result scale must be a scalar or 1D tensor of size 1 T4 QLinearConv const T* onnxruntime::Tensor::Data() const [with T = int] void onnxruntime::math::Im2colNd<T, 2>::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, bool, T) [with T = unsigned char; int64_t = long int] Compute virtual onnxruntime::common::Status onnxruntime::QLinearConv::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime11QLinearConvE 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() Ret onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper<Ret, UnsupportedPolicy>::Get() [with Ret = onnxruntime::common::Status; UnsupportedPolicy = onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<onnxruntime::common::Status>] 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 :Please, install necessary language-pack-XX and configure locales Failed to construct locale with name: /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&) onnxruntime::StringNormalizer::StringNormalizer(const onnxruntime::OpKernelInfo&) N11onnxruntime16StringNormalizerE TfIdfVectorizer 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 Got weights of size: but ngram_indexes size: must be of equal size pool_strings !impl_->pool_strings_.empty() pool_int64s -grams (items % ngram_size == 0) -grams detected pool_int64s duplicate pool_strings duplicate Input shape must have either [C] or [B,C] dimensions with B > 0. Invalid type of the input argument /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 (before_insert + ngrams) == impl_->int64_set_.size() (before_insert + ngrams) == impl_->str_set_.size() 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* 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] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::TfIdfVectorizer::TfIdfVectorizer(const onnxruntime::OpKernelInfo&) N11onnxruntime15TfIdfVectorizerE G G G Gcenter_point_box NonMaxSuppression boxes_tensor scores_tensor boxes must be a 3D tensor. scores must be a 3D tensor. Not satisfied: ret.IsOK()
+ output != nullptr iou_threshold must be in range [0, 1]. /onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.cc Not satisfied: (iou_threshold >= 0 && iou_threshold <= 1.f)
+ 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_ Not satisfied: boxes_shape.NumDimensions() == 3
+ Not satisfied: scores_shape.NumDimensions() == 3
+ boxes and scores should have same num_batches. Not satisfied: boxes_dims[0] == scores_dims[0]
+ boxes and scores should have same spatial_dimension. Not satisfied: boxes_dims[1] == scores_dims[2]
+ The most inner dimension in boxes must have 4 data. Not satisfied: boxes_dims[2] == 4
+ virtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const Compute GetThresholdsFromInputs 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] 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 output_height output_width sampling_ratio sampling_ratio_ >= 0 Invalid mode of value 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 /onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/roialign.h Sampling ratio should be >=0, but it was specified. It should be either avg or max 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<T>::RoiAlignBase(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::RoiAlignBase<T>::RoiAlignBase(const onnxruntime::OpKernelInfo&) [with T = float] *ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIdEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUliE_ *ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIfEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUliE_ N11onnxruntime12RoiAlignBaseIdEE N11onnxruntime12RoiAlignBaseIfEE N11onnxruntime8RoiAlignIfEE N11onnxruntime8RoiAlignIdEE ReduceL1 ReduceL2 ReduceLogSum ReduceLogSumExp ReduceMax ReduceMin ReduceProd ReduceSumSquare ArgMax ArgMin has_value_ in_dim != 0 /onnxruntime_src/include/onnxruntime/core/common/optional.h /onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.h info.GetAttr("keepdims", &keepdims).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/containers.h /onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.cc Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape: bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, onnxruntime::FastAllocVector<T>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = long int; onnxruntime::FastAllocVector<T> = std::vector<long int, onnxruntime::OrtStlAllocator<long int> >; int64_t = long int] onnxruntime::OrtStlAllocator<T1> onnxruntime::GetAllocator(const onnxruntime::OpKernelContext&) [with T = long int] bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, onnxruntime::FastAllocVector<T>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = double; onnxruntime::FastAllocVector<T> = std::vector<double, onnxruntime::OrtStlAllocator<double> >; int64_t = long int] onnxruntime::OrtStlAllocator<T1> onnxruntime::GetAllocator(const onnxruntime::OpKernelContext&) [with T = double] bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, onnxruntime::FastAllocVector<T>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = float; onnxruntime::FastAllocVector<T> = std::vector<float, onnxruntime::OrtStlAllocator<float> >; int64_t = long int] onnxruntime::OrtStlAllocator<T1> onnxruntime::GetAllocator(const onnxruntime::OpKernelContext&) [with T = float] bool onnxruntime::PrepareForReduce(onnxruntime::OpKernelContext*, onnxruntime::FastAllocVector<T>&, onnxruntime::Tensor**, int64_t&, int64_t&, const std::vector<long int>&, bool, bool) [with T = int; onnxruntime::FastAllocVector<T> = std::vector<int, onnxruntime::OrtStlAllocator<int> >; int64_t = long int] onnxruntime::OrtStlAllocator<T1> onnxruntime::GetAllocator(const onnxruntime::OpKernelContext&) [with T = int] onnxruntime::ReduceKernelBase<allow_multi_axes>::ReduceKernelBase(const onnxruntime::OpKernelInfo&, onnxruntime::optional<long int>) [with bool allow_multi_axes = false] T& onnxruntime::optional<T>::value() [with T = long int] onnxruntime::ReduceKernelBase<allow_multi_axes>::ReduceKernelBase(const onnxruntime::OpKernelInfo&, onnxruntime::optional<long int>) [with bool allow_multi_axes = true] N11onnxruntime16ReduceKernelBaseILb1EEE N11onnxruntime16ReduceKernelBaseILb0EEE N11onnxruntime12ReduceKernelILb1EEE N11onnxruntime12ReduceKernelILb0EEE N11onnxruntime8ReduceL1IfEE N11onnxruntime8ReduceL1IiEE N11onnxruntime8ReduceL2IfEE N11onnxruntime8ReduceL2IiEE N11onnxruntime12ReduceLogSumIfEE N11onnxruntime12ReduceLogSumIiEE N11onnxruntime15ReduceLogSumExpIfEE N11onnxruntime15ReduceLogSumExpIiEE N11onnxruntime9ReduceMaxIfEE N11onnxruntime9ReduceMaxIiEE N11onnxruntime9ReduceMaxIlEE N11onnxruntime10ReduceMeanIfEE N11onnxruntime10ReduceMeanIiEE N11onnxruntime9ReduceMinIfEE N11onnxruntime9ReduceMinIiEE N11onnxruntime9ReduceMinIlEE N11onnxruntime10ReduceProdIfEE N11onnxruntime10ReduceProdIiEE N11onnxruntime9ReduceSumIfEE N11onnxruntime9ReduceSumIiEE N11onnxruntime9ReduceSumIlEE N11onnxruntime9ReduceSumIdEE N11onnxruntime15ReduceSumSquareIfEE N11onnxruntime15ReduceSumSquareIiEE N11onnxruntime15ReduceSumSquareIdEE N11onnxruntime6ArgMaxIfEE N11onnxruntime6ArgMaxIiEE N11onnxruntime6ArgMinIfEE N11onnxruntime6ArgMinIiEE 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 Invalid 'direction' argument of ' '. Must be one of 'forward', 'reverse', or 'bidirectional'. /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] 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&) N3gsl15narrowing_errorE N11onnxruntime12DeepCpuGruOpE ZN11onnxruntime10IAllocator13MakeUniquePtrIfEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPfE_ ZN11onnxruntime10IAllocator13MakeUniquePtrIiEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPiE_ }. Actual: Input P must have shape { std::future_error DEEPCPU_LSTM input_forget rows[ Processing batch Input initial_c must have shape { /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.cc /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.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 ValidateInputs 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] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] ComputeImpl virtual onnxruntime::common::Status onnxruntime::DeepCpuLstmOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&) onnxruntime::DeepCpuLstmOp::DeepCpuLstmOp(const onnxruntime::OpKernelInfo&) NSt13__future_base7_ResultIvEE N11onnxruntime13DeepCpuLstmOpE NSt13__future_base11_State_base7_SetterIvNS0_19__exception_ptr_tagEEE NSt13__future_base11_State_base7_SetterIvvEE St23_Sp_counted_ptr_inplaceINSt13__future_base11_State_baseESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE ZN11onnxruntime3rnn6detail23ExecuteLambdaInParallelIZNS_6detail18UniDirectionalLstmIfE7ComputeERKN3gsl4spanIKfEERKNS7_IKiEEiSB_SB_RNS7_IfEESH_SH_EUliE_EEvRKSsT_iiRNS_11concurrency10ThreadPoolERKNS_7logging6LoggerEEUlvE_ St23_Sp_counted_ptr_inplaceISt7promiseIvESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE RNN Affine ThresholdedRelu ScaledTanh HardSigmoid Elu Softsign /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) 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: affine leakyrelu thresholdedrelu scaledtanh hardsigmoid softsign softplus * Input W must have shape { Input R must have shape { Input B must have shape { 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 Invalid activation function of Invalid LSTM merge activation function of Invalid GRU reset gate activation function: Invalid GRU hidden gate activation function: Input X must have 3 dimensions only. Actual: Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length= Input sequence_lens must have shape { Input initial_h must have shape { 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) 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] 7.7.7.7. A Anew_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 Tensor size ( ) != new size ( Y != nullptr Unsupported 'dtype' value: SequenceEmpty SequenceLength SequenceConstruct SequenceInsert SequenceErase SequenceAt SplitToSequence 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 yet shape_.Size() == new_shape.Size() SequenceEmpty: Got nullptr for output sequence /onnxruntime_src/onnxruntime/core/providers/cpu/sequence/sequence_ops.cc SequenceLength: Got nullptr for output tensor Got nullptr input for index tensor ) specified for sequence of size ( SequenceAt: 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 tdTD4$ttl 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>] 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] void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&) 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. Tensor type mismatch. type == dtype_ allocator != nullptr len > 0 buffer stol stoul stoi -INF stoull stod /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/cast_op.cc Casting from 'float16' to 'string' is not supported yet. Cast op must have 'to' argument of type DataType Unexpected 'to' argument value: Input is missing. The operator Cast expects one and only one input DD$L,\4\ $ ll \D
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+D ,t <*+,/4'()*11l55t%&:;\<=L=\=,>D>@@@?? : void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = signed char] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = double] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = float] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = long unsigned int] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = unsigned int] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = short unsigned int] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = unsigned char] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = long int] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = int] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = short int] onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = std::basic_string<char>] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = signed char; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = double; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void* onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType) const void* onnxruntime::Tensor::DataRaw(onnxruntime::MLDataType) const void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = long unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = short unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = unsigned char; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = long int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = short int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = bool; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = onnxruntime::MLFloat16] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = double] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = double; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = double] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = float] onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = bool] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = bool] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = bool; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = bool] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = long int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = long int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = long int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = short int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = short int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = short int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = signed char] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = signed char; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = signed char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = long unsigned int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = long unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = long unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = unsigned int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = short unsigned int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = short unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = short unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned char] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = unsigned char; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = unsigned char] N11onnxruntime4CastIhEE N11onnxruntime4CastItEE N11onnxruntime4CastIjEE N11onnxruntime4CastImEE N11onnxruntime4CastIaEE N11onnxruntime4CastIsEE N11onnxruntime4CastIiEE N11onnxruntime4CastIlEE N11onnxruntime4CastIbEE N11onnxruntime4CastIfEE N11onnxruntime4CastIdEE N11onnxruntime4CastINS_9MLFloat16EEE N11onnxruntime4CastISsEE 8 O O O O A A A ACompress /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: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.h /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.cc Not satisfied: input_count >= 1
+ Not satisfied: inputs_0_rank > 0
+ 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 Not satisfied: dim_value == inputs_0_dims[axis_index]
+ Not satisfied: data_n.DataType() == p.output_tensor->DataType()
+ 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 CEyeLike T1 != nullptr EyeLike : Input tensor dimension is not 2 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/eye_like.cc ԕ 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 Type for Tind not supported yet in Gather. 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 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: Data type for 'indices' tensor must be 'int32_t' and 'int64_t' /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.cc GatherElements op: Value in indices must be within bounds [ GatherElements op: Data type of input 'data' should match the data type of the output std::vector<long int> onnxruntime::parse_and_validate_indices_tensor(const onnxruntime::Tensor*, int64_t, const onnxruntime::TensorShape&) T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] 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&) N11onnxruntime14GatherElementsE GatherND invalid index found, index = GatherND op: Input count mismatch /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_nd.cc input_tensor != nullptr && indices_tensor != nullptr indices tensor must has rank larger than 0 last dimension of indices must not be larger than rank of input tensor const T* onnxruntime::Tensor::Data() const [with T = long int] onnxruntime::common::Status onnxruntime::GatherNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::GatherNDBase::Prepare&) const [with Tind = long int] const T* onnxruntime::Tensor::Data() const [with T = int] onnxruntime::common::Status onnxruntime::GatherNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::GatherNDBase::Prepare&) const [with Tind = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime12GatherNDBaseE N11onnxruntime8GatherNDE /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 Data type X must be float or double, but instead got 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] virtual onnxruntime::common::Status onnxruntime::IsInf::Compute(onnxruntime::OpKernelContext*) const 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! /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 'axis' attribute must have a value in the range [ Compute Compute Compute Compute Compute Compute Compute Compute Compute Compute Compute N11onnxruntime8OneHotOpIlllEE N11onnxruntime8OneHotOpIfllEE N11onnxruntime8OneHotOpIlSslEE N11onnxruntime8OneHotOpIfSslEE N11onnxruntime8OneHotOpIlflEE N11onnxruntime8OneHotOpIifiEE N11onnxruntime8OneHotOpIiffEE N11onnxruntime8OneHotOpIfffEE N11onnxruntime8OneHotOpIlifEE N11onnxruntime8OneHotOpIlffEE N11onnxruntime8OneHotOpIlfiEE >Unexpected mode of Pad 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->IsDataType<T>() && value_tensor->Shape().Size() == 1 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 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = float] onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = double] onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = int] onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = long int] onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] onnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo&) N11onnxruntime7PadBaseE N11onnxruntime3PadIfEE N11onnxruntime3PadIdEE N11onnxruntime3PadIiEE N11onnxruntime3PadIlEE y_zero_point x_scale x_zero_point DequantizeLinear /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/quantize_linear.cc IsScalarOr1ElementVector(&y_scale) IsScalarOr1ElementVector(&y_zero_point) y_scale.Shape().NumDimensions() == 1 && y_scale.Shape().Size() == broadcastDim x_zero_point must be 1D tensor with size y_zero_point.Shape().NumDimensions() == 1 && y_zero_point.Shape().Size() == broadcastDim x_scale must be a scalar or 1D tensor or size 1. x_zero_point must be a scalar or 1D tensor or size 1. x_scale must be 1D tensor with size x_scale.Shape().NumDimensions() == 1 && x_scale.Shape().Size() == broadcastDim x_zero_point.Shape().NumDimensions() == 1 && x_zero_point.Shape().Size() == broadcastDim IsScalarOr1ElementVector(&x_scale) IsScalarOr1ElementVector(&x_zero_point) onnxruntime::common::Status onnxruntime::DequantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::common::Status onnxruntime::DequantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] onnxruntime::common::Status onnxruntime::QuantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = signed char] onnxruntime::common::Status onnxruntime::QuantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char] N11onnxruntime16DequantizeLinearIhEE N11onnxruntime16DequantizeLinearIaEE N11onnxruntime14QuantizeLinearIhEE N11onnxruntime14QuantizeLinearIaEE BAttribute shape is not set. requested_shape[i] >= -1 unknown_dim == -1 , requested shape: Reshape_1 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape.h A dimension cannot be less than -1. /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: (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 nearest cubic mode attribute is . It can only be (default) or scales extrapolation_value asymmetric pytorch_half_pixel tf_half_pixel_for_nn align_corners tf_crop_and_resize coordinate_transform_mode:[ ] is not supportted! round_prefer_floor nearest_mode round_prefer_ceil nearst_mode:[ 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 and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the 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() coordinate_transformation_mode exclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to scales size should be greater than 0. const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor*, std::vector<float>&) const onnxruntime::ResizeNearestMode onnxruntime::UpsampleBase::StringToNearstMode(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&) 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_ @ ?ReverseSequence Unknown tensor type of . batch_size= batch_axis Invalid batch_axis of . Must be 0 or 1 batch_axis < 2 Invalid time_axis of time_axis < 2 batch_axis != time_axis /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.cc sequence_lens shape must be {batch_size}. Got: /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 )3{G#)}k 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 = 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] 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::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 . Input rank= Indices dim= at pos= is greater than input dim= jE ւgB ցjjjޅoJ% ۄlG" 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 ScatterND input_tensor != nullptr indice_tensor != nullptr update_tensor != nullptr , indices shape: updates shape: , data shape: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter_nd.cc 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]:]. invalid indice found, indice = T* onnxruntime::Tensor::MutableData() [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::common::Status onnxruntime::ScatterNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::ScatterNDBase::Prepare&) const [with Tind = long int] Compute N11onnxruntime13ScatterNDBaseE 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 Starts must be a 1-D array Ends must be a 1-D array 'axes' has duplicates 'step' value cannot be 0 output == output_end 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 nullptr != start_tensor && start_tensor->Shape().NumDimensions() == 1 nullptr != ends_tensor && ends_tensor->Shape().NumDimensions() == 1 start_tensor->Shape() == ends_tensor->Shape() nullptr == axes_tensor || start_tensor->Shape() == axes_tensor->Shape() nullptr == steps_tensor || start_tensor->Shape() == steps_tensor->Shape() Data type for starts and ends inputs' need to be int32_t or int64_t, but instead got Starts and ends shape mismatch Starts and axes shape mismatch Starts and steps shape mismatch 'axes' has an axis outside of the tensor dimension count Missing input tensor to be processed Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long int; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = double; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = bool; bool dynamic = false] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>; bool dynamic = false] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = unsigned char]::__lambda47 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = short unsigned int]::__lambda47 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = short unsigned int] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = unsigned int]::__lambda47 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = long unsigned int]::__lambda47 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = signed char]::__lambda47 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = signed char] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = short int]::__lambda47 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = short int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = short int; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = int]::__lambda47 void onnxruntime::SliceIterator<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] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = int; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = long int]::__lambda47 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = long int; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = float]::__lambda47 void onnxruntime::SliceIterator<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] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = double]::__lambda47 void onnxruntime::SliceIterator<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] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = double; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = onnxruntime::MLFloat16]::__lambda47 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = onnxruntime::MLFloat16] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = bool]::__lambda47 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = bool] T* onnxruntime::Tensor::MutableData() [with T = bool] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = bool; bool dynamic = true] onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = std::basic_string<char>]::__lambda47 void onnxruntime::SliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] Compute onnxruntime::common::Status onnxruntime::Slice<T, dynamic>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>; bool dynamic = true] void onnxruntime::SliceBase::FillVectorsFromInput(const onnxruntime::OpKernelContext*, std::vector<long int>&, std::vector<long int>&, std::vector<long int>&, std::vector<long int>&) const int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) onnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo&, bool) N11onnxruntime9SliceBaseE N11onnxruntime5SliceIhLb0EEE N11onnxruntime5SliceItLb0EEE N11onnxruntime5SliceIjLb0EEE N11onnxruntime5SliceImLb0EEE N11onnxruntime5SliceIaLb0EEE N11onnxruntime5SliceIsLb0EEE N11onnxruntime5SliceIiLb0EEE N11onnxruntime5SliceIlLb0EEE N11onnxruntime5SliceIfLb0EEE N11onnxruntime5SliceIdLb0EEE N11onnxruntime5SliceINS_9MLFloat16ELb0EEE N11onnxruntime5SliceIbLb0EEE N11onnxruntime5SliceISsLb0EEE N11onnxruntime5SliceIhLb1EEE N11onnxruntime5SliceItLb1EEE N11onnxruntime5SliceIjLb1EEE N11onnxruntime5SliceImLb1EEE N11onnxruntime5SliceIaLb1EEE N11onnxruntime5SliceIsLb1EEE N11onnxruntime5SliceIiLb1EEE N11onnxruntime5SliceIlLb1EEE N11onnxruntime5SliceIfLb1EEE N11onnxruntime5SliceIdLb1EEE N11onnxruntime5SliceINS_9MLFloat16ELb1EEE N11onnxruntime5SliceIbLb1EEE N11onnxruntime5SliceISsLb1EEE blocksize CRD DCR DepthToSpace SpaceToDepth 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 *> Axis= NumOutputs= Input shape= Split 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 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.cc 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 T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] ComputeImpl T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] ComputeImpl T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] ComputeImpl 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 onnxruntime::TensorShape&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] 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 have an implementation yet for the type: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/tile.cc 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 Mismatched data types between input and output Tensors. /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 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.cc does not align with rank of input data: is outside range. is repeated. [ perm: 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 T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<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] N11onnxruntime6UniqueE 'axes' has a duplicate axis 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' has an out of range axis 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::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::UnsqueezeBase::Prepare&) const onnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo&) N11onnxruntime13UnsqueezeBaseE N11onnxruntime9UnsqueezeE Upsample Resize: unexpected mode Upsample: unexpected mode Invalid roi input index. roi_input_idx_ > 0 scales != nullptr sizes == nullptr 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. 'Linear' mode only support 2-D inputs ('Bilinear') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the 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] 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 @ A @Where condition && X && Y condition, X, and Y inputs are required! /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/where_op.cc T* onnxruntime::Tensor::MutableData() [with T = unsigned char] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = unsigned char] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = int] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = int] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = long int] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = long int] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = float] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = std::basic_string<char>] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>] N11onnxruntime5WhereIhEE N11onnxruntime5WhereIiEE N11onnxruntime5WhereIlEE N11onnxruntime5WhereIfEE N11onnxruntime5WhereISsEE /onnxruntime_src/onnxruntime/contrib_ops/cpu_contrib_kernels.cc RegisterCpuContribKernels RegisterNchwcKernels gamma Selu 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] N11onnxruntime7SigmoidIfEE N11onnxruntime4TanhIfEE N11onnxruntime3EluIfEE N11onnxruntime11HardSigmoidIfEE N11onnxruntime9LeakyReluIfEE N11onnxruntime4ReluIfEE N11onnxruntime4SeluIfEE N11onnxruntime18ParametricSoftplusIfEE N11onnxruntime8SoftsignIfEE N11onnxruntime15ThresholdedReluIfEE
+#<}-?_}?L>V If 'If' node has outputs. then_branch info == nullptr did not. else_branch ' attribute. 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. Subgraph must have the shape set for all outputs but Failed to create output tensor for 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 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_ N11onnxruntime2IfE Expected: Loop inputs. Found: 'Loop' node has but has info_ == nullptr 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 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() Execute ConcatenateLoopOutput 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&) FN11onnxruntime6common6StatusERSt6vectorI8OrtValueSaIS3_EEPvmE N11onnxruntime4LoopE PFN11onnxruntime6common6StatusERSt6vectorI8OrtValueSaIS3_EEPvmE 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] FN11onnxruntime6common6StatusERKSt6vectorImSaImEERKNS_6TensorERS7_E *ZN11onnxruntime4ScanILi9EEC1ERKNS_12OpKernelInfoEEUlPvmE_ N11onnxruntime4ScanILi9EEE PFN11onnxruntime6common6StatusERKSt6vectorImSaImEERKNS_6TensorERS7_E Number of entries in ' ' was Invalid values in ' is not compatible with !is_concrete_shape_ 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 num_entries < 0 || gsl::narrow_cast<int64_t>(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 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>&, int64_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 ParametricSoftplus 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] ZNK11onnxruntime7contrib4GeluIfE7ComputeEPNS_15OpKernelContextEEUliE_ N11onnxruntime7contrib10ScaledTanhIfEE N11onnxruntime7contrib4GeluIfEE 5?AttnLSTM DEEPCPU_ATTN_LSTM }, 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 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= /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention.cc info.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0 Input 0 is expected to have 3 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute. Input 1 is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0 Input 1 dimension 1 should be 3 times of dimension 0 Input 2 is expected to have 1 dimension, got Input 2 dimension 0 should have same length as dimension 1 of input 1 Input 3 is expected to have 1 dimension, got Inputs 3 and 0 shall have same length at dimension 0 T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] Compute const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::AttentionBase::AttentionBase(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib13AttentionBaseE ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUliE_ ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUliE0_ ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUliE1_ ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUliE2_ N11onnxruntime7contrib9AttentionIfEE @ @ @ @ Shape of Input 0 is expected to have at least 1 dimension, got Input 1 is expected to have 1 dimension, got dimension 0 of Input 1 should have same length as the last dimension of input 0 /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/bias_gelu_fusion.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] Compute ZNK11onnxruntime7contrib8BiasGeluIfE7ComputeEPNS_15OpKernelContextEEUliE_ N11onnxruntime7contrib8BiasGeluIfEE 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 ZNK11onnxruntime7contrib14EmbedLayerNormIfE7ComputeEPNS_15OpKernelContextEEUliE_ 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 position_embedding shall have same dimension 1 word_embedding and segment_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] sqeuclidean CDist /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] 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 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] ZN11onnxruntime7contrib20CropAndResizeForwardIfEEvRKNS_11TensorShapeEPKT_fllS7_lPS5_RKSsPKiPNS_11concurrency10ThreadPoolEEUliE_ N11onnxruntime7contrib13CropAndResizeIfEE DynamicSlice Scale /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 leaky_relu_alpha onnxruntime::Gemm<T>::Gemm(const onnxruntime::OpKernelInfo&) [with T = float] 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 /onnxruntime_src/onnxruntime/contrib_ops/cpu/layer_norm.cc op_kernel_info.GetAttr("axis", &axis_).IsOK() T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] 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>::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::contrib::LayerNorm<T>::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = float] ZNK11onnxruntime7contrib9LayerNormIdE7ComputeEPNS_15OpKernelContextEEUliE_ ZNK11onnxruntime7contrib9LayerNormIfE7ComputeEPNS_15OpKernelContextEEUliE_ N11onnxruntime7contrib9LayerNormIfEE N11onnxruntime7contrib9LayerNormIdEE 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 const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime7contrib15MaxpoolWithMaskE 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 /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 X_rank == 4 channels_ <= X_shape[1] X_shape.NumDimensions() == 4 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() 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) Not satisfied: Y->Shape() == sum_shape
+ output and sum shape must match 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 const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] virtual onnxruntime::common::Status onnxruntime::contrib::ReorderInput::Compute(onnxruntime::OpKernelContext*) const 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 y_scale 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 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] ZNK11onnxruntime7contrib13SkipLayerNormIdE7ComputeEPNS_15OpKernelContextEEUliE_ ZNK11onnxruntime7contrib13SkipLayerNormIfE7ComputeEPNS_15OpKernelContextEEUliE_ N11onnxruntime7contrib13SkipLayerNormIfEE N11onnxruntime7contrib13SkipLayerNormIdEE q=̼+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 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 embedding_size attribute: conv filter size: conv_window_size attribute: conv kernal size 1: Char embedding size: Conv kernal size 2 : 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. /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 hӥhӥhӥhReal 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 unimplemented activation: activation_params count mismatch SSE, 12x4, depth 2 allocation failure gemmlowp error: %s
+ N8gemmlowp10KernelBaseE N8gemmlowp24SSE4_64_Kernel12x4Depth2E N8gemmlowp17DefaultKernelImplILb0ELb0EEE N8gemmlowp4TaskE N8gemmlowp13DefaultKernelINS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEES3_EEEE N8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE1ELSD_1ELSD_1ENS_9VectorDupIKiLNS_11VectorShapeE0EEENSE_ISF_LSG_1EEESt5tupleIINS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEE N8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE1ELSD_1ELSD_1ENS_9VectorDupIKiLNS_11VectorShapeE0EEENSE_ISF_LSG_1EEESt5tupleIINS_23OutputStageBiasAdditionINS_9VectorMapISF_LSG_0EEEEENS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEE N8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE0ELSD_0ELSD_0ENS_9VectorDupIKiLNS_11VectorShapeE1EEENSE_ISF_LSG_0EEESt5tupleIINS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEE N8gemmlowp21GemmWithPackedRhsTaskINS_12KernelFormatINS_16KernelSideFormatINS_10CellFormatILi4ELi2ELNS_9CellOrderE1EEELi3EEENS2_IS5_Li1EEEEEhhNS_14BitDepthParamsINS_12OperandRangeILi0ELi255EEESB_EELNS_8MapOrderE0ELSD_0ELSD_0ENS_9VectorDupIKiLNS_11VectorShapeE1EEENSE_ISF_LSG_0EEESt5tupleIINS_23OutputStageBiasAdditionINS_9VectorMapISF_LSG_1EEEEENS_40OutputStageQuantizeDownInt32ByFixedPointENS_32OutputStageSaturatingCastToUint8EEENS_11GemmContextEEE @ @ Gemv float found an unexpected CBLAS_TRANSPOSE input of /onnxruntime_src/onnxruntime/core/util/math_cpu.cc void onnxruntime::math::Im2colNd<T, 2>::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, bool, T) [with T = float; int64_t = long int] OrtMemoryInfo: [ id: mem_type: alloc_type: Cuda CudaPinned Specified device is not supported. /onnxruntime_src/onnxruntime/core/framework/allocator.cc CalcMemSizeForArrayWithAlignment N11onnxruntime12CPUAllocatorE St14default_deleteIN11onnxruntime8BFCArenaEE St19_Sp_counted_deleterIPN11onnxruntime8BFCArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE /onnxruntime_src/onnxruntime/core/framework/bfc_arena.cc /onnxruntime_src/onnxruntime/core/framework/bfc_arena.h !c->in_use() && (c->bin_num != kInvalidBinNum) !c->in_use() && (c->bin_num == kInvalidBinNum) p_int < base_int + memory_size_ !c1->in_use() && !c2->in_use() bin->free_chunks.count(h) == 1 BinForSize(bin_size) == BinFromIndex(b) BinForSize(bin_size + 255) == BinFromIndex(b) BinForSize(bin_size * 2 - 1) == BinFromIndex(b) BinForSize(bin_size * 2) != BinFromIndex(b) 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: BFC Arena ran out of memory trying . Current allocation summary follows. reserved_chunks_.find(ptr) == reserved_chunks_.end() Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: MaxAllocSize: Could not find Region for h < chunks_.size() Size: | Requested Size: | in_use: p_int >= base_int c2->prev == h1 h != kInvalidChunkHandle c->bin_num == bin_num Creating BFCArena for Creating bins of max chunk size !chunk->in_use() cudaMalloc 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: , prev: , next: 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: to allocate 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) AllocateRawInternal virtual void* onnxruntime::BFCArena::Reserve(size_t) int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void*) const RegionFor onnxruntime::BFCArena::AllocationRegion::AllocationRegion(void*, size_t) Extend bool onnxruntime::BFCArena::Extend(size_t) onnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::ChunkFromHandle(onnxruntime::BFCArena::ChunkHandle) onnxruntime::BFCArena::BFCArena(std::unique_ptr<onnxruntime::IDeviceAllocator>, size_t) BFCArena N11onnxruntime15IArenaAllocatorE N11onnxruntime8BFCArenaE fff?/onnxruntime_src/onnxruntime/core/framework/data_transfer.cc nullptr != src && nullptr != dst src.SizeInBytes() == dst.SizeInBytes() virtual onnxruntime::common::Status onnxruntime::CPUDataTransfer::CopyTensor(const onnxruntime::Tensor&, onnxruntime::Tensor&, int) const CopyTensors virtual onnxruntime::common::Status onnxruntime::IDataTransfer::CopyTensors(const onnxruntime::Tensor*, onnxruntime::Tensor*, int) const N11onnxruntime13IDataTransferE N11onnxruntime15CPUDataTransferE Device: [ memory_type: device_id: Tensor size mismatch There's no data transfer registered for copying tensors from data_transfer registered is nullptr. (null) tensor(bfloat16) Not implemented sparse tensor type elem_proto != nullptr value_proto != nullptr MLDataType for: dIJJIIIIIIIIXJHJ8JdIdI(J /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 XdXXX XWXWDW\`\\\ \[\[P[ddtďԏ 4Ldd< 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 Caught exception while destructing CustomOpsLoader with message: /onnxruntime_src/onnxruntime/core/framework/ex_lib_loader.cc Caught exception while loading custom ops with message: Unloading DSO Failed to unload DSO: A dso with name has already been loaded. LoadExternalLib ~ExLibLoader N11onnxruntime11ExLibLoaderE 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_ /onnxruntime_src/onnxruntime/core/framework/execution_frame.cc ort_value_idx >= 0 && static_cast<size_t>(ort_value_idx) < alloc_plan.size() TraceAllocation for ort_value_idx= OrtValue shape verification failed. Current shape: shape && tensor.Shape() == *shape 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. entries which doesn't match the number of fetches the frame was initialized with of Fetches vector passed to GetOutputs contains feeds.size() == feed_mlvalue_idxs.size() fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() 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() 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 , block not found in target location. 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(mem_patterns_->locations[i]) == buffers_.end() size= TraceFree for ort_value_idx= Requested shape: invalid index ort_value.Fence() == nullptr For ort_value with index: but the actually size is: Invalid allocation kind: mlvalue.Fence() == nullptr 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) T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] AllocateMLValueTensorPreAllocateBuffer AllocateMLValueTensorSelfOwnBufferHelper onnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue&, int, onnxruntime::MLDataType, const OrtMemoryInfo&, const onnxruntime::TensorShape&, bool) 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&) int onnxruntime::NodeIndexInfo::GetMLValueIndex(int) const const OrtValue& onnxruntime::IExecutionFrame::GetMLValue(int) const const T& OrtValue::Get() const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(int, const onnxruntime::TensorShape*, OrtValue*&, size_t) onnxruntime::IExecutionFrame::IExecutionFrame(const std::vector<int>&, const std::vector<OrtValue>&, const std::unordered_map<int, OrtValue>&, const std::vector<int>&, const std::vector<OrtValue>&, const onnxruntime::OrtValueNameIdxMap&, const onnxruntime::NodeIndexInfo&) N11onnxruntime15IExecutionFrameE N11onnxruntime14ExecutionFrameE duplicated allocator /onnxruntime_src/onnxruntime/core/framework/execution_provider.cc 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 /onnxruntime_src/onnxruntime/core/framework/func_kernel.h create_func(&context, &func_state_) == 0 /onnxruntime_src/onnxruntime/core/framework/graph_partitioner.cc Provider doesn't return correct number of compiled functions node_compute_funcs.size() == nodes_need_compile.size() No provider specified. 1 == capability->nodes.size() onnxruntime::FunctionKernel::FunctionKernel(const onnxruntime::OpKernelInfo&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const onnxruntime::Node* onnxruntime::PlaceNode(onnxruntime::Graph&, std::unique_ptr<onnxruntime::IndexedSubGraph>, const onnxruntime::KernelRegistryManager&, const string&, int&) onnxruntime::common::Status onnxruntime::GraphPartitioner::Partition(onnxruntime::Graph&, bool, onnxruntime::FuncManager&) const Partition N11onnxruntime14FunctionKernelE Op: Incompatible types. kernel def can't be NULL Failed to add kernel for Version mismatch. node_version: kernel start version: kernel_end_version: kernel is not supported in Failed to find kernel for : Conflicting with a registered kernel with op versions. /onnxruntime_src/onnxruntime/core/framework/kernel_registry.cc len <= op_schema.inputs().size() Failed to create kernel for op: since it was ill-formed during registration Encountered following errors: ( TryFindKernel void onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::__lambda10; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::__lambda11] void onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::__lambda8; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::__lambda9] The node is not placed on any Execution Provider, therefore, can't find a suitable kernel for The node is not placed on any Execution Provider (node 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&) /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]::__lambda9 onnxruntime::NodeIndexInfo::Init(const TValidNodes&, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap&) [with TValidNodes = onnxruntime::GraphNodes; onnxruntime::NodeIndex = long unsigned int]::__lambda9 ZN11onnxruntime13NodeIndexInfo4InitINS_10GraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ ZN11onnxruntime13NodeIndexInfo4InitINS_10GraphNodesEEEvRKT_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 \++**\*+ Tensor types should have been handled already 0,P,0,0,++0,,P,8L988<8<88$9L9 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:' attr != nullptr 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()) Attibute name and type don't match /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()) 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. static onnxruntime::OrtValueTensorSlicer<T> onnxruntime::OrtValueTensorSlicer<T>::Create(T&, int64_t, int64_t) [with T = const OrtValue; 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] node_index_info_ with error: duplicated ort_value index: SaveMLValueNameIndexMapping is used by node ) and node Entry exists in node for attribute SetGraphAndCreateKernels must be called prior to GetExecutionInfo. /onnxruntime_src/onnxruntime/core/framework/session_state.cc Failed to find input name in the mapping: Failed to find output name in the mapping: Could not create kernel for node: as there's no execution provider allocated. Kernel creation failed for node: . Do you have duplicated calls to SessionState::AddInitializedTensor function? Done saving OrtValue mappings. 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() 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&) SetGraph /onnxruntime_src/onnxruntime/core/framework/session_state_initializer.cc OrtValue indexes should have been populated. ort_value_name_idx_map.MaxIdx() > -1 Invalid allocation info. Provider name = string tensor is not supported for copying between allocators Internal error. The preallocated buffer is too small. Requires Failed to copy tensor to execution provider: Done saving initialized tensors Execution plan was not found in SessionState. CreatePlan must be called first. Saving initialized tensors. , Got Deserialize tensor Subgraph Graph exec_plan_ptr input with name is not used by any node. ForEachWithIndex operator() operator() SaveInputOutputNamesToNodeMapping DeserializeTensorProto onnxruntime::common::Status onnxruntime::SaveInitializedTensors(const onnxruntime::Env&, const std::basic_string<char>&, const onnxruntime::Graph&, const onnxruntime::ExecutionProviders&, const onnxruntime::OrtValueNameIdxMap&, onnxruntime::ITensorAllocator*, const T&, const onnxruntime::logging::Logger&, const onnxruntime::DataTransferManager&) [with T = onnxruntime::SessionStateInitializer::CreatePlan(const onnxruntime::Node*, const onnxruntime::ConstPointerContainer<std::vector<onnxruntime::NodeArg*> >*, ExecutionMode)::__lambda18] SaveInitializedTensors onnxruntime::common::Status onnxruntime::SessionStateInitializer::CreatePlan(const onnxruntime::Node*, const onnxruntime::ConstPointerContainer<std::vector<onnxruntime::NodeArg*> >*, ExecutionMode) CreatePlan *ZN11onnxruntimeL33SaveInputOutputNamesToNodeMappingERKNS_5GraphERKNS_21KernelRegistryManagerERNS_12SessionStateEPKNS_21ConstPointerContainerISt6vectorIPNS_7NodeArgESaISB_EEEEEUlRKSA_mE_ *ZN11onnxruntimeL33SaveInputOutputNamesToNodeMappingERKNS_5GraphERKNS_21KernelRegistryManagerERNS_12SessionStateEPKNS_21ConstPointerContainerISt6vectorIPNS_7NodeArgESaISB_EEEEEUlRKSA_mE0_ N11onnxruntime25ISequentialPlannerContextE N11onnxruntime24SequentialPlannerContextE 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>, ptrdiff_t) 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 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
+
const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] OrtStatus* OrtApis::GetTensorTypeAndShape(const OrtValue*, OrtTensorTypeAndShapeInfo**) Invalid TensorProto ) in proto data overflow UnpackTensor: the pre-allocate size does not match the size in proto source and destination buffer size mismatch /onnxruntime_src/onnxruntime/core/framework/endian_utils.h Not satisfied: source_bytes.size_bytes() == destination.size_bytes()
+ UnpackTensor: the pre-allocated size does not match the raw data size, expected /onnxruntime_src/onnxruntime/core/framework/tensorprotoutils.cc ) 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: :ݝݝݝ::ݝJ..՞՞.՞՞JJ]7}
t I
+
GetFileContent TensorProtoToMLValue ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData ReadLittleEndian UnpackTensorWithRawData NnapiExecutionProvider invalid allocator copy_info.size() == num_feeds /onnxruntime_src/onnxruntime/core/framework/utils.cc feed_locations.size() == copy_info.size() fetch_alloc_info.size() == copy_info.size() Only one thread was configured for parallel execution. Hence will use sequential execution. CopyOutputsAcrossDevices CopyInputsAcrossDevices onnxruntime::common::Status onnxruntime::utils::CopyInputsAcrossDevices(const std::vector<OrtValue>&, std::vector<OrtValue>&, const std::vector<onnxruntime::MLValueCopyInfo>&, const onnxruntime::DataTransferManager&) ExecuteGraphImpl ExecuteGraph T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] CopyMLValue const T& OrtValue::Get() const [with T = onnxruntime::Tensor] bool onnxruntime::utils::FinalizeCopyInfoForFetches(const onnxruntime::SessionState&, 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 /onnxruntime_src/onnxruntime/core/framework/allocation_planner.cc 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() kernel_create_info != nullptr && kernel_create_info->kernel_def != nullptr (ort_value_idx) output_name : <allocation plan>
No suitable kernel definition found for op Can not find the execution provider Allocate AllocateStatically PreExisting Reuse AllocateOutput Share nullptr != tensor_type_base reused != reused_for Can not find the node 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 onnxruntime::OrtValueIndex& onnxruntime::PlannerImpl::Buffer(onnxruntime::OrtValueIndex) 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&) ForEachWithIndex ComputeUseCounts CreatePlan N11onnxruntime17ExecutionPlanBaseE N11onnxruntime23SequentialExecutionPlanE ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEmE_ ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEbE0_ ZN11onnxruntime11PlannerImpl22GeneratePlanForWeightsEvEUlRKNS_7NodeArgEmE_ func info for node: already exist. Can't use func with null ptr /onnxruntime_src/onnxruntime/core/framework/fuse_nodes_funcs.cc GetFuncs *ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlS6_S9_SB_E_ *ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlSG_SH_E0_ *ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlS6_E1_ 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 SequentialExecutor::Execute /onnxruntime_src/onnxruntime/core/framework/sequential_executor.cc const T& OrtValue::Get() const [with T = onnxruntime::Tensor] 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 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! /onnxruntime_src/onnxruntime/core/graph/automl_ops/automl_defs.cc The input represents a number of seconds passed since the epoch, suitable to properly constructan instance of std::chrono::system_clock::time_point The output which is a Microsoft::DateTimeFeaturizer::TimePoint structure Constrain input type to int64 scalar tensor. Constrain output type to an AutoML specific Microsoft::Featurizers::TimePoint typecurrently not part of ONNX standard. When it becomes a part of the standard we will adjust thiskernel definition and move it to ONNX repo opaque(com.microsoft.automl,DateTimeFeaturizer_TimePoint) [ShapeInferenceError] Input's shape must be 4-D ) + bottom_border ( ) + right_border ( ) + scale[0] ( ) + scale[1] ( tensor rank too small Number of attention heads mask_index input_ids segment_ids 2D with shape (,hidden_size) word_embedding 2D with shape (, hidden_size) position_embedding segment_embedding bias tensor skip 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 Strings to tokenize Tokenized strings N-dimensional matrix A N-dimensional matrix B ReduceSumInteger An input tensor. Reduced output tensor. reduced 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 Scale tensor. Bias tensor. Output data tensor. inv_std_var The input data as Tensor. The output. The normal input data. Attribute should specify a shape Input axis is invalid: Constrain input and output types to float tensors 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 '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 inputs are expected to have tensor type and output type should not be null. both data and indices tensor need to have rank larger than zero. last dimension of indices must not be larger and rank of data tensor 'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64 /onnxruntime_src/onnxruntime/core/graph/contrib_ops/contrib_defs.cc Constrain input and output types to float/quantized tensors 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 index with shape (batch_size) 3D output tensor with shape (batch_size, sequence_length, hidden_size) Constrain input and output types to float tensors. Constrain mask index to integer types 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 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 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 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 'y', 'x_scale' to float tensors. Constrain 'x_zero_point' and 'x' to 8-bit integer tensors. 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 (http://pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set. an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string. Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be 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). 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 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). The epsilon value to use to avoid division by zero. Input data tensor from the previous layer. Saved mean used during training to speed up gradient computation Saved inverse standard variance used during training to speed up gradient computation. Constrain input and output types (except mean and inv_std_var) to float tensors. Constrain mean and inv_std_var to be float tensors. The bias input data that is a 1D tensor. Inputs 0 shall be 2 dimensions word_embedding should have 2 dimensions and dimension size is known. Negative values are not allowed in a shape specification FvRN4onnx8OpSchemaEE *ZN11onnxruntime7contrib26NchwcPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ *ZN11onnxruntime7contrib32NchwcGlobalPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ *ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE10_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE11_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE12_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE13_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE14_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE15_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE16_ PFvRN4onnx8OpSchemaEE 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() Not satsified: dims_eq()
- Constant !found node_arg Removing initializer ' Can't remove node Target= Dimension= ' source: target: Output: Type mismatch. Current= Input= but different TensorProto. existing->second == &tensor graph_proto cannot be null graph_proto != nullptr constant_attribute.has_t() 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 ( does not match its type. does not match. Outer scope node arg name ' Fatal error: ) attribute ( inputs but subgraph has inputs and requires Subgraph input missing type. ldDH@ /onnxruntime_src/onnxruntime/core/graph/graph.cc This API is not supported when model is loaded from proto file right now. /onnxruntime_src/include/onnxruntime/core/common/const_pointer_container.h Graph attribute inferencing failed: 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. 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. No Graph instance was found for attribute Error: Duplicate definition-site for ( Failed to find existing initializer with name Not satsified: name_to_initializer_it != name_to_initial_tensor_.end()
- Replacement tensor's data type does not match. Not satsified: old_initializer.data_type() == new_initializer.data_type()
- graph_proto_ is not in sync with name_to_initial_tensor_ existing_entry != mutable_initializers.pointer_end() Replacement tensor's dimensions do not match. This is an invalid model. Error: two nodes with same node name ( This is an invalid model. Error: Duplicate definition of name ( This is an invalid model. Error: the graph is not acyclic. graph_proto_ is not in sync with name_to_initial_tensor_. Graph ctor should have created NodeArg for initializer. This is an invalid model. Graph output ( ) does not exist in the graph. 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. 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= 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. AddInitializedTensor already has tensor with name Only 'value' attribute is supported within a 'Constant' node in ORT nullptr != sub_graph && nullptr != sub_graph->GetMetaDef() 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 ( This is an invalid model. Model input ( ) does not have type information. Type Error: Value of initializer Type Error: Shape of initializer '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() void onnxruntime::Graph::SetOutputs(const std::vector<const onnxruntime::NodeArg*>&) void onnxruntime::Graph::SetInputs(const std::vector<const onnxruntime::NodeArg*>&) InlineFunction onnxruntime::Node& onnxruntime::Graph::FuseSubGraph(std::unique_ptr<onnxruntime::IndexedSubGraph>, const string&) gsl::not_null<onnxruntime::Node*> onnxruntime::Graph::AllocateNode() onnxruntime::common::Status onnxruntime::Graph::SetGraphInputsOutputs() CleanUnusedInitializers void onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto&) const bool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex) void onnxruntime::Graph::RemoveInitializedTensor(const string&) void onnxruntime::Graph::AddInitializedTensor(const onnx::TensorProto&) onnxruntime::common::Status onnxruntime::Graph::Resolve(bool) Resolve ForThisAndAllSubgraphs PerformTypeAndShapeInferencing InitInputsInitializersOutputs VerifyNodeAndOpMatch 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] onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const BuildConnections void onnxruntime::Graph::RemoveEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int) void onnxruntime::Graph::AddEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int) SetOuterScopeNodeArgs Graph onnxruntime::Graph::Graph(onnx::GraphProto*, const std::unordered_map<std::basic_string<char>, int>&, onnxruntime::Version, onnxruntime::IOnnxRuntimeOpSchemaCollectionPtr, onnxruntime::Graph*, const onnxruntime::Node*, const onnxruntime::logging::Logger&, const std::unordered_map<std::basic_string<char>, const onnx::FunctionProto*>&) MergeShapeInfo UpdateTypeAndShape FN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_E *ZN11onnxruntime5Graph7ResolveEbEUlRS0_E0_ *ZN11onnxruntime5Graph7ResolveEbEUlRS0_E1_ *ZN11onnxruntime5Graph7ResolveEbEUlRS0_E2_ N4onnx15GraphInferencerE N4onnx16InferenceContextE N11onnxruntime5GraphE N11onnxruntime19GraphInferencerImplE N11onnxruntime20InferenceContextImplE PFN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_E ExplicitInputs: . Index: Invalid input index for node ImplicitInputs: Implicit input name cannot be safely updated to in one of the subgraphs. -> /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) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const *ZN11onnxruntime11GraphViewerC1ERKNS_5GraphEEUlPKNS_4NodeEE_ N11onnxruntime11NodeCompareE Protobuf parsing failed. ModelProto was null. <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 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. /onnxruntime_src/onnxruntime/core/graph/model.cc No graph was found in the protobuf. Failed to load model with error: Protobuf serialization failed. Save SaveModel Load Model St15_Sp_counted_ptrIPN11onnxruntime5ModelELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime21SchemaRegistryManagerESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE Domain already set in registry , but it its version is higher than the operator set version /onnxruntime_src/onnxruntime/core/graph/schema_registry.cc RegisterOpSet N11onnxruntime27OnnxRuntimeOpSchemaRegistryE N11onnxruntime21SchemaRegistryManagerE sequence_lens initial_h initial_c QW MW memory_seq_lens AW Y_h Y_c 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. 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. Couple the input and forget gates if 1, default 0. Number of neurons in the hidden layer. Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional. 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]`. 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. 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]`. The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`. fused_function_subgraph input_arg->Type() != nullptr Resolve subgraph failed: 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 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::FunctionImpl::FunctionImpl(const onnxruntime::Graph&, const NodeIndex&, const onnx::FunctionProto&, const onnxruntime::logging::Logger&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const onnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph&, std::unique_ptr<onnxruntime::IndexedSubGraph>, const onnxruntime::logging::Logger&) *ZN11onnxruntime12FunctionImplC1ERKNS_5GraphERKmRKN4onnx13FunctionProtoERKNS_7logging6LoggerEEUlRNS6_16InferenceContextEE_ N11onnxruntime8FunctionE N11onnxruntime12FunctionImplE Default logger already set. ISink must be provided. System onnxruntime 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. N@VIWEF is not a valid year is not a valid day is not a valid date N11onnxruntime7logging11OStreamSinkE Writing profiler data to file /onnxruntime_src/onnxruntime/core/common/profiler.cc Maximum number of events reached, could not record profile event. [
+ ) , use fence when async Index out-of-range! nullptr != node Free ml-values: INVALID INDEX vector<bool>::_M_fill_insert onnxruntime::OrtValueIndex& onnxruntime::PlannerImpl::Buffer(onnxruntime::OrtValueIndex) 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&) ForEachWithIndex ComputeUseCounts CreatePlan N11onnxruntime17ExecutionPlanBaseE N11onnxruntime23SequentialExecutionPlanE ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEmE_ ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEbE0_ ZN11onnxruntime11PlannerImpl22GeneratePlanForWeightsEvEUlRKNS_7NodeArgEmE_ 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 *ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlS6_S9_SB_E_ *ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlSG_SH_E0_ *ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlS6_E1_ 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 SequentialExecutor::Execute /onnxruntime_src/onnxruntime/core/framework/sequential_executor.cc const T& OrtValue::Get() const [with T = onnxruntime::Tensor] 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 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!
+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. 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 First input does not have rank 2 Second input does not have rank 2 '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 ( inputs are expected to have tensor type and output type should not be null. both data and indices tensor need to have rank larger than zero. last dimension of indices must not be larger and rank of data tensor 'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64 /onnxruntime_src/onnxruntime/core/graph/contrib_ops/contrib_defs.cc 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 index with shape (batch_size) 3D output tensor with shape (batch_size, sequence_length, hidden_size) Constrain input and output types to float tensors. Constrain mask index to integer types 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 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 'y', 'x_scale' to float tensors. Constrain 'x_zero_point' and 'x' to 8-bit integer tensors. 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 (http://pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set. an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string. Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be 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). 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. 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. Constrain input types to 8 bit signed and unsigned tensors. Constrain output to 32 bit tensor Constrain output types to 32 bit tensors. Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad. The size of the kernel along each axis. Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis. Whether to use ceil or floor (default) to compute the output shape. Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...]. Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used Constrain input and output types to 8 bit tensors. 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). The epsilon value to use to avoid division by zero. 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 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 inputs are expected to have tensor type. Inputs 0 shall be 2 dimensions word_embedding should have 2 dimensions and dimension size is known. 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 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. {name} {additionalDocumentation} First operand. A_scale A_zero_point Second operand. B_scale B_zero_point C_scale C_zero_point [ShapeInferenceError] Input's shape must be 4-D ) + bottom_border ( ) + right_border ( ) + scale[0] ( ) + scale[1] ( Number of attention heads mask_index input_ids segment_ids 2D with shape (,hidden_size) word_embedding 2D with shape (, hidden_size) position_embedding segment_embedding bias tensor skip 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 Strings to tokenize Tokenized strings N-dimensional matrix A N-dimensional matrix B ReduceSumInteger An input tensor. Reduced output tensor. reduced addition QLinearAdd multiplication QLinearMul QLinearReduceMean data_scale data_zero_point reduced_scale reduced_zero_point QLinearAveragePool 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 Scale tensor. Bias tensor. Output data tensor. The input data as Tensor. The output. The normal input data. Attribute should specify a shape Input axis is invalid: Incompatible dimensions *ZZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_ENKUlRN4onnx8OpSchemaEE_clES5_EUlRNS3_16InferenceContextEE_ *ZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_EUlRN4onnx8OpSchemaEE_ *ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE10_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE11_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE12_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE13_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE14_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE15_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE16_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE17_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE18_ tensor rank too small /onnxruntime_src/onnxruntime/core/graph/contrib_ops/nchwc_schema_defs.cc FvRN4onnx8OpSchemaEE *ZN11onnxruntime7contrib26NchwcPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ *ZN11onnxruntime7contrib32NchwcGlobalPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ *ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE0_ PFvRN4onnx8OpSchemaEE 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() Not satisfied: dims_eq()
+ Constant !found node_arg Removing initializer ' Can't remove node Target= Dimension= ' source: target: Output: Type mismatch. Current= Input= but different TensorProto. existing->second == &tensor graph_proto cannot be null graph_proto != nullptr constant_attribute.has_t() 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 ( ' has element type does not match. Outer scope node arg name ' Fatal error: ) attribute ( inputs but subgraph has inputs and requires Subgraph input missing type. Dd< /onnxruntime_src/onnxruntime/core/graph/graph.cc This API is not supported when model is loaded from proto file right now. /onnxruntime_src/include/onnxruntime/core/common/const_pointer_container.h Graph attribute inferencing failed: 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. 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. No Graph instance was found for attribute Error: Duplicate definition-site for ( Failed to find existing initializer with name Not satisfied: name_to_initializer_it != name_to_initial_tensor_.end()
+ Replacement tensor's data type does not match. Not satisfied: old_initializer.data_type() == new_initializer.data_type()
+ graph_proto_ is not in sync with name_to_initial_tensor_ existing_entry != mutable_initializers.pointer_end() Replacement tensor's dimensions do not match. This is an invalid model. Error: two nodes with same node name ( This is an invalid model. Error: Duplicate definition of name ( This is an invalid model. Error: the graph is not acyclic. graph_proto_ is not in sync with name_to_initial_tensor_. Graph ctor should have created NodeArg for initializer. This is an invalid model. Graph output ( ) does not exist in the graph. 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. 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= 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. AddInitializedTensor already has tensor with name Only 'value' attribute is supported within a 'Constant' node in ORT nullptr != sub_graph && nullptr != sub_graph->GetMetaDef() 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 ( 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 '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() void onnxruntime::Graph::SetOutputs(const std::vector<const onnxruntime::NodeArg*>&) void onnxruntime::Graph::SetInputs(const std::vector<const onnxruntime::NodeArg*>&) InlineFunction onnxruntime::Node& onnxruntime::Graph::FuseSubGraph(std::unique_ptr<onnxruntime::IndexedSubGraph>, const string&) gsl::not_null<onnxruntime::Node*> onnxruntime::Graph::AllocateNode() onnxruntime::common::Status onnxruntime::Graph::SetGraphInputsOutputs() CleanUnusedInitializers void onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto&) const bool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex) void onnxruntime::Graph::RemoveInitializedTensor(const string&) void onnxruntime::Graph::AddInitializedTensor(const onnx::TensorProto&) onnxruntime::common::Status onnxruntime::Graph::Resolve(bool) Resolve ForThisAndAllSubgraphs PerformTypeAndShapeInferencing InitInputsInitializersOutputs VerifyNodeAndOpMatch 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] onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const BuildConnections void onnxruntime::Graph::RemoveEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int) void onnxruntime::Graph::AddEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int) SetOuterScopeNodeArgs Graph onnxruntime::Graph::Graph(onnx::GraphProto*, const std::unordered_map<std::basic_string<char>, int>&, onnxruntime::Version, onnxruntime::IOnnxRuntimeOpSchemaCollectionPtr, onnxruntime::Graph*, const onnxruntime::Node*, const onnxruntime::logging::Logger&, const std::unordered_map<std::basic_string<char>, const onnx::FunctionProto*>&) MergeShapeInfo UpdateTypeAndShape FN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_E *ZN11onnxruntime5Graph7ResolveEbEUlRS0_E0_ *ZN11onnxruntime5Graph7ResolveEbEUlRS0_E1_ *ZN11onnxruntime5Graph7ResolveEbEUlRS0_E2_ N4onnx15GraphInferencerE N4onnx16InferenceContextE N11onnxruntime5GraphE N11onnxruntime19GraphInferencerImplE N11onnxruntime20InferenceContextImplE PFN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_E ExplicitInputs: . Index: Invalid input index for node ImplicitInputs: Implicit input name cannot be safely updated to in one of the subgraphs. -> /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) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const *ZN11onnxruntime11GraphViewerC1ERKNS_5GraphEEUlPKNS_4NodeEE_ N11onnxruntime11NodeCompareE Protobuf parsing failed. <p_fd> less than 0. failed. File doesn't exist Load model system error number ModelProto was null. <p_fd> is less than 0. Invalid istream object. Null model_proto ptr. 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. /onnxruntime_src/onnxruntime/core/graph/model.cc No graph was found in the protobuf. Failed to load model with error: Protobuf serialization failed. Save SaveModel Load Model St15_Sp_counted_ptrIPN11onnxruntime5ModelELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime21SchemaRegistryManagerESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE Domain already set in registry , but it its version is higher than the operator set version /onnxruntime_src/onnxruntime/core/graph/schema_registry.cc RegisterOpSet N11onnxruntime27OnnxRuntimeOpSchemaRegistryE N11onnxruntime21SchemaRegistryManagerE sequence_lens initial_h initial_c QW MW memory_seq_lens AW Y_h Y_c 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. 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. Couple the input and forget gates if 1, default 0. Number of neurons in the hidden layer. Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional. 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]`. 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. 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]`. The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`. fused_function_subgraph input_arg->Type() != nullptr Resolve subgraph failed: 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 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::FunctionImpl::FunctionImpl(const onnxruntime::Graph&, const NodeIndex&, const onnx::FunctionProto&, const onnxruntime::logging::Logger&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const onnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph&, std::unique_ptr<onnxruntime::IndexedSubGraph>, const onnxruntime::logging::Logger&) *ZN11onnxruntime12FunctionImplC1ERKNS_5GraphERKmRKN4onnx13FunctionProtoERKNS_7logging6LoggerEEUlRNS6_16InferenceContextEE_ N11onnxruntime8FunctionE N11onnxruntime12FunctionImplE Default logger already set. ISink must be provided. System onnxruntime 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. N@VIWEF is not a valid year is not a valid day is not a valid date N11onnxruntime7logging11OStreamSinkE Writing profiler data to file /onnxruntime_src/onnxruntime/core/common/profiler.cc Maximum number of events reached, could not record profile event. [
]
{"cat" : " "pid" : "tid" : "dur" : "ts" : "ph" : "X", "name" :" "args" : { }
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- 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] p_N=,
/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&) *ZN11onnxruntime11concurrency10ThreadPool11ParallelForEiSt8functionIFviEEEUliE_ *ZN11onnxruntime11concurrency10ThreadPool11ParallelForEiSt8functionIFviEEEUlvE0_ *ZN11onnxruntime11concurrency10ThreadPool16BatchParallelForEiSt8functionIFviEEiEUliE_ NSt6thread10_Impl_baseE NSt6thread5_ImplISt12_Bind_simpleIFSt8functionIFvvEEvEEEE ZN5Eigen15ThreadPoolTemplINS_20StlThreadEnvironmentEEC1EibS1_EUlvE_ St23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFSt8functionIFvvEEvEEEESaIS8_ELN9__gnu_cxx12_Lock_policyE2EE lib .so Failed to get symbol Got null library handle file " " failed: close open Failed to load library munmap failed. error code: - error code: fstat Not satsified: file_path
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- mmap lseek read File: , offset: , length: Failed to unload library with error: /onnxruntime_src/onnxruntime/core/platform/posix/env.cc Failed to close file descriptor GetFileLength: input is not a regular file Not satsified: length <= buffer.size()
- ReadFileIntoBuffer - unexpected end of file. ReadFileIntoBuffer CleanUp UnmapFile MapFileIntoMemory *N11onnxruntime12_GLOBAL__N_18PosixEnvE N11onnxruntime3EnvE mutex lock failed OrtCondVar wait failed: mutex not locked OrtCondVar wait failed, error code= OrtCondVar timed_wait failed, error code= <stacktrace not implemented> N11onnxruntime9TelemetryE *N11onnxruntime12_GLOBAL__N_112PosixEnvTimeE N11onnxruntime7EnvTimeE 0A >
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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 <*=*=*=*=*>*>*>*>>>>> *Z19MlasExecuteThreadedPFvPviES_iPN11onnxruntime11concurrency10ThreadPoolEEUliE_ 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 ' Sparse tensor indices ( ) has values, but NNZ is ] not in sorted order. ) index value at position [ Sparse tensor ( ] out of range [0, ] 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. op_type NodeProto (name: , type: ATen ConstantFill No opset import for domain ' No Op registered for with domain_version of Warning: was a removed as possible. ' of node:
+ 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] ~m\K=/! /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&) *ZN11onnxruntime11concurrency10ThreadPool11ParallelForEiSt8functionIFviEEEUliE_ *ZN11onnxruntime11concurrency10ThreadPool11ParallelForEiSt8functionIFviEEEUlvE0_ *ZN11onnxruntime11concurrency10ThreadPool16BatchParallelForEiSt8functionIFviEEiEUliE_ NSt6thread10_Impl_baseE NSt6thread5_ImplISt12_Bind_simpleIFSt8functionIFvvEEvEEEE ZN5Eigen15ThreadPoolTemplINS_20StlThreadEnvironmentEEC1EibS1_EUlvE_ St23_Sp_counted_ptr_inplaceINSt6thread5_ImplISt12_Bind_simpleIFSt8functionIFvvEEvEEEESaIS8_ELN9__gnu_cxx12_Lock_policyE2EE lib .so Failed to get symbol Got null library handle file " " failed: close open Failed to load library munmap failed. error code: - error code: fstat Not satisfied: file_path
+ Not satisfied: offset >= 0
+ mmap lseek read File: , offset: , length: Failed to unload library with error: /onnxruntime_src/onnxruntime/core/platform/posix/env.cc Failed to close file descriptor GetFileLength: input is not a regular file Not satisfied: length <= buffer.size()
+ ReadFileIntoBuffer - unexpected end of file. ReadFileIntoBuffer CleanUp UnmapFile MapFileIntoMemory *N11onnxruntime12_GLOBAL__N_18PosixEnvE N11onnxruntime3EnvE N11onnxruntime9TelemetryE *N11onnxruntime12_GLOBAL__N_112PosixEnvTimeE N11onnxruntime7EnvTimeE 0A >
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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 <*=*=*=*=*>*>*>*>>>>> *Z19MlasExecuteThreadedPFvPviES_iPN11onnxruntime11concurrency10ThreadPoolEEUliE_ 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. op_type NodeProto (name: , type: ATen ConstantFill No opset import for domain ' No Op registered for with domain_version of Warning: was a removed as possible. ' of node:
Bad node spec:
==> Context: attr Attribute (name: since_version function ( 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. ) has zero input and zero output. is deprecated in domain_version of experimental ops. In the future, we may directly reject this operator. Please update your model as soon ' has been used as graph input names multiple times. Graph must be in single static assignment (SSA) form, however ' in initializer but not in graph input 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. 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 ' 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 ' . 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: `H` N4onnx7checker15ValidationErrorE Input type was null Input element type of axis value All Tensor types Condition for the if Only bool v_initial v_final_and_scan_outputs initial_state_and_scan_inputs final_state_and_scan_outputs Int64 tensor Scan input was not a tensor. outputs. Expected was not If node has but subgraphs produce Mismatched type for output then= else= 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 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]. 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 ( Graph attribute inferencing returned type information for Scan 'body' subgraph outputs should all be tensors but output then_branch and else_branch produce different number of outputs. Mismatched tensor element type for output Loop 'body' subgraph outputs should all be tensors but 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 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. bfloat16 double uint8 uint16 uint32 uint64 complex64 complex128 seq( map( sparse_tensor( seq opaque sparse_tensor NJNN4MKN$NIsparse_value not specified Value of attribute Invalid shape value: Constrain input types. Number of times to sample. Input tensor must have rank 2 loop_body_attribute cond_out current 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. 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. 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 tensor must be 2-dimensional Input to 'Range' op should be scalars (Tensor with only one element and shape empty) All inputs to 'Range' op must be of the same type 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. 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. 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 usethe 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. 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_ver11EEENS_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. /onnxruntime_src/cmake/external/onnx/onnx/defs/generator/old.cc *ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_
+ is neither output of any previous nodes nor input of the function. Function must be in single static assignment (SSA) form, however ' . 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: \\\d\P0 N4onnx7checker15ValidationErrorE Input type was null Input element type of axis value All Tensor types Condition for the if Only bool v_initial v_final_and_scan_outputs initial_state_and_scan_inputs final_state_and_scan_outputs Int64 tensor Scan input was not a tensor. outputs. Expected was not If node has but subgraphs produce Mismatched type for output then= else= 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 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]. 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 ( Graph attribute inferencing returned type information for Scan 'body' subgraph outputs should all be tensors but output then_branch and else_branch produce different number of outputs. Mismatched tensor element type for output Loop 'body' subgraph outputs should all be tensors but 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 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. bfloat16 double uint8 uint16 uint32 uint64 complex64 complex128 seq( map( sparse_tensor( seq opaque sparse_tensor ,LG,L,LtJ I,LdKFsparse_value not specified Value of attribute Invalid shape value: Constrain input types. Number of times to sample. Input tensor must have rank 2 loop_body_attribute cond_out current 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. 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. should be of integer type and specify a type. does not specify a valid type. Input tensor must be 2-dimensional Input to 'Range' op should be scalars (Tensor with only one element and shape empty) All inputs to 'Range' op must be of the same type Attribute expected to have tensor type Attribute expected to have a one-dim tensor 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. 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. 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 usethe 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. 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_ver11EEENS_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. /onnxruntime_src/cmake/external/onnx/onnx/defs/generator/old.cc *ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_
Returns the tensor resulted from performing the `{name}` logical operation
elementwise on the input tensors `A` and `B` (with Numpy-style broadcasting support).
{broadcast_doc}
- This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). 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.
+ This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). 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/output to boolean tensors. Constrains input types to all numeric tensors.
Bitwise shift operator performs element-wise operation. For each input element, if the
attribute "direction" is "RIGHT", this operator moves its binary representation toward
the right side so that the input value is effectively decreased. If the attribute "direction"
is "LEFT", bits of binary representation moves toward the left side, which results the
increase of its actual value. The input X is the tensor to be shifted and another input
@@ -5771,11 +6021,11 @@
and S is [1, 1], the corresponding output Z would be [0, 2]. If "direction" is "LEFT" with
X=[1, 2] and S=[1, 2], the corresponding output Y would be [2, 8].
Because this operator supports Numpy-style broadcasting, X's and Y's shapes are
not necessarily identical.
- 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). Constrains input/output to boolean tensors. {name} {broadcast_doc} Result tensor. equal Z less xor greater Incompatible dimensions *ZZN4onnx23BinaryLogicDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx23BinaryLogicDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_19BitShift_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_
+ 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). {broadcast_doc} Result tensor. xor less greater equal Z *ZZN4onnx23BinaryLogicDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx23BinaryLogicDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_19BitShift_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_
Returns the tensor resulted from performing the `{name}` logical operation
elementwise on the input tensors `A` and `B`.
If broadcasting is enabled, the right-hand-side argument will be broadcasted
to match the shape of left-hand-side argument. See the doc of `Add` for a
@@ -5801,11 +6051,11 @@
{broadcast_doc}
This operator supports **unidirectional broadcasting** ( should be unidirectional broadcastable to ); for more details please check [the doc](Broadcasting.md).
Performs element-wise binary {name} (with Numpy-style broadcasting support).
{broadcast_doc}
- Result, has same element type as two inputs Constrain input and output types to high-precision numeric tensors. /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 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
+ Constrain input and output types to high-precision numeric tensors. /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 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
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.
First operand, base of the exponent. Second operand, power of the exponent. Output tensor (same size as X)
PRelu takes input data (Tensor<T>) and slope tensor as input, and produces one
@@ -5821,11 +6071,11 @@
Compute Y = alpha * A' * B' + beta * C, where input tensor A has shape (M, K) or (K, M),
input tensor B has shape (K, N) or (N, K), input tensor C is broadcastable to shape (M, N),
and output tensor Y has shape (M, N). A will be transposed before doing the
computation if attribute transA is non-zero, same for B and transB.
- 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). Constrain input and output types to numeric tensors. 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'. Scale tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'. Constrain input A data type to 8-bit integer tensor. Constrain input B data type to 8-bit integer tensor. Constrain output Y data type as 32-bit integer tensor. If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements. If set to 1 will perform the sums in reverse direction. An input tensor that is to be processed. (Optional) A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back. Output tensor of the same type as 'x' with cumulative sums of the x's elements axis tensor can be int32 or int64 only Constrain input and output types to floating-point tensors. The inner-most 2 dimensions must have the same size (mat_w: 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 Incompatible dimensions for matrix multiplication Input tensors of wrong rank (0). inputs are expected to have tensor type. input and zero_point pair is expected to have be same type. input and zero_point pair is expected to have same type. {description} List of tensors for data_0 Output tensor. 'axis' must be in [ ]. Its actual value is: First operand. Second operand. addition subtraction Dividend tensor Divisor tensor Remainder tensor multiplication division Coefficient of leakage. Coefficient of ELU. input tensor X tensor slope Value of alpha. Value of beta. normalized exponential log of softmax logsoftmax hardmax tensor A * B Values Indices scale of quantized input a a_scale a_zero_point scale of quantized input b b_scale b_zero_point scale of quantized output y Input rank must be >= 2. != mat_h: *ZZN4onnx16MathDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx16MathDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_EUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_13Pow_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx30ElementwiseMultiOpDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx30ElementwiseMultiOpDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24QLinearMatMul_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24MatMulInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Det_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_
+ 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). Constrain input and output types to numeric tensors. 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'. Scale tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'. Constrain input A data type to 8-bit integer tensor. Constrain input B data type to 8-bit integer tensor. Constrain output Y data type as 32-bit integer tensor. If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements. If set to 1 will perform the sums in reverse direction. An input tensor that is to be processed. (Optional) A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back. Output tensor of the same type as 'x' with cumulative sums of the x's elements axis tensor can be int32 or int64 only Constrain input and output types to floating-point tensors. The inner-most 2 dimensions must have the same size (mat_w: 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 Incompatible dimensions for matrix multiplication Input tensors of wrong rank (0). input and zero_point pair is expected to have be same type. input and zero_point pair is expected to have same type. {description} List of tensors for data_0 Output tensor. 'axis' must be in [ ]. Its actual value is: subtraction Dividend tensor Divisor tensor Remainder tensor division Coefficient of leakage. Coefficient of ELU. input tensor X tensor slope Value of alpha. Value of beta. normalized exponential log of softmax logsoftmax hardmax tensor A * B Values Indices scale of quantized input a scale of quantized input b b_scale b_zero_point scale of quantized output y Input rank must be >= 2. != mat_h: *ZZN4onnx16MathDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx16MathDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_EUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_13Pow_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx30ElementwiseMultiOpDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx30ElementwiseMultiOpDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24QLinearMatMul_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24MatMulInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Det_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_
The operator computes the {name} ({description}) values for each layer in the batch
of the given input. The input is a 2-D tensor (Tensor<float>) of size
(batch_size x input_feature_dimensions). The output tensor has the same shape
and contains the {name} values of the corresponding input.
@@ -5887,11 +6137,11 @@
And pad shape will be following if `SAME_UPPER` or `SAME_LOWER`:
```
pad_shape[i] = (output_spatial_shape[i] - 1) * strides_spatial_shape[i] + {kernelSpatialShape} - input_spatial_shape[i]
```
{additionalDescription}
- 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. Wether 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
+ Wether to use ceil or floor (default) to compute the output shape.
{name} consumes an input tensor X and applies Lp pooling across
the tensor according to kernel sizes, stride sizes, and pad lengths.
Lp pooling consisting of computing the Lp norm on all values of a subset
of the input tensor according to the kernel size and downsampling the
data into the output tensor Y for further processing. p value of the Lp norm used to pool over the input data. 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.
@@ -5914,11 +6164,11 @@
Global{op_type} consumes an input tensor X and applies {op} pooling across
the values in the same channel. This is equivalent to {op_type} with kernel size
equal to the spatial dimension of input tensor. 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.
ROI {name} pool consumes an input tensor X and region of interests (RoIs) to
apply {name} pooling across each RoI, to produce output 4-D tensor of shape
- (num_rois, channels, pooled_shape[0], pooled_shape[1]). ROI pool output shape (height, width). Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling. Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...]. RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]). The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero). Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad. /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). Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization. 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. Scale tensor for input 'w'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M) Constrain input x and its zero point data type to 8-bit integer tensor. Constrain input w and its zero point data type to 8-bit integer tensor. Constrain output y data type to 32-bit integer tensor. dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis. Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.
+ (num_rois, channels, pooled_shape[0], pooled_shape[1]). ROI pool output shape (height, width). Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling. Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...]. RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]). The 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). Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization. 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. Scale tensor for input 'w'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M) Constrain input x and its zero point data type to 8-bit integer tensor. Constrain input w and its zero point data type to 8-bit integer tensor. Constrain output y data type to 32-bit integer tensor. dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis. Stride along each spatial axis. If not present, the stride defaults to 1 along each axis.
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)
@@ -5930,11 +6180,11 @@
Dropout takes one input floating tensor and produces two tensor outputs,
output (floating tensor) and mask (`Tensor<bool>`). Depending on whether it is
in test mode or not, the output Y will either be a random dropout, or a simple
copy of the input. Note that our implementation of Dropout does scaling in
the training phase, so during testing nothing needs to be done.
- 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 weight and zero_point pair is expected to have same type. MaxUnpool op must have either two or three inputs. Input tensor X must have atleast 2 dimensions. Attribute pads has incorrect size. Attribute strides has incorrect size. Attribute kernel_shape has incorrect size. Attribute kernel_shape must be specified. 'output_shape' must be rank 1 tensor. 'output_shape' must have same number of elements as the shape of input tensor X. Input tensor must have at least 2 dimensions RoIs tensor must have 2 dimensions Attribute pooled_shape has incorrect length Attribute pooled_shape must be specified 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 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 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. kernel_spatial_shape[i] {opName} {additionalDescription} {kernelSpatialShape} {filter_desc} {op_type} {op} average a filter w_scale w_zero_point lp pool momentum Scale tensor of shape (C). Bias tensor of shape (C). saved_mean saved_var Input matrix Matrix after normalization The ratio of random dropout 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_bENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bEUlRNS_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_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25TfIdfVectorizer_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27StringNormalizer_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_
+ 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 weight and zero_point pair is expected to have same type. MaxUnpool op must have either two or three inputs. Input tensor X must have atleast 2 dimensions. Attribute pads has incorrect size. Attribute strides has incorrect size. Attribute kernel_shape has incorrect size. Attribute kernel_shape must be specified. 'output_shape' must be rank 1 tensor. 'output_shape' must have same number of elements as the shape of input tensor X. Input tensor must have at least 2 dimensions RoIs tensor must have 2 dimensions Attribute pooled_shape has incorrect length Attribute pooled_shape must be specified 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 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. kernel_spatial_shape[i] {opName} {additionalDescription} {kernelSpatialShape} {filter_desc} {op_type} {op} average a filter w_scale w_zero_point lp pool momentum Scale tensor of shape (C). Bias tensor of shape (C). saved_var Input matrix Matrix after normalization The ratio of random dropout 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_bENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bEUlRNS_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_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25TfIdfVectorizer_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27StringNormalizer_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_
{name} consumes an input tensor X and applies {opName} pooling across
the tensor according to kernel sizes, stride sizes, and pad lengths.
{opName} pooling consisting of computing the {opName} on all values of a
subset of the input tensor according to the kernel size and downsampling the
data into the output tensor Y for further processing. The output spatial shape will be following:
@@ -5965,86 +6215,25 @@
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)
- 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 *ZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_ *ZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_bENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_bEUlRNS_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_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 scores 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's a scalar, which means a per-tensor/layer quantization. Zero point for input 'x'. It's a scalar, which means a per-tensor/layer quantization. It's optional. 0 is the default value when it's not specified. Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor. /onnxruntime_src/cmake/external/onnx/onnx/defs/quantization/defs.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. Constrain 'x' to float or int32 tensor. Constrain 'y_zero_point' and 'y' to 8-bit integer tensor. Output scale. It's a scalar, which means a per-tensor/layer quantization. Output zero point. It's a scalar, which means a per-tensor/layer quantization. Constrain 'x' to float tensor. Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor. 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 Quantized output tensor *ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_
+ 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 *ZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_ *ZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_bENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_bEUlRNS_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_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 scores 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's a scalar, which means a per-tensor/layer quantization. Zero point for input 'x'. It's a scalar, which means a per-tensor/layer quantization. It's optional. 0 is the default value when it's not specified. Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor. /onnxruntime_src/cmake/external/onnx/onnx/defs/quantization/defs.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. 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 X_Max_Adjusted X_Range Min_Scaled Initial_ZeroPoint_FP Clipped_ZeroPoint_FP Rounded_ZeroPoint_FP Zeropoint Quantized output tensor *ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_
Computes the {name} of the input tensor's element along the provided axes. The resulted
tensor has the same rank as the input if keepdims equal 1. If keepdims equal 0, then
the resulted tensor have the reduced dimension pruned.
The above behavior is similar to numpy, with the exception that numpy default keepdims to
False instead of True. 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).
Computes the indices of the {name} elements of the input tensor's element along the
provided axis. The resulted tensor has the same rank as the input if keepdims equal 1.
If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.
-The type of the output tensor is integer. The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data). Reduced output tensor with integer data type. 'axis' must be in [-rank(indices), rank(indices)-1] axis must be in [-rank, rank-1]. input rank was /onnxruntime_src/cmake/external/onnx/onnx/defs/reduction/defs.cc product L2 norm sum square log sum L1 norm log sum exponent *ZZN4onnx18ReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx18ReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_
+The type of the output tensor is integer. The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data). Reduced output tensor with integer data type. 'axis' must be in [-rank(indices), rank(indices)-1] /onnxruntime_src/cmake/external/onnx/onnx/defs/reduction/defs.cc log sum log sum exponent L1 norm sum square product L2 norm *ZZN4onnx18ReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx18ReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_
Computes the indices of the {name} elements of the input tensor's element along the
provided axis. The resulted tensor has the same rank as the input if keepdims equal 1.
If keepdims equal 0, then the resulted tensor have the reduced dimension pruned.
-The type of the output tensor is integer. The axis in which to compute the arg indices. /onnxruntime_src/cmake/external/onnx/onnx/defs/reduction/old.cc *ZZN4onnx25ReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx25ReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ Number of neurons in the hidden layer The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`. Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`. 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]`. Constrain seq_lens to integer tensor. 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.
-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)
- 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
+The type of the output tensor is integer. The axis in which to compute the arg indices. /onnxruntime_src/cmake/external/onnx/onnx/defs/reduction/old.cc *ZZN4onnx25ReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx25ReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ Number of neurons in the hidden layer The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`. Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`. 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]`. Constrain seq_lens to integer tensor.
Computes an one-layer LSTM. This operator is usually supported via some
custom implementation such as CuDNN.
Notations:
@@ -6121,11 +6310,11 @@
- Ct = ft (.) Ct-1 + it (.) ct
- ot = f(Xt*(Wo^T) + Ht-1*(Ro^T) + Po (.) Ct + Wbo + Rbo)
- Ht = ot (.) h(Ct)
- Couple the input and forget gates if 1.
+ Couple the input and forget gates if 1. /onnxruntime_src/cmake/external/onnx/onnx/defs/rnn/defs.cc
Computes an one-layer GRU. This operator is usually supported via some custom
implementation such as CuDNN.
Notations:
@@ -6194,11 +6383,72 @@
- 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
- 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 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. Attribute ' has unknown expected type ' is missing. Required attribute ' ) than declared ( ) has more inputs ( ) in op definition. )'s input ) has more outputs ( )'s output ' appeared multiple times. for operator Unrecognized attribute: ONNX Schema (inputs_.size() - 1) == i (outputs_.size() - 1) == i !(it.GetName().empty()) (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. حPP *ZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_ *ZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_ N4onnx16OpSchemaRegistryE 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)) ], 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. 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 positive. 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 the same elem type. Sequence= SequenceConstruct is expected to have at least 1 input. 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_ axis must be in [-r, r-1] 'input' must have rank >= 2 Invalid attribute perm { }, input shape = { Invalid position of 0 Invalid dimension value: Input tensor to be cast. Specified shape for output. Reshaped data. reshaped Shape of the input tensor length of each output 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. output (always 2D tensor) Tensor of rank r >= 2. inverse_indices constant_value Input axes has invalid data 'step' cannot be 0 Blocksize must be positive 'Repeats' input must be 1D tensor of type int64 'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions. axis must be in [-rank, rank-1]. All inputs to Concat must have same rank data tensor must have rank >= 1 Indices tensor must have rank >= 1 OneHot node must have three inputs. Input 'depth' must be a scalar or rank 1 tensor. Input 'depth' must have exactly one element. Input 'values' must be rank 1 tensor. Input 'values' must have exactly two elements. 'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64 Pads has incorrect number of values '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 Target shape may not have multiple -1 dimensions Invalid Target shape product of 0 Dimension could not be inferred: incompatible shapes values in 'axes' are beyond the bounds of the computed output shape 'axes' attribute must not contain any duplicates 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 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`. Default 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. 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). Tensors with at least max(dims) dimensions. 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.
+ 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.
+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)
+ 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()) (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. pؤםpС *ZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_ *ZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_ N4onnx16OpSchemaRegistryE 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)) ], 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. 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 positive. 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 the same elem type. Sequence= SequenceConstruct is expected to have at least 1 input. 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_ axis must be in [-r, r-1] 'input' must have rank >= 2 Invalid attribute perm { }, input shape = { Invalid position of 0 Invalid dimension value: Input tensor to be cast. Specified shape for output. Reshaped data. reshaped Shape of the input tensor length of each output 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. output (always 2D tensor) Tensor of rank r >= 2. inverse_indices constant_value Input axes has invalid data 'step' cannot be 0 Blocksize must be positive 'Repeats' input must be 1D tensor of type int64 'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions. axis must be in [-rank, rank-1]. All inputs to Concat must have same rank data tensor must have rank >= 1 Indices tensor must have rank >= 1 OneHot node must have three inputs. Input 'depth' must be a scalar or rank 1 tensor. Input 'depth' must have exactly one element. Input 'values' must be rank 1 tensor. Input 'values' must have exactly two elements. 'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64 '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 Target shape may not have multiple -1 dimensions Invalid Target shape product of 0 Dimension could not be inferred: incompatible shapes values in 'axes' are beyond the bounds of the computed output shape 'axes' attribute must not contain any duplicates 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 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`. Default 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. 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). Tensors with at least max(dims) dimensions. 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.
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/>
@@ -6216,29 +6466,39 @@
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). 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'. 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. 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' 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). Slice op must have either three, four or five inputs. 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Which axis to split on 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. '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. Attribute value for pads is required Attribute pads has incorrect length Incorrect or missing attribute value for starts and ends Attribute axes has incorrect length Invalid value of attribute 'axis' Constrain input types. Casting from strings and complex are not supported. Constrain output types. Casting to strings and complex are not supported. /onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/old.cc 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` 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. *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_ ) 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. 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 input type must be a tensor of a numeric type. 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. An ordered collection of tensors, all with the same element type. The output array, elements ordered as the inputs. The size of each input in the input list 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_ is out of bounds ) vs ( inferred= type case mismatch. existing= type mismatch. existing= does not contain a graph. no name (op_type: , name: inputs but Graph has were provided Graph input # 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. is tensor type, but provided type is N4onnx15shape_inference19GraphInferencerImplE N4onnx15shape_inference20InferenceContextImplE Func_ Input for function node Output for function node onnx.AttributeProto onnx.ValueInfoProto onnx.NodeProto 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.FunctionProto onnx.TensorShapeProto.Dimension /build/Release/onnx/onnx-ml.pb.cc $4Dܻ
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+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'. 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. 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' 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). 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_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_18Scatter_Onnx_ver11EEENS_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_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Ranks inferred ( Which axis to concat on outputs... Which axis to split on 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. '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. Attribute value for pads is required Attribute pads has incorrect length Incorrect or missing attribute value for starts and ends Attribute axes has incorrect length Invalid value of attribute 'axis' Constrain input types. Casting from strings and complex are not supported. Constrain output types. Casting to strings and complex are not supported. /onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/old.cc 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` 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. *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_ ) 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. 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 input type must be a tensor of a numeric type. 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. An ordered collection of tensors, all with the same element type. The output array, elements ordered as the inputs. The size of each input in the input list 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_ is out of bounds ) vs ( inferred= type case mismatch. existing= type mismatch. existing= does not contain a graph. no name (op_type: , name: inputs but Graph has were provided Graph input # 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. is tensor type, but provided type is N4onnx15shape_inference19GraphInferencerImplE N4onnx15shape_inference20InferenceContextImplE Func_ Input for function node Output for function node onnx.AttributeProto onnx.ValueInfoProto onnx.NodeProto 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.FunctionProto onnx.TensorShapeProto.Dimension /build/Release/onnx/onnx-ml.pb.cc @p@@@ `P 8d $ |L T\L P ( h (
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-u
-
- x ?r'2'2J2,3d33D444$5\555666D7|777[[t888 9t99D:t:::\;4<d<<====>[[>OPP|Q\R<SSTTUYWX VWZT[LM[[tN>?t?@@TAABBC|CClDEEFdFDG[[GlHHHI4IdIIIDJtJJ$KTKKKKLDL[[tL2 /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 google/protobuf/io/coded_stream.h. CHECK failed: (buffer_size) >= (0): /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/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 (cannot determine missing fields for lite message) /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/message_lite.cc CHECK failed: !coded_out.HadError(): " because it is missing required fields: CHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): was modified concurrently during serialization. CHECK failed: (bytes_produced_by_serialization) == (byte_size_before_serialization): Byte size calculation and serialization were inconsistent. This may indicate a bug in protocol buffers or it may be caused by concurrent modification of This shouldn't be called if all the sizes are equal. Exceeded maximum protobuf size of 2GB: Can't parse message of type " N6google8protobuf11MessageLiteE [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 " N6google8protobuf7ClosureE N6google8protobuf8internal16FunctionClosure0E N6google8protobuf14FatalExceptionE , 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 @` `Am 3IW+ @h 0
+ N4onnx14AttributeProtoE N4onnx14ValueInfoProtoE N4onnx9NodeProtoE N4onnx10ModelProtoE N4onnx22StringStringEntryProtoE N4onnx16TensorAnnotationE N4onnx10GraphProtoE N4onnx19TensorProto_SegmentE N4onnx11TensorProtoE N4onnx17SparseTensorProtoE N4onnx26TensorShapeProto_DimensionE N4onnx16TensorShapeProtoE N4onnx16TypeProto_TensorE N4onnx18TypeProto_SequenceE N4onnx13TypeProto_MapE N4onnx22TypeProto_SparseTensorE N4onnx16TypeProto_OpaqueE N4onnx9TypeProtoE N4onnx18OperatorSetIdProtoE N4onnx13FunctionProtoE N6google8protobuf8internal29InternalMetadataWithArenaBaseISsNS1_29InternalMetadataWithArenaLiteEE9ContainerE /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 <lL,lL\\,d L| <l,\\\d| Lll<,\\ l t4\L\\ T4l T,d T\\&&%%,%$D$$##$#""D"!T! &&t l D d t <l&&T4dT4d&&
T
4
+d
+ l T < $ &&TTtDd4d &&$ t /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 G>
pparsing Can't happen /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/parse_context.h آ[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 8Xx8xY 5Ka-oC basic_string::at 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'): @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@0@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@`@@@P@@@@@0@@@ @@@@@@@@@@ @@@` 0123456789abcdef ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/ >456789:;<=
+
? !"#$%&'()*+,-./0123>?456789:;<=
+
!"#$%&'()*+,-./012300010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 ? 4444p4(57889(77p9
- /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 parsing serializing '%s' String field contains invalid UTF-8 data when a protocol bytes. N6google8protobuf8internal12FieldSkipperE N6google8protobuf8internal29CodedOutputStreamFieldSkipperE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/extension_set.cc 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). Multiple extension registrations for type " CHECK failed: type == WireFormatLite::TYPE_MESSAGE || type == WireFormatLite::TYPE_GROUP: CHECK failed: (type) == (WireFormatLite::TYPE_ENUM): CHECK failed: (type) != (WireFormatLite::TYPE_ENUM): CHECK failed: (type) != (WireFormatLite::TYPE_MESSAGE): CHECK failed: (type) != (WireFormatLite::TYPE_GROUP): can't reach here. Can't get here. Extension not found. ", field number R````````$, 4l<\| 0H`x@( oy8 FFFF7]YLcmmmm\L\LGTPL\'e H0<H0N6q0HYT44444444DD@ @ @ @ @ @ @ @ wXX},4,-<-d----4,,,<ABHBC@8Ax<AAP@??0?>`>>=0=<gDC:9;:D D9<<<<{9;bCCv;:@L0M@MXMpMMMMMMLL OJMQ R4SDTtUV$P 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/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.
-
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 \LL2002d2L2L24242422100000001|10 N3re26Regexp6WalkerIiEE N3re217NumCapturesWalkerE N3re219NamedCapturesWalkerE N3re218CaptureNamesWalkerE \%03o basic_string::erase GrowStack() failed: njob_ = job_.size() = Unexpected opcode: arg Bad arg in kInstAltMatch: Bad arg in kInstCapture: /onnxruntime_src/cmake/external/re2/re2/bitstate.cc DCompiler::Copy called! should never happen No ranges in char class Missing case in Compiler: hello, world /onnxruntime_src/cmake/external/re2/re2/compile.cc 4 D d D $ d < $ 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 W!VVVHVVVunhandled 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: h PH-P e`x /onnxruntime_src/cmake/external/re2/re2/onepass.cc Cannot use SearchOnePass for unanchored matches. L##t#t#)#t#" 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 YZZZZZZZ[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[~Z[[[[lZ[[[[[[[ZZ[[[[[Z[HZ[NY|̧ |l$ԨɯVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVVNVVVVFVVVVVVV>VVV6V.VV N3re216RepetitionWalkerE 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
+ /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. Ni\i\i\i\i\i\i\i\iiiTnhkjll;mhkj
+nnnnnbn|ljhkmmj kj|k|k\k kjkk
+lkkl\kj kklmjjypppjjnnnHooo0pjjPn nrqqqqqqqqqqr(sstPsxsss(s@tprrllllllll|(8xЋ8PBUB"iʞmt @8
0^:R
+dn˯\Eì}4&i (Px $`2aaaa3Z,_V'[P[h? |<l L tLtS?;FAdt 8X@xx8h 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.
+
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 ̺ ttt|\aat$ aaaaaaa<T 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 ܆Ԃ$Compiler::Copy called! should never happen No ranges in char class Missing case in Compiler: hello, world /onnxruntime_src/cmake/external/re2/re2/compile.cc $D$D |d 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 (unhandled 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: Hr w0u(vu`uu
u0 `СССEج@X /onnxruntime_src/cmake/external/re2/re2/onepass.cc Cannot use SearchOnePass for unanchored matches. q,TT T 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 p p p p p p p ^ L : ( .\NlOlOlOMMMLlOlO\OlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOLOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOONlONlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlOlO|NlNU6W6W6W6W6W6W6W6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V6V.W6V6V6V6V&W6V6V6V6V6V6V6V W6V6V6VW6VW6VV6VhV N3re216RepetitionWalkerE 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 [: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
%d+ %s
[%02x-%02x] -> %d
- /onnxruntime_src/cmake/external/re2/re2/prog.cc Unexpected opcode in IsMatch: (h88 ((( 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 ,,,,888,,,,,,,,, H H H H H H H H H @705050505p5p5060606660505050505050505X505 N3re26Regexp6WalkerIPS0_EE N3re214CoalesceWalkerE N3re214SimplifyWalkerE []^-\ \r \t \n \f \x%02x \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 `ldDd$daNatatfLffefffeedeDe$edddll\llllll 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 | ~ ~
?AJVoqPP=Pptv 4 6 F ,,,, 3 7 ? & ) f j z V ` x T V
+ /onnxruntime_src/cmake/external/re2/re2/prog.cc Unexpected opcode in IsMatch: oopHphppopxxxe 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 ȷȷ` h((n(hh((((((h PP8 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
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+ mu not held on entry to nsync_cv_wait_with_deadline()
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+ 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()
+ nsync_mu_wait woke but condition not true
+ future Broken promise Future already retrieved Promise already satisfied No associated state Unknown error *N12_GLOBAL__N_121future_error_categoryE St12future_error NSt13__future_base12_Result_baseE NSt13__future_base11_State_baseE bad_function_call St17bad_function_call ;<= ' ( a Fpb ch j~ Ȟp ➗H |p ؟ 4x Ԡ 0( ` ⡗ > R P c P z0 `0
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-ABD < 4 XqB O BBD I(DPN
-(A ABBA $ L hrv AG z
-AA $ t r^ AG b
-AA $ r^ AG b
-AA 4 0s> BDG DPO
- AABD < $ 8te BED G(D`O
-(A ABBE < d hue BED G(D`O
-(A ABBE < ve _ BED G(D`O
-(A ABBE < w ? AGDPO
+ABD < B BBD I(DPN
+(A ABBA $ v AG z
+AA $ x^ AG b
+AA $ ^ AG b
+AA , 蚗 ADDpl
+AAA , o ADDpt
+AAA 4 > [ BDG DPO
+ AABD < e C BED G(D`O
+(A ABBE < \ Оe # BED G(D`O
+(A ABBE < e BED G(D`O
+(A ABBE < 0 AGDPO
AAC X
-CAA < $ x AGDPO
+CAA < AGDPO
AAC X
-CAA < d hy ~ AGDPO
+CAA < \ Т AGDPO
AAC X
-CAA 4 8z> ~ BDG DPO
- AABD 4 @{> ~ BDG DPO
- AABD $ H|~ AG B
-AA $ |k AG o
-AA L d |[ _~ BBB E(D0D8GP
-8A0A(B BBBI T } ;~ BBB E(D0I8Gz
-8D0A(B BBBA D ~ BKB D(A0G`d
-0A(A BBBA L T } BBE B(D0A8Dx
-8D0A(B BBBG , } ADDpl
-AAA , X } ADDpt
-AAA T 8 } BEE E(D0A8G
-8D0A(B BBBK D 4
- BEB A(A0D@
-0A(A BBBA |
- x p } BBB E(A0D8D
+CAA 4 > BDG DPO
+ AABD 4 > k BDG DPO
+ AABD , S ADKP[
+AAA , < C AGJP[
+AAA , l P 3 AGJP[
+AAA , # ADKP[
+AAA , ADKP[
+AAA $ ~ AG B
+AA $ k AG o
+AA L L `[ BBB E(D0D8GP
+8A0A(B BBBI T p BEE E(D0A8G|
+8D0A(B BBBJ D BEB A(A0D@
+0A(A BBBA p T h BBB E(A0D8D
8A0A(B BBBB U
8A0A(B BBBA U
-8A0A(B BBBA , 0 AI
FK
- C D L BBE A(A0D@P
-0A(A BBBA , AAK0i
-AAA T
- | BBE E(A0A8G~
-8A0A(B BBBA T D | BEE E(K0A8G
-8A0A(B BBBA L +| BEE E(K0A8G
-8A0A(B BBBA $ Pm AAG0aAA$ u AAG0iAAT < " { BBB E(A0A8G
-8D0A(B BBBA D Ț { BBB A(A0Gy
-0A(A BBBH < { BDD g
+8A0A(B BBBA ,
+ 0 AI
FK
+ C D
+ BBE A(A0D@P
+0A(A BBBA ,
+ x AAK0i
+AAA T
+ 賗8 BBE E(A0A8Gu
+8A0A(B BBBJ T
+ и BEE E(K0A8G
+8A0A(B BBBA L 4 X ' BEE E(K0A8G
+8A0A(B BBBA $ , m AAG0aAA$ T пu AAG0iAAT (" BBB E(A0A8G
+8D0A(B BBBA < , BDD g
ABD k
-ABF T
c{ BBE E(A0A8G
-8D0A(B BBBI < t
H6 &{ BBA A(J
-(A ABBA <
H6 { BBA A(J
-(A ABBA D
H z BBB A(A0J
-0A(A BBBA < ТN BED D(D@\
-(D ABBA T | ࢚ z BBE E(A0A8G
-8D0A(B BBBI < hV Vz BBA A(J
-(C ABBA 4 J BDA l
+ABF T l BBE E(A0A8G
+8D0A(B BBBI < 8ŗ6 F BBA A(J
+(A ABBA <
8Ɨ6 % BBA A(J
+(A ABBA D D
8Ǘ BBB A(A0J
+0A(A BBBA < 4 ȗN BED D(D@\
+(D ABBA T
ȗ BBE E(A0A8G
+8D0A(B BBBI < $ X˗V v BBA A(J
+(C ABBA 4 x̗J BDA l
ABJ A
-ABA $ = A{ L l E y BBB B(A0A8J
-8A0A(B BBBJ 4 Z BDA y
+ABA D ̗= A{ L ̗) BBB B(A0A8J
+8A0A(B BBBJ 4 ЗZ BDA y
ABM A
-ABA < 詚 BDD k
+ABA < З BDD k
ABH k
-ABA d h BBB B(A0A8D`
+ABA d , 8ї BBB B(A0A8D`
8A0A(B BBBE d
-8A0A(B BBBA T x BBE E(A0A8G
-8D0A(B BBBI D hB x BBB I(A0DD
-0D(A BBBA 4 pz AEG `
+8A0A(B BBBA T җ BBE E(A0A8G
+8D0A(B BBBI D D 8B BBB I(A0DD
+0D(A BBBA 4 4 @֗z AEG `
AAA p
-AAB L t Bx BBE E(D0C8Io
-8D0A(B BBBF T ( x BBE E(D0D8Ft
-8C0A(B BBBD D p w BBB A(A0I
-0A(A BBBF T d 8 w BBB B(A0A8J
-8D0A(B BBBA L Ú, w BBB B(A0D8D`
-8A0A(B BBBI < Ě BBG A(G@
-(A ABBG L Ś w D i
-C < D @ƚ BBG A(G@
-(A ABBG ǚ ;w D i
-C T ǚ 3w BEJ E(D0D8G
-8D0A(B BBBK \ $ ɚ v BEB D(A0Dc
-0A(A BBBC U
-0A(A BBBA < ʚ" v BBA A(GU
-(A ABBA d ˚> v BBE E(D0A8GP
+AAB L ֗ b BBE E(D0C8Io
+8D0A(B BBBF T ؗ 8 BBE E(D0D8Ft
+8C0A(B BBBD D l @ۗ BBB A(A0I
+0A(A BBBF T ߗ BBB B(A0A8J
+8D0A(B BBBA , AID0y
+DAA D 0/ BBE A(A0D@
+0A(A BBBA < BBD A(G@,
+(A ABBA < BBA G(D`O
+(A ABBK L X BBB B(A0A8G
+8A0A(B BBBH $ s AAG0gAAL | P, k BBB B(A0D8D`
+8A0A(B BBBI < t 0 BBG A(G@
+(A ABBG D i
+C < BBG A(G@
+(A ABBG l P D i
+C T BEJ E(D0D8G
+8D0A(B BBBK < X" f BBA A(GU
+(A ABBA \ $ H G BEB D(A0D@[
+0A(A BBBK D
+0M(A BBBE < " BBA A(GU
+(A ABBA \ & BBB A(D0G
+0A(A BBBC j
+0C(A BBBA d $ > BBE E(D0A8GP
8A0A(B BBBE D
-8M0A(B BBBE T , ̚k Wv BBB B(A0A8J
-8A0A(B BBBA \ Κ ;v BBB A(A0G|
-0A(A BBBE h
-0C(A BBBA \ XК v BBB A(A0Dh
+8M0A(B BBBE T k c BBB B(A0A8J
+8A0A(B BBBA \ G BBB A(A0Dh
0A(A BBBD D
-0C(A BBBA \ D Қ u BBB A(A0Dh
+0C(A BBBA \ D X % BBB A(A0Dh
0A(A BBBD D
-0C(A BBBA d | Ӛ BLB B(A0A8D`c
+0C(A BBBA d L BLB B(A0A8D`c
8A0A(B BBBK
-8A0A(B BBBD L ՚ BOB A(A0]
+8A0A(B BBBD L ` BOB A(A0]
(A BBBG Y
-(A BBBH 4 \ ՚R u ]GB A(A0D L l ֚ BOB A(A0]
+(A BBBH 4 \ R K ]GB A(A0D L < BOB A(A0]
(A BBBG Y
-(A BBBH 4 ךR t ]GB A(A0D L @ؚ BOB A(A0]
+(A BBBH 4 hR ]GB A(A0D L BOB A(A0]
(A BBBG Y
-(A BBBH 4 l ؚR It ]GB A(A0D 4 ٚR 2t ]GB A(A0D 4 ۚR t ]GB A(A0D 4 (ܚR t ]GB A(A0D d $ Pݚ BBB E(A0A8DPm
-8A0A(B BBBH
-8A0A(B BBBB T ޚ s BBE E(A0A8G
-8D0A(B BBBI , ` ADD0e
-AAA L ] BBB H(A0D8DP
-8A0A(B BBBG T r BBE B(A0D8G
-8D0A(B BBBG T x_ r BBB E(A0A8GT
-8A0A(B BBBF T < r BBB E(A0A8G"
-8A0A(B BBBH T 8 r BBE E(A0A8G
-8D0A(B BBBE < ' nr BBA A(JU
-(A ABBA < , p' Qr BBA A(JU
-(A ABBA L l ` 7r BBA D(GpS
+(A BBBH 4 l R } ]GB A(A0D 4 (R f ]GB A(A0D 4 PR O ]GB A(A0D 4 x R 8 ]GB A(A0D d
+f BBE B(A0A8DP
+8A0A(B BBBE D
+8A0A(B BBBB T BBE E(A0A8G
+8D0A(B BBBI , 0 ADD0e
+AAA L ] BBB H(A0D8DP
+8A0A(B BBBG T 2 BBE B(A0D8G
+8D0A(B BBBG T _ BBB E(A0A8GT
+8A0A(B BBBF T < BBB E(A0A8G*
+8A0A(B BBBH T X BBE E(A0A8G
+8D0A(B BBBE < ' BBA A(JU
+(A ABBA < , !' BBA A(JU
+(A ABBA L l " k BBA D(GpS
(A ABBG g
-(A ABBC < ' r BBA A(JU
-(A ABBA < ' q BBA A(JU
-(A ABBA < BBG A(G@
-(A ABBG < T P BBG A(G@
-(A ABBD < BBG A(G@
-(A ABBD < " q BBA A(GU
-(A ABBA < < " p BBA A(GU
-(A ABBA < | p" p BBA A(GU
-(A ABBA < `" p BBA A(GU
-(A ABBA < P" p BBA A(GU
-(A ABBA < < @" tp BBA A(GU
-(A ABBA < | 0" Sp BBA A(GU
-(A ABBA < " 2p BBA A(GU
-(A ABBA < " p BBA A(GU
-(A ABBA < < " o BBA A(GU
-(A ABBA < | " o BBA A(GU
-(A ABBA < " o BBA A(GU
-(A ABBA < " o BBA A(GU
-(A ABBA T <! r lo BBB B(A0A8J
-8A0A(B BBBD D l"
BBB A(A0G@R
-0A(A BBBA " DR
-E A d " P BLB B(A0A8D@q
+(A ABBC < #' G BBA A(JU
+(A ABBA < $' * BBA A(JU
+(A ABBA < % BBG A(G@
+(A ABBG < $ p& BBG A(G@
+(A ABBD < d ' BBG A(G@
+(A ABBD < '" M BBA A(GU
+(A ABBA < < (" , BBA A(GU
+(A ABBA < | )" BBA A(GU
+(A ABBA < *" BBA A(GU
+(A ABBA < p+" BBA A(GU
+(A ABBA < < `," BBA A(GU
+(A ABBA < | P-" BBA A(GU
+(A ABBA < @." f BBA A(GU
+(A ABBA < 0/" E BBA A(GU
+(A ABBA < <! 0" $ BBA A(GU
+(A ABBA < |! 1" BBA A(GU
+(A ABBA < ! 2" BBA A(GU
+(A ABBA < ! 2" BBA A(GU
+(A ABBA T <" 3r BBB B(A0A8J
+8A0A(B BBBD D <# < BBB A(A0G@R
+0A(A BBBA # * DR
+E A d # p< BLB B(A0A8D@q
8A0A(B BBBE @
-8A0A(B BBBF D d" n BBJ A(D0
-(A BBEA L " n BLE B(A0D8D@
-8A0A(B BBBG L # P BBB B(A0A8Gp8A0A(B BBB $$ DR
-E A d D$ BLB B(A0A8D@q
+8A0A(B BBBF D d# = BBJ A(D0
+(A BBEA L # 0? BLE B(A0D8D@
+8A0A(B BBBG L $ pA BBB B(A0A8Gp8A0A(B BBB $ ( DR
+E A d % B BLB B(A0A8D@q
8A0A(B BBBE @
-8A0A(B BBBF D # m BBJ A(D0
-(A BBEA L $ {m BLE B(A0D8D@
-8A0A(B BBBG L l$ ( Km BBB E(D0D8JV
-8A0A(B BBBC < $ @" o BBA A(GU
-(A ABBA < $ A" zo BBA A(GU
-(A ABBA < <% B" Yo BBA A(GU
-(A ABBA < |% C" 8o BBA A(GU
-(A ABBA < % D" o BBA A(GU
-(A ABBA < % E" n BBA A(GU
-(A ABBA < <& F" n BBA A(GU
-(A ABBA < |& pG" n BBA A(GU
-(A ABBA < & `H" n BBA A(GU
-(A ABBA < & PI" rn BBA A(GU
-(A ABBA < <' @J" Qn BBA A(GU
-(A ABBA < |' 0K" 0n BBA A(GU
-(A ABBA < ' L" n BBA A(GU
-(A ABBA < ' M m BBD D(D
-(D ABBK L <( ^3 m BBH E(D0D8DPj
-8D0A(B BBBD L d) _9 BBB B(A0D8D 8A0A(B BBB L ) b BBB B(A0D8Dm8A0A(B BBB L * d BBB B(A0D8Dm8A0A(B BBB L T* g BBB B(A0D8Dm8A0A(B BBB L * @i BBB B(A0A8Gp8A0A(B BBB L * j BBB B(A0A8Gp8A0A(B BBB L l* @l* k BBB B(A0A8Jq
-8A0A(B BBBA + o KD
-E L + p BEB B(A0A8DP
-8A0A(B BBBA L ,+ R gm BBI B(H0A8DP
-8A0A(B BBBC < |+ Лy _m BJA A(DP
-(A ABBA , " DN
-N A 4 + m BID D0]
- AABA , % , ,, , l AGG XCA , \, 0 l ADG _CA , $ l D_ - Н - ȝ - - , - , l AGG XCA . ,. D. r \. t. L . BHB E(A0A8D`
-8A0A(B BBBE 4 . 'k BAD G@W
- CABE / . K^ ,/ , D0g D/ . - j Hd |/ ( A\ / ( / / / / 0 ,0 D0 / 4 i Hg |0 0 0 ( 0 0 0 8 0 @ 1 H $1 P <1 X HS L \1 X AAG ]
+8A0A(B BBBF D $ (D BBJ A(D0
+(A BBEA L % E BLE B(A0D8D@
+8A0A(B BBBG L l% @G' BBB E(D0D8JV
+8A0A(B BBBC < % n" BBA A(GU
+(A ABBA < % o" BBA A(GU
+(A ABBA < <& p" BBA A(GU
+(A ABBA < |& q" h BBA A(GU
+(A ABBA < & r" G BBA A(GU
+(A ABBA < & ps" & BBA A(GU
+(A ABBA < <' `t" BBA A(GU
+(A ABBA < |' Pu" BBA A(GU
+(A ABBA < ' @v" BBA A(GU
+(A ABBA < ' 0w" BBA A(GU
+(A ABBA < <( x" BBA A(GU
+(A ABBA < |( y" ` BBA A(GU
+(A ABBA < ( z" ? BBA A(GU
+(A ABBA T ( zb BBB B(D0D8D
+8D0A(B BBBG L T) 3 BBH E(D0D8DPj
+8D0A(B BBBD L L* 9 BBB B(A0D8D 8A0A(B BBB L * 萘 BBB B(A0D8Dm8A0A(B BBB L * ( BBB B(A0D8Dm8A0A(B BBB L * " BBB B(A0D8J8A0A(B BBB L + BBB B(A0D8Dm8A0A(B BBB L + X BBB B(A0A8Gp8A0A(B BBB L ,, ؘ BBB B(A0A8Gp8A0A(B BBB L + X. BBB B(A0A8J q
+8A0A(B BBBA , T KH , , - ,- D- \- t- T , . BBE B(D0A8Gc
+8D0A(B BBBI T <- | BFG B(D0A8G
+8A0A(B BBBE <. T. L - ( BBE B(D0A8Du
+8D0A(B BBBJ . Ę D ,. Ę BBB A(A0Gy
+0A(A BBBH / . K^ L 4/ Ř BEB B(A0A8DP
+8A0A(B BBBA L . @ȘR BBI B(H0A8DP
+8A0A(B BBBC < ,/ P˘y BJA A(DP
+(A ABBA 0 ̘" DN
+N A 4 / ̘ BID D0]
+ AABA l0 (͘% , / @͘, AGG XCA , 0 @͘0 { ADG _CA <0 @͘$ O D_ 1 P͘ 1 H͘ 41 @͘ L1 8͘ , 0 0͘, AGG XCA 1 0͘ 1 (͘ 1 ͘r 1 ͘ 1 ͘ L 2 x͘ BHB E(A0A8D`
+8A0A(B BBBE 4 1 Ϙ BAD G@W
+ CABE 2 P. K^ 2 Ϙ, D0g 2 Ϙ 42 Ϙ- c Hd 2 Ϙ A\ 3 Ϙ 43 Ϙ L3 Ϙ d3 Ϙ |3 Ϙ 3 Ϙ 3 xϘ 3 pϘ 43 xϘ4 g Hg 3 Ϙ 4 Ϙ ,4 Ϙ D4 Ϙ \4 Ϙ t4 Ϙ 4 Ϙ 4 Ϙ 4 Ϙ HS L 4 Ϙ AAG ]
CAF [
ACA R
-CAJ JCA , 1 b D\
+CAJ JCA , ,5 Иb D\
H P
H Q
-G I , 1 Ƞb D\
+G I , \5 HИb D\
H P
H Q
-G I 2 - $2 - <2 8- < T2 P BAA a
+G I 5 И- 5 И- 5 И- < 5 И BAA a
ABH i
-ABL < 1 {g BED A(Dpz
-(A ABBH < 1 ?g BED A(Dpz
-(A ABBH 3 ' Ae 43 У' Ae |2 ࣛ f AN < 2 УP f BEJ D(DPn(C ABB L 2 ࣛ kf BEE E(A0A8D
-8A0A(B BBBG D ,3 2 f BBB D(A0Dn
-0D(A BBBA L L4 x BBB B(A0A8D
-8A0A(B BBBA L 4 BBB B(A0A8D
-8A0A(B BBBA T 4 \ re BBE E(D0A8G
-8D0A(B BBBF $ l4 Xe AFD@ L l5 (W BBB E(A0A8DP
-8A0A(B BBBA L 5 8 BBB B(A0A8Gp8A0A(B BBB L 45 d BEB E(I0H8DP
-8D0A(B BBBD 4 \6 H@ BAD D0
- AABA D 6 PB BBE A(A0D@m
-0A(A BBBA $ 6 XV ADD JAA< ,6 ^ c BBA D(G0
-(A ABBA 4 l6 c BFD D0I
- DABA D 6 8 c BBB D(A0G@:
-0A(A BBBA L 6 ๛] c BBB B(D0A8D`
-8A0A(B BBBA 8
,8 . K^ D D8 м" BBB A(A0G@m
-0A(A BBBA T 7 | b BBL B(A0A8D
-8A0A(B BBBA D 8 ě b BBJ A(D0G@q
-0D(A BBBA T T8 HǛ
- b BEB B(A0A8G
-8A0A(B BBBA L 8 ћ #c BLB B(D0A8DP
-8A0A(B BBBE T 8 `ӛ c BBB B(A0A8G
-8A0A(B BBBA L ,: ܛ BBE B(A0A8D`
-8A0A(B BBBA T 9 Hޛk _b BBB B(D0A8G
-8A0A(B BBBE : ` : X ; P ; X 4; `+ Hb T; p2 He t; ; $ ; g AG k
-AA $ ; o AG s
-AA 4 ; a BAA G@I
- AABA ,< D< \< x , ; " sa AAG0i
-AAD < T KH < h < ` F < = F = - AG cA $ <= g AG k
-AA $ d= g AG k
-AA $ = 8o AG s
-AA $ = o AG s
-AA L = > #` BBB B(A0A8GZ
-8D0A(B BBBH T= _ D> N D \> BBE A(A0D@l
-0A(A BBBA < = P _ BBD A(G0T
-(A ABBA , > t _ AID {
-DAA 4 <> P {_ BFD D0
- DABA , L? ADD Z
-AAA 4 |? BDA w
+ABL < l5 ј 3 BED A(Dpz
+(A ABBH < 5 pҘ BED A(Dpz
+(A ABBH 6 @Ә' Ae 6 PӘ' Ae ,6 `Ә { AN < L6 PӘP _ BEJ D(DPn(C ABB L 6 `Ә # BEE E(A0A8D
+8A0A(B BBBG D 6 2 BBB D(A0Dn
+0D(A BBBA L 7 ֘ BBB B(A0A8D
+8A0A(B BBBA L 8 ؘ BBB B(A0A8D
+8A0A(B BBBA T 7 8ژ\ * BBE E(D0A8G
+8D0A(B BBBF $ 8 @ߘ AFD@ L 8 ߘW BBB E(A0A8DP
+8A0A(B BBBA L <9 BBB B(A0A8Gp8A0A(B BBB L 8 8 W BEB E(I0H8DP
+8D0A(B BBBD 4 9 @ BAD D0
+ AABA D : B BBE A(A0D@m
+0A(A BBBA $ \: V ADD JAA< 9 ^ BBA D(G0
+(A ABBA 4 : 0 o BFD D0I
+ DABA D T: _ BBB D(A0G@:
+0A(A BBBA L : `] W BBB B(D0A8D`
+8A0A(B BBBA ; p
; h. K^ D ; P" BBB A(A0G@m
+0A(A BBBA T d; 8| BBL B(A0A8D
+8A0A(B BBBA D ; ` ? BBJ A(D0G@q
+0D(A BBBA T <
+ _ BEB B(A0A8G
+8A0A(B BBBA L \< 0 BLB B(D0A8DP
+8A0A(B BBBE T < BEB B(A0A8D
+8A0A(B BBBA L = ( BBE B(A0A8D`
+8A0A(B BBBA T T=
9) BBB B(D0A8G
+8A0A(B BBBB T> 6 l> x6 > p6 > x6 > 6+ Hb > 62 He > 6 ? 6 $ $? 6g AG k
+AA $ L? 6o AG s
+AA 4 > 07 BAA G@I
+ AABA ? 7 ? 7 ? 7 , L? 7" 3 AAG0i
+AAD $@ T KH <@ 8 T@ 8F l@ 8 @ 8F @ J - AG cA $ @ 8g AG k
+AA $ @ 9g AG k
+AA $ A X9o AG s
+AA $ 4A 9o AG s
+AA L @ 9> BBB B(A0A8GZ
+8D0A(B BBBH A < N D A < BBE A(A0D@l
+0A(A BBBA < dA x= [ BBD A(G0T
+(A ABBA , A (?t c AID {
+DAA 4 A x? O BFD D0
+ DABA , B @@ ADD Z
+AAA 4 B @ BDA w
AEA {
-AFI T > ] _ BEB B(A0A8G
-8D0A(B BBBD T 4? ^ BEB B(A0A8G
-8D0A(B BBBA T ? ] ^ BEB B(A0A8G
-8D0A(B BBBD L ? % ^ BCA G`w
+AFI T tB 8A] BEB B(A0A8G
+8D0A(B BBBD T B @F] ļ BEB B(A0A8G
+8D0A(B BBBD T $C HK] BEB B(A0A8G
+8D0A(B BBBD L |C PP% BCA G`w
AABH C
- AABI A . K^ $A P <A X 4 |@ ` 5 ^ ADG
+ AABI tD 0. K^ D T D T 4 D (T5 ] ADG
-AAA , @ h+ gb ADGv
-AAA , @ +Q db ADGj
-AAA , A ,Q Db ADGj
-AAA , DA H,Q $b ADGj
-AAA 4 tA x,u b ADGO
-AAA 4 A 0 "c ADG
-AAA , A H4 d ADG
-AAA 4 B (55 1d ADG4
-AAA 4 LB 09 De ADG&
-AAA 4 B ; e ADG
-AAA 4 B Ef \h ADG
-AAA , B HGQ h ADGj
-AAA T $C xG h BIB B(A0A8DI
-8A0A(B BBBA < TD O BEE A(D@M
-(A ABBA L C P h BEB E(I0H8D@
-8D0A(B BBBD L D Q: h BJE B(A0A8D@
-8A0A(B BBBA L 4E R BBE B(A0A8D`
-8A0A(B BBBA L D PS 3h BEB E(I0H8D@
-8D0A(B BBBD L D T h BJE B(A0A8D`8A0A(B BBB D LE `Uc g BBB A(C0K@
-0A(A BBBA L E X g BBH E(D0D8D`
-8D0A(B BBBH l E ]2 g BBB E(D0I8D6
+AAA , LD 0c ; ADGv
+AAA , |D cQ 8 ADGj
+AAA , D cQ ADGj
+AAA , D dQ ADGj
+AAA 4 E @du ؿ ADGO
+AAA 4 DE h ADG
+AAA , |E l ADG
+AAA 4 E l5 ADG4
+AAA 4 E p ADG&
+AAA 4 F ps ADG
+AAA 4 TF }f D ADG
+AAA , F PQ ADGj
+AAA T F BIB B(A0A8DD
+8A0A(B BBBA < G BEE A(D@M
+(A ABBA L TG BEB E(I0H8D@
+8D0A(B BBBD L G : BJE B(A0A8D@
+8A0A(B BBBA L H BBE B(A0A8D`
+8A0A(B BBBA L DH X BEB E(I0H8D@
+8D0A(B BBBD L H ، BJE B(A0A8D`8A0A(B BBB D H hc BBB A(C0K@
+0A(A BBBA L ,I BBH E(D0D8D`
+8D0A(B BBBH l |I 2 k BBB E(D0I8D6
8D0A(B BBBA }
-8D0A(B BBBA T TF c _g BEB B(A0A8J
-8A0A(B BBBA T F @m: g BBB B(D0A8G
-
-8D0A(B BBBE , G (yE BIA rAE H Hy $H @y <H 8y TH 0y lH (y H 0y
H (y H y H y9 Hp H 8y I 0y I (y 4I 0y LI (y( Af lI 8y I @y I Hy I Py@ Hs I py I xy J y J y3 Aq <J y- TJ y- AG cA tJ y- AG cA L J y AAG ]
+8D0A(B BBBA T I Л G BEB B(A0A8J
+8A0A(B BBBA T DJ H* BBB B(D0A8G
+
+8D0A(B BBBJ $ DK > AIG gDA lK 8 K 0 K ( K K K K L ,L ( Af LL dL |L ( L 0 L 8 L @ $ 4L Hv [ AI M
+DA M - M - AG cA <M ȱ- AG cA L \M ر AAG ]
CAF [
ACA R
-CAJ JCA , J zb D\
+CAJ JCA , M b D\
H P
H Q
-G I L <J Pzr d BBB B(E0A8J@
-8A0A(B BBBA dK 0 De
-E A < J `{ xd BBA A(D@(A ABB < K | BEB A(A0v
-(A BBBA 4 ,K | c BAD D@Z
- AABA , dK } c AID o
-DAA lL }- , K } c AAD@s
-AAA L 0~ D |
-H l T K ~ ]c BBB E(A0A8D+
-8A0A(B BBBA ,M 2 DN
-E A LM AL < L p b BED A(G@|
-(A ABBA $ L 8 b RFD@ 4 L b BFA K0
- AABA N h- AG cA ,N x2 Al L tM b BFB E(A0A8GP
-8A0A(B BBBH $ N 腜_ AAG LDAD M a BEE A(C0G@n
-0A(A BBBC L 4N 膜+ a BBI A(D0MZ
-0D(A BBBD L N Ȉ
- a BBB H(D0M2
-0D(A BBBD L N
- a BBB H(D0M2
-0D(A BBBD < $O H pa BBA D(Jz
-(D ABBJ \ dO 莜9 a BBB A(A0J
-0A(A BBBE s
-0A(A BBBE D O ȑ ba BBB A(A0J
-0A(A BBBH T P 0 Fa BEB E(A0A8G
-8A0A(B BBBI < dP % Za BEA D(G
-(D ABBA L P x 3a BEE E(A0C8J
-8A0A(B BBBB Q 蜜E < Q _ a BED D(G
-(D ABBH $ LQ @ ` AFD@ < tQ _ ` BED D(G
-(D ABBH $ Q ȟ ` AFD@ < Q 0Z ` BDA z
+G I L 4M Hr g BBB B(E0A8J@
+8A0A(B BBBA ,N 0 De
+E A < M X BBA A(D@(A ABB < N BEB A(A0v
+(A BBBA 4 $N BAD D@Z
+ AABA , \N j AID o
+DAA 4O - , N 4 AAD@s
+AAA |O ( D |
+H l T N ȶ BBB E(A0A8D+
+8A0A(B BBBA O P DN
+E A P AL < O p ] BED A(G@|
+(A ABBA $ O 0 - RFD@ 4 O BFA K0
+ AABA P `- AG cA P p2 Al L lP BFB E(A0A8GP
+8A0A(B BBBH $ dQ ཙ_ AAG LDAD P k BEE A(C0G@n
+0A(A BBBC L ,Q ྙ+ C BBI A(D0MZ
+0D(A BBBD L |Q + . BBI A(D0MZ
+0D(A BBBD L Q 3 BBI A(D0MZ
+0D(A BBBD L R ę3 BBI A(D0MZ
+0D(A BBBD < lR ƙ BBA D(Jz
+(D ABBJ \ R ə9
+ BBB A(A0J
+0A(A BBBC {
+0A(A BBBE D S ̙ BBB A(A0J
+0A(A BBBH T TS hΙ ſ BEB E(A0A8G
+8A0A(B BBBI < S ә% ٿ BEA D(G
+(D ABBA L S ԙ BEE E(A0C8J
+8A0A(B BBBB T יE < TT XיZ BDA z
ABL A
-ABA < R P G` BBI A(D0
-(A ABBA < \R ` BBD D(J@F
-(D ABBA \ R PG _ BBB D(D0Du
+ABA < T xי [ BBD D(J@F
+(D ABBA \ T יG ' BBB D(D0Du
0D(A BBBF K
-0D(A BBBA T R @+ _ BEL B(A0D8G
-8D0A(B BBBD L TS _ BBB B(D0A8D`
-8D0A(B BBBI 4 |T بZ BDA y
+0D(A BBBA T 4U ٙ+ BEL B(A0D8G
+8D0A(B BBBD L U ܙ BBB B(D0A8D`
+8D0A(B BBBI 4 V PߙZ BDA y
ABM A
-ABA 4 T Z BDA y
+ABA 4 V xߙZ BDA y
ABM A
-ABA d T ( BEB B(A0A8D@{
+ABA d V ߙ BEB B(A0A8D@{
8A0A(B BBBJ D
-8A0A(B BBBA L |T
^ BEE A(A0G
-0A(A BBBA U < U x BEB D(A0M
-(A BBBF 4 U Z BDA y
+8A0A(B BBBA , V 8> AAL W
+DAA L V H
BEE A(A0G
+0A(A BBBA W < W BEB D(A0M
+(A BBBF 4 4X Z BDA y
ABM A
-ABA T \U P ^ BBB B(A0A8Gw
-8A0A(B BBBA , U ] AID0y
-DAA 4 V x BEA A(D@q(A ABB$ V аf AAK0VAA< DV ] BBD A(G@,
-(A ABBA 4 \W BFD A(D@(A ABBT V 0 G] BEB A(A0[
+ABA T W P , BBB B(A0A8Gw
+8A0A(B BBBA $ X f AAK0VAA4 X BFD A(D@(A ABBT |X BEB A(A0[
(A BBBK A
-(A BBBA d W h; BLB B(A0A8D`q
+(A BBBA d |Y ; BLB B(A0A8D`q
8A0A(B BBBE S
-8A0A(B BBBK L TX @M BBB B(A0D8Dp18A0A(B BBB X @I AG A L W p \ BLB A(A0Jv
-0A(A BBBF Y Q AG GA4 4Y @u BEA A(D@a(A ABB lY A AK sA Y I AK {A d Y 軜 BEE F(A0A8D@c
-8F0A(B BBBF
-8D0A(B BBGA 4 Z u BEA A(D@a(A ABB, LZ Ƚv BDA YAT $ |Z V ADD JAAL Z P) BEB B(A0A8D`
-8A0A(B BBBA 4 Z 0 BEA A(D@o(A ABB< ,[ BEE A(D0(A BBB D Z Y BBB A(D0D@
-0A(A BBBA [ " DN
-N A D [ BBB B(A0A8D`8A0A(B BBBT D[ XÜ
Y BBB E(A0A8GX
-8A0A(B BBBA , [ Мe Y AID l
-DAA D [ `М Y BBB D(A0&
-(A BBBA D \ ќ Y BJB A(A0Gp
-0A(A BBBA < 4] Ҝ BEB A(A0{
-(A BBRA 4 t] BBD A(D@(A ABB< \ ҜE X BBA I(D0
-(A DBBA , ] Ӝ> BDA sAB L ^ Ӝ BBB B(A0A8Gp8A0A(B BBB 4 l^ X՜ BBH A(D@(A ABB4 ^ ֜ BEA A(D@o(A ABBT ^ X֜ W BBB B(A0A8J
-8A0A(B BBBK 4_ ۜ AL L T_ ۜ BBB B(A0D8D`O
-8A0A(B BBBF T ^ 0ݜ 'W BBD D(D@%
+8A0A(B BBBK L Y M BBB B(A0D8Dp18A0A(B BBB 4Z I AG A L Y BLB A(A0J
+0A(A BBBH Z `Q AG GAD Z BBE A(A0D@l
+0A(A BBBA [ 8A AK sA ,[ hI AK {A L L[ BFB B(A0A8D@f
+8D0A(B BBBK d [ BFB E(D0A8D`x
+8F0A(B BBBA +
+8D0A(B BBGA D \ BBE A(A0D@l
+0A(A BBBA , L\ Xv BDA YAT $ |\ V ADD JAAL \ ) BEB B(A0A8D`
+8A0A(B BBBA 4 \ BEA A(D@o(A ABB< ,] BEE A(D0(A BBB L \ C BBB B(A0D8DP
+8A0A(B BBBA ] " DN
+N A D ] BBB B(A0A8D`8A0A(B BBBL |] p BEB B(I0A8D`O
+8A0A(B BBBF T ] C BBB E(A0A8G8
+8A0A(B BBBA , $^ Xe AID l
+DAA D T^ ϸ BBB D(A0&
+(A BBBA D ^ BJB A(A0Gp
+0A(A BBBA < _ BEB A(A0{
+(A BBRA 4 _ BBD A(D@(A ABB< \_ E BBA I(D0
+(A DBBA < D` BEA A(D0o
+(A ABBA L ` BBB B(A0A8Gp8A0A(B BBB 4 ` BBH A(D@(A ABB4 a BEA A(D@o(A ABBT ` BBB B(A0D8G
+8A0A(B BBBA a h AL L a h BBB B(A0D8D`O
+8A0A(B BBBF T da K BBD D(D@%
(D ABBB ]
-(D ABBJ 4 $_ ޜJ V BGA G`k
- DABE , 4` ߜv BAA i
-ABA 4 d` @~ BEA A(D@j(A ABBL ` BLB B(A0A8D`
-8A0A(B BBBJ < ` X BBG A(G@
-(A ABBH L T` U BEE E(D0A8G
-8A0A(B BBBF \ |a x BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBT a K XU BBE B(D0A8G
-8D0A(B BBBH \ 4b BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBL a T BEB E(I0H8DP
-8D0A(B BBBD T b T BGB D(A0z
+(D ABBJ 4 a J BGA G`k
+ DABE , b v BAA i
+ABA 4 b ~ BEA A(D@j(A ABB< c @ BBG A(G@
+(A ABBG L b K BEE E(D0A8G
+8A0A(B BBBF L c `# BLB B(A0A8D`
+8A0A(B BBBJ < c 0& BBG A(G@
+(A ABBG \ $d & BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBT c @'K < BBE B(D0A8G
+8D0A(B BBBH \ d 8) BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBL d ) BEB E(I0H8DP
+8D0A(B BBBD T d H+ BGB D(A0z
(A BBDE H
-(A BBGA T db ( kT BFB E(A0D8Jb
-8A0A(B BBBA b ` nT BBB A(D0G`
+(A BBGA T <e + O BFE B(D0D8JX
+8D0A(B BBBJ e 0 Q BBB A(D0G`
0D(A BBBF ]
0D(A BBBH m
0D(A BBBH ]
-0D(A BBBA \ d BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBL c S BBL B(D0A8G
-8A0A(B BBBA T c +T BEB B(D0D8J
-
-8D0A(B BBBI < Ld P T BLD D(G0
-(A ABBE 4 d = kT BFD D0
- DABF < d
- [T BED I(K@`
-(D ABBH T e
-` ;T BBB B(A0D8G
-8A0A(B BBBG < \e
T BLD D(G0
-(A ABBE < e S BED I(KPc
-(D ABBE T e P S BBB B(A0D8G
-8A0A(B BBBF g AL T Tf gS BBB E(A0D8J ~
-8D0A(B BBBK T f S BBD A(Gp
+0D(A BBBA \ f p1 BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBL |f 1 BBL B(D0A8G
+8A0A(B BBBA T f P>
+ BBE B(D0D8J
+
+8D0A(B BBBD < $g H BLD D(G0
+(A ABBE 4 dg I= w BFD D0
+ DABF < g J g BED I(K@`
+(D ABBH T g pK` G BBB B(A0D8G
+8A0A(B BBBG < 4h xN BLD D(G0
+(A ABBE < th 8O* BED I(KPc
+(D ABBE T h (P Ӳ BBB B(A0D8G
+8A0A(B BBBF i T AL T ,i pT s BBB E(A0D8J }
+8D0A(B BBBD T i ] BBD A(Gp
(D ABBK F
-(D ABBA g ( ( AK ZA L $g 8 S BEB E(I0H8D@
-8D0A(B BBBD L tg * WS BJE B(A0A8D@
-8A0A(B BBBA L g /S BEB B(I0D8Ge
-8D0A(B BBBG L h ) S BBB B(A0D8J(
-8D0A(B BBBA 4 <i H<h BEA A(G@Q(A ABB\ ti < BBD A(D@V
+(D ABBA D j 0^ BBE A(A0D@e
+0A(A BBBA L $j ^ BEB E(I0H8D@
+8D0A(B BBBD L tj x`* W BJE B(A0A8D@
+8A0A(B BBBA L j Xa / BEB B(I0D8Ge
+8D0A(B BBBG L k l BBB B(A0D8J(
+8D0A(B BBBD 4 l Hh BEA A(G@Q(A ABB\ Dl BBD A(D@V
(A ABBG Y
(A ABBA a
-(A ABBI T h < wT BBB B(A0A8G]
-8A0A(B BBBH T Ti S U BBB B(A0A8N
-8A0A(B BBBA T i _V U BBB B(A0A8N
-8A0A(B BBBD $ j j~ -V AG0qD T ,j j V BBB B(A0C8J
-8A0A(B BBBD , \k AHD0
-AAA k o k o% HX k o- H` k p% HX l p- H` $l p <l p Tl p ll p
l p l p l p l p l p l (p% HX m 8p- H` <m Hp% HX \m Xp- H` |m hp% HX m xp- H` m p% HX m p- H` m p- , n p0 AF
FK
- D $ Dn q^ AG b
-AA L m q R BVB D(A0D3
-0D(A BBBG T m s R BGB B(A0D8G
-8D0A(B BBBH L <n x` R BLB E(A0A8Gp
-8D0A(B BBBA L n y` xR BLB E(A0A8Gp
-8D0A(B BBBA L n 0z` KR BLB E(A0A8Gp
-8D0A(B BBBA , p @{ ADD0e
-AAA , 4p { AAG0M
-AAH 4 o 0| Q BAA G0c
- AABF T o | Q BBE E(A0A8G
-8D0A(B BBBA L p ~E OQ BBB B(A0A8G
-8E0A(B BBBF , lp Y GQ ADG i
-AAA T p #Q AAG@r
+(A ABBI T k BBB B(D0A8J
+8A0A(B BBBH T Tl 8 BBB B(A0A8N
+8A0A(B BBBA T l G ; BBB B(A0A8Ny
+8A0A(B BBBA $ m ~ v AG0qD T ,m চ R BBB B(A0C8J
+8A0A(B BBBD ,n ԖT KH Dn \n % HX |n - H` n % HX n (- H` n 8 n 0 o ( $o
<o To lo ( o 0 o 8 o @% HX o P- H` o `% HX p p- H` 4p % HX Tp - H` tp % HX p - H` p - , p ج0 AF
FK
+ D $ p ح^ AG b
+AA L |p # BVB D(A0D3
+0D(A BBBG T p Я BGB B(A0D8G
+8D0A(B BBBH L $q (` BLB E(A0A8Gp
+8D0A(B BBBA L tq 8` ٰ BLB E(A0A8Gp
+8D0A(B BBBA L q H` BLB E(A0A8Gp
+8D0A(B BBBA , r X ADD0e
+AAA , r ȷ AAG0M
+AAH 4 tr H BAA G0c
+ AABF T r и BBE E(A0A8G
+8D0A(B BBBA L s E BBB B(A0A8G
+8E0A(B BBBF , Ts Y ADG i
+AAA T s 8 AAG@r
AAC G
AAG y
AAE ]
-AAA L p h= P BBB B(D0A8DP
-8D0A(B BBBA T Dq X4 P BBE E(K0A8G
-8D0A(B BBBG < q @5 P NAG JH
-CAA < q @5 P KAG MH
-CAA D r @ P AAD@e
+AAA L s = J BBB B(D0A8DP
+8D0A(B BBBA T ,t pw BBE E(K0A8G
+8D0A(B BBBF < t ƚ5 NAG JH
+CAA < t ɚ5 KAG MH
+CAA D u ̚ AAD@e
AAC H
AAF h
-AAE <s L |r xP BBB B(A0A8Gi
-8A0A(B BBBD L r \P BBB B(D0A8GA{
-8A0A(B BBBG < s p7 BBA A(G0\
-(A ABBA d 4t p BEB E(A0D8Gp
+AAE u p͚ L du h͚ خ BBB B(A0A8Gi
+8A0A(B BBBD L u К| BBB B(D0A8GA{
+8A0A(B BBBG < v H7 BBA A(G0\
+(A ABBA d v H BEB E(A0D8Gp
8A0A(B BBBD f
-8A0A(B BBBA t ȷp \ Sd t T BIB I(D0H8G
+8A0A(B BBBA Tw p \ Sd lw T BIB I(D0H8G
8A0A(B BBBJ Q
-8A0A(B BBGH d u BBE F(A0H8GP
+8A0A(B BBGH d w BBE F(A0H8GP
8C0A(B BBBD
-8F0A(B BBBE L t л P BFB B(E0A8GP{
-8A0A(B BBBB < u @Ý BBG A(G@
-(A ABBG v ĝ $ Tu Ýb P AY
-F A L |u @ĝ% O BBB B(A0A8Dp
-8D0A(B BBBF < v ȝ BBA A(G@d
-(A ABBI v ȝ O BFB A(E0G@
+8F0A(B BBBE d w @ BIB B(A0E8DP
+8A0A(B BBBJ \
+8F0A(B BBBE < x BBG A(G@
+(A ABBG x @ $ Tx 8b ] AY
+F A L |x % 9 BBB B(A0A8Dp
+8D0A(B BBBF < ty ` BBA A(G@d
+(A ABBI y 0 BFB A(E0G@
0F(A BBBH y
0A(A BBBG
0F(A BBBD
-0F(A BBBD d tw ̝ BBB A(A0
+0F(A BBBD d Dz BBB A(A0
(A BBBA
(A BBBE N
-(A BBBE , w ϝC N AID \
-DAA L 4w ϝ N BBB B(A0D8DP
-8A0A(B BBBF 4 \x (ѝ AAG0^
+(A BBBE , z C AID \
+DAA L 4z
Ӭ BBB B(A0D8DP
+8A0A(B BBBF 4 ,{ h AAG0^
AAG H
-AAF < x ҝ BBG A(G@
-(A ABBG L x ҝ= BBB H(A0D8DP
-8A0A(B BBBE L $y ӝ= BBB H(A0D8DP
-8A0A(B BBBE , ty ԝ AG ]
+AAF < d{ P BBG A(G@
+(A ABBG L { = BBB H(A0D8DP
+8A0A(B BBBE L { = BBB H(A0D8DP
+8A0A(B BBBE , D| AG ]
AJ D
-AK L y ՝@ BBB H(A0D8D@
-8A0A(B BBBJ L y ֝= BBB H(A0D8DP
-8A0A(B BBBE L ly ם kL BBE B(A0D8D@
-8A0A(B BBBF L z ٝ= BBB H(A0D8DP
-8A0A(B BBBE L z ٝM BBB H(A0D8DP
-8A0A(B BBBE 4{ ڝ5 D tz K BAC GPF
+AK L t| @ BBB H(A0D8D@
+8A0A(B BBBJ L | = BBB H(A0D8DP
+8A0A(B BBBE L l| BBE B(A0D8D@
+8A0A(B BBBF L d} @= BBB H(A0D8DP
+8A0A(B BBBE L } 0M BBB H(A0D8DP
+8A0A(B BBBE ~ 05 D t} X ۩ BAC GPF
EABE
- AABD D z pݝ ]K BAA GP9
- ECBB X
- CABB < { ߝ %K AAGPF
+ AABD < } AAGPF
EAK
-AAD D D{ J BAA GPA
+AAD D } BAA GPA
ECBJ X
- CABB D { ` J BAA GP9
- ECBB X
- CABB D { J BAC GPF
+ CABB D D~ X K BAC GPF
EABE
- AABI D | @ cJ BAA GP9
- ECBB X
- CABB d <} E BBB B(A0A8DP
+ AABI d 4 !E BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l | p= I BBB E(I0D8D#
+8F0A(B BBBA l ~ "= BBB E(I0D8D#
8D0A(B BBBD
-8D0A(B BBBA L ~ @' BFB E(A0A8Dp
-8F0A(B BBBH T } #I BBB B(A0D8J
-8C0A(B BBBH , } I AID0H
-DAA D ~ l I BBE A(D0K@
-0D(A BBBA \~ 0 I BBB B(D0A8D@Q
+8D0A(B BBBA L %' BFB E(A0A8Dp
+8F0A(B BBBH T & BBB B(A0D8J
+8C0A(B BBBH , = AID0H
+DAA D < p=l BBE A(D0K@
+0D(A BBBA > ۨ BBB B(D0A8D@Q
8A0A(B BBBD L
8A0A(B BBBJ N
8A0A(B BBBH *
8A0A(B BBBD K
-8A0A(B BBBC 4
-> AAG0}
+8A0A(B BBBC 4 p@> AAG0}
FAC
-FAA T L
7I BBB B(A0A8O
-8F0A(B BBBE 4 |
BAA G`{
- FABA T H BBB B(A0D8J
-8A0A(B BBBD T 4 h$ I BBE E(A0A8J
-8A0A(B BBBK T , I BBB B(A0D8J
F
-8D0A(B BBBF $ ( AHD0AA< 4 BDA \
+FAA T t xA
/ BBB B(A0A8O
+8F0A(B BBBE 4 t 0D BAA G`{
+ FABA T E § BBB B(A0D8J
+8A0A(B BBBD T \ ` BBE E(A0A8J
+8A0A(B BBBK T hq BBB B(A0D8J
5
+8D0A(B BBBG . K^ < ̃ p BDA \
ABJ i
-ABL L $ 4 BBB B(A0A8D
-8A0A(B BBBA t @. K^ \ AG RAT ԁ X9; I BKD D(D`
+ABL L hq BBB B(A0A8D
+8A0A(B BBBA | \ v BBE B(G0L8DPu
+8A0A(B BBBG S
+8E0A(B BBBG W
+8E0A(B BBBK ܄ . K^ ݕ\ AG RAT l @w; BKD D(D`
(D ABBH Q
-(D ABBF ࠙. K^ l D (; H BBB K(A0A8Dg
+(D ABBF l (. K^ l ܄ y BBB K(A0A8Dg
8A0A(B BBBH
-8D0A(B BBBE L < _H BEB E(I0H8DP
-8D0A(B BBBD L (> 7H BJE B(A0A8D@
-8A0A(B BBBA L T > H BEB E(I0H8D@
-8D0A(B BBBD L | @] BBB H(A0D8DP
-8A0A(B BBBG T A G BBE B(D0D8D
-8D0A(B BBBE $ . K^ < D Y , T D0 AC
FK
- E , ED AKD i
-DAA @EY } N $ ̅ Eg AG k
-AA 4 E F BRA GP
- DABA 4 , FZ BDA y
+8D0A(B BBBE L L z ; BEB E(I0H8DP
+8D0A(B BBBD L | BJE B(A0A8D@
+8A0A(B BBBA L | BEB E(I0H8D@
+8D0A(B BBBD L ~] BBB H(A0D8DP
+8A0A(B BBBG T s BBE B(D0D8D
+8D0A(B BBBE 8. K^ Y Y } N $ ԇ Pg AG k
+AA , 0 AC
FK
+ E 4 M BRD DP
+ DABA 4 d Z BDA y
ABM A
-ABA L d F BBB B(A0A8GPl
-8A0A(B BBBI L H BBB B(A0A8GPl
-8A0A(B BBBI L PI BBB B(A0A8GPl
-8A0A(B BBBI L T J BBB B(A0A8GPl
-8A0A(B BBBI L K BBB B(A0A8GPl
-8A0A(B BBBI L @M BBB B(A0A8GPl
-8A0A(B BBBI L D N BBB B(A0A8GPl
-8A0A(B BBBI L O BBB B(A0A8GPl
-8A0A(B BBBI L Q BBB B(A0A8GPl
-8A0A(B BBBI L 4 pR BBB B(A0A8GPl
-8A0A(B BBBI L S BBB B(A0A8GPl
-8A0A(B BBBI L ԉ U BBB B(A0A8GPl
-8A0A(B BBBI L $ `V BBB B(A0A8GPl
-8A0A(B BBBI L t W BBB B(A0A8GPl
-8A0A(B BBBI L Ċ X BBB B(A0A8GPl
-8A0A(B BBBI L < @Z A BBB B(M0A8GP7
-8A0A(B BBBA < [' kA BED I(D0
-(A ABBA D ̊ \ CA BBB I(A0D
-0A(A EBBA L x]] BBB H(A0D8DP
-8A0A(B BBBG < ^= T | ^4 @ BBB E(A0A8G
-8A0A(B BBBA T ԋ g @ BBB B(D0A8G1
-8A0A(B BBBA L , i @ BHI E(A0A8G`
-8A0A(B BBBE T | jm @ BBB J(A0D8Dd
-8D0A(B BBBA $ l= ADG nAA T lk @ BEB B(A0A8G
-8A0A(B BBBA , . K^ $ D ~0 AGJ XDA l ~ ~Y } N $ ~g AG k
-AA 4 @ ;@ BHA G@h
- DABA x. K^ Z\ AG RAD 4 7 BBE A(A0D@
-0A(A BBBA D | 7 BBE A(A0D@
-0A(A BBBA D ď C BBE A(A0D@
-0A(A BBBA D C BBE A(A0D@
-0A(A BBBA , T U AGG m
-AAB l > BBB B(A0A8D1
+ABA L BBB B(A0A8GPl
+8A0A(B BBBI L ( BBB B(A0A8GPl
+8A0A(B BBBI L < x BBB B(A0A8GPl
+8A0A(B BBBI L Ȉ BBB B(A0A8GPl
+8A0A(B BBBI L ܉ BBB B(A0A8GPl
+8A0A(B BBBI L , h BBB B(A0A8GPl
+8A0A(B BBBI L | BBB B(A0A8GPl
+8A0A(B BBBI L ̊ BBB B(A0A8GPl
+8A0A(B BBBI L H BBB B(A0A8GPl
+8A0A(B BBBI L l BBB B(A0A8GPl
+8A0A(B BBBI L ؑ BBB B(A0A8GPl
+8A0A(B BBBI L ( BBB B(A0A8GPl
+8A0A(B BBBI L \ x BBB B(A0A8GPl
+8A0A(B BBBI L ȕ BBB B(A0A8GPl
+8A0A(B BBBI L BBB B(A0A8GPl
+8A0A(B BBBI L L h BBB B(A0A8GPl
+8A0A(B BBBI L ? BBB B(M0A8GP7
+8A0A(B BBBA < D 8' ' BED I(D0
+(A ABBA D (@ BBB I(A0D
+0A(A EBBA L t ] BBB H(A0D8DP
+8A0A(B BBBG Ď 0= T 4 X"
+ W BBB E(A0A8G
+8A0A(B BBBA T 0 ǟ BBE B(D0A8G:
+8D0A(B BBBB L h BHI E(A0A8G`@
+8A0A(B BBBE T 4 k BBB J(A0D8Dy
+8D0A(B BBBA T A BBB B(A0A8GK
+8A0A(B BBBA h. K^ $ `0 AGJ XDA ̐ h `Y } N $ g AG k
+AA 4 | _ BHA G@h
+ DABA \ Ȳ. K^ t Е\ AG RAD `7 BBE A(A0D@
+0A(A BBBA D ܑ XÛ7 BBE A(A0D@
+0A(A BBBA D $ PěC BBE A(A0D@
+0A(A BBBA D l XśC BBE A(A0D@
+0A(A BBBA , `ƛU AGG m
+AAB l < ƛ BBB B(A0A8D1
8A0A(B BBBA Z
-8A0A(B BBBA T P G> BEE E(D0A8G
-8D0A(B BBBE L L x BBB B(A0A8Gp8A0A(B BBB $ V ADD JAAL đ 0 BBB B(A0A8Gp8A0A(B BBB < BBG A(G@
-(A ABBF l T `y BBF D(A0G@I
+8A0A(B BBBA T ʛ k BEE E(D0A8G
+8D0A(B BBBE L (ϛ BBB B(A0A8Gp8A0A(B BBB $ ЛV ADD JAAL $ Л BBB B(A0A8Gp8A0A(B BBB < t `қ BBG A(G@
+(A ABBF l ӛy BBF D(A0G@I
0A(A BBBI w
0A(A BBBA D
-0F(A BBBG l Ē py BBF D(A0G@I
+0F(A BBBG l $ ՛y BBF D(A0G@I
0A(A BBBI w
0A(A BBBA D
-0F(A BBBG L \ G< BEB E(I0H8D@
-8D0A(B BBBA D < BKI D(A0G@L
-0A(A BBBA T
; BBB B(D0D8J
-8A0A(B BBBF $ . K^ L d + 3< BEB E(I0H8DP
-8D0A(B BBBD d < BBB B(I0A8G`
+0F(A BBBG L 0כ k BEB E(I0H8D@
+8D0A(B BBBA D < ؛ C BKI D(A0G@L
+0A(A BBBA T 8ڛ
# BBB B(D0D8J
+8A0A(B BBBF Э. K^ L X+ W BEB E(I0H8DP
+8D0A(B BBBD d D 8 / BBB B(I0A8G`
8F0A(B BBBF g
-8F0A(B BBBA T ; BEB B(A0D8G
-8D0A(B BBBA L t M ; BEB B(A0A8G
-8A0A(B BBBA $ . AAG0AA ĕ \ AG RAD 7 BBE A(A0D@
-0A(A BBBA D , ÞC BBE A(A0D@
-0A(A BBBA t Ğ+ \N < ĞB 6; BBA A(Jd
-(A ABBA | ̖ Ş BBE B(G0L8DPu
+8F0A(B BBBA T ` Ԛ BEB B(A0D8G
+8D0A(B BBBA L M ݚ BEB B(A0A8G
+8A0A(B BBBA $ ʕ AAG0AA $ ,˕\ AG RAD D `7 BBE A(A0D@
+0A(A BBBA D XC BBE A(A0D@
+0A(A BBBA Ԙ `+ \N < D xB Z BBA A(Jd
+(A ABBA | , BBE B(G0L8DPu
8A0A(B BBBG S
8E0A(B BBBG W
-8E0A(B BBBK , L zV BAA D0I AAB< | Ǟ BBG A(G@
-(A ABBG < Ǟ BBG A(G@x
-(A ABBG < 8Ȟ BBG A(G@
-(A ABBD < < Ȟ BBG A(G@
-(A ABBD T xɞ f9 BBB E(A0A8J
-8A0A(B BBBH Ԙ ڞ% T (ڞ) 9 BEE B(A0G8G Y
-8A0A(B BBBA < l ; BIA A(D`9
-(A ABBA \ AG RAD @7 BBE A(A0D@
-0A(A BBBA D 8C BBE A(A0D@
-0A(A BBBA < 4 @ BBG A(G@
-(A ABBH < t BBG A(G@
-(A ABBG 5 ̚ T KH4 J BDA m
+8E0A(B BBBK , ʕV BAA D0I AAB< ܙ BBG A(G@
+(A ABBG < h
+ BBG A(G@x
+(A ABBG < \
+ BBG A(G@
+(A ABBD < BBG A(G@
+(A ABBD < ܚ ( BBG A(G@
+(A ABBH T t = J BBB E(A0A8J
+8A0A(B BBBH t % < BBG A(G@
+(A ABBG ̛ 5 < < g BIA A(D`9
+(A ABBA d | p O BBB E(D0A8Dp
+8A0A(B BBBF k
+8A0A(B BBBA T ( Q+ BEE B(A0G8G Y
+8A0A(B BBBA Ǖ\ AG RAD K7 BBE A(A0D@
+0A(A BBBA D L LC BBE A(A0D@
+0A(A BBBA < M BBG A(G@
+(A ABBH < ԝ M BBG A(G@
+(A ABBG pN5 , XT KH4 D NJ BDA m
ABI A
-ABA T D /: BEE B(A0G8GM
-8A0A(B BBBA D t p BBE A(A0D@
-0A(A BBBA \ AG RA, ܛ ( AAG0
-AAA D 7 BBE A(A0D@
-0A(A BBBA $ T Q AG L
-DH oA, ( 9 AAG@G
-AAF - AU
-J L L r9 BEB B(A0K8D
-8A0A(B BBBD T D " _9 BBE B(A0A8Gv
-8A0A(B BBBD 4 t (3 BAD
-ABE AAB ** A[
-L A L ̝ * BBB B(A0D8G@^
-8A0A(B BBBD < + Ac
+ABA T ԝ N BEE B(A0G8GM
+8A0A(B BBBA Ԟ @bL Ad
+K W D pb
+ BBE A(A0D@
+0A(A BBBA D < 8c BBE A(A0D@
+0A(A BBBA ĕ\ AG RA, c AAG0
+AAA $ ԟ dQ AG L
+DH oA, T d AAG@G
+AAF t , ei BBA D(D@g
+(D ABBC [
+(D ABBD
+(D ABBG T
+(D ABBC L h& BBB B(A0D8DP
+8D0A(B BBBG L o BBB B(A0D8DP
+8D0A(B BBBG L D v- BBB B(A0D8DP
+8D0A(B BBBG t | BBD A(D0]
+(D ABBE T
+(D ABBK U
+(D ABBB Z
+(D ABBE L d ~ BEB B(A0K8D
+8A0A(B BBBD T BBE B(A0A8G>
+8A0A(B BBBD 4 3 BAD
+ABE AAB 舜* A[
+L A , d M ADG0_
+DAA < < Ac
D ]
K E
K H
H H
H E
-K C < \ + BDA k
+K C D Ԣ x ' BGB D(M0B
+(A BBBK < ģ BDA k
ABK @
-ABA L H, BOB A(A0]
+ABA L 0 BOB A(A0]
(A BBBG Y
-(A BBBH < ,4 BBB A(A0d
-(A BBBE L , -K BBB B(A0A8G@d
-8A0A(B BBBA | .T , /@ 67 BDA b
-ABA L / 7 BEB E(I0H8D@
-8D0A(B BBBA d < 0j 6 BBB E(D0A8GP
+(A BBBH < T 4 BBB A(A0d
+(A BBBE L K BBB B(A0A8G@d
+8A0A(B BBBA T < d Γ BBB B(D0A8G
+8D0A(B BBBA < T T T , Ĥ H@ U BDA b
+ABA L X 7 BEB E(I0H8D@
+8D0A(B BBBA d D j BBB E(D0A8GP
8A0A(B BBBK D
-8A0A(B BBBA L 1 6 BEB E(I0H8D@
-8D0A(B BBBA < ̠ 83 BDI ~
+8A0A(B BBBA L BEB E(I0H8D@
+8D0A(B BBBA < BDI ~
AGK K
-ABB < 3 BBG A(G@
-(A ABBG L L x4 BBB B(A0D8GP>
-8A0A(B BBBD , 5 BAD b
-ABD ̡ hT KH 6" DR
-J A , 06t AAG0v
-AAA d \ 6 5 BBB E(D0A8Dp
-8A0A(B BBBE k
-8A0A(B BBBA T ġ 78 4 BDB B(D0D8G
-8A0A(B BBBA T < 4 BBE E(D0A8G
-8A0A(B BBBA l t @ 4 BBB L(A0D8DM
+ABB < BBG A(G@
+(A ABBG L $ BBB B(A0D8GP>
+8A0A(B BBBD , t BAD b
+ABD L q BBB E(A0K8GQ
+8A0A(B BBBG 1 Dl ОT KH , " DR
+J A , L Țt AAG0v
+AAA T ԧ BDB B(D0D8G
+8A0A(B BBBA T , `g BBE E(D0A8G
+8A0A(B BBBA l x BBB L(A0D8DM
8A0A(B BBBA
-8A0A(B BBBA 8B A4 BDB B(D0D8DpL
+8A0A(B BBBA BDB B(D0D8DpL
8A0A(B BBBD P
-8A0A(B BBBF
-8A0A(B BBBA L l D_ 3 BBB B(A0A8JU
-8A0A(B BBBE L F 3 BBB E(A0A8J
-8A0A(B BBBB L PKZ
3 BBB B(A0D8Jd
-8A0A(B BBBK T \ `X 4 BBB B(A0A8Q<
-8A0A(B BBBG T c'
- 4 BEE E(A0D8G
-8A0A(B BBBA mY } N $ mg AG k
-AA 4 L 0n _4 BHA G@h
- DABA L n@ C4 BBB B(D0A8D
-8A0A(B BBBG , ԥ q> "4 BDI X
-ABA , ܦ q AAG0
-AAA L r BBB A(A0
-(A BBBK Y
-(A EBBM L \ Xs BBB A(A0
-(A BBBG Y
-(A EBBE L t BBB A(A0
-(A BBBK Y
-(A EBBM L t BBB A(A0
-(A BBBG Y
-(A EBBE L t xu 2 BJB A(A0W
-(A BBDH ^(A BBBT u0 BAA G0@
+8A0A(B BBBF
+8A0A(B BBBA L | F BBB B(A0A8JU
+8A0A(B BBBE L ̩ ' BBB E(A0A8J
+8A0A(B BBBB L
+ BBB B(A0D8Jd
+8A0A(B BBBK L l W
+ w BBB B(A0A8JU
+8A0A(B BBBE L Ü ِ BBB B(A0A8Q
+8A0A(B BBBF T Μ BEE E(A0D8G}
+8A0A(B BBBA ֜Y } N $ $ P֜g AG k
+AA 4 ֜ K BHA G@h
+ DABA L ܫ @ל@ / BBB B(D0A8D
+8A0A(B BBBG , , 0ڜ> BDI X
+ABA , @ڜ AAG0
+AAA L ۜ BJB A(A0W
+(A BBDH ^(A BBBT Pۜ0 BAA G0@
AABA
AABG e
- AABG T | 1 BHB B(D0D8JW
-8A0A(B BBBG L H~T KHT G2 BBE B(A0A8G
-8A0A(B BBBJ \ AG RAD ܩ 7 BBE A(A0D@
-0A(A BBBA D $ C BBE A(A0D@
-0A(A BBBA , l AAG0s
-AAA D ĩ ;2 BGB D(M0B
-(A BBBK L X 2 BIN B(A0C8Gpg
-8A0A(B BBBI L 4 n BBB B(A0A8Dp
-8A0A(B BBBA L 0 BBB A(A0
-(A BBGG A
-(A BBDA ԫ |T KHT x6 /1 BBB B(A0A8J
+ AABG T 4 ( BHB B(D0D8JW
+8A0A(B BBBG 4 T KHT h! s BBB B(A0A8G
+8A0A(B BBBH \ AG RAD Į 7 BBE A(A0D@
+0A(A BBBA D C BBE A(A0D@
+0A(A BBBA , T AAG0s
+AAA D ܮ BGB D(M0B
+(A BBBK L $ xX _ BIN B(A0C8Gpg
+8A0A(B BBBI L n BBB B(A0A8Dp
+8A0A(B BBBA L l BBB A(A0E
+(A BBGG I
+(A BBDA 薖T KHT , 6 BBB B(A0A8J
-8C0A(B BBBH T l `ßv 1 BEE E(A0G8G
+8C0A(B BBBH T .v BEE E(A0G8G
-8A0A(B BBBA \ AG RAD hԟ BBB D(A0D@
-0A(A BBBA D ֟ BBB D(A0D@
-0A(A BBBA d t Xٟ 1 BBB E(D0D8D`
+8A0A(B BBBA \ AG RAD ? BBB D(A0D@
+0A(A BBBA D hB BBB D(A0D@
+0A(A BBBA d D BBB E(D0D8D`
8D0A(B BBBD O
-8A0A(B BBBA T ܬ ڟN 21 BBE E(G0A8Gb
-8A0A(B BBBG < ޟ BBG A(G@
-(A ABBF L t ߟ 0 BEB E(I0H8DP
-8D0A(B BBBD T ĭ x3 0 BBE E(A0A8D
-8A0A(B BBBE `y. K^ HY } N $ $ g AG k
-AA l t C0 BBB B(D0K8G
+8A0A(B BBBA T EN BBE E(G0A8Gb
+8A0A(B BBBG < J BBG A(G@
+(A ABBF L 0K K BEB E(I0H8DP
+8D0A(B BBBD T ܲ M3 # BBE E(A0A8D
+8A0A(B BBBE ܳ (. K^ OY } N $ Pg AG k
+AA l `P BBB B(D0K8G
8C0A(B BBBE |
-8A0A(B BBBA 4 h / BHA G@h
- DABA xT KHT 4 \ / BEE B(A0A8G?
-8D0A(B BBBA d (\ AG RAD 7 BBE A(A0D@
-0A(A BBBA D ̰ C BBE A(A0D@
-0A(A BBBA < BEA A(G@[
-(A ABBG < T i BEA A(G@[
-(A ABBG T ?! g. BHE B(A0A8Ga
-8A0A(B BBBA 4 v w/ BHA D@
- AABA $ $ 0 AGJ XDA $ L D XYOC 4 t c AAG \
-CAG dFA Y } N $ IJ Pg AG k
-AA 4 . BHA G@h
- DABA L L @| . BBB E(A0A8J
-8C0A(B BBBB t 0wT KHL X$ AAG e
+8A0A(B BBBA 4 Q G BHA G@h
+ DABA ܴ XT KHT L R\ BEE B(A0A8G>
+8D0A(B BBBA L &\ AG RAD l hU7 BBE A(A0D@
+0A(A BBBA D `VC BBE A(A0D@
+0A(A BBBA < hW BEA A(G@[
+(A ABBG < < HXi BEA A(G@[
+(A ABBG T Ե xY?! Ì BHE B(A0A8Ga
+8A0A(B BBBA 4 , Ӎ BHA D@
+ AABA $ (z0 AGJ XDA $ 4 0zD XYOC 4 \ Xzc AAG \
+CAG dFA zY } N $ zg AG k
+AA 4 , { BHA G@h
+ DABA L d {| ߌ BBB E(A0A8J
+8C0A(B BBBB \ T KHL t AAG e
CAF s
ACA R
-CAJ JCA L ܳ $ AAG ]
+CAJ JCA L ĸ 0 AAG ]
CAF c
CAI R
-CAJ JCA L , $ AAG ]
+CAJ JCA L p AAG ]
CAF c
CAI R
-CAJ JCA | l\ AG RA4 % BDD G@g
- AABD D Դ `% BBE A(A0D@
-0A(A BBBA D (' BBE A(A0D@
-
-0A(A BBBA T )| , BBE E(D0A8G
-8D0A(B BBBE L (. BBB B(A0A8Gp8A0A(B BBB L / BBB B(A0A8Gp8A0A(B BBB L \ (1 BBB B(A0A8Gp8A0A(B BBB L 2; BBB B(A0A8Gp 8A0A(B BBB $ 4 AG
-AA L $ @5 BFE E(A0A8DP
-8D0A(B BBBJ d t 6r BFB E(D0A8DP
+CAJ JCA d j\ AG RA4 BDD G@g
+ AABD D 萝 BBE A(A0D@
+0A(A BBBA D BBE A(A0D@
+0A(A BBBA T H| J BBE E(D0A8G
+8D0A(B BBBE L p BBB B(A0A8Gp8A0A(B BBB L BBB B(A0A8Gp8A0A(B BBB L D p BBB B(A0A8Gp8A0A(B BBB L ; BBB B(A0A8Gp 8A0A(B BBB $ AG
+AA L BFE E(A0A8DP
+8D0A(B BBBJ d \ Xr BFB E(D0A8DP
8F0A(B BBBG
-8D0A(B BBGA d ܷ (72 BFE E(A0A8DP
+8D0A(B BBGA d ļ p2 BFE E(A0A8DP
8F0A(B BBBD
-8D0A(B BBGD D 9 d \ 95 BBE B(D0I8DP
+8D0A(B BBGD , H d D 05 BBE B(D0I8DP
8D0A(B BBBC q
-8D0A(B BBBB T < ) BBB B(A0A8G
-8A0A(B BBBB L A BBB B(D0A8D@d
-8D0A(B BBBF L hC ;) BEB E(I0H8DP
-8D0A(B BBBD d (EI BBE E(A0A8D@
+8D0A(B BBBB T ( BBB B(A0A8G
+8A0A(B BBBB L @ BBB B(D0A8D@d
+8D0A(B BBBF L BEB E(I0H8DP
+8D0A(B BBBD d pI BBE E(A0A8D@
8F0A(B BBBC S
-8F0A(B BBBF L $ F BBB E(A0D8D@x
-8A0A(B BBBJ T F [( BBH E(A0A8D{
-8D0A(B BBBA L ̺ XI BBB E(A0D8D@x
-8A0A(B BBBJ , J ADD ]
-AAA D t J ' BBJ A(D0
-(A BBEA , `K ' GA A(D L LG x' BBE H(A0A8J~
-8A0A(B BBBA n. K^ , W" DR
-J A T t WV ' BLB E(G0A8J&
-8A0A(B BBBI T ̻ ^ ' BBB B(A0A8J
-8C0A(B BBBF T $ xj* ' BBB B(A0I8Gl
-8D0A(B BBBA T | Por ' BBE E(D0A8J
-8A0A(B BBBA xt! A[ ̽ tY } N $ tg AG k
-AA 4 4 u ' BHA G@h
- DABA D l. K^ T u & BEE E(A0A8Gu
-8A0A(B BBBG $ AAG0AA ܾ H& `$ A[
-D C $ pd AW
+8F0A(B BBBF L X BBB E(A0D8D@x
+8A0A(B BBBJ T BBH E(A0A8D{
+8D0A(B BBBA L BBB E(A0D8D@x
+8A0A(B BBBJ , ` ADD ]
+AAA D е BBJ A(D0
+(A BBEA , Կ GA A(D L G ԅ BBE H(A0A8J~
+8A0A(B BBBA . K^ Ý" DR
+J A T ÝV BLB E(G0A8J&
+8A0A(B BBBI T ʝ BBB B(A0A8J
+8C0A(B BBBF T < ՝* H BBB B(A0I8Gl
+8D0A(B BBBA T ڝr BBE E(D0A8J
+8A0A(B BBBA ߝ! A[ ߝY } N $ g AG k
+AA 4 L ` { BHA G@h
+ DABA , . K^ T G BEE E(A0A8Gs
+8A0A(B BBBI $ AAG0AA & $ A[
+D C $ d AW
H y
-G C T d & BED G(GP
+G C \ $ BBB E(A0A8DP{
+8D0A(B BBBG D8F0A(B BBBL Pp BBA D(D@E
+(C ABBF D(F ABB T , p S BED G(GP
(C ABBE I
-(C CBBE D l& BDG GP3
+(C CBBE D L BDG GP3
CABC I
- CCBG | ܿ X BBB B(D0A8DP
+ CCBG | t BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI L ؊] % BBB B(A0D8Jpi
-8D0A(B BBBC jV Ko
-A l ȍ BBB A(A0G`]
+8A0A(B BBBI L L @] o BBB B(A0D8Jpi
+8D0A(B BBBC D ЄV Ko
+A l d 0 BBB A(A0G`]
0A(A BBBD c
-0C(A BBBK D0F(A BBB , < Z BDD D@G AAB, l ._ BDD D@L AAB< ] BEE D(D0(A BBB $ Є AAG0AA4 , Hg 4$ BAD D
-ABA < x\ AG RAD \ BBE A(A0D@
-0A(A BBBA D 7 BBE A(A0D@
-0A(A BBBA , ؎ X# AAG@G
-AAF % AQ
-F L < . Aa
-F E < \ Ac
+0C(A BBBK D0F(A BBB , RZ BDD D@G AAB, |_ BDD D@L AAB< 4 BEE D(D0(A BBB $ t AAG0AA4 g BAD D
+ABA ơ\ AG RAD p BBE A(A0D@
+0A(A BBBA D < H7 BBE A(A0D@
+0A(A BBBA , @ AAG@G
+AAF % AQ
+F L . Aa
+F E < Ac
D ]
K E
K H
H H
H E
-K C D " BGB D(M0B
-(A BBBK D p " BGB D(M0B
-(A BBBK , ؐm A~
-A l d L (= BIB B(A0A8D`
+K C D p f BGB D(M0B
+(A BBBK | m A~
+A l d (= BIB B(A0A8D`
8F0A(B BBBI
-8D0A(B BBBA L BBB B(A0A8D
-8A0A(B BBBA L 8 BBB B(A0D8D@
-8A0A(B BBBJ < T BBG A(G@
-(A ABBG \ @ KFF E(D0D8G@8A0A(B BBBG@ < BBG A(G@
-(A ABBG 4 % L xeT KHL BHB E(D0D8J8D0A(B BBB T PTM BBB B(A0A8G`
+8D0A(B BBBA L BBB B(A0A8D
+8A0A(B BBBA L T 8 BBB B(A0D8D@
+8A0A(B BBBJ < BBG A(G@
+(A ABBG \ @ KFF E(D0D8G@8A0A(B BBBG@ < D BBG A(G@
+(A ABBG % T KHL B } BHB E(D0D8J8D0A(B BBB T \ PTM } BBB B(A0A8G`
-8A0A(B BBBE T 4 X $ BBE B(A0D8M
-8A0A(B BBBA d \ AG RAD p7 BBE A(A0D@
-0A(A BBBA D hC BBE A(A0D@
-0A(A BBBA T < p N$ BEE B(A0G8G%
-8A0A(B BBBA l cT KH \ AG RAD BBE A(A0D@
-0A(A BBBA D X BBE A(A0D@
-0A(A BBBA D p # BBB A(D0D@%
-0A(A BBBD L ( p# BJB A(A0W
-(A BBDH ^(A BBBD x BBE A(A0D@l
-0A(A BBBA D @bT KHT W" " BBB B(G0A8G
-8A0A(B BBBI \ AG RAD .7 BBE A(A0D@
-0A(A BBBA D /C BBE A(A0D@
-0A(A BBBA T 0O7 k# BEE B(A0G8G
-8A0A(B BBBA (aT KH gY } N $ hg AG k
-AA $ AAG0AA4 d (h $ BHA G@h
- DABA L t hf BBB B(A0D8DP
-8D0A(B BBBG T o {$ BEB B(A0A8JU
-8A0A(B BBBJ L D hy r$ BBB E(K0A8D
-8A0A(B BBBA l _T KHL ~u D$ BBB E(A0D8J
-8D0A(B BBBD Y } N $ g AG k
-AA , P AAG0
-AAA 4 l _$ BHA G@h
- DABA L | ȋ& BBB B(A0D8DP
-8D0A(B BBBG L BBB B(A0D8DP
-8D0A(B BBBG L ~
+8A0A(B BBBE T XS BBE B(A0D8M
+8A0A(B BBBA (\ AG RAD [7 BBE A(A0D@
+0A(A BBBA D x\C BBE A(A0D@
+0A(A BBBA T ]n 2 BEE B(A0G8G%
+8A0A(B BBBA ~T KH n xn 4\ AG RAD $ `n BBE A(A0D@
+0A(A BBBA D l q BBE A(A0D@
+0A(A BBBA D sp BBB A(D0D@%
+0A(A BBBD L T x a BJB A(A0W
+(A BBDH ^(A BBBD L (y BBE A(A0D@l
+0A(A BBBA |T KHT y0" BBB B(G0A8G
+8A0A(B BBBI \ AG RAD $ `7 BBE A(A0D@
+0A(A BBBA D l XC BBE A(A0D@
+0A(A BBBA T `6 [ BEE B(A0G8G
+8A0A(B BBBA {T KH $ ӞY } N $ < XӞg AG k
+AA $ d AAG0AA4 xӞ ˂ BHA G@h
+ DABA T Ԟ BEB B(A0A8JU
+8A0A(B BBBJ L ݞf BBB B(A0D8DP
+8D0A(B BBBG L V BBB E(K0A8D
+8A0A(B BBBA 8zT KHL , e ( BBB E(A0D8J
+8D0A(B BBBD $ Y } N $ < Hg AG k
+AA , d AAG0
+AAA 4 ` C BHA G@h
+ DABA L ~
BEE B(A0A8G`
-8A0A(B BBBB L 8* S# BBB E(K0A8D
-8C0A(B BBBF L BEB A(D0l
+8A0A(B BBBB L t 8* ׁ BBB E(K0A8D
+8C0A(B BBBF L l BEB A(D0l
(A BBBG
-(A BBBA ]T KHT L ЪU " BBB B(A0D8J]
-8A0A(B BBBB | ġY } N $ šg AG k
-AA , hš AAG0
-AAA 4 8ơ # BHA G@h
- DABA L L ơ s# BBB E(K0A8D
-8A0A(B BBBA t \T KHL ̡ E# BEB B(A0D8G
-8A0A(B BBBF $ 0 AGJ XDA Y } N $ g AG k
-AA d l P # BEL B(D0A8D
+(A BBBA xT KHT , Z BBB B(A0D8J]
+8A0A(B BBBB , h%Y } N $ D %g AG k
+AA $ l AAG0AA4 % BHA G@h
+ DABA L $ x& BBB E(K0A8D
+8A0A(B BBBA wT KHL +9 с BEB E(A0A8G
+8A0A(B BBBB $ `d0 AGJ XDA hdY } N $ dg AG k
+AA d D d x BEL B(D0A8D
8C0A(B BBBE c
-8D0A(B BBBA 4 s# BHA G@h
- DABA [. K^ 4 $ g ?# BAD D
-ABA 4 \ Pg # BAD D
-ABA l }\ AG RAD h BBE A(A0D@
-0A(A BBBA D @7 BBE A(A0D@
-0A(A BBBA 4 D F}W 7" BAD JA AAB T E lX , 8M " ADG0_
-DAA < X BBG A(G@
-(A ABBG % 4 } BED C(D@(A ABBL T v~
- b! BBB E(D0K8G
-8D0A(B BBB | YT KHL Ո ! BBB H(D0A8G8A0A(B BBB T p9 ! BEE B(A0G8GrA
-8A0A(B BBBA D < 8'O BBI A(A0D@
-0A(A BBBA D @(a BBB H(A0D@
-0A(A BBBA l h) {$ BBB B(A0D8Gp
+8D0A(B BBBA 4 0f + BHA G@h
+ DABA v. K^ 4 fg BAD D
+ABA 4 4 fg ˁ BAD D
+ABA ԛ\ AG RAD 4 g BBE A(A0D@
+0A(A BBBA D | g7 BBE A(A0D@
+0A(A BBBA 4 W BAD JA AAB hE lX h% 4 , o BED C(D@(A ABBL
+ z BBB E(D0K8G
+8D0A(B BBB tT KHL $ ր BBB H(D0A8G8A0A(B BBB T t h8 BEE B(A0G8GqT
+8A0A(B BBBE t |\ AG RAD `$ BEL D(D0Dt
+0A(A BBBE L 4 H6 BEE L(D0D8Dt
+8A0A(B BBBF D , 87 BBE A(A0D@
+0A(A BBBA D t 0C BBE A(A0D@
+0A(A BBBA L 8
+ BLB B(A0D8J
+8D0A(B BBBD T d 0 BLB B(D0D8J5
+8D0A(B BBBJ t d 0 KHE A(GP(A ABBEP
+(A ABBM 4
+(A ABBF 4 4 q BHA D@
+ AABA T l BEE B(A0G8GJ
+8A0A(B BBBA D l ПO BBI A(A0D@
+0A(A BBBA D џa BBB H(A0D@
+0A(A BBBA l T Hҟ G BBB B(A0D8Gp
8F0A(B BBBA
-8D0A(B BBBA T d + +$ BBB B(A0A8Lq
-8A0A(B BBBG `W. K^ T H/ # BBB B(A0A8GQ
-8A0A(B BBBA
-\ AG RAD $ `;7 BBE A(A0D@
-0A(A BBBA D l X<C BBE A(A0D@
-0A(A BBBA T `=n G# BBE B(A0D8Mr
-8A0A(B BBBG 4 4 V # BHA D@
- AABA D &\ AG RAD d P7 BBE A(A0D@
-0A(A BBBA D QC BBE A(A0D@
-0A(A BBBA T RP '# BEE B(A0G8G
-8A0A(B BBBA < t V K$ BIA A(D`9
-(A ABBA :\ AG RAD k7 BBE A(A0D@
-0A(A BBBA D lC BBE A(A0D@
-0A(A BBBA < WT KHT | m k# BEE B(A0G8G m
-8A0A(B BBBA ؈ AZ ؈ HS L AAG ]
+8D0A(B BBBA T ԟ BBB B(A0A8Lq
+8A0A(B BBBG q. K^ T 4 (؟ BBB B(A0A8GQ
+8A0A(B BBBA 4 \ AG RAD T @7 BBE A(A0D@
+0A(A BBBA D 8C BBE A(A0D@
+0A(A BBBA T < @n BBE B(A0D8Mr
+8A0A(B BBBG 4 Xp BHA D@
+ AABA t 4\ AG RAD 7 BBE A(A0D@
+0A(A BBBA D C BBE A(A0D@
+0A(A BBBA < | BGD D(K0l
+(A ABBB , d V BAA D0I AABT ` BEE B(A0G8G
+C
+8A0A(B BBBA < D hp BIA A(D`9
+(A ABBA , .\ AG RAD L X7 BBE A(A0D@
+0A(A BBBA D PC BBE A(A0D@
+0A(A BBBA xqT KHT L @" BEE B(A0G8G
+8A0A(B BBBA L 3 AZ l 3 3 HS L 3 AAG ]
CAF c
CAI R
-CAJ JCA $ T ^ AG b
-AA 4 | X BAA G0y
- AABH $ DX
+CAJ JCA $ `3^ AG b
+AA 4 3 BAA G0y
+ AABH $ T 5 DX
D n
-B T 8 7# BKI B(A0A8G
-8A0A(B BBBD T \ " BHE B(D0A8L{
-8D0A(B BBBF L ȗ O# BEB E(I0H8D@
-8D0A(B BBBA L H '# BEB E(I0H8D@
-8D0A(B BBBA , , ؚ BCD D0 AABL x " BEB E(I0H8D@
-8D0A(B BBBA L " BEB E(I0H8D@
-8D0A(B BBBA L $ " BEB E(I0H8D@
-8D0A(B BBBA T t 8 W" BBB B(H0A8Gx
-8A0A(B BBBF L p o# BEB E(I0H8DP
-8D0A(B BBBD d 0, G# BGE E(A0A8D@
+B T x5 BKI B(A0A8G
+8A0A(B BBBD L , 9 g BEB E(I0H8D@
+8D0A(B BBBA L | P; ? BEB E(I0H8D@
+8D0A(B BBBA , t < BCD D0 AABL = BEB E(I0H8D@
+8D0A(B BBBA L L ? BEB E(I0H8D@
+8D0A(B BBBA L @ BEB E(I0H8D@
+8D0A(B BBBA T @B o BHE B(D0A8L{
+8D0A(B BBBF T D J Ӏ BBI B(A0A8G8
+8A0A(B BBBF L p` BEB E(I0H8DP
+8D0A(B BBBD d 0b, Á BGE E(A0A8D@
8F0A(B BBBJ g
-8F0A(B BBBA T # BBB B(A0A8G/
-8A0A(B BBBF RT KH H @$ Ab \ AG RA, $ 0 AAG0W
-AAF 4 | _" BDA D0
- AABA 4 +" BDA D0
- AABA L P¢ ! BBE B(A0A8DPn
-8A0A(B BBBA T < Ţ ! BBB I(A0A8G
-8A0A(B BBBA T XȢ k! BBB I(A0A8G
-8A0A(B BBBA 4 ˢc +! BAA G`
- DABB D $ ͢ 0! BBA D(GD
-(D ABBC D `P. K^ \ Ϣ t Ϣ Ϣ Ϣ Ϣ Ϣ Ϣ Ϣ Ϣ- 4 4 Ϣ BGI _
-ABD NAB , ТE BDC rAH T 8Т BBB J(A0A8G
-8D0A(B BBBG L բ/ BEB B(A0A8D`u
-8A0A(B BBBH , l ע o AAG0}
-CAF t `N. K^ 8آ 0آF hآ `آ \ AG RAL 4 HآX BBB B(A0A8Ji
-8A0A(B BBBI T X BBE B(D0A8G
-8D0A(B BBBG T , BBE B(D0A8G
-8D0A(B BBBK T 4 BBB B(D0D8G
-8A0A(B BBBH D d v~ BBB D(A0D@0A(A BBB T 8 BIB E(A0A8G5
-8D0A(B BBBA L , k BDE E(G0A8G
-8D0A(B BBBH L | # BBB I(A0A8JY
-8D0A(B BBBG `KT KHL BBE B(D0A8G
-8A0A(B BBBA , 4 g AID @
-DAA < H , Hc \ X 3 Hf | x , Hc 3 Hf , Hc 3 Hf < Hs < Hs < C Hv \ H C Hv $ | x ^ AG b
-AA T , BBE B(D0A8G
-8D0A(B BBBH T $ x"L BBB B(A0D8R
-8A0A(B BBBH , | p-> AID W
-DAA T -L BBB B(A0D8R
-8A0A(B BBBH , x8> AID W
-DAA T 4 8g BBB B(D0A8R|
-8A0A(B BBBK , @> @ AID W
-DAA T @g BBB B(D0A8R|
-8A0A(B BBBK , H> f AID W
-DAA T D H F BBB B(A0I8G
-8D0A(B BBBF T N f BBE B(D0A8G
-8D0A(B BBBG T R L BBE B(D0A8G
-8D0A(B BBBH D $ W BBB D(A0D@
-0A(A BBBA D Y| BIA D(D
-(D ABBA T @\ BIB E(A0A8D
-8D0A(B BBBA T 4 ` BIB E(A0A8D
-8D0A(B BBBA L e BIB A(D0D
-0D(A BBBA T i f BIB E(A0A8D
-8D0A(B BBBA L 4 m ? BBB B(G0D8G
-8A0A(B BBBA < p ? AAG L
+8F0A(B BBBA T T b BBB B(A0A8G/
+8A0A(B BBBF T `lT KH l Hj @j$ Ab \ AG RA, 0j AAG0W
+AAF 4 L j ۀ BDA D0
+ AABA 4 k BDA D0
+ AABA L Pl s BBE B(A0A8DPn
+8A0A(B BBBA T o ' BBB I(A0A8G
+8A0A(B BBBA T d Xr BBB I(A0A8G
+8A0A(B BBBA 4 u BAA GP
+ DABB D w BBA D(GD
+(D ABBC 0j. K^ Xy Py , Hy D @y \ 8y t @y Hy Py Xy- 4 py BGI _
+ABD NAB , d yE f~ BDC rAH T y :~ BBB J(A0A8G6
+8D0A(B BBBD L / BEB B(A0A8D`u
+8A0A(B BBBH , < } AAG0}
+CAF 0h. K^ , D F \ Ȃ t \ AG RAL X } BBB B(A0A8Ji
+8A0A(B BBBI T T 7} BBE B(D0A8G
+8D0A(B BBBG T , } BBE B(D0A8G
+8D0A(B BBBK T X } BBB B(D0D8G
+8A0A(B BBBH D j BBB D(A0D@0A(A BBB T ~ BIB E(A0A8G5
+8D0A(B BBBA L ೠ ~ BDE E(G0A8G
+8D0A(B BBBH L L P# + BBB I(A0A8JY
+8D0A(B BBBG D 0eT KHL à BBE B(D0A8G
+8A0A(B BBBA , hƠg AID @
+DAA Ơ Ơ Ơ $ Ơ < Ơ T Ơ l Ơ0 Hg Ơ7 Hj Ơ0 Hg Ơ7 Hj $ Ǡ^ AG b
+AA 4 l @Ǡ } BGB A(A0D T Ǡ } BBE B(D0A8G
+8D0A(B BBBH T ̠ } BBB B(A0D8R
+8A0A(B BBBK , T נ> "~ AID W
+DAA T נ ~ BBB B(A0D8R
+8A0A(B BBBK , `> ~ AID W
+DAA T p? v~ BBB B(D0A8R]
+8A0A(B BBBJ , d X> ~ AID W
+DAA T h? ~ BBB B(D0A8R]
+8A0A(B BBBJ , P> ~ AID W
+DAA T `g ~ BBB B(A0I8G
+8D0A(B BBBI T t x ~ BBE B(D0A8G
+8D0A(B BBBG T @ ~ BBE B(D0A8G
+8D0A(B BBBH D BBB D(A0D@
+0A(A BBBA T l ` c~ BIB E(A0A8D
+8D0A(B BBBA T <~ BIB E(A0A8D
+8D0A(B BBBA T ~ BIB E(A0A8D
+8D0A(B BBBA T t x } BIB E(A0A8D
+8D0A(B BBBA L @ } BIB A(D0D
+0D(A BBBA L } BBB B(G0D8G
+8A0A(B BBBA < l } AAG L
AAI p
-AAB L xpq BBB A(A0G
-0D(A BBBF < wx BBA N(M0{
-(D ABBA T T w BBB G(D0G8G
-8D0A(B BBBH D P{ BBA A(G
-(D ABBE < x BBA N(M0{
-(D ABBA T 4 BBB G(D0G8G
-8D0A(B BBBH < d `g BBA A(DV(A ABB T g BBE B(A0A8L
-8A0A(B BBBA hA. K^ Е , ȕ , l Е[ _ AID t
-DAA , [ ? AID t
-DAA < tD BBD A(D@0(A ABB T & BIB E(A0A8G
-8D0A(B BBBA T d Ț& BIB E(A0A8G
-8D0A(B BBBA 4 BGB A(C0G T y BBE B(D0A8G
-8D0A(B BBBB L L PU _ BBB B(F0G8G
-8D0A(B BBBK T `\& BDB B(G0D8G
>
-8D0A(B BBBI >. K^ Pң Hң @ң , Hң , l Pң[ ' AID t
-DAA T ң BBB B(A0I8G
-8D0A(B BBBD D r BBB D(A0D@0A(A BBB T < p֣\ BIE B(A0A8DI
-8D0A(B BBBA T xۣb BIE B(A0A8DQ
-8D0A(B BBBA T g BDE B(A0G8G
-8D0A(B BBBI T D H w BBB G(D0G8G
-8D0A(B BBBB t P<. K^ x 4 L nq\ AG RAT x BBE E(D0D8G2
-8A0A(B BBBA , BGD l
-ABD L `q BBA D(G@n
+AAB L @q o} BBB A(A0G
+0D(A BBBF < p"r D} BBA K(M0x
+(D ABBA T < " } BBB G(D0G8G
+8D0A(B BBBH D & /} BBA A(G
+(D ABBE < )r } BBA K(M0x
+(D ABBA T ) | BBB G(D0G8G
+8D0A(B BBBH < H-g BBA A(DV(A ABB T x1 | BBE B(A0A8L
+8A0A(B BBBA [. K^ @ @ , T @[ | AID t
+DAA , @[ | AID t
+DAA < \ D BBD A(D@0(A ABB T @& @| BIB E(A0A8G
+8D0A(B BBBA T L E& | BIB E(A0A8G
+8D0A(B BBBA 4 J { BGB A(C0G T pK { BBE B(D0A8G
+8D0A(B BBBB L 4 8PU { BBB B(F0G8G
+8D0A(B BBBK T HW\& | BDB B(G0D8G
>
+8D0A(B BBBI X. K^ 8} 0} (} 0} , T 8}[ ~ AID t
+DAA T h} k~ BBB B(A0I8G
+8D0A(B BBBD D BBB D(A0D@0A(A BBB T $ X\ /~ BIE B(A0A8DI
+8D0A(B BBBA T | `b } BIE B(A0A8DQ
+8D0A(B BBBA T x } BDE B(A0G8G
+8D0A(B BBBI T , 0 ~ BBB G(D0G8G
+8D0A(B BBBB , V. K^ D \ x t p h ` h p x $ ^ AG b
+AA , , BGD l
+ABD L \ 8q BBA D(G@n
(A ABBD
-(A ABBH T l BBB E(A0A8KP
-8A0A(B BBBF , 8> AID W
-DAA T H BBB E(A0A8KP
-8A0A(B BBBF , L > m AID W
-DAA T | M BBB E(A0A8KP
-8A0A(B BBBF , > AID W
-DAA T BBB E(A0A8KP
-8A0A(B BBBF , \ `> AID W
-DAA D d p BBB D(A0D@
-0A(A BBBA D [ BIB A(D0D`+
-0D(A BBBA D 9 BIB A(D0D`+
-0D(A BBBA D d 8 BIB A(D0D`+
-0D(A BBBA D BIB A(D0D`+
-0D(A BBBA T BEJ E(D0D8G:
-8D0A(B BBBD $ @* A[
-L A D P* A[
-L A d `* A[
-L A p* A[
-L A * A[
-L A * A[
-L A * A[
-L A * A[
-L A T L BBB B(A0A8Jk
-8A0A(B BBBA T x BEE B(A0D8JR
-8A0A(B BBBA T j BBB B(A0A8Jk
-8A0A(B BBBA T T g BBB B(A0A8Jk
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+DAA T } BBB E(A0A8KP
+8A0A(B BBBF , < > | AID W
+DAA T l | BBB E(A0A8KP
+8A0A(B BBBF , h> v| AID W
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+8A0A(B BBBA T l x BEB B(A0A8Je
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+DAA , $0 ٢[ [ AID t
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+DAA , 0 (ڢ[ C[ AID t
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+DAA , 1 ڢ[ Z AID t
+DAA , D1 ڢ[ Z AID t
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+DAA , 1 Hۢ[ Z AID t
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+DAA , 42 ۢ[ #Z AID t
+DAA , d2 ܢ[ Z AID t
+DAA , 2 8ܢ[ Y AID t
+DAA , 2 hܢ[ Y AID t
+DAA , 2 ܢ[ Y AID t
+DAA , $3 ܢ[ Y AID t
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+DAA , 3 (ݢ[ CY AID t
+DAA , 3 Xݢ[ #Y AID t
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+DAA , 4 ݢ[ X AID t
+DAA , D4 ݢ[ X AID t
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+DAA , 4 Hޢ[ X AID t
+DAA , 4 xޢ[ cX AID t
+DAA , 5 ޢ[ CX AID t
+DAA , 45 ޢ[ #X AID t
+DAA , d5 ߢ[ X AID t
+DAA , 5 8ߢ[ W AID t
+DAA , 5 hߢ[ W AID t
+DAA , 5 ߢ[ W AID t
+DAA , $6 ߢ[ W AID t
+DAA , T6 ߢ[ cW AID t
+DAA , 6 ([ CW AID t
+DAA , 6 X[ #W AID t
+DAA , 6 [ W AID t
+DAA , 7 [ V AID t
+DAA , D7 [ V AID t
+DAA , t7 [ V AID t
+DAA , 7 H[ V AID t
+DAA , 7 x[ cV AID t
+DAA , 8 [ CV AID t
+DAA , 48 [ #V AID t
+DAA , d8 [ V AID t
+DAA , 8 8[ U AID t
+DAA , 8 h[ U AID t
+DAA , 8 [ U AID t
+DAA , $9 [ U AID t
+DAA , T9 [ cU AID t
+DAA , 9 ([ CU AID t
+DAA , 9 X[ #U AID t
+DAA , 9 [ U AID t
+DAA , : [ T AID t
+DAA , D: [ T AID t
+DAA , t: [ T AID t
+DAA , : H[ T AID t
+DAA , : x[ cT AID t
+DAA , ; [ CT AID t
+DAA , 4; [ #T AID t
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H P
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- DABA 4 ; T7 # BHA G`
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- DABA 4 |; V7 BHA G`
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- DABA 4 ; X7 BHA G`
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- DABA 4 <= _7 BHA G`
- DABA 4 t= `7 BHA G`
- DABA 4 = a7 BHA G`
- DABA 4 = b7 BHA G`
- DABA 4 > c7 q BHA G`
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- DABA 4 > 0e7 3 BHA G`
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- DABA < B x\ BIA A(Gp
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+(A ABBA T? l? 2 Ah
+G A ? r Af
+I AL ? (" P BBB J(A0A8G
+8D0A(B BBBE D T? P BGB D(D0J
+(A BBBD T ? p XP BBE E(D0A8G/
+8D0A(B BBBJ T ? P BBE E(D0A8G
+8D0A(B BBBE D @ BBB D(A0D@
+0A(A BBBA 4 @ O BDA H
+ABA 4 @ P7 kO BHA G`
+ DABA 4 A X7 LO BHA G`
+ DABA 4 <A `7 -O BHA G`
+ DABA 4 tA h7 O BHA G`
+ DABA 4 A p7 N BHA G`
+ DABA 4 A x 7 N BHA G`
+ DABA 4 B 7 N BHA G`
+ DABA 4 TB 7 N BHA G`
+ DABA 4 B 7 sN BHA G`
+ DABA 4 B 7 TN BHA G`
+ DABA 4 B 7 5N BHA G`
+ DABA 4 4C 7 N BHA G`
+ DABA 4 lC 7 M BHA G`
+ DABA 4 C 7 M BHA G`
+ DABA 4 C 7 M BHA G`
+ DABA 4 D
+7 M BHA G`
+ DABA 4 LD 7 {M BHA G`
+ DABA 4 D 7 \M BHA G`
+ DABA 4 D
7 =M BHA G`
+ DABA 4 D 7 M BHA G`
+ DABA 4 ,E 7 L BHA G`
+ DABA 4 dE 7 L BHA G`
+ DABA 4 E 7 L BHA G`
+ DABA 4 E 7 L BHA G`
+ DABA 4 F 7 L BHA G`
+ DABA 4 DF 7 dL BHA G`
+ DABA 4 |F 7 EL BHA G`
+ DABA 4 F (7 &L BHA G`
+ DABA 4 F 07 L BHA G`
+ DABA 4 $G 87 K BHA G`
+ DABA 4 \G @7 K BHA G`
+ DABA 4 G H7 K BHA G`
+ DABA 4 G P 7 K BHA G`
+ DABA 4 H X 7 lK BHA G`
+ DABA 4 <H ` 7 MK BHA G`
+ DABA 4 tH h 7 .K BHA G`
+ DABA 4 H p 7 K BHA G`
+ DABA 4 H x!7 J BHA G`
+ DABA 4 I "7 J BHA G`
+ DABA 4 TI #7 J BHA G`
+ DABA 4 I $7 J BHA G`
+ DABA 4 I %B tJ BHA G`
+ DABA 4 I &7 UJ BHA G`
+ DABA 4 4J 'B 6J BHA G`
+ DABA 4 lJ (7 J BHA G`
+ DABA 4 J )B I BHA G`
+ DABA 4 J *7 I BHA G`
+ DABA 4 K +7 I BHA G`
+ DABA < LK -\ I BIA A(Gp
+(D ABBA < K .\ tI BIA A(Gp
+(D ABBA < K @/U MI BIA A(Gp
+(D ABBA < L `0U &I BIA A(Gp
+(D ABBA < LL 1\ H BIA A(Gp
+(D ABBA < L 2U H BIA A(Gp
+(D ABBA < L 3U H BIA A(Gp
+(D ABBA < M 4\ H BIA A(Gp
+(D ABBA < LM 6\ cH BIA A(Gp
+(D ABBA < M 7\ <H BIA A(Gp
+(D ABBA < M @8U H BIA A(Gp
+(D ABBA < N `9U G BIA A(Gp
+(D ABBA < LN :U G BIA A(Gp
+(D ABBA < N ;U G BIA A(Gp
+(D ABBA 4 N <B yG BHA G`
+ DABA 4 O =7 ZG BHA G`
+ DABA 4 <O >7 ;G BHA G`
+ DABA 4 tO ?7 G BHA G`
+ DABA 4 O @7 F BHA G`
+ DABA 4 O A7 F BHA G`
+ DABA < P CU F BIA A(Gp
+(D ABBA < \P DU F BIA A(Gp
+(D ABBA < P @EU qF BIA A(Gp
+(D ABBA < P `FU JF BIA A(Gp
+(D ABBA 4 Q G7 #F BHA G`
+ DABA 4 TQ H7 F BHA G`
+ DABA 4 Q I7 E BHA G`
+ DABA 4 Q J7 E BHA G`
+ DABA 4 Q K7 E BHA G`
+ DABA 4 4R L7 E BHA G`
+ DABA 4 lR M7 iE BHA G`
+ DABA 4 R N7 JE BHA G`
+ DABA 4 R O7 +E BHA G`
+ DABA 4 S P7 E BHA G`
+ DABA 4 LS Q7 D BHA G`
+ DABA 4 S R7 D BHA G`
+ DABA 4 S SB D BHA G`
+ DABA 4 S T7 D BHA G`
+ DABA 4 ,T V7 qD BHA G`
+ DABA 4 dT W7 RD BHA G`
+ DABA 4 T X7 3D BHA G`
+ DABA 4 T Y7 D BHA G`
+ DABA 4 U Z7 C BHA G`
+ DABA 4 DU ([7 C BHA G`
+ DABA 4 |U 0\7 C BHA G`
+ DABA 4 U 8]7 C BHA G`
+ DABA 4 U @^7 yC BHA G`
+ DABA 4 $V H_7 ZC BHA G`
+ DABA 4 \V P`7 ;C BHA G`
+ DABA 4 V Xa) C AHJ
+DAA < tW Pc BBA A(G@a
+(A ABBD D W dY B BBB A(A0Gj
+0A(A BBBG < TW (f B BBA A(JE
+(A ABBE d <X gB BBB B(A0A8G@x
8A0A(B BBBE
-8A0A(B BBBF T dO x p BBE E(D0A8G
-8D0A(B BBBI L O M 3 BBB B(A0D8N
-8A0A(B BBBH , P > 7 AID W
-DAA L <P M BBB B(A0D8N
-8A0A(B BBBH , P > AID W
-DAA L P 0M BBB B(A0D8N
-8A0A(B BBBH , Q 0> AID W
-DAA L <Q @t BGB B(A0D8G
-8A0A(B BBBA L Q p¥s BDB B(A0D8G
-8A0A(B BBBA D Q ĥS BEB A(A0Gh
-0A(A BBBA < $R ťS BBD A(JX
-(D ABBA < <S ƥ BEG A(D0M
-(D ABBG L |S xɥJ BBB B(A0A8GP
-8A0A(B BBBK < S xʥ BHB A(A0n(A BBL L T ʥz BBB B(A0A8GPJ
-8A0A(B BBBK < \T ̥ BHB A(A0~(A BBL < T Xͥ BHB A(A0~(A BBL < T ͥ BHB A(A0~(A BBL L U Υ BBB B(A0A8GP
-8A0A(B BBBK < lU ϥj BEG A(D0A
-(D ABBC < U ѥ BEG A(D0J
-(D ABBJ < U ӥ BEG A(D0J
-(D ABBJ < ,V X֥ BHB A(A0n(A BBL < lV ֥ BHB A(A0n(A BBL < V ֥ BHB A(A0n(A BBL < V Hץ BHB A(A0n(A BBL < ,W ץ BHB A(A0n(A BBL < lW ץ BHB A(A0n(A BBL L W 8إ BBB B(A0A8G
-8A0A(B BBBD L W ݥ BBB B(A0A8G
-8A0A(B BBBD < tW (G BBA D(Go
-(A ABBA L W 8 z BBE A(G0D
-0D(A BBBE D X m BBE A(G0D
-0D(A BBBA D LX ` h BBE G(A0D
-0D(A BBBF L X (b c BBE G(A0D4
-0D(A BBBD D X H V BBE A(G0D
-0D(A BBBA L ,Y K Q BBE A(G0D
-0D(A BBBG L |Y ] D BBE G(A0D
-0D(A BBBB | Z BEB B(D0A8Dp
+8A0A(B BBBF T W h %B BBE E(D0A8G
+8D0A(B BBBI L TX XkM A BBB B(A0D8N
+8A0A(B BBBH , X Xn> A AID W
+DAA L X hnM A BBB B(A0D8N
+8A0A(B BBBH , $Y hq> A AID W
+DAA L TY xqM A BBB B(A0D8N
+8A0A(B BBBH , Y xt> A AID W
+DAA L Y tt A BGB B(A0D8G
+8A0A(B BBBA L $Z vs A BDB B(A0D8G
+8A0A(B BBBA D tZ xS A BEB A(A0Gh
+0A(A BBBA < Z zS yA BBD A(JX
+(D ABBA < [ { BEG A(D0M
+(D ABBG L [ }J BBB B(A0A8GP
+8A0A(B BBBK < 4\ ~ BHB A(A0n(A BBL L t\ z BBB B(A0A8GPJ
+8A0A(B BBBK < \ @ BHB A(A0~(A BBL < ] BHB A(A0~(A BBL < D] BHB A(A0~(A BBL L ] ` BBB B(A0A8GP
+8A0A(B BBBK < ] 0j BEG A(D0A
+(D ABBC < ^ ` BEG A(D0J
+(D ABBJ < T^ BEG A(D0J
+(D ABBJ < ^ BHB A(A0n(A BBL < ^ BHB A(A0n(A BBL < _ @ BHB A(A0n(A BBL < T_ BHB A(A0n(A BBL < _ BHB A(A0n(A BBL < _ 0 BHB A(A0n(A BBL L ` BBB B(A0A8G
+8A0A(B BBBD L d` BBB B(A0A8G
+8A0A(B BBBD < ` pG N< BBA D(Go
+(A ABBA L L` 2< BBE A(G0D
+0D(A BBBE D ` P %< BBE G(A0D
+0D(A BBBF D ` < BBE A(G0D
+0D(A BBBA L ,a pb < BBE G(A0D4
+0D(A BBBD D |a < BBE A(G0D
+0D(A BBBA L a 覣K < BBE A(G0D
+0D(A BBBG L b 誣] ; BBE G(A0D
+0D(A BBBB | c BEB B(D0A8Dp
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA < LZ PH BBA D(Gp
-(A ABBA | d[ ` BEB B(D0A8Dp
+8A0A(B BBBA < b H o; BBA D(Gp
+(A ABBA | c BEB B(D0A8Dp
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA d [ BBB B(D0A8DP
+8A0A(B BBBA d Ld H BBB B(D0A8DP
8A0A(B BBBF
-8A0A(B BBBH T t[ h BEB E(A0A8J'
-8A0A(B BBBE , [ y AEG V
-AAA T [ 0] BBB B(A0A8J
-8A0A(B BBBI ,] 8 AO D t\ 8 v BBB A(E0J{
-0A(A BBBA T \ w BBB B(A0A8J
-8A0A(B BBBH ] 8" AO T 4] 8" BBB B(A0A8J
-8A0A(B BBBG d^ - AO T ] - BBB B(A0A8J
-8A0A(B BBBE ^ X8 AO T $^ X8l T BBB B(A0H8G
-8A0A(B BBBA L |^ p>G ] BBB H(A0G
-0A(A BBBA < ^ p@ O BEA A(G@}
-(D ABBJ < _ PA BEG A(D0
-(D ABBC < $` 0Bm BEA G(D0
-(D ABBB < d` `Dm BEA G(D0
-(D ABBB L ` FZ BBB B(A0A8GP2
-8A0A(B BBBC < ` H BHB A(A0n(A BBL L \` HG BBB H(A0G
-0A(A BBBA D ` J BBB A(E0J{
-0A(A BBBA L a HM& BEE B(A0A8D
-8A0A(B BBBA L Da (P u BBB H(A0G<
-0A(A BBBA L lb XU& BEE B(A0A8D
-8A0A(B BBBA L a 8X m BBB H(A0G<
-0A(A BBBA L c h]z BBE E(A0A8DP
-8A0A(B BBBA L b _ e BBB H(A0G<
-0A(A BBBA | c dY BEE B(A0A8D
+8A0A(B BBBH T d k: BEE B(D0A8G
+8A0A(B BBBD , dd hãy : AEG V
+AAA T d ã] : BBB B(A0A8J
+8A0A(B BBBI e ˣ AO D e ˣ .: BBB A(E0J{
+0A(A BBBA T Te Σ /: BBB B(A0A8J
+8A0A(B BBBH Tf գ AO T e գ 9 BBB B(A0A8J
+8A0A(B BBBG f ( AO T Df ( m9 BBB B(A0A8J
+8A0A(B BBBE Dg AO T f l 9 BBB B(A0H8G
+8A0A(B BBBA L g G 9 BBB H(A0G
+0A(A BBBA < dg 9 BEA A(G@}
+(D ABBJ < Lh BEG A(D0
+(D ABBC < h m BEA G(D0
+(D ABBB < h m BEA G(D0
+(D ABBB L i Z BBB B(A0A8GP2
+8A0A(B BBBC < \i ( BHB A(A0n(A BBL L h xG 7 BBB H(A0G
+0A(A BBBA D Di x |7 BBB A(E0J{
+0A(A BBBA L 4j & BEE B(A0A8D
+8A0A(B BBBA L i -7 BBB H(A0G<
+0A(A BBBA L j & BEE B(A0A8D
+8A0A(B BBBA L |j %7 BBB H(A0G<
+0A(A BBBA L tk z BBE E(A0A8DP
+8A0A(B BBBA L k 7 BBB H(A0G<
+0A(A BBBA | l PY BEE B(A0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA L Tc g - BBB H(A0G=
-0A(A BBBA | |d l BBE E(A0A8DP
+8A0A(B BBBA L k 0 6 BBB H(A0G=
+0A(A BBBA | l ` BBE E(A0A8DP
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB L $d xn BBB H(A0G=
-0A(A BBBA | Le sY BEE B(A0A8D
+8A0A(B BBBA 8A0A(B BBB L l " 6 BBB H(A0G=
+0A(A BBBA | m 0'Y BEE B(A0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA L d v BBB H(A0G=
-0A(A BBBA | f { BBE E(A0A8DP
+8A0A(B BBBA L m * u6 BBB H(A0G=
+0A(A BBBA | n @/ BBE E(A0A8DP
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB L e } BBB H(A0G=
-0A(A BBBA | f Y BEE B(A0A8D
+8A0A(B BBBA 8A0A(B BBB L \n p1 =6 BBB H(A0G=
+0A(A BBBA | To 6Y BEE B(A0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA L f M BBB H(A0G=
-0A(A BBBA | g ( BBE E(A0A8DP
+8A0A(B BBBA L ,o 9 6 BBB H(A0G=
+0A(A BBBA | $p > BBE E(A0A8DP
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB L dg Ȍ BBB H(A0G=
-0A(A BBBA < g H ] BBA D(Gp
-(A ABBA | h BEE B(A0A8Dp
+8A0A(B BBBA 8A0A(B BBB L o P@ 5 BBB H(A0G=
+0A(A BBBA < Lp EH 6 BBA D(Gp
+(A ABBA | 4q F BEE B(A0A8Dp
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA L th BBB H(A0G=
-0A(A BBBA | i ؚ BBE E(A0A8DP
+8A0A(B BBBA L q 0I y5 BBB H(A0G=
+0A(A BBBA | r `N BBE E(A0A8DP
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB L Di 蜦 BBB H(A0G=
-0A(A BBBA lj BEE B(A0A8D_
+8A0A(B BBBA 8A0A(B BBB L q pP A5 BBB H(A0G=
+0A(A BBBA r U BEE B(A0A8D_
8A0A(B BBBC
8A0A(B BBBA
-8A0A(B BBBA L j I BBB H(A0G=
-0A(A BBBA | Dk ଦ. BBE E(A0A8DP
+8A0A(B BBBA L r 8[ 5 BBB H(A0G=
+0A(A BBBA | s h`. BBE E(A0A8DP
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB L j BBB H(A0G=
-0A(A BBBA | l R BEE B(A0A8D
+8A0A(B BBBA 8A0A(B BBB L s b 4 BBB H(A0G=
+0A(A BBBA | |t HgR BEE B(A0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA | l BBE E(A0A8DP
+8A0A(B BBBA | t (j BBE E(A0A8DP
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB $ m ฦ) KA|A T dl 躦 1 BBB B(D0A8Gm
-8A0A(B BBBA T l # BBB B(A0A8G[
-8A0A(B BBBA $ m h̦- ADK WDA T <m p̦ BBB B(D0A8Gm
-8A0A(B BBBA 4 ln Ц BBA A(^
- ABBG T m Ҧ BBB B(D0A8G.
-8A0A(B BBBD T $n h٦ BBB B(A0A8G
-8A0A(B BBBA $ To - ADK WDA T n BBB B(A0A8G
-8A0A(B BBBA < n BBE D(Mi
-(D ABBD T <o BBB B(D0A8G
-8A0A(B BBBA T o H BBB B(A0J8G1
-8A0A(B BBBA D o ) BBG D(D
-(D ABBF < q q BEG A(D0
-(D ABBF < Lq * BEA G(D0
-(D ABBA T p BBB B(D0G8GY
-8D0A(B BBBH T q BBB B(A0D8G8
-8A0A(B BBBJ < dq ( BBG D(D
-(D ABBA T q BBB E(G0A8G
-8D0A(B BBBG T q P BBB B(D0G8G
-8D0A(B BBBJ T Tr %k BBB B(A0D8G
-8A0A(B BBBA T r ) BBB B(A0D8G
-8A0A(B BBBA T s . { BBB B(A0D8G
-8A0A(B BBBI T \s 3 k BBB B(A0D8G}
-8A0A(B BBBE T s H; [ BBB B(A0D8G@
-8A0A(B BBBB T t A K BBB B(A0D8G}
-8A0A(B BBBE T dt H ; BBB B(A0D8G@
-8A0A(B BBBB T t O + BBB B(A0D8G
-8A0A(B BBBA L u TT > BBB H(A0G
-0A(A BBBA L du WT N BBB H(A0G
-0A(A BBBA L u Z% ^ BBE E(A0A8Jc
-8A0A(B BBBI L v `4 ^ BEB A(G0GJ
-0D(A BBBC L Tv d y BBE B(A0A8J
-8A0A(B BBBI T v (j g BEE B(G0D8GJ
-8D0A(B BBBA w pn DX L w pn" h BBE B(A0A8J
-8A0A(B BBBD T lw Pt V BEE B(G0D8GJ
-8D0A(B BBBA x x DX L w x W BBE B(A0A8J
-8A0A(B BBBJ T 4x E BEE B(G0D8GH
-8D0A(B BBBA dy @ DX | y @ BBE E(A0A8D`t
+8A0A(B BBBA 8A0A(B BBB $ |u hl) KA|A T t pn 3 BBB B(D0A8Gm
+8A0A(B BBBA T Tu r# 3 BBB B(A0A8G[
+8A0A(B BBBA $ Tv - ADK WDA T u 3 BBB B(D0A8Gm
+8A0A(B BBBA 4 v BBA A(^
+ ABBG T dv 8 i3 BBB B(D0A8G.
+8A0A(B BBBD T v m3 BBB B(A0A8G
+8A0A(B BBBA $ w (- ADK WDA T <w 0 M3 BBB B(A0A8G
+8A0A(B BBBA < w h U3 BBE D(Mi
+(D ABBD T w H i3 BBB B(D0A8G
+8A0A(B BBBA T ,x а m3 BBB B(A0J8G1
+8A0A(B BBBA D x 8) O3 BBG D(D
+(D ABBF < ty q BEG A(D0
+(D ABBF < y `* BEA G(D0
+(D ABBA T Ly P 2 BBB B(D0G8GY
+8D0A(B BBBH T y ä 2 BBB B(A0D8G8
+8A0A(B BBBJ < y ʤ 2 BBG D(D
+(D ABBC < <z p̤ j2 BBG D(D
+(D ABBA T |z ͤ O2 BBB E(G0A8G
+8D0A(B BBBG T z Ӥ 82 BBB B(D0G8G
+8D0A(B BBBJ T ,{ ڤk !2 BBB B(A0D8G
+8A0A(B BBBA T { ߤ (2 BBB B(A0D8G
+8A0A(B BBBA T { ` 2 BBB B(A0D8G
+8A0A(B BBBI T 4| ( 2 BBB B(A0D8G}
+8A0A(B BBBE T | 1 BBB B(A0D8G@
+8A0A(B BBBB T | 1 BBB B(A0D8G}
+8A0A(B BBBE T <} 0 1 BBB B(A0D8G@
+8A0A(B BBBB L } hT 1 BBB H(A0G
+0A(A BBBA L } xT 1 BBB H(A0G
+0A(A BBBA L 4~
+% 1 BBE E(A0A8Jc
+8A0A(B BBBI L ~ h4 1 BEB A(G0GJ
+0D(A BBBC T ~ X 2 BBB B(A0D8G
+8A0A(B BBBA L , 1 BBE B(A0A8J
+8A0A(B BBBI T | P 1 BEE B(G0D8GJ
+8D0A(B BBBA | " DX L "" 1 BBE B(A0A8J
+8A0A(B BBBD T D x( 1 BEE B(G0D8GJ
+8D0A(B BBBA D , DX L , 1 BBE B(A0A8J
+8A0A(B BBBJ T 6 1 BEE B(G0D8GH
+8D0A(B BBBA h: DX | , h: BBE E(A0A8D`t
8A0A(B BBBF {
8A0A(B BBBC
-8A0A(B BBBA T ,y i BBB B(G0D8G
-8D0A(B BBBA \z ' AJ0WD | |z ȑ BBE E(A0A8D@
+8A0A(B BBBA T Ci @1 BBB B(G0D8G
+8D0A(B BBBA E' AJ0WD | $ E BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB T $z (i
- BBB B(G0D8G
-8D0A(B BBBA T{ @' AJ0WD | t{ P BBE E(A0A8DP
+8A0A(B BBBA 8A0A(B BBB T PGi 0 BBB B(G0D8G
+8D0A(B BBBA hI' AJ0WD | xI BBE E(A0A8DP
8A0A(B BBBI
8A0A(B BBBE
-8A0A(B BBBK L { N BBB G(D0GF
-0D(A BBBA D| ' AJ0WD | d| o BBE E(A0A8D@,
+8A0A(B BBBK L P / BBB G(D0GF
+0D(A BBBA 8U' AJ0WD | HUo BBE E(A0A8D@,
8A0A(B BBBF K
8A0A(B BBBK s
-8A0A(B BBBC L | BBB G(D0GF
-0D(A BBBA 4} `' AJ0WD | T} p BBE E(A0A8D@
+8A0A(B BBBC L 8\ K/ BBB G(D0GF
+0D(A BBBA ܅ `' AJ0WD | ` BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB T | Эi T BBB B(G0D8G
-8D0A(B BBBA ,~ 诧' AJ0WD | L~ BBE E(A0A8DP
+8A0A(B BBBA 8A0A(B BBB T ԅ ai . BBB B(G0D8G
+8D0A(B BBBA Ԇ d' AJ0WD | d BBE E(A0A8DP
8A0A(B BBBJ
8A0A(B BBBE
-8A0A(B BBBK L } BBB G(D0GF
-0D(A BBBA ػ' AJ0WD | < 軧 BBE E(A0A8DP
+8A0A(B BBBK L ̆ k . BBB G(D0GF
+0D(A BBBA ć p' AJ0WD | p BBE E(A0A8DP
8A0A(B BBBH A
8A0A(B BBBE
-8A0A(B BBBI T ~ (ħi BBB B(G0D8G
-8D0A(B BBBA @Ƨ' AJ0WD | 4 PƧ BBE E(A0A8D@
+8A0A(B BBBI T Pxi - BBB B(G0D8G
+8D0A(B BBBA hz' AJ0WD | ܈ xz BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB T ǧi _ BBB B(G0D8G
-8D0A(B BBBA ɧ' AJ0WD | , ɧ BBE E(A0A8DP
+8A0A(B BBBA 8A0A(B BBB T {i , BBB B(G0D8G
+8D0A(B BBBA }' AJ0WD | ԉ ~ BBE E(A0A8DP
8A0A(B BBBI
8A0A(B BBBF
-8A0A(B BBBK L Ԁ hѧ BBB G(D0GF
-0D(A BBBA է' AJ0WD | է\
+8A0A(B BBBK L , BBB G(D0GF
+0D(A BBBA ' AJ0WD | Ċ \
BBE E(A0A8D`
8A0A(B BBBG w
8A0A(B BBBG
-8A0A(B BBBH T ā ߧi & BBB B(G0D8G
-8D0A(B BBBA ' AJ0WD | BBE E(A0A8DP
+8A0A(B BBBH T Гi + BBB B(G0D8G
+8D0A(B BBBA 蕥' AJ0WD | BBE E(A0A8DP
8A0A(B BBBG
8A0A(B BBBF
-8A0A(B BBBI T i j BBB B(G0D8G
-8D0A(B BBBA ' AJ0WD D 4 (S . BEB A(A0Gh
-0A(A BBBA L T @ BEE B(A0A8D`6
-8A0A(B BBBD L l BEE B(A0A8Dp
-8A0A(B BBBC T p BBB B(A0A8G#
-8A0A(B BBBA $ L ; ADK@eDA | t BBE E(A0A8D
+8A0A(B BBBI T (i * BBB B(G0D8G
+8D0A(B BBBA @' AJ0WD D PS * BEB A(A0Gh
+0A(A BBBA L h BEE B(A0A8D`6
+8A0A(B BBBD L L (l BEE B(A0A8Dp
+8A0A(B BBBC T H ) BBB B(A0A8G#
+8A0A(B BBBA $ ඥ; ADK@eDA | BBE E(A0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA L BBE G(D0GX
-0D(A BBBA 4 l BAD M
- DABH | | X BBE E(A0A8D
+8A0A(B BBBA L P) BBE G(D0GX
+0D(A BBBA 4 D *) BAD M
+ DABH | $ BBE E(A0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA L $ h - BBE G(D0GX
-0D(A BBBA 4 t
BAD M
- DABH | BBE E(A0A8D@
+8A0A(B BBBA L ( BBE G(D0GX
+0D(A BBBA 4 L 0 ( BAD M
+ DABH | , ĥ BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB T , `r BBB B(G0D8G!
-8D0A(B BBBA \ ' AJ0WD | | BBE E(A0A8D@
+8A0A(B BBBA 8A0A(B BBB T ťr ' BBB B(G0D8G!
+8D0A(B BBBA ǥ' AJ0WD | $ ǥ BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB L $ BBB G(D0GH
-0D(A BBBA L H' AJ0WD L l X BEE B(A0A8D`
-8A0A(B BBBE D U { BBD G(G
-(D ABBA !' AJ0WD L $ !- BEE B(A0A8Dp
-8A0A(B BBBH D *U BBD G(G
-(D ABBA +' AJ0WD | ܊ + BBB B(A0D8GP
+8A0A(B BBBA 8A0A(B BBB L ɥ B' BBB G(D0GH
+0D(A BBBA pͥ' AJ0WD L ͥ BEE B(A0A8D`
+8A0A(B BBBE D ԥU & BBD G(G
+(D ABBA ե' AJ0WD L ̒ ֥- BEE B(A0A8Dp
+8A0A(B BBBH D t ޥU [& BBD G(G
+(D ABBA d ' AJ0WD | BBB B(A0D8GP
8A0A(B BBBB ;
8A0A(B BBBK
-8A0A(B BBBJ T x/r BBB B(G0D8G!
-8D0A(B BBBA 1' AJ0WD L ԋ 1 BEE B(A0A8D`
-8A0A(B BBBE L L ; BBB G(D0GH
-0D(A BBBA t P?' AJ0WD L `? BEE B(A0A8D`
-8A0A(B BBBE D FU > BBD G(G
-(D ABBA , G' AJ0WD L L G- BEE B(A0A8Dp~
-8A0A(B BBBD D Č PU BBD G(G
-(D ABBA Q' AJ0WD | Qw BBB B(A0D8GP
+8A0A(B BBBJ T \ r % BBB B(G0D8G!
+8D0A(B BBBA \ ' AJ0WD L | BEE B(A0A8D`
+8A0A(B BBBE L $ ( % BBB G(D0GH
+0D(A BBBA x' AJ0WD L < BEE B(A0A8D`
+8A0A(B BBBE D U $ BBD G(G
+(D ABBA ԕ ' AJ0WD L - BEE B(A0A8Dp~
+8A0A(B BBBD D U $ BBD G(G
+(D ABBA ' AJ0WD | w BBB B(A0D8GP
8A0A(B BBBF
8A0A(B BBBH
-8A0A(B BBBJ T Tr BBB B(G0D8G!
-8D0A(B BBBA W' AJ0WD L (Wq BEE B(A0A8D`d
-8A0A(B BBBF L t X_ N BBB G(D0GH
-0D(A BBBA c' AJ0WD L c BEE B(A0A8D`
-8A0A(B BBBE D 4 kU BBD G(G
-(D ABBA T 0l' AJ0WD L t @l- BEE B(A0A8Dp
-8A0A(B BBBH D uU g BBD G(G
-(D ABBA 8v' AJ0WD | , Hvw BBB B(A0D8GP
+8A0A(B BBBJ T r T# BBB B(G0D8G!
+8D0A(B BBBA @ ' AJ0WD L P q BEE B(A0A8D`d
+8A0A(B BBBF L L " BBB G(D0GH
+0D(A BBBA D ' AJ0WD L d BEE B(A0A8D`
+8A0A(B BBBE D @ U {" BBD G(G
+(D ABBA X ' AJ0WD L h - BEE B(A0A8Dp
+8A0A(B BBBH D Ę H)U ! BBD G(G
+(D ABBA `*' AJ0WD | ԙ p*w BBB B(A0D8GP
8A0A(B BBBJ
8A0A(B BBBK
-8A0A(B BBBJ T Ԑ Hyr BBB B(G0D8G!
-8D0A(B BBBA p{' AJ0WD L $ {a BEE B(A0A8D`\
-8A0A(B BBBF L BBB G(D0GH
-0D(A BBBA Ē ' AJ0WD L BEE B(A0A8D`
-8A0A(B BBBE D \ `U BBD G(G
-(D ABBA | x' AJ0WD L BBB B(D0A8J
-8A0A(B BBBF D U * BBD G(G
-(D ABBA 4 ' AJ0WD T 0 T Ș BBB B(A0A8G#
-8A0A(B BBBA $ Ĕ `; ADK@eDA . K^ $ `m AG q
-AA $ , u AG y
-AA T bח\ AG RAT & BBB B(D0A8J
-8A0A(B BBBJ T ( BBB B(D0A8N1
-8A0A(B BBBA , L > AID W
-DAA , | > AID W
-DAA , > AID W
-DAA D ^֗ BBB D(A0D@0A(A BBB T $ 6 BIB E(A0A8D6
-8D0A(B BBBA T | BIB E(A0A8DF
-8D0A(B BBBA T Ԗ ҿ BIB E(A0A8DF
-8D0A(B BBBA D P¨X BBB B(A0A8D0A(B BBB D L hè8 BBB B(A0A8$0A(B BBB T `Ĩ, BBE B(D0A8G
-8D0A(B BBBK D 8ɨ3 BDA DPN
+8A0A(B BBBJ T p-r ! BBB B(G0D8G!
+8D0A(B BBBA /' AJ0WD L ̚ /a BEE B(A0A8D`\
+8A0A(B BBBF L t 7 BBB G(D0GH
+0D(A BBBA l <' AJ0WD L (< BEE B(A0A8D`
+8A0A(B BBBE D 4 CU > BBD G(G
+(D ABBA $ D' AJ0WD L D D BBB B(D0A8J
+8A0A(B BBBF D 0KU BBD G(G
+(D ABBA ܜ HL' AJ0WD XL T l L B BBB B(A0A8G+
+8A0A(B BBBA $ l Q; ADK@eDA `. K^ $ Qm AG q
+AA $ ԝ Qu AG y
+AA \ AG RAD . BBB D(A0D@0A(A BBB T Q X BIB E(A0A8DF
+8D0A(B BBBA T V & BIB E(A0A8DF
+8D0A(B BBBA T l PZ BIB E(A0A8D6
+8D0A(B BBBA T Ğ ^ BBB B(D0A8N1
+8A0A(B BBBA , g> AID W
+DAA , L g> AID W
+DAA , | g> AID W
+DAA T 0g^ BBB B(D0A8J
+8A0A(B BBBH D 8vX BBB B(A0A8D0A(B BBB D Pw8 BBB B(A0A8$0A(B BBB T Hx, BBE B(D0A8G
+8D0A(B BBBK D }3 p BDA DPN
AABC
- AABB $ 4 0Ϩ) SAzA \ 8Ѩ AO
-H T Ө
{ BBB G(G0A8JE
-8D0A(B BBBG ԙ . K^ ( T D (r BBA I(Dp
+ AABB $ ܡ ) SAzA AO
+H T |
BBB G(G0A8JE
+8D0A(B BBBG | . K^ T r S BBA I(Dp
(D ABBH L
-(D ABBA T Pr BBA I(Dp
+(D ABBA T t 8r BBA I(Dp
(D ABBH L
-(D ABBA T x1 _ BEE E(D0A8GY
-8D0A(B BBBE D $ ӗ BBB D(A0D@0A(A BBB T L BIE B(A0A8D9
-8D0A(B BBBA T R ˽ BIE B(A0A8DA
-8D0A(B BBBA T D BBB H(A0A8L
-8D0A(B BBBA t p. K^ Ԝ 4 L d | , Ԝ [ AID t
-DAA , [ AID t
-DAA , 4 ([ ϼ AID t
-DAA , d X[ AID t
-DAA , [ AID t
-DAA , ĝ [ o AID t
-DAA , [ O AID t
-DAA , $ [ / AID t
-DAA L T Hr BBB B(E0A8J@
-8A0A(B BBBA $ | x^ AG b
-AA L >
BEB B(A0A8G
8A0A(B BBB < BEB D(A0M
-(A BBBF T \ BBE E(A0A8G
-8D0A(B BBBE T x ߺ BBE E(A0A8G
-8D0A(B BBBE T BBE E(A0A8G
-8D0A(B BBBE T d ( e BBE E(A0A8G
-8D0A(B BBBE D BBB D(A0D@
-0A(A BBBA D BIB A(D0D`+
-0D(A BBBA D L BIB A(D0D`+
-0D(A BBBA D H" BIB A(D0D`+
-0D(A BBBA D ܡ % z BIB A(D0D`+
-0D(A BBBA D $ ' X BIB A(D0D`+
-0D(A BBBA D l p* 6 BIB A(D0D`+
-0D(A BBBA D (- BIB A(D0D`2
-0D(A BBBA D / BIB A(D0D`2
-0D(A BBBA \ 2m BBD D(J@
+(D ABBA T ̢ `1 BEE E(D0A8GY
+8D0A(B BBBE D ̣ L BBB D(A0D@0A(A BBB T l R y BIE B(A0A8DA
+8D0A(B BBBA T ģ L G BIE B(A0A8D9
+8D0A(B BBBA T BBB H(A0A8L
+8D0A(B BBBA 8. K^ 4 L d | ĥ ܥ $ < , [ AID t
+DAA , ܥ ദ[ k AID t
+DAA , [ K AID t
+DAA , < @[ + AID t
+DAA , l p[ AID t
+DAA , [ AID t
+DAA , ̦ е[ AID t
+DAA , [ AID t
+DAA L , 0r BBB B(E0A8J@
+8A0A(B BBBA $ \ AG RAL D @>
BEB B(A0A8G
8A0A(B BBB < 0Ħ BEB D(A0M
+(A BBBF T , Ŧ BBE E(A0A8G
+8D0A(B BBBF T 8Ȧ c BBE E(A0A8G
+8D0A(B BBBF T ܨ ʦ & BBE E(A0A8G
+8D0A(B BBBF D ܩ Hͦ BBB D(A0D@
+0A(A BBBA D | Φ BIB A(D0D`+
+0D(A BBBA D ĩ XѦ BIB A(D0D`+
+0D(A BBBA D Ԧ ] BIB A(D0D`+
+0D(A BBBA D T ֦ ; BIB A(D0D`+
+0D(A BBBA D ٦ BIB A(D0D`+
+0D(A BBBA D 8ܦ BIB A(D0D`+
+0D(A BBBA D , ަ BIB A(D0D`2
+0D(A BBBA D t BIB A(D0D`2
+0D(A BBBA \ d m BBD D(J@
(A ABBD T
(A ABBF _
-(A ABBK d | 3?
+(A ABBK d Ĭ ?
BBB B(A0A8GP
8G0A(B BBBP ^
-8A0A(B BBBH T =2 BHB B(A0A8GH
-8A0A(B BBBG < X A
-J AT Y| 6 BDB E(A0A8J
-8A0A(B BBBH T ܤ @_| X BDB E(A0A8J
-8A0A(B BBBH T 4 he| z BDB E(A0A8J
-8A0A(B BBBH T k| BDB E(A0A8J
-8A0A(B BBBH T q| BDB E(A0A8J
-8A0A(B BBBH T < w| BDB E(A0A8J
-8A0A(B BBBH l . K^ } } } ̧ } 4 } BAI a
-ABA 4 D X~ g BAI a
-ABA T Zȗ\ AG RA\ ~ $ BBB A(A0Dj
+8A0A(B BBBH T h BBE E(A0A8G{
+8D0A(B BBBA T ܬ BEB B(A0A8G
+8A0A(B BBBI ܭ ( A
+J AT T | BDB E(A0A8J
+8A0A(B BBBB T | BDB E(A0A8J
+8A0A(B BBBB T | BDB E(A0A8J
+8A0A(B BBBB T \ 0#| 9 BDB E(A0A8J
+8A0A(B BBBB T X)| [ BDB E(A0A8J
+8A0A(B BBBB T /| } BDB E(A0A8J
+8A0A(B BBBB x. K^ $ 5 < 5 T 5 l 5 4 ܯ 5 ' BAI a
+ABA 4 5 BAI a
+ABA N\ AG RA\ l @6 BBB A(A0Dj
0C(A BBBH V
-0C(A BBBH < Ԩ 6ȗD BBD A(D@0(A ABB T < ĸ BIB B(A0D8Df
-8D0A(B BBBA T H BIB B(A0D8Df
-8D0A(B BBBA T
- ` BGB B(A0A8J
-8A0A(B BBBD T D 8 ܸ BHB B(A0A8J
-8D0A(B BBBK t Ћ. K^ 蝩 , 蝩[ AID t
-DAA D bǗ BBB D(A0D@0A(A BBB T \ Н y BIB B(A0D8Df
-8D0A(B BBBA T 8 G BDB E(A0A8G
-8A0A(B BBBI . K^ , D \ t , [ AID t
-DAA , ȶ[ AID t
-DAA , [ ˸ AID t
-DAA D Ɨ BBB D(A0D@0A(A BBB T ඩL c BIE B(A0A8D9
-8D0A(B BBBA T ػL 1 BIE B(A0A8D9
-8D0A(B BBBA T < L BIE B(A0A8D9
-8D0A(B BBBA T ũ Ϸ BDH B(A0D8Gl
-8D0A(B BBBA T ʩ BDH B(A0D8Gd
-8D0A(B BBBA T D XΩ / BDH B(A0D8Gd
-8D0A(B BBBA t 0T KH ҩ ҩ , ҩ[ AID t
-DAA D ŗ BBB D(A0D@0A(A BBB T \ pҩ; BIB E(A0A8G(
-8D0A(B BBBA D Xة BBE A(H0D
-0A(A BBBA < کb BBA D(K
-(A ABBA T < ܩ BBE B(D0A8G
-8D0A(B BBBH L n BBE E(A0D8Gx
-8A0A(B BBBD T , K BDB B(D0A8M/
-8D0A(B BBBH . K^ , D \ t D BEE D(A0D`n
-0A(A BBBE Բ pj D o BHB A(D0J^
-0A(A BBBA T \ 0 M BBD A(Kp
+0C(A BBBH < t *D BBD A(D@0(A ABB T 9 a BIB B(A0D8Df
+8D0A(B BBBA T d = / BIB B(A0D8Df
+8D0A(B BBBA T PB
+ BGB B(A0A8J
+8A0A(B BBBF T L y BHB B(A0A8J
+8D0A(B BBBE . K^ , U D U , U[ ~ AID t
+DAA D V BBB D(A0D@0A(A BBB T , pU BIB B(A0D8Df
+8D0A(B BBBA T Y BDB E(A0A8G
+8A0A(B BBBC `. K^ m m ̴ l l l m , m[ AID t
+DAA , 8m[ j AID t
+DAA , hm[ J AID t
+DAA D BBB D(A0D@0A(A BBB T \ PmL BIE B(A0A8D9
+8D0A(B BBBA T HrL BIE B(A0A8D9
+8D0A(B BBBA T @wL ~ BIE B(A0A8D9
+8D0A(B BBBA T d 8| O BDH B(A0D8Gl
+8D0A(B BBBA T BDH B(A0D8Gd
+8D0A(B BBBA T Ȅ BDH B(A0D8Gd
+8D0A(B BBBA T KH , D , [ AID t
+DAA D BBB D(A0D@0A(A BBB T , ; / BIB E(A0A8G(
+8D0A(B BBBA D Ȏ
+ BBE A(H0D
+0A(A BBBA < ̸ 0b BBA D(K
+(A ABBA T ` BBE B(D0A8G
+8D0A(B BBBH L d ( BBE E(A0D8Gx
+8A0A(B BBBD T , BDB B(D0A8M/
+8D0A(B BBBH . K^ ̺ H @ 8 0 , ( D 0 \ 8 t @ D H BEE D(A0D`n
+0A(A BBBE Ի j D D 8 BHB A(D0J^
+0A(A BBBA 4 T x U BBD A(Kp
(A ABBE L
-(A ABBA , > AID W
-DAA , > AID W
-DAA T ̷ BBD A(Kp
+(A ABBA , > AID W
+DAA , , > AID W
+DAA T \ 0 BBD A(Kp
(A ABBE L
-(A ABBA , l > AID W
-DAA , > k AID W
-DAA < BBD A(D@(A ABB 4 k BHA GP
- DABA 4 D
-k BHA GP
- DABA 4 | r Ͷ BHA GP
- DABA 4 8
r BHA GP
- DABA $ ĵ AG y
-AF AM T Xh BIA
+(A ABBA , ȿ> AID W
+DAA , ؿ> s AID W
+DAA T 迧 S BBD A(Kp
+(A ABBE L
+(A ABBA , l > AID W
+DAA , > AID W
+DAA T ̽ BBD A(Kp
+(A ABBE L
+(A ABBA , $ 8> AID W
+DAA , T H> q AID W
+DAA < , X BBD A(D@(A ABB 4 ľ 8§B BHA G`
+ DABA 4 Pç7 BHA G`
+ DABA 4 4 XħB BHA G`
+ DABA 4 l pŧ7 BHA G`
+ DABA 4 xƧB BHA G`
+ DABA 4 ܿ ǧ7 v BHA G`
+ DABA 4 ȧB W BHA G`
+ DABA 4 L ɧ7 8 BHA G`
+ DABA $ , ʧ AG y
+AF T ̧ AM $ t ̧ AG y
+AF ͧ AM D hΧ A
+G @
+H
+I
+B
+I O
+Q L ѧS BBE B(A0A8G
+8A0A(B BBBD L T ӧ BEB B(A0A8D)
+8A0A(B BBBD L է BBB B(A0A8D#
+8A0A(B BBBE T קh BIA
ABD @
ABE
ABF U
-ABP L d pS BBE B(A0A8G
-8A0A(B BBBD L BEB B(A0A8D)
-8A0A(B BBBD < BBG A(G@
-(A ABBF D 5 < \ BEA A(D`H
-(A ABBE L S BBE B(A0A8G
-8A0A(B BBBD $ AGpZ
-AE { d , BBB B(A0A8D_
+ABP L L (ۧS BBE B(A0A8G
+8A0A(B BBBD L 8ݧ BEB B(A0A8D)
+8A0A(B BBBD < ާ BBG A(G@
+(A ABBF , ߧ5 $ D ߧQ ADD@EAA$ l ߧQ ADD@EAAL 0S BBE B(A0A8G
+8A0A(B BBBD $ @ AGpZ
+AE L (S BBE B(A0A8G
+8A0A(B BBBD $ \ 8 AGpZ
+AE { d BBB B(A0A8D_
8A0A(B BBBE
-8A0A(B BBBA L o BEB B(A0D8J
-8A0A(B BBBA L #' BBB B(A0D8Jm
-8A0A(B BBBB d 4 ) BBB B(A0A8D_
+8A0A(B BBBA L o BEB B(A0D8J
+8A0A(B BBBA L T 0' BBB B(A0D8Jm
+8A0A(B BBBB d BBB B(A0A8D_
8A0A(B BBBE
-8A0A(B BBBA L (+o BEB B(A0D8J
-8A0A(B BBBA L H0' BBB B(A0D8Jm
-8A0A(B BBBB d < (6 BBB B(A0A8D_
+8A0A(B BBBA L o BEB B(A0D8J
+8A0A(B BBBA L \ ' BBB B(A0D8Jm
+8A0A(B BBBB d BBB B(A0A8D_
8A0A(B BBBE
-8A0A(B BBBA L 7o BEB B(A0D8J
-8A0A(B BBBA L <' BBB B(A0D8Jm
-8A0A(B BBBB D B \ (C{ t C L (D BHB B(A0D8G
-8A0A(B BBBA , ܻ Fj AAG@O
-FAA l FX BBE D(A0DP
+8A0A(B BBBA L o BEB B(A0D8J
+8A0A(B BBBA L d @' BBB B(A0D8Jm
+8A0A(B BBBB { d BBB B(A0A8D_
+8A0A(B BBBE
+8A0A(B BBBA L 4 o BEB B(A0D8J
+8A0A(B BBBA L 0' BBB B(A0D8Jm
+8A0A(B BBBB d BBB B(A0A8D_
+8A0A(B BBBE
+8A0A(B BBBA L < o BEB B(A0D8J
+8A0A(B BBBA L ' BBB B(A0D8Jm
+8A0A(B BBBB d % BBB B(A0A8D_
+8A0A(B BBBE
+8A0A(B BBBA L D 'o BEB B(A0D8J
+8A0A(B BBBA L @,' BBB B(A0D8Jm
+8A0A(B BBBB 2 2{ 3 , x3{ D 3 L \ h4 BHB B(A0D8Ge
+8A0A(B BBBA , 6j AAG@O
+FAA 6 L 7 BHB B(A0D8G
+8A0A(B BBBA , D :j AAG@O
+FAA D t @: BBE D(A0DP
+0E(A BBBH D <3 BBB A(D0G@
+0E(A BBBF L =i BEB B(A0A8JG8A0A(B BBB l T ?X BBE D(A0DP
0D(A BBBJ ^
0A(E BBEC L
-0A(A FBBD L | H BEE F(A0A8Fpv8A0A(B BBB L ̼ J BEB B(A0A8J8A0A(B BBB l LD BBB A(D0G@
+0A(A FBBD L A BEE F(A0A8Fpv8A0A(B BBB L B BEB B(A0A8J8A0A(B BBB l d ED BBB A(D0G@
0D(A BBBK `
0A(E BBEA L
-0A(A FBBD L M BEB B(A0A8Jn8A0A(B BBB 4 ܽ O BBA A(q
- ABBD T < R ? BBB E(A0D8Gb
-8A0A(B BBBA T W* BBB B(A0A8J
-8A0A(B BBBJ 4 ľ [ BBA A(q
- ABBD T $ H^ | BIB I(A0D8G9
-8A0A(B BBBA T | bz 7 BBB B(A0A8J0
-8A0A(B BBBB < f BFB D(A0
-(A BBBE L hK BBB B(D0A8J
-8A0A(B BBBA T d l* d BBB B(A0A8J
-8A0A(B BBBB 4 `p BBA A(q
- ABBD T s BIB I(A0D8G9
-8A0A(B BBBA T L `w BBB B(A0A8JP
-8A0A(B BBBB L | { BBG E(A0A8M7
-8D0A(B BBBE L | BBG E(A0A8M>
-8D0A(B BBBF u. K^ 4 ~ L ~ d ~ | } ~ ~ T ~n BBB B(A0D8N+
-8A0A(B BBBH , D (> > AID W
-DAA L t 8Y BBB E(A0A8N
-8A0A(B BBBK , H> AID W
-DAA T X BBB B(D0A8N
-8A0A(B BBBF , L > AID W
-DAA D T 8 BBB D(A0D@0A(A BBB T h BIB E(A0A8D^
-8D0A(B BBBA T { BIB E(A0A8D^
-8D0A(B BBBA T t I BIB E(A0A8DV
-8D0A(B BBBA d p BLB B(A0A8D@c
+0A(A FBBD L pF BEB B(A0A8Jn8A0A(B BBB , $ HV BAA
+ABB T J BBB E(A0D8Gb
+8A0A(B BBBA T P* BBB B(A0A8J
+8A0A(B BBBJ , SV BAA
+ABB T V 6 BIB I(A0D8G9
+8A0A(B BBBA T xZ BBB B(A0A8J
+8A0A(B BBBD L _{ BBG E(A0A8M1
+8D0A(B BBBC < 4 P` BFB A(A0
+(A BBBG L t aK BBB B(D0A8J
+8A0A(B BBBA T e* BBB B(A0A8J
+8A0A(B BBBB , hV BAA
+ABB T (k \ BIB I(A0D8G9
+8A0A(B BBBA T o BBB B(A0A8JP
+8A0A(B BBBB L s{ BBG E(A0A8M-
+8D0A(B BBBG 4 L t BBA A(q
+ ABBD T w L BBB E(A0D8Gb
+8A0A(B BBBA T 4 |* BBB B(A0A8J
+8A0A(B BBBJ 4 4 BBA A(q
+ ABBD T X BIB I(A0D8G9
+8A0A(B BBBA T z D BBB B(A0A8J0
+8A0A(B BBBB L ؋{ BBG E(A0A8M1
+8D0A(B BBBC < l BFB D(A0
+(A BBBE L ȎK BBB B(D0A8J
+8A0A(B BBBA T T Ȓ* ! BBB B(A0A8J
+8A0A(B BBBB 4 T BBA A(q
+ ABBD T H BIB I(A0D8G9
+8A0A(B BBBA T < a BBB B(A0A8JP
+8A0A(B BBBB L < 表{ BBG E(A0A8M-
+8D0A(B BBBG . K^ 袨 ࢨ 袨 4 L T n BBB B(A0D8N+
+8A0A(B BBBH , > AID W
+DAA L D (Y BBB E(A0A8N
+8A0A(B BBBK , 8> AID W
+DAA T H BLB E(A0A8G
+8A0A(B BBBA , Z BHI s
+ABA , L > AID W
+DAA , | ЮZ BHI s
+ABA , > AID W
+DAA D BBB D(A0D@
+0A(A BBBA L $ h BIB B(A0D8DpE
+8D0A(B BBBA L t 8 BIB B(A0D8DpE
+8D0A(B BBBA L " BIB B(A0D8DpL
+8D0A(B BBBA L 踨" BIB B(A0D8DpL
+8D0A(B BBBA T d Ȼ s BEE E(D0A8Gn
+8D0A(B BBBH d d BLB B(A0A8D@c
8A0A(B BBBK |
-8A0A(B BBBJ $ t EA
-AG D 4 ` BHB E(A0D8D`8A0A(B BBB$ | | EA
-AD D ` BHB E(A0D8D`8A0A(B BBB< BBG A(G@
-(A ABBF , ȝ5 D \ Ȟ t 9 L ؠ BKE B(A0A8DPa8A0A(B BBB I L P BKE B(A0A8DPq8A0A(B BBB T l & w BBB B(A0A8J
-8A0A(B BBBA T ˪R BBB G(D0A8M
-8D0A(B BBBC L Ϊ BBB G(D0A8M
-8D0A(B BBBJ L l Pժ W BBB G(D0A8M
-8D0A(B BBBJ m. K^ xܪ pܪ hܪ `ܪ Xܪ $ Pܪ < Xܪ T `ܪ l hܪ pܪ , xܪ[ æ AID t
-DAA , ܪ[ AID t
-DAA , $ ܪ[ AID t
-DAA , T ݪ[ c AID t
-DAA , 8ݪ[ C AID t
-DAA D έ BBB D(A0D@0A(A BBB T ݪ ۥ BIB E(A0A8D6
-8D0A(B BBBA T T X BIB E(A0A8D6
-8D0A(B BBBA T w BIB E(A0A8D6
-8D0A(B BBBA T E BIB E(A0A8D6
-8D0A(B BBBA T \ BIB E(A0A8D6
-8D0A(B BBBA T 8 BEE E(D0A8G*
-8D0A(B BBBD T Ǥ BDB B(A0D8G&
-8A0A(B BBBA T d 8U Ӥ BDE H(A0A8G$
-8D0A(B BBBA T @U ? BDE H(A0A8G$
-8D0A(B BBBA T HU BDE H(A0A8G$
-8D0A(B BBBA T l PU BDE H(A0A8G$
-8D0A(B BBBA g. K^ @! 8! D @!b ; BBB A(D0Kp
-0A(A BBBJ , T h">
- AID W
-DAA D \ ̪ BBB D(A0D@0A(A BBB T 0"L BIE B(A0A8D9
-8D0A(B BBBA L $ ('l s BBE B(F0G8G
-8D0A(B BBBI L xf. K^ d 0. | (. , 0.0 AF
FK
- D T 0/| O BBE E(D0A8G
-8D0A(B BBBE T D X4| 5 BBE E(D0A8G
-8D0A(B BBBE D t BBB D(A0D@0A(A BBB T 89 Ӥ BIB E(A0A8GJ
-8D0A(B BBBA T < > BBE B(D0A8G
-8D0A(B BBBG T CE
- BBB E(A0A8N
-8A0A(B BBBA , M> % AID W
-DAA T M. BBB B(A0F8J
-8A0A(B BBBB T t hU w BBB B(A0F8J
-8A0A(B BBBF T \ BBB H(F0A8J
-8D0A(B BBBD T $ b2 / BBE G(A0G8J
-8D0A(B BBBD T | ph s BBB B(A0D8L
-8A0A(B BBBG T n BGB B(A0D8J
-8A0A(B BBBA T , u BBB G(A0D8M
-8D0A(B BBBB \ a. K^ D t | BBE A(A0D@
-0A(A BBBA D BBE A(A0D@
-0A(A BBBA D BBB D(A0D@0A(A BBB T t 聫u , BIB E(A0A8G=
-8D0A(B BBBA T t BBE B(A0G8L-
-8D0A(B BBBG < $ 8 BLD D(G0
-(A ABBE < d * w BED I(KPc
-(D ABBE < 蒫 W BLD D(G0
-(A ABBE < * ; BED I(KP_
-(D ABBI T $ - BBM B(A0D8G
-8A0A(B BBBD , | p> צ AID W
-DAA ^. K^ h0 Hg x7 Hj 0 Hg 7 Hj ȧ0 Hg < ا7 Hj D \ BBE A(A0D@g
-0A(A BBBA D BBE A(A0D@g
-0A(A BBBA D BBE A(A0D@g
-0A(A BBBA D 4 BBE A(A0D@o
-0A(A BBBA D | ( BBE A(A0D@o
-0A(A BBBA D BBE A(A0D@o
-0A(A BBBA L 4 X / BBJ B(A0A8G
-8D0A(B BBBD L BBJ B(A0A8G
-8D0A(B BBBD L BBJ B(A0A8G
-8D0A(B BBBD L $ x BBB B(A0I8G
-8D0A(B BBBD L t ȴ ۣ BBB B(A0I8G
-8D0A(B BBBD L ƣ BBB B(A0I8G
-8D0A(B BBBD < D BBD A(D@0(A ABB T T ( q BIB E(A0A8DV
-8D0A(B BBBA T ? BIB E(A0A8DV
-8D0A(B BBBA T
BIB E(A0A8DV
-8D0A(B BBBA T \ 0ƫ ۢ BIB E(A0A8DV
-8D0A(B BBBA T ʫ BIB E(A0A8DV
-8D0A(B BBBA T Ϋ w BIB E(A0A8DV
-8D0A(B BBBA T d 8ӫ G BBB E(F0A8My
-8D0A(B BBBA T k BBB E(F0A8Mx
-8D0A(B BBBA T Xܫ BBG B(A0A8J
-8A0A(B BBBA T l BBB B(D0F8G2
-8A0A(B BBBK T BBB B(D0F8G1
-8A0A(B BBBD T P BBB B(D0A8L6
-8A0A(B BBBG L V. K^ d 0 Hg 7 Hj T Ӣ BBB J(A0A8G?
-8D0A(B BBBK T $ 8 BEE E(D0A8G
-8D0A(B BBBH T | BBE B(D0A8G
-8D0A(B BBBG D * BBB D(A0D@0A(A BBB T @ : BIB E(A0A8G
-8D0A(B BBBA T t 5 BBB B(F0A8G
-8A0A(B BBBE S. K^ > Hu E Hx \ AG RAT D
- BBB B(D0A8N
-8A0A(B BBBD , `+> J AID W
-DAA D \ BBB D(A0D@0A(A BBB T (+ BIB E(A0A8D
-8D0A(B BBBA T l 0u BBE E(A0A8GV
-8D0A(B BBBF T 6U BBE B(D0A8GH
-8D0A(B BBBD T < 3 BBB G(D0A8M
-8D0A(B BBBE L hQ. K^ $ d 0@ ADD0AA$ @ ADD0AA$ @A ADD0AA$ A ADD0AAD PB1 BBE A(A0D@
-0A(A BBBA D L HC> BBB D(A0D@
-0A(A BBBA D @DF BBB D(A0D@
-0A(A BBBA D HEN BBB D(A0D@
-0A(A BBBA $ $ PF^ AG b
-AA L t F2 G BGB B(A0K8U
-8A0A(B BBBA < xG BQA A(N`
-(A ABBA < H BQA A(N`
-(A ABBA < D H BQA A(N`
-(A ABBA L XI2 BGB B(A0K8U
-8A0A(B BBBA < HJ M BQA A(N`
-(A ABBA D J BBB D(A0D@
-0A(A BBBA < \ @L ן BHA J
- DABA D N ȟ BIA D(G
-(D ABBA D xR BIA D(G
-(D ABBA D , PV BIA D(G
-(D ABBA < t (Z BHA J
- DABA < \ BHA J
- DABA < ^ BHA J
- DABA D ha BBE A(A0D@l
-0A(A BBBA $ T bi AAG0]AA$ | Hbq AAG0eAAD b BBE A(A0D@l
-0A(A BBBA $ 8c| AAG0pAA ci AG _A 4 cq AG gA$ T @d AAG0xAAT d
- BEJ E(D0D8G
-8D0A(B BBBG T phR ŝ BBB I(A0A8JN
-8A0A(B BBBA , T xo> AID W
-DAA \ \ o BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBL o l BBB E(A0G8L
-8D0A(B BBBA < 4 t s BGB D(A0~
-(A BBBK T t HuM
- [ BBB M(A0D8Gg
-8A0A(B BBBA , @> AID W
-DAA T P { BBB E(A0H8K
-8A0A(B BBBA , T ȅ> AID W
-DAA T BBB E(I0A8K}
-8D0A(B BBBF T P [ BBB B(A0H8JH
-8A0A(B BBBA , 4 > AID W
-DAA L d ( c BEB E(I0H8DP
-8D0A(B BBBD L 蒬 ; BEB E(I0H8D@
-8D0A(B BBBA T h( BBB I(A0A8J
-8A0A(B BBBA , \ @> : AID W
-DAA T P BBB E(I0A8K}
-8D0A(B BBBF T F BBB B(A0D8N
-8A0A(B BBBA , < > AID W
-DAA \ D BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBT ६ BBB M(A0A8J
-8A0A(B BBBA , $ h> AID W
-DAA < T xH Ü BBA A(Gp
-(D ABBA < H BBA A(Gp
-(D ABBA l BBB E(A0F8DQ
+8A0A(B BBBJ $ t EA
+AG D BHB E(A0D8D`8A0A(B BBB$ < | EA
+AD D d è BHB E(A0D8D`8A0A(B BBBd è BLB B(A0A8D@c
+8A0A(B BBBK |
+8A0A(B BBBJ < @Ĩ BBG A(G@
+(A ABBF T Ũ5 l (Ũ ƨ < ƨi BFI G(D0
+(A BBBF L Ȩ BHB E(A0D8DP
+8A0A(B BBBB < , Ȩi BFI G(D0
+(A BBBF L l ɨ BHB E(A0D8DP
+8A0A(B BBBB hʨ9 L ˨ BKE B(A0A8DPa8A0A(B BBB $ ̨I L < Ψ BKE B(A0A8DPq8A0A(B BBB L XϨ BBB B(A0D8J
+8A0A(B BBBA D 4 9 BFD JPs
+ DABH F
+ DABA d | ` BGA D(J`
+(D ABBI F
+(D ABBA z
+(D ABBE L BBB B(A0D8Jp
+8D0A(B BBBI < X BBG A(G@
+(A ABBF 5 4 @ WC T WC t L y BKE B(A0A8DPT8A0A(B BBB L y BKE B(A0A8DPT8A0A(B BBB L r 5 BBB B(A0D8G
+8A0A(B BBBA @t. K^ ( $ < T l , ([ ~ AID t
+DAA , $ X[ ^ AID t
+DAA , T [ > AID t
+DAA , [ AID t
+DAA , [ AID t
+DAA D BBB D(A0D@0A(A BBB T , BIB E(A0A8D6
+8D0A(B BBBA T d BIB E(A0A8D6
+8D0A(B BBBA T @" 2 BIB E(A0A8D6
+8D0A(B BBBA T 4 x& BIB E(A0A8D6
+8D0A(B BBBA T * BIB E(A0A8D6
+8D0A(B BBBA T . BEE E(D0A8G*
+8D0A(B BBBD T < 4 BDB B(A0D8G&
+8A0A(B BBBA T 9U BDE H(A0A8G$
+8D0A(B BBBA T BU BDE H(A0A8G$
+8D0A(B BBBA T D KU g BDE H(A0A8G$
+8D0A(B BBBA T UU BDE H(A0A8G$
+8D0A(B BBBA hn. K^ ] ] D < ]b BBB A(D0Kp
+0A(A BBBJ , _> AID W
+DAA D \ BBB D(A0D@0A(A BBB T ^L ^ BIE B(A0A8D9
+8D0A(B BBBA L T cl / BBE B(F0G8G
+8D0A(B BBBI L l. K^ d j | j , j0 AF
FK
+ D T k| BBE E(D0A8G
+8D0A(B BBBE T t q| BBE E(D0A8G
+8D0A(B BBBE D t H BBB D(A0D@0A(A BBB T u BIB E(A0A8GJ
+8D0A(B BBBA T l p{ u BBE B(D0A8G
+8D0A(B BBBG T 8E
+ [ BBB E(A0A8N
+8A0A(B BBBA , 0> AID W
+DAA T L @. BBB B(A0F8J
+8A0A(B BBBB T 3 BBB B(A0F8J
+8A0A(B BBBF T BBB H(F0A8J
+8D0A(B BBBD T T 82 BBE G(A0G8J
+8D0A(B BBBD T / BBB B(A0D8L
+8A0A(B BBBG T s BGB B(A0D8J
+8A0A(B BBBA T \ 0 BBB G(A0D8M
+8D0A(B BBBB \ h. K^ D t BBE A(A0D@
+0A(A BBBA D 8 BBE A(A0D@
+0A(A BBBA D BBB D(A0D@0A(A BBB T u BIB E(A0A8G=
+8D0A(B BBBA T ĩt BBE B(A0G8L-
+8D0A(B BBBG < T ͩ O BLD D(G0
+(A ABBE < Ω* 3 BED I(KPc
+(D ABBE < ϩ BLD D(G0
+(A ABBE < XЩ* BED I(KP_
+(D ABBI T T Hѩ- BBM B(A0D8G
+8A0A(B BBBD , > AID W
+DAA e. K^ 0 Hg (7 Hj H0 Hg X7 Hj x0 Hg < 7 Hj D \ BBE A(A0D@g
+0A(A BBBA D 0 BBE A(A0D@g
+0A(A BBBA D BBE A(A0D@g
+0A(A BBBA D 4 @ BBE A(A0D@o
+0A(A BBBA D | BBE A(A0D@o
+0A(A BBBA D p BBE A(A0D@o
+0A(A BBBA D Lv BBB D(A0D@^0A(A BBB T BIB E(A0A8DV
+8D0A(B BBBA T q BIB E(A0A8DV
+8D0A(B BBBA T \ p ? BIB E(A0A8DV
+8D0A(B BBBA T
BIB E(A0A8DV
+8D0A(B BBBA T BIB E(A0A8DV
+8D0A(B BBBA T d x BIB E(A0A8DV
+8D0A(B BBBA L w BBJ B(A0A8G
+8D0A(B BBBD L 0 b BBJ B(A0A8G
+8D0A(B BBBD L \ M BBJ B(A0A8G
+8D0A(B BBBD L 8 BBB B(A0I8G
+8D0A(B BBBD L @ # BBB B(A0I8G
+8D0A(B BBBD L L
BBB B(A0I8G
+8D0A(B BBBD T BBB E(F0A8My
+8D0A(B BBBA T x BBB E(F0A8Mx
+8D0A(B BBBA T L C BBG B(A0A8J
+8A0A(B BBBA T H s BBB B(D0F8G2
+8A0A(B BBBK T # BBB B(D0F8G1
+8A0A(B BBBD T T ( BBB B(D0A8L6
+8A0A(B BBBG T p\. K^ l H.0 Hg X.7 Hj T x. BBB J(A0A8G?
+8D0A(B BBBK T \ 1 j BEE E(D0A8G
+8D0A(B BBBH T h7 P BBE B(D0A8G
+8D0A(B BBBG D BBB D(A0D@0A(A BBB T T ; BIB E(A0A8G
+8D0A(B BBBA T `A5 BBB B(F0A8G
+8A0A(B BBBE HZ. K^ 0]> Hu P]E Hx \ AG RAT | `]
+ BBB B(D0A8N
+8A0A(B BBBD , h> AID W
+DAA D L BBB D(A0D@0A(A BBB T L g BIB E(A0A8D
+8D0A(B BBBA T 8mu o BBE E(A0A8GV
+8D0A(B BBBF T `sU BBE B(D0A8GH
+8D0A(B BBBD T T hy BBB G(D0A8M
+8D0A(B BBBE T W. K^ $ l | ADD0AA$ `} ADD0AA$ } ADD0AA$ p~ ADD0AA$ ~ ADD0AA$ 4 ADD0AAD \ 1 BBE A(A0D@
+0A(A BBBA D > BBB D(A0D@
+0A(A BBBA D F BBB D(A0D@
+0A(A BBBA D 4 N BBB D(A0D@
+0A(A BBBA $ | ^ AG b
+AA L @2 BGB B(A0K8U
+8A0A(B BBBA < L 0 s BQA A(N`
+(A ABBA < Ѕ E BQA A(N`
+(A ABBA < p BQA A(N`
+(A ABBA L 2 BGB B(A0K8U
+8A0A(B BBBA < \ BQA A(N`
+(A ABBA < BQA A(N`
+(A ABBA D @ BBB D(A0D@
+0A(A BBBA < $
BHA J
+ DABA < d BHA J
+ DABA < X BHA J
+ DABA < BHA J
+ DABA < $ BHA J
+ DABA < d x BHA J
+ DABA < ؘ BHA J
+ DABA < 8 BHA J
+ DABA D BBE A(A0D@l
+0A(A BBBA $ Pi AAG0]AA$ < q AAG0eAAD d BBE A(A0D@l
+0A(A BBBA $ | AAG0pAA i AG _A 0q AG gA$
+ AAG0xAAT % BEJ E(D0D8G
+8D0A(B BBBG T R BBB I(A0A8JN
+8A0A(B BBBA , D
+ ȫ> AID W
+DAA \ ث BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBL
+ ( BBB E(A0G8L
+8D0A(B BBBA < $ BGB D(A0~
+(A BBBK T d
+ s BBB M(A0A8J
+8A0A(B BBBA , > AID W
+DAA T 0 BBB E(A0H8K
+8A0A(B BBBA , D ª> AID W
+DAA T t ª BBB E(I0A8K}
+8D0A(B BBBF T 0Ū s BBB B(A0H8JH
+8A0A(B BBBA , $
ͪ> AID W
+DAA L T
Ϊ { BEB E(I0H8DP
+8D0A(B BBBD L
Ϫ S BEB E(I0H8D@
+8D0A(B BBBA T
HѪ( + BBB I(A0A8J
+8A0A(B BBBA , L ت> R AID W
+DAA T | 0ت 3 BBB E(I0A8K}
+8D0A(B BBBF T hڪF BBB B(A0D8N
+8A0A(B BBBA , , `> 2 AID W
+DAA L \ p BEB E(I0H8D@
+8D0A(B BBBA T L BBI B(A0A8J.
+8A0A(B BBBA , > AID W
+DAA \ BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBT H BBB M(A0A8J
+8A0A(B BBBA , > AID W
+DAA < H BBA A(Gp
+(D ABBA < \ H BBA A(Gp
+(D ABBA L l BEB B(G0A8Ip
+8D0A(B BBBH < H E BBA A(Gp
+(D ABBA l , 0 ) BBB E(A0F8DQ
8A0A(B BBBG j
-8A0A(B BBBA < D 8H 3 BBA A(Gp
-(D ABBA < HH BBA A(Gp
-(D ABBA L Xe BEB B(G0A8Ip
-8D0A(B BBBC < xH BBA A(Gp
-(D ABBA l T @ BEB B(A0A8I
+8A0A(B BBBA < H BBA A(Gp
+(D ABBA L e BEB B(G0A8Ip
+8D0A(B BBBC < , H w BBA A(Gp
+(D ABBA l l @ [ BEB B(A0A8I
8A0A(B BBBA @
-8A0A(B BBBA T X^ G BBB E(A0F8G
-8A0A(B BBBA < `H BBA A(Gp
-(D ABBA L \ pg BEB B(G0A8Ip
-8D0A(B BBBE T BBH E(A0F8DG
-8D0A(B BBBH @. K^ ( KN $ \ AG RAD D BBE A(A0D@
-0A(A BBBA D ؽ BBE A(A0D@
-0A(A BBBA D BBE A(A0D@
-0A(A BBBA D h BBE A(A0D@
-0A(A BBBA D d 0 BBE A(A0D@
-0A(A BBBA D BBE A(A0D@
-0A(A BBBA D BBE A(A0D@
-0A(A BBBA D < ¬ BBE A(A0D@
-0A(A BBBA < D BBD A(D@0(A ABB T Pì BIB E(A0A8DN
-8D0A(B BBBA T D Ǭ Q BIB E(A0A8DN
-8D0A(B BBBA T ˬ BIB E(A0A8DN
-8D0A(B BBBA T (Ь BIB E(A0A8DN
-8D0A(B BBBA $ pԬ% T d Ԭ
BEB B(H0A8GP
-8A0A(B BBBK , > AID W
-DAA T
BEB B(H0A8GP
-8A0A(B BBBK , D > [ AID W
-DAA T t
; BEB B(H0A8GP
-8A0A(B BBBK , P> AID W
-DAA T `
BEB B(H0A8GP
-8A0A(B BBBK , T > AID W
-DAA < " ӗ BBA A(GU
-(A ABBA
-% L
- BEE D(A0D
-0A(A BBBH L , p BEE D(A0D
-0A(A BBBH L | 0 l BEE D(A0D
-0A(A BBBH T $ T BBB G(A0D8G
-8A0A(B BBBH T $ ,g F BBB G(A0D8G&
-8A0A(B BBBG T | 5 V BBB G(A0D8G
-8A0A(B BBBH T = H BBB G(A0D8G
-8A0A(B BBBH $ , 7 : AK
-AA , (E> Hu L HEE Hx l \ AG RAT XE
- Ė BBB B(D0A8N
-8A0A(B BBBA , O>
- AID W
-DAA D BBB D(A0D@0A(A BBB T xOL BIE B(A0A8D9
-8D0A(B BBBA T pTb s BBB B(F0D8G
-8A0A(B BBBA 5. K^ $ pd < hd T | pd BBB B(D0D8G_
-8A0A(B BBBH T 8m, BBE B(D0A8G
-8D0A(B BBBK L , r BBB J(A0A8G
-8D0A(B BBBH D T D BBB D(A0D@0A(A BBB T u& F BIB E(A0A8G
-8D0A(B BBBA T pz BBE B(D0A8G
-8D0A(B BBBH T t 8
- BBB B(F0A8G
-8A0A(B BBBB T p
- / BBB B(F0A8G
-8A0A(B BBBJ T $ 蓭
- W BBB B(F0A8G
-8A0A(B BBBB T |
- BBB B(F0A8G
-8A0A(B BBBB T BBB G(D0A8M
-8D0A(B BBBB 2. K^ J\ AG RAD < } BBB D(A0D@
-0A(A BBBA D } BBB D(A0D@
-0A(A BBBA D (} BBB D(A0D@
-0A(A BBBA D `} BBB D(A0D@
-0A(A BBBA D \ BBB D(A0D@
-0A(A BBBA D ಭ BBB D(A0D@
-0A(A BBBA D ( BBB D(A0D@
-0A(A BBBA D 4
- p BBB D(A0D@
-0A(A BBBA D |
- Fv BBB D(A0D@^0A(A BBB T p BIB E(A0A8D6
-8D0A(B BBBA T D
- BIB E(A0A8D6
-8D0A(B BBBA T
- ྭ BIB E(A0A8D6
-8D0A(B BBBA T
- í BIB E(A0A8D6
-8D0A(B BBBA D $ Pǭ BBE A(A0D@l
-0A(A BBBA $ l ǭV ADD JAAL ȭ BEB E(I0H8D@
-8D0A(B BBBA L ɭ BJE B(A0A8D@
-8A0A(B BBBA T \ pʭ BBB B(A0D8L7
-8A0A(B BBBF T ϭ\ BBB B(H0A8J
-8A0A(B BBBG ,
ڭ> AID W
-DAA T <
0ڭ\ BBB B(H0A8J
-8A0A(B BBBG ,
8> G AID W
-DAA T
H ' BBB B(H0A8J
-8A0A(B BBBG , > AID W
-DAA T L \ g BBB B(H0A8J
-8A0A(B BBBG , > Ǔ AID W
-DAA T BBB H(A0D8L
-8D0A(B BBBH T , ` BBB H(A0D8L
-8D0A(B BBBH T BBB H(A0D8L
-8D0A(B BBBH ). K^ > Hu ( > Hu H > Hu , h > Hu L E Hx l E Hx E Hx
-E Hx l{\ AG RA (
-_ AF
-I T 4 h
-
- v BEB B(A0A8N
-8A0A(B BBBH , 0> AID W
-DAA T @
- BEB B(A0A8N
-8A0A(B BBBH , > AID W
-DAA T D
- BEB B(A0A8N
-8A0A(B BBBH , '> AID W
-DAA T '
- BEB B(A0A8N
-8A0A(B BBBH , $ 1> * AID W
-DAA D , hy BBB D(A0D@0A(A BBB T 1 BIB E(A0A8DF
-8D0A(B BBBA T 5 BIB E(A0A8DF
-8D0A(B BBBA T L : ^ BIB E(A0A8DF
-8D0A(B BBBA T X> , BIB E(A0A8DF
-8D0A(B BBBA T B BBG E(A0G8GY
-8D0A(B BBBK T T K K BBG E(A0G8GY
-8D0A(B BBBK T T BBG E(A0G8GY
-8D0A(B BBBK T (^
- BBG E(A0G8Go
-8D0A(B BBBE 4 @#. K^ L Hhd D L
-H G l h KN $ h^ AG b
-AA D hv BBB A(D0D@
-0A(A BBBA D jv BBB A(D0D@
-0A(A BBBA D < Hkv BBB A(D0D@
-0A(A BBBA D lv BBB A(D0D@
-0A(A BBBA D m~ BBB A(D0D@
-0A(A BBBA D n~ BBB A(D0D@
-0A(A BBBA D \ (p~ BBB A(D0D@
-0A(A BBBA D `q~ BBB A(D0D@
-0A(A BBBA D r BGB D(M0B
-(A BBBK D 4 s BBB D(A0D@
-0A(A BBBA L Xt BIB A(D0D
-0D(A BBBA L w BIB A(D0D
-0D(A BBBA L D z ď BIB A(D0D
-0D(A BBBA L H~ BIB A(D0D
-0D(A BBBA 4 Z BDA G`
- DABJ 4 Z b BDA G`
- DABJ D T 胮 > BEB A(A0G`l
-0D(A BBBA T BBB B(D0D8G
-8A0A(B BBBA T ( BBB B(D0D8G
-8A0A(B BBBA T L BBB B(D0D8G
-8A0A(B BBBA T BBB B(D0D8G
-8A0A(B BBBA T ` BBB B(H0A8JZ
-8A0A(B BBBA T T 蘮' BBB B(D0A8NS
-8A0A(B BBBH , > AID W
-DAA T Щ' BBB B(D0A8NS
-8A0A(B BBBH , 4 > AID W
-DAA T d ' BBB B(D0A8NS
-8A0A(B BBBH , ˮ> AID W
-DAA T ˮ' BBB B(D0A8NS
-8A0A(B BBBH , D xܮ> AID W
-DAA L L ܮ; BBB B(A0D8DPd
-8A0A(B BBBA L x߮ BBA D(G0o
-(A ABBK K
-(A ABBG L K BBB B(A0A8G@d
-8A0A(B BBBA d d BBA A(GO
+8A0A(B BBBA T ^ BBB E(A0F8G
+8A0A(B BBBA < 4 H BBA A(Gp
+(D ABBA L t g BEB B(G0A8Ip
+8D0A(B BBBE T e BBH E(A0F8DG
+8D0A(B BBBH D. K^ d D L
+H G KN , \ AG RAD L BBE A(A0D@
+0A(A BBBA D BBE A(A0D@
+0A(A BBBA D x BBE A(A0D@
+0A(A BBBA D $ @ BBE A(A0D@
+0A(A BBBA D l BBE A(A0D@
+0A(A BBBA D BBE A(A0D@
+0A(A BBBA D BBE A(A0D@
+0A(A BBBA D D BBE A(A0D@
+0A(A BBBA < D BBD A(D@0(A ABB T $ (
+ BIB E(A0A8DN
+8D0A(B BBBA T | p BIB E(A0A8DN
+8D0A(B BBBA T BIB E(A0A8DN
+8D0A(B BBBA T , BIB E(A0A8DN
+8D0A(B BBBA , H% T `
? BEB B(H0A8GP
+8A0A(B BBBK , (> AID W
+DAA T $ (
BEB B(H0A8GP
+8A0A(B BBBK , | 5> AID W
+DAA T 5
BEB B(H0A8GP
+8A0A(B BBBK , (C> C AID W
+DAA T 4 8C
# BEB B(H0A8GP
+8A0A(B BBBK , pP> AID W
+DAA d P% L P W BEE D(A0Gp
+0A(A BBBH L $ Y F BEE D(A0Gp
+0A(A BBBH T t xb$ / BBB G(A0D8G
+8A0A(B BBBE T Pj ! BBB G(A0D8G
+8A0A(B BBBE T $ r BBB G(A0D8G
+8A0A(B BBBE T | y BBB G(A0D8G
+8A0A(B BBBE $ < AK
+AA > Hu E Hx \ AG RAT \
+ BBB B(D0A8N
+8A0A(B BBBA , > AID W
+DAA D 6 BBB D(A0D@0A(A BBB T , ЋL _ BIE B(A0A8D9
+8D0A(B BBBA T Ȑ / BBB B(F0D8G
+8A0A(B BBBA :. K^ x p T $ x W BBB B(D0D8G_
+8A0A(B BBBH T | @, k BBE B(D0A8G
+8D0A(B BBBK L Q BBB J(A0A8G
+8D0A(B BBBH D ! ĉ BBB D(A0D@0A(A BBB T l! & BIB E(A0A8G
+8D0A(B BBBA T ! x BBE B(D0A8G
+8D0A(B BBBH T " @
+ BBB B(F0A8G
+8A0A(B BBBB T t" xƫ
+ BBB B(F0A8G
+8A0A(B BBBJ T " Ы
+ # BBB B(F0A8G
+8A0A(B BBBB T $# ۫
+ S BBB B(F0A8G
+8A0A(B BBBB T |# { BBB G(D0A8M
+8D0A(B BBBB |$ 7. K^ $ ʈ\ AG RAD $ } BBB D(A0D@
+0A(A BBBA D $ } BBB D(A0D@
+0A(A BBBA D D% 0} BBB D(A0D@
+0A(A BBBA D % h} BBB D(A0D@
+0A(A BBBA D % BBB D(A0D@
+0A(A BBBA D & BBB D(A0D@
+0A(A BBBA D d& 0 BBB D(A0D@
+0A(A BBBA D & x BBB D(A0D@
+0A(A BBBA D & Ɔ BBB D(A0D@0A(A BBB T & x BIB E(A0A8D6
+8D0A(B BBBA T & BIB E(A0A8D6
+8D0A(B BBBA T D' BIB E(A0A8D6
+8D0A(B BBBA T ' U BIB E(A0A8D6
+8D0A(B BBBA D ( X BBE A(A0D@l
+0A(A BBBA T <( BBB B(A0D8L7
+8A0A(B BBBF T ( BBB H(A0D8L
+8D0A(B BBBH T ( BBB H(A0D8L
+8D0A(B BBBH T D) X BBB H(A0D8L$
+8D0A(B BBBH $ D* V ADD JAAL ) BEB E(I0H8D@
+8D0A(B BBBA L * x BJE B(A0A8D@
+8A0A(B BBBA T d* H\ BBB B(H0A8J
+8A0A(B BBBG , * P&> ' AID W
+DAA T * `& BBB B(H0A8J
+8A0A(B BBBG , D+ 1> g AID W
+DAA T t+ 1\ G BBB B(H0A8J
+8A0A(B BBBG , + => AID W
+DAA T + =\ BBB B(H0A8J
+8A0A(B BBBG , T, H> AID W
+DAA ,- .. K^ D- H> Hu d- 0H> Hu - PH> Hu - pH> Hu - HE Hx - HE Hx . HE Hx $. IE Hx D. D\ AG RA d. 0I_ AF
+I T - pI
+ o BEB B(A0A8N
+8A0A(B BBBH , 4. 8S> AID W
+DAA T d. HS
+ BEB B(A0A8N
+8A0A(B BBBH , . ]> AID W
+DAA T . ]
+ BEB B(A0A8N
+8A0A(B BBBH , D/ f> AID W
+DAA T t/ f
+ BEB B(A0A8N
+8A0A(B BBBH , / p> # AID W
+DAA D 0 @v BBB D(A0D@^0A(A BBB T D0 p BIB E(A0A8DF
+8D0A(B BBBA T 0 t BIB E(A0A8DF
+8D0A(B BBBA T 0 y W BIB E(A0A8DF
+8D0A(B BBBA T L1 `} % BIB E(A0A8DF
+8D0A(B BBBA T 1 BBG E(A0G8GY
+8D0A(B BBBK T 1 C BBG E(A0G8GY
+8D0A(B BBBK T T2 BBG E(A0G8GY
+8D0A(B BBBK T 2 0
+ BBG E(A0G8Go
+8D0A(B BBBE 3 8(. K^ $ 3 P^ AG b
+AA D 3 v BBB A(D0D@
+0A(A BBBA D 44 v BBB A(D0D@
+0A(A BBBA D |4 v BBB A(D0D@
+0A(A BBBA D 4 0v BBB A(D0D@
+0A(A BBBA D 5 h~ BBB A(D0D@
+0A(A BBBA D T5 ~ BBB A(D0D@
+0A(A BBBA 5 خ H 5 H D 45 8 BGB D(M0B
+(A BBBK D $6 BBB D(A0D@
+0A(A BBBA L 5 BIB A(D0D
+0D(A BBBA L 6 H c BIB A(D0D
+0D(A BBBA L d6 D BIB A(D0D
+0D(A BBBA L 6 軬 % BIB A(D0D
+0D(A BBBA 4 7 8Z BDA G`
+ DABJ 4 <7 `Z BDA G`
+ DABJ D t7 BEB A(A0G`l
+0D(A BBBA T 7 `¬ BBB B(D0D8G
+8A0A(B BBBA T 8 Ŭ BBB B(D0D8G
+8A0A(B BBBA T l8 0ɬ BBB B(D0D8G
+8A0A(B BBBA T 8 ̬ z BBB B(D0D8G
+8A0A(B BBBA T 9 Ь u BBB B(H0A8JZ
+8A0A(B BBBA T t9 ֬7 , BBB B(D0A8Nc
+8A0A(B BBBH , 9 p> = AID W
+DAA T 9 7 BBB B(D0A8Nc
+8A0A(B BBBH , T: h> . AID W
+DAA T : x BBB B(A0D8Ns
+8A0A(B BBBH , : > AID W
+DAA T ; 7 BBB B(D0A8Nc
+8A0A(B BBBH , d; > AID W
+DAA d ; BBA A(GO
(A ABBF M
(A ABBE P
-(F ABBA d p 9 BBA A(GX
+(F ABBA d ; P BBA A(GX
(C ABBC w
(C ABBA v
-(C ABBA L 4! BEE D(A0D
-0A(A BBBH T ! 5 BBB B(D0A8Lp
-8A0A(B BBBE T ! p ݐ BBB B(D0A8Lp
-8A0A(B BBBE T 4" I BBB B(D0A8Lp
-8A0A(B BBBE T " E ݐ BBB B(D0A8Lp
-8A0A(B BBBE # . K^ # *R HI # *Y HL $ Nk\ AG RAT \# + b BBB B(D0A8N
-8A0A(B BBBI , # X?> * AID W
-DAA D $ k BBB D(A0D@0A(A BBB T ,$ ?L BIE B(A0A8D9
-8D0A(B BBBA T $ D BBB B(D0A8L
-8A0A(B BBBA % . K^ % O KN % k HN % k\ AG RAD & XO BBB D(A0D@
-0A(A BBBA L % P W BIB A(D0D
-0D(A BBBA L % T 8 BIB A(D0D
-0D(A BBBA L ,& PW BIB A(D0D
-0D(A BBBA L |& Z BIB A(D0D
-0D(A BBBA L & ] ۏ BBB E(A0A8G
-8D0A(B BBBB L ' P_ BBB B(A0A8Gp8A0A(B BBB $ D( `V ADD JAA l( a Hw ( hb Hw ( c Hw ( (e Hw ( f Hz ) g Hz ,) hi Hz L) j Hz L l) Hl BOB A(A0]
+(C ABBA T = U BAA G0
+ AABA
+ AABK f
+ AABF T d= !O BAA G05
+ AABD
+ AABD e
+ AABO T = & BBB E(A0A8L
+8A0A(B BBBI T l= p5 BBB E(A0A8L
+8A0A(B BBBI T = D BBB E(A0A8L
+8A0A(B BBBI T > R BBB E(A0A8L
+8A0A(B BBBI ? . K^ 4? aR HI T? `aY HL t? q\ AG RAT > a BBB B(D0A8N
+8A0A(B BBBI , D? u> AID W
+DAA D @ q BBB D(A0D@0A(A BBB T ? uL g BIE B(A0A8D9
+8D0A(B BBBA T @ zB
+ 7 BBB E(A0A8L
+8A0A(B BBBA A 0. K^ ,A x KN DA ?r HN \A >r\ AG RAD |A H BBB D(A0D@
+0A(A BBBA L A BIB A(D0D
+0D(A BBBA L lA BIB A(D0D
+0D(A BBBA L A @ BIB A(D0D
+0D(A BBBA L B b BIB A(D0D
+0D(A BBBA L \B C BBB E(A0A8G
+8D0A(B BBBB L TC @ BBB B(A0A8Gp8A0A(B BBB $ C V ADD JAA C Hw C X Hw D Hw ,D Hw LD x Hz lD 蜭 Hz D X Hz D ȟ Hz L D 8 BOB A(A0]
(A BBBG Y
-(A BBBH d ) l0 BBE B(D0E8D@P
+(A BBBH d E 0 BBE B(D0E8D@P
8F0A(B BBBA
-8D0A(B BBGA $* mT < <* m BBG A(G@
-(A ABBH < |* hn BBE A(D0
-(A BBBA , * Ho8 WEAG , * Xp8 WEAG , + hq8 WEAG , L+ xr8 WEAG |+ s L * u BEB E(I0H8D@
-8D0A(B BBBA 4 + v AIG
+8D0A(B BBGA E pT < E BBG A(G@
+(A ABBH < E X BBE A(D0
+(A BBBA , F 88 WEAG , LF H8 WEAG , |F X8 WEAG , F h8 WEAG F x 4 F AIG
FAI I
-FAH , 8w 4, x L, hz 4 d, | FEH G(
+FAH ,G DG @ \G ح 4 tG p FEH G(
- ABBG L , }U BKB E(D0A8D
-8A0A(B BBBH D , ~ BBE D(A0M`0A(A BBB T \, `'
BBB B(A0A8J
+ ABBG L G U BKB E(D0A8D
+8A0A(B BBBH D G BBE D(A0M`0A(A BBB T G г'
3 BBB B(A0A8J
-8A0A(B BBBF T , 85 ڊ BBB B(D0A8N }
-8A0A(B BBBF , - > Ɍ AID W
-DAA 4 . 0 FEH G(
+8A0A(B BBBF T G 5 j BBB B(D0A8N }
+8A0A(B BBBF , LH ۭ> Y AID W
+DAA 4 $I ۭ FEH G(
- ABBG L L. U BKB E(D0A8D
-8A0A(B BBBH D . Ȫ BBE D(A0M`0A(A BBB T . '
ً BBB B(A0A8J
+ ABBG L \I (ݭU BKB E(D0A8D
+8A0A(B BBBH D I 8ޭ BBE D(A0M`0A(A BBB T LI '
i BBB B(A0A8J
-8A0A(B BBBF T d. h5 BBB B(D0A8N }
-8A0A(B BBBF , . Pԯ> AID W
-DAA 4 / `ԯ FEH G(
+8A0A(B BBBF T I 5 BBB B(D0A8N }
+8A0A(B BBBF , I > AID W
+DAA 4 J FEH G(
- ABBG L / կU BKB E(D0A8D
-8A0A(B BBBH D L0 ֯ BBE D(A0M`0A(A BBB T / د'
BBB B(A0A8J
+ ABBG L K X U BKB E(D0A8D
+8A0A(B BBBH D \K h
+ BBE D(A0M`0A(A BBB T J 0 '
BBB B(A0A8J
-8A0A(B BBBF T 0 5 F BBB B(D0A8N }
-8A0A(B BBBF , l0 > 5 AID W
-DAA 4 t1 FEH G(
+8A0A(B BBBF T TK 5 BBB B(D0A8N }
+8A0A(B BBBF , K 3> AID W
+DAA 4 L 4 FEH G(
- ABBG L 1 U BKB E(D0A8D
-8A0A(B BBBH D 1 ( BBE D(A0M`0A(A BBB T l1 '
E BBB B(A0A8J
+ ABBG L L 5U BKB E(D0A8D
+8A0A(B BBBH D M 6 BBE D(A0M`0A(A BBB T L `8'
BBB B(A0A8J
-8A0A(B BBBF T 1 5 | BBB B(D0A8N }
-8A0A(B BBBF , 2 ,> k AID W
-DAA d $3 ,A BBB E(D0E8D@P
+8A0A(B BBBF T M 8E5 BBB B(D0A8N }
+8A0A(B BBBF , \M `> AID W
+DAA d 4N 0`A BBB E(D0E8D@P
8F0A(B BBBA
-8D0A(B BBGA , 3 - ADD ]
-AAA 3 .T T 2 `. BBB B(A0D8GE
-8A0A(B BBBE T T3 3 z BBB B(A0D8GE
-8A0A(B BBBE T 3 8 Y BBB B(A0D8GE
-8A0A(B BBBE T 4 > 8 BBB B(A0D8GE
-8A0A(B BBBE 45 PC BBF D(A0G@I
+8D0A(B BBGA , N a ADD ]
+AAA N aT T <N a + BBB B(A0D8GE
+8A0A(B BBBE T N g
+ BBB B(A0D8GE
+8A0A(B BBBE T N @l BBB B(A0D8GE
+8A0A(B BBBE T DO xq BBB B(A0D8GE
+8A0A(B BBBE DP v BBF D(A0G@I
0A(A BBBI }
0D(A BBBH D
0F(A BBBG c
-0A(A BBBE D 5 hD BBE E(D0D@
-0E(A BBBA T ,5 0E G BBB E(D0F8G
-8A0A(B BBBC T 5 P i BBB E(D0F8G
-8A0A(B BBBC T 5 \ BBB E(D0F8G
-8A0A(B BBBC T 46 hg BBB E(D0F8G
-8A0A(B BBBC d7 . K^ $ |7 r^ AG b
-AA D 7 r BBB D(A0D@
-0A(A BBBA D 7 Ht G BIB A(D0D`7
-0D(A BBBA D \7 w % BIB A(D0D`7
-0D(A BBBA |8 yD H; 8 {D H; 8 8|K H> 8 h}K H> 8 ~T 9 ~ ,9 xo 4 D9 Ѐ BDD D0
- AABH 4 |9 x AIG
+0A(A BBBE D P w BBE E(D0D@
+0E(A BBBA T lP x BBB E(D0F8G
+8A0A(B BBBC T P BBB E(D0F8G
+8A0A(B BBBC T Q p BBB E(D0F8G
+8A0A(B BBBC T tQ ؚ = BBB E(D0F8G
+8A0A(B BBBC tR
+. K^ $ R (^ AG b
+AA D R ` BBB D(A0D@
+0A(A BBBA D TR BIB A(D0D`7
+0D(A BBBA D R BIB A(D0D`7
+0D(A BBBA S HD H; S xD H; S K H> S ذK H> T T $T P <T 賮o 4 TT @ BDD D0
+ AABH 4 T 贮 AIG
FAI I
-FAH 9 9 o 4 9 BDD D0
- AABH 4 : FEH G(
+FAH T T o 4 T p BDD D0
+ AABH 4 ,U FEH G(
- ABBG L T: 0U BKB E(D0A8D
-8A0A(B BBBH D : @ BBE D(A0M`0A(A BBB T : & BBE B(A0A8NB
-8A0A(B BBBA , l: `> AID W
-DAA 4 t; p FEH G(
+ ABBG L dU U BKB E(D0A8D
+8A0A(B BBBH D U BBE D(A0M`0A(A BBB T TU x& # BBE B(A0A8NB
+8A0A(B BBBA , U > AID W
+DAA 4 V FEH G(
- ABBG L ; U BKB E(D0A8D
-8A0A(B BBBH D ; BBE D(A0M`0A(A BBB T l; г& * BBE B(A0H8G4
-8A0A(B BBBA , ; ڰ> AID W
-DAA $ < ڰV ADD JAA < PڰT L 4< ڰ O BEB E(I0H8D@
-8D0A(B BBBA , \= ܰ BFA D0 AAB4 = ܰ AIG
+ ABBG L V hU BKB E(D0A8D
+8A0A(B BBBH D W x BBE D(A0M`0A(A BBB T V @n' BBE B(A0A8N
+8A0A(B BBBA , W X> < AID W
+DAA $ W hV ADD JAA X T L tW BEB E(I0H8D@
+8D0A(B BBBA , lX h BFA D0 AAB4 X AIG
FAA I
-FAA d = 0ݰ BBB B(A0A8Ge
+FAA d X BBB B(A0A8Ge
8A0A(B BBBH
-8A0A(B BBBA T T= W BBE B(A0A8Lq
-8A0A(B BBBA L > ` BBB E(A0A8GX
-8D0A(B BBBA T = BBE B(A0A8Lq
-8A0A(B BBBA ,? . K^ D? R\ AG RAD d? BBE A(A0D@u
-0A(A BBBA D ? BBE A(A0D@}
-0A(A BBBA T ? W BBI B(A0A8J
-8A0A(B BBBA , t? > AID W
-DAA D |@ Q BBB D(A0D@0A(A BBB T ? BIB E(A0A8D
-8D0A(B BBBA \ A KLE B(A0A8DPi8A0A(B BBBIP \ |A KLE B(A0A8DPi8A0A(B BBBIP d A HE BBB B(A0A8DP
+8A0A(B BBBA T X BBE B(A0A8Lq
+8A0A(B BBBA L Y BBB E(A0A8GX
+8D0A(B BBBA T <Y P r BBE B(A0A8Lq
+8A0A(B BBBA <Z h. K^ TZ Y\ AG RAD tZ # BBE A(A0D@u
+0A(A BBBA D Z h$ BBE A(A0D@}
+0A(A BBBA T \Z %W BBI B(A0A8J
+8A0A(B BBBA , Z -> AID W
+DAA D [ X BBB D(A0D@0A(A BBB T ,[ , 7 BIB E(A0A8D
+8D0A(B BBBA \ ,\ 82 KLE B(A0A8DPi8A0A(B BBBIP \ \ h4 KLE B(A0A8DPi8A0A(B BBBIP d \ 6E BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l lA 0B [ BBB J(D0D8D4
+8F0A(B BBBA l \ 7B BBB J(D0D8D4
8D0A(B BBBC i
-8A0A(B BBBE L A BBB B(A0A8J
-8A0A(B BBBA C `. K^ C hO HF <C V HI T B < BBE B(D0A8G
-8D0A(B BBBB T B < i BBE B(D0A8G
-8D0A(B BBBB D D P BBB D(A0D@0A(A BBB T |C `$ BIB E(A0A8DV
-8D0A(B BBBA T C ( Պ BIB E(A0A8D^
-8D0A(B BBBA T ,D -{ BGE B(A0A8G
-8A0A(B BBBI T D 8G BBF B(D0A8Nk
-8A0A(B BBBA , D P> V AID W
-DAA , E P> 6 AID W
-DAA F . K^ ,F P DF P L E Pr BBB B(E0A8J@
-8A0A(B BBBA F JO\ AG RAT E Q s BBB B(A0I8G
-8D0A(B BBBG T LF T a BBB B(A0I8G
-8D0A(B BBBG T F (X O BEE E(D0A8G
-8D0A(B BBBF T F P] 5 BBE B(D0A8G
-8D0A(B BBBH T TG b BBE B(D0A8G
-8D0A(B BBBG T G f BEE E(D0A8G
-8D0A(B BBBF T H lE BBE B(A0A8G
-8D0A(B BBBC D 4I M BBB D(A0D@0A(A BBB T H V BIB B(A0D8D
-8D0A(B BBBA T H ` % BIB B(A0D8D
-8D0A(B BBBA L TI BBB B(D0A8G
-8A0A(B BBBA |J H. K^ J @ DV $ J H^ AG b
-AA $ J AG u
-AA $ J AG }
-AA T LJ c BBB E(D0D8J
-8D0A(B BBBD T J H4
w BBB B(D0A8J
-8D0A(B BBBH | K 0 BBB B(D0A8DP
+8A0A(B BBBE L ] `; BBB B(A0A8J
+8A0A(B BBBA ^ . K^ ,^ NO HF L^ NV HI T ] (O< BBE B(D0A8G
+8D0A(B BBBB T ^ T< BBE B(D0A8G
+8D0A(B BBBB D _ W BBB D(A0D@0A(A BBB T ^ X BIB E(A0A8D^
+8D0A(B BBBA T _ ] e BIB E(A0A8DV
+8D0A(B BBBA T l_ `a 3 BBF B(D0A8Nk
+8A0A(B BBBA , _ j> AID W
+DAA , _ j> f AID W
+DAA T $` j G BGE B(A0A8G
+8A0A(B BBBI $a . K^ <a 8 Ta 0 la ( a 0 L ` 8r 3 BBB B(E0A8J@
+8A0A(B BBBA $ a h^ AG b
+AA 4 b l ADG b
+AAH l
+AAA 4 Lb ؆l ADG b
+AAH l
+AAA T a ` BBB B(A0I8G
+8D0A(B BBBE T 4b 8 O BBB B(A0I8G
+8D0A(B BBBE T b ` > BEE E(D0A8G
+8D0A(B BBBF T b $ BBE B(D0A8G
+8D0A(B BBBH T <c P
+ BBE B(D0A8G
+8D0A(B BBBG T c BEE E(D0A8G
+8D0A(B BBBF T c @e BBB B(D0A8G
+8D0A(B BBBD < d SD BBD A(D@0(A ABB T d BIB E(A0A8DF
+8D0A(B BBBA T d ` BIB E(A0A8DF
+8D0A(B BBBA L 4e & BGB E(A0A8G
+8A0A(B BBBA L e ɯ2 BGB E(A0A8G
+8A0A(B BBBA |f . K^ f `ۯ DV $ f hۯ^ AG b
+AA $ f ۯ AG u
+AA $ f 8ܯ AG }
+AA T |f ܯ4
BBB B(D0A8J
+8D0A(B BBBH T f x . BBB B(D0D8J&
+8D0A(B BBBC L g ; BBB B(A0D8DPd
+8A0A(B BBBA $ $h r DS
+I H
+E | Lh ( BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI TL < lL KD BBD A(D@0(A ABB T K x BIB E(A0A8DF
-8D0A(B BBBA T ,L BIB E(A0A8D6
-8D0A(B BBBA T L | Z BBB L(A0A8J
-8A0A(B BBBG 4 L Աx y BAI H
-ABA 4 M hԱx U BAI H
-ABA T LM Ա 1 BBB B(A0A8G
-8A0A(B BBBK L M (ڱ. ' BBB E(A0A8J
-8A0A(B BBBG N (. K^ $ N ^ AG b
-AA $ O ( AG u
-AA $ 4O AG }
-AA D \O J BBB D(A0D@0A(A BBB T N BIB B(A0D8Df
-8D0A(B BBBA 4 $O xq BAI A
-ABA L \O U BBE B(A0A8J
-
-8A0A(B BBBA P . K^ L O r P BBB B(E0A8J@
-8A0A(B BBBA $ P
^ AG b
-AA $ Q AG
-AA $ <Q AG
-AA , P AAG@A
-AAA $ Q AG M
-AA T P H I BBE E(A0A8G
-8D0A(B BBBE D R BBB D(A0D@
-0A(A BBBA T Q ć BIB E(A0A8D6
-8D0A(B BBBA D Q P BIB A(D0D`2
-0D(A BBBA T $R W p BHE E(D0A8GG
-8A0A(B BBBA T |R # c BBB B(A0I8G
-8D0A(B BBBF T R h' R BBB B(A0I8G
-8D0A(B BBBF \ T +m BBD D(J@
+8A0A(B BBBI h D h NP BBB D(A0D@0A(A BBB T h h BIB E(A0A8D6
+8D0A(B BBBA T h BIB E(A0A8DF
+8D0A(B BBBA T 4i | BBB L(A0A8J
+8A0A(B BBBG 4 i x BAI H
+ABA 4 i Xx BAI H
+ABA T i BBB B(A0A8G
+8A0A(B BBBK L Tj BBI E(A0A8J
+8D0A(B BBBC Lk . K^ $ dk 4^ AG b
+AA $ k 4 AG u
+AA $ k 5 AG }
+AA k 6T < k `8 BBA A(G@a
+(A ABBD < 4l ND BBD A(D@0(A ABB T k 8 BIB B(A0D8Df
+8D0A(B BBBA L $l 8=( BBE B(A0A8J
+
+8A0A(B BBBG 4 tl Vq BAI A
+ABA Tm @. K^ L l HVr BBB B(E0A8J@
+8A0A(B BBBA $ m xW^ AG b
+AA $ m W AG
+AA $ n hX AG
+AA , m Y AAG@A
+AAA $ dn pY AG M
+AA T m YR | BBE B(D0A8G
+8D0A(B BBBF < n 4MD BBD A(D@0(A ABB T |n ^ " BIB E(A0A8DF
+8D0A(B BBBA T n b BIB E(A0A8D6
+8D0A(B BBBA T ,o g BBB B(A0I8G
+8D0A(B BBBG T o le BBB B(A0I8G
+8D0A(B BBBF \ p qm BBD D(J@
(A ABBD T
(A ABBF _
-(A ABBK T S ,P BBE E(D0A8M
-8D0A(B BBBG T =Z AK
-L AT T =2 a BBB E(A0D8I
-8A0A(B BBBA 4U K AN TU . K^ lU K U K L T Kq BBB B(A0I8G
-8D0A(B BBBD L U Mq BBB B(A0I8G
-8D0A(B BBBD L dU Pq φ BBB B(A0I8G
-8D0A(B BBBD T U HR BBH B(D0F8Gd
-8D0A(B BBBH D V ND BBB D(A0D@0A(A BBB T TV hX BIB E(A0A8DF
-8D0A(B BBBA T V \ BIB E(A0A8DO
-8D0A(B BBBA T W (a [ BIB E(A0A8DN
-8D0A(B BBBA 4X P. K^ LX Xe dX Pe T W Xe BBE E(A0A8G
-8D0A(B BBBE L W |q BBB B(A0I8G
-8D0A(B BBBD D $Y C BBB D(A0D@0A(A BBB T X ~L ` BIE B(A0A8D9
-8D0A(B BBBA T X Ѓ / BBB B(A0A8J"
-8A0A(B BBBH Z . K^ 4Z ` LZ X , dZ ` MPAC , Z JXAD T Y Зj o BBB B(A0D8R
-8A0A(B BBBD , DZ 蜲> AID W
-DAA D L[ C BBB D(A0D@0A(A BBB T Z L , BIE B(A0A8D9
-8D0A(B BBBA L [ BBE G(A0G8G
-8D0A(B BBBG <\ . K^ T\ P l\ H T [ Px 7 BBB B(D0A8N9
-8A0A(B BBBB , \ x> AID W
-DAA D ] C BBB D(A0D@0A(A BBB T |\ @L BIE B(A0A8D9
-8D0A(B BBBA T \ 8 S BBB B(A0A8J
-8A0A(B BBBA ^ ߗ. K^ L ^ xDz BBB B(A0A8Dpn
-8A0A(B BBBA L l^ Ȳ BBB B(A0D8G
-8A0A(B BBBG L ^ ˲~ BBB E(A0A8G
-8A0A(B BBBG L _ ϲ BBE B(A0A8D`t8A0A(B BBB L \_ в BEB B(A0A8Dd
-8A0A(B BBBA L _ 8Ӳ BBE B(A0A8Di8A0A(B BBB $ _ xֲ^ AG b
-AA $ $` ֲ AG r
-AA $ L` 8ײ AG r
-AA $ t` ײ AG r
-AA $ ` Hز AG r
-AA $ ` ز AG z
-AA $ ` hٲ AG z
-AA $ a ڲ AG z
-AA $ <a ڲ AG z
-AA D ` 0۲ _ BEB A(A0Nj
-0A(A BBBA L ` ܲ A BBB L(D0C8M
-8A0A(B BBBJ T $a K BBE E(A0A8G
-8D0A(B BBBE $ Tb P AG L
-AA \ a $ BBB A(I0D@
+(A ABBK T <p rF 2 BBE E(A0C8L
+8A0A(B BBBH <q Z AK
+L AT p BBB E(A0D8I
+8A0A(B BBBA q AN q . K^ q X r P L tq Xq BBB B(A0I8G
+8D0A(B BBBD L q q BBB B(A0I8G
+8D0A(B BBBD L r q { BBB B(A0I8G
+8D0A(B BBBD T dr 袰 g BBH B(D0F8Gd
+8D0A(B BBBH D ds I BBB D(A0D@0A(A BBB T s k BIB E(A0A8DN
+8D0A(B BBBA T \s P 9 BIB E(A0A8DG
+8D0A(B BBBA T s ȱ BIB E(A0A8DN
+8D0A(B BBBA t @. K^ t t T Tt BBE E(A0A8G
+8D0A(B BBBE L t Ͱq j BBB B(A0I8G
+8D0A(B BBBD D u I BBB D(A0D@0A(A BBB T Du xϰL BIE B(A0A8D9
+8D0A(B BBBA T u p BBB B(A0A8J"
+8A0A(B BBBH v . K^ v v , v MPAC , w JXAD T v pj BBB B(A0D8R
+8A0A(B BBBD , v > @ AID W
+DAA D w ,I BBB D(A0D@0A(A BBB T lw PL BIE B(A0A8D9
+8D0A(B BBBA L w H BBE G(A0G8G
+8D0A(B BBBG x . K^ x x T \x x BBB B(D0A8N9
+8A0A(B BBBB , x > AID W
+DAA D y :I BBB D(A0D@0A(A BBB T ,y L 1 BIE B(A0A8D9
+8D0A(B BBBA T y BBB B(A0A8J
+8A0A(B BBBA z . K^ L z x BBB B(A0A8Dpn
+8A0A(B BBBA L z BBB B(A0D8G
+8A0A(B BBBG L <{ ~ BBB E(A0A8G
+8A0A(B BBBG L { BBE B(A0A8D`t8A0A(B BBB L { ! BEB B(A0A8Dd
+8A0A(B BBBA L ,| 8$ BBE B(A0A8Di8A0A(B BBB $ || x' AG r
+AA $ | ( AG r
+AA $ | ( AG r
+AA $ | ) AG r
+AA $ } ) AG z
+AA $ D} 0* AG z
+AA $ l} * AG z
+AA $ } `+ AG z
+AA D } + ; BBB D(A0Np
+0A(A BBBG L \} -5 BBB L(D0C8M
+8A0A(B BBBJ T } ? ( BBE E(A0A8G
+8D0A(B BBBD T ~ B BBE E(A0A8G
+8D0A(B BBBF $ D AG L
+AA \ ~ E$ BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ b $ BBB A(I0D@
+0D(A BBBA \ ~ E$ G BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ db X$ h BBB A(I0D@
+0D(A BBBA \ D F$ BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ b ($ ) BBB A(I0D@
+0D(A BBBA \ xG$ BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ $c $ BBB A(I0D@
+0D(A BBBA \ HH$ BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ c $ BBB A(I0D@
+0D(A BBBA \ d I$ K BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ c $ l BBB A(I0D@
+0D(A BBBA \ Ā I$ BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ Dd h$ - BBB A(I0D@
+0D(A BBBA \ $ J$ BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ d 8$ BBB A(I0D@
+0D(A BBBA \ K$ BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ e $ BBB A(I0D@
+0D(A BBBA \ XL$ O BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ de $ p BBB A(I0D@
+0D(A BBBA \ D (M$ BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ e $ 1 BBB A(I0D@
+0D(A BBBA \ M$ BBB A(I0D@
0D(A BBBG W
-0D(A BBBA D f x BBB D(A0D@
-0A(A BBBA 4 lf k BHA GP
- DABA 4 f k BHA GP
- DABA 4 f @k l BHA GP
- DABA 4 g xk M BHA GP
- DABA 4 Lg k . BHA GP
- DABA 4 g r BHA GP
- DABA 4 g 0r BHA GP
- DABA 4 g xr BHA GP
- DABA 4 ,h r BHA GP
- DABA < dh BIA A(G`
-(D ABBA < h X l BIA A(G`
-(D ABBA < h E BIA A(G`
-(D ABBA T i U BAA G0
- AABA
- AABK f
- AABF T |i ~ BEE B(G0D8G
-8D0A(B BBBD T i E ~ BBE G(A0G8G.
-8D0A(B BBBF k ! AR $k ! AY T lj !B ~ BBB E(D0A8J{
-8D0A(B BBBF T j , BEB H(A0F8GR
-8D0A(B BBBB k Pї. K^ $ l (5^ AG b
-AA $ 4l `5 AG u
-AA $ \l 5 AG }
-AA < l 5D BBD A(D@0(A ABB T k P6 W~ BIB B(A0D8D
-8D0A(B BBBA 4 Dl :q %~ BAI A
-ABA T |l ;z ~ BBB B(A0A8J
-8A0A(B BBBH m ϗ. K^ m 0X m (X T m 0X ( BBB B(A0D8N
-8A0A(B BBBC , tm `> AID W
-DAA D |n 25 BBB D(A0D@0A(A BBB T m P`L A BIE B(A0A8D9
-8D0A(B BBBA T Dn He" BBB B(A0A8J
-8A0A(B BBBA to 0Η. K^ o o T n y ' BBB B(A0I8G>
-8A0A(B BBBG < p h5D BBD A(D@0(A ABB T |o BIB E(A0A8DF
-8D0A(B BBBA D o ؏d BEI A(D0D
-0D(A BBBA < p r BBH D(J
-(D ABBA D \p @ BBE H(D0D
-0D(A BBBF D p 薳 BBE H(D0D
-0D(A BBBF D p BBE D(H0D
-0D(A BBBA D 4q X BBE D(H0D
-0D(A BBBA D |q BBE D(H0D
-0D(A BBBA D q BBE D(H0D
-0D(A BBBA D r P BBE D(H0D
-0D(A BBBA D Tr BBE D(H0D
-0D(A BBBA D r BBE D(H0D
-0D(A BBBA L r HV BBE D(H0D
-0D(A BBBI T 4s X BBB E(G0A8L
+0D(A BBBA D N BBB D(A0D@
+0A(A BBBA D L P J BIB A(D0D`+
+0D(A BBBA D R ( BIB A(D0D`+
+0D(A BBBA D ܃ U BIB A(D0D`+
+0D(A BBBA D $ HX BIB A(D0D`+
+0D(A BBBA D l [ BIB A(D0D`+
+0D(A BBBA D ] BIB A(D0D`2
+0D(A BBBA D ` ~ BIB A(D0D`2
+0D(A BBBA D D Hc \ BIB A(D0D`2
+0D(A BBBA D f : BIB A(D0D`2
+0D(A BBBA L ԅ h BIB B(A0D8DpE
+8D0A(B BBBA L $ k" BIB B(A0D8DpL
+8D0A(B BBBA L t n" BIB B(A0D8DpL
+8D0A(B BBBA T Ć hq BEE B(G0D8G
+8D0A(B BBBD T yE BBE G(A0G8G.
+8D0A(B BBBF ȁ AR < ȁ AY T ȁB BBB E(D0A8J{
+8D0A(B BBBF T
+ BBB E(G0F8G*
+8D0A(B BBBJ Ք. K^ $ $ @^ AG b
+AA $ L x AG u
+AA $ t AG }
+AA < :D BBD A(D@0(A ABB T 4 h Q BIB B(A0D8D
+8D0A(B BBBA 4 q BAI A
+ABA T ĉ 8 BBB B(A0A8J
+8A0A(B BBBH Ċ Ԕ. K^ ܊ غ к T d غ # BBB B(A0D8N
+8A0A(B BBBC , 0ñ> AID W
+DAA D D: BBB D(A0D@0A(A BBB T 4 ±L < BIE B(A0A8D9
+8D0A(B BBBA T DZ" BBB B(A0A8J
+8A0A(B BBBA Ҕ. K^ P H T , Py # BBB B(A0I8G>
+8A0A(B BBBG < , z:D BBD A(D@0(A ABB T Č 8 BIB E(A0A8DF
+8D0A(B BBBA D d BEI A(D0D
+0D(A BBBA < d r BBH D(J
+(D ABBA D BBE H(D0D
+0D(A BBBF D BBE H(D0D
+0D(A BBBF D 4 X BBE D(H0D
+0D(A BBBA D | BBE D(H0D
+0D(A BBBA D Ď BBE D(H0D
+0D(A BBBA D P BBE D(H0D
+0D(A BBBA D T BBE D(H0D
+0D(A BBBA D BBE D(H0D
+0D(A BBBA D H BBE D(H0D
+0D(A BBBA L , V BBE D(H0D
+0D(A BBBI T | BBB E(G0A8L
-8D0A(B BBBJ dt pɗ. K^ $ |t ^ AG b
-AA D t BBB D(A0D@
-0A(A BBBA D t x BBB D(A0D@%
-0A(A BBBA D \t ' BBB D(A0Gn
-0D(A BBBE D t ³ ~ BBB D(A0Gn
-0D(A BBBE T t ij ~ BEE E(D0A8G2
-8D0A(B BBBD d v ɳ BLB B(A0A8D@c
+8D0A(B BBBJ | ͔. K^ $ P ^ AG b
+AA D BBB D(A0D@
+0A(A BBBA D ! BBB D(A0D@%
+0A(A BBBA D # # BBB D(A0Gn
+0D(A BBBE D @% BBB D(A0Gn
+0D(A BBBE T 4 & BEE E(D0A8G2
+8D0A(B BBBD d 4 , BLB B(A0A8D@c
8A0A(B BBBK s
-8A0A(B BBBC < v <0D BBD A(D@0(A ABB T u ʳ ~ BIB E(A0A8D6
-8D0A(B BBBA d Dv Xγ } BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d v γ s} BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB < w Hϳ BBG A(G@
-(A ABBF L ,x ϳ BHB E(A0A8D`
-8A0A(B BBBE d w xѳ | BJE B(A0A8D@
+8A0A(B BBBC < N5D BBD A(D@0(A ABB T 4 , BIB E(A0A8D6
+8D0A(B BBBA d 1 BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB d x1 o BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB < 1 BBG A(G@
+(A ABBF L D 2 BHB E(A0A8D`
+8A0A(B BBBE d 4 { BJE B(A0A8D@
8F0A(B BBBA b
-8F0A(B BBBA L x 0ҳ ?| BEB E(I0H8DP
-8D0A(B BBBD < \x ӳ; | BBA A(JO
-(A ABBA T x Գ | BBE B(A0A8M =
-8A0A(B BBBA , x > ?} AID W
-DAA T $y } BBE B(D0A8GR
-8A0A(B BBBE T |y I} BBE B(D0A8GS
-8A0A(B BBBD T y g} BBE B(D0A8GS
-8A0A(B BBBD L ,z I
} BBB B(A0A8G
-8A0A(B BBBA $ T{ AHD0AA$ |{ h^ AG b
-AA < { ~ BEA A(D0j(A ABB { AL T ,{ C O} BBE E(D0A8G
-8D0A(B BBBH T { 5} BBE B(D0A8G
-8D0A(B BBBF T { p } BBE B(D0A8G
-8D0A(B BBBF T 4| "3 } BBE E(D0A8G:
-8D0A(B BBBG T | $C | BBE E(D0A8G
-8D0A(B BBBH T | )C | BBE E(D0A8G
-8D0A(B BBBH T <} .C | BBE E(D0A8G
-8D0A(B BBBH D l~ 3 BBB D(A0D@
-0A(A BBBA L } 05" .| BIB B(A0D8DpH
-8D0A(B BBBA T ,~ 8 | BIB E(A0A8DV
-8D0A(B BBBA L ~ h<" { BIB B(A0D8DpH
-8D0A(B BBBA L H? BGB A(A0J
-(A BBDH x(A BBB L ? BBB B(A0A8GPd
-8A0A(B BBBA T t B { BBB E(A0D8G"
-8A0A(B BBBE T J z BBB B(A0A8G1
-8A0A(B BBBA T $ 8R z BBB B(A0A8G1
-8A0A(B BBBA d T Y: BEB B(D0A8D`
-8C0A(B BBBH D8D0A(B BBB T Z z BBB B(A0A8G1
-8A0A(B BBBA L < b z BBB G(D0A8J
-8D0A(B BBBE D pg* z BGE D(D0
-(A BBBA L ԁ Xh y BEB E(I0H8D@
-8D0A(B BBBD L $ i@ y BGB E(D0A8GP
-8D0A(B BBBA L t j y BEB E(I0H8D@
-8D0A(B BBBD T Ă hl4 {y BEB B(A0A8J
-8A0A(B BBBA , > | AID W
-DAA , L Р> c| AID W
-DAA , | ࠴> C| AID W
-DAA P. K^ ؠ Р 4 ̄ 8#> BDA f
-AFE AAB , , { AAG q
-AAA L 4 @ BBB B(A0D8G@r
-8A0A(B BBBH "\ AG RA< BHH R
+8F0A(B BBBA L T 4 ; BEB E(I0H8DP
+8D0A(B BBBD < 6; BBA A(JO
+(A ABBA T 7 BBE B(A0A8M =
+8A0A(B BBBA , < PM> ; AID W
+DAA T l `M BBE B(D0A8GR
+8A0A(B BBBE T Ė V E BBE B(D0A8GS
+8A0A(B BBBD T p_ c BBE B(D0A8GS
+8A0A(B BBBD L t 8hI
BBB B(A0A8G
+8A0A(B BBBA $ l ǔ AHD0AA$ u^ AG b
+AA < Hu~ BEA A(D0j(A ABB v AL T t vC K BBE E(D0A8G
+8D0A(B BBBH T ̘ { 1 BBE B(D0A8G
+8D0A(B BBBF T $ G BEE E(D0A8G
+8D0A(B BBBH T | BBE B(D0A8G
+8D0A(B BBBF T ԙ G BEE E(D0A8G
+8D0A(B BBBH T , C BBE E(D0A8G
+8D0A(B BBBH T C BBE E(D0A8G
+8D0A(B BBBH T ܚ C BBE E(D0A8G
+8D0A(B BBBH T 4 C { BBE E(D0A8G
+8D0A(B BBBH < 4 -D BBD A(D@0(A ABB T ̛ @& ! BIB E(A0A8G
+8D0A(B BBBA L ̜ BGB A(A0J
+(A BBDH x(A BBB L x BBB B(A0A8GPd
+8A0A(B BBBA T Ĝ Ȫ Z BEE B(A0A8G@
+8A0A(B BBBG T P L BBB B(A0A8G1
+8A0A(B BBBA T t Ⱥ BBB B(A0A8G1
+8A0A(B BBBA d t @²: BEB B(D0A8D`
+8C0A(B BBBH D8D0A(B BBB T 4 ò BBB B(A0A8G1
+8A0A(B BBBA L ʲ s BBB G(D0A8J
+8D0A(B BBBE D ܞ в* BGE D(D0
+(A BBBA L $ в [ BEB E(I0H8D@
+8D0A(B BBBD L t xҲ@ 3 BGB E(D0A8GP
+8D0A(B BBBA L ğ hӲ BEB E(I0H8D@
+8D0A(B BBBD T Բ. BEB B(A0A8J
+8A0A(B BBBA , l > AID W
+DAA D . K^ \ t 4 )> BDA f
+AFE AAB , ` / AAG q
+AAA L BBB B(A0D8G@r
+8A0A(B BBBH D l)\ AG RA< d BHH R
ABI >
-ABG L `, BFL H(A0A8DxK
-8A0A(B BBBI T \ @ z BBB B(D0A8Gm
-8D0A(B BBBB L z BBB B(I0A8G
-8D0A(B BBBE L X z BBB B(I0A8G
-8D0A(B BBBE T T < lz BBE B(D0A8G
-8D0A(B BBBB T < Rz BBE B(D0A8G
-8D0A(B BBBB T x< 8z BBE B(D0A8G
-8D0A(B BBBB D 4 j BBB D(A0D@0A(A BBB T ôc y BIB J(A0A8D
-8D0A(B BBBA T 0Ǵr y BIB J(A0A8D!
-8D0A(B BBBA T T X˴% sy BBE E(I0A8G
-8D0A(B BBBI < 01 BBG A(GP
-(A ABBF T 0 z BIE B(A0G8G.
-8A0A(B BBBA 贗. K^ 4 L x d p | x d BHB B(A0A8G
+ABG L , BFL H(A0A8DxK
+8A0A(B BBBI T L
+ ' BBB B(D0A8Gm
+8D0A(B BBBB L $ BBB B(I0A8G
+8D0A(B BBBE L BBB B(I0A8G
+8D0A(B BBBE T D h< BBE B(D0A8G
+8D0A(B BBBB T P < BBE B(D0A8G
+8D0A(B BBBB T 8!< BBE B(D0A8G
+8D0A(B BBBB D ' BBB D(A0D@0A(A BBB T %c b BIB J(A0A8D
+8D0A(B BBBA T )r 0 BIB J(A0A8D!
+8D0A(B BBBA T D .% BBE E(I0A8G
+8D0A(B BBBI < D H1 BBG A(GP
+(A ABBF T ܥ I O BIE B(A0G8G.
+8A0A(B BBBA ܦ . K^ @^ 8^ $ 0^ < 8^ d T @^ BHB B(A0A8G
8D0A(B BBBM
-8A0A(B BBBA d W BHB B(A0A8GN
+8A0A(B BBBA f d ԧ fW BHB B(A0A8GN
8D0A(B BBBN
-8A0A(B BBBA | L ADD e
+8A0A(B BBBA < n L T n ADD e
CAF {
ACI V
-CAF JCA L ` ADD e
+CAF JCA L o ADD e
CAF {
ACI V
-CAF JCA < \
\ x BBA A(D0K(A ABB <
\ x BBA A(D0K(A ABB \ AG RAD
Yx BAA GP
+CAF JCA < L o\ } BBA A(D0K(A ABB < o\ A BBA A(D0K(A ABB t f$\ AG RAD o BAA GP
DABK
- DABG D BBB D(A0D@0A(A BBB T w BIB E(A0A8DV
-8D0A(B BBBA T w BIB E(A0A8DV
-8D0A(B BBBA T < @| w BBE E(D0A8G
-8D0A(B BBBE T h vw BBE B(D0A8G
-8D0A(B BBBG T 0" \w BBB L(A0D8G
-8A0A(B BBBB 4 D +q w BAI A
-ABA T | + `w BBB L(A0D8G
-8A0A(B BBBB 4 Ԏ (5q w BAI A
-ABA T p5W gw BGB B(A0A8J
-8A0A(B BBBB T d xHW {x BGB B(A0A8J
-8A0A(B BBBB . K^ h[0 Hg ̐ x[7 Hj [0 Hg [7 Hj , [0 Hg L [7 Hj l [0 Hg \7 Hj (\0 Hg ̑ 8\7 Hj X\0 Hg h\7 Hj , \0 Hg L \7 Hj l \0 Hg \7 Hj \0 Hg ̒ \7 Hj ]0 Hg (]7 Hj , H]0 Hg L X]7 Hj l x]0 Hg ]7 Hj ]0 Hg ̓ ]7 Hj ]0 Hg ]7 Hj , ^0 Hg L ^7 Hj l 8^0 Hg H^7 Hj h^0 Hg ̔ x^7 Hj ^0 Hg ^7 Hj , ^0 Hg L ^7 Hj l ^0 Hg _7 Hj (_0 Hg ̕ 8_7 Hj X_0 Hg h_7 Hj , _0 Hg L _7 Hj l _0 Hg _7 Hj _0 Hg ̖ _7 Hj `0 Hg (`7 Hj , H`0 Hg L X`7 Hj l x`0 Hg `7 Hj `0 Hg ̗ `7 Hj `0 Hg `7 Hj , a0 Hg L a7 Hj < l 8a BBA A(G@k
-(A ABBB zPR x , L $ b BBB B(D0A8D@{
-8A0A(B BBBJ L t d BBB B(A0D8D@[
-8A0A(B BBBJ L 8f BBB B(A0D8DP
-8A0A(B BBBB L j BBB B(A0A8G@0
-8A0A(B BBBE L d xl BBB B(A0A8GP
-8A0A(B BBBB D \ p BBB D(A0D@
-0A(A BBBA 4 ̙ @rB o BHA G`
- DABA 4 XsB `o BHA G`
- DABA 4 < pt7 Ao BHA G`
- DABA 4 t xu7 "o BHA G`
- DABA 4 vB o BHA G`
- DABA 4 wB n BHA G`
- DABA 4 x7 n BHA G`
- DABA 4 T y7 n BHA G`
- DABA 4 zB n BHA G`
- DABA 4 ě {B hn BHA G`
- DABA 4 |7 In BHA G`
- DABA 4 4 }7 *n BHA G`
- DABA 4 l B n BHA G`
- DABA 4 B m BHA G`
- DABA 4 ܜ 07 m BHA G`
- DABA 4 87 m BHA G`
- DABA 4 L @B m BHA G`
- DABA 4 XB pm BHA G`
- DABA 4 p7 Qm BHA G`
- DABA 4 x7 2m BHA G`
- DABA 4 , B m BHA G`
- DABA 4 d 7 l BHA G`
- DABA 4 B l BHA G`
- DABA 4 Ԟ B l BHA G`
- DABA 4 Ћ7 l BHA G`
- DABA 4 D ،7 xl BHA G`
- DABA 4 | B Yl BHA G`
- DABA 4 B :l BHA G`
- DABA 4 7 l BHA G`
- DABA 4 $ 7 k BHA G`
- DABA 4 \ B k BHA G`
- DABA 4 87 k BHA G`
- DABA 4 ̠ @B k BHA G`
- DABA 4 XB k BHA G`
- DABA 4 < p7 ak BHA G`
- DABA 4 t x7 Bk BHA G`
- DABA 4 B #k BHA G`
- DABA 4 B k BHA G`
- DABA 4 7 j BHA G`
- DABA 4 T 7 j BHA G`
- DABA 4 B j BHA G`
- DABA 4 Ģ ؝7 j BHA G`
- DABA 4 B ij BHA G`
- DABA 4 4 7 Jj BHA G`
- DABA 4 l B +j BHA G`
- DABA 4 B j BHA G`
- DABA 4 ܣ 07 i BHA G`
- DABA 4 87 i BHA G`
- DABA 4 L @B i BHA G`
- DABA 4 X7 i BHA G`
- DABA 4 `B qi BHA G`
- DABA 4 xB Ri BHA G`
- DABA 4 , 7 3i BHA G`
- DABA 4 d 7 i BHA G`
- DABA 4 B h BHA G`
- DABA 4 ԥ B h BHA G`
- DABA 4 Э7 h BHA G`
- DABA 4 D خ7 h BHA G`
- DABA T | ௵3 yh BBB B(K0A8Jl
-8A0A(B BBBD 4 Ԧ ȳq kh BAI A
-ABA 4 q Gh BAI A
-ABA 4 D Xq #h BAI A
-ABA 4 | q g BAI A
-ABA 4 贵q g BAI A
-ABA 4 0q g BAI A
-ABA 4 $ xq g BAI A
-ABA 4 \ q og BAI A
-ABA 4 q Kg BAI A
-ABA 4 ̨ Pq 'g BAI A
-ABA 4 q g BAI A
-ABA 4 < ඵq f BAI A
-ABA 4 t (q f BAI A
-ABA 4 pq f BAI A
-ABA 4 q sf BAI A
-ABA 4 q Of BAI A
-ABA 4 T Hq +f BAI A
-ABA 4 q f BAI A
-ABA 4 Ī ظq e BAI A
-ABA 4 q e BAI A
-ABA 4 4 hq e BAI A
-ABA 4 l q we BAI A
-ABA 4 q Se BAI A
-ABA 4 ܫ @q /e BAI A
-ABA 4 q e BAI A
-ABA 4 L кq d BAI A
-ABA 4 q d BAI A
-ABA 4 `q d BAI A
-ABA 4 q {d BAI A
-ABA 4 , q Wd BAI A
-ABA 4 d 8q 3d BAI A
-ABA 4 q d BAI A
-ABA 4 ԭ ȼq c BAI A
-ABA 4 q c BAI A
-ABA 4 D Xq c BAI A
-ABA 4 | q c BAI A
-ABA 4 轵q [c BAI A
-ABA 4 0q 7c BAI A
-ABA 4 $ xq c BAI A
-ABA 4 \ q b BAI A
-ABA 4 q b BAI A
-ABA 4 ̯ Pq b BAI A
-ABA 4 q b BAI A
-ABA 4 < q _b BAI A
-ABA 4 t (q ;b BAI A
-ABA 4 pq b BAI A
-ABA 4 q a BAI A
-ABA 4 q a BAI A
-ABA 4 T Hq a BAI A
-ABA 4 q a BAI A
-ABA T ı
- ca BBB B(A0I8G
-8D0A(B BBBF T ĵ
- Ra BBB B(A0I8G
-8D0A(B BBBF T t Hǵ
- Aa BBB B(A0I8G
-8D0A(B BBBF T ̲ ʵ
- 0a BBB B(A0I8G
-8D0A(B BBBF T $ ̵
- a BBB B(A0I8G
-8D0A(B BBBF T | pϵ
- a BBB B(A0I8G
-8D0A(B BBBF T Գ (ҵ
- ` BBB B(A0I8G
-8D0A(B BBBF T , Ե
- ` BBB B(A0I8G
-8D0A(B BBBF D ` BBF K(H0G@Q
-0A(A BBBC D ̴ ٵ ` BGB D(D0J
-(A BBBD < hٵ/ h` BEI D(G
-(D ABBG < , Xڵ BED A(D0Y
-(A ABBA d l ڵ BBB B(A0D8DPJ
-8A0A(B BBBK
-8A0A(B BBBD d Զ BBB B(A0D8DPZ
-8A0A(B BBBK
-8A0A(B BBBH d < h BBB B(D0A8DP
-8A0A(B BBBE
-8A0A(B BBBG E
-K < ķ BKD A(G0(A ABB < P BDD
+ DABG D ܩ Z$v BBB D(A0D@^0A(A BBB T | Pr BIB E(A0A8DV
+8D0A(B BBBA T ԩ v N BIB E(A0A8DV
+8D0A(B BBBA T , {| BBE E(D0A8G
+8D0A(B BBBE T ( BBE B(D0A8G
+8D0A(B BBBG T ܪ BBB L(A0D8G
+8A0A(B BBBB 4 4 Hq BAI A
+ABA T l BBB L(A0D8G
+8A0A(B BBBB 4 ī 藳q BAI A
+ABA T 0W BGB B(A0A8J
+8A0A(B BBBB T T 8W BGB B(A0A8J
+8A0A(B BBBB T P. K^ l (0 Hg 87 Hj X0 Hg ̭ h7 Hj 0 Hg 7 Hj , 0 Hg L Ⱦ7 Hj l 辳0 Hg 7 Hj 0 Hg ̮ (7 Hj H0 Hg X7 Hj , x0 Hg L 7 Hj l 0 Hg 7 Hj ؿ0 Hg ̯ 迳7 Hj 0 Hg 7 Hj , 80 Hg L H7 Hj l h0 Hg x7 Hj 0 Hg ̰ 7 Hj 0 Hg 7 Hj , 0 Hg L 7 Hj l (0 Hg 87 Hj X0 Hg ̱ h7 Hj 0 Hg 7 Hj , 0 Hg L 7 Hj l 0 Hg 7 Hj ³0 Hg ̲ (³7 Hj H³0 Hg X³7 Hj , x³0 Hg L ³7 Hj l ³0 Hg ³7 Hj ³0 Hg ̳ ³7 Hj ó0 Hg ó7 Hj , 8ó0 Hg L Hó7 Hj l hó0 Hg xó7 Hj ó0 Hg ̴ ó7 Hj ó0 Hg ó7 Hj < , ó BBA A(G@k
+(A ABBB zPR x d, L $ ų BBB B(A0D8D@[
+8A0A(B BBBJ L t 8dz BBB B(A0A8G@0
+8A0A(B BBBE , \ AG RAL ȳ BBB B(A0A8GP
+8A0A(B BBBB , (ͳC AAG n
+DAA D ̶ Hͳ BBB D(A0D@
+0A(A BBBA 4 l γB [ BHA G`
+ DABA 4 ϳB < BHA G`
+ DABA 4 ܶ г7 BHA G`
+ DABA 4 ѳ7 BHA G`
+ DABA 4 L ҳB BHA G`
+ DABA 4 ӳB BHA G`
+ DABA 4 ճ7 BHA G`
+ DABA 4 ֳ7 BHA G`
+ DABA 4 , ׳B c BHA G`
+ DABA 4 d 8سB D BHA G`
+ DABA 4 Pٳ7 % BHA G`
+ DABA 4 Ը Xڳ7 BHA G`
+ DABA 4 `۳B BHA G`
+ DABA 4 D xܳB Ⱥ BHA G`
+ DABA 4 | ݳ7 BHA G`
+ DABA 4 7 BHA G`
+ DABA 4 ߳B k BHA G`
+ DABA 4 $ B L BHA G`
+ DABA 4 \ 7 - BHA G`
+ DABA 4 7 BHA G`
+ DABA 4 ̺ B BHA G`
+ DABA 4 7 й BHA G`
+ DABA 4 < B BHA G`
+ DABA 4 t B BHA G`
+ DABA 4 07 s BHA G`
+ DABA 4 87 T BHA G`
+ DABA 4 @B 5 BHA G`
+ DABA 4 T XB BHA G`
+ DABA 4 p7 BHA G`
+ DABA 4 ļ x7 ظ BHA G`
+ DABA 4 B BHA G`
+ DABA 4 4 7 BHA G`
+ DABA 4 l B { BHA G`
+ DABA 4 B \ BHA G`
+ DABA 4 ܽ 7 = BHA G`
+ DABA 4 7 BHA G`
+ DABA 4 L B BHA G`
+ DABA 4 B BHA G`
+ DABA 4 7 BHA G`
+ DABA 4 7 BHA G`
+ DABA 4 , B BHA G`
+ DABA 4 d 87 d BHA G`
+ DABA 4 @B E BHA G`
+ DABA 4 Կ X7 & BHA G`
+ DABA 4 `B BHA G`
+ DABA 4 D xB BHA G`
+ DABA 4 | 7 ɶ BHA G`
+ DABA 4 7 BHA G`
+ DABA 4 B BHA G`
+ DABA 4 $ 7 l BHA G`
+ DABA 4 \ B M BHA G`
+ DABA 4 B . BHA G`
+ DABA 4 7 BHA G`
+ DABA 4 7 BHA G`
+ DABA 4 < B ѵ BHA G`
+ DABA 4 t B BHA G`
+ DABA 4 0
+7 BHA G`
+ DABA 4 8 7 t BHA G`
+ DABA , @ U LGA A(D T L
B BBB B(K0A8J
+8A0A(B BBBB 4 u 4 BAI E
+ABA 4 u BAI E
+ABA 4 Hu BAI E
+ABA 4 L u ȴ BAI E
+ABA 4 u BAI E
+ABA 4 u BAI E
+ABA 4 hu \ BAI E
+ABA 4 , u 8 BAI E
+ABA 4 d u BAI E
+ABA 4 @u BAI E
+ABA 4 u ̳ BAI E
+ABA 4 u BAI E
+ABA 4 D u BAI E
+ABA 4 | `u ` BAI E
+ABA 4 u < BAI E
+ABA 4 u BAI E
+ABA 4 $ 8u BAI E
+ABA 4 \ u в BAI E
+ABA 4 u BAI E
+ABA 4 u BAI E
+ABA 4 Xu d BAI E
+ABA 4 < u @ BAI E
+ABA 4 t u BAI E
+ABA 4 0u BAI E
+ABA 4 xu Ա BAI E
+ABA 4 u BAI E
+ABA 4 T u BAI E
+ABA 4 Pu h BAI E
+ABA 4 u D BAI E
+ABA 4 u BAI E
+ABA 4 4 (u BAI E
+ABA 4 l pu ذ BAI E
+ABA 4 u BAI E
+ABA 4 u BAI E
+ABA 4 Hu l BAI E
+ABA 4 L u H BAI E
+ABA 4 u $ BAI E
+ABA 4 u BAI E
+ABA 4 hu ܯ BAI E
+ABA 4 , u BAI E
+ABA 4 d u BAI E
+ABA 4 @ u p BAI E
+ABA 4 u L BAI E
+ABA 4 u ( BAI E
+ABA 4 D u BAI E
+ABA 4 | ` u BAI E
+ABA 4 u BAI E
+ABA 4 u BAI E
+ABA 4 $ 8 u t BAI E
+ABA 4 \ u P BAI E
+ABA T 2 , BBB B(A0I8G%
+8D0A(B BBBE T !2 BBB B(A0I8G%
+8D0A(B BBBE T D $2
+ BBB B(A0I8G%
+8D0A(B BBBE T ' BBB B(A0I8G
+8D0A(B BBBF T H*2 BBB B(A0I8G%
+8D0A(B BBBE T L 0-2 BBB B(A0I8G%
+8D0A(B BBBE T 02 ƭ BBB B(A0I8G%
+8D0A(B BBBE T 3 BBB B(A0I8G
+8D0A(B BBBF < T 5 BBA A(J
+(D ABBA < x7 BBA A(J
+(D ABBA < (9 | BBA A(J
+(D ABBA < : h BBA A(J
+(D ABBA $ T <* T A_
+H A D | < 0 BBF K(H0G@Q
+0A(A BBBC D = BGB D(D0J
+(A BBBD < `> BDA k
+ABK @
+ABA < L >/ } BEI D(G
+(D ABBG $ ?* O A_
+H A $ ?* + A_
+H A $ ?* A_
+H A ?: L C BBB B(A0D8D8A0A(B BBB pDG L , E BBB B(A0D8D8A0A(B BBB < | XF BED A(D0Y
+(A ABBA F KBB A(D0G@y0A(A BBBL@a
+0A(A BBBO (
+0A(A BBBH L (K KBB A(D0G@y0A(A BBBL@f
+0A(A BBBJ ,
+0A(A BBBL O KBB A(D0G@a0A(A BBBL@1
+0A(A BBBG ,
+0A(A BBBL l S KBB A(D0G@a0A(A BBBL@-
+0A(A BBBK *
+0A(A BBBN W E
+K < X BKD A(G0(A ABB < \ (Z BDD
ABA L
-ABA T l "^ BBB B(D0A8G
-8C0A(B BBBB L ķ h { v^ BIE B(A0G8G
-8D0A(B BBBE < 4^ BID D(D
-(D ABBA < T ^ BID D(D
-(D ABBA T 8 ] BBB B(D0A8G
-8C0A(B BBBB 4 / $^ BHA J
- DABA 4 $ / ] BHA J
- DABA T \ ] BIB B(A0A8Gw
-8A0A(B BBBA L (% ] BIE B(A0G8G
-8D0A(B BBBE T D] BIB B(A0A8J
-8D0A(B BBBA L \ \ BIE B(A0G8G
-8D0A(B BBBE T 8 \ BIB B(A0A8J
-8D0A(B BBBA T ! n\ BIB B(A0A8G
-8A0A(B BBBA T \ & $\ BBB B(D0A8G
-8C0A(B BBBB $ 6( x\ AQp
-DA $ ܻ 7( ^\ AQp
-DA L 8 D\ BIH A(A0Dc
-0D(A BBBA L T : \ BIE B(A0G8G
-8D0A(B BBBE T < [ BIB B(A0A8J
-8D0A(B BBBA L P? v[ BIE B(A0G8G
-8D0A(B BBBE T L B 4[ BIB B(A0A8JE
-8D0A(B BBBA T hD Z BIB B(A0A8D
-8A0A(B BBBG T 0G Z BBB B(D0A8G
-8C0A(B BBBB L T W Z BIE B(A0G8G
-8D0A(B BBBE T Z Z BIB B(A0A8D
-8A0A(B BBBG L Կ @] BBB B(A0A8DF
-8A0A(B BBBB $ L e( Z AT
-DA D L f BBB B(A0A8
-0A(B BBBF $ i( Y AT
-DA D j BBB B(A0A8
-0A(B BBBA T , lL TY BIB B(D0D8G
-8D0A(B BBBE D \ xss BBB B(A0A8I
-0A(B BBBA T u X BIB B(D0D8G
-8D0A(B BBBA D hx BBB B(A0A8
-0A(B BBBA T l zl 0X BIB B(D0D8G
-8D0A(B BBBE D s BBB B(A0A8I
-0A(B BBBA T 0 W BIB B(D0D8G
-8D0A(B BBBA D < 膶 BBB B(A0A8
-0A(B BBBF $ ( W AT
-DA D Ȋ1 BBB B(A0A8
-0A(B BBBF $ ( V AT
-DA H{. K^ 4 L d | HS / A L BBB B(A0A8GP8A0A(B BBB L @ BEB B(A0A8DP8A0A(B BBB D T BBE A(A0D@g
-0A(A BBBA D h BBE A(A0D@o
-0A(A BBBA $ ^ AG b
-AA D 8 BBE A(A0D@P
-0A(A BBBA $ | XT A
+ABA T Z BBB B(D0A8G3
+8C0A(B BBBE L L jf 6 BHE A(A0G+
+0D(A BBBA L lf BHE A(A0G+
+0D(A BBBA T m BBB B(G0D8G
+8A0A(B BBBA T D n^ w BBB B(D0A8M
+8A0A(B BBBC T q" 2 BBB H(D0A8G
+8A0A(B BBBA T t BBE B(D0A8M
+8D0A(B BBBA T L 0vx BBB H(D0A8G
+8A0A(B BBBA T Xy c BBB B(G0D8G;
+8A0A(B BBBA T ~ BBB B(A0A8Jf
+8C0A(B BBBJ T T a BBB E(A0D8M9
+8D0A(B BBBE L BHE A(D0D&
+0D(A BBBA L ߥ BHE A(D0D&
+0D(A BBBA T L BBB B(D0A8G3
+8C0A(B BBBE L Xz BHE A(A0G;
+0D(A BBBA L z BHE A(A0G;
+0D(A BBBA T D k BBB E(G0D8G
+8D0A(B BBBA T `M & BBB B(D0A8M
+8A0A(B BBBC T X BBB H(D0A8G
+8A0A(B BBBA T L a BBB B(D0A8M
+8A0A(B BBBC T 8 W BBB H(D0A8G
+8A0A(B BBBA T вR BBB B(G0D8G
+8A0A(B BBBD T T ص ͣ BBB B(A0A8Jf
+8C0A(B BBBJ T ƴ} BBB E(A0D8M9
+8D0A(B BBBE T ɴB ˣ BBB B(G0D8G
+8A0A(B BBBD L ̴ BBB B(A0A8DF
+8A0A(B BBBB L մ
6 BBE G(A0J
+0D(A BBBE D ִ BBB B(A0A8
+0A(B BBBF L D 8ٴ
BBE G(A0J
+0D(A BBBE D < ڴ BBB B(A0A8
+0A(B BBBA T pݴ , BBB E(A0A8Mf
+8A0A(B BBBF D s BBB B(A0A8I
+0A(B BBBA T | R BBE B(A0D8M
+8D0A(B BBBA D | BBB B(A0A8
+0A(B BBBA T p BBB E(A0A8M
+8A0A(B BBBF D s BBB B(A0A8I
+0A(B BBBA T R BBE B(A0D8M
+8D0A(B BBBA D 1 BBB B(A0A8
+0A(B BBBF L \
BBE G(A0J
+0D(A BBBE T J L t ` BBB B(A0A8DP^
+8A0A(B BBBJ L
K BBE G(A0J
+0D(A BBBE z. K^ , X D P \ H t P HS t/ A L 8 BBB B(A0A8GP8A0A(B BBB L BEB B(A0A8DP8A0A(B BBB D L BBE A(A0D@g
+0A(A BBBA D BBE A(A0D@o
+0A(A BBBA $ ^ AG b
+AA D BBE A(A0D@P
+0A(A BBBA $ X ֜ A
G A
-A L | 8 AAG {
+A L t
+ AAG {
CAH z
CAB R
CAJ J
-CAA L AAG {
+CAA L ` AAG {
CAH z
CAB R
CAJ J
-CAA T D 8 S BBB E(D0A8G
-8D0A(B BBBA D t p BBB D(A0D@
-0A(A BBBA L ț S BIB A(D0D
-0D(A BBBA L 4 ) R BBB B(A0D8D`
-8A0A(B BBBA L ) R BBB B(A0D8D`
-8A0A(B BBBA T آ R BEB B(H0A8G 6
+CAA L l BBE D(A0G
+0D(A BBBA D d BBB D(A0D@
+0A(A BBBA L x BIB A(D0D
+0D(A BBBA L T ) s BBB B(A0D8D`
+8A0A(B BBBA L ) C BBB B(A0D8D`
+8A0A(B BBBA T BEB B(H0A8G 6
-8A0A(B BBBE , , 0> S AID W
-DAA $ \ @G wS As
+8A0A(B BBBE , L ,> AID W
+DAA $ | ,G As
L A
-A T h SS BEB E(A0D8G&
-8D0A(B BBBC $ G S As
+A T - ۛ BBE E(A0D8G
+8D0A(B BBBD $ 0G As
L A
-A T ( R BFA A(GP
+A T $ 0 t BFA A(GP
(A ABBE S
-(A ABBA l \ ¶ R BBB B(A0A8G
+(A ABBA l | P6 f BBB B(A0A8G
8A0A(B BBBD
-8A0A(B BBBF T ζ }R BBE E(A0D8G
-8D0A(B BBBE L Ѷ BBB B(A0A8J8A0A(B BBB < t HҶB Q BBA A(G
-(A ABBA , Xն R PGA A(D , (ֶ
-R ]GA A(D , Q PGA A(D < D B Q BBA A(G
-(A ABBA T ڶ^ R BEB B(A0D8J
-8A0A(B BBBA T Q BGH B(A0A8J
-8D0A(B BBBH T 4 F R BBB H(F0D8G
-8D0A(B BBBA d 0r. K^ | $ < * ` , T M AAD z
-AAA HS ޖ/ A L AAG ]
+8A0A(B BBBF T A BBE E(A0D8G
+8D0A(B BBBF L 8E BBB B(A0A8J8A0A(B BBB < EB r BBA A(G
+(A ABBA , I PGA A(D , I ]GA A(D , 4 J PGA A(D < d KB l BBA A(G
+(A ABBA T N^ BEB B(A0D8J
+8A0A(B BBBA T _ W BGH B(A0A8J
+8D0A(B BBBH T T hoF BBB H(F0D8G
+8D0A(B BBBA T q. K^ l Ht Pt Ht @t 8t @t Ht Pt , Xt* ` , D ptM AAD z
+AAA t t t HS ߓ/ A L t AAG ]
CAF c
CAI R
-CAJ JCA L $ AAG ]
+CAJ JCA L t AAG ]
CAF [
ACA R
-CAJ JCA t @@ Dz 4 h O BDA D0e
- AABA - - $ ^ AG b
-AA D H BBL A(A0D@p
-0A(A BBBA D d BBL A(A0D@x
-0A(A BBBA , 0 AC
FK
- E , A N ADL
-AAA < 4 N BBP J(G
-(D ABBH L " L d AAG
+CAJ JCA d t@ Dz 4 u b BDA D0e
+ AABA u- u- $ u^ AG b
+AA D u BBL A(A0D@p
+0A(A BBBA D T v BBL A(A0D@x
+0A(A BBBA , 8w0 AC
FK
+ E , $ 8xA ( ADL
+AAA < T Xy BBP J(G
+(D ABBH < 8}" L T P} AAG
CAD V
CAF R
CAJ J
-CAA T @
-. /N BBB B(A0A8D
-8A0A(B BBBA D BBB D(A0D@
-0A(A BBBA L | p
M BIB A(D0D
-0D(A BBBA T M BBB B(D0A8N
+CAA T }. BBB B(A0A8D
+8A0A(B BBBA D BBB D(A0D@
+0A(A BBBA L 7 BIB A(D0D
+0D(A BBBA T p BBB B(D0A8N
d
-8A0A(B BBBG , $ x > HN AID W
-DAA $ T g (N AR
+8A0A(B BBBG , D (> Ж AID W
+DAA $ t 8g AR
M A
-A T | !_ N BBB E(D0A8G
-8D0A(B BBBA T #I M BBB E(D0A8G
-8D0A(B BBBA < , %H ~M BBA A(Gp
-(D ABBA D l & bM BEA G@
+A L g BBE D(A0G
+0D(A BBBA L P Q BBE D(A0G
+0D(A BBBA < < H BBA A(Gp
+(D ABBA D | BEA G@
AABH
- AABA l - vM BBB B(A0A8G@=
+ AABA l h BBB B(A0A8G@=
8A0A(B BBBH u
-8A0A(B BBBA , $ ; M ZGA A(D , T < M ZGA A(D T = L BBB B(A0A8G;
-8A0A(B BBBJ T I L BBB B(A0A8G
-8A0A(B BBBA L HQf BEB A(A0|
+8A0A(B BBBA , 4 ȯ ZGA A(D , d ZGA A(D T BBB B(A0A8G;
+8A0A(B BBBJ T P k BBB B(A0A8G
+8A0A(B BBBA L ŵf BEB A(A0|
(A BBBJ A
-(A BBBA d \ hQc BLB B(A0A8DPc
+(A BBBA d < 8ŵc BLB B(A0A8DPc
8A0A(B BBBK
-8A0A(B BBBG L pR BBB B(A0D8D`4
-8A0A(B BBBA T < S5 K BDB B(A0A8I
-8A0A(B BBBK T Z K BEB B(A0A8J
-8A0A(B BBBA T i K BHB B(F0A8J
-8D0A(B BBBG T D } K BBB G(G0D8Gw
-8D0A(B BBBA t Pg. K^ h p h ` h ` h 4 `F L ) G] d J W c | 耷 D k
-Q z X H G
-A H0W
-I $ x^ AG b
-AA D , J BBE A(A0D@
-0A(A BBBA $ t g I AG k
-AA , ЃY I ADG i
-AAA < PҖD BBD A(D@0(A ABB T PI BIB E(A0A8DV
-8D0A(B BBBA D < | BBB A(D0D@
-0A(A BBBA D P BBB A(D0D@
-0A(A BBBA T e H BEB B(A0A8GL
-8A0A(B BBBA T L \H BBB B(A0A8G
-8A0A(B BBBG L | 菷 BBF B(A0A8D@
-8D0A(B BBBB D x G BBE A(A0D@
-0A(A BBBA L < @ G BEB E(I0H8D@
-8D0A(B BBBD L w G BHI E(A0A8Gp
-8A0A(B BBBF L G BJE B(A0A8D@
-8A0A(B BBBA L , V WG BBB B(A0D8J
-8D0A(B BBBE T | Л -G BBB B(A0D8J
-8A0A(B BBBA d HE BBB B(A0A8DP
+8A0A(B BBBG L @Ƶ BBB B(A0D8D`4
+8A0A(B BBBA T L ǵ5 G BDB B(A0A8I
+8A0A(B BBBK T hε _ BEB B(A0A8J
+8A0A(B BBBA T ܵ # BHB B(F0A8J
+8D0A(B BBBG T T x f BBB G(G0D8Gw
+8D0A(B BBBA T f. K^ l ( 0 ( ( ( F , X) G] D pJ W c \ D k
+Q z | H G
+A H0W
+I $ 8^ AG b
+AA D < p BBE A(A0D@
+0A(A BBBA $ Hg p AG k
+AA , Y L ADG i
+AAA < ӓD BBD A(D@0(A ABB T BIB E(A0A8DV
+8D0A(B BBBA D | BBB A(D0D@
+0A(A BBBA D d BBB A(D0D@
+0A(A BBBA T Xe & BEB B(A0A8GL
+8A0A(B BBBA T \ p BBB B(A0A8G
+8A0A(B BBBG L \ BBF B(A0A8D@
+8D0A(B BBBB $ 8 DL
+A V
+I D , h BBE A(A0D@
+0A(A BBBA L t ' BEB E(I0H8D@
+8D0A(B BBBD L w BHI E(A0A8Gp
+8A0A(B BBBF L 8 BJE B(A0A8D@
+8A0A(B BBBA L d
+V Ǐ BBB B(A0D8J
+8D0A(B BBBE T
+ BBB B(A0D8J
+8A0A(B BBBA d 'E BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA T < 0 /G BBB B(A0A8N
-8A0A(B BBBA , Ʒ> 'H AID W
-DAA X`. K^ Ʒ x˷r ϷJ ѷe p , ݷ7 D 0ݷZ \ x t PB p XJ L r F BBB B(E0A8J@
-8A0A(B BBBA D $ BBE A(A0D@u
-0A(A BBBA D l X BBE A(A0D@u
-0A(A BBBA J (J L ` BBB B(A0A8D`
-8A0A(B BBBA , \ E AAD@W
-AAA , 0 `E AAD@W
-AAA , <E AAD@W
-AAA , E AAD@W
-AAA , P D AAD@W
-AAA , L D AAD@W
-AAA , | D AAD@W
-AAA , p D AAD@W
-AAA $ RMIp L H BBB B(A0A8D`
-8A0A(B BBBA , T C AAD@W
-AAA , C AAD@W
-AAA , x C AAD@W
-AAA , C AAD@W
-AAA , 8 \C AAD@W
-AAA , D 8C AAD@W
-AAA , t C AAD@W
-AAA , X B AAD@W
-AAA L BBB B(A0A8Dp
-8A0A(B BBBA , $ x |B AAD@W
-AAA , T XB AAD@W
-AAA , 8 4B AAD@W
-AAA , B AAD@W
-AAA , A AAD@W
-AAA , X A AAD@W
-AAA , D A AAD@W
-AAA , t A AAD@W
-AAA < | x BBA A(D@i
-(A ABBA , A AAD@W
-AAA , @ AAD@W
-AAA , D x @ AAD@W
-AAA , t @ AAD@W
-AAA , 8 @ AAD@W
-AAA , h@ AAD@W
-AAA , D@ AAD@W
-AAA , 4 X @ AAD@W
-AAA T d ? BBB B(D0D8G
-8D0A(B BBBJ T
- ? BEE E(D0A8G
-8D0A(B BBBD T X, ? BBE B(D0A8G
-8D0A(B BBBK T l 0J ? BIA A(GT
+8F0A(B BBBA L t ( BJB B(D0A8GPx
+8A0A(B BBBB T X*
s BBB B(H0A8G k
+8A0A(B BBBA , <> _ AID W
+DAA p_. K^ < $ @r < HEJ T Fe l L pR7 RZ V [B \ a cJ L hr BBB B(E0A8J@
+8A0A(B BBBA D | (j BBE A(A0D@u
+0A(A BBBA D j BBE A(A0D@u
+0A(A BBBA XkJ $ kJ L < k BBB B(A0A8D`
+8A0A(B BBBA , 8l AAD@W
+AAA , l AAD@W
+AAA , D l t AAD@W
+AAA , t Xm P AAD@W
+AAA , m , AAD@W
+AAA , n AAD@W
+AAA , xn AAD@W
+AAA , 4 n AAD@W
+AAA $ 8o RMIp L 4 p BBB B(A0A8D`
+8A0A(B BBBA , q $ AAD@W
+AAA , q AAD@W
+AAA , < q ܋ AAD@W
+AAA , l @r AAD@W
+AAA , r AAD@W
+AAA , s p AAD@W
+AAA , `s L AAD@W
+AAA , , s ( AAD@W
+AAA L t BBB B(A0A8Dp
+8A0A(B BBBA , t AAD@W
+AAA , @u AAD@W
+AAA , u l AAD@W
+AAA , < v H AAD@W
+AAA , l `v $ AAD@W
+AAA , v AAD@W
+AAA , w ܉ AAD@W
+AAA , w AAD@W
+AAA < w BBA A(D@i
+(A ABBA , l x T AAD@W
+AAA , x 0 AAD@W
+AAA , x AAD@W
+AAA , @y AAD@W
+AAA , ,
+ y Ĉ AAD@W
+AAA , \
+ z AAD@W
+AAA ,
+ `z | AAD@W
+AAA ,
+ z X AAD@W
+AAA T
+ { 4 BBB B(D0D8G
+8D0A(B BBBJ T D h BEE E(D0A8G
+8D0A(B BBBD T , BBE B(D0A8G
+8D0A(B BBBK T J Ӈ BIA A(GT
(A ABBB ^
-(A ABBD T (, c? BBE B(D0A8G
-8D0A(B BBBK T J I? BIA A(GT
+(A ABBD T L , BBE B(D0A8G
+8D0A(B BBBK T hJ BIA A(GT
(A ABBB ^
-(A ABBD T t , ? BBE B(D0A8G
-8D0A(B BBBK T "J > BIA A(GT
+(A ABBD T `, I BBE B(D0A8G
+8D0A(B BBBK T T
8J / BIA A(GT
(A ABBB ^
-(A ABBD T $ $, > BBE B(D0A8G
-8D0A(B BBBK T | )J > BIA A(GT
+(A ABBD T
0, BBE B(D0A8G
+8D0A(B BBBK T J ݆ BIA A(GT
(A ABBB ^
-(A ABBD T +~ m> BBB B(D0D8GL
-8D0A(B BBBH L , ; > BBB B(D0A8G`8
-8A0A(B BBBJ T | > > BBB B(D0A8G
-8A0A(B BBBA L @ w> BEB E(I0H8DP
-8D0A(B BBBD L $ HB O> BHI E(A0A8Gps
-8A0A(B BBBB L t D ;> BEB E(I0H8DP
-8D0A(B BBBD L F > BHI E(A0A8Gpr
-8A0A(B BBBC T 8Q = BIB B(A0A8GS
-8A0A(B BBBA D I \ I T I = BBB J(A0A8G
-8D0A(B BBBD D l BBB D(A0D@0A(A BBB T < LL <= BIE B(A0A8D9
-8D0A(B BBBA < l Q BBG A(G@
-(A ABBG T Q < BEB B(G0A8G
-8D0A(B BBBJ ST KH XX 4 PX L HX d @X | 8X 0X (X X (X 0X 8X $ @X < HX T PX , XX[ ; AID t
-DAA , X[ w; AID t
-DAA , X[ W; AID t
-DAA , $ X[ 7; AID t
-DAA , T Y[ ; AID t
-DAA \ HY2 I 4 t pY BAA s
-AEK AAB L Yr : BBB B(E0A8J@
-8A0A(B BBBA $ Z^ AG b
-AA $ 0[ d d 8[ ,: BBB B(A0D8Op
+(A ABBD T \ & BBB B(D0D8G
+8D0A(B BBBH L ذ BBB B(D0A8G`8
+8A0A(B BBBJ T 8 BBB B(D0A8G
+8A0A(B BBBA L \ BEB E(I0H8DP
+8D0A(B BBBD L ` BHI E(A0A8Gps
+8A0A(B BBBB L s BEB E(I0H8DP
+8D0A(B BBBD L L л K BHI E(A0A8Gpr
+8A0A(B BBBC T PP 7 BIB B(A0A8GS
+8A0A(B BBBA ( T $ ( ΅ BBB J(A0A8G
+8D0A(B BBBD D $ BBB D(A0D@0A(A BBB T 8L t BIE B(A0A8D9
+8D0A(B BBBA < 0ƶ BBG A(G@
+(A ABBG T \ ƶ BEB B(G0A8G
+8D0A(B BBBJ \ (RT KH t pͶ hͶ `Ͷ XͶ PͶ HͶ @Ͷ 8Ͷ 4 @Ͷ L HͶ d PͶ | XͶ `Ͷ hͶ , pͶ[ σ AID t
+DAA , L Ͷ[ AID t
+DAA , | Ͷ[ AID t
+DAA , ζ[ o AID t
+DAA , 0ζ[ O AID t
+DAA `ζ2 I 4 ζ BAA s
+AEK AAB L \ ζr ߂ BBB B(E0A8J@
+8A0A(B BBBA $ T ж^ AG b
+AA | Hж d Pж d BBB B(A0D8Op
8A0A(B BBBH U
-8A0A(B BBBA , [> 9 AID W
-DAA T \ 9 BBB B(D0A8K#
-8A0A(B BBBK , T h]> 9 AID W
-DAA D x] g9 BBB A(A0JR
-0A(A BBBA D P^ B9 BBB D(E0Ml
-0A(A BBBH , c O9 qGA A(D L D ct =9 BBB D(D0J
-0D(A BBBG T (h~ G9 BBA A(Ik
+8A0A(B BBBA , T Ѷ> AID W
+DAA T Ѷ BBB B(D0A8K#
+8A0A(B BBBK , Ҷ> AID W
+DAA D Ҷ BBB A(A0JR
+0A(A BBBA D T hӶ z BBB D(E0Ml
+0A(A BBBH , ض qGA A(D L ٶt u BBB D(D0J
+0D(A BBBG T @ݶ~ BBA A(Ik
(C ABBF
-(A ABBA T Pi 9 BEE E(D0D8G(
-8D0A(B BBBK d j= BLB B(A0A8DPq
+(A ABBA T t h L BEE E(D0D8G(
+8D0A(B BBBK d t ߶= BLB B(A0A8DPq
8A0A(B BBBE
-8A0A(B BBBE < kg g8 BED D(G
-(D ABBD D l BBB D(A0D@
-0A(A BBBA D 4 8n 7 BIB A(D0D`+
-0D(A BBBA L | p 7 BIB B(A0D8DpE
-8D0A(B BBBA L s 7 BIB B(A0D8DpE
-8D0A(B BBBA < v {7 BHA Gi
- DABA < \ `x `7 BHA Gi
- DABA D 0z$ E7 BIA A(G
-(D ABBA < | "7 BHA J
- DABA L $ ~ 7 BBB B(G0D8G
-8A0A(B BBBA D t 4 7 BDB A(G0G
-0A(D BBBA \ " 07 BBB A(A0G
+8A0A(B BBBE < 4 g BED D(G
+(D ABBD D BBB D(A0D@
+0A(A BBBA D P ) BIB A(D0D`+
+0D(A BBBA L BIB B(A0D8DpE
+8D0A(B BBBA L T BIB B(A0D8DpE
+8D0A(B BBBA < BHA Gi
+ DABA < x BHA Gi
+ DABA D $ H$ } BIA A(G
+(D ABBA < l 0 Z BHA J
+ DABA L K BBB B(G0D8G
+8A0A(B BBBA D 4 K BDB A(G0G
+0A(D BBBA \ D " h BBB A(A0G
0A(A BBBI O
-0A(A BBBA T f 6 BDB H(A0D8G
-8D0A(B BBBG < t ؉0
-7 BBA A(Jc
-(A ABBG T Ȋa 6 BEE B(D0A8G
-8D0A(B BBBI L B BLH B(D0A8DP
-8A0A(B BBBB 4 9 Af
-I D L | _6 BHB E(A0A8Gn
-8D0A(B BBBK T @6 BDB E(A0A8G
-8A0A(B BBBJ T $ 84 6 BDB B(D0A8G
-8A0A(B BBBH T | 6 BBB B(A0D8G
-8A0A(B BBBA < h 6 BBH A(Jx
-(A ABBK T (w 6 BBB B(A0E8G
-8A0A(B BBBA T l Pw 6 BBB B(A0E8G
-8A0A(B BBBA T xw M7 BBB B(A0E8G
-8A0A(B BBBA T ̸ 7 BBB B(A0E8G
-8A0A(B BBBA L t 8ٸ 8 BBB G(A0D8M
-8D0A(B BBBG DV Ko
-A xܸ pܸ hܸ `ܸ Xܸ 4 Pܸ L Hܸ d @ܸ | 8ܸ 0ܸ (ܸ ܸ ܸ ܸ ܸ $ ܸ < (ܸ T 0ܸ l 8ܸ @ܸ Hܸ Pܸ Xܸ `ܸ hܸ pܸ L T xܸr b5 BBB B(E0A8J@
-8A0A(B BBBA $ | ݸ^ AG b
-AA T ݸL 4 BBB E(A0D8G
-8A0A(B BBBH L $ * 5 BBB J(A0A8G
-8D0A(B BBBA L t 4 BBB J(A0A8G
-8D0A(B BBBA L X* 4 BBB J(A0A8G
-8D0A(B BBBA L 8 4 BBB J(A0A8G
-8D0A(B BBBA L d 4 BBB J(A0A8G
-8D0A(B BBBA L x 4 BBB J(A0A8G
-8D0A(B BBBA L 4 BBB J(A0A8G
-8D0A(B BBBA L T m4 BBB J(A0A8G
-8D0A(B BBBA L X W4 BBB J(A0A8G
-8D0A(B BBBA L A4 BBB J(A0A8G
-8D0A(B BBBA L D +4 BBB J(A0A8G
-8D0A(B BBBA L 8 4 BBB J(A0A8G
-8D0A(B BBBA L 3 BBB J(A0A8G
-8D0A(B BBBA T 4 x 3 BBE E(D0A8G/
-8D0A(B BBBJ < " 3 BBA A(JU
-(A ABBA < " 3 BBA A(JU
-(A ABBA T j3 BBE E(A0A8G
-8D0A(B BBBA D < 8 BBB D(A0D@
-0A(A BBBA D 2 BIB A(D0D`A
-0D(A BBBA D h 2 BIB A(D0D`H
-0D(A BBBA D < @ 2 BIB A(D0D`H
-0D(A BBBA D 2 BIB A(D0D`H
-0D(A BBBA D ]2 BIB A(D0D`H
-0D(A BBBA D ;2 BIB A(D0D`H
-0D(A BBBA D \ 2 BIB A(D0D`H
-0D(A BBBA D x 1 BIB A(D0D`H
-0D(A BBBA D P 1 BIB A(D0D`H
-0D(A BBBA D 4
- ( 1 BIB A(D0D`H
-0D(A BBBA D |
- 1 BIB A(D0D`H
-0D(A BBBA D
- o1 BIB A(D0D`H
-0D(A BBBA D " M1 BIB A(D0D`H
-0D(A BBBA < T % +1 BBA A(G
-(A ABBA D (' 1 BBE G(A0D
-0A(A BBBA T (U 0 BBB E(A0A8G
-8A0A(B BBBD T 4 - 0 BBB E(A0A8G
-8A0A(B BBBK T 45 1 BBB E(A0A8G
-8A0A(B BBBD L 8 1 BBB B(A0G8J
-8A0A(B BBBA T 4
h;U 1 BBB E(A0A8G
-8A0A(B BBBF T
p@ 91 BBB E(A0A8G
-8A0A(B BBBA T
(C ?1 BBB E(A0A8G
-8A0A(B BBBE L < pH F1 BBB B(A0G8J
-8A0A(B BBBA T J5 :1 BBB E(A0A8G
-8A0A(B BBBD L N A1 BBB B(A0G8J
-8A0A(B BBBA T 4 hQ 51 BBB E(A0A8G-
-8A0A(B BBBE T TE <1 BBB E(A0A8G
-8A0A(B BBBA T Wu C1 BBB E(A0A8G
-8A0A(B BBBC L < ]t h1 BBB B(A0D8M
-8A0A(B BBBA T 0_ [1 BBB E(A0A8G
-8A0A(B BBBE T xd b1 BBG B(D0G8G
-8D0A(B BBBD T < q *2 BBB B(A0D8M
-8A0A(B BBBA T ud (2 BBB G(D0G8G
-8D0A(B BBBD T *2 BBB B(D0A8M
-8A0A(B BBBA T D (2 BBB B(A0D8M
-8A0A(B BBBA T &2 BBB B(A0D8M
-8A0A(B BBBA T 螹 $2 BBB B(A0D8M
-8A0A(B BBBA T L ` "2 BBB B(D0A8M
-8A0A(B BBBA T ا 2 BBB B(A0D8M
-8A0A(B BBBA T @ 2 BBB B(A0D8M
-8A0A(B BBBA T T 2 BBB B(A0D8M
-8A0A(B BBBA L e 2 BBB B(A0D8M
-8A0A(B BBBH T 0X &2 BBB G(D0G8G
-8D0A(B BBBD L T 8ϹU 2 BBB B(A0D8M
-8A0A(B BBBC T HԹ5 2 BBB E(A0A8G
-8A0A(B BBBH T 0ع $2 BBB G(D0G8G0
-8D0A(B BBBD T T &2 BBB E(A0A8G1
-8A0A(B BBBA T Pm -2 BBB G(D0G8G
-8D0A(B BBBD T h5 /2 BBB E(A0A8G
-8A0A(B BBBA T \ P 62 BBB G(D0G8G
-8D0A(B BBBD T e 82 BBB E(A0A8G
-8A0A(B BBBH T t ?2 BBB G(D0G8G
-8D0A(B BBBD T d H65 A2 BBB E(A0A8G
-8A0A(B BBBH T 0:b H2 BBB G(D0G8G
-8D0A(B BBBD T HO J2 BBB E(A0A8G
-8A0A(B BBBG T l R= Q2 BBB G(D0G8G
-8D0A(B BBBD T f S2 BBB E(A0A8G
-8A0A(B BBBA T @i
Y2 BBB G(D0G8G
-8D0A(B BBBD T t v [2 BBB E(A0A8G
-8A0A(B BBBA T y a2 BBB G(D0G8G`
-8D0A(B BBBD T $ Ȃ c2 BBB E(A0A8G)
-8A0A(B BBBI T | @ j2 BBB G(D0G8GH
-8D0A(B BBBD T Ț l2 BBG E(G0A8G
-8D0A(B BBBD , , '< s3 AK
-AA 4 @ L 8 L @m '3 BBB B(A0I8Dp
-8D0A(B BBBF L `m 2 BBB B(A0I8Dp
-8D0A(B BBBF D BBB D(A0D@0A(A BBB T t 8l w2 BIE B(A0A8DY
-8D0A(B BBBA T Pr E2 BIE B(A0A8Da
-8D0A(B BBBA T $ x 2 BBB B(A0F8G
-8A0A(B BBBA T &. K^ l Ǻ Ǻ 8\ AG RAL xǺ BLB B(A0A8Dq
-8A0A(B BBBE T 4 Ⱥ 2 BEE E(D0A8G
-8D0A(B BBBE L d κ BOB A(A0k
+0A(A BBBA T f ' BDB H(A0D8G
+8D0A(B BBBG < 0 B BBA A(Jc
+(A ABBG T < a BEE B(D0A8G
+8D0A(B BBBI L < B BLH B(D0A8DP
+8A0A(B BBBB 9 Af
+I D L ~ BHB E(A0A8Gn
+8D0A(B BBBK T T x~ BDB E(A0A8G
+8A0A(B BBBJ T P4 ~ BDB B(D0A8G
+8A0A(B BBBH T 8 ~ BBB B(A0D8G
+8A0A(B BBBA < \ ~ BBH A(Jx
+(A ABBK T @ w ~ BBB B(A0E8G
+8A0A(B BBBA T h)w # BBB B(A0E8G
+8A0A(B BBBA T L 5w BBB B(A0E8G
+8A0A(B BBBA T A BBB B(A0E8G
+8A0A(B BBBA L PN I BBB G(A0D8M
+8D0A(B BBBG ! CV Ko
+A " Q ," Q D" Q \" xQ t" pQ " hQ " `Q " XQ " PQ " HQ # @Q # 8Q 4# 0Q L# (Q d# 0Q |# 8Q # @Q # HQ # PQ # XQ # `Q $ hQ $$ pQ <$ xQ T$ Q l$ Q L # Qr } BBB B(E0A8J@
+8A0A(B BBBA $ $ R^ AG b
+AA T T$ R 7} BBE E(A0A8G4
+8A0A(B BBBK L $ `W* ?} BBB J(A0A8G
+8D0A(B BBBA L $ @Y )} BBB J(A0A8G
+8D0A(B BBBA L L% Z } BBB J(A0A8G
+8D0A(B BBBA L % \ | BBB J(A0A8G
+8D0A(B BBBA L % ^ | BBB J(A0A8G
+8D0A(B BBBA L <& _ | BBB J(A0A8G
+8D0A(B BBBA L & `a | BBB J(A0A8G
+8D0A(B BBBA L & c | BBB J(A0A8G
+8D0A(B BBBA L ,' d | BBB J(A0A8G
+8D0A(B BBBA L |' @f* y| BBB J(A0A8G
+8D0A(B BBBA L ' h c| BBB J(A0A8G
+8D0A(B BBBA L ( i* M| BBB J(A0A8G
+8D0A(B BBBA L l( k 7| BBB J(A0A8G
+8D0A(B BBBA T ( @m !| BBE E(D0A8G/
+8D0A(B BBBJ < ) o" { BBA A(JU
+(A ABBA < T) p" { BBA A(JU
+(A ABBA T ) q { BBE E(A0A8G
+8D0A(B BBBA D * t BBB D(A0D@
+0A(A BBBA D 4* Xu { BIB A(D0D`A
+0D(A BBBA D |* 0x z BIB A(D0D`H
+0D(A BBBA D * { z BIB A(D0D`H
+0D(A BBBA D + } z BIB A(D0D`H
+0D(A BBBA D T+ z BIB A(D0D`H
+0D(A BBBA D + sz BIB A(D0D`H
+0D(A BBBA D + h Qz BIB A(D0D`H
+0D(A BBBA D ,, @ /z BIB A(D0D`H
+0D(A BBBA D t,
z BIB A(D0D`H
+0D(A BBBA D , y BIB A(D0D`H
+0D(A BBBA D - ȑ y BIB A(D0D`H
+0D(A BBBA D L- y BIB A(D0D`H
+0D(A BBBA D - x y BIB A(D0D`H
+0D(A BBBA < - P cy BBA A(G
+(A ABBA D . Hy BBE G(A0D
+0A(A BBBA T d. x %y BBB E(A0A8G
+8A0A(B BBBD T . t )y BBB E(A0A8GG
+8A0A(B BBBK T / ةu Vy BBB E(A0A8G
+8A0A(B BBBF L l/ Zy BBB B(A0G8J
+8A0A(B BBBA T / D Ny BBB E(A0A8G,
+8A0A(B BBBF L 0 E {y BBB E(A0A8G
+8A0A(B BBBA T d0 y BBB E(A0A8Gc
+8A0A(B BBBG L 0 y BBB B(A0G8J
+8A0A(B BBBA T 1 u ~y BBB E(A0A8G
+8A0A(B BBBF T d1 ķd y BBB E(A0A8GO
+8A0A(B BBBC T 1 ɷu y BBB E(A0A8G3
+8A0A(B BBBA T 2 ̷ y BBB E(A0A8G
+8A0A(B BBBG L l2 зt y BBB B(A0D8M
+8A0A(B BBBA T 2 ҷ y BBB E(A0A8Gc
+8A0A(B BBBG T 3 hط y BBG B(D0G8G
+8D0A(B BBBD T l3 vz BBB B(A0D8M
+8A0A(B BBBA T 3 xd tz BBB G(D0G8G
+8D0A(B BBBD T 4 vz BBB B(D0A8M
+8A0A(B BBBA T t4
+ tz BBB B(A0D8M
+8A0A(B BBBA T 4 p rz BBB B(A0D8M
+8A0A(B BBBA T $5 pz BBB B(A0D8M
+8A0A(B BBBA T |5 P nz BBB B(D0A8M
+8A0A(B BBBA T 5 lz BBB B(A0D8M
+8A0A(B BBBA T ,6 0 jz BBB B(A0D8M
+8A0A(B BBBA T 6 $ hz BBB B(A0D8M
+8A0A(B BBBA L 6 )e fz BBB B(A0D8M
+8A0A(B BBBH T ,7 .X rz BBB G(D0G8G
+8D0A(B BBBD L 7 (CU ]z BBB B(A0D8M
+8A0A(B BBBC T 7 8He iz BBB E(A0A8G
+8A0A(B BBBH T ,8 PL mz BBB G(D0G8G0
+8D0A(B BBBD T 8 ` oz BBB E(A0A8G
+8A0A(B BBBH T 8 dm sz BBB G(D0G8G
+8D0A(B BBBD T 49 wu uz BBB E(A0A8G(
+8A0A(B BBBA T 9 z yz BBB G(D0G8G
+8D0A(B BBBD T 9 {z BBB E(A0A8G*
+8A0A(B BBBH T <: t z BBB G(D0G8G
+8D0A(B BBBD T : u z BBB E(A0A8G
+8A0A(B BBBH T : @b z BBB G(D0G8G
+8D0A(B BBBD T D; Xĸ z BBB E(A0A8Gl
+8A0A(B BBBF T ; Ǹ= z BBB G(D0G8G
+8D0A(B BBBD L ; ۸5 z BBB E(A0A8G
+8A0A(B BBBA T D<
z BBB G(D0G8G
+8D0A(B BBBD L < @E z BBB E(A0A8G
+8A0A(B BBBA T < @U
+ z BBB G(D0G8G`
+8D0A(B BBBD T D= H z BBB E(A0A8G
+8A0A(B BBBH T = z BBB G(D0G8GH
+8D0A(B BBBD T = z BBG E(G0A8G0
+8D0A(B BBBD , L> P'< { AK
+AA $? # <? # L > #m { BBB B(A0I8Dp
+8D0A(B BBBF L > @$m i{ BBB B(A0I8Dp
+8D0A(B BBBF D ? BBB D(A0D@0A(A BBB T ? %r z BIE B(A0A8Da
+8D0A(B BBBA T ? @*l z BIE B(A0A8DY
+8D0A(B BBBA T D@ X/ z BBE B(A0A8G
+8A0A(B BBBA DA @&. K^ \A @ tA @ A \ AG RAL A @ BLB B(A0A8Dq
+8A0A(B BBBE L TA HB z BBB B(I0A8G
+8D0A(B BBBF L A D qz BBB B(I0A8G
+8D0A(B BBBF T A F [z BEE E(D0A8G
+8D0A(B BBBE L B L BOB A(A0k
(A BBBI b
-(A BBBG T κ< 1 BBE B(D0A8G
-8D0A(B BBBB D ! $ BBB D(A0D@0A(A BBB T | @Ӻ C1 BIB E(A0A8DF
-8D0A(B BBBA T 1 BIB E(A0A8D6
-8D0A(B BBBA L " ۺ BBB E(D0D8G@L
-8D0A(B BBBE < T" ݺ BLH D(A0
-(A BBBF T ! ݺ O0 BBB B(A0A8G 0
-8A0A(B BBBE T " /1 BEB I(D0A8GM
-8D0A(B BBBH L l" C1 BBB B(I0A8G
-8D0A(B BBBF L " -1 BBB B(I0A8G
-8D0A(B BBBF # `". K^ # H $ @ , T# H[ 0 AID t
-DAA D \$ BBB D(A0D@0A(A BBB T # 0L g0 BIE B(A0A8D9
-8D0A(B BBBA T $$ ( 70 BJB B(A0D8G
-8D0A(B BBBA T% !. K^ l% h % ` T $ h 0 BBB B(I0A8D'
-8D0A(B BBBF D % ؏ BBB D(A0D@0A(A BBB T d% 0 BIB E(A0A8G
-8D0A(B BBBA L % 0 BBB B(A0D8Jo
-8D0A(B BBBE L & / BBB B(A0D8Jo
-8D0A(B BBBE L \& @# / BBB B(A0D8Jo
-8D0A(B BBBE L & & / BBB B(A0D8Jo
-8D0A(B BBBE L & ) / BBB B(A0D8Jo
-8D0A(B BBBE T L' p,
c/ BBB G(D0G8GN
-8D0A(B BBBF |( ( V Ko
-A ( 9 ( 9 L ' 9 / BBB B(D0A8DP
-8A0A(B BBBG L D( X: O/ BBB B(D0A8DP
-8A0A(B BBBH L ( (; / BBB B(I0A8G
-8D0A(B BBBF L ( h= . BBB B(I0A8G
-8D0A(B BBBF D * BBB D(A0D@0A(A BBB T |) `? . BIE B(A0A8D
-8D0A(B BBBA T ) D h. BIE B(A0A8D
-8D0A(B BBBA T ,* `JD C. BBH E(A0A8L
-8D0A(B BBBE T * XS . BBB B(A0A8G
-8D0A(B BBBK T * [ . BBB B(A0A8G
-8D0A(B BBBE L 4+ b . BFB B(A0A8JJ
-8D0A(B BBBA \, . K^ t, e , d L + e X. BBB B(A0I8G
-8D0A(B BBBD T , @g B. BEE E(D0A8G*
-8D0A(B BBBD T t, l (. BBE B(D0A8G
-8D0A(B BBBG D , q . BAA G@u
+(A BBBG T B L< y BBE B(D0A8G
+8D0A(B BBBB D C BBB D(A0D@0A(A BBB T <C @Q y BIB E(A0A8DF
+8D0A(B BBBA T C U ]y BIB E(A0A8D6
+8D0A(B BBBA L D Y BBB E(D0D8G@L
+8D0A(B BBBE < D [ BLH D(A0
+(A BBBF T |D [ x BBB B(A0A8G 0
+8A0A(B BBBE T D 8v y BEB I(D0A8GM
+8D0A(B BBBH E !. K^ E X{ F P{ , tE X{[ Ky AID t
+DAA D LF $ BBB D(A0D@0A(A BBB T E @{L x BIE B(A0A8D9
+8D0A(B BBBA T DF 8 x BDB H(D0A8GH
+8D0A(B BBBG DG . K^ \G ؊ tG Њ T F ؊ x BBB B(I0A8D'
+8D0A(B BBBF D G Z BBB D(A0D@0A(A BBB T G x BIB E(A0A8G
+8D0A(B BBBA L G ` ix BBB B(A0D8Jo
+8D0A(B BBBE L ,H Ix BBB B(A0D8Jo
+8D0A(B BBBE L |H )x BBB B(A0D8Jo
+8D0A(B BBBE L H ` x BBB B(A0D8Jo
+8D0A(B BBBE L I w BBB B(A0D8Jo
+8D0A(B BBBE T lI ࣹ
w BBB G(D0G8GN
+8D0A(B BBBF lJ V Ko
+A J J L J x BBB B(D0A8DP
+8A0A(B BBBG L dJ ȱ w BBB B(D0A8DP
+8A0A(B BBBH L J kw BBB B(I0A8G
+8D0A(B BBBF L K ش Uw BBB B(I0A8G
+8D0A(B BBBF D K BBB D(A0D@0A(A BBB T K ж v BIE B(A0A8D
+8D0A(B BBBA T K H v BIE B(A0A8D
+8D0A(B BBBA T LL D v BBH E(A0A8L
+8D0A(B BBBE T L ʹ #w BBB B(A0A8G
+8D0A(B BBBK T L pҹ .w BBB B(A0A8G
+8D0A(B BBBE L TM ڹ 9w BFB B(A0A8JJ
+8D0A(B BBBA LN (. K^ dN pܹ |N hܹ L M pܹ v BBB B(A0I8G
+8D0A(B BBBD T <N v BEE E(D0A8G*
+8D0A(B BBBD T N H v BBE B(D0A8G
+8D0A(B BBBG D N vv BAA G@u
DABI
- DABF D - | BBB D(A0D@0A(A BBB T \- s - BIB E(A0A8D
-8D0A(B BBBA T - wj - BEE B(D0A8N[
-8D0A(B BBBG L . | - BHG B(A0D8G
-8D0A(B BBBJ 4/ . K^ L/ d/ , . [ '. AID t
-DAA , . ȓ[ . AID t
-DAA < / BBB A(A0(A BBB < D/ X - BBB A(A0(A BBB D \0 ؔ
- BBB B(D0A8DP8A0A(B BBBL / #- BEB B(A0A8D@y8A0A(B BBB D 0 BBB B(A0D8D@8A0A(B BBBL d0 , BEB B(A0A8D@y8A0A(B BBB $ 1 薻< AAN0fDA $ 1 < AAN0fDA D 1 Z BBB D(A0D@0A(A BBB T L1 Жl + BIE B(A0A8DY
-8D0A(B BBBA T 1 蛻 y+ BIE B(A0A8D
-8D0A(B BBBA T 1 ` R+ BBE B(D0A8G
-8D0A(B BBBG T ,3 (U BAA G0
+ DABF D O BBB D(A0D@0A(A BBB T |O v BIB E(A0A8D
+8D0A(B BBBA T O Xj u BEE B(D0A8N[
+8D0A(B BBBG L ,P p u BHG B(A0D8G
+8D0A(B BBBJ $Q . K^ <Q TQ , P [ v AID t
+DAA , P 8 [ ov AID t
+DAA < Q h BBB A(A0(A BBB < dQ v BBB A(A0(A BBB D LR H
+ BBB B(D0A8DP8A0A(B BBBL Q
u BEB B(A0A8D@y8A0A(B BBB D R `
BBB B(A0D8D@8A0A(B BBBL R t BEB B(A0A8D@y8A0A(B BBB $ |S X< AAN0fDA $ S p< AAN0fDA D S ܊ BBB D(A0D@0A(A BBB T lS @l t BIE B(A0A8DY
+8D0A(B BBBA T S X s BIE B(A0A8D
+8D0A(B BBBA T T s BBE B(D0A8G
+8D0A(B BBBG T U U BAA G0
AABA
AABK f
- AABF | 3 0 BBB B(D0A8DP
+ AABF | tU ! BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI T ,3 c* BDE E(G0A8G
-8D0A(B BBBE T 3 8 + BDE E(G0A8G
-8D0A(B BBBE T 3 Że + BBB J(D0A8M(
-8D0A(B BBBI 5 X. K^ $5 ɻ <5 ɻ T5 ɻ l5 ɻ , 4 ɻ[ [+ AID t
-DAA , 4 ɻ[ ;+ AID t
-DAA , 5 ʻ[ + AID t
-DAA D 6 BBB D(A0D@0A(A BBB T 5 ʻ\ * BIE B(A0A8DI
-8D0A(B BBBA T 5 ϻ * BIB E(A0A8G
-8D0A(B BBBA T 46 Ի Z* BIB E(A0A8G
-8D0A(B BBBA L 6 0ڻz 3* BDE H(A0A8G
-8D0A(B BBBI L 6 `/ o* BDH B(A0D8G
-8D0A(B BBBD 8 . K^ 8 ( 48 T t7 ( c* BBB B(A0D8R:
-8A0A(B BBBA , 7 > j* AID W
-DAA T 7 J* BBE B(D0A8G
-8D0A(B BBBG D ,9 BBB D(A0D@0A(A BBB T 8 ) BIB E(A0A8D
-8D0A(B BBBA 8 E ) BEE E(G0G
+8A0A(B BBBI T LU $ r BDE E(G0A8G
+8D0A(B BBBE T U 0 s BDE E(G0A8G
+8D0A(B BBBE T U 0=e 3t BBB J(D0A8M(
+8D0A(B BBBI V . K^ W 0A ,W (A DW A \W (A , V 0A[ s AID t
+DAA , V `A[ s AID t
+DAA , ,W A[ s AID t
+DAA D X r BBB D(A0D@0A(A BBB T W xA\ s BIE B(A0A8DI
+8D0A(B BBBA T W F r BIB E(A0A8G
+8D0A(B BBBA T TX L r BIB E(A0A8G
+8D0A(B BBBA L X Qz r BDE H(A0A8G
+8D0A(B BBBI L X V/ r BDH B(A0D8G
+8D0A(B BBBD Y . K^ Z [ $Z [ T Y [ r BBB B(A0D8R:
+8A0A(B BBBA , Y ``> r AID W
+DAA T Z p` r BBE B(D0A8G
+8D0A(B BBBG D [ ( BBB D(A0D@0A(A BBB T Z d Pr BIB E(A0A8D
+8D0A(B BBBA [ hj
)r BEE E(G0G>
0A(A BBBK Q
-0A(A BBBG ,
-0A(A BBBD
-0A(A BBBG 9
) BEE E(G0G>
-0A(A BBBK Q
0A(A BBBG
0A(A BBBE K
-0A(A BBBE T : ) BBB G(D0A8M5
-8D0A(B BBBG D; . K^ \; t; ; ; , : [ O) AID t
-DAA , ; [ /) AID t
-DAA D < BBB D(A0D@0A(A BBB T ; l ( BIE B(A0A8DY
-8D0A(B BBBA T ; l ( BIE B(A0A8DY
-8D0A(B BBBA T << c( BDB E(A0G8G
-8D0A(B BBBF T < x!J ( BDB B(D0G8G
-8D0A(B BBBI = 0
-. K^ = X( = P( > H( $> P( \ d= X(j S( BBB A(I0DW
+0A(A BBBE [ wE %r BEE E(G0G
+0A(A BBBK Q
+0A(A BBBG ,
+0A(A BBBD
+0A(A BBBG T 4\ #r BBB G(D0A8M5
+8D0A(B BBBG 4] . K^ L] 8 d] 0 |] ( ] 0 , ] 8[ q AID t
+DAA , 4] h[ q AID t
+DAA D ^ BBB D(A0D@0A(A BBB T ] Pl /q BIE B(A0A8DY
+8D0A(B BBBA T ^ hl p BIE B(A0A8DY
+8D0A(B BBBA T \^ p BDB E(A0G8G
+8D0A(B BBBF T ^ 蘺J p BDB B(D0G8G
+8D0A(B BBBI _ . K^ _ ȟ _ _ ` \ _ ȟj p BBB A(I0DW
0D(A BBBJ n
-0D(A BBBG D > ҃ BBB D(A0D@0A(A BBB T > ,L ' BIE B(A0A8D9
-8D0A(B BBBA L d> 1 ' BBE B(A0A8G
-8A0A(B BBBG ? . K^ ? O ? O ? O ? xO @ pO @ hO 4@ pO L@ xO d@ O |@ O , ? O[ ' AID t
-DAA , ? O[ & AID t
-DAA , @ O[ & AID t
-DAA , L@ P[ & AID t
-DAA , |@ PP[ & AID t
-DAA D A BBB D(A0D@0A(A BBB T @ 8P #& BIB E(A0A8D6
-8D0A(B BBBA T LA pT % BIB E(A0A8D6
-8D0A(B BBBA T A X % BIB E(A0A8D6
-8D0A(B BBBA T A \ % BIB E(A0A8D6
-8D0A(B BBBA T TB a [% BIB E(A0A8D6
-8D0A(B BBBA T B PeA
- )% BDB B(A0A8G
-8A0A(B BBBA T C Ho)
- z% BDB B(A0A8G
-8A0A(B BBBA T \C y
- % BDB B(A0A8G
-8A0A(B BBBA T C
- $& BDB B(A0A8G
-8A0A(B BBBA T D e
- y& BDB B(A0A8G
-8A0A(B BBBA <E . K^ TE lE E E x E p E h E ` E X F P ,F H DF @ \F 8 tF @ F H F P F X F ` F h G p G x 4G LG \ F , $ BBB A(I0Dp
+0D(A BBBG D ` T BBB D(A0D@0A(A BBB T ,` L 6p BIE B(A0A8D9
+8D0A(B BBBA L ` p BBE B(A0A8G
+8A0A(B BBBG |a . K^ a Ǻ a ƺ a ƺ a ƺ a ƺ b ƺ $b ƺ <b ƺ Tb ƺ lb ƺ , a Ǻ[ so AID t
+DAA , b 0Ǻ[ So AID t
+DAA , <b `Ǻ[ 3o AID t
+DAA , lb Ǻ[ o AID t
+DAA , b Ǻ[ n AID t
+DAA D tc : BBB D(A0D@0A(A BBB T c Ǻ n BIB E(A0A8D6
+8D0A(B BBBA T lc ˺ Yn BIB E(A0A8D6
+8D0A(B BBBA T c к 'n BIB E(A0A8D6
+8D0A(B BBBA T d PԺ m BIB E(A0A8D6
+8D0A(B BBBA T td غ m BIB E(A0A8D6
+8D0A(B BBBA T d ܺA
+ m BDB B(A0A8G
+8A0A(B BBBA T $e )
+ m BDB B(A0A8G
+8A0A(B BBBA T |e
+ 7n BDB B(A0A8G
+8A0A(B BBBA T e H
+ n BDB B(A0A8G
+8A0A(B BBBA T ,f e
+ n BDB B(A0A8G
+8A0A(B BBBA ,g . K^ Dg \g
tg
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0D(A BBBG ^
-0D(A BBBA \ F `, ]$ BBB A(I0Dp
+0D(A BBBA \ i , l BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ LG 0, $ BBB A(I0Dp
+0D(A BBBA \ li , l BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ G , # BBB A(I0Dp
+0D(A BBBA \ i p, =l BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ H Й, # BBB A(I0Dp
+0D(A BBBA \ ,j @, k BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ lH , M# BBB A(I0Dp
+0D(A BBBA \ j , k BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ H p, # BBB A(I0Dp
+0D(A BBBA \ j , qk BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ ,I @, " BBB A(I0Dp
+0D(A BBBA \ Lk , -k BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ I , " BBB A(I0Dp
+0D(A BBBA \ k , j BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ I , =" BBB A(I0Dp
+0D(A BBBA \ l P, j BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ LJ , ! BBB A(I0Dp
+0D(A BBBA \ ll , aj BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ J , ! BBB A(I0Dp
+0D(A BBBA \ l , j BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ K P, q! BBB A(I0Dp
+0D(A BBBA \ ,m , i BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ lK , -! BBB A(I0Dp
+0D(A BBBA \ m , i BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ K , BBB A(I0Dp
+0D(A BBBA \ m `, Qi BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ ,L , BBB A(I0Dp
+0D(A BBBA \ Ln 0,
i BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ L , a BBB A(I0Dp
+0D(A BBBA \ n , h BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ L `, BBB A(I0Dp
+0D(A BBBA \ o , h BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ LM 0, BBB A(I0Dp
+0D(A BBBA \ lo , Ah BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ M , BBB A(I0Dp
+0D(A BBBA \ o p , g BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ N Ц, Q BBB A(I0Dp
+0D(A BBBA \ ,p @ , g BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ lN ,
BBB A(I0Dp
+0D(A BBBA \ p , ug BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA $ O p^ AG b
-AA D O BEE D(A0D`n
-0A(A BBBE $ P @ Az
+0D(A BBBA $ q ^ AG b
+AA D q BEE D(A0D`n
+0A(A BBBE $ r Az
E
-A D <P BBB D(A0D@
-0A(A BBBA D O BIB A(A0G`
-0D(A BBBA D O x BIB A(A0G`
-0D(A BBBA D <P BIB A(A0G`
-0D(A BBBA D P H \ BIB A(A0G`
-0D(A BBBA D P - BIB A(A0G`
-0D(A BBBA D Q BIB A(A0G`
-0D(A BBBA D \Q BIB A(A0G`
-0D(A BBBA D Q 贼 BIB A(A0G`
-0D(A BBBA D Q P q BIB A(A0G`
-0D(A BBBA D 4R B BIB A(A0G`
-0D(A BBBA D |R BIB A(A0G`
-0D(A BBBA D R BIB A(A0G`#
-0D(A BBBA D S BIB A(A0G`#
-0D(A BBBA D TS x BIB A(A0G`#
-0D(A BBBA D S W BIB A(A0G`#
-0D(A BBBA D S h ( BIB A(A0G`#
-0D(A BBBA D ,T BIB A(A0G`#
-0D(A BBBA D tT Xü BIB A(A0G`#
-0D(A BBBA D T ļ BIB A(A0G`#
-0D(A BBBA D U HƼ l BIB A(A0G`#
-0D(A BBBA D LU Ǽ = BIB A(A0G`#
-0D(A BBBA D U 8ɼ BIB A(A0G`#
-0D(A BBBA L V ʼ BOB A(A0]
+A D ,r (! BBB D(A0D@
+0A(A BBBA D q " Qf BIB A(A0G`
+0D(A BBBA D r # "f BIB A(A0G`
+0D(A BBBA D \r P% e BIB A(A0G`
+0D(A BBBA D r & e BIB A(A0G`
+0D(A BBBA D r ( e BIB A(A0G`
+0D(A BBBA D 4s ) fe BIB A(A0G`
+0D(A BBBA D |s * 7e BIB A(A0G`
+0D(A BBBA D s X, e BIB A(A0G`
+0D(A BBBA D t - d BIB A(A0G`
+0D(A BBBA D Tt (/ d BIB A(A0G`
+0D(A BBBA D t 0 {d BIB A(A0G`
+0D(A BBBA D t 1 Ld BIB A(A0G`#
+0D(A BBBA D ,u p3 d BIB A(A0G`#
+0D(A BBBA D tu 4 c BIB A(A0G`#
+0D(A BBBA D u `6 c BIB A(A0G`#
+0D(A BBBA D v 7 c BIB A(A0G`#
+0D(A BBBA D Lv P9 ac BIB A(A0G`#
+0D(A BBBA D v : 2c BIB A(A0G`#
+0D(A BBBA D v @< c BIB A(A0G`#
+0D(A BBBA D $w = b BIB A(A0G`#
+0D(A BBBA D lw 0? b BIB A(A0G`#
+0D(A BBBA D w @ vb BIB A(A0G`#
+0D(A BBBA L x B BOB A(A0]
(A BBBG Y
-(A BBBH L W ˼ BBB B(D0D8G@H
-8A0A(B BBBG D TW p̼E BAA GP
- AABB AAB L W xμ
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+8A0A(B BBBG D Dy CE BAA GP
+ AABB AAB L y E
BKB B(A0A8G}
-8A0A(B BBB $ W ؼ AD@
-AE $ X @ۼ MSgv D <X ۼE BAA GP
- AABB AAB L X BKB B(A0A8Gu 8A0A(B BBB D X PE BAA GP
- AABB AAB L Y X* BKB B(A0A8G 8A0A(B BBB D lY 8E BAA GP
- AABB AAB L Y @
+8A0A(B BBB $ y HP AD@
+AE $ z R MSgv D ,z hSE BAA GP
+ AABB AAB L tz pU BKB B(A0A8Gu 8A0A(B BBB D z ^E BAA GP
+ AABB AAB L { `* BKB B(A0A8G 8A0A(B BBB D \{ kE BAA GP
+ AABB AAB L { m
BKB B(A0A8G
-8A0A(B BBB D Z E BAA GP
- AABB AAB $ LZ AD@
-AE $ tZ MSgv D Z E BAA GP
- AABB AAB L Z BHB B(A0A8D`
-8A0A(B BBBA $ 4[ IP c
-DH L \[ X BHB B(A0A8D`
-8A0A(B BBBA L [ (
! BHB B(A0A8DX
-8A0A(B BBBA L [ BHB B(A0A8D`
-8A0A(B BBBA L L\ - BHB B(A0A8Dh
-8A0A(B BBBA L \ 5 BEB B(A0A8Dp
-8A0A(B BBBA , \ BTA _DG T D\ w BBB B(A0A8G
-8A0A(B BBBG T \ p 3 BEB H(D0A8Gk
-8A0A(B BBBA T \ BEB H(D0A8Gk
-8A0A(B BBBA T L] ' BEB H(D0A8G
-8A0A(B BBBA T ] 3 [ BEB H(D0A8G
-8A0A(B BBBA T ] ?' BEH E(A0A8G5
-8A0A(B BBBA T T^ M BEB H(D0A8G
-8A0A(B BBBA _ Z1 Dl \ _ Z BBD D(G0G
+8A0A(B BBB D { xE BAA GP
+ AABB AAB $ <| (z AD@
+AE $ d| | MSgv D | H}E BAA GP
+ AABB AAB L | P BHB B(A0A8D`
+8A0A(B BBBA $ $} IP c
+DH L L} Ȃ BHB B(A0A8D`
+8A0A(B BBBA L } ! BHB B(A0A8DX
+8A0A(B BBBA L } x BHB B(A0A8D`
+8A0A(B BBBA L <~ H- BHB B(A0A8Dh
+8A0A(B BBBA L ~ (5 BEB B(A0A8Dp
+8A0A(B BBBA , ~ BTA _DG T d~ x [ BBB B(A0A8G
+8A0A(B BBBG T ~ [ BEB H(D0A8Gk
+8A0A(B BBBA T h S[ BEB H(D0A8Gk
+8A0A(B BBBA T l [ BEB H(D0A8G
+8A0A(B BBBA T ( Z BEB H(D0A8G
+8A0A(B BBBA T `' {Z BEH E(A0A8G5
+8A0A(B BBBA T t 8Ż 3Z BEB H(D0A8G
+8A0A(B BBBA t pѻ1 Dl \ ѻ BBD D(G0G
(E ABBD o
-(E ABBG L(A EBB L ,_ Z
+(E ABBG L(A EBB L L ѻ
- BHB B(A0A8J
-8D0A(B BBBI L |_ @d BBB B(A0G8J
-8D0A(B BBBI L _ j BBB B(G0A8J
-8D0A(B BBBI L ` q BBB B(G0A8J
-8D0A(B BBBI L l` @w BBB B(G0A8J
-8D0A(B BBBI L ` } BBB B(G0A8J
-8D0A(B BBBI L a BBB B(A0G8J
-8D0A(B BBBI L \a @ BBB B(A0G8J
-8D0A(B BBBI L a BBB B(A0G8J
-8D0A(B BBBI L a BBB B(A0G8J
-8D0A(B BBBI L Lb ` BBB B(G0A8J
-8D0A(B BBBI tc . K^ D c BEB E(D0D8PP~8A0A(B BBB c > Hu c > Hu d 0> Hu 4d P> Hu Td pE Hx td E Hx d ФE Hx d E Hx d 6a\ AG RA< d _ BBB D(A0
-(A BBBH 4e 0d T td ' BBE E(A0A8G
-8D0A(B BBBC T d BBE E(A0A8G
-8D0A(B BBBA L $e k AAGPa
+ kY BHB B(A0A8J
+8D0A(B BBBI L ۻ pY BBB B(A0G8J
+8D0A(B BBBI L 0 oY BBB B(G0A8J
+8D0A(B BBBI L < p nY BBB B(G0A8J
+8D0A(B BBBI L mY BBB B(G0A8J
+8D0A(B BBBI L ܂ lY BBB B(G0A8J
+8D0A(B BBBI L , 0 kY BBB B(A0G8J
+8D0A(B BBBI L | jY BBB B(A0G8J
+8D0A(B BBBI L ̃ iY BBB B(A0G8J
+8D0A(B BBBI L P iY BBB B(A0G8J
+8D0A(B BBBI L l hY BBB B(G0A8J
+8D0A(B BBBI d . K^ D | BEB E(D0D8PP~8A0A(B BBB ą `> Hu > Hu > Hu $ > Hu D E Hx d E Hx @ E Hx p E Hx Ć b\ AG RA< _ BBB D(A0
+(A BBBH $ d T W BBE E(A0A8G
+8D0A(B BBBC T 0 RW BBE E(A0A8G
+8D0A(B BBBA L D h"k W AAGPa
DAA A
DAJ }
-DAA D Lf BBB D(A0D@
-0A(A BBBA D e p 2 BIB A(D0D`+
-0D(A BBBA D f ( BIB A(D0D`+
-0D(A BBBA D Lf ಽ
BIB A(D0D`+
-0D(A BBBA D f
BIB A(D0D`+
-0D(A BBBA D f P
BIB A(D0D`+
-0D(A BBBA D $g
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-0D(A BBBA < Dh н[ BBE H(A0
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BBB B(K0A8Jo
-8A0A(B BBBI 4 h hĽq ,
BAI A
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BAI A
-ABA 4 th Ľq BAI A
-ABA 4 h @Žq BAI A
-ABA 4 h Žq BAI A
-ABA 4 i Žq x BAI A
-ABA , Ti ƽ T ADG0
-AAB < \j ǽ BDA k
-ABK @
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+DAA D < # BBB D(A0D@
+0A(A BBBA D ܇ $ V BIB A(D0D`+
+0D(A BBBA D $ ' xV BIB A(D0D`+
+0D(A BBBA D l P* VV BIB A(D0D`+
+0D(A BBBA D - 4V BIB A(D0D`+
+0D(A BBBA D / V BIB A(D0D`+
+0D(A BBBA D D x2 U BIB A(D0D`2
+0D(A BBBA < 4 @5[ BBE H(A0
+(A BBBF T ̉ `6 U BBB B(K0A8Jo
+8A0A(B BBBI 4 $ ;q U BAI A
+ABA 4 \ <q pU BAI A
+ABA 4 h<q LU BAI A
+ABA 4 ̊ <q (U BAI A
+ABA 4 <q U BAI A
+ABA 4 < @=q T BAI A
+ABA , t = T ADG0
+AAB $ L ? A
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+K $ t ? A
A e
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+K $ x@ A
A e
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+K $ Č 0A A
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-8A0A(B BBBA T j E BEE E(A0A8G>
-8D0A(B BBBA T k # BBB B(A0A8G
-8A0A(B BBBA T lk p
E BEE E(A0A8G9
-8D0A(B BBBA T k h! BBB B(A0A8J
-8A0A(B BBBA T l 4E N
BEE E(A0A8G>
-8D0A(B BBBA T tl :
BBB B(A0A8G
-8A0A(B BBBA T l UE BEE E(A0A8G<
-8D0A(B BBBA m ږ. K^ n Z ,n Z Dn Z \n Z tn Z n Z n Z n Z L m Zu
BBB B(A0I8Dp
-8D0A(B BBBF L Ln \u M
BBB B(A0I8Dp
-8D0A(B BBBF L n P]u
BBB B(A0I8Dp
-8D0A(B BBBF L n ^u BBB B(A0I8Dp
-8D0A(B BBBF < p RVD BBD A(D@0(A ABB T |o p_ q BIB B(A0D8Df
-8D0A(B BBBA T o c ? BIB B(A0D8Df
-8D0A(B BBBA T ,p @h
BIB B(A0D8D
-8D0A(B BBBA T p l BIB B(A0D8D
-8D0A(B BBBA T p Pq BBB B(D0A8G\
-8A0A(B BBBA T 4q z # BBB B(D0A8Ga
-8A0A(B BBBA T q % BGB B(D0A8G
-8A0A(B BBBA T q
BGB B(D0A8G
-8A0A(B BBBA s Ֆ. K^ ,s 8 Ds 0 \s 80 Hg |s H7 Hj , r h[ AID t
-DAA L r BBB J(A0A8G
-8D0A(B BBBA T Ds 蚾| BBE E(D0A8G
-8D0A(B BBBF D s BBE A(D0G
-0A(A BBBA D t R BBB D(A0D@0A(A BBB T ,t P BIB E(A0A8DN
-8D0A(B BBBA T t x BIB E(A0A8Df
-8D0A(B BBBA T t G BBE B(A0D8J
-8D0A(B BBBF T 4u c BGH B(A0D8G
-8D0A(B BBBH dv Җ. K^ |v Ⱥ v $ v غ
v к1 v i v P w X AL ,w H DI Dw P DI \w X DI tw ` AL w P AL w @# TI w XA I[
-L K , w b D\
+K T D A2 S BBE B(A0A8G
+8A0A(B BBBA T [E rT BEE E(A0A8G>
+8D0A(B BBBA T a# T BBB B(A0A8G
+8A0A(B BBBA T L pE 4U BEE E(A0A8G9
+8D0A(B BBBA T h! ~U BBB B(A0A8J
+8A0A(B BBBA T ଼E U BEE E(A0A8G>
+8D0A(B BBBA T T ز @V BBB B(A0A8G
+8A0A(B BBBA T ͼE V BEE E(A0A8G<
+8D0A(B BBBA xړ. K^ ď Ҽ Ҽ Ҽ Ҽ $ Ҽ < Ҽ T Ҽ l Ҽ L Ҽu )V BBB B(A0I8Dp
+8D0A(B BBBF L , Լu U BBB B(A0I8Dp
+8D0A(B BBBF L | Pռu U BBB B(A0I8Dp
+8D0A(B BBBF L ̐ ּu U BBB B(A0I8Dp
+8D0A(B BBBF D đ Xv BBB D(A0D@^0A(A BBB T d h U BIB B(A0D8Df
+8D0A(B BBBA T ۼ T BIB B(A0D8Df
+8D0A(B BBBA T 8 T BIB B(A0D8D
+8D0A(B BBBA T l {T BIB B(A0D8D
+8D0A(B BBBA T Ē H IT BBB B(D0A8G
+8A0A(B BBBA T T BBB B(D0A8G
+8A0A(B BBBA T t ;U BGB B(D0A8G
+8A0A(B BBBA T ̓ % U BGB B(D0A8G
+8A0A(B BBBA ̔ Փ. K^ p h p0 Hg 4 7 Hj , [ U AID t
+DAA L ܔ oU BBB J(A0A8G
+8D0A(B BBBA T , | ZU BBE E(D0A8G
+8D0A(B BBBF D H @U BBE A(D0G
+0A(A BBBA D t T BBB D(A0D@0A(A BBB T HU BIB E(A0A8DN
+8D0A(B BBBA T l U BIB E(A0A8Df
+8D0A(B BBBA T Ė 8$ T BBE B(A0D8J
+8D0A(B BBBF T ( U BGH B(A0D8G
+8D0A(B BBBH hғ. K^ 4 2 L 1$ d 2
| 21 02i 2 Ę 2 AL 2 DI 2 DI 2 DI , 2 AL L 2 AL l x2# TI 2A I[
+L K , 2b D\
H P
H Q
-G I , x Ȼb D\
+G I , ԙ 3b D\
H P
H Q
-G I , Lx b D\
+G I , @3b D\
H P
H Q
-G I , |x Hb D\
+G I , 4 3b D\
H P
H Q
-G I , x b D\
+G I , d 3b D\
H P
H Q
-G I , x ȼb D\
+G I , 4b D\
H P
H Q
-G I , y b D\
+G I , Ě @4b D\
H P
H Q
-G I , <y Hb D\
+G I , 4b D\
H P
H Q
-G I , ly b D\
+G I , $ 4b D\
H P
H Q
-G I , y Ƚb D\
+G I , T 5b D\
H P
H Q
-G I , y b D\
+G I , @5b D\
H P
H Q
-G I $ $y Hr c H\
-L A $ Ly r ? H\
-L A $ ty r H\
-L A $ y Pr H\
-L A $ y r H\
-L A $ y r H\
-L A z Xq Hd 4z q o Hd Tz q S Hd tz xq 7 Hd z q Hd z 8¾q Hd { M\ AG RAT z x¾t BBE B(D0A8G
-8D0A(B BBBH D L{ Ǿ BBB A(A0G\
-0A(A BBBE $ { ʾR AI
-F A D | @Mv BBB D(A0D@^0A(A BBB T | ʾ P BIB E(A0A8DF
-8D0A(B BBBA T \| Ͼ BIB E(A0A8D6
-8D0A(B BBBA T | XӾ BIB E(A0A8D6
-8D0A(B BBBA T } BIB E(A0A8DF
-8D0A(B BBBA T d} ۾ BIB E(A0A8D6
-8D0A(B BBBA T } V BIB E(A0A8DF
-8D0A(B BBBA d ~ XB BBB B(A0A8G@x
+G I $ 5r R H\
+L A $ 4 5r Q H\
+L A $ \ 06r Q H\
+L A $ 6r Q H\
+L A $ 6r sQ H\
+L A $ ԛ 87r OQ H\
+L A 7q +Q Hd 7q Q Hd < P8q P Hd \ 8q P Hd | 9q P Hd p9q P Hd d O\ AG RAT ܜ 9t cP BBE B(D0A8G
+8D0A(B BBBH D 4 > IP BBB A(A0G\
+0A(A BBBE $ | BR \P AI
+F A < L ,OD BBD A(D@0(A ABB T B O BIB E(A0A8DF
+8D0A(B BBBA T < `F O BIB E(A0A8D6
+8D0A(B BBBA T J O BIB E(A0A8D6
+8D0A(B BBBA T N bO BIB E(A0A8DF
+8D0A(B BBBA T D S 0O BIB E(A0A8D6
+8D0A(B BBBA T PW N BIB E(A0A8DF
+8D0A(B BBBA d [B BBB B(A0A8G@x
8A0A(B BBBE
-8A0A(B BBBF T |~ @! BBB B(D0A8J [
-8A0A(B BBBD < ~ BBI A(Ga
-(D ABBA < H BBI A(Ga
-(D ABBA < T d BBI A(Ga
-(D ABBA < ; BBI A(Ga
-(D ABBA < h BBI A(Ga
-(D ABBA < BBI A(Ga
-(D ABBA , Ȗ. K^ D
\
t < BBB A(A0(A BBB D <
-k BBB A(A08
-(A BBBI < BBB A(A0(A BBB L ā h
\ BBB B(A0D8DP 8A0A(B BBB D 8k BBB A(A08
-(A BBBI D \ ` BBB A(A0
-(A BBBA D BBB A(A0
-(A BBBA < p D BBB A(A0(A BBB < , BBB A(A0(A BBB < l 0 BBB A(A0(A BBB < BBB A(A0(A BBB < T BBB A(A0(A BBB < , @ BBB A(A0(A BBB L l BEB B(A0A8DP 8A0A(B BBB D s BBB A(A0A
-(A BBBH < b L BBA A(G0/
-(A ABBF < D ( BBB A(A0y(A BBB < x BBA A(G0(A ABB D ą ( BBB A(A0
-(A BBBH < ! T BBA A(G0(A ABB < L `" BBA A(G0(A ABB D # BBB A(A0
-(A BBBH < Ԇ $ BBB A(A0y(A BBB < % \ BBA A(G0(A ABB < T & BBA A(G0(A ABB < (' BBB A(A0(A BBB T ԇ ( BBE B(D0A8G
-8D0A(B BBBB T , 0-0
BBE B(A0A8N
+8A0A(B BBBF T \ \! dN BBB E(A0A8J c
+8A0A(B BBBD < (~ ^P BBI A(Ga
+(D ABBA < ~ 5P BBI A(Ga
+(D ABBA < 4 ~ P BBI A(Ga
+(D ABBA < t H O BBI A(Ga
+(D ABBA < O BBI A(Ga
+(D ABBA < O BBI A(Ga
+(D ABBA ܢ Ǔ. K^ P
H
$ @ < H T P l H < ܢ P N BBB A(A0(A BBB D Ёk N BBB A(A08
+(A BBBI < d @N BBB A(A0(A BBB L N BBB B(A0D8DP 8A0A(B BBB D xk M BBB A(A08
+(A BBBI D < tM BBB A(A0
+(A BBBA D ( 0M BBB A(A0
+(A BBBA < ̤ L BBB A(A0(A BBB < L BBB A(A0(A BBB < L p tL BBB A(A0(A BBB < 8L BBB A(A0(A BBB < ̥ Ў K BBB A(A0(A BBB < K BBB A(A0(A BBB L L 0 K BEB B(A0A8DP 8A0A(B BBB D s 8K BBB A(A0A
+(A BBBH < 8b J BBA A(G0/
+(A ABBF < $ h J BBB A(A0(A BBB < d ؖ |J BBA A(G0(A ABB < x @J BBA A(G0(A ABB < J BBA A(G0(A ABB < $ Ș I BBB A(A0y(A BBB D d I BBB A(A0
+(A BBBH D HI BBB A(A0
+(A BBBH < H I BBB A(A0y(A BBB < 4 H BBA A(G0(A ABB < t 8 H BBA A(G0(A ABB < \ `DD BBD A(D@0(A ABB T H BIB E(A0A8D6
+8D0A(B BBBA T L У. G BBB B(A0D8G
+8A0A(B BBBA T 2 G BBG E(A0A8G
+8A0A(B BBBC T @ڽ 3J BBE E(A0A8G[
+8D0A(B BBBA T T ݽ0
J BBE B(A0A8N
I
-8A0A(B BBBB , :> AID W
-DAA < AD BBD A(D@0(A ABB T 9 BIB E(A0A8D6
-8D0A(B BBBA T L >. v BBB B(A0D8G
-8A0A(B BBBA T A2 c BBG E(A0A8G
-8A0A(B BBBC Ԋ . K^ ht `t L D htr BBB B(E0A8J@
-8A0A(B BBBA T u H BEB E(A0A8G;
-8D0A(B BBBA T x BEB E(A0A8G;
-8D0A(B BBBA L D hz) BBB B(A0I8G
-8D0A(B BBBA L H|) BBB B(A0I8G
-8D0A(B BBBA T (~ BBE E(A0A8G
-8D0A(B BBBH D ` BBB D(A0D@
-0A(A BBBA L BIB A(D0D
-0D(A BBBA L Ԍ BIB A(D0D
-0D(A BBBA < xQ BEH H(A0n(A BBE T d BEB B(A0D8J
-8A0A(B BBBA T Ќ r BEB B(A0D8J
-8A0A(B BBBA T E BEB B(A0D8J
-8A0A(B BBBA T l @ BEB B(A0D8J
-8A0A(B BBBA T Ď x BEB B(A0D8J
-8A0A(B BBBA T BEB B(A0D8J
-8A0A(B BBBA T t 衿 BEB B(A0D8J
-8A0A(B BBBA T ̏ d BEB B(A0D8J
-8A0A(B BBBA T $ X 7 BEB B(A0D8J
-8A0A(B BBBA T |
- BEB B(A0D8J
-8A0A(B BBBA T Ԑ Ȳr BEB B(A0D8J
-8A0A(B BBBA T , BEB B(A0D8J
-8A0A(B BBBA T (r BEB B(A0D8J
-8A0A(B BBBA T ܑ P V BEB B(A0D8J
-8A0A(B BBBA T 4 ÿ / BEB B(A0D8J
-8A0A(B BBBA T ȿ BEB B(A0D8J
-8A0A(B BBBA T H̿ BEB B(A0D8J
-8A0A(B BBBA T < п BEB B(A0D8J
-8A0A(B BBBA T Կ { BEB B(A0D8J
-8A0A(B BBBA T ؿ N BEB B(A0D8J
-8A0A(B BBBA T D (ݿ ! BEB B(A0D8J
-8A0A(B BBBA T ` BEB B(A0D8J
-8A0A(B BBBA T BEB B(A0D8J
-8A0A(B BBBA T L r BEB B(A0D8J
-8A0A(B BBBA T r m BEB B(A0D8J
-8A0A(B BBBA T @ BEB B(A0D8J
-8A0A(B BBBA T T X BEB B(A0D8J
-8A0A(B BBBA T BEB B(A0D8J
-8A0A(B BBBA L BBH B(D0F8G
-8D0A(B BBBD , x. K^ D
\
,
[ AID t
-DAA < P
BBB A(A0(A BBB <
BBB A(A0(A BBB 4 $ 0 BBA A(G0(A ABB< BBB D(A0n(A BBB $ (< AAN0fDA $ ę @< AAN0fDA D 2 BBB D(A0D@0A(A BBB T \ l > BIE B(A0A8DY
-8D0A(B BBBA T (( BDH E(A0A8G
&
-8D0A(B BBBA < ;p BBD A(G
-(D ABBG $ . K^ < < T < , <[ AID t
-DAA D 2 BBB D(A0D@0A(A BBB T <l 9 BIE B(A0A8DY
-8D0A(B BBBA T d A BDE H(A0A8G<
-8D0A(B BBBK p. K^ F Ĝ F , F[ AID t
-DAA D 3 BBB D(A0D@0A(A BBB T | pFK { BIB E(A0A8G8
-8D0A(B BBBA L Ԝ hLH W BDB B(D0A8M
-8D0A(B BBBA 8. K^ PP- AG cA 4 `PO HF T PO HF t PO HF PO HF QO HF Ԟ PQO HF QO HF QO HF 4 QO HF T RO HF t @RO HF pRO HF RO HF ԟ RO HF SO HF 0SO HF 4 `SO HF T SO HF t SO HF SO HF TO HF Ԡ PTO HF TO HF TO HF 4 TO HF T UO HF t @UV HI UV HI UV HI ԡ VV HI @VV HI VV HI 4 VV HI T WV HI t @WV HI WV HI WV HI Ԣ XV HI @XV HI XV HI 4 XV HI T YV HI t @YV HI YV HI YV HI ԣ ZV HI @ZV HI ZV HI 4 ZV HI T [V HI t @[V HI [V HI L ܣ [T BBB B(A0A8JR
-8A0A(B BBBH 4 , _s BAI C
-ABA 4 d `s BAI C
-ABA 4 ``s BAI C
-ABA 4 Ԥ `s t BAI C
-ABA 4 `s P BAI C
-ABA 4 D 8as , BAI C
-ABA 4 | as BAI C
-ABA 4 as BAI C
-ABA 4 bs BAI C
-ABA 4 $ Xbs BAI C
-ABA 4 \ bs x BAI C
-ABA 4 bs T BAI C
-ABA 4 ̦ 0cs 0 BAI C
-ABA , xc AID
-AAA , 4 c AID
-AAA , d xd AID
-AAA , d AID
-AAA , ħ xe AID
-AAA , e v AID
-AAA , $ xf X AID
-AAA , T f : AID
-AAA , xg AID
-AAA , g AID
-AAA , xh AID
-AAA , h AID
-AAA , D xi AID
-AAA , t i AID
-AAA , xj h AID
-AAA , ԩ j J AID
-AAA , xk , AID
-AAA , 4 k AID
-AAA , d xl AID
-AAA , l AID
-AAA , Ī xm AID
-AAA , m AID
-AAA , $ xn x AID
-AAA , T n Z AID
-AAA , xo < AID
-AAA , o AID
-AAA L xpK BBB B(A0A8G@d
-8A0A(B BBBA d xq BEB B(A0D8DPi
+8A0A(B BBBB , p> J AID W
+DAA `. K^ h ` L $ hr J BBB B(E0A8J@
+8A0A(B BBBA T t I BEB E(A0A8G;
+8D0A(B BBBA T ̬ I BEB E(A0A8G;
+8D0A(B BBBA L $ h) kI BBB B(A0I8G
+8D0A(B BBBA L t H) UI BBB B(A0I8G
+8D0A(B BBBA T ĭ ( ?I BBE E(A0A8G
+8D0A(B BBBH D Į ` BBB D(A0D@
+0A(A BBBA L d H BIB A(D0D
+0D(A BBBA L H BIB A(D0D
+0D(A BBBA < xQ BEH H(A0n(A BBE T D <H BEB B(A0D8J
+8A0A(B BBBA T H BEB B(A0D8J
+8A0A(B BBBA T G BEB B(A0D8J
+8A0A(B BBBA T L @ G BEB B(A0D8J
+8A0A(B BBBA T x G BEB B(A0D8J
+8A0A(B BBBA T [G BEB B(A0D8J
+8A0A(B BBBA T T .G BEB B(A0D8J
+8A0A(B BBBA T G BEB B(A0D8J
+8A0A(B BBBA T X F BEB B(A0D8J
+8A0A(B BBBA T \ $ F BEB B(A0D8J
+8A0A(B BBBA T ( zF BEB B(A0D8J
+8A0A(B BBBA T -r MF BEB B(A0D8J
+8A0A(B BBBA T d (1r F BEB B(A0D8J
+8A0A(B BBBA T P5 E BEB B(A0D8J
+8A0A(B BBBA T 9 E BEB B(A0D8J
+8A0A(B BBBA T l = E BEB B(A0D8J
+8A0A(B BBBA T Ĵ 8B rE BEB B(A0D8J
+8A0A(B BBBA T F EE BEB B(A0D8J
+8A0A(B BBBA T t J E BEB B(A0D8J
+8A0A(B BBBA T ̵ N D BEB B(A0D8J
+8A0A(B BBBA T $ (Sr D BEB B(A0D8J
+8A0A(B BBBA T | PWr D BEB B(A0D8J
+8A0A(B BBBA T Զ x[ dD BEB B(A0D8J
+8A0A(B BBBA T , _ 7D BEB B(A0D8J
+8A0A(B BBBA T c
+D BEB B(A0D8J
+8A0A(B BBBA T ܷ h C BEB B(A0D8J
+8A0A(B BBBA T 4 Xl C BEB B(A0D8J
+8A0A(B BBBA T p C BEB B(A0D8J
+8A0A(B BBBA L t \C BBH B(D0F8G
+8D0A(B BBBD ܹ 8. K^ x , | [ C AID t
+DAA < T BBB A(A0(A BBB < #C BBB A(A0(A BBB 4 Ժ BBA A(G0(A ABB< d 8 B BBB D(A0n(A BBB $ L < AAN0fDA $ t < AAN0fDA D d4 BBB D(A0D@0A(A BBB T < pl A BIE B(A0A8DY
+8D0A(B BBBA T ( A BDH E(A0A8G
&
+8D0A(B BBBA < @p C BBD A(G
+(D ABBG Լ p. K^ X P , t X[ =C AID t
+DAA D L 4 BBB D(A0D@0A(A BBB T @l B BIE B(A0A8DY
+8D0A(B BBBA T D X B BDE H(A0A8G<
+8D0A(B BBBK D 0. K^ \ 輾 t ༾ , 輾[ B AID t
+DAA D 4 BBB D(A0D@0A(A BBB T \ мK B BIB E(A0A8G8
+8D0A(B BBBA L ¾H A BDB B(D0A8M
+8D0A(B BBBA . K^ Ŀ ƾ- AG cA ƾO HF ƾO HF $ ǾO HF D PǾO HF d ǾO HF ǾO HF ǾO HF ȾO HF @ȾO HF pȾO HF $ ȾO HF D ȾO HF d ɾO HF 0ɾO HF `ɾO HF ɾO HF ɾO HF ɾO HF $ ʾO HF D PʾO HF d ʾO HF ʾO HF ʾO HF ˾O HF @˾O HF p˾O HF $ ˾V HI D ˾V HI d ̾V HI `̾V HI ̾V HI ̾V HI ;V HI `;V HI $ ;V HI D ;V HI d ξV HI `ξV HI ξV HI ξV HI ϾV HI `ϾV HI $ ϾV HI D ϾV HI d оV HI `оV HI оV HI оV HI ѾV HI `ѾV HI $ ѾV HI D ѾV HI L ҾT c; BBB B(A0A8JR
+8A0A(B BBBH 4 0־s |; BAI C
+ABA 4 D x־s X; BAI C
+ABA 4 | ־s 4; BAI C
+ABA 4 s ; BAI C
+ABA 4 Ps : BAI C
+ABA 4 $ s : BAI C
+ABA 4 \ s : BAI C
+ABA 4 (ؾs : BAI C
+ABA 4 pؾs \: BAI C
+ABA 4 ؾs 8: BAI C
+ABA 4 < پs : BAI C
+ABA 4 t Hپs 9 BAI C
+ABA 4 پs 9 BAI C
+ABA , پ 9 AID
+AAA , Xھ 9 AID
+AAA , D ھ l9 AID
+AAA , t X۾ N9 AID
+AAA , ۾ 09 AID
+AAA , Xܾ 9 AID
+AAA , ܾ 8 AID
+AAA , 4 Xݾ 8 AID
+AAA , d ݾ 8 AID
+AAA , X 8 AID
+AAA , |8 AID
+AAA , X߾ ^8 AID
+AAA , $ ߾ @8 AID
+AAA , T X "8 AID
+AAA , 8 AID
+AAA , X 7 AID
+AAA , 7 AID
+AAA , X 7 AID
+AAA , D 7 AID
+AAA , t X n7 AID
+AAA , P7 AID
+AAA , X 27 AID
+AAA , 7 AID
+AAA , 4 X 6 AID
+AAA , d 6 AID
+AAA , X 6 AID
+AAA L l K BBB B(A0A8G@d
+8A0A(B BBBA d BEB B(A0D8DPi
8A0A(B BBBI
-8G0A(B BBBJ < t s BBD A(D@(A ABB 4 ܬ tB BHA G`
- DABA 4 uB BHA G`
- DABA 4 L vB BHA G`
- DABA 4 wB BHA G`
- DABA 4 yB BHA G`
- DABA 4 zB m BHA G`
- DABA 4 , 0{B N BHA G`
- DABA 4 d H|B / BHA G`
- DABA 4 `}B BHA G`
- DABA 4 Ԯ x~B BHA G`
- DABA 4 B BHA G`
- DABA 4 D B BHA G`
- DABA 4 | B BHA G`
- DABA < u BHA GZ
- DABA < x Z BHA GZ
- DABA < 4 ? BHA GZ
- DABA < t $ BHA GZ
- DABA < X BHA GZ
- DABA < BHA GZ
- DABA < 4 BHA GZ
- DABA < t 8 BHA GZ
- DABA < ؏ BHA GZ
- DABA < x BHA GZ
- DABA < 4 g BHA GZ
- DABA < t L BHA GZ
- DABA < X 1 BHA GZ
- DABA < BHA GS
- DABA < 4 BHA GS
- DABA < t 8 BHA GS
- DABA < BHA GS
- DABA < x BHA GS
- DABA < 4 BHA GS
- DABA < t t BHA GS
- DABA < X Y BHA GS
- DABA < > BHA GS
- DABA < 4 # BHA GS
- DABA < t 8 BHA GS
- DABA < ة BHA GS
- DABA < x BHA GS
- DABA $ V ADD JAAT \ P BEB M(A0C8G
-8A0A(B BBBF L BEB E(I0H8D@
-8D0A(B BBBA < ܷ BFA D0}
- FABE D FAB T D e { BHB B(A0A8G
-8A0A(B BBBI T m BEE E(A0A8GK
-8A0A(B BBBA T ; BBB B(D0A8J
-8A0A(B BBBE $ P* A[
-L A < l `G BBA D(G
-(A ABBG p* A[
-L A D ̸ W BBA A(D
-(A ABBK * A[
-L A D 4 W c BBA D(G
-(A ABBG D | W < BBA D(G
-(A ABBG D Ĺ g BBA D(G
-(A ABBH < W BBA D(G
-(A ABBG < L G BBA D(G
-(A ABBG d 0* A[
-L A D @B BBA D(G
-(A ABBE ̻ H* A[
-L A D Xg I BBA D(G
-(A ABBH 4 * A[
-L A < | W BBA D(G
-(A ABBG * A[
-L A < ܻ G BBA D(G
-(A ABBG * A[
-L A D < B BBA D(G
-(A ABBE \ * A[
-L A D = BBA A(Dq
-(A ABBA L J BEE H(D0D8GR
-8D0A(B BBBF T < + BGB H(A0A8G
-8D0A(B BBBH T " BBB G(G0A8G
-8D0A(B BBBH D ` BBA A(Dl
-(A ABBA L 4 J BEE H(D0D8GR
-8D0A(B BBBF T
- BGB H(A0A8G
-8D0A(B BBBH T ܾ @ BBB G(G0A8G
-8D0A(B BBBH D 4 BBA A(Dm
-(A ABBA L | PJ BEE H(D0D8GR
-8D0A(B BBBF T ̿ P BGB H(A0A8G
-8D0A(B BBBH T $ BBB G(G0A8G
-8D0A(B BBBH D | 0$ BBA A(Dq
-(A ABBA L %J BEE H(D0D8GR
-8D0A(B BBBF T + BGB H(A0A8G
-8D0A(B BBBH T l 1 BBB G(G0A8G
-8D0A(B BBBH D 4 BBA A(Dq
-(A ABBA L 6J BEE H(D0D8GR
-8D0A(B BBBF T \ < BGB H(A0A8G
-8D0A(B BBBH T xA BBB G(G0A8G
-8D0A(B BBBH D E BBA A(Dq
-(A ABBA L T FJ ` BEE H(D0D8GR
-8D0A(B BBBF T L w BGB H(A0A8G
-8D0A(B BBBH T Q n BBB G(G0A8G
-8D0A(B BBBH D T hU e BBA A(Dq
-(A ABBA L VJ < BEE H(D0D8GR
-8D0A(B BBBF T \ S BGB H(A0A8G
-8D0A(B BBBH T D Hb J BBB G(G0A8G
-8D0A(B BBBH D e A BBA A(Dm
-(A ABBA L XgJ BEE H(D0D8GR
-8D0A(B BBBF T 4 Xm / BGB H(A0A8G
-8D0A(B BBBH T r & BBB G(G0A8G
-8D0A(B BBBH D 8v BBA A(Dl
-(A ABBA L , wJ BEE H(D0D8GR
-8D0A(B BBBF T | } BGB H(A0A8G
-8D0A(B BBBH T BBB G(G0A8G
-8D0A(B BBBH D , BBA A(Dq
-(A ABBA L t (J BEE H(D0D8GR
-8D0A(B BBBF T ( BGB H(A0A8G
-8D0A(B BBBH T BBB G(G0A8G
-8D0A(B BBBH D t BBA A(Dq
-(A ABBA L J BEE H(D0D8GR
-8D0A(B BBBF T BGB H(A0A8G
-8D0A(B BBBH T d BBB G(G0A8G
-8D0A(B BBBH D p BBA A(Dm
-(A ABBA L J BEE H(D0D8GR
-8D0A(B BBBF T T BGB H(A0A8G
-8D0A(B BBBH T P BBB G(G0A8G
-8D0A(B BBBH D ط BBA A(Dl
-(A ABBA L L `J d BEE H(D0D8GR
-8D0A(B BBBF T ` { BGB H(A0A8G
-8D0A(B BBBH T r BBB G(G0A8G
-8D0A(B BBBH $ @|. K^ < ( T l L ( BBB B(A0I8G
-8D0A(B BBBF T D xg BBB B(I0A8Gc
-8D0A(B BBBG T g BBB B(I0A8Gc
-8D0A(B BBBG D , BBB D(A0D@0A(A BBB T < ` { BIB E(A0A8D6
-8D0A(B BBBA T I BIB E(A0A8D6
-8D0A(B BBBA T BIB E(A0A8DF
-8D0A(B BBBA L BBB B(A0A8K`
-8A0A(B BBBH L l BGB B(A0A8Gv
-8A0A(B BBBA L (] BBB B(A0A8G
-8A0A(B BBBA L 8m BMB E(A0A8G)
-8A0A(B BBBF d \ X- BBB B(A0A8G
+8G0A(B BBBJ < $ BBD A(D@(A ABB 4 B 5 BHA G`
+ DABA 4 B 5 BHA G`
+ DABA 4 , 0B f5 BHA G`
+ DABA 4 d HB G5 BHA G`
+ DABA 4 `B (5 BHA G`
+ DABA 4 xB 5 BHA G`
+ DABA 4 B 4 BHA G`
+ DABA 4 D B 4 BHA G`
+ DABA 4 | B 4 BHA G`
+ DABA 4 B 4 BHA G`
+ DABA 4 B n4 BHA G`
+ DABA 4 $ B O4 BHA G`
+ DABA 4 \ B 04 BHA G`
+ DABA < 8 4 BHA GZ
+ DABA < 3 BHA GZ
+ DABA < x 3 BHA GZ
+ DABA < T 3 BHA GZ
+ DABA < 3 BHA GZ
+ DABA < X 3 BHA GZ
+ DABA < o3 BHA GZ
+ DABA < T T3 BHA GZ
+ DABA < 8 93 BHA GZ
+ DABA < 3 BHA GZ
+ DABA < x 3 BHA GZ
+ DABA < T 2 BHA GZ
+ DABA < 2 BHA GZ
+ DABA < X 2 BHA GS
+ DABA < 2 BHA GS
+ DABA < T |2 BHA GS
+ DABA < 8 a2 BHA GS
+ DABA < F2 BHA GS
+ DABA < x +2 BHA GS
+ DABA < T 2 BHA GS
+ DABA < 1 BHA GS
+ DABA < X 1 BHA GS
+ DABA < 1 BHA GS
+ DABA < T 1 BHA GS
+ DABA < 8 1 BHA GS
+ DABA < ! n1 BHA GS
+ DABA $ x#V ADD JAAT < # +1 BEB M(A0C8G
+8A0A(B BBBF L h/ 1 BEB E(I0H8D@
+8D0A(B BBBA < 0 BFA D0}
+ FABE D FAB T $ X1e 1 BHB B(A0A8G
+8A0A(B BBBI T | p9 1 BEE E(A0A8GK
+8A0A(B BBBA T 8@ 0 BBB B(D0A8J
+8A0A(B BBBE C* A[
+L A < L CG 0 BBA D(G
+(A ABBG 4 F* A[
+L A D FW F0 BBA A(D
+(A ABBK I* A[
+L A D JW / BBA D(G
+(A ABBG D \ MW / BBA D(G
+(A ABBG D 8Pg / BBA D(G
+(A ABBH < `SW / BBA D(G
+(A ABBG < , VG k/ BBA D(G
+(A ABBG Y* A[
+L A D YB ,/ BBA D(G
+(A ABBE | \* A[
+L A D \g . BBA D(G
+(A ABBH _* A[
+L A < \ _W . BBA D(G
+(A ABBG D c* A[
+L A < cG _. BBA D(G
+(A ABBG 0f* A[
+L A D @fB . BBA D(G
+(A ABBE Hi* A[
+L A D Xi - BBA A(Dq
+(A ABBA L jJ - BEE H(D0D8GR
+8D0A(B BBBF T p - BGB H(A0A8G
+8D0A(B BBBH T t 8v - BBB G(G0A8G
+8D0A(B BBBH D y - BBA A(Dl
+(A ABBA L H{J - BEE H(D0D8GR
+8D0A(B BBBF T d H - BGB H(A0A8G
+8D0A(B BBBH T - BBB G(G0A8G
+8D0A(B BBBH D ( - BBA A(Dm
+(A ABBA L \ J h- BEE H(D0D8GR
+8D0A(B BBBF T - BGB H(A0A8G
+8D0A(B BBBH T v- BBB G(G0A8G
+8D0A(B BBBH D \ m- BBA A(Dq
+(A ABBA L J D- BEE H(D0D8GR
+8D0A(B BBBF T [- BGB H(A0A8G
+8D0A(B BBBH T L p R- BBB G(G0A8G
+8D0A(B BBBH D I- BBA A(Dq
+(A ABBA L J - BEE H(D0D8GR
+8D0A(B BBBF T < 7- BGB H(A0A8G
+8D0A(B BBBH T ط .- BBB G(G0A8G
+8D0A(B BBBH D ` %- BBA A(Dq
+(A ABBA L 4 輿J , BEE H(D0D8GR
+8D0A(B BBBF T ¿ - BGB H(A0A8G
+8D0A(B BBBH T @ȿ
+- BBB G(G0A8G
+8D0A(B BBBH D 4 ˿ - BBA A(Dq
+(A ABBA L | PͿJ , BEE H(D0D8GR
+8D0A(B BBBF T Pӿ , BGB H(A0A8G
+8D0A(B BBBH T $ ؿ , BBB G(G0A8G
+8D0A(B BBBH D | 0ܿ , BBA A(Dm
+(A ABBA L ݿJ , BEE H(D0D8GR
+8D0A(B BBBF T , BGB H(A0A8G
+8D0A(B BBBH T l , BBB G(G0A8G
+8D0A(B BBBH D , BBA A(Dl
+(A ABBA L J , BEE H(D0D8GR
+8D0A(B BBBF T \ , BGB H(A0A8G
+8D0A(B BBBH T x , BBB G(G0A8G
+8D0A(B BBBH D , BBA A(Dq
+(A ABBA L T J l, BEE H(D0D8GR
+8D0A(B BBBF T , BGB H(A0A8G
+8D0A(B BBBH T z, BBB G(G0A8G
+8D0A(B BBBH D T h
q, BBA A(Dq
+(A ABBA L J H, BEE H(D0D8GR
+8D0A(B BBBF T _, BGB H(A0A8G
+8D0A(B BBBH T D H V, BBB G(G0A8G
+8D0A(B BBBH D M, BBA A(Dm
+(A ABBA L X J $, BEE H(D0D8GR
+8D0A(B BBBF T 4 X% ;, BGB H(A0A8G
+8D0A(B BBBH T * 2, BBB G(G0A8G
+8D0A(B BBBH D 8. ), BBA A(Dl
+(A ABBA L , /J , BEE H(D0D8GR
+8D0A(B BBBF T | 5 , BGB H(A0A8G
+8D0A(B BBBH T ; , BBB G(G0A8G
+8D0A(B BBBH |. K^ > > x> 4 p> L x> d > L > ]+ BBB B(A0I8G
+8D0A(B BBBF T $ @g G+ BBB B(I0A8Gc
+8D0A(B BBBG T | Eg S+ BBB B(I0A8Gc
+8D0A(B BBBG D | BBB D(A0D@0A(A BBB T J + BIB E(A0A8D6
+8D0A(B BBBA T t N * BIB E(A0A8D6
+8D0A(B BBBA T 0S * BIB E(A0A8DF
+8D0A(B BBBA L xW BBB B(A0A8K`
+8A0A(B BBBH L (_ BGB B(A0A8Gv
+8A0A(B BBBA L l r] BBB B(A0A8G
+8A0A(B BBBA L {m BMB E(A0A8G)
+8A0A(B BBBF d - BBB B(A0A8G
8A0A(B BBBA =
-8A0A(B BBBE T % = BEB H(D0A8G
-8A0A(B BBBA T D xA BEB B(F0A8G
-8A0A(B BBBE T I% s BBG B(A0G8G
-8D0A(B BBBH v. K^ R0 Hg R7 Hj $ R2 I $ < R^ AG b
-AA T S< k BBB B(I0A8GD
-8D0A(B BBBF T Y< Z BBB B(I0A8GD
-8D0A(B BBBF T < ^
I BBB B(D0D8G
-8D0A(B BBBF T 8l| z BBE E(D0A8G
-8D0A(B BBBI D BBB D(A0D@0A(A BBB T 4 q
BIB E(A0A8G
-8D0A(B BBBA T u BIB E(A0A8G
-8D0A(B BBBA T z BEB B(D0D8G
-8A0A(B BBBA T < BEB B(D0D8G
-8A0A(B BBBA T 7 BEB B(D0D8G.
-8A0A(B BBBA L p" BBB G(D0A8M
-8D0A(B BBBH r. K^ , 80 Hg L H7 Hj l (\ AG RAT H BEE E(D0A8G'
-8D0A(B BBBG D BBB D(A0D@0A(A BBB T T i BIB E(A0A8DF
-8D0A(B BBBA T 7 BIB E(A0A8DN
-8D0A(B BBBA L ( BBB B(I0A8D
-8D0A(B BBBD T T E BBB B(A0F8G
-8A0A(B BBBG L k BBB B(I0A8D
-8D0A(B BBBD p. K^ , D [ AID t
-DAA L L BEB E(A0A8G>
-8A0A(B BBBA T X BBE B(D0A8G
-8D0A(B BBBG D BBB D(A0D@0A(A BBB T d ػC * BIB E(A0A8G0
-8D0A(B BBBA L
BDB B(A0D8M
-8D0A(B BBBA @n. K^ 0 Hg 7 Hj , < ( A|
+8A0A(B BBBE T % ( BEB H(D0A8G
+8A0A(B BBBA T $ ط ( BEB B(F0A8G
+8A0A(B BBBE T | 0% ) BBG B(A0G8G
+8D0A(B BBBE | v. K^ 0 Hg 7 Hj 2 I $ H^ AG b
+AA T l < ) BBB B(I0A8GD
+8D0A(B BBBF T h< ( BBB B(I0A8GD
+8D0A(B BBBF T P ( BBB B(D0D8Gt
+8D0A(B BBBH T t | ) BBE E(D0A8G
+8D0A(B BBBI D t BBB D(A0D@0A(A BBB T ( BIB E(A0A8G
+8D0A(B BBBA T l p ( BIB E(A0A8G
+8D0A(B BBBA T 8 `( BEB B(D0D8G
+8A0A(B BBBA T }( BEB B(D0D8G
+8A0A(B BBBA T t ( ( BEB B(D0D8G
+8A0A(B BBBA T ( BBE E(E0D8G
+8A0A(B BBBA L $ h ( BBB G(D0A8M
+8D0A(B BBBG r. K^ 4 0 Hg T 7 Hj t \ AG RAT ( BEE E(D0A8G'
+8D0A(B BBBG D v BBB D(A0D@0A(A BBB T P %( BIB E(A0A8DF
+8D0A(B BBBA T ' BIB E(A0A8DN
+8D0A(B BBBA L < ' BBB B(I0A8D
+8D0A(B BBBD T !E ' BBB B(A0F8G
+8A0A(B BBBG L - '( BBB B(I0A8D
+8D0A(B BBBD o. K^ @1 81 , | @1[ ' AID t
+DAA L T p1 BEB E(A0A8G>
+8A0A(B BBBA T 3 H' BBE B(D0A8G
+8D0A(B BBBG D 4 BBB D(A0D@0A(A BBB T 7C & BIB E(A0A8G0
+8D0A(B BBBA L =
& BDB B(A0D8M
+8D0A(B BBBA m. K^ J0 Hg $ J7 Hj , D J A|
C K
E R
-A $ l ^ AG b
-AA T 0| BBE E(D0A8G
-8D0A(B BBBI T X| BBE E(D0A8G
-8D0A(B BBBI D D H BBB D(A0D@0A(A BBB T 8 BIB E(A0A8D6
-8D0A(B BBBA d pB BBB B(A0A8G@x
+A $ t K^ AG b
+AA T K| & BBE E(D0A8G
+8D0A(B BBBI T L Q| & BBE E(D0A8G
+8D0A(B BBBI D L BBB D(A0D@0A(A BBB T U S& BIB E(A0A8D6
+8D0A(B BBBA d (ZB BBB B(A0A8G@x
8A0A(B BBBE
-8A0A(B BBBF L L X BEB A(D0l
+8A0A(B BBBF L T [ BEB A(D0l
(A BBBG
-(A BBBA L ( BEB A(D0l
+(A BBBA L [ BEB A(D0l
(A BBBG
-(A BBBA L BEB A(D0l
+(A BBBA L \ BEB A(D0l
(A BBBG
-(A BBBA T d
BBE E(E0A8G
-8A0A(B BBBA L BEB A(D0l
+(A BBBA T ] $ BBE E(E0A8G
+8A0A(B BBBA L 8c BEB A(D0l
(A BBBG
-(A BBBA L P BEB A(D0l
+(A BBBA L d BEB A(D0l
(A BBBG
-(A BBBA L 4 BEB A(D0l
+(A BBBA L < d BEB A(D0l
(A BBBG
-(A BBBA T BBE B(E0D8G
-8A0A(B BBBA T BFE E(A0A8G
-8A0A(B BBBA T \ x BBB B(D0D8J}
-8D0A(B BBBD L
BBB B(K0A8J
-8A0A(B BBBF 4 q BAI A
-ABA T < BBB B(A0A8L
+(A BBBA T e # BBE B(E0D8G
+8A0A(B BBBA T < `k q# BFE E(A0A8G
+8A0A(B BBBA T x # BBB B(D0D8J}
+8D0A(B BBBD L Ј # BBB B(K0A8J
+8A0A(B BBBF 4 < @q # BAI A
+ABA T t Z # BBB B(A0A8L'
-8A0A(B BBBC l g. K^ @& 8& 4 @&g BAD D
-ABA T x& BBB B(A0I8D
-8D0A(B BBBE D D @( BBB D(A0D@
-0A(A BBBA T ) X BIB E(A0A8D6
-8D0A(B BBBA 4 -J BDA q
+8A0A(B BBBI t Pg. K^ x p 4 xg # BAD D
+ABA T L # BBB B(A0I8D
+8D0A(B BBBE D L x BBB D(A0D@
+0A(A BBBA T Х # BIB E(A0A8D6
+8D0A(B BBBA 4 J BDA q
ABE A
-ABA d -g BLB B(A0A8D@q
+ABA d $ g BLB B(A0A8D@q
8A0A(B BBBE
-8A0A(B BBBF < .F BBA A(Gq
-(A ABBA L 0 BBB B(A0A8Gp8A0A(B BBB 1% L , 1M BBB B(A0D8Dp18A0A(B BBB L | 3 BBB B(A0A8Gp8A0A(B BBB L 5M BBB B(A0D8Dp18A0A(B BBB L 7 BBB B(A0A8Gp8A0A(B BBB < 8F BBA A(Gq
-(A ABBA L 9M BBB B(A0D8Dp18A0A(B BBB L ; BBB B(A0A8D
-8A0A(B BBBA L L H@ BBB E(D0A8D`
-8A0A(B BBBA L @ BLB H(A0D8DPV
-8A0A(B BBBG hBm , B ADD X
-AAA d 4 0C BHD G0W
+8A0A(B BBBF < (F >" BBA A(Gq
+(A ABBA L 8 BBB B(A0A8Gp8A0A(B BBB L M BBB B(A0D8Dp18A0A(B BBB L l BBB B(A0A8Gp8A0A(B BBB L 8M BBB B(A0D8Dp18A0A(B BBB L 8 BBB B(A0A8Gp8A0A(B BBB < F BBA A(Gq
+(A ABBA L ȵM BBB B(A0D8Dp18A0A(B BBB L ȷ BBB B(A0A8D
+8A0A(B BBBA L <
+ h BBB E(D0A8D`
+8A0A(B BBBA L
+ BLB H(A0D8DPV
+8A0A(B BBBG
+ m ,
+ ADD X
+AAA d $ P BHD G0W
AABH F
AABF g
AABE D
- FABK , C ADD ]
-AAA d XD BBB E(I0D8DP
+ FABK , ADD ]
+AAA d x BBB E(I0D8DP
8D0A(B BBBC |
-8D0A(B BBBA , 4 E ADD [
-AAA t d F BIA D(G0C
+8D0A(B BBBA , $ ADD [
+AAA t T BIA D(G0C
(A ABBH o
(A ABBC g
(A ABBC D
-(F ABBA d xFE BBB B(A0A8DP
+(F ABBA d E BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l l `G BBB E(I0D8D
+8F0A(B BBBA l BBB E(I0D8D
8A0A(B BBBG
-8D0A(B BBBE Ju L xJ BEB B(A0A8DP
-8A0A(B BBBH l D K' W BBB E(D0I8D`M
+8D0A(B BBBE
0u L
BEB B(A0A8DP
+8A0A(B BBBH l d
(' ' BBB E(D0I8D`M
8D0A(B BBBJ
-8D0A(B BBBB T MU BBB B(A0A8J
-8A0A(B BBBA d^ L e BEB B(A0A8DP
-8A0A(B BBBH l t e' BBB E(D0I8D`M
+8D0A(B BBBB T
U BBB B(A0A8J
+8A0A(B BBBA ^ L 8 BEB B(A0A8DP
+8A0A(B BBBH l ' BBB E(D0I8D`M
8D0A(B BBBJ
-8D0A(B BBBB T hh
BBB B(A0A8JX
-8A0A(B BBBA V L , h BEB B(A0A8DP
-8A0A(B BBBH l BBB E(D0I8D`:
+8D0A(B BBBB T
BBB B(A0A8JX
+8A0A(B BBBA @V L BEB B(A0A8DP
+8A0A(B BBBH l BBB E(D0I8D`:
8D0A(B BBBE
-8D0A(B BBBJ T xZ BBB B(A0A8J
-8A0A(B BBBA d D ! BHF K(H0A8D`}
-8A0A(B BBBA d8C0A(B BBB L H BEB B(A0A8DP
-8A0A(B BBBH | f BIB B(A0D8GPz
+8D0A(B BBBJ T 4 Z g BBB B(A0A8J
+8A0A(B BBBA d 4 ! BHF K(H0A8D`}
+8A0A(B BBBA d8C0A(B BBB L h BEB B(A0A8DP
+8A0A(B BBBH | f BIB B(A0D8GPz
8A0A(B BBBA v
8A0A(B BBBH D
-8F0A(B BBBE d ȝ BBJ B(A0A8G`
+8F0A(B BBBE d BBJ B(A0A8G`
8D0A(B BBBE N
-8A0A(B BBBH T 4 BBB B(A0A8J]
-8A0A(B BBBA D d h ; BGA D(M
-(D ABBF Z. K^ 0 Hg 7 Hj L ( BBB B(A0I8G
-8D0A(B BBBD L T BBB B(A0I8G
-8D0A(B BBBD T BEG E(A0G8G-
-8D0A(B BBBD \ BFE A(A0D`W
+8A0A(B BBBH T , 4 BBB B(A0A8J]
+8A0A(B BBBA D 3 BGA D(M
+(D ABBF t Y. K^ 80 Hg (87 Hj L $ H8 BBB B(A0I8G
+8D0A(B BBBD L t : { BBB B(A0I8G
+8D0A(B BBBD T = g BEG E(A0G8G-
+8D0A(B BBBD \ 0D BFE A(A0D`W
0D(A BBBK ^
-0D(A BBBG D 4 & BBB D(A0D@0A(A BBB T b V BIE B(A0A8DQ
-8D0A(B BBBA T \ $ BIE B(A0A8DI
-8D0A(B BBBA , W. K^ D $ \ ^ AG b
-AA 0$ < D BBD A(D@0(A ABB T B BIB E(A0A8DF
-8D0A(B BBBA T \ P BIB E(A0A8DF
-8D0A(B BBBA T BIB E(A0A8DF
-8D0A(B BBBA L BBB B(A0A8G
-8A0A(B BBBA < \ @ BEA A(Gc
-(D ABBA < BEA A(Gc
-(D ABBA < BEA A(Gc
-(D ABBA , `Y y ADG i
-AAA , L Y U ADG i
-AAA T N l R < BBG A(G@
-(A ABBD p% T BHB B(A0A8GI
-8A0A(B BBBA T \ m BHB B(A0A8J
-8A0A(B BBBA T X 1 BHB B(A0A8GU
-8A0A(B BBBA $ V ADD JAA4 ( BEA A(D0(A ABBd l 1 BHB B(D0A8Dpv
+0D(A BBBG D $ BBB D(A0D@0A(A BBB T Eb & BIE B(A0A8DQ
+8D0A(B BBBA T 0J\ BIE B(A0A8DI
+8D0A(B BBBA W. K^ 4 O $ L O^ AG b
+AA t PO$ < D BBD A(D@0(A ABB T $ (P BIB E(A0A8DF
+8D0A(B BBBA T | pT BIB E(A0A8DF
+8D0A(B BBBA T X BIB E(A0A8DF
+8D0A(B BBBA L , ]6 | BBB B(A0A8Gb
+8A0A(B BBBC < | _ n BEA A(Gc
+(D ABBA < P` D BEA A(Gc
+(D ABBA < ` BEA A(Gc
+(D ABBA , < aY ADG i
+AAA , l @aY ADG i
+AAA D pa N \ xa R < t a BBG A(G@
+(A ABBD b% T $ 8b BBB B(A0A8G
+8A0A(B BBBA T | kZ
+ BBB B(A0A8Js
+8A0A(B BBBA $ | uV ADD JAA4 u BEA A(D0(A ABBd 4 xv1 l BHB B(D0A8Dpv
8C0A(B BBBG {
-8D0A(B BBBA T 3 BBB E(I0A8K}
-8D0A(B BBBF L , BEB B(D0A8Gpu
-8A0A(B BBBA L | BEB B(D0A8Gpu
-8A0A(B BBBA T `
- BBB E(I0A8K}
-8D0A(B BBBF L $ * W BEB B(D0A8Gp
-8A0A(B BBBA $ L V ADD JAA, t JP
+8D0A(B BBBA T Pw BBB B(A0A8M
+8A0A(B BBBA T BBB E(I0A8K}
+8D0A(B BBBF L L Ђ BEB B(D0A8Gpu
+8A0A(B BBBA L p BEB B(D0A8Gpu
+8A0A(B BBBA T p + BBB E(I0A8K}
+8D0A(B BBBF L D * BEB B(D0A8Gp
+8A0A(B BBBA $ < ؉V ADD JAA, d JP
I ^
B j
F k
-A , JP
+A , JP
I ^
B j
F k
-A L BBB B(A0A8G}
-8A0A(B BBBH < L ) BLD D(G0
-(A ABBE D d * BHI H(D0D@
-0A(A BBBA T H+X BBB B(A0A8GC
-8A0A(B BBBA T , P;
- BGE E(G0D8G<
-8D0A(B BBBB T E
BBB H(D0A8L"
-8D0A(B BBBB , 0S JP
+A L 0 W BBB B(A0A8G}
+8A0A(B BBBH < l BLD D(G0
+(A ABBE D T BHI H(D0D@
+0A(A BBBA T xX c BBB B(A0A8GC
+8A0A(B BBBA T L
+ F BGE E(G0D8G<
+8D0A(B BBBB T
BBB H(D0A8L"
+8D0A(B BBBB , ` JP
I ^
B j
F k
-A , U JP
+A , JP
I ^
B j
F k
-A L < PX _ BBB B(A0A8G~
-8A0A(B BBBG T n
- BGE E(G0D8G<
-8D0A(B BBBB T Hy
BBB H(D0A8L"
-8D0A(B BBBB , І JP
+A L \ BBB B(A0A8G~
+8A0A(B BBBG T
+ u BGE E(G0D8G<
+8D0A(B BBBB T x
BBB H(D0A8L"
+8D0A(B BBBB , ! JP
I ^
B j
F k
-A , D ` JP
+A , 4! JP
I ^
B j
F k
-A L BBB B(A0A8G~
-8A0A(B BBBG T
- BGE E(G0D8G<
-8D0A(B BBBB T D ج
A BBB H(D0A8L"
-8D0A(B BBBB t pK. K^ H @ 8 0 ( ( 4 0 L 8 d @ , H[ AID t
-DAA , x[ AID t
-DAA , [ AID t
-DAA , 4 غ[ AID t
-DAA , d [ AID t
-DAA l ܕ- AG cA " DN
-N A < ( BBD A(D@(A ABB 4 k BHA GP
- DABA 4 L @k BHA GP
- DABA 4 xk BHA GP
- DABA 4 k BHA GP
- DABA 4 k BHA GP
- DABA D , S ` BEB A(A0Gh
-0A(A BBBA D t 8S B BEB A(A0Gh
-0A(A BBBA T Pl
- $ BBL B(A0A8JL
-8A0A(B BBBD T hW BDB B(A0A8J
-8A0A(B BBBJ T l p H BEB B(A0G8J
-8A0A(B BBBD T X 8 BDB B(A0A8JV
-8A0A(B BBBJ T 7 BEB I(A0D8G
-8A0A(B BBBA T t '
- BDB B(A0A8J
-8A0A(B BBBA T BEB B(A0G8JM
-8A0A(B BBBD T $ X-
- BDB B(A0A8J
-8A0A(B BBBD T | 0X + BEB B(A0G8J
-8A0A(B BBBD T 8 BDB B(A0A8J
-8A0A(B BBBK D. K^ ~֕\ AG RAT d 3 ; BBB I(A0A8G
-8A0A(B BBBA T 7 BBB I(A0A8GL
-8A0A(B BBBA XC. K^ T , : BBB E(A0A8G
-8D0A(B BBBA \ C. K^ t > = = = = =
- =
- = 4
- = L
- = d
- = |
- =
- =
- =
- =
- =
- = = $ = < = , |
- >[ x AID t
-DAA ,
- 0>[ X AID t
-DAA ,
- `>[ 8 AID t
-DAA , >[ AID t
-DAA D < > BBB A(I0Dp
-0D(A BBBK D @ BBB A(I0Dp
-0D(A BBBK D PA BBB A(I0Dp
-0D(A BBBK T B t BBB B(I0A8D$
-8D0A(B BBBI T l E D BBB B(I0A8D$
-8D0A(B BBBI T hG BBB B(I0A8D$
-8D0A(B BBBI D
IJ BBB D(A0D@
-0A(A BBBA D d
K BIB A(D0D`7
-0D(A BBBA T
N z BIB E(A0A8DF
-8D0A(B BBBA T R H BIB E(A0A8DF
-8D0A(B BBBA T \ 0W BIB E(A0A8DF
-8D0A(B BBBA T x[ BIB E(A0A8DF
-8D0A(B BBBA D _ BIB A(D0D`7
-0D(A BBBA D T b BIB A(D0D`7
-0D(A BBBA D Pe n BIB A(D0D`7
-0D(A BBBA D h L BIB A(D0D`7
-0D(A BBBA T , j * BIB E(A0A8DF
-8D0A(B BBBA L \ (o BBB B(A0F8Gp
-8A0A(B BBBG L q BGE G(A0DG
-0D(A BBBD T $ u BDB B(G0D8GE
-8D0A(B BBBA T | y BDB B(G0D8GE
-8D0A(B BBBA D (~ BGE G(A0D
-0D(A BBBA L d BBH G(D0A8G
-8D0A(B BBBA L l BDB D(G0D`
-0D(A BBBF L p BEG A(A0J]
-0D(A BBBF L Њr BGH A(D0G
-0D(A BBBA D \ BDG D(D
-(D ABBG | (8. K^ L AAG ]
+A L 6 BBB B(A0A8G~
+8A0A(B BBBG T !
+ BGE E(G0D8G<
+8D0A(B BBBB T d! (
BBB H(D0A8L"
+8D0A(B BBBB d" J. K^ |" x5 " p5 " h5 " `5 " X5 " P5 # X5 $# `5 <# h5 T# p5 , " x5[ AID t
+DAA , " 5[ AID t
+DAA , $# 5[ AID t
+DAA , T# 6[ c AID t
+DAA , # 86[ C AID t
+DAA \$ ݒ- AG cA |$ H6" DN
+N A < $ X6 BBD A(D@(A ABB 4 4$ 87k BHA GP
+ DABA 4 l$ p8k BHA GP
+ DABA 4 $ 9k e BHA GP
+ DABA 4 $ :k F BHA GP
+ DABA 4 % <k ' BHA GP
+ DABA D L% P=S BEB A(A0Gh
+0A(A BBBA D % h>S BEB A(A0Gh
+0A(A BBBA T % ?l
+ BBL B(A0A8JL
+8A0A(B BBBD T 4& IW BDB B(A0A8J
+8A0A(B BBBJ T & YX BEB B(A0G8J
+8A0A(B BBBD T & a BDB B(A0A8J
+8A0A(B BBBK T <' `u 8 BEB B(A0G8JM
+8A0A(B BBBD T ' y-
+ " BDB B(A0A8J
+8A0A(B BBBD T ' 7 [ BEB I(A0D8G
+8A0A(B BBBA T D( '
+ i BDB B(A0A8J
+8A0A(B BBBA T ( BEB B(A0G8J
+8A0A(B BBBD T ( X BDB B(A0A8JV
+8A0A(B BBBJ ) C. K^ * ؒ\ AG RAT ) BBB I(A0A8G
+8A0A(B BBBA T ) @ BBB I(A0A8GL
+8A0A(B BBBA * B. K^ * + $+ <+ ص T+ е l+ ȵ + + + + + + , ,, D, \, ȵ t, е , ص , , , ,, [ w AID t
+DAA , \, [ W AID t
+DAA , , P[ 7 AID t
+DAA , , [ AID t
+DAA D , BBB A(I0Dp
+0D(A BBBK D 4- BBB A(I0Dp
+0D(A BBBK D |- @ BBB A(I0Dp
+0D(A BBBK T - s BBB B(I0A8D$
+8D0A(B BBBI T . C BBB B(I0A8D$
+8D0A(B BBBI T t. X BBB B(I0A8D$
+8D0A(B BBBI D t/ J BBB D(A0D@
+0A(A BBBA T / BIB E(A0A8DF
+8D0A(B BBBA T l/ i BIB E(A0A8DF
+8D0A(B BBBA T / X 7 BIB E(A0A8DF
+8D0A(B BBBA D 0 BIB A(D0D`7
+0D(A BBBA T d0 h BIB E(A0A8DF
+8D0A(B BBBA D 0 BIB A(D0D`7
+0D(A BBBA T 1 x BIB E(A0A8DF
+8D0A(B BBBA T \1 ] BIB E(A0A8DF
+8D0A(B BBBA D 1 + BIB A(D0D`7
+0D(A BBBA T 1 BIB E(A0A8DF
+8D0A(B BBBA L 2 BBB B(A0F8Gp
+8A0A(B BBBG L 2 BGE G(A0DG
+0D(A BBBD T 2 { BDB B(G0D8GE
+8D0A(B BBBA T L3 BDB B(G0D8GE
+8D0A(B BBBA D 3 BGE G(A0D
+0D(A BBBA L 3 d BBH G(D0A8G
+8D0A(B BBBA L <4 BDB D(G0D`
+0D(A BBBF L 4 ` BEG A(A0J]
+0D(A BBBF L 4 r BGH A(D0G
+0D(A BBBA T ,5 BDB E(A0G8D
+8D0A(B BBBC ,6 7. K^ L D6 p
AAG ]
CAF k
ACA R
-CAJ JCA $ В^ AG b
-AA D BBE A(A0D@n
-0A(A BBBA D T BBB D(A0D@
-0A(A BBBA L h BBB B(D0A8DP9
-8A0A(B BBBA 8 AL 8, d $ P BBB B(A0A8D`
+CAJ JCA $ 6
^ AG b
+AA D 6
BBE A(A0D@n
+0A(A BBBA D 7 BBB D(A0D@
+0A(A BBBA L7 , L d7 BBB B(D0A8DP
+8A0A(B BBBA 7 AL d 7 BBB B(A0A8D`
8A0A(B BBBE d
-8A0A(B BBBA T ț BBE B(D0A8G
-8D0A(B BBBC L `i s BBE E(D0A8Dp2
-8D0A(B BBBJ D 4 BBB D(A0D@
-0A(A BBBA T إ BIB E(A0A8DV
-8D0A(B BBBA L 0$ BIB B(A0D8DpO
-8D0A(B BBBA T L BBB B(D0A8J
-8A0A(B BBBA , Hq z BDA TAT L BOB B(A0A8Dp
-8A0A(B BBBJ < ( BBG A(G@
-(A ABBG T d BEB B(A0A8JB
-8A0A(B BBBE L BOB B(A0A8Dp
-8A0A(B BBBJ T $ BBB B(A0A8J
-8A0A(B BBBH D | & BDA D@
+8A0A(B BBBA T 7 ( ]
BBE B(D0A8G
+8D0A(B BBBC L 7 i C
BBE E(D0A8Dp2
+8D0A(B BBBJ D 8 BBB D(A0D@
+0A(A BBBA T 8 8 BIB E(A0A8DV
+8D0A(B BBBA L 8 $$ BIB B(A0D8DpO
+8D0A(B BBBA T ,9 p' c BBB B(D0A8J
+8A0A(B BBBA , 9 ,q J BDA TAT L \: , BOB B(A0A8Dp
+8A0A(B BBBJ < : 1 BBG A(G@
+(A ABBG T D: H2 BEB B(A0A8J
+8A0A(B BBBE D; : L \; : BOB B(A0A8Dp
+8A0A(B BBBJ T ; X? e BBB B(A0A8J
+8A0A(B BBBH D \; F& Q BDA D@
DABE L
- DABA L W BEB E(I0H8D@
-8D0A(B BBBA T / BBB B(A0D8G\
-8A0A(B BBBA T l 3 BBB B(A0D8G
-8A0A(B BBBI 81T KHT BBB E(A0A8NH
-8A0A(B BBBC , 4 x> AID W
-DAA , d > AID W
-DAA l AU F $ ^ AG b
-AA D BBB D(A0D@
-0A(A BBBA D < Z BBE A(A0G@
-0A(A BBBC d BJE B(A0A8D@Y
-8C0A(B BBBB i8A0A(B BBB 4 BDA D0
- AABE L $ } BBB B(A0D8DP
-8A0A(B BBBA L AL L BEE B(A0D8G
-8A0A(B BBBA d 8" BBB B(A0A8DpO
+ DABA L ; G ' BEB E(I0H8D@
+8D0A(B BBBA T ; HI
+ BBB B(A0D8G\
+8A0A(B BBBA T L< O BBB B(A0D8G
+8A0A(B BBBI L= 0T KHT < ^ BBB E(A0A8NH
+8A0A(B BBBC , = 8c> AID W
+DAA , D= Hc> m AID W
+DAA > Xc AU <> XcF $ T> c^ AG b
+AA D |> c BBB D(A0D@
+0A(A BBBA D > @eZ
+ BBE A(A0G@
+0A(A BBBC d d> Xf
+ BJE B(A0A8D@Y
+8C0A(B BBBB i8A0A(B BBB 4 t? f BDA D0
+ AABE L ? Hh} BBB B(A0D8DP
+8A0A(B BBBA ? xi AL L t? xi BEE B(A0D8G
+8A0A(B BBBA d l@ j" BBB B(A0A8DpO
8A0A(B BBBI j
-8A0A(B BBBA d $ r BBB B(A0A8Dp
+8A0A(B BBBA d @ lr BBB B(A0A8Dp
8A0A(B BBBI j
-8A0A(B BBBA D BBB D(A0D@
-0A(A BBBA T pB e BIB B(A0D8Dg
-8D0A(B BBBA T T hI 3 BIB B(A0D8Dn
-8D0A(B BBBA T ` BBB B(A0A8J
-8A0A(B BBBA D 8 BFB A(D0J
-0A(A BBBB < L BEA A(G@}
-(D ABBJ d d! i BBB B(A0A8Dm
+8A0A(B BBBA D <A n BBB D(A0D@
+0A(A BBBA T @ 0pB 5 BIB B(A0D8Dg
+8D0A(B BBBA T 4A (sI BIB B(A0D8Dn
+8D0A(B BBBA T A v BBB B(A0A8J
+8A0A(B BBBA D A }8 BFB A(D0J
+0A(A BBBB < ,B BEA A(G@}
+(D ABBJ d C pi BBB B(A0A8Dm
8A0A(B BBBA
-8A0A(B BBBG L
-
- BEB B(A0A8J
-8A0A(B BBBD L " 8 BEE B(A0A8D`
-8A0A(B BBBB , l" BDA n
-ABH " 5 T ! 3 BBB B(A0A8J
-8A0A(B BBBG # H$ T L" @$ q BBE E(D0A8D
-8D0A(B BBBE T " &U U BBB B(A0A8J
-8A0A(B BBBA T " 4 BBB B(K0D8G
-8A0A(B BBBH ,$ )T KHT l# 0: BBE B(A0A8N
-8A0A(B BBBA , # => ; AID W
-DAA , # => AID W
-DAA $ = AU % =F 4% = AU T% =F l% (> A[ $ $ (> BFA D@ , % >b D\
+8A0A(B BBBG L B x
+ W BEB B(A0A8J
+8A0A(B BBBD L C BEE B(A0A8D`
+8A0A(B BBBB , D H BDA n
+ABH LD 5 T C Ж BBB B(A0A8J
+8A0A(B BBBG D H T ,D @ A BBE E(D0A8D
+8D0A(B BBBE T D U % BBB B(A0A8J
+8A0A(B BBBA T D BBB B(K0D8G
+8A0A(B BBBH E (T KHT LE 0 BBE B(A0A8NE
+8A0A(B BBBA , E ظ> AID W
+DAA , E > } AID W
+DAA F AU F F F 0 AU G 0F G h A[ $ F h BFA D@ , dG йb D\
H P
H Q
-G I , % >b D\
+G I , G b D\
H P
H Q
-G I < & ?( BHE m
+G I < G P( BHE m
ABI A
-ABD L |% @r A BBB B(E0A8J@
-8A0A(B BBBA $ & 0A^ AG b
-AA , & hA( BHE u
-ABI < $& hB^ BDA n
+ABD L \G @r BBB B(E0A8J@
+8A0A(B BBBA $ TH p^ AG b
+AA , |H ( BHE u
+ABI < H ^ ~ BDA n
EBD C
-EBA $ d& Bb ~ A]
-B A , & B Z BHI kAB ' 0D AL L & 0D BBB B(A0A8J
-8D0A(B BBBA ( E 4 ( EJ BDA o
+EBA $ DH Ƚb N A]
+B A , lH * BHI kAB DI p AL L H p BBB B(A0A8J
+8D0A(B BBBA I 4 I J BDA o
ABG A
-ABA d T( EA BLB B(A0A8Dq
+ABA d J 0A BLB B(A0A8Dq
8A0A(B BBBE &
-8A0A(B BBBH D ( L BBB D(A0D@
-0A(A BBBA L ,( 0N$ BIB B(A0D8DpO
-8D0A(B BBBA L |( Qg ú BBE B(D0D8Gp
-8A0A(B BBBD \ ) 0Wm BBD D(J@
+8A0A(B BBBH D lJ BBB D(A0D@
+0A(A BBBA L J p$ BIB B(A0D8DpO
+8D0A(B BBBA L \J Pg BBE B(D0D8Gp
+8A0A(B BBBD \ TK pm BBD D(J@
(A ABBD T
(A ABBF _
-(A ABBK D ,) @X BBB D(A0J
+(A ABBK D K k BBB D(A0J
-0D(A BBBJ L t) `P Ӻ BEB E(A0D8DP
-8D0A(B BBBA d ) b9 BLB B(D0A8D@q
+0D(A BBBJ L TK HP BEB E(A0D8DP
+8D0A(B BBBA d K H9 o BLB B(D0A8D@q
8A0A(B BBBJ
-8A0A(B BBBA L ,* b
- _ BLB B(D0D8D@
-8A0A(B BBBG L T+ e BBD D(D0O
+8A0A(B BBBA L L
+ / BLB B(D0D8D@
+8A0A(B BBBG L M BBD D(D0O
(A ABBK \
-(A ABBE < + Pg BAD
+(A ABBE < TM BAD
ABE
-ABJ T + k BBB B(A0A8D~
-8A0A(B BBBJ d <, s BBB B(A0A8D7
+ABJ T L w BBB B(A0A8D~
+8A0A(B BBBJ d M H BBB B(A0A8D7
8A0A(B BBBI l
-8A0A(B BBBA 4 , pxJ BDA l
+8A0A(B BBBA 4 TN J BDA l
ABJ A
-ABA \ , x% GLE B(A0A8DPV8A0A(B BBBHP T d, Xz o BBB B(A0D8G
-8A0A(B BBBA T , Ё BBB E(A0A8Ge
-8A0A(B BBBA - T KHT ,- BBB B(A0D8N
-8A0A(B BBBA , - ȑ> AID W
-DAA d . ؑ BEB B(A0A8D@
+ABA \ N % GLE B(A0A8DPV8A0A(B BBBHP T DN ? BBB B(A0D8G
+8A0A(B BBBA T N b BBB E(A0A8Ge
+8A0A(B BBBA O 8 T KHT O ` P BBB B(A0D8N
+8A0A(B BBBA , dO > k AID W
+DAA d <P BEB B(A0A8D@
8A0A(B BBBE I
-8A0A(B BBBA T . p BFB B(A0D8JT
-8A0A(B BBBA d L/ ( BEB B(A0A8D@
+8A0A(B BBBA T O P
BFB B(A0D8JT
+8A0A(B BBBA d P BEB B(A0A8D@
8A0A(B BBBE I
-8A0A(B BBBA L / Е BBE B(A0A8D`
-8A0A(B BBBA L ,/ S BBE B(A0A8J
-8A0A(B BBBC < |/ @ R BEA D(DP
-(D ABBC T / D @ BBB B(A0A8J
-8D0A(B BBBA 0 AU 1 F $1 A[ , D1 b D\
+8A0A(B BBBA L dQ BBE B(A0A8D`
+8A0A(B BBBA L Q ` # BBE B(A0A8J
+8A0A(B BBBC < \Q " BEA D(DP
+(D ABBC T Q D BBB B(A0A8J
+8D0A(B BBBA R AU R F R A[ , R b D\
H P
H Q
-G I L 0 r Ŷ BBB B(E0A8J@
-8A0A(B BBBA $ 1 P^ AG b
-AA , 1 b BHI kAB 2 AL L d1 BBB B(A0A8J
-8D0A(B BBBA 2 T 1 M ݵ BBB E(A0A8GI
-8A0A(B BBBI L $2 r BBE E(D0D8I{
-8D0A(B BBBI D L3 BBB D(A0D@
-0A(A BBBA L 2 BIB B(A0D8DpE
-8D0A(B BBBA L 3 " BIB B(A0D8DpL
-8D0A(B BBBA T \3 ^ BBB B(D0A8JW
-8D0A(B BBBE T 3 1 BBB B(A0A8J
-8A0A(B BBBA 4 L 4 BLB B(A0A8D
-8A0A(B BBBA L t4 BEB B(D0D8G
-8A0A(B BBBE T 4 x BBB B(A0D8G'
-8A0A(B BBBK 5 T KHT 45 BBB B(D0A8N
-8A0A(B BBBB , 5 > C AID W
-DAA , 5 > # AID W
-DAA T 5 BBF A(A0
+G I L |R r BBB B(E0A8J@
+8A0A(B BBBA $ tS 0 ^ AG b
+AA , R h 2 BHI kAB S AL L DS BBB B(A0A8J
+8D0A(B BBBA <T x! T S p!M BBB E(A0A8GI
+8A0A(B BBBI L T h(r BBE E(D0D8I{
+8D0A(B BBBI D T * BBB D(A0D@
+0A(A BBBA L T + BIB B(A0D8DpE
+8D0A(B BBBA L T ." X BIB B(A0D8DpL
+8D0A(B BBBA T <U 1 . BBB B(D0A8JW
+8D0A(B BBBE T U 3 BBB B(A0A8J
+8A0A(B BBBA V ; L V x= BLB B(A0A8D
+8A0A(B BBBA L TV A BEB B(D0D8G
+8A0A(B BBBE T V XH BBB B(A0D8G'
+8A0A(B BBBK W T KHT W M BBB B(D0A8N
+8A0A(B BBBB , lW [> AID W
+DAA , W [> AID W
+DAA T W \ BBF A(A0
(A BBBG W
-(A BBBJ d 7 X BBB B(A0A8DPB
+(A BBBJ d X 8] BBB B(A0A8DPB
8A0A(B BBBE L
-8A0A(B BBBA L 6 h S BBE B(A0A8J
-8A0A(B BBBC D 6 $ R BBE A(D0D`
-0D(A BBBD T D7 D BBB B(A0A8J
-8A0A(B BBBA L t8 P AAG ]
+8A0A(B BBBA L X `^h # BBE B(A0A8J
+8A0A(B BBBC D X c$ " BBE A(D0D`
+0D(A BBBD T $Y he BBB B(A0A8J
+8A0A(B BBBA L $Z 0j AAG ]
CAF k
ACA R
-CAJ JCA $ 8 ^ AG b
-AA < 8 ( BHE m
+CAJ JCA $ tZ pj^ AG b
+AA < Z j( BHE m
ABI A
-ABD , ,9 ( BHE u
-ABI D \9 BBB D(A0D@
-0A(A BBBA 9 05 9 X= 4 8 SGB A(A0D 4 49 XX ܲ cGB A(A0D L l9 Ų BHB B(G0G8Ga
-8A0A(B BBBB \ 9 ˲ BBB A(A0Do
+ABD , Z k( BHE u
+ABI D [ l BBB D(A0D@
+0A(A BBBA T[ n5 l[ 8n= 4 Z `n SGB A(A0D 4 [ 8oX cGB A(A0D L L[ `p BHB B(G0G8Ga
+8A0A(B BBBB \ [ w BBB A(A0Do
0A(A BBBE i
-0E(A BBBA L : P BOB A(A0]
+0E(A BBBA L \ 0y BOB A(A0]
(A BBBG Y
-(A BBBH T l: u BBE E(D0A8G)
-8D0A(B BBBH < : X 8 ACG@s
+(A BBBH T L\ y E BBE E(D0A8G)
+8D0A(B BBBH < \ 8| ACG@s
DAE J
-DAA 4 ; J BDA o
+DAA 4 ] |J BDA o
ABG A
-ABA < 0 FP
-A < \; 0 BEA D(G@)
-(D ABBK \ ; BBB D(D0J
+ABA ] } FP
+A < <] } BEA D(G@)
+(D ABBK \ |] ` { BBB D(D0J
0A(A BBBF
-0A(A BBBG T ; / s BBB B(A0A8J
-8A0A(B BBBA \ ,=
KOB B(A0A8Dpk8A0A(B BBBGp d = H3 BOB B(A0A8D`E
+0A(A BBBG T ] C BBB B(A0A8J
+8A0A(B BBBA \ ^ 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 l= b C BEB F(D0D8J
-8D0A(B BBBC T = B BBB B(A0A8GS
-8A0A(B BBBA L > (F BEE B(D0D8D`
-8A0A(B BBBF L D? ) BEE B(G0D8G`C
-8A0A(B BBBC L > * BLB B(A0D8D`
-8A0A(B BBBD L ? / BBD D(D0O
+8D0A(B BBBE L _ Ѝ BLB E(A0A8DpC
+8A0A(B BBBH T L_ b
BEB F(D0D8J
+8D0A(B BBBC T _ BBB B(A0A8GS
+8A0A(B BBBA L ` СF BEE B(D0D8D`
+8A0A(B BBBF L ` Т BEE B(G0D8G`C
+8A0A(B BBBC L ` ` c BLB B(A0D8D`
+8A0A(B BBBD L a BBD D(D0O
(A ABBK \
-(A ABBE T \? 1R 3 BBB B(A0A8J
-8A0A(B BBBH D ? 8w c BBB A(D0J
-0D(A BBBD D ? ; BBB D(A0D
-0D(A BBBI L D@ h> ~ BEE E(A0D8D`
-8D0A(B BBBF lA T KH| @ @} 9 BFE E(D0D8Gp
+(A ABBE T <a PR BBB B(A0A8J
+8A0A(B BBBH D a Xw / BBB A(D0J
+0D(A BBBD D a K BBB D(A0D
+0D(A BBBI L $b J BEE E(A0D8D`
+8D0A(B BBBF c x T KH| b `} BFE E(D0D8Gp
8A0A(B BBBF T
8A0A(B BBBB |
-8A0A(B BBBA T ,A B خ BBE E(G0A8GJ
-8D0A(B BBBD D \B K GBA A(G@H(A ABBH@ L B PL BEE E(D0A8G@p
-8D0A(B BBBF B . K^ C L $C L <C L TC L | lC L BEB B(A0A8D@6
+8A0A(B BBBA T c ` BBE E(G0A8GJ
+8D0A(B BBBD D d GBA A(G@H(A ABBH@ L Td BEE E(D0A8G@p
+8D0A(B BBBF d P . K^ d X d P d H e P | e X BEB B(A0A8D@6
8A0A(B BBBG @
-8A0A(B BBBF x8D0A(I BBB , C (O[ w AID t
-DAA L D XO AAG e
+8A0A(B BBBF x8D0A(I BBB , d [ C AID t
+DAA L e AAG e
CAF s
ACA R
-CAJ JCA D C O BBB A(I0Dp
-0D(A BBBK T C P ۬ BBB B(I0A8D$
-8D0A(B BBBI T 4D XS BBB B(I0A8D$
-8D0A(B BBBI D dE BBB D(A0D@0A(A BBB T D xU 3 BIB E(A0A8D6
-8D0A(B BBBA T ,E Y BIB E(A0A8DF
-8D0A(B BBBA T E ] ϫ BIB E(A0A8DF
-8D0A(B BBBA T E @b BIB E(A0A8DN
-8D0A(B BBBA D G f BBE G(F0D@
-0D(A BBBI T |F `j # BDB B(A0A8J
-8A0A(B BBBD G 8. K^ G / A G u- AG cA L $G ur W BBB B(E0A8J@
-8A0A(B BBBA D LH v BBL A(A0D@p
-0A(A BBBA D H w BBL A(A0D@x
-0A(A BBBA D H 8x BBB D(A0D@
-0A(A BBBA L LH y D BIB A(D0D
-0D(A BBBA T H |r % BBB B(D0A8N
-E
-8A0A(B BBBF , H > ݪ AID W
-DAA I R HB
-F A$ DI Xg AR
+CAJ JCA D te X BBB A(I0Dp
+0D(A BBBK T e BBB B(I0A8D$
+8D0A(B BBBI T f w BBB B(I0A8D$
+8D0A(B BBBI D g T BBB D(A0D@0A(A BBB T f ( BIB E(A0A8D6
+8D0A(B BBBA T g ` BIB E(A0A8DF
+8D0A(B BBBA T dg BIB E(A0A8DN
+8D0A(B BBBA T g i BIB E(A0A8DF
+8D0A(B BBBA D h 8 BBE G(F0D@
+0D(A BBBI T \h BDB B(A0A8J
+8A0A(B BBBD \i . K^ ti / A i h- AG cA L i xr # BBB B(E0A8J@
+8A0A(B BBBA D i BBL A(A0D@p
+0A(A BBBA D Dj @ BBL A(A0D@x
+0A(A BBBA D j BBB D(A0D@
+0A(A BBBA L ,j @ BIB A(D0D
+0D(A BBBA T |j 7 BBB B(D0A8N
+
+8A0A(B BBBK , j x> AID W
+DAA k R HB
+F A$ $k g i AR
M A
-A DJ W HJ T I Y BEB E(A0D8G+\
-8D0A(B BBBE T I 8 m BBG B(A0A8J)
-8A0A(B BBBB T <J D BBB G(A0D8M
-8D0A(B BBBA lK . K^ L K @ BBB B(A0A8GP8A0A(B BBB L K BEB B(A0A8DP8A0A(B BBB , $L 0 AC
FK
- E $ TL ~ Al
+A k W HJ T lk P % BEB E(A0D8G+\
+8D0A(B BBBE T k 9 BBG B(A0A8J)
+8A0A(B BBBB T l p? BBB G(A0D8M
+8D0A(B BBBA m 8. K^ L 4m A BBB B(A0A8GP8A0A(B BBB L m `C BEB B(A0A8DP8A0A(B BBB , m E0 AC
FK
+ E $ n F~ Al
C V
-J < |L ADG v
+J < ,n XF ADG v
GAF
-AAF L 8S L L + ULB A(A0@(A BBBD0 T LL ` j BBB E(D0A8G$
-8D0A(B BBBH T L ( ' BBB E(D0A8G
-
-8D0A(B BBBB D L U BFB A(A0GP
-0A(A BBBD d DM 9 BBB B(A0A8G@s
+AAF ln HS L n H+ ULB A(A0@(A BBBD0 L ,n M 6 BBE D(A0G,
+0D(A BBBD L |n P BBE D(A0G
+0D(A BBBK D n `SU BFB A(A0GP
+0A(A BBBD d o xa9 BBB B(A0A8G@s
8A0A(B BBBB _
-8A0A(B BBBG T M BBB B(A0A8J
-8A0A(B BBBK T N D BEB B(A0A8G
-8A0A(B BBBA 4O . K^ LO
dO
L |O
9 BME F(A0A8Dp8A0A(B BBB < O BBD A(DP
-(A ABBA L P BBB B(A0A8DP
-8A0A(B BBBH L \P AAG e
+8A0A(B BBBG T |o Ph | BBB B(A0A8J
+8A0A(B BBBK T o wD o BEB B(A0A8G
+8A0A(B BBBA p . K^ p x q p L q x9 BME F(A0A8Dp8A0A(B BBB < lq h BBD A(DP
+(A ABBA L q 8 BBB B(A0A8DP
+8A0A(B BBBH L q AAG e
CAF S
ACA R
-CAJ JCA L P AAG e
+CAJ JCA L Lr AAG e
CAF S
ACA R
-CAJ JCA L P AAG ]
+CAJ JCA L r AAG ]
CAF c
CAI R
-CAJ JCA L LQ 8 AAG e
+CAJ JCA L r ȍ AAG e
CAF S
ACA R
-CAJ JCA D P K BBB A(A0DP1
-0A(A BBBA Q \ AG RAL ,Q BBB B(A0A8Dt8A0A(B BBB L TR u BBA A(D@
+CAJ JCA D r 8K BBB A(A0DP1
+0A(A BBBA s \ AG RAL r s BBB B(A0A8Dt8A0A(B BBB L s `u BBA A(D@
(A ABBG
-(A ABBH L Q " BEB B(A0A8Q
-8A0A(B BBBB 4 R q ֥ BAI A
-ABA T TR ( BBE B(D0A8G
-8D0A(B BBBG T R BBE B(D0A8G
-8D0A(B BBBG D S r BBB D(A0D@0A(A BBB T LS p% 6 BIB E(A0A8D6
-8D0A(B BBBA L S )
- BBB G(D0G8G
-8D0A(B BBBD D S 3 BBB D(A0D
-0A(A BBBA L <T 5B BBB A(A0DD
-0A(A BBBA L T 7 L BEI B(A0A8JR
-8D0A(B BBBC U V Ko
-A U L U L < V L. BAD
+(A ABBH L s " BEB B(A0A8Q
+8A0A(B BBBB 4 s pq BAI A
+ABA T $t BBE B(D0A8G
+8D0A(B BBBG T |t t BBE B(D0A8G
+8D0A(B BBBG D |u D BBB D(A0D@0A(A BBB T u BIB E(A0A8D6
+8D0A(B BBBA L tu 8
+ BBB G(D0G8G
+8D0A(B BBBD D u x BBB D(A0D
+0A(A BBBA L v @B \ BBB A(A0DD
+0A(A BBBA L \v @* ( BEI B(A0A8JS
+8D0A(B BBBB Tw `V Ko
+A tw w < w . BAD
ABG n
-ABG , lU O[ AID t
-DAA L tV @O AAG e
+ABG , <w [ ` AID t
+DAA L x AAG e
CAF {
ACI R
-CAJ JCA L U O BBB B(A0A8D@~8A0A(B BBB D W BBB D(A0D@0A(A BBB T V RL BIE B(A0A8D9
-8D0A(B BBBA T V W BDE B(A0A8G r
-8A0A(B BBBF X . K^ $X s <X s , |W s[ AID t
-DAA 4 X s ADD 6
+CAJ JCA L w BBB B(A0A8D@~8A0A(B BBB D x ڍ BBB D(A0D@0A(A BBB T Tx L \ BIE B(A0A8D9
+8D0A(B BBBA T x + BDE B(A0A8G r
+8A0A(B BBBF y h. K^ y y , Ly [ 7 AID t
+DAA 4 $z 0 ADD 6
AAG K
-AAK L X xv ADD e
+AAK L \z ADD e
CAF {
ACI V
-CAF JCA L Y w_ BBB B(A0A8D`
-8A0A(B BBBI D \Y BBB D(A0D@0A(A BBB T X yL BIE B(A0A8D9
-8D0A(B BBBA L $Y ~) { BEB B(D0A8J L
-8D0A(B BBBA LZ . K^ T Y BBE B(D0A8G
-8D0A(B BBBG T Y ج BBE B(D0A8G
-8D0A(B BBBG T <Z BEE E(D0A8G
-8D0A(B BBBE T Z ȶ i BBE B(D0A8G
-8D0A(B BBBG T Z O BEE E(D0A8G
-8D0A(B BBBE T D[ - 7 BBB B(A0A8L Y
-8A0A(B BBBG t\ T KH \ \ \ \ D \ ̉ BBB D(A0D@0A(A BBB T \\ W BIB E(A0A8D6
-8D0A(B BBBA T \ % BIB E(A0A8D6
-8D0A(B BBBA L ] 0 BBB J(A0A8G
-8D0A(B BBBA L \] BBB J(A0A8G
-8D0A(B BBBA T ] BBE B(D0A8G
-8D0A(B BBBB T ^ ӧ BBE B(D0A8G
-8D0A(B BBBB T \^ `| BBE E(D0A8G
-8D0A(B BBBF L _ BBB B(A0A8D
-8A0A(B BBBF D _
. BEB B(A0A8
-0A(B BBBG L L_
- BBB B(D0G8L
-8D0A(B BBBE L t` BBB B(A0A8D
-8A0A(B BBBF D ` 0 BEB B(A0A8
-0A(B BBBG L 4`
- BBB B(D0G8L
-8D0A(B BBBE \a 8. K^ ta @( AQ a \ AG RA$ a ( AG
-AA $ a ( AG
-AA D b ކ BBB D(A0D@0A(A BBB T ta H)L BIE B(A0A8D9
-8D0A(B BBBA T a @. e BBB B(A0I8G
-8D0A(B BBBK b . K^ c 32 Hb
-F A 4c 37 Hj Tc \\ AG RAT b 3f ֥ BBB B(I0A8G
-8D0A(B BBBF T b 9i BBE B(D0A8G
-8D0A(B BBBG D $d 膕 BBB D(A0D@0A(A BBB T c J ѥ BIB E(A0A8D6
-8D0A(B BBBA L c O
- BBB G(D0A8G
-8A0A(B BBBA e . K^ L ,e XY BHB B(A0A8GI
-8A0A(B BBBA L |e ` ADD e
+CAF JCA L z X_ BBB B(A0A8D`
+8A0A(B BBBI D z ` BBB D(A0D@0A(A BBB T z L BIE B(A0A8D9
+8D0A(B BBBA L z ) BEB B(D0A8J L
+8D0A(B BBBA { X. K^ T \{ P% BBE B(D0A8G
+8D0A(B BBBG T { * BBE B(D0A8G
+8D0A(B BBBG T | . BEE E(D0A8G
+8D0A(B BBBE T d| 4 BBE B(D0A8G
+8D0A(B BBBG T | 8 BEE E(D0A8G
+8D0A(B BBBE T } =- BBB B(A0A8L Y
+8A0A(B BBBG ~ `T KH ,~ Hk D~ @k \~ 8k t~ @k L } Hk BBB J(A0A8G
+8D0A(B BBBA L 4~ n BBB J(A0A8G
+8D0A(B BBBA D , BBB D(A0D@0A(A BBB T ~ q BIB E(A0A8D6
+8D0A(B BBBA T $ u k BIB E(A0A8D6
+8D0A(B BBBA T | z 9 BBE B(D0A8G
+8D0A(B BBBB T ~ BBE B(D0A8G
+8D0A(B BBBB T , | BBE E(D0A8G
+8D0A(B BBBF L , Ȉ BBB B(A0A8D
+8A0A(B BBBF D | . BEB B(A0A8
+0A(B BBBG L
+ S BBB B(D0G8L
+8D0A(B BBBE L @ BBB B(A0A8D
+8A0A(B BBBF D d p BEB B(A0A8
+0A(B BBBG L H
+ G BBB B(D0G8L
+8D0A(B BBBE . K^ AQ 4 Ĉ\ AG RA$ T ` AG
+AA $ | AG
+AA D BBB D(A0D@0A(A BBB T D L BIE B(A0A8D9
+8D0A(B BBBA T BBB B(A0I8G
+8D0A(B BBBK h. K^ 2 Hb
+F A Ԅ 07 Hj .\ AG RAT l 0f " BBB B(I0A8G
+8D0A(B BBBF T Ą H BBE B(D0A8G
+8D0A(B BBBG D ą BBB D(A0D@0A(A BBB T d h BIB E(A0A8D6
+8D0A(B BBBA L
+ BBB G(D0A8G
+8A0A(B BBBA . K^ L ̆ BHB B(A0A8GI
+8A0A(B BBBA L 8 ADD e
CAF s
ACA V
-CAF JCA $ e 8am AG q
-AA $ e au AG y
-AA f \ AG RAT de a? Ϥ BBB B(A0A8G
-8A0A(B BBBA D f BBB D(A0D@0A(A BBB T f Xi 3 BIB E(A0A8DV
-8D0A(B BBBA T \f m| BBE E(D0A8G
-8D0A(B BBBE T f rT BBB B(I0A8G
-8D0A(B BBBH T g w BBB B(A0G8J
-8D0A(B BBBE <h . K^ , |g ? AID
-AAA , g ! AID
-AAA , g AID
-AAA , h AID
-AAA , <h Ǥ AID
-AAA , lh AID
-AAA , h AID
-AAA , h m AID
-AAA , h O AID
-AAA , ,i 1 AID
-AAA , \i AID
-AAA , i AID
-AAA , i ף AID
-AAA D j J BBB D(A0D@
-0A(A BBBA L 4j q BIB A(D0D
-0D(A BBBA L j h R BIB A(D0D
-0D(A BBBA T j 3 BIE B(A0A8D
-8D0A(B BBBA L ,k 0 BIB A(D0D
-0D(A BBBA T |k BIE B(A0A8D
-8D0A(B BBBA L k Ƣ BIB A(D0D
-0D(A BBBA T $l H BIE B(A0A8D
-8D0A(B BBBA T |l BIE B(A0A8D
-8D0A(B BBBA T l 8 Y BIE B(A0A8D
-8D0A(B BBBA T ,m 2 BIE B(A0A8D
-8D0A(B BBBA T m ( BIE B(A0A8D
-8D0A(B BBBA T m BIE B(A0A8D
-8D0A(B BBBA T 4n BIE B(A0A8D
-8D0A(B BBBA do . K^ |o x o p o h o p L o xq BBB B(A0I8G
-8D0A(B BBBD T To BBB B(A0I8G
-8D0A(B BBBD D p ~ BBB D(A0D@0A(A BBB T o L ̠ BIE B(A0A8D9
-8D0A(B BBBA T Lp L BIE B(A0A8D9
-8D0A(B BBBA T p Z k BBB G(D0G8G
-8D0A(B BBBD T p
- BBB G(D0G8G
-8D0A(B BBBE ,r Xߕ. K^ Dr 0 \r ( , q 0[ s AID t
-DAA T q `A S BEE E(D0A8GY
-8D0A(B BBBE D r } BBB D(A0D@0A(A BBB T lr | BIE B(A0A8Di
-8D0A(B BBBA T r 8h BGH B(A0D8G
-8D0A(B BBBI s ݕ. K^ $ t 8 AG u
-AA $ 4t AG }
-AA D \t D~ BBB D(A0D@0A(A BBB T s L ˟ BIE B(A0A8D9
-8D0A(B BBBA T $t 8 BBB J(A0A8G>
-8D0A(B BBBA Tu ܕ. K^ $ lu m AG q
-AA $ u 0u AG y
-AA u ~\ AG RAT u h
BBB B(I0A8G
-8D0A(B BBBA < 4v ~D BBD A(D@0(A ABB T u ? BIB E(A0A8D6
-8D0A(B BBBA v Hە. K^ v !0 Hg w !7 Hj T Lv @! BBB B(A0I8G6
-8D0A(B BBBD D |w ~ BBB D(A0D@0A(A BBB T v @%L z BIE B(A0A8D9
-8D0A(B BBBA T Dw 8*' K BBB E(A0A8Ga
-8A0A(B BBBA tx ٕ. K^ x ? x ? x ? x ? < x ?0 BEA A(G@
-(A ABBF < ,y A| BEA A(G@
-(A ABBI L ly (D AAG e
+CAF JCA $ l m AG q
+AA $ u AG y
+AA \ AG RAT 4 H? BBB B(A0A8G
+8A0A(B BBBA D 4 t BBB D(A0D@0A(A BBB T ԇ { BIB E(A0A8DV
+8D0A(B BBBA T , @| I BBE E(D0A8G
+8D0A(B BBBE T hT / BBB B(I0A8G
+8D0A(B BBBH T ܈ p + BBB B(A0G8J
+8D0A(B BBBE ܉ . K^ , L AID
+AAA , | i AID
+AAA ,
+ K AID
+AAA , ܉
+ - AID
+AAA , AID
+AAA , < AID
+AAA , l AID
+AAA , AID
+AAA , ̊
AID
+AAA ,
y AID
+AAA , , [ AID
+AAA , \ = AID
+AAA , AID
+AAA D d J BBB D(A0D@
+0A(A BBBA L BIB A(D0D
+0D(A BBBA L T BIB A(D0D
+0D(A BBBA L H { BIB A(D0D
+0D(A BBBA T \ BIE B(A0A8D
+8D0A(B BBBA L L ! 5 BIB A(D0D
+0D(A BBBA T `$ BIE B(A0A8D
+8D0A(B BBBA L ) BIB A(D0D
+0D(A BBBA T D (- BIE B(A0A8D
+8D0A(B BBBA T 2 BIE B(A0A8D
+8D0A(B BBBA L 8 BIB A(D0D
+0D(A BBBA L D h; c BIB A(D0D
+0D(A BBBA L > D BIB A(D0D
+0D(A BBBA L B % BIB A(D0D
+0D(A BBBA ܐ . K^ @E 8E $ 0E < 8E L @Eq BBB B(A0I8G
+8D0A(B BBBD T pG x BBB B(A0I8G
+8D0A(B BBBD D z BBB D(A0D@0A(A BBB T `KL < BIE B(A0A8D9
+8D0A(B BBBA T XPL
+ BIE B(A0A8D9
+8D0A(B BBBA T L PUZ BBB G(D0G8G
+8D0A(B BBBD T X\
+ BBB G(D0G8G
+8D0A(B BBBE ߒ. K^ b ԓ b , D b[ AID t
+DAA T t (cA BEE E(D0A8GY
+8D0A(B BBBE D t BBB D(A0D@0A(A BBB T h| a BIE B(A0A8Di
+8D0A(B BBBA T l nh / BGH B(A0D8G
+8D0A(B BBBI l ݒ. K^ $ y AG u
+AA $ y AG }
+AA D ԕ > BBB D(A0D@0A(A BBB T t yL ; BIE B(A0A8D9
+8D0A(B BBBA T ̕ ~8 BBB J(A0A8G>
+8D0A(B BBBA ̖ Xܒ. K^ $ m AG q
+AA $ u AG y
+AA 4 \ AG RAT 0
c BBB B(I0A8G
+8D0A(B BBBA D BBB D(A0D@0A(A BBB T L 0 BIB E(A0A8D6
+8D0A(B BBBA L ے. K^ d P0 Hg `7 Hj T BBB B(A0I8G6
+8D0A(B BBBD D ‒ BBB D(A0D@0A(A BBB T L BIE B(A0A8D9
+8D0A(B BBBA T x' BBB E(A0A8Ga
+8A0A(B BBBA ْ. K^ 8 $ 0 < ( T 0 < l 8@ BEA A(G@
+(A ABBJ < 8 BEA A(G@
+(A ABBE L AAG e
CAF S
ACA R
-CAJ JCA L y D AAG e
+CAJ JCA L < AAG e
CAF S
ACA R
-CAJ JCA z }\ AG RAL ,z D BBB B(A0A8DPV
-8A0A(B BBBA L |z HG BBB B(A0A8D`
-8A0A(B BBBA T y Ii BBB B(A0D8R
-8A0A(B BBBF , Lz N> Ɯ AID W
-DAA T |z Ni BBB B(A0D8R
-8A0A(B BBBF , z 8R> AID W
-DAA D { \| BBB D(A0D@0A(A BBB T L{ R E BIB E(A0A8D6
-8D0A(B BBBA T { 8V BIB E(A0A8D6
-8D0A(B BBBA < | pZ BBG A(G@
-(A ABBG T <| [ BHB E(A0A8G
-8D0A(B BBBA T | m I BHB E(A0A8G
-8D0A(B BBBA } ԕ. K^ } } , 4} [ AID t
-DAA < <~ H BBB A(A0
-(A BBBA D |~ = BEB B(A0A8
-0A(B BBBA D ~ BBB D(A0D@
-0A(A BBBA T 4~ HB BIB B(A0D8Dg
-8D0A(B BBBA < d @ BBD D(G0b
-(A ABBE L K BBB B(A0A8D@28A0A(B BBB d S BBB E(D0D8D
+CAJ JCA \ AG RAL H
BBB B(A0A8D`
+8A0A(B BBBA L BBB B(A0A8D`V
+8A0A(B BBBA T xi ' BBB B(A0D8R
+8A0A(B BBBF , > . AID W
+DAA T , i BBB B(A0D8R
+8A0A(B BBBF , > AID W
+DAA D \ ~ BBB D(A0D@0A(A BBB T BIB E(A0A8D6
+8D0A(B BBBA T T { BIB E(A0A8D6
+8D0A(B BBBA < T BBG A(G@
+(A ABBG T BHB E(A0A8G
+8D0A(B BBBA T D BHB E(A0A8G
+8D0A(B BBBA D pԒ. K^ \ H t @ , H[ AID t
+DAA < x BBB A(A0
+(A BBBA D (= BEB B(A0A8
+0A(B BBBA D D BBB D(A0D@
+0A(A BBBA T x BIB B(A0D8Df
+8D0A(B BBBA < BBD D(G0b
+(A ABBE L $ PK BBB B(A0A8D@28A0A(B BBB d t P S BBB E(D0D8D
8A0A(B BBBD
-8A0A(B BBBG L \ BBB B(A0A8Dxv
-8A0A(B BBBA L
+8A0A(B BBBG L ܡ H
BBB B(A0A8Dxv
+8A0A(B BBBA L ,
BIB B(A0A8G
-8A0A(B BBB < $ x AC
FM-
+8A0A(B BBB < ԡ AC
FM-
A X
- C T d X Ù BBA A(Gp
+ C T % BBA A(Gp
(A ABBG u
-(A ABBE 4 а o AF
P
- G 4 ́ 8 AC
BGM
+(A ABBE 4 l p' a AF
P
+ G 4 L X,8 AC
BGM
H
- F l , BBB B(A0D8Gc
+ F l ܢ `0 BBB B(A0D8Gc
8A0A(B BBBG
-8A0A(B BBBG T p Ƙ BEB B(D0D8Je
-8A0A(B BBBD T v BDH B(A0A8J
-8D0A(B BBBI $ ϕ. K^ L < BBB E(A0A8D8A0A(B BBB L Xx BEB B(A0A8GM
-8A0A(B BBBE L ܃ BBB E(A0A8G
-8A0A(B BBBJ $ , 8 AG r
-AA $ T AG z
-AA T X | BBE B(D0A8G
-8D0A(B BBBB D Ԅ 2u BBB D(A0D@0A(A BBB T D BIB E(A0A8D6
-8D0A(B BBBA < BBA I(D@e
-(D ABBA T ܄ '! ǖ BBE B(A0A8L
+8A0A(B BBBG T L @ BEB B(D0D8Je
+8A0A(B BBBD T xGv BDH B(A0A8J
+8D0A(B BBBC @ϒ. K^ L O BBB E(A0A8D8A0A(B BBB L Px BEB B(A0A8GM
+8A0A(B BBBE L \ (T BBB E(A0A8G
+8A0A(B BBBJ $ X AG r
+AA $ ԥ `Y AG z
+AA < T Y n BBA I(D@e
+(D ABBA D < w BBB D(A0D@0A(A BBB T ܥ 0Z BIB E(A0A8D6
+8D0A(B BBBA T 4 h^'! BBE B(A0A8L
-8A0A(B BBBD ̕. K^ T L P
# BBB B(A0I8G
-8D0A(B BBBD D | Xu BBB D(A0D@0A(A BBB T @R BIE B(A0A8DA
-8D0A(B BBBA ˕. K^ 4 0 L ( T 0 m BBB B(I0A8D
-8D0A(B BBBE ԇ " < BGE D(A0j
-(A BFBH D , vu BBB D(A0D@0A(A BBB T BIB E(A0A8GM
-8D0A(B BBBA L ( k BHB B(F0D8G
-8D0A(B BBBB ʕ. K^ 4 $ L $ d $ | $ $ DV , Ԉ $[ ; AID t
-DAA ܉ %} AG sA x%} AG sA % AG {A < H& AG {A \ & AG vA | ' AG ~AL ĉ 'r [ BBB B(E0A8J@
-8A0A(B BBBA $ (^ AG b
-AA T < ( BBB B(A0I8GV
-8D0A(B BBBA L + BBB B(I0A8D
-8D0A(B BBBA L (- BBB B(I0A8D
-8D0A(B BBBA L 4 . ̔ BBB B(I0A8D
-8D0A(B BBBA L H0 BBB B(I0A8D
-8D0A(B BBBA D 1 BBB D(A0D@
-0A(A BBBA 4 03k X BHA GP
- DABA 4 T h4k 9 BHA GP
- DABA 4 5k BHA GP
- DABA 4 Č 6k BHA GP
- DABA 4 8k ܓ BHA GP
- DABA 4 4 H9k BHA GP
- DABA 4 l :z BHA GP
- DABA T ; BBB E(D0A8G
-8A0A(B BBBK T `Dw ` BBG E(G0A8G+
-8D0A(B BBBI D T G g BDB D(G0Dy
-0D(A BBBA T @I a BBB J(A0A8G&
-8D0A(B BBBA T J C BHB B(A0D8G
-8D0A(B BBBF T L PYU BBB H(D0F8G
-8D0A(B BBBE | X` AR X` AY . K^ L @`u ӓ BBB B(A0I8Dp
-8D0A(B BBBF L L pau BBB B(A0I8Dp
-8D0A(B BBBF L bu k BBB B(A0I8Dp
-8D0A(B BBBF D đ m BBB D(A0D@0A(A BBB T 4 c BIB B(D0A8D
-8D0A(B BBBA T h BIB B(D0A8D
-8D0A(B BBBA T Xn BIB B(D0A8D
-8D0A(B BBBA . K^ , s D s , s[ AID t
-DAA D m BBB D(A0D@0A(A BBB T s\ BIE B(A0A8DI
-8D0A(B BBBA T T x { BDB E(G0A8G3
-8D0A(B BBBD @. K^ ~ ~ ̔ } ~ < ~? AAG0
+8A0A(B BBBD 4 ̒. K^ T ( / BBB B(A0I8G
+8D0A(B BBBD D x BBB D(A0D@0A(A BBB T D R BIE B(A0A8DA
+8D0A(B BBBA D ̒. K^ \ t T y BBB B(I0A8D
+8D0A(B BBBE p h" < BGE D(A0j
+(A BFBH D T x BBB D(A0D@0A(A BBB T Ȋ BIB E(A0A8GM
+8D0A(B BBBA L L w BHB B(F0D8G
+8D0A(B BBBB D 0ʒ. K^ \ t , [ _ AID t
+DAA AG {AL d Xr BBB B(E0A8J@
+8A0A(B BBBA $ \ ^ AG b
+AA $ AG u
+AA $ X AG r
+AA $ ԫ AG r
+AA $ h AG z
+AA $ $ AG }
+AA T C BBB B(A0D8Ra
+8A0A(B BBBA , > AID W
+DAA L , BBB B(I0A8D
+8D0A(B BBBA L | 0 BBB B(I0A8D
+8D0A(B BBBA L ̬ BBB B(I0A8D
+8D0A(B BBBA L P BBB B(I0A8D
+8D0A(B BBBA T l BBE E(A0A8G
+8D0A(B BBBE D l 8 BBB D(A0D@
+0A(A BBBA D BIB A(D0D`+
+0D(A BBBA D T H BIB A(D0D`+
+0D(A BBBA D BIB A(D0D`+
+0D(A BBBA D BIB A(D0D`+
+0D(A BBBA D , p BIB A(D0D`+
+0D(A BBBA D t ( l BIB A(D0D`+
+0D(A BBBA D J BIB A(D0D`:
+0D(A BBBA T ( BBE H(A0F8DE
+8D0A(B BBBB D \ `& BDB D(G0D
+0D(A BBBA T H J BBB J(A0A8G&
+8D0A(B BBBA T , BBB B(A0D8G
+8A0A(B BBBI T T ( BBB B(A0A8L]
+8A0A(B BBBC T ` AR t ` AY . K^ L Hu BBB B(A0I8Dp
+8D0A(B BBBF L T xu BBB B(A0I8Dp
+8D0A(B BBBF L u BBB B(A0I8Dp
+8D0A(B BBBF D o BBB D(A0D@0A(A BBB T < - BIB B(D0A8D
+8D0A(B BBBA T BIB B(D0A8D
+8D0A(B BBBA T ` BIB B(D0A8D
+8D0A(B BBBA 迒. K^ , [ R AID t
+DAA D d o BBB D(A0D@0A(A BBB T \ BIE B(A0A8DI
+8D0A(B BBBA T \ BDB E(G0A8G3
+8D0A(B BBBD \ . K^ t < Զ ? AAG0
AAD a
-AAE < < AAG0
+AAE < AAG0
AAD a
-AAE , X[ AID t
-DAA , Ԕ [ AID t
-DAA L ܕ AAG e
+AAE , `[ AID t
+DAA , ܶ [ AID t
+DAA L AAG e
CAF K
ACI R
-CAJ JCA L , ( AAG e
+CAJ JCA L 0 AAG e
CAF K
ACI R
-CAJ JCA D | BBB A(A0DP
-0A(A BBBE D Ė BBB A(A0DPO
-0A(A BBBE D l BBB D(A0D@0A(A BBB T | BIB E(A0A8D6
-8D0A(B BBBA T Ԗ Ɏ BIB E(A0A8D6
-8D0A(B BBBA T , BBE B(D0A8G
-8D0A(B BBBG L ~ } BBB B(A0A8G
-8A0A(B BBBI L ԗ ~ F BBB B(A0A8G
-8A0A(B BBBI T $ HE BDB H(A0A8J
-8D0A(B BBBK T | @D C BDB H(A0A8J
-8D0A(B BBBG H. K^ ę k DN
-E A $ ^ AG b
-AA < 8 BEA A(D@
-(A ABBA < L BEA A(D@
-(A ABBA L p BEA A(D0H
+CAJ JCA D T BBB A(A0DP
+0A(A BBBE D ( BBB A(A0DPO
+0A(A BBBE D m BBB D(A0D@0A(A BBB T ; BIB E(A0A8D6
+8D0A(B BBBA T ܸ
BIB E(A0A8D6
+8D0A(B BBBA T 4 ( BBE B(D0A8G
+8D0A(B BBBG L ~ BBB B(A0A8G
+8A0A(B BBBI L ܹ ~ BBB B(A0A8G
+8A0A(B BBBI T , PE O BDB H(A0A8J
+8D0A(B BBBK T H+D BDB H(A0A8J
+8D0A(B BBBG . K^ m DN
+E A $ ?^ AG b
+AA < @? BEA A(D@
+(A ABBA < $ ? BEA A(D@
+(A ABBA d @ L | Bp BEA A(D0H
(A ABBE D
-(A ABBA T k BEE E(D0A8G
-8D0A(B BBBE T t , Q BBE B(D0A8G
-8D0A(B BBBK 4 J BDA q
+(A ABBA T $ B BEE E(D0A8G
+8D0A(B BBBE T | H, BBE B(D0A8G
+8D0A(B BBBK 4 | LJ BDA q
ABE A
-ABA d ܛ g BLB B(A0A8D@q
+ABA d Lg BLB B(A0A8D@q
8A0A(B BBBE
-8A0A(B BBBF D D h BBB D(A0D@0A(A BBB T O BIB E(A0A8D6
-8D0A(B BBBA < BBG A(G@
-(A ABBF L $ ~ BLB H(A0D8D@
-8A0A(B BBBD < t BBG A(G@
-(A ABBF L m BBB H(A0D8DP
-8A0A(B BBBA T , BBB B(H0D8G
+8A0A(B BBBF D j BBB D(A0D@0A(A BBB T M BIB E(A0A8D6
+8D0A(B BBBA < Q BBG A(G@
+(A ABBF L R~ BLB H(A0D8D@
+8A0A(B BBBD < L S BBG A(G@
+(A ABBF L Tm BBB H(A0D8DP
+8A0A(B BBBA T 4 U 9 BBB B(H0D8G
Z
-8A0A(B BBBA , X> 3 AID W
-DAA L h BBB E(D0D8J
-8A0A(B BBBA T ( * BBE B(A0H8J
-8A0A(B BBBB L 4 ` BLB H(A0D8DPV
-8A0A(B BBBG T
- ԍ BEE B(A0A8N
-8A0A(B BBBB T x ۍ BBB G(D0A8M
-8D0A(B BBBC 4 . K^ L d , [ AID t
-DAA D f BBB D(A0D@0A(A BBB T L S BIE B(A0A8D9
-8D0A(B BBBA $ L V ADD JAAd 1 BHB B(D0A8Dpv
+8A0A(B BBBA , `m> o AID W
+DAA L pm O BBB E(D0D8J
+8A0A(B BBBA T 0t f BBE B(A0H8J
+8A0A(B BBBB L { BLB H(A0D8DPV
+8A0A(B BBBG T }
+ BEE B(A0A8N
+8A0A(B BBBB T BBB G(D0A8M
+8D0A(B BBBC X. K^ $ @ < 8 , @[ AID t
+DAA D h BBB D(A0D@0A(A BBB T $ (L BIE B(A0A8D9
+8D0A(B BBBA $ $ V ADD JAAd X1 = BHB B(D0A8Dpv
8C0A(B BBBG {
-8D0A(B BBBA T BBB E(I0A8K}
-8D0A(B BBBF d \ o BEB B(D0A8Gp
+8D0A(B BBBA T 0 BBB E(I0A8K}
+8D0A(B BBBF d d h BEB B(D0A8Gp
8E0A(B BBBC
-8E0A(B BBBA T ġ Hl BDB B(A0A8Gp
-8A0A(B BBBA `. K^ H# $ @# < Te\ AG RAT (# g BBB B(A0I8G
-8D0A(B BBBE D & BBB B(A0A8D`8A0A(B BBB 8'o T < ' BBB E(D0A8G
-8D0A(B BBBE \ 5N BBB A(D0GP{
-0D(A BBBH
-0A(A BBBA T 6 BBB B(D0A8G
-8A0A(B BBBA D $ c BBB D(A0D@0A(A BBB T B P BIB E(A0A8DV
-8D0A(B BBBA T pFE BBB E(A0G8G>
-8D0A(B BBBK h. K^ 4 P[ L H[ , P[[ AID t
-DAA D &d BBB D(A0D@0A(A BBB T 8[l BIE B(A0A8DY
-8D0A(B BBBA L \ P` BDB B(D0D8G
-8D0A(B BBBC 0. K^ , ej ADG`
-AAH ̧ g gY l $ Xg O BBB B(D0A8G
+8E0A(B BBBA T l W BDB B(A0A8Gp
+8A0A(B BBBA Ȱ. K^ Lg\ AG RAT p BBB B(A0I8G
+8D0A(B BBBE D BBB B(A0A8D`8A0A(B BBB o T D ب ' BBB E(D0A8G
+8D0A(B BBBE T X ] BBA D(G`
+(A ABBK T
+(D ABBA T BBB B(D0A8G
+8A0A(B BBBA D e BBB D(A0D@0A(A BBB T x BIB E(A0A8DV
+8D0A(B BBBA T k BBB E(A0G8G>
+8D0A(B BBBK ح. K^ , j ADG`
+AAH 4 @ L HY l 7 BBB B(D0A8G
8A0A(B BBBD Z
-8A0A(B BBBD l x. K^ `m Xm `m ̨ hm `m Xm L `mg BEB B(A0A8J
-8A0A(B BBBA 4 s BHE n
-ABH 4 Ĩ (u ډ BHE q
-ABM T v BEB B(A0D8J
-8A0A(B BBBA , . T l BBB B(A0A8J
-8A0A(B BBBB x. K^ $ Љ^ AG b
-AA L ^ BBE B(A0D8G
-8D0A(B BBBF , . K^ D 0 \ ( t ԫ 4 L ؍ d Ѝ | ؍ HS ؍ HS Ԭ ؍" D] , , s YX
-G n
-A 4 L " AZ
-B L BBB A(A0D
-0A(A BBBA D $ BBB A(A0Dg
-0A(A BBBA " D] X. K^ 4 L ؒ , d В BBB wBB 0 ( Į ܮ $ < T l ( 0 , ̯ 8 AAD0[
-AAE ؖ ` , S D 0 \ 8 t G P D $ AG a
-AF ܰ F < % BBF G(F0n
-(A BBDB , 4 О> ADD c
-AAB d BEB B(A0A8D@
+8A0A(B BBBD . K^ 4 L d L | g BEB B(A0A8J
+8A0A(B BBBA 4 $ BHE n
+ABH 4 \ ` BHE q
+ABM T BEB B(A0D8J
+8A0A(B BBBA . T BBB B(A0A8J
+8A0A(B BBBB . K^ $ ^ AG b
+AA L @ F BBE B(A0D8G
+8D0A(B BBBF . K^ h ` , X BBB wBB $ < T l , , D AAD0[
+AAE t ` p S H
+ G D $ , AG a
+AF T 0F < l h% BBF G(F0n
+(A BBDB , X> ADD c
+AAB d 4 h BEB B(A0A8D@
8A0A(B BBBN D
-8A0A(B BBBA < ̱ h BBG A(G@
-(A ABBG L (i BBB B(A0A8Gp
+8A0A(B BBBA < D BBG A(G@
+(A ABBG L i BBB B(A0A8Gp
-8A0A(B BBBK , \ H BOA
-cB| Pc * , Բ BJJ AB < BBG A(G@
-(A ABBG D p5 , \ B BAA :AB 4 7 BKJ A( ABB 4 ij U BBA A(I ABB D BHB B(A0A8D`8A0A(B BBBD D BEB B(A0A8DP8A0A(B BBB ( L BBB B(A0A8G8A0A(B BBB L @ BBB B(A0A8G8A0A(B BBB L D о BBB E(A0A8J8A0A(B BBB L ` BBB E(A0A8J8A0A(B BBB I D 8 BBB D(A0J0A(A BBB D D BBE A(A0J0A(A BBB D BBE A(A0J0A(A BBB Զ ` H $ 0 ELE L , BBB E(A0C8G@
-8A0A(B BBBH L | H BDB B(A0D8DP8A0A(B BBB ̷ < D@w L BEB E(A0A8Iv8A0A(B BBB L L BDB B(A0A8G`8A0A(B BBB 8< D@w 4 `^ BTM F(( ABI L BEB E(A0A8Iv8A0A(B BBB L < BDB B(A0D8D`8A0A(B BBB 8< D@w 4 ` BRA C( ABI L ܹ 8 BBB E(A0C8G@
-8A0A(B BBBH L , BDB B(D0A8DP8A0A(B BBB | < D@w @ < r BBA A(GP^(A ABB < XX BBA A(GPD(A ABB , x $ D BAG ABL l BEB B(A0A8J
+8A0A(B BBBK , BOA
+cB| @ c 4 0* , L H BJJ AB < | 8 BBG A(G@
+(A ABBG 5 , B BAA :AB 4 @ 7 BKJ A( ABB 4 < H U BBA A(I ABB D t p# BHB B(A0A8D`8A0A(B BBBD ($ BEB B(A0A8DP8A0A(B BBB $ L (% BBB B(A0A8G8A0A(B BBB L l * BBB B(A0A8G8A0A(B BBB L X0 BBB E(A0A8J8A0A(B BBB L 6 BBB E(A0A8J8A0A(B BBB \ =I D t = BBB D(A0J0A(A BBB D XA BBE A(A0J0A(A BBB D F BBE A(A0J0A(A BBB L J d K $ | L ELE L pM BBB E(A0C8G@
+8A0A(B BBBH L O BDB B(A0D8DP8A0A(B BBB D @Q< D@w \ hQ L t S BEB E(A0A8Iv8A0A(B BBB L `U BDB B(A0A8G`8A0A(B BBB V< D@w 4 , V^ BTM F(( ABI L d X BEB E(A0A8Iv8A0A(B BBB L `Z BDB B(A0D8D`8A0A(B BBB [< D@w 4 [ BRA C( ABI L T \ BBB E(A0C8G@
+8A0A(B BBBH L 0_ BDB B(D0A8DP8A0A(B BBB `< D@w ` < $ ar BBA A(GP^(A ABB < d bX BBA A(GPD(A ABB d $ d BAG ABL e BEB B(A0A8J
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-8A0A(B BBBA L BEB B(A0A8G
-8A0A(B BBBA L 4 " y BBB B(A0A8Jy
-8A0A(B BBBA L \
BBE B(A0A8G8A0A(B BBB $ 8 BAG ABL Լ BEE B(A0A8G
-8A0A(B BBBA L $ ` BEB B(A0A8G
-8A0A(B BBBA L $ w BBB B(A0A8Jy
-8A0A(B BBBA 4 Ľ BBA Gk
- ABBD L H" BBE B(A0A8G8A0A(B BBB $ L $ ADG uAAL t P%O BEB B(A0A8J
-8A0A(B BBBA L ľ P6 BEB B(A0A8G
-8A0A(B BBBA L < :" 9v BBB B(A0A8Jy
-8A0A(B BBBA L d = BBE B(A0A8G8A0A(B BBB $ @ ADG uAAL ܿ @ BEE B(A0A8G
-8A0A(B BBBA L , M BEB B(A0A8G
-8A0A(B BBBA L HQ$ t BBB B(A0A8Jy
-8A0A(B BBBA , (U{ BAGm
-ABF 4 $ xV t AAG
-AAB L 4 X BBE B(A0A8G8A0A(B BBB D [
BBB B(A0A8
-0A(B BBBA < H\ BBB A(A0
-(A BBBA < \ BBB A(A0
-(A BBBA L L h] BIB B(A0A8OA
-8A0A(B BBBA < n+ r AF
Pf
+8A0A(B BBBA L 4 w BEB B(A0A8G
+8A0A(B BBBA L 0{" G BBB B(A0A8Jy
+8A0A(B BBBA L BBE B(A0A8G8A0A(B BBB $ $ BAG ABL L 8 BEE B(A0A8G
+8A0A(B BBBA L BEB B(A0A8G
+8A0A(B BBBA L D $ BBB B(A0A8Jy
+8A0A(B BBBA 4 < h BBA Gk
+ ABBD L t Г BBE B(A0A8G8A0A(B BBB $ p ADG uAAL ؖO BEB B(A0A8J
+8A0A(B BBBA L < ا BEB B(A0A8G
+8A0A(B BBBA L " w BBB B(A0A8Jy
+8A0A(B BBBA L h BBE B(A0A8G8A0A(B BBB $ , ADG uAAL T BEE B(A0A8G
+8A0A(B BBBA L 0 BEB B(A0A8G
+8A0A(B BBBA L L $ + BBB B(A0A8Jy
+8A0A(B BBBA , D { BAGm
+ABF 4 AAG
+AAB L h BBE B(A0A8G8A0A(B BBB D
BBB B(A0A8
+0A(B BBBA < D BBB A(A0
+(A BBBA < ` BBB A(A0
+(A BBBA L BIB B(A0A8OA
+8A0A(B BBBA < l 0+ 7 AF
Pf
C
- A sR D@M sW D@R (tX D@S $ pt, $ < t AA $ d w< VAA 4 (y] BED H(I@z(A ABB 4 Py BEA A(M
- ABBE 4 { BHA I(
- ABBG $ 4 @} HN
+ A T R D@M l hW D@R X D@S , $ AA $ < VAA 4 ] BED H(I@z(A ABB 4 < BEA A(M
+ ABBE 4 t ` BHA I(
+ ABBG $ HN
J W
-Y L \ BRI B(A0D8G!
-8A0A(B BBBJ H' ` ؇w L @ BLE B(A0A8GQ
-8A0A(B BBBA T l Ў cp BEE B(A0D8J
-8A0A(B BBBA L x BIB B(A0A8Gd
-8A0A(B BBBA L ت BIB B(A0A8Gd
-8A0A(B BBBA d < 8S BBB E(D0D8D
+Y L BRI B(A0D8G!
+8A0A(B BBBJ $ ' < T `w T BEE B(A0D8J
+8A0A(B BBBA L p BIB B(A0A8J
+8A0A(B BBBF L BIB B(A0A8Gd
+8A0A(B BBBA L d ` BIB B(A0A8Gd
+8A0A(B BBBA d ,S BBB E(D0D8D
8A0A(B BBBD
-8A0A(B BBBG L 0 BBB B(A0A8Gd
-8A0A(B BBBA d S BBB E(D0D8D
+8A0A(B BBBG L 0 BBB B(A0A8Gd
+8A0A(B BBBA d l 7S BBB E(D0D8D
8A0A(B BBBD
-8A0A(B BBBG L \ BBB B(A0A8Gd
-8A0A(B BBBA d S BBB E(D0D8D
+8A0A(B BBBG L ; BBB B(A0A8Gd
+8A0A(B BBBA d $ pAS BBB E(D0D8D
8A0A(B BBBD
-8A0A(B BBBG < < m AC
BEO
+8A0A(B BBBG < hE / AC
BEO
A +
- G T | m BBA A(Gp
+ G T $ (N BBA A(Gp
(A ABBE w
-(A ABBK 4 (8 AC
BGM
+(A ABBK 4 $ O8 AC
BGM
H
- F L 0- BBE B(A0A8GK
-8A0A(B BBBG , 4 ( AC
BN
- E T l BBA A(Gp
+ F L \ S- BBE B(A0A8GK
+8A0A(B BBBG , h( AC
BN
+ E T 4 i BBA A(Gp
(A ABBG u
-(A ABBE 4 X AC
BGM
+(A ABBE 4 4 kX AC
BGM
J
- F T /l BKE E(D0A8J
-8A0A(B BBBA L L - BBE B(A0A8GK
-8A0A(B BBBG , #( AC
BN
- E D $ BBB B(A0A8
-0A(B BBBA < ( BBB A(A0
-(A BBBA L T @) BEB B(A0A8Gy
-8A0A(B BBBA < 9 j AC
BEO+
+ F T 8o k BKE E(D0A8J
+8A0A(B BBBA L ~- BBE B(A0A8GK
+8A0A(B BBBG , `( AC
BN
+ E D D ` BBB B(A0A8
+0A(B BBBA < BBB A(A0
+(A BBBA L Ș BEB B(A0A8Gy
+8A0A(B BBBA < t 8 ˷ AC
BEO+
A
- G T D cj BBA A(Gp
+ G T BBA A(Gp
(A ABBG u
-(A ABBE 4 < EX AC
BGM
+(A ABBE 4 X AC
BGM
J
- F T J# i BEE E(D0D8M
-8A0A(B BBBG L PZ BEB B(A0A8G3
-8A0A(B BBBG , f* AC
BN
- D D L g
BBB B(A0A8
-0A(B BBBA L g BEB B(A0A8G
-8A0A(B BBBA < px{
- wh AC
BEO
+ F T D # BEE E(D0D8M
+8A0A(B BBBG L D Z BEB B(A0A8G3
+8A0A(B BBBG , * AC
BN
+ D D
BBB B(A0A8
+0A(B BBBA L X BEB B(A0A8G
+8A0A(B BBBA < {
+ AC
BEO
A {
- G T L Kh BBA A(Gp
+ G T 8 BBA A(Gp
(A ABBE w
-(A ABBK < 8 g AF
P
+(A ABBK < L . AF
P
K j
- A 4 8 AC
BGM
+ A 4 4 8 AC
BGM
H
- F T g BHE B(D0D8J,
-8A0A(B BBBG L L J BEB B(A0A8G#
-8A0A(B BBBG , * AC
BN
- D T f BHE B(D0D8J,
-8A0A(B BBBG $ . K^ < 8 T 0 l ( L 0 f BED A(Gpq
-(C ABBD G
-(C ABBA D e BAC G`^
- AABA b
- CABA d D ( e BEE B(D0A8Dj
-8A0A(B BBBE I
-8C0A(B BBBA % < _e ADD`y
-AAD O
-CAE 4 =e BAA GPf
- AABK , < ^ e BKI ^
-ABA D 0 AL d 0 | (! < @ d ADD`y
-AAD O
-CAE 4 ж wd BAA GPf
- AABK , 4 H^ Cd BKI ^
-ABA < x AL \ x. K^ t ` X P H , @I c AEG yAA ` AL $ ` < X T P l H5 Hl h< Ho 4 4 (. K^ L ob BEA D(F0k
+ F T Ŵ BHE B(D0D8J,
+8A0A(B BBBG L 0J BEB B(A0A8G#
+8A0A(B BBBG , 0* AC
BN
+ D T 0 BHE B(D0D8J,
+8A0A(B BBBG 舒. K^ "4 "4 Ј. K^ L T " U BEA D(F0k
(D ABBJ L
-(D ABBK D \ 8 Ho | (? Hr H @ T 8 a BBE B(D0A8G`
-8D0A(B BBBD $ L a AG@q
-AA D t 85 a BIC Q0v
+(D ABBK L H# d @#8 Ho `#? Hr # x# T , p# BBE B(D0A8G`
+8D0A(B BBBD $ ( AG@q
+AA D p)5 k BIC Q0v
MABK |
- CABF 0 ( T l g $ . K^ < 8 T 0 l 8 0 8 @ H P X- p- < T _ BBA D(D`
-(D ABBG l AT L
+_ BBB F(A0D8G`
-8A0A(B BBBH L L _ BBB B(A0A8JC
-8A0A(B BBBG t
- , # ^ }GA A(D $ * ADG [AA T [ ^ BBE B(D0A8G
-8D0A(B BBBF < d (= ^ BBD D(D
-(A ABBA l ( y^ BBB E(A0A8G
+ CABF h* `* X* P* H* @*T l g < *6 BBA A(G
+(A ABBG l x. K^ p, h, p, x, ,- < T , BBA D(D`
+(D ABBD < / T / l / / AT / DV < /w L BLD A(G@|
+(A ABBA L T 0
BBB F(A0D8G`
+8A0A(B BBBH T 2S BBE B(D0A8G
+8D0A(B BBBD L 8l BBB B(A0A8JC
+8A0A(B BBBG ?
+ , d ># }GA A(D $ < ?* ADG [AA T @[ BBE B(D0A8G
+8D0A(B BBBF < E= BBD D(D
+(A ABBA l T F BBB E(A0A8G
8A0A(B BBBC
-8A0A(B BBBB < +^ BBD D(DU
-(A ABBA T T ( -^ BBE B(D0A8G
-8A0A(B BBBA T b ^ BIB B(A0A8G
-8A0A(B BBBA 4 R BDA r
+8A0A(B BBBB < xH > BBD D(DU
+(A ABBA T J @ BBE B(D0A8G
+8A0A(B BBBA T \ K ' BIB B(A0A8G
+8A0A(B BBBA 4 \ hSR BDA r
ABL A
-ABA T < g c^ BBB E(A0A8J
-8A0A(B BBBA L l BBB B(A0A8Gp8A0A(B BBB 4 ^ BHH w
-ABA AL \ h KLA A(G@(A ABBI@a
-(A ABBI 4 t AAG d
+ABA T S K BBE B(A0A8J A
+8A0A(B BBBF T D 8e BBE B(D0A8G
+8D0A(B BBBG L D j BBB B(A0A8Gp8A0A(B BBB 4 k BHH k
+ABA l AL \ lh KLA A(G@(A ABBI@a
+(A ABBI 4 L n AAG d
DAF K
-DAH T ] BEB B(D0A8J
-8A0A(B BBBI l , @ ^ KEB B(V0Q8L
-8A0A(B BBBH {CL t ` BBB B(A0A8Gp8A0A(B BBB d BBB E(A0A8DP
+DAH T `o î BEB B(D0A8J
+8A0A(B BBBI l 4 (v KEB B(V0Q8L
+8A0A(B BBBE {CL L Hx BBB B(A0A8Gp8A0A(B BBB d y BBB E(A0A8DP
8A0A(B BBBE !
-8A0A(B BBBF T T X \ BBB E(A0A8J
-8A0A(B BBBE L BBF E(A0D8DPV
-8D0A(B BBBE T " ^ BBB B(A0A8JS
-8A0A(B BBBA D T H$ ] BBA D(G
-(A ABBH L % ] BBB B(A0D8J
-8A0A(B BBBG T + ^ BEA A(D0
+8A0A(B BBBF T \ @}e BBB E(A0A8G
+8A0A(B BBBG L \ X BBF E(A0D8DPV
+8D0A(B BBBE T W BBB B(A0A8JS
+8A0A(B BBBA D \ > BBA D(G
+(A ABBH L BBB B(A0D8J
+8A0A(B BBBG T H [ BEA A(D0
(D ABBK D
-(A ABBF l x,y BBF D(A0G@I
+(A ABBF l y BBF D(A0G@I
0A(A BBBI w
0A(A BBBA D
-0F(A BBBG T .; _] BBB B(A0A8J$)
-8A0A(B BBBA T j O` BBB B(A0A8J
-8A0A(B BBBG < hr L | `r ` BEB E(I0H8D@
-8D0A(B BBBA \ s [` BBB D(A0D`
-0D(A BBBF
-0C(A BBBB v AO $ v AP D v \ v t r. K^ u DM u u u u u L L uG _ BBE B(A0D8GD
-8D0A(B BBBA t r. K^ T v{ ^ BBE B(D0A8G
-8D0A(B BBBH T | ^ BEE E(D0A8G
-8D0A(B BBBE L d (h ^ BEB I(A0A8G}
-8D0A(B BBBK H AO L H] BBB H(A0D8DP
-8A0A(B BBBG X D ~
+0F(A BBBG T ) BBB B(A0A8G!
+8A0A(B BBBA L h BBB B(A0A8Jo
+8A0A(B BBBC L | ? BEB E(I0H8D@
+8D0A(B BBBA \ 0 BBB D(A0Da
+0D(A BBBE
+0C(A BBBB p AO p AP p , h D y. K^ \ H DM | H @ 8 0 8 L L @) ȯ BBB B(A0D8G
+8A0A(B BBBA L G BBE B(A0D8GD
+8D0A(B BBBA x. K^ T { d BBE B(D0A8G
+8D0A(B BBBH T \ 0 P BEE E(D0A8G
+8D0A(B BBBE L Xh 6 BEB I(A0A8G}
+8D0A(B BBBK x AO L x] BBB H(A0D8DP
+8A0A(B BBBG D ~
F b
-F p. K^ 4 L d | x p h $ ` < X T P l H @ 8 0 ( , D \ t Ѓ ȃ 4 L d | x p h $ ` < X T P l H @ 8 0 ( , D \ t Ђ Ȃ 4 L d | x p h $ ` < X T P l H @ 8 0 ( , D \ t Ё ȁ Ё 4 L d | ( $ 0 < 8 T @ l 8 0 ( , D \ t , AG bA * De * De " DN
-N A L ( AAG ]
+F < 8w. K^ T l , D \ t x p h ` X P H 4 @ L 8 d 0 | ( $ < T l , D \ t x p h ` X P H 4 @ L 8 d 0 | ( $ < T l , D \ t x p h ` X P H 4 @ L 8 d 0 | ( $ < T l
+ ,
+ D
+ \
+ t
+
+
+
+
+
+ ( 4 0 L ( d | $ < T l , AG bA * De * De " DN
+N A L AAG ]
CAF [
ACA R
-CAJ JCA L L X AAG ]
-CAF [
-ACA R
-CAJ JCA 6 Dq 6 Dq 6 Dq 6 Dq (6 Dq P6 Dq , x6 Dq D 6 Dq \ ȃ: Dk
-I A 4 | Z BGA w
+CAJ JCA ,
H6 Dq D
p6 Dq \
6 Dq t
6 Dq
6 Dq
6 Dq
86 Dq
`6 Dq
: Dk
+I A 4 Z BGA w
AEI A
-ABA $ ^ ADD NAA4 Hy BDD q
-AEO aAB 4 y BDD q
-AEO aAB < L BEE A(D0c
-(J BBBL h
- `v fN ȅ
- , g "O BHH j
-ABA $ D N YX
+ABA $ D ^ ADD NAA4 l y BDD q
+AEO aAB 4 Py BDD q
+AEO aAB
+ v fN
+ , | g BHH j
+ABA T 0 $ ( V YX
G z
-A D ` , Xg N BHH j
-ABA $ N YX
+A , g # BHH j
+ABA $ L YX
G z
-A , g LN BHH j
-ABA 0 $ T ( N YX
+A ( , g BHH j
+ABA d ` $ X YX
G z
-A T , g M BHH j
-ABA ȇ t I DN
+A , g M BHH j
+ABA t I DN
F H
H H
H H
H H
H H
@@ -11031,3930 +11561,4167 @@
H H
H H
H H
H H
H H
-A F , z cV D \ t z cV H @ 8 0 (z cV 4 L d | x pz cV ؉ Љ ȉ z cV ( $ < T l z cV p h ` X Pz cV , D \ z cV t z cV H @ 8 4 0 L (z cV d | x pz cV ؋ Ћ ȋ $ < z cV T l z cV h ` X P , Hz cV D \ t , { AAG r
-DAH 4 F BGB A(A0D 4 4 nF BGB A(A0D < l p_ SF BJD D(G
-(D ABBC $ %F AFD@ $ F AFD@ $ ` E AFD@ $ ȐD E BDA A(DL
+A F | z cV 0 ( z cV x $ p < h T ` l Xz cV z cV , D z cV \ X t P H @ 8z cV 4 z cV L d | z cV 0 ( $ z cV < x T p l h ` Xz cV z cV , D \ t z cV P H @ 8 0z cV 4 L d | xz cV , { AAG r
+DAH 4 | BGB A(A0D 4 ږ BGB A(A0D < _ BJD D(G
+(D ABBC $ , AFD@ $ T ( u AFD@ $ | Y AFD@ D = BDA A(DL
(A ABBB l
(C ABBD m
(A ABBE }
(A ABBE }
(A ABBE }
(A ABBE }
-(A ABBE \ h IE rEA A(Dx
-(A ABBE p T 4 E BBA A(Gl
+(A ABBE \ T rEA A(Dx
+(A ABBE p T 8 } BBA A(Gl
(A ABBA D
-(F ABBA T p D BBA A(GZ
+(F ABBA T e BBA A(GZ
(A ABBC d
-(F ABBA T ؚ D BBA A(GZ
+(F ABBA T d N BBA A(GZ
(A ABBC t
-(F ABBA L < ` D ZBA A(Db(A ABBL d 0 | ( L L % C ZBA A(Dj(A ABBN t П ȟ L $ C ZBA A(DW(A ABBG N D 4 zC BEJ D(D0G
-0D(A BBBA D | w DC BEJ D(D0G
-0D(A BBBE $ V ADD JAA< P BBG A(G@
-(A ABBH < BBG A(G@
-(A ABBH L l Фy gB BBB B(A0A8G
-8A0A(B BBBA < o DB BJD D(G
-(D ABBG L 0 B BEB E(I0H8D@
-8D0A(B BBBA D L J A BBB D(G0G
-0A(A BBBE l * De * De ح6 Dq 6 Dq (6 Dq P6 Dq , x6 Dq D 6 Dq \ Ȯ6 Dq t 6 Dq " DR
-J A (" DR
-J A 4 8Z BGA z
+(F ABBA L 8 ZBA A(Db(A ABBL ` X P H @ , 8 D 0 \ ( L % i ZBA A(Dj(A ABBN N D d BEJ D(D0G
+0D(A BBBA D 0 w ݓ BEJ D(D0G
+0D(A BBBE $ h
+V ADD JAA<
+ BBG A(G@
+(A ABBH ` - < x BBG A(G@
+(A ABBH l 8 BBB B(A0D8D
+8A0A(B BBBH K
+8A0A(B BBBK < $ o BJD D(G
+(D ABBG L d { BEB E(I0H8D@
+8D0A(B BBBA D J S BBB D(G0G
+0A(A BBBE * De * De 6 Dq 6 Dq 6 Dq 4 @6 Dq L h6 Dq d 6 Dq | 6 Dq 6 Dq " DR
+J A " DR
+J A 4 (Z BGA z
AEF A
-ABA `" DR
-J A $ p" DR
-J A D " DR
-J A d " DR
-J A 4 Z BGA z
+ABA $! P" DR
+J A D! `" DR
+J A d! p" DR
+J A ! " DR
+J A 4 ! Z BGA z
AEF A
-ABA ȯ" DR
-J A د" DR
-J A NT KH$ ЯE AAG yAA < AL \ t HS HS HS HS < T HS t ( HS ( 0 HS 0 8 HS
- 8 4
- @ HS T
- @ l
- H HS
- H
- P HS
- P
- X HS
- X ` HS 4 ` L h HS ,
- h ; YX
+ABA ! " DR
+J A ! " DR
+J A " UT KH$ 4" E AAG yAA \" AL |" " HS " " HS " # HS $# <# HS \# t# HS # # HS # # HS $ $ ( HS <$ ( T$ 0 HS t$ 0 $ 8 HS $ 8 $ @ HS $ @ $ H HS % H 4% P HS T% P l% X HS , $ X l YX
G
-B ( ,
- ; YX
+B % , ,% 7 YX
G
-B , $ ر ; YX
+B & , t% YX
G
-B , , l Q; YX
+B L& , % ͋ YX
G
-B t P , H ; YX
+B & @ , & 8 YX
G
-B , : YX
+B & , L& c YX
G
-B
, D : YX
+B $' , & . YX
G
-B L
x , p }: YX
+B l' h , & ` YX
G
-B
0 , ( H: YX
+B ' , $' Ċ YX
G
-B
,
: YX
+B ' , l' YX
G
-B $ , d
9 YX
+B D( , ' Z YX
G
-B l X ,
P 9 YX
+B ( H , ' @ % YX
G
-B ,
t9 YX
+B ( , D( YX
G
-B ȹ , < ?9 YX
+B ) , ( YX
G
-B D , x
-9 AADP~
-AAJ 4 (u 8 YKM
+B d) p! , ( h! AADP~
+AAJ 4 ) #u j YKM
HG
-HA 4 p 8 YKM
+HA 4 <) `$ F YKM
HG
-HA 4 $ k 8 YKpM
+HA 4 t) %k # YKpM
HG
-HA $ 4 E AAG yAA \ AL | HS ( HS ( 0 HS $ 0 < 8 HS \ 8 t @ HS @ H HS H P HS P X HS < X T ` HS t ` h HS h p HS p x HS x 4 HS , | c5 YX
+HA $ T* &E AAG yAA |* ' AL * ' * ' HS * ' * ' HS + ' $+ ' HS D+ ' \+ (' HS |+ (' + 0' HS + 0' + 8' HS + 8' , @' HS $, @' <, H' HS \, H' t, P' HS , P' , X' HS , X' , `' HS - `' - h' HS <- h' T- p' HS , , p' ߅ YX
G
-B @ , 8 .5 YX
+B - 0( , - (( YX
G
-B , 4 YX
+B - ( , \- ( u YX
G
-B , T 4 YX
+B 4. ) , - ) @ YX
G
-B \ h , ` 4 YX
+B |. X* , - P* YX
G
-B , Z4 YX
+B . + , 4. + ք YX
G
-B , , %4 YX
+B / + , |. + YX
G
-B 4 , t 3 YX
+B T/ , , . x, l YX
G
-B | H , @ 3 YX
+B / 8- , / 0- 7 YX
G
-B , 3 YX
+B / - , T/ - YX
G
-B , L Q3 YX
+B ,0 . , / . ̓ YX
G
-B T p , h 3 YX
+B t0 `/ , / X/ YX
G
-B ( , 2 YX
+B 0 0 , ,0 0 c YX
G
-B $ E AAG yAA $ AL D \ HS | HS HS HS $ < ( HS \ ( t 0 HS 0 8 HS 8 @ HS @ H HS < H T P HS t P X HS X ` HS ` h HS h 4 p HS D | p Z/ ZIA A(GP
-(H ABBG D +/ ZIA A(GP
-(H ABBG D < . ZIA A(GP
-(H ABBG \ X D P . ZIA A(GP
-(H ABBG D . ZIA A(GP
-(H ABBG D \ o. ZIA A(GP
-(H ABBG | 8 D 0 @. ZIA A(GP
-(H ABBG D . ZIA A(GP
-(H ABBG < x D | p - ZIA A(GP
-(H ABBG D - ZIA A(GP
-(H ABBG D < - ZIA A(GP
-(H ABBG \ H D @ U- ZIA A(GP
-(H ABBG D &- ZIA A(GP
-(H ABBG x D \ p , ZIA A(GP
-(H ABBG | 4 , YKM
+B 1 0 $ 1 0E AAG yAA D1 0 AL d1 0 |1 0 HS 1 0 1 1 HS 1 1 1 1 HS 2 1 $2 1 HS D2 1 \2 1 HS |2 1 2 1 HS 2 1 2 (1 HS 2 (1 3 01 HS $3 01 <3 81 HS \3 81 t3 @1 HS 3 @1 3 H1 HS 3 H1 3 P1 HS 4 P1 4 X1 HS <4 X1 T4 `1 HS D 3 `1 ZIA A(GP
+(H ABBG 4 3 D ,4 3 ZIA A(GP
+(H ABBG 5 4 D 4 4 x ZIA A(GP
+(H ABBG |5 H6 D 4 @6 I ZIA A(GP
+(H ABBG 5 7 D L5 7 ZIA A(GP
+(H ABBG <6 x9 D 5 p9 ~ ZIA A(GP
+(H ABBG 6 ; D 6 ; ~ ZIA A(GP
+(H ABBG 6 < D l6 < ~ ZIA A(GP
+(H ABBG \7 X> D 6 P> ^~ ZIA A(GP
+(H ABBG 7 ? D ,7 ? /~ ZIA A(GP
+(H ABBG 8 A D 7 A ~ ZIA A(GP
+(H ABBG |8 (C D 7 C } ZIA A(GP
+(H ABBG 8 D D L8 D } ZIA A(GP
+(H ABBG <9 XF D 8 PF s} ZIA A(GP
+(H ABBG 9 G 4 9 G D} YKM
HG
-HA $ HE AAG yAA p AL p , x HS L x d HS HS HS ! HS ,! D! HS d! |! HS ! ! HS ! ! HS " $" HS D" \" HS |" " HS " " HS " # HS $# <# HS \# t# HS # # HS # # HS $ $ HS <$ T$ HS D # ( ' ZIA A(GP
-(H ABBG $ D # ( ' ZIA A(GP
-(H ABBG % D \$ 7 ' ZIA A(GP
-(H ABBG |% D $ p' ZIA A(GP
-(H ABBG % h D % `7 A' ZIA A(GP
-(H ABBG <& X D |% P ' ZIA A(GP
-(H ABBG & D % 7 & ZIA A(GP
-(H ABBG & D <& 7 & ZIA A(GP
-(H ABBG \' D & 7 & ZIA A(GP
-(H ABBG ' D & 7 V& ZIA A(GP
-(H ABBG ( 4( 5 A/ 4 |' & BFA K`
- AABA 4 ' Xx % BAA G@
- AABK < ' % BAD h
-ADD A
-AFH 4 ) @Q BHD A(D0t(D ABB , <) hd BAA N
-ABK l) ) ) ) L ) BBB B(A0A8D
-8A0A(B BBBA T D) () $ BBB B(A0A8G
-8A0A(B BBBA t* AL L * BBF B(A0A8D@
-8D0A(B BBBB d * E BBB B(A0A8DP
+HA $ 9 8IE AAG yAA : `I AL 4: `I L: hI HS l: hI : pI HS : pI : xI HS : xI : I HS ; I ,; I HS L; I d; I HS ; I ; I HS ; I ; I HS ; I < I HS ,< I D< I HS d< I |< I HS < I < I HS < I < I HS = I $= I HS D= I \= I HS |= I = I HS = I = I HS = I > I HS $> I <> I HS \> I t> J HS D = J( yx ZIA A(GP
+(H ABBG > K D L> K7 Jx ZIA A(GP
+(H ABBG <? M D > M7 x ZIA A(GP
+(H ABBG ? O D ? O7 w ZIA A(GP
+(H ABBG ? Q D l? Q7 w ZIA A(GP
+(H ABBG \@ S D ? S7 w ZIA A(GP
+(H ABBG @ U D ,@ U _w ZIA A(GP
+(H ABBG A 8W D @ 0W( 0w ZIA A(GP
+(H ABBG |A Y D @ Y7 w ZIA A(GP
+(H ABBG A [ D LA [ v ZIA A(GP
+(H ABBG <B \ TB \5 A/ \ A _ v BBB A(D0DPk
+0C(A BBBD H
+0A(A BBBH D ,B pa Hv BBB A(A0G`
+0A(A BBBG C c 4C c LC c < B ci u BHD A(D0A
+(A ABBF , C cq BAD V
+ABH C d L C d BBB B(A0A8D
+8A0A(B BBBA T C h) u BBB B(A0A8G
+8A0A(B BBBA D | AL L D | BBF B(A0A8D@
+8D0A(B BBBB d E }E BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l t* xB s$ BBB J(D0D8D4
+8F0A(B BBBA l D ~B t BBB J(D0D8D4
8D0A(B BBBC i
-8A0A(B BBBE L * X #$ BFE B(H0D8M
-8D0A(B BBBD , AP $ ,, ^ AG b
-AA d T, 0 BBB B(A0A8D`<
+8A0A(B BBBE L 4E t BFE B(H0D8M
+8D0A(B BBBD ,F AP $ LF ^ AG b
+AA d tF BBB B(A0A8D`<
8A0A(B BBBL d
-8A0A(B BBBA , AL , DJ , DJ - L \, ! " BEE B(A0D8GP
-8A0D(B BBBA - "9 AX
-G X < , " " BBA A(DU
-(E ABFA < - #
- " BBA A(D[
-(A ABBA $. $$ AX
-G C D. $ < - $; 7" BBA A(G
-(E AFEA T - % " BBB B(A0A8Jz
-8A0A(B BBBH L . )V " BBB A(A0J
-0A(A BBBI T l. 0,4 " BBE E(D0F8J
-8D0A(B BBBA T . 1+ " BBE E(D0A8G2
-8D0A(B BBBG T / 3 ! BBB B(A0A8G
-8A0A(B BBBK T t/ < /" BBB B(D0D8G
-8D0A(B BBBA L / ? ! BED A(G@x
+8A0A(B BBBA F h AL F h DJ G X DJ <G H L F 0 ss BEE B(A0D8GP
+8A0D(B BBBA G `9 AX
+G X < G 3s BBA A(DU
+(E ABFA < \G P
+ s BBA A(D[
+(A ABBA DH $ AX
+G C dH 0 < G (; r BBA A(G
+(E AFEA T H ( r BBB B(A0A8Jz
+8A0A(B BBBH L lH V r BBB A(A0J
+0A(A BBBI T H 4 r BBE E(D0F8J
+8D0A(B BBBA T I + r BBE E(D0A8G2
+8D0A(B BBBG T lI Wr BBB B(A0A8G
+8A0A(B BBBK T I H r BBB B(D0D8G
+8D0A(B BBBA L J yr BED A(G@x
(D ABBD V
-(D ABBA T 0 @@ ! BEB E(A0A8G
-8D0A(B BBBD L L1 xD| BBE E(D0D8D.
-8A0A(B BBBF T 0 I B! BEE E(D0D8J
-8A0A(B BBBA D 1 M _! BEE A(A0
-(A BBBA < <2 N BFB A(A0W
-(A EBBC T 1 8O BBB B(D0C8G
-8D0A(B BBBE 2 V( AG [D T 2 W BBE E(D0D8Gu
-8D0A(B BBBA L3 8c AL L 2 (c ! BEB E(I0H8D@
-8D0A(B BBBA < 3 d BBG A(G@
-(A ABBF D 3 he BBE B(A0D8D@8A0A(B BBB D4 e" DR
-J A , 3 e BRE AE 4 f AL L 4 f BBE E(A0A8D@l
-8A0A(B BBBF l ,4 `g 3 BBB E(I0D8DP
-8D0A(B BBBD
-8D0A(B BBBI T 4 j BEE B(D0D8JX
-8A0A(B BBBF 5 !V Ko
-A 5 v 6 v 6 v 46 v L6 u d6 v AV 6 v AV 6 v < 6 xv BBG A(G@
-(A ABBF l 6 (wx BBF D(A0G@F
+(D ABBA T lJ 9r BEB E(A0A8G
+8D0A(B BBBD L lK | BBE E(D0D8D.
+8A0A(B BBBF T K H q BEE E(D0D8J
+8A0A(B BBBA D lK q BEE A(A0
+(A BBBA < \L 8 BFB A(A0S
+(A EBBG T K ض [q BBB B(D0C8G b
+8D0A(B BBBK L p( AG [D T lL % sq BBE E(D0D8Gu
+8D0A(B BBBA lM X AL L L H r BEB E(I0H8D@
+8D0A(B BBBA < M BBG A(G@
+(A ABBF D N BBE B(A0D8D@8A0A(B BBB dN " DR
+J A , M Oq BRE AE N AL L N BBE E(A0A8D@i
+8A0A(B BBBI l |N p p BBB E(I0D8D`
+8D0A(B BBBE
+8D0A(B BBBA T N Kp BEE B(D0D8JX
+8A0A(B BBBF O (V Ko
+A P H $P @ <P 8 TP 0 lP ( P 0 AV P 0 AV P 0 < P BBG A(G@
+(A ABBF l Q Xx BBF D(A0G@F
0A(A BBBD v
0A(A BBBJ D
-0F(A BBBG T 6 8y e BBB B(D0A8K*
-8A0A(B BBBD 7 . K^ L 7 Ԕw BBB E(A0A8DP[8A0A(B BBB L ,8 | BBB B(A0A8DT
-8A0A(B BBBA L |8 h BBB B(A0A8DI
-8A0A(B BBBG T 7 A / BBB E(A0A8G
-8A0A(B BBBA $9 Ք\ AG RAT l8 % BBA A(D^
+0F(A BBBG T P h n BBB B(D0A8K*
+8A0A(B BBBD Q &. K^ L Q ܑw BBB E(A0A8DP[8A0A(B BBB L LR 8 BBB B(A0A8DT
+8A0A(B BBBA L R BBB B(A0A8DI
+8A0A(B BBBG T DR
+ m BBB E(A0A8G
+8A0A(B BBBA DS ݑ\ AG RAT R @% Xm BBA A(D^
(A ABBB P
-(A ABBA T 8 | BBE E(D0A8G
-8D0A(B BBBE T 9 4 BBB B(D0D8G
-8A0A(B BBBK D t9 _ BFE D(A0G
-0D(A BBBH \ : KBA A(G@(A ABBE@r
-(A ABBH : @ BIB E(D0A8F`w
+(A ABBA T S | m BBE E(D0A8G
+8D0A(B BBBE T lS @4 m BBB B(D0D8G
+8A0A(B BBBK D S ( l BFE D(A0G
+0D(A BBBH \ T p KBA A(G@(A ABBE@r
+(A ABBH lT ol BIB E(D0A8F`w
8A0A(B BBBB
8A0A(B BBBA V
-8A0A(B BBBA T : BEG E(A0A8J
-8D0A(B BBBA ; . K^ , ; (D BDD n
-DBA $ < H^ AG b
-AA 4 l; o BHE
-ABH 4 ; H ; BHE
-ABE 4 < d BBD G(D0G(D ABBd < H BBB B(A0A8DP
+8A0A(B BBBA T T 8
+ Cl BEG E(A0A8J
+8D0A(B BBBA U ". K^ , V D BDD n
+DBA $ <V ^ AG b
+AA 4 U k BHE
+ABH 4 U k BHE
+ABE 4 V d BBD G(D0G(D ABBd dV Ok BBB B(A0A8DP
8A0A(B BBBH D
-8A0A(B BBBA D T= J BBB D(A0D@
-0A(A BBBA L < # BJB A(A0W
-(A BBDH ^(A BBB< = BBG A(G@
-(A ABBG < ,> BBG A(G@
-(A ABBG T = x W BJB B(A0A8G@
-8D0A(B BBBJ > P. K^ L > Дw BBB E(A0A8DP[8A0A(B BBB T T> ȳ BBB E(A0A8G
-8A0A(B BBBH ? AO , > AAG@s
-DAA ? AS , ? AAG@w
-DAA $@ p AS , l? p l AAG@w
-DAA D t@ BBE A(A0D@z
-0A(A BBBA 4 ? - BFD D0
- DABB L @ m BBB B(D0A8GP
-8A0A(B BBBA DA L @ Y BBB B(A0D8G^
-8A0A(B BBBA < A BBG A(G@
-(A ABBF A x] D r
-J B ] D r
-J L TA BEB E(I0H8DP
-8D0A(B BBBD L A BBJ B(D0A8DpS
-8D0A(B BBBA 4 A 8 BDD DPv
- AABA L ,B BBE J(A0A8DP9
-8A0A(B BBBD 4 |B s BDD DPv
- AABA D C BBE A(A0D@
-0A(A BBBA D B @ BBB A(G0D`T
-0C(A BBBH D D ͔v BBB D(A0D@^0A(A BBB d dD ` BFB B(A0A8DP
+8A0A(B BBBA D tW J BBB D(A0D@
+0A(A BBBA L W ( j BJB A(A0W
+(A BBDH ^(A BBB< X x BBG A(G@
+(A ABBG < LX 8 BBG A(G@
+(A ABBG T W i BJB B(A0A8G@
+8D0A(B BBBJ X . K^ L X bؑw BBB E(A0A8DP[8A0A(B BBB T X H ri BBB E(A0A8G
+8A0A(B BBBH Y ; AO , Y ; jj AAG@s
+DAA Y 0< AS , lY 0< +j AAG@w
+DAA DZ < AS , Y < i AAG@w
+DAA D Z = BBE A(A0D@z
+0A(A BBBA 4 4Z =- i BFD D0
+ DABB L lZ >m wi BBB B(D0A8GP
+8A0A(B BBBA d[ @ L Z AY Oi BBB B(A0D8G^
+8A0A(B BBBA < [ G BBG A(G@
+(A ABBF \ H] D r
+J ,\ H] D r
+J L [ (I h BEB E(I0H8DP
+8D0A(B BBBD L [ J ch BBJ B(D0A8DpS
+8D0A(B BBBA 4 D\ hL ?h BDD DPv
+ AABA L |\ L h BBE J(A0A8DP9
+8A0A(B BBBD 4 \ @O g BDD DPv
+ AABA D ] O BBE A(A0D@
+0A(A BBBA D L] pP g BBB A(G0D`T
+0C(A BBBH < <^ ՑD BBD A(D@0(A ABB d |^ Q BFB B(A0A8DP
8A0A(B BBEK
-8D0A(B BBBH L C + BBB E(I0D8DP^
-8D0A(B BBBA T DD ( BBE E(A0A8D
-8D0A(B BBBA T D V BBE B(D0A8G
-8D0A(B BBBE d E BFB B(A0A8DP
+8D0A(B BBBH L <^ R f BBB E(I0D8DP^
+8D0A(B BBBA T ^ `T f BBE E(A0A8D
+8D0A(B BBBA T ^ YV rf BBE B(D0A8G
+8D0A(B BBBE d _ \ BFB B(A0A8DP
8A0A(B BBEK
-8D0A(B BBBH L \E 0 G BEB E(I0H8DP
-8D0A(B BBBD d E { BBB J(A0D8G`
+8D0A(B BBBH L _ h] e BEB E(I0H8DP
+8D0A(B BBBD d _ (_{ e BBB J(A0D8G`
8F0A(B BBBF B
-8F0A(B BBBA T F BEB B(A0A8G
-8A0A(B BBBA T lF k BEE B(A0A8M
-8D0A(B BBBI L F 8 BEB E(H0J8G
-8D0A(B BBBB G X. K^ 4 H p BAD D0~
- AABC 4 <H } AAG R
+8F0A(B BBBA T \` @` ce BEB B(A0A8G5
+8A0A(B BBBA T ` r e BEE B(A0A8M
+8D0A(B BBBI L a kf BEB E(H0J8G
+8D0A(B BBBB b . K^ 4 b H BAD D0~
+ AABC 4 Tb } AAG R
GAM D
-AAA T G P $ BBB E(D0A8G^
-8A0A(B BBBA L G - ~ BBB E(D0A8G
-8A0A(B BBBA T DH H5 BFE E(D0E8D
-8C0A(B BBBH 4 tI 6 BAD D0~
- AABC L H (7 3 BEB E(I0H8D@
-8D0A(B BBBD d $I 8 BBB B(I0A8G`
+AAA T a ( e BBB E(D0A8G^
+8A0A(B BBBA L <b f BBB E(D0A8G
+8A0A(B BBBA T b -f BFE E(D0E8D
+8C0A(B BBBH 4 c h BAD D0~
+ AABC L c e BEB E(I0H8D@
+8D0A(B BBBD d lc e BBB B(I0A8G`
8F0A(B BBBF r
-8F0A(B BBBG T I @: BBB B(A0A8G
-8A0A(B BBBG J
. K^ < I B BBA A(DV
-(A ABBA K
. K^ ,K C DK C L \K C AAG ]
+8F0A(B BBBG T c 8e BBB B(A0A8G
+8A0A(B BBBG d P. K^ < Dd ȫ e BBA A(DV
+(A ABBA ,e (. K^ De \e x L te p AAG ]
CAF [
ACA R
-CAJ JCA L K C AAG ]
+CAJ JCA L e AAG ]
CAF k
ACA R
-CAJ JCA L K D AAG ]
+CAJ JCA L f AAG ]
CAF [
ACA R
-CAJ JCA L LL 8D AAG ]
+CAJ JCA L df AAG ]
CAF k
ACA R
-CAJ JCA L Ɣ\ AG RAL K XD BBB B(A0A8Ji
-8A0A(B BBBI L 4L F BBB B(A0A8Ji
-8A0A(B BBBI L L Ho BBB B(A0A8D
-8A0A(B BBBA < L Q BBD D(G0d
-(A ABBA L M Qo ` BBB B(A0A8D
-8A0A(B BBBA , dM Y T ADG0N
-AAA lN h
-. K^ N Y N Y N Y N Y N Y" DN
-N A ,N Y BBB E(A0A8DJ
-8C0A(B BBBI c
-8C0A(B BBBA q
-8D0A(B BBBB U
-8A0A(B BBBA c
-8C0A(B BBBA T N (^t BBE B(D0A8G
-8D0A(B BBBH T <O Pc BBD D(G
-(A ABBK
-(C ABBF lP T KH P "Ô\ AG RA< P f BBD D(I@
-(D ABBF P Ph P Hh$ A]
-B C 4 Q XhY ACG
-AAD DCA TQ h 4 lQ xha ACG E
-AAF DCA Q h 4 Q hY ACG
-AAD DCA Q h DQ R h DQ 4 $R hS AAF h
-CAD RCA 4 \R i[ AAF n
-CAF RCA 4 R 0iS AAF h
-CAD RCA T Q Xi BBA A(Ir
+CAJ JCA f fΑ\ AG RAL ,f 0 Yc BBB B(A0A8Ji
+8A0A(B BBBI L |f @c BBB B(A0A8Ji
+8A0A(B BBBI L f бo 'c BBB B(A0A8D
+8A0A(B BBBA < g c BBD D(G0g
+(A ABBA L \g `o b BBB B(A0A8D
+8A0A(B BBBA , g b ADG0Q
+AAA h . K^ h < h \ BBD D(D@`
+(D ABBA h dh ~ 3b D M
+A $ h H2 +b D]
+O A Ti `. K^ T h H a BBA A(Di
+(A ABBG e
+(A ABBE i 4 i R BAD D0`
+ DABA li ~ [a D M
+A $ i p* Sa D\
+H A \j . K^ D i ` a BAA D`R
+ AABB U
+ AABG j * j & j & k 0& $ k Hv AG z
+AA Dk \k tk k k k " DN
+N A \ k BBA A(G@P
+(C ABBK \
+(D ABBK
+(A ABBE k \ a_ BBB E(A0A8DJ
+8D0A(B BBBH e
+8A0A(B BBBA q
+8D0A(B BBBB n
+8C0A(B BBBF T 4l t ^ BBE B(D0A8G
+8D0A(B BBBH T l ^ BBD D(G
+(A ABBK
+(C ABBF m T KH m Ǒ\ AG RA< m @ BBD D(I@
+(D ABBF n n $ A]
+B C 4 <n Y ACG
+AAD DCA tn 4 n a ACG E
+AAF DCA n 4 n Y ACG
+AAD DCA o @ DQ ,o H DQ 4 Do PS AAF h
+CAD RCA 4 |o x[ AAF n
+CAF RCA 4 o S AAF h
+CAD RCA T Do m\ BBA A(Ir
(C ABBG E
-(A ABBA < LR jW _ BBA A(J
-(A ABBA D R k" I BBB A(D0G
-0A(A BBBA < R nR \ BDA A(G
-(A ABBH T S o ? BEE E(D0A8G
-8D0A(B BBBE < lS tS % BBA A(Gx
-(A ABBA T . K^ T u2 T v6 G n T Hv AQ T Hv U @v < U 8vD BEE D(D0h(A BBB D T Hv$ .
BLB A(A0GS
-0A(A BBBD T T 0xk
BBB B(A0A8G
-8A0A(B BBBA U . K^ V 0{ ,V ({ L lU { BEB A(A0g
-(A BBDE _(A BBBT U {| Q BBE E(D0A8G
-8D0A(B BBBE D V 7 BJD G@o
+(A ABBA < o W @\ BBA A(J
+(A ABBA D o 0" *\ BBB A(D0G
+0A(A BBBA < $p R =\ BDA A(G
+(A ABBH T dp 8 \ BEE E(D0A8G
+8D0A(B BBBE < p `S \ BBA A(Gx
+(A ABBA q . K^ q h2 q 6 G n q AQ r $r < <r D BEE D(D0h(A BBB D q $ [ BLB A(A0GS
+0A(A BBBD T r k Z BBB B(A0A8G
+8A0A(B BBBA s . K^ 4s Ls L r ~Z BEB A(A0g
+(A BBDE _(A BBBT s | 2Z BBE E(D0A8G
+8D0A(B BBBE D ds Z BJD G@o
DABC ]
- DABD D \V BJD G@l
+ DABD D s Y BJD G@l
DABF ]
- DABD D V X{ BJD G@s
+ DABD D s { Y BJD G@s
DABG @
- DABA T W BHE A(D0D@|
-0D(A BBBA m0D(A BBB T X BHE A(D0D@y
-0D(A BBBD m0D(A BBB L W
- BHE B(A0D8D`I
-8D0A(B BBBA X T KHD X X`
- BJD JT
+ DABA T t BHE A(D0D@|
+0D(A BBBA m0D(A BBB T <u BHE A(D0D@y
+0D(A BBBD m0D(A BBB L t X BHE B(A0D8D`
+8D0A(B BBBA u T KHD Tu ` X BJD JT
DABK `
- DABI D LX p` u
- BJD JT
+ DABI D u ` UX BJD JT
DABK `
- DABI L lY BBB B(A0A8Gp8A0A(B BBB $ Y V ADD JAA< Y @! BLG A(G@
-(A ABBF d $Z 0 BBB J(A0D8GP&
-8F0A(B BBBG
-8D0A(B BBGA T Y X % BFB E(A0A8D
-8A0A(B BBBA < Z BFB A(A0W
-(A EBBC T $[ P BBB D(C0GP
-0D(A BBBD n0D(A BBB T Z T N BBB E(D0D8Gz
-8D0A(B BBBG < [ 1 BLG A(G@
-(A ABBH L <[ BEB E(I0H8D@
-8D0A(B BBBA L [ BLB B(A0D8Dp#
-8A0A(B BBBA l [ g BBE B(A0A8DJ
+ DABI L v BBB B(A0A8Gp8A0A(B BBB $ v XV ADD JAA< w ! BLG A(G@
+(A ABBF d Dw BBB J(A0D8GP
+8F0A(B BBBG
+8D0A(B BBGG T w W BFB E(A0A8D
+8A0A(B BBBA < x BFB A(A0S
+(A EBBG \ w V BBB D(C0GP
+0D(A BBBD n0D(A BBB \ w @y 3V BEB D(D0Gp
+0D(A BBBH f
+0D(A BBBG < y `1 BLG A(G@
+(A ABBH L x ` U BEB E(I0H8D@
+8D0A(B BBBA L x U BLB B(A0D8Dp#
+8A0A(B BBBA l <y P CU BBE B(A0A8DJ
8A0A(B BBBA T
-8A0A(B BBBA $] . K^ T d\
- BBE B(A0D8J
-8A0A(B BBBA T \ ! BBE B(A0D8J
-8A0A(B BBBA T ] x( ; BBB A(A0U
+8A0A(B BBBA Tz . K^ T y T BBB B(D0D8J
+8A0A(B BBBK T z
T BBB B(D0D8J
+8A0A(B BBBK T tz X( U BBB A(A0U
(A BBBD h
-(H BBBJ T l] P( BBB A(A0U
+(H BBBJ T z 0( T BBB A(A0U
(A BBBD h
-(H BBBJ T ] ( BBE B(D0A8G
-8D0A(B BBBG T ^ BBE E(D0A8G
-8D0A(B BBBK T t^ ( BEE E(D0A8G
-8D0A(B BBBE D ^ P BBB A(D0J
-0A(A BBBD D _ BBB A(D0J
-0A(A BBBD 4` . K^ $ t_ AL n
-AA t` ` , _ 1 AAG cCA ` ` a
- a x 4a p La h da `2 |a a a x a p $ a h `FD@ b b 4 4b BAD F0q
- DABK lb X b P T a H BEB F(A0G
-(A BBDH ~(A BBB b 4 4b PGB A(A0D T lb n BEE E(D0A8GI
-8D0A(B BBBE < c ( BEB D(A0
-(A EBBD L c % BBB E(D0A8Gp
-8D0A(B BBBB T Tc BBE B(D0A8G
-8D0A(B BBBF L c 0 BJB A(A0W
-(A BBDH ^(A BBB, d s AAG0u
-AAA L e 6 BBE B(A0A8DP
-8A0A(B BBBA < Te BGB A(A0
-(A BBBA < e BBG A(G@
-(A ABBF d e p BBB B(D0A8DP
+(H BBBJ T ${ T BBE B(D0A8G
+8D0A(B BBBG T |{ T BBE E(D0A8G
+8D0A(B BBBK T { " T BEE E(D0A8G
+8D0A(B BBBE D ,| 0' T BBB A(D0J
+0A(A BBBD D t| + T BBB A(D0J
+0A(A BBBD d} . K^ $ | / T AL n
+AA } p0 } h0 , ,} `01 T AAG cCA ~ p0 ~ h0 4~ `0
+ L~ X0 d~ P0 |~ H0 ~ @02 ~ h0 ~ `0 ~ X0 ~ P0 $ d~ H0 S `FD@ 4 0 L 0 4 ~ 0 8S BAD F0q
+ DABK 82 02 T $ (2 R BEB F(A0G
+(A BBDH ~(A BBB $ 2 4 2 mR PGB A(A0D T 3 SR BEE E(D0A8GI
+8D0A(B BBBE < ̀ 8 BEB D(A0
+(A EBBD L d 9% Q BBB E(D0A8Gp
+8D0A(B BBBB T x: Q BBE B(D0A8G
+8D0A(B BBBF L ? Q BJB A(A0W
+(A BBDH ^(A BBB, `?s AAG0u
+AAA L 4 ?6 BBE B(A0A8DP
+8A0A(B BBBA < @ BGB A(A0
+(A BBBA < Ă pA BBG A(G@
+(A ABBF d PB BBB B(D0A8DP
8A0A(B BBBC
-8A0A(B BBBH T de BBB B(D0A8G\
-8A0A(B BBBA , e ~ AID E
-DAA L e BEB E(I0H8D@
-8D0A(B BBBA D g ` BCD D0
+8A0A(B BBBH T Ă G O BBB B(D0A8G\
+8A0A(B BBBA , pN~ O AID E
+DAA L L N O BEB E(I0H8D@
+8D0A(B BBBA D D @P BCD D0
FABE D
- FABA T f 8R K BEE E(A0D8I
-8D0A(B BBBG , g @ ADD ]
-AAA g BBF D(A0G@I
+ FABA T QR /O BEE E(A0D8I
+8D0A(B BBBG , V ADD ]
+AAA V BBF D(A0G@I
0A(A BBBI }
0D(A BBBH D
0F(A BBBG c
-0A(A BBBE d lh BBB E(D0A8L`x
+0A(A BBBE d W BBB E(D0A8L`x
8D0A(B BBBG
-8D0A(B BBBF T g 6 BBB B(D0D8G
-8D0A(B BBBA L Th BJE B(A0A8Dp
-8C0A(B BBBB T h z BBB B(D0A8J
-8A0A(B BBBA $ i `^ AG b
-AA L $i o BEB E(I0H8D@
-8D0A(B BBBD T ti G BBB E(D0A8G
-8A0A(B BBBD T i & BJE B(A0A8G
-8A0A(B BBBA , j s AAG0u
-AAA L Tj BEB E(I0H8D@
-8D0A(B BBBD d j X O BBB B(I0A8G`
-8F0A(B BBBF g8F0A(B BBB T k @ / BEE E(F0A8Jt
-8A0A(B BBBA <l H. K^ Tl P ll H L l @ AAG e
+8D0A(B BBBF T \ Y6 M BBB B(D0D8G
+8D0A(B BBBA L [5 M BJE B(A0A8Dp
+8C0A(B BBBC T \ ^M BBB B(D0A8J
+8A0A(B BBBA $ pd^ AG b
+AA L d SM BEB E(I0H8D@
+8D0A(B BBBD T Ԇ (f +M BBB E(D0A8G
+8A0A(B BBBD T , h& L BJE B(A0A8G
+8A0A(B BBBA , , js AAG0u
+AAA L j L BEB E(I0H8D@
+8D0A(B BBBD d hlO kL BBB B(I0A8G`
+8F0A(B BBBF g8F0A(B BBB T l Pm L BEE E(F0A8Jt
+8A0A(B BBBA l (. K^ `s Xs L Ps AAG e
CAF K
ACI R
-CAJ JCA L l AAG e
+CAJ JCA L s AAG e
CAF C
ACA R
-CAJ JCA $m DV $ <m ^ AG b
-AA \ dm P BEG O(G0
+CAJ JCA T t DV $ l (t^ AG b
+AA \ `t BEG O(G0
(A ABBA Q
(E ABBE Q
-(E ABBE L l BBE B(D0D8FP
-8D0A(B BBBA $ n p
-: ACG lAA L dm
-5 N BBB E(D0A8Gp
-8D0A(B BBBG L m x / BBB B(D0G8Dc
-8D0A(B BBBI L n X
2 BBB B(D0G8Dc
-8D0A(B BBBI < ,o H BBG A(G@
-(A ABBG L n ~ BJB A(A0W
-(A BBDH ^(A BBBL n X 3 BEB E(I0H8D@
-8D0A(B BBBA T 4o BBB B(A0A8J
-8A0A(B BBBA T o 2 c BBB B(A0A8G
-8A0A(B BBBE p T KHT o G BBE B(D0D8G]
-8A0A(B BBBG ,q . K^ 4 lp `K BAD `
-ABF D |q (M BBB A(D0DPa
-0A(A BBBH < p M J AAG @
+(E ABBE L L u J BBE B(D0D8FP
+8D0A(B BBBA $ D v: ACG lAA L Ċ v5 2J BBB E(D0A8Gp
+8D0A(B BBBG L w/ I BBB B(D0G8Dc
+8D0A(B BBBI L d hy2 I BBB B(D0G8Dc
+8D0A(B BBBI L \ X| BFB B(A0A8D@f
+8D0A(B BBBK < | BBG A(G@
+(A ABBG L D } I BJB A(A0W
+(A BBDH ^(A BBBL } H BEB E(I0H8D@
+8D0A(B BBBA T X H BBB B(A0A8J
+8A0A(B BBBA T < I BBB B(A0A8G
+8A0A(B BBBE < T KHT @ J BBE B(D0D8G]
+8A0A(B BBBG x. K^ 4 :J BAD `
+ABF D BBB A(D0DPa
+0A(A BBBH < P I AAG @
AAE p
-AAB L ,q 0N BBB A(D0H
-0D(E BBBH D |q O BEB A(D0?
-(D BBBI T q Qt BBE B(D0A8G
-8D0A(B BBBH L r V BEE B(D0D8M
-8A0A(B BBBE D lr Y BBB A(A0G
-0A(A BBBC T r h[ f BBE E(D0D8J
-8A0A(B BBBB s `. K^ s _ t _ ,t ֟\ AG RAT ts _ BBA D(GPR
+AAB D 0 I BBB A(A0Gz
+0A(A BBBA D $ XI BEB A(D0?
+(D BBBI T l pt I BBE B(D0A8G
+8D0A(B BBBH L ď H BEE B(D0D8M
+8A0A(B BBBE D H H BBB A(A0G
+0A(A BBBC T \ H BBE E(D0D8JK
+8A0A(B BBBK \ . K^ t .\ AG RAT mH BBA D(GPR
(D ABBE P
-(D ABBG L s ` BEB B(A0A8G[
-8D0A(B BBBD d t ps BBB B(A0A8DP
+(D ABBG L t =H BEB B(A0A8G[
+8D0A(B BBBD d l X BBB B(A0A8DP
8A0A(B BBBE D
-8A0A(B BBBA L \u s8 BBE B(D0A8DP
-8D0A(B BBBE L t t BEE B(D0D8D@
-8D0A(B BBBJ d u xv BEE F(A0A8D@c
-8F0A(B BBBF
-8D0A(B BBGA L dv x BBB B(A0A8Gp8A0A(B BBB L v y BLB B(A0A8DP
-8A0A(B BBBA L w @z BLB B(A0A8DP
-8A0A(B BBBE L |v { BLB E(A0A8D`
-8A0A(B BBBF T v @} BBB B(A0D8G
-8A0A(B BBBF 4 $w BEH G0x
- DABK 4x @T KH, Lx 0 AC
FK
- E d |x BBB E(A0A8G@p
+8A0A(B BBBA L Ԓ BEB B(A0A8DP
+8D0A(B BBBG L | H BEE B(D0D8D@
+8D0A(B BBBJ d t @
BEE F(A0A8D@`
+8F0A(B BBBI
+8D0A(B BBGG L ܓ BBB B(A0A8Gp8A0A(B BBB L , h BLB B(A0A8DP
+8A0A(B BBBA L | BLB B(A0A8DP
+8A0A(B BBBE L $ }F BLB E(A0A8D`
+8A0A(B BBBF T t 5F BBB B(A0D8G
+8A0A(B BBBF 4 ̔ LF BEH G0x
+ DABK T KH, ĕ `0 AC
FK
+ E d ` BBB E(A0A8G@p
8A0A(B BBBB i
-8A0A(B BBBA T x BBB A(D0o
+8A0A(B BBBA T E BBB A(D0o
(A BBBG E
-(A BBBD L dx h BGB E(A0K8Dp
-8D0A(B BBBA y W y 0 $ x 8 k AG@q
-AA D y 2 T BBB A(A0D
-0A(A BBBA ,z HP T ty
- BEE E(D0A8G
-8D0A(B BBBG < y 0< BEA A(Gh
-(A ABBA < z 07 BBA A(Gf
-(A ABBA ${ T KH$ dz AL b
-AA D d{ BBE A(A0D@n
-0A(A BBBA { ( $ z / AG@c
-AA { ؗ | З | ؗ0 Dk 4| H t{ ( AR l| ( | 0' DR
-J F $ { @ k AG0S
-AD { Ș [ K IN L | 8 S BBB B(D0A8D`
-8D0A(B BBBH T d| G BEB B(A0A8D
-8A0A(B BBBD L | ; BBB E(D0D8FS
-8A0A(B BBBJ T } ; BBD A(J`
+(A BBBD L @ 6E BGB E(A0K8Dp
+8D0A(B BBBA $ \ E AG@q
+AA D 82 D BBB A(A0D
+0A(A BBBA t 0 HP < 0) ]SN
+AAD DHAO ԗ T D ( SD BEE E(D0A8G
+8D0A(B BBBG < < 9D BEA A(Gh
+(A ABBA < ܗ 7 D BBA A(Gf
+(A ABBA Ę T KH$ 4 C AL b
+AA D BBE A(A0D@n
+0A(A BBBA L $ {C AG@c
+AA X P X0 Dk ԙ pH D C AR $ ' DR
+J F $ B AG0S
+AD ę H B K IN L B BBB B(D0A8D`
+8D0A(B BBBH T 4 B BEB B(A0A8D
+8A0A(B BBBD L p B BBB E(D0D8FS
+8A0A(B BBBJ T ܚ ; KB BBD A(J`
(A ABBD U
-(A ABBA L d} hN BBB A(D0G
-0A(A BBBE \ } h BBB D(D0G`
+(A ABBA L 4 N B BBB A(D0G
+0A(A BBBE \ CB BBB D(D0G`
0A(A BBBG U
-0A(A BBBK ~ Hݔ. K^ $ ^ AG b
-AA , D AAG0q
-AAA T ~ @ # BBB E(A0D8GJ
-8D0A(B BBBB \ BBG A(D0d
-(D ABBC ]
-(D ABBJ f(D ABB < , h BBE D(D0_
-(A BBBI l ? T GBB A(D0D@G0A(A BBBB@ ܀ x AV x T T x BEE E(D0A8G
-8D0A(B BBBH T BEE E(D0A8G
-8D0A(B BBBE 4 ܁ ( ADG
-DAH V
-DAA T < p O BEE E(D0A8G
-8D0A(B BBBE l ` 4 g ADG
-DAH G
-DAA T < BBE B(D0A8G
-8D0A(B BBBB T < BBB B(A0D8D}
-8D0A(B BBBE L X AAJm
-DAB
-DAI ]
-DAF T w BBB B(A0D8D}
-8D0A(B BBBE L < p J AAJm
-DAB
-DAI ]
-DAF T @ # BBB B(A0D8D}
-8D0A(B BBBE < AAGm
-DAE
-DAF T $ BBB B(A0D8D}
-8D0A(B BBBE < | p AAGm
-DAE
-DAF T BBB B(A0D8D}
-8D0A(B BBBE < H n AAJm
-DAB
-DAK T T W BBB B(A0D8D}
-8D0A(B BBBE < p * AAJm
-DAB
-DAK T @ BBB B(A0D8D}
-8D0A(B BBBE L D AAJm
-DAB
-DAG ]
-DAF T h BBB B(A0D8D}
-8D0A(B BBBE L AAJm
-DAB
-DAD ]
-DAF T < k BBB B(A0D8D}
-8D0A(B BBBE L > AAJm
-DAB
-DAD ]
-DAF T BBB B(A0D8D}
-8D0A(B BBBE L < 8 AAJm
-DAB
-DAF ]
-DAF T BBB B(A0D8D}
-8D0A(B BBBE L H
- AAGp
-DAC
-DAH n
-DAE T 4 d BBB B(A0D8D}
-8D0A(B BBBE < 7 AAJp,
-DAC f
-DAE T ̉ P BBB B(A0D8D}
-8D0A(B BBBE < $ AAJp,
-DAC f
-DAE T d BHB E(D0D8JD
-8D0A(B BBBD ДT KHT Ԋ X- BBB B(A0D8G
-8D0A(B BBBB / / $ 4 /^ AG b
-AA , \ 00 AC
FK
- E T 1 , BBB B(A0I8Dr
-8A0A(B BBBA T p3 BBB B(I0A8D
-8A0A(B BBBA , < (5f AG d
+0A(A BBBK D A BEF A(A0G`
+0A(A BBBI Ԝ p. K^ $ ^ AG b
+AA , , AAG0q
+AAA T 6A BBB E(A0D8GJ
+8D0A(B BBBB \ @ BBA D(D@h
+(D ABBB B
+(D ABBE O
+(D ABBH < BBE D(D0_
+(A BBBI T ? T l GBB A(D0D@G0A(A BBBB@ Ğ AV T l ? BEE E(D0A8G
+8D0A(B BBBH T Ğ # ? BEE E(D0A8G
+8D0A(B BBBE L ğ (] BEE E(A0A8DPz
+8D0A(B BBBB L )Q BEE E(A0A8DPz
+8D0A(B BBBB T * > BEE E(D0A8G
+8D0A(B BBBE T /< > BBE B(D0A8G
+8D0A(B BBBB T l 4 > BBB B(A0D8D}
+8D0A(B BBBE T Ġ 88 > BBA D(Gm
+(D ABBJ E
+(D ABBB T ; k> BBB B(A0D8D}
+8D0A(B BBBE T t ? >> BBA D(Gm
+(D ABBJ
+(D ABBE T ̡ A > BBB B(A0D8D}
+8D0A(B BBBE T $ 8E = BBA D(Gm
+(D ABBJ
+(D ABBD T | H = BBB B(A0D8D}
+8D0A(B BBBE T Ԣ XK = BBA D(Gm
+(D ABBJ
+(D ABBD T , N Z= BBB B(A0D8D}
+8D0A(B BBBE T xQ -= BBA D(Gm
+(D ABBJ 4
+(D ABBC T ܣ U < BBB B(A0D8D}
+8D0A(B BBBE T 4 HY < BBA D(Gm
+(D ABBJ 4
+(D ABBC T ] < BBB B(A0D8D}
+8D0A(B BBBE T a w< BBA D(Gm
+(D ABBJ
+(D ABBE T < c I< BBB B(A0D8D}
+8D0A(B BBBE T 8g < BBA D(Gm
+(D ABBJ
+(D ABBH T j ; BBB B(A0D8D}
+8D0A(B BBBE T D Xm ; BBA D(Gm
+(D ABBJ
+(D ABBH T p ; BBB B(A0D8D}
+8D0A(B BBBE T xs( f; BBA D(Gm
+(D ABBJ
+(D ABBG T L Pv 8; BBB B(A0D8D}
+8D0A(B BBBE D y ; BAD Gv
+ DABE f
+ DABC T P~ : BBB B(A0D8D}
+8D0A(B BBBE D D : BAD GE
+ DABF f
+ DABC T : BBB B(A0D8D}
+8D0A(B BBBE D x `: BAD GE
+ DABF f
+ DABC T , U 7: BBE E(D0A8JN
+8A0A(B BBBK , HӑT KHL S; BEB B(A0D8Gp
+8D0A(B BBBC ` X $ Ī `^ AG b
+AA , 0 AC
FK
+ E T t : BBB B(A0I8Dr
+8A0A(B BBBA T ̪ u: BBB B(I0A8D
+8A0A(B BBBA , ̫ f AG d
AC D
-CA l h5 L `58 BBE B(D0A8DP
-8D0A(B BBBE ԍ P6 AY
-F a, 6# ACG0
-AAI L L 7 BBE B(D0A8Ds
-8D0A(B BBBD T 9 BEB B(A0A8G
-8A0A(B BBBI T > BEB B(A0A8J
-8A0A(B BBBA T L C BEE B(D0A8G
-8D0A(B BBBC D K, BBB D(D0IPb
-0D(A BBBD T N BBE E(D0D8J
-8D0A(B BBBG L XS~ BOB B(A0A8Dp~
-8A0A(B BBBE T W
BEE B(A0A8J
-8A0A(B BBBK Đ e1 AG0dD L @eF BBE E(D0A8Gl
-8A0A(B BBBH 4 ˔T KH L (l d 0l$ | Hl @l HlZ HQ L ̑ l AAG e
+CA L BEB B(A0A8DP
+8D0A(B BBBG d AY
+F a, ܫ # 9 ACG0
+AAI L q9 BBE B(D0A8Ds
+8D0A(B BBBD T \ V :9 BBE B(A0A8G
+8A0A(B BBBG T *9 BEB B(A0A8J
+8A0A(B BBBA T @ ?9 BEE B(D0A8G
+8D0A(B BBBC D d , _9 BBB D(D0IPb
+0D(A BBBD T O9 BBE E(D0D8J
+8D0A(B BBBG L ~ BOB B(A0A8Dp~
+8A0A(B BBBE T T H
8 BEE B(A0A8J
+8A0A(B BBBK T 1 AG0dD L ̮ F )9 BBE E(D0A8Gl
+8A0A(B BBBH į ΑT KH ܯ $ $ < Z HQ L \ H AAG e
CAF {
ACI R
-CAJ JCA L l AAG ]
+CAJ JCA L AAG ]
CAF [
ACA R
-CAJ JCA L l m AAG e
+CAJ JCA L AAG e
CAF s
ACA R
-CAJ JCA hm Ԓ na HT $ Pn^ AG b
-AA $ n; F^
-D K 4 D nM BDF o
-ABJ AAB L n ^ BJB A(A0G
-0A(A BBBA < hq$ F BBA A(D
-(A ABBA < 4 Xr $ BBA A(D
-(E ABGA < t 8s3 BBA A(D
-(E AFEA < 8t BBA A(D
-(E ABGA L u$ BBB H(D0D8J
-8A0A(B BBBJ l D wY BEB E(A0F8DW
+CAJ JCA L ( d a HT $ ^ AG b
+AA $ H; F^
+D K 4 Ա `M BDF o
+ABJ AAB L d x 6 BJB A(A0G
+0A(A BBBA < ($ 6 BBA A(D
+(A ABBA < 6 BBA A(D
+(E ABGA < 4 3 v6 BBA A(D
+(E AFEA < t U6 BBA A(D
+(E ABGA L $ 36 BBB H(D0D8J
+8A0A(B BBBJ l Y %6 BEB E(A0F8DW
8A0A(B BBBA
-8A0A(B BBBA < yM U BBA A(D
-(A ABBA T z 8 BBB B(A0A8J
-8A0A(B BBBE D L } BBA A(D
-(A ABBF d l BLB B(A0A8D@c
+8A0A(B BBBA < t M 5 BBA A(D
+(A ABBA T 5 BBB B(A0A8J
+8A0A(B BBBE D p {5 BBA A(D
+(A ABBF d BLB B(A0A8D@c
8A0A(B BBBK |
-8A0A(B BBBJ T BBE E(A0A8G
-8D0A(B BBBA l T BBB B(A0D8D;
+8A0A(B BBBJ T p N5 BBE E(A0A8G
+8D0A(B BBBA l 5 BBB B(A0D8D;
8A0A(B BBBJ W
-8A0A(B BBBA T Ė A BBE E(A0A8G
-8D0A(B BBBE 4 J BDA q
+8A0A(B BBBA T H 4 BBE E(A0A8G
+8D0A(B BBBE 4 J BDA q
ABE A
-ABA L T n BBB B(D0A8D
-8D0A(B BBBE T 8 BBE B(A0A8G
-8A0A(B BBBA l o BEB B(A0A8D
+ABA L n @4 BBB B(D0A8D
+8D0A(B BBBE T d 4 BBE B(A0A8G
+8A0A(B BBBA l 3 BEB B(A0A8D
8D0A(B BBBA C
-8A0A(B BBBJ T l / BBE E(G0A8GF
-8D0A(B BBBH d X BEE F(A0A8D@c
-8F0A(B BBBF
-8D0A(B BBGA L , BBE F(A0A8F`
-8D0A(B BBBH D T GBA A(G@Q(A ABBG@ \ m KLA A(G@(A ABBI@n
-(A ABBD BBD G(R@
+8A0A(B BBBJ T , @/ 3 BBE E(G0A8GF
+8D0A(B BBBH d , BEE F(A0A8D@`
+8F0A(B BBBI
+8D0A(B BBGG L
+ 63 BBE F(A0A8F`
+8D0A(B BBBH D GBA A(G@Q(A ABBG@ \ , h
m KLA A(G@(A ABBI@n
+(A ABBD x BBD G(R@
(D ABBD Q
(D ABBF Q
(E ABBE Q
(E ABBE Q
-(E ABBA L
Q BBB H(A0A8Dp-
-8D0A(B BBBB d BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB < < (1 BLG A(G@
-(A ABBH D (g w BBE A(A0J`g
-0D(A BBBI < Ĝ P BBG A(G@
-(A ABBF L , BEB E(I0H8DP
-8D0A(B BBBA L T Щ
+(E ABBA L l
1 BBB H(A0A8Dp-
+8D0A(B BBBB d 1 BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB < ̺ 1 BLG A(G@
+(A ABBH D d g 0 BBE A(A0J`g
+0D(A BBBI < T 0 BBG A(G@
+(A ABBF L 0 BEB E(I0H8DP
+8D0A(B BBBA L
BBB E(D0D8G
-8A0A(B BBBE T ̜ | BBB B(A0I8G
-8A0A(B BBBF L $ Ⱦ BBB E(D0A8Dp
-8D0A(B BBBH L t 84 BEE E(D0D8L
-8D0A(B BBBA T KH ̞ . K^ A[ 4 L L d AAG ]
+8A0A(B BBBE T !| 0 BBB B(A0I8G
+8A0A(B BBBF L , I0 BBB E(D0A8Dp
+8D0A(B BBBH L 4 04 $0 BEE E(D0D8L
+8D0A(B BBBA , T KH D 0 \ 0 4 ̼ 0 / BGB A(A0D $ 1 Dw
+E c
+M Խ . K^ 82 A[ 82 $ @2 L < 82 AAG ]
CAF [
ACA R
-CAJ JCA L 0 AAG ]
+CAJ JCA L h2 AAG ]
CAF [
ACA R
-CAJ JCA L ` AAG ]
+CAJ JCA L ܾ 2 AAG ]
CAF [
ACA R
-CAJ JCA T
-t\ AG RA$ p AG Y
-AA T ğ ` w BBE E(D0A8G
-8A0A(B BBBA , AID J
-DAA , L P AAG0i
-AAA L | BEB E(I0H8D@
-8D0A(B BBBD L ̠ W BJE B(A0A8D`8A0A(B BBB T 9 BBE E(A0A8J
-8D0A(B BBBA T t BEE E(A0D8J
-8D0A(B BBBA . K^ (0 Hg ܢ 87 Hj L X AAG e
+CAJ JCA , w\ AG RA$ 2 - AG Y
+AA T ̾ 3` - BBE E(D0A8G
+8A0A(B BBBA , $ (> . AID J
+DAA , T > - AAG0i
+AAA L 8? - BEB E(I0H8D@
+8D0A(B BBBD L Կ @ - BJE B(A0A8D`8A0A(B BBB T $ HA9 T- BBE E(A0A8J
+8D0A(B BBBA T | 0F "- BEE E(A0D8J
+8D0A(B BBBA | H. K^ `L0 Hg pL7 Hj L L AAG e
CAF s
ACA R
-CAJ JCA d L BBB B(A0A8DP
+CAJ JCA d $ L BBB B(A0A8DP
8A0A(B BBBE D
-8A0A(B BBBA $ 0^ AG b
-AA , ܣ h ADD0e
-AAA \ 4 \ BBB A(D0GPw
-0A(A BBBG w
-0A(A BBBA L l BOB A(A0]
+8A0A(B BBBA $ hM^ AG b
+AA , M ADD0e
+AAA \ < N + BBB A(D0GPq
+0A(A BBBE w
+0A(A BBBA L D N BOB A(A0]
(A BBBG Y
-(A BBBH T | BBE E(D0A8G
-8D0A(B BBBE D 0 BBE A(A0D@d
-0A(A BBBA T 1 g BEE E(D0A8GI
-8D0A(B BBBE < ܤ o M BBA D(Gp
-(D ABBA L # BBB B(A0D8D`b
-8A0A(B BBBC < D BBG A(G@
-(A ABBG T BBE B(A0A8G
-8A0A(B BBBA T 82 BBB B(A0A8DI
-8A0A(B BBBG 4 . K^ < t W BGD A(K0
-(A ABBE D ( e BBE A(A0D@
-0A(A BBBA T P 3 BEE B(D0A8J
-8A0A(B BBBF D , t BBB D(A0D@
-0A(A BBBA 4 t P Y BEA A(G0(D ABB x Ĩ p \ ܨ l BBB B(A0A8DP
-8A0A(B BBBE D8A0A(B BBB$ < ^ AG b
-AA d . K^ T 8 K$ BBB B(A0D8J
-8A0A(B BBBA ԩ 0C (C DV L 0C, BEB B(A0A8DP8A0A(B BBB L T D3 BBE B(A0A8DP8A0A(B BBB < E BEH D(A0H@0A(A EBBT EJ / BHG B(A0D8J
-8D0A(B BBBF < T KHT | p\| BBE E(D0A8G
-8D0A(B BBBE . K^ T aJ BBE B(D0A8G
-8A0A(B BBBJ . K^ T \ `hw
{ RIB B(A0A8GP
-8A0A(B BBBA u uF , uD 1 AHGP
-AAA , vA AHG`
-AAA x , 4 xb D\
+(A BBBH T @O|
+ BBE E(D0A8G
+8D0A(B BBBE D hT BBE A(A0D@d
+0A(A BBBA T U1 * BEE E(D0A8GI
+8D0A(B BBBE < q * BBA D(Gp
+(D ABBA L $ Z g* BBB B(A0D8D`b
+8A0A(B BBBC < 8] BBG A(G@
+(A ABBG T ]^ ) BBE B(A0A8G
+8A0A(B BBBA T u" + BBB B(A0A8D:
+8A0A(B BBBF 踑. K^ < | w * BBD A(D0P
+(A ABBE D d py4 BBE A(A0D@
+0A(A BBBA T hz o* BEE B(D0A8J
+8A0A(B BBBF D @ BBE A(A0D@
+0A(A BBBA 4 L Y BEA A(G0(D ABB Ќ Ȍ \ o BBB B(A0A8DP
+8A0A(B BBBE D8A0A(B BBB$ p^ AG b
+AA < 趑. K^ T ) BBB B(A0D8Jh
+8A0A(B BBBA 8 0 DV L 8, BEB B(A0A8DP8A0A(B BBB L , 3 BBE B(A0A8DP8A0A(B BBB < | BEH D(A0H@0A(A EBBT J C* BHG B(A0D8J
+8D0A(B BBBF @T KHT x| * BBE E(D0A8G
+8D0A(B BBBE 0. K^ T J * BBE B(D0A8G
+8A0A(B BBBJ D L Q * BBA A(N
+(A ABBA L < AAG ]
+CAF c
+CAI R
+CAJ JCA , b D\
H P
H Q
-G I , d 0yb D\
+G I , b D\
H P
H Q
-G I , pyb D\
+G I , Xb D\
H P
H Q
-G I , ĭ yb D\
+G I , b D\
H P
H Q
-G I , yb D\
+G I , L b D\
H P
H Q
-G I , $ 0zb D\
+G I , | b D\
H P
H Q
-G I , T pzb D\
+G I , Xb D\
H P
H Q
-G I , zb D\
+G I , b D\
H P
H Q
-G I , zb D\
+G I , b D\
H P
H Q
-G I , 0{b D\
+G I , < b D\
H P
H Q
-G I , p{b D\
+G I , l Xb D\
H P
H Q
-G I , D {b D\
+G I , b D\
H P
H Q
-G I , t {b D\
+G I , b D\
H P
H Q
-G I , 0|b D\
+G I , b D\
H P
H Q
-G I , ԯ p|b D\
+G I , , Xb D\
H P
H Q
-G I , |b D\
+G I , \ b D\
H P
H Q
-G I , 4 |b D\
+G I , b D\
H P
H Q
-G I , d 0}b D\
+G I , b D\
H P
H Q
-G I , p}b D\
+G I , Xb D\
H P
H Q
-G I , İ }b D\
+G I , b D\
H P
H Q
-G I , }b D\
+G I , L b D\
H P
H Q
-G I , $ 0~b D\
+G I , | b D\
H P
H Q
-G I , T p~b D\
-H P
-H Q
-G I ~m A~
-A l L ̰ BBB F(A0A8J@X
-8A0A(B BBBA 4 ADG X
-CAH [AA L T " BBB B(A0A8DW
-8D0A(B BBBF < Ȃ BEA G(GPE
-(A ABBG < x AAGPQ
+G I Xm A~
+A l L $ % BBB F(A0A8J@X
+8A0A(B BBBA 4 ( ADG X
+CAH [AA L % BBB B(A0A8DW
+8D0A(B BBBF , h g% BDD y
+ABA < , C% BEA G(GPE
+(A ABBG < l % AAGPQ
AAD O
-AAG 8+ A[ H( A[ < X( A[ \ h t `( A[ L p - BBB B(A0C8JY
-8A0A(B BBBG L Z D BDB B(A0I8G
-8A0A(B BBBG \ \ BDB A(A0LP~
+AAG T ( A[ t ( A[ < u $ BBA C(JU
+(A ABBC \ , $ BDB A(A0LP~
0A(A BBBD
-0A(A BBBD < @B AADPU
+0A(A BBBD < B $ AADPU
AAC
-AAA 4 Դ P ACO a
+AAA 4 t ACO a
AAJ
-GAO 4 ) AAJ P
+GAO 4 ) AAJ P
AAB
-GAO D l BAA G@
+GAO D < $ BAA G@
AABH
- AABH L E
- BDB J(A0A8Ib
-8A0A(B BBBG L BCA LP
+ AABH L h5 # BDB B(A0C8OZ
+8A0A(B BBBG L X # BCA LP
AABH )
- AABK T T BBB B(A0A8G
-8A0A(B BBBG T p BBB B(A0A8G
-8A0A(B BBBG \ e BDB A(A0LP^
-0A(A BBBD 8
-0A(A BBBH T d h < BBB B(A0A8G
-8A0A(B BBBG D BED D(G$
-(D ABBE < (a BBA A(JP
-(A ABBI D D X BED D(G$
-(D ABBE L t BDB B(A0C8LZ
-8A0A(B BBBB T ܷ p O BDB B(A0I8G
-8A0A(B BBBA T KHT L ! BBA A(G^
-(A ABBG
-(A ABBA D BAA Gw
- AABJ
- AABI T ~{ BBB B(A0A8G]
-8A0A(B BBBH L D r BDB M(A0A8Gi
-8A0A(B BBBG T Xy BBB B(A0A8J
-8A0A(B BBBA L }d BDB B(A0A8L
-8A0A(B BBBD L < d BDB B(A0A8L
-8A0A(B BBBD L @C BDJ B(A0A8GZ
-8A0A(B BBBA L ܺ @Y BDB J(A0A8G\
-8A0A(B BBBG , Pb D\
+ AABK D $ # BED D(G$
+(D ABBE D l @ # BED D(G$
+(D ABBE L \ BBB A(A0E
+(A BBGG I
+(A BBDA L = # BDB B(A0I8G
+8A0A(B BBBG D T # BED D(G$
+(D ABBE D p ^# BED D(G$
+(D ABBE D )# BED D(G$
+(D ABBE < , @a " BBA A(JP
+(A ABBI D l p " BED D(G$
+(D ABBE T " BDB B(A0A8L
+8A0A(B BBBJ < P! BBG A(G@
+(A ABBG L L " *" BDB B(A0A8O\
+8A0A(B BBBG D T KHD 'b
! BAA Gw
+ AABJ
+ AABI T 4 3" BBB B(A0A8G
+8A0A(B BBBA T T * BBE E(D0D8J
+
+8A0A(B BBBG L # @* BDB B(I0A8G
+8A0A(B BBBA , D* ACL@F
+AAH L , ,* BDB M(A0A8Gi
+8A0A(B BBBG T | ** BEB B(A0A8J
+8A0A(B BBBA L HS
* BDB B(A0A8O
+8A0A(B BBBD L $ XS * BDB B(A0A8O
+8A0A(B BBBD L t hC * BDJ B(A0A8GZ
+8A0A(B BBBA L hY ) BDB J(A0A8G\
+8A0A(B BBBG L x ) BFB B(A0C8J
+]
+8A0A(B BBBG T d W* BDB B(A0A8O
+8A0A(B BBBA T R* BBB B(A0A8MZ
+8A0A(B BBBA F , D D * AHGP
+AAA , 4 b D\
H P
H Q
-G I , \ R AALPd
-DAA T e 6 BEE B(A0A8G
-8A0A(B BBBF $ 8 . JFD@ $ GFD@ $ 4 ؖ GFD@ $ \ /Y BFA D@ $ x JFD@ T Pu BEE B(A0A8G
-8A0A(B BBBE T xe BEE B(A0A8G
-8A0A(B BBBF 4 0T KHL t x BBB A(A0M
-0A(A BBBH ̾
, D AX d F | د , \ AAG o
-AAA , b ADG r
-AAA < L `P BBD A(D0|(A ABB , d pb D\
+G I , d @b D\
H P
H Q
-G I , b D\
+G I , b D\
H P
H Q
-G I L AAG ]
+G I , b D\
+H P
+H Q
+G I , L } 2) AHG`
+AAA 4 | P& &) BAA J`w
+ AABG 4 H& ') BAA J`z
+ AABD T @e () BEE B(A0A8G
+8A0A(B BBBF XT KHL \ @5 ) BBB B(A0A8G^
+8A0A(B BBBG T P o* BBB B(A0G8G
+8A0A(B BBBG , HYb D\
+H P
+H Q
+G I , 4 Y j* AALPd
+DAA T d [e N* BEE B(A0A8G
+8A0A(B BBBF $ 0a F* JFD@ $ b 1* GFD@ $ b * GFD@ < 4 c * AAD@j
+AAF
+FAA $ t Hd ) JFD@ T eu ) BEE B(A0A8G
+8A0A(B BBBE T Hke ) BEE B(A0A8G
+8A0A(B BBBF T KHL d Hq ) BBB A(A0M
+0A(A BBBH \ | t | { { { |
{ { AX $ {F < (| T 0| l 8| , @|\ ( AAG o
+AAA , p|b ( ADG r
+AAA , |b D\
+H P
+H Q
+G I , |b D\
+H P
+H Q
+G I L D 0} AAG ]
CAF [
ACA R
-CAJ JCA Y , , ({ BFA i
-ABA 4 x BKE
-AJA 4 L BKE
-AJE J n N L r BBB B(E0A8J@
-8A0A(B BBBA $ 4 ^ AG b
-AA \ HB Av
-I A | x p4 Af
-I C % G DB4 شM AAG0o
+CAJ JCA `} Y , h}{ BFA i
+ABA 4 4 } ' BKE
+AJA 4 l P~ ' BKE
+AJE L ~r O' BBB B(E0A8J@
+8A0A(B BBBA $ ^ AG b
+AA PB Av
+I A x4 Af
+I C 4 % L G DB4 d M AAG0o
AAF D
-AAJ $ 4 V AD s
-AG \ ( t
-
-
- AO < T l ص < е BDD R
+AAJ $ V AD s
+AG 0 (
+ $ < T
+ l
+ AO < BDD R
ABA h
-CEH @ 8 0 ( $ < T l DR L , BJB A(A0_
-(A BBDH _(A BBBd T ` BBE B(D0A8G@
+CEH , H D @ \ 8 t 0 ( DR $ < T L D# BJB A(A0_
+(A BBDH _(A BBBd h BBE B(D0A8G@
8A0A(B BBBJ
-8A0A(B BBBK $ AIK0AA e , $ ADD@n
-AAG L T Ⱥ2 BBB B(A0D8D
-8D0A(B BBBH | T BBB E(A0D8GY
-8D0A(B BBBC X2 D \ 5 BBB D(A0G
-0A(A BBBE D BBB D(A0G
-0A(A BBBE T 9 BBA A(Dq
+8A0A(B BBBK $ $ AIK0AA L e , P" ADD@n
+AAG L І2 >" BBB B(A0D8D
+8D0A(B BBBH T ! BBB E(A0D8GY
+8D0A(B BBBC `2 D ! BBB D(A0G
+0A(A BBBE D < X! BBB D(A0G
+0A(A BBBE T 9 ,! BBA A(Dq
(C ABBE L
-(A ABBA < BEB D(A0M
-(A BBBF D o d BEE D(D0G
-0D(A BBBB 4 @Z BDA y
+(A ABBA < BEB D(A0M
+(A BBBF D o BEE D(D0G
+0D(A BBBB 4 HZ BDA y
ABM A
-ABA hQ , Y ADG i
-AAA T L BBB B(A0A8G
-8A0A(B BBBD L XJ BEB B(A0A8G
-8D0A(B BBBA 4 XJ BDA l
+ABA D pQ , Y - ADG i
+AAA T BBB B(A0A8G
+8A0A(B BBBD L < `J BEB B(A0A8G
+8D0A(B BBBA 4 4 `J BDA l
ABJ A
-ABA , , pY - ADG i
-AAA T \ F BBB B(A0A8G>
-8A0A(B BBBG T BEE E(D0D8G
-8D0A(B BBBD T BEE E(D0D8G9
-8D0A(B BBBJ L < X BBB B(A0A8Gp8A0A(B BBB + Ae 4 I ADG q
-AAA DCA T BEB B(A0D8Gh
-8A0A(B BBBA D < x BBE A(A0D@l
-0A(A BBBA $ ADD AA4 BFA b
-ABA $ pV ADD JAAL 4 BJB A(A0W
-(A BBDH ^(A BBB< BBD A(G0L
-(A ABBA $ c AG g
-AA D BBE A(A0D@l
-0A(A BBBA < 4 x # BBD A(G0T
-(A ABBA d L ( BBE F(D0A8D@
+ABA , xY | ADG i
+AAA T F X BBB B(A0A8G>
+8A0A(B BBBG T L " BEE E(D0D8G
+8D0A(B BBBD T ء BEE E(D0D8G9
+8D0A(B BBBJ L ` BOB A(A0]
+(A BBBG Y
+(A BBBH L У BBB B(A0A8Gp8A0A(B BBB D P+ Ae 4 d `I ADG q
+AAA DCA T x BEB B(A0D8Gh
+8A0A(B BBBA D BBE A(A0D@l
+0A(A BBBA $ < ADD AA4 0 BFA b
+ABA $ V ADD JAAL BJB A(A0W
+(A BBDH ^(A BBB< l p BBD A(G0L
+(A ABBA $ T c AG g
+AA D | X BBE A(A0D@l
+0A(A BBBA < BBD A(G0T
+(A ABBA d BBE F(D0A8D@
8F0A(B BBBC
-8D0A(B BBGH T BEB B(A0D8J
+8D0A(B BBGH T 8 BEB B(A0D8J
-8A0A(B BBBA L 4 x BJB A(A0W
-(A BBDH ^(A BBB< \ BBG A(G@
-(A ABBF < BBG A(G@
-(A ABBF $ HV ADD JAA4 U BDA w
-ABO AAB < T d | BBB E(A0A8DPD
+8A0A(B BBBA L BJB A(A0W
+(A BBDH ^(A BBB< @ BBG A(G@
+(A ABBF < T BBG A(G@
+(A ABBF $ V ADD JAA4 U BDA w
+ABO AAB T d d h BBB E(A0A8DPD
8A0A(B BBBA x
-8A0A(B BBBF D BBE A(A0D@l
-0A(A BBBA < , BBA D(K@C
-(A ABBA L l 0 BJB A(A0W
-(A BBDH ^(A BBBL ] BBB H(A0D8DP
-8A0A(B BBBG , v BAA i
-ABA D BBE A(A0D@l
-0A(A BBBA \ x5 L t BBB B(A0D8D@
-8A0A(B BBBB L @ p BJB A(A0W
-(A BBDH ^(A BBB, < m ' AID t
-DAA < l BBD A(G0$
-(A ABBA L ] BBB H(A0D8DP
-8A0A(B BBBG L 0 BJE B(A0A8D@8A0A(B BBB , L o AID0|
-DAA L T P] BBB H(A0D8DP
-8A0A(B BBBG L ` BBB B(D0D8Dp
-8D0A(B BBBD L BFE B(D0A8D@v
-8D0A(B BBBE L D BBB B(D0A8D@m
-8A0A(B BBBH T R 8 BEB E(A0A8G
-8A0A(B BBBF < BBG A(G@
-(A ABBG L T h c BBB B(A0A8Dp#
-8A0A(B BBBA l BNB B(A0D8G
+8A0A(B BBBF D t BBE A(A0D@l
+0A(A BBBA < ( BBA D(K@C
+(A ABBA L T BJB A(A0W
+(A BBDH ^(A BBBL L ] BBB H(A0D8DP
+8A0A(B BBBG , v BAA i
+ABA D X BBE A(A0D@l
+0A(A BBBA 5 L , BBB B(A0D8D@
+8A0A(B BBBB L l BJB A(A0W
+(A BBDH ^(A BBB, $ m # AID t
+DAA < T H BBD A(G0$
+(A ABBA L < ] BBB H(A0D8DP
+8A0A(B BBBG L BJE B(A0A8D@8A0A(B BBB , 4 h k AID0|
+DAA L ] BBB H(A0D8DP
+8A0A(B BBBG L BBB B(D0D8Dp
+8D0A(B BBBD L X BFE B(D0A8D@v
+8D0A(B BBBE L BBB B(D0A8D@m
+8A0A(B BBBH T R 4 BEB E(A0A8G
+8A0A(B BBBF < BBG A(G@
+(A ABBG L < c BBB B(A0A8Dp#
+8A0A(B BBBA l BNB B(A0D8G
8A0A(B BBBA n
-8A0A(B BBBA D , BJB K(A0j
-(_ aBBA D \ g BBJ A(D0
-(A BBEA L | ] BBB H(A0D8DP
-8A0A(B BBBG < BBA D(GP
-(D ABBG < ( BBG A(G@
-(A ABBG L t BEB E(I0H8DP
-8D0A(B BBBD L g BJE B(A0A8D@
-8A0A(B BBBA < x
- BBG A(G@
-(A ABBG L , 8 BBB B(A0D8DP
-8A0A(B BBBA < | BED A(D0Y
-(A ABBA < h BBG A(G@
-(A ABBG L $ (
/ BEB E(I0H8DP
-8D0A(B BBBD T t z BEE I(A0A8D
-8D0A(B BBBA < BED D(GPf
-(A ABBF 4 pY AIG t
-AAI DCA T D T BHB B(A0A8J
-8A0A(B BBBE \ t ` BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB\ BBE B(A0D8D@r
-8D0A(B BBBE D8C0A(B BBB4 \ i t ADD N
-AAG DCAL 8 C BEB E(I0H8DP
-8D0A(B BBBD < BLD D(G0
-(A ABBE \ BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 Y AIG t
-AAI DCA < 0e k BID ~
-AFD AEF < ` / BGB D(A0~
-(A BBBK L < BEB E(I0H8DP
-8D0A(B BBBD T * BLB B(A0A8Gq
-8A0A(B BBBA L BEB E(I0H8D@
-8D0A(B BBBA < " BFA D0}
- FABE D FAB L " N L " BEB E(I0H8D@
-8D0A(B BBBA < $ BFA D0}
- FABE D FAB L $ BBB B(A0D8G@j
-8A0A(B BBBH L l & BEB E(I0H8DP
-8D0A(B BBBD d ', BGE E(A0A8D@
+8A0A(B BBBA D , BJB K(A0j
+(_ aBBA D D h c BBJ A(D0
+(A BBEA L 4 @] BBB H(A0D8DP
+8A0A(B BBBG < P BBA D(GP
+(D ABBG < BBG A(G@
+(A ABBG L \ ` BEB E(I0H8DP
+8D0A(B BBBD L c BJE B(A0A8D@
+8A0A(B BBBA < BBG A(G@
+(A ABBG L BBB B(A0D8DP
+8A0A(B BBBA < 4 p BED A(D0Y
+(A ABBA L k BEB E(I0H8DP
+8D0A(B BBBD T z C BEE I(A0A8D
+8D0A(B BBBA < BED D(GPf
+(A ABBF 4 (Y AIG t
+AAI DCA T P BHB B(A0A8J
+8A0A(B BBBE \ BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB\ L h BBE B(A0D8D@r
+8D0A(B BBBE D8C0A(B BBB4 i ADD N
+AAG DCAL < BEB E(I0H8DP
+8D0A(B BBBD < W BLD D(G0
+(A ABBE \ t p BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 , Y AIG t
+AAI DCA < d e BID ~
+AFD AEF < k BGB D(A0~
+(A BBBK L S BEB E(I0H8DP
+8D0A(B BBBD T 4 x* + BLB B(A0A8Gq
+8A0A(B BBBA L P ; BEB E(I0H8D@
+8D0A(B BBBA < BFA D0}
+ FABE D FAB @ N L 4 H BEB E(I0H8D@
+8D0A(B BBBA < , BFA D0}
+ FABE D FAB L l 8 BBB B(A0D8G@j
+8A0A(B BBBH L BEB E(I0H8DP
+8D0A(B BBBD d d , BGE E(A0A8D@
8F0A(B BBBI g
-8F0A(B BBBA < ( BBG A(G@
-(A ABBF < d H) BLA D(G@
-(D ABBC | * T * BBE B(A0A8G
-8A0A(B BBBH 8 AO L 4 8 BLE I(D0D8D
-8A0A(B BBBC T 8:2 BBB E(A0D8G
-8A0A(B BBBA T <M } BHB B(D0D8G
-8A0A(B BBBA T 4 A BEB B(A0A8Gd
-8A0A(B BBBF d PG T HG } BBB B(A0E8M
-8A0A(B BBBH L E
-K < M BKD A(G0(A ABB T \ O BEB B(A0A8Gu
-8A0A(B BBBA L f t BBL E(H0A8D`
-8D0A(B BBBA hj AO hj T < `j BBE B(A0A8G
-8A0A(B BBBA , l x< BDG kAB x1 Dl yV Ko
-A $ xY AK Y
-AA < , y BED I(K@c
-(D ABBE D l y BGB D(A0G@~
-0A(A BBBC hz1 Ak < z _ BED D(G
-(A ABBH < ({ 1 BED D(G
-(A ABBI D T { BEE A(D0G
-0A(A BBBI T |' ξ BBB B(A0D8G
-8A0A(B BBBB D X~ BBE A(A0D@u
-0A(A BBBA L BBB B(A0A8D`
-8A0A(B BBBA L d ` BEB B(A0A8D
-8A0A(B BBBA L BDB E(A0A8DP
-8A0A(B BBBK T , Py Q BBE E(A0A8GZ
-8A0A(B BBBA T x * BBJ B(A0A8G
-8A0A(B BBBA T Е BBB B(A0D8D
-8D0A(B BBBF < 4 h1 BGA D(G@
-(A ABBB d t h м BBB F(D0D8J`g
+8F0A(B BBBA < t P BBG A(G@
+(A ABBF < [ BLA D(G@
+(D ABBC T d BBE B(A0A8G
+8A0A(B BBBH d ` AO L ` BLE I(D0D8D
+8A0A(B BBBC T , 2
BBB E(A0D8G
+8A0A(B BBBA T M
BHB B(D0D8G
+8A0A(B BBBA T
BEB B(A0A8Gd
+8A0A(B BBBF T L
BBB B(A0E8M
+8A0A(B BBBH L h E
+K < l H BKD A(G0(A ABB T ,
BEB B(A0A8Gu
+8A0A(B BBBA L \ 2
BBL E(H0A8D`
+8D0A(B BBBA T 6 AO t 6 T 6 S
BBE B(A0A8G
+8A0A(B BBBA , pD< BDG kAB D1 Dl 4 wV Ko
+A $ T DY AK Y
+AA < D BED I(K@c
+(D ABBE D xE BGB D(A0G@~
+0A(A BBBC
+ F1 Ak < | @F BED D(G
+(A ABBH < F m BED D(G
+(A ABBI D G ? BEE A(D0G
+0A(A BBBI L D
+ 8H,
+ BEE B(A0D8G
+8A0A(B BBBA D < I BBE A(A0D@u
+0A(A BBBA L I BBB B(A0A8D`
+8A0A(B BBBA L L BEB B(A0A8D
+8A0A(B BBBA L $ O BDB E(A0A8DP
+8A0A(B BBBK T Ry
+ BBE E(A0A8GZ
+8A0A(B BBBA T $ 8U n
+ BBJ B(A0A8G
+8A0A(B BBBA T | ` [
+ BBB B(A0D8D
+8D0A(B BBBF < (i1 P
+ BGA D(G@
+(A ABBB d
(j
+ BBB F(D0D8J`g
8A0A(B BBBA K
-8A0A(B BBBK " DR
-J A L & BBE B(A0A8DP
-8A0A(B BBBA T L BBB B(A0D8G:
-8D0A(B BBBE L | BBB L(A0A8D`@
-8A0A(B BBBA ( AL , ( AAG f
-AAG T D ( F RHB B(A0A8G{
-8A0A(B BBBD L Ы
BEB B(A0A8G\
-8D0A(B BBBC L O BBB B(A0D8JS
-8D0A(B BBBI 4 < F BAA G@
- DABB $ L XY AK Y
-AA L BEB E(I0H8D@
-8D0A(B BBBD L 2 Ϻ BJE B(A0A8DP
-8A0A(B BBBA D < BGF D(A0JI
-0A(A BBBA D \ X BBE A(A0D@u
-0A(A BBBA T W BEE E(D0D8G
-8A0A(B BBBA , $ AADP}
-AAK T T X غ BBE E(D0A8L!
-8A0A(B BBBF l BGB B(A0D8GF
+8A0A(B BBBK $ k" DR
+J A L D k& BBE B(A0A8DP
+8A0A(B BBBA T
l O BEE B(D0A8G/
+8D0A(B BBBA L n BBB L(A0A8D`@
+8A0A(B BBBA < s AL , \ s AAG f
+AAG T ht( RHB B(A0A8G{
+8A0A(B BBBD L < @v
? BEB B(A0A8G\
+8D0A(B BBBC L BBB B(A0D8JS
+8D0A(B BBBI 4 p BAA G@
+ DABB $ Y AK Y
+AA L < P 3 BEB E(I0H8D@
+8D0A(B BBBD L Њ2 BJE B(A0A8DP
+8A0A(B BBBA D BGF D(A0JI
+0A(A BBBA D BBE A(A0D@u
+0A(A BBBA T l BEE E(D0D8G
+8A0A(B BBBA , H 6 AADP}
+AAK T BBE E(D0A8L!
+8A0A(B BBBF l L BGB B(A0D8GF
8A0A(B BBBG (
-8A0A(B BBBF T o BEE E(A0H8G;
-8A0A(B BBBJ T t U W BBB B(A0A8J}
-8D0A(B BBBA D y BBB B(A0G8GPW8A0A(B BBBT 7 BEB B(D0A8T7
-8D0A(B BBBH T l BBB B(A0A8G
-8A0A(B BBBA T hS ӹ BBE B(A0A8G
-8A0A(B BBBA L p BEB B(A0A8G
-8A0A(B BBBJ D l 0s & BBB A(D0D@
-0A(A BBBA 4 hD BEA A(D0p(A ABB T BBB B(D0A8J
-8A0A(B BBBA # HZ L d $ BBB E(D0A8KPI
-8A0A(B BBBA T %p S BJB B(A0A8G
-8A0A(B BBBA 4 3 ˺ BDA G0x
- DABC T D 84 BBE B(D0A8G?
-8A0A(B BBBH L ; BBB B(A0D8J_
-8D0A(B BBBE B AN T A պ BBB E(A0A8G
-8A0A(B BBBA < 8D, A^
-A K D \ HD A
+8A0A(B BBBF T BEE E(A0H8G;
+8A0A(B BBBJ T xU BBB B(A0A8J}
+8D0A(B BBBA D y BBB B(A0G8GPW8A0A(B BBBT 7 V BEB B(D0A8T7
+8D0A(B BBBH T # BBB B(A0A8G
+8A0A(B BBBA T d (S BBE B(A0A8G
+8A0A(B BBBA L 0 W BEB B(A0A8G
+8A0A(B BBBD < b BBB A(H0(A BBB 4 D BEA A(D0p(A ABB T 8 BBB B(D0A8J
+8A0A(B BBBA p# HZ L % BBB E(D0A8KP
+8A0A(B BBBA T L `
BJB B(A0A8G
+8A0A(B BBBA 4 BDA G0x
+ DABC T BBE B(D0A8G?
+8A0A(B BBBH L 4 BBB B(A0D8J_
+8D0A(B BBBE , H
+ AN T 8
+ BBB E(A0A8G
+8A0A(B BBBA , A^
+A K D A
A O
A O
E G
I K
A ]
A G
-A S \ EL ADD
+A S \ h
L ADD
AAD N
AAH N
AAH N
AAA `
-AAA 4 F ADD H
-AAE HAA L < F~ BED D(G0Z
-(C ABBH m(G ABB L F M BEB A(A0g
-(A BBDE _(A BBB4 GW BDH q
-AGI AAD @G d T 8H BEL B(A0A8D`
-8C0A(B BBBF L8A0A(B BBB d H V BEL B(A0A8D`
-8C0A(B BBBF L8A0A(B BBB L (Ip BEH A(D0y
-(D ABBJ D(C DBB L HI' DQ
-K F D XI) BBB A(A0D
-0A(A BBBA D @J) a BBB A(A0D
-0A(A BBBA L (K BEB B(A0A8D`\
-8A0A(B BBBA T t K, BBE B(D0A8G
-8D0A(B BBBK T PA Ͷ BEE E(D0A8Go
-8D0A(B BBBG T $ V BBA A(JK
+AAA 4 l X ADD H
+AAE HAA L ~ BED D(G0Z
+(C ABBH m(G ABB L L BEB A(A0g
+(A BBDE _(A BBB4 D `W BDH q
+AGI AAD | d BEL B(A0A8D`
+8C0A(B BBBF L8A0A(B BBB d T BEL B(A0A8D`
+8C0A(B BBBF L8A0A(B BBB L d pp BEH A(D0y
+(D ABBJ D(C DBB ' DQ
+K F D , ) BBB A(A0D
+0A(A BBBA D t ) BBB A(A0D
+0A(A BBBA L d p BEB B(A0A8D`\
+8A0A(B BBBA T , # BBE B(D0A8G
+8D0A(B BBBK T d A BEE E(D0A8Go
+8D0A(B BBBG T BBA A(JK
(A ABBG K
-(A ABBA L T W BOB A(A0]
+(A ABBA L ( BOB A(A0]
(A BBBG Y
-(A BBBH T PX) & BEE B(A0D8Dn
-8C0A(B BBBA T $ ([ RBE B(G0A8Dv
-8A0A(B BBBA T | \ BBB B(A0A8G#
-8C0A(B BBBA c0 AP
-G W \ d? BEA C(GPf
+(A BBBH T d ) b BEE B(A0D8Dn
+8C0A(B BBBA T p# RBE B(G0A8Dv
+8A0A(B BBBA T % BBB B(A0A8G
+8C0A(B BBBA *0 AP
+G W \ 4 *? BEA C(GPf
(A ABBB {
(A ABBG
-(A ABBF , e DR D e AW < d e{ BDD G0V
- AABE D CAB < 0f BBG A(G@
-(A ABBG T fa BBB E(A0G8J
-8A0A(B BBBA L < lc BKB B(A0A8Dpa
-8A0A(B BBBF T (n O BKB B(D0A8Dz
-8D0A(B BBBA L ow BBB B(D0A8Dg
-8A0A(B BBBA L \ s ֳ BBB B(D0A8Dr
-8A0A(B BBBA L PvV BBB B(D0A8D
-8D0A(B BBBA L `w} f BBB B(D0H8Kp
-8A0A(B BBBA , $ x7 BDD eAB T | x BBB B(A0A8G
-8A0A(B BBBA D X|c BBB A(D0GB
-0A(A BBBD } BBB E(A0D8D
+(A ABBF , DR , AW < ,{ BDD G0V
+ AABE D CAB < , BBG A(G@
+(A ABBG T -a BBB E(A0G8J
+8A0A(B BBBA L 2c BKB B(A0A8Dpa
+8A0A(B BBBF T L 4 BKB B(D0A8Dz
+8D0A(B BBBA L 6w < BBB B(D0A8Dg
+8A0A(B BBBA L 9 BBB B(D0A8Dr
+8A0A(B BBBA L D! =V BBB B(D0A8D
+8D0A(B BBBA L ! (>} BBB B(D0H8Kp
+8A0A(B BBBA , " X?7 BDD eAB T " h? ( BBB B(A0A8G
+8A0A(B BBBA D l" Cc BBB A(D0GB
+0A(A BBBD " HD BBB E(A0D8D
8F0A(B BBBE r
8C0A(B BBBA u
-8A0A(B BBDA | ^T KH Ѐ" DR
-J A T U BBB B(A0A8G_
-8A0A(B BBBF T 4 K BBB B(D0D8J
-8F0A(B BBBA T BBB I(A0A8G
-8A0A(B BBBJ L x BBB B(D0A8Jr
-8A0A(B BBBE T 4 , BEB B(A0A8G
-8D0A(B BBBA $ d К) AGD ZAA L ؚ AAG ]
+8A0A(B BBDA # ]T KHT T# Gb [ BBB B(A0A8G_
+8A0A(B BBBF T # LK F BBB B(D0D8J
+8F0A(B BBBA T $ Q [ BBB I(A0A8G
+8A0A(B BBBJ L \$ @X a BBB B(D0A8Jr
+8A0A(B BBBE T $ ] | BEB B(A0A8G
+8D0A(B BBBA $ % a) AGD ZAA L % a AAG ]
CAF [
ACA R
-CAJ JCA , b D\
+CAJ JCA , $& ab D\
H P
H Q
-G I H
- $ @
- < 8
- T 0 l ( L BJB A(A0_
-(A BBDH _(A BBB< D hB n BBA A(Gg
-(A ABBA $ \ xN AD p
-AJ T & BBB B(A0D8G
-8A0A(B BBBA Z. K^ @ 8 $ 0 < ( T l (" @ H P- $ h^ AG b
-AA $ < T l 4 xk BDD D0u
- DABF D AB $ Z AK
-L AL l BJE B(A0A8D@
-8A0A(B BBBA T o BBB J(H0D8JY
-8A0A(B BBBA L X BJE B(A0A8D@
-8A0A(B BBBA T d (| BBE E(D0A8G
-8D0A(B BBBE T P| i BBE E(D0A8G
-8D0A(B BBBE T x O BBE B(D0A8G
-8D0A(B BBBH L l @ 7 BEB E(I0H8D@
-8D0A(B BBBD L " BJE B(A0A8D@
-8A0A(B BBBA T
- BBE B(D0A8N1
-8A0A(B BBBG D d
- BEE D(I0D@{
-0A(A BBBA L
- P k BBB B(D0A8Gp
-8D0A(B BBBG TT KH< 7 S BBD A(JX
-(D ABBD T T n 7 BBD D(G
+G I T& b
+ l& b
+ & b
+ & a & a & a & a & a L l& a
+ BJB A(A0_
+(A BBDH _(A BBB< & 0bB BBA A(Gg
+(A ABBA $ ' @cN AD p
+AJ T $' hc v BBB B(A0D8G
+8A0A(B BBBA $( Y. K^ <( h T( h l( g ( g ( g ( g" ( h ( h ( h- $ ) 0h^ AG b
+AA <) hh T) ph l) hh ) `h ) Xh ) `h ) Xh ) Ph ) Hh 4 * @hk BDD D0u
+ DABF $ L* xh^ C\
+A Z
+F ] t* hD AB * hZ AK
+L AL * i BJE B(A0A8D@
+8A0A(B BBBA T \* i BBB J(H0D8JY
+8A0A(B BBBA L * Xu BJE B(A0A8D@
+8A0A(B BBBA T + (v| BBE E(D0A8G
+8D0A(B BBBE T \+ P{| BBE E(D0A8G
+8D0A(B BBBE T + x w BBE B(D0A8G
+8D0A(B BBBH < , @ _ BED A(J
+(D ABBG L L, s BEB E(I0H8D@
+8D0A(B BBBD L , " K BJE B(A0A8D@
+8A0A(B BBBA < , p # BAD '
+ABG O
+ABA T ,- c BBE B(D0A8ND
+8A0A(B BBBA L - (
+ BBB B(A0A8Gp
+8D0A(B BBBF |. xST KH< - Т7 BBD A(JX
+(D ABBD T ,. Уn BBD D(G
(D ABBJ
-(D ABBA D BAD Dh
- DABF r
- DABA < BED A(J
-(D ABBG
-\ AG RAl T H ۬ BEE E(D0H8D`i
+(D ABBA ,/ \ AG RAl . Ȧ S BEE E(D0H8D`i
8A0A(B BBBD
-8A0A(B BBBB d o BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d ,
BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB L l x BBB B(A0D8G@
-8A0A(B BBBB H_ L
? BEB E(I0H8DP
-8D0A(B BBBD L L P BJE B(A0A8D@
-8A0A(B BBBA L 9 BEB F(G0D8D`
-8D0A(B BBBA L BBB I(I0A8DPv
-8D0A(B BBBH d < s BBB J(A0A8G`
+8A0A(B BBBB d / BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB d |/ BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB L 0 BBB B(A0D8G@
+8A0A(B BBBB 0 ȫ_ L L0 BEB E(I0H8DP
+8D0A(B BBBD L 0 Э BJE B(A0A8D@
+8A0A(B BBBA L 0 9 g BEB F(G0D8D`
+8D0A(B BBBA L <1 + BBB I(I0A8DPv
+8D0A(B BBBH d 1 @s BBB J(A0A8G`
8F0A(B BBBF B
-8F0A(B BBBA d ] K BBB B(I0A8G`
+8F0A(B BBBA d 1 X] BBB B(I0A8G`
8F0A(B BBBF A
-8F0A(B BBBA T BBB B(A0I8N
-8D0A(B BBBF | < H BBB B(D0A8DP
+8F0A(B BBBA T \2 P h BBB B(A0I8N
+8D0A(B BBBF | \3 ȷ BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI L BEB B(A0A8DP4
-8A0A(B BBBA , BAH D` AAB, < BAH D` AAB l N. K^ l Y BBB J(D0A8DR
+8A0A(B BBBI L 3 H BEB B(A0A8DP4
+8A0A(B BBBA , ,4 BAH D` AAB, \4 BAH D` AAB 4 M. K^ l 3 Y BBB J(D0A8DR
8F0A(B BBBA b
-8D0A(B BBGA L BBE B(A0A8D`
-8A0A(B BBBA L D p BBE B(A0A8D`
-8A0A(B BBBA T
-0 BBB B(A0D8J
-8A0A(B BBBA ' AL \ 4 ' [ BBE D(A0P@|
+8D0A(B BBGA L 5 p BBE B(A0A8D`
+8A0A(B BBBA L d5 BBE B(A0A8D`
+8A0A(B BBBA T 5 ` BBB B(A0D8Jb
+8A0A(B BBBA 6 AL \ 5 BBE D(A0P@|
0D(A BBBK d
-0D(A BBBA L X( BBE E(D0C8J
-8A0A(B BBBF 4 -y BKA Dp
- AABA PL. K^ 3 $ 3 < 3 T 3 , 3b F ADG r
-AAA H3 L @3 AAG ]
+0D(A BBBA T 5 x~ BBE E(D0C8J>
+8A0A(B BBBI 4 <6 y M BKA Dp
+ AABA 7 hK. K^ 47 L7 d7 |7 , 6 b ADG r
+AAA 7 L 7 AAG ]
CAF [
ACA R
-CAJ JCA $ p3^ AG b
-AA , , 30 AF
FK
- D \ 4 4 t 4= BDA Y
-JBL AAB 4 4J BDA o
+CAJ JCA $ ,8 (^ AG b
+AA , T8 `0 AF
FK
+ D 8 ` 4 8 X= BDA Y
+JBL AAB 4 8 `J BDA o
ABG A
-ABA < 4 BDD k
+ABA < 9 x BDD k
ABH V
-ABA L $ 56 BEB B(A0A8D`h
-8A0A(B BBBA < t 64 BBB A(A0d
-(A BBBE T 7
- BBE E(A0A8G
-8D0A(B BBBD D H9 BBE A(A0D@l
-0A(A BBBA d | 9 BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d X: ! BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB L L : BEB E(I0H8DP
-8D0A(B BBBD L < BJE B(A0A8D@
-8A0A(B BBBA `=5 \ = BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBL d = BHF E(D0D8GP`
-8A0A(B BBBB < ? BGD A(G0~
-(A ABBG T @b BEB B(D0D8GF
-8A0A(B BBBA $ PFT KH, < C AAK0i
-AAA l C" DN
-N A < CB BBA A(K@*(A ABB D AL L D
BEE A(A0]
+ABA L L9 6 BEB B(A0A8D`h
+8A0A(B BBBA < 9 4 BBB A(A0d
+(A BBBE T 49 t BBE E(A0A8G
+8D0A(B BBBD D 4: BBE A(A0D@l
+0A(A BBBA d 9 BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB d <: BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB L : ' BEB E(I0H8DP
+8D0A(B BBBD L : H BJE B(A0A8D@
+8A0A(B BBBA \ ; BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 ; hY w AIG t
+AAI DCA L ; C BHF E(D0D8GP`
+8A0A(B BBBB < ,< P / BGD A(G0~
+(A ABBG T l< j BBE E(A0A8II
+8A0A(B BBBA l= HET KH, = AAK0i
+AAA = @ " DN
+N A < = P B BBA A(K@*(A ABB > `
+ AL L 4> `
+
BEE A(A0]
(A BBBF
-(E BBBK T d hE g BEB B(A0A8G
-8A0A(B BBBG T U BBB B(A0I8D
-8D0A(B BBBA L X BBB E(A0A8DP
-8A0A(B BBBA < \" DR
-J A d (\| BGB E(D0A8Dp
+(E BBBK T = BBB B(A0A8J3
+8A0A(B BBBA T 4> ^ BBB B(A0I8D
+8D0A(B BBBA L 4? BBB E(A0A8DP
+8A0A(B BBBA ? !" DR
+J A T > !F BBE I(D0A8JF
+8A0A(B BBBA D T? 1 { BBB D(I0GP
+0D(A BBBA d ? 2| F BGB E(D0A8Dp
8A0A(B BBBH k
-8A0A(B BBBA T @]6 ? BBE I(D0A8J6
-8A0A(B BBBA D D (m BBB D(I0GP
-0D(A BBBA T n BBE B(D0D8G
-8A0A(B BBBA D # s BBB A(D0D@
-0A(A BBBA 4 p ZHA G HAB 4 d (F BAA G0_
- AABB L @ â BEH B(D0D8Dp`
-8D0A(B BBBA ML , j AAD@l
-AAA 4 < p Q BAA GP
- AABA D L ( BBB D(D0G@T
-0A(A BBBG x DP DP < ӡ BBA A(G@z
-(A ABBC T D ! BBB B(A0D8G
-8A0A(B BBBA T
- BEB B(A0A8G
-8A0A(B BBBA < BBA A(DO
-(A ABBA , 4! OGA A(D 4 d! BJD K@g
- AABJ $ t" c AK c
-AA L ! $ 6 BBB B(A0A8GpH
-8A0A(B BBBA T " s BLB B(D0A8G
-8A0A(B BBBA T l" ϡ BHF E(D0A8G
-8A0A(B BBBD T " BEL B(D0D8J
-8A0A(B BBBE # =. K^ $ `\ AG RA ,$ D$ \$ p cL t$ D # q O BBB A(D0Gpt
-0D(A BBBG D # @ . BBB D(D0D`
-0A(A BBBA D D$ BBB D(D0De
-0A(A BBBA d% P |% H % @ % 8 % @ L % H AAG ]
+8A0A(B BBBA T @ 3 BEB B(D0D8G
+8A0A(B BBBA D \@ 0R# BBB A(D0D@
+0A(A BBBA 4 LA T ZHA G HAB 4 @ T ? BAA G0_
+ AABB L A xU BEH B(D0D8Dp`
+8D0A(B BBBA B V ML , A V AAD@l
+AAA 4 A V BAA GP
+ AABB D B Y BBB D(D0G@T
+0A(A BBBG B XY B PY DP C XY DP < |B `Y BBA A(G@z
+(A ABBC T B Y! BBB B(A0D8G
+8A0A(B BBBA T C z
+ BEB B(A0A8G
+8A0A(B BBBA < lC BBA A(DO
+(A ABBA , C OGA A(D 4 C BJD K@g
+ AABJ $ D Xc AK c
+AA L <D BBB B(A0A8Gp@
+8A0A(B BBBA T D ps ; BLB B(D0A8G
+8A0A(B BBBA T D BHF E(D0A8G
+8A0A(B BBBD T <E ` BEL B(D0D8J
+8A0A(B BBBE <F <. K^ TF <\ AG RA tF ` F h F pp cL F Ș D ,F Иq BBB A(D0Gpt
+0D(A BBBG D tF ~ BBB D(D0D`
+0A(A BBBA D F g BBB D(D0De
+0A(A BBBA G G G G H L $H AAG ]
CAF k
ACA R
-CAJ JCA L ,& AAG ]
+CAJ JCA L tH P AAG ]
CAF c
CAI R
-CAJ JCA L |& AAG ]
+CAJ JCA L H AAG ]
CAF c
CAI R
-CAJ JCA L & AAG ]
+CAJ JCA L I П AAG ]
CAF c
CAI R
-CAJ JCA ' H- 4' ` U L' h W , d' pU BAA H
-ABA 4 ' AAD {
+CAJ JCA dI - |I ( U I 0 W , I 8U BAA H
+ABA 4 I h AAD {
AAE p
-AAB , ' BHE s
-ABK < $' x h AAG `
+AAB , J Р BHE s
+ABK < I @ AAG `
AAE p
-AAB 4 d' , BHE X
-ABF $ t( Ag
+AAB 4 I | BHE X
+ABF $ J H Ag
H l
E U
-E , ' = Н AAG`
-AAK ( ( 1 Ae
-A T ,( | BBB B(D0A8Gy
-8A0A(B BBBI T ( a BAA DP
+E , <J = AAG`
+AAK K ,K 1 Ae
+A T J أ BBB B(D0A8Gy
+8A0A(B BBBI T J p BAA DP
AABH k
AABA l
- AABH d ( @ - BBB B(A0A8DP
+ AABH d TK } BBB B(A0A8DP
8A0A(B BBBE D
-8A0A(B BBBA T D) ɜ BBB B(D0C8J
-8A0A(B BBBA t* 0 D T T ) 8 z BFE E(D0A8D
-8C0A(B BBBA T * 6 BEB B(A0K8G
-8A0A(B BBBH <+ T+
l+
+ + + + + " $ + G AG a
-DC RD $, 0 4 <, ( BAD D0
- AABA t, D + BBE D(D0D
-0D(A BBBD \ + ` h BEB D(D0Gp
+8A0A(B BBBA T K BBB B(D0C8J
+8A0A(B BBBA L D T T ,L BFE E(D0A8D
+8C0A(B BBBA T L x BEB B(A0K8G
+8A0A(B BBBH M M
M
M M M N ,N " $ DN G AG a
+DC RD lN 4 N BAD D0
+ AABA N X D ,N P BBE D(D0D
+0D(A BBBD \ tN (h BEB D(D0Gp
0D(A BBBK L
-0D(A BBBA D \, p
k BAD GPl
+0D(A BBBA D N 8 BAD GPl
DABG
- DABA T , a ; BEE I(A0A8G ^
-8A0A(B BBBJ , , 3 AAI@T
-DAD , . pT BKI _
-ABH , 4. D BHD ^
-ABA D - C BBB D(D0Gpn
-0D(A BBBB L - c BBB B(D0A8G_
-8A0A(B BBBC L $. h BBB B(D0A8GI8A0A(B BBB L t. BFH B(A0D8Jl
-8D0A(B BBBF , . h3 k YX
+ DABA T O a BEE I(A0A8G ^
+8A0A(B BBBJ , tO AAI@T
+DAD , LP 8T BKI _
+ABH , |P hD BHD ^
+ABA D P C BBB D(D0Gpn
+0D(A BBBB L LP c BBB B(D0A8G_
+8A0A(B BBBC L P h BBB B(D0A8GI8A0A(B BBB L P ` BFH B(A0D8Jl
+8D0A(B BBBF , <Q 0 YX
G Z
-A T . 3< I BBE B(D0A8G
-8D0A(B BBBB T L/ 8< / BBE B(D0A8G
-8D0A(B BBBB T / ="( BEB B(A0A8G
-8A0A(B BBBG T / e< BBB B(A0A8G
-8A0A(B BBBA L T0 ȡ9 BEE B(D0D8D
-8A0A(B BBBF |1 4 0 BAA D@O
- AABA 1 ( 1 1 4 <1 h E BAA G[
- AABA 4 t1 H C BHA G@h
- DABA 2 2 2 2 2 2 3 ,3 إ < D3 J BEE D(D0\(Q BBB 3 HT 3 # DT 3 3 3 4 4 # D ^ , \3 YX
+A T lQ < BBE B(D0A8G
+8D0A(B BBBB T Q < BBE B(D0A8G
+8D0A(B BBBB T R "( g BEB B(A0A8G
+8A0A(B BBBG T tR X*< BBB B(A0A8G
+8A0A(B BBBA tS f S f S f S f S f S xf T pf T hf 4T `f LT Xf dT Pf |T Hf T @f < T HfJ BEE D(D0\(Q BBB T Xf HT U Xf# DT $U pf <U hf TU `f lU hf U pf# D ^ , T f YX
G H
-A d4 $ |4 H
-A 4 4 H- 4 v D 4 (X BIB B(A0A85
-0A(B BBBA 4 45 @ BFA A(
- ABBA D l5 Ȭ, BIE B(A0A8
-0A(B BBBA , 5 AC
TS
- A 5 @e G@]L 5 { BEE E(A0D8J
-8A0A(B BBBI d L6 ȸ BJB B(D0D8G
-8A0A(B BBBD B8A0A(B BBB 6 D 6 h BFB B(A0A8H
-0A(B BBBA 4 7 BFA A(
- ABBA L L7 ؿ BEB B(A0A8G
-8A0A(B BBBA , 7 R AC
T
- A 7 d G@\L 7 { BEE E(A0D8J
-8A0A(B BBBI d 48 @ BHB B(D0D8G
-8A0A(B BBBF B8A0A(B BBB L 8 BEB B(A0A8Dj
-8A0A(B BBBA L 8 H BBB B(A0A8J
-8A0A(B BBBH L <9 BEB A(A0z
+A U f $ U f H
+A V Hi $V - <V (iv D TV iX BIB B(A0A85
+0A(B BBBA 4 V j BFA A(
+ ABBA D V 0m, BIE B(A0A8
+0A(B BBBA , W q AC
TS
+ A LW ue G@]L dW v{ BEE E(A0D8J
+8A0A(B BBBI d W 0y BJB B(D0D8G
+8A0A(B BBBD B8A0A(B BBB X y D 4X `zh BFB B(A0A8H
+0A(B BBBA 4 |X { BFA A(
+ ABBA L X @ BEB B(A0A8G
+8A0A(B BBBA 4 Y 3 AC
T
+ A
+ F <Y d G@\L TY P{ BEE E(A0D8J
+8A0A(B BBBI d Y BHB B(D0D8G
+8A0A(B BBBF B8A0A(B BBB 4 Z 8B AC
Tv
+ B , DZ P AC
TV
+ B , tZ AGDP
+AAH L Z К BEB B(A0A8Dj
+8A0A(B BBBA L Z BBB B(A0A8J
+8A0A(B BBBH L D[ p BEB A(A0z
(A BBGG [
-(A BBBF L 9 8 BBB B(A0A8D
-8A0A(B BBBA L 9 BBB B(D0A8G
-8A0A(B BBBH L ,: ( BEB A(A0*
+(A BBBF L [ BBB B(A0A8D
+8A0A(B BBBA L [ p BBB B(D0A8G
+8A0A(B BBBH L 4\ BEB A(A0*
(A BBGG [
-(A BBBF 4 |: xA AC
Tv
- B , : AC
Tf
- B L : P BDB B(A0D8G
-8A0A(B BBBB L 4; BME B(A0A8DX
-8A0A(B BBBG L ; BNE B(A0G8DQ
-8A0A(B BBBB L ; ` BEE B(A0A8G:
-8A0A(B BBBA $< M G EL << BBE B(A0A8J
-8A0A(B BBBH d < (p BBB B(A0A8G`
+(A BBBF < \ P] BBE A(A0
+(A BBBJ \ p D \ D L ] P BDB B(A0D8G
+8A0A(B BBBB L T] Щ BME B(A0A8DX
+8A0A(B BBBG L ] BNE B(A0G8DQ
+8A0A(B BBBB L ] ` BEE B(A0A8G:
+8A0A(B BBBA D^ M G EL \^ BBE B(A0A8J
+8A0A(B BBBH d ^ (p BBB B(A0A8G`
8A0A(B BBBI ~
-8A0A(B BBBH D < 0 BEB E(A0A80A(B BBB D <= BEB B(D0A8
-0A(B BBBD L = P BEB B(A0A8D
-8A0A(B BBBB D = BBB K(A0A8
-0A(B BBBD L > 2 BBB B(A0D8DD
-8A0A(B BBBD L l> p BEB B(A0A8D
-8A0A(B BBBE > > L > X BBB E(A0A8GBb
-8A0A(B BBBA L <?
@ BBB B(A0D8GA
-8A0A(B BBBE L ? BBE B(A0A8GA
-8A0A(B BBBA L ? H BBB B(A0A8G@6
-8A0A(B BBBG < T? BBA A(G0(A ABB L l@ ( BBB B(A0A8Gv
-8A0A(B BBBG L @ d BBB B(A0A8G=
-8A0A(B BBBA L A "u BBB B(A0A8JG
-8A0A(B BBBA L \A & BBB B(A0D8J8A0A(B BBB L A ( BBB B(A0D8G8A0A(B BBB A X* L B P* BBE B(A0C8G;
-8A0A(B BBBE L dB -p BBB B(D0A8J
-8A0A(B BBBH L B 3 BBB B(A0C8J
-8A0A(B BBBG L C @D
+8A0A(B BBBH D _ 0 BEB E(A0A80A(B BBB D \_ BEB B(D0A8
+0A(B BBBD L _ P BEB B(A0A8D
+8A0A(B BBBB D _ BBB K(A0A8
+0A(B BBBD L <` 2 BBB B(A0D8DD
+8A0A(B BBBD L ` p BEB B(A0A8D
+8A0A(B BBBE ` C ` a L $a BBB E(A0A8GBb
+8A0A(B BBBA L ta @ BBB B(A0D8GA
+8A0A(B BBBE L a BBE B(A0A8GA
+8A0A(B BBBA L b BBB B(A0A8G@6
+8A0A(B BBBG L db AAG e
+CAF C
+ACA R
+CAJ JCA , b ` BAA AB T <b BEB B(A0A8G2
+8A0A(B BBBH L <c d BBB B(A0A8G=
+8A0A(B BBBA L c u BBB B(A0A8JG
+8A0A(B BBBA L c , BBB B(A0A8G
+8A0A(B BBBG L ,d BBB B(A0D8J8A0A(B BBB L |d H BBB B(A0D8G8A0A(B BBB d L d BBE B(A0C8G;
+8A0A(B BBBE L 4e @p BBB B(D0A8J
+8A0A(B BBBH L e ` BBB B(A0C8J
+8A0A(B BBBG L e
BBB B(A0C8J_
-8A0A(B BBBA L TC N BBB B(A0C8J
-8A0A(B BBBA < C X BFB A(A0m
-(A BBBH < C n BBA A(G?
-(A ABBF , $D p AAG
-AAC TD p KIA D(G@(A ABBF@
+8A0A(B BBBA L $f BBB B(A0C8J
+8A0A(B BBBA < tf BFB A(A0m
+(A BBBH < f 06 BBA A(G?
+(A ABBF , f 7 AAG
+AAC $g @8 KIA D(G@(A ABBF@
(A ABBO
-(G EBBH D(G EBB D w D xy E py ,E H{ 4 DE @{ BDD G0 AAB |E E < D b BBA D(D0N(A ABB L E ( AAG ]
+(G EBBH D(G EBB g @? g (A g A g B 4 h B BDD G0 AAB Lh F dh F |h xG h h < h hb < BBA D(D0N(A ABB L h (i AAG ]
CAF c
CAI R
-CAJ JCA D dE h+ BAA FP
+CAJ JCA D h hi+ BAA FP
AABG z
- AABJ F F 6 AG lA F > AG tA , F D AKD i
-DAA , G { BFA i
-ABA L dF h BBB F(A0A8J@X
-8A0A(B BBBA , F Hp r AHK s
-AAA L F G R BEE D(D0G
-0D(A BBBA T 4G q BEE E(D0D8G0
-8D0A(B BBBA T G q · BEE E(D0D8G0
-8D0A(B BBBA T G q BEE E(D0D8G0
-8D0A(B BBBA T <H q B BEE E(D0D8G0
-8D0A(B BBBA T H (
-q BEE E(D0D8G0
-8D0A(B BBBA T H P q BEE E(D0D8G0
-8D0A(B BBBA < DI X p BBA A(J
-(A ABBC T I 8 q dž BEE E(D0D8G0
-8D0A(B BBBA L I `
G BEE D(D0G
-0D(A BBBA T ,J `Z C BEE E(D0D8G
-8D0A(B BBBA T J hz BEE E(D0D8G9
-8D0A(B BBBA L J G BEE D(D0G
-0D(A BBBA L ,K G y BEE D(D0G
-0D(A BBBA L |K G ; BEE D(D0G
-0D(A BBBA L K G BEE D(D0G
-0D(A BBBA L L BEE E(D0D8G
-8D0A(B BBBA L lL BEE E(D0D8G
-8D0A(B BBBA T L Z C BEE E(D0D8G
-8D0A(B BBBA T M BEB B(A0A8G
-8A0A(B BBBH T lM `v BEE E(D0D8G5
-8D0A(B BBBA L M R BBE D(A0G
-0A(A BBBA L N 8G Q BEE D(D0G
-0D(A BBBA L dN 8G BEE D(D0G
-0D(A BBBA T N 8v Ճ BEE E(D0D8G5
-8D0A(B BBBA L O BBE A(D0G
-0A(A BBBA T \O q BEE E(D0D8G0
-8D0A(B BBBA L O 8G M BEE D(D0G
-0D(A BBBA L P 8 G BEE D(D0G
-0D(A BBBA L TP 8 G т BEE D(D0G
-0D(A BBBA L P 8 G BEE D(D0G
-0D(A BBBA d P ( U BBA A(J
+ AABJ i i 6 AG lA i > AG tA , i iD AKD i
+DAA , j j{ BFA i
+ABA L i hj BBB F(A0A8J@X
+8A0A(B BBBA L i k BBE A(A0J
+0A(A BBBA , 4j p AHK s
+AAA L dj G { BEE D(D0G
+0D(A BBBA T j q = BEE E(D0D8G0
+8D0A(B BBBA T k @q BEE E(D0D8G0
+8D0A(B BBBA T dk hq BEE E(D0D8G0
+8D0A(B BBBA T k q k BEE E(D0D8G0
+8D0A(B BBBA T l q % BEE E(D0D8G0
+8D0A(B BBBA T ll q BEE E(D0D8G0
+8D0A(B BBBA < l o BBA A(J
+(A ABBC T m q BEE E(D0D8G0
+8D0A(B BBBA L \m G BEE D(D0G
+0D(A BBBA T m Z l BEE E(D0D8G
+8D0A(B BBBA L n G & BEE D(D0G
+0D(A BBBA L Tn G BEE D(D0G
+0D(A BBBA L n G BEE D(D0G
+0D(A BBBA L n G l BEE D(D0G
+0D(A BBBA L Do . BEE E(D0D8G
+8D0A(B BBBA L o BEE E(D0D8G
+8D0A(B BBBA T o Z BEE E(D0D8G
+8D0A(B BBBA T <p r l BBB B(A0A8G
+8A0A(B BBBB L p G BEE D(D0G
+0D(A BBBA L p G BEE D(D0G
+0D(A BBBA T 4q ȁ_ BBE B(A0D8G
+8A0A(B BBBA T q pq BEE E(D0D8G0
+8D0A(B BBBA L q G S BEE D(D0G
+0D(A BBBA L 4r G BEE D(D0G
+0D(A BBBA L r G BEE D(D0G
+0D(A BBBA L r G BEE D(D0G
+0D(A BBBA d $s 8 [ BBA A(J
(A ABBA U
(A ABBA U
-(A ABBA T \Q q d BEE E(D0D8G0
-8D0A(B BBBA T Q q BEE E(D0D8G0
-8D0A(B BBBA L R !G BEE D(D0G
-0D(A BBBA L \R "G BEE D(D0G
-0D(A BBBA L R #G \ BEE D(D0G
-0D(A BBBA T R $q BEE E(D0D8G0
-8D0A(B BBBA T TS H%q BEE E(D0D8G0
-8D0A(B BBBA L S p&G BEE D(D0G
-0D(A BBBA L S p'G ` BEE D(D0G
-0D(A BBBA T LT p(q " BEE E(D0D8G0
-8D0A(B BBBA T T )q BEE E(D0D8G0
-8D0A(B BBBA L T *G BEE D(D0G
-0D(A BBBA L LU +G X BEE D(D0G
-0D(A BBBA L U ,G BEE D(D0G
-0D(A BBBA L U -G ~ BEE D(D0G
-0D(A BBBA L <V .G ~ BEE D(D0G
-0D(A BBBA L V /G `~ BEE D(D0G
-0D(A BBBA W 0 4 W 0i ADG S
+(A ABBA T s 0q j BEE E(D0D8G0
+8D0A(B BBBA T s Xq $ BEE E(D0D8G0
+8D0A(B BBBA L <t G BEE D(D0G
+0D(A BBBA L t G BEE D(D0G
+0D(A BBBA L t G b BEE D(D0G
+0D(A BBBA T ,u q $ BEE E(D0D8G0
+8D0A(B BBBA T u q BEE E(D0D8G0
+8D0A(B BBBA L u G BEE D(D0G
+0D(A BBBA L ,v G f BEE D(D0G
+0D(A BBBA T |v q ( BEE E(D0D8G0
+8D0A(B BBBA T v q BEE E(D0D8G0
+8D0A(B BBBA L ,w G BEE D(D0G
+0D(A BBBA L |w G ^ BEE D(D0G
+0D(A BBBA L w G BEE D(D0G
+0D(A BBBA L x G BEE D(D0G
+0D(A BBBA L lx G BEE D(D0G
+0D(A BBBA L x G f BEE D(D0G
+0D(A BBBA y 4 y i ADG S
CAE ]
-FCB T ,W 0d ~ BEB E(A0C8J
-8A0A(B BBBA | \X 6 BIE B(A0A8GP
+FCB T \y Pd BEB E(A0C8J
+8A0A(B BBBA | \z h BIE B(A0A8GP
8A0A(B BBBD B
8D0A(B BBBA
-8A0A(B BBBG T X e } BBE B(A0A8GV
-8A0A(B BBBD T \X 0 _~ BBE B(A0A8G
-8A0A(B BBBF L X ~ BBB D(A0D
-0A(A BBBG T Y Xi o BBB B(A0A8G&
-8A0A(B BBBG < \Y p" BJA A(D
-(A ABBA T Y ` BBB E(A0A8G
-8A0A(B BBBA Z H. K^ T Z 1 BBB B(A0A8G
-8A0A(B BBBF <[ 4m A~
-A l T Z 8~ BBB B(A0A8G
-8A0A(B BBBF L Z BBB B(A0A8JW
-8A0A(B BBBK T ,[ 0 BEJ E(D0D8G
-8D0A(B BBBD < [ h| AAD`
+8A0A(B BBBG T 4z (U BBE B(A0A8GV
+8A0A(B BBBD T z 0 w BBE B(A0A8G
+8A0A(B BBBA L z Ȗ
+ BBB D(A0D
+0A(A BBBG T 4{ i BBB B(A0A8G&
+8A0A(B BBBG < { " BJA A(D
+(A ABBA T { BBB E(A0A8G
+8A0A(B BBBA | . K^ T <| 1 BBB B(A0A8G
+8A0A(B BBBF <} 8m A~
+A l L | E BBB B(A0A8JW
+8A0A(B BBBK T } U c BEJ E(D0D8G
+8D0A(B BBBD < \} W| AAD`
AAH p
-AAF T [ ! BEJ E(D0D8Gn
-8D0A(B BBBH L \ M BBB B(D0A8Gi
-8A0A(B BBBI T l\ 1 ( BEJ E(D0D8G
-8D0A(B BBBH 4 \ 2 ~ BCA GPc
- AABA L ] BBB A(A0
-(A BBGG A
-(A BBDA d L] M n~ BLB B(A0A8DPq
+AAF T } Y! BEJ E(D0D8Gn
+8D0A(B BBBH L } Z BBB B(D0A8Gi
+8A0A(B BBBI T D~ x]1 BEJ E(D0D8G
+8D0A(B BBBH 4 ~ K BCA GPc
+ AABA d ~ (_M & BLB B(A0A8DPq
8A0A(B BBBE
-8A0A(B BBBG T ] ! ~ BEJ E(D0D8Go
-8D0A(B BBBG < ^ BBG A(G@
-(A ABBG L L^ } BLB B(D0A8DP
-8A0A(B BBBG L ^ ^ k} BLH B(D0A8G@
-8A0A(B BBBB _ 5 < _ ( BBG A(G@
-(A ABBG ` (. K^ L \_ С; o~ BIB B(A0A8Gpf
-8A0A(B BBBA T _ 1R W~ BIE B(A0A8G
-8D0A(B BBBD T ` 5 G~ BIE B(A0A8G<
-8D0A(B BBBD T \` 9 7~ BIE B(A0A8G\
-8D0A(B BBBD T ` > '~ BBE B(A0A8G
-8A0A(B BBBA T a pY BIB E(A0A8G)
-8A0A(B BBBB T da iu
w BEB B(A0A8G
-8A0A(B BBBB b m A~
-A l T a
BIE B(A0A8G
-8A0A(B BBBD T 4b 8 BEJ E(D0D8GP
-8D0A(B BBBF T b BEJ E(D0D8GP
-8D0A(B BBBF 4 b Ȱ BCA GPc
- AABA < c pQ BDD v
-ABE AAB 4d @. K^ T tc 8 | BIB B(D0A8G
-8D0A(B BBBA T c p I BIB B(D0A8G
-8D0A(B BBBA T $d ȷ BIB B(D0A8G
-8D0A(B BBBA T |d 0 BIB B(D0A8G8
-8D0A(B BBBA 4 e D BEA A(D0p(A ABB L e x BBB E(A0C8GP#
-8A0A(B BBBE T \e ; BBB B(A0A8JZ
-8A0A(B BBBH T e BEB B(A0A8G
-8A0A(B BBBA T f @ ́ BEB B(A0A8G
-8A0A(B BBBB D <g & BBE A(A0D@I
-0A(A BBBA < f YHD M
+8A0A(B BBBG T < ` BEB B(A0A8G
+8A0A(B BBBE < < c BBG A(G@
+(A ABBG L d c BLB B(D0A8DP
+8A0A(B BBBG L $ e^ 3 BLH B(D0A8G@
+8A0A(B BBBB f5 < 4 g BBG A(G@
+(A ABBG t . K^ D g 7 BIB A(A0G`d
+0A(A BBBA T , R ' BIE B(A0A8G
+8D0A(B BBBD T BIE B(A0A8G<
+8D0A(B BBBD T ܁ BIE B(A0A8G\
+8D0A(B BBBD T 4 ( BBE B(A0A8G
+8A0A(B BBBA T BIB E(A0A8G1
+8A0A(B BBBJ T 0
G BEB B(A0A8G(
+8A0A(B BBBJ `im A~
+A l T \ i
k BIE B(A0A8G
+8A0A(B BBBD T BEJ E(D0D8GP
+8D0A(B BBBF T BEJ E(D0D8GP
+8D0A(B BBBF 4 d xv j BCA GPc
+ AABA < Q BDD v
+ABE AAB 0. K^ T v L BIB B(D0A8G
+8D0A(B BBBA T L z BIB B(D0A8G
+8D0A(B BBBA T x} BIB B(D0A8G
+8D0A(B BBBA T Ѐ0 BIB B(D0A8G8
+8D0A(B BBBA 4 D BEA A(D0p(A ABB L H BBB E(A0C8GP#
+8A0A(B BBBE T ܆ p BBB B(A0A8JZ
+8A0A(B BBBH T 4 8 BEB B(A0A8G
+8A0A(B BBBA T BEB B(A0A8G
+8A0A(B BBBB D BBE A(A0D@I
+0A(A BBBA < , YHD M
HAE
-HAA g L g n BBB B(A0A8G@
-8D0A(B BBBA ,h Dh \h th h x h p h x h p h x& PT i / SZ i L 4i e BEA A(D0s
-(C ABBH D(G ABB i ' AR
-E N i L i m BEA A(D0~
-(C ABBE D(G ABB j ' AR
-E N $ ,j ! AAG SCA Tj lj ( $ j 01 ADJ [AA $ j H AAG`AA j B j k AZ $k <k Tk L j % } BJB A(A0W
-(A BBDH ^(A BBBT j '~ BGB I(A0A8G
-8A0A(B BBBD $ <k ~ YX
+HAA L x > BBB B(A0A8G@
+8D0A(B BBBA | 8 @ 8 ĉ 0 ܉ ( ( $ < (& PT T @/ SZ l X L `e BEA A(D0s
+(C ABBH D(G ABB Ԋ ' AR
+E N L m BEA A(D0~
+(C ABBE D(G ABB \ ' AR
+E N $ | ȼ! AAG SCA м ؼ $ ԋ 1 ADJ [AA $ AAG`AA $ B < T AZ t L h BJB A(A0W
+(A BBDH ^(A BBBT d H BGB I(A0A8G
+8A0A(B BBBD $ YX
G z
-A $ <l v ACG eDAT k '
- ~ BBB E(A0D8D
-8D0A(B BBBG L k ~ BEB A(A0d
-(A BBDH [(A BBGL 4l / ~ BBE B(A0A8DP
-8A0A(B BBBA \m H. K^ D tm P$ BBE A(A0D@~
-0A(A BBBA T l t w~ BBB B(A0G8G
-8A0A(B BBBF T <m v~ BIB B(A0D8DH
-8D0A(B BBBC , m U X~ ADD`v
-AAA , n $b D\
+A $ 8v ACG eDAT '
+ p BBB E(A0D8D
+8D0A(B BBBG L d h BEB A(A0d
+(A BBDH [(A BBGL / s BBE B(A0A8DP
+8A0A(B BBBA 8. K^ D Ď BBE A(A0D@~
+0A(A BBBA T d Ht G BBB B(A0G8G
+8A0A(B BBBF T p F BIB B(A0D8DH
+8D0A(B BBBC , 8U ( ADD`v
+AAA , b D\
H P
H Q
-G I , n X$b D\
+G I , b D\
H P
H Q
-G I , n $b D\
+G I , L Hb D\
H P
H Q
-G I , ,o $b D\
+G I , | b D\
H P
H Q
-G I , \o %b D\
+G I , b D\
H P
H Q
-G I , o X%b D\
+G I , ܐ b D\
H P
H Q
-G I , o %b D\
+G I , Hb D\
H P
H Q
-G I , o %b D\
+G I , < b D\
H P
H Q
-G I , p &b D\
+G I , l b D\
H P
H Q
-G I 4 to X& | BAD D
-ABA , o y o| AAG@D
-AAA , o Pz Q| AAG@D
-AAA 4 p zy | BDD G0y
- AABA < Dp 8{ | ACDP
+G I 4 [ BAD D
+ABA , , E ? AAG@D
+AAA , \ pF ! AAG@D
+AAA 4 Gy g BDD G0y
+ AABA < đ XG | ACDP
AAA O
-AAA T p % | BBB B(A0A8J
-8A0A(B BBBA < p { | AADP
+AAA T l BBB B(A0A8J
+8A0A(B BBBA < \ G _ AADP
AAA O
-AAA D q | | BJD D(G$
-(D ABBH D dq ~ j| BJD D(G$
-(D ABBH < q p 5| AADP
-AAI O
-AAA T q P +| BEJ E(D0D8GT
-8D0A(B BBBJ T Dr { BEJ E(D0D8GT
-8D0A(B BBBJ T r { BEJ E(D0D8GT
-8D0A(B BBBJ T r \{ BED D(G`~
+AAA T H o BEJ E(D0D8GT
+8D0A(B BBBJ T J * BEJ E(D0D8GT
+8D0A(B BBBJ T L PL BEJ E(D0D8GT
+8D0A(B BBBJ T N BED D(G`~
(A ABBF
-(A ABBA \ Ls )n F{ BBB A(A0GP
+(A ABBA \ n BBB A(A0GP
0A(A BBBD j
-0A(A BBBA \ s *n { BBB A(A0GP
+0A(A BBBA \ \ n F BBB A(A0GP
0A(A BBBD j
-0A(A BBBA D t @ z BEE E(J0J@X
-0A(A BBBK D Tt z BJD D(G$
-(D ABBH D t *W rz BBB A(A0GP
-0A(A BBBH 4 u (J BDA l
+0A(A BBBA D O BEE E(J0J@X
+0A(A BBBK T xP BEE E(A0A8G2
+8D0A(B BBBG D \ W BBB A(A0GP
+0A(A BBBH 4 L RJ BDA l
ABJ A
-ABA D u @ z BJD D(G$
-(D ABBH 4 <v AAG }
-ADE o
-AAG tv pm A~
-A l v m A~
-A l T u sy BBB B(A0A8G
-8A0A(B BBBG 4 4v h =y AAD
-DAA 4 lv )y AAD$
-DAA 4 v y AAD
-DAA L v * y BDB A(A0L
-0A(A BBBE T ,w @. x BBB B(A0A8J*
-8A0A(B BBBA \x x. K^ 4 w g y BDD G~
- AABA 4 w (5 y BAD G
-ABA 4 x J BDA o
+ABA D ܕ R ] BJD D(G$
+(D ABBH ̖ XTm A~
+A l Tm A~
+A l D d T BJD D(G$
+(D ABBH 4 `V AAD
+DAA 4 W AAD$
+DAA 4 Y AAD
+DAA L T h w BDB A(A0L
+0A(A BBBE T _ BBB B(A0D8Gm
+8A0A(B BBBA p. K^ 4 Yg BDD G~
+ AABA 4 L p { BAD G
+ABA 4 , YJ BDA o
ABG A
-ABA L Dx H5j |z BBB I(A0G
-0D(A BBBA < x h7 Wz BAI G|
- DABA < x 9 0z BAI G
- DABA < y :0 z BAI G
- DABA < Ty <0 y BAI G
- DABA T y >4 y BBB B(I0A8Gg
-8D0A(B BBBA T y A4 y BBB B(I0A8Gg
-8D0A(B BBBA < Dz D; ay BAI G
- DABA 4 \{ xv BDA D0a
- AABA L { BBB B(A0A8Dp
-8A0A(B BBBA L { Pz x BBB E(D0D8GP
-8A0A(B BBBC < \{ E x BBK A(DPn
-(A ABBA < { `F x BBK A(DPn
-(A ABBA T { F_ x BBB B(A0D8G
-8A0A(B BBBD T 4| N x BBB B(A0A8G 4
-8A0A(B BBBA D | Pj| 3z BBI A(G
-(D ABBA , } b D\
+ABA L j H BBB I(A0G
+0D(A BBBA < # BAI G|
+ DABA < L ` BAI G
+ DABA < @ 0 BAI G
+ DABA < ̙ 0 0 BAI G
+ DABA T
4 BBB B(I0A8Gg
+8D0A(B BBBA T d 4 Z BBB B(I0A8Gg
+8D0A(B BBBA < ; - BAI G
+ DABA 4 pWv BDA D0a
+ AABA L ܛ W BBB B(A0A8Dp
+8A0A(B BBBA L HXz BBB E(D0D8GP
+8A0A(B BBBC < ԛ BBK A(DPn
+(A ABBA < BBK A(DPn
+(A ABBA T T 8 c BBB B(A0A8J
+8A0A(B BBBD T w BBB B(A0A8G \
+8A0A(B BBBI D x7t BBI A(G
+(D ABBA , Xb D\
H P
H Q
-G I , } Hb D\
+G I , $ @Xb D\
H P
H Q
-G I T 4} y BBB B(A0A8G
-8A0A(B BBBJ T } e y BEE B(A0A8G
-8A0A(B BBBF T } ȕ4 y BBB B(A0A8G{
-8A0A(B BBBJ . K^ T T~ 4 #{ BBB B(A0A8G
-8A0A(B BBBA T ~ { BBB B(A0A8Gu
-8A0A(B BBBA L AAG ]
+G I T X { BBB B(A0A8G
+8A0A(B BBBJ T e Y BEE B(A0A8G
+8A0A(B BBBF T \ [% Q BBB B(A0A8Gk
+8A0A(B BBBJ \ . K^ T ̞ p_4 BBB B(A0A8G
+8A0A(B BBBA T $ Xm BBB B(A0A8Gu
+8A0A(B BBBA L $ AAG ]
CAF [
ACA R
-CAJ JCA , , b D\
+CAJ JCA , t b D\
H P
H Q
-G I , \ (b D\
+G I , b D\
H P
H Q
-G I L h z BBB A(A0N
-0A(A BBBA , e xz ADG r
-DAA D 4 ?6 D{ BCA GPg
+G I L , @ c BBB A(A0N
+0A(A BBBA , | e H ADG r
+DAA D 6 BCA GPg
AABH C
- AABI T | Be ({ BEE B(A0A8G
-8A0A(B BBBF < I BBG A(G@
-(A ABBG H. K^ T , P | BIB B(D0A8G
-8D0A(B BBBA T | BIB B(D0A8G
-8D0A(B BBBA T ܁ o| BBB B(I0A8G'
-8D0A(B BBBC T 4 X s| BIE B(A0A8G
-8D0A(B BBBF T 7 | BBB B(A0A8G
-8A0A(B BBBA T
| BIB B(A0A8G
-8A0A(B BBBA T < ` {| BIB B(A0A8G
-8A0A(B BBBA T
| BIB B(A0A8G
-8A0A(B BBBA T + | BBB B(A0A8M
-8A0A(B BBBA L D 3 | BBB B(A0A8Mi
-8A0A(B BBBA L l HF AAG ]
+ AABI . K^ T BIB B(D0A8G
+8D0A(B BBBA T d xf BBB B(A0D8G
+8D0A(B BBBA T BIE B(A0A8G
+8D0A(B BBBF T H BIE B(A0A8G
+8D0A(B BBBF T l
; BIB B(A0A8G
+8A0A(B BBBA T Ģ h
% BIB B(A0A8G
+8A0A(B BBBA T i BBB B(A0A8G1
+8A0A(B BBBA T t
BIB B(A0A8G
+8A0A(B BBBA T ̣ P BBB B(A0A8M
+8A0A(B BBBA L $ BBB B(A0A8Mi
+8A0A(B BBBA L ( AAG ]
CAF [
ACA R
-CAJ JCA L xF AAG ]
+CAJ JCA L l X AAG ]
CAF [
ACA R
-CAJ JCA , Fb D\
+CAJ JCA , b D\
H P
H Q
-G I D d F { BBA A(N
-(A ABBA L 0I { BBB A(A0N
-0A(A BBBA , Ke { ADG r
-DAA , , Le { ADG r
-DAA D \ 06 { BCA GPg
+G I D D W BBA A(N
+(A ABBA L 3 BBB A(A0N
+0A(A BBBA , ܥ
+e ADG r
+DAA ,
+e ADG r
+DAA D < `6 BCA GPg
AABH C
- AABI | ۓ. K^ T K
} BIB B(A0A8G
-8A0A(B BBBA T XY
} BIB B(A0A8G
-8A0A(B BBBA T l f
} BIB B(A0A8G
-8A0A(B BBBA T ć (t
i} BIB B(A0A8G
-8A0A(B BBBA T
S} BIB B(A0A8G
-8A0A(B BBBA T t
=} BIB B(A0A8G
-8A0A(B BBBA T ̈ `
'} BIB B(A0A8G
-8A0A(B BBBA T $ ȩ
} BIB B(A0A8G
-8A0A(B BBBA T | 0
| BIB B(A0A8G
-8A0A(B BBBA L ԉ | BBB B(A0A8Mi
-8A0A(B BBBA D $ | BBA K(D|
-(A ABBA L D p AAG ]
+ AABI , xݐ. K^ T
+ BIB B(D0A8G
+8D0A(B BBBA T
BIB B(A0A8G
+8A0A(B BBBA T L
BIB B(A0A8G
+8A0A(B BBBA T X+
BIB B(A0A8G
+8A0A(B BBBA T 8
BIB B(A0A8G
+8A0A(B BBBA T T (F
BIB B(A0A8G
+8A0A(B BBBA T S
BIB B(A0A8G
+8A0A(B BBBA T `
u BIB B(A0A8G
+8A0A(B BBBA T \ `n
_ BIB B(A0A8G
+8A0A(B BBBA L { I BBB B(A0A8Mi
+8A0A(B BBBA D ؆ S BBA K(D|
+(A ABBA L AAG ]
CAF [
ACA R
-CAJ JCA , b D\
+CAJ JCA , D Јb D\
H P
H Q
-G I L ċ AAG ]
+G I L t AAG ]
CAF c
CAI R
-CAJ JCA , b D\
+CAJ JCA , ī Pb D\
H P
H Q
-G I , D `b D\
+G I , b D\
H P
H Q
-G I , t b D\
+G I , $ Љb D\
H P
H Q
-G I L AAG ]
+G I L T AAG ]
CAF [
ACA R
-CAJ JCA , b D\
+CAJ JCA , @b D\
H P
H Q
-G I , $ Pb D\
+G I , Ԭ b D\
H P
H Q
-G I , T b D\
+G I , b D\
H P
H Q
-G I , b D\
+G I , 4 b D\
H P
H Q
-G I , b D\
+G I , d @b D\
H P
H Q
-G I , Pb D\
+G I , b D\
H P
H Q
-G I , b D\
+G I , ĭ b D\
H P
H Q
-G I , D b D\
+G I , b D\
H P
H Q
-G I t m A~
-A l T ` y BBB B(K0A8G
-8A0A(B BBBA L y BBB B(A0A8DW
-8D0A(B BBBF L d h [y BLE A(D0D
-0D(A BBBA , Xe 9y ADG r
-DAA , h y BDD y
-ABA , e x ADG r
-DAA L D Xy BLB B(A0A8G\
-8A0A(B BBBG T sy BBB L(A0A8G
-8A0A(B BBBA T y BBB B(A0A8G
-8A0A(B BBBG L D h Yy BDB J(A0A8Ib
-8A0A(B BBBG T Ȕ iy BBB B(A0A8G
-8A0A(B BBBG T 3y BBB B(A0A8G
-8A0A(B BBBG T D x x BBB B(A0A8G
-8A0A(B BBBG t О ѓ. K^ 4 ̑ z BKA G
- DABA 4 X z BKA G
- DABA T < e z BBB B(I0A8GM
-8D0A(B BBBA 4 ( az BKA G
- DABA 4 ̒ @z BKA G
- DABA < z BAD GC
- DABA T D y BBB B(D0A8G
-8D0A(B BBBA < p y BAD GC
- DABA < ܓ y BAD GC
- DABA < iy BAD GC
- DABA < \ ` =y BAD GC
- DABA < y BAD GC
- DABA < ܔ x BAD Gk
- DABA < x BAD Gk
- DABA < \ x BAD G
- DABA < ax BAD Gk
- DABA < ܕ 5x BAD GC
- DABA < p x BAD GC
- DABA < \ w BAD GC
- DABA D w BBA I(G)
-(D ABBI D v w BBA D(G
-(D ABBA < , rw BAD GC
- DABA < l P Fw BAD G
- DABA 4
- w BKA G
- DABA 4 v BKA G
- DABA 4 ` v BKA G
- DABA 4 T
v BKA G
- DABA <
v BAD G
- DABA 4 ̘ jv BRA G
- DABA 4 H Iv BRA G
- DABA 4 < (v BRA G
- DABA < t v BAD GC
- DABA < u BAD GC
- DABA D X u BBA I(G
-(D ABBF < < u BAI Gp
- DABA T | ` u BBB B(I0A8G
-8D0A(B BBBA < Ԛ ( Su BAD GC
- DABA < x 'u BAD GC
- DABA < T t BAD GC
- DABA < " t BAD GC
- DABA < ԛ h# t BAD GC
- DABA < $ wt BAD GC
- DABA T T &y Kt BBB B(I0A8GQ
-8D0A(B BBBI < 0, ?t BAD GC
- DABA < - t BAD GC
- DABA < , . s BAD GC
- DABA < l 0 s BAD GC
- DABA < p1 s BAD GC
- DABA T 2 cs BBB B(D0A8G
-8D0A(B BBBA T D H5 1s BBB B(D0A8G
-8D0A(B BBBA < 7Y r BAI G
- DABA < ܞ : r BAI G"
- DABA < @=5 r BAD G
- DABA < \ @? r BAD GC
- DABA < @ Yr BAI G<
- DABA T ܟ n 2r BDB E(A0C8G
-8A0A(B BBBA < 4 r BEA D(D`s
-(A ABBG D t hA q BDB A(I0F`K
-0A(A BBBE T BV q BDA C(L`^
+G I $ @m A~
+A l T BBB B(K0A8G
+8A0A(B BBBA L HD BBB B(A0A8DW
+8D0A(B BBBF L D H BLE A(D0D
+0D(A BBBA , e ADG r
+DAA , Į ȏh y BDD y
+ABA , e U ADG r
+DAA L $ H BLB B(A0A8G\
+8A0A(B BBBG T t BBB L(A0A8G
+8A0A(B BBBA T ̯ H BBB B(A0A8G
+8A0A(B BBBG L $ BDB J(A0A8Ib
+8A0A(B BBBG T t K BBB B(A0A8G
+8A0A(B BBBG T ̰ PO BBB B(A0A8G
+8A0A(B BBBG ̱ R Ґ. K^ 4 T ( BKA G
+ DABA 4 k BKA G
+ DABA T ı e J BBB B(I0A8GM
+8D0A(B BBBA 4 BKA G
+ DABA 4 T h BKA G
+ DABA < BAD GC
+ DABA T ̲ p BBB B(D0A8G
+8D0A(B BBBA < $ } BAD GC
+ DABA < d H Q BAD GC
+ DABA < % BAD GC
+ DABA < BAD GC
+ DABA < $ 8 BAD GC
+ DABA < d BAD Gk
+ DABA < u BAD Gk
+ DABA < I BAD G
+ DABA < $ ( BAD Gk
+ DABA < d BAD GC
+ DABA < BAD GC
+ DABA < H BAD GC
+ DABA D $ m BBA I(G)
+(D ABBI D l Pv V BBA D(G
+(D ABBA < . BAD GC
+ DABA < BAD G
+ DABA 4 4 x BKA G
+ DABA 4 l 0 BKA G
+ DABA 4 BKA G
+ DABA 4 ܷ s BKA G
+ DABA < X R BAD G
+ DABA 4 T & BRA G
+ DABA 4 BRA G
+ DABA 4 ĸ BRA G
+ DABA < @ BAD GC
+ DABA < < BAD GC
+ DABA D | k BBA I(G
+(D ABBF < Ĺ H b BAI Gp
+ DABA T ; BBB B(I0A8G
+8D0A(B BBBA < \ BAD GC
+ DABA < BAD GC
+ DABA < ܺ P BAD GC
+ DABA < BAD GC
+ DABA < \ _ BAD GC
+ DABA < @ 3 BAD GC
+ DABA T ܻ y BBB B(I0A8GQ
+8D0A(B BBBI < 4 BAD GC
+ DABA < t BAD GC
+ DABA < X BAD GC
+ DABA < w BAD GC
+ DABA < 4 K BAD GC
+ DABA T t H BBB B(D0A8G
+8D0A(B BBBA T ̽ BBB B(D0A8G
+8D0A(B BBBA < $ XY BAI G
+ DABA < d x BAI G"
+ DABA < 5 m BAD G
+ DABA < A BAD GC
+ DABA < $ BAI G<
+ DABA T d xDn BDB E(A0C8G
+8A0A(B BBBA < J BEA D(D`s
+(A ABBG D BDB A(I0F`K
+0A(A BBBE T D XV s BDA C(L`^
(A ABBF
-(A ABBE T DV ~q BDA C(L`^
+(A ABBE T `V : BDA C(L`^
(A ABBF
-(A ABBE T l FV Eq BDA C(L`^
+(A ABBE T hV BDA C(L`^
(A ABBF
-(A ABBE L ġ HQ q BDB B(A0A8OZ
-8A0A(B BBBA L PQ q BDB B(A0A8Or
-8A0A(B BBBI T d a cq BDJ B(A0A8G
-8A0A(B BBBA L f
Sq BBB B(D0G8JP
-8A0A(B BBBI 0t AZ 4 , 0tT yq BAA J@
- AABB < d Xva zq BBA A(JP
-(A ABBI L Sq BBB A(K0D
-0A(A BBBA D ȕ 'q BBB A(A0Np
-0A(A BBBA L AAG ]
+(A ABBE L L p Q BDB B(A0A8OZ
+8A0A(B BBBA L Q BDB B(A0A8Or
+8A0A(B BBBI T BDJ B(A0A8G
+8A0A(B BBBA L D
BBB B(D0G8JP
+8A0A(B BBBI < + AZ 4 +T 5 BAA J@
+ AABB < -a 6 BBA A(JP
+(A ABBI L , G BBB A(K0D
+0A(A BBBA D | I BBB A(A0Np
+0A(A BBBA L l hK AAG ]
CAF c
CAI R
-CAJ JCA L d З AAG ]
+CAJ JCA L K AAG ]
CAF [
ACA R
-CAJ JCA L AAG ]
+CAJ JCA L K AAG ]
CAF [
ACA R
-CAJ JCA , 0b D\
+CAJ JCA , \ Lb D\
H P
H Q
-G I , 4 pb D\
+G I , HLb D\
H P
H Q
-G I , d b D\
+G I , Lb D\
H P
H Q
-G I , b D\
+G I , Lb D\
H P
H Q
-G I , Ħ 0b D\
+G I , Mb D\
H P
H Q
-G I , pb D\
+G I , L HMb D\
H P
H Q
-G I $ m A~
-A l T l h n BBL B(A0A8G\
-8A0A(B BBBG , Ħ h o BDD y
-ABA , Xe n ADG r
-DAA , $ e n ADG r
-DAA T ء Do BDB B(A0C8Lpo
+G I | Mm A~
+A l T Mh BBL B(A0A8G\
+8A0A(B BBBG , L Th ; BDD y
+ABA , | 0Ue ADG r
+DAA , pUe ADG r
+DAA U BDB B(A0C8Lpo
8A0A(B BBBE 4
8A0A(B BBBJ /
-8A0A(B BBBG T ܧ p eo BBB B(A0A8G
-8A0A(B BBBG L 4 ( /o BDB B(A0C8OZ
-8A0A(B BBBG L ( -o BDB B(A0C8OZ
-8A0A(B BBBG . K^ 4 q BRA G
- DABA 4 $ x p BRA G
- DABA 4 \ 0 p BRA G
- DABA 4 p BKA G
- DABA 4 ̩ p BKA G
- DABA 4 X op BKA G
- DABA 4 < Np BKA G
- DABA 4 t Ȳ -p BKA G
- DABA 4 p BKA G
- DABA 4 8 o BKA G
- DABA 4 o BKA G
- DABA D T o BBA D(G8
-(D ABBA 4 - o ADG
-DAA 4 ԫ - \o ADG
-DAA 4 - 7o ADG
-DAA 4 D - o ADG
-DAA 4 | - n ADG
-DAA 4 - n ADG
-DAA 4 - n ADG
-DAA 4 $ U ~n ADG0
-DAA 4 \ } Yn ADGX
-DAA 4 XU 4n ADG0
-DAA 4 ̭ - n ADG
-DAA 4 x- m ADG
-DAA 4 < p- m ADG
-DAA 4 t ha m ADG<
-DAA < {m BAD Gw
- DABA D 0v Om BBA D(G
-(D ABBA 4 4 h- 'm ADG
-DAA 4 l `} m ADGX
-DAA 4 0 l ADG
-DAA 4 ܯ 0 l ADG
-DAA 4 0 l ADG
-DAA 4 L 0 nl ADG
-DAA 4 i Il ADGD
-DAA 4 B $l ADG
-DAA < k BAI Gr
- DABA D 4 x k BBA I(G
-(D ABBA D | k BBA I(G
-(D ABBA < ı k BAD GF
- DABA < X kk BAD GF
- DABA < D ?k BAD GF
- DABA < k BAD GF
- DABA < IJ H j BAI Gp
- DABA < I j BAI G
- DABA < D \ j BAI G
- DABA < tj BAD G
- DABA L ij j BDB B(A0A8O|
-8A0A(B BBBG L j BDB B(A0A8O|
-8A0A(B BBBG T d j BFB B(A0A8J
-8A0A(B BBBD L k BBB A(A0Nf
-0A(A BBBA D < j BBA A(N
-(A ABBA D 85 j BBA A(N
-(A ABBA D ̵ 0 q j BBA A(N
-(A ABBA D h j BBA K(DU
-(A ABBA D \ !N j BBA K(D
-(A ABBA D #D j BBA A(N
-(A ABBA L ķ % AAG e
+8A0A(B BBBG T d H ! BBB B(A0A8G
+8A0A(B BBBG L Y( BDB B(A0C8OZ
+8A0A(B BBBG L ]( BDB B(A0C8OZ
+8A0A(B BBBG . K^ 4 t a BRA G
+ DABA 4 Pb BRA G
+ DABA 4 c BRA G
+ DABA 4 c m BKA G
+ DABA 4 T xd L BKA G
+ DABA 4 0e + BKA G
+ DABA 4 e
+ BKA G
+ DABA 4 f BKA G
+ DABA 4 4 Xg ȿ BKA G
+ DABA 4 l h BKA G
+ DABA 4 h BKA G
+ DABA D i e BBA D(G8
+(D ABBA 4 $ k- = ADG
+DAA 4 \ l- ADG
+DAA 4 m- ADG
+DAA 4 n- ξ ADG
+DAA 4 o- ADG
+DAA 4 < p- ADG
+DAA 4 t q- _ ADG
+DAA 4 rU : ADG0
+DAA 4 s} ADGX
+DAA 4 0uU ADG0
+DAA 4 T Xv- ˽ ADG
+DAA 4 Pw- ADG
+DAA 4 Hx- ADG
+DAA 4 @ya \ ADG<
+DAA < 4 xz 7 BAD Gw
+ DABA D t |v BBA D(G
+(D ABBA 4 @~- ADG
+DAA 4 8} ADGX
+DAA 4 , 0 ADG
+DAA 4 d x0 t ADG
+DAA 4 p0 O ADG
+DAA 4 h0 * ADG
+DAA 4 `i ADGD
+DAA 4 D B ADG
+DAA < | BAI Gr
+ DABA D P BBA I(G
+(D ABBA D t BBA I(G
+(D ABBA < L S BAD GF
+ DABA < 0 ' BAD GF
+ DABA < BAD GF
+ DABA < Д Ϻ BAD GF
+ DABA < L BAI Gp
+ DABA < I | BAI G
+ DABA < Й\ V BAI G
+ DABA < 0 BAD G
+ DABA L L G BDB B(A0A8O|
+8A0A(B BBBG L ` b BDB B(A0A8O|
+8A0A(B BBBG T BFB B(A0A8J
+8A0A(B BBBD X L t Ϻ BBB A(A0Nf
+0A(A BBBA D BBA A(N
+(A ABBA D 5 BBA A(N
+(A ABBA D T q BBA A(N
+(A ABBA D @ BBA K(DU
+(A ABBA D N a BBA K(D
+(A ABBA D , D I BBA A(N
+(A ABBA L AAG e
CAF s
ACA R
-CAJ JCA L `% AAG ]
+CAJ JCA L l 8 AAG ]
CAF Z
ACB R
-CAJ JCA , d %b D\
+CAJ JCA , hb D\
H P
H Q
-G I L % AAG ]
+G I L AAG ]
CAF [
ACA R
-CAJ JCA , &b D\
+CAJ JCA , < b D\
H P
H Q
-G I L @& AAG ]
+G I L l AAG ]
CAF [
ACA R
-CAJ JCA , d p&b D\
+CAJ JCA , Hb D\
H P
H Q
-G I L & AAG ]
+G I L AAG ]
CAF [
ACA R
-CAJ JCA , &b D\
+CAJ JCA , < b D\
H P
H Q
-G I , 'b D\
+G I , l b D\
H P
H Q
-G I , D `'b D\
+G I , 8b D\
H P
H Q
-G I L t ' AAG ]
+G I L x AAG ]
CAF [
ACA R
-CAJ JCA , ĺ 'b D\
+CAJ JCA , b D\
H P
H Q
-G I L ( AAG ]
+G I L L AAG ]
CAF c
CAI R
-CAJ JCA , D P(b D\
+CAJ JCA , (b D\
H P
H Q
-G I L t ( AAG ]
+G I L h AAG ]
CAF c
CAI R
-CAJ JCA , Ļ (b D\
+CAJ JCA , b D\
H P
H Q
-G I , )b D\
+G I , L b D\
H P
H Q
-G I , $ P)b D\
+G I , | (b D\
H P
H Q
-G I , T )b D\
+G I , hb D\
H P
H Q
-G I , )b D\
+G I , b D\
H P
H Q
-G I , *b D\
+G I , b D\
H P
H Q
-G I , P*b D\
+G I , < (b D\
H P
H Q
-G I , *b D\
+G I , l hb D\
H P
H Q
-G I D *m A~
-A l 4 + d BAD D
-ABA < ļ x+ d BBD A(G@a
-(A ABBA L , d BBB F(A0A8J@X
-8A0A(B BBBA D T -~ fd BEE D(D0A
-(A BBBA , -e *d ADG r
-DAA , ̽ .e d ADG r
-DAA , P.e c ADG r
-DAA , , .e c ADG r
-DAA , \ .h c BDD y
-ABA , /h vc BDD y
-ABA P/ A`
-G b / A`
-G b Կ 00 A`
-G b< 0 c ACLPo
+G I m A~
+A l 4 BAD D
+ABA < L P o BBD A(G@a
+(A ABBA L W BBB F(A0A8J@X
+8A0A(B BBBA D p~ " BEE D(D0A
+(A BBBA , $ e ADG r
+DAA , T e ³ ADG r
+DAA , (e ADG r
+DAA , he z ADG r
+DAA , h V BDD y
+ABA , h 2 BDD y
+ABA ( A`
+G b A`
+G b , A`
+G b< x ACLPo
AAG
-AAE D \ c BED D(G$
-(D ABBE D c BED D(G$
-(D ABBE D c BED D(G$
-(D ABBE L X BBB B(A0A8Gp8A0A(B BBB , \ ADD Z
-AAA $ h d A
-E D c BPR M(D0c
-(A BBBA 4 $ 0 e B\A G
- DABA < \ 1 d B_A G5
- DABA < 2 d BAI G,
- DABA 4 P5 d BKA G
- DABA 4 6 d BKA G
- DABA 4 L 6 bd BKA G
- DABA D x7 Ad BBA I(G4
-(D ABBA D < d BBA I(G
-(D ABBA 4 X? c BKA G
- DABA 4 L @ c BRA G
- DABA 4 @ c BRA G
- DABA 4 A c BRA G
- DABA T 8B kc BBB B(A0A8G
-8A0A(B BBBJ < L G9 ?c BAI G
- DABA < I c BAI G
- DABA D pK b BBA I(G2
-(D ABBA T N b BBE B(A0A8G<
-8D0A(B BBBC T l 0S b BBJ B(A0A8G
-8D0A(B BBBE < X& b BAD G
- DABA < Z b BAI G~
- DABA T D 8] ab BBB B(A0I8G
-8D0A(B BBBA T `R sb BBE E(D0D8L
-8A0A(B BBBK , v BCA x
-ABO w( A[ w( A[ 4 d (wd b BAA J@
- AABJ < `ya b BBA A(JP
-(A ABBA T z- b BBB B(A0D8G
-8A0A(B BBBG h L L `% ?c BFB B(A0C8J]
-8A0A(B BBBG t @ d 8{ Lc BFB B(A0A8Gp_
+AAE D BED D(G$
+(D ABBE D , Z BED D(G$
+(D ABBE D t h BED D(G$
+(D ABBE L d Д BBB B(A0A8Gp8A0A(B BBB , P ADD Z
+AAA $ < P dz A
+E D d BPR M(D0c
+(A BBBA 4 д B\A G
+ DABA < h B_A G5
+ DABA < $ BAI G,
+ DABA 4 d ( ` BKA G
+ DABA 4 ? BKA G
+ DABA 4 BKA G
+ DABA D P BBA I(G4
+(D ABBA D T Գ BBA I(G
+(D ABBA 4 0 BKA G
+ DABA 4 BRA G
+ DABA 4 i BRA G
+ DABA 4 D X H BRA G
+ DABA T | ' BBB B(A0A8G
+8A0A(B BBBJ < 9 BAI G
+ DABA < Բ BAI G
+ DABA D T H BBA I(G2
+(D ABBA T BBE B(A0A8G<
+8D0A(B BBBC T { BBJ B(A0A8G
+8D0A(B BBBE < L p & o BAD G
+ DABA < ` D BAI G~
+ DABA T BBB B(A0I8G
+8D0A(B BBBA T $ X / BEE B(D0D8L
+8A0A(B BBBK , $ * BCA x
+ABO T *( A[ t *( A[ 4 *d BAA J@
+ AABJ < $ ,a BBA A(JP
+(A ABBA T d (.- i BBB B(A0D8G
+8A0A(B BBBG d A L @% BFB B(A0C8J]
+8A0A(B BBBG G d < G{ BFB B(A0A8Gp_
8A0A(B BBBB
-8A0A(B BBBA P H T L @1 b BBA A(MT
+8A0A(B BBBA L I d I T I1 BBA A(MT
(A ABBC \
-(A ABBF L ( b BAA JV
+(A ABBF L , O } BAA JV
AABH
- AABD L b BFB B(A0C8J]
-8A0A(B BBBG T \ %c BBE E(A0C8G^
-8D0A(B BBBD L X b BDB B(A0A8O\
-8A0A(B BBBG T b BBB E(A0G8G
-8D0A(B BBBJ T \ @ b BIB B(A0A8G
-8A0A(B BBBD D h gd BBA A(N
-(A ABBA L Ud BBB A(A0Nf
-0A(A BBBA D L <d BBA A(N
-(A ABBA D 5 *d BBA A(N
-(A ABBA D q d BBA A(N
-(A ABBA L ( AAG ]
+ AABD L | SI x BFB B(A0C8J]
+8A0A(B BBBG t c T 8 ܲ BBE E(A0C8G^
+8D0A(B BBBD L < 0c BDB B(A0A8O\
+8A0A(B BBBG T BBB E(A0G8G
+8D0A(B BBBJ T gU BIB B(A0A8G ]
+8A0A(B BBBA D < BBA A(N
+(A ABBA L H
BBB A(A0Nf
+0A(A BBBA D BBA A(N
+(A ABBA D p5 BBA A(N
+(A ABBA D d hq г BBA A(N
+(A ABBA L T AAG ]
CAF k
ACA R
-CAJ JCA L L h AAG e
+CAJ JCA L AAG e
CAF s
ACA R
-CAJ JCA , b D\
+CAJ JCA , 0b D\
H P
H Q
-G I L AAG ]
+G I L $ p AAG ]
CAF Z
ACB R
-CAJ JCA , (b D\
+CAJ JCA , t b D\
H P
H Q
-G I L L h AAG ]
+G I L AAG ]
CAF [
ACA R
-CAJ JCA , b D\
+CAJ JCA , b D\
H P
H Q
-G I L AAG ]
+G I L $ P AAG ]
CAF [
ACA R
-CAJ JCA , b D\
+CAJ JCA , t b D\
H P
H Q
-G I L L H AAG ]
+G I L AAG ]
CAF [
ACA R
-CAJ JCA , xb D\
+CAJ JCA , b D\
H P
H Q
-G I , b D\
+G I , $ 0b D\
H P
H Q
-G I , b D\
+G I , T pb D\
H P
H Q
-G I , , 8b D\
+G I , b D\
H P
H Q
-G I , \ xb D\
+G I , b D\
H P
H Q
-G I m A~
-A l < { ` BED D(G0z
-(D ABBA D H~ K` BEE D(D0A
-(A BBBA , \ e ` ADG r
-DAA , e _ ADG r
-DAA , e _ ADG r
-DAA D @2 0` BCA L@Z
+G I 0m A~
+A l < \ { 7 BED D(G0z
+(D ABBA D ~ BEE D(D0A
+(A BBBA , e ǯ ADG r
+DAA , 8e ADG r
+DAA , D xe ADG r
+DAA D t 2 BCA L@Z
AABH
- AABA D 4 82 ` BCA L@Z
+ AABA D 2 BCA L@Z
AABH
- AABA T | l ` BEJ E(D0D8GP
-8D0A(B BBBF . K^ 4 a BYA G
- DABA 4 $ x a BYA G
- DABA 4 \ ` ja B\A G
- DABA 4 H Ia BYA G
- DABA < t (a BYA G
- DABA < @ a B_A G5
- DABA < L ` BAI G,
- DABA L ` BIB A(D0G
-0D(A BBBA 4 y` BKA G
- DABA 4 x X` BKA G
- DABA 4 L 0 7` BKA G
- DABA 4 7 ` ADG
-DAA T _ BDB I(A0A8Np
-8A0A(B BBBE 4 x _ ADG
-DAA 4 L _ ADG
-DAA <
- k_ BAD G
- DABA D h ?_ BBA D(G
-(D ABBA T V _ BBB B(A0A8G
-8A0A(B BBBD < d ^ BAI Ge
- DABA T hX ^ BBJ B(A0A8G
-8D0A(B BBBI T p ^ BBB B(A0A8G.
-8A0A(B BBBG T T P ^ BEE B(D0D8L
-8A0A(B BBBK , 7 ACL d
-JAQ , 7 BCA x
-ABO 8( A[ 8( A[ T L 08" ^ BBB B(A0D8G
-8A0A(B BBBE | L L L X_ BFB B(A0C8J]
-8A0A(B BBBG [ L $ [M _ BDB B(A0A8O\
-8A0A(B BBBG L t _M _ BDB B(A0A8O\
-8A0A(B BBBG , eb D\
+ AABA T ! BEJ E(D0D8GP
+8D0A(B BBBF . K^ 4 t 8 d BYA G
+ DABA 4 C BYA G
+ DABA 4 ث " B\A G
+ DABA 4 BYA G
+ DABA < T xt BYA G
+ DABA < B_A G5
+ DABA < BAI G,
+ DABA L x i BIB A(D0G
+0D(A BBBA 4 d 8 1 BKA G
+ DABA 4 BKA G
+ DABA 4 BKA G
+ DABA 4 `7 ί ADG
+DAA T D h BDB I(A0A8Np
+8A0A(B BBBE 4 m ADG
+DAA 4 x H ADG
+DAA < # BAD G
+ DABA D L BBA D(G
+(D ABBA T 8V Ϯ BBB B(A0A8G
+8A0A(B BBBD < @ BAI Ge
+ DABA T , X s BBJ B(A0A8G
+8D0A(B BBBI T g BBB B(A0A8G.
+8A0A(B BBBG T P < BEE B(D0D8L
+8A0A(B BBBK , ACL d
+JAQ , BCA x
+ABO < ( A[ \ ( A[ T " y BBB B(A0D8G
+8A0A(B BBBE L D x BFB B(A0C8J]
+8A0A(B BBBG < L M BDB B(A0A8O\
+8A0A(B BBBG L M BDB B(A0A8O\
+8A0A(B BBBG , b D\
H P
H Q
-G I , fb D\
+G I , $ b D\
H P
H Q
-G I T $ xE q_ BBE E(A0A8G6
-8A0A(B BBBI T | p j_ BBE E(A0A8G
-8A0A(B BBBK T E b_ BBE E(A0A8G6
-8A0A(B BBBI . K^ T D (ep C_ BBB B(A0A8G
-8A0A(B BBBA T @u7 5_ BBB B(A0A8G
-8A0A(B BBBA T ({ !_ BDB B(A0A8J
-8A0A(B BBBH $ P < d H ` BBA A(J`x
-(A ABBB | xQ Az
-E P , b D\
+G I T VE # BBE E(A0A8G6
+8A0A(B BBBI T x] BBE E(A0A8G
+8A0A(B BBBK T \ dE BBE E(A0A8G6
+8A0A(B BBBI \ ؉. K^ T 0p BBB B(A0A8G
+8A0A(B BBBA T $ H*7 BBB B(A0A8G
+8A0A(B BBBA T | 09{ Ӯ BDB B(A0A8J
+8A0A(B BBBH | XQ < PQ ȯ BBA A(J`x
+(A ABBB iQ Az
+E P , ib D\
H P
H Q
-G I X. K^ T 7 _ BBB B(A0A8G
-8A0A(B BBBA , < b D\
+G I $ @. K^ T i7 ; BBB B(A0A8G
+8A0A(B BBBA , xb D\
H P
H Q
-G I , l b D\
+G I , yb D\
H P
H Q
-G I 4 H _ BAD D
-ABA D /` BAA Jp
+G I 4 L Py Ǯ BAD D
+ABA D y ߯ BAA Jp
AABH
- AABC D D hl 2` BAA JPV
+ AABC D p}l BAA JPV
AABH
- AABC d . K^ T x a BBB B(A0I8G_
-8D0A(B BBBA T a BBB B(A0A8G
-8A0A(B BBBA T T xA b BIB B(A0A8G+"
-8A0A(B BBBI L @ AAG ]
+ AABC ؆. K^ T , BBB B(A0I8G_
+8D0A(B BBBA T BBB B(A0I8G
+8D0A(B BBBA T A { BIB B(A0A8G+"
+8A0A(B BBBI L x AAG ]
CAF [
ACA R
-CAJ JCA , p b D\
+CAJ JCA , , b D\
H P
H Q
-G I L AAG ]
+G I L \ AAG ]
CAF [
ACA R
-CAJ JCA , T b D\
+CAJ JCA , b D\
H P
H Q
-G I T !
- e BBB L(A0A8G
-8A0A(B BBBA T X+ 'e BBB L(A0A8G
-8A0A(B BBBI , \ 7e 3e ADG r
-DAA , 8e e ADG r
-DAA T Уu e BEE B(A0A8G
-8A0A(B BBBD T e e BEE B(A0A8G
-8A0A(B BBBF L l 7 e BBB B(A0G8Dh
-8A0A(B BBBJ T = e BBB B(G0A8Gx
-8A0A(B BBBA . K^ T , F g BIB B(D0A8GV
-8D0A(B BBBA T K xg BIB B(D0A8GV
-8D0A(B BBBA T P ag BIB B(D0A8GV
-8D0A(B BBBA T 4 XV Jg BIB B(D0A8GV
-8D0A(B BBBA T [ 3g BIB B(D0A8GV
-8D0A(B BBBA T (a g BIB B(D0A8GV
-8D0A(B BBBA T < f g BIB B(D0A8GV
-8D0A(B BBBA T k f BIB B(D0A8GV
-8D0A(B BBBA T `q f BIB B(D0A8GV
-8D0A(B BBBA T D v|
f BIB B(A0A8GH
-8A0A(B BBBA T |
f BIB B(A0A8GH
-8A0A(B BBBA T |
f BIB B(A0A8GH
-8A0A(B BBBA L $ 0 AAG ]
+G I T 4 X
+ ϴ BBB L(A0A8G
+8A0A(B BBBA T ۴ BBB L(A0A8G
+8A0A(B BBBI , e ADG r
+DAA , Xe ô ADG r
+DAA T D Pu BEE B(A0A8G
+8A0A(B BBBD T We BEE B(A0A8G
+8A0A(B BBBF L BBB B(A0G8Dh
+8A0A(B BBBJ T D 8p r BBB B(G0A8Gx
+8A0A(B BBBA D . K^ T 8 C BIB B(D0A8GV
+8D0A(B BBBA T , BIB B(D0A8GV
+8D0A(B BBBA T d BIB B(D0A8GV
+8D0A(B BBBA T p BIB B(D0A8GV
+8D0A(B BBBA T BIB B(D0A8GV
+8D0A(B BBBA T l @ ж BIB B(D0A8GV
+8D0A(B BBBA T BIB B(D0A8GV
+8D0A(B BBBA T BIB B(D0A8GV
+8D0A(B BBBA T t x BIB B(D0A8GV
+8D0A(B BBBA T #|
t BIB B(A0A8GH
+8A0A(B BBBA T $ 1|
] BIB B(A0A8GH
+8A0A(B BBBA T | 0>|
F BIB B(A0A8GH
+8A0A(B BBBA L | HX AAG ]
CAF [
ACA R
-CAJ JCA , t `b D\
+CAJ JCA , xXb D\
H P
H Q
-G I L AAG ]
+G I L X AAG ]
CAF [
ACA R
-CAJ JCA , Ыb D\
+CAJ JCA , L Xb D\
H P
H Q
-G I T L
- {e BBB L(A0A8G
-8A0A(B BBBA T H e BBB L(A0A8G
-8A0A(B BBBI , e e ADG r
-DAA , , e oe ADG r
-DAA L \ P5 ;f BBB B(A0G8Dh
-8A0A(B BBBJ T @_ f BBB B(G0A8Gx
-8A0A(B BBBA y. K^ T 0 g BIB B(D0A8GV
-8D0A(B BBBA T t g BIB B(D0A8GV
-8D0A(B BBBA T g BIB B(D0A8GV
-8D0A(B BBBA T $ h g BIB B(D0A8GV
-8D0A(B BBBA T | g BIB B(D0A8GV
-8D0A(B BBBA T 8 hg BIB B(D0A8GV
-8D0A(B BBBA T , Qg BIB B(D0A8GV
-8D0A(B BBBA T |
:g BIB B(A0A8GH
-8A0A(B BBBA T 0|
#g BIB B(A0A8GH
-8A0A(B BBBA T 4 X|
g BIB B(A0A8GH
-8A0A(B BBBA T |
f BIB B(A0A8GH
-8A0A(B BBBA T *|
f BIB B(A0A8GH
-8A0A(B BBBA , 7b D\
+G I T (Y
+ / BBB L(A0A8G
+8A0A(B BBBA T , `c ; BBB L(A0A8G
+8A0A(B BBBI , oe G ADG r
+DAA , (pe # ADG r
+DAA L hp5 BBB B(A0G8Dh
+8A0A(B BBBJ T 4
+ XuP е BBB B(G0A8Gx
+8A0A(B BBBA 4 {. K^ T
+ 8} BIB B(D0A8GV
+8D0A(B BBBA T
+ x BIB B(D0A8GV
+8D0A(B BBBA T T a BIB B(D0A8GV
+8D0A(B BBBA T p J BIB B(D0A8GV
+8D0A(B BBBA T ؒ 3 BIB B(D0A8GV
+8D0A(B BBBA T \ @ BIB B(D0A8GV
+8D0A(B BBBA T |
BIB B(A0A8GH
+8A0A(B BBBA T
Ъ|
BIB B(A0A8GH
+8A0A(B BBBA T d
|
BIB B(A0A8GH
+8A0A(B BBBA T
|
BIB B(A0A8GH
+8A0A(B BBBA T H|
BIB B(A0A8GH
+8A0A(B BBBA T l p|
BIB B(A0A8GH
+8A0A(B BBBA , l b D\
H P
H Q
-G I , l 8 f AAN`V
-AAA , :_ f AAG o
-DAA @u. K^ T :_ _i BIB B(A0A8GF
+G I , K AAN`V
+AAA , $ _ = AAG o
+DAA (w. K^ T l ; BIB B(A0A8Gu
+8A0A(B BBBA T BIB B(A0A8G
+8A0A(B BBBA T _ BIB B(A0A8GF
-8A0A(B BBBA T < M; i BIB B(A0A8Gu
-8A0A(B BBBA T ] yi BIB B(A0A8G
-8A0A(B BBBA T m5 oi BBB B(D0A8G
-8A0A(B BBBB , b D\
+8A0A(B BBBA T t !5 # BBB B(D0A8G
+8A0A(B BBBB , t 7b D\
H P
H Q
-G I , L 8b D\
+G I , 8b D\
H P
H Q
-G I , x +j AANP4
-AAA ,
j AAN`}
-AAA , Ȉ_ i AAG o
-DAA , 4 _ i AAG o
-DAA < r. K^ T | ' _l BIB B(A0A8G
-8A0A(B BBBA T w Il BIB B(A0A8GA
-8A0A(B BBBA T , F 3l BIB B(A0A8G
-8A0A(B BBBA T l BIB B(A0A8G
-8A0A(B BBBA T 7l BBB B(A0A8J N
-8A0A(B BBBD (A $ S < S T 0A L h m BEE B(D0D8K`
-8A0A(B BBBA ( 4 L d | ^ cz 0
- ( , k AAG@
-DAF AP , AL , t h Lk ADDP~
-AAA $ | $ ACK ODA 4 r k BDD DPA
- AABA H AP , $ Hh j ADDP~
-AAA , D \ AZ L | p BBB B(A0A8G@
-8A0A(B BBBB T i BBB E(D0A8D
-8D0A(B BBBK < L p j YHD M
+G I , , @8 ߹ AANP4
+AAA , \ : AAN`}
+AAA , =_ AAG o
+DAA , =_ AAG o
+DAA t. K^ T =F BIB B(A0A8G
+8A0A(B BBBA T \ K' BIB B(A0A8G
+8A0A(B BBBA T Z BIB B(A0A8G
+8A0A(B BBBA T Plw BIB B(A0A8GA
+8A0A(B BBBA T d xz BBB B(A0A8J N
+8A0A(B BBBD d A | A L 0 ü BEE B(D0D8K`
+8A0A(B BBBA , D \ ؑ t Б ȑ ^ cz
+ , t z AAG@
+DAF L d AP AL , h ADDP~
+AAA $ $ ACK ODA 4 T Ȓr BDD DPA
+ AABA 4 AP , h p ADDP~
+AAA P H P AZ < Pr BBB A(D0:
+(A BBBA T l BBB B(D0A8D
+8A0A(B BBBF < H չ YHD M
HAE
-HAA d P D | H BBE E(G0D8DP~8D0A(B BBB, `O BAA
-ABA L Qi BEB B(D0D8KPQ
-8A0A(B BBBA T l O_ i BJE E(D0D8G
-8D0A(B BBBA P N T P{ h BJE E(D0D8G5
-8D0A(B BBBA T 4 Q{ jh BJE E(D0D8G5
-8D0A(B BBBA T S $h BJE E(D0D8GZ
-8D0A(B BBBA T HT{ g BJE E(D0D8G5
-8D0A(B BBBA T < pU g BJE E(D0D8GZ
-8D0A(B BBBA T V{ Rg BJE E(D0D8G5
-8D0A(B BBBA L W g BJE D(D0G!
-0D(A BBBD T < Yc f BJE E(D0D8G
-8D0A(B BBBA L 8Z f BJE D(D0G!
-0D(A BBBD L x[ Lf BEE I(D0G
-0D(A BBBE L 4 \ f BJE D(D0G!
-0D(A BBBD L ]L e BJE D(D0G
-0D(A BBBA ^ AL ^0 AG fA $ _F AAG0zAA 0_ D T _ e BBJ A(D0
-(A BBEA T e BDD u
+HAA ( D BBE E(G0D8DP~8D0A(B BBB, H BAA
+ABA L X BEB B(D0D8KPQ
+8A0A(B BBBA T _ Ѹ BJE E(D0D8G
+8D0A(B BBBA T < { BJE E(D0D8G5
+8D0A(B BBBA T { E BJE E(D0D8G5
+8D0A(B BBBA T BJE E(D0D8GZ
+8D0A(B BBBA T D ({ BJE E(D0D8G5
+8D0A(B BBBA T P s BJE E(D0D8GZ
+8D0A(B BBBA T { - BJE E(D0D8G5
+8D0A(B BBBA L L L BJE D(D0G
+0D(A BBBA T
+c BJE E(D0D8G
+8D0A(B BBBA L c BEE I(D0G
+0D(A BBBE L D
L & BJE D(D0G
+0D(A BBBA L L BJE D(D0G
+0D(A BBBA AL 0 AG fA $ (F AAG0zAA P D d BBJ A(D0
+(A BBEA T BDD u
ADD
ABE U
ABH I
-ABA T h Ve BDD u
+ABA T j BDD u
ADD
ABE U
ABH I
-ABA < L 0 e BDA G
- DABG < d BDA G
- DABG L P_ d BHE B(D0D8DP
-8D0A(B BBBB L ` BLB B(A0A8Dz
-8A0A(B BBBA L l P) Gd ZHA DPM
+ABA < \ p % BDA G
+ DABG < Л BDA G
+ DABG L p ˴ BHE B(D0D8DP
+8D0A(B BBBB L BLB B(A0A8Dz
+8A0A(B BBBA L | ) [ ZHA DPM
HABA v
- HABA T 0 Od BBB B(A0A8G
-8A0A(B BBBA \ b BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 t bY [e AIG t
-AAI DCA L c 'e BEB E(I0H8D@
-8D0A(B BBBA T T d BBB B(A0A8G
-8A0A(B BBBA D T F d BLB A(A0Gp@
-0A(A BBBA $ t cF AAG0zAA L d d BEB E(I0H8D@
-8D0A(B BBBD \ e] d BBB I(D0D@
+ HABA T p BBB B(D0A8G
+8A0A(B BBBA \ BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 Y AIG t
+AAI DCA L c BEB E(I0H8D@
+8D0A(B BBBA T ! @T ; BBB B(A0A8G
+8A0A(B BBBA D d! HF BLB A(A0Gp@
+0A(A BBBA $ T" F AAG0zAA L ! ( BEB E(I0H8D@
+8D0A(B BBBD \ $" ] ״ BBB I(D0D@
0F(A BBBK U
-0F(A BBBA T t 8! cd BLH B(A0A8G
-8D0A(B BBBA T Od BLB B(A0A8G
-8A0A(B BBBE L $ eU Kd BBB E(E0E8G`
-8A0A(B BBBA l t f= d BEB B(A0A8D
+0F(A BBBA T " x! BLH B(A0A8G
+8D0A(B BBBA T " P BLB B(A0A8G
+8A0A(B BBBE L 4# U BBB E(E0E8G`
+8A0A(B BBBA l # = W BEB B(A0A8D
8A0A(B BBBG U
-8A0A(B BBBA T @ c BEE B(A0D8G
-8D0A(B BBBD L < 7 c BEB F(A0D8D
-8A0A(B BBBA T c BEE B(A0D8Gf
-8D0A(B BBBC L 8 7 >c BEB F(A0D8D
-8A0A(B BBBA 8_. K^ $ $ PhM AAK0}AA D L xh BEB B(A0A8DP8A0A(B BBBL @i
BEE A(A0]
+8A0A(B BBBA T # @ 3 BEE B(A0D8G
+8D0A(B BBBD L L$ 7 BEB F(A0D8D
+8A0A(B BBBA T $ BEE B(A0D8Gf
+8D0A(B BBBC L $ x7 z BEB F(A0D8D
+8A0A(B BBBA % a. K^ $ & pM AAK0}AA D ,& BEB B(A0A8DP8A0A(B BBBL t& `
BEE A(A0]
(A BBBF
-(E BBBJ P D Q T $ X 0 c BBE B(A0A8J]
-8D0A(B BBBA < | 0 c BBD A(G
-(D ABBA L Wc BHF E(D0A8Dm
-8A0A(B BBBA T u c BBB B(A0A8GM
-8D0A(B BBBE L d " b BHE E(D0A8Dm
-8A0A(B BBBA T H#u b BBB B(A0A8GM
-8D0A(B BBBE L p% b BHE E(D0A8Dm
-8A0A(B BBBA T \ & Pb BBB B(A0A8G]
-8D0A(B BBBE L H( "b BHE E(D0A8Dm
-8A0A(B BBBA T
- ( a BBB B(A0A8GR
-8D0A(B BBBH T \
- + a BBB B(A0A8GR
-8D0A(B BBBH T
- h- a BBB B(A0A8G
-8A0A(B BBBA T / da BBB B(A0A8G
-8A0A(B BBBA T d h2T ;a BBB B(A0A8G
-8A0A(B BBBA T p5 a BBB B(A0A8G
-8A0A(B BBBA T 7 ` BBB B(A0A8G
-8A0A(B BBBA D l : ` BBB A(A0G
-0D(A BBBA $
Xd3 AAJ ZGA ,
pdf AAJ m
-GAO ,
db D\
+(E BBBJ & D Q T 4& 0 BBE B(A0A8J]
+8D0A(B BBBA < & p BBD A(G
+(D ABBA L & @ BHF E(D0A8Dm
+8A0A(B BBBA T ' u Z BBB B(A0A8GM
+8D0A(B BBBE L t' , BHE E(D0A8Dm
+8A0A(B BBBA T ' u BBB B(A0A8GM
+8D0A(B BBBE L ( Ų BHE E(D0A8Dm
+8A0A(B BBBA T l( @ BBB B(A0A8G]
+8D0A(B BBBE L ( ^ BHE E(D0A8Dm
+8A0A(B BBBA T ) % BBB B(A0A8GR
+8D0A(B BBBH T l) ` BBB B(A0A8GR
+8D0A(B BBBH T ) ɱ BBB B(A0A8G
+8A0A(B BBBA T * BBB B(A0A8G
+8A0A(B BBBA T t* o w BBB B(A0A8GJ
+8A0A(B BBBA T * N BBB B(A0A8G
+8A0A(B BBBA T $+ H & BBB B(A0A8G
+8A0A(B BBBA D |+ BBB A(A0G
+0D(A BBBA $ l, x3 AAJ ZGA , , f AAJ m
+GAO , , b D\
H P
H Q
-G I , db D\
+G I , , b D\
H P
H Q
-G I , D 0eb D\
+G I , $- Pb D\
H P
H Q
-G I , t peb D\
+G I , T- b D\
H P
H Q
-G I , eb D\
+G I , - b D\
H P
H Q
-G I , eb D\
+G I , - b D\
H P
H Q
-G I , 0fb D\
+G I , - Pb D\
H P
H Q
-G I , 4 pfb D\
+G I , . b D\
H P
H Q
-G I L @> ^ BBB B(A0A8GZ
-8D0A(B BBBH T 0e ^ BEE B(A0A8G
-8A0A(B BBBF 4 4 f" ^ BAA GP
- AABG T l e ~^ BEE B(A0A8G
-8A0A(B BBBF T (e v^ BEE B(A0A8G
-8A0A(B BBBF T @ n^ BBE E(A0A8G
-8A0A(B BBBH T t f^ BBE E(A0A8G
-8A0A(B BBBH T e ^^ BEE B(A0A8G
-8A0A(B BBBF T $ e V^ BEE B(A0A8G
-8A0A(B BBBF T | N^ BBE E(A0A8G
-8A0A(B BBBF T He ^ BEE B(A0A8G
-8A0A(B BBBF T , ` ^ BBE E(A0A8G2
-8A0A(B BBBE L dU ^ BBB B(A0A8J
-8A0A(B BBBB | BBB B(A0D8DP
+G I L - i> BBB B(A0A8GZ
+8D0A(B BBBH T - le Ϯ BEE B(A0A8G
+8A0A(B BBBF 4 D. (" Ǯ BAA GP
+ AABG T |. re BEE B(A0A8G
+8A0A(B BBBF T . xe BEE B(A0A8G
+8A0A(B BBBF T ,/ ~ BBE E(A0A8G
+8A0A(B BBBH T / X BBE E(A0A8G
+8A0A(B BBBH T / e BEE B(A0A8G
+8A0A(B BBBF T 40 e BEE B(A0A8G
+8A0A(B BBBF T 0 З BBE E(A0A8G
+8A0A(B BBBF T 0 e ֮ BEE B(A0A8G
+8A0A(B BBBF T <1 0 ή BBE E(A0A8G2
+8A0A(B BBBE L 1 U Ǯ BBB B(A0A8J
+8A0A(B BBBB | 2 BBB B(A0D8DP
8A0A(B BBBB
8A0A(B BBBE "
-8A0A(B BBBD L T xj4 ^ BBB B(A0A8M
-8A0A(B BBBH | Q. K^ T Pv ;` BBJ B(A0A8G_
-8D0A(B BBBK T z, /` BBJ B(A0A8G
-8D0A(B BBBF T l p K` BBB B(I0A8G%
-8D0A(B BBBE T ؇ W` BBJ B(A0A8Gd
-8D0A(B BBBF T v s` BBJ B(A0A8G
-8D0A(B BBBD T t 8 g` BBJ B(A0A8G
-8D0A(B BBBK T [` BBJ B(A0A8GT
-8D0A(B BBBF T $ (u c` BBJ B(A0A8G
-8D0A(B BBBC T | P, g` BBB B(A0A8M
-8A0A(B BBBG 4 ( ADD K
-AAJ ]DA T 5 _ BEB B(A0A8J
-8A0A(B BBBG < T
, l b D\
+8A0A(B BBBD L d2 4 E BBB B(A0A8M
+8A0A(B BBBH \3 XT. K^ T 2 p& { BBJ B(A0A8G_
+8D0A(B BBBK T $3 *, o BBJ B(A0A8G
+8D0A(B BBBF T |3 1 BBJ B(A0A8G4
+8D0A(B BBBF T 3 7} BBB B(I0A8G
+8D0A(B BBBE T ,4 > BBJ B(A0A8G
+8D0A(B BBBK T 4 B BBJ B(A0A8G
+8D0A(B BBBK T 4 @G BBJ B(A0A8GT
+8D0A(B BBBF T 45 M BBB B(I0A8G
+8D0A(B BBBJ T 5 T, BBB B(A0A8M
+8A0A(B BBBG 4 6 Y ADD K
+AAJ ]DA T 6 @Z $ BEB B(A0A8J
+8A0A(B BBBG 7 47
, L7 b D\
H P
H Q
-G I , b D\
+G I , |7 ȫb D\
H P
H Q
-G I , 8b D\
+G I , 7 b D\
H P
H Q
-G I , xb D\
+G I , 7 Hb D\
H P
H Q
-G I , , b D\
+G I , 8 b D\
H P
H Q
-G I , \ b D\
+G I , <8 Ȭb D\
H P
H Q
-G I , 8b D\
+G I , l8 b D\
H P
H Q
-G I , xb D\
+G I , 8 Hb D\
H P
H Q
-G I , b D\
+G I , 8 b D\
H P
H Q
-G I , b D\
+G I , 8 ȭb D\
H P
H Q
-G I , L 8b D\
+G I , ,9 b D\
H P
H Q
-G I , | xb D\
+G I , \9 Hb D\
H P
H Q
-G I , b D\
+G I , 9 b D\
H P
H Q
-G I , b D\
+G I , 9 Ȯb D\
H P
H Q
-G I , 8b D\
+G I , 9 b D\
H P
H Q
-G I , < xb D\
+G I , : Hb D\
H P
H Q
-G I , l b D\
+G I , L: b D\
H P
H Q
-G I , b D\
+G I , |: ȯb D\
H P
H Q
-G I , 8 b D\
+G I , : b D\
H P
H Q
-G I , x b D\
+G I , : Hb D\
H P
H Q
-G I , , b D\
+G I , ; b D\
H P
H Q
-G I , \ b D\
+G I , <; Ȱb D\
H P
H Q
-G I , 8b D\
+G I , l; b D\
H P
H Q
-G I , xb D\
+G I , ; Hb D\
H P
H Q
-G I , b D\
+G I , ; b D\
H P
H Q
-G I , b D\
+G I , ; ȱb D\
H P
H Q
-G I , L 8b D\
+G I , ,< b D\
H P
H Q
-G I , | xb D\
+G I , \< Hb D\
H P
H Q
-G I , b D\
+G I , < b D\
H P
H Q
-G I , b D\
+G I , < Ȳb D\
H P
H Q
-G I 8m A~
-A l 4 , AAJ v
-AAD fAA L Y BBB F(A0A8J@X
-8A0A(B BBBA < BAA M
+G I < m A~
+A l 4 = X AAJ v
+AAD fAA L < BBB F(A0A8J@X
+8A0A(B BBBA < = ` BAA M
ABD
-ABD T jZ BDB B(A0I8G
-8A0A(B BBBH L x
A`
-G b l
AW
A`
-G b X A`
-G mT e Y BDB B(I0A8G
-8A0A(B BBBA , L ! Y ADDP
-AAE < | Y AAJPh
+ABD T ,= BDB B(A0I8G
+8A0A(B BBBH ,> H A`
+G b L> AW l> A`
+G b > ( A`
+G mL > J BDB B(I0A8G
+8A0A(B BBBA , T> ! ADDP
+AAE < > AAJPh
AAJ
-AAH < Y AAJPh
+AAH < > 8 AAJPh
AAJ
-AAH L 7 Y BAA N
+AAH L ? 7 ѩ BAA N
AABH
- AABF D L Y BED D(G$
-(D ABBE D x Y BED D(G$
-(D ABBE L p Y BDB B(A0A8L`\
-8A0A(B BBBB L ,! |Y BDB J(A0A8GL
-8A0A(B BBBA D |! @ Y BED D(G$
-(D ABBE D ! jY BED D(G$
-(D ABBE T " ( 5Y BDB B(A0I8D(
-8A0A(B BBBF D d" Y BED D(G$
-(D ABBE < " , X BDA A(LP`
-(A ABBF T " `. X BEB B(A0A8Jn
-8A0A(B BBBA T D# (: 5Y BDB B(A0A8O
-8A0A(B BBBA < # > Y AEL@Z
+ AABF D T? Ȣ BED D(G$
+(D ABBE D ? 0 BED D(G$
+(D ABBE L ? ( BDB B(A0A8L`\
+8A0A(B BBBB L 4@ % BDB J(A0A8GL
+8A0A(B BBBA D @ ۩ BED D(G$
+(D ABBE D @ ` BED D(G$
+(D ABBE T A q BDB B(A0I8D(
+8A0A(B BBBF D lA p P BED D(G$
+(D ABBE < A . BDA A(LP`
+(A ABBF T A H BEB B(A0A8Jn
+8A0A(B BBBA T LB q BDB B(A0A8O
+8A0A(B BBBA < B V AEL@Z
AAB
-AAE D # Y BED D(G$
-(D ABBE D $$ X X BED D(G$
-(D ABBE L l$ 0@ X BDB B(A0A8L
-8A0A(B BBBJ L $ D vX BBB B(A0C8JA
-8A0A(B BBBG T % PJ jX BFB B(A0D8Gd
-8A0A(B BBBA <& Q T |% Q cX BDB B(A0I8G
-8A0A(B BBBF L % ]
- Y BDB J(A0A8G
-8A0A(B BBBA L $& h Y BBB B(A0A8Je
-8D0A(B BBBJ L' X?. K^ < & k Z BAI G
- DABA L & @m uZ BBB I(A0G
-0D(A BBBA L ' o PZ BBB I(A0G
-0D(A BBBA L l' q +Z BBB I(A0G
-0D(A BBBA T ' t Z BBB B(I0A8G
-
-8D0A(B BBBA L ( v Y BBB I(A0G
-0D(A BBBA L d( HyW Y BBB I(A0G
-0D(A BBBA L ( X|} Y BBB I(A0G
-0D(A BBBA T ) ~5 jY BBB B(I0A8Gn
-8D0A(B BBBA L \) p =Y BBB I(A0G
-0D(A BBBA T ) ( Y BBB B(I0A8Ga
-8D0A(B BBBA T * Ȇ X BBB B(I0A8G-
-8D0A(B BBBA T \* p X BBB B(I0A8G-
-8D0A(B BBBA T * X BBB B(I0A8G
-8D0A(B BBBA L + dX BBB I(A0G
-0D(A BBBA L \+ ?X BBB I(A0G
-0D(A BBBA T + @ X BBB B(I0A8G
-8D0A(B BBBA L , W BBB I(A0G
-0D(A BBBA T T, X W BBB B(I0A8G/
-8D0A(B BBBA T , E W BBJ B(A0A8G
-8D0A(B BBBA T - W BBB B(D0A8G
-8D0A(B BBBA L \- ZW BBB I(A0G
-0D(A BBBA T - 0V 5W BBJ B(A0A8G
-8D0A(B BBBA < . 8 ,W BAI G^
- DABA < D. ȫ W BAI G
- DABA L . V BBB I(A0G)
-0D(A BBBA T . H V BBB B(I0A8G
-8D0A(B BBBA T ,/ V BBB B(I0A8G?
-8D0A(B BBBA T / a `V BBB B(I0A8G
-8D0A(B BBBA L / 3V BBB I(A0G
-0D(A BBBA T ,0 C V BBB B(I0A8G^
-8D0A(B BBBA L 0 U BBB B(A0A8J
-8A0A(B BBBA 1 x L 0 pL qV BDB B(A0C8R_
-8A0A(B BBBA T <1 p mV BFB B(A0D8G
-8A0A(B BBBH l2 T 1 iV BFB B(A0D8G
-8A0A(B BBBH 2 H 2 3 , $3 b D\
+AAE D B A BED D(G$
+(D ABBE D ,C BED D(G$
+(D ABBE L tC ר BDB B(A0A8L
+8A0A(B BBBJ L C h BBB B(A0C8JA
+8A0A(B BBBG T D 8 BFB B(A0D8Gd
+8A0A(B BBBA E T D BDB B(A0I8G
+8A0A(B BBBF L D
+ ˩ BDB J(A0A8G
+8A0A(B BBBA L ,E Щ BBB B(A0A8Je
+8D0A(B BBBJ $F A. K^ < E x BAI G
+ DABA L E ( BBB I(A0G
+0D(A BBBA L $F x BBB I(A0G
+0D(A BBBA L tF ! w BBB I(A0G
+0D(A BBBA L F # R BBB I(A0G
+0D(A BBBA L G 8& - BBB I(A0G
+0D(A BBBA L dG (W BBB I(A0G
+0D(A BBBA L G +} BBB I(A0G
+0D(A BBBA T H -5 BBB B(I0A8Gn
+8D0A(B BBBA L \H 0 BBB I(A0G
+0D(A BBBA T H P3( l BBB B(I0A8Ga
+8D0A(B BBBA T I (6 ? BBB B(I0A8G-
+8D0A(B BBBA T \I 8 BBB B(I0A8G-
+8D0A(B BBBA L I x;} BBB I(A0G
+0D(A BBBA L J = BBB I(A0G
+0D(A BBBA L TJ ? BBB I(A0G
+0D(A BBBA T J B v BBB B(I0A8G
+8D0A(B BBBA L J D O BBB I(A0G
+0D(A BBBA T LK G * BBB B(I0A8G/
+8D0A(B BBBA T K IE BBJ B(A0A8G
+8D0A(B BBBA T K N BBB B(D0A8G
+8D0A(B BBBA L TL hQ BBB I(A0G
+0D(A BBBA T L SV BBJ B(A0A8G
+8D0A(B BBBA < L Y BAI G^
+ DABA < <M Z a BAI G
+ DABA L |M p\ ; BBB I(A0G)
+0D(A BBBA T M _ BBB B(I0A8G
+8D0A(B BBBA T $N a BBB B(I0A8G?
+8D0A(B BBBA T |N pda BBB B(I0A8G
+8D0A(B BBBA L N g BBB I(A0G
+0D(A BBBA T $O iC j BBB B(I0A8G^
+8D0A(B BBBA L |O l C BBB B(A0A8J
+8A0A(B BBBA tP @ L O 8L ͦ BDB B(A0C8R_
+8A0A(B BBBA T 4P 8 ɦ BFB B(A0D8G
+8A0A(B BBBH 4Q T P Ŧ BFB B(A0D8G
+8A0A(B BBBH Q Q Q М , Q Ȝb D\
H P
H Q
-G I , T3 @b D\
+G I , R b D\
H P
H Q
-G I , 3 b D\
+G I , LR Hb D\
H P
H Q
-G I , 3 b D\
+G I , |R b D\
H P
H Q
-G I , 3 b D\
+G I , R ȝb D\
H P
H Q
-G I , 4 @b D\
+G I , R b D\
H P
H Q
-G I , D4 b D\
+G I , S Hb D\
H P
H Q
-G I , t4 b D\
+G I , <S b D\
H P
H Q
-G I , 4 b D\
+G I , lS Ȟb D\
H P
H Q
-G I , 4 @b D\
+G I , S b D\
H P
H Q
-G I , 5 b D\
+G I , S Hb D\
H P
H Q
-G I , 45 b D\
+G I , S b D\
H P
H Q
-G I , d5 b D\
+G I , ,T ȟb D\
H P
H Q
-G I , 5 @b D\
+G I , \T b D\
H P
H Q
-G I , 5 b D\
+G I , T Hb D\
H P
H Q
-G I , 5 b D\
+G I , T b D\
H P
H Q
-G I $6 m A~
-A l L l5 P S BBB F(A0A8J@X
-8A0A(B BBBA 6 6 A`
-G bT 5 8 S BDB B(A0A8O
-8A0A(B BBBG 4 L6 S BAA N
- AABD T 6 H RT BEB B(A0A8Jn
-8A0A(B BBBA L 6 = T BDB B(I0A8GL
-8A0A(B BBBA T ,7 T BBB J(A0A8G
-8A0A(B BBBI L 7
U BDJ A(A0Ge
-0A(A BBBJ T 7 H" U BDB B(A0I8D(
-8A0A(B BBBF T ,8 &e T BDB B(A0A8Oo
-8A0A(B BBBD L 8 / T BBB B(A0C8JA
-8A0A(B BBBG T 8 5 T BBB B(A0A8J=
-8D0A(B BBBB L ,9 P: U BDB J(A0A8D
-8A0A(B BBBA T: ,. K^ 4 9 ?R V ADG-
-DAA < 9 @ V BAI G
- DABA 4 : B0 |V ADG
-DAA L D: C WV BBB I(A0G
-0D(A BBBA 4 : E 2V ADG`
-DAA < : G
V BHD Gt
- DABA 4 ; HK U ADG&
-DAA < D; I3 U BAI G
- DABA < ; K U BHD Gz
- DABA T ; HM qU BBB B(I0A8G
-8D0A(B BBBA T < O JU BBB B(I0A8G'
-8D0A(B BBBA T t< R #U BBB B(I0A8G'
-8D0A(B BBBA L < `U T BBB I(A0G
-0D(A BBBA L = WW T BBB I(A0G
-0D(A BBBA T l= Z( T BBB B(I0A8Ga
-8D0A(B BBBA L = ] T BBB I(A0G
-0D(A BBBA T > _ `T BBB B(I0A8G-
-8D0A(B BBBA L l> b} 3T BBB I(A0G
-0D(A BBBA L > d T BBB I(A0G
-0D(A BBBA T ? fV S BBJ B(A0A8G
-8D0A(B BBBA L d? k S BIE A(A0G
-0D(A BBBA L ? n S BBB I(A0G
-0D(A BBBA < @ pR S BAI G
- DABA L D@ sB lS BBB B(A0A8J
-8A0A(B BBBA lA T @ 3
T BFB B(A0D8G0
-8A0A(B BBBF A D A { BBE E(A0A8D@\8A0A(B BBBT dA 5 S BBE E(A0A8G$
-8A0A(B BBBK L A А S BEB E(A0A8F
-8A0A(B BBBB L B S BEB E(A0A8F
-8A0A(B BBBB T \B @% TS BBE E(A0A8G"
-8A0A(B BBBE T B e MS BEE B(A0A8G
-8A0A(B BBBF T C 0e S BEE B(A0A8G
-8A0A(B BBBF T dC He S BEE B(A0A8G
-8A0A(B BBBF L C S BBB E(A0A8JX
-8A0A(B BBBG \ D P S BDB A(A0Js
+G I T Ƞm A~
+A l L dT s BBB F(A0A8J@X
+8A0A(B BBBA \U tU A`
+G b4 T [ BAA N
+ AABD T $U x ¤ BEB B(A0A8Jn
+8A0A(B BBBA L |U = BDB B(I0A8GL
+8A0A(B BBBA T U 0 BBB J(A0A8G
+8A0A(B BBBJ L $V X g BDB B(A0A8O\
+8A0A(B BBBG L tV h BDJ A(A0G
+0A(A BBBA T V u BDB B(A0I8D(
+8A0A(B BBBF T W 0e T BDB B(A0A8Oo
+8A0A(B BBBD L tW Hf ^ BBB B(A0C8JA
+8A0A(B BBBG T W h R BBB B(A0A8J=
+8D0A(B BBBB L X W BDB J(A0A8D
+8A0A(B BBBA Y /. K^ 4 X R 0 ADG-
+DAA < X 0 BAI G
+ DABA 4 X 0 ADG
+DAA L 4Y BBB I(A0G
+0D(A BBBA 4 Y ADG`
+DAA < Y p u BHD Gt
+ DABA 4 Y K M ADG&
+DAA < 4Z 3 ( BAI G
+ DABA < tZ BHD Gz
+ DABA T Z ٥ BBB B(I0A8G
+8D0A(B BBBA T [ @ BBB B(I0A8G'
+8D0A(B BBBA T d[ BBB B(I0A8G'
+8D0A(B BBBA L [ d BBB I(A0G
+0D(A BBBA L \ W ? BBB I(A0G
+0D(A BBBA T \\ 0 ( BBB B(I0A8Ga
+8D0A(B BBBA L \ BBB I(A0G
+0D(A BBBA T ] 8 Ȥ BBB B(I0A8G-
+8D0A(B BBBA L \] } BBB I(A0G
+0D(A BBBA L ] v BBB I(A0G
+0D(A BBBA T ] @V Q BBJ B(A0A8G
+8D0A(B BBBA L T^ H H BIE A(A0G
+0D(A BBBA L ^ BBB I(A0G
+0D(A BBBA < ^ H R BAI G
+ DABA L 4_ h!B ԣ BBB B(A0A8J
+8A0A(B BBBA ,` h8 T _ `83 u BFB B(A0D8G0
+8A0A(B BBBF ` H? D ` @?{ BBE E(A0A8D@\8A0A(B BBBT T` HR5 / BBE E(A0A8G$
+8A0A(B BBBK L ` ? ( BEB E(A0A8F
+8A0A(B BBBB L ` p@ BEB E(A0A8F
+8A0A(B BBBB T La X% BBE E(A0A8G"
+8A0A(B BBBE T a h_e BEE B(A0A8G
+8A0A(B BBBF T a ee BEE B(A0A8G
+8A0A(B BBBF T Tb ke BEE B(A0A8G
+8A0A(B BBBF L b `@ BBB E(A0A8JX
+8A0A(B BBBG \ b H F BDB A(A0Js
0A(A BBBA
-0A(A BBBE DE !. K^ L \E BBB A(A0
-(A BBBG Y
-(A EBBE | E BBB B(D0A8DP
-8A0A(B BBBB U
-8G0A(B BBBK
-8A0A(B BBBI L TE S BBB B(A0A8GpN
-8D0A(B BBBD L E X R BBB B(A0A8GpN
-8D0A(B BBBD | F x BBB B(D0A8DP
-8A0A(B BBBB U
-8G0A(B BBBK
-8A0A(B BBBI L tF h -R BBB B(A0A8GpN
-8D0A(B BBBD | G BBB B(D0A8DP
-8A0A(B BBBB U
-8G0A(B BBBK
-8A0A(B BBBI L DG x xQ BBB B(A0A8GpW
-8D0A(B BBBK lH . K^ , G } ,Q ADG0A
-DAF , G Pm Q ADG r
-DAE , H m P ADG q
-DAF , <H m P ADG s
-DAD , lH P ADG
-DAH , H p P ADG0M
-DAB 4 H mP BDA
-ABF L I HP BBB B(D0D8DP
-8D0A(B BBBG L TI P BBB E(A0H8DP
-8D0A(B BBBA L I O BBB B(D0D8DP
-8D0A(B BBBG L I O BBB B(D0D8DP
-8D0A(B BBBG L DJ 8 WO BBB B(D0D8DP
-8D0A(B BBBG L J O BBB B(D0D8DP(
-8D0A(B BBBG D J N BEB A(A0Q
-(A BBBA , L @b D\
+0A(A BBBE d @$. K^ L tc pV O BBB B(A0A8GpB
+8D0A(B BBBH L c sV BBB B(A0A8GpB
+8D0A(B BBBH L d wV BBB B(A0A8GpB
+8D0A(B BBBH L dd zV £ BBB B(A0A8GpH
+8D0A(B BBBJ \e #. K^ , d }} { ADG0@
+DAG , d `}m [ ADG q
+DAF , ,e }m ; ADG p
+DAG , \e }m ADG r
+DAE , e ~{ ADG ~
+DAA , e p~ ۢ ADG0L
+DAC , e ~P ADG k
+DAA < f ~ BBA H(G@x
+(D ABBA L \f ` n BBB E(A0A8KP
+8D0A(B BBBK D f 1 BBB A(D0q
+(A BBBA D f X BBB A(H0j
+(D BBBA D <g š BBB A(K0
+(D BBBA < g BBA K(G@
+(D ABBA < g } b BEE A(G0L
+(D ABBA , h b D\
H P
H Q
-G I , 4L b D\
+G I , h b D\
H P
H Q
-G I , dL b D\
+G I , i Xb D\
H P
H Q
-G I , L b D\
+G I , <i b D\
H P
H Q
-G I , L @b D\
+G I , li b D\
H P
H Q
-G I , L b D\
+G I , i b D\
H P
H Q
-G I , $M b D\
+G I , i Xb D\
H P
H Q
-G I , TM b D\
+G I , i b D\
H P
H Q
-G I D M @ AAJ X
+G I D ,j AAJ X
AAJ O
AAG X
-AAF 4 M AAJ o
+AAF 4 tj P AAJ o
AAC O
-AAG D ,M @g L BAA G@
+AAG D j g 3 BAA G@
AABD b
- AABB T tM h wL BBB L(A0A8D
-8A0A(B BBBB T M 0% SL BBB L(A0A8D
-8A0A(B BBBB T $N j /L BBB L(A0A8D
-8A0A(B BBBI T |N Be cM BEE B(A0A8G
-8A0A(B BBBF T N G M BBB B(A0D8Gg
-8A0A(B BBBK P P H. K^ 4 \O C Q ADG
-DAA 4 O 7 P ADG
-DAA D O & P BBA I(G
-(D ABBA < P P BAI G
- DABA < TP 8 ~P BAI G
- DABA 4 P WP ADGw
-DAA < P p d 2P BAI G
- DABA D Q k P BBI A(G
-(D ABBA < TQ O BHD G
- DABA < Q O BHD G,
- DABA 4 Q t O ADGO
-DAA 4 R PK sO ADG&
-DAA D DR h? NO BBA I(G1
-(D ABBA L R ` ? *O BIB D(A0Gi
-0D(A BBBA < R P s N BAI G
- DABA D S # N BIA D(G6
-(D ABBA < dS X' N BAI Gx
- DABA T S ) N BIB B(A0D8G
-8D0A(B BBBA T S . oN BIB B(D0A8G7
-8A0A(B BBBD ,U < D DU `C AAJ X
+ AABB T Lj BBB L(A0A8D
+8A0A(B BBBJ T j ߞ BBB L(A0A8D
+8A0A(B BBBJ T j P9 BBB L(A0A8D
+8A0A(B BBBA T Tk He BEE B(A0A8G
+8A0A(B BBBF T k BBB B(A0D8G7
+8A0A(B BBBK l X l . K^ 4 4l 8C ADG
+DAA 4 ll P7 ADG
+DAA D l X& n BBA I(G
+(D ABBA < l @ K BAI G
+ DABA < ,m Э * BAI G
+ DABA 4 lm ADGw
+DAA < m d ޢ BAI G
+ DABA D m 8k BBI A(G
+(D ABBA < ,n ` BHD G
+ DABA < ln 0 l BHD G,
+ DABA 4 n t D ADGO
+DAA 4 n K ADG&
+DAA D o ? BBA I(G1
+(D ABBA L do ? ֡ BIB D(A0Gi
+0D(A BBBA < o s BAI G
+ DABA D o ( BIA D(G6
+(D ABBA < <p X BAI Gx
+ DABA T |p 1 BIB B(A0D8G
+8D0A(B BBBA T p 8u BIB B(D0A8G
+8A0A(B BBBD q ` D q AAJ X
AAJ O
AAG X
-AAF , U Cb D\
+AAF , 4r @b D\
H P
H Q
-G I U . K^ T T D ON BBB B(A0A8G
-8A0A(B BBBA ,V H DV P! \V h tV ` L U h M BJB A(A0W
-(A BBDH _(A BBBD V BBB A(D0D@
-0A(A BBBA $ LV pi MM aFA Dp $ tV c DM [FA Dp $ V ~ ;M vFA Dp L V Q1 3M BBB A(D0JH
-0A(A BBBK T W e HM BEE B(A0A8G
-8A0A(B BBBF T lW @M BBE E(A0A8G
-8A0A(B BBBI T W 8M BBE E(A0A8G
-8A0A(B BBBE T X 0M BBE E(A0A8G
-8A0A(B BBBE < tX XU (M AAD`
+G I dr p. K^ T q h BBB B(A0A8G
+8A0A(B BBBA r + r +! s + s + L r + BJB A(A0W
+(A BBDH _(A BBBD s p+ BBB A(D0D@
+0A(A BBBA s (/ AL $ Ds (/i aFA Dp $ ls p0c ܟ [FA Dp $ s 1~ ӟ vFA Dp L s 1 ˟ BBB A(D0JH
+0A(A BBBK T t 2e BEE B(A0A8G
+8A0A(B BBBF T dt 8 ؟ BBE E(A0A8G
+8A0A(B BBBI T t P? П BBE E(A0A8G
+8A0A(B BBBE T u E ȟ BBE E(A0A8G
+8A0A(B BBBE < lu AAD`
AAK Q
-AAE T X H M BBE E(A0A8G
-8A0A(B BBBC D Y pV M BBE A(A0G
-0A(A BBBD D ,Z HY* BDA
+AAE T u L BBE E(A0A8G
+8A0A(B BBBC D v BBE A(A0G
+0A(A BBBD D v * BDA
ABM A
-ABD EAG T Y @% L BBE E(A0A8G"
-8A0A(B BBBE D Z BBE A(A0D@l
-0A(A BBBA D [ BBB D(A0D@
-0A(A BBBA D \[ BBE A(A0D@
-0A(A BBBA < [ BBG A(G@
-(A ABBG D [ BBE A(A0D@l
-0A(A BBBA < T[ 8 K BBD A(G0T
-(A ABBA d [ K BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d [ ` _K BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB L d\ ؼ J BEB E(I0H8DP
-8D0A(B BBBD L \ J BJE B(A0A8D@
-8A0A(B BBBA L ] h J BEB E(I0H8DP
-8D0A(B BBBD l T] ( J BBB E(A0D8D\
-8A0A(B BBBF k
-8A0A(B BBBA L ] wJ BEB E(I0H8DP
-8D0A(B BBBD L ^ h OJ BJE B(A0A8D@
-8A0A(B BBBA L d^ 8 J BEB E(I0H8DP
-8D0A(B BBBD d ^ wJ BBB J(A0D8G`
+ABD EAG T v Q% 2 BBE E(A0A8G"
+8A0A(B BBBE D w X BBE A(A0D@l
+0A(A BBBA D w hY BBE A(A0D@
+0A(A BBBA < $x 0Z BBG A(G@
+(A ABBG D dx Z BBE A(A0D@l
+0A(A BBBA < x [ BBD A(G0T
+(A ABBA d Dx 8] BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB d x ] ? BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB L y (^ BEB E(I0H8DP
+8D0A(B BBBD L dy _ BJE B(A0A8D@
+8A0A(B BBBA L y ` BEB E(I0H8DP
+8D0A(B BBBD L z xb c BJE B(A0A8D@
+8A0A(B BBBA l Tz Hc ; BEE B(D0A8G
+8A0A(B BBBA d
+8A0A(B BBBA L z j ; BEB E(I0H8DP
+8D0A(B BBBD L { l BJE B(A0A8D@
+8A0A(B BBBA $ | Xmg AG k
+AA L { m ; BEB E(I0H8D@
+8D0A(B BBBD d { o BBB J(A0D8G`
8F0A(B BBBF `
-8F0A(B BBBA T _ P 3J BBB B(D0A8D
-8D0A(B BBBH , t_ h J BGD c
-ABA |` H. K^ T _ T I BBB E(A0A8G
-8A0A(B BBBA T ` hd WJ BBE B(D0D8G
-8A0A(B BBBJ T l` p J BBE G(D0A8D
-8A0A(B BBBA T ` r3 _J BBB B(A0A8G`
-8A0A(B BBBE T a t GJ BBE B(D0A8G
-8D0A(B BBBA Lb . K^ 4 a @ J BDD H
-ABK 4 a I BDD H
-ABK T a I BED A(G0Q
+8F0A(B BBBA T D| hp Ϝ BBB B(D0A8D
+8D0A(B BBBH , | sh BGD c
+ABA t} . K^ T | ( { BBB B(A0A8Gd
+8A0A(B BBBA T <} ߜ BEB E(A0D8G
+8A0A(B BBBJ T } H 7 BBE G(D0A8D
+8A0A(B BBBA T } BBB B(A0A8G
+8A0A(B BBBD T D~ X ۜ BBE B(D0A8G
+8D0A(B BBBA D . K^ 4 ~ Xq BDD H
+ABK 4 ~ q BDD H
+ABK T $ r ^ BED A(G0Q
(D ABBC k
-(D ABBA 4 Tb X I BDD Q
-ABB 4 b ^I BDD Q
-ABB L b ( 8I BBB B(D0D8GP-
-8D0A(B BBBG L c H BBB B(D0D8GP%
-8D0A(B BBBG D dc H BBB D(D06
-(A BBBA < c ` H BBD D(G0
-(D ABBA < c @ \H BBD D(G0
-(D ABBA < ,d /H BED A(G0N
-(D ABBF D ld H BEE A(D0DPt
-0D(A BBBA L d H G BLB B(A0A8D`g
-8A0A(B BBBA d e x G BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d le [G BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB f x. K^ T e G BHE B(A0A8GA
-
-8A0A(B BBBK g p 4g p Lg p dg |g g g g g g h x $h p <h h Th ` lh X h P h H h @ h 8 h 0 h ( i ,i Di n \i n ti n i n i n i n i n i n j n j xn 4j pn Lj hn dj `n |j Xn j Pn j Hn j @n j 8n j 0n k (n $k 0" AN ND Dk @" AN ND dk P" AN ND k `" AN ND k p" AN ND k " AN ND k " AN ND l " AN ND $l " AN ND Dl " AN ND dl " AN ND l " AN ND l " AN ND l " AN ND l " AN ND m " AN ND $m 0" AN ND Dm @" AN ND dm P" AN ND m `" AN ND m k m k m k m k n k n xk 4n pk Ln hk dn `k |n Xk n Pk n Hk n @k n 8k n 0k o (k $o k <o k To k lo k < n P ? BBD A(D0|(A ABB 4 o lS BBA D(A0B(A ABB4 o S BBA D(A0B(A ABB< 4p 0g BBA D(D0Y
-(A ABBD 4 tp dS BBA D(A0B(A ABB4 p S BBA D(A0B(A ABB< p g BBA D(D0Y
-(A ABBD 4 $q \S BBA D(A0B(A ABB< \q g BBA D(D0Y
-(A ABBD < q g BBA D(D0Y
-(A ABBD 4 q S BBA D(A0B(A ABB4 r S BBA D(A0B(A ABB, Lr BDD h
-AEH , |r 8u BDA g
-AEL , r u BDA g
-AEL , r ADG l
-DAK , s Ht BDA g
-AEL , <s W ADG _
-DAH , ls t BDA g
-AEL , s t BDA g
-AEL , s hu BDA g
-AEL , s ADG J
-DAE , ,t u BDA g
-AEL < \t BBB A(D0
-(D BBBG < t e BBB D(D0
-(A EBBK < t BBD D(D0
-(D ABBI < u BEA D(D0R
-(D ABBE < \u BEA D(D0Z
-(D ABBM < u X BBB A(D0
-(D BBBH < u
BBB D(D0
-(D BBBK < v t BEA D(F0H
-(D ABBE L \v (dY BEE E(D0A8DP
-8A0A(B BBBA v 8eJ n N L v p BBB B(D0A8DPY
-8A0A(B BBBD $ w e< H\
-D O
-A <w 8eI D c
-I X \w he8 HZ
-F G |w eM < w [ BBB D(D0\
-(D BBBL v e 7 $ w D
-A K
-I x P ,x H Dx @ \x 8 tx 0 x (2 x P[ 4 x BAA G0
- AABA x p y h $ VM $y <y 4 Ty BAA G0q
- AABA , x H
- 5 AAG0u
-AAA y
- AL y
- y
-$ VM z
- $z
- 4 <z
- BAD D0
- AABA tz @ z 8 $ VM z P z X 9 4 z BAA G0
- AABA { ${ $ VM <{ ( T{ 4 l{ BDA D0X
- AABA , z 3 AAG0u
-AAA { AL { | | Ep
-A ,| x$ VM D| \| $ t| g AD n
-AA < { 2 BBA A(G@O
-(A ABBF | AL | < } BEA A(D0
-(D ABBA T} $ VM l} ( } 4 } ( BDA D0^
- AABA } } $ VM ~ ~ 4~ , t} H1 AAG0u
-AAA |~ H AL ~ H ~ @h E\
-A ~ $ VM ~ 3 4 ` BAA G0
- AABA , |~ D0 AAG0
-AAF AL < BBA D(D0
-(D ABBJ h$ VM , D 2 Ha
-G A , 8/ AAG0K
-AAJ P AL P , ̀ H BAA I
-DBE $ VM , D $ \ Ex
-C c
-A p$ VM 4 AAG0Q
+(D ABBA 4 | pr BDD Q
+ABB 4 r BDD Q
+ABB < @s ̛ BBD D(G0
+(D ABBA < , s BBD D(G0
+(D ABBA < l `t r BEA D(G0m
+(D ABBA < t D BBD D(G0
+(D ABBA < u BBD D(G0
+(D ABBA < , v BED A(G0N
+(D ABBF D l w BEE A(D0DPt
+0D(A BBBA L w BLB B(A0A8D`
+8A0A(B BBBA d w BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB d l BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB | . K^ T y ? BEB B(A0A8G
+8A0A(B BBBB H @ 8 4 L d | Ą ܄ x $ p < h T ` l X P H @ ̅ 8 0 ( P , H D @ \ 8 t 0 ( Ԇ 4 L d | ć ܇ @" AN ND P" AN ND 4 `" AN ND T p" AN ND t " AN ND " AN ND " AN ND Ԉ " AN ND " AN ND Ў" AN ND 4 " AN ND T " AN ND t " AN ND " AN ND " AN ND ԉ 0" AN ND @" AN ND P" AN ND 4 `" AN ND T p" AN ND t 0 ( Ԋ 4 L d | ċ ܋ $ < < T g BBA D(D0Y
+(A ABBD 4 S BBA D(A0B(A ABB< ̌ g BBA D(D0Y
+(A ABBD 4 S BBA D(A0B(A ABB4 D S BBA D(A0B(A ABB< | Xg BBA D(D0Y
+(A ABBD 4 S BBA D(A0B(A ABB< Pg BBA D(D0Y
+(A ABBD < 4 g BBA D(D0Y
+(A ABBD 4 t 0S BBA D(A0B(A ABB4 LS BBA D(A0B(A ABB, < @C AID _
+AAA J n N D , H BHF E(E0G@>
+0A(A BBBA D t BHF E(E0G@>
+0A(A BBBA , AAG0p
+AAA h AL , h AAG0[
+AAA < 8 AL , \ 8 AAG0H
+AAA AL , AAG0H
+AAA ܐ 8 AL , 8 AAG0H
+AAA , AL , L AAG0H
+AAA | 8 AL & A^
+A A L BED A(G0_
+(A ABBH (A ABB 4 H BDA
+AEA jAB L D BED A(G0_
+(A ABBH (A ABB L Е8 BED A(G0v
+(A ABBA (A ABB $ AX
+G g
+I 4 i BDD h
+ABK DB L D З BHA D(D0
+(A ABBK j(A ABB L p BHA D(D0
+(A ABBK M(A ABB PP T , $ HD
+D E
+A l 2 AL cA $ ` HD
+D E
+A ̔ 82 AL cA X $ \ P HD
+D E
+A , ؚ2 AL cA L $ ĉ HD
+D E
+A x2 AL cA $ v HD
+D E
+A 2 AL cA 82 $ ` < X T P l H @$ VM X ` ̖ X P$ VM h ` , X$ VM D p \ x t p h$ VM x ԗ py D@
+D М$ VM $ < < BEA A(F0
+(D ABBA | H$ VM ` X Ę P$ VM ܘ h `5 ; Au , D g Dx
+D d $ VM | d * BEB B(D0A8GP{
+8D0A(B BBBA
+8A0A(B BBBE Х < , ȥ" BBB A(H0
+(A EBBA l $ VM Ш ب Ш L ̚ Ȩ~ BEB D(D0
+(A BBBE z(A LEB , 4 BAA |
+DBJ d p$ VM | x AG
+H ̛ $ VM 4 AAG0Q
AAD v
-AAA , ԁ { ADD0}
-AAA , , 0 - AAG0u
-AAA 4 AL T l < ā - BBA A(G@O
-(A ABBF ܂ X AL X < P BEA A(D0h
-(D ABBA T $ VM l " , ܂ + AAG0u
-AAA @ AL @ , 8 AAG Q
-DAA L $ VM d | " , ԃ + AAG0u
-AAA ܄ 8 AL 8 0 $ VM , H D P \ H " , ` <* AAG0u
-AAA AL ą ܅ $ VM $ " , d x) AAG0u
-AAA l h AL h , ` AAG Q
-DAA Ԇ $ VM 4 BDA D0X
- AABA , | ` d( AAG0u
-AAA AL | Ep
-A ܇ (!$ VM @! 8! $ @! , < 8! AAG
-DAE , l ! AAG Q
-DAA , X" AAG
-DAC < ̈ " BBA A(G0e
-(D ABBA < 8%H BBA A(G0q
-(D ABBI < L H( BEA A(D0
-(D ABBA , )_ AAG J
-DAA )$ VM ԉ *8 Av $ 0*( ADG YAA $ 8*( ADG YAA $ D @*( ADG YAA $ l H*( ADG YAA $ P*( ADG YAA , X*{ ADD0}
-AAA , * $ AAG0u
-AAA + AL < + T + $ l +g AD n
-AA , `+ ,$ AAG0u
-AAA ċ + AL + +r Ef
-A (,$ VM 4 @, L 8,( 4 d P, BDA D0[
- AABA , < , BBD A(D0
-(D ABBA < .( BBA D(D0
-(D ABBD 4 0$ VM 4 L (P AAG0R
+AAA h 4 ` < L X BEA A(F0^
+(D ABBA $ VM ȫ 4 Ԝ U BGA
+ABE DB , $ BAC m
+DBG T P$ VM l h p h `$ VM ̝ x x p$ VM , D 4 \ U BGA
+ABE DB , BAC m
+DBG ܞ $ VM 0 ( $ y D@
+D D $ VM \ t x ԟ p L h BEA D(G0
+(A ABBG (A ABB L < , BED A(G0
+(A ABBB (A ABB L BEB A(D08
+(A BBBC (A BEB d ܠ $ BEB B(A0D8GP)
+8D0A(B BBBC
+8A0A(B BBBE d D BBF H(A0D8GP
+8D0A(B BBBC 8A0A(B BBB d ? BBB E(D0A8GP
+8D0A(B BBBK 8A0A(B BBB L BED A(G0
+(A ABBJ D
+(A ABBE 4 d BGA l
+ABG tDB 4 U BGA
+ABE DB Ԣ , ACG {
+DAE , x BAC m
+DBG , L \ ACG \
+DAD , | ACG j
+DAF $ VM ģ 8 Av $ ( ADG YAA $ ( ADG YAA $ 4 ( ADG YAA $ \ ( ADG YAA $ ( ADG YAA Ĥ ܤ o D@
+D 8$ VM P , H D @$ VM < X y AAG y
+AAD Y
+DAA 4 ̥ AAG0R
AAA q
-AAA , p0t @" AAG }
-AAA 0; Au < 0 ! AAG y
+AAA , \ @ Xy AAG L
+AAA 4 ; Au < y AAG y
AAD Y
-DAA O , O , l 1t ! AAG }
-AAA t P1; Au < p1 X! AAG y
+DAA , y x AAG D
+AAA 0; Au < l P px AAG y
AAD Y
-DAA Ԏ PO HO , , 1t AAG }
-AAA 4 1; Au < | 2 AAG y
+DAA T l , ܦ p x AAG [
+AAA ; Au < , w AAG y
AAD Y
-DAA N N , 2t T AAG }
-AAA p2; Au < < 2 AAG y
+DAA , x D [ HR d ` AL , ܧ `| -w AAG G
+AAA ; Au < , v AAG y
AAD Y
-DAA T N l N , 2t AAG }
-AAA 3; Au < 3 l AAG y
+DAA , , y v AAG D
+AAA t @; Au < ` Ev AAG y
AAD Y
-DAA pM , hM , l @3t AAG }
-AAA < 3 AAG y
+DAA ԩ , \ r u AAG }
+AAA < u AAG y
AAD Y
-DAA L ̑ L , 3t AAG }
-AAA < < 4 d AAG y
+DAA t x , y au AAG D
+AAA < , @ =u AAG y
AAD Y
-DAA T pL l hL , 4t AAG }
-AAA p4; Au < 4 AAG y
+DAA , , `y t AAG D
+AAA t ; Au < t AAG y
AAD Y
-DAA K , K , l 4t ` AAG }
-AAA t 5; Au < 5 AAG y
+DAA ԫ ` X , \ y 9t AAG D
+AAA 4 @; Au < ` s AAG y
AAD Y
-DAA ԓ 0K (K , , @5t AAG }
-AAA $ 4 5 ARD hAA< 5 p AAG y
+DAA , | s AAG G
+AAA $ ARD hAA< t 8 Is AAG y
AAD Y
-DAA J J , 6t AAG }
-AAA h6; Au < D 6 AAG y
+DAA \ t , X} r AAG H
+AAA ; Au < 4 r AAG y
AAD Y
-DAA \ I t I < 6 l BBA A(G0
-(A ABBK ̕ 88 AL , 88 AAG
-AAA < D 9 AAG y
+DAA x 4 p 4 L BAA G0
+ AABA AL 4 BAA G0~
+ AABA ܮ AL , T q AAG0c
+AAA , AL 4 L BAA G0~
+ AABA AL , p AAG0c
+AAA ԯ ` AL 4 L ` p BHI
+ABA < p AAG y
AAD Y
-DAA \ I t I < (9 BBA A(G0b
-(A ABBK ̖ 9 AL , 9 3 AAG [
-AAA :; Au < d 8: AAG y
+DAA l H @ < BBA A(G@|
+(A ABBA ܰ x AL < T x o BBA E(G0~
+(A ABBA < ( o AAG y
AAD Y
-DAA | H H , Ԗ X: AAG
-AAA ܗ (;; Au < $ H; O AAG y
+DAA | X P < HF BBA A(G@
+(A ABBE X AL , d X n AAG ^
+AAA < ; Au < n AAG y
AAD Y
-DAA < hG T `G < h; BBA A(G0o
-(A ABBF 8< AL , 8< AAG
-AAA (=; Au < D H= R AAG y
+DAA X P , ̲ AAG0a
+AAA x AL 4 t x m BHI $
+ABA T ; Au < ̲ 0 m AAG y
AAD Y
-DAA \ hF t `F < h= BBA A(G0o
-(A ABBF ̙ = AL , = AAG 0
-AAA < D ? u AAG y
+DAA ` ̳ X 4 P BAA G0-
+ AABA AL , # l AAG
+AAA l ; Au < ( l AAG y
AAD Y
-DAA \ E t E < ?G BBA A(G0
-(A ABBD ̚ @ AL , @ AAG [
-AAA 8A; Au < d XA v AAG y
+DAA ̴ h ` < H BBA A(G@8
+(A ABBE < AL D k BIF A(E0
+(A BBBA ; Au < k AAG y
AAD Y
-DAA | D D xD% ě D% , HA AAG S
-AAA A; Au < T A AAG y
+DAA P H 4 4 + BAA G0
+ AABA l AL , j AAG =
+AAA $ ARD hAA< < j AAG y
AAD Y
-DAA l D D , ě Ap C AAG
-AAA $ ̜ 8C ARD hAA< C AAG y
+DAA $ P < H < T BBA A(G@
+(A ABBG AL , i AAG S
+AAA ; Au < \ 0 i AAG y
AAD Y
-DAA 4 pC L hC d `C% | xC% C% , Ԝ xC R AAG
-AAA ܝ D; Au < $ E AAG y
-AAD Y
-DAA < C T C , l C ADD c
-ADG C2 W V ܝ C0 i D]
-E , Ԟ D AG s
-AD X
-AE pG $ D xC AG0y
-AA < l Ca AAG0_
-AAF Q
-AAA L G BBB B(D0A8K
-8C0A(B BBBI 4 ԟ Cj BAD a
-ADC rAB D 4 8H G BBB D(A0Kp
-0C(A BBBB D | IP BBB D(A0Kp
-0C(A BBBG L ğ K BBB B(D0A8K
-8C0A(B BBBI D (M BBB D(A0Kp
-0C(A BBBH T \ Na BBB B(D0A8K~
-8C0A(B BBBA T P \ BBB B(D0A8K>
-8C0A(B BBBF T R ! BBB B(D0A8K
-8C0A(B BBBE T d XT BBB B(D0A8K
-8C0A(B BBBE D V BBB D(A0Kp
-0C(A BBBG L W BBB B(D0A8N
-8C0A(B BBBK 4 , @j BAD a
-ADC rAB L [ BBB B(D0A8K
-8C0A(B BBBH 4 @j BAD a
-ADC rAB D 8]Q BBB D(A0Kp
-0C(A BBBB , 4 P^ AG C
-AD X
-AE d ^ , | ^ AG s
-AD X
-AE (_ , Ĥ 0_ AG C
-AD X
-AE _ , _ AG C
-AD X
-AE < ` , T ` AG
-AH X
-AE ` , ` AG S
-AD X
-AE ̥ a , a AG
-AH X
-AE a , , a AG
-AH X
-AE \ b , t b AG C
-AD X
-AE b , b AG O
-AH X
-AE c , c AG O
-AH X
-AE 4 c , L c AG K
-AD X
-AE | d , d AG [
-AD X
-AE ħ d , ܧ d AG k
-AD X
-AE Xe , $ `e= AG
-AH X
-AE T pf , l xf AG 3
-AD X
-AE g < ܧ g BBA D(G0d
-(A ABBA , @h} AG #
-AD X
-AE $ i , < i~ ADG0h
-AAJ l i , i= AG
-AD X
-AE k 4 ̩ k BDD D0U
- AABI l , l AG S
-AD X
-AE L o T d o BDA G0e
+DAA D P \ H < t P BBA D(D0D
+(A ABBI AL $ Ը I De
+G I
+G H D _ BIB D(C0G@~
+0A(A BBBG L D BHF F(A0C8G`
+8A0A(B BBBG L + BEF F(A0D8G`
+8A0A(B BBBA L u BBB E(H0D8GPx
+8D0A(B BBBE 4 DK < L ^ BBB A(L0[
+(A EBBH < BBF A(G0
+(A EBBF < ̺ BBA D(F0s
+(D ABBE < ( BBF A(G0
+(A EBBB < L 5 BBA D(F0
+(D ABBG L BIA A(G0r
+(A ABBD d(C ABB L ܻ BEF F(E0D8GP
+8A0A(B BBBA L , BIA A(G0r
+(A ABBD d(C ABB L | 8 BEF B(D0D8GP
+8D0A(B BBBK L ̼ BEF B(D0D8GP
+8D0A(B BBBK L BIB E(H0D8G`
+8D0A(B BBBD L l 8 BIA A(G0r
+(A ABBD d(C ABB L H BEF F(E0D8G@
+8D0A(B BBBA L X BIA A(G0r
+(A ABBD d(C ABB < \ A BFD D(G0
+(D ABBA L
+ BEF B(D0D8GP
+8D0A(B BBBK , 8 ADD c
+ADG 2 W V 0 |b D]
+E L T BEE B(A0A8G
+8A0A(B BEBG L BIA A(G0r
+(A ABBD d(C ABB L p BFF B(D0D8G`
+8D0A(B BBBA L D BIA A(G0r
+(A ABBD d(C ABB L BIE B(A0A8GpG
+8A0A(B BBEA L I BEE B(A0A8K`
+8D0A(B BBBD L 4 BIA A(G0r
+(A ABBD d(C ABB L BEB B(H0A8G`
+8D0A(B BBBD L BIA A(G0r
+(A ABBD d(C ABB L $ (h BIB F(A0D8Gp
+8A0A(B BBEC L t , BIE E(A0A8D
+8A0A(B BEBD , P3 ADG0y
+AAI , 6 ADG0E
+AAE , $ 6 ADG0u
+AAE , T 6 ADG0E
+AAE , p7 ADG0E
+AAE , 7 ADG0A
+AAA , `8 ADG0U
+AAE , 8 ADG0A
+AAA , D `9 ADG0A
+AAA , t 9 ADG0E
+AAE , P: ADG0Q
+AAA , : ADG0Q
+AAA , P; ADG0M
+AAE , 4 ; ADG0]
+AAE , d `< ADG0m
+AAE , =D ADG0
+AAE , > ADG09
+AAI , ? ADG0-
+AAE < | @ [ BBA D(G0d
+(A ABBA , d PA~ ADG0h
+AAJ A , AL ADG0
+AAI 4 C BDD D0U
+ AABI PD , , HD ADG0Y
+AAI T \ F BDA G0e
AABI D
AABH T
- FABE o T Ԫ o BDA G0e
+ FABE pG T hG BDA G0e
AABI D
AABH T
- FABE , 8p < l 0p ~ BBE D(G0Z
-(A ABBA D p& ADG0p
+ FABE $ H < G Y BBE D(G0Z
+(A ABBA D | xH& ADG0p
DAO a
AAE Q
-DAJ ̫ q < q BFA D(G0p
-(A ABBA < L r BFA D(G0p
-(A ABBA rL 4 | r7 ADG N
-GAN DAA T ܫ r < BFA D(G0
+DAJ `I < 4 XI X BFA D(G0p
+(A ABBA < t I X BFA D(G0p
+(A ABBA xJL X 4 t J7 ADG N
+GAN DAA $ JU GX Ac
+D d 4 J7 ADG N
+GAN DAA T d J W BFA D(G0
(A ABBI [
-(A ABBA $ 4 sU Ac
-D d 4 4 s7 ADG N
-GAN DAA 4 s AAG i
-AAD KAA4 Pt7 ADG N
-GAN DAA 4 Xt + AAG i
-AAD KAA4 t7 ADG N
-GAN DAA $ t tU Ac
-D d 4 t t7 ADG N
-GAN DAA $ ԭ tU g Ac
-D d 4 0u G AAG o
-AAF [AA$ 4 uE A[
-D \ $ \ uM Ab
-E \ L uy BEA A(D0@
-(A ABBE V(A ABB $ Ԯ (vM Ab
-E \ $ PvM o Ab
-E \ L $ xv O BEA A(D0G
-(A ABBF z(A ABB T t v BFA H(D0
-(A ABBE p
-(A ABBJ 4 ̯ xz AAG p
-AAE sAA T XxL BFA H(D0
-(A ABBE ~
-(A ABBL L 4 Py BBA D(D0
+(A ABBA 4 K W AAG i
+AAD KAA4 L7 ADG N
+GAN DAA 4 , L 6W AAG i
+AAD KAA4 xL7 ADG N
+GAN DAA L L V BEA A(D0F
+(A ABBG g(E ABB T Lb V BBA H(D0
+(A ABBB \
+(E ABBJ 4 D Mz 8V AAG p
+AAE sAA $ | 0NU V Ac
+D d 4 hN U AAG o
+AAF [AAL N BBA D(D0
(A ABBJ d
-(E ABBJ T { BFA H(D0
+(E ABBJ L P BBA H(D0
(A ABBA \
-(E ABBJ T |' BFA H(D0
-(A ABBB z
-(A ABBH T \ } R BBA D(D0
+(E ABBJ T | @R, U BIA A(D0x
+(A ABBA l
+(E ABBJ L S T BBH A(D0x
+(A ABBA d(F ABB L S BBA D(D0
(A ABBG g
-(E ABBO L X2 BBB B(A0D8G`c
-8A0A(B BBBG L ܲ 3 BBA D(G0a
-(C ABBG (F ABB T T h~ c BBB B(D0A8Nz
-8D0A(B BBBF 4 04j BAD a
-ADC rAB L BBB B(D0A8K
-8C0A(B BBBE 4 4j BAD a
-ADC rAB T l PU BBB B(D0A8N=
-8D0A(B BBBA T ij Xt Y BBB B(D0A8K
-8C0A(B BBBE 4 3j BAD a
-ADC rAB T T H BBB B(D0A8K
-8C0A(B BBBA 4 3j BAD a
-ADC rAB T u BBB B(D0A8K
-8D0A(B BBBK L < 0 ; BBB B(D0A8N
-8A0A(B BBBG d d 3 BBB B(A0A8D`P
-8A0A(B BBBH
-8A0A(B BBBA ̶ x4 p4 | BEB B(D0A8D`E
+(E ABBO $ t UE :T A[
+D \ $ @UM T Ab
+E \ $ hUU S Ac
+D d 4 U7 ADG N
+GAN DAA L Uk BEA A(D0
+(A ABBF D(E ABB $ t UM RS Ab
+E \ $ UM 2S Ab
+E \ l | U BBB B(D0A8D`H
8A0A(B BBBM D
8A0A(B BBBB
-8F0A(B BBBE | X 4 P7 ADG N
-GAN DAA L X! BBB E(D0A8D`
-8A0A(B BBBK L 8 BBB B(A0D8G`
-8A0A(B BBBG l h 4 `7 ADG N
-GAN DAA D hR BAD G0
- AABF D
- AABH 4 x7 ADG N
-GAN DAA T 2 T h) # BEB E(A0A8Dy
-8A0A(B BBBI T @ * BIF E(D0d
-(A BBBK
-(A BBBA X1 d 4 pf BBB B(A0A8G`^
+8F0A(B BBBE W 4 4 W7 ADG N
+GAN DAA L W R BBB E(D0A8D`
+8A0A(B BBBK L X} BBB B(A0D8G`
+8A0A(B BBBH \ 4 $ \7 ADG N
+GAN DAA D \ \R BAD G0
+ AABK D
+ AABH ^ 4 ^7 ADG N
+GAN DAA p T d ] P BBB B(A0D8G,
+8A0A(B BBBF T c P BIF E(D0`
+(A BBBG
+(A BBBA d df BBB B(A0A8G`^
8A0A(B BBBG
-8A0A(B BBBA x 4 p7 ADG N
-GAN DAA 4 x BDD D0G
- AABG $ 4 < 7 ADG N
-GAN DAA 4 t BDD D0G
- AABG 4 Ļ 7 ADG N
-GAN DAA d ADG0
+8A0A(B BBBA < e 4 T e7 ADG N
+GAN DAA 4 e BDD D0G
+ AABG `f 4 Xf7 ADG N
+GAN DAA 4 `f BDD D0G
+ AABG L f 4 d f7 ADG N
+GAN DAA d f ADG0
DAH {
DAH {
DAH {
DAH {
DAH D
-AAJ 4 d BDD D0G
- AABG 4 7 ADG N
-GAN DAA D / BAD G0
+AAJ 4 `i BDD D0G
+ AABG < i 4 T i7 ADG N
+GAN DAA D i/ BAD G0
AABL D
- AABH 4 4 L 7 ADG N
-GAN DAA | BBB B(A0D8G`
-8A0A(B BBBM D
-8A0A(B BBBB
-8H0A(B BBBE 4 7 ADG N
-GAN DAA T 0- L l BBB B(A0D8G`w
-8A0A(B BBBK h 4 Ծ `7 ADG N
-GAN DAA , | $ P BBB B(A0D8Gp
-8A0A(B BBBH D
-8A0A(B BBBB
-8E0A(B BBBE 4 7 ADG N
-GAN DAA L BBB B(D0A8Gp
-8A0A(B BBBH D 4 \ 7 ADG N
-GAN DAA + L x
BBB B(A0D8G`X
-8A0A(B BBBJ 8 4 07 ADG N
-GAN DAA L t 88 BBB E(A0A8G`?
-8A0A(B BBBC L (S
- BBB E(A0A8G`
-8A0A(B BBBG L 8{ BBB E(A0A8G`h
-8A0A(B BBBJ L d h BBB B(D0A8GpK
-8A0A(B BBBG L Y BBB E(A0A8G`1
-8A0A(B BBBA 4
- ADG0j
+ AABH j 4 j7 ADG N
+GAN DAA d $ j BBB B(A0D8G`
+8A0A(B BBBO D
+8A0A(B BBBB 8s 4 0s7 ADG N
+GAN DAA L s6 BBB B(A0D8G`I
+8A0A(B BBBI D y 4 \ y7 ADG N
+GAN DAA | x BBB B(D0A8G`
+8A0A(B BBBL D
+8A0A(B BBBJ 0
+8E0A(B BBBE , { 4 D {7 ADG N
+GAN DAA L {S
+ J BBB E(A0A8G`
+8A0A(B BBBG T $ u J BBB B(D0A8G5
+8A0A(B BBBE L $ @L BBB B(A0D8G`
+8A0A(B BBBH t @ 4 87 ADG N
+GAN DAA L ( BBB B(A0D8G`(
+8A0A(B BBBJ , x 4 D p7 ADG N
+GAN DAA L x{ EI BBB E(A0A8G`h
+8A0A(B BBBJ L $ ,I BBB I(A0A8G
+8A0A(B BBBE L t ( I BBB I(A0A8Gp
+8A0A(B BBBB 4 l ADG0j
AAH D
-AAJ P 4 , H 7 ADG N
-GAN DAA d P 4 | H 7 ADG N
-GAN DAA P Y BBE D(G0
+AAJ 4 7 ADG N
+GAN DAA 4 7 ADG N
+GAN DAA Y H BBE D(G0
(A ABBA y
(A ABBA y
(A ABBA y
(A ABBA u
-(A ABBE T d (
] BFA D(G0
+(A ABBE T $ G BFA D(G0
(A ABBC a
-(A ABBA L
BBE D(G0}
+(A ABBA L | NG BBE D(G0}
(A ABBI a
-(A ABBA < BBE D(G0C
-(A ABBC l L BBB B(D0A8D`d
+(A ABBA < P G BBE D(G0C
+(A ABBC l F BBB B(D0A8D`d
8A0A(B BBBA "
-8A0A(B BBBA < m BBE D(G0C
-(A ABBC < 0 F BBE D(G0C
-(A ABBC @. K^ , % D l % BEB D(A0GpY
-0A(A BBBJ $ H&[ ADG LAA4 & BDA G0x
- AABF ' '6 $ 8'. ACD `DA < D @'L BBA A(D0t
-(D ABBA , P'< BDD kAE 4 `' BEA A(D0(A ABB ' '# Aa L ' . AL D 'D , \ ( AFG |
-AAD $ (+ AAG \DA 4 ( BDD D0I
- AABE )Q Ad
-A c H). Ac
-A D < , X)o ADG x
-AAA P
-GAE ADA 4 l )v BDD D0|
- AABA L ) BED D(D@Q
-(A ABBA S
-(G ABEE 4 @*B ADG b
-AAA JAA D , X*x BDD z
+8A0A(B BBBA < | p F BBE D(G0C
+(A ABBC < uF BBE D(G0C
+(A ABBC K D 4 F BEB D(A0GpY
+0A(A BBBJ $ $ [ ADG LAA4 L BDA G0x
+ AABF x6 $ . ACD `DA < L BBA A(D0t
+(D ABBA , < BDD kAE , L ȴb BAA ZAB | # Aa ]D AL D , X AFG |
+AAD $ ȵ+ AAG \DA 4 D е BDD D0I
+ AABE | HQ Ad
+A c . Ac
+A D 4 v BDD D0|
+ AABA L BED D(D@R
+(A ABBA T
+(G ABEE 4 D PB ADG b
+AAA JAA D | hx BDD z
ABA Y
-ABE AGB 4 t *O AAG n
-DDI DAA *" . K^ *J n N *4 $ *8 Ds d D + BBB B(A0A8DP
-8A0A(B BBBA h
-8A0A(B BBBA M . . . $ 4 x.t J QQ
+ABE AGB < r ADG x
+AAA S
+GAE ADA 4 O AAG n
+DDI DAA < " T . K^ 4 l L AAD r
+AAF DFA L BIF H(H0A8D`
+8C0A(B BBBD иz Em
+A FL 0 KGF B(D0D8I`y8F0A(B BBB d 0. K^ | % 4 p BAD H
+ABF rAB 4 $ ȹ I@ BPH DPW
+ AABA 4 \ "@ BPH DPW
+ AABA < xJ n N T l X P $ DE
+G W
+I V t $ , < ( ADF0w
+AAD , l #/ ACG W
+AAA , #1 ADG X
+AAA D # ACG a
+AAB v
+AAH P
+AAA D @$ ADG b
+AAH z
+AAD P
+AAA \ ȿ $ t = QQ
F b
-S 4 . L Z d k 4 | 8 ADG `
-AAJ D
-AAJ p. h. DT , h.l v ATDP}
-AAA . 4 . L . d . | . . x. .% , .4 AAG a
-AAD < 4 .E f ADD`r
+S D @$ ADD k
+AAJ u
+AAI O
+AAA D $ ADD q
+AAD y
+AAE O
+AAA < D % BDA _
+ABG M
+ABH P DR < P < BLD D(D`
+(A ABBA , < l q< ATDP}
+AAA , D \ ؿ t п ȿ IT , b ACG D
+AAA , 4 AAG a
+AAD 4 | q A; BAD L
+ABF < XE ; ADD`r
AAC ~
-AAA , L AG w
-DL ED L /- BBB B(A0A8G
-8A0A(B BBBA \ AT , H ADG
-AAA Ȏ 4 4 \L AAG d
-DAF NDA D l ' BDD G0a
- IABR t
- GABE , ~ ADG0R
-AAH L @\, BBB B(A0A8D
-8A0A(B BBBG 4 @ L h( < d BDD G0
- AABH | DAB . K^ ( FR (C AA $ X % ` , ȏ5 D EN $ d AAG QAC AL ؏& M (& , @| 1 ARDPO
-AAA 4 $ BAA S
+AAA # 4 PL AAG d
+DAF NDA D h#] BDD G0z
+ AIBI |
+ DABM L 4 , BBB B(A0A8D`
+8A0A(B BBBG ` $( \ @ BED A(G@y
+(A ABBF
+(A ABBD `
+(A ABBA , # AJ T
+HI l
+DP T @[. P9 BBB B(A0A8G
+8A0A(B BBBK H AT < H ! ADG
+AAH }
+AAA ^AA Ȣ. K^ #C AA $ u8 L $ d $5 | $ EN $ $ AAG QAC $ AL $& $M $& , $| 7 ARDPO
+AAA 4 \ H% BAA S
CBD |
-CBG < BBA A(Gn
-(A ABBG < Hl BEE A(D0S(A BBB , xN AAG0f
-AAG , N AAG0h
-AAE $ < P AG ]
-AJ L d BED C(G0T
+CBG < % )7 BBA A(Gn
+(A ABBG $ 'P AG ]
+AJ < ('l BEE A(D0S(A BBB , < X'N AAG0f
+AAG , l x'N AAG0h
+AAE L ' BED C(G0T
(C ABBG L
-(A ABBA $ 0 D X
-D Y
-G ؔ AO $ ؔ D b
+(A ABBA $ ' D D
+H I
+G + AO $ 4 + D b
J
E K
-E $ $ ЕT Ag
+E $ \ ,T Ag
H F
J K
-E L " D] d , | ȗx Ac
+E ," D] , , 0x Ac
D O
I o
-A G N D P BEB D(A0
-(A BBBA < s FAD I
-ABI AAB L H+ Ai X P AY $ PL AK e
-AF RA4 xQ ADF r
-FAD DCA $ H AG Y
-AF 4 , Țe BBD C(G0L(A ABB$ d J AG p
-AG DA (9 d T P: d U x? F x A G y ؛K Hr
-F J
- $ 5 < . K^ T l x , b ARDPs
-CAA `. K^ x : H @: , h D D `b BBB A(C0G B
-0C(A BBBA 4 L 4 Лb BAC G`a
- AABF 4 BAC G`k
- AABD 4 b BAC G`a
- AABF , 4 AAG`T
-AAA D d BAC G`q
+A G 0N D T (2 4 BEB D(A0
+(A BBBA < D 2s FAD I
+ABI AAB 3 FS $ 3/ AAD cDA | (3 BFE E(E0A8Dp
+8A0A(B BBBA ]
+8A0A(B EBBA K
+8A0A(B IBBA $ L 84> AM
+B U
+K $ t P4J EJ
+A h
+H L x4 BBB B(A0A8F`d
+8C0A(B BBBH d (5 BEB A(D0T
+(A BBBG n
+(A BBBK S
+(D BBBC < T 5 BHD B
+ABE [
+ABJ L 6V BFE B(E0A8Dp
+8A0A(B BBBA < 7 BBE D(C0c(E BBB \ $ `7 BED D(G0F
+(A ABBF W
+(E ABBA A(A ABB < 7 ACG0
+EAF T
+GAE uGA4 `8 ADF0|
+EAC h
+GAE L 9 BEE D(GP}
+(A ABBF |
+(A ABBF x9 0 EM l x9p AP
+G h. K^ F xF 9 x9 4 p9o BAA Z
+ABG AAD 4 < 9 BAA o
+ABJ AAD t E E 9 AL 9 AL E L T 9 . BED A(Gpq
+(C ABBD G
+(C ABBA D 8: . BAC G`^
+ AABA b
+ CABA d : . BEE B(D0A8Dj
+8A0A(B BBBE I
+8C0A(B BBBA h;: Ax ;& AT < ;' QU < ; - AADPX
+AAH D
+AAA ;% < < - ADD`y
+AAD O
+CAE < 4 d < c- BAA GPf
+ AABK , =^ /- BKI ^
+ABA t @= AL @= 8=! < P= , ADD`y
+AAD O
+CAE $ =- AAK [AC 4 = u, BAA GPf
+ AABK , `>^ A, BKI ^
+ABA > AL > , $ >^ + BKI ^
+ABA > , l >^ + BKI ^
+ABA D ? \ Ȕ. K^ t > > > > > > > > , >I * AEG yAA d > AL , >I U* AEG yAA > AL > > > > 4 > L > d 0?5 Hl P?< Ho p?5 Hl ?< Ho R ?: ? , ?: D ? D \ ?b BBB A(C0G B
+0C(A BBBA @ ? $ , ?L ( Ai
+F [ 4 @E ADG ]
+AAE RAA 4 4 0@Y ADG f
+CAJ VAA l XQ PQ HQ @Q ? AL (Q 0Q 8Q 4 ?b & BAC G`a
+ AABF 4 @ & BAC G`k
+ AABD 4 `Ab & BAC G`a
+ AABF , 4 B & AAG`T
+AAA D d B & BAC G`q
AABF ]
- AABG D ` BAC G`b
+ AABG D @D & BAC G`b
AABE
- AABA 4 d BAD G`k
- AABC l , BBE E(A0D8Du
+ AABA 4 Ed & BAD G`k
+ AABC l , F & BBE E(A0D8Du
8F0A(B BBBE U
-8C0A(B BBBG t M HD @ AL D @ f BAC G`a
+8C0A(B BBBG D GM HD d H AL 4 HQ AAG w
+AAF FAA HH- AY
+F L D 4 XH % BAC G`a
AABF e
- AABG Ȥ, , , D \ F t 0, AK
-D W , @ AAG`h
-AAE L BBA A(G0O
+ AABG $ H, < H, T I l IF PI, AK
+D W , `I % AAG`h
+AAE L I BBA A(G0O
(C ABBD z
-(A ABBH 0? , X, AK
-D W L h" , d f AI V
+(A ABBH $ PJ? < xJ, AK
+D W \ J" , t Jf AI V
AG e
-AB R HI AL 4 V BDD v
-ABE JAB $ . K^ < T l " AN ND ! $ 3 ADG SDK L اB BBB B(D0D8Jq
-8A0A(B BBBK D D ب \ BGB A(K0G`
-0D(A BBBA L y 3 BEE D(A0Dj
-0A(A BBBA 4 P ? BAD D`P
- CABG 4 BAD G`\
- CABH $ $ Э' ADG WAA , t ح AAGp
-CAA , x AAGp
-CAA 4 BDD Dp
- CABG 4 Я y BDD Dp
- CABG < D U BBA A(J
-(D ABBJ , 0 AAGp
-CAA < BEA A(J
-(D ABBA , ADGp
-CAA \ $ M BBB D(A0G`}
-0A(A BBBI
-0C(A BBBA \ x , pK d ADDPl
-AAA , K D ADDPl
-AAA T $ BBD A(G`j
-(D ABBE d
-(D ABBA , h , l `0 AFJ RGA , `0 AFJ RGA L `t BEE A(C0R
-(C BBBE z(C BBB , 4 _ AAM0n
-DAA , d _ AAM0n
-DAA l $ ض, AAG ]DA . K^ ع L $ BBB A(A0
+AB JR $ HI K AL K 4 KV BDD v
+ABE JAB @K? # H`
+A T . K^ l e e e
e K e e e , XK" AN ND D L e\ BDD R
+ABA c
+ABJ HAB e3 $ KG AFGsDAL , 0KH ! BNE A(A0D
+0A(A BBBA T $ 0Mh BAD G
+ FABA D
+ CABF H
+ FABG | HN8 AQ
+F _ $ hN# ADG JGA $ pN# ADG JGA $ xN^ ADG`OAA\ l N5 BBB A(D0G
+0C(A BBBG
+0F(A BBDC t R T R BBD A(G
+(C ABBJ T
+(A ABEA S T T S BBA D(G
+(C ABBG o
+(F ABBA T U , U0 4 AFJ RGA , U0 AFJ RGA d $ U BED A(G\
+(A ABBK M
+(F ABBH
+(A ABBA 4
+ 0V , (V_ ~ AAM0n
+DAA , XV_ Z AAM0n
+DAA
+ V
+ V
+ V 4 L
+ Vl RAA
+AIH L , ha5 BED D(D
+(A ABBG T(C ABB $ | hW) ADG ZAA , pW& BDD XAB 4 pW] ADG `
+CAH ^CC 4 WS BDD ^
+ADC [CD D W@ A[
+D _ 4 d pa AAJ
+AAE DCA $ W# ADG JGA 4 WU ADG [
+CAE ^CC 4 a BDD D
+ AABF 4
W FQ , L
W$ BDC WAB |
W@ CY
+D _ 4
WS BDC ]
+ADE [CD $
W, AAG ]DA <
a BBG A(G@
+(A ABBF D
WZ BEE A(A0D@
+0A(A BBBF X 聏. K^ , X_ R AIDpp
+CAF , < X_ 2 AIDpp
+CAF D l Y BID Dy
+ AABH T
+ AABA , XYP AIDl
+AAA , xY AIDK
+CAA , Y AIDK
+CAA 4 D 8Z | BID DpS
+ CABA 4 | Z U BID DpS
+ CABA \ X` t P` H`
+ 4 @` AJ0_
+AE e
+AJ kD L ` BBB B(I0D8G@'
+8A0A(B BBBA L , 8b BBJ B(D0A8GP<
+8D0A(E BBBE | c~ d dQ @em L e BIA A(G0t
+(A ABBB d(C ABB , f D D f BBB A(A0G@v
+0D(A BBBH L Xh BHD A(G0c
+(A ABBA X
+(A ABBA 4 hm AFG F
+CAH DDD d l h v BBE B(G0D8I@
+8C0A(B BBBH U8E0A(B BBB \ | i BBE B(D0G8G@
+8C0A(B BBBH U8E0A(B BBB j A[ $ T j AJ@Z
+AJ L $ j BHD A(D0f
+(G ABEH n(A ABB d t kL BEB B(A0G8D@
+8A0A(B BBBH Z
+8A0A(B BBBA d kL BEB B(A0G8D@
+8A0A(B BBBH Z
+8A0A(B BBBA d D lL BEB B(A0G8D@
+8A0A(B BBBH Z
+8A0A(B BBBA d mL BEB B(A0G8D@
+8A0A(B BBBH Z
+8A0A(B BBBA d nL BEB B(A0G8D@
+8A0A(B BBBH Z
+8A0A(B BBBA d | oL BEB B(A0G8D@
+8A0A(B BBBH Z
+8A0A(B BBBA L < pu
+ BBB B(A0A8K
+8D0A(B BBBG < 4 AGG h
+CDB N
+DAE iDD t o D P o D R d p BBB L(D0A8GP
+8C0A(B BBBA
+8D0A(B BBBA d Hq BBH F(D0A8G`
+8C0A(B BBBH
+8D0A(B BBBA $ t r@ AF R
+CE L 8G BBH A(D0
+(A BBDD (A BBB hr GO pr GO L G BBH A(D0
+(A BBDD (A BBB l (r GO L F BHB A(D0
+(A BBDC (A BBB q GO L F BHB A(D0
+(A BBDC (A BBB < q GO L T Z BHB D(A0
+(A BBDJ (A BBB Pq GO L V BHB D(A0
+(A BBDE (A BBB q GO L $ B BHB A(D0
+(A BBDA (A BBB t p GO (u. K^ ` XJ n N L P BBB A(A0
(A BBGG Q
-(A FBGG t B 4 M ADG T
+(A FBGG $ < (B 4 T `M ADG T
AAF
-AAG ȃ. K^ $ < T l DZ и D[ ظ AL ظ AL ظB Es 4 x7 AAG Z
-AAK DAA $ 4 иg AG k
-AA \ AL , | PAQqDA l D ]
-A ع AP $ ع- ADJ XDA , < AAG@C
-DAG , l p AAG@o
-DAA T к BBE B(A0A8G
-8D0A(B BBBH , H AVGaDA , ATGaDA , , ATGaDA , \ h AUGaDA , AUGaDA , ( AUGaDA , AUGaDA , AUGaDA , L H AUGaDA | K o N 4 BAA S
-ABA L $ g BEB B(D0A8FY
-8A0A(B BBBG L ) $ d C Am
-E H
-A $ C Y Am
-E H
-A 0 ($ 4 @l RAA
-AIH < BBG A(G@
-(A ABBF D 8Z BEE A(A0D@
-0A(A BBBF P HL DC }. K^ l o ! BEE E(D0D8G
-8A0A(B BBBJ
-8A0A(B BBBE \ _ AG0UA | ` AG0VAT H BBE B(A0D8G
-8A0A(B BBBF |. K^ K o N $ 4 d d BAA G@Y
- AABH t @ H AY H5 fN < p BBA A(G`p
-(D ABBJ $ , 0 AG0q
-DA , , { BOA AB \ t , P AG
-AA aA k Ae
-A 4 $ @ AFD0{
+AAG ؇% L BBB A(A0
+(A BBBB Q
+(A EBBE % L $ BBB A(A0
+(A BBBB Q
+(A EBBE , t @= ADG [
+AAG P X% L p BBB A(A0
+(A BBBB Q
+(A EBBE $ < % L T 0 BBB A(A0
+(A BBBJ Q
+(A EBBE , Њ= ADG ]
+AAE % L BBB A(A0
+(A BBBJ Q
+(A EBBE , T = ADG ]
+AAE % L Ћ BBB A(A0
+(A BBBB Q
+(A EBBE , p= ADG _
+AAC 4 # L % L d BBB A(A0
+(A BBBJ Q
+(A EBBE , P= ADG _
+AAC `# x ! d ,! BBB B(A0A8G`j
+8A0A(B BBBK )
+8A0A(B BBBA L ! 0 BBB B(D0A8DP^
+8A0A(B BBBG ! ! ؏ " Џ
," ȏ
4 D" AAG }
+ADE o
+AAG < |" BDA
+ABA l
+ABI " 8$ < ," PQ BBD A(D0}(A ABB d # p BBB B(A0A8D`P
+8A0A(B BBBH
+8A0A(B BBBA |# ؒ# # # # m. K^ # # $ ؒ $$ <$ ؒB Es \$ AL |$ AL 4 $ 7 AAG Z
+AAK DAA $ $ Вg AG k
+AA $ AL , % PAQqDA L% AP $ l% - ADJ XDA , $ + AAG@C
+DAG , %
AAG@o
+DAA T L% BBE B(A0A8G
+8D0A(B BBBH , L& AVGaDA , |& X ATGaDA , & ATGaDA , & AUGaDA , ' x AUGaDA , <' ؛ AUGaDA , l' 8 AUGaDA , ' AUGaDA , ' AUGaDA ' X ( `K o N , ' AAG Z
+AAK \( L ' g BEB B(D0A8FY
+8A0A(B BBBG ( 0) ( H ( @ ) 8 $) 0$ <) g. K^ t ( p BEE D(D0G`w
+0A(A BBBF
+0A(A BBBF B
+0A(A BBBF ) _ AG0UA ) ` AG0VAT d) 8 w BBE B(A0D8G
+8A0A(B BBBF d* f. K^ |* K o N * 4 * X BAA G@Y
+ AABH * 0 * 8 AY + 85 fN < * ` BBA A(G`p
+(D ABBJ $ * AG0q
+DA , + e BOA AB \ + BIB E(A0A8I
+0A(B BBBA
+0A(B IBBK d ,, H BGB B(A0C8D`S
+8A0A(B BBBA
+8A0A(B BBBG , > QR YA , 8 T c D , 8 BBE E(A0A8D`8D0A(B BBB - e6 Kj D ,- BEB E(D0A8DP]8A0A(B BBB| , BBB B(A0D8D@
+8F0A(B BBBL D
+8A0A(B BBBB
+8F0A(B BBBG d L- [ BDE E(D0A8D@s
+8D0A(B BBBE ~8G0A(B BBB , - R AAT0Z
+DAA L - ع4 BBE E(A0A8D
+8A0A(B BBBA . ȺY . < d. BED D(DpH
+(C ABBE L/ 4 . s L BDD D0E
+ DABA d / % BHE B(E0D8DP
+8F0A(B BBBB D
+8C0A(B BBBH L 0 BBD D(G0!
+(A ABBF d
+(G AEBE , / L l AAJ0^
+DAA < / @ H BEA D(D@Y
+(D ABBA < 0 BEA D(D@Y
+(D ABBA d 1 BEB D(D0K
+(A BBBE F
+(A BBBK d
+(A BBBE \ l1 H BEB D(D0C
+(A BBBE D
+(A BBDC L(A BBB 1 x 1 p 4 T1 8 BAD F`x
+ AABG 42 F L2
+ d2 |2 2 4 2 AAI
+AAK DAA 2 2 - AI@^D < 3 BBA F(D`V
+(D ABBB \3 h. AJ@^D < |3 x BBA G(D`V
+(D ABBA 3 . AJ@^D < 3 BBA G(D`V
+(D ABBA 4 4 h AFG N
+AAB GA T 3 BBD D(Gpa
+(A ABBF f
+(F ABBG , 4 N BDA G0{ AAB D 44 p X BBB A(A0J`
+0A(A BBBF D |4 5 BBB A(G0D`
+0A(A BBBF D 4 H BBB A(A0Jh
+0A(A BBBF D 5 BBB A(G0D`
+0A(A BBBF 4 5 x DJ0
+DF Z
+DJ N
+DF 46 @+ AG@^D T6 PI 4 5 \ BDD G0^
+ DABA < 5 1 BED D(G@y
+(D ABBA D <6 p BEE D(D0G@
+0D(A BBBA L 6 BEE E(D0D8GP
+8D0A(B BBBA L 6 BEE E(D0D8GP
+8D0A(B BBBA L $7 E A BEE E(D0D8GP
+8D0A(B BBBA L t7 n BEE E(D0D8GP$
+8D0A(B BBBA L 7 BEE E(D0D8GPM
+8D0A(B BBBA 8 < ,8 s c BBD D(G0U(A ABB < l8 0 + BBE D(D0`(L BFB L 8 BEB E(D0D8G@8A0A(B BBB l 8 BEE E(A0A8D
+8A0A(B BBBF D
+8C0A(B BBBA : E ,:
+ \ 9 , BBB A(A0D`
+0F(A BBBG
+0D(A BBBE : 0 T : ( BED I(G0
+(F ABBE X
+(C ABBH T l: BED I(G0
+(F ABBE X
+(C ABBH l : Xh : JEA K(D`
+(A ABBH D
+(C ABBD XH` ; X ; ` L d; X BEE B(E0H8G@\
+8F0A(B BBBK \< t< <
+ < <
+ < < BDA A(G@X
+(C ABBA = ` L ,= h BED A(D0
+(A ABBH }
+(G ABBG D < J BDA G@G
+ AABG [
+ AABA L = ` BBB B(A0D8Gb
+8A0A(B BBBH 4 > AK0{
+DE J
+DB v
+DF L> + AG@^D , l> e ADG0|
+AAF L = BBB B(D0D8Gp
+8A0A(B BBBB L D> x( U BBE E(D0A8Fp;
+8A0A(B BBBA <? 8S. K^ T? ( l? ( ? ? x( ? ( T ? < BDC D0
+ AABH _
+ AABE _
+ AABA $@ k Ae
+A 4 D@ AFD0{
AAH I
-FAH \
- t 4 ADG Z
-AAH D
-AFA < 0 BIA A(FPK
-(C ABBJ 4 L d | , AM
-AA aD , AN
-AA cG , < X AM }
-AD ^F , l AN
-AB ^G , AM
-AK iF , H AN
-AA dG , AM F
-AK VC , , X AN N
-AJ VD , \ AM F
-AK VC , AN N
-AJ VD 4 x AAN0Q
-AAM LFA , z AAN0L
-GAL , $ AM Z
-AG ^G , T AM
-AA aD 4 0M n BHD G`
- AABB D Hm V BHD G`
- AABK I
- AABC 4 , pM . BHD G`
- AABB D d m BHD G`
- AABK I
- AABC 4 BDJ
-ABA TAB , H AKG
-AAB < BIJ
-ABE _
-ABN 4 , : AKG0
-AAK R
-AAL D d BAD h
-ADD L
-ADG L
-CDE T x$ BAA G0|
- CABC J
- FADK O
- CADI T , P n BBE B(A0C8D:
-8C0A(B BBBA \ C t @1 h; C 4 4 B L P BBE D(A0j
-(A BBDG
-(A BBGH d T BBE E(D0D8GPj
-8C0A(B BBBE
-8F0A(B BBBF 8r. K^ 4 p HS T p AL 4 t Pw ADD [
-DAG DAA % % Ȅ% % % $ % < (% < T @t BBA D(D0]
-(A ABBH $ E D
-I A
-G L BBB B(D0A8DP^
-8A0A(B BBBG L X BBB B(D0A8DPk
-8A0A(B BBBJ $ \ XY
-G p , s AAD y
-CAA < p BED H(G`o
-(A ABBA $ T d _ n BBA A(G0q
+FAH |@ h
+ @ ` < @ X BDA `
+ABF D
+ALA < D@ X s BIA A(F`K
+(C ABBJ ,A ( DA 0 d \A ( BBE E(D0D8GPj
+8C0A(B BBBE X
+8F0A(B BBBA , A PL AAN l
+AAA , A pL AAN l
+AAA , $B L AAN l
+AAA , TB L AAN l
+AAA , B L AAN l
+AAA , B L AAN l
+AAA , B G ADG i
+AAA D C 0. AHG0
+AAF ^
+AAH _
+AAA D \C . AHG0
+AAF ^
+AAH _
+AAA D C & AHF0
+AAD ^
+AAH _
+AAA D C . AHG0
+AAF ^
+AAH _
+AAA D 4D . ACK0
+AAD ^
+AAH _
+AAA D |D > ADK0
+AAF ^
+AAH _
+AAA , D ACK0Z
+AAE , D @
+ ADK0[
+AAC , $E
+ ACK0Z
+AAE , TE ` ADK0[
+AAC 4 E BAH G0i
+ AABA 4 E x BAH G0j
+ AABH , E
ACK0[
+AAD D $F
. AHG0
+AAF ^
+AAH _
+AAA < E x BBD H(Gp
+(A ABBD T F ( BBD H(Gp
+(A ABBF `
+(A ABBJ < \F BBD H(Gp
+(A ABBD T F BBD H(Gp
+(A ABBF `
+(A ABBJ \ G hW BEA H(N0
+(A ABBI _
+(A ABBK `
+(A ABBA , G h= BID
+ABF ,H x AU \ LH xg BJH D(G0
+(A ABBK _
+(A ABBK `
+(A ABBA , H ) BDH
+AEN 4 H Z ADG b
+CAF K
+FDE 4 I AAG0n
+CAE K
+FDK T H U BLB E(A0A8D+
+8A0A(B BBBA $ I H ADF0zAA I H B I 0 I : J B ,J 3 DJ 3 \J H A tJ 0H. K^ J J J J J HS K x AL ,K DK \K tK K K K x K p K h" DN
+N A 4 L w ADD [
+DAG DAA DL @R DG
+E A< dL t BBA D(D0]
+(A ABBH l K w BBB E(A0D8D
+8A0A(B BBBH
+8A0A(B BBBA $ M ! UL
+G p L L x! C BBE E(D0D8Fc
+8A0A(B BBBG L L " 6 BEE E(D0D8F
+8A0A(B BBBA L 4M x# BHE E(D0D8F
+8A0A(B BBBA ,N $ DN $ \N ($ , tN 0$s AAD y
+CAA < M $ N BED H(G`o
+(A ABBA N % d TN % BBA A(G0q
(A ABBD M
-(A ABBE , m AAD x
-AAA < P
BBD D(G`
-(A ABBF < 2 BED A(D0
-(A ABBA 4 l BAD DP
- DABE , | h{ BAA |
-DBA < : AADPs
-AAE J
-AAD | (j BBB B(A0A8DPf
-8A0A(B BBBB j
-8A0A(B BBBD n
-8A0A(B BBBH < l ADD Z
+(A ABBE w
+(A ABBK , dO %m AAD x
+AAA 4 N & ~ BAD DP
+ DABE , O -{ BAA |
+DBA , TO - , AADPs
+AAE \ ,P . BBA A(D@j
+(A ABBF j
+(A ABBH h
+(A ABBJ < P 0 ADD Z
AAK Z
-AAD HAA4 x ADD
+AAD HAA4 P (1 ADD
AAF D
-FAA N ' DR
-J F 4 ' DQ
-K F , | AADP[
-AAE P AZ 4 P q BAD D`\
- AABE A] 4 $ : BAD D`]
- AABD 4 @ AZ 4 | @ BAD D`\
- AABE A] 4 ̿ BAD D`]
- AABD 0) D d 4 $ H BAD D`^
- AABC 4 ) D d 4 t n BAD D`^
- AABC P A[ 4 P 7 BAD D`]
- AABD AQ
-F D AZ 4 D BAD D`\
- AABE T @ A] 4 @ BAD D`]
- AABD $ 5 AW
+FAA Q 1N Q 1 4Q @2' DR
+J F TQ P2' DQ
+K F , P `2 AADP[
+AAE Q 3 AZ 4 Q 3 BAD D`[
+ AABF Q x3 A] 4 tQ x3 ] BAD D`\
+ AABE TR 3 AZ 4 Q 3 & BAD D`[
+ AABF R h4 A] 4 $R h4 BAD D`\
+ AABE S 4) D d 4 tR 4 BAD D`]
+ AABD TS p5) D d 4 R 5 BAD D`]
+ AABD S 6 A[ 4 S 6 Z BAD D`\
+ AABE S x6 AQ
+F D T x6 AZ 4 S x6 BAD D`[
+ AABF tT 6 A] 4 S 6 BAC D`[
+ AABG $ T h75 AW
H G
-I D 4 j BAD D`]
- AABD H N $ < % DQ
-K D < BBD D(Dp]
-(A ABBE < x BBD D(Dp]
-(A ABBE < Ž BBD D(Dp]
-(A ABBE < D X BBD D(Dp]
-(A ABBE 4 BAD D`j
- AABG 4 P! q BAD D`j
- AABG < ! [ BBD D(Dp]
-(A ABBE , 4 H" < AAD`T
-AAD < d " * BBD D(Dp]
-(A ABBE 4 (# BAD D`]
- AABD 4 # BAD D`]
- AABD , $ ݼ AAD`D
-AAD , D % AAD`C
-AAE T t % ʼ BBD D(Dp[
-(A ABBG _
-(A ABBC d '[ BBB B(D0A8D
-8A0A(B BBBB
-8A0A(B BBBL , 4 + / YX
+I D 4 LT 7 BAD D`]
+ AABD ,U 7N DU 08 \U 8% DQ
+K D < T 8 & BBD D(Dp\
+(A ABBF < U (9 BBD D(Dp\
+(A ABBF < TU 9 BBD D(Dp\
+(A ABBF < U : BBD D(Dp\
+(A ABBF 4 U x: BAD D`i
+ AABH 4 V ; BAD D`i
+ AABH < DV ; ~ BBD D(Dp\
+(A ABBF , V ; _ AAD`T
+AAD < V h< M BBD D(Dp\
+(A ABBF 4 V < . BAC D`[
+ AABG 4 ,W P= BAD D`]
+ AABD , dW = AAD`D
+AAD , W > AAD`C
+AAE T W 8? BBD D(DpL
+(A ABBF Q
+(A ABBA d X @2 BBB B(A0D8D
+8A0A(B BBBJ >
+8A0A(B BBBH , X hE N YX
G
-A , d , YX
-G T
-A 4 l |w ADD [
-DAG DAA $ P|E D
-I A
-G L x} BBB B(D0A8DPk
-8A0A(B BBBJ L 8~ BBB B(A0A8Gp8A0A(B BBB D h, BAD G
-ABG D
-ABE KAB , p= BDA rAB L BBB A(A0
-(A BBBK Y
-(A EBBM L 4 @ BBB A(A0~
-(A BBBC Q
-(A EBBE d ЀQ BBB E(D0E8D@O
+A < X XF - YHD T
+AAE B
+AAA T X Fi+ BEE B(A0C8G
+8D0A(B BBBG < Y q< BEG A(D@
+(D ABBA \ Y r BEE D(A0G`[
+0A(A BBBE
+0E(A BBBE 4 Z pt BDA D0F
+ DABA , Z te XGH _ID , Z u{ BAA |
+DBA ,[ 8R DG
+E A L[ x" DQ
+K A L l[ BBB B(A0A8Gp8A0A(B BBB D [ t
BAD G
+ABG J
+ABE KAB , \ = BDA rAB d 4\ Q BBB E(D0E8D@O
8F0A(B BBBA
-8D0A(B BBGA D +\ BDC D`
+8D0A(B BBGA D \ t\ BDC D`
AABA U
- AABA $ 4
- ,$ ADG QCC $ \
- ,2 AFF0bAA D
- , BHE D(D0D@
-0A(A BBBK $
- -4 AFG0cAA D
- - BHE D(D0D@
-0A(A BBBK $ < h.2 AFF0bAA D d . BHE D(D0D@
-0A(A BBBK $ 8/4 AFG0cAA D P/ BHE D(D0D@
-0A(A BBBK 0> AI0rA < < (0 BHD D(DP
-(A ABBE | 0> AI0rA < 1 BHD D(DP
-(A ABBE $ 12 AFF0bAA D
1 BHE D(D0D@
-0A(A BBBK < L
2B BEG D(D@
-(A ABBJ $
52 AFF0bAA D
5 BHE D(D0D@
-0A(A BBBK 4
x6 BGA G0f
- AABE D 4 7 BGD D0
- AAEG o
- AABE \ | 8 BBG D(G@e
+ AABA $ \ u$ ADG QCC $ ] u2 AFF0bAA D 4] u BHE D(D0D@
+0A(A BBBD $ |] v4 AFG0cAA D ] v BHE D(D0D@
+0A(A BBBD $ ] xw2 AFF0bAA D ^ w BHE D(D0D@
+0A(A BBBD $ \^ Hx4 AFG0cAA D ^ `x BHE D(D0D@
+0A(A BBBD ^ y> AI0rA < ^ 8y BHD D(DP
+(A ABBF ,_ y> AI0rA < L_ z BHD D(DP
+(A ABBF $ _ z2 AFF0bAA D _ z BHE D(D0D@
+0A(A BBBD < T_ { BEG D(D@
+(A ABBJ $ <` 2 AFF0bAA D d` BHE D(D0D@
+0A(A BBBD 4 ` BGA G0f
+ AABE 4 <` ` BGD D0f
+ AABE \ a BBG D(G@e
(A ABBG r
-(A ABBH P(A ABB d 80 BBB B(D0C8GP}
+(A ABBH P(A ABB d |a (0 BBB B(D0C8GP}
8A0A(B BBBC A
-8A0A(B BBBE D D 9 BAD G0s
+8A0A(B BBBE D a BAD G0s
AABK y
- AABC L (|l BBE F(A0A8D@
-8A0A(B BBBJ , 9 AG r
+ AABC L ,b 8l BBE F(A0A8D@
+8A0A(B BBBJ , |b AG r
AA G
-AA < X: BED D(D0D
-(A ABBC < L :t BED D(D0h
-(A ABBG 4 |
ADG H
+AA < b BED D(D0D
+(A ABBC < b t BED D(D0h
+(A ABBG 4 ,c
ADG H
AAJ k
-AAC L :5 BBG D(GP
+AAC L dc 5 BBG D(GP
(A ABBB T
-(A ABBF L < ;8 H BBE E(D0D8GO
-8A0A(B BBBJ T d L} BHE D(A0Gpv
-0A(A BBBG T0A(A BBB 4 L} BAA DPT
- AABA 4 M BAA DPY
- AABA L , hMe BBB B(A0D8G`p
-8A0A(B BBBB L | _ BBB B(D0D8D@
-8A0A(B BBBK D (a BAD G@P
+(A ABBF L c A BEE E(D0A8J
+8A0A(B BBBF T \c ? BEE E(A0C8J0
+8D0A(B BBBD \d L c ( BBE E(D0D8GO
+8A0A(B BBBJ T d } BHE D(A0Gpv
+0A(A BBBG T0A(A BBB 4 td } D BAA DPT
+ AABA 4 d P BAA DPY
+ AABA L d r BBB E(A0C8G
+8A0A(B BBBG < e ؤ BED D(G0K
+(A ABBI L te 8 w BEH E(A0A8G
+8A0A(B BBBA D e
+ 9 BBB D(D0G`
+0A(A BBBD L f @ BBB B(D0D8D@
+8A0A(B BBBK D \f BAD G@P
AABF t
- AABA L < a BBB B(D0D8Dp
-8A0A(B BBBE L z K BEB E(I0H8D@
-8D0A(B BBBD \ bK # BBB A(C0G*
-0A(A BBBE
-0A(A BBBE L < e BHE E(D0D8F
-8A0A(B BBBA L f BEE E(D0D8F
-8A0A(B BBBA L 0g BBE E(D0D8Fc
-8A0A(B BBBG V. K^ | 4 | L { d { | z- {" AN ND {8 Ds | |
| 4 < ze m BHA D`~
- AABA $ L {u AG y
-AA $ t 0| DG
+ AABA L f X BBB B(D0D8Dp
+8A0A(B BBBE g 8+. K^ g g g g x h - ,h X DZ Dh `" AN ND dh p8 Ds g p h
h 7 Al
+A A $ h u AG y
+AA , h `d AEG L
+DAA $ $i DG
E p
-A $ |m AG q
-AA ys D d
-A pT. K^ L | BBE B(A0D8JW
-8A0A(B BBBE , t h} E AAJ
-DAA , ~ $ AAJDA ~ G D 2 ݬ BEA D(J?
-(D ABBA pS. K^ \ , BIB E(A0A8I
-0A(B BBBA
-0A(B IBBK d x BGB B(A0C8D`S
-8A0A(B BBBA
-8A0A(B BBBG > QR YA 08 T c D , X BBE E(A0A8D`8D0A(B BBB t @R6 Kj $ F An
+A $ Li Xm AG q
+AA ti Ps D d
+A i p). K^ i i , 4i 8b ARDPs
+CAA j (). K^ L $j BBE B(A0D8JW
+8A0A(B BBBE , i @ AAJ
+DAA , i k AAJDA j p G D Lj 2 $ BEA D(J?
+(D ABBA <k ((. K^ $ Tk F An
A L
-D $ ЄF An
+D $ |k F An
A L
-D D< p# AAJ@j
+D k D< k # AAJ@j
DAE Y
-DAB L \ BBF B(D0E8GPr
-8A0A(B BBBH L BBB B(E0A8Gp
-8A0A(B BBBK L c BLB B(A0A8Jm
-8A0A(B BBBK $ # DZ < I L | Ќ 7 BBB B(A0A8J
-8A0A(B BBBD T 0 - BBB E(D0A8J
-8A0A(B BBBE 4 $ BAF GP
- AABH 4 \ | BFA GP
- AABA 4 x S BFA G@
- AABA 4 * BAA G0x
- AABA $ n AG Z
-AA - KQJ $ < n AG Z
-AA $ d `n AG Z
-AA L P BBB B(D0D8DP
-8D0A(B BBBE l 8, BBB E(A0A8D_
+DAB L Tk T g BBF B(D0E8GPr
+8A0A(B BBBH L k V 2 BBB B(E0A8Gp
+8A0A(B BBBK L k
+ BLB B(A0A8Jm
+8A0A(B BBBK l # DZ m I L tl BBB B(A0A8J
+8A0A(B BBBD T l H BBB E(D0A8J
+8A0A(B BBBE 4 m L BAF GP
+ AABH 4 Tm # BFA GP
+ AABA 4 m BFA G@
+ AABA 4 m 8 BAA G0x
+ AABA $ n n AG Z
+AA n n - KQJ $ o 0n AG Z
+AA $ ,o xn AG Z
+AA L n BBB B(D0D8DP
+8D0A(B BBBE l n P, BBB E(A0A8D_
8A0A(B BBBF D
-8C0A(B BBBA L t Z* Ȩ BBB B(A0D8G[
-8D0A(B BBBD L ^ V BEB B(A0A8GZ
-8A0A(B BBBH (6 AU ^A < H3 AR ^A , \ h Dl
+8C0A(B BBBA L lo Z* o BBB B(A0D8G[
+8D0A(B BBBD L o ^ BEB B(A0A8GZ
+8A0A(B BBBH p 0% p 8% p @%6 AU ^A q `%3 AR ^A , $q % Dl
H _
A Z
-A Z b L ` BEB B(D0A8G
-8A0A(B BBBG T BBB E(G0D8G
-8C0A(B BBBC T t x BBB E(A0A8J#
-8A0A(B BBBD T s BBE E(G0D8G
-8A0A(B BBBF h ! `. LW ,! x D! & \! & t! & L ! R BBB B(A0A8GK
-8A0A(B BBBJ L ! d BBB B(A0A8GK
-8A0A(B BBBJ $ ," > AG _
-AH LA$ T" > AG ^
-AI LA$ |" (= AG ^
-AI KA$ " @= AG ]
-AJ KAL " XR BBB B(A0A8GK
-8A0A(B BBBJ L # hd BBB B(A0A8GK
-8A0A(B BBBJ D l# BBB D(A0GN
-0A(A BBBH D # BBB D(A0GN
-0A(A BBBH #
- $
- ,$
- D$ \$
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- $
- $ $
- $
- $
- % %
- 4%
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- d% |%
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- % % x
- % p
- & h
- $& ` <& X
- T& P
- l& H
- & @ & 8
- & 0
- & (
- & L & BBB B(A0A8D
-8A0A(B BBBA L L'
BBB B(A0A8D
-8A0A(B BBBA 4 & x BAA G0
- AABG L ' BBB B(A0A8Gp8A0A(B BBB l $( x BBF D(A0G@F
+A Z Tq &b L p x& E BEB B(D0A8G
+8A0A(B BBBG T q 84 BBB E(G0D8G
+8C0A(B BBBC T lq 6 ] BBB E(A0A8J#
+8A0A(B BBBD T q : BBE E(G0D8G
+8A0A(B BBBF r < r x<. LW r < s <& $s <& <s <& L Ts <R BBB B(A0A8GK
+8A0A(B BBBJ L s =d BBB B(A0A8GK
+8A0A(B BBBJ $ s ?> AG _
+AH LA$ t (?> AG ^
+AI LA$ Dt @?= AG ^
+AI KA$ lt X?= AG ]
+AJ KAL t p?R BBB B(A0A8GK
+8A0A(B BBBJ L t @d BBB B(A0A8GK
+8A0A(B BBBJ D 4u A BBB D(A0GN
+0A(A BBBH D |u B BBB D(A0GN
+0A(A BBBH u 0D
+ u (D
+ u D
+ v D $v D
+ <v D
+ Tv D
+ lv C v C
+ v C
+ v C
+ v C v C
+ v C
+ w C
+ ,w C Dw C
+ \w C
+ tw C
+ w C w C
+ w C
+ w C
+ w xC x pC
+ x hC
+ 4x `C
+ Lx XC dx PC
+ |x HC
+ x @C
+ x 8C L x N BBB B(A0A8D
+8A0A(B BBBA L y R BBB B(A0A8D
+8A0A(B BBBA 4 x B 0 BAA G0
+ AABG L y W BBB B(A0A8Gp8A0A(B BBB l y Xx BBF D(A0G@F
0A(A BBBD v
0A(A BBBJ D
-0F(A BBBG T ' BLB E(A0A8D
-8C0A(B BBBA ( $ ) 7 HTTF ,) x D) \) x t) p ) ^ AW
-A L ( ; BDB B(A0A8GQ
-8A0A(B BBBB T $) BEB B(A0A8G:
-8D0A(B BBBE T |) z BEB B(A0A8G:
-8D0A(B BBBE T ) H g BEB B(A0A8G:
-8D0A(B BBBE + DM
-A $+ ; D <+ BDA
+0F(A BBBG T y C < BLB E(A0A8D
+8C0A(B BBBA z E $ z E7 HTTF z { ${ <{ T{ Y^ AW
+A L z Y; ? BDB B(A0A8GQ
+8A0A(B BBBB T { 5 BEB B(A0A8G:
+8D0A(B BBBE T t{ " BEB B(A0A8G:
+8D0A(B BBBE T { ` BEB B(A0A8G:
+8D0A(B BBBE | DM
+A | _; D } _ BDA
ABA O
-ABF ]AB , * pD ԟ BDH _
-ABA * BBB E(A0A8F
+ABF ]AB , | `D | BDH _
+ABA | ` \ BBB E(A0A8F
8A0A(B BBBD
8A0A(B BBBF
8D0A(B BBBH W
-8A0A(B BBBG T, l, , < + ` BEI C(D0l
-(D ABBA < , c ў BEJ D(C0p
-(A BBBA , D, D BDH _
-ABA d t, BED A(D0O
+8A0A(B BBBG ~ c 4~ c L~ c < } c` BEI C(D0l
+(D ABBA < } cc y BEJ D(C0p
+(A BBBA , <~ dD I BDH _
+ABA d l~ d ) BED A(D0O
(D ABBH F
(C ABBJ a
-(D ABBA , , p F 5 BDI `
-ABA - n AL@_A . 4 AS [D \ L- # ՝ BBB A(D0G`u
+(D ABBA , ~ dF ݽ BDI `
+ABA dn AL@_A d4 AS [D \ D e# } BBB A(D0G`u
0D(A BBBF K
-0D(A BBBA . . 8 . !A . 8! L . 0!w - BBB B(A0A8G`
-8D0A(B BBBA 4/ `"2 Gj L/ " d/ " A
-A / #C < / X#s BBE E(A0X
-(A BBBA L / 8 BBB B(A0A8Gp8A0A(B BBB L T/ H# BBB B(A0A8Je
-8A0A(B BBBE |0 )! A_ < 0 H BBG A(G@
-(A ABBG L 0 BEB A(D0l
+0D(A BBBA L e d e8 | fA Pf L Hfw ռ BBB B(A0A8G`
+8D0A(B BBBA xg2 Gj g , g A
+A L 8hC < d phs BBE E(A0X
+(A BBBA L P BBB B(A0A8Gp8A0A(B BBB L L `h BBB B(A0A8Je
+8A0A(B BBBE D n! A_ < d ` BBG A(G@
+(A ABBG L BEB A(D0l
(A BBBG
-(A BBBA L T0 ()
- 1 BBB E(D0A8Gd
-8A0A(B BBBK T 0 < BBB B(A0A8J
-8A0A(B BBBG < 0 u ) BHE L
-AEO AAB < <1 `u BHE L
-AEO AAB < |1 | BEA A(D0a(D ABB < 1 | u BEA A(D0a(D ABB < 1 9 BHE |
-AEO AAB < <2 BBD A(D0(D ABB < |2 BHE |
-AEO AAB < 2 p BBD A(D0(D ABB d 3 2 BBB E(D0E8D@P
+(A BBBA L L @n
+ ٺ BBB E(D0A8Gd
+8A0A(B BBBK T BBB B(A0A8J
+8A0A(B BBBG < 8u Ѻ BHE L
+AEO AAB < 4 xu BHE L
+AEO AAB < t | Y BEA A(D0a(D ABB < | BEA A(D0a(D ABB < 8 BHE |
+AEO AAB < 4 BBD A(D0(D ABB < t i BHE |
+AEO AAB < - BBD A(D0(D ABB d 2 BBB E(D0E8D@P
8F0A(B BBBA
-8D0A(B BBGA d <4 0 BBE B(A0A8FP[
+8D0A(B BBGA d u BBE B(A0A8FP[
8C0A(B BBBA W
-8F0A(B BBBA , 4 2; BDE lAB d 4 2 BBB E(D0E8D@P
+8F0A(B BBBA , l w; BDE lAB d 82 BBB E(D0E8D@P
8F0A(B BBBA
-8D0A(B BBGA L <5 2? BBB B(A0C8DP
-8A0A(B BBBA l 5 y BBF D(A0G@I
+8D0A(B BBGA L w? BBB B(A0C8DP
+8A0A(B BBBA l T y BBF D(A0G@I
0A(A BBBI w
0A(A BBBA D
-0F(A BBBG L $5 3W ! BBB B(A0A8GPQ
-8C0A(B BBBB L t5 4 BBB B(A0A8G`U
-8D0A(B BBBE d 5 6 BEB B(F0D8Je
+0F(A BBBG L xW ɶ BBB B(A0A8GPQ
+8C0A(B BBBB L l 0y BBB B(A0A8G`U
+8D0A(B BBBE d { M BEB B(F0D8Je
8A0A(B BBBB S
-8C0A(B BBBA L 7 BBB B(A0A8Gp8A0A(B BBB d |6 p9
BBB B(A0A8GPZ
+8C0A(B BBBA L ̈ BBB B(A0A8Gp8A0A(B BBB d t ~
BBB B(A0A8GPZ
8A0A(B BBBK D
-8A0A(B BBBJ T 6 = BBB B(A0A8JS
-8A0A(B BBBA T <7 PD BBD C(G0J
+8A0A(B BBBJ T ܈ 0 W BBB B(A0A8JS
+8A0A(B BBBA T 4 h [ BBD C(G0J
(A ABBF d
-(D ABBA l8 E 8 E 8 D l 8 Фx BBF D(A0G@F
+(D ABBA 4 L d l | x BBF D(A0G@F
0A(A BBBD v
0A(A BBBJ D
-0F(A BBBG L $9 BBB B(A0C8D`I
-8C0A(B BBBK < t9 BBG A(G@
-(A ABBF T 8 C 0 BLB E(A0A8D
-8A0A(B BBBG D 49 ب/ BJB K(A0j
-(c `BBA D |9 M BFA Dp
+0F(A BBBG L BBB B(A0C8D`I
+8C0A(B BBBK < < BBG A(G@
+(A ABBF T Ԋ س BLB E(A0A8D
+8A0A(B BBBG D , / BJB K(A0j
+(c `BBA D t M BFA Dp
AABH O
- AABA d : ȫE BBB B(A0A8DP
+ AABA d d E BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l ,: B S BBB J(D0D8D4
+8F0A(B BBBA l $ B BBB J(D0D8D4
8D0A(B BBBC i
-8A0A(B BBBE L t; N BBB B(A0A8DV
-8A0A(B BBBB L ; 2 BBB B(A0D8G@@
-8A0A(B BBBB T <; pD
c BBB B(A0D8Jc
-8A0A(B BBBD < ; (S BBD A(DP
-(D ABBA T ; S | BBB B(D0A8J
-8A0A(B BBBD < ,< Pe Ȓ BBA D(DP
-(D ABBA T l< e BBB B(A0D8J+
-8A0A(B BBBD < < (v BBA D(DP
-(D ABBA = + Ai = Ȳ < > в ZBA A(%
- DBFA T> `) 4 l> x bGHX
-AG > @X > AS > > P ? D0W $? B , <? HK BAC G0w CAB L l? h| BAC G0L
+8A0A(B BBBE L < N BBB B(A0A8DV
+8A0A(B BBBB L 2 BBB B(A0D8G@@
+8A0A(B BBBB T 4
BBB B(A0D8Jc
+8A0A(B BBBD < @ R BBD A(DP
+(D ABBA T ̍ $ BBB B(D0A8J
+8A0A(B BBBD < $ h p BBA D(DP
+(D ABBA T d B BBB B(A0D8J+
+8A0A(B BBBD < @ BBA D(DP
+(D ABBA + Ai ď < ZBA A(%
+ DBFA x) 4 4 bGHX
+AG l XX AS h Ԑ D0W ( B , ` K BAC G0w CAB L 4 | BAC G0L
AABC D
- CABF D DAB L ? BEE B(A0A8G
-8A0A(B BBBF L @ h BBB E(A0A8GP
-8D0A(B BBBA , \@ AAG j
-AAA , ? x AAJ
-DAA , ? AAJAA @ G A s , $A _ AAG N
-AAA L |@ ^ BBB E(A0D8GG
-8A0A(B BBBH T @ P X BDB B(A0D8G
+ CABF D DAB L BEE B(A0A8G
+8A0A(B BBBF L ԑ BBB E(A0A8GP
+8D0A(B BBBA , $ 0 AAG j
+AAA , AAJ
+DAA , ܑ 0 AAJAA G Ԓ 0s , _ AAG N
+AAA L t BBB E(A0D8GG
+8A0A(B BBBH T Ē h BDB B(A0D8G
-8A0A(B BBBH T A BBB A(D0F
+8A0A(B BBBH T ē 0! BBB A(D0F
(A BBBH :
-(E BBBH L |A BEE B(D0D8G
-8C0A(B BBBF B B B B L ,B p BBE B(A0A8D`s
-8D0A(B BBBG L |B BBE B(A0A8D`s
-8D0A(B BBBG C C C A < C h BGD A(D0T
-(A ABBD < ,D 8 BBA D(D0
-(A ABBG lD D DM T C
BEB B(A0A8GG
-8A0A(B BBBK \ D BIH D(G0Q
+(E BBBH L t # 7 BEE B(D0D8G
+8C0A(B BBBF l $ ] ] ] L $ $ BBE B(A0A8D`s
+8D0A(B BBBG L t 8& j BBE B(A0A8D`s
+8D0A(B BBBG l ' ' 'A < (h BGD A(D0T
+(A ABBD < P( BBA D(D0
+(A ABBG 4 ( L ( DM T (
i BEB B(A0A8GG
+8A0A(B BBBK \ . BIH D(G0Q
(A ABBK s
-(C ABEB I(C AEB L TE BEA D(G0O
+(C ABEB I(C AEB L (/ BEA D(G0O
(A ABBH E
-(G AFEH L E ` BEB I(D0^
-(A BFEF a(A BBB E A\ L F q BEE I(D0v
-(A BFEC I(A BBB 4 dF _ AID i
-JDK LAA L F ( BBA D(G0^
+(G AFEH L l x/ BEB I(D0^
+(A BFEF a(A BBB / A\ L ܗ /q BEE I(D0v
+(A BFEC I(A BBB 4 , 0_ AID i
+JDK LAA L d @0 BBA D(G0^
(A ABBD a
-(A ABBA 4 F E BID a
-ABE IAB 4 $G ] BID n
-EEI IAB \ \G BBB B(A0D8G@p
-8G0A(B BFEE L8A0A(B BBB$ G (l ADD `AA4 G pb BNA o
-ABE TCB H 4H 5 T tG H [ BBB E(D0D8J
-8A0A(B BBBC | H , BBB B(D0C8GP
+(A ABBA 4 0E BID a
+ABE IAB 4 0] BID n
+EEI IAB \ $ 0 BBB B(A0D8G@p
+8G0A(B BFEE L8A0A(B BBB$ @1l ADD `AA4 1b BNA o
+ABE TCB 1 825 T l `2 BBB E(D0D8J
+8A0A(B BBBC | l 8, BBB B(D0C8GP
8A0A(B BBBH
8C0A(B BBBD A
-8D0A(B BBBJ D $I 0 BAA L
+8D0A(B BBBJ D H: BAA L
ABE G
ABF K
-ABJ lI x I pB X^ 4 I BPD A(K`Z(A ABB| I BBB A(A0GPU
+ABJ 4 : L :B X^ 4 l : BPD A(K`Z(A ABB| ; BBB A(A0GPU
0C(A BBBB R
0A(A BBBF o
-0D(A BBBF N0A(A BBB \J hn 4 tJ AAG
-AAB tCA J h$ Ab T I . BBB B(A0A8J+
-8A0A(B BBBG < LJ pu BHE L
-AEO AAB < J | ߇ BEA A(D0a(D ABB < J BHH
-AEG AAB K p AL L ,K G BEB E(I0H8D@
-8D0A(B BBBA L TL 4 BIF A(R0
-(A ABBA K(D ABB d L W BDB B(A0A8Dpq
+0D(A BBBF N0A(A BBB $ ;n 4 < ; AAG
+AAB tCA t <$ Ab T U ֧ BBB B(A0A8J+
+8A0A(B BBBG < D \u ç BHE L
+AEO AAB < \| BEA A(D0a(D ABB < Ĝ ; K BHH
+AEG AAB < AL L $ \ BEB E(I0H8D@
+8D0A(B BBBA L 8<4 BIF A(R0
+(A ABBA K(D ABB d l (=W BDB B(A0A8Dpq
8A0A(B BBBE
-8A0A(B BBBA M T LL x O BBB B(A0A8J
-8A0A(B BBBG D L U OMB L(E0Y
-(g ~BBA D L M g BFA Dp
+8A0A(B BBBA Ԟ @ T D (@x BBB B(A0A8J
+8A0A(B BBBG D ]U / OMB L(E0Y
+(g ~BBA D ^M BFA Dp
AABH O
- AABA 4 4M 8 BAA
-ABA L DN BBB B(A0D8G@
-8A0A(B BBBD T M
- څ BBB E(D0H8J
-8A0A(B BBBG L N h W BBE D(C0G
-0A(A BBBG <O x
-
- TO p
-
L N h
-c v BBE D(D0G
-0D(A BBBK O L N & # BBB F(A0E8GPg
-8A0A(B BBBA $P p DM
-A l lO ' BFB B(A0D8G`"
+ AABA 4 , M BAA
+ABA L _ BBB B(A0D8G@
+8A0A(B BBBD T a
+ BBB E(D0H8J
+8A0A(B BBBG L NW BBE D(C0G
+0A(A BBBG O
+ O
L Oc BBE D(D0G
+0D(A BBBK P L (k ˤ BBB F(A0E8GPg
+8A0A(B BBBA DM
+A l d l BFB B(A0D8G`"
8A0A(B BBBD V
-8A0A(B BBBH , O +e ׄ AAD I
-AAA , P +[ AAD
-AAA < <P , AAD R
+8A0A(B BBBH , ԡ pe AAD I
+AAA , q[ _ AAD
+AAA < 4 8q ? AAD R
AAF d
-AAA 4 |P , r BKD D0
- DABA < P -b H BEA D(DP
-(D ABBE $ Q 8. ADD AA, Q .+ ADD
-AAA $R /% DW
-A $ DR /5 AJ
+AAA 4 t q BKD D0
+ DABA < rb BEA D(DP
+(D ABBE $ Ps ADD AA, s+ ADD
+AAA t% DW
+A $ t5 AJ
E ]
-A lR / DP
-A R /K AI 4 R 0 ADG Z
-AAP bAA R p0 R x0< , S 0( AG o
+A 4 t DP
+A T tK AI 4 t u ADG Z
+AAP bAA u Ĥ u< , ܤ u( AG o
AH V
-AI DS 1" DQ
-K A dS 1 T R 1M k BBB H(A0A8Jr
-8A0A(B BBBJ < S 9 BEE D(D0e(A BBB T <S 9f $ BBB E(D0A8GP
-8A0A(B BBBG 4 lT B BED D(F0(A ABB$ T V ADD JAAL S H Á BEB E(I0H8D@
-8D0A(B BBBA < U ȗ BDI ~
+AI v" DQ
+K A , v T vM BBB H(A0A8Jr
+8A0A(B BBBJ < ~ BEE D(D0e(A BBB T 4 f ̡ BBB E(D0A8GP
+8A0A(B BBBG 4 4 BED D(F0(A ABB$ l (V ADD JAAL ` k BEB E(I0H8D@
+8D0A(B BBBA < BDI ~
AGK K
-ABB L T H [ BEB E(I0H8D@
-8D0A(B BBBD d U A? BBB B(H0G8Fp0
+ABB L | ` BEB E(I0H8D@
+8D0A(B BBBD d t ? BBB B(H0G8Fp0
8D0A(B BBBF W
-8A0A(B BBBG L <U `DM ˀ BBB B(A0D8GQ
-8A0A(B BBBA L dV BBB B(A0D8GP>
-8A0A(B BBBD V P E
-G < V BHD A(D0(A ABB d W I BBB B(K0D8Fpb
+8A0A(B BBBG L 4 xM s BBB B(A0D8GQ
+8A0A(B BBBA L , ( BBB B(A0D8GP>
+8A0A(B BBBD | h E
+G < 8 BHD A(D0(A ABB d ܨ Ȏ BBB B(K0D8Fpb
8A0A(B BBBG j
-8A0A(B BBBA T V XO BBB B(D0A8Jo
-8A0A(B BBBH < V `f * BED D(H0m
-(D ABBA T <W Z BHB E(A0A8GP
-8A0A(B BBBD lX _ T W _ BBB E(A0A8G
-8A0A(B BBBF X 8f X 0f Y (f $Y f T dX f M BBB E(A0A8G
-8A0A(B BBBF Y pl Y hl Y `l Y Xl L Y Pl ~ BBB B(A0D8I@L
-8C0A(B BBBB T lY mA
~ BBB B(A0A8J
-8D0A(B BBBH L Y z ~ BBB B(A0A8Ji
-8A0A(B BBBI L Z x{m BBB E(A0D8GR
-8A0A(B BBBE L dZ } 3~ BBE E(A0A8GV
-8D0A(B BBBF , [ , BDD WAB L Z
- } BBE B(A0A8IR
-8C0A(B BBBA \ h AO , ,\ Ȟ BDA AK T [ x
- } BDB B(A0A8G$
-8A0A(B BBBA T [ $ } BBB B(A0A8Gs
-8A0A(B BBBA < 4\ } BED C(G@
-(D ABBC T t\ xs } BBB B(A0A8J
-8A0A(B BBBE ] @2 Ap < \ ` } BBB A(C0Z
-(A BBBA < ,] @ } BBA A(G0d
-(A ABBA L l] } BEH E(D0D8Gr
-8A0A(B BBBA ^ 2 Ap T ] ` 5} BLE B(A0A8G
-8C0A(B BBBC T 4^
- ,} BLB E(A0A8G
-8A0A(B BBBA d_ j |_ H T ^ @z | BBB B(A0D8G
-8A0A(B BBBG d _ 8' BBB B(A0A8GPe
+8A0A(B BBBA T p U BBB B(D0A8Jo
+8A0A(B BBBH < (f ҟ BED D(H0m
+(D ABBA T 4 Z BHB E(A0A8GP
+8A0A(B BBBD 4 T p BBB E(A0A8G
+8A0A(B BBBF P H Ԫ @ 8 T \ 0 BBB E(A0A8G
+8A0A(B BBBF \ t x p L h z BBB B(A0D8I@L
+8C0A(B BBBB T d 8A
8 BBB B(A0A8J
+8D0A(B BBBH L 0 h BBB B(A0A8Ji
+8A0A(B BBBI L m BBB E(A0D8GR
+8A0A(B BBBE L \ BBE E(A0A8GV
+8D0A(B BBBF , T , BDD WAB L ܬ
+ BBE B(A0A8IR
+8C0A(B BBBA ԭ AO , BDA AK T |
+ [ BDB B(A0A8G$
+8A0A(B BBBA T ԭ ($ ^ BBB B(A0A8Gs
+8A0A(B BBBA < , \ BED C(G@
+(D ABBC T l s * BBB B(A0A8J
+8A0A(B BBBE l X'2 Ap < x' BBB A(C0Z
+(A BBBA < $ X ` BBA A(G0d
+(A ABBA L d ; BEH E(D0D8Gr
+8A0A(B BBBA \ '2 Ap T ԯ x ݜ BLE B(A0A8G
+8C0A(B BBBC T ,
+ Ԝ BLB E(A0A8G
+8A0A(B BBBA , 'j D `' T X'z BBB B(A0D8G
+8A0A(B BBBG d PB' BBB B(A0A8GPe
8D0A(B BBBM
-8A0A(B BBBB L |_ | BBB B(A0A8Jk
-8C0A(B BBBE ` A , ` BDD E
-ABF ` H% L a ` BBD A(D@|
+8A0A(B BBBB L t 2 B BBB B(A0A8Jk
+8C0A(B BBBE l CA , F BDD E
+ABF `F% L ̲ xF BBD A(D@|
(A ABBA D
-(F ABBA T |` /| BBB B(A0A8G3
-8C0A(B BBBH L ` h | BEB B(A0A8I_
-8A0A(B BBBA a
: < <a 0
{ BBA A(D@z
-(A ABBA Tb u T a h { BEB B(A0A8G<
-8A0A(B BBBF b h b H; H^
-J F b h c `S L Tb /{ BDA }
+(F ABBA T t F כ BBB B(A0A8G3
+8C0A(B BBBH L ̲ L ě BEB B(A0A8I_
+8A0A(B BBBA ij R: < 4 HR BBA A(D@z
+(A ABBA Su T S M BEB B(A0A8G<
+8A0A(B BBBF Zh `Z; H^
+J F Ĵ Z ܴ xZS L L Z ך BDA }
ABI \
ABA G
-ABF |c Hy L b z BBB B(A0D8I
-8A0A(B BBBG d c ` @ BBB B(D0A8D@l
+ABF D `[y L [ BBB B(A0D8I
+8A0A(B BBBG d xc@ BBB B(D0A8D@l
8A0A(B BBBI
-8C0A(B BBBA < tc 8 , wz BDA D(L0u
-(A ABGI , c ( G Vz BDI ]
-ABE d H d P 4 d X B AAG L
-FAL WAA < Ld p y BAA x
+8C0A(B BBBA < l Pd, BDA D(L0u
+(A ABGI , @eG BDI ]
+ABE `e he 4 peB AAG L
+FAL WAA < D e v BAA x
ABI A
-ABJ < d y BBD A(L@e
-(A ABBH , d p!Q y AID c
-DDE T d !b by BBD D(D0m
+ABJ < f P BBD A(L@e
+(A ABBH , Ķ fQ * AID c
+DDE T fb
+ BBD D(D0m
(A ABBE
-(D ABBG D ,f " BND `
+(D ABBG D g BND `
GBE o
-IBL _AB L tf # BBB B(A0A8DPj
-8D0A(B BBBK L f # BEB B(A0A8Gp
-8C0A(B BBBH \ g p( BEB E(D0D8G@i
-8A0A(B BBBH T8C0A(B BBBT f ( w BEB B(A0D8G
-8A0A(B BBBG D f 3| w BDE A(G0DP\0A(A BBB 4 h 3l AAD Y
+IBL _AB L < 8h BBB B(A0A8DPj
+8D0A(B BBBK L h BEB B(A0A8Gp
+8C0A(B BBBH \ ܸ m BEB E(D0D8G@i
+8A0A(B BBBH T8C0A(B BBBT m BEB B(A0D8G
+8A0A(B BBBG D x| BDE A(G0DP\0A(A BBB 4 ܹ xl AAD Y
DAD v
-DAE d tg 41 vw BBA D(G0
+DAE d l y1 BBA D(G0
(A ABGC `
(A ABBJ S
-(F ABBB l g 4; !w BEB B(A0A8D`
+(F ABBB l Թ y; ɖ BEB B(A0A8D`
8A0A(B BBBO C
-8A0A(B BBBA , Lh 6k v AAG @
-IAE $ Ti 6 Aw
+8A0A(B BBBA , D {k m AAG @
+IAE $ | Aw
H h
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+H D |( Ab 4 d | ADD0|
AAI }
-GAK i 8N Ad
-K ] i P8 v BBE D(A0Gp
+GAK 8}N Ad
+K ] h} BBE D(A0Gp
0A(A BBBH o
0A(A BBBI
0A(A BBBG d
-0A(A BBBA d |j X: BGB B(A0A8FI
+0A(A BBBA d D p BGB B(A0A8FI
8A0A(B BBBH Y
-8F0A(B BBBH L j = )u BBB B(D0A8JG
-8A0A(B BBBH < \j u Au BHE L
-AEO AAB < j | u BEA A(D0a(D ABB L k , BLB B(D0A8D`
-8A0A(B BBBJ < l 1 BBG A(GP
-(A ABBF L Dl @Bs BBB B(A0A8G
-8A0A(B BBBF L l pD1 BBB E(A0C8I
-8A0A(B BBBA T l `F,
- s BBB B(A0A8G
-8A0A(B BBBA T dl 8P s BBB B(A0A8G
-8D0A(B BBBA T l 8 t BEE B(A0C8J
-8A0A(B BBBG L m i t BBE E(D0C8Dp
-8C0A(B BBBK T dm ls t BBB E(D0C8G
-8D0A(B BBBD n $ Ab D n ' BKK
+8F0A(B BBBH L є BBB B(D0A8JG
+8A0A(B BBBH < T u BHE L
+AEO AAB < | BEA A(D0a(D ABB L | 8, BLB B(D0A8D`
+8A0A(B BBBJ < ̽ 1 BBG A(GP
+(A ABBF L Xs BBB B(A0A8G
+8A0A(B BBBF L \ 1 BBB E(A0C8I
+8A0A(B BBBA T x,
+ E BBB B(A0A8G
+8A0A(B BBBA T \ P a BBB B(A0A8G
+8D0A(B BBBA T 8 \ BEE B(A0C8J
+8A0A(B BBBG L ` BBE E(D0C8Dp
+8C0A(B BBBK T \ 0s / BBB E(D0C8G
+8D0A(B BBBD \ $ Ab D | ' BKK
ABH B
-ABC sAB < n BHD A(D0(A ABB L dn XR s BGB B(A0A8GJ
-8A0A(B BBBF o h o o o o p p 4p D Lp 3 Aw
+ABC sAB < BHD A(D0(A ABB L \ pR BGB B(A0A8GJ
+8A0A(B BBBF T l ( D 3 Aw
H [
E [
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-8D0A(B BBBA L p @8 r BEE B(D0A8Dp}
-8D0A(B BBBG 4q 0 tp ? ur A} 4 p ؗ Yr BFD D0u
- DABA $ q Pm AG Y
-AA q
T q 7 q BBA D(G`
+K R L BBB E(D0A8F
+8D0A(B BBBA L X8 p BEE B(D0A8Dp}
+8D0A(B BBBG H l ? A} 4 BFD D0u
+ DABA $ l hm AG Y
+AA
T 7 BBA D(G`
(D ABBE L
-(D ABBC T dq x7 q BBA D(G`
+(D ABBC T \ 7 V BBA D(G`
(D ABBE L
-(D ABBC L q `e mq BLB B(A0D8Dp
-8A0A(B BBBA r 2 Ap T ,r `u q BEE B(A0D8J
-8A0A(B BBBJ d \s B BBB B(A0A8G@x
+(D ABBC L xe BLB B(A0D8Dp
+8A0A(B BBBA 2 Ap T $ xu BEE B(A0D8J
+8A0A(B BBBJ d $ B BBB B(A0A8G@x
8A0A(B BBBE
-8A0A(B BBBF s ] D X
+8A0A(B BBBF 8] D X
D `
-H 4 s ` BAA D@
- AABD L t ȫ? BBB B(A0D8D@
-8A0A(B BBBA < lt t BEE D(A0S(A GBE T s o BGE B(A0A8Q
-8A0A(B BBBA < u @ BBA A(G@_
-(A ABBF L Du ~ BLB H(A0D8D@
-8A0A(B BBBD < u BBG A(G@
-(A ABBH L u z BHE B(G0A8J
-8A0A(B BBBC < $v p BBG A(G0
-(A ABBB L dv - BEE B(D0A8Gp
-8A0A(B BBBC L u { m BBB B(A0A8JM
-8A0A(B BBBE w w 4w Lw H dw @ |w 8 T v @ m BBB B(A0A8G@
-8A0A(B BBBE T w um BBB B(A0A8G@
-8A0A(B BBBE L lw . gm BBB B(A0A8Gh
-8A0A(B BBBE 4 x pJ AAG P
+H 4 x BAA D@
+ AABD L ? BBB B(A0D8D@
+8A0A(B BBBA < 4 t BEE D(A0S(A GBE T m BGE B(A0A8Q
+8A0A(B BBBA < X BBA A(G@_
+(A ABBF L
~ BLB H(A0D8D@
+8A0A(B BBBD < \ 8 BBG A(G@
+(A ABBH L z BHE B(G0A8J
+8A0A(B BBBC < BBG A(G0
+(A ABBB L , - BEE B(D0A8Gp
+8A0A(B BBBC L { { BBB B(A0A8JM
+8A0A(B BBBE 8P 0P (P ` , X D P T X + BBB B(A0A8G@
+8A0A(B BBBE T BBB B(A0A8G@
+8A0A(B BBBE L d . BBB B(A0A8Gh
+8A0A(B BBBE 4 \ J AAG P
AAE
-FAE T w $m BEE B(A0A8G
-8A0A(B BBBH Lx qm BBB B(A0A8G@
+FAE T ! ̌ BEE B(A0A8G
+8A0A(B BBBH D . BBB B(A0A8G@
8A0A(B BBBH t
8D0A(B BBBO
-8D0A(B BBBG < x o m BEI D(D0z
-(D ABBA L y o l BEE B(D0D8G
-8A0A(B BBBJ D dy
-m AAD V
+8D0A(B BBBG < 1o BEI D(D0z
+(D ABBA L 1o BEE B(D0D8G
+8A0A(B BBBJ D \ ; AAD V
AAJ d
DAG d
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-8D0A(B BBBE T y l BBB B(A0A8J+
-8A0A(B BBBG < Tz 8u l BHE L
-AEO AAB < z xu l BHE L
-AEO AAB < z u ol BHE L
-AEO AAB < { | 3l BEA A(D0a(D ABB < T{ 8| k BEA A(D0a(D ABB < { x| k BEA A(D0a(D ABB D { U k OMB L(E0Y
-(g ~BBA D | M _k BFA Dp
+DAA L X; BBB B(A0A8JB
+8D0A(B BBBE T L BBB B(A0A8J+
+8A0A(B BBBG < L PSu BHE L
+AEO AAB < Su S BHE L
+AEO AAB < Su BHE L
+AEO AAB < T| ۋ BEA A(D0a(D ABB < L PT| BEA A(D0a(D ABB < T| c BEA A(D0a(D ABB D TU ' OMB L(E0Y
+(g ~BBA D UM BFA Dp
AABH O
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-8A0A(B BBBG T } j BBB B(A0A8GE
-8D0A(B BBBE L d} j BBH B(A0D8Jn
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+ AABA L W BBB B(A0D8G@
+8A0A(B BBBD T YH
+ BBE B(A0D8J]
+8A0A(B BBBG T @ BBB B(A0A8GE
+8D0A(B BBBE L \ B Q BBH B(A0D8Jn
+8A0A(B BBBK T p T l b AGF n
FAM b
DAI D
KAP D
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-8D0A(B BBBD T | / 7j BBB E(A0A8J
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+KAP mFA 4 c BGD o
+IBI VAB L T d BBB B(D0C8JS
+8C0A(B BBBH < u BHE L
+AEO AAB < | ߉ BEA A(D0a(D ABB L $ o BBB B(A0A8JC
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+8A0A(B BBBG ` X L P BEE E(A0D8D
+8A0A(B BBBG d $ BEB B(A0A8DpJ
+8A0A(B BBBK C
+8A0A(B BBBC \ X* BBB B(A0A8G`J
+8A0A(B BBBK 8A0A(B BBB (& D a @ H 4 @ L 8: d ` | h) DS
+I H x DT x x L 4 BEE F(D0A8G
+8A0A(B BBBA A $ s AK@V
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+A D0[
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+8D0A(B BBBF 8 , 0s AAG@_
+AAF D BBE A(D0G`f
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+8D0A(B BBBA L l " BFB B(A0G8DP
+8D0A(B BBBM 2 Ai
+F A L D@D @ L 8 BBB B(D0D8G
+8A0A(B BBBC \ x| D s
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_ZNK11onnxruntime10ReduceMeanIfE7ComputeEPNS_15OpKernelContextE._omp_fn.5 _ZNK11onnxruntime10ReduceMeanIiE7ComputeEPNS_15OpKernelContextE._omp_fn.4 _ZNKSt14default_deleteIN11onnxruntime9KernelDefEEclEPS1_.isra.314 _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceL1_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceL1_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ReduceL1_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceL1_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceL2_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceL2_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ReduceL2_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceL2_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceLogSum_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_62kCpuExecutionProvider_ReduceLogSum_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceLogSum_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceLogSum_kOnnxDomain_ver11_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_61kCpuExecutionProvider_ReduceLogSumExp_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_63kCpuExecutionProvider_ReduceLogSumExp_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver1_10_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver11_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver11_int32_tEEENS_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_55kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver1_10_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver11_int64_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_56kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver11_int32_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_55kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver1_10_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver1_10_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_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_61kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_63kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_64kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver1_10_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_62kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver11_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_52kCpuExecutionProvider_ArgMax_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ArgMax_kOnnxDomain_ver11_int32_tEEENS_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_52kCpuExecutionProvider_ArgMin_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ArgMin_kOnnxDomain_ver11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime16ReduceKernelBaseILb1EEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime16ReduceKernelBaseILb0EEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime16PrepareForReduceIlEEbPNS_15OpKernelContextERSt6vectorIT_SaIS4_EEPPNS_6TensorERlSB_RKS3_IlSaIlEEbbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime16PrepareForReduceIiEEbPNS_15OpKernelContextERSt6vectorIT_SaIS4_EEPPNS_6TensorERlSB_RKS3_IlSaIlEEbbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime16PrepareForReduceIfEEbPNS_15OpKernelContextERSt6vectorIT_SaIS4_EEPPNS_6TensorERlSB_RKS3_IlSaIlEEbbE19__PRETTY_FUNCTION__ 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_ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE11_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE12_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE12_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE13_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE13_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE14_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE14_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE15_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE15_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE16_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE16_ _ZN6google8protobuf8internal20RepeatedPtrFieldBase3AddINS0_16RepeatedPtrFieldIN4onnx26TensorShapeProto_DimensionEE11TypeHandlerEEEPNT_4TypeESB_.constprop.421 _ZNSt18basic_stringstreamIcSt11char_traitsIcESaIcEEC1ESt13_Ios_Openmode.constprop.422 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlS2_E0_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E2_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E3_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E4_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E15_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E1_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E16_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E7_E9_M_invokeERKSt9_Any_dataS2_ 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_ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28BatchNormalization_Onnx_ver7EEENS0_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_17Flatten_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Flatten_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_ _ZTSZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_bENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_bEUlRNS_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_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.201 _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_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_ _ZNSt6vectorISsSaISsEEC2ESt16initializer_listISsERKS0_.constprop.235 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25QuantizeLinear_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_27DequantizeLinear_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_managerIZN4onnx18ReduceDocGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx18ReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx18ReduceDocGeneratorEPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx18ReduceDocGeneratorEPKcENKUlRNS_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_18ReduceDocGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_18ReduceDocGeneratorEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_21ArgReduceDocGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_21ArgReduceDocGeneratorEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZZN4onnx18ReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx18ReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx25ReduceDocGenerator_opset1EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx25ReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx25ReduceDocGenerator_opset1EPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx25ReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_25ReduceDocGenerator_opset1EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_25ReduceDocGenerator_opset1EPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_28ArgReduceDocGenerator_opset1EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_28ArgReduceDocGenerator_opset1EPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZZN4onnx25ReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx25ReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS_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_ schema.cc _ZNSt17_Function_handlerIFbiEZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_E9_M_invokeERKSt9_Any_datai _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_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_24SequenceErase_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_21SequenceAt_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _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_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_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Cast_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Reshape_Onnx_ver5EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Shape_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Size_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Concat_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Split_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Slice_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Transpose_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Scatter_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_20ScatterND_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26ScatterElements_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Gather_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25GatherElements_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Squeeze_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_20Unsqueeze_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_22SpaceToDepth_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_23DepthToSpace_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Tile_Onnx_ver6EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Upsample_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Resize_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Compress_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _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_15IsNaN_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_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_17NonZero_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_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_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Unique_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19GatherND_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Pad_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZN6google8protobuf8internal20RepeatedPtrFieldBase3AddINS0_16RepeatedPtrFieldIN4onnx26TensorShapeProto_DimensionEE11TypeHandlerEEEPNT_4TypeESB_.constprop.541 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Size_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt18basic_stringstreamIcSt11char_traitsIcESaIcEEC1ESt13_Ios_Openmode.constprop.542 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15Shape_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Tile_Onnx_ver6EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Scatter_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_20ScatterND_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_26ScatterElements_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25GatherElements_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Squeeze_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17NonZero_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15IsNaN_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16IsInf_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Cast_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Unique_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Concat_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Gather_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19Compress_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17OneHot_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Pad_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_26ReverseSequence_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19GatherND_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16Split_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.517 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19Transpose_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Reshape_Onnx_ver5EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_20Unsqueeze_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_ENKUlPKNS_11TensorProtoEE_clES8_.isra.520 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16Slice_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_22SpaceToDepth_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_23DepthToSpace_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15Where_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.530 _ZZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.531 _ZZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.532 _ZTSZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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_ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMean_kOnnxDomain_ver11_int32_tEEENS_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_55kCpuExecutionProvider_ReduceMin_kOnnxDomain_ver11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ 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_ZZN11onnxruntime7contrib22RegisterContribSchemasEvE29op_schema_register_onceGelu50 ._199 ._200 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE38op_schema_register_onceCropAndResize48 ._198 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceCDist47 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE31op_schema_register_onceUnique46 ._197 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE28op_schema_register_oncePad45 ._195 ._196 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceWordConvEmbedding44 ._194 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceGatherND43 ._192 ._193 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceMurmurHash342 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E12_E9_M_invokeERKSt9_Any_dataS2_ ._191 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE43op_schema_register_onceQLinearAveragePool41 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E11_E9_M_invokeERKSt9_Any_dataS2_ ._189 ._190 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceMulInteger40 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E10_E9_M_invokeERKSt9_Any_dataS2_ ._188 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceQLinearReduceMean39 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceQLinearMul38 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceQLinearAdd37 ._186 ._187 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE41op_schema_register_onceReduceSumInteger36 ._183 ._184 ._185 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceMatMulInteger1635 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_ZTSZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE0_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE10_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE11_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE12_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE13_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE14_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE15_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE16_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE17_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE18_ nchwc_schema_defs.cc 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_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_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_31InstanceNormalization_Onnx_ver6EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_31InstanceNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25LpNormalization_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25LpNormalization_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_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_18Flatten_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25TfIdfVectorizer_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25TfIdfVectorizer_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_27StringNormalizer_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_27StringNormalizer_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZN6google8protobuf8internal20RepeatedPtrFieldBase3AddINS0_16RepeatedPtrFieldIN4onnx26TensorShapeProto_DimensionEE11TypeHandlerEEEPNT_4TypeESB_.constprop.465 _ZNSt6vectorISsSaISsEEC2ESt16initializer_listISsERKS0_.constprop.463 _ZNSt6vectorISsSaISsEEC2ESt16initializer_listISsERKS0_.constprop.462 _ZNSt18basic_stringstreamIcSt11char_traitsIcESaIcEEC1ESt13_Ios_Openmode.constprop.464 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25LpNormalization_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_31InstanceNormalization_Onnx_ver6EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28BatchNormalization_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Dropout_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZN4onnxL16mvn_default_axesE _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_20MaxUnpool_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_22QLinearConv_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_22ConvInteger_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ 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_ZTSZN4onnx24RoiPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx21ConvOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx21ConvOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZTSZN4onnx11GetOpSchemaINS_22QLinearConv_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_22ConvInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZZN4onnx30ConvTransposeOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx30ConvTransposeOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_ _ZTSZZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_ _ZTSZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_31InstanceNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25LpNormalization_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25TfIdfVectorizer_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_27StringNormalizer_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_23PoolOpSchemaGenerator_9EPKcS5_S5_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_23PoolOpSchemaGenerator_9EPKcS5_S5_ENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx23PoolOpSchemaGenerator_9EPKcS3_S3_ENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_24PoolOpSchemaGenerator_10EPKcS5_S5_bEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_24PoolOpSchemaGenerator_10EPKcS5_S5_bENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx24PoolOpSchemaGenerator_10EPKcS3_S3_bENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_26LpPoolOpSchemaGenerator_10EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_26LpPoolOpSchemaGenerator_10EPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ 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_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_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_ 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_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.201 _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_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_ _ZNSt6vectorISsSaISsEEC2ESt16initializer_listISsERKS0_.constprop.235 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25QuantizeLinear_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_27DequantizeLinear_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_managerIZN4onnx18ReduceDocGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx18ReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx18ReduceDocGeneratorEPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx18ReduceDocGeneratorEPKcENKUlRNS_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_18ReduceDocGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_18ReduceDocGeneratorEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_21ArgReduceDocGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_21ArgReduceDocGeneratorEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZZN4onnx18ReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx18ReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx25ReduceDocGenerator_opset1EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx25ReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx25ReduceDocGenerator_opset1EPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx25ReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_25ReduceDocGenerator_opset1EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_25ReduceDocGenerator_opset1EPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_28ArgReduceDocGenerator_opset1EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_28ArgReduceDocGenerator_opset1EPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZZN4onnx25ReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx25ReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS_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_ schema.cc _ZNSt17_Function_handlerIFbiEZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_E9_M_invokeERKSt9_Any_datai _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_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_24SequenceErase_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_21SequenceAt_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _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_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_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Cast_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Reshape_Onnx_ver5EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Shape_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Size_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Concat_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Split_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Slice_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Transpose_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Scatter_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_20ScatterND_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26ScatterElements_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Gather_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25GatherElements_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Squeeze_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_20Unsqueeze_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_22SpaceToDepth_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_23DepthToSpace_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Tile_Onnx_ver6EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Upsample_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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_ZTIZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_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_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Unique_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19GatherND_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Pad_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZN6google8protobuf8internal20RepeatedPtrFieldBase3AddINS0_16RepeatedPtrFieldIN4onnx26TensorShapeProto_DimensionEE11TypeHandlerEEEPNT_4TypeESB_.constprop.542 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Size_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt18basic_stringstreamIcSt11char_traitsIcESaIcEEC1ESt13_Ios_Openmode.constprop.541 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15Shape_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Tile_Onnx_ver6EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ 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