vendor/libonnxruntime.so in onnxruntime-0.5.1 vs vendor/libonnxruntime.so in onnxruntime-0.5.2
- old
+ new
@@ -1,1078 +1,1179 @@
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_ZTTSt19basic_ostringstreamIcSt11char_traitsIcESaIcEE _ZTVSt9basic_iosIcSt11char_traitsIcEE _ZNSt8ios_baseD2Ev memset _ZNSt19basic_ostringstreamIcSt11char_traitsIcESaIcEED1Ev _ZNSs4_Rep9_S_createEmmRKSaIcE _ZNSolsEi _ZNSs4swapERSs _ZNSt9exceptionD2Ev __cxa_allocate_exception __cxa_throw __cxa_free_exception _ZNSs6appendEPKcm _ZNSt8ios_baseC2Ev _ZNSt9basic_iosIcSt11char_traitsIcEE4initEPSt15basic_streambufIcS1_E _ZNSt6localeC1Ev _ZSt20__throw_length_errorPKc _ZNKSt8__detail20_Prime_rehash_policy11_M_next_bktEm _ZSt25__throw_bad_function_callv _ZSt11_Hash_bytesPKvmm _ZNKSs7compareEPKc _ZNSs6assignERKSs vsnprintf _ZNSs6appendERKSs _ZSt18_Rb_tree_incrementPKSt18_Rb_tree_node_base _ZSt20__throw_out_of_rangePKc _ZSt29_Rb_tree_insert_and_rebalancebPSt18_Rb_tree_node_baseS0_RS_ _ZSt18_Rb_tree_decrementPSt18_Rb_tree_node_base _ZNKSs7compareERKSs _ZNSt8ios_base4InitC1Ev _ZNSt8ios_base4InitD1Ev __cxa_atexit _ZTVN10__cxxabiv117__class_type_infoE _ZTVN10__cxxabiv120__si_class_type_infoE _ZTISt11logic_error _ZTISt9exception __cxa_pure_virtual __pthread_key_create _ZNKSs12find_last_ofEPKcmm _ZNSsC1ERKSsmm _ZSt19__throw_logic_errorPKc _ZSt4clog _ZNSs9_M_mutateEmmm _ZSt4cout _ZNSt11logic_errorD2Ev _ZNSs6insertEmPKc _ZNSs6appendEPKc _ZNSt11logic_errorC2ERKSs _ZNKSt8__detail20_Prime_rehash_policy14_M_need_rehashEmmm _ZNSs12_M_leak_hardEv _ZSt20__throw_system_errori _ZNKSt11logic_error4whatEv pthread_mutex_unlock pthread_mutex_lock __cxa_guard_acquire __cxa_guard_release __cxa_guard_abort _ZTVN10__cxxabiv121__vmi_class_type_infoE _ZNSt13runtime_errorD2Ev strcmp _ZTTSt18basic_stringstreamIcSt11char_traitsIcESaIcEE _ZTVSt18basic_stringstreamIcSt11char_traitsIcESaIcEE _ZNSt18basic_stringstreamIcSt11char_traitsIcESaIcEED1Ev _ZNSdD2Ev _ZNSt13runtime_errorC2ERKSs _ZNKSt13runtime_error4whatEv _ZSt18_Rb_tree_incrementPSt18_Rb_tree_node_base _ZNSt18basic_stringstreamIcSt11char_traitsIcESaIcEEC1ESt13_Ios_Openmode _ZStlsISt11char_traitsIcEERSt13basic_ostreamIcT_ES5_PKc 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_ZTVSt14basic_ofstreamIcSt11char_traitsIcEE _ZNSsC1EPKcmRKSaIcE _ZNSt14basic_ofstreamIcSt11char_traitsIcEEC1ERKSsSt13_Ios_Openmode _ZNSo5writeEPKcl _ZNSt14basic_ofstreamIcSt11char_traitsIcEED1Ev _ZTVSt13runtime_error _ZNSs9push_backEc _ZSt28_Rb_tree_rebalance_for_erasePSt18_Rb_tree_node_baseRS_ __errno_location strtod strtoull strtoll localeconv _ZTISt12out_of_range sqrtf sqrt _ZNSs6assignEPKc logf exp _ZTVSt9bad_alloc _ZTISt9bad_alloc _ZNSt9bad_allocD1Ev omp_get_thread_num omp_get_num_threads omp_get_max_threads GOMP_parallel_start GOMP_parallel_end _ZNSs5eraseEN9__gnu_cxx17__normal_iteratorIPcSsEES2_ _ZNKSs4findEPKcmm pow powf floorf atanhf acoshf asinhf atanf acosf asinf fmodf fmod sin rint fmax floor log2 strtof _ZSt24__throw_invalid_argumentPKc roundf _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 _ZNSt6localeaSERKS_ _ZNSbIwSt11char_traitsIwESaIwEE12_M_leak_hardEv _ZSt9use_facetISt5ctypeIwEERKT_RKSt6locale _ZNSsD1Ev _ZNSbIwSt11char_traitsIwESaIwEEC1EPKwRKS1_ _ZNSbIwSt11char_traitsIwESaIwEED1Ev _ZNSo3putEc _ZNSo5flushEv _ZNSo9_M_insertIdEERSoT_ _ZNKSt5ctypeIcE13_M_widen_initEv _ZSt16__throw_bad_castv _ZNSo9_M_insertIxEERSoT_ _ZNSo9_M_insertIbEERSoT_ strtol strtoul _ZNSolsEs _ZTIb fesetround nearbyintf __udivti3 _ZNKSs7compareEmmPKc _ZNSt6thread20hardware_concurrencyEv tanh _ZNKSs4findEPKcm _ZNSo9_M_insertIPKvEERSoT_ _ZTIf _ZTId _ZTIh _ZTIa _ZTIj _ZTIt _ZTIm _ZTIs _ZTIl _ZTIi _ZNSs6appendEmc _ZNSt8__detail15_List_node_base9_M_unhookEv bsearch _ZNSsC1EmcRKSaIcE posix_memalign _ZSt18_Rb_tree_decrementPKSt18_Rb_tree_node_base gmtime_r mktime difftime syscall _ZNSt9basic_iosIcSt11char_traitsIcEE5imbueERKSt6locale _ZNSt6localeC1ERKS_ _ZSt9use_facetISt8numpunctIcEERKT_RKSt6locale _ZNSs7replaceEmmPKcm GOMP_loop_dynamic_start GOMP_loop_dynamic_next GOMP_loop_end_nowait pthread_self strdup dirname getenv getpid __xstat nanosleep pthread_join dlerror dlsym dlclose strerror_r realpath _ZNKSs13find_first_ofEPKcmm mkdir dlopen __fxstat pthread_attr_init pthread_create pthread_setaffinity_np pthread_attr_setstacksize remove munmap nftw mmap sysconf lseek read gettimeofday ldexpf _ZTTSt13basic_fstreamIcSt11char_traitsIcEE _ZTVSt13basic_fstreamIcSt11char_traitsIcEE _ZNSt13basic_fstreamIcSt11char_traitsIcEED1Ev isspace _ZNSt15basic_stringbufIcSt11char_traitsIcESaIcEE7_M_syncEPcmm _ZSt7getlineIcSt11char_traitsIcESaIcEERSt13basic_istreamIT_T0_ES7_RSbIS4_S5_T1_ES4_ _ZNSt11logic_errorC1ERKSs _ZNSt11logic_errorD1Ev _ZNSs6resizeEmc strerror _ZTIc stderr fprintf fflush _ZNSs14_M_replace_auxEmmmc strpbrk _ZNSs6appendERKSsmm sprintf strchr _ZNKSs17find_first_not_ofEPKcmm fwrite pthread_rwlock_init pthread_rwlock_unlock 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-Ht$YHCHxH9t0H=l HWt PJHЅ
-Ht$ HHxH9t0H=0 HWt PJHЅ
-Ht$HH[]ÐUSHHH H H H H H{hHt身H{PHt謫Hs0H{ ? HCH- HxH9t0H={ HWt PJHЅ
-Ht$+HCHxH9t0H=> HWt PJHЅ
-Ht$HHxH9t0H= HWt PJHЅ
-Ht$販HڪH[]ÐUSHHH H H H H H{hHt茪H{PHt~Hs0H{ HCH-ƞ HxH9t0H=M HWt PJHЅ
-Ht$HCHxH9t0H= HWt PJHЅ
-Ht$HHxH9t0H=ԟ HWt PJHЅ
-Ht$脨H謩H[]ÐUSHHH H H H H H{hHt^H{PHtPHs0H{ HCH- HxH9t0H= HWt PJHЅ
-Ht$ϧHCHxH9t0H=➮ HWt PJHЅ
-Ht$蒧HHxH9t0H= HWt PJHЅ
-Ht$VH~H[]ÐSHHHtIHGHxH;= t0H=H HWt PJHЅ
-Ht$H H[USHHH H H 5 H H " H{hHtԧH{PHtƧHs0H{ Y HCH- HxH9t0H= HWt PJHЅ
-Ht$EHCHxH9t0H=X HWt PJHЅ
-Ht$HHxH9t0H= HWt PJHЅ
-Ht$̥HH[]ÐSHHHtIHGHxH;=0 t0H= HWt PJHЅ
-Ht$nH薦H[USHHH H H H H H{hHtJH{PHt<Hs0H{ HCH- HxH9t0H= HWt PJHЅ
-Ht$軤HCHxH9t0H=Λ HWt PJHЅ
-Ht$~HHxH9t0H= HWt PJHЅ
-Ht$BHjH[]ÐSHHHtIHGHxH;= t0H=4 HWt PJHЅ
-Ht$H H[USHHH H H ! H H H{hHtH{PHt貤Hs0H{ E HCH- HxH9t0H= HWt PJHЅ
-Ht$1HCHxH9t0H=D HWt PJHЅ
-Ht$HHxH9t0H= HWt PJHЅ
-Ht$踢HH[]ÐUSHHH H H H H H{hHt蒣H{PHt脣Hs0H{ HCH-̗ HxH9t0H=S HWt PJHЅ
-Ht$HCHxH9t0H= HWt PJHЅ
-Ht$ơHHxH9t0H=ژ HWt PJHЅ
-Ht$芡H貢H[]ÐSHHHtIHGHxH;= t0H=| HWt PJHЅ
-Ht$,HTH[USHHH H H i H H V H{hHtH{PHtHs0H{ HCH-B HxH9t0H=ɗ HWt PJHЅ
-Ht$yHCHxH9t0H= HWt PJHЅ
-Ht$<HHxH9t0H=P HWt PJHЅ
-Ht$ H(H[]ÐSHHHtIHGHxH;=d t0H= HWt PJHЅ
-Ht$袟HʠH[USHHH H H H H H{hHt~H{PHtpHs0H{ HCH- HxH9t0H=? HWt PJHЅ
-Ht$HCHxH9t0H= HWt PJHЅ
-Ht$貞HHxH9t0H=ƕ HWt PJHЅ
-Ht$vH螟H[]ÐSHHHtIHGHxH;=ړ t0H=h HWt PJHЅ
-Ht$H@H[USHHH H H U H H B H{hHtH{PHtHs0H{ y HCH-. HxH9t0H= HWt PJHЅ
-Ht$eHCHxH9t0H=x HWt PJHЅ
-Ht$(HHxH9t0H=< HWt PJHЅ
-Ht$HH[]Q1Ʌy߱1HyHްII t td HHH u8t wމ2
-w҉2XÐUSHHH H H H H H{hHtpH{PHtbHs0H{ HCH- HxH9t0H=1 HWt PJHЅ
-Ht$HCHxH9t0H= HWt PJHЅ
-Ht$褛HHxH9t0H= HWt PJHЅ
-Ht$hH萜H[]H5\t P蜕ZSHHHtIHGHxH;= t0H=D HWt PJHЅ
-Ht$H H[USHHH H H 1 H H H{hHtЛH{PHtHs0H{ Hl$P HCHHxPHCHHx@HHHx1HyH[]SHHHtIHGHxH;= t0H=D HWt PJHЅ
-Ht$H H[USHHH H H 1 H H H{hHtКH{PHtHs0H{ U HCH-
- HxH9t0H= HWt PJHЅ
-Ht$AHCHxH9t0H=T HWt PJHЅ
-Ht$HHxH9t0H= HWt PJHЅ
-Ht$ȘHH[]ÐSHHHtIHGHxH;=, t0H= HWt PJHЅ
-Ht$jH蒙H[USHHH H H H H H{hHtFH{PHt8Hs0H{ HCH- HxH9t0H= HWt PJHЅ
-Ht$跗HCHxH9t0H=ʎ HWt PJHЅ
-Ht$zHHxH9t0H= HWt PJHЅ
-Ht$>HfH[]ÐUSHHH H H y H H f H{hHtH{PHt
-Hs0H{ 蝿 HCH-R HxH9t0H=ٍ HWt PJHЅ
-Ht$艖HCHxH9t0H= HWt PJHЅ
-Ht$LHHxH9t0H=` HWt PJHЅ
-Ht$H8H[]ÐUSHHH H H K H H 8 H{hHtH{PHtܖHs0H{ o HCH-$ HxH9t0H= HWt PJHЅ
-Ht$[HCHxH9t0H=n HWt PJHЅ
-Ht$ HHxH9t0H=2 HWt PJHЅ
-Ht$H
-H[]ÐUSHHH H H H H
- H{hHt輕H{PHt讕Hs0H{ A HCH- HxH9t0H=} HWt PJHЅ
-Ht$-HCHxH9t0H=@ HWt PJHЅ
-Ht$HHxH9t0H= HWt PJHЅ
-Ht$贓HܔH[]ÐUSHHH H H H H H{hHt莔H{PHt耔Hs0H{ HCH-Ȉ HxH9t0H=O HWt PJHЅ
-Ht$HCHxH9t0H= HWt PJHЅ
-Ht$HHxH9t0H=։ HWt PJHЅ
-Ht$膒H讓H[]ÐUSHHH H H H H H{hHt`H{PHtRHs0H{ HCH- HxH9t0H=! HWt PJHЅ
-Ht$ёHCHxH9t0H=䈮 HWt PJHЅ
-Ht$蔑HHxH9t0H= HWt PJHЅ
-Ht$XH耒H[]ÐUSHHH H H 蓿 H H 耿 H{hHt2H{PHt$Hs0H{ 跹 HCH-l HxH9t0H= HWt PJHЅ
-Ht$裐HCHxH9t0H= HWt PJHЅ
-Ht$fHHxH9t0H=z HWt PJHЅ
-Ht$*HRH[]ÐUSHHH H H e H H R H{hHtH{PHtHs0H{ 艸 HCH-> HxH9t0H=ņ HWt PJHЅ
-Ht$uHCHxH9t0H= HWt PJHЅ
-Ht$8HHxH9t0H=L HWt PJHЅ
-Ht$H$H[]ÐUSHHH H H 7 H H $ H{hHt֏H{PHtȏHs0H{ [ HCH- HxH9t0H= HWt PJHЅ
-Ht$GHCHxH9t0H=Z HWt PJHЅ
+HHxH9t0H= HWt PJHЅ
+Ht$μHH[]ÐSHHHtIHGHxH;= t0H=8 HWt PJHЅ
+Ht$pH訽H[USHHH H H ] H H J H{hHt\H{PHtNHs0H{ HCH- HxH9t0H= HWt PJHЅ
+Ht$轻HCHxH9t0H=H HWt PJHЅ
+Ht$耻HHxH9t0H= HWt PJHЅ
+Ht$DH|H[]ÐHSHtM H[][ÐSHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$̺HH[HSHtM H[[ÐSHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$VH莻H[USHHH H H C H H 0 H{hHtBH{PHt4Hs0H{ g HCH-ܷ HxH9t0H=k HWt PJHЅ
+Ht$裹HCHxH9t0H=. HWt PJHЅ
+Ht$fHHxH9t0H= HWt PJHЅ
+Ht$*HbH[]ÐSHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$̸HH[USHHH H H H H H{hHt踹H{PHt誹Hs0H{ HCH-R HxH9t0H=᷶ HWt PJHЅ
+Ht$HCHxH9t0H= HWt PJHЅ
+Ht$ܷHHxH9t0H=h HWt PJHЅ
+Ht$蠷HظH[]ÐSHHHtIHGHxH;=t t0H=
+ HWt PJHЅ
+Ht$BHzH[USHHH H H / H H H{hHt.H{PHt Hs0H{ S HCH-ȴ HxH9t0H=W HWt PJHЅ
+Ht$菶HCHxH9t0H= HWt PJHЅ
+Ht$RHHxH9t0H= HWt PJHЅ
+Ht$HNH[]ÐSHHHtIHGHxH;=곶 t0H= HWt PJHЅ
+Ht$踵HH[USHHH H H H H H{hHt褶H{PHt薶Hs0H{ HCH-> HxH9t0H=ʹ HWt PJHЅ
+Ht$HCHxH9t0H= HWt PJHЅ
+Ht$ȴHHxH9t0H=T HWt PJHЅ
+Ht$茴HĵH[]ÐUSHHH H H w H H d H{hHtvH{PHthHs0H{ HCH- HxH9t0H= HWt PJHЅ
+Ht$׳HCHxH9t0H=b HWt PJHЅ
+Ht$蚳HHxH9t0H=& HWt PJHЅ
+Ht$^H薴H[]ÐUSHHH H H I H H 6 H{hHtHH{PHt:Hs0H{ m HCH-ⰶ HxH9t0H=q HWt PJHЅ
+Ht$該HCHxH9t0H=4 HWt PJHЅ
+Ht$lHHxH9t0H= HWt PJHЅ
+Ht$0HhH[]ÐUSHHH H H H H H{hHtH{PHt Hs0H{ ? HCH- HxH9t0H=C HWt PJHЅ
+Ht${HCHxH9t0H= HWt PJHЅ
+Ht$>HHxH9t0H=ʰ HWt PJHЅ
+Ht$H:H[]ÐUSHHH H H H H H{hHtH{PHtޱHs0H{ HCH- HxH9t0H= HWt PJHЅ
+Ht$MHCHxH9t0H=د HWt PJHЅ
+Ht$HHxH9t0H= HWt PJHЅ
+Ht$ԯH H[]ÐUSHHH H H H H H{hHt辰H{PHt谰Hs0H{ HCH-X HxH9t0H=箶 HWt PJHЅ
+Ht$ HCHxH9t0H= HWt PJHЅ
+Ht$HHxH9t0H=n HWt PJHЅ
+Ht$覮HޯH[]ÐUSHHH H H H H ~ H{hHt萯H{PHt肯Hs0H{ HCH-* HxH9t0H= HWt PJHЅ
+Ht$HCHxH9t0H=| HWt PJHЅ
+Ht$购HHxH9t0H=@ HWt PJHЅ
+Ht$xH谮H[]ÐUSHHH H H c H H P H{hHtbH{PHtTHs0H{ HCH- HxH9t0H= HWt PJHЅ
+Ht$ìHCHxH9t0H=N HWt PJHЅ
+Ht$膬HHxH9t0H= HWt PJHЅ
+Ht$JH肭H[]ÐUSHHH H H 5 H H " H{hHt4H{PHt&Hs0H{ Y HCH-Ω HxH9t0H=] HWt PJHЅ
+Ht$蕫HCHxH9t0H= HWt PJHЅ
+Ht$XHHxH9t0H=䪶 HWt PJHЅ
+Ht$ HTH[]ÐUSHHH H H H H H{hHtH{PHtHs0H{ + HCH- HxH9t0H=/ HWt PJHЅ
+Ht$gHCHxH9t0H= HWt PJHЅ
+Ht$*HHxH9t0H= HWt PJHЅ
+Ht$H&H[]ÐUSHHH H H H H H{hHtتH{PHtʪHs0H{ HCH-r HxH9t0H= HWt PJHЅ
+Ht$9HCHxH9t0H=Ĩ HWt PJHЅ
+Ht$HHxH9t0H= HWt PJHЅ
+Ht$HH[]ÐSHHHtIHGHxH;= t0H=* HWt PJHЅ
+Ht$bH蚩H[USHHH H H O H H < H{hHtNH{PHt@Hs0H{ s HCH-襶 HxH9t0H=w HWt PJHЅ
+Ht$诧HCHxH9t0H=: HWt PJHЅ
+Ht$rHHxH9t0H= HWt PJHЅ
+Ht$6HnH[]ÐSHHHtIHGHxH;=
+ t0H= HWt PJHЅ
+Ht$ئHH[USHHH H H H H H{hHtħH{PHt趧Hs0H{ HCH-^ HxH9t0H= HWt PJHЅ
+Ht$%HCHxH9t0H= HWt PJHЅ
+Ht$HHxH9t0H=t HWt PJHЅ
+Ht$謥HH[]ÐSHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$NH膦H[USHHH H H ; H H ( H{hHt:H{PHt,Hs0H{ _ HCH-Ԣ HxH9t0H=c HWt PJHЅ
+Ht$蛤HCHxH9t0H=& HWt PJHЅ
+Ht$^HHxH9t0H=ꣶ HWt PJHЅ
+Ht$"HZH[]ÐUSHHH H H
H H H{hHt H{PHtHs0H{ 1 HCH- HxH9t0H=5 HWt PJHЅ
+Ht$mHCHxH9t0H= HWt PJHЅ
+Ht$0HHxH9t0H= HWt PJHЅ
+Ht$H,H[]ÐSHHHtIHGHxH;=Ƞ t0H=^ HWt PJHЅ
+Ht$薢HΣH[USHHH H H H H p H{hHt肣H{PHttHs0H{ HCH- HxH9t0H= HWt PJHЅ
+Ht$HCHxH9t0H=n HWt PJHЅ
+Ht$覡HHxH9t0H=2 HWt PJHЅ
+Ht$jH袢H[]ÐSHHHtIHGHxH;=> t0H=Ԡ HWt PJHЅ
+Ht$ HDH[USHHH H H H H H{hHtH{PHtHs0H{ HCH- HxH9t0H=! HWt PJHЅ
+Ht$YHCHxH9t0H=䟶 HWt PJHЅ
+Ht$ HHxH9t0H= HWt PJHЅ
+Ht$HH[]ÐSHHHtIHGHxH;= t0H=J HWt PJHЅ
+Ht$肟H躠H[Q1Ʌy߱1HyHްII t t;, HHH u8t wމ2
+w҉2XUSHHH H H H H H{hHtH{PHt
+Hs0H{ = HCH- HxH9t0H=A HWt PJHЅ
+Ht$yHCHxH9t0H= HWt PJHЅ
+Ht$<HHxH9t0H=ȝ HWt PJHЅ
+Ht$ H8H[]ÐUSHHH H H H H H{hHtH{PHtܞHs0H{ HCH- HxH9t0H= HWt PJHЅ
+Ht$KHCHxH9t0H=֜ HWt PJHЅ
+Ht$HHxH9t0H= HWt PJHЅ
+Ht$ҜH
+H[]H5\y PZSHHHtIHGHxH;= t0H=& HWt PJHЅ
+Ht$^H薝H[USHHH H H K H H 8 H{hHtJH{PHt<Hs0H{ Hl$j HCHHx躙HCHHx誙HHHx蛙HH[]SHHHtIHGHxH;= t0H=& HWt PJHЅ
+Ht$^H薜H[USHHH H H K H H 8 H{hHtJH{PHt<Hs0H{ o HCH-䘶 HxH9t0H=s HWt PJHЅ
+Ht$諚HCHxH9t0H=6 HWt PJHЅ
+Ht$nHHxH9t0H= HWt PJHЅ
+Ht$2HjH[]ÐSHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$ԙH H[USHHH H H H H H{hHtH{PHt貚Hs0H{ HCH-Z HxH9t0H=阶 HWt PJHЅ
+Ht$!HCHxH9t0H= HWt PJHЅ
+Ht$HHxH9t0H=p HWt PJHЅ
+Ht$記HH[]ÐUSHHH H H H H H{hHt蒙H{PHt脙Hs0H{ HCH-, HxH9t0H= HWt PJHЅ
+Ht$HCHxH9t0H=~ HWt PJHЅ
+Ht$趗HHxH9t0H=B HWt PJHЅ
+Ht$zH貘H[]ÐUSHHH H H e H H R H{hHtdH{PHtVHs0H{ HCH- HxH9t0H= HWt PJHЅ
+Ht$ŖHCHxH9t0H=P HWt PJHЅ
+Ht$舖HHxH9t0H= HWt PJHЅ
+Ht$LH脗H[]ÐUSHHH H H 7 H H $ H{hHt6H{PHt(Hs0H{ [ HCH-Г HxH9t0H=_ HWt PJHЅ
+Ht$藕HCHxH9t0H=" HWt PJHЅ
+Ht$ZHHxH9t0H=收 HWt PJHЅ
+Ht$ HVH[]ÐUSHHH H H H H H{hHtH{PHtHs0H{ - HCH- HxH9t0H=1 HWt PJHЅ
+Ht$iHCHxH9t0H= HWt PJHЅ
+Ht$,HHxH9t0H= HWt PJHЅ
+Ht$H(H[]ÐUSHHH H H H H H{hHtڔH{PHt̔Hs0H{ HCH-t HxH9t0H= HWt PJHЅ
+Ht$;HCHxH9t0H=ƒ HWt PJHЅ
+Ht$HHxH9t0H= HWt PJHЅ
+Ht$HH[]ÐUSHHH H H H H H{hHt謓H{PHt螓Hs0H{ HCH-F HxH9t0H=Ց HWt PJHЅ
+Ht$
HCHxH9t0H= HWt PJHЅ
+Ht$БHHxH9t0H=\ HWt PJHЅ
+Ht$蔑H̒H[]ÐUSHHH H H H H l H{hHt~H{PHtpHs0H{ HCH- HxH9t0H= HWt PJHЅ
+Ht$ߐHCHxH9t0H=j HWt PJHЅ
+Ht$袐HHxH9t0H=. HWt PJHЅ
+Ht$fH螑H[]ÐUSHHH H H Q H H > H{hHtPH{PHtBHs0H{ u HCH-ꍶ HxH9t0H=y HWt PJHЅ
+Ht$豏HCHxH9t0H=< HWt PJHЅ
+Ht$tHHxH9t0H= HWt PJHЅ
+Ht$8HpH[]ÐUSHHH H H # H H H{hHt"H{PHtHs0H{ G HCH- HxH9t0H=K HWt PJHЅ
+Ht$胎HCHxH9t0H= HWt PJHЅ
+Ht$FHHxH9t0H=ҍ HWt PJHЅ
Ht$
-HHxH9t0H= HWt PJHЅ
-Ht$HH[]ÐUSHHH H H H H H{hHt討H{PHt蚎Hs0H{ - HCH-₮ HxH9t0H=i HWt PJHЅ
-Ht$HCHxH9t0H=, HWt PJHЅ
-Ht$܌HHxH9t0H= HWt PJHЅ
-Ht$蠌HȍH[]ÐUSHHH H H ۺ H H Ⱥ H{hHtzH{PHtlHs0H{ HCH- HxH9t0H=; HWt PJHЅ
-Ht$HCHxH9t0H= HWt PJHЅ
-Ht$讋HHxH9t0H= HWt PJHЅ
-Ht$rH蚌H[]ÐUSHHH H H 譹 H H 蚹 H{hHtLH{PHt>Hs0H{ ѳ HCH- HxH9t0H=
HWt PJHЅ
-Ht$轊HCHxH9t0H=Ё HWt PJHЅ
-Ht$耊HHxH9t0H= HWt PJHЅ
-Ht$DHlH[]ÐUSHHH H H H H l H{hHt H{PHtHs0H{ 裲 HCH-X HxH9t0H=߀ HWt PJHЅ
-Ht$菉HCHxH9t0H= HWt PJHЅ
-Ht$RHHxH9t0H=f HWt PJHЅ
-Ht$H>H[]ÐUSHHH H H Q H H > H{hHtH{PHtHs0H{ u HCH-*~ HxH9t0H= HWt PJHЅ
-Ht$aHCHxH9t0H=t HWt PJHЅ
-Ht$$HHxH9t0H=8 HWt PJHЅ
-Ht$HH[]ÐUSHHH H H # H H H{hHtH{PHt贈Hs0H{ G HCH-| HxH9t0H=~ HWt PJHЅ
-Ht$3HCHxH9t0H=F~ HWt PJHЅ
-Ht$HHxH9t0H=
-~ HWt PJHЅ
-Ht$躆HH[]ÐUSHHH H H H H H{hHt蔇H{PHt膇Hs0H{ HCH-{ HxH9t0H=U} HWt PJHЅ
-Ht$HCHxH9t0H=} HWt PJHЅ
-Ht$ȅHHxH9t0H=| HWt PJHЅ
-Ht$茅H贆H[]ÐUSHHH H H dz H H 贳 H{hHtfH{PHtXHs0H{ HCH-z HxH9t0H='| HWt PJHЅ
-Ht$ׄHCHxH9t0H={ HWt PJHЅ
-Ht$蚄HHxH9t0H={ HWt PJHЅ
-Ht$^H膅H[]ÐSHHHtIHGHxH;=y t0H=P{ HWt PJHЅ
-Ht$ H(H[USHHH H H = H H * H{hHt܄H{PHt΄Hs0H{ a HCH-y HxH9t0H=z HWt PJHЅ
-Ht$MHCHxH9t0H=`z HWt PJHЅ
-Ht$HHxH9t0H=$z HWt PJHЅ
-Ht$ԂHH[]ÐSHHHtIHGHxH;=8x t0H=y HWt PJHЅ
-Ht$vH螃H[SHHHtIHGHxH;=w t0H=jy HWt PJHЅ
-Ht$HBH[USHHH H H W H H D H{hHtH{PHtHs0H{ { HCH-0w HxH9t0H=x HWt PJHЅ
-Ht$gHCHxH9t0H=zx HWt PJHЅ
-Ht$*HHxH9t0H=>x HWt PJHЅ
-Ht$HH[]ÐUSHHH H H ) H H H{hHtȁH{PHt躁Hs0H{ M HCH-v HxH9t0H=w HWt PJHЅ
-Ht$9HCHxH9t0H=Lw HWt PJHЅ
-Ht$HHxH9t0H=w HWt PJHЅ
-Ht$HH[]ÐUSHHH H H H H H{hHt蚀H{PHt茀Hs0H{ HCH-t HxH9t0H=[v HWt PJHЅ
-Ht$ HCHxH9t0H= v HWt PJHЅ
-Ht$~HHxH9t0H=u HWt PJHЅ
-Ht$~HH[]ÐUSHHH H H ͬ H H 躬 H{hHtlH{PHt^Hs0H{ HCH-s HxH9t0H=-u HWt PJHЅ
-Ht$}HCHxH9t0H=t HWt PJHЅ
-Ht$}HHxH9t0H=t HWt PJHЅ
-Ht$d}H~H[]ÐUSHHH H H 蟫 H H 茫 H{hHt>~H{PHt0~Hs0H{ å HCH-xr HxH9t0H=s HWt PJHЅ
-Ht$|HCHxH9t0H=s HWt PJHЅ
-Ht$r|HHxH9t0H=s HWt PJHЅ
-Ht$6|H^}H[]ÐUSHHH H H q H H ^ H{hHt}H{PHt}Hs0H{ 蕤 HCH-Jq HxH9t0H=r HWt PJHЅ
-Ht${HCHxH9t0H=r HWt PJHЅ
-Ht$D{HHxH9t0H=Xr HWt PJHЅ
-Ht${H0|H[]ÐUSHHH H H C H H 0 H{hHt{H{PHt{Hs0H{ g HCH- p HxH9t0H=q HWt PJHЅ
-Ht$SzHCHxH9t0H=fq HWt PJHЅ
-Ht$zHHxH9t0H=*q HWt PJHЅ
-Ht$yH{H[]ÐUSHHH H H H H H{hHtzH{PHtzHs0H{ 9 HCH-n HxH9t0H=up HWt PJHЅ
-Ht$%yHCHxH9t0H=8p HWt PJHЅ
-Ht$xHHxH9t0H=o HWt PJHЅ
-Ht$xHyH[]ÐUSHHH H H H H Ԧ H{hHtyH{PHtxyHs0H{ HCH-m HxH9t0H=Go HWt PJHЅ
-Ht$wHCHxH9t0H=
-o HWt PJHЅ
-Ht$wHHxH9t0H=n HWt PJHЅ
-Ht$~wHxH[]ÐUSHHH H H 蹥 H H 覥 H{hHtXxH{PHtJxHs0H{ ݟ HCH-l HxH9t0H=n HWt PJHЅ
-Ht$vHCHxH9t0H=m HWt PJHЅ
-Ht$vHHxH9t0H=m HWt PJHЅ
-Ht$PvHxwH[]ÐUSHHH H H 苤 H H x H{hHt*wH{PHt wHs0H{ 诞 HCH-dk HxH9t0H=l HWt PJHЅ
-Ht$uHCHxH9t0H=l HWt PJHЅ
-Ht$^uHHxH9t0H=rl HWt PJHЅ
-Ht$"uHJvH[]ÐUSHHH H H ] H H J H{hHtuH{PHtuHs0H{ 聝 HCH-6j HxH9t0H=k HWt PJHЅ
-Ht$mtHCHxH9t0H=k HWt PJHЅ
-Ht$0tHHxH9t0H=Dk HWt PJHЅ
-Ht$sH uH[]ÐUSHHH H H / H H H{hHttH{PHttHs0H{ S HCH-i HxH9t0H=j HWt PJHЅ
-Ht$?sHCHxH9t0H=Rj HWt PJHЅ
-Ht$sHHxH9t0H=j HWt PJHЅ
-Ht$rHsH[]ÐUSHHH H H H H H{hHtsH{PHtsHs0H{ % HCH-g HxH9t0H=ai HWt PJHЅ
-Ht$rHCHxH9t0H=$i HWt PJHЅ
-Ht$qHHxH9t0H=h HWt PJHЅ
-Ht$qHrH[]ÐUSHHH H H ӟ H H H{hHtrrH{PHtdrHs0H{ HCH-f HxH9t0H=3h HWt PJHЅ
-Ht$pHCHxH9t0H=g HWt PJHЅ
-Ht$pHHxH9t0H=g HWt PJHЅ
-Ht$jpHqH[]ÐUSHHH H H 襞 H H 蒞 H{hHtDqH{PHt6qHs0H{ ɘ HCH-~e HxH9t0H=g HWt PJHЅ
-Ht$oHCHxH9t0H=f HWt PJHЅ
-Ht$xoHHxH9t0H=f HWt PJHЅ
-Ht$<oHdpH[]ÐUSHHH H H w H H d H{hHtpH{PHtpHs0H{ 蛗 HCH-Pd HxH9t0H=e HWt PJHЅ
-Ht$nHCHxH9t0H=e HWt PJHЅ
-Ht$JnHHxH9t0H=^e HWt PJHЅ
-Ht$nH6oH[]ÐH H9vPiHPoATUSHHH H H ) H H H{hHtnH{PHtnHs0H{ Ld$H HCLHxHkHCH-b HxH9t.H=td HWt PJHЅL&mHHxH9t.H=:d HWt PJHЅLlHnH[]A\ÐUSHHH H H % H H H{hHtmH{PHtmHs0H{ I HCH-a HxH9t0H=c HWt PJHЅ
-Ht$5lHCHxH9t0H=Hc HWt PJHЅ
-Ht$kHHxH9t0H= c HWt PJHЅ
-Ht$kHlH[]ÐUSHHH H H H H H{hHtlH{PHtlHs0H{ HCH-` HxH9t0H=Wb HWt PJHЅ
-Ht$kHCHxH9t0H=b HWt PJHЅ
-Ht$jHHxH9t0H=a HWt PJHЅ
-Ht$jHkH[]ÐSHHHtIHGHxH;=_ t0H=a HWt PJHЅ
-Ht$0jHXkH[USHHH H H m H H Z H{hHt kH{PHtjHs0H{ 葒 HCH-F_ HxH9t0H=` HWt PJHЅ
-Ht$}iHCHxH9t0H=` HWt PJHЅ
-Ht$@iHHxH9t0H=T` HWt PJHЅ
-Ht$iH,jH[]ÐUSHHH H H ? H H , H{hHtiH{PHtiHs0H{ c HCH-^ HxH9t0H=_ HWt PJHЅ
-Ht$OhHCHxH9t0H=b_ HWt PJHЅ
-Ht$hHHxH9t0H=&_ HWt PJHЅ
-Ht$gHhH[]ÐUSHHH H H H H H{hHthH{PHthHs0H{ 5 HCH-\ HxH9t0H=q^ HWt PJHЅ
-Ht$!gHCHxH9t0H=4^ HWt PJHЅ
-Ht$fHHxH9t0H=] HWt PJHЅ
-Ht$fHgH[]ÐUSHHH H H H H Д H{hHtgH{PHttgHs0H{ HCH-[ HxH9t0H=C] HWt PJHЅ
-Ht$eHCHxH9t0H=] HWt PJHЅ
-Ht$eHHxH9t0H=\ HWt PJHЅ
-Ht$zeHfH[]ÐUSHHH H H 赓 H H 袓 H{hHtTfH{PHtFfHs0H{ ٍ HCH-Z HxH9t0H=\ HWt PJHЅ
-Ht$dHCHxH9t0H=[ HWt PJHЅ
-Ht$dHHxH9t0H=[ HWt PJHЅ
-Ht$LdHteH[]ÐUSHHH H H 臒 H H t H{hHt&eH{PHteHs0H{ 諌 HCH-`Y HxH9t0H=Z HWt PJHЅ
-Ht$cHCHxH9t0H=Z HWt PJHЅ
-Ht$ZcHHxH9t0H=nZ HWt PJHЅ
-Ht$ cHFdH[]ÐUSHHH H H Y H H F H{hHtcH{PHtcHs0H{ } HCH-2X HxH9t0H=Y HWt PJHЅ
-Ht$ibHCHxH9t0H=|Y HWt PJHЅ
-Ht$,bHHxH9t0H=@Y HWt PJHЅ
-Ht$aHcH[]ÐUSHHH H H + H H H{hHtbH{PHtbHs0H{ O HCH-W HxH9t0H=X HWt PJHЅ
-Ht$;aHCHxH9t0H=NX HWt PJHЅ
-Ht$`HHxH9t0H=X HWt PJHЅ
-Ht$`HaH[]ÐUSHHH H H H H H{hHtaH{PHtaHs0H{ ! HCH-U HxH9t0H=]W HWt PJHЅ
-Ht$
`HCHxH9t0H= W HWt PJHЅ
-Ht$_HHxH9t0H=V HWt PJHЅ
-Ht$_H`H[]ÐSHHHtIHGHxH;=T t0H=V HWt PJHЅ
-Ht$6_H^`H[USHHH H H s H H ` H{hHt`H{PHt`Hs0H{ 藇 HCH-LT HxH9t0H=U HWt PJHЅ
-Ht$^HCHxH9t0H=U HWt PJHЅ
-Ht$F^HHxH9t0H=ZU HWt PJHЅ
+HBH[]ÐUSHHH H H H H H{hHtH{PHtHs0H{ HCH- HxH9t0H= HWt PJHЅ
+Ht$UHCHxH9t0H= HWt PJHЅ
+Ht$HHxH9t0H= HWt PJHЅ
+Ht$܌HH[]ÐUSHHH H H H H H{hHtƍH{PHt踍Hs0H{ HCH-` HxH9t0H= HWt PJHЅ
+Ht$'HCHxH9t0H= HWt PJHЅ
+Ht$HHxH9t0H=v HWt PJHЅ
+Ht$讋HH[]ÐUSHHH H H H H H{hHt蘌H{PHt芌Hs0H{ HCH-2 HxH9t0H= HWt PJHЅ
+Ht$HCHxH9t0H= HWt PJHЅ
+Ht$輊HHxH9t0H=H HWt PJHЅ
+Ht$耊H踋H[]ÐUSHHH H H k H H X H{hHtjH{PHt\Hs0H{ HCH- HxH9t0H= HWt PJHЅ
+Ht$ˉHCHxH9t0H=V HWt PJHЅ
+Ht$莉HHxH9t0H= HWt PJHЅ
+Ht$RH芊H[]ÐUSHHH H H = H H * H{hHt<H{PHt.Hs0H{ a HCH-ֆ HxH9t0H=e HWt PJHЅ
+Ht$蝈HCHxH9t0H=( HWt PJHЅ
+Ht$`HHxH9t0H=쇶 HWt PJHЅ
+Ht$$H\H[]ÐUSHHH H H H H H{hHtH{PHt Hs0H{ 3 HCH- HxH9t0H=7 HWt PJHЅ
+Ht$oHCHxH9t0H= HWt PJHЅ
+Ht$2HHxH9t0H= HWt PJHЅ
+Ht$H.H[]ÐUSHHH H H H H H{hHtH{PHt҇Hs0H{ HCH-z HxH9t0H= HWt PJHЅ
+Ht$AHCHxH9t0H=̅ HWt PJHЅ
+Ht$HHxH9t0H= HWt PJHЅ
+Ht$ȅH H[]ÐSHHHtIHGHxH;= t0H=2 HWt PJHЅ
+Ht$jH袆H[USHHH H H W H H D H{hHtVH{PHtHHs0H{ { HCH- HxH9t0H= HWt PJHЅ
+Ht$跄HCHxH9t0H=B HWt PJHЅ
+Ht$zHHxH9t0H= HWt PJHЅ
+Ht$>HvH[]ÐSHHHtHGHt$HxHGH[ÐSHHHtIHGHxH;=䁶 t0H=z HWt PJHЅ
+Ht$貃HH[USHHH H H H H H{hHt螄H{PHt萄Hs0H{ HCH-8 HxH9t0H=ǂ HWt PJHЅ
+Ht$HCHxH9t0H= HWt PJHЅ
+Ht$HHxH9t0H=N HWt PJHЅ
+Ht$膂H较H[]ÐSHHHtIHGHxH;=Z t0H= HWt PJHЅ
+Ht$(H`H[USHHH H H H H H{hHtH{PHtHs0H{ 9 HCH- HxH9t0H== HWt PJHЅ
+Ht$uHCHxH9t0H= HWt PJHЅ
+Ht$8HHxH9t0H=Ā HWt PJHЅ
+Ht$H4H[]ÐSHHHtIHGHxH;=~ t0H=f HWt PJHЅ
+Ht$螀HցH[USHHH H H H H x H{hHt芁H{PHt|Hs0H{ 诿 HCH-$~ HxH9t0H= HWt PJHЅ
+Ht$HCHxH9t0H=v HWt PJHЅ
+Ht$HHxH9t0H=: HWt PJHЅ
+Ht$rH誀H[]ÐUSHHH H H ] H H J H{hHt\H{PHtNHs0H{ 聾 HCH-| HxH9t0H=~ HWt PJHЅ
+Ht$~HCHxH9t0H=H~ HWt PJHЅ
+Ht$~HHxH9t0H= ~ HWt PJHЅ
+Ht$D~H|H[]ÐUSHHH H H / H H H{hHt.H{PHt Hs0H{ S HCH-{ HxH9t0H=W} HWt PJHЅ
+Ht$}HCHxH9t0H=} HWt PJHЅ
+Ht$R}HHxH9t0H=| HWt PJHЅ
+Ht$}HN~H[]ÐUSHHH H H H H H{hHt ~H{PHt}Hs0H{ % HCH-z HxH9t0H=)| HWt PJHЅ
+Ht$a|HCHxH9t0H={ HWt PJHЅ
+Ht$$|HHxH9t0H={ HWt PJHЅ
+Ht${H }H[]ÐUSHHH H H H H H{hHt|H{PHt|Hs0H{ HCH-ly HxH9t0H=z HWt PJHЅ
+Ht$3{HCHxH9t0H=z HWt PJHЅ
+Ht$zHHxH9t0H=z HWt PJHЅ
+Ht$zH{H[]ÐUSHHH H H 西 H H 蒿 H{hHt{H{PHt{Hs0H{ ɹ HCH->x HxH9t0H=y HWt PJHЅ
+Ht$zHCHxH9t0H=y HWt PJHЅ
+Ht$yHHxH9t0H=Ty HWt PJHЅ
+Ht$yHzH[]ÐUSHHH H H w H H d H{hHtvzH{PHthzHs0H{ 蛸 HCH-w HxH9t0H=x HWt PJHЅ
+Ht$xHCHxH9t0H=bx HWt PJHЅ
+Ht$xHHxH9t0H=&x HWt PJHЅ
+Ht$^xHyH[]ÐUSHHH H H I H H 6 H{hHtHyH{PHt:yHs0H{ m HCH-u HxH9t0H=qw HWt PJHЅ
+Ht$wHCHxH9t0H=4w HWt PJHЅ
+Ht$lwHHxH9t0H=v HWt PJHЅ
+Ht$0wHhxH[]LLO11M)IIL9}H9tLI<I<HHHHOH)HHD HAHu$H<LcHOH>H J; uH HHyÐATIUHSHHH=' "1I; $}AH} uerHHHUH= r2 Ht$1HH$sHr2 oH[]A\ÐUSHHH H H 1 H H H{hHt0wH{PHt"wHs0H{ U HCH-s HxH9t0H=Yu HWt PJHЅ
+Ht$uHCHxH9t0H= u HWt PJHЅ
+Ht$TuHHxH9t0H=t HWt PJHЅ
+Ht$uHPvH[]AWIAVIAUIATULSHHh dx HHD$0v H$ HD$8IEI] I+]HHT$PH1HD$Hǁ2 IFLHHD$(蒻 H|$(IHD$@0M IUIcMH4L$ 1HHHH$ H$ H9! Ic H9L$`| H9 X Lt$ppHL$`HH$ L
"{ H5{ LH $HPL@H
{ 82 H$ L$ HQY L$P H
A{ H5{ Iغ LJ 1LLHZ L H HD$pLHxYqH"
+ H5 H{H IH LHHD$pLHxqHH*r Iv I+vH|$(HHM HD$LcIFI}0IcHHD$ HPL$ H$ H$ HHDŽ$ L
+ HD$ E1M9H$ A Hl$H$ u H$ HD$H$ HD$$ H$ HD$H$ HD$H$ HD$XH H\$PH|$LH
HT$0H$ HHD$H$ HT$8HxH$ 蛣 HD$@Ht$HH|$XD$ L$ H$ o H$ H$ 1H+$ L$ HDŽ$ HDŽ$ H$ H$ HDŽ$ HH$ H$ H$ t$H H9vmH< rHHH$ H$ H$ H$ H$ H)HHtH HwHHH$ H+$ 1HDŽ$8 HDŽ$@ H$ H$ HDŽ$H HH$ $ $( H$ H$0 t$H H9v"lH< qHHH$ H$8 H$@ H$H H$ H)HHtH HvHHٿh HDŽ$ H$@ H$ H$P UqHH$ 1HH$ HCL$ L+$ H$ HC HC HC( HCItD H$ HH p H I9v kJ< pHJHKHK HC(H$ H$ H)HItH HuHJ1HC H$ HC0$( L$@ L+$8 C8H$0 HCH HCP IHCX HC@t)H I9v
+Nj J< oHJHKHHKPHCXH$8 H$@ H)HItH H uHJH$ H$ HCPH$P HC`Hi2 H$ H$( Hx2 HH$ FNH{HHt
+oHHHoHH$ H H$ H H$ Ht
HHH$ He2 H|$H\$ e2 Ht$(H|$IA]Ht$PH$ LHT$0H$ I~0HD$@Ht$HD$ L$ H$ HT$8H$ H$ 胞 H$ H|$Iv0L$8 H$ H$ H$ H$ H$ H$ H$ $ $ H$ H$ H$8 P HDŽ$@ H$ mHH$ Ht$H{0HH$ HCH$ HCH$ HC$ C H$ HC(蓝 H$ H$0 H|$HCHH3f2 H$0 H$H Hg2 HH$@
+H$@ Ht
HHH$ HtlH$ Ll$ HtlHt$(H$ HDd$ bHH$ Ht?tl8HHglHHH$@ H H$0 H|H$ H $l H$ HHt
HHHH$ b2 HH$ b2 NH$@ HHt
HHHH$ Ht
+kHH$ Ht
+kHH$ Ht
+~kHH$ HtgkH rHh []A\A]A^A_ÐUSHHH H H H H H{hHtkH{PHtjHs0H{ + HCH-g HxH9t0H=/i HWt PJHЅ
+Ht$giHCHxH9t0H=h HWt PJHЅ
+Ht$*iHHxH9t0H=h HWt PJHЅ
+Ht$hH&jH[]ÐUSHHH H H ٭ H H ƭ H{hHtiH{PHtiHs0H{ HCH-rf HxH9t0H=h HWt PJHЅ
+Ht$9hHCHxH9t0H=g HWt PJHЅ
+Ht$gHHxH9t0H=g HWt PJHЅ
+Ht$gHhH[]ÐUSHHH H H 諬 H H 蘬 H{hHthH{PHthHs0H{ Ϧ HCH-De HxH9t0H=f HWt PJHЅ
+Ht$ gHCHxH9t0H=f HWt PJHЅ
+Ht$fHHxH9t0H=Zf HWt PJHЅ
+Ht$fHgH[]ÐUSHHH H H } H H j H{hHt|gH{PHtngHs0H{ 补 HCH-d HxH9t0H=e HWt PJHЅ
+Ht$eHCHxH9t0H=he HWt PJHЅ
+Ht$eHHxH9t0H=,e HWt PJHЅ
+Ht$deHfH[]ÐUSHHH H H O H H < H{hHtNfH{PHt@fHs0H{ s HCH-b HxH9t0H=wd HWt PJHЅ
+Ht$dHCHxH9t0H=:d HWt PJHЅ
+Ht$rdHHxH9t0H=c HWt PJHЅ
+Ht$6dHneH[]ÐUSHHH H H ! H H H{hHt eH{PHteHs0H{ E HCH-a HxH9t0H=Ic HWt PJHЅ
+Ht$cHCHxH9t0H= c HWt PJHЅ
+Ht$DcHHxH9t0H=b HWt PJHЅ
+Ht$cH@dH[]ÐH H9vP^HZdUSHHH H H է H H § H{hHtcH{PHtcHs0H{ HCH-n` HxH9t0H=a HWt PJHЅ
+Ht$5bHCHxH9t0H=a HWt PJHЅ
+Ht$aHHxH9t0H=a HWt PJHЅ
+Ht$aHbH[]ÐUSHHH H H 触 H H 蔦 H{hHtbH{PHtbHs0H{ ˠ HCH-@_ HxH9t0H=` HWt PJHЅ
+Ht$aHCHxH9t0H=` HWt PJHЅ
+Ht$`HHxH9t0H=V` HWt PJHЅ
+Ht$`HaH[]ÐUSHHH H H y H H f H{hHtxaH{PHtjaHs0H{ 蝟 HCH-^ HxH9t0H=_ HWt PJHЅ
+Ht$_HCHxH9t0H=d_ HWt PJHЅ
+Ht$_HHxH9t0H=(_ HWt PJHЅ
+Ht$`_H`H[]ÐSHHHtIHGHxH;=4] t0H=^ HWt PJHЅ
+Ht$_H:`H[USHHH H H H H ܣ H{hHt_H{PHt_Hs0H{ HCH-\ HxH9t0H=^ HWt PJHЅ
+Ht$O^HCHxH9t0H=] HWt PJHЅ
+Ht$^HHxH9t0H=] HWt PJHЅ
+Ht$]H_H[]ÐUSHHH H H H H 订 H{hHt^H{PHt^Hs0H{ HCH-Z[ HxH9t0H=\ HWt PJHЅ
+Ht$!]HCHxH9t0H=\ HWt PJHЅ
+Ht$\HHxH9t0H=p\ HWt PJHЅ
+Ht$\H]H[]ÐUSHHH H H 蓡 H H 耡 H{hHt]H{PHt]Hs0H{ Hl$貛 HCHHxZHCHHxYHHHxYH;]H[]USHHH H H H H ܠ H{hHt\H{PHt\Hs0H{ HCH-Y HxH9t0H=[ HWt PJHЅ
+Ht$O[HCHxH9t0H=Z HWt PJHЅ
+Ht$[HHxH9t0H=Z HWt PJHЅ
+Ht$ZH\H[]ÐUSHHH H H H H 讟 H{hHt[H{PHt[Hs0H{ HCH-ZX HxH9t0H=Y HWt PJHЅ
+Ht$!ZHCHxH9t0H=Y HWt PJHЅ
+Ht$YHHxH9t0H=pY HWt PJHЅ
+Ht$YHZH[]ÐUSHHH H H 蓞 H H 耞 H{hHtZH{PHtZHs0H{ 跘 HCH-,W HxH9t0H=X HWt PJHЅ
+Ht$XHCHxH9t0H=~X HWt PJHЅ
+Ht$XHHxH9t0H=BX HWt PJHЅ
+Ht$zXHYH[]ÐUSHHH H H e H H R H{hHtdYH{PHtVYHs0H{ Hl$脗 HCHHxUHCHHxUHHHxUH
YH[]USHHH H H H H 讜 H{hHtXH{PHtXHs0H{ HCH-ZU HxH9t0H=V HWt PJHЅ
+Ht$!WHCHxH9t0H=V HWt PJHЅ
+Ht$VHHxH9t0H=pV HWt PJHЅ
+Ht$VHWH[]ÐUSHHH H H 蓛 H H 耛 H{hHtWH{PHtWHs0H{ 跕 HCH-,T HxH9t0H=U HWt PJHЅ
+Ht$UHCHxH9t0H=~U HWt PJHЅ
+Ht$UHHxH9t0H=BU HWt PJHЅ
+Ht$zUHVH[]ÐSHHHtIHGHxH;=NS t0H=T HWt PJHЅ
+Ht$ UHTVH[USHHH H H H H H{hHtVH{PHtUHs0H{ - HCH-R HxH9t0H=1T HWt PJHЅ
+Ht$iTHCHxH9t0H=S HWt PJHЅ
+Ht$,THHxH9t0H=S HWt PJHЅ
+Ht$SH(UH[]ÐUSHHH H H ۘ H H Ș H{hHtTH{PHtTHs0H{ HCH-tQ HxH9t0H=S HWt PJHЅ
+Ht$;SHCHxH9t0H=R HWt PJHЅ
+Ht$RHHxH9t0H=R HWt PJHЅ
+Ht$RHSH[]ÐUSHHH H H 譗 H H 蚗 H{hHtSH{PHtSHs0H{ ё HCH-FP HxH9t0H=Q HWt PJHЅ
+Ht$
RHCHxH9t0H=Q HWt PJHЅ
+Ht$QHHxH9t0H=\Q HWt PJHЅ
+Ht$QHRH[]ÐUSHHH H H H H l H{hHt~RH{PHtpRHs0H{ 裐 HCH-O HxH9t0H=P HWt PJHЅ
+Ht$PHCHxH9t0H=jP HWt PJHЅ
+Ht$PHHxH9t0H=.P HWt PJHЅ
+Ht$fPHQH[]ÐUSHHH H H Q H H > H{hHtPQH{PHtBQHs0H{ u HCH-M HxH9t0H=yO HWt PJHЅ
+Ht$OHCHxH9t0H=<O HWt PJHЅ
+Ht$tOHHxH9t0H= O HWt PJHЅ
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+M H5 HfH= HH=I۵ HL H5s HfH=b HH=۵ HL H5D HfH=3 HH=ڵ HzL H5 HrfH= HH=ڵ HJL H5 HBfH= HPH=ڵ HL H5 HfH= H H=Yڵ HK H5 HfH=w HH=)ڵ HK H5Y HfH=H HH=ٵ HK H5* HfH= HH=ٵ HZK H5 HRfH= H`H=ٵ H*K H5 H"fH= H0H=iٵ HJ H5 HfH= H H=9ٵ HJ H5n HfH=] HH= ٵ HJ H5? HfH=. HH=ص HjJ H5 HbfH= HpH=ص H:J H5 H2fH= H@H=yص H
+J H5 HfH= HH=Iص HI H5 HfH=r HH=ص HI H5T HfH=C HH= HzI H5% HrfH= HH= HJI H5 HBfH= HPH= HI H5 HfH= H H=Y HH H5 HfH= HH=) HH H5i HfH=X HH=ֵ HH H5: HfH=) HH=ֵ HZH H5 HRfH= H`H=ֵ H*H H5 H"fH= H0H=iֵ HG H5 HfH= H H=9ֵ HG H5~ HfH=m HH= ֵ HG H5O HfH=> HH=յ HjG H5 HbfH= HpH=յ H:G H5 H2fH= H@H=yյ H
+G H5 HfH= HH=Iյ HF H5 HfH= HH=յ HF H5d HfH=S HH=Ե HzF H55 HrfH=$ HH=Ե HJF H5 HBfH= HPH=Ե HF H5 HfH= H H=YԵ HE H5 HfH= HH=)Ե HE H5y HfH=h HH=ӵ HE H5J HfH=9 HH=ӵ HZE H5 HRfH=
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+- HhLpH}H$ L73L%- H{@HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ ID$HDŽ$ H$ ID$@H$ H, HH$ 18H- HsH{XDŽ$ HH$ H$, HH$ S2H5̇g . H0H4$H胦7 H|$0HsH<$-ID$H$ ID$@H$ H- HH$ H$ HxH;=+ H, H{@Ld$`HH$ Z2HEH$ H{XL HC+ HH$ (L2 H H$ H
g H5$g Mນ H輩H $1HL˳H#L[HD$0HHx*HH5 Lt4IM $ML$PH0HpPHL $1E1J< IM~Cf D IL LI D HHH9uIMI9MLIHI?HtqI H|$@Htp-H(4 LdHD$HHL$P`H$ LHFHD$HHL$PH$ LH$XLGdH\$(ID$LlH$ CHHH 5L*=H=(+ HWtIH$ d+HHD$0HHHx()LH蛰PJH볿X Lt$@Ld$`'H5Ig LLIs-H$ HH H$ H
Dg H5Ղg Iq HmHVg LHLxHаHHD$`HHxw(H@H5ɡ L!2H:IHL苰HïHD$`LHx2(LJ)HB2HHHHH( H{XHHH$ %oH{IPHEH$ LL AWHFAVAUATIUSHX H^ HD$HFHT$XHL$PH)HH~ H9H X L$ &&HHD$HT$XL
g LD$PH
-g H5g LH$H$ H3H L$ H
Tg H5%g Iغ L轥Hg LLHȯL HXH$ LHx&HH5 Hn0f D H<H9<H4 Ht$ H4
H9M HT$@HL$HIHL$8HHE m HCLsH9L L9 H HDŽ$ HDŽ$ HDŽ$ HHD$)IH$ HD$1LLD$(I HH$ HL$0%L9t$8HL$0LD$(H$ 2 M9a HD$HKHD$Ht$@E1It HD$811 D L9t
H9tIHHH9uHAoE H$ Hf$ H$8 H!H$0 IEHtH=& HP H$ H$ L$ HT$H$ E1L<$ILHHD$(H$ Ht H$0 Ht
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+ Hu H$ L$ L<$E1HHVH] H$ HDŽ$ HHE t-H3< H&H$ HtH3< H%H$ HtH$0 Ht
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`vg I 1HL}< Ll$pH5$ H
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ƈg H5_.f Iغ LOLt$Hwg LLLUL譣HH$ LHxQH$ LHx=H$ LHx)H$ LHxH$ LHxHD$pLHxH$ LHxHH5. L$HH$ Ht MtL H} HtaHE H$H$ H gH$ HHtH$0 Ht
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+HBH)HH X H$p H$ 4H5ug HHIL$ L_G H$ H
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rg ILDŽ$ DŽ$ H$ HG H$p H
%vg H5mg IغW HN1LHL^H趛HH$ H$ HxUH H5 L HH$ Ht0ZHDŽ$ I>HtYI H$ HtXH HȿX L$ LC0H
f H f H5 f LHH$P HG L|$HH
tg H5M%f Iغ L=Hv%f LLHHL蠚HؙH$ LHxDH
H5 HX H$ ME0H
f H
+ f H5 f HH(L$0 LG L$ H
tg H5$f Mທ L肐H$f HLH荚LL H$ Ht$HHxHPH5ي H1X L$ LC0H
Q f HM f H5I f LHkL$ L;G H$ H
\sg H5#f M躗 HŏH#f LHHЙH(L`H$ Ht$HHxHH5 HtX H$p H$ ZH5[ng HHIL$ LG H$ H
rg H5wjg M9 HIHD$HH8ng HLL!X H$p H$ H5lmg HHIL$ LG H$ H
_rg H5ig M8 HIHD$H耎HImg HLL苘LX H$p H$ IH52lg HHIL$ LtG H$ H
qg H5fig M6 HIHD$HHlg HLLqHL1H)H|$HH<LtH$ HHxHH @ Hf D f. f. LHӖL H$ Ht$HHxuHJHHHH|$HI茖HLH$ H$ Hx(IHHHH茕H$ H$ Hx
L HHIHLLFH$ Ht$HHx
6HH ; @ f. f. Of. AWAVAUIATUSHH8 HA0H9F0LL$X H
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HD$HHHHH$ H$ tHt$xH0HPL$ H9H$ HtL`H$ HBHt$HH$ H$ Ht$PH9H$ 2 HtH(HH$ HC0HL$XLHT$PHt$HH$ sHC0x IE0HHSPx I IM IMPHC0xA H$ LLCPHx HH$x H$ Hl$(L|$ Ld$LL$`HHD$8H$p HD$0HD$xHD$HD$pHD$HD$hH$VH$ H$ HDŽ$ Htt
H8 L[]A\A]A^A_ Ht$HH|$PY @ H$ H$ HHt$PHHD$H+H$ H$ kX L$ THIF HT$HH$ H
gg ILDŽ$ DŽ$ TH$ HTG H$p H
og H5Fcg IغW Hއ1LHLHFH~H$ H$ HxHH57 LHH$ HtOHDŽ$ I>HtOI H$ Ht HHȿX L$ $LC0H
Vf HRf H5Nf LHpH$P H@G L|$HH
mg H5f Iغ L͆Hf LLHؐL0HhH$ LHxHH5& H~X H$ lME0H
f Hf H5f HHL$0 LG L$ H
mg H5"f Mທ LHKf HLH LuL譎H$ Ht$HHxHH5i HX L$ LC0H
f Hf H5f LHL$ LG H$ H
Llg H5ef M躗 HUHf LHH`H踎LH$ Ht$HHxZH#H5 HX H$p H$ H5cg HHI
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kg H5`g M9 HIHD$H藄Hcg HLL袎!X H$p H$ cH5bg HHI
+L$ LG H$ H
Okg H5_g M8 HIHD$HHbg HLLLX H$p H$ H5ag HHI L$ LG H$ H
jg H5^g M6 HIHD$H膃Hag HLL葍qHLHH|$HȞLH$ HHxpHH @ Hf D f. f. LHcL蛋H$ Ht$HHxH JHHHH|$HI HLQH$ H$ HxIHHHH H$ H$ HxLHHIHL螋L֊H$ Ht$HHx@6HH ; @ f. f. Of. AWAVAUIATUSHH8 HA0H9F0LL$X H
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5]g ILDŽ$ DŽ$ H$ HG H$p H
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f H f H5 f LH H$P HG L|$HH
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Zg H5Ug M9 HIHD$H'zHXYg HLL2!X H$p H$ H5Xg HHIL$ L G H$ H
Zg H5Ug M8 HIHD$HyHiXg HLL諃LX H$p H$ iH5RWg HHI$L$ LG H$ H
Yg H5Tg M6 HIHD$HyH/Wg HLL!qHLQHIH|$HH\L蔁H$ HHx HH @ Hf D f. f. LHL+H$ Ht$HHxHJHHHH|$HI謁HLH$ H$ HxHIHHHH謀H$ H$ HxL+HHIHL.LfH$ Ht$HHx6HH ; @ f. f. Of. AWAVAUIATUSHH8 HA0H9F0LL$X H
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Rg ILDŽ$ DŽ$ tH$ HtG H$p H
u]g H5fNg IغW Hr1LHL}Hf|H{H$ H$ HxHΌH5Wm LHH$ Ht:HDŽ$ I>Ht:I H$ HtHHȿX L$ DLC0H
vf Hrf H5nf LHH$P H`G L|$HH
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f H f H5 f HHL$0 LG L$ H
i[g H5Bf Mທ L2qHkf HLH={LzLyH$ Ht$HHx7H H5k HX L$ LC0H
f He H5e LHL$ LG H$ H
Zg H5f M躗 HupHf LHHzHyLyH$ Ht$HHxzHCH5j H$X H$p H$
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6Zg H5'Kg M9 HIHD$HoHNg HLLy!X H$p H$ H5 Ng HHI>L$ LG H$ H
Yg H5Jg M8 HIHD$H0oHMg HLL;yLX H$p H$ H5Lg HHIL$ L$G H$ H
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H HHEHD$ H I HHoEHMLEfD$Hh Hp H4$H$Hx H|$HT$H Hh Lx HHp HD$HT$H t
H HH([]A\A] HL$1 @ HL$E1oHL$HHt
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HHH;HtWH Lf. ATHW# USHH0HD$Hb>HD$(Hf>HD$ f0 HIaH5)_ LH1 H5\ H1 H5!c HA1 HH5(~[ HB 1 H8H-1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-SHD$ HtH|$ HH0H[]A\f D 1HH<$HtHD$ HtH|$ HHHHKIHt
HHH;HtDzH Lf. ATH USHH0HD$H<HD$(H<HD$ `0 HIћH5l(_ LH1 H5x\ H 1 H5c H1 HH5|[ HB X1 H8H1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-ñHD$ HtH|$ HH0H[]A\f D 1HH<$Ht胱HD$ HtH|$ HHHHKIHt
HHH;Ht7H Lf. ATHw USHH0HD$HB;HD$(HF;HD$ |[0 HIAH5&_ LHo1 H5\ Hp1 H5c H!1 HH5{[ HB 1 H8H
1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-3HD$ HtH|$ HH0H[]A\f D 1HH<$HtHD$ HtH|$ HHbHHKIHt
HHH;Ht觯H Lf. ATH
USHH0HD$H9HD$(H9HD$ Z0 HI豘H5L%_ LH1 H552\ H1 H5qc H1 HH5xy[ HB
81 H8H}1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-裮HD$ HtH|$ HH0H[]A\f D 1HH<$HtcHD$ HtH|$ HHHHKIHt
HHH;HtH Lf. ATHW USHH0HD$H"8HD$(H&8HD$ \c0 HI!H5#_ LHO1 H5\ HP1 H5c H1 HH5w[ HB 1 H8H1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-HD$ HtH|$ HH0H[]A\f D 1HH<$HtӬHD$ HtH|$ HHBHHKIHt
HHH;Ht臬H Lf. ATHx USHH0HD$H6HD$(H6HD$ ]0 HI葕H5,"_ LH1 H5>\ H1 H5Qc Hq1 HH5Xv[ HB 1 H8H]1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-胫HD$ HtH|$ HH0H[]A\f D 1HH<$HtCHD$ HtH|$ HHHHKIHt
HHH;HtH Lf. ATHWUSHH0HD$H5HD$(H5HD$ <]0 HIH5 _ LH/1 H5\a H01 H5c H1 HH5t[ HB 1 H8HͿ1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-HD$ HtH|$ HH0H[]A\f D 1HH<$Ht賩HD$ HtH|$ HH"HHKIHt
HHH;HtgH Lf. ATHUSHH0HD$Hr3HD$(Hv3HD$ [0 HIqH5 _ LH1 H5+\ H1 H51c HQ1 HH58s[ HB
1 H8H=1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-cHD$ HtH|$ HH0H[]A\f D 1HH<$Ht#HD$ HtH|$ HH蒿HHKIHt
HHH;HtקH Lf. ATHUSHH0HD$H1HD$(H1HD$ U0 HIH5| _ LH1 H5\ H1 H5c H1 HH5q[ HB h1 H8H譼1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-ӦHD$ HtH|$ HH0H[]A\f D 1HH<$Ht蓦HD$ HtH|$ HHHHKIHt
HHH;HtGH Lf. ATHUSHH0HD$HR0HD$(HV0HD$ S0 HIQH5_ LH1 H5q\ H耽1 H5c H11 HH5p[ HB ؾ1 H8H 1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-CHD$ HtH|$ HH0H[]A\f D 1HH<$HtHD$ HtH|$ HHrHHKIHt
HHH;Ht跤H Lf. ATHUSHH0HD$H.HD$(H.HD$ V0 HIH5\_ LH1 H5c H1 H5c H衼1 HH5n[ HB H1 H8H荹1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-賣HD$ HtH|$ HH0H[]A\f D 1HH<$HtsHD$ HtH|$ HHHHKIHt
HHH;Ht'H Lf. ATHUSHH0HD$H2-HD$(H6-HD$ lT0 HI1H5_ LH_1 H5%\ H`1 H5c H1 HH5l[ HB
踻1 H8H1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-#HD$ HtH|$ HH0H[]A\f D 1HH<$HtHD$ HtH|$ HHRHHKIHt
HHH;Ht藡H Lf. ATH7USHH0HD$H+HD$(H+HD$ N0 HI衊H5<_ LH1 H5\\ Hи1 H5ac H聹1 HH5hk[ HB (1 H8Hm1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-蓠HD$ HtH|$ HH0H[]A\f D 1HH<$HtSHD$ HtH|$ HH·HHKIHt
HHH;HtH Lf. ATHGUSHH0HD$H*HD$(H*HD$ LM0 HIH5_ LH?1 H5\ H@1 H5ѭc H1 HH5i[ HB 蘸1 H8Hݴ1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-HD$ HtH|$ HH0H[]A\f D 1HH<$HtÞHD$ HtH|$ HH2HHKIHt
HHH;HtwH Lf. ATHUSHH0HD$H(HD$(H(HD$ P0 HI聇H5 _ LH1 H5fPa H谵1 H5Ac Ha1 HH5Hh[ HB 1 H8HM1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-sHD$ HtH|$ HH0H[]A\f D 1HH<$Ht3HD$ HtH|$ HH袴HHKIHt
HHH;HtH Lf. ATHUSHH0HD$H&HD$(H&HD$ ,G0 HIH5_ LH 1 H5u \ H 1 H5c HѴ1 HH5f[ HB
x1 H8H轱1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-HD$ HtH|$ HH0H[]A\f D 1HH<$Ht裛HD$ HtH|$ HHHHKIHt
HHH;HtWH Lf. ATHwUSHH0HD$Hb%HD$(Hf%HD$ Q0 HIaH5_ LH1 H5[c H萲1 H5!c HA1 HH5(e[ HB 1 H8H-1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-SHD$ HtH|$ HH0H[]A\f D 1HH<$HtHD$ HtH|$ HH肱HHKIHt
HHH;HtǙH Lf. ATHUSHH0HD$H#HD$(H#HD$ G0 HIтH5l_ LH1 H5\ H 1 H5c H豱1 HH5c[ HB X1 H8H蝮1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-ØHD$ HtH|$ HH0H[]A\f D 1HH<$Ht胘HD$ HtH|$ HHHHKIHt
HHH;Ht7H Lf. ATHUSHH0HD$HB"HD$(HF"HD$ |J0 HIAH5
_ LHo1 H5;c Hp1 H5c H!1 HH5b[ HB Ȱ1 H8H
1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-3HD$ HtH|$ HH0H[]A\f D 1HH<$HtHD$ HtH|$ HHbHHKIHt
HHH;Ht觖H Lf. ATHUSHH0HD$H HD$(H HD$ C0 HIH5L _ LH1 H5Jc H1 H5qc H葮1 HH5x`[ HB 81 H8H}1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-裕HD$ HtH|$ HH0H[]A\f D 1HH<$HtcHD$ HtH|$ HHҬHHKIHt
HHH;HtH Lf. ATHUSHH0HD$H" HD$(H& HD$ \B0 HI!~H5
-_ LHO1 H5\ HP1 H5c H1 HH5^[ HB 設1 H8H1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-HD$ HtH|$ HH0H[]A\f D 1HH<$HtӓHD$ HtH|$ HHBHHKIHt
HHH;Ht臓H Lf. ATHUSHH0HD$H HD$(H HD$ @0 HI|H5, _ LH追1 H5\ H1 H5Qc Hq1 HH5X][ HB
1 H8H]1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-胒HD$ HtH|$ HH0H[]A\f D 1HH<$HtCHD$ HtH|$ HH販HHKIHt
HHH;HtH Lf. ATHUSHH0HD$H HD$(H HD$ <@0 HI{H5_ LH/1 H5\ H01 H5c H1 HH5[[ HB
航1 H8Hͦ1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-HD$ HtH|$ HH0H[]A\f D 1HH<$Ht賐HD$ HtH|$ HH"HHKIHt
HHH;HtgH Lf. ATHUSHH0HD$HrHD$(HvHD$ =0 HIqyH5 _ LH蟺1 H5?\ H蠧1 H51c HQ1 HH58Z[ HB 1 H8H=1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-cHD$ HtH|$ HH0H[]A\f D 1HH<$Ht#HD$ HtH|$ HH蒦HHKIHt
HHH;HtH Lf. ATHWUSHH0HD$HHD$(HHD$ E0 HIwH5|_ LH1 H5\ H1 H5c H1 HH5X[ HB h1 H8H譣1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-ӍHD$ HtH|$ HH0H[]A\f D 1HH<$Ht蓍HD$ HtH|$ HHHHKIHt
HHH;HtGH Lf. ATH\ USHH0HD$HRHD$(HVHD$ <0 HIQvH5_ LH1 H5\ H耤1 H5c H11 HH5W[ HB إ1 H8H 1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-CHD$ HtH|$ HH0H[]A\f D 1HH<$HtHD$ HtH|$ HHrHHKIHt
HHH;Ht跋H Lf. ATHUSHH0HD$HHD$(HHD$ 80 HItH5\_ LH1 H5\ H1 H5c H衣1 HH5U[ HB H1 H8H荠1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-賊HD$ HtH|$ HH0H[]A\f D 1HH<$HtsHD$ HtH|$ HHHHKIHt
HHH;Ht'H Lf. ATH USHH0HD$H2HD$(H6HD$ l70 HI1sH5^ LH_1 H5\ H`1 H5c H1 HH5S[ HB 踢1 H8H1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-#HD$ HtH|$ HH0H[]A\f D 1HH<$HtHD$ HtH|$ HHRHHKIHt
HHH;Ht藈H Lf. ATH USHH0HD$HHD$(HHD$ >0 HIqH5<^ LHϲ1 H5D\ HП1 H5ac H聠1 HH5hR[ HB (1 H8Hm1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-蓇HD$ HtH|$ HH0H[]A\f D 1HH<$HtSHD$ HtH|$ HHHHKIHt
HHH;HtH Lf. ATHG USHH0HD$HHD$(HHD$ L50 HIpH5^ LH?1 H5\ H@1 H5єc H1 HH5P[ HB 蘟1 H8Hݛ1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-HD$ HtH|$ HH0H[]A\f D 1HH<$HtÅHD$ HtH|$ HH2HHKIHt
HHH;HtwH Lf. ATHUSHH0HD$HHD$(HHD$ ;0 HInH5 ^ LH误1 H5jc H谜1 H5Ac Ha1 HH5HO[ HB 1 H8HM1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-sHD$ HtH|$ HH0H[]A\f D 1HH<$Ht3HD$ HtH|$ HH袛HHKIHt
HHH;HtH Lf. ATH USHH0HD$H
HD$(H
HD$ ,/0 HIlH5^ LH 1 H5\ H 1 H5c Hћ1 HH5M[ HB x1 H8H轘1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-HD$ HtH|$ HH0H[]A\f D 1HH<$Ht裂HD$ HtH|$ HHHHKIHt
HHH;HtWH Lf. ATH' USHH0HD$Hb HD$(Hf HD$ 70 HIakH5^ LH菬1 H5\ H萙1 H5!c HA1 HH5(L[ HB 1 H8H-1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-SHD$ HtH|$ HH0H[]A\f D 1HH<$HtHD$ HtH|$ HH肘HHKIHt
HHH;HtǀH Lf. ATH USHH0HD$H
-HD$(H
-HD$ 20 HIiH5l^ LH1 H5t\ H 1 H5c H豘1 HH5J[ HB X1 H8H蝕1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-HD$ HtH|$ HH0H[]A\f D 1HH<$HtHD$ HtH|$ HHHHKIHt
HHH;Ht7H Lf. ATH USHH0HD$HB HD$(HF HD$ |.0 HIAhH5^ LHo1 H5\ Hp1 H5c H!1 HH5I[ HB ȗ1 H8H
1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-3~HD$ HtH|$ HH0H[]A\f D 1HH<$Ht}HD$ HtH|$ HHbHHKIHt
HHH;Ht}H Lf. ATH USHH0HD$HHD$(HHD$ *0 HIfH5L^ LHߧ1 H5@\ H1 H5qc H葕1 HH5xG[ HB 81 H8H}1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-|HD$ HtH|$ HH0H[]A\f D 1HH<$Htc|HD$ HtH|$ HHғHHKIHt
HHH;Ht|H Lf. ATH USHH0HD$H"HD$(H&HD$ \*0 HI!eH5^ LHO1 H5\ HP1 H5c H1 HH5E[ HB 訔1 H8H1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-{HD$ HtH|$ HH0H[]A\f D 1HH<$HtzHD$ HtH|$ HHBHHKIHt
HHH;HtzH Lf. ATH USHH0HD$HHD$(HHD$ ,0 HIcH5,^ LH迤1 H5 \ H1 H5Qc Hq1 HH5XD[ HB 1 H8H]1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-yHD$ HtH|$ HH0H[]A\f D 1HH<$HtCyHD$ HtH|$ HH貐HHKIHt
HHH;HtxH Lf. ATHUSHH0HD$HHD$(HHD$ <&0 HIbH5^ LH/1 H5c H01 H5c H1 HH5B[ HB 舑1 H8H͍1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-wHD$ HtH|$ HH0H[]A\f D 1HH<$HtwHD$ HtH|$ HH"HHKIHt
HHH;HtgwH Lf. ATHUSHH0HD$HrHD$(HvHD$ -0 HIq`H5 ^ LH蟡1 H5a)a H蠎1 H51c HQ1 HH58A[ HB 1 H8H=1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-cvHD$ HtH|$ HH0H[]A\f D 1HH<$Ht#vHD$ HtH|$ HH蒍HHKIHt
HHH;HtuH Lf. ATHWUSHH0HD$HHD$(HHD$ !0 HI^H5|^ LH1 H5c H1 H5c H1 HH5?[ HB h1 H8H譊1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-tHD$ HtH|$ HH0H[]A\f D 1HH<$HttHD$ HtH|$ HHHHKIHt
HHH;HtGtH Lf. ATHUSHH0HD$HRHD$(HVHD$ !0 HIQ]H5^ LH1 H5A&a H耋1 H5c H11 HH5>[ HB ،1 H8H 1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-CsHD$ HtH|$ HH0H[]A\f D 1HH<$HtsHD$ HtH|$ HHrHHKIHt
HHH;HtrH Lf. ATHUSHH0HD$HHD$(HHD$ 0 HI[H5\^ LH1 H5$a H1 H5c H衊1 HH5<[ HB H1 H8H荇1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-qHD$ HtH|$ HH0H[]A\f D 1HH<$HtsqHD$ HtH|$ HHHHKIHt
HHH;Ht'qH Lf. ATHUSHH0HD$H2HD$(H6HD$ l 0 HI1ZH5^ LH_1 H5+c H`1 H5~c H1 HH5:[ HB 踉1 H8H1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-#pHD$ HtH|$ HH0H[]A\f D 1HH<$HtoHD$ HtH|$ HHRHHKIHt
HHH;HtoH Lf. ATHUSHH0HD$HHD$(HHD$ 0 HIXH5<^ LHϙ1 H5c HІ1 H5a}c H聇1 HH5h9[ HB (1 H8Hm1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-nHD$ HtH|$ HH0H[]A\f D 1HH<$HtSnHD$ HtH|$ HH
HHKIHt
HHH;HtnH Lf. ATHUSHH0HD$HHD$(HHD$ L0 HIWH5^ LH?1 H5 a H@1 H5{c H1 HH57[ HB 蘆1 H8H݂1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-mHD$ HtH|$ HH0H[]A\f D 1HH<$HtlHD$ HtH|$ HH2HHKIHt
HHH;HtwlH Lf. ATHUSHH0HD$HHD$(HHD$ "0 HIUH5 ^ LH诖1 H5f a H调1 H5Azc Ha1 HH5H6[ HB 1 H8HM1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-skHD$ HtH|$ HH0H[]A\f D 1HH<$Ht3kHD$ HtH|$ HH袂HHKIHt
HHH;HtjH Lf. ATH'USHH0HD$HHD$(HHD$ ,0 HISH5^ LH 1 H5 a H 1 H5xc Hт1 HH54[ HB x1 H8H1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-iHD$ HtH|$ HH0H[]A\f D 1HH<$HtiHD$ HtH|$ HHHHKIHt
HHH;HtWiH Lf. ATH USHH0HD$HbHD$(HfHD$ 0 HIaRH5^ LH菓1 H58\ H萀1 H5!wc HA1 HH5(3[ HB 1 H8H-~1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-ShHD$ HtH|$ HH0H[]A\f D 1HH<$HthHD$ HtH|$ HHHHKIHt
HHH;HtgH Lf. ATH USHH0HD$HHD$(HHD$ 0 HIPH5l^ LH1 H5Ѿc H 1 H5uc H1 HH51[ HB X1 H8H|1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-fHD$ HtH|$ HH0H[]A\f D 1HH<$HtfHD$ HtH|$ HH}HHKIHt
HHH;Ht7fH Lf. ATH USHH0HD$HBHD$(HFHD$ |0 HIAOH5^ LHo1 H50c Hp}1 H5tc H!~1 HH50[ HB ~1 H8H
{1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-3eHD$ HtH|$ HH0H[]A\f D 1HH<$HtdHD$ HtH|$ HHb|HHKIHt
HHH;HtdH Lf. ATH USHH0HD$HHD$(HHD$ 0 HIMH5L^ LHߎ1 H5c H{1 H5qrc H|1 HH5x.[ HB 8}1 H8H}y1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-cHD$ HtH|$ HH0H[]A\f D 1HH<$HtccHD$ HtH|$ HHzHHKIHt
HHH;HtcH Lf. ATHUSHH0HD$H"HD$(H&HD$ \0 HI!LH5^ LHO1 H5_ HPz1 H5pc H{1 HH5,[ HB {1 H8Hw1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-bHD$ HtH|$ HH0H[]A\f D 1HH<$HtaHD$ HtH|$ HHByHHKIHt
HHH;HtaH Lf. ATH7 USHH0HD$HHD$(HHD$ 0 HIJH5,^ LH迋1 H5m\ Hx1 H5Qoc Hqy1 HH5X+[ HB z1 H8H]v1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-`HD$ HtH|$ HH0H[]A\f D 1HH<$HtC`HD$ HtH|$ HHwHHKIHt
HHH;Ht_H Lf. ATHW USHH0HD$HHD$(HHD$ <
0 HIIH5^ LH/1 H5ֻ[ H0w1 H5mc Hw1 HH5)[ HB x1 H8Ht1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-^HD$ HtH|$ HH0H[]A\f D 1HH<$Ht^HD$ HtH|$ HH"vHHKIHt
HHH;Htg^H Lf. ATHW USHH0HD$HrHD$(HvHD$ 0 HIqGH5 ^ LH蟈1 H5F[ Hu1 H51lc HQv1 HH58([ HB v1 H8H=s1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-c]HD$ HtH|$ HH0H[]A\f D 1HH<$Ht#]HD$ HtH|$ HHtHHKIHt
HHH;Ht\H Lf. ATH USHH0HD$HHD$(HHD$
-0 HIEH5|^ LH1 H5ȕ\ Ht1 H5jc Ht1 HH5&[ HB hu1 H8Hq1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
-[HD$ HtH|$ HH0H[]A\f D 1HH<$Ht[HD$ HtH|$ HHsHHKIHt
HHH;HtG[H Lf. ATHUSHH0HD$HRHD$(HVHD$ 0 HIQDH5^ LH1 H5>\ Hr1 H5ic H1s1 HH5%[ HB B s1 H8Hp1 HE HE HC HHD$ HteHkHt$ HfoD$ HD$ CH<$HC( Ht
-<ZHD$ HtH|$ HH0H[]A\ 1HH<$HtYHD$ HtH|$ HHjqHHKIHt
HHH;HtYH L ATH7USHH0HD$HHD$(HHD$ 0 HIBH5\^ LH1 H5E[ Hp1 H5gc Hq1 HH5#[ HB B Ar1 H8Hn1 HE HE HC HHD$ HteHkHt$ HfoD$ HD$ CH<$HC( Ht
-XHD$ HtH|$ HH0H[]A\ 1HH<$HtkXHD$ HtH|$ HHoHHKIHt
HHH;Ht XH L ATHUSHH0HD$H2HD$(H6HD$ l
0 HI1AH5^ LH_1 H5[ H`o1 H5ec Hp1 HH5![ HB B p1 H8Hl1 HE HE HC HHD$ HteHkHt$ HfoD$ HD$ CH<$HC( Ht
- WHD$ HtH|$ HH0H[]A\ 1HH<$HtVHD$ HtH|$ HHJnHHKIHt
HHH;HtVH L ATHUSHH0HD$HHD$(HHD$ 0 HI?H5<^ LHπ1 H5c Hm1 H5adc Hn1 HH5h [ HB B !o1 H8Hfk1 HE HE HC HHD$ HteHkHt$ HfoD$ HD$ CH<$HC( Ht
-UHD$ HtH|$ HH0H[]A\ 1HH<$HtKUHD$ HtH|$ HHlHHKIHt
HHH;HtTH L ATHUSHH0HD$HHD$(HHD$ L0 HI>H5^ LH?1 H5#jd H@l1 H5bc Hl1 HH5 [ HB B m1 H8Hi1 HE HE HC HHD$ HteHkHt$ HfoD$ HD$ CH<$HC( Ht
-SHD$ HtH|$ HH0H[]A\ 1HH<$HtSHD$ HtH|$ HH*kHHKIHt
HHH;HtoSH L ATHUSHH0HD$HHD$(HHD$ / HI<H5 ^ LH}1 H5[ Hj1 H5Aac Hak1 HH5H [ HB B l1 H8HFh1 HE HE HC HHD$ HteHkHt$ HfoD$ HD$ CH<$HC( Ht
-lRHD$ HtH|$ HH0H[]A\ 1HH<$Ht+RHD$ HtH|$ HHiHHKIHt
HHH;HtQH L ATHUSHH0HD$HHD$(HHD$ ,0 HI:H5^ LH |1 H5u[ H i1 H5_c Hi1 HH5[ HB B qj1 H8Hf1 HE HE HC HHD$ HteHkHt$ HfoD$ HD$ CH<$HC( Ht
-PHD$ HtH|$ HH0H[]A\ 1HH<$HtPHD$ HtH|$ HH
-hHHKIHt
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3_ H5`\ H$LHl$`HF= H$ H
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3_ H5`\ H$LHl$`HF= H$ H
7] H5H\ I躦 HSH\ LHL]:X Ld$P'LC0H
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b H5\ H$L4Hl$`H= H$ H
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\ LD$@H
3_ H5`\ H$LHl$`HF= H$ H
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Z HZ H5Z LISHl$`H= H$ H
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b H5\ H$L4Hl$`H= H$ H
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\ LD$@H
3_ H5`
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Z HZ H5Z LISHl$`H= H$ H
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b H5 \ H$L3Hl$PH= H\$pH
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LLHD$`HtH|$P HH{qH []A\A]A^ D LH H H; HDŽ$ ;HT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ HHHt$PH|$XH$ HdH$ HD$`HT$hH$ H D HDŽ$ 蒏HT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ HHLL$PLT$XH$ H3cH$ HD$`HT$h
-H$ HHD$`HHD$`HtH|$P HH{JpHRHH$ HHt LLH$ HHt LLH$ HHtH$ HlH|$HHtjvHL$`HHTH|$P H@H$ HHH$ H AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I HefM I LH KfIT$A H,M4$H9II$t7*$ @ $HIZ跌,AVH9uI$I9T$kHH[]A\A]A^A_H H8 HFH9tHGhLHHD$HD$0HD$HD$ HD$f D I I H|$eD I M L$H\eI$I4ID$$H<HI$H{ Ht$0H|$8HHT$ HD$(LCHT$Ht$AID$I9$dHH[]A\A]A^A_HGHD$HGhH$HFH9 I H<$I dI L<H|$I HdM4$H IL$H,I H9I $t2 @ A*HIIZ,ANH9uI $I9L$oHH[]A\A]A^A_Å AVIAULjATIUSH H$ H$ H$ HLH{oH$ H$h H$ H H9HNH$ PH$ H$ 英Hl$H H$ HOJH1Lr@1 H|$IHtPHl$0H$ LHh HD$` eHD$PHLHD$hHHD$`3 H HD$`HHDŽ$ Ld$pt#Ld$pHt$P LfoD$`f$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{kH []A\A]A^ D LH .] .@[ 4. HDŽ$ NHT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ HHHt$PH|$XH$ HO`H$ HD$`HT$hH$ HC HDŽ$ 褈HT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ HHLL$PLT$XH$ H^H$ HD$`HT$hH$ HHD$`H$ HHtH$ HHD$`HtH|$P HH{=iHEH$ HHtH$ HHH$ HHt LLH$ HHx LLfHL$`HHnH|$P HZH|$HHIچ?Hf f. AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I Hu_M I LH [_IT$A H,M4$H9II$t4*$ @ HI$ʅ,AVH9uI$I9T$nHH[]A\A]A^A_H H8 HFH9tHGhLHHD$HD$0HD$HD$ HD$I I H|$^D I M L$Ht^I$I4ID$$H<HI$H{ Ht$0H|$8HHT$ HD$(LCHT$Ht$AID$I9$dHH[]A\A]A^A_HGHD$HGhH$HFH9 I H<$I ]I L<H|$I H]M4$H IL$H,I H9I $t/ @ A*HII ,ANH9uI $I9L$rHH[]A\A]A^A_~f. @ AVIAULjATIUSH H$ H$ H$ HLH{H$ H$h H$ H H9HNH$ `H$ H$ ˫Hl$H H$ H_CH1L91 H|$IHt`Hl$0H$ LHx HD$` uHD$PHYLHD$hH:HD$` 3 H HD$`HHDŽ$ Ld$pt#Ld$pHt$P LfoD$`f$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{%dH []A\A]A^ D L航 f.] f.] 2, HDŽ$ \HT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ HHHt$PH|$XH$ H[H$ HD$`HT$hH$ Hf D < HDŽ$ 謁HT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ HHLL$PLT$XH$ HYH$ HD$`HT$hH$ HHD$`H$ HHtH$ HHD$`HtH|$P HH{EbHMH$ HHtH$ HH @ H$ HHt LLH$ HHt LLbHL$`HHjH|$P HVH|$HHE;HAUATUSHHHHHH LA0A@% ~Ct uYHqHHH[]A\A] HHHH[]A\A]f. uHCHHH[]A\A] D L. Hl$ HT$H5[ HD$HZjLd$0H L^< H+HD$0HD$0 HHt?HELl$LHx{H~Hl$0HtHELHx`{H~HD$ LHxG{HHH[]A\A]f HHHH[]A\A]HHD$ Ht$Hx{H;HtH HH AUATIHzULSHH It$Ht$HL$3 Ht$HHHOHD$0HL$@H tGHH?upHH?ueH9|`H9[H|$1HHH 1HHH HC0xv HHCPHHI$ID$Hĸ []A\A]ÿX Ld$PxIHD$0HT$L
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-RM4$H IL$H,I H9I $t1 @ H*A*xH,HIIINH9uI $I9L$pHH[]A\A]A^A_<sf. fAVIAULjATIUSH H$ H$ H$ HLH{ߟH$ H$h H$ H H9HNH$ H$ H$ 諩Hl$H H$ H7H1L-1 H|$IHtwHl$0H$ LHؑ HD$` wHD$PH LHD$hH
-HD$` 3 H HD$`HHDŽ$ Ld$pt#Ld$pHt$P LfoD$`f$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{XH []A\A]A^ D Lh > HDŽ$ vHT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ H HHt$PH|$XH$ H/RH$ HD$`HT$hH$ H HDŽ$ *vHT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ HHLL$PLT$XH$ HPH$ HD$`HT$hH$ HHD$`HHD$`HtH|$P HH{VH{HH$ HHt LLH$ HHt LLH$ HHtH$ HlH|$HHttj D qHL$`HHOH|$P H;H$ HHH$ Hf. AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I HLM I LH LIT$I H,M4$H9II$t6H*$ $H* HIIsH,IVH9uI$I9T$lHH[]A\A]A^A_H H8 HFH9tHGHHD$HGhH$HD$0HD$HD$ HD$ I H<$I LM M H|$Hl LKI$I4ID$J<HI$H{ Ht$0H|$8HHT$ HD$(HLKHT$Ht$AID$I9$kHH[]A\A]A^A_HGHD$HGhH$HFH9f D I H<$I IKI L<H|$I H*KM4$H IL$H,I H9I $t1 @ H*I*qH,HIIINH9uI $I9L$pHH[]A\A]A^A_\lf. fAVIAULjATIUSH H$ H$ H$ HLH{H$ H$h H$ H H9HNH$ H$ H$ Hl$H H$ H0H1L'1 H|$IHtpHl$0H$ LH HD$` pHD$PHLHD$hHHD$`02 H HD$`HHDŽ$ Ld$pt#Ld$pHt$P LfoD$`f$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{QH []A\A]A^ D L訕H H H; HDŽ$ oHT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ HHHt$PH|$XH$ H<MH$ HD$`HT$hH$ H D HDŽ$ BoHT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ HHLL$PLT$XH$ HKH$ HD$`HT$h
-H$ HHD$`HHD$`HtH|$P HH{OHuHH$ HHt LLH$ HHt LLH$ HHtH$ HlH|$HHtmjvHL$`HHTH|$P H@H$ HHH$ H AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I HFM I LH EIT$I H,M4$H9II$t8H*$ $HIZglH,IVH9uI$I9T$jHH[]A\A]A^A_H H8 HFH9tHGhLHHD$HD$0HD$HD$ HD$ D I I H|$0ED I M L$H EI$I4ID$$H<HI$H{
Ht$0H|$8HHT$ HD$(LCHT$Ht$AID$I9$dHH[]A\A]A^A_HGHD$HGhH$HFH9 I H<$I aDI L<H|$I HBDM4$H IL$H,I H9I $t4 @ AH*HIIZjH,INH9uI $I9L$mHH[]A\A]A^A_qeAVIAULjATIUSH H$ H$ H$ HLH{ H$ H$h H$ H H9HNH$ H$ H$ ;Hl$H H$ H)H1L" 1 H|$IHt jHl$0H$ LH HD$` jHD$PH LHD$hH*HD$`P2 H HD$`HHDŽ$ Ld$pt#Ld$pHt$P LfoD$`f$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{JH []A\A]A^ D L .] .@ [ 4. HDŽ$ hHT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ HHHt$PH|$XH$ H?HH$ HD$`HT$hH$ HC HDŽ$ ThHT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ HHLL$PLT$XH$ HFH$ HD$`HT$hH$ HHD$`H$ HHtH$ HHD$`HtH|$P HH{HHmH$ HHtH$ HHH$ HHt LLH$ HHx LLfHL$`HHnH|$P HZH|$HHIf?Hf f. AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I H%?M I LH ?IT$I H,M4$H9II$t5H*$ HI$zeH,IVH9uI$I9T$mHH[]A\A]A^A_H H8 HFH9tHGhLHHD$HD$0HD$HD$ HD$I I H|$H>D I M L$H$>I$I4ID$$H<HI$H{
- Ht$0H|$8HHT$ HD$(LCHT$Ht$AID$I9$dHH[]A\A]A^A_HGHD$HGhH$HFH9 I H<$I y=I L<H|$I HZ=M4$H IL$H,I H9I $t1 @ AH*HIIcH,INH9uI $I9L$pHH[]A\A]A^A_^f. fAVIAULjATIUSH H$ H$ H$ HLH{/H$ H$h H$ H H9HNH$ 0H$ H$ {Hl$H H$ H#H1L21 H|$IHtcHl$0H$ LH(} HD$` %cHD$PHyLHD$hHHD$``2 H HD$`HHDŽ$ Ld$pt#Ld$pHt$P LfoD$`f$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{CH []A\A]A^ D L8 f.] f.] 2, HDŽ$ bHT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ HHHt$PH|$XH$ H=CH$ HD$`HT$hH$ Hf D < HDŽ$ \aHT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ HHLL$PLT$XH$ HAH$ HD$`HT$hH$ HHD$`H$ HHtH$ HHD$`HtH|$P HH{AHfH$ HHtH$ HH @ H$ HHt LLH$ HHt LLbHL$`HHjH|$P HVH|$HHE_;HAUATUSHHHHHH LA0A@% ~Ct uYHqHHH[]A\A] HHHH[]A\A]f. uHCHHH[]A\A] D LȻ. Hl$ HT$H5ň[ HD$H
-JLd$0H L>< H+HD$0HD$0 HHt?HELl$LHx2[Hz^Hl$0HtHELHx[HX^HD$ LHxZHHH[]A\A]f HHHH[]A\A]HHD$ Ht$HxZH;HtGH HdH AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I H6M I LH {6IT$AM4$H,H9II$t8Z$$$* HI\ffZAVH9uI$I9T$hHH[]A\A]A^A_H H8 HFH9tHGHHD$HGhH$HD$0HD$HD$ HD$ I H<$I 5M M H|$l L5I$I4ID$J<HI$H{ Ht$0H|$8HT$ HD$(HLKHT$Ht$AID$I9$mHH[]A\A]A^A_HGHD$HGhH$HFH9I H<$I 4I L<H|$I H4M4$H IL$H,I H9I $t8 @ A*HIIZ;[ffZA^H9uI $I9L$iHH[]A\A]A^A_U D AVIAULjATIUSH H$ H$ H$ HLH{蟂H$ H$h H$ H H9HNH$ ЁH$ H$ kHl$H H$ HH1L1 H|$IHtZHl$0H$ LHw HD$` ZHD$PH9~LHD$hHHD$`2 H HD$`HHDŽ$ Ld$pt#Ld$pHt$P LfoD$`f$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{E;H []A\A]A^ D L( > HDŽ$ YHT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ HKHHt$PH|$XH$ H<H$ HD$`HT$hH$ H HDŽ$ XHT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ H7HLL$PLT$XH$ H;;H$ HD$`HT$hH$ HHD$`HHD$`HtH|$P HH{9H^HH$ HHt LLH$ HHt LLH$ HHtH$ HlH|$HHtjWj D qHL$`HHOH|$P H;H$ HHH$ Hf. AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I H/M I LH /IT$AM4$H,H9II$t9Z$$$H* HI VffZAVH9uI$I9T$gHH[]A\A]A^A_H H8 HFH9tHGHHD$HGhH$HD$0HD$HD$ HD$f D I H<$I .M M H|$Hl L.I$I4ID$J<HI$H{ Ht$0H|$8HHT$ HD$(HLKHT$Ht$AID$I9$kHH[]A\A]A^A_HGHD$HGhH$HFH9f D I H<$I .I L<H|$I H-M4$H IL$H,I H9I $t8 @ I*HIIZSTffZA^H9uI $I9L$iHH[]A\A]A^A_
Of. AVIAULjATIUSH H$ H$ H$ HLH{{H$ H$h H$ H H9HNH$ zH$ H$ yHl$H H$ HH1L 1 H|$IHtSHl$0H$ LHp HD$` SHD$PHwLHD$hH
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LLHD$`HtH|$P HH{U4H []A\A]A^ D LXxH H H; HDŽ$ RHT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ H8HHt$PH|$XH$ H<8H$ HD$`HT$hH$ H D HDŽ$ QHT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ H HLL$PLT$XH$ H6H$ HD$`HT$h
-H$ HHD$`HHD$`HtH|$P HH{2HWHH$ HHt LLH$ HHt LLH$ HHtH$ HlH|$HHtrPjvHL$`HHTH|$P H@H$ HHH$ H AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I H(M I LH (IT$AM4$H,$H9II$t+f D HI$JTAFH9uI$I9T$pHH[]A\A]A^A_H H8 HFH9tHGhLHHD$HD$0HD$HD$ HD$ I I H|$'D I M L$H'I$I4ID$$H<HI$H{ Ht$0H|$8HHT$ HD$(LCHT$Ht$AID$I9$dHH[]A\A]A^A_HGHD$HGhH$HFH9 I H<$I 'I L<H|$I H&M4$H IL$H,I H9I $t. @ AHICIRAFH9uI $I9L$sHH[]A\A]A^A_/Hf. D AVIAULjATIUSH H$ H$ H$ HLH{tH$ H$h H$ H H9HNH$ tH$ H$ sHl$H H$ H H1L1 H|$IHtLHl$0H$ LHi HD$` LHD$PH LHD$hHHD$` 2 H HD$`HHDŽ$ Ld$pt#Ld$pHt$P LfoD$`f$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{u-H []A\A]A^ D Lr .qw] .[ 4. HDŽ$ KHT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ H+HHt$PH|$XH$ H3H$ HD$`HT$hH$ HC HDŽ$ KHT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ HHLL$PLT$XH$ H1H$ HD$`HT$hH$ HHD$`H$ HHtH$ HHD$`HtH|$P HH{+HPH$ HHtH$ HHH$ HHt LLH$ HHx LLfHL$`HHnH|$P HZH|$HHI:I?Hf f. AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I H!M I LH !IT$AM4$H,H9II$t8Z$$ HI$*HffZAVH9uI$I9T$hHH[]A\A]A^A_H H8 HFH9tHGhLHHD$HD$0HD$HD$ HD$ I I H|$ D I M L$H I$I4ID$$H<HI$H{ Ht$0H|$8HHT$ HD$(LCHT$Ht$AID$I9$dHH[]A\A]A^A_HGH$HGhHD$HFH9 I H|$I I L<H<$I H M4$H IL$H,I H9I $t8 @ AHIIZsFffZA^H9uI $I9L$iHH[]A\A]A^A_-Af. AVIAULjATIUSH H$ H$ H$ HLH{mH$ H$h H$ H H9HNH$ mH$ H$ nHl$H H$ HH1L0 H|$IHtEHl$0H$ LHb HD$` EHD$PHLHD$hH
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LLHD$`HtH|$P HH{u&H []A\A]A^ D Ll f.<s] f.>s] 2, HDŽ$ DHT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ H HHt$PH|$XH$ H.H$ HD$`HT$hH$ Hf D < HDŽ$ CHT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ HHLL$PLT$XH$ H
-H$ HD$`HT$hH$ HHD$`H$ HHtH$ HHD$`HtH|$P HH{$HIH$ HHtH$ HH @ H$ HHt LLH$ HHt LLbHL$`HHjH|$P HVH|$HHE.B;HAUATUSHHHHHH LA0A@% ~Ct uYHqHHH[]A\A] HxHHH[]A\A]f. uH3HHH[]A\A] D Lh. Hl$ HT$H5ek[ HD$H,Ld$0H L !< H+HD$0HD$0 HHt?HELl$LHx=HAHl$0HtHELHx=H@HD$ LHx=HHH[]A\A]f HHHH[]A\A]HHD$ Ht$HxT=H;HtH HHGH AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I H5M I LH IT$AM4$H,$H9II$t+f D $* HI?AFH9uI$I9T$pHH[]A\A]A^A_H H8 HFH9tHGHHD$HGhH$HD$0HD$HD$ HD$ I H<$I QM M H|$l L2I$I4ID$J<HI$H{ Ht$0H|$8HT$ HD$(HLKHT$Ht$AID$I9$mHH[]A\A]A^A_HGHD$HGhH$HFH9 I H<$I I L<H|$I HjM4$H IL$H,I H9I $t- @ HA*II=AFH9uI $I9L$tHH[]A\A]A^A_8AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I HM I LH IT$AM4$H,$H9II$t,f D $H* HI<AFH9uI$I9T$oHH[]A\A]A^A_H H8 HFH9tHGHHD$HGhH$HD$0HD$HD$ HD$f D I H<$I M M H|$Hl LI$I4ID$J<HI$H{ Ht$0H|$8HHT$ HD$(HLKHT$Ht$AID$I9$kHH[]A\A]A^A_HGHD$HGhH$HFH9f D I H<$I I L<H|$I HM4$H IL$H,I H9I $t- @ HI*IIN;AFH9uI $I9L$tHH[]A\A]A^A_6AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I HM I LH IT$AM4$H,$H9II$t.f D $HIZg:AFH9uI$I9T$mHH[]A\A]A^A_H H8 HFH9tHGhLHHD$HD$0HD$HD$ HD$I I H|$8D I M L$HI$I4ID$$H<HI$H{ Ht$0H|$8HHT$ HD$(LCHT$Ht$AID$I9$dHH[]A\A]A^A_HGHD$HGhH$HFH9 I H<$I iI L<H|$I HJM4$H IL$H,I H9I $t0 @ AHIIZ8AFH9uI $I9L$qHH[]A\A]A^A_}3f. AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I HuM I LH [IT$AM4$H,$H9II$t+f D HI$7AFH9uI$I9T$pHH[]A\A]A^A_H H8 HFH9tHGhLHHD$HD$0HD$HD$ HD$ I I H|$D I M L$HtI$I4ID$$H<HI$H{ Ht$0H|$8HHT$ HD$(LCHT$Ht$AID$I9$dHH[]A\A]A^A_HGHD$HGhH$HFH9 I H<$I I L<H|$I HM4$H IL$H,I H9I $t. @ AHICI 6AFH9uI $I9L$sHH[]A\A]A^A_0f. D AUIATUSHHHAL&oHf$tHPH=+ X 6HE1LLHH|2 H+Hl$Ht L%+ HEMtVЃtHH[]A\A]f HE HPMHE t0ЃuHE HP뺋EPU몃@XE PU HH'5H|$HtH;Hf. AUATIUHHzSLH HuHt$HL$s2 Ht$HHHOHD$HHL$0H tGHH?utHH?uiH9|dH9_H|$1HHH 1HHH LfHT$LHHE QHT$0HHEHĨ []A\A]ÿX Ld$@/IHD$HHT$L
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V_ H5\[ H$L:Hl$PHi = H\$pH
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LLHE HxH[]A\A]A^A_Ll$X ,LH~H\$ Hq = Ld$@H
[ H5\[ Iغ_ L>H ][ LLHIL衣H٢HD$LHx-HH5 H"7HL$PHHt
LLH`7HL$PHHtH|$@ HILM$HLYHD$Ht$Hx-H .IH @ Hf AWAVAUATUHHSHHxL`1HIL0 LLkI葸2 HH HH9 LWLI迤 IHD$P /L`LpHH Ld$@H?^] L ^] LHHH+D$ HT$@HT$ D$(T$0HD$XHSHD$P/< HD$PHt
LLHE HxH[]A\A]A^A_Ll$X i*LH.H\$ H! = Ld$@H
[ H5Z[ Iغ_ LHZ[ LLHLQH艠HD$LHx8+HH5Z H4HL$PHHt
LLH5HL$PHHtH|$@ HILMԠHL HD$Ht$Hx*H+IH @ Hf AWAVAUATUHHSHHxL`1HIL0 LLkIA2 HH HH9 LULIo IHD$P -L`LpHH Ld$@H[] L\[ LHHHKD$ HT$@HT$ D$(T$0HD$XHHD$P-< HD$PHt
LLHE HxH[]A\A]A^A_Ll$X (LHH\$ H= Ld$@H
[ H5FX[ Iغ_ L螕HX[ LLH詟LH9HD$LHx(HqH5
- H2HL$PHHt
LLH2HL$PHHtH|$@ HILM脞HL蹝HD$Ht$Hxf(H~)IH @ Hf AWAVAUATUHHSHHxDL`1HILp0 LLkI2 HH HH9 LZSLI IHD$P I+L`LpHH7 Ld$@HY] L [ LHHH D$ HT$@HT$ D$(T$0HD$XH3HD$PI+< HD$PHt
LLHE HxH[]A\A]A^A_Ll$X %LHH\$ H= Ld$@H
[ H5U[ Iغ_ LNH/V[ LLHYL豜HHD$LHx&H!H5 H20HL$PHHt
LLHp0HL$PHHtH|$@ HILM4HLiHD$Ht$Hx&H.'IH @ Hf AVIHAUATUSHL`1HHL&0 LH諱2 H3QH{I藱2 HH1HI~ fAD !AHH9u[]A\A]LI A^fAUATUHSHHzH HsHt$HD$ "2 HHD$@HD$HHD$ Ht?HH?ukHH?u`H9|[Ht$1HH 1HHH H$HT$ HHHHT$HHHCHĨ []A\A]ÿX Ld$0V#IHD$@HT$ L
P[ LD$HH
b H5P[ H$L+Hl$PH= H\$pH
X\ H5R[ I躣 H賐HS[ LHL辚HHNHD$0HHx#HH5 L-X Ld$0"IHD$HT$ L
S[ LD$HH
_ H5BP[ H$L-Hl$PH(= H\$pH
X\ H5-R[ I躦 HHS[ LHL =IHLMH腘HD$0Ht$ Hx2#LJ$H2-HHH f. f. @ AVIAULjATIUSH H$ H$ H$ HLH{MH$ H$h H$ H H9HNH$ NH$ H$ MHl$H H$ HH1L0 H|$IHt%Hl$0H$ LH HD$` %HD$PH)LHD$hH~HD$`Э2 H HD$`HHDŽ$ Ld$pt#Ld$pHt$P LfoD$`f$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{EH []A\A]A^ D LL f. S] f.S] 2, HDŽ$ |$HT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ HYHHt$PH|$XH$ HmH$ HD$`HT$hH$ Hf D < HDŽ$ #HT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ H9HLL$PLT$XH$ HH$ HD$`HT$hH$ HHD$`H$ HHtH$ HHD$`HtH|$P HH{eHm)H$ HHtH$ HH @ H$ HHt LLH$ HHt LLbHL$`HHjH|$P HVH|$HHE!;HAVIAULjATIUSH H$ H$ H$ HLH{OIH$ H$h H$ H H9HNH$ IH$ H$ kHHl$H H$ H/H1LR0 H|$IHt0!Hl$0H$ LHh HD$` E!HD$PH)LHD$hHyHD$`耩2 H HD$`HHDŽ$ Ld$pt#Ld$pHt$P LfoD$`f$ D$ $ LHH!H$ Ht
LLHD$`HtH|$P HH{H []A\A]A^ D L(G .K] .pZ 4. HDŽ$ . HT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ H+HHt$PH|$XH$ HH$ HD$`HT$hH$ HC HDŽ$ HT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ HHLL$PLT$XH$ HH$ HD$`HT$hH$ HHD$`H$ HHtH$ HHD$`HtH|$P HH{ H%%H$ HHtH$ HHH$ HHt LLH$ HHx LLfHL$`HHnH|$P HZH|$HHI ?Hf f. AVIAULjATIUSH H$ H$ H$ HLH{DH$ H$h H$ H H9HNH$ `EH$ H$ BHl$H H$ HH1L0 H|$IHt Hl$0H$ LH HD$` HD$PHALHD$hHuHD$`02 H HD$`HHDŽ$ Ld$pt#Ld$pHt$P LfoD$`f$ D$ $ LHHAH$ Ht
LLHD$`HtH|$P HH{H []A\A]A^ D LAH H H; HDŽ$ HT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ HHHt$PH|$XH$ H H$ HD$`HT$hH$ H D HDŽ$ BHT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ HHLL$PLT$XH$ H3H$ HD$`HT$h
-H$ HHD$`HHD$`HtH|$P HH{H!HH$ HHt LLH$ HHt LLH$ HHtH$ HlH|$HHtjvHL$`HHTH|$P H@H$ HHH$ H AVIAULjATIUSH H$ H$ H$ HLH{@H$ H$h H$ H H9HNH$ AH$ H$ JHl$H H$ HH1L0 H|$IHtHl$0H$ LH HD$` HD$PH=LHD$hHjpHD$`2 H HD$`HHDŽ$ Ld$pt#Ld$pHt$P LfoD$`f$ D$ $ LHHqH$ Ht
LLHD$`HtH|$P HH{eH []A\A]A^ D LHI > HDŽ$ HT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ H{HHt$PH|$XH$ HH$ HD$`HT$hH$ H HDŽ$
-HT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ HzHLL$PLT$XH$ H[H$ HD$`HT$hH$ HHD$`HHD$`HtH|$P HH{H HH$ HHt LLH$ HHt LLH$ HHtH$ HlH|$HHtj D qHL$`HHOH|$P H;H$ HHH$ Hf. AUATUSHHHHHH LA0A@% ~Ct uYHHHH[]A\A] H(HHH[]A\A]f. uHHHH[]A\A] D Lhq. Hl$ HT$H5e>[ HD$HLd$0H L ; H+HD$0HD$0 HHt?HELl$LHxHHl$0HtHELHxHHD$ LHxHHH[]A\A]f HhHHH[]A\A]HHD$ Ht$HxTH;HteH HHH AUATUHSHHHh8 IH H I|$0w tu f2 H|$0HLHH+HD$0HD$0 HHt.HELd$PLHxHHl$0H HhH[]A\A]fH|$HLH-H+HD$HD$ HHtHELd$PLHx/HwHl$H ucH|$ HLH(H+HD$ HD$ HH[HELd$PLHxHHl$ H + n. Hl$@HT$0H5;[ HD$0HLd$PH L; H+HD$PHD$P HHt?HELl$ LHx=HHl$PHtHELHxHcHD$@LHxHhH[]A\A] @ HHLH_H+H$H$ HHDHELd$PLHx
HH,$HHELHx
HHhH[]A\A]HHD$@Ht$ Hx^
H;HtbH HRHf. AVAUATIHVUSH H$ H$ H$ HwH{7H$ H$h H$ H H9HNH$ F8H$ H$ 18Hl$@H H$ HH1L0 H|$@HHtHtML$ H$ HLLHD$ D$ $ 4H{4H []A\A]A^ÿX Lt$0
-HT$/H58[ LI~Ld$`L< H$ H
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H$ H8 : HD$(H9D$ H] @ L$8 H$0 H H|$0It HD$ LLT$ IHIAI9LOL)HH?HHLE1M~ BBIM9uI9J }&f. fHfB)IL9LT$ H9~ HH9uL9T$("H}I$ H<$Ht+ HH L[]A\A]A^A_f D I1<HD$(H9D$ tH]f D H$0 HL$8 HT$ LD$0AD N L\$ HLHL9IOH)HH?HHHH HH<H?H9HHHFH H HB HB HB HB B(A H9tBII)MIMMt(f(H<1fHHf)OL9rMM9tBH9}$f(ff f) HH9L\$ I9 H4I)HH<H?L9IGIIFHt`HHy HHy HHytzHHytkHHyt\HI9t>I)LHIMt(f(H41fHHf)NH9rLM9tL9\$(vH 1L f D HuA1E1A A vA kA `A UHH|$@HHtH H}H<$Ht1 HHHX Ll$ I{H5l6[ HLI&
-H\$@H)< Hl$`H
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LLHE HxH[]A\A]A^A_Ll$X YLH H\$ H< Ld$@H
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LLH
HL$PHHtH|$@ HILMxHLwHD$Ht$HxHIH @ Hf AWAVAUATUHHSHHxL`1HIL谻0 LLkI12 HH HH9 L7LI诂 IHD$P L`LpHHۖ Ld$@H3] LLZ LHHHKD$ HT$@HT$ D$(T$0HD$XH3zHD$P< HD$PHt
LLHE HxH[]A\A]A^A_Ll$X LHΚH\$ H< Ld$@H
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LLH
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LLHE HxH[]A\A]A^A_Ll$X LH~H\$ Hq< Ld$@H
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LLH`HL$PHHtH|$@ HILM$tHLYsHD$Ht$HxH IH @ Hf AWAVAUATUHHSHHxL`1HIL0 LLkI葉2 HH HH9 L&LIo IHD$P L`LpHHז Ld$@H?/] LZ LHHHAD$ HT$@HT$ D$(T$0HD$XHtHD$P < HD$PHt
LLHE HxH[]A\A]A^A_Ll$X iLH.H\$ H!< Ld$@H
v[ H5+[ Iغ_ LhH+[ LLHrLQrHqHD$LHx8HH5Z HHL$PHHt
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-] M9^H8[]A\A]A^A_f(]HH9u 1Hf D 1f D (]HH9uH H8 HFH9tHGWH_hHD$ I I H)d$I H|$ \ I $rI6LD MN $(d$@N$M&K HLHHڃL9IOH)HyHHHHHH Hd IxH9H~AI9@AE (I1IE1 N (((IA 8AL8TUV>D>HM9wL9t>(I{C]H9B~&(IL9A]~(C]BH9}0(I< (])HHH9M&I9 HH<MHT I)HBH9HGAH9A I LHL, MtI(1E1 (((I LTUVDHI9rLM9t9(HAA]I9~"(HI9A]~ A] M9fHGhWHD$ HGHD$(HFH9f I H|$ )$M I H|$(I M HD$II~It ($H,I. IHIIAI9LOL)LBHIHHLM LFLYL9AL9AE LCL9AL9AE Ef I\ ME1E1IL $I((IBB BDBL]BBDIL9$wM9tHBMCB]M9B~-BIB]M9B~BB]BI9 N E1J,N I IA(C ]C)IL9ML$H9 LD$HHMT H)IO LIAM`I9AM9A IBI9M9AD A H IIN, MtD1E1((IAA ADAL]A AD HM9wLL9t?HB]H9~'H]H9~]H$I9F? f. ]HH9uf D (A]HI9u @ 1Lf D I1]f D E1 D BB]BIM9uI9H,$Q 1f D (A]HH9u, @ AVHAUATUSHHĀH HG Hl$H£0 H|$ u
H[]A\A]A^ÿX Lt$ HIC; LHXLd$0L;< Hl$PH
\ H5@ [ M H^HZ LHLhHkgLfHD$ HHxRHwH5tu LHH|$HtFH{HtlH$IHLgL>fHD$ Ht$HxLHHHHf. AWIAVIAUATUSH(HG(H8 HGhHD$HGH$HFH9 I H|$I I H<$II L$I6MN\ N @M HLHL9IOH)HH?HHHH H
- ID$H9HF@I9@ f(HE1H1fL C f(f(HfCLffTfUfVfBfBDIH9wI9tf(C]BH9}2f(If D ff(f]f)HHH9M I9 ILMJ<I)HGI9IBH9 I
- MILHtRf(11f f(f(HfLffTfUfVfAfADHL9rHI9t A] M9^H([]A\A]A^A_ f(A]HI9uf D 1L7f D 1f D f(A]HH9uH H8 6 HFH9{HGH_hHD$ I I H*I H|$\ I $I6LL MV $@N,M.] HLHL9IOH)HH?HHHH H
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OI HfB*fB f]fA)HH9MI9 H$HLM$H)HH HHCLjH9AL9A ID$H9M9AD At{H vuIIMMtA1E1IA fDfALf]ffDHM9wLL9tD A]M9Nf D D A]HI9u @ f(A]HI9uP 1Lf D L1f D 1f D f(A]HH9u$ 1f D D A]HH9u6f AVHAUATUSHHĀH HG Hl$HR0 H|$ u
H[]A\A]A^ÿX Lt$ HI; LHLd$0L< Hl$PH
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H[]A\A]A^ÿX Lt$ HI; LHLd$0L< Hl$PH
O\ H5[ M HPHyZ LHLZHYL#YHD$ HHxH[jH5g LlHH|$HtJ9H{HtHIHLYLXHD$ Ht$HxkLHHHHf. AWIAVIAUATUSH(HG(H8 HGhHD$HGHD$HFH9 I H|$I @I H|$IM H\ I6MFA| N,@M.S HLHHڃL9IOH)HHHHIHHH H$ HCH9HFAH9A <$IE1fn<$IE1J fp Bo foIfofffffBIM9rH9t@9<ADN D LHL9~(B9<AFNHH9F~9<ADN D H9}J<$Hfn4$fp fo @ ofoHfffoffffHH9M.I9 IL MJT I)HBI9IAAH9A I LHL$ MtJ<$1E1fn<$fp o foIfofffffAHI9rLM9t49<NHAI9~ 9<NHI9ȉ~
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H[]A\A]A^ÿX Lt$ HIӸ; LHLd$0L< Hl$PH
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H[]A\A]A^ÿX Lt$ >HI; LHLd$0L< Hl$PH
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H[]A\A]A^ÿX Lt$ HIê; LHLd$0L軻< Hl$PH
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+f(f]f)HHH9I9 IHMND M I)I@I9IAI9t|I
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+ HWH9HVAH9A| f(I1IE1fO, f(f(IfLffTfUfVffDHM9wL9tf(B]BH9}-f(Hf f
+f(f]f)HHH9I9 IHMND M I)I@I9IAI9t|I
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+] []A\A] f(]HI9u L1Vf D 1f D f(]HH9uf. fAWAVAUATUSHL^HLp0H L`(H HF@Hn0H^ HHD$HFMI<LhIt @ HLHL9IOH)HH?HHHH HGLVH9AL9A I@H9M9AD A H I1E1IO<A I fAD fLf]ffDHM9wL9tCB]BH9}:L 1O<NIfHfB8fB f]fA)HH9I9 HD$HHI,I MI)HIT HELbH9AL9A HCH9I9AD AtrI vlMIMMt@1E1D I fDfLf]ffDHM9wLM9tA][]A\A]A^A_ A]HI9u D L1%f D 1f D A]HH9uf. ATIUHSHH/2 Ht$LHHHD$rHH[]A\f D HH~HH HV0HHH0LQH H@( PHFHV@H@fL$fL$fL$fL$H4P~7LT$LL$1fA@fL$f9fT$LIBfFHH9u D HH~HH HV HHH(LQH H@0 PHFHV@H@fL$fL$fL$fL$H4P~7LT$LL$1fA@fL$f9fT$LIBfFHH9u D HH~HH HV0HH0LQH HV H@(HL PHFHV@H@H4P~<L\$LT$1fAAA @fT$f9LIBfL$fFHH9uATIUHSHH*%2 Ht$LHHHD$2pHH[]A\f D AVAUATUSHH^@LNHHh0L H H@(LV0DHFJ| L`I4@T HLHHL9IOH)HJHHHHHH H% HOH9HNAH9A DD$I1fnt$IE1N, fp o foIfofffffHM9wL9tBF9DBN B IMH9~)D9EDN IL9D ~F9DBN B H9}FDD$H fnl$fp foo foHfffoffffHH9I9 IHMJL MI)HAI9IBAH9A I LHH, HtMDD$1E1fn|$fp o foIfofffffAHI9rHI9t;D9DNHBI9~$D9DN HI9щ ~
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+tc } HZ_I}HD$PH`HD$XHpHD$`Ե4 H@`H)cHL$PL8X H9 I}HD$PHHD$XHHD$`胵4 H@`HbHL$PL8虶f HE HH[]A\A]A^ HI}HD$PHIHD$XH=HD$`4 H@`Hh`HL$PL8) HI}HD$PHHD$XH=BHD$`ô4 H@`HcHL$PL8ٵA uqHVI}HD$PH"VHD$XHfgHD$`l4 H@`HA`HL$PL8肵LH HHt$PH荭 D
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+tc } HJZI}HD$PHXHD$XHjHD$`4 H@`H]HL$PL8X HI}HD$PH9HD$XH=HD$`賱4 H@`H\HL$PL8ɲf HE HH[]A\A]A^ HI}HD$PHHD$XHMHD$`C4 H@`H^HL$PL8Y HI}HD$PHyHD$XH];HD$`4 H@`H_HL$PL8 A uqH"SI}HD$PHTHD$XHbHD$`蜰4 H@`Hq]HL$PL8貱LH HHt$PH轩 D
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?b H5F` I HHa LHL &Ha%H$HD$ HHxH5H5Z L貦HLHIHL%HI$HD$ LHx踜HHf. fATIUHSHH
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+(_)HHH9I9 IIMJNL I)HBI9IAAH9A I MIJ HtN(W1E1 ((I L(TUVAADHM9wHI9t6(HA_I9~ (HI9_~
+_ []A\A]Ð(_HI9u L1f D 1f D (_HH9uf. AUATUSHL^@LFHHX(L H H@0LN HFJ<HhJt @% HLHHL9IOH)HJHHIHHH H HWH9HVAH9A (I1IW N$ E1((I L(TUVDHM9wL9t>(IT$B_H9B~%(IL9_~(B_BH9}'(H f
+(_)HHH9I9 IIMJNL I)HBI9IAAH9A I MIJ HtN(W1E1 ((I L(TUVAADHM9wHI9t6(HA_I9~ (HI9_~
+_ []A\A]Ð(_HI9u L1f D 1f D (_HH9uf. AWAVAUATUSHH^HLx0L Lh(H HF@Lf0Hn HHD$HFOI| LpI4@v HHHHڃH9HOH)HHHHIHHH HGLVH9AL9A I@H9M9AD A H IWI1E1N ((IA AD L_DHM9wL9tFCIBB_H9B~+AI_L9~CB_BH9}7L 1O NI HBB _A)HH9H9 HD$IIO HI)M\ HIIALbH9@M9 ICH9M9AD @ I MIN, MtDW11(H(AA ADAL_DHI9wLM9tBAHA_H9~)AH_H9~A_[]A\A]A^A_ @ A_HH9u D H1f D 1f D A_HH9u]f. ATIUHSHHJ 2 Ht$LHHHD$OHH[]A\f D AUATUSHH^@LVHHh0L H H@(LN0 HFJ| L`I4@ HLHL9IOH)HH?HHHH H
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+_ []A\A] f(_HI9u L1Vf D 1f D f(_HH9uf. fAUATUSHH^@LVHHh(L H H@0LN HFJ| L`I4@ HLHL9IOH)HH?HHHH H
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՜ HHHSPHQHaILMJHD$HLHxWLXIH @ f. @ UHh SH [HHHHH[]HHZHrafUHh SHZHHHHH[]HHzZH2afAWAVAUATUHSHHH HԜ HHGqh3 H HHCX HC` Hl$@HHHT HHCPh3 H@ HzHH$ yH|$@YH<$ uHĨ []A\A]A^A_H
a H` AT H1W7A L|$X THLILd$ L-6A Ll$pH
1 a H5` MT L1LLLL"LZHD$LHxUHH5Ϝ Ls_HH<$HtBHH H
Ӝ HHHSPHQH_ILMHD$HLHxVULnVIH @ f. @ UHh SHXHHHHH[]HHZXH_fUHh SH}XHHHHH[]HHXH^fAWAVAUATUHSHHH HҜ HHGf3 H HHCX HC` Hl$@HHH HHCPWf3 H@ HxHH$ wH|$@~WH<$ uHĨ []A\A]A^A_H
a Hw` AT H14A L|$X RHLILd$ L3A Ll$pH
a H5*` MT LZ1LLLjLLHD$LHxiSH2H5̜ L]HH<$HtHHX H
1ќ HHHSPHQH:]ILMHD$HLHxRLTIH @ f. @ UHh SH]VHHHHH[]HHUH\fUHh SH VHHHHH[]HHUHr\fAWAVAUATUHSHHH H.М HHGc3 Hz HHCX HC` Hl$@HHH HHCPc3 H@ H(vHH$ (uH|$@ UH<$ uHĨ []A\A]A^A_H
a H` AT H12A L|$X 8PHLIZLd$ Lm1A Ll$pH
a H5` MT L1LLL
+LbLHD$LHx QHH5[ʜ LZHH<$HtHH H
Μ HHHSPHQHZILM*HD$HLHxPLQIH @ f. @ UHh SHSHHHHH[]HHSHRZfUHh SHSHHHHH[]HHZSHZfAWAVAUATUHSHHH H͜ HHGQa3 H HHCX HC` Hl$@HHHt HHCPa3 H@ HsHH$ rH|$@RH<$ uHĨ []A\A]A^A_H
a H` AT H170A L|$X MHLILd$ L
/A Ll$pH
a H5j` MT L1LLLLL:HD$LHxNHrH5ǜ LSXHH<$Ht"HH H
q̜ HHHSPHQHzXILMHD$HLHx6NLNOIH @ f. @ UHh SHQHHHHH[]HH:QHWfUHh SH]QHHHHH[]HHPHWfAWAVAUATUHSHHH Hn˜ HHG^3 H: HHCX HC` Hl$@HHH HHCP7_3 H@ HhqHH$ hpH|$@^PH<$ uHĨ []A\A]A^A_H
a HW` AT H1-A L|$X xKHLILd$ L,A Ll$pH
` H5
+` MT L:1LLLJLLHD$LHxILHH5Ŝ LUHH<$HtHHx H
ʜ HHHSPHQHVILMjHD$HLHxKLLIH @ f. @ UHh SH=OHHHHH[]HHNHUfUHh SHNHHHHH[]HHNHRUfAWAVAUATUHSHHH Hɜ HHG\3 H HHCX HC` Hl$@HHH4 HHCP\3 H@ HoHH$ nH|$@MH<$ uHĨ []A\A]A^A_H
n` H` AT H1w+A L|$X IHLI:Ld$ LM*A Ll$pH
` H5` MT L1LLLLBLzHD$LHxIHH5;Ü LSHH<$HtbHH H
ǜ HHHSPHQHSILM
+HD$HLHxvILJIH @ f. @ UHh SHLHHHHH[]HHzLH2SfUHh SHLHHHHH[]HH:LHRfAWAVAUATUHSHHH HƜ HHG1Z3 H HHCX HC` Hl$@HHH HHCPwZ3 H@ HlHH$ kH|$@KH<$ uHĨ []A\A]A^A_H
N` H` AT H1)A L|$X FHLILd$ L'A Ll$pH
q` H5J` MT Lz1LLLLLHD$LHxGHRH5 L3QHH<$HtHH8 H
QŜ HHHSPHQHZQILMHD$HLHxGL.HIH @ f. @ UHh SH}JHHHHH[]HHJHPfUHh SH=JHHHHH[]HHIHPfAWAVAUATUHSHHH HNĜ HHGW3 H HHCX HC` Hl$@HHH HHCPX3 H@ HHjHH$ HiH|$@>IH<$ uHĨ []A\A]A^A_H
.` H7` AT H1&A L|$X XDHLIzLd$ L%A Ll$pH
Q` H5` MT L1LLL*LLHD$LHx)EHH5{ LNHH<$HtHH H
HHHSPHQHNILMJHD$HLHxDLEIH @ f. @ UHh SH HHHHHH[]HHGHrNfUHh SHGHHHHH[]HHzGH2NfAWAVAUATUHSHHH H HHGqU3 H: HHCX HC` Hl$@HHH HHCPU3 H@ HgHH$ fH|$@FH<$ uHĨ []A\A]A^A_H
` H` AT H1W$A L|$X AHLILd$ L-#A Ll$pH
1` H5` MT L1LLLL"LZHD$LHxBHH5 LsLHH<$HtBHHx H
HHHSPHQHLILMHD$HLHxVBLnCIH @ f. @ UHh SHEHHHHH[]HHZEHLfUHh SH}EHHHHH[]HHEHKfAWAVAUATUHSHHH H HHGS3 HZ HHCX HC` Hl$@HHH HHCPWS3 H@ HeHH$ dH|$@~DH<$ uHĨ []A\A]A^A_H
` Hw` AT H1!A L|$X ?HLI躺Ld$ L A Ll$pH
` H5*` MT LZ1LLLjLLHD$LHxi@H2H5 LJHH<$HtHH H
1 HHHSPHQH:JILMHD$HLHx?LAIH @ f. @ UHh SH]CHHHHH[]HHBHIfUHh SH CHHHHH[]HHBHrIfAWAVAUATUHSHHH H. HHGP3 Hz
HHCX HC` Hl$@HHH
HHCPP3 H@ H(cHH$ (bH|$@ BH<$ uHĨ []A\A]A^A_H
` H` AT H1 A L|$X 8=HLIZLd$ Lm A Ll$pH
` H5` MT L1LLL
+LbLHD$LHx >HH5[ LGHH<$HtHH H
ѻ HHHSPHQHGILM*HD$HLHx=L>IH @ f. @ UHh SH@HHHHH[]HH@HRGfUHh SH@HHHHH[]HHZ@HGfAWAVAUATUHSHHH Hκ HHGQN3 H HHCX HC` Hl$@HHH4 HHCPN3 H@ H`HH$ _H|$@?H<$ uHĨ []A\A]A^A_H
` H` AT H17 A L|$X :HLILd$ L
A Ll$pH
` H5j` MT L蚺1LLLLL:HD$LHx;HrH5 LSEHH<$Ht"HHX H
q HHHSPHQHzEILMHD$HLHx6;LN<IH @ f. @ UHh SH>HHHHH[]HH:>HDfUHh SH]>HHHHH[]HH=HDfAWAVAUATUHSHHH Hn HHGK3 H HHCX HC` Hl$@HHHT HHCP7L3 H@ Hh^HH$ h]H|$@^=H<$ uHĨ []A\A]A^A_H
` HW` AT H1A L|$X x8HLI蚳Ld$ LA Ll$pH
` H5
+` MT L:1LLLJLLHD$LHxI9HH5 LBHH<$HtHH H
HHHSPHQHCILMjHD$HLHx8L9IH @ f. @ UHh SH=<HHHHH[]HH;HBfUHh SH;HHHHH[]HH;HRBfAWAVAUATUHSHHH H HHGI3 H HHCX HC` Hl$@HHHt HHCPI3 H@ H\HH$ [H|$@:H<$ uHĨ []A\A]A^A_H
n` H` AT H1wA L|$X 6HLI:Ld$ LMA Ll$pH
` H5` MT Lڵ1LLLLBLzHD$LHx6HH5; L@HH<$HtbHH H
HHHSPHQH@ILM
+HD$HLHxv6L7IH @ f. @ UHh SH9HHHHH[]HHz9H2@fUHh SH9HHHHH[]HH:9H?fAWAVAUATUHSHHH H HHG1G3 H HHCX HC` Hl$@HHH HHCPwG3 H@ HYHH$ XH|$@8H<$ uHĨ []A\A]A^A_H
N` H` AT H1A L|$X 3HLIڮLd$ LA Ll$pH
q` H5J` MT Lz1LLL芽LLHD$LHx4HRH5ۭ L3>HH<$HtHH8 H
Q HHHSPHQHZ>ILM誻HD$HLHx4L.5IH @ f. @ UHh SH}7HHHHH[]HH7H=fUHh SH=7HHHHH[]HH6H=fAWAVAUATUHSHHH HN HHGD3 H HHCX HC` Hl$@HHH HHCPE3 H@ HHWHH$ HVH|$@>6H<$ uHĨ []A\A]A^A_H
.` H7` AT H1A L|$X X1HLIzLd$ LA Ll$pH
Q` H5` MT L1LLL*L肺L躹HD$LHx)2HH5{ L;HH<$HtHH H
HHHSPHQH;ILMJHD$HLHx1L2IH @ f. @ UHh SH 5HHHHH[]HH4Hr;fUHh SH4HHHHH[]HHz4H2;fAWAVAUATUHSHHH H HHGqB3 H: HHCX HC` Hl$@HHH HHCPB3 H@ HTHH$ SH|$@3H<$ uHĨ []A\A]A^A_H
` H` AT H1WA L|$X .HLILd$ L-A Ll$pH
1` H5` MT L躮1LLLʸL"LZHD$LHx/HH5 Ls9HH<$HtBHHx H
HHHSPHQH9ILMHD$HLHxV/Ln0IH @ f. @ UHh SH2HHHHH[]HHZ2H9fUHh SH}2HHHHH[]HH2H8fAWAVAUATUHSHHH H HHG@3 H HHCX HC` Hl$@HHHt HHCPW@3 H@ HRHH$ QH|$@~1H<$ uHĨ []A\A]A^A_H
` Hw` AT H1A L|$X ,HLI躧Ld$ L
A Ll$pH
` H5*` MT LZ1LLLjLµLHD$LHxi-H2H5 L7HH<$HtHH H
1 HHHSPHQH:7ILM芴HD$HLHx,L.IH @ f. @ UHh SH]0HHHHH[]HH/H6fUHh SH 0HHHHH[]HH/Hr6fAWAVAUATUHSHHH H. HHG=3 H: HHCX HC` Hl$@HHH HHCP=3 H@ H(PHH$ (OH|$@ /H<$ uHĨ []A\A]A^A_H
` H` AT H1 A L|$X 8*HLIZLd$ Lm A Ll$pH
` H5ʱ` MT L1LLL
+LbL蚲HD$LHx +HH5[ L4HH<$HtHH H
Ѩ HHHSPHQH4ILM*HD$HLHx*L+IH @ f. @ UHh SH-HHHHH[]HH-HR4fUHh SH-HHHHH[]HHZ-H4fAWAVAUATUHSHHH HΧ HHGQ;3 HZ HHCX HC` Hl$@HHH HHCP;3 H@ HMHH$ LH|$@,H<$ uHĨ []A\A]A^A_H
` H` AT H17
+A L|$X 'HLILd$ L
A Ll$pH
` H5j` MT L蚧1LLL誱LL:HD$LHx(HrH5 LS2HH<$Ht"HHX H
q HHHSPHQHz2ILMʯHD$HLHx6(LN)IH @ f. @ UHh SH+HHHHH[]HH:+H1fUHh SH]+HHHHH[]HH*H1fHH8HH fUSHHtb~@tuH?*H1[] D H. *HU HHH[1]ÅuHe HH1[]f HHH1[]ÐATHW UHSHHH HtHP H Hǃ HtH{xHtHPH Hǃ Ht L%U( HEMtcЃt$H{`Ht)Hg HH[]A\ HE HPMHE t'ЃuHE HP뭋EPU띋E PU ATHg UHSHHH HtHP H Hǃ HtH{xHtHPH Hǃ Ht L%e' HEMtkЃt,H{`Ht(Hw HHH[]A\( @ HE HPMHE t'ЃuHE HP륋EPU땋E PU f. AVH9AUATIUSHtDHNL.H?HCHL)H)HHH9wqLCLH)HH9w H H{H[]A\A]A^ D HIt H)H<HHtH Lz-HH<H{ D H H H9 L4 L'HI H;Ht'K<4L#H{H{V HLH,HH;H{1f. H L,LCH;IL$LH)H<HI4$H)HH D HLL,H;HVVf E1E15 D ;!f. H H AUIATIUHSHXHPHHhHhH HP HD$@ ZL$&HHl$0HT$HD$0HKL` HL$LHD$HH8T$T$L$ HD$@@ HD$@Ht
HHHHP HX[]A\A]HHHP H|,HL$@IHt
HHLHL$@HHtH|$0 Hf. D ATH UHSH@HD$ H HD$8H HD$00 Ho%Hƹ 1HL%! HHHF(F F IL$H|$HF8HF@H HHNH H H HNHt$H H H5*a 2 H5E8` Hp2 H5f Hq2 HH5._ HB
Dr2 H8Hn2 HH HE HE HD$0H H]Ht$ HfoD$0HD$0EH\$HE( H H H gH H gH{hHt#H{PHt#Hs0H{ aHCHxL9 HCHxL9uoHHxL9u:H#HD$0HtH|$ HH@H[]A\f D 17H=! HW Ht$!H=! HW pHt$!aH=X! HWtB7Ht$!(HHD$0HtH|$ HHp)PJHHMIHt
HHH} HtfHE LH|$HtfHPJH PJH$f. ATHG UHSH@HD$ HHD$8HHD$0l0 H/"Hƹ 1HL% HHHF(F F IL$H|$HF8HF@H HHNH H H HNHt$H H H5&a 諀2 H55` Hm2 H5Xf H]n2 HH5+_ HB B
+ n2 H8HBk2 HH HE HE HD$0H H]Ht$ HfoD$0HD$0EH\$HE( H H H dH H dH{hHt H{PHt Hs0H{ ^HCHxL9 HCHxL9upHHxL9u;HD HD$0HtH|$ HH@H[]A\ 16H=i HW Ht$ H=@ HW oHt$} `H= HWtB6Ht$Q 'HHD$0HtH|$ HH(&PJHHMIHt
HHH} HtcHE LH|$HtcH D PJHPJH f. @ ATH7 UHSH@HD$ HBHD$8HFHD$0 0 H Hƹ 1HL%K HHHF(F F IL$H|$HF8HF@H HHNH H H HNHt$H H H5r#a [}2 H51` H\j2 H5f H
k2 HH5/(_ HB B k2 H8Hg2 HH HE HE HD$0H H]Ht$ HfoD$0HD$0EH\$HE( H H H BaH H /aH{hHtA H{PHt3 Hs0H{ f[HCHxL9 HCHxL9upHHxL9u;H HD$0HtH|$ HH@H[]A\ 16H= HW Ht$ZH= HW oHt$-`H= HWtB6Ht$'HHD$0HtH|$ HH"PJHHMIHt
HHH} Ht\`HE LH|$HtB`H D PJHPJH f. @ ATHg UHSH@HD$ HHD$8HHD$00 HHƹ 1HL% HHHF(F F IL$H|$HF8HF@H HHNH H H HNHt$H H H5" a z2 H5e.` H g2 H5f Hg2 HH5$_ HB B ]h2 H8Hd2 HH HE HE HD$0H H]Ht$ HfoD$0HD$0EH\$HE( H H H ]H H ]H{hHtH{PHtHs0H{ XHCHxL9 HCHxL9upHHxL9u;HHD$0HtH|$ HH@H[]A\ 16H= HW Ht$
+H= HW oHt$`H=p HWtB6Ht$'HHD$0HtH|$ HH PJHHMIHt
HHH} Ht ]HE LH|$Ht\H D PJHPJH f. @ AWAVAUATIUSHHH H L$ Hl$ HHG4&3 H H5` HC` HCh HCp LHCx Hǃ HHHǃ Hǃ HH|$HHLI
.3 L|$M IGH-4 HxH9O L_HD$ HxH9 M% H<$L|$PH5` LLpCPPH|$@LLL-3 Lt$@MtIFHxH9@ LHD$PHxH9 M H<$L$ H5'e LL{XLpCTH|$pLLL+3 Lt$pMtIFHxH9 LnH$ HxH9f M L$ H5e LL{\LbH$ LLL|+3 L$ MtID$HxH9~ LH$ HxH9 Mu'H []A\A]A^A_f H-y UL$ X LIL$0 L7@ H$ H
` H5` M HH` LHL̚H$L\H$ HHxHH5 LrHH HtHP H{xHǃ 詎H{`HtHt HHHH= HW* H$ WL$ X LIH$ H@ H
` H5` I L觏H` LLL貙L
+HBH$ H$ HxHrH5 LSH=K HW8 yH$ gLt$`X LIH$ HF@ L$ H
` H5 ` I LЎHy` LLLۘL3HkHD$`H$ HxHH5' LLt$0X pLIuH$ H@ L$P H
F` H5` I L/H` LLL:L蒗HʖHD$0H$ Hx4HH5 LH= HW H$ H= HW RH$ @H=p HW H$ ~H== HW NH$ w<H=
+ HWc H$ DH= HWtmeH$ SILM"HLWHD$0H$ Hx
LHŐHIPJHILMɕHLH$ H$ Hxe
L}IHHPJHPJH$PJHPJH7IHL;LsH$ LHx L
#HHHILMHL*HD$`H$ Hx IH D HH$ H$ Hx_ HHD$ H$ HxA HHD$PH$ Hx# oHH$ H$ Hx NPJHNPJHqPJHf. UH SHMHHHHH[]HHHfUH SH
HHHHH[]HHHbfUH SHHHH?HH[]HHjH"fUH SHHHHHH[]HH*HfAWAVAUATIUSHH( HHFHG H)HHpH Hl$@X 6 HI;L$ Lk@ L$0 H
` H5E` M
LHv` HLL LXL萑HD$@H$ Hx HâH5L L @ I4$I|$H)HHI M H HH EHC H. HkH9 L= L$ H
b` I 1LL訏H` 1LRH-[ HL$ LCLLHUHL$H
b` H 1WHB` 1LHHUH$ LHHL$d5 L$ I|$XH Ƅ$ LHDŽ$ Ƅ$ 1HDŽ$ HDŽ$ HHDŽ$ HDŽ$ H$ H HHH@HyH$ HL$HD$ HH0 I|$@HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHDŽ$ H$ H H@H$ HE HH$ H It$I|$XDŽ$ HH$ HEH$
H5` L H$ LHV HD$LHPH HD$LHPH H$ H}H|$pH L$ L$ L9 L9o M L)H$` 1HI IIHH HLLLD$(LD$(HI9 L$ Lt$pH$ LL9H$ HxH9 HU HH$ HB H@H$ H HH$ H$ HxH9 H I|$@HH$ HD$HT$ I|$XH$ H@H H HH$ >H|$`L G? LsHD$`HD$` MHCt<IFHxH9> LLt$`MtIFHxH9 LHD$pHxH9r H$ HxH9/ HD$HxH9 H$ HxH9t HD$HxH92 H$ HxH9 HkIEI+E L#H H$ H
#` ? LcH$ HDŽ$ MHCtHID$H- HxH9 LL$ MtID$HxH9` LH; H{ H{ H( []A\A]A^A_f Hn1T D H H H! Ld5 HtIU IEH)HH HHI9C HH9j?f. M9 M @ Z M)H$` 1LIIHHt`HLLLD$(uLD$(HI9 L$ Lt$pH$ LL v AA@9 AA@f D HHHAf HAHHH9*WfHt HHH{HtIHC Hg H= HW H$ 9 @ Lt$pIt$PLf MH=8B_ L$ HHD$pHt$?HxLU
H$ LHxH|$LHH$ LHx|H|$LHkH$ LHxWfH=A_ T @ HJHH9L$ X LIH$ H@ L$ H
(` H5` I, LH/` LLL蜉LH,H$ H$ Hx H\H5y L=
+Lt$PX .LI3H$ Hc@ L$` H
` H5=` I L~HI` LLLLPH與HD$PH$ HxHH5Dy L H= HW~ LH=f HG` ЅLH=6 HWC kLu^H= HGF Ѕ&LEH= HW L$ LH= HW L H=t HW mL `H=F HW3 H$0 H= HW LR H= HW L$ A@ M8CD8 A@ M8CD8 JH= HWt\H$ qLHمLHD$@H$ Hx{L HPJHHL$ PJHyHL$APQPJHHHD$pLL$ HxCHL$APQHH$ H$0 HxPJHILMHLHD$PH$ HxLIHHPJHRPJHxPJHPJHHHHL$ hHH I|$XL$ HH$ q'HL$ PHL$ ,LIHL&H$ H$ Hx
IH PJH;I|$H\HD$HL$ H$ H@H 3OPJH @ AWWɸ AVAUATIUSH .D M HI IHHL[5 H IU IEH)HH H: HzE I@ H1HH H | IT$H9HU@I9@] IWI11J< (HAADD DHI9wH9t6HWAD H9~!HAH9D ~ AD H9I}f D H)D HH9H9 H4 II)I4HHFH9HB@H9@ I MIN Mt1W11(HDDHI9wLM9t8HAAD H9~#HAH9D ~AD fHĘ []A\A]A^A_f D M~HH IT$H1L H|$H$HT$HK<MPMHWLD$HO,O 1I H Mt/11(HAADDHH9t$wI9t=Ht$AL9H1D ~$JAE L9D ~J AD HH$HL9H~HPI9@H;D$@P1f D AHH9uf. AD HH9uf D HHHHH9HOH)HJHHIHfMWHLL< HHE1L H<$1L\$MkMc H Mt+1I IHH94$ BBDwL9t9Ht$ L9H 7D ~!J / L9D ~ J'D IHHM9LHILH~LHH9AL9@AO HAH9u @ I@A$\ H1HH HHƃHHكH9HFHg HE HEy HE` HE G HE. E H9teII)MIN Mt'(H| 1 HH)OI9wLM9t$HqD H9~HD H9~D H9}( @ )L HH9H9Ht HH)HHHH9HGHHFHtuHHz8 HD Hz$ HD Hz HD Hz HD Hz D HH9uH)IIJ< Ht*(H41 HH)NI9wHH94HBD H9!HD H9D 1f D AD HH9uf D HHHHH9HOH)HJHHHH{f. HH 11 X Ll$/Lt$0H5` LLIHl$@H @ H\$`H
` H5` IP HqHf` LHL{H{HFzHD$0HHxH~H5l L_HIHLzHzHD$0LHxpLHHHHAWAVAUATUHSHH8HLT$pLl$xH$ L$ L$ uH8[]A\A]A^A_f D HtD$(LD$AAMLHHL\$(LT$ LL$L$ML$HD$T$LL$LT$ L\$(t<Ld$xL$ HLT$pH8H[]A\A]DDIA^A_(. D Wf. AWAVAUATUSHH HF H+FHHt1HĘ []A\A]A^A_f D HvILAL$HL$EI W LxL0LL@ HtH|$01L1H$3 H|$0H$HHRLl$8IHT$0MtH=O IE HuLMHt!H} HtHHT$L $PHT$L $HEHU LmHt(H- HPH
+ʃ MtLL $UL $HC0EMMDƃx! H HKPALLp@ H\$8Ht$H-} HCH Ѓt nf D L8LpHHPHHC c ЃuHHPHLL$HH$RH$HLL$HP tN
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^ H^ H5^ LIHl$@H@ H\$`H
` H55_ I躗 H%mH^_ LHL0wHvHuHD$ HHx/HH5g LPJHC PS IHL/vHguHD$ Ht$ HxLHHH ULSHHA tH HH[]f. H1҃~ToHNxH~LF`HE f. AWAVAUIHATUHSH1HH HBHD$82 HI HxH HP0 I$HD$(E1H H2 HI9 HxH HP0 M$$M It$I|$ HDŽ$ HDŽ$ HDŽ$ H)HH E1E111HMH$ H$ H$ tHL.HLLd$0H$ E1{ToHC`INL$ H$ MAMHD1HD$({PoHpH$ Ht%L$( M JHA$It$H{AD$C+HCHxH;= HH$( H HËE HuH{ECH$( I] Ht HEHxH;=8 t HjHH L[]A\A]A^A_ D X I|$IY0 H$ H$ H5^ H$ HL$ L@ L$ H
s` H5^ MW L<iH!e HLLGsLrLqH$ LHxCH H5c L D X I|$IY0 H$ H$ H5^ H$ H*L$ L@ L$ H
` H5^ MW LthHYe HLLrLqLqH$ LHx{HDH5b L% D H H9 H HHT$0It$MD$ HHT$0I)IN< fH$ L$ LH$ 1HHDŽ$ HD$@H$ H$ HL$HL$ H$ 2 M| IE H$( k HDŽ$ HDŽ$ HDŽ$ HD$0 Cf D 11 HbIQHH7 aX H$ H}IH2 H@HHxL: H5` HH0Hl$pHHpH$ H@ L$ H
ѽ` H5J
+` Iغn LZf1HLLjpLoHnHD$pLHxiH$ LHxUH H5` LHHC mX H$ H}IH2 H@HH8K: H5` HH=Hl$pHH}H$ H@ L$ H
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W H5xV I H@=HV LHLKGHFHEHD$ HHxHWH5T L$Ld$ X %LIiHl$0H8 H\$PH
1W H5V I H<H+V LHLFeHL$`HHt
HHHHL$`HHtH|$P HHIHLEHDHD$ Ht$ HxLHHHHL$`HHtH|$P HHL$`HHtH|$P HwHL$`HHB HH0HL$`HH HH
4bf Yf. Nf. AWAVAUATIUHSH f. Y D$0% HHH觔HSH$ H$ HpH$ H{NH$0 H$ H$ H H9HNH$ H$ H$ jL$ H H$ L+L1HN, L$ H$ HLSH$ HtHD$ D$ $ LH{蟲I$ H L[]A\A]A^A_fHH$ 萓 IHpINH$ IH@H{L$ HHL$HHHD$@ H$0 H$ H$ H H9HNH$ MH$ H$ 8Ll$`H H$ LL1H , H|$`HD$(Ht
-HD$(HxL$ X. HHD$8 MHm L9 1LIH H|$@FX. LIH|$HHD$@,X. LIH|$(IDCLL$@HT$8MD$0D$ D$ D$ LL,$HHH{Cf HDŽ$ ZHL$(HMLhHHH$ HhH$ H[H$ I*YX MZ$ $ I*HD$81H$ YD$0L$ ILHH$ H 7 H$ H LL LLHH H*XU LLHH H*XcH$ HHt
LLH{蕯HH$ HHtH$ HLt$PX LIcH$ HϺ8 L$ H
`V H5V I2 L6HV LLL@L?H4?HD$PLHxHlPH5N L}H'ILM?HL>HD$PH$ HxLIHHf D H$ HHH|$`HHuf. @ SHHH V D$ D$ $ :H H[ÐAWAVAUATUHh SH rHHQ HH{HHŏ, H HHCX HC` Hl$pHHH HHCP, H8 H蜟HH$ 蜞H|$pH<$ uHĨ H[]A\A]A^A_H
X HPV AT H1踹8 L|$X HLI*Ld$ L莸8 Lt$@H
X H5V MT L[41LLLk>L=L<HD$HHxH3NH5K LDHH<$Ht# H̙ HHCPH=P HHHHfILM<HD$HLHx4LLHHfIH @ AWAVAUATUHh SH HHO HH{HHՍ, H^ HHCX HC` Hl$pHHH HHCP , H8 H謝HH$ 謜H|$pH<$ uHĨ H[]A\A]A^A_H
X H`V AT H1ȷ8 L|$X HLI(Ld$ L螶8 Lt$@H
X H5V MT Lk21LLL{<L;L ;HD$HHxHCLH5I LTHH<$Ht3H HHCPHMN HHHHvILM:HD$HLHxDL\HHfIH @ AWAVAUATUHh SH HHM HH{HH, H. HHCX HC` Hl$pHHHȠ HHCP, H8 H輛HH$ 輚H|$pH<$ uHĨ H[]A\A]A^A_H
/ X HpV AT H1ص8 L|$X HLI&Ld$ L讴8 Lt$@H
B
-X H5#V MT L{01LLL:L9L9HD$HHxHSJH5G LdHH<$HtCHl HHCPH]L HHHHILM8HD$HLHxTLlHHfIH @ AWAVAUATUHh SH HHK HH{HH, H> HHCX HC` Hl$pHHHآ HHCP+, H8 H̙HH$ ̘H|$pH<$ uHĨ H[]A\A]A^A_H
X HV AT H18 L|$X HLI$Ld$ L農8 Lt$@H
X H53V MT L.1LLL8L7L+7HD$HHxHcHH5E LtHH<$HtSH HHCPHmJ HHHHILM6HD$HLHxdL|HHfIH @ AWAVAUATUHh SH HHI HH{HH, HΡ HHCX HC` Hl$pHHHh HHCP;, H8 HܗHH$ ܖH|$pH<$ uHĨ H[]A\A]A^A_H
?X HV AT H18 L|$X HLI #Ld$ Lΰ8 Lt$@H
b X H5CV MT L,1LLL6L6L;5HD$HHxHsFH5 D LHH<$HtcH HHCPH}H HHHHILM4HD$HLHxtLHHfIH @ AWAVAUATUHh SH HHG HH{HH, H^ HHCX HC` Hl$pHHH HHCPK, H8 HHH$ H|$pH<$ uHĨ H[]A\A]A^A_H
X HV AT H18 L|$X )HLI!Ld$ Lޮ8 Lt$@H
2X H5SV MT L*1LLL4L4LK3HD$HHxHDH5 B LHH<$HtsH HHCPHF HHHHILM2HD$HLHx脽L蜾HHfIH @ AWAVAUATUHh SH HHF HH{HH%, H HHCX HC` Hl$pHHH HHCP[, H8 HHH$ H|$pH<$ uHĨ H[]A\A]A^A_H
_ X HV AT H18 L|$X 9HLI+ Ld$ L8 Lt$@H
W H5cV MT L(1LLL2L#2L[1HD$HHx
-HBH5,@ LHH<$HtH, HHCPHD HHHHILM0HD$HLHx蔻L謼HHfIH @ AWAVAUATUHh SH HHD HH{HH5, H HHCX HC` Hl$pHHH HHCPk, H8 H HH$ H|$p"H<$ uHĨ H[]A\A]A^A_H
oW HV AT H1(8 L|$X IHLI; Ld$ L8 Lt$@H
W H5sV MT L&1LLL0L30Lk/HD$HHxH@H5<> LHH<$HtH< HHCPHB HHH HILM.HD$HLHx褹L輺HHfIH @ AWAVAUATUHh SH HH(B HH{HHE, HN HHCX HC` Hl$pHHH蕒 HHCP{, H8 H HH$ H|$p2H<$ uHĨ H[]A\A]A^A_H
W HV AT H188 L|$X YHLIKLd$ L8 Lt$@H
W H5V MT L$1LLL.LC.L{-HD$HHx*H>H5L< LHH<$Ht
H HHCPH@ HHH.HILM-HD$HLHx贷L̸HHfIH @ AWAVAUATUHh SH HH8@ HH{HHU~, H HHCX HC` Hl$pHHH HHCP~, H8 H,HH$ ,H|$pBH<$ uHĨ H[]A\A]A^A_H
W HV AT H1H8 L|$X iHLI[Ld$ L 8 Lt$@H
2W H5V MT L"1LLL,LS,L+HD$HHx:H<H5\: LԿHH<$Ht H\ HHCPH> HHH>HILM+HD$HLHxĵLܶHHfIH @ AWAVAUATUHh SH HHH> HH{HHe|, H HHCX HC` Hl$pHHHH HHCP|, H8 H<HH$ <H|$pRH<$ uHĨ H[]A\A]A^A_H
W HV AT H1X8 L|$X yHLIkLd$ L.8 Lt$@H
W H5V MT L 1LLL +Lc*L)HD$HHxJH:H5l8 LHH<$Ht Hl HHCPH< HHHNHILM()HD$HLHxԳLHHfIH @ AWAVAUATUHh SH "HHX< HH{HHuz, H> HHCX HC` Hl$pHHHؔ HHCPz, H8 HLHH$ LH|$pbH<$ uHĨ H[]A\A]A^A_H
/W H V AT H1h8 L|$X 艱HLI{Ld$ L>8 Lt$@H
RW H5V MT L 1LLL)Ls(L'HD$HHxZH8H5|6 LHH<$HtH| HHCPH: HHH^HILM8'HD$HLHxLHHfIH @ AWAVAUATUHh SH 2HHh: HH{HHx, Hΐ HHCX HC` Hl$pHHHh HHCPx, H8 H\HH$ \H|$prH<$ uHĨ H[]A\A]A^A_H
W HV AT H1x8 L|$X 虯HLILd$ LN8 Lt$@H
"W H5V MT L 1LLL+'L&L%HD$HHxjH6H54 LHH<$HtHL HHCPH8 HHHnH&ILMH%HD$HLHxL HHfIH @ AWAVAUATUHh SH BHHx8 HH{HHv, H^ HHCX HC` Hl$pHHH HHCPv, H8 HlHH$ lH|$p育H<$ uHĨ H[]A\A]A^A_H
OW H V AT H1舠8 L|$X 詭HLILd$ L^8 Lt$@H
rW H5V MT L+1LLL;%L$L#HD$HHxzH5H52 LHH<$HtH܇ HHCPH
7 HHH~H6ILMX#HD$HLHxL HHfIH @ AWAVAUATUHh SH RHH6 HH{HHt, H HHCX HC` Hl$pHHH HHCPt, H8 H|HH$ |H|$p蒰H<$ uHĨ H[]A\A]A^A_H
W H0V AT H1蘞8 L|$X 蹫HLILd$ Ln8 Lt$@H
BW H5V MT L;1LLLK#L"L!HD$HHx芬H3H50 L$HH<$HtH~ HHCPH 5 HHH莯HFILMh!HD$HLHxL,HHfIH @ AWAVAUATUHh SH bHH4 HH{HHr, H> HHCX HC` Hl$pHHH؎ HHCPr, H8 H茂HH$ 茁H|$p袮H<$ uHĨ H[]A\A]A^A_H
oW H@V AT H1訜8 L|$X ɩHLI
Ld$ L~8 Lt$@H
W H5V MT LK1LLL[!L L HD$HHx蚪H#1H5. L4HH<$Ht H HHCPH-3 HHH螭HVILMx HD$HLHx$L<HHfIH @ AWAVAUATUHh SH rHH2 HH{HHp, H HHCX HC` Hl$pHHHh HHCPp, H8 H蜀HH$ H|$p責H<$ uHĨ H[]A\A]A^A_H
?W HPV AT H1踚8 L|$X ٧HLI Ld$ L莙8 Lt$@H
bW H5V MT L[1LLLk L L HD$HHx誨H3/H5, LDHH<$Ht#Hz HHCPH=1 HHH讫HfILM HD$HLHx4LLHHfIH @ AWAVAUATUHh SH 肫HH0 HH{HHn, Hދ HHCX HC` Hl$pHHHx HHCP o, H8 H~HH$ }H|$pªH<$ uHĨ H[]A\A]A^A_H
W H`V AT H1Ș8 L|$X HLI Ld$ L螗8 Lt$@H
W H5V MT Lk1LLL{ L L HD$HHx躦HC-H5* LTHH<$Ht3Hx HHCPHM/ HHH辩HvILMHD$HLHxDL\HHfIH @ AWAVAUATUHh SH 蒩HH. HH{HHl, Hn~ HHCX HC` Hl$pHHH~ HHCPm, H8 H|HH$ {H|$pҨH<$ uHĨ H[]A\A]A^A_H
W HpV AT H1ؖ8 L|$X HLILd$ L讕8 Lt$@H
W H5#V MT L{1LLLLLHD$HHxʤHS+H5( LdHH<$HtCH| HHCPH]- HHHΧH膮ILMHD$HLHxTLlHHfIH @ AWAVAUATUHh SH 袧HH, HH{HHj, H>} HHCX HC` Hl$pHHH| HHCP+k, H8 HzHH$ yH|$pH<$ uHĨ H[]A\A]A^A_H
/W HV AT H18 L|$X HLILd$ L输8 Lt$@H
RW H53V MT L1LLLLL+HD$HHxڢHc)H5& LtHH<$HtSH|{ HHCPHm+ HHHޥH薬ILMHD$HLHxdL|HHfIH @ AWAVAUATUHh SH 貥HH* HH{HHi, Hy HHCX HC` Hl$pHHHhy HHCP;i, H8 HxHH$ wH|$pH<$ uHĨ H[]A\A]A^A_H
W HV AT H18 L|$X HLI Ld$ LΑ8 Lt$@H
W H5CV MT L
1LLLLL;HD$HHxHs'H5 % L脪HH<$HtcH x HHCPH}) HHHH親ILMHD$HLHxtL茡HHfIH @ AWAVAUATUHh SH £HH( HH{HHg, H z HHCX HC` Hl$pHHHy HHCPKg, H8 HvHH$ uH|$pH<$ uHĨ H[]A\A]A^A_H
W HV AT H18 L|$X )HLILd$ Lޏ8 Lt$@H
W H5SV MT L 1LLLLLKHD$HHxH%H5 # L蔨HH<$HtsH\x HHCPH' HHHH趨ILMHD$HLHx脞L蜟HHfIH @ AWAVAUATUHh SH ҡHH' HH{HH%e, Hx HHCX HC` Hl$pHHHx HHCP[e, H8 HtHH$ sH|$pH<$ uHĨ H[]A\A]A^A_H
W HV AT H18 L|$X 9HLI+ Ld$ L8 Lt$@H
W H5cV MT L 1LLLL#L[HD$HHx
-H#H5,! L褦HH<$HtH,w HHCPH% HHHHƦILMHD$HLHx蔜L謝HHfIH @ H SHHHH`HtHP H% HC` HH[ HH8HH fH SHHHH`HtHP H$ HC` HHH[0USHHtb~@tuH?H1[] D H. 3HU HHH[1]ÅuHՍ HH1[]f HHH1[]ÐAWAVAUATIUSHH( HHFHG H)HHpH Hl$@X ֙HI4L$ L苋8 L$0 H
X H5}X M
LUHX HLL`LLHD$@H$ Hx蚚H#!H5 L4 @ I4$I|$H)HHI M H HH EHC H. HkH9 L=$ L$ H
V I 1LLH$X 1LH- HL$ LCLLHUHL$H
V H 1
HX 1L葖HHUH$ LHHL$b . L$ I|$XpH Ƅ$ LHDŽ$ Ƅ$ 1HDŽ$ HDŽ$ HHDŽ$ HDŽ$ H$ H HHH@HyH$ HL$HD$ H/H` I|$@HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHDŽ$ H$ H H@H$ Hu HH$ $HE It$I|$XDŽ$ HH$ HEH$ kH5@X L臜H$ LHVsHD$LHPH_HD$LHPHKH$ H}H|$pH L$ L$ L9 L9o M L)H$` 1HI螢IIHH HLLLD$(芙LD$(HI9 L$ Lt$pH$ LLɠH$ HxH9 H HH$ Hr H@H$ HϏ HH$ H$ HxH9 H I|$@HH$ 艝HD$HT$ I|$XH$ H@H H HH$ ΓH|$`L Wy7 LsHD$`HD$` MHCt<IFHxH9> LSLt$`MtIFHxH9 L0HD$pHxH9r H$ HxH9/ HD$HxH9 H$ HxH9t HD$HxH92 H$ HxH9 HkIEI+E L#H H$ H
[X $z7 LcH$ HDŽ$ MHCtHID$H- HxH9 LBL$ MtID$HxH9` LH; H{ H{ H( []A\A]A^A_f Hn1T D H H H! LH . HtIU IEH)HH HHI9C HH9j?f. M9 M @ Z M)H$` 1LIIHHt`HLLLD$(LD$(HI9 L$ Lt$pH$ LLDv AA@9 AA@f D HHHAf HAHHH9*WfHt HHH{HtHC HH=' HW H$ ٔ @ Lt$pIt$PL^f MH=U 苓L$ HHD$pHt$?Hx]L՝H$ LHxAH|$LH0H$ LHx H|$LH H$ LHxfH=QU @ HJHH9L$ X rLI7+H$ H'8 L$ H
X H5X I, LHX LLLLTHH$ H$ Hx3HH5U L͚Lt$PX ΏLI*H$ H胁8 L$` H
X H5uX I LMHX LLLXLHHD$PH$ Hx蒐HH5 L,H= HW~ LsH= HG` ЅLCH=^ HWC kL^H=0 HGF Ѕ&LH= HW L$ L译H=ʈ HW L聑H= HW mLS`H=n HW3 H$0 H=; HW LH=
HW LĐA@ M8CD8 A@ M8CD8 JH= HWt\H$ iqLH9LqHD$@H$ HxL3 HPJHHL$ PJHyHL$APQPJHHHD$pLL$ Hx蝍CHL$APQHH$ H$0 HxiPJHILMFHL{HD$PH$ Hx%L=IHHPJHRPJHxPJHPJHHHHL$ hHH7 I|$XL$ HH$ 'HL$ PHL$ ,LIQHLH$ H$ Hx-
IH PJH;I|$H<HD$HL$ H$ H@H 3OPJH @ AWAVAUATIUSHHH Hn L$ Hl$ HHGR, H
~ H5X HC` LHHHH|$HHLIZ, L|$M IGH-݂ HxH9+ LhHD$ HxH9 M H<$L|$PH5jX LLpCPYH|$@LLLZ, Lt$@MtIFHxH9 LHD$PHxH9i M H<$L$ H56\ LL{XLpCTݎH|$pLLLjX, Lt$pMtIFHxH9m LwH$ HxH9B M L$ H5\ LL{\LkH$ LLLW, L$ MtID$HxH9Z LH$ HxH9 Mu H []A\A]A^A_fH-) \L$ X "LI"L$0 Ly8 H$ H
X H5qX M' HH*X LHLHL<H$ HHxHqH5
-
L肒HH{`HtHP H HC` HHH貒H=⁓ HW) H$ 蔊L$ X LI!H$ Hx8 H
X H5tX I& LHX LLLLH?H$ H$ HxHoH5 L耑H= HW7 H$ Lt$`X NLI!H$ Hx8 L$ H
X H5X I# LHX LLLL0HhHD$`H$ HxH
H54 L謐Lt$0X 譅LIr H$ Hbw8 L$P H
X H5X I L,H%X LLL7LHHD$0H$ HxqH H5
- L H= HW 4H$ M"H=h HW vH$ dH=5 HW H$ H= HW rH$ 贇`H=~ HWb H$ 聇H=~ HWtlH$ RwILM HLTHD$0H$ HxL"HHIËPJHILMHLH$ H$ Hx裄L軅IHHPJHPJH%PJHPJH8IHL9LqH$ LHx L5AHHHILMHL(HD$`H$ Hx҃IH D HH$ H$ Hx蝃HHD$ H$ HxHHD$PH$ HxaHH$ H$ Hx@lPJHOPJHrPJHf. UHh SH}HHH HH[]HHHҌfUHh SH=HHHHH[]HHڅH蒌fUHh SHHHHHH[]HH蚅HRfATHUHSH@HD$ HHD$8HHD$0 "* H菅Hƹ 1HL%y HHHF(F F IL$H|$HF8HF@H HHNH H H HNHt$H H H5X 蛞+ H5%AV H蜋+ H5-] HM+ HH54>U HB + H8H9+ HH HE HE HD$0H H]Ht$ HfoD$0HD$0EH\$HE( H H H YH H FH{hHtH{PHtHs0H{ }HCHxL9 HCHxL9uoHHxL9u:H諃HD$0HtH|$ HH@H[]A\f D 17H=iy HW Ht$"H=@y HW pHt$aH=y HWtB7Ht$Ɂ(HHD$0HtH|$ HH萉PJHHMIHt
HHH} Ht`HE LH|$HtFHPJH PJH$f. ATH7UHSH@HD$ HHD$8HHD$0 * HOHƹ 1HL%[v HHHF(F F IL$H|$HF8HF@H HHNH H H HNHt$H H H5X [+ H5=V H\+ H5~] H
+ HH5:U HB B
- 證+ H8H+ HH HE HE HD$0H H]Ht$ HfoD$0HD$0EH\$HE( H H H H H H{hHt豀H{PHt裀Hs0H{ 6HCHxL9 HCHxL9upHHxL9u;HdHD$0HtH|$ HH@H[]A\ 16H=!v HW Ht$~H=u HW oHt$~`H=u HWtB6Ht$~'HHD$0HtH|$ HHHPJHHMIHt
HHH} HtHE LH|$HtH D PJHPJH f. @ ATHUHSH@HD$ HRHD$8HVHD$0* H~Hƹ 1HL% s HHHF(F F IL$H|$HF8HF@H HHNH H H HNHt$H H H5rX + H5:V H + H5{] H轅+ HH57U HB B ]+ H8H袂+ HH HE HE HD$0H H]Ht$ HfoD$0HD$0EH\$HE( H H H ªH H 诪H{hHta}H{PHtS}Hs0H{ HCHxL9 HCHxL9upHHxL9u;H}HD$0HtH|$ HH@H[]A\ 16H=r HW Ht${H=r HW oHt$]{`H=xr HWtB6Ht$1{'HHD$0HtH|$ HHPJHHMIHt
HHH} HtHE LH|$HtH D PJHPJH f. @ AWAVAUATUSHHHHLvH|$HM HL< HHE1E1H, HL$Hl$LmLe D H Ht,1I IHH9t$ BBDwH9t6Ht$ L9I0~ K( L9~
K IIHM9Lt=HI LX~L9AH9@AY HBH9u @ HD$[]A\A]A^A_ AWAVAUI1ATIHUSHH HBHD$(3,, HHG
HxH2 HP0$ HE HD$ HE1+, HHt HxH HP0 Lu H+, HHS HxHN HP0@ L} M+ IwI HDŽ$P HDŽ$X HDŽ$` H)HH E1111HMH$P H$X H$` tHH~HHL|$ H$X E1A}ToHD$L$P INH$ HpMA1A}Po-H$P HtxH$ Hj xHËE HuH{ECtHCHxH;=l * HxxH$ H/ xHËE HuH{ECtH$ I $Ht HEHxH;=l HxHĈ L[]A\A]A^A_ X ^sH}I) H$ H$ H54U H$ HkL$ Ld8 L$P H
X H5u4U MZ LH>\ HLLLLPH$ LHxsHH5 L}f D X rH}I) H$ H$ H53U H$ HL$ L#d8 L$P H
4X H53U MZ LH=\ HLLLPLH$ LHx4sHH5V L|f D X qH}I2) H$ H$ H52U H$ HL$ L[c8 L$P H
lX H52U MZ L%H<\ HLL0LLH$ LHxlrHH5 L|f D H H9e H HHT$ uIwMG HHT$ I)IJ, @ H$ H$ LH$ 1HHDŽ$X HD$0H$ H$ HL$8H$( HD$@H$P p-, HHuI$ H$ H|$0 tHD$ H H貛HD$HHD$ HHD$XHC0x HH[PAU\T$TH$ HHD$ IF0x IHD$hIFPHD$pHD$H@0x HD$H|$H A]X\$L0H@PHD$xAETD$`AEPD$d Wd$T .Dl H|$X a H|$Xn- H, HD$XHH@H)HH H: Hz Ht$0HD$ h HD$ HHHH9HOH)HJHHIHHH H 3 LT$HL\$ LHI9L@HI9@ IWI11N (HAADAADHI9wL9tIH|$HL\$ IPH9BC~*IL9A~HT$HBHT$ BHT$HH9H|$ H}fH)HH9H9 HT$HH H$ II)HHHHGH9HBAH9AN I D MIN Mt2W1E1(IDDHM9wLM9tIH\$HHT$ HAH9~,HH9~HD$HHD$ D L$TPHDŽ$P HDŽ$` HDŽ$X HD$ &f D 11 W D HD$(LD$8HHT$0t$`|$dD$HD$HD$ HD$HD$hHD$pH$HD$xM ' I}`HHPHHD$ Ll$0HHCHCHLP HDŽ$ Zl$pHH$ LH$ HLH|$(5X l$HH$ H $P $X $` H$ p7 H$ Ht
HHHHP HH jX =kH{IH$` H3, H@@H8yZ2 H55V HHGL$ HLkH$ H\8 H$ H
X H5^7V Iغn HI{1LLLLHH$ LHxkH$` LHxkH<H5 LMuHH iX AjH{IH$` H2, H@@Hx|Y2 H54V HHJL$ HLjH$ H[8 H$ H
X H5a6V Iغn HI~1LLLLH H$ LHxjH$` LHxjH?H5 LPtHH" hX DiH{IH$` H1, H@@HxX2 H53V HHML$ HLiH$ HZ8 H$ H
X H5d5V Iغn HI1LLLLH!H$ LHxiH$` LHxiHBH5 LSs HD$ fHD$H HD$X *H|$0 EHHt$ L$ HL$ L$ L HD$XHD$HHL E1LMZL$ HJ HxHT$@HIH$ H|$HJ H$ WHL$ 1HL$ L$ H Mt.1E1(IDDHL;d$XrL9tFH$ AHL9AD ~*H;l$@J AD ~HD$@AHAD ILLHL;D$0t>H~HBH9AH;T$HAH1f D HH9uL$ L$ L$ L$ H1HD$HHL$ LHt$@H$` H$ HD$0H$P H$( H$X KHD$HHT$ f HH9u Ht$0HD$HHD$ HD$ HHHH9HOH)HJHHHHHH HL$ HǃHHكH9HFHI HL$ H j HIP HIb HI H HI IA H9trII)MIN Mt+(HL$ H<1HH)GL9rMM9t-H|$ IHH9B ~I L9~ HL$ B H9} (HL$ @ )HH9H9LL$ HH)LЃHHH9HGHHFHH& H\$ HHB vEH HBv6H HBv'H HBvH HBv HBH9 H)IIN Mt*(I 1 HH)AI9wLI9HL$ HPH9 H H9HL$ H|$HLD$ 1f D AHH9uZH11E1HHH4A A A A A H=p] HWtOGL+f:H=F] HWu LeaPJHHILMH$ H$ HxcLdHLHH$ Ht,H|mHHfHH$P HHtfLHI7LMlH$ H$ HxcL+dHLcHIHIHIHH H$ H$@ HHxbH$` HHxbLckHlHHH$@ HHkH HHH$ H$@ HHxJbH$` HHx6bLNcH낿X L$ `H$ H5FX LI{fH$` H{R8 H
TX H5 X Iغ8 LMHKX LLLXLHH$ LHxaH H5 L.k @ f. H H
HH$@ LI(HL]H$ Ht$@HxaL btIHfHHILaHL1HIH$ H$ Hx`HILMLHIHIHILHIcH$ IHt
HHLHHHP H$ HHtH$ H˿X L$ ^MF0H
U H U H5 U LHKH$ HSP8 L$ H
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X @ X Hs@W HH)HH HnH\$X HH Ll$ LO8 Ld$@H
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Z^Y H5[Y M躲 LcrHYY HLHn|LH{LzHD$Ht$HxcH{HsHHH @ f. f. @ AWAVMAUATIUSHHX HrHB LL$@H)HH LnHHD$pHB0x HH*HjP}MHD$8 LɉHD$HL7 H3AHD$pH H HcSxH9 IcH9Ch L$ HshLOzD$ H$ LHHD$PE9ENMcLHt$pl}HL$p8 HDŽ$ HL$XHt$@H|$PHL$XHh H$ HDpHpHxHHH(Lh0LH$ HeH$ HVrH$ YbH$ Ht
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LLH\$@HL$HH$ Ht$8HD$pD$ H$ H$ H $HL$ H$ H$ H$ Lt$HT$HL$Ht$ HD$(ЕH|$PH_H$ Ht
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UY H5uQY M躒 L
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V H5V HILB0HV L$ L8 L$ H
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ZY H5W In HZ1LHLdH dHTcHD$0HHxHD$ HHxH{tH5U L\ @ E1oH
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EY H5AY I躙 HWHbAY LHLaH`H4`HD$HHxHlqH5Q LMHLHIHL`H_HD$Ht$HxQHH D AWAVAUATUHSH H H$ Ht$@HHD$HHHt$H01`HC0HcKHHH9 L`LIDH$L9$$J HD$' HT$HHD$@H9t @ @ HH9uL;E1IoPMoHHL)HH @ LHCKt HS8LHwHH0H;H8 D H|$@HT$HH1H)HH9 HHXABH;H8uH;HoPHWHHIIH)HI9sIkHC(Ht$1H{H0HHC Ht$HELIH0IHD$L;$$H|$@HtHĸ []A\A]A^A_ LLlLd$0X LH#H\$`HV8 L|$H
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X @ X Hs@W HH)HH HnH\$X HH~Ll$ L/8 Ld$@H
3GY H5$;Y M\ LQH;Y HLH[L [LWZHD$LHxHkH5L Hp@7 IpHsH)H70 tJ1 .W HG(H H@1f D HE 0 @ .X HG8 D HCH)HH B HC.yX HG(H H@ 끐 x X u9IpHsH)H[ D @XF@ X p HG@ 1 t-.eW HG(H HG(H H .X HG(H 3 D SH;HsHH̬ @ L`H;HtAH+HLcHkH\$X ~HH|Ll$ L趰8 Ld$@H
EY H58Y M躲 LCOH5Y HLHNYLHXLWHD$Ht$HxCH[HSHHH @ f. f. @ AWMAVIAUATIUSHHX HJHB LL$@H)HH LiHHHD$p LHZMHD$8HD$H t
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- ! H8H! HH HE HE HD$0H H]Ht$ HfoD$0HD$0EH\$HE( H H H 3H H H{hHtH{PHtHs0H{ Ld$RHCLHxRHCLHxBHLHx3H{HD$0HtH|$ HH@H[]A\f D 1-HMIHt
HHH} Ht 4HE LH|$Ht4HD$0HtH|$ HHHHf. AWAVAUIATUSH LbMpH $$@ H|$(I8Ht$LD$LL$H$8 L$WH
-I)II)IM9; H$LLpM)IM9 H\$HLsI)IM9 H$0 O<$HLvI)IM9 M8 M9HD$p MN H$ 1Ht$ Ht$pHt$X X IMMMI*IM)I*^X L H,HD$pHT$pH HEH;E HtHHHEHL9 IM IEHD$p H)HH4H$0 HH@H)HH9 H9L H<$HD$LH|$H H?Ht$LHcM L<L4L 6, YI/ KDHIHILL)Ȁ|$W tHH HH?HHH)II @ H|$ H
LO LD$HLL$@L\$8LT$0٨- H$ LD$HLL$@L\$8LT$0HTHt$ H
O HqL A 14. H$ HH$ PH$ H
O HO A 1. H$ HL$(HH I I D H|$ H
O LD$HLL$@L\$8LT$0- H$ LT$0L\$8LL$@LD$H H$ H$ H8H5O * HDLHYH5"Q H%LH:HsHH HPH@H$ Hh H$ HH$ H$ HxH;=鹉 HL H{@HH$ Hع H{XHPH@H$ HRH H HH$ XH|$(H - H$ HHxHD$(H []A\A]A^A_f D Ht$XHof D H$ H$ HH5O / HLHH5Q HLHHsHfH HPH@H$ H H$ HH$ H$ HxH;= H三 H{@HH$ Hp H{XHPH@H$ HRH H HH$ H|$(H y- @ H$ H$ HH5O , HLHH5Q HLHHsH>H HPH@H$ Hظ H$ HH$ H$ HxH;=Y < H H{@HH$ HH H{XHPH@H$ HRH H HH$ ȼH|$(H Q- k @ HPHH?HHfH$ H$ HhH5O ' HtLH艿H5RQ HULHjHsHH&H|$(H 诡- f. HD$(H H$ LHEE$ HO 1H?HH$ H|$( HH$ Hȵ HH '- H$ Ht$oHx+H={K 0HH$ Ht$oHxǽH$ HHx賽HHH$ HHHHH$ HHtaHyH= HW Ht$p\ D H=l HWt7Ht$p%H$ HHvvPJH<H= HWt3Ht$pξPJHFPJH AWIH~AVIAUATUSHHx ̓" LhHL+h@1H[ HIIGIHHD$X 1 ID$Mn`HLHH@HD$pHEHT$`LH HD$hH$ HHD$PH$ tBHt$PH
O HoO A 1B. H$ IHx L[]A\A]A^A_fHEH|$PH$ LHDŽ$ HDŽ$ HHDŽ$ H$ HL$x#H$ thHt$PH
]O HO A 1訬. H$ IH$ HUGKf HHD$Xf H$ I HH$ }H$ H9$ " H$ I HH$ }H$ H9$ H$ IvxHH$ z}H$ H9$ M HD$` HDŽ$ HDŽ$ HDŽ$ H$ HD$hH$ |Ht$PHH HH$ H$ H$ H$ H$ ID$HH$ Hƺ H$ H$ !D$ H$ LLl$PL$ L$ H$ HD$H$ HL$xLH$ D$ H$H$ H
FO HO A L1莪. H$ IH$ Ht1H$ Ht H$ Ht
H$ HtH$ HH$ H$ HIH$ {L1Hq" H$ H$ Ht
薻H$ LhH$@ LHH$ B$ LD$ H I H$@ H<H$ H+$ HT$PH$ HDŽ$ HE|H$ H+$ HT$PH$ HDŽ$ HH$ H+$ HT$PH$ HDŽ$ HH4 H|$PH$p HHDŽ$p HDŽ$x h" H$ tFHt$PH
+O HO A 1v. H$ IH$x HrLH|$XH$ H
H$ H$ -H$ H$ H$ HCH)HH$ HFH\ HD$pINhL$ H<$H|$PHHIH$ HD$@H$ HD$8IFPHD$0HD$hHHHT$`HD$(H$@ HD$ H$ HD$H$ HD$H$ HD$!/ H$ H$ E1H$ H H$x L$p Hv H舺H耺HL$ H$ L$ pHH$ H$ L$ IH$ H$ H|$PH$H/ H$ I H$ @$ H$ I@$ H$ Ht$xH$ HwH@$ H$ HD$XHtHD$pLHI~hLL$ k@$ H$ H$ HJHH9HI~hH$0 ?@$ INhHH$p HH$H HD$pHHH$ HD$XHH$ HHHHJy 0f D HH
- HH H4 HH"H L$x IH$p Mf
- LHD$X襸L蝸HD$XLL$h L$p H$` nH$p H$ H$ H$ =$ H$ Ht$P HH$ H$ HH$ H$H H$@ HH$ H|$` g IFPHDŽ$ H$P I~h HDŽ$( HDŽ$ H$ H$ H$ HH$ H$ H)HHHHD$xH$ H$ H$ H$8 H$ H$@ H$ H$ H$ H$ HHH$
- HH<H?H9HGHHFHH HHH$ v
- HAHH$ U
- HAHH$
- HAHH$
- HA HH$ ^
- HA(HH$ =
- HA0H$ H;T$x LD$xI)LHHH H$ foKK H 1fo fofoHfoHfH9fs ffs fffs frfoH$ foHfsfofofofs fffs fffs ffL$XHT$XI9H$ t H$ H$ HH$ E1E1H|$x HH$ p H$ S HD$xL$ H$X L$8 H$@ HD$p HPL$8 L$@ H$ HHH IDH$ HH IDH$ HT$XH$ D L$ MxJHL$xHt$pH$ ! HAL9}
HHH|HHHHH|HITuHD$pHH$ H$ fH|$x H$ L$ E1LD$pHP )H9AE LLHHIIH;D$Xt8ILItItHH|I+|HH:yA f. EW H$ B H$ xrH$ H$ H0HH9Q HHD$xH$ H9t0* fHtH
-HH9 HHHH9 HH uHD$pH$ H9D$pQH$X L$8 L$@ MtL MtLH$0 L$ HH$ IH HHD$hLH$H WL$ HL I~hH$ H\$o H$ L H$ HT$H $o H>+ H$( H$( I;FhH$ YL$ H$ HL$hH$ H$P MH+/ IFhH$ H$ H$ HH$0 HH$ HH$ HD$`H9$ H$ Ht贯H$ Ht袯H$` %AL$ LD$p H$ BH$ B HHX 貪H\$PHHRL$` HsL H_ H$ HPH@H$h H H$ HH$ H$` Hx胫H H{@HH$ gH( H{XH$ HPH@H$ HRH Hɢ HH$h 蠨H訛. L$ H
O H5O Iغ4 LrHZO LLH}!L H
H$` Ht$PHx跪H@1H5. HQHMtLMtLԭH$ HtH$ Ht谭H$` 3H$x HteH$@ HtH$ HtmH$ Ht[H$ HtIH$ Ht7H$ Ht%H$ HtH˳H H9D$xi HD$xHHI"HL1I¨LLl$XHT$X1HI裨3H4 HDŽ$ 1H$` HDŽ$h L$p HDŽ$ uL$ HDŽ$ L$ H$p H$ HL`HDŽ$ HHAIH$ H耸HH@ H$ HH'e IHL H$` Ht$PHxLHdHH$ I* H$ HH_HHHHH$ HHHH @ H$ HHHH$ HHlH$ HH4N* HHDŽ$ H## H|$PH`HHGHQ AWAVAUIH5L ATUSHHh H-K HG L$ Lt$PHG HG( HG0 HEHG8 HG@ HGH HGP LHGX HG`HGp HGx HLJ LHD$0蓪HD$@L|$0LLLHHD$Sv" HD$PHxH9v 1H|$@ Lt$pH5L LL{pL6H|$`LLLcx" Lt$`MCt IFH$0 Hx耥LȨHD$pHxH9a L$ H5hL LL{LȩH$ LLLw" Lt$@H$ HDŽ$ MHD$@tEIFL$0 LHxL?L$ MtIFLHxҤLH$ HxH9 H|$@ HsL{ HCxH+CpMI)IHL9 sHHC L$ H5uL LL{0LڨH$ LLLw" Lt$@H$ HDŽ$ MHD$@tMINH$0 HH$HyLML$ Mt IFHxH$HܣL$H$ HxH9 H|$@ tDHCxH+CpHs8HK0HDŽ$0 HHH)H HH9 s HHHC8L$ H5}L LL{HLէH$ LLLu" Lt$@H$ HDŽ$ MHD$@tEIFL$0 LHxLLL$ MtIFLHxߢL'H$ HxH9 H|$@ HsHL{PHCxH+CpMI)IHL9 sHHCPL$ H5U LL{LH$ LLLq" Ld$@H$ HDŽ$ MHD$@tGID$L$0 LHxL]L$ MtID$LHxL7H$ HxH9 Ld$@Mt&ID$H$0 HC Hx誡LHD$0HD$@ HxH9 Hh []A\A]A^A_ D H$0 H)L]r HD$0Hx u 1j D H5K L虝tH5tK L膝¸ 4H59K Lh¸ H5&K LJ L{PL;{H-Lf. L{ L;{Lf. L)HH$HC(L)HH9 LH<H?H9HGHHHr HI IWvTHIG IWvBHIG IWv0HIG IW v HIG IW(v IW0IG( H9$tEH<$H)HHIMt)foK I 1HHfAH9wI9JtH H$IHC f L)HH$HCXL)HH9 LH<H?H9HGHHHJ HI IWvTHIG IWvBHIG IWv0HIG IW v HIG IW(v IW0IG( H;$tEH<$H)HHIMt)foK I 1HHfAH9wL9JtH H$IHCPf H HL)H9$p H$HD$L9IBL H|$诡HsLIH)HH|$KH $HH<IH?H9HGH- IH HJvTIHB HJvBIHB HJv0IHB HJ v IHB HJ(v HJ0HB( L9 $tJH$L)IIMMt.foK N 1 @ HIfACI9wI9J tH Lt$IHtLLIH$MI HC E1L)HHt!L4 HLL $LɥL $HH{IHt
L $
L $LKLL$Ls LK(+ H HL)H9$K H$HD$L9IBL H|$HsHLIH)HH|$KH $HH<IH?H9HGH IH HJvTIHB HJvBIHB HJv0IHB HJ v IHB HJ(v HJ0HB( L9 $tJH$L)IIMMt.foK N 1 @ HIfACI9wI9J tH Lt$IHtLLDIH$MI HCPE1L)HHt!L4 HLL $L L $HH{HIHt
L $ML $LKHLL$LsPLKX^ H 1L D H| 1LH E1H*H] E1HH9[HL Lt$LHD$ E1YH9H Lt$LHD$ E1H=w HW TH$0 )BH=D HW $H$0 H= HW )H$0 ÛH=ޒ HW H$0 萛H= HW` iH$0 ]WH=x HWM EH$0 *3H=E HWt-H$0 pH=L PJHͿX L$ kHT$/H5"O LIL . H$0 H
-O H56L MD HH"K LHLHIL
H$ HHx-H H5O LǡHIHLL9
H$ H$ HxLH|$@Ht蹳HD$@ HD$0Ht$.Hx譗H{pHtHC`Ht$Hx荗H{HHtϚH{0HtH{Ht賚HkHRHRHcHIHHD$PJHHH$ H$0 Hx HH$ H$0 HxؖHHD$pH$0 Hx躖HHH$ H$0 Hx葖HH$ H$0 HxpHHD$PH$0 HxRHHD$@HD${H=LL OHHD$I7PJHQPJHPJHPJHTPJHf ATUH S?L%x HHH{ID$H\" Hڣ H{`HHHCP H[]A\HH識HeIT$HH ATUH S还L% HHH{ID$H\" HZ H{`HHH7CP H[]A\HH-HIT$HH AWAVAUATLgXUSHLH賑H4 ƃ8 HHǃ0 ƃ9 1Hǃ@ HǃH HHǃP HǃX HCXH HhLhH}H+L/茚H H{@HC HC HC LsHC( HC0 HPH@HC8 HCXH HHHC誟Hˌ CH LLHHCHW HHCPH[]A\A]A^A_HH LHHCXHyLIHUH+LL,f. @ SHHHtHGHxH;=Ԋ uHjH[H=T HWtHt$͋PJHfSHHH HHH Ht H HxH;=W uDH HtH HtӕH{hHtŕH> HHH[H= HWtHt$^뗋PJHf. D SHHH HHH HtIH HxH;= uLH Ht$H HtH{hHtH~ HHHH[H=ي HWtHt$薓돋PJH LG HWLH)HHHH H:F Hz Hz Hzt6 fHz Hx* Hx/ Hx4 HHB HHuLH)HH# H H LG8HW0LH)HHHHv H: Hz [ Hz J Hz t:8 f D Hz Hx Hx Hx HHB HHuLH)HH H H H H1I9. H(f. H0f. H8H>HuHH>HuHH>uHI9HHHHyH(I9H8H0H> uHH> uHH> uHHHHf. fAUH7 ATUHSHHHD$ H HD$8H HD$0, H2 I2 I=Hƹ 1HLHHC F H|$F HHHFHFHF(Ht$HF8HF@H H H H H H H5WL J! H5WL LH8! H5[N HH&! H5QO H'! H5S Hؘ! HH5JK HB
- ! H8Hĕ! HH HE HE HD$0H H]Ht$ HfoD$0HD$0EH\$HE( H H H H H ѽH{hHt胐H{PHtuHs0H{ Ld$HCLHxHCLHxHLHxH,HD$0HtH|$ HHHH[]A\A] D 1,HMIHt
HHH} HtHE LH|$HtHD$0HtH|$ HHWHHf. AWE1AVAUATUSHH H|$hH~Ht$pvS" L`HL+`@1H HHD$xIIHƄ$ Y HT$xHHBH8H@H$ HEH$ H H$ HD$pL`PH$p LHIH$ @H$p tEH
O HO A6 L1|. H$p H\$hHHD$hH []A\A]A^A_L$ HEL$ LHDŽ$ HDŽ$ LHHDŽ$ LHD$`貲H$p D H
O H
O A9 L15|. H$p H\$hHH$ HMύCf. HsHP H+PIHH L賻 I$ H2HP H+PIHHz HX X L$ H$ 覈H5O LHHQL$@ LQz. H$p H
"O H5O M/ HIH$ HQO HLH LsLH$ LHxWHH5y
HH X L$ H$ ܇H5O LHH臍L$@ Ly. H$p H
XO H59O M* HIH$ FH_O HLHQ1 @ Hx$ H D LD8HD$pH H$ HH$ KH$ H9$ 4 HD$pH H$ HH$ QKH$ H9$ HD$pHphH$ HH$ KH$ H9$( O H$ HDŽ$@ HDŽ$H HDŽ$P H$p H$ H$x H$ Lp HH$@ L$P MHT$xH$` L$H HH$ Hr $ H$ L$@ L$ L$ H$ LH$ D$ LLt$H$cH$p H$ H
'O HO AJ 1Ax. H$p H\$hHH$` Ht߉H$@ Ht͉H$ Ht軉H$ Ht詉H$ H蓉H$ LHIXIL1H{?" H$ IHtVH$ IFHH$ Hƺ H$ z$ H$ $ H HD$hH H$ HHx$ HT:H$ H+$ H$ H$ HDŽ$p HwH$ H+$ H$ H$ HDŽ$p H覵H$ H+$ H$ H$ HDŽ$p HH4 hH$ $ H$ H$@ $ H$ H$ LHIGL{$ H$ H$ HtLl$pH$ IEXH$H C$ H$P IFH|$`H H$X IEXH$` $ H$h IEXHDŽ$ HDŽ$ HDŽ$ HDŽ$ HH$p H$ HDŽ$ HDŽ$ HHH$ HIH$ H$ H$ H$ H)$ H$ H H$ H$@ HHDŽ$@ HDŽ$H 5" H$p tiH$ H
O HwO Ae 1t. H$H H$p H\$hHHt@>H$ Ht.H$ 豒L$ H$@ HMHP
- vf D L8 HDŽ$ HCH|$xH$x HH$ γLH$ H$ H$ c $ HD$pHDŽ$0 H@X$ Hc$ H H{H$ H H$ HzH$ H\$`HZH$ H$ A߉$ HIHDŽ$8 HDŽ$( H$ G H$ w H$ H$ L$ L$ H$ H$ H$ HHH$ HD$xLx[ HH<H?H9HGHHFHH HHH$ HGHH$ HGHH$ _ HGHH$ > HG HH$ Y HG(HH$ 8 HG0H$ H9$ H$ H)HHIM H$ foeK H 1fo fofoHfoHfH9fs ffs fffs fwfoH$ foLfsfofofofs fffs fffs ff$ H$ I9H$ t H$ H$ HH$ E11H$ HH$ H$ H$ L$ HDŽ$ HH$ H$ LH$ H$ LH$ H$ HxBH$ H$ H;$ }
HHH|HHHH<HT HHuH$ HH$ H$ H$ L$ H$ E1+ H9AE H<LHIHH;D$`t4ITHLILHItI+tHItyA @ E/ $ H$ 8H$ xwH$ H0H$ HH9 HHH$ H9H$ t+ HtH
-HH9 HHH9 HH HuH$ H$ H9$ KMtLnHtHaHD$pL$ LXXH$h H$ H$ D$ L$D$ HH$p AH$( H H$ H$ HH\$ I$ T$HH$x HD$0H$ D$(D$謿 H$ HH$H H$@ H$ H$P HH$X HH$` HH\$pH$ H$8 L[XH$8 H$( L9cLH$0 H$ H9$0 HD$hH H$ L$ H$ H$ B : HHX :zH$ IHL$ HsL|Hs L$@ LHPH@H$ H2t H$p HH$x H$ Hx{H s H{@HH$x Hr H{XH$ HPH@H$p HRHp HKr HH$ "xH*k. H
O H5|O Iغ4 LHO LLLL_HH$ H$ Hx>zH H5` LHMtLh}HtH[}H$ HtI}H$ ̉H$ Ht*}H$` Ht}H$@ Ht}H$ Ht|H$ Ht|H$ Ht|H$ Ht|Hvf D H H9$ L$ L|HL1HhxLL$ |H$ 1HICxH$` $ H$ L$ H$ L$ LF\$PH$ HH$ IH\$HIRHT$@IHT$8HQHT$0HQHT$(HQHT$ HHT$IQHT$IHT$HWH$LHHH$ H$
,L$ 0HDŽ$ 1LH ? H EJ4(L$H L$@ MtH=,q IL$l H$ L$p L$x HDŽ$ H$ H2 H$ LHtHPL$p H$x L$ HDŽ$x HDŽ$p L$ H$ Mt&H=p IE ȃ H$ MtLQ2H$ u/H$H Ht42H$ H$ KDH$ H+$ L$ LHHp}H$ L{H$ H$ LH$ otLIE LPH=o IE t3ȃIE LP AEHAMAE HAM H^H$ HL*H$ LHxuHvHH,H H$ HHxH D HH$H Hl0bAD$ HH$p HHt|ILMHL+H$ H$ HxtLuHDŽ$ XHwrIHH$ H
HH HHb @ H~E1HiHH$p HHMCH$p HHH$p HHkaH$ HHg ATUH S?wL%x HHH{ID$H:" H: H{PHHHH[]A\HHvHl}IT$HHf. D AWAVAUATLgXUSHLH3pHj ƃ8 HHǃ0 ƃ9 1Hǃ@ HǃH HHǃP HǃX HCXHj HhLhH}H+L/ yH=k H{@HC HC HC LsHC( HC0 HPH@HC8 HCXHzj HHHC*~HKk CH LLHHCHi HHCPzxH[]A\A]A^A_HHi LHHCXqoH{LI HUH+LL,f. @ SHHHtHGHxH;=Ti uHtH[H=j HWtHt$s͋PJHfSHHHa HHH HttH HtxtH HtgtH HxH;=h uDH HtBtH Ht1tH{hHt#tH HHH[H=i HWtHt$r뗋PJHf. SHHH HHH HtsH HtsH HtsH HxH;=g uLH HtbsH HtQsH{hHtCsH HHH-sH[H=i HWtHt$q돋PJH D SHHHpHtrHC`HxH;=+g u0H{HHtrH{0HtrH{HtrH[H=h HWtHt$Fq뫋PJH AWAVIAUATIUSH H\$0H$H{XkHxf Ƅ$h HHDŽ$` Ƅ$i 1HDŽ$p HDŽ$x HHDŽ$ HDŽ$ H$ HDf HhLxH}Hl$0L?tL-f H{@HD$@ HD$H HD$P HD$X HD$` IEHD$h HD$0IE@H$ H&f HHD$8yHf HsH{XD$x HHD$8He HH$ tM' L lLHH1rH$HH0HV rH7e HxHD$`I<$H HT$PLt$XH9. L9 ML L)HT$ 1HH$vxH<$IHHt H$HLLD$`oLD$HL;d c Lt$ LHL$ LvHD$ HxH;=d IEHD$0IE@H$ He HHD$8H$ HxH;=Hd Hd H{@HHD$8ysHEHl$0H{XL|0Hc HH$ iHĘ L[]A\A]A^A_f. L9 M L)1HH$HT$ HEwH<$IHHtWH$HLLD$3nLD$HL;c P Lt$ LHL$ Lyu @ AA@ AA@f D HD$0HHxw u @ Hm H HsPL qw HH=ZK ;lLt$ HI<$LHkHvHu H=aZK lH=4d HW AHt$ l2H=d HWtmHt$ lH$A@ I AD H$A@ I AD HHD$ Ht$ HxNj&PJH덋PJHQH{I^HEHl$0LL|0Ha H{XHH$ qgHsH @ ATH
- UHSH@HD$ HHD$8HHD$0 H/mHƹ 1HHHH5a F H|$F HHHF(HHNHNHF8H HF@H Ht$H H H H H5N <! H5O H=s! H5iS Hs! HH5%K HB t! H8Hp! HH HE HE HD$0H H]Ht$ HfoD$0HD$0EH\$HE( H H H H H H{hHtkH{PHtkHs0H{ Ld$HCLHxhHCLHx hHLHxgHBkHD$0HtH|$ HH@H[]A\ D 1.HMIHt
HHH} HtDHE LH|$Ht*HD$0HtH|$ HHoqHH D ATH UHSH@HD$ HHD$8HHD$0, HjHƹ 1HHHH^ F H|$F HHHF(HHNHNHF8H HF@H Ht$H H H H H5N 謃! H5O Hp! H5>gS H^q! HH5E#K HB B
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+" Lt$PM IFHLd$0HxL_HD$@HHxX 褅LI2Lf0 H
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zR H5Q Mq HCH$Q LHLN HLHD$0HHxMwHH5 L HD$@HLt$ HxwH$ H5ѬQ LLCh{H$ H$ LL" L$ M IFHHxvLE1zHD$ HHxvDsiLt$H5oQ LL{H$ H$ LLM" L$ MtpID$HHxOvLE1yHD$HHx3vH, DcjHHH( H[]A\A]A^A_ @ L|$pH$ f D H$ A띐H$ Af D H+CPHH H H9H H$ H<$HH{PHCXIH)HH ItM8Ht HHLD$Y~H{PLD$HHMxtHL$xHL$H$HKPL{XHHC` @ H$nHH{PHtYxH HHHCxH~H96fIHD$HLHxtIHD$ HLHxtIHD$@HLHxtyHHD$`H$ Hxgt[HLwuKHHD$0H$ Hx9tHLIH_LH'fAWAVAUATIp USH( wHH LH{HHe" L$ Hl$`H5p<S HCP HCX HC` LH.xL$ HL$pHLLc" L$ Mz IGH$ HHxZsLE1vHD$`HHx>sHCXH;C`x HtL8HHCXL|$@H5ҨQ LHDŽ$ LwH|$PLLLʌ" Lt$PM IFHLd$0HxrL vHD$@HHxrX dqLIi LR0 H
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N H5N Iغ HH#N LHL H% H] HD$0H$ HxǃH H5 LqX Ll$@bHT$/H5N LIH$ Hc- H$ H
lN H5mN Iغ HHֻN LHL Hx H
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N H5<N Im HHN LHLHGH H$ HHxxHH5= L蕂IHLHH< HD$pH$ Hxx HH%IHL HHHD$`H$ HxcxHH @ IHL} HH$ H$ Hx xHH AWIAVAUATEUSHH Ht$1HL$HD$` HD$h HD$p
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N H5@N Iغ` HHN LHLHKHHD$@H$ HxjHH5? LtHH$ Ht"nHtLl$PX kiLIpH$ HJ- H$ H
N H5N Iغk H*HN LHL5HHHD$PH$ Hx/jHH5 LsIHLKHHHD$@H$ HxiLk HH D HH$@ HHt
HHHH$@ HHH$0 HLl$`X *hLI/H$ H_I- H$ H
N H5AN Iغl HHN LHLHLHHD$`H$ HxhHH5@ LrLl$pX gLIH$ HH- H$ H
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N H5N Iغ HIHD$H2N LLHL HUHD$PLHxdHH5ވ Hnnf D HH HHHP H)HH HL)计H$ H$ JHHt$HH$ H9$ t L$ L$ Ll$`Ht$LL$ H$ MLLH謩H$ HFguH$ LLHDŽ$ HDŽ$ HHDŽ$ IwL1Hh H$ H:HD$fHD$&L$ LL H踡HH}WM .vX x2M WHH$ Ht^fH$ HtLfH$ Ht:fH|$`Ht+fHl EHT$1Hf HI-t! HILHHLHdH$ H7e-X Ll$@aHT$/H5N LIfH$ H8B- H$ H
N H5N Iغ HHN LHLH%H]HD$@H$ HxaHH5ۈ LqkX Ll$0b`HT$.H5N LIfH$ HA- H$` H
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N H5N Iغ` H:HaN LHLEHHHD$@H$ Hx?UHH5Έ L^HH$ HttXH,_Ll$`X SLI H$ H4- H$ H
SN H5ԋN Iغl H|H5N LHLHHHD$`H$ HxTHJH5͈ L+^Ll$pX SLI! H$ HQ4- H$ H
N H53N Im HHN LHLH>HvHD$pHHxSHH57͈ L]Ll$PX RLIH$ H3- H$ H
N H5N Iغk H?HЊN LHLJHHHD$PH$ HxDSH
H5̈ L\IHL`HHD$pH$ HxSLTHfIHL HHUHD$PH$ HxRHH D HH @ H$@ HHdH$0 HMH$@ HHt
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)N H5jN Iغ HIHD$
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N H5݂N Iغ HHN LHLHH HD$0H$ HxKHSH5Ĉ L4UH$ HHNIHLHHHD$0H$ Hx'KL?LHHfHIHL;HHpHD$@H$ HxJHHH|$IHL4HD$PH$ HxJHKIHHfH)H$ HHf. AVHPAUIATUSHH DvHHD$ HD$( HD$0 HD$8 HD$@ HD$H HD$P HD$X HD$` HD$x HDŽ$ HDŽ$ HD$h HD$p HDŽ$ HDŽ$ N}Hl$ E1LHLHH<$HtLH HxHĠ H[]A\A]A^Hl$ HHUHMSH<$HHt|Lf. AWIAVIAUATMUHHwSHH L$ LD$L|LLHD$ LHD$0腩! LSL+IIM HEH+E HI9q HHL# IL$HHx It$pH ID$8I+D$0HH HHH$ H HHH H*$ HH*H*f(Y$ H*Y¿ HDŽ$@ YX$ VKH$ L H$ H$0 H$0 HPHT$ HHPHT$0HPHӭH$H HH$@ LHI|$pHH|$H:, H$@ H HH IO H֧! Ll$ H$ H$ H$ IL|$0H$ H$ L$ L$ MAfW~,11 @ AD HD)*L,L9u*C,AE7H$ HtIH []A\A]A^A_ÐLHH^IL$HHtI|$p tHH*XHHH H*X H$@ HHtH$0 HH$ Ht,IHOHX Ll$@pDHT$ H5||N LI)JH$ H%- H$P H
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HHHLl$PX CLIH$ H$- H$ H
N H5{N Iغk H]H{N LHLhHHHD$PH$ HxbDH+H5 L NLl$`X BLIH$ H2$- H$ H
N H5{N Iغl HHu{N LHLH HWHD$`H$ HxCHH5 LkMLl$pX \BLIaH$ H#- H$ H
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N H5vN Iغ HIHD$JHuN LLHULHHD$PLHxT?H H5 HHf D H{H HHHP H)HH HL)>|H$ H$ JHHt$H*H$ H9$ T L$ L$ Ll$`Ht$LL$ H$ MLLH<H$ HAuH$ LLHDŽ$ HDŽ$ HHDŽ$ IsRL1HvC H$ H:HD$kAHD$&L$ LLq HHHU HH$ HtAH$ Ht@H$ Ht@H|$`Ht@HG EHT$1HHA HIN! HIbLHHLH?H$ HWs@MX Ll$@;HT$/H5SuN LIxAH$ H - H$ H
N H5sN Iغ HrH3uN LHL}HH
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ivN H5jeN Iغ HIHD$
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sN H5bN Iغ H踩HaN LHLóHHSHD$0H$ Hx*HH5 Lg4X Ll$@X)HT$/H5bN LI/H$ H
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M H"M AI 1H$p + H$p IH$ HtH$ HtH$ HtH$p HH$ t4H$ w H$ H8H~H 1H!H$ t4H$ w H$ HH~H 1HH$ t4H$ Zw H$ H躡H~H 1HI f H|$X Ht$XH$ bHt$xHi H$ aL$( L$ LL)HAD$H$ H$ L$@ @H$0 HcHHH)IH$ HHHt$`HHDHH$8 KIDH$H H$P HHD$XTaH H$` H AD$Ht$hAHHHHD$x H$p
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NM HM A 1+ H$P H$ HH$ Ht.H$ Ht,H$ HH$ H$ H$ H$ HHDŽ$ HDŽ$ HhHD$pL H$ Ht$xH$ H$ HDŽ$ H$ H$ H$ H$ LHDŽ$ H$ wH$ HH$ H$ H
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$L HL A L1* H$ H$ HH$P Ht3H$ H []A\A]A^A_H$ HHH$ cH|$XHDŽ$ H$ HDŽ$ HDŽ$ wnH$ H$ H H$P HD$@ 1HD$H HL$xH$ H$ H$ HL$PH$ H$ $ t
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K HK A L1w) H$ H$ HH$P Ht
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4K HK A L1) H$ H$ HH$p HtH$ H8 []A\A]A^A_H$p HeL$x H$p LH)HHHH
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Lg L9v=Da Db Lg L9v+Da Db Lg
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vIJIHILM9 HH)H IIE1IMIfH HVI9IPH9 H M 11oHAHL9rL9KJ L% D)D*LoL9 DiDjLoL9 DiDjLoL9 DiDjLoL9 DiDjLoL9 DiDjLoL9 DiDjLoL9 DiDjLoL9vsDiDjLo L9vaDi Dj Lo
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Db
Lg L9v=Da Db Lg L9v+Da Db Lg
L9vDa
HH9Db
vIJIHILM9]H HH)H IIE1IMIf D H HVI9IPH9 H M 11oHAHL9rI9KJ L% D)D*LoL9 DiDjLoL9 DiDjLoL9 DiDjLoI9 DiDjLoI9 DiDjLoL9 DiDjLoL9 DiDjLoL9vsDiDjLo L9vaDi Dj Lo
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sIJIHILL9]HL|$HH$ HPHH$ AH$ H 1HL 1HL 1f D A HH9uof1f D A HH9u1H<I<H<I<H<HI9uEH|$8H1 Ht$0VP- 1H H9j
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HX L$P \BH$ H5 LC LIHH$ H4! H$ H
sNC H5KC Iغ H̯HlJC LHLH/HgH$P H$@ HxCHH50z LLX L$` AH$ H5KC LILGH$ HL3! H$ H
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.LC H5EC Iغp L觩1LLH跳LHGH$ H$@ Hx<HwH5z HFIHL
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EC H5)BC Iغ HIH@C LHLTH謯HH$0 H$@ Hx9HH5z L%CX L$@ #8H$ H5AC LI=H$P H)! H$ H
:EC H5sAC Iغ H蓥H%@C LHL螯HH.H$@ H$@ Hx8H^H5z LoB6HIHL褮HH٭H$@ H$@ Hx8L9HHHIHLVHH苭H$0 H$@ Hx28HHHŐvHy }5f D Hz HHÐHz HHÐHz HHÐHz HHÐHz HH:f. Hiz HH:f. HIz HH:f. H)z HH:f. AWAVAUATUHX SH8:L=z HHH{IGH Hq{ Ld$HT$H5/C LsPLHH];Ll$ LLHL Hl$ HtMHEL%b.| HxL9ueH9HD$HxL9u,HtHCP H8H[]A\A]A^A_ @ L%.| H=/| HWtrLf8H=/| HWtaHt$A8sIWHHHZ9H@HHD$Ht$Hx5HˋPJHHËPJH AWAVAUATUHX SH859L=nz HHH{IGH Ho{ Ld$HT$H5C LsPLHH9Ll$ LLHL Hl$ HtMHEL%,| HxL9ueHq8HD$HxL9u,HtHCP H8H[]A\A]A^A_ @ L%,| H='.| HWtrL6H=.| HWtaHt$6sIWHHH7H>HHD$Ht$Hxl4HˋPJHHËPJH AWAVAUATUHX SH87L=z HHH{IGH Hqn{ Ld$HT$H5/C LsPLHH]8Ll$ LLHL Hl$ HtMHEL%b+| HxL9ueH6HD$HxL9u,HtHCP H8H[]A\A]A^A_ @ L%+| H=,| HWtrLf5H=,| HWtaHt$A5sIWHHHZ6H=HHD$Ht$Hx2HˋPJHHËPJH AWAVAUATUHX SH856L=nz HHH{IGH Hl{ Ld$HT$H5C LsPLHH6Ll$ LLHL Hl$ HtMHEL%)| HxL9ueHq5HD$HxL9u,HtHCP H8H[]A\A]A^A_ @ L%)| H='+| HWtrL3H=+| HWtaHt$3sIWHHH4H;HHD$Ht$Hxl1HˋPJHHËPJH AWAVAUATUHX SH84L=z HHH{IGH Hk{ Ld$HT$H5/C LsPLHH]5Ll$ LLHL Hl$ HtMHEL%b(| HxL9ueH3HD$HxL9u,HtHCP H8H[]A\A]A^A_ @ L%(| H=)| HWtrLf2H=)| HWtaHt$A2sIWHHHZ3H:HHD$Ht$Hx/HˋPJHHËPJH AWAVAUATUHX SH853L=nz HHH{IGH H1j{ Ld$HT$H5C LsPLHH3Ll$ LLHL Hl$ HtMHEL%&| HxL9ueHq2HD$HxL9u,HtHCP H8H[]A\A]A^A_ @ L%&| H='(| HWtrL0H=(| HWtaHt$0sIWHHH1H8HHD$Ht$Hxl.HˋPJHHËPJH AWAVAUATUHX SH81L=z HHH{IGH H1h{ Ld$HT$H5/C LsPLHH]2Ll$ LLHL Hl$ HtMHEL%b%| HxL9ueH0HD$HxL9u,HtHCP H8H[]A\A]A^A_ @ L%%| H=&| HWtrLf/H=&| HWtaHt$A/sIWHHHZ0H7HHD$Ht$Hx,HˋPJHHËPJH AWAVAUATUHX SH850L=nz HHH{IGH Hqg{ Ld$HT$H5C LsPLHH0Ll$ LLHL Hl$ HtMHEL%#| HxL9ueHq/HD$HxL9u,HtHCP H8H[]A\A]A^A_ @ L%#| H='%| HWtrL-H=%| HWtaHt$-sIWHHH.H5HHD$Ht$Hxl+HˋPJHHËPJH AWAVIAUATIUSH H\$0H$H{X'(H"| Ƅ$h HHDŽ$` Ƅ$i 1HDŽ$p HDŽ$x HHDŽ$ HDŽ$ H$ Ht"| HhLxH}Hl$0L?0L-$#| H{@HD$@ HD$H HD$P HD$X HD$` IEHD$h HD$0IE@H$ HV"| HHD$86H)#| HsH{XD$x HHD$8H!| HH$ P0M LO(LHHa.H$HH0"-Hk!| HxHD$`I<$H
HT$PLt$XH9* L9 M@ L)HT$ 1HH$4H<$IHHp H$HLLD$+LD$HL; | W Lt$ LHL$ L2HD$ HxH;= | IEHD$0IE@H$ H!| HHD$8H$ HxH;=| | H | H{@HHD$8/HEHl$0H{XL|0H, | HH$ &HĘ L[]A\A]A^A_f D L9 M L)1HH$HT$ H}3H<$IHHtWH$HLLD$k*LD$HL; | H Lt$ LHL$ L1 @ AA@ AA@f D HD$0HHxw A1 @ HqH HsPLL- H!H=> {(Lt$ HI<$LHS'H2HS1 H=> D(H=t | HW MHt$ ))>H=D | HWtmHt$ (H$A@ I AD H$A@ I AD HHD$ Ht$ Hx&&PJH덋PJHQH{IvHEHl$0LL|0H | H{XHH$ #H90H @ AWAVIAUATIUSH H\$0H$H{X#H | Ƅ$h HHDŽ$` Ƅ$i 1HDŽ$p HDŽ$x HHDŽ$ HDŽ$ H$ HT | HhLxH}Hl$0L?+L- | H{@HD$@ HD$H HD$P HD$X HD$` IEHD$h HD$0IE@H$ H6 | HHD$80H | HsH{XD$x HHD$8H | HH$ 0+M L/#LHHA)H$HH0(HK | HxHD$`I<$H
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;= H5?:= Iغ LߺH:= LLLLBHzHD$pLHx;HH5;u LEHXILMHL2HD$pHHx;L<HEIHHILHML|$pX :LH"H$ HR L$ H
:= H5<9= Iغ LܹH9= LLH Gf. ?f. sf. f. f. f. Hu HHÐHyu HHÐHiu HH=f. HIu HH=f. Hiv SHHHHPHtp=H u HH[H9v SHHHHPHt@=Hٷu HHH[)=f UHP SHM=HHu HH{HH K Hv HHHH[]HH<HC D SHHHQv HHH Ht<H Ht<H Htw<H{pHti<HCPHt$Hx8Hu HHH[ SHHHv HHH Ht<H Ht<H Ht;H{pHt;HCPHt$Hxw8Hpu HHH[ SHHHv HHH Ht;H Ht;H Htw;H{pHti;HCPHt$Hx7Hu HHHA;H[f. SHHHv HHH Ht ;H Ht:H Ht:H{pHt:HCPHt$Hxg7H`u HHH:H[f. SHHHv HHH Hty:H Ht$Hx7H HtS:H HtB:H{hHt4:Hʹu HHH[SHHH1v HHH Ht9H Ht$Hx6H Ht9H Ht9H{hHt9HMu HHH9H[ AWAVAUATLgXUSHLH33H6w ƃ8 HHǃ0 ƃ9 1Hǃ@ HǃH HHǃP HǃX HCXH5w HhLhH}H+L/;H6w H{@HC HC HC LsHC( HC0 HPH@HC8 HCXH5w HHHC:AH6w CH LLHHCH75w HHCPj;H[]A\A]A^A_HH4w LHHCXq2H>LI>HUH+LL,f. @ SHHHtHGHxH;=4w uH7H[H=<6w HWtHt$6͋PJHfAWAVAUATUHSHHLsPHx H*u L$ Ld$ HHGE HIv H5R0= LLHH8H|$LLHM Lt$L$@ M IFLHl$0Hx3L
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:= H5k2= I LH1= LLL&L~H趸HD$@LHx%1HH5wu L:LHDL|HD$pLHx0L2HHHD$ H$@ Hx0HILMHL!HD$@LHx0IHHŐHrHHD$0LHxb0Lz1HHHL·LHuH-HHD$`LHx0HHf. UH` SH3HHHHH[]HH*3H9fAWAVAUATUHSHHLsPHH Hu L$ Ll$HHGA H|v H58 HCP HCX HC` LLHH3HLLH[ L,$H$ M IEHLd$ Hx
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Ld$X LI良Hl$ H H\$@H
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= H&< A H1 H$ HL$HHH$p HtH$P HtH$0 HtH$ HtH$ H{qHT$`1L| HIaHL$hIH I}IE HqHQ H)H)HHH9 H$ H$ H52< HZL$ H
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@@ -5479,1126 +5762,1435 @@
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HHHޤH=S HW PHt$d֤AH=S HW'
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HHHoؤH=S HW PHt$TФAH=oS HW'
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HHH_ҤH=S HW PHt$DʤAH=_S HW'
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HHHO̤H=S HW PHt$4ĤAH=OS HW'
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HHH?ƤH=oS HW PHt$$AH=?S HW'
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HHH/H=_S HW PHt$AH=/S HW'
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LLHĘ []A\A]A^A_LlPLL؞?H$ HHt
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- DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( ԞH H$ H9 L, L0מHL1IҞ L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` ֞H$P H HDŽ$ H$h HQH$` t֞H$p HHP HDŽ$ H$ HH$ ) HD$ HDŽ$p HxH;=:M HD$0HxH;=$M K HHm H5E Hh H5 Hi
Hxg H5I H4i HL"HAHD$PHt
HHHĈ L[]A\A]A^A_ϞIH H{p(H{X H$ H{ LHD$xLHxўHD$pLHxўHD$hLHxxўHD$`LHxgўHD$ Ht$ HxTўHD$0H4$HxBўHD$PHt
HHH3۞H=cM HW Ht$ӞH=3M HW& NLҞAH$ IHtH HH$` HtH HH
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- DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( ΞH H$ H9 L, L`ўHL1I ͞ L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` ОH$P H HDŽ$ H$h HH$` ОH$p HߡHP HDŽ$ H$ HH$ $ HD$ HDŽ$p HxH;=jM HD$0HxH;=TM K HHg H5G? Hb H5E H%d
Ha H5y Hdc HL HqHD$PHt
HHHĈ L[]A\A]A^A_ɞIH AH{pXH{XOH$ H{ L
HD$xLHx˞HD$pLHx˞HD$hLHx˞HD$`LHx˞HD$ Ht$ Hx˞HD$0H4$Hxr˞HD$PHt
HHHc՞H=M HW Ht$H͞H=cM HW& NL͞AH$ IHtH HH$` HtH HH :H$ ͞H HHPJH6IH
LIH$ HtH0 HH$ HWH HAH$` IHKH H5PJHf. AWH5 1AVAUATIUSH Hl$@HHT$ H|$0H5 ΞHD$ HL$ H5 HHH$HL$͞HD$ HT$0H\$`Lt$ H5 H{HD$`HM HT$hLHHD$ HD$0HD$p͞HC(H
- DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( )ɞH H$ H9 L, L˞HL1I.Ǟ L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` ˞H$P H/ڡ HDŽ$ H$h HܡH$` ʞH$p HڡHP HDŽ$ H$ HܡH$ D HD$ HDŽ$p HxH;=M HD$0HxH;=M K HHa H5^9 H"] H5s HS^
H[ H5 x H] HLH HD$PHt
HHHĈ L[]A\A]A^A_ĞIH oH{pH{X}H$ H{ L HD$xLHxŞHD$pLHxŞHD$hLHxŞHD$`LHxŞHD$ Ht$ HxŞHD$0H4$HxŞHD$PHt
HHHϞH=M HW Ht$vǞH=M HW& NLHǞAH$ IHtH HH$` HtH HH hH$ ȞH HHPJH6IHLIH$ HtH0 HH$ HWH HAH$` IHKH H5PJHf AWH5[ 1AVAUATIUSH Hl$@HHT$ H|$0H5? @ȞHD$ HL$ H5) HHH$HL$ȞHD$ HT$0H\$`Lt$ H5H H{HD$`H M HT$hLHHD$ HD$0HD$pǞHC(H
- DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( YÞH H$ H9 L, LŞHL1I^ L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` >ŞH$P H_ԡ HDŽ$ H$h H֡H$` ŞH$p H3ԡHP HDŽ$ H$ HסH$ t HD$ HDŽ$p HxH;=ȸM HD$0HxH;=M K HH!\ H563 HRW H5 HX
HV H5 HW HL7HHD$PHt
HHHĈ L[]A\A]A^A_>IH H{pߡH{XߡH$ H{ L9HD$xLHx(HD$pLHxHD$hLHxHD$`LHxHD$ Ht$ HxHD$0H4$HxпHD$PHt
HHHɞH=M HW Ht$H=M HW& NLxAH$ IHtH HH$` HtH HH H$ KH HHPJH6IHLIH$ HtH0 HH$ HWH HAH$` IHKH H5PJHf AWH5 1AVAUATIUSH Hl$@HHT$ H|$0H5o pHD$ HL$ H5Y HHH$HL$KHD$ HT$0H\$`Lt$ H5x H{HD$`HPM 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$( 艽H H$ H9 L, LHL1I莻 L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` nH$P HΡ HDŽ$ H$h HѡH$` 2H$p HcΡHP HDŽ$ H$ HAѡH$ HD$ HDŽ$p HxH;=M HD$0HxH;=M K HHQV H5o- HQ H5ӻ HR
H6P H5 HQ HLg HHD$PHt
HHHĈ L[]A\A]A^A_nIH ۡH{p١H{X١H$ H{ LiHD$xLHxXHD$pLHxGHD$hLHx6HD$`LHx%HD$ Ht$ HxHD$0H4$Hx HD$PHt
HHHÞH=!M HW Ht$ֻH=M HW& NL註AH$ IHtH HH$` HtH HH H$ {H HHPJH6IH#LIH$ HtH0 HH$ HWH HAH$` IHKH H5PJHf AWH5 1AVAUATIUSH Hl$@H6HT$ H|$0H5 蠼HD$ HL$ H5 HHH$HL${HD$ HT$0H\$`Lt$ H5 H{HD$`HM HT$hLHHD$ HD$0HD$p0HC(H
- DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( 蹷H H$ H9 L, L HL1I辵 L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` 螹H$P Hȡ HDŽ$ H$h H?ˡH$` bH$p HȡHP HDŽ$ H$ HqˡH$ HD$ HDŽ$p HxH;=(M HD$0HxH;=M K HHP H5' HK H5 HL
HfJ H57 s H"L HLH/HD$PHt
HHHĈ L[]A\A]A^A_螲IH աH{pԡH{X
ԡH$ H{ LHD$xLHx舴HD$pLHxwHD$hLHxfHD$`LHxUHD$ Ht$ HxBHD$0H4$Hx0HD$PHt
HHH!H=QM HW Ht$H=!M HW& NLصAH$ IHtH HH$` HtH HH ܡH$ 諶H HHPJH6IHSLIH$ HtH0 HH$ HWH HAH$` IHKH H5PJHf AWH5 AVAUATIUSH Hl$@HcHT$ H|$0H5̫ ͶHD$ HL$ H5 HHH$HL$訶HD$ HT$0H\$`Lt$ H5ղ H{HD$`HM 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$( 汞H H$ H9 L, LKHL1I믞 L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` ˳H$P H¡ HDŽ$ H$h HlšH$` 菳H$p H¡HP HDŽ$ H$ HšH$ HD$ HDŽ$p HxH;=UM HD$0HxH;=?M K HHJ H5! HE H50 HG
HD H5d i HOF HLH\HD$PHt
HHHĈ L[]A\A]A^A_ˬIH ,СH{pCΡH{X:ΡH$ H{ LHD$xLHx赮HD$pLHx褮HD$hLHx蓮HD$`LHx肮HD$ Ht$ HxoHD$0H4$Hx]HD$PHt
HHHNH=~M HW Ht$3H=NM HW& NLAH$ IHtH HH$` HtH HH %סH$ ذH HHPJH6IHLIH$ HtH0 HH$ HWH HAH$` IHKH H5PJHf D AWH5
AVAUATIUSH Hl$@H蓾HT$ H|$0H5 HD$ HL$ H5 HHH$HL$ذHD$ HT$0H\$`Lt$ H5 H{HD$`HݣM HT$hLHHD$ HD$0HD$p荰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{HL1I L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` H$P H HDŽ$ H$h HH$` 迭H$p HHP HDŽ$ H$ HοH$ 1 HD$ HDŽ$p HxH;=M HD$0HxH;=oM K HHD H5, H@ H5` H@A
H> H5 n H@ HLHHD$PHt
HHHĈ L[]A\A]A^A_IH \ʡH{psȡH{XjȡH$ H{ LHD$xLHx娞HD$pLHxԨHD$hLHxèHD$`LHx貨HD$ Ht$ Hx蟨HD$0H4$Hx荨HD$PHt
HHH~H=M HW Ht$cH=~M HW& NL5AH$ IHtH HH$` HtH HH UѡH$ H HHPJH6IHLIH$ HtH0 HH$ HWH HAH$` IHKH H5PJHf D AWH5[ 1AVAUATIUSH Hl$@HƸHT$ H|$0H5/ 0HD$ HL$ H5 HHH$HL$ HD$ HT$0H\$`Lt$ H58 H{HD$`HM 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$( IH H$ H9 L, L讨HL1IN L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` .H$P HO HDŽ$ H$h HϹH$` H$p H#HP HDŽ$ H$ HH$ d HD$ HDŽ$p HxH;=M HD$0HxH;=M K HH? H5E HB: H5 Hs;
H8 H5Dz H: HL'HHD$PHt
HHHĈ L[]A\A]A^A_.IH ġH{p¡H{X¡H$ H{ L)HD$xLHxHD$pLHxHD$hLHxHD$`LHx增HD$ Ht$ HxҢHD$0H4$HxHD$PHt
HHH豬H=M HW Ht$薤H=M HW& NLhAH$ IHtH HH$` HtH HH ˡH$ ;H HHPJH6IHLIH$ HtH0 HH$ HWH HAH$` IHKH H5PJHf AWH5 1AVAUATIUSH Hl$@HHT$ H|$0H5_ `HD$ HL$ H5I HHH$HL$;HD$ HT$0H\$`Lt$ H5h H{HD$`H@M 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$( yH H$ H9 L, LޢHL1I~ L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` ^H$P H HDŽ$ H$h HH$` "H$p HSHP HDŽ$ H$ H1H$ HD$ HDŽ$p HxH;=M HD$0HxH;=ҕM K HHA9 H5h Hr4 H5Þ H5
H&3 H5 H4 HLWHHD$PHt
HHHĈ L[]A\A]A^A_^IH 迾H{pּH{XͼH$ H{ LYߡHD$xLHxHHD$pLHx7HD$hLHx&HD$`LHxHD$ Ht$ HxHD$0H4$HxHD$PHt
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- DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( 詚H H$ H9 L, LHL1I讘 L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` 莜H$P H HDŽ$ H$h H/H$` RH$p HHP HDŽ$ H$ HaH$ HD$ HDŽ$p HxH;=M HD$0HxH;=M K HHq3 H5 H. H5 H/
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+ HD$H HD$P HD$X D$` ?HD$h HD$ xH HD$HH9 H, H 1HHHH\$@H\$0IuhHH\$UHt$pLHD$p HD$x HDŽ$ HDŽ$ Ƅ$ Ƅ$ HDŽ$ DŽ$ H$ Ƅ$ HDŽ$ HDŽ$ Ƅ$ GA L$ I I` I11LHP IEHHD$(,X DD$EV D$ H|$H1LHHD$@HHHt2HHqf L9 H HtHq1HHH9t tHtH L`@ H|$ HLH HH H5 1L H|$H5 1 D H|$H|$PHufHH HuHD$HH|$@1H wH|$@HD$X HD$P {<f D E1 H9 H|$H1LHHD$@HHHt*HHq D L9t[H HtHq1HHH9t HHtH L`@ H|$ HLH HH H LLyHtSHt Hth@
ub1(f D H v p t6LL)HHuE1H|$( t7 H|$PHu @ HH %HuHD$HH|$@1H H|$11HD$X HD$P L$ HHPL$ E1H|$H1LHHD$@HHHt-HHq @ L9R H HtHq1HHH9t HtH L`@ H|$ HLH*
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+ HDŽ$ HDŽ$ HDŽ$ Hx DŽ$ ?HDŽ$ HD$H H$ H9_ H, H$HH1I@ 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
H$ H$ HWH9 HH$( t HH$ HH$ I6FXHcH< HD$ 1HD$0 LD$0H~x1f D Hc=
: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 tOHtH HT$`@ HPH|$HHH% HHAE IL9d$1HD$8Hx t.HD$H9 P HD$8۾ IDHP M$ tLNH|$0HtNH$ Ht8H$ H$ L`I9vH} HQNI9wH$ ?NH$ HuHH/%NHuH$ H$ 1H IH$ HDŽ$ HDŽ$ M1H|$@NH?H9 H, HMH<(1HHD$0H|$H{II6 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$ ~LH$ HPH$ H@H H$ H$ H$ H$ H$ HH+$ HH)H LLHCH$ H\$`HtHH\$`H$ HPH$ H@H H$ H$ HL= uHD$0 HH:QH|$H1Ҿ H$ TH?H9 HKH 1HI*GMHtL%KH|$0HtKH$ Ht7H$ H$ L`I9vH;HJI9wH$ JHH|$ H$ JH|$@.HfQ!EHENHHWPHoEH$ rJHDŽ$ HDŽ$ NDHHPoH$ H$ HH+$ HH)Hvb LJHCH$ H$( HtHH$ HPH$ H@H H$ H$ H$ H$ 1Ҿ H$ HxHaDSHgHH[;f. HwGuHIEf UHSHHHu @ HH IHuHEH} 1H DHE HE H[]f. AWIAVAUIATI̹ UHH SHHWHwLwLK H HH96 L< LH1HLI^DHu1HE LUHuU D IHIH2Ht?HHF1HHMMMuHEHHuMH> HI4HuH} G1LLu HH]IIK>HHt8HI$H L HEHL[]A\A]A^A_Lu IK>HHuHUI$LeI$HtHB1HuLM$HE HUHf HAH;BLu(rKH
-MH"BL*GUKHLHM D ATIUH SH9GHH H LLPA$GLM1LIHE HHHt*HLC D M9t;HHtLC1LIH9tHLH[] A\f. kFH1[]A\ÐL% { AWIAVAUIATI̹ UHH SHHWHwLwH H HH9. L< L.F1HLIAHu1HE LUHuU D IHIH2Ht?HHF1HHMMMuHEHHuMH> HI4HuH} gEL1H]HLu IIMl$K>HHt/HI$H L HEHL[]A\A]A^A_ Lu fHUI$LeI$HtHB1HuLM$HE HUH D H K?H?Lu(HHJH?LDHHeJHMKf. AWAVAUATUHSHHHOHHw(HH)HL`HGM<,K<?H9vUL)H1HHEH,H+H9 HH)HH H HII f D H9HHCLlH I9 J< T$ HCT$ LIL)1Hs(HHEH,HCHLHPH)HHt HHIH;nCL3LkIHk(HU JD%HCHHSH HS H HC8H HC@H[]A\A]A^A_ HIH)HHtH J|% H)Hp=HBSIL HH?HHHH9|
+Hq @ H9 H HtHq1HIH9t HtH HT$`@ HPH|$HHH% HHAE IL9d$1HD$8Hx t.HD$H9 P HD$8۾ IDHP M$ tLH|$0HtH$ Ht8H$ H$ L`I9vH} HI9wH$ H$ HuHH/eHuH$ H$ 1H !H$ HDŽ$ HDŽ$ 1H|$@NH?H9 H, H&H<(1HHD$0H|$HI6 Hw A I BD(xAS D Hc@ H$ HT$(Ht$ H|$$ D$P H$ H$ HWH9 HH\$`tHH$ H\$`HL$ H$ 1HIH$ HL# D Hc@E H$ H$ HPH$ H@H H$ H$ H$ H$ H$ HH+$ HH)H HCH$ H\$`HtHH\$`H$ HPH$ H@H H$ H$ HL= uHD$0 HH:QH|$H1Ҿ H$ TH?H9 HH 1HIZMHtLeH|$0HtVH$ Ht7H$ H$ L`I9vH;H$I9wH$ HH|$ H$ H|$@.H aHHHHH$ HDŽ$ HDŽ$ HHSoH$ H$ HH+$ HH)Hvb HCH$ H$( HtHH$ HPH$ H@H H$ H$ H$ H$ 1Ҿ H$ HxHaASHgHH[;f. HuHyf UHSHHHu @ HH ]HuHEH} 1H !HE HE H[]f. AWIAVAUIATI̹ UHH SHHWHwLw H HH96 L< L1HLIHu1HE LUHuU D IHIH2Ht?HHF1HHMMMuHEHHuMH> HI4HuH} 71LLu HH]IIK>HHt8HI$H L HEHL[]A\A]A^A_Lu IK>HHuHUI$LeI$HtHB1HuLM$HE HUHf HHkLu(HJHRLjH-H D ATIUH SHyHH H LLPA$GLM1LIHE HHHt*HLC D M9t;HHtLC1LIH9tHLH[] A\f. H1[]A\ÐL% { AWIAVAUIATI̹ UHH SHHWHwLw H HH9. L< Ln1HLIHu1HE LUHuU D IHIH2Ht?HHF1HHMMMuHEHHuMH> HI4HuH} L1H]HLu IIMl$K>HHt/HI$H L HEHL[]A\A]A^A_ Lu fHUI$LeI$HtHB1HuLM$HE HUH D H HLu(HHLHHf. AWAVAUATUHSHHHOHHw(HH)HL`HGM<,K<?H9vUL)H1HHEH,H+H9 HH)HH H HI f D H9HHCLlH I9 J< T$ H9T$ LIL)1Hs(HHEH,HCHLHPH)HHt HHVH;L3LkIHk(HU JD%HCHHSH HS H HC8H HC@H[]A\A]A^A_ HIH)HHtH J|% H)HBSIL HH?HHHH9|
6 D IMZKIN EF;D } HL EH9F|uHII?LHH9t\HPII?IIH9~;FD90 DD9~ LIDI@HH?HHL9|N A [f D HD THAL f. AUHH)ATIUSHHCr HI HoHAL$II~9 9 9 E $A<$DNED$A $HIf. A9L} HDA9|HA9~ HDA9H9sD LSD DHDCA $LH$HL)HC MtJH49|%9UA $A$
-DNED$A $NE$A $ED$DN8HHHhIHHA LHLHuHA$ H1LL)HHHH[]A\A]f D AWDIAVAUEATUSH DD$$LF(H6H|$T$!L$#HI_ItIHHD$H H$HxA@
HD$ D$" E1I9 HD$@L$HD$8 HD$(HD$pHD$0 f|$! ELD$ Aw0LHH)@HHLD$B +HH|$HcHI H@ HF A
t3HCLsELDIGPHt Ht$~tm|$# D$"I9\ IL;$GEzkHtAw0L)LHnDHLD$} HXf. qxH9HcL qLX MdJ|f. HWC;Ps)L@LcODMcE9sC;t
ELc C u|$#
MwH $ H|$LƉLD$ HHLD$=dB@ t,HD$8Ht"HH)HD$H HHH9 H$Ht$H|$(LD$螌LD$Ht$H|$0L臌Iw8H|$)H|$( HH$> H|$0H+ H|$H`HHo H|$0HL$ŌH|$(軌HL$H\$8H H MgH H []A\A]A^A_ IGPHtH|$ ID$" I7|$$ tdIwIGIGH9 .H|$HcHDxHHHa H H[MwHD$"VI7LLI9w nf. I_H %AxEMHcL A*Lp M OLf D IAQ;Ps)LPLcODIc*D9sA;t
ELcC uHD$ H Hc@f D @
t+IWPD$"HtH|$ I_HD$"JLI7LLAG@H|$0赊H|$(諊1H|$HΉH $HH $Ht$Ll$pHLωIw8H|$qLiH H|$HH> LH$,H$ H/H0HcH< LR MtqHt>f. HFt6;Bs)HzLcJ<DLcE9sC;t
DHc:AuD$"LGAG@1HD>
-HxtуuH$ H{H{H5
a 9H3AG@oHH|$0!H|$(H?@HH2HHH$ Hu{H{H5` e9H2AG@L1,HL讈H?HH{2Hf D ATIUSH HtH;I$ Hu*f HH{H+Ht ;H8HuI$ Ht8I|$pIDŽ$ Ht8I|$`ID$p Htj8I|$HID$` HtR8I|$8ID$H Ht:8ID$8 []A\ff. AWAVAUATUSH ҉L$0LD$@LL$8H$@ AHH D$XH$ ID$ HD$H$ Ht$HF HD$ H$ HD$PHD$pHD$H l$HcD$HHLbM Ht1V҉ L$0L=` I h HS HcH ;CZ L LH{LcD; sIB;
+DNED$A $NE$A $ED$DN8HHHhIHHA LHLHuHA$ H1LL)HHHH[]A\A]f D AWDIAVAUEATUSH DD$$LF(H6H|$T$!L$#HI_ItIHHD$H H$HxA@
HD$ D$" E1I9 HD$@L$HD$8 HD$(HD$pHD$0 f|$! ELD$ Aw0LHH)HHLD$B +HH|$HcHI H@ HF A
t3HCLsELDIGPHt Ht$~tm|$# D$"I9\ IL;$GEzkHtAw0L)LHHLD$} HXf. qxH9HcL qLX MdJ|f. HWC;Ps)L@LcODMcE9sC;t
ELc C u|$#
MwH $ H|$LƉLD$ HHLD$=@ t,HD$8Ht"HH)HD$H HHH9 H$Ht$H|$(LD$螌LD$Ht$H|$0L臌Iw8H|$)H|$( HH$> H|$0H+ H|$H`HHo H|$0HL$ŌH|$(軌HL$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赊H|$(諊1H|$HΉH $HH $Ht$Ll$pHLωIw8H|$qLiH H|$HH> LH$,H$ H/H0HcH< LR MtqHt>f. HFt6;Bs)HzLcJ<DLcE9sC;t
DHc:AuD$"LGAG@1HD>
+HxtуuH$ H{H{H5͢ H3AG@oHH|$0!H|$(HHH2HHH$ Hu{H{H5 H2AG@L1,HL讈HHH{2Hf D ATIUSH HtI$ Hu*f HH{H+Ht\HHuI$ HtI|$pIDŽ$ HtI|$`ID$p HtI|$HID$` HtI|$8ID$H HtzID$8 []A\ff. 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=` I h HS HcH ;CZ L LH{LcD; sIB;
* D DDMcAIB
-DHc?HHD: LH{LK HcI} HPHLL4A6Ic<Lf D H|$ Ƅ$ F0H*@ Ƅ$ 1HL$HDŽ$ Ƅ$ HDŽ$ HHDŽ$ HDŽ$ H$ H*@ HDŽ$ LpIFL$ H|Hu*@ LxL?9H2+@ HL$HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHyHHDŽ$ H$ H*@ HDŽ$ H@H$ HC*@ HH$ =H+@ HL$H|$ DŽ$ HHqH$ H)@ HH$ .8HD$H5
-^ . HxD6HD$H5
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- Hx5HD$Hx;H5] H5$ H)@ HH$ H)@ H@H$ H*@ HH$ H$ HxH;=(@ q H(@ HH$ HD$HxH7IFL$ H|$ L H,(@ HH$ .D$ 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.
- Hx2H%@ HPH$ HT$pH
L$ H$ L9 H9 H H)HT$o1HI8IIHH E AGL;=E%@ Ht$PH|$HH$ H57H$ HxH;=%@ H|$pH@&@ HWH8HD$pƄ$ HxH;=$@ H$@ LpLx D I9 H& I)HT$o1L$8IIHHu_E AGL;=x$@ Ht$PH|$HH$ Hh6. H []A\A]A^A_HLH.HHLH.H떿 L$\DT$4DL$(LD$/H IcE LD$H< L9HHG+'AU HEHLD$LcL$(DT$4L$\H.HYHD$H|$HHpX1tMH=( ,H= ,H$ HD$pHHx+(H$ Ht$nHx{+AG M7CD7 HD$pHt$oHxP+H=$@ HWt(Ht$nE-I
PJHH=K$@ HWtLHt$P-HHk"@ H|$HH`H$ 9(H4PJHH|$HH6H4HHD$HxzIFL$ L H=#@ HWt%pHt$P[,aPJHAG M7CD7 LH|$H&H 4 AWAVAUATUSH H^ Ht$Hc6L$4HT$(LD$8LL$ HHH2 LL$ IHHHT$@IW`HT$HT$pHT$H' HL$Hc1HA HHHHH9 HkHtAt$@t(A$ t I$ HUHH9 D M $HcIyPLAAAA A A H|$Ƅ$ %H` @ Ƅ$ 1HDŽ$ Ƅ$ HDŽ$ HDŽ$ HHDŽ$ HDŽ$ H$ H/ @ LpIFL$ I|H @ H@HHD$.H @ IHHDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHDŽ$ H$ Hl @ H@H$ H @ HH$ 3H @ H|$IwDŽ$ HH$ H2 @ HH$ -IH5S . +IH5
- +I] 1H5 H+IH5R
- +ID0H5R H+$ Ll$ Hn @ HH$ H[ @ H@H$ H @ HH$ H$ HxH;=A @ H @ IHHH$ o-IFL$ H|$L H @ HH$ #E E I$ Ht$HE I$ HF Hc6 H9uHcҋЃt @tAD$ HuI$ ~.HP1HL L LLLLHH9uM $AƄ$ E E HD$HHt$HP H
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+ Hx)HD$HxYH5ў H$ HX@ HH$ HE@ H@H$ H@ HH$ H$ HxH;=+@ q H@ HH$ HD$HxHIFL$ H|$ L H@ HH$ C݉D$ 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$H5h HxH_@ HPH$ HT$pH
L$ H$ L9 H9 H H)HT$o1HIEIIHH E AGL;=@ Ht$PH|$HH$ HuH$ HxH;=@ H|$pH@ HWHHD$pƄ$ HxH;=w@ H@ LpLx D I9 H& I)HT$o1LtIIHHu_E AGL;=@ Ht$PH|$HH$ H. H []A\A]A^A_HLHމHHLH݉H떿 L$\DT$4DL$(LD$߉H IcE LD$H< L9HHGk։AU HEHLD$LcL$(DT$4L$\H.HYHD$H|$HHpXtMH=H ۉH=< ۉH$ HD$pHHxډH$ Ht$nHxډAG M7CD7 HD$pHt$oHxډH=6@ HWt(Ht$nu܉I
PJHH=@ HWtLHt$P2܉HH @ H|$HH`H$ yHPJHH|$HHcHHHD$Hx;IFL$ L H=L@ HWt%pHt$PۉaPJHAG M7CD7 LH|$H&HI AWAVAUATUSH H^ Ht$Hc6L$4HT$(LD$8LL$ HHH2 LL$ IHHHT$@IW`HT$HT$pHT$H' HL$Hc1HA HHHHH9 HkHtAt$@t(A$ t I$ HUHH9 D M $HcIyPLAAAA A A H|$Ƅ$ ՉH @ Ƅ$ 1HDŽ$ Ƅ$ HDŽ$ HDŽ$ HHDŽ$ HDŽ$ H$ H@ LpIFL$ I|H@ H@HHD$݉Hk@ IHHDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHDŽ$ H$ H @ H@H$ H@ HH$ HQ@ H|$IwDŽ$ HH$ H@ HH$ ݉IH5n . ,ۉIH52B ۉI] IH5 HډIH5
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H9u HV H
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H H= H`H(1Ҹ $ t@$Pu5 tD$tD$HM> H0.f D H(é tH=m H= HH 1.5.2 out of index internal error not enough space: expected , got RegisterCustomOps was false. Stacktrace:
- Integer overflow opaque( ml_type != nullptr non_tensor_base != nullptr invalid location range IsTensorSequence() vector::reserve onnxruntime_profile_ lengths allocation failed elem_type_ != nullptr input name cannot be empty CPUExecutionProvider output name cannot be empty ORT_LOAD_CONFIG_FROM_MODEL != T Tensor type mismatch. output buffer is too small input array is too short shape is invalid ++index < c.size() vector::_M_default_append i < tensors_.size() vector::_M_range_check Encountered nullptr. value_type != nullptr in[idx]->IsTensor() Unsupported input type FlatBuffers 1.12.0 tried creating tensor with negative value in shape RegisterCustomOpsLibrary: Failed to load library RegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in library /onnxruntime_src/onnxruntime/core/common/safeint.h Specified domain and type names combination does not refer to a registered opaque type /onnxruntime_src/onnxruntime/core/session/onnxruntime_c_api.cc Opaque type is not a non_tensor type!!! Trying to get a Tensor, but got: /onnxruntime_src/include/onnxruntime/core/framework/ml_value.h this API does not support strings location dimensions do not match shape size Trying to get a TensorSeq, but got: is not supported in this function string buffer allocation failed Output buffer allocation failed Tensor sequence must contain only primitive types /onnxruntime_src/onnxruntime/core/framework/TensorSeq.h /onnxruntime_src/include/onnxruntime/core/framework/tensor.h utils::IsPrimitiveDataType<T>(dtype_) input array doesn't equal tensor size offsets buffer is not equal to tensor size element index is out of bounds buffer size is too small for string Sequence is missing type entry for its element /onnxruntime_src/include/onnxruntime/core/framework/data_types_internal.h Map is missing type entry for its value onnxruntime::DataTypeImpl::GetType<T>() == type_ Input is not of one of the supported sequence types. Input is not of type sequence or map. 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 ؐ0א@אPא`אpאאאאאאאאא ؐ ؐ ,<L\l|d 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] static void SafeIntExceptionHandler<onnxruntime::OnnxRuntimeException>::SafeIntOnOverflow() const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] St11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EE PFvPvE *ZN7OrtApis19GetBoundOutputNamesEPK12OrtIoBindingP12OrtAllocatorPPcPPmS7_EUlS7_E_ *ZN7OrtApis19GetBoundOutputNamesEPK12OrtIoBindingP12OrtAllocatorPPcPPmS7_EUlS5_E0_ *ZN7OrtApis20GetBoundOutputValuesEPK12OrtIoBindingP12OrtAllocatorPPP8OrtValuePmEUlS7_E_ St16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EE N11onnxruntime23NotImplementedExceptionE N11onnxruntime20OnnxRuntimeExceptionE N11onnxruntime10IAllocatorE N11onnxruntime16AllocatorWrapperE St19_Sp_counted_deleterIPvPFvS0_ESaIiELN9__gnu_cxx12_Lock_policyE2EE St19_Sp_make_shared_tag St23_Sp_counted_ptr_inplaceIN11onnxruntime16AllocatorWrapperESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE /\ basic_string::substr env_ptr == p_instance_ basic_string::_S_construct null not valid /onnxruntime_src/onnxruntime/core/session/ort_env.cc static void OrtEnv::Release(OrtEnv*) N11onnxruntime7logging5ISinkE 14LoggingWrapper N11onnxruntime7logging8CLogSinkE /onnxruntime_src/onnxruntime/core/session/IOBinding.cc BindInput execution_mode is not valid graph_optimization_level is not valid WARNING: Since openmp is enabled in this build, this API cannot be used to configure intra op num threads. Please use the openmp environment variables to control the number of threads.
- /onnxruntime_src/onnxruntime/core/session/abi_session_options.cc Result buffer is not large enough /onnxruntime_src/onnxruntime/core/session/custom_ops.cc %u Unsupported version ' ' in custom op ' is not implemented Description onnxruntime::CustomOpKernel::CustomOpKernel(const onnxruntime::OpKernelInfo&, OrtCustomOp&) CreateCustomRegistry ComputeAsync *ZN11onnxruntime20CreateCustomRegistryERKSt6vectorIP17OrtCustomOpDomainSaIS2_EERSt10shared_ptrINS_14CustomRegistryEEEUlRKNS_12OpKernelInfoEE_ N4onnx15ISchemaRegistryE N11onnxruntime8OpKernelE N11onnxruntime30IOnnxRuntimeOpSchemaCollectionE N11onnxruntime14CustomOpKernelE N4onnx8OpSchema19num_inputs_allowed_MUliE_E N4onnx8OpSchema20num_outputs_allowed_MUliE_E St23_Sp_counted_ptr_inplaceIN11onnxruntime14KernelRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime27OnnxRuntimeOpSchemaRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime14CustomRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE 12OrtAllocator N3Ort9ExceptionE 16OrtAllocatorImpl 19OrtDefaultAllocator Cpu Env is null OrtMemoryInfo is null No requested allocator available Only CPU devices are supported for now. Received invalid value for arena extend strategy. Valid values can be either 0, 1 or -1. *ZN7OrtApis26CreateAndRegisterAllocatorEP6OrtEnvPK13OrtMemoryInfoPK11OrtArenaCfgEUliE_ *ZN7OrtApis26CreateAndRegisterAllocatorEP6OrtEnvPK13OrtMemoryInfoPK11OrtArenaCfgEUliE0_ tensor(uint8) tensor(uint16) tensor(uint32) tensor(uint64) tensor(int8) tensor(int16) tensor(int32) tensor(int64) tensor(bfloat16) tensor(float16) tensor(float) tensor(double) tensor(string) tensor(bool) tensor(complex64) tensor(complex128) unknown Element type of input [TypeInferenceError] expected to have tensor type Input Output com.microsoft com.microsoft.nchwc com.microsoft.mlfeaturizers (domain: ) from file , but it its domain is not known by the checker. , but it its version is not in the inclusive range [ ] (usually, this means you in onnx/defs/schema.h). Schema error: intra-op inter-op MemcpyFromHost MemcpyToHost Exception caught: Allocator with this OrtMemoryInfo is already registered. Trying to register schema with name , but it is already registered from file bumped the operator version but forgot to update the version range in DomainToVersionRange Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type. /onnxruntime_src/onnxruntime/core/session/environment.cc expected to have sequence type expected to have type but instead is null FvON4onnx8OpSchemaEE FvRN4onnx16InferenceContextEE N4onnx14InferenceErrorE N4onnx11SchemaErrorE PFvRN4onnx16InferenceContextEE PFvON4onnx8OpSchemaEE Load model from failed. Only bytes were able to be read. Model was not loaded Model was not loaded. Profiler is disabled. ) , expected: ( Output vector pointer is NULL p_fetches->size(): Invalid Output Name: other_error [json.exception. %Y-%m-%d_%H-%M-%S .json Session was not initialized Session not initialized. Compute_ Create_State_ Release_State_ Invalid rank for input: Expected: for the following indices
- 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 model_loading_proto model_loading_istream Provider has already been registered. DmlExecutionProvider CUDAExecutionProvider Node placements ]. Provider: [ MemcpyTransformer are '0' and '1'. is_model_proto_parsed CastFloat16Transformer st.IsOK() Error during EndProfiling(): ORTM 1.4.0 session.load_model_format ORT model_loading_array Initializing session. session.use_env_allocators session.save_model_format session_initialization /onnxruntime_src/onnxruntime/core/session/inference_session.cc Received nullptr for graph transformer Graph transformers must be registered before the session is initialized. Attempt to use DefaultLogger but none has been registered. /onnxruntime_src/include/onnxruntime/core/common/logging/logging.h Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast<int>(logging::Severity::kFATAL) Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast<int>(logging::Severity::kFATAL) Could not write a profile because no model was loaded. /onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.h Unexpected input data type. Actual: ( At least one output should be requested. Output vector incorrectly sized: output_names.size(): timeout_in_ms >0 is not supported session->NewIOBinding(&binding_).IsOK() Exceeded max transformer level. Current level is set to graph_optimization_level <= TransformerLevel::MaxLevel Received nullptr for custom registry Please fix either the inputs or the model. Got invalid dimensions for input: Size mismatch: feed_names has is not expected to be of type tensor. is not expected to be of type sparse tensor. Trying to get a SparseTensor, but got: is not expected to be of type tensor sequence. Encountered unknown exception in Run() This session already contains a loaded model. Encountered unknown exception in Load() ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load(). ModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction. model_loading_from_saved_proto /onnxruntime_src/onnxruntime/core/framework/execution_providers.h Received nullptr for exec provider Execution providers must be registered before the session is initialized. Execution providers must be registered before the session is initialized. Having memory pattern enabled is not supported while using the DML Execution Provider. So disabling it for this session since it uses the DML Execution Provider. Parallel execution mode does not support the DML Execution Provider. So making the execution mode sequential for this session since it uses the DML Execution Provider. Parallel execution mode does not support the CUDA Execution Provider. So making the execution mode sequential for this session since it uses the CUDA Execution Provider. Could not find an implementation for the node All nodes have been placed on [ The only supported values for the environment variable The environment variable contained the value: Reading the provided model for the ORT config ModelProto needs to be parsed to check for ORT config within it Could not finalize session options while constructing the inference session. Error Message: graph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK() Creating and using per session threadpools since use_per_session_threads_ is true Failed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIAL Using global/env threadpools since use_per_session_threads_ is false When the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API. session_env.EnvCreatedWithGlobalThreadPools() Given model could not be parsed while creating inference session. Error message: Could not parse model successfully while constructing the inference session Unknown error during EndProfiling() Failed to load model because protobuf parsing failed. This session has already been initialized. Not satisfied: fbs::VerifyInferenceSessionBuffer(verifier)
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+ Integer overflow opaque( ml_type != nullptr non_tensor_base != nullptr invalid location range IsTensorSequence() vector::reserve lengths allocation failed elem_type_ != nullptr input name cannot be empty CPUExecutionProvider output name cannot be empty onnxruntime_profile_ ORT_LOAD_CONFIG_FROM_MODEL != T Tensor type mismatch. output buffer is too small input array is too short shape is invalid ++index < c.size() vector::_M_default_append i < tensors_.size() vector::_M_range_check Encountered nullptr. value_type != nullptr in[idx]->IsTensor() Unsupported input type FlatBuffers 1.12.0 CUDA execution provider is not enabled. tried creating tensor with negative value in shape RegisterCustomOpsLibrary: Failed to load library RegisterCustomOpsLibrary: Entry point RegisterCustomOps not found in library /onnxruntime_src/onnxruntime/core/common/safeint.h Specified domain and type names combination does not refer to a registered opaque type /onnxruntime_src/onnxruntime/core/session/onnxruntime_c_api.cc Opaque type is not a non_tensor type!!! Trying to get a Tensor, but got: /onnxruntime_src/include/onnxruntime/core/framework/ml_value.h this API does not support strings location dimensions do not match shape size Trying to get a TensorSeq, but got: is not supported in this function string buffer allocation failed Output buffer allocation failed Tensor sequence must contain only primitive types /onnxruntime_src/onnxruntime/core/framework/TensorSeq.h /onnxruntime_src/include/onnxruntime/core/framework/tensor.h utils::IsPrimitiveDataType<T>(dtype_) input array doesn't equal tensor size offsets buffer is not equal to tensor size element index is out of bounds buffer size is too small for string Sequence is missing type entry for its element /onnxruntime_src/include/onnxruntime/core/framework/data_types_internal.h Map is missing type entry for its value onnxruntime::DataTypeImpl::GetType<T>() == type_ Input is not of one of the supported sequence types. Input is not of type sequence or map. 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 HxȘؘ蘉(8HH̼Լ伉$4DTdtlOrtStatus* 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] static void SafeIntExceptionHandler<onnxruntime::OnnxRuntimeException>::SafeIntOnOverflow() const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] St11_Mutex_baseILN9__gnu_cxx12_Lock_policyE2EE PFvPvE *ZN7OrtApis19GetBoundOutputNamesEPK12OrtIoBindingP12OrtAllocatorPPcPPmS7_EUlS7_E_ *ZN7OrtApis19GetBoundOutputNamesEPK12OrtIoBindingP12OrtAllocatorPPcPPmS7_EUlS5_E0_ *ZN7OrtApis20GetBoundOutputValuesEPK12OrtIoBindingP12OrtAllocatorPPP8OrtValuePmEUlS7_E_ St16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EE N11onnxruntime23NotImplementedExceptionE N11onnxruntime20OnnxRuntimeExceptionE N11onnxruntime10IAllocatorE N11onnxruntime16AllocatorWrapperE St19_Sp_counted_deleterIPvPFvS0_ESaIiELN9__gnu_cxx12_Lock_policyE2EE St19_Sp_make_shared_tag St23_Sp_counted_ptr_inplaceIN11onnxruntime16AllocatorWrapperESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE /\ basic_string::substr env_ptr == p_instance_ basic_string::_S_construct null not valid /onnxruntime_src/onnxruntime/core/session/ort_env.cc static void OrtEnv::Release(OrtEnv*) N11onnxruntime7logging5ISinkE 14LoggingWrapper N11onnxruntime7logging8CLogSinkE /onnxruntime_src/onnxruntime/core/session/IOBinding.cc BindInput execution_mode is not valid graph_optimization_level is not valid WARNING: Since openmp is enabled in this build, this API cannot be used to configure intra op num threads. Please use the openmp environment variables to control the number of threads.
+ /onnxruntime_src/onnxruntime/core/session/abi_session_options.cc Result buffer is not large enough /onnxruntime_src/onnxruntime/core/session/custom_ops.cc %u Unsupported version ' ' in custom op ' is not implemented Description onnxruntime::CustomOpKernel::CustomOpKernel(const onnxruntime::OpKernelInfo&, const OrtCustomOp&) CreateCustomRegistry ComputeAsync *ZN11onnxruntime20CreateCustomRegistryERKSt6vectorIP17OrtCustomOpDomainSaIS2_EERSt10shared_ptrINS_14CustomRegistryEEEUlRKNS_12OpKernelInfoEE_ N4onnx15ISchemaRegistryE N11onnxruntime8OpKernelE N11onnxruntime30IOnnxRuntimeOpSchemaCollectionE N11onnxruntime14CustomOpKernelE N4onnx8OpSchema19num_inputs_allowed_MUliE_E N4onnx8OpSchema20num_outputs_allowed_MUliE_E St23_Sp_counted_ptr_inplaceIN11onnxruntime14KernelRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime27OnnxRuntimeOpSchemaRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime14CustomRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE 12OrtAllocator N3Ort9ExceptionE 16OrtAllocatorImpl 19OrtDefaultAllocator No requested allocator available Env is null OrtMemoryInfo is null Cpu tensor(uint8) tensor(uint16) tensor(uint32) tensor(uint64) tensor(int8) tensor(int16) tensor(int32) tensor(int64) tensor(bfloat16) tensor(float16) tensor(float) tensor(double) tensor(string) tensor(bool) tensor(complex64) tensor(complex128) unknown Element type of input [TypeInferenceError] expected to have tensor type Input Output com.microsoft com.microsoft.nchwc com.microsoft.mlfeaturizers (domain: ) from file , but it its domain is not known by the checker. , but it its version is not in the inclusive range [ ] (usually, this means you in onnx/defs/schema.h). Schema error: intra-op inter-op MemcpyFromHost MemcpyToHost Exception caught: Allocator with this OrtMemoryInfo is already registered. Only CPU devices are supported for now. Received invalid value for arena extend strategy. Valid values can be either 0, 1 or -1. Trying to register schema with name , but it is already registered from file bumped the operator version but forgot to update the version range in DomainToVersionRange Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type. /onnxruntime_src/onnxruntime/core/session/environment.cc expected to have sequence type expected to have type but instead is null FvON4onnx8OpSchemaEE FvRN4onnx16InferenceContextEE *ZN11onnxruntime11Environment26CreateAndRegisterAllocatorERK13OrtMemoryInfoPK11OrtArenaCfgEUliE_ *ZN11onnxruntime11Environment26CreateAndRegisterAllocatorERK13OrtMemoryInfoPK11OrtArenaCfgEUliE0_ N4onnx14InferenceErrorE N4onnx11SchemaErrorE PFvRN4onnx16InferenceContextEE PFvON4onnx8OpSchemaEE Load model from failed. Only bytes were able to be read. off Model was not loaded Model was not loaded. Profiler is disabled. ) , expected: ( other_error [json.exception. %Y-%m-%d_%H-%M-%S .json Session was not initialized Session not initialized. Invalid rank for input: Expected: for the following indices
+ Compute_ Create_State_ Release_State_ Output vector pointer is NULL p_fetches->size(): Invalid Output Name: elements, but feeds has elements. Invalid Feed Input Name: Input with name: IsSparseTensor() Running with tag: model_run Exception during loading: Unknown exception in Load() model_loading_uri model_loading_proto model_loading_istream Provider has already been registered. DmlExecutionProvider CUDAExecutionProvider Node placements ]. Provider: [ MemcpyTransformer are '0' and '1'. is_model_proto_parsed session.set_denormal_as_zero CastFloat16Transformer st.IsOK() Error during EndProfiling(): ORTM 1.4.0 session.load_model_format ORT model_loading_array Initializing session. session.use_env_allocators session.save_model_format session_initialization /onnxruntime_src/onnxruntime/core/session/inference_session.cc Flush-to-zero and denormal-as-zero are Received nullptr for graph transformer Graph transformers must be registered before the session is initialized. Attempt to use DefaultLogger but none has been registered. /onnxruntime_src/include/onnxruntime/core/common/logging/logging.h Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast<int>(logging::Severity::kFATAL) Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast<int>(logging::Severity::kFATAL) Could not write a profile because no model was loaded. /onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.h Unexpected input data type. Actual: ( timeout_in_ms >0 is not supported session->NewIOBinding(&binding_).IsOK() Exceeded max transformer level. Current level is set to graph_optimization_level <= TransformerLevel::MaxLevel Please fix either the inputs or the model. Got invalid dimensions for input: Received nullptr for custom registry At least one output should be requested. Output vector incorrectly sized: output_names.size(): Size mismatch: feed_names has is not expected to be of type tensor. is not expected to be of type sparse tensor. Trying to get a SparseTensor, but got: is not expected to be of type tensor sequence. Encountered unknown exception in Run() This session already contains a loaded model. Encountered unknown exception in Load() ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load(). ModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction. model_loading_from_saved_proto /onnxruntime_src/onnxruntime/core/framework/execution_providers.h Received nullptr for exec provider Execution providers must be registered before the session is initialized. Execution providers must be registered before the session is initialized. Having memory pattern enabled is not supported while using the DML Execution Provider. So disabling it for this session since it uses the DML Execution Provider. Parallel execution mode does not support the DML Execution Provider. So making the execution mode sequential for this session since it uses the DML Execution Provider. Parallel execution mode does not support the CUDA Execution Provider. So making the execution mode sequential for this session since it uses the CUDA Execution Provider. Could not find an implementation for the node All nodes have been placed on [ The only supported values for the environment variable The environment variable contained the value: Reading the provided model for the ORT config ModelProto needs to be parsed to check for ORT config within it Could not finalize session options while constructing the inference session. Error Message: graph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK() Creating and using per session threadpools since use_per_session_threads_ is true Failed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIAL Using global/env threadpools since use_per_session_threads_ is false When the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API. session_env.EnvCreatedWithGlobalThreadPools() Given model could not be parsed while creating inference session. Error message: Could not parse model successfully while constructing the inference session Unknown error during EndProfiling() Failed to load model because protobuf parsing failed. This session has already been initialized. Not satisfied: fbs::VerifyInferenceSessionBuffer(verifier)
InferenceSession is null. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_session
Serialized version info is null. Invalid ORT format model. Satisfied, but should not be: fbs_ort_model_version == nullptr
The ORT format model version [ ] is not supported this build Not satisfied: IsOrtModelVersionSupported(fbs_ort_model_version->str())
Missing Model. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_model
SessionState is null. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_sess_state
- Session has already been initialized. Adding default CPU execution provider. Serializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED. The generated model may contain hardware and execution provider specific optimizations, and should only be used in the same environment the model was optimized for. Session successfully initialized. Exception during initialization: Encountered unknown exception in Initialize() virtual void onnxruntime::InferenceSession::AddPredefinedTransformers(onnxruntime::GraphTransformerManager&, onnxruntime::TransformerLevel, const std::vector<std::basic_string<char> >&) void onnxruntime::InferenceSession::InitLogger(onnxruntime::logging::LoggingManager*) const onnxruntime::logging::Logger& onnxruntime::InferenceSession::CreateLoggerForRun(const RunOptions&, std::unique_ptr<onnxruntime::logging::Logger>&) EndProfiling NewIOBinding GetModelOutputs GetOverridableInitializers GetModelInputs GetModelMetadata onnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector<std::basic_string<char> >&, const std::vector<std::basic_string<char> >&, const onnxruntime::OrtValueNameIdxMap&) FeedsFetchesInfo Run const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor] ValidateInputs operator() operator() Initialize LoadOrtModel LoadOrtModelBytes operator() TransformGraph Load SaveToOrtFormat AddCustomOpDomains Add RegisterExecutionProvider operator() ~InferenceSession onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, const void*, int) onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, const string&) static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() onnxruntime::common::Status onnxruntime::FinalizeSessionOptions(const onnxruntime::SessionOptions&, const onnx::ModelProto&, bool, onnxruntime::SessionOptions&) FinalizeSessionOptions ConstructorCommon void onnxruntime::InferenceSession::ConstructorCommon(const onnxruntime::SessionOptions&, const onnxruntime::Environment&) St14default_deleteIN11onnxruntime5ModelEE *ZN11onnxruntime16InferenceSession4LoadEPKviEUlRSt10shared_ptrINS_5ModelEEE_ *ZN11onnxruntime16InferenceSession4LoadEvEUlRSt10shared_ptrINS_5ModelEEE_ *ZN11onnxruntime16InferenceSession12LoadOrtModelERKSsEUlvE_ *ZN11onnxruntime16InferenceSession12LoadOrtModelEPKviEUlvE_ N11onnxruntime16GraphTransformerE N11onnxruntime16InferenceSessionE N8nlohmann6detail9exceptionE N8nlohmann6detail11other_errorE ZN11onnxruntime20CPUExecutionProviderC1ERKNS_24CPUExecutionProviderInfoEEUliE_ ZN11onnxruntime16InferenceSession4LoadIcEENS_6common6StatusERKSbIT_St11char_traitsIS4_ESaIS4_EEEUlRSt10shared_ptrINS_5ModelEEE_ St19_Sp_counted_deleterIPN11onnxruntime5ModelESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceISt13unordered_mapISsN11onnxruntime11FuncManager8FuncInfoESt4hashISsESt8equal_toISsESaISt4pairIKSsS3_EEESaISC_ELN9__gnu_cxx12_Lock_policyE2EE _ _ 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: XLΓΓΓ$D|td444$Ě T蛔(888HXhxțT 잔D|ԠDlT䧔ԧħٔؔהההההٔٔٔpٔӔHٔ0ٔٔ ٔؔؔؔؔؔpؔXؔ֔֔֔x֔`֔H֔0֔֔ ֔ՔՔՔٔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔՔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔӔhהhהhהhהhהhהhהhהhהhהhהhהhהhהhהhהhהhהhהhהhהhהhהhהhהhהhהhהhהhהXה ה ה ה ה ה ה ה ה ה ה ה ה֔ ה הHؔ ؔ ؔ ؔ`ٔ֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔֔w֔֔֔֔֔֔e֔֔֔֔S֔֔֔֔֔֔֔֔A֔֔֔֔/֔֔ ֔Qє۔ ڔ۔X۔h۔۔۔۔x۔D:*********** T^
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- 0 | , )\/|/-D/ ./+*d())))$' SetEnableProfiling SetGraphOptimizationLevel SetExecutionMode SetInterOpNumThreads SetIntraOpNumThreads ParseSessionOptionsFromModelProto operator() ParseOrtConfigJsonInModelProto N8nlohmann6detail11parse_errorE N8nlohmann6detail16invalid_iteratorE N8nlohmann6detail10type_errorE N8nlohmann6detail12out_of_rangeE N8nlohmann6detail22input_adapter_protocolE N8nlohmann6detail20input_buffer_adapterE St23_Sp_counted_ptr_inplaceIN8nlohmann6detail20input_buffer_adapterESaIS2_ELN9__gnu_cxx12_Lock_policyE2EE /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer.cc Apply /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_mgr.cc This transformer is already registered ApplyTransformers Level _RuleBasedTransformer NchwcTransformer MatMulScaleFusion EliminateIdentity EliminateSlice UnsqueezeElimination EliminateDropout ExpandElimination CastElimination FuseReluClip ShapeToInitializer ConvAddFusion ConvMulFusion ConvBNFusion Unsupported level MatMulAddFusion ReshapeFusion GemmActivationFusion DynamicQuantizeMatMulFusion ACLExecutionProvider ConvActivationFusion AttentionFusion EmbedLayerNormFusion BiasGeluFusion SkipLayerNormFusion FastGeluFusion Unsupported level GeluApproximation /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_utils.cc CommonSubexpressionElimination std::vector<std::unique_ptr<onnxruntime::GraphTransformer> > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, gsl::span<const onnxruntime::FreeDimensionOverride>, const onnxruntime::IExecutionProvider&, const std::vector<std::basic_string<char> >&) std::vector<std::unique_ptr<onnxruntime::RewriteRule> > onnxruntime::optimizer_utils::GenerateRewriteRules(onnxruntime::TransformerLevel, const std::vector<std::basic_string<char> >&) N11onnxruntime11RewriteRuleE N11onnxruntime17EliminateIdentityE Max: node_index < nodes_.size() Inserted_Cast dtype Validating no unexpected access using an invalid node_index. Got: /onnxruntime_src/include/onnxruntime/core/graph/graph.h /onnxruntime_src/include/onnxruntime/core/optimizer/graph_transformer.h /onnxruntime_src/onnxruntime/core/optimizer/insert_cast_transformer.cc cast node to cast from float16 to float32 on cpu InsertCastTransformer works on the assumption that `dtype` attribute holds an integer. dtype_attribute->second.has_i() RemoveDuplicateCastTransformer virtual onnxruntime::common::Status onnxruntime::InsertCastTransformer::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) const ApplyImpl Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime21InsertCastTransformerE N11onnxruntime30RemoveDuplicateCastTransformerE _Map_base::at size is different size_ == size unsupported data type: ai.onnx Sqrt Pow fused LayerNorm subgraphs epsilon /onnxruntime_src/onnxruntime/core/optimizer/initializer.h ReadExternalRawData() failed: /onnxruntime_src/onnxruntime/core/optimizer/layer_norm_fusion.cc Y 4 F F<B/B<B<B<B<B"BB<B<BBA<B<B<B<BBInitializer onnxruntime::Initializer::Initializer(const onnx::TensorProto&, const onnxruntime::Path&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl '7
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 has_value_ Unsupported data type: div_inputs.size() == 2 mul_inputs.size() == 2 TransposeMatMul Fused MatMul and Scale _FusedMatMulAndScale /onnxruntime_src/include/onnxruntime/core/common/optional.h /onnxruntime_src/onnxruntime/core/optimizer/matmul_scale_fusion.cc Constant initializer NodeArg shape should not be null. NodeArg: input_node.InputDefs().size() == 2 && scale_and_index.value().second < 2 output_node.OutputDefs().size() == 1 std::vector<onnxruntime::{anonymous}::ScaleMergeInfo> onnxruntime::{anonymous}::GetOutputNodeMerges(onnxruntime::Graph&, onnxruntime::Node&, const std::unordered_set<std::basic_string<char> >&) const T& onnxruntime::optional<T>::value() const [with T = std::pair<float, int>] const T& onnxruntime::optional<T>::value() const [with T = float] Ret onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<Ret>::operator()(int32_t) const [with Ret = onnxruntime::common::Status; int32_t = int] operator() operator() operator() operator() operator() operator() operator() operator() GetScalarConstantInitializer onnxruntime::optional<float> onnxruntime::{anonymous}::GetScalarConstantInitializer(const onnxruntime::Graph&, const onnxruntime::NodeArg&) onnxruntime::optional<std::pair<float, int> > onnxruntime::{anonymous}::GetScaleFromNode(const onnxruntime::Graph&, const onnxruntime::Node&, const std::unordered_set<std::basic_string<char> >&) std::vector<onnxruntime::{anonymous}::ScaleMergeInfo> onnxruntime::{anonymous}::GetInputNodeMerges(onnxruntime::Graph&, onnxruntime::Node&, const std::unordered_set<std::basic_string<char> >&) Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
N11onnxruntime17MatMulScaleFusionE ?`hxX|tȢȢȢȢȢȢȢȢȢȢȢ䢖䢖䢖䢖䢖̢䢖䢖ܢ䢖䢖䢖䢖ܢ Т آ袖 Ȣ 좖 䢖 88888 880 88880T$TTTT,<TTL TTTTLp@ppppHXpph8pppph\dtTЪH Ыtttttttij4tttt p8pads strides dilations kernel_shape auto_pad SAME_UPPER SAME_LOWER NOTSET VALID ReorderOutput channels_last perm reorder ReorderInput activation asymmetric Upsample scales nearest nearest_mode bn_scale bn_B _bn_nchwc spatial FusedConv Concat BatchNormalization GlobalMaxPool GlobalAveragePool coordinate_transformation_mode /onnxruntime_src/onnxruntime/core/optimizer/nchwc_transformer.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
+ Session has already been initialized. Adding default CPU execution provider. This session will use the allocator registered with the environment. Unable to serialize model as it contains compiled nodes. Please disable any execution providers which generate compiled nodes. Serializing optimized model with Graph Optimization level greater than ORT_ENABLE_EXTENDED. The generated model may contain hardware and execution provider specific optimizations, and should only be used in the same environment the model was optimized for. Session successfully initialized. Exception during initialization: Encountered unknown exception in Initialize() virtual void onnxruntime::InferenceSession::AddPredefinedTransformers(onnxruntime::GraphTransformerManager&, onnxruntime::TransformerLevel, const std::vector<std::basic_string<char> >&) void onnxruntime::InferenceSession::InitLogger(onnxruntime::logging::LoggingManager*) const onnxruntime::logging::Logger& onnxruntime::InferenceSession::CreateLoggerForRun(const RunOptions&, std::unique_ptr<onnxruntime::logging::Logger>&) EndProfiling NewIOBinding GetModelOutputs GetOverridableInitializers GetModelInputs GetModelMetadata onnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector<std::basic_string<char> >&, const std::vector<std::basic_string<char> >&, const onnxruntime::OrtValueNameIdxMap&) FeedsFetchesInfo Run const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor] ValidateInputs operator() operator() Initialize LoadOrtModel LoadOrtModelBytes operator() PartitionOrtFormatModel TransformGraph Load SaveToOrtFormat AddCustomOpDomains Add RegisterExecutionProvider operator() ~InferenceSession onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, const void*, int) onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, const string&) operator() static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() onnxruntime::common::Status onnxruntime::FinalizeSessionOptions(const onnxruntime::SessionOptions&, const onnx::ModelProto&, bool, onnxruntime::SessionOptions&) FinalizeSessionOptions ConstructorCommon void onnxruntime::InferenceSession::ConstructorCommon(const onnxruntime::SessionOptions&, const onnxruntime::Environment&) St14default_deleteIN11onnxruntime5ModelEE *ZN11onnxruntime16InferenceSession4LoadEPKviEUlRSt10shared_ptrINS_5ModelEEE_ *ZN11onnxruntime16InferenceSession4LoadEvEUlRSt10shared_ptrINS_5ModelEEE_ *ZN11onnxruntime16InferenceSession12LoadOrtModelERKSsEUlvE_ *ZN11onnxruntime16InferenceSession12LoadOrtModelEPKviEUlvE_ N11onnxruntime16GraphTransformerE N11onnxruntime16InferenceSessionE N8nlohmann6detail9exceptionE N8nlohmann6detail11other_errorE ZN11onnxruntime20CPUExecutionProviderC1ERKNS_24CPUExecutionProviderInfoEEUliE_ ZN11onnxruntime16InferenceSession4LoadIcEENS_6common6StatusERKSbIT_St11char_traitsIS4_ESaIS4_EEEUlRSt10shared_ptrINS_5ModelEEE_ St19_Sp_counted_deleterIPN11onnxruntime5ModelESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceISt13unordered_mapISsN11onnxruntime11FuncManager8FuncInfoESt4hashISsESt8equal_toISsESaISt4pairIKSsS3_EEESaISC_ELN9__gnu_cxx12_Lock_policyE2EE _ _ 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: hhhhh\hhhhgiij4jiiiiTonootoDoDoDo4o$ooooo,qdooppo(p8pHpHpHpXphpxpppppppdtr,sssTrwuTvv$w|u|d{|||}}}|`@(Ȭ8 ȮhP8 ح@(્ȫh88P888888888888888888888888888888888888888888888888888888888H88888888888888888888888888888888888 XXXX------------ -------------------------------------------------嫍---ӫ-----------ѦP` 0ppp@gggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggg/gggggggggggvgl`ڍ8ߍ ߍލ0ލݍݍX܍ۍHٍ`ڍ`ڍ`ڍ`ڍ؍T4T| SetEnableProfiling SetGraphOptimizationLevel SetExecutionMode SetInterOpNumThreads SetIntraOpNumThreads ParseSessionOptionsFromModelProto operator() ParseOrtConfigJsonInModelProto N8nlohmann6detail11parse_errorE N8nlohmann6detail16invalid_iteratorE N8nlohmann6detail10type_errorE N8nlohmann6detail12out_of_rangeE N8nlohmann6detail22input_adapter_protocolE N8nlohmann6detail20input_buffer_adapterE St23_Sp_counted_ptr_inplaceIN8nlohmann6detail20input_buffer_adapterESaIS2_ELN9__gnu_cxx12_Lock_policyE2EE /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer.cc Apply /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_mgr.cc This transformer is already registered ApplyTransformers Level _RuleBasedTransformer NhwcTransformer NchwcTransformer MatMulScaleFusion EliminateIdentity EliminateSlice UnsqueezeElimination EliminateDropout ExpandElimination CastElimination FuseReluClip ShapeToInitializer ConvAddFusion ConvMulFusion ConvBNFusion Unsupported level MatMulAddFusion ReshapeFusion GemmActivationFusion MatMulIntegerToFloatFusion DynamicQuantizeMatMulFusion ACLExecutionProvider ArmNNExecutionProvider ConvActivationFusion SimplifiedLayerNormFusion AttentionFusion EmbedLayerNormFusion BiasGeluFusion BiasSoftmaxFusion SkipLayerNormFusion FastGeluFusion Unsupported level GeluApproximation /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_utils.cc CommonSubexpressionElimination std::vector<std::unique_ptr<onnxruntime::GraphTransformer> > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, gsl::span<const onnxruntime::FreeDimensionOverride>, const onnxruntime::IExecutionProvider&, const std::vector<std::basic_string<char> >&) std::vector<std::unique_ptr<onnxruntime::RewriteRule> > onnxruntime::optimizer_utils::GenerateRewriteRules(onnxruntime::TransformerLevel, const std::vector<std::basic_string<char> >&) N11onnxruntime11RewriteRuleE N11onnxruntime17EliminateIdentityE Max: node_index < nodes_.size() Inserted_Cast dtype Validating no unexpected access using an invalid node_index. Got: /onnxruntime_src/include/onnxruntime/core/graph/graph.h RemoveDuplicateCastTransformer /onnxruntime_src/include/onnxruntime/core/optimizer/graph_transformer.h /onnxruntime_src/onnxruntime/core/optimizer/insert_cast_transformer.cc cast node to cast from float16 to float32 on cpu InsertCastTransformer works on the assumption that `dtype` attribute holds an integer. dtype_attribute->second.has_i() virtual onnxruntime::common::Status onnxruntime::InsertCastTransformer::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) const ApplyImpl Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime21InsertCastTransformerE N11onnxruntime30RemoveDuplicateCastTransformerE hrzcmc_Map_base::at size is different size_ == size unsupported data type: ai.onnx Sqrt Pow fused LayerNorm subgraphs epsilon SimplifiedLayerNormalization /onnxruntime_src/onnxruntime/core/optimizer/initializer.h ReadExternalRawData() failed: /onnxruntime_src/onnxruntime/core/optimizer/layer_norm_fusion.cc 8AApplyImpl Initializer onnxruntime::Initializer::Initializer(const onnx::TensorProto&, const onnxruntime::Path&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl '7
N11onnxruntime15LayerNormFusionE N11onnxruntime25SimplifiedLayerNormFusionE fused Matmul and Add gemm /onnxruntime_src/onnxruntime/core/optimizer/matmul_add_fusion.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
N11onnxruntime15MatMulAddFusionE MatMulInteger MatMulIntegerToFloat /onnxruntime_src/onnxruntime/core/optimizer/matmul_integer_to_float.cc onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl
N11onnxruntime26MatMulIntegerToFloatFusionE bad optional access Unsupported data type: div_inputs.size() == 2 mul_inputs.size() == 2 FusedMatMul TransposeMatMul Fused MatMul and Scale _FusedMatMulAndScale /onnxruntime_src/onnxruntime/core/optimizer/matmul_scale_fusion.cc Constant initializer NodeArg shape should not be null. NodeArg: input_node.InputDefs().size() == 2 && scale_and_index.value().second < 2 output_node.OutputDefs().size() == 1 std::vector<onnxruntime::{anonymous}::ScaleMergeInfo> onnxruntime::{anonymous}::GetOutputNodeMerges(onnxruntime::Graph&, onnxruntime::Node&, const std::unordered_set<std::basic_string<char> >&) Ret onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<Ret>::operator()(int32_t) const [with Ret = onnxruntime::common::Status; int32_t = int] operator() operator() operator() operator() operator() operator() operator() operator() GetScalarConstantInitializer nonstd::optional_lite::optional<float> onnxruntime::{anonymous}::GetScalarConstantInitializer(const onnxruntime::Graph&, const onnxruntime::NodeArg&) nonstd::optional_lite::optional<std::pair<float, int> > onnxruntime::{anonymous}::GetScaleFromNode(const onnxruntime::Graph&, const onnxruntime::Node&, const std::unordered_set<std::basic_string<char> >&) std::vector<onnxruntime::{anonymous}::ScaleMergeInfo> onnxruntime::{anonymous}::GetInputNodeMerges(onnxruntime::Graph&, onnxruntime::Node&, const std::unordered_set<std::basic_string<char> >&) Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
N11onnxruntime17MatMulScaleFusionE N6nonstd13optional_lite19bad_optional_accessE ?44444 44,4444,P PPPP(8PPHPPPPHl<llllDTlld4lllldX`pPt|lܛܛܛܛܛěܛܛԛܛܛܛܛԛțЛ䛏웏 ܛ 0 000000(0000(,D,,,,,, ,,,,D詏 詏詏詏詏xp詏詏8詏詏詏詏 䰏4 pads strides dilations kernel_shape auto_pad SAME_UPPER SAME_LOWER NOTSET VALID ReorderOutput channels_last perm reorder ReorderInput activation asymmetric Upsample scales nearest nearest_mode spatial bn_scale bn_B _bn_nchwc FusedConv Concat BatchNormalization GlobalMaxPool coordinate_transformation_mode /onnxruntime_src/onnxruntime/core/optimizer/nchwc_transformer.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
- N11onnxruntime16NchwcTransformerE data type is not supported. min FuseReluClip_ _min_zero_constant data type is not supported Unexpected data type for Clip 'min' input of /onnxruntime_src/onnxruntime/core/optimizer/relu_clip_fusion.cc @;9@;@;@;@;9P:@;@;::@;@;@;@;`9YXYYYYXVYYUTWYYYYU\\\\\\\\\\\\\\\\\.]!].].].].]]].].]\\.].].].]\U]H]U]U]U]U];]3]U]U]&]]U]U]U]U]&] 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 Squeeze Unsqueeze Gather Reshape Fused reshape node: Cannot replace concat node with initializer: /onnxruntime_src/onnxruntime/core/optimizer/reshape_fusion.cc Total fused reshape node count: Fuse_Subgraph Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
N11onnxruntime13ReshapeFusionE /onnxruntime_src/onnxruntime/core/optimizer/rule_based_graph_transformer.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl ApplyRulesOnNode N11onnxruntime25RuleBasedGraphTransformerE
N11onnxruntime18ShapeToInitializerE SkipLayerNormalization /onnxruntime_src/onnxruntime/core/optimizer/skip_layer_norm_fusion.cc fused SkipLayerNorm subgraphs Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl ̼+
N11onnxruntime19SkipLayerNormFusionE starts ends ?LLLL2%LLLLLLrP]]]]C6]]) ]]]])
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N11onnxruntime14EliminateSliceE TensorrtExecutionProvider VitisAIExecutionProvider NGRAPHExecutionProvider ' doesn't support memcpy Execution type ' Memcpy Copy from/to host memory DnnlExecutionProvider NupharExecutionProvider OpenVINOExecutionProvider NnapiExecutionProvider ArmNNExecutionProvider /onnxruntime_src/onnxruntime/core/optimizer/transformer_memcpy.cc dup_replacements.find(&arg) == dup_replacements.end() 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 xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxvector::_M_range_insert RandomUniform RandomNormal RandomUniformLike RandomNormalLike Multinomial 3&333333슘3333슘u`uuuuK;uu&uuuu&r '7w+2NhhhhX`hhIShhhhIJddddT\ddEOddddEStart ValidateGemmInitializer Pass ValidateGemmInitializer Expected value: Where Expand Failed to find mask path where const not matched. Failed to find shape path equal const not matched. gather indices not matched. Start CheckNodesInPathQ q_reshape const not matched qk_div const not matched. Pass CheckNodesInPathQ Start CheckNodesInPathK k_reshape const not matched Pass CheckNodesInPathK Start MatchInputMaskSubgraph Failed to find Softmax node Failed to find path for mask Pass MatchInputMaskSubgraph Start CheckNodesInPathV Pass CheckNodesInPathV Start MatchGemmSubgraph Faild to match gemm path Gemm does not have 3 inputs Gemm bias is not constant Gemm bias shape not expected Pass MatchGemmSubgraph Start MatchUnidirMaskSubgraph Pass MatchUnidirMaskSubgraph Start MatchPastSubgraph Failed to match v_concat past_v_gather indices != 1 past_k_gather indices != 0 Pass MatchPastSubgraph Mask_Int32 Cast mask from int64 to int32 MaskCast Start FuseGptAttention Faild to find path v to Split Failed to find path for q Failed to find path for k Fused Attention subgraphs num_heads unidirectional qkv_weights qkv_bias k root is not layer norm Faild to find path v Fused an attention node. Gemm bias is not constant initializer Gemm bias shape is not expected Gemm weight is not constant initializer Gemm weight shape is not expected /onnxruntime_src/onnxruntime/core/optimizer/attention_fusion_helper.h input_indices.size() == expected_values.size() && input_indices.size() > 0 Slice does not have enough number of inputs Slice ends is less than INT_MAX Slice parameter is not expected. Input index: Start MatchInputMaskSubgraphDistilBert Output edge count not expected for mask nodes Softmax attribute axis is expected to be 3 Failed to find reshape shape path 1 Failed to find reshape shape path 2 Pass MatchInputMaskSubgraphDistilBert q_transpose perm attribute not matched Output edge count not expected for Softmax mask_unsqueeze_1 axes not matched. Expect: 1 mask_unsqueeze_2 axes not matched. Expect: 2 mask_sub const input not matched mask_mul const input not matched Output edge count not expected for nodes in path v Failed in match Transpose attribute perm. Expected: 0, 2, 1, 3 Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3 hidden_size != num_heads * head_size v_reshape initializer value is not expected reshape initializer value is not expected Input of reshape_before_gemm is not the input of subgraph Output edge count not expected for nodes in gemm path CheckSliceParameters return false concat first input value is not -1 Faild to match concat node for Gather paths concat_after_gather does not have expected number of inputs or output edges concat_after_gather input 2 does not have expected value Faild to match gemm gather path Output edge count not expected for nodes in gemm gather path unsqueeze_after_gather axes value not expected gather axis value not expected gather input 1 value is not expected Faild to match the path (Div-->Where-->Add) for unidirectional mask Faild to match path 1 for unidirectional mask Output edge count not expected for nodes in path 1 of unidirectional mask Div and Shape1 does not have edge CheckSliceParameters returns false for last_slice CheckSliceParameters returns false for mask_slice CheckSliceParameters returns false for slice1 Faild to match path 2 for unidirectional mask Output edge count not expected for unsqueeze2 of unidirectional mask Faild to match path 3 for unidirectional mask Output edge count not expected for unsqueeze3 of unidirectional mask Faild to match path 4 for unidirectional mask Div and Shape does not have edge Output edge count not expected for squeeze_2/slices2/shape2 of unidirectional mask CheckSliceParameters return false for slice2 Failed to find path for past_k Failed to find path for present_k Failed to find path for present_v and past_v past_k_transpose perm attribute not matched present_k_transpose perm attribute not matched present_k_unsqueeze axes value not expected present_v_unsqueeze axes value not expected past_v_gather and past_k_gather does not have same past input Output edge count not expected for nodes in past subgraph Faild to find path to qkv_matmul CheckNodesInPathV return false MatchInputMaskSubgraph returns false MatchUnidirMaskSubgraph returns NULL q and v are not from same Split node CheckNodesInPathQ returns false k and v are not from same Split node CheckNodesInPathK returns false MatchPastSubgraph returns false Fused an attention node for GPT. q root should be layer normalization q_matmul and q_add shape not matched k_matmul and k_add shape not matched Failed to load Q, K and V weights, or data type is not float or float16. Failed to load Q, K and V bias tensors, or data type is not float or float16. Mask shape is unknown or not 2D, or data type unknown Mask data type is not int32 or int64 or float32 Failed to convert mask to int32 /onnxruntime_src/onnxruntime/core/optimizer/attention_fusion.cc Output edge count not expected for Add or MatMul in path v Failed in match v_matmul and v_add input shape Failed in match input mask subgraph shape of layer norm bias tensor not expected Total fused Attention node count: FuseSubGraphQKDistilBert ConvertMaskToInt32 FuseSubGraphQKImpl FuseSubGraphQK FuseSubGraph Recurse ApplyImpl FuseGptAttention CheckNodesInPathK CheckNodesInPathQ CheckNodesInPathV CheckDistilBertReshapeShape onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const MatchPastSubgraph MatchInputMaskSubgraph MatchInputMaskSubgraph MatchUnidirMaskSubgraph ValidateGemmInitializer MatchGemmSubgraph CheckSliceParameters bool onnxruntime::AttentionFusionHelper::CheckSliceParameters(const onnxruntime::Graph&, const onnxruntime::Node&, const std::vector<int>&, const std::vector<long int>&, const onnxruntime::logging::Logger&)
+ N11onnxruntime16NchwcTransformerE DequantizeLinear _nhwc QLinearConv QLinearAdd QLinearMul QLinearLeakyRelu QLinearSigmoid QLinearGlobalAveragePool /onnxruntime_src/onnxruntime/core/optimizer/nhwc_transformer.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
+
+
N11onnxruntime15NhwcTransformerE data type is not supported. min FuseReluClip_ _min_zero_constant data type is not supported Unexpected data type for Clip 'min' input of /onnxruntime_src/onnxruntime/core/optimizer/relu_clip_fusion.cc [Y[[[[@ZZ[[ [0[[[[[Yyxyyyy<xwyyv?vyyyyv{{{{{{k{[{{{F{1{{{{{F{{{{{{{{{{{{}{{{{{{{{{{{{{{{{{{{{{{{ onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ToProto onnxruntime::Initializer::Initializer(onnx::TensorProto_DataType, const string&, const std::vector<long int>&) virtual onnxruntime::common::Status onnxruntime::FuseReluClip::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const
N11onnxruntime12FuseReluClipE Unsqueeze Gather Reshape Squeeze starts ends Fused reshape node: Cannot replace concat node with initializer: /onnxruntime_src/onnxruntime/core/optimizer/reshape_fusion.cc Total fused reshape node count: Fuse_Subgraph Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
N11onnxruntime13ReshapeFusionE /onnxruntime_src/onnxruntime/core/optimizer/rule_based_graph_transformer.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl ApplyRulesOnNode N11onnxruntime25RuleBasedGraphTransformerE
N11onnxruntime18ShapeToInitializerE SkipLayerNormalization /onnxruntime_src/onnxruntime/core/optimizer/skip_layer_norm_fusion.cc fused SkipLayerNorm subgraphs Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl ̼+
N11onnxruntime19SkipLayerNormFusionE 9999999999v999999 666665566*6 66666*6Z7g7g7g7g7M7@7g7g737&7g7g7g7g737b7o7o7o7o7U7H7o7o7;7.7o7o7o7o7;75555555555w555555!8.8.8.8.888.8.877.8.8.8.87
+
+
N11onnxruntime14EliminateSliceE /onnxruntime_src/onnxruntime/core/optimizer/transformer_memcpy.cc dup_replacements.find(&arg) == dup_replacements.end() TensorrtExecutionProvider VitisAIExecutionProvider ' doesn't support memcpy Execution type ' Memcpy Copy from/to host memory onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&)::__lambda11 bool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&) ForEachWithIndex void onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node&, const onnxruntime::KernelRegistryManager&, onnxruntime::InitializedTensorSet&) Recurse ApplyImpl *ZN11onnxruntime21TransformerMemcpyImpl11ProcessDefsERNS_4NodeERKNS_21KernelRegistryManagerERSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSA_EEEEUlRKNS_7NodeArgEmE_ *ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE_ *ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE0_ N11onnxruntime17MemcpyTransformerE UnsqueezeElimination_ UnsqueezeElimination cannot remove node /onnxruntime_src/onnxruntime/core/optimizer/unsqueeze_elimination.cc Apply N11onnxruntime20UnsqueezeEliminationE 0 000000(0000(L LLLL$4LLDLLLLDvector::_M_range_insert RandomUniform RandomNormal RandomUniformLike RandomNormalLike Multinomial 㧑֧㧑㧑㧑㧑㧑㧑㧑㧑㧑㧑%%%%%맑%%֧ɧ%%%%֧cVccccI<cc/"cccc/ '7w+2\*\*\*\*\\"\*\*\ \\*\*\*\*\ \ \&\&\&\&\\ \&\&\\\&\&\&\&\\Start ValidateGemmInitializer Pass ValidateGemmInitializer Expected value: Where Expand Failed to find mask path where const not matched. Failed to find shape path equal const not matched. gather indices not matched. Start CheckNodesInPathQ q_reshape const not matched qk_div const not matched. Pass CheckNodesInPathQ Start CheckNodesInPathK k_reshape const not matched Pass CheckNodesInPathK Start MatchInputMaskSubgraph Failed to find Softmax node Failed to find path for mask Pass MatchInputMaskSubgraph Start CheckNodesInPathV Pass CheckNodesInPathV unidir mask is not constant Start MatchGemmSubgraph Faild to match gemm path Gemm does not have 3 inputs Gemm bias is not constant Gemm bias shape not expected Pass MatchGemmSubgraph Start MatchPastSubgraph Failed to match v_concat past_v_gather indices != 1 past_k_gather indices != 0 Pass MatchPastSubgraph Start MatchUnidirMaskSubgraph Pass MatchUnidirMaskSubgraph Mask_Int32 Cast mask from int64 to int32 MaskCast Start FuseGptAttention Faild to find path v to Split Failed to find path for q Failed to find path for k Fused Attention subgraphs num_heads unidirectional qkv_weights qkv_bias k root is not layer norm Faild to find path v Fused an attention node. Gemm bias is not constant initializer Gemm bias shape is not expected Gemm weight is not constant initializer Gemm weight shape is not expected /onnxruntime_src/onnxruntime/core/optimizer/attention_fusion_helper.h input_indices.size() == expected_values.size() && input_indices.size() > 0 Slice does not have enough number of inputs Slice ends is less than INT_MAX Slice parameter is not expected. Input index: Start MatchInputMaskSubgraphDistilBert Output edge count not expected for mask nodes Softmax attribute axis is expected to be 3 Failed to find reshape shape path 1 Failed to find reshape shape path 2 Pass MatchInputMaskSubgraphDistilBert q_transpose perm attribute not matched Output edge count not expected for Softmax mask_unsqueeze_1 axes not matched. Expect: 1 mask_unsqueeze_2 axes not matched. Expect: 2 mask_sub const input not matched mask_mul const input not matched Output edge count not expected for nodes in path v Failed in match Transpose attribute perm. Expected: 0, 2, 1, 3 Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3 hidden_size != num_heads * head_size v_reshape initializer value is not expected reshape initializer value is not expected unidir mask shape not expected This optimizer does not support external data for unidirectional mask right now Mask is neither unidirectional nor all ones Expect mask data type is uint8 or float Input of reshape_before_gemm is not the input of subgraph Output edge count not expected for nodes in gemm path CheckSliceParameters return false concat first input value is not -1 Faild to match concat node for Gather paths concat_after_gather does not have expected number of inputs or output edges concat_after_gather input 2 does not have expected value Faild to match gemm gather path Output edge count not expected for nodes in gemm gather path unsqueeze_after_gather axes value not expected gather axis value not expected gather input 1 value is not expected Failed to find path for past_k Failed to find path for present_k Failed to find path for present_v and past_v past_k_transpose perm attribute not matched present_k_transpose perm attribute not matched present_k_unsqueeze axes value not expected present_v_unsqueeze axes value not expected past_v_gather and past_k_gather does not have same past input Output edge count not expected for nodes in past subgraph Faild to match the path (Div-->Where-->Add) for unidirectional mask Faild to match path 1 for unidirectional mask Output edge count not expected for nodes in path 1 of unidirectional mask Div and Shape1 does not have edge CheckSliceParameters returns false for last_slice CheckSliceParameters returns false for mask_slice ValidateUnidirMask returns false for mask_slice CheckSliceParameters returns false for slice1 Faild to match path 2 for unidirectional mask Output edge count not expected for unsqueeze2 of unidirectional mask Faild to match path 3 for unidirectional mask Output edge count not expected for unsqueeze3 of unidirectional mask Faild to match path 4 for unidirectional mask Div and Shape does not have edge Output edge count not expected for squeeze_2/slices2/shape2 of unidirectional mask CheckSliceParameters return false for slice2 Faild to find path to qkv_matmul CheckNodesInPathV return false MatchInputMaskSubgraph returns false MatchUnidirMaskSubgraph returns NULL q and v are not from same Split node CheckNodesInPathQ returns false k and v are not from same Split node CheckNodesInPathK returns false MatchPastSubgraph returns false Fused an attention node for GPT. q root should be layer normalization q_matmul and q_add shape not matched k_matmul and k_add shape not matched Failed to load Q, K and V weights, or data type is not float or float16. Failed to load Q, K and V bias tensors, or data type is not float or float16. Mask shape is unknown or not 2D, or data type unknown Mask data type is not int32 or int64 or float32 Failed to convert mask to int32 /onnxruntime_src/onnxruntime/core/optimizer/attention_fusion.cc Output edge count not expected for Add or MatMul in path v Failed in match v_matmul and v_add input shape Failed in match input mask subgraph shape of layer norm bias tensor not expected Total fused Attention node count: FuseSubGraphQKDistilBert ConvertMaskToInt32 FuseSubGraphQKImpl FuseSubGraphQK FuseSubGraph Recurse ApplyImpl FuseGptAttention CheckNodesInPathK CheckNodesInPathQ CheckNodesInPathV CheckDistilBertReshapeShape onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const MatchPastSubgraph MatchInputMaskSubgraph MatchInputMaskSubgraph MatchUnidirMaskSubgraph ValidateUnidirMask ValidateGemmInitializer MatchGemmSubgraph CheckSliceParameters bool onnxruntime::AttentionFusionHelper::CheckSliceParameters(const onnxruntime::Graph&, const onnxruntime::Node&, const std::vector<int>&, const std::vector<long int>&, const onnxruntime::logging::Logger&)
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N11onnxruntime15AttentionFusionE @ FastGelu BiasGelu fused Add and Gelu /onnxruntime_src/onnxruntime/core/optimizer/bias_gelu_fusion.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
N11onnxruntime14BiasGeluFusionE N11onnxruntime15CastEliminationE ~ǙǙ ș\Ǚ~Ǚ~ǙƙLƙǙNot eliminating output of node /onnxruntime_src/onnxruntime/core/optimizer/common_subexpression_elimination.cc representative.output_index != kInvalidOutputIndex ] because it's the graph's output. 6˙̙̙̙6˙6˙@̙˙̙onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse virtual onnxruntime::common::Status onnxruntime::CommonSubexpressionElimination::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) const ApplyImpl N11onnxruntime30CommonSubexpressionEliminationE ConstantFolding can't constant fold Unsupported output type of . Can't constant fold /onnxruntime_src/onnxruntime/core/optimizer/constant_folding.cc Could not find OrtValue with name ' Could not find a CPU kernel and hence fetches.size() == node->OutputDefs().size() const T& OrtValue::Get() const [with T = onnxruntime::Tensor] Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const virtual onnxruntime::common::Status onnxruntime::ConstantFolding::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) const ApplyImpl N11onnxruntime15OpKernelContextE N11onnxruntime15ConstantFoldingE fused Conv with activation fused activation_params /onnxruntime_src/onnxruntime/core/optimizer/conv_activation_fusion.cc Unexpected data type for Clip input of Y~YYYYYYMYYNNYYYYNWWWWWWYYWWxYkYWWWWxYXXXXXXXXXX5X XXXXX5XbXUXbXbXbXbX@X0XbXbXXXbXbXbXbXX onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const onnxruntime::{anonymous}::GetClipConstantMinMax(const onnxruntime::Graph&, const onnxruntime::Node&, float&, float&)::__lambda7 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 c cccccb>bccaPaccccajijjjj$kkjjlljjjjjmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmmnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnToProto 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 X~(~X~X~X~X~0~@~X~X~P~ ~X~X~X~X~P~t~D~t~t~t~t~L~\~t~t~l~<~t~t~t~t~l~~`~~~~~h~x~~~~X~~~~~~~|~~~~~~~~~~t~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ~ ~~ ~~ ~ ~ ~ ~ 88888 880 88880T$TTTT,<TTL TTTTLbn_scale_tensor_proto bn_B_tensor_proto bn_mean_tensor_proto bn_var_tensor_proto ConvBnFusion_W_ ConvBnFusion_BN_B_ /onnxruntime_src/onnxruntime/core/optimizer/conv_bn_fusion.cc & Ȏ F c] 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 ȪЪચ䪚쪚 ܪ 0 000000(0000(L LLLL$4LLDLLLLDh8hhhh@Phh`0hhhh`T\l|L|pxhثثثثثثثЫثثثثЫī̫ܫ쫚쫚mul_B_tensor_proto ConvMulFusion_W_ ConvMulFusion_Mul_B_ /onnxruntime_src/onnxruntime/core/optimizer/conv_mul_fusion.cc [AӮTSĚTTTTUÚTT?TTTT˿̚]˚̚̚̚̚ʚ
ʚ̚̚hɚȚ̚̚̚̚hɚToProto onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const virtual onnxruntime::common::Status onnxruntime::ConvMulFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const
N11onnxruntime13ConvMulFusionE
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N11onnxruntime16EliminateDropoutE MatMulInteger DynamicQuantizeLinear DynamicQuantizeMatMul MatMulIntegerToFloat nullptr != fused_node /onnxruntime_src/onnxruntime/core/optimizer/dynamic_quantize_matmul_fusion.cc onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse virtual onnxruntime::common::Status onnxruntime::DynamicQuantizeMatMulFusion::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) const ApplyImpl
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N11onnxruntime27DynamicQuantizeMatMulFusionE Input shape is unknown or not 2D, or data type unknown Input data type is not int32 or int64 /onnxruntime_src/onnxruntime/core/optimizer/embed_layer_norm_fusion.cc Failed to find path 1 of position shape. Output edge count not expected for nodes in path 1 of position shape. Second input of Gather in path 1 of position shape should be a constant with value 0. Failed to find path 2 of position shape. Output edge count not expected for nodes in path 2 of position shape. Gather node in path 2 is not linked to another subgraph. Second input of Gather in path 2 of position shape should be a constant with value 1. The parent of two shape nodes are expected to be input_ids. two paths share the same shape Output edge count not expected for nodes in path1. The first input of Range should be a constant with value 0. The third input of Range should be a constant with value 1. Second input of Gather should be a constant with value 1. The parent of shape nodes are expected to be input_ids. Optional position subgraph nodes number of outputs unexpected. Optional position subgraph nodes Where node is expected to be the parent of Reshape. Failed to match position subgraph. Cast Input from int64 to int32 fused EmbedLayerNorm subgraphs Word embedding shape not expected. Failed to match position embedding subgraph. Failed to get position embedding weights. Position embedding shape is not expected. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). 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. Input_ids and segment id should have the same shape. NonZero ConstantOfShape Failed to match Shape node. mask_index EmbedLayerNormalization Input id is not valid. position_embeddings Segment id is not valid. SSSSSSRRSSRRSSSSRRRRRRRRyRRRlR_RRRRRlRFuseSubGraph CheckInput MatchInputToConcatSubgraph MatchPositionEmbeddingSubgraphsFromGather FuseSubGraphDistilBert Recurse ApplyImpl ̼+
N11onnxruntime20EmbedLayerNormFusionE N11onnxruntime17ExpandEliminationE fast_gelu_output fused GPT2Gelu subgraphs GPT2Gelu /onnxruntime_src/onnxruntime/core/optimizer/fast_gelu_fusion.cc Recurse ApplyImpl onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const
N11onnxruntime14FastGeluFusionE @@'7=*BL? ?FreeDimensionOverrideTransformer Invalid free dimension override. /onnxruntime_src/onnxruntime/core/optimizer/free_dim_override_transformer.cc which does not equal the specified override of Conflicting free dimension overrides. The model has input ' ' with a fixed dimension size ApplyImpl onnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformer(gsl::span<const onnxruntime::FreeDimensionOverride>) N11onnxruntime32FreeDimensionOverrideTransformerE Gelu approximation /onnxruntime_src/onnxruntime/core/optimizer/gelu_approximation.cc Total Gelu Approximation (FastGelu) node count: Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
N11onnxruntime17GeluApproximationE Erf fused Gelu subgraphs /onnxruntime_src/onnxruntime/core/optimizer/gelu_fusion.cc onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl
N11onnxruntime10GeluFusionE ??Elu HardSigmoid Selu Softsign ScaledTanh ParametricSoftplus ThresholdedRelu fused Gemm FusedGemm activation_ /onnxruntime_src/onnxruntime/core/optimizer/gemm_activation_fusion.cc onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl
+
N11onnxruntime15AttentionFusionE @ FastGelu BiasGelu fused Add and Gelu /onnxruntime_src/onnxruntime/core/optimizer/bias_gelu_fusion.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
N11onnxruntime14BiasGeluFusionE is not in valid range [- axis fused and into softmax(input + bias) BiasSoftmax softmax_axis broadcast_axis /onnxruntime_src/onnxruntime/core/optimizer/bias_softmax_fusion.cc /onnxruntime_src/onnxruntime/core/providers/common.h axis >= -tensor_rank && axis <= tensor_rank - 1 int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime17BiasSoftmaxFusionE N11onnxruntime15CastEliminationE ~
+
+ \
+~
+~
+ L
+Not eliminating output of node /onnxruntime_src/onnxruntime/core/optimizer/common_subexpression_elimination.cc representative.output_index != kInvalidOutputIndex ] because it's the graph's output. vvv)%onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse virtual onnxruntime::common::Status onnxruntime::CommonSubexpressionElimination::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) const ApplyImpl N11onnxruntime30CommonSubexpressionEliminationE ConstantFolding can't constant fold Unsupported output type of . Can't constant fold /onnxruntime_src/onnxruntime/core/optimizer/constant_folding.cc Could not find OrtValue with name ' Could not find a CPU kernel and hence fetches.size() == node->OutputDefs().size() const T& OrtValue::Get() const [with T = onnxruntime::Tensor] Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const virtual onnxruntime::common::Status onnxruntime::ConstantFolding::ApplyImpl(onnxruntime::Graph&, bool&, int, const onnxruntime::logging::Logger&) const ApplyImpl N11onnxruntime15OpKernelContextE N11onnxruntime15ConstantFoldingE fused Conv with activation activation_params Unexpected data type for Clip input of /onnxruntime_src/onnxruntime/core/optimizer/conv_activation_fusion.cc yyyyyyyyyynyYyyyyynyyyyyyyyyyyyyyyyyy onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const onnxruntime::{anonymous}::GetClipConstantMinMax(const onnxruntime::Graph&, const onnxruntime::Node&, float&, float&)::__lambda9 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 JЗJJJJ_JJ JJJJDTDDDDԟDD4DDDDTjajjjjUIjj@7jjjj@~u~~~~i]~~TK~~~~T}qh_h|s|jajjjjUIjj@7jjjj@~u~~~~i]~~TK~~~~TToProto 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 Ȱаఓ䰓찓 ܰ 0 000000(0000(L LLLL$4LLDLLLLDh8hhhh@Phh`0hhhh`T\l|L|pxhرررررررбررررбı̱ܱ챓챓bn_scale_tensor_proto bn_B_tensor_proto bn_mean_tensor_proto bn_var_tensor_proto ConvBnFusion_W_ ConvBnFusion_BN_B_ /onnxruntime_src/onnxruntime/core/optimizer/conv_bn_fusion.cc ɓX־ɓƓɓɓɓɓēȓɓɓēɓɓɓɓ 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 ۓXۓۓۓۓۓ`ۓpۓۓۓۓPۓۓۓۓۓۓۓtۓۓۓۓۓ|ۓۓۓۓۓlۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓۓܓۓܓܓܓܓۓۓܓܓ ܓۓܓܓܓܓ ܓ0ܓ ܓ0ܓ0ܓ0ܓ0ܓܓܓ0ܓ0ܓ(ܓۓ0ܓ0ܓ0ܓ0ܓ(ܓLܓ ܓLܓLܓLܓLܓ$ܓ4ܓLܓLܓDܓܓLܓLܓLܓLܓDܓhܓ8ܓhܓhܓhܓhܓ@ܓPܓhܓhܓ`ܓ0ܓhܓhܓhܓhܓ`ܓܓTܓܓܓܓܓ\ܓlܓܓܓ|ܓLܓܓܓܓܓ|ܓmul_B_tensor_proto ConvMulFusion_W_ ConvMulFusion_Mul_B_ |ߓ_|ߓ|ߓ|ߓ|ߓ/|ߓ|ߓ\|ߓ|ߓ|ߓ|ߓߓ/onnxruntime_src/onnxruntime/core/optimizer/conv_mul_fusion.cc XSXXXX XXXXXX?S^^^^^^^ 9 ^^^^ S F S S S S 9 , S S S S S S 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 DynamicQuantizeMatMul /onnxruntime_src/onnxruntime/core/optimizer/dynamic_quantize_matmul_fusion.cc onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl N11onnxruntime27DynamicQuantizeMatMulFusionE Input shape is unknown or not 2D, or data type unknown Input data type is not int32 or int64 /onnxruntime_src/onnxruntime/core/optimizer/embed_layer_norm_fusion.cc Failed to find path 1 of position shape. Output edge count not expected for nodes in path 1 of position shape. Second input of Gather in path 1 of position shape should be a constant with value 0. Failed to find path 2 of position shape. Output edge count not expected for nodes in path 2 of position shape. Gather node in path 2 is not linked to another subgraph. Second input of Gather in path 2 of position shape should be a constant with value 1. The parent of two shape nodes are expected to be input_ids. two paths share the same shape Output edge count not expected for nodes in path1. The first input of Range should be a constant with value 0. The third input of Range should be a constant with value 1. Second input of Gather should be a constant with value 1. The parent of shape nodes are expected to be input_ids. Optional position subgraph nodes number of outputs unexpected. Optional position subgraph nodes Where node is expected to be the parent of Reshape. Failed to match position subgraph. Cast Input from int64 to int32 fused EmbedLayerNorm subgraphs Word embedding shape not expected. Input is expected to have dim value in all dimensions. Failed to get initializer tensor. Position embedding shape not matched. Position embedding data type shall be float or float16. Failed to match position embedding subgraph. Failed to get position embedding weights. Position embedding shape is not expected. Input_ids and segment id should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). NonZero ConstantOfShape Failed to match Shape node. mask_index EmbedLayerNormalization position_embeddings Input id is not valid. Segment id is not valid. rieiririririhhririhhririririhviiivivivivi\iOiviviBi5iviviviviBiFuseSubGraph CheckInput MatchInputToConcatSubgraph MatchPositionEmbeddingSubgraphsFromGather FuseSubGraphDistilBert Recurse ApplyImpl ̼+
N11onnxruntime20EmbedLayerNormFusionE N11onnxruntime17ExpandEliminationE fast_gelu_output fused GPT2Gelu subgraphs GPT2Gelu /onnxruntime_src/onnxruntime/core/optimizer/fast_gelu_fusion.cc Recurse ApplyImpl onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const
N11onnxruntime14FastGeluFusionE *BL?'7= @@ ?FreeDimensionOverrideTransformer Invalid free dimension override. /onnxruntime_src/onnxruntime/core/optimizer/free_dim_override_transformer.cc which does not equal the specified override of Conflicting free dimension overrides. The model has input ' ' with a fixed dimension size ApplyImpl onnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformer(gsl::span<const onnxruntime::FreeDimensionOverride>) N11onnxruntime32FreeDimensionOverrideTransformerE Gelu approximation /onnxruntime_src/onnxruntime/core/optimizer/gelu_approximation.cc Total Gelu Approximation (FastGelu) node count: Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
N11onnxruntime17GeluApproximationE Erf fused Gelu subgraphs /onnxruntime_src/onnxruntime/core/optimizer/gelu_fusion.cc onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl
N11onnxruntime10GeluFusionE ??Elu HardSigmoid Selu Softsign ScaledTanh ParametricSoftplus ThresholdedRelu fused Gemm FusedGemm activation_ /onnxruntime_src/onnxruntime/core/optimizer/gemm_activation_fusion.cc onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl
N11onnxruntime20GemmActivationFusionE Not satisfied: tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED && tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING
model_path must not be empty. Ensure that a path is provided when the model is created or loaded. /onnxruntime_src/onnxruntime/core/optimizer/initializer.cc Satisfied, but should not be: model_path.IsEmpty()
TensorProto external data size mismatch. Not satisfied: external_data_length == 0 || external_data_length == actual_tensor_data_length
- External data type must not be UNDEFINED or STRING. Computed size: , external_data.length: ReadExternalRawData /onnxruntime_src/onnxruntime/core/optimizer/optimizer_execution_frame.cc Tried to allocate without valid type information, ort_value index= Failed to get allocator for optimizer allocator_ptr_ void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType) ForEachWithIndex operator() Info onnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector<const onnxruntime::Node*>&, const InitializedTensorSet&, const onnxruntime::Path&, const onnxruntime::IExecutionProvider&) *ZN11onnxruntime23OptimizerExecutionFrame4InfoC1ERKSt6vectorIPKNS_4NodeESaIS5_EERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSE_EEERKNS_4PathERKNS_18IExecutionProviderEEUlRKNS_7NodeArgEmE_ N11onnxruntime23OptimizerExecutionFrameE /onnxruntime_src/onnxruntime/core/providers/cpu/cpu_execution_provider.cc RegisterCPUKernels virtual std::shared_ptr<onnxruntime::KernelRegistry> onnxruntime::CPUExecutionProvider::GetKernelRegistry() const GetKernelRegistry RegisterOnnxMLOperatorKernels RegisterOnnxOperatorKernels N11onnxruntime20CPUExecutionProviderE N11onnxruntime25IExecutionProviderFactoryE N11onnxruntime18CpuProviderFactoryE St23_Sp_counted_ptr_inplaceIN11onnxruntime18CpuProviderFactoryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE 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 |Ŝ̜̜<̜˜˜d˜ ˜|Ŝʜʜ<ʜɜ|ɜ 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&) FPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEE N11onnxruntime19ConstantOfShapeBaseE N11onnxruntime15ConstantOfShapeE PFPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEE seed Invalid dtype of high sample_size FLOAT16 is not supported Invalid data type of input count mismatch batch_size is < 1 num_classes is < 1 num_samples is < 1 /onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.h info.GetAttr<float>("mean", &mean_).IsOK() info.GetAttr<float>("scale", &scale_).IsOK() info.GetAttr<int64_t>("dtype", &dtype).IsOK() ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINED info.GetAttrs<int64_t>("shape", shape).IsOK() info.GetAttr<float>("high", &high_).IsOK() info.GetAttr<float>("low", &low_).IsOK() info.GetAttr<int64_t>("sample_size", &num_samples_).IsOK() ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINED /onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.cc Could not infer data type from input tensor with data type Empty dimensions for input tensor T* onnxruntime::Tensor::MutableData() [with T = long int] MultinomialCompute T* onnxruntime::Tensor::MutableData() [with T = int] MultinomialCompute const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::RandomUniformCompute(float, float, std::default_random_engine&, onnx::TensorProto::DataType, onnxruntime::Tensor&) onnxruntime::common::Status onnxruntime::RandomNormalCompute(float, float, std::default_random_engine&, onnx::TensorProto::DataType, onnxruntime::Tensor&) onnxruntime::Multinomial::Multinomial(const onnxruntime::OpKernelInfo&) onnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo&) onnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo&) onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo&) onnxruntime::RandomNormal::RandomNormal(const onnxruntime::OpKernelInfo&) N11onnxruntime12RandomNormalE N11onnxruntime16RandomNormalLikeE N11onnxruntime13RandomUniformE N11onnxruntime17RandomUniformLikeE N11onnxruntime11MultinomialE 0 A C ? "~+@"~+@W6}+W6}++eG?+eG? ? ? ? ? @.? @.?ʫy>ʫy>K͉ ?K͉ ?~, ?~, ?_6.>_6.>9d?9d?t ?t ? @ @ delta in Range operator can not be zero! start in Range operator should be scalar like tensor, yet got shape: limit in Range operator should be scalar like tensor, yet got shape: delta in Range operator should be scalar like tensor, yet got shape: T* onnxruntime::Tensor::MutableData() [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] N11onnxruntime5RangeE min_ <= max_ min should be a scalar. max should be a scalar. helper.called_ < 2 helper.called_ == 1 /onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.h /onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.cc min->Shape().NumDimensions() == 0 max->Shape().NumDimensions() == 0 Check for duplicate types in MLTypeCallDispatcher T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = float] void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor*&, const onnxruntime::Tensor*&, const onnxruntime::Tensor*&, onnxruntime::Tensor*&}; Fn = onnxruntime::Clip::ComputeImpl; Types = {float, double, signed char, unsigned char, 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] onnxruntime::clip_internal::Clip_6Base<T>::Clip_6Base(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime13clip_internal10Clip_6BaseIfEE N11onnxruntime4ClipE N11onnxruntime6Clip_6IfEE 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() dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size() Axis tensor must be provided to the CumSum op Axis tensor should be 0D or 1D Axis tensor should be of type `int32_t` or `int64_t` /onnxruntime_src/onnxruntime/core/providers/common.h axis >= -tensor_rank && axis <= tensor_rank - 1 dims.size() == extents_.size() Cannot apply CumSum operator on a scalar /onnxruntime_src/onnxruntime/core/providers/cpu/math/cumsum.cc void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::common::Status onnxruntime::CumSum<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] Compute void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] onnxruntime::common::Status onnxruntime::CumSum<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] Compute void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int] T* onnxruntime::Tensor::MutableData() [with T = int] onnxruntime::common::Status onnxruntime::CumSum<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] Compute void onnxruntime::SliceIteratorBase::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int] onnxruntime::SliceSkips::SliceSkips(const onnxruntime::TensorShape&, gsl::span<const long int>, gsl::span<const long int>) T* onnxruntime::Tensor::MutableData() [with T = long int] onnxruntime::common::Status onnxruntime::CumSum<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] Compute int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) N11onnxruntime6CumSumIfEE N11onnxruntime6CumSumIdEE N11onnxruntime6CumSumIiEE N11onnxruntime6CumSumIlEE u4udu4uv4udu4u4u4u4udu4u4u4u4u4v4u4u4u4u4u4u4uTvDv4uu4u4u4uu4u4uu4u4u4uu4u4u4u4u4u4u4u4utudvtuu$v$u4uvtuu$uuuuutvvvuvvv4u4u4u4u4u4u4u4u4u4u4u4u4u4u4u4u4udu4u4u4u4u4uuduu4u4u4u4u4u4u4u4u4u4u4u4u4u4u4uutuu$uuuu$u$utuu$uuu$uuuuu4u4uvDet 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@/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum.cc Einsum op: An implementation for the input type Einsum op: There must be atleast one input There was a problem acquiring temporary memory allocator in Einsum op /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum.h info.GetAttr<std::string>("equation", &equation_).IsOK() is not supported yet Einsum equation Missing 'equation' attribute -> DeviceCompute const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::Einsum::Einsum(const onnxruntime::OpKernelInfo&) FN11onnxruntime6common6StatusEPKiS3_PimmmmmmmPNS_11concurrency10ThreadPoolEPvE FN11onnxruntime6common6StatusEPKdS3_PdmmmmmmmPNS_11concurrency10ThreadPoolEPvE FN11onnxruntime6common6StatusEPKlS3_PlmmmmmmmPNS_11concurrency10ThreadPoolEPvE FSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_llSt10shared_ptrINS0_10IAllocatorEEE FN11onnxruntime6common6StatusERKSt6vectorImSaImEERKNS_6TensorERS7_PKNS_11TensorShapeEPvE FN11onnxruntime6common6StatusEPKfS3_PfmmmmmmmPNS_11concurrency10ThreadPoolEPvE FN11onnxruntime6TensorERKS0_RKSt6vectorIlSaIlEEbSt10shared_ptrINS_10IAllocatorEEPKNS_11TensorShapeEPNS_11concurrency10ThreadPoolEPvE FN11onnxruntime6common6StatusERKNS_6TensorERS2_E N11onnxruntime6EinsumE PFSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_llSt10shared_ptrINS0_10IAllocatorEEE PFN11onnxruntime6common6StatusERKSt6vectorImSaImEERKNS_6TensorERS7_PKNS_11TensorShapeEPvE PFN11onnxruntime6common6StatusEPKfS3_PfmmmmmmmPNS_11concurrency10ThreadPoolEPvE PFN11onnxruntime6TensorERKS0_RKSt6vectorIlSaIlEEbSt10shared_ptrINS_10IAllocatorEEPKNS_11TensorShapeEPNS_11concurrency10ThreadPoolEPvE PFN11onnxruntime6common6StatusERKNS_6TensorERS2_E PFN11onnxruntime6common6StatusEPKiS3_PimmmmmmmPNS_11concurrency10ThreadPoolEPvE PFN11onnxruntime6common6StatusEPKdS3_PdmmmmmmmPNS_11concurrency10ThreadPoolEPvE PFN11onnxruntime6common6StatusEPKlS3_PlmmmmmmmPNS_11concurrency10ThreadPoolEPvE Einsum op: Transpose failed: %ld Tensor size ( ) != new size ( for input shape and Einsum op: The candidate output does not match the actual output's shape /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_auxiliary_ops.cc output.SizeInBytes() == input.SizeInBytes() The rank of the input must match permutation size for Transpose input_rank == permutation.size() Length of permutation must match the rank of the input to be permutated The innermost dims should have the same dim value to parse the diagonal elements input_dims[rank - 2] == input_dims[rank - 1] Einsum op: Unsupported data type for Diagonal rank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2] shape_.Size() == new_shape.Size() Cannot parse the diagonal elements along dims Data types of the inputs must match for MatMul input_1.DataType() == input_2.DataType() input_shape_1_override.size() == 3 && input_shape_2_override.size() == 3 Batch dimension should match for MatMul; input_shape_1_override[0] == input_shape_2_override[0] Incompatible matrix dimensions for matMul input_shape_1_override[2] == input_shape_2_override[1] Einsum op: Exception during MatMul operation: Only 1 batch dimension is allowed for MatMul T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector<long int>&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul<T>&) [with T = long int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>; onnxruntime::EinsumOp::DeviceHelpers::MatMul<T> = std::function<onnxruntime::common::Status(const long int*, const long int*, long int*, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, onnxruntime::concurrency::ThreadPool*, void*)>] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector<long int>&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul<T>&) [with T = double; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>; onnxruntime::EinsumOp::DeviceHelpers::MatMul<T> = std::function<onnxruntime::common::Status(const double*, const double*, double*, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, onnxruntime::concurrency::ThreadPool*, void*)>] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector<long int>&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul<T>&) [with T = int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>; onnxruntime::EinsumOp::DeviceHelpers::MatMul<T> = std::function<onnxruntime::common::Status(const int*, const int*, int*, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, onnxruntime::concurrency::ThreadPool*, void*)>] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector<long int>&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul<T>&) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>; onnxruntime::EinsumOp::DeviceHelpers::MatMul<T> = std::function<onnxruntime::common::Status(const float*, const float*, float*, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, onnxruntime::concurrency::ThreadPool*, void*)>] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::Transpose(const onnxruntime::Tensor&, const std::vector<long int>&, const std::vector<long unsigned int>&, onnxruntime::AllocatorPtr, void*, const Transpose&) bool onnxruntime::EinsumOp::IsTransposeRequired(size_t, const std::vector<long unsigned int>&) void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&) std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDims(const onnxruntime::Tensor&, bool, onnxruntime::AllocatorPtr) std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonal(const onnxruntime::Tensor&, int64_t, int64_t, onnxruntime::AllocatorPtr) onnxruntime::common::Status onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopy(const onnxruntime::Tensor&, onnxruntime::Tensor&) dim_iter == rank . The shape is: in the same dimension vector::_M_fill_insert Inputs have ellipses in them but the provided output subscript does not contain an ellipsis /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_compute_preprocessor.cc num_broadcasted_indices < num_of_ellipsis_dims_ The broadcasted dimensions of the inputs are incompatible Number of subscripts in the input equation does not match number of input tensors Found a '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the input Ellipsis must indicate a fixed number of dimensions across all inputs Found '.' not part of an ellipsis in input: Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand is incompatible in the dimension Another operand has a dim value of Einsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z) Found a '.' not part of an ellipsis in the output subscript provided Found '.' not part of an ellipsis in the output subscript provided The only subscript labels allowed in the output subscript are lowercase letters (a-z) Output subscript contains repeated letters Output subscript contains letters not seen in the inputs Rank of the input must match number of subscript labels corresponding to the input void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&) onnxruntime::common::Status onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDims() Run Left shape override: Left shape: Right shape override: Right shape: left_rank == right_rank left_dim == right_dim Einsum op: The candidate output cannot be reshaped into the op's output /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_typed_compute_processor.cc candidate_output.Shape().Size() == output_shape.Size() Not all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: candidate_output_dims[iter] == 1 Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: The override dims are not compatible with given tensor's shape. left.Shape().Size() == left_shape_override.Size() right.Shape().Size() == right_shape_override.Size() Ranks of pair-wise operands must be equal. Einsum op: Input dimensions must be equal along an axis to be reduced across all inputs Einsum op: Input shapes do not align void onnxruntime::EinsumTypedComputeProcessor<T>::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&) [with T = long int] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumTypedComputeProcessor<T>::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, bool) [with T = long int] void onnxruntime::EinsumTypedComputeProcessor<T>::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&) [with T = double] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumTypedComputeProcessor<T>::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, bool) [with T = double] void onnxruntime::EinsumTypedComputeProcessor<T>::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&) [with T = int] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumTypedComputeProcessor<T>::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, bool) [with T = int] void onnxruntime::EinsumTypedComputeProcessor<T>::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&) [with T = float] void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&) std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumTypedComputeProcessor<T>::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, bool) [with T = float] fmod (fmod == 0) || (fmod == 1) Less Greater Not And Or Xor Mod Neg Acosh Atan Sinh Abs Atanh BitShift Cosh Asin Tan Cos Asinh Sin Acos Ceil Reciprocal Floor PRelu axis == 1 || axis == largest direction LEFT RIGHT Invalid direction value of ' cur1 == end1 cur_out == end_out ) for tensor of length: Invalid start/ending offset [ Broadcast Output range [ offset % span_size_ == 0 Can broadcast 0 by 0 or 1. largest <= 1 failed to get first output! input1_size == output_size input0_size == output_size Unsupported Y type: Unsupported X type: Must have 1 or more inputs input_count >= 1 vs fmod_ inputCount >= 1 data_n.Shape() == shape data_1.Shape() == shape p_ml_value 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'. /onnxruntime_src/onnxruntime/core/providers/cpu/element_wise_ranged_transform.h input_size < std::numeric_limits<std::ptrdiff_t>::max() start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= len ) are not at boundary of span with size: start_offset % span_size == 0 && real_end % span_size == 0 TBroadcaster can only start at span boundary! (output_size % span_size) == 0 Shape must be 1 dimensional as it's tensor data is a shape tensor_shape.Shape().GetDims().size() == 1 X and Y input types do not match: fmod attribute must be true for float, float16 and double types All inputs must have the same shape Please fetch output tensor with specified shape. void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda92; Input1Scalar = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda93; General = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda94; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda92; Input1Scalar = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda93; General = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda94] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = double; TOutput = double; Input0Scalar = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda92; Input1Scalar = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda93; General = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda94; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = double; TOutput = double; Input0Scalar = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda92; Input1Scalar = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda93; General = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda94] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = int; TOutput = int; Input0Scalar = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda92; Input1Scalar = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda93; General = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda94; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = int; TOutput = int; Input0Scalar = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda92; Input1Scalar = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda93; General = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda94] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = long int; TOutput = long int; Input0Scalar = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda92; Input1Scalar = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda93; General = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda94; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = long int; TOutput = long int; Input0Scalar = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda92; Input1Scalar = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda93; General = onnxruntime::Add<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda94] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda95; Input1Scalar = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda96; General = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda97; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda95; Input1Scalar = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda96; General = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda97] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = double; TOutput = double; Input0Scalar = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda95; Input1Scalar = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda96; General = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda97; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = double; TOutput = double; Input0Scalar = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda95; Input1Scalar = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda96; General = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda97] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = int; TOutput = int; Input0Scalar = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda95; Input1Scalar = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda96; General = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda97; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = int; TOutput = int; Input0Scalar = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda95; Input1Scalar = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda96; General = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda97] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = long int; TOutput = long int; Input0Scalar = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda95; Input1Scalar = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda96; General = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda97; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = long int; TOutput = long int; Input0Scalar = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda95; Input1Scalar = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda96; General = onnxruntime::Sub<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda97] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda98; Input1Scalar = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda99; General = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda100; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda98; Input1Scalar = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda99; General = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda100] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = double; TOutput = double; Input0Scalar = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda98; Input1Scalar = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda99; General = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda100; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = double; TOutput = double; Input0Scalar = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda98; Input1Scalar = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda99; General = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda100] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = int; TOutput = int; Input0Scalar = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda98; Input1Scalar = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda99; General = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda100; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = int; TOutput = int; Input0Scalar = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda98; Input1Scalar = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda99; General = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda100] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = long int; TOutput = long int; Input0Scalar = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda98; Input1Scalar = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda99; General = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda100; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = long int; TOutput = long int; Input0Scalar = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda98; Input1Scalar = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda99; General = onnxruntime::Mul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda100] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda101; Input1Scalar = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda102; General = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda103; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda101; Input1Scalar = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda102; General = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda103] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = double; TOutput = double; Input0Scalar = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda101; Input1Scalar = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda102; General = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda103; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = double; TOutput = double; Input0Scalar = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda101; Input1Scalar = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda102; General = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda103] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = int; TOutput = int; Input0Scalar = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda101; Input1Scalar = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda102; General = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda103; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = int; TOutput = int; Input0Scalar = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda101; Input1Scalar = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda102; General = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda103] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = long int; TOutput = long int; Input0Scalar = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda101; Input1Scalar = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda102; General = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda103; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = long int; TOutput = long int; Input0Scalar = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda101; Input1Scalar = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda102; General = onnxruntime::Div<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda103] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<signed char>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<short int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<long int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<unsigned char>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<short unsigned int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<unsigned int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<long unsigned int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg<signed char>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg<int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg<long int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Floor<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Ceil<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Reciprocal<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sqrt<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sqrt<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Exp<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Exp<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Log<float>] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = bool; Input0Scalar = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda135; Input1Scalar = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda136; General = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda137; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = float; TOutput = bool; Input0Scalar = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda135; Input1Scalar = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda136; General = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda137] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = double; TOutput = bool; Input0Scalar = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda135; Input1Scalar = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda136; General = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda137; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = double; TOutput = bool; Input0Scalar = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda135; Input1Scalar = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda136; General = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda137] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = int; TOutput = bool; Input0Scalar = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda135; Input1Scalar = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda136; General = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda137; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = int; TOutput = bool; Input0Scalar = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda135; Input1Scalar = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda136; General = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda137] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = long int; TOutput = bool; Input0Scalar = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda135; Input1Scalar = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda136; General = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda137; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = long int; TOutput = bool; Input0Scalar = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda135; Input1Scalar = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda136; General = onnxruntime::Less<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda137] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = bool; Input0Scalar = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda138; Input1Scalar = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda139; General = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda140; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = float; TOutput = bool; Input0Scalar = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda138; Input1Scalar = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda139; General = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda140] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = double; TOutput = bool; Input0Scalar = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda138; Input1Scalar = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda139; General = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda140; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = double; TOutput = bool; Input0Scalar = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda138; Input1Scalar = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda139; General = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda140] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = int; TOutput = bool; Input0Scalar = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda138; Input1Scalar = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda139; General = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda140; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = int; TOutput = bool; Input0Scalar = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda138; Input1Scalar = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda139; General = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda140] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = long int; TOutput = bool; Input0Scalar = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda138; Input1Scalar = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda139; General = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda140; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = long int; TOutput = bool; Input0Scalar = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda138; Input1Scalar = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda139; General = onnxruntime::Greater<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda140] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = bool; TOutput = bool; Input0Scalar = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool]::__lambda132; Input1Scalar = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool]::__lambda133; General = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool]::__lambda134; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = bool; TOutput = bool; Input0Scalar = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool]::__lambda132; Input1Scalar = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool]::__lambda133; General = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool]::__lambda134] void onnxruntime::TBroadcaster<T0, T1>::AdvanceBy(size_t) [with T0 = int; T1 = int; size_t = long unsigned int] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = int; TOutput = bool; Input0Scalar = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda132; Input1Scalar = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda133; General = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda134; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = int; TOutput = bool; Input0Scalar = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda132; Input1Scalar = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda133; General = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int]::__lambda134] void onnxruntime::TBroadcaster<T0, T1>::AdvanceBy(size_t) [with T0 = long int; T1 = long int; size_t = long unsigned int] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = long int; TOutput = bool; Input0Scalar = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda132; Input1Scalar = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda133; General = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda134; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = long int; TOutput = bool; Input0Scalar = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda132; Input1Scalar = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda133; General = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int]::__lambda134] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = bool; Input0Scalar = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda132; Input1Scalar = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda133; General = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda134; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = float; TOutput = bool; Input0Scalar = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda132; Input1Scalar = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda133; General = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda134] void onnxruntime::TBroadcaster<T0, T1>::AdvanceBy(size_t) [with T0 = double; T1 = double; size_t = long unsigned int] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = double; TOutput = bool; Input0Scalar = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda132; Input1Scalar = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda133; General = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda134; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = double; TOutput = bool; Input0Scalar = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda132; Input1Scalar = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda133; General = onnxruntime::Equal<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double]::__lambda134] void onnxruntime::TBroadcaster<T0, T1>::AdvanceBy(size_t) [with T0 = unsigned char; T1 = unsigned char; size_t = long unsigned int] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = unsigned char; TOutput = unsigned char; Input0Scalar = onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda144; Input1Scalar = onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda145; General = onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda146; int64_t = long int] onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda146 onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = unsigned char; TOutput = unsigned char; Input0Scalar = onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda144; Input1Scalar = onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda145; General = onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda146] void onnxruntime::TBroadcaster<T0, T1>::AdvanceBy(size_t) [with T0 = unsigned int; T1 = unsigned int; size_t = long unsigned int] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = unsigned int; TOutput = unsigned int; Input0Scalar = onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda144; Input1Scalar = onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda145; General = onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda146; int64_t = long int] onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda146 onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = unsigned int; TOutput = unsigned int; Input0Scalar = onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda144; Input1Scalar = onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda145; General = onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda146] void onnxruntime::TBroadcaster<T0, T1>::AdvanceBy(size_t) [with T0 = long unsigned int; T1 = long unsigned int; size_t = long unsigned int] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = long unsigned int; TOutput = long unsigned int; Input0Scalar = onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda144; Input1Scalar = onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda145; General = onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda146; int64_t = long int] onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda146 onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = long unsigned int; TOutput = long unsigned int; Input0Scalar = onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda144; Input1Scalar = onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda145; General = onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda146] 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] 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] 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] 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] 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] onnxruntime::TBroadcastOutput<T>::TBroadcastOutput(size_t, onnxruntime::Tensor&, int64_t, int64_t) [with T = short int; size_t = long unsigned int; int64_t = long 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] onnxruntime::TBroadcastOutput<T>::TBroadcastOutput(size_t, onnxruntime::Tensor&, int64_t, int64_t) [with T = short unsigned int; size_t = long unsigned int; int64_t = long 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] onnxruntime::TBroadcastOutput<T>::TBroadcastOutput(size_t, onnxruntime::Tensor&, int64_t, int64_t) [with T = signed char; size_t = long unsigned int; int64_t = long int] 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] onnxruntime::TBroadcastOutput<T>::TBroadcastOutput(size_t, onnxruntime::Tensor&, int64_t, int64_t) [with T = unsigned char; size_t = long unsigned int; int64_t = long int] 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}] 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 void onnxruntime::mod_internal::BroadCastMFloat16FMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) onnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo&) void onnxruntime::TBroadcaster<T0, T1>::AdvanceBy(size_t) [with T0 = float; T1 = float; size_t = long unsigned int] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::PRelu<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda159; Input1Scalar = onnxruntime::PRelu<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda160; General = onnxruntime::PRelu<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda161; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::PRelu<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda159; Input1Scalar = onnxruntime::PRelu<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda160; General = onnxruntime::PRelu<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda161] 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]::__lambda141; Input1Scalar = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda142; General = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda143] onnxruntime::common::Status onnxruntime::Mean_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = bool; TOutput = bool; Input0Scalar = onnxruntime::Xor::Compute(onnxruntime::OpKernelContext*) const::__lambda129; Input1Scalar = onnxruntime::Xor::Compute(onnxruntime::OpKernelContext*) const::__lambda130; General = onnxruntime::Xor::Compute(onnxruntime::OpKernelContext*) const::__lambda131; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = bool; TOutput = bool; Input0Scalar = onnxruntime::Xor::Compute(onnxruntime::OpKernelContext*) const::__lambda129; Input1Scalar = onnxruntime::Xor::Compute(onnxruntime::OpKernelContext*) const::__lambda130; General = onnxruntime::Xor::Compute(onnxruntime::OpKernelContext*) const::__lambda131] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = bool; TOutput = bool; Input0Scalar = onnxruntime::Or::Compute(onnxruntime::OpKernelContext*) const::__lambda126; Input1Scalar = onnxruntime::Or::Compute(onnxruntime::OpKernelContext*) const::__lambda127; General = onnxruntime::Or::Compute(onnxruntime::OpKernelContext*) const::__lambda128; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = bool; TOutput = bool; Input0Scalar = onnxruntime::Or::Compute(onnxruntime::OpKernelContext*) const::__lambda126; Input1Scalar = onnxruntime::Or::Compute(onnxruntime::OpKernelContext*) const::__lambda127; General = onnxruntime::Or::Compute(onnxruntime::OpKernelContext*) const::__lambda128] void onnxruntime::TBroadcaster<T0, T1>::AdvanceBy(size_t) [with T0 = bool; T1 = bool; size_t = long unsigned int] void onnxruntime::BroadcastOneSpan(onnxruntime::concurrency::ThreadPool*, double, TOutput*, int64_t, const TInput*, int64_t, const TInput*, int64_t, Input0Scalar, Input1Scalar, General) [with TInput = bool; TOutput = bool; Input0Scalar = onnxruntime::And::Compute(onnxruntime::OpKernelContext*) const::__lambda123; Input1Scalar = onnxruntime::And::Compute(onnxruntime::OpKernelContext*) const::__lambda124; General = onnxruntime::And::Compute(onnxruntime::OpKernelContext*) const::__lambda125; int64_t = long int] onnxruntime::TBroadcastOutput<T>::TBroadcastOutput(size_t, onnxruntime::Tensor&, int64_t, int64_t) [with T = bool; size_t = long unsigned int; int64_t = long int] onnxruntime::common::Status onnxruntime::BroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with TInput = bool; TOutput = bool; Input0Scalar = onnxruntime::And::Compute(onnxruntime::OpKernelContext*) const::__lambda123; Input1Scalar = onnxruntime::And::Compute(onnxruntime::OpKernelContext*) const::__lambda124; General = onnxruntime::And::Compute(onnxruntime::OpKernelContext*) const::__lambda125] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = long unsigned int; TOutput = long unsigned int; Input0Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda120; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda121; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda122] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = long int; TOutput = long int; Input0Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = long int]::__lambda120; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = long int]::__lambda121; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = long int]::__lambda122] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = unsigned int; TOutput = unsigned int; Input0Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda120; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda121; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda122] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = int; TOutput = int; Input0Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = int]::__lambda120; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = int]::__lambda121; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = int]::__lambda122] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = onnxruntime::MLFloat16; TOutput = onnxruntime::MLFloat16; Input0Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16]::__lambda120; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16]::__lambda121; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16]::__lambda122] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = double; TOutput = double; Input0Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = double]::__lambda120; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = double]::__lambda121; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = double]::__lambda122] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = float]::__lambda120; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = float]::__lambda121; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = float]::__lambda122] onnxruntime::common::Status onnxruntime::Max_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] 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>] onnxruntime::TBroadcastOutput<T>::TBroadcastOutput(size_t, onnxruntime::Tensor&, int64_t, int64_t) [with T = long unsigned int; size_t = long unsigned int; int64_t = long int] onnxruntime::TensorAllocator<T>::TensorAllocator(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] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = long unsigned int; TOutput = long unsigned int; Input0Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda117; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda118; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda119] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = long int] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = long int; TOutput = long int; Input0Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = long int]::__lambda117; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = long int]::__lambda118; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = long int]::__lambda119] onnxruntime::TBroadcastOutput<T>::TBroadcastOutput(size_t, onnxruntime::Tensor&, int64_t, int64_t) [with T = unsigned int; size_t = long unsigned int; int64_t = long int] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] const T* onnxruntime::Tensor::Data() const [with T = unsigned int] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = unsigned int; TOutput = unsigned int; Input0Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda117; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda118; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda119] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = int] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = int; TOutput = int; Input0Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = int]::__lambda117; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = int]::__lambda118; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = int]::__lambda119] onnxruntime::TBroadcastOutput<T>::TBroadcastOutput(size_t, onnxruntime::Tensor&, int64_t, int64_t) [with T = onnxruntime::MLFloat16; size_t = long unsigned int; int64_t = long int] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = onnxruntime::MLFloat16] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = onnxruntime::MLFloat16; TOutput = onnxruntime::MLFloat16; Input0Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16]::__lambda117; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16]::__lambda118; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16]::__lambda119] 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::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = double]::__lambda117; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = double]::__lambda118; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = double]::__lambda119] onnxruntime::common::Status onnxruntime::BroadcastVariadic(const onnxruntime::Node&, onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General) [with TInput = float; TOutput = float; Input0Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = float]::__lambda117; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = float]::__lambda118; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = float]::__lambda119] onnxruntime::common::Status onnxruntime::Min_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] 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]::__lambda114; Input1Scalar = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda115; General = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda116] onnxruntime::common::Status onnxruntime::Sum_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::TBroadcastOutput<T>::TBroadcastOutput(size_t, onnxruntime::Tensor&, int64_t, int64_t) [with T = double; size_t = long unsigned int; int64_t = long int] onnxruntime::TBroadcastOutput<T>::TBroadcastOutput(size_t, onnxruntime::Tensor&, int64_t, int64_t) [with T = float; size_t = long unsigned int; int64_t = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] onnxruntime::TBroadcastOutput<T>::TBroadcastOutput(size_t, onnxruntime::Tensor&, int64_t, int64_t) [with T = long int; size_t = long unsigned int; int64_t = long int] T* onnxruntime::Tensor::MutableData() [with T = int] onnxruntime::TBroadcastOutput<T>::TBroadcastOutput(size_t, onnxruntime::Tensor&, int64_t, int64_t) [with T = int; size_t = long unsigned int; int64_t = long int] 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>&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = long unsigned int] onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned int] onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned char] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Log<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Exp<double>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Exp<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Sqrt<double>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Sqrt<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Reciprocal<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Ceil<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Floor<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg<long int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg<int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg<signed char>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg<double>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<long unsigned int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<unsigned int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<short unsigned int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<unsigned char>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<long int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<short int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<signed char>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<double>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<float>] 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ZN11onnxruntime12pow_internal7PowImplIlfEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEEfE2_ ZN11onnxruntime12pow_internal7PowImplIldEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEEdE1_ ZN11onnxruntime12pow_internal7PowImplIldEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEEdE2_ ZN11onnxruntime12pow_internal7PowImplIfiEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIfEENS8_IKfEEiE1_ ZN11onnxruntime12pow_internal7PowImplIfiEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIfEENS8_IKfEEiE2_ ZN11onnxruntime12pow_internal7PowImplIflEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIfEENS8_IKfEElE1_ ZN11onnxruntime12pow_internal7PowImplIflEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIfEENS8_IKfEElE2_ ZN11onnxruntime12pow_internal7PowImplIffEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIfEENS8_IKfEEfE1_ ZN11onnxruntime12pow_internal7PowImplIffEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIfEENS8_IKfEEfE2_ ZN11onnxruntime12pow_internal7PowImplIfdEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIfEENS8_IKfEEdE1_ ZN11onnxruntime12pow_internal7PowImplIfdEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIfEENS8_IKfEEdE2_ ZN11onnxruntime12pow_internal7PowImplIdiEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIdEENS8_IKdEEiE1_ ZN11onnxruntime12pow_internal7PowImplIdiEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIdEENS8_IKdEEiE2_ ZN11onnxruntime12pow_internal7PowImplIdlEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIdEENS8_IKdEElE1_ ZN11onnxruntime12pow_internal7PowImplIdlEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIdEENS8_IKdEElE2_ ZN11onnxruntime12pow_internal7PowImplIdfEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIdEENS8_IKdEEfE1_ 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B B B B5?5?5?5?!=!=!=!=뽸뽸뽸O]O]O]O]>>>>L>L>L>L>%=%=%=%=P*P*P*P*>>>>^^^^ 1? 1? 1? 1? "?"?"?"? @K @K @K @K ɿ ɿ ɿ ɿ .4 .4 .4 .4t.t.t.t. ,F ,F ,F ,F s7 s7 s7 s7nnnn*=*=*=*= pMpMpMpMӅ<Ӆ<Ӆ<Ӆ<**** F F F F @ .@ @-DT!< @ @ right.NumDimensions() == 2 N: K: GEMM: Dimension mismatch, W: M_ >= 0 && K_ > 0 && N_ >= 0 transA transB c_shape != nullptr /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() c_shape is required if c_data is provided static void onnxruntime::Gemm<T>::ComputeGemm(CBLAS_TRANSPOSE, CBLAS_TRANSPOSE, int64_t, int64_t, int64_t, float, const T*, const T*, float, const T*, const onnxruntime::TensorShape*, T*, onnxruntime::concurrency::ThreadPool*) [with T = float; int64_t = long int] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] 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] N11onnxruntime4GemmIfE11CallWrapperE N11onnxruntime4GemmIfEE 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
+ External data type must not be UNDEFINED or STRING. Computed size: , external_data.length: ReadExternalRawData /onnxruntime_src/onnxruntime/core/optimizer/optimizer_execution_frame.cc Tried to allocate without valid type information, ort_value index= Failed to get allocator for optimizer allocator_ptr_ void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType) ForEachWithIndex operator() Info onnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector<const onnxruntime::Node*>&, const InitializedTensorSet&, const onnxruntime::Path&, const onnxruntime::IExecutionProvider&) *ZN11onnxruntime23OptimizerExecutionFrame4InfoC1ERKSt6vectorIPKNS_4NodeESaIS5_EERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSE_EEERKNS_4PathERKNS_18IExecutionProviderEEUlRKNS_7NodeArgEmE_ N11onnxruntime23OptimizerExecutionFrameE /onnxruntime_src/onnxruntime/core/providers/cpu/cpu_execution_provider.cc RegisterCPUKernels virtual std::shared_ptr<onnxruntime::KernelRegistry> onnxruntime::CPUExecutionProvider::GetKernelRegistry() const GetKernelRegistry RegisterOnnxMLOperatorKernels RegisterOnnxOperatorKernels N11onnxruntime20CPUExecutionProviderE N11onnxruntime25IExecutionProviderFactoryE N11onnxruntime18CpuProviderFactoryE St23_Sp_counted_ptr_inplaceIN11onnxruntime18CpuProviderFactoryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE 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 LەT |4LەT ߕLߕ 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&) FPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEE N11onnxruntime19ConstantOfShapeBaseE N11onnxruntime15ConstantOfShapeE PFPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEE seed Invalid dtype of high sample_size FLOAT16 is not supported Invalid data type of input count mismatch batch_size is < 1 num_classes is < 1 num_samples is < 1 /onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.h info.GetAttr<float>("mean", &mean_).IsOK() info.GetAttr<float>("scale", &scale_).IsOK() info.GetAttr<int64_t>("dtype", &dtype).IsOK() ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINED info.GetAttrs<int64_t>("shape", shape).IsOK() info.GetAttr<float>("high", &high_).IsOK() info.GetAttr<float>("low", &low_).IsOK() info.GetAttr<int64_t>("sample_size", &num_samples_).IsOK() ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINED /onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.cc Could not infer data type from input tensor with data type Empty dimensions for input tensor T* onnxruntime::Tensor::MutableData() [with T = long int] MultinomialCompute T* onnxruntime::Tensor::MutableData() [with T = int] MultinomialCompute const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::RandomUniformCompute(float, float, std::default_random_engine&, onnx::TensorProto::DataType, onnxruntime::Tensor&) onnxruntime::common::Status onnxruntime::RandomNormalCompute(float, float, std::default_random_engine&, onnx::TensorProto::DataType, onnxruntime::Tensor&) onnxruntime::Multinomial::Multinomial(const onnxruntime::OpKernelInfo&) onnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo&) onnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo&) onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo&) onnxruntime::RandomNormal::RandomNormal(const onnxruntime::OpKernelInfo&) N11onnxruntime12RandomNormalE N11onnxruntime16RandomNormalLikeE N11onnxruntime13RandomUniformE N11onnxruntime17RandomUniformLikeE N11onnxruntime11MultinomialE 0 A C ? "~+@"~+@W6}+W6}++eG?+eG? ? ? ? ? @.? @.?ʫy>ʫy>K͉ ?K͉ ?~, ?~, ?_6.>_6.>9d?9d?t ?t ? @ @ delta in Range operator can not be zero! start in Range operator should be scalar like tensor, yet got shape: limit in Range operator should be scalar like tensor, yet got shape: delta in Range operator should be scalar like tensor, yet got shape: T* onnxruntime::Tensor::MutableData() [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] N11onnxruntime5RangeE /onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.h /onnxruntime_src/onnxruntime/core/providers/cpu/math/clip.cc min->Shape().NumDimensions() == 0 max->Shape().NumDimensions() == 0 Check for duplicate types in MLTypeCallDispatcher min_ <= max_ min should be a scalar. max should be a scalar. helper.called_ < 2 helper.called_ == 1 T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::Clip::ComputeImpl<T>::operator()(const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*) const [with T = float] void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor*&, const onnxruntime::Tensor*&, const onnxruntime::Tensor*&, onnxruntime::Tensor*&}; Fn = onnxruntime::Clip::ComputeImpl; Types = {float, double, signed char, unsigned char, 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] onnxruntime::clip_internal::Clip_6Base<T>::Clip_6Base(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime13clip_internal10Clip_6BaseIfEE N11onnxruntime4ClipE N11onnxruntime6Clip_6IfEE exclusive reverse CumSum starts.size()= dims.size()= dims.size() == starts.size() extents.size()= steps.size()= dims.size() == steps.size() /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/utils.h dims.size() == extents.size() && dims.size() >= steps.size() dims.size() == starts.size() && dims.size() == extents_.size() && dims.size() >= steps.size() Axis tensor must be provided to the CumSum op Axis tensor should be 0D or 1D Axis tensor should be of type `int32_t` or `int64_t` dims.size() == extents_.size() Cannot apply CumSum operator on a scalar /onnxruntime_src/onnxruntime/core/providers/cpu/math/cumsum.cc void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::common::Status onnxruntime::CumSum<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] Compute void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] onnxruntime::common::Status onnxruntime::CumSum<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] Compute void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = int] T* onnxruntime::Tensor::MutableData() [with T = int] onnxruntime::common::Status onnxruntime::CumSum<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] Compute void onnxruntime::SliceIteratorBase::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) void onnxruntime::WritableSliceIterator<T>::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) [with T = long int] onnxruntime::SliceSkips::SliceSkips(const onnxruntime::TensorShape&, gsl::span<const long int>, gsl::span<const long int>) T* onnxruntime::Tensor::MutableData() [with T = long int] onnxruntime::common::Status onnxruntime::CumSum<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] Compute int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) N11onnxruntime6CumSumIfEE N11onnxruntime6CumSumIdEE N11onnxruntime6CumSumIiEE N11onnxruntime6CumSumIlEE d䏗䏗䏗ԐĐt$DD䐗lT$4,4<L\䏗d䏗DTTt$4Det 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@/onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum.cc Einsum op: An implementation for the input type Einsum op: There must be atleast one input There was a problem acquiring temporary memory allocator in Einsum op /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum.h info.GetAttr<std::string>("equation", &equation_).IsOK() is not supported yet Einsum equation Missing 'equation' attribute -> DeviceCompute const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::Einsum::Einsum(const onnxruntime::OpKernelInfo&) FN11onnxruntime6TensorERKS0_RKSt6vectorIlSaIlEEbSt10shared_ptrINS_10IAllocatorEEPKNS_11TensorShapeEPNS_11concurrency10ThreadPoolEPvE FN11onnxruntime6common6StatusERKNS_6TensorERS2_E FN11onnxruntime6common6StatusEPKiS3_PimmmmmmmPNS_11concurrency10ThreadPoolEPvE FN11onnxruntime6common6StatusEPKdS3_PdmmmmmmmPNS_11concurrency10ThreadPoolEPvE FN11onnxruntime6common6StatusEPKlS3_PlmmmmmmmPNS_11concurrency10ThreadPoolEPvE FSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_llSt10shared_ptrINS0_10IAllocatorEEE FN11onnxruntime6common6StatusERKSt6vectorImSaImEERKNS_6TensorERS7_PKNS_11TensorShapeEPvE FN11onnxruntime6common6StatusEPKfS3_PfmmmmmmmPNS_11concurrency10ThreadPoolEPvE N11onnxruntime6EinsumE PFSt10unique_ptrIN11onnxruntime6TensorESt14default_deleteIS1_EERKS1_llSt10shared_ptrINS0_10IAllocatorEEE PFN11onnxruntime6common6StatusERKSt6vectorImSaImEERKNS_6TensorERS7_PKNS_11TensorShapeEPvE PFN11onnxruntime6common6StatusEPKfS3_PfmmmmmmmPNS_11concurrency10ThreadPoolEPvE PFN11onnxruntime6TensorERKS0_RKSt6vectorIlSaIlEEbSt10shared_ptrINS_10IAllocatorEEPKNS_11TensorShapeEPNS_11concurrency10ThreadPoolEPvE PFN11onnxruntime6common6StatusERKNS_6TensorERS2_E PFN11onnxruntime6common6StatusEPKiS3_PimmmmmmmPNS_11concurrency10ThreadPoolEPvE PFN11onnxruntime6common6StatusEPKdS3_PdmmmmmmmPNS_11concurrency10ThreadPoolEPvE PFN11onnxruntime6common6StatusEPKlS3_PlmmmmmmmPNS_11concurrency10ThreadPoolEPvE Einsum op: Transpose failed: %ld Tensor size ( ) != new size ( for input shape Einsum op: The candidate output does not match the actual output's shape /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_auxiliary_ops.cc output.SizeInBytes() == input.SizeInBytes() The rank of the input must match permutation size for Transpose input_rank == permutation.size() Length of permutation must match the rank of the input to be permutated The innermost dims should have the same dim value to parse the diagonal elements input_dims[rank - 2] == input_dims[rank - 1] Einsum op: Unsupported data type for Diagonal rank >= 2 && dim_1 != dim_2 && input_dims[dim_1] == input_dims[dim_2] shape_.Size() == new_shape.Size() Cannot parse the diagonal elements along dims Data types of the inputs must match for MatMul input_1.DataType() == input_2.DataType() input_shape_1_override.size() == 3 && input_shape_2_override.size() == 3 Batch dimension should match for MatMul; input_shape_1_override[0] == input_shape_2_override[0] Incompatible matrix dimensions for matMul input_shape_1_override[2] == input_shape_2_override[1] Einsum op: Exception during MatMul operation: Only 1 batch dimension is allowed for MatMul T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector<long int>&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul<T>&) [with T = long int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>; onnxruntime::EinsumOp::DeviceHelpers::MatMul<T> = std::function<onnxruntime::common::Status(const long int*, const long int*, long int*, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, onnxruntime::concurrency::ThreadPool*, void*)>] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector<long int>&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul<T>&) [with T = double; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>; onnxruntime::EinsumOp::DeviceHelpers::MatMul<T> = std::function<onnxruntime::common::Status(const double*, const double*, double*, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, onnxruntime::concurrency::ThreadPool*, void*)>] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector<long int>&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul<T>&) [with T = int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>; onnxruntime::EinsumOp::DeviceHelpers::MatMul<T> = std::function<onnxruntime::common::Status(const int*, const int*, int*, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, onnxruntime::concurrency::ThreadPool*, void*)>] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::MatMul(const onnxruntime::Tensor&, const std::vector<long int>&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, void*, onnxruntime::EinsumOp::DeviceHelpers::MatMul<T>&) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>; onnxruntime::EinsumOp::DeviceHelpers::MatMul<T> = std::function<onnxruntime::common::Status(const float*, const float*, float*, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, long unsigned int, onnxruntime::concurrency::ThreadPool*, void*)>] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::Transpose(const onnxruntime::Tensor&, const std::vector<long int>&, const std::vector<long unsigned int>&, onnxruntime::AllocatorPtr, void*, const Transpose&) bool onnxruntime::EinsumOp::IsTransposeRequired(size_t, const std::vector<long unsigned int>&) void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&) std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DiagonalInnermostDims(const onnxruntime::Tensor&, bool, onnxruntime::AllocatorPtr) std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::Diagonal(const onnxruntime::Tensor&, int64_t, int64_t, onnxruntime::AllocatorPtr) onnxruntime::common::Status onnxruntime::EinsumOp::DeviceHelpers::CpuDeviceHelpers::DataCopy(const onnxruntime::Tensor&, onnxruntime::Tensor&) dim_iter == rank . The shape is: in the same dimension vector::_M_fill_insert Inputs have ellipses in them but the provided output subscript does not contain an ellipsis /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_compute_preprocessor.cc num_broadcasted_indices < num_of_ellipsis_dims_ The broadcasted dimensions of the inputs are incompatible Number of subscripts in the input equation does not match number of input tensors Found a '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the input Ellipsis must indicate a fixed number of dimensions across all inputs Found '.' not part of an ellipsis in input: Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand is incompatible in the dimension Another operand has a dim value of Einsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z) Found a '.' not part of an ellipsis in the output subscript provided Found '.' not part of an ellipsis in the output subscript provided Output subscript contains repeated letters Output subscript contains letters not seen in the inputs Rank of the input must match number of subscript labels corresponding to the input void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&) onnxruntime::common::Status onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDims() Run Left shape override: Left shape: Right shape override: Right shape: left_rank == right_rank left_dim == right_dim Einsum op: The candidate output cannot be reshaped into the op's output /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_typed_compute_processor.cc candidate_output.Shape().Size() == output_shape.Size() Not all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: candidate_output_dims[iter] == 1 Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: The override dims are not compatible with given tensor's shape. left.Shape().Size() == left_shape_override.Size() right.Shape().Size() == right_shape_override.Size() Ranks of pair-wise operands must be equal. Einsum op: Input dimensions must be equal along an axis to be reduced across all inputs Einsum op: Input shapes do not align void onnxruntime::EinsumTypedComputeProcessor<T>::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&) [with T = long int] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumTypedComputeProcessor<T>::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, bool) [with T = long int] void onnxruntime::EinsumTypedComputeProcessor<T>::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&) [with T = double] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumTypedComputeProcessor<T>::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, bool) [with T = double] void onnxruntime::EinsumTypedComputeProcessor<T>::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&) [with T = int] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumTypedComputeProcessor<T>::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, bool) [with T = int] void onnxruntime::EinsumTypedComputeProcessor<T>::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&) [with T = float] void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&) std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumTypedComputeProcessor<T>::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, bool) [with T = float] offset % span_size_ == 0 fmod (fmod == 0) || (fmod == 1) ) for tensor of length: Invalid start/ending offset [ Broadcast Output range [ cur1 == end1 cur_out == end_out Abs Neg Floor Ceil Reciprocal Less Greater BitShift Not And Or Xor Sin Cos Tan Asin Acos Atan Sinh Cosh Asinh Acosh Atanh PRelu Mod axis == 1 || axis == largest direction LEFT RIGHT Invalid direction value of ' Can broadcast 0 by 0 or 1. largest <= 1 !helper.HaveTwoTensorInputs() fmod_ p_ml_value Unsupported Y type: Unsupported X type: Must have 1 or more inputs inputCount >= 1 data_n.Shape() == shape input_count >= 1 data_1.Shape() == shape InputBroadcaster can only start at span boundary! /onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.h fmod must have value either 0 or 1 /onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.cc start_offset >= 0 && real_end >= 0 && start_offset <= real_end && real_end <= len ) are not at boundary of span with size: start_offset % span_size == 0 && real_end % span_size == 0 BroadcastLooper requires two tensors as input. Attempting to broadcast an axis by a dimension other than 1. '. Valid values are 'LEFT' or 'RIGHT'. /onnxruntime_src/onnxruntime/core/providers/cpu/element_wise_ranged_transform.h input_size < std::numeric_limits<std::ptrdiff_t>::max() Tensor with shape information must be 1 dimensional. shape_data_tensor.Shape().GetDims().size() == 1 ExpandBroadcastLooper should only have a shape for the second input. fmod attribute must be true for float, float16 and double types Please fetch output tensor with specified shape. All inputs must have the same shape Invalid usage. Input 1 is a shape with no data. onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<signed char>] T* onnxruntime::Tensor::MutableData() [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = short int] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<short int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<long int>] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<unsigned char>] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] const T* onnxruntime::Tensor::Data() const [with T = short unsigned int] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<short unsigned int>] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] const T* onnxruntime::Tensor::Data() const [with T = unsigned int] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<unsigned int>] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Abs<long unsigned int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg<signed char>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg<int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Neg<long int>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Floor<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Ceil<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Reciprocal<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sqrt<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sqrt<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Exp<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Exp<double>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Log<float>] onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda207 onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda207 onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda207 void onnxruntime::ExpandBroadcastLooper(onnxruntime::BroadcastHelper&, const onnxruntime::ProcessBroadcastSpanFuncs&) void onnxruntime::UntypedExpand(onnxruntime::OpKernelContext&, const onnxruntime::ProcessBroadcastSpanFuncs&) onnxruntime::Expand_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>]::__lambda226 void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {const bool&, 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}] virtual onnxruntime::common::Status onnxruntime::Mod::Compute(onnxruntime::OpKernelContext*) const onnxruntime::Mod::Mod(const onnxruntime::OpKernelInfo&) T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] T* onnxruntime::OpKernelContext::Output(int) [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::Mean_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = bool] onnxruntime::common::Status onnxruntime::Max_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] 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>] onnxruntime::common::Status onnxruntime::Min_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] void onnxruntime::InputBroadcaster::AdvanceBy(size_t) void onnxruntime::BroadcastLooper(TBroadcastHelper&, const onnxruntime::ProcessBroadcastSpanFuncs&) [with TBroadcastHelper = onnxruntime::BroadcastHelper] onnxruntime::OutputBroadcaster::OutputBroadcaster(size_t, onnxruntime::Tensor&, int64_t, int64_t) 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::TensorAllocator(onnxruntime::OpKernelContext&) onnxruntime::common::Status onnxruntime::Sum_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = long unsigned int] onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned int] onnxruntime::BitShift<T>::BitShift(const onnxruntime::OpKernelInfo&) [with T = unsigned char] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Log<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Exp<double>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Exp<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Sqrt<double>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Sqrt<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Reciprocal<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Ceil<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Floor<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg<long int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg<int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg<signed char>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg<double>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Neg<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<long unsigned int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<unsigned int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<short unsigned int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<unsigned char>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<long int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<short int>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<signed char>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<double>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Abs<float>] ElementWiseKernel ( ( `( `(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfO *ZN11onnxruntime19UntypedBroadcastTwoERNS_15OpKernelContextERKNS_25ProcessBroadcastSpanFuncsEdPvEUlllE_ *ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE_ *ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE0_ *ZN11onnxruntimeL21ParallelizeSingleSpanINS_15BroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE1_ N11onnxruntime3PowE N11onnxruntime5Min_8E N11onnxruntime5Max_8E N11onnxruntime3NotE N11onnxruntime3AndE N11onnxruntime2OrE N11onnxruntime3XorE N11onnxruntime8functors26ElementWiseRangedTransformIfEE N11onnxruntime8functors3ExpIfEE N11onnxruntime3AddIfEE N11onnxruntime3AddIdEE N11onnxruntime3AddIiEE N11onnxruntime3AddIlEE N11onnxruntime3SubIfEE N11onnxruntime3SubIdEE N11onnxruntime3SubIiEE N11onnxruntime3SubIlEE N11onnxruntime3MulIfEE N11onnxruntime3MulIdEE N11onnxruntime3MulIiEE N11onnxruntime3MulIlEE N11onnxruntime3DivIfEE N11onnxruntime3DivIdEE N11onnxruntime3DivIiEE N11onnxruntime3DivIlEE N11onnxruntime8functors3AbsIfEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIfEEEE N11onnxruntime8functors26ElementWiseRangedTransformIdEE N11onnxruntime8functors3AbsIdEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIdEEEE N11onnxruntime8functors26ElementWiseRangedTransformIaEE N11onnxruntime8functors3AbsIaEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIaEEEE N11onnxruntime8functors26ElementWiseRangedTransformIsEE N11onnxruntime8functors3AbsIsEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIsEEEE N11onnxruntime8functors26ElementWiseRangedTransformIiEE N11onnxruntime8functors3AbsIiEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIiEEEE N11onnxruntime8functors26ElementWiseRangedTransformIlEE N11onnxruntime8functors3AbsIlEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIlEEEE N11onnxruntime8functors26ElementWiseRangedTransformIhEE N11onnxruntime8functors3AbsIhEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIhEEEE N11onnxruntime8functors26ElementWiseRangedTransformItEE N11onnxruntime8functors3AbsItEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsItEEEE N11onnxruntime8functors26ElementWiseRangedTransformIjEE N11onnxruntime8functors3AbsIjEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsIjEEEE N11onnxruntime8functors26ElementWiseRangedTransformImEE N11onnxruntime8functors3AbsImEE N11onnxruntime17ElementWiseKernelINS_8functors3AbsImEEEE N11onnxruntime8functors3NegIfEE N11onnxruntime17ElementWiseKernelINS_8functors3NegIfEEEE N11onnxruntime8functors3NegIdEE N11onnxruntime17ElementWiseKernelINS_8functors3NegIdEEEE N11onnxruntime8functors3NegIaEE N11onnxruntime17ElementWiseKernelINS_8functors3NegIaEEEE N11onnxruntime8functors3NegIiEE N11onnxruntime17ElementWiseKernelINS_8functors3NegIiEEEE N11onnxruntime8functors3NegIlEE N11onnxruntime17ElementWiseKernelINS_8functors3NegIlEEEE N11onnxruntime8functors5FloorIfEE N11onnxruntime17ElementWiseKernelINS_8functors5FloorIfEEEE N11onnxruntime8functors4CeilIfEE N11onnxruntime17ElementWiseKernelINS_8functors4CeilIfEEEE N11onnxruntime8functors10ReciprocalIfEE N11onnxruntime17ElementWiseKernelINS_8functors10ReciprocalIfEEEE N11onnxruntime8functors4SqrtIfEE N11onnxruntime17ElementWiseKernelINS_8functors4SqrtIfEEEE N11onnxruntime8functors4SqrtIdEE N11onnxruntime17ElementWiseKernelINS_8functors4SqrtIdEEEE N11onnxruntime17ElementWiseKernelINS_8functors3ExpIfEEEE N11onnxruntime8functors3ExpIdEE N11onnxruntime17ElementWiseKernelINS_8functors3ExpIdEEEE N11onnxruntime8functors3LogIfEE N11onnxruntime17ElementWiseKernelINS_8functors3LogIfEEEE N11onnxruntime5Sum_6IfEE N11onnxruntime5Sum_8IfEE N11onnxruntime5Max_6IfEE N11onnxruntime5Min_6IfEE N11onnxruntime4LessIfEE N11onnxruntime4LessIdEE N11onnxruntime4LessIiEE N11onnxruntime4LessIlEE N11onnxruntime7GreaterIfEE N11onnxruntime7GreaterIdEE N11onnxruntime7GreaterIiEE N11onnxruntime7GreaterIlEE N11onnxruntime5EqualIbEE N11onnxruntime5EqualIiEE N11onnxruntime5EqualIlEE N11onnxruntime5EqualIfEE N11onnxruntime5EqualIdEE N11onnxruntime6Mean_6IfEE N11onnxruntime6Mean_8IfEE N11onnxruntime8BitShiftIhEE N11onnxruntime8BitShiftIjEE N11onnxruntime8BitShiftImEE N11onnxruntime3ErfIfEE N11onnxruntime3SinIfEE N11onnxruntime3SinIdEE N11onnxruntime3CosIfEE N11onnxruntime3TanIfEE N11onnxruntime4AsinIfEE N11onnxruntime4AcosIfEE N11onnxruntime4AtanIfEE N11onnxruntime4SinhIfEE N11onnxruntime4CoshIfEE N11onnxruntime5AsinhIfEE N11onnxruntime5AcoshIfEE N11onnxruntime5AtanhIfEE N11onnxruntime5PReluIfEE N11onnxruntime8Expand_8ISsEE N11onnxruntime3ModE pA @ ,F F ? ? ? ? ? ? ? ? ? ? ? ? B B B B5?5?5?5?!=!=!=!=뽸뽸뽸O]O]O]O]>>>>L>L>L>L>%=%=%=%=P*P*P*P*>>>>^^^^ 1? 1? 1? 1? "?"?"?"? @K @K @K @K ɿ ɿ ɿ ɿ .4 .4 .4 .4t.t.t.t. ,F ,F ,F ,F s7 s7 s7 s7nnnn*=*=*=*= pMpMpMpMӅ<Ӆ<Ӆ<Ӆ<**** F F F F @ @ @ .@-DT!< @/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 c_shape is required if c_data is provided /onnxruntime_src/onnxruntime/core/providers/cpu/math/gemm.cc transA transB right.NumDimensions() == 2 N: K: GEMM: Dimension mismatch, W: M_ >= 0 && K_ > 0 && N_ >= 0 c_shape != nullptr void onnxruntime::GemmBroadcastBias(int64_t, int64_t, float, const T*, const onnxruntime::TensorShape*, T*) [with T = float; int64_t = long int] onnxruntime::GemmHelper::GemmHelper(const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&, bool, const onnxruntime::TensorShape&) const T& 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] onnxruntime::Gemm<T>::Gemm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime4GemmIfEE ZNK11onnxruntime4GemmIfE17ComputeActivationEPfmPNS_11concurrency10ThreadPoolEEUlllE_ Hardmax . N= , D= /onnxruntime_src/onnxruntime/core/providers/cpu/math/hardmax.cc Hardmax inputs N, D and N * D must be < T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime7HardmaxIfEE MatMul dimension mismatch /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 Compute Compute Compute T* onnxruntime::Tensor::MutableData() [with T = float] Compute Compute N11onnxruntime6MatMulIfEE N11onnxruntime6MatMulIdEE N11onnxruntime6MatMulIiEE N11onnxruntime6MatMulIlEE T3 /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_integer.cc IsScalarOr1ElementVector(a_zero_point) IsScalarOr1ElementVector(b_zero_point) MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1 MatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1 T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext*) const Compute N11onnxruntime13MatMulIntegerE QLinearMatMul /onnxruntime_src/onnxruntime/core/providers/cpu/math/quantize_linear_matmul.cc IsScalarOr1ElementVector(a_offset) IsScalarOr1ElementVector(b_offset) IsScalarOr1ElementVector(y_offset) IsScalarOr1ElementVector(a_scale) IsScalarOr1ElementVector(b_scale) IsScalarOr1ElementVector(y_scale) QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1 QLinearMatmul : weight zero point must be a scalar or 1D tensor of size 1 QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1 QLinearMatmul : input scale must be a scalar or 1D tensor of size 1 QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1 QLinearMatmul : result scale must be a scalar or 1D tensor of size 1 T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] virtual onnxruntime::common::Status onnxruntime::QLinearMatMul::Compute(onnxruntime::OpKernelContext*) const Compute N11onnxruntime13QLinearMatMulE Round T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime5RoundINS_9MLFloat16EEE N11onnxruntime5RoundIfEE N11onnxruntime5RoundIdEE Sign 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 const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7SoftmaxIfEE N11onnxruntime7SoftmaxIdEE SoftmaxCPU inputs N, D and N * D must be < ;k_temp > 0 sorted I TopK k argument [ /onnxruntime_src/onnxruntime/core/providers/cpu/math/top_k.cc op_kernel_info.GetAttr<int64_t>("k", &k_temp).IsOK() op_kernel_info.GetAttr<int64_t>("axis", &axis_temp).IsOK() op_kernel_info.GetAttr<int64_t>("largest", &largest_temp).IsOK() op_kernel_info.GetAttr<int64_t>("sorted", &sorted_temp).IsOK() ] should not be greater than specified axis dim value [ output count mismatch, expected 2 outputs to be present for TopK operator input count mismatch, expected 1 input - the tensor to be processed input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k value value of k must not be negative k tensor should be a 1D tensor of size 1 void onnxruntime::TopkOpset11ConstructorCommon(const onnxruntime::OpKernelInfo&, int&, bool&, bool&) 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] *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ N11onnxruntime4TopKILi9EfEE N11onnxruntime4TopKILi10EfEE N11onnxruntime4TopKILi11EfEE N11onnxruntime4TopKILi11ElEE 333333? ArrayFeatureExtractor ai.onnx.ml ) >= Invalid argument: X input has empty dimensions. Invalid Y argument: num_indices = 0 Invalid Y argument: index is out of range: Y[ T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] N11onnxruntime2ml23ArrayFeatureExtractorOpIfEE N11onnxruntime2ml23ArrayFeatureExtractorOpIdEE N11onnxruntime2ml23ArrayFeatureExtractorOpIiEE N11onnxruntime2ml23ArrayFeatureExtractorOpIlEE N11onnxruntime2ml23ArrayFeatureExtractorOpISsEE Binarizer Input data with index: is NaN T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime2ml11BinarizerOpIfEE 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 CastMap %f stof stoll Invalid input type of value: Unexpected CAST_TO value of /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> onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = std::basic_string<char>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = long int] const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, std::basic_string<char> >] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = std::map<long int, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, float>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = float] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] onnxruntime::ml::PACK_MAP onnxruntime::ml::MakePack(const string&) onnxruntime::ml::CAST_TO onnxruntime::ml::MakeCast(const string&) onnxruntime::ml::CastMap::CastMap(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml7CastMapE CategoryMapper cats_strings cats_int64s default_string default_int64 Input of string must have output of int64 Input of int64 must have output of string /onnxruntime_src/onnxruntime/core/providers/cpu/ml/category_mapper.h info.GetAttrs<std::string>("cats_strings", string_categories).IsOK() info.GetAttrs<int64_t>("cats_int64s", int_categories).IsOK() info.GetAttr<std::string>("default_string", &default_string_).IsOK() info.GetAttr<int64_t>("default_int64", &default_int_).IsOK() num_entries == int_categories.size() T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::CategoryMapper::CategoryMapper(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml14CategoryMapperE DictVectorizer string_vocabulary int64_vocabulary /onnxruntime_src/onnxruntime/core/providers/cpu/ml/dictvectorizer.h info.GetAttrs(std::is_same<AttrType, std::string>::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK() T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, long int>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, long int>] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, float>] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, double>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, double>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, std::basic_string<char> >] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = std::map<long int, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, float>] T* onnxruntime::Tensor::MutableData() [with T = double] const T& OrtValue::Get() const [with T = std::map<long int, double>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, double>] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = double] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = float] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = std::basic_string<char>] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = double] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = float] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = long int] N11onnxruntime2ml16DictVectorizerOpISslEE N11onnxruntime2ml16DictVectorizerOpISsfEE N11onnxruntime2ml16DictVectorizerOpISsdEE N11onnxruntime2ml16DictVectorizerOpIlSsEE N11onnxruntime2ml16DictVectorizerOpIlfEE N11onnxruntime2ml16DictVectorizerOpIldEE inputdimensions FeatureVectorizer input_tensor_ptr != nullptr Invalid input type: Number of inputs ( /onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.h status.IsOK() && !input_dimensions_.empty() inputdimensions attribute must be provided /onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.cc input_count >= 0 && static_cast<size_t>(input_count) == input_dimensions_.size() ) does not match number of inputdimensions values ( const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = float] virtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml17FeatureVectorizerE imputed_value_floats imputed_value_int64s replaced_value_float replaced_value_int64 Imputer Empty input dimensions. Invalid type Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specified /onnxruntime_src/onnxruntime/core/providers/cpu/ml/imputer.cc Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specified Must provide imputed_values_float_ or imputed_values_int64_ but not both. imputed_values_float_.empty() ^ imputed_values_int64_.empty() Empty value of imputed values. T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml9ImputerOpE keys_floats values_strings _Unused keys_strings values_floats default_float keys_int64s values_int64s LabelEncoder attribtues in LabelEncoder The values is and the number of ) must have the same length. num_keys == num_values classes_strings /onnxruntime_src/onnxruntime/core/providers/cpu/ml/label_encoder.h info.GetAttrs<TKey>(_key_field_name, keys).IsOK() info.GetAttrs<TValue>(_value_field_name, values).IsOK() However, the number of key is Input of tensor(string) must have output of tensor(int64) Input of tensor(int64) must have output of tensor(string) info.GetAttrs<std::string>("classes_strings", string_classes).IsOK() gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = std::basic_string<char>] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = std::basic_string<char>] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string<char>; TValue = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = std::basic_string<char>] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = float] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string<char>; TValue = float] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = long int] onnxruntime::ml::LabelEncoder::LabelEncoder(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml12LabelEncoderE N11onnxruntime2ml14LabelEncoder_2IfSsEE N11onnxruntime2ml14LabelEncoder_2ISsfEE N11onnxruntime2ml14LabelEncoder_2IlfEE N11onnxruntime2ml14LabelEncoder_2IflEE N11onnxruntime2ml14LabelEncoder_2IlSsEE N11onnxruntime2ml14LabelEncoder_2ISslEE N11onnxruntime2ml14LabelEncoder_2IllEE multi_class NONE post_transform LOGISTIC SOFTMAX SOFTMAX_ZERO intercepts classlabels_strings classlabels_ints coefficients !coefficients_.empty() LinearClassifier shape_size == out.length() /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearclassifier.cc scores.size() == static_cast<size_t>(expected_num_scores) Unexpected value for 'add_second_class' of Scores output is incorrect size. Expected: scores_output_data.length() >= scores_output_size Input shape needs to be at least a single dimension. Unsupported input element type of const T* onnxruntime::Tensor::Data() const [with T = double] void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span<float>&) [with SrcType = double] const T* onnxruntime::Tensor::Data() const [with T = long int] void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span<float>&) [with SrcType = long int] const T* onnxruntime::Tensor::Data() const [with T = int] void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span<float>&) [with SrcType = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] void onnxruntime::ml::batched_update_scores_inplace(gsl::span<T>, int64_t, int64_t, onnxruntime::ml::POST_EVAL_TRANSFORM, int, bool, onnxruntime::concurrency::ThreadPool*) [with T = float; int64_t = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] void onnxruntime::ml::LinearClassifier::ComputeImpl(gsl::span<const float>, int64_t, int64_t, int64_t, const std::vector<float>&, const std::vector<float>&, onnxruntime::Tensor&, onnxruntime::Tensor&, onnxruntime::ml::POST_EVAL_TRANSFORM, bool, onnxruntime::concurrency::ThreadPool*) const onnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml16LinearClassifierE ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_EEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE_ ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_EEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE0_ ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_EEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE1_ @@3ֳLinearRegressor Unsupported data type of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearregressor.cc info.GetAttr<int64_t>("targets", &num_targets_).IsOK() info.GetAttrs<float>("coefficients", coefficients_).IsOK() Input shape had more than 2 dimension. Dims= T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::LinearRegressor::LinearRegressor(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml15LinearRegressorE Invalid normalize value of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.h info.GetAttr<std::string>("norm", &norm).IsOK() Rank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of const T* onnxruntime::Tensor::Data() const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::NORMALIZE onnxruntime::ml::MakeNormalize(const string&) onnxruntime::ml::Normalizer::Normalizer(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml10NormalizerE 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 /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.cc /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.h info.GetAttrs<float>("kernel_params", kernel_params).IsOK() 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 SVMClassifier LINEAR kernel_type POLY RBF kernel_params vectors_per_class prob_a prob_b support_vectors rho coefficients_.size() > 0 T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] static void onnxruntime::Gemm<T>::ComputeGemm(CBLAS_TRANSPOSE, CBLAS_TRANSPOSE, int64_t, int64_t, int64_t, float, const T*, const T*, float, const T*, const onnxruntime::TensorShape*, T*, onnxruntime::concurrency::ThreadPool*) [with T = float; int64_t = long int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo&) onnxruntime::ml::SVMClassifier::SVMClassifier(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml9SVMCommonE N11onnxruntime2ml13SVMClassifierE ? 4
-ף;n_supports one_class SVMRegressor Unexpected mode: /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmregressor.cc num_features == feature_count_ static void onnxruntime::Gemm<T>::ComputeGemm(CBLAS_TRANSPOSE, CBLAS_TRANSPOSE, int64_t, int64_t, int64_t, float, const T*, const T*, float, const T*, const onnxruntime::TensorShape*, T*, onnxruntime::concurrency::ThreadPool*) [with T = float; int64_t = long int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::common::Status onnxruntime::ml::SVMRegressor<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo&) onnxruntime::ml::SVMRegressor<T>::SVMRegressor(const onnxruntime::OpKernelInfo&) [with T = float] ZN11onnxruntime10IAllocator13MakeUniquePtrIfEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPfE_ N11onnxruntime2ml12SVMRegressorIfEE BRANCH_EQ TreeEnsembleClassifier n_targets_or_classes > 0 AVERAGE SUM MIN BRANCH_LEQ LEAF BRANCH_LT BRANCH_GTE BRANCH_GT is already there. in tree Node (truenode). Unable to find node (falsenode). (weights). classlabels_int64s class_weights class_treeids class_nodeids class_ids nodes_values nodes_truenodeids nodes_treeids nodes_nodeids nodes_modes nodes_hitrates nodes_featureids nodes_falsenodeids base_values aggregate_function predictions.size() == 2 X dims is empty. /onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_common.h nodes_falsenodeids.size() == nodes_featureids.size() nodes_falsenodeids.size() == nodes_modes.size() nodes_falsenodeids.size() == nodes_nodeids.size() nodes_falsenodeids.size() == nodes_treeids.size() nodes_falsenodeids.size() == nodes_truenodeids.size() nodes_falsenodeids.size() == nodes_values.size() target_class_ids.size() == target_class_nodeids.size() target_class_ids.size() == target_class_treeids.size() One falsenode is pointing either to itself, either to another tree. nodes_missing_value_tracks_true /onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_aggregator.h it->i < (int64_t)predictions.size() predictions.size() == predictions2.size() classes.size() == 2 || classes.size() == 1 r{ l4<l,z
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- int64_t onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::_set_score_binary(int&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = float; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = float; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = float; OTYPE = float] int64_t onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::_set_score_binary(int&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = double; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = double; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = double; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = double; OTYPE = float] int64_t onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::_set_score_binary(int&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = long int; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = long int; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = long int; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = long int; OTYPE = float] int64_t onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::_set_score_binary(int&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = int; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = int; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = int; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = int; OTYPE = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::TreeEnsembleCommon(int, int, const string&, const std::vector<IT>&, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const std::vector<long int>&, const std::vector<std::basic_string<char> >&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const string&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&) [with ITYPE = int; OTYPE = float; std::string = std::basic_string<char>; int64_t = long int] onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::TreeEnsembleCommon(int, int, const string&, const std::vector<IT>&, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const std::vector<long int>&, const std::vector<std::basic_string<char> >&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const string&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&) [with ITYPE = long int; OTYPE = float; std::string = std::basic_string<char>; int64_t = long int] onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::TreeEnsembleCommon(int, int, const string&, const std::vector<IT>&, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const std::vector<long int>&, const std::vector<std::basic_string<char> >&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const string&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&) [with ITYPE = double; OTYPE = float; std::string = std::basic_string<char>; int64_t = long int] onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::TreeEnsembleCommon(int, int, const string&, const std::vector<IT>&, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const std::vector<long int>&, const std::vector<std::basic_string<char> >&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const string&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&) [with ITYPE = float; OTYPE = float; std::string = std::basic_string<char>; int64_t = long int] ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE2_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE2_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE2_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE2_ St23_Sp_counted_ptr_inplaceIN11onnxruntime12CPUAllocatorESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE N11onnxruntime2ml22TreeEnsembleClassifierIfEE N11onnxruntime2ml22TreeEnsembleClassifierIdEE N11onnxruntime2ml22TreeEnsembleClassifierIlEE N11onnxruntime2ml22TreeEnsembleClassifierIiEE TreeEnsembleRegressor target_weights target_treeids target_nodeids target_ids n_targets predictions.size() == (size_t)n_targets_or_classes_ this->base_values_.size() == predictions.size() Unknown aggregation function in TreeEnsemble. void onnxruntime::ml::detail::TreeAggregatorMax<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeAggregator<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = float; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorMin<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorAverage<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = float; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::compute(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorMax<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = double; OTYPE = float] void onnxruntime::ml::detail::TreeAggregator<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = double; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorMin<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = double; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorAverage<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = double; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = double; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = double; OTYPE = float] void onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::compute(onnxruntime::concurrency::ThreadPool*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = double; OTYPE = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE2_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE2_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE2_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE2_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE2_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE2_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE2_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE2_ N11onnxruntime2ml21TreeEnsembleRegressorIfEE N11onnxruntime2ml21TreeEnsembleRegressorIdEE ZipMap Input features_per_batch[ ] != number of classlabels[ Must provide classlabels_strings or classlabels_int64s but not both. /onnxruntime_src/onnxruntime/core/providers/cpu/ml/zipmap.cc classlabels_strings_.empty() ^ classlabels_int64s_.empty() Zipmap does not support empty dim count Zipmap only supports 1D or 2D input tensors T* OrtValue::GetMutable() [with T = std::vector<std::map<long int, float> >] T* OrtValue::GetMutable() [with T = std::vector<std::map<std::basic_string<char>, float> >] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::ZipMapOp::ZipMapOp(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml8ZipMapOpE MaxUnpool Unsupported pooling size. inferred output shape: No kernel shape is set. kernel_shape_[dim] > 0 Input dimension cannot be less than 3. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/Unpool.cc Not satisfied: X_shape.NumDimensions() >= 3
+ /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul.cc Compute Compute Compute T* onnxruntime::Tensor::MutableData() [with T = float] Compute Compute N11onnxruntime6MatMulIfEE N11onnxruntime6MatMulIdEE N11onnxruntime6MatMulIiEE N11onnxruntime6MatMulIlEE T3 /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_integer.cc IsScalarOr1ElementVector(a_zero_point) IsScalarOr1ElementVector(b_zero_point) MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1 MatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1 T* onnxruntime::Tensor::MutableData() [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::MatMulInteger::Compute(onnxruntime::OpKernelContext*) const Compute N11onnxruntime17MatMulIntegerBaseE N11onnxruntime13MatMulIntegerE QLinearMatMul /onnxruntime_src/onnxruntime/core/providers/cpu/math/quantize_linear_matmul.cc IsScalarOr1ElementVector(a_offset) IsScalarOr1ElementVector(b_offset) IsScalarOr1ElementVector(y_offset) IsScalarOr1ElementVector(a_scale) IsScalarOr1ElementVector(b_scale) IsScalarOr1ElementVector(y_scale) QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1 QLinearMatmul : weight zero point must be a scalar or 1D tensor of size 1 QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1 QLinearMatmul : input scale must be a scalar or 1D tensor of size 1 QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1 QLinearMatmul : result scale must be a scalar or 1D tensor of size 1 T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] virtual onnxruntime::common::Status onnxruntime::QLinearMatMul::Compute(onnxruntime::OpKernelContext*) const Compute N11onnxruntime13QLinearMatMulE Round T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime5RoundINS_9MLFloat16EEE N11onnxruntime5RoundIfEE N11onnxruntime5RoundIdEE Sign 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 /onnxruntime_src/onnxruntime/core/providers/cpu/math/softmax.cc ComputeImplOpset13 const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] ComputeImplOpset13 const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7SoftmaxIfEE N11onnxruntime7SoftmaxIdEE SoftmaxCPU inputs N, D and N * D must be < ;k_temp > 0 sorted I TopK k argument [ /onnxruntime_src/onnxruntime/core/providers/cpu/math/top_k.cc op_kernel_info.GetAttr<int64_t>("k", &k_temp).IsOK() op_kernel_info.GetAttr<int64_t>("axis", &axis_temp).IsOK() op_kernel_info.GetAttr<int64_t>("largest", &largest_temp).IsOK() op_kernel_info.GetAttr<int64_t>("sorted", &sorted_temp).IsOK() ] should not be greater than specified axis dim value [ output count mismatch, expected 2 outputs to be present for TopK operator input count mismatch, expected 1 input - the tensor to be processed input count mismatch, expected 2 inputs - the tensor to be processed and a tensor containing k value value of k must not be negative k tensor should be a 1D tensor of size 1 void onnxruntime::TopkOpset11ConstructorCommon(const onnxruntime::OpKernelInfo&, int&, bool&, bool&) 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] *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIfEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_15GreaterValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE0_ *ZN11onnxruntimeL16FindTopKElementsINS_14LesserValueCmpIlEEEEvPKNS_6TensorERKNS_11TensorShapeEPS3_S9_S8_jbjPNS_11concurrency10ThreadPoolEEUllE1_ N11onnxruntime4TopKILi9EfEE N11onnxruntime4TopKILi10EfEE N11onnxruntime4TopKILi11EfEE N11onnxruntime4TopKILi11ElEE 333333? ArrayFeatureExtractor ai.onnx.ml ) >= Invalid argument: X input has empty dimensions. Invalid Y argument: num_indices = 0 Invalid Y argument: index is out of range: Y[ T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] N11onnxruntime2ml23ArrayFeatureExtractorOpIfEE N11onnxruntime2ml23ArrayFeatureExtractorOpIdEE N11onnxruntime2ml23ArrayFeatureExtractorOpIiEE N11onnxruntime2ml23ArrayFeatureExtractorOpIlEE N11onnxruntime2ml23ArrayFeatureExtractorOpISsEE Binarizer Input data with index: is NaN T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime2ml11BinarizerOpIfEE 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 CastMap %f stof stoll Invalid input type of value: Unexpected CAST_TO value of /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> onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = std::basic_string<char>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = long int] const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, std::basic_string<char> >] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = std::map<long int, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, float>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = float] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] onnxruntime::ml::PACK_MAP onnxruntime::ml::MakePack(const string&) onnxruntime::ml::CAST_TO onnxruntime::ml::MakeCast(const string&) onnxruntime::ml::CastMap::CastMap(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml7CastMapE CategoryMapper cats_strings cats_int64s default_string default_int64 Input of string must have output of int64 Input of int64 must have output of string /onnxruntime_src/onnxruntime/core/providers/cpu/ml/category_mapper.h info.GetAttrs<std::string>("cats_strings", string_categories).IsOK() info.GetAttrs<int64_t>("cats_int64s", int_categories).IsOK() info.GetAttr<std::string>("default_string", &default_string_).IsOK() info.GetAttr<int64_t>("default_int64", &default_int_).IsOK() num_entries == int_categories.size() T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::CategoryMapper::CategoryMapper(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml14CategoryMapperE DictVectorizer string_vocabulary int64_vocabulary /onnxruntime_src/onnxruntime/core/providers/cpu/ml/dictvectorizer.h info.GetAttrs(std::is_same<AttrType, std::string>::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK() T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, long int>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, long int>] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, float>] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, double>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, double>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, std::basic_string<char> >] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = std::map<long int, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, float>] T* onnxruntime::Tensor::MutableData() [with T = double] const T& OrtValue::Get() const [with T = std::map<long int, double>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, double>] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = double] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = float] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = std::basic_string<char>] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = double] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = float] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = long int] N11onnxruntime2ml16DictVectorizerOpISslEE N11onnxruntime2ml16DictVectorizerOpISsfEE N11onnxruntime2ml16DictVectorizerOpISsdEE N11onnxruntime2ml16DictVectorizerOpIlSsEE N11onnxruntime2ml16DictVectorizerOpIlfEE N11onnxruntime2ml16DictVectorizerOpIldEE inputdimensions FeatureVectorizer input_tensor_ptr != nullptr Invalid input type: Number of inputs ( /onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.h status.IsOK() && !input_dimensions_.empty() inputdimensions attribute must be provided /onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.cc input_count >= 0 && static_cast<size_t>(input_count) == input_dimensions_.size() ) does not match number of inputdimensions values ( const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = float] virtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml17FeatureVectorizerE imputed_value_floats imputed_value_int64s replaced_value_float replaced_value_int64 Imputer Empty input dimensions. Invalid type Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specified /onnxruntime_src/onnxruntime/core/providers/cpu/ml/imputer.cc Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specified Must provide imputed_values_float_ or imputed_values_int64_ but not both. imputed_values_float_.empty() ^ imputed_values_int64_.empty() Empty value of imputed values. T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml9ImputerOpE keys_floats values_strings _Unused keys_strings values_floats default_float keys_int64s values_int64s LabelEncoder attribtues in LabelEncoder The values is and the number of ) must have the same length. num_keys == num_values classes_strings /onnxruntime_src/onnxruntime/core/providers/cpu/ml/label_encoder.h info.GetAttrs<TKey>(_key_field_name, keys).IsOK() info.GetAttrs<TValue>(_value_field_name, values).IsOK() However, the number of key is Input of tensor(string) must have output of tensor(int64) Input of tensor(int64) must have output of tensor(string) info.GetAttrs<std::string>("classes_strings", string_classes).IsOK() gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = std::basic_string<char>] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = std::basic_string<char>] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string<char>; TValue = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = std::basic_string<char>] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = float] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string<char>; TValue = float] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = long int] onnxruntime::ml::LabelEncoder::LabelEncoder(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml12LabelEncoderE N11onnxruntime2ml14LabelEncoder_2IfSsEE N11onnxruntime2ml14LabelEncoder_2ISsfEE N11onnxruntime2ml14LabelEncoder_2IlfEE N11onnxruntime2ml14LabelEncoder_2IflEE N11onnxruntime2ml14LabelEncoder_2IlSsEE N11onnxruntime2ml14LabelEncoder_2ISslEE N11onnxruntime2ml14LabelEncoder_2IllEE multi_class NONE post_transform LOGISTIC SOFTMAX SOFTMAX_ZERO intercepts classlabels_strings classlabels_ints coefficients !coefficients_.empty() LinearClassifier shape_size == out.length() /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearclassifier.cc scores.size() == static_cast<size_t>(expected_num_scores) Unexpected value for 'add_second_class' of Scores output is incorrect size. Expected: scores_output_data.length() >= scores_output_size Input shape needs to be at least a single dimension. Unsupported input element type of const T* onnxruntime::Tensor::Data() const [with T = double] void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span<float>&) [with SrcType = double] const T* onnxruntime::Tensor::Data() const [with T = long int] void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span<float>&) [with SrcType = long int] const T* onnxruntime::Tensor::Data() const [with T = int] void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span<float>&) [with SrcType = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] void onnxruntime::ml::batched_update_scores_inplace(gsl::span<T>, int64_t, int64_t, onnxruntime::ml::POST_EVAL_TRANSFORM, int, bool, onnxruntime::concurrency::ThreadPool*) [with T = float; int64_t = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] void onnxruntime::ml::LinearClassifier::ComputeImpl(gsl::span<const float>, int64_t, int64_t, int64_t, const std::vector<float>&, const std::vector<float>&, onnxruntime::Tensor&, onnxruntime::Tensor&, onnxruntime::ml::POST_EVAL_TRANSFORM, bool, onnxruntime::concurrency::ThreadPool*) const onnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml16LinearClassifierE ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_EEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE_ ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_EEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE0_ ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_EEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE1_ @@3ֳLinearRegressor Unsupported data type of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearregressor.cc info.GetAttr<int64_t>("targets", &num_targets_).IsOK() info.GetAttrs<float>("coefficients", coefficients_).IsOK() Input shape had more than 2 dimension. Dims= T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::LinearRegressor::LinearRegressor(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml15LinearRegressorE Invalid normalize value of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.h info.GetAttr<std::string>("norm", &norm).IsOK() Rank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of const T* onnxruntime::Tensor::Data() const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::NORMALIZE onnxruntime::ml::MakeNormalize(const string&) onnxruntime::ml::Normalizer::Normalizer(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml10NormalizerE 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] ZNK11onnxruntime2ml8ScalerOpIiE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime2ml8ScalerOpIiE7ComputeEPNS_15OpKernelContextEEUllE1_ ZNK11onnxruntime2ml8ScalerOpIlE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime2ml8ScalerOpIlE7ComputeEPNS_15OpKernelContextEEUllE1_ ZNK11onnxruntime2ml8ScalerOpIdE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime2ml8ScalerOpIdE7ComputeEPNS_15OpKernelContextEEUllE1_ ZNK11onnxruntime2ml8ScalerOpIfE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime2ml8ScalerOpIfE7ComputeEPNS_15OpKernelContextEEUllE1_ N11onnxruntime2ml8ScalerOpIfEE N11onnxruntime2ml8ScalerOpIdEE N11onnxruntime2ml8ScalerOpIlEE N11onnxruntime2ml8ScalerOpIiEE SVMClassifier LINEAR kernel_type POLY RBF kernel_params vectors_per_class prob_a prob_b support_vectors rho coefficients_.size() > 0 /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.h info.GetAttrs<float>("kernel_params", kernel_params).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmclassifier.cc info.GetAttrs<float>("rho", rho_).IsOK() proba_.size() == probb_.size() info.GetAttrs<std::string>("classlabels_strings", classlabels_strings_).IsOK() || info.GetAttrs<int64_t>("classlabels_ints", classlabels_ints_).IsOK() classlabels_strings_.size() > 0 || classlabels_ints_.size() > 0 T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo&) onnxruntime::ml::SVMClassifier::SVMClassifier(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml9SVMCommonE N11onnxruntime2ml13SVMClassifierE ? 4
+ף;n_supports one_class SVMRegressor Unexpected mode: /onnxruntime_src/onnxruntime/core/providers/cpu/ml/svmregressor.cc num_features == feature_count_ gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::common::Status onnxruntime::ml::SVMRegressor<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::ml::SVMCommon::SVMCommon(const onnxruntime::OpKernelInfo&) onnxruntime::ml::SVMRegressor<T>::SVMRegressor(const onnxruntime::OpKernelInfo&) [with T = float] ZN11onnxruntime10IAllocator13MakeUniquePtrIfEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPfE_ N11onnxruntime2ml12SVMRegressorIfEE BRANCH_EQ TreeEnsembleClassifier n_targets_or_classes > 0 AVERAGE SUM MIN BRANCH_LEQ LEAF BRANCH_LT BRANCH_GTE BRANCH_GT is already there. in tree Node (truenode). Unable to find node (falsenode). (weights). classlabels_int64s class_weights class_treeids class_nodeids class_ids nodes_values nodes_truenodeids nodes_treeids nodes_nodeids nodes_modes nodes_hitrates nodes_featureids nodes_falsenodeids base_values aggregate_function predictions.size() == 2 X dims is empty. /onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_common.h nodes_falsenodeids.size() == nodes_featureids.size() nodes_falsenodeids.size() == nodes_modes.size() nodes_falsenodeids.size() == nodes_nodeids.size() nodes_falsenodeids.size() == nodes_treeids.size() nodes_falsenodeids.size() == nodes_truenodeids.size() nodes_falsenodeids.size() == nodes_values.size() target_class_ids.size() == target_class_nodeids.size() target_class_ids.size() == target_class_treeids.size() One falsenode is pointing either to itself, either to another tree. nodes_missing_value_tracks_true /onnxruntime_src/onnxruntime/core/providers/cpu/ml/tree_ensemble_aggregator.h it->i < (int64_t)predictions.size() predictions.size() == predictions2.size() classes.size() == 2 || classes.size() == 1 R[Ll&& &%l%'4%$%%$$D%/&0J/.a.0,. . .--<.8]98;87977|7 7\77 int64_t onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::_set_score_binary(int&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = float; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = float; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = float; OTYPE = float] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::ml::detail::TreeEnsembleCommonClassifier<ITYPE, OTYPE>::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = float; OTYPE = float] compute int64_t onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::_set_score_binary(int&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = double; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = double; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = double; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = double; OTYPE = float] const T* onnxruntime::Tensor::Data() const [with T = double] void onnxruntime::ml::detail::TreeEnsembleCommonClassifier<ITYPE, OTYPE>::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = double; OTYPE = float] compute int64_t onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::_set_score_binary(int&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = long int; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = long int; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = long int; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = long int; OTYPE = float] void onnxruntime::ml::detail::TreeEnsembleCommonClassifier<ITYPE, OTYPE>::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = long int; OTYPE = float] compute T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] int64_t onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::_set_score_binary(int&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = int; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorClassifier<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = int; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = int; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = int; OTYPE = float] const T* onnxruntime::Tensor::Data() const [with T = int] void onnxruntime::ml::detail::TreeEnsembleCommonClassifier<ITYPE, OTYPE>::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = int; OTYPE = float] compute const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::TreeEnsembleCommon(int, int, const string&, const std::vector<IT>&, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const std::vector<long int>&, const std::vector<std::basic_string<char> >&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const string&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&) [with ITYPE = int; OTYPE = float; std::string = std::basic_string<char>; int64_t = long int] onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::TreeEnsembleCommon(int, int, const string&, const std::vector<IT>&, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const std::vector<long int>&, const std::vector<std::basic_string<char> >&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const string&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&) [with ITYPE = long int; OTYPE = float; std::string = std::basic_string<char>; int64_t = long int] onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::TreeEnsembleCommon(int, int, const string&, const std::vector<IT>&, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const std::vector<long int>&, const std::vector<std::basic_string<char> >&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const string&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&) [with ITYPE = double; OTYPE = float; std::string = std::basic_string<char>; int64_t = long int] onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::TreeEnsembleCommon(int, int, const string&, const std::vector<IT>&, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const std::vector<long int>&, const std::vector<std::basic_string<char> >&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&, const string&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<IT>&) [with ITYPE = float; OTYPE = float; std::string = std::basic_string<char>; int64_t = long int] ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIifE10ComputeAggINS1_24TreeAggregatorClassifierIifEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIlfE10ComputeAggINS1_24TreeAggregatorClassifierIlfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_24TreeAggregatorClassifierIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_24TreeAggregatorClassifierIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ N11onnxruntime2ml22TreeEnsembleClassifierIfEE N11onnxruntime2ml22TreeEnsembleClassifierIdEE N11onnxruntime2ml22TreeEnsembleClassifierIlEE N11onnxruntime2ml22TreeEnsembleClassifierIiEE TreeEnsembleRegressor target_weights target_treeids target_nodeids target_ids n_targets this->base_values_.size() == predictions.size() predictions.size() == (size_t)n_targets_or_classes_ Unknown aggregation function in TreeEnsemble. void onnxruntime::ml::detail::TreeAggregatorMax<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeAggregator<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = float; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorMin<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorAverage<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = float; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = float; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorMax<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = double; OTYPE = float] void onnxruntime::ml::detail::TreeAggregator<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = double; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorMin<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = double; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorAverage<ITYPE, OTYPE>::FinalizeScores(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, OTYPE*, int, int64_t*) const [with ITYPE = double; OTYPE = float; int64_t = long int] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::ProcessTreeNodePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const onnxruntime::ml::detail::TreeNodeElement<OTYPE>&) const [with ITYPE = double; OTYPE = float] void onnxruntime::ml::detail::TreeAggregatorSum<ITYPE, OTYPE>::MergePrediction(std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&, const std::vector<onnxruntime::ml::detail::ScoreValue<OTYPE> >&) const [with ITYPE = double; OTYPE = float] void onnxruntime::ml::detail::TreeEnsembleCommon<ITYPE, OTYPE>::compute(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, onnxruntime::Tensor*, onnxruntime::Tensor*) const [with ITYPE = double; OTYPE = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_21TreeAggregatorAverageIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorSumIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMinIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIdfE10ComputeAggINS1_17TreeAggregatorMaxIdfEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_21TreeAggregatorAverageIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorSumIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMinIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE0_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE1_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE3_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE4_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE5_ ZNK11onnxruntime2ml6detail18TreeEnsembleCommonIffE10ComputeAggINS1_17TreeAggregatorMaxIffEEEEvPNS_11concurrency10ThreadPoolEPKNS_6TensorEPSA_SD_RKT_EUllE6_ N11onnxruntime2ml21TreeEnsembleRegressorIfEE N11onnxruntime2ml21TreeEnsembleRegressorIdEE ZipMap Input features_per_batch[ ] != number of classlabels[ Must provide classlabels_strings or classlabels_int64s but not both. /onnxruntime_src/onnxruntime/core/providers/cpu/ml/zipmap.cc classlabels_strings_.empty() ^ classlabels_int64s_.empty() Zipmap does not support empty dim count Zipmap only supports 1D or 2D input tensors T* OrtValue::GetMutable() [with T = std::vector<std::map<long int, float> >] T* OrtValue::GetMutable() [with T = std::vector<std::map<std::basic_string<char>, float> >] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::ZipMapOp::ZipMapOp(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml8ZipMapOpE MaxUnpool Unsupported pooling size. inferred output shape: No kernel shape is set. kernel_shape_[dim] > 0 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. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/unpool.h info.GetAttrs<int64_t>("kernel_shape", kernel_shape_).IsOK() pads_[dim] < kernel_shape_[dim] && pads_[dim + kernel_shape_.size()] < kernel_shape_[dim] strides_.size() == kernel_shape_.size() Pad should be smaller than kernel. T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = float] Compute onnxruntime::MaxUnpool::MaxUnpool(const onnxruntime::OpKernelInfo&) N11onnxruntime9MaxUnpoolE Invalid input scale: dimension != Invalid input B: Invalid input mean: Invalid input var: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/batch_norm.h Invalid input X: The rank of input X must be atleast 2. Got rank: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input var: NumDimensions() != Invalid input var: 0th dimension != T* onnxruntime::Tensor::MutableData() [with T = float] Compute T* onnxruntime::Tensor::MutableData() [with T = double] Compute onnxruntime::BatchNorm<T>::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::BatchNorm<T>::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime9BatchNormIfEE N11onnxruntime9BatchNormIdEE 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. Not enough elements in strides. Expected: Not enough elements in kernel shape. Expected: Not enough elements in dilations. Expected: Not enough elements in pads. Expected: Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER. ComputePad: pad type not supported. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_attributes.h /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv.cc /onnxruntime_src/onnxruntime/core/util/math.h X: C: kernel channels: group: M: kernel_shape: Invalid input shape: d_iter[d_i] < d_max Unknown AutoPadType String 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] ComputePadAndOutputShape 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 filter number not equal to input channel number. Input channels is not divisible by group. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.h /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose.cc void onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape(int64_t, int64_t, int64_t, int64_t, int64_t, onnxruntime::AutoPadType, int64_t*, int64_t*, int64_t*) const void onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(onnxruntime::TensorShape, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, int64_t, std::vector<long int>*, std::vector<long int>*) const const T* onnxruntime::Tensor::Data() const [with T = long int] PrepareForCompute DoConvTranspose N11onnxruntime13ConvTransposeIfEE ratio input should have a single value. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/dropout_op.h ratio_tensor->Shape().Size() == 1 ratio must be in the range [0, 1) 0.0f <= ratio_value && ratio_value < 1.0f !mask || mask->Shape() == X_shape X and mask should have the same shape onnxruntime::common::Status onnxruntime::Dropout<T1, T2, trainable_dropout>::Compute(onnxruntime::OpKernelContext*) const [with T1 = float; T2 = float; bool trainable_dropout = false] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] onnxruntime::common::Status onnxruntime::Dropout<T1, T2, trainable_dropout>::Compute(onnxruntime::OpKernelContext*) const [with T1 = float; T2 = double; bool trainable_dropout = false] const T* onnxruntime::Tensor::Data() const [with T = float] float onnxruntime::{anonymous}::GetRatioOrDefault(const onnxruntime::Tensor*) [with T2 = float] onnxruntime::common::Status onnxruntime::Dropout<T1, T2, trainable_dropout>::Compute(onnxruntime::OpKernelContext*) const [with T1 = double; T2 = float; bool trainable_dropout = false] const T* onnxruntime::Tensor::Data() const [with T = bool] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = bool] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] float onnxruntime::{anonymous}::GetRatioOrDefault(const onnxruntime::Tensor*) [with T2 = double] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double] onnxruntime::common::Status onnxruntime::Dropout<T1, T2, trainable_dropout>::Compute(onnxruntime::OpKernelContext*) const [with T1 = double; T2 = double; bool trainable_dropout = false] N11onnxruntime7DropoutIffLb0EEE N11onnxruntime7DropoutIfdLb0EEE N11onnxruntime7DropoutIdfLb0EEE N11onnxruntime7DropoutIddLb0EEE Flatten /onnxruntime_src/onnxruntime/core/providers/cpu/nn/flatten.h info.GetAttr<int64_t>("axis", &axis_).IsOK() The rank of input tensor must be >= axis gsl::narrow_cast<int64_t>(X_shape.NumDimensions()) >= axis onnxruntime::Flatten::Flatten(const onnxruntime::OpKernelInfo&) int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Flatten::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7FlattenE Invalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count /onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.h op_kernel_info.GetAttr<float>("epsilon", &epsilon_).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.cc vs. InstanceNormalization const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::InstanceNorm<T>::InstanceNorm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime12InstanceNormIfEE LpNormalization p_ == 1 || p_ == 2 /onnxruntime_src/onnxruntime/core/providers/cpu/nn/lp_norm.h op_kernel_info.GetAttr<int64_t>("axis", &axis_).IsOK() op_kernel_info.GetAttr<int64_t>("p", &p_).IsOK() const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] 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 N11onnxruntime8functors4PowxIfEE t@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() info.GetAttr("storage_order", &storage_order).IsOK() pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim] strides.size() == kernel_shape.size() Dilations dimensions should match kernel shape dilations.size() == kernel_shape.size() Invalid input shape. Only N can be zero. Got: input_shape.Size() > 0 || input_shape[0] == 0 /onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool.cc Not satisfied: input_dims >= 3
+ 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 Invalid input scale: dimension != Invalid input B: Invalid input mean: Invalid input var: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/batch_norm.h Invalid input X: The rank of input X must be atleast 2. Got rank: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input var: NumDimensions() != Invalid input var: 0th dimension != T* onnxruntime::Tensor::MutableData() [with T = float] Compute T* onnxruntime::Tensor::MutableData() [with T = double] Compute onnxruntime::BatchNorm<T>::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::BatchNorm<T>::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime9BatchNormIfEE N11onnxruntime9BatchNormIdEE kernel_shape: Invalid input shape: X: C: kernel channels: group: M: Unknown AutoPadType String kernel_shape num_dims is not compatible with W num_dims. kernel_shape is not compatible with W shape. Not enough elements in strides. Expected: Not enough elements in kernel shape. Expected: Not enough elements in dilations. Expected: Not enough elements in pads. Expected: Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER. ComputePad: pad type not supported. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_attributes.h X num_dims does not match W num_dims. Input channels C is not equal to kernel channels * group. Output channels M is not divisible by group. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv.cc onnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&) const T* onnxruntime::Tensor::Data() const [with T = float] ComputePadAndOutputShape InferOutputShape Compute N11onnxruntime4ConvIfEE ConvInteger /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_integer.cc IsScalarOr1ElementVector(X_Zero_Point) IsScalarOr1ElementVector(W_Zero_Point) Must be a scalar or 1D tensor or size 1. Non per-tensor quantization is not supported now. Compute virtual onnxruntime::common::Status onnxruntime::ConvInteger::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime11ConvIntegerE ConvTranspose output_padding output_shape group count is <= 0 filter_number: num_input_channels: *out_size >= 0 pad type not supported dim_size > 0 filter number not equal to input channel number. Input channels is not divisible by group. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.h /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose.cc void onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape(int64_t, int64_t, int64_t, int64_t, int64_t, onnxruntime::AutoPadType, int64_t*, int64_t*, int64_t*) const void onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(onnxruntime::TensorShape, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, int64_t, std::vector<long int>*, std::vector<long int>*) const const T* onnxruntime::Tensor::Data() const [with T = long int] PrepareForCompute DoConvTranspose N11onnxruntime13ConvTransposeIfEE ratio input should have a single value. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/dropout_op.h ratio_tensor->Shape().Size() == 1 ratio must be in the range [0, 1) 0.0f <= ratio_value && ratio_value < 1.0f !mask || mask->Shape() == X_shape X and mask should have the same shape onnxruntime::common::Status onnxruntime::Dropout<T1, T2, trainable_dropout>::Compute(onnxruntime::OpKernelContext*) const [with T1 = float; T2 = float; bool trainable_dropout = false] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] onnxruntime::common::Status onnxruntime::Dropout<T1, T2, trainable_dropout>::Compute(onnxruntime::OpKernelContext*) const [with T1 = float; T2 = double; bool trainable_dropout = false] const T* onnxruntime::Tensor::Data() const [with T = float] float onnxruntime::{anonymous}::GetRatioOrDefault(const onnxruntime::Tensor*) [with T2 = float] onnxruntime::common::Status onnxruntime::Dropout<T1, T2, trainable_dropout>::Compute(onnxruntime::OpKernelContext*) const [with T1 = double; T2 = float; bool trainable_dropout = false] const T* onnxruntime::Tensor::Data() const [with T = bool] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = bool] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] float onnxruntime::{anonymous}::GetRatioOrDefault(const onnxruntime::Tensor*) [with T2 = double] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double] onnxruntime::common::Status onnxruntime::Dropout<T1, T2, trainable_dropout>::Compute(onnxruntime::OpKernelContext*) const [with T1 = double; T2 = double; bool trainable_dropout = false] N11onnxruntime7DropoutIffLb0EEE N11onnxruntime7DropoutIfdLb0EEE N11onnxruntime7DropoutIdfLb0EEE N11onnxruntime7DropoutIddLb0EEE Flatten /onnxruntime_src/onnxruntime/core/providers/cpu/nn/flatten.h info.GetAttr<int64_t>("axis", &axis_).IsOK() The rank of input tensor must be >= axis gsl::narrow_cast<int64_t>(X_shape.NumDimensions()) >= axis onnxruntime::Flatten::Flatten(const onnxruntime::OpKernelInfo&) int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Flatten::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7FlattenE Invalid input data: number of dimensions is less than 3: Invalid input scale: number of dimensions is not 1: Mismatch between input data and scale: size of scale != input channel count Invalid input B: number of dimensions is not 1: Mismatch between input data and B: size of B != input channel count /onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.h op_kernel_info.GetAttr<float>("epsilon", &epsilon_).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/nn/instance_norm.cc vs. InstanceNormalization const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::InstanceNorm<T>::InstanceNorm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime12InstanceNormIfEE LpNormalization p_ == 1 || p_ == 2 /onnxruntime_src/onnxruntime/core/providers/cpu/nn/lp_norm.h op_kernel_info.GetAttr<int64_t>("axis", &axis_).IsOK() op_kernel_info.GetAttr<int64_t>("p", &p_).IsOK() const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] 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 N11onnxruntime8functors4PowxIfEE t@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() info.GetAttr("storage_order", &storage_order).IsOK() pads[dim] < kernel_shape[dim] && pads[dim + kernel_shape.size()] < kernel_shape[dim] strides.size() == kernel_shape.size() Dilations dimensions should match kernel shape dilations.size() == kernel_shape.size() Invalid input shape. Only N can be zero. Got: input_shape.Size() > 0 || input_shape[0] == 0 /onnxruntime_src/onnxruntime/core/providers/cpu/nn/pool.cc 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 = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] ComputeImpl T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] ComputeImpl T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] ComputeImpl ComputeImpl void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) const void onnxruntime::PoolAttributes::InferOutputSize(const std::vector<long int>&, std::vector<long int>*, std::vector<long int>*) const std::vector<long int> onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, std::vector<long int>*) const Compute N11onnxruntime8PoolBaseE N11onnxruntime10Pool1DTaskIfNS_6LpPoolEEE N11onnxruntime10Pool2DTaskIfNS_6LpPoolEEE N11onnxruntime10Pool3DTaskIfNS_6LpPoolEEE N11onnxruntime9MaxPoolV8E N11onnxruntime4PoolIfNS_7MaxPoolILi1EEEEE N11onnxruntime4PoolIfNS_11AveragePoolEEE N11onnxruntime4PoolIfNS_6LpPoolEEE N11onnxruntime13MaxPool1DTaskIfEE N11onnxruntime13MaxPool2DTaskIfEE N11onnxruntime13MaxPool3DTaskIfEE N11onnxruntime13MaxPool1DTaskIdEE N11onnxruntime13MaxPool2DTaskIdEE N11onnxruntime13MaxPool3DTaskIdEE N11onnxruntime13MaxPool1DTaskIaEE N11onnxruntime13MaxPool2DTaskIaEE N11onnxruntime13MaxPool3DTaskIaEE N11onnxruntime13MaxPool1DTaskIhEE N11onnxruntime13MaxPool2DTaskIhEE N11onnxruntime13MaxPool3DTaskIhEE QLinearConv : input zero point must be a scalar or 1D tensor of size 1 /onnxruntime_src/onnxruntime/core/providers/cpu/nn/qlinearconv.cc IsScalarOr1ElementVector(X_zero_point) QLinearConv : filter zero point must be a scalar or 1D tensor of size 1 IsScalarOr1ElementVector(W_zero_point) QLinearConv : result zero point must be a scalar or 1D tensor of size 1 IsScalarOr1ElementVector(Y_zero_point) QLinearConv : input scale must be a scalar or 1D tensor of size 1 IsScalarOr1ElementVector(X_scale) QLinearConv : filter scale must be a scalar or 1D tensor of size 1 IsScalarOr1ElementVector(W_scale) QLinearConv : result scale must be a scalar or 1D tensor of size 1 IsScalarOr1ElementVector(Y_scale) T4 QLinearConv 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 /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() Shrink lambd 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 Failed to construct locale with name: :Please, install necessary language-pack-XX and configure locales /onnxruntime_src/onnxruntime/core/providers/cpu/nn/string_normalizer.cc attribute is_case_sensitive is not set attribute case_change_action is not set attribute case_change_action has invalid value Duplicate stopwords not allowed Stopword contains invalid utf8 chars Input contains invalid utf8 chars at: Single dimension value must be greater than 0 Input dimensions are either[C > 0] or [1][C > 0] allowed C o n v e r s i o n E r r o r const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::string_normalizer::Locale::Locale(const string&)::__lambda8 onnxruntime::StringNormalizer::StringNormalizer(const onnxruntime::OpKernelInfo&) N11onnxruntime16StringNormalizerE TfIdfVectorizer id: p.first->second->id_ == 0 mode is required TF TFIDF mode: min_gram_length min_gram_length is required impl_->min_gram_length_ > 0 max_gram_length >= max_skip_count max_skip_count is required impl_->max_skip_count_ >= 0 ngram_counts <= ngram_indexes must be of equal size but ngram_indexes size: Got weights of size: pool_strings !impl_->pool_strings_.empty() pool_int64s -grams (items % ngram_size == 0) Input shape must have either [C] or [B,C] dimensions with B > 0. Duplicate ngram detected, size: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/tfidfvectorizer.cc is unrecognized, acceptable values are TF,IDF,TFIDF impl_->weighting_criteria_ != kNone Required min_gram_length must be positive: min_gram_length >= max_gram_length required: impl_->max_gram_length_ >= impl_->min_gram_length_ max_skip_count must be non-negative: status.IsOK() && !impl_->ngram_counts_.empty() min_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size() max_gram_length must be inbounds of ngram_counts: size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size() status.IsOK() && !impl_->ngram_indexes_.empty() Negative ngram_indexes values are not allowed std::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; }) impl_->weights_.size() == impl_->ngram_indexes_.size() pool_strings must not be empty if specified status.IsOK() && !pool_int64s.empty() n-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_items Number of items must compose whole Non-empty ngram_counts is required Non-empty ngram_indexes is required non-empty pool_int64s is required if pool_strings not provided const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = float] size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map&) [with K = std::basic_string<char>; ForwardIter = __gnu_cxx::__normal_iterator<std::basic_string<char>*, std::vector<std::basic_string<char> > >; Map = std::unordered_map<std::reference_wrapper<const std::basic_string<char> >, std::unique_ptr<onnxruntime::ngram_details::NgramPart<std::basic_string<char> > >, std::hash<std::basic_string<char> >, std::equal_to<std::basic_string<char> > >; size_t = long unsigned int] size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map&) [with K = long int; ForwardIter = __gnu_cxx::__normal_iterator<long int*, std::vector<long int> >; Map = std::unordered_map<long int, std::unique_ptr<onnxruntime::ngram_details::NgramPart<long int> > >; size_t = long unsigned int] onnxruntime::TfIdfVectorizer::TfIdfVectorizer(const onnxruntime::OpKernelInfo&) *ZNK11onnxruntime15TfIdfVectorizer7ComputeEPNS_15OpKernelContextEEUllE_ N11onnxruntime15TfIdfVectorizerE 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()
+ Compute void onnxruntime::PoolProcessContext::init(const onnxruntime::OpKernelInfo&) onnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&) onnxruntime::PoolAttributes::PoolAttributes(const onnxruntime::OpNodeProtoHelper<onnxruntime::ProtoHelperNodeContext>&, const string&, int) T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] ComputeImpl T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] ComputeImpl T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] ComputeImpl ComputeImpl void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) const void onnxruntime::PoolAttributes::InferOutputSize(const std::vector<long int>&, std::vector<long int>*, std::vector<long int>*) const std::vector<long int> onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, std::vector<long int>*) const Compute N11onnxruntime8PoolBaseE N11onnxruntime10Pool1DTaskIfNS_6LpPoolEEE N11onnxruntime10Pool2DTaskIfNS_6LpPoolEEE N11onnxruntime10Pool3DTaskIfNS_6LpPoolEEE N11onnxruntime9MaxPoolV8E N11onnxruntime4PoolIfNS_7MaxPoolILi1EEEEE N11onnxruntime4PoolIfNS_11AveragePoolEEE N11onnxruntime4PoolIfNS_6LpPoolEEE N11onnxruntime13MaxPool1DTaskIfEE N11onnxruntime13MaxPool2DTaskIfEE N11onnxruntime13MaxPool3DTaskIfEE N11onnxruntime13MaxPool1DTaskIdEE N11onnxruntime13MaxPool2DTaskIdEE N11onnxruntime13MaxPool3DTaskIdEE N11onnxruntime13MaxPool1DTaskIaEE N11onnxruntime13MaxPool2DTaskIaEE N11onnxruntime13MaxPool3DTaskIaEE N11onnxruntime13MaxPool1DTaskIhEE N11onnxruntime13MaxPool2DTaskIhEE N11onnxruntime13MaxPool3DTaskIhEE /onnxruntime_src/onnxruntime/core/providers/cpu/nn/qlinearconv.cc IsScalarOr1ElementVector(X_zero_point) IsScalarOr1ElementVector(Y_zero_point) QLinearConv : filter zero point must be constant IsScalarOr1ElementVector(X_scale) IsScalarOr1ElementVector(Y_scale) QLinearConv : input zero point must be a scalar or 1D tensor of size 1 QLinearConv : result zero point must be a scalar or 1D tensor of size 1 QLinearConv : filter zero point shape invalid QLinearConv : input scale must be a scalar or 1D tensor of size 1 QLinearConv : result scale must be a scalar or 1D tensor of size 1 QLinearConv : filter scale shape invalid T4 const T* onnxruntime::Tensor::Data() const [with T = unsigned char] Compute virtual onnxruntime::common::Status onnxruntime::QLinearConv::Compute(onnxruntime::OpKernelContext*) const *ZNK11onnxruntime11QLinearConv7ComputeEPNS_15OpKernelContextEEUllE_ N11onnxruntime11QLinearConvE 0A >pooled_shape pooled_shape.size() == 2 pooled_height_ > 0 pooled_width_ > 0 spatial_scale spatial_scale_ > 0 MaxRoiPool R->Shape()[1] == 5 roi_batch_id >= 0 roi_batch_id < batch_size /onnxruntime_src/onnxruntime/core/providers/cpu/nn/roi_pool.h info.GetAttrs<int64_t>("pooled_shape", pooled_shape).IsOK() info.GetAttr<float>("spatial_scale", &spatial_scale_).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/nn/roi_pool.cc T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::RoiPool<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::RoiPool<T>::RoiPool(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7RoiPoolIfEE lambd Shrink /onnxruntime_src/onnxruntime/core/providers/cpu/nn/shrink.h op_kernel_info.GetAttr<float>("bias", &bias_temp).IsOK() op_kernel_info.GetAttr<float>("lambd", &lambd_temp).IsOK() 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 Failed to construct locale with name: :Please, install necessary language-pack-XX and configure locales /onnxruntime_src/onnxruntime/core/providers/cpu/nn/string_normalizer.cc attribute is_case_sensitive is not set attribute case_change_action is not set attribute case_change_action has invalid value Duplicate stopwords not allowed Stopword contains invalid utf8 chars Input contains invalid utf8 chars at: Single dimension value must be greater than 0 Input dimensions are either[C > 0] or [1][C > 0] allowed C o n v e r s i o n E r r o r const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::string_normalizer::Locale::Locale(const string&)::__lambda10 onnxruntime::StringNormalizer::StringNormalizer(const onnxruntime::OpKernelInfo&) N11onnxruntime16StringNormalizerE TfIdfVectorizer id: p.first->second->id_ == 0 mode is required TF TFIDF mode: min_gram_length min_gram_length is required impl_->min_gram_length_ > 0 max_gram_length >= max_skip_count max_skip_count is required impl_->max_skip_count_ >= 0 ngram_counts <= ngram_indexes must be of equal size but ngram_indexes size: Got weights of size: pool_strings !pool_strings.empty() pool_int64s -grams (items % ngram_size == 0) Input shape must have either [C] or [B,C] dimensions with B > 0. Duplicate ngram detected, size: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/tfidfvectorizer.cc is unrecognized, acceptable values are TF,IDF,TFIDF impl_->weighting_criteria_ != kNone Required min_gram_length must be positive: min_gram_length >= max_gram_length required: impl_->max_gram_length_ >= impl_->min_gram_length_ max_skip_count must be non-negative: status.IsOK() && !impl_->ngram_counts_.empty() min_gram_length must be inbounds of ngram_counts: size_t(impl_->min_gram_length_) <= impl_->ngram_counts_.size() max_gram_length must be inbounds of ngram_counts: size_t(impl_->max_gram_length_) <= impl_->ngram_counts_.size() status.IsOK() && !impl_->ngram_indexes_.empty() Negative ngram_indexes values are not allowed std::all_of(impl_->ngram_indexes_.cbegin(), impl_->ngram_indexes_.cend(), [](int64_t i) { return i >= 0; }) impl_->weights_.size() == impl_->ngram_indexes_.size() pool_strings must not be empty if specified status.IsOK() && !pool_int64s.empty() n-gram counts out of bounds for end_idx >= start_idx && end_idx <= total_items Number of items must compose whole Non-empty ngram_counts is required Non-empty ngram_indexes is required non-empty pool_int64s is required if pool_strings not provided const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = float] size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map&) [with K = std::basic_string<char>; ForwardIter = __gnu_cxx::__normal_iterator<std::reference_wrapper<const std::basic_string<char> >*, std::vector<std::reference_wrapper<const std::basic_string<char> > > >; Map = std::unordered_map<std::reference_wrapper<const std::basic_string<char> >, std::unique_ptr<onnxruntime::ngram_details::NgramPart<std::basic_string<char> > >, std::hash<std::basic_string<char> >, std::equal_to<std::basic_string<char> > >; size_t = long unsigned int] size_t onnxruntime::ngram_details::PopulateGrams(ForwardIter, size_t, size_t, size_t, Map&) [with K = long int; ForwardIter = const long int*; Map = std::unordered_map<long int, std::unique_ptr<onnxruntime::ngram_details::NgramPart<long int> > >; size_t = long unsigned int] onnxruntime::TfIdfVectorizer::TfIdfVectorizer(const onnxruntime::OpKernelInfo&) *ZNK11onnxruntime15TfIdfVectorizer7ComputeEPNS_15OpKernelContextEEUllE_ N11onnxruntime15TfIdfVectorizerE 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
- T* onnxruntime::Tensor::MutableData() [with T = long int] virtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const Compute GetThresholdsFromInputs const T* onnxruntime::Tensor::Data() const [with T = float] const T& 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 Invalid mode of value output_height output_width sampling_ratio sampling_ratio_ >= 0 Number of dimensions for batch indices should be exactly 1 Number of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't match specified. It should be either avg or max /onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/roialign.h Sampling ratio should be >=0, but it was const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::RoiAlignBase::RoiAlignBase(const onnxruntime::OpKernelInfo&) *ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIdEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUlllE_ *ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIfEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUlllE_ N11onnxruntime12RoiAlignBaseE N11onnxruntime8RoiAlignIfEE N11onnxruntime8RoiAlignIdEE ReduceL1 ReduceL2 ReduceLogSum ReduceLogSumExp ReduceMax ReduceMin ReduceProd ReduceSum ReduceSumSquare ArgMax ArgMin keepdims noop_with_empty_axes select_last_index Input to be reduced is null in_dim != 0 /onnxruntime_src/onnxruntime/core/providers/cpu/containers.h /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 The input shape override's size does not match the input tensor's shape size input_tensor_ptr->Shape().Size() == input_shape_override->Size() Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape: bool onnxruntime::PrepareForReduce(const onnxruntime::Tensor*, onnxruntime::FastAllocVector<T>&, int64_t&, int64_t&, const std::vector<long int>&, bool, std::vector<long int>&, bool, const onnxruntime::TensorShape*) [with T = signed char; onnxruntime::FastAllocVector<T> = std::vector<signed char, onnxruntime::OrtStlAllocator<signed char> >; int64_t = long int] onnxruntime::OrtStlAllocator<T1> onnxruntime::GetAllocator(const onnxruntime::OpKernelContext&) [with T = signed char] bool onnxruntime::PrepareForReduce(const onnxruntime::Tensor*, onnxruntime::FastAllocVector<T>&, int64_t&, int64_t&, const std::vector<long int>&, bool, std::vector<long int>&, bool, const onnxruntime::TensorShape*) [with T = unsigned char; onnxruntime::FastAllocVector<T> = std::vector<unsigned char, onnxruntime::OrtStlAllocator<unsigned char> >; int64_t = long int] onnxruntime::OrtStlAllocator<T1> onnxruntime::GetAllocator(const onnxruntime::OpKernelContext&) [with T = unsigned char] bool onnxruntime::PrepareForReduce(const onnxruntime::Tensor*, onnxruntime::FastAllocVector<T>&, int64_t&, int64_t&, const std::vector<long int>&, bool, std::vector<long int>&, bool, const onnxruntime::TensorShape*) [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(const onnxruntime::Tensor*, onnxruntime::FastAllocVector<T>&, int64_t&, int64_t&, const std::vector<long int>&, bool, std::vector<long int>&, bool, const onnxruntime::TensorShape*) [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(const onnxruntime::Tensor*, onnxruntime::FastAllocVector<T>&, int64_t&, int64_t&, const std::vector<long int>&, bool, std::vector<long int>&, bool, const onnxruntime::TensorShape*) [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] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) bool onnxruntime::PrepareForReduce(const onnxruntime::Tensor*, onnxruntime::FastAllocVector<T>&, int64_t&, int64_t&, const std::vector<long int>&, bool, std::vector<long int>&, bool, const onnxruntime::TensorShape*) [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] 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 N11onnxruntime9ReduceMaxIaEE N11onnxruntime9ReduceMaxIhEE N11onnxruntime10ReduceMeanIfEE N11onnxruntime10ReduceMeanIiEE N11onnxruntime9ReduceMinIfEE N11onnxruntime9ReduceMinIiEE N11onnxruntime9ReduceMinIlEE N11onnxruntime9ReduceMinIaEE N11onnxruntime9ReduceMinIhEE N11onnxruntime10ReduceProdIfEE N11onnxruntime10ReduceProdIiEE N11onnxruntime10ReduceProdIlEE N11onnxruntime9ReduceSumIfEE N11onnxruntime9ReduceSumIiEE N11onnxruntime9ReduceSumIlEE N11onnxruntime9ReduceSumIdEE N11onnxruntime15ReduceSumSquareIfEE N11onnxruntime15ReduceSumSquareIiEE N11onnxruntime15ReduceSumSquareIdEE N11onnxruntime6ArgMaxIfEE N11onnxruntime6ArgMaxIiEE N11onnxruntime6ArgMaxIdEE 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 '. Must be one of 'forward', 'reverse', or 'bidirectional'. Invalid 'direction' argument of ' /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.h activation_func_names.size() == static_cast<size_t>(num_directions_) * 2 lda >= K && ldb >= K && ldc >= N A + (M * lda - (lda - K)) <= A_end B + (N * ldb - (ldb - K)) <= B_end C + (M * ldc - (ldc - N)) <= C_end offset + size <= size_t(span.size()) /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.cc GRU operator does not support double yet Invalid data type for GRU operator of T* onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span<T>::iterator, typename gsl::span<T>::iterator, size_t) [with T = float; typename gsl::span<T>::iterator = float*; size_t = long unsigned int] const T* onnxruntime::rnn::detail::SafeRawConstPointer(typename gsl::span<T>::const_iterator, typename gsl::span<T>::const_iterator, size_t) [with T = float; typename gsl::span<T>::const_iterator = const float*; size_t = long unsigned int] T* onnxruntime::rnn::detail::SafeRawPointer(gsl::span<T>, size_t, size_t) [with T = float; size_t = long unsigned int] void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = float*; TSpanBIter = const float*; TSpanCIter = float*] void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = const float*; TSpanBIter = const float*; TSpanCIter = float*] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] ComputeImpl virtual onnxruntime::common::Status onnxruntime::DeepCpuGruOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&) onnxruntime::DeepCpuGruOp::DeepCpuGruOp(const onnxruntime::OpKernelInfo&) N11onnxruntime12DeepCpuGruOpE ZN11onnxruntime10IAllocator13MakeUniquePtrIiEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPiE_ }. Actual: Input P must have shape { rows[ input_forget Input initial_c must have shape { /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.cc LSTM operator does not support double yet Invalid data type for LSTM operator of /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.h activation_func_names.size() == static_cast<size_t>(num_directions_) * 3 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&) *ZN11onnxruntimeL23ExecuteLambdaInParallelIZNS_6detail18UniDirectionalLstmIfE7ComputeERKN3gsl4spanIKfEERKNS5_IKiEEiS9_S9_RNS5_IfEESF_SF_EUliE_EEvT_iidPNS_11concurrency10ThreadPoolEEUlllE_ N11onnxruntime13DeepCpuLstmOpE /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.cc /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.h info.GetAttr("direction", &direction_).IsOK() allowed_directions.find(direction_) != allowed_directions.end() info.GetAttrs("activations", activations_).IsOK() info.GetAttr("hidden_size", &hidden_size_).IsOK() activations_.size() == static_cast<size_t>(num_directions) RNN op: Invalid activation attribute - allowed_activations.find(activations_[direction]) != allowed_activations.end() RNN Affine 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 B must have shape { * Input W must have shape { Input R 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: Input sequence_lens must have shape { Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length= Input initial_h must have shape { 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] %%%%~3a*~3a*~3a*~3a*7.7.7.7.A \3A \3A \3A \3JCy7JCy7JCy7JCy7
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':Y;Ӡ5֕8;Y; 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 Y != nullptr Unsupported 'dtype' value: SequenceLength SequenceConstruct SequenceInsert SequenceErase SequenceAt SplitToSequence SequenceEmpty I != nullptr Invalid sequence index ( split_size_sum ( ) != split_dim_size ( S != nullptr Got nullptr for input tensor. ), input tensor data type ( num_inputs >= 1 Split should be > 0 split_scalar > 0 SequenceEmpty: Got nullptr for output sequence /onnxruntime_src/onnxruntime/core/providers/cpu/sequence/sequence_ops.cc 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 < , | l < < 4 int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = int] ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = double] ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] void onnxruntime::GetSplitSizesInput(const onnxruntime::Tensor&, std::vector<long int>&) int64_t onnxruntime::GetScalarSplitInput(const onnxruntime::Tensor&) ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = float] virtual onnxruntime::common::Status onnxruntime::SplitToSequence::Compute(onnxruntime::OpKernelContext*) const virtual onnxruntime::common::Status onnxruntime::SequenceConstruct::Compute(onnxruntime::OpKernelContext*) const virtual onnxruntime::common::Status onnxruntime::SequenceErase::Compute(onnxruntime::OpKernelContext*) const virtual onnxruntime::common::Status onnxruntime::SequenceInsert::Compute(onnxruntime::OpKernelContext*) const CreateCopyAndAppendCpuTensor void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType) T* OrtValue::GetMutable() [with T = onnxruntime::TensorSeq] virtual onnxruntime::common::Status onnxruntime::SequenceEmpty::Compute(onnxruntime::OpKernelContext*) const const onnxruntime::Tensor& onnxruntime::TensorSeq::Get(size_t) const int64_t onnxruntime::GetSeqIdx(const onnxruntime::Tensor&) virtual onnxruntime::common::Status onnxruntime::SequenceAt::Compute(onnxruntime::OpKernelContext*) const T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq] virtual onnxruntime::common::Status onnxruntime::SequenceLength::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime14SequenceLengthE N11onnxruntime10SequenceAtE N11onnxruntime13SequenceEmptyE N11onnxruntime14SequenceInsertE N11onnxruntime13SequenceEraseE N11onnxruntime17SequenceConstructE N11onnxruntime15SplitToSequenceE Attribute to is not set. -INF stod stoi stoul stol stoull /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/cast_op.cc Unsupported type in cast op: from String to void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = bool; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = long unsigned int; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = long int; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = unsigned int; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = int; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = short unsigned int; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = short int; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = unsigned char; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = signed char; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = double; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = float; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {int&, const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; Fn = onnxruntime::Cast::SrcDispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] void onnxruntime::{anonymous}::CastFromStringData(const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&) [with DstType = bool] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = bool] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char] void onnxruntime::{anonymous}::CastFromStringData(const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&) [with DstType = onnxruntime::MLFloat16] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::MLFloat16] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {bool&, const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; Fn = onnxruntime::Cast::StringDispatcher; Types = {float, double, onnxruntime::MLFloat16, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo&) N11onnxruntime4CastE N11onnxruntime9MLFloat16E 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
+ T* onnxruntime::Tensor::MutableData() [with T = long int] virtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const Compute GetThresholdsFromInputs const T* onnxruntime::Tensor::Data() const [with T = float] const T& 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 Invalid mode of value output_height output_width sampling_ratio sampling_ratio_ >= 0 Number of dimensions for batch indices should be exactly 1 Number of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't match specified. It should be either avg or max /onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/roialign.h Sampling ratio should be >=0, but it was const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::RoiAlignBase::RoiAlignBase(const onnxruntime::OpKernelInfo&) *ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIdEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUlllE_ *ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIfEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUlllE_ N11onnxruntime12RoiAlignBaseE N11onnxruntime8RoiAlignIfEE N11onnxruntime8RoiAlignIdEE ReduceL1 ReduceL2 ReduceLogSum ReduceLogSumExp ReduceMin ReduceProd ReduceSumSquare ArgMax ArgMin keepdims noop_with_empty_axes select_last_index count == 1 Input to be reduced is null Axes input is null axes_tensor != nullptr keepdims_ keep_dims /onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.h info.GetAttr("keepdims", &keepdims).IsOK() Reduction on all axes, output size should be 1. /onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.cc last_results.last_loop_red_size > 0 last_results.last_loop_size > 0 last_results.projected_index.size() > 0 The input shape override's size does not match the input tensor's shape size input_tensor_ptr->Shape().Size() == input_shape_override->Size() An axes tensor must be a vector tensor. axes_tensor->Shape().NumDimensions() == 1 Can't reduce on dim with value of 0 if 'keepdims' is false. Invalid output shape would be produced. input_shape: void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorL1<float, float>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorL1<float, float>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorL1<int, int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorL1<int, int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorL2<float, float>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorL2<float, float>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorL2<int, int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorL2<int, int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorLogSum<float, float>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorLogSum<float, float>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorLogSum<int, int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorLogSum<int, int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorLogSumExp<float, float>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorLogSumExp<float, float>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorLogSumExp<int, int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorLogSumExp<int, int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorMax<float, float>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorMax<float, float>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorMax<int, int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorMax<int, int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = long int; AGG = onnxruntime::ReduceAggregatorMax<long int, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = long int; AGG = onnxruntime::ReduceAggregatorMax<long int, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = signed char; AGG = onnxruntime::ReduceAggregatorMax<signed char, signed char>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = signed char; AGG = onnxruntime::ReduceAggregatorMax<signed char, signed char>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = unsigned char; AGG = onnxruntime::ReduceAggregatorMax<unsigned char, unsigned char>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = unsigned char; AGG = onnxruntime::ReduceAggregatorMax<unsigned char, unsigned char>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorMean<float, float>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorMean<float, float>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorMean<int, int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorMean<int, int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorMin<float, float>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorMin<float, float>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorMin<int, int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorMin<int, int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = long int; AGG = onnxruntime::ReduceAggregatorMin<long int, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = long int; AGG = onnxruntime::ReduceAggregatorMin<long int, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = signed char; AGG = onnxruntime::ReduceAggregatorMin<signed char, signed char>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = signed char; AGG = onnxruntime::ReduceAggregatorMin<signed char, signed char>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = unsigned char; AGG = onnxruntime::ReduceAggregatorMin<unsigned char, unsigned char>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = unsigned char; AGG = onnxruntime::ReduceAggregatorMin<unsigned char, unsigned char>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorProd<float, float>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorProd<float, float>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorProd<int, int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorProd<int, int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = long int; AGG = onnxruntime::ReduceAggregatorProd<long int, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = long int; AGG = onnxruntime::ReduceAggregatorProd<long int, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorSumSquare<float, float>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorSumSquare<float, float>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorSumSquare<int, int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorSumSquare<int, int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = double; AGG = onnxruntime::ReduceAggregatorSumSquare<double, double>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = double; AGG = onnxruntime::ReduceAggregatorSumSquare<double, double>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorArgMax<float, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorArgMax<float, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex<float, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex<float, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorArgMax<int, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorArgMax<int, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex<int, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex<int, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = double; AGG = onnxruntime::ReduceAggregatorArgMax<double, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = double; AGG = onnxruntime::ReduceAggregatorArgMax<double, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = double; AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex<double, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = double; AGG = onnxruntime::ReduceAggregatorArgMaxLastIndex<double, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorArgMin<float, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorArgMin<float, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorArgMinLastIndex<float, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorArgMinLastIndex<float, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorArgMin<int, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorArgMin<int, long int>; int64_t = long int] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorArgMinLastIndex<int, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorArgMinLastIndex<int, long int>; int64_t = long int] static onnxruntime::Tensor onnxruntime::ReduceSum<T>::Impl(const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, bool, const onnxruntime::TensorShape*) [with T = long int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = long int; AGG = onnxruntime::ReduceAggregatorSum<long int, long int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = long int; AGG = onnxruntime::ReduceAggregatorSum<long int, long int>; int64_t = long int] static onnxruntime::Tensor onnxruntime::ReduceSum<T>::Impl(const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, bool, const onnxruntime::TensorShape*) [with T = double; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = double; AGG = onnxruntime::ReduceAggregatorSum<double, double>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = double; AGG = onnxruntime::ReduceAggregatorSum<double, double>; int64_t = long int] static onnxruntime::Tensor onnxruntime::ReduceSum<T>::Impl(const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, bool, const onnxruntime::TensorShape*) [with T = int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = int; AGG = onnxruntime::ReduceAggregatorSum<int, int>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = int; AGG = onnxruntime::ReduceAggregatorSum<int, int>; int64_t = long int] static onnxruntime::Tensor onnxruntime::ReduceSum<T>::Impl(const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*, bool, const onnxruntime::TensorShape*) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] void onnxruntime::NoTransposeReduce(onnxruntime::Tensor*, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, onnxruntime::ResultsNoTransposePrepareForReduce&) [with T = float; AGG = onnxruntime::ReduceAggregatorSum<float, float>] void onnxruntime::CommonReduce(onnxruntime::OpKernelContext*, std::vector<long int>, int64_t, onnxruntime::ResultsNoTransposePrepareForReduce&, bool) [with T = float; AGG = onnxruntime::ReduceAggregatorSum<float, float>; int64_t = long int] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) bool onnxruntime::SetupForReduce(const onnxruntime::Tensor*, const std::vector<long int>&, std::vector<long int>&, onnxruntime::TensorShape&, std::vector<long int>&, bool&, const onnxruntime::TensorShape*) onnxruntime::ReduceKernelBase<allow_multi_axes>::ReduceKernelBase(const onnxruntime::OpKernelInfo&, nonstd::optional_lite::optional<long int>) [with bool allow_multi_axes = false] onnxruntime::ReduceKernelBase<allow_multi_axes>::ReduceKernelBase(const onnxruntime::OpKernelInfo&, nonstd::optional_lite::optional<long int>) [with bool allow_multi_axes = true] N11onnxruntime16ReduceKernelBaseILb1EEE N11onnxruntime16ReduceKernelBaseILb0EEE ZN11onnxruntime17NoTransposeReduceIiNS_31ReduceAggregatorArgMinLastIndexIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_22ReduceAggregatorArgMinIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_31ReduceAggregatorArgMinLastIndexIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_22ReduceAggregatorArgMinIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIdNS_31ReduceAggregatorArgMaxLastIndexIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIdNS_22ReduceAggregatorArgMaxIdlEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_31ReduceAggregatorArgMaxLastIndexIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_22ReduceAggregatorArgMaxIilEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_31ReduceAggregatorArgMaxLastIndexIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_22ReduceAggregatorArgMaxIflEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIdNS_25ReduceAggregatorSumSquareIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_25ReduceAggregatorSumSquareIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_25ReduceAggregatorSumSquareIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIlNS_20ReduceAggregatorProdIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_20ReduceAggregatorProdIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_20ReduceAggregatorProdIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIhNS_19ReduceAggregatorMinIhhEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIaNS_19ReduceAggregatorMinIaaEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIlNS_19ReduceAggregatorMinIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_19ReduceAggregatorMinIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_19ReduceAggregatorMinIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_20ReduceAggregatorMeanIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_20ReduceAggregatorMeanIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIhNS_19ReduceAggregatorMaxIhhEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIaNS_19ReduceAggregatorMaxIaaEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIlNS_19ReduceAggregatorMaxIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_19ReduceAggregatorMaxIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_19ReduceAggregatorMaxIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_25ReduceAggregatorLogSumExpIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime17NoTransposeReduceIfNS_25ReduceAggregatorLogSumExpIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE_ ZN11onnxruntime17NoTransposeReduceIiNS_22ReduceAggregatorLogSumIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_22ReduceAggregatorLogSumIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_18ReduceAggregatorL2IiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_18ReduceAggregatorL2IffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_18ReduceAggregatorL1IiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIfNS_18ReduceAggregatorL1IffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ N11onnxruntime12ReduceKernelILb1EEE N11onnxruntime12ReduceKernelILb0EEE N11onnxruntime8ReduceL1IfEE N11onnxruntime8ReduceL1IiEE N11onnxruntime8ReduceL2IfEE N11onnxruntime8ReduceL2IiEE N11onnxruntime12ReduceLogSumIfEE N11onnxruntime12ReduceLogSumIiEE N11onnxruntime15ReduceLogSumExpIfEE N11onnxruntime15ReduceLogSumExpIiEE N11onnxruntime9ReduceMaxIfEE N11onnxruntime9ReduceMaxIiEE N11onnxruntime9ReduceMaxIlEE N11onnxruntime9ReduceMaxIaEE N11onnxruntime9ReduceMaxIhEE N11onnxruntime10ReduceMeanIfEE N11onnxruntime10ReduceMeanIiEE N11onnxruntime9ReduceMinIfEE N11onnxruntime9ReduceMinIiEE N11onnxruntime9ReduceMinIlEE N11onnxruntime9ReduceMinIaEE N11onnxruntime9ReduceMinIhEE N11onnxruntime10ReduceProdIfEE N11onnxruntime10ReduceProdIiEE N11onnxruntime10ReduceProdIlEE N11onnxruntime9ReduceSumIfEE N11onnxruntime9ReduceSumIiEE N11onnxruntime9ReduceSumIlEE N11onnxruntime9ReduceSumIdEE N11onnxruntime15ReduceSumSquareIfEE N11onnxruntime15ReduceSumSquareIiEE N11onnxruntime15ReduceSumSquareIdEE N11onnxruntime6ArgMaxIfEE N11onnxruntime6ArgMaxIiEE N11onnxruntime6ArgMaxIdEE N11onnxruntime6ArgMinIfEE N11onnxruntime6ArgMinIiEE ZN11onnxruntime17NoTransposeReduceIfNS_19ReduceAggregatorSumIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIiNS_19ReduceAggregatorSumIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIdNS_19ReduceAggregatorSumIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ ZN11onnxruntime17NoTransposeReduceIlNS_19ReduceAggregatorSumIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEEUlllE0_ GRU hidden_size activations activation_alpha activation_beta clip clip_ > 0.f forward bidirectional linear_before_reset cur + size <= end /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.h info.GetAttr("direction", &direction).IsOK() info.GetAttr("linear_before_reset", &int64_value).IsOK() info.GetAttr("hidden_size", &int64_value).IsOK() && int64_value > 0 '. Must be one of 'forward', 'reverse', or 'bidirectional'. Invalid 'direction' argument of ' /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.h activation_func_names.size() == static_cast<size_t>(num_directions_) * 2 lda >= K && ldb >= K && ldc >= N A + (M * lda - (lda - K)) <= A_end B + (N * ldb - (ldb - K)) <= B_end C + (M * ldc - (ldc - N)) <= C_end offset + size <= size_t(span.size()) /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_gru.cc GRU operator does not support double yet Invalid data type for GRU operator of T* onnxruntime::rnn::detail::SafeRawPointer(typename gsl::span<T>::iterator, typename gsl::span<T>::iterator, size_t) [with T = float; typename gsl::span<T>::iterator = float*; size_t = long unsigned int] const T* onnxruntime::rnn::detail::SafeRawConstPointer(typename gsl::span<T>::const_iterator, typename gsl::span<T>::const_iterator, size_t) [with T = float; typename gsl::span<T>::const_iterator = const float*; size_t = long unsigned int] T* onnxruntime::rnn::detail::SafeRawPointer(gsl::span<T>, size_t, size_t) [with T = float; size_t = long unsigned int] void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = float*; TSpanBIter = const float*; TSpanCIter = float*] void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, TSpanAIter, TSpanAIter, int, TSpanBIter, TSpanBIter, int, float, TSpanCIter, TSpanCIter, int, onnxruntime::concurrency::ThreadPool*) [with TSpanAIter = const float*; TSpanBIter = const float*; TSpanCIter = float*] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] ComputeImpl virtual onnxruntime::common::Status onnxruntime::DeepCpuGruOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&) onnxruntime::DeepCpuGruOp::DeepCpuGruOp(const onnxruntime::OpKernelInfo&) N11onnxruntime12DeepCpuGruOpE ZN11onnxruntime10IAllocator13MakeUniquePtrIiEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPiE_ input_forget /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/lstm_base.h activation_func_names.size() == static_cast<size_t>(num_directions_) * 3 LSTM operator does not support double yet Invalid data type for LSTM operator of /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.cc const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::DeepCpuLstmOp::Compute(onnxruntime::OpKernelContext*) const const T* onnxruntime::Tensor::Data() const [with T = float] onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&) onnxruntime::LSTMBase::LSTMBase(const onnxruntime::OpKernelInfo&) N11onnxruntime8LSTMBaseE N11onnxruntime13DeepCpuLstmOpE Input B must have shape { }. Actual: Input P must have shape { Input X must have 3 dimensions only. Actual: Input sequence_lens must have shape { Invalid value/s in sequence_lens. All values must be > 0 and < seq_length. seq_length= Input initial_h must have shape { Input initial_c must have shape { /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/lstm_base.cc ComputeImpl const T* onnxruntime::Tensor::Data() const [with T = int] ComputeImpl gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] RNN Affine /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.cc /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn.h info.GetAttr("direction", &direction_).IsOK() allowed_directions.find(direction_) != allowed_directions.end() info.GetAttrs("activations", activations_).IsOK() info.GetAttr("hidden_size", &hidden_size_).IsOK() activations_.size() == static_cast<size_t>(num_directions) RNN op: Invalid activation attribute - allowed_activations.find(activations_[direction]) != allowed_activations.end() const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = float] Compute onnxruntime::RNN<T>::RNN(const onnxruntime::OpKernelInfo&) [with T = float] FffffE N11onnxruntime3RNNIfEE PFffffE Dump matrix: Input W must have shape { Input R must have shape { A + (M * K) <= A_end weights.quant_para_ affine leakyrelu thresholdedrelu scaledtanh hardsigmoid softsign softplus Expecting activation to be one of Affine, Relu, LeakyRelu, ThresholdedRelu, Tanh, ScaledTanh, Sigmoid, HardSigmoid, Elu, Softsign, Softplus. Got /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/rnn_helpers.cc Quantized GEMM only support alpha equal to 1.0f and beta equal to 0.0f or 1.0f alpha == 1.0f && (beta == 0.0f || beta == 1.0f) Invalid activation function of Invalid LSTM merge activation function of Invalid GRU reset gate activation function: Invalid GRU hidden gate activation function: void (* onnxruntime::rnn::detail::deepcpu::GruOutputGateFuncByName(const string&))(float*, const float*, const float*, float*, int, float, float) void (* onnxruntime::rnn::detail::deepcpu::GruResetGateFuncByName(const string&))(const float*, float*, float*, int, float, float) void (* onnxruntime::rnn::detail::deepcpu::LstmMergeGatesFuncByName(const string&))(const float*, float*, const float*, float*, int, float, float) void (* onnxruntime::rnn::detail::deepcpu::ActivationFuncByName(const string&))(float*, int, float, float) void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, const float*, const float*, const onnxruntime::rnn::detail::GemmWeights<unsigned char>&, float, float*, float*, int, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*) void onnxruntime::rnn::detail::ComputeGemm(int, int, int, float, const float*, const float*, const onnxruntime::rnn::detail::GemmWeights<float>&, float, float*, float*, int, onnxruntime::AllocatorPtr, onnxruntime::concurrency::ThreadPool*) std::string onnxruntime::rnn::detail::NormalizeActivationArgumentAndGetAlphaBetaCount(const string&, std::vector<float>::const_iterator&, const const_iterator&, std::vector<float>::const_iterator&, const const_iterator&, float&, float&) gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] 27MLAS_QGEMM_OUTPUT_PROCESSOR %%%%~3a*~3a*~3a*~3a*7.7.7.7.A \3A \3A \3A \3JCy7JCy7JCy7JCy7
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':Y;Ӡ5֕8;Y; A C rows[ const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span<T>, size_t, size_t) [with T = float; size_t = long unsigned int] const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span<T>, size_t, size_t) [with T = const float; size_t = long unsigned int] *ZN11onnxruntime4lstmL23ExecuteLambdaInParallelIZNS0_18UniDirectionalLstmIfE7ComputeIfEEvRKN3gsl4spanIKfEERKNS6_IKiEEiRKNS_3rnn6detail11GemmWeightsIT_EESL_RNS6_IfEESN_SN_EUliPNS_11concurrency10ThreadPoolEE_EEvT_iidSQ_EUlllE_ *ZN11onnxruntime4lstmL23ExecuteLambdaInParallelIZNS0_18UniDirectionalLstmIfE7ComputeIhEEvRKN3gsl4spanIKfEERKNS6_IKiEEiRKNS_3rnn6detail11GemmWeightsIT_EESL_RNS6_IfEESN_SN_EUliPNS_11concurrency10ThreadPoolEE_EEvT_iidSQ_EUlllE_ new_axis ConcatFromSequence Must have valid 'axis' attribute /onnxruntime_src/onnxruntime/core/providers/cpu//tensor/concat.h Got nullptr for sequence input. /onnxruntime_src/onnxruntime/core/providers/cpu/sequence/concat_from_sequence.cc const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq] virtual onnxruntime::common::Status onnxruntime::ConcatFromSequence::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo&, bool) N11onnxruntime10ConcatBaseE N11onnxruntime18ConcatFromSequenceE Y != nullptr Unsupported 'dtype' value: SequenceErase SequenceInsert SequenceAt SplitToSequence SequenceConstruct SequenceLength SequenceEmpty I != nullptr Invalid sequence index ( split_size_sum ( ) != split_dim_size ( S != nullptr Got nullptr for input tensor. ), input tensor data type ( num_inputs >= 1 Split should be > 0 split_scalar > 0 SequenceEmpty: Got nullptr for output sequence /onnxruntime_src/onnxruntime/core/providers/cpu/sequence/sequence_ops.cc Got nullptr input for index tensor ) specified for sequence of size ( SequenceAt: Got nullptr for output tensor SequenceLength: Got nullptr for output tensor Invalid data type for split tensor Data type of the input tensor MUST be same as that of the input sequence. Sequence data type ( SequenceInsert: Got nullptr for output sequence SequenceErase: Got nullptr for output sequence SequenceConstruct: Got nullptr for output sequence Violation of the requirment that all input tensors must have the same data type. Invalid value in 'split' input. All values must be >= 0 std::all_of(split_sizes.cbegin(), split_sizes.cend(), [](int64_t value) { return value >= 0; }) SplitToSequence operator does not support 씳ܔ̔|l\L<, int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = std::basic_string<char>] ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = int] ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = double] ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] void onnxruntime::GetSplitSizesInput(const onnxruntime::Tensor&, std::vector<long int>&) int64_t onnxruntime::GetScalarSplitInput(const onnxruntime::Tensor&) ComputeImpl onnxruntime::common::Status onnxruntime::SplitToSequence::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&, const onnxruntime::Tensor*) const [with T = float] virtual onnxruntime::common::Status onnxruntime::SplitToSequence::Compute(onnxruntime::OpKernelContext*) const virtual onnxruntime::common::Status onnxruntime::SequenceConstruct::Compute(onnxruntime::OpKernelContext*) const virtual onnxruntime::common::Status onnxruntime::SequenceErase::Compute(onnxruntime::OpKernelContext*) const virtual onnxruntime::common::Status onnxruntime::SequenceInsert::Compute(onnxruntime::OpKernelContext*) const CreateCopyAndAppendCpuTensor void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType) T* OrtValue::GetMutable() [with T = onnxruntime::TensorSeq] virtual onnxruntime::common::Status onnxruntime::SequenceEmpty::Compute(onnxruntime::OpKernelContext*) const const onnxruntime::Tensor& onnxruntime::TensorSeq::Get(size_t) const int64_t onnxruntime::GetSeqIdx(const onnxruntime::Tensor&) virtual onnxruntime::common::Status onnxruntime::SequenceAt::Compute(onnxruntime::OpKernelContext*) const T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::TensorSeq] virtual onnxruntime::common::Status onnxruntime::SequenceLength::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime14SequenceLengthE N11onnxruntime10SequenceAtE N11onnxruntime13SequenceEmptyE N11onnxruntime14SequenceInsertE N11onnxruntime13SequenceEraseE N11onnxruntime17SequenceConstructE N11onnxruntime15SplitToSequenceE Attribute to is not set. -INF stod stoi stoul stol stoull /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/cast_op.cc Unsupported type in cast op: from String to void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = bool; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = long unsigned int; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = long int; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = unsigned int; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = int; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = short unsigned int; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = short int; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = unsigned char; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = signed char; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = double; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcherWithCarriedType<TCarried, Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; TCarried = float; Fn = onnxruntime::Cast::Dispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {int&, const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; Fn = onnxruntime::Cast::SrcDispatcher; Types = {float, double, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] void onnxruntime::{anonymous}::CastFromStringData(const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&) [with DstType = bool] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = bool] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char] void onnxruntime::{anonymous}::CastFromStringData(const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&) [with DstType = onnxruntime::MLFloat16] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::MLFloat16] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {bool&, const onnxruntime::Tensor&, onnxruntime::Tensor&, const onnxruntime::TensorShape&}; Fn = onnxruntime::Cast::StringDispatcher; Types = {float, double, onnxruntime::MLFloat16, signed char, unsigned char, short int, short unsigned int, int, unsigned int, long int, long unsigned int, bool}] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::Cast::Cast(const onnxruntime::OpKernelInfo&) N11onnxruntime4CastE N11onnxruntime9MLFloat16E 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.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 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 D4DDDD$DDDD 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 ZN11onnxruntime14GatherCopyDataIiEENS_6common6StatusEPKNS_6TensorEPKhPhbmlllllRKNS_11TensorShapeElPNS_11concurrency10ThreadPoolEEUlllE0_ ZN11onnxruntime14GatherCopyDataIlEENS_6common6StatusEPKNS_6TensorEPKhPhbmlllllRKNS_11TensorShapeElPNS_11concurrency10ThreadPoolEEUlllE0_ GatherElements ]. Actual value is , /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.h GatherElements op: Cannot operate on scalar input GatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices' GatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: 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 batch_dims GatherND invalid index found, index = indices tensor must has rank larger than 0 GatherNDBase PrepareForCompute: Input count mismatch /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_nd.cc input_tensor != nullptr && indices_tensor != nullptr last dimension of indices must not be larger than rank of input tensor indices tensor data type not supported const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::GatherND::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime12GatherNDBaseE *ZNK11onnxruntime8GatherND12GatherNumberERKNS_12GatherNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ *ZNK11onnxruntime8GatherND12GatherStringERKNS_12GatherNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ N11onnxruntime8GatherNDE ZNK11onnxruntime12GatherNDBase17PrepareForComputeIiEENS_6common6StatusERKNS_11TensorShapeEPKNS_6TensorElRNS0_7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ ZNK11onnxruntime12GatherNDBase17PrepareForComputeIlEENS_6common6StatusERKNS_11TensorShapeEPKNS_6TensorElRNS0_7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ Tensor type mismatch. type == dtype_ /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/identity_op.h onnxruntime::common::Status onnxruntime::IdentityOp<is_dropout>::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = true] void* onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::IdentityOp<is_dropout>::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = false] N11onnxruntime10IdentityOpILb1EEE N11onnxruntime10IdentityOpILb0EEE detect_positive detect_negative IsInf Failed to obtain detect_positive /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/isinf.cc Failed to obtain detect_negative 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 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/nonzero_op.cc X input is required! const T* onnxruntime::Tensor::Data() const [with T = bool] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = int] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = long int] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = float] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] N11onnxruntime7NonZeroIbEE N11onnxruntime7NonZeroIhEE N11onnxruntime7NonZeroIiEE N11onnxruntime7NonZeroIlEE N11onnxruntime7NonZeroIfEE OneHot Depth is negative. Invalid argument for values; either it's rank is more than 1 or it has more than 2 elements Invalid argument for depth; it's not a scalar. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/onehot.cc Compute Compute Compute Compute Compute Compute Compute Compute Compute Compute Compute int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) N11onnxruntime8OneHotOpIlllEE N11onnxruntime8OneHotOpIfllEE N11onnxruntime8OneHotOpIlSslEE N11onnxruntime8OneHotOpIfSslEE N11onnxruntime8OneHotOpIlflEE N11onnxruntime8OneHotOpIifiEE N11onnxruntime8OneHotOpIiffEE N11onnxruntime8OneHotOpIfffEE N11onnxruntime8OneHotOpIlifEE N11onnxruntime8OneHotOpIlffEE N11onnxruntime8OneHotOpIlfiEE >Unexpected mode of 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->DataType() == data_type && value_tensor->Shape().Size() == 1 Unsupported input data type of Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank] Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensor PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = unsigned char] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = long unsigned int] void onnxruntime::SliceIteratorBase::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) onnxruntime::SliceSkips::SliceSkips(const onnxruntime::TensorShape&, gsl::span<const long int>, gsl::span<const long int>) PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = unsigned int] onnxruntime::PadValue onnxruntime::PadValueFromFloat(float, onnxruntime::MLDataType) virtual onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext*) const onnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo&) N11onnxruntime7PadBaseE N11onnxruntime3PadE DequantizeLinear /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/quantize_linear.cc zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr) scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dim zero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim) x_zero_point must be null or a scalar or 1D tensor or size 1. scale must be 1D tensor with size x_zero_point must be null or 1D tensor with size T* onnxruntime::Tensor::MutableData() [with T = float] T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) void onnxruntime::PrepareForQDQ(const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::Tensor*, int64_t, int64_t&, int64_t&, int64_t&) N11onnxruntime16DequantizeLinearIaEE N11onnxruntime16DequantizeLinearIhEE N11onnxruntime14QuantizeLinearIaEE N11onnxruntime14QuantizeLinearIhEE Attribute 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, got /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape_helper.h At most one dimension can be -1. The dimension with value zero exceeds the dimension size of the input tensor. i < input_shape.NumDimensions() The input tensor cannot be reshaped to the requested shape. Input shape: size != 0 && (input_shape.Size() % size) == 0 gsl::narrow_cast<int64_t>(input_shape.Size()) == size A shape tensor must be a vector tensor. shapeTensor->Shape().NumDimensions() == 1 onnxruntime::Reshape_1::Reshape_1(const onnxruntime::OpKernelInfo&) onnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape&, std::vector<long int>&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = long int] virtual onnxruntime::common::Status onnxruntime::Reshape::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7ReshapeE N11onnxruntime9Reshape_1E Resize operator Upsample operator scale >= 1 scale > 0 cubic mode attribute is . It can only be (default) or extrapolation_value pytorch_half_pixel tf_half_pixel_for_nn align_corners tf_crop_and_resize coordinate_transform_mode:[ ] is not supportted! round_prefer_floor round_prefer_ceil nearest_mode:[ ] is not supported! cubic_coeff_a exclude_outside scales_size > 0 Scale value should be greater than or equal to 1. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.h Scale value should be greater than 0. 'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) || scales.size() == 3 || (scales.size() == 5 && scales[0] == 1 && scales[1] == 1) 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) info.GetAttr<std::string>("mode", &mode).IsOK() info.GetAttrs<float>("scales", scales_).IsOK() 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::StringToNearestMode(const string&) onnxruntime::ResizeCoordinateTransformationMode onnxruntime::UpsampleBase::StringToCoordinateTransformationMode(const string&) void onnxruntime::UpsampleBase::ScalesValidation(const std::vector<float>&, onnxruntime::UpsampleMode) const onnxruntime::UpsampleMode onnxruntime::UpsampleBase::StringToUpsampleMode(const string&) onnxruntime::UpsampleBase::UpsampleBase(onnxruntime::OpKernelInfo) N11onnxruntime12UpsampleBaseE N11onnxruntime8UpsampleIfEE N11onnxruntime6ResizeIfEE N11onnxruntime8UpsampleIiEE N11onnxruntime6ResizeIiEE N11onnxruntime8UpsampleIhEE N11onnxruntime6ResizeIhEE ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE0_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE1_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE2_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE3_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE4_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE0_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE1_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE2_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE3_ @ ?batch_axis . Must be 0 or 1 Invalid batch_axis of batch_axis < 2 Invalid time_axis of time_axis < 2 batch_axis != time_axis ReverseSequence Unknown tensor type of . batch_size= /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.h info.GetAttr<int64_t>("batch_axis", &batch_axis).IsOK() info.GetAttr<int64_t>("time_axis", &time_axis).IsOK() time_axis and batch_axis must have different values but both are /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.cc sequence_lens shape must be {batch_size}. Got: AºF R뤽mQAA' gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::BFloat16] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::BFloat16] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::MLFloat16] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::MLFloat16] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = short unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = short int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned char] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = std::basic_string<char>] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = std::basic_string<char>] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = double] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = bool] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = bool] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] virtual onnxruntime::common::Status onnxruntime::ReverseSequenceOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ReverseSequenceOp::ReverseSequenceOp(const onnxruntime::OpKernelInfo&) N11onnxruntime17ReverseSequenceOpE /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter.cc data type is different from updates type Indices and updates must have the same rank Indices vs updates dimensions differs at position= Indices must have the same rank as Input. Indices rank= Expecting indices to be either int32_t or int64_t ScatterElements Scatter . Input rank= Indices dim= at pos= is greater than input dim= )aa```p`K`&``___s_Q_)a)a)_ccxcSc.c cbbbubPb+bbacca 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 *ZNK11onnxruntime9ScatterND13ScatterNumberERKNS_13ScatterNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ *ZNK11onnxruntime9ScatterND13ScatterStringERKNS_13ScatterNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ N11onnxruntime9ScatterNDE T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime5ShapeE Size T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime4SizeE Unexpected element size of output == output_end Starts must be a 1-D array Ends must be a 1-D array 'axes' has duplicates 'step' value cannot be 0 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 start_tensor.Shape().NumDimensions() == 1 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 onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&) [with T = unsigned char]::__lambda10 onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&) [with T = short unsigned int]::__lambda10 onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&) [with T = long unsigned int]::__lambda10 onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&) [with T = unsigned int]::__lambda10 void* onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep(void*) onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&) [with T = std::basic_string<char>]::__lambda10 Compute onnxruntime::common::Status onnxruntime::SliceBase::Compute(onnxruntime::OpKernelContext*) const static void onnxruntime::SliceBase::FillVectorsFromInput(const onnxruntime::Tensor&, const onnxruntime::Tensor&, const onnxruntime::Tensor*, const onnxruntime::Tensor*, std::vector<long int>&, std::vector<long int>&, std::vector<long int>&, std::vector<long int>&) int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) onnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo&, bool) N11onnxruntime9SliceBaseE N11onnxruntime6Slice1E N11onnxruntime7Slice10E blocksize CRD DCR SpaceToDepth DepthToSpace Attribute blocksize is not set. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.h info.GetAttr("blocksize", &blocksize_).IsOK() DepthToSpace op: only 'DCR' and 'CRD' modes are supported /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/space_depth_ops.cc input.Shape().NumDimensions() == 4 input_depth % (blocksize_ * blocksize_) == 0 input_shape.NumDimensions() == 4 input_height % this->blocksize_ == 0 input_width % this->blocksize_ == 0 onnxruntime::common::Status onnxruntime::DepthToSpace<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::SpaceToDepth<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::DepthToSpace<T>::DepthToSpace(const onnxruntime::OpKernelInfo&) [with T = float] onnxruntime::SpaceDepthBase::SpaceDepthBase(const onnxruntime::OpKernelInfo&) N11onnxruntime14SpaceDepthBaseE N11onnxruntime12SpaceToDepthIfEE N11onnxruntime12DepthToSpaceIfEE e@*> Axis= NumOutputs= Input shape= Invalid value in 'split' attribute. All values must be > 0 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.h std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; }) Input cannot be split evenly on selected axis. Input shape= Cannot split using values in 'split' attribute. Axis= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was /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 is outside range. is repeated. [ perm: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.h v >= 0 && static_cast<uint64_t>(v) <= std::numeric_limits<size_t>::max() Attribute perm of Transpose has an invalid value. Value Mismatched data types between input and output Tensors. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.cc does not align with rank of input data: onnxruntime::TransposeBase::TransposeBase(const onnxruntime::OpKernelInfo&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext*) const 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 nullptr != p.output_tensor Missing/Invalid 'axes' attribute value /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.h info.GetAttrs("axes", axes_).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.cc 'axes' 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 Dimension: Input dim value: Output dim value: output_dims[i] == 0 Resize: unexpected mode Upsample: unexpected mode Invalid roi input index. roi_input_idx_ > 0 scales != nullptr sizes == nullptr Input dim is zero but required output dim is non-zero. Cannot scale 0 by any factor to generate a non-zero value. Resize: input/output value is nullptr Upsample: input/output value is nullptr Resize: input/output value's dimension mismatch Upsample: input/output value's dimension mismatch Resize: input shape needs to be at least a single dimension Upsample: input shape needs to be at least a single dimension. Resize: input tensor's dimension does not match the scales. Upsample: input tensor's dimension does not match the scales. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.cc Rank of input and output tensor should be same. output_dims.size() == dims.size() Resize: size of roi array should be 2 * N where N is the rank of input tensor X. Only one of scales or sizes must be provided as input. sizes != nullptr && sizes->Shape().Size() != 0 Resize: input tensor's rank does not match the output tensor's rank. X->Shape().GetDims().size() == output_dims.size() Either scales or sizes MUST be provided as input. BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = float] onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = int] onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = unsigned char] void onnxruntime::UpsampleBase::ParseScalesDataFromOutputSize(const std::vector<long int>&, const std::vector<long int>&, std::vector<float>&) const onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor*, std::vector<float>&) const @condition && X && Y condition, X, and Y inputs are required! /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/where_op.cc onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = unsigned char] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = int] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] 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::TBroadcastOutput<T>::TBroadcastOutput(size_t, onnxruntime::Tensor&, int64_t, int64_t) [with T = std::basic_string<char>; size_t = long unsigned int; int64_t = long int] 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/cpu_contrib_kernels.cc RegisterCpuContribKernels RegisterQuantizationKernels RegisterNchwcKernels border input dimensions instead Input's height ( ) + bottomBorder ( Input's width ( ) + rightBorder ( ) + scale_[0] ( ) + scale_[1] ( Crop Attribute border needs to be specified with four border elements, got Input is expected to have four dimensions corresponding to [N,C,H,W], got ) needs to be greater than or equal to the topBorder ( ) needs to be greater than or equal to the leftBorder ( /onnxruntime_src/onnxruntime/contrib_ops/cpu/crop.h T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime7contrib8CropBaseE N11onnxruntime7contrib4CropIfEE bilinear CropAndResize Null crop_size_ptr /onnxruntime_src/onnxruntime/contrib_ops/cpu/crop_and_resize.h specified. It should be either bilinear or nearest Number of dimensions for crop size should be exactly 1 const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::CropAndResize<T>::CropAndResize(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib13CropAndResizeIfEE DynamicSlice /onnxruntime_src/onnxruntime/contrib_ops/cpu/element_wise_ops.h info.GetAttr("scale", &scale_).IsOK() info.GetAttr("alpha", &alpha_).IsOK() info.GetAttr("beta", &beta_).IsOK() onnxruntime::contrib::Scale<T>::Scale(const onnxruntime::OpKernelInfo&) [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::Affine<T>::Affine(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib6AffineIfEE N11onnxruntime7contrib5ScaleIfEE ExpandDims Axis must be within range [ Axis is /onnxruntime_src/onnxruntime/contrib_ops/cpu/expand_dims.h axis_tensor->Shape().IsScalar() axis <= X_NumDims && axis >= -X_NumDims An axis tensor must be a scalar tensor. const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = int] virtual onnxruntime::common::Status onnxruntime::contrib::ExpandDims::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7contrib10ExpandDimsE /onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_conv.cc GetFusedActivationAttr(info, activation_).IsOK() onnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib14FusedConvFloatE /onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_gemm.cc onnxruntime::contrib::FusedGemm<T>::FusedGemm(const onnxruntime::OpKernelInfo&) [with T = float] FusedGemm N11onnxruntime7contrib9FusedGemmIfEE ImageScaler Bias size ( /onnxruntime_src/onnxruntime/contrib_ops/cpu/image_scaler.h info.GetAttrs<float>("bias", bias_).IsOK() ) does not match the number of channels ( T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::ImageScaler<T>::ImageScaler(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib11ImageScalerIfEE Inverse T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor* const&, onnxruntime::Tensor* const&, long int&, const long int&, const long int&}; Fn = onnxruntime::contrib::Inverse::ComputeImpl; Types = {float, double, onnxruntime::MLFloat16}] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] *ZNK11onnxruntime7contrib7Inverse7ComputeEPNS_15OpKernelContextEEUllE_ N11onnxruntime7contrib7InverseE /onnxruntime_src/onnxruntime/contrib_ops/cpu/layer_norm.cc op_kernel_info.GetAttr("axis", &axis_).IsOK() 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] 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 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 input_num_bytes % 4 == 0 /onnxruntime_src/onnxruntime/contrib_ops/cpu/murmur_hash3.cc Invalid assumption of output element size sizeof(uint32_t) == output_element_bytes const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::contrib::MurmurHash3::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7contrib11MurmurHash3E invalid channel count channels_ > 0 scales_.size() == 4 X_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() info.GetAttrs<int64_t>("scales", scales_).IsOK() scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1 pool_attrs_.kernel_shape.size() == 2 /onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.cc (X_shape[1] % MlasNchwcGetBlockSize()) == 0 (static_cast<size_t>(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0) Not satisfied: Y->Shape() == sum_shape
- output and sum shape must match virtual onnxruntime::common::Status onnxruntime::contrib::NchwcUpsample::Compute(onnxruntime::OpKernelContext*) const void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) const void onnxruntime::PoolAttributes::InferOutputSize(const std::vector<long int>&, std::vector<long int>*, std::vector<long int>*) const std::vector<long int> onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, std::vector<long int>*) const onnxruntime::common::Status onnxruntime::contrib::NchwcPoolBase::NchwcPool(onnxruntime::OpKernelContext*, MLAS_POOLING_KIND) const virtual onnxruntime::common::Status onnxruntime::contrib::NchwcConv::Compute(onnxruntime::OpKernelContext*) const Compute virtual onnxruntime::common::Status onnxruntime::contrib::ReorderOutput::Compute(onnxruntime::OpKernelContext*) const T* onnxruntime::Tensor::MutableData() [with T = float] virtual onnxruntime::common::Status onnxruntime::contrib::ReorderInput::Compute(onnxruntime::OpKernelContext*) const onnxruntime::contrib::NchwcUpsample::NchwcUpsample(const onnxruntime::OpKernelInfo&) onnxruntime::contrib::NchwcPoolBase::NchwcPoolBase(const onnxruntime::OpKernelInfo&) onnxruntime::contrib::NchwcConv::NchwcConv(const onnxruntime::OpKernelInfo&) onnxruntime::contrib::ReorderOutput::ReorderOutput(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib13NchwcPoolBaseE N11onnxruntime7contrib12ReorderInputE N11onnxruntime7contrib13ReorderOutputE N11onnxruntime7contrib9NchwcConvE N11onnxruntime7contrib12NchwcMaxPoolE N11onnxruntime7contrib16NchwcAveragePoolE N11onnxruntime7contrib13NchwcUpsampleE QLinearMul QLinearAdd MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1 /onnxruntime_src/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc IsScalarOr1ElementVector(tensor_a_scale) tensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point) MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1 IsScalarOr1ElementVector(tensor_b_scale) tensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point) MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1 IsScalarOr1ElementVector(tensor_c_scale) tensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point) MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if given MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if given MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if given onnxruntime::common::Status onnxruntime::contrib::QLinearBroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with T = signed char; Input0Scalar = onnxruntime::contrib::QLinearAdd<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]::__lambda15; Input1Scalar = onnxruntime::contrib::QLinearAdd<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]::__lambda16; General = onnxruntime::contrib::QLinearAdd<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]::__lambda17] onnxruntime::common::Status onnxruntime::contrib::QLinearBroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with T = unsigned char; Input0Scalar = onnxruntime::contrib::QLinearAdd<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda15; Input1Scalar = onnxruntime::contrib::QLinearAdd<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda16; General = onnxruntime::contrib::QLinearAdd<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda17] void onnxruntime::TBroadcaster<T0, T1>::AdvanceBy(size_t) [with T0 = signed char; T1 = signed char; size_t = long unsigned int] onnxruntime::common::Status onnxruntime::contrib::QLinearBroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with T = signed char; Input0Scalar = onnxruntime::contrib::QLinearMul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]::__lambda18; Input1Scalar = onnxruntime::contrib::QLinearMul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]::__lambda19; General = onnxruntime::contrib::QLinearMul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]::__lambda20] void onnxruntime::TBroadcaster<T0, T1>::AdvanceBy(size_t) [with T0 = unsigned char; T1 = unsigned char; size_t = long unsigned int] onnxruntime::common::Status onnxruntime::contrib::QLinearBroadcastTwo(onnxruntime::OpKernelContext&, Input0Scalar, Input1Scalar, General, double) [with T = unsigned char; Input0Scalar = onnxruntime::contrib::QLinearMul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda18; Input1Scalar = onnxruntime::contrib::QLinearMul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda19; General = onnxruntime::contrib::QLinearMul<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda20] ZN11onnxruntime7contrib19QLinearBroadcastTwoIhZNKS0_10QLinearMulIhE7ComputeEPNS_15OpKernelContextEEUlN3gsl4spanIhEERKhNS7_IS9_EEfffhhhE_ZNKS3_7ComputeES5_EUlS8_SB_SA_fffhhhE0_ZNKS3_7ComputeES5_EUlS8_SB_SB_fffhhhE1_EENS_6common6StatusERS4_T0_T1_T2_dEUlllE_ ZN11onnxruntime7contrib19QLinearBroadcastTwoIaZNKS0_10QLinearMulIaE7ComputeEPNS_15OpKernelContextEEUlN3gsl4spanIaEERKaNS7_IS9_EEfffaaaE_ZNKS3_7ComputeES5_EUlS8_SB_SA_fffaaaE0_ZNKS3_7ComputeES5_EUlS8_SB_SB_fffaaaE1_EENS_6common6StatusERS4_T0_T1_T2_dEUlllE_ ZN11onnxruntime7contrib19QLinearBroadcastTwoIhZNKS0_10QLinearAddIhE7ComputeEPNS_15OpKernelContextEEUlN3gsl4spanIhEERKhNS7_IS9_EEfffhhhE_ZNKS3_7ComputeES5_EUlS8_SB_SA_fffhhhE0_ZNKS3_7ComputeES5_EUlS8_SB_SB_fffhhhE1_EENS_6common6StatusERS4_T0_T1_T2_dEUlllE_ ZN11onnxruntime7contrib19QLinearBroadcastTwoIaZNKS0_10QLinearAddIaE7ComputeEPNS_15OpKernelContextEEUlN3gsl4spanIaEERKaNS7_IS9_EEfffaaaE_ZNKS3_7ComputeES5_EUlS8_SB_SA_fffaaaE0_ZNKS3_7ComputeES5_EUlS8_SB_SB_fffaaaE1_EENS_6common6StatusERS4_T0_T1_T2_dEUlllE_ ZN11onnxruntime7contrib23QLinearBroadcastOneSpanIhZNKS0_10QLinearMulIhE7ComputeEPNS_15OpKernelContextEEUlN3gsl4spanIhEERKhNS7_IS9_EEfffhhhE_ZNKS3_7ComputeES5_EUlS8_SB_SA_fffhhhE0_ZNKS3_7ComputeES5_EUlS8_SB_SB_fffhhhE1_EEvPNS_11concurrency10ThreadPoolEdNS7_IT_EENS7_IKSI_EESL_T0_T1_T2_fffSI_SI_SI_EUlllE_ ZN11onnxruntime7contrib23QLinearBroadcastOneSpanIhZNKS0_10QLinearMulIhE7ComputeEPNS_15OpKernelContextEEUlN3gsl4spanIhEERKhNS7_IS9_EEfffhhhE_ZNKS3_7ComputeES5_EUlS8_SB_SA_fffhhhE0_ZNKS3_7ComputeES5_EUlS8_SB_SB_fffhhhE1_EEvPNS_11concurrency10ThreadPoolEdNS7_IT_EENS7_IKSI_EESL_T0_T1_T2_fffSI_SI_SI_EUlllE0_ ZN11onnxruntime7contrib23QLinearBroadcastOneSpanIhZNKS0_10QLinearMulIhE7ComputeEPNS_15OpKernelContextEEUlN3gsl4spanIhEERKhNS7_IS9_EEfffhhhE_ZNKS3_7ComputeES5_EUlS8_SB_SA_fffhhhE0_ZNKS3_7ComputeES5_EUlS8_SB_SB_fffhhhE1_EEvPNS_11concurrency10ThreadPoolEdNS7_IT_EENS7_IKSI_EESL_T0_T1_T2_fffSI_SI_SI_EUlllE1_ ZN11onnxruntime7contrib23QLinearBroadcastOneSpanIaZNKS0_10QLinearMulIaE7ComputeEPNS_15OpKernelContextEEUlN3gsl4spanIaEERKaNS7_IS9_EEfffaaaE_ZNKS3_7ComputeES5_EUlS8_SB_SA_fffaaaE0_ZNKS3_7ComputeES5_EUlS8_SB_SB_fffaaaE1_EEvPNS_11concurrency10ThreadPoolEdNS7_IT_EENS7_IKSI_EESL_T0_T1_T2_fffSI_SI_SI_EUlllE_ ZN11onnxruntime7contrib23QLinearBroadcastOneSpanIaZNKS0_10QLinearMulIaE7ComputeEPNS_15OpKernelContextEEUlN3gsl4spanIaEERKaNS7_IS9_EEfffaaaE_ZNKS3_7ComputeES5_EUlS8_SB_SA_fffaaaE0_ZNKS3_7ComputeES5_EUlS8_SB_SB_fffaaaE1_EEvPNS_11concurrency10ThreadPoolEdNS7_IT_EENS7_IKSI_EESL_T0_T1_T2_fffSI_SI_SI_EUlllE0_ ZN11onnxruntime7contrib23QLinearBroadcastOneSpanIaZNKS0_10QLinearMulIaE7ComputeEPNS_15OpKernelContextEEUlN3gsl4spanIaEERKaNS7_IS9_EEfffaaaE_ZNKS3_7ComputeES5_EUlS8_SB_SA_fffaaaE0_ZNKS3_7ComputeES5_EUlS8_SB_SB_fffaaaE1_EEvPNS_11concurrency10ThreadPoolEdNS7_IT_EENS7_IKSI_EESL_T0_T1_T2_fffSI_SI_SI_EUlllE1_ ZN11onnxruntime7contrib23QLinearBroadcastOneSpanIhZNKS0_10QLinearAddIhE7ComputeEPNS_15OpKernelContextEEUlN3gsl4spanIhEERKhNS7_IS9_EEfffhhhE_ZNKS3_7ComputeES5_EUlS8_SB_SA_fffhhhE0_ZNKS3_7ComputeES5_EUlS8_SB_SB_fffhhhE1_EEvPNS_11concurrency10ThreadPoolEdNS7_IT_EENS7_IKSI_EESL_T0_T1_T2_fffSI_SI_SI_EUlllE_ ZN11onnxruntime7contrib23QLinearBroadcastOneSpanIhZNKS0_10QLinearAddIhE7ComputeEPNS_15OpKernelContextEEUlN3gsl4spanIhEERKhNS7_IS9_EEfffhhhE_ZNKS3_7ComputeES5_EUlS8_SB_SA_fffhhhE0_ZNKS3_7ComputeES5_EUlS8_SB_SB_fffhhhE1_EEvPNS_11concurrency10ThreadPoolEdNS7_IT_EENS7_IKSI_EESL_T0_T1_T2_fffSI_SI_SI_EUlllE0_ ZN11onnxruntime7contrib23QLinearBroadcastOneSpanIhZNKS0_10QLinearAddIhE7ComputeEPNS_15OpKernelContextEEUlN3gsl4spanIhEERKhNS7_IS9_EEfffhhhE_ZNKS3_7ComputeES5_EUlS8_SB_SA_fffhhhE0_ZNKS3_7ComputeES5_EUlS8_SB_SB_fffhhhE1_EEvPNS_11concurrency10ThreadPoolEdNS7_IT_EENS7_IKSI_EESL_T0_T1_T2_fffSI_SI_SI_EUlllE1_ ZN11onnxruntime7contrib23QLinearBroadcastOneSpanIaZNKS0_10QLinearAddIaE7ComputeEPNS_15OpKernelContextEEUlN3gsl4spanIaEERKaNS7_IS9_EEfffaaaE_ZNKS3_7ComputeES5_EUlS8_SB_SA_fffaaaE0_ZNKS3_7ComputeES5_EUlS8_SB_SB_fffaaaE1_EEvPNS_11concurrency10ThreadPoolEdNS7_IT_EENS7_IKSI_EESL_T0_T1_T2_fffSI_SI_SI_EUlllE_ ZN11onnxruntime7contrib23QLinearBroadcastOneSpanIaZNKS0_10QLinearAddIaE7ComputeEPNS_15OpKernelContextEEUlN3gsl4spanIaEERKaNS7_IS9_EEfffaaaE_ZNKS3_7ComputeES5_EUlS8_SB_SA_fffaaaE0_ZNKS3_7ComputeES5_EUlS8_SB_SB_fffaaaE1_EEvPNS_11concurrency10ThreadPoolEdNS7_IT_EENS7_IKSI_EESL_T0_T1_T2_fffSI_SI_SI_EUlllE0_ ZN11onnxruntime7contrib23QLinearBroadcastOneSpanIaZNKS0_10QLinearAddIaE7ComputeEPNS_15OpKernelContextEEUlN3gsl4spanIaEERKaNS7_IS9_EEfffaaaE_ZNKS3_7ComputeES5_EUlS8_SB_SA_fffaaaE0_ZNKS3_7ComputeES5_EUlS8_SB_SB_fffaaaE1_EEvPNS_11concurrency10ThreadPoolEdNS7_IT_EENS7_IKSI_EESL_T0_T1_T2_fffSI_SI_SI_EUlllE1_ N11onnxruntime7contrib10QLinearAddIaEE N11onnxruntime7contrib10QLinearAddIhEE N11onnxruntime7contrib10QLinearMulIaEE N11onnxruntime7contrib10QLinearMulIhEE QLinearSigmoid QLinearLeakyRelu /onnxruntime_src/onnxruntime/contrib_ops/cpu/qlinear_lookup_table.cc IsScalarOr1ElementVector(tensor_x_scale) tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point) IsScalarOr1ElementVector(tensor_y_scale) tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point) QLinearLeakyRelu : input X_scale must be a scalar or 1D tensor of size 1 QLinearLeakyRelu : input X_zero_point must be a scalar or 1D tensor of size 1 QLinearLeakyRelu : input Y_scale must be a scalar or 1D tensor of size 1 QLinearLeakyRelu : input Y_zero_point must be a scalar or 1D tensor of size 1 T* onnxruntime::Tensor::MutableData() [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] void onnxruntime::contrib::QlinearBuildLookupTable(uint8_t*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const LookupTableArrayTransformer&) [with T = unsigned char; uint8_t = unsigned char; onnxruntime::contrib::LookupTableArrayTransformer = std::function<void(const float*, float*, long unsigned int)>] const T* onnxruntime::Tensor::Data() const [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::contrib::QlinearBuildLookupTable(uint8_t*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const LookupTableArrayTransformer&) [with T = signed char; uint8_t = unsigned char; onnxruntime::contrib::LookupTableArrayTransformer = std::function<void(const float*, float*, long unsigned int)>] ZNK11onnxruntime7contrib14QLinearSigmoidIhE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_ ZNK11onnxruntime7contrib17QLinearLookupBaseIhE11ComputeBaseIZNKS0_14QLinearSigmoidIhE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_EENS_6common6StatusES7_T_EUlllE_ ZNK11onnxruntime7contrib14QLinearSigmoidIaE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_ ZNK11onnxruntime7contrib17QLinearLookupBaseIaE11ComputeBaseIZNKS0_14QLinearSigmoidIaE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_EENS_6common6StatusES7_T_EUlllE_ ZNK11onnxruntime7contrib16QLinearLeakyReluIhE7ComputeEPNS_15OpKernelContextEEUlfE_ ZNK11onnxruntime7contrib17QLinearLookupBaseIhE11ComputeBaseIZNKS0_16QLinearLeakyReluIhE7ComputeEPNS_15OpKernelContextEEUlfE_EENS_6common6StatusES7_T_EUlllE_ ZNK11onnxruntime7contrib16QLinearLeakyReluIaE7ComputeEPNS_15OpKernelContextEEUlfE_ ZNK11onnxruntime7contrib17QLinearLookupBaseIaE11ComputeBaseIZNKS0_16QLinearLeakyReluIaE7ComputeEPNS_15OpKernelContextEEUlfE_EENS_6common6StatusES7_T_EUlllE_ *ZN11onnxruntime7contribL23QlinearBuildLookupTableIaEEvPhPKNS_6TensorES5_S5_S5_RKSt8functionIFffEEEUlPKfPfmE_ *ZN11onnxruntime7contribL23QlinearBuildLookupTableIhEEvPhPKNS_6TensorES5_S5_S5_RKSt8functionIFffEEEUlPKfPfmE_ N11onnxruntime7contrib17QLinearLookupBaseIaEE N11onnxruntime7contrib16QLinearLeakyReluIaEE N11onnxruntime7contrib17QLinearLookupBaseIhEE N11onnxruntime7contrib16QLinearLeakyReluIhEE N11onnxruntime7contrib14QLinearSigmoidIaEE N11onnxruntime7contrib14QLinearSigmoidIhEE ZN11onnxruntime7contrib16QLinearLeakyReluIaEC1ERKNS_12OpKernelInfoEEUlfE_ ZN11onnxruntime7contrib16QLinearLeakyReluIhEC1ERKNS_12OpKernelInfoEEUlfE_ ZN11onnxruntime7contrib14QLinearSigmoidIaEC1ERKNS_12OpKernelInfoEEUlPKfPfmE_ ZN11onnxruntime7contrib14QLinearSigmoidIhEC1ERKNS_12OpKernelInfoEEUlPKfPfmE_
+ /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 invalid expand shape T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] const T* onnxruntime::Tensor::Data() const [with T = short unsigned int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] const T* onnxruntime::Tensor::Data() const [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = bool] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] ZNK11onnxruntime6ExpandINS_9MLFloat16EE7ComputeEPNS_15OpKernelContextEEUllE_ ZNK11onnxruntime6ExpandINS_9MLFloat16EE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime6ExpandIbE7ComputeEPNS_15OpKernelContextEEUllE_ ZNK11onnxruntime6ExpandIbE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime6ExpandImE7ComputeEPNS_15OpKernelContextEEUllE_ ZNK11onnxruntime6ExpandImE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime6ExpandIjE7ComputeEPNS_15OpKernelContextEEUllE_ ZNK11onnxruntime6ExpandIjE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime6ExpandItE7ComputeEPNS_15OpKernelContextEEUllE_ ZNK11onnxruntime6ExpandItE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime6ExpandIhE7ComputeEPNS_15OpKernelContextEEUllE_ ZNK11onnxruntime6ExpandIhE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime6ExpandIlE7ComputeEPNS_15OpKernelContextEEUllE_ ZNK11onnxruntime6ExpandIlE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime6ExpandIiE7ComputeEPNS_15OpKernelContextEEUllE_ ZNK11onnxruntime6ExpandIiE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime6ExpandIsE7ComputeEPNS_15OpKernelContextEEUllE_ ZNK11onnxruntime6ExpandIsE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime6ExpandIaE7ComputeEPNS_15OpKernelContextEEUllE_ ZNK11onnxruntime6ExpandIaE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime6ExpandIdE7ComputeEPNS_15OpKernelContextEEUllE_ ZNK11onnxruntime6ExpandIdE7ComputeEPNS_15OpKernelContextEEUllE0_ ZNK11onnxruntime6ExpandIfE7ComputeEPNS_15OpKernelContextEEUllE_ ZNK11onnxruntime6ExpandIfE7ComputeEPNS_15OpKernelContextEEUllE0_ N11onnxruntime6ExpandIfEE N11onnxruntime6ExpandIdEE N11onnxruntime6ExpandIaEE N11onnxruntime6ExpandIsEE N11onnxruntime6ExpandIiEE N11onnxruntime6ExpandIlEE N11onnxruntime6ExpandIhEE N11onnxruntime6ExpandItEE N11onnxruntime6ExpandIjEE N11onnxruntime6ExpandImEE N11onnxruntime6ExpandIbEE N11onnxruntime6ExpandINS_9MLFloat16EEE EyeLike T1 != nullptr EyeLike : Input tensor dimension is not 2 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/eye_like.cc L<LLLL, LLL L䂶 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 ZN11onnxruntime14GatherCopyDataIiEENS_6common6StatusEPKNS_6TensorEPKhPhbmlllllRKNS_11TensorShapeElPNS_11concurrency10ThreadPoolEEUlllE0_ ZN11onnxruntime14GatherCopyDataIlEENS_6common6StatusEPKNS_6TensorEPKhPhbmlllllRKNS_11TensorShapeElPNS_11concurrency10ThreadPoolEEUlllE0_ GatherElements ]. Actual value is , /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.h GatherElements op: Cannot operate on scalar input GatherElements op: Rank of input 'data' needs to be equal to rank of input 'indices' GatherElements op: 'indices' shape should have values within bounds of 'data' shape. Invalid value in indices shape is: GatherElements op: Value in indices must be within bounds [ /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_elements.cc GatherElements op: Data type of input 'data' should match the data type of the output onnxruntime::core_impl(const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, int64_t, onnxruntime::concurrency::ThreadPool*) [with bool is_string = false; T = signed char; Tin = long int; int64_t = long int]::__lambda11 onnxruntime::core_impl(const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, int64_t, onnxruntime::concurrency::ThreadPool*) [with bool is_string = false; T = signed char; Tin = int; int64_t = long int]::__lambda11 onnxruntime::core_impl(const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, int64_t, onnxruntime::concurrency::ThreadPool*) [with bool is_string = true; T = std::basic_string<char>; Tin = long int; int64_t = long int]::__lambda11 onnxruntime::core_impl(const onnxruntime::Tensor*, const onnxruntime::Tensor*, onnxruntime::Tensor*, int64_t, onnxruntime::concurrency::ThreadPool*) [with bool is_string = true; T = std::basic_string<char>; Tin = int; int64_t = long int]::__lambda11 int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::GatherElements::GatherElements(const onnxruntime::OpKernelInfo&) *ZN11onnxruntimeL9core_implILb1ESsiEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL9core_implILb1ESsiEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE3_ *ZN11onnxruntimeL9core_implILb1ESslEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE1_ *ZN11onnxruntimeL9core_implILb1ESslEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE3_ *ZN11onnxruntimeL9core_implILb0EaiEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE2_ *ZN11onnxruntimeL9core_implILb0EaiEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE4_ *ZN11onnxruntimeL9core_implILb0EalEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE2_ *ZN11onnxruntimeL9core_implILb0EalEEvPKNS_6TensorES3_PS1_lPNS_11concurrency10ThreadPoolEEUllE4_ N11onnxruntime14GatherElementsE batch_dims GatherND invalid index found, index = indices tensor must has rank larger than 0 GatherNDBase PrepareForCompute: Input count mismatch /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_nd.cc input_tensor != nullptr && indices_tensor != nullptr last dimension of indices must not be larger than rank of input tensor indices tensor data type not supported const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::GatherND::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime12GatherNDBaseE *ZNK11onnxruntime8GatherND12GatherNumberERKNS_12GatherNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ *ZNK11onnxruntime8GatherND12GatherStringERKNS_12GatherNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ N11onnxruntime8GatherNDE ZNK11onnxruntime12GatherNDBase17PrepareForComputeIiEENS_6common6StatusERKNS_11TensorShapeEPKNS_6TensorElRNS0_7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ ZNK11onnxruntime12GatherNDBase17PrepareForComputeIlEENS_6common6StatusERKNS_11TensorShapeEPKNS_6TensorElRNS0_7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ Tensor type mismatch. type == dtype_ /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/identity_op.h onnxruntime::common::Status onnxruntime::IdentityOp<is_dropout>::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = true] void* onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::IdentityOp<is_dropout>::Compute(onnxruntime::OpKernelContext*) const [with bool is_dropout = false] N11onnxruntime10IdentityOpILb1EEE N11onnxruntime10IdentityOpILb0EEE detect_positive detect_negative IsInf Failed to obtain detect_positive /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/isinf.cc Failed to obtain detect_negative 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 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/nonzero_op.cc X input is required! failed to get first output! const T* onnxruntime::Tensor::Data() const [with T = bool] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = int] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = long int] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = float] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] N11onnxruntime7NonZeroIbEE N11onnxruntime7NonZeroIhEE N11onnxruntime7NonZeroIiEE N11onnxruntime7NonZeroIlEE N11onnxruntime7NonZeroIfEE OneHot Depth is negative. Invalid argument for values; either it's rank is more than 1 or it has more than 2 elements Invalid argument for depth; it's not a scalar. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/onehot.cc Compute Compute Compute Compute Compute Compute Compute Compute Compute Compute Compute int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) N11onnxruntime8OneHotOpIlllEE N11onnxruntime8OneHotOpIfllEE N11onnxruntime8OneHotOpIlSslEE N11onnxruntime8OneHotOpIfSslEE N11onnxruntime8OneHotOpIlflEE N11onnxruntime8OneHotOpIifiEE N11onnxruntime8OneHotOpIiffEE N11onnxruntime8OneHotOpIfffEE N11onnxruntime8OneHotOpIlifEE N11onnxruntime8OneHotOpIlffEE N11onnxruntime8OneHotOpIlfiEE >Unexpected mode of 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->DataType() == data_type && value_tensor->Shape().Size() == 1 Unsupported input data type of Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank] Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensor PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = unsigned char] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = long unsigned int] void onnxruntime::SliceIteratorBase::Init(const std::vector<long int>&, gsl::span<const long int>, gsl::span<const long int>) onnxruntime::SliceSkips::SliceSkips(const onnxruntime::TensorShape&, gsl::span<const long int>, gsl::span<const long int>) PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = unsigned int] onnxruntime::PadValue onnxruntime::PadValueFromFloat(float, onnxruntime::MLDataType) virtual onnxruntime::common::Status onnxruntime::Pad::Compute(onnxruntime::OpKernelContext*) const onnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo&) N11onnxruntime7PadBaseE N11onnxruntime3PadE /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/quantize_linear.cc zero_point_ptr == nullptr || IsScalarOr1ElementVector(zero_point_ptr) scale.Shape().NumDimensions() == 1 && scale.Shape()[0] == broadcast_dim zero_point_ptr == nullptr || (zero_point_ptr->Shape().NumDimensions() == 1 && zero_point_ptr->Shape()[0] == broadcast_dim) x_zero_point must be null or a scalar or 1D tensor or size 1. scale must be 1D tensor with size x_zero_point must be null or 1D tensor with size T* onnxruntime::Tensor::MutableData() [with T = float] T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) void onnxruntime::PrepareForQDQ(const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::Tensor*, int64_t, int64_t&, int64_t&, int64_t&) N11onnxruntime16DequantizeLinearIaEE N11onnxruntime16DequantizeLinearIhEE N11onnxruntime14QuantizeLinearIaEE N11onnxruntime14QuantizeLinearIhEE Attribute shape is not set. requested_shape[i] >= -1 unknown_dim == -1 , requested shape: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape.h A dimension cannot be less than -1, got /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reshape_helper.h At most one dimension can be -1. The dimension with value zero exceeds the dimension size of the input tensor. i < input_shape.NumDimensions() The input tensor cannot be reshaped to the requested shape. Input shape: size != 0 && (input_shape.Size() % size) == 0 gsl::narrow_cast<int64_t>(input_shape.Size()) == size A shape tensor must be a vector tensor. shapeTensor->Shape().NumDimensions() == 1 onnxruntime::Reshape_1::Reshape_1(const onnxruntime::OpKernelInfo&) onnxruntime::ReshapeHelper::ReshapeHelper(const onnxruntime::TensorShape&, std::vector<long int>&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = long int] virtual onnxruntime::common::Status onnxruntime::Reshape::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7ReshapeE N11onnxruntime9Reshape_1E Resize operator Upsample operator scale >= 1 scale > 0 cubic mode attribute is . It can only be (default) or extrapolation_value pytorch_half_pixel tf_half_pixel_for_nn align_corners tf_crop_and_resize coordinate_transform_mode:[ ] is not supportted! yet this opset round_prefer_floor round_prefer_ceil nearest_mode:[ ] is not supported! cubic_coeff_a exclude_outside scales_size > 0 Scale value should be greater than or equal to 1. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.h Scale value should be greater than 0. 'Linear' mode only support 2-D inputs or 3-D inputs ('Bilinear', 'Trilinear') or 4-D inputs or 5-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) || scales.size() == 3 || (scales.size() == 5 && scales[0] == 1 && scales[1] == 1) 'Cubic' mode only support 2-D inputs ('Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) info.GetAttr<std::string>("mode", &mode).IsOK() info.GetAttrs<float>("scales", scales_).IsOK() `tf_half_pixel_for_nn` is deprecated since opset 13, model uses the deprecated attribute exclude_outside can be set to 1 only when mode is CUBIC. Current mode is set to scales size should be greater than 0. XGBFFF G const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor*, std::vector<float>&) const onnxruntime::ResizeNearestMode onnxruntime::UpsampleBase::StringToNearestMode(const string&) static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() onnxruntime::ResizeCoordinateTransformationMode onnxruntime::UpsampleBase::StringToCoordinateTransformationMode(const string&) void onnxruntime::UpsampleBase::ScalesValidation(const std::vector<float>&, onnxruntime::UpsampleMode) const onnxruntime::UpsampleMode onnxruntime::UpsampleBase::StringToUpsampleMode(const string&) UpsampleBase onnxruntime::UpsampleBase::UpsampleBase(onnxruntime::OpKernelInfo) N11onnxruntime12UpsampleBaseE N11onnxruntime8UpsampleIfEE N11onnxruntime6ResizeIfEE N11onnxruntime8UpsampleIiEE N11onnxruntime6ResizeIiEE N11onnxruntime8UpsampleIhEE N11onnxruntime6ResizeIhEE ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE0_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE1_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE2_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE3_ ZN11onnxruntime12UpsampleBase42GetOriginalCoordinateFromResizedCoordinateENS_34ResizeCoordinateTransformationModeEEUlffffffE4_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE0_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE1_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE2_ ZN11onnxruntime12UpsampleBase27GetNearestPixelFromOriginalENS_17ResizeNearestModeEEUlfbE3_ @ ?batch_axis . Must be 0 or 1 Invalid batch_axis of batch_axis < 2 Invalid time_axis of time_axis < 2 batch_axis != time_axis ReverseSequence Unknown tensor type of . batch_size= /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.h info.GetAttr<int64_t>("batch_axis", &batch_axis).IsOK() info.GetAttr<int64_t>("time_axis", &time_axis).IsOK() time_axis and batch_axis must have different values but both are /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.cc sequence_lens shape must be {batch_size}. Got: ?T')q3m??N gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::BFloat16] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::BFloat16] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::MLFloat16] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::MLFloat16] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = short unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = short int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned char] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = std::basic_string<char>] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = std::basic_string<char>] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = double] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = bool] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = bool] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] virtual onnxruntime::common::Status onnxruntime::ReverseSequenceOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ReverseSequenceOp::ReverseSequenceOp(const onnxruntime::OpKernelInfo&) N11onnxruntime17ReverseSequenceOpE /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/scatter.cc data type is different from updates type Indices and updates must have the same rank Indices vs updates dimensions differs at position= Indices must have the same rank as Input. Indices rank= Expecting indices to be either int32_t or int64_t ScatterElements Scatter vs . Input rank= Indices dim= at pos= is greater than input dim= YYYcY7Y YXXX[X/XXWWYYW\\\T\(\[[[x[L[ [ZZZ\\pZ 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 = 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 *ZNK11onnxruntime9ScatterND13ScatterNumberERKNS_13ScatterNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ *ZNK11onnxruntime9ScatterND13ScatterStringERKNS_13ScatterNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ N11onnxruntime9ScatterNDE T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime5ShapeE Size T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime4SizeE Unexpected element size of output == output_end Starts must be a 1-D array Ends must be a 1-D array 'axes' has duplicates 'step' value cannot be 0 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 start_tensor.Shape().NumDimensions() == 1 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 onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&) [with T = unsigned char]::__lambda13 onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&) [with T = short unsigned int]::__lambda13 onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&) [with T = long unsigned int]::__lambda13 onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&) [with T = unsigned int]::__lambda13 void* onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep(void*) onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, onnxruntime::SliceOp::PrepareForComputeMetadata&) [with T = std::basic_string<char>]::__lambda13 Compute onnxruntime::common::Status onnxruntime::SliceBase::Compute(onnxruntime::OpKernelContext*) const static void onnxruntime::SliceBase::FillVectorsFromInput(const onnxruntime::Tensor&, const onnxruntime::Tensor&, const onnxruntime::Tensor*, const onnxruntime::Tensor*, std::vector<long int>&, std::vector<long int>&, std::vector<long int>&, std::vector<long int>&) int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) onnxruntime::SliceBase::SliceBase(const onnxruntime::OpKernelInfo&, bool) N11onnxruntime9SliceBaseE N11onnxruntime6Slice1E N11onnxruntime7Slice10E 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 SpaceToDepth DepthToSpace blocksize CRD DCR onnxruntime::common::Status onnxruntime::DepthToSpace<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::SpaceToDepth<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::DepthToSpace<T>::DepthToSpace(const onnxruntime::OpKernelInfo&) [with T = float] onnxruntime::SpaceDepthBase::SpaceDepthBase(const onnxruntime::OpKernelInfo&) N11onnxruntime14SpaceDepthBaseE N11onnxruntime12SpaceToDepthIfEE N11onnxruntime12DepthToSpaceIfEE e@*> Axis= NumOutputs= Input shape= Invalid value in 'split' attribute. All values must be > 0 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.h std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; }) Input cannot be split evenly on selected axis. Input shape= Cannot split using values in 'split' attribute. Axis= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was An split tensor must be a vector tensor. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.cc split_tensor->Shape().NumDimensions() == 1 Split operator does not support T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] ComputeImpl onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&) const [with T = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = long int] ComputeImpl onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&) const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] ComputeImpl onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&) const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] ComputeImpl onnxruntime::common::Status onnxruntime::Split::ComputeImpl(onnxruntime::OpKernelContext&, const onnxruntime::Tensor&) const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Split::Compute(onnxruntime::OpKernelContext*) const int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) onnxruntime::SplitBase::SplitBase(const onnxruntime::OpKernelInfo&) N11onnxruntime9SplitBaseE N11onnxruntime5SplitE . shape= must be 1 instead of Dimension of input input_shape[i] == 1 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/squeeze.h int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) static std::vector<long int> onnxruntime::SqueezeBase::ComputeOutputShape(const onnxruntime::TensorShape&, const std::vector<long int>&) const T* onnxruntime::Tensor::Data() const [with T = long int] virtual onnxruntime::common::Status onnxruntime::Squeeze::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime11SqueezeBaseE N11onnxruntime7SqueezeE Tile Input count of Tile OP mismatch, the first one is empty Input count of Tile OP mismatch, the second one is empty the tensor to be tiled using Tile OP must be atleast 1 dimensional 'repeat' input tensor must be 1 dimensional 'repeat' input tensor must have the same length as the 'input' tensor Tile doesn't 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 is outside range. is repeated. naxes > 0 num_axes > 0 [ perm: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.h v >= 0 && static_cast<uint64_t>(v) <= std::numeric_limits<size_t>::max() Attribute perm of Transpose has an invalid value. Value Method IncrementIndexAndComputeOffset assumes this value is strictly positive. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.cc Transpose not implemented for empty tensors. (local_source >= source) && (local_source < source + num_blocks * num_elts_in_block) (local_source >= source) && (local_source < source + num_blocks) (local_source >= source) && (local_source < source + sizeof(T) * num_blocks) (local_source >= source) && (local_source < source + num_blocks * blocksize) Mismatched data types between input and output Tensors. does not align with rank of input data: onnxruntime::TransposeBase::TransposeBase(const onnxruntime::OpKernelInfo&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext*) const void onnxruntime::DoTransposeImpl(int64_t, const std::vector<long int>&, size_t, size_t, const std::vector<long unsigned int>&, const uint8_t*, uint8_t*, size_t) void onnxruntime::DoTransposeImpl(int64_t, const std::vector<long int>&, size_t, size_t, const std::vector<long unsigned int>&, const string*, std::string*) void onnxruntime::DoTransposeEltWise(int64_t, const std::vector<long int>&, size_t, const std::vector<long unsigned int>&, const string*, std::string*) T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] void onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector<long int>&, size_t, const std::vector<long unsigned int>&, const uint8_t*, uint8_t*) [with T = unsigned char; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char] void onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector<long int>&, size_t, const std::vector<long unsigned int>&, const uint8_t*, uint8_t*) [with T = short unsigned int; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char] void onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector<long int>&, size_t, const std::vector<long unsigned int>&, const uint8_t*, uint8_t*) [with T = unsigned int; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char] void onnxruntime::IncrementIndexAndComputeOffsetSetup(onnxruntime::MultiIndex&, size_t, const std::vector<long int>&, const std::vector<long unsigned int>&, size_t) void onnxruntime::TypedDoTransposeEltWise(int64_t, const std::vector<long int>&, size_t, const std::vector<long unsigned int>&, const uint8_t*, uint8_t*) [with T = long unsigned int; int64_t = long int; size_t = long unsigned int; uint8_t = unsigned char] N11onnxruntime13TransposeBaseE N11onnxruntime9TransposeE Unique Unsupported tensor type of gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) N11onnxruntime6UniqueE 'axes' has a duplicate axis nullptr != p.output_tensor Missing/Invalid 'axes' attribute value /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.h info.GetAttrs("axes", axes_).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.cc axes_tensor->Shape().NumDimensions() == 0 || axes_tensor->Shape().NumDimensions() == 1 An axes tensor must be a scalar or a 1-D tensor. 'axes' has an out of range axis Compute int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::UnsqueezeBase::Prepare&) const onnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo&) N11onnxruntime13UnsqueezeBaseE N11onnxruntime9UnsqueezeE Dimension: Input dim value: Output dim value: output_dims[i] == 0 Resize: unexpected mode Upsample: unexpected mode Invalid roi input index. roi_input_idx_ > 0 sizes == nullptr Input dim is zero but required output dim is non-zero. Cannot scale 0 by any factor to generate a non-zero value. Resize: input/output value is nullptr Upsample: input/output value is nullptr Resize: input/output value's dimension mismatch Upsample: input/output value's dimension mismatch Resize: input shape needs to be at least a single dimension Upsample: input shape needs to be at least a single dimension. Resize: input tensor's dimension does not match the scales. Upsample: input tensor's dimension does not match the scales. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.cc Rank of input and output tensor should be same. output_dims.size() == dims.size() Resize: size of roi array should be 2 * N where N is the rank of input tensor X. Only one of scales or sizes must be provided as input. sizes != nullptr && sizes->Shape().Size() != 0 Resize: input tensor's rank does not match the output tensor's rank. X->Shape().GetDims().size() == output_dims.size() Either scales or sizes MUST be provided as input. BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = float] onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = int] onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = unsigned char] void onnxruntime::UpsampleBase::ParseScalesDataFromOutputSize(const std::vector<long int>&, const std::vector<long int>&, std::vector<float>&) const onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor*, std::vector<float>&) const ZN11onnxruntime16UpsampleBilinearIhEEvllllllffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEESt8functionIFfffffffEEPNS_11concurrency10ThreadPoolEEUllE_ ZN11onnxruntime17UpsampleTrilinearIhEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEESt8functionIFfffffffEEPNS_11concurrency10ThreadPoolEEUllE_ ZN11onnxruntime16UpsampleBilinearIiEEvllllllffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEESt8functionIFfffffffEEPNS_11concurrency10ThreadPoolEEUllE_ ZN11onnxruntime17UpsampleTrilinearIiEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEESt8functionIFfffffffEEPNS_11concurrency10ThreadPoolEEUllE_ ZN11onnxruntime16UpsampleBilinearIfEEvllllllffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEESt8functionIFfffffffEEPNS_11concurrency10ThreadPoolEEUllE_ ZN11onnxruntime17UpsampleTrilinearIfEEvllllllllfffRKSt6vectorIfSaIfEEbfPKT_PS6_RSt10shared_ptrINS_10IAllocatorEESt8functionIFfffffffEEPNS_11concurrency10ThreadPoolEEUllE_ @ A @ N11onnxruntime5WhereIhEE N11onnxruntime5WhereIiEE N11onnxruntime5WhereIlEE N11onnxruntime5WhereIfEE N11onnxruntime5WhereISsEE /onnxruntime_src/onnxruntime/contrib_ops/cpu/cpu_contrib_kernels.cc RegisterCpuContribKernels RegisterQuantizationKernels RegisterNchwcKernels border input dimensions instead Input's height ( ) + bottomBorder ( Input's width ( ) + rightBorder ( ) + scale_[0] ( ) + scale_[1] ( Crop Attribute border needs to be specified with four border elements, got Input is expected to have four dimensions corresponding to [N,C,H,W], got ) needs to be greater than or equal to the topBorder ( ) needs to be greater than or equal to the leftBorder ( /onnxruntime_src/onnxruntime/contrib_ops/cpu/crop.h T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime7contrib8CropBaseE N11onnxruntime7contrib4CropIfEE bilinear CropAndResize Null crop_size_ptr /onnxruntime_src/onnxruntime/contrib_ops/cpu/crop_and_resize.h specified. It should be either bilinear or nearest Number of dimensions for crop size should be exactly 1 const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::CropAndResize<T>::CropAndResize(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib13CropAndResizeIfEE DynamicSlice /onnxruntime_src/onnxruntime/contrib_ops/cpu/element_wise_ops.h info.GetAttr("scale", &scale_).IsOK() info.GetAttr("alpha", &alpha_).IsOK() info.GetAttr("beta", &beta_).IsOK() onnxruntime::contrib::Scale<T>::Scale(const onnxruntime::OpKernelInfo&) [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::Affine<T>::Affine(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib6AffineIfEE N11onnxruntime7contrib5ScaleIfEE ExpandDims Axis must be within range [ Axis is /onnxruntime_src/onnxruntime/contrib_ops/cpu/expand_dims.h axis_tensor->Shape().IsScalar() axis <= X_NumDims && axis >= -X_NumDims An axis tensor must be a scalar tensor. const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = int] virtual onnxruntime::common::Status onnxruntime::contrib::ExpandDims::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7contrib10ExpandDimsE /onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_conv.cc GetFusedActivationAttr(info, activation_).IsOK() onnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib14FusedConvFloatE /onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_gemm.cc onnxruntime::contrib::FusedGemm<T>::FusedGemm(const onnxruntime::OpKernelInfo&) [with T = float] FusedGemm N11onnxruntime7contrib9FusedGemmIfEE ImageScaler Bias size ( /onnxruntime_src/onnxruntime/contrib_ops/cpu/image_scaler.h info.GetAttrs<float>("bias", bias_).IsOK() ) does not match the number of channels ( T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::ImageScaler<T>::ImageScaler(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib11ImageScalerIfEE Inverse T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {const onnxruntime::Tensor* const&, onnxruntime::Tensor* const&, long int&, const long int&, const long int&}; Fn = onnxruntime::contrib::Inverse::ComputeImpl; Types = {float, double, onnxruntime::MLFloat16}] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] *ZNK11onnxruntime7contrib7Inverse7ComputeEPNS_15OpKernelContextEEUllE_ N11onnxruntime7contrib7InverseE /onnxruntime_src/onnxruntime/contrib_ops/cpu/layer_norm.cc op_kernel_info.GetAttr("axis", &axis_).IsOK() Compute T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] Compute Compute T* onnxruntime::Tensor::MutableData() [with T = double] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T* onnxruntime::Tensor::Data() const [with T = double] Compute onnxruntime::contrib::LayerNorm<T, simplified>::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = double; bool simplified = true] onnxruntime::contrib::LayerNorm<T, simplified>::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = double; bool simplified = false] onnxruntime::contrib::LayerNorm<T, simplified>::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = float; bool simplified = true] onnxruntime::contrib::LayerNorm<T, simplified>::LayerNorm(const onnxruntime::OpKernelInfo&) [with T = float; bool simplified = false] N11onnxruntime7contrib9LayerNormIfLb0EEE N11onnxruntime7contrib9LayerNormIfLb1EEE N11onnxruntime7contrib9LayerNormIdLb0EEE N11onnxruntime7contrib9LayerNormIdLb1EEE MatMulInteger16 A != nullptr && B != nullptr /onnxruntime_src/onnxruntime/contrib_ops/cpu/matmul_integer16.cc const T* onnxruntime::Tensor::Data() const [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = int] Compute onnxruntime::common::Status onnxruntime::contrib::MatMulInteger16<T1, T2, T3>::Compute(onnxruntime::OpKernelContext*) const [with T1 = short int; T2 = short int; T3 = int] N11onnxruntime7contrib15MatMulInteger16IssiEE MaxpoolWithMask /onnxruntime_src/onnxruntime/contrib_ops/cpu/maxpool_with_mask.h T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) const void onnxruntime::PoolAttributes::InferOutputSize(const std::vector<long int>&, std::vector<long int>*, std::vector<long int>*) const std::vector<long int> onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, std::vector<long int>*) const Compute N11onnxruntime7contrib15MaxpoolWithMaskE across_channels normalize_variance /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/mean_variance_normalization.h info.GetAttr<int64_t>("across_channels", &across_channels_).IsOK() info.GetAttr<int64_t>("normalize_variance", &normalize_variance_).IsOK() onnxruntime::MeanVarianceNormalization_0<T>::MeanVarianceNormalization_0(const onnxruntime::OpKernelInfo&, bool) [with T = float] MurmurHash3 keys input_num_bytes % 4 == 0 /onnxruntime_src/onnxruntime/contrib_ops/cpu/murmur_hash3.cc Invalid assumption of output element size sizeof(uint32_t) == output_element_bytes const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::contrib::MurmurHash3::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7contrib11MurmurHash3E invalid channel count channels_ > 0 scales_.size() == 4 X_shape.NumDimensions() == 4 X_rank == 4 channels_ <= X_shape[1] Unsupported convolution size. /onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.h info.GetAttr<int64_t>("channels", &channels_).IsOK() info.GetAttr<int64_t>("channels_last", &channels_last_).IsOK() info.GetAttrs<int64_t>("scales", scales_).IsOK() scales_[0] == 1 && scales_[1] == 1 && scales_[2] >= 1 && scales_[3] >= 1 pool_attrs_.kernel_shape.size() == 2 /onnxruntime_src/onnxruntime/contrib_ops/cpu/nchwc_ops.cc (X_shape[1] % MlasNchwcGetBlockSize()) == 0 (static_cast<size_t>(X_shape[1]) < nchwc_block_size) || ((X_shape[1] % nchwc_block_size) == 0) Not satisfied: Y->Shape() == sum_shape
+ output and sum shape must match virtual onnxruntime::common::Status onnxruntime::contrib::NchwcUpsample::Compute(onnxruntime::OpKernelContext*) const void onnxruntime::PoolAttributes::ComputeSizePadDilations(int64_t, int64_t, int64_t, int64_t*, int64_t*, int64_t, int64_t*) const void onnxruntime::PoolAttributes::InferOutputSize(const std::vector<long int>&, std::vector<long int>*, std::vector<long int>*) const std::vector<long int> onnxruntime::PoolAttributes::SetOutputSize(const onnxruntime::TensorShape&, int64_t, std::vector<long int>*) const onnxruntime::common::Status onnxruntime::contrib::NchwcPoolBase::NchwcPool(onnxruntime::OpKernelContext*, MLAS_POOLING_KIND) const virtual onnxruntime::common::Status onnxruntime::contrib::NchwcConv::Compute(onnxruntime::OpKernelContext*) const Compute virtual onnxruntime::common::Status onnxruntime::contrib::ReorderOutput::Compute(onnxruntime::OpKernelContext*) const virtual onnxruntime::common::Status onnxruntime::contrib::ReorderInput::Compute(onnxruntime::OpKernelContext*) const onnxruntime::contrib::NchwcUpsample::NchwcUpsample(const onnxruntime::OpKernelInfo&) onnxruntime::contrib::NchwcPoolBase::NchwcPoolBase(const onnxruntime::OpKernelInfo&) onnxruntime::contrib::NchwcConv::NchwcConv(const onnxruntime::OpKernelInfo&) onnxruntime::contrib::ReorderOutput::ReorderOutput(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib13NchwcPoolBaseE N11onnxruntime7contrib12ReorderInputE N11onnxruntime7contrib13ReorderOutputE N11onnxruntime7contrib9NchwcConvE N11onnxruntime7contrib12NchwcMaxPoolE N11onnxruntime7contrib16NchwcAveragePoolE N11onnxruntime7contrib13NchwcUpsampleE /onnxruntime_src/onnxruntime/contrib_ops/cpu/qlinear_binary_op.cc IsScalarOr1ElementVector(tensor_a_scale) tensor_a_zero_point == nullptr || IsScalarOr1ElementVector(tensor_a_zero_point) IsScalarOr1ElementVector(tensor_b_scale) tensor_b_zero_point == nullptr || IsScalarOr1ElementVector(tensor_b_zero_point) IsScalarOr1ElementVector(tensor_c_scale) tensor_c_zero_point == nullptr || IsScalarOr1ElementVector(tensor_c_zero_point) MatmulInteger : input1 A_scale must be a scalar or 1D tensor of size 1 MatmulInteger : input1 A_zero_point must be a scalar or 1D tensor of size 1 if given MatmulInteger : input1 B_scale must be a scalar or 1D tensor of size 1 MatmulInteger : input1 B_zero_point must be a scalar or 1D tensor of size 1 if given MatmulInteger : input1 C_scale must be a scalar or 1D tensor of size 1 MatmulInteger : input1 C_zero_point must be a scalar or 1D tensor of size 1 if given const T* onnxruntime::Tensor::Data() const [with T = signed char] void onnxruntime::contrib::{anonymous}::QLinearImpl(onnxruntime::OpKernelContext&, double, const onnxruntime::ProcessBroadcastSpanFuncs&) [with T = signed char] void onnxruntime::InputBroadcaster::AdvanceBy(size_t) void onnxruntime::BroadcastLooper(TBroadcastHelper&, const onnxruntime::ProcessBroadcastSpanFuncs&) [with TBroadcastHelper = onnxruntime::contrib::{anonymous}::QLinearBroadcastHelper] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::contrib::{anonymous}::QLinearImpl(onnxruntime::OpKernelContext&, double, const onnxruntime::ProcessBroadcastSpanFuncs&) [with T = unsigned char] *ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE_ *ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE0_ *ZN11onnxruntimeL21ParallelizeSingleSpanINS_7contrib12_GLOBAL__N_122QLinearBroadcastHelperEEEvRT_RKNS_25ProcessBroadcastSpanFuncsEEUlllE1_ N11onnxruntime7contrib10QLinearAddIaEE N11onnxruntime7contrib10QLinearAddIhEE N11onnxruntime7contrib10QLinearMulIaEE N11onnxruntime7contrib10QLinearMulIhEE /onnxruntime_src/onnxruntime/contrib_ops/cpu/qlinear_global_average_pool.cc IsScalarOr1ElementVector(tensor_x_scale) IsScalarOr1ElementVector(tensor_x_zero_point) IsScalarOr1ElementVector(tensor_y_scale) IsScalarOr1ElementVector(tensor_y_zero_point) Not satisfied: x_shape.size() >= 3
+ Input x_scale must be a scalar or 1D tensor of size 1 input x_zero_point must be a scalar or 1D tensor of size 1 if given input y_scale must be a scalar or 1D tensor of size 1 input y_zero_point must be a scalar or 1D tensor of size 1 if given const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute virtual onnxruntime::common::Status onnxruntime::contrib::QLinearGlobalAveragePool::Compute(onnxruntime::OpKernelContext*) const *ZN11onnxruntime7contrib18ComputeAveragePoolEPKhfhPhfhlllbPNS_11concurrency10ThreadPoolEEUlllE_ *ZN11onnxruntime7contrib18ComputeAveragePoolEPKhfhPhfhlllbPNS_11concurrency10ThreadPoolEEUlllE0_ *ZN11onnxruntime7contrib18ComputeAveragePoolEPKhfhPhfhlllbPNS_11concurrency10ThreadPoolEEUlllE1_ N11onnxruntime7contrib24QLinearGlobalAveragePoolE P@/onnxruntime_src/onnxruntime/contrib_ops/cpu/qlinear_lookup_table.cc tensor_x_zero_point == nullptr || IsScalarOr1ElementVector(tensor_x_zero_point) tensor_y_zero_point == nullptr || IsScalarOr1ElementVector(tensor_y_zero_point) QLinearLeakyRelu : input X_scale must be a scalar or 1D tensor of size 1 QLinearLeakyRelu : input X_zero_point must be a scalar or 1D tensor of size 1 QLinearLeakyRelu : input Y_scale must be a scalar or 1D tensor of size 1 QLinearLeakyRelu : input Y_zero_point must be a scalar or 1D tensor of size 1 T* onnxruntime::Tensor::MutableData() [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] void onnxruntime::contrib::QlinearBuildLookupTable(uint8_t*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const LookupTableArrayTransformer&) [with T = unsigned char; uint8_t = unsigned char; onnxruntime::contrib::LookupTableArrayTransformer = std::function<void(const float*, float*, long unsigned int)>] const T* onnxruntime::Tensor::Data() const [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::contrib::QlinearBuildLookupTable(uint8_t*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const LookupTableArrayTransformer&) [with T = signed char; uint8_t = unsigned char; onnxruntime::contrib::LookupTableArrayTransformer = std::function<void(const float*, float*, long unsigned int)>] ZNK11onnxruntime7contrib14QLinearSigmoidIhE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_ ZNK11onnxruntime7contrib17QLinearLookupBaseIhE11ComputeBaseIZNKS0_14QLinearSigmoidIhE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_EENS_6common6StatusES7_T_EUlllE_ ZNK11onnxruntime7contrib14QLinearSigmoidIaE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_ ZNK11onnxruntime7contrib17QLinearLookupBaseIaE11ComputeBaseIZNKS0_14QLinearSigmoidIaE7ComputeEPNS_15OpKernelContextEEUlPKfPfmE_EENS_6common6StatusES7_T_EUlllE_ ZNK11onnxruntime7contrib16QLinearLeakyReluIhE7ComputeEPNS_15OpKernelContextEEUlfE_ ZNK11onnxruntime7contrib17QLinearLookupBaseIhE11ComputeBaseIZNKS0_16QLinearLeakyReluIhE7ComputeEPNS_15OpKernelContextEEUlfE_EENS_6common6StatusES7_T_EUlllE_ ZNK11onnxruntime7contrib16QLinearLeakyReluIaE7ComputeEPNS_15OpKernelContextEEUlfE_ ZNK11onnxruntime7contrib17QLinearLookupBaseIaE11ComputeBaseIZNKS0_16QLinearLeakyReluIaE7ComputeEPNS_15OpKernelContextEEUlfE_EENS_6common6StatusES7_T_EUlllE_ *ZN11onnxruntime7contribL23QlinearBuildLookupTableIaEEvPhPKNS_6TensorES5_S5_S5_RKSt8functionIFffEEEUlPKfPfmE_ *ZN11onnxruntime7contribL23QlinearBuildLookupTableIhEEvPhPKNS_6TensorES5_S5_S5_RKSt8functionIFffEEEUlPKfPfmE_ N11onnxruntime7contrib17QLinearLookupBaseIaEE N11onnxruntime7contrib16QLinearLeakyReluIaEE N11onnxruntime7contrib17QLinearLookupBaseIhEE N11onnxruntime7contrib16QLinearLeakyReluIhEE N11onnxruntime7contrib14QLinearSigmoidIaEE N11onnxruntime7contrib14QLinearSigmoidIhEE ZN11onnxruntime7contrib16QLinearLeakyReluIaEC1ERKNS_12OpKernelInfoEEUlfE_ ZN11onnxruntime7contrib16QLinearLeakyReluIhEC1ERKNS_12OpKernelInfoEEUlfE_ ZN11onnxruntime7contrib14QLinearSigmoidIaEC1ERKNS_12OpKernelInfoEEUlPKfPfmE_ ZN11onnxruntime7contrib14QLinearSigmoidIhEC1ERKNS_12OpKernelInfoEEUlPKfPfmE_
#< QAttention /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention_cpu_base.h /onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/attention_quant.cc Not satisfied: IsScalarOr1ElementVector(input_scale_tensor)
Not satisfied: IsScalarOr1ElementVector(weight_scale_tensor)
Not satisfied: IsScalarOr1ElementVector(i_zp_tensor)
Not satisfied: IsScalarOr1ElementVector(w_zp_tensor)
- weight must be a scalar or 1D tensor of size 1 input zero point must be a scalar or 1D tensor of size 1. weight zero point must be a scalar or 1D tensor of size 1. T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] ApplyAttention const T* onnxruntime::Tensor::Data() const [with T = float] Compute N11onnxruntime7contrib13AttentionBaseE N11onnxruntime7contrib16AttentionCPUBaseE ZNK11onnxruntime7contrib10QAttentionIfE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime7contrib16AttentionCPUBase21ComputeAttentionProbsIfEEvPT_PKS3_S6_PKiPKSt6vectorIlSaIlEES4_iiiiS6_S4_PNS_11concurrency10ThreadPoolEEUlllE_ ZNK11onnxruntime7contrib16AttentionCPUBase23ComputeVxAttentionScoreIfEEvPT_S4_PKS3_S6_iiiiiS6_S4_PNS_11concurrency10ThreadPoolEEUlllE_ N11onnxruntime7contrib10QAttentionIfEE @ F @ @ @ @ @ F @ F @ F @ F/onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_matmul.cc IsScalarOr1ElementVector(b_scale_tensor) IsScalarOr1ElementVector(b_zero_point_tensor) DynamicQuantizeMatMul : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet. DynamicQuantizeMatMul : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet. IsScalarOr1ElementVector(a_scale_tensor) IsScalarOr1ElementVector(a_zero_point_tensor) MatMulIntegerToFloat : input A scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet. MatMulIntegerToFloat : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet. MatMulIntegerToFloat : input A zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet. MatMulIntegerToFloat : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet. const T* onnxruntime::Tensor::Data() const [with T = unsigned char] virtual onnxruntime::common::Status onnxruntime::contrib::MatMulIntegerToFloat::Compute(onnxruntime::OpKernelContext*) const Compute virtual onnxruntime::common::Status onnxruntime::contrib::DynamicQuantizeMatMul::Compute(onnxruntime::OpKernelContext*) const const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] ComputeCommon N11onnxruntime7contrib24MatMulIntegerToFloatBaseE N11onnxruntime7contrib21DynamicQuantizeMatMulE N11onnxruntime7contrib20MatMulIntegerToFloatE SampleOp T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7contrib8SampleOpIfEE epsilon_ >= 0 /onnxruntime_src/onnxruntime/contrib_ops/cpu/skip_layer_norm.cc input is expected to have 3 dimensions, got gamma is expected to have 1 dimension, got Last dimension of gamma and input does not match beta is expected to have 1 dimension, got Last dimension of beta and input does not match bias is expected to have 1 dimension, got Last dimension of bias and input does not match skip is expected to have same shape as input const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::SkipLayerNorm<T>::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::contrib::SkipLayerNorm<T>::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib13SkipLayerNormIfEE N11onnxruntime7contrib13SkipLayerNormIdEE Tokenizer mark attribute mark is not set pad_value mincharnum mincharnum_ > 0 separators tokenexp !tokenexp.empty() separators must not be empty !separators.empty() Can not digest separators: Can not digest tokenexp: /onnxruntime_src/onnxruntime/contrib_ops/cpu/tokenizer.cc attribute pad_value is not set attribute mincharnum is not set attribute mincharnum must have a positive value Either one of the separators OR tokenexp attributes required but none is set Expecting a non-empty tokenexp mincharnum is too big for char level tokenezation !char_tokenezation_ || mincharnum_ < 2 Input string contains invalid utf8 chars: Match contains invalid utf8 chars: tensor(string) expected as input Input dimensions are either [C] or [N][C] allowed const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::Tokenizer::Tokenizer(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib9TokenizerE /onnxruntime_src/onnxruntime/contrib_ops/cpu/transpose_scale_matmul.cc 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::contrib::TransposeMatMul::TransposeMatMul(const onnxruntime::OpKernelInfo&) TransposeMatMul N11onnxruntime7contrib15TransposeMatMulE upper Trilu IsScalarOr1ElementVector(k) /onnxruntime_src/onnxruntime/contrib_ops/cpu/trilu.h info.GetAttr<int64_t>("upper", &temp).IsOK() /onnxruntime_src/onnxruntime/contrib_ops/cpu/trilu.cc k should be a 1-D or 0-D tensor. const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::contrib::Trilu::Compute(onnxruntime::OpKernelContext*) const onnxruntime::contrib::Trilu::Trilu(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib5TriluE Input tensor to Unique op should be 1D T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7contrib6UniqueIfEE conv_window_size char_embedding_size WordConvEmbedding embedding_size attribute: conv filter size: conv_window_size attribute: conv kernal size 1: Char embedding size: Conv kernal size 2 : Conv filter size does not match embedding_size attribute. Conv kernal size 1 does not match conv_window_size attribute . Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size does not match conv kernal size 2. /onnxruntime_src/onnxruntime/contrib_ops/cpu/word_conv_embedding.cc const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime7contrib17WordConvEmbeddingE hӥhӥhӥh'is defined. No attribute with name:' unknown kernel type Attribute name and type don't match for ' /onnxruntime_src/onnxruntime/core/providers/cpu/activation/activations.h /onnxruntime_src/onnxruntime/contrib_ops/cpu/activations.h onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Elu<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::HardSigmoid<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::LeakyRelu<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Selu<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sigmoid<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Softplus<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Softsign<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Tanh<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ThresholdedRelu<float>] Init Init onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ThresholdedRelu<float>] ElementWiseKernel Init onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Selu<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::LeakyRelu<float>] ElementWiseKernel Init onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::HardSigmoid<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Elu<float>] ElementWiseKernel N11onnxruntime8functors7SigmoidIfEE N11onnxruntime8functors4TanhIfEE N11onnxruntime8functors3EluIfEE N11onnxruntime17ElementWiseKernelINS_8functors3EluIfEEEE N11onnxruntime8functors11HardSigmoidIfEE N11onnxruntime17ElementWiseKernelINS_8functors11HardSigmoidIfEEEE N11onnxruntime8functors9LeakyReluIfEE N11onnxruntime17ElementWiseKernelINS_8functors9LeakyReluIfEEEE N11onnxruntime8functors4ReluIfEE N11onnxruntime17ElementWiseKernelINS_8functors4ReluIfEEEE N11onnxruntime8functors4SeluIfEE N11onnxruntime17ElementWiseKernelINS_8functors4SeluIfEEEE N11onnxruntime17ElementWiseKernelINS_8functors7SigmoidIfEEEE N11onnxruntime8functors8SoftplusIfEE N11onnxruntime17ElementWiseKernelINS_8functors8SoftplusIfEEEE N11onnxruntime8functors8SoftsignIfEE N11onnxruntime17ElementWiseKernelINS_8functors8SoftsignIfEEEE N11onnxruntime17ElementWiseKernelINS_8functors4TanhIfEEEE N11onnxruntime8functors15ThresholdedReluIfEE N11onnxruntime17ElementWiseKernelINS_8functors15ThresholdedReluIfEEEE N11onnxruntime8functors18ParametricSoftplusIfEE N11onnxruntime8functors10ScaledTanhIfEE A AO@O9BBBB¥¥¥;?;?;?;?giP9giP9giP9giP9C:C:C:C:<<<<*=*=*=*=*>*>*>*> B B B BO@O@O@O@OOOO99997777 9@ >@If 'If' node has outputs. then_branch info == nullptr else_branch ' attribute. session_state Failed to create output tensor for If output outputs which doesn't match the subgraph's /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/if.cc num_subgraph_outputs == static_cast<size_t>(num_outputs) SetupSubgraphExecutionInfo should only be called once for each subgraph. Failed to create output tensor for 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_ N11onnxruntime11controlflow18IControlFlowKernelE 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 T* onnxruntime::Tensor::MutableData() [with T = bool] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = long int] Compute virtual onnxruntime::common::Status onnxruntime::Loop::Compute(onnxruntime::OpKernelContext*) const onnxruntime::Loop::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&) SetupSubgraphExecutionInfo virtual onnxruntime::common::Status onnxruntime::Loop::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] 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] *ZN11onnxruntime4ScanILi9EEC1ERKNS_12OpKernelInfoEEUlRKSt6vectorImSaImEERKNS_6TensorERSA_E_ *ZN11onnxruntime4ScanILi9EEC1ERKNS_12OpKernelInfoEEUlPvmE0_ N11onnxruntime4ScanILi9EEE Number of entries in ' ' was Invalid values in ' is not compatible with !is_concrete_shape_ did not. Misuse of LoopStateVariable. Attempt to move beyond end of sequence /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.cc iteration_num_ < sequence_len_ Expected AllocateFinalOutput to have been called to before we increment the iterator 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 Subgraph must have the shape set for all outputs but onnxruntime::scan::detail::OutputIterator& onnxruntime::scan::detail::OutputIterator::operator++() typename std::enable_if<(! std::is_const<T&>::value), T&>::type onnxruntime::OrtValueTensorSlicer<T>::Iterator::operator*() [with T = OrtValue; typename std::enable_if<(! std::is_const<T&>::value), T&>::type = OrtValue&] OrtValue& onnxruntime::scan::detail::OutputIterator::operator*() AllocateFinalOutput onnxruntime::common::Status onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput(const onnxruntime::TensorShape&) AllocateFinalBuffer const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Initialize void onnxruntime::scan::detail::LoopStateVariable::Next() const T& OrtValue::Get() const [with T = onnxruntime::Tensor] operator() typename std::enable_if<(! std::is_const<T&>::value), T&>::type onnxruntime::OrtValueTensorSlicer<T>::Iterator::operator*() [with T = const OrtValue; typename std::enable_if<(! std::is_const<T&>::value), T&>::type = const OrtValue&] IterateSequence CreateFeedsFetchesManager void onnxruntime::scan::detail::ReadDirections(const onnxruntime::OpKernelInfo&, const string&, std::vector<long int>&, 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 onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ParametricSoftplus<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ScaledTanh<float>] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ThresholdedRelu<float>] ElementWiseKernel Init onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ScaledTanh<float>] ElementWiseKernel Init onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ParametricSoftplus<float>] ElementWiseKernel N11onnxruntime17ElementWiseKernelINS_8functors18ParametricSoftplusIfEEEE N11onnxruntime17ElementWiseKernelINS_8functors10ScaledTanhIfEEEE N11onnxruntime7contrib4GeluIfEE 5? @AttnLSTM }, actural: ], while found! , am_attn_size}, Got: }, Got: }. Got: , aw_attn_size}. Got: /onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.h static_cast<int>(activation_func_names.size()) == num_directions_ * 3 Attention mechanism memory shape error! Expected: { Attention mechanism memory sequence lengths must have shape { Attention mechanism memory sequence lengths value must in (0, Attention memory layer weight shape error! Expected:{ Attention query layer weight shape error! Expected:{ Attention v weight shape error! Expected:{ Attention layer weight shape error! Expected: { /onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.cc ValidateInputs gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] ComputeImpl virtual onnxruntime::common::Status onnxruntime::contrib::DeepCpuAttnLstmOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&) onnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib17DeepCpuAttnLstmOpE N11onnxruntime7contrib16AttentionWrapperIfEE row[ [seqno= Expect to have present state output when past state input is given /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention.cc info.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0 Input 'input' is expected to have 3 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute. Input 'weights' is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0 Input 'weights' dimension 1 should be 3 times of dimension 0 Input 'bias' is expected to have 1 dimension, got Input 'bias' dimension 0 should have same length as dimension 1 of input 'weights' Input 'past' is only allowed for unidirectional Input 'past' is expected to have 5 dimension, got Inputs 'past' dimension 0 shall have length of 2 Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0 Inputs 'past' dimension 2 shall have length of num_heads Inputs 'past' dimension 2 shall have length of Input 'mask_index' is expected to have 1 or 2 dimensions, got Inputs 'mask_index' dimension 0 shall have length of batch_size or 2 * batch_size Inputs 'mask_index' with raw attention mask shall have shape batch_size x (past_sequence_length + sequence_length) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::Tensor* onnxruntime::contrib::AttentionBase::GetPresent(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, int, int, int, int&) const onnxruntime::contrib::AttentionBase::AttentionBase(const onnxruntime::OpKernelInfo&) ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUlllE_ N11onnxruntime7contrib9AttentionIfEE use_approximation /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/bias_gelu.cc Compute const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::common::Status onnxruntime::contrib::BiasGelu<T, use_approximation>::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool use_approximation = false] Compute N11onnxruntime7contrib8BiasGeluIfLb0EEE N11onnxruntime7contrib8BiasGeluIfLb1EEE y"=y"=y"=y"=*BL?*BL?*BL?*BL?y"= Input 0 is expected to have 1 or more dimensions, got Input 1 is expected to have 1 dimensions, got Input 1 dimension 0 should have same length as the last dimension of input 0 const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] input index out of range /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/embed_layer_norm.cc T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::contrib::EmbedLayerNorm<T>::EmbedLayerNorm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib14EmbedLayerNormIfEE input_ids is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got segment_embedding is expected to have 2 dimensions, got word_embedding and segment_embedding shall have same dimension 1 word_embedding and position_embedding shall have same dimension 1 beta is expected to have 1 dimensions, got beta is expected to have size of gamma is expected to have 1 dimensions, got gamma is expected to have size of Input 0 and 1 shall have same shape Input 0 and 7 (mask) shall have same shape const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] CDist sqeuclidean /onnxruntime_src/onnxruntime/contrib_ops/cpu/cdist.h info.GetAttr<std::string>("metric", &metric).IsOK() The second input of CDist kernel has wrong shape: Input shape dimensions mismatch: The first input of CDist kernel has wrong shape: T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::CDist<T>::CDist(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::contrib::CDist<T>::CDist(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib5CDistIfEE N11onnxruntime7contrib5CDistIdEE ConvTransposeWithDynamicPads N11onnxruntime7contrib28ConvTransposeWithDynamicPadsIfEE unimplemented activation: activation_params count mismatch Real memory steps is not in (0, not support normalize yet. !normalize_ /onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/bahdanau_attention.cc mem_steps <= max_memory_steps_ && mem_steps > 0 void onnxruntime::contrib::BahdanauAttention<T>::PrepareMemory(const gsl::span<const T>&, const gsl::span<const int>&) [with T = float] onnxruntime::contrib::BahdanauAttention<T>::BahdanauAttention(onnxruntime::AllocatorPtr, const onnxruntime::logging::Logger&, int, int, int, int, int, bool, onnxruntime::concurrency::ThreadPool*) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] N11onnxruntime7contrib19IAttentionMechanismIfEE N11onnxruntime7contrib17BahdanauAttentionIfEE Gemv found an unexpected CBLAS_TRANSPOSE input of /onnxruntime_src/onnxruntime/core/util/math_cpu.cc void onnxruntime::math::Gemv(CBLAS_TRANSPOSE, int, int, float, const T*, const T*, float, T*, Provider*) [with T = double; Provider = onnxruntime::CPUMathUtil] 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] Received null OrtThreadingOptions Received invalid value for allow_spinning. Valid values are 0 or 1 Device:[ DeviceType: MemoryType: DeviceId: OrtMemoryInfo:[ name: id: OrtMemType: OrtAllocatorType: Cuda CudaPinned Specified device is not supported. /onnxruntime_src/onnxruntime/core/framework/allocator.cc operator() N11onnxruntime12CPUAllocatorE Received invalid value of arena_extend_strategy /onnxruntime_src/onnxruntime/core/framework/allocatormgr.cc static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() CreateAllocator St14default_deleteIN11onnxruntime10IAllocatorEE St14default_deleteIN11onnxruntime8BFCArenaEE St19_Sp_counted_deleterIPN11onnxruntime8BFCArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE St19_Sp_counted_deleterIPN11onnxruntime10IAllocatorESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE cudaMalloc Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: MaxAllocSize: Could not find Region for h < chunks_.size() Size: | Requested Size: | in_use: , prev: , next: p_int >= base_int c2->prev == h1 h != kInvalidChunkHandle c->bin_num == bin_num Creating BFCArena for max_dead_bytes_per_chunk: memory limit: arena_extend_strategy Creating bins of max chunk size !chunk->in_use() Available memory of Extended allocation by bytes. Total allocated bytes: Allocated memory at Could not find chunk in bin Chunk Free Allocator: : Chunks . Bytes Requested Bin for bytes has max bytes of , Chunk State: Overall chunks summary: chunks of size . Total Sum Total of in-use chunks: Stats:
- tried to allocate 0 bytes Extending BFCArena for . bin_num: rounded_bytes: /onnxruntime_src/onnxruntime/core/framework/bfc_arena.cc /onnxruntime_src/onnxruntime/core/framework/bfc_arena.h p_int < base_int + memory_size_ !c->in_use() && (c->bin_num != kInvalidBinNum) !c->in_use() && (c->bin_num == kInvalidBinNum) !c1->in_use() && !c2->in_use() bin->free_chunks.count(h) == 1 with following configs: initial_chunk_size_bytes: BinForSize(bin_size) == BinFromIndex(b) BinForSize(bin_size + 255) == BinFromIndex(b) BinForSize(bin_size * 2 - 1) == BinFromIndex(b) BinForSize(bin_size * 2) != BinFromIndex(b) is smaller than requested bytes of Incorrect arena extend strategy. 0 == memory_size % kMinAllocationSize Failed to allocate memory for requested buffer of size BinFromIndex(c->bin_num)->free_chunks.erase(h) > 0 c->in_use() && (c->bin_num == kInvalidBinNum) Bin size: Chunks in_use/total (if not zero). Allocated bytes in_use/total. Requested bytes. b->free_chunks.size() == bin_info.total_chunks_in_bin - bin_info.total_chunks_in_use Diff between in-use and requested bytes is Summary of in-use chunks by size: Failed to find a free memory block despite calling Extend. rounded_bytes= BFC Arena ran out of memory trying to allocate . Current allocation summary follows. reserved_chunks_.find(ptr) == reserved_chunks_.end() void onnxruntime::BFCArena::DumpMemoryLog(size_t) DumpMemoryLog std::array<onnxruntime::BFCArena::BinDebugInfo, 21ul> onnxruntime::BFCArena::get_bin_debug_info() void onnxruntime::BFCArena::FreeAndMaybeCoalesce(onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::RemoveFreeChunkFromBin(onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::RemoveFreeChunkIterFromBin(onnxruntime::BFCArena::Bin::FreeChunkSet*, const iterator&) void onnxruntime::BFCArena::InsertFreeChunkIntoBin(onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::Merge(onnxruntime::BFCArena::ChunkHandle, onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::DeallocateRawInternal(void*) void onnxruntime::BFCArena::SplitChunk(onnxruntime::BFCArena::ChunkHandle, size_t) void* onnxruntime::BFCArena::FindChunkPtr(onnxruntime::BFCArena::BinNum, size_t, size_t) void* onnxruntime::BFCArena::AllocateRawInternal(size_t, bool) AllocateRawInternal virtual void* onnxruntime::BFCArena::Reserve(size_t) int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void*) const RegionFor onnxruntime::BFCArena::AllocationRegion::AllocationRegion(void*, size_t) onnxruntime::BFCArena::Extend(size_t)::__lambda3 Extend onnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::ChunkFromHandle(onnxruntime::BFCArena::ChunkHandle) static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() onnxruntime::BFCArena::BFCArena(std::unique_ptr<onnxruntime::IAllocator>, size_t, onnxruntime::ArenaExtendStrategy, int, int) BFCArena N11onnxruntime15IArenaAllocatorE N11onnxruntime8BFCArenaE fff?/onnxruntime_src/onnxruntime/core/framework/data_transfer.cc src.SizeInBytes() == dst.SizeInBytes() virtual onnxruntime::common::Status onnxruntime::CPUDataTransfer::CopyTensor(const onnxruntime::Tensor&, onnxruntime::Tensor&, int) const CopyTensors N11onnxruntime13IDataTransferE N11onnxruntime15CPUDataTransferE Tensor size mismatch There's no data transfer registered for copying tensors from /onnxruntime_src/onnxruntime/core/framework/data_transfer_manager.cc data_transfer registered is nullptr. CopyTensors (null) Not implemented sparse tensor type elem_proto != nullptr value_proto != nullptr MLDataType for: X |L<,XX /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 \<XH\7\6l6|666666667,7D7\7\746 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 N11onnxruntime8BFloat16E St3mapISsfSt4lessISsESaISt4pairIKSsfEEE St3mapIlfSt4lessIlESaISt4pairIKlfEEE Unloading DSO Failed to unload DSO: A dso with name has already been loaded. Caught exception while destructing CustomOpsLoader with message: /onnxruntime_src/onnxruntime/core/framework/ex_lib_loader.cc Caught exception while loading custom ops with message: LoadExternalLib operator() static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() ~ExLibLoader N11onnxruntime11ExLibLoaderE Requested shape: size= TraceFree for ort_value_idx= invalid index ort_value.Fence() == nullptr For ort_value with index: but the actually size is: Invalid allocation kind: mlvalue.Fence() == nullptr Memory type should only appear once. returned nullptr failed. Error: Memory pattern planner is not enabled on this execution framework. /onnxruntime_src/onnxruntime/core/framework/execution_frame.h ort_value_index >= 0 && static_cast<size_t>(ort_value_index) < all_values_size_ /onnxruntime_src/onnxruntime/core/framework/node_index_info.h offset >= 0 && static_cast<size_t>(offset) < node_values_size_ OrtValue shape verification failed. Current shape: /onnxruntime_src/onnxruntime/core/framework/execution_frame.cc shape && tensor.Shape() == *shape ort_value_idx >= 0 && static_cast<size_t>(ort_value_idx) < alloc_plan.size() TraceAllocation for ort_value_idx= node_index_info and ort_value_idx_map are out of sync and cannot be used node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx() Shape mismatch attempting to re-use buffer. . Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model. feeds.size() == feed_mlvalue_idxs.size() fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() entries which doesn't match the number of fetches the frame was initialized with of Fetches vector passed to GetOutputs contains Trying to allocate memory for unused optional inputs/outputs Tensor shape cannot contain any negative value , block in memory pattern size is: , fall back to default allocation behavior ort_value_index >= 0 && static_cast<size_t>(ort_value_index) < alloc_plan.size() We don't expect custom allocators for non-tensor types, so a shape is mandatory here. Allocation of tensor types requires a shape. buffers_.find(location) == buffers_.end() static_activation_memory_sizes_in_byte_.find(location.name) == static_activation_memory_sizes_in_byte_.end() Allocation of memory pattern buffer for TraceFree void onnxruntime::ExecutionFrame::TraceFree(int) TraceAllocate const onnxruntime::AllocPlanPerValue& onnxruntime::ExecutionFrame::GetAllocationPlan(int) ReleaseMLValueImpl onnxruntime::common::Status onnxruntime::AllocateSparseTensor(OrtValue&, const onnxruntime::DataTypeImpl&, onnxruntime::AllocatorPtr, const onnxruntime::TensorShape&, size_t, bool, const onnxruntime::SessionState&) AllocateAsPerAllocationPlan onnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan(OrtValue&, int, const onnxruntime::TensorShape*, size_t) AllocateMLValueTensorPreAllocateBuffer AllocateMLValueTensorSelfOwnBufferHelper onnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue&, int, onnxruntime::MLDataType, const OrtMemoryInfo&, const onnxruntime::TensorShape&, bool) operator() ExecutionFrame onnxruntime::ExecutionFrame::ExecutionFrame(const std::vector<int>&, const std::vector<OrtValue>&, const std::vector<int>&, const std::vector<OrtValue>&, const std::unordered_map<long unsigned int, std::function<onnxruntime::common::Status(const onnxruntime::TensorShape&, const OrtMemoryInfo&, OrtValue&, bool&)> >&, const onnxruntime::SessionState&) T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] Init void onnxruntime::IExecutionFrame::Init(const std::vector<int>&, const std::vector<OrtValue>&, const std::unordered_map<int, OrtValue>&, const std::vector<OrtValue>&) int onnxruntime::NodeIndexInfo::GetMLValueIndex(int) const const OrtValue& onnxruntime::IExecutionFrame::GetMLValue(int) const onnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(int, const onnxruntime::TensorShape*, OrtValue*&, size_t) onnxruntime::IExecutionFrame::IExecutionFrame(const onnxruntime::OrtValueNameIdxMap&, const onnxruntime::NodeIndexInfo&, const std::vector<int>&) N11onnxruntime15IExecutionFrameE N11onnxruntime14ExecutionFrameE 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 kernel def can't be NULL Failed to add kernel for and type ( Version mismatch. node_version: kernel start version: kernel_end_version: (node_version: ), Op with name ( kernel not found in No matching hash for kernel is not supported in Kernel not found : Conflicting with a registered kernel with op versions. /onnxruntime_src/onnxruntime/core/framework/kernel_registry.cc len <= op_schema.inputs().size() Found kernel for Op with name ( in the supported version range However the types are incompatible. This op has been implemented only for the following types ( but the node in the model has the following type ( Encountered following errors: ( TryCreateKernel void onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::__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. (node ). Failed to find kernel for found duplicated provider in KernelRegistryManager nullptr != type_proto /onnxruntime_src/onnxruntime/core/framework/mldata_type_utils.cc const onnxruntime::DataTypeImpl* onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg&) $
%,5y</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 T T OrtValue is TensorSequence type but has no element Tensor DataType. Tensor types should have been handled already (H(((H\\\\4 const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] TempSpace allocator not found Execution frame was null frame != nullptr OpKernel was null kernel != nullptr . Num args is Invalid arg_num of arg_num < arg_counts.size() /onnxruntime_src/onnxruntime/core/framework/op_kernel.cc node_offsets_index < node_offsets_size_ OrtValue* onnxruntime::OpKernelContext::GetOrCreateOutputMLValue(int) int onnxruntime::OpKernelContext::NumVariadicInputs(size_t) const OrtValue* onnxruntime::OpKernelContext::OutputMLValue(int, const onnxruntime::TensorShape&, size_t) T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] int onnxruntime::NodeIndexInfo::GetNodeOffset(onnxruntime::NodeIndex) const onnxruntime::OpKernelContext::OpKernelContext(onnxruntime::IExecutionFrame*, const onnxruntime::OpKernel*, onnxruntime::concurrency::ThreadPool*, const onnxruntime::logging::Logger&) cannot find allocator /onnxruntime_src/onnxruntime/core/framework/op_kernel_info.cc const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const OrtMemoryInfo& onnxruntime::OpKernelInfo::GetMemoryInfo(int, OrtMemType) const attr != nullptr Attibute name and type don't match No attribute with this name is defined. /onnxruntime_src/onnxruntime/core/framework/op_node_proto_helper.cc values.size() == static_cast<size_t>(attr->floats_size()) values.size() == static_cast<size_t>(attr->ints_size()) values.size() == static_cast<size_t>(attr->strings_size()) /onnxruntime_src/include/onnxruntime/core/framework/op_node_proto_helper.h values.size() == static_cast<size_t>(attr->tensors_size()) values.size() == static_cast<size_t>(attr->graphs_size()) ort_value.IsTensor() ort_value.IsAllocated() . Shape: . Dimension 0 is Invalid dim0_offset of dim0_offset < dim0_size /onnxruntime_src/onnxruntime/core/framework/ort_value_tensor_slicer.cc Insufficient dimensions to slice on gsl::narrow_cast<int64_t>(tensor_shape.NumDimensions()) >= slice_dimension Can't slice a non-tensor OrtValue. Type was OrtValue has not been allocated so can't be sliced. onnxruntime::OrtValueTensorSlicer<T>::Iterator::Iterator(T&, size_t, size_t, int64_t, onnxruntime::OrtValueTensorSlicer<T>::Iterator::Direction) [with T = const OrtValue; size_t = long unsigned int; int64_t = long int] static onnxruntime::OrtValueTensorSlicer<T> onnxruntime::OrtValueTensorSlicer<T>::Create(T&, int64_t, int64_t) [with T = const OrtValue; int64_t = long int] onnxruntime::OrtValueTensorSlicer<T>::Iterator::Iterator(T&, size_t, size_t, int64_t, onnxruntime::OrtValueTensorSlicer<T>::Iterator::Direction) [with T = OrtValue; size_t = long unsigned int; int64_t = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] static onnxruntime::OrtValueTensorSlicer<T> onnxruntime::OrtValueTensorSlicer<T>::Create(T&, int64_t, int64_t) [with T = OrtValue; int64_t = long int] Session Config with key [ ] already exists with value [ ]. It will be overwritten Config value is longer than maximum length 1024 Config key is empty or longer than maximum length 128 /onnxruntime_src/onnxruntime/core/framework/session_options.cc static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() AddConfigEntry /onnxruntime_src/onnxruntime/core/framework/session_state.cc SetGraphAndCreateKernels must be called prior to GetExecutionInfo. entry != kernel_create_info_map_.cend() . Do you have duplicated calls to SessionState::AddInitializedTensor function? Done saving OrtValue mappings. Allocator already registered for Kernel create info is null. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_kcis
+ weight must be a scalar or 1D tensor of size 1 input zero point must be a scalar or 1D tensor of size 1. weight zero point must be a scalar or 1D tensor of size 1. T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] ApplyAttention const T* onnxruntime::Tensor::Data() const [with T = float] Compute N11onnxruntime7contrib13AttentionBaseE N11onnxruntime7contrib16AttentionCPUBaseE ZNK11onnxruntime7contrib10QAttentionIfE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime7contrib16AttentionCPUBase21ComputeAttentionProbsIfEEvPT_PKS3_S6_PKiPKSt6vectorIlSaIlEES4_iiiiS6_S4_PNS_11concurrency10ThreadPoolEEUlllE_ ZNK11onnxruntime7contrib16AttentionCPUBase23ComputeVxAttentionScoreIfEEvPT_S4_PKS3_S6_iiiiiS6_S4_PNS_11concurrency10ThreadPoolEEUlllE_ N11onnxruntime7contrib10QAttentionIfEE @ F @ @ @ @ @ F @ F @ F @ FDynamicQuantizeLSTM DynamicQuantizeLSTM : Weight point must be constant Recurrent W_zero_point , 4* W_scale Weight zero point must be zero } for per-tensor/layer quantization or shape { } for per-channel quantization. Actual: const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7contrib19DynamicQuantizeLSTME /onnxruntime_src/onnxruntime/contrib_ops/cpu/quantization/dynamic_quantize_matmul.cc IsScalarOr1ElementVector(b_scale_tensor) IsScalarOr1ElementVector(b_zero_point_tensor) DynamicQuantizeMatMul : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet. DynamicQuantizeMatMul : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet. IsScalarOr1ElementVector(a_scale_tensor) IsScalarOr1ElementVector(a_zero_point_tensor) MatMulIntegerToFloat : input A scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet. MatMulIntegerToFloat : input B scale must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet. MatMulIntegerToFloat : input A zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet. MatMulIntegerToFloat : input B zero point must be a scalar or 1D tensor of size 1. Per-Channel is not supported yet. const T* onnxruntime::Tensor::Data() const [with T = unsigned char] virtual onnxruntime::common::Status onnxruntime::contrib::MatMulIntegerToFloat::Compute(onnxruntime::OpKernelContext*) const Compute virtual onnxruntime::common::Status onnxruntime::contrib::DynamicQuantizeMatMul::Compute(onnxruntime::OpKernelContext*) const const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] ComputeCommon N11onnxruntime7contrib24MatMulIntegerToFloatBaseE N11onnxruntime7contrib21DynamicQuantizeMatMulE N11onnxruntime7contrib20MatMulIntegerToFloatE SampleOp T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7contrib8SampleOpIfEE epsilon_ >= 0 /onnxruntime_src/onnxruntime/contrib_ops/cpu/skip_layer_norm.cc input is expected to have 3 dimensions, got gamma is expected to have 1 dimension, got Last dimension of gamma and input does not match beta is expected to have 1 dimension, got Last dimension of beta and input does not match bias is expected to have 1 dimension, got Last dimension of bias and input does not match skip is expected to have same shape as input const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::SkipLayerNorm<T>::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::contrib::SkipLayerNorm<T>::SkipLayerNorm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib13SkipLayerNormIfEE N11onnxruntime7contrib13SkipLayerNormIdEE Tokenizer mark attribute mark is not set pad_value mincharnum mincharnum_ > 0 separators tokenexp !tokenexp.empty() separators must not be empty !separators.empty() Can not digest separators: Can not digest tokenexp: /onnxruntime_src/onnxruntime/contrib_ops/cpu/tokenizer.cc attribute pad_value is not set attribute mincharnum is not set attribute mincharnum must have a positive value Either one of the separators OR tokenexp attributes required but none is set Expecting a non-empty tokenexp mincharnum is too big for char level tokenezation !char_tokenezation_ || mincharnum_ < 2 Input string contains invalid utf8 chars: Match contains invalid utf8 chars: tensor(string) expected as input Input dimensions are either [C] or [N][C] allowed const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::Tokenizer::Tokenizer(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib9TokenizerE upper Trilu IsScalarOr1ElementVector(k) /onnxruntime_src/onnxruntime/contrib_ops/cpu/trilu.h info.GetAttr<int64_t>("upper", &temp).IsOK() /onnxruntime_src/onnxruntime/contrib_ops/cpu/trilu.cc k should be a 1-D or 0-D tensor. const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::contrib::Trilu::Compute(onnxruntime::OpKernelContext*) const onnxruntime::contrib::Trilu::Trilu(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib5TriluE Input tensor to Unique op should be 1D T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7contrib6UniqueIfEE conv_window_size char_embedding_size WordConvEmbedding embedding_size attribute: conv filter size: conv_window_size attribute: conv kernal size 1: Char embedding size: Conv kernal size 2 : Conv filter size does not match embedding_size attribute. Conv kernal size 1 does not match conv_window_size attribute . Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size does not match conv kernal size 2. /onnxruntime_src/onnxruntime/contrib_ops/cpu/word_conv_embedding.cc const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime7contrib17WordConvEmbeddingE hӥhӥhӥh'is defined. No attribute with name:' unknown kernel type Attribute name and type don't match for ' /onnxruntime_src/onnxruntime/core/providers/cpu/activation/activations.h /onnxruntime_src/onnxruntime/contrib_ops/cpu/activations.h onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Elu<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::HardSigmoid<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::LeakyRelu<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Relu<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Selu<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Sigmoid<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Softplus<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Softsign<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::Tanh<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ThresholdedRelu<float>] Init Init onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ThresholdedRelu<float>] ElementWiseKernel Init onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Selu<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::LeakyRelu<float>] ElementWiseKernel Init onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::HardSigmoid<float>] ElementWiseKernel onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::Elu<float>] ElementWiseKernel N11onnxruntime8functors7SigmoidIfEE N11onnxruntime8functors4TanhIfEE N11onnxruntime8functors3EluIfEE N11onnxruntime17ElementWiseKernelINS_8functors3EluIfEEEE N11onnxruntime8functors11HardSigmoidIfEE N11onnxruntime17ElementWiseKernelINS_8functors11HardSigmoidIfEEEE N11onnxruntime8functors9LeakyReluIfEE N11onnxruntime17ElementWiseKernelINS_8functors9LeakyReluIfEEEE N11onnxruntime8functors4ReluIfEE N11onnxruntime17ElementWiseKernelINS_8functors4ReluIfEEEE N11onnxruntime8functors4SeluIfEE N11onnxruntime17ElementWiseKernelINS_8functors4SeluIfEEEE N11onnxruntime17ElementWiseKernelINS_8functors7SigmoidIfEEEE N11onnxruntime8functors8SoftplusIfEE N11onnxruntime17ElementWiseKernelINS_8functors8SoftplusIfEEEE N11onnxruntime8functors8SoftsignIfEE N11onnxruntime17ElementWiseKernelINS_8functors8SoftsignIfEEEE N11onnxruntime17ElementWiseKernelINS_8functors4TanhIfEEEE N11onnxruntime8functors15ThresholdedReluIfEE N11onnxruntime17ElementWiseKernelINS_8functors15ThresholdedReluIfEEEE N11onnxruntime8functors18ParametricSoftplusIfEE N11onnxruntime8functors10ScaledTanhIfEE A AOO@9BBBB¥¥¥;?;?;?;?giP9giP9giP9giP9C:C:C:C:<<<<*=*=*=*=*>*>*>*> B B B BO@O@O@O@OOOO99997777 9@ >@If 'If' node has outputs. then_branch info == nullptr else_branch ' attribute. session_state Failed to create output tensor for If output outputs which doesn't match the subgraph's /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/if.cc num_subgraph_outputs == static_cast<size_t>(num_outputs) SetupSubgraphExecutionInfo should only be called once for each subgraph. Failed to create output tensor for Only tensors or sequence of tensors are suppported then_feeds_fetches_manager_ && else_feeds_fetches_manager_ Subgraph SessionState was not found for ' CreateFeedsFetchesManager must be called prior to execution of graph. info.GetAttr<ONNX_NAMESPACE::GraphProto>("then_branch", &proto).IsOK() info.GetAttr<ONNX_NAMESPACE::GraphProto>("else_branch", &proto).IsOK() Execute T* OrtValue::GetMutable() [with T = onnxruntime::TensorSeq] Initialize const T* onnxruntime::Tensor::Data() const [with T = bool] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute virtual onnxruntime::common::Status onnxruntime::If::Compute(onnxruntime::OpKernelContext*) const onnxruntime::If::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&) SetupSubgraphExecutionInfo virtual onnxruntime::common::Status onnxruntime::If::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) onnxruntime::If::If(const onnxruntime::OpKernelInfo&) *ZN11onnxruntime6IfImpl7ExecuteERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK13OrtMemoryInfoR8OrtValueRbE_ N11onnxruntime11controlflow18IControlFlowKernelE N11onnxruntime2IfE Expected: Loop inputs. Found: 'Loop' node has but has Unable to get an allocator body feeds_fetches_manager_ Inconsistent shape in loop output for output. Graph in 'body' attribute of Loop should have /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/loop.cc static_cast<size_t>(num_subgraph_inputs) == subgraph_inputs.size() outputs so the subgraph requires num_subgraph_outputs - 1 == num_outputs 'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' input 'cond' should be a scalar tensor. Got shape of last_outputs[j + 1].IsTensor() All scan outputs MUST be tensors Loop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}. Subgraph SessionState was not found for 'body' attribute. info.GetAttr<ONNX_NAMESPACE::GraphProto>("body", &proto).IsOK() T* OrtValue::GetMutable() [with T = onnxruntime::TensorSeq] const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] onnxruntime::LoopImpl::Execute(const onnxruntime::FeedsFetchesManager&)::__lambda12 Execute ConcatenateLoopOutput void onnxruntime::LoopImpl::SaveOutputsAndUpdateFeeds(const std::vector<OrtValue>&, std::vector<OrtValue>&) T* onnxruntime::Tensor::MutableData() [with T = bool] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = long int] Compute virtual onnxruntime::common::Status onnxruntime::Loop::Compute(onnxruntime::OpKernelContext*) const onnxruntime::Loop::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&) SetupSubgraphExecutionInfo virtual onnxruntime::common::Status onnxruntime::Loop::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) onnxruntime::Loop::Loop(const onnxruntime::OpKernelInfo&) 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] *ZN11onnxruntime4ScanILi9EEC1ERKNS_12OpKernelInfoEEUlRKSt6vectorImSaImEERKNS_6TensorERSA_E_ *ZN11onnxruntime4ScanILi9EEC1ERKNS_12OpKernelInfoEEUlPvmE0_ N11onnxruntime4ScanILi9EEE Number of entries in ' ' was Invalid values in ' is not compatible with !is_concrete_shape_ did not. Misuse of LoopStateVariable. Attempt to move beyond end of sequence /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.cc iteration_num_ < sequence_len_ Expected AllocateFinalOutput to have been called to before we increment the iterator 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 Subgraph must have the shape set for all outputs but onnxruntime::scan::detail::OutputIterator& onnxruntime::scan::detail::OutputIterator::operator++() typename std::enable_if<(! std::is_const<T&>::value), T&>::type onnxruntime::OrtValueTensorSlicer<T>::Iterator::operator*() [with T = OrtValue; typename std::enable_if<(! std::is_const<T&>::value), T&>::type = OrtValue&] OrtValue& onnxruntime::scan::detail::OutputIterator::operator*() AllocateFinalOutput onnxruntime::common::Status onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput(const onnxruntime::TensorShape&) AllocateFinalBuffer const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Initialize void onnxruntime::scan::detail::LoopStateVariable::Next() const T& OrtValue::Get() const [with T = onnxruntime::Tensor] operator() typename std::enable_if<(! std::is_const<T&>::value), T&>::type onnxruntime::OrtValueTensorSlicer<T>::Iterator::operator*() [with T = const OrtValue; typename std::enable_if<(! std::is_const<T&>::value), T&>::type = const OrtValue&] IterateSequence CreateFeedsFetchesManager void onnxruntime::scan::detail::ReadDirections(const onnxruntime::OpKernelInfo&, const string&, std::vector<long int>&, 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 onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ParametricSoftplus<float>] onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::ScaledTanh<float>] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ThresholdedRelu<float>] ElementWiseKernel Init onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ScaledTanh<float>] ElementWiseKernel Init onnxruntime::ElementWiseKernel<F>::ElementWiseKernel(const onnxruntime::OpKernelInfo&) [with F = onnxruntime::functors::ParametricSoftplus<float>] ElementWiseKernel N11onnxruntime17ElementWiseKernelINS_8functors18ParametricSoftplusIfEEEE N11onnxruntime17ElementWiseKernelINS_8functors10ScaledTanhIfEEEE N11onnxruntime7contrib4GeluIfEE 5? @AttnLSTM }, actural: ], while found! , am_attn_size}, Got: }, Got: }. Got: , aw_attn_size}. Got: /onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.h static_cast<int>(activation_func_names.size()) == num_directions_ * 3 Attention mechanism memory shape error! Expected: { Attention mechanism memory sequence lengths must have shape { Attention mechanism memory sequence lengths value must in (0, Attention memory layer weight shape error! Expected:{ Attention query layer weight shape error! Expected:{ Attention v weight shape error! Expected:{ Attention layer weight shape error! Expected: { /onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/deep_cpu_attn_lstm.cc ValidateInputs ComputeImpl virtual onnxruntime::common::Status onnxruntime::contrib::DeepCpuAttnLstmOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&) onnxruntime::contrib::DeepCpuAttnLstmOp::DeepCpuAttnLstmOp(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib17DeepCpuAttnLstmOpE N11onnxruntime7contrib16AttentionWrapperIfEE row[ [seqno= Expect to have present state output when past state input is given /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention.cc info.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0 Input 'input' is expected to have 3 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute. Input 'weights' is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0 Input 'weights' dimension 1 should be 3 times of dimension 0 Input 'bias' is expected to have 1 dimension, got Input 'bias' dimension 0 should have same length as dimension 1 of input 'weights' Input 'past' is expected to have 5 dimension, got Inputs 'past' dimension 0 shall have length of 2 Inputs 'past' dimension 1 shall have same length as dimension 0 of input 0 Inputs 'past' dimension 2 shall have length of num_heads Inputs 'past' dimension 2 shall have length of Input 'mask_index' is expected to have 1, 2 or 3 dimensions, got Inputs 'mask_index' dimension 0 shall have length of batch_size or 2 * batch_size Inputs 'mask_index' with raw attention mask shall have shape batch_size x (past_sequence_length + sequence_length) Inputs 'mask_index' of 3d shall have shape batch_size x sequence_length x (past_sequence_length + sequence_length) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::Tensor* onnxruntime::contrib::AttentionBase::GetPresent(onnxruntime::OpKernelContext*, const onnxruntime::Tensor*, int, int, int, int&) const onnxruntime::contrib::AttentionBase::AttentionBase(const onnxruntime::OpKernelInfo&) ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUlllE_ N11onnxruntime7contrib9AttentionIfEE use_approximation /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/bias_gelu.cc Compute const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::common::Status onnxruntime::contrib::BiasGelu<T, use_approximation>::Compute(onnxruntime::OpKernelContext*) const [with T = float; bool use_approximation = false] Compute N11onnxruntime7contrib8BiasGeluIfLb0EEE N11onnxruntime7contrib8BiasGeluIfLb1EEE y"=y"=y"=y"=*BL?*BL?*BL?*BL?y"= Input 0 is expected to have 1 or more dimensions, got Input 1 is expected to have 1 dimensions, got Input 1 dimension 0 should have same length as the last dimension of input 0 const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] input index out of range /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/embed_layer_norm.cc T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::contrib::EmbedLayerNorm<T>::EmbedLayerNorm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib14EmbedLayerNormIfEE input_ids is expected to have 2 dimensions, got word_embedding is expected to have 2 dimensions, got position_embedding is expected to have 2 dimensions, got segment_embedding is expected to have 2 dimensions, got word_embedding and segment_embedding shall have same dimension 1 word_embedding and position_embedding shall have same dimension 1 beta is expected to have 1 dimensions, got beta is expected to have size of gamma is expected to have 1 dimensions, got gamma is expected to have size of Input 0 and 1 shall have same shape Input 0 and 7 (mask) shall have same shape const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] CDist sqeuclidean /onnxruntime_src/onnxruntime/contrib_ops/cpu/cdist.h info.GetAttr<std::string>("metric", &metric).IsOK() The second input of CDist kernel has wrong shape: Input shape dimensions mismatch: The first input of CDist kernel has wrong shape: const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::CDist<T>::CDist(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::contrib::CDist<T>::CDist(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib5CDistIfEE N11onnxruntime7contrib5CDistIdEE ConvTransposeWithDynamicPads N11onnxruntime7contrib28ConvTransposeWithDynamicPadsIfEE unimplemented activation: activation_params count mismatch Real memory steps is not in (0, not support normalize yet. !normalize_ /onnxruntime_src/onnxruntime/contrib_ops/cpu/attnlstm/bahdanau_attention.cc mem_steps <= max_memory_steps_ && mem_steps > 0 void onnxruntime::contrib::BahdanauAttention<T>::PrepareMemory(const gsl::span<const T>&, const gsl::span<const int>&) [with T = float] onnxruntime::contrib::BahdanauAttention<T>::BahdanauAttention(onnxruntime::AllocatorPtr, const onnxruntime::logging::Logger&, int, int, int, int, int, bool, onnxruntime::concurrency::ThreadPool*) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] N11onnxruntime7contrib19IAttentionMechanismIfEE N11onnxruntime7contrib17BahdanauAttentionIfEE dims[d_i] < d_max /onnxruntime_src/onnxruntime/core/util/math_cpu.cc Gemv found an unexpected CBLAS_TRANSPOSE input of bool onnxruntime::math::NextPosition(int64_t, const int64_t*, int64_t*) void onnxruntime::math::Gemv(CBLAS_TRANSPOSE, int, int, float, const T*, const T*, float, T*, Provider*) [with T = double; Provider = onnxruntime::CPUMathUtil] Received null OrtThreadingOptions Received invalid value for allow_spinning. Valid values are 0 or 1 Device:[ DeviceType: MemoryType: DeviceId: OrtMemoryInfo:[ name: id: OrtMemType: OrtAllocatorType: Cuda CudaPinned Specified device is not supported. /onnxruntime_src/onnxruntime/core/framework/allocator.cc operator() N11onnxruntime12CPUAllocatorE Received invalid value of arena_extend_strategy /onnxruntime_src/onnxruntime/core/framework/allocatormgr.cc static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() CreateAllocator St14default_deleteIN11onnxruntime10IAllocatorEE St14default_deleteIN11onnxruntime8BFCArenaEE St19_Sp_counted_deleterIPN11onnxruntime8BFCArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE St19_Sp_counted_deleterIPN11onnxruntime10IAllocatorESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE cudaMalloc hipMalloc Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: MaxAllocSize: Could not find Region for h < chunks_.size() Size: | Requested Size: | in_use: , prev: , next: p_int >= base_int c2->prev == h1 h != kInvalidChunkHandle c->bin_num == bin_num Creating BFCArena for max_dead_bytes_per_chunk: memory limit: arena_extend_strategy Creating bins of max chunk size !chunk->in_use() Could not find chunk in bin Chunk Free Allocator: : Chunks . Bytes Requested Bin for bytes has max bytes of , Chunk State: Overall chunks summary: chunks of size . Total Sum Total of in-use chunks: Stats:
+ Available memory of Extended allocation by bytes. Total allocated bytes: Allocated memory at tried to allocate 0 bytes Extending BFCArena for . bin_num: rounded_bytes: /onnxruntime_src/onnxruntime/core/framework/bfc_arena.cc /onnxruntime_src/onnxruntime/core/framework/bfc_arena.h p_int < base_int + memory_size_ !c->in_use() && (c->bin_num != kInvalidBinNum) !c->in_use() && (c->bin_num == kInvalidBinNum) !c1->in_use() && !c2->in_use() bin->free_chunks.count(h) == 1 with following configs: initial_chunk_size_bytes: BinForSize(bin_size) == BinFromIndex(b) BinForSize(bin_size + 255) == BinFromIndex(b) BinForSize(bin_size * 2 - 1) == BinFromIndex(b) BinForSize(bin_size * 2) != BinFromIndex(b) 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: reserved_chunks_.find(ptr) == reserved_chunks_.end() is smaller than requested bytes of Incorrect arena extend strategy. 0 == memory_size % kMinAllocationSize Failed to allocate memory for requested buffer of size Failed to find a free memory block despite calling Extend. rounded_bytes= BFC Arena ran out of memory trying to allocate . Current allocation summary follows. void onnxruntime::BFCArena::DumpMemoryLog(size_t) DumpMemoryLog std::array<onnxruntime::BFCArena::BinDebugInfo, 21ul> onnxruntime::BFCArena::get_bin_debug_info() void onnxruntime::BFCArena::FreeAndMaybeCoalesce(onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::RemoveFreeChunkFromBin(onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::RemoveFreeChunkIterFromBin(onnxruntime::BFCArena::Bin::FreeChunkSet*, const iterator&) void onnxruntime::BFCArena::InsertFreeChunkIntoBin(onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::Merge(onnxruntime::BFCArena::ChunkHandle, onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::DeallocateRawInternal(void*) void onnxruntime::BFCArena::SplitChunk(onnxruntime::BFCArena::ChunkHandle, size_t) void* onnxruntime::BFCArena::FindChunkPtr(onnxruntime::BFCArena::BinNum, size_t, size_t) void* onnxruntime::BFCArena::AllocateRawInternal(size_t, bool) AllocateRawInternal virtual void* onnxruntime::BFCArena::Reserve(size_t) int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void*) const RegionFor onnxruntime::BFCArena::AllocationRegion::AllocationRegion(void*, size_t) onnxruntime::BFCArena::Extend(size_t)::__lambda3 Extend onnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::ChunkFromHandle(onnxruntime::BFCArena::ChunkHandle) static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() onnxruntime::BFCArena::BFCArena(std::unique_ptr<onnxruntime::IAllocator>, size_t, onnxruntime::ArenaExtendStrategy, int, int) BFCArena N11onnxruntime15IArenaAllocatorE N11onnxruntime8BFCArenaE fff?/onnxruntime_src/onnxruntime/core/framework/data_transfer.cc src.SizeInBytes() == dst.SizeInBytes() virtual onnxruntime::common::Status onnxruntime::CPUDataTransfer::CopyTensor(const onnxruntime::Tensor&, onnxruntime::Tensor&, int) const CopyTensors N11onnxruntime13IDataTransferE N11onnxruntime15CPUDataTransferE Tensor size mismatch There's no data transfer registered for copying tensors from /onnxruntime_src/onnxruntime/core/framework/data_transfer_manager.cc data_transfer registered is nullptr. CopyTensors (null) Not implemented sparse tensor type elem_proto != nullptr value_proto != nullptr MLDataType for: |l\L<, ܪ̪/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 444\4̷000h0 ػ ,<L\t 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 N11onnxruntime8BFloat16E St3mapISsfSt4lessISsESaISt4pairIKSsfEEE St3mapIlfSt4lessIlESaISt4pairIKlfEEE Unloading DSO Failed to unload DSO: A dso with name has already been loaded. Caught exception while destructing CustomOpsLoader with message: /onnxruntime_src/onnxruntime/core/framework/ex_lib_loader.cc Caught exception while loading custom ops with message: LoadExternalLib operator() static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() ~ExLibLoader N11onnxruntime11ExLibLoaderE Requested shape: size= TraceFree for ort_value_idx= invalid index ort_value.Fence() == nullptr For ort_value with index: but the actually size is: Invalid allocation kind: mlvalue.Fence() == nullptr Memory type should only appear once. returned nullptr failed. Error: Memory pattern planner is not enabled on this execution framework. /onnxruntime_src/onnxruntime/core/framework/execution_frame.h ort_value_index >= 0 && static_cast<size_t>(ort_value_index) < all_values_size_ /onnxruntime_src/onnxruntime/core/framework/node_index_info.h offset >= 0 && static_cast<size_t>(offset) < node_values_size_ OrtValue shape verification failed. Current shape: /onnxruntime_src/onnxruntime/core/framework/execution_frame.cc shape && tensor.Shape() == *shape ort_value_idx >= 0 && static_cast<size_t>(ort_value_idx) < alloc_plan.size() TraceAllocation for ort_value_idx= node_index_info and ort_value_idx_map are out of sync and cannot be used node_index_info_.GetMaxMLValueIdx() == ort_value_idx_map.MaxIdx() Shape mismatch attempting to re-use buffer. . Validate usage of dim_value (values should be > 0) and dim_param (all values with the same string should equate to the same size) in shapes in the model. feeds.size() == feed_mlvalue_idxs.size() fetches.empty() || fetches.size() == fetch_mlvalue_idxs_.size() entries which doesn't match the number of fetches the frame was initialized with of Fetches vector passed to GetOutputs contains Trying to allocate memory for unused optional inputs/outputs Tensor shape cannot contain any negative value , block in memory pattern size is: , fall back to default allocation behavior ort_value_index >= 0 && static_cast<size_t>(ort_value_index) < alloc_plan.size() We don't expect custom allocators for non-tensor types, so a shape is mandatory here. Allocation of tensor types requires a shape. buffers_.find(location) == buffers_.end() static_activation_memory_sizes_in_byte_.find(location.name) == static_activation_memory_sizes_in_byte_.end() Allocation of memory pattern buffer for TraceFree void onnxruntime::ExecutionFrame::TraceFree(int) TraceAllocate const onnxruntime::AllocPlanPerValue& onnxruntime::ExecutionFrame::GetAllocationPlan(int) ReleaseMLValueImpl onnxruntime::common::Status onnxruntime::AllocateSparseTensor(OrtValue&, const onnxruntime::DataTypeImpl&, onnxruntime::AllocatorPtr, const onnxruntime::TensorShape&, size_t, bool, const onnxruntime::SessionState&) AllocateAsPerAllocationPlan onnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan(OrtValue&, int, const onnxruntime::TensorShape*, size_t) AllocateMLValueTensorPreAllocateBuffer AllocateMLValueTensorSelfOwnBufferHelper onnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue&, int, onnxruntime::MLDataType, const OrtMemoryInfo&, const onnxruntime::TensorShape&, bool) operator() ExecutionFrame onnxruntime::ExecutionFrame::ExecutionFrame(const std::vector<int>&, const std::vector<OrtValue>&, const std::vector<int>&, const std::vector<OrtValue>&, const std::unordered_map<long unsigned int, std::function<onnxruntime::common::Status(const onnxruntime::TensorShape&, const OrtMemoryInfo&, OrtValue&, bool&)> >&, const onnxruntime::SessionState&) T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] Init void onnxruntime::IExecutionFrame::Init(const std::vector<int>&, const std::vector<OrtValue>&, const std::unordered_map<int, OrtValue>&, const std::vector<OrtValue>&) int onnxruntime::NodeIndexInfo::GetMLValueIndex(int) const const OrtValue& onnxruntime::IExecutionFrame::GetMLValue(int) const onnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(int, const onnxruntime::TensorShape*, OrtValue*&, size_t) onnxruntime::IExecutionFrame::IExecutionFrame(const onnxruntime::OrtValueNameIdxMap&, const onnxruntime::NodeIndexInfo&, const std::vector<int>&) N11onnxruntime15IExecutionFrameE N11onnxruntime14ExecutionFrameE 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 1 == capability.nodes.size() No provider specified. /onnxruntime_src/onnxruntime/core/framework/func_kernel.h compute_info_->create_state_func(&context, &func_state_) == 0 /onnxruntime_src/onnxruntime/core/framework/graph_partitioner.cc did not return correct number of compiled functions . Execution Provider must generate unique names across the entire model. Existing entry in compiled kernel hashes for Must use Function based fusion when exporting compiled nodes to dll. fusion_style == IExecutionProvider::FusionStyle::Function Compiled kernel hashes must be provided compiled_kernel_hashes != nullptr onnxruntime::common::Status onnxruntime::GraphPartitioner::Partition(onnxruntime::Graph&, bool, onnxruntime::FuncManager&, onnxruntime::GraphPartitioner::Mode, std::unordered_map<std::basic_string<char>, long unsigned int>*) const Partition onnxruntime::common::Status onnxruntime::PartitionOrtFormatModelImpl(onnxruntime::Graph&, onnxruntime::FuncManager&, onnxruntime::KernelRegistryManager&, onnxruntime::KernelRegistry&, onnxruntime::IExecutionProvider&, std::unordered_map<std::basic_string<char>, long unsigned int>&, int&) PartitionOrtFormatModelImpl PartitionOrtFormatModel InlineNodes onnxruntime::FunctionKernel::FunctionKernel(const onnxruntime::OpKernelInfo&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const onnxruntime::Node* onnxruntime::PlaceNode(onnxruntime::Graph&, const onnxruntime::IndexedSubGraph&, const onnxruntime::KernelRegistryManager&, const string&, onnxruntime::IExecutionProvider::FusionStyle, onnxruntime::GraphPartitioner::Mode, int&) onnxruntime::common::Status onnxruntime::PartitionOnnxFormatModelImpl(onnxruntime::Graph&, bool, onnxruntime::FuncManager&, onnxruntime::KernelRegistryManager&, onnxruntime::KernelRegistry&, onnxruntime::IExecutionProvider&, onnxruntime::GraphPartitioner::Mode, int&) PartitionOnnxFormatModelImpl PartitionOnnxFormatModel N11onnxruntime14FunctionKernelE kernel def can't be NULL Failed to add kernel for and type ( Version mismatch. node_version: kernel start version: kernel_end_version: (node_version: ), Op with name ( kernel not found in No matching hash for kernel is not supported in Kernel not found : Conflicting with a registered kernel with op versions. /onnxruntime_src/onnxruntime/core/framework/kernel_registry.cc len <= op_schema.inputs().size() Found kernel for Op with name ( in the supported version range However the types are incompatible. This op has been implemented only for the following types ( but the node in the model has the following type ( Encountered following errors: ( TryCreateKernel void onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::__lambda11; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::Resolve(const string&) const::__lambda12] void onnxruntime::{anonymous}::TraverseFormalParametersWithTypeProto(const onnxruntime::Node&, ParamFilterFn, TraverseFn) [with ParamFilterFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::__lambda9; TraverseFn = onnxruntime::{anonymous}::TypeBindingResolver::TypeBindingResolver(const onnxruntime::Node&, bool)::__lambda10] The node is not placed on any Execution Provider. (node ). Failed to find kernel for found duplicated provider in KernelRegistryManager nullptr != type_proto /onnxruntime_src/onnxruntime/core/framework/mldata_type_utils.cc const onnxruntime::DataTypeImpl* onnxruntime::utils::GetMLDataType(const onnxruntime::NodeArg&) d?FM5U\elu(Ӯ/|/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]::__lambda11 onnxruntime::NodeIndexInfo::Init(const TValidNodes&, onnxruntime::NodeIndex, const onnxruntime::OrtValueNameIdxMap&) [with TValidNodes = onnxruntime::ConstGraphNodes; onnxruntime::NodeIndex = long unsigned int]::__lambda11 ZN11onnxruntime13NodeIndexInfo4InitINS_15ConstGraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ ZN11onnxruntime13NodeIndexInfo4InitINS_15ConstGraphNodesEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE_ ZN11onnxruntime13NodeIndexInfo4InitINS_10ValidNodesIKSt6vectorIPKNS_4NodeESaIS6_EEEEEEvRKT_mRKNS_18OrtValueNameIdxMapEEUlRKNS_7NodeArgEbE0_ type_proto is not of type map!
+
type_proto is not of type sequence! :tensor type <|< OrtValue is TensorSequence type but has no element Tensor DataType. Tensor types should have been handled already x@h const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] TempSpace allocator not found Execution frame was null frame != nullptr OpKernel was null kernel != nullptr . Num args is Invalid arg_num of arg_num < arg_counts.size() /onnxruntime_src/onnxruntime/core/framework/op_kernel.cc node_offsets_index < node_offsets_size_ OrtValue* onnxruntime::OpKernelContext::GetOrCreateOutputMLValue(int) int onnxruntime::OpKernelContext::NumVariadicInputs(size_t) const OrtValue* onnxruntime::OpKernelContext::OutputMLValue(int, const onnxruntime::TensorShape&, size_t) T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] int onnxruntime::NodeIndexInfo::GetNodeOffset(onnxruntime::NodeIndex) const onnxruntime::OpKernelContext::OpKernelContext(onnxruntime::IExecutionFrame*, const onnxruntime::OpKernel*, onnxruntime::concurrency::ThreadPool*, const onnxruntime::logging::Logger&) cannot find allocator /onnxruntime_src/onnxruntime/core/framework/op_kernel_info.cc const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const OrtMemoryInfo& onnxruntime::OpKernelInfo::GetMemoryInfo(int, OrtMemType) const is defined. No attribute with name: but is of type: expected to be of type: Attribute: attr != nullptr is expected to have type: Requested attribute: Attibute name and type don't match No attribute with this name is defined. /onnxruntime_src/onnxruntime/core/framework/op_node_proto_helper.cc values.size() == static_cast<size_t>(attr->floats_size()) values.size() == static_cast<size_t>(attr->ints_size()) values.size() == static_cast<size_t>(attr->strings_size()) /onnxruntime_src/include/onnxruntime/core/framework/op_node_proto_helper.h values.size() == static_cast<size_t>(attr->tensors_size()) values.size() == static_cast<size_t>(attr->graphs_size()) !using_counters_ current <= buffer_size_ /onnxruntime_src/onnxruntime/core/framework/mem_pattern_planner.h void onnxruntime::MemPatternPlanner::TraceAllocation(int, size_t) ort_value.IsTensor() ort_value.IsAllocated() . Shape: . Dimension 0 is Invalid dim0_offset of dim0_offset < dim0_size /onnxruntime_src/onnxruntime/core/framework/ort_value_tensor_slicer.cc Insufficient dimensions to slice on gsl::narrow_cast<int64_t>(tensor_shape.NumDimensions()) >= slice_dimension Can't slice a non-tensor OrtValue. Type was OrtValue has not been allocated so can't be sliced. onnxruntime::OrtValueTensorSlicer<T>::Iterator::Iterator(T&, size_t, size_t, int64_t, onnxruntime::OrtValueTensorSlicer<T>::Iterator::Direction) [with T = const OrtValue; size_t = long unsigned int; int64_t = long int] static onnxruntime::OrtValueTensorSlicer<T> onnxruntime::OrtValueTensorSlicer<T>::Create(T&, int64_t, int64_t) [with T = const OrtValue; int64_t = long int] onnxruntime::OrtValueTensorSlicer<T>::Iterator::Iterator(T&, size_t, size_t, int64_t, onnxruntime::OrtValueTensorSlicer<T>::Iterator::Direction) [with T = OrtValue; size_t = long unsigned int; int64_t = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] static onnxruntime::OrtValueTensorSlicer<T> onnxruntime::OrtValueTensorSlicer<T>::Create(T&, int64_t, int64_t) [with T = OrtValue; int64_t = long int] Provider_SetHost GetProvider libonnxruntime_providers_shared.so /onnxruntime_src/onnxruntime/core/framework/provider_bridge_ort.cc OrtSessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared library OrtSessionOptionsAppendExecutionProvider_Dnnl: Failed to load shared library SessionOptionsAppendExecutionProvider_OpenVINO: Failed to load shared library OrtSessionOptionsAppendExecutionProvider_Tensorrt: Failed to load shared library libonnxruntime_providers_dnnl.so libonnxruntime_providers_openvino.so libonnxruntime_providers_tensorrt.so Ensure Get const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&) static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() N11onnxruntime32Provider_NodeAttributes_IteratorE N11onnxruntime44Provider_TensorShapeProto_Dimension_IteratorE N11onnxruntime27Provider_Node__NodeIteratorE N11onnxruntime27Provider_Node__EdgeIteratorE N11onnxruntime34Provider_IExecutionProvider_RouterE N11onnxruntime12ProviderHostE N11onnxruntime49Provider_TensorShapeProto_Dimension_Iterator_ImplE N11onnxruntime37Provider_NodeAttributes_Iterator_ImplE N11onnxruntime32Provider_Node__NodeIterator_ImplE N11onnxruntime32Provider_Node__EdgeIterator_ImplE N11onnxruntime19OpKernel_TranslatorE N11onnxruntime39Provider_IExecutionProvider_Router_ImplE N11onnxruntime16ProviderHostImplE N11onnxruntime36IExecutionProviderFactory_TranslatorE ZN11onnxruntime16ProviderHostImpl33Provider_KernelRegistry__RegisterEPNS_14KernelRegistryEONS_25Provider_KernelCreateInfoEEUlRKNS_12OpKernelInfoEE_ St23_Sp_counted_ptr_inplaceIN11onnxruntime36IExecutionProviderFactory_TranslatorESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE Session Config with key [ ] already exists with value [ ]. It will be overwritten Received nullptr for name. Config value is longer than maximum length 1024 Config key is empty or longer than maximum length 128 /onnxruntime_src/onnxruntime/core/framework/session_options.cc Received nullptr for OrtValue. Buffer containing the initializer must be owned by the user. An OrtValue for this name has already been added. Received OrtValue is not a tensor. Only tensors are supported. const T& OrtValue::Get() const [with T = onnxruntime::Tensor] static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() AddConfigEntry /onnxruntime_src/onnxruntime/core/framework/session_state.cc SetGraphAndCreateKernels must be called prior to GetExecutionInfo. entry != kernel_create_info_map_.cend() . Do you have duplicated calls to SessionState::AddInitializedTensor function? Done saving OrtValue mappings. Allocator already registered for Kernel create info is null. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_kcis
Kernel create info node indices are null. Invalid ORT format model. Satisfied, but should not be: nullptr == node_indices
Kernel create info hashes are null. Invalid ORT format model. Satisfied, but should not be: nullptr == kernel_def_hashes
Size mismatch for kernel create info node indexes and hashes. Invalid ORT format model. Not satisfied: node_indices->size() == kernel_def_hashes->size()
- Satisfied, but should not be: node == nullptr
- SessionState for subgraphs is null. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_sub_graph_session_states
+ Satisfied, but should not be: compiled_kernel_hashes.empty()
+ Unable to find compiled kernel hash for node ' Satisfied, but should not be: hash_info == compiled_kernel_hashes.cend()
+ SessionState for subgraphs is null. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_sub_graph_session_states
Subgraph SessionState entry for Satisfied, but should not be: nullptr == fbs_sub_graph_ss
Satisfied, but should not be: nullptr == fbs_sub_session_state
- Failed to find output name in the mapping: Failed to find input name in the mapping: Missing session state for subgraph. Node:' entry != node_to_subgraph_ss.second.cend() Using an input in multiple nodes on different devices is not supported currently. Input: Only one node should produce an output. Existing entry for output_names_to_nodeinfo.empty() existing_entries.find(attribute_name) == existing_entries.cend() Main Graph instance should have populated all subgraphs when being resolved. Could not find OrtValue with idx ' node_index_info_ duplicated ort_value index: SaveMLValueNameIndexMapping . Ignoring allocator from Can't find node with index . Invalid ORT format model. Subgraph SessionState for session.disable_prepacking ' OpType: Attribute: p_op_kernel is used by node ) and node Entry exists in node for attribute onnxruntime::common::Status onnxruntime::SessionState::FinalizeSessionStateImpl(const std::basic_string<char>&, onnxruntime::KernelRegistryManager&, const onnxruntime::Node*, const onnxruntime::SessionOptions&, bool) FinalizeSessionStateImpl FinalizeSessionState LoadFromOrtFormat CreateSubgraphSessionState onnxruntime::common::Status onnxruntime::SessionState::CreateSubgraphSessionState() GetSubGraphSessionStatesOrtFormat SaveToOrtFormat void onnxruntime::SessionState::UpdateToBeExecutedNodes(const std::vector<int>&) const onnxruntime::NodeIndexInfo& onnxruntime::SessionState::GetNodeIndexInfo() const void onnxruntime::SessionState::AddSubgraphSessionState(onnxruntime::NodeIndex, const string&, std::unique_ptr<onnxruntime::SessionState>) void onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const string&, const onnxruntime::SessionState::NodeInfo&) PrepackInitializedConstantTensors const onnxruntime::KernelCreateInfo& onnxruntime::SessionState::GetNodeKernelCreateInfo(onnxruntime::NodeIndex) const PopulateKernelCreateInfo CreateGraphInfo SetupAllocators *ZN11onnxruntime12SessionState33PrepackInitializedConstantTensorsEvEUlRKNS_7NodeArgEbE_ *ZN11onnxruntime12SessionState23UpdateToBeExecutedNodesERKSt6vectorIiSaIiEEEUlPKNS_4NodeEE_ *ZN11onnxruntime12SessionState24FinalizeSessionStateImplERKSsRNS_21KernelRegistryManagerEPKNS_4NodeERKNS_14SessionOptionsEbEUliRK8OrtValueRKNS_11OrtCallbackEbE0_ *ZN11onnxruntime12SessionState15SetupAllocatorsEvEUli10OrtMemTypeE_ N11onnxruntime25ISequentialPlannerContextE N11onnxruntime24SequentialPlannerContextE Graph input with name is not used by any node. Saving initialized tensors. bytes for , Got Failed to copy tensor to Deserialize tensor /onnxruntime_src/onnxruntime/core/framework/session_state_utils.cc OrtValue indexes should have been populated. ort_value_name_idx_map.MaxIdx() > -1 [Memory] SessionStateInitializer statically allocates string tensor is not supported for copying between allocators Internal error. The preallocated buffer is too small. Requires Done saving initialized tensors ForEachWithIndex operator() operator() SaveInputOutputNamesToNodeMapping DeserializeTensorProto onnxruntime::common::Status onnxruntime::session_state_utils::SaveInitializedTensors(const onnxruntime::Env&, const std::basic_string<char>&, const onnxruntime::GraphViewer&, const OrtMemoryInfo&, const onnxruntime::OrtValueNameIdxMap&, onnxruntime::ITensorAllocator&, const std::function<onnxruntime::common::Status(int, const OrtValue&, const onnxruntime::OrtCallback&, bool)>&, const onnxruntime::logging::Logger&, const onnxruntime::DataTransferManager&) SaveInitializedTensors *ZN11onnxruntime19session_state_utils33SaveInputOutputNamesToNodeMappingERKNS_11GraphViewerERNS_12SessionStateERKSt6vectorIPKNS_7NodeArgESaIS9_EEEUlRS8_mE_ *ZN11onnxruntime19session_state_utils33SaveInputOutputNamesToNodeMappingERKNS_11GraphViewerERNS_12SessionStateERKSt6vectorIPKNS_7NodeArgESaIS9_EEEUlRS8_mE0_ tensor size overflow shape.Size() must >=0 dtype_ != nullptr p_type != nullptr /onnxruntime_src/onnxruntime/core/framework/tensor.cc Tensor is expected to contain one of the primitive data types. Got: tensor failed memory size calculation void onnxruntime::Tensor::Init(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, onnxruntime::AllocatorPtr, ptrdiff_t) size_t onnxruntime::Tensor::SizeInBytes() const onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, std::shared_ptr<onnxruntime::IAllocator>, 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 model format error! Need a key for the external data info model format error! Need a value for the external data info model format error! Missing 'location' checksum model format error! Invalid dimension of dimension <= num_dims /onnxruntime_src/onnxruntime/core/framework/tensor_shape.cc dimstart <= dimend && dimend <= size() Invalid tensor shape slice argument. for SizeFromDimension. Tensor has onnxruntime::TensorShape onnxruntime::TensorShape::Slice(size_t, size_t) const int64_t onnxruntime::TensorShape::SizeFromDimension(size_t) const int64_t onnxruntime::TensorShape::SizeToDimension(size_t) const OrtValue is not a Tensor Argument is not a tensor /onnxruntime_src/onnxruntime/core/framework/tensor_type_and_shape.cc
-
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 in 'Constant' node ' data overflow Unsupported type: 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( Unsupported sparse tensor data type of rank == dims.size() && rank > 0 cur_index == &*indices_data.cend() Invalid SparseTensor indices. Should be rank 0 or 1. Got: Unsupported attribute value type of 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: 66644444`444R0 ////000t000.lKtFFDGGHHHlKTII$JJKlKlK FXVYU/XVuWTWpVWTUBTSXXS
-
onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto&, gsl::span<const long int>, std::function<void(long unsigned int, long unsigned int)>) [with T = std::basic_string<char>; size_t = long unsigned int] onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto&, gsl::span<const long int>, std::function<void(long unsigned int, long unsigned int)>) [with T = long unsigned int; size_t = long unsigned int] onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto&, gsl::span<const long int>, std::function<void(long unsigned int, long unsigned int)>) [with T = unsigned int; size_t = long unsigned int] SparseTensorProtoToDenseTensorProto onnxruntime::common::Status onnxruntime::utils::ConstantNodeProtoToTensorProto(const onnx::NodeProto&, onnx::TensorProto&) ConstantNodeProtoToTensorProto 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 *ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERNS1_11TensorProtoEEUlmmE_ *ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERNS1_11TensorProtoEEUlmmE0_ *ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERNS1_11TensorProtoEEUlmmE1_ RknpuExecutionProvider exec_plan_ptr allocator != nullptr 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() Failed to find allocator for device Only one thread was configured for parallel execution. Hence will use sequential execution. CopyOutputsAcrossDevices CopyInputsAcrossDevices onnxruntime::common::Status onnxruntime::utils::CopyInputsAcrossDevices(const onnxruntime::SessionState&, const std::vector<OrtValue>&, std::vector<OrtValue>&, const std::vector<onnxruntime::MLValueCopyInfo>&) ExecuteGraphImpl ExecuteGraph T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] BatchOrCopyMLValue onnxruntime::common::Status onnxruntime::utils::BatchOrCopyMLValue(const onnxruntime::SessionState&, const onnxruntime::MLValueCopyInfo&, const OrtValue&, OrtValue&, std::vector<onnxruntime::IDataTransfer::SrcDstPair>*) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] CopyOneInputAcrossDevices bool onnxruntime::utils::FinalizeCopyInfoForFetches(const std::vector<const OrtMemoryInfo*>&, std::vector<onnxruntime::MLValueCopyInfo>&) bool onnxruntime::utils::FinalizeCopyInfoForFeeds(const std::vector<OrtDevice>&, std::vector<onnxruntime::MLValueCopyInfo>&) CalculateStaticCopyInfoForFeed CalculateStaticCopyInfoForFeeds InitializeFeedFetchCopyInfo const OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::OrtValueNameIdxMap&, const onnxruntime::SequentialExecutionPlan&, const string&) FindMemoryInfoForValue const OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::SessionState&, const string&) N11onnxruntime9IExecutorE Allocate AllocateStatically PreExisting Reuse AllocateOutput Share nullptr != tensor_type_base reused != reused_for Can not find the node p_provider Allocation Plan:
+ Failed to find input name in the mapping: Failed to find output name in the mapping: Missing session state for subgraph. Node:' entry != node_to_subgraph_ss.second.cend() Using an input in multiple nodes on different devices is not supported currently. Input: Only one node should produce an output. Existing entry for output_names_to_nodeinfo.empty() existing_entries.find(attribute_name) == existing_entries.cend() Main Graph instance should have populated all subgraphs when being resolved. Could not find OrtValue with idx ' node_index_info_ duplicated ort_value index: SaveMLValueNameIndexMapping . Ignoring allocator from Can't find node with index . Invalid ORT format model. '. Subgraph SessionState for session.disable_prepacking ' OpType: Attribute: p_op_kernel is used by node ) and node Entry exists in node for attribute onnxruntime::common::Status onnxruntime::SessionState::FinalizeSessionStateImpl(const std::basic_string<char>&, onnxruntime::KernelRegistryManager&, const onnxruntime::Node*, const onnxruntime::SessionOptions&, bool, std::unordered_map<std::basic_string<char>, long unsigned int>&) FinalizeSessionStateImpl FinalizeSessionState operator() LoadFromOrtFormat CreateSubgraphSessionState onnxruntime::common::Status onnxruntime::SessionState::CreateSubgraphSessionState() GetSubGraphSessionStatesOrtFormat SaveToOrtFormat void onnxruntime::SessionState::UpdateToBeExecutedNodes(const std::vector<int>&) const onnxruntime::NodeIndexInfo& onnxruntime::SessionState::GetNodeIndexInfo() const void onnxruntime::SessionState::AddSubgraphSessionState(onnxruntime::NodeIndex, const string&, std::unique_ptr<onnxruntime::SessionState>) void onnxruntime::SessionState::AddOutputNameToNodeInfoMapping(const string&, const onnxruntime::SessionState::NodeInfo&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] PrepackConstantInitializedTensors const onnxruntime::KernelCreateInfo& onnxruntime::SessionState::GetNodeKernelCreateInfo(onnxruntime::NodeIndex) const PopulateKernelCreateInfo CreateGraphInfo SetupAllocators *ZN11onnxruntime12SessionState23UpdateToBeExecutedNodesERKSt6vectorIiSaIiEEEUlPKNS_4NodeEE_ *ZN11onnxruntime12SessionState24FinalizeSessionStateImplERKSsRNS_21KernelRegistryManagerEPKNS_4NodeERKNS_14SessionOptionsEbRSt13unordered_mapISsmSt4hashISsESt8equal_toISsESaISt4pairIS1_mEEEEUliRK8OrtValueRKNS_11OrtCallbackEbE0_ *ZN11onnxruntime12SessionState15SetupAllocatorsEvEUli10OrtMemTypeE_ N11onnxruntime25ISequentialPlannerContextE N11onnxruntime24SequentialPlannerContextE Graph input with name is not used by any node. Saving initialized tensors. bytes for , Got Failed to copy tensor to Deserialize tensor /onnxruntime_src/onnxruntime/core/framework/session_state_utils.cc OrtValue indexes should have been populated. ort_value_name_idx_map.MaxIdx() > -1 Cannot use user supplied initializer with name: ( ) because the ORT planned memory location device ) is different from what is supplied ( entry != initialized_tensors_to_allocate.end() [Memory] SessionStateInitializer statically allocates Using user supplied initializer with name ( string tensor is not supported for copying between allocators Internal error. The preallocated buffer is too small. Requires Done saving initialized tensors ForEachWithIndex operator() operator() SaveInputOutputNamesToNodeMapping DeserializeTensorProto operator() onnxruntime::common::Status onnxruntime::session_state_utils::SaveInitializedTensors(const onnxruntime::Env&, const std::basic_string<char>&, const onnxruntime::GraphViewer&, const OrtMemoryInfo&, const onnxruntime::OrtValueNameIdxMap&, const std::vector<int>&, onnxruntime::ITensorAllocator&, const std::function<onnxruntime::common::Status(int, const OrtValue&, const onnxruntime::OrtCallback&, bool)>&, const onnxruntime::logging::Logger&, const onnxruntime::DataTransferManager&, const onnxruntime::ExecutionPlanBase&, const onnxruntime::SessionOptions&) SaveInitializedTensors *ZN11onnxruntime19session_state_utils33SaveInputOutputNamesToNodeMappingERKNS_11GraphViewerERNS_12SessionStateERKSt6vectorIPKNS_7NodeArgESaIS9_EEEUlRS8_mE_ *ZN11onnxruntime19session_state_utils33SaveInputOutputNamesToNodeMappingERKNS_11GraphViewerERNS_12SessionStateERKSt6vectorIPKNS_7NodeArgESaIS9_EEEUlRS8_mE0_ tensor size overflow shape.Size() must >=0 dtype_ != nullptr p_type != nullptr /onnxruntime_src/onnxruntime/core/framework/tensor.cc Tensor is expected to contain one of the primitive data types. Got: tensor failed memory size calculation void onnxruntime::Tensor::Init(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, onnxruntime::AllocatorPtr, ptrdiff_t) size_t onnxruntime::Tensor::SizeInBytes() const onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, std::shared_ptr<onnxruntime::IAllocator>) onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, const OrtMemoryInfo&, ptrdiff_t) Internal error. Mem pattern for initializer ' is not found ' failed duplicated location /onnxruntime_src/onnxruntime/core/framework/tensor_allocator_with_mem_pattern.h Weight buffer for initializer ' Get preallocated buffer for initializer ' Failed to get allocator for location: Trace FinalizePlan N11onnxruntime16ITensorAllocatorE N11onnxruntime29TensorAllocatorWithMemPatternE model format error! Need a key for the external data info model format error! Need a value for the external data info model format error! Missing 'location' checksum model format error! Invalid dimension of dimension <= num_dims /onnxruntime_src/onnxruntime/core/framework/tensor_shape.cc dimstart <= dimend && dimend <= size() Invalid tensor shape slice argument. for SizeFromDimension. Tensor has onnxruntime::TensorShape onnxruntime::TensorShape::Slice(size_t, size_t) const int64_t onnxruntime::TensorShape::SizeFromDimension(size_t) const int64_t onnxruntime::TensorShape::SizeToDimension(size_t) const OrtValue is not a Tensor Argument is not a tensor /onnxruntime_src/onnxruntime/core/framework/tensor_type_and_shape.cc
+
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 Unsupported type: Indicies expected to be INT64 in 'Constant' node ' Must have a valid data type HasDataType(dense_proto) UnpackTensor: the pre-allocate size does not match the size in proto Unsupported sparse tensor data type of 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( Not satisfied: indices.data_type() == ONNX_NAMESPACE ::TensorProto_DataType_INT64
+ Sparse Indicies raw data size does not match expected. Not satisfied: indices.raw_data().size() == (elements * sizeof(int64_t))
+ Invalid SparseTensor indices. Should either have raw or int64 data rank == dims.size() && rank > 0 cur_index == &*indices_data.cend() Invalid SparseTensor indices. Should be rank 0 or 1. Got: BUG! Report to onnxruntime team. Unsupported attribute value type of 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: 6T6s6s65656T666s6566T6666567j777N7N7j7-7-77N7-7j7-777N7,4tԕD,tTĘ,,̓P0@ H^4zu G
+
DenseTensorToSparseTensorProto onnxruntime::common::Status onnxruntime::utils::DenseTensorToSparseTensorProto(const onnx::TensorProto&, onnx::SparseTensorProto&) onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto&, gsl::span<const long int>, std::function<void(long unsigned int, long unsigned int)>) [with T = unsigned int; size_t = long unsigned int] CopySparseData onnxruntime::common::Status onnxruntime::utils::CopySparseData(size_t, const onnx::TensorProto&, gsl::span<const long int>, std::function<void(long unsigned int, long unsigned int)>) [with T = unsigned char; size_t = long unsigned int] CopySparseData onnxruntime::common::Status onnxruntime::utils::SparseTensorProtoToDenseTensorProto(const onnx::SparseTensorProto&, onnx::TensorProto&) SparseTensorProtoToDenseTensorProto onnxruntime::common::Status onnxruntime::utils::ConstantNodeProtoToTensorProto(const onnx::NodeProto&, onnx::TensorProto&) ConstantNodeProtoToTensorProto 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 *ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERNS1_11TensorProtoEEUlmmE_ *ZN11onnxruntime5utils35SparseTensorProtoToDenseTensorProtoERKN4onnx17SparseTensorProtoERNS1_11TensorProtoEEUlmmE0_ DnnlExecutionProvider NupharExecutionProvider OpenVINOExecutionProvider NnapiExecutionProvider RknpuExecutionProvider exec_plan_ptr allocator != nullptr copy_info.size() == num_feeds /onnxruntime_src/onnxruntime/core/framework/utils.cc InternalTestingExecutionProvider feed_locations.size() == copy_info.size() fetch_alloc_info.size() == copy_info.size() Failed to find allocator for device Only one thread was configured for parallel execution. Hence will use sequential execution. CopyOutputsAcrossDevices CopyInputsAcrossDevices onnxruntime::common::Status onnxruntime::utils::CopyInputsAcrossDevices(const onnxruntime::SessionState&, const std::vector<OrtValue>&, std::vector<OrtValue>&, const std::vector<onnxruntime::MLValueCopyInfo>&) ExecuteGraphImpl ExecuteGraph T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] BatchOrCopyMLValue onnxruntime::common::Status onnxruntime::utils::BatchOrCopyMLValue(const onnxruntime::SessionState&, const onnxruntime::MLValueCopyInfo&, const OrtValue&, OrtValue&, std::vector<onnxruntime::IDataTransfer::SrcDstPair>*) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] CopyOneInputAcrossDevices bool onnxruntime::utils::FinalizeCopyInfoForFetches(const std::vector<const OrtMemoryInfo*>&, std::vector<onnxruntime::MLValueCopyInfo>&) bool onnxruntime::utils::FinalizeCopyInfoForFeeds(const std::vector<OrtDevice>&, std::vector<onnxruntime::MLValueCopyInfo>&) CalculateStaticCopyInfoForFeed CalculateStaticCopyInfoForFeeds InitializeFeedFetchCopyInfo const OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::OrtValueNameIdxMap&, const onnxruntime::SequentialExecutionPlan&, const string&) FindMemoryInfoForValue const OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::SessionState&, const string&) N11onnxruntime9IExecutorE Allocate AllocateStatically PreExisting Reuse AllocateOutput Share NotSet nullptr != tensor_type_base reused != reused_for Can not find the node No matching 'start' entry. end >= starts_.back() p_provider Allocation Plan:
Execution Plan:
- ) , use fence when async Index out-of-range! nullptr != node Free ml-values: INVALID INDEX vector<bool>::_M_fill_insert p_kernel_def /onnxruntime_src/onnxruntime/core/framework/allocation_planner.cc 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() SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex: entry != kernel_create_info_map.cend() (ort_value_idx) output_name : <allocation plan>
- Should not have entry in kernel create info with nullptr for kernel_def Can not find the execution provider 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 const onnxruntime::KernelCreateInfo& onnxruntime::GetKernelCreateInfo(const std::unordered_map<long unsigned int, gsl::not_null<const onnxruntime::KernelCreateInfo*> >&, onnxruntime::NodeIndex) ComputeUseCounts onnxruntime::common::Status onnxruntime::PlannerImpl::ComputeUseCounts() CreatePlan N11onnxruntime17ExecutionPlanBaseE N11onnxruntime23SequentialExecutionPlanE ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEmE_ ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEbE0_ ZN11onnxruntime11PlannerImpl22GeneratePlanForWeightsEvEUlRKNS_7NodeArgEmE_ 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 graph_index exec_plan_index activation_size parameter_size SequentialExecutor::Execute /onnxruntime_src/onnxruntime/core/framework/sequential_executor.cc [Memory] ExecutionFrame statically allocates [Memory] ExecutionFrame dynamically allocates ReleaseNodeMLValues onnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState&, onnxruntime::IExecutionFrame&, const onnxruntime::OpKernel&, const onnxruntime::logging::Logger&, const bool&) Execute N11onnxruntime18SequentialExecutorE invalid ort_value_index: ', location: /onnxruntime_src/onnxruntime/core/framework/simple_tensor_allocator.cc Failed to get allocator for initializer ' GetPreallocatedBuffer N11onnxruntime21SimpleTensorAllocatorE
+ ) , use fence when async Index out-of-range! nullptr != node Free ml-values: INVALID INDEX vector<bool>::_M_fill_insert p_kernel_def /onnxruntime_src/onnxruntime/core/framework/allocation_planner.cc Invalid program_counter entries at index entry.program_counter.HasValidEntries() 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() SessionState should have saved the KernelCreateInfo prior to this running. NodeIndex: entry != kernel_create_info_map.cend() AllocPlan(ml_value_idx).program_counter.Ends().back() == program_counter Previous entry was not terminated. /onnxruntime_src/onnxruntime/core/framework/sequential_execution_plan.h starts_.size() == ends_.size() starts_.empty() || start > ends_.back() Invalid 'start'. Value is smaller than previous 'end'. starts_.size() == ends_.size() + 1 Invalid 'end'. Value is larger than 'start'. (ort_value_idx) output_name : <allocation plan>
+ Should not have entry in kernel create info with nullptr for kernel_def Can not find the execution provider void onnxruntime::PlannerImpl::VerifyMemoryTimeSchedule() void onnxruntime::PlannerImpl::GenerateDeallocationPlan() void onnxruntime::AllocPlanPerValue::ProgramCounter::AddEnd(size_t) onnxruntime::OrtValueIndex& onnxruntime::PlannerImpl::Buffer(onnxruntime::OrtValueIndex) void onnxruntime::AllocPlanPerValue::ProgramCounter::AddStart(size_t) void onnxruntime::PlannerImpl::Reuse(onnxruntime::OrtValueIndex, onnxruntime::OrtValueIndex, onnxruntime::AllocKind) static size_t onnxruntime::PlannerImpl::GetElementSize(const string* const&) OrtMemoryInfo onnxruntime::PlannerImpl::GetLocationForNodeInput(size_t, const onnxruntime::Node&) GeneratePlanForWeights ComputeReusePlan onnxruntime::AllocPlanPerValue& onnxruntime::PlannerImpl::AllocPlan(onnxruntime::OrtValueIndex) int& onnxruntime::PlannerImpl::UseCount(onnxruntime::OrtValueIndex) void onnxruntime::PlannerImpl::ProcessDef(onnxruntime::OrtValueIndex, const onnxruntime::NodeArg*) onnxruntime::OrtValueIndex onnxruntime::PlannerImpl::Index(const OrtValueName&) ForEachWithIndex const onnxruntime::KernelCreateInfo& onnxruntime::GetKernelCreateInfo(const std::unordered_map<long unsigned int, gsl::not_null<const onnxruntime::KernelCreateInfo*> >&, onnxruntime::NodeIndex) ComputeUseCounts onnxruntime::common::Status onnxruntime::PlannerImpl::ComputeUseCounts() CreatePlan N11onnxruntime17ExecutionPlanBaseE N11onnxruntime23SequentialExecutionPlanE ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEmE_ ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEbE0_ ZN11onnxruntime11PlannerImpl22GeneratePlanForWeightsEvEUlRKNS_7NodeArgEmE_ Divide by zero static void SafeIntExceptionHandler<onnxruntime::OnnxRuntimeException>::SafeIntOnDivZero() func info for node: already exist. Can't use func with null ptr /onnxruntime_src/onnxruntime/core/framework/fuse_nodes_funcs.cc GetFuncs *ZNK11onnxruntime11FuncManager8GetFuncsERKSsRPNS_15NodeComputeInfoEEUlPvPK6OrtApiP16OrtKernelContextE_ *ZNK11onnxruntime11FuncManager8GetFuncsERKSsRPNS_15NodeComputeInfoEEUlPNS_14ComputeContextEPPvE0_ *ZNK11onnxruntime11FuncManager8GetFuncsERKSsRPNS_15NodeComputeInfoEEUlPvE1_ 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 Begin execution does not. entry != nullptr op_name _fence_before node. Name:' ' Status Message: _kernel_time _fence_after Multiple errors were found. ParallelExecutor::Execute onnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState&, onnxruntime::IExecutionFrame&, const onnxruntime::OpKernel&, const onnxruntime::logging::Logger&, const bool&) onnxruntime::common::Status onnxruntime::ParallelExecutor::RunNodeAsync(size_t, const onnxruntime::SessionState&, const onnxruntime::logging::Logger&) RunNodeAsync const T& OrtValue::Get() const [with T = onnxruntime::Tensor] Execute *ZN11onnxruntime16ParallelExecutor11EnqueueNodeEmRKNS_12SessionStateERKNS_7logging6LoggerEEUlvE_ N11onnxruntime16ParallelExecutorE N11onnxruntime23OpKernelContextInternalE graph_index exec_plan_index activation_size parameter_size output_size SequentialExecutor::Execute /onnxruntime_src/onnxruntime/core/framework/sequential_executor.cc [Memory] ExecutionFrame statically allocates [Memory] ExecutionFrame dynamically allocates ReleaseNodeMLValues onnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState&, onnxruntime::IExecutionFrame&, const onnxruntime::OpKernel&, const onnxruntime::logging::Logger&, const bool&) Execute N11onnxruntime18SequentialExecutorE invalid ort_value_index: ', location: /onnxruntime_src/onnxruntime/core/framework/simple_tensor_allocator.cc Failed to get allocator for initializer ' GetPreallocatedBuffer N11onnxruntime21SimpleTensorAllocatorE Number of attention heads past present input_scale weight_scale input_zero_point weight_zero_point LongformerAttention window global_weight global_bias global Constrain to integer types input_ids segment_ids 2D with shape (,hidden_size) word_embedding 2D with shape (, hidden_size) position_embedding segment_embedding bias tensor skip inv_std_var Value of alpha Value of beta 1D input tensor 1D output tensor The scale to apply. Threshold value Input tensor Output tensor Sliced data tensor. GivenTensorFill The shape of filled tensor The filled tensor input_as_shape extra_shape Input data to be scaled Output data after scaling GRUUnit drop_states hidden_prev gates seq_lengths hidden Scaling value Rfft signal_ndim normalized onesided Irfft ComplexMul input_0 input_1 ComplexMulConj activation_gamma Strings to tokenize Tokenized strings N-dimensional matrix A N-dimensional matrix B Matrix multiply results An input tensor to hash. 32-bit hash value. Tensor of rank r >= 1. Tensor of rank q >= 1. Specify weights of conv Specify bias of conv Constrain to tensor(int32). Constrain to tensor(float). Input tensor. Tensor after padding. idx 2D matrix with shape (M,N) 2D matrix with shape (K,N) rois batch_indices crop_size stash_type Scale tensor. Bias tensor. Output data tensor. The input data as Tensor. The output. The normal input data. The bias (or mask) as Tensor. [ShapeInferenceError] Attribute should specify a shape Input's shape must be 4-D ) + bottom_border ( ) + right_border ( ) + scale[0] ( ) + scale[1] ( Input axis is invalid: Input rank must be >= 2. != mat_h: Incompatible dimensions inputs are expected to have tensor type and output type should not be null. /onnxruntime_src/onnxruntime/core/graph/contrib_ops/contrib_defs.cc Whether every token can only attend to previous tokens. Default value is 0. 3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_size 2D input tensor with shape (hidden_size, 3 * hidden_size) 1D input tensor with shape (3 * hidden_size) Attention mask with shape (batch_size, past_sequence_length + sequence_length) or (batch_size, sequence_length, past_sequence_length + sequence_length), or index with shape (batch_size) or (2 * batch_size). past state for key and value with shape (2, batch_size, num_heads, past_sequence_length, head_size). 3D output tensor with shape (batch_size, append_length, hidden_size) present state for key and value with shape (2, batch_size, num_heads, past_sequence_length + sequence_length, head_size) Constrain input and output types to float tensors. Constrain mask index to integer types scale of quantized input tensor. It's a scalar, which means a per-tensor/layer quantization. scale of weight scale. It's a scalar, which means a per-tensor/layer quantization. Attention mask index with shape (batch_size) zero point of quantized input tensor.It's a scalar, which means a per-tensor/layer quantization. zero point of quantized weight tensor. It's a scalar, which means a per-tensor/layer quantization. 3D output tensor with shape (batch_size, sequence_length, hidden_size) Constrain input and output types to int8 tensors. One sided attention windows length W, or half of total window length Attention mask with shape (batch_size, sequence_length) Global attention flags with shape (batch_size, sequence_length) The epsilon value to use to avoid division by zero. 2D words IDs with shape (batch_size, sequence_length) 2D segment IDs with shape (batch_size, sequence_length) 1D gamma tensor for layer normalization with shape (hidden_size) 1D beta tensor for layer normalization with shape (hidden_size) 2D attention mask with shape (batch_size, sequence_length) 1D mask_index tensor with shape (batch_size) Constrain input and output integer tensors types Constrain input and output float tensors types. Constrain input and output types to float or half tensors. 3D input tensor with shape (batch_size, sequence_length, hidden_size) 3D skip tensor with shape (batch_size, sequence_length, hidden_size) 1D input tensor with shape (hidden_size) 1D skip tensor with shape (hidden_size 1D bias tensor with shape (hidden_size Saved mean used during training to speed up gradient computation Saved inverse standard variance used during training to speed up gradient computation. Constrain mean and inv_std_var to float tensors. Bias applied to each channel, same size as C. Input tensor of shape [N,C,H,W] Result, has same shape and type as input A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder). A 1-D values of (height, width). Result, has same type as input, with H and W dimensions reduced. Constrain input and output types to all tensor types. Tensor of data to extract slices from. 1-D tensor of starting indices of corresponding axis in `axes` 1-D tensor of ending indices (exclusive) of corresponding axis in axes 1-D tensor of axes that `starts` and `ends` apply to. Constrain indices to integer types Bool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length. The previous GRU hidden state. Unactivated gate outputs from forget, update, and output gates, pre-activation. Array of sequence lengths. len(seq_lengths) should equal batch size N. The timestep for this operation. The new GRU hidden state calculated by this op. If 1, mean and variance are computed across channels. Default is 0. If 0, normalize the mean only. Default is 1. The scaled hyperbolic tangent values of the input tensor computed element-wise Constrain input0 and output types to float tensors Constrain input and output types to float tensors Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero. Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero. Input tensor C. The shape of C should be unidirectional broadcastable to (M, N). Output tensor of shape (M, N). Constrain input and output types to float/int tensors. Whether A should be transposed Whether B should be transposed Scalar multiplier for the product of input tensors A * B. Scalar multiplier for input tensor C. Specified axis to insert a dimension Input/Output is a string tensor Boolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03). The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers. An optional string. Token's regular expression in basic POSIX format (pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set. an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string. Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignored Matrix multiply results from A * B Constrain input A data types as 16-bit integer tensor Constrain input B data types as 16-bit integer tensor Constrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16). Scalar multiplier for the product of the input tensors. Whether A should be transposed on the last two dimensions before doing multiplication Whether B should be transposed on the last two dimensions before doing multiplication Constrain input type to unsigned or signed 32-bit integer tensor, or string tensor. It should be utf-8 encoded if using unicode. Constrain output type to unsigned and signed 32-bit integer tensor. Seed for the hashing algorithm, unsigned 32-bit integer, default to 0. If value is 1, output type is uint32_t, else int32_t. Default value is 1. Constrain input and output types to any tensor type. Tensor of rank q-1+r-indices[-1]. Constrain indice type to int32 or int64 Integer representing the embedding vector size for each word.If not provide, use the fileter size of conv weight This operator applies convolution to word from left to right with window equal to conv_window_size and stride to 1.Take word 'example' for example, with conv_window_size equal to 2, conv is applied to [ex],[xa], [am], [mp]...If not provide, use the first dimension of conv kernal shape. Integer representing the embedding vector size for each char.If not provide, use the char embedding size of embedding vector. Specify batchs of sequence words to embedding Specify embedding vector of char Three modes: `constant`(default) - pads with a given constant value, `reflect` - pads with the reflection of the vector mirrored on the first and last values of the vector along each axis, `edge` - pads with the edge values of array Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank]. `pads` format (1D example) should be as follow [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. (Optional) A scalar or rank 1 tensor containing a single value to be filled if the mode chosen is `constant` (by default it is 0.0). Input can be of any tensor type. A 1-D input tensor that is to be processed. A 1-D tensor of the same type as 'x' containing all the unique values in 'x' sorted in the same order that they occur in the input 'x' A 1-D INT64 tensor of the same size as 'x' containing the indices for each value in 'x' in the output 'uniques' A 1-D INT64 tensor containing the the count of each element of 'uniques' in the input 'x' The distance metric to use. If a string, the distance function can be "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "wminkowski", "yule". A 2D Matrix that represents the distance between each pair of the two collections of inputs. Constrains input to only numeric types. The pooling method. Two modes are supported: 'bilinear' and 'nearest'. Default is 'bilinear'. Value used for extrapolation, when applicable. Default is 0.0f. Input data tensor from the previous operator; 4-D feature map of shape (N, C, H, W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[y1, x1, y2, x2], ...]. The RoIs' coordinates are normalized in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input. 1-D tensor of shape (num_rois,) with each element denoting the index of the corresponding image in the batch. 1-D tensor of 2 elements: [crop_height, crop_width]. All cropped image patches are resized to this size. Both crop_height and crop_width need to be positive. RoI pooled output, 4-D tensor of shape (num_rois, C, crop_height, crop_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1]. Constrain types to float tensors. Constrain types to int tensors. The first normalization dimension: normalization will be performed along dimensions axis : rank(inputs). type used for stash mean/inv_std_var Input data tensor from the previous layer. Constrain input and output types (except mean and inv_std_var) to float tensors. Constrain mean and inv_std_var to be float tensors. The bias input data that is a 1D tensor. Input tensor. Every matrix in the batch must be invertible. Output tensor of the same type and shape as the input tensor. Boolean. Indicates whether upper or lower part of matrix is retained. Default is true. Input tensor of rank 2 or higher. A 0-D tensor containing a single value corresponding to the number diagonals above or the main diagonal to exclude or include.Default value is 0 if it's not specified. Constrain input and output types to all numeric tensors and bool tensors. apply softmax to elements for dimensions softmax_axis or higher broadcast bias across input for dimensions broadcast_axis to softmax_axis-1 Negative values are not allowed in a shape specification 'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64 Pads has incorrect number of values Inputs 0 shall be 3 dimensions Inputs 4 shall be 5 dimensions Inputs 0 shall be 2 dimensions word_embedding should have 2 dimensions and dimension size is known. 'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border) 'Scale' must contain exactly 2 values - (height, width) ) needs to be greater than or equal to the top_border ( ) needs to be greater than or equal to the left_border ( First input does not have rank 2 Second input does not have rank 2 both data and indices tensor need to have rank larger than zero. last dimension of indices must not be larger and rank of data tensor 'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64 first input tensor has wrong dimension rois input tensor has wrong dimension batch_indices shape input tensor has wrong dimension crop_size shape input tensor has wrong dimension The inner-most 2 dimensions must have the same size (mat_w: Input tensors of wrong rank (0). Incompatible dimensions for matrix multiplication ̼+̼+ *ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE1_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE10_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE11_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE12_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE13_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE14_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE15_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE16_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE17_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE18_ tensor rank too small invalid scales dimension invalid scales value /onnxruntime_src/onnxruntime/core/graph/contrib_ops/nchwc_schema_defs.cc FvRN4onnx8OpSchemaEE *ZN11onnxruntime7contrib26NchwcPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ *ZN11onnxruntime7contrib32NchwcGlobalPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ *ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE1_ PFvRN4onnx8OpSchemaEE /onnxruntime_src/onnxruntime/core/graph/contrib_ops/nhwc_schema_defs.cc Input data tensor from the previous operator; According to channels_last, dimensions for image case are (N x C x H x W), or (N x H x W x C) where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), or (N x D1 X D2 ... Dn x C) where N is the batch size. Scale of quantized input 'X'. It must be a scalar. Zero point tensor for input 'X'. It must be a scalar. Scale of quantized output 'Y'. It must be a scalar. Zero point tensor for output 'Y'. It must be a scalar. Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. with the N and C value keep it value, while the otherdimensions are all 1. Constrain input and output types to singed/unsigned int8 tensors. Input tensor must have at least 2 dimensions Output tensor must have at least 2 dimensions inputs are expected to have tensor type. input and zero_point pair is expected to have be same type. weight and zero_point pair is expected to have same type. x_scale x_zero_point w_scale w_zero_point y_scale y_zero_point *ZN11onnxruntime7contrib19RegisterNhwcSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib19RegisterNhwcSchemasEvEUlRN4onnx16InferenceContextEE0_ N4onnx16InferenceContextE N11onnxruntime7contrib20NhwcInferenceContextE
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. inputs are expected to have tensor type and output type should not be null. /onnxruntime_src/onnxruntime/core/graph/contrib_ops/contrib_defs.cc Whether every token can only attend to previous tokens. Default value is 0. 3D input tensor with shape (batch_size, sequence_length, hidden_size), hidden_size = num_heads * head_size 2D input tensor with shape (hidden_size, 3 * hidden_size) 1D input tensor with shape (3 * hidden_size) Attention mask with shape (batch_size, past_sequence_length + sequence_length), or index with shape (batch_size) or (2 * batch_size). past state for key and value with shape (2, batch_size, num_heads, past_sequence_length, head_size). 3D output tensor with shape (batch_size, append_length, hidden_size) present state for key and value with shape (2, batch_size, num_heads, past_sequence_length + sequence_length, head_size) Constrain input and output types to float tensors. Constrain mask index to integer types scale of quantized input tensor. It's a scalar, which means a per-tensor/layer quantization. scale of weight scale. It's a scalar, which means a per-tensor/layer quantization. Attention mask index with shape (batch_size) zero point of quantized input tensor.It's a scalar, which means a per-tensor/layer quantization. zero point of quantized weight tensor. It's a scalar, which means a per-tensor/layer quantization. 3D output tensor with shape (batch_size, sequence_length, hidden_size) Constrain input and output types to int8 tensors. The epsilon value to use to avoid division by zero. 2D words IDs with shape (batch_size, sequence_length) 2D segment IDs with shape (batch_size, sequence_length) 1D gamma tensor for layer normalization with shape (hidden_size) 1D beta tensor for layer normalization with shape (hidden_size) 2D attention mask with shape (batch_size, sequence_length) 1D mask_index tensor with shape (batch_size) Constrain input and output integer tensors types Constrain input and output float tensors types. Constrain input and output types to float or half tensors. 3D input tensor with shape (batch_size, sequence_length, hidden_size) 3D skip tensor with shape (batch_size, sequence_length, hidden_size) 1D input tensor with shape (hidden_size) 1D skip tensor with shape (hidden_size 1D bias tensor with shape (hidden_size Saved mean used during training to speed up gradient computation Saved inverse standard variance used during training to speed up gradient computation. Constrain mean and inv_std_var to float tensors. Bias applied to each channel, same size as C. Input tensor of shape [N,C,H,W] Result, has same shape and type as input A 1-D values of (leftBorder, topBorder, rightBorder, bottomBorder). A 1-D values of (height, width). Result, has same type as input, with H and W dimensions reduced. Constrain input and output types to all tensor types. Tensor of data to extract slices from. 1-D tensor of starting indices of corresponding axis in `axes` 1-D tensor of ending indices (exclusive) of corresponding axis in axes 1-D tensor of axes that `starts` and `ends` apply to. Constrain indices to integer types Bool to determine if hidden state is zeroes or passed along for timesteps past the given sequence_length. The previous GRU hidden state. Unactivated gate outputs from forget, update, and output gates, pre-activation. Array of sequence lengths. len(seq_lengths) should equal batch size N. The timestep for this operation. The new GRU hidden state calculated by this op. If 1, mean and variance are computed across channels. Default is 0. If 0, normalize the mean only. Default is 1. The scaled hyperbolic tangent values of the input tensor computed element-wise Constrain input0 and output types to float tensors Constrain input and output types to float tensors Input tensor A. The shape of A should be (M, K) if transA is 0, or (K, M) if transA is non-zero. Input tensor B. The shape of B should be (K, N) if transB is 0, or (N, K) if transB is non-zero. Input tensor C. The shape of C should be unidirectional broadcastable to (M, N). Output tensor of shape (M, N). Constrain input and output types to float/int tensors. Whether A should be transposed Whether B should be transposed Scalar multiplier for the product of input tensors A * B. Scalar multiplier for input tensor C. Specified axis to insert a dimension The axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors. N-D full precision Input tensor to be quantized. Scale for doing quantization to get 'y'. It could be a scalar or a 1-D tensor,which means a per-tensor or per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. Zero point for doing quantization to get 'y'. It could be a scalar or a 1-D tensor, which means a per-tensoror per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. N-D quantized output tensor. It has same shape as input 'x'. Constrain 'x', 'y_scale' to float tensors. Constrain 'y_zero_point' and 'y' to 8-bit integer tensors. N-D quantized Input tensor to be de-quantized. Scale for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. Zero point for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. N-D full precision output tensor. It has same shape as input 'x'. Constrain 'x' and 'x_zero_point' to 8-bit integer tensors. Constrain 'y', 'x_scale' to float tensors. Input/Output is a string tensor Boolean whether to mark the beginning/end character with start of text character (0x02)/end of text character (0x03). The string used to pad output tensors when the tokens extracted doesn't match the maximum number of tokens found. If start/end markers are needed, padding will appear outside the markers. An optional string. Token's regular expression in basic POSIX format (pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set. an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string. Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignored Matrix multiply results from A * B Constrain input A data types as 16-bit integer tensor Constrain input B data types as 16-bit integer tensor Constrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16). Scale of quantized input 'B'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'. Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'. 1D input tensor, whose dimension is same as B's last dimension Constrain input A, b_scale and output Y data type as float tensor. Constrain input B data type to 8-bit integer tensor. Scale of quantized input 'A'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'. Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'. Constrain input A data type to 8-bit integer tensor. Constrain input a_scale, b_scale and output Y data type as float tensor. Scalar multiplier for the product of the input tensors. Whether A should be transposed on the last two dimensions before doing multiplication Whether B should be transposed on the last two dimensions before doing multiplication Constrain input type to 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. Input X's scale. It's a scalar, which means a per-tensor/layer quantization. Input X's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Output Y's scale. It's a scalar, which means a per-tensor/layer quantization. Output Y's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. 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). Input data tensor from the previous layer. Constrain input and output types (except mean and inv_std_var) to float tensors. Constrain mean and inv_std_var to be float tensors. The bias input data that is a 1D tensor. Input tensor. Every matrix in the batch must be invertible. Output tensor of the same type and shape as the input tensor. Boolean. Indicates whether upper or lower part of matrix is retained. Default is true. Input tensor of rank 2 or higher. A 0-D tensor containing a single value corresponding to the number diagonals above or the main diagonal to exclude or include.Default value is 0 if it's not specified. Constrain input and output types to all numeric tensors and bool tensors. 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. Negative values are not allowed in a shape specification inputs are expected to have tensor type. 'pads' input must be a 1D (shape: [2 * n_input_dims]) tensor of type int64 Pads has incorrect number of values Inputs 0 shall be 3 dimensions Inputs 4 shall be 5 dimensions Inputs 0 shall be 2 dimensions word_embedding should have 2 dimensions and dimension size is known. 'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border) 'Scale' must contain exactly 2 values - (height, width) ) needs to be greater than or equal to the top_border ( ) needs to be greater than or equal to the left_border ( First input does not have rank 2 Second input does not have rank 2 axis must be in [-rank, rank-1]. input rank was both data and indices tensor need to have rank larger than zero. last dimension of indices must not be larger and rank of data tensor 'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64 first input tensor has wrong dimension rois input tensor has wrong dimension batch_indices shape input tensor has wrong dimension crop_size shape input tensor has wrong dimension The inner-most 2 dimensions must have the same size (mat_w: Input tensors of wrong rank (0). Incompatible dimensions for matrix multiplication 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 Number of attention heads past present input_scale weight_scale input_zero_point weight_zero_point input_ids segment_ids 2D with shape (,hidden_size) word_embedding 2D with shape (, hidden_size) position_embedding segment_embedding bias tensor skip inv_std_var Value of alpha Value of beta 1D input tensor 1D output tensor The scale to apply. Threshold value Input tensor Output tensor Sliced data tensor. GivenTensorFill The shape of filled tensor The filled tensor input_as_shape extra_shape Input data to be scaled Output data after scaling GRUUnit drop_states hidden_prev gates seq_lengths hidden Scaling value Rfft signal_ndim normalized onesided Irfft ComplexMul input_0 input_1 ComplexMulConj activation_gamma y_scale y_zero_point x_scale x_zero_point Strings to tokenize Tokenized strings N-dimensional matrix A N-dimensional matrix B b_scale b_zero_point Matrix multiply results ReduceSumInteger An input tensor. Reduced output tensor. reduced addition multiplication QLinearReduceMean data_scale data_zero_point reduced_scale reduced_zero_point QLinearAveragePool Coefficient of leakage. X_scale X_zero_point Y_scale Y_zero_point 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. 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+ Input A's scale. It's a scalar, which means a per-tensor/layer quantization. Input A zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Input B's scale. It's a scalar, which means a per-tensor/layer quantization. Input B zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Output scale. It's a scalar, which means a per-tensor/layer quantization. Output zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Result, has same element type as two inputs Constrain input and output types to 8 bit signed and unsigned tensors. /onnxruntime_src/onnxruntime/core/graph/contrib_ops/quantization_defs.cc The axis along which same quantization parameters are applied. It's optional.If it's not specified, it means per-tensor quantization and input 'x_scale' and 'x_zero_point' must be scalars.If it's specified, it means per 'axis' quantization and input 'x_scale' and 'x_zero_point' must be 1-D tensors. N-D full precision Input tensor to be quantized. Scale for doing quantization to get 'y'. It could be a scalar or a 1-D tensor,which means a per-tensor or per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. Zero point for doing quantization to get 'y'. It could be a scalar or a 1-D tensor, which means a per-tensoror per-axis quantization. If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. N-D quantized output tensor. It has same shape as input 'x'. Constrain 'x', 'y_scale' to float tensors. Constrain 'y_zero_point' and 'y' to 8-bit integer tensors. N-D quantized Input tensor to be de-quantized. Scale for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. Zero point for input 'x'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-axis quantization.If it's a 1-D tensor, its number of elements should be equal to the dimension value of 'axis' dimension of input 'x'. N-D full precision output tensor. It has same shape as input 'x'. Constrain 'x' and 'x_zero_point' to 8-bit integer tensors. Constrain 'y', 'x_scale' to float tensors. Constrain input type to 8-bit integer tensor. Constrain output data type to 32-bit integer tensor.T2 must be tensor(uint32) when T1 is tensor(uint8),or must be tensor(int32) when T1 is tensor(int8). A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Keep the reduced dimension or not, default 1 mean keep reduced dimension. Constrain output to 32 bit tensor Constrain input types to 8 bit signed and unsigned tensors. Constrain output types to 32 bit tensors. Scale of quantized input 'B'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'. Zero point tensor for input 'B'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'B'. 1D input tensor, whose dimension is same as B's last dimension Constrain input A, b_scale and output Y data type as float tensor. Constrain input B data type to 8-bit integer tensor. Scale of quantized input 'A'. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'. Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-column quantization. If it's a 1-D tensor, its number of elements should be equal to the number of columns of input 'A'. Constrain input A data type to 8-bit integer tensor. Constrain input a_scale, b_scale and output Y data type as float tensor. C = (A_scale * (A - A_zero_point) + B_scale * (B - B_zero_point))/C_scale + C_zero_point C = ((A - A_zero_point) * (B - B_zero_point)) * (A_scale * B_scale)/C_scale + C_zero_point Input scale. It's a scalar, which means a per-tensor/layer quantization. Input zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Whether include pad pixels when calculating values for the edges. Default is 0, doesn't count include pad. The size of the kernel along each axis. Stride along each spatial axis. If not present, the stride defaults to 1 along each spatial axis. auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaults to 0 along start and end of each spatial axis. Whether to use ceil or floor (default) to compute the output shape. Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...]. Output data tensor from average or max pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. Floor value of the dimension is used Constrain input and output types to 8 bit tensors. Input X's scale. It's a scalar, which means a per-tensor/layer quantization. Input X's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Output Y's scale. It's a scalar, which means a per-tensor/layer quantization. Output Y's zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Specify if the RNN is forward, reverse, or bidirectional. Must be one of forward (default), reverse, or bidirectional. Number of neurons in the hidden layer Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators.For example with LeakyRelu, the default alpha is 0.01. Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. Default values are the same as of corresponding ONNX operators. Cell clip threshold. Clipping bounds the elements of a tensor in the range of [-threshold, +threshold] and is applied to the input of activations. No clip if not specified. A list of 3 (or 6 if bidirectional) activation functions for input, output, forget, cell, and hidden. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified. Couple the input and forget gates if 1. The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]`. The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, input_size, 4*hidden_size]`. The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, hidden_size, 4*hidden_size]`. The bias tensor for input gate. Concatenation of `[Wb[iofc], Rb[iofc]]`, and `[WBb[iofc], RBb[iofc]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 8*hidden_size]`. Optional: If not specified - assumed to be 0. Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]`. Optional initial value of the hidden. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`. Optional initial value of the cell. If not specified - assumed to be 0. It has shape `[num_directions, batch_size, hidden_size]`. The weight tensor for peepholes. Concatenation of `P[iof]` and `PB[iof]` (if bidirectional) along dimension 0. It has shape `[num_directions, 3*hidde_size]`. Optional: If not specified - assumed to be 0. W's scale. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size. W's zero point. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size. R's scale. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size. R's zero point. Its size is [num_directions] for per-tensor/layer quantization, or [num_directions, 4*hidden_size] for per-channel quantization on the axis input_size. A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. The last output value of the cell. It has shape `[num_directions, batch_size, hidden_size]`. Constrain seq_lens to integer tensor. Constrain weights types to 8 bit tensors. Input data type does not match the expected data type Input data type does not match the expected data type. Current data type is Scale and Zero-point must be a scalar Scale and Zero-point must be of rank 1 Scale and Zero-point must be of rank 1 and the number of elements should be equal to the number of rows of the corresponding input. axis must be in [-rank, rank-1]. input rank was {name} {additionalDocumentation} First operand. A_scale A_zero_point Second operand. B_scale B_zero_point C_scale C_zero_point ReduceSumInteger An input tensor. Reduced output tensor. reduced b_scale b_zero_point addition multiplication QLinearReduceMean data_scale data_zero_point reduced_scale reduced_zero_point QLinearAveragePool Coefficient of leakage. X_scale X_zero_point Y_scale Y_zero_point sequence_lens initial_h initial_c R_scale R_zero_point Y_h Y_c *ZZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_ENKUlRN4onnx8OpSchemaEE_clES5_EUlRNS3_16InferenceContextEE_ *ZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_EUlRN4onnx8OpSchemaEE_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE1_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE2_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE3_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE4_ *ZN11onnxruntime7contrib27RegisterQuantizationSchemasEvEUlRN4onnx16InferenceContextEE5_ start limit delta 1-D Tensor of the range. Unsupported type: Constrain input and output types. Tensor(scalar, or dims=[1]). First entry in the range. Tensor(scalar, or dims=[1]). Upper limit of sequence, exclusive. Tensor(scalar, or dims=[1]). Number that increments start. Defaults to 1. Can not get shape initializer data! Unsupported non-raw-data data type! *ZN11onnxruntime7contrib21RegisterRangeOpSchemaEON4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ index < data_.size() Inputs:
Nodes:
) -> Outputs:
- index < data_.size() input index: input edges output edges ai.onnx.training ai.onnx.preview.training node_arg_name cannot be null ] op_type [ ], could not find NodeArg _token_ Node [ Not satisfied: dims_eq()
- Input to set must exist. input->Exists() node_arg Constant !found Removing initializer ' fbs_attr cannot be null Not satisfied: subgraph
- Can't remove node Target= Dimension= ' source: target: Output: Type mismatch. Current= Input= ' has element type does not match. but different TensorProto. existing->second == &tensor graph_proto cannot be null graph_proto != nullptr Initializer NoOp ) input arg ( ' of input parameter ( ) of operator ( ) in node ( ) is invalid. Type Error: Type parameter ( ) bound to different types ( Node ( ) output arg ( ) type inference failed Type Error: Type ( ) of output arg ( ) of node ( Node: ) Op ( Outer scope node arg name ' Fatal error: ) attribute ( inputs but subgraph has inputs and requires Subgraph input missing type. /onnxruntime_src/onnxruntime/core/graph/graph.cc /onnxruntime_src/include/onnxruntime/core/common/const_pointer_container.h Graph attribute inferencing failed: info->Name() != new_value_info->Name() Error: trying to add an existing value info. Invalid source node arg slot specified when adding edge. Invalid destination node arg slot specified when adding edge. Argument type mismatch when adding edge. Invalid node indexes specified when adding edge. Node::LoadEdgesFromOrtFormat, edge is missing for Satisfied, but should not be: nullptr == fbs_edge
+ input index: input edges output edges ai.onnx.training ai.onnx.preview.training node_arg_name cannot be null ] op_type [ ], could not find NodeArg _token_ Node [ node_arg Not satisfied: dims_eq()
+ Input to set must exist. input->Exists() Constant !found Removing initializer ' fbs_attr cannot be null Not satisfied: subgraph
+ Can't remove node nullptr != func_meta_def Target= Dimension= ' source: target: Output: Type mismatch. Current= Input= ' has element type does not match. but different TensorProto. existing->second == &tensor graph_proto cannot be null graph_proto != nullptr ' Model is invalid. utils::HasName(sparse_tensor) . Please, fix your model. Initializer NoOp ) input arg ( ' of input parameter ( ) of operator ( ) in node ( ) is invalid. Type Error: Type parameter ( ) bound to different types ( Node ( ) output arg ( ) type inference failed Type Error: Type ( ) of output arg ( ) of node ( Node: ) Op ( Node index is out of range Outer scope node arg name ' Fatal error: ) attribute ( inputs but subgraph has inputs and requires Subgraph input missing type. /onnxruntime_src/onnxruntime/core/graph/graph.cc /onnxruntime_src/include/onnxruntime/core/common/const_pointer_container.h Graph attribute inferencing failed: info->Name() != new_value_info->Name() Error: trying to add an existing value info. Invalid source node arg slot specified when adding edge. Invalid destination node arg slot specified when adding edge. Argument type mismatch when adding edge. Invalid node indexes specified when adding edge. Node::LoadEdgesFromOrtFormat, edge is missing for Satisfied, but should not be: nullptr == fbs_edge
is not the same as this node's index: Satisfied, but should not be: fbs_node_edges.node_index() != index_
- This is an invalid model. The sum of input arg count is not equal to size of input defs in node ( Invalid source node arg slot specified when removing edge. Invalid destination node arg slot specified when removing edge. Argument mismatch when removing edge. Invalid node indexes specified when removing edge. fbs_node_arg_names cannot be null Satisfied, but should not be: nullptr == fbs_node_arg_names
+ This is an invalid model. The sum of input arg count is not equal to size of input defs in node ( Invalid source node arg slot specified when removing edge. Invalid destination node arg slot specified when removing edge. Argument mismatch when removing edge. Invalid node indexes specified when removing edge. Some nodes are not included in the topological sort, graph have a cycle. fbs_node_arg_names cannot be null Satisfied, but should not be: nullptr == fbs_node_arg_names
Satisfied, but should not be: nullptr == node_arg_name
LoadNodeArgsFromOrtFormat: Node [ Satisfied, but should not be: nullptr == node_arg
NodeArg Name is missing. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_node_arg_name
Error: Duplicate definition-site for ( No Graph instance was found for attribute Serialization of fused function body is not currently supported, does not have the graph for key Not satisfied: it != attr_to_subgraph_map_.cend()
- Failed to find existing initializer with name Not satisfied: name_to_initializer_it != name_to_initial_tensor_.end()
+ graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty() Graph ctor should have created NodeArg for initializer. Missing: This is an invalid model. Graph output ( ) does not exist in the graph. Graph state to be loaded into must be empty. This is an invalid model. Error: two nodes with same node name ( This is an invalid model. Error: Duplicate definition of name ( Failed to find existing initializer with name 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. graph_inputs_excluding_initializers_.empty() && graph_inputs_including_initializers_.empty() && value_info_.empty() && graph_outputs_.empty() Graph ctor should have created NodeArg for initializer. Missing: This is an invalid model. Graph output ( ) does not exist in the graph. Graph state to be loaded into must be empty. This is an invalid model. Error: two nodes with same node name ( This is an invalid model. Error: Duplicate definition of name ( This is an invalid model. Error: the graph is not acyclic. graph_proto_ is not in sync with name_to_initial_tensor_. must be either specified in graph inputs or graph initializers. '. It is not used by any node and should be removed from the model. '. It is no longer used by any node. Satisfied, but should not be: nullptr == fbs_attr
+ 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: the graph is not acyclic. sparse_tensor_names_ not in sync with name_to_initial_tensor_ sparse_tensor_names_.count(tensor_name) == 0 graph_proto_ is not in sync with name_to_initial_tensor_. must be either specified in graph inputs or graph initializers. '. It is not used by any node and should be removed from the model. '. It is no longer used by any node. Satisfied, but should not be: nullptr == fbs_attr
Serialization error. Graph attribute was serialized without Graph instance Node::LoadFromOrtFormat, input_arg_counts is missing Satisfied, but should not be: nullptr == fbs_input_arg_counts
- 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()) Initializer tensor is missing. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_tensor
- NodeArg is missing. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_value_info
+ 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. This is an invalid model. Model input ( ) does not have type information. Type Error: Data in initializer ' but usage of initializer in graph expects Type Error: Shape of initializer AddInitializedTensor already has tensor with name ALLOW_RELEASED_ONNX_OPSET_ONLY are '0' and '1'. The environment variable contained the value: /onnxruntime_src/onnxruntime/core/graph/model_load_utils.h Duplicate constant node sparse initializer name: ' Sparse initializer must have a name. This model is invalid Duplicate sparse_tensor_initializer: ' Duplicate initializer (dense, sparse or ConstantNode): ' ' the model will use the latest encountered initializer This is an invalid model. Tensor does not have type information. appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py. This is an invalid model. Node ( ) does not have type information set by parent node. This is an invalid model. Type Error: Type ' ) does not match expected type ( UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphs node.GetAttributeNameToMutableSubgraphMap().empty() Initializer tensor is missing. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_tensor
+ Duplicate initializer (dense or ConstantNode): ' Sparse Initializer tensor is missing. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_sparse_tensor
+ NodeArg is missing. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_value_info
Node is missing. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_node
+ Satisfied, but should not be: node->Index() >= fbs_graph.max_node_index()
NodeEdge is missing. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_node_edge
- Mismatch between number of source and target dimensions. Source= Can't merge shape info. Both source and target dimension have values but they differ. Source= Error merging shape info for output. ' . Falling back to lenient merge. Tensor element type mismatch. SparseTensor element type mismatch. This is an invalid model. Model input ( ) does not have type information. Type Error: Data in initializer ' but usage of initializer in graph expects Type Error: Shape of initializer AddInitializedTensor already has tensor with name ALLOW_RELEASED_ONNX_OPSET_ONLY are '0' and '1'. The environment variable contained the value: /onnxruntime_src/onnxruntime/core/graph/model_load_utils.h This is an invalid model. Tensor does not have type information. appears in graph inputs and will not be treated as constant value/weight. This may prevent some of the graph optimizations, like const folding. Move it out of graph inputs if there is no need to override it, by either re-generating the model with latest exporter/converter or with the tool onnxruntime/tools/python/remove_initializer_from_input.py. 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 ( UpdateTypeShapeInference is not intended to be used with control flow nodes containing subgraphs node.GetAttributeNameToMutableSubgraphMap().empty() 'was added but does not exist. is not a registered function/op ) is required but not specified. This is an invalid model. Error in Node: Size mismatch validating subgraph inputs. Got inputs. Either provide all subgraph inputs, or just the required inputs. Shouldn't be possible to have NodeArgs that haven't been handled already. outer_scope_node_args_consumed.empty() 's number of inputs is different from function body graph's number of input. 's number of outputs is different from function body graph's number of outputs. node.MutableOutputDefs().size() == subgraph.GetOutputs().size() VU V V VU VUUoperator() LoadFromOrtFormat LoadFromOrtFormat void onnxruntime::Graph::SetInputs(const std::vector<const onnxruntime::NodeArg*>&) InlineFunction onnxruntime::common::Status onnxruntime::Graph::InlineFunction(onnxruntime::Node&) onnxruntime::Node& onnxruntime::Graph::FuseSubGraph(std::unique_ptr<onnxruntime::IndexedSubGraph>, const string&) gsl::not_null<onnxruntime::Node*> onnxruntime::Graph::AllocateNode() CleanUnusedInitializers void onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto&) const bool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex) SaveToOrtFormat void onnxruntime::Graph::RemoveInitializedTensor(const string&) void onnxruntime::Graph::AddInitializedTensor(const onnx::TensorProto&) onnxruntime::common::Status onnxruntime::Graph::Resolve(const onnxruntime::Graph::ResolveOptions&) Resolve ForThisAndAllSubgraphs PerformTypeAndShapeInferencing InitInputsInitializersOutputs VerifyNodeAndOpMatch InferAndVerifyTypeMatch onnxruntime::common::Status onnxruntime::Graph::UpdateShapeInference(onnxruntime::Node&) InferAndVerifySubgraphTypes const T* onnxruntime::ConstPointerContainer<Container>::at(size_t) const [with Container = std::vector<onnxruntime::NodeArg*>; onnxruntime::ConstPointerContainer<Container>::T = onnxruntime::NodeArg; size_t = long unsigned int] BuildConnections void onnxruntime::Graph::RemoveEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int) void onnxruntime::Graph::AddEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int) SetOuterScopeNodeArgs void onnxruntime::Graph::InitializeStateFromModelFileGraphProto() bool onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet() Graph onnxruntime::Graph::Graph(const onnxruntime::Model&, onnx::GraphProto*, const std::unordered_map<std::basic_string<char>, int>&, onnxruntime::Version, onnxruntime::IOnnxRuntimeOpSchemaCollectionPtr, onnxruntime::Graph*, const onnxruntime::Node*, const onnxruntime::logging::Logger&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const operator() LoadEdgesFromOrtFormat operator() LoadFromOrtFormat SaveToOrtFormat MergeShapeInfo UpdateTypeAndShape FN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_RKNS5_14ResolveOptionsEE *ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E0_ *ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E1_ *ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E2_ N4onnx15GraphInferencerE N4onnx16InferenceContextE N4onnx24FunctionBodyBuildContextE N4onnx28FunctionBodyBuildContextImplE N11onnxruntime5GraphE N11onnxruntime19GraphInferencerImplE N11onnxruntime20InferenceContextImplE PFN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_RKNS5_14ResolveOptionsEE St23_Sp_counted_ptr_inplaceIN11onnxruntime21SchemaRegistryManagerESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE We do not support type [ ] for now is missing type info. is not supported currently Null type info for sequence_type map_type UNDEFINED INT FLOATS INTS STRINGS GRAPHS SPARSE_TENSOR SPARSE_TENSORS /onnxruntime_src/onnxruntime/core/graph/graph_flatbuffers_utils.cc SaveValueInfoOrtFormat: value_info_proto for Graph attribute value was null. Invalid ORT format model. Satisfied, but should not be: nullptr == graph
+ Satisfied, but should not be: fbs_node_edge->node_index() >= fbs_graph.max_node_index()
+ 'was added but does not exist. is not a registered function/op ) is required but not specified. This is an invalid model. Error in Node: Size mismatch validating subgraph inputs. Got inputs. Either provide all subgraph inputs, or just the required inputs. Shouldn't be possible to have NodeArgs that haven't been handled already. outer_scope_node_args_consumed.empty() 's number of inputs is different from function body graph's number of input. 's number of outputs is different from function body graph's number of outputs. node.MutableOutputDefs().size() == subgraph.GetOutputs().size() Failed to convert dense initializer to sparse E EEEpE8EE`E Eoperator() LoadFromOrtFormat LoadFromOrtFormat void onnxruntime::Graph::SetInputs(const std::vector<const onnxruntime::NodeArg*>&) InlineFunction onnxruntime::common::Status onnxruntime::Graph::InlineFunction(onnxruntime::Node&) void onnxruntime::Graph::FinalizeFuseSubGraph(const onnxruntime::IndexedSubGraph&, onnxruntime::Node&) onnxruntime::Node& onnxruntime::Graph::CreateFusedSubGraphNode(const onnxruntime::IndexedSubGraph&, const string&) gsl::not_null<onnxruntime::Node*> onnxruntime::Graph::AllocateNode() CleanUnusedInitializers void onnxruntime::Graph::ToGraphProtoInternal(onnx::GraphProto&) const onnx::GraphProto onnxruntime::Graph::ToGraphProto() const bool onnxruntime::Graph::RemoveNode(onnxruntime::NodeIndex) SaveToOrtFormat void onnxruntime::Graph::RemoveInitializedTensor(const string&) void onnxruntime::Graph::AddInitializedTensor(const onnx::TensorProto&) onnxruntime::common::Status onnxruntime::Graph::Resolve(const onnxruntime::Graph::ResolveOptions&) Resolve ForThisAndAllSubgraphs PerformTypeAndShapeInferencing InitInputsInitializersOutputs VerifyNodeAndOpMatch InferAndVerifyTypeMatch onnxruntime::common::Status onnxruntime::Graph::UpdateShapeInference(onnxruntime::Node&) InferAndVerifySubgraphTypes const T* onnxruntime::ConstPointerContainer<Container>::at(size_t) const [with Container = std::vector<onnxruntime::NodeArg*>; onnxruntime::ConstPointerContainer<Container>::T = onnxruntime::NodeArg; size_t = long unsigned int] void onnxruntime::Graph::KahnsTopologicalSort(const std::function<void(const onnxruntime::Node*)>&, const std::function<bool(const onnxruntime::Node*, const onnxruntime::Node*)>&) const BuildConnections void onnxruntime::Graph::RemoveEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int) void onnxruntime::Graph::AddEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int) SetOuterScopeNodeArgs void onnxruntime::Graph::InitializeStateFromModelFileGraphProto() bool onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet() Graph onnxruntime::Graph::Graph(const onnxruntime::Model&, onnx::GraphProto*, const std::unordered_map<std::basic_string<char>, int>&, onnxruntime::Version, onnxruntime::IOnnxRuntimeOpSchemaCollectionPtr, onnxruntime::Graph*, const onnxruntime::Node*, const onnxruntime::logging::Logger&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const operator() LoadEdgesFromOrtFormat operator() LoadFromOrtFormat SaveToOrtFormat MergeShapeInfo UpdateTypeAndShape FN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_RKNS5_14ResolveOptionsEE *ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E0_ *ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E1_ *ZN11onnxruntime5Graph7ResolveERKNS0_14ResolveOptionsEEUlRS0_E2_ N4onnx15GraphInferencerE N4onnx24FunctionBodyBuildContextE N4onnx28FunctionBodyBuildContextImplE N11onnxruntime5GraphE N11onnxruntime19GraphInferencerImplE N11onnxruntime20InferenceContextImplE PFN11onnxruntime6common6StatusERKNS_4NodeERNS_5GraphERKSt6vectorIPKN4onnx9TypeProtoESaISB_EERSD_RKNS5_14ResolveOptionsEE St23_Sp_counted_ptr_inplaceIN11onnxruntime21SchemaRegistryManagerESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE UNDEFINED INT FLOATS INTS STRINGS GRAPHS SPARSE_TENSOR SPARSE_TENSORS /onnxruntime_src/onnxruntime/core/graph/graph_flatbuffers_utils.cc Graph attribute value was null. Invalid ORT format model. Satisfied, but should not be: nullptr == graph
SaveAttributeOrtFormat: Unsupported attribute type: Missing dimensions for initializer. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_dims
Missing string data for initializer. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_str_data
Missing raw data for initializer. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_raw_data
- Null tensor type info. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_tensor_type
- Null entry in dimensions. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_dim
- dim_param value with no name. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_dim_param
- Null sequence type info. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_sequence_type
- Null value type info in fbs::SequenceType. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_type_info
- Null map type info. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_map_type
- Null value type info in fbs::MapType. Invalid ORT format model. Satisfied, but should not be: !value_info_proto.name().empty()
- Null string attribute. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_str
+ Missing values for sparse initializer. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_values_tensor
+ Missing name for SparseTensor initializer. Invalid ORT format model. Satisfied, but should not be: values_tensor->name().empty()
+ Missing indicies for sparse initializer: Satisfied, but should not be: nullptr == fbs_indicies_tensor
+ Missing dims for sparse initializer: Null string attribute. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_str
Null tensor attribute. Invalid ORT format model. Null graph attribute. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_graph
Empty graph proto from deserialization of ORT format model Null floats attribute. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_floats
Null ints attribute. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_ints
Null strings attribute. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_strings
Null string in strings attribute. Invalid ORT format model. Null tensors attribute. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_tensors
- Null tensor in tensors attribute. Invalid ORT format model. Dl| d4T< LoadAttributeOrtFormat LoadMapTypeOrtFormat LoadSequenceTypeOrtFormat LoadTensorDimensionOrtFormat LoadTensorTypeAndShapeOrtFormat LoadTypeInfoOrtFormat LoadValueInfoOrtFormat LoadInitializerOrtFormat SaveAttributeOrtFormat SaveInitializerOrtFormat SaveMapTypeOrtFormat SaveSequenceTypeOrtFormat SaveTensorTypeAndShapeOrtFormat SaveTypeInfoOrtFormat SaveValueInfoOrtFormat itr != node.InputDefs().end() ExplicitInputs: . Index: Invalid input index for node ImplicitInputs: Implicit input name cannot be safely updated to in one of the subgraphs. Failed since multiple edges matched: /onnxruntime_src/onnxruntime/core/graph/graph_utils.cc Attempting to get index for an input which does not exist. 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. Can only add a new input at the end of the current ones. num_explicit_inputs == static_cast<size_t>(target_input_idx) std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0 Node must only have one used output std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; }) Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here. Initializer with same name exists. Name: !graph.GetInitializedTensor(new_initializer.name(), existing) FindPath void onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&) void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&) onnxruntime::NodeArg& onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph&, const onnx::TensorProto&) void onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph(onnxruntime::Node&, const string&, const string&) bool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(onnxruntime::Graph&, onnxruntime::Node&) bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph&, onnxruntime::Node&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const CanUpdateImplicitInputNameInSubgraphs const string& onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node&, int) const string& onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node&, int) int onnxruntime::graph_utils::GetNodeInputIndexFromInputName(const onnxruntime::Node&, const string&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const *ZN11onnxruntime11GraphViewerC1ERKNS_5GraphEEUlPKNS_4NodeEE_ N11onnxruntime11NodeCompareE /onnxruntime_src/onnxruntime/core/graph/model.cc ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain ModelProto does not have a graph. Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported. ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators. Unknown model file format version. No graph was found in the protobuf. Failed to load model with error: Model must have opset imports. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_op_set_ids
- opset import domain is null. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_domain
- Graph is null. Invalid ORT format model. Protobuf serialization failed. Protobuf parsing failed. is till opset <p_fd> is less than 0. Invalid istream object. Null model_proto ptr. <p_fd> less than 0. failed. File doesn't exist Load model system error number LoadFromOrtFormat SaveToOrtFormat Save Load SaveModel Load Model onnxruntime::Model::Model(onnx::ModelProto&&, const PathString&, const IOnnxRuntimeOpSchemaRegistryList*, const onnxruntime::logging::Logger&) ValidateOpsetForDomain void onnxruntime::model_load_utils::ValidateOpsetForDomain(const std::unordered_map<std::basic_string<char>, int>&, const onnxruntime::logging::Logger&, bool, const string&, int) bool onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet() N6google8protobuf2io19ZeroCopyInputStreamE St15_Sp_counted_ptrIPN11onnxruntime5ModelELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime5ModelESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE Domain already set in registry /onnxruntime_src/onnxruntime/core/graph/schema_registry.cc , but it its version is higher than the operator set version static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() RegisterOpSchemaInternal RegisterOpSet N11onnxruntime27OnnxRuntimeOpSchemaRegistryE N11onnxruntime21SchemaRegistryManagerE 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 /onnxruntime_src/onnxruntime/core/common/logging/logging.cc default_logger_id must be provided if instance_type is InstanceType::Default Only one instance of LoggingManager created with InstanceType::Default can exist at any point in time. void onnxruntime::logging::LoggingManager::CreateDefaultLogger(const string&) onnxruntime::logging::LoggingManager::LoggingManager(std::unique_ptr<onnxruntime::logging::ISink>, onnxruntime::logging::Severity, bool, onnxruntime::logging::LoggingManager::InstanceType, const string*, int) N@VIWEF is not a valid year is not a valid day is not a valid date N11onnxruntime7logging11OStreamSinkE Failed to parse path root: src root: , dst root: /onnxruntime_src/onnxruntime/core/common/path.cc Not satisfied: has_trailing_separator
+ Null tensor in tensors attribute. Invalid ORT format model. t $$ ,-$21\2./\00, LoadAttributeOrtFormat LoadSparseInitializerOrtFormat LoadInitializerOrtFormat SaveAttributeOrtFormat SaveSparseInitializerOrtFormat SaveInitializerOrtFormat itr != node.InputDefs().end() ExplicitInputs: . Index: Invalid input index for node ImplicitInputs: Implicit input name cannot be safely updated to in one of the subgraphs. Failed since multiple edges matched: /onnxruntime_src/onnxruntime/core/graph/graph_utils.cc Attempting to get index for an input which does not exist. 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. Can only add a new input at the end of the current ones. num_explicit_inputs == static_cast<size_t>(target_input_idx) std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0 Node must only have one used output std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; }) Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here. Initializer with same name exists. Name: !graph.GetInitializedTensor(new_initializer.name(), existing) FindPath void onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&) void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&) onnxruntime::NodeArg& onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph&, const onnx::TensorProto&) void onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph(onnxruntime::Node&, const string&, const string&) bool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(onnxruntime::Graph&, onnxruntime::Node&) bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph&, onnxruntime::Node&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const CanUpdateImplicitInputNameInSubgraphs const string& onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node&, int) const string& onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node&, int) int onnxruntime::graph_utils::GetNodeInputIndexFromInputName(const onnxruntime::Node&, const string&) Invalid ExecutionOrder filter_info_ == nullptr nodearg /onnxruntime_src/onnxruntime/core/graph/graph_viewer.cc Not supported with filtered graph. IndexedSubGraph contains values not present in the Graph graph_->GetNode(idx) != nullptr Mismatch between Graph and IndexedSubGraph. Input not found: Mismatch between Graph and IndexedSubGraph. Output not found: Mismatch between Graph and IndexedSubGraph. Node not found: const std::vector<long unsigned int>& onnxruntime::GraphViewer::GetRootNodes() const const std::vector<long unsigned int>& onnxruntime::GraphViewer::GetNodesInTopologicalOrder(onnxruntime::ExecutionOrder) const onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const onnxruntime::GraphViewer::GraphViewer(const onnxruntime::Graph&, const onnxruntime::IndexedSubGraph*) *ZN11onnxruntime11GraphViewerC1ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlmE_ *ZN11onnxruntime11GraphViewerC1ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlPKNS_4NodeEE0_ *ZN11onnxruntime11GraphViewerC1ERKNS_5GraphEPKNS_15IndexedSubGraphEEUlPKNS_4NodeEE1_ N11onnxruntime11NodeCompareE N11onnxruntime19PriorityNodeCompareE Protobuf parsing failed. is till opset <p_fd> is less than 0. Invalid istream object. Null model_proto ptr. <p_fd> less than 0. failed. File doesn't exist Load model system error number /onnxruntime_src/onnxruntime/core/graph/model.cc ONNX Runtime only *guarantees* support for models stamped with official released onnx opset versions. Opset is under development and support for this is limited. The operator schemas and or other functionality may change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain is under development and support for this is limited. The operator schemas and or other functionality could possibly change before next ONNX release and in this case ONNX Runtime will not guarantee backward compatibility. Current official support for domain ModelProto does not have a graph. Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported. ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators. Unknown model file format version. No graph was found in the protobuf. Failed to load model with error: Graph is null. Invalid ORT format model. Protobuf serialization failed. LoadFromOrtFormat SaveToOrtFormat Save Load SaveModel Load Model onnxruntime::Model::Model(onnx::ModelProto&&, const PathString&, const IOnnxRuntimeOpSchemaRegistryList*, const onnxruntime::logging::Logger&) ValidateOpsetForDomain void onnxruntime::model_load_utils::ValidateOpsetForDomain(const std::unordered_map<std::basic_string<char>, int>&, const onnxruntime::logging::Logger&, bool, const string&, int) bool onnxruntime::model_load_utils::IsAllowReleasedONNXOpsetsOnlySet() N6google8protobuf2io19ZeroCopyInputStreamE St15_Sp_counted_ptrIPN11onnxruntime5ModelELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime5ModelESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE Domain already set in registry /onnxruntime_src/onnxruntime/core/graph/schema_registry.cc , but it its version is higher than the operator set version static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() RegisterOpSchemaInternal RegisterOpSet N11onnxruntime27OnnxRuntimeOpSchemaRegistryE N11onnxruntime21SchemaRegistryManagerE QW MW memory_seq_lens AW Couple the input and forget gates if 1, default 0. Number of neurons in the hidden layer. Constrain seq_lens to integral tensors. The input sequences packed (and potentially padded) into one 3-D tensor with the shape of `[seq_length, batch_size, input_size]` The weight tensor for the gates. Concatenation of `W[iofc]` and `WB[iofc]` (if bidirectional) along dimension 0. The tensor has shape `[num_directions, 4*hidden_size, input_size]`. The recurrence weight tensor. Concatenation of `R[iofc]` and `RB[iofc]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 4*hidden_size, hidden_size]`. Optional tensor specifying lengths of the sequences in a batch. If not specified - assumed all sequences in the batch to have length `seq_length`. It has shape `[batch_size]` The weight tensor of the query layer in the attention mechanism. Should be of shape `[num_directions, am_query_depth(hidden_size of lstm), am_attn_size]` The weight tensor of the memory layer in the attention mechanism. Should be of shape `[num_directions, memory_depth, am_attn_size]` The attention_v tensor in the attention mechanism. Should be of shape `[num_directions, am_attn_size]` The sequence of the memory (input) for attention mechanism. Should be of `[batch_size, max_memory_step, memory_depth]` The sequence length of the input memory for the attention mechanism. Should be of `[batch_size]` The weights of attention layer in the attention wrapper. If exists, should be of shape `[num_directions, memory_depth+hidden_size, aw_attn_size]. Please note that attention mechanism context depth is also memory_depth in the attention mechanism.` A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]` The last output value of the hidden. It has shape `[num_directions, batch_size, hidden_size]`. Not supported input_arg->Type() != nullptr fused_function_subgraph Resolve subgraph failed: /onnxruntime_src/onnxruntime/core/graph/function_impl.h A node with a function body within a subgraph within another function body is currently not supported in ORT /onnxruntime_src/onnxruntime/core/graph/function.cc 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 std::unique_ptr<onnx::OpSchema> onnxruntime::CreateSchema(const onnxruntime::Graph&, const onnxruntime::IndexedSubGraph&) onnxruntime::FunctionImpl::FunctionImpl(const onnxruntime::Graph&, const onnxruntime::IndexedSubGraph&, const onnxruntime::logging::Logger&) virtual const onnxruntime::Graph& onnxruntime::ViewerFunctionImpl::Body() const *ZN11onnxruntime12FunctionImplC1ERKNS_5GraphERKmRKN4onnx13FunctionProtoERKNS_7logging6LoggerEEUlRNS6_16InferenceContextEE_ N11onnxruntime8FunctionE N11onnxruntime12FunctionImplE N11onnxruntime18ViewerFunctionImplE Default logger already set. ISink must be provided. System onnxruntime /onnxruntime_src/onnxruntime/core/common/logging/logging.cc default_logger_id must be provided if instance_type is InstanceType::Default Only one instance of LoggingManager created with InstanceType::Default can exist at any point in time. void onnxruntime::logging::LoggingManager::CreateDefaultLogger(const string&) onnxruntime::logging::LoggingManager::LoggingManager(std::unique_ptr<onnxruntime::logging::ISink>, onnxruntime::logging::Severity, bool, onnxruntime::logging::LoggingManager::InstanceType, const string*, int) N@VIWEF is not a valid year is not a valid day is not a valid date N11onnxruntime7logging11OStreamSinkE Failed to parse path root: src root: , dst root: /onnxruntime_src/onnxruntime/core/common/path.cc Not satisfied: has_trailing_separator
Cannot concatenate with a string containing a path separator. String: first_separator == value.end() Paths must have the same root to compute a relative path. Not satisfied: src.GetRootPathString() == dst.GetRootPathString()
static onnxruntime::Path onnxruntime::Path::Parse(const PathString&) ParsePathRoot Parse Writing profiler data to file /onnxruntime_src/onnxruntime/core/common/profiler.cc Maximum number of events reached, could not record profile event. [
]
{"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] n]L;- /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&) fn != nullptr n >= 0 total >= 0 degree_of_parallelism >= 1 /onnxruntime_src/onnxruntime/core/common/threadpool.cc static void onnxruntime::concurrency::ThreadPool::TryParallelFor(onnxruntime::concurrency::ThreadPool*, std::ptrdiff_t, const onnxruntime::TensorOpCost&, const std::function<void(long int, long int)>&) void onnxruntime::concurrency::ThreadPool::Schedule(std::function<void()>) onnxruntime::concurrency::ThreadPool::ThreadPool(onnxruntime::Env*, const onnxruntime::ThreadOptions&, const NAME_CHAR_TYPE*, int, bool) ZN11onnxruntime15ThreadPoolTemplINS_3EnvEE13RunInParallelESt8functionIFvvEEjEUlvE_ N5Eigen19ThreadPoolInterfaceE N11onnxruntime11concurrency27ExtendedThreadPoolInterfaceE N11onnxruntime15ThreadPoolTemplINS_3EnvEEE ZN11onnxruntime15ThreadPoolTemplINS_3EnvEE10WorkerLoopEiEUlvE_ ZN11onnxruntime15ThreadPoolTemplINS_3EnvEE10WorkerLoopEiEUlvE0_ ? @{Gz? ? j@? AFailed to get symbol with error: lib .so Got null library handle file " " failed: realpath close open \/ Failed to load library Invalid fd was supplied: fstat pthread_attr_init failed pthread_create failed pthread_setaffinity_np failed remove() failed. Error code: , path: munmap failed. error code: - error code: Not satisfied: result == 0
+ 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] `_```}`l`[`M`?`1`#` /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&) n >= 0 total >= 0 /onnxruntime_src/onnxruntime/core/common/threadpool.cc static void onnxruntime::concurrency::ThreadPool::TryParallelFor(onnxruntime::concurrency::ThreadPool*, std::ptrdiff_t, const onnxruntime::TensorOpCost&, const std::function<void(long int, long int)>&) ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE20RunInParallelSectionERNS0_25ThreadPoolParallelSectionESt8functionIFvjEEjEUljE_ ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE13SummonWorkersERNS3_9PerThreadERNS0_25ThreadPoolParallelSectionEjRKSt8functionIFvjEEEUlvE_ N5Eigen19ThreadPoolInterfaceE N11onnxruntime11concurrency27ExtendedThreadPoolInterfaceE N11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEEE ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE10WorkerLoopEiEUlvE_ ZN11onnxruntime11concurrency15ThreadPoolTemplINS_3EnvEE10WorkerLoopEiEUlvE0_ ? @{Gz? j@? AFailed to get symbol with error: lib .so Got null library handle file " " failed: realpath close open \/ Failed to load library Invalid fd was supplied: fstat pthread_attr_init failed pthread_create failed pthread_setaffinity_np failed - error code: remove() failed. Error code: , path: munmap failed. error code: Not satisfied: result == 0
Not satisfied: file_path
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mmap lseek read File: , offset: , length: Failed to unload library with error: Received negative size from stat call /onnxruntime_src/onnxruntime/core/platform/posix/env.cc pthread_attr_setstacksize failed Failed to close file descriptor DeleteFolder(): nftw() failed with error: Not satisfied: length <= buffer.size()
- ReadFileIntoBuffer - unexpected end of file. onnxruntime::{anonymous}::PosixThread::PosixThread(const char*, int, unsigned int (*)(int, Eigen::ThreadPoolInterface*), Eigen::ThreadPoolInterface*, const onnxruntime::ThreadOptions&) ReadFileIntoBuffer CleanUp UnmapFile MapFileIntoMemory static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() nftw_remove DeleteFolder *N11onnxruntime12_GLOBAL__N_111PosixThreadE *N11onnxruntime12_GLOBAL__N_18PosixEnvE N11onnxruntime9EnvThreadE N11onnxruntime3EnvE N11onnxruntime9TelemetryE *N11onnxruntime12_GLOBAL__N_112PosixEnvTimeE N11onnxruntime7EnvTimeE ? ? 0A >packing unavailable
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=ppppu>u>u>u> l? l? l? l?7777fȹfȹfȹfȹ~;~;~;~;ƼƼƼƼX=X=X=X=P"?P"?P"?P"?>>>> r1 r1 r1 r1mL:mL:mL:mL:e9 <e9 <e9 <e9 <*=*=*=*=*>*>*>*>>>>> rB¥B @K;? r1 :n/ <*=(*>> ? ?µµµrBrBrBrB : : : :n/ <n/ <n/ <n/ <*=*=*=*=(*>(*>(*>(*> *Z19MlasExecuteThreadedPFvPviES_iPN11onnxruntime11concurrency10ThreadPoolEEUllE_ Sparse tensor indices ( ) has values, but NNZ is Sparse tensor ( ) index value at position [ ] out of range [0, ] not in sorted order. is required to be non-empty. value_info ' of Field ' is required but missing. elem_type key_type value_type data_type ) to UNDEFINED is not allowed float_data int32_data string_data raw_data double_data uint64_data TensorProto ( tensor name: ) should be stored in TensorProto (tensor name: STRING data (tensor name: ' instead of ' ' should be stored in field ' values of data_type ' ] out of range. sparse_tensor_proto ) must have rank 1. Sparse tensor values ( ) must have a dense-rank > 0 ) must have INT64 type. ) must have rank 1 or 2. ) has no index values. , elem_type: Sequence ( Structure name: Map (name: ' of node:
+ ReadFileIntoBuffer - unexpected end of file. onnxruntime::{anonymous}::PosixThread::PosixThread(const char*, int, unsigned int (*)(int, Eigen::ThreadPoolInterface*), Eigen::ThreadPoolInterface*, const onnxruntime::ThreadOptions&) ReadFileIntoBuffer CleanUp UnmapFile MapFileIntoMemory static const onnxruntime::logging::Logger& onnxruntime::logging::LoggingManager::DefaultLogger() nftw_remove DeleteFolder *N11onnxruntime12_GLOBAL__N_111PosixThreadE *N11onnxruntime12_GLOBAL__N_18PosixEnvE N11onnxruntime9EnvThreadE N11onnxruntime3EnvE N11onnxruntime9TelemetryE *N11onnxruntime12_GLOBAL__N_112PosixEnvTimeE N11onnxruntime7EnvTimeE ? ? packing unavailable
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=ppppu>u>u>u> l? l? l? l?7777fȹfȹfȹfȹ~;~;~;~;ƼƼƼƼX=X=X=X=P"?P"?P"?P"?>>>> r1 r1 r1 r1mL:mL:mL:mL:e9 <e9 <e9 <e9 <*=*=*=*=*>*>*>*>>>>> rB¥B @K;? r1 :n/ <*=(*>> ? ?µµµrBrBrBrB : : : :n/ <n/ <n/ <n/ <*=*=*=*=(*>(*>(*>(*> 38MLAS_QGEMM_SCALE_BIAS_OUTPUT_PROCESSOR ElementSize must be power of 2 and less or equal than 16! QLinearGlobalAveragePool ImageSize too large! QLinearGlobalAveragePool parameter out of computation range! ͢\ڞ / C *Z19MlasExecuteThreadedPFvPviES_iPN11onnxruntime11concurrency10ThreadPoolEEUllE_ We do not support type [ ] for now is missing type info. is not supported currently Null type info for sequence_type map_type /onnxruntime_src/onnxruntime/core/flatbuffers/flatbuffers_utils.cc SaveValueInfoOrtFormat: value_info_proto for Model must have opset imports. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_op_set_ids
+ opset id is null. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_op_set_id
+ opset import domain is null. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_domain
+ Null tensor type info. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_tensor_type
+ Null entry in dimensions. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_dim
+ dim_param value with no name. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_dim_param
+ Null sequence type info. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_sequence_type
+ Null value type info in fbs::SequenceType. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_type_info
+ Null map type info. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_map_type
+ Null value type info in fbs::MapType. Invalid ORT format model. Satisfied, but should not be: !value_info_proto.name().empty()
+ LoadOpsetImportOrtFormat LoadMapTypeOrtFormat LoadSequenceTypeOrtFormat LoadTensorDimensionOrtFormat LoadTensorShapeOrtFormat LoadTensorTypeAndShapeOrtFormat LoadTypeInfoOrtFormat LoadValueInfoOrtFormat SaveMapTypeOrtFormat SaveSequenceTypeOrtFormat SaveTensorTypeAndShapeOrtFormat SaveTypeInfoOrtFormat SaveValueInfoOrtFormat Sparse tensor indices ( ) has values, but NNZ is Sparse tensor ( ) index value at position [ ] out of range [0, ] not in sorted order. is required to be non-empty. value_info ' of Field ' is required but missing. elem_type key_type value_type data_type ) to UNDEFINED is not allowed float_data int32_data string_data raw_data double_data uint64_data TensorProto ( tensor name: ) should be stored in TensorProto (tensor name: STRING data (tensor name: ' instead of ' ' should be stored in field ' values of data_type ' ] out of range. sparse_tensor_proto ) must have rank 1. Sparse tensor values ( ) must have a dense-rank > 0 ) must have INT64 type. ) must have rank 1 or 2. ) has no index values. , elem_type: Sequence ( Structure name: Map (name: init ' of node:
Bad node spec:
-==> Context: attr Attribute (name: op_type NodeProto (name: , type: No opset import for domain ' No Op registered for with domain_version of since_version function ( ATen ConstantFill Unable to open model file: Unrecognized type value case (value_info name: setting data_type field (tensor name: ) is stored externally and should not have data field. Data of TensorProto ( tensor name: ) is stored externally but doesn't have a location. , but it doesn't exist or is not accessible. ) is 0-element but contains data! ) should contain one and only one value field. ) should not be stored in raw_data field Unrecognized data_type (tensor name: ) first dimension size does not equal NNZ. ) second dimension size does not match rank of tensor. ] not in lexicographic sorted order. ) dimensions are not positive. ) is not have a valid element type. setting key_type field (map name: ) to invalid TensorProto key_type Length of map keys and map values are not the same (map name: ) should not contain more than one keys field. ' has been used as graph input names multiple times. Graph must be in single static assignment (SSA) form, however ' in initializer but not in graph input Nodes in a graph must be topologically sorted, however input '
+==> Context: attr Attribute (name: op_type NodeProto (name: , type: No opset import for domain ' No Op registered for with domain_version of since_version function ( ATen ConstantFill Unable to open model file: Unrecognized type value case (value_info name: setting data_type field (tensor name: ) is stored externally and should not have data field. Data of TensorProto ( tensor name: ) is stored externally but doesn't have a location. , but it doesn't exist or is not accessible. ) is 0-element but contains data! ) should contain one and only one value field. ) should not be stored in raw_data field Unrecognized data_type (tensor name: ) first dimension size does not equal NNZ. ) second dimension size does not match rank of tensor. ] not in lexicographic sorted order. ) dimensions are not positive. ) is not have a valid element type. setting key_type field (map name: ) to invalid TensorProto key_type Length of map keys and map values are not the same (map name: ) should not contain more than one keys field. ' has been used as graph input names multiple times. Graph must be in single static assignment (SSA) form, however ' Tensor initializers must have a non-empty name initializer name is not unique in initializer but not in graph input Sparse tensor initializers must have a non-empty name sparse initializer name is not unique across initializers and sparse_initializers Nodes in a graph must be topologically sorted, however input '
is not output of any previous nodes. ' has been used as output names multiple times. ) should not contain more than one value field. type field and data field mismatch in attribute ) should refer to attribute in parent node. ) has zero input and zero output. Warning: Checker does not support models with experimental ops: is deprecated in domain_version of ' has been used multiple times. ) should not have duplicate outputs specified. ) should not have duplicate attributes specified. Nodes in a function must be topologically sorted, however input '
- is neither output of any previous nodes nor input of the function. Function must be in single static assignment (SSA) form, however ' The model does not have an ir_version set properly. Your model ir_version is higher than the checker's. Your model has duplicate keys in metadata_props. model with IR version >= 3 must specify opset_import for ONNX model with IR version < 3 cannot have opset_import specified . Please check if it is a valid file. . Please check if it is a valid protobuf file of model. Unable to parse model from file: 4<,hhhhhhhh N4onnx7checker15ValidationErrorE seq(tensor(uint8)) seq(tensor(uint16)) seq(tensor(uint32)) seq(tensor(uint64)) seq(tensor(int8)) seq(tensor(int16)) seq(tensor(int32)) seq(tensor(int64)) seq(tensor(float16)) seq(tensor(float)) seq(tensor(double)) seq(tensor(string)) seq(tensor(bool)) seq(tensor(complex64)) seq(tensor(complex128)) Input type was null Input element type of axis value All Tensor and Sequence types Condition for the if Only bool v_initial v_final_and_scan_outputs All Tensor types initial_state_and_scan_inputs final_state_and_scan_outputs Int64 tensor Scan input was not a tensor. outputs. Expected was not Mismatched type: source= target= If node has but subgraphs produce Input was expected to have tensor type. Got Element type of input was unknown does not match existing output type of Output was expected to have tensor type. Got is invalid for a tensor of rank Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible. Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch. Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch. /onnxruntime_src/cmake/external/onnx/onnx/defs/controlflow/defs.cc A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip. A boolean termination condition. Optional. Pass empty string to skip. The initial values of any loop-carried dependencies (values that change across loop iterations) Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors. The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations. tensor of int64, which should be a scalar. tensor of bool, which should be a scalar. Initial values of the loop's N state variables followed by M scan_inputs Final values of the loop's N state variables followed by K scan_outputs The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations. An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction. An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration. An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1]. Input was expected to have sequence type. Got Input was expected to have either tensor or sequence type. Got 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 Mismatched tensor element type: source sequence type missing element type. target sequence type missing element type. then_branch and else_branch produce different number of outputs. Loop 'body' subgraph outputs should all be tensors or sequences but output Loop 'body' subgraph scan outputs should all be tensors but output then= Mismatched type for output else= Mismatched tensor element type for output Optional tensor specifying lengths of the sequences in a batch. If this input is not specified, all sequences are assumed to be of the maximum sequence length (the dimension of the sequence axis of the scan_input tensors). /onnxruntime_src/cmake/external/onnx/onnx/defs/controlflow/old.cc Final N loop carried dependency values then K scan_outputs An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type. Loop 'body' subgraph outputs should all be tensors but output bfloat16 double uint8 uint16 uint32 uint64 complex64 complex128 seq( map( sparse_tensor( seq opaque sparse_tensor at]aa `^a a\ not specified Value of attribute Invalid shape value: sparse_value value_int value_ints value_string value_strings Constrain input types. Number of times to sample. Input tensor must have rank 2 loop_body_attribute cond_out 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. Attribute expected to have tensor type Attribute expected to have a one-dim tensor should be of integer type and specify a type. does not specify a valid type. Input to 'Range' op should be scalars (Tensor with only one element and shape empty) Shape input must be a one-dimensional tensor. The value for the elements of the output tensor. The value for the elements of the output tensor in sparse format. The value for the sole element for the scalar, int64, output tensor. The values for the elements for the 1D, int64, output tensor. The value for the sole element for the scalar, float32, output tensor. The values for the elements for the 1D, float32, output tensor. The value for the sole element for the scalar, UTF-8 string, output tensor. The values for the elements for the 1D, UTF-8 string, output tensor. Output tensor containing the same value of the provided tensor. /onnxruntime_src/cmake/external/onnx/onnx/defs/generator/defs.cc (Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32 1D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. All values must be >= 0. Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32. Constrain output types to be numerics. (Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal. (Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'. 2D input tensor to copy shape, and optionally, type information from. Output tensor, same shape as input tensor T1. Constrain input types. Strings and complex are not supported. Constrain output types. Strings and complex are not supported. Lower boundary of the output values. Upper boundary of the output values. (Optional) Seed to the random generator, if not specified we will auto generate one. The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT. The shape of the output tensor. Output tensor of random values drawn from uniform distribution Constrain output types to float tensors. The mean of the normal distribution. The standard deviation of the normal distribution. The data type for the elements of the output tensor. Default is TensorProto::FLOAT. Output tensor of random values drawn from normal distribution (Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor. Input tensor to copy shape and optionally type information from. (Optional) The data type for the elements of the output tensor, if not specified, we will use int32. Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch. Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch. Constrain input types to float tensors. Constrain output types to integral tensors. One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node. Attribute 'value_int' expect an integer. Attribute 'value_ints' expect a list of integers. Attribute 'value_float' expect a float. Attribute 'value_floats' expect a list of floats. Attribute 'value_string' expect a string. Attribute 'value_strings' expect a list of strings. TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached. Input tensor must be 2-dimensional All inputs to 'Range' op must be of the same type Output type must be int32 or int64 Scalar. First entry for the range of output values. Scalar. Exclusive upper limit for the range of output values. A 1-D tensor with same type as the inputs containing generated range of values. Constrain input types to common numeric type tensors. *ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25ConstantOfShape_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17EyeLike_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_23RandomUniform_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22RandomNormal_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27RandomUniformLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26RandomNormalLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_21Multinomial_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Range_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Attribute 'value' of Constant node must exist with 'Tensor' data. Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node. One of the attributes 'value' or 'sparse_value' must be specified for a Constant node. /onnxruntime_src/cmake/external/onnx/onnx/defs/generator/old.cc *ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Result tensor. greater less Z xor O1 O2 less_equal LessOrEqual greater_equal GreaterOrEqual First input operand for the logical operator. Second input operand for the logical operator. Constrains input to boolean tensor. Constrains output to boolean tensor. /onnxruntime_src/cmake/external/onnx/onnx/defs/logical/defs.cc Constrains input types to all numeric tensors. Constrains input/output to boolean tensors. First operand, input to be shifted. Second operand, amounts of shift. Constrain input and output types to integer tensors. Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift). *ZZN4onnx23BinaryLogicDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx23BinaryLogicDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_19BitShift_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Enable broadcasting broadcast If set, defines the broadcast dimensions. Left input tensor for the logical operator. Right input tensor for the logical operator. Constrains input to float tensors. /onnxruntime_src/cmake/external/onnx/onnx/defs/logical/old.cc Constrains input to integral tensors. *ZZN4onnx31BinaryLogicDocGenerator_opset12EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx31BinaryLogicDocGenerator_opset12EPKcEUlRNS_8OpSchemaEE_ *ZN4onnx30BinaryLogicDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx30BinaryLogicDocGenerator_opset7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx30BinaryLogicDocGenerator_opset7EPKcEUlRNS_8OpSchemaEE_ List of tensors for data_0 Output tensor. Rank of input basic_string::at subtraction Dividend tensor Divisor tensor Remainder tensor division Coefficient of ELU. Celu slope Value of alpha. Value of beta. normalized exponential log of softmax logsoftmax hardmax Values Indices scale of quantized input a scale of quantized input b scale of quantized output y loss reduction NegativeLogLikelihoodLoss Einsum expression string. Operands Inputs scores labels log_prob SoftmaxCrossEntropyLoss X_Max X_Sub X_Exp X_RS X_Div X_Log 'axis' must be in [ ]. Its actual value is: Weight rank must be 1. none X_alpha Elu_Result const_zero const_one expanded_target input_gather_element loss_NCdd loss_N1dd loss_Ndd weight_gather loss_unweighted loss_sum weight_gather_sum const_ignore_index const_zero_target_typed expanded_target_int64 transform_targets const_zero_float squeeze_mask const_one_float weight_gather_temp weight_gather_temp_1 Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). The input tensor that's coerced into a 2D matrix of size (NxD) as described above. The output values with the same shape as input tensor (the original size without coercion). Constrain input and output types to high-precision numeric tensors. Number of input tensors does not match the operands in the equation. Ellipsis represents incompatible dimensions. does not match the equation indices. /onnxruntime_src/cmake/external/onnx/onnx/defs/math/defs.cc Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment Constrain input and output types to signed numeric tensors. Constrain input and output types to all numeric tensors. Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717). Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946). The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0. Constrain input and output types to float32 tensors. The exponential of the input tensor computed element-wise The natural log of the input tensor computed element-wise The hyperbolic tangent values of the input tensor computed element-wise First operand, base of the exponent. Second operand, power of the exponent. Output tensor (same size as X) Constrain input X and output types to float/int tensors. Constrain input Y types to float/int tensors. Slope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to X Constrain input and output types to numeric tensors. Input tensor whose elements to be clipped Minimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape). Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape). Output tensor with clipped input elements 1 for the first maximum value, and 0 for all others The softsign (x/(1+|x|)) values of the input tensor computed element-wise Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N). Tensor of shape [a_1, a_2, ..., a_n, r] A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieve Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensor Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values. Constrain index tensor to int64 Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). Whether to return the top-K largest or smallest elements. Whether to return the elements in sorted order. The sine of the input tensor computed element-wise The cosine of the input tensor computed element-wise The tangent of the input tensor computed element-wise The arcsine of the input tensor computed element-wise The arccosine of the input tensor computed element-wise The arctangent of the input tensor computed element-wise Constrain input and output types to all tensors. A 1-D tensor indicates the shape you want to expand to, following the broadcast rule The hyperbolic sine values of the input tensor computed element-wise The hyperbolic cosine values of the input tensor computed element-wise The hyperbolic arcsine values of the input tensor computed element-wise The hyperbolic arccosine values of the input tensor computed element-wise The hyperbolic arctangent values of the input tensor computed element-wise The sign of the input tensor computed element-wise. It has the same shape and type of the input. The error function of the input tensor computed element-wise. It has the same shape and type of the input. N-dimensional quantized matrix a zero point of quantized input a N-dimensional quantized matrix b zero point of quantized input b zero point of quantized output y Quantized matrix multiply results from a * b Constrain input a and its zero point data type to 8-bit integer tensor. Constrain input b and its zero point data type to 8-bit integer tensor. Constrain output y and its zero point data type to 8-bit integer tensor. Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-row quantization. If it's a 1-D tensor, its number of elements should be equal to the number of rows of input 'A'. Constrain output Y data type as 32-bit integer tensor. If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements. If set to 1 will perform the sums in reverse direction. An input tensor that is to be processed. A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back. Output tensor of the same type as 'x' with cumulative sums of the x's elements axis tensor can be int32 or int64 only Constrain input and output types to floating-point tensors. Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk). Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index. Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones. The negative log likelihood loss Type of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights. Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value. Constrain input, weight, and output types to floating-point tensors. Constrain target to integer types Constrain input and output types to all numerical tensor types. The predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions. The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index. A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones. Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar. Log probability tensor. If the output of softmax is prob, its value is log(prob). Target rank must be 1 less than the input rank. Input and target dimension value mismatch. input_gather_element_transform Invalid value for attribute axis K input must be a one-dimensional tensor of size 1. K input must be of type int64. Axis has less than the requested k elements. 'shape' input must be 1D tensor of type INT64 input and zero_point pair is expected to have be same type. input and zero_point pair is expected to have same type. Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output. FbRKN4onnx24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE *ZZN4onnx16MathDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx16MathDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_EUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx30ElementwiseMultiOpDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx30ElementwiseMultiOpDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17MatMul_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Expand_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24QLinearMatMul_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24MatMulInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Det_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Einsum_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ PFbRKN4onnx24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE Pass 1 to enable broadcasting consumed_inputs 1-D input tensor Value of alpha default to 0.2 Value of beta default to 0.5 List of tensors for Max. List of tensors for Min List of tensors for Sum. List of tensors for Mean. Input tensor A Input tensor B Input tensor C Invalid value for attribute k Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size legacy optimization attribute. If set, defines the broadcast dimensions. See doc for details. First operand, should share the type with the second operand. Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size. Result, has same dimensions and type as A /onnxruntime_src/cmake/external/onnx/onnx/defs/math/old.cc
+ is neither output of any previous nodes nor input of the function. Function must be in single static assignment (SSA) form, however ' The model does not have an ir_version set properly. Your model ir_version is higher than the checker's. Your model has duplicate keys in metadata_props. model with IR version >= 3 must specify opset_import for ONNX model with IR version < 3 cannot have opset_import specified . Please check if it is a valid file. . Please check if it is a valid protobuf file of model. Unable to parse model from file: LTD0((0 N4onnx7checker15ValidationErrorE seq(tensor(uint8)) seq(tensor(uint16)) seq(tensor(uint32)) seq(tensor(uint64)) seq(tensor(int8)) seq(tensor(int16)) seq(tensor(int32)) seq(tensor(int64)) seq(tensor(float16)) seq(tensor(float)) seq(tensor(double)) seq(tensor(string)) seq(tensor(bool)) seq(tensor(complex64)) seq(tensor(complex128)) Input type was null Input element type of axis value All Tensor and Sequence types Condition for the if Only bool v_initial v_final_and_scan_outputs All Tensor types initial_state_and_scan_inputs final_state_and_scan_outputs Int64 tensor Scan input was not a tensor. outputs. Expected was not target= source= Mismatched type: If node has but subgraphs produce Input was expected to have tensor type. Got Element type of input was unknown does not match existing output type of Output was expected to have tensor type. Got is invalid for a tensor of rank Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible. Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch. Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch. /onnxruntime_src/cmake/external/onnx/onnx/defs/controlflow/defs.cc A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip. A boolean termination condition. Optional. Pass empty string to skip. The initial values of any loop-carried dependencies (values that change across loop iterations) Final N loop carried dependency values then K scan_outputs. Scan outputs must be Tensors. The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations. tensor of int64, which should be a scalar. tensor of bool, which should be a scalar. Initial values of the loop's N state variables followed by M scan_inputs Final values of the loop's N state variables followed by K scan_outputs The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations. An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction. An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration. An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1]. Input was expected to have sequence type. Got Input was expected to have either tensor or sequence type. Got ) is not equal to number of scan inputs ( Number of scan input axes specified ( ) is not equal to number of scan outputs ( Number of scan output axes specified ( Graph attribute inferencing returned type information for Scan 'body' subgraph outputs should all be tensors but output Mismatched tensor element type: source sequence type missing element type. target sequence type missing element type. then_branch and else_branch produce different number of outputs. Loop 'body' subgraph outputs should all be tensors or sequences but output Loop 'body' subgraph scan outputs should all be tensors but output then= Mismatched type for output else= Mismatched tensor element type for output Optional tensor specifying lengths of the sequences in a batch. If this input is not specified, all sequences are assumed to be of the maximum sequence length (the dimension of the sequence axis of the scan_input tensors). /onnxruntime_src/cmake/external/onnx/onnx/defs/controlflow/old.cc Final N loop carried dependency values then K scan_outputs An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type. Loop 'body' subgraph outputs should all be tensors but output bfloat16 double uint8 uint16 uint32 uint64 complex64 complex128 seq( map( sparse_tensor( seq opaque sparse_tensor zuzz\xvzLyt not specified Value of attribute Invalid shape value: sparse_value value_int value_ints value_string value_strings Constrain input types. Number of times to sample. Input tensor must have rank 2 loop_body_attribute cond_out sub_result sub_result_casted delta_casted div_result ceil_result ceil_result_relu ceil_result_relu_int ceil_result_relu_bool variadic_output Scalar. Value to step by. Attribute expected to have tensor type Attribute expected to have a one-dim tensor should be of integer type and specify a type. does not specify a valid type. Input to 'Range' op should be scalars (Tensor with only one element and shape empty) Shape input must be a one-dimensional tensor. The value for the elements of the output tensor. The value for the elements of the output tensor in sparse format. The value for the sole element for the scalar, int64, output tensor. The values for the elements for the 1D, int64, output tensor. The value for the sole element for the scalar, float32, output tensor. The values for the elements for the 1D, float32, output tensor. The value for the sole element for the scalar, UTF-8 string, output tensor. The values for the elements for the 1D, UTF-8 string, output tensor. Output tensor containing the same value of the provided tensor. /onnxruntime_src/cmake/external/onnx/onnx/defs/generator/defs.cc (Optional) The value of the output elements.Should be a one-element tensor. If not specified, it defaults to a tensor of value 0 and datatype float32 1D tensor. The shape of the expected output tensor. If empty tensor is given, the output would be a scalar. All values must be >= 0. Output tensor of shape specified by 'input'.If attribute 'value' is specified, the value and datatype of the output tensor is taken from 'value'.If attribute 'value' is not specified, the value in the output defaults to 0, and the datatype defaults to float32. Constrain output types to be numerics. (Optional) Index of the diagonal to be populated with ones. Default is 0. If T2 is the output, this op sets T2[i, i+k] = 1. k = 0 populates the main diagonal, k > 0 populates an upper diagonal, and k < 0 populates a lower diagonal. (Optional) The data type for the elements of the output tensor. If not specified,the data type of the input tensor T1 is used. If input tensor T1 is also notspecified, then type defaults to 'float'. 2D input tensor to copy shape, and optionally, type information from. Output tensor, same shape as input tensor T1. Constrain input types. Strings and complex are not supported. Constrain output types. Strings and complex are not supported. Lower boundary of the output values. Upper boundary of the output values. (Optional) Seed to the random generator, if not specified we will auto generate one. The data type for the elements of the output tensor. If not specified, default is TensorProto::FLOAT. The shape of the output tensor. Output tensor of random values drawn from uniform distribution Constrain output types to float tensors. The mean of the normal distribution. The standard deviation of the normal distribution. The data type for the elements of the output tensor. Default is TensorProto::FLOAT. Output tensor of random values drawn from normal distribution (Optional) The data type for the elements of the output tensor, if not specified, we will use the data type of the input tensor. Input tensor to copy shape and optionally type information from. (Optional) The data type for the elements of the output tensor, if not specified, we will use int32. Input tensor with shape [batch_size, class_size], where class_size is the number of all possible outcomes. Each value along the axis zero represents the unnormalized log-probability of each corresponding outcome in a batch. Output tensor with shape [batch_size, sample_size], where sample_size is the number of times to sample. Each value along the axis zero represents the outcome of the corresponding sample in a batch. Constrain input types to float tensors. Constrain output types to integral tensors. One and only one of the attributes 'value', 'value_*' or 'sparse_value' must be specified for a Constant node. Attribute 'value_int' expect an integer. Attribute 'value_ints' expect a list of integers. Attribute 'value_float' expect a float. Attribute 'value_floats' expect a list of floats. Attribute 'value_string' expect a string. Attribute 'value_strings' expect a list of strings. TypeAndShapeInferenceFunction implementation incomplete: this line should never be reached. Input tensor must be 2-dimensional All inputs to 'Range' op must be of the same type Output type must be int32 or int64 Scalar. First entry for the range of output values. Scalar. Exclusive upper limit for the range of output values. A 1-D tensor with same type as the inputs containing generated range of values. Constrain input types to common numeric type tensors. *ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25ConstantOfShape_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17EyeLike_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_23RandomUniform_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22RandomNormal_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27RandomUniformLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26RandomNormalLike_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_21Multinomial_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Range_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Attribute 'value' of Constant node must exist with 'Tensor' data. Only one of the attributes 'value' or 'sparse_value' must be specified for a Constant node. One of the attributes 'value' or 'sparse_value' must be specified for a Constant node. /onnxruntime_src/cmake/external/onnx/onnx/defs/generator/old.cc *ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Result tensor. xor greater less Z O1 O2 greater_equal GreaterOrEqual less_equal LessOrEqual First input operand for the logical operator. Second input operand for the logical operator. Constrains input to boolean tensor. Constrains output to boolean tensor. /onnxruntime_src/cmake/external/onnx/onnx/defs/logical/defs.cc Constrains input types to all numeric tensors. Constrains input/output to boolean tensors. First operand, input to be shifted. Second operand, amounts of shift. Constrain input and output types to integer tensors. Direction of moving bits. It can be either "RIGHT" (for right shift) or "LEFT" (for left shift). *ZZN4onnx23BinaryLogicDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx23BinaryLogicDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_19BitShift_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Enable broadcasting broadcast If set, defines the broadcast dimensions. Left input tensor for the logical operator. Right input tensor for the logical operator. Constrains input to float tensors. /onnxruntime_src/cmake/external/onnx/onnx/defs/logical/old.cc Constrains input to integral tensors. *ZZN4onnx31BinaryLogicDocGenerator_opset12EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx31BinaryLogicDocGenerator_opset12EPKcEUlRNS_8OpSchemaEE_ *ZN4onnx30BinaryLogicDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx30BinaryLogicDocGenerator_opset7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx30BinaryLogicDocGenerator_opset7EPKcEUlRNS_8OpSchemaEE_ List of tensors for data_0 Output tensor. Rank of input basic_string::at subtraction Dividend tensor Divisor tensor Remainder tensor division Coefficient of ELU. Celu slope Value of alpha. Value of beta. normalized exponential log of softmax hardmax Values Indices scale of quantized input a scale of quantized input b scale of quantized output y loss reduction NegativeLogLikelihoodLoss Einsum expression string. Operands Inputs scores labels log_prob SoftmaxCrossEntropyLoss 'axis' must be in [ ]. Its actual value is: Weight rank must be 1. none X_alpha Elu_Result X_ReduceMax X_Sub X_Exp X_ReduceSum X_Log const_zero const_one expanded_target input_gather_element loss_NCdd loss_N1dd loss_Ndd weight_gather loss_unweighted loss_sum weight_gather_sum const_ignore_index const_zero_target_typed expanded_target_int64 transform_targets const_zero_float squeeze_mask const_one_float weight_gather_temp weight_gather_temp_1 X_Max X_RS X_Div The input tensor that's coerced into a 2D matrix of size (NxD) as described above. The output values with the same shape as input tensor (the original size without coercion). Constrain input and output types to high-precision numeric tensors. Number of input tensors does not match the operands in the equation. Ellipsis represents incompatible dimensions. does not match the equation indices. /onnxruntime_src/cmake/external/onnx/onnx/defs/math/defs.cc Whether the operator should behave like fmod (default=0 meaning it will do integer mods); Set this to 1 to force fmod treatment Constrain input and output types to signed numeric tensors. Constrain input and output types to all numeric tensors. Coefficient of SELU default to 1.67326319217681884765625 (i.e., float32 approximation of 1.6732632423543772848170429916717). Coefficient of SELU default to 1.05070102214813232421875 (i.e., float32 approximation of 1.0507009873554804934193349852946). The Alpha value in Celu formula which control the shape of the unit. The default value is 1.0. Constrain input and output types to float32 tensors. The exponential of the input tensor computed element-wise The natural log of the input tensor computed element-wise The hyperbolic tangent values of the input tensor computed element-wise First operand, base of the exponent. Second operand, power of the exponent. Constrain input X and output types to float/int tensors. Constrain input Y types to float/int tensors. Slope tensor. The shape of slope can be smaller then first input X; if so, its shape must be unidirectional broadcastable to X Output tensor (same size as X) Constrain input and output types to numeric tensors. Input tensor whose elements to be clipped Minimum value, under which element is replaced by min. It must be a scalar(tensor of empty shape). Maximum value, above which element is replaced by max. It must be a scalar(tensor of empty shape). Output tensor with clipped input elements Softmax(input, axis) = Exp(input) / ReduceSum(Exp(input), axis=axis, keepdims=1) LogSoftmax(input, axis) = Log(Softmax(input, axis=axis)) Hardmax(element in input, axis) = 1 if the element is the first maximum value along the specified axis, 0 otherwise The softsign (x/(1+|x|)) values of the input tensor computed element-wise Optional input tensor C. If not specified, the computation is done as if C is a scalar 0. The shape of C should be unidirectional broadcastable to (M, N). Tensor of shape [a_1, a_2, ..., a_n, r] A 1-D tensor containing a single positive value corresponding to the number of top elements to retrieve Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing top K values from the input tensor Tensor of shape [a_1, a_2, ..., a_{axis-1}, k, a_{axis+1}, ... a_n] containing the corresponding input tensor indices for the top K values. Constrain index tensor to int64 Dimension on which to do the sort. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). Whether to return the top-K largest or smallest elements. Whether to return the elements in sorted order. The sine of the input tensor computed element-wise The cosine of the input tensor computed element-wise The tangent of the input tensor computed element-wise The arcsine of the input tensor computed element-wise The arccosine of the input tensor computed element-wise The arctangent of the input tensor computed element-wise Constrain input and output types to all tensors. A 1-D tensor indicates the shape you want to expand to, following the broadcast rule The hyperbolic sine values of the input tensor computed element-wise The hyperbolic cosine values of the input tensor computed element-wise The hyperbolic arcsine values of the input tensor computed element-wise The hyperbolic arccosine values of the input tensor computed element-wise The hyperbolic arctangent values of the input tensor computed element-wise The sign of the input tensor computed element-wise. It has the same shape and type of the input. The error function of the input tensor computed element-wise. It has the same shape and type of the input. N-dimensional quantized matrix a zero point of quantized input a N-dimensional quantized matrix b zero point of quantized input b zero point of quantized output y Quantized matrix multiply results from a * b Constrain input a and its zero point data type to 8-bit integer tensor. Constrain input b and its zero point data type to 8-bit integer tensor. Constrain output y and its zero point data type to 8-bit integer tensor. Zero point tensor for input 'A'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor or per-row quantization. If it's a 1-D tensor, its number of elements should be equal to the number of rows of input 'A'. Constrain output Y data type as 32-bit integer tensor. If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements. If set to 1 will perform the sums in reverse direction. An input tensor that is to be processed. A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back. Output tensor of the same type as 'x' with cumulative sums of the x's elements axis tensor can be int32 or int64 only Constrain input and output types to floating-point tensors. Input tensor of shape (N, C) or (N, C, d1, d2, ..., dk). Target tensor of shape (N) or (N, d1, d2, ..., dk). Target element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the target values should either be in the range [0, C) or have the value ignore_index. Optional rescaling weight tensor. If given, it has to be a tensor of size C. Otherwise, it is treated as if having all ones. The negative log likelihood loss Type of reduction to apply to loss: none, sum, mean (default). 'none': the output is the loss for each sample. 'sum': the output will be summed. 'mean': the sum of the output will be divided by the sum of applied weights. Specifies a target value that is ignored and does not contribute to the input gradient. It's an optional value. Constrain input, weight, and output types to floating-point tensors. Constrain target to integer types Constrain input and output types to all numerical tensor types. The predicted outputs with shape [batch_size, class_size], or [batch_size, class_size, D1, D2 , ..., Dk], where K is the number of dimensions. The ground truth output tensor, with shape [batch_size], or [batch_size, D1, D2, ..., Dk], where K is the number of dimensions. Labels element value shall be in range of [0, C). If ignore_index is specified, it may have a value outside [0, C) and the label values should either be in the range [0, C) or have the value ignore_index. A manual rescaling weight given to each class. If given, it has to be a 1D Tensor assigning weight to each of the classes. Otherwise, it is treated as if having all ones. Weighted loss float Tensor. If reduction is 'none', this has the shape of [batch_size], or [batch_size, D1, D2, ..., Dk] in case of K-dimensional loss. Otherwise, it is a scalar. Log probability tensor. If the output of softmax is prob, its value is log(prob). Target rank must be 1 less than the input rank. Input and target dimension value mismatch. input_gather_element_transform Invalid value for attribute axis K input must be a one-dimensional tensor of size 1. K input must be of type int64. Axis has less than the requested k elements. 'shape' input must be 1D tensor of type INT64 input and zero_point pair is expected to have same type. Type of reduction to apply to loss: none, sum, mean(default). 'none': no reduction will be applied, 'sum': the output will be summed. 'mean': the sum of the output will be divided by the number of elements in the output. FbRKN4onnx24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE *ZZN4onnx16MathDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx16MathDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_S1_EUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx30ElementwiseMultiOpDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx30ElementwiseMultiOpDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_18Softmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_ *ZN4onnx11GetOpSchemaINS_21LogSoftmax_Onnx_ver13EEENS_8OpSchemaEvEUlRKNS_24FunctionBodyBuildContextERKS2_RNS_13FunctionProtoEE_ *ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17MatMul_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Expand_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24QLinearMatMul_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24MatMulInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Det_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Einsum_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ PFbRKN4onnx24FunctionBodyBuildContextERKNS_8OpSchemaERNS_13FunctionProtoEE Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). legacy optimization attribute. If set, defines the broadcast dimensions. See doc for details. First operand, should share the type with the second operand. Second operand. With broadcasting can be of smaller size than A. If broadcasting is disabled it should be of the same size. Result, has same dimensions and type as A Describes the axis of the inputs when coerced to 2D; defaults to one because the 0th axis most likely describes the batch_size /onnxruntime_src/cmake/external/onnx/onnx/defs/math/old.cc 1 for the first maximum value, and 0 for all others
Pow takes input data (Tensor<T>) and exponent Tensor, and
produces one output data (Tensor<T>) where the function `f(x) = x^exponent`,
is applied to the data tensor elementwise.
Input tensor of any shape, base of the exponent. Input tensor of any shape broadcastable to X shape, the exponent component. This operator supports **multidirectional (i.e., Numpy-style) broadcasting**; for more details please check [the doc](Broadcasting.md). Coefficient of leakage default to 0.01. Coefficient of SELU default to 1.6732. Coefficient of SELU default to 1.0507. Coefficient of ELU default to 1.0. Slope tensor. If `Slope` is of size 1, the value is sharedacross different channels Output tensor. Same dimension as inputs. Minimum value, under which element is replaced by min Maximum value, above which element is replaced by max Input tensor C, can be inplace. Whether C should be broadcasted Scalar multiplier for the product of input tensors A * B, the default value is 1.0. Scalar multiplier for input tensor C, the default value is 1.0. Number of top elements to retrieve Dimension on which to do the sort.
If necessary the right-hand-side argument will be broadcasted to match the
@@ -7367,26 +8031,26 @@
shape(A) = (2, 3, 4, 5), shape(B) = (4, 5)
shape(A) = (2, 3, 4, 5), shape(B) = (3, 4), with axis=1
shape(A) = (2, 3, 4, 5), shape(B) = (2), with axis=0
Attribute `broadcast=1` needs to be passed to enable broadcasting.
- *ZZN4onnx24MathDocGenerator_opset_7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx24MathDocGenerator_opset_7EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_EUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx32SoftmaxFamilyDocGenerator_opset1EPKcS1_EUlRNS_8OpSchemaEE_ *ZN4onnx20MathDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx27MathDocGenerator_old_opset6EPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_13Pow_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14TopK_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_ p value of the Lp norm used to pool over the input data. Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. ROI pool output shape (height, width). Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling. Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...]. RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]). Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...]. The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. Optional 1D bias to be added to the convolution, has size of M. Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths. The shape of the convolution kernel. If not present, should be inferred from input W. dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis. Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis. number of groups input channels and output channels are divided into. Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn) The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array) Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array) The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate pads Additional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks. dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis. Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1. Constrain input and output types to float and 8 bit tensors. The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero). /onnxruntime_src/cmake/external/onnx/onnx/defs/nn/defs.cc The output of each pooling window is maximum number of elements exclude pad. The storage order of the tensor. 0 is row major, and 1 is column major. Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis. Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn). Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...]. Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn). The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored. Output data tensor that contains the result of the unpooling. Scale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization. Zero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization. Scale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M). Zero point tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M). Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization. Zero point tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization. Optional 1D bias to be added to the convolution, has size of M. Bias must be quantized using scale = x_scale * w_scale and zero_point = 0 Constrain filter type to 8-bit integer tensor. Constrain output type to 8-bit integer tensor. Constrain bias type to 32-bit integer tensor. The shape of the convolution kernel. If not present, should be inferred from input 'w'. Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis. number of groups input channels and output channels are divided into. default is 1. Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization. Zero point tensor for input 'w'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M) Constrain input x and its zero point data type to 8-bit integer tensor. Constrain input w and its zero point data type to 8-bit integer tensor. Constrain output y data type to 32-bit integer tensor. dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis. Stride along each spatial axis. If not present, the stride defaults to 1 along each axis. This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
+ Pass 1 to enable broadcasting consumed_inputs logsoftmax 1-D input tensor Value of alpha default to 0.2 Value of beta default to 0.5 List of tensors for Max. List of tensors for Min List of tensors for Sum. List of tensors for Mean. Input tensor A Input tensor B Input tensor C Invalid value for attribute k *ZZN4onnx24MathDocGenerator_opset_7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx24MathDocGenerator_opset_7EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_EUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx32SoftmaxFamilyDocGenerator_opset1EPKcS1_EUlRNS_8OpSchemaEE_ *ZN4onnx20MathDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx27MathDocGenerator_old_opset6EPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_13Pow_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14TopK_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_ p value of the Lp norm used to pool over the input data. Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Output data tensor from Lp pooling across the input tensor. Dimensions will vary based on various kernel, stride, and pad sizes. ROI pool output shape (height, width). Multiplicative spatial scale factor to translate ROI coordinates from their input scale to the scale used when pooling. Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. RoIs (Regions of Interest) to pool over. Should be a 2-D tensor of shape (num_rois, 5) given as [[batch_id, x1, y1, x2, y2], ...]. RoI pooled output 4-D tensor of shape (num_rois, channels, pooled_shape[0], pooled_shape[1]). Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn). Optionally, if dimension denotation is in effect, the operation expects input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...]. The weight tensor that will be used in the convolutions; has size (M x C/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the kernel shape will be (M x C/group x k1 x k2 x ... x kn), where (k1 x k2 x ... kn) is the dimension of the kernel. Optionally, if dimension denotation is in effect, the operation expects the weight tensor to arrive with the dimension denotation of [FILTER_OUT_CHANNEL, FILTER_IN_CHANNEL, FILTER_SPATIAL, FILTER_SPATIAL ...]. X.shape[1] == (W.shape[1] * group) == C (assuming zero based indices for the shape array). Or in other words FILTER_IN_CHANNEL should be equal to DATA_CHANNEL. Optional 1D bias to be added to the convolution, has size of M. Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, and pad lengths. The shape of the convolution kernel. If not present, should be inferred from input W. dilation value along each spatial axis of the filter. If not present, the dilation defaults is 1 along each spatial axis. Stride along each spatial axis. If not present, the stride defaults is 1 along each spatial axis. number of groups input channels and output channels are divided into. Input data tensor from previous layer; has size (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and width. Note that this is for the 2D image. Otherwise the size is (N x C x D1 x D2 ... x Dn) The weight tensor that will be used in the convolutions; has size (C x M/group x kH x kW), where C is the number of channels, and kH and kW are the height and width of the kernel, and M is the number of feature maps. For more than 2 dimensions, the weight shape will be (C x M/group x k1 x k2 x ... x kn), where (k1 x k2 x ... x kn) is the dimension of the kernel. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array) Output data tensor that contains the result of the convolution. The output dimensions are functions of the kernel size, stride size, pad lengths and group count. The number of channels in the output should be equal to W.shape[1] * group (assuming zero based indices of the shape array) The shape of the output can be explicitly set which will cause pads values to be auto generated. If output_shape is specified pads values are ignored. See doc for details for equations to generate pads Additional elements added to the side with higher coordinate indices in the output. Each padding value in "output_padding" must be less than the corresponding stride/dilation dimension. By default, this attribute is a zero vector. Note that this attribute doesn't directly affect the computed output values. It only controls the selection of the computed values, so changing this attribute only adds or removes output elements. If "output_shape" is explicitly provided, "output_padding" does not contribute additional size to "output_shape" but participates in the computation of the needed padding amount. This is also called adjs or adjustment in some frameworks. dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each spatial axis. Output data tensor from pooling across the input tensor. The output tensor has the same rank as the input. The first two dimensions of output shape are the same as the input (N x C), while the other dimensions are all 1. Constrain input and output types to float and 8 bit tensors. The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero). /onnxruntime_src/cmake/external/onnx/onnx/defs/nn/defs.cc The output of each pooling window is maximum number of elements exclude pad. The storage order of the tensor. 0 is row major, and 1 is column major. Dilation value along each spatial axis of filter. If not present, the dilation defaults to 1 along each spatial axis. Indices tensor from max pooling across the input tensor. The dimensions of indices are the same as output tensor. The values in indices of are the indices of the selected values during pooling. The indices are computed as flatten 1-D tensor, and the indices do not consider padding. So the values in indices are in [0, N x C x D1 x ... x Dn). Input data tensor that has to be unpooled. This tensor is typically the first output of the MaxPool op.Dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non-image case, the dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Optionally, if dimension denotation is in effect, the operation expects the input data tensor to arrive with the dimension denotation of [DATA_BATCH, DATA_CHANNEL, DATA_FEATURE, DATA_FEATURE ...]. Input data tensor containing the indices corresponding to elements in the first input tensor X.This tensor is typically the second output of the MaxPool op.Dimensions must be the same as input tensor X. The indices are linear, i.e. computed considering the tensor as flattened 1-D tensor, assuming row-major storage. Also, the linear indices should not consider padding. So the values in indices are in the range [0, N x C x D1 x ... x Dn). The shape of the output can be explicitly set which will cause pads values to be auto generated. If 'output_shape' is specified, 'pads' values are ignored. Output data tensor that contains the result of the unpooling. Scale tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization. Zero point tensor for input 'x'. It's a scalar, which means a per-tensor/layer quantization. Scale tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M). Zero point tensor for input 'w'. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M). Scale tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization. Zero point tensor for output 'y'. It's a scalar, which means a per-tensor/layer quantization. Optional 1D bias to be added to the convolution, has size of M. Bias must be quantized using scale = x_scale * w_scale and zero_point = 0 Constrain filter type to 8-bit integer tensor. Constrain output type to 8-bit integer tensor. Constrain bias type to 32-bit integer tensor. The shape of the convolution kernel. If not present, should be inferred from input 'w'. Padding for the beginning and ending along each spatial axis, it can take any value greater than or equal to 0.The value represent the number of pixels added to the beginning and end part of the corresponding axis.`pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number ofpixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`.This attribute cannot be used simultaneously with auto_pad attribute. If not present, the padding defaultsto 0 along start and end of each spatial axis. number of groups input channels and output channels are divided into. default is 1. Zero point tensor for input 'x'. It's optional and default value is 0. It's a scalar, which means a per-tensor/layer quantization. Zero point tensor for input 'w'. It's optional and default value is 0. It could be a scalar or a 1-D tensor, which means a per-tensor/layer or per output channel quantization. If it's a 1-D tensor, its number of elements should be equal to the number of output channels (M) Constrain input x and its zero point data type to 8-bit integer tensor. Constrain input w and its zero point data type to 8-bit integer tensor. Constrain output y data type to 32-bit integer tensor. dilation value along each spatial axis of the filter. If not present, the dilation defaults to 1 along each axis. Stride along each spatial axis. If not present, the stride defaults to 1 along each axis. This operator has **optional** inputs/outputs. See [the doc](IR.md) for more details about the representation of optional arguments. An empty string may be used in the place of an actual argument's name to indicate a missing argument. Trailing optional arguments (those not followed by an argument that is present) may also be simply omitted.
Carries out batch normalization as described in the paper
https://arxiv.org/abs/1502.03167. Depending on the mode it is being run,
there are multiple cases for the number of outputs, which we list below:
Output case #1: Y, mean, var, saved_mean, saved_var (training mode)
Output case #2: Y (test mode)
For previous (depreciated) non-spatial cases, implementors are suggested
to flatten the input shape to (N x C*D1*D2 ..*Dn) before a BatchNormalization Op.
- Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum). Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1 running (training) or estimated (testing) mean tensor of shape (C). running (training) or estimated (testing) variance tensor of shape (C). The output tensor of the same shape as X The running mean after the BatchNormalization operator. The running variance after the BatchNormalization operator. Saved mean used during training to speed up gradient computation. Saved variance used during training to speed up gradient computation. The input 1-dimensional scale tensor of size C. The input 1-dimensional bias tensor of size C. The output tensor of the same shape as input. The axis on which to apply normalization, -1 mean last axis. The order of the normalization, only 1 or 2 are supported. The ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5. If set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones. Constrain input 'ratio' types to float tensors. Constrain output 'mask' types to boolean tensors. The lambd value for the Shrink formulation. Default is 0.5. The bias value added to output. Default is 0. Constrain input and output to all tensor types. A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output. Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). The number of channels to sum over Output tensor, which has the shape and type as input tensor Constrain input and output types to float tensors. Input is ether string UTF-8 or int32/int64 Maximum n-gram length. If this value is 3, 3-grams will be used to generate the output. Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams. Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1 List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector. List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector. The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity. list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor. list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts. The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF) Boolean. Whether the identification of stop words in X is case-sensitive. Default is false string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE" List of stop words. If not set, no word would be removed from X. Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation. Input tensor must have atleast 2 dimensions Attribute dilations has incorrect size Attribute strides has incorrect size Attribute kernel_shape has incorrect size Attribute kernel_shape must be specified Attribute pads has incorrect size Second input tensor has wrong dimension ngram_indexes must be non-empty with no negative values Input tensor must have rank 1 or 2 Input shape must have either [C] or [1,C] dimensions where C > 0 MaxUnpool op must have either two or three inputs. Input tensor X must have atleast 2 dimensions. Attribute pads has incorrect size. Attribute strides has incorrect size. Attribute kernel_shape has incorrect size. Attribute kernel_shape must be specified. 'output_shape' must be rank 1 tensor. 'output_shape' must have same number of elements as the shape of input tensor X. 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 weight and zero_point pair is expected to have same type. Ratio of Dropout must be a scalar. training_mode of Dropout must be a scalar. 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. 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 training_mode The output mask. A tensor of rank >= axis. Scaling parameter. The exponent. Input for n-gram extraction Ngram results 1-D tensor of floats UTF-8 strings to normalize UTF-8 Normalized strings Invalid value( ) for attribute 'axis' Exponent Epsilon X_RM EX_squared E_Xsquared Variance X_variance Processed_STD *ZZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_20MaxUnpool_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx23LpPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx23LpPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx24RoiPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx24RoiPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx21ConvOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx21ConvOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_22QLinearConv_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22ConvInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx30ConvTransposeOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx30ConvTransposeOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_ *ZZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_31InstanceNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25LpNormalization_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25TfIdfVectorizer_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27StringNormalizer_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Stride along each spatial axis. dilation value along each spatial axis of the filter. The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks. /onnxruntime_src/cmake/external/onnx/onnx/defs/nn/old.cc The output of each pooling window is divided by the number of elements exclude pad. The output of each pooling window is maximum number of elements exclude pad. Dilation value along each spatial axis of filter. p value of the Lp norm used to pool over the input data, default is 2.0. Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1 If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1. If set to nonzero, run spatial batch normalization in test mode, default is 0. The epsilon value to use to avoid division by zero, default is 1e-5f. Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f. The input 4-dimensional tensor of shape NCHW. The scale as a 1-dimensional tensor of size C to be applied to the output. The bias as a 1-dimensional tensor of size C to be applied to the output. The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C. The running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C. The output 4-dimensional tensor of the same shape as X. The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing. The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing. Saved mean used during training to speed up gradient computation. Should not be used for testing. Saved variance used during training to speed up gradient computation. Should not be used for testing. The output 4-dimensional tensor of the same shape as input. (float, default 0.5) the ratio of random dropout (int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X. The output mask. If is_test is nonzero, this output is not filled. Constrain output mask types to boolean tensors. The output tensor of the same shape as X. Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch. If spatial is true, the dimension of scale is (C). If spatial is false, the dimensions of scale are (C x D1 x ... x Dn) If spatial is true, the dimension of bias is (C). If spatial is false, the dimensions of bias are (C x D1 x ... x Dn) If spatial is true, the dimension of the running mean (training) or the estimated mean (testing) is (C). If spatial is false, the dimensions of the running mean (training) or the estimated mean (testing) are (C x D1 x ... x Dn). If spatial is true, the dimension of the running variance(training) or the estimated variance (testing) is (C). If spatial is false, the dimensions of the running variance(training) or the estimated variance (testing) are (C x D1 x ... x Dn). auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute. Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. Stride along each axis. is_test The ratio of random dropout *ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_ *ZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx26LpPoolOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx26LpPoolOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx24ConvOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx24ConvOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. default 1; Pooled output Y's height. default 1; Pooled output Y's width. Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0. The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'. RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input. RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1]. /onnxruntime_src/cmake/external/onnx/onnx/defs/object_detection/defs.cc An input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box. An input tensor with shape [num_batches, num_classes, spatial_dimension] Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output. Float representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0. Float representing the threshold for deciding when to remove boxes based on score. It is a scalar. selected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index]. Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models. Dimension mismatch in unification between boxes max_output_boxes_per_class iou_threshold score_threshold selected_indices expected to have rank but has rank expected to have rank > *ZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ /onnxruntime_src/cmake/external/onnx/onnx/defs/object_detection/old.cc *ZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ N-D quantized input tensor to be de-quantized. Scale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization. Zero point for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization. It's optional. 0 is the default value when it's not specified. (Optional) The axis of the dequantizing dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input) Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor. /onnxruntime_src/cmake/external/onnx/onnx/defs/quantization/defs.cc Scale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D Tensor for per-axis quantization. Zero point for doing quantization to get 'y'. It can be a scalar, which means a per-tensor/layer quantization, or a 1-D tensor for per-axis quantization. Default value is uint8 typed 0 if it's not specified. (Optional) The axis of the quantization dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input) Constrain 'x' to float or int32 tensor. Constrain 'y_zero_point' and 'y' to 8-bit integer tensor. Output zero point. It's a scalar, which means a per-tensor/layer quantization. Constrain 'x' to float tensor. Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor. Q_Min Q_Max X_Min X_Min_Adjusted X_Max_Adjusted X_Range Min_Scaled Initial_ZeroPoint_FP Clipped_ZeroPoint_FP Rounded_ZeroPoint_FP Zeropoint Quantized output tensor *ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Scale for input 'x'. It's a scalar, which means a per-tensor/layer quantization. Zero point for input 'x'. It's a scalar, which means a per-tensor/layer quantization. It's optional. 0 is the default value when it's not specified. /onnxruntime_src/cmake/external/onnx/onnx/defs/quantization/old.cc Scale for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. Zero point for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. Default value is uint8 typed 0 if it's not specified. *ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data). Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index). Reduced output tensor with integer data type. 'axis' must be in [-rank(indices), rank(indices)-1] Constrain input and output types to high-precision and 8 bit numeric tensors. A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data). /onnxruntime_src/cmake/external/onnx/onnx/defs/reduction/defs.cc sum square L1 norm L2 norm log sum exponent log sum product *ZZN4onnx18ReduceDocGeneratorEPKcbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx18ReduceDocGeneratorEPKcbEUlRNS_8OpSchemaEE_ *ZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ The axis in which to compute the arg indices.
+ Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum). Input data tensor from the previous operator; dimensions are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size, C is the number of channels. Statistics are computed for every channel of C over N and D1 to Dn dimensions. For image data, input dimensions become (N x C x H x W). The op also accepts single dimension input of size N in which case C is assumed to be 1 running (training) or estimated (testing) mean tensor of shape (C). running (training) or estimated (testing) variance tensor of shape (C). The output tensor of the same shape as X The running mean after the BatchNormalization operator. The running variance after the BatchNormalization operator. Saved mean used during training to speed up gradient computation. Saved variance used during training to speed up gradient computation. The input 1-dimensional scale tensor of size C. The input 1-dimensional bias tensor of size C. The output tensor of the same shape as input. The axis on which to apply normalization, -1 mean last axis. The order of the normalization, only 1 or 2 are supported. The ratio of random dropout, with value in [0, 1). If this input was not set, or if it was set to 0, the output would be a simple copy of the input. If it's non-zero, output will be a random dropout of the scaled input, which is typically the case during training. It is an optional value, if not specified it will default to 0.5. If set to true then it indicates dropout is being used for training. It is an optional value hence unless specified explicitly, it is false. If it is false, ratio is ignored and the operation mimics inference mode where nothing will be dropped from the input data and if mask is requested as output it will contain all ones. Constrain input 'ratio' types to float tensors. Constrain output 'mask' types to boolean tensors. The lambd value for the Shrink formulation. Default is 0.5. The bias value added to output. Default is 0. Constrain input and output to all tensor types. A 2D tensor with the contents of the input tensor, with input dimensions up to axis flattened to the outer dimension of the output and remaining input dimensions flattened into the inner dimension of the output. Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [-r, r], where r is the rank of the input tensor. Negative value means counting dimensions from the back. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). The number of channels to sum over Output tensor, which has the shape and type as input tensor Constrain input and output types to float tensors. Input is ether string UTF-8 or int32/int64 Maximum n-gram length. If this value is 3, 3-grams will be used to generate the output. Minimum n-gram length. If this value is 2 and max_gram_length is 3, output may contain counts of 2-grams and 3-grams. Maximum number of items (integers/strings) to be skipped when constructing an n-gram from X. If max_skip_count=1, min_gram_length=2, max_gram_length=3, this operator may generate 2-grams with skip_count=0 and skip_count=1, and 3-grams with skip_count=0 and skip_count=1 List of strings n-grams learned from the training set. Either this or pool_int64s attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector. List of int64 n-grams learned from the training set. Either this or pool_strings attributes must be present but not both. It's an 1-D tensor starting with the collections of all 1-grams and ending with the collections of n-grams. The i-th element in pool stores the n-gram that should be mapped to coordinate ngram_indexes[i] in the output vector. The starting indexes of 1-grams, 2-grams, and so on in pool. It is useful when determining the boundary between two consecutive collections of n-grams. For example, if ngram_counts is [0, 17, 36], the first index (zero-based) of 1-gram/2-gram/3-gram in pool are 0/17/36. This format is essentially identical to CSR (or CSC) sparse matrix format, and we choose to use this due to its popularity. list of int64s (type: AttributeProto::INTS). This list is parallel to the specified 'pool_*' attribute. The i-th element in ngram_indexes indicate the coordinate of the i-th n-gram in the output tensor. list of floats. This attribute stores the weight of each n-gram in pool. The i-th element in weights is the weight of the i-th n-gram in pool. Its length equals to the size of ngram_indexes. By default, weights is an all-one tensor.This attribute is used when mode is "IDF" or "TFIDF" to scale the associated word counts. The weighting criteria. It can be one of "TF" (term frequency), "IDF" (inverse document frequency), and "TFIDF" (the combination of TF and IDF) Boolean. Whether the identification of stop words in X is case-sensitive. Default is false string enum that cases output to be lowercased/uppercases/unchanged. Valid values are "LOWER", "UPPER", "NONE". Default is "NONE" List of stop words. If not set, no word would be removed from X. Environment dependent string that denotes the locale according to which output strings needs to be upper/lowercased.Default en_US or platform specific equivalent as decided by the implementation. Input tensor must have atleast 2 dimensions Attribute dilations has incorrect size Attribute strides has incorrect size Attribute kernel_shape has incorrect size Attribute kernel_shape must be specified Attribute pads has incorrect size Second input tensor has wrong dimension ngram_indexes must be non-empty with no negative values Input tensor must have rank 1 or 2 Input shape must have either [C] or [1,C] dimensions where C > 0 MaxUnpool op must have either two or three inputs. Input tensor X must have atleast 2 dimensions. Attribute pads has incorrect size. Attribute strides has incorrect size. Attribute kernel_shape has incorrect size. Attribute kernel_shape must be specified. 'output_shape' must be rank 1 tensor. 'output_shape' must have same number of elements as the shape of input tensor X. RoIs tensor must have 2 dimensions Attribute pooled_shape has incorrect length Attribute pooled_shape must be specified Ratio of Dropout must be a scalar. training_mode of Dropout must be a scalar. The pads attribute cannot be used simultaneously with auto_pad attribute A list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance. average a filter lp pool momentum Scale tensor of shape (C). Bias tensor of shape (C). saved_mean saved_var Input matrix Matrix after normalization training_mode The output mask. A tensor of rank >= axis. Scaling parameter. The exponent. Input for n-gram extraction Ngram results 1-D tensor of floats UTF-8 strings to normalize UTF-8 Normalized strings Invalid value( ) for attribute 'axis' Exponent Epsilon X_RM EX_squared E_Xsquared Variance X_variance Processed_STD *ZZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_20MaxUnpool_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx23LpPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx23LpPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx24RoiPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx24RoiPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx21ConvOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx21ConvOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_22QLinearConv_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22ConvInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx30ConvTransposeOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx30ConvTransposeOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_ *ZZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_31InstanceNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25LpNormalization_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25TfIdfVectorizer_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27StringNormalizer_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Stride along each spatial axis. dilation value along each spatial axis of the filter. The zero-padding added to one side of the output. This is also called adjs/adjustment in some frameworks. /onnxruntime_src/cmake/external/onnx/onnx/defs/nn/old.cc The output of each pooling window is divided by the number of elements exclude pad. The output of each pooling window is maximum number of elements exclude pad. Dilation value along each spatial axis of filter. p value of the Lp norm used to pool over the input data, default is 2.0. Input data tensor from the previous operator; dimensions for image case are (N x C x H x W), where N is the batch size, C is the number of channels, and H and W are the height and the width of the data. For non image case, the dimension are in the form of (N x C x D1 x D2 ... Dn), where N is the batch size. Output data tensor from pooling across the input tensor. Dimensions will be N x C x 1 x 1 If true, compute the mean and variance across all spatial elements If false, compute the mean and variance across per feature.Default is 1. If set to nonzero, run spatial batch normalization in test mode, default is 0. The epsilon value to use to avoid division by zero, default is 1e-5f. Factor used in computing the running mean and variance.e.g., running_mean = running_mean * momentum + mean * (1 - momentum), default is 0.9f. The input 4-dimensional tensor of shape NCHW. The scale as a 1-dimensional tensor of size C to be applied to the output. The bias as a 1-dimensional tensor of size C to be applied to the output. The running mean (training) or the estimated mean (testing) as a 1-dimensional tensor of size C. The running variance (training) or the estimated variance (testing) as a 1-dimensional tensor of size C. The output 4-dimensional tensor of the same shape as X. The running mean after the BatchNormalization operator. Must be in-place with the input mean. Should not be used for testing. The running variance after the BatchNormalization operator. Must be in-place with the input var. Should not be used for testing. Saved mean used during training to speed up gradient computation. Should not be used for testing. Saved variance used during training to speed up gradient computation. Should not be used for testing. The output 4-dimensional tensor of the same shape as input. (float, default 0.5) the ratio of random dropout (int, default 0) if nonzero, run dropout in test mode where the output is simply Y = X. The output mask. If is_test is nonzero, this output is not filled. Constrain output mask types to boolean tensors. The output tensor of the same shape as X. Indicate up to which input dimensions (exclusive) should be flattened to the outer dimension of the output. The value for axis must be in the range [0, R], where R is the rank of the input tensor. When axis = 0, the shape of the output tensor is (1, (d_0 X d_1 ... d_n), where the shape of the input tensor is (d_0, d_1, ... d_n). If true, compute the mean and variance across per activation. If false, compute the mean and variance across per feature over each mini-batch. If spatial is true, the dimension of scale is (C). If spatial is false, the dimensions of scale are (C x D1 x ... x Dn) If spatial is true, the dimension of bias is (C). If spatial is false, the dimensions of bias are (C x D1 x ... x Dn) If spatial is true, the dimension of the running mean (training) or the estimated mean (testing) is (C). If spatial is false, the dimensions of the running mean (training) or the estimated mean (testing) are (C x D1 x ... x Dn). If spatial is true, the dimension of the running variance(training) or the estimated variance (testing) is (C). If spatial is false, the dimensions of the running variance(training) or the estimated variance (testing) are (C x D1 x ... x Dn). auto_pad must be either NOTSET, SAME_UPPER, SAME_LOWER or VALID. Where default value is NOTSET, which means explicit padding is used. SAME_UPPER or SAME_LOWER mean pad the input so that the output size match the input.In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER. VALID mean no padding. DEPRECATION NOTE: auto_pad is only intended to support legacy uses, and for framework authors, one is explicitly encouraged to use explicit padding specified in the pads attribute. Padding for the beginning and ending along each axis, it can take any value greater than or equal to 0. The value represent the number of pixels added to the beginning and end part of the corresponding axis. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. This attribute cannot be used simultaneously with auto_pad attribute. Stride along each axis. is_test The ratio of random dropout *ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_ *ZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx26LpPoolOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx26LpPoolOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx24ConvOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx24ConvOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Multiplicative spatial scale factor to translate ROI coordinates from their input spatial scale to the scale used when pooling, i.e., spatial scale of the input feature map X relative to the input image. E.g.; default is 1.0f. default 1; Pooled output Y's height. default 1; Pooled output Y's width. Number of sampling points in the interpolation grid used to compute the output value of each pooled output bin. If > 0, then exactly sampling_ratio x sampling_ratio grid points are used. If == 0, then an adaptive number of grid points are used (computed as ceil(roi_width / output_width), and likewise for height). Default is 0. The pooling method. Two modes are supported: 'avg' and 'max'. Default is 'avg'. RoIs (Regions of Interest) to pool over; rois is 2-D input of shape (num_rois, 4) given as [[x1, y1, x2, y2], ...]. The RoIs' coordinates are in the coordinate system of the input image. Each coordinate set has a 1:1 correspondence with the 'batch_indices' input. RoI pooled output, 4-D tensor of shape (num_rois, C, output_height, output_width). The r-th batch element Y[r-1] is a pooled feature map corresponding to the r-th RoI X[r-1]. /onnxruntime_src/cmake/external/onnx/onnx/defs/object_detection/defs.cc An input tensor with shape [num_batches, spatial_dimension, 4]. The single box data format is indicated by center_point_box. An input tensor with shape [num_batches, num_classes, spatial_dimension] Integer representing the maximum number of boxes to be selected per batch per class. It is a scalar. Default to 0, which means no output. Float representing the threshold for deciding whether boxes overlap too much with respect to IOU. It is scalar. Value range [0, 1]. Default to 0. Float representing the threshold for deciding when to remove boxes based on score. It is a scalar. selected indices from the boxes tensor. [num_selected_indices, 3], the selected index format is [batch_index, class_index, box_index]. Integer indicate the format of the box data. The default is 0. 0 - the box data is supplied as [y1, x1, y2, x2] where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. Mostly used for TF models. 1 - the box data is supplied as [x_center, y_center, width, height]. Mostly used for Pytorch models. Dimension mismatch in unification between boxes max_output_boxes_per_class iou_threshold score_threshold selected_indices expected to have rank but has rank expected to have rank > *ZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ /onnxruntime_src/cmake/external/onnx/onnx/defs/object_detection/old.cc *ZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ N-D quantized input tensor to be de-quantized. Scale for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization. Zero point for input 'x'. It can be a scalar, which means a per-tensor/layer dequantization, or a 1-D tensor for per-axis dequantization. It's optional. 0 is the default value when it's not specified. (Optional) The axis of the dequantizing dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input) Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor. /onnxruntime_src/cmake/external/onnx/onnx/defs/quantization/defs.cc Scale for doing quantization to get 'y'. It can be a scalar, which means per-tensor/layer quantization, or a 1-D Tensor for per-axis quantization. Zero point for doing quantization to get 'y'. It can be a scalar, which means a per-tensor/layer quantization, or a 1-D tensor for per-axis quantization. Default value is uint8 typed 0 if it's not specified. (Optional) The axis of the quantization dimension of the input tensor. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input) Constrain 'x' to float or int32 tensor. Constrain 'y_zero_point' and 'y' to 8-bit integer tensor. Output zero point. It's a scalar, which means a per-tensor/layer quantization. Constrain 'x' to float tensor. Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor. Q_Min Q_Max X_Min X_Min_Adjusted X_Max_Adjusted X_Range Min_Scaled Initial_ZeroPoint_FP Clipped_ZeroPoint_FP Rounded_ZeroPoint_FP Zeropoint Quantized output tensor *ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Scale for input 'x'. It's a scalar, which means a per-tensor/layer quantization. Zero point for input 'x'. It's a scalar, which means a per-tensor/layer quantization. It's optional. 0 is the default value when it's not specified. /onnxruntime_src/cmake/external/onnx/onnx/defs/quantization/old.cc Scale for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. Zero point for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. Default value is uint8 typed 0 if it's not specified. *ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ The axis in which to compute the arg indices. Accepted range is [-r, r-1] where r = rank(data). Whether to select the last index or the first index if the {name} appears in multiple indices, default is False (first index). Reduced output tensor with integer data type. 'axis' must be in [-rank(indices), rank(indices)-1] Constrain input and output types to high-precision and 8 bit numeric tensors. Defines behaviour if 'axes' is empty. Default behaviour with 'false' is to reduce all axes. When axes is empty and this attribute is set to true, input tensor will not be reduced,and the output tensor would be equivalent to input tensor. Optional input list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor if 'noop_with_empty_axes' is false, else act as an Identity op when 'noop_with_empty_axes' is true. Accepted range is [-r, r-1] where r = rank(data). A list of integers, along which to reduce. The default is to reduce over all the dimensions of the input tensor. Accepted range is [-r, r-1] where r = rank(data). /onnxruntime_src/cmake/external/onnx/onnx/defs/reduction/defs.cc axes as an input and attribute cannot be specified at the same time. sum square product L1 norm L2 norm log sum exponent log sum *ZZN4onnx18ReduceDocGeneratorEPKcbbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx18ReduceDocGeneratorEPKcbbEUlRNS_8OpSchemaEE_ *ZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ The axis in which to compute the arg indices.
Computes the indices of the {name} elements of the input tensor's element along the
provided axis. The resulting tensor has the same rank as the input if keepdims equal 1.
If keepdims equal 0, then the resulting tensor have the reduced dimension pruned.
-The type of the output tensor is integer. /onnxruntime_src/cmake/external/onnx/onnx/defs/reduction/old.cc *ZZN4onnx26ReduceDocGenerator_opset12EPKcbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx26ReduceDocGenerator_opset12EPKcbEUlRNS_8OpSchemaEE_ *ZZN4onnx29ArgReduceDocGenerator_opset12EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx29ArgReduceDocGenerator_opset12EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx25ReduceDocGenerator_opset1EPKciENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx25ReduceDocGenerator_opset1EPKciEUlRNS_8OpSchemaEE_ *ZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx29ArgReduceDocGenerator_opset11EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx29ArgReduceDocGenerator_opset11EPKcEUlRNS_8OpSchemaEE_ 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. Couple the input and forget gates if 1. /onnxruntime_src/cmake/external/onnx/onnx/defs/rnn/defs.cc A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified. When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate. The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`. The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`. The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0 One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified. The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`. The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`. The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0. First input tensor must have rank 3 *ZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_ foward output_sequence Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. The sequence output for the hidden is optional if 0. Default 0. A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0. /onnxruntime_src/cmake/external/onnx/onnx/defs/rnn/old.cc *ZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_ Attributes: (no type) (no doc) (unnamed) Outputs: Defined at (no documentation yet) Operator ' ) has input size not in range [min= , max= not in allowed input sizes. ) has output size not in allowed output sizes. ) than declared ( ) has more inputs ( ) in op definition. ) has more outputs ( ' appeared multiple times. Attribute ' Unrecognized attribute: for operator has unknown expected type Required attribute ' ' is missing. )'s input )'s output ONNX Schema (inputs_.size() - 1) == i (outputs_.size() - 1) == i !(it.GetName().empty()) , has unsupported type: typestr: has inconsistent type has unsupported type (no explicit description available) ' has been deprecated since version Mismatched attribute type in ' ' is expected to have field 't' ' is expected to have field 'sparse_tensor' ' is expected to have field 'g' ' is expected to have field 'floats' ' is expected to have field 'ints' ' is expected to have field 'strings' ' is expected to have field 'tensors' ' is expected to have field 'graphs' is marked single but has an empty string in the graph : failed validating the check: Duplicate type constraint name Attribute specification type mismatch. @@ؒ *ZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_ *ZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_ N4onnx16OpSchemaRegistryE ], Value= sum of split values= Empty sequence. Tensors. Constrain to any tensor type. Input sequence. input_sequence position The tensor to split Concatenated tensor concat_result Tensor= Attribute dtype should be of integer type and specify a type. Input type for input at index 0 is null. Type info is expected. Required attribute axis is missing new_axis must be either 0 or 1 Invalid value of attribute 'axis'. Accepted range=[ Invalid value of attribute 'axis'. Rank= Only supports `int32_t` or `int64_t` inputs for split Input 'split' can not be empty. Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size= Constrain output types to any tensor type. (Optional) The data type of the tensors in the output sequence. The default type is 'float'. /onnxruntime_src/cmake/external/onnx/onnx/defs/sequence/defs.cc Constrain input types to any tensor type. Sequence enclosing the input tensors. Input tensor to be inserted into the input sequence. Position in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape). Output sequence that contains the inserted tensor at given position. Constrain position to integral tensor. It must be a scalar(tensor of empty shape). Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape). Output tensor at the specified position in the input sequence. Output sequence that has the tensor at the specified position removed. Length of input sequence. It must be a scalar(tensor of empty shape). Constrain output to integral tensor. It must be a scalar(tensor of empty shape). Constrain output types to all tensor types. Constrain input types to all tensor types. Length of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be >= 0. One or more outputs forming a sequence of tensors after splitting Constrain split size to integral tensor. Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1]. Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored. Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. Insert and concatenate on a new axis or not, default 0 means do not insert new axis. Sequence of tensors for concatenation Input Sequence and Tensor are expected to have type info. Current type is null. Input Sequence and Tensor are expected to have the same elem type. Sequence= SequenceConstruct is expected to have at least 1 input. Input type for input at index is null. Type info is expected. Element type of inputs are expected to be the same. *ZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Input tensor to be cast. Specified shape for output. Reshaped data. reshaped Shape of the input tensor steps Transposed output. transposed updates Tensor of rank q + (r - 1). squeezed Original tensor expanded Input tensor of any shape. repeats N-D tensor N-D tensor after resizing roi sizes Tensor to copy input into. condition depth Constrain to boolean tensors. Tensor of rank r >= 2. inverse_indices constant_value Invalid position of 0 Invalid dimension value: Invalid attribute perm { }, input shape = { axis must be in [-r, r-1] 'input' must have rank >= 2 ) and outputs ( Input axes has invalid data 'step' cannot be 0 Blocksize must be positive Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto Output tensor with the same shape as input with type specified by the 'to' argument Constrain input types. Casting from complex is not supported. Constrain output types. Casting to complex is not supported. /onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/defs.cc Input tensor can be of arbitrary type. Constrain output to int64 tensor. Total number of elements of the input tensor Constrain output to int64 tensor, which should be a scalar though. Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs).. List of tensors for concatenation 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). length of each output. Values should be >= 0. 1-D tensor of ending indices (exclusive) of corresponding axis in `axes` 1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). 1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1. A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. Input and output types can be of any tensor type. Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Tensor of rank r >=1 (same rank and shape as indices) Tensor of rank r >= 1 (same rank as input). Tensor of rank q + r - indices_shape[-1] - 1. Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Tensor of the same shape as indices. 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.
+The type of the output tensor is integer. /onnxruntime_src/cmake/external/onnx/onnx/defs/reduction/old.cc *ZZN4onnx26ReduceDocGenerator_opset12EPKcbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx26ReduceDocGenerator_opset12EPKcbEUlRNS_8OpSchemaEE_ *ZZN4onnx29ArgReduceDocGenerator_opset12EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx29ArgReduceDocGenerator_opset12EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx25ReduceDocGenerator_opset1EPKciENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx25ReduceDocGenerator_opset1EPKciEUlRNS_8OpSchemaEE_ *ZZN4onnx28ArgReduceDocGenerator_opset1EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx28ArgReduceDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx29ArgReduceDocGenerator_opset11EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx29ArgReduceDocGenerator_opset11EPKcEUlRNS_8OpSchemaEE_ /onnxruntime_src/cmake/external/onnx/onnx/defs/rnn/defs.cc A list of 2 (or 4 if bidirectional) activation functions for update, reset, and hidden gates. The activation functions must be one of the activation functions specified above. Optional: See the equations for default if not specified. When computing the output of the hidden gate, apply the linear transformation before multiplying by the output of the reset gate. The weight tensor for the gates. Concatenation of `W[zrh]` and `WB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, input_size]`. The recurrence weight tensor. Concatenation of `R[zrh]` and `RB[zrh]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 3*hidden_size, hidden_size]`. The bias tensor for the gates. Concatenation of `[Wb[zrh], Rb[zrh]]` and `[WBb[zrh], RBb[zrh]]` (if bidirectional) along dimension 0. This tensor has shape `[num_directions, 6*hidden_size]`. Optional: If not specified - assumed to be 0 One (or two if bidirectional) activation function for input gate. The activation function must be one of the activation functions specified above. Optional: Default `Tanh` if not specified. The weight tensor for input gate. Concatenation of `Wi` and `WBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, input_size]`. The recurrence weight tensor. Concatenation of `Ri` and `RBi` (if bidirectional). The tensor has shape `[num_directions, hidden_size, hidden_size]`. The bias tensor for input gate. Concatenation of `[Wbi, Rbi]` and `[WBbi, RBbi]` (if bidirectional). The tensor has shape `[num_directions, 2*hidden_size]`. Optional: If not specified - assumed to be 0. First input tensor must have rank 3 *ZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_ foward output_sequence Optional scaling values used by some activation functions. The values are consumed in the order of activation functions, for example (f, g, h) in LSTM. The sequence output for the hidden is optional if 0. Default 0. A tensor that concats all the intermediate output values of the hidden. It has shape `[seq_length, num_directions, batch_size, hidden_size]`. It is optional if `output_sequence` is 0. /onnxruntime_src/cmake/external/onnx/onnx/defs/rnn/old.cc *ZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_ Attributes: (no type) (no doc) (unnamed) Outputs: Defined at (no documentation yet) Operator ' ) has input size not in range [min= , max= not in allowed input sizes. ) has output size not in allowed output sizes. ) than declared ( ) has more inputs ( ) in op definition. ) has more outputs ( ' appeared multiple times. Attribute ' has unknown expected type Required attribute ' ' is missing. )'s input )'s output for operator Unrecognized attribute: ONNX Schema (inputs_.size() - 1) == i (outputs_.size() - 1) == i !(it.GetName().empty()) , has unsupported type: typestr: has inconsistent type has unsupported type (no explicit description available) ' has been deprecated since version Mismatched attribute type in ' ' is expected to have field 't' ' is expected to have field 'sparse_tensor' ' is expected to have field 'g' ' is expected to have field 'floats' ' is expected to have field 'ints' ' is expected to have field 'strings' ' is expected to have field 'tensors' ' is expected to have field 'graphs' is marked single but has an empty string in the graph : failed validating the check: Duplicate type constraint name Attribute specification type mismatch. ^UUU]@]\ZU@ZYWU *ZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_ *ZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_ N4onnx16OpSchemaRegistryE ], Value= sum of split values= Empty sequence. Tensors. Constrain to any tensor type. Input sequence. input_sequence position The tensor to split Concatenated tensor concat_result Tensor= Attribute dtype should be of integer type and specify a type. Input type for input at index 0 is null. Type info is expected. Required attribute axis is missing new_axis must be either 0 or 1 Invalid value of attribute 'axis'. Accepted range=[ Invalid value of attribute 'axis'. Rank= Only supports `int32_t` or `int64_t` inputs for split Input 'split' can not be empty. Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size= Constrain output types to any tensor type. (Optional) The data type of the tensors in the output sequence. The default type is 'float'. /onnxruntime_src/cmake/external/onnx/onnx/defs/sequence/defs.cc Constrain input types to any tensor type. Sequence enclosing the input tensors. Input tensor to be inserted into the input sequence. Position in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape). Output sequence that contains the inserted tensor at given position. Constrain position to integral tensor. It must be a scalar(tensor of empty shape). Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape). Output tensor at the specified position in the input sequence. Output sequence that has the tensor at the specified position removed. Length of input sequence. It must be a scalar(tensor of empty shape). Constrain output to integral tensor. It must be a scalar(tensor of empty shape). Constrain output types to all tensor types. Constrain input types to all tensor types. Length of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be >= 0. One or more outputs forming a sequence of tensors after splitting Constrain split size to integral tensor. Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1]. Keep the split dimension or not. Default 1, which means we keep split dimension. If input 'split' is specified, this attribute is ignored. Which axis to concat on. Accepted range in `[-r, r - 1]`, where `r` is the rank of input tensors. When `new_axis` is 1, accepted range is `[-r - 1, r]`. Insert and concatenate on a new axis or not, default 0 means do not insert new axis. Sequence of tensors for concatenation Input Sequence and Tensor are expected to have type info. Current type is null. Input Sequence and Tensor are expected to have the same elem type. Sequence= SequenceConstruct is expected to have at least 1 input. Input type for input at index is null. Type info is expected. Element type of inputs are expected to be the same. *ZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Input tensor to be cast. Specified shape for output. Reshaped data. reshaped Shape of the input tensor steps Transposed output. transposed updates Tensor of rank q + (r - 1). squeezed Original tensor expanded Input tensor of any shape. repeats N-D tensor N-D tensor after resizing roi sizes Tensor to copy input into. condition depth Constrain to boolean tensors. Tensor of rank r >= 2. inverse_indices constant_value Invalid position of 0 Invalid dimension value: Invalid attribute perm { }, input shape = { axis must be in [-r, r-1] 'input' must have rank >= 2 ) and outputs ( Input axes has invalid data 'step' cannot be 0 Blocksize must be positive Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto Output tensor with the same shape as input with type specified by the 'to' argument Constrain input types. Casting from complex is not supported. Constrain output types. Casting to complex is not supported. /onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/defs.cc Input tensor can be of arbitrary type. Constrain output to int64 tensor. Total number of elements of the input tensor Constrain output to int64 tensor, which should be a scalar though. Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs).. List of tensors for concatenation Optional length of each output. Values should be >= 0.Sum of the values must be equal to the dim value at 'axis' specified. One or more outputs forming list of tensors after splitting Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input). 1-D tensor of ending indices (exclusive) of corresponding axis in `axes` 1-D tensor of axes that `starts` and `ends` apply to. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). 1-D tensor of slice step of corresponding axis in `axes`. Negative value means slicing backward. 'steps' cannot be 0. Defaults to 1. A list of integers. By default, reverse the dimensions, otherwise permute the axes according to the values given. Input and output types can be of any tensor type. Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Tensor of int32/int64 indices, of r >= 1 (same rank as input). All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Tensor of rank r >=1 (same rank and shape as indices) Tensor of rank r >= 1 (same rank as input). Tensor of rank q + r - indices_shape[-1] - 1. Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Tensor of int32/int64 indices, with the same rank r as the input. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Tensor of the same shape as indices. Tensors with at least max(dims) dimensions. List of integers indicating the dimensions to squeeze. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(data). Reshaped tensor with same data as input. List of integers indicating the dimensions to be inserted. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(expanded). Blocks of [blocksize, blocksize] are moved. Input tensor of [N,C,H,W], where N is the batch axis, C is the channel or depth, H is the height and W is the width. Output tensor of [N, C * blocksize * blocksize, H/blocksize, W/blocksize]. DCR (default) for depth-column-row order re-arrangement. Use CRD for column-row-depth order. Output tensor of [N, C/(blocksize * blocksize), H * blocksize, W * blocksize]. 1D int64 tensor of the same length as input's dimension number, includes numbers of repeated copies along input's dimensions. Output tensor of the same dimension and type as tensor input. output_dim[i] = input_dim[i] * repeats[i] Constrain repeat's type to int64 tensors. Constrain input 'X' and output 'Y' to all tensor types. Two interpolation modes: nearest (default), and linear (including bilinear, trilinear, etc) The scale array along each dimension. It takes value greater than or equal to 1. The number of elements of 'scales' should be the same as the rank of input 'X'. Three interpolation modes: nearest (default), linear and cubic. The "linear" mode includes linear interpolation for 1D tensor and N-linear interpolation for N-D tensor (for example, bilinear interpolation for 2D tensor). The "cubic" mode includes cubic interpolation for 1D tensor and N-cubic interpolation for N-D tensor (for example, bicubic interpolation for 2D tensor). The coefficient 'a' used in cubic interpolation. Two common choice are -0.5 (in some cases of TensorFlow) and -0.75 (in PyTorch). Check out Equation (4) in https://ieeexplore.ieee.org/document/1163711 for the details. This attribute is valid only if "mode" is "cubic". If set to 1, the weight of sampling locations outside the tensor will be set to 0 and the weight will be renormalized so that their sum is 1.0. The default value is 0.
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/>
@@ -7400,36 +8064,63 @@
x_original = x_resized * (length_original - 1) / (length_resized - 1), <br/>
if coordinate_transformation_mode is "asymmetric", <br/>
x_original = x_resized / scale, <br/>
+if coordinate_transformation_mode is "tf_crop_and_resize", <br/>
+x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1). Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest". When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f. 1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize" The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. One of 'scales' and 'sizes' MUST be specified and it is an error if both are specified. If 'sizes' is needed, the user can use an empty string as the name of 'scales' in this operator's input list. The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified. Constrain roi type to float or double. (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded. Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1. Constrains to boolean tensors. (Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices). Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use. Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use. Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used. Constrain output types to boolean tensors. (Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false. (Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false. When True (nonzero), yield X, otherwise yield Y values selected at indices where condition is True values selected at indices where condition is False Tensor of shape equal to the broadcasted shape of condition, X, and Y. Constrain to all tensor types. (Optional) Specify which axis is time axis. Must be one of 0 (default), or 1. (Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0. Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`. Tensor with same shape of input. (Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default). (Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). A N-D input tensor that is to be processed. A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X' A 1-D INT64 tensor containing indices of 'Y' elements' first occurance in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. A 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X' The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:] Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Constrains input and output to only numeric types. Supported modes: `constant`(default), `reflect`, `edge` Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`. (Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0). OneHot node must have three inputs. Input 'depth' must be a scalar or rank 1 tensor. Input 'depth' must have exactly one element. Input 'values' must be rank 1 tensor. Input 'values' must have exactly two elements. Indices tensor must have rank >= 1 Target shape may not have multiple -1 dimensions Invalid Target shape product of 0 Dimension could not be inferred: incompatible shapes axis must be in [-rank, rank-1]. All inputs to Concat must have same rank data tensor must have rank >= 1 values in 'axes' are beyond the bounds of the computed output shape 'axes' attribute must not contain any duplicates 'Repeats' input must be 1D tensor of type int64 'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions. 'sequence_lens' must have rank of 1 Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0. Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensor 'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64 Mismatch between number of splits ( Mismatch between the sum of 'split' ( ) and the split dimension of the input ( The input is not evenly splittable Slice op must have either three, four or five inputs. Incorrect or missing input value for starts and ends Input axes has incorrect length Input steps has incorrect length Input tensor must be 4-dimensional *ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20Transpose_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_23SpaceToDepth_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Tile_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16IsNaN_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18NonZero_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Which axis to concat on outputs... Which axis to split on length of each output paddings New shape tiles Axis along which to repeat. width_scale height_scale 4-D tensor, [N,C,H,W] Which axis to split on. Ranks inferred ( /onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/old.cc length of each output. Values should be >= 0.
+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/>
+
+if coordinate_transformation_mode is "half_pixel", <br/>
+x_original = (x_resized + 0.5) / scale - 0.5, <br/>
+
+if coordinate_transformation_mode is "pytorch_half_pixel", <br/>
+x_original = length_resized > 1 ? (x_resized + 0.5) / scale - 0.5 : 0, <br/>
+
+if coordinate_transformation_mode is "align_corners", <br/>
+x_original = x_resized * (length_original - 1) / (length_resized - 1), <br/>
+
+if coordinate_transformation_mode is "asymmetric", <br/>
+x_original = x_resized / scale, <br/>
+
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 specified, then set scales to empty data (zero shape) in this operator's input list. The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified. Constrain roi type to float or double. (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded. Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1. Constrains to boolean tensors. (Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices). Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use. Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use. Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used. Constrain output types to boolean tensors. (Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false. (Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false. When True (nonzero), yield X, otherwise yield Y values selected at indices where condition is True values selected at indices where condition is False Tensor of shape equal to the broadcasted shape of condition, X, and Y. Constrain to all tensor types. (Optional) Specify which axis is time axis. Must be one of 0 (default), or 1. (Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0. Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`. Tensor with same shape of input. (Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default). (Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). A N-D input tensor that is to be processed. A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X' A 1-D INT64 tensor containing indices of 'Y' elements' first occurance in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. A 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X' The number of batch dimensions. The gather of indexing starts from dimension of data[batch_dims:] Tensor of rank q >= 1. All index values are expected to be within bounds [-s, s-1] along axis of size s. It is an error if any of the index values are out of bounds. Constrains input and output to only numeric types. Supported modes: `constant`(default), `reflect`, `edge` Tensor of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D input tensor, it is the number of pixels. `pads` should be a 1D tensor of shape [2 * input_rank]. `pads` format should be: [x1_begin, x2_begin,...,x1_end, x2_end,...], where xi_begin is the number of pad values added at the beginning of axis `i` and xi_end, the number of pad values added at the end of axis `i`. (Optional) A scalar value to be used if the mode chosen is `constant` (by default it is 0). OneHot node must have three inputs. Input 'depth' must be a scalar or rank 1 tensor. Input 'depth' must have exactly one element. Input 'values' must be rank 1 tensor. Input 'values' must have exactly two elements. Indices tensor must have rank >= 1 Target shape may not have multiple -1 dimensions Invalid Target shape product of 0 Dimension could not be inferred: incompatible shapes axis must be in [-rank, rank-1]. All inputs to Concat must have same rank data tensor must have rank >= 1 values in 'axes' are beyond the bounds of the computed output shape 'axes' attribute must not contain any duplicates 'Repeats' input must be 1D tensor of type int64 'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions. 'sequence_lens' must have rank of 1 Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0. Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensor 'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64 Mismatch between number of splits ( Mismatch between the sum of 'split' ( ) and the split dimension of the input ( The input is not evenly splittable Slice op must have either three, four or five inputs. Incorrect or missing input value for starts and ends Input axes has incorrect length Input steps has incorrect length Input tensor must be 4-dimensional *ZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20Transpose_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_23SpaceToDepth_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Tile_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16IsNaN_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18NonZero_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Which axis to concat on outputs... Which axis to split on length of each output paddings New shape tiles Axis along which to repeat. width_scale height_scale 4-D tensor, [N,C,H,W] Which axis to split on. Ranks inferred ( /onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/old.cc The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified. If 'size' is needed, the user can use an empty string as the name of 'scales' in this operator's input list. Constrain input types. Casting from strings and complex are not supported. Constrain output types. Casting to strings and complex are not supported. Which axis to concat on. Default value is 1. Optional list of output lengths (see also arg 'split') List of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. Three modes: constant(default), reflect, edge One float, indicates the value to be filled, default is 0 Number of repeated copies to make of the input tensor. Output tensor of same shape and type as input. Constrain tiles and axis's type to int64 tensors. The scale along width dimension. It takes value greater than or equal to 1. The scale along height dimension. It takes value greater than or equal to 1. Two interpolation modes: nearest(default), bilinear 4-D tensor after resizing, [N,C,H,W] Constrain output types to bool, int32, int64, float16, float, double tensors. The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1]. Starting indices of corresponding axis in `axes` Ending indices (exclusive) of corresponding axis in axes` 1-D tensor of slice step of corresponding axis in `axes`. Default to 1. Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] Tensor of int32/int64 indices, of r >= 1 (same rank as input). Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds. List of non-negative integers, indicate the dimensions to squeeze. List of non-negative integers, indicate the dimensions to be inserted (Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Input tensor containing indices. The values must be non-negative integers. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use. Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use. (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length alone the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded. List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. One float, indicates the value to be filled. 'axis' must be in [-rank(indices)-1, rank(indices)] rank must be greater than axis ) is not equal to the existing rank value ( Number of elements of attribute 'scales' must be same as rank of input 'X' Attribute 'scales' must have floats type. Attribute 'scales' is required. Incorrect or missing attribute value for starts and ends Attribute axes has incorrect length Invalid value of attribute 'axis' Attribute value for pads is required Attribute pads has incorrect length *ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_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_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ ) is not equal to the existing dim value ( Number of elements of input 'sizes' must be same as rank of input 'X' Input 'sizes' must have int64 element type. Number of elements of input 'scales' must be same as rank of input 'X' Input 'scales' must have float element type. Dimension value inferred ( Data to be selected Data to be binarized Binarized output data Input data A dictionary. Data to be processed. Imputed output data Value(s) to change to A value that needs replacing. Value(s) to change to. Output data. A list of ints. A list of floats. A string. An integer. A float. Data to be classified. A collection of intercepts. Data to be regressed. Weights of the model(s). Encoded output data One of 'MAX,' 'L1,' 'L2' Data to be encoded. Data to be scaled. Scaled output data. Chosen support vectors Support vector coefficients. Input of shape [N,F] N, Top class for each point Tree id for each node. Feature id for each node. N classes The node id of each weight The weight for each target The total number of targets. The input values The output map seq(map(string, float)) seq(map(int64, float)) map(string, int64) map(int64, string) map(int64, float) map(int64, double) map(string, float) map(string, double) Label encoder has only one input. Label encoder has only one output. Only one of keys_*'s can be set in label encoder. Input type is not string tensor but key_strings is set Input type is not int64 tensor but keys_int64s is set Input type is not float tensor but keys_floats is set Only one of values_*'s can be set in label encoder. The indices, based on 0 as the first index of any dimension. Selected output data as an array The input must be a tensor of a numeric type or string. The output will be of the same tensor type. /onnxruntime_src/cmake/external/onnx/onnx/defs/traditionalml/defs.cc The input must be a tensor of a numeric type. The output will be of the same tensor type. Values greater than this are mapped to 1, others to 0. The input map that is to be cast to a tensor A tensor representing the same data as the input map, ordered by their keys The input must be an integer map to either string or float. The output is a 1-D tensor of string, float, or integer. A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'. Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).<br>One of 'DENSE', 'SPARSE'. If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor. Output data. If strings are input, the output values are integers, and vice versa. The input must be a tensor of strings or integers, either [N,C] or [C]. The output is a tensor of strings or integers. Its shape will be the same as the input shape. The strings of the map. This sequence must be the same length as the 'cats_int64s' sequence The integers of the map. This sequence must be the same length as the 'cats_strings' sequence. A string to use when an input integer value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined. An integer to use when an input string value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined. A 1-D tensor holding values from the input dictionary. The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same. The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary. A string vocabulary array.<br>One and only one of the vocabularies must be defined. An integer vocabulary array.<br>One and only one of the vocabularies must be defined. An ordered collection of tensors, all with the same element type. The output array, elements ordered as the inputs. The input type must be a tensor of a numeric type. The size of each input in the input list The input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape. Input data. It can be either tensor or scalar. The input type is a tensor of any shape. Output type is determined by the specified 'values_*' attribute. A list of strings. One and only one of 'keys_*'s should be set. A list of strings. One and only one of 'value_*'s should be set. Classification outputs (one class per example). Classification scores ([N,E] - one score for each class and example The input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C] The output will be a tensor of strings or integers. A collection of weights of the model(s). Indicates whether to do OvR or multinomial (0=OvR is the default). Class labels when using string labels. One and only one 'classlabels' attribute must be defined. Class labels when using integer labels. One and only one 'classlabels' attribute must be defined. Indicates the transform to apply to the scores vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' Regression outputs (one per target, per example). The input must be a tensor of a numeric type. Indicates the transform to apply to the regression output vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' Weights of the intercepts, if used. The total number of regression targets, 1 if not defined. Data to be encoded, a tensor of shape [N,C] or [C] Encoded output data, having one more dimension than X. List of categories, ints.<br>One and only one of the 'cats_*' attributes must be defined. List of categories, strings.<br>One and only one of the 'cats_*' attributes must be defined. If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail. First, offset by this.<br>Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count. Second, multiply by this.<br>Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.<br>Must be same length as 'offset' Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores. The input must be a tensor of a numeric type, either [C] or [N,C]. The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input. The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'. List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel. First set of probability coefficients. Second set of probability coefficients. This array must be same size as prob_a.<br>If these are provided then output Z are probability estimates, otherwise they are raw scores. Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' Class labels if using string labels.<br>One and only one of the 'classlabels_*' attributes must be defined. Class labels if using integer labels.<br>One and only one of the 'classlabels_*' attributes must be defined. Regression outputs (one score per target per example). The input type must be a tensor of a numeric type, either [C] or [N,C]. Flag indicating whether the regression is a one-class SVM or not. The number of support vectors. Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.' The class score for each class, for each point, a tensor of shape [N,E]. The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Node id for each node. Ids may restart at zero for each tree, but it not required to. Thresholds to do the splitting on for each node. Popularity of each node, used for performance and may be omitted. The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.<br>One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF' Child node if expression is true. Child node if expression is false. For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.<br>This attribute may be left undefined, and the defalt value is false (0) for all nodes. The id of the tree that this node is in. node id that this weight is for. The index of the class list that each weight is for. The weight for the class in class_id. Indicates the transform to apply to the score. <br> One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.' Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0) Node id for each node. Node ids must restart at zero for each tree and increase sequentially. Child node if expression is true Child node if expression is false For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.<br>This attribute may be left undefined and the defalt value is false (0) for all nodes. The id of the tree that each node is in. The index of the target that each weight is for Defines how to aggregate leaf values within a target. <br>One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.' The output will be a sequence of string or integer maps to float. The keys when using string keys.<br>One and only one of the 'classlabels_*' attributes must be defined. The keys when using int keys.<br>One and only one of the 'classlabels_*' attributes must be defined. Input's shape should be 1D or 2D *ZN4onnx11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26CategoryMapper_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26DictVectorizer_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28LinearClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25SVMClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Input data. A list of labels. The input type must be a tensor of integers or strings, of any shape. The output type will be a tensor of strings or integers, and will have the same shape as the input. /onnxruntime_src/cmake/external/onnx/onnx/defs/traditionalml/old.cc *ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ , node name: (op_type: is out of bounds opaque_type sparse_tensor_type NOT_SET ) vs ( inferred= type case mismatch. existing= does not contain a graph. Shape inference error(s): inputs but Graph has were provided Sfff-@ Inferred elem type differs from existing elem type: ( Inferred shape and existing shape differ in rank: ( Inferred shape and existing shape differ in dimension type case unsupported. existing= GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. Warning: Shape inference does not support models with experimental operators: Warning: Unsupported operator . No schema registered for this operator. N4onnx15shape_inference19GraphInferencerImplE N4onnx15shape_inference20InferenceContextImplE Func_ Input for function node Output for function node onnx.AttributeProto onnx.ValueInfoProto onnx.NodeProto onnx.TrainingInfoProto onnx.ModelProto onnx.StringStringEntryProto onnx.TensorAnnotation onnx.GraphProto onnx.TensorProto.Segment onnx.TensorProto onnx.SparseTensorProto onnx.TensorShapeProto onnx.TypeProto.Tensor onnx.TypeProto.Sequence onnx.TypeProto.Map onnx.TypeProto.SparseTensor onnx.TypeProto.Opaque onnx.TypeProto onnx.OperatorSetIdProto onnx.TensorShapeProto.Dimension /build/Release/external/onnx/onnx/onnx-ml.pb.cc 0L`L0L0LpLL0LLPLN|NNN|N|NN|N|Nppp@dĭܬ|$tLHȸ8дhؾ(XH``Ph0h0@,tuVVuVuV W@WuVV@V IR_VERSIONIR_VERSION_2017_10_10IR_VERSION_2017_10_30IR_VERSION_2017_11_3IR_VERSION_2019_1_22IR_VERSION_2019_3_18IR_VERSION_2019_9_19_START_VERSION DEFAULTEXTERNAL
+x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1). The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified. If 'size' is needed, the user can use an empty string as the name of 'scales' in this operator's input list. Constrain input types. Casting from strings and complex are not supported. Constrain output types. Casting to strings and complex are not supported. Which axis to concat on. Default value is 1. Optional list of output lengths (see also arg 'split') List of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. Three modes: constant(default), reflect, edge One float, indicates the value to be filled, default is 0 Number of repeated copies to make of the input tensor. Output tensor of same shape and type as input. Constrain tiles and axis's type to int64 tensors. The scale along width dimension. It takes value greater than or equal to 1. The scale along height dimension. It takes value greater than or equal to 1. Two interpolation modes: nearest(default), bilinear 4-D tensor after resizing, [N,C,H,W] Constrain output types to bool, int32, int64, float16, float, double tensors. The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1]. Starting indices of corresponding axis in `axes` Ending indices (exclusive) of corresponding axis in axes` 1-D tensor of slice step of corresponding axis in `axes`. Default to 1. Which axis to scatter on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] Tensor of int32/int64 indices, of r >= 1 (same rank as input). Which axis to gather on. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] Tensor of int32/int64 indices, of any rank q. All index values are expected to be within bounds. It is an error if any of the index values are out of bounds. List of non-negative integers, indicate the dimensions to squeeze. List of non-negative integers, indicate the dimensions to be inserted (Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Input tensor containing indices. The values must be non-negative integers. Any entries in the 'indices' input tensor with values outside the range [0, depth) will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use. Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [0, depth). In case 'depth' is of non-integer type, it will be casted to int64 before use. (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length alone the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded. List of integers indicating the number of padding elements to add or remove (if negative) at the beginning and end of each axis. For 2D it is the number of pixels. `pads` rank should be double of the input's rank. `pads` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. One float, indicates the value to be filled. 'axis' must be in [-rank(indices)-1, rank(indices)] rank must be greater than axis ) is not equal to the existing rank value ( Number of elements of attribute 'scales' must be same as rank of input 'X' Attribute 'scales' must have floats type. Attribute 'scales' is required. Incorrect or missing attribute value for starts and ends Attribute axes has incorrect length Invalid value of attribute 'axis' Attribute value for pads is required Attribute pads has incorrect length *ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ ) is not equal to the existing dim value ( Number of elements of input 'sizes' must be same as rank of input 'X' Input 'sizes' must have int64 element type. Number of elements of input 'scales' must be same as rank of input 'X' Input 'scales' must have float element type. Dimension value inferred ( Data to be selected Data to be binarized Binarized output data Input data A dictionary. Data to be processed. Imputed output data Value(s) to change to A value that needs replacing. Value(s) to change to. Output data. A list of ints. A list of floats. A string. An integer. A float. Data to be classified. A collection of intercepts. Data to be regressed. Weights of the model(s). Encoded output data One of 'MAX,' 'L1,' 'L2' Data to be encoded. Data to be scaled. Scaled output data. Chosen support vectors Support vector coefficients. Input of shape [N,F] N, Top class for each point Tree id for each node. Feature id for each node. N classes The node id of each weight The weight for each target The total number of targets. The input values The output map seq(map(string, float)) seq(map(int64, float)) map(string, int64) map(int64, string) map(int64, float) map(int64, double) map(string, float) map(string, double) Label encoder has only one input. Label encoder has only one output. Only one of keys_*'s can be set in label encoder. Input type is not string tensor but key_strings is set Input type is not int64 tensor but keys_int64s is set Input type is not float tensor but keys_floats is set Only one of values_*'s can be set in label encoder. The indices, based on 0 as the first index of any dimension. Selected output data as an array The input must be a tensor of a numeric type or string. The output will be of the same tensor type. /onnxruntime_src/cmake/external/onnx/onnx/defs/traditionalml/defs.cc The input must be a tensor of a numeric type. The output will be of the same tensor type. Values greater than this are mapped to 1, others to 0. The input map that is to be cast to a tensor A tensor representing the same data as the input map, ordered by their keys The input must be an integer map to either string or float. The output is a 1-D tensor of string, float, or integer. A string indicating the desired element type of the output tensor, one of 'TO_FLOAT', 'TO_STRING', 'TO_INT64'. Indicates whether to only output as many values as are in the input (dense), or position the input based on using the key of the map as the index of the output (sparse).<br>One of 'DENSE', 'SPARSE'. If the value of map_form is 'SPARSE,' this attribute indicates the total length of the output tensor. Output data. If strings are input, the output values are integers, and vice versa. The input must be a tensor of strings or integers, either [N,C] or [C]. The output is a tensor of strings or integers. Its shape will be the same as the input shape. The strings of the map. This sequence must be the same length as the 'cats_int64s' sequence The integers of the map. This sequence must be the same length as the 'cats_strings' sequence. A string to use when an input integer value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined. An integer to use when an input string value is not found in the map.<br>One and only one of the 'default_*' attributes must be defined. A 1-D tensor holding values from the input dictionary. The input must be a map from strings or integers to either strings or a numeric type. The key and value types cannot be the same. The output will be a tensor of the value type of the input map. It's shape will be [1,C], where C is the length of the input dictionary. A string vocabulary array.<br>One and only one of the vocabularies must be defined. An integer vocabulary array.<br>One and only one of the vocabularies must be defined. An ordered collection of tensors, all with the same element type. The output array, elements ordered as the inputs. The input type must be a tensor of a numeric type. The size of each input in the input list The input type must be a tensor of a numeric type, either [N,C] or [C]. The output type will be of the same tensor type and shape. Input data. It can be either tensor or scalar. The input type is a tensor of any shape. Output type is determined by the specified 'values_*' attribute. A list of strings. One and only one of 'keys_*'s should be set. A list of strings. One and only one of 'value_*'s should be set. Classification outputs (one class per example). Classification scores ([N,E] - one score for each class and example The input must be a tensor of a numeric type, and of of shape [N,C] or [C]. In the latter case, it will be treated as [1,C] The output will be a tensor of strings or integers. A collection of weights of the model(s). Indicates whether to do OvR or multinomial (0=OvR is the default). Class labels when using string labels. One and only one 'classlabels' attribute must be defined. Class labels when using integer labels. One and only one 'classlabels' attribute must be defined. Indicates the transform to apply to the scores vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' Regression outputs (one per target, per example). The input must be a tensor of a numeric type. Indicates the transform to apply to the regression output vector.<br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' Weights of the intercepts, if used. The total number of regression targets, 1 if not defined. Data to be encoded, a tensor of shape [N,C] or [C] Encoded output data, having one more dimension than X. List of categories, ints.<br>One and only one of the 'cats_*' attributes must be defined. List of categories, strings.<br>One and only one of the 'cats_*' attributes must be defined. If true and category is not present, will return all zeros; if false and a category if not found, the operator will fail. First, offset by this.<br>Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count. Second, multiply by this.<br>Can be length of features in an [N,F] tensor or length 1, in which case it applies to all features, regardless of dimension count.<br>Must be same length as 'offset' Class scores (one per class per example), if prob_a and prob_b are provided they are probabilities for each class, otherwise they are raw scores. The input must be a tensor of a numeric type, either [C] or [N,C]. The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Its size will match the bactch size of the input. The kernel type, one of 'LINEAR,' 'POLY,' 'RBF,' 'SIGMOID'. List of 3 elements containing gamma, coef0, and degree, in that order. Zero if unused for the kernel. First set of probability coefficients. Second set of probability coefficients. This array must be same size as prob_a.<br>If these are provided then output Z are probability estimates, otherwise they are raw scores. Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT' Class labels if using string labels.<br>One and only one of the 'classlabels_*' attributes must be defined. Class labels if using integer labels.<br>One and only one of the 'classlabels_*' attributes must be defined. Regression outputs (one score per target per example). The input type must be a tensor of a numeric type, either [C] or [N,C]. Flag indicating whether the regression is a one-class SVM or not. The number of support vectors. Indicates the transform to apply to the score. <br>One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.' The class score for each class, for each point, a tensor of shape [N,E]. The output type will be a tensor of strings or integers, depending on which of the the classlabels_* attributes is used. Node id for each node. Ids may restart at zero for each tree, but it not required to. Thresholds to do the splitting on for each node. Popularity of each node, used for performance and may be omitted. The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.<br>One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF' Child node if expression is true. Child node if expression is false. For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.<br>This attribute may be left undefined, and the defalt value is false (0) for all nodes. The id of the tree that this node is in. node id that this weight is for. The index of the class list that each weight is for. The weight for the class in class_id. Indicates the transform to apply to the score. <br> One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.' Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0) Node id for each node. Node ids must restart at zero for each tree and increase sequentially. Child node if expression is true Child node if expression is false For each node, define what to do in the presence of a NaN: use the 'true' (if the attribute value is 1) or 'false' (if the attribute value is 0) branch based on the value in this array.<br>This attribute may be left undefined and the defalt value is false (0) for all nodes. The id of the tree that each node is in. The index of the target that each weight is for Defines how to aggregate leaf values within a target. <br>One of 'AVERAGE,' 'SUM,' 'MIN,' 'MAX.' The output will be a sequence of string or integer maps to float. The keys when using string keys.<br>One and only one of the 'classlabels_*' attributes must be defined. The keys when using int keys.<br>One and only one of the 'classlabels_*' attributes must be defined. Input's shape should be 1D or 2D *ZN4onnx11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26CategoryMapper_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26DictVectorizer_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28LinearClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25SVMClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Input data. A list of labels. The input type must be a tensor of integers or strings, of any shape. The output type will be a tensor of strings or integers, and will have the same shape as the input. /onnxruntime_src/cmake/external/onnx/onnx/defs/traditionalml/old.cc *ZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ , node name: (op_type: is out of bounds opaque_type sparse_tensor_type NOT_SET ) vs ( inferred= type case mismatch. existing= does not contain a graph. Shape inference error(s): inputs but Graph has were provided Ў Inferred elem type differs from existing elem type: ( Inferred shape and existing shape differ in rank: ( Inferred shape and existing shape differ in dimension type case unsupported. existing= GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. Warning: Shape inference does not support models with experimental operators: Warning: Unsupported operator . No schema registered for this operator. Unable to save inferred model to the target path: N4onnx15shape_inference19GraphInferencerImplE N4onnx15shape_inference20InferenceContextImplE Func_ Input for function node Output for function node onnx.AttributeProto onnx.ValueInfoProto onnx.NodeProto onnx.TrainingInfoProto onnx.ModelProto onnx.StringStringEntryProto onnx.TensorAnnotation onnx.GraphProto onnx.TensorProto.Segment onnx.TensorProto onnx.SparseTensorProto onnx.TensorShapeProto onnx.TypeProto.Tensor onnx.TypeProto.Sequence onnx.TypeProto.Map onnx.TypeProto.SparseTensor onnx.TypeProto.Opaque onnx.TypeProto onnx.OperatorSetIdProto onnx.TensorShapeProto.Dimension /build/Release/external/onnx/onnx/onnx-ml.pb.cc
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BFLOAT16BOOLCOMPLEX128COMPLEX64DOUBLEFLOATFLOAT16INT16INT32INT64INT8STRINGUINT16UINT32UINT64UINT8UNDEFINED
FLOATFLOATSGRAPHGRAPHSINTINTSSPARSE_TENSORSPARSE_TENSORSSTRINGSTRINGSTENSORTENSORSUNDEFINED
- N4onnx14AttributeProtoE N4onnx14ValueInfoProtoE N4onnx9NodeProtoE N4onnx17TrainingInfoProtoE N4onnx10ModelProtoE N4onnx22StringStringEntryProtoE N4onnx16TensorAnnotationE N4onnx10GraphProtoE N4onnx19TensorProto_SegmentE N4onnx11TensorProtoE N4onnx17SparseTensorProtoE N4onnx26TensorShapeProto_DimensionE N4onnx16TensorShapeProtoE N4onnx16TypeProto_TensorE N4onnx18TypeProto_SequenceE N4onnx13TypeProto_MapE N4onnx22TypeProto_SparseTensorE N4onnx16TypeProto_OpaqueE N4onnx9TypeProtoE N4onnx18OperatorSetIdProtoE N6google8protobuf8internal29InternalMetadataWithArenaBaseISsNS1_29InternalMetadataWithArenaLiteEE9ContainerE onnx.FunctionProto onnx.OperatorProto onnx.OperatorSetProto /build/Release/external/onnx/onnx/onnx-operators-ml.pb.cc H0؛h(h(h(ȟ EXPERIMENTALSTABLE N4onnx13FunctionProtoE N4onnx13OperatorProtoE N4onnx16OperatorSetProtoE onnx.SequenceProto onnx.MapProto /build/Release/external/onnx/onnx/onnx-data.pb.cc P t MAPSEQUENCESPARSE_TENSORTENSORUNDEFINED N4onnx13SequenceProtoE N4onnx8MapProtoE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/arena.cc CHECK failed: (min_bytes) <= (std::numeric_limits<size_t>::max() - kBlockHeaderSize): /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/generated_message_util.cc CHECK failed: (scc->visit_status.load(std::memory_order_relaxed)) == (SCCInfoBase::kRunning): Not implemented field number with type t D|$\ D|,"",TDt4d""L4D !$ T $"4""|t $
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DL""l,d <t\<""4 S|SLSSR RQ|QDQ QP\P,PO4ON NTMdTdTLlLKKK4KJdJ<J JIlI<IIH<HG,G|FdTdT F|ED$DTCB,BA A|@?\?>,>=L==l<;dTdT,;:L:98<87\766666 6l54|43D3dTdT,32L2 211L100|0D0//l/</.\.,.-dTdT-, /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 wnC=Ļparsing Can't happen /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/parse_context.h @ [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 ( H h Hh ) U k 1 ? 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 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 P 0 0 0 @ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 P 0123456789abcdef ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/ >456789:;<=
+ N4onnx14AttributeProtoE N4onnx14ValueInfoProtoE N4onnx9NodeProtoE N4onnx17TrainingInfoProtoE N4onnx10ModelProtoE N4onnx22StringStringEntryProtoE N4onnx16TensorAnnotationE N4onnx10GraphProtoE N4onnx19TensorProto_SegmentE N4onnx11TensorProtoE N4onnx17SparseTensorProtoE N4onnx26TensorShapeProto_DimensionE N4onnx16TensorShapeProtoE N4onnx16TypeProto_TensorE N4onnx18TypeProto_SequenceE N4onnx13TypeProto_MapE N4onnx22TypeProto_SparseTensorE N4onnx16TypeProto_OpaqueE N4onnx9TypeProtoE N4onnx18OperatorSetIdProtoE N6google8protobuf8internal29InternalMetadataWithArenaBaseISsNS1_29InternalMetadataWithArenaLiteEE9ContainerE onnx.FunctionProto onnx.OperatorProto onnx.OperatorSetProto /build/Release/external/onnx/onnx/onnx-operators-ml.pb.cc [[@[ZZ(ZYXxX0W@bbaxa@a``x`X_^ EXPERIMENTALSTABLE N4onnx13FunctionProtoE N4onnx13OperatorProtoE N4onnx16OperatorSetProtoE onnx.SequenceProto onnx.MapProto /build/Release/external/onnx/onnx/onnx-data.pb.cc @P ė\d$ MAPSEQUENCESPARSE_TENSORTENSORUNDEFINED N4onnx13SequenceProtoE N4onnx8MapProtoE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/arena.cc CHECK failed: (min_bytes) <= (std::numeric_limits<size_t>::max() - kBlockHeaderSize): /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/generated_message_util.cc CHECK failed: (scc->visit_status.load(std::memory_order_relaxed)) == (SCCInfoBase::kRunning): Not implemented field number with type L,dL,l <l$TDt4 t4$4TddtD4<dL 4Ld|4lL| D|4t,T\Ll
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+
+ t |L |T|lL4d l \ lD l,| ,\4<l\, | l< /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/coded_stream.cc A protocol message was rejected because it was too big (more than bytes). To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in third_party/protobuf/src/google/protobuf/io/coded_stream.h. CHECK failed: (buffer_size) >= (0): /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl.cc Can't BackUp() after failed Next(). CHECK failed: !is_closed_: close() failed: N6google8protobuf2io19CopyingOutputStreamE N6google8protobuf2io15FileInputStream22CopyingFileInputStreamE N6google8protobuf2io15FileInputStreamE N6google8protobuf2io16FileOutputStream23CopyingFileOutputStreamE N6google8protobuf2io16FileOutputStreamE N6google8protobuf2io18IstreamInputStream25CopyingIstreamInputStreamE N6google8protobuf2io18IstreamInputStreamE N6google8protobuf2io19OstreamOutputStream26CopyingOstreamOutputStreamE N6google8protobuf2io19OstreamOutputStreamE N6google8protobuf2io24ConcatenatingInputStreamE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl_lite.cc CHECK failed: (last_returned_size_) > (0): BackUp() can only be called after a successful Next(). CHECK failed: (count) <= (last_returned_size_): CHECK failed: (count) >= (0): CHECK failed: target_ != NULL: CHECK failed: (buffer_used_) == (buffer_size_): BackUp() can only be called after Next(). CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next(). CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: Parameter to BackUp() can't be negative. CHECK failed: (count) <= (target_->size()): Cannot allocate buffer larger than kint32max for CHECK failed: (backup_bytes_) == (0): StringOutputStream. N6google8protobuf2io16ArrayInputStreamE N6google8protobuf2io17ArrayOutputStreamE N6google8protobuf2io18StringOutputStreamE N6google8protobuf2io18CopyingInputStreamE N6google8protobuf2io25CopyingInputStreamAdaptorE N6google8protobuf2io26CopyingOutputStreamAdaptorE N6google8protobuf2io19LimitingInputStreamE (cannot determine missing fields for lite message) " because it is missing required fields: /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/message_lite.cc exceeded maximum protobuf size of 2GB: CHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): was modified concurrently during serialization. CHECK failed: (bytes_produced_by_serialization) == (byte_size_before_serialization): Byte size calculation and serialization were inconsistent. This may indicate a bug in protocol buffers or it may be caused by concurrent modification of This shouldn't be called if all the sizes are equal. MessageLite at 0x Can't parse message of type " N6google8protobuf11MessageLiteE N6google8protobuf24ZeroCopyCodedInputStreamE zzz}zzzparsing Can't happen /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/parse_context.h H``X[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 (Hh(H$i$/E[q=S basic_string::erase CHECK failed: dest: \x%02x \%03o CHECK failed: i >= 0: 0123456789abcdef nullptr output boolean given. %.*g CHECK failed: s != nullptr: Logic problem? szsrc = %.1f CHECK failed: (size) <= (6): /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/strutil.cc CHECK failed: result != nullptr: FastHexToBuffer() wants non-negative integers, not CHECK failed: value != nullptr: This can't happen; base64 decoder state = CHECK failed: (temp[0]) == ('1'): CHECK failed: (temp[size - 1]) == ('5'): ppppppppppppppppppppppppppppppppp`ppppHpppppppp ppppppppppppppppppppppppppppppppppppppp`pppPppppppp@ppp0p p p 0123456789abcdef ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/ >456789:;<=
? !"#$%&'()*+,-./0123>?456789:;<=
-
!"#$%&'()*+,-./012300010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 ? @dhddd ddHg(hhff i
+
!"#$%&'()*+,-./012300010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 ? """P"@""%h&'$$@'
- /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. ޘ4LHQ˜H HX< ,P,mSp Kp)<؞*pddddddddt80XУ Ⱥ 0H`xȺp)ZP/C5G"xuA|[O3333S
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l , l < $ T$K! /! zzzzoF j-/$/</T/l///40./--1,1(22(3-30 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 , lqq4 qqqqqqqLd 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 Դ|$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: Ф P0ݢ pp0px@)/onnxruntime_src/cmake/external/re2/re2/onepass.cc Cannot use SearchOnePass for unanchored matches. Byc 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 N,-------I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.I.-I.I.I.I.-I.I.I.I.I.I.I.-I.I.I.7.I.%.I.x-I.~,z{{{Lz z zly{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{{t{4{{${{{{{{{{{{{{{{{{{{{{{{{{{{{{{zzNNNNNNNF>6xp ܂ N3re216RepetitionWalkerE 0 9 A F a f 0 9 A Z _ _ a z A Z
! / : @ [ ` { ~ ~ a z ! ~ 0 9 A Z a z 0 9 A Z a z 0 9 A Z _ _ a z
+ /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/wire_format_lite.cc CHECK failed: (value.size()) <= (kint32max): buffer. Use the 'bytes' type if you intend to send raw '%s' String field contains invalid UTF-8 data when a protocol bytes. serializing N6google8protobuf8internal12FieldSkipperE N6google8protobuf8internal29CodedOutputStreamFieldSkipperE Multiple extension registrations for type " /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/extension_set.cc CHECK failed: (type) != (WireFormatLite::TYPE_ENUM): CHECK failed: (type) != (WireFormatLite::TYPE_MESSAGE): CHECK failed: (type) != (WireFormatLite::TYPE_GROUP): CHECK failed: (type) == (WireFormatLite::TYPE_ENUM): CHECK failed: type == WireFormatLite::TYPE_MESSAGE || type == WireFormatLite::TYPE_GROUP: Non-primitive types can't be packed. Don't lookup extension types if they aren't present (1). Don't lookup extension types if they aren't present (2). CHECK failed: extension != NULL: Index out-of-bounds (field is empty). Invalid message set extension. /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/extension_set_inl.h ", field number can't reach here. Can't get here. Extension not found. W,W,W,W,W,W,W,W,WtWW$\8YXZZ [8YX[[[[[2\LZX8Y[Q[|XXXLYLY,YXXYlYYYlYY,YXXYY[XXI^^^XXi\|\\]j]] ^XX \[___________x``aa aHapaa`b@`Bw<w<w<w<w<w<w<w<wLw\wxyHy`ypyyyyyx yT%i9=ߍǒD˓o݇ YuԆ.b[
+"ډ4>U՜U՛,ssssM}Y@hнp~P"QQQQ#xJ O
+FK@|KX/l,\ <|d<d Co/
++61Td|t(H0hh( X N6google8protobuf8internal20RepeatedPtrFieldBaseE N6google8protobuf8internal15ExtensionFinderE N6google8protobuf8internal24GeneratedExtensionFinderE N6google8protobuf8internal12ExtensionSet8KeyValueE N6google8protobuf13RepeatedFieldIiEE N6google8protobuf13RepeatedFieldIlEE N6google8protobuf13RepeatedFieldIjEE N6google8protobuf13RepeatedFieldImEE N6google8protobuf13RepeatedFieldIfEE N6google8protobuf13RepeatedFieldIdEE N6google8protobuf13RepeatedFieldIbEE N6google8protobuf16RepeatedPtrFieldISsEE N6google8protobuf16RepeatedPtrFieldINS0_11MessageLiteEEE St3mapIiN6google8protobuf8internal12ExtensionSet9ExtensionESt4lessIiESaISt4pairIKiS4_EEE N6google8protobuf8internal19ImplicitWeakMessageE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream.cc This ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug. N6google8protobuf2io20ZeroCopyOutputStreamE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/stringprintf.cc CHECK failed: (v.size()) <= (kStringPrintfVectorMaxArgs): StringPrintfVector currently only supports up to Feel free to add support for more if you need it. arguments. Error reverse compiling ' Unknown encoding Error parsing ' ': Error compiling ' \x00 Invalid RE2: startpos: endpos: text size: DFA out of memory: size bytemap range list count SearchDFA inconsistency SearchOnePass inconsistency SearchBitState inconsistency SearchNFA inconsistency requested group in regexp invalid rewrite pattern: /onnxruntime_src/cmake/external/re2/re2/re2.cc pattern too large - reverse compile failed pattern too large - compile failed RE2: invalid startpos, endpos pair. [ Rewrite schema error: '\' not allowed at end. Rewrite schema error: '\' must be followed by a digit or '\'. Rewrite schema requests %d matches, but the regexp only has %d parenthesized subexpressions.
+
Regexp not destroyed. Stack not empty. Bad reference count Walk NULL no error unexpected error invalid escape sequence invalid character class invalid character class range missing ] missing ) trailing \ invalid repetition size bad repetition operator invalid perl operator invalid UTF-8 invalid named capture group /onnxruntime_src/cmake/external/re2/re2/regexp.cc Unexpected op in Regexp::Equal: NumCapturesWalker::ShortVisit called NamedCapturesWalker::ShortVisit called CaptureNamesWalker::ShortVisit called /onnxruntime_src/cmake/external/re2/re2/walker-inl.h no argument for repetition operator l\\ħħħ̦ܧ t\\DDD 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 usr$poondnCompiler::Copy called! should never happen No ranges in char class Missing case in Compiler: hello, world /onnxruntime_src/cmake/external/re2/re2/compile.cc 4TܯdİdDı\D,4 N3re26Regexp6WalkerINS_4FragEEE N3re28CompilerE /onnxruntime_src/cmake/external/re2/re2/dfa.cc StateSaver failed to restore state. Unexpected special state in RunStateOnByte RunStateOnByteUnlocked failed after ResetCache RunStateOnByteUnlocked failed after Reset Failed to analyze start state. %s%d (%p) flag=%#x || unhandled opcode: DeadState in RunStateOnByte NULL state in RunStateOnByte context does not contain text A8h8unhandled in AddToThreadq Unhandled in step (?,?) (%d,?) (%d,%d) Bad args: nsubmatch= in ComputeFirstByte in Prog::Fanout() /onnxruntime_src/cmake/external/re2/re2/nfa.cc Unexpected opcode in short circuit: X^c@a8bapaa a@p% 8i/onnxruntime_src/cmake/external/re2/re2/onepass.cc Cannot use SearchOnePass for unanchored matches. !ܴ 0 0 0 0 Bad hex digit Concat of Any RE2: unexpected op: unknown round: /onnxruntime_src/cmake/external/re2/re2/parse.cc RepetitionWalker::ShortVisit called AddFoldedRange recurses too much. Bad call to ParseState::ParsePerlFlags we 9 : : :8\8L87 : : : : : : : : : : : : : : : : : : :9 : : : : : : : : : : : : : : : : : : : : : : : : : : :9t9 :d9 : : : : : : : : : : : : : : : : : : : : : : : : : : : :,9 9Y@AAAAAAA@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@A@@@@~A@@@@@@@vA@@@A@A@`A@ A 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: 8Xx؛(x(u 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 0P8VP>h888 P 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 154411 2 766L6d7D775,6 6555T545,=,==T=T=T=T=d= 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: xYYYYZ8ZXYXZX}X}|||h|~h|PXXX 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 ؠؠ(((p xج88~8xx888888x0 `` H N3re26Regexp6WalkerIPS0_EE N3re214CoalesceWalkerE N3re214SimplifyWalkerE []^-\ \r \t \n \f \x{%x} (){}[]*+?|.^$\ [^\x00-\x{10ffff}] (?:) Bad final char: ? + {%d,} {%d} {%d,%d} \C ^ $ (?-m:^) (?-m:$) \z \b \B (?HaveMatch:%d) (?: kRegexpCapture cap() == 0 ?P< [truncated] /onnxruntime_src/cmake/external/re2/re2/tostring.cc T, 6\\4lL, tllD N3re214ToStringWalkerE A Z . @2 6 @9 G @J v @x x y } @ @ O @ @ @ @ @ @ ~" 2 @: : +* ; ; = = ]> > (* A A C C =D D E E E G F N @E E t p r @v v t & % @ ? @ ~ P / ` @ @ @ . @1 V 0 ` ` ` É @ @ @ A @ ( / 8 ? H M Y Y [ [ ] ] _ _ h o &! &! *! *! A+! +! 2! 2! `! o! ! ! $ $ , ., 0 `, `, b, b, c, c, d, d, g, k, @m, m, n, n, o, o, p, p, r, r, u, u, ~, , , , @, , @, , @ l @ @" . @2 n @y { @} } u~ @ Z @ @ Z Z Z Z Z Z Z Z @p 0h! : ' ( ( @ @n _n ! " ] A Z a j k k l r s s t z F y / 2 7 9 H J w x x y ~ O a 8 ~" 3 : : +* ; < = = ]> > (* ? @ ?* A B C C =D D E E E G F O P P * Q Q * R R * S S .T T 2V W 3Y Y 6[ [ 5\ \ O ` ` 3a a K c c 1e e ( f f D h h /i i -j j D k k ) l l A o o -q q ) r r +u u *} } ) & & * & ' % E E T p s v w { } t & % @ ? @ 6 0 ] # ~ P / 0 1 2 2 N 3 3 4 4 M 5 = > > D ? @ A B B C I J J < K O P _ ` b c c $ d / 1 V 0 a ` ` ` З @ @y y } } ` a a : b A ' ( / 0 7 8 ? @ E H M Q Q S S U U W W Y Y [ [ ] ] _ _ ` g h o p q J r u V v w d x y z { p | } ~ &! &! *! *! !+! +! 2! 2! N! N! `! o! p! ! ! ! $ $ $ $ , ., 0 0, ^, `, a, b, b, c, c, d, d, e, e, f, f, g, l, m, m, n, n, o, o, p, p, r, s, u, v, ~, , , , , , , , - %- '- '- -- -- @ J K K =vL m " / 2 o y | } } u~ Z Z Z Z Z Z Z Z Z S S `p 0h! : A Z ' ( ( O ( @ @n _n `n n ! " " C 68<>>@ACDFO 5 9 ? P ` h 8j ` ~ ! " $ $ ' ' ) 2 4 7 9 9 ; ; B B G G I I K K M O Q R T T W W Y Y [ [ ] ] _ _ a b d d g j l r t w y | ~ ~
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9 ; > @ D F F J P R q + 0 k p ; @ H P Q ` e s G P Y ` > @ p s v z z | ` m @ [ ` { tt~~ @@d e ??56 d f p t ~ !%!'!)!,!1!3!M!O!_!!!!&$@$J$`$' )s+v++++++ .N.// 00000 00070<0?00000001111 2_222X33MM !09..ϩϩ[[>?0RTfhk ;@[epp @j ^j `j ij nj oj ` @SФ -0> 0 c o o 0 Y 0 .4 9 ? ? 7 ; I M O H ; 6 @ U ` g
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@@ -7823,7794 +8514,8002 @@
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
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-8D0A(B BBBB 4 $ Q _]
- BGA P@B
- AABH 4 $ Q C]
- BDA P@E
- AABH T $ H ']
- BBE E(A0A8G
-8D0A(B BBBE < L% ' \
- BBA A(JU
-(A ABBA < % ' \
- BBA A(JU
-(A ABBA L % p \
- BBA D(GpS
+8A0A(B BBBB T L$ X~ : BBE E(A0A8G
+8D0A(B BBBI , \%
+~ ADD0e
+AAA L % P ~] BBB H(A0D8DP
+8A0A(B BBBG T $% ` ~& D9 BBB E(A0A8D
+8D0A(B BBBB 4 |% 8~Q 9 BGA P@B
+ AABH 4 % `~Q 8 BDA P@E
+ AABH T % ~ 8 BBE E(A0A8G
+8D0A(B BBBE < D& ~' 8 BBA A(JU
+(A ABBA < & ~' 8 BBA A(JU
+(A ABBA L & ~ o8 BBA D(GpS
(A ABBG g
-(A ABBC < & ຄ' \
- BBA A(JU
-(A ABBA < \& л' r\
- BBA A(JU
-(A ABBA < T' BBG A(G@
-(A ABBG < ' ` BBG A(G@
-(A ABBD < ' BBG A(G@
-(A ABBD < \' " [
- BBA A(GU
-(A ABBA < ' " t[
- BBA A(GU
-(A ABBA < ' " S[
- BBA A(GU
-(A ABBA < ( p" 2[
- BBA A(GU
-(A ABBA < \( `" [
- BBA A(GU
-(A ABBA < ( PÄ" Z
- BBA A(GU
-(A ABBA < ( @Ą" Z
- BBA A(GU
-(A ABBA < ) 0ń" Z
- BBA A(GU
-(A ABBA < \) Ƅ" Z
- BBA A(GU
-(A ABBA < ) DŽ" lZ
- BBA A(GU
-(A ABBA < ) Ȅ" KZ
- BBA A(GU
-(A ABBA < * Ȅ" *Z
- BBA A(GU
-(A ABBA < \* Ʉ" Z
- BBA A(GU
-(A ABBA T * ʄr Y
- BBB B(A0A8J
-8A0A(B BBBD D + ҄ BBB A(A0G@R
-0A(A BBBA + DR
-E A d , `ӄ BLB B(A0A8D@q
+(A ABBC < ' ~' K8 BBA A(JU
+(A ABBA < T' ~' .8 BBA A(JU
+(A ABBA < L( ~ BBG A(G@
+(A ABBG < ( ~ BBG A(G@
+(A ABBD < ( @~ BBG A(G@
+(A ABBD < T( ~" Q7 BBA A(GU
+(A ABBA < ( ~" 07 BBA A(GU
+(A ABBA < ( ~" 7 BBA A(GU
+(A ABBA < ) ~" 6 BBA A(GU
+(A ABBA < T) ~" 6 BBA A(GU
+(A ABBA < ) ~" 6 BBA A(GU
+(A ABBA < ) ~" 6 BBA A(GU
+(A ABBA < * p ~" j6 BBA A(GU
+(A ABBA < T* `!~" I6 BBA A(GU
+(A ABBA < * P"~" (6 BBA A(GU
+(A ABBA < * @#~" 6 BBA A(GU
+(A ABBA < + 0$~" 5 BBA A(GU
+(A ABBA < T+ %~" 5 BBA A(GU
+(A ABBA T + &~r 5 BBB B(A0A8J
+8A0A(B BBBD D , 8.~ BBB A(A0G@R
+0A(A BBBA , { DR
+E A d - .~ BLB B(A0A8D@q
8A0A(B BBBE @
-8A0A(B BBBF D + Ԅ +Y
- BBJ A(D0
-(A BBEA L , ք 'Y
- BLE B(A0D8D@
-8A0A(B BBBG L - ` BBB B(A0A8Gp8A0A(B BBB d- > DR
-E A d - ل BLB B(A0A8D@q
+8A0A(B BBBF D , /~ 4 BBJ A(D0
+(A BBEA L - `1~ 4 BLE B(A0D8D@
+8A0A(B BBBG L . 3~ BBB B(A0A8Gp8A0A(B BBB \. { DR
+E A d |. 5~ BLB B(A0A8D@q
8A0A(B BBBE @
-8A0A(B BBBF D 4- ۄ X
- BBJ A(D0
-(A BBEA L |- ۄ W
- BLE B(A0D8D@
-8A0A(B BBBG L - 0ބ& W
- BBB E(D0D8JV
-8A0A(B BBBC < . " Z
- BBA A(GU
-(A ABBA < \. " Y
- BBA A(GU
-(A ABBA < . " Y
- BBA A(GU
-(A ABBA < . " Y
- BBA A(GU
-(A ABBA < / p" Y
- BBA A(GU
-(A ABBA < \/ ` " nY
- BBA A(GU
-(A ABBA < / P
-" MY
- BBA A(GU
-(A ABBA < / @ " ,Y
- BBA A(GU
-(A ABBA < 0 0 " Y
- BBA A(GU
-(A ABBA < \0
" X
- BBA A(GU
-(A ABBA < 0 " X
- BBA A(GU
-(A ABBA < 0 " X
- BBA A(GU
-(A ABBA < 1 " X
- BBA A(GU
-(A ABBA T \1 b fX
- BBB B(D0D8D
-8D0A(B BBBG L 1 "3 +X
- BBH E(D0D8DPj
-8D0A(B BBBD L 2 #9 BBB B(A0D8D 8A0A(B BBB L 3 & BBB B(A0D8Dm8A0A(B BBB L \3 ) BBB B(A0D8Dm8A0A(B BBB L 2 W
- BBB B(A0D8J8A0A(B BBB L 3 + BBB B(A0D8Dm8A0A(B BBB L L4 H- BBB B(A0A8Gp8A0A(B BBB L 4 . BBB B(A0A8Gp8A0A(B BBB L 44 H0. SW
- BBB B(A0A8J q
-8A0A(B BBBA <5 uT KH T5 N l5 N 5 N 5 M 5 M 5 M 5 N L 5 ʦ BEE A(A0
-(A BMBE A(A BBB T 5 M. X
- BBE B(D0A8Gc
-8D0A(B BBBI T 5 S| X
- BFG B(D0A8G
-8A0A(B BBBE 6 V 7 V L t6 V
- W
- BBE E(D0A8Gx
-8D0A(B BBBA 4 6 8a@ W
- BAA G0d
- DABJ T 6 @b W
- BBB B(D0A8G
-8A0A(B BBBA 8 sT KH $8 \ AG RA D8 d| < \8 d BDA \
+8A0A(B BBBF D ,. X6~ 3 BBJ A(D0
+(A BBEA L t. 07~ 3 BLE B(A0D8D@
+8A0A(B BBBG L . p9~& 3 BBB E(D0D8JV
+8A0A(B BBBC < / _~" 5 BBA A(GU
+(A ABBA < T/ `~" 5 BBA A(GU
+(A ABBA < / a~" 5 BBA A(GU
+(A ABBA < / b~" l5 BBA A(GU
+(A ABBA < 0 c~" K5 BBA A(GU
+(A ABBA < T0 d~" *5 BBA A(GU
+(A ABBA < 0 e~" 5 BBA A(GU
+(A ABBA < 0 f~" 4 BBA A(GU
+(A ABBA < 1 pg~" 4 BBA A(GU
+(A ABBA < T1 `h~" 4 BBA A(GU
+(A ABBA < 1 Pi~" 4 BBA A(GU
+(A ABBA < 1 @j~" d4 BBA A(GU
+(A ABBA < 2 0k~" C4 BBA A(GU
+(A ABBA T T2 l~b "4 BBB B(D0D8D
+8D0A(B BBBG L 2 8~~3 3 BBH E(D0D8DPj
+8D0A(B BBBD L 3 (~9 BBB B(A0D8D 8A0A(B BBB L 4 ~ BBB B(A0D8Dm8A0A(B BBB L T4 X~ BBB B(A0D8Dm8A0A(B BBB L 3 c{ 2 BBB B(A0D8J8A0A(B BBB L 4 H~ BBB B(A0D8Dm8A0A(B BBB L D5 ~ BBB B(A0A8Gp8A0A(B BBB L 5 ~ BBB B(A0A8Gp8A0A(B BBB L ,5 ~. 3 BBB B(A0A8J q
+8A0A(B BBBA 46 |T KH L6 P~ d6 H~ |6 @~ 6 8~ 6 0~ 6 8~ 6 @~ L 6 { BEE A(A0
+(A BMBE A(A BBB T 6 ~. 3 BBE B(D0A8Gc
+8D0A(B BBBI T 6 Ю~| 3 BFG B(D0A8G
+8A0A(B BBBE 7 ~ 8 ~ L l7 ~ t3 BBB E(A0A8DP
+8A0A(B BBBA L 7 H~
+ (3 BBE E(D0A8Gx
+8D0A(B BBBA 8 Ƚ~ AO D ,8 Ƚ~] 3 BBB A(A0Gy
+0A(A BBBH ,9 |T KH D9 {\ AG RA d9 ~| < |9 ~ BDA \
ABJ i
-ABL L 8 eg BEB B(A0A8Dp\
-8A0A(B BBBI L 8 g BEB B(A0A8Dpw
-8A0A(B BBBF <9 8j T9 0j l9 (j 9 j 9 j L 9 j BBB B(A0A8D
-8A0A(B BBBA | : n BBE B(G0L8DPu
+ABL L 9 ~g BEB B(A0A8Dp\
+8A0A(B BBBI L : ~ BEB B(A0A8Dpw
+8A0A(B BBBF \: P~ t: H~ : @~ : 8~ : 0~ L : (~ BBB B(A0A8D
+8A0A(B BBBA | $; ~ BBE B(G0L8DPu
8A0A(B BBBG S
8E0A(B BBBG W
-8E0A(B BBBK L : p~ BOB B(A0A8Dp~
-8A0A(B BBBE T : Pt! T
- BEB E(A0D8G;
-8D0A(B BBBF < ,; (} BBG A(G@
-(A ABBB L l; }j BBE B(D0D8G`,
-8D0A(B BBBE ; AO 4 $; [ S
- BDA GP
- DABD < po. K^ ,< % , ; 0, fS
- AGG XCA , ; 00 :S
- ADG _CA ; 0$ S
- D_ < @ < 8 < 0 = ( , l< , R
- AGG XCA T= l= = r < x# R
- D^ = =
- $ 4= x Q
- AM@F
-AA < \= Q
- BBI A(DP
-(A ABBA L = p` Q
- BBB I(H0A8DP
-8A0A(B BBBH 4 = Q
- BID D0]
- AABA L > BBB E(A0A8DP<
-8A0A(B BBBA ,? x" DN
-N A L L? BRB E(A0A8D`
-8A0A(B BBBE 4 > 8 P
- BAD GPo
- CABE 4 ? P
- BAD GPo
- CABE @ k. K^ $@ <@ / D j T@ ? - O
- Hd @ A\ @ @ @ @ A $A <A x TA p @ x4 N
- Hg A A A A A B B ȏ 4B Џ LB ؏ HS lB ؏- L B AAG ]
+8E0A(B BBBK L ; 8~~ BOB B(A0A8Dp~
+8A0A(B BBBE T <; h~! '0 BEB E(A0D8G;
+8D0A(B BBBF < L< @~ BBG A(G@
+(A ABBB L < ~j BBE B(D0D8G`,
+8D0A(B BBBE < ~ AO 4 D< ~[ J/ BDA GP
+ DABD 4= H|. K^ L= 0~% , < H~, . AGG XCA , < H~0 . ADG _CA = H~$ . D_ = X~ = P~ > H~ ,> @~ , = 8~, . AGG XCA t> 8~ > 0~ > (~r > ~# - D^ > ~ > ~
+ $ T> ~ \- AM@F
+AA $ |> ~ 8- AM@N
+AC L > ~ - BBB I(H0A8DP
+8A0A(B BBBH 4 > ~ - BID D0]
+ AABA D ,? x~, - BBB I(A0DP
+0A(A BBBA L ,@ `~ BBB B(D0A8DPl
+8A0A(B BBBA |@ ~" DN
+N A L @ ~ BRB E(A0A8D`
+8A0A(B BBBE 4 4@ ~ 3, BAD GPo
+ CABE 4 l@ X~ + BAD GPo
+ CABE \A P|. K^ tA ~ A ~/ D j A ~ A ~- k+ Hd A ~ A\ A ~ B ~ ,B ~ DB ~ \B ~ tB ~ B ~ B ~ B ~4 o* Hg B ~ B ~ C ~ $C ~ <C ~ TC (~ lC 0~ C 8~ C @~ HS L C @~ AAG ]
CAF [
ACA R
-CAJ JCA , B b D\
+CAJ JCA , D p~b D\
H P
H Q
-G I , C `b D\
+G I , <D ~b D\
H P
H Q
-G I 4C - LC - < dC А BAA a
+G I lD ~- D ~- D ~- < D 8~ BAA a
ABH i
-ABL < B L
- BED A(Dpz
-(A ABBH < ,C p L
- BED A(Dpz
-(A ABBH $D @' Ae DD P' Ae C ` L
- AN < C PP K
- BEJ D(DPn(C ABB L D ` K
- BEE E(A0A8D
-8A0A(B BBBG L \D kK
- BBB B(D0A8Gq
-8D0A(B BBBA L dE BBB B(A0A8D
-8A0A(B BBBA L E BBB B(A0A8D
-8A0A(B BBBA T LE К\ J
- BBE E(D0A8G
-8D0A(B BBBF $ E ؟ J
- AFD@ $ F @V ADD JAAL F xW BBB E(A0A8DP
-8A0A(B BBBA L F BBB B(A0A8D
-8A0A(B BBBA L LG ئ BBB B(A0A8Gp8A0A(B BBB 4 F X tI
- BAD D0
- AABA L G CI
- BEB E(I0H8DP
-8D0A(B BBBD L lG I
- BEB E(I0H8DP
-8D0A(B BBBD \ tH 0 BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 H @ BAD D0
- AABA D I B BBE A(A0D@m
-0A(A BBBA < H ^ H
- BBA D(G0
-(A ABBA 4 H G
- BFD D0I
- DABA D I 8 G
- BBB D(A0G@:
-0A(A BBBA L \I ಅ G
- BBB B(A0A8GP0
-8A0A(B BBBA dJ
|J ha. K^ D J " BBB A(A0G@m
-0A(A BBBA T $J 赅 G
- BBE B(A0A8D
-8A0A(B BBBA L 4K ` BBB B(A0A8Gp8A0A(B BBB L K 0 BBE B(A0A8D`
-8A0A(B BBBA D K +F
- BBJ A(D0G@q
-0D(A BBBA T dK
5 KF
- BEB B(A0A8G
+ABL < <D ~ ;( BED A(Dpz
+(A ABBH < |D ~ ' BED A(Dpz
+(A ABBH tE ~' Ae E ~* Ah D ~ ' AN < E ~P g' BEJ D(DPn(C ABB L \E ~ +' BEE E(A0A8D
+8A0A(B BBBG L E x~ & BBB B(D0A8Gq
+8D0A(B BBBA L F ~ BBB B(A0A8D
+8A0A(B BBBA L G ~ BBB B(A0A8D
+8A0A(B BBBA T F H~\ 6& BBE E(D0A8G
+8D0A(B BBBF $ F P~ & AFD@ $ G ~V ADD JAAL G ~W BBB E(A0A8DP
+8A0A(B BBBA L LH ~ BBB B(A0A8D
+8A0A(B BBBA L H P BBB B(A0A8Gp8A0A(B BBB , H 6 AAG0
+AAA 4 dH $ BAD D0
+ AABA L H $ BEB E(I0H8DP
+8D0A(B BBBD L H (
+ _$ BEB E(I0H8DP
+8D0A(B BBBD \ I BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 TJ @ BAD D0
+ AABA D J
B BBE A(A0D@m
+0A(A BBBA < J ^ W# BBA D(G0
+(A ABBA 4 \J 8 ?# BFD D0I
+ DABA D J /# BBB D(A0G@:
+0A(A BBBA L J h '# BBB B(A0A8GP0
+8A0A(B BBBA K
K |. K^ D L " BBB A(A0G@m
+0A(A BBBA T K p c" BBE B(A0A8D
+8A0A(B BBBA L L ( BBB B(A0A8Gp8A0A(B BBB L M BBE B(A0A8D`
+8A0A(B BBBA D L h o! BBJ A(D0G@q
+0D(A BBBA T L 5 ! BEB B(A0A8G
-8A0A(B BBBH L K ̅- F
- BLB B(D0A8DP
-8A0A(B BBBF T L ΅ F
- BBB B(D0A8G
-8A0A(B BBBD M H 4M @ LM 8 dM @ |M H+ Hb M X2 He M x M p $ M hg AG k
-AA $ N o AG s
-AA 4 M GF
- BAA G@I
- AABA tN p N h N ` , N h" E
- AAG0i
-AAD N (]T KH O P
O H
4O @ L LO 8 AAG ]
-CAF [
+8A0A(B BBBH L <M +- " BLB B(D0A8DP
+8A0A(B BBBF T M -T ! BBB B(D0A8G
+8A0A(B BBBD N C N C N C N C N C+ Hb O C2 He <O C TO C $ lO Cg AG k
+AA $ O Do AG s
+AA 4 O PD ! BAA G@I
+ AABA O D P D $P D , O D" ! AAG0i
+AAD lP |T KH P E
P E
P E P E P E P E < \P xE _ BEA A(MP^
+(A ABBF $ P G ; AD@t
+AF 4 |Q G BDA r
+ABL y
+ABA Q |T KH Q H Q HF Q PH R HHF L ,R H AAG ]
+CAF k
ACA R
-CAJ JCA , O hb D\
+CAJ JCA |R {- AG cA , R Hb D\
H P
H Q
-G I $ O ] AID IAD O U AS P ,P DP < O SD
- BEA A(MP^
-(A ABBF T O w /D
- BBB B(A0D8GH
-8A0A(B BBBJ 4 P BDA r
-ABL y
-ABA ,Q H[T KH DQ \Q F tQ @ Q 8F Q - AG cA $ Q Pg AG k
-AA $ Q g AG k
-AA $ R o AG s
-AA $ <R (o AG s
-AA dR p9 dT L Q T kB
- BBB B(A0A8GZ
-8D0A(B BBBH T R CB
- BBB B(A0A8G
-8A0A(B BBBF T lR p
B
- BBB B(A0A8G
-8A0A(B BBBF T R 8 A
- BBB B(A0A8G
-8A0A(B BBBF < S A
- AAGPQ
+G I $ R HZ AID FAD R IU AS $ S XIg AG k
+AA $ <S Ig AG k
+AA $ dS Io AG s
+AA $ S 0Jo AG s
+AA S xJ9 dT L S JT BBB B(A0A8GZ
+8D0A(B BBBH T dS M BBB B(A0A8G
+8A0A(B BBBF T S xQ i BBB B(A0A8G
+8A0A(B BBBF T T @U 3 BBB B(A0A8G
+8A0A(B BBBF < lT Y AAGPQ
AAD O
-AAG T \S A
- BBB B(A0A8G
-8A0A(B BBBF lT N D T BBE A(A0D@l
-0A(A BBBA < T ( A
- BBD A(G0T
-(A ABBA d U BBB B(A0D8D`~
-8A0A(B BBBG t
-8A0A(B BBBB L tU P
BBE E(A0A8DP
-8D0A(B BBBH , U t W@
- AID {
-DAA T <U v C@
- BIE E(D0A8D
-8A0A(B BBBA 4 U H @
- BFD D0
- DABA , V ADD Z
-AAA V VT KH V h V p 4 DV x5 ?
- ADG
+AAG T T Z BBB B(A0A8G
+8A0A(B BBBF U ^ N D U ^ BBE A(A0D@l
+0A(A BBBA < dU 0_ c BBD A(G0T
+(A ABBA d \V `j BBB B(A0D8D`^
+8A0A(B BBBE
+8A0A(B BBBA L V d BBE E(A0A8DP
+8D0A(B BBBH T \V hg BBB B(A0A8Ja
+8D0A(B BBBF , V 0mt AID {
+DAA T V mv BIE E(D0A8D
+8A0A(B BBBA 4 <W o g BFD D0
+ DABA , ,X pp ADD Z
+AAA \X p|T KH tX p X p 4 W p5 ADG
-AAA , |V " qC
- ADGv
-AAA , V #G nC
- ADGe
-AAA , V #G NC
- ADGe
-AAA , W @#G .C
- ADGe
-AAA 4 <W `#u C
- ADGO
-AAA 4 tW ' ,D
- ADG
-AAA , W 0+ E
- ADG
-AAA 4 W ,u ;E
- ADGO
-AAA 4 X X0 bF
- ADG&
-AAA 4 LX 2 G
- ADG
-AAA 4 X 8<f zI
- ADG
-AAA 4 X p> J
- ADGz
-AAA 4 X Jf BM
- ADG
-AAA , ,Y MG M
- ADGe
-AAA T \Y 0M-
- M
- BIB B(A0A8D
-8A0A(B BBBF < lZ W BEE A(D@M
-(A ABBA L Y hW N
- BEB E(I0H8D@
-8D0A(B BBBD L DZ X: M
- BJE B(A0A8D@
-8A0A(B BBBA L L[ Y BBE B(A0A8D`
-8A0A(B BBBA L Z Z wM
- BEB E(I0H8D@
-8D0A(B BBBD L 4[ (\ OM
- BJE B(A0A8D`8A0A(B BBB D [ \n M
- BBB A(C0K@
-0A(A BBBA l [ _ L
- BBE B(D0I8D&
+AAA , $X ADGv
+AAA , TX `G ADGe
+AAA , X G ADGe
+AAA , X G z ADGe
+AAA 4 X u Z ADGO
+AAA 4 Y x ADG
+AAA , TY j ADG
+AAA 4 Y p5 ADG4
+AAA 4 Y x ! ADG&
+AAA 4 Y ?" ADG
+AAA 4 ,Z Xf $ ADG
+AAA 4 dZ A% ADG_
+AAA 4 Z f o( ADG
+AAA , Z G ( ADGe
+AAA T [ @ ( BIB B(A0A8D
+8A0A(B BBBH < \ BEE A(D@M
+(A ABBA L [ +) BEB E(I0H8D@
+8D0A(B BBBD L [ : ) BJE B(A0A8D@
+8A0A(B BBBA L \ BBE B(A0A8D`
+8A0A(B BBBA L \ X ( BEB E(I0H8D@
+8D0A(B BBBD L \ ظ c( BJE B(A0A8D`8A0A(B BBB D ,] hn ( BBB A(C0K@
+0A(A BBBA l t] ( BBE B(D0I8D&
8D0A(B BBBA }
-8D0A(B BBBA L <\ Pb L
- BBH E(D0D8D`
-8D0A(B BBBF T \ h L
- BEB B(A0A8Jq
-8A0A(B BBBA T \ 8q
L
- BBE B(D0D8J
-8D0A(B BBBJ T <] ~g ?M
- BBB E(D0I8D
-8D0A(B BBBA T ] 耆
- L
- BBB B(D0A8J
-8A0A(B BBBA < ] @ M
- ACG`I
+8D0A(B BBBA L ] ' BBH E(D0D8D`
+8D0A(B BBBF T 4^ P ' BEB B(A0A8Jq
+8A0A(B BBBA T ^ ' BBE B(D0D8J
+8D0A(B BBBJ T ^ g S( BBB E(D0I8D
+8D0A(B BBBA T <_
+ ( BBB B(D0A8J
+8A0A(B BBBA < _ (( ACG`I
AAJ
-AAG ^ Ѝ ^ ȍ _ ,_ ȍ D_ Ѝ \_ ؍ t_ 0 An _ > A| _ - L _ ( AAG ]
+AAG ` ` ` ` ` a a 4a 0 An Ta > A| L ta AAG ]
CAF k
ACA R
-CAJ JCA L ` h AAG ]
+CAJ JCA L a ( AAG ]
CAF k
ACA R
-CAJ JCA l` } DN
-E A ` - AG cA ` - AG cA L ` AAG ]
+CAJ JCA b { DN
+E A 4b H- AG cA Tb X- AG cA L tb h AAG ]
CAF [
ACA R
-CAJ JCA L a ؎ AAG ]
+CAJ JCA L b AAG ]
CAF c
CAI R
-CAJ JCA , la b D\
+CAJ JCA , c b D\
H P
H Q
-G I L a X AAG ]
+G I L Dc AAG ]
CAF [
ACA R
-CAJ JCA a [ L La Џr I
- BBB B(E0A8J@
-8A0A(B BBBA < a I
- BBA A(D@(A ABB b U AS b - L b ;I
- BEE E(A0D8G
-8D0A(B BBBC c < |b P +I
- AAG0M
+CAJ JCA c H[ L b r $ BBB B(E0A8J@
+8A0A(B BBBA < Dc $ BBA A(D@(A ABB <d `- Td xU AS td - L c 7$ BEE E(A0D8G
+8D0A(B BBBC < $d p ?$ AAG0M
DAE K
-DAA < b p H
- BED A(G@|
-(A ABBA D b И H
- BAA G0c
+DAA < dd p $ BED A(G@|
+(A ABBA D d # BAA G0c
DABK k
- DABA D c XS BBB D(A0D@
-0A(A BBBA L c pf NH
- BBB B(A0D8GP^
-8A0A(B BBBD L c 2 H
- BBB B(A0A8GP
-8A0A(B BBBB L ,d G
- BBB B(A0A8GPd
-8A0A(B BBBA 4e Le 8{ de |e ( e s L e BBB B(A0D8MP
-8A0A(B BBBH 4 De F
- BFA K0
- AABA 4f p Lf h AS lf h- AG cA L e x OF
- BFB E(A0A8GP
-8A0A(B BBBH f Ȥ2 Al $ f 褆_ AAG LDAD lf E
- BEE A(C0G@n
-0A(A BBBC L f 襆 E
- BBB H(D0M2
-0D(A BBBD L g E
- BBB H(D0M2
-0D(A BBBD L Tg h
- E
- BBB H(D0M2
-0D(A BBBD L g (
- E
- BBB H(D0M2
-0D(A BBBD 4 g 謆 oE
- SGB A(A0D \ ,h 9 OE
- BBB A(A0J
+ DABA 4 e x BEA A(D@(A ABBL $e 0f r# BBB B(A0D8GP^
+8A0A(B BBBD L te P2 6# BBB B(A0A8GP
+8A0A(B BBBB L e @ " BBB B(A0A8GPd
+8A0A(B BBBA f f f g s L ,g p BBB B(A0D8MPD
+8A0A(B BBBH 4 f @
" BFA K0
+ AABA g g AS g - AG cA L Tg ! BFB E(A0A8GP
+8A0A(B BBBH \h ` 2 Al $ |h _ AAG LDAD g '! BEE A(C0G@n
+0A(A BBBC T 4h
BBA D(Gl
+(A ABBF 8
+(A ABBA Di 8 DW L h @ BBB H(D0M2
+0D(A BBBD L h BBB H(D0M2
+0D(A BBBD L Di
+ BBB H(D0M2
+0D(A BBBD L i
+ BBB H(D0M2
+0D(A BBBD 4 i @
+ l SGB A(A0D \ j 9 L BBB A(A0J
0A(A BBBE s
-0A(A BBBE D h &E
- BBB A(A0J
-0A(A BBBA < h 貆
-E
- BBA D(Jz
-(D ABBJ d i BEB B(A0A8D@{
+0A(A BBBE D |j
# BBB A(A0J
+0A(A BBBA < j @ BBA D(Jz
+(D ABBJ d k BEB B(A0A8D@{
8A0A(B BBBJ D
-8A0A(B BBBA < |i % D
- BEA D(G
-(D ABBA L i D
- BEE E(A0C8J
-8A0A(B BBBB d j D
- BEB B(A0A8D@
+8A0A(B BBBA < lk x% BEA D(G
+(D ABBA L k h BEE E(A0C8J
+8A0A(B BBBC d k BEB B(A0A8D@
8A0A(B BBBJ D
-8A0A(B BBBA < tj 8Z /D
- BDA z
+8A0A(B BBBA < dl Z + BDA z
ABL A
-ABA L j X C
- BEB B(A0A8GP
-8A0A(B HBBH k B k PB L k > BBB B(A0D8MPd
-8A0A(B BBBA <l xB D Tl ; BDA G0
- AABF N
- AABF l Æc L l ĆM BBA G(J0
-(G ABBF D(C ABB d m Ɔ BBB E(A0A8DPs
-8C0A(B BBBH
-8F0A(B BBBA d lm XȆ BBB B(D0A8DP
-8A0A(B BBBH 8F0A(B BBB L m ʆ- BBB B(A0D8MP
-8A0A(B BBBH < $n Άm BBB A(G0E
-(A BBBA < dn Ԇ BBB A(G05
-(A BBBA L n ؆C BBB B(D0A8MP|
-8A0A(B BBBH < n ۆ BBA G(J0
-(A ABBD < |n `܆ F@
- BBD D(J@F
-(D ABBA \ n ܆G @
- BBB D(D0Du
+ABA L l BEB B(A0A8GP
+8A0A(B HBBH m `B m B L m > BBB B(A0D8MPd
+8A0A(B BBBA ,n B d m e " BBB E(D0A8G`
+8A0A(B BBBG N
+8A0A(B BBBH n "c < n X" BBB D(G0
+(A EBBE d o (% BBB B(D0A8DP
+8A0A(B BBBH 8F0A(B BBB L lo & BBB B(A0D8MP
+8A0A(B BBBH < o - BBB A(G0e
+(A BBBA < o 2 BBB A(G05
+(A BBBA L <p P7C BBB B(D0A8MP|
+8A0A(B BBBH < p P: BBA G(J0
+(A ABBD < p 0; BBD D(J@F
+(D ABBA \ Tp ;G { BBB D(D0Du
0D(A BBBF K
-0D(A BBBA T o ކ ?
- BBB B(D0A8J
-8D0A(B BBBA T to + ?
- BEL B(A0D8G
-8D0A(B BBBD L o . ?
- BBB B(D0A8D`
-8D0A(B BBBE d p BEB B(A0A8D@{
+0D(A BBBA T p p=+ V BEL B(A0D8G
+8D0A(B BBBD L q H@. ? BBB B(D0A8D`
+8D0A(B BBBE d r (C BEB B(A0A8D@{
8A0A(B BBBJ D
-8A0A(B BBBA 4 <q 8Z BDA y
+8A0A(B BBBA 4 |r CZ BDA y
ABM A
-ABA , tq `Y BDA U
-AEF L p
>
- BEE A(A0G
-0A(A BBBA q P < r H BEB D(A0M
-(A BBBF 4 Lr Z BDA y
+ABA , r CY BDA U
+AEF L ,r D
g BEE A(A0G
+0A(A BBBA 4s E < Ls E BEB D(A0M
+(A BBBF 4 s PGZ BDA y
ABM A
-ABA 4 r u BEA A(D@a(A ABB$ r 8f AAK0VAAD ,r =
- BGB A(A0K@
-0A(A BBBA < ,s O BBD A(D@;(A ABB $ ls (i AAG0]AA4 s p BFD A(D@(A ABBd s ; BLB B(A0A8D`q
+ABA 4 s xGu BEA A(D@a(A ABB$ s Gf AAK0VAAD ls H L BGB A(A0K@
+0A(A BBBA $ lt Ii AAG0]AA4 t I BFD A(D@(A ABBd t J; BLB B(A0A8D`q
8A0A(B BBBE S
-8A0A(B BBBK L |s <
- BBE B(H0D8M
-8D0A(B BBBH L t @M BBB B(A0D8Dp18A0A(B BBB L t @ BBB B(A0A8Gp8A0A(B BBB L $u P BBL B(A0A8DP
-8A0A(B BBBA L tu X BBB L(A0A8DP
-8A0A(B BBBA T u ;
- BBE J(A0A8Gi
-8D0A(B BBBF L du H ;
- BJB A(A0W
-(A BBDH ^(A BBBT u :
- BBB B(A0A8G
-8A0A(B BBBA L v :
- BJB A(A0W
-(A BBDH ^(A BBB4 w ` u BEA A(D@a(A ABB Lw AL lw A AK sA w I AK {A $ w V ADD JAA, w @T 9
- BGA FAB , x pv BDA kAB $ 4x V ADD JAAL \x ) BEB B(A0A8D`
-8A0A(B BBBA < x BHB D(A0(A BBB 4 x X BEA A(D@o(A ABB< $y BEE A(D0(A BBB 4 dy 0 BEA D(D0(A ABBD x 7
- BBB A(D0D@
-0A(A BBBA y p" DN
-N A D z BBB B(A0A8D`8A0A(B BBBL y H '7
- BBE E(A0I8Dp>
-8D0A(B BBBI , y e 7
- AID l
-DAA D z {7
- BBB D(A0&
-(A BBBA D \z g7
- BJB A(A0Gp
-0A(A BBBA < \{ BEB A(A0
-(A BBBA 4 { h BEA A(D@|(A ABB< { BEA A(D0o
-(A ABBA L | ` BBB B(A0A8Gp8A0A(B BBB , { z 06
- AAG c
-AAB 4 | 0 BEA A(D@o(A ABBL | BBB B(A0D8D`O
-8A0A(B BBBF T d| |5
- BBD D(D@%
+8A0A(B BBBK L |t XL \ BBE B(H0D8M
+8D0A(B BBBH L u NM BBB B(A0D8Dp18A0A(B BBB L u P BBB B(A0A8Gp8A0A(B BBB L $v 8RP BBL B(A0A8DP
+8A0A(B BBBA L tv 8SX BBB L(A0A8DP
+8A0A(B BBBA T v HT BBE J(A0A8Gi
+8D0A(B BBBF L dv W BJB A(A0W
+(A BBDH ^(A BBBT v X BBB B(A0A8G
+8A0A(B BBBA 4 w [u BEA A(D@a(A ABB w [ AL x [A AK sA <x \I AK {A 4 \x 0\u BEA A(D@a(A ABB4 x x\ BBH A(D@(A ABBT x @]K t BEE B(D0A8G
+8D0A(B BBBE < $y 8_ BBG A(G@
+(A ABBG L x _ BEE E(D0A8G
+8A0A(B BBBF $ y XcV ADD JAA, $y cT BGA FAB , z cv BDA kAB $ <z dV ADD JAAL dz Hd) BEB B(A0A8D`
+8A0A(B BBBA < z (e BHB D(A0(A BBB 4 z e BEA A(D@o(A ABB< ,{ f BEE A(D0(A BBB 4 l{ f BEA D(D0(A ABB, z fe AID l
+DAA D { 8g BBB D(A0&
+(A BBBA D d{ h BJB A(A0Gp
+0A(A BBBA < d| Xi BEB A(A0
+(A BBBA 4 | i BEA A(D@|(A ABB< | j BEA A(D0o
+(A ABBA L } j BBB B(A0A8Gp8A0A(B BBB , | @lz AAG c
+AAB 4 } lu BEA A(D@a(A ABB4 } l BEA A(D@o(A ABBL ~ 0m BBB B(A0D8D`O
+8A0A(B BBBF T } n BBD D(D@%
(D ABBB ]
-(D ABBJ < | z C5
- BBG A(G`k
-(D ABBI , } v BAA i
-ABA 4 } @ ~ BEA A(D@j(A ABBL ~ BEB A(D0
+(D ABBJ < } Xpz g BBG A(G`k
+(D ABBI , ~ qv BAA i
+ABA 4 $ q~ BEA A(D@j(A ABBL \ 0r BEB A(D0
(A BBBB O
-(A BBBJ , l~ H ADD ]
-DAE < ~ BBD I(D0m
-(D ABBE < ~ BBG A(G@
-(A ABBG L d~ 3
- BEE E(D0A8G
-8A0A(B BBBF L ~ 8$ 3
- BEB E(I0H8DP
-8D0A(B BBBD 4 % s3
- BFD D0
- DABA < & BBG A(G@
-(A ABBG \ 4 P' BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBT ' 2
- BGB D(A0z
-(A BBDE H
-(A BBGA L 4 ((a 2
- BBB B(A0D8Kp
-8A0A(B BBBA \ < H) BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBL ) 1
- BBL B(D0A8G
-8A0A(B BBBA L 4 (6s w2
- BBB E(D0D8J
+(A BBBJ , r ADD ]
+DAE < `s BBD I(D0m
+(D ABBE L d s BEB E(I0H8DP
+8D0A(B BBBD d Pu, BGE E(A0A8D@
+8F0A(B BBBI g
+8F0A(B BBBA 4 v BFD D0
+ DABA < v BBG A(G@
+(A ABBG \ L w BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBL wa BBB B(A0D8Kp
+8A0A(B BBBA $ yV ADD JAAD l Hy(
BBB A(D0D@
+0A(A BBBE l 0}" DN
+N A D @} BBB B(A0A8D`8A0A(B BBBL ~|
BBE E(A0I8Dp
+8D0A(B BBBJ \ $ 8 BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBT ̂ S
BBE B(D0A8G
+8D0A(B BBBK \ ܃ BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBL I
s BBB B(D0A8G
+8A0A(B BBBA L ԃ s
BBB E(D0D8J
-8D0A(B BBBI D < XA BBB B(A0G8G`c8D0A(B BBB< ́ A 2
- BLD D(G0
-(A ABBE 4 `B= 2
- BFD D0
- DABF < D hC {2
- BED I(K@`
-(D ABBH T D [2
- BBB B(A0D8J
-8A0A(B BBBG < ܂ @G #2
- BLD D(G0
-(A ABBE < H 2
- BED I(KPc
-(D ABBE d \ H 1
- BJE B(A0A8D@u
-8F0A(B BBBC ^8F0A(B BBB L | (I BBB E(A0A8G
-8A0A(B BBBF ̄ L AL T 4 L= 1
- BEB E(A0D8J 6
-8D0A(B BBBH T S w1
- BHE E(A0A8G
-8D0A(B BBBG T Uw c1
- BBD A(GpO
+8D0A(B BBBI D ܄ BBB B(A0G8G`c8D0A(B BBB< l X ?
BLD D(G0
+(A ABBE 4 = #
BFD D0
+ DABF <
BED I(K@`
+(D ABBH T $ Ш BBB B(A0D8J
+8A0A(B BBBG < | BLD D(G0
+(A ABBE < BED I(KPc
+(D ABBE d x BJE B(A0A8D@u
+8F0A(B BBBC ^8F0A(B BBB L ୀ BBB E(A0A8G
+8A0A(B BBBF l p AL T Ԇ `= BEB E(A0D8J 6
+8D0A(B BBBH T , H BHE E(A0A8G
+8D0A(B BBBG T w BBD A(GpO
(D ABBH
-(D ABBA W1 AK cA L \ @W 1
- BEB E(I0H8D@
-8D0A(B BBBD L X 0
- BJE B(A0A8D`8A0A(B BBB T PY> 0
- BFE E(D0D8G
-8D0A(B BBBJ T T 8] 0
- BBB B(D0D8GT
-8D0A(B BBBH D d b BBE A(A0D@`
-0A(A BBBA L 8c o0
- BEB E(I0H8D@
-8D0A(B BBBD L D d* G0
- BJE B(A0A8D@
-8A0A(B BBBA L eW 0
- BEB B(A0D8Oe
-8D0A(B BBBG 4 qh BEA A(G@Q(A ABB\ Ԉ q BBD A(D@V
+(D ABBA ػ1 AK cA L BEB E(I0H8D@
+8D0A(B BBBD L L x BJE B(A0A8D`8A0A(B BBB T > < BFE E(D0D8G
+8D0A(B BBBJ T BBB B(D0D8GT
+8D0A(B BBBH D hǀ BBE A(A0D@`
+0A(A BBBA L ǀ BEB E(I0H8D@
+8D0A(B BBBD L pɀ*
+ BJE B(A0A8D@
+8A0A(B BBBA L 4 Pʀ'
+ BBE B(I0D8Gw
+8A0A(B BBBH 4 < 0׀h BEA A(G@Q(A ABB\ t h׀ BBD A(D@V
(A ABBG Y
(A ABBA a
-(A ABBI T | @r 0
- BBE B(A0D8J
-8A0A(B BBBG #. K^ 腇% HX ܉ - H` % HX - H` T (
- F0
- BBB J(D0A8K
-8A0A(B BBBA T ܉ 0
- BJB B(D0D8N
-8A0A(B BBBA T 4 ( 0
- BBB B(D0A8N
-8A0A(B BBBA T W1
- BBB B(A0D8G
-8A0A(B BBBD ( AL < ( 1
- BEA D(G
-(D ABBD جz AD sAL d 8 0
- BBE B(H0G8J!
-8D0A(B BBBG 4 1
- BDD L@]
- DABA L 1
- BBE L(D0A8D
-8D0A(B BBBI 4 < p 1
- BDA LPz
- DABA T t Ç p1
- BEB L(D0A8Gi
-8D0A(B BBBI T ̌ Ň
- S1
- BEB B(D0A8G
-8D0A(B BBBE L $ Ї G1
- BBB B(A0D8J%
-8D0A(B BBBD , D \ x t p
h p x Ԏ % HX $ - H` D % HX d - H` % HX - H` ď % HX - H` - , (0 AF
FK
- D $ L (^ AG b
-AA L ` [0
- BVB D(A0D3
-0D(A BBBG T 50
- BGB B(A0D8G
-8D0A(B BBBH L d x` >0
- BLB E(A0A8Gp
-8D0A(B BBBA L ` 0
- BLB E(A0A8Gp
-8D0A(B BBBA L ` /
- BLB E(A0A8Gp
-8D0A(B BBBA , ADD0e
-AAA , < AAG0M
-AAH 4 W/
- BAA G0c
- AABF T #/
- BBE E(A0A8G
-8D0A(B BBBA L D XE .
- BBB B(A0A8G
-8E0A(B BBBF , X\ .
- ADG l
-AAA T Ē .
- AAG@r
+(A ABBI T ׀o
+ BIB B(A0D8J
+8A0A(B BBBD , v|. K^ D \ % HX | - H` % HX - H` T $
+
+ BBB J(D0A8K
+8A0A(B BBBA T | X . BJB B(D0D8N
+8A0A(B BBBA T Ԍ BBB B(D0A8N
+8A0A(B BBBA T , BBB B(A0D8G
+8A0A(B BBBD < AL < BEA D(G
+(D ABBD z AD sAL g BBE B(H0G8J!
+8D0A(B BBBG 4 T * BDD L@]
+ DABA L h+ i BBE L(D0A8D
+8D0A(B BBBI 4 , = BDA LPz
+ DABA L p- BEB L(D0A8G
+8D0A(B BBBG T d /
+ BEB B(D0A8G
+8D0A(B BBBE L H:; BBB B(A0D8J)
+8D0A(B BBBD Đ 8R ܐ 0R (R R
$ R < R T (R l 0R 8R @R% HX PR- H` ܑ `R% HX pR- H` R% HX < R- H` \ R% HX | R- H` R- , R0 AF
FK
+ D $ S^ AG b
+AA L T T 7 BVB D(A0D3
+0D(A BBBG T U BGB B(A0D8G
+8D0A(B BBBH L (Z` BLB E(A0A8Gp
+8D0A(B BBBA L L 8[`
+ BLB E(A0A8Gp
+8D0A(B BBBA L H\`
+ BLB E(A0A8Gp
+8D0A(B BBBA , X] ADD0e
+AAA , Ԕ ] AAG0L
+AAI 4 L x^ 3
+ BAA G0c
+ AABF T _ BBE E(A0A8G
+8D0A(B BBBA L ܔ 8aE BBB B(A0A8G
+8E0A(B BBBF , , 8d\ ADG l
+AAA T \ hd AAG@r
AAC G
AAG y
AAE ]
-AAA L = .
- BBB B(D0A8DP
-8D0A(B BBBA T l 4 A.
- BBE E(K0A8G
-8D0A(B BBBG < ē 5 T.
- NAG JH
-CAA < 5 P.
- KAG MH
-CAA t D L.
- BBB A(A0De
+AAA L e= ^ BBB B(D0A8DP
+8D0A(B BBBA T f4 BBE E(K0A8G
+8D0A(B BBBG < \ l5 0 NAG JH
+CAA < o5 , KAG MH
+CAA t ܖ r ( BBB A(A0De
0A(A BBBG H
0A(A BBBH (
-0A(A BBFA t L Ԕ -
- BBB B(A0A8Gi
-8A0A(B BBBD L $ h
- -
- BBB B(D0A8GA{
-8A0A(B BBBG < , *7 BBA A(G0\
-(A ABBA d l + BBB E(D0D8Gp
+0A(A BBFA t L l t BBB B(A0A8Gi
+8A0A(B BBBD L Hwc BBB B(D0A8GA{
+8A0A(B BBBG < Ę h7 BBA A(G0\
+(A ABBA d h BBB E(D0D8Gp
8A0A(B BBBD f
-8A0A(B BBBA Ԗ `.y \ \d .T BIB I(D0H8G
+8A0A(B BBBA l y \ \d T BIB I(D0H8G
8A0A(B BBBJ Q
-8A0A(B BBGH d T 0 BBE F(A0H8GP
+8A0A(B BBGH d BBE F(A0H8GP
8C0A(B BBBD
-8F0A(B BBBE d x2@ 9.
- BIB B(A0E8DP
+8F0A(B BBBE d ȟ@ BIB B(A0E8DP
8A0A(B BBBJ \
-8F0A(B BBBE < $ P; BBG A(G@
-(A ABBG d < $ ė <b -
- AY
-F A L P<E d-
- BBB B(A0A8D
-8D0A(B BBBF < PA BBA A(G@d
-(A ABBI | B $-
- BFB A(E0G@
+8F0A(B BBBE < BBG A(G@
+(A ABBG ` $ \ Xb j AY
+F A L % F BBB B(A0A8Dp
+8D0A(B BBBF < BBA A(G@d
+(A ABBI P BFB A(E0G@
0F(A BBBH y
0A(A BBBG
0F(A BBBD
-0F(A BBBD d ę E BBB A(A0
+0F(A BBBD d \ ు BBB A(A0
(A BBBA
(A BBBE N
-(A BBBE , t HC 5,
- AID \
-DAA L H ,
- BBB B(A0D8DP
-8A0A(B BBBI < XJ BBG A(G@
-(A ABBG L K= BBB H(A0D8DP
-8A0A(B BBBE L < L= BBB H(A0D8DP
-8A0A(B BBBE , L AG ]
+(A BBBE , C AID \
+DAA L < ( BBB B(A0D8DP
+8A0A(B BBBI < D BBG A(G@
+(A ABBG L H= BBB H(A0D8DP
+8A0A(B BBBE L ԝ 8= BBB H(A0D8DP
+8A0A(B BBBE , $ ( AG ]
AJ D
-AK L M@ BBB H(A0D8D@
-8A0A(B BBBJ L N= BBB H(A0D8DP
-8A0A(B BBBE L O 7*
- BBE B(A0D8D@
-8A0A(B BBBI L HQ= BBB H(A0D8DP
-8A0A(B BBBE L 8RM BBB H(A0D8DP
-8A0A(B BBBE L 8S5 D `S W)
- BAC GPF
+AK L T @ BBB H(A0D8D@
+8A0A(B BBBJ L = BBB H(A0D8DP
+8A0A(B BBBE L < BBE B(A0D8D@
+8A0A(B BBBI L D x= BBB H(A0D8DP
+8A0A(B BBBE L hM BBB H(A0D8DP
+8A0A(B BBBE h5 D D BAC GPF
EABE
- AABJ < U #)
- AAGPF
+ AABJ < AAGPF
EAK
-AAB D 4 (X (
- BAA GPI
+AAB D ̟ Xā BAA GPI
ECBB X
- CABB D | Z (
- BAC GPF
+ CABB D Ɓ BAC GPF
EABE
- AABG d | (]E BBB B(A0A8DP
+ AABG d XɁE BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l , ^ 3(
- BBB J(D0D8D0
+8F0A(B BBBA l Ġ @ʁ BBB J(D0D8D0
8D0A(B BBBG w
-8A0A(B BBBG L T a' BFB E(A0A8Dp
-8F0A(B BBBH T pb5 '
- BBB B(A0D8J
-8C0A(B BBBH , D X{ k(
- AID0H
-DAA D t {l _(
- BBE A(D0K@
-0D(A BBBA | S(
- BBB B(D0A8D@Q
+8A0A(B BBBG L ́' BFB E(A0A8Dp
+8F0A(B BBBH T 5 [ BBB B(A0D8J
+8C0A(B BBBH , ܡ 3 AID0H
+DAA D l ' BBE A(D0K@
+0D(A BBBA T 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 ģ > AAG0}
FAC
-FAA T '
- BBB B(A0A8O
-8F0A(B BBBE 4 BAA G`{
- FABA T < :'
- BBB B(A0D8J
-8A0A(B BBBD T h `(
- BBE E(A0A8J
-8A0A(B BBBK T o(
- BBB B(A0D8J
2
-8D0A(B BBBJ h
-. K^ n;\ AG RAT | `F (
- BKD D(Dp
+FAA T D o BBB B(A0A8O
+8F0A(B BBBE 4 T BAA G`{
+ FABA T ԣ BBB B(A0D8J
+8A0A(B BBBD T , ( BBE E(A0A8J
+8A0A(B BBBK T P1 7 BBB B(A0D8J
2
+8D0A(B BBBJ \|. K^ {\ AG RAT F [ BKD D(Dp
(D ABBD Q
-(D ABBF
-. K^ @
l 8 5(
- BBB K(A0A8Dg
-8A0A(B BBBH
-8D0A(B BBBE L t '
- BEB E(I0H8DP
-8D0A(B BBBD L ģ 8 '
- BJE B(A0A8D@
-8A0A(B BBBA L '
- BEB E(I0H8D@
-8D0A(B BBBD L ] BBB H(A0D8DP
-8A0A(B BBBG T ȸc '
- BBE B(D0D8D8
-8D0A(B BBBD ĥ . K^ ܥ Ȼ Y , л0 AC
FK
- E $ мY } N $ < g AG k
-AA 4 ` =&
- BRA GP
- DABA 4 Z BDA y
+(D ABBF $ h\|. K^ <
l BBB K(F0A8DZ
+8A0A(B BBBH '
+8D0A(B BBBA L BEB E(I0H8DP
+8D0A(B BBBD L \ ! s BJE B(A0A8D@
+8A0A(B BBBA L ! K BEB E(I0H8D@
+8D0A(B BBBD L #] BBB H(A0D8DP
+8A0A(B BBBG T L $c BBE B(D0D8D8
+8D0A(B BBBD \ `Z|. K^ t ' Y '@ , '0 AC
FK
+ E Ԩ (Y } N $ )g AG k
+AA 4 \ h) BRA GP
+ DABA 4 L *Z BDA y
ABM A
-ABA L @ %
- BBE J(A0D8Dp
-8D0A(B BBBA < l V %
- BED I(D@
-(A ABBA D : %
+ABA L ̨ H* BBE J(A0D8Dp
+8D0A(B BBBA < *V O BED I(D@
+(A ABBA D \ ,: , BBB I(A0D
+0A(A EBBA D -:
BBB I(A0D
-0A(A EBBA L J%
+0A(A EBBA L .
BJB D(A0D
-0D(A BBBA L X] BBB H(A0D8DP
-8A0A(B BBBG L hÈ= T È2
- $
+0D(A BBBA L X/] BBB H(A0D8DP
+8A0A(B BBBG D h0= T 02
+ +
BBB E(A0A8G
-8A0A(B BBBA T x͈ #%
+8A0A(B BBBA T x:
BBB B(D0A8G1
-8A0A(B BBBA L \ Έ $
+8A0A(B BBBA L T ; W
BHI E(A0A8G`@
-8A0A(B BBBE $ d Ј= ADG nAA < Јa BBA A(G@l
-(A ABBA < ̩ шa BBA A(G@l
-(A ABBA < ӈa BBA A(G@l
-(A ABBA < L 8Ԉa BBA A(G@l
-(A ABBA < hՈa BBA A(G@l
-(A ABBA < ̪ ֈa BBA A(G@l
-(A ABBA < a BBA A(G@l
-(A ABBA < L ؈a BBA A(G@l
-(A ABBA < (ڈa BBA A(G@l
-(A ABBA < ̫ Xۈa BBA A(G@l
-(A ABBA < ܈a BBA A(G@l
-(A ABBA < L ݈a BBA A(G@l
-(A ABBA < ވa BBA A(G@l
-(A ABBA < ̬ a BBA A(G@l
-(A ABBA < Ha BBA A(G@l
-(A ABBA < L xa BBA A(G@l
-(A ABBA < a BBA A(G@l
-(A ABBA < ̭ a BBA A(G@l
-(A ABBA < a BBA A(G@l
-(A ABBA T 8
- BBB B(A0A8G
-8A0A(B BBBA P. K^ $ 0 AGJ XDA Y } N $ g AG k
-AA 4 h
+8A0A(B BBBE $ \ == ADG nAA D =@ BBB A(A0G@m
+0A(A BBBD D ̬ >@ BBB A(A0G@m
+0A(A BBBD D ?@ BBB A(A0G@m
+0A(A BBBD D \ @@ BBB A(A0G@m
+0A(A BBBD D A@ BBB A(A0G@m
+0A(A BBBD D B@ BBB A(A0G@m
+0A(A BBBD D 4 xC@ BBB A(A0G@m
+0A(A BBBD D | pD@ BBB A(A0G@m
+0A(A BBBD D Į hE@ BBB A(A0G@m
+0A(A BBBD D `F@ BBB A(A0G@m
+0A(A BBBD D T XG@ BBB A(A0G@m
+0A(A BBBD D PH@ BBB A(A0G@m
+0A(A BBBD D HI@ BBB A(A0G@m
+0A(A BBBD D , @J@ BBB A(A0G@m
+0A(A BBBD D t 8K@ BBB A(A0G@m
+0A(A BBBD D 0Le BBB A(A0G@m
+0A(A BBBD D XM@ BBB A(A0G@m
+0A(A BBBD D L PN@ BBB A(A0G@m
+0A(A BBBD D HO@ BBB A(A0G@m
+0A(A BBBD D ܱ @P@ BBB A(A0G@m
+0A(A BBBD D $ 8Q@ BBB A(A0G@m
+0A(A BBBD D l 0R@ BBB A(A0G@m
+0A(A BBBD D (S@ BBB A(A0G@m
+0A(A BBBD T D T
+ BBB B(A0A8G
+8A0A(B BBBA T O|. K^ $ l n0 AGJ XDA n nY } N $ ij ng AG k
+AA 4 4 @o
BHA G@h
- DABA t . K^ R/\ AG RAD 7 BBE A(A0D@
-0A(A BBBA D 7 BBE A(A0D@
-0A(A BBBA D < C BBE A(A0D@
-0A(A BBBA D C BBE A(A0D@
-0A(A BBBA 4 ̰ V AGG k
-CAB SAA T L
- BBB B(A0A8D
-8A0A(B BBBA T x
+ DABA $ N|. K^ $ < o^ AG b
+AA D d p7 BBE A(A0D@
+0A(A BBBA D q7 BBE A(A0D@
+0A(A BBBA D qC BBE A(A0D@
+0A(A BBBA D < sC BBE A(A0D@
+0A(A BBBA 4 tV AGG k
+CAB SAA L 0t
+ BBB B(A0A8G
+8A0A(B BBBA , x' AX
+G Y
+G
+A D y
+ BBB D(A0D@
+0A(A BBBA T ̵ x{ P
BEE E(D0A8G
-8D0A(B BBBE L BBB B(A0A8Gp8A0A(B BBB $
V ADD JAAL , X
BBB B(A0A8Gp8A0A(B BBB L ı
+8D0A(B BBBE < $ R 6
+ BBA A(Jj
+(A ABBA L d U
+ BGE A(A0DT
+0A(A BBBA L l Ѓ BBB B(A0A8Gp8A0A(B BBB $ PV ADD JAAL BBB B(A0A8Gp8A0A(B BBB L |
BJB A(A0Z
-(A BBDE ^(A BBB< ̲ ( BBG A(G@
-(A ABBF l y BBF D(A0G@I
+(A BBDE ^(A BBB< X BBG A(G@
+(A ABBF l ĸ y BBF D(A0G@I
0A(A BBBI w
0A(A BBBA D
-0F(A BBBG l | y BBF D(A0G@I
+0F(A BBBG l 4 y BBF D(A0G@I
0A(A BBBI w
0A(A BBBA D
-0F(A BBBG L 4 s
+0F(A BBBG L (
BEB E(I0H8D@
-8D0A(B BBBA D x K
+8D0A(B BBBA D <
BKI D(A0G@L
-0A(A BBBA T ̳ ;
+
- BBB B(D0D8J
-8A0A(B BBBA ܴ x. K^ L < #+ c
- BEB E(I0H8DP
-8D0A(B BBBD d % ;
+0A(A BBBA T 0\ k
+ BBB B(D0D8J[
+8A0A(B BBBA H|. K^ , t AAG0v
+AAA L $ p {
+ BEB E(I0H8D@
+8D0A(B BBBD , , t AAG0v
+AAA d @ #
BBB B(I0A8G`
8F0A(B BBBF g
-8F0A(B BBBA T &
- BEB B(K0C8G
-8D0A(B BBBA L L .?
- BEB B(A0A8G
-8A0A(B BBBA T (\ AG RAD t C7 BBE A(A0D@
-0A(A BBBA D DC BBE A(A0D@
-0A(A BBBA $ E AG }
-AA , F+ \N T F,
+8F0A(B BBBA T h
+ BEB B(K0C8G
+8D0A(B BBBA T d
+ BBB B(D0A8G
+8A0A(B BBBA t j{; j{\ AG RAD @7 BBE A(A0D@
+0A(A BBBA D 87 BBE A(A0D@
+0A(A BBBA D < 0C BBE A(A0D@
+0A(A BBBA D 8C BBE A(A0D@
+0A(A BBBA $ ̽ @ AG }
+AA ػ+ \N D T
+ BGB D(D0J
+(A BBBD T X, ^
BBE B(D0A8G
-8D0A(B BBBK < xKR u
- BBA A(Jj
-(A ABBA < ܷ L BBG A(G@
-(A ABBH | 8M BBE B(G0L8DPu
+8D0A(B BBBK D 0 D
+ BGB D(M0B
+(A BBBK < BBG A(G@
+(A ABBH | 4 8 BBE B(G0L8DPu
8A0A(B BBBG S
8E0A(B BBBG W
-8E0A(B BBBK , &V BAA D0I AABL xN f
+8E0A(B BBBK < Âv BAA D0S
+ CABG D FAB L < Â
BJB A(A0Z
-(A BBDE ^(A BBB< d N
- BJB A(A0(A BBB < \ (O BBG A(G@
-(A ABBG < O BBG A(G@x
-(A ABBG < ܹ HP BBG A(G@
-(A ABBD < P BBG A(G@
-(A ABBD < \ Q BBG A(G@
-(A ABBH T (RZ
+(A BBDE ^(A BBB< 8Ă
+ BJB A(A0(A BBB < Ă BBG A(G@
+(A ABBG < 8ł BBG A(G@x
+(A ABBG < ł BBG A(G@
+(A ABBD < D XƂ BBG A(G@
+(A ABBD < Ƃ BBG A(G@
+(A ABBH T ǂ G
BEB B(D0A8Jm
-8A0A(B BBBA 0e% T T Hel/ '
- BEE B(A0G8G
-8A0A(B BBBA <
+8A0A(B BBBA `ڂ% T | xڂZ
+ BBB B(G0A8J D
+8A0A(B BBBA T
+ BEE B(A0D8J
+8A0A(B BBBA < , @| g
BIA A(D`)
-(A ABBA H$\ AG RAD Ļ 7 BBE A(A0D@
-0A(A BBBA D C BBE A(A0D@
-0A(A BBBA < T BBG A(G@
-(A ABBG 5 . K^ 4 ļ ЖJ BDA m
+(A ABBA $ d{\ AG RAD D 7 BBE A(A0D@
+0A(A BBBA D C BBE A(A0D@
+0A(A BBBA <
BBG A(G@
+(A ABBG 5 , A|. K^ 4 D J BDA m
ABI A
-ABA T D 薉
- BBE B(D0G8Gg
-8A0A(B BBBA < T F BBA A(G@q
-(A ABBD D BBE A(A0D@
-0A(A BBBA ܽ l"\ AG RAD ح BBE A(A0D@
-0A(A BBBA D P* A[
-L A T `,
+ABA T
+ BBE B(D0G8G
+8A0A(B BBBA Fc{\ AG RAD !7 BBE A(A0D@
+0A(A BBBA D < "C BBE A(A0D@
+0A(A BBBA ?|. K^ T "
+ BEE B(A0G8J
+8A0A(B BBBA X8 `8 HS < , `8F BBA A(G@q
+(A ABBD l
+b{\ AG RAD P9 BBE A(A0D@
+0A(A BBBA D ; BBE A(A0D@
+0A(A BBBA , d `> S
+ AHG@A
+AAA T >, 4
BBE B(D0A8G
-8D0A(B BBBK , 8 x
- LGA A(D , 4 e
- LGA A(D , ضv BAA i
-ABA L L (4 BBB E(A0A8D`
-8A0A(B BBBA 4
- BGB A(C0G D 4
- BED D(G
-(D ABBI \ |
- BBB A(G0Je
-0C(A BBBI C
-0A(A BFFA T ܿ ĉ
- BBB E(A0A8Qg
-8A0A(B BBBA L 4 Xɉ9
+8D0A(B BBBK $ CK
+ KFA , Cv BAA i
+ABA L D 0D
+ BJB A(A0W
+(A BBDH ^(A BBBL L D4 BBB E(A0A8D`
+8A0A(B BBBA pE L hE9
BEB B(A0A8G`
-8D0A(B BBBD d < HˉE BBB B(A0A8DP
+8D0A(B BBBD 4 XG BDA w
+AEA {
+AFI 4 G
+ BGB A(C0G D H
+ BED D(G
+(D ABBI D `Q
+ BBB D(D0Mx
+0D(A BBBB T L T
+ BIB E(A0G8QN
+8D0A(B BBBJ d \ pWE BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA | 0̉R BIE B(A0A8GP
+8F0A(B BBBA | XXR BIE B(A0A8GP
8A0A(B BBBC
8D0A(B BBBD o
-8A0A(B BBBG $ `. K^ $ < ͉Y AK Y
-AA L 0Ή
+8A0A(B BBBG D h:|. K^ $ \ ZY AK Y
+AA L XZ
BEB E(I0H8D@
-8D0A(B BBBA L ω?
+8D0A(B BBBA L [?
BJE B(A0A8DP
-8A0A(B BBBA D Љ BBE A(A0D@u
-0A(A BBBA < hщ.
+8A0A(B BBBA D $ \ BBE A(A0D@u
+0A(A BBBA < ]. ?
BBD A(G@l
-(A ABBA T XӉ(# '
- BBE B(A0G8GA
-8A0A(B BBBA 0L Ad
-K W `[ [ 4 [ L 8[ d [ | [ [ X[ [ [ D 0 BBE A(A0D@
-0A(A BBBA < h\ AG RAD \ 7 BBE A(A0D@
-0A(A BBBA $ Q AG L
-DH oA, ?
+(A ABBA T _r" S
+ BEB B(A0G8G
+8A0A(B BBBA L Ad
+K W $ [ < [ T h[ l [ [ @[ [ Ѓ[ [ `[ D BBE A(A0D@
+0A(A BBBA D \
+ BBE A(A0D@
+0A(A BBBA .Z{\ AG RA$ (Q AG L
+DH oA, 4 ` ?
AAG@G
-AAF d BBA D(DP
+AAF d BBA D(DP
(D ABBK
(D ABBH S
-(D ABBD L d BBB B(A0D8D`
-8D0A(B BBBG L ` BBB B(A0D8D`
-8D0A(B BBBG L
- BBB B(A0D8D`
-8D0A(B BBBG t T p BBD A(DP
+(D ABBD L X BBB B(A0D8D`
+8D0A(B BBBG L ` BBB B(A0D8D`
+8D0A(B BBBG L $ BBB B(A0D8D`
+8D0A(B BBBG t t BBD A(DP
(D ABBK y
(D ABBF @
(D ABBG N
-(D ABBA L Q
+(D ABBA L 4 ` Q
BEB B(A0K8D
-8A0A(B BBBD T d >
+8A0A(B BBBD T >
BBE B(A0A8G>
-8A0A(B BBBD 4 t 3 BAD
-ABE AAB , M
+8A0A(B BBBD 4 83 BAD
+ABE AAB , @M
ADG0_
-DAA D $
- BGB D(M0B
-(A BBBK < $ BDA k
+DAA < ` BDA k
ABK @
-ABA L d BOB A(A0]
+ABA L < BOB A(A0]
(A BBBG Y
-(A BBBH < @ 4 BBB A(A0d
-(A BBBE L @ K BBB B(A0A8G@d
-8A0A(B BBBA T @ d
+(A BBBH < 4 BBB A(A0d
+(A BBBE L K BBB B(A0A8G@d
+8A0A(B BBBA T d d
BBB B(D0A8G
-8D0A(B BBBA X!T L ! #
+8D0A(B BBBA t 8T T L Ȭ G
BEB E(I0H8D@
-8D0A(B BBBA d L @#j
+8D0A(B BBBA d < hj
BBB E(D0A8GP
8A0A(B BBBK D
-8A0A(B BBBA L H$
+8A0A(B BBBA L p
BEB E(I0H8D@
-8D0A(B BBBA < % BDI ~
+8D0A(B BBBA < BDI ~
AGK K
-ABB < H& BBG A(G@
-(A ABBG L < ' BBB B(A0D8GP>
-8A0A(B BBBD , H( BAD b
-ABD L (
+ABB < p BBG A(G@
+(A ABBG L , 0 BBB B(A0D8GP>
+8A0A(B BBBD , | p BAD b
+ABD L
BDB B(A0D8JT
-8A0A(B BBBI (-1 Dl , . K^ D 0-" DR
-J A , d @-t AAG0v
-AAA d -
+8A0A(B BBBI `1 Dl /|. K^ 4 h" DR
+J A d x Z
BBB E(D0A8Dp
8A0A(B BBBE k
-8A0A(B BBBA T D .
- BDB B(D0D8G
-8A0A(B BBBA T 4g
+8A0A(B BBBA T
+ BDB B(D0D8G
+8A0A(B BBBA T \ g
+
BBE E(D0A8G
-8A0A(B BBBA l 7
+8A0A(B BBBA l
BBB L(A0D8DM
8A0A(B BBBA
-8A0A(B BBBA d 89r A
- BDB B(D0D8DpL
-8A0A(B BBBD I
-8A0A(B BBBE
-8A0A(B BBBA L 0;2
- BDB B(A0D8JT
-8A0A(B BBBI L < C
- BBB B(A0A8JU
-8A0A(B BBBE L E
- BBB E(A0A8J
-8A0A(B BBBB L J9
+8A0A(B BBBA d $ 0ă
+ BBB B(H0D8Dp^
+8A0A(B BBBH
+8A0A(B BBBH L Xƃ ]
+ BBB B(A0H8J
+8A0A(B BBBG L
+ BBB B(A0A8JX
+8A0A(B BBBJ T , xЃ f
+ BBE B(A0A8J
+8A0A(B BBBH L ԃ
+ H
BBB B(A0D8Jd
-8A0A(B BBBK L , Uc
- E
- BBB B(A0A8JU
-8A0A(B BBBE T | _%
- BBB B(A0A8Q\
-8A0A(B BBBG T n
- BEE E(A0D8G
-8A0A(B BBBA vY } N $ vg AG k
-AA 4 l w g
+8A0A(B BBBK L ߃3
+
+ BBB B(A0A8JX
+8A0A(B BBBJ T $
+
+ BPD A(Q`n
+(A ABBD x
+(A ABBB T | X
+ BEE E(A0D8G{
+8A0A(B BBBA N{\ AG RAD 7 BBE A(A0D@
+0A(A BBBA D C BBE A(A0D@
+0A(A BBBA T Y k
+ BEB B(A0A8GR
+8A0A(B BBBH , BDA AG T d 2
+ BBB B(D0A8G
+8D0A(B BBBA L d
+ BEB E(I0H8D@
+8D0A(B BBBA l )|. K^ T 8[
+ BGB B(A0D8J
+8A0A(B BBBH T $ @W
+ BBE E(A0A8D
+8A0A(B BBBA \ 4 HA BBE A(D0r
+(A BBBI
+(A BBBF I(A BBB 8q AN
+A \l 8
+ BDB B(A0D8GL
+8A0A(B BBBD
+8A0A(B BBBG L l 8C
+ BBB B(A0D8J
+8A0A(B BBBG d 8
+ BPD A(QP_
+(A ABBC O
+(A ABBK r
+(A ABBH T $
+ BEE E(A0I8QU
+8A0A(B BBBA \ | H)
+ BIL K(A0J@i
+0A(A BBBB g
+0C(A BBBA )Y } N $ *g AG k
+AA 4 h*
BHA G@h
- DABA L w6 K
- BBB B(D0A8D
-8A0A(B BBBG , z> *
+ DABA , T +>
BDI X
-ABA L $ z
-
+ABA L +
BJB A(A0W
-(A BBDH ^(A BBBT , {0 BAA G0@
+(A BBDH ^(A BBBT p+0 BAA G0@
AABA
AABG e
- AABG T g
+ AABG T , H2 7
BHE B(D0D8J4
-8A0A(B BBBG ݂. K^ T <
- BBB B(A0D8G
-8A0A(B BBBJ L \ AG RAD l 谊7 BBE A(A0D@
-0A(A BBBA D ొC BBE A(A0D@
-0A(A BBBA , 貊 AAG0s
-AAA , t h
+8A0A(B BBBG < %|. K^ T C c
+ BEB B(D0A8G ~
+8D0A(B BBBF G{\ AG RAD p_7 BBE A(A0D@
+0A(A BBBA D h`C BBE A(A0D@
+0A(A BBBA , \ pa AAG0s
+AAA , a S
AG0L
AK D
-CA L ȳR
+CA L PbR 3
BIN B(A0C8Gpg
-8A0A(B BBBI L صn BBB B(A0A8Dp
-8A0A(B BBBA T D
- BBB B(A0A8G
-8A0A(B BBBF L T BBB A(A0E
+8A0A(B BBBI L `dn BBB B(A0A8Dp
+8A0A(B BBBA L \ e BBB A(A0E
(A BBGG I
-(A BBDA T
+(A BBDA T e
BBB B(A0A8G0
-8A0A(B BBBE L D ϊ ;
+8A0A(B BBBE T L u
+
+ BBB B(A0A8G
+8A0A(B BBBF L `
BBB B(A0A8Jw
-8A0A(B BBBK L ق. K^ L `.
- BBB B(I0A8G
-8D0A(B BBBA T @ _
- BEE E(A0G8G
-
-8A0A(B BBBA P \ AG RAD , h BBB D(A0D@
-0A(A BBBA D t BBB D(A0D@
-0A(A BBBA d X
- /
+8A0A(B BBBK !|. K^ L
+ BJB B(A0A8Gl
+8C0A(B BBBG T \
+ BEE B(A0D8J
+8A0A(B BBBA l zD{\ AG RAD BBB D(A0D@
+0A(A BBBA D h BBB D(A0D@
+0A(A BBBA d d ไ
BBB E(D0D8D`
8D0A(B BBBD O
-8A0A(B BBBA T l >
- BBE E(G0A8GR
-8A0A(B BBBG < | BBG A(G@
-(A ABBF L
+8A0A(B BBBA T
+ BBE E(G0A8G
+8A0A(B BBBH < 0 BBG A(G@
+(A ABBF L d ?
BEB E(I0H8DP
-8D0A(B BBBD T T h3 o
+8D0A(B BBBD T Ä3
BBE E(A0A8D
-8A0A(B BBBE d ւ. K^ | 8Y } N $ g AG k
-AA l
- BBB B(D0K8G
-8C0A(B BBBE |
-8A0A(B BBBA 4 t X
+8A0A(B BBBE |. K^ < Ƅ BRK J0]
+ CABD | AAB DŽY } N $ 4 hDŽg AG k
+AA 4 DŽ S
BHA G@h
- DABA d ւ. K^ T \ _
+ DABA 8 |. K^ T @Ȅ\
BEE B(A0A8G>
-8D0A(B BBBA \ AG RAD 7 BBE A(A0D@
-0A(A BBBA D < C BBE A(A0D@
-0A(A BBBA D / BEE A(A0D@Z
-0A(A BBBD < i BEA A(D@Z
-(A ABBC < T Ԃ
+8D0A(B BBBA >A{\ AG RAD $ (˄7 BBE A(A0D@
+0A(A BBBA D l ̄C BBE A(A0D@
+0A(A BBBA D (̈́/ BEE A(A0D@Z
+0A(A BBBD < ΄i BEA A(D@Z
+(A ABBC < |
BIA A(D`)
-(A ABBA T /
- BHE B(A0A8G
-8A0A(B BBBC $ @N0 AGJ XDA $ HND XYOC 4 pNc AAG \
-CAG dFA , NY } N $ D Ng AG k
-AA 4 8O
+(A ABBA T τ/
+ BEB B(A0A8M
+
+8A0A(B BBBJ $ 0 AGJ XDA $ D XYOC 4 $ c AAG \
+CAG dFA \ Y } N $ t 8g AG k
+AA 4 3
BHA G@h
- DABA L OT g
- BBB E(A0A8J
-8C0A(B BBBC Ԃ. K^ L b AAG e
+ DABA L (
+ BIB L(A0G8Qq
+8C0A(B BBBA $ |. K^ L < AAG e
CAF s
ACA R
-CAJ JCA L \ (c AAG ]
+CAJ JCA L AAG ]
CAF c
CAI R
-CAJ JCA L hc AAG ]
+CAJ JCA L AAG ]
CAF c
CAI R
-CAJ JCA \ AG RA4 c BDD G@g
- AABD D T c BBE A(A0D@
-0A(A BBBA D e BBE A(A0D@
-
-0A(A BBBA T , g|
+CAJ JCA , r={\ AG RA4 L 0 BDD G@g
+ AABD D BBE A(A0D@
+0A(A BBBA D BBE A(A0D@
+0A(A BBBA L \ ^
+ BBE B(D0D8FP
+8D0A(B BBBA T X| !
BBE E(D0A8G
-8D0A(B BBBE , l
- GA A(D L l n BBB B(A0A8Gp8A0A(B BBB L o BBB B(A0A8Gp8A0A(B BBB L q BBB B(A0A8Gp8A0A(B BBB L \ r; BBB B(A0A8Gp 8A0A(B BBB $ t AG
-AA L 0u BFE E(A0A8DP
-8D0A(B BBBJ d $ vH BBB F(D0D8D@r
+8D0A(B BBBE L BBB B(A0A8Gp8A0A(B BBB L BBB B(A0A8Gp8A0A(B BBB L \ BBB B(A0A8Gp8A0A(B BBB L !; BBB B(A0A8Gp 8A0A(B BBB $ " AG
+AA D $ # BBE A(A0D@l
+0A(A BBBA L l 0$ BFE E(A0A8DP
+8D0A(B BBBJ d %H BBB F(D0D8D@r
8F0A(B BBBG z
-8D0A(B BBGD d v2 BFE E(A0A8DP
+8D0A(B BBGD d $ %2 BFE E(A0A8DP
8F0A(B BBBD
-8D0A(B BBGD x d y5 BBE B(D0I8DP
+8D0A(B BBGD ' d (5 BBE B(D0I8DP
8D0A(B BBBC q
-8D0A(B BBBB T |n
- BBB B(A0A8G
-8A0A(B BBBB L BBB B(D0A8D@d
-8D0A(B BBBF L d
+8D0A(B BBBB L T +l
+ BBB B(A0A8G
+8A0A(B BBBB L \ 1 BBB B(D0A8D@d
+8D0A(B BBBF L 3 C
BEB E(I0H8DP
-8D0A(B BBBD d l ȄI BBE E(A0A8D@
+8D0A(B BBBD d 4I BBE E(A0A8D@
8F0A(B BBBC S
-8F0A(B BBBF L BBB E(A0D8D@x
-8A0A(B BBBJ T l p
+8F0A(B BBBF L d 5 BBB E(A0D8D@x
+8A0A(B BBBJ T x6 c
BBH B(D0A8DY
-8D0A(B BBBC L | BBB E(A0D8D@x
-8A0A(B BBBJ L ȉ
- BBB E(A0D8Qv
-8A0A(B BBBA D d
+8D0A(B BBBC L 9 BBB E(A0D8D@x
+8A0A(B BBBJ , \ 9 ADD ]
+AAA D @:
BBJ A(D0
-(A BBEA d @̂. K^ | ؕ" DR
-J A T 蕋
- BIE E(G0A8J
-8A0A(B BBBI T < l
- BBB B(A0D8G
-8C0A(B BBBA T *
- BBB B(A0I8Gl
-8D0A(B BBBA T
- BBE E(D0A8J/
-8A0A(B BBBA 貋! A[ Y } N $ 4 @g AG k
-AA 4
+(A BBEA , ;
+ GA A(D L L <K
+
+ BBB E(A0D8Qv
+8A0A(B BBBA T |. K^ l pF" DR
+J A T FV
+ BLB E(G0A8J&
+8A0A(B BBBI T , M3
+ BBB B(A0A8J
+8C0A(B BBBC T pX>
+ BBB B(A0I8Go
+8D0A(B BBBA T X\)
+ BBE E(D0A8J<
+8A0A(B BBBA 0b! A[ @bY } N $ $ bg AG k
+AA 4 b '
BHA G@h
- DABA @ʂ. K^ T
-
+ DABA |. K^ T `c
+
BEE B(A0D8Gs
-8A0A(B BBBI о[ [ L 4 ` BBB E(A0A8D`
-8A0A(B BBBA & $ A[
-D C ^ $ @d AW
+8A0A(B BBBI n[ `n[ L $ n BBB E(A0A8D`
+8A0A(B BBBA t p& 0p$ A[
+D C @p^ $ pd AW
H y
-G C \ BBB E(A0A8DP{
-8D0A(B BBBG D8F0A(B BBBD \ ' BBE A(A0D@
-0A(A BBBA l [
+G C \ p BBB E(A0A8DP{
+8D0A(B BBBG D8F0A(B BBBD L 0q' BBE A(A0D@
+0A(A BBBA l r[
BBB E(A0A8D
8A0A(B BBBA v
-8A0A(B BBBA L ċp BBA D(D@E
-(C ABBF D(F ABB L d ċ BBB E(A0A8D`
-8A0A(B BBBA d @Ƌ ~
+8A0A(B BBBA L tp BBA D(D@E
+(C ABBF D(F ABB L T (t BBB E(A0A8D`
+8A0A(B BBBA d u
BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d d Ƌ
+8D0A(B BBBG D8C0A(B BBB d T v 2
BJB B(D0A8D@Y
-8C0A(B BBBB p8A0A(B BBB | Nj BBB B(D0A8DP
+8C0A(B BBBB p8A0A(B BBB < Xvi
+ BDG Jg
+ CABD < { BED G(JB
+(C ABBJ | h} BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI 4 ɋ BDA w
-AEA {
-AFI < ʋi
- BDG Jg
- CABD < | Hϋ BED G(JB
-(C ABBJ L (ы
+8A0A(B BBBI L
BBB B(D0D8Jt
-8C0A(B BBBF L T Ӌ W
+8C0A(B BBBF L H
BEB E(I0H8D@
-8D0A(B BBBD L Ջw /
+8D0A(B BBBD L \ ȃw
BHI E(A0A8Gp
-8A0A(B BBBF D XĂ>
+8A0A(B BBBF D |> o
BIA A(G
-(A ABBA l BBB A(A0G`]
+(A ABBA l BBB A(A0G`]
0A(A BBBD c
-0C(A BBBK D0F(A BBB , d PZ BDD D@G AAB, _ BDD D@L AAB ; ; < BEE D(D0(A BBB 4 | @g
+0C(A BBBK D0F(A BBB , Z BDD D@G AAB, L @_ BDD D@L AAB | ~,{; ,{; < ,{ BEE D(D0(A BBB 4 4 g
BAD D
-ABA 4 xg
+ABA 4 l 8g
BAD D
-ABA \ AG RAD BBE A(A0D@
-0A(A BBBA D h؋7 BBE A(A0D@
-0A(A BBBA L `ً
BBB F(A0D8G`
-8A0A(B BBBH L ܋B BEB E(A0A8GX
-8C0A(B BBBE D < BGB D(M0B
-(A BBBK T t BEB E(A0D8G
-8D0A(B BBBI D BGB D(M0B
-(A BBBK T $ B _ BBB B(D0A8G,
-8C0A(B BBBF L | \ `
+ABA \ ,{\ AG RAD | P BBE A(A0D@
+0A(A BBBA D (7 BBE A(A0D@
+0A(A BBBA L T
+ BBB F(A0D8G`
+8A0A(B BBBH L
+ BEB E(A0A8GX
+8C0A(B BBBE D
+ BGB D(M0B
+(A BBBK T < t
+ BEB E(A0D8G
+8D0A(B BBBI D 0
+ BGB D(M0B
+(A BBBK T C
+ BBB B(D0A8G,
+8C0A(B BBBF L 4 \
BEE B(I0A8Gw
-8C0A(B BBBF L ^
+8C0A(B BBBF L
BBE E(D0A8G
-8D0A(B BBBG L I
+8D0A(B BBBG L 0I
BEE E(A0A8J
-8D0A(B BBBC T l
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+8D0A(B BBBC T $ 0
BBB B(D0D8J/g
-8A0A(B BBBE L
-
+8A0A(B BBBE L | Xͅ
+
BEE E(A0A8G M
-8D0A(B BBBD (H DPCD , (
+8D0A(B BBBD (ׅH DPC, `ׅ BGD l
+ABD , ׅ BGD l
+ABD L D `f
+ BBE H(D0D8Gq
+8C0A(B BBBC D ޅ F
BGB D(M0B
-(A BBBK | , X) BBB B(D0A8DP
+(A BBBK | ޅ BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI < + f
+8A0A(B BBBI < \ h
BGD D(K0l
-(A ABBB L h, BBF B(A0A8D@
-8D0A(B BBBB d < ,= BIB B(A0A8D`
+(A ABBB L T BBF B(A0A8D@
+8D0A(B BBBB d = BIB B(A0A8D`
8F0A(B BBBI
-8D0A(B BBBA L . BBB B(A0A8D
-8A0A(B BBBA < p3 BBG A(G@
-(A ABBG 4 4% T (4] BBB B(D0C8GE
-8C0A(B BBBE $ 0R AG0
-AF D S BDD G0r
+8D0A(B BBBA L ` BBB B(A0A8D
+8A0A(B BBBA < \ BBG A(G@
+(A ABBG % T
+ BBB B(D0C8GE
+8C0A(B BBBK $ AG0
+AF D 4 BDD G0r
AABI
- AABA < U BBG A(G@
-(A ABBG L T @V8 BBB B(A0D8D@
-8A0A(B BBBJ < 0W BBG A(G@
-(A ABBG \ W KFF E(D0D8G@8A0A(B BBBG@ < D X BBG A(G@
-(A ABBF t PY KHE A(GP(A ABBEP
+ AABA < | @ BBG A(G@
+(A ABBG L 8 BBB B(A0D8D@
+8A0A(B BBBJ < BBG A(G@
+(A ABBG \ L
KFF E(D0D8G@8A0A(B BBBG@ < ` BBG A(G@
+(A ABBF t KHE A(GP(A ABBEP
(A ABBM 4
-(A ABBF \ [m BBD D(J@
+(A ABBF \ d m BBD D(J@
(A ABBD T
(A ABBF _
-(A ABBK T \ BBB B(A0A8JF%
-8A0A(B BBBE T 0{ r
+(A ABBK T f
BBB E(A0A8G6
-8D0A(B BBBE 8. K^ $ " DR
-J A T
- BEB B(A0D8G
-8A0A(B BBBK T X
+8D0A(B BBBE T d ) L
+ BBB B(D0A8GJ
+8D0A(B BBBF t
+ (|. K^
+ pG" DR
+J A T G
+ BEB B(A0D8G
+8A0A(B BBBG T L
+ HP
BEG D(GPa
(A ABBH P
-(D ABBA T <
- BEB B(D0A8GA
-8D0A(B BBBI T L H
+(D ABBA T
+ P
+ BEB E(D0D8GAL
+8C0A(B BBBK T
+ {L H
BEE E(D0D8J
-8A0A(B BBBA L 9
+8A0A(B BBBA L T p 9
BBB B(A0D8I:S
-8C0A(B BBBK T < PՌ
+8C0A(B BBBK T
BBB E(A0A8G*X
-8D0A(B BBBG T
- BBB B(D0A8M
-
-8A0A(B BBBA \ AG RAD @7 BBE A(A0D@
-0A(A BBBA D 8C BBE A(A0D@
-0A(A BBBA T @q G
- BEE B(A0G8G%
-8A0A(B BBBD ȶ. K^ $ P 0 AGJ XDA X Y } N $ g AG k
-AA 4 t C
+8D0A(B BBBG T
+ BEB B(A0G8G #
+8A0A(B BBBA
&{\ AG RAD ,
7 BBE A(A0D@
+0A(A BBBA D t
C BBE A(A0D@
+0A(A BBBA T
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+ BEE B(A0G8G
+8A0A(B BBBA {. K^ , %{\ AG RAD L P7 BBE A(A0D@
+0A(A BBBA D HC BBE A(A0D@
+0A(A BBBA T $ P1
+ BEE E(D0A8GY
+8D0A(B BBBE T | 8ņP
+ BBB B(A0A8M ]
+8A0A(B BBBB p{. K^ $ 0 AGJ XDA Y } N $ hg AG k
+AA 4 T
BDA MPO
- AABI 4
+ AABI 4
BHA G@h
- DABA . K^ 4 `
J BDA q
+ DABA | {. K^ 4 (J BDA q
ABE A
-ABA < x
BBG A(G@
-(A ABBG L t 8 s
+ABA < @ BBG A(G@
+(A ABBG L T
BEB E(I0H8D@
-8D0A(B BBBD L K
+8D0A(B BBBD L
BEB E(I0H8D@
-8D0A(B BBBD \ AG RAL v BBE B(A0A8Dp
-8A0A(B BBBA , < BDA D0 AABD l 7 BBE A(A0D@
-0A(A BBBA L ;
+8D0A(B BBBD "{\ AG RAL v BBE B(A0A8Dp
+8A0A(B BBBA , "{ BDA D0 AABL
BBB B(D0A8Dp
-8A0A(B BBBI D C BBE A(A0D@
-0A(A BBBA d L ' BEB B(A0D8D
+8A0A(B BBBI D 7 BBE A(A0D@
+0A(A BBBA D C BBE A(A0D@
+0A(A BBBA d , ' BEB B(A0D8D
8C0A(B BBBH M
-8D0A(B BBBA < G
+8D0A(B BBBA < h
BLD D(G0
-(A ABBE D ` BKM D(A0D@
-0A(A BBBA T 0-
- BEB B(A0A8G
-8A0A(B BBBA @. K^ J J \ AG RAD J BBE A(A0D@
-0A(A BBBA D D `M BBE A(A0D@
-0A(A BBBA D Pe
+(A ABBE D ( BKM D(A0D@
+0A(A BBBA T d / w
+ BEB B(A0A8G
+8A0A(B BBBA t {. K^ ! x! p! $ x!^ AG b
+AA D ! BBE A(A0D@
+0A(A BBBA D D X$ BBE A(A0D@
+0A(A BBBA D 'e
BBB A(D0D@
-0A(A BBBG D 0V
+0A(A BBBG D (- O
BEE K(A0GPl
-0A(A BBBA D V BBE A(A0D@l
-0A(A BBBA d . K^ T HW]# ?
- BBH B(A0A8G
-8A0A(B BBBA X\ AG RAD 0z7 BBE A(A0D@
-0A(A BBBA D < ({C BBE A(A0D@
-0A(A BBBA T 0|5
- BEE B(A0G8G
-8A0A(B BBBA X. K^ аY } N $ g AG k
-AA 4 | `
+0A(A BBBA p{. K^ T | -o#
+ BBH B(A0A8G
+8A0A(B BBBA * {\ AG RAD P7 BBE A(A0D@
+0A(A BBBA D QC BBE A(A0D@
+0A(A BBBA L RM
+ BBB B(A0D8Gz
+8C0A(B BBBF T ^"
+ BEE B(A0G8G
+8A0A(B BBBA {. K^ `uY } N $ ug AG k
+AA 4 u W
BHA G@h
- DABA T
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+ DABA T v
+ ;
BEB B(A0D8G<
-8A0A(B BBBC t Y BBB A(D0DP
+8A0A(B BBBC t PY BBB A(D0DP
0D(A BBBA q
0D(A BBBD
-0D(A BBBE L v
- BBB E(K0A8D
-8A0A(B BBBA ح. K^ L Ǎ
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+0D(A BBBE L 8l
+ BPB H(A0K8Do
+8A0A(B BBBK X{. K^ L @
+
BBB E(A0D8J
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+8D0A(B BBBC [ 8[ , [ D ȕ[ \ [ t X[ [ 薇[ 0[ x[ Y } N $ g AG k
+AA 4 t P
BHA G@h
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+ DABA L
+ BPB H(A0K8Dp
+8D0A(B BBBG L BEB A(D0l
(A BBBG
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BEB B(A0A8GV
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Y } N $ t
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-AA 4 X [
+8A0A(B BBBD t h[ [ [ @Ç[ Ç[ Ç[ ć[ `ć[ 4 ć[ L ć[ d 8ŇY } N $ | Ňg AG k
+AA 4 Ň
BHA G@h
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l BEE B(A0A8Dp
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-8A0A(B BBBA t P. K^ L
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- BEB B(D0D8G
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-8D0A(B BBBE $ 10 AGJ XDA 1Y } N $ 2g AG k
-AA d P2 2
- BEL B(D0A8D
-8C0A(B BBBE c
-8D0A(B BBBA 4 3
+ DABA L pƇl BEE B(A0A8Dp
+8A0A(B BBBB L t χl g
+ BPB H(A0K8Do
+8A0A(B BBBK | {. K^ L ҇)
+ BEE B(A0A8G
+
+8A0A(B BBBF $ h0 AGJ XDA D p BRK J0]
+ AABF Z
+ AABB T Y } N $ l g AG k
+AA 4 H C
BHA G@h
- DABA . K^ ݁\ AG RAD 37 BBE A(A0D@
-0A(A BBBA D d 4C BBE A(A0D@
-0A(A BBBA x. K^ T 5% BEB B(A0A8G?
-8A0A(B BBBA 4 d x[g
+ DABA {. K^ .{\ AG RAD 7 BBE A(A0D@
+0A(A BBBA D L C BBE A(A0D@
+0A(A BBBA {. K^ T G
+ BEE B(A0D8J,
+8A0A(B BBBA 4 L ( g
BAD D
-ABA 4 [g
+ABA 4 ` g a
BAD D
-ABA D [
+ABA D t!
BBE A(A0D@
-0A(A BBBA D \ BBE A(A0D@
-0A(A BBBA ہ\ AG RA < h]% AQ
-F L \ x]. Aa
-F E 4 ] BAD Jd
- AABG T p_q BBB E(A0A8G%
-8C0A(B BBBI T T m H
- BBB B(A0A8G6
-8D0A(B BBBF d m A~
-A l , @
+0A(A BBBA D ! ` BBE A(A0D@
+0A(A BBBA " j{\ AG RA $"
% AQ
+F L D" (
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+F E 4 ! 8
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+ BAD Jd
+ AABG " m A~
+A l , " p #
AID0|
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- BBB B(A0A8Q7
-8C0A(B BBBB H. K^ L l ځv
- BBB H(D0A8GQ8A0A(B BBB T B߁
- BBB E(D0D8G
-8A0A(B BBBA T
-
- BBE B(K0A8G C
-8A0A(B BBBE T l p% n
- BBB B(D0A8G9M
-8A0A(B BBBE T HU s
- BEE B(A0G8GS
-8A0A(B BBBF $ P0 AGJ XDA XY } N $ g AG k
-AA 4 躎 [
+DAA T 4" q
+ BBB E(A0A8G%
+8C0A(B BBBI T "
+ BBB B(A0A8G6
+8D0A(B BBBF T " <
+ BBB B(A0A8Q;
+8C0A(B BBBF # {. K^ L T# {z l
+ BBB H(D0A8GU8A0A(B BBB T # {
+ BBB E(D0D8G
+8A0A(B BBBA T # hAa
+ BBB B(A0A8J9M
+8A0A(B BBBE T T$ W
+ BEE B(A0G8G!
+8A0A(B BBBH $ d% Hj0 AGJ XDA % PjY } N $ % jg AG k
+AA 4 % j
BHA G@h
- DABA t . K^ L x '
+ DABA & {. K^ L d% pk
BBB H(A0J8DQ
-8C0A(B BBBF \ AG RAD 7 BBE A(A0D@
-0A(A BBBA D D C BBE A(A0D@
-0A(A BBBA D C
+8C0A(B BBBF l& {\ AG RAD & n7 BBE A(A0D@
+0A(A BBBA D & oC BBE A(A0D@
+0A(A BBBA D d& p
BEE E(J0J@X
-0A(A BBBK L J
+0A(A BBBK L & pZ
BEB L(D0D8G\
-8A0A(B BBBF L l Î
-
- BLB B(A0D8J
-8D0A(B BBBH T Ύ A
- BLB E(A0D8J8
-8D0A(B BBBG t ڎ KHE A(GP(A ABBEP
+8A0A(B BBBF L & sJ
+ BIL L(A0D8JI
+8D0A(B BBBH L L' ~
+ BSL E(D0A8JE
+8D0A(B BBBI t T( X KHE A(GP(A ABBEP
(A ABBM 4
-(A ABBF D p -
+(A ABBF D ( p{
BIB A(A0D`*
-0A(A BBBA T ݎ
- BEE B(A0G8G
-
-8A0A(B BBBA D pi BBB H(A0D@
-0A(A BBBA D , q BBB H(A0D@
-0A(A BBBA L t BBF B(A0A8D@
-8D0A(B BBBB d `E BBB B(A0A8DP
+0A(A BBBA T \( z
+ BEE B(A0G8G
+8A0A(B BBBA D l) Тi BBB H(A0D@
+0A(A BBBA D ) q BBB H(A0D@
+0A(A BBBA L ) 0 BBF B(A0A8D@
+8D0A(B BBBB d L* E BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA T t H
+8F0A(B BBBA T ) +
BBE B(A0A8O
-8A0A(B BBBG . K^ T O
+8A0A(B BBBG + {. K^ T l*
BBB B(A0A8G
-8A0A(B BBBJ ݁\ AG RAD 7 BBE A(A0D@
-0A(A BBBA D \ C BBE A(A0D@
-0A(A BBBA T
- BBE B(A0D8Ms
-8A0A(B BBBF < D O
+8A0A(B BBBJ |+ {\ AG RAD + @̈7 BBE A(A0D@
+0A(A BBBA D + 8͈C BBE A(A0D@
+0A(A BBBA T t+ @Έ ;
+ BEB B(D0A8M
+
+8A0A(B BBBH < + {
BIA A(D`)
-(A ABBA < ܁\ AG RAD \ 17 BBE A(A0D@
-0A(A BBBA D 2C BBE A(A0D@
-0A(A BBBA < 4 3
+(A ABBA , {\ AG RAD , ߈7 BBE A(A0D@
+0A(A BBBA D ,- C BBE A(A0D@
+0A(A BBBA < ,
BGD D(K0l
-(A ABBB , , V܁V BAA D0I AABT 84 &
- BEE B(A0G8G
-
-8A0A(B BBBA <
+(A ABBB , - b{V BAA D0I AABT ,- x< z
+ BEE B(A0G8G
+8A0A(B BBBA < - { W
BIA A(D`)
-(A ABBA 4 ہL BDA v
-ABE AAD , ہ\ AG RAD L P7 BBE A(A0D@
-0A(A BBBA D QC BBE A(A0D@
-0A(A BBBA X. K^ T < QT1 w
- BEE B(A0G8G D
-8A0A(B BBBA L 4ہ\ AG RAD l BBE A(A0D@e
-0A(A BBBA T <
+(A ABBA 4 |. {L BDA v
+ABE AAD . {\ AG RAD . 7 BBE A(A0D@
+0A(A BBBA D / C BBE A(A0D@
+0A(A BBBA d/ X{. K^ T . 2 ?
+ BEE B(A0G8G
+8A0A(B BBBA / @{\ AG RAD / H+ BBE A(A0D@e
+0A(A BBBA T / ,< [
BBE B(D0A8G
-8D0A(B BBBB T T F3
+8D0A(B BBBB T / 0F3 C
BBB B(A0A8J
-8A0A(B BBBG d! . K^ |! h A^ ! h ! p HS ! p AT L ! p AAG ]
+8A0A(B BBBG 0 {. K^ 1 c A^ $1 c <1 c HS \1 c AT L |1 c AAG ]
CAF k
ACA R
-CAJ JCA D" ف\ AG RAT ! S
+CAJ JCA $ 1 d^ AG b
+AA T <1 Xd
BEB B(A0H8G
-8A0A(B BBBC T " ȿ
+8A0A(B BBBC T 1 h
BBE B(A0D8G
-8D0A(B BBBH L \" Pŏ
+8D0A(B BBBH L 1 n
BEB E(I0H8DP
-8D0A(B BBBA L " Ǐ
+8D0A(B BBBA L <2 o
BEB E(I0H8D@
-8D0A(B BBBA L " ȏ
+8D0A(B BBBA L 2 Xq g
BEB E(I0H8D@
-8D0A(B BBBA , $ ʏ BCD D0 AABL |# ʏ [
+8D0A(B BBBA , 3 r BCD D0 AABL 3 s
BEB E(I0H8D@
-8D0A(B BBBA L # `̏ 3
+8D0A(B BBBA L \3 (u
BEB E(I0H8D@
-8D0A(B BBBA d $ ͏,
+8D0A(B BBBA d 3 v,
BGE E(A0A8D@
8F0A(B BBBJ g
-8F0A(B BBBA T $ Ώ
-
+8F0A(B BBBA T 4 pw
BBE B(A0A8G
-8A0A(B BBBF T $ p؏
+8A0A(B BBBF T l4 r s
BHE B(D0A8L
-8D0A(B BBBA T 4% m #
- BBE B(A0A8J
-8A0A(B BBBD D& P. K^ \& t& & & & $ Ab & \Ձ\ AG RA, & AAG0W
-AAF L t& '
+8D0A(B BBBA T 4
+ BBE B(A0A8J
+8A0A(B BBBA 5 H{. K^ 5 6 6 46 L6 d6 $ Ab 6
{\ AG RA, 6 AAG0W
+AAF L 6 H' y
BBB B(D0A8D`B
-8A0A(B BBBA L & /
+8A0A(B BBBA L l6 (/ -
BBB B(D0A8D`J
-8A0A(B BBBA T ' x E
+8A0A(B BBBA T 6
BBB I(A0A8K]
-8A0A(B BBBE T l'
+8A0A(B BBBE T 7
BBB I(A0A8K]
-8A0A(B BBBE L ' J BBB A(D0DF
-0D(A BBBH ( . K^ ( ( x ) p ,) h D) ` \) h t) p ) x ) - 4 ) BGI _
-ABD NAB , <) E BDC rAH T l) BBB B(I0A8G
-8D0A(B BBBD L |* / BEB B(A0A8D`u
-8A0A(B BBBH , * AAG0
-CAH * . K^ + @ ,+ 8F D+ p \+ h t+ с\ AG RAL * PX S BBB B(A0A8Ji
-8A0A(B BBBI T ,+ ` s BBE B(D0A8G
-8D0A(B BBBG T + ( , Y BBE B(D0A8G
-8D0A(B BBBK T + " ? BBB B(D0D8G
-8A0A(B BBBH , , Ё- AAG0!AA < d, (99 ( BHD G
- DABA L , (= BDE E(G0A8G
-8D0A(B BBBH L , C3 k BBB I(A0A8JY
-8D0A(B BBBG - (. K^ L \- L W BBE B(D0A8G
-8A0A(B BBBA , - Pg ` AID @
-DAA . PP . HP . @P . 8P . @P / HP $/ PP0 Hg D/ `P7 Hj d/ P0 Hg / P7 Hj / z\ AG RAT / P BBE B(D0A8G
-8D0A(B BBBC T d/ (U BBB B(A0D8R
-8A0A(B BBBK , / _> AID W
-DAA T / ` j BBB B(A0D8R
-8A0A(B BBBK , D0 j> AID W
-DAA T t0 j? BBB B(D0A8R]
-8A0A(B BBBJ , 0 r> # AID W
-DAA T 0 r/ BBB B(A0D8Rh
-8A0A(B BBBG , T1 y> H AID W
-DAA T 1 yg ( BBB B(A0I8G
-8D0A(B BBBI T 1 ~ G BBE B(D0A8G
-8D0A(B BBBB T 42 h - BBE B(D0A8G
-8D0A(B BBBC , D3 6ˁ- AAG0!AA < 2 Ї BHD DW
- DABA < 2 BHD DW
- DABA < <3 P BHD Dq
- DABA < |3 0 BHD Dq
- DABA < 3 w BHD Dq
- DABA L 3 _ BBB B(G0D8G
-8A0A(B BBBA < L4 p c AAG L
+8A0A(B BBBE L l7 (J e
+ BBB A(D0DF
+0D(A BBBH t8 {. K^ 8 8 8 8 8 9 9 49 L9 - 4 d9 ( BGI _
+ABD NAB , 8 E L
+ BDC rAH T 9
+ BBB B(I0A8G
+8D0A(B BBBD L $: H/ BEB B(A0A8D`u
+8A0A(B BBBH , 9 (
+ AAG0
+CAH : {. K^ : д : ȴF : ; ; {\ AG RAL : ഉX
+ BBB B(A0A8Ji
+8A0A(B BBBI T :
+ BBE B(D0A8G
+8D0A(B BBBG T ,; ,
+ BBE B(D0A8G
+8D0A(B BBBK T ; lj
+ BBB B(D0D8G
+8A0A(B BBBH , < {- AAG0!AA < < މ9
+ BHD G
+ DABA L L<
+ BDE E(G0A8G
+8D0A(B BBBH L < x3
+ BBB I(A0A8JY
+8D0A(B BBBG = {. K^ L = P
+ BBE B(D0A8G
+8A0A(B BBBA , T= g
+ AID @
+DAA <> T> l> > > > > 0 Hg > 7 Hj ? 0 Hg ,? 7 Hj $ L? @^ AG b
+AA T > x
+ BBE B(D0A8G
+8D0A(B BBBC T ?
+ BBB B(A0D8R
+8A0A(B BBBK , l? >
+ AID W
+DAA T ?
+ BBB B(A0D8R
+8A0A(B BBBK , ? >
+ AID W
+DAA T $@ r
+ BBB B(D0A8Ra
+8A0A(B BBBF , |@ h>
+ AID W
+DAA T @ x/
+ BBB B(A0D8Rh
+8A0A(B BBBG , A P >
+ AID W
+DAA T 4A ` g
+ BBB B(A0I8G
+8D0A(B BBBI T A x$
+ BBE B(D0A8G
+8D0A(B BBBB T A )
+ BBE B(D0A8G
+8D0A(B BBBC , B b{- AAG0!AA < lB x- w
+ BHD DW
+ DABA < B 80 \
+ BHD DW
+ DABA < B 2 A
+ BHD Dq
+ DABA < ,C 5 &
+ BHD Dq
+ DABA < lC 8
+ BHD Dq
+ DABA L C ;
+ BBB B(G0D8G
+8A0A(B BBBA < C >
+ AAG L
AAI p
-AAB L 4 q ' BBB A(A0G
-0D(A BBBF < 4 r BBA K(M0x
-(D ABBA T 5 P BBB G(D0G8G
-8D0A(B BBBH D t5 BBA A(G
-(D ABBE < 5 r BBA K(M0x
-(D ABBA T 5 BBB G(D0G8G
-8D0A(B BBBH < 7 g BBA A(DV(A ABB T 6 د w BBB E(A0A8L
-8A0A(B BBBA 7 . K^ 7 7 , 47 [ p AID t
-DAA , d7 ([ P AID t
-DAA , L8 \ǁ- AAG0!AA < 7 ( BHD Gr
- DABA < 8 BHD Gr
- DABA T D8 HƐ BBE B(D0A8G
-8D0A(B BBBB L 8 ː BBB B(F0G8G
-8D0A(B BBBK T 8 Ґ# BDB B(G0D8G
-8D0A(B BBBE 9 . K^ : ,: D: \: , 9 [ < AID t
-DAA , 9 [ AID t
-DAA , : Ɓ- AAG0!AA 4 L: E BHD D`
- DABD 4 : (E BHD D`
- DABD D : @ BIA D(G
-(D ABBA T ; n BBE B(D0A8G
-8D0A(B BBBH T \; T BBB B(A0I8G(
-8D0A(B BBBB \ ; u C BEE A(A0De
-0A(A BBBA j
-0A(A BBBF \ < E BEE A(A0D=
+AAB L <D >q
+ BBB A(A0G
+0D(A BBBF < D Er
+ BBA K(M0x
+(D ABBA T D E c
+ BBB G(D0G8G
+8D0A(B BBBH D $E I
+ BBA A(G
+(D ABBE < lE XMr \
+ BBA K(M0x
+(D ABBA T E M /
+ BBB G(D0G8G
+8D0A(B BBBH < F PQg BBA A(DV(A ABB T DF US
+ BBB E(A0A8L
+8A0A(B BBBA TG {. K^ lG pd G hd , F pd[
+ AID t
+DAA , G d[
+ AID t
+DAA , G z- AAG0!AA < tG d
+ BHD Gr
+ DABA < G 0h z
+ BHD Gr
+ DABA T G k `
+ BBE B(D0A8G
+8D0A(B BBBB T LH p[* F
+ BDB B(G0D8G
+8D0A(B BBBE \I {. K^ tI x I p I h I p , I x[
+ AID t
+DAA , LI [
+ AID t
+DAA , |I ؚ[
+ AID t
+DAA , dJ Nz- AAG0!AA 4 I ؚE }
+ BHD D`
+ DABD D J _
+ BIA D(DX
+(D ABBA D \J X <
+ BIA D(G
+(D ABBA D J
+ BIA D(G
+(D ABBA T J
+ BBE B(D0A8G
+8D0A(B BBBH T DK `
+ BBB B(A0I8G(
+8D0A(B BBBB \ K E
+ BEE A(A0D=
0A(A BBBI j
-0A(A BBBF D t< BEE A(A0D
-0A(A BBBD D < ; BEE A(A0D
-0A(A BBBD L = #5 BBB A(D0G
-0A(A BBBB T T= + BDB B(A0G8J
-8D0A(B BBBD T = ;
+0A(A BBBF \ K u
+ BEE A(A0De
+0A(A BBBA j
+0A(A BBBF D \L _
+ BEE A(A0D
+0A(A BBBD D L @Ɗ
+ BEE A(A0D
+0A(A BBBD L L ˊ5 +
+ BBB A(D0G
+0A(A BBBB T <M ӊ
+ BDB B(A0G8J
+8D0A(B BBBD T M p
BGB B(D0G8G
-8D0A(B BBBB > . K^ > @ > @ ? @ ? @ 4? @ L? @ d? @ |? @ $ ? @^ AG b
-AA , ? A BGD l
-ABD L ? Aq BBA D(G@n
+8D0A(B BBBB N {. K^ N N N O x O p 4O x LO dO $ |O ^ AG b
+AA , O BGD l
+ABD L O Hq BBA D(G@n
(A ABBD
-(A ABBH T ? B9 v BEE E(A0A8G
-8A0A(B BBBA T ? D H BEE E(A0A8G
-8A0A(B BBBA T 4@ F ! BBB E(A0A8KP
-8A0A(B BBBF , @ H> AID W
-DAA T @ H BBB E(A0A8KP
-8A0A(B BBBF , A HJ> AID W
-DAA T DA XJ w BBB E(A0A8KP
-8A0A(B BBBF , A L> B AID W
-DAA T A L " BBB E(A0A8KP
-8A0A(B BBBF , $B M> AID W
-DAA $ C M AAG0AA< |B PN BHA G
- DABA < B P BHA G
- DABA < B R o BHA G
- DABA < <C @U T BHD Dq
- DABA D |C X 9 BBB A(A0J`
-0D(A BBBC |D h[* A[
-L A D x[* A[
-L A D [* A[
-L A D [* A[
-L A D [* A[
-L A T dD [ BEJ E(D0D8G(
-8D0A(B BBBF T D @] R BEJ E(D0D8G(
-8D0A(B BBBF T E ^
BEJ E(D0D8G(
-8D0A(B BBBF T lE P`B BBB B(A0A8J
-8A0A(B BBBA T E HfN BEB E(A0D8G
-8A0A(B BBBF T F @h BEB E(A0A8G
-8A0A(B BBBF T tF l x BEE B(A0A8JN
-8A0A(B BBBF L F Pr
- : BBB B(A0D8G
-8A0A(B BBBG T G |B BBB B(A0A8J
-8A0A(B BBBA T tG N BEB E(A0D8G
-8A0A(B BBBF T G BEB E(A0A8G
-8A0A(B BBBF T $H ؇ B BEE B(A0A8JN
-8A0A(B BBBF L |H
- BBB B(A0D8G
-8A0A(B BBBG T H B \ BBB B(A0A8J
-8A0A(B BBBA T $I 蝑N BEB E(A0D8G
-8A0A(B BBBF T |I P BEB E(A0A8G
-8A0A(B BBBF T I BEE B(A0A8JN
-8A0A(B BBBF L ,J
- BBB B(A0D8G
-8A0A(B BBBG T |J B & BBB B(A0A8J
-8A0A(B BBBA T J N _ BEB E(A0D8G
-8A0A(B BBBF T ,K BEB E(A0A8G
-8A0A(B BBBF T K x BEE B(A0A8JN
-8A0A(B BBBF L K ő
- BBB B(A0D8G
-8A0A(B BBBG L s. K^ L xϑ M pϑ , tL xϑ[ AID t
-DAA D \M ϑ BBB B(A0A8s
-0A(B BBBA L M ё BBB B(A0A8J@
-8A0A(B BBBA M ֑2 I N ֑ AN
-A < ,N ֑ AM
+(A ABBH T lO x9
+ BEE E(A0A8G
+8A0A(B BBBA T O `
+ BEE E(A0A8G
+8A0A(B BBBA T P
+ BBB E(A0A8KP
+8A0A(B BBBF , tP P> x
+ AID W
+DAA T P ` X
+ BBB E(A0A8KP
+8A0A(B BBBF , P > #
+ AID W
+DAA T ,Q
+ BBB E(A0A8KP
+8A0A(B BBBF , Q >
+ AID W
+DAA T Q
+ BBB E(A0A8KP
+8A0A(B BBBF , R x> y
+ AID W
+DAA $ R AAG0AA< dR 1
+ BHA G
+ DABA < R `
+ BHD Dq
+ DABA < R @
+ BHA G
+ DABA < $S
+ BHD Dq
+ DABA D dS p
+ BBB A(A0J`
+0D(A BBBC dT * A[
+L A T * A[
+L A T * A[
+L A T * A[
+L A T ,T C
+ BEJ E(D0D8G(
+8D0A(B BBBF T T
+ BEJ E(D0D8G(
+8D0A(B BBBF T T
+ BEJ E(D0D8G(
+8D0A(B BBBF T 4U B t
+ BBB B(A0A8J
+8A0A(B BBBA T U N
+ BEB E(A0D8G
+8A0A(B BBBF T U h
+ BEB E(A0A8G
+8A0A(B BBBF T <V H $
+ BEE B(A0A8JN
+8A0A(B BBBF L V
+
+ BBB B(A0D8G
+8A0A(B BBBG T V `%B >
+ BBB B(A0A8J
+8A0A(B BBBA T <W X+N w
+ BEB E(A0D8G
+8A0A(B BBBF T W P- 2
+ BEB E(A0A8G
+8A0A(B BBBF T W 1
+ BEE B(A0A8Jz
+8A0A(B BBBB L DX 7
+
+ BBB B(A0D8G
+8A0A(B BBBG T X `AB
+ BBB B(A0A8J
+8A0A(B BBBA T X XGN A
+ BEB E(A0D8G
+8A0A(B BBBF T DY PI
+ BEB E(A0A8G
+8A0A(B BBBF T Y M
+ BEE B(A0A8JN
+8A0A(B BBBF L Y `S
+ z
+ BBB B(A0D8G
+8A0A(B BBBG T DZ 0]B
+ BBB B(A0A8J
+8A0A(B BBBA T Z (cN
+ BEB E(A0D8G
+8A0A(B BBBF T Z e
+ BEB E(A0A8G
+8A0A(B BBBF T L[ h
+ BEE B(A0A8Jg
+8A0A(B BBBE L [ @o
+ D
+ BBB B(A0D8G
+8A0A(B BBBG \ {. K^ \ x \ x , <\ x[ T
+ AID t
+DAA D $] (y BBB B(A0A8s
+0A(B BBBA L l] z BBB B(A0A8J@
+8A0A(B BBBA ] 02 I ] X AN
+A < ] X AM
AF
AH
-EH , lN - AAG0!AA 4 M ۑ H BHD D`
- DABF , N `ݑ ADT
+EH , 4^ z- AAG0!AA 4 ]
+ BHD D`
+ DABF , ^ ADT
AF
-AA D O ޑH BBB B(A0A8
-0A(B BBBE L LO (] BBB B(D0A8G
-8A0A(B BBBA d O 8c BBB E(D0D8D
+AA D ^ H BBB B(A0A8
+0A(B BBBE L _ ] BBB B(D0A8G
+8A0A(B BBBA d d_ c BBB E(D0D8D
8A0A(B BBBD
-8A0A(B BBBK L P @Y BEB B(A0A8DX/
-8A0A(B BBBA L TP P BEE E(D0A8D
-8A0A(B BBBA 4 O Z AF
P
- H D P BBE B(A0A8
-0A(B BBBA 4 lP P
- AC
PG
- A \ \Q BBE B(A0A8
-0A(B BBBA 0A(B BBB L Q 8% BBE B(A0A8D
-8A0A(B BBBA , R #$ AM
DL
- E d <R $c BBB E(D0D8D
+8A0A(B BBBK L _ Y BEB B(A0A8DX/
+8A0A(B BBBA L ` И BEE E(D0A8D
+8A0A(B BBBA 4 _
+ AF
P
+ H D ` BBE B(A0A8
+0A(B BBBA 4 4` г
+ AC
PG
+ A \ $a BBE B(A0A8
+0A(B BBBA 0A(B BBB L a % BBE B(A0A8D
+8A0A(B BBBA , a ̋$ AM
DL
+ E d b ͋c BBB E(D0D8D
8A0A(B BBBD
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+8A0A(B BBBK d lb ыH BEE B(E0A8DX
8A0A(B BBBA I
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- AC
FM
+8A0A(B BBBA < b D
+
+ AC
FM
A
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+ D T \b
+ BBA A(G`
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BGM
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BGM
C
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-8A0A(B BBBA L 4T PF BBB B(A0A8G
-8A0A(B BBBB L S K BBB B(D0A8GP
-8A0A(B BBBB T T 0O BBA A(DU
+ F T b
+ BEB B(D0A8Jd
+8A0A(B BBBA L c BBB B(A0A8G
+8A0A(B BBBB L c 0 v
+ BBB B(D0A8GP
+8A0A(B BBBB T c _
+ BBA A(DU
(A ABBK
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+ BGB B(A0A8G
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+8A0A(B BBBF L Lg
+\ BBB E(A0A8DP
+8A0A(B BBBF D f
/
+ BBB D(A0D@
+0A(A BBBI < ,g x
+ AAG
AAF p
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+ AAG
AAF p
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+ AAG
AAF p
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+ AAG
AAF p
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BAA M@z
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- DABI 4 ] 0 BHA G@n
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- DABI D ] h BBE A(D0G`
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-8D0A(B BBBD T _ BEE E(A0D8Jg
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BBB E(A0A8J<
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+ BAA P0[
+ DABJ 4 |h (
+ BAA M@z
+ DABF T h l
+ BEE E(A0A8G
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+ BHD Db
+ DABA < 4j X, BBG A(G@
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+ BEE B(A0D8Gu
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+ C
+ BBB B(A0A8O6
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+ BEE E(D0A8G'
+8D0A(B BBBG T 4k `CT
+ BEB E(D0A8G
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+ BHA G@n
+ DABI 4 l K
+ BHA G@n
+ DABI 4 m XL h
+ BHA G@n
+ DABI 4 Tm M >
+ BHA G@n
+ DABI D m M
+ BBE A(D0G`
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+ BEE E(A0D8Jg
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+ BEE E(A0D8Jg
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+ BEE E(A0D8Jg
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+ BEB E(D0A8G
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+ BBB D(G0GA
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+ BEB E(A0D8G
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+ BEB B(D0A8G
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+ BFB B(D0A8G
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+
+ BBB E(A0A8J<
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+ BBB E(A0A8J<
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+ BBB E(A0A8J<
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+ BBE B(D0A8Dp
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8A0A(B BBBE
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+8A0A(B BBBF T t '
+ BBE B(D0A8G
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8A0A(B BBBK
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+8A0A(B BBBF d v g BLB B(A0A8D@q
8A0A(B BBBE
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BLE B(A0D8D@
-8A0A(B BBBI T Tf (' _ BBB E(A0A8G
-8A0A(B BBBA L dg BLB H(A0D8DPV
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-8A0A(B BBBF d h & BBB B(D0A8DP
+8A0A(B BBBF L u ?
+ BLE B(A0D8D@
+8A0A(B BBBI T v '
+ BBB E(A0A8G
+8A0A(B BBBA L ,w BLB H(A0D8DPV
+8A0A(B BBBG L v PÌb ȹ
+ BEB B(A0A8J
+8A0A(B BBBF d w pь BBB B(D0A8DP
8A0A(B BBBF
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- DABC i W. K^ i v DN
-E A i hC" DN
-N A i xC Y 4 \i Cg BAD D
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+ BBB E(A0A8J
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+8A0A(B BBBA T $x ܌ @
+ BBB B(A0H8G
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+ DABC ly {. K^ y z DN
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-8A0A(B BBBG l U < l U BBA A(G@a
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+(A ABBB < z BBA A(G@b
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+AA D dz
+ BBB D(E0Ml
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+ BEB E(D0D8G`B
+8A0A(B BBBG L z 8#
+ BEB E(D0D8G`B
+8A0A(B BBBG L L{ #
+ BEB E(D0D8G`B
+8A0A(B BBBG L { #
+ BEB E(D0D8G`B
+8A0A(B BBBG | < | BBA A(G@a
+(A ABBD T D| Z
+ BEE E(D0A8Gi
+8D0A(B BBBE T | ö
+ BBE E(D0A8G,
+8D0A(B BBBE T | @Z
+ BEE E(D0A8Gf
+8D0A(B BBBH d ~ H
+g BBB B(A0A8GP_
8A0A(B BBBF <
-8A0A(B BBBJ L n @c; BBB B(A0D8DPd
-8A0A(B BBBA d n 0fg BBB B(A0A8GP_
+8A0A(B BBBJ L l~ P; BBB B(A0D8DPd
+8A0A(B BBBA d ~ @g BBB B(A0A8GP_
8A0A(B BBBF <
-8A0A(B BBBJ | \o 8j BBB B(D0A8DP
+8A0A(B BBBJ | $ H BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI T $o l BBE E(A0A8G
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-A T o n BBE E(A0A8G
-8D0A(B BBBD T o (q _ BBB B(D0D8J
-8A0A(B BBBA T Lp z BBB B(D0D8J
-8A0A(B BBBA T p BBB B(D0D8J
-8A0A(B BBBA T p 0 j BBB B(D0D8J
-8A0A(B BBBA T Tq E BEB E(A0A8G
-8A0A(B BBBI T q # BBB B(A0A8J
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-8A0A(B BBBI T \r ӓ# f BBB B(A0A8J
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-8A0A(B BBBI T s p# $ BBB B(A0A8J
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-CAF s
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-CAF s
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-CAJ JCA L | XQ AAG e
-CAF s
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-CAJ JCA L ̘ Q AAG e
-CAF s
-ACA R
-CAJ JCA L Q AAG e
-CAF s
-ACA R
-CAJ JCA L l HR AAG e
-CAF s
-ACA R
-CAJ JCA L R AAG e
-CAF s
-ACA R
-CAJ JCA L R AAG e
-CAF s
-ACA R
-CAJ JCA L \ 8S AAG e
-CAF s
-ACA R
-CAJ JCA L S AAG e
-CAF s
-ACA R
-CAJ JCA L S AAG e
-CAF s
-ACA R
-CAJ JCA L L (T AAG e
-CAF s
-ACA R
-CAJ JCA L xT AAG e
-CAF s
-ACA R
-CAJ JCA , 4 T[ AID t
-DAA , d T[ AID t
-DAA , (U[ i AID t
-DAA , ě XU[ I AID t
-DAA , U[ ) AID t
-DAA , $ U[ AID t
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-N A l (f Y , 0fb D\
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+8A0A(B BBBA T ́ xa 1
+ BEB E(A0A8K
+8A0A(B BBBI T $ ~#
+ BBB B(A0A8J
+8A0A(B BBBA T | ȄE
+ BEB E(A0A8G
+8A0A(B BBBI T Ԃ # Է
+ BBB B(A0A8J
+8A0A(B BBBA T ,
+ BEB E(A0A8K
+8A0A(B BBBI T #
+ BBB B(A0A8J
+8A0A(B BBBA ȑ{. K^ Ǎ Ą Ǎ ܄ Ǎ Ǎ Ǎ $ xǍ < pǍ T hǍ l `Ǎ XǍ PǍ HǍ ̅ @Ǎ 8Ǎ 0Ǎ (Ǎ , Ǎ D Ǎ \ Ǎ t Ǎ Ǎ ƍ ƍ Ԇ ƍ ƍ ƍ ƍ 4 ƍ L ƍ d ƍ | ƍ ƍ ƍ ć ƍ ܇ ƍ ƍ ƍ $ xƍ < pƍ T hƍ l `ƍ Xƍ Pƍ Hƍ ̈ @ƍ 8ƍ 0ƍ (ƍ , ƍ D ƍ \ ƍ t ƍ ƍ ō ō ԉ ō ō ō ō 4 ō L ō d ō | ō ō ō Ċ ō ܊ ō ō ō $ ō < ō T ō l ō ō ō ō ̋ ō ō ō ō , ō D ō \ ō t ō ƍ ƍ ƍ Ԍ ƍ ƍ (ƍ 0ƍ 4 8ƍ L 0ƍ d (ƍ | ƍ ƍ ƍ č ƍ ܍ ƍ ō ō $ ō < ō T ō l ō ō ō ō ̎ ō ō ō ō , ō D ō \ ō t xō pō
+ hǍ `Ǎ ԏ XǍ PǍ3 xǍ pǍ 4 ȍ L ȍ d ɍ | ɍ ʍ ʍ Đ ʍ ܐ ʍ ʍ ʍ $ ʍ < ʍ T ʍ
+ l ̍3 ̍ p͍" ̑ PЍ ЍA э ԍ , ԍ D ԍ \ Ս t Ս Ս Ս Ս Ԓ (Ս 0Ս 8Ս @Ս 4 HՍ L PՍ d XՍ | `Ս hՍ pՍ ē xՍ ܓ Ս Ս Ս $ Ս < Ս T Ս l Ս Ս Ս Ս ̔ Ս Ս Ս Ս , Ս D Ս \ ֍ t ֍ ֍ ֍ ֍ ԕ (֍ 0֍ 8֍ @֍ 4 H֍ L P֍ d X֍ | `֍ h֍ p֍ Ė x֍ ܖ ֍ ֍ ֍ $ ֍ < ֍ T ֍ l ֍ ֍ ֍ ֍ ̗ ֍ ֍" ֍ ֍" , D " \ 0 t 8" P X" p Ԙ x" " 4 " L d " | " ؍ ę ؍" ܙ 0؍ 8؍" P؍ $ X؍" < p؍ T x؍" l ؍ ؍" ؍ ؍" ̚ ؍ ؍" ؍ ؍" , ٍ D ٍ" \ 0ٍ t 8ٍ" Pٍ Xٍ" pٍ ԛ xٍ" ٍ ٍ" ٍ. Al < ٍ. Al \ ٍ. Al | ٍ. Al ٍ. Al ڍ. Al ܜ ڍ. Al ڍ. Al 0ڍ. Al < @ڍ. Al \ Pڍ. Al | `ڍ. Al pڍ. Al ڍ. Al ܝ ڍ. Al ڍ. Al ڍ. Al < ڍ. Al \ ڍ. Al | ڍ. Al ڍ. Al ۍ. Al ܞ ۍ. Al ۍ (ۍ 4 , ۍV BJD G(F0r(A ABB L d Hۍ AAG e
+CAF {
+ACI R
+CAJ JCA , ۍ[ Z
+ AID t
+DAA , , ۍ[ :
+ AID t
+DAA , \ ܍[
+ AID t
+DAA , 8܍[
+ AID t
+DAA , h܍[ ڜ
+ AID t
+DAA , ܍[
+ AID t
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+ AID t
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+ AID t
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+ AID t
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+ AID t
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+ AID t
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+ AID t
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+ AID t
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+ AID t
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+ AID t
+DAA , ̡ xލ[ z
+ AID t
+DAA , ލ[ Z
+ AID t
+DAA , , ލ[ :
+ AID t
+DAA , \ ߍ[
+ AID t
+DAA , 8ߍ[
+ AID t
+DAA , hߍ[ ښ
+ AID t
+DAA , ߍ[
+ AID t
+DAA , ߍ[
+ AID t
+DAA , L ߍ[ z
+ AID t
+DAA , | ([ Z
+ AID t
+DAA , X[ :
+ AID t
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+ AID t
+DAA , [
+ AID t
+DAA , < [ ڙ
+ AID t
+DAA , l [
+ AID t
+DAA , H[
+ AID t
+DAA , ̤ x[ z
+ AID t
+DAA , [ Z
+ AID t
+DAA , , [ :
+ AID t
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+ AID t
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+ AID t
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+ AID t
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+ AID t
+DAA , [
+ AID t
+DAA , L [ z
+ AID t
+DAA , | ([ Z
+ AID t
+DAA , X[ :
+ AID t
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+ AID t
+DAA , [
+ AID t
+DAA , < [ ڗ
+ AID t
+DAA , l [
+ AID t
+DAA , H[
+ AID t
+DAA , ̧ x[ z
+ AID t
+DAA , [ Z
+ AID t
+DAA , , [ :
+ AID t
+DAA , \ [
+ AID t
+DAA , 8[
+ AID t
+DAA , h[ ږ
+ AID t
+DAA , [
+ AID t
+DAA , [
+ AID t
+DAA , L [ z
+ AID t
+DAA , | ([ Z
+ AID t
+DAA , X[ :
+ AID t
+DAA , ܩ [
+ AID t
+DAA , [
+ AID t
+DAA , < [ ڕ
+ AID t
+DAA , l [
+ AID t
+DAA , H[
+ AID t
+DAA , ̪ x[ z
+ AID t
+DAA , [ Z
+ AID t
+DAA , , [ :
+ AID t
+DAA , \ [
+ AID t
+DAA , 8[
+ AID t
+DAA , h[ ڔ
+ AID t
+DAA , [
+ AID t
+DAA , [
+ AID t
+DAA , L [ z
+ AID t
+DAA , | ([ Z
+ AID t
+DAA , X[ :
+ AID t
+DAA , ܬ [
+ AID t
+DAA , [
+ AID t
+DAA , < [ ړ
+ AID t
+DAA , l [
+ AID t
+DAA , H[
+ AID t
+DAA , ̭ x[ z
+ AID t
+DAA , [ Z
+ AID t
+DAA , , [ :
+ AID t
+DAA , \ [
+ AID t
+DAA , 8[
+ AID t
+DAA , h[ ڒ
+ AID t
+DAA , [
+ AID t
+DAA , [
+ AID t
+DAA , L [ z
+ AID t
+DAA , | ([ Z
+ AID t
+DAA , X[ :
+ AID t
+DAA , ܯ [
+ AID t
+DAA , [
+ AID t
+DAA , < [ ڑ
+ AID t
+DAA , l [
+ AID t
+DAA , H[
+ AID t
+DAA , ̰ x[ z
+ AID t
+DAA , [ Z
+ AID t
+DAA , , [ :
+ AID t
+DAA , \ [
+ AID t
+DAA , 8[
+ AID t
+DAA , h[ ڐ
+ AID t
+DAA , [
+ AID t
+DAA , [
+ AID t
+DAA , L [ z
+ AID t
+DAA , | ([ Z
+ AID t
+DAA , X[ :
+ AID t
+DAA " DN
+N A - AG cA L Գ AAG ]
+CAF c
+CAI R
+CAJ JCA L $ AAG ]
+CAF c
+CAI R
+CAJ JCA L t ( AAG ]
+CAF c
+CAI R
+CAJ JCA , Ĵ h BAA D0 AAB, BAA D0} AAB4 $ ( BBA A(D@(A ABBL \ AAG x
CAK r
CAJ R
-CAJ JCA L Զ r AAG x
+CAJ JCA L AAG x
CAK r
CAJ R
-CAJ JCA L $ s AAG x
+CAJ JCA L p AAG x
CAK r
CAJ R
-CAJ JCA L t s AAG x
+CAJ JCA L L AAG x
CAK r
CAJ R
-CAJ JCA L ķ s AAG x
+CAJ JCA L P AAG x
CAK r
CAJ R
-CAJ JCA L `t AAG x
+CAJ JCA L AAG x
CAK r
CAJ R
-CAJ JCA L d t AAG x
+CAJ JCA L < 0 AAG x
CAK r
CAJ R
-CAJ JCA L @u AAG x
+CAJ JCA L AAG x
CAK r
CAJ R
-CAJ JCA L u AAG x
+CAJ JCA L ܷ AAG x
CAK r
CAJ R
-CAJ JCA L T v AAG x
+CAJ JCA L , AAG x
CAK r
CAJ R
-CAJ JCA L v AAG x
+CAJ JCA L | AAG x
CAK r
CAJ R
-CAJ JCA L w AAG x
+CAJ JCA L ̸ ` AAG x
CAK r
CAJ R
-CAJ JCA L D pw AAG x
+CAJ JCA L AAG x
CAK r
CAJ R
-CAJ JCA L w AAG x
+CAJ JCA L l @ AAG x
CAK r
CAJ R
-CAJ JCA L Px AAG x
+CAJ JCA L AAG x
CAK r
CAJ R
-CAJ JCA L 4 x AAG x
+CAJ JCA L AAG x
CAK r
CAJ R
-CAJ JCA L 0y AAG x
+CAJ JCA L \ AAG x
CAK r
CAJ R
-CAJ JCA L Ի y AAG x
+CAJ JCA L AAG x
CAK r
CAJ R
-CAJ JCA L $ z AAG x
+CAJ JCA L p AAG x
CAK r
CAJ R
-CAJ JCA L t z AAG x
+CAJ JCA L L AAG x
CAK r
CAJ R
-CAJ JCA L ļ z AAG x
+CAJ JCA L P AAG x
CAK r
CAJ R
-CAJ JCA L `{ AAG x
+CAJ JCA L AAG x
CAK r
CAJ R
-CAJ JCA L d { AAG x
+CAJ JCA L < 0 AAG x
CAK r
CAJ R
-CAJ JCA L @| AAG e
-CAF C
-ACA R
-CAJ JCA L | AAG e
-CAF C
-ACA R
-CAJ JCA L T } AAG e
-CAF C
-ACA R
-CAJ JCA L `} AAG e
-CAF s
-ACA R
-CAJ JCA L } AAG e
-CAF s
-ACA R
-CAJ JCA L D ~ AAG e
-CAF s
-ACA R
-CAJ JCA L P~ AAG e
-CAF s
-ACA R
-CAJ JCA L ~ AAG e
-CAF s
-ACA R
-CAJ JCA L 4 ~ AAG e
-CAF s
-ACA R
-CAJ JCA L @ AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L $ AAG e
-CAF s
-ACA R
-CAJ JCA L t 0 AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L Ѐ AAG e
-CAF s
-ACA R
-CAJ JCA L d AAG e
-CAF s
-ACA R
-CAJ JCA L p AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L T AAG e
-CAF s
-ACA R
-CAJ JCA L ` AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L D AAG e
-CAF s
-ACA R
-CAJ JCA L P AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L 4 AAG e
-CAF s
-ACA R
-CAJ JCA L @ AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L $ AAG e
-CAF s
-ACA R
-CAJ JCA L t 0 AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L Ѕ AAG e
-CAF s
-ACA R
-CAJ JCA L d AAG e
-CAF s
-ACA R
-CAJ JCA L p AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L T AAG e
-CAF s
-ACA R
-CAJ JCA L ` AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L D AAG e
-CAF s
-ACA R
-CAJ JCA L P AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L 4 AAG e
-CAF s
-ACA R
-CAJ JCA L @ AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L $ AAG e
-CAF s
-ACA R
-CAJ JCA L t 0 AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L Њ AAG e
-CAF s
-ACA R
-CAJ JCA L d AAG e
-CAF s
-ACA R
-CAJ JCA L p AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L T AAG e
-CAF s
-ACA R
-CAJ JCA L ` AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L D AAG e
-CAF s
-ACA R
-CAJ JCA L P AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L 4 AAG e
-CAF s
-ACA R
-CAJ JCA L @ AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L $ AAG e
-CAF s
-ACA R
-CAJ JCA L t 0 AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L Џ AAG e
-CAF s
-ACA R
-CAJ JCA L d AAG e
-CAF s
-ACA R
-CAJ JCA L p AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L T AAG e
-CAF s
-ACA R
-CAJ JCA L ` AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L D AAG e
-CAF s
-ACA R
-CAJ JCA L P AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L 4 AAG e
-CAF s
-ACA R
-CAJ JCA L @ AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L $ AAG e
-CAF s
-ACA R
-CAJ JCA L t 0 AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L Д AAG e
-CAF s
-ACA R
-CAJ JCA L d AAG e
-CAF s
-ACA R
-CAJ JCA L p AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L T AAG e
-CAF s
-ACA R
-CAJ JCA L ` AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L D AAG e
-CAF s
-ACA R
-CAJ JCA L P AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L 4 AAG e
-CAF s
-ACA R
-CAJ JCA L @ AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L $ AAG e
-CAF s
-ACA R
-CAJ JCA L t 0 AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L Й AAG e
-CAF s
-ACA R
-CAJ JCA L d AAG e
-CAF s
-ACA R
-CAJ JCA L p AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L T AAG e
-CAF s
-ACA R
-CAJ JCA L ` AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L D AAG e
-CAF s
-ACA R
-CAJ JCA L P AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L 4 AAG e
-CAF s
-ACA R
-CAJ JCA L @ AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L $ AAG e
-CAF s
-ACA R
-CAJ JCA L t 0 AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L О AAG e
-CAF s
-ACA R
-CAJ JCA L d AAG e
-CAF s
-ACA R
-CAJ JCA L p AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L T AAG e
-CAF s
-ACA R
-CAJ JCA L ` AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L D AAG e
-CAF s
-ACA R
-CAJ JCA L P AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L 4 AAG e
-CAF s
-ACA R
-CAJ JCA L @ AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L $ AAG e
-CAF s
-ACA R
-CAJ JCA L t 0 AAG e
-CAF s
-ACA R
-CAJ JCA L AAG e
-CAF s
-ACA R
-CAJ JCA L У AAG e
-CAF s
-ACA R
-CAJ JCA L d AAG e
-CAF s
-ACA R
-CAJ JCA p" A > 覔 , h < D BLA
+CAJ JCA " r Լ r L L xr
+ BBB B(E0A8J@
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ABF y
-ABD p , xU BAA D0H AAB, J BAA D0} AAB , ȬJ BAA D0} AAB , , 謔R BAA D0E AAB, \ ) BLA
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+ ABBF D XB \ B t Y [ X Կ ha b c p` 4 ` L c d Xc | l o `o ` , $ X BAA D0u AAB, T BAA D0u AAB, BAA D0u AAB, x BAA D0u AAB, BAA D0u AAB, 8 BAA D0 AAB, D BAA D0u AAB, t BAA D0x AAB h p x" @& (R ,S 4 h, L 00 d 2S | 3R X6 7 7 8 9 P: $ ;r < p=W T = t H@ A C D xE L 4 pF ~
+ BBB B(A0A8D`~
+8A0A(B BBBA L FY ~
+ BBB B(A0A8D
+8A0A(B BBBA , G BAA D0 AAB, `H BAA D0v AAB, H BAA D0 AAB, 0I BAA D0u AAB, L I BAA D0| AAB, | I BAA D0w AAB, PJ BAA D0y AAB, J BAA D0z AAB, K BAA D0r AAB, < `K BAA D0r AAB, l K BAA D0r AAB, L BAA D0r AAB, PL BAA D0z AAB, L BAA D0w AAB, , M BAA D0{ AAB< \ pM BBA A(D@
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ABC y
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ABD z
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+ABC L 0j BJH E(A0A8D@
+8A0A(B BBBG l A pl l , m < D n BLA
ABG y
-ABD < 8. BLA
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ABE z
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+ AABI E
+ AABG
+ AABH T 4 (u BAA D0)
+ AABK H
+ AABD %
+ AABG T w BAA D0(
+ AABD }
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-8A0A(B BBBG @ = X < . BLA
+ABD < | ~. BLA
+ABE z
+ABC T BAA D0"
+ AABB
+ AABF AAB L ( BAA D0
+ AABH
+ AABI AABL d BAA D0
+ AABG
+ AABH AABT 臎 BAA D0!
+ AABC
+ AABG AAB T B BAA D0
+ AABG
+ AABF
+ AABJ T d xB BAA D0
+ AABG
+ AABF
+ AABJ T p BAA D0
+ AABD
+ AABH
+ AABG T BAA D0
+ AABD
+ AABH
+ AABG T l BAA D0
+ AABE
+
+ AABJ
+ AABH T BAA D0
+ AABE
+
+ AABJ
+ AABH T BAA D0
+ AABJ
+ AABD
+ AABA T t BAA D0
+ AABJ
+ AABD
+ AABA 蝎 HS , : D : \ t S Dp Dp Dp S h 4
+ L S d Ⱥ | `
+ D Î BBB B(A0A8
+0A(B BBBF D (Ǝ BBB B(A0A8
+0A(B BBBE D < Ȏ BBB B(A0A8
+0A(B BBBE D ˎ BBB B(A0A8
+0A(B BBBF L Ύ
+ BJH E(A0A8D@
+8A0A(B BBBG Ύ < @юW T ю l pӎ Վ َ (ێW pێ[ ێ[ [ 4 H[ L [ d [ | ݎ[ hݎ[ L ݎ2 BJH E(A0A8D@
+8A0A(B BBBG ߎ2 2 , 2 D 2 \ @ t < . BLA
ABD z
-ABC < T BLA
+ABC < BLA
ABF y
-ABD , 0v BLA
-ABA , BLA
-ABJ , ` BLA
-ABF , $ 0~ BLA
-ABE , T BLA
-ABF , P BLA
-ABF , @ BLA
-ABF , BLA
-ABF , } BLA
-ABE , D 0v BLA
-ABA L t
- BJH E(A0A8D@
-8A0A(B BBBG , @1 BLA
-ABD 4 P BHA A(
- ABBH , < D BLA
+ABD L BJH E(A0A8D@
+8A0A(B BBBG 4 \ BBA A(
+ ABBF 4 BBA A(
+ ABBF 4 % BBA A(
+ ABBF 4 BBA A(+
+ ABBJ 4 < BBA A(/
+ ABBF 4 t x BBA A(
+ ABBB 4 ` BBA A(+
+ ABBJ 4 BBA A(
+ ABBF 4 BBA A(+
+ ABBJ 4 T BBA A(+
+ ABBJ 4 BBA A(
+ ABBF 4 h BBA A(+
+ ABBJ 4 BBA A(+
+ ABBJ 4 4 8 BBA A(+
+ ABBJ 4 l BBA A(+
+ ABBJ 4 BBA A(/
+ ABBF , p) BLA
+ABD 4 pE BBA A(
+ ABBE 4 D BBA A(D
+ ABBA 4 | BBA A(D
+ ABBA 4 x BBA A(D
+ ABBA 4 E BBA A(
+ ABBE 4 $ BBA A(D
+ ABBA 4 \ E BBA A(
+ ABBE 4 E BBA A(
+ ABBE " < # BLA
ABC y
-ABD 4 0a BMA A(
- ABBB 4 ha BMA A(
- ABBE 4 q BMA A(
- ABBE 4 ,
-a BMA A(
- ABBE d
| A 0 , ~ BLA
-ABE , BLA
-ABJ , BLA
-ABF , < } BLA
-ABE l ( 4 x BMA A(V
- ABBD 4 BMA A(V
- ABBD " < $ "` BMB A(A0
-(A BBBD < d $ BMB A(A0l
-(A BBBJ < '` BMB A(A0
-(A BBBD < )` BMB A(A0
-(A BBBD < $ + BMB A(A0l
-(A BBBJ < d . BMB A(A0l
-(A BBBJ < 01` BMB A(A0
-(A BBBD P3
- H5
- 4 @7V BJD G(F0r(A ABB < L h7 BMB A(A0!
-(A BBBE < 9 BMB A(A0!
-(A BBBE (<B `? A E , H D J; \ J t PM O R; SB PV XY; Y 8]; 4 `] L _R d 0`; | X` @c e; e hz 0lz o < $ r BMB A(A0
-(A BBBC d tR | u v Hx z o |H |H } o 4 p L L H BJH E(A0A8D@
-8A0A(B BBBG L : BJH E(A0A8D@
-8A0A(B BBBG , L AD,
-AF A , (L AD,
-AF A , L H{ WGAF , | { WGAF T 葕 BBE A(G0
-(F LBBG
-(A BBBG T BBE A(G0
-(F LBBG
-(A BBBG < \ 8T BLA q
-ABE
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-8A0A(B BBBA } A{ , 蝕> AG
-AF |A | < BBE E(A0A8DP
-8A0A(B BBBC
-8A0A(B BBBA
-8A0A(B BBBF 2 Ah
-G A r Af
-I AL D " nh BBB J(A0A8G
-8D0A(B BBBE D Yh BGB D(D0J
-(A BBBD `E lX T h BBE E(D0A8G/
-8D0A(B BBBJ T L @ g BBE E(D0A8G
-8D0A(B BBBE T ȭ g BBE E(A0A8G
-8D0A(B BBBI 4 P
- Qg BAD
-ABA $ ( AAG0AA4 \ g BAD K
-ABA < i f BIA A(Gp
-(D ABBA < 8i f BIA A(Gp
-(D ABBA < hb f BIA A(Gp
-(D ABBA < T b }f BIA A(Gp
-(D ABBA < ȶb Vf BIA A(Gp
-(D ABBA < b /f BIA A(Gp
-(D ABBA < (i f BIA A(Gp
-(D ABBA < T Xi e BIA A(Gp
-(D ABBA < b e BIA A(Gp
-(D ABBA < b e BIA A(Gp
-(D ABBA < 轕b le BIA A(Gp
-(D ABBA < T b Ee BIA A(Gp
-(D ABBA < Hi e BIA A(Gp
-(D ABBA < xi d BIA A(Gp
-(D ABBA < i d BIA A(Gp
-(D ABBA < T Õi d BIA A(Gp
-(D ABBA < ŕi d BIA A(Gp
-(D ABBA < 8ƕb [d BIA A(Gp
-(D ABBA < hǕb 4d BIA A(Gp
-(D ABBA < T ȕb
d BIA A(Gp
-(D ABBA < ɕb c BIA A(Gp
-(D ABBA < ʕb c BIA A(Gp
-(D ABBA < (̕b c BIA A(Gp
-(D ABBA < T X͕b qc BIA A(Gp
-(D ABBA < Εb Jc BIA A(Gp
-(D ABBA < ϕb #c BIA A(Gp
-(D ABBA < Еt b BHA J
- DABA < T (ӕt b BHA J
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-DAA 4 b BHA GP
- DABG 4 tb BHA GP
- DABG 4 < @ Ub BHA GP
- DABG 4 t 6b BHA GP
- DABG 4 b BHA GP
- DABG 4 H a BHA GP
- DABG 4 a BHA GP
- DABG 4 T a BHA GP
- DABG 4 P a BHA GP
- DABG 4 |a BHA GP
- DABG 4 ]a BHA GP
- DABG 4 4 X >a BHA GP
- DABG 4 l a BHA GP
- DABG 4 a BHA GP
- DABG 4 ` ` BHA GP
- DABG 4
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- DABG 4 L
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- DABG 4
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- DABG 4
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- DABG 4
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- DABG 4 , p '` BHA GP
- DABG 4 d ` BHA GP
- DABG 4 _ BHA GP
- DABG 4 x _ BHA GP
- DABG 4 _ BHA GP
- DABG 4 D ( _ BHA GP
- DABG 4 | m_ BHA GP
- DABG 4 N_ BHA GP
- DABG 4 0 /_ BHA GP
- DABG 4 $
_ BHA GP
- DABG 4 \
^ BHA GP
- DABG 4
8 ^ BHA GP
- DABG 4
^ BHA GP
- DABG 4
^ BHA GP
- DABG 4 < @ u^ BHA GP
- DABG 4 t V^ BHA GP
- DABG 4 7^ BHA GP
- DABG 4 H ^ BHA GP
- DABG 4 ] BHA GP
- DABG 4 T ] BHA GP
- DABG 4 P ] BHA GP
- DABG 4 ] BHA GP
- DABG 4 }] BHA GP
- DABG 4 4 X ^] BHA GP
- DABG 4 l ?] BHA GP
- DABG 4 ] BHA GP
- DABG 4 ` ] BHA GP
- DABG 4 \ BHA GP
- DABG 4 L " \ BHA GP
- DABG 4 h# \ BHA GP
- DABG 4 $ \ BHA GP
- DABG 4 & f\ BHA GP
- DABG 4 , p' G\ BHA GP
- DABG 4 d ( (\ BHA GP
- DABG 4 * \ BHA GP
- DABG 4 x+ [ BHA GP
- DABG 4 , [ BHA GP
- DABG 4 D (. [ BHA GP
- DABG 4 | / [ BHA GP
- DABG 4 0 n[ BHA GP
- DABG 4 02 O[ BHA GP
- DABG 4 $ 3 0[ BHA GP
- DABG 4 \ 4 [ BHA GP
- DABG 4 86 Z BHA GP
- DABG 4 7 Z BHA GP
- DABG 4 8 Z BHA GP
- DABG 4 < @: Z BHA GP
- DABG 4 t ; vZ BHA GP
- DABG 4 < WZ BHA GP
- DABG 4 H> 8Z BHA GP
- DABH 4 ? Z BHA GP
- DABH 4 T @ Y BHA GP
- DABH 4 PB Y BHA GP
- DABH 4 C Y BHA GP
- DABH 4 E Y BHA GP
- DABH 4 4 XF ~Y BHA GP
- DABH 4 l G _Y BHA GP
- DABH 4 I @Y BHA GP
- DABH 4 `J !Y BHA GP
- DABH 4 K Y BHA GP
- DABH 4 L M X BHA GP
- DABH 4 hN X BHA GP
- DABH 4 O X BHA GP
- DABH 4 Q X BHA GP
- DABH 4 , pR gX BHA GP
- DABH 4 d S HX BHA GP
- DABH < T U BBA A(G@a
-(A ABBD D UY W BBB A(A0Gj
-0A(A BBBG < $ W W BBA A(JE
-(A ABBE d YB BBB B(A0A8G@x
+ABD $ % < < )p BBB A(A0
+(A BBBD < | +p BBB A(A0
+(A BBBF < .p BBB A(A0
+(A BBBF < P0x BBB A(A0
+(A BBBF < 2A T 2 4 l P3 BBA A(/
+ ABBF 4 5 BBA A(
+ ABBB 4 7 BBA A(/
+ ABBF 4 :% BBA A(
+ ABBF L <" < d > BBB A(A0t
+(A BBBE < @ BBB A(A0t
+(A BBBE < XC BBB A(A0f
+(A BBBK < $ E BBB A(A0j
+(A BBBG < d H BBB A(A0f
+(A BBBK < 8K BBB A(A0j
+(A BBBG D M BBB B(A0A8
+0A(B BBBF D , P BBB B(A0A8
+0A(B BBBF D t hR BBB B(A0A8
+0A(B BBBE D 0U BBB B(A0A8
+0A(B BBBF D xW BBB B(A0A8
+0A(B BBBE D L @Z BBB B(A0A8
+0A(B BBBE D ] BBB B(A0A8
+0A(B BBBE D _ BBB B(A0A8
+0A(B BBBE D $ b BBB B(A0A8
+0A(B BBBF D l d BBB B(A0A8
+0A(B BBBE 4 gV BJD G(F0r(A ABB D g BBB B(A0A8
+0A(B BBBG D 4 j BBB B(A0A8
+0A(B BBBG D | m BBB B(A0A8U
+0A(B BBBH D p BBB B(A0A8U
+0A(B BBBH r $ s < s T s D l pt BBB B(A0A8
+0A(B BBBF L Hw BJH E(A0A8D@
+8A0A(B BBBG L y: BJH E(A0A8D@
+8A0A(B BBBG T {} A{ , t h{> AG
+AF |A x|2 Ah
+G A |r Af
+I AL | BEA A(D0F
+(G AEFJ x(A ABB D | X}3 S
+ BGB A(D0Gh
+0A(A BBBA L P~ S
+ BBB B(D0A8N
+8A0A(B BBBG , > S
+ AID W
+DAA , D > `S
+ AID W
+DAA , E lX L H (S
+ BEE E(D0D8G
+8D0A(B BBBD D R
+ BEE D(D0G
+0D(A BBBF T $ R
+ BEE E(D0D8G;
+8D0A(B BBBH T | X pR
+ BEE E(D0D8G;
+8D0A(B BBBH < +R
+ BBD D(J
+(A ABBE < P -R
+ BBA D(J
+(A ABBD < T " /R
+ BBA A(D
+(A ABBG < R -R
+ BBA A(D
+(A ABBJ < D +R
+ BBA A(D
+(A ABBD , )R
+ AAG@k
+DAA , D 0
+R
+ AAG`
+DAA < t g Q
+ BED D(G
+(D ABBD , l BAA D0u AAB, @ BAA D0u AAB4 BBA A(D0p(A ABB, BAA D0u AAB, 4 X BAA D0u AAB4 d BBA A(D0p(A ABBt P
+ BBB A(A0G
+0A(A BBBE c
+0A(A BBBE C
+0A(A BBBE T \ eP
+ BEE E(D0C8G
+8A0A(B BBBH l D ؞S JP
+ BEB A(A0Gh
+0A(A BBBA $ AAG0AA4 < x P
+ BAD K
+ABA 4 t РK O
+ BHA G`
+ DABA 4 衏K O
+ BHA G`
+ DABA 4 K O
+ BHA G`
+ DABA 4 K |O
+ BHA G`
+ DABA 4 T 0D ]O
+ BHA G`
+ DABA 4 HD >O
+ BHA G`
+ DABA 4 `D O
+ BHA G`
+ DABA 4 xD O
+ BHA G`
+ DABA 4 4 K N
+ BHA G`
+ DABA 4 l K N
+ BHA G`
+ DABA 4 K N
+ BHA G`
+ DABA 4 جK N
+ BHA G`
+ DABA 4 D eN
+ BHA G`
+ DABA 4 L D FN
+ BHA G`
+ DABA 4 D 'N
+ BHA G`
+ DABA 4 8D N
+ BHA G`
+ DABA 4 PK M
+ BHA G`
+ DABA 4 , hK M
+ BHA G`
+ DABA 4 d K M
+ BHA G`
+ DABA 4 K M
+ BHA G`
+ DABA 4 D mM
+ BHA G`
+ DABA 4 ȷD NM
+ BHA G`
+ DABA 4 D ฏD /M
+ BHA G`
+ DABA 4 | D M
+ BHA G`
+ DABA 4 K L
+ BHA G`
+ DABA 4 (K L
+ BHA G`
+ DABA 4 $ @K L
+ BHA G`
+ DABA 4 \ XK L
+ BHA G`
+ DABA 4 pD uL
+ BHA G`
+ DABA 4 D VL
+ BHA G`
+ DABA 4 D 7L
+ BHA G`
+ DABA 4 < D L
+ BHA G`
+ DABA 4 t ÏK K
+ BHA G`
+ DABA 4 ďK K
+ BHA G`
+ DABA 4 ƏK K
+ BHA G`
+ DABA 4 ǏK K
+ BHA G`
+ DABA 4 T 0ȏK }K
+ BHA G`
+ DABA 4 HɏK ^K
+ BHA G`
+ DABA 4 `ʏK ?K
+ BHA G`
+ DABA 4 xˏK K
+ BHA G`
+ DABA 4 4 ̏K K
+ BHA G`
+ DABA 4 l ͏K J
+ BHA G`
+ DABA 4 ΏD J
+ BHA G`
+ DABA 4 ϏD J
+ BHA G`
+ DABA 4 ЏD J
+ BHA G`
+ DABA 4 L ҏD fJ
+ BHA G`
+ DABA 4 ӏD GJ
+ BHA G`
+ DABA 4 8ԏD (J
+ BHA G`
+ DABA 4 PՏD J
+ BHA G`
+ DABA 4 , h֏D I
+ BHA G`
+ DABA 4 d D I
+ BHA G`
+ DABA 4 ؏D I
+ BHA G`
+ DABA 4 ُK I
+ BHA G`
+ DABA 4 ڏK nI
+ BHA G`
+ DABA 4 D ۏK OI
+ BHA G`
+ DABA 4 | K 0I
+ BHA G`
+ DABA 4 ޏK I
+ BHA G`
+ DABA 4 (ߏD H
+ BHA G`
+ DABA 4 $ @D H
+ BHA G`
+ DABA 4 \ XD H
+ BHA G`
+ DABA 4 pD H
+ BHA G`
+ DABA 4 D vH
+ BHA G`
+ DABA 4 K WH
+ BHA G`
+ DABA 4 < D 8H
+ BHA G`
+ DABA 4 t K H
+ BHA G`
+ DABA 4 D G
+ BHA G`
+ DABA 4 K G
+ BHA G`
+ DABA 4 D G
+ BHA G`
+ DABA 4 T 0K G
+ BHA G`
+ DABA 4 HK ~G
+ BHA G`
+ DABA 4 `D _G
+ BHA G`
+ DABA 4 xD @G
+ BHA G`
+ DABA 4 4 K !G
+ BHA G`
+ DABA 4 l K G
+ BHA G`
+ DABA 4 D F
+ BHA G`
+ DABA 4 D F
+ BHA G`
+ DABA 4 K F
+ BHA G`
+ DABA 4 L D F
+ BHA G`
+ DABA 4 K gF
+ BHA G`
+ DABA 4 8K HF
+ BHA G`
+ DABA 4 PD )F
+ BHA G`
+ DABA 4 , hK
+F
+ BHA G`
+ DABA 4 d K E
+ BHA G`
+ DABA < i E
+ BIA A(Gp
+(D ABBA < i E
+ BIA A(Gp
+(D ABBA < i ~E
+ BIA A(Gp
+(D ABBA < \ (i WE
+ BIA A(Gp
+(D ABBA < X i 0E
+ BIA A(Gp
+(D ABBA < i E
+ BIA A(Gp
+(D ABBA <
+ b D
+ BIA A(Gp
+(D ABBA < \
+ b D
+ BIA A(Gp
+(D ABBA <
+ b D
+ BIA A(Gp
+(D ABBA <
+ Hb mD
+ BIA A(Gp
+(D ABBA < xi FD
+ BIA A(Gp
+(D ABBA < \ i D
+ BIA A(Gp
+(D ABBA < i C
+ BIA A(Gp
+(D ABBA < i C
+ BIA A(Gp
+(D ABBA < 8 i C
+ BIA A(Gp
+(D ABBA < \ h
i C
+ BIA A(Gp
+(D ABBA < b \C
+ BIA A(Gp
+(D ABBA < b 5C
+ BIA A(Gp
+(D ABBA <
b C
+ BIA A(Gp
+(D ABBA < \
(b B
+ BIA A(Gp
+(D ABBA <
Xi B
+ BIA A(Gp
+(D ABBA <
i B
+ BIA A(Gp
+(D ABBA < i rB
+ BIA A(Gp
+(D ABBA < \ i KB
+ BIA A(Gp
+(D ABBA < i $B
+ BIA A(Gp
+(D ABBA < Hi A
+ BIA A(Gp
+(D ABBA < xi A
+ BIA A(Gp
+(D ABBA < \ i A
+ BIA A(Gp
+(D ABBA < i A
+ BIA A(Gp
+(D ABBA < i aA
+ BIA A(Gp
+(D ABBA < 8 b :A
+ BIA A(Gp
+(D ABBA < \ h b A
+ BIA A(Gp
+(D ABBA < !b @
+ BIA A(Gp
+(D ABBA < "b @
+ BIA A(Gp
+(D ABBA < #b @
+ BIA A(Gp
+(D ABBA 4 \ (%K w@
+ BHA G`
+ DABA 4 @&K X@
+ BHA G`
+ DABA 4 X'D 9@
+ BHA G`
+ DABA 4 p(D @
+ BHA G`
+ DABA 4 < )D ?
+ BHA G`
+ DABA 4 t *D ?
+ BHA G`
+ DABA 4 +K ?
+ BHA G`
+ DABA 4 ,D ?
+ BHA G`
+ DABA < -b ?
+ BIA A(Gp
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+ BIA A(Gp
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+ BIA A(Gp
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+?
+ BIA A(Gp
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+ BHA G`
+ DABA 4 T 3D >
+ BHA G`
+ DABA 4 4D >
+ BHA G`
+ DABA 4 5D >
+ BHA G`
+ DABA 4 7D g>
+ BHA G`
+ DABA 4 4 8D H>
+ BHA G`
+ DABA 4 l 89D )>
+ BHA G`
+ DABA 4 P:D
+>
+ BHA G`
+ DABA 4 h;D =
+ BHA G`
+ DABA 4 <D =
+ BHA G`
+ DABA 4 L =D =
+ BHA G`
+ DABA 4 >D =
+ BHA G`
+ DABA 4 ?K o=
+ BHA G`
+ DABA 4 @D P=
+ BHA G`
+ DABA 4 , AK 1=
+ BHA G`
+ DABA 4 d CD =
+ BHA G`
+ DABA < (Dt <
+ BHA J
+ DABA < hFt <
+ BHA J
+ DABA D H <
+ BIA A(J+
+(D ABBA D d J <
+ BIA A(J$
+(D ABBA D K <
+ BIA A(J+
+(D ABBA D M f<
+ BIA A(J$
+(D ABBA D < N A<
+ BIA A(J+
+(D ABBA D P <
+ BIA A(J$
+(D ABBA < xQd ;
+ BHA J
+ DABA 4 S9 ;
+ AHJ
+DAA 4 D U4 ;
+ AHJ
+DAA < 4 W BBA A(G@a
+(A ABBD < hX ;
+ BBA A(J]
+(A ABBE < Y i;
+ BBA A(JE
+(A ABBE d h[B BBB B(A0A8G@x
8A0A(B BBBE
-8A0A(B BBBF T Z DW BBE E(D0A8G
-8D0A(B BBBI L $ (]M W BBB B(A0D8N
-8A0A(B BBBH , t (`> W AID W
-DAA L 8`M V BBB B(A0D8N
-8A0A(B BBBH , 8c> V AID W
-DAA L $ HcM V BBB B(A0D8N
-8A0A(B BBBH , t Hf> V AID W
-DAA L Xf V BDB B(A0D8G
-8A0A(B BBBA D hS V BEB A(A0Gh
-0A(A BBBA < < i V BKA A(Dp
-(A ABBG < | k& V BIA A(D}
-(A ABBD < m4 V BIA A(D
-(A ABBJ < ol BBE A(A0Y(A BBB < o BHB A(A0~(A BBL < 4 Pp BHB A(A0~(A BBL < t p BHB A(A0~(A BBL T qG U BBB B(A0G8G
-8D0A(B BBBA T T sG qU BBB B(A0G8G
-8D0A(B BBBA T uG OU BBB B(A0G8G
-8D0A(B BBBA T vG -U BBB B(A0G8G
-8D0A(B BBBA T \ xG U BBB B(A0G8G
-8D0A(B BBBA T zG T BBB B(A0G8G
-8D0A(B BBBA T ! |G T BBB B(A0G8G
-8D0A(B BBBA T d! ~G T BBB B(A0G8G
-8D0A(B BBBA < t" Ѐ BHB A(A0n(A BBL < " BHB A(A0n(A BBL < " p BHB A(A0n(A BBL < 4# BHB A(A0n(A BBL < t# BHB A(A0n(A BBL < # ` BHB A(A0n(A BBL L # BBB B(A0A8G
-8A0A(B BBBD L D$ 0 BBB B(A0A8G
-8A0A(B BBBD 4 $ BEG A(D0m(D ABBT $ v +R BBB B(A0G8G
-8D0A(B BBBA T l$ v >R BBB B(A0G8G
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-8A0A(B BBBH L , ( BEB E(A0D8G
-8A0A(B BBBA 4 L U ( BEE E(D0D8J
-8A0A(B BBBC T ( BBB B(D0D8G)
-8D0A(B BBBC / AJ0_D | w BBB B(A0D8GP
-8A0A(B BBBJ
-8A0A(B BBBK
-8A0A(B BBBJ L ܓ &( BEB E(A0D8G
-8A0A(B BBBA L D U ' BEE E(D0D8J
-8A0A(B BBBC T ( BBB B(D0D8G
-8D0A(B BBBK #/ AJ0_D L ĕ #a BEE B(A0A8D`\
-8A0A(B BBBF L \ @+ ' BEB E(A0D8G
-8A0A(B BBBA d , L ĕ ,U m' BEE E(D0D8J
-8A0A(B BBBC T 0 ' BBB B(D0D8G)
-8D0A(B BBBC $ @6/ AJ0_D L D P6 BEE B(A0A8D`
-8A0A(B BBBE L ܖ = "' BEB E(A0D8G
-8A0A(B BBBA `? L D X?U & BEE E(D0D8J
-8A0A(B BBBC T hC ' BBB B(D0D8G)
-8D0A(B BBBC H/ AJ0_D L Ę H BBB B(D0A8J
-8A0A(B BBBF L \ @O & BEB E(A0D8G
-8A0A(B BBBA d P L Ę PU i& BEE E(D0D8J
-8A0A(B BBBC T T & BBB B(D0D8G)
-8D0A(B BBBC $ @Z/ AJ0_D D &. K^ L 8Z V& BBB B(A0I8G
-8D0A(B BBBA L [ ;& BBB B(A0I8G
-8D0A(B BBBA L D x] & BBB B(A0I8G
-8D0A(B BBBA L _ & BBB B(A0I8G
-8D0A(B BBBA L ` % BBB B(A0I8G
-8D0A(B BBBA L 4 Xb % BBB B(A0I8G
-8D0A(B BBBA L c % BBB B(A0I8G
-8D0A(B BBBA L ԛ e % BBB B(A0I8G
-8D0A(B BBBA L $ 8g ~% BBB B(A0I8G
-8D0A(B BBBA L t h c% BBB B(A0I8G
-8D0A(B BBBA L Ĝ xj H% BBB B(A0I8G
-8D0A(B BBBA L l -% BBB B(A0I8G
-8D0A(B BBBA L d m % BBB B(A0I8G
-8D0A(B BBBA L Xo $ BBB B(A0I8G
-8D0A(B BBBA L p $ BBB B(A0I8G
-8D0A(B BBBA L T r $ BBB B(A0I8G
-8D0A(B BBBA L 8t $ BBB B(A0I8G
-8D0A(B BBBA L u $ BBB B(A0I8G
-8D0A(B BBBA L D xw p$ BBB B(A0I8G
-8D0A(B BBBA L y U$ BBB B(A0I8G
-8D0A(B BBBA L z :$ BBB B(A0I8G
-8D0A(B BBBA L 4 X| $ BBB B(A0I8G
-8D0A(B BBBA L } $ BBB B(A0I8G
-8D0A(B BBBA 9 Hp ġ @ Hs L AAG ]
+8A0A(B BBBF D P\o :
+ BEE D(D0G
+0D(A BBBB L x]7 :
+ BBB B(D0A8N
+8A0A(B BBBG , < h`> :
+ AID W
+DAA L l x`7 :
+ BBB B(D0A8N
+8A0A(B BBBG , hc> :
+ AID W
+DAA L xc7 x:
+ BBB B(D0A8N
+8A0A(B BBBG , < hf> |:
+ AID W
+DAA , $ xf BAA D0w AABT T f BAA D0
+ AABE
+
+ AABJ
+ AABH 4 `i BBA A(D0q(A ABB, i BAA D0x AABT j BAA D0
+ AABE
+
+ AABJ
+ AABH 4 l l BBA A(D0q(A ABB, l BAA D0z AABT Xm BAA D0!
+ AABC
+ AABG AAB 4 , o BBA A(D0t(A ABB, d Hp BAA D0w AABT p BAA D0"
+ AABB
+ AABF AAB 4 @s BBA A(D0q(A ABB, $! s BAA D0y AABT T! s BAA D0
+ AABJ
+ AABD
+ AABA 4 ! v BBA A(D0t(A ABB, ! xv BAA D0z AABT " v BAA D0
+ AABJ
+ AABD
+ AABA 4 l" y BBA A(D0t(A ABB, " Xy BAA D0| AABL " y BAA D0
+ AABH
+ AABI AAB4 $# | BBA A(D0w(A ABB, \# p| BAA D0y AABL # | BAA D0
+ AABG
+ AABH AAB4 # 0 BBA A(D0t(A ABB, $ BAA D0 AABT D$ BAA D0
+ AABD
+ AABH
+ AABG 4 $ BBA A(D0z(A ABB, $ BAA D0 AABT % H BAA D0
+ AABD
+ AABH
+ AABG 4 \% Ѕ BBA A(D0z(A ABB, % ( BAA D0u AABT % BAA D0(
+ AABD }
+ AABG
+ AABF 4 & BBA A(D0p(A ABB, T& X BAA D0 AABT & ȉ BAA D0)
+ AABK H
+ AABD %
+ AABG 4 & ` BBA A(D0z(A ABB, ' BAA D0u AABT D' B BAA D0
+ AABG
+ AABF
+ AABJ 4 ' BBA A(D0o(A ABB, ' X BAA D0v AABT ( B BAA D0
+ AABG
+ AABF
+ AABJ 4 \( BBA A(D0o(A ABB, ( BAA D0x AABT ( XR BAA D0
+ AABI E
+ AABG
+ AABH 4 ) ` BBA A(D0r(A ABB, T) BAA D0u AABT ) R BAA D0
+ AABB }
+ AABG
+ AABH 4 ) BBA A(D0p(A ABB< \) x
l.
+ BEA A(G@z
+(D ABBE , T* H: BDD G0a DAB , * X: BDD G0a DAB , * h: BDD G0a DAB , * x: BDD G0a DAB , + : BDD G0a DAB , D+ : BDD G0a DAB , t+ : BDD G0a DAB , + : BDD G0a DAB , + Ș: BDD G0a DAB , , ؘ: BDD G0a DAB , 4, 蘐: BDD G0a DAB , d, : BDD G0a DAB , , : BDD G0a DAB , , : BDD G0a DAB , , (: BDD G0a DAB , $- 8: BDD G0a DAB < T- Hn BDE A(A0Y(A BBB < - x BJB A(A0|(A BBL < - ؙ BJB A(A0|(A BBL < . 8 BJB A(A0|(A BBL T - G ?*
+ BDB B(A0G8G
+8D0A(B BBBA T - G *
+ BDB B(A0G8G
+8D0A(B BBBA T L. G )
+ BDB B(A0G8G
+8D0A(B BBBA T . G )
+ BDB B(A0G8G
+8D0A(B BBBA T . xG )
+ BDB B(A0G8G
+8D0A(B BBBA T T/ pG )
+ BDB B(A0G8G
+8D0A(B BBBA T / hG s)
+ BDB B(A0G8G
+8D0A(B BBBA T 0 `G Q)
+ BDB B(A0G8G
+8D0A(B BBBA < 1 X BJB A(A0l(A BBL < T1 BJB A(A0l(A BBL < 1 BJB A(A0l(A BBL < 1 H BJB A(A0l(A BBL < 2 BJB A(A0l(A BBL < T2 諐 BJB A(A0l(A BBL L 2 8 BDB B(A0A8G
+8A0A(B BBBD L 2 BDB B(A0A8G
+8A0A(B BBBD D |2 ( '
+ BDE A(G0D
+0D(A BBBA T 2 v
+'
+ BDB B(A0G8G
+8D0A(B BBBA T 3 v '
+ BDB B(A0G8G
+8D0A(B BBBA T t3 пv 0'
+ BDB B(A0G8G
+8D0A(B BBBA T 3 v C'
+ BDB B(A0G8G
+8D0A(B BBBA T $4 Ɛv V'
+ BDB B(A0G8G
+8D0A(B BBBA T |4 Hɐ i'
+ BEE B(D0A8G
+8A0A(B BBBD T 4 Ґ '
+ BBB E(A0A8O
+8A0A(B BBBG 5 xאF AJ0vD T L5 א '
+ BBB E(I0A8J
+8A0A(B BBBD \6 ڐ6 K0j T 5 Hڐ% H'
+ BBB G(D0G8G~
+8D0A(B BBBF T 6 '
+ BBB B(A0F8G
+8A0A(B BBBA $ $7 a AKK@DDAL 6 ?'
+ BIB A(D0N0
+0D(A BBBE T 6 -'
+ BBB B(A0D8G
+8A0A(B BBBJ d <7 h3 '
+ AAMv
+DAF
+DAD D
+DAG
+DAG
+DAA d 7 @3 &
+ AAMv
+DAF
+DAD D
+DAG
+DAG
+DAA d 8 3 h&
+ AAMv
+DAF
+DAD D
+DAG
+DAG
+DAA d t8 3 &
+ AAMv
+DAF
+DAD D
+DAG
+DAG
+DAA \ 8 S %
+ BCD J
+ DABE
+ DABJ D
+ DABA 9 P AS0wD : P AS0wD 4: (P AS0wD T: XP AS0wD t: R AJ0BD : R AJ0BD : R AJ0BD : HP AL0~D : xP AL0~D ; P AL0~D 4; P AL0~D T; P AL0~D t; 8P AL0~D ; hP AL0~D ; P AL0~D ; P AL0~D ; P AL0~D < (P AL0~D 4< XP AL0~D T< P AL0~D t< P AL0~D < P AL0~D < P AL0~D < HP AL0~D < xP AL0~D = P AL0~D 4= P AL0~D T= P AL0~D t= 8P AL0~D = hP AL0~D = P AL0~D = P AL0~D = P AL0~D > ( P AL0~D 4> X P AL0~D T> P AL0~D , t> : BDD G0a DAB 4 > BBA A(C
+ ABBB 4 > @ BBA A(C
+ ABBB D ? BBB B(A0A8
+0A(B BBBE , \? : BDD G0a DAB 4 ? D BBA A(
+ ABBD 4 ?
+D BBA A(
+ ABBD D ? BBB B(A0A8
+0A(B BBBD , D@ : BDD G0a DAB t@ @ @ , @ p: BDD G0a DAB < @ BBB A(A0i
+(A BBBH < ,A BBB A(A0i
+(A BBBH D lA BBB B(A0A8
+0A(B BBBG , A : BDD G0a DAB A B A B B Z , ,B ` : BDD G0a DAB \B p [ tB [ B d , B X : BDD G0a DAB B h [ B [ C d D dB P e
+ BBG D(G0D
+0D(A BBBI D B ! C
+ BBG D(G0D
+0D(A BBBI , C `%: BDD G0a DAB 4 C p% BBA A(C
+ ABBB 4 D ' BBA A(C
+ ABBB D LD `* BBB B(A0A8
+0A(B BBBE , D (-: BDD G0a DAB 4 D 8-D BBA A(
+ ABBD 4 D P/D BBA A(
+ ABBD D 4E h1 BBB B(A0A8
+0A(B BBBD , |E 3: BDD G0a DAB E 3 E 84 E 4 , E 5: BDD G0a DAB < $F (5 BBB A(A0e
+(A BBBD < dF 7 BBB A(A0e
+(A BBBD D F H: BBB B(A0A8
+0A(B BBBG , F =: BDD G0a DAB G 0=: 4G X>: LG ?Z , dG @: BDD G0a DAB G @[ G A[ G hAd , G A: BDD G0a DAB H A[ $H B[ <H `Bd , TH B BAA D0r AAB, H C BAA D0r AAB4 H XC BBA A(D0p(A ABB H CY I C[ I @Da , 4I D BAA D0r AAB, dI D BAA D0r AAB4 I 8E BBA A(D0p(A ABB I E~ I E I pF A , J F BAA D0{ AAB, LJ @G BAA D0{ AAB4 |J G BBA A(D0x(A ABB J Gb J PHc J Ho , J I BAA D0{ AAB, ,K `I BAA D0{ AAB4 \K I BBA A(D0x(A ABB K J K J K K , K K BAA D0{ AAB, L K BAA D0{ AAB4 <L PL BBA A(D0x(A ABB tL L` L L` L 8Ml , L M BAA D0w AAB, L M BAA D0w AAB4 M PN BBA A(D0q(A ABB TM N lM O M O < M P BBA A(D@
+(A ABBD < M Q BBA A(D@
+(A ABBD < N Q BEL A(A0(A BBB \N Rc tN Sc N `So , N S BAA D0z AAB, N T BAA D0z AAB4 O xT BBA A(D0t(A ABB <O T TO HU lO U $ O HV) KA|A T N PX !
+ BBB B(D0A8Gm
+8A0A(B BBBA T LO [
+ BBB B(D0A8Gm
+8A0A(B BBBA 4 \P _ BBA A(^
+ ABBG T O 8b
+ BBB B(D0A8G.
+8A0A(B BBBD $ P h$ A]A Q j D`l
+H X 4Q xk D`s
+I X T TQ k BBB A(A0D
+0A(A BBBI u0A(A BBB T Q l BBB A(A0D
+0A(A BBBI u0A(A BBB R mf D`i
+C , $R o BAA j
+ABG T Q xr ]
+ BBB B(D0A8G
+8A0A(B BBBF L Q zY a
+ BBB B(A0A8D
+8A0A(B BBBA 4 DR { 4
+ BGB A(A0D 4S { G L R {
+ BBB B(A0A8D`~
+8A0A(B BBBA S 0| S | < S }R
+ BBA A(D
+(A ABBJ T $T H < S D \
+ BBA A(D
+(A ABBD |T T < S " *
+ BBA A(D
+(A ABBG T U ȉ U <U R TU hR lU U S U S U 8W U U U V X ,V ` DV hr \V ЦS tV S V `W V V ` V V : W ȱ W P <W з TW x
+ lW p W XS W S W 近W W 0 W W ő X (Ǒ ,X ʑ: DX ͑ dX XБ
+ |X Pӑ X Ց X בX X (ؑX X pؑa X ؑU Y ّU $Y Xّ^ 4 <Y ّ$ BBA A(
+ ABBE 4 tY ۑ BBA A(
+ ABBA D Y ݑ BBB B(A0A8
+0A(B BBBD 4 Y ߑ BBA A(.
+ ABBG 4 ,Z 0 BBA A(.
+ ABBG D dZ BBB B(A0A8
+0A(B BBBE Z ` Z H Z 0 < Z ( BBB A(A0s
+(A BBBF < 4[ BBB A(A0s
+(A BBBF D t[ h BBB B(A0A8
+0A(B BBBG 4 [ @ BBA A(
+ ABBE 4 [ ( BBA A(
+ ABBE D ,\ BBB B(A0A8
+0A(B BBBD 4 t\ X BBA A(*
+ ABBK 4 \ BBA A(*
+ ABBK D \ ( BBB B(A0A8
+0A(B BBBE ,] D] \] ` < t] (y BBB A(A0
+(A BBBG < ] hq BBB A(A0
+(A BBBF D ] BBB B(A0A8T
+0A(B BBBI 4 <^ $ BBA A(
+ ABBE 4 t^ BBA A(
+ ABBA D ^
BBB B(A0A8
+0A(B BBBD 4 ^ H BBA A(.
+ ABBG 4 ,_ BBA A(.
+ ABBG D d_ BBB B(A0A8
+0A(B BBBE _ _ _ P < _ q BBB A(A0
+(A BBBG < 4` Xq BBB A(A0
+(A BBBG D t` BBB B(A0A8T
+0A(B BBBI 4 ` BBA A(
+ ABBE 4 ` ! BBA A(
+ ABBE D ,a # BBB B(A0A8
+0A(B BBBD 4 ta (& BBA A(*
+ ABBK 4 a ( BBA A(*
+ ABBK D a * BBB B(A0A8
+0A(B BBBE T ta -G BDB B(A0G8G
+8D0A(B BBBA T a /G BDB B(A0G8G
+8D0A(B BBBA T $b 1G BDB B(A0G8G
+8D0A(B BBBA T |b 3G BDB B(A0G8G
+8D0A(B BBBA < c 5 BJB A(A0l(A BBL T c 5G BDB B(A0G8G
+8D0A(B BBBA T lc 7G BDB B(A0G8G
+8D0A(B BBBA T c 9G BDB B(A0G8G
+8D0A(B BBBA T d ;G BDB B(A0G8G
+8D0A(B BBBA L td == BDB B(A0G8G
+8D0A(B BBBA L d ?= ~ BDB B(A0G8G
+8D0A(B BBBA e z. K^ L ,e A L BBB B(A0D8N
+8A0A(B BBBA , |e (C> , AID W
+DAA , e 8C> AID W
+DAA L e HC BBB B(A0D8N
+8A0A(B BBBA , ,f D> AID W
+DAA , \f D> AID W
+DAA L f D BBB B(A0D8N
+8A0A(B BBBA , f F> l AID W
+DAA , g F> L AID W
+DAA L <g F , BBB B(A0D8N
+8A0A(B BBBA , g 8H> AID W
+DAA , g HH> AID W
+DAA L g XH BBB B(A0D8N
+8A0A(B BBBA , <h I> AID W
+DAA , lh I> AID W
+DAA L h J l BBB B(A0D8N
+8A0A(B BBBA , h K> L AID W
+DAA , i K> , AID W
+DAA L Li K BBB B(A0D8N
+8A0A(B BBBA , i HM> AID W
+DAA , i XM> AID W
+DAA L i hM BBB B(A0D8N
+8A0A(B BBBA , Lj N> AID W
+DAA , |j O> l AID W
+DAA L j O L BBB B(A0D8N
+8A0A(B BBBA , j P> , AID W
+DAA , ,k P> AID W
+DAA L \k P BBB B(A0D8N
+8A0A(B BBBA , k XR> AID W
+DAA , k hR> AID W
+DAA L l xR BBB B(A0D8N
+8A0A(B BBBA , \l T> l AID W
+DAA , l T> L AID W
+DAA L l (T , BBB B(A0D8N
+8A0A(B BBBA , m U> AID W
+DAA , <m U> AID W
+DAA L lm U BBB B(A0D8N
+8A0A(B BBBA , m hW> AID W
+DAA , m xW> AID W
+DAA L n W l BBB B(A0D8N
+8A0A(B BBBA , ln Y> L AID W
+DAA , n (Y> , AID W
+DAA L n 8Y BBB B(A0D8N
+8A0A(B BBBA , o Z> AID W
+DAA , Lo Z> AID W
+DAA L |o Z BBB B(A0D8N
+8A0A(B BBBA , o x\> AID W
+DAA , o \> l AID W
+DAA L ,p \ L BBB B(A0D8N
+8A0A(B BBBA , |p (^> , AID W
+DAA , p 8^> AID W
+DAA L p H^ BBB B(A0D8N
+8A0A(B BBBA , ,q _> AID W
+DAA , \q _> AID W
+DAA L q _ BBB B(A0D8N
+8A0A(B BBBA , q a> l AID W
+DAA , r a> L AID W
+DAA L <r a , BBB B(A0D8N
+8A0A(B BBBA , r 8c> AID W
+DAA , r Hc> AID W
+DAA L r Xc BBB B(A0D8N
+8A0A(B BBBA , <s d> AID W
+DAA , ls d> AID W
+DAA L s e l BBB B(A0D8N
+8A0A(B BBBA , s f> L AID W
+DAA , t f> , AID W
+DAA L Lt f BBB B(A0D8N
+8A0A(B BBBA , t Hh> AID W
+DAA , t Xh> AID W
+DAA u hh L u `h AAG ]
CAF [
ACA R
-CAJ JCA 4 c\ AG RAT !# BBB B(D0A8J
-8A0A(B BBBJ T ( |# BBB B(D0A8N
-8A0A(B BBBC , L > # AID W
-DAA , | > # AID W
-DAA , > # AID W
-DAA , b- AAG0!AA 4 5 H# BHD D`
- DABG 4 D B *# BHD D`
- DABH 4 | B # BHD D`
- DABH D l Ƞ8 BBB B(A0A8$0A(B BBB T 7 " BBB G(G0A8JD
-8D0A(B BBBH ( . K^ $ < T r # BBA I(Dp
-(D ABBH L
-(D ABBA T r W# BBA I(Dp
-(D ABBH L
-(D ABBA T L ཛ1 # BEE E(D0A8GY
-8D0A(B BBBE , \ z`- AAG0!AA 4 ԥ Û5 " BHD D`
- DABG 4 ƛB " BHD D`
- DABH T D ɛ_ " BBB H(A0A8L
-8D0A(B BBBA T . K^ l כ כ כ כ ̧ כ כ , D כ[ " AID t
-DAA , t כ[ w" AID t
-DAA , ؛[ W" AID t
-DAA , ԧ 8؛[ 7" AID t
-DAA , h؛ " AID d
-DAA , 4 ؛ ! AID d
-DAA 4 d (ٛ ! BHE l
-ABB 4 ٛ ! BHE o
-ABO L Ԩ hڛr q! BBB B(E0A8J@
-8A0A(B BBBA ܩ (^\ AG RAL xۛ>
BEB B(A0A8G
8A0A(B BBB L h A
-J A< l BEB D(A0M
-(A BBBF T x f BBE E(A0A8G
-8D0A(B BBBF T L ) BBE B(D0A8G
-8D0A(B BBBE T BBE B(D0A8G
-8D0A(B BBBE , \- AAG0!AA 4 , @ BHD D`
- DABF 4 d BHD D`
- DABF 4 BHD D`
- DABG 4 ԫ X k BHD D`
- DABG < M BHD DW
- DABA < L 2 BHD DW
- DABA L @k BBB B(A0D8GT
-8D0A(B BBBK d `?
+CAJ JCA 4 du h D BHE
+ABD 4 u Hi BHE
+ABI v y\ AG RA< v i BEB D(A0M
+(A BBBF L 4v pk1 | RED D(D(A ABBB , <w Hy- AAG0!AA 4 v 0l5 BHD D`
+ DABG 4 v 8oB BHD D`
+ DABH 4 $w PrB BHD D`
+ DABH 4 \w huB BHD D`
+ DABH T w x BBB B(D0A8NQ
+8A0A(B BBBJ , w H> AID W
+DAA , x X> AID W
+DAA , Lx h> AID W
+DAA , |x x> AID W
+DAA T x BBB B(D0A8J
+8A0A(B BBBJ T y А BJB B(D0A8G
+8A0A(B BBBG d z H BBB B(A0D8Gpk
+8A0A(B BBBG }
+8D0A(B BKBM L y ? BBB B(A0A8J[
+8A0A(B BBBG , z 0U ADG T
+DAK T Dz ` BBB H(A0D8L.
+8D0A(B BBBF T{ hz. K^ l{ { { y\ AG RAT { BBA I(Dp
+(D ABBA L
+(D ABBA T \{ BBA I(Dp
+(D ABBA L
+(D ABBA T { P BBA I(Dp
+(D ABBA L
+(D ABBA , | Jy- AAG0!AA 4 <| X5 BHD D`
+ DABG 4 t| `B BHD D`
+ DABH 4 | xB BHD D`
+ DABH T | P" BBB B(A0D8L
+D
+8A0A(B BBBI } z. K^ ~ p֒ $~ h֒ <~ `֒ T~ X֒ l~ `֒ ~ h֒ , ~ p֒Y ADG T
+DAK , ~ ֒[ W AID t
+DAA , D~ ֒[ 7 AID t
+DAA , t~ ג[ AID t
+DAA , ~ 0ג[ AID t
+DAA , ~ `ג[ AID t
+DAA , ג[ AID t
+DAA , 4 ג[ AID t
+DAA 4 d ג w BHE l
+ABB 4 ؒ C BHE o
+ABO L 0ْr BBB B(E0A8J@
+8A0A(B BBBA ܀ `y\ AG RAL @ڒ>
BEB B(A0A8G
8A0A(B BBB L 0 A
+J AT D BBB B(A0I8D
+8D0A(B BBBA T " BBB B(A0I8D
+8D0A(B BBBA T d p BBB B(I0A8D
+8D0A(B BBBA T BBE E(A0A8G
+8D0A(B BBBF T BBE E(A0A8G
+8D0A(B BBBF T l d BBE E(A0A8G
+8D0A(B BBBF T Ă ' BBE E(A0A8G
+8D0A(B BBBF , ԃ 6 AAG0
+AAA 4 L ( BHD D`
+ DABF 4 BHD D`
+ DABF 4 ~ BHD D`
+ DABG 4 @ ` BHD D`
+ DABG 4 , B BHD D`
+ DABG 4 d $ BHD D`
+ DABG 4 xd BHA Gp
+ DABA < Ԅ
+ BHD D_
+ DABA < p
BHD DX
+ DABA 4 T 0i BHA Gp
+ DABA d D h?
BBB B(A0A8GP
8G0A(B BBBP ^
-8A0A(B BBBH T D 8E x BEB B(A0A8G
-8A0A(B BBBI T 00 BEH B(A0F8I
-8D0A(B BBBH T 5 BGB B(A0A8J
-8A0A(B BBBG T L 09 v BGB B(A0A8J
-8A0A(B BBBG T < > BGB B(A0A8J
-8A0A(B BBBG . K^ ̯ (@ @ , D (@[ AID t
-DAA T t X@< BBE B(D0A8G
-8D0A(B BBBB ,
-Y- AAG0!AA D EY T BIA D(D
-(D ABBA T D (H 3 BDB B(A0G8J
-8D0A(B BBBB T p. K^ l V V , V[ AID t
-DAA ̱ X\ AG RA, ,Y- AAG0!AA < d V C BIA D(Dp-
-(D ABBF T (Y BDB B(A0D8J
-8A0A(B BBBA @. K^ ̲ Hn @n 8n 0n , 8n D @n , Hn[ AID t
-DAA , Բ xn[ AID t
-DAA , n[ AID t
-DAA , ZX- AAG0!AA 4 d n5 = BHD D`
- DABG 4 q5 BHD D`
- DABG 4 Գ t5 BHD D`
- DABG T w BDH B(A0D8Gl
-8D0A(B BBBA T d H| BDH B(A0D8Gd
-8D0A(B BBBA T Ѐ [ BDH B(A0D8Gd
-8D0A(B BBBA ̵ X . K^ @ 8 , \ @[ O AID t
-DAA , p[ / AID t
-DAA , t W- AAG0!AA < p" BHD G
- DABA < , `) BHD G
- DABA D l P BBE A(H0D
-0A(A BBBA < b BBA D(K
-(A ABBA L 葜 BBE E(A0D8Gx
-8A0A(B BBBD T D BDB B(D0A8M5
-8D0A(B BBBB T
-. K^ l ز в Ȳ в ̸ U\ AG RAD 4 BBB A(I0Dp
-0D(A BBBG D | 0 BBB A(I0Dp
-0D(A BBBG D ĸ BBB A(I0Dp
-0D(A BBBG D } BBB A(I0Dp
-0D(A BBBG D T W BBB A(I0Dp
-0D(A BBBG D 1 BBB A(I0Dp
-0D(A BBBG D BBB A(I0Dp
-0D(A BBBG D , BBB A(I0Dp
-0D(A BBBG , , S- AAG0!AA 4 H5 BHD D`
- DABG 4 ܺ P5 q BHD D`
- DABG 4 XĜB S BHD D`
- DABH 4 L pǜB 5 BHD D`
- DABH 4 ʜE BHD D`
- DABK 4 ͜E BHD D`
- DABK 4 МE BHD D`
- DABD 4 , ӜE BHD D`
- DABD T d ֜G BBB G(D0A8M
-8D0A(B BBBH T ܜG BBB G(D0A8M
-8D0A(B BBBH ̽ . K^ - Ak T L BBE B(A0H8G
-8A0A(B BBBD \ X. K^ D t 8 BBB B(A0A8
-0A(B BBBA D x8 BBB B(A0A8
-0A(B BBBA D pB BBB B(A0A8
-0A(B BBBA D L xB BBB B(A0A8
-0A(B BBBA x Ŀ p ܿ h ` h $ p < x L T AAG e
+8A0A(B BBBH T @ B BEB B(A0A8G
+8A0A(B BBBG T L 9 BBB B(D0A8L
+8A0A(B BBBD T p? l BGB B(A0A8J
+8A0A(B BBBG T B 4 BGB B(A0A8J
+8A0A(B BBBG T T F BGB B(A0A8J
+8A0A(B BBBG d z. K^ , ć I AID g
+DAA 4 PJ BHE r
+ABD 4 , J Y BHE r
+ABD 4 d K % BHE u
+ABI 4 (L BHE u
+ABI L Ԉ L BBB B(A0D8GT
+8D0A(B BBBK , ܉ y- AAG0!AA D T Nd V BIA D(D
+(D ABBA T R 3 BBB B(D0A8L
+8A0A(B BBBH z. K^ Ċ ] ܊ ] , < ][ c AID t
+DAA $ y\ AG RA, D y- AAG0!AA < ]' BHD D
+ DABA T x`
BDB B(A0D8J
+8A0A(B BBBF pz. K^ $ u < u T u l u u u , u[ J AID t
+DAA , , Hu[ * AID t
+DAA , \ xu[
+ AID t
+DAA , D y- AAG0!AA 4 xu5 BHD D`
+ DABG 4 x5 BHD D`
+ DABG 4 , {5 ~ BHD D`
+ DABG T d ~ c BDH B(A0D8Gl
+8D0A(B BBBA T BDH B(A0D8Gd
+8D0A(B BBBA T BDH B(A0D8Gd
+8D0A(B BBBA $ z. K^ < T , [ AID t
+DAA , @[ AID t
+DAA , ̏ y- AAG0!AA < D @" _ BHD G
+ DABA < 0) F BHD G
+ DABA D ď - BBE A(H0D
+0A(A BBBA < b ) BBA D(K
+(A ABBA L L + BBE E(A0D8Gx
+8A0A(B BBBD T BDB B(D0A8M5
+8D0A(B BBBB 0z. K^ đ ܑ $ $ ^ AG b
+AA D ๓ g BBB A(I0Dp
+0D(A BBBH D ܑ h A BBB A(I0Dp
+0D(A BBBH D $ BBB A(I0Dp
+0D(A BBBH D l x BBB A(I0Dp
+0D(A BBBH D BBB A(I0Dp
+0D(A BBBH D BBB A(I0Dp
+0D(A BBBH D D Ó BBB A(I0Dp
+0D(A BBBH D ē ] BBB A(I0Dp
+0D(A BBBH D ԓ Ɠ 7 BBB A(I0Dp
+0D(A BBBH D Ǔ BBB A(I0Dp
+0D(A BBBH D d 0ɓ BBB A(I0Dp
+0D(A BBBH D ʓ BBB A(I0Dp
+0D(A BBBH , y- AAG0!AA 4 $ ̓5 o BHD D`
+ DABG 4 \ ϓ5 Q BHD D`
+ DABG 4 ғB 3 BHD D`
+ DABH 4 ̕ 8ՓB BHD D`
+ DABH 4 PؓB BHD D`
+ DABH 4 < hۓB BHD D`
+ DABH 4 t ޓE BHD D`
+ DABK 4 E BHD D`
+ DABK 4 E BHD D`
+ DABD 4 E a BHD D`
+ DABD 4 T E C BHD D`
+ DABD 4 E % BHD D`
+ DABD T ė BGB B(D0D8G\
+8D0A(B BBBK T h BBB G(A0D8M+
+8D0A(B BBBI T t BGB B(D0D8G\
+8D0A(B BBBK T ̘ _ BBB G(A0D8M+
+8D0A(B BBBI ܙ 0~z. K^ ( - Ak T \ 8 ? BBE B(A0H8G
+8A0A(B BBBD l }z. K^ D *8 BBB B(A0A8
+0A(B BBBA D ̚ +8 BBB B(A0A8
+0A(B BBBA D ,B BBB B(A0A8
+0A(B BBBA D \ -B BBB B(A0A8
+0A(B BBBA . . ԛ . . . . 4 . L . L d . AAG e
CAF S
ACA R
-CAJ JCA L ADD e
+CAJ JCA L X/ ADD e
CAF [
ACI R
-CAJ JCA L p ADD e
+CAJ JCA L / ADD e
CAF [
ACI R
-CAJ JCA L D AAG e
+CAJ JCA L T X0 AAG e
CAF S
ACA R
-CAJ JCA L ` ADD e
+CAJ JCA L 0 ADD e
CAF [
ACI R
-CAJ JCA L ADD e
+CAJ JCA L H1 ADD e
CAF [
ACI R
-CAJ JCA L 4 ` AAG e
+CAJ JCA L D 1 AAG e
CAF S
ACA R
-CAJ JCA L ADD e
+CAJ JCA L 82 ADD e
CAF [
ACI R
-CAJ JCA L P ADD e
+CAJ JCA L 2 ADD e
CAF [
ACI R
-CAJ JCA L $ AAG e
+CAJ JCA L 4 83 AAG e
CAF S
ACA R
-CAJ JCA L t @ ADD e
+CAJ JCA L 3 ADD e
CAF [
ACI R
-CAJ JCA L ADD e
+CAJ JCA L ԟ (4 ADD e
CAF [
ACI R
-CAJ JCA 4 @ BEA A(X
- ABBJ 4 L BEA A(X
- ABBJ , BDA
-ABD , BDA z
-ABD < , q BBA A(D0[
-(A ABBA T l T BBB B(A0D8N/
-8A0A(B BBBA , h > y AID W
-DAA L x Y Y BBB E(A0A8N
-8A0A(B BBBK , D > > AID W
-DAA T t r BEL B(D0A8G5
-8A0A(B BBBA , Z 7 BHI s
-ABA , > AID W
-DAA , , Z BHI s
-ABA , \ 0> AID W
-DAA , D H- AAG0!AA 4 BHD D`
- DABD 4 i BHD D`
- DABD 4 , K BHD D`
- DABE 4 d x - BHD D`
- DABE T Z BEE E(D0A8Gk
-8D0A(B BBBK T | BEB E(A0D8JR
-8D0A(B BBBD D L # BFD JPs
+CAJ JCA 4 $ 4 BEA A(X
+ ABBJ 4 \ 5 BEA A(X
+ ABBJ , X6 BDA
+ABD , Ġ 87 BDA z
+ABD < < 8q BBA A(D0[
+(A ABBA T | H8 BBB B(A0D8N/
+8A0A(B BBBA , Ԡ <> AID W
+DAA L <Y BBB E(A0A8N
+8A0A(B BBBK , T >> v AID W
+DAA T ?r V BEL B(D0A8G5
+8A0A(B BBBA , ܡ (CZ o BHI s
+ABA , XC> O AID W
+DAA , < hCZ / BHI s
+ABA , l C> AID W
+DAA , T y- AAG0!AA 4 ̢ xC BHD D`
+ DABD 4 E BHD D`
+ DABD 4 < hH BHD D`
+ DABE 4 t J e BHD D`
+ DABE T XMZ G BEE E(D0A8Gk
+8D0A(B BBBK T `P|
+ BEB E(A0D8JR
+8D0A(B BBBD D \ _ I BFD JPs
DABH F
- DABA T # BGA D(J`
+ DABA T ` BGA D(J`
(D ABBF x
-(D ABBG L $ BBB B(A0D8Jp
-8D0A(B BBBF T < & BBE B(D0G8G
-8A0A(B BBBB T h+ Y BBE B(D0G8G
-8A0A(B BBBB L 0/ - BBB B(A0D8M
-8D0A(B BBBB < 6 BDA k
+(D ABBG L Ha BBB B(A0D8Jp
+8D0A(B BBBF T L b BBE B(D0G8G
+8A0A(B BBBB T g BBE B(D0G8G
+8A0A(B BBBB L m e BBB B(A0D8M
+8D0A(B BBBG < r BDA k
ABK @
-ABA L 4 6Q BBB B(A0A8D
-8A0A(B BBBH L :Q BBB B(A0A8D
-8A0A(B BBBH L ?f BBB B(A0A8GM
-8A0A(B BBBH L $ Cf BBB B(A0A8GM
-8A0A(B BBBH t @G D H BHB B(A0A8DP8A0A(B BBB I D XK BHB B(A0A8DP8A0A(B BBB, 4 LR EKH
-ABA D d PM BEE B(A0A8DP8A0A(B BBB, NR EKH
-ABA D HO BEE B(A0A8DP8A0A(B BBB, $ P EG
+ABA L D sQ BBB B(A0A8D
+8A0A(B BBBH L wQ BBB B(A0A8D
+8A0A(B BBBH L ({f BBB B(A0A8GM
+8A0A(B BBBH L 4 Hf BBB B(A0A8GM
+8A0A(B BBBH h D BHB B(A0A8DP8A0A(B BBB D BHB B(A0A8DP8A0A(B BBB, D HR EKH
+ABA D t x BEE B(A0A8DP8A0A(B BBB, @R EKH
+ABA D p BEE B(A0A8DP8A0A(B BBB, 4 8 EG
AE o
-AH D T Q BHE E(D0A8DP8A0A(B BBBL xQ= BHE E(A0A8D@8A0A(B BBB L hS BBB B(A0A8GO
-8A0A(B BBBA , < X EG
+AH D d ( BHE E(D0A8DP8A0A(B BBBL = BHE E(A0A8D@8A0A(B BBB L BBB B(A0A8GO
+8A0A(B BBBA , L Д EG
AE o
-AH D l Y BHE E(D0A8DP8A0A(B BBBL ZM BHE E(A0A8D@(8A0A(B BBB L \ BBB B(A0A8GO
-8A0A(B BBBA < T Pa FDK
+AH D | BHE E(D0A8DP8A0A(B BBBL ī 8M BHE E(A0A8D@(8A0A(B BBB L 8 BBB B(A0A8GO
+8A0A(B BBBA < d x FDK
ABB w
-ABF L 0b BHE E(D0A8DP
-8A0A(B BBBK L b@ BHE E(A0A8D@
-8A0A(B BBBA L 4 d BBB B(A0A8Gl
-8A0A(B BBBA < j FDK
+ABF L X BHE E(D0A8DP
+8A0A(B BBBK L ؞@ BHE E(A0A8D@
+8A0A(B BBBA L D Ƞ BBB B(A0A8Gl
+8A0A(B BBBA < ( FDK
ABB w
-ABF L j BHE E(D0A8DP
-8A0A(B BBBK L `k@ BHE E(A0A8D@
-8A0A(B BBBA L d Pm BBB B(A0A8Gl
-8A0A(B BBBA 0. K^ r r r r , xr D pr \ xr t r r r , r[ AID t
-DAA , 4 r[ ` AID t
-DAA , d r[ @ AID t
-DAA , (s[ AID t
-DAA , Xs[ AID t
-DAA , <- AAG0!AA 4 $ Xs5
- BHD D`
- DABG 4 \ `v5
- BHD D`
- DABG 4 hy5 t
- BHD D`
- DABG 4 p|5 V
- BHD D`
- DABG 4 x5 8
- BHD D`
- DABG T <
- BEE E(D0A8G*
-8D0A(B BBBD T
- BDB B(A0D8G&
-8A0A(B BBBA T
- BDE H(A0A8G$
-8D0A(B BBBA T D Ȗ
- BDE H(A0A8G,
-8D0A(B BBBA T
- BDE H(A0A8G$
-8D0A(B BBBA T ئ + BDE H(A0A8G$
-8D0A(B BBBA . K^ x 4 p D xb
BBB A(D0Kp
-0A(A BBBJ , >
- AID W
-DAA , 9- AAG0!AA 4 < 5
- BHD D`
- DABG L t o
- BBE B(F0G8G
-8D0A(B BBBI | . K^ , 0 AF
FK
- D T < | S
- BBE E(D0A8G
-8D0A(B BBBE T (| 9
- BBE E(D0A8G
-8D0A(B BBBE T PĝE
-
- BBB E(A0A8N
-8A0A(B BBBA , D HΝ>
- AID W
-DAA , , 8- AAG0!AA < (Νp U
- BHD G
- DABA T Xҝ F
- BBE B(D0A8G
-8D0A(B BBBG L < ם /
- BBB B(D0F8Gb
-8A0A(B BBBK T ٝk 7
- BBB B(A0F8J`
-8A0A(B BBBE L ޝ K
- BBB B(D0F8Gb
-8A0A(B BBBK T 4 8 S
- BBB B(A0F8JD
-8A0A(B BBBI T g
- BBH E(F0A8G?
-8D0A(B BBBE T
- BBB E(F0A8J>
-8A0A(B BBBD T <
- BBB B(F0D8Gv
-8A0A(B BBBG T H BBB B(D0F8Jy
-8A0A(B BBBA T [ BBB G(A0D8M
-8D0A(B BBBB x. K^ D BBE A(A0D@
-0A(A BBBA D \ BBE A(A0D@
-0A(A BBBA , @5- AAG0!AA <
-`
- BHD G
- DABA T \ @
- BBB E(A0G8L@
-8D0A(B BBBA < BLD D(G0
-(A ABBE < *
- BED I(KPc
-(D ABBE < 4
- BLD D(G0
-(A ABBE < t h*
- BED I(KP_
-(D ABBI T X-
- BBM B(A0D8G
-8A0A(B BBBD , 0-> K AID W
-DAA . K^ (-0 Hg , 8-7 Hj L X-0 Hg l h-7 Hj -0 Hg -7 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 P/ BBE A(A0D@o
-0A(A BBBA D / BBE A(A0D@o
-0A(A BBBA D 4 0 BBE A(A0D@o
-0A(A BBBA , | 1- AAG0!AA < 0U s BIA D(Dp
+ABF L ԭ BHE E(D0A8DP
+8A0A(B BBBK L $ @ BHE E(A0A8D@
+8A0A(B BBBA L t x BBB B(A0A8Gl
+8A0A(B BBBA Į iz. K^ ܮ $ < T l , [ AID t
+DAA , D [ AID t
+DAA , t [ x AID t
+DAA , P[ X AID t
+DAA , ԯ [ 8 AID t
+DAA , Ly- AAG0!AA 4 4 5 BHD D`
+ DABG 4 l 5 BHD D`
+ DABG 4 5 BHD D`
+ DABG 4 ܰ 5 BHD D`
+ DABG 4 5 p BHD D`
+ DABG T L R BEE E(D0A8G*
+8D0A(B BBBD T @Ĕ 8 BDB B(A0D8G&
+8A0A(B BBBA T ɔ D BDE H(A0A8G,
+8D0A(B BBBA T T pД o BDE H(A0A8G$
+8D0A(B BBBA T ٔ BDE H(A0A8G,
+8D0A(B BBBA T BDE H(A0A8G$
+8D0A(B BBBA dz. K^ , D D b C BBB A(D0Kp
+0A(A BBBJ , > AID W
+DAA , Դ by- AAG0!AA 4 L 5 BHD D`
+ DABG L BBE B(F0G8G
+8D0A(B BBBI @cz. K^ 8 0 , Ե 80 AF
FK
+ D T L 8| BBE E(D0A8G
+8D0A(B BBBE T `| q BBE E(D0A8G
+8D0A(B BBBE T E
+ W BBB E(A0A8N
+8A0A(B BBBA , T
+> AID W
+DAA , < (y- AAG0!AA < `
+p BHD G
+ DABA T ~ BBE B(D0A8G
+8D0A(B BBBG L L X g BBB B(D0F8Gb
+8A0A(B BBBK T k o BBB B(A0F8J`
+8A0A(B BBBE L BBB B(D0F8Gb
+8A0A(B BBBK T D p BBB B(A0F8JD
+8A0A(B BBBI T (" BBH E(F0A8G?
+8D0A(B BBBE T ' BBB E(F0A8J>
+8A0A(B BBBD T L (- BBB B(F0D8Gv
+8A0A(B BBBG T 3 W BBB B(D0F8Jy
+8A0A(B BBBA T 9 BBB G(A0D8M
+8D0A(B BBBB ]z. K^ D $ 8A BBE A(A0D@
+0A(A BBBA D l C BBE A(A0D@
+0A(A BBBA , ޟy- AAG0!AA < , XF` BHD G
+ DABA T l xJ BBB E(A0G8L@
+8D0A(B BBBA < Ļ 0S ? BLD D(G0
+(A ABBE < S* # BED I(KPc
+(D ABBE < D T BLD D(G0
+(A ABBE < U* BED I(KP_
+(D ABBI T ļ V- BBM B(A0D8G
+8A0A(B BBBD , hi> AID W
+DAA ([z. K^ `i0 Hg < pi7 Hj \ i0 Hg | i7 Hj i0 Hg i7 Hj D ܾ i BBE A(A0D@g
+0A(A BBBA D $ xj BBE A(A0D@g
+0A(A BBBA D l k BBE A(A0D@g
+0A(A BBBA D k BBE A(A0D@o
+0A(A BBBA D l BBE A(A0D@o
+0A(A BBBA D D l BBE A(A0D@o
+0A(A BBBA , 4y- AAG0!AA < mU BIA D(Dp
-(D ABBI < 4 4U M BIA D(Dp
+(D ABBI < D @pU BIA D(Dp
-(D ABBI < t (7U ' BIA D(Dp
+(D ABBI < `sU _ BIA D(Dp
-(D ABBI < H:U BIA D(Dp
+(D ABBI < vU 9 BIA D(Dp
-(D ABBI < h=U BIA D(Dp
+(D ABBI < yU BIA D(Dp
-(D ABBI < 4 @U BIA D(Dp
+(D ABBI < D |U BIA D(Dp
-(D ABBI L t C BBJ B(A0A8G
-8D0A(B BBBD L F z BBJ B(A0A8G
-8D0A(B BBBD L hH e BBJ B(A0A8G
-8D0A(B BBBD L d J P BBB B(A0I8G
-8D0A(B BBBD L M ; BBB B(A0I8G
-8D0A(B BBBD L hO & BBB B(A0I8G
-8D0A(B BBBD T T Q BBE B(A0F8M9
-8D0A(B BBBA T 0V 7 BBE B(A0F8M0
-8D0A(B BBBA T Zq [ BBG B(A0A8J
-8A0A(B BBBA T \ _v BBB E(A0A8L
-8A0A(B BBBK T dm BBB E(A0A8L
-8A0A(B BBBD T j} BBB B(D0A8L
-8A0A(B BBBG ـ. K^ 4 o0 Hg T o7 Hj T @o BBB J(A0A8G?
-8D0A(B BBBK T r BEE E(D0A8G
-8D0A(B BBBH T l 0x h BBE B(D0A8G
-8D0A(B BBBG , | +- AAG0!AA < | BHD Db
- DABA T 4 Hc BBB B(F0A8G
-8A0A(B BBBE D ׀. K^ \ H> Hu | hE Hx +\ AG RAT x
- BBB B(D0A8N
-8A0A(B BBBD , \ > > AID W
-DAA , D +- AAG0!AA < BHD DN
- DABA T ` BBE E(A0A8GV
-8D0A(B BBBF T T Ȱ BBE B(D0A8GH
-8D0A(B BBBD T S BBB G(D0A8M
-8D0A(B BBBE xՀ. K^ $ ADD0AA$ H ADD0AA$ $ л ADD0AA$ L X ADD0AA$ t ༞ ADD0AA$ h ADD0AAD 1 BBE A(A0D@
-0A(A BBBA D 辞> BBB D(A0D@
-0A(A BBBA D T F BBB D(A0D@
-0A(A BBBA D N BBB D(A0D@
-0A(A BBBA $ ^ AG b
-AA L T (2 BGB B(A0K8U
-8A0A(B BBBA < Þ BQA A(N`
-(A ABBA < Þ BQA A(N`
-(A ABBA < $ XĞ BQA A(N`
-(A ABBA L d Ğ2 Y BGB B(A0K8U
-8A0A(B BBBA < Ş ! BQA A(N`
-(A ABBA < ƞ BQA A(N`
-(A ABBA $ (Ǟ AAG0AA< \ Ǟ BHA JI
- DABA < ʞ BHA JI
- DABA < P͞ BHA JI
- DABA < О p BHA JI
- DABA < \ Ҟ a BHA JI
- DABA < ՞ R BHA JI
- DABA < ؞ C BHA JI
- DABA < `۞4 4 BHA Ju
- DABA D `ޞ BBE A(A0D@l
-0A(A BBBA $ \ ޞi AAG0]AA$ @ߞq AAG0eAAD ߞ BBE A(A0D@l
-0A(A BBBA $ 0| AAG0pAA i AG _A < q AG gA$ \ 8 AAG0xAAT BEJ E(D0D8G
-8D0A(B BBBG T $ hM p BBB I(A0A8JN
-8A0A(B BBBA , | `> AID W
-DAA \ d p BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBL BBB E(A0G8I[
-8D0A(B BBBA < \ BGB D(A0~
-(A BBBK T 0M
- BBB M(A0D8Gg
-8A0A(B BBBA , (> ; AID W
-DAA T $ 8 BBB E(A0H8K
-8A0A(B BBBA , | > A AID W
-DAA T # BBB E(I0A8K}
-8D0A(B BBBF T ( BBB B(A0H8JH
-8A0A(B BBBA , \ > " AID W
-DAA L
BEB E(I0H8DP
-8D0A(B BBBD L BEB E(I0H8D@
-8D0A(B BBBA T , @' BBB I(A0A8J
-8A0A(B BBBA , > AID W
-DAA T ( BBB E(I0A8K}
-8D0A(B BBBF T `= BBB B(A0D8N
-8A0A(B BBBA , d H!> AID W
-DAA L X! BEB E(I0H8D@
-8D0A(B BBBA T "H s BBI B(A0A8J.
-8A0A(B BBBA , < )> AID W
-DAA \ $ ) BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBT 0* BBB M(A0A8J
-8A0A(B BBBA , $ 1> c AID W
-DAA < T 1H C BBA A(Gp
-(D ABBA < 2H ' BBA A(Gp
-(D ABBA T 38 BEB F(G0DP
-0D(A BBBA l0D(A BBB < , 4H BBA A(Gp
-(D ABBA d l 5/ BBB E(A0F8Dp
-8A0A(B BBBF d8A0A(B BBB < 6H < BBA A(Gp
-(D ABBA T 79 BEB G(A0IP
-0D(A BBBE m0D(A BBB < l 8H BBA A(Gp
-(D ABBA d 9 BGB B(A0D8D
-8A0A(B BBBA @8A0A(B BBB d X;# R BBB B(D0F8D
-8A0A(B BBBA E8A0A(B BBB < | =H BBA A(Gp
-(D ABBA T t 0>; BEB F(G0DP
-0D(A BBBE o0D(A BBB L ? BBH E(A0F8D`
-8D0A(B BBBH H. K^ 4 ? L ? , d ?b D\
+(D ABBI L BBJ B(A0A8G
+8D0A(B BBBD L @ BBJ B(A0A8G
+8D0A(B BBBD L $ BBJ B(A0A8G
+8D0A(B BBBD L t BBB B(A0I8G
+8D0A(B BBBD L P s BBB B(A0I8G
+8D0A(B BBBD L ^ BBB B(A0I8G
+8D0A(B BBBD T d K BBE B(A0F8M9
+8D0A(B BBBA T h o BBE B(A0F8M0
+8D0A(B BBBA T Жq BBG B(A0A8J
+8A0A(B BBBA T l v BBB E(A0A8L
+8A0A(B BBBK T m BBB E(A0A8L
+8A0A(B BBBD T 8} BBB B(D0A8L
+8A0A(B BBBG , 0Sz. K^ D H0 Hg d X7 Hj T x BBB J(A0A8G?
+8D0A(B BBBK T $ க BEE E(D0A8G
+8D0A(B BBBH T | h BBE B(D0A8G
+8D0A(B BBBG , by- AAG0!AA < V BHD Db
+ DABA T D c ; BBB B(F0A8G
+8A0A(B BBBE T 8Qz. K^ l ؕ> Hu ؕE Hx py\ AG RAT ؕ
+ + BBB B(D0A8N
+8A0A(B BBBD , l X> v AID W
+DAA , T $y- AAG0!AA < 8 & BHD DN
+ DABA T BBE E(A0A8GV
+8D0A(B BBBF T d K BBE B(D0A8GH
+8D0A(B BBBD T H BBB G(D0A8M
+8D0A(B BBBE Nz. K^ $ ADD0AA$ ADD0AA$ 4 ADD0AA$ \ ADD0AA$ ADD0AA$ ADD0AAD (1 BBE A(A0D@
+0A(A BBBA D > BBB D(A0D@
+0A(A BBBA D d F BBB D(A0D@
+0A(A BBBA D N BBB D(A0D@
+0A(A BBBA $ (^ AG b
+AA L d `2 S BGB B(A0K8U
+8A0A(B BBBA < P BQA A(N`
+(A ABBA < BQA A(N`
+(A ABBA < 4 BQA A(N`
+(A ABBA L t 02 BGB B(A0K8U
+8A0A(B BBBA < Y BQA A(N`
+(A ABBA < + BQA A(N`
+(A ABBA $ ` AAG0AA< l BHA JI
+ DABA < BHA JI
+ DABA < BHA JI
+ DABA < , X BHA JI
+ DABA < l ( BHA JI
+ DABA < BHA JI
+ DABA < { BHA JI
+ DABA < , 4 l BHA Ju
+ DABA D $ BBE A(A0D@l
+0A(A BBBA $ l 0i AAG0]AA$ xq AAG0eAAD BBE A(A0D@l
+0A(A BBBA $ h | AAG0pAA , i AG _A L q AG gA$ l p AAG0xAAT BEJ E(D0D8G
+8D0A(B BBBG T 4 !M BBB I(A0A8JN
+8A0A(B BBBA , (> AID W
+DAA \ t ( BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBL ( O BBB E(A0G8I[
+8D0A(B BBBA < l , K BGB D(A0~
+(A BBBK T h-
+ 3 BBB M(A0A8J
+8A0A(B BBBA , 7> s AID W
+DAA T 4 8 S BBB E(A0H8K
+8A0A(B BBBA , h>> y AID W
+DAA T x> [ BBB E(I0A8K}
+8D0A(B BBBF T @ 3 BBB B(A0H8JH
+8A0A(B BBBA , l I> Z AID W
+DAA L I ; BEB E(I0H8DP
+8D0A(B BBBD L K BEB E(I0H8D@
+8D0A(B BBBA T < M' BBB I(A0A8J
+8A0A(B BBBA , S> AID W
+DAA T S BBB E(I0A8K}
+8D0A(B BBBF T (V= BBB B(A0D8N
+8A0A(B BBBA , t ^> AID W
+DAA L ^ BEB E(I0H8D@
+8D0A(B BBBA T _H BBI B(A0A8J.
+8A0A(B BBBA , L f> AID W
+DAA \ 4 f BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBT f S BBB M(A0A8J
+8A0A(B BBBA , 4 n> AID W
+DAA < d nH { BBA A(Gp
+(D ABBA < oH _ BBA A(Gp
+(D ABBA T p8 BEB F(G0DP
+0D(A BBBA l0D(A BBB < < qH BBA A(Gp
+(D ABBA d | r/ BBB E(A0F8Dp
+8A0A(B BBBF d8A0A(B BBB < psH t BBA A(Gp
+(D ABBA T t9 BEB G(A0IP
+0D(A BBBE m0D(A BBB < | huH BBA A(Gp
+(D ABBA d xv BGB B(A0D8D
+8A0A(B BBBA @8A0A(B BBB d $ x# BBB B(D0F8D
+8A0A(B BBBA E8A0A(B BBB < yH 0 BBA A(Gp
+(D ABBA T z; BEB F(G0DP
+0D(A BBBE o0D(A BBB L $ { BBH E(A0F8D`
+8D0A(B BBBH , ;z. K^ D | \ | , t |b 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 , @ AJ@
-AG MA $ \ AG RAD D A BBE A(A0D@
-0A(A BBBA D A BBE A(A0D@
-0A(A BBBA T B BEB B(H0A8G`
-8A0A(B BBBK , t XS> AID W
-DAA T hS BEE E(D0A8G
-8D0A(B BBBE , - AAG0!AA < , `X{ BHD G
- DABA $ l \ ~ AL0p
-DA $ (] d AL0p
-DA $ ] J AL0p
-DA d 8^ 0 KGB B(A0A8R8A0A(B BBBG T L j 3 BBB B(A0A8G
-8A0A(B BBBK T 8{ BBH B(F0A8J
-8D0A(B BBBG ཀ. K^ H> Hu hE Hx v\ AG RAT t x
- BBB B(D0A8N
-8A0A(B BBBJ , > E AID W
-DAA , *- AAG0!AA 4 , И5 BHD D`
- DABG T d ؛4 BBB B(D0F8G
-
-8A0A(B BBBC t P. K^ T , ? BBE B(D0A8G
-8D0A(B BBBK L \ % BBB J(A0A8G
-8D0A(B BBBH , d - AAG0!AA < BHD Dn
- DABA T BBE B(D0A8G
-8D0A(B BBBG T t hƟ BBE B(D0A8G
-8D0A(B BBBH \
- 0˟^ KLY H(D0I8J8A0A(B BBBG \
- 0Ο KEJ E(J0I8J8A0A(B BBBG \ D ϟ KLQ I(G0I8M8A0A(B BBBI L
- pҟ BHB E(A0F8G
-8D0A(B BBBD L < ޟf BEB G(A0G8G
-8D0A(B BBBD L BEB G(A0A8J
-8D0A(B BBBG L BEB G(A0A8M
-8D0A(B BBBD T , BBB G(D0G8G
-8D0A(B BBBG <
. K^ T
\ AG RAD t
} BBB D(A0D@
-0A(A BBBA D
X} BBB D(A0D@
-0A(A BBBA D } BBB D(A0D@
-0A(A BBBA D L } BBB D(A0D@
-0A(A BBBA D BBB D(A0D@
-0A(A BBBA D H BBB D(A0D@
-0A(A BBBA D $ BBB D(A0D@
-0A(A BBBA D l BBB D(A0D@
-0A(A BBBA , - AAG0!AA 4 , 5 BHD D`
- DABG 4 d 5 BHD D`
- DABG 4 5 BHD D`
- DABG 4 5 BHD D`
- DABG D BBE A(A0D@l
-0A(A BBBA L T W BJE B(A0A8D@
-8A0A(B BBBA $ \ xV ADD JAAL BEB E(I0H8D@
-8D0A(B BBBA T 0 BBB B(A0D8L7
-8A0A(B BBBF T t xE BBB B(H0A8J
-8A0A(B BBBG , p > ; AID W
-DAA T E BBB B(H0A8J
-8A0A(B BBBG , T x+> { AID W
-DAA T +E [ BBB B(H0A8J
-8A0A(B BBBG , 6> AID W
-DAA T 6E BBB B(H0A8J
-8A0A(B BBBG , d A> AID W
-DAA T AW BBB H(A0D8L
-8D0A(B BBBH T EW BBB H(A0D8L
-8D0A(B BBBH T D IL BBB H(A0D8L
-8D0A(B BBBH T Я. K^ l M> Hu M> Hu M> Hu M> Hu NE Hx 8NE Hx , hNE Hx L NE Hx l X \ AG RA N_ AF
-I T N
- e BEB B(A0A8N
-8A0A(B BBBH , L X> AID W
-DAA T | X
- BEB B(A0A8N
-8A0A(B BBBH , b> AID W
-DAA T b
- BEB B(A0A8N
-8A0A(B BBBH , \ `l> AID W
-DAA T pl
- BEB B(A0A8N
-8A0A(B BBBH , 8v> AID W
-DAA , T- AAG0!AA 4 D vE BHD D`
- DABK 4 | 0yE BHD D`
- DABK 4 H|E BHD D`
- DABK 4 `E o BHD D`
- DABK T $ x S BBG E(A0G8GY
-8D0A(B BBBK T | @E
- BBG E(A0G8GY
-8D0A(B BBBK T 8
- BBG E(A0G8Go
-8D0A(B BBBE T , П5
- O BBG E(A0G8GY
-8D0A(B BBBK < . K^ T l \ AG RAD v BBB A(D0D@
-0A(A BBBA D ~ BBB A(D0D@
-0A(A BBBA L BBB B(A0D8DPd
-8A0A(B BBBA L l hK BBB B(A0A8G@d
-8A0A(B BBBA , - AAG0!AA < 4 8{ BHD G
- DABA T , xO BAA G05
+G I , h} AJ@
+AG MA 4 ry\ AG RAD T } BBE A(A0D@
+0A(A BBBA D ~ BBE A(A0D@
+0A(A BBBA T , BEB B(H0A8G?
+8A0A(B BBBD , > / AID W
+DAA T BEE E(D0A8G
+8D0A(B BBBE , >y- AAG0!AA < < 蔖{ BHD G
+ DABA $ | ( AL0p
+DA $ AL0p
+DA $ 8 AL0p
+DA d h KGB B(A0A8R8A0A(B BBBG T \ X k BBB B(A0A8G
+8A0A(B BBBG T BBH B(F0A8J
+8D0A(B BBBG X7z. K^ 0Ö> Hu PÖE Hx y\ AG RAT `Ö
+ 7 BBB B(D0A8N
+8A0A(B BBBJ , ͖> } AID W
+DAA , ~y- AAG0!AA 4 < ͖5 - BHD D`
+ DABG T t ЖH BBB B(A0D8L
+8A0A(B BBBK 5z. K^ T , o BBE B(D0A8G
+8D0A(B BBBK L l x U BBB J(A0A8G
+8D0A(B BBBH , t F~y- AAG0!AA < . BHD Dn
+ DABA T , BBE B(D0A8G
+8D0A(B BBBG T ` BBE B(D0A8G
+8D0A(B BBBH \ (^ KLY H(D0I8J8A0A(B BBBG \ ( KEJ E(J0I8J8A0A(B BBBG \ T KLQ I(G0I8M8A0A(B BBBI L h BHB E(A0F8G
+8D0A(B BBBD L L
+f BEB G(A0G8G
+8D0A(B BBBD L BEB G(A0A8M
+8D0A(B BBBD L x BEB G(A0A8J
+8D0A(B BBBG T < BBB G(D0G8G
+8D0A(B BBBG L 01z. K^ d {y\ AG RAD %} BBB D(A0D@
+0A(A BBBA D P&} 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 4 , BBB D(A0D@
+0A(A BBBA D | - BBB D(A0D@
+0A(A BBBA , yy- AAG0!AA 4 < .5 G BHD D`
+ DABG 4 t 15 ) BHD D`
+ DABG 4 45 BHD D`
+ DABG 4 85 BHD D`
+ DABG L ; BJE B(A0A8D@
+8A0A(B BBBA $ $ ;V ADD JAAL < BEB E(I0H8D@
+8D0A(B BBBA T = W BBB B(A0D8L7
+8A0A(B BBBF T < AE S BBB B(H0A8J
+8A0A(B BBBG , L> AID W
+DAA T LE BBB B(H0A8J
+8A0A(B BBBG , W> AID W
+DAA T L W BBB B(H0A8J
+8A0A(B BBBG , pc> 3 AID W
+DAA T cE BBB B(H0A8J
+8A0A(B BBBG , , xn> s AID W
+DAA T \ nW S BBB H(A0D8L
+8D0A(B BBBH T rW I BBB H(A0D8L
+8D0A(B BBBH T vL ? BBB H(A0D8L
+8D0A(B BBBH )z. K^ 4 xz4 L z) d z5 | z* zK 0{? X{5 {) {> Hu {> Hu 4 {> Hu T {> Hu L t | AAG e
+CAF s
+ACA R
+CAJ JCA L h| AAG ]
+CAF k
+ACA R
+CAJ JCA L | AAG e
+CAF s
+ACA R
+CAJ JCA L d | AAG ]
+CAF k
+ACA R
+CAJ JCA L 8} AAG e
+CAF s
+ACA R
+CAJ JCA L } AAG ]
+CAF k
+ACA R
+CAJ JCA L T } AAG e
+CAF s
+ACA R
+CAJ JCA L ~ AAG ]
+CAF k
+ACA R
+CAJ JCA X~E Hx ~E Hx 4 ~E Hx T ~E Hx < q BBA A(D0[
+(A ABBA py\ AG RA 8_ AF
+I T < x
+ a BEB B(A0A8N
+8A0A(B BBBH , @> AID W
+DAA T P
+ BEB B(A0A8N
+8A0A(B BBBH , > AID W
+DAA T L (
+ BEB B(A0A8N
+8A0A(B BBBH , > AID W
+DAA T
+ BEB B(A0A8N
+8A0A(B BBBH , , Ȧ> AID W
+DAA , ny- AAG0!AA 4 E BHD D`
+ DABK 4 E BHD D`
+ DABK 4 جE BHD D`
+ DABK 4 4 E k BHD D`
+ DABK T l
O BBB E(A0G8L
+8D0A(B BBBD T `
BBB E(A0G8L
+8D0A(B BBBD T ͗ BBB E(A0G8L
+8D0A(B BBBE T t Pڗ 3 BBB E(A0G8L
+8D0A(B BBBE X z. K^ Hmy\ AG RAD v BBB A(D0D@
+0A(A BBBA D ~ BBB A(D0D@
+0A(A BBBA d lyV ATL | BBB B(A0D8DPd
+8A0A(B BBBA l BBE E(A0A8G
+8A0A(B BBBA
+8A0A(B BBBA L < K BBB B(A0A8G@d
+8A0A(B BBBA , "ly- AAG0!AA < { BHD G
+ DABA T O BAA G05
AABD
AABD e
- AABO SV ATT Xr
t BEB B(D0A8Je
-8A0A(B BBBA T < Ơ N BOB B(A0A8G]
-8A0A(B BBBA < Ԡg ^ BBA A(D0V(A ABB T Ԡ " BBE E(A0A8J#
-8A0A(B BBBA T , p
- c BBB B(A0D8M
-8D0A(B BBBA T (! BBB B(D0A8N E
-8A0A(B BBBF , > = AID W
-DAA . K^ R HI
Y HL $ 4 @
^ AG b
-AA T \ x
- BDA ]
+ AABO T ! D BBB B(A0D8N E
+8A0A(B BBBF , > AID W
+DAA T $ BOB B(A0A8G]
+8A0A(B BBBA < | X*g BBA A(D0V(A ABB T * BBE E(A0A8J#
+8A0A(B BBBA T 7
+ BBB B(A0D8M
+8D0A(B BBBA $ z. K^ < @ T @R HI t @Y HL $ (A^ AG b
+AA T `A- BDA ]
ADG w
ADD Q
ABD I
-ABD T P - BBB B(D0A8N
-8A0A(B BBBD , T "> AID W
-DAA , <! - AAG0!AA 4 "5 BHD D`
- DABG $ %G As
+ABD T \ 8B # BBB B(D0A8N
+8A0A(B BBBD , V> AID W
+DAA , @iy- AAG0!AA 4 V5 BHD D`
+ DABG $ L YG } As
L A
-A L ! %r c BBB B(A0A8G~
-8A0A(B BBBG T d! . ; BBG B(A0D8G
-8A0A(B BBBH t" @. K^ " H " H " H " H " Hd D L
-H G # hH G t
-E < t" Iq W BBA A(D0[
-(A ABBA l# HI2 AF iA # hIi AX
-G # HN L # I AAG e
+A T t Y [ BBG B(A0D8G
+8A0A(B BBBH z. K^ sd D L
+H G ps G t
+E < $ tq BBA A(D0[
+(A ABBA Pt2 AF iA < pti AX
+G \ hy HN L t t AAG e
+CAF K
+ACI R
+CAJ JCA L u AAG e
CAF C
ACA R
-CAJ JCA L $ J AAG e
+CAJ JCA L xu AAG e
+CAF {
+ACI R
+CAJ JCA L d u AAG e
+CAF K
+ACI R
+CAJ JCA L Hv AAG e
+CAF K
+ACI R
+CAJ JCA L v AAG e
CAF S
ACA R
-CAJ JCA L d$ pJ AAG e
+CAJ JCA L T (w AAG e
+CAF K
+ACI R
+CAJ JCA L w AAG e
CAF C
ACA R
-CAJ JCA L $ J AAG e
+CAJ JCA L w AAG e
+CAF {
+ACI R
+CAJ JCA L D Xx AAG e
+CAF K
+ACI R
+CAJ JCA L x AAG e
+CAF K
+ACI R
+CAJ JCA L 8y AAG e
CAF S
ACA R
-CAJ JCA L % @K AAG e
+CAJ JCA L 4 y AAG e
+CAF K
+ACI R
+CAJ JCA L z AAG e
CAF C
ACA R
-CAJ JCA L T% K AAG e
+CAJ JCA L xz AAG e
+CAF {
+ACI R
+CAJ JCA L $ z AAG e
+CAF K
+ACI R
+CAJ JCA L t H{ AAG e
+CAF K
+ACI R
+CAJ JCA L { AAG e
CAF S
ACA R
-CAJ JCA L % L AAG e
+CAJ JCA L (| AAG e
+CAF K
+ACI R
+CAJ JCA L d | AAG e
CAF C
ACA R
-CAJ JCA L % pL AAG e
+CAJ JCA L | AAG e
+CAF {
+ACI R
+CAJ JCA L X} AAG e
+CAF K
+ACI R
+CAJ JCA L T } AAG e
+CAF K
+ACI R
+CAJ JCA L 8~ AAG e
CAF S
ACA R
-CAJ JCA D& L- \& \ AG RA< |& L BLF A(A0
-(H BFEA < & xM BLF A(A0
-(H BFEA < & N BLF A(A0
-(H BFEA < <' N BLF A(A0
-(H BFEA < |' XO BII A(A0
-(A BJBE < ' O BII A(A0
-(A BJBE < ' P BII A(A0
-(A BJBE < <( 8Q BII A(A0
-(A BJBE $ |( R AAG0AA< ' Q BHD Dq
- DABA < ,( T BHD Dq
- DABA < l( pW BHD Dq
- DABA < ( PZ BHD Dq
- DABA D ( 0] w BEB A(A0G`l
-0D(A BBBA 4 4) ^Z B BDA G`
- DABJ 4 l) 0_Z BDA G`
- DABJ L ) X` BBB E(A0A8G
-8D0A(B BBBB L * af BEB A(A0|
+CAJ JCA ~ Y
+ ay\ AG RA< ,
+ ~ BLF A(A0
+(H BFEA < l
+ 0 BLF A(A0
+(H BFEA <
+ BLF A(A0
+(H BFEA <
+ p BLF A(A0
+(H BFEA < , BII A(A0
+(A BJBE < l BII A(A0
+(A BJBE < P BII A(A0
+(A BJBE < BII A(A0
+(A BJBE $ , P_y AAG0AA< h c BHD Dq
+ DABA < H H BHD Dq
+ DABA < ( - BHD Dq
+ DABA < \ BHD Dq
+ DABA D 莘 BEB A(A0G`l
+0D(A BBBA 4 Z · BDA G`
+ DABJ 4
萘Z BDA G`
+ DABJ L T
{ BBB E(A0A8G
+8D0A(B BBBB L \ `f BEB A(A0|
(A BBBJ A
-(A BBBA L D* a BBB B(D0A8D`
-8D0A(B BBBI L L+ HdK BBB B(A0A8G@d
-8A0A(B BBBA < + He BEB D(A0M
-(A BBBF T $+ fR BEE E(D0A8Gj
-8D0A(B BBBD T |+ iR BEE E(D0A8Gj
-8D0A(B BBBD T + lR m BEE E(D0A8Gj
-8D0A(B BBBD T ,, oR 0 BEE E(D0A8Gj
-8D0A(B BBBD L <- r BBB B(A0A8Gp8A0A(B BBB d - ht0 BBE B(D0E8D@P
+(A BBBA L
BBB B(D0A8D`
+8D0A(B BBBI L K BBB B(A0A8G@d
+8A0A(B BBBA < L BEB D(A0M
+(A BBBF T R g BEE E(D0A8Gj
+8D0A(B BBBD T , R * BEE E(D0A8Gj
+8D0A(B BBBD T R BEE E(D0A8Gj
+8D0A(B BBBD T R BEE E(D0A8Gj
+8D0A(B BBBD L BBB B(A0A8Gp8A0A(B BBB d < 0 BBE B(D0E8D@P
8F0A(B BBBA
-8D0A(B BBGA \ - 0u KLB B(D0A8DPX8A0A(B BBBJP \ T. w BLE A(A0]
+8D0A(B BBGA \ 覘 KLB B(D0A8DPX8A0A(B BBBJP \ x BLE A(A0]
(A BBBG B
-(A BBBG A(A BBB L . x BBB B(A0A8Gp8A0A(B BBB / y{ < / y BEB A(F0z
-(A BBBG 4 \/ xz ADD S
+(A BBBG A(A BBB L d BBB B(A0A8Gp8A0A(B BBB 8{ d z BBB E(D0E8D@S
+8F0A(B BBBF
+8D0A(B BBGA < 4 BEB A(F0z
+(A BBBG 4 t H ADD S
AAB I
-FAH T / { BBB D(D0D@
-0D(A BBGK D
-0C(A BBBB < / { BBG A(G@
-(A ABBG \ ,0 x|8 BFD D(G0~
+FAH < BBG A(G@
+(A ABBG \ 8 BFD D(G0~
(A ABBE i
-(A ABBA \(F ABB T / X} BBB B(A0D8J
-8A0A(B BBBD T ,0 N BBB B(A0D8N
-8A0A(B BBBJ , 0 h> AID W
-DAA T 0 x BBB B(A0D8J
-8A0A(B BBBD T 1 ǡ BBB B(A0D8N
-8A0A(B BBBJ , d1 ݡ> / AID W
-DAA T 1 ݡ BBB B(A0D8J
-8A0A(B BBBD T 1 0 BBB B(A0D8N
-8A0A(B BBBJ , D2
> e AID W
-DAA T t2
E BBB B(A0D8J
-8A0A(B BBBD T 2 P' BBB B(A0D8N
-8A0A(B BBBJ , $3 => AID W
-DAA 4 = D $4 @ BBB B(A0A8D@8A0A(B BBBT 3 (A BBE B(A0A8GU
-8A0A(B BBBA L 4 D BBD A(G0o
+(A ABBA \(F ABB T BBB B(A0D8J
+8A0A(B BBBD T ɘ BBB B(A0D8N
+8A0A(B BBBJ , D ߘ> i AID W
+DAA T t ߘ I BBB B(A0D8J
+8A0A(B BBBD T 8 BBB B(A0D8N
+8A0A(B BBBJ , $ > AID W
+DAA T T BBB B(A0D8J
+8A0A(B BBBD T X) * BBB B(A0D8N
+8A0A(B BBBJ , ?> ո AID W
+DAA T 4 ? BBB B(A0D8J
+8A0A(B BBBD T xY ` BBB B(A0D8N
+8A0A(B BBBJ , o> AID W
+DAA p D r BBB B(A0A8D@8A0A(B BBBL , Psb BBB B(A0A8Dp,
+8A0A(B BBBA L | pt BBD A(G0o
(A ABBK X
-(A ABBA 5 E) d < t4 (E6 BBA A(DG
-(A ABBA , 4 (F AAG d
-AAA < 5 F BDD k
+(A ABBA t) d < t BDD k
ABH M
-ABA 5 F D 5 I BBB B(A0A8D@8A0A(B BBBT 5 HJ BBE B(A0A8GU
-8A0A(B BBBA < 5 M6 BBA A(DG
-(A ABBA , 6 N AAG d
-AAA 7 0O D 7 R BBB B(A0A8D@8A0A(B BBBT 6 S % BBE B(A0A8GU
-8A0A(B BBBA < 7 V6 " BBA A(DG
-(A ABBA , D7 W AAG d
-AAA ,8 XQ D D8 P[ BBB B(A0A8D@8A0A(B BBBT 7 [ ~ BBE B(A0A8GU
-8A0A(B BBBA < ,8 _6 { BBA A(DG
-(A ABBA , l8 ` Z AAG d
-AAA < T9 `d BBB A(A0d
-(A BBBE 9 a% t 9 a BBE A(D0D@"
+ABA , l Xu AAG m
+AAA , u p AAG d
+AAA < 8v6 M BBA A(DG
+(A ABBA < 8w BDD k
+ABH [
+ABA 4 wJ BDA q
+ABE A
+ABA , wb wGA A(D l y D { BBB B(A0A8D@8A0A(B BBBL P|b BBB B(A0A8Dp,
+8A0A(B BBBA , d p} AAG m
+AAA , } ҷ AAG d
+AAA < P~6 BBA A(DG
+(A ABBA , Pb wGA A(D D ( BBB B(A0A8D@8A0A(B BBBL L b BBB B(A0A8Dp,
+8A0A(B BBBA , ϶ AAG m
+AAA , AAG d
+AAA < D 6 BBA A(DG
+(A ABBA , b h wGA A(D l Q D BBB B(A0A8D@8A0A(B BBBL `b BBB B(A0A8Dp,
+8A0A(B BBBA , d AAG m
+AAA , AAG d
+AAA < `6 c BBA A(DG
+(A ABBA , `b B wGA A(D | BBB B(D0A8DP
+8A0A(B BBBB
+8A0A(B BBBE
+8A0A(B BBBB T BBB B(A0A8J
+8A0A(B BBBA L hB BEB A(E0K
+0A(A BBBA L \ hB W BEB A(E0K
+0A(A BBBA L hB - BEB A(E0K
+0A(A BBBA L hB BEB A(E0K
+0A(A BBBA T L h ٳ BBB B(A0A8J
+8A0A(B BBBA T BBB B(A0A8J
+8A0A(B BBBA T X u BBB B(A0A8J
+8A0A(B BBBA " Ф% t $" 褙 BBE A(D0D@"
0A(A BBBD
0A(A BBBE 7
-0A(A BBBI < l9 k g BED D(LP
-(A ABBH L 9 plC : BBB B(A0A8D*8A0A(B BBB < 9 pm BED D(LP
-(A ABBH L <: nC BBB B(A0A8D*8A0A(B BBB < : o u BED D(LP
-(A ABBH L : 0qC H BBB B(A0A8D*8A0A(B BBB < ; 0r BED D(LP
-(A ABBH L \; sC BBB B(A0A8D*8A0A(B BBB < d< t BBG A(G@
-(A ABBD < Pu5 < xu BBE A(A0G@
+0A(A BBBI < ! BED D(LP
+(A ABBH < " BBB A(A0
+(A BBBA L d" @? F BBB B(A0A8D&8A0A(B BBB < " 0 BED D(LP
+(A ABBH < # BBB A(A0
+(A BBBA L 4# P? BBB B(A0A8D&8A0A(B BBB < # @ A BED D(LP
+(A ABBH < |$ BBB A(A0
+(A BBBA L $ `? BBB B(A0A8D&8A0A(B BBB < T$ P BED D(LP
+(A ABBH < L% BBB A(A0
+(A BBBA L $ p? BBB B(A0A8D&8A0A(B BBB < % ` BBG A(G@
+(A ABBD & 5 4& H1 Dl < T& hC BBA A(G0d
+(A ABBI & x BBE A(A0G@
0A(A BBBC
0A(A BBBE
0A(A BBBE R
-0A(A BBBF D < {r BBA A(G
-(A ABBD T < ( BBB B(A0D8G
-8A0A(B BBBF T ,= : x BEB E(A0A8J
-8A0A(B BBBA L = + q BEE E(A0A8J
-8A0A(B BBBH L = C BBB G(A0G8J
-8D0A(B BBBH D $> r K BBA A(G
-(A ABBD T l> 0 G BBB B(A0D8G
-8A0A(B BBBF T > ț: BEB E(A0A8J
-8A0A(B BBBA L ? + BEE E(A0A8J
-8A0A(B BBBH L l? BBB G(A0G8J
-8D0A(B BBBH D ? r BBA A(G
-(A ABBD T @ 8 BBB B(A0D8G
-8A0A(B BBBF T \@ г: BEB E(A0A8J
-8A0A(B BBBA L @ + BEE E(A0A8J
-8A0A(B BBBH L A BBB G(A0G8J
-8D0A(B BBBH D TA Ģr BBA A(G
-(A ABBD T A @ɢ BBB B(A0D8G
-8A0A(B BBBF T A ˢ: p BEB E(A0A8J
-8A0A(B BBBA L LB Ԣ+ i BEE E(A0A8J
-8A0A(B BBBH L B آ ; BBB G(A0G8J
-8D0A(B BBBH C @. K^ C ۢ G t
-E C ܢd D L
-H G < DC ܢq BBA A(D0[
-(A ABBA <D (ݢ2 AF iA L \D Hݢ AAG e
+0A(A BBBF D d& R BBA D(G
+(A ABBK T & ș BBE B(D0A8J-
+8A0A(B BBBA L ' P֙ ٮ BEB E(D0A8J
+8A0A(B BBBG L T' ܙ BBB G(A0G8J
+8D0A(B BBBI L ' ߙk BBB A(A0Dg
+0A(A BBBA T ' ɮ BBB B(A0D8G
+8A0A(B BBBD D L( (R BBA D(G
+(A ABBK T ( @ BBE B(D0A8J-
+8A0A(B BBBA L ( BEB E(D0A8J
+8A0A(B BBBG L <) H BBB G(A0G8J
+8D0A(B BBBI L ) k BBB A(A0Dg
+0A(A BBBA T ) ݮ BBB B(A0D8G
+8A0A(B BBBD D 4* `R BBA D(G
+(A ABBK T |* x
+w
BEE B(D0A8J(
+8A0A(B BBBA L * ݮ BEE B(D0A8J
+8A0A(B BBBA L $+ 0 Ӯ BBB G(A0G8J
+8D0A(B BBBI L t+ k ۮ BBB A(A0Dg
+0A(A BBBA T + ! BBB B(A0D8G
+8A0A(B BBBD D , H$R BBA D(G
+(A ABBK T d, `) BBE E(A0A8J-
+8A0A(B BBBA L , 7 Ԯ BEE B(D0A8J
+8A0A(B BBBA L - h< ˮ BBB G(A0G8J
+8D0A(B BBBI L \- ?k Ӯ BBB A(A0Dg
+0A(A BBBA T - A BBB B(A0D8G
+8A0A(B BBBD . y. K^ . hD G t
+E . Ed D L
+H G < \. XEq * BBA A(D0[
+(A ABBA T/ E2 AF iA L t/ E AAG e
+CAF K
+ACI R
+CAJ JCA L / (F AAG e
CAF C
ACA R
-CAJ JCA L D ݢ AAG e
+CAJ JCA L 0 F AAG e
+CAF {
+ACI R
+CAJ JCA L d0 F AAG e
+CAF K
+ACI R
+CAJ JCA L 0 XG AAG e
+CAF K
+ACI R
+CAJ JCA L 1 G AAG e
CAF S
ACA R
-CAJ JCA L D ޢ AAG e
+CAJ JCA L T1 8H AAG e
+CAF K
+ACI R
+CAJ JCA L 1 H AAG e
CAF C
ACA R
-CAJ JCA L LE xޢ AAG e
+CAJ JCA L 1 I AAG e
+CAF {
+ACI R
+CAJ JCA L D2 hI AAG e
+CAF K
+ACI R
+CAJ JCA L 2 I AAG e
+CAF K
+ACI R
+CAJ JCA L 2 HJ AAG e
CAF S
ACA R
-CAJ JCA L E ޢ AAG e
+CAJ JCA L 43 J AAG e
+CAF K
+ACI R
+CAJ JCA L 3 (K AAG e
CAF C
ACA R
-CAJ JCA L E Hߢ AAG e
+CAJ JCA L 3 K AAG e
+CAF {
+ACI R
+CAJ JCA L $4 K AAG e
+CAF K
+ACI R
+CAJ JCA L t4 XL AAG e
+CAF K
+ACI R
+CAJ JCA L 4 L AAG e
CAF S
ACA R
-CAJ JCA L <F ߢ AAG e
+CAJ JCA L 5 8M AAG e
+CAF K
+ACI R
+CAJ JCA L d5 M AAG e
CAF C
ACA R
-CAJ JCA L F AAG e
+CAJ JCA L 5 N AAG e
+CAF {
+ACI R
+CAJ JCA L 6 hN AAG e
+CAF K
+ACI R
+CAJ JCA L T6 N AAG e
+CAF K
+ACI R
+CAJ JCA L 6 HO AAG e
CAF S
ACA R
-CAJ JCA L F AAG e
+CAJ JCA L 6 O AAG e
+CAF K
+ACI R
+CAJ JCA L D7 (P AAG e
CAF C
ACA R
-CAJ JCA L ,G AAG e
+CAJ JCA L 7 P AAG e
+CAF {
+ACI R
+CAJ JCA L 7 P AAG e
+CAF K
+ACI R
+CAJ JCA L 48 XQ AAG e
+CAF K
+ACI R
+CAJ JCA L 8 Q AAG e
CAF S
ACA R
-CAJ JCA L |G X AAG e
+CAJ JCA L 8 8R AAG e
+CAF K
+ACI R
+CAJ JCA L $9 R AAG e
CAF C
ACA R
-CAJ JCA L G AAG e
+CAJ JCA L t9 S AAG e
+CAF {
+ACI R
+CAJ JCA L 9 hS AAG e
+CAF K
+ACI R
+CAJ JCA L : S AAG e
+CAF K
+ACI R
+CAJ JCA L d: HT AAG e
CAF S
ACA R
-CAJ JCA L H ( AAG e
+CAJ JCA L : T AAG e
+CAF K
+ACI R
+CAJ JCA L ; (U AAG e
CAF C
ACA R
-CAJ JCA L lH AAG e
+CAJ JCA L T; U AAG e
+CAF {
+ACI R
+CAJ JCA L ; U AAG e
+CAF K
+ACI R
+CAJ JCA L ; XV AAG e
+CAF K
+ACI R
+CAJ JCA L D< V AAG e
CAF S
ACA R
-CAJ JCA L H AAG e
+CAJ JCA L < 8W AAG e
+CAF K
+ACI R
+CAJ JCA L < W AAG e
CAF C
ACA R
-CAJ JCA L I X AAG e
+CAJ JCA L 4= X AAG e
+CAF {
+ACI R
+CAJ JCA L = hX AAG e
+CAF K
+ACI R
+CAJ JCA L = X AAG e
+CAF K
+ACI R
+CAJ JCA L $> HY AAG e
CAF S
ACA R
-CAJ JCA < \I BLF A(A0p
-(H BFEA < I 8 BLF A(A0p
-(H BFEA < I BLF A(A0p
-(K BFEE < J BLF A(A0p
-(K BFEE \J g0
-A |J g0
-A J \ AG RA, J - AAG0!AA 4 4J 8 BHD D`
- DABB 4 lJ BHD D`
- DABB T J BBE B(A0A8J
-8A0A(B BBBB , J `> w AID W
-DAA T ,K p W BBE B(A0A8J
-8A0A(B BBBB , K > AID W
-DAA D lL BBB B(A0A8D@8A0A(B BBBL L 0 BBB B(A0A8D`
-8A0A(B BBBF D M BBB B(A0A8D@8A0A(B BBBL LM H BBB B(A0A8D`
-8A0A(B BBBG 4 M BBA A(D0(A ABBL M `F BBB B(A0A8D`(
-8A0A(B BBBH 4 $N ` BBA A(D0(A ABBL \N F BBB B(A0A8D`(
-8A0A(B BBBH T M BBE B(A0A8GU
-8A0A(B BBBA T LN P# BBE B(A0A8GU
-8A0A(B BBBA T N & BBB E(A0A8I^
-8A0A(B BBBA T N P) BBB E(A0A8I^
-8A0A(B BBBA , TO + Y AAG d
-AAA , O H, 6 AAG d
-AAA , O , AAG d
-AAA D P (- BBB B(A0A8D@8A0A(B BBBL P - BBB B(A0A8D`
-8A0A(B BBBF D 4Q @/ BBB B(A0A8D@8A0A(B BBBL |Q / BBB B(A0A8D`
-8A0A(B BBBG 4 Q 81 BBA A(D0(A ABBL R 1F BBB B(A0A8D`(
-8A0A(B BBBH 4 TR 3 BBA A(D0(A ABBL R (4F BBB B(A0A8D`(
-8A0A(B BBBH T $R (6 BBE B(A0A8GU
-8A0A(B BBBA T |R 9 BBE B(A0A8GU
-8A0A(B BBBA T R = BBB E(A0A8I^
-8A0A(B BBBA T ,S ? BBB E(A0A8I^
-8A0A(B BBBA , S (B ^ AAG d
-AAA , S B ; AAG d
-AAA , S C AAG d
-AAA T xC BBE A(A0G@
+CAJ JCA < t> Y BLF A(A0p
+(H BFEA < > (Z BLF A(A0p
+(H BFEA < > Z BLF A(A0p
+(K BFEE < 4? [ BLF A(A0p
+(K BFEE L t? x[ BBB B(D0A8DPd
+8A0A(B BBBA ? \ g0
+A ? ] g0
+A @ ,y\ AG RAD $@ ^ BBB A(A0D@
+0A(A BBBA D l@ ` BBB A(A0D@
+0A(A BBBA D @ Hb< BBB A(A0D@
+0A(A BBBA D @ @d< BBB A(A0D@
+0A(A BBBA D DA 8f< BBB A(A0D@
+0A(A BBBA D A 0h< BBB A(A0D@
+0A(A BBBA D A (j< BBB A(A0D@
+0A(A BBBA D B l< BBB A(A0D@
+0A(A BBBA , dB *y- AAG0!AA 4 A m BHD D`
+ DABB 4 B Pp BHD D`
+ DABB T LB r s BBE B(A0A8J
+8A0A(B BBBB , B > ' AID W
+DAA T B BBE B(A0A8J
+8A0A(B BBBB , ,C x> AID W
+DAA D D BBB B(A0A8D@8A0A(B BBBL \D b BBB B(A0A8Dp,
+8A0A(B BBBA L D BBB B(A0A8D`
+8A0A(B BBBC D D Р BBB B(A0A8D@8A0A(B BBBL DE (b BBB B(A0A8Dp,
+8A0A(B BBBA L E H BBB B(A0A8D`
+8A0A(B BBBD 4 E 裚 BBA A(D0(A ABBL F `A BBB E(A0A8DP
+8A0A(B BBBA L lF `K BBB B(A0A8Dp8
+8A0A(B BBBH 4 F ` BBA A(D0(A ABBL F اA BBB E(A0A8DP
+8A0A(B BBBA L DG بK BBB B(A0A8Dp8
+8A0A(B BBBH L F ت BEB B(A0E8H
+8A0A(B BBBA T ,G BBB B(A0A8Jl
+8A0A(B BBBA L G Ю BEB B(A0E8H
+8A0A(B BBBA T G BBB B(A0A8Jl
+8A0A(B BBBA D H Ȳ BEB A(E0H@
+0A(A BBBA L ,I } BBB B(A0D8D`J
+8A0A(B BBBA D |I BEB A(E0H@
+0A(A BBBA L I } BBB B(A0D8D`J
+8A0A(B BBBA , \I | 1 GA A(D , I | " GA A(D D tJ X BBB B(A0A8D@8A0A(B BBBL J b BBB B(A0A8Dp,
+8A0A(B BBBA L K к BBB B(A0A8D`
+8A0A(B BBBC D \K BBB B(A0A8D@8A0A(B BBBL K b BBB B(A0A8Dp,
+8A0A(B BBBA L K BBB B(A0A8D`
+8A0A(B BBBD 4 DL BBA A(D0(A ABBL |L 0A BBB E(A0A8DP
+8A0A(B BBBA L L 0K BBB B(A0A8Dp8
+8A0A(B BBBH 4 M 0Ú BBA A(D0(A ABBL TM ÚA BBB E(A0A8DP
+8A0A(B BBBA L M ĚK BBB B(A0A8Dp8
+8A0A(B BBBH L <M ƚ BEB B(A0E8H
+8A0A(B BBBA T M hȚ h BBB B(A0A8Jl
+8A0A(B BBBA L M ʚ 6 BEB B(A0E8H
+8A0A(B BBBA T 4N `̚ BBB B(A0A8Jl
+8A0A(B BBBA D DO Κ BEB A(E0H@
+0A(A BBBA L O `Ϛ} BBB B(A0D8D`J
+8A0A(B BBBA D O К BEB A(E0H@
+0A(A BBBA L $P Xњ} BBB B(A0D8D`J
+8A0A(B BBBA , O Қ| GA A(D , O Ӛ| GA A(D P (՚ BBE A(A0G@
0A(A BBBC
0A(A BBBE
0A(A BBBE R
-0A(A BBBF < T I m BEA A(Dh
-(A ABBE T T PK R BEB E(A0A8Jo
-8A0A(B BBBA T 4U T ; BEB E(A0A8Jo
-8A0A(B BBBA T U ^ $ BBB B(D0A8Dd
-8A0A(B BBBA T U h_ BBB B(D0A8Dd
-8A0A(B BBBA < <V ` BEA A(Dh
-(A ABBE T |V 0b BEB E(A0A8Jo
-8A0A(B BBBA T V k h BEB E(A0A8Jo
-8A0A(B BBBA T ,W t Q BBB B(D0A8Dd
-8A0A(B BBBA T W Hv BBB B(D0A8Dd
-8A0A(B BBBA T W wH BBB B(A0D8G
-8A0A(B BBBH T 4X zH BBB B(A0D8G
-8A0A(B BBBH T X }`
- y BBE E(A0A8Ja
-8A0A(B BBBA T X `
- v BBE E(A0A8Ja
-8A0A(B BBBA T <Y s BBB B(A0D8G;
-8A0A(B BBBG T Y H j BBB B(A0D8G;
-8A0A(B BBBG T Y T a BBE B(A0D8Ja
-8A0A(B BBBA T DZ 訣 BBB E(G0A8L
-8D0A(B BBBD T Z `T BBE B(A0D8Ja
-8A0A(B BBBA T Z h BBB E(G0A8L
-8D0A(B BBBD \ j. K^ \ \ AG RAD <\ ã BBE A(A0D@u
-0A(A BBBA D \ @ģ BBE A(A0D@}
-0A(A BBBA T \ ģJ G BBI B(A0A8J
-8A0A(B BBBA , l\ ̣> _ AID W
-DAA , T] - AAG0!AA < \ ̣ BHD DN
- DABA \ ] У KLE B(A0A8DPi8A0A(B BBBIP \ $^ @ң KLE B(A0A8DPi8A0A(B BBBIP l ^ pԣy BBF D(A0G@I
+0A(A BBBF < P ۚ BEA A(D
+(A ABBG T P `ݚ= BEB B(D0D8J%
+8A0A(B BBBA L <Q HW BBB A(A0DS
+0A(A BBBA T Q X BBB E(A0A8G
+8A0A(B BBBG < Q ȏ BEA A(Dh
+(A ABBE T $R @V
BBB E(A0D8J"
+8A0A(B BBBA T |R HV
Ï BBB E(A0D8J"
+8A0A(B BBBA T R P s ُ BBB B(A0A8Dk
+8A0A(B BBBA T ,S x
BBB B(D0A8Dd
+8A0A(B BBBA T S s b BBB B(A0A8Dk
+8A0A(B BBBA T S 0 BBB B(D0A8Dd
+8A0A(B BBBA < 4T p BEA A(D
+(A ABBG T tT 0= ю BEB B(D0D8J%
+8A0A(B BBBA L T "W BBB A(A0DS
+0A(A BBBA T U ($ BBB E(A0A8G
+8A0A(B BBBG < tU & BEA A(Dh
+(A ABBE T U (
BBB E(A0D8J#
+8A0A(B BBBA T V H5V
BBB E(A0D8J"
+8A0A(B BBBA T dV PBs BBB B(A0A8Dk
+8A0A(B BBBA T V xD BBB B(D0A8Dd
+8A0A(B BBBA T W Es J BBB B(A0A8Dk
+8A0A(B BBBA T lW H BBB B(D0A8Dd
+8A0A(B BBBA T W pIH Ӎ BBB B(A0D8G
+8A0A(B BBBH T X hLH BBB B(A0D8G
+8A0A(B BBBH L tX `O BBB A(A0D
+0A(A BBBA L X Q [ BBB A(A0D
+0A(A BBBA T Y T~ 1 BEE B(D0D8J
+8A0A(B BBBA T lY Hc c BBB E(G0A8L
+8D0A(B BBBC T Y i~ BEE B(D0D8J
+8A0A(B BBBA T Z x BBB E(G0A8L
+8D0A(B BBBC ,[ py. K^ D[ fy\ AG RAD d[ ( BBE A(A0D@u
+0A(A BBBA D [ BBE A(A0D@}
+0A(A BBBA T <[ XJ BBI B(A0A8J
+8A0A(B BBBA , [ P> # AID W
+DAA , |\ y- AAG0!AA < [ 0 ӌ BHD DN
+ DABA \ \ KLE B(A0A8DPi8A0A(B BBBIP \ L] KLE B(A0A8DPi8A0A(B BBBIP l ] y BBF D(A0G@I
0A(A BBBI w
0A(A BBBA D
-0F(A BBBG d ^ ֣E BBB B(A0A8DP
+0F(A BBBG d ^ E BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l ^ hףB _ BBB J(D0D8D4
+8F0A(B BBBA l ] 蒛B # BBB J(D0D8D4
8D0A(B BBBC i
-8A0A(B BBBE L _ Hۣ# BBB B(A0A8J
-8A0A(B BBBA ` (f. K^ 4` O HF T` @V HI T _ < BBE B(D0A8G
-8D0A(B BBBB T ` h< u BBE B(D0A8G
-8D0A(B BBBB , $a b- AAG0!AA < ` U + BIA D(Dp
+8A0A(B BBBE L <^ Ȗ# ӊ BBB B(A0A8J
+8A0A(B BBBA D_ y. K^ \_ O HF |_ V HI T ^ < S BBE B(D0A8G
+8D0A(B BBBB T <_ 诛< 9 BBE B(D0A8G
+8D0A(B BBBB , L` y- AAG0!AA < _ U BIA D(Dp
-(D ABBI < ` @U BIA D(Dp
-(D ABBB T a ` BGE B(A0A8G
-8A0A(B BBBA T ta BBF B(A0D8N{
-8A0A(B BBBA , a > ^ AID W
-DAA , a 0 > > AID W
-DAA b c. K^ b ( c ,c Dc L b ( r BBB B(E0A8J@
-8A0A(B BBBA $ c X!^ AG b
-AA 4 c !l ADG b
+(D ABBI < ` U Ɋ BIA D(Dp
+(D ABBB T D` ປ4 BGE B(A0A8G
+8A0A(B BBBI T ` ՛ BBF B(A0D8Ns
+8A0A(B BBBA , ` ޛ> b AID W
+DAA , $a ޛ> B AID W
+DAA b y. K^ $b ޛ <b ޛ Tb ޛ lb ޛ L a ޛr BBB B(E0A8J@
+8A0A(B BBBA $ b ߛ^ AG b
+AA 4 b l ADG b
AAH l
-AAA 4 d !l ADG b
+AAA 4 4c 8l ADG b
AAH l
-AAA T c " BBB B(A0I8G
-8D0A(B BBBE T c (% BBB B(A0I8G
-8D0A(B BBBE T <d P( BEE E(D0A8G
-8D0A(B BBBF T d x- BBE B(D0A8G
-8D0A(B BBBH T d @2 } BBE B(D0A8G
-8D0A(B BBBG T De 7 c BBB B(D0A8G
-8D0A(B BBBD , Tf `- AAG0!AA < e `I BIA D(Dp
-(D ABBE 4 f K BHD D`
- DABG L Df XN< BGB E(A0A8G
-8A0A(B BBBA L f H^G BGB E(A0A8G
-8A0A(B BBBA g _. K^ g 0p DV L g 8pr ^ BBB B(E0A8J@
-8A0A(B BBBA $ h hq^ AG b
-AA $ Dh q AG u
-AA $ lh 8r AG }
-AA T g r
- BBB B(A0D8J$
-8D0A(B BBBH T 4h | BBB B(D0D8J
-8D0A(B BBBC L Di ; BBB B(A0D8DPd
-8A0A(B BBBA $ i Їr DS
+AAA T b p BBB B(A0I8G
+8D0A(B BBBE T c Ɗ BBB B(A0I8G
+8D0A(B BBBE T dc BBB B(A0I8G
+8D0A(B BBBE T c BBB B(A0I8G
+8D0A(B BBBE T d BBE B(D0A8G
+8D0A(B BBBH T ld y BBE B(D0A8G
+8D0A(B BBBG T d B _ BBB B(D0A8G
+8D0A(B BBBD , e
+y- AAG0!AA 4 Le h ϊ BHD D`
+ DABF 4 e
BHD D`
+ DABH 4 e X BHD D`
+ DABG 4 e u BHD D`
+ DABG L ,f (< W BGB E(A0A8G
+8A0A(B BBBA L |f %G ] BGB E(A0A8G
+8A0A(B BBBA g y. K^ g 7 DV L f 7r 3 BBB B(E0A8J@
+8A0A(B BBBA $ h 88^ AG b
+AA $ ,h p8 AG u
+AA $ Th 9 AG }
+AA T g 9 BBB B(D0D8J
+8D0A(B BBBC L h A; BBB B(A0D8DPd
+8A0A(B BBBA $ $i Dr DS
I H
-E , i &- AAG0!AA 4 4i
BHD D`
- DABF 4 li P BHD D`
- DABG T i BBB E(A0A8G
-8A0A(B BBBG L i ` BIE B(A0A8J
-8D0A(B BBBC T Lj Ы BBB L(A0A8J"
-8A0A(B BBBF 4 j xx BAI H
-ABA 4 j x BAI H
-ABA k [. K^ L ,k r \ BBB B(E0A8J@
-8A0A(B BBBA $ 4l ^ AG b
-AA $ \l X AG u
-AA $ l AG }
-AA l T , l L- AAG0!AA < <l a BIA D(Dp-
-(D ABBF L |l 0Ĥm ; BEB B(A0A8J
-
-8A0A(B BBBA 4 l Pܤq BAI A
-ABA m HY. K^ L m ܤr BBB B(E0A8J@
-8A0A(B BBBA $ $n ݤ^ AG b
-AA $ Ln ݤ AG
-AA $ tn ޤ AG
-AA $ n Xߤ AG M
-AA T n ߤ, BBE B(D0A8G
-8D0A(B BBBK , o "- AAG0!AA 4 n h BHD D`
- DABF 4 n BHD D`
- DABG T o (e BBB B(A0I8G
-8D0A(B BBBF T \o @ i BBB B(A0I8G
-8D0A(B BBBG \ lp m BBD D(J@
+E , Li Dy- AAG0!AA 4 h E5 BHD D`
+ DABG 4 h HB Ȉ BHD D`
+ DABH T 4i 0KW
+ BBB E(A0A8G
+8A0A(B BBBG T i 8U BEE B(A0A8G6
+8A0A(B BBBI T i j BBB L(A0A8J"
+8A0A(B BBBF 4 <j x{x BAI H
+ABA 4 tj {x BAI H
+ABA dk y. K^ L j {r O BBB B(E0A8J@
+8A0A(B BBBA $ k }^ AG b
+AA $ k X} AG u
+AA $ l } AG }
+AA Dl ~T , \l by- AAG0!AA D k u T BIB A(D0Dp$
+0D(A BBBK T l & BBE B(A0A8G
+8A0A(B BBBH 4 tl Pq BAI A
+ABA dm (y. K^ L l r BBB B(E0A8J@
+8A0A(B BBBA $ m ^ AG b
+AA $ m 蚜 AG
+AA $ n AG
+AA $ Dn X AG M
+AA T m , ׇ BBE B(D0A8G
+8D0A(B BBBK , n (y- AAG0!AA 4 <n h BHD D`
+ DABF 4 tn o BHD D`
+ DABG T n (e Q BBB B(A0I8G
+8D0A(B BBBF T o @ ; BBB B(A0I8G
+8D0A(B BBBG \ p m BBD D(J@
(A ABBD T
(A ABBF _
-(A ABBK T p T BBE E(A0C8L
-8A0A(B BBBE $q
Z AK
-L AT p BBB E(A0C8J
-8A0A(B BBBK q H AN q xU. K^ q 8 Ho q @ ? Hr r ` 8 Ho 4r ? Hr Tr 8 Ho tr ? Hr r 8 Ho r ? Hr D r BBB A(I0Dp
-0D(A BBBF D dr a BBB A(I0Dp
-0D(A BBBF D r 0 < BBB A(I0Dp
-0D(A BBBF D r ! BBB A(I0Dp
-0D(A BBBF , s x- AAG0!AA D ls #u BIB A(D0Dp$
-0D(A BBBK D s H&u BIB A(D0Dp$
-0D(A BBBK D s )u f BIB A(D0Dp$
-0D(A BBBK D Dt ,u 8 BIB A(D0Dp$
-0D(A BBBK \ t /
- BBB A(A0GP
+(A ABBK T o ȱ ņ BBE B(D0C8L
+8A0A(B BBBH p ̜Z AK
+L AT 4p @̜x
BBB B(D0D8J
+8A0A(B BBBH Dq hٜ AN dq Xy. K^ |q @ٜ8 Ho q `ٜ? Hr q ٜ8 Ho q ٜ? Hr q ٜ8 Ho r ٜ? Hr <r ڜ8 Ho \r ڜ? Hr D q @ڜ W BBB A(I0Dp
+0D(A BBBF D r ۜ 2 BBB A(I0Dp
+0D(A BBBF D Tr Pݜ
BBB A(I0Dp
+0D(A BBBF D r ޜ BBB A(I0Dp
+0D(A BBBF D r ` Å BBB A(I0Dp
+0D(A BBBF D ,s BBB A(I0Dp
+0D(A BBBF D ts p y BBB A(I0Dp
+0D(A BBBF D s T BBB A(I0Dp
+0D(A BBBF , t ^ y- AAG0!AA D 4t Pu BIB A(D0Dp$
+0D(A BBBK D |t u ф BIB A(D0Dp$
+0D(A BBBK D t u BIB A(D0Dp$
+0D(A BBBK D u u u BIB A(D0Dp$
+0D(A BBBK D Tu 0u G BIB A(D0Dp+
+0D(A BBBD D u hu BIB A(D0Dp+
+0D(A BBBD D u u BIB A(D0Dp+
+0D(A BBBD D ,v u BIB A(D0Dp+
+0D(A BBBD \ tv BBB A(A0GP
0A(A BBBI L
-0A(A BBBA \ t 2 BBB A(A0GL
+0A(A BBBA \ v BBB A(A0GL
0A(A BBBE L
-0A(A BBBA T Lu 57 BBE B(A0A8G
-8A0A(B BBBG T u =7 O BBE B(A0A8G
-8A0A(B BBBG T u F\
- BBE B(A0A8G
-8A0A(B BBBG T Tv P\
- BBE B(A0A8G
-8A0A(B BBBG dw P. K^ |w Z w Z L v Zq BBB B(A0I8G
-8D0A(B BBBD L Dw ]q BBB B(A0I8G
-8D0A(B BBBD L w 8_q BBB B(A0I8G
-8D0A(B BBBD T w ha BBH B(D0F8G
-8D0A(B BBBA , x - AAG0!AA 4 lx gE BHD D`
- DABK 4 x jE BHD D`
- DABD 4 x mE BHD D`
- DABD y M. K^ y p y p T \y p A BBE E(A0A8G
-8D0A(B BBBE , lz \- AAG0!AA 4 y 5 BHD D`
- DABG L z q BBB B(A0I8G
-8D0A(B BBBD T lz P BBB B(A0A8J"
-8A0A(B BBBH |{ HL. K^ { { , { MRAC , { p JXAD , $| ҵ- AAG0!AA 4 { 5 BHD D`
- DABG T { j BBB B(A0D8R
-8A0A(B BBBD , ,| > AID W
-DAA L \| 05 BBE G(A0G8G
-8D0A(B BBBD d} J. K^ |} } < } t BBB I(M0F@I0A(A BBBL } H AAG e
+0A(A BBBA T 4w 07 BBE B(A0A8G
+8A0A(B BBBG T w 7 ԃ BBE B(A0A8G
+8A0A(B BBBG T w 7 BBE B(A0A8G
+8A0A(B BBBG T <x L
+ b BBE B(A0A8G
+8A0A(B BBBG Ly y. K^ dy ) |y ) L x )q z BBB B(A0I8G
+8D0A(B BBBD L ,y +q d BBB B(A0I8G
+8D0A(B BBBD L |y (.q N BBB B(A0I8G
+8D0A(B BBBD L y X0q 8 BBB B(A0I8G
+8D0A(B BBBD T z 2 # BBH B(D0F8G
+8D0A(B BBBA , ,{ x- AAG0!AA 4 z 8E 7 BHD D`
+ DABK 4 z ;E BHD D`
+ DABD 4 { >E BHD D`
+ DABD 4 L{ AE ݃ BHD D`
+ DABD <| y. K^ T| D l| D T { D w BBE E(A0A8G
+8D0A(B BBBE , | x- AAG0!AA 4 T| P\5 $ BHD D`
+ DABG L | X_q BBB B(A0I8G
+8D0A(B BBBD T | a BBB B(A0A8J"
+8A0A(B BBBH } `y. K^ ~ t ~ t , 4~ t
JUAL , d~ u J\AK , ~ x- AAG0!AA 4 ~ v5 BHD D`
+ DABG T D~ yj BBB B(A0D8R
+8A0A(B BBBD , ~ ~> AID W
+DAA L ~ ~5 BBE G(A0G8G
+8D0A(B BBBD y. K^ < t BBB I(M0F@I0A(A BBBL \ І AAG e
CAF s
ACA R
-CAJ JCA T } x { BBB B(D0A8N9
-8A0A(B BBBB , } > 1 AID W
-DAA , ~ `- AAG0!AA 4 <~ 5 BHD D`
- DABG T t~ BBB B(A0A8G
-8A0A(B BBBA H. K^ L Х ADD e
+CAJ JCA T x BBB B(D0A8N9
+8A0A(B BBBB , L H> i AID W
+DAA , | X> I AID W
+DAA , d nx- AAG0!AA 4 ܀ 85 BHD D`
+ DABG 4 @B ۃ BHD D`
+ DABH T L X BBB B(A0A8G
+8A0A(B BBBA \ Py. K^ L t ADD e
CAF
ACA V
-CAF JCA L ѥ ADD e
+CAF JCA L Ă H ADD e
CAF
ACA V
-CAF JCA L < Hҥ ADD e
+CAF JCA L ADD e
CAF
ACA V
-CAF JCA L ҥ ADD e
+CAF JCA L d ADD e
CAF
ACA V
-CAF JCA L ܀ ӥ AAG ]
+CAF JCA L X AAG ]
CAF b
CAJ R
-CAJ JCA L , ӥ ADD e
+CAJ JCA L ADD e
CAF k
ACI V
-CAF JCA L | xԥo BBB B(A0D8D`G
-8A0A(B BBBA L ́ ե BBB B(A0D8Dt
-8A0A(B BBBA L إ BBB B(A0D8G
-8A0A(B BBBF L l ۥr BBB B(A0D8D`I
-8A0A(B BBBA L ܥ BBB B(A0D8D}
-8A0A(B BBBH L Xߥ BBB B(A0D8G
-8A0A(B BBBF < \ E BBB D(A0#
-(A BBBA L (u BBB B(A0D8DB
-8A0A(B BBBC L X BBB B(A0D8G8A0A(B BBB < < B BBB D(A0#
-(A BBBA L | r BBB B(A0D8DB
-8A0A(B BBBC L ̄ BBB B(A0D8G8A0A(B BBB L X} BBB B(A0D8D`a8A0A(B BBB L l U BBB B(A0D8D%
-8A0A(B BBBH L BBB B(A0D8D8A0A(B BBB H: AD0sA , h: AD0sA L : AD0sA l : AD0sA : AD0sA : AD0sA ̆ : AD0sA (: AD0sA H: AD0sA , h: AD0sA L : AD0sA l : AD0sA : AD0sA : AD0sA ̇ : AD0sA L ( AAG ]
+CAF JCA L T (o BBB B(A0D8D`G
+8A0A(B BBBA L H BBB B(A0D8Dt
+8A0A(B BBBA L BBB B(A0D8G
+8A0A(B BBBF L D Xr BBB B(A0D8D`I
+8A0A(B BBBA L BBB B(A0D8D}
+8A0A(B BBBH L BBB B(A0D8G
+8A0A(B BBBF < 4 ȺE BBB D(A0#
+(A BBBA L t ػu BBB B(A0D8DB
+8A0A(B BBBC L Ć BBB B(A0D8G8A0A(B BBB < hB BBB D(A0#
+(A BBBA L T xr BBB B(A0D8DB
+8A0A(B BBBC L ĝ BBB B(A0D8G8A0A(B BBB L ȝ} BBB B(A0D8D`a8A0A(B BBB L D 8ɝU BBB B(A0D8D%
+8A0A(B BBBH L H˝ BBB B(A0D8D8A0A(B BBB ͝: AD0sA Ν: AD0sA $ 8Ν: AD0sA D XΝ: AD0sA d xΝ: AD0sA Ν: AD0sA Ν: AD0sA ĉ Ν: AD0sA Ν: AD0sA ϝ: AD0sA $ 8ϝ: AD0sA D Xϝ: AD0sA d xϝ: AD0sA ϝ: AD0sA ϝ: AD0sA L Ċ ϝ AAG ]
CAF b
CAJ R
-CAJ JCA L < h ADD e
+CAJ JCA L Н ADD e
CAF k
ACI V
-CAF JCA L AAG ]
+CAF JCA L d Н AAG ]
CAF b
CAJ R
-CAJ JCA L ܈ 8 ADD e
+CAJ JCA L Н ADD e
CAF k
ACI V
-CAF JCA L , AAG ]
+CAF JCA L xѝ AAG ]
CAF b
CAJ R
-CAJ JCA L | ADD e
+CAJ JCA L T ѝ ADD e
CAF k
ACI V
-CAF JCA L ̉ AAG ]
+CAF JCA L Hҝ AAG ]
CAF b
CAJ R
-CAJ JCA L ADD e
+CAJ JCA L ҝ ADD e
CAF k
ACI V
-CAF JCA L l h ADD e
+CAF JCA L D ӝ ADD e
CAF
ACA V
-CAF JCA $ ^ AG b
-AA $ @ AG r
-AA $ AG r
-AA $ 4 P AG r
-AA $ \ AG r
-AA $ ` AG z
-AA $ AG z
-AA $ ԋ AG z
-AA $ ( AG z
-AA $ $ AG L
-AA D ( BBB D(A0Np
-0A(A BBBG L ܋ r j BEB L(D0A8M
-8A0A(B BBBD < , BBA I(D@e
-(D ABBA < l n BBA I(D@e
-(D ABBA \ $ J BBB A(I0D@
+CAF JCA $ ӝ^ AG b
+AA $ ӝ AG r
+AA $ xԝ AG r
+AA $ ՝ AG r
+AA $ 4 ՝ AG r
+AA $ \ ֝ AG z
+AA $ ֝ AG z
+AA $ @ם AG z
+AA $ Ԏ ם AG z
+AA $ p؝ AG L
+AA D l ؝ w BBB D(A0Np
+0A(A BBBG L ڝr fw BEB L(D0A8M
+8A0A(B BBBD < x BBA I(D@e
+(D ABBA < D P jx BBA I(D@e
+(D ABBA \ $ Fx BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ $ BBB A(I0D@
+0D(A BBBA \ $ x BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ l $ BBB A(I0D@
+0D(A BBBA \ D p$ w BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ ̍ $ BBB A(I0D@
+0D(A BBBA \ @$ w BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ , `$ N BBB A(I0D@
+0D(A BBBA \ $ Jw BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ 0$ BBB A(I0D@
+0D(A BBBA \ d $ w BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ $ BBB A(I0D@
+0D(A BBBA \ đ $ v BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ L $ BBB A(I0D@
+0D(A BBBA \ $ $ v BBB A(I0D@
0D(A BBBG W
-0D(A BBBA \ $ R BBB A(I0D@
+0D(A BBBA \ P$ Nv BBB A(I0D@
0D(A BBBG W
-0D(A BBBA $ Đ p AAG0AA< 4 BHA G
- DABA < t H BHA G
- DABA 4 " BHA GPh
- DABG 4 p$ BHD D`
- DABF 4 $ & y BHD D`
- DABF 4 \ ) [ BHA GPo
- DABH 4 * = BHA GPo
- DABH 4 ̑ , BHA GPo
- DABH 4 x. BHD D`
- DABG 4 < 0 BHD D`
- DABF 4 t 83 BHD D`
- DABF T d 5U BAA G0
+0D(A BBBA $ AAG0AA< u BHA G
+ DABA < L u BHA G
+ DABA 4 X u BHA GPh
+ DABG 4 ē u BHA GPh
+ DABG 4 uu BHD D`
+ DABF 4 4 @ Wu BHA GPo
+ DABH 4 l 9u BHA GPo
+ DABH 4 u BHA GPo
+ DABH 4 ܔ t BHD D`
+ DABG 4 t BHD D`
+ DABF 4 L X
+ t BHD D`
+ DABF T < U BAA G0
AABA
AABK f
- AABF T 9 O BEE B(G0D8G
-8D0A(B BBBD T \ `DE u BBE G(A0G8G.
-8D0A(B BBBF l XL AR XL AY T XL ` BBE E(A0A8G
-8D0A(B BBBE T L N # BBB E(D0C8J
-8D0A(B BBBC T Y
= BBB B(D0G8L2
-8D0A(B BBBB T 0g
BBB B(D0G8L2
-8D0A(B BBBB T T ht
BBB B(D0G8L2
-8D0A(B BBBB T r N BBF G(GP
+ AABF T ܕ Kt BEE B(G0D8G
+8D0A(B BBBD T 4 E qt BBE G(A0G8G.
+8D0A(B BBBF D x# AR d x# AY T ̖ x# \t BBE E(A0A8G
+8D0A(B BBBE T $ % t BBB E(D0C8J
+8D0A(B BBBH T | (1
9t BBB B(D0G8L2
+8D0A(B BBBB T ԗ `>
t BBB B(D0G8L2
+8D0A(B BBBB T , K
t BBB B(D0G8L2
+8D0A(B BBBB T Xr Ju BBF G(GP
(D ABBI L
-(D ABBK T Ȃ BBB E(G0A8L3
-8D0A(B BBBA @1. K^ L t xr BBB B(E0A8J@
-8A0A(B BBBA $ | ^ AG b
-AA $ AG u
-AA $ ̗ x AG }
-AA Q tU
-A , >- AAG0!AA D BIB A(D0DpG
-0D(A BBBH 4 ԗ Ȓq BAI A
-ABA $ Ę DL
+(D ABBK T ܘ Y u BBB E(G0A8L3
+8D0A(B BBBA y. K^ L d BBB B(A0A8G8
+8A0A(B BBBE , hil t AAG
+AAA L i AAG ]
+CAF b
+CAJ R
+CAJ JCA < iq Pt BBA A(D0[
+(A ABBA L \ (jr t BBB B(E0A8J@
+8A0A(B BBBA $ d Xk^ AG b
+AA 4 Ԛ k\ s BAA G0F
+ AABK 4 m\ }s BAA G0N
+ AABC T D o{ Is BBB B(A0D8J
+8D0A(B BBBB 4 u 6s BAD v
+ABH L ԛ u
+ s BBB B(A0A8Gk
+8A0A(B BBBA , ܜ $x- AAG0!AA D T 0|G r BIA D(D
+(D ABBA D 8G {r BIA D(D
+(D ABBA d @B BBB B(A0A8G@x
+8A0A(B BBBE
+8A0A(B BBBF T L ( q BBB B(A0I8D#
+8D0A(B BBBJ T Є q BBB B(A0I8D#
+8D0A(B BBBJ $ x DL
A V
-I T 4 i BBE B(A0A8G
-8A0A(B BBBK D @/. K^ \ t T Ԙ } BBB B(A0D8N
-8A0A(B BBBC , , > AID W
-DAA , l- AAG0!AA 4 и5 BHD D`
- DABG T ę ػd BBB B(A0A8J
-8A0A(B BBBA Ԛ -. K^ ۦ ۦ , - AAG0!AA 4 ۦE _ BHD D`
- DABK D ̚ ަd A BEI A(D0D
-0D(A BBBA < r < BBH D(J
-(D ABBA D T ( > BBE H(D0D
-0D(A BBBF D 9 BBE H(D0D
-0D(A BBBF D 4 BBE D(H0D
-0D(A BBBA D , @ / BBE D(H0D
-0D(A BBBA D t * BBE D(H0D
-0D(A BBBA D % BBE D(H0D
-0D(A BBBA D 8 BBE D(H0D
-0D(A BBBA D L BBE D(H0D
-0D(A BBBA D BBE D(H0D
-0D(A BBBA L ܝ 0V BBE D(H0D
-0D(A BBBI T , @y BBB B(A0I8G>
-8A0A(B BBBG T h
BBB E(G0A8L
+I T $ ! ^q BBB G(A0C8J
E
+8A0A(B BBBF 4 ~y. K^ L @ d 8 T Ğ @ Rr BBB B(A0D8N
+8A0A(B BBBC , > r AID W
+DAA , *x- AAG0!AA 4 | x5 r BHD D`
+ DABG T d gr BBB B(A0A8J
+8A0A(B BBBA Ġ x}y. K^ ܠ Ӟ xӞ T T Ӟy cs BBB B(A0I8G>
+8A0A(B BBBG , d x- AAG0!AA 4 ܠ xמE ?s BHD D`
+ DABK D ڞd !s BEI A(D0D
+0D(A BBBA < \ ܞr s BBH D(J
+(D ABBA D ޞ s BBE H(D0D
+0D(A BBBF D s BBE H(D0D
+0D(A BBBF D , h s BBE D(H0D
+0D(A BBBA D t s BBE D(H0D
+0D(A BBBA D
+s BBE D(H0D
+0D(A BBBA D ` s BBE D(H0D
+0D(A BBBA D L s BBE D(H0D
+0D(A BBBA D r BBE D(H0D
+0D(A BBBA D ܣ X r BBE D(H0D
+0D(A BBBA L $ V r BBE D(H0D
+0D(A BBBI T t
r BBB E(G0A8L
-8D0A(B BBBJ P). K^ $ ^ AG b
-AA D ԟ @ 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 d p BLB B(A0A8D@c
+8D0A(B BBBJ xy. K^ $ ^ AG b
+AA D ĥ BBB D(A0D@
+0A(A BBBA D BBB D(A0D@%
+0A(A BBBA D ( r BBB D(A0Gn
+0D(A BBBE D
]r BBB D(A0Gn
+0D(A BBBE d BLB B(A0A8D@c
8A0A(B BBBK s
-8A0A(B BBBC , \ - AAG0!AA 4 Ԡ 5 BHD D`
- DABG d BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB < , H BBG A(G@
-(A ABBF L l BHB E(A0A8D`
-8A0A(B BBBE L x BEB E(I0H8DP
-8D0A(B BBBD 4 T 8 ^ BBB A(A0O T ` 5 s BBE B(A0A8M M
-8A0A(B BBBA , H6> AID W
-DAA T X6 BBE B(D0A8GO
-8A0A(B BBBH T l >^
- BBE B(D0A8GI
-8A0A(B BBBF T ģ H BBB B(D0A8J_
-8A0A(B BBBH L Q
BBB B(A0A8G
-8A0A(B BBBA $ $ # AHD0AAL L _ AAG e
+8A0A(B BBBC , L >x- AAG0!AA 4 Ħ p5 q BHD D`
+ DABG d x yq BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB < BBG A(G@
+(A ABBF L \ BHB E(A0A8D`
+8A0A(B BBBE L p BEB E(I0H8DP
+8D0A(B BBBD 4 D ^ _p BBB A(A0O T | ~ Gp BBE B(A0A8M e
+8A0A(B BBBA , Ԩ 0.> q AID W
+DAA T @. oq BBE B(D0A8GO
+8A0A(B BBBH T \ 6^
+ q BBE B(D0A8GI
+8A0A(B BBBF T @ q BBB B(D0A8J_
+8A0A(B BBBH L I
q BBB B(A0A8G
+8A0A(B BBBA $ sy AHD0AAL < W AAG e
CAF s
ACA R
-CAJ JCA < h_q BBA A(D0[
-(A ABBA T $ _| BBE E(D0A8G
-8D0A(B BBBE T | d, BBE B(D0A8G
-8D0A(B BBBK T ԥ i BEE E(D0A8G
-8D0A(B BBBE T , n, m BBE B(D0A8G
-8D0A(B BBBK T s S BEE E(D0A8G
-8D0A(B BBBE T ܦ x 9 BEE E(D0A8G
-8D0A(B BBBG T 4 X~| BBE E(D0A8G
-8D0A(B BBBE , D Ɛ- AAG0!AA < P BHD D{
- DABA L 0 BBB B(A0A8D`8A0A(B BBB L BBB B(A0A8D`8A0A(B BBB L T ` BBB E(A0A8DP
-8A0A(B BBBA AL T BEE E(D0A8G
-8D0A(B BBBE L d ( BEB B(A0D8O8A0A(B BBB l T ̩ 0 BEE B(A0A8G<
-8A0A(B BBBC d ܪ H BBB B(A0D8DPJ
+CAJ JCA < Ԫ PWq q BBA A(D0[
+(A ABBA T W| q BBE E(D0A8G
+8D0A(B BBBE T l \, uq BBE B(D0A8G
+8D0A(B BBBK T ī a [q BEE E(D0A8G
+8D0A(B BBBE T f, Aq BBE B(D0A8G
+8D0A(B BBBK T t k 'q BEE E(D0A8G
+8D0A(B BBBE T ̬ p
q BEE E(D0A8G
+8D0A(B BBBG T $ @v| p BBE E(D0A8G
+8D0A(B BBBE , 4 x- AAG0!AA D 8{ p BIA D(Gu
+(D ABBA L ~ BBB B(A0A8D`8A0A(B BBB L BBB B(A0A8D`8A0A(B BBB $ L p ADD dAA t ȁ AL T ܮ ȁ o BEE E(D0A8G
+8D0A(B BBBE L 4 o BEB B(A0D8O8A0A(B BBB < T Ȋ $o BEB E(A0A8G=
+8A0A(B BBBB d 0 BBB B(A0D8DPJ
8A0A(B BBBK
-8A0A(B BBBD T BBB G(D0A8M
-8D0A(B BBBC L ئ BEB E(I0H8D@
-8D0A(B BBBA T 4 J BBE B(A0A8G
-8A0A(B BBBA L BEB E(I0H8D@
-8D0A(B BBBD T ܫ g BBB B(A0D8GW
-8A0A(B BBBA T 4 8- Q BBB I(A0A8J
-8A0A(B BBBA , pݧ> AID W
-DAA t p . K^ hݧ `ݧ $ hݧ EDf
-AH < Pߧ BBA A(GP
-(A ABBE T l 0 ( BBB E(A0D8GJ
-8D0A(B BBBB T ĭ BBB B(D0A8G]
-8D0A(B BBBB L P BBB B(I0A8G
-8D0A(B BBBE L l BBB B(I0A8G
-8D0A(B BBBE T < BBE B(D0A8G
-8D0A(B BBBB T < BBE B(D0A8G
-8D0A(B BBBB , $ - AAG0!AA 4 BHI D`
- DABA 4 ԯ x% o BHI D`
- DABJ T p S BEB E(I0A8G
-8D0A(B BBBD < 8 1 BBG A(GP
-(A ABBF T 8 V k BIB B(A0G8G
-8A0A(B BBBA `. K^ ̱ (/ / / / L , (/ BGB B(A0A8G
-8A0A(B BBBC L | 7~ BFB B(A0A8G
-8A0A(B BBBD L ̲ ? ADD e
+8A0A(B BBBD T \ h n BBB G(D0A8M
+8D0A(B BBBC L n BEB E(I0H8D@
+8D0A(B BBBA T pJ n BBE B(A0A8G
+8A0A(B BBBA L \ h n BEB E(I0H8D@
+8D0A(B BBBD T [n BBE B(A0A8GP
+8A0A(B BBBA T p+ En BBB I(A0A8J_
+8A0A(B BBBA , \ ӟ> p AID W
+DAA D (ly. K^ \ ӟ t ӟ $ ӟ EDf
+AH < ՟ BBA A(GP
+(A ABBE T < ؟ p BBB E(A0D8GJ
+8D0A(B BBBB T `ޟ p BBB B(D0A8G]
+8D0A(B BBBB L p BBB B(I0A8G
+8D0A(B BBBE L < ( o BBB B(I0A8G
+8D0A(B BBBE T x< o BBE B(D0A8G
+8D0A(B BBBB T `< o BBE B(D0A8G
+8D0A(B BBBB , x- AAG0!AA 4 l o BHI D`
+ DABA 4 % co BHI D`
+ DABJ T ܵ 5 Go BEB E(I0A8G
+8D0A(B BBBD < 1 BBG A(GP
+(A ABBF T t V _p BIB B(A0G8G
+8A0A(B BBBA hy. K^ % % ̷ % % L % BGB B(A0A8G
+8A0A(B BBBC L L `.~ BFB B(A0A8G
+8A0A(B BBBD L 6 ADD e
CAF {
ACI V
-CAF JCA L @ ADD e
+CAF JCA L 07 ADD e
CAF {
ACI V
-CAF JCA l \ AG RAD Բ A BAA GP
+CAF JCA < x\ AG RAD 7 n BAA GP
DABK
- DABG T C| BBE E(D0A8G
-8D0A(B BBBE T t H BBE B(D0A8G
-8D0A(B BBBG , >- AAG0!AA < MU | BIA D(Dp
+ DABG T h:| n BBE E(D0A8G
+8D0A(B BBBE T D ? n BBE B(D0A8G
+8D0A(B BBBG , T x- AAG0!AA < ̹ (DU pn BIA D(Dp
-(D ABBI < < PU V BIA D(Dp
+(D ABBI < HGU Jn BIA D(Dp
-(D ABBI T | S 0 BBB L(A0D8G
-8A0A(B BBBB 4 Դ (]q X BAI A
-ABA 4 p]q 4 BAI A
-ABA L D ]: BGB B(A0G8J
-8D0A(B BBBJ L m: ' BGB B(A0G8J
-8D0A(B BBBJ . K^ }0 Hg Զ }7 Hj }0 Hg }7 Hj 4 }0 Hg T }7 Hj t ~0 Hg ~7 Hj @~0 Hg Է P~7 Hj p~0 Hg ~7 Hj 4 ~0 Hg T ~7 Hj t ~0 Hg ~7 Hj 0 Hg Ը 7 Hj 00 Hg @7 Hj 4 `0 Hg T p7 Hj t 0 Hg 7 Hj 0 Hg Թ 7 Hj 0 Hg 7 Hj 4 0 Hg T 07 Hj t P0 Hg `7 Hj 0 Hg Ժ 7 Hj 0 Hg 7 Hj 4 0 Hg T 7 Hj t 0 Hg 7 Hj @0 Hg Ի P7 Hj p0 Hg 7 Hj 4 0 Hg T 7 Hj t Ё0 Hg 7 Hj 0 Hg Լ 7 Hj 00 Hg @7 Hj 4 `0 Hg T p7 Hj t 0 Hg 7 Hj 0 Hg Խ Ђ7 Hj 0 Hg 7 Hj 4 0 Hg T 07 Hj t P0 Hg `7 Hj 0 Hg Ծ 7 Hj 0 Hg 7 Hj 4 0 Hg T 7 Hj t 0 Hg 7 Hj @0 Hg Կ P7 Hj < p BBA A(D@_
-(A ABBA 4 ^ zPR x- L $ H BBB B(A0A8D@V
-8A0A(B BBBB D ؇ BFB B(A0A8
-0A(B BBBA L BBB B(A0A8D@6
-8A0A(B BBBB L @ BBB B(A0A8D@n
-8A0A(B BBBJ L{\ AG RAD ЌD BHB A(D0J^
-0A(A BBBA d ؎b BBB B(A0A8D
-8A0A(B BBBG E
-8A0A(B BBBA $ t i AJ@V
-AF A$ (i AJ@V
-AF A, pC AAG n
-DAA $ AAG0AA4 d K BHA G`
- DABA 4 0K · BHA G`
- DABA 4 HD BHA G`
- DABA 4 `D BHA G`
- DABA 4 D xK e BHA G`
- DABA 4 | K F BHA G`
- DABA 4 D ' BHA G`
- DABA 4 D BHA G`
- DABA 4 $ ؚK BHA G`
- DABA 4 \ K ʶ BHA G`
- DABA 4 D BHA G`
- DABA 4 D BHA G`
- DABA 4 8K m BHA G`
- DABA 4 < PK N BHA G`
- DABA 4 t hD / BHA G`
- DABA 4 D BHA G`
- DABA 4 K BHA G`
- DABA 4 K ҵ BHA G`
- DABA 4 T ȥK BHA G`
- DABA 4 নK BHA G`
- DABA 4 K u BHA G`
- DABA 4 K V BHA G`
- DABA 4 4 (D 7 BHA G`
- DABA 4 l @D BHA G`
- DABA 4 XD BHA G`
- DABA 4 pD ڴ BHA G`
- DABA 4 D BHA G`
- DABA 4 L K BHA G`
- DABA 4 K } BHA G`
- DABA 4 бD ^ BHA G`
- DABA 4 貨D ? BHA G`
- DABA 4 , K BHA G`
- DABA 4 d K BHA G`
- DABA 4 0K BHA G`
- DABA 4 HK ó BHA G`
- DABA 4 `K BHA G`
- DABA 4 D xK BHA G`
- DABA 4 | D f BHA G`
- DABA 4 D G BHA G`
- DABA 4 D ( BHA G`
- DABA 4 $ ؽD BHA G`
- DABA 4 \ D BHA G`
- DABA 4 K ˲ BHA G`
- DABA 4 K BHA G`
- DABA 4 8¨D BHA G`
- DABA 4 < PèD n BHA G`
- DABA 4 t hĨK O BHA G`
- DABA 4 ŨD 0 BHA G`
- DABA 4 ƨK BHA G`
- DABA 4 ǨK BHA G`
- DABA 4 T ȨD ӱ BHA G`
- DABA 4 ɨD BHA G`
- DABA 4 ʨK BHA G`
- DABA 4 ̨D v BHA G`
- DABA 4 4 (ͨK W BHA G`
- DABA 4 l @ΨD 8 BHA G`
- DABA 4 XϨK BHA G`
- DABA 4 pШK BHA G`
- DABA 4 ѨD ۰ BHA G`
- DABA 4 L ҨD BHA G`
- DABA 4 ӨK BHA G`
- DABA 4 ԨD ~ BHA G`
- DABA 4 ըK _ BHA G`
- DABA 4 , רK @ BHA G`
- DABA 4 d بK ! BHA G`
- DABA 4 0٨K BHA G`
- DABA 4 HڨK BHA G`
- DABA 4 `ۨD į BHA G`
- DABA 4 D xܨD BHA G`
- DABA 4 | ݨD BHA G`
- DABA 4 ިK g BHA G`
- DABA 4 ߨK H BHA G`
- DABA 4 $ K ) BHA G`
- DABA 4 \ K
- BHA G`
- DABA 4 D BHA G`
- DABA 4 D ̮ BHA G`
- DABA < 8 BBA A(J
-(D ABBA < D BBA A(J
-(D ABBA < BBA A(J
-(D ABBA < H q BBA A(J
-(D ABBA $ * ] A_
-H A D , 9 BBF K(H0G@Q
-0A(A BBBC D t h BGB D(D0J
-(A BBBD < / ƭ BEI D(G
-(D ABBG $ * A_
-H A $ $ * t A_
-H A $ L * P A_
-H A , t , LGA A(D T p BBB B(K0A8J
-8A0A(B BBBC 4 u # BAI E
-ABA 4 4 u BAI E
-ABA 4 l Pu ۬ BAI E
-ABA 4 u BAI E
-ABA 4 u BAI E
-ABA 4 (u o BAI E
-ABA 4 L pu K BAI E
-ABA 4 u ' BAI E
-ABA 4 u BAI E
-ABA 4 Hu ߫ BAI E
-ABA 4 , u BAI E
-ABA 4 d u BAI E
-ABA 4 u s BAI E
-ABA 4 hu O 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 $ u w BAI E
-ABA 4 \ `u S BAI E
-ABA 4 u / BAI E
-ABA 4 u BAI E
-ABA 4 8u BAI E
-ABA 4 < u é BAI E
-ABA 4 t u BAI E
-ABA 4 u { BAI E
-ABA 4 Xu W BAI E
-ABA 4 u 3 BAI E
-ABA 4 T u BAI E
-ABA 4 0u BAI E
-ABA 4 xu Ǩ BAI E
-ABA 4 u BAI E
-ABA 4 4 u BAI E
-ABA 4 l Pu [ BAI E
-ABA 4 u 7 BAI E
-ABA 4 u BAI E
-ABA 4 (u BAI E
-ABA 4 L pu ˧ BAI E
-ABA 4 u BAI E
-ABA 4 u BAI E
-ABA 4 H u _ BAI E
-ABA 4 , u ; BAI E
-ABA 4 d u BAI E
-ABA 4 u BAI E
-ABA 4 hu Ϧ BAI E
-ABA 4 u BAI E
-ABA 4 D u BAI E
-ABA 4 | @u c BAI E
-ABA 4 u ? BAI E
-ABA 4 u BAI E
-ABA 4 $ u BAI E
-ABA 4 \ `u ӥ BAI E
-ABA 4 u BAI E
-ABA 4 u BAI E
-ABA 4 8u g BAI E
-ABA 4 < u C BAI E
-ABA 4 t u BAI E
-ABA 4 u BAI E
-ABA 4 Xu פ BAI E
-ABA 4 u BAI E
-ABA 4 T u BAI E
-ABA T 0j k BBB L(A0A8J
-8A0A(B BBBH 4 H
-u i BAI E
-ABA 4
-u E BAI E
-ABA 4 T
-u ! BAI E
-ABA 4 u BAI E
-ABA 4 h u ٣ BAI E
-ABA 4 u BAI E
-ABA T 4 BBB B(I0A8G)
-8D0A(B BBBA T P BBB B(I0A8G)
-8D0A(B BBBA T BBB B(I0A8G)
-8D0A(B BBBA T < BBB B(I0A8G)
-8D0A(B BBBA T X BBB B(I0A8G)
-8D0A(B BBBA T ! BBB B(I0A8G)
-8D0A(B BBBA T D & BBB B(I0A8G)
-8D0A(B BBBA T `* ɣ BBB B(I0A8G)
-8D0A(B BBBA .G / KBB A(D0G@y0A(A BBBL@f
-0A(A BBBJ ,
-0A(A BBBL < T `4 BED A(D0Y
-(A ABBA 4 KBB A(D0G@y0A(A BBBL@a
-0A(A BBBO (
-0A(A BBBH $ 09 KBB A(D0G@a0A(A BBBL@1
-0A(A BBBG ,
-0A(A BBBL P= KBB A(D0G@a0A(A BBBL@-
-0A(A BBBK *
-0A(A BBBN D `A d \ XAb BBB B(D0A8D`
-8A0A(B BBBB *
-8A0A(B BBBD L `E BBB B(A0A8D8A0A(B BBB L E BBB B(A0A8D8A0A(B BBB d d Fz BBB B(A0A8D
-8A0A(B BBBK E
-8A0A(B BBBA H E
-K < xI BKD A(G0(A ABB < , J BDD
-ABA L
-ABA L xK BBB B(A0A8G
-8C0A(B BBBI T 8\_ m BBB E(G0D8G
-8D0A(B BBBA T \ @^ ( BHB E(A0D8G
-8D0A(B BBBI T ` BEE B(D0D8JO
-8D0A(B BBBD T @cU BBH B(A0A8J
-8A0A(B BBBD T d Hg i BBE H(D0A8G
-8D0A(B BBBA T pj % BBE H(D0A8G
-8D0A(B BBBA T m BBH B(D0A8G~
-8A0A(B BBBF T l q BBE H(A0A8G-
-8A0A(B BBBG L u W BBB B(A0A8G
-8C0A(B BBBI T BHB E(A0D8G
-8D0A(B BBBI T l ` o BEE B(D0D8JO
-8D0A(B BBBD T f 9 BBB B(G0D8G
-8A0A(B BBBA T A BBH B(A0A8J
-8A0A(B BBBJ T t [ BBH E(D0A8G
-8D0A(B BBBA T [ k BBH E(D0A8G
-8D0A(B BBBA T $ 9 ' BHB E(D0A8Gp
-8D0A(B BBBF T | e
- BBB H(A0A8J
-8A0A(B BBBD T X
- BBB H(A0A8J
-8A0A(B BBBA T , x
- X BBB H(A0A8J*
-8A0A(B BBBA T 跩C BBB H(A0A8J
-8A0A(B BBBE L ཀྵ Κ BBB B(A0A8G
-8C0A(B BBBI T , Ω * BHB E(A0D8G
-8D0A(B BBBI T hЩ BEE B(D0D8JO
-8D0A(B BBBD T ө BBB E(G0D8G
-8D0A(B BBBA L 4 ש l BBB B(A0A8G
-8C0A(B BBBI T Ț BHB E(A0D8G
-8D0A(B BBBI T BEE B(D0D8JO
-8D0A(B BBBD T 4 N BEH B(A0D8G
-8D0A(B BBBH T p BEH B(A0D8Gc
-8D0A(B BBBA T 8 ę BEH B(A0D8Gc
-8D0A(B BBBA < < BBA A(J
-(D ABBA < | k BBA A(J
-(D ABBA $ `* W A_
-H A $ h* 3 A_
-H A d pq BBB B(D0A8DP
-8A0A(B BBBB _
-8H0A(B BBBH L t BBB B(A0A8G
-8C0A(B BBBI T
- BBE H(A0A8G
-8A0A(B BBBH T BBE H(A0A8G
-8A0A(B BBBH d , 8q BBB B(D0A8DP
-8A0A(B BBBB _
-8H0A(B BBBH L P BBB B(A0A8G
-8C0A(B BBBI T , ) m BBE H(A0A8G0
-8A0A(B BBBD T 1 ( BBE H(A0A8GH
-8A0A(B BBBD p8 T x8 ˗ BBH B(A0D8Gh
-8A0A(B BBBA T L @C BBB B(G0A8Jb
-8A0A(B BBBJ D \ R BBB B(A0A8
-0A(B BBBF T 0U BHB B(A0D8J
-8D0A(B BBBI D xX BBB B(A0A8
-0A(B BBBF T Zh l BHB B(D0A8J
-8D0A(B BBBF D ^s BBB B(A0A8I
-0A(B BBBA T , @` ߕ BHB B(D0A8J(
-8D0A(B BBBA D < cs BBB B(A0A8I
-0A(B BBBA T e R BHB B(D0A8J(
-8D0A(B BBBA D Hi BBB B(A0A8
-0A(B BBBF T l lh Ŕ BHB B(D0A8J[
-8D0A(B BBBK D | 8o BBB B(A0A8
-0A(B BBBF T qx 8 BHB B(D0A8J
-8D0A(B BBBF X. K^ 4 t D L t BBE A(A0D@g
-0A(A BBBA D `u BBE A(A0D@o
-0A(A BBBA p<\ AG RAT u- BDA ]
+(D ABBI T L hJ $n BBB L(A0D8G
+8A0A(B BBBB 4 Sq Ln BAI A
+ABA 4 ܺ Tq (n BAI A
+ABA L `T: n BGB B(A0G8J
+8D0A(B BBBJ L d Pd: o BGB B(A0G8J
+8D0A(B BBBJ l `cy. K^ D (t) BEB B(A0A80A(B BBB D ̼ u& BBB E(A0A80A(B BBB D u* BEB B(A0A80A(B BBB D \ v BBB B(D0A8
+0A(B BBBA D w^ BBB B(A0A8F
+0A(B BBBA D x^ BBB B(A0A8F
+0A(B BBBA D 4 yK BBE B(A0A80
+0A(B BBBA D | z> BBE B(A0A8#
+0A(B BBBA D ľ {[ BBE B(A0A8@
+0A(B BBBA D }J BBE B(A0A8/
+0A(B BBBA D T ~^ BBB B(A0A8F
+0A(B BBBA D ^ BBB B(A0A8F
+0A(B BBBA D 8S BBE B(A0A88
+0A(B BBBA D , PF BBE B(A0A8+
+0A(B BBBA D t X2 BEB B(A0A80A(B BBB D P5 BBB B(D0A8 0A(B BBB D H1 BEB B(A0A80A(B BBB D L @ BBB B(D0A8
+0A(B BBBA D 2 BBB E(A0A80A(B BBB D 1 BEB B(A0A80A(B BBB D $ BBE E(A0A80A(B BBB D l BBE E(A0A80A(B BBB D x( BBE B(A0A80A(B BBB D `A BBB B(D0A8*0A(B BBB D D h3 BEB B(A0A8 0A(B BBB D `3 BBB B(A0D8 0A(B BBB D X5 BEB B(A0A8 0A(B BBB D P BBE E(A0A80A(B BBB D d BBE E(A0A80A(B BBB D ( BBE B(A0A80A(B BBB D A BBB B(D0A8*0A(B BBB D < 3 BEB B(A0A8 0A(B BBB D @ BEB B(A0A8)0A(B BBB D P BEB B(A0A890A(B BBB 0 Hg 4 7 Hj T ؔ0 Hg t 蔠7 Hj 0 Hg 7 Hj 80 Hg H7 Hj h0 Hg 4 x7 Hj T 0 Hg t 7 Hj ȕ0 Hg ؕ7 Hj 0 Hg 7 Hj (0 Hg 4 87 Hj T X0 Hg t h7 Hj 0 Hg 7 Hj 0 Hg Ȗ7 Hj 薠0 Hg 4 7 Hj T 0 Hg t (7 Hj H0 Hg X7 Hj x0 Hg 7 Hj 0 Hg 4 7 Hj T ؗ0 Hg t 藠7 Hj 0 Hg 7 Hj 80 Hg H7 Hj h0 Hg 4 x7 Hj T 0 Hg t 7 Hj Ș0 Hg ؘ7 Hj 0 Hg 7 Hj (0 Hg 4 87 Hj T X0 Hg t h7 Hj 0 Hg 7 Hj 0 Hg ș7 Hj 虠0 Hg 4 7 Hj T 0 Hg t (7 Hj H0 Hg X7 Hj x0 Hg 7 Hj 0 Hg 4 7 Hj T ؚ0 Hg t 蚠7 Hj 0 Hg 7 Hj 80 Hg H7 Hj h0 Hg 4 x7 Hj D T BBB B(A0A8JP8A0A(B BBBL p BEB B(A0A8D8A0A(B BBB L BEB B(A0A8Dp8A0A(B BBB D < BHB B(A0A8GP8A0A(B BBBL ; BBB B(A0A8G`
+8A0A(B BBBA L L BBE B(A0A8G`
+8A0A(B BBBA $ zx- AG cA D Y L \ AAG e
+CAF s
+ACA R
+CAJ JCA L ࢠ AAG e
+CAF s
+ACA R
+CAJ JCA L 0 AAG e
+CAF s
+ACA R
+CAJ JCA L L AAG e
+CAF s
+ACA R
+CAJ JCA L У AAG e
+CAF s
+ACA R
+CAJ JCA L AAG e
+CAF s
+ACA R
+CAJ JCA L < p AAG e
+CAF s
+ACA R
+CAJ JCA L AAG e
+CAF s
+ACA R
+CAJ JCA L AAG e
+CAF s
+ACA R
+CAJ JCA L , ` AAG e
+CAF s
+ACA R
+CAJ JCA L | AAG e
+CAF s
+ACA R
+CAJ JCA L AAG e
+CAF s
+ACA R
+CAJ JCA L P AAG e
+CAF s
+ACA R
+CAJ JCA L l AAG e
+CAF s
+ACA R
+CAJ JCA L AAG e
+CAF s
+ACA R
+CAJ JCA L @ AAG e
+CAF s
+ACA R
+CAJ JCA L \ AAG e
+CAF s
+ACA R
+CAJ JCA L ৠ AAG e
+CAF s
+ACA R
+CAJ JCA L 0 AAG e
+CAF s
+ACA R
+CAJ JCA L L AAG e
+CAF s
+ACA R
+CAJ JCA L Ш AAG e
+CAF s
+ACA R
+CAJ JCA L AAG e
+CAF s
+ACA R
+CAJ JCA L < p AAG e
+CAF s
+ACA R
+CAJ JCA L AAG e
+CAF s
+ACA R
+CAJ JCA L AAG e
+CAF s
+ACA R
+CAJ JCA L , ` AAG e
+CAF s
+ACA R
+CAJ JCA L | AAG e
+CAF s
+ACA R
+CAJ JCA L AAG e
+CAF s
+ACA R
+CAJ JCA L P AAG e
+CAF s
+ACA R
+CAJ JCA L l AAG e
+CAF s
+ACA R
+CAJ JCA L AAG e
+CAF s
+ACA R
+CAJ JCA L @ AAG e
+CAF s
+ACA R
+CAJ JCA L \ AAG e
+CAF s
+ACA R
+CAJ JCA L ଠ AAG e
+CAF s
+ACA R
+CAJ JCA L 0 AAG e
+CAF s
+ACA R
+CAJ JCA L L AAG e
+CAF s
+ACA R
+CAJ JCA L Э AAG e
+CAF s
+ACA R
+CAJ JCA L AAG e
+CAF s
+ACA R
+CAJ JCA L < p AAG e
+CAF s
+ACA R
+CAJ JCA L AAG e
+CAF s
+ACA R
+CAJ JCA , qfIn
+B C B Av
+I A $ , AAG0AA4 xK GN BHA G`
+ DABA 4 K (N BHA G`
+ DABA 4 K N BHA G`
+ DABA 4 D K M BHA G`
+ DABA 4 | شD M BHA G`
+ DABA 4 D M BHA G`
+ DABA 4 K M BHA G`
+ DABA 4 $ K nM BHA G`
+ DABA 4 \ 8K OM BHA G`
+ DABA 4 PK 0M BHA G`
+ DABA 4 hD M BHA G`
+ DABA 4 D L BHA G`
+ DABA 4 < K L BHA G`
+ DABA 4 t K L BHA G`
+ DABA 4 ȿK L BHA G`
+ DABA 4 K vL BHA G`
+ DABA 4 D WL BHA G`
+ DABA 4 T àD 8L BHA G`
+ DABA 4 (ĠK L BHA G`
+ DABA 4 @ŠK K BHA G`
+ DABA 4 XƠK K BHA G`
+ DABA 4 4 pǠK K BHA G`
+ DABA 4 l ȠD K BHA G`
+ DABA 4 ɠD ~K BHA G`
+ DABA 4 ʠK _K BHA G`
+ DABA 4 ˠK @K BHA G`
+ DABA 4 L ̠K !K BHA G`
+ DABA 4 ΠK K BHA G`
+ DABA 4 ϠK J BHA G`
+ DABA 4 0РK J BHA G`
+ DABA 4 , HѠK J BHA G`
+ DABA 4 d `ҠK J BHA G`
+ DABA 4 xӠK gJ BHA G`
+ DABA 4 ԠK HJ BHA G`
+ DABA 4 ՠK )J BHA G`
+ DABA 4 D ֠D
+J BHA G`
+ DABA 4 | נD I BHA G`
+ DABA 4 ؠD I BHA G`
+ DABA 4 ڠD I BHA G`
+ DABA 4 $ ۠D I BHA G`
+ DABA 4 \ 8ܠK oI BHA G`
+ DABA 4 PݠK PI BHA G`
+ DABA 4 hޠK 1I BHA G`
+ DABA 4 ߠK I BHA G`
+ DABA 4 < D H BHA G`
+ DABA 4 t D H BHA G`
+ DABA 4 K H BHA G`
+ DABA 4 K H BHA G`
+ DABA 4 K wH BHA G`
+ DABA 4 T K XH BHA G`
+ DABA 4 (K 9H BHA G`
+ DABA 4 @K H BHA G`
+ DABA 4 XK G BHA G`
+ DABA 4 4 pK G BHA G`
+ DABA 4 l K G BHA G`
+ DABA 4 K G BHA G`
+ DABA 4 K G BHA G`
+ DABA 4 D `G BHA G`
+ DABA 4 L D AG BHA G`
+ DABA 4 D "G BHA G`
+ DABA 4 D G BHA G`
+ DABA 4 0D F BHA G`
+ DABA 4 , HK F BHA G`
+ DABA 4 d `K F BHA G`
+ DABA 4 xK F BHA G`
+ DABA 4 K hF BHA G`
+ DABA 4 K IF BHA G`
+ DABA 4 D K *F BHA G`
+ DABA 4 | D F BHA G`
+ DABA 4 D E BHA G`
+ DABA 4 D E BHA G`
+ DABA 4 $ K E BHA G`
+ DABA 4 \ 8K E BHA G`
+ DABA 4 P K pE BHA G`
+ DABA 4 hK QE BHA G`
+ DABA 4 K 2E BHA G`
+ DABA 4 < K E BHA G`
+ DABA 4 t K D BHA G`
+ DABA 4 K D BHA G`
+ DABA 4 D D BHA G`
+ DABA 4 D D BHA G`
+ DABA 4 T D xD BHA G`
+ DABA 4 (
+D YD BHA G`
+ DABA 4 @ K :D BHA G`
+ DABA 4 X K D BHA G`
+ DABA 4 4 p
K C BHA G`
+ DABA 4 l K C BHA G`
+ DABA 4 K C BHA G`
+ DABA 4 K C BHA G`
+ DABA 4 D C BHA G`
+ DABA 4 L D aC BHA G`
+ DABA 4 D BC BHA G`
+ DABA 4 K #C BHA G`
+ DABA 4 0K C BHA G`
+ DABA 4 , HK B BHA G`
+ DABA 4 d `K B BHA G`
+ DABA 4 xK B BHA G`
+ DABA 4 D B BHA G`
+ DABA 4 D iB BHA G`
+ DABA 4 D D JB BHA G`
+ DABA 4 | K +B BHA G`
+ DABA 4 K B BHA G`
+ DABA 4 K A BHA G`
+ DABA 4 $ !K A BHA G`
+ DABA 4 \ 8"D A BHA G`
+ DABA 4 P#D A BHA G`
+ DABA , h$ qA AGJ@|
+AAH T % TA BBE E(A0A8G
+8D0A(B BBBC < @' BED A(D0Y
+(A ABBA T 'q @ BEB B(A0D8M
+8A0A(B BBBA T 0) @ BBB L(A0A8J
+8A0A(B BBBD 4 D 5 @ BAI D0Q
+ DABA 4 | 6 @ BAI D0Q
+ DABA 4 `6 s@ BAI D0Q
+ DABA 4 6 O@ BAI D0Q
+ DABA 4 $ 7 +@ BAI D0Q
+ DABA 4 \ h7 @ BAI D0Q
+ DABA 4 7 ? BAI D0Q
+ DABA 4 8 ? BAI D0Q
+ DABA 4 p8 ? BAI D0Q
+ DABA 4 < 8 w? BAI D0Q
+ DABA 4 t 9 S? BAI D0Q
+ DABA 4 x9 /? BAI D0Q
+ DABA 4 9 ? BAI D0Q
+ DABA 4 (: > BAI D0Q
+ DABA 4 T : > BAI D0Q
+ DABA 4 : > BAI D0Q
+ DABA 4 0; {> BAI D0Q
+ DABA 4 ; W> BAI D0Q
+ DABA 4 4 ; 3> BAI D0Q
+ DABA 4 l 8< > BAI D0Q
+ DABA 4 < = BAI D0Q
+ DABA 4 < = BAI D0Q
+ DABA 4 @= = BAI D0Q
+ DABA 4 L = = BAI D0Q
+ DABA 4 = [= BAI D0Q
+ DABA 4 H> 7= BAI D0Q
+ DABA 4 > = BAI D0Q
+ DABA 4 , > < BAI D0Q
+ DABA 4 d P? < BAI D0Q
+ DABA 4 ? < BAI D0Q
+ DABA 4 @ < BAI D0Q
+ DABA 4 X@ _< BAI D0Q
+ DABA 4 D @ ;< BAI D0Q
+ DABA 4 | A < BAI D0Q
+ DABA 4 `A ; BAI D0Q
+ DABA 4 A ; BAI D0Q
+ DABA 4 $ B ; BAI D0Q
+ DABA 4 \ hB ; BAI D0Q
+ DABA 4 B c; BAI D0Q
+ DABA 4 C ?; BAI D0Q
+ DABA 4 pC ; BAI D0Q
+ DABA 4 < C : BAI D0Q
+ DABA 4 t D : BAI D0Q
+ DABA 4 xD : BAI D0Q
+ DABA 4 D : BAI D0Q
+ DABA 4 (E g: BAI D0Q
+ DABA 4 T E C: BAI D0Q
+ DABA 4 E : BAI D0Q
+ DABA 4 0F 9 BAI D0Q
+ DABA 4 F 9 BAI D0Q
+ DABA 4 4 F 9 BAI D0Q
+ DABA 4 l 8G 9 BAI D0Q
+ DABA 4 G k9 BAI D0Q
+ DABA 4 G G9 BAI D0Q
+ DABA 4 @H #9 BAI D0Q
+ DABA 4 L H 8 BAI D0Q
+ DABA 4 H 8 BAI D0Q
+ DABA 4 HI 8 BAI D0Q
+ DABA 4 I 8 BAI D0Q
+ DABA 4 , I o8 BAI D0Q
+ DABA 4 d PJ K8 BAI D0Q
+ DABA T J '8 BEE B(H0D8J
+8A0A(B BBBG T pR m8 BEE B(H0D8J
+8A0A(B BBBD T L Y 8 BBB B(I0A8G
+8D0A(B BBBE T ^ 8 BBB B(I0A8G
+8D0A(B BBBE T hc 8 BBB B(I0A8G
+8D0A(B BBBE T T h 8 BBB B(I0A8G
+8D0A(B BBBE T l 8 BBB B(I0A8G
+8D0A(B BBBE T q 8 BBB B(I0A8G
+8D0A(B BBBE T \ Hv 8 BBB B(I0A8G
+8D0A(B BBBE T { 8 BBB B(I0A8G
+8D0A(B BBBE T 8 BBB B(I0A8G
+8D0A(B BBBE T d p 8 BBB B(I0A8G
+8D0A(B BBBE T ( 8 BBB B(I0A8G
+8D0A(B BBBE T 9 BBB B(I0A8G
+8D0A(B BBBE T l 9 BBB B(I0A8G
+8D0A(B BBBE T P 9 BBB B(I0A8G
+8D0A(B BBBE T = 9 BBB B(A0I8G
+8D0A(B BBBD T t = 9 BBB B(A0I8G
+8D0A(B BBBD T إ= %9 BBB B(A0I8G
+8D0A(B BBBD T $
+ = -9 BBB B(A0I8G
+8D0A(B BBBD T |
+ = 59 BBB B(A0I8G
+8D0A(B BBBD T
+ = =9 BBB B(A0I8G
+8D0A(B BBBD T , x= E9 BBB B(A0I8G
+8D0A(B BBBD T `= M9 BBB B(A0I8G
+8D0A(B BBBD T Há= U9 BBB B(A0I8G
+8D0A(B BBBD T 4 0ȡ= ]9 BBB B(A0I8G
+8D0A(B BBBD T ͡= e9 BBB B(A0I8G
+8D0A(B BBBD T ҡ= m9 BBB B(A0I8G
+8D0A(B BBBD T <
֡= u9 BBB B(A0I8G
+8D0A(B BBBD T
ۡ= }9 BBB B(A0I8G
+8D0A(B BBBD T
= 9 BBB B(A0I8G
+8D0A(B BBBD T D = 9 BBB B(A0I8G
+8D0A(B BBBD T = 9 BBB B(A0I8G
+8D0A(B BBBD T p= 9 BBB B(A0I8G
+8D0A(B BBBD T L X= 9 BBB B(A0I8G
+8D0A(B BBBD T @= 9 BBB B(A0I8G
+8D0A(B BBBD T (= 9 BBB B(A0I8G
+8D0A(B BBBD T T = 9 BBB B(A0I8G
+8D0A(B BBBD T = 9 BBB B(A0I8G
+8D0A(B BBBD T = 9 BBB B(A0I8G
+8D0A(B BBBD T \ = 9 BBB B(A0I8G
+8D0A(B BBBD T = 9 BBB B(A0I8G
+8D0A(B BBBD T = 9 BBB B(A0I8G
+8D0A(B BBBD T d = 9 BBB B(A0I8G
+8D0A(B BBBD T h%= 9 BBB B(A0I8G
+8D0A(B BBBD T P*= 9 BBB B(A0I8G
+8D0A(B BBBD T l 8/= : BBB B(A0I8G
+8D0A(B BBBD | 4 E
+K < 5 BKD A(G0(A ABB L $ 6 9 BBB B(A0A8Gq
+8A0A(B BBBD T t P?f 9 BEB B(D0A8Jq
+8A0A(B BBBK L hF? : BFB B(D0A8J
+8D0A(B BBBA T XG 9 BBE B(D0D8N
+8D0A(B BBBF T t Nn 9 BEB B(D0A8Jq
+8A0A(B BBBK L U? : BFB B(D0A8J
+8D0A(B BBBA T V 9 BBE B(D0D8N
+8D0A(B BBBF , 0^G T h_ 9 BEE B(H0D8J
+8A0A(B BBBE T fn /: BEB B(D0A8Jq
+8A0A(B BBBK L < m? j: BFB B(D0A8J
+8D0A(B BBBA T n /: BBE B(D0D8N
+8D0A(B BBBF pu L huR BBD A(DP
+(A ABBF *
+(A ABBH T L xy 9 BEE B(H0D8J
+8A0A(B BBBH T f ): BEB B(D0A8Jq
+8A0A(B BBBK L (? d: BFB B(D0A8J
+8D0A(B BBBA T L ): BBE B(D0D8N
+8D0A(B BBBF T p E: BEE B(D0H8J6
+8A0A(B BBBD T ؕf : BEB B(D0A8Jq
+8A0A(B BBBK T T : BEE B(H0D8J
+8A0A(B BBBG T xf ; BEB B(D0A8Jq
+8A0A(B BBBK D F; BBB D(A0J
+0D(A BBBD T L ث ; BEE B(D0H8JD
+8A0A(B BBBF T Pf e; BEB B(D0A8Jq
+8A0A(B BBBK T h ; BEE B(H0D8J
+8A0A(B BBBF T T f ; BEB B(D0A8Jq
+8A0A(B BBBK D Ǣ !< BBB D(A0J
+0D(A BBBD T `Ȣ ; BEE B(D0H8JK
+8A0A(B BBBG T L f @< BEB B(D0A8Jq
+8A0A(B BBBK T բ {< BEE B(H0D8J
+8A0A(B BBBF T ܢf < BEB B(D0A8Jq
+8A0A(B BBBK D T < BBB D(A0J
+0D(A BBBD T < BEE B(D0H8J6
+8A0A(B BBBD T Pf = BEB B(D0A8Jq
+8A0A(B BBBK T L h U= BEE B(H0D8J
+8A0A(B BBBG T f = BEB B(D0A8Jq
+8A0A(B BBBK D = BBB D(A0J
+0D(A BBBD T D P = BEE B(D0H8JD
+8A0A(B BBBF T f = BEB B(D0A8Jq
+8A0A(B BBBK T 0> BEE B(H0D8J
+8A0A(B BBBF T L! xf v> BEB B(D0A8Jq
+8A0A(B BBBK D ! > BBB D(A0J
+0D(A BBBD T ! > BEE B(H0D8J
+8A0A(B BBBD T D" 0%v > BEB B(D0A8Jq
+8A0A(B BBBK D " X,6 ? BFE A(A0J
+0D(A BBBA T " P- > BEE B(H0D8J
+8A0A(B BBBE T <# 4v ? BEB B(D0A8Jq
+8A0A(B BBBK D # ;6 Y? BFE A(A0J
+0D(A BBBA T # < &? BEE B(H0D8J
+8A0A(B BBBD T 4$ Ef l? BEB B(D0A8Jq
+8A0A(B BBBK D $ (L6 ? BFE A(A0J
+0D(A BBBA T $ M< t? BEE B(H0D8J
+8A0A(B BBBE T ,% Yf ? BEB B(D0A8Jq
+8A0A(B BBBK D % `6 ? BFE A(A0J
+0D(A BBBA T % a ? BEE B(H0D8J
+
+8A0A(B BBBH T $& hn @ BEB B(D0A8Jq
+8A0A(B BBBK D |& o6 C@ BFE A(A0J
+0D(A BBBA T & p @ BEE B(D0H8J0
+8A0A(B BBBB T ' hyf V@ BEB B(D0A8Jq
+8A0A(B BBBK D t' 6 @ BFE A(A0J
+0D(A BBBA T ' xX ^@ BEE B(D0H8J
+8A0A(B BBBJ T ( f @ BEB B(D0A8Jq
+8A0A(B BBBK D l( 6 @ BFE A(A0J
+0D(A BBBA T ( @ BEE B(D0H8J+
+8A0A(B BBBG T ) 蘣f @ BEB B(D0A8Jq
+8A0A(B BBBK D d) 6 ,A BFE A(A0J
+0D(A BBBA T ) w @ BEE B(H0D8J
+8A0A(B BBBI T * f ?A BEB B(D0A8Jq
+8A0A(B BBBK D \* 86 zA BFE A(A0J
+0D(A BBBA T * 0 GA BEE B(H0D8J
+8A0A(B BBBK T * ȼn A BEB B(D0A8Jq
+8A0A(B BBBK D T+ ã6 A BFE A(A0J
+0D(A BBBA T + ģ A BEE B(H0D8J@
+8A0A(B BBBB T + `ˣf A BEB B(D0A8Jq
+8A0A(B BBBK D L, xң6 B BFE A(A0J
+0D(A BBBA T , pӣ A BEE B(H0D8J
+8A0A(B BBBA T , ڣn )B BEB B(D0A8Jq
+8A0A(B BBBK D D- 6 dB BFE A(A0J
+0D(A BBBA T - 1B BEE B(D0H8J0
+8A0A(B BBBB T - f wB BEB B(D0A8Jq
+8A0A(B BBBK D <. 6 B BFE A(A0J
+0D(A BBBA T . h B BEE B(D0H8J
+8A0A(B BBBB T . f B BEB B(D0A8Jq
+8A0A(B BBBK D 4/ 6 C BFE A(A0J
+0D(A BBBA T |/ B BEE B(D0H8J+
+8A0A(B BBBG T / @ f C BEB B(D0A8Jq
+8A0A(B BBBK D ,0 X6 MC BFE A(A0J
+0D(A BBBA T t0 P C BEE B(H0D8J
+8A0A(B BBBK T 0 n `C BEB B(D0A8Jq
+8A0A(B BBBK D $1 "6 C BFE A(A0J
+0D(A BBBA T l1 " hC BEE B(H0D8J
+8A0A(B BBBE T 1 )v C BEB B(D0A8Jq
+8A0A(B BBBK D 2 06 C BFE A(A0J
+0D(A BBBA T d2 1 C BEE B(H0D8J
+8A0A(B BBBH T 2 H8v C BEB B(D0A8Jq
+8A0A(B BBBK D 3 p?6 7D BFE A(A0J
+0D(A BBBA T \3 h@ D BEE B(H0D8J
+8A0A(B BBBE T 3 G JD BEB B(D0A8Jq
+8A0A(B BBBK D 4 O6 D BFE A(A0J
+0D(A BBBA T T4 P RD BEE B(H0D8J
+8A0A(B BBBG T 4 HXv D BEB B(D0A8Jq
+8A0A(B BBBK D 5 p_6 D BFE A(A0J
+0D(A BBBA T L5 h` D BEE B(H0D8J
+8A0A(B BBBE T 5 g D BEB B(D0A8Jq
+8A0A(B BBBK D 5 o6 !E BFE A(A0J
+0D(A BBBA L 6 p BBD A(D@
+(A ABBF
+(A ABBE T 6 u D BEE B(H0D8J8
+8A0A(B BBBJ T 6 H|f D BEB B(D0A8Jq
+8A0A(B BBBK D D7 `6 E BFE A(A0J
+0D(A BBBA T 7 X D BEE B(D0H8J
+8A0A(B BBBB T 7 f 2E BEB B(D0A8Jq
+8A0A(B BBBK D <8 (6 mE BFE A(A0J
+0D(A BBBA <9 c T 8 x "E BEE B(D0H8J
+8A0A(B BBBH T 8 @ hE BEB B(D0A8Jq
+8A0A(B BBBK D L9 Ȣ6 E BFE A(A0J
+0D(A BBBA L: T 9 ȣ XE BEE B(H0D8J
+8A0A(B BBBK T : P E BEB B(D0A8Jq
+8A0A(B BBBK D \: 6 E BFE A(A0J
+0D(A BBBA T : E BEE B(H0D8J
+8A0A(B BBBG T : ¤f E BEB B(D0A8Jq
+8A0A(B BBBK D T; ʤ6 'F BFE A(A0J
+0D(A BBBA T< x. K^ l< ʤ D < ʤ BBE A(A0D@g
+0A(A BBBA D < pˤ BBE A(A0D@o
+0A(A BBBA = Fx\ AG RAT 4= ˤ- BDA ]
ADG w
ADD Q
ABD I
-ABD T v| BBE E(D0A8G
-8D0A(B BBBE D { BBE A(A0D@P
-0A(A BBBA $ < P| Y A
+ABD T < ̤| D BBE E(D0A8G
+8D0A(B BBBE D = Ѥ BBE A(A0D@P
+0A(A BBBA $ t= `Ҥ ZD A
G A
-A , ;- AAG0!AA D I BIA D(Do
-(D ABBA L 4 BBB B(A0D8D`
-8A0A(B BBBD L , 4 BBB B(A0D8D`
-8A0A(B BBBD T | { BEB I(A0A8G
+A , T> Ex- AAG0!AA < = դ D BHD Dq
+ DABA L > ؤ4 C BBB B(A0D8D`
+8A0A(B BBBD L \> ڤ4 C BBB B(A0D8D`
+8A0A(B BBBD T > xܤ{ C BEB I(A0A8G
-8A0A(B BBBE , > w AID W
-DAA T țm W BBE E(A0D8G>
-8D0A(B BBBK $ \ G As
+8A0A(B BBBE , ? > D AID W
+DAA T 4? m _D BBE E(A0D8G>
+8D0A(B BBBK $ ? G D As
L A
-A T BFA A(GP
+A T ? C BFA A(GP
(A ABBF k
-(A ABBA l BBB B(A0A8G
+(A ABBA l @ C BBB B(A0A8G
8A0A(B BBBD
-8A0A(B BBBF T L U BBE E(A0D8G,
-8D0A(B BBBE D \ ( BEB B(A0A80A(B BBB < B BBA A(G
-(A ABBA , , ) PGA A(D , \ p ]GA A(D , P PGA A(D < B BBA A(G
-(A ABBA T 0 # BBB B(D0D8J
-8A0A(B BBBA T T Ҫ/ ې BHB B(A0F8J
-8D0A(B BBBJ T f BBB H(F0D8G
-8D0A(B BBBA . K^ L AAG ]
-CAF c
-CAI R
-CAJ JCA D $ BBE A(A0D@g
-0A(A BBBA D l x BBE A(A0D@o
-0A(A BBBA $ ^ AG b
-AA , H0 AC
FK
- E < T H BBP J(G
-(D ABBD $ A
-K A
-A , t b5- AAG0!AA < BHD Dq
- DABA D , R BEA G@
- AABI
- AABA l t l
BBB B(A0A8G@'
-8A0A(B BBBF x
-8A0A(B BBBF , N ZGA A(D ,
- ; ZGA A(D T D
- h ( BBB B(A0A8G;
-8A0A(B BBBJ L T 0 BBB B(A0A8Go8A0A(B BBB T
- p% BEB B(A0A8J
-8A0A(B BBBA T D (4 BHB B(F0A8J
-8D0A(B BBBE T IF BBB H(F0D8G
-8D0A(B BBBA T N ώ BEB B(H0A8G
-8A0A(B BBBD , L `e> AID W
-DAA 4
. K^ L
Xe d
`e |
Xe
Pe
Xe
Pe
Xe
PeF e) G] $ eJ W c < e D k
-Q z \ Hf H G
-A | f H0W
-I $ hg^ AG b
-AA D g BBE A(A0D@
-0A(A BBBA $ T xhg Í AG k
-AA , | h\ ADG l
-AAA , d 0- AAG0!AA < hU K BIA D(Dp
+8A0A(B BBBF T |@ U C BBE E(A0D8G,
+8D0A(B BBBE D A BEB B(A0A80A(B BBB < A xB B BBA A(G
+(A ABBA , \A 1C PGA A(D , A X C ]GA A(D , A 8 C PGA A(D < A B B BBA A(G
+(A ABBA T ,B +C BBB B(D0D8J
+8A0A(B BBBA T B (/ B BHB B(A0F8J
+8D0A(B BBBB T B X; zC BBB H(F0D8G
+8D0A(B BBBA C @x. K^ D RAx\ AG RAD lC XD ?C BBB A(D0D@[
+0A(A BBBF D C F B BBB A(D0D@c
+0A(A BBBF T C H
+ B BBB B(D0D8G
+8A0A(B BBBI , E @x- AAG0!AA D D R B BIA D(Dh
+(D ABBA T D U B BBE B(A0A8J]
+
+8A0A(B BBBB 4 $E k oC BAI
+ABA L \E Hl NC BBB E(A0A8Jh
+8D0A(B BBBD dF ok D \
+H T E (p C BBB H(D0F8G;
+8D0A(B BBBI F x. K^ F ?x\ AG RAT \F A C BEE E(D0A8Gg
+8D0A(B BBBG < F _ C AAG
+AAI p
+AAB < F H EC BBA A(Gp
+(D ABBA T 4G Ћ )C BEB B(A0A8G]
+8A0A(B BBBE T G u C BHB B(F0A8J
+8D0A(B BBBD T G u D BHB B(F0A8J
+8D0A(B BBBD H x. K^ I Υ $I Υ <I Υ TI Υ lI Υ I Υ I Υ I ΥF I Υ) G] I ϥJ W c I @ϥ D k
+Q z J ϥ H G
+A <J 0Х H0W
+I $ \J Х^ AG b
+AA D I ѥ
C BBE A(A0D@
+0A(A BBBA $ J ѥg B AG k
+AA , <J (ҥ\ B ADG l
+AAA , $K <x- AAG0!AA < J (ҥU QB BIA D(Dp
-(D ABBI D k| BBB A(D0D@
-0A(A BBBA D m BBB A(D0D@
-0A(A BBBA T `n, BBE B(D0A8G
-8D0A(B BBBK L 8s BBF B(A0A8D@
-8D0A(B BBBB D T s + BBE A(A0D@
-0A(A BBBA L t BEB E(I0H8D@
-8D0A(B BBBD L vw BHI E(A0A8Gp
-8A0A(B BBBF L < @x BJE B(A0A8D@
-8A0A(B BBBA L y^ BBB B(A0D8J
-8D0A(B BBBE T e ] BEB B(A0A8GL
-8A0A(B BBBA T 4 8 + BBB B(A0A8G
-8A0A(B BBBG T p BBB B(A0D8J
-8A0A(B BBBA d țE BBB B(A0A8DP
+(D ABBI D K Hե| BBB A(D0D@
+0A(A BBBA D K ֥ BBB A(D0D@
+0A(A BBBA T lK ץ, A BBE B(D0A8G
+8D0A(B BBBK T K ܥe A BEB B(A0A8GL
+8A0A(B BBBA T L ޥ OA BBB B(A0A8G
+8A0A(B BBBG L ,M BBF B(A0A8D@
+8D0A(B BBBB D L @ BBE A(A0D@
+0A(A BBBA L M H @ BEB E(I0H8D@
+8D0A(B BBBD L \M w @ BHI E(A0A8Gp
+8A0A(B BBBF L M o@ BJE B(A0A8D@
+8A0A(B BBBA L M ^ G@ BBB B(A0D8J
+8D0A(B BBBE T LN @ BBB B(A0D8J
+8A0A(B BBBA d \O 0E BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA T L BBB B(A0A8N
-8A0A(B BBBA , x> AID W
-DAA x. K^ p Hr J 身e @ ƫ7 4 ǫZ L H˫ d ЫB | Xѫ @֫ (ثJ L `ݫr BBB B(E0A8J@
-8A0A(B BBBA D ޫ BBE A(A0D@u
-0A(A BBBA D \ (߫ BBE A(A0D@u
-0A(A BBBA ߫J ߫J L 0 BBB B(A0A8D`
-8A0A(B BBBA , l \ AAD@W
-AAA , 8 AAD@W
-AAA , ` AAD@W
-AAA , AAD@W
-AAA , , ̈ AAD@W
-AAA , \ AAD@W
-AAA , AAD@W
-AAA , @ ` AAD@W
-AAA $ RMIp L BBB B(A0A8D`
-8A0A(B BBBA , d ć AAD@W
-AAA , AAD@W
-AAA , H | AAD@W
-AAA , X AAD@W
-AAA , $ 4 AAD@W
-AAA , T h AAD@W
-AAA , AAD@W
-AAA , ( Ȇ AAD@W
-AAA L BBB B(A0A8Dp
-8A0A(B BBBA , 4 H T AAD@W
-AAA , d 0 AAD@W
-AAA , AAD@W
-AAA , h AAD@W
-AAA , ą AAD@W
-AAA , $ ( AAD@W
-AAA , T | AAD@W
-AAA , X AAD@W
-AAA < l H BBA A(D@i
-(A ABBA , AAD@W
-AAA , $ Є AAD@W
-AAA , T H AAD@W
-AAA , AAD@W
-AAA , d AAD@W
-AAA , h @ AAD@W
-AAA , AAD@W
-AAA , D ( AAD@W
-AAA T t ԃ BBB B(D0D8G
-8D0A(B BBBJ T BEE E(D0A8G
-8D0A(B BBBD T $ (, BBE B(D0A8G
-8D0A(B BBBK T | J s BIA A(GT
+8F0A(B BBBA T O @ BBB B(A0A8N
+8A0A(B BBBA , dO > A AID W
+DAA LP px. K^ dP |P r P #J P P$e P * P @07 P h0Z Q 4 $Q 9B <Q : TQ ? lQ AJ D Q F BBE A(A0D@u
+0A(A BBBA D Q `G BBE A(A0D@u
+0A(A BBBA L \Q Gr ? BBB B(E0A8J@
+8A0A(B BBBA dR (IJ |R `IJ L R I BBB B(A0A8D`
+8A0A(B BBBA , ,R J d> AAD@W
+AAA , \R hJ @> AAD@W
+AAA , R J > AAD@W
+AAA , R (K = AAD@W
+AAA , R K = AAD@W
+AAA , S K = AAD@W
+AAA , LS HL = AAD@W
+AAA , |S L h= AAD@W
+AAA $ dT M RMIp L T N BBB B(A0A8D`
+8A0A(B BBBA , $T N < AAD@W
+AAA , TT PO < AAD@W
+AAA , T O < AAD@W
+AAA , T P `< AAD@W
+AAA , T pP << AAD@W
+AAA , U P < AAD@W
+AAA , DU 0Q ; AAD@W
+AAA , tU Q ; AAD@W
+AAA L \V Q BBB B(A0A8Dp
+8A0A(B BBBA , U R \; AAD@W
+AAA , $V S 8; AAD@W
+AAA , TV pS ; AAD@W
+AAA , V S : AAD@W
+AAA , V 0T : AAD@W
+AAA , V T : AAD@W
+AAA , W T : AAD@W
+AAA , DW PU `: AAD@W
+AAA < ,X U BBA A(D@i
+(A ABBA , W U 9 AAD@W
+AAA , W PV 9 AAD@W
+AAA , X V 9 AAD@W
+AAA , DX W 9 AAD@W
+AAA , tX pW l9 AAD@W
+AAA , X W H9 AAD@W
+AAA , X 0X $9 AAD@W
+AAA , Y X 9 AAD@W
+AAA T 4Y X 8 BBB B(D0D8G
+8D0A(B BBBJ T Y 8]c 8 BEE E(D0A8G
+8D0A(B BBBD T Y Pa r8 BBB B(D0D8G
+8D0A(B BBBH T <Z qR 8 BIA H(G
+(A ABBE B
+(A ABBA T Z s 8 BIE B(D0A8G
+8A0A(B BBBE T Z , 8 BBE B(D0A8G
+8D0A(B BBBK T D[ J 8 BIA A(GT
(A ABBB ^
-(A ABBD T , ; BBE B(D0A8G
-8D0A(B BBBK T , J ! BIA A(GT
+(A ABBD T [ 舦, 8 BBE B(D0A8G
+8D0A(B BBBK T [ J 8 BIA A(GT
(A ABBB ^
-(A ABBD T , BBE B(D0A8G
-8D0A(B BBBK T J ς BIA A(GT
+(A ABBD T L\ , P8 BBE B(D0A8G
+8D0A(B BBBK T \ J 68 BIA A(GT
(A ABBB ^
-(A ABBD T 4 , BBE B(D0A8G
-8D0A(B BBBK T pJ } BIA A(GT
+(A ABBD T \ , 7 BBE B(D0A8G
+8D0A(B BBBK T T] `J 7 BIA A(GT
(A ABBB ^
-(A ABBD T h2 E BBB E(A0D8G/
-8D0A(B BBBE L <! P) BBB B(D0A8G`8
-8A0A(B BBBJ T ! + BBB B(D0A8G
-8A0A(B BBBA L ! . c BEB E(I0H8DP
-8D0A(B BBBD L 4" / ; BHI E(A0A8Gps
-8A0A(B BBBB L " 2 ' BEB E(I0H8DP
-8D0A(B BBBD L " H4 BHI E(A0A8Gpr
-8A0A(B BBBC T $# X BIB B(A0A8GS
-8A0A(B BBBA 4$ 6 L$ 6 T # 6 BBB J(A0A8G
-8D0A(B BBBD , $ v - AAG0!AA 4 4$ 95 @ BHD D`
- DABG T l$ < # BEB B(G0A8G
-8D0A(B BBBJ |% . K^ % pC % hC % `C % XC % PC & HC $& @C <& 8C T& @C l& HC & PC & XC & `C & hC , ,& pC[ AID t
-DAA , \& C[ AID t
-DAA , & C[ AID t
-DAA , & D[ AID t
-DAA , & 0D[ { AID t
-DAA 4 ' `D BAA s
-AEK AAB L T' Dr # BBB B(E0A8J@
-8A0A(B BBBA $ \( E^ AG b
-AA ( F d ' (F ~ BBB B(A0D8Op
+(A ABBD L ] X 7 BBB B(D0A8G`8
+8A0A(B BBBJ T ] 7 BBB B(D0A8G
+8A0A(B BBBA L T^ [7 BEB E(I0H8DP
+8D0A(B BBBD L ^ ࣦ 37 BHI E(A0A8Gps
+8A0A(B BBBB L ^ 7 BEB E(I0H8DP
+8D0A(B BBBD L D_ P 6 BHI E(A0A8Gpr
+8A0A(B BBBC T _ x 6 BIB B(A0A8GS
+8A0A(B BBBA L ` AAG ]
+CAF c
+CAI R
+CAJ JCA L ` 誦 AAG ]
+CAF c
+CAI R
+CAJ JCA , Da (0 AC
FK
+ E ta (? A} < a H ADG v
+GAF
+AAF a S L a + ULB A(A0@(A BBBD0 D a e 5 BFB A(A0GP
+0A(A BBBC d a 迦i 5 BBB B(A0A8G@*
+8A0A(B BBBK _
+8A0A(B BBBG b Ħ , Lb Ŧ 4 ZGA A(D , |b Ʀ 4 ZGA A(D T b hǦ
4 BBB B(A0A8G<
+8A0A(B BBBI T c Ԧ f4 BEB B(A0A8G\
+8A0A(B BBBA T \c :4 BEB B(A0A8G\
+8A0A(B BBBA T c 4 BBB B(A0A8Gl
+8A0A(B BBBA T d 3 BBB B(A0A8Gl
+8A0A(B BBBA e x. K^ 4e Le T d 63 BBB J(A0A8G
+8D0A(B BBBD , e "x- AAG0!AA 4 4e 5 2 BHD D`
+ DABG T le 2 BEB B(G0A8G
+8D0A(B BBBJ |f x. K^ f X f P f H f @ f 8 g 0 $g ( <g Tg ( lg 0 g 8 g @ g H g P , ,g X[ 1 AID t
+DAA , \g [ 1 AID t
+DAA , g [ o1 AID t
+DAA , g [ O1 AID t
+DAA , g [ /1 AID t
+DAA 4 h H BAA s
+AEK AAB L Th r 0 BBB B(E0A8J@
+8A0A(B BBBA $ \i ^ AG b
+AA i d h \0 BBB B(A0D8Op
8A0A(B BBBH U
-8A0A(B BBBA , L( F> W~ AID W
-DAA T |( F 7~ BBB B(D0A8K#
-8A0A(B BBBK , ( XH> ~ AID W
-DAA D ) hH } BBB A(A0JR
-0A(A BBBA L L) @I{ } BBB D(D0J
-0D(A BBBG , ) pM } qGA A(D T ) `N~ } BBA A(Ik
-(C ABBF
-(A ABBA T $* O } BEE E(D0D8G(
-8D0A(B BBBK d 4+ Q= BLB B(A0A8DPq
+8A0A(B BBBA , Li > 0 AID W
+DAA T |i / BBB B(D0A8K#
+8A0A(B BBBK , i @> / AID W
+DAA D j P / BBB A(A0JR
+0A(A BBBA L Lj ({ r/ BBB D(D0J
+0D(A BBBG , j X |/ qGA A(D T j H j/ BBA A(In
+(C ABBK
+(A ABBA T $k ! 7/ BEE E(D0D8G(
+8D0A(B BBBK d 4l #= BLB B(A0A8DPq
8A0A(B BBBE
-8A0A(B BBBE < * Qg | BED D(G
-(D ABBD $ + S AAG0AA4 L+ S
| BHA GP
- DABE 4 + xU
b| BHA GP
- DABE < + PW D| BHA G
- DABA < + Y )| BHA G
- DABA < <, [ | BHA G
- DABA < |, P^T { BHA J
- DABA 4 , pa { BHD D`
- DABF L , c { BBB B(G0D8G
-8A0A(B BBBA D D- Hf { BDB D(A0J~
-0D(A BBBA \ - i" { BBB A(A0G
+8A0A(B BBBE < k #g . BED D(G
+(D ABBD $ l % AAG0AA< Ll % 4. BHA G
+ DABA < l ' . BHA G
+ DABA < l 8* - BHA G
+ DABA < m ,R - BHA J
+ DABA 4 Lm / - BHD D`
+ DABD 4 m 02 - BHD D`
+ DABD 4 m 4 - BHD D`
+ DABF L m 7 {- BBB B(G0D8G
+8A0A(B BBBA \ Dn 9" - BBB A(A0G
0A(A BBBI O
-0A(A BBBA D - jD { BDH D(A0G
-0D(A BBBD < 4. n0 { BBA A(Jc
-(A ABBG T t. oa { BEE B(D0A8G
-8D0A(B BBBI L / wB BLH B(D0A8DP
-8A0A(B BBBB / x9 Af
-I D L </ y { BHB E(A0A8Gn
-8D0A(B BBBK T / { z BDB E(A0A8Gu
-8A0A(B BBBK T / 胬l R{ BDB B(D0A8G6
-8A0A(B BBBJ T <0 q{ BBB B(A0D8G
-8A0A(B BBBA < 0 H g{ BBH A(Jx
-(A ABBK T 0 E
G{ BBB B(A0E8G
-8A0A(B BBBA T ,1 E
{ BBB B(A0E8G
-8A0A(B BBBA T 1 E
| BBB B(A0E8G
-8A0A(B BBBA T 1 2
m| BBB B(A0A8K
-8A0A(B BBBA L 42 | BBB G(A0D8M
-8D0A(B BBBG <3 . K^ T3 Ŭ l3 Ŭ 3 Ŭ L 2 (Ŭr Q| BBB B(E0A8J@
-8A0A(B BBBA $ 3 XƬ^ AG b
-AA L \3 Ƭ* { BBB J(A0A8G
-8D0A(B BBBA L 3 pȬ* { BBB J(A0A8G
-8D0A(B BBBA T 3 PʬX { BBA D(G`
+0A(A BBBA D n P;D >- BDH D(A0G
+0D(A BBBD D n X? b- BDB D(A0J~
+0D(A BBBA < 4o B0 y- BBA A(Jc
+(A ABBG T to Ca W- BEE B(D0A8G
+8D0A(B BBBI L p (KB BLH B(D0A8DP
+8A0A(B BBBB p (L9 Af
+I D L <p HL, , BBB E(A0A8Mn
+8D0A(B BBBK T p (N , BDB E(A0A8G
+8A0A(B BBBJ T p Ul , BDB B(D0A8G6
+8A0A(B BBBJ T <q Z - BBB B(A0D8G
+8A0A(B BBBA < q @^ - BBH A(Jx
+(A ABBK T q `l , BBB B(A0E8G
+8A0A(B BBBA T ,r lE
G- BBB B(A0E8G
+8A0A(B BBBA T r yl - BBB B(A0E8G
+8A0A(B BBBA T r (l . BBB B(A0E8G
+8A0A(B BBBA T 4s @"
_. BBB B(A0A8K
+8A0A(B BBBA L s . BBB G(A0D8M
+8D0A(B BBBE t x. K^ t t t L <t r D. BBB B(E0A8J@
+8A0A(B BBBA $ Du 袧^ AG b
+AA L t * - BBB J(A0A8G
+8D0A(B BBBA L u * - BBB J(A0A8G
+8D0A(B BBBA T Tu ধX - BBA D(G`
(A ABBK T
-(D ABBA , 5 T
- AAG0!AA 4 4 (ˬ N{ BHI D`
- DABH 4 4 ͬ 0{ BHI D`
- DABI D 4 (Ь { BBE G(A0D
-0A(A BBBA < <5 Ѭx z BBD A(Jg
-(A ABBA L |5 Ҭ z BBB B(A0D8M
-8A0A(B BBBH T 5 z BBE E(A0A8G
-8D0A(B BBBF T $6 8ڬ6 z BBB H(A0D8J
-8A0A(B BBBG L |6 | BBB E(D0D8G
-8A0A(B BBBI L 6 0$u | BBB E(D0D8G
-8A0A(B BBBI L 7 `7 | BBE E(D0A8G
-8A0A(B BBBI L l7 I | BBE E(D0A8G
-8A0A(B BBBI L 7 @Z | BBE E(D0A8G
-8A0A(B BBBI L 8 k | BBE E(D0A8G
-8A0A(B BBBI L \8 P| | BBB E(D0D8G
-8A0A(B BBBI L 8
v| BBB E(D0D8G
-8A0A(B BBBI L 8 j| BBB E(D0D8G
-8A0A(B BBBI L L9 g6 ^| BEE E(D0A8G
-8A0A(B BBBI T 9 0խ | BGB B(D0A8M
-8D0A(B BBBE , 9 ؔF } AK
-AA : : L T: m } BBB B(A0I8Dp
-8D0A(B BBBF L : m w} BBB B(A0I8Dp
-8D0A(B BBBF , ; - AAG0!AA < $; U } BIA D(Dp
+(D ABBA , dv 4x- AAG0!AA 4 u A- BHI D`
+ DABH 4 v 0 #- BHI D`
+ DABI D Lv - BBE G(A0D
+0A(A BBBA < v @x , BBD A(Jg
+(A ABBA L v , BBB B(A0D8M
+8A0A(B BBBH T $w , BBE E(A0A8G
+8D0A(B BBBF T |w ȶ%7 ~, BBB H(A0D8J
+8A0A(B BBBG L w . BBB E(D0D8G
+8A0A(B BBBI L $x u . BBB E(D0D8G
+8A0A(B BBBI L tx . BBE E(D0A8G
+8A0A(B BBBI L x % . BBE E(D0A8G
+8A0A(B BBBI L y 7 . BBE E(D0A8G
+8A0A(B BBBI L dy H . BBE E(D0A8G
+8A0A(B BBBI L y Y v. BBB E(D0D8G
+8A0A(B BBBI L z e
j. BBB E(D0D8G
+8A0A(B BBBI L Tz pr ^. BBB E(D0D8G
+8A0A(B BBBI L z {g6 R. BEE E(D0A8G
+8A0A(B BBBI T z . BGB B(D0A8M
+8D0A(B BBBE , L{ hxF / AK
+AA 4| xŨ L| pŨ L { xŨm / BBB B(A0I8Dp
+8D0A(B BBBF L { ƨm c/ BBB B(A0I8Dp
+8D0A(B BBBF , }
x- AAG0!AA < || ǨU . BIA D(Dp
-(D ABBI < d; U | BIA D(Dp
-(D ABBB T ; | BBB E(A0A8G
-8A0A(B BBBA < . K^ < < L < BLB B(A0A8D
-8A0A(B BBBE L < | BBB B(I0A8G
-8D0A(B BBBF L < | BBB B(I0A8G
-8D0A(B BBBF T 4= 8 | BEE E(D0A8G
-8D0A(B BBBE L D> ` BOB A(A0k
+(D ABBI < | ʨU . BIA D(Dp
+(D ABBB T | ͨ . BBB E(A0A8G
+8A0A(B BBBA ~ x. K^ $~ ߨ <~ ߨ L T~ ߨ BLB B(A0A8D
+8A0A(B BBBE T } h . BEE E(D0A8G
+8D0A(B BBBE L ~ BOB A(A0k
(A BBBI b
-(A BBBG T = < ]| BBE B(D0A8G
-8D0A(B BBBB , > - AAG0!AA 4 d> 5 | BHD D`
- DABG 4 > B { BHD D`
- DABH L ? BBB E(D0D8G@L
-8D0A(B BBBE < ? ( BLH D(A0
-(A BBBF T d? G{ BBB B(A0A8G
-8A0A(B BBBF T ? 8 ;| BBB I(D0A8GU
-8D0A(B BBBC @ 8. K^ @ 0> @ (> , \@ 0>[ | AID t
-DAA , DA - AAG0!AA 4 @ 0>5 { BHD D`
- DABG T @ 8At { BDB H(A0A8J
-8D0A(B BBBJ B 0. K^ B HJ 4B @J T A HJ { BBB B(I0A8D'
-8D0A(B BBBF , B t- AAG0!AA < B L { BHD G6
- DABA L \B P6 { BBB B(A0D8Jo
-8D0A(B BBBE L B Sg ^{ BBB B(A0D8Jo
-8D0A(B BBBE L B Vs 3{ BBB B(A0D8Go
-8D0A(B BBBH L LC ZV { BBB B(A0D8Jo
-8D0A(B BBBE L C 0\V z BBB B(A0D8Jo
-8D0A(B BBBE T C @^ z BBB G(D0G8GN
-8D0A(B BBBF D h. K^ E k ,E k < DE k BBD A(D0
-(A ABBF < E l BBD A(D0
-(A ABBD L E `m AAG ]
+(A BBBG T ~ < u. BBE B(D0A8G
+8D0A(B BBBB , P x- AAG0!AA 4 5 +. BHD D`
+ DABG 4 T B
. BHD D`
+ DABH 4 B - BHD D`
+ DABH L | BBB E(D0D8G@L
+8D0A(B BBBE < ̀ p BLH D(A0
+(A BBBF T T 0 A- BBB B(A0A8G 0
+8A0A(B BBBE T h #. BBB I(D0A8GU
+8D0A(B BBBC L ?. BBB B(I0A8G
+8D0A(B BBBF L T p ). BBB B(I0A8G
+8D0A(B BBBF L . BBB B(I0A8G
+8D0A(B BBBF @x. K^ Ă ܂ , < [ - AID t
+DAA , $ x- AAG0!AA 4 5 e- BHD D`
+ DABG T Ԃ ! G- BDB H(A0A8J
+8D0A(B BBBB 8x. K^ `* X* , P* D H* \ @* t 8* 0* (* * Ԅ * * * * 4 * L * d * | * (* 0* ą 8* ܅ @* H* P* $ X* \ < `* zBB B(A0A8D@w8A0A(B BBBI@ L * BBB B(A0A8D@
+8A0A(B BBBG \ + zBB B(A0A8D@w8A0A(B BBBI@ \ L , zBB B(A0A8D@w8A0A(B BBBI@ \ , zBB B(A0A8D@w8A0A(B BBBI@ L 0- BBB B(A0A8D@
+8A0A(B BBBG \ \ - zBB B(A0A8D@w8A0A(B BBBI@ \ P. zBB B(A0A8D@w8A0A(B BBBI@ \ . zBB B(A0A8D@w8A0A(B BBBI@ L | p/ BBB B(A0A8D@
+8A0A(B BBBG \ ̉ 0 zBB B(A0A8D@w8A0A(B BBBI@ \ , 0 zBB B(A0A8D@w8A0A(B BBBI@ < ԉ 1q ;' BBA A(D0[
+(A ABBA ̊ `12 AF iA , 4 1[ & AID t
+DAA , d 1[ & AID t
+DAA , 1[ & AID t
+DAA , Ċ 2[ & AID t
+DAA , @2[ _& AID t
+DAA , $ p2[ ?& AID t
+DAA , T 2[ & AID t
+DAA , 2[ % AID t
+DAA , 3[ % AID t
+DAA , 03[ % AID t
+DAA , `3[ % AID t
+DAA , D 3[ % AID t
+DAA , t 3[ _% AID t
+DAA , 3[ ?% AID t
+DAA , Ԍ 4[ % AID t
+DAA , P4[ $ AID t
+DAA , 4 4[ $ AID t
+DAA , d 4[ $ AID t
+DAA , 4[ $ AID t
+DAA , č 5[ $ AID t
+DAA , @5[ _$ AID t
+DAA , $ p5[ ?$ AID t
+DAA , T 5[ $ AID t
+DAA , 5[ # AID t
+DAA L l 6 ADD e
+CAF [
+ACI R
+CAJ JCA L 6 AAG e
+CAF {
+ACI R
+CAJ JCA L 6 ADD e
+CAF [
+ACI R
+CAJ JCA L \ `7 AAG e
+CAF {
+ACI R
+CAJ JCA L 7 ADD e
+CAF [
+ACI R
+CAJ JCA L @8 AAG e
+CAF {
+ACI R
+CAJ JCA L L 8 ADD e
+CAF [
+ACI R
+CAJ JCA L 9 AAG e
+CAF {
+ACI R
+CAJ JCA L 9 ADD e
+CAF [
+ACI R
+CAJ JCA L < : AAG e
+CAF {
+ACI R
+CAJ JCA L `: ADD e
+CAF [
+ACI R
+CAJ JCA L ܒ : AAG e
+CAF {
+ACI R
+CAJ JCA L , @; ADD e
+CAF [
+ACI R
+CAJ JCA L | ; AAG e
+CAF {
+ACI R
+CAJ JCA L ̓ < ADD e
+CAF [
+ACI R
+CAJ JCA L < AAG e
+CAF {
+ACI R
+CAJ JCA L l = ADD e
+CAF [
+ACI R
+CAJ JCA L = AAG e
+CAF {
+ACI R
+CAJ JCA L = ADD e
+CAF [
+ACI R
+CAJ JCA L \ `> AAG e
+CAF {
+ACI R
+CAJ JCA L > ADD e
+CAF [
+ACI R
+CAJ JCA L @? AAG e
+CAF {
+ACI R
+CAJ JCA L L ? ADD e
+CAF [
+ACI R
+CAJ JCA L @ AAG e
+CAF {
+ACI R
+CAJ JCA , @ BDA |
+ABA , @ BDA |
+ABA , L @A BDA |
+ABA , | A BDA
+ABA , B BDA
+ABA , ܗ B BDA
+ABA , B BDA
+ABA , < `C BDA
+ABA , l C BDA
+ABA , @D BDA
+ABA , ̘ D BDA
+ABA , E BDA
+ABA , , E6 AAG0
+AAA 4 F BHA GPh
+ DABG 4 ܘ hH BHA GPh
+ DABG 4 0J BHA GPh
+ DABG 4 L K BHA GPh
+ DABG 4 M w BHA GPh
+ DABG 4 O Y BHA GPh
+ DABG 4 PQ ; BHA GPh
+ DABG 4 , S BHA GPh
+ DABG 4 d T BHA GPh
+ DABG 4 V BHA GPh
+ DABG 4 Ԛ pX BHA GPo
+ DABH 4 8Z BHA GPo
+ DABH 4 D \ BHA GPo
+ DABH 4 | ] i BHA GPo
+ DABH 4 _ K BHA GPo
+ DABH 4 Xa - BHA GPo
+ DABH 4 $ c BHA GPo
+ DABH 4 \ d BHA GPo
+ DABH 4 f BHA GPo
+ DABH 4 ̜ xh BHA GPo
+ DABH 4 @j BHA GPo
+ DABH 4 < l y BHA GPo
+ DABH 4 t m [ BHA GPh
+ DABG 4 o5 = BHD D`
+ DABG T rm BDB B(A0A8JY
+8A0A(B BBBA T < ~] X BDB B(A0A8JH
+8A0A(B BBBA T w BDB B(A0A8Ja
+8A0A(B BBBA T 薩w BDB B(A0A8J_
+8A0A(B BBBA T D m BDB B(A0A8JY
+8A0A(B BBBA T (] < BDB B(A0A8JH
+8A0A(B BBBA T 0? u BDB B(A0A8JQ
+8A0A(B BBBA T L Ʃw BDB B(A0A8J_
+8A0A(B BBBA T @ҩm BDB B(A0A8JY
+8A0A(B BBBA T Xީ] BDB B(A0A8JH
+8A0A(B BBBA T T `w 6 BDB B(A0A8Jc
+8A0A(B BBBA T w o BDB B(A0A8Jc
+8A0A(B BBBA x. K^ Ԣ T L ` BBB B(I0A8D'
+8D0A(B BBBF , \ w- AAG0!AA < Ԣ BHD G6
+ DABA L 0 6 BBB B(A0D8Jo
+8D0A(B BBBE L d g BBB B(A0D8Jo
+8D0A(B BBBE L @s BBB B(A0D8Go
+8D0A(B BBBH L pV q BBB B(A0D8Jo
+8D0A(B BBBE L T V F BBB B(A0D8Jo
+8D0A(B BBBE T BBB G(D0G8GN
+8D0A(B BBBF ȁx. K^ ̥ 0$ ($ < 0$ BBD A(D0
+(A ABBF < < $ BBD A(D0
+(A ABBD L | % AAG ]
CAF [
ACA R
-CAJ JCA L F m AAG ]
+CAJ JCA L ̦ % AAG ]
CAF [
ACA R
-CAJ JCA L E m y BBB B(I0A8G
-8D0A(B BBBF L E p y BBB B(I0A8G
-8D0A(B BBBF , G B- AAG0!AA < |F r {y BHD Dh
- DABA < F u `y BHD Do
- DABA T F y Gy BBH E(A0A8L
-8D0A(B BBBE T TG P y BBB B(A0A8G
-8D0A(B BBBE T G P y BBB B(A0A8G
-8D0A(B BBBG L H y BFB B(D0A8JS
-8D0A(B BBBE I . K^ $I @ <I 8 < TI @ BBB D(A0&
-(A BBBH L H 2y BBB B(A0I8G
-8D0A(B BBBD T ,I К y BEE E(D0A8G*
-8D0A(B BBBD T I h y BBE B(D0A8G
-8D0A(B BBBG D I 0 x BAA G@u
- DABI
- DABF , J ~- AAG0!AA < TJ Ȧ x BHD Dh
- DABA T J H }x BEE B(D0A8N[
-8D0A(B BBBG L J ம kx BHG B(A0D8G
-8D0A(B BBBJ K Ѕ. K^ T L hŮZ MGB B(A0H8)0A(B BBBB8T dL pƮ MGB B(A0A8`0A(B BBBB8 L Ǯ L Ǯ 4 L Ǯs BBA A(D0b(A ABBD $M Ǯ BBB B(A0A8DP8A0A(B BBBL lM hȮ AAG ]
+CAJ JCA L d & ? BBB B(I0A8G
+8D0A(B BBBF L P( ) BBB B(I0A8G
+8D0A(B BBBF L * BBB B(I0A8G
+8D0A(B BBBF , rw- AAG0!AA < , BHD Dh
+ DABA < ħ 0 BHD Do
+ DABA < 3 BHD Do
+ DABA T D @7 BBH E(A0A8L
+8D0A(B BBBE T @P BBB B(A0A8G
+8D0A(B BBBE T IP
BBB B(A0A8G
+8D0A(B BBBG L L R # BFB B(D0A8JS
+8D0A(B BBBE T X}x. K^ l ?w4 [wJ 0U (U ̪ 0U* A[
+L A @U* A[
+L A PU* A[
+L A , `U* A[
+L A L pUU d UL | U] 8VT VM ī VE ܫ V] 8WQ < T Wq : BBA A(D0[
+(A ABBA , L wk BDD D0X AABt ī W BBD A(D0C
+(C ABBH
+(C ABBG R
+(C ABBF J
+(C ABBF t < X i BBD A(D0C
+(C ABBH
+(C ABBE R
+(C ABBF J
+(C ABBF t Z BBD A(D0C
+(C ABBH
+(C ABBD R
+(C ABBF J
+(C ABBF t , h[ BBD A(D0C
+(C ABBH
+(C ABBF R
+(C ABBF J
+(C ABBF t \{ : BBD A(D0I
+(C ABBB )
+(A ABDG V
+(C ABBJ J
+(C ABBF t ^{ BBD A(D0I
+(C ABBB *
+(A ABBH V
+(C ABBJ J
+(C ABBF t `k BBD A(D0I
+(C ABBB (
+(A ABBB V
+(C ABBB J
+(C ABBF t bk A BBD A(D0I
+(C ABBB '
+(A ABDA V
+(C ABBB J
+(C ABBF < < d BBB D(A0&
+(A BBBH L į f BBB B(A0I8G
+8D0A(B BBBD L @h BBB B(A0I8G
+8D0A(B BBBD T d jB BBE B(D0A8G
+8D0A(B BBBB d xo h BAA G@u
+ DABI k
+ DABF K
+ DABF F
+ DABA , ܱ w- AAG0!AA < T p BHD Dq
+ DABA < s BHD Dx
+ DABA T Ա v BEE E(D0A8G&
+8D0A(B BBBH T , { BEE E(D0A8G'
+8D0A(B BBBG L w
+ BEE E(A0D8J
+8A0A(B BBB T Բ BBE F(D0D8G
+8A0A(B BBBB T , x BEB B(D0D8J
+8A0A(B BBBB T C BLE B(E0D8G
+8A0A(B BBBJ L ܳ x BBE B(G0F8G
+8D0A(B BBBH rx. K^ T Z MGB B(A0H8)0A(B BBBB8T T ȸ MGB B(A0A8`0A(B BBBB8 ĵ 4 ܵ s BBA A(D0b(A ABBD H BBB B(A0A8DP8A0A(B BBBL \ AAG ]
CAF [
ACA R
-CAJ JCA L M Ȯ AAG ]
+CAJ JCA L AAG ]
CAF [
ACA R
-CAJ JCA L N Ȯ AAG ]
+CAJ JCA L AAG ]
CAF [
ACA R
-CAJ JCA L \N Ȯ AAG ]
+CAJ JCA L L P AAG ]
CAF [
ACA R
-CAJ JCA L M (ɮx v BBB B(A0I8Dp
-8D0A(B BBBJ L DN Xʮx _v BBB B(A0I8Dp
-8D0A(B BBBJ L N ˮx +v BBB B(A0I8Dp
-8D0A(B BBBJ 4 N ̮ u BDI Gp
- DABG 4 O ͮ u BDI Gp
- DABG , P ~- AAG0!AA < O XήU u BIA D(Dp
+CAJ JCA L x W BBB B(A0I8Dp
+8D0A(B BBBJ L 4 x # BBB B(A0I8Dp
+8D0A(B BBBJ L ཪx BBB B(A0I8Dp
+8D0A(B BBBJ L Է x BBB B(A0I8Dp
+8D0A(B BBBJ 4 $ @ BDI Gp
+ DABG 4 \ ( h BDI Gp
+ DABG , L tw- AAG0!AA < ĸ U BIA D(Dp
-(D ABBI < O xѮU cu BIA D(Dp
-(D ABBB < P Ԯ =u BHD Dh
- DABA T DP خ "u BBE B(D0A8G
-8D0A(B BBBG T TQ ܮU BAA G0
+(D ABBI < ŪU
BIA D(Dp
+(D ABBB < D ȪU
BIA D(Dp
+(D ABBB < @˪
BHD Dh
+ DABA T Ĺ Ϊ
BBE B(D0A8G
+8D0A(B BBBG T Ժ ӪU BAA G0
AABA
AABK f
- AABF L P " t BBE B(D0A8G|
-8A0A(B BBBC L DQ " t BBE B(D0A8G|
-8A0A(B BBBC | LR BBB B(D0A8DP
+ AABF L t ת"
BBE B(D0A8G|
+8A0A(B BBBC L ĺ pݪ" "
BBE B(D0A8G|
+8A0A(B BBBC | ̻ P BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI T R ( Ct BBB H(D0A8G
-8D0A(B BBBH $S ~. K^ <S TS lS S , R [ ct AID t
-DAA , S [ Ct AID t
-DAA , DS [ #t AID t
-DAA T tS 8 t BEE E(D0A8G
-8D0A(B BBBE , T L~- AAG0!AA 4 S 0 E s BHD D`
- DABK D 4T H s BIA D(G|
-(D ABBA D |T xs BIA D(G|
-(D ABBA L T x
- Ws BDE H(A0A8G
-8D0A(B BBBK L U l s BDB H(D0A8G
-8D0A(B BBBB V |. K^ 4V LV T U s BBB B(A0D8R:
-8A0A(B BBBA , V > s AID W
-DAA T 4V ~s BBE B(D0A8G
-8D0A(B BBBG , DW ~- AAG0!AA D V P 4s BIA D(Dh
-(D ABBA W !
s BEE E(G0GE
+8A0A(B BBBI T BBB H(D0A8G
+8D0A(B BBBH hkx. K^ P Լ H @ H , d P[ AID t
+DAA , [ AID t
+DAA , ļ [ AID t
+DAA , [ k AID t
+DAA T $ K BEE E(D0A8G
+8D0A(B BBBE , 4 w- AAG0!AA 4 E BHD D`
+ DABK 4 E BHD D`
+ DABD D 8 BIA D(G|
+(D ABBA D d BIA D(G|
+(D ABBA L h BDE H(A0A8G
+8D0A(B BBBK L l BDB H(D0A8G
+8D0A(B BBBB 8hx. K^ 4 T BBB B(A0D8R:
+8A0A(B BBBA , > AID W
+DAA T BBE B(D0A8G
+8D0A(B BBBG , , w- AAG0!AA D @ \ BIA D(Dh
+(D ABBA
9 BEE E(G0GE
0A(A BBBD Q
0A(A BBBG
0A(A BBBE K
-0A(A BBBE W (/E
s BEE E(G0G
+0A(A BBBE | )E 5 BEE E(G0G
0A(A BBBK Q
0A(A BBBG ,
0A(A BBBD
-0A(A BBBG T $X :n s BBB G(D0A8M5
-8D0A(B BBBG 4Y y. K^ LY > dY > |Y > Y > , X >[ r AID t
-DAA , $Y ?[ r AID t
-DAA , Z ~- AAG0!AA < Y ?U 7r BIA D(Dp
+0A(A BBBG T 4n 3 BBB G(D0A8M5
+8D0A(B BBBG Pex. K^ 4 8 L 8 d 8 | 8 , 8[
+ AID t
+DAA , 9[
+ AID t
+DAA , < 89[
+ AID t
+DAA , l h9[ o
+ AID t
+DAA , T w- AAG0!AA < h9U
+ BIA D(Dp
-(D ABBI < Y 8BU r BIA D(Dp
+(D ABBI < <U BIA D(Dp
-(D ABBI T Z XE
- q BDB E(A0G8G
-8D0A(B BBBF T \Z M +r BDB B(D0G8G
-8D0A(B BBBI l[ w. K^ [ @T [ 8T [ 0T [ 8T , [ v~- AAG0!AA 4 \[ T5 q BHD D`
- DABG \ [ Wj q BBB A(I0DW
+(D ABBI < L ?U BIA D(Dp
+(D ABBB < BU BIA D(Dp
+(D ABBB T E
+ BDB E(A0G8G
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+8D0A(B BBBI 4 hbx. K^ L T d T | T T , w- AAG0!AA 4 $ T5 _ BHD D`
+ DABG 4 \ WB A BHD D`
+ DABH \ Zj # BBB A(I0DW
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-DAA , |] y[ p AID t
-DAA , ] y[ p AID t
-DAA , ] z[ wp AID t
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- DABG 4 ^ X}5 o BHD D`
- DABG 4 ^ `5 o BHD D`
- DABG 4 _ h5 o BHD D`
- DABG 4 L_ p5 o BHD D`
- DABG T _ x qo BDB B(A0A8G
-8A0A(B BBBA T _ o BDB B(A0A8G
-8A0A(B BBBA T 4` o BDB B(A0A8G
-8A0A(B BBBA T ` p BDB B(A0A8G
-8A0A(B BBBA T ` h Fp BDB B(A0A8G
-8A0A(B BBBA a p. K^ b $b <b Tb lb b b x b p b h b ` b X c P ,c X Dc ` \c h tc p c x c c c c d d ~ PE \ |c , :n BBB A(I0Dp
+0D(A BBBG \ ^j BBB A(I0DW
+0D(A BBBJ n
+0D(A BBBG b L l c BBB E(A0A8G
+8A0A(B BBBE t X`x. K^ Ё ȁ 4 L d ȁ , Ё[ AID t
+DAA , [ AID t
+DAA , $ 0[ AID t
+DAA , T `[ AID t
+DAA , [ AID t
+DAA , [ _ AID t
+DAA , [ ? AID t
+DAA , [ AID t
+DAA , D P[ AID t
+DAA , t [ AID t
+DAA , \ Jw- AAG0!AA 4 5 BHD D`
+ DABG 4 5 q BHD D`
+ DABG 4 D 5 S BHD D`
+ DABG 4 | 5 5 BHD D`
+ DABG 4 5 BHD D`
+ DABG 4 B BHD D`
+ DABH 4 $ B BHD D`
+ DABH 4 \ ؘB BHD D`
+ DABH 4 B BHD D`
+ DABH 4 B BHD D`
+ DABH T c BDB B(A0A8G
+8A0A(B BBBA T \ BDB B(A0A8G
+8A0A(B BBBA T 0 BDB B(A0A8G
+8A0A(B BBBA T BDB B(A0A8G
+8A0A(B BBBA T d ī 8 BDB B(A0A8G
+8A0A(B BBBA t Yx. K^ P̫ H̫ @̫ 8̫ 0̫ (̫ ̫ 4 ̫ L ̫ d ̫ | ̫ ˫ ̫ ̫ ̫ ̫ ̫ $ (̫ < 0̫ T 8̫ l @̫ H̫ 8w PE \ 8̫, , BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ c `, m BBB A(I0Dp
+0D(A BBBA \ \ ͫ, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ <d 0, m BBB A(I0Dp
+0D(A BBBA \ ͫ, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ d , nm BBB A(I0Dp
+0D(A BBBA \ Ϋ, ` BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ d ж, *m BBB A(I0Dp
+0D(A BBBA \ | xϫ, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ \e , l BBB A(I0Dp
+0D(A BBBA \ HЫ, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ e p, l BBB A(I0Dp
+0D(A BBBA \ < ѫ, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ f @, ^l BBB A(I0Dp
+0D(A BBBA \ ѫ, P BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ |f , l BBB A(I0Dp
+0D(A BBBA \ ҫ, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ f ຯ, k BBB A(I0Dp
+0D(A BBBA \ \ ӫ, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ <g , k BBB A(I0Dp
+0D(A BBBA \ Xԫ, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ g , Nk BBB A(I0Dp
+0D(A BBBA \ (ի, @ BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ g P,
-k BBB A(I0Dp
+0D(A BBBA \ | ի, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ \h , j BBB A(I0Dp
+0D(A BBBA \ ֫, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ h , j BBB A(I0Dp
+0D(A BBBA \ < , t BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ i , >j BBB A(I0Dp
+0D(A BBBA \ hث, 0 BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ |i , i BBB A(I0Dp
+0D(A BBBA \ 8٫, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ i `, i BBB A(I0Dp
+0D(A BBBA \ \ ګ, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ <j 0¯, ri BBB A(I0Dp
+0D(A BBBA \ ګ, d BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ j ï, .i BBB A(I0Dp
+0D(A BBBA \ ۫, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ j ï, h BBB A(I0Dp
+0D(A BBBA \ | xܫ, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ \k į, h BBB A(I0Dp
+0D(A BBBA \ Hݫ, BBB A(I0Dp
0D(A BBBG ^
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-0A(A BBBE l @ƯV $ l Ư Az
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E
-A $ $m ǯ AAG0AAD l ǯ g BIB A(A0G`,
-0D(A BBBC D l ɯ [g BIB A(A0G`,
-0D(A BBBC D $m xʯ ,g BIB A(A0G`,
-0D(A BBBC D lm ˯ f BIB A(A0G`,
-0D(A BBBC D m hͯ f BIB A(A0G`,
-0D(A BBBC D m ί f BIB A(A0G`,
-0D(A BBBC D Dn XЯ pf BIB A(A0G`,
-0D(A BBBC D n ѯ Af BIB A(A0G`,
-0D(A BBBC D n Hӯ f BIB A(A0G`,
-0D(A BBBC D o ԯ e BIB A(A0G`,
-0D(A BBBC D do 8֯ e BIB A(A0G`,
-0D(A BBBC D o ׯ e BIB A(A0G`3
-0D(A BBBD D o 8ٯ Ve BIB A(A0G`3
-0D(A BBBD D <p گ 'e BIB A(A0G`3
-0D(A BBBD D p Hܯ d BIB A(A0G`3
-0D(A BBBD D p ݯ d BIB A(A0G`3
-0D(A BBBD D q X߯ d BIB A(A0G`3
-0D(A BBBD D \q kd BIB A(A0G`3
-0D(A BBBD D q h <d BIB A(A0G`3
-0D(A BBBD D q
d BIB A(A0G`3
-0D(A BBBD D 4r x c BIB A(A0G`3
-0D(A BBBD D |r c BIB A(A0G`3
-0D(A BBBD L |s BBB B(D0D8G@H
-8A0A(B BBBG L s BBB B(D0D8G@H
-8A0A(B BBBG T ds (V b BBE E(D0E8J$
-8D0A(B BBBF L tt 0 BOB A(A0]
+A $ ߫ AAG0AAD 0 | BIB A(A0G`,
+0D(A BBBC D \ M BIB A(A0G`,
+0D(A BBBC D BIB A(A0G`,
+0D(A BBBC D BIB A(A0G`,
+0D(A BBBC D 4 BIB A(A0G`,
+0D(A BBBC D | BIB A(A0G`,
+0D(A BBBC D b BIB A(A0G`,
+0D(A BBBC D x 3 BIB A(A0G`,
+0D(A BBBC D T BIB A(A0G`,
+0D(A BBBC D h BIB A(A0G`,
+0D(A BBBC D BIB A(A0G`,
+0D(A BBBC D , X w BIB A(A0G`3
+0D(A BBBD D t H BIB A(A0G`3
+0D(A BBBD D h BIB A(A0G`3
+0D(A BBBD D BIB A(A0G`3
+0D(A BBBD D L x BIB A(A0G`3
+0D(A BBBD D BIB A(A0G`3
+0D(A BBBD D ] BIB A(A0G`3
+0D(A BBBD D $ . BIB A(A0G`3
+0D(A BBBD D l BIB A(A0G`3
+0D(A BBBD D BIB A(A0G`3
+0D(A BBBD D BIB A(A0G`3
+0D(A BBBD L 0 BBB B(D0D8G@H
+8A0A(B BBBG L L BBB B(D0D8G@H
+8A0A(B BBBG T V BBE E(D0E8J$
+8D0A(B BBBF L BOB A(A0]
(A BBBG Y
-(A BBBH < t GDPE
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AAE
-AAG 4 u g ADD H
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-8A0A(B BBBE L u `* ya BGB B(A0G8J|
-8D0A(B BBBH L \u @ qa BBH B(F0A8J{
-8D0A(B BBBI L dv BBB B(A0A8Dxi
-8A0A(B BBBG L v 0 BBB B(A0A8DxN
-8A0A(B BBBJ < w
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-(A BBBG L Dw 0 BBB B(D0A8GP
-8A0A(B BBBH D w 8 ADG m
+AAG 4 ( g ADD H
+AAM DAA L ` c BBH B(F0A8J{
+8D0A(B BBBI , BTA gAB T BBB B(A0A8G
+8A0A(B BBBE L (* o BGB B(A0G8J|
+8D0A(B BBBH L BBB B(A0A8Dxi
+8A0A(B BBBG L 4 BBB B(A0A8DxN
+8A0A(B BBBJ < $ BEB A(F0z
+(A BBBG L $ BBB B(D0A8GP
+8A0A(B BBBH D %8 ADG m
AAE V
-DAE UAA L w x
O BEB B(A0A8Q
-8A0A(B BBBA T tw x2 _ BBB B(D0A8Gn
-8A0A(B BBBD L w `* d_ BGB B(A0G8J|
-8D0A(B BBBH L x @ 9 \_ BGB B(A0G8J|
-8D0A(B BBBH L lx 0!* T_ BGB B(A0G8J|
-8D0A(B BBBH L ty &: BEB B(A0A8Q
-8A0A(B BBBA T y *2 ^ BBB B(D0A8Gn
-8A0A(B BBBD L dy /Q ^ BGB B(A0G8J|
-8D0A(B BBBH L lz 3b BEB B(A0A8Q
-8A0A(B BBBA T z 82 [^ BBB B(D0A8Gn
-8A0A(B BBBD L \z >/ ^ BGB B(A0G8J|
-8D0A(B BBBH L z AB
-^ BGB B(A0G8J|
-8D0A(B BBBH L { E BBB B(A0H8J
-8A0A(B BBEA T L{ 0J2 ] BBB B(D0A8Gn
-8A0A(B BBBD L { P* i] BGB B(A0G8J|
-8D0A(B BBBH D | T8 ADG m
+DAE UAA L \ &O BEB B(A0A8Q
+8A0A(B BBBA T *2 BBB B(D0A8Gn
+8A0A(B BBBD L L 0* V BGB B(A0G8J|
+8D0A(B BBBH L 59 N BGB B(A0G8J|
+8D0A(B BBBH L :* F BGB B(A0G8J|
+8D0A(B BBBH L x?: BEB B(A0A8Q
+8A0A(B BBBA T hC2 BBB B(D0A8Gn
+8A0A(B BBBD L PIQ BGB B(A0G8J|
+8D0A(B BBBH L `Mb BEB B(A0A8Q
+8A0A(B BBBA T Q2 M BBB B(D0A8Gn
+8A0A(B BBBD L hW/ BGB B(A0G8J|
+8D0A(B BBBH L , H[B BGB B(A0G8J|
+8D0A(B BBBH L 4 H_ BBB B(A0H8J
+8A0A(B BBEA T c2 BBB B(D0A8Gn
+8A0A(B BBBD L $ i* [ BGB B(A0G8J|
+8D0A(B BBBH D , `n8 ADG m
AAE V
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-8A0A(B BBBA T | 0YX \ BBB B(A0D8G
-8A0A(B BBBA L | 8aQ \ BGB B(A0G8J|
-8D0A(B BBBH L } He BEB B(A0A8QN
-8A0A(B BBBA T } hX (\ BBB B(A0D8G
-8A0A(B BBBA L } p* [ BGB B(A0G8J|
-8D0A(B BBBH ~ 0T. K^ ~ hu> Hu uE Hx D < u BEB E(D0D8PP~8A0A(B BBB$ v^ AG b
-AA d Xvq BEB B(A0A8G@
+DAE UAA L t Xo BEB B(A0A8Q5
+8A0A(B BBBA T rX BBB B(A0D8G
+8A0A(B BBBA L d zQ r BGB B(A0G8J|
+8D0A(B BBBH L l ~ BEB B(A0A8QN
+8A0A(B BBBA T X BBB B(A0D8G
+8A0A(B BBBA L \ * BGB B(A0G8J|
+8D0A(B BBBH d <x. K^ | Ў> Hu E Hx D BEB E(D0D8PP~8A0A(B BBB$ ^ AG b
+AA d , q BEB B(A0A8G@
8A0A(B BBBC
-8A0A(B BBBF T \ pwu Z BEE B(A0D8J
-8A0A(B BBBA T | Z BBE E(A0A8G
-8D0A(B BBBC T ~ qZ BBE E(A0A8G
-8D0A(B BBBA L d k 4Z AAGPa
+8A0A(B BBBF T ؐu BEE B(A0D8J
+8A0A(B BBBA T 4 BBE E(A0A8G
+8D0A(B BBBD T h c BBE E(A0A8G
+8D0A(B BBBB L Кk & AAGPa
DAA A
DAJ }
-DAA T ( Z BBE E(A0A8G
-8D0A(B BBBH , ā ~ BAA G0 AAB4 < 0 Y BHD D`
- DABF < t vY BHD D^
- DABA < HB [Y BHD G
- DABA < X[ BBE D(A0
-(A BBBF , 4 x Y ADG0
-AAB T d 2 X BBB B(I0A8G
-8D0A(B BBBG T 2 X BBB B(I0A8G
-8D0A(B BBBG T Ț2 Y BBB B(I0A8G
-8D0A(B BBBG T l " Y BBB B(A0A8J~
-8A0A(B BBBA T ă h°> eY BEE B(A0A8G
+DAA T 4 BBE E(A0A8G
+8D0A(B BBBA , D w- AAG0!AA 4 ( BHD D`
+ DABF < h BHD D^
+ DABA < 4 @B M BHD G
+ DABA < t PH 3 BHD G
+ DABA < l `[ BBE D(A0
+(A BBBF T BBB B(K0A8Jo
+8A0A(B BBBI 4 L q BAI A
+ABA 4 @q BAI A
+ABA 4 q BAI A
+ABA , а s ADG0
+AAB T $ PC R BBB B(I0A8G
+8D0A(B BBBC T | H" b BBB B(A0A8J~
+8A0A(B BBBA T ڬ> BEE B(A0A8G
-8A0A(B BBBA Ԅ pN. K^ 4 L d | L x `Z BBB B(A0I8Dp
-8D0A(B BBBE L D x ,Z BBB B(A0I8Dp
-8D0A(B BBBE L x Y BBB B(A0I8Dp
-8D0A(B BBBE L 8x Y BBB B(A0I8Dp
-8D0A(B BBBE L 4 hx Y BBB B(A0I8Dp
-8D0A(B BBBE L x \Y BBB B(A0I8Dp
-8D0A(B BBBE L Ԇ x (Y BBB B(A0I8Dp
-8D0A(B BBBE L $ x X BBB B(A0I8Dp
-8D0A(B BBBE T t (
- X BBE B(D0A8G
-8D0A(B BBBG T ̇ X BBE B(D0A8G
-8D0A(B BBBG T $ X BEE B(D0D8Jw
-8A0A(B BBBG , 4 ~- AAG0!AA 4 5 X BHD D`
- DABG 4 5 dX BHD D`
- DABG 4 B FX BHD D`
- DABH 4 T 8!B (X BHD D`
- DABH < P$U
-X BIA D(Dp
+8A0A(B BBBA x6x. K^ @ 8 , 0 D ( \ t ( 0 8 L @x BBB B(A0I8Dp
+8D0A(B BBBE L T px ~ BBB B(A0I8Dp
+8D0A(B BBBE L x J BBB B(A0I8Dp
+8D0A(B BBBE L x BBB B(A0I8Dp
+8D0A(B BBBE L D x BBB B(A0I8Dp
+8D0A(B BBBE L 0 x BBB B(A0I8Dp
+8D0A(B BBBE L ` x z BBB B(A0I8Dp
+8D0A(B BBBE L 4 x F BBB B(A0I8Dp
+8D0A(B BBBE T ! BBE B(D0A8G
+8D0A(B BBBG T & BBE B(D0A8G
+8D0A(B BBBG T 4 P+ BEE B(D0D8Jw
+8A0A(B BBBG , D ܮw- AAG0!AA 4 /5 BHD D`
+ DABG 4 25 BHD D`
+ DABG 4 , 5B BHD D`
+ DABH 4 d 8B z BHD D`
+ DABH < ;U \ BIA D(Dp
-(D ABBI < ̉ p'U W BIA D(Dp
+(D ABBI < ?U 6 BIA D(Dp
-(D ABBI < *U W BIA D(Dp
-(D ABBB < L -U W BIA D(Dp
-(D ABBB T 0y rW BBG B(A0A8J(
-8A0A(B BBBE T 2y @W BBG B(A0A8J(
-8A0A(B BBBE T < 5f W BBB B(A0A8JI
-8A0A(B BBBI T 8<v W BBB B(A0A8JY
-8A0A(B BBBI F. K^ HC Ԍ @C HC0 Hg XC7 Hj , t xC[ V AID t
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-8D0A(B BBBA T E| aV BBE E(D0A8G
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-8A0A(B BBBA , \ ~- AAG0!AA 4 ԍ QE XV BHD D`
- DABD < Tb :V BIA D(Dp
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-8D0A(B BBBH C. K^ ̏ f f$ f
f1 , fi D Pg \ Xg AL | Hg DI Pg DI Xg DI Đ `g AL Pg AL @g# TI XgA I[
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+8A0A(B BBBE T Jy BBG B(A0A8J(
+8A0A(B BBBE T L Lf ` BBB B(A0A8JI
+8A0A(B BBBI T Sv h BBB B(A0A8JY
+8A0A(B BBBI .x. K^ Z Z Z0 Hg Z7 Hj , [[ AID t
+DAA , @[[ AID t
+DAA L p[ BBB J(A0A8G
+8D0A(B BBBA T 4 ]| BBE E(D0A8G
+8D0A(B BBBF L b y BBE A(D0G
+0A(A BBBA , w- AAG0!AA 4 HhE BHD D`
+ DABD < D `kb t BIA D(Dp
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+8D0A(B BBBF T v+
+ O BGH B(A0D8G
+8D0A(B BBBH , +x. K^ D x \ p$ t
1 i AL DI DI $ DI < AL \ AL | # TI A I[
+L K , 8b D\
H P
H Q
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+G I , xb D\
H P
H Q
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+G I , b D\
H P
H Q
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+G I , D b D\
H P
H Q
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+G I , t 8b D\
H P
H Q
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+G I , xb D\
H P
H Q
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+G I , b D\
H P
H Q
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+G I , b D\
H P
H Q
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+G I , 4 8b D\
H P
H Q
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+G I , d xb D\
H P
H Q
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+G I , b D\
H P
H Q
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-L A $ jr 'S H\
-L A $ jr S H\
-L A $ Pkr R H\
-L A $ 4 kr R H\
-L A $ \ lr R H\
-L A Xlq sR Hd lq WR Hd ē mq ;R Hd xmq R Hd mq R Hd $ 8nq Q Hd :~\ AG RAT d xnt Q BBE B(D0A8G
-8D0A(B BBBH T st Q BBE B(D0A8G
-8D0A(B BBBH D x wQ BBB A(A0Gb
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-F A , < V~- AAG0!AA 4 }5 IQ BHD D`
- DABG 4 5 +Q BHD D`
- DABG 4 $ 5
Q BHD D`
- DABG 4 \ B P BHD D`
- DABH 4 ȉB P BHD D`
- DABH 4 ̖ B P BHD D`
- DABH d B BBB B(A0A8G@x
-8A0A(B BBBE
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-8A0A(B BBBK < ė %R BBI A(Ga
-(D ABBA < Q BBI A(Ga
-(D ABBA < D x Q BBI A(Ga
-(D ABBA < س Q BBI A(Ga
-(D ABBA < Ę 8 Q BBI A(Ga
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D д \ ش t റ ش T റ| P BBE E(D0A8G
-8D0A(B BBBI T D | mP BBE E(D0A8G
-8D0A(B BBBI T 0 SP BBE B(D0A8G
-8D0A(B BBBG T ñ 9P BBB B(A0D8NH
-8A0A(B BBBC , L @˱> P AID W
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- DABG T (α^ -P BBB B(A0D8G3
-8A0A(B BBBA T < 0Ա P BBB B(A0D8Gs
-8A0A(B BBBA T xڱ P BBB B(A0D8Gs
-8A0A(B BBBA T O BBB B(A0D8G
-8A0A(B BBBA T D 55 O BBB B(F0A8J
-8A0A(B BBBG T 5. K^ l X P L Xr ^Q BBB B(E0A8J@
-8A0A(B BBBA T 4 #Q BEB E(A0A8G;
-8D0A(B BBBA T P BEB E(A0A8G;
-8D0A(B BBBA L X!) P BBB B(A0I8G
-8D0A(B BBBA L 4 8#) P BBB B(A0I8G
-8D0A(B BBBA T % }P BBE E(A0A8G
-8D0A(B BBBA , Z~- AAG0!AA < P' P BHD D}
- DABA < L 0*" O BHD D
- DABA < D -Q BEH H(A0n(A BBE T ̠ @- O BEB B(A0D8J
-8A0A(B BBBA T $ 1 mO BEB B(A0D8J
-8A0A(B BBBA T | 5 @O BEB B(A0D8J
-8A0A(B BBBA T ԡ : O BEB B(A0D8J
-8A0A(B BBBA T , `> N BEB B(A0D8J
-8A0A(B BBBA T B N BEB B(A0D8J
-8A0A(B BBBA T ܢ F N BEB B(A0D8J
-8A0A(B BBBA T 4 8K _N BEB B(A0D8J
-8A0A(B BBBA T pO 2N BEB B(A0D8J
-8A0A(B BBBA T S N BEB B(A0D8J
-8A0A(B BBBA T < X M BEB B(A0D8J
-8A0A(B BBBA T H\ M BEB B(A0D8J
-8A0A(B BBBA T ` ~M BEB B(A0D8J
-8A0A(B BBBA T D d QM BEB B(A0D8J
-8A0A(B BBBA T @i *M BEB B(A0D8J
-8A0A(B BBBA T m L BEB B(A0D8J
-8A0A(B BBBA T L q L BEB B(A0D8J
-8A0A(B BBBA T v L BEB B(A0D8J
-8A0A(B BBBA T `z vL BEB B(A0D8J
-8A0A(B BBBA T T ~ IL BEB B(A0D8J
-8A0A(B BBBA T L BEB B(A0D8J
-8A0A(B BBBA T ( K BEB B(A0D8J
-8A0A(B BBBA T \ ` K BEB B(A0D8J
-8A0A(B BBBA T K BEB B(A0D8J
-8A0A(B BBBA T hK BEB B(A0D8J
-8A0A(B BBBA T d 8 ;K BEB B(A0D8J
-8A0A(B BBBA T K BEB B(A0D8J
-8A0A(B BBBA T Ƞ J BEB B(A0D8J
-8A0A(B BBBA L l @ J BBH B(D0F8G
-8D0A(B BBBD t (. K^ h ` 4 hv BBA A(D0e(A ABBD BBB B(A0A8DP8A0A(B BBB, ([ aJ AID t
-DAA L l X AAG ]
+G I $ r _ H\
+L A $ 4 Pr ; H\
+L A $ \ r H\
+L A $ r H\
+L A $ Xr H\
+L A $ r H\
+L A q Hd hq k Hd < Ȇq O Hd \ (q 3 Hd | q Hd 臭q Hd t w\ AG RAT (t BBE B(D0A8G
+8D0A(B BBBH T 4 Pt BBE B(D0A8G
+8D0A(B BBBH D x BBB A(A0Gb
+0A(A BBBG $ R AI
+F A , $w- AAG0!AA 4 , Ȗ5 b BHD D`
+ DABG 4 d Й5 D BHD D`
+ DABG 4 5 & BHD D`
+ DABG 4 B BHD D`
+ DABH 4 B BHD D`
+ DABH 4 D B BHD D`
+ DABH 4 | (B BHD D`
+ DABH 4 @B BHD D`
+ DABH 4 XB r BHD D`
+ DABH T $ p% W BEB B(A0A8Ju
+8A0A(B BBBJ < | ( BBI A(Ga
+(D ABBA < b BBI A(Ga
+(D ABBA < 9 BBI A(Ga
+(D ABBA < < Hح BBI A(Ga
+(D ABBA < | ح BBI A(Ga
+(D ABBA < ٭ BBI A(Ga
+(D ABBA < h٭ BBI A(Ga
+(D ABBA < < ٭ l BBI A(Ga
+(D ABBA < | (ڭ C BBI A(Ga
+(D ABBA t x x. K^ pڭ
hڭ
`ڭ hڭ pڭ
+ hڭ T d pڭ| r BBE E(D0A8G
+8D0A(B BBBI T ߭| X BBE E(D0A8G
+8D0A(B BBBI T
+ > BBE B(D0A8G
+8D0A(B BBBG T l
+ $ BBB B(A0D8NH
+8A0A(B BBBC ,
+ > AID W
+DAA , Zw- AAG0!AA 4 $ 5 6 BHD D`
+ DABG T \ ^ BBB B(A0D8G3
+8A0A(B BBBA T E BBB B(A0D8G2
+8A0A(B BBBA T ; BBB B(A0D8G(
+8A0A(B BBBA T d 3 BBB B(A0D8G
+8A0A(B BBBA T u< BBB B(A0F8J
+8A0A(B BBBD
P x. K^
A
A L \
Ar H BBB B(E0A8J@
+8A0A(B BBBA L
BX
BEE D(D0G
+0D(A BBBA T
C BEB E(A0A8G;
+8D0A(B BBBA L T @F) BBB B(A0I8G
+8D0A(B BBBA L H) } BBB B(A0I8G
+8D0A(B BBBA L J) g BBB B(A0I8G
+8D0A(B BBBA T D K Q BBE E(A0A8G
+8D0A(B BBBH $ T w AAG0AA< M BHD D}
+ DABA < P" BHD D
+ DABA < D S" BHD D
+ DABA < < VQ BEH H(A0n(A BBE T V [ BEB B(A0D8J
+8A0A(B BBBA T [ . BEB B(A0D8J
+8A0A(B BBBA T t `_ BEB B(A0D8J
+8A0A(B BBBA T c BEB B(A0D8J
+8A0A(B BBBA T $ g BEB B(A0D8J
+8A0A(B BBBA T | 8l z BEB B(A0D8J
+8A0A(B BBBA T p M BEB B(A0D8J
+8A0A(B BBBA T , t BEB B(A0D8J
+8A0A(B BBBA T y BEB B(A0D8J
+8A0A(B BBBA T X} BEB B(A0D8J
+8A0A(B BBBA T 4 BEB B(A0D8J
+8A0A(B BBBA T 腮 l BEB B(A0D8J
+8A0A(B BBBA T ? BEB B(A0D8J
+8A0A(B BBBA T < X BEB B(A0D8J
+8A0A(B BBBA T BEB B(A0D8J
+8A0A(B BBBA T 8 BEB B(A0D8J
+8A0A(B BBBA T D BEB B(A0D8J
+8A0A(B BBBA T ȟ d BEB B(A0D8J
+8A0A(B BBBA T 7 BEB B(A0D8J
+8A0A(B BBBA T L x
+ BEB B(A0D8J
+8A0A(B BBBA T Ь BEB B(A0D8J
+8A0A(B BBBA T BEB B(A0D8J
+8A0A(B BBBA T T ` BEB B(A0D8J
+8A0A(B BBBA T V BEB B(A0D8J
+8A0A(B BBBA T ) BEB B(A0D8J
+8A0A(B BBBA T \ h® BEB B(A0D8J
+8A0A(B BBBA T Ʈ BEB B(A0D8J
+8A0A(B BBBA T ʮ BEB B(A0D8J
+8A0A(B BBBA L d Ϯ
{ BHE B(A0F8G
+8D0A(B BBBA l x. K^ ݮ ݮ 4 ݮv BBA A(D0e(A ABBD Pݮ BBB B(A0A8DP8A0A(B BBB, | ݮ[ AID t
+DAA , ݮ[ AID t
+DAA L (ޮ AAG ]
CAF [
ACA R
-CAJ JCA L AAG ]
+CAJ JCA L Xޮ AAG ]
CAF [
ACA R
-CAJ JCA 4 T I BDI Gp
- DABG 4 I BDI Gp
- DABG , | ~- AAG0!AA < XU 3I BIA D(Dp
+CAJ JCA 4 | ޮ < BDI Gp
+ DABG 4 p߮ BDI Gp
+ DABG , 4w- AAG0!AA < (U BIA D(Dp
-(D ABBI T 4 x6# I BDB B(D0A8G
-8A0A(B BBBI D `ܲ{ J BBB D(A0G
-0D(A BBBG %. K^ ݲ xݲ , ݲ[ J AID t
-DAA , F~- AAG0!AA < | ݲU 2J BIA D(Dp
+(D ABBI < \ HU BIA D(Dp
+(D ABBB T h BGB B(A0A8G
+8A0A(B BBBK D 0{ + BBB D(A0G
+0D(A BBBG x. K^ P $ H , P[ AID t
+DAA , [ AID t
+DAA , jw- AAG0!AA < U F BIA D(Dp
-(D ABBI T J BDB E(G0A8Gw
-8D0A(B BBBA ̯ $. K^ , \ [ I AID t
-DAA , [ I AID t
-DAA , t ~- AAG0!AA D < oI BIA D(G
-(D ABBA D 4 D NI BIA D(G
-(D ABBA L | /I BDB B(D0A8M
-8D0A(B BBBA @#. K^ 8O HF hO HF ܱ V HI V HI $ ^ AG b
-AA D PB Aw
-H A L T {H BBB B(A0A8JU
-8A0A(B BBBE 4 s H BAI C
-ABA 4 4 v pH BAI F
-ABA 4 l v LH BAI F
-ABA L h (H BBB A(A0M
-0D(A BBBF T G BBB B(A0D8J
-8A0A(B BBBG T L G BBB B(A0D8J
-8A0A(B BBBG T 8 G BBB B(A0D8J
-8A0A(B BBBG T iG BBB B(A0D8J
-8A0A(B BBBG T T h 7G BBB B(A0D8J
-8A0A(B BBBG d d BEB B(A0D8DPi
+(D ABBI < T U BIA D(Dp
+(D ABBB T BDB E(G0A8Gw
+8D0A(B BBBA
+x. K^ , 4 [ AID t
+DAA , d [ AID t
+DAA , L! w- AAG0!AA D < [ BIA D(G
+(D ABBA D ! D : BIA D(G
+(D ABBA L T! BDB B(D0A8M
+8D0A(B BBBA \" x. K^ t" X O HF " O HF " V HI " V HI $ " 8 ^ AG b
+AA # p B Aw
+H A L " T g BBB B(A0A8JU
+8A0A(B BBBE 4 " !s BAI C
+ABA 4 # !v \ BAI F
+ABA 4 D# @"v 8 BAI F
+ABA 4 |# "v BAI F
+ABA L # " BBB A(A0M
+0D(A BBBF T $ p) BBB B(A0D8J
+8A0A(B BBBG T \$ , BBB B(A0D8J
+8A0A(B BBBG T $ . c BBB B(A0D8J
+8A0A(B BBBG T % 81 1 BBB B(A0D8J
+8A0A(B BBBG T d% 3 BBB B(A0D8J
+8A0A(B BBBG d t& h6 BEB B(A0D8DPi
8A0A(B BBBI
-8G0A(B BBBJ , ̵ ڦ~- AAG0!AA 4 D
mF BHD D`
- DABG < | OF BHD Dq
- DABA < ` 4F BHD Dx
- DABA $ @V ADD JAAT $ x E BBB B(A0D8G
-8A0A(B BBBB L | " GF BEB E(I0H8D@
-8D0A(B BBBA < # BFA D0}
- FABE D FAB T $ E BBE B(D0A8G
-8A0A(B BBBA T d X- E BEH B(A0D8G
-8A0A(B BBBA T 3! E BGH E(A0A8G
-8D0A(B BBBE ̸ U AP U AP . K^ $ U < T T T l T T T L U F BBB B(A0I8G
-8D0A(B BBBF T L PWg F BBB B(I0A8Gc
-8D0A(B BBBG T h\g F BBB B(I0A8Gc
-8D0A(B BBBG , ~- AAG0!AA 4 , Pa5 E BHD D`
- DABG 4 d Xd5 E BHD D`
- DABG 4 `gB E BHD D`
- DABH L xj BBB B(A0A8G&
-8A0A(B BBBA L ܻ (y BEB E(D0A8Gp
-8A0A(B BBBD T t D BJB B(A0A8G
+8G0A(B BBBJ $ & w AAG0AA4 L& 8 = BHD D`
+ DABG < & : BHD Dq
+ DABA < & = BHD Dx
+ DABA < ' @ BHD Dx
+ DABA $ ' CV ADD JAAT l' C BBB B(A0D8G
+8A0A(B BBBB L ' `P BEB E(I0H8D@
+8D0A(B BBBA < ( Q BFA D0}
+ FABE D FAB T T( PRE BEB B(D0A8G
+8A0A(B BBBI T ( HZ BEH B(A0D8G
+8A0A(B BBBA T ) `! N BGH E(A0A8G
+8D0A(B BBBE * AP 4* AP T* x. K^ l* * 聯 * * * * 聯 , *
+w- AAG0!AA 4 t* 5 BHD D`
+ DABG 4 * Ȅ5 v BHD D`
+ DABG 4 * ЇB X BHD D`
+ DABH 4 + 芯B : BHD D`
+ DABH 4 T+ B BHD D`
+ DABH L + BBB B(A0I8G
+8D0A(B BBBF L + h BBB B(A0I8G
+8D0A(B BBBF T ,, g BBB B(I0A8Gc
+8D0A(B BBBG T , Кg BBB B(I0A8Gc
+8D0A(B BBBG T , 蟯g BBB B(I0A8Gc
+8D0A(B BBBG L - BBB B(A0A8G&
+8A0A(B BBBA L <. BEB E(D0A8Gp
+8A0A(B BBBD T - @ï V BJB B(A0A8G
-8A0A(B BBBE T ̻ E D BBB E(F0G8GO
-8D0A(B BBBE T $ E BGB B(D0G8G;
-8D0A(B BBBI 4 . K^ L 0 Hg l 7 Hj $ ^ AG b
-AA T < 'E BBB B(I0A8GD
-8D0A(B BBBF T T س< E BBB B(I0A8GD
-8D0A(B BBBF T
E BBB B(D0D8G
-8D0A(B BBBF T dz| 0E BBE E(D0A8G
-8D0A(B BBBI , ~- AAG0!AA < ̳ D BHD Db
- DABA < ̾ ϳ D BHD Di
- DABA T ӳ D BEB B(D0D8G
-8A0A(B BBBA T d xٳ D BEB B(D0D8G
-8A0A(B BBBA T ߳ D BEB B(D0D8G
-8A0A(B BBBA T h D BBE E(E0D8G
-8A0A(B BBBA L l + E BBB G(D0A8M
-8D0A(B BBBG t . K^ 0 Hg 7 Hj d~\ AG RAT 4 D BEE E(D0A8G'
-8D0A(B BBBG , D H~- AAG0!AA 4 E mD BHD D`
- DABK 4 E OD BHD D`
- DABD L , 0 1D BBB B(I0A8D
-8D0A(B BBBD T | d D BBB B(A0F8G
-8A0A(B BBBK L D BBB B(I0A8D
-8D0A(B BBBD . K^ , l [ D AID t
-DAA L T BEB E(A0A8GN
-8A0A(B BBBA T C BBE B(D0A8G
-8D0A(B BBBG , ~- AAG0!AA D t 4 ZC BIA D(G
-(D ABBA L D ;C BDB B(A0D8M
-8D0A(B BBBA . K^ *0 Hg +7 Hj (+ BEB E(D0E8D
-8A0A(B BBBA |
-8A0A(B BBBA p
-8A0A(B BBBA
-8A0A(B BBBA q8A0A(B BBB . BEE F(D0D8D
-8A0A(B BBBA t
-8A0A(B BBBA p
-8A0A(B BBBA
-8A0A(B BBBA q8A0A(B BBB , | 0 A|
+8A0A(B BBBE T ,. ׯE BBB E(F0G8GO
+8D0A(B BBBE T . ߯ BGB B(D0G8G;
+8D0A(B BBBI / w. K^ / 0 Hg / 7 Hj $ / @^ AG b
+AA L \/ x8 BBB B(A0I8G
+8D0A(B BBBH L / h8 BBB B(A0I8G
+8D0A(B BBBH L / X8 } BBB B(A0I8G
+8D0A(B BBBH T L0 H
t BBE B(A0D8G
+8D0A(B BBBI T 0 | BBE E(D0A8G
+8D0A(B BBBI , 1 w- AAG0!AA < ,1 I BHD Db
+ DABA < l1 x . BHD Di
+ DABA < 1 BHD Di
+ DABA \ 2 xm BBD D(J@
+(A ABBD T
+(A ABBF _
+(A ABBK T L2 w BEF B(D0D8Gd
+8A0A(B BBBA T 2 w BEF B(D0D8Gd
+8A0A(B BBBA T 2 'h BEF B(D0D8Gd
+8A0A(B BBBA T T3 .M ! BEF B(D0D8Gl
+8A0A(B BBBA L 3 6 S BBB G(D0A8M
+8D0A(B BBBG 4 w. K^ 4 p=0 Hg 4 =7 Hj 5 V}w\ AG RA ,5 =_ , D5 =_ ADG k
+DAD T 4 = BEE E(D0A8G'
+8D0A(B BBBG , 5 |w- AAG0!AA 4 D5 `CE q BHD D`
+ DABK 4 |5 xFE S BHD D`
+ DABD 4 5 IE 5 BHD D`
+ DABD L 6 L BBB B(D0A8G@
+8A0A(B BBBA D <6 xN BBB A(I0D
+0D(A BBBE D 6 @P BBB A(I0D
+0D(A BBBE D 6 R } BBB A(I0D
+0D(A BBBE T 7 S7 X BBE B(A0A8L
+8A0A(B BBBG $8 xw. K^ <8 b T8 b , 7 b[ AID t
+DAA , 7 b[ y AID t
+DAA L 8 c BEB E(A0A8GN
+8A0A(B BBBA T d8 d BBE B(D0A8G
+8D0A(B BBBG , t9 xzw- AAG0!AA D 8 8i4 BIA D(G
+(D ABBA D 49 0m9 BIA D(G
+(D ABBA L |9 (qD BDB B(A0D8M
+8D0A(B BBBA : Hw. K^ : 0 Hg : 7 Hj : @ BEE F(D0A8D1
+8A0A(B BBBA
+8A0A(B BBBA D
+8A0A(B BBBA
+8A0A(B BBBA 8A0A(B BBB ; @ BEF E(D0D8D.
+8A0A(B BBBA
+8A0A(B BBBA D
+8A0A(B BBBA
+8A0A(B BBBA 8A0A(B BBB , << @ A|
C K
E R
-A $ 1^ AG b
-AA T 2| A BBE E(D0A8G
-8D0A(B BBBI T t (7| A BBE E(D0A8G
-8D0A(B BBBI L P< A BBB B(K0A8J
-8A0A(B BBBF 4 Cq A BAI A
-ABA , ܙ~- AAG0!AA 4 C5 A BHD D`
- DABG d t FB BBB B(A0A8G@x
+A $ l< ^ AG b
+AA < H2 Ai
+F A < hK T < | BBE E(D0A8G
+8D0A(B BBBI T l< ȋ| BBE E(D0A8G
+8D0A(B BBBI L < BBB B(K0A8J
+8A0A(B BBBF 4 = `q BAI A
+ABA 4 L= q BAI A
+ABA , <> vw- AAG0!AA 4 = 5 q BHD D`
+ DABG 4 = țB S BHD D`
+ DABH d > B BBB B(A0A8G@x
8A0A(B BBBE
-8A0A(B BBBF T $ G A BBE F(A0A8G
-8A0A(B BBBA T | ] $A BBB B(D0D8J}
-8D0A(B BBBD T Xek A BBB B(A0A8L"
+8A0A(B BBBF D > ȟ" BEE D(D0G
+0A(A BBBE T > BBB B(A0A8G'
+8A0A(B BBBA T ,? 4 BBB B(A0A8G
+8A0A(B BBBA T ? ੰ
+ BBB B(D0D8J
+8A0A(B BBBA T ? $ BEF B(A0A8G
+8A0A(B BBBA T 4@ * BBB B(D0D8J
+8D0A(B BBBD T @ ð3 BBB B(A0A8L
-8A0A(B BBBF . K^ Xw Pw 4 t Xwg OA BAD D
-ABA T w #A BBB B(I0A8D%
-8D0A(B BBBH , Z~- AAG0!AA 4 4 x @ BHD D`
- DABF 4 $ @{J BDA q
+8A0A(B BBBC A `w. K^ A ְ A ְ 4 ,A ְg 7 BAD D
+ABA T dA ְ BBB B(I0A8D%
+8D0A(B BBBH L A Xذ BHB B(A0D8J^
+8A0A(B BBBA , B sw- AAG0!AA 4 <B ۰ BHD D`
+ DABF 4 ,C ްJ BDA q
ABE A
-ABA < X{F n@ BBA A(Gq
-(A ABBA L h| BBB B(A0A8Gp8A0A(B BBB L }M BBB B(A0D8Dp18A0A(B BBB L < BBB B(A0A8Gp8A0A(B BBB L hM BBB B(A0D8Dp18A0A(B BBB L h BBB B(A0A8Gp8A0A(B BBB < t 脴F > BBA A(Gq
-(A ABBA L l M BBB B(A0D8Dp18A0A(B BBB D BBB A(A0G@R
-0A(A BBBA L BBB E(D0A8D`
-8A0A(B BBBA T 0m , l ADD X
-AAA d BHD G0W
+ABA < B ްF 3 BBA A(Gq
+(A ABBA L C (߰ BBB B(A0A8Gp8A0A(B BBB L C M BBB B(A0D8Dp18A0A(B BBB L DD BBB B(A0A8Gp8A0A(B BBB L D (M BBB B(A0D8Dp18A0A(B BBB L D ( BBB B(A0A8Gp8A0A(B BBB < |D F BBA A(Gq
+(A ABBA L tE M BBB B(A0D8Dp18A0A(B BBB D E BBB A(A0G@R
+0A(A BBBA L F @ BBB E(D0A8D`
+8A0A(B BBBA \F m , tF H ADD X
+AAA d F BHD G0W
AABH F
AABF g
AABE D
- FABK , ADD ]
-AAA d 4 BBB E(I0D8DP
+ FABK , G p ADD ]
+AAA d <G BBB E(I0D8DP
8D0A(B BBBC |
-8D0A(B BBBA , X ADD [
-AAA t Ȍ BIA D(G0C
+8D0A(B BBBA , G ADD [
+AAA t G BIA D(G0C
(A ABBH o
(A ABBC g
(A ABBC D
-(F ABBA d D @E BBB B(A0A8DP
+(F ABBA d LH E BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l ( g; BBB E(I0D8D
+8F0A(B BBBA l G + BBB E(I0D8D
8A0A(B BBBG
-8D0A(B BBBE ؐu L 4 @ BEB B(A0A8DP
-8A0A(B BBBH l Б' : BBB E(D0I8D`M
+8D0A(B BBBE $I u L <I BEB B(A0A8DP
+8A0A(B BBBH l H ' s BBB E(D0I8D`M
8D0A(B BBBJ
-8D0A(B BBBB T < M c: BBB B(A0A8J
-8A0A(B BBBA L ^ L d Ъ BEB B(A0A8DP
-8A0A(B BBBH l `' : BBB E(D0I8D`M
+8D0A(B BBBB T DI Pn ' BBB B(A0A8JI
+8A0A(B BBBA TJ h
^ L lJ
BEB B(A0A8DP
+8A0A(B BBBH l J @' BBB E(D0I8D`M
8D0A(B BBBJ
-8D0A(B BBBB T l 9 BBB B(A0A8J
-8A0A(B BBBA | ôV L Ĵ BEB B(A0A8DP
-8A0A(B BBBH l , Ĵ 9 BBB E(D0I8D`:
+8D0A(B BBBB T tJ BBB B(A0A8J
+8A0A(B BBBA K &V L K & BEB B(A0A8DP
+8A0A(B BBBH l 4K ' W BBB E(D0I8D`:
8D0A(B BBBE
-8D0A(B BBBJ T Ǵx C9 BBB B(A0A8J
-8A0A(B BBBA d Hߴ! BHF K(H0A8D`}
-8A0A(B BBBA d8C0A(B BBB L BEB B(A0A8DP
-8A0A(B BBBH | d f BIB B(A0D8GPz
+8D0A(B BBBJ T K * BBB B(A0A8J
+8A0A(B BBBA d L A! BHF K(H0A8D`}
+8A0A(B BBBA d8C0A(B BBB L M pB BEB B(A0A8DP
+8A0A(B BBBH | lM Cf BIB B(A0D8GPz
8A0A(B BBBA v
8A0A(B BBBH D
-8F0A(B BBBE d , +8 BBJ B(A0A8G`
+8F0A(B BBBE d 4M C BBJ B(A0A8G`
8D0A(B BBBE N
-8A0A(B BBBH T H 7 BBB B(A0A8J4
-8A0A(B BBBA D )8 BGA D(M
-(D ABBF (~. K^ 0 Hg $ 7 Hj L 7 BBB B(A0I8G
-8D0A(B BBBD L 0 7 BBB B(A0I8G
-8D0A(B BBBD L , 7 BBB B(A0I8G
-8D0A(B BBBD T | o7 BEG E(A0G8G-
-8D0A(B BBBD \ 7 BFE A(A0D`W
+8A0A(B BBBH T M E BBB B(A0A8J4
+8A0A(B BBBA D M p\ BGA D(M
+(D ABBF N 8w. K^ O a0 Hg ,O a7 Hj L N 0ai ~ BBB B(A0I8G]
+8D0A(B BBBE L N Pci i BBB B(A0I8G]
+8D0A(B BBBE L 4O pei T BBB B(A0I8G]
+8D0A(B BBBE , <P :gw- AAG0!AA 4 O `gE BHD D`
+ DABK 4 O xjE BHD D`
+ DABD 4 $P mE BHD D`
+ DABD T \P p BEB B(D0G8L
+8D0A(B BBBI \ P @} BFE A(A0D`W
0D(A BBBK f
-0D(A BBBG , X~- AAG0!AA 4 d E n7 BHD D`
- DABK 4 E P7 BHD D`
- DABD 4 E 27 BHD D`
- DABD ~. K^ L BLA A(D0]
+0D(A BBBG Q w. K^ D Q X~/ BBB B(A0A80A(B BBB D ,R @ BBB B(A0A80A(B BBB D tR 7 BBB B(A0A8#0A(B BBB D R Ѕ BBB B(A0A80A(B BBB D S x BBB B(A0A8 0A(B BBB D LS P BBB B(A0A80A(B BBB < R ؎q BBA A(D0[
+(A ABBA L S AAG ]
+CAF b
+CAJ R
+CAJ JCA L $T X AAG ]
+CAF b
+CAJ R
+CAJ JCA L tT AAG ]
+CAF b
+CAJ R
+CAJ JCA L T ؏ AAG ]
+CAF b
+CAJ R
+CAJ JCA L U AAG ]
+CAF b
+CAJ R
+CAJ JCA L dU X AAG ]
+CAF b
+CAJ R
+CAJ JCA L U BLA A(D0]
(A ABBI X
-(A ABBB $ , (^ AG b
-AA T `$ l xH Gj
+(A ABBB $ V ^ AG b
+AA ,V @$ DV XH Gj
G F
-E L w L6 BBB B(A0A8G
-8A0A(B BBBA , ~- AAG0!AA 4 T 6 BHD D`
- DABG 4 # 5 BHD D`
- DABG 4 x% 5 BHD D`
- DABG L '6 5 BBB B(A0A8Gb
-8A0A(B BBBC < L * 5 BEA A(Gc
-(D ABBA < 0+ 5 BEA A(Gc
-(D ABBA < + `5 BEA A(Gc
-(D ABBA , +\ 65 ADG l
-AAA , < ,\ 5 ADG l
-AAA L $ P, BBB E(A0A8G`
-8A0A(B BBBA L t - BBB E(A0A8G`
-8A0A(B BBBA L / BBB E(A0A8G`
-8A0A(B BBBA p0 N < , x0 BDD k
+E L U BBB B(A0A8G
+8A0A(B BBBA , V aw- AAG0!AA 4 ,V t BHD D`
+ DABG 4 dV V BHD D`
+ DABG 4 V h 8 BHD D`
+ DABG 4 V Н BHD D`
+ DABG 4 W 8 BHD D`
+ DABG 4 DW BHD D`
+ DABG L |W 6 BBB B(A0A8Gb
+8A0A(B BBBC < W BEA A(Gc
+(D ABBA < X X BEA A(Gc
+(D ABBA < LX ^ BEA A(Gc
+(D ABBA < X 4 BEA A(Gc
+(D ABBA < X x
+ BEA A(Gc
+(D ABBA < Y ة BEA A(Gc
+(D ABBA , LY 8\ ADG l
+AAA , |Y h\ ADG l
+AAA L dZ BBB E(D0D8L`Y
+8A0A(B BBBF L Z H BBB E(D0D8L`Y
+8A0A(B BBBF L [ BBB E(D0D8L`Y
+8A0A(B BBBF T[ N < l[ BDD k
ABH [
-ABA L 07 3 BEB B(A0D8Gm
-8A0A(B BBBA L ?" 3 BEB B(A0A8Jm
-8A0A(B BBBA L T O
-4 BBB B(A0A8Jp
-8A0A(B BBBA < \ H_ BBG A(G@
-(A ABBD _% 4 ` BEA A(D0(A ABBd 4 `1 3 BHB B(D0A8Dpv
+ABA D Z & BBF K(H0G@Q
+0A(A BBBC L <[ ! BEB B(A0D8Gm
+8A0A(B BBBA L [ h" BEB B(A0A8Jm
+8A0A(B BBBA L [ Hб P BBB B(A0A8Jp
+8A0A(B BBBA < \ ߱ BBG A(G@
+(A ABBD $] x% 4 <] BEA A(D0(A ABBd \ 1 BHB B(D0A8Dpv
8C0A(B BBBG {
-8D0A(B BBBA T `a H3 BEB B(A0A8J
-8A0A(B BBBA T j
*3 BBB B(D0A8J
-8A0A(B BBBA T L x 3 BBE B(A0A8J+
-8A0A(B BBBA T
2 BBB B(D0A8J,
-8A0A(B BBBA T 0
- 2 BBE B(A0A8G
-8A0A(B BBBA T T h
2 BBB B(D0A8J
-8A0A(B BBBA T 2 BBB E(I0A8K}
-8D0A(B BBBF L 訵 2 BEB B(D0A8Gpu
-8A0A(B BBBA L T 8 <2 BEB B(D0A8Gpu
-8A0A(B BBBA T 1 BBB E(I0A8K}
-8D0A(B BBBF L * 1 BEB B(D0A8Gp
-8A0A(B BBBA $ V ADD JAA< t ( 1 BLD D(G0
-(A ABBE D l 谵 BHI H(D0D@
-0A(A BBBA L O 1 BBB B(A0A8J08A0A(B BBB L L µ 1 BHE E(A0D8G
-8D0A(B BBBA T ϵ*
e1 BBB H(D0A8L"
-8D0A(B BBBB L ۵ "2 BHE E(A0D8G
-8D0A(B BBBA T D H*
2 BBB H(D0A8L"
-8D0A(B BBBB L ?3 BHE E(A0D8G
-8D0A(B BBBA T *
3 BBB H(D0A8L"
-8D0A(B BBBB x~. K^ @ , 8 D 0 \ ( t ( 0 8 , L @[ T3 AID t
-DAA , | p[ 43 AID t
-DAA , [ 3 AID t
-DAA , [ 2 AID t
-DAA , [ 2 AID t
-DAA }~- AG cA " DN
-N A $ 4 AAG0AA4 L2 BHA GPh
- DABG 4 p .2 BHA GPh
- DABG 4 8 2 BHA GPh
- DABG 4 L 1 BHA GPh
- DABG 4 1 BHA GPh
- DABG D S 1 BEB A(A0Gh
-0A(A BBBA T b 1 BBE B(K0A8G
-8A0A(B BBBI T \ d1 BDB B(A0A8J
-8A0A(B BBBF T 8-( ~1 BEB B(A0D8M
-8A0A(B BBBD T 4y P1 BDB B(A0D8Gs
-8A0A(B BBBE < d 8F 1 BBH D(J
-(A ABBA T I 1 BEI B(A0A8J
-8A0A(B BBBA T N' 1 BDB B(A0A8J
-8A0A(B BBBA T T W 1 BEB B(A0D8M
-8A0A(B BBBE T 0^ 1 BDB B(A0D8G,
-8A0A(B BBBD T o 1 BEB B(D0A8M
-8A0A(B BBBA T \ s 1 BDB B(D0A8G
-8A0A(B BBBA l 8~. K^ p{ 2x~\ AG RAT X{ 1 BBB I(A0A8KL
-8A0A(B BBBF T \ L1 BBB I(A0A8KL
-8A0A(B BBBF T H 1 BBB I(A0A8K
-8A0A(B BBBG ~. K^ 2 Hb
-F A 7 Hj w~\ AG RAT f `0 BBB B(I0A8G
-8D0A(B BBBF T Ў/ D0 BBE B(A0A8G{
-8D0A(B BBBD , v~- AAG0!AA 4 d x5 0 BHD D`
- DABG L \
- g0 BBB E(F0A8G
-8A0A(B BBBA `~. K^ T x 0 BBB B(A0A8G
-8A0A(B BBBA $ @m AG q
-AA $ < u AG y
-AA d Pv~\ AG RA, v~- AAG0!AA < U / BIA D(Dp
+8D0A(B BBBA T $] = BBB B(A0A8G=
+8A0A(B BBBH T |] | BBB B(A0A8G
+8A0A(B BBBA T ] @= p BBB B(A0A8G=
+8A0A(B BBBH T ,^ ( ^ BBB B(A0A8G
+8A0A(B BBBA T ^ = R BBB B(A0A8G=
+8A0A(B BBBH T ^ x @ BBB B(A0A8G
+8A0A(B BBBA T 4_ ! 7 BBB E(I0A8K}
+8D0A(B BBBF L _ $ BEB B(D0A8Gpu
+8A0A(B BBBA L _ h% ȿ BEB B(D0A8Gpu
+8A0A(B BBBA T ,` & BBB E(I0A8K}
+8D0A(B BBBF L ` @)* [ BEB B(D0A8Gp
+8A0A(B BBBA $ a +V ADD JAA< ` X+ BLD D(G0
+(A ABBE D a , BHI H(D0D@
+0A(A BBBA L a ,_ BBB B(A0A8J@8A0A(B BBB L a =7
BHE E(D0A8G
+8D0A(B BBBA T $b JW BBB H(D0A8L
+8D0A(B BBBJ L |b X,
BHE E(D0A8G
+8D0A(B BBBA T b eW BBB H(D0A8L
+8D0A(B BBBJ L $c s,
BHE E(D0A8G
+8D0A(B BBBA T tc W BBB H(D0A8L
+8D0A(B BBBJ d w. K^ d d d d d e x ,e De \e te , d [ AID t
+DAA , e Ў[ i AID t
+DAA , 4e [ I AID t
+DAA , de 0[ ) AID t
+DAA , e `[ AID t
+DAA |f VSw DN
+E A 4 e p ɿ BAA D@
+ AABA f 8r f c g 2 g 4g Lg " dg " |g Кz g 8j g i g 蛲 g po g Ȟ h @ $h <h Th (z lh " 4 h BAA i
+ABH rAB h z T h BBB A(A0
+(V BBBI
+(A BBBG T ,i 0 BBB A(A0
+(V BBBG
+(A BBBF 4 i HA
+AA d
+AK i g DPb i g DPb4 i p AC
+AF
+AA $j e DP` <j Pe DP`4 Tj BAA i
+ABH rAB L i BBB B(A0A8Dp
+8A0A(B BBBA L $j ` BBB B(A0A8Dp
+8A0A(B BBBA L tj [ { BBB B(A0A8D`
+8A0A(B BBBA , |k pNw- AAG0!AA 4 j BHD D`
+ DABF 4 ,k 軲 BHD D`
+ DABF 4 dk @ ٺ BHD D`
+ DABF 4 k BHD D`
+ DABF 4 k ² BHD D`
+ DABF L l HŲ= BBB D(D0Gk
+0A(A BBBH T \l 8Ȳ J BEG E(D0A8M
+8D0A(B BBBD D l ˲ BEA D(G
+(D ABBG T l Ͳ BEG E(D0A8M
+8D0A(B BBBD D Tm pв BEA D(G
+(D ABBG T m Ѳ BEG E(D0A8M
+8D0A(B BBBD D m Pղ O BEA D(G
+(D ABBG T <n ֲ $ BEG E(D0A8M
+8D0A(B BBBD D n 0ڲ BEA D(G
+(D ABBG T n ۲ ¸ BEG E(D0A8M
+8D0A(B BBBD D 4o ߲ BEA D(G
+(D ABBG 4p w. K^ Lp dp Jw\ AG RAT o h BBB I(A0A8KL
+8A0A(B BBBF T $p з BBB I(A0A8KL
+8A0A(B BBBF T |p X BBB I(A0A8K]
+8A0A(B BBBE q `w. K^ q 2 Hb
+F A q 7 Hj q Iw\ AG RAT Lq f BBB B(I0A8G
+8D0A(B BBBF T q ʶ BBE B(A0A8G{
+8D0A(B BBBD , r Iw- AAG0!AA 4 ,r 5
+ BHD D`
+ DABG L dr
+\
+ BBB E(F0A8G
+8A0A(B BBBA ls w. K^ T r / BBB B(A0A8G
+8A0A(B BBBA $ s m AG q
+AA $ t u AG y
+AA ,t Hw\ AG RA, Lt Iw- AAG0!AA < s U ; BIA D(Dp
-(D ABBI T < | / BBE E(D0A8G
-8D0A(B BBBE T T s/ BBB B(I0A8G
-8D0A(B BBBH T Ŷ o/ BBB B(A0G8J
-8D0A(B BBBE 8~. K^ , \ p۶ 0 AID
-AAA , D u~- AAG0!AA < ۶ 0 BHD Dh
- DABA ~. K^ 8߶ 0߶ (߶ 0߶ , , @v~- AAG0!AA 4 ߶5 / BHD D`
- DABG 4 5 / BHD D`
- DABG L q / BBB B(A0I8G
-8D0A(B BBBD T d H p/ BBB B(A0I8G
-8D0A(B BBBD T / BBB G(D0G8G
-8D0A(B BBBD T J / BBB G(D0G8G
-8D0A(B BBBE $ p~. K^ < T , [ / AID t
-DAA T A w/ BEE E(D0A8GY
-8D0A(B BBBE , t~- AAG0!AA < l b -/ BIA D(Dp
-(D ABBE T / BGH B(A0D8G
-8D0A(B BBBI ~. K^ $ AG u
-AA $ AG }
-AA , $ t~- AAG0!AA 4 5 3/ BHD D`
- DABG T 8 / BBB J(A0A8G>
-8D0A(B BBBA ~. K^ X9 Hp x@ Hs < t~\ AG RA, \ t~- AAG0!AA 4 H5 O. BHD D`
- DABG `~. K^ T $ 8 . BBE B(A0A8J
-8A0A(B BBBA , | > C. AID W
-DAA d 0 Hg 7 Hj , t~- AAG0!AA 4 5 - BHD D`
- DABG T T # - BBB B(A0I8G6
-8D0A(B BBBD T P' - BBB E(A0A8G
+(D ABBI T t | BBE E(D0A8G
+8D0A(B BBBE T \t H%T BBB B(I0A8G
+8D0A(B BBBH T t P* BBB B(A0G8J
+8D0A(B BBBE u w. K^ , $u ? [ AID
+AAA , v ~Hw- AAG0!AA < u @@
BHD Dh
+ DABA |v w. K^ v C v C v C v C , v Hw- AAG0!AA 4 lv xC5 J BHD D`
+ DABG 4 v F5 , BHD D`
+ DABG L v Iq BBB B(A0I8G
+8D0A(B BBBD T ,w K BBB B(A0I8G
+8D0A(B BBBD T w O BBB G(D0G8G
+8D0A(B BBBD T w HWJ 7 BBB G(D0G8G
+8D0A(B BBBE x w. K^ y (^ y ^ , |x (^[ AID t
+DAA T x X^A BEE E(D0A8GY
+8D0A(B BBBE , y *Gw- AAG0!AA < 4y db BIA D(Dp
+(D ABBE T ty Pg BGH B(A0D8G
+8D0A(B BBBI z Xw. K^ $ z r AG u
+AA $ z s AG }
+AA , z (Gw- AAG0!AA 4 dz s5 BHD D`
+ DABG T z v8 BBB J(A0A8G>
+8D0A(B BBBA { `w. K^ 4 { x g BHE
+ABD 4 D{ y 3 BHE
+ABI 4| Gw\ AG RA, T| JGw- AAG0!AA 4 { y5 BHD D`
+ DABG | w. K^ T | | { BBE B(A0A8J
+8A0A(B BBBA , t| p> AID W
+DAA T | BBB B(I0A8D
+8D0A(B BBBH T | ( a BBB B(I0A8D
+8D0A(B BBBH , ~ Fw- AAG0!AA 4 } 5 BHD D`
+ DABG 4 } 5 BHD D`
+ DABG ~ w. K^ ~ 0 Hg ~ 7 Hj , Fw- AAG0!AA 4 |~ 5 K BHD D`
+ DABG T ~ - BBB B(A0I8G6
+8D0A(B BBBD T 蚳 ; BBB E(A0A8G
-8A0A(B BBBD ~. K^ ; x; L ; AAG e
+8A0A(B BBBD w. K^ 4 L L d AAG e
CAF {
ACI R
-CAJ JCA , ;[ - AID t
-DAA < <q - BBA A(D0[
-(A ABBA L P< BBB B(A0A8J@
-8A0A(B BBBA D B BEB B(A0A8
-0A(B BBBA L \ G
BIE E(D0A8J
-8A0A(B BBBA xU% X% [% ^% L a BBB B(D0D8G@o
-8A0A(B BBBH \ k t m T nf + BDB B(D0G8GA
-8D0A(B BBBF , q~- AAG0!AA < \ pr + BHD Db
- DABA T @u AY t @u AY @u AY < @uy BDD H
+CAJ JCA , x[ k AID t
+DAA < , q K BBA A(D0[
+(A ABBA L $ 诳 BBB B(A0A8J@
+8A0A(B BBBA D t BEB B(A0A8
+0A(B BBBA L `
BIE E(D0A8J
+8A0A(B BBBA ɳ% $ (̳% < @ϳ% T Xҳ% L l pճ BBB B(D0D8G@o
+8A0A(B BBBH ߳ Ԃ T 4 f G BDB B(D0G8GA
+8D0A(B BBBF , D Cw- AAG0!AA < # BHD Db
+ DABA AY ԃ AY AY < y BDD H
ABK A
-ABL < u BBD D(G0b
-(A ABBE 4 ue aC < T 0w BBD D(G0b
-(A ABBE , w AET
+ABL < T BBD D(G0b
+(A ABBE xe aC < BBD D(G0b
+(A ABBE , 8 AET
AE
-AA D px BBB B(A0A80A(B BBB L { BIB B(A0D8G
-8D0A(B BBBD d \ XS BBB E(D0D8D
+AA D $ BBB B(A0A80A(B BBB L l BIB B(A0D8G
+8D0A(B BBBD d S BBB E(D0D8D
8A0A(B BBBH
-8A0A(B BBBG D P BBB B(A0A8
-0A(B BBBA D c BEB B(A0A8H
-0A(B BBBA D T 0. BBE B(A0A8
-0A(B BBBA L BBB B(A0A8G
-8A0A(B BBBD L BEB B(A0A8G
-8A0A(B BBBA 4 @D p' AC
P\
- A L Xķ BBI B(A0A8G
-8A0A(B BBBA , ҷ AC
BEH
- D d HշS BBB E(D0D8D
+8A0A(B BBBG D $ BBB B(A0A8
+0A(B BBBA D l c BEB B(A0A8H
+0A(B BBBA D . BBE B(A0A8
+0A(B BBBA L BBB B(A0A8G
+8A0A(B BBBD L d ( BEB B(A0A8G
+8A0A(B BBBA 4 +D AC
P\
+ A L 7 BBI B(A0A8G
+8A0A(B BBBA , < F AC
BEH
+ D d l HS BBB E(D0D8D
8A0A(B BBBI
-8A0A(B BBBG L t @ٷ BBB B(D0A8J:
-8A0A(B BBBA D BEB B(A0A8
-0A(B BBBF L hu BBB B(D0A8G
-8A0A(B BBBA d \ 3 BBB E(D0D8D
+8A0A(B BBBG L Ԉ L BBB B(D0A8J:
+8A0A(B BBBA D $ _ BEB B(A0A8
+0A(B BBBF L l bu BBB B(D0A8G
+8A0A(B BBBA d 0f3 BBB E(D0D8D
8A0A(B BBBB
-8A0A(B BBBK D p BBB B(A0A8
-0A(B BBBA D 8w BEB B(A0A8Y
-0A(B BBBA D T p BBB B(A0A8
-0A(B BBBA < H BBB A(A0
-(A BBBA D $ # AC
BEO"
+8A0A(B BBBK D $ j BBB B(A0A8
+0A(B BBBA D l jw BEB B(A0A8Y
+0A(B BBBA D l BBB B(A0A8
+0A(B BBBA < m BBB A(A0
+(A BBBA D n AC
BEO"
A f
A
- K T l # BBA A(Gp
+ K T ̊ hy [ BBA A(Gp
(A ABBG
-(A ABBK 4 | X~ AC
BGM
+(A ABBK 4 ܋ z~ AC
BGM
I
- F t 7# BBB A(A0JW
+ F t \ 8 Ϩ BBB A(A0JW
0A(A BBBG
0A(A BBBB @
-0A(A BBBH T t H " BEB B(A0A8Jp
-8A0A(B BBBA D
- BBB A(A0G`
-0A(A BBBD D
- x!| C" BBB D(A0@
-(A BBBF 4 \
- # 3" AC
PC
- I D L 85 BKB B(G0A8t
-0A(B BBBA L <% BEE E(D0A8G
-8A0A(B BBBJ 4 , LP r! AC
P\
- A \ U BBB B(A0A8
-0A(B BBBA 0A(B BBB L | YT
+0A(A BBBH T ԋ g BEB B(A0A8Jp
+8A0A(B BBBA D h BBB A(A0G`
+0A(A BBBD L t ۧ BBB B(D0A8GP:
+8A0A(B BBBH 4 Č ` AC
PC
+ I D 訴 BKB B(G0A8t
+0A(B BBBA L p% BEE E(D0A8G
+8A0A(B BBBJ 4 PP AC
P\
+ A \ hɴ BBB B(A0A8
+0A(B BBBA 0A(B BBB L ̴T
BBB B(A0A8D
-8A0A(B BBBA , c$ AM
DL
- E d dS BBB E(D0D8D
+8A0A(B BBBA , 4 ִ$ AM
DL
+ E d d ״S BBB E(D0D8D
8A0A(B BBBB
-8A0A(B BBBC d d
h BEE B(E0A8DX7
+8A0A(B BBBC d ̏ ۴ BEE B(E0A8DX7
8A0A(B BBBA S
-8A0A(B BBBA 4
n
- AC
P
- A D py BBB B(A0A8j
-0A(B BBBA D
z> ( AC
BEOB
+8A0A(B BBBA 4 | X
+ # AC
P
+ A D l BBB B(A0A8j
+0A(B BBBA D h> AC
BEOB
A f
A
- G T
BBA A(G`
+ G T D ` BBA A(G`
(A ABBD {
-(A ABBG 4 (H AC
BGM
+(A ABBG 4 T H AC
BGM
E
- F T l @5 s BEB B(D0A8J
-8A0A(B BBBA T ( 5 BBB E(A0A8G
-8A0A(B BBBB 4
- AC
P^
- A T T ئ BBB B(A0A8J
-8A0A(B BBBA T u BBB B(A0A8J"
-8A0A(B BBBA Ƚ3 D0n ~. K^ ؽ н Ƚ 4 н L L ؽ BBB B(A0A8D`/
-8A0A(B BBBI L X BBB B(A0A8D`s
-8A0A(B BBBE a~\ AG RA, ,b~- AAG0!AA 4 ¸5 4 BHD D`
- DABG 4 Ÿ5 BHD D`
- DABG T ȸi BBB B(A0D8R
-8A0A(B BBBF , L ̸> AID W
-DAA T | i BBB B(A0D8R
-8A0A(B BBBF , Ѹ> AID W
-DAA < (Ѹ BBG A(G@
-(A ABBG T D Ѹ BHB E(A0A8G
-8D0A(B BBBA T p# V BHB E(A0A8G
-8D0A(B BBBA ~. K^ 0 ( , < 0[ AID t
-DAA < $ ` BBB A(A0
-(A BBBA L d 0 BBB B(A0A8JHv
-8A0A(B BBBA , _~- AAG0!AA < , ` BIA D(Dp-
-(D ABBF < $ BBD D(G0b
-(A ABBE L d `K BBB B(A0A8D@28A0A(B BBB d ` S BBB E(D0D8D
+ F T Ԑ 5 BEB B(D0A8J
+8A0A(B BBBA T , BBB E(A0A8G
+8A0A(B BBBB 4 0
+ AC
P^
+ A T } BBB B(A0A8J
+8A0A(B BBBA T P u i BBB B(A0A8J"
+8A0A(B BBBA $ x13 D0n < w. K^ T 1 l 1 x1 p1 h1 ̓ p1 x1 1 L 1_ BBB B(A0A8D`
+8A0A(B BBBA L d 3 BBB B(A0A8D`4
+8A0A(B BBBD L 6 BBB B(A0A8D`
+8A0A(B BBBG L x8 BBB B(A0A8D`x
+8A0A(B BBBH T 3w\ AG RA, t >3w- AAG0!AA 4 :5 BHD D`
+ DABG 4 $ >5 BHD D`
+ DABG 4 \ A5 BHD D`
+ DABG 4 D5 e BHD D`
+ DABG T ̕ Gi G BBB B(A0D8R
+8A0A(B BBBF , $ 0K> N AID W
+DAA T T @Ki . BBB B(A0D8R
+8A0A(B BBBF , XO> 5 AID W
+DAA T ܖ hOi BBB B(A0D8R
+8A0A(B BBBF , 4 S> AID W
+DAA T d Si BBB B(A0D8R
+8A0A(B BBBF , W> AID W
+DAA < W BBG A(G@
+(A ABBG T , XX BHB E(A0A8G
+8D0A(B BBBA T f = BHB E(A0A8G
+8D0A(B BBBA T ܘ xq BHB E(A0A8G
+8D0A(B BBBA T 4 | ס BHB E(A0A8G
+8D0A(B BBBA D w. K^ \ t x , ԙ [ $ AID t
+DAA < BBB A(A0
+(A BBBA L BBB B(A0A8JHv
+8A0A(B BBBA , L .w- AAG0!AA < Ě D BIA D(Dp-
+(D ABBF < @ BBD D(G0b
+(A ABBE L K BBB B(A0A8D@28A0A(B BBB d L S BBB E(D0D8D
8A0A(B BBBD
-8A0A(B BBBG L X
BBB B(A0A8Dxv
-8A0A(B BBBA L l
+8A0A(B BBBG L BBB B(A0A8Dxv
+8A0A(B BBBA L
BIB B(A0A8G
-8A0A(B BBB < ( : AC
FM-
+8A0A(B BBB < x AC
FM-
A X
- C T D & BBA A(Gp
+ C T ܜ X Z BBA A(Gp
(A ABBG u
-(A ABBE 4 ' AF
P
- G 4 h,8 AC
BGM
+(A ABBE 4 4 й AF
P
+ G 4 $ 8 AC
BGM
H
- F l p0O Y BBB B(D0A8Gc
+ F l µO BBB B(D0A8Gc
8A0A(B BBBG P
-8A0A(B BBBN T | P@ BEB B(D0D8Ju
-8A0A(B BBBD T G BDH B(A0A8J
-8D0A(B BBBC ~. K^ L O BBB B(A0D8D
-8A0A(B BBBA L L xQ] BBB B(A0D8G
-8A0A(B BBBC L T BBB B(A0D8G
-8A0A(B BBBE $ X AG r
-AA $ Y AG z
-AA < Z BBA I(D@e
-(D ABBA T Z BBE B(D0A8G
-8D0A(B BBBB , Z~- AAG0!AA 4 L 0_5 Y BHD D`
- DABG T 8b ; BBE B(A0A8L 6
-8A0A(B BBBG ~. K^ , [~- AAG0!AA 4 $ x~B BHD D`
- DABH T \ BBB B(A0I8G
-8D0A(B BBBD l ػ~. K^ T BBB B(I0A8D
-8D0A(B BBBE $ " L < ( BEE B(D0A8G@i
-8A0A(B BBBC , dZ~- AAG0!AA < x BHD G/
- DABA L D ،U BGB B(A0D8G
-8A0A(B BBBC L (~. K^ d Г | ȓ ȓ Г0 Hg 7 Hj , L [ [ AID t
-DAA 4 0} AG sA T } AG sA t AG {A ` AG {A Е AG vA 0 AG ~AL < r { BBB B(E0A8J@
-8A0A(B BBBA $ D! З^ AG b
-AA T C BBB B(A0D8Ra
-8A0A(B BBBA , ! > 7 AID W
-DAA T <! BBB B(A0D8R9
-8A0A(B BBBA , ! > 1 AID W
-DAA L ! ȟ BBB B(I0A8D}
-8D0A(B BBBA L " X BBB B(I0A8D}
-8D0A(B BBBA L d" 袹 BBB B(I0A8D}
-8D0A(B BBBA L " x BBB B(I0A8D}
-8D0A(B BBBA $ # AAG0AA4 ,# BHA GPh
- DABG 4 d# X s BHA GPh
- DABG 4 # U BHA GPh
- DABG 4 # 諹 7 BHA GPh
- DABG 4 $ BHA GPh
- DABG 4 D$ x BHA GPh
- DABG 4 |$ @ BHA GPh
- DABG 4 $ BHA GPw
- DABH T $ ഹ BBB E(D0A8G
-8A0A(B BBBC D D% h BEG A(I
-(D ABBA L % BBG D(G0D
-0D(A BBBA D % @¹ BDB D(G0D
-0D(A BBBA T $& Ĺ BBB J(A0A8G&
-8D0A(B BBBA T |& ƹb t BBB B(A0D8G
-8A0A(B BBBG T & (Թ BBB E(G0F8G
-8D0A(B BBBJ ' ٹ AR ( ٹ AY $( ~. K^ <( ٹ T( ٹ l( ٹ ( ٹ ( ٹ ( ٹ ( ٹ ( ٹ ( ٹT D O ) ڹT D O ,) XڹW D R D) ڹT D O \) ڹT D O t) 0۹W D R ) x۹T D O ) ۹T D O ) ܹW D R ) PܹT D O ) ܹT D O * ܹW D R, d) (ݹ[ AID t
-DAA , ) Xݹ[ AID t
-DAA , ) ݹ[ AID t
-DAA , ) ݹ[ AID t
-DAA L * ݹ AAG e
-CAF C
+8A0A(B BBBN T ҵ ] BEB B(D0D8Ju
+8A0A(B BBBD T l ڵ + BDH B(A0A8J
+8D0A(B BBBC | w. K^ L H BBB B(A0D8D
+8A0A(B BBBA L ] BBB B(A0D8G
+8A0A(B BBBC L 4 BBB B(A0D8G
+8A0A(B BBBE $ H AG r
+AA $ AG z
+AA T h BBE B(D0A8G
+8D0A(B BBBB , , )w- AAG0!AA 4 5 ɜ BHD D`
+ DABG T ܠ BBE B(A0A8L 6
+8A0A(B BBBG < 4 BBA I(D@e
+(D ABBA , 0w. K^ , D )w- AAG0!AA 4 B / BHD D`
+ DABH T BBB B(A0I8G
+8D0A(B BBBD w. K^ 4 T ՜ BBB B(I0A8D
+8D0A(B BBBE h `" L ԣ x BEE B(D0A8G@i
+8A0A(B BBBC , $ F)w- AAG0!AA < BHD G/
+ DABA L ܣ ( U BGB B(A0D8G
+8A0A(B BBBC ؊w. K^ & & , & D & \ &0 Hg | 0&7 Hj , P&[ AID t
+DAA ̥ &} AG sA &} AG sA @' AG {A , ' AG {A L ( AG vA l ( AG ~AL ԥ (r ǚ BBB B(E0A8J@
+8A0A(B BBBA $ ܦ *^ AG b
+AA T L X*C d BBB B(A0D8Ra
+8A0A(B BBBA , P.> AID W
+DAA T Ԧ `. c BBB B(A0D8R9
+8A0A(B BBBA , , 2> } AID W
+DAA L \ 2 ] BBB B(I0A8D}
+8D0A(B BBBA L 3 G BBB B(I0A8D}
+8D0A(B BBBA L 85 1 BBB B(I0A8D}
+8D0A(B BBBA L L 6 BBB B(I0A8D}
+8D0A(B BBBA $ T X8 AAG0AA4 Ĩ 8 ݙ BHA GPh
+ DABG 4 : BHA GPh
+ DABG 4 4 p< BHA GPh
+ DABG 4 l 8> BHA GPh
+ DABG 4 @ e BHA GPh
+ DABG 4 ܩ A G BHA GPh
+ DABG 4 C ) BHA GPh
+ DABG 4 L XE BHA GPw
+ DABH T 0G BBB E(D0A8G
+8A0A(B BBBC D ܪ O Θ BDB D(G0Dy
+0D(A BBBA D $ pQ Ș BEG A(I
+(D ABBA L l S Ș BBG D(G0D
+0D(A BBBA T HV Ƙ BBB J(A0A8G&
+8D0A(B BBBA T W BBE B(A0A8G
+8A0A(B BBBE T l f B BBB E(G0F8G
+8D0A(B BBBJ | k AR k AY 0w. K^ ԭ k k k k 4 k L k d k | k , ܭ k[ U AID t
+DAA , k[ 5 AID t
+DAA , < l[ AID t
+DAA , l Hl[ AID t
+DAA L T xl AAG ]
+CAF c
+CAI R
+CAJ JCA L l AAG ]
+CAF c
+CAI R
+CAJ JCA L l AAG ]
+CAF c
+CAI R
+CAJ JCA D 8m D d m D n D xoj D e oj D e (pj D e pj D e ps D0n @qs D0n 4 qs D0n L rs D0n d xrs D0n | rs D0n Hss D0n ss D0nt t e BBB A(A0G'
+0A(A BBBB
+0A(A BBBB
+0A(A BBBB , < \ w- AAG0!AA 4 ` Z BHD D`
+ DABF 4 < BHD D`
+ DABF 4 $ BHD D`
+ DABF 4 \ h BHD D`
+ DABF L s D0n d (s D0n | j D e 茶s D0n Ps D0n ij j D e ܳ s D0n xs D0n j D e $ 8s D0n < s D0n T j D eT `5
BBB B(A0A8J
+8A0A(B BBBH Ĵ HF AJ0vD xF AJ0vD T L
G BBB B(A0A8J
+8A0A(B BBBB \ F AJ0vD | F AJ0vD zw. K^ 8 ̵ 0 L 8 AAG e
+CAF {
+ACI R
+CAJ JCA L 4 AAG e
+CAF S
ACA R
-CAJ JCA L ,+ H AAG e
+CAJ JCA L AAG e
CAF C
ACA R
-CAJ JCA L |+ AAG e
-CAF C
-ACA R
-CAJ JCA L + ߹ AAG e
-CAF C
-ACA R
-CAJ JCA L , h߹ ADD e
-CAF x
-ACD V
-CAF JCA L l, ADD e
-CAF x
-ACD V
-CAF JCA L , ADD e
-CAF x
-ACD V
-CAF JCA L - H ADD e
-CAF x
-ACD V
-CAF JCA L \- ADD e
-CAF x
-ACD V
-CAF JCA L - ADD e
-CAF x
-ACD V
-CAF JCA L - ( ADD e
-CAF x
-ACD V
-CAF JCA L L. ADD e
-CAF x
-ACD V
-CAF JCA L . h ADD e
-CAF x
-ACD V
-CAF JCA L . ADD e
-CAF x
-ACD V
-CAF JCA L </ ADD e
-CAF x
-ACD V
-CAF JCA L / H ADD e
-CAF x
-ACD V
-CAF JCA $ / BJ~ AAG0AA4 L/ Y
BHD D`
- DABF 4 / ;
BHD D`
- DABF 4 / p
BHD D`
- DABF 4 / BHD D`
- DABF T ,0 % BEB B(A0A8JO
-8A0A(B BBBH D 0 2 BGB A(D0Ge
-0A(A BBBA T 0 % BEB B(A0A8JO
-8A0A(B BBBH D $1 2 l BGB A(D0Ge
-0A(A BBBA T l1 % C BEB B(A0A8JO
-8A0A(B BBBH T 1 % BEB B(A0A8JO
-8A0A(B BBBH T 2 p
- BBB E(A0A8R
-8D0A(B BBBB ,3 / AJ0_D T 2 W BBE B(D0A8Jw
-8A0A(B BBBE 3 T 3 U BBB B(D0A8R
-8D0A(B BBBD 4 / AJ0_D T |3 k BBE B(D0A8Jw
-8A0A(B BBBE 4 x T 3 p BBB B(A0D8R
-8D0A(B BBBG 4 '/ AJ0_D T d4 ' z BBE B(D0A8Jw
-8A0A(B BBBE t5 * T 4 * BBB B(A0D8R
-8D0A(B BBBG 5 5/ AJ0_D T L5 6 BBE B(D0A8Jw
-8A0A(B BBBE \6 8 t6 `~. K^ 6 8 6 8 , 6 8 BGA
-ABA 6 X9 7 `9 7 h90 Hg <7 x97 Hj \7 90 Hg |7 97 Hj 7 90 Hg 7 97 Hj 7 90 Hg 7 :7 Hj 8 (:0 Hg <8 8:7 Hj \8 X:0 Hg |8 h:7 Hj L 8 : BBB B(D0A8D@b
-8A0A(B BBBA 8 : 9 : 9 : 49 : L L9 : AAG ]
+CAJ JCA d h BBE E(D0G8GPO
+8D0A(B BBBL |
+8A0A(B BBBA d ~ V BHB B(D0A8Dp
+8D0A(B BBBM T
+8A0A(B BBBA d BBE E(D0A8D`
+8D0A(B BBBK d
+8A0A(B BBBA D T 0q ̒ BBB A(I0Dp
+0D(A BBBC T h BEE E(D0D8G
+8D0A(B BBBG T t BBE B(D0A8G
+8D0A(B BBBH , w- AAG0!AA 4 | * BHI D`
+ DABA T + BBB B(A0A8J H
+8A0A(B BBBJ Ĺ vw. K^ ܹ $ , < BGA
+ABA l p0 Hg 7 Hj 0 Hg ̺ 7 Hj 0 Hg 7 Hj , 0 Hg L 7 Hj l 00 Hg @7 Hj `0 Hg ̻ p7 Hj L BBB B(D0A8D@b
+8A0A(B BBBA < T l L AAG ]
CAF [
ACA R
-CAJ JCA L 9 : AAG ]
+CAJ JCA L AAG ]
CAF [
ACA R
-CAJ JCA L 9 ; AAG ]
+CAJ JCA L < AAG ]
CAF [
ACA R
-CAJ JCA L <: 8; AAG ]
+CAJ JCA L @ AAG ]
CAF [
ACA R
-CAJ JCA , : h;b D\
+CAJ JCA , ܽ pb 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 L ; (< AAG e
+G I L l 0 AAG e
CAF s
ACA R
-CAJ JCA , l; x<b D\
+CAJ JCA , b D\
H P
H Q
-G I L ; < AAG e
+G I L AAG e
CAF s
ACA R
-CAJ JCA L ; = AAG ]
+CAJ JCA L < AAG ]
CAF [
ACA R
-CAJ JCA L << 8= AAG e
+CAJ JCA L @ AAG e
CAF s
ACA R
-CAJ JCA L < = AAG ]
+CAJ JCA L ܿ AAG ]
CAF [
ACA R
-CAJ JCA L < = AAG e
+CAJ JCA L , AAG e
CAF s
ACA R
-CAJ JCA L ,= > BBB B(D0A8D@b
-8A0A(B BBBA , |= H> BGA
-ABA , = > BGA
-ABA , = ? BGA
-ABA , > <~- AAG0!AA 4 = ?5 e BHD D`
- DABG 4 = C5 G BHD D`
- DABG 4 = F5 ) BHD D`
- DABG 4 ,> I5 BHD D`
- DABG L d> Lg BBE D(D0JU
-0A(A BBBH \ > 8U z BBB G(D0O
+CAJ JCA L | BBB B(D0A8D@b
+8A0A(B BBBA , P BGA
+ABA , BGA
+ABA , , BGA
+ABA , \ w- AAG0!AA 4 5 BHD D`
+ DABG 4 5 BHD D`
+ DABG 4 D 5 ˌ BHD D`
+ DABG 4 | 5 BHD D`
+ DABG L g BBE D(D0JU
+0A(A BBBH \ @ BBB G(D0O
0A(A BBBB
-0A(A BBBC 4 ? xW = BAI D0\
- DABA L L? W[ BBE B(G0D8R
-8A0A(B BBBG L ? [ BBB B(A0D8K
-8A0A(B BBBG , ? `\> AID W
-DAA L @ p\7 BBE D(D0J2
-0A(A BBBA \ l@ `d ) BBB G(D0O
+0A(A BBBC 4 d ߌ BAI D0\
+ DABA L [ BBE B(G0D8R
+8A0A(B BBBG L BBB B(A0D8K
+8A0A(B BBBG , < h
+> ^ AID W
+DAA L l x
+7 > BBE D(D0J2
+0A(A BBBA \ h ˌ BBB G(D0O
0A(A BBBB
-0A(A BBBC 4 @ f BAI D0\
- DABA L A g[ BBE B(G0D8R
-8A0A(B BBBG L TA (j BBB B(A0D8K
-8A0A(B BBBG , A k> k AID W
-DAA T A kd K BBH G(A0A8J
-8D0A(B BBBI B o AG MD T LB od 7 BBH G(A0A8J
-8D0A(B BBBI \C s AG MD T B sL # BBH G(D0A8J
-8D0A(B BBBJ C x AO T <C xL BBH G(D0A8J
-8D0A(B BBBJ LD } AO lD ~. K^ L D } BBB B(A0D8D8A0A(B BBB L D P ADD e
+0A(A BBBC 4 BAI D0\
+ DABA L T [ l BBE B(G0D8R
+8A0A(B BBBG L 0 @ BBB B(A0D8K
+8A0A(B BBBG , >
AID W
+DAA T $ d BBH G(A0A8J
+8D0A(B BBBI 4 AG MD T d ً BBH G(A0A8J
+8D0A(B BBBI ! AG MD T !L ŋ BBH G(D0A8J
+8D0A(B BBBJ $ & AO T &L BBH G(D0A8J
+8D0A(B BBBJ + AO hw. K^ L + BBB B(A0D8G8A0A(B BBB L $ x- ADD e
CAF s
ACA V
-CAF JCA L $E ADD e
+CAF JCA L t . ADD e
CAF
ACA V
-CAF JCA L tE ADD e
+CAF JCA L . ADD e
CAF
ACA V
-CAF JCA 4 E BAI w
-ABA L E BBB B(A0D8Dp8A0A(B BBB $ LF ^ AG b
-AA 4 E 0 " BHE l
-ABB 4 E ȃ BHE o
-ABO F p\ DX
-D L G BBE B(A0A8D`
-8A0A(B BBBA , TG 4~- AAG0!AA L F BIB A(D0D
-0D(A BBBA L G BBB B(D0A8GT
-8D0A(B BBBK T lG Ѝ< BBE B(D0A8G
-8D0A(B BBBB L |H BBB E(A0A8G
-8A0A(B BBBH L H P BBB B(D0D8J
-8D0A(B BBBF T dH +
BHG B(D0A8J
-8D0A(B BBBF tI ~. K^ , H Ǻ AID g
-DAA , I 8Ⱥ AID g
-DAA 4 4I Ⱥ ` BHE r
-ABD 4 lI 0ɺ , BHE r
-ABD 4 I ɺ BHE r
-ABD 4 I `ʺ BHE u
-ABI 4 J ˺ BHE u
-ABI 4 LJ ˺ \ BHE u
-ABI <K 1~\ AG RAL J 8̺ BBB B(A0D8GT
-8D0A(B BBBK , K 1~- AAG0!AA < $K Xκ BHD Dq
- DABA < dK 8Ѻ' BHD D
- DABA T K (Ժ7 k BEB E(G0A8J
-8A0A(B BBBD T K ں M BGB B(G0A8J
-8D0A(B BBBJ T TL h5 BBE G(G0A8J
-8D0A(B BBBG dM ~. K^ L L 8x
- BBB B(A0I8Dp
-8D0A(B BBBE L M hx BBB B(A0I8Dp
-8D0A(B BBBE L dM x BBB B(A0I8Dp
-8D0A(B BBBE L M x n BBB B(A0I8Dp
-8D0A(B BBBE , N /~- AAG0!AA < 4N U
- BIA D(Dp
+CAF JCA 4 \ H/ ] BAI w
+ABA L L / BBB B(A0D8Dp8A0A(B BBB $ 1^ AG b
+AA 4 X1 ĉ BHE l
+ABB 4 D 1 BHE o
+ABO 4 2\ DX
+D L T 2 BBE B(A0A8D`
+8A0A(B BBBA , ~w- AAG0!AA L 4 BIB A(D0D
+0D(A BBBA L l 7 BBB B(D0A8GT
+8D0A(B BBBK T ;< \ BBE B(D0A8G
+8D0A(B BBBB L @ BBB E(A0A8G
+8A0A(B BBBA L d L BBB B(D0D8J
+8D0A(B BBBF T c BHG B(D0A8J
+8D0A(B BBBF cw. K^ L $ xr / BBB B(E0A8J@
+8A0A(B BBBA D t y BBB A(D0D@[
+0A(A BBBF D | BBB A(D0D@c
+0A(A BBBF T `~ l BEE E(D0A8G
+8D0A(B BBBG T \ 脷 R BEE E(D0A8G
+8D0A(B BBBG L p 8 BBB B(D0A8Gf
+8D0A(B BBBI o D \
+H , tw- AAG0!AA D T @ BIA D(D
+(D ABBA 4 ؑ BAI
+ABA T h o BEB B(D0D8J
+8D0A(B BBBI `w. K^ , D AID g
+DAA , t AID g
+DAA 4 ` q BHE r
+ABD 4 = BHE r
+ABD 4 BHE u
+ABI 4 L 8 Շ BHE u
+ABI < Bw\ AG RA, \ ~w- AAG0!AA < Q BHD Dq
+ DABA < p' 6 BHD D
+ DABA T T ` BBB E(G0D8J
+8A0A(B BBBK T BGB B(G0A8J
+8D0A(B BBBJ T P5 B BBE G(G0A8J
+8D0A(B BBBG ]w. K^ L t ɷx BBB B(A0I8Dp
+8D0A(B BBBE L Pʷx BBB B(A0I8Dp
+8D0A(B BBBE L ˷x R BBB B(A0I8Dp
+8D0A(B BBBE L d ̷x BBB B(A0I8Dp
+8D0A(B BBBE , l w- AAG0!AA < ͷU BIA D(Dp
-(D ABBI < tN U BIA D(Dp
+(D ABBI < $ зU BIA D(Dp
-(D ABBI < N U BIA D(Dp
+(D ABBI < d ӷU n BIA D(Dp
-(D ABBI < N (U BIA D(Dp
+(D ABBI < U H BIA D(Dp
-(D ABBI O ~. K^ P 0 P ( , |O 0[ * AID t
-DAA , dP r/~- AAG0!AA 4 O 0E BHD D`
- DABK T P HB BDB E(G0A8G3
-8D0A(B BBBD $Q ~. K^ <Q ( TQ lQ Q < Q ( BBA A(D@
-(A ABBK < Q BBA A(DPC
-(A ABBE , R .~- AAG0!AA 4 Q H 5 BHD D`
- DABG 4 Q P 5 BHD D`
- DABG L R Xh BBB B(D0A8R
-8A0A(B BBBK , TR x> AID W
-DAA L R h v BBB B(D0A8R
-8A0A(B BBBK , R > y AID W
-DAA T S Y BBE B(D0A8G
-8D0A(B BBBG T \S ? BBE B(D0A8G
-8D0A(B BBBG T S H % BBE B(D0A8G
-8D0A(B BBBG T T $ BBB B(A0G8Jx
-8D0A(B BBBA T dT 3e # BBG B(A0G8J
-8D0A(B BBBG tU ~. K^ U ,~ DN
-E A $ U E^ AG b
-AA < U E BEA A(D@
-(A ABBA < V F BEA A(D@
-(A ABBA TV `G L lV hIp BEA A(D0H
+(D ABBI [w. K^ ڷ ڷ , , ڷ[ څ AID t
+DAA , "w- AAG0!AA 4 ڷE BHD D`
+ DABK T 0ݷB o BDB E(G0A8G3
+8D0A(B BBBD Zw. K^ 4 < L BBA A(D@
+(A ABBK < BBA A(DPC
+(A ABBE , w- AAG0!AA 4 D 05 BHD D`
+ DABG 4 | 85 a BHD D`
+ DABG L @h C BBB B(D0A8R
+8A0A(B BBBK , `> F AID W
+DAA L 4 ph & BBB B(D0A8R
+8A0A(B BBBK , > ) AID W
+DAA T BBE B(D0A8G
+8D0A(B BBBG T h BBE B(D0A8G
+8D0A(B BBBG T d 0 Ճ BBE B(D0A8G
+8D0A(B BBBG T BBB B(A0G8Jx
+8D0A(B BBBA T u ӄ BBG B(A0G8J
+8D0A(B BBBG $ xVw. K^ < Vv DN
+E A $ \ $^ AG b
+AA < $ BEA A(D@
+(A ABBA < % BEA A(D@
+(A ABBA X& L `(p BEA A(D0H
(A ABBE D
-(A ABBA T V I 3 BEE E(D0A8G
-8D0A(B BBBE T \V N, BBE B(D0A8G
-8D0A(B BBBK 4 lW SJ BDA q
+(A ABBA T ( BEE E(D0A8G
+8D0A(B BBBE T -, Ʉ BBE B(D0A8G
+8D0A(B BBBK 4 2J BDA q
ABE A
-ABA d W Sg BLB B(A0A8D@q
+ABA d T 2g BLB B(A0A8D@q
8A0A(B BBBE
-8A0A(B BBBF , X @*~- AAG0!AA 4 W xT5 / BHD D`
- DABG < tX W BBG A(G@
-(A ABBF L X PX~ BLB H(A0D8D@
-8A0A(B BBBG < Y Y BBG A(G@
-(A ABBF L DY @Zm BBB H(A0D8DP
-8A0A(B BBBA T X `[ BBB B(H0A8J
+8A0A(B BBBF , v- AAG0!AA 4 4 p35 ߃ BHD D`
+ DABG < $ x6 BBG A(G@
+(A ABBF L d H7~ BLB H(A0D8D@
+8A0A(B BBBG < x8 BBG A(G@
+(A ABBF L 89m BBB H(A0D8DP
+8A0A(B BBBA T X:W BBB B(H0A8J
"
-8A0A(B BBBA , 4Y (q> ' AID W
-DAA L dY 8q BBB E(D0D8J
-8A0A(B BBBA T Y w BBE B(A0H8J
-8A0A(B BBBB L Z BLB H(A0D8DPV
-8A0A(B BBBG T \Z p
- BEE B(A0A8N
-8A0A(B BBBB T Z " BBB G(D0A8M
-8D0A(B BBBC [ `~. K^ [ ؐ [ А \ n'~\ AG RAT t[ BBB B(A0D8R3
-8A0A(B BBBA , [ > AID W
-DAA , \ "'~- AAG0!AA 4 ,\ 5 m BHD D`
- DABG T d\ 蘻r
O BBG B(A0A8J<
-8A0A(B BBBA t] }~. K^ ] ] L ] BBB J(A0A8G
-8A0A(B BBBG , ^ &~- AAG0!AA D ] q BIA D(De
-(D ABBA T ] H O BBB G(A0A8J
-8A0A(B BBBA ^ |~. K^ ^ `Ż _ XŻ , l^ `Ż[ AID t
-DAA , T_ &~- AAG0!AA 4 ^ `Ż5 BHD D`
- DABG $ _ hȻV ADD JAAd ,_ Ȼ1 Y BHB B(D0A8Dpv
+8A0A(B BBBA , `P> ׃ AID W
+DAA L pP$ BBB E(D0D8J
+8A0A(B BBBA T d PV BBE B(A0H8J
+8A0A(B BBBB L t ] BLB H(A0D8DPV
+8A0A(B BBBG T ^
+ a BEE B(A0A8N
+8A0A(B BBBB T d i" g BBB G(D0A8M
+8D0A(B BBBC t XPw. K^ `o Xo L `o O BBB J(A0A8G
+8A0A(B BBBG , v- AAG0!AA D pq BIA D(De
+(D ABBA T tH BBB G(A0A8J
+8A0A(B BBBA Ow. K^ Ȏ , l Ȏ[ AID t
+DAA , T v- AAG0!AA 4 Ȏ5 7 BHD D`
+ DABG $ БV ADD JAAd , 1 BHB B(D0A8Dpv
8C0A(B BBBG {
-8D0A(B BBBA T _ xɻ BBB E(I0A8K}
-8D0A(B BBBF d _ ˻ BEB B(D0A8Gp
+8D0A(B BBBA T BBB E(I0A8K}
+8D0A(B BBBF d g BEB B(D0A8Gp
8E0A(B BBBC
-8E0A(B BBBA T T` ̻ s BDB B(A0A8Gp
-8A0A(B BBBA da Pz~. K^ |a (ۻ a ۻ a %~\ AG RAT a ۻ BBB B(A0I8G
-8D0A(B BBBE D $b BBB B(A0A8D`8A0A(B BBB lb o T a p W BBB B(D0A8G
-8A0A(B BBBA , b $~- AAG0!AA < Tb U BIA D(Dp
+8E0A(B BBBA T T @ BDB B(A0A8Gp
+8A0A(B BBBA d Lw. K^ | v\ AG RAT p s BBB B(A0I8G
+8D0A(B BBBE D $ BBB B(A0A8D`8A0A(B BBB l o T ب BBB B(D0A8G
+8A0A(B BBBA , v- AAG0!AA < T PU BIA D(Dp
-(D ABBI T b U BBE E(A0A8G9
-8D0A(B BBBC T b 7 BBB B(A0G8J?
-8D0A(B BBBJ c w~. K^ d ,d Dd \d x td p d h d ` d X d P d H e @ e 8 4e 0 Le 8 de @ |e H e P e X e ` e h e p f x $f <f x Tf p lf h f ` f X f P f H f @ f 8 g 0 ,g ( Dg \g (" tg @ g H" g ` g h" g g " h h " 4h Lh " dh |h " h h " h h (" h @ i H" $i ` <i h Ti pB A@ ti B A@ i 8 Av i . Al i . Al i . Al j . Al 4j 0B A@ Tj `. Al tj p8 Av j B A@ j 8 Av j j k k L 4k AAG k
+(D ABBI T p c BBE E(A0A8G7
+8D0A(B BBBE T Ǹ ׁ BBB B(A0G8J?
+8D0A(B BBBJ `Jw. K^ 8߸ , 0߸ D (߸ \ ߸ t ߸ ߸ ߸ ߸ 4 L d | ߸ ߸ ߸ ߸ ߸ $ (߸ < ߸ T ߸ l ߸ ߸ ߸ , D \ " t " ߸ ߸" (߸ 0߸" H߸ P߸" 4 h߸ L p߸" d ߸ | ߸" ߸ ߸" ߸ ߸" ߸ ߸" $ < T B A@ t HB A@ x8 Av . Al . Al . Al . Al 4 B A@ T . Al t 8 Av 8B A@ h8 Av L 4 AAG k
CAH |
CAH R
-CAB JCA L k @ AAG k
+CAB JCA L AAG k
CAH r
CAJ R
-CAJ JCA L k AAG k
+CAJ JCA L H AAG k
CAH r
CAJ R
-CAJ JCA L $l AAG k
+CAJ JCA L $ AAG k
CAH r
CAJ R
-CAJ JCA L tl ` AAG k
+CAJ JCA L t AAG k
CAH r
CAJ R
-CAJ JCA L l AAG k
+CAJ JCA L h AAG k
CAH F
CAF R
-CAJ JCA L m 0 AAG k
+CAJ JCA L AAG k
CAH r
CAJ R
-CAJ JCA L dm AAG k
+CAJ JCA L d 8 AAG k
CAH |
CAH R
-CAB JCA L m AAG k
+CAB JCA L AAG k
CAH F
CAF R
-CAJ JCA L n ` AAG k
+CAJ JCA L AAG k
CAH |
CAH R
-CAB JCA Tn R ln n " 4 n t BJD G(D@R(A ABB n ` 4 n X BLD G AAB $o 0 , n ( AID [
-DAA , n o AID [
-DAA , n O AID [
-DAA , o H / AID [
-DAA , Do AID [
-DAA , to AID [
-DAA 4 \p h | BLD I\ AAB L p ! BHB J(D0A8DPe
-8A0A(B BBBB p $ < p # BLA "
+CAB JCA T hR l " 4 t BJD G(D@R(A ABB 4 BLD G AAB $ , /x AID [
+DAA , 0 x AID [
+DAA , w AID [
+DAA , w AID [
+DAA , D P w AID [
+DAA , t w AID [
+DAA , ow AID [
+DAA , p Ow AID [
+DAA 4 | BLD I\ AAB L BHB J(D0A8DPe
+8A0A(B BBBB D h < \ ` BLA "
ABD q
-ABD < <q X& BLA
+ABD < BLA
ABG N
-ABG |q ) q )2 q * q *5 q /" < q 0 BLA "
+ABG 0 (2 P $ H" < < ` BLA "
ABD q
-ABD $ 4r 2^ AG b
-AA D q 2w BEJ D(D0G
-0D(A BBBE D q 3w BEJ D(D0G
-0D(A BBBE L 4r (5~ e BBB E(D0A8Lp
-8D0A(B BBBH $ <s X6 AAG0AA4 r 6 BHA GPt
- DABC 4 r 8 BHA GPt
- DABC 4 s p: BHA GPt
- DABC 4 Ts 8< BHA GPt
- DABC 4 s > BHA GPt
- DABC 4 s ? v BHA GPt
- DABC 4 s A X BHA GPt
- DABC 4 4t XC : BHA GPt
- DABC 4 lt E BHA GPt
- DABC 4 t F BHA GPt
- DABC 4 t H BHA GP{
- DABD L u J8 BBB B(A0I8G
-8D0A(B BBBH L du xM8 BBB B(A0I8G
-8D0A(B BBBH L u hP' BBB B(A0I8G
-8D0A(B BBBA L v HR' BBB B(A0I8G
-8D0A(B BBBA L Tv (T' BBB B(A0I8G
-8D0A(B BBBA L v Vl BBL E(A0A8Gy
-8D0A(B BBBD L v (Wl g BBL E(A0A8Gy
-8D0A(B BBBD L Dw HXl @ BBL E(A0A8Gy
-8D0A(B BBBD L w hYl BBL E(A0A8Gy
-8D0A(B BBBD D w Z BBE D(K0J`
-0D(A BBBC T ,x `g BBG B(A0G8J"
-8D0A(B BBBA T x bG BDB B(A0G8G
-8D0A(B BBBA T x dG BDB B(A0G8G
-8D0A(B BBBA T 4y fG p BDB B(A0G8G
-8D0A(B BBBA T y hG N BDB B(A0G8G
-8D0A(B BBBA T y iy , BBB G(A0G8J1
-8D0A(B BBBA T <z lG
- BDB B(A0G8G
-8D0A(B BBBA T z ng BBG B(A0G8J"
-8D0A(B BBBA T z 0p BBB G(A0G8J9
-8D0A(B BBBA T D{ hrg BBG B(A0G8J"
-8D0A(B BBBA T| _~. K^ L l| ht AAG ]
+ABD $ | ^ AG b
+AA D E BBJ G(D0DP
+0A(A BBBD D L w t BEJ D(D0G
+0D(A BBBE D w t BEJ D(D0G
+0D(A BBBE L H~ }t BBB E(D0A8Lp
+8D0A(B BBBH $ x AAG0AA4 T $t BHA GPt
+ DABC 4 t BHA GPt
+ DABC 4 s BHA GPt
+ DABC 4 X s BHA GPt
+ DABC 4 4 s BHA GPt
+ DABC 4 l s BHA GPt
+ DABC 4 ps BHA GPt
+ DABC 4 x
Rs BHA GPt
+ DABC 4 @ 4s BHA GPt
+ DABC 4 L s BHA GPt
+ DABC 4 r BHA GP{
+ DABD 4 r BHA GP{
+ DABD 4 r BHA GP{
+ DABD L , X' r BBB B(A0I8G
+8D0A(B BBBA L | 8' r BBB B(A0I8G
+8D0A(B BBBA L ' vr BBB B(A0I8G
+8D0A(B BBBA L l br BBL E(A0A8Gy
+8D0A(B BBBD L l ;r BBB B(A0I8G~
+8D0A(B BBBA L l !r BBL E(A0A8Gy
+8D0A(B BBBD L ! q BBB B(A0I8G~
+8D0A(B BBBA L \ 8#l q BBL E(A0A8Gy
+8D0A(B BBBD L X$l q BBL E(A0A8Gy
+8D0A(B BBBD D x% q BBE D(K0J`
+0D(A BBBC T D *g vq BBG B(A0G8J"
+8D0A(B BBBA T -G Tq BDB B(A0G8G
+8D0A(B BBBA T /G 2q BDB B(A0G8G
+8D0A(B BBBA T L 0G q BDB B(A0G8G
+8D0A(B BBBA T 2G p BDB B(A0G8G
+8D0A(B BBBA T 4y p BBB G(A0G8J1
+8D0A(B BBBA T T 7G p BDB B(A0G8G
+8D0A(B BBBA T 9g p BBG B(A0G8J"
+8D0A(B BBBA T ; fp BBB G(A0G8J9
+8D0A(B BBBA T \ X=g Dp BBG B(A0G8J"
+8D0A(B BBBA l 1w. K^ L X? AAG ]
CAF k
ACA R
-CAJ JCA $ | t^ AG b
-AA D | t BBE A(A0D@n
-0A(A BBBA D ,} xu BBB D(A0D@
-0A(A BBBA L t} vh BBB B(D0A8DP9
-8A0A(B BBBA } | AL } |, T D} (| BBE E(D0A8G
-8D0A(B BBBE d T~ ` BBB B(A0A8D`
+CAJ JCA $ ?^ AG b
+AA D ? BBE A(A0D@n
+0A(A BBBA D D h@ BBB D(A0D@
+0A(A BBBA A, L A BBB B(D0A8DP
+8A0A(B BBBA E AL d E BBB B(A0A8D`
8A0A(B BBBE d
-8A0A(B BBBA T ~ X BBE B(D0A8G
-8D0A(B BBBH L \~ i > BBE E(D0A8Dp2
-8D0A(B BBBJ , d ~- AAG0!AA 4 ~ BHD D`
- DABD 4 BHD D`
- DABE T L BBB B(D0A8J
-8A0A(B BBBA , q } BDA fAB < BBG A(G@
-(A ABBG T Ȗ@ BBB E(A0A8J;
-8A0A(B BBBD L $ BOB B(A0A8Dp
-8A0A(B BBBJ T 0 BBB B(A0D8G
-8A0A(B BBBA D & BDA D@
+8A0A(B BBBA T 0G n BBE B(D0A8G
+8D0A(B BBBF T K m BBE B(D0A8G
+8D0A(B BBBC L t `Pi m BBE E(D0A8Dp2
+8D0A(B BBBJ , | Dv- AAG0!AA 4 PT rm BHD D`
+ DABD 4 , V Tm BHD D`
+ DABE 4 d @Y 6m BHD D`
+ DABE T [ m BBB B(D0A8J
+8A0A(B BBBA , `q l BDA fAB < @a BBG A(G@
+(A ABBG T d bP l BBB E(A0A8JK
+8A0A(B BBBD L t i BOB B(A0A8Dp
+8A0A(B BBBJ xn L $ o[ Rl BBB B(A0A8J
+8A0A(B BBBH D t w& ml BDA D@
DABE L
- DABA L \ BEB E(I0H8D@
-8D0A(B BBBA T s BBB B(A0D8G\
-8A0A(B BBBA T W
- w BBB B(A0D8G
-8A0A(B BBBH 0Y~. K^ T t BBB E(A0A8N@
-8A0A(B BBBK , ̂ @ü> AID W
-DAA , Pü> AID W
-DAA `ü AU `üF $ ü^ AG b
-AA D D ü BBB D(A0D@
-0A(A BBBA D ԃ HżZ BBE A(A0G@
-0A(A BBBC d `Ƽ BJE B(A0A8D@Y
-8C0A(B BBBB i8A0A(B BBB L Ƽ( BBB B(D0A8DPf
-8A0A(B BBBA ȼ AL T ȼ BEB B(D0A8G
-8A0A(B BBBA d 0˼" BBB B(A0A8DpO
+ DABA L w Cl BEB E(I0H8D@
+8D0A(B BBBA T y l BBB B(A0D8G\
+8A0A(B BBBA T d l BBB B(A0D8G
+8A0A(B BBBA t H*w. K^ T P l BBB E(A0A8N@
+8A0A(B BBBK , , > l AID W
+DAA , \ > l AID W
+DAA , (> ql AID W
+DAA t 8 AU 8F L pr l BBB B(E0A8J@
+8A0A(B BBBA $ ^ AG b
+AA D l ؕZ k BBE A(A0G@
+0A(A BBBC d k BJE B(A0A8D@Y
+8C0A(B BBBB i8A0A(B BBB 4
+ Hw AAG Y
+DAI DAA , T
+ j BHE AB < @ AL L
+ @ j BEE B(A0D8G
+8A0A(B BBBA <
+ 6 }j BBA A(J
+(A ABBA < 4 6 \j BBA A(J
+(A ABBA d , " BBB B(A0A8DpO
8A0A(B BBBI j
-8A0A(B BBBA d l ̼r BBB B(A0A8Dp
+8A0A(B BBBA d xr BBB B(A0A8Dp
8A0A(B BBBI j
-8A0A(B BBBA , Ԇ D~- AAG0!AA < L μ BIA D(Dp-
-(D ABBF < pѼ BIA D(Dp4
-(D ABBG T ̆ ԼO BBB B(A0A8J
-8A0A(B BBBA D $ ܼ8 BFB A(D0J
-0A(A BBBB < l BEA A(G@}
-(D ABBJ L BBE D(C0Ju
-0D(A BBBF L BBE D(C0Ju
-0D(A BBBF d Pi BBB B(A0A8Dm
+8A0A(B BBBA $ v AAG0AA< l h Ci BIA D(Dp-
+(D ABBF < i BIA D(Dp4
+(D ABBG < h BIA D(Dp4
+(D ABBG T ,
" h BBB B(A0A8J
+8A0A(B BBBA D
8 h BFB A(D0J
+0A(A BBBB <
诹
h BEA A(G@z
+(D ABBE L h BBE D(C0JU
+0D(A BBBF L \ h h BBE D(C0JU
+0D(A BBBF d d i BBB B(A0A8Dm
8A0A(B BBBA
-8A0A(B BBBG L X C BEB B(A0A8L
-8A0A(B BBBD L BEE B(A0A8D`
-8A0A(B BBBB , X BDA n
-ABH T BBB B(A0D8G
-8D0A(B BBBH T ܉ P BBE E(D0A8D
-8D0A(B BBBE T 4 BBE B(A0A8G
-8A0A(B BBBA T + BBB B(K0D8G
-8A0A(B BBBH P~. K^ T P * BBE B(A0A8N7
-8A0A(B BBBA , T >
AID W
-DAA , > AID W
-DAA l AU F @ AU Č @ F ܌ x A[ $ D x = BFA D@ , $ b D\
+8A0A(B BBBG L Sh BEB B(A0A8L
+8A0A(B BBBI T d ) ?h BBB E(A0D8G
+8A0A(B BBBA , t ù BDA n
+ABH ǹ5 T 0ǹ2 g BBB B(A0A8J
+8A0A(B BBBE L \ Ϲ g BBE B(D0D8G_
+8A0A(B BBBE T Թ g BBE E(D0A8D
+8D0A(B BBBE T P> g BBB B(A0A8J
+8A0A(B BBBA T \ 8 h BBB B(K0D8G
+8A0A(B BBBH l w. K^ T xL h BBE B(A0A8N
+8A0A(B BBBA , $ p> g AID W
+DAA , T > g AID W
+DAA , > g AID W
+DAA l AU F AU F A[ $ D g BFA D@ , $ xb D\
H P
H Q
-G I , T b D\
+G I , T b D\
H P
H Q
-G I < ` ( BHE m
+G I < ( BHE m
ABI A
-ABD , č P ( BHE u
-ABI L < P r Q BBB B(E0A8J@
-8A0A(B BBBA $ D ^ AG b
-AA < ^ BDA n
+ABD , ( BHE u
+ABI L < r f BBB B(E0A8J@
+8A0A(B BBBA $ D ^ AG b
+AA < P^ e BDA n
EBD C
-EBA $ b A]
-B A L ! BBB B(A0A8J
-8D0A(B BBBA , l "z y BHE gAB T $ AL 4 t $J BDA o
+EBA $ pb e A]
+B A L fe BBB B(A0A8J
+8D0A(B BBBA , l hz Ee BHE gAB T AL 4 t J BDA o
ABG A
-ABA T 8$ BBE E(A0A8G
-8D0A(B BBBB d & BLB B(A0A8Dq
+ABA d BLB B(A0A8Dq
8A0A(B BBBE
-8A0A(B BBBJ $ l } AAG0AA4 0/ ( BHD D`
- DABE L 1 BBE B(D0D8Gt
-8A0A(B BBBH \ h8m BBD D(J@
-(A ABBD T
-(A ABBF _
-(A ABBK D Đ x9 BBB D(A0J
+8A0A(B BBBJ $ ~v AAG0AA4 ` 1d BHD D`
+ DABE L d BBE B(D0D8Gp
+8A0A(B BBBD D ( Sd BBB D(A0J
-0D(A BBBJ L @AP # BEB E(A0D8DP
-8D0A(B BBBA d \ @C9 BLB B(D0A8D@q
+0D(A BBBJ L T P d BEB E(A0D8DP
+8D0A(B BBBA d 9 Wd BLB B(D0A8D@q
8A0A(B BBBJ
-8A0A(B BBBA L đ D
- BLB B(D0D8D@
-8A0A(B BBBG L ̒ F BBD D(D0O
+8A0A(B BBBA L
+ d BLB B(D0D8D@
+8A0A(B BBBG L BBD D(D0O
(A ABBK \
-(A ABBE < H BAD
+(A ABBE < d 8 BAD
ABE
-ABJ T M BBB B(A0A8D
-8A0A(B BBBJ d pV BBB B(A0A8D7
+ABJ T _c BBB B(A0A8D
+8A0A(B BBBJ d " BBB B(A0A8D7
8A0A(B BBBI l
-8A0A(B BBBA 4 [J BDA l
+8A0A(B BBBA 4 d 'J BDA l
ABJ A
-ABA \ T [% GLE B(A0A8DPV8A0A(B BBBHP T ] BBB B(A0D8G
-8A0A(B BBBA T T e BBB E(A0A8Ge
-8A0A(B BBBA d @G~. K^ T Ĕ l BBB B(A0D8N
-8A0A(B BBBA , pu> AID W
-DAA d u BEB B(A0A8D@
+ABA \ '% GLE B(A0A8DPV8A0A(B BBBHP T D p) 'b BBB B(A0D8G
+8A0A(B BBBA T 1| Kb BBB E(A0A8G+
+8A0A(B BBBA pw. K^ T 8 9b BBB B(A0D8N
+8A0A(B BBBA , d `A> Tb AID W
+DAA d L pA BEB B(A0A8D@
8A0A(B BBBE
-8A0A(B BBBA T wR _ BFB B(A0D8JT
-8A0A(B BBBA d Ė z BEB B(A0A8D@
+8A0A(B BBBA T CR a BFB B(A0D8JT
+8A0A(B BBBA d F BEB B(A0A8D@
8A0A(B BBBF
-8A0A(B BBBA L , { BBE B(A0A8D`
-8A0A(B BBBC L Ė ~ BBE B(A0A8J
-8A0A(B BBBC < ȃ BEA D(DP
-(D ABBC T T 8 BBB B(A0A8J
-8D0A(B BBBG d AX A[ , b D\
+8A0A(B BBBA L t G BBE B(A0A8D`
+8A0A(B BBBC L J a BBE B(A0A8J
+8A0A(B BBBC < \ O a BEA D(DP
+(D ABBC T (Q a BBB B(A0A8J
+8D0A(B BBBG U AX U A[ , Ub D\
H P
H Q
-G I , Ԙ b D\
+G I , Ub D\
H P
H Q
-G I L L 0r BBB B(E0A8J@
-8A0A(B BBBA $ T `^ AG b
-AA L Ę BBB B(A0A8J
-8D0A(B BBBA , 8z u BHE gAB AL T d ) BBB E(A0A8GQ
-8A0A(B BBBA L Pr _ BBE E(D0D8I{
-8D0A(B BBBI $ Ě V} AAG0AA4 4 X BHD D`
- DABD 4 l И BHD D`
- DABE T H BBB B(D0A8JW
-8D0A(B BBBE T BBB B(A0A8J
-8A0A(B BBBA H L $ BLB B(A0A8D
-8A0A(B BBBA L Z BEB B(D0D8G
-8A0A(B BBBE T T BBB B(A0D8G
-8A0A(B BBBF ?~. K^ T | 0 ( BBB B(A0D8N
-8A0A(B BBBG , Ԝ ǽ> AID W
-DAA , ǽ> AID W
-DAA T 4 ǽ v BBF A(A0
+G I L Vr i` BBB B(E0A8J@
+8A0A(B BBBA $ PW^ AG b
+AA L W ` BBB B(A0A8J
+8D0A(B BBBA , \ 8Yz _ BHE gAB D Z AL T Z _ BBB E(A0A8GQ
+8A0A(B BBBA L P`r _ BBE E(D0D8I{
+8D0A(B BBBI $ ! *v AAG0AA4 | Xb _ BHD D`
+ DABD 4 d d_ BHD D`
+ DABE T Hg F_ BBB B(D0A8JW
+8D0A(B BBBE T D! i _ BBB B(A0A8J
+8A0A(B BBBA T" Hq L l" s BLB B(A0A8D
+8A0A(B BBBA L " w ^ BEB B(D0D8G
+8A0A(B BBBE T T" ~ ^ BBB B(A0D8GL
+8A0A(B BBBF d# w. K^ T " 0 ^ BBB B(A0D8N
+8A0A(B BBBG , # > &_ AID W
+DAA , L# > _ AID W
+DAA T |# ^ BBF A(A0
(A BBBG W
-(A BBBJ d D Ƚ BBB B(A0A8DPB
+(A BBBJ d $ BBB B(A0A8DPB
8A0A(B BBBE L
-8A0A(B BBBA L ʽh BBE B(A0A8J
-8A0A(B BBBC D D 8Ͻ$ BBE A(D0D`
-0D(A BBBD T ѽ% BBB B(A0A8J
-8A0A(B BBBK L ֽ AAG ]
+8A0A(B BBBA L <$ h 6^ BBE B(A0A8J
+8A0A(B BBBC D $ 8$ 5^ BBE A(D0D`
+0D(A BBBD T $ % '^ BBB B(A0A8J
+8A0A(B BBBK L % AAG ]
CAF k
ACA R
-CAJ JCA $ 8^ AG b
-AA < p( BHE m
+CAJ JCA $ 4& 8^ AG b
+AA < \& p( BHE m
ABI A
-ABD , T `ؽ( BHE u
-ABI D `ٽ BBB D(A0D@
-0A(A BBBA 4 ̠ ڽ BDA D0
- AABE pܽ5 ܽ= 4 | ܽ 2 SGB A(A0D 4 ݽX cGB A(A0D L W BBB E(D0A8Si
-8A0A(B BBBJ \ < BBB A(A0Do
+ABD , & `( BHE u
+ABI D & ` BBB D(A0D@
+0A(A BBBA 4 ' ئ BDA D0
+ AABE L' p5 d' = 4 & \ SGB A(A0D 4 & X \ cGB A(A0D L 4' K q\ BBB E(D0A8Sf
+8A0A(B BBBE \ ' }\ BBB A(A0Do
0A(A BBBE i
-0E(A BBBA L T BOB A(A0]
+0E(A BBBA L ( BOB A(A0]
(A BBBG Y
-(A BBBH T p# BBE E(D0A8G,
-8D0A(B BBBE < D H u ACG@s
+(A BBBH T 4( `# '\ BBE E(D0A8G,
+8D0A(B BBBE < ( 8 [ ACG@s
DAE J
-DAA 4 < J BDA o
+DAA 4 ) J BDA o
ABG A
-ABA t AU
-A < ܢ BEA D(G@)
-(D ABBK T p BBB B(D0D8J"
-8A0A(B BBBJ T t l BBB B(A0A8J
-8A0A(B BBBA \ KOB B(A0A8Dpk8A0A(B BBBGp d `3 BOB B(A0A8D`E
+ABA ) AU
+A < $) g[ BEA D(G@&
+(D ABBF \ d) ` [[ BBB D(D0J
+0A(A BBBF
+0A(A BBBG T ) | #[ BBB B(A0A8J
+8A0A(B BBBA \ * ƺ KOB B(A0A8Dpk8A0A(B BBBGp d 4+ xʺ3 BOB B(A0A8D`E
8A0A(B BBBF |
-8D0A(B BBBE L L 8 BLB E(A0A8DpC
-8A0A(B BBBH T 7 BEB F(D0D8J
-8D0A(B BBBC T < % u BBB B(A0A8Ga
-8A0A(B BBBD L L F BEE B(D0D8D`
-8A0A(B BBBF L BEE B(G0D8G`C
-8A0A(B BBBC L 4 8 BLB B(A0D8D`
-8A0A(B BBBD L < x BBD D(D0O
+8D0A(B BBBE L + P̺ BLB E(A0A8DpC
+8A0A(B BBBH T 4+ к7 Y BEB F(D0D8J
+8D0A(B BBBC T + Ժ% Y BBB B(A0A8Ga
+8A0A(B BBBD L , F BEE B(D0D8D`
+8A0A(B BBBF L , BEE B(G0D8G`C
+8A0A(B BBBC L , P Y BLB B(A0D8D`
+8A0A(B BBBD L - BBD D(D0O
(A ABBK \
-(A ABBE T Ԧ ( BBB B(A0D8G
-8A0A(B BBBF L , p(F BBB B(A0D8J
-8D0A(B BBBJ D | p- + BBB D(A0D
-0D(A BBBD L ħ 0 + BEE E(A0D8G
-8D0A(B BBBA ̨ 84~. K^ | , p4} BFE E(D0D8Gp
+(A ABBE T $- @ 'Y BBB B(A0D8G
+8A0A(B BBBF D |- cY BBB A(D0J
+0D(A BBBD D - Y BBB D(A0D
+0D(A BBBD L . h Y BEE E(A0D8G
+8D0A(B BBBA / hw. K^ | t. } `Y BFE E(D0D8Gp
8A0A(B BBBF T
8A0A(B BBBB |
-8A0A(B BBBA T p6
BBE E(G0A8GJ
-8D0A(B BBBD D (? GBA A(G@H(A ABBH@ L p@ BEE E(D0A8G@p
-8D0A(B BBBF T 2~. K^ l @ @ @ @ ̪ @" @" , D A[ AID t
-DAA L t 8AU r BBB B(A0A8D`
-8A0A(B BBBJ L | HD AAG k
+8A0A(B BBBA T . X BBE E(G0A8GJ
+8D0A(B BBBD D 0 X GBA A(G@H(A ABBH@ L L0 BEE E(D0A8G@p
+8D0A(B BBBF 0 w. K^ 0 0 0 0
+ 1
+" ,1
+" , 0 8
+[ W AID t
+DAA L 0 h
+U W BBB B(A0A8D`
+8A0A(B BBBJ L 1 x
AAG k
CAH F
CAF R
-CAJ JCA L ̫ D AAG k
+CAJ JCA L 2
AAG k
CAH F
CAF R
-CAJ JCA (E 4 E5 L r}\ AG RA, l }- AAG0!AA 4 H5 BHD D`
- DABG 4 LE BHD D`
- DABK 4 T OE BHD D`
- DABK 4 0RE BHD D`
- DABD T Ĭ HU BBB B(A0I8G6
-8D0A(B BBBD T Z BBB B(A0I8G6
-8D0A(B BBBD T t `^ BBB B(A0A8O
-8D0A(B BBBG T ̭ d BBB G(A0G8J
-8D0A(B BBBE T $ xjU BDB B(D0G8G
-8D0A(B BBBA T | o ; BBB G(A0G8J
-8D0A(B BBBE -~. K^ D u BBE A(A0D@g
-0A(A BBBA D Hv BBE A(A0D@o
-0A(A BBBA L | vr BBB B(E0A8J@
-8A0A(B BBBA $ x^ AG b
-AA $ HxG A4
+CAJ JCA d2 X |2 P5 2 Fv\ AG RA, 2 v- AAG0!AA 4 ,2 (5 bV BHD D`
+ DABG 4 d2 0E DV BHD D`
+ DABK 4 2 HE &V BHD D`
+ DABK 4 2 `E V BHD D`
+ DABD T 3 x U BBB B(A0I8G6
+8D0A(B BBBD T d3 # U BBB B(A0I8G6
+8D0A(B BBBD T 3 H)^ V BBB B(A0A8O
+8D0A(B BBBG T 4 P- U BBB G(A0G8J
+8D0A(B BBBE T l4 3U cV BDB B(D0G8G
+8D0A(B BBBA T 4 8 V BBB G(A0G8J
+8D0A(B BBBE 5 v. K^ D 5 > BBE A(A0D@g
+0A(A BBBA D 46 x? BBE A(A0D@o
+0A(A BBBA L 5 @r SV BBB B(E0A8J@
+8A0A(B BBBA $ 6 @A^ AG b
+AA $ <6 xAG U A4
K A
-A $ pyG p A<
-E , L}- AAG0!AA < t hz BHD Dq
- DABA T H}Z BEB B(H0A8G H
-8A0A(B BBBC , P> AID W
-DAA $ < `' A
-N A
-A < d hH BBA A(Gp
-(D ABBA T xy BEB B(A0D8G+y
-8D0A(B BBBC T BBG B(A0A8J)
-8A0A(B BBBB T T XҾ~ E BBB G(A0D8M
-8D0A(B BBBA d 0*~. K^ , | hվ0 AC
FK
- E $ h־~ Al
+A $ d6 BG U A<
+E , D7 v- AAG0!AA < 6 C xU BHD Dq
+ DABA T 6 xFZ _U BEB B(H0A8G H
+8A0A(B BBBC , T7 Z> ?V AID W
+DAA $ 7 Z7 V A#
+L A
+A T 7 ] U BEB B(A0D8G+y
+8D0A(B BBBC T 8 P{ W BBG B(A0A8J)
+8A0A(B BBBB T \8 ~ W BBB G(A0D8M
+8D0A(B BBBA l9 v. K^ , 9 0 AC
FK
+ E $ 9 ~ Al
C V
-J < Գ ־ ADG v
+J < 9 p ADG v
GAF
-AAF پS L , Xپ+ ULB A(A0@(A BBBD0 L ij 8 BBE D(A0G,
-0D(A BBBD D u BFB A(A0GP
-0A(A BBBD d \ @9 BBB B(A0A8G@s
+AAF : S L 4: + ULB A(A0@(A BBBD0 L 9 襻 V BBE D(A0G,
+0D(A BBBD D : u HV BFB A(A0GP
+0A(A BBBD d d: 9 \V BBB B(A0A8G@s
8A0A(B BBBB _
-8A0A(B BBBG T Ĵ BBB B(A0A8J
-8A0A(B BBBK T ` BBB B(A0D8G~
-8A0A(B BBBA , '~. K^ L D BBE B(A0A8D
-8A0A(B BBBD L P AAG e
+8A0A(B BBBG T : Ƚ V BBB B(A0A8J
+8A0A(B BBBK T $; ͻ U BBB B(A0D8G~
+8A0A(B BBBA 4< v. K^ L L< @ BBE B(A0A8D
+8A0A(B BBBD L < AAG e
CAF S
ACA R
-CAJ JCA }\ AG RA4 L x BHE l
-ABB 4 8 D BHE o
-ABO L BKB B(D0D8G
-8D0A(B BBBA L ^ BEL B(A0A8G
-8A0A(B BBBH 4 \ BAI w
-ABA T 8 BBE B(D0A8G
-8D0A(B BBBG T $ BBE B(D0A8G
-8D0A(B BBBH T D ( BBE B(D0A8G
-8D0A(B BBBH T - h BBE B(D0A8G
-8D0A(B BBBG T X2b
N BBB E(A0A8G
-8D0A(B BBBD , }- AAG0!AA 4 | @?5 BHD D`
- DABG L HB BBB B(D0A8GT
-8D0A(B BBBK T Fn c BBB B(G0D8L
-8D0A(B BBBH "~. K^ , X D X \ X t X , Ժ X[ AID t
-DAA L X BBB B(A0A8D`^
-8A0A(B BBBJ }\ AG RA\ , \ BEB D(A0C
+CAJ JCA < v\ AG RA4 T< P T BHE l
+ABB 4 < T BHE o
+ABO L < T BKB B(D0D8G
+8D0A(B BBBA L = `^ iT BEL B(A0A8G
+8A0A(B BBBH 4 d= p OT BAI w
+ABA T = .T BBE B(D0A8G
+8D0A(B BBBG T = T BBE B(D0A8G
+8D0A(B BBBH T L> x S BBE B(D0A8G
+8D0A(B BBBH T > @ S BBE B(D0A8G
+8D0A(B BBBG T >
S BBB E(A0A8G
+8D0A(B BBBD , @ v- AAG0!AA 4 ? 05 +T BHD D`
+ DABG L ? 8
+
T BBB B(D0A8GT
+8D0A(B BBBK T @ n S BBB B(G0D8L
+8D0A(B BBBH A Pv. K^ 4A LA dA |A , @ [ T AID t
+DAA L A gT BBB B(A0A8D`^
+8A0A(B BBBJ B v\ AG RA\ 4B $ BEB D(A0C
(A BBBH n
-(A BBBK Q(A BBB< Ի ^ # BBA A(D0(A ABB \ ̼ (_X BBD A(G0
+(A BBBK Q(A BBB< A & S BBA A(D0(A ABB \ B 'X BBD A(G0
(A ABBB q
-(A ABBI d(G ABB < t (b BBA A(D0(A ABB , l }- AAG0!AA 4 Xb5 BHD D`
- DABG L `ee
BBH B(D0A8JK
-8D0A(B BBBK L l r
BBH B(D0A8JK
-8D0A(B BBBK t ~. K^ T Խ u BBB G(A0G8G
-8D0A(B BBBD p ~. K^ D , BBB A(A0D@
-0A(A BBBB , t }- AAG0!AA 4 5 BHD D`
- DABG L $ 薿h i BBB B(D0A8R
-8A0A(B BBBK , t > l AID W
-DAA L & O BBB B(A0D8J T
-8D0A(B BBBA ~. K^ T BEE E(D0A8G
-8D0A(B BBBE T d ſ i BEE E(D0A8G
-8D0A(B BBBE T ʿ90 O BBB B(D0A8OJ
-8D0A(B BBBE ~. K^ , , [ 7 AID t
-DAA , }- AAG0!AA 4 5 BHD D`
- DABG | h ~. K^ , }- AAG0!AA 4 l 5 ! BHD D`
- DABG 4 5 BHD D`
- DABG L BBB J(A0A8G
-8D0A(B BBBA L , BBB J(A0A8G
-8D0A(B BBBA T | 0
- BBE B(D0A8G
-8D0A(B BBBB T BBE B(D0A8G
-8D0A(B BBBB T , | BBE E(D0A8G
-8D0A(B BBBF < " T r l h O_ L ( f C BBH B(A0D8G
-8D0A(B BBBE $ H+ TAA L L @, BBH B(A0D8G
-8D0A(B BBBE T ~. K^ l < AQ }\ AG RA$ h< AG
-AA $ = AG
-AA , t}- AAG0!AA 4 t =5 BHD D`
- DABG T @ y BBB B(A0I8G
-8D0A(B BBBK ~. K^ $ @F^ AG b
-AA L D xF " BBE B(A0D8G
-8D0A(B BBBF L (~. K^ , d Jj ADG`
-AAH K KY l 0L BBB B(D0A8G
+(A ABBI d(G ABB < |B * R BBA A(D0(A ABB , tC v- AAG0!AA 4 B H*5 R BHD D`
+ DABG L $C P-e
wR BBH B(D0A8JK
+8D0A(B BBBK L tC p:
7S BBH B(D0A8JK
+8D0A(B BBBK |D v. K^ T C Gu S BBB G(A0G8G
+8D0A(B BBBD D v. K^ E X E X D 4E X BBB A(A0D@
+0A(A BBBB , |E *v- AAG0!AA 4 D [5 S BHD D`
+ DABG L ,E ^h S BBB B(D0A8R
+8A0A(B BBBK , |E a> S AID W
+DAA L E b& S BBB B(A0D8J T
+8D0A(B BBBA F Hv. K^ T F V BEE E(D0A8G
+8D0A(B BBBE T lF V BEE E(D0A8G
+8D0A(B BBBE T F 90 V BBB B(D0A8OJ
+8D0A(B BBBE G Xv. K^ , 4G ¼[ Y AID t
+DAA , H v- AAG0!AA 4 G ¼5 _Y BHD D`
+ DABG H v. K^ H ż H ż H ż H ż , H v- AAG0!AA 4 tH pż5 X BHD D`
+ DABG 4 H xȼ5 {X BHD D`
+ DABG L H ˼ ]X BBB J(A0A8G
+8D0A(B BBBA L 4I μ ZX BBB J(A0A8G
+8D0A(B BBBA T I Ҽ WX BBE B(D0A8G
+8D0A(B BBBB T I ּ =X BBE B(D0A8G
+8D0A(B BBBB T 4J ۼ| #X BBE E(D0A8G
+8D0A(B BBBF DK " \K r tK X O_ L J c W BBH B(A0D8G
+8D0A(B BBBE $ K 8 TAA L TK 0 /X BBH B(A0D8G
+8D0A(B BBBE \L 0v. K^ tL X AQ L v\ AG RA$ L 8 AG
+AA $ L AG
+AA , M v- AAG0!AA 4 |L x5 W BHD D`
+ DABG T L W BBB B(A0I8G
+8D0A(B BBBK M v. K^ $ M
^ AG b
+AA L LM H
~W BBE B(A0D8G
+8D0A(B BBBF TN v. K^ , lN pj ADG`
+AAH N N Y l N V BBB B(D0A8G
8A0A(B BBBD Z
-8A0A(B BBBD 4 p~. K^ L 8R d 0R | 8R @R 8R 0R L 8Rg BEB B(A0A8J
-8A0A(B BBBA 4 t XX V BHE n
-ABH 4 Z " BHE q
-ABM T [ BEB B(A0D8J
-8A0A(B BBBA g. T T (g BBB B(A0A8J
-8A0A(B BBBB d p~. K^ D | Xn
BBB B(A0A8
-0A(B BBBA < o BBB A(A0
-(A BBBA oR D@M oR D@M 4 @pW D@R L pX D@S d p, $ | p AA L ps BEE B(D0A8D@
-8A0A(B BBBF $ 0u< VAA 4 Hw BHA I(
- ABBG , T x{ BLI
-ABI $ z HN
+8A0A(B BBBD <O v. K^ TO lO O O O O L O g BEB B(A0A8J
+8A0A(B BBBA 4 |O ( U BHE n
+ABH 4 O ~U BHE q
+ABM T O x" KU BEB B(A0D8J
+8A0A(B BBBA P -. T \P - WU BBB B(A0A8J
+8A0A(B BBBB lQ v. K^ D Q (5
BBB B(A0A8
+0A(B BBBA < Q 5 BBB A(A0
+(A BBBA L R 6 BIB B(A0A8GW
+8A0A(B BBBA < Q E+ NT AF
Pf
+ C
+ A R JR D@M R KR D@M R PKW D@R R KX D@S R K, $ S K AA L <S N BEE B(D0A8D@
+8A0A(B BBBF $ S @P< VAA 4 S XR BHA I(
+ ABBG , S S{ BLI
+ABI $ T U HN
J W
-Y L | BRI B(A0D8G!
-8A0A(B BBBJ ' , w L D BIB B(A0A8J
-8A0A(B BBBF T BBE B(D0A8G
-8D0A(B BBBH T 4 X+
- BEE B(A0D8J
-8A0A(B BBBA L D 0 BIB B(A0A8Gd
-8A0A(B BBBA L BBB B(A0A8Gd
-8A0A(B BBBA d S BBB E(D0D8D
+Y L DT W BRI B(A0D8G!
+8A0A(B BBBJ T _' T 0_ T _w T $T `7 Q BBB E(A0A8M
+8A0A(B BBBJ D 4U e BHE D(G0D0A(A BBB L |U `f BIB B(A0A8J
+8A0A(B BBBF T U jB Q BBB E(A0A8M
+8A0A(B BBBJ L $V p/ BEE B(A0A8G
+8A0A(B BBBE T U s P BBB E(D0C8GY
+8A0A(B BBBA T V P{ P BBE B(D0A8G
+8D0A(B BBBH L $W BIB B(A0A8Gd
+8A0A(B BBBA L tW x BBB B(A0A8Gd
+8A0A(B BBBA d W ؘS BBB E(D0D8D
8A0A(B BBBD
-8A0A(B BBBG L L - BBE B(A0A8GK
-8A0A(B BBBG , ( AC
BN
- E T BBA A(Gp
-(A ABBG u
-(A ABBE 4 $ @X AC
BGM
- J
- F T h BKE E(D0A8J
-8A0A(B BBBA L Z BEB B(A0A8G3
-8A0A(B BBBG , * AC
BN
- D L 4 BEB B(A0A8G
-8A0A(B BBBA < P+
- w AC
BEO
- A h
- J T @ F BBA A(Gp
+8A0A(B BBBG < tW М O AC
BEO#
+ A +
+ G T W SO BBA A(Gp
(A ABBE w
-(A ABBK < d AF
P
- F R
- A 4 \ X8 AC
BGM
+(A ABBK 4 X (8 AC
BGM
H
- F T ` BHE B(D0D8J,
-8A0A(B BBBG , # AF
K
- E T d # BBB E(G0G8J9
-8A0A(B BBBJ t ~. K^ H,? p,? x ~. K^ ,+ Ai , , $ , < ,? < T , BEB D(A0M
-(A BBBF L @. BBB B(A0A8JS
-8D0A(B BBBD 0 0 0 , 0 T 0s ] BBB B(D0A8G
-8A0A(B BBBD $ < AG@q
-AA D == | BLF K0
+ F L X 0- BBE B(A0A8GK
+8A0A(B BBBG , LY ( AC
BN
+ E T X GN BBA A(Gp
+(A ABBG u
+(A ABBE 4 Y ½X AC
BGM
+ J
+ F T TY ƽ M BKE E(D0A8J
+8A0A(B BBBA L dZ սZ BEB B(A0A8G3
+8A0A(B BBBG , Z * AC
BN
+ D T ,Z L BHE B(D0D8J,
+8A0A(B BBBG , <[ AF
K
+ E T Z p {L BBB E(G0G8J9
+8A0A(B BBBJ [ v. K^ [ ? [ v. K^ T[ 4 'L Ar ,\ D\ \\ t\ (? \ P < \ X BEB D(A0M
+(A BBBF L ,\ SK BBB B(A0A8JS
+8D0A(B BBBD 4] H L] @ d] H |] @ T \ 8s J BBB B(D0A8G
+8A0A(B BBBD $ 4] `
J AG@q
+AA D \]
= J BLF K0
MABJ |
- CABF > $ > < > T > l = =T l g D }D BBB A(D0G
-0A(A BBBA $ , =h D0
+ CABF \^ t^ ^ ^ ^ ^ T l g D 4^ vD J BBB A(D0G
+0A(A BBBA $ |^ h I D0
A W
-I X~. K^ $ ? < ? T ? l ? ? (? 0? 8? @?- X?- T \ p? k BBB B(A0D8G<
-8D0A(B BBBC l N N N AT , &}{ AAD@rAA N DV < L Nw { BLD A(G@|
-(A ABBA $ D N^ AG b
-AA L O
# BBB F(A0D8G`
-8A0A(B BBBH T QS BBE B(D0A8G
-8D0A(B BBBD W
- L t W BBB B(A0A8JC
-8A0A(B BBBG , 8\# }GA A(D $ 8]* ADG [AA T @] BBE E(A0A8G
-8D0A(B BBBH < t c BBA A(D\
-(D AEFA < 8e= BBD D(D
-(A ABBA l 8f BBB E(A0A8G
+I \_ v. K^ t_ _ _ _ _ _ ` ` 4` - L` - T _ ( H BBB B(A0D8G<
+8D0A(B BBBC ` ` ` AT 4 T` v I BAD DP AAB Da H DV < ` P w H BLD A(G@|
+(A ABBA $ a ^ AG b
+AA L a
H BBB F(A0D8G`
+8A0A(B BBBH T \a "S wH BBE B(D0A8G
+8D0A(B BBBD lb (
+ L a ( OH BBB B(A0A8JC
+8A0A(B BBBG , b ,# SH }GA A(D $ c -* ADG [AA T tb - H BBE E(A0A8G
+8D0A(B BBBH < b 4 H BBA A(D\
+(D AEFA < c 5= H BBD D(D
+(A ABBA l Lc 6 G BBB E(A0A8G
8A0A(B BBBK
-8A0A(B BBBB < d h = BBD D(DU
-(A ABBA T Hj ? BBE B(D0A8G
-8A0A(B BBBA T k ' BIB B(A0A8G
-8A0A(B BBBA 4 sR BDA r
+8A0A(B BBBB < c h9 G BBD D(DU
+(A ABBA T c : G BBE B(D0A8G
+8A0A(B BBBA T Td < G BIB B(A0A8G
+8A0A(B BBBA 4 de XDR BDA r
ABL A
-ABA T s K BBE B(D0D8J #
-8A0A(B BBBF T 8 BBE B(D0A8G
-8D0A(B BBBH T < BBE B(D0A8G
-8D0A(B BBBG L L ȍ BBB B(A0A8Gp8A0A(B BBB 4 H BHH k
-ABA AL \ h KLA A(G@(A ABBI@a
-(A ABBI 4 T AAG d
+ABA T d D G BBE B(D0D8J #
+8A0A(B BBBF T <e T {H BBE B(D0A8G
+8D0A(B BBBH T e Y aH BBE B(D0A8G
+8D0A(B BBBG L f x^ BBB B(A0A8Gp8A0A(B BBB 4 <f _ G BHH k
+ABA ,g Pa AL \ Lg Pah KLA A(G@(A ABBI@a
+(A ABBI 4 g `c AAG d
DAF K
-DAH T ( BEB B(D0A8J
-8A0A(B BBBI l , KEB B(V0Q8L
-8A0A(B BBBJ {CL T BBB B(A0A8Gp8A0A(B BBB d BBB E(A0A8DP
+DAH T ,g c G BEB B(D0A8J
+8A0A(B BBBI l g j WG KEB B(V0Q8L
+8A0A(B BBBJ {CL h l BBB B(A0A8Gp8A0A(B BBB d h @n BBB E(A0A8DP
8A0A(B BBBE !
-8A0A(B BBBF T T BBB E(A0D8G
-8A0A(B BBBE L d BBF E(A0D8DPV
-8D0A(B BBBE T / BBB B(A0A8J[
-8A0A(B BBBA D T X BBA D(G
-(A ABBH D ( BBB A(D0J
-0A(A BBBI T 3 BEA A(D0
+8A0A(B BBBF L di q BBF E(A0D8DPV
+8D0A(B BBBE T h s F BBB B(A0A8J[
+8A0A(B BBBA D Ti Pu E BBA D(G
+(A ABBH D i v( E BBB A(D0J
+0A(A BBBI T i z
+F BEA A(D0
(D ABBK D
-(A ABBF l 0y BBF D(A0G@I
+(A ABBF l j (|y BBF D(A0G@I
0A(A BBBI w
0A(A BBBA D
-0F(A BBBG T @$ BBB B(A0A8G k
-8A0A(B BBBA L ~ _ BBB B(A0A8J l
-8A0A(B BBBF 8 L l 0 BEB E(I0H8D@
-8D0A(B BBBA \ BBB D(A0Da
+0F(A BBBG T j 8~$ WE BBB B(A0A8G k
+8A0A(B BBBA L k 7G BEB E(I0H8D@
+8D0A(B BBBA \ Tk p G BBB D(A0Da
0D(A BBBE
-0C(A BBBB AO AP , D P}. K^ \ DM | d < % x BBB B(A0A8Dp
+0C(A BBBB T k F BBB E(A0D8G
+8A0A(B BBBI L l H~ cH BBB B(A0A8J l
+8A0A(B BBBF m xϾ ,m pϾ AO Lm pϾ AP lm pϾ m hϾ m pv. K^ m HϾ DM m HϾ m @Ͼ n 8Ͼ n 0Ͼ 4n 8Ͼ d m @Ͼ% G BBB B(A0A8Dp
8D0A(B BBBE P
-8A0A(B BBBA L R # BBE E(D0A8DM
-8D0A(B BBBA }. K^ }\ AG RAT , `{ BBE B(D0A8G
-8D0A(B BBBH T BEE E(D0A8G
-8D0A(B BBBE L h BEB I(A0A8G}
-8D0A(B BBBK
AO T L
4 B BBB B(A0A8GK
-8A0A(B BBBJ L \ ] BBB H(A0D8DP
-8A0A(B BBBG D ~
+8A0A(B BBBA L m оR ~G BBE E(D0A8DM
+8D0A(B BBBA o 8v. K^ o v\ AG RAT n о{ G BBE B(D0A8G
+8D0A(B BBBH T n ־ G BEE E(D0A8G
+8D0A(B BBBE L 4o 0۾h F BEB I(A0A8G}
+8D0A(B BBBK <p Pݾ AO T o Pݾ4 F BBB B(A0A8GK
+8A0A(B BBBJ L p 8] BBB H(A0D8DP
+8A0A(B BBBG q H D ~
F b
-F (}. K^ ! ! ! , ! D ! \ t 4 L d | x p h $ ` < X T P l H @ 8 0 ( , D \ t 4 L d | x p h $ ` < X T P l H @ 8 0 ( , D \ t 4 L d | x p h $ ` < X T P l H @ 8 0 ( , D \ t ( 0 8 4 @ L H d P | X ` h p h ` X $ P < H T @ l 8 0 ( , AG bA * De 4 0 * De L H " DN
-N A L l X AAG ]
+F $q Hv. K^ <q Tq lq q q q x q p q h q ` r X ,r P Dr H \r @ tr 8 r 0 r ( r r r s s 4s Ls ds |s s s s s s t $t <t Tt lt t t t x t p t h t ` u X ,u P Du H \u @ tu 8 u 0 u ( u u u v v 4v Lv dv |v v v v v v w $w <w Tw lw w w w x w p w h w ` x X ,x P Dx H \x @ tx 8 x 0 x ( x x x y y 4y Ly dy |y y y y y y z $z <z Tz lz z z z x z z z { ,{ D{ \{ t{ { { { { { | | 4| L| d| || | | | | | } $} <} T} , AG bA t} * De } * De } " DN
+N A L } AAG ]
CAF [
ACA R
-CAJ JCA 6 Dq 6 Dq 6 Dq 6 Dq ( 6 Dq 4 P 6 Dq L x 6 Dq d 6 Dq | : Dk
-I A 4 Z BGA w
+CAJ JCA ~ 6 Dq ,~ 06 Dq D~ X6 Dq \~ 6 Dq t~ 6 Dq ~ 6 Dq ~ 6 Dq ~ 6 Dq ~ H: Dk
+I A 4 ~ hZ BGA w
AEI A
-ABA $ ^ ADD NAA4 H y BDD q
-AEO aAB 4 4 y BDD q
-AEO aAB l
- v fN 8!
- , 0!g @ BHH j
-ABA p! $ D h! YX
+ABA $ , ^ ADD NAA4 T y BDD q
+AEO aAB 4 y BDD q
+AEO aAB X
+ Pv fN
+ , T g 7 BHH j
+ABA < $ c7 YX
G z
-A $ ! , !g հ BHH j
-ABA l " $ " YX
+A | P , Hg 07 BHH j
+ABA Ā $ $ 6 YX
G z
-A h" , `"g j BHH j
-ABA " $ T " 2 YX
+A , d g 6 BHH j
+ABA L $ 6 YX
G z
-A 4 # , "g BHH j
-ABA | 8# t 0#I DN
+A , xg Z6 BHH j
+ABA ԁ t I DN
F H
H H
H H
H H
H H
@@ -15621,5531 +16520,6604 @@
H H
H H
H H
H H
H H
-A F $z cV $ p$ < h$ T `$ l X$ P$z cV $ $ $ $ $z cV % , $ D $ \ $ t $z cV H% @% 8% 0%z cV % % % 4 % L x%z cV d % | % % % %z cV (& & & $ & < &z cV T p& l h& `& X& P&z cV & & & & , &z cV D ' \ & t & & &z cV H' @' 8' 0' ('z cV 4 ' L ' d ' | x' p'z cV ' ' ' ' 'z cV $ ( < ( T ( l ( , ({ AAG r
-DAH 4 P( BGB A(A0D 4 4 ) BGB A(A0D < l )_ q BJD D(G
-(D ABBC $ + C AFD@ $ h+ ' AFD@ $ + AFD@ $ 8,D BDA A(DL
+A F d z cV | Ă ܂ z cV 8 0 $ ( < T z cV l x p h ̃ `z cV , z cV D \ t z cV ` Ԅ X P H @z cV 4 L d | z cV ą ܅ z cV $ 8 < 0 T ( l z cV x ̆ p h `z cV , D \ t z cV ԇ z cV X P 4 H L @ d 8z cV | Ĉ , ܈ { AAG r
+DAH 4 T / BGB A(A0D 4 . BGB A(A0D < Ĉ `_ . BJD D(G
+(D ABBC $ . AFD@ $ , . AFD@ $ T P f. AFD@ | D J. BDA A(DL
(A ABBB l
(C ABBD m
(A ABBE }
(A ABBE }
(A ABBE }
(A ABBE }
-(A ABBE \ / g rEA A(Dx
-(A ABBE p T 4 x3 / BBA A(Gl
+(A ABBE \ , X - rEA A(Dx
+(A ABBE p T - BBA A(Gl
(A ABBA D
-(F ABBA T 4 BBA A(GZ
+(F ABBA T ` r- BBA A(GZ
(A ABBC d
-(F ABBA T H6 BBA A(GZ
+(F ABBA T < [- BBA A(GZ
(A ABBC t
-(F ABBA L < 7 ZBA A(Db(A ABBL D 9 \ 9 t 9 9 9 x9 p9 h9 L L `9% ZBA A(Dj(A ABBN T @; l 8; 0; N D 8; ť BEJ D(D0G
-0D(A BBBA D , p<w BEJ D(D0G
-0D(A BBBE $ , =V ADD JAA< T = BBG A(G@
-(A ABBH >- < > BBG A(G@
-(A ABBH l 4 x? BBB B(A0D8D
+(F ABBA L P E- ZBA A(Db(A ABBL ̌ , D L % v, ZBA A(Dj(A ABBN
+ č
+ ܍
+ N D <
+ , BEJ D(D0G
+0D(A BBBA D w + BEJ D(D0G
+0D(A BBBE $ (
V ADD JAA< `
BBG A(G@
+(A ABBH - < 8 BBG A(G@
+(A ABBH l * BBB B(A0D8D
8A0A(B BBBH K
-8A0A(B BBBK < (Bo X BJD D(G
-(D ABBG L XC + BEB E(I0H8D@
-8D0A(B BBBA D 4 DJ BBB D(G0G
-0A(A BBBE 4 G L G* De d G* De | H6 Dq 0H6 Dq XH6 Dq H6 Dq H6 Dq H6 Dq
- H6 Dq $
- I6 Dq <
- HI" DR
-J A \
- XI" DR
-J A 4 |
- hIZ BGA z
+8A0A(B BBBK < o * BJD D(G
+(D ABBG < BBG A(G@
+(A ABBG L | G* BEB E(I0H8D@
+8D0A(B BBBA D ̏ J * BBB D(G0G
+0A(A BBBE ̐ * De 0* De H6 Dq , p6 Dq D 6 Dq \ 6 Dq t 6 Dq 6 Dq 86 Dq `6 Dq ԑ " DR
+J A " DR
+J A 4 Z BGA z
AEF A
-ABA
- I" DR
-J A
- I" DR
-J A
- I" DR
-J A I" DR
-J A 4 4 IZ BGA z
+ABA L " DR
+J A l " DR
+J A " DR
+J A " DR
+J A 4 ̒ Z BGA z
AEF A
-ABA l I" DR
-J A J" DR
-J A x}. K^ $ JE AAG yAA (J AL (J $ 0J HS D 0J \ 8J HS | 8J @J HS @J HJ HS HJ
PJ HS $
PJ <
XJ HS \
XJ t
`J HS
`J
hJ HS
hJ
pJ HS pJ xJ HS < xJ T J HS t J J HS J J HS J J HS , d J YX
+ABA 8" DR
+J A $ H" DR
+J A D Xv. K^ $ \ @E AAG yAA h AL h p HS ܓ p x HS x , HS L d HS HS Ԕ HS HS , D HS d | HS HS ԕ HS $ HS D \ HS | HS , 8$ YX
G
-B L XK , PK YX
+B , D $ YX
G
-B L , L YX
+B , P , H # YX
G
-B L , < L } YX
+B t , Ԗ # YX
G
-B $ M , xM H YX
+B , d# YX
G
-B l 8N , 0N YX
+B x , d p /# YX
G
-B N , N ޜ YX
+B L 0 , ( " YX
G
-B O , \ O YX
+B , " YX
G
-B D `P , XP t YX
+B ܘ , < " YX
G
-B Q , Q ? YX
+B $ X! , P! [" YX
G
-B Q , 4 Q
- YX
+B l " , ̘ " &" YX
G
-B R , | R ՛ YX
+B " , " ! YX
G
-B d @S , 8S YX
+B # , \ x# ! YX
G
-B S , S k YX
+B D 8$ , 0$ ! YX
G
-B T , T T 6 AADP~
-AAJ 4 XVu YKM
+B $ , $ R! AADP~
+AAJ 4 &u 6! YKM
HG
-HA 4 W YKM
-HG
-HA 4 Xk Ӛ YKpM
+HA < T ' ! YHD0M
+HAE
+HAG 4 `)k YKpM
HG
-HA $ ZE AAG yAA HZ AL , HZ D PZ HS d PZ | XZ HS XZ `Z HS `Z hZ HS hZ $ pZ HS D pZ \ xZ HS | xZ Z HS Z Z HS Z Z HS $ Z < Z HS \ Z t Z HS Z Z HS Z Z HS , L Z YX
+HA $ *E AAG yAA * AL ̛ * * HS * * HS < * T * HS t * * HS * Ĝ * HS * * HS * 4 * HS T * l + HS + + HS ĝ + ܝ + HS + + HS 4 + L + HS l + (+ HS , (+ YX
G
-B 4 p[ , h[ Z YX
+B Ԟ + , 4 + YX
G
-B | (\ , \ % YX
+B , , | , J YX
G
-B \ , $ \ YX
+B d X- , Ğ P- YX
G
-B ] , l ] YX
+B . , . YX
G
-B T P^ , H^ YX
+B . , T . YX
G
-B _ , _ Q YX
+B < / , x/ v YX
G
-B _ , D _ YX
+B 80 , 00 A YX
G
-B , x` , p` YX
+B ̠ 0 , , 0 YX
G
-B t 0a , (a YX
+B 1 , t 1 YX
G
-B a , a } YX
+B \ `2 , X2 YX
G
-B b , d b H YX
+B 3 , 3 m YX
G
-B L Xc , Pc YX
+B 3 , L 3 8 YX
G
-B d $ dE AAG yAA 0d AL 0d 8d HS , 8d D @d HS d @d | Hd HS Hd Pd HS Pd Xd HS Xd $ `d HS D `d \ hd HS | hd pd HS pd xd HS xd d HS $ d < d HS \ d t d HS d d HS d d HS D L d ZIA A(GP
-(H ABBG L Hf D @f W ZIA A(GP
-(H ABBG g D g ( ZIA A(GP
-(H ABBG i D l i ZIA A(GP
-(H ABBG l (k D k ʐ ZIA A(GP
-(H ABBG l D , l ZIA A(GP
-(H ABBG , Xn D Pn l ZIA A(GP
-(H ABBG o D o = ZIA A(GP
-(H ABBG q D L q ZIA A(GP
-(H ABBG L! s D s ߏ ZIA A(GP
-(H ABBG ! t D ! t ZIA A(GP
-(H ABBG " Xv D l! Pv ZIA A(GP
-(H ABBG l" w D ! w R ZIA A(GP
-(H ABBG " y D ," y # ZIA A(GP
-(H ABBG ,# 8{ 4 " 0{ YKM
-HG
-HA $ |# x|E AAG yAA # | AL # | # | HS # | $ | HS 4$ | L$ | HS l$ | $ | HS $ | $ | HS $ | $ | HS % | ,% | HS L% | d% | HS % | % | HS % | % | HS % | & | HS ,& | D& } HS d& } |& } HS & } & } HS & } & } HS ' } $' } HS D' } \' (} HS |' (} ' 0} HS ' 0} ' 8} HS ' 8} ( @} HS D l' @}( ) ZIA A(GP
-(H ABBG l( ( D ' 7 ZIA A(GP
-(H ABBG ( D ,( 7 ˉ ZIA A(GP
-(H ABBG ,) D ( 7 ZIA A(GP
-(H ABBG ) D ( 7 m ZIA A(GP
-(H ABBG ) D L) > ZIA A(GP
-(H ABBG L* D ) 7 ZIA A(GP
-(H ABBG * x D * p( ZIA A(GP
-(H ABBG + X D l* P7 ZIA A(GP
-(H ABBG l+ H D * @ ZIA A(GP
-(H ABBG + + 5 A/ \ L+ 3 BBB A(D0DPk
+B 4 4 $ L 4E AAG yAA t 4 AL 4 4 HS ̢ 4 4 HS 4 4 HS < 4 T 4 HS t 4 4 HS 4 ģ 4 HS 4 4 HS 4 4 4 HS T 4 l 4 HS 4 4 HS Ĥ 4 ܤ 5 HS 5 5 HS 4 5 L 5 HS l 5 5 HS D 5 ZIA A(GP
+(H ABBG 6 D L 6 | ZIA A(GP
+(H ABBG L `8 D X8 M ZIA A(GP
+(H ABBG : D 9 ZIA A(GP
+(H ABBG ; D l ; ZIA A(GP
+(H ABBG l 0= D ̦ (= ZIA A(GP
+(H ABBG ̧ > D , > ZIA A(GP
+(H ABBG , `@ D X@ b ZIA A(GP
+(H ABBG B D A 3 ZIA A(GP
+(H ABBG C D L C ZIA A(GP
+(H ABBG L @E D 8E ZIA A(GP
+(H ABBG F D F ZIA A(GP
+(H ABBG pH D l hH w ZIA A(GP
+(H ABBG l J D ̩ J H ZIA A(GP
+(H ABBG ̪ K 4 , Ku YKM
+HG
+HA < d L YHD0M
+HAE
+HAG T pN ZIB B(A0A8D@M
+8H0A(B BBBE $ QE AAG yAA ܫ @Q AL @Q HQ HS 4 HQ L PQ HS l PQ XQ HS XQ `Q HS ܬ `Q hQ HS hQ , pQ HS L pQ d xQ HS xQ Q HS Q ԭ Q HS Q Q HS , Q D Q HS d Q | Q HS Q Q HS Ԯ Q Q HS Q $ Q HS D Q \ Q HS | Q Q HS Q ̯ Q HS Q Q HS $ Q < Q HS D Q7 ZIA A(GP
+(H ABBG S D S( ZIA A(GP
+(H ABBG U D d U7 ZIA A(GP
+(H ABBG d W D İ W7 z ZIA A(GP
+(H ABBG ı Y D $ Y7 K ZIA A(GP
+(H ABBG $ [ D [7 ZIA A(GP
+(H ABBG x] D p] ZIA A(GP
+(H ABBG _ D D _7 ZIA A(GP
+(H ABBG D a D a ZIA A(GP
+(H ABBG b D b( ` ZIA A(GP
+(H ABBG d d5 A/ \ g BBB A(D0DPk
0C(A BBBD H
-0A(A BBBH D + BBB A(A0G`
-0A(A BBBG , , , < <, y BED A(D0I
-(A ABBA , 4- q BDA X
-ABF d- p |- >l}\ AG RAL - H BBB B(A0A8D
-8A0A(B BBBA T 4- BBB B(A0A8GP
-8A0A(B BBBA D. ` AL L d. ` BBF B(A0A8D@
-8D0A(B BBBB d . E BBB B(A0A8DP
+0A(A BBBH D Pi
BBB A(A0G`
+0A(A BBBG k k k < t ky {
BED A(D0I
+(A ABBA , l kq BDA X
+ABF l 9v\ AG RAL Ե k BBB B(A0A8D
+8A0A(B BBBA T l p BBB B(A0A8GP
+8A0A(B BBBA | AL L BBF B(A0A8D@
+8D0A(B BBBB d E BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l d. ضB BBB J(D0D8D4
+8F0A(B BBBA l xB BBB J(D0D8D4
8D0A(B BBBC i
-8A0A(B BBBE L . ~ φ BFB F(G0D8M
-8D0A(B BBBC / / AW 0 AP 40 i} DN
-E A L / r O BBB B(E0A8J@
-8A0A(B BBBA $ 0 ^ AG b
-AA d 0 ( BBB B(A0A8D`<
+8A0A(B BBBE L X~ BFB F(G0D8M
+8D0A(B BBBC , AW L AP l B7v DN
+E A L Է `r + BBB B(E0A8J@
+8A0A(B BBBA $ ܸ ^ AG b
+AA d Ȕ BBB B(A0A8D`<
8A0A(B BBBL d
-8A0A(B BBBA 41 AL T1 7 dO l1 DJ 1 DJ 1 1 9 AX
-G X < ,1 Ԅ BBA A(DU
-(E ABFA < l1
- BBA A(D[
-(A ABBA d2 $ AX
-G C 2 T 1 C X BBE E(D0F8J(
-8D0A(B BBBI 4 2 ADG _
-CAA PFC < t2 ; BBA A(G
-(E AFEA T 2 BBB B(A0A8Jc
-8A0A(B BBBG T 3 ` BBB B(A0D8G
-8A0A(B BBBG L d3 BEB E(D0D8G
-8A0A(B BBBB L 3 x ރ BEE E(D0D8G
-8D0A(B BBBD L 4 BEE B(A0D8GP
-8A0D(B BBBA T T4 V BBE B(A0A8G
-8A0A(B BBBC D 4 Ã BBD D(G
+8A0A(B BBBA l p AL p7 dO DJ Ĺ DJ x `9 AX
+G X < d
+ BBA A(DU
+(E ABFA < P
+
+ BBA A(D[
+(A ABBA $ AX
+G C 0 T (C 4
+ BBE E(D0F8J(
+8D0A(B BBBI 4 , ADG _
+CAA PFC < x; BBA A(G
+(E AFEA T x BBB B(A0A8Jc
+8A0A(B BBBG T D BBB B(A0D8G
+8A0A(B BBBG L H BEB E(D0D8G
+8A0A(B BBBB L BEE E(D0D8G
+8D0A(B BBBD L < X BEE B(A0D8GP
+8A0D(B BBBA T (V c BBE B(A0A8G
+8A0A(B BBBC D 0 BBD D(G
-(D ABBJ L 4 ( BED A(G@x
+(D ABBJ L , Ȳ p BED A(G@x
(D ABBD V
-(D ABBA T D5 x W BBB B(A0A8Ge
-8A0A(B BBBH T 5 BEB E(A0A8G
-8A0A(B BBBA , 5 q { BDA fAB , $6 k O BGA ]AB < 7 H BFB A(A0S
-(A EBBG L 6 BJE B(A0A8D@8A0A(B BBB T 6 BBB B(A0A8G
-8A0A(B BBBD 7 ( AG [D T \7 e
BEB E(D0D8G
-8D0A(B BBBA l8 AL L 7 s BEB E(I0H8D@
-8D0A(B BBBA < 8 x BBG A(G@
-(A ABBF D 9 ( BBE B(A0D8D@8A0A(B BBB d9 " DR
-J A , 9 ' BRE AE 9 AL L 9 BBE E(A0A8D@i
-8A0A(B BBBI l l9 `z BBB E(I0D8DP
+(D ABBA T | 3 BBB B(A0A8Ge
+8A0A(B BBBH T Խ BEB E(A0A8G
+8A0A(B BBBA , , Xq W BDA fAB , \ k + BGA ]AB < D 辿 BFB A(A0S
+(A EBBG L ̾ BJE B(A0A8D@8A0A(B BBB T 8 w BBB B(A0A8G
+8A0A(B BBBG , pȿ( AG [D T ȿe
BEB E(D0D8G
+8D0A(B BBBA տ AL L տ O BEB E(I0H8D@
+8D0A(B BBBA < BBG A(G@
+(A ABBF D T BBE B(A0D8D@8A0A(B BBB ؿ" DR
+J A , 0ؿ' BRE AE 0ٿ AL L 0ٿ BBE E(A0A8D@i
+8A0A(B BBBI l ڿz BBB E(I0D8DP
8D0A(B BBBA
-8D0A(B BBBA T 9 p} BEE B(A0D8J9
-8A0A(B BBBH : h}. K^ ; ; x 4; p L; h d; ` |; X ; ` AV ; ` AV ; ` < ; BFB A(A0S
-(A EBBG L ,< x O BEB B(A0A8D`
-8A0A(B BBBE l |< x x BBF D(A0G@F
+8D0A(B BBBA T ݿ w BEE B(A0D8J9
+8A0A(B BBBH $ tv. K^ < T l AV AV AV , < D x BFB A(A0S
+(A EBBG L O BEB B(A0A8D`
+8A0A(B BBBE l x BBF D(A0G@F
0A(A BBBD v
0A(A BBBJ D
-0F(A BBBG L < BBB E(H0A8DPI
-8A0A(B BBBE L <= " BBE E(A0A8D@i
-8A0A(B BBBI t = #< BFE D(D0Y
+0F(A BBBG L D ( BBB E(H0A8DPI
+8A0A(B BBBE L h BBE E(A0A8D@i
+8A0A(B BBBI t 8< BFE D(D0Y
(A BBBK }
(A BBJD l
(A BBBE F
-(A BBBK T L= `% C BBE B(A0H8G
-8A0A(B BBBA \> (}. K^ L t> [}w BBB E(A0A8DP[8A0A(B BBB L > ) BBB B(A0A8DT
-8A0A(B BBBA L ? . BBB B(A0A8DI
-8A0A(B BBBG T > 3X ~ BBE B(A0A8D
-8A0A(B BBBA ? \}\ AG RAT $? ;| } BBE E(D0A8G
-8D0A(B BBBE T |? @% } BBA A(D^
+(A BBBK T BBE B(A0H8G
+8A0A(B BBBA Hqv. K^ L (vw BBB E(A0A8DP[8A0A(B BBB L ` BBB B(A0A8DT
+8A0A(B BBBA L l BBB B(A0A8DI
+8A0A(B BBBG T @M
+ BBB B(A0A8J
+8A0A(B BBBJ ,*v\ AG RAT | | { BBE E(D0A8G
+8D0A(B BBBE T @% a BBA A(D^
(A ABBB P
-(A ABBA T ? A4 j} BBB B(D0D8G
-8A0A(B BBBK D ,@ pD D} BFE D(A0G
-0D(A BBBH D ,A H GBA A(G@X(A ABBH@ @ J | BIB E(D0A8F`w
+(A ABBA T , 4 ( BBB B(D0D8G
+8A0A(B BBBK D BFE D(A0G
+0D(A BBBH D H GBA A(G@X(A ABBH@ BIB E(D0A8F`w
8A0A(B BBBB
8A0A(B BBBA V
-8A0A(B BBBA T DA N | BEG E(A0A8J
-8D0A(B BBBA TB `}. K^ , lB VD BDD n
-DBA $ B (V^ AG b
-AA 4 B `V k| BHE
-ABH 4 DB (W 7| BHE
-ABE 4 4C WT BBD D(D0z(D ABB d B X { BBB B(A0A8DP
+8A0A(B BBBA T (% BEG E(A0A8J
+8D0A(B BBBA mv. K^ , ,F BDD p
+DBA , ,5 BAD fAB $ ,Y Af
+I 4 - BHE Y
+ABE 4 - BHE \
+ABJ $ .^ AG b
+AA < $ &v s BBA D(D@z(A ABB d d . 7 BBB B(A0A8DP
8A0A(B BBBH D
-8A0A(B BBBA , C pX6 AAG0
-AAA L LC Y 7{ BJB A(A0W
-(A BBDH ^(A BBB< TD Y BBG A(G@
-(A ABBG < D Z BBG A(G@
-(A ABBG < D P[ BBG A(G@
-(A ABBG T \D \ +z BJB B(A0A8GK
-8D0A(B BBBG lE x}. K^ L E W}w BBB E(A0A8DP[8A0A(B BBB T E paE y BBB B(A0A8J
-8A0A(B BBBH T tE h z BHB B(A0A8GF
-8A0A(B BBBA F AO , E {z AAG@s
-DAA F AS , <F <z AAG@w
-DAA $G AS , F y AAG@w
-DAA tG p L G hf BBB B(A0A8Dh
-8A0A(B BBBA < G BBG A(G@
-(A ABBF H H] D r
-J <H ] D r
-J d \H ȊE BBB B(A0A8DP
+8A0A(B BBBA T h.R BBE B(D0A8G
+8D0A(B BBBF L $ p3H BBE B(A0A8D`
+8A0A(B BBBA l t p5- m BLB B(A0A8Dc
+8A0A(B BBBK 6
+8A0A(B BBBA T 08C
+ BBE E(D0A8G
+8D0A(B BBBH T < (= BBB B(A0D8N
+8A0A(B BBBI , A> AID W
+DAA , A> AID W
+DAA , B> AID W
+DAA , B6 AAG0
+AAA , T Ci ^ ADG U
+AAA < < `C BBG A(G@
+(A ABBG < | D BBG A(G@
+(A ABBG L D BFE E(E0A8G`
+8D0A(B BBBH T T Fh w BBE B(A0A8D
+8A0A(B BBBA < d 8I BBG A(G@
+(A ABBF < I BBG A(G@
+(A ABBG T , JV BIH B(D0D8G
+8D0A(B BBBG $ MR z AF
+I A d MI W BLB B(A0A8Dpz
+8A0A(B BBBD
+8A0A(B BBBA fv. K^ d P BLB B(A0A8De
+8A0A(B BBBI
+8A0A(B BBBH T PU BLB B(D0A8G{
+8A0A(B BBBE d a BEB E(A0D8D
+8D0A(B BBBK P
+8A0A(B BBBA L T Pb BBB F(A0A8G S
+8A0A(B BBBF L y BEB B(A0D8G
+8A0A(B BBBG T { BBB B(D0D8GH
+8D0A(B BBBD T L ( BHB B(A0A8GF
+8A0A(B BBBA \ AO , 7 AAG@s
+DAA P AS , P AAG@w
+DAA AS , d AAG@w
+DAA L 0 L d (f BBB B(A0A8Dh
+8A0A(B BBBA < H BBG A(G@
+(A ABBF ] D r
+J H] D r
+J d 4 E BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l H s x BBB J(D0D8D
+8F0A(B BBBA l ps K BBB J(D0D8D
8D0A(B BBBI |
-8A0A(B BBBJ T |H ?x BEB B(A0A8G'
-8A0A(B BBBK 4 H ( w BDD DPv
- AABA L I BBB B(D0A8D%
-8A0A(B BBBH 4 \I w BDD DPv
- AABA LJ ȗ}. K^ D dJ BBE A(A0D@
-0A(A BBBA , J T}- AAG0!AA d J BFB B(A0A8DP
+8A0A(B BBBJ T T BEB B(A0A8G'
+8A0A(B BBBK 4 BDD DPv
+ AABA L ` BBB B(D0A8D%
+8A0A(B BBBH 4 4 ? BDD DPv
+ AABA $ h`v. K^ D < @ BBE A(A0D@
+0A(A BBBA , v- AAG0!AA d Ȕ BFB B(A0A8DP
8A0A(B BBEK
-8D0A(B BBBH d DK ` BFB B(A0A8DP
+8D0A(B BBBH d BFB B(A0A8DP
8A0A(B BBEK
-8D0A(B BBBH L J u BBB E(I0D8DP^
-8D0A(B BBBA T DK ( u BBE B(D0A8D
-8D0A(B BBBK L K 9 u BBE D(D0G
-0D(A BBBA L K u BEB E(I0H8DP
-8D0A(B BBBD d <L @{ [u BBB J(A0D8G`
+8D0A(B BBBH L x BBB E(I0D8DP^
+8D0A(B BBBA T BBE B(D0A8D
+8D0A(B BBBK L t P9 n BBE D(D0G
+0D(A BBBA L @ ? BEB E(I0H8DP
+8D0A(B BBBD d { BBB J(A0D8G`
8F0A(B BBBF B
-8F0A(B BBBA T L X# u BBE B(A0A8G
-8D0A(B BBBA T L 0 du BLE B(A0A8NE
-8A0A(B BBBD N AR L tM ] hu BEE I(D0A8JP
-8D0A(B BBBG |N ȓ}. K^ L N BEB H(A0D8G]
-8D0A(B BBBA 4 N 0 BAD D0~
- AABC 4 O } AAG R
+8F0A(B BBBA T | 3 BBE B(A0A8G
+8D0A(B BBBA T BLE B(A0A8NE
+8A0A(B BBBD AR L L ] $ BEE I(D0A8JP
+8D0A(B BBBG T h\v. K^ L l BEB H(A0D8G]
+8D0A(B BBBA 4 BAD D0~
+ AABC 4 } AAG R
GAM D
-AAA T N Rt BEB B(A0A8GI
-8A0A(B BBBI O 8 AQ T O 8 t BFE E(E0D8G
-8C0A(B BBBC 4 $P BAD D0~
- AABC L O 8 t BEB E(I0H8D@
-8D0A(B BBBD d O s BBB B(I0A8G`
+AAA L t BJB A(A0W
+(A BBDH ^(A BBBT 0 BEB B(A0A8GI
+8A0A(B BBBI X AQ T < X BFE E(E0D8G
+8C0A(B BBBC 4 L BAD D0~
+ AABC L X BEB E(I0H8D@
+8D0A(B BBBD d c BBB B(I0A8G`
8F0A(B BBBF r
-8F0A(B BBBG T \P P s BBB B(A0A8G
-8A0A(B BBBG lQ }. K^ < P ss BBA A(DV
-(A ABBA Q }. K^ Q X Fq
-A Q ( R L ,R AAG ]
+8F0A(B BBBG T p BBB B(A0A8G
+8A0A(B BBBG XYv. K^ < BBA A(DV
+(A ABBA 0Yv. K^ x Fq
+A $ H < @ L T 8 AAG ]
CAF [
ACA R
-CAJ JCA L |R H AAG ]
+CAJ JCA L h AAG ]
CAF k
ACA R
-CAJ JCA L R AAG ]
+CAJ JCA L AAG ]
CAF [
ACA R
-CAJ JCA L S AAG ]
+CAJ JCA L D AAG ]
CAF k
ACA R
-CAJ JCA lS >M}\ AG RAL R q BBB B(A0A8Ji
-8A0A(B BBBI L $S ( tq BBB B(A0A8Ji
-8A0A(B BBBI L tS x" [q BBB B(A0A8D
-8A0A(B BBBA < S X Jq BBD D(G0g
-(A ABBA L T " q BBB B(A0A8D
-8A0A(B BBBA , TT q ADG0Q
-AAA <U }. K^ TU < lU \ BBD D(D@`
-(D ABBA U U ~ _p D M
-A $ ,U p2 Wp D]
-O A V }. K^ T lU p p BBA A(Di
+CAJ JCA v\ AG RAL BBB B(A0A8Ji
+8A0A(B BBBI L L H BBB B(A0A8Ji
+8A0A(B BBBI T =
+ BBB B(D0A8GS
+8A0A(B BBBG < BBD D(G0g
+(A ABBA T 4 =
+ BBB B(D0A8GS
+8A0A(B BBBG 4 BDA GPv
+ AABA | Uv. K^ X < `\ BBD D(D@`
+(D ABBA L x~ D M
+A $ l 2 D]
+O A L 0Uv. K^ T BBA A(Di
(A ABBG e
-(A ABBE |V 4 V R BAD D0`
- DABA V 8~ o D M
-A $ 4V * o D\
-H A W }. K^ D tV Co BAA D`R
+(A ABBE 4 xR BAD D0`
+ DABA T ~ D M
+A $ t * D\
+H A T XTv. K^ D BAA D`R
AABB U
- AABG tW * W (& W @& W X& $ W pv AG z
-AA W X ,X DX \X tX " DN
-N A \ X BBA A(G@P
+ AABG x* & & & $ v AG z
+AA < 0 T ( l 0 8 @ H " DN
+N A \ X BBA A(G@P
(C ABBK \
(D ABBK
-(A ABBE <X \ m BBB E(A0A8DJ
+(A ABBE |
+\ BBB E(A0A8DJ
8D0A(B BBBH e
8A0A(B BBBA q
8D0A(B BBBB n
-8C0A(B BBBF T X
-t #m BBE B(D0A8G
-8D0A(B BBBH T 4Y f m BBD D(G
+8C0A(B BBBF T Ht BBE B(D0A8G
+8D0A(B BBBH T t pf } BBD D(G
(A ABBK
-(C ABBF DZ }. K^ \Z F}\ AG RA< |Z BBD D(I@
-(D ABBF Z h Z `$ A]
-B C 4 Z pY ACG
-AAD DCA ,[ 4 D[ a ACG E
-AAF DCA |[ 4 [ Y ACG
-AAD DCA [ DT 4 [ S AAF h
-CAD RCA 4 \ [ AAF n
-CAF RCA 4 T\ @S AAF h
-CAD RCA T [ h j BBA A(Ir
-(C ABBG E
-(A ABBA < ,\ W j BBA A(J
-(A ABBA < l\ R j BDA A(G
-(A ABBH < \ zj BED A(G`
-(A ABBC L \ Ij BDB E(D0C8Dp
-8A0A(B BBBD D <] `
j BBB A(D0G
-0A(A BBBA T ] 8 j BEE E(D0A8G
-8D0A(B BBBE < ] `$S j BBA A(Gx
-(A ABBA ^ }. K^ ^ h%2 _ %6 G n _ % AQ <_ % T_ % l_ %& Ad D ^ %& /i BLB A(A0GQ
-0A(A BBBF T _ 'k i BBB B(A0A8G
-8A0A(B BBBA ,` }. K^ D` * \` * L _ * h BJB A(A0b
-(A BBDE _(A BBBD ` * Mh BJD G@o
+(C ABBF XQv. K^
v\ AG RA< P BBD D(I@
+(D ABBF $ A]
+B C 4 4 Y ACG
+AAD DCA l 4 a ACG E
+AAF DCA 0 4 (Y ACG B
+AAA DCA P DT 4 $ XS AAF h
+CAD RCA 4 \ [ AAF n
+CAF RCA 4 [ AAF k
+CAI RCA T N BBA A(Iu
+(C ABBD E
+(A ABBA < l W ! BBA A(J
+(A ABBA < 8 R BDA A(G
+(A ABBH < X BED A(G`
+(A ABBC L , ( BDB E(D0C8Dp
+8A0A(B BBBD D | BBB A(D0G
+0A(A BBBA T # BEE E(D0A8G
+8D0A(B BBBE < (S z BBA A(Gx
+(A ABBA Lv. K^ , )2 D )6 G n \ * AQ | * * * * A^ D , *& BLB A(A0GQ
+0A(A BBBF T t +k h BBB B(A0A8G
+8A0A(B BBBA Kv. K^ . . L . BJB A(A0b
+(A BBDE _(A BBBD d P/ BJD G@o
DABC ]
- DABD D T` + h BJD G@l
+ DABD D (0 t BJD G@l
DABF ]
- DABD D ` ,{ g BJD G@s
+ DABD D 1{ ? BJD G@s
DABG @
- DABA T a - BHE A(D0D@|
-0D(A BBBA m0D(A BBB T a `. BHE A(D0D@y
-0D(A BBBD m0D(A BBB L a . g BHE B(A0D8D`
-8D0A(B BBBA b X}. K^ D a 0` f BJD JT
+ DABA T < 82 BEE E(D0A8G6
+8D0A(B BBBH T 7q BHD D(G`
+(D ABBI `
+(D ABBG T 9q BHD D(G`
+(D ABBI `
+(D ABBG T D 0: BEE E(D0A8G6
+8D0A(B BBBH T T ? BHE A(D0D@|
+0D(A BBBA m0D(A BBB T `@ BHE A(D0D@y
+0D(A BBBD m0D(A BBB L L @ BHE B(A0D8D`
+8D0A(B BBBA L B_ BHB B(D0D8G
+8D0A(B BBBE Gv. K^ D E` J BJD JT
DABK `
- DABI D Db 1` f BJD JT
+ DABI D L F` BJD JT
DABK `
- DABI $ Dc 2V ADD JAA< lc 2! BLG A(G@
-(A ABBF d c 3 BBB J(A0D8GP
+ DABI $ L GV ADD JAA< t G9 BLA F(G@
+(A ABBF d H BBB J(A0D8GP
8F0A(B BBBG
-8D0A(B BBGG T \c 6 e BFB E(A0A8D
-8A0A(B BBBA < ld X7 BFB A(A0S
-(A EBBG \ c 7 e BBB D(C0GP
-0D(A BBBD n0D(A BBB \ Td 8y d BEB D(D0Gp
-0D(A BBBH f
-0D(A BBBG < le :1 BLG A(G@
-(A ABBH L d ; 7d BEB E(I0H8D@
-8D0A(B BBBA L De 8= d BLB B(D0A8Dp8
-8A0A(B BBBA l e @ c BBE B(A0A8DJ
+8D0A(B BBGG T d 0K BIB E(A0A8D/
+8A0A(B BBBA < t L BFB A(A0S
+(A EBBG L HM BBB D(C0y
+(A MBBH k(A EBBT L M Y BEB E(A0C8J
+8D0A(B BBBE < \ @R1 BLG A(G@
+(A ABBH L @S # BEB E(I0H8D@
+8D0A(B BBBA L 4 T BLB B(D0A8Dp8
+8A0A(B BBBA l PX BBE B(A0A8DJ
8A0A(B BBBA T
-8A0A(B BBBA f h}}. K^ T f `D) jc BBB B(D0D8J
-8A0A(B BBBC T tf 8J) c BBB B(D0D8J
-8A0A(B BBBC T f P( c BBB A(A0U
+8A0A(B BBBA Cv. K^ T [) V BBB B(D0D8J
+8A0A(B BBBC T d a) BBB B(D0D8J
+8A0A(B BBBC T g( BBB A(A0U
(A BBBD h
-(H BBBJ T $g R( c BBB A(A0U
+(H BBBJ T pj( BBB A(A0U
(A BBBD h
-(H BBBJ T |g U wc BBE B(D0A8G
-8D0A(B BBBG T g Z ]c BBE E(D0A8G
-8D0A(B BBBK T ,h _ Cc BEE E(D0A8G
-8D0A(B BBBE D h d )c BBB A(D0J
-0A(A BBBD D h Pi nc BBB A(D0J
-0A(A BBBD i z}. K^ $ ,i mp c YX
+(H BBBJ T l Hm c BBE B(D0A8G
+8D0A(B BBBG T r I BBE E(D0A8G
+8D0A(B BBBK T Hw / BEE E(D0A8G
+8D0A(B BBBE D t p| BBB A(D0J
+0A(A BBBD D Z BBB A(D0J
+0A(A BBBD Av. K^ ( 4 L d | Є Ȅ $ < T AT t AT AT AT AT < T l AT x p h ` h ` 4 X L P d X | P X ` X P H @
$ 8 < 0 T ( l
,
D \ t Ѓ
+ Ѓ 4 ȃ L Ѓ d | Ѓ
ȃ
ȃ
$ < T l x , p D h
+ \ ` t X P H @ 8 0 ( 4 L d |
+ Ђ $ Ȃ < T l 6 AG lA 4 9 Hp T l Ȃ Ђ , D \ @ Hs | $ G AIF qAD $ HN ACL xDA , D p< _ AAL hDA , t ? 3 AKL aDA $ \ 9 AAL eDA / Am 9 \ \ . T Y . T Y
4 L AQ l AP AP AP AO $ - ADG [DA AL 4 L d | AO AO $
+ S AD W
+AC qA ,
+ " DQ
+K A L
+ 0 d
+ (6 Ad
+K E
+ H
+ @m A~
+A l
+ m A
+H d
+ m A~
+A l
+ 0m A
+H d 4 x L p d h | ` AO ` $ X, ADG ]AA ` X AX X , P D H \ @ t 8 $ 0, ADG ZDA 4 8s BAA S
+ABA , L xH o AAG0yDA , | H C AHG0rDA , H AAG0yDA , H AHG0rDA
$ < Ѕ. AJ0^D \ t - Ѕ ȅ 4 L d |
+ x p
$ h AU D h AU d h ` DR ` X A
+E Q 5 As , < @ BDA b
+ABA , l > s AAL W
+DAA , > S AAL W
+DAA $ (Z AIG CAD< ` BFD D(DPV
+(A ABBA І- , L AAL
+AAA 4 hm A
+H d < BBA I(D0~
+(A DBBA T 8 Y BDA B
+ADB E
+ADF Q
+ABD I
+ABA D 4 Ј BBB D(A0D@
+0A(A BBBA $ 4 ^ AG b
+AA , \ BHE ~
+ABH , 0 BHE A
+ABM L G BJE B(D0D8D`
+8D0A(B BBBE @/ $ X/ $ < p AD0A L 0 BGB B(A0A8JM
+8A0A(B BBBH D | BBB A(A0GP
+0A(A BBBI T D F BBB B(A0D8O
+8A0A(B BBBA D BBB A(I0D@
+0A(D BBBA 4 Й SGB A(A0D 4 SGB A(A0D L T `" m BBB B(A0D8GP
+8D0A(B BBBG 4 \ @J BDA o
+ABG A
+ABA , X\ E ADG l
+AAA $ j ! AD C
+AA < 4 П* AKG0
+DAH T
+ADA D t BBE D(I0D@Q
+0A(A BBBA L t H BBB B(D0A8DP-
+8A0A(B BBBA L BBB B(D0A8DP5
+8A0A(B BBBA , $ D AAG0AA$ l u AAG0AA, H BAD D0 AABL W BFE A(D0G
+0D(A BBBB d h/ BBE E(E0A8D@P
+8C0A(B BBBA
+8D0A(B BBGA | 0% G ] H% G ] T `_ BJB E(D0A8G
+8D0A(B BBBA h" DU
+G A d l x 6 BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB , " TGA A(G , " TGA A(G L BBB B(A0D8D`O
+8A0A(B BBBF , h H AAG0x
+DAJ < BLD D(G0
+(A ABBE \ Ȱ BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB\ $ BBE B(A0D8D@r
+8D0A(B BBBE D8C0A(B BBB, hX C ADG y
+EAA 4 t BDD Z
+ABI v- A * DY
+K A , б D D ȱ BBB A(D0D@
+0A(A BBBA D BBB A(A0D@
+0A(A BBBA < x+ BBA A(G`
+(A ABBH , h BAA G0 AAB< BBA A(L@Y
+(D ABBA 4 x BBA A(G@(A ABBD S BBB D(A0G@C
+0A(A BBBC L L ; BBB E(D0D8GP
+8A0A(B BBBB T x DR 4 l D BEA A(D0p(A ABB 4 o BGA GC
+ DABA D и4 BBB A(D0D@
+0A(A BBBA 4 l ȹo 8 BGA GC
+ DABA T 7 BEH D(G`@
+(A ABBH
+(A ABBA $ p YX
G `
-A j m $j `z} DP $ i m Oc AL r
-AA dj Pn |j Hn , i @n1 c AAG cCA j Pn j Hn j @n
- k 8n $k 0n L j (n kb BBB E(D0A8GPx
-8D0A(B BBBD L j n b BEB E(I0H8D@
-8D0A(B BBBD L $k (p a BJE B(A0A8D@8A0A(B BBB T tk p* a BEE E(A0G8G
-8D0A(B BBBG l l AV l A Dz l l - AG cA L m AAG ]
+A 0 8v DP $ T AL r
+AA 4! L! , 1 K AAG cCA ! ! !
+ ! ! x L T! p BBB E(D0A8GPx
+8D0A(B BBBD L ! [ BEB E(I0H8D@
+8D0A(B BBBD L ! p 3 BJE B(A0A8D@8A0A(B BBB T D" e BEE E(A0G8G
+8D0A(B BBBG L " 8 BEB E(I0H8DP
+8D0A(B BBBD d " , BGE E(A0A8D@
+8F0A(B BBBI g
+8F0A(B BBBA L T# " BBB E(A0A8Jy
+8D0A(B BBBC \$ v. K^ t$ X $ P $ H AV $ HA Dz $ $ x- AG cA L % AAG ]
CAF [
ACA R
-CAJ JCA L \m AAG ]
+CAJ JCA L d% AAG ]
CAF [
ACA R
-CAJ JCA L m H AAG ]
+CAJ JCA L % AAG ]
CAF [
ACA R
-CAJ JCA L m x AAG ]
-CAF [
-ACA R
-CAJ JCA Ln S < m w #` BLD A(G@|
-(A ABBA n 02 De
-G A , n PD AKD i
-DAA L <n pr _ BBB B(E0A8J@
-8A0A(B BBBA , Do ADD \
-AAI L to BBB B(A0C8Gp
-8A0A(B BBBF < o BEA H(G0
-(A ABBE p ` p X 4p P Lp H 4 dp @S BDA J
-ABD wAB p h p ` < p X ] BVA A(G`
-(D ABBA 4 Tp ] PGB A(A0D < p D ] BGA A(D
-(A ABBA T p |] BEE E(D0D8G
-8D0A(B BBBD < q BEB D(A0
-(A EBBD , r BGD l
-ABD L Lr ( BOB A(A0]
+CAJ JCA & S L d% `r BBB B(E0A8J@
+8A0A(B BBBA < % w \ BLD A(G@|
+(A ABBA , & D AKD i
+DAA & Y Af
+I & 0Y Af
+I , ' p ADD \
+AAI L L' BBB B(A0C8Gp
+8A0A(B BBBF < ' P BEA H(G0
+(A ABBE ' 0 ' ( ( $( 4 <( [ BDA J
+ABD AB t( 8 ( 0 < ' ( 4 BVA A(G`
+(D ABBA 4 ,( PGB A(A0D < d( D BGA A(D
+(A ABBA T ( O л BEE E(D0D8Gi
+8D0A(B BBBJ < ) BEB D(A0
+(A EBBD L ) H BOB A(A0]
(A BBBG Y
-(A BBBH L q w\ BHI E(D0D8HPo
-8D0A(B BBBD L r 8$ BDB A(D0
+(A BBBH L ) BHI E(D0D8KP|
+8D0A(B BBBD L * h$ BDB A(D0
(A BBBB A
-(A BBDA L <s BFB B(A0A8D@f
-8D0A(B BBBK 4 r [ BHD G0y
- DABA d s W[ BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB < ,t h BBG A(G@
-(A ABBF $ lt HV ADD JAA4 s 0 Z BAD
-ABA < t x BBG A(G@
-(A ABBG < u 8 BBG A(G@
-(A ABBG d Lu BBB B(D0A8DP
+(A BBDA L * H BFB B(A0A8D@f
+8D0A(B BBBK 4 |* BKD G0|
+ DABA d * 0 ۹ BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB < + BBG A(G@
+(A ABBF , * A^
+I A 4 |+ 0 BAD
+ABA D + BBB D(A0D@;
+0A(A BBBA < , BBG A(G@
+(A ABBG < , BBG A(G@
+(A ABBG , |, xi ; ADG U
+AAA d d- BBB B(D0A8DP
8A0A(B BBBC
-8A0A(B BBBH D u ` BCD D0
+8A0A(B BBBH D - BCD D0
FABE D
- FABA T Du 89 CY BEE E(A0D8I
-8D0A(B BBBG Tv AV 4 u X BFD y
-ABH , v h BDA ]AB v (" DN
-N A $ v 8B AAG0vAA L lv `P cX BEB E(I0H8DP
-8D0A(B BBBA < tw ` BED A(D0U
-(A ABBE < w Р BBG A(G@
-(A ABBH < w BBG A(G@
-(A ABBH \ 4x 0 BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 w Y W AIG t
-AAI DCA x ) CF
-G X T 4x V BBB B(A0A8O
-8A0A(B BBBA L x 0 V BBB B(D0A8Dp
-8A0A(B BBBA L x V BEB E(I0H8D@
-8D0A(B BBBA , y 0 BCA AB D \y d 3V BGB E(D0G`w
-0A(A BBBF T y U BEB B(A0A8Gc
-8A0A(B BBBA L z p BHB B(A0A8D`q
-8A0A(B BBBA { @ BFB B(D0D8GPv
+ FABA T \- 9 _ BEE E(A0D8I
+8D0A(B BBBG l. AV 4 - BFD y
+ABH , . h BDA ]AB . " DN
+N A $ / B AAG0vAA L . P BEB E(I0H8DP
+8D0A(B BBBA < / BED A(D0U
+(A ABBE < / BBG A(G@
+(A ABBH < 0 P BBG A(G@
+(A ABBH \ L0 BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 / @Y 7 AIG t
+AAI DCA T ,0 h[ BBB B(D0D8D
+8A0A(B BBBF T 0 pN BIB B(D0A8D
+8A0A(B BBBA T 0 h BBB B(A0A8G
+8A0A(B BBBA L 41 { BEB E(I0H8D@
+8D0A(B BBBA , <2 P BCA AB D 1 d # BGB E(D0G`w
+0A(A BBBF T 1 BBB E(A0A8Gn
+8A0A(B BBBA L 3 BHB B(A0A8D`q
+8A0A(B BBBA \3 ` BFB B(D0D8GPv
8A0A(B BBBE S
8A0A(B BBBC w
8A0A(B BBBG D
-8F0A(B BBBE T z 3 T BED I(D@
+8F0A(B BBBE T <3 3 BED I(D@
(D ABBK c
-(D ABBD T <{ c T BBE B(A0A8G>
-8A0A(B BBBD L { T BEB E(I0H8D@
-8D0A(B BBBA T { 8 T BBE B(A0A8G
-8A0A(B BBBA T <| KT BBB B(A0A8G
-8A0A(B BBBF , L} ADD ]
-AAA l | D U BBB E(I0D8DP
+(D ABBD T 3 f BBE B(A0A8GF
+8A0A(B BBBD L 3
BEB E(I0H8D@
+8D0A(B BBBA T <4 X% w BBB E(A0A8G
+8A0A(B BBBB T 4 0O a BBB B(A0A8J
+8A0A(B BBBF , 5 (. ADD ]
+AAA l 5 .D k BBB E(I0D8DP
8D0A(B BBBG
-8D0A(B BBBA T 4} d T BDB E(A0A8G
-8D0A(B BBBF , D~ ADD ]
-AAA t t~ p BIA D(G0B
+8D0A(B BBBA T 5 x0d BDB E(A0A8G
+8D0A(B BBBF , 6 4 ADD ]
+AAA t 6 5 BIA D(G0B
(A ABBA o
(A ABBK g
(A ABBC D
-(F ABBA d ~ BBB E(D0A8L`x
+(F ABBA d D7 x5 BBB E(D0A8L`x
8D0A(B BBBG
-8D0A(B BBBF T ~ @6 S BBB B(D0D8G
-8D0A(B BBBA \ ( BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBT T x R BED D(GPT
+8D0A(B BBBF T 6 66 BBB B(D0D8G
+8D0A(B BBBA \ 8 9 BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBT 7 : C BED D(GPT
(D ABBE C
-(D ABBA \ d BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 `Y >R AIG t
-AAI DCA z \Y
-A T |
Q BBB B(D0A8J
-8A0A(B BBBF $ ^ AG b
-AA L ?R BEB E(I0H8D@
-8D0A(B BBBD < L ` R BGB D(A0~
-(A BBBK T Q BBB E(D0N8GU
-8D0A(B BBBJ D , Q BIB A(D0JF
-0A(A BBBF $ B AAG0vAA L T Q BEB E(I0H8D@
-8D0A(B BBBD \ h [Q BBB I(D0D@
-0F(A BBBD U0F(A BBB T Q BBE B(F0D8Mc
-8A0A(B BBBA T \ Q BBB B(D0A8G1
+(D ABBA \ 8 : BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 d8 :Y AIG t
+AAI DCA T9 ;z \Y
+A t9 x; T 8 p;t
2 BBB B(D0A8J
+8A0A(B BBBF 9 H- AG cA L L9 H BEB E(I0H8D@
+8D0A(B BBBD < 9 (J BGB D(A0~
+(A BBBK T 9 J g BBB E(D0N8GU
+8D0A(B BBBJ D 4: L, . BIB A(D0JF
+0A(A BBBF $ 4; NB AAG0vAA L : N BEB E(I0H8D@
+8D0A(B BBBD \ : 0P ï BBB I(D0D@
+0F(A BBBD U0F(A BBB T T; P s BBE B(F0D8Mc
+8A0A(B BBBA T ; T BLB B(D0A8G
-8A0A(B BBBA L ( B Q BBE B(D0D8GpS
-8D0A(B BBBF $ (
- AAD AAD
- BBB E(A0A8HP8A0A(B BBBL , X g BBE B(A0A8Dp+
-8A0A(B BBBJ | x E
-D < H
BKD A(G0(A ABB ܅ ^ < BDB D(C0E
-(A EBBA L | ?P BBH E(D0D8DPU
-8D0A(B BBBA d @ BBE B(A0A8DP
+8A0A(B BBBA L < d7 _ BBE E(D0A8Ga
+8D0A(B BBBH $ = f AAD AAD 4= Hg BBB E(A0A8HP8A0A(B BBBL |= hg BBE B(A0A8Dp+
+8A0A(B BBBJ = i E
+D < = i BKD A(G0(A ABB ,> Pk^ < D> k BDB D(C0E
+(A EBBA L = Hm ˮ BBH E(D0D8DPU
+8D0A(B BBBA d > n BBE B(A0A8DP
8F0A(B BBBC [
-8F0A(B BBBF T 4 H O BBB B(D0A8J
-8A0A(B BBBH L D BLB I(D0A8D@
-8A0A(B BBBD L ` BHB E(D0D8Gpl8A0A(B BBB L BHB B(A0A8D
-8A0A(B BBBA T | `%m
N BLE B(A0A8G
-8A0A(B BBBA \}. K^ L `2 AAG e
+8F0A(B BBBF T > o( 7 BBB B(D0A8J
+8A0A(B BBBH L ? Xx BLB I(D0A8D@
+8A0A(B BBBD L ? z BHB E(D0D8Gpl8A0A(B BBB L 4@ 8| BHB B(A0A8D
+8A0A(B BBBA T ? m
u BLE B(A0A8G
+8A0A(B BBBA @ u. K^ L @ AAG e
CAF K
ACI R
-CAJ JCA L 2 AAG e
+CAJ JCA L DA X AAG e
CAF C
ACA R
-CAJ JCA $ D 03^ AG b
-AA L l h3 BBE A(D0
+CAJ JCA $ A ^ AG b
+AA L A BBE A(D0
(E BBED V
-(A BBEH L 4 M BBE B(D0D8FP
-8D0A(B BBBA L T 55 M BBB E(D0A8Gp
-8D0A(B BBBG L 6& EM BBB B(D0G8Dc
-8D0A(B BBBI L h8 M BBB B(D0G8Dc
-8D0A(B BBBI L D : L BJB A(A0W
-(A BBDH ^(A BBBT H; L BBB B(D0A8J
-8A0A(B BBBA L D L BEB E(I0H8D@
-8D0A(B BBBA T < @F" L BBB B(A0A8J
-8A0A(B BBBA L X}. K^ T f N BBE B(D0D8G]
-8A0A(B BBBG HX}. K^ 4 i M BAD `
-ABF D hk BBB A(D0DPa
-0A(A BBBH < l RM AAG @
+(A BBEH L TA 5 J BBB E(D0A8Gp
+8D0A(B BBBG L A p& BBB B(D0G8Dc
+8D0A(B BBBI L A P ٫ BBB B(D0G8Dc
+8D0A(B BBBI $ B V ADD JAA< lB m BBA D(G0$
+(A ABBA L dC H BBB B(A0A8Gp8A0A(B BBB d C ș/ BBE B(D0E8D@O
+8F0A(B BBBA
+8D0A(B BBGA L dC ê BJB A(A0W
+(A BBDH ^(A BBBT C v
+ w BBB B(D0A8J
+8A0A(B BBBA L D BEB E(I0H8D@
+8D0A(B BBBA , E ACG i
+ADG T D 5# K BBB B(D0A8G
+
+8A0A(B BBBD E u. K^ T D BBE B(D0D8GW
+8A0A(B BBBE F u. K^ 4 lE v BAD `
+ABF D \F P BBB A(D0DPa
+0A(A BBBH < E AAG @
AAE p
-AAB D ܌ pl0 M BBB A(A0Gz
-0A(A BBBA D $ Xm L BEB A(D0?
-(D BBBI T l 0ot L BBE B(D0A8G
-8D0A(B BBBH L č Xt L BEE B(D0D8M
-8A0A(B BBBE D w zL BBB A(A0G
-0A(A BBBC T \ x ^L BBE B(D0D8JI
-8A0A(B BBBH l U}. K^ P} H} }\ AG RAT 0} L BBA D(GPR
+AAB D ,F X0 BBB A(A0Gz
+0A(A BBBA D tF @ BEB A(D0?
+(D BBBI T F t P BBE B(D0A8G
+8D0A(B BBBH L G @ 6 BEE B(D0D8M
+8A0A(B BBBE D dG " BBB A(A0G
+0A(A BBBC L G BBE D(D0J,
+0A(A BBBA H Xu. K^ H H H u\ AG RAT dH BBA D(GPR
(D ABBE P
-(D ABBG L t h~ K BEB B(A0A8G[
-8D0A(B BBBD d | BBB B(A0A8DP
+(D ABBG L H BEB B(A0A8G[
+8D0A(B BBBD d I BBB B(A0A8DP
8A0A(B BBBE D
-8A0A(B BBBA AP L L K BEE B(D0D8D@
-8D0A(B BBBJ d T
BEE F(A0A8D@`
+8A0A(B BBBA ,J AP L I t BEE B(D0D8D@
+8D0A(B BBBJ d J
BEE F(A0A8D@`
8F0A(B BBBI
-8D0A(B BBGG L Ȕ BBB B(A0A8Gp8A0A(B BBB L H BLB B(A0A8DP
-8A0A(B BBBA L \ BLB B(A0A8DP
-8A0A(B BBBE L @J BLB E(A0A8D`
-8A0A(B BBBF L D I BBE B(A0D8Gj
-8A0A(B BBBE L R}. K^ T | I BBE E(D0A8G
-8D0A(B BBBE Q}. K^ T J I BBE B(D0A8G
-8A0A(B BBBJ , , 0 AC
FK
- E d \ BBB E(A0A8G@p
+8D0A(B BBGG L K x BBB B(A0A8Gp8A0A(B BBB L TK BLB B(A0A8DP
+8A0A(B BBBA L K BLB B(A0A8DP
+8A0A(B BBBE L <K h BLB E(A0A8D`
+8A0A(B BBBF L K BBE B(A0D8Gj
+8A0A(B BBBE L u. K^ T K 0| BBE E(D0A8G
+8D0A(B BBBE M hu. K^ T dL @
+J a BBE B(D0A8G
+8A0A(B BBBJ , tM 80 AC
FK
+ E d M 8 BBB E(A0A8G@p
8A0A(B BBBB i
-8A0A(B BBBA T (I BBB A(D0o
+8A0A(B BBBA T TM ئ BBB A(D0o
(A BBBG E
-(A BBBD L d h H BGB E(A0K8Dp
-8D0A(B BBBA $ H AG@q
-AA D ܔ `2 H BBB A(A0D
-0A(A BBBA ܕ X AW < X) ]SN
-AAD DHAO < H T P G BEE E(D0A8G
-8D0A(B BBBG < ظ< G BEA A(Gh
-(A ABBA < 4 ع7 G BBA A(Gf
-(A ABBA , N}. K^ $ G AL b
-AA D l 8 BBE A(A0D@n
-0A(A BBBA л $ Ȼ #G AG@c
-AA x $ 0 Dk < H м F AR t м ؼ' DR
-J F $ _F AG0S
-AD p OF K IN T < GF BEB B(A0A8D
-8A0A(B BBBD L 8 ;F BBB E(D0D8FS
-8A0A(B BBBJ T ; E BBD A(J`
+(A BBBD L M BGB E(A0K8Dp
+8D0A(B BBBA $ M d AG@q
+AA D $N 2 M BBB A(A0D
+0A(A BBBA $O AW < DO ) ]SN
+AAD DHAO O T N BEE E(D0A8G
+8D0A(B BBBG < <O < BEA A(Gh
+(A ABBA < |O 7 u BBA A(Gf
+(A ABBA tP (u. K^ $ O p C AL b
+AA D P BBE A(A0D@n
+0A(A BBBA P $ \P x Ӥ AG@c
+AA <Q 0 TQ ( lQ 0 0 Dk Q H H P c AR Q Q ' DR
+J F $ <Q AG0S
+AD dQ ! K IN T Q ! BEB B(A0A8D
+8A0A(B BBBD L Q & BBB E(D0D8FS
+8A0A(B BBBJ T ,R x(; BBD A(J`
(A ABBD U
-(A ABBA L < 3 E BBB A(D0G
-0A(A BBBE \ E BBB D(D0G`
+(A ABBA L R `)N k BBB A(D0G
+0A(A BBBE \ R `. BBB D(D0G`
0A(A BBBG U
-0A(A BBBK D E BEF A(A0G`
-0A(A BBBI K}. K^ 4 L L AAG ]
+0A(A BBBK D 4S / [ BEF A(A0G`
+0A(A BBBI 4T u. K^ LT P0 dT X0 |T `0 L T h0 AAG ]
CAF k
ACA R
-CAJ JCA L AAG ]
+CAJ JCA L T 0 AAG ]
CAF k
ACA R
-CAJ JCA L ( AAG ]
-CAF c
-CAI R
-CAJ JCA L hr "D BBB B(E0A8J@
-8A0A(B BBBA $ ^ AG b
-AA , AAG0q
-AAA T , P1 C BBE B(A0D8G
-8D0A(B BBBA , < 8k AAF Q
-CAC \ l x BBA D(D@h
+CAJ JCA L |T 0r " BBB B(E0A8J@
+8A0A(B BBBA $ U 2^ AG b
+AA , U P2 AAG0q
+AAA T $U 21 BBE B(A0D8G
+8D0A(B BBBA , 4V 5k AAF Q
+CAC dV 5 DI \ |V 6 BBA D(D@h
(D ABBB B
(D ABBE O
-(D ABBH < ̝ ( BBE D(D0_
-(A BBBI ? T $ GBB A(D0D@G0A(A BBBB@ | 8 AV 8 @ , ̞ 8N BHA p
-AHE T D XZ A BEE E(D0A8Gk
-8D0A(B BBBK L T `Q BEE E(A0A8DPz
-8D0A(B BBBB L p] BEE E(A0A8DPz
-8D0A(B BBBB T < @ BBE E(A0A8G
-8D0A(B BBBB T @ BBE E(D0A8G/
-8D0A(B BBBJ T H@ BBE E(A0A8G
-8D0A(B BBBD T D @ BBE E(A0A8G
-8D0A(B BBBF T ? BBB E(A0A8D~
-8D0A(B BBBD t e ? BBA D(Gm
+(D ABBH < V 6 BBE D(D0_
+(A BBBI W @7? T 4W h7 GBB A(D0D@G0A(A BBBB@ W 7 AV W 7 W 7 T $W 7Z BEE E(D0A8Gk
+8D0A(B BBBK < |W : z BBA D(Gg
+(D ABBH T W x< _ BBE E(A0A8G
+8D0A(B BBBH L X >] BEE E(A0A8DPz
+8D0A(B BBBB L Y ?Q BEE E(A0A8DPz
+8D0A(B BBBB T X @ BBE E(A0A8G
+8D0A(B BBBA T Y C E BBE E(D0A8G/
+8D0A(B BBBJ T dY E BBE E(A0A8G
+8D0A(B BBBE T Y H ˝ BBE E(A0A8G
+8D0A(B BBBE T Z `J BBE E(A0A8G
+8D0A(B BBBC T lZ L Q BBE E(A0A8G
+8D0A(B BBBE T Z N BBB E(A0A8D~
+8D0A(B BBBD t [ HQe BBA D(Gm
(D ABBJ
(D ABBF i
(D ABBF
-(D ABBB T l S? BBB E(A0A8D~
-8D0A(B BBBD t ġ &? BBA D(Gm
+(D ABBB T [ @T BBB E(A0A8D~
+8D0A(B BBBD t [ V l BBA D(Gm
(D ABBJ
(D ABBF l
(D ABBC r
-(D ABBE T < @ > BBB E(A0A8Dv
-8D0A(B BBBD t > BBA D(Gm
+(D ABBE T d\ X BBB E(A0A8Dv
+8D0A(B BBBD t \ ([ BBA D(Gm
(D ABBJ
(D ABBF i
(D ABBF {
-(D ABBD T ]> BBB E(A0A8Dv
-8D0A(B BBBD t d 0> BBA D(Gm
+(D ABBD T 4] `] BBB E(A0A8Dv
+8D0A(B BBBD t ] _ t BBA D(Gm
(D ABBJ
(D ABBF i
(D ABBF {
-(D ABBD T ܣ @ = BBB E(A0A8D~
-8D0A(B BBBD | 4 U = BBA D(Gm
+(D ABBD T ^ a & BBB E(A0A8D~
+8D0A(B BBBD | \^ HdU BBA D(Gm
(D ABBJ
(D ABBF i
(D ABBF
-(D ABBC T h _= BBB E(A0A8D~
-8D0A(B BBBD | U 2= BBA D(Gm
+(D ABBC T ^ (h BBB E(A0A8D~
+8D0A(B BBBD | 4_ jU v BBA D(Gm
(D ABBJ
(D ABBF i
(D ABBF
-(D ABBC T < BBB E(A0A8D~
-8D0A(B BBBD t < BBA D(Gm
+(D ABBC T _ `n BBB E(A0A8D~
+8D0A(B BBBD t ` p BBA D(Gm
(D ABBJ
(D ABBF i
(D ABBF z
-(D ABBE T \ a< BBB E(A0A8D~
-8D0A(B BBBD t X 4< BBA D(Gm
+(D ABBE T ` r BBB E(A0A8D~
+8D0A(B BBBD t ` Hu x BBA D(Gm
(D ABBJ
(D ABBF i
(D ABBF o
-(D ABBH T , ; BBB E(A0A8D~
-8D0A(B BBBD t
; BBA D(Gm
+(D ABBH T Ta w * BBB E(A0A8D~
+8D0A(B BBBD t a y BBA D(Gm
(D ABBJ
(D ABBF l
(D ABBC o
-(D ABBH T k; BBB E(A0A8D~
-8D0A(B BBBD t T X >; BBA D(Gm
+(D ABBH T $b | BBB E(A0A8D~
+8D0A(B BBBD t |b h~ BBA D(Gm
(D ABBJ
(D ABBF l
(D ABBC @
-(D ABBG T ̨ ! : BEB B(A0A8G d
-8A0A(B BBBA L $ 6 < BBB E(A0A8G
-8D0A(B BBBD T t ; < BBB E(A0A8Dv
-8D0A(B BBBD T ̩ > < BAD G
+(D ABBG T b 4 BBB E(A0A8Dv
+8D0A(B BBBD T Lc BAD G
DABG
DABE n
- DABC T $ hB M< BBB E(A0A8D~
-8D0A(B BBBD T | D < BAD G
+ DABC T c p Η BBB E(A0A8D~
+8D0A(B BBBD T c ȉ BAD G
DABG [
DABF n
- DABC T Ԫ F ; BBB E(A0A8D~
-8D0A(B BBBD T , 0I ; BAD G
+ DABC T Td h BBB E(A0A8D~
+8D0A(B BBBD T d X ; BAD G
DABG [
DABF n
- DABC < hK ; BBA D(G`
-(D ABBC T ī S5 W; BBE E(G0D8J4
-8D0A(B BBBD Ԭ P9}. K^ L 4 f w< BBE B(A0D8G`
-8D0A(B BBBA < h T h $ l h^ AG b
-AA , @h0 AC
FK
- E 4 @i ; BDA GPg
- DABD T D i ; BBB B(A0I8Dr
-8A0A(B BBBA T @l t; BBB B(I0A8D
-8A0A(B BBBA , mf AG d
+ DABC < e BBA D(G`
+(D ABBC T De ֖ BBE B(A0A8G
+8A0A(B BBBA D e Z BBE A(A0G
+0D(A BBBF T e 5 BBE E(G0D8J4
+8D0A(B BBBD f u. K^ L Tf BBE B(A0D8G`
+8D0A(B BBBA T f M BBB B(D0A8G
+8A0A(B BBBA g g $ g ^ AG b
+AA , h 00 AC
FK
+ E 4 g 0 , BDA GPg
+ DABD T g BBB B(A0I8Dr
+8A0A(B BBBA T h 0 ܘ BBB B(I0A8D
+8A0A(B BBBA , $i f AG d
AC D
-CA ܮ (n n AY
-F d \ nV : KBA A(DX(A ABBGN
-(A ABBD T Į q : BEB B(A0A8J
-8A0A(B BBBA T pv : BEE B(D0A8G
-8D0A(B BBBC T t ~, : BBE B(D0A8G
-8D0A(B BBBK d BLB B(A0A8D@q
+CA Ti li AY
+F d h V I KBA A(DX(A ABBGN
+(A ABBD T <i % BEB B(A0A8J
+8A0A(B BBBA T i ; BEE B(D0A8G
+8D0A(B BBBC T i , [ BBE B(D0A8G
+8D0A(B BBBK d j BLB B(A0A8D@q
8A0A(B BBBE
-8A0A(B BBBA < h BLG A(G@
-(A ABBH T t Hm 3: BEE E(D0D8J^
-8D0A(B BBBB L ̰ `W {: BBB B(D0D8J
-8D0A(B BBBA L Ա p~ BOB B(A0A8Dp~
-8A0A(B BBBE T l 3 : BBE B(A0A8J
-8A0A(B BBBE | 9 AG0lD L r: BBE E(D0A8Gw
-8A0A(B BBBE h3}. K^ $ 4 L d Z HQ L ` AAG e
+8A0A(B BBBA < dk BLG A(G@
+(A ABBH T j xm BEE E(D0D8J^
+8D0A(B BBBB L Dk BBB B(D0D8J
+8D0A(B BBBE L Ll ~ BOB B(A0A8Dp~
+8A0A(B BBBE T k @ t BBE B(A0A8J
+8A0A(B BBBK l 9 AG0lD L \l f Ɨ BBE E(D0A8Gw
+8A0A(B BBBE dm u. K^ |m m $ m m L m AAG e
CAF {
ACI R
-CAJ JCA L Գ AAG ]
+CAJ JCA L ,n ` AAG ]
CAF [
ACA R
-CAJ JCA L $ AAG e
+CAJ JCA L |n AAG e
CAF s
ACA R
-CAJ JCA t @ ذa HT $ (^ AG b
-AA L ` 8 BBB B(D0A8D
-8D0A(B BBBK $ $ ; F^
-D K 4 38 BAA GP
- AABE 4 PM BDF o
-ABJ AAB L hg 7 BJB A(A0G%
-0A(A BBBA < T $ 7 BBA A(D
-(A ABBA < x 7 BBA A(D
-(E ABGA < Ե X3 y7 BBA A(D
-(E AFEA < X X7 BBA A(D
-(E ABGA L T 8$ 67 BBB H(D0D8J
-8A0A(B BBBJ < M (7 BBA A(D
-(A ABBA D ( 7 BBA A(D
-(A ABBF L , F7 BEB B(A0A8Gi
-8A0A(B BBBI T | P 7 BBE B(D0A8G
-8D0A(B BBBB < Է W 7 BGA A(G
-(A ABBA L ̸ BBE H(D0A8D`
-8A0A(B BBBH d BLB B(A0A8D@c
+CAJ JCA n L n x BEB B(A0A8DP
+8A0A(B BBBA L 4o ! BBB E(A0A8DP
+8A0A(B BBBE $ o !^ AG b
+AA D o ! BGA
+ABD
+ABA A
+ALE $ o X$; F^
+D K 4 do p$A BAA GP
+ AABD 4 Tp %M BDF o
+ABJ AAB L o %_ BJB A(A0G
+0A(A BBBA d $p ' WBB B(A0A8I8A0A(B BBBB < p *$ N BBA A(D
+(A ABBA < p + , BBA A(D
+(E ABGA < q ,3
+ BBA A(D
+(E AFEA r -' Ae < lq - ɓ BBA A(D
+(E ABGA L q .$ BBB H(D0D8J
+8A0A(B BBBJ L r x1 BBB B(A0A8GPf
+8A0A(B BBBG D Lr 3 I BBA A(G
+(A ABBG T r 5 l BBE E(A0A8G
+8D0A(B BBBA < r (8W / BGA A(G
+(A ABBA L s H9 BBE H(D0A8D`
+8A0A(B BBBH d 4t : BLB B(A0A8D@c
8A0A(B BBBK |
-8A0A(B BBBJ T ̸ 0 +6 BBE E(A0A8G
-8D0A(B BBBA l $ 5 BBB B(A0D8D;
+8A0A(B BBBJ T s p; W BBE E(A0A8G
+8D0A(B BBBH l <t = BBB B(A0D8D;
8A0A(B BBBJ W
-8A0A(B BBBA 4 L J BDA q
+8A0A(B BBBA 4 du @J BDA q
ABE A
-ABA T ̹ ) Z5 BBE B(A0A8G
-8A0A(B BBBJ l $ E5 BBB B(A0A8G
-8D0A(B BBBA ]
-8A0A(B BBBA d L BEE F(A0A8D@`
+ABA L t 0@Z BBB B(D0A8D
+8D0A(B BBBJ T 4u @A I BBB E(A0A8G
+8A0A(B BBBA T u G ; BGB B(A0A8K
+8A0A(B BBBA L u J BBB A(A0D
+0A(A BBBI D 4v K BBB A(A0DT
+0A(A BBBH l |v M BBB B(A0A8G
+8D0A(B BBBA
+8A0A(B BBBA d w U BEE F(A0A8D@`
8F0A(B BBBI
-8D0A(B BBGG L P 4 BBE F(A0A8F`
-8D0A(B BBBH D GBA A(G@Q(A ABBG@ \ L m KLA A(G@(A ABBI@n
-(A ABBD BBD G(R@
+8D0A(B BBGG L Tw V - BBE F(A0A8F`
+8D0A(B BBBH D \x X GBA A(G@Q(A ABBG@ | x hY; KLB B(D0A8D`8A0A(B BBBK`
+8A0A(B BBBK $y (] BBD G(R@
(D ABBD Q
(D ABBF Q
(E ABBE Q
(E ABBE Q
-(E ABBA L | ` 3 BBB H(A0A8Dp
-8D0A(B BBBF d ̼ 2 BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB < 1 BLG A(G@
-(A ABBH L t V 72 BBB B(G0D8G[
-8D0A(B BBBF < | BBG A(G@
-(A ABBF L h%
- BBB B(D0D8Go
-8A0A(B BBBH T T H, 1 BBB B(A0I8Gf
-8A0A(B BBBG L 1 BBB E(D0A8Dp
-8D0A(B BBBH L 4 1 BEE E(D0D8L
-8D0A(B BBBA &}. K^ h 4 p 4 h F1 BGB A(A0D $ Dw
+(E ABBA L x ^ BBB H(A0A8Dp
+8D0A(B BBBF d Dy @` e BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB < dz `1 BLG A(G@
+(A ABBH L y a Í BBB B(G0D8G[
+8D0A(B BBBF < z e BBG A(G@
+(A ABBF d 4{ Hff BBE B(A0D8Dp
+8A0A(B BBBB }
+8A0A(B BBBI T z Pm BBB B(I0A8Gf
+8A0A(B BBBG T <{ xR d BBB B(A0A8G
+8A0A(B BBBC L { |4 6 BEE E(D0D8L
+8D0A(B BBBA | u. K^ | | | | 4 ,| | BGB A(A0D $ } } Dw
E c
-M &}. K^ A[ L AAG ]
+M D} Hu. K^ \} ~ A[ |} ~ } (~ L } ~ AAG ]
CAF [
ACA R
-CAJ JCA L d AAG ]
+CAJ JCA L } P~ AAG ]
CAF [
ACA R
-CAJ JCA L AAG ]
+CAJ JCA L L~ ~ AAG ]
CAF [
ACA R
-CAJ JCA |\ AG RA$ l / AG Y
-AA T k b/ BBE E(D0A8G
-8A0A(B BBBG , / AID J
-DAA , / AAG0i
-AAA L L k/ BEB E(I0H8D@
-8D0A(B BBBD L @ C/ BJE B(A0A8D`8A0A(B BBB T 9 . BBE E(A0A8J
-8D0A(B BBBA T D . BEE E(A0D8J
-8D0A(B BBBA T "}. K^ l "0 Hg #7 Hj L (# AAG e
+CAJ JCA $ ~ ~^ AG b
+AA $ ~ ~ AG Y
+AA T 4~ `F Ŋ BBE E(D0D8G
+8D0A(B BBBA , ~ X AID J
+DAA , ~ ۊ AAG0i
+AAA L ~ h BEB E(I0H8D@
+8D0A(B BBBD L < BJE B(A0A8D`8A0A(B BBB T x) @ BBE E(A0A8J
+8D0A(B BBBA T P_ BBE E(A0D8J
+8D0A(B BBBA Ƚu. K^ @ $ 80 Hg D H7 Hj L d h AAG e
CAF s
ACA R
-CAJ JCA d x# BBB B(A0A8DP
+CAJ JCA d BBB B(A0A8DP
8A0A(B BBBE D
-8A0A(B BBBA $ d $^ AG b
-AA , 8$ ADD0e
-AAA \ $ H- BBB A(D0GPq
-0A(A BBBE w
-0A(A BBBA L h% BOB A(A0]
+8A0A(B BBBA $ @^ AG b
+AA , D x ADD0e
+AAA \ ] BBB A(D0Gpq
+0A(A BBBE
+0A(A BBBD L Ԃ 8 BOB A(A0]
(A BBBG Y
-(A BBBH T %| , BBE E(D0A8G
-8D0A(B BBBE D + BBE A(A0D@d
-0A(A BBBA T T +1 S, BEE E(D0A8GI
-8D0A(B BBBE 4 | 9, BAD G`
- DABA L X1 , BBB B(A0D8D`b
-8A0A(B BBBC < 3 BBG A(G@
-(A ABBG T t 4C + BEB B(A0A8G
u
-8A0A(B BBBA T K" , BBB B(A0A8D:
-8A0A(B BBBF 8 }. K^ , < PN , AAG
-AAI D $ O4 BBE A(A0D@
-0A(A BBBA T P /, BEE B(D0A8J
-8A0A(B BBBF D `b@ BBE A(A0D@
-0A(A BBBA Xc $ Pc d < Xc BBB B(A0A8DP
+(A BBBH T l | և BBE E(D0A8G
+8D0A(B BBBE D | Н BBE A(A0D@d
+0A(A BBBA T x1 t BEE E(D0A8GI
+8D0A(B BBBE 4 d ru Z BAD G`
+ DABA L ( 7 BBB B(A0D8D`b
+8A0A(B BBBC < BBG A(G@
+(A ABBG T , h φ BEB B(A0A8G
+8A0A(B BBBA T " BBB B(A0A8D:
+8A0A(B BBBF Xu. K^ L pq BBB E(A0A8DP
+8A0A(B BBBA D 4 BBE A(A0D@
+0A(A BBBA T 3 BEE B(D0A8J
+8A0A(B BBBF D 0@ BBE A(A0D@
+0A(A BBBA ( d ( BBB B(A0A8DP
8A0A(B BBBE D
-8A0A(B BBBA $ c^ AG b
-AA , d0 AC
FK
- E T D e< + BBE B(D0A8G
-8D0A(B BBBF T }. K^ T i- {+ BBB B(A0D8J
-8A0A(B BBBI @ DV L H, BEB B(A0A8DP8A0A(B BBB L , (3 BBE B(A0A8DP8A0A(B BBB L | , BGB B(D0D8D
-8D0A(B BBBA < BEH D(A0H@0A(A EBBT T o, BKB E(A0D8K
-8D0A(B BBBE d }. K^ D ȬW , BBA A(N
-(A ABBA L AAG ]
-CAF c
-CAI R
-CAJ JCA , b D\
+8A0A(B BBBA $ | ^ AG b
+AA , 0 AC
FK
+ E T < BBE B(D0A8G
+8D0A(B BBBF , u. K^ T - BBB B(A0D8J
+8A0A(B BBBF DV L , BEB B(A0A8DP8A0A(B BBB L 3 BBE B(A0A8DP8A0A(B BBB L T , BGB B(D0D8D
+8D0A(B BBBA < BEH D(A0H@0A(A EBBT , X s BKB E(A0D8K
+8D0A(B BBBE < u. K^ , T b 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 , b D\
+G I , X!b D\
H P
H Q
-G I , b D\
+G I , !b D\
H P
H Q
-G I , `b D\
+G I , D !b D\
H P
H Q
-G I , 4 b D\
+G I , t "b D\
H P
H Q
-G I , d b D\
+G I , X"b D\
H P
H Q
-G I , b D\
+G I , ԋ "b D\
H P
H Q
-G I , `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 X#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 , Č #b D\
H P
H Q
-G I , b D\
+G I , $b D\
H P
H Q
-G I , b D\
+G I , $ X$b D\
H P
H Q
-G I , b D\
+G I , T $b D\
H P
H Q
-G I , D `b D\
+G I , $b D\
H P
H Q
-G I , t b D\
+G I , %b D\
H P
H Q
-G I , b D\
+G I , X%b D\
H P
H Q
-G I , b D\
+G I , %b D\
H P
H Q
-G I , `b D\
+G I , D %b D\
H P
H Q
-G I , 4 b D\
+G I , t &b D\
H P
H Q
-G I , d b D\
+G I X&m A~
+A l L & G BBB F(A0A8J@X
+8A0A(B BBBA 4 (( ADG X
+CAH [AA L ( ۂ BBB B(A0A8DW
+8D0A(B BBBF L +> BBB B(A0A8GZ
+8D0A(B BBBH 4 4 .Y BAG GP}
+ AABA < l /B v AADPU
+AAC
+AAA D 0 O BAA G@
+ AABH
+ AABH D 2 BED D(G$
+(D ABBE L 3 BBB A(A0E
+(A BBGG I
+(A BBDA D X4 BED D(G$
+(D ABBE D Ԑ 5 _ BED D(G$
+(D ABBE T (7 * BBB B(A0A8G
+8A0A(B BBBG T t : BBB B(A0A8G
+8A0A(B BBBG T ̑ = BBB B(A0A8G
+8A0A(B BBBG < $ 0Aa BBA A(JP
+(A ABBI D d `B ` BED D(G$
+(D ABBE D C + BED D(G$
+(D ABBE < 0E BBG A(G@
+(A ABBG E xu. K^ L d E` BAA GQ
+ AABH k
+ AABI T ] BBB B(A0A8G0
+8A0A(B BBBE T ( BEB B(A0A8J
+8A0A(B BBBA < d " AFGPe
+AAK T
+AAJ L j Ç BDB B(A0C8J
+_
+8A0A(B BBBG L f BBB B(A0A8Lv
+8A0A(B BBBJ L D
E H BBB H(A0A8GT
+8A0A(B BBBK L < BBB B(A0A8O
+8A0A(B BBBH L @ > BBB B(A0A8O
+8A0A(B BBBH L 4 = @ BBB B(A0I8G
+8A0A(B BBBG L -u ψ BBB B(A0C8O\
+8A0A(B BBBG L Ԗ 3C ӈ BBJ B(A0A8GZ
+8A0A(B BBBC l $ 7H BBB J(A0A8D^
+8A0A(B BBBB
+8A0A(B BBBD T : a BBB B(A0A8L
+8A0A(B BBBJ T x>o 4 BBB B(A0A8O-
+8A0A(B BBBH L D C BAA LP
+ AABH
+ AABK < Gu BBA C(JU
+(A ABBC L Ԙ `N} BBB B(A0A8O^
+8A0A(B BBBG \ $ R BBB A(A0LP@
+0A(A BBBD
+0A(A BBBH l Uq BBB B(A0A8Lp
+8A0A(B BBBJ
+8A0A(B BBBA < Y ` AAJPH
+AAJ j
+AAD , 4 Z) : AAJP~
+AAD , d [) # AAJP~
+AAD < \2 AAJPH
+AAJ
+AAI L Ԛ ^ BDE B(A0A8G
+8A0A(B BBBA ܛ t t t $ tF , @tT 7 AHG`
+AAA l pu , hub D\
H P
H Q
-G I , b D\
+G I , ub D\
H P
H Q
-G I , `b D\
+G I , ub D\
H P
H Q
-G I , b D\
+G I , (vb D\
H P
H Q
-G I , $ b D\
+G I , D hvb D\
H P
H Q
-G I , T b D\
+G I , v AHGp
+AAA T y
BBE E(A0A8G
+8A0A(B BBBE T D p BBE E(A0A8G
+8A0A(B BBBE T e BEE B(A0A8G
+8A0A(B BBBF T e BEE B(A0A8G
+8A0A(B BBBF u. K^ T d Յ BBA A(Gk
+(A ABBB
+(A ABBC D xG BBB A(A0JE
+0A(A BBBA T 7 ׆ BBA D(Gi
+(A ABBA |
+(A ABBA D \ hW BBB A(A0JH
+0A(A BBBF L i BDB B(A0I8G\
+8A0A(B BBBG L j BDB B(A0I8G\
+8A0A(B BBBG x t p
Y D h `
- t X P X ` , ԡ hb D\
H P
H Q
-G I `m A~
-A l t AR
-E UL & BBB F(A0A8J@X
-8A0A(B BBBA 4 ADG X
-CAH [AA L & BBB B(A0A8DW
-8D0A(B BBBF L 8> % BBB B(A0A8GZ
-8D0A(B BBBH , 4 (h % BDD y
-ABA 4 d hY % BAG GP}
- AABA < B % AADPU
-AAC
-AAA 4 ) AAJ P
-AAB
-GAO D 7% BAA G@
- AABH
- AABH D \ % BED D(G$
-(D ABBE D $ BED D(G$
-(D ABBE L BBB A(A0E
-(A BBGG I
-(A BBDA D < ` G$ BED D(G$
-(D ABBE D $ BED D(G$
-(D ABBE D 0 # BED D(G$
-(D ABBE D # BED D(G$
-(D ABBE D \ s# BED D(G$
-(D ABBE D h ># BED D(G$
-(D ABBE < a # BBA A(JP
-(A ABBI D , " BED D(G$
-(D ABBE D t h " BED D(G$
-(D ABBE < t BBG A(G@
-(A ABBG
}. K^ L , p " BAA GD
- AABE x
- AABD T | d" BBB B(A0A8G7
-8A0A(B BBBF T ؗE
- , BBE E(D0D8J
-8A0A(B BBBG T , С ', BEB B(A0A8J
-8A0A(B BBBA T
-, BBB B(D0A8Je
-8A0A(B BBBA T + BBB B(A0A8MZ
-8A0A(B BBBA < 4 " + AFGPe
-AAK T
-AAJ L t + BDB B(A0C8J
-_
-8A0A(B BBBG L U, BBB B(A0A8Lv
-8A0A(B BBBJ T 7 7, BBB B(A0A8O
-8A0A(B BBBA L l 3, BBB H(A0A8GX
-8A0A(B BBBG L ', BBB B(A0A8O
-8A0A(B BBBH L ), BBB B(A0A8O
-8A0A(B BBBH L \ pM +, BBB J(A0A8G
-8A0A(B BBBG L pu , BBB B(A0C8O\
-8A0A(B BBBG L , BBJ B(A0A8G^
-8A0A(B BBBG l L H , BBB J(A0A8D^
-8A0A(B BBBB
-8A0A(B BBBD L # E, BBB B(I0A8G
-8A0A(B BBBA , I, AAL@H
-AAH L < 1, BBB B(A0C8L\
-8A0A(B BBBB L , BBB B(A0A8O^
-8A0A(B BBBG L p + BAA LP
- AABH
- AABK < , u + BBA C(JU
-(A ABBC L l @} + BBB B(A0A8O^
-8A0A(B BBBG \ p + BBB A(A0LP@
-0A(A BBBD
-0A(A BBBD , ) + AAJPy
-AAI < L {+ AAJPH
-AAJ j
-AAD < U+ AAJPH
-AAJ
-AAJ , !! /+ AAJPv
-AAD , ") + AAJP~
-AAD , , #) + AAJP~
-AAD < \ $2 * AAJPH
-AAJ
-AAI L & * BDB B(A0A8J
-8A0A(B BBBF 5 5 5F , 4 H5T * AHG`
-AAA x6 , 4 p6b D\
+G I , b D\
H P
H Q
-G I , d 6b D\
+G I T | e Y BEE B(A0A8G
+8A0A(B BBBF T ԡ e Q BEE B(A0A8G
+8A0A(B BBBF T , e I BEE B(A0A8G
+8A0A(B BBBF T 0e A BEE B(A0A8G
+8A0A(B BBBF T ܢ He 9 BEE B(A0A8G
+8A0A(B BBBF u. K^ L L H; BBB B(A0A8G]
+8A0A(B BBBH T 85 Hl t X< Ho T ܣ x: BBB B(A0A8G2
+8A0A(B BBBC \ 4 ` BBB A(A0Jo
+0A(A BBBG Q
+0A(A BBBG L z BBB B(A0A8M
+8A0A(B BBBG D W BBA A(N
+(A ABBA L AAG ]
+CAF c
+CAI R
+CAJ JCA , 4 (b D\
H P
H Q
-G I , 6b D\
+G I , d hb D\
H P
H Q
-G I , 07b D\
+G I , b D\
H P
H Q
-G I , p7b D\
+G I , Ħ b D\
H P
H Q
-G I , l 7 ) AHGp
-AAA T : ) BBE E(A0A8G
-8A0A(B BBBE T x@ ) BBE E(A0A8G
-8A0A(B BBBE T L Fe ) BEE B(A0A8G
-8A0A(B BBBF T Le ) BEE B(A0A8G
-8A0A(B BBBF }. K^ T R ) BBA A(Gk
-(A ABBB
-(A ABBC D l gG 2* BBB A(A0JE
-0A(A BBBA T m7 * BBA D(Gi
-(A ABBA |
-(A ABBA D psW * BBB A(A0JH
-0A(A BBBF L T y + BDB B(A0I8G\
-8A0A(B BBBG L + BDB B(A0I8G\
-8A0A(B BBBG , b D\
+G I , (b D\
H P
H Q
-G I , $ ȅ + AAL`
-DAA T T xe * BEE B(A0A8G
-8A0A(B BBBF $ * JFD@ $ h * GFD@ $ 0 * GFD@ $ $ | * BFA D@ $ L Џ * JFD@ T t u w* BEE B(A0A8G
-8A0A(B BBBE T Жe o* BEE B(A0A8G
-8A0A(B BBBF (|. K^ L < М O* BBB A(A0M
-0A(A BBBB D \ t x p x
x p A] $ pF < T l , D Ȭ\ ) AAG o
-AAA , t b _) ADG r
-AAA $ \ 8A ACH pCA $ `A ACH pCA - AG cA , b D\
+G I , $ hb D\
H P
H Q
-G I , حb D\
+G I , T b D\
H P
H Q
-G I L , AAG ]
+G I , b D\
+H P
+H Q
+G I (m A~
+A l , xh ̄ BDD y
+ABA D L l BAA JPV
+ AABH
+ AABC L u. K^ L s- b BBB B(A0A8G
+8A0A(B BBBI T C ݅ BBE E(D0C8J
+
+8A0A(B BBBG D T @Yr B BBB A(A0J@
+0A(A BBBF D x], Z BCA OP_
+ AABH
+ AABC L `_6 , BDB B(A0A8O
+8A0A(B BBBA , 4 Pj = ACL@F
+AAH D d kr % BBB A(A0J}
+0A(A BBBI T o = BDB E(A0C8GR
+8A0A(B BBBA T Pw4 0 BDA A(L`
+(A ABBF
+(A ABBB T \ 8{ BBA A(Jpd
+(A ABBF
+(A ABBA , l ~b D\
+H P
+H Q
+G I , AAL`
+DAA T Ѐe BEE B(A0A8G
+8A0A(B BBBF $ l JFD@ $ u GFD@ $ ` GFD@ $ ;Ku K BFA D@ $ ( 5 JFD@ T 4 u BEE B(A0A8G
+8A0A(B BBBE T (e BEE B(A0A8G
+8A0A(B BBBF u. K^ L ( BBB A(A0M
+0A(A BBBB 4 إ L Х d إ |
إ Х A] ̮ ХF , D \ t , Ԯ \ \ AAG o
+AAA , Pb 8 ADG r
+AAA $ A ACH pCA $ A ACH pCA < Y Af
+I , \ b D\
+H P
+H Q
+G I , `b D\
+H P
+H Q
+G I L AAG ]
CAF [
ACA R
-CAJ JCA | H Y PS m A~
-A l < w ' BLD A(G@|
-(A ABBA (- , $ @{ BFA i
-ABA , T { BFA i
-ABA 4 #' BKE
-AJA 4 x & BKE
-AJE L < & BBB B(A0A8GPd
-8A0A(B BBBA L P & BJB A(A0W
-(A BBDH _(A BBBL 3& BBB B(A0A8GP
-8A0A(B BBBA L , pr % BBB B(E0A8J@
-8A0A(B BBBA T | % BBB B(D0A8J]
-8D0A(B BBBA < BED A(G0
-(A ABBA x B Av
-I A , A^
-A K < , A^
-A K \ ȹ t % عG DB 2 D]
-G I 4 0M AAG0o
+CAJ JCA Ч Y $ اm A~
+A l < (w BLD A(G@|
+(A ABBA h- , { BFA i
+ABA , ̱ Ш{ BFA i
+ABA , { BFA i
+ABA , pY Af
+I 4 ā BKE
+AJA 4 ̱ H BKE
+AJE L \ BBB B(A0A8GPd
+8A0A(B BBBA L T BJB A(A0W
+(A BBDH _(A BBBL p Ԁ BBB B(A0A8GP
+8A0A(B BBBA L @r BBB B(E0A8J@
+8A0A(B BBBA T D p d BBB B(D0A8J]
+8D0A(B BBBA $ T ر^ AG b
+AA L ij BFB E(A0A8GP
+8A0A(B BBBH < ̴ p BED A(G0
+(A ABBA B Av
+I A , D , A^
+A K d , A^
+A K ( % 8G DB ̵ p2 D]
+G I 4 M AAG0o
AAF D
-AAJ $ HV AD s
-AG $ < x
- T p l h
- `
- X AO X P H @ D[ $ @ < 8 T @ $ l H AIK0AAd BBE B(D0A8G@
-8A0A(B BBBJ
-8A0A(B BBBK 4 \ P " BDA D@u
- AABD L 2 ! BBB B(A0D8D
-8D0A(B BBBH : d D f! BBB B(D0A8D`H
+AAJ $ $ V AD s
+AG 4 L R ADJ Y
+DAK MDA
+ ̶
+
+ AO 4 ض L ж D[ l ж ض $ AIK0AAd ķ x BBE B(D0A8G@
+8A0A(B BBBG
+8A0A(B BBBC , 4 o| BDA D@u
+ AABD L ķ 2 P| BBB B(A0D8D
+8D0A(B BBBH ̸ : , AV d { BBB B(D0A8D`H
8A0A(B BBBE
-8A0A(B BBBK d p ! BBB B(D0A8D`H
+8A0A(B BBBK d a{ BBB B(D0A8D`H
8A0A(B BBBE
-8A0A(B BBBK T 9 BBA A(Dq
+8A0A(B BBBK T d `9 { BBA A(Dq
(C ABBE L
-(A ABBA T l BEE E(D0D8G
-8D0A(B BBBI L | ( BOB A(A0]
+(A ABBA T H z BEE E(D0D8G
+8D0A(B BBBI L ̺ BOB A(A0]
(A BBBG Y
-(A BBBH D ? BEE D(D0G
-0D(A BBBA < BEB D(A0M
-(A BBBF D o | BEE D(D0G
-0D(A BBBB 4 8Z BDA y
+(A BBBH D d 0? @z BEE D(D0G
+0D(A BBBA < d ( BEB D(A0M
+(A BBBF D o y BEE D(D0G
+0D(A BBBB 4 Z BDA y
ABM A
-ABA `Q N , L \ ADG l
-AAA T | BBB B(A0A8G
-8A0A(B BBBD T XZ BEB B(A0A8G
+ABA $ Q < @ N , H\ ,y ADG l
+AAA T ̻ x y BBB B(A0A8G
+8A0A(B BBBD T $ Z x BEB B(A0A8G
-8D0A(B BBBE 4 `J BDA l
+8D0A(B BBBE 4 4 J BDA l
ABJ A
-ABA , d x\ & ADG l
-AAA T F BBB B(A0A8G>
-8A0A(B BBBG T BEE E(D0D8G
-8D0A(B BBBD T D / BEE E(D0D8GZ
-8D0A(B BBBI T B BEE E(D0D8G9
-8D0A(B BBBJ T 8 BEE E(D0D8G
-8D0A(B BBBI ' Ae d $ i BEB B(A0A8Dp
+ABA , \ tx ADG l
+AAA T @F Px BBB B(A0A8G>
+8A0A(B BBBG T < 8 x BEE E(D0D8G
+8D0A(B BBBD T p/ w BEE E(D0D8GZ
+8D0A(B BBBI T H w BEE E(D0D8G9
+8D0A(B BBBJ T D Kw BEE E(D0D8G
+8D0A(B BBBI D H? w BEE D(D0G
+0D(A BBBA @' Ae d Pi BEB B(A0A8Dp
8F0A(B BBBA /
-8D0A(B BBBA d T BEB B(D0D8DS
+8D0A(B BBBA d $ XT BEB B(D0D8DS
8A0A(B BBBD W
-8A0A(B BBBG L < BBE B(A0A8DP
-8A0A(B BBBA L BBB B(A0A8Gp
-8A0A(B BBBA D BBE A(A0D@l
-0A(A BBBA < $ BBA D(K@J
-(A ABBA < d BBD A(G0L
-(A ABBA D \ BBI A(A0D@
-0A(A BBBA `2 AG hA , = BGA oAB + Ae D BBE A(A0D@l
-0A(A BBBA $ \ 8 ADD AA4 BFA b
-ABA $ V ADD JAA< BBG A(G@
-(A ABBH < $ BEB A(A0D@0A(A BBBD d BBE A(A0D@l
-0A(A BBBA 5 L BBB B(A0D8D@
-8A0A(B BBBB L \ BJB A(A0W
-(A BBDH ^(A BBBL = BJB A(A0W
-(A BBDH ^(A BBB< BBG A(G@
-(A ABBF < BBG A(G@
-(A ABBF 4 4 U BDA w
-ABO AAB l T d # BBB E(A0A8DPD
+8A0A(B BBBG L Կ P u BEB B(A0A8Dp
+8A0A(B BBBA L $ u BBB B(A0A8Gp
+8A0A(B BBBA D , BBE A(A0D@l
+0A(A BBBA < h Du BBA D(K@J
+(A ABBA < u BBD A(G0L
+(A ABBA D BBI A(A0D@
+0A(A BBBA < @2 AG hA L \ ` BBB B(A0A8Gp8A0A(B BBB + Ae D BBE A(A0D@l
+0A(A BBBA $ ADD AA4 0 s BFA b
+ABA $ t V ADD JAA< BBG A(G@
+(A ABBH $ V ADD JAA< BEB A(A0D@0A(A BBBD D BBE A(A0D@l
+0A(A BBBA @5 L h BBB B(A0D8D@
+8A0A(B BBBB L < ir BJB A(A0W
+(A BBDH ^(A BBBL X r BJB A(A0W
+(A BBDH ^(A BBB< BBG A(G@
+(A ABBF < h BBG A(G@
+(A ABBF $ (V ADD JAA4 < `U BDA w
+ABO AAB , i p ADG U
+AAA T d p BBB E(A0A8DPD
8A0A(B BBBA x
-8A0A(B BBBF D 8 BBE A(A0D@l
-0A(A BBBA L | BJB A(A0W
-(A BBDH ^(A BBBL ] BBB H(A0D8DP
-8A0A(B BBBG < 0 BBG A(G@
-(A ABBH < BBG A(G@
-(A ABBH < T BBG A(G@
-(A ABBH , pm W AID t
-DAA < C BBD A(G0$
-(A ABBA L ] BBB H(A0D8DP
-8A0A(B BBBG D l BBE A(A0D@l
-0A(A BBBA < BBG A(G@
-(A ABBG L < P G BEB E(I0H8DP
-8D0A(B BBBD L BJE B(A0A8D@8A0A(B BBB 4 BFD D0
- DABA < BLG A(G@
-(A ABBH T T hB BLB E(A0A8DK
-8C0A(B BBBF L d `] BBB H(A0D8DP
-8A0A(B BBBG | pU BBB E(A0A8DP
+8A0A(B BBBF D $ 8 BBE A(A0D@l
+0A(A BBBA L ?p BJB A(A0W
+(A BBDH ^(A BBBL ] BBB H(A0D8DP
+8A0A(B BBBG 0 DV < $ 8 BBG A(G@
+(A ABBH < d BBG A(G@
+(A ABBH < BBG A(G@
+(A ABBH < x BBG A(G@
+(A ABBH , l 8m n AID t
+DAA < x wn BBD A(G0$
+(A ABBA L ] BBB H(A0D8DP
+8A0A(B BBBG D BBE A(A0D@l
+0A(A BBBA < D X BBG A(G@
+(A ABBG L {m BEB E(I0H8DP
+8D0A(B BBBD L Sm BJE B(A0A8D@8A0A(B BBB 4 l m BFD D0
+ DABA < \ ` BLG A(G@
+(A ABBH T 0B l BLB E(A0A8DK
+8C0A(B BBBF L (] BBB H(A0D8DP
+8A0A(B BBBG | D 8U BBB E(A0A8DP
8A0A(B BBBA |
8A0A(B BBBJ "
-8A0A(B BBBD , 4 P ADD v
-AAG T d 0 BEB A(D0D@
+8A0A(B BBBD , ADD v
+AAG T BEB A(D0D@
0D(A BBGE D
-0C(A BBBJ T BEB B(A0D8J
-8A0A(B BBBA T \ BBB B(A0A8J
-8A0A(B BBBC T h BEB E(D0A8G
-8D0A(B BBBH L # BBB B(D0A8Gp
-8D0A(B BBBG
- p&V L ,
- ' BBB B(D0A8D@m
-8A0A(B BBBH T 8(b a BEB E(A0A8G
-8A0A(B BBBF <
- P, BBG A(G@
-(A ABBG L \
- - BBK I(K0G8Mp
-8D0A(B BBBA L
- .m BBB B(A0A8Dp#
-8A0A(B BBBA l
- 0 BNB B(A0D8G
+0C(A BBBJ T Fk BEB B(A0D8J
+8A0A(B BBBA T x Ik BBB B(A0A8J
+8A0A(B BBBC T D 7k BBE E(D0A8Gn
+8D0A(B BBBK L x$ 8k BBB B(D0A8Gp
+8D0A(B BBBG 'V L 0) BBB B(D0A8D@m
+8A0A(B BBBH T T )b j BEB E(A0A8G
+8A0A(B BBBF < d - BBG A(G@
+(A ABBG L . Qj BBB B(A0A8G
+8A0A(B BBBI L < / j BBK I(K0G8Mp
+8D0A(B BBBA L 1m i BBB B(A0A8Dp#
+8A0A(B BBBA l 2 i BNB B(A0D8G
8A0A(B BBBA n
-8A0A(B BBBA D l 1, BJB K(A0
-(A BBBA D h2 c BBJ A(D0
-(A BBEA $ @3 AHG tAAL 3] BBB H(A0D8DP
-8A0A(B BBBG < ,
4 BBG A(G@
-(A ABBG L x5 BEB E(I0H8DP
-8D0A(B BBBD L
87 [ BJE B(A0A8D@
-8A0A(B BBBA < 8 BBG A(G@
-(A ABBG L L 8 BBB B(A0D8DP
-8A0A(B BBBA < 9 BBG A(G@
-(A ABBH < H: BED A(D0Y
-(A ABBA L d : # BEB E(I0H8DP
-8D0A(B BBBD L x<
BJE B(A0A8D@
-8A0A(B BBBA < H= BBG A(G@
-(A ABBG < D >
BGD A(G0~
-(A ABBG L > [
BGB A(A0N
-0A(A BBBA \ B BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 HCI ADG q
-AAA DCA T l `Ck BBB B(D0A8G
-8A0A(B BBBC L xI
BBB B(A0A8J
-8A0A(B BBBC < K BFA D0}
- FABE D FAB \ 8L BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 LY R AIG t
-AAI DCA T L BEE I(A0A8D
-8D0A(B BBBA < M BED D(GPf
-(A ABBF T HN BEH B(A0A8G
-8A0A(B BBBA T P k BBB B(G0A8G
-8A0A(B BBBA T 4 HR 7 BHB B(A0A8J
-8A0A(B BBBE \ D V BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 `Va
- AIG
-AAF DCA D $ V
- BBB A(D0GP
-0D(A BBBK T l W;
- Z
- BBB B(D0D8G
-8A0A(B BBBE T a2
- j
- BLB B(A0A8J
-8A0A(B BBBE L k
- BEB E(I0H8DP
-8D0A(B BBBD \ l m W
- BEH D(I0D@
+8A0A(B BBBA L 84 BEE E(A0A8G@Q
+8A0A(B BBBK L 4Y 4i BLB E(H0G8D
+8A0A(B BBBH L 5 h BEB B(D0A8D`c
+8A0A(B BBBA L < 6 h BEB E(I0H8D@
+8D0A(B BBBA , D 7 BFA D0 AABT 81 gh BBB B(A0D8G#
+8A0A(B BBBA D @, h BJB K(A0
+(A BBBA D \ @ kh BBJ A(D0
+(A BBEA $ \ A AHG tAAL (B] BBB H(A0D8DP
+8A0A(B BBBG < 8C BBG A(G@
+(A ABBG L \ C g BEB E(I0H8DP
+8D0A(B BBBD L E cg BJE B(A0A8D@
+8A0A(B BBBA < F BBG A(G@
+(A ABBG D HG BBB H(G0G0A(A BBB < < H BBG A(G@
+(A ABBH < | H BED A(D0Y
+(A ABBA L @I 3f BEB E(I0H8DP
+8D0A(B BBBD L T K f BJE B(A0A8D@
+8A0A(B BBBA < \ K BBG A(G@
+(A ABBG < L e BGD A(G0~
+(A ABBG L $ M ke BGB A(A0N
+0A(A BBBA < t Q e BGB D(A0~
+(A BBBK 4 l RI ADG q
+AAA DCA T 8Rk Ke BBB B(D0A8G
+8A0A(B BBBC L D PXD pe BBB B(A0A8J
+8A0A(B BBBC < L PZ BFA D0}
+ FABE D FAB 4 ZY e AIG t
+AAI DCA T Z d BEE I(A0A8D
+8D0A(B BBBA < \ BED D(GPf
+(A ABBF T \ Sd BEH B(A0A8G
+8A0A(B BBBA T H^ d BBB B(G0A8G
+8A0A(B BBBA T T ` c BHB B(A0A8J
+8A0A(B BBBE \ d Hd BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 da kc AIG
+AAF DCA D D d 7c BBB A(D0GP
+0D(A BBBK T (f c BBB B(D0D8G
+8A0A(B BBBH L o c BEB E(I0H8DP
+8D0A(B BBBD \ 4 pq b BEH D(I0D@
0F(A BBBF U
-0F(A BBBA \ 0n BBE B(A0D8D@r
-8D0A(B BBBE D8C0A(B BBB4 , ni AID G
-AAI DCA< d n BLD D(G0
-(A ABBE \ \ xo BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 oY AIG t
-AAI DCA < < om BID D
-AFF AEF \ 4 p? BEA C(GPf
-(A ABBB {
-(A ABBG
-(A ABBF r4 Af
-I C T rb BBB B(A0K8J+
-8A0A(B BBBA < T 8t BLD D(G0
-(A ABBE < t BGB D(A0~
-(A BBBK L u BEB E(I0H8DP
-8D0A(B BBBD d $ Xw, BGE E(A0A8D@
+0F(A BBBA 4 ri b AID G
+AAI DCAL Xr b BEB E(I0H8DP
+8D0A(B BBBD d t, _b BGE E(A0A8D@
8F0A(B BBBI g
-8F0A(B BBBA T x%
S BBB B(A0A8G
-8A0A(B BBBA L BEB E(I0H8DP
-8D0A(B BBBD T 4 BLB B(A0A8Ga
-8A0A(B BBBA L BEB E(I0H8D@
-8D0A(B BBBA < BFA D0}
- FABE D FAB L p BBB B(A0D8G@j
-8A0A(B BBBH < l BLD D(G0
-(A ABBE $ d q ADD0eAAL BEB E(I0H8D@
-8D0A(B BBBD L $ k BJE B(A0A8D`8A0A(B BBB < , ( BBG A(G@
-(A ABBF < ؓ BLA D(G@
-(D ABBC T l BBB B(A0A8G
-8A0A(B BBBA AO L BLE I(D0D8D
-8A0A(B BBBC T X2 k BBB E(A0D8G
-8A0A(B BBBA L , @ A BHB B(A0D8J
-8A0A(B BBBI T | 7 BBB B(D0D8Gc
-8A0A(B BBBA , Z T AAD@o
-AAA ȯ T BBB B(A0H8M
-8A0A(B BBBH , H E
-K < L ( BKD A(G0(A ABB L BEB B(A0A8Ga
-8A0A(B BBBA L $ BBL E(H0A8D`
-8D0A(B BBBA ,! AO L! T
- : BBB B(A0A8J _
-8A0A(B BBBA , ! E BAG wAB ! 1 Dl " (|. K^ D l! BGB D(A0G@~
-0A(A BBBC l" 1 Ak T ! BHB B(D0A8Jy
-8A0A(B BBBH < ," ` BED D(G
-(A ABBH < l" BED D(G
-(A ABBI D " BEE A(D0G
-0A(A BBBI L " X, M BEE B(A0D8G
-8A0A(B BBBA L D# 8, BEE B(A0D8G
-8A0A(B BBBA < L$ BBA D(D@
-(A ABBA L $ & BBB B(A0A8D`
-8A0A(B BBBA L $ : BEB B(A0A8Dp
-8A0A(B BBBA L ,% BDB E(A0A8DP
-8A0A(B BBBK \ $ - BBB D(D0G
+8F0A(B BBBA T tD
b BBB B(A0A8G
+8A0A(B BBBA L sb BEB E(I0H8DP
+8D0A(B BBBD T , Kb BLB B(A0A8Nc
+8A0A(B BBBA L [b BEB E(I0H8D@
+8D0A(B BBBA < ` BFA D0}
+ FABE D FAB T Њ a BBE B(D0D8J
+8A0A(B BBBA L l a BHB B(A0D8J
+8A0A(B BBBI T ؎ a BBB B(D0D8Gc
+8A0A(B BBBA , pZ a AAD@o
+AAA L BBB B(A0D8G@j
+8A0A(B BBBH \ L 0 BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 Y a AIG t
+AAI DCA < , m ` BID D
+AFF AEF L $ ؗ BEA D(FPf
+(A ABBB L
+(A ABBF t 4 Af
+I C T 8b "` BBB B(A0K8J+
+8A0A(B BBBA < 4 P _ BLD D(G0
+(A ABBE \ t _ BEH D(I0D@
+0F(A BBBF U
+0F(A BBBA \ _ BEH D(I0D@
+0F(A BBBF U
+0F(A BBBA \ 4 p c_ BEH D(I0D@
+0F(A BBBF U
+0F(A BBBA < '_ BGB D(A0~
+(A BBBK \ BBE B(A0D8D@m
+8D0A(B BBBB D8C0A(B BBB< 4 ^ BLD D(G0
+(A ABBE < t Р ^ BED I(K@`
+(D ABBH T s^ BBB D(I0j
+(A BGBG R
+(A BBGB < +^ BLD D(G0
+(A ABBE $ q ADD0eAAL t ] BEB E(I0H8D@
+8D0A(B BBBD L ] BJE B(A0A8D`8A0A(B BBB < BBG A(G@
+(A ABBF < T 8] BLA D(G@
+(D ABBC L T \ BBB E(A0A8G
+8A0A(B BBBE AO T < \ BBB B(K0A8G
+8A0A(B BBBH L E
+K < l BKD A(G0(A ABB L
f\ BBB B(A0A8Gf
+8A0A(B BBBA T D \ BBB B(A0K8K
+8A0A(B BBBG T x AO t x T p
+ \ BEE B(A0A8G m
+8A0A(B BBBA , E BAG wAB 1 Dl 4 8Wu. K^ D [ BGB D(A0G@~
+0A(A BBBC 1 Ak T g[ BHB B(D0A8J
+8A0A(B BBBI < T [ BED D(G
+(A ABBH < [ BED D(G
+(A ABBI D Z[ BEE A(D0G
+0A(A BBBI L x, %[ BEE B(A0D8G
+8A0A(B BBBA L l X, Z BEE B(A0D8G
+8A0A(B BBBA < t 8 BBA D(D@
+(A ABBA L & BBB B(A0A8D`
+8A0A(B BBBA L : BEB B(A0A8Dp
+8A0A(B BBBA L T BDB E(A0A8DP
+8A0A(B BBBK \ - {Y BBB D(D0G
0A(A BBBA G
-0C(A BBBA T $% W
t BBB B(A0A8GN
-8A0A(B BBBA \ 4& GEB B(A0A8Dp|8A0A(B BBBDp L % 0 % BBB A(D0D
-0D(A BBBA L ,& } " BEE B(D0D8Dp
-8D0A(B BBBA < |& 1 BGA D(G@
-(A ABBB d &
- BBB F(D0D8J`g
+0C(A BBBA T L f
LY BBB B(A0A8G3
+8A0A(B BBBA T hY BEB B(A0A8G[
+8A0A(B BBBA \ A GEB B(A0A8D|8A0A(B BBBD T \ x
+K X BBB B(A0D8D
+8D0A(B BBBF L p X BBE E(D0C8Dp
+8D0A(B BBBA < 1 X BGA D(G@
+(A ABBB d D X BBB F(D0D8J`g
8A0A(B BBBA K
-8A0A(B BBBK < $' x k BDE C
+8A0A(B BBBK < ,X BDE C
ABG H
-ABE ( " DR
-J A < <( BEB A(A0D@0A(A BBBT ' 8
BEB B(A0A8J
-8A0A(B BBBK T ( l BEE B(D0A8G/
-8D0A(B BBBA , ,) AAG f
-AAG T ( RHB B(A0A8G{
-8A0A(B BBBD L ( " BEB B(A0A8G \
-8D0A(B BBBC L L) 01R 7 BEE B(A0D8JS
-8D0A(B BBBC 4 ) @6 D BAA G@
- DABJ $ * 6Y AK Y
-AA L ) 07 BEB E(I0H8D@
-8D0A(B BBBD L L* 82 BJE B(A0A8DP
-8A0A(B BBBA L * 9 BEB B(A0A8G
-8A0A(B BBBD D * 0@ BGI D(A0JL
-0A(A BBBK 4 + C~ BEA A(D@j(A ABBT l+ C g BEE E(D0A8J
-8A0A(B BBBA , + (_ AADP~
-AAJ , + _ ` AGDP
-AAG l $, ` ? BGB B(A0D8GF
+ABE " DR
+J A < BEB A(A0D@0A(A BBBT L x W BEE B(D0A8G/
+8D0A(B BBBA , \ AAG f
+AAG T @W RHB B(A0A8G{
+8A0A(B BBBD L , X
V BEB B(A0A8G \
+8D0A(B BBBC L | ,R ;W BEE B(A0D8JS
+8D0A(B BBBC 4 (1 HW BAA G@
+ DABJ $ 1Y AK Y
+AA L , 2 V BEB E(I0H8D@
+8D0A(B BBBD L | 32 V BJE B(A0A8DP
+8A0A(B BBBA L 4 V BEB B(A0A8G
+8A0A(B BBBD D ;$ V BGI A(D0JL
+0A(A BBBK 4 >~ BEA A(D@j(A ABBL H> fV BBL B(A0A8D`M
+8A0A(B BBBA B AL L B5
+ U BEE F(A0A8G`p
+8A0A(B BBBK T \ xL$ SV BEE E(D0D8Ga
+8A0A(B BBBA , p X AADP~
+AAJ , q W AGDP
+AAG l r W BGB B(A0D8GF
8A0A(B BBBG (
-8A0A(B BBBF T , c BEE E(A0H8G;
-8A0A(B BBBJ T , k BBB B(A0A8J~
-8D0A(B BBBA T D- hq7 BEB B(D0A8T7
-8D0A(B BBBH T - Pt BBB B(A0A8G
-8A0A(B BBBA L . zM BBL B(A0A8D`
-8A0A(B BBBA . ~ AL L d. ~U ? BEE F(A0A8G`
-8A0A(B BBBJ L . BEE B(D0D8LPT
-8D0A(B BBBE T / O BFE B(A0A8G
-8A0A(B BBBA D \/ P5 BBB A(A0Dr
-0D(A BBBA D \0 H BBI A(A0D@
-0A(A BBBA D 0 BBI A(A0D@
-0A(A BBBA < 40 BBB A(K0(A BBB 4 ,1 hD BEA A(D0p(A ABB T 0 v j BBB E(A0A8J
-8A0A(B BBBA 1 # Aa L $1 0 BBB E(D0A8GPu
-8A0A(B BBBA T t1 H BBB B(A0A8G`
-8A0A(B BBBE 2 б* AM
-B Y T 1 T BBB B(A0A8Gw
-8A0A(B BBBA < D2 / BED A(G@x
-(D ABBD T 2 hI BBB B(D0D8J?
-8A0A(B BBBE L 2 ` BBB B(D0D8JX
-8D0A(B BBBA L ,3 h ] BEE E(A0D8L
-8D0A(B BBBD T |3 4 BBB B(A0A8J
-8A0A(B BBBK L 3 r \ BBB B(E0A8J@
-8A0A(B BBBA $ 4 ^ AG b
-AA L L4 @ BBB B(A0A8GPd
-8A0A(B BBBA d 4 BBB B(A0A8G`I
-8A0A(B BBBD j
-8A0A(B BBBL L 5 i BBB E(A0C8G`x
-8A0A(B BBBH L T5 8 - BBE E(A0C8GP
-8A0A(B BBBA d 5 BBB E(D0A8G`
+8A0A(B BBBF T t W BEE E(A0H8G;
+8A0A(B BBBJ T } W BBB B(A0A8J~
+8D0A(B BBBA T 4 07 W BEB B(D0A8T7
+8D0A(B BBBH T ZW BBB B(A0A8G
+8D0A(B BBBA < Њ NW BBD A(G@
+(D ABBG T $ `b #W BFE B(A0A8G
+8A0A(B BBBA D | x5 wX BBB A(A0Dr
+0D(A BBBA D | p BBI A(A0D@
+0A(A BBBA D H BBI A(A0D@
+0A(A BBBA < T W BBB A(K0(A BBB T `
W BLB B(A0D8G
+8A0A(B BBBC T W BEB B(A0A8J~
+8A0A(B BBBA L D CY BEE B(D0D8LPT
+8D0A(B BBBE 4 L D BEA A(D0p(A ABB T X BBB B(D0A8J
+8A0A(B BBBA 0# Aa L D @% X BBB E(D0A8KP
+8A0A(B BBBA T wX BBB B(A0A8G`
+8A0A(B BBBE * AM
+B Y T T kX BBB B(A0A8Gw
+8A0A(B BBBA < d X BED A(G@x
+(D ABBD T @I X BBB B(D0D8J?
+8A0A(B BBBE L 8 X BBB B(D0D8JX
+8D0A(B BBBA L L x X BEE E(A0D8L
+8D0A(B BBBD T X BBB B(A0A8J+
+8A0A(B BBBA L b X BBB B(D0A8Gr
+8A0A(B BBBH D D
X BBE A(A0G@
+0D(A BBBA D x DV L r X BBB B(E0A8J@
+8A0A(B BBBA $ ^ AG b
+AA L 8X BBB B(A0A8GPd
+8A0A(B BBBA L l ( W BBB E(A0C8G`x
+8A0A(B BBBH T W BBB B(A0A8G
+8A0A(B BBBA d @ W BBB E(D0A8G`
8A0A(B BBBD N
-8A0A(B BBBH T 6 0 BBB B(D0A8G
-8A0A(B BBBH d d6 BBB B(D0A8D`H
+8A0A(B BBBH T | TW BBB B(D0A8J7
+8A0A(B BBBH d `' JW BBB B(D0A8D`H
8A0A(B BBBE
-8A0A(B BBBK D 6 0o T BEE D(D0G
-0D(A BBBB D 7 X? BEE D(D0G
-0D(A BBBA D \7 P? BEE D(D0G
-0D(A BBBA < \8 H BBG A(G@
-(A ABBH T 7 r BBB B(A0D8J_
-8A0A(B BBBA T <8 BBB B(A0D8Gk
-8A0A(B BBBG T 8 : J BEB B(D0D8M
-8D0A(B BBBD 9 |. K^ T 9 BHB E(A0D8J
-8D0A(B BBBE T \9 !% 0 BEB B(D0D8J
-8A0A(B BBBG T 9 x4 5 BBB E(G0D8Jd
-8D0A(B BBBG T : 08u ! BEE B(G0D8J\
-8D0A(B BBBD L d: XJ 4 BBB B(D0A8Dp
-8C0A(B BBBA l; M ; N 4 ; O AEG @
-AAA HAA 4 ; O_ BAD U
-ADG oAB L T; P S BJB A(A0b
-(A BBDE _(A BBB4 \< xPT AAK q
-CAF DFA < P < Q < Q DR < Q AW d D< Q g BEL B(A0A8D`
-8C0A(B BBBF L8A0A(B BBB d < R BEL B(A0A8D`
-8C0A(B BBBF L8A0A(B BBB 4 = RS AAK q
-CAF DEA > R$ DQ
-K L $> R BEB B(A0A8D`\
-8A0A(B BBBA T = PS, BBE B(D0A8G
-8D0A(B BBBK T > (XA BEE E(D0A8Go
-8D0A(B BBBG L $? ^ BOB A(A0]
+8A0A(B BBBK d < ) V BBB B(D0A8D`H
+8A0A(B BBBE
+8A0A(B BBBK d , V BBB B(D0A8D`H
+8A0A(B BBBE
+8A0A(B BBBK L h. QV BBB B(A0D8Gp
+8D0A(B BBBC L \ 3_ (V BBE E(A0D8G]
+8D0A(B BBBD T 8 U BBE E(A0A8G
+8D0A(B BBBF T @; U BBE E(A0A8G
+8D0A(B BBBE T \ = |U BBE E(A0A8G
+8D0A(B BBBE T ? ?U BEB E(A0D8MW
+8D0A(B BBBD =u. K^ T $ `U% U BBH E(A0A8J
+
+8D0A(B BBBD T | 8]w
0V BEB B(A0D8J
+8D0A(B BBBE T `j V BEH B(A0D8J
+8D0A(B BBBE L , } W BBB B(D0A8Dp
+8C0A(B BBBA 4 x L ` 4 d H AEG @
+AAA HAA 4 _ BAD U
+ADG oAB D 5 W BHE D(A0Dp_
+0A(A BBBA L d Ѓ V BJB A(A0b
+(A BBDE _(A BBB4 l 0T AAK q
+CAF DFA X P L H BBD D(F@v
+(A ABBB g(A ABB $ DR < AW d U BEL B(A0A8D`
+8C0A(B BBBF L8A0A(B BBB d 8 =U BEL B(A0A8D`
+8C0A(B BBBF L8A0A(B BBB 4 , S AAK q
+CAF DEA d ؆$ DQ
+K L BEB B(A0A8D`\
+8A0A(B BBBA T x, :T BBE B(D0A8G
+8D0A(B BBBK T t PA T BEE E(D0A8Go
+8D0A(B BBBG L H BOB A(A0]
(A BBBG Y
-(A BBBH D > ^ BEB D(D0D@
-0D(A BBBA T ? 8` L RBE B(G0A8Dv
-8A0A(B BBBA @ a. [R < ,@ a{ BDD G0V
- AABE D CAB < l@ (b BBG A(G@
-(A ABBG T ? b o BBB B(D0G8J
-8A0A(B BBBA L A Phc BKB B(A0A8Dpa
-8A0A(B BBBF L TA pj BBB B(A0D8G@j
-8A0A(B BBBH @ l" BBB E(A0D8D
+(A BBBH D S BEB D(D0D@
+0D(A BBBA l d ` }S BBB B(A0A8DX
+8A0A(B BBBA k
+8A0A(B BBBA Ж. [R < BBG A(G@
+(A ABBG T , R BBB B(D0G8J
+8A0A(B BBBA L <
+ c BKB B(A0A8Dpa
+8A0A(B BBBF L
+ 0 BBB B(A0D8G@j
+8A0A(B BBBH $
+ " +R BBB E(A0D8D
8A0A(B BBDA
8F0A(B BBBE p
-8C0A(B BBBA D tA or w BBE A(D0DU
-0A(A BBBA D A p) P BBB A(A0D
-0A(A BBBA D B q) & BBB A(A0D
-0A(A BBBA T LB r BBB I(D0D8D)
-8D0A(B BBBA L B tV BBB B(D0A8D
-8D0A(B BBBA L B (um { BBB B(D0H8Kp
-8A0A(B BBBA , C Hv0 BDD ^AB T tC Hv
BBE E(A0A8G
-8A0A(B BBBA T C y BBB E(A0F8G*
-8C0A(B BBBA T $D 8 BBA A(JK
+8C0A(B BBBA D
+ hr Q BBE A(D0DU
+0A(A BBBA D
+ ) Q BBB A(A0D
+0A(A BBBA D < 2 Q BBB A(A0D
+0A(A BBBA T YQ BBB I(D0D8D)
+8D0A(B BBBA L V Q BBB B(D0A8D
+8D0A(B BBBA L , m P BBB B(D0H8Kp
+8A0A(B BBBA , 4
0 BDD ^AB T jP BBE E(A0A8G
+8A0A(B BBBA T
p .P BBB E(A0F8G*
+8C0A(B BBBA T \
O BBA A(JK
(A ABBG K
-(A ABBA D |D c c BBB A(D0GB
-0A(A BBBD T D C BBB B(A0A8G
-8C0A(B BBBA L E pd o BBB B(D0A8Dg
-8A0A(B BBBA T lE 9 BBB B(D0A8D
-8A0A(B BBBA L E ؎< BBB B(D0M8JL
-8D0A(B BBBD F ȡ|. K^ F " DR
-J A T LF BBB E(A0A8G5
-8A0A(B BBBE T F Hw BBB B(D0D8J
-8F0A(B BBBA T F p` BIB B(A0A8G
-8A0A(B BBBA L TG xw BBB B(A0D8Jr
-8A0A(B BBBE T G BEB B(A0A8G
-8D0A(B BBBA L H ` AAG ]
+(A ABBA D
Pc O BBB A(D0GB
+0A(A BBBD T
x O BBB B(A0A8G
+8C0A(B BBBA L T @d O BBB B(D0A8Dg
+8A0A(B BBBA T ` O BBB B(D0A8D
+8A0A(B BBBA 1u. K^ " DR
+J A T 4 " #O BBB E(A0A8G
+8A0A(B BBBF T xw
O BBB B(D0D8J
+8F0A(B BBBA T ` "O BIB B(A0A8G
+8A0A(B BBBA L < w "O BBB B(A0D8Jr
+8A0A(B BBBE T O BEB B(A0A8G
+8D0A(B BBBA L AAG ]
CAF [
ACA R
-CAJ JCA , I b D\
+CAJ JCA L AAG ]
+CAF [
+ACA R
+CAJ JCA L < AAG ]
+CAF [
+ACA R
+CAJ JCA , b D\
H P
H Q
-G I 4I Ъ
- LI Ȫ dI
- |I
- I
- I
- I I I J
- L lI ( BJB A(A0_
-(A BBDH _(A BBB tJ J DU < I R BBA A(Gm
-(A ABBA J > D k
-A T LJ 0 m BBB B(A0D8G
-8A0A(B BBBA \K h|. K^ tK а K Ȱ K K K K L " L ذ 4L < K w S BLD A(G@|
-(A ABBA L (- $ L @^ AG b
-AA L L x BBB B(A0A8GPd
-8A0A(B BBBA L dL BBB B(A0A8GPl
-8A0A(B BBBI lM M M M M M M N ,N , DN c ADG0s
-DAD d M c BBB B(D0D8G`
-8A0A(B BBBH N
-8A0A(B BBBH L $N H BBB A(A0G9
-0C(A BBBA D tN ظw BEJ D(D0G
-0D(A BBBE D N w BEJ D(D0G
-0D(A BBBE T O H BBE E(A0A8G
-8D0A(B BBBA < P BBG A(G@
-(A ABBH T O @
- BBE B(A0A8J
-8A0A(B BBBA T O xD BBE E(I0D8J
-8A0A(B BBBK < LP p BGB D(A0~
-(A BBBK L P " BJE B(A0A8D@
-8A0A(B BBBA < P BAD '
+G I , `b D\
+H P
+H Q
+G I
+ 4 d aM BEB B(D0A8DPn
+8C0A(B BBBB g8D0A(B BBB x
+ , " D \ t DU T L SGB B(A0A8GP 4 D K NGB A(A0D d | K BBA A(G
+(A ABBD m
+(A ABBE G
+(C ABBA T , K BBE B(D0A8G
+8D0A(B BBBK T < X, K BBE B(D0A8G
+8D0A(B BBBK T 0 jK BBE B(D0A8G
+8D0A(B BBBG , D\
+H > D k
+A (> D k
+A T L H J BBB E(A0D8G
+8A0A(B BBBB D J BKI D(A0G@L
+0A(A BBBA d X, J BGE E(A0A8D@
+8F0A(B BBBI g
+8F0A(B BBBA T T gJ BEB B(D0A8J
+8A0A(B BBBA d X | P h(u. K^ 0 ( $ < " T 8 l @ H- $ `^ AG b
+AA L I BBB B(A0A8GPd
+8A0A(B BBBA L \ I BBB B(A0A8GPl
+8A0A(B BBBI d ( | 0 ( $ T H BBB B(A0A8G
+8A0A(B BBBA d a H BBB B(A0C8G`
+8A0A(B BBBD N
+8A0A(B BBBH T D /H BBE E(D0A8G
+8D0A(B BBBI T (# G BBE E(D0A8G
+8D0A(B BBBI T % G BBE E(A0A8G
+8D0A(B BBBA < ' BBG A(G@
+(A ABBH T (j 8G BEB B(A0A8J
+8A0A(B BBBA L 0" 'G BJE B(A0A8D@
+8A0A(B BBBA T 4 1d F BBE E(I0D8J
+8A0A(B BBBB < 7 #G BAD '
ABG O
-ABA Q |. K^ T 4Q x^
- BEE B(A0D8JU
-8A0A(B BBBD T Q ^ BBE E(D0A8J
-8A0A(B BBBI D R BBE A(A0D@
-0A(A BBBA T ,R `] S BBB B(D0A8G
-8D0A(B BBBI 4 R h [ BAA
-ABA T R 0 6 BBB B(A0A8J
-8A0A(B BBBE D S BBB A(A0D@
-0A(A BBBA D T 4 BBB A(A0D@
-0A(A BBBA D S N BBE A(A0Jo
-0D(A BBBA D S % BDD Gp
+ABA "u. K^ T P9^
+ F BEE B(A0D8JU
+8A0A(B BBBD T < XC +G BBE E(D0A8J
+8A0A(B BBBI D L R BBE A(A0D@
+0A(A BBBA T R] G BBB B(D0A8G
+8D0A(B BBBI 4 4 U G BAA
+ABA D $ V BBB A(A0D@
+0A(A BBBA D l W4 BBB A(A0D@
+0A(A BBBA T xXR F BBJ B(D0D8Gj
+8D0A(B BBBJ 4 T a +G BDA JD
+ DABD D aN G BBE A(A0Jo
+0D(A BBBA D b% F BDD Gp
DABF ~
- DABC < 4T x BBD D(G
-(D ABBJ < tT BED A(J
-(D ABBB L T BEE E(A0D8Gg
-8D0A(B BBBG T U h BEE E(A0D8J%
-8D0A(B BBBF l \U
- BEE E(D0H8D`i
+ DABC < ! d F BBD D(G
+(D ABBJ < \! HfW F BED A(J
+(D ABBE L ! hj F BEE E(A0D8Gg
+8D0A(B BBBG T ! l F BEE E(A0D8J)
+8D0A(B BBBJ l D" p F BEE E(D0H8D`i
8A0A(B BBBD
-8A0A(B BBBB V M|\ AG RAd U BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d TV 8
BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB L V
O BJB A(A0W
-(A BBDH ^(A BBBL W BBB B(A0D8G@
-8A0A(B BBBB X _ L tW BBB I(I0A8DPv
-8D0A(B BBBH L W w BEB E(I0H8DP
-8D0A(B BBBD L X O BJE B(A0A8D@
-8A0A(B BBBA L dX X9 ' BEB F(G0D8D`
-8D0A(B BBBA d X Hs BBB J(A0A8G`
+8A0A(B BBBB l# t\ AG RAd " s #F BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB d <# xs E BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB L # s [E BJB A(A0W
+(A BBDH ^(A BBBL $ @t BBB B(A0D8G@
+8A0A(B BBBB $ v_ L \$ Xv D BBB I(I0A8DPv
+8D0A(B BBBH L $ x D BEB E(I0H8DP
+8D0A(B BBBD L $ y [D BJE B(A0A8D@
+8A0A(B BBBA L L% z9 3D BEB F(G0D8D`
+8D0A(B BBBA d % {s C BBB J(A0A8G`
8F0A(B BBBF B
-8F0A(B BBBA T Y ` BBB B(A0A8J
-8A0A(B BBBC d tY ] b BBB B(I0A8G`
+8F0A(B BBBA T & | C BBB B(A0A8J
+8A0A(B BBBC d \& ~] nC BBB B(I0A8G`
8F0A(B BBBF A
-8F0A(B BBBA T Y t BBJ B(A0H8Fa
-8D0A(B BBBC T 4Z t BBJ B(A0H8Fa
-8D0A(B BBBC | D[ BBB B(D0A8DP
+8F0A(B BBBA T & t C BBJ B(A0H8Fa
+8D0A(B BBBC T ' t B BBJ B(A0H8Fa
+8D0A(B BBBC | ,( 0 BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI L [ p BEB B(A0A8DP4
-8A0A(B BBBA , \ # BAH D` AAB, D\ @$ BAH D` AAB t\ |. K^ \ [ $ [ BBB I(D0D@
+8A0A(B BBBI L ( BEB B(A0A8DP4
+8A0A(B BBBA , ( BAH D` AAB, ,) BAH D` AAB \) u. K^ \ ( gA BBB I(D0D@
0F(A BBBA `
-0D(A BBGA L \ h+ BBE B(A0A8D`
-8A0A(B BBBA L <] - BBE B(A0A8D`
-8A0A(B BBBA T \ X0 BHB B(D0A8J
-8D0A(B BBBB ] H AL \ L] H [ BBE D(A0P@|
+0D(A BBGA L ) BBE B(A0A8D`
+8A0A(B BBBA L $* ( BBE B(A0A8D`
+8A0A(B BBBA T ) @ BHB B(D0A8J
+8D0A(B BBBB * AL \ 4* Ю gA BBE D(A0P@|
0D(A BBBK d
-0D(A BBBA T ] 0I~ BBE E(D0C8J>
-8A0A(B BBBI 4 ^ XO BKA Dp
- AABA ^ `|. K^ _ U $_ U <_ U T_ U , ^ Ub D ADG r
-AAA _ HU L _ @U AAG ]
+0D(A BBBA T * p~ $A BBE E(D0C8J>
+8A0A(B BBBI 4 * @ BKA Dp
+ AABA + u. K^ + X , P $, H <, @ , + Hb P@ ADG r
+AAA $ , - AAG VAA , L , AAG ]
CAF [
ACA R
-CAJ JCA $ ` pU^ AG b
-AA , ,` U0 AF
FK
- D \` V 4 t` V= BDA Y
-JBL AAB 4 ` VJ BDA o
-ABG A
-ABA < ` V BDD k
+CAJ JCA $ - ^ AG b
+AA , <- 0 AF
FK
+ D 4 , D? BGB A(A0D < - BDD k
ABH V
-ABA L $a W6 BEB B(A0A8D`h
-8A0A(B BBBA < ta X4 BBB A(A0d
-(A BBBE T ` Y BBE E(A0A8G
-8D0A(B BBBD D b H[ BBE A(A0D@l
-0A(A BBBA d a [ BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d b X\ BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB L lb \ BEB E(I0H8DP
-8D0A(B BBBD L b ^ BJE B(A0A8D@
-8A0A(B BBBA c `_5 4 $c _Y S AIG t
-AAI DCA 4 \c _Y AIG t
-AAI DCA T c _ BBB B(A0D8I|
-8A0A(B BBBA d |. K^ , d c AAK0i
-AAA d xc" DN
-N A L e c
BEE A(A0]
+ABA L - 6 BEB B(A0A8D`h
+8A0A(B BBBA < 4. 4 BBB A(A0d
+(A BBBE T - T> BBE E(A0A8G
+8D0A(B BBBD D . 0 BBE A(A0D@l
+0A(A BBBA L \. = BJB A(A0W
+(A BBDH ^(A BBBd . = BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB d / = BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB L |/ < BJB A(A0W
+(A BBDH ^(A BBBL / X o< BEB E(I0H8DP
+8D0A(B BBBD L 0 G< BJE B(A0A8D@
+8A0A(B BBBA $1 5 T 0 < BBE B(A0D8I
+8A0A(B BBBA 1 (u. K^ , 1 ` AAK0i
+AAA 1 " DN
+N A $ 1 M AAG AAA $2 AL L D2
BEE A(A0]
(A BBBF
-(E BBBK T d Hd BBB B(A0A8Jg
-8A0A(B BBBA T d t# BBB B(A0I8D
-8D0A(B BBBA L f w BBB E(A0A8DP
-8A0A(B BBBA \f ({" DR
-J A d e 8{| % BGB E(D0A8Dp
-8A0A(B BBBH k
-8A0A(B BBBA T ,f P| BIB B(G0D8G
-8A0A(B BBBA D f ، BBB D(I0GP
-0D(A BBBA D g BBB A(K0D@
-0A(A BBBA g AL T 4g BBE E(H0D8G
-
-8A0A(B BBBA D g # BBB A(D0D@
-0A(A BBBA 4 h ZHA G HAB 4 h # BAA G0_
- AABB L Dh X BEH B(D0D8Dp`
-8D0A(B BBBA Li AX D li BBB D(D0G@T
-0A(A BBBG i P i H DP i P DP T Di XM BBB B(D0D8G
-8A0A(B BBBB D i P k BBB A(A0Dl
-0A(A BBBA D i ( @ BBB A(A0J
-0A(A BBBA < ,j T BBA A(G@z
-(A ABBC T lj ! BBB B(A0D8G
-8A0A(B BBBA |k k k <8|\ AG RA< k X] AAG0O
+(E BBBK T 1 y : BBB B(A0A8Gz
+8A0A(B BBBA T 42 # ; BBB B(A0I8D
+8D0A(B BBBA T 2 t; BEB B(A0A8G
+8A0A(B BBBA , 2 ; AAG0d
+AAA 3 " DR
+J A T 43 B O; BBB H(H0A8G
+
+8A0A(B BBBA < 3 T ; BEE D(A0h
+(A BBBA D 4 (x BBB A(K0D@
+0A(A BBBA 4 ` AL T 44 `8 +; BEI E(A0A8J
+M
+8A0A(B BBBA D5 H D
+E d5 8 4 0 < 5 (7 AF0nA D 4 H# < BBB A(D0D@
+0A(A BBBA 4 5 0 ZHA G HAB 4 |5 < BAA G0_
+ AABB L 5 ; BEH B(D0D8Dp`
+8D0A(B BBBA 6 0 AX D 6 0 BBB D(D0G@T
+0A(A BBBG $7 <7 DP T7 DP T 6 M : BBB B(D0D8G
+8A0A(B BBBB D 7 ' _; BBB A(A0Dl
+0A(A BBBA D T7 `( 4; BBB A(A0J
+0A(A BBBA < 7 + H; BBA A(G@z
+(A ABBC T 7 H,! ; BBB B(A0D8G
+8A0A(B BBBA 8 M 9 M 9 Ŀt\ AG RA< 8 N] ; AAG0O
DAC [
-DAA D Tk x BEB A(A0G@y
-0D(A BBBA Tl ll D k 7 BEE A(C0G@
-0D(A BBBA L l X BBB B(A0D8D`m
-8A0A(B BBBH T dl BBB B(A0A8G
-8A0A(B BBBB D l 0t j BEB A(A0GC
-0D(A BBBA m h@ O BBB E(A0A8D
+DAA D 8 N ; BEB A(A0G@y
+0D(A BBBA 9 8O 9 @O D <9 8O +; BEE A(C0G@
+0D(A BBBA L 9 P ; BBB B(A0D8D`m
+8A0A(B BBBH T 9 S : BBB B(A0A8G
+8A0A(B BBBB D ,: hmt ^; BEB A(A0GC
+0D(A BBBA t: n@ C; BBB E(A0A8D
8A0A(B BBBI
8A0A(B BBBG
-8A0A(B BBBI L Dn f BBB B(D0G8D`u
-8D0A(B BBBG T m @
- BEB B(A0A8G
-8A0A(B BBBA < 4n BBA A(DO
-(A ABBA , tn x OGA A(D 4 n H BJD K@g
- AABJ $ o c AK c
-AA L o X ^ BBB B(A0A8Gp@
-8A0A(B BBBA T To ( s BLB B(D0A8G
-8A0A(B BBBA T o P BHF E(D0A8G
-8A0A(B BBBD T p " BEL B(D0D8J
-8A0A(B BBBE q x|. K^ ,q 3|\ AG RA Lq + dq + |q (+p cL q + D p +q w BBB A(D0Gpt
-0D(A BBBG D <q . V BBB D(D0D`
-0A(A BBBA D q H0 ? BBB D(D0De
-0A(A BBBA r 1 r 1 r 1 r 1 r 1( Af s 1 s 1 L 4s 1 AAG ]
+8A0A(B BBBI L ; Xsf BBB B(D0G8D`u
+8D0A(B BBBG T L; xt
+ : BEB B(A0A8G
+8A0A(B BBBA < ; ~ : BBA A(DO
+(A ABBA , ; : OGA A(D 4 < : BJD K@g
+ AABJ $ = Hc AK c
+AA L t< R: BBB B(A0A8Gp@
+8A0A(B BBBA T < `s : BLB B(D0A8G
+8A0A(B BBBA T = 9 BHF E(D0A8G
+8A0A(B BBBD T t= P 9 BEL B(D0D8J
+8A0A(B BBBE > hu. K^ > t\ AG RA > P > X > `p cL ? D d> q k9 BBB A(D0Gpt
+0D(A BBBG D > J9 BBB D(D0D`
+0A(A BBBA D > 39 BBB D(D0De
+0A(A BBBA ? @ $@ <@ T@ l@ ( Af @ @ Y AW @ 8 L @ 0 AAG ]
CAF k
ACA R
-CAJ JCA L s 2 AAG ]
+CAJ JCA L ,A p AAG ]
CAF [
ACA R
-CAJ JCA L s 02 AAG ]
-CAF c
-CAI R
-CAJ JCA D $t p2 BEB A(D0O@d0D(A BBB L s 3 @ BOB B(A0A8Dp
-8A0A(B BBBA < t 4 BBD D(DPT
-(A ABBA 4 Dt 4 BAA
-ABD $ |t P5b Ag
-H ^
-E T t 5 BBB B(A0A8J
-8A0A(B BBBC T t @= Y BBE B(A0A8D2
-8A0A(B BBBA T Tu @ BBB E(A0A8D!
-8A0A(B BBBA d u `D BBA A(D
+CAJ JCA L |A AAG ]
+CAF [
+ACA R
+CAJ JCA $ A Й` AAh
+AE A " DV
+F A < \A 6 BBD D(DPT
+(A ABBA D TB BEB A(D0O@d0D(A BBB B Л1 Aj
+A L B f6 BOB B(A0A8Dp
+8A0A(B BBBA 4 TB P 6 BAA
+ABD , DC u ACD A
+AAE t B h, 5 BBA A(G0L
+(C ABBG V
+(C ABBJ R
+(C ABBF J
+(C ABBA T 4C _5 BBB E(A0A8Jc
+8A0A(B BBBD \ C Ƞ 5 BBB D(A0G
+0A(A BBBD m
+0A(A BBBK T C 4 BBE B(A0A8D2
+8A0A(B BBBA T DD 4 BBB E(A0A8D!
+8A0A(B BBBA d D ` ,4 BBA A(D
(A ABBA U
-(A ABBA
-(C ABBA v H2 Ad
-K A v H) DS
-I D T Tv H BBA A(D
-(A ABBE J
-(A ABBA t v 0J BBA A(GM
-(A ABBH ]
-(A ABBE ^
-(A ABBD ]
-(A ABBA $w L' A[
-A d Dw L BBB B(A0A8DP
-8A0A(B BBBE D8A0A(B BBB 4 dx @M] BDA A
-ABM AAB T w hMr BBE E(D0G8J
-8A0A(B BBBA T <x S BLB E(A0A8I
-8A0A(B BBBA Ly d FP < dy d BBG A(G@
-(A ABBF T x d BBB B(A0A8I5
-8A0A(B BBBA < Dy Xtv BEA A(G0D
-(D ABBA <z t
Tz t
lz t z t z xt z pt z ht z pt" AN ND { t { t 4{ t" $ L{ tG AG a
-DC RD t{ t , z tb ' AAG V
-CAA { u8 Gp4 { 0u BAD D0
- AABA | u $ l{ u4 AN cA $ { u< AN kA t| uH L | u BBA D(D@x
+(A ABBA
+(C ABBA E 2 Ad
+K A < $E 3 AAD@y
+AAG Q
+AAA F P |E X m3 AW TF X) DS
+I D tF h DO d E p 3 BBA A(GO
+(A ABBF w
+(A ABBK ^
+(A ABBA d <F 2 BBB B(A0A8DP
+8A0A(B BBBE D8A0A(B BBB 4 \G p] BDA A
+ABM AAB L F J2 BBE E(D0D8J`
+8A0A(B BBBA T ,G غ 2 BLB B(D0A8I
+8A0A(B BBBA <H @ FP TH H" DV
+F A < tH X BBG A(G@
+(A ABBF L G 1 BBB B(A0C8J
+8A0A(B BBBA L LH 1 BEB D(C0G
+0A(A BBBA T H (a f1 BBB E(A0C8J(
+8A0A(B BBBE < H @v /1 BEA A(G0D
+(D ABBA I
J x
J p 4J h LJ ` dJ X |J P J X" AN ND J h J p J x" $ J G AG a
+DC RD $K , J b / AAG V
+CAA lK 8 Gp4 K BAD D0
+ AABA K $ K x4 // AN cA $ DK < / AN kA L $L BBA D(D@x
(D ABBB
-(D ABBG | Xx Q \ <| `xh BEB D(D0Gp
+(D ABBG \ K h . BEB D(D0Gp
0D(A BBBK L
-0D(A BBBA D | pz BBE D(D0D
-0D(A BBBD D | H~ BAD GPl
+0D(A BBBA D L W. BBE D(D0D
+0D(A BBBD D dL F. BAD GPl
DABG
- DABA T ,} a b BEE I(A0A8G ^
-8A0A(B BBBJ L } Z BGD D(D0z
+ DABA T L Ha . BEE I(A0A8G ^
+8A0A(B BBBJ L M ` . BGD D(D0z
(D ABBH V
-(D ABBA , } AAI@T
-DAD , ~ T BKI _
-ABH , ~ ȆD BHD ^
-ABA L d~ BBB E(D0A8F
-8A0A(B BBBH D ~ C g BBB D(D0Gpn
-0D(A BBBB L ~ @ BBB B(A0C8J
F
-8D0A(B BBBG , L YX
+(D ABBA , TM - AAI@T
+DAD , <N @T BKI _
+ABH , lN pD BHD ^
+ABA L M M- BBB E(D0A8F
+8A0A(B BBBH D 4N `C - BBB D(D0Gpn
+0D(A BBBB L |N h , BBB B(A0C8J
F
+8D0A(B BBBG , N ( A- YX
G Z
-A L | k BLE E(A0D8J
-8A0A(B BBBE < `d BJD D(D@o
-(D ABBA L [ BBE B(A0A8GS
-8D0A(B BBBD L \ @r BBB B(D0A8G_
-8A0A(B BBBC L p BBB B(D0A8GQ
-8A0A(B BBBA < d BGF A(J0i
-(D ABBA T < < BBE B(D0A8G
-8D0A(B BBBB T < BBE B(D0A8G
-8D0A(B BBBB L BVB B(D0A8G^
-8D0A(B BBBE T < 0( BEE B(A0A8G
-8D0A(B BBBE T
-6 k BBB B(A0A8G
-8A0A(B BBBA p* h* ԃ p* h* `* X* 4 P* L H* d @* | 8* 0* (* Ą * ܄ * * < *J BEE D(D0\(Q BBB L (* HT l (*# DT @* 8* 0* ̅ 8* @*# D ^ , D X* ; YX
+A L N - BLE E(A0D8J
+8A0A(B BBBE < LO d =- BJD D(D@o
+(D ABBA L O 8 - BBB B(D0A8GQ
+8A0A(B BBBA < O d G- BGF A(J0i
+(D ABBA L P - BBE B(A0A8GS
+8D0A(B BBBD L lP r [- BBB B(D0A8G_
+8A0A(B BBBC T P < k- BBE B(D0A8G
+8D0A(B BBBB T Q < Q- BBE B(D0A8G
+8D0A(B BBBB L lQ % 7- BVB B(D0A8G^
+8D0A(B BBBE T Q 3( - BEE B(A0A8G
+8D0A(B BBBE T R `\v6 / BBB B(A0A8G
+8A0A(B BBBA $S <S TS lS S x S p S h S ` S X S P T H ,T @ DT 8 \T 0 tT ( < T 0J BEE D(D0\(Q BBB T @ HT T @# DT U X U P 4U H LU P dU X# D ^ , T p . YX
G H
-A , * $ D *T HD
-D l . `c| .v D @/X BIB B(A0A85
-0A(B BBBA 4 X0 BFA A(
- ABBA D 4 2, BIE B(A0A8
-0A(B BBBA , | 6 AC
TS
- A X;e G@]L ć ; BEE E(D0D8J
-8A0A(B BBBI d > BJB B(D0D8G
-8A0A(B BBBD B8A0A(B BBB | 8? D ?h BFB B(A0A8H
-0A(B BBBA 4 ܈ @ BFA A(
- ABBA L E BEB B(A0A8G
-8A0A(B BBBA 4 d @MM AC
T
+A U , U ؒ IGK 8
+ABJ U H V t $V ( D <V h BFB B(A0A8H
+0A(B BBBA 4 V Ș BFA A(
+ ABBA L V BEB B(A0A8G
+8A0A(B BBBA 4 W 0M AC
T
A
- H L XS BEB B(A0A8Gb
-8A0A(B BBBA L V BHB B(A0A8Dp
-8A0A(B BBBD < < (X< BBE A(A0
-(A BBBI , | (Y AKFAA , Y AKFjAA ܊ Y- Ae L Y BBF F(D0A8D`}
-8A0A(B BBBH , L Z) G
-A j
-F `
-H P | [ L \ BBB B(A0A8Dh
-8A0A(B BBBA ]B L ^ BBB B(A0A8J
-8A0A(B BBBJ \ L HaQ BEB B(A0A8
+ H L DW H BEB B(A0A8Gb
+8A0A(B BBBA L W خ BHB B(A0A8Dp
+8A0A(B BBBD < W < BBE A(A0
+(A BBBI , $X AKFAA , TX AKFjAA X ر- Ae L X BBF F(D0A8D`}
+8A0A(B BBBH , X G4
+E g
+I `
+H P $Y , <Y AC
T , lY AC
T L Y BBB B(A0A8Dh
+8A0A(B BBBA Y B L Z ( BBB B(A0A8J
+8A0A(B BBBJ \ TZ hQ BEB B(A0A8
0A(B BBBB V
-0A(B BBBA , Hc AA
-AA A , ܌ e AC
T , j AC
T < < Hp< BBE A(A0
-(A BBBI , | Hq AKJAA , q AKJAA , ܍ hr AKJAA $ s Cb
+0A(B BBBA < Z h< BBE A(A0
+(A BBBI , Z h AKJAA , $[ AKJAA $ T[ h Cb
K E
-K 4 | psY AC
BEHS
- J , l v AF
T
- J 4 X{ AF
T~
- G N , Ԏ = AF
T
- G L BDB B(A0D8G
-8A0A(B BBBB L T BME B(A0A8DX
-8A0A(B BBBG L BNE B(A0G8DQ
-8A0A(B BBBB L 0 BEE B(A0A8G:
-8A0A(B BBBA D M G EL \ X BBE B(A0A8J
-8A0A(B BBBH d Ș BBB B(A0A8GPl
+K 4 Z Y ( AC
BEHS
+ J , [ AC
T
+ G 4 [ AF
TL
+ I c , \ E AF
T
+ A L L\ BDB B(A0D8G
+8A0A(B BBBB L \ BME B(A0A8DX
+8A0A(B BBBG L \ p BNE B(A0G8DQ
+8A0A(B BBBB L <] BEE B(A0A8G:
+8A0A(B BBBA ] M G EL ] 8X BBE B(A0A8J
+8A0A(B BBBH d ] H BBB B(A0A8GPl
8A0A(B BBBI
-8A0A(B BBBI 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 ܒ Ȩ 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 < Ƚ BBA A(D0(A ABB T ԓ ( BBB B(A0A8G
-8A0A(B BBBJ L `d BBB B(A0A8G=
-8A0A(B BBBA L 4 u BBB B(A0A8JG
-8A0A(B BBBA L , BBB B(A0A8G
-8A0A(B BBBG L ԕ BBB B(A0D8J8A0A(B BBB L $ BBB B(A0D8G8A0A(B BBB t L { BBE B(A0C8G3
-8A0A(B BBBE L ܖ ` BBB B(D0A8J
-8A0A(B BBBH L ,
+8A0A(B BBBI L \^ BBB B(A0A8H
+8A0A(B BBBF L ^ ` BBB B(A0A8Hn
+8A0A(B BBBF D ^ BEB E(A0A80A(B BBB D D_ h BEB B(D0A8
+0A(B BBBD L _ BEB B(A0A8D
+8A0A(B BBBB D _ BBB K(A0A8
+0A(B BBBD L $` h2 BBB B(A0D8DD
+8A0A(B BBBD L t` X p BEB B(A0A8D
+8A0A(B BBBE ` x ` P
L ` BBB E(A0A8GBb
+8A0A(B BBBA L Da H@ BBB B(A0D8GA
+8A0A(B BBBE L a 8 BBE B(A0A8GA
+8A0A(B BBBA L a BBB B(A0A8G@6
+8A0A(B BBBG < |a x! P" BBA A(D0(A ABB T a ! " BBB B(A0A8G
+8A0A(B BBBJ L b ' BBB E(A0A8DH
+8A0A(B BBBA L c *d BBB B(A0A8G=
+8A0A(B BBBA L lc ,u BBB B(A0A8JG
+8A0A(B BBBA L c 0, BBB B(A0A8G
+8A0A(B BBBG L d 2 BBB B(A0D8J8A0A(B BBB L \d 4 BBB B(A0D8G8A0A(B BBB d P6 L d H6{ BBE B(A0C8G3
+8A0A(B BBBE L e x9` BBB B(D0A8J
+8A0A(B BBBH L de ?
BBB B(A0C8JO
-8A0A(B BBBA L | BBB B(A0C8J
-8A0A(B BBBG L ̗ h BBB B(A0C8J
-8A0A(B BBBA , ` BDA
-ABD < L H t BBA A(G
-(A ABBH , { AAGoAA L T BBB B(A0D8G58A0A(B BBB KIA D(G@(A ABBF@
+8A0A(B BBBA L e HI BBB B(A0C8J
+8A0A(B BBBG L f Y BBB B(A0C8J
+8A0A(B BBBA , Tf d` BDA
+ABD < f gt BBA A(G
+(A ABBH , f h{ AAGoAA L f HiT BBB B(A0D8G58A0A(B BBB Dg Xk KIA D(G@(A ABBF@
(A ABBO
-(G EBBH D(G EBB
- ̙ 4 BDD G0 AAB 4 p L L h` BDB B(A0A8Dp
-8A0A(B BBBD xO ̚ ; f , , D q BDD GP[ AAB, t @] FLQ )
-ABA p h ԛ 4 ! D L % BKB A(A0D`Q
-0A(A BBBB D p. BKB A(A0D`a
-0A(A BBBB $ ܜ AC
- I , _ AG
FEO
- H , 4 e AG
FEO
- H d 0 < Ĝ 8q BBA A(D0[
-(A ABBA L x AAG ]
+(G EBBH D(G EBB g Xr g @t h 8t h v 4 4h v BDD G0 AAB lh y L h y` BDB B(A0A8Dp
+8A0A(B BBBD h zO h | i |; i @~ 4i 8f Li di h , |i `q BDD GP[ AABD i BEB B(A0A8_
+0A(B BBBK i H j @ $j <j Tj lj j Ȋ D j Ў BKB A(A0D`Q
+0A(A BBBB D j H BKB A(A0D`a
+0A(A BBBB D ,k ПD BBB B(A0A8n
+0A(B BBBA $ j ت ) mFD@ T j p BBE B(A0A8G
+8A0A(B BBBE T <k BBB B(A0A8G
+8A0A(B BBBB Ll v D dl hX BIB B(A0A85
+0A(B BBBA 4 l BFA A(
+ ABBA D l , BIE B(A0A8
+0A(B BBBA , ,m AC
TS
+ A \m e G@]L tm BEE E(D0D8J
+8A0A(B BBBI d m BJB B(D0D8G
+8A0A(B BBBD B8A0A(B BBB $ ,n PP AC
+ I , Tn AG
FEO
+ H , n AG
FEO
+ H n < n q : BBA A(D0[
+(A ABBA L o AAG ]
CAF c
CAI R
-CAJ JCA < T q AAG`
+CAJ JCA < n 0 AAG`
AAJ M
-AAA L xi d i6 AG lA i> AG tA , { BFA i
-ABA L ` BBB F(A0A8J@X
-8A0A(B BBBA L l BBE A(A0J
-0A(A BBBA , hp AHK s
-AAA \ P BBB B(A0A8D@
-8C0A(B BBEA D8C0A(B BBBL L hG BEE D(D0G
-0D(A BBBA T iq BEE E(D0D8G0
-8D0A(B BBBA T jq BEE E(D0D8G0
-8D0A(B BBBA T L lq E BEE E(D0D8G0
-8D0A(B BBBA T Hmq BEE E(D0D8G0
-8D0A(B BBBA T pnq BEE E(D0D8G0
-8D0A(B BBBA T T oq s BEE E(D0D8G0
-8D0A(B BBBA < ` - BBA A(J
-(A ABBC T pq BEE E(D0D8G0
-8D0A(B BBBA L D qG > BEE D(D0G
-0D(A BBBA T rZ BEE E(D0D8G
-8D0A(B BBBA L sG BEE D(D0G
-0D(A BBBA L < tG | BEE D(D0G
-0D(A BBBA L uG > BEE D(D0G
-0D(A BBBA L ܣ vG BEE D(D0G
-0D(A BBBA L , w ½ BEE E(D0D8G
-8D0A(B BBBA L | @x BEE E(D0D8G
-8D0A(B BBBA T ̤ xZ F BEE E(D0D8G
-8D0A(B BBBA T $ x BBB B(A0A8G
-8A0A(B BBBB T | yq BEE E(D0D8G0
-8D0A(B BBBA L ԥ z a BEE E(D0D8Gp
-8D0A(B BBBA L $ {G # BEE D(D0G
-0D(A BBBA L t |G BEE D(D0G
-0D(A BBBA L Ħ }G BEE D(D0G
-0D(A BBBA L ~G i BEE D(D0G
-0D(A BBBA L d G + BEE D(D0G
-0D(A BBBA T _ BBE B(A0D8G
-8A0A(B BBBA L G BEE D(D0G
-0D(A BBBA L \ G BEE D(D0G
-0D(A BBBA L G s BEE D(D0G
-0D(A BBBA L G 5 BEE D(D0G
-0D(A BBBA d L BBA A(J
+AAA $ o ^ AG b
+AA L o Y BBB B(A0A8GPd
+8A0A(B BBBA T \o X RBB B(A0A8G`/
+8A0A(B EEBH , lp 2 FDH LGA p d o ȱ5 BBB B(D0A8D`H
+8A0A(B BBBE g
+8A0A(B BBBG T dp \ BBE E(D0A8G#
+8D0A(B BBBF < tq h BBG A(G@
+(A ABBH T p ( BBB B(D0D8Gy
+8A0A(B BBBA T Tq
BEB B(A0A8Gn
+8A0A(B BBBD < q BLD D(G0
+(A ABBE T q X BBB E(A0A8G
+8A0A(B BBBA r 0t. K^ T \r x BEB B(A0D8J(
+8A0A(B BBBD T r BBE E(A0A8M
+8D0A(B BBBF s t s t6 AG lA s t> AG tA , t @{ BFA i
+ABA L s BBB F(A0A8J@X
+8A0A(B BBBA L s j BBE A(A0J
+0A(A BBBA , 4t @tp o AHK s
+AAA \ u BBB B(A0A8D@
+8C0A(B BBEA D8C0A(B BBBL t tG BEE D(D0G
+0D(A BBBA T u uq BEE E(D0D8G0
+8D0A(B BBBA T lu Hvq { BEE E(D0D8G0
+8D0A(B BBBA T u pwq 5 BEE E(D0D8G0
+8D0A(B BBBA T v xq BEE E(D0D8G0
+8D0A(B BBBA T tv yq BEE E(D0D8G0
+8D0A(B BBBA T v zq c BEE E(D0D8G0
+8D0A(B BBBA < $w
+ BBA A(J
+(A ABBC T dw {q t BEE E(D0D8G0
+8D0A(B BBBA L w |G . BEE D(D0G
+0D(A BBBA T x }Z BEE E(D0D8G
+8D0A(B BBBA L dx G BEE D(D0G
+0D(A BBBA L x G l BEE D(D0G
+0D(A BBBA L y G . BEE D(D0G
+0D(A BBBA L Ty G BEE D(D0G
+0D(A BBBA L y BEE E(D0D8G
+8D0A(B BBBA L y t BEE E(D0D8G
+8D0A(B BBBA T Dz Z 6 BEE E(D0D8G
+8D0A(B BBBA T z
BBB B(A0A8G
+8A0A(B BBBB T z Єq BEE E(D0D8G0
+8D0A(B BBBA L L{ Q BEE E(D0D8Gp
+8D0A(B BBBA T { hq BEE E(D0D8G0
+8D0A(B BBBA L { G BEE D(D0G
+0D(A BBBA L D| G BEE D(D0G
+0D(A BBBA T | q Q BEE E(D0D8G0
+8D0A(B BBBA L | G BEE D(D0G
+0D(A BBBA L <} G BEE D(D0G
+0D(A BBBA L } G BEE D(D0G
+0D(A BBBA T } x_ Q BBE B(A0D8G
+8A0A(B BBBA T 4~ `q S BEE E(D0D8G0
+8D0A(B BBBA L ~ G
BEE D(D0G
+0D(A BBBA L ~ G
BEE D(D0G
+0D(A BBBA L , G
BEE D(D0G
+0D(A BBBA L | G S
BEE D(D0G
+0D(A BBBA d
BBA A(J
(A ABBA U
(A ABBA U
-(A ABBA T H BDA DPm
+(A ABBA T 4 ! $
BDA DPm
AABD x
AABD
- AABA D ں BBA A(J
-(A ABBA L T G BEE D(D0G
-0D(A BBBA L G ź BEE D(D0G
-0D(A BBBA L G BEE D(D0G
-0D(A BBBA T D q U BEE E(D0D8G0
-8D0A(B BBBA L G BEE D(D0G
-0D(A BBBA L G ѹ BEE D(D0G
-0D(A BBBA T < q BEE E(D0D8G0
-8D0A(B BBBA L G M BEE D(D0G
-0D(A BBBA L G BEE D(D0G
-0D(A BBBA L 4 G Ѹ BEE D(D0G
-0D(A BBBA L G BEE D(D0G
-0D(A BBBA L ԭ G U BEE D(D0G
-0D(A BBBA L $ G BEE D(D0G
-0D(A BBBA , t \ # BEH m
-ABA \ 8 t 4 i ADG S
+ AABA D #u BBA A(J
+(A ABBA T Ԁ q
BEE E(D0D8G0
+8D0A(B BBBA T , q BEE E(D0D8G0
+8D0A(B BBBA L ГG BEE D(D0G
+0D(A BBBA L ԁ ДG O BEE D(D0G
+0D(A BBBA L $ ЕG BEE D(D0G
+0D(A BBBA T t Жq BEE E(D0D8G0
+8D0A(B BBBA T ̂ q BEE E(D0D8G0
+8D0A(B BBBA L $ G S BEE D(D0G
+0D(A BBBA T t q BEE E(D0D8G0
+8D0A(B BBBA L ̃ HG
+ BEE D(D0G
+0D(A BBBA T Hq
+ BEE E(D0D8G0
+8D0A(B BBBA L t pG K
+ BEE D(D0G
+0D(A BBBA L Ą pG
+ BEE D(D0G
+0D(A BBBA L pG BEE D(D0G
+0D(A BBBA L d pG BEE D(D0G
+0D(A BBBA L pG S BEE D(D0G
+0D(A BBBA L pG BEE D(D0G
+0D(A BBBA , T p\ BEH m
+ABA < T ( 4 l i ADG S
CAE ]
-FCB T @ ; BBE B(A0A8G
-8A0A(B BBBA L d '
- BBB D(A0GP
-0A(A BBBF T (2 c BBB B(A0D8G1
-8A0A(B BBBA T 9i BBB E(A0A8G
-8A0A(B BBBA T d D| & BBE B(A0A8G^
-8A0A(B BBBD < L" e BJA A(D
-(A ABBA T f ? BEB E(A0C8J
-8A0A(B BBBA | BIE B(A0A8GP
+FCB d X _ BJE B(A0A8D@
+8F0A(B BBBI l8F0A(B BBB T T p! BBB B(A0A8Gh
+8A0A(B BBBA L (1
+ BBB D(A0D
+0A(A BBBG T :
+ BBB B(A0D8G1
+8A0A(B BBBA T T B
+ BBB E(A0A8G
+8A0A(B BBBA T 8M| \ BBE B(A0A8G^
+8A0A(B BBBD < `U" BJA A(D
+(A ABBA T D f w BEB E(A0C8J
+8A0A(B BBBA | T BIE B(A0A8GP
8A0A(B BBBD B
8D0A(B BBBA
-8A0A(B BBBG T Ա h7| BIB B(A0A8G?
-8A0A(B BBBA T , L1 o BBB B(A0A8G
-8A0A(B BBBE < 9|. K^ 4 BDD H
-ABK 4 Բ ؔ ^ BDD H
-ABK T 0 6 BED A(G0Q
+8A0A(B BBBG T t
+ BIB B(A0A8G?
+8A0A(B BBBA T t U1
+ BBB B(A0A8G
+8A0A(B BBBE t. K^ 4
+ BDD H
+ABK 4 ب
+ BDD H
+ABK T T 0 n
+ BED A(G0Q
(D ABBC k
-(D ABBA 4 d BDD Q
-ABB 4 ʸ BDD Q
-ABB < Գ h BBD D(G0
-(D ABBA < w BBD D(G0
-(D ABBA < T J BEA D(G0m
-(D ABBA < BBD D(G0
-(D ABBA < Դ ؘ BBD D(G0
-(D ABBA D · BEE D(A0Q
-(A BBBG 0 AQ 4 0m A~
-A l L O BBB F(A0A8J@X
-8A0A(B BBBA L p S BBB B(A0A8GZ
-8D0A(B BBBH D < N BED D(G$
-(D ABBE D h BED D(G$
-(D ABBE < ̶ 5 BBA A(JW
-(A ABBC L BEE D(D0G0
-0D(A BBBJ T \ @ Q BEE E(D0D8GU
-8D0A(B BBBA < | AAD`
+(D ABBA 4 (
+ BDD Q
+ABB 4
+ BDD Q
+ABB < h BBD D(G0
+(D ABBA < \ BBD D(G0
+(D ABBA < BEA D(G0m
+(D ABBA < ܌ T BBD D(G0
+(D ABBA < ج ' BBD D(G0
+(D ABBA D \ BEE D(A0Q
+(A BBBG \ 0 AQ | 0m A~
+A l L BBB F(A0A8J@X
+8A0A(B BBBA L 4 p BBB B(A0A8GZ
+8D0A(B BBBH D BED D(G$
+(D ABBE D ̎ h Q BED D(G$
+(D ABBE < 5 BBA A(JW
+(A ABBC L T A BEE D(D0G0
+0D(A BBBJ T @ BEE E(D0D8GU
+8D0A(B BBBA < | C AAD`
AAH p
-AAF T BEE E(D0D8GE
-8D0A(B BBBF L L BBB B(D0A8Gi
-8A0A(B BBBI D z BED D(G$
-(D ABBE T E BEE E(D0D8GH
-8D0A(B BBBA 4 < BCA GPc
- AABA d t M ڵ BLB B(A0A8DPq
+AAF T < BEE E(D0D8GE
+8D0A(B BBBF L BBB B(D0A8Gi
+8A0A(B BBBI T BEE E(D0D8GD
+8D0A(B BBBA T < l BEE E(D0D8GD
+8D0A(B BBBA D & BED D(G$
+(D ABBE T ܑ h BEE E(D0D8GH
+8D0A(B BBBA 4 4 BCA GPc
+ AABA T l h BEE E(D0D8GB
+8D0A(B BBBA T Ē @ BEE E(D0D8GB
+8D0A(B BBBA d M BLB B(A0A8DPq
8A0A(B BBBE
-8A0A(B BBBG T ܹ BEE E(D0D8GX
-8D0A(B BBBK < x BBG A(G@
-(A ABBG L t 8 ' BLB B(D0A8DP
-8A0A(B BBBJ L ĺ ^ BLH B(D0A8G@
-8A0A(B BBBB ̻ 5 < BBG A(G@
-(A ABBG l W BBB E(A0A8D
+8A0A(B BBBG L < BOB A(A0]
+(A BBBG Y
+(A BBBH T ԓ 0 P BEE E(D0D8GE
+8D0A(B BBBF < BBG A(G@
+(A ABBG L l H BLB B(D0A8DP
+8A0A(B BBBJ L ^ BLH B(D0A8G@
+8A0A(B BBBB ĕ 5 < ܕ BBG A(G@
+(A ABBG d ' BBB E(A0A8D
8A0A(B BBBI n
8A0A(B BBBH
-8A0A(B BBBI H0|. K^ D BIB A(A0G`o
-0A(A BBBA T T % BIB B(D0A8GW
-8D0A(B BBBA T p a BIB B(D0A8G
-8D0A(B BBBA L Ȝ3 : BIB D(A0G
-0D(A BBBA < T BBA A(J
-(A ABBJ 4 8 BAA GP_
- AABB T ̽ @ | BEB B(A0A8G
-8A0A(B BBBA L $ 8 _ BBB B(A0A8J]
-8A0A(B BBBE T t X o BIB B(D0A8G
-8A0A(B BBBA T ̾
x BBB B(D0A8G
-8A0A(B BBBJ ܿ Z m A~
-A l L \ ` BBB F(A0A8J@X
-8A0A(B BBBA L 5 BBB B(A0A8Gv
-8A0A(B BBBG T WV I BEE E(D0D8G
-8D0A(B BBBA T T XV BEE E(D0D8G
-8D0A(B BBBA 4 BCA GPc
- AABA T Y BEE E(D0D8GB
-8D0A(B BBBA T < [ v BEE E(D0D8GB
-8D0A(B BBBA T 8\r _ BEE E(D0D8G1
-8D0A(B BBBA T `]r BEE E(D0D8G1
-8D0A(B BBBA T D ^r Ӷ BEE E(D0D8G1
-8D0A(B BBBA T _ BEE E(D0D8GU
-8D0A(B BBBA T ` G BEE E(D0D8GU
-8D0A(B BBBA T L BIB B(A0A8J$
-8A0A(B BBBG < aQ S BDD v
-ABE AAB (|. K^ L p ط BIB D(A0Gq
-0D(A BBBA L L @: BIB D(A0G
-0D(A BBBA L 0: BIB D(A0G
-0D(A BBBA L 3 W BIB D(A0G
-0D(A BBBA L < , BIB D(A0G
-0D(A BBBA L BIB D(A0G
-0D(A BBBA 4 D BEA A(D0p(A ABB L _j w BBB E(A0C8DP
-8A0A(B BBBA T d : BBB B(A0A8J
-8A0A(B BBBA T '$ BEB B(A0A8G
+8A0A(B BBBI t. K^ D k BIB A(A0G`o
+0A(A BBBA T L % X BIB B(D0A8GW
+8D0A(B BBBA T 1 BIB B(D0A8G
+8D0A(B BBBA L 3
+ BIB D(A0G
+0D(A BBBA < L P BBA A(J
+(A ABBJ 4 H \ BAA GP_
+ AABB T ė L BEB B(A0A8G
+
+8A0A(B BBBA L H' 0 BBB B(A0A8J]
+8A0A(B BBBE T l 4 BIB B(D0A8G
+8A0A(B BBBA T Ę @
= BBB B(A0D8G
+8A0A(B BBBJ ԙ xZ m A~
+A l L T W BBB F(A0A8J@X
+8A0A(B BBBA L BBB B(A0A8Gv
+8A0A(B BBBG T lV BEE E(D0D8G
+8D0A(B BBBA T L mV BEE E(D0D8G
+8D0A(B BBBA 4 ` BCA GPc
+ AABA T ܚ nr BEE E(D0D8G1
+8D0A(B BBBA T 4 or BEE E(D0D8G1
+8D0A(B BBBA T qr S BEE E(D0D8G1
+8D0A(B BBBA T 0r
BEE E(D0D8GU
+8D0A(B BBBA T < xs BEE E(D0D8GU
+8D0A(B BBBA T p BIB B(A0A8J$
+8A0A(B BBBG < htQ BDD v
+ABE AAB t. K^ L D X BIB D(A0Gq
+0D(A BBBA L #: - BIB D(A0G
+0D(A BBBA L &: BIB D(A0G
+0D(A BBBA L 4 )3 BIB D(A0G
+0D(A BBBA L p, BIB D(A0G
+0D(A BBBA L Ԟ . BIB D(A0G
+0D(A BBBA 4 ܟ P1D BEA A(D0p(A ABB L \ Xrj BBB E(A0C8DP
+8A0A(B BBBA T 1 BBB B(A0A8J
+8A0A(B BBBA T <$ BEB B(A0A8G
-8A0A(B BBBA T 6% M BEB B(A0A8G
-8A0A(B BBBA T l >% X BEB B(A0A8G
-8A0A(B BBBA T F c BIB B(A0D8G$
-8A0A(B BBBG D BBE A(A0D@~
-0A(A BBBA D BBE A(A0D@I
-0A(A BBBA < ^ YHD M
+8A0A(B BBBA T \ XLG BEB B(A0A8G
+8A0A(B BBBA T PTG BEB B(A0A8G
+8A0A(B BBBA T H\ BIB B(A0D8G$
+8A0A(B BBBG D ` BBE A(A0D@~
+0A(A BBBA D d BBE A(A0D@I
+0A(A BBBA < 0q w YHD M
HAE
-HAA _ L _ BBB B(A0A8G@
-8D0A(B BBBG ` $ ` < ` T ` l ` ` ` ` `& PT `/ SZ ` L `e BEA A(D0s
-(C ABBH D(G ABB d a' AR
-E N a L (am BEA A(D0~
-(C ABBE D(G ABB Ha' AR
-E N $ Xa! AAG SCA 4 `a L ha $ d pa1 ADJ [AA $ a AAG`AA bB Hb @b AZ @b 8b 4 0b L ( < BJB A(A0W
-(A BBDH ^(A BBBT a ˴ BGB I(A0A8G
-8A0A(B BBBD $ < `q YX
+HAA r L L r . BBB B(A0A8G@
+8D0A(B BBBG T Hs l Ps Hs @s 8s ̣ 0s 8s 0s 8s& PT , Ps/ SZ D hs L \ pse BEA A(D0s
+(C ABBH D(G ABB s' AR
+E N ̤ s L sm BEA A(D0~
+(C ABBE D(G ABB 4 s' AR
+E N $ T s! AAG SCA | s s $ s1 ADJ [AA $ ԥ t AAG`AA tB t , t AZ L t d t | t L ܥ BJB A(A0W
+(A BBDH ^(A BBBT , Xt K BGB I(A0A8G
+8A0A(B BBBD $ YX
G z
-A L q BBD D(D@v
-(D ABBA X(D ABB T 8r'
- BBB E(A0D8D
-8D0A(B BBBG L | g BEB A(A0d
-(A BBDH [(A BBGL \ p|/ BBE B(A0A8DP
-8A0A(B BBBA d |. K^ T 8}t BBB B(A0G8G
-8A0A(B BBBF T ` BIB B(A0D8DH
-8D0A(B BBBC , t (U Ĵ ADD`v
-AAA , \ b D\
+A L d H BBD D(D@v
+(D ABBA X(D ABB T '
+ BBB E(A0D8D
+8D0A(B BBBG L T BEB A(A0d
+(A BBDH [(A BBGL / BBE B(A0A8DP
+8A0A(B BBBA t. K^ T t c BBB B(A0G8G
+8A0A(B BBBF T d b BIB B(A0D8DH
+8D0A(B BBBC , U D ADD`v
+AAA , 8b D\
H P
H Q
-G I , b D\
+G I , ԩ xb D\
H P
H Q
-G I , 8b D\
+G I , b D\
H P
H Q
-G I , xb D\
+G I , 4 b D\
H P
H Q
-G I , b D\
+G I , d 8b D\
H P
H Q
-G I , L b D\
+G I , xb D\
H P
H Q
-G I , | 8b D\
+G I , Ī b D\
H P
H Q
-G I , xb D\
+G I , b D\
H P
H Q
-G I , b D\
+G I , $ 8b D\
H P
H Q
-G I 4 T ~ BAD D
-ABA L @ ۲ BBB F(A0A8J@X
-8A0A(B BBBA , AAG@B
-AAC , p AAG@B
-AAC , ADG [
-AAG 4 l Py BDD G0y
- AABA < ACDP
+G I 4 x~ w BAD D
+ABA L Ԫ [ BBB F(A0A8J@X
+8A0A(B BBBA , $
+ & AAG@B
+AAC , T @ AAG@B
+AAC < , T ADG [
+AAG < ̫ ' ACDP
AAA O
-AAA < x AADP
+AAA <
AADP
AAA O
-AAA D $ X ϲ BED D(G$
-(D ABBE D l BED D(G$
-(D ABBE < ( e AADP
+AAA D L : BED D(G$
+(D ABBE D BED D(G$
+(D ABBE < ܬ AADP
AAI O
-AAA T [ BEE E(D0D8GT
-8D0A(B BBBG T L BEE E(D0D8GT
-8D0A(B BBBG T x ѱ BEE E(D0D8GT
-8D0A(B BBBG < 0 BED C(GP{
-(A ABBJ D < Ʊ BED D(G$
-(D ABBE 4 < J BDA l
+AAA T h BEE E(D0D8GT
+8D0A(B BBBG T t BEE E(D0D8GT
+8D0A(B BBBG T ̭ < BEE E(D0D8GT
+8D0A(B BBBG < $ BED C(GP{
+(A ABBJ D d R BED D(G$
+(D ABBE 4 d hJ BDA l
ABJ A
-ABA t m A~
-A l pm A~
-A l D BED D(G$
-(D ABBE 4 D ( AAD
-DAA 4 | а AAD$
-DAA 4 AAD
-DAA T ] BBB B(A0A8JB
-8A0A(B BBBA |. K^ 4 \
-g BDD G~
- AABA 4 h o BAD G
-ABA 4
-J BDA o
+ABA m A~
+A l m A~
+A l D $ BED D(G$
+(D ABBE 4 l p AAD
+DAA 4 \ AAD$
+DAA 4 ܯ 8 H AAD
+DAA T Pb 4 BBB B(A0A8JB
+8A0A(B BBBA $ Ȕt. K^ 4 !g # BDD G~
+ AABA 4 BAD G
+ABA 4 !J BDA o
ABG A
-ABA T BBB B(A0I8G
-8D0A(B BBBF < \ BAI G
- DABA < " α BAI G
- DABA < 8 BAI G
- DABA < 8 BAI G
- DABA T \ D Y BBB B(I0A8Gw
-8D0A(B BBBA T D , BBB B(I0A8Gw
-8D0A(B BBBA < K BAI G
- DABA 4 v BDA D0a
- AABA L < BBB B(A0A8Dp
-8A0A(B BBBA L X z S BBB E(D0D8GP
-8A0A(B BBBC T $ Ȩ 3 BIB B(A0A8J9
-8A0A(B BBBB 4 P < H @ BBK A(DPq
-(A ABBA < ؼ BBK A(DPq
-(A ABBA D hW g BBB A(A0GP
-0A(A BBBH \ \ n M BBB A(A0GP
+ABA T , 8 BBB B(A0I8G
+8D0A(B BBBF < BAI G
+ DABA < ı `" Z BAI G
+ DABA < P8 3 BAI G
+ DABA < D P8 BAI G
+ DABA T PD BBB B(I0A8Gw
+8D0A(B BBBA T ܲ HD BBB B(I0A8Gw
+8D0A(B BBBA < 4 @K BAI G
+ DABA 4 , v BDA D0a
+ AABA L d ( BBB B(A0A8Dp
+8A0A(B BBBA T Ȼ BIB B(A0A8J9
+8A0A(B BBBB P < l H BBK A(DPq
+(A ABBA < BBK A(DPq
+(A ABBA D hW BBB A(A0GP
+0A(A BBBH \ 4 n BBB A(A0GP
0A(A BBBD j
-0A(A BBBA \ n BBB A(A0GP
+0A(A BBBA \ n BBB A(A0GP
0A(A BBBD j
-0A(A BBBA L Ű BDB A(A0L
-0A(A BBBE T l 0 BBB B(A0A8J.
-8A0A(B BBBD T h BBB B(A0A8G
-8A0A(B BBBI D G BBI A(G
-(D ABBA , Hb D\
+0A(A BBBA L n BDB A(A0L
+0A(A BBBE T D 0 V BBB B(A0A8J.
+8A0A(B BBBD T hF c BBB B(A0A8G
+8A0A(B BBBK D ` BBI A(G
+(D ABBA , x b D\
H P
H Q
-G I , L b D\
+G I , $ b D\
H P
H Q
-G I , | b D\
+G I , T b D\
H P
H Q
-G I , b D\
+G I , 8 b D\
H P
H Q
-G I T $ H
_ BBB B(A0A8G
-8A0A(B BBBJ T | ge = BEE B(A0A8G
-8A0A(B BBBF T me ձ BEE B(A0A8G
-8A0A(B BBBF T , se ͱ BEE B(A0A8G
-8A0A(B BBBF T ye ű BEE B(A0A8G
-8A0A(B BBBF T e BEE B(A0A8G
-8A0A(B BBBF T 4 e BEE B(A0A8G
-8A0A(B BBBF T V BBB B(A0A8Gk
-8A0A(B BBBJ T ȋe BEE B(A0A8G
-8A0A(B BBBF T <
BBB B(A0A8G
-8A0A(B BBBB L h
-|. K^ T x { BBB B(A0I8G
-8D0A(B BBBA T 8
g BBJ B(A0A8G
-8D0A(B BBBD T \ p(
BBJ B(A0A8G
-8D0A(B BBBD T 5e ϳ BBB B(A0A8G
+G I T x
BBB B(A0A8G
+8A0A(B BBBJ T T |e BEE B(A0A8G
+8A0A(B BBBF T e y BEE B(A0A8G
+8A0A(B BBBF T e q BEE B(A0A8G
+8A0A(B BBBF T \ e i BEE B(A0A8G
+8A0A(B BBBF T e a BEE B(A0A8G
+8A0A(B BBBF T 8e Y BEE B(A0A8G
+8A0A(B BBBF T d V Q BBB B(A0A8Gk
+8A0A(B BBBJ T e @ BEE B(A0A8G
+8A0A(B BBBF T " 8 BBB B(A0A8G
+8A0A(B BBBB $ t. K^ T @*x BBB B(A0I8G
+8D0A(B BBBA T ܻ h0
BBJ B(A0A8G
+8D0A(B BBBD T 4 =
? BBJ B(A0A8G
+8D0A(B BBBD T Je s BBB B(A0A8G
-8A0A(B BBBA T K
- BIB B(A0A8J
-8A0A(B BBBJ b , 4 b D\
+8A0A(B BBBA T a
+ BIB B(A0A8J
+8A0A(B BBBJ w , Фb D\
H P
H Q
-G I , d b D\
+G I , < b D\
H P
H Q
-G I , b D\
+G I , l Pb D\
H P
H Q
-G I < ` BBD A(G@a
-(A ABBA 4 L ~ BAD D
-ABA , Xk g AYLp
-AAA L BBB B(A0A8DW
-8D0A(B BBBF , H_ o AAG o
-DAA < 4 ACG`i
+G I 4 ~ ? BAD D
+ABA < إ # BBD A(G@a
+(A ABBA , \ k AYLp
+AAA L X BBB B(A0A8DW
+8D0A(B BBBF , ܾ x_ AAG o
+DAA < x ACG`i
AAJ
-AAA , |. K^ 4 & ĸ BKA G
- DABA 4 & BKA G
- DABA T BIE B(A0A8G&
-8D0A(B BBBB T T BIE B(A0A8G&
-8D0A(B BBBB 4 @& BKA G
- DABA < 8 w BAD GS
- DABA < $ L BAI G
- DABA T d x % BIB B(D0A8G
-8D0A(B BBBA T 0 BIB B(A0A8G p
-
-8A0A(B BBBF T { BIB B(A0A8G x
-8A0A(B BBBF L l BBB B(A0A8Mi
-8A0A(B BBBA L BBB B(A0A8MF
-8A0A(B BBBA , b D\
+AAA T L ( e BEE B(A0A8G
+8A0A(B BBBF \ t. K^ 4 & BKA G
+ DABA 4 & BKA G
+ DABA 4 , & BKA G
+ DABA T d o BIE B(A0A8G&
+8D0A(B BBBB T x w BIE B(A0A8G&
+8D0A(B BBBB < BAD GS
+ DABA < T p T BAI G
+ DABA T P - BIB B(D0A8G
+8D0A(B BBBA T BIB B(A0A8G o
+8A0A(B BBBG T D pM BIB B(A0A8G .
+8A0A(B BBBH L h BBB B(A0A8Mi
+8A0A(B BBBA L 8 BBB B(A0A8MF
+8A0A(B BBBA , b D\
H P
H Q
-G I , 0b D\
+G I , $ b D\
H P
H Q
-G I , $ pb D\
+G I , T ( b D\
H P
H Q
-G I , T b D\
+G I , h b D\
H P
H Q
-G I , b D\
+G I , b D\
H P
H Q
-G I , 0k AYLp
-AAA , , pk AYLp
-AAA , \ ϸ AMQ`:
-AAA , _ AAG o
-DAA , P_ AAG o
-DAA , _ l AAG o
-DAA < ` ACG`i
+G I , , k AYLp
+AAA , \ (k AYLp
+AAA , h AMQ`:
+AAA , _ AAG o
+DAA , _ AAG o
+DAA , 8_ x AAG o
+DAA < L h ACG`i
AAJ
-AAA |. K^ T t X
- BIB B(D0A8G
-8D0A(B BBBA T
- BIB B(D0A8G
-8D0A(B BBBA T $
- k BIB B(D0A8G
-8D0A(B BBBA T |
- U BIB B(D0A8G
-8D0A(B BBBA T 8
- ? BIB B(D0A8G
-8D0A(B BBBA T ,
- ) BIB B(D0A8G
-8D0A(B BBBA T
- BIB B(D0A8G
-8D0A(B BBBA T `
- BIB B(D0A8G
-8D0A(B BBBA T 4
- BIB B(D0A8G
-8D0A(B BBBA T $
- ѹ BIB B(D0A8G
-8D0A(B BBBA T * BIE B(A0A8G"
-8D0A(B BBBF T < @; BIE B(A0A8G"
-8D0A(B BBBF L K # BBB B(A0A8Mi
-8A0A(B BBBA L S * BBB B(A0A8Mi
-8A0A(B BBBA H PF T d X^ BBB B(K0A8G
-8A0A(B BBBA , a AMQp
-AAA , bb D\
+AAA D 8t. K^ T
+
+ BIB B(D0A8G
+8D0A(B BBBA T
+
+ BIB B(D0A8G
+8D0A(B BBBA T T
+ w
+ BIB B(D0A8G
+8D0A(B BBBA T 8"
+ a
+ BIB B(D0A8G
+8D0A(B BBBA T '
+ K
+ BIB B(D0A8G
+8D0A(B BBBA T \ -
+ 5
+ BIB B(D0A8G
+8D0A(B BBBA T `3
+
+ BIB B(D0A8G
+8D0A(B BBBA T 9
+
+ BIB B(D0A8G
+8D0A(B BBBA T d >
+ BIB B(D0A8G
+8D0A(B BBBA T D
+ BIB B(D0A8G
+8D0A(B BBBA T @J BIE B(A0A8G"
+8D0A(B BBBF T l Z BIE B(A0A8G"
+8D0A(B BBBF L k /
+ BBB B(A0A8Mi
+8A0A(B BBBA L s 6
+ BBB B(A0A8Mi
+8A0A(B BBBA `6 4 h6F T ~
+ BBB B(K0A8G
+8A0A(B BBBA T N BBB B(A0D8G
+8A0A(B BBBA , Ђb D\
H P
H Q
-G I , bb D\
+G I , , b D\
H P
H Q
-G I , cb D\
+G I , \ Pb D\
H P
H Q
-G I , 4 @cb D\
+G I , b D\
H P
H Q
-G I , d cb D\
+G I , Ѓb D\
H P
H Q
-G I , cb D\
+G I , b D\
H P
H Q
-G I L d AAG ]
+G I L P AAG ]
CAF [
ACA R
-CAJ JCA , 0db D\
+CAJ JCA , l b D\
H P
H Q
-G I , D pdb D\
+G I , b D\
H P
H Q
-G I , t db D\
+G I , b D\
H P
H Q
-G I , db D\
+G I , @b D\
H P
H Q
-G I , 0eb D\
+G I , , b D\
H P
H Q
-G I , peb D\
+G I , \ b D\
H P
H Q
-G I , 4 eb D\
+G I , b D\
H P
H Q
-G I , d eb D\
+G I , @b D\
H P
H Q
-G I , 0fb D\
+G I , b D\
H P
H Q
-G I , pfb D\
+G I , b D\
H P
H Q
-G I , fb D\
+G I , L b D\
H P
H Q
-G I 4 l fg R BAD D
-ABA \ (gm A~
-A l < xg BBD A(G@a
-(A ABBA 4 (h~ BAD D
-ABA T < ph ӵ BZB J(A0A8G)
-8A0A(B BBBD L X ۵ BBB B(A0A8DW
-8D0A(B BBBF , m_ AAG o
-DAA , m_ AAG o
-DAA , D ne k ADG r
-DAA T t h W BBB B(A0A8Gw
-8A0A(B BBBF T O BBB B(A0A8G
-8A0A(B BBBG T $ ; BBB B(A0A8G
-8A0A(B BBBG T | ` BBB B(A0A8G
-8A0A(B BBBG T li 5 BBB B(A0D8G K
-8A0A(B BBBG `{. K^ , D H6 ADG T
-AAA 4 t } BAD G
-ABA L BLH B(D0D8D@
-8A0A(B BBBG 4 } , BKA G
- DABA 4 4 ~ BKA G
- DABA T l @ BBB J(A0A8G
-8D0A(B BBBD 4 BKA G
- DABA 4 BKA G
- DABA < 4 x BAD GS
- DABA T t h k BBE B(A0A8G>
-8D0A(B BBBA < S BAD GS
- DABA < P ( BAD GS
- DABA < L BAD GS
- DABA < Ҷ BAD GS
- DABA < p BAD GS
- DABA < Џ | BAD G{
- DABA < L ` P BAD G{
- DABA < $ BAD G
- DABA < BAD G{
- DABA D 0' ̵ BBA D(G
-(D ABBA < T BAD GS
- DABA < x n BAD GS
- DABA < ؚ C BAD GS
- DABA < 8 BAI G
- DABA < T BAD G
- DABA < ƴ BAD GS
- DABA < BAD G
- DABA T Ȣ o BIE B(A0A8G
-8D0A(B BBBG T l @ [ BIE B(A0A8G
-8D0A(B BBBG 4 G BKA G
- DABA 4 & BKA G
- DABA T 4 h BBB E(A0A8G
-8D0A(B BBBI 4 BRA G
- DABA 4 ȭ γ BRA G
- DABA 4 BRA G
- DABA < 4 8 BAD GS
- DABA < t a BAD GS
- DABA < 6 BAI Gl
- DABA < BAI G
- DABA T 4 XR BBB J(A0A8G
-8D0A(B BBBB < ` BAD GS
- DABA < BAD GS
- DABA < BAD GS
- DABA < L b BAD GS
- DABA < 7 BAD GS
- DABA < @ BAD GS
- DABA T BBJ B(A0A8G
-8D0A(B BBBJ < d H ױ BAD GS
- DABA < BAD GS
- DABA < BAD GS
- DABA < $ h V BAD GS
- DABA < d + BAD GS
- DABA T (h BBE B(A0A8G>
-8D0A(B BBBA T @h BBE B(A0A8G>
-8D0A(B BBBA < T X Ӱ BAI G4
- DABA < BAI GJ
- DABA < HM BAD G
- DABA < X Y BAD GS
- DABA < T . BAI GL
- DABA D 8B BBA I(G
-(D ABBA T @R ߯ BBB J(A0A8G
-8D0A(B BBBB D 4 H ӯ BBA I(G
-(D ABBA L 4
- BLB B(A0A8D
-8A0A(B BBBA
- T (5 C BBB B(A0A8J v
-8F0A(B BBBA d <
- i BDB B(A0A8L\
-8A0A(B BBBB
-8A0A(B BBBI L
- t BDB J(A0A8Ib
-8A0A(B BBBG T
-
BDA I(Dp
+G I , | @b D\
+H P
+H Q
+G I , b D\
+H P
+H Q
+G I m A~
+A l 4 D ~ BAD D
+ABA < | X BBD A(G@a
+(A ABBA T BZB J(A0A8G)
+8A0A(B BBBD 1 L , 1 BBB B(A0A8DW
+8D0A(B BBBF , | 8_ [ AAG o
+DAA , h_ 7 AAG o
+DAA , e ADG r
+DAA T 3 BBB B(A0A8Gw
+8A0A(B BBBF T d 8 5 BBB B(A0A8G
+8A0A(B BBBG T ( e BBB B(A0D8G
+8A0A(B BBBG st. K^ , , ;6 G ADG T
+AAA L \ ; ' BLH B(D0D8D@
+8A0A(B BBBG 4 H H BKA G
+ DABA 4 ' BKA G
+ DABA T BBB J(A0A8G
+8D0A(B BBBD 4 t BKA G
+ DABA 4 8 BKA G
+ DABA < BAD GS
+ DABA T $ Ph BBE B(A0A8G>
+8D0A(B BBBA < | h o BAD GS
+ DABA < Ȫ D BAD GS
+ DABA < ( BAD GS
+ DABA < < BAD GS
+ DABA < | BAD GS
+ DABA < H BAD G{
+ DABA < ر l BAD G{
+ DABA < < h @ BAD G
+ DABA < | BAD G{
+ DABA D ' BBA D(G
+(D ABBA < BAD GS
+ DABA < D BAD GS
+ DABA < P _ BAD GS
+ DABA < 4 BAI G
+ DABA <
BAD G
+ DABA < D 0 BAD GS
+ DABA < BAD G
+ DABA T @ BIE B(A0A8G
+8D0A(B BBBG T w BIE B(A0A8G
+8D0A(B BBBG 4 t 0 c BKA G
+ DABA 4 B BKA G
+ DABA T # BBB E(A0A8G
+8D0A(B BBBI < < BBI A(G
+(D ABBA < | BBI A(G
+(D ABBA 4 ( BYA G
+ DABA < BAD GS
+ DABA < 4 @ y BAD GS
+ DABA < t N BAI Gl
+ DABA < @ ( BAI G
+ DABA T R BBB J(A0A8G
+8D0A(B BBBB < L BAD GS
+ DABA < h BAD GS
+ DABA < BAD GS
+ DABA < ( z BAD GS
+ DABA < L O BAD GS
+ DABA < $ BAD GS
+ DABA T H BBJ B(A0A8G
+8D0A(B BBBJ < $ BAD GS
+ DABA < d P BAD GS
+ DABA < BAD GS
+ DABA < n BAD GS
+ DABA < $ p C BAD GS
+ DABA T d h BBE B(A0A8G>
+8D0A(B BBBA T h BBE B(A0A8G>
+8D0A(B BBBA < BAI G4
+ DABA < T p BAI GJ
+ DABA < M BAD G
+ DABA < q BAD GS
+ DABA < ` F BAI GL
+ DABA D T B BBA I(G
+(D ABBA T R BBB J(A0A8G
+8D0A(B BBBB D BBA I(G
+(D ABBA 4 < 8 BAD G
+ABA L , , BLB B(A0A8D
+8A0A(B BBBA | @0 l (Y BDB B(A0A8L\
+8A0A(B BBBB
+8A0A(B BBBD L L BDB J(A0A8Ib
+8A0A(B BBBG T BDA I(Dp
(A ABBA U
-(A ABBE T L 0 U BDB E(A0C8GR
-8A0A(B BBBA D x H BDB A(I0F`K
-0A(A BBBE T V BDA C(L`^
+(A ABBE L BDB B(A0I8Dpk
+8A0A(B BBBC T D V BDA C(L`^
(A ABBF
-(A ABBE T D V ۰ BDA C(L`^
+(A ABBE T V BDA C(L`^
(A ABBF
-(A ABBE T V BDA C(L`^
+(A ABBE T V H BDA C(L`^
(A ABBF
-(A ABBE L i BDB B(A0A8O\
-8A0A(B BBBG L D
i i BLB B(A0A8IZ
-8A0A(B BBBG ,
- AALP
-DAJ T
x.
BBB B(A0D8Ju
-8F0A(B BBBA , @; E AALP
-DAD , L ; . AALP
-DAK T | <d BBE B(A0A8Gx#
-8F0A(B BBBA L (b
BDB B(A0A8O|
-8A0A(B BBBG L $ H7 F BDJ B(A0A8Ir
-8A0A(B BBBG < t 8G ; BDA A(L
-(A ABBF L H + BBB B(D0G8JP
-8A0A(B BBBI AZ 4 $ T ] BAA J@
- AABB < \ a ^ BBA A(JP
-(A ABBI , T b D\
+(A ABBE L L ! BDB B(A0A8O\
+8A0A(B BBBG L X)i BLB B(A0A8IZ
+8A0A(B BBBG , x9 \ AALP
+DAJ , 8: E AALP
+DAD , L : . AALP
+DAK T | ; BBB B(D0A8J
+8F0A(B BBBA T K BBB B(A0A8J
+8F0A(B BBBA c T D c' BEB B(A0A8J
+8F0A(B BBBA T X} T P}Z BBE B(A0A8Gs@
+8F0A(B BBBE T BBB B(A0A8J
+8F0A(B BBBI L d b
BDB B(A0A8O|
+8A0A(B BBBG L 7 BDJ B(A0A8Ir
+8A0A(B BBBG < G BDA A(L
+(A ABBF L D BBB B(D0G8JP
+8A0A(B BBBI L @ AZ 4 @T
BAA J@
+ AABB < ha
BBA A(JP
+(A ABBI , , ) AMQp
+AAA , \ X+q AMQP5
+AAA , D ,b D\
H P
H Q
-G I , @b D\
+G I , t ,b D\
H P
H Q
-G I , b D\
+G I , (-b D\
H P
H Q
-G I , b D\
+G I , h-b D\
H P
H Q
-G I , b D\
+G I , -b D\
H P
H Q
-G I , D @b D\
+G I , 4 -b D\
H P
H Q
-G I L t AAG ]
+G I L d (. AAG ]
CAF [
ACA R
-CAJ JCA , b D\
+CAJ JCA , X.b D\
H P
H Q
-G I , b D\
+G I , .b D\
H P
H Q
-G I , $ 0b D\
+G I , .b D\
H P
H Q
-G I , T pb D\
+G I , D /b D\
H P
H Q
-G I , b D\
+G I , t X/b D\
H P
H Q
-G I , b D\
+G I , /b D\
H P
H Q
-G I , 0b D\
+G I , /b D\
H P
H Q
-G I , pb D\
+G I , 0b D\
H P
H Q
-G I , D b D\
+G I , 4 X0b D\
H P
H Q
-G I , t b D\
+G I , d 0b D\
H P
H Q
-G I , 0b D\
+G I , 0b D\
H P
H Q
-G I , pb D\
+G I , 1b D\
H P
H Q
-G I , b D\
+G I , X1b D\
H P
H Q
-G I , 4 b D\
+G I , $ 1b D\
H P
H Q
-G I , d 0b D\
+G I , T 1b D\
H P
H Q
-G I L p AAG ]
+G I , 2b D\
+H P
+H Q
+G I L X2 AAG ]
CAF [
ACA R
-CAJ JCA , b D\
+CAJ JCA , 2b D\
H P
H Q
-G I m A~
-A l T | 0 W BBB B(K0A8G
-8A0A(B BBBA T > ; BBB B(K0A8G
-8A0A(B BBBA 4 , ~ BAD D
-ABA , d (Z AMQ`
-AAA , Xv AMQp
-AAA , b Ӷ AMQP&
-AAA , _ ۶ AAG o
-DAA , $ _ AAG o
-DAA , T He ADG r
-DAA , _ o AAG o
-DAA , _ K AAG o
-DAA , _ ' AAG o
-DAA , e ADG r
-DAA T D X Ƕ BZB B(I0A8G
-8A0A(B BBBJ T BNB O(A0A8GT
-8A0A(B BBBH T + BBB B(A0A8G
-8A0A(B BBBG p{. K^ 4 d @ BKA G
- DABA 4 ` BKA G
- DABA 4 BKA G
- DABA 4 ݸ BKA G
- DABA 4 D BRA G
- DABA 4 | @ BRA G
- DABA 4 z BRA G
- DABA T
- Y BBB B(I0A8Gm
-8D0A(B BBBA < D
, BAD GS
- DABA T H BBB B(D0A8G
-8D0A(B BBBA < Ϸ BAD GS
- DABA < P BAD GS
- DABA < \ y BAD GS
- DABA < N BAD GS
- DABA < p # BAD GS
- DABA < BAD GS
- DABA < \ 0 Ͷ BAD GS
- DABA < BAD GS
- DABA < w BAI G
- DABA < P BAD GS
- DABA T \ 0 U % BIB E(A0A8Ga
-8D0A(B BBBA T 8"U BIB E(A0A8Ga
-8D0A(B BBBA 4 @$ õ BKA G
- DABA 4 D % BKA G
- DABA T | %t BBB B(D0A8G
-8D0A(B BBBA < ) O BAI Gl
- DABA < + ) BAI G
- DABA T T x- BBJ B(A0A8G
-8D0A(B BBBJ T 3 BBB B(D0A8G
-8D0A(B BBBA T ! 5 Ŵ BBB B(D0A8G
-8D0A(B BBBA D \! `8 BBA I(G
-(D ABBA 4 ! ; k BRA G
- DABA 4 ! `< J BRA G
- DABA 4 " = ) BRA G
- DABA 4 L" = BKA G
- DABA 4 " > BKA G
- DABA 4 " @? Ƴ BKA G
- DABA 4 " ? BKA G
- DABA 4 ,# @ BKA G
- DABA 4 d# hA c BKA G
- DABA 4 # B B BKA G
- DABA 4 # B ! BKA G
- DABA D $ C BBA D(G6
-(D ABBA L T$ F ز BBB A(I0G,
-0D(A BBBB 4 $ H= ADG
-DAA 4 $ I= ADG
-DAA 4 % J= o ADG
-DAA 4 L% K= J ADG
-DAA 4 % L= % ADG
-DAA 4 % M= ADG
-DAA 4 % N= ۱ ADG
-DAA 4 ,& Oe ADG@
-DAA 4 d& (Q ADGh
-DAA 4 & Re l ADG@
-DAA 4 & S= G ADG
-DAA 4 ' T= " ADG
-DAA 4 D' U= ADG
-DAA 4 |' Vy ذ ADGT
-DAA < ' X BAD G
- DABA < ' Y BAD G
- DABA 4 4( X[= ] ADG
-DAA 4 l( `\ 8 ADGh
-DAA 4 ( ]@ ADG
-DAA 4 ( ^@ ADG
-DAA 4 ) _@ ɯ ADG
-DAA 4 L) `@ ADG
-DAA 4 ) ay ADGT
-DAA 4 ) cb Z ADG=
-DAA < ) Xe 5 BAI G
- DABA < 4* 8h BAI Gi
- DABA < t* j BAI Gi
- DABA < * xm î BAD GV
- DABA < * n BAD GV
- DABA < 4+ 8p m BAD GV
- DABA < t+ q B BAD GV
- DABA < + r BAI G
- DABA < + ta BAI G
- DABA < 4, v| ʭ BAI G
- DABA < t, (y BAD G
- DABA < , z x BAD G
- DABA 4 , | M BKA G
- DABA 4 ,- p} , BKA G
- DABA T d- (~R BBB B(A0I8J ;
-8F0A(B BBBA L - 0x ۮ BBB B(F0A8Jp
-8A0A(B BBBE d . `i [ BBB B(A0A8L^
+G I 4 | 2g BAD D
+ABA l 3m A~
+A l T P3 y BBB B(K0A8G
+8A0A(B BBBA T , 6> ] BBB B(K0A8G
+8A0A(B BBBA 4 9~ C BAD D
+ABA < H9 ' BBD A(G@a
+(A ABBA T 9 BZB J(A0A8G)
+8A0A(B BBBD T T ? BNO B(A0A8G|
+8A0A(B BBBH , EZ AMQ`
+AAA , 8F_ + AAG o
+DAA , hF_ AAG o
+DAA , < Fe ADG r
+DAA , l F_ AAG o
+DAA , G_ AAG o
+DAA , 8G_ w AAG o
+DAA , hGe S ADG r
+DAA T , BBB B(A0A8G
+8A0A(B BBBG T @ BEE B(A0A8G
+8A0A(B BBBF T BEE B(A0A8G
+8A0A(B BBBF T 4 P BEE B(A0A8G
+8A0A(B BBBF D Qt. K^ 4 0F BAD G
+ABA 4 F
+ BKA G
+ DABA 4 @G BKA G
+ DABA 4 L G BKA G
+ DABA 4 H BKA G
+ DABA 4 hI BRA G
+ DABA 4 J w BRA G
+ DABA 4 , J V BRA G
+ DABA T d K 7 BBB J(A0A8G
+8D0A(B BBBD < XO # BAD GS
+ DABA T Ph BBE B(A0A8G>
+8D0A(B BBBA < T S BAD GS
+ DABA < 0U BAD GS
+ DABA < V BAD GS
+ DABA < W b BAD GS
+ DABA < T PY 7 BAD GS
+ DABA < Z BAD GS
+ DABA < \ BAD GS
+ DABA < p] BAD GS
+ DABA < T ^ BAI G
+ DABA < ` d BAD GS
+ DABA T b ; BIE B(A0A8G
+8D0A(B BBBG T , d ' BIE B(A0A8G
+8D0A(B BBBG 4 g BKA G
+ DABA 4 g BKA G
+ DABA T h BBB E(A0A8G
+8D0A(B BBBI < L Xl BAI Gl
+ DABA < n BAI G
+ DABA T p o BBB J(A0A8G
+8D0A(B BBBE T $ Pwh w BBE B(A0A8G>
+8D0A(B BBBA T | hzh _ BBE B(A0A8G>
+8D0A(B BBBA D }B G BBA I(G
+(D ABBA D BBA I(G
+(D ABBA 4 d Ѓ BRA G
+ DABA 4 BRA G
+ DABA 4 @ BRA G
+ DABA 4 BKA G
+ DABA 4 D s BKA G
+ DABA 4 | h R BKA G
+ DABA 4 1 BKA G
+ DABA 4 ؈ BKA G
+ DABA 4 $ BKA G
+ DABA 4 \ H BKA G
+ DABA 4 BKA G
+ DABA D BBA D(G6
+(D ABBA L 0 d BBB A(I0G,
+0D(A BBBB 4 d = E ADG
+DAA 4 = ADG
+DAA 4 = ADG
+DAA 4 = ADG
+DAA 4 D = ADG
+DAA 4 | = ADG
+DAA 4 = g ADG
+DAA 4 e B ADG@
+DAA 4 $ P ADGh
+DAA 4 \ e ADG@
+DAA 4 = ADG
+DAA 4 = ADG
+DAA 4 = ADG
+DAA 4 < y d ADGT
+DAA < t @ ? BAD G
+ DABA < BAD G
+ DABA 4 = ADG
+DAA 4 , ADGh
+DAA 4 d @ ADG
+DAA 4 @ z ADG
+DAA 4 @ U ADG
+DAA 4 @ 0 ADG
+DAA 4 D y ADGT
+DAA 4 | Hb ADG=
+DAA < BAI G
+ DABA < ` BAI Gi
+ DABA < 4 u BAI Gi
+ DABA < t O BAD GV
+ DABA < $ BAD GV
+ DABA < ` BAD GV
+ DABA < 4 BAD GV
+ DABA < t BAI G
+ DABA < a | BAI G
+ DABA < | V BAI G
+ DABA < 4 P 0 BAD G
+ DABA < t BAD G
+ DABA 4 BKA G
+ DABA 4 BKA G
+ DABA T $ P BBB B(A0A8J
+8F0A(B BBBA L | xx 3 BBB B(F0A8Jp
+8A0A(B BBBE d i BBB B(A0A8L^
8A0A(B BBBB
-8A0A(B BBBI L t. h( BBB B(A0C8O\
-8A0A(B BBBG . H BBB B(A0C8Lpq
-8A0A(B BBBE 4
-8A0A(B BBBJ /
-8A0A(B BBBG L L/ ( ծ BBB B(A0C8O\
-8A0A(B BBBG L / ( Ӯ BBB B(A0C8O\
-8A0A(B BBBG D / " BBB A(I0F`E
-0A(A BBBE D 40 BBB A(I0F`E
-0A(A BBBE T |0 PV BBA C(L``
+8A0A(B BBBI L 4
+ ( p BBB B(A0C8O\
+8A0A(B BBBG
+ l n BBB B(A0C8Lo
+8A0A(B BBBG
+8A0A(B BBBJ
+8A0A(B BBBE L x( > BBB B(A0C8O\
+8A0A(B BBBG L \ X( < BBB B(A0C8O\
+8A0A(B BBBG < s BEA D(D`s
+(A ABBG D A BBB A(I0F`E
+0A(A BBBE D 4 P
BBB A(I0F`E
+0A(A BBBE T | V BBA C(L``
(A ABBF
-(A ABBE T 0 XV BBA C(L``
+(A ABBE T V BBA C(L``
(A ABBF
-(A ABBE T ,1 `V H BBA C(L``
+(A ABBE T ,
V g BBA C(L``
(A ABBF
-(A ABBE T 1 hV BBA C(L``
+(A ABBE T
+V . BBA C(L``
(A ABBF
-(A ABBE L 1 pK ֭ BBJ B(A0A8Il
-8A0A(B BBBG < ,2 pG ĭ BBA A(L
-(A ABBF L l2
- BBB B(A0A8Ov
-8A0A(B BBBG L 2 @ ϭ BBB B(A0A8Ov
-8A0A(B BBBG T 3
- " BDB B(D0A8G,
-8A0A(B BBBD 4 L |3 c
- 7 BBB B(D0D8MP
-8A0A(B BBBI 4 AX 4 p < 4 W 3 BSA Q
- AABA , D4 ASQpe
-AAA < t4 BYA Lt
- AABA < 4 XQ BYA L
- AABA , 4 x` ӭ AYLp
-AAA , $5 AMQp
-AAA L 6 AAG ]
+(A ABBE L
BJB B(A0A8I\
+8A0A(B BBBG L , (. + BBJ B(A0A8Il
+8A0A(B BBBG L | BBB B(A0A8L
+8A0A(B BBBF l H# BBB B(A0A8Lv
+8A0A(B BBBJ [
+8A0A(B BBBK L < ' BBB B(A0A8Ov
+8A0A(B BBBG L 81
+ ( BDB B(D0A8I_
+8A0A(B BBBG ; L ; E BBB B(D0D8MP
+8A0A(B BBBI F AX , d F e AALP
+DAJ , @G N AALP
+DAD , G 7 AALP
+DAK T Haa BBE B(A0A8G"
+8F0A(B BBBA X < d W BSA Q
+ AABA , ASQpe
+AAA < BYA Lt
+ AABA < pQ x BYA L
+ AABA , T ` _ AYLp
+AAA , I AMQp
+AAA L l AAG ]
CAF [
ACA R
-CAJ JCA L \6 8 AAG ]
+CAJ JCA L P AAG ]
CAF Z
ACB R
-CAJ JCA , 6 h b D\
+CAJ JCA , b D\
H P
H Q
-G I , 6 b D\
+G I , < b D\
H P
H Q
-G I , 7 b D\
+G I , l b D\
H P
H Q
-G I , <7 ( b D\
+G I , @b D\
H P
H Q
-G I , l7 h b D\
+G I , b D\
H P
H Q
-G I , 7 b D\
+G I , b D\
H P
H Q
-G I , 7 b D\
+G I , , b D\
H P
H Q
-G I , 7 (
b D\
+G I , \ @b D\
H P
H Q
-G I , ,8 h
b D\
+G I , b D\
H P
H Q
-G I , \8
b D\
+G I , b D\
H P
H Q
-G I , 8
b D\
+G I , b D\
H P
H Q
-G I , 8 (b D\
+G I , @b D\
H P
H Q
-G I , 8 hb D\
+G I , L b D\
H P
H Q
-G I , 9 b D\
+G I , | b D\
H P
H Q
-G I , L9 b D\
+G I , b D\
H P
H Q
-G I , |9 (b D\
+G I , @b D\
H P
H Q
-G I , 9 hb D\
+G I , b D\
H P
H Q
-G I , 9 b D\
+G I , < b D\
H P
H Q
-G I , : b D\
+G I , l b D\
H P
H Q
-G I , <: (b D\
+G I , @b D\
H P
H Q
-G I , l: hb D\
+G I , b D\
H P
H Q
-G I , : b D\
+G I , b D\
H P
H Q
-G I : m A~
-A l 4 4: 8~ ˨ BAD D
-ABA < l: BBD A(G@a
-(A ABBA L : 0 BBB F(A0A8J@X
-8A0A(B BBBA T : c BBB E(A0A8D
-8D0A(B BBBA T T; xK O BTB B(A0A8QK
-8A0A(B BBBA , ; ph 0 BDD y
-ABA , ; _ AAG o
-DAA , < _ AAG o
-DAA , << _ ħ AAG o
-DAA , l< @_ AAG o
-DAA , < p_ | AAG o
-DAA , < _ X AAG o
-DAA D < BED D(G$
-(D ABBE D D= BED D(G$
-(D ABBE D = P BED D(G$
-(D ABBE L > BBB B(A0A8Gp8A0A(B BBB , > 8 ADD Z
-AAA $ T> { ; A
-E D |> ' BPR M(D0c
-(A BBBA 4 > H B\D G
- DABA < > ' B_G G=
- DABA < <? ` BAI GL
- DABA 4 |? ب BKA G
- DABA 4 ? BKA G
- DABA 4 ? P BKA G
- DABA D $@ u BBA I(G
-(D ABBA D l@ $ L BBA I(G9
-(D ABBA 4 @ h( # BKA G
- DABA 4 @ ) BRA G
- DABA 4 $A ) BRA G
- DABA 4 \A * BRA G
- DABA T A H+
- BBB B(A0A8G>
-8A0A(B BBBG < A 1Y s BAI G
- DABA < ,B 3 L BAI G
- DABA < lB 4 & BAI Gk
- DABA T B 7 BBE B(A0A8GO
-8D0A(B BBBH T C < BBJ B(A0A8G
-8D0A(B BBBJ < \C pB6 BAD G
- DABA < C pD BAI G
- DABA T C 0G BBB B(A0I8G
-8D0A(B BBBA T 4D J BEE B(D0D8L
-8A0A(B BBBK < D P`a t BBA A(JP
-(A ABBA T D a1 L BBA A(MT
+G I , m A~
+A l < P W BBD A(G@a
+(A ABBA 4 ~ ? BAD D
+ABA L H # BBB F(A0A8J@X
+8A0A(B BBBA T \ BBB E(A0A8D
+8D0A(B BBBA T K BTB B(A0A8QK
+8A0A(B BBBA , h BDD y
+ABA , < _ AAG o
+DAA , l _ t AAG o
+DAA , (_ P AAG o
+DAA , X_ , AAG o
+DAA , _ AAG o
+DAA , , _ AAG o
+DAA D \ h 5 BED D(G$
+(D ABBE , \ Б ADD Z
+AAA $ +t A
+E D k BPR M(D0c
+(A BBBA 4 D | B\D G
+ DABA < | [ B_G G=
+ DABA < X 3 BAI GL
+ DABA 4 BKA G
+ DABA 4 4 BKA G
+ DABA 4 l H BKA G
+ DABA D BBA I(G
+(D ABBA D BBA I(G9
+(D ABBA 4 4 ` W BKA G
+ DABA 4 l 6 BRA G
+ DABA 4 BRA G
+ DABA 4 BRA G
+ DABA T @
+ BBB B(A0A8G>
+8A0A(B BBBG < l Y BAI G
+ DABA < BAI G
+ DABA < Z BAI Gk
+ DABA T , 3 BBE B(A0A8G
+8D0A(B BBBI T 7 BBB J(A0A8G
+8D0A(B BBBD < x6 ? BAD G
+ DABA < x BAI G
+ DABA T \ 8 BBB B(A0I8G
+8D0A(B BBBA T R BBE E(D0D8L
+8A0A(B BBBK < ! $a BBA A(JP
+(A ABBA T L! %1 v BBA A(MT
(A ABBC \
-(A ABBF L $E hg D BAA JV
+(A ABBF L ! + n BAA JV
AABH
- AABD , ,F 8k BCA x
-ABO T E k BBB B(A0D8G
-8A0A(B BBBE F } F }( A[ L 4F }% % BFB B(A0C8J]
-8A0A(B BBBG <G TG ( A[ 4 F d BAA J@
- AABJ L F 8 BFB B(A0A8Gp
-8A0A(B BBBJ G H < tG ACLPj
+ AABD , " / BCA x
+ABO T $" 0 BBB B(A0D8G
+8A0A(B BBBE 4# XA L# PA( A[ L " `A% O BFB B(A0C8J]
+8A0A(B BBBG # @H # 8H( A[ 4 <# HHd < BAA J@
+ AABJ L t# J = BFB B(A0A8Gp
+8A0A(B BBBJ |$ PL $ HL < # @L ACLPj
AAD I
-AAE lH A`
-G b H A`
-G b H h A`
-G bL H ، C BFB B(A0C8J]
-8A0A(B BBBG I ( T |H BBE E(A0C8G^
-8D0A(B BBBD L H Ȝ x BDB B(A0A8O\
-8A0A(B BBBG T $I _ BBB E(A0G8G
-8D0A(B BBBJ T |I & BIB B(A0A8G
-8A0A(B BBBF L I 8 BTB A(A0N
-0A(A BBBA < $J x0 BSA Q
- AABA < dJ hW BSA Q
- AABA < J BYA Lt
- AABA < J XQ s BYA L
- AABA , K xb D\
+AAE $ O A`
+G b % @P A`
+G b ,% P A`
+G bL $ QG m BFB B(A0C8J]
+8A0A(B BBBG % c T $ 8 BBE E(A0C8G^
+8D0A(B BBBD L T% b BDB B(A0A8O\
+8A0A(B BBBG T % BBB B(A0G8J
+8D0A(B BBBA T % f BIB B(A0A8G
+8A0A(B BBBF L T& BTB A(A0N
+0A(A BBBA < & Џ0 BSA Q
+ AABA < & W BSA Q
+ AABA < $' BYA Lt
+ AABA < d' Q BYA L
+ AABA , \( Йb D\
H P
H Q
-G I , L b D\
+G I , ( b D\
H P
H Q
-G I , <L b D\
+G I , ( Pb D\
H P
H Q
-G I , lL 8b D\
+G I , ( b D\
H P
H Q
-G I L L x AAG ]
+G I L ) К AAG ]
CAF [
ACA R
-CAJ JCA L L AAG ]
+CAJ JCA L l) AAG ]
CAF Z
ACB R
-CAJ JCA , <M b D\
+CAJ JCA , ) 0b D\
H P
H Q
-G I , lM b D\
+G I , ) pb D\
H P
H Q
-G I , M Xb D\
+G I , * b D\
H P
H Q
-G I , M b D\
+G I , L* b D\
H P
H Q
-G I , M b D\
+G I , |* 0b D\
H P
H Q
-G I , ,N b D\
+G I , * pb D\
H P
H Q
-G I , \N Xb D\
+G I , * b D\
H P
H Q
-G I , N b D\
+G I , + b D\
H P
H Q
-G I , N b D\
+G I , <+ 0b D\
H P
H Q
-G I , N b D\
+G I , l+ pb D\
H P
H Q
-G I , O Xb D\
+G I , + b D\
H P
H Q
-G I , LO b D\
+G I , + b D\
H P
H Q
-G I |O m A~
-A l < N ( BBD A(G@a
-(A ABBA 4 $O ~ BAD D
-ABA , \O _ AAG o
-DAA , O Ph k BDD y
-ABA , O _ G AAG o
-DAA , O _ # AAG o
-DAA , P _ AAG o
-DAA T LP BEJ E(D0D8GP
-8D0A(B BBBF $ P { A
-E < P BAI Gk
- DABA L Q @ BBB I(A0G
-0D(A BBBA < \Q 6 Ц BAD G
- DABA 4 Q BYA G
- DABA 4 Q h BYA G
- DABA 4 R P! c B_D G
- DABA 4 DR H B BYA G
- DABA < |R | ! BYA G
- DABA < R @ B_G G=
- DABA < R ѥ B_G G=
- DABA < <S BAI GL
- DABA L |S BIB A(D0G
-0D(A BBBA 4 S p J BKA G
- DABA 4 T ( ) BKA G
- DABA 4 <T BKA G
- DABA 4 tT G ADG"
-DAA T T ¤ BEE B(A0H8N
-8D0A(B BBBF 4 U ADG
-DAA 4 <U a ADG
-DAA < tU (- < BAD G
- DABA < U BAD G
- DABA < U B BAI G
- DABA T 4V BJB B(A0A8GE
-8D0A(B BBBE < V BAI Gu
- DABA L V `
- ` BBB I(A0G
-0D(A BBBA T W ; BJB B(A0A8G
-8D0A(B BBBE < tW X ACL@j
-AAD
-AAG lX A`
-G b X x A`
-G b< W O ACLPo
+G I + 0m A~
+A l < d+ BBD A(G@a
+(A ABBA 4 + 0~ BAD D
+ABA , + x_ AAG o
+DAA , , h BDD y
+ABA , <, _ { AAG o
+DAA , l, _ W AAG o
+DAA , , H_ 3 AAG o
+DAA T , XH # BEJ E(D0D8GP
+8D0A(B BBBF $ $- t A
+E < L- BAI Gk
+ DABA T - BBJ B(A0A8Gd
+8D0A(B BBBF < - 6 BAD G
+ DABA 4 $. BYA G
+ DABA 4 \. Ȩ BYA G
+ DABA 4 . ! z B_D G
+ DABA 4 . Y BYA G
+ DABA < / `| 8 BYA G
+ DABA < D/ B_G G=
+ DABA < / B_G G=
+ DABA < / BAI GL
+ DABA L 0 BIB A(D0G
+0D(A BBBA 4 T0 г a BKA G
+ DABA 4 0 @ BKA G
+ DABA 4 0 @ BKA G
+ DABA 4 0 G ADG"
+DAA T 41 BEE B(A0H8N
+8D0A(B BBBF 4 1 H ADG
+DAA 4 1 x ADG
+DAA < 1 - S BAD G
+ DABA < <2 x ' BAD G
+ DABA < |2 HB BAI G
+ DABA T 2 X BBB B(A0A8G
+8A0A(B BBBD < 3 BAI Gu
+ DABA T T3 ` w BBJ B(A0A8Gd
+8D0A(B BBBF T 3 v c BBB B(A0A8G
+8A0A(B BBBD < 4 t ACL@a
+AAE
+AAG 4 @ A`
+G b 5 A`
+G b< 4 C ACLPo
AAG
-AAE L 4X
S BDB B(A0A8OZ
-8A0A(B BBBA L X x E BDB B(A0A8OZ
-8A0A(B BBBA L X ( 7 BDB B(A0A8OZ
-8A0A(B BBBA T $Y "H BIB B(A0A8G
-8A0A(B BBBD T |Y = BEE B(D0D8L
-8A0A(B BBBK , Z V ACL d
-JAQ , Z V BCA x
-ABO Z W( A[ [ W( A[ T tZ (W ( BBB B(A0D8G
-8A0A(B BBBE [ l L Z l ˥ BFB B(A0C8J]
-8A0A(B BBBG [ (} , \ 0b D\
+AAE L 4
G BDB B(A0A8OZ
+8A0A(B BBBA L 5 9 BDB B(A0A8OZ
+8A0A(B BBBA L d5 ` + BDB B(A0A8OZ
+8A0A(B BBBA T 5 z s BIB B(A0A8G
+8A0A(B BBBD T 6 8 BEE B(D0D8L
+8A0A(B BBBK , 7 p ACL d
+JAQ , L7 BCA x
+ABO |7 p( A[ 7 ( A[ T 7 BBB B(A0D8G
+8A0A(B BBBE 8 - L t7 - BFB B(A0C8J]
+8A0A(B BBBG |8 P= , 8 X?b D\
H P
H Q
-G I , 4\ pb D\
+G I , 8 ?b D\
H P
H Q
-G I T [ E Υ BBE E(A0A8G6
-8A0A(B BBBI T \ ǥ BBE E(A0A8G
-8A0A(B BBBK T \\ E BBE E(A0A8G6
-8A0A(B BBBI l] ؒ{. K^ T \ ~ BBB B(A0A8G
+G I T <8 E BBE E(A0A8G6
+8A0A(B BBBI T 8 BBE E(A0A8G
+8A0A(B BBBK T 8 8E BBE E(A0A8G6
+8A0A(B BBBI 9 t. K^ T \9 > t BBB B(A0A8G
-8A0A(B BBBA T $] BBB B(A0A8GX
-8A0A(B BBBA T |] @y ~ BDB B(A0A8J
-8A0A(B BBBH ^ h < ] ` Ȧ BBA A(JPx
-(A ABBB ^ Q Az
-E P , _ b D\
+8A0A(B BBBA T 9 O f BBB B(A0A8GX
+8A0A(B BBBA T : h^y R BDB B(A0A8J
+8A0A(B BBBH ; z < |: z BBA A(JPx
+(A ABBB t; Q Az
+E P , ; b D\
H P
H Q
-G I 4_ @{. K^ T ^ ; BBB B(A0A8GX
-8A0A(B BBBA , _ @b D\
+G I ;
t. K^ T $; BBB B(A0A8GX
+8A0A(B BBBA , 4< hb D\
H P
H Q
-G I , _ b D\
+G I , d< b D\
H P
H Q
-G I 4 L_ ~ ǥ BAD D
-ABA D _ l ߦ BAA JPV
+G I 4 ; ~ BAD D
+ABA D < 0l o BAA JPV
AABH
- AABC ` {. K^ T _ D BBB B(A0I8G
-8D0A(B BBBA T <` f BBB B(A0I8G
-8D0A(B BBBA \ ` ( ˩ BBB A(A0MJ
+ AABC = t. K^ T t< @D ( BBB B(A0I8G
+8D0A(B BBBA T < 8f BBB B(A0I8G
+8D0A(B BBBA \ $= P BBB A(A0MJ
0A(A BBBI
-0A(A BBBD T ` = BIB B(A0A8G+ "
-8A0A(B BBBA , b 7b D\
+0A(A BBBD T = D BIB B(A0A8G*,
+8A0A(B BBBD , > hb D\
H P
H Q
-G I , 4b 7b D\
+G I , > b D\
H P
H Q
-G I D a 8l BAA JPV
+G I D <> l BAA JPV
AABH
- AABC b ({. K^ T b : BBB B(A0A8G
-8A0A(B BBBA T db H' BBB B(A0A8G
-8A0A(B BBBA \ b W ߯ BBB A(A0MJ
+ AABC <?
+t. K^ T >
+ BBB B(A0A8G
+8A0A(B BBBA T > '
+ BBB B(A0A8G
+8A0A(B BBBA \ L? BBB A(A0MJ
0A(A BBBI
-0A(A BBBD L c 0_ AAG ]
-CAF Z
-ACB R
-CAJ JCA , $d `_b D\
+0A(A BBBD L d@ ' AAG ]
+CAF [
+ACA R
+CAJ JCA , @ H'b D\
H P
H Q
-G I , Td _b D\
+G I , @ 'b D\
H P
H Q
-G I , d _b D\
+G I , A 'b D\
H P
H Q
-G I T c `
- C BNO B(A0A8G
-8A0A(B BBBH , Td jd K ACG r
-DAA , d j_ ' AAG o
-DAA T d u BEE B(A0A8G
-8A0A(B BBBD L e j d BBB B(A0G8Dh
-8A0A(B BBBJ T \e oo W BBB B(A0A8J
-8D0A(B BBBK T e ~ BQB B(K0A8G
-8A0A(B BBBA f @{. K^ T $f H BIB B(D0A8Gb
-8D0A(B BBBA T |f BIB B(D0A8Gb
-8D0A(B BBBA T f 8 BKB B(D0A8Gb
-8D0A(B BBBA T ,g Ҳ BKB B(D0A8Gb
-8D0A(B BBBA T g ( BKB B(D0A8Gb
-8D0A(B BBBA T g BKB B(D0A8Gb
-8D0A(B BBBA T 4h BNB B(D0A8Gb
-8D0A(B BBBA T h v BNB B(D0A8Gb
-8D0A(B BBBA T h _ BKB B(D0A8Gb
-8D0A(B BBBA T <i H BKB B(D0A8Gb
-8D0A(B BBBA T i 1 BKB B(D0A8Gb
-8D0A(B BBBA T i p BKB B(D0A8Gb
-8D0A(B BBBA L Dj J O BBB B(G0A8GH
-8A0A(B BBBA L Lk h AAG ]
+G I T @ (
+ [ BNO B(A0A8G
+8A0A(B BBBH , @ p2g c BDC y
+ABA , A 2_ ? AAG o
+DAA T DA u BEE B(A0A8G
+8A0A(B BBBD L A 2 BBB B(A0G8Dh
+8A0A(B BBBJ T A 7o BBB B(A0A8J
+8D0A(B BBBK T DB E2 BNB B(K0A8GX
+8A0A(B BBBG \ TC m BBD D(J@
+(A ABBD T
+(A ABBF _
+(A ABBK C t. K^ 4 C `I d
BRA G
+ DABA 4 LC J C
BOA G
+ DABA 4 C J "
BOA G
+ DABA T C K
BIB B(D0A8G[
+8D0A(B BBBA T D Q BIB B(D0A8G[
+8D0A(B BBBA T lD xV BPB B(D0A8G[
+8D0A(B BBBA T D [ BMB B(D0A8G[
+8D0A(B BBBA T E ha BMB B(D0A8G[
+8D0A(B BBBA T tE f BMB B(D0A8G[
+8D0A(B BBBA T E Xl w BMB B(D0A8G[
+8D0A(B BBBA T $F q ` BPB B(D0A8G[
+8D0A(B BBBA T |F Hw I BMB B(D0A8G[
+8D0A(B BBBA L F | ~ BBB B(A0A8MH
+8A0A(B BBBA L G AAG ]
CAF Z
ACB R
-CAJ JCA , k b D\
+CAJ JCA , ,H b D\
H P
H Q
-G I , k b D\
+G I , \H `b D\
H P
H Q
-G I , k b D\
+G I , H b D\
H P
H Q
-G I L ,l X AAG ]
+G I L H AAG ]
CAF Y
ACC R
-CAJ JCA , |l b D\
+CAJ JCA , I b D\
H P
H Q
-G I , l b D\
+G I , <I Pb D\
H P
H Q
-G I , l b D\
+G I , lI b D\
H P
H Q
-G I L m H AAG ]
+G I L I Б AAG ]
CAF [
ACA R
-CAJ JCA , \m xb D\
+CAJ JCA , I b D\
H P
H Q
-G I m 7{Z AX T l BNB L(A0A8G
-8A0A(B BBBH T Lm BNB O(A0A8G
-8A0A(B BBBG T m x BNB O(A0A8Ge
-8A0A(B BBBG T m D BLB B(A0A8G
-8A0A(B BBBB , Tn d ACG r
-DAA , n _ ϯ AAG o
-DAA , n (c ACG q
-DAA , n h_ AAG o
-DAA , o e c ADG r
-DAA L Do / BBB B(A0G8Dh
-8A0A(B BBBJ L o BBB B(A0G8Dh
-8A0A(B BBBJ L o BBB B(A0G8Dh
-8A0A(B BBBJ \ 4p BBE A(A0Da
+G I J |sZ AX T I w
+ BNB L(A0A8G
+8A0A(B BBBH T I
+ BNB O(A0A8G
+8A0A(B BBBG T 4J
+ BNB O(A0A8Ge
+8A0A(B BBBG T J HD {
+ BLB B(A0A8G
+8A0A(B BBBB , J @d s
+ ACG r
+DAA , K _ O
+ AAG o
+DAA , DK c +
+ ACG q
+DAA , tK _
+ AAG o
+DAA , K e ADG r
+DAA L K `
+ BBB B(A0G8Dh
+8A0A(B BBBJ L $L , BBB B(A0G8Dh
+8A0A(B BBBJ L tL p BBB B(A0G8Dh
+8A0A(B BBBJ \ L @ BBE A(A0Da
0D(A BBBE
-0D(A BBBH T p { BQB B(K0A8G
-8A0A(B BBBA q {. K^ 4 q ( H BPA G
- DABA 4 <q ' BPA G
- DABA 4 tq BMA G
- DABA 4 q P BMA G
- DABA 4 q IJ BMA G
- DABA 4 r BMA G
- DABA 4 Tr x BMA G
- DABA 4 r 0 a BMA G
- DABA 4 r @ BMA G
- DABA 4 r BMA G
- DABA 4 4s X BKA G
- DABA 4 ls ݱ BKA G
- DABA 4 s BPA G
- DABA 4 s BPA G
- DABA 4 t 8 z BPA G
- DABA 4 Lt Y BPA G
- DABA 4 t 8 BPA G
- DABA 4 t ` BPA G
- DABA 4 t BPA G
- DABA 4 ,u հ BPA G
- DABA 4 du BPA G
- DABA 4 u @
- BPA G
- DABA 4 u
- r BPA G
- DABA 4 v Q BPA G
- DABA 4 Dv h 0 BKA G
- DABA 4 |v
BKA G
- DABA 4 v
BKA G
- DABA 4 v ͯ BKA G
- DABA L $w H E BDB B(I0A8G\
-8A0A(B BBBA l tw 4 BDB B(I0A8D\
+0D(A BBBH T $M p BQB B(K0A8G
+8A0A(B BBBA 4N hs. K^ 4 M
BPA G
+ DABA 4 M h
BPA G
+ DABA 4 N
BMA G
+ DABA 4 <N ػ e
BMA G
+ DABA 4 tN D
BMA G
+ DABA 4 N H #
BMA G
+ DABA 4 N
BMA G
+ DABA 4 O BMA G
+ DABA 4 TO p BMA G
+ DABA 4 O ( BMA G
+ DABA 4 O ~ BKA G
+ DABA 4 O ] BKA G
+ DABA 4 4P P < BPA G
+ DABA 4 lP BPA G
+ DABA 4 P BPA G
+ DABA 4 P x BPA G
+ DABA 4 Q 0 BPA G
+ DABA 4 LQ BPA G
+ DABA 4 Q v BPA G
+ DABA 4 Q X U BPA G
+ DABA 4 Q 4 BPA G
+ DABA 4 ,R BPA G
+ DABA 4 dR
+ BPA G
+ DABA 4 R 8
+ BPA G
+ DABA 4 R
+ BKA G
+ DABA 4 S
+ BKA G
+ DABA 4 DS ` n
+ BKA G
+ DABA 4 |S M
+ BKA G
+ DABA L S
+ BDB B(I0A8G\
+8A0A(B BBBA l T
+ BDB B(I0A8D\
8A0A(B BBBA
-8A0A(B BBBA , x b D\
+8A0A(B BBBA , ,U b D\
H P
H Q
-G I , x
- ï AANp~
-AAA , Dx _ AAG o
-DAA ,y 8x{. K^ T x BIB B(D0A8G-
-8D0A(B BBBA T x (& m BIB B(A0A8G
-8A0A(B BBBA T <y 4C W BIB B(A0A8G
-8A0A(B BBBA T y FW$ o BBB B(A0A8J
-8A0A(B BBBK , z jb D\
+G I , T `
+ C
+ AANp~
+AAA , T @_ 5
+ AAG o
+DAA U s. K^ T U X BIB B(D0A8G-
+8D0A(B BBBA T tU
BIB B(A0A8G
+8A0A(B BBBA T U (C
BIB B(A0A8G
+8A0A(B BBBA T $V W$
BBB B(A0A8J
+8A0A(B BBBK , 4W ()b D\
H P
H Q
-G I , z jb D\
+G I , dW h)b D\
H P
H Q
-G I , Lz k S AAN`\
-AAA , |z m1 5 AANp
-AAA , z p_ ' AAG o
-DAA , z q_ AAG o
-DAA { u{. K^ T ${ qC BIB B(A0A8G
-8A0A(B BBBA T |{ BIB B(A0A8Ga
-8A0A(B BBBA T { X BIB B(A0A8GQ
-8A0A(B BBBA T ,| k BIB B(A0A8G
-8A0A(B BBBA T | $ U BBB B(A0A8J n
-8A0A(B BBBD } PA } xS } pS } XS } A ~ $~ <~ T~ l~ ~ ~ ~ ~ ~ ^ cz ~
- 4 t~ BAD G@
- DABK d | x AL , ~ hh ADDP~
-AAA $ $ ACK ODA 4 < r Y BDD DPA
- AABA , AP , h ADDP~
-AAA | 8 4 0 ٷ BAD G@
- DABK ̀ + 4 BIA A(G0(D ABB< BEI A(A0(A BBB \ H t P AZ L P@ BBB B(D0D8D@
-8A0A(B BBBK T , @" BBB E(D0A8D
-8D0A(B BBBK < YHD M
+G I , V ) AAN`\
+AAA , W H,1 AANp
+AAA , <W X/_ AAG o
+DAA , lW /_ AAG o
+DAA TX s. K^ T W /C BIB B(A0A8G
+8A0A(B BBBA T X A BIB B(A0A8Ga
+8A0A(B BBBA T dX O BIB B(A0A8GQ
+8A0A(B BBBA T X _ BIB B(A0A8G
+8A0A(B BBBA T Y m$ BBB B(A0A8J n
+8A0A(B BBBD $Z ؑA <Z TZ lZ DV Z S Z 0A Z h Z ` Z X Z P [ H ,[ @ D[ 8 \[ 0 t[ ( [ ^ cz [ h
+ [ ` $ [ X k AG0D [ В \ Ȓ AL , |[ h ADDP~
+AAA $ d\ $ ACK ODA 4 [ r BDD DPA
+ AABA \ H AP , ,\ Hh ADDP~
+AAA ] $ t\ G AG0D T] + 4 l] BIA A(G0(D ABB< ] BEI A(A0(A BBB ] X ] ` AZ L ^ `@ BBB B(D0D8D@
+8A0A(B BBBK T ] P BBB E(D0A8D
+8D0A(B BBBK < ^ YHD M
HAE
-HAA | D BBE E(G0D8DP~8D0A(B BBB, ܂ xN BAA
-ABA T T O BEE J(D0D8G>
-8D0A(B BBBA L @P BJE D(D0G%
-0D(A BBBH T Q{ i BJE E(D0D8G5
-8D0A(B BBBA T T R{ # BJE E(D0D8G5
-8D0A(B BBBA T S ݴ BJE E(D0D8GZ
-8D0A(B BBBA T U{ BJE E(D0D8G5
-8D0A(B BBBA T \ @V Q BJE E(D0D8GZ
-8D0A(B BBBA T W{ BJE E(D0D8G5
-8D0A(B BBBA L X ų BEE I(D0G
-0D(A BBBE L \ Y BEE I(D0G
-0D(A BBBE L 0[ K BEE I(D0G
-0D(A BBBE L p\ BEE I(D0G
-0D(A BBBE ]0 AG fA $ $ ]F AAG0zAA L ]7 Dh
-D F l ^ D ̆ ^ / BBJ A(D0
-(A BBEA T h BDD u
+HAA _ ؞ D _ О BBE E(G0D8DP~8D0A(B BBB, d_
+ BAA
+ABA T ^ h Z BEE J(D0D8G>
+8D0A(B BBBA L 4_ BJE D(D0G%
+0D(A BBBH T _
{ BJE E(D0D8G5
+8D0A(B BBBA T _ { BJE E(D0D8G5
+8D0A(B BBBA T 4` 0 K BJE E(D0D8GZ
+8D0A(B BBBA T ` x{ BJE E(D0D8G5
+8D0A(B BBBA T ` BJE E(D0D8GZ
+8D0A(B BBBA T <a { y BJE E(D0D8G5
+8D0A(B BBBA L a 3 BEJ D(D0G4
+0D(A BBBA T a pc
BJE E(D0D8G
+8D0A(B BBBA L <b
BEJ D(D0G4
+0D(A BBBA L b s
BEE I(D0G
+0D(A BBBE L b 8 6
BEE I(D0G
+0D(A BBBE c x0 AG fA $ d F AAG0zAA ,d 7 Dh
+D F Ld D c X W
BBJ A(D0
+(A BBEA T c /
BDD u
ADD
ABE U
ABH I
-ABA T l 0 ² BDD u
+ABA T Ld BDD u
ADD
ABE U
ABH I
-ABA < ć } BDA G
- DABG < X P BDA G
- DABG L D (^ # BHE B(D0D8DP
-8D0A(B BBBB L L x_ BLB B(A0A8Dz
-8A0A(B BBBA $ 1 YX
+ABA < d BDA G
+ DABG < d x BDA G
+ DABG L $e K BHE B(D0D8DP
+8D0A(B BBBB L ,f @ BLB B(A0A8Dz
+8A0A(B BBBA $ e Y YX
G z
-A T g BBB B(D0A8Gb
-8A0A(B BBBA \ a BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 ĉ aY ~ AIG t
-AAI DCA \ b BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 \ PbY AIG t
-AAI DCA L xb BEB E(I0H8D@
-8D0A(B BBBA T hD BBB B(A0A8G
-8A0A(B BBBA D < `F P BLB A(A0GpG
-0A(A BBBA $ < XcF AAG0zAA L c S BEB E(I0H8D@
-8D0A(B BBBD \ e] + BBB I(D0D@
+A T e U BBB B(D0A8G
+8A0A(B BBBA \ f P BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 f Y
AIG t
+AAI DCA \ g BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 <g Y
AIG t
+AAI DCA L tg @ BEB E(I0H8D@
+8D0A(B BBBA T g D BBB B(A0A8G
+8A0A(B BBBA D h F | BLB A(A0GpG
+0A(A BBBA $ i !F AAG0zAA L h H! BEB E(I0H8D@
+8D0A(B BBBD \ h "] W BBB I(D0D@
0F(A BBBK U
-0F(A BBBA T \ ! BLH B(A0A8G
-8D0A(B BBBA T h ߱ BLB B(A0A8G
-8A0A(B BBBE T + BEB B(A0A8O
-8A0A(B BBBI L d dU 7 BBB E(E0E8G`
-8A0A(B BBBA l fM BEB B(A0A8D
+0F(A BBBA T <i ! BLH B(A0A8G
+8D0A(B BBBA T i BLB B(A0A8G
+8A0A(B BBBE \ i #= / BEH D(I0D@
+0F(A BBBG U
+0F(A BBBA T Lj # BEB B(A0A8O
+8A0A(B BBBF L j #U / BBB E(E0E8G`
+8A0A(B BBBA l j $M BEB B(A0A8D
8A0A(B BBBF Y
-8A0A(B BBBE T $ BBB E(A0F8G-
-8D0A(B BBBE L | = BBB F(A0D8D
-8A0A(B BBBA T ̎ _ s BBB E(A0F8G
-8D0A(B BBBF L $ = ; BBB F(A0D8D
-8A0A(B BBBA , a{. K^ $ D gM AAK0}AA D l g BEB B(A0A8DP8A0A(B BBBL ph
BEE A(A0]
+8A0A(B BBBE T dk P BBB E(A0F8G-
+8D0A(B BBBE L k = BBB F(A0D8D
+8A0A(B BBBA T l _ k BBB E(A0F8G
+8D0A(B BBBF L dl = 3 BBB F(A0D8D
+8A0A(B BBBA lm s. K^ $ m (&M AAK0}AA D m P& BEB B(A0A8DP8A0A(B BBBL m '
BEE A(A0]
(A BBBF
-(E BBBJ D Q T d z BBB B(A0A8J
-8A0A(B BBBG < @ | BBD A(G
-(D ABBA L ! O BHF E(D0A8Dm
-8A0A(B BBBA T L !u BBB B(A0A8GM
-8D0A(B BBBE L # BHE E(D0A8Dm
-8A0A(B BBBA T X$u BBB B(A0A8GM
-8D0A(B BBBE L L & BHE E(D0A8Dm
-8A0A(B BBBA T ' H BBB B(A0A8G]
-8D0A(B BBBE L X) BHE E(D0A8Dm
-8A0A(B BBBA T D ) BBB B(A0A8GR
-8D0A(B BBBH T 0, BBB B(A0A8GR
-8D0A(B BBBH T x. BBB B(A0A8G
-8A0A(B BBBA T L 0 \ BBB B(A0A8G
-8A0A(B BBBA T x3o 3 BBB B(A0A8GJ
-8A0A(B BBBA T 5
- BBB B(A0A8G
-8A0A(B BBBA T T 8 BBB B(A0A8G
-8A0A(B BBBA , d cb D\
+(E BBBJ Dn D Q T m z BBB B(A0A8J
+8A0A(B BBBG < m t BBD A(G
+(D ABBA L <n G BHF E(D0A8Dm
+8A0A(B BBBA T n Hu BBB B(A0A8GM
+8D0A(B BBBE L n p
+ BHE E(D0A8Dm
+8A0A(B BBBA T 4o u
+ BBB B(A0A8GM
+8D0A(B BBBE L o ( y
+ BHE E(D0A8Dm
+8A0A(B BBBA T o @
+ BBB B(A0A8G]
+8D0A(B BBBE L 4p
+ BHE E(D0A8Dm
+8A0A(B BBBA T p BBB B(A0A8GR
+8D0A(B BBBH T p BBB B(A0A8GR
+8D0A(B BBBH T 4q } BBB B(A0A8G
+8A0A(B BBBA T q T BBB B(A0A8G
+8A0A(B BBBA T q o + BBB B(A0A8GJ
+8A0A(B BBBA T <r 8 BBB B(A0A8G
+8A0A(B BBBA T r BBB B(A0A8G
+8A0A(B BBBA , s x"b D\
H P
H Q
-G I , db D\
+G I , s "b D\
H P
H Q
-G I , Ė Pdb D\
+G I , t "b D\
H P
H Q
-G I , db D\
+G I , 4t 8#b D\
H P
H Q
-G I , $ db D\
+G I , dt x#b D\
H P
H Q
-G I , T eb D\
+G I , t #b D\
H P
H Q
-G I , Peb D\
+G I , t #b D\
H P
H Q
-G I , eb D\
+G I , t 8$b D\
H P
H Q
-G I T , P# : BEE B(A0A8G
-8A0A(B BBBI 4 xe" 3 BAA GP
- AABG T * BBB B(A0A8G
-8A0A(B BBBD T # BEE B(A0A8G
-8A0A(B BBBI T l `# BEE B(A0A8G
-8A0A(B BBBI T Ę 8v BBB B(A0A8G7
-8A0A(B BBBF T `# BEE B(A0A8G
-8A0A(B BBBI , t e
- AAJ@}
-AAE , f AAJ@j
-AAA T ԙ # v BEE B(A0A8G
-8A0A(B BBBI L , fu
- BBB B(A0A8M
-8A0A(B BBBG | 4 ` BBB B(A0D8DP
+G I T lt # 2 BEE B(A0A8G
+8A0A(B BBBI 4 t $" + BAA GP
+ AABG T t ( " BBB B(A0A8G
+8A0A(B BBBD T Tu # BEE B(A0A8G
+8A0A(B BBBI T u # BEE B(A0A8G
+8A0A(B BBBI T v pv BBB B(A0A8G7
+8A0A(B BBBF T \v # BEE B(A0A8G
+8A0A(B BBBI , v `$
+ AAJ@}
+AAE , v @% AAJ@j
+AAA T w # n BEE B(A0A8G
+8A0A(B BBBI T lw # f BEE B(A0A8G
+8A0A(B BBBI L w @%u
+ BBB B(A0A8M
+8A0A(B BBBG | x p BBB B(A0D8DP
8A0A(B BBBB
8A0A(B BBBE "
-8A0A(B BBBD T BEB B(A0A8G=
-8D0A(B BBBB L T Hp BBB B(A0A8M
-8A0A(B BBBG \ U{. K^ T BBJ B(A0A8Gi
-8D0A(B BBBA T x BBB B(A0I8G
-8D0A(B BBBA T l @{ Ԯ BBB B(A0I8G
-8D0A(B BBBA T Ĝ h; BBB B(A0I8Ge
-8D0A(B BBBA T P BBJ B(A0A8G
-8D0A(B BBBI T t V BBJ B(A0A8G
-8D0A(B BBBA T ̝ BBB B(A0I8G
-8D0A(B BBBA T $ A n BBB B(A0I8Gh
-8D0A(B BBBA T | S BBB B(A0A8M
-8A0A(B BBBG 4 ADD K
-AAJ ]DA T k BEB B(A0A8J
-8A0A(B BBBG 4
, L b D\
+8A0A(B BBBD L x . q BBB B(A0A8M
+8A0A(B BBBG y s. K^ T x XC BBJ B(A0A8Gi
+8D0A(B BBBA T Ty G\ BBB B(A0I8G
+8D0A(B BBBA T y (L{ BBB B(A0I8G
+8D0A(B BBBA T z PQ; BBB B(A0I8Ge
+8D0A(B BBBA T \z 8V w BBJ B(A0A8G
+8D0A(B BBBI T z ZV k BBJ B(A0A8G
+8D0A(B BBBA T { ^ _ BBB B(A0I8G
+8D0A(B BBBA T d{ cA F BBB B(A0I8Gh
+8D0A(B BBBA T { h + BBB B(A0A8M
+8A0A(B BBBG 4 | n ADD K
+AAJ ]DA T L| n BEB B(A0A8J
+8A0A(B BBBG \} t} x
, } pb D\
H P
H Q
-G I , | b D\
+G I , } b D\
H P
H Q
-G I , Xb D\
+G I , } b D\
H P
H Q
-G I , ܠ b D\
+G I , ~ 0b D\
H P
H Q
-G I , b D\
+G I , L~ pb D\
H P
H Q
-G I , < b D\
+G I , |~ b D\
H P
H Q
-G I , l Xb D\
+G I , ~ b D\
H P
H Q
-G I , b D\
+G I , ~ 0b D\
H P
H Q
-G I , ̡ b D\
+G I , pb D\
H P
H Q
-G I , b D\
+G I , < b D\
H P
H Q
-G I , , Xb D\
+G I , l b D\
H P
H Q
-G I , \ b D\
+G I , 0b D\
H P
H Q
-G I , b D\
+G I , pb D\
H P
H Q
-G I , b D\
+G I , b D\
H P
H Q
-G I , Xb D\
+G I , , b D\
H P
H Q
-G I , b D\
+G I , \ 0b D\
H P
H Q
-G I , L b D\
+G I , pb D\
H P
H Q
-G I , | b D\
+G I , b D\
H P
H Q
-G I , Xb D\
+G I , b D\
H P
H Q
-G I , ܣ b D\
+G I , 0b D\
H P
H Q
-G I , b D\
+G I , L pb D\
H P
H Q
-G I , < b D\
+G I , | b D\
H P
H Q
-G I , l Xb D\
+G I , b D\
H P
H Q
-G I , b D\
+G I , ܁ 0b D\
H P
H Q
-G I , ̤ b D\
+G I , pb D\
H P
H Q
-G I , b D\
+G I , < b D\
H P
H Q
-G I , , Xb D\
+G I , l b D\
H P
H Q
-G I , \ b D\
+G I , 0b D\
H P
H Q
-G I , b D\
+G I , ̂ pb D\
H P
H Q
-G I , b D\
+G I , b D\
H P
H Q
-G I Xm A~
-A l 4 AAJ v
-AAD fAA L 0 BBB F(A0A8J@X
-8A0A(B BBBA < ] BAA m
+G I , Y D m A~
+A l 4 d H AAJ v
+AAD fAA L е BBB F(A0A8J@X
+8A0A(B BBBA < P] BAA m
ABD
-ABD , ! ADDP
-AAE , L D AAJPl
-AAF , | D AAJPl
-AAF 4 BAA N
- AABD D BED D(G$
-(D ABBE D , BED D(G$
-(D ABBE D t X BED D(G$
-(D ABBE D Ĩ BED D(G$
-(D ABBE D ( BED D(G$
-(D ABBE D L BED D(G$
-(D ABBE D K BED D(G$
-(D ABBE `1 Dl L p BBB B(A0A8Je
-8D0A(B BBBJ H{. K^ < d BAI G
- DABA L ٩ BBB I(A0G
-0D(A BBBA L BBB I(A0G
-0D(A BBBA L D x BBB I(A0G
-0D(A BBBA L j BBB I(A0G
-0D(A BBBA L 8 E BBB I(A0G
-0D(A BBBA L 4
BBB I(A0G
-0D(A BBBA L BBB I(A0G
-0D(A BBBA T ԫ ha ֨ BBB B(I0A8G
-8D0A(B BBBA L , BBB I(A0G3
-0D(A BBBA T | 0T BBB B(I0A8G
-8D0A(B BBBA T Ԭ 8 W BBB B(I0A8GK
-8D0A(B BBBA T , * BBB B(I0A8GK
-8D0A(B BBBA L ! BBB I(A0G
-0D(A BBBA L ԭ $ ا BBB I(A0G
-0D(A BBBA L $ x& BBB I(A0G
-0D(A BBBA T t ( BBB B(I0A8G*
-8D0A(B BBBA L ̮ + g BBB I(A0G
-0D(A BBBA T 0.8 B BBB B(I0A8GM
-8D0A(B BBBA T t 1m BBJ B(A0A8G(
-8D0A(B BBBA L ̯ 06b BBB D(A0G
-0D(A BBBA L P8 ֦ BBB I(A0G7
-0D(A BBBA T l ; BBJ B(A0A8G
-8D0A(B BBBA < İ (@ BAI Gn
- DABA < A- BAI G
- DABA L D C [ BBB I(A0GU
-0D(A BBBA L F 6 BBB I(A0G
-0D(A BBBA T H$ BBB B(I0A8GW
-8D0A(B BBBA T < K BBB B(I0A8G
-8D0A(B BBBA T N BBB B(I0A8G)
-8D0A(B BBBA T Qo BBB B(I0A8G
-8D0A(B BBBA T D T BEB B(A0A8JP
-8A0A(B BBBG < a" AFGPe
+ABD , t p! ADDP
+AAE , pD g AAJPl
+AAF , ԃ D P AAJPl
+AAF 4 BAA N
+ AABD D < BED D(G$
+(D ABBE D BED D(G$
+(D ABBE D ̄ BED D(G$
+(D ABBE D BED D(G$
+(D ABBE D \ X S BED D(G$
+(D ABBE D D BED D(G$
+(D ABBE D ( BED D(G$
+(D ABBE 1 Dl L T BBB B(A0A8Je
+8D0A(B BBBJ \ 0s. K^ < x BAI G
+ DABA L 8 BBB I(A0G
+0D(A BBBA L L x BBB I(A0G
+0D(A BBBA L S BBB I(A0G
+0D(A BBBA L h . BBB I(A0G
+0D(A BBBA L < BBB I(A0G,
+0D(A BBBA L x BBB I(A0G
+0D(A BBBA L ܈ BBB I(A0G
+0D(A BBBA T , a BBB B(I0A8G
+8D0A(B BBBA L 0 m BBB I(A0G3
+0D(A BBBA T ԉ T H BBB B(I0A8G
+8D0A(B BBBA T , BBB B(I0A8GK
+8D0A(B BBBA T BBB B(I0A8GK
+8D0A(B BBBA L ܊ x BBB I(A0G
+0D(A BBBA L , BBB I(A0G
+0D(A BBBA L | X w BBB I(A0G
+0D(A BBBA T ̋ R BBB B(I0A8G*
+8D0A(B BBBA L $ + BBB I(A0G
+0D(A BBBA T t 8 BBB B(I0A8GM
+8D0A(B BBBA T ̌ s BBJ B(A0A8G.
+8D0A(B BBBA L $ b BBB D(A0G
+0D(A BBBA L t @ BBB I(A0G7
+0D(A BBBA T č u BBJ B(A0A8G
+8D0A(B BBBA < l BAI Gn
+ DABA < \ - E BAI G
+ DABA L BBB I(A0GU
+0D(A BBBA L x BBB I(A0G
+0D(A BBBA T < $ BBB B(I0A8GW
+8D0A(B BBBA T BBB B(I0A8G
+8D0A(B BBBA T { BBB B(I0A8G)
+8D0A(B BBBA T D o N BBB B(I0A8G
+8D0A(B BBBA T BEB B(A0A8JP
+8A0A(B BBBG < "" AFGPe
AAK T
-AAJ T ܳ b BBB B(A0I8G
-8A0A(B BBBF L 4 m BBB J(A0A8GF
-8A0A(B BBBA T u 0 BDB B(A0D8G^
-8A0A(B BBBA } } A`
-G b ̵ } A`
-G b } A`
-G bT T h~ ɥ BBB B(A0I8D*
-8A0A(B BBBF d A`
-G mL ̵ @> BBJ B(A0A8G
-8A0A(B BBBA L 0
- n BBB J(A0A8G
-8A0A(B BBBA T l r BBB B(A0I8G
-8A0A(B BBBH < Ķ X d BBA A(LPW
-(A ABBA < 8 T ACL@\
+AAJ T 4 # BBB B(A0I8G
+8A0A(B BBBF L . BBB J(A0A8GF
+8A0A(B BBBA T ܑ x6 BDB B(A0D8G
+8A0A(B BBBA > > A`
+G b $ (? A`
+G b D ? A`
+G bT @ BBB B(A0I8D*
+8A0A(B BBBF `D A`
+G mL $ D; d BBB B(I0A8GT
+8A0A(B BBBA T t Ic
+ K BBB J(A0A8G
+8A0A(B BBBA T ̓ S G BBB B(A0I8G
+8A0A(B BBBH < $ PZ 9 BBA A(LPW
+(A ABBA < d 0\ ) ACL@\
AAB
-AAE L D ? BBB B(A0A8L`N
-8A0A(B BBBB T BBB B(A0A8L
-8A0A(B BBBH T P BBB B(A0A8O
-8A0A(B BBBA T D ٤ BBB B(A0C8J
-8A0A(B BBBG L BBB B(A0A8J
-8A0A(B BBBA , ť AGD`
-AAH T 4 BDB B(A0D8G
-8A0A(B BBBH D T BDB B(A0D8G
-8A0A(B BBBH ` ̺ h ` X P
, , Hb D\
+AAE L ^ BBB B(A0A8L`N
+8A0A(B BBBB T b BBB B(A0A8L
+8A0A(B BBBH T L Hg BBB B(A0A8O
+8A0A(B BBBA T l
BBB B(A0C8J
+8A0A(B BBBG L x BBB B(A0A8J
+8A0A(B BBBA , d AGD`
+AAH T BDB B(A0D8G
+8A0A(B BBBH T BDB B(A0D8G
+8A0A(B BBBH ,
D \ , t xb D\
H P
H Q
-G I , \ b D\
+G I , b D\
H P
H Q
-G I , b D\
+G I , Ԙ b D\
H P
H Q
-G I , b D\
+G I , 8b D\
H P
H Q
-G I , Hb D\
+G I , 4 xb D\
H P
H Q
-G I , b D\
+G I , d b D\
H P
H Q
-G I , L b D\
+G I , b D\
H P
H Q
-G I , | b D\
+G I , ę 8b D\
H P
H Q
-G I , Hb D\
+G I , xb D\
H P
H Q
-G I , ܼ b D\
+G I , $ b D\
H P
H Q
-G I , b D\
+G I , T b D\
H P
H Q
-G I , < b D\
+G I , 8b D\
H P
H Q
-G I , l Hb D\
+G I , xb 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 , b D\
+G I , D 8b D\
H P
H Q
-G I , , Hb D\
+G I , t xb D\
H P
H Q
-G I , \ b D\
+G I , b D\
H P
H Q
-G I , b D\
+G I , ԛ b D\
H P
H Q
-G I , b D\
+G I , 8b D\
H P
H Q
-G I , Hb D\
+G I , 4 xb D\
H P
H Q
-G I , b D\
+G I , d b D\
H P
H Q
-G I , L b D\
+G I , b D\
H P
H Q
-G I , | b D\
+G I , Ĝ 8b D\
H P
H Q
-G I , Hb D\
+G I , xb D\
H P
H Q
-G I , ܿ b D\
+G I , $ b D\
H P
H Q
-G I , b D\
+G I , T b D\
H P
H Q
-G I , < b D\
+G I , 8b D\
H P
H Q
-G I , l Hb D\
+G I , xb 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 , b D\
+G I , D 8b D\
H P
H Q
-G I , , Hb D\
+G I , t xb D\
H P
H Q
-G I , \ b D\
+G I , b D\
H P
H Q
-G I , b D\
+G I , Ԟ b D\
H P
H Q
-G I , b D\
+G I , 8b D\
H P
H Q
-G I , Hb D\
+G I , 4 xb D\
H P
H Q
-G I , b D\
+G I , d b D\
H P
H Q
-G I , L b D\
+G I , b D\
H P
H Q
-G I , | b D\
-H P
-H Q
-G I , Hb D\
-H P
-H Q
-G I m A~
-A l 4 AAJ v
-AAD fAA L | ` S BBB F(A0A8J@X
-8A0A(B BBBA < BAA M
+G I ğ 8m A~
+A l 4 AAJ v
+AAD fAA L d BBB F(A0A8J@X
+8A0A(B BBBA < l BAA M
ABD
-ABD , `! ADDP
-AAE < < ` AAJPh
+ABD , ! T ADDP
+AAE < $ ; AAJPh
AAJ
-AAH 4 | BAA N
- AABD 4 h 8 BAA N
- AABD D p\ BED D(G$
-(D ABBE D 4 ] ܞ BED D(G$
-(D ABBE D | @_ ͞ BED D(G$
-(D ABBE T `! BEE E(D0D8Go
-8D0A(B BBBD T BBB B(A0A8G:
-8D0A(B BBBH T t BBB B(A0A8G:
-8D0A(B BBBH +{. K^ < | BAI G
- DABA L $ U BBB I(A0G
-0D(A BBBA L t 8 0 BBB I(A0G
-0D(A BBBA L BBB I(A0G
-0D(A BBBA L BBB I(A0G
-0D(A BBBA L d X BBB I(A0G
-0D(A BBBA L BBB I(A0G
-0D(A BBBA L 8 w BBB I(A0G
-0D(A BBBA T T a R BBB B(I0A8G
-8D0A(B BBBA L % BBB I(A0G3
-0D(A BBBA T P T BBB B(I0A8G
-8D0A(B BBBA T T X Ӟ BBB B(I0A8GK
-8D0A(B BBBA T " BBB B(I0A8GK
-8D0A(B BBBA L $ y BBB I(A0G
-0D(A BBBA L T 8' T BBB I(A0G
-0D(A BBBA T ): / BBB B(I0A8G%
-8D0A(B BBBA T , BBB B(I0A8G*
-8D0A(B BBBA L T H/ BBB I(A0G
-0D(A BBBA T 18 BBB B(I0A8GM
-8D0A(B BBBA T 4m BBJ B(A0A8G(
-8D0A(B BBBA T T 9 t BBB B(D0A8G
+AAH 4 d a BAA N
+ AABD 4 BAA N
+ AABD D Ԡ P a BED D(G$
+(D ABBE D , BED D(G$
+(D ABBE D d BED D(G$
+(D ABBE T ! BEE E(D0D8Go
+8D0A(B BBBD T ` BBB B(A0A8G:
+8D0A(B BBBH T \ BBB B(A0A8G:
+8D0A(B BBBH l Ps. K^ < ̢ BAI G
+ DABA L x BBB I(A0G
+0D(A BBBA L \ BBB I(A0G
+0D(A BBBA L H [ BBB I(A0G
+0D(A BBBA L 6 BBB I(A0G
+0D(A BBBA L L BBB I(A0G
+0D(A BBBA L BBB I(A0G
+0D(A BBBA L BBB I(A0G
+0D(A BBBA L < 8 BBB I(A0G3
+0D(A BBBA T T } BBB B(I0A8G
+8D0A(B BBBA T P BBB B(I0A8GK
+8D0A(B BBBA T < # BBB B(I0A8GK
+8D0A(B BBBA T 2 BBB B(I0A8G
+8D0A(B BBBA L h BBB I(A0G
+0D(A BBBA L < BBB I(A0G
+0D(A BBBA T ( BBB B(I0A8G*
+8D0A(B BBBA L X BBB I(A0G
+0D(A BBBA T 4 m 3 BBJ B(A0A8G(
+8D0A(B BBBA T BBB B(D0A8G
-8D0A(B BBBA < < B BAI Gn
- DABA T > BBB B(I0A8G
-8D0A(B BBBA T D @ BBB B(I0A8G)
-8D0A(B BBBA T Co BBB B(I0A8G
-8D0A(B BBBA 4 Fb ADG=
-DAA < , G u BAI G
- DABA 4 l I@ N ADG
-DAA T JB ) BBB B(I0A8G-
-8D0A(B BBBA 4 M ADGx
-DAA < 4 O כ BHD G
- DABA 4 t P[ ADG6
-DAA < QS BAI G
- DABA < S c BHD G
- DABA T , U ; BBB B(I0A8G*
-8D0A(B BBBA T HX0 BBB B(I0A8GE
-8D0A(B BBBA T [0 BBB B(I0A8GE
-8D0A(B BBBA L 4 ] ƚ BBB I(A0G
-0D(A BBBA L ` BBB I(A0G
-0D(A BBBA T cT | BBB B(I0A8G
-8D0A(B BBBA T , f: O BBB B(I0A8G%
-8D0A(B BBBA T i " BBB B(I0A8GK
-8D0A(B BBBA L l BBB I(A0G
-0D(A BBBA T , n: Й BBB B(I0A8G%
-8D0A(B BBBA T q BBJ B(A0A8G
-8D0A(B BBBA L w BIE A(A0G
-0D(A BBBA L , y l BBB I(A0G
-0D(A BBBA < | 0|b G BAI G
- DABA L `~ & BBB I(A0G
-0D(A BBBA T Ѐ
Y BEB B(A0A8JP
-8A0A(B BBBG < d H" AFGPe
+8D0A(B BBBA < @ BAI Gn
+ DABA T $ BBB B(I0A8G
+8D0A(B BBBA T | BBB B(I0A8G)
+8D0A(B BBBA T ԩ Po e BBB B(I0A8G
+8D0A(B BBBA 4 , hb > ADG=
+DAA < d BAI G
+ DABA 4 `@ ADG
+DAA L ܪ h BBB I(A0G
+0D(A BBBA 4 , ADGx
+DAA < d 0
+ BHD G
+ DABA 4 [ [ ADG6
+DAA < ܫ S 6 BAI G
+ DABA < BHD G
+ DABA T \ BBB B(I0A8G*
+8D0A(B BBBA T p0 BBB B(I0A8GE
+8D0A(B BBBA T H0 BBB B(I0A8GE
+8D0A(B BBBA L d r BBB I(A0G
+0D(A BBBA L M BBB I(A0G
+0D(A BBBA T T ( BBB B(I0A8G
+8D0A(B BBBA T \ ": BBB B(I0A8G%
+8D0A(B BBBA T % BBB B(I0A8GK
+8D0A(B BBBA L ' BBB I(A0G
+0D(A BBBA L \ * | BBB I(A0G
+0D(A BBBA T x, W BBJ B(A0A8G
+8D0A(B BBBA L 1 N BIE A(A0G
+0D(A BBBA L T @4 BBB I(A0G
+0D(A BBBA < 6b BAI G
+ DABA L 9 BBB I(A0G
+0D(A BBBA T 4 p;
BEB B(A0A8JP
+8A0A(B BBBG < H" c AFGPe
AAK T
-AAJ T 8 BBB B(A0I8G
-8A0A(B BBBF L ę BBB B(A0I8GF
-8A0A(B BBBA T L P ș BDB B(A0D8G^
-8A0A(B BBBA \ t A`
-G b ` A`
-G b Ш A`
-G bT @ a BBB B(A0I8D*
-8A0A(B BBBF , A`
-G mL BBB B(A0A8O
-8A0A(B BBBA L
- BBB J(A0A8G
-8A0A(B BBBA T 4 X BBB B(A0I8G
-8A0A(B BBBH T BBB B(A0A8L
-8A0A(B BBBH T ֘ BBB B(A0A8O
-8A0A(B BBBA L < P BBB B(A0I8GF
-8A0A(B BBBA T BBB J(A0A8G
-8A0A(B BBBD T h8 BBB B(A0A8Oq
-8A0A(B BBBD P A`
-G bT \ BBB B(A0I8D*
-8A0A(B BBBF L BBB B(A0A8L
-8A0A(B BBBH L h BBB J(A0A8D
-8A0A(B BBBA L T J BBB B(A0C8JA
-8A0A(B BBBG L
u BBJ A(A0Gg
-0A(A BBBJ L o BBB B(A0A8L
-8A0A(B BBBD < D ! J AAJPg
+AAJ T ̱ I < BBB B(A0I8G
+8A0A(B BBBF L $ U x BBB B(A0I8GF
+8A0A(B BBBA T t Z | BDB B(A0D8G^
+8A0A(B BBBA b b A`
+G b b A`
+G bT $ `c 5 BBB B(A0I8D*
+8A0A(B BBBF 4 g A`
+G mL 8h BBB B(A0A8O
+8A0A(B BBBA L lG BBB J(A0A8G
+8A0A(B BBBA T < u BBB B(A0I8G
+8A0A(B BBBH T | BBB B(A0A8L
+8A0A(B BBBH T BBB B(A0A8O
+8A0A(B BBBA L D BBB B(A0I8GF
+8A0A(B BBBA T p BBB J(A0A8G
+8A0A(B BBBD T 8 BBB B(A0A8Oq
+8A0A(B BBBD A`
+G bT d BBB B(A0I8D*
+8A0A(B BBBF L h BBB B(A0A8L
+8A0A(B BBBH L BBB J(A0A8D
+8A0A(B BBBA L \ HJ Z BBB B(A0C8JA
+8A0A(B BBBG L H
I BBJ A(A0Gg
+0A(A BBBJ L C BBB B(A0A8L
+8A0A(B BBBD < L H! AAJPg
AAK S
-AAK T # BBB B(A0C8J
-8A0A(B BBBG L `g Q BBB B(A0A8J
-8A0A(B BBBA L D x ј BBB B(A0A8J
-8A0A(B BBBA L 81 T 01 T BDB B(A0D8G
-8A0A(B BBBH 6 T 6 P BDB B(A0D8G
-8A0A(B BBBH , < T < L BDB B(A0D8G
-8A0A(B BBBH hA D H{ BBE E(A0A8D@\8A0A(B BBBT D (d5 BBE E(A0A8G$
-8A0A(B BBBK L H BEB E(A0A8F
-8A0A(B BBBB L I Ø BEB E(A0A8F
-8A0A(B BBBB T < pj% BBE E(A0A8G"
-8A0A(B BBBE T Hqe BEE B(A0A8G
-8A0A(B BBBF {. K^ L XJ V BBB G(A0A8JF
-8A0A(B BBBG \ T V' BDB A(A0JC
+AAK T 8 BBB B(A0C8J
+8A0A(B BBBG L g % BBB B(A0A8J
+8A0A(B BBBA L L x BBB B(A0A8J
+8A0A(B BBBA T T # BDB B(A0D8G
+8A0A(B BBBH ĺ h T $ ` BDB B(A0D8G
+8A0A(B BBBH 4 T BDB B(A0D8G
+8A0A(B BBBH ( D `{ BBE E(A0A8D@\8A0A(B BBBT L 5 BBE E(A0A8G$
+8A0A(B BBBK L @ BEB E(A0A8F
+8A0A(B BBBB L BEB E(A0A8F
+8A0A(B BBBB T D 0#% \ BBE E(A0A8G"
+8A0A(B BBBE T *e U BEE B(A0A8G
+8A0A(B BBBF @s. K^ L % BBB G(A0A8JF
+8A0A(B BBBG \ \ ' BDB A(A0JC
0A(A BBBA
-0A(A BBBB L vV BBB B(A0A8GpB
-8D0A(B BBBH L yV ] BBB B(A0A8GpB
-8D0A(B BBBH L T |V . BBB B(A0A8GpB
-8D0A(B BBBH L V BBB B(A0A8GpH
-8D0A(B BBBJ {. K^ , } ADG0@
-DAG , < m ADG q
-DAF , l Pm x ADG p
-DAG , m X ADG r
-DAE , Ѓ{ 8 ADG ~
-DAA , ADG0L
-DAC , , P ADG k
-DAA < \ ٘ BBA H(G@x
-(D ABBA L BBB E(A0A8KP
-8D0A(B BBBK D n BBB A(D0q
-(A BBBA D 4 8 BBB A(H0j
-(D BBBA D | ` BBB A(K0
-(D BBBA < Ȇ ̗ BBA K(G@
-(D ABBA < H} BEE A(G0L
-(D ABBA , b D\
+0A(A BBBB L X/V [ BBB B(A0A8GpB
+8D0A(B BBBH L h2V , BBB B(A0A8GpB
+8D0A(B BBBH L \ x5V BBB B(A0A8GpB
+8D0A(B BBBH L 8V BBB B(A0A8GpH
+8D0A(B BBBJ hs. K^ , ;} ADG0@
+DAG , D ;m g ADG q
+DAF , t <m G ADG p
+DAG , P<m ' ADG r
+DAE , Կ <{ ADG ~
+DAA , < ADG0L
+DAC , 4 @=P ADG k
+DAA < d `= BBA H(G@x
+(D ABBA L = z BBB E(A0A8KP
+8D0A(B BBBK D p> = BBB A(D0q
+(A BBBA D < > BBB A(H0j
+(D BBBA D ? BBB A(K0
+(D BBBA < ? BBA K(G@
+(D ABBA < @} n BEE A(G0L
+(D ABBA , H@b D\
H P
H Q
-G I , , ȇb 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 , Hb D\
+G I , Ab D\
H P
H Q
-G I , b D\
+G I , HAb D\
H P
H Q
-G I , Ȉb D\
+G I , Ab D\
H P
H Q
-G I , b D\
+G I , $ Ab D\
H P
H Q
-G I , L Hb D\
+G I , T Bb D\
H P
H Q
-G I D | AAJ X
+G I D HB AAJ X
AAJ O
AAG X
-AAF 4 AAJ o
+AAF 4 B AAJ o
AAC O
-AAG D D g o BAA G@
+AAG D L HCg ? BAA G@
AABD b
- AABB T ; BBB L(A0A8D
-8A0A(B BBBJ T X BBB L(A0A8D
-8A0A(B BBBJ T < 9 BBB L(A0A8D
-8A0A(B BBBA T e / BEE B(A0A8G
-8A0A(B BBBF T \ ǖ BBB B(A0D8G
-8A0A(B BBBC {. K^ 4 t x[ ADG6
-DAA 4 G י ADG"
-DAA D 9 BBA I(G+
-(D ABBA < , BAI G
- DABA < l P n BAI G
- DABA 4 0@ G ADG
-DAA 4 8 " ADG
-DAA < w BAI G
- DABA D \ ܘ BBI A(G
-(D ABBA < 8 BHD G
- DABA < BHD G<
- DABA 4 $ c ADG_
-DAA 4 \ [ > ADG6
-DAA D W BBA I(GI
-(D ABBA L 0O BIB D(A0Gy
-0D(A BBBA < , 0 × BAI G
- DABA D l BIA D(GF
-(D ABBA < X x BAI G
- DABA T
S BIB B(A0D8G
-8A0A(B BBBD P D AAJ X
+ AABB T pD BBB L(A0A8D
+8A0A(B BBBJ T H BBB L(A0A8D
+8A0A(B BBBJ T D K9 BBB L(A0A8D
+8A0A(B BBBA T ؠe BEE B(A0A8G
+8A0A(B BBBF T PQ\ BBB B(A0D8G
+8A0A(B BBBC Xd 0s. K^ 4 | 8d[ ADG6
+DAA 4 `eG ADG"
+DAA D xf9 BBA I(G+
+(D ABBA < 4 pi _ BAI G
+ DABA < t l > BAI G
+ DABA 4 m@ ADG
+DAA 4 n ADG
+DAA < $ ppw BAI G
+ DABA D d s BBI A(G
+(D ABBA < v BHD G
+ DABA < x [ BHD G<
+ DABA 4 , Xz 3 ADG_
+DAA 4 d {[ ADG6
+DAA D |W BBA I(GI
+(D ABBA L O BIB D(A0Gy
+0D(A BBBA < 4 BAI G
+ DABA D t @ r BIA D(GF
+(D ABBA < H BAI G
+ DABA T ؍
# BIB B(A0D8G
+8A0A(B BBBD D $ x AAJ X
AAJ O
AAG X
-AAF , d 0b D\
+AAF , l b D\
H P
H Q
-G I {. K^ T X ? BBB B(A0A8G=
-8A0A(B BBBA 4 4! 4 4 L 4 d z AS L 4 BJB A(A0W
-(A BBDH _(A BBB, 5? m AEK X
-AAA < BEA A(G@
-(A ABBF T HM BBE B(D0D8DZ
-8D0A(B BBBB $ @6 AAD mAA $ X4i aFA Dp $ , 5c [FA Dp $ T 6~ vFA Dp 4 ۱z AG DL ! ~ BBB A(D0JH
-0A(A BBBK T 7e BEE B(A0A8G
-8A0A(B BBBF T D = BBE E(A0A8G
-8A0A(B BBBI T `D BBE E(A0A8G
-8A0A(B BBBC T J { BBE E(A0A8G
-8A0A(B BBBE \ L @ s BBB A(D0GZ
+G I ~s. K^ T BBB B(A0A8G=
+8A0A(B BBBA ` $ h! < T x l ,s AS L h BJB A(A0W
+(A BBDH _(A BBB, ? = AEK X
+AAA < BEA A(G@
+(A ABBF T M BBE B(D0D8DZ
+8D0A(B BBBB $ 6 AAD mAA $ i aFA Dp $ 4 Pc [FA Dp $ \ ~ v vFA Dp < *s AG DL ! N BBB A(D0JH
+0A(A BBBK , BGD l
+ABD T $ 3 BBE B(A0A8Gs
+8A0A(B BBBG D 4 x BBB D(A0D@
+0A(A BBBA | AL D BBE A(A0D@
+0A(A BBBA < BBG A(G@
+(A ABBG D $ h BBE A(A0D@l
+0A(A BBBA < { BBD A(G0T
+(A ABBA L BJB A(A0W
+(A BBDH ^(A BBB< BBG A(G@
+(A ABBG d BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB d 8 BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB L T W BJE B(A0A8D@
+8A0A(B BBBA L W BEB E(I0H8DP
+8D0A(B BBBD L @
+ / BJE B(A0A8D@
+8A0A(B BBBA l D BEE B(D0A8G
+8A0A(B BBBA d
+8A0A(B BBBA L BEB E(I0H8DP
+8D0A(B BBBD L P BJE B(A0A8D@
+8A0A(B BBBA 5 T l H ' BEB B(A0A8G=
+8D0A(B BBBB T m BEB B(A0A8G
+8D0A(B BBBH T n BBE B(A0A8G
+8D0A(B BBBC T t #~ x BBE B(A0A8G
+8D0A(B BBBI \ e BBB A(D0GZ
0A(A BBBD
-0A(A BBBA D d BAD
+0A(A BBBA D ȴ BAD
ABS A
-ABD EEB , P BGD l
-ABD T $ 8Q% Δ BBE E(A0A8G"
-8A0A(B BBBE D 4 X BBB D(A0D@
-0A(A BBBA D | xZ BBB D(A0D@
-0A(A BBBA D \ BBE A(A0D@
-0A(A BBBA < ] BBG A(G@
-(A ABBG D L h^ BBE A(A0D@l
-0A(A BBBA < _ BBD A(G0T
-(A ABBA L ` BJB A(A0W
-(A BBDH ^(A BBB< $ a BBG A(G@
-(A ABBG d a k BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d 8b BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB L | b ˒ BJE B(A0A8D@
-8A0A(B BBBA L c ˒ BEB E(I0H8DP
-8D0A(B BBBD L @e BJE B(A0A8D@
-8A0A(B BBBA l l f { BEE B(D0A8G
-8A0A(B BBBA d
-8A0A(B BBBA L m { BEB E(I0H8DP
-8D0A(B BBBD L , Po S BJE B(A0A8D@
-8A0A(B BBBA 4 p5 $ L Hpg AG k
-AA L p ; BEB E(I0H8D@
-8D0A(B BBBD d r BBB J(A0D8G`
+ABD EEB T t p( BBE B(A0A8G
+8D0A(B BBBC $ -g AG k
+AA L 0. BEB E(I0H8D@
+8D0A(B BBBD d D / BBB J(A0D8G`
8F0A(B BBBF `
-8F0A(B BBBA T t Xs ϑ BBB B(D0A8D
-8D0A(B BBBH , uh BGD c
-ABA z. K^ T 8 { BEB B(A0A8G
-8A0A(B BBBA T l p
-r BBE E(A0D8G
-P
-8A0A(B BBBA T BBE F(F0D8D
-8A0A(B BBBA T "A BBB B(A0A8Gs
-8A0A(B BBBB T t $ BEE B(A0D8Dm
-8D0A(B BBBG D `t w BEE A(D0DPt
-0D(A BBBA L u C BLB B(A0A8D`
-8A0A(B BBBA d d 7 BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d ӑ BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB z. K^ T L v # BEB B(A0A8G
-8A0A(B BBBG \ t 4 L d | x p h ` X P H $ @ < 8 T 0 l ( , D \ t x p h 4 ` L X d @" AN ND P" AN ND `" AN ND p" AN ND " AN ND " AN ND $ " AN ND D " AN ND d " AN ND Ѝ" AN ND " AN ND " AN ND " AN ND " AN ND $ " AN ND D 0" AN ND d @" AN ND P" AN ND `" AN ND p" AN ND , D \ t x p h 4 ` L X d P | H @ 8 < g BBA D(D0Y
-(A ABBD 4 ̜zS BBA D(A0B(A ABB< < g BBA D(D0Y
-(A ABBD 4 | zS BBA D(A0B(A ABB< g BBA D(D0Y
-(A ABBD 4 zS BBA D(A0B(A ABB4 ,
- zS BBA D(A0B(A ABB4 d
- zS BBA D(A0B(A ABB<
- g BBA D(D0Y
-(A ABBD 4
- zS BBA D(A0B(A ABB4 zS BBA D(A0B(A ABB,
- ؋C ˈ AID _
-AAA | xJ n N D BHF E(E0G@>
-0A(A BBBA D 8 BHF E(E0G@>
-0A(A BBBA , $ AAG0p
-AAA T AL t & A^
-A A L BED A(G0_
-(A ABBH (A ABB 4 H BDA
-AEA jAB L
Ȑ BED A(G0_
-(A ABBH (A ABB $ l
AX
+8F0A(B BBBA T 0 K BBB B(D0A8D
+8D0A(B BBBH , 3h ' BGD c
+ABA rs. K^ T L ȳu BEB B(A0A8G
+8A0A(B BBBA T _ BBE E(A0D8J
+
+8A0A(B BBBA L x c BBE F(F0D8D
+8A0A(B BBBA T L (A ' BBB B(A0A8Gs
+8A0A(B BBBB T
+ BBB B(A0A8G
+8A0A(B BBBI T _ BEE B(A0A8G
+8D0A(B BBBG D T 1 3 BEE A(D0DPt
+0D(A BBBA L X2 BLB B(A0A8D`
+8A0A(B BBBA d L BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB os. K^ T l 4 G BEB B(A0A8G
+8A0A(B BBBG | L L L L $ xL < pL T hL l `L XL PL HL @L 8L 0L (L , L D L \ L t L L Ȱ 4 L d | x p h ` X P $ H < @ T 8 l 0 J" AN ND (J" AN ND 8J" AN ND HJ" AN ND XJ" AN ND $ hJ" AN ND D xJ" AN ND d J" AN ND J" AN ND J" AN ND J" AN ND J" AN ND J" AN ND $ J" AN ND D J" AN ND d K" AN ND K" AN ND (K" AN ND 8K" AN ND HK" AN ND 4 L d | x p h ` X P $ H < @ T 8 l 0 ( < xIg BBA D(D0Y
+(A ABBD 4 $ sS BBA D(A0B(A ABB< \ pIg BBA D(D0Y
+(A ABBD 4 sS BBA D(A0B(A ABB< hIg BBA D(D0Y
+(A ABBD 4 \sS BBA D(A0B(A ABB4 L xsS BBA D(A0B(A ABB4 sS BBA D(A0B(A ABB< Hg BBA D(D0Y
+(A ABBD 4 psS BBA D(A0B(A ABB4 4 sS BBA D(A0B(A ABB, HC AID _
+AAA PJ n N D H BHF E(E0G@>
+0A(A BBBA D J BHF E(E0G@>
+0A(A BBBA , D hK AAG0p
+AAA t K AL & A^
+A A L K BED A(G0_
+(A ABBH (A ABB 4 LH BDA
+AEA jAB L < M BED A(G0_
+(A ABBH (A ABB $ AX
G g
-I 4
pi BDD h
-ABK DB L
BHA D(D0
-(A ABBK j(A ABB 4 H BGA h
-ABK WAB L T BHA D(D0
-(A ABBK M(A ABB P k $ L 7 HD
+I 4 HNi BDD h
+ABK DB L O BHA D(D0
+(A ABBK j(A ABB 4 < P BGA h
+ABK WAB L t P BHA D(D0
+(A ABBK M(A ABB xP $ xQ Q $ l xQ [ HD
D E
-A , (2 AL cA L H $ @ HD
+A L R2 AL cA l R $ R
HD
D E
-A ȕ2 AL cA $ HD
+A R2 AL cA R $ , R HD
D E
-A h2 AL cA $ l M HD
+A @S2 AL cA , `S $ XS q HD
D E
-A L 2 AL cA l (2 P H @ 8 0$ VM H P , H D @$ VM \ X t P H$ VM ` h ` X P$ VM h 4 p L h d `$ VM | x p hy AC
-D ȗ$ VM ؗ < З BEA A(F0
-(D ABBA T @$ VM l X P H$ VM ` X5 ; Au g A{
-D < $ VM T l d * BEB B(D0A8GP{
+A l S2 AL cA T2 (T T T T T$ VM T 4 (T L T d T$ VM | 0T (T T$ VM 8T @T 8T 0T $ (T$ VM < @T T HT l @T 8T$ VM PT HT @Ty AC
+D T$ VM T T < 4 T BEA A(F0
+(D ABBA t U$ VM 0U (U U$ VM 8U 0U5 XU; Au $ xU < pUg A{
+D \ U$ VM t U U d U* BEB B(D0A8GP{
8D0A(B BBBA
-8A0A(B BBBE Ƞ < " BBB A(H0
-(A EBBA D $ VM \ ȣ t У ȣ L ~ BEB D(D0
-(A BBBE z(A LEB , BAA |
-DBJ < h$ VM T l x p AG
-H $ VM 4 AAG0Q
+8A0A(B BBBE ] < $ ]" BBB A(H0
+(A EBBA d `$ VM | ` ` ` L `~ BEB D(D0
+(A BBBE z(A LEB a , , a BAA |
+DBJ \ @b$ VM t Xb Pb Hb AG
+H b$ VM 4 b AAG0Q
AAD v
-AAA ` X < $ P BEA A(F0^
-(D ABBA d $ VM | 4 U BGA
-ABE DB , ا BAC m
-DBG , H$ VM D ` \ h t ` X$ VM p x p h$ VM 4 4 U BGA
-ABE DB l , BAC m
-DBG $ VM ( y AC
-D x$ VM 4 L d | x p h L ` BEA D(G0
-(A ABBG (A ABB L , BED A(G0
-(A ABBB (A ABB L d BEB A(D08
-(A BBBC (A BEB d $ BEB B(A0D8GP)
+AAA 8c , 0c < D (c BEA A(F0^
+(D ABBA xc$ VM c c 4 cU BGA
+ABE DB d , d BAC m
+DBG L e$ VM d 8e | @e 8e 0e$ VM He Pe He @e$ VM $ Xe < `e 4 T XeU BGA
+ABE DB f , xf BAC m
+DBG f$ VM g f fy AC
+D < Pg$ VM T hg l `g Xg Pg Hg @g L 8g BEA D(G0
+(A ABBG (A ABB L 4 i, BED A(G0
+(A ABBB (A ABB L j BEB A(D08
+(A BBBC (A BEB d o$ BEB B(A0D8GP)
8D0A(B BBBC
-8A0A(B BBBE d BBF H(A0D8GP
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8A0A(B BBBM D
8A0A(B BBBB
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-GAN DAA D L TR BAD G0
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+GAN DAA L l'
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} BBB B(A0D8G`
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+ x 4 (
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AABK D
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-8A0A(B BBBF T DM H[ J BIF E(D0`
+ AABH \)
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+GAN DAA )
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8A0A(B BBBG
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DAH {
DAH {
DAH {
DAH {
DAH D
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AABL D
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+GAN DAA d/
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+GAN DAA 0
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+GAN DAA 0
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8A0A(B BBBL D
8A0A(B BBBJ 0
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+GAN DAA L 1
+ 6S
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+ @ BBB E(A0A8G`
8A0A(B BBBD =
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+8A0A(B BBBI L 1
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+8A0A(B BBBB T d2
+ Ju 4 BBB B(D0A8G5
+8A0A(B BBBE 4 t3
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AAH D
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-GAN DAA W H 4 W @7 ADG N
-GAN DAA tW HY AA BBE D(G0
+AAJ 3
+ R 4 3
+ R7 ADG N
+GAN DAA 3
+ R 4 4
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+GAN DAA 3
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8A0A(B BBBA "
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-AAA _ P; Au < _ p a: AAG y
+A :
+ o2 AL cA :
+ p :
+ p :
+ p$ VM :
+ p ;
+ p $;
+ p A
+J D;
+ p$ VM \;
+ p t;
+ p ;
+ p$ VM , :
+ p Ȗ AAG S
+AAA ;
+ (q; Au < <;
+ Hq AAG y
AAD Y
-DAA ` 0 ,` ( D _ R : BIF A(E0
-(A BBBA ` ; Au < _ ȵ 9 AAG y
+DAA 4<
+ L<
+ D ;
+ hqR % BIF A(E0
+(A BBBA <
+ r; Au < <
+ r ە AAG y
AAD Y
-DAA ` x a p , d` W9 AAG
-AAA La ; Au < ` ȶ 9 AAG y
+DAA =
+ P $=
+ H , <
+ r { AAG
+AAA l=
+ s; Au < <
+ s : AAG y
AAD Y
-DAA a a L a BFE F(D0D8GP
-8D0A(B BBBD , ,b h ADG0
-AAE , \b ( ADG0a
-AAA , b L ADG0
-AAE 4 b ؼ BDD D0
- AABC b 4 Tb 7 AAG o
-AAF cAA4 Dc 7 ADG N
-GAN DAA < b ( 7 BFA D(G0~
-(A ABBA L c Ⱦ BBB B(A0D8G`
-8A0A(B BBBH d ( T lc > 6 BBB I(A0A8GP
-8A0A(B BBBF |d ( d L d BBB B(A0D8G`@
-8A0A(B BBBB d L \d 5 BBB E(A0A8G`O
-8A0A(B BBBC de zu Ki4 |e D BEA A(D0p(A ABB T d 0Z R5 BBA H(D0
+DAA =
+ =
+ L =
+ s BFE F(D0D8GP
+8D0A(B BBBD , L>
+ @v ADG0
+AAE , |>
+ x ADG0a
+AAA , >
+ xL ADG0
+AAE 4 >
+ y BDD D0
+ AABC ?
+ z 4 t>
+ z AAG o
+AAF cAA4 d?
+ z7 ADG N
+GAN DAA < >
+ { > BFA D(G0~
+(A ABBA L ?
+ { BBB B(A0D8G`
+8A0A(B BBBH ,@
+ T ?
+ ~> BBB I(A0A8GP
+8A0A(B BBBF @
+ @
+ L @
+ BBB B(A0D8G`@
+8A0A(B BBBB A
+ ` L |@
+ X BBB E(A0A8G`O
+8A0A(B BBBC A
+ 8su Ki4 A
+ D BEA A(D0p(A ABB T A
+ Z v BBA H(D0
(A ABBE z
-(A ABBH 4 f 87 ADG N
-GAN DAA L e @ 4 BBD A(D0
-(A ABBE (A ABB 4 f 7 ADG N
-GAN DAA 4 f O BEA A(D0{(A ABB L g BEE B(A0A8Kp
-8A0A(B BBEE L Tg K BEE B(D0A8Hp
-8A0A(B BBED d g w BBB E(D0A8GP
+(A ABBH 4 ,B
+ 7 ADG N
+GAN DAA L A
+ BBD A(D0
+(A ABBE (A ABB 4 B
+ x7 ADG N
+GAN DAA 4 B
+ O BEA A(D0{(A ABB L $C
+ BEE B(A0A8Kp
+8A0A(B BBEE L tC
+ XK BEE B(D0A8Hp
+8A0A(B BBED d C
+ Xw BBB E(D0A8GP
8D0A(B BBBJ r
-8A0A(B BBBE L h BHA A(G0
+8A0A(B BBBE L ,D
+ p BHA A(G0
(A ABBH
-(A ABBE < \h (x BBF A(G0%
-(A EBBG < h h BBA D(F0
-(D ABBH h h i $i <i " AN ND \i " AN ND |i i 4 i ?zS BBA D(A0B(A ABB4 i ?zS BBA D(A0B(A ABB4 j ?zS BBA D(A0B(A ABB4 Tj @zS BBA D(A0B(A ABB, j AAG0
-AAA j AL < j BBA A(G@
-(A ABBB k AL <k $ j / HD
+(A ABBE < |D
+ x BBF A(G0%
+(A EBBG < D
+ @ BBA D(F0
+(D ABBH D
+ E
+ ,E
+ DE
+ \E
+ " AN ND |E
+ " AN ND E
+ E
+ 4 E
+ rS BBA D(A0B(A ABB4 F
+ rS BBA D(A0B(A ABB4 <F
+ иrS BBA D(A0B(A ABB4 tF
+ rS BBA D(A0B(A ABB, F
+ AAG0
+AAA F
+ AL < F
+ BBA A(G@
+(A ABBB <G
+ Щ AL \G
+ Щ $ F
+ ȩ HD
D E
-A |k x2 AL cA k k k $ VM k k l ,l $ VM 4 k . BHI b
-ABA < k 8 . AAG y
+A G
+ P2 AL cA G
+ p G
+ h G
+ `$ VM H
+ x H
+ 4H
+ x LH
+ p$ VM 4 G
+ BHI b
+ABA < G
+ AAG y
AAD Y
-DAA l l , 4l X+ _. AAG
-AAA $ m Xg ARD IAA< l . AAG y
+DAA H
+ p H
+ h , TH
+ 0+ AAG
+AAA $ <I
+ 0g ARD IAA< H
+ x : AAG y
AAD Y
-DAA m m , m T ADG0
-AAE , m ADG0
-AAI n L ,n BBB B(A0D8G`
-8A0A(B BBBE |n x 4 n pO BAD G0
- AABG n n L Dn h n, BBB E(A0A8G`
-8A0A(B BBBG L n U, BBB B(E0D8G@
-8A0A(B BBBA o 8z K4 o @D BEA A(D0p(A ABB L 4o X1 + BBA D(D0
-(A ABBE n(A ABB 4 <p H7 ADG N
-GAN DAA 4 o P b+ AAK |
-AAE CAA4 p 7 ADG N
-GAN DAA 4 p D BEA A(D0p(A ABB L q 1 BEB E(D0A8Hp
-8A0A(B BBEC L lq H BIA A(G0r
-(A ABBD d(C ABB L q x BIE B(E0E8G`
-8A0A(B BBEB L r BEA D(G0
+DAA I
+ I
+ , I
+ T ADG0
+AAE , J
+ ȭ ADG0
+AAI 4J
+ X L LJ
+ BBB B(A0D8G`
+8A0A(B BBBE J
+ P 4 J
+ HO BAD G0
+ AABG J
+ ` K
+ L dJ
+ @ BBB E(A0A8G`
+8A0A(B BBBG L J
+ y BBB B(E0D8G@
+8A0A(B BBBA K
+ s K4 K
+ D BEA A(D0p(A ABB L TK
+ 01 BBA D(D0
+(A ABBE n(A ABB 4 \L
+ 7 ADG N
+GAN DAA 4 K
+ ( AAK |
+AAE CAA4 L
+ 7 ADG N
+GAN DAA 4 M
+ D BEA A(D0p(A ABB L <M
+ 1 BEB E(D0A8Hp
+8A0A(B BBEC L M
+ BIA A(G0r
+(A ABBD d(C ABB L M
+ P BIE B(E0E8G`
+8A0A(B BBEB L ,N
+ BEA D(G0
(A ABBI
-(A ABBE d \r h
A BEB B(D0D8DP
+(A ABBE d |N
+ @A BEB B(D0D8DP
8D0A(B BBBB
-8A0A(B BBBE < r P BBA D(F0
-(D ABBH < s , BIB E(A0
-(D BBBC Ds @ D r 8 ( BEB D(A0GpY
-0A(A BBBJ $ s [ ADG LAA4 s ( BDA G0x
- AABF t t 6 $ 4t . ACD `DA < \t L BBA A(D0t
-(D ABBA , t < BDD kAE , t b BAA ZAB t H u P# Aa |t ` & AL Tu `D , lu AFG |
-AAD $ u + AAG \DA 4 u BDD D0I
- AABE u xQ Ad
-A c v . Ac
-A D 4 <v v BDD D0|
- AABA L tv BED D(D@R
+8A0A(B BBBE < N
+ ( BBA D(F0
+(D ABBH < $O
+ , BIB E(A0
+(D BBBC dO
+ D N
+ BEB D(A0GpY
+0A(A BBBJ $ O
+ [ ADG LAA4 O
+ BDA G0x
+ AABF $P
+ <P
+ 6 $ TP
+ . ACD `DA < |P
+ L BBA A(D0t
+(D ABBA , P
+ < BDD kAE , P
+ b BAA ZAB Q
+ 4Q
+ # Aa P
+ ( AL tQ
+ (D , Q
+ ` AFG |
+AAD $ Q
+ + AAG \DA 4 Q
+ BDD D0I
+ AABE R
+ @Q Ad
+A c <R
+ . Ac
+A D 4 \R
+ v BDD D0|
+ AABA L R
+ BED D(D@R
(A ABBA T
-(G ABEE 4 v B ADG b
-AAA JAA D v x BDD z
+(G ABEE 4 R
+ HB ADG b
+AAA JAA D S
+ `x BDD z
ABA Y
-ABE AGB < Dw r ADG x
+ABE AGB < dS
+ r ADG x
AAA S
-GAE ADA 4 w O AAG n
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-AAF DFA L $x P BIF H(H0A8D`
-8C0A(B BBBD tx z Em
-A FL x p KGF B(D0D8I`y8F0A(B BBB x z. K^ x 4 y BAD H
-ABF rAB 4 x " BPH DPW
- AABA 4 x ` " BPH DPW
- AABA y J n N y y z @ z 4z Lz H $ dz DE
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+ O AAG n
+DDI DAA S
+ " S
+ (r. K^ 4 T
+ L AAD r
+AAF DFA L DT
+ BIF H(H0A8D`
+8C0A(B BBBD T
+ z Em
+A FL T
+ 8 KGF B(D0D8I`y8F0A(B BBB U
+ Hr. K^ U
+ C 4 4U
+ x BAD H
+ABF rAB 4 T
+ ~ BPH DPW
+ AABA 4 T
+ ( ~ BPH DPW
+ AABA U
+ J n N U
+ V
+ ` $V
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+ TV
+ X lV
+ $ V
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G W
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+I V V
+ t V
+ , V
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+AAD , W
+ A/ ACG W
+AAA , <W
+ A1 ADG X
+AAA D lW
+ B ACG a
AAB v
AAH P
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+AAA D W
+ HB ADG b
AAH z
AAD P
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+AAA W
+ $ \W
+ t S| QQ
F b
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+S <X
+ D TX
+ HB ADD k
AAJ u
AAI O
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+AAA D X
+ B ADD q
AAD y
AAE O
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+ C BDA _
ABG M
-ABH } DR < l| BLD D(D`
-(A ABBA , | l ATDP}
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-AAA , t~ @!4 AAG a
-AAD 4 } P!q BAD L
-ABF < $~ !E ADD`r
+ABH $Y
+ X DR < X
+ X /{ BLD D(D`
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+ l { ATDP}
+AAA Y
+ Y
+ Y
+ Y
+ Z
+ ,Z
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+ A\ , dZ
+ b ACG D
+AAA , Z
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+AAD 4 Z
+ q y BAD L
+ABF < DZ
+ `E y ADD`r
AAC ~
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+AAA <[
+ A 4 T[
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+DAF NDA D [
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AIBI |
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+ DABM L [
+ (, BBB B(A0A8D`
+8A0A(B BBBG $\
+ h <\
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+ H BED A(G@y
(A ABBF
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HI l
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+8A0A(B BBBK <]
+ P: AT < \]
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AAH }
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+ r. K^ ]
+ AC AA ]
+ B w ]
+ B ^
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+ B AAG QAC d^
+ B AL ^
+ B& ^
+ BM ^
+ B& , ^
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+ PC BAA S
CBD |
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-(A ABBG $ T @P AG ]
-AJ < | hl BEE A(D0S(A BBB , N AAG0f
-AAG , N AAG0h
-AAE < ؈ BED C(G0Y
-(A ABBA $ \ D D
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+ EP AG ]
+AJ < _
+ 0El BEE A(D0S(A BBB , _
+ `EN AAG0f
+AAG , `
+ EN AAG0h
+AAE < <`
+ E BED C(G0Y
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H I
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J
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H F
J K
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D O
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+A G ta
+ NN D `
+ P s BEB D(A0
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+ Ps FAD I
+ABI AAB b
+ Q FS $ 4b
+ Q/ AAD cDA | \b
+ Q BFE E(E0A8Dp
8A0A(B BBBA ]
8A0A(B EBBA K
-8A0A(B IBBA $ X> AM
+8A0A(B IBBA $ b
+ R> AM
B U
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+K $ c
+ 8RJ AN
A h
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+H T ,c
+ `R BBB A(A0FPb
0A(A BBBH
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+0C(A BBBB \ c
+ S BEA A(G0T
(A ABBF g
(F ABBF X
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+ S BDD F
ABE [
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+ABJ L $d
+ SV BFE B(E0A8Dp
+8A0A(B BBBA < td
+ U BBE D(C0c(E BBB \ d
+ HU BED D(G0F
(A ABBF W
-(E ABBA A(A ABB < ACG0
+(E ABBA A(A ABB < e
+ U ACG0
EAF T
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+GAE uGA4 Te
+ HV ADF0|
EAC h
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+GAE L e
+ V BEE D(GP}
(A ABBF |
-(A ABBF AQ ܉ p AP
-G Hqz. K^ , D \ 4 t o BAA Z
-ABG AAD 4 Ț BAA o
-ABJ AAD AL 4 AL T L ؚ h BED A(Gpq
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+ `W 8o AQ e
+ `Wp AP
+G f
+ r. K^ 4f
+ hd Lf
+ `d df
+ hW |f
+ `W 4 f
+ XWo BAA Z
+ABG AAD 4 f
+ W BAA o
+ABJ AAD g
+ c g
+ c 4g
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+ W AL tg
+ xc L f
+ W m BED A(Gpq
(C ABBD G
-(C ABBA D X 6 BAC G`^
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+ X Zm BAC G`^
AABA b
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8A0A(B BBBE I
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+8C0A(B BBBA h
+ PY: Ax h
+ pY& AT h
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+ Y l AADPX
AAH D
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AAD O
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+CAE i
+ Z 4 h
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+ AABK , i
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+ABA j
+ ([ AL $j
+ ([ <j
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AAD O
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AEG yAA Ԑ AL , < I AEG yAA $ AL D \ t ԑ P5 Hl p< Ho 5 Hl 4 < Ho T l : ؠ: D ̒ b BBB A(C0G B
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+CAE $ j
+ [- AAK [AC 4 j
+ [ k BAA GPf
+ AABK , <j
+ H\^ j BKI ^
+ABA $k
+ x\ AL Dk
+ x\ , j
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+ABA k
+ \ , j
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+ \ k
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+ \ l
+ \ 4l
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+ \ dl
+ \ |l
+ \ l
+ \ l
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+ \I Ai AEG yAA l
+ \ AL , \l
+ \I h AEG yAA Dm
+ \ AL dm
+ \ |m
+ \ m
+ \ m
+ \ m
+ \ m
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+ 8]< Ho 4n
+ X]5 Hl Tn
+ x]< Ho tn
+ p n
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+ ] n
+ ]: n
+ ] D n
+ ]b BBB A(C0G B
+0C(A BBBA 4o
+ ] Lo
+ ] $ n
+ ]L f Ai
+F [ 4 o
+ ^E ADG ]
+AAE RAA 4 o
+ ^Y ADG f
+CAJ VAA o
+ @o p
+ 8o ,p
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+ (o \p
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+ o p
+ o p
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+ ]b Me BAC G`a
+ AABF 4 Dp
+ ^ We BAC G`k
+ AABD 4 |p
+ H_b 7e BAC G`a
+ AABF , p
+ ` Ae AAG`T
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+ ` &e BAC G`q
AABF ]
- AABG D ` BAC G`b
+ AABG D ,q
+ (b %e BAC G`b
AABE
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+ AABA 4 tq
+ cd *e BAD G`k
+ AABC l q
+ d 2e BBE E(A0D8Du
8F0A(B BBBE U
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+ 0f- AY
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AABF e
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+ AABG s
+ f, s
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+D W , |s
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FABA D
CABF H
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+ FABG x
+ 0l8 AQ
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+ Pl# ADG JGA $ Tx
+ Xl# ADG JGA $ |x
+ `l^ ADG`OAA\ w
+ l5 _ BBB A(D0G
0C(A BBBG
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+0F(A BBDC y
+ xp T dx
+ pp _ BBD A(G
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+ r , Dy
+ r0 ^ AFJ RGA , ty
+ r0 ^ AFJ RGA d y
+ r ^ BED A(G\
(A ABBK M
(F ABBH
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+8A0A(B BBBJ d L6 'B BBB B(A0A8G@x
8A0A(B BBBE
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- xP *p BGE B(A0A8Q
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+ BAA D@
+ AABD L 7 ? BBB B(A0D8D@
+8A0A(B BBBA < \7
t BEE D(A0S(A GBE T 6 @
N BGE B(A0A8Q
+8A0A(B BBBA < 7 (' BBA A(G@_
+(A ABBF L 48 '~ BLB H(A0D8D@
+8A0A(B BBBG < 8 ) BBG A(G@
+(A ABBH L 8 V BHB E(G0A8G
+8A0A(B BBBA < 9 X) BBG A(G0
+(A ABBB L T9 - BEE B(D0A8Gp
+8A0A(B BBBC L 8 > \ BBB B(A0A8JM
+8A0A(B BBBE 9 k : k $: j <: 0) T: () l: ) T 9 () BBB B(A0A8G@
+8A0A(B BBBE T $: . BBB B(A0A8G@
+8A0A(B BBBE L |: x4. BBB B(A0A8Gh
+8A0A(B BBBE 4 ; X;J AAG P
AAE
-FAE T ^
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-8A0A(B BBBH <_
- m BBB B(A0A8G@
+FAE T ; p< BEE B(A0A8G
+8A0A(B BBBH \; H BBB B(A0A8G@
8A0A(B BBBH t
8D0A(B BBBO
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AAJ d
DAG d
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+DAA L < (V U BBB B(A0A8JB
+8D0A(B BBBE T = hg r BBB B(A0A8J+
+8A0A(B BBBG < d= nu _ BHE L
+AEO AAB < = `nu # BHE L
+AEO AAB < = nu BHE L
+AEO AAB < $> n| BEA A(D0a(D ABB < d> o| o BEA A(D0a(D ABB < > `o| 3 BEA A(D0a(D ABB D > oU OMB L(E0
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FAM b
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KAP D
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+AEO AAB < A x| BEA A(D0a(D ABB L <B ؉ BBB B(A0A8JC
+8D0A(B BBBD T B X BBB E(A0A8J
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8A0A(B BBBK C
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_ZZN11onnxruntime21BuildKernelCreateInfoINS_45kCpuExecutionProvider_Min_kOnnxDomain_ver8_11EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_43kCpuExecutionProvider_Min_kOnnxDomain_ver12EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_45kCpuExecutionProvider_Max_kOnnxDomain_ver8_11EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_43kCpuExecutionProvider_Max_kOnnxDomain_ver12EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_42kCpuExecutionProvider_Not_kOnnxDomain_ver1EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_42kCpuExecutionProvider_And_kOnnxDomain_ver7EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ 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_ZZN11onnxruntime16BroadcastOneSpanIbbZNKS_3And7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIbLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEEbNS5_IKS7_Li0ES9_EEE_ZNKS1_7ComputeES3_EUlSA_SC_bE0_ZNKS1_7ComputeES3_EUlSA_SC_SC_E1_EEvPNS_11concurrency10ThreadPoolEdPT0_lPKT_lSN_lT1_T2_T3_E19__PRETTY_FUNCTION__ _ZN11onnxruntime13BroadcastLoopINS_12TBroadcasterIbbEENS_16TBroadcastOutputIbEEZNKS_2Or7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS8_6MatrixIbLin1ELi1ELi0ELin1ELi1EEELi0ENS8_6StrideILi0ELi0EEEEEbNS9_IKSB_Li0ESD_EEE_ZNKS5_7ComputeES7_EUlSE_SG_bE0_ZNKS5_7ComputeES7_EUlSE_SG_SG_E1_EEvRT_RT0_T1_T2_T3_.isra.1531 _ZZN11onnxruntime12BroadcastTwoIbbZNKS_2Or7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIbLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEEbNS5_IKS7_Li0ES9_EEE_ZNKS1_7ComputeES3_EUlSA_SC_bE0_ZNKS1_7ComputeES3_EUlSA_SC_SC_E1_EENS_6common6StatusERS2_T1_T2_T3_dENKUlllE_clEll 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_ZTSZN11onnxruntime16BroadcastOneSpanIbbZNKS_3And7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIbLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEEbNS5_IKS7_Li0ES9_EEE_ZNKS1_7ComputeES3_EUlSA_SC_bE0_ZNKS1_7ComputeES3_EUlSA_SC_SC_E1_EEvPNS_11concurrency10ThreadPoolEdPT0_lPKT_lSN_lT1_T2_T3_EUlllE_ _ZTSZN11onnxruntime16BroadcastOneSpanIbbZNKS_3And7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIbLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEEbNS5_IKS7_Li0ES9_EEE_ZNKS1_7ComputeES3_EUlSA_SC_bE0_ZNKS1_7ComputeES3_EUlSA_SC_SC_E1_EEvPNS_11concurrency10ThreadPoolEdPT0_lPKT_lSN_lT1_T2_T3_EUlllE0_ _ZTSZN11onnxruntime16BroadcastOneSpanIbbZNKS_3And7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIbLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEEbNS5_IKS7_Li0ES9_EEE_ZNKS1_7ComputeES3_EUlSA_SC_bE0_ZNKS1_7ComputeES3_EUlSA_SC_SC_E1_EEvPNS_11concurrency10ThreadPoolEdPT0_lPKT_lSN_lT1_T2_T3_EUlllE1_ _ZTSZN11onnxruntime16BroadcastOneSpanIbbZNKS_2Or7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIbLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEEbNS5_IKS7_Li0ES9_EEE_ZNKS1_7ComputeES3_EUlSA_SC_bE0_ZNKS1_7ComputeES3_EUlSA_SC_SC_E1_EEvPNS_11concurrency10ThreadPoolEdPT0_lPKT_lSN_lT1_T2_T3_EUlllE_ _ZTSZN11onnxruntime16BroadcastOneSpanIbbZNKS_2Or7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIbLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEEbNS5_IKS7_Li0ES9_EEE_ZNKS1_7ComputeES3_EUlSA_SC_bE0_ZNKS1_7ComputeES3_EUlSA_SC_SC_E1_EEvPNS_11concurrency10ThreadPoolEdPT0_lPKT_lSN_lT1_T2_T3_EUlllE0_ _ZTSZN11onnxruntime16BroadcastOneSpanIbbZNKS_2Or7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIbLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEEbNS5_IKS7_Li0ES9_EEE_ZNKS1_7ComputeES3_EUlSA_SC_bE0_ZNKS1_7ComputeES3_EUlSA_SC_SC_E1_EEvPNS_11concurrency10ThreadPoolEdPT0_lPKT_lSN_lT1_T2_T3_EUlllE1_ _ZTSZN11onnxruntime16BroadcastOneSpanIbbZNKS_3Xor7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIbLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEEbNS5_IKS7_Li0ES9_EEE_ZNKS1_7ComputeES3_EUlSA_SC_bE0_ZNKS1_7ComputeES3_EUlSA_SC_SC_E1_EEvPNS_11concurrency10ThreadPoolEdPT0_lPKT_lSN_lT1_T2_T3_EUlllE_ _ZTSZN11onnxruntime16BroadcastOneSpanIbbZNKS_3Xor7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIbLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEEbNS5_IKS7_Li0ES9_EEE_ZNKS1_7ComputeES3_EUlSA_SC_bE0_ZNKS1_7ComputeES3_EUlSA_SC_SC_E1_EEvPNS_11concurrency10ThreadPoolEdPT0_lPKT_lSN_lT1_T2_T3_EUlllE0_ _ZTSZN11onnxruntime16BroadcastOneSpanIbbZNKS_3Xor7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIbLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEEbNS5_IKS7_Li0ES9_EEE_ZNKS1_7ComputeES3_EUlSA_SC_bE0_ZNKS1_7ComputeES3_EUlSA_SC_SC_E1_EEvPNS_11concurrency10ThreadPoolEdPT0_lPKT_lSN_lT1_T2_T3_EUlllE1_ _ZTSZN11onnxruntime16BroadcastOneSpanIffZNKS_5PReluIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS5_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS5_6StrideILi0ELi0EEEEEfNS6_IKS8_Li0ESA_EEE_ZNKS2_7ComputeES4_EUlSB_SD_fE0_ZNKS2_7ComputeES4_EUlSB_SD_SD_E1_EEvPNS_11concurrency10ThreadPoolEdPT0_lPKT_lSO_lT1_T2_T3_EUlllE_ _ZTSZN11onnxruntime16BroadcastOneSpanIffZNKS_5PReluIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS5_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS5_6StrideILi0ELi0EEEEEfNS6_IKS8_Li0ESA_EEE_ZNKS2_7ComputeES4_EUlSB_SD_fE0_ZNKS2_7ComputeES4_EUlSB_SD_SD_E1_EEvPNS_11concurrency10ThreadPoolEdPT0_lPKT_lSO_lT1_T2_T3_EUlllE0_ _ZTSZN11onnxruntime16BroadcastOneSpanIffZNKS_5PReluIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS5_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS5_6StrideILi0ELi0EEEEEfNS6_IKS8_Li0ESA_EEE_ZNKS2_7ComputeES4_EUlSB_SD_fE0_ZNKS2_7ComputeES4_EUlSB_SD_SD_E1_EEvPNS_11concurrency10ThreadPoolEdPT0_lPKT_lSO_lT1_T2_T3_EUlllE1_ _GLOBAL__sub_I_softmax_shared.cc _ZZN11onnxruntime2ml21BuildKernelCreateInfoINS0_62kCpuExecutionProvider_LabelEncoder_kMLDomain_ver2_string_int64EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES6_ _ZZN11onnxruntime2ml21BuildKernelCreateInfoINS0_62kCpuExecutionProvider_LabelEncoder_kMLDomain_ver2_int64_stringEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES6_ _ZZN11onnxruntime2ml21BuildKernelCreateInfoINS0_61kCpuExecutionProvider_LabelEncoder_kMLDomain_ver2_int64_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES6_ _ZZN11onnxruntime2ml21BuildKernelCreateInfoINS0_61kCpuExecutionProvider_LabelEncoder_kMLDomain_ver2_float_int64EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES6_ _ZZN11onnxruntime2ml21BuildKernelCreateInfoINS0_61kCpuExecutionProvider_LabelEncoder_kMLDomain_ver2_int64_int64EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES6_ _ZZN11onnxruntime2ml21BuildKernelCreateInfoINS0_62kCpuExecutionProvider_LabelEncoder_kMLDomain_ver2_float_stringEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES6_ _ZZN11onnxruntime2ml21BuildKernelCreateInfoINS0_62kCpuExecutionProvider_LabelEncoder_kMLDomain_ver2_string_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES6_ _ZZN11onnxruntime2ml21BuildKernelCreateInfoINS0_51kCpuExecutionProvider_LabelEncoder_kMLDomain_ver1_1EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES6_ _ZZN11onnxruntime2ml14LabelEncoder_2IlSsEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml12LabelEncoderC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml14LabelEncoder_2ISslEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml14LabelEncoder_2IfSsEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml14LabelEncoder_2IlfEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml14LabelEncoder_2IflEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml14LabelEncoder_2IllEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml14LabelEncoder_2ISsfEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _GLOBAL__sub_I_label_encoder.cc _ZNK11onnxruntime13MaxPool1DTaskIfEclEll._omp_fn.0 _ZNK11onnxruntime13MaxPool2DTaskIfEclEll._omp_fn.1 _ZNK11onnxruntime13MaxPool3DTaskIfEclEll._omp_fn.2 _ZNK11onnxruntime13MaxPool1DTaskIdEclEll._omp_fn.3 _ZNK11onnxruntime13MaxPool2DTaskIdEclEll._omp_fn.4 _ZNK11onnxruntime13MaxPool3DTaskIdEclEll._omp_fn.5 _ZNK11onnxruntime13MaxPool1DTaskIaEclEll._omp_fn.6 _ZNK11onnxruntime13MaxPool2DTaskIaEclEll._omp_fn.7 _ZNK11onnxruntime13MaxPool3DTaskIaEclEll._omp_fn.8 _ZNK11onnxruntime13MaxPool1DTaskIhEclEll._omp_fn.9 _ZNK11onnxruntime13MaxPool2DTaskIhEclEll._omp_fn.10 _ZNK11onnxruntime13MaxPool3DTaskIhEclEll._omp_fn.11 _ZNK11onnxruntime10Pool1DTaskIfNS_6LpPoolEEclEll._omp_fn.12 _ZNK11onnxruntime10Pool2DTaskIfNS_6LpPoolEEclEll._omp_fn.13 _ZNK11onnxruntime10Pool3DTaskIfNS_6LpPoolEEclEll._omp_fn.14 _ZNKSt14default_deleteIN11onnxruntime6common6Status5StateEEclEPS3_.isra.76 _ZZN11onnxruntime18PoolProcessContext4initERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime14PoolAttributesC1ERKNS_17OpNodeProtoHelperINS_22ProtoHelperNodeContextEEERKSsiE19__PRETTY_FUNCTION__ _ZZN11onnxruntime21BuildKernelCreateInfoINS_49kCpuExecutionProvider_MaxPool_kOnnxDomain_ver8_11EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_47kCpuExecutionProvider_MaxPool_kOnnxDomain_ver12EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_48kCpuExecutionProvider_MaxPool_kOnnxDomain_ver1_7EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ 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_ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE1_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE1_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE0_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE0_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE10_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE10_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE11_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _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_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE17_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE17_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE18_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE18_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE19_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE19_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE20_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE20_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE21_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE21_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE22_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE22_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE23_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE23_ _ZN6google8protobuf8internal20RepeatedPtrFieldBase3AddINS0_16RepeatedPtrFieldIN4onnx26TensorShapeProto_DimensionEE11TypeHandlerEEEPNT_4TypeESB_.constprop.527 _ZN4onnx13hasInputShapeERNS_16InferenceContextEm.constprop.528 _ZNSt18basic_stringstreamIcSt11char_traitsIcESaIcEEC1ESt13_Ios_Openmode.constprop.530 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E7_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E16_E9_M_invokeERKSt9_Any_dataS2_ 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_ZZN11onnxruntime7contrib19RegisterBertSchemasEvE47op_schema_register_onceEmbedLayerNormalization2 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib19RegisterBertSchemasEvEUlS2_E0_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib19RegisterBertSchemasEvE34op_schema_register_onceQAttention1 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib19RegisterBertSchemasEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib19RegisterBertSchemasEvE33op_schema_register_onceAttention0 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceAffine5 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceParametricSoftplus6 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceImageScaler7 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE28op_schema_register_onceCrop8 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_ZZN11onnxruntime7contrib22RegisterContribSchemasEvE32op_schema_register_onceInverse62 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E20_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE38op_schema_register_onceCropAndResize58 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceCDist57 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E19_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE31op_schema_register_onceUnique56 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E18_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE28op_schema_register_oncePad55 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceWordConvEmbedding54 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_ZNSt14_Function_base13_Base_managerIZN4onnx24MathDocGenerator_opset_7EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx24MathDocGenerator_opset_7EPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx24MathDocGenerator_opset_7EPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx24MathDocGenerator_opset_7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS3_EUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_EUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS3_ENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Mod_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Pow_Onnx_ver12EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Gemm_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16MatMul_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Expand_Onnx_ver8EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_34SoftmaxCrossEntropyLoss_Onnx_ver12EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx32SoftmaxFamilyDocGenerator_opset1EPKcS3_EUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx32SoftmaxFamilyDocGenerator_opset1EPKcS1_EUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx20MathDocGenerator_oldEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx20MathDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx27MathDocGenerator_old_opset6EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx27MathDocGenerator_old_opset6EPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_13Pow_Onnx_ver7EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_13Pow_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Gemm_Onnx_ver6EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Gemm_Onnx_ver7EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Gemm_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16MatMul_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14TopK_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14TopK_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15TopK_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZN6google8protobuf8internal20RepeatedPtrFieldBase3AddINS0_16RepeatedPtrFieldIN4onnx26TensorShapeProto_DimensionEE11TypeHandlerEEEPNT_4TypeESB_.constprop.491 _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_37ElementwiseMultiOpDocGenerator_opset8EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_37ElementwiseMultiOpDocGenerator_opset8EPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_34ElementwiseMultiOpDocGenerator_oldEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_34ElementwiseMultiOpDocGenerator_oldEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt6vectorISsSaISsEEC2ESt16initializer_listISsERKS0_.constprop.490 _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_32SoftmaxFamilyDocGenerator_opset1EPKcS5_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_34SoftmaxFamilyDocGenerator_opset_11EPKcS5_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ 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_ZTSZZN4onnx24MathDocGenerator_opset_7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx24MathDocGenerator_opset_7EPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx34SoftmaxFamilyDocGenerator_opset_11EPKcS1_EUlRNS_8OpSchemaEE_ _ZTSZN4onnx11GetOpSchemaINS_14Mod_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx37ElementwiseMultiOpDocGenerator_opset8EPKcEUlRNS_8OpSchemaEE_ _ZTSZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx32SoftmaxFamilyDocGenerator_opset1EPKcS1_EUlRNS_8OpSchemaEE_ _ZTSZN4onnx20MathDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_ _ZTSZN4onnx27MathDocGenerator_old_opset6EPKcEUlRNS_8OpSchemaEE_ _ZTSZN4onnx11GetOpSchemaINS_13Pow_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_14Gemm_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_14TopK_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx34ElementwiseMultiOpDocGenerator_oldEPKcEUlRNS_8OpSchemaEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_23LpPoolOpSchemaGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_23LpPoolOpSchemaGeneratorEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx23LpPoolOpSchemaGeneratorEPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_24RoiPoolOpSchemaGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_24RoiPoolOpSchemaGeneratorEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx24RoiPoolOpSchemaGeneratorEPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_21ConvOpSchemaGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_21ConvOpSchemaGeneratorEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx21ConvOpSchemaGeneratorEPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_30ConvTransposeOpSchemaGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_30ConvTransposeOpSchemaGeneratorEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx30ConvTransposeOpSchemaGeneratorEPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_30GlobalPoolingOpSchemaGeneratorEPKcS5_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_30GlobalPoolingOpSchemaGeneratorEPKcS5_ENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS3_ENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_32GlobalLpPoolingOpSchemaGeneratorEPKcS5_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_32GlobalLpPoolingOpSchemaGeneratorEPKcS5_ENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ 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_ZTIZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28BatchNormalization_Onnx_ver6EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Flatten_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Flatten_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28BatchNormalization_Onnx_ver7EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZN6google8protobuf8internal20RepeatedPtrFieldBase3AddINS0_16RepeatedPtrFieldIN4onnx26TensorShapeProto_DimensionEE11TypeHandlerEEEPNT_4TypeESB_.constprop.448 _ZNSt6vectorISsSaISsEEC2ESt16initializer_listISsERKS0_.constprop.446 _ZNSt6vectorISsSaISsEEC2ESt16initializer_listISsERKS0_.constprop.447 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Dropout_Onnx_ver12EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Flatten_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19MaxUnpool_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Dropout_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28BatchNormalization_Onnx_ver6EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Flatten_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Flatten_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28BatchNormalization_Onnx_ver7EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_ _ZTSZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biEUlRNS_8OpSchemaEE_ _ZTSZN4onnx11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZZN4onnx26LpPoolOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx26LpPoolOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx24ConvOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx24ConvOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTSZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19RoiAlign_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28NonMaxSuppression_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19RoiAlign_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28NonMaxSuppression_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.219 _ZTSZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28NonMaxSuppression_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28NonMaxSuppression_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25QuantizeLinear_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_27DequantizeLinear_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt6vectorISsSaISsEEC2ESt16initializer_listISsERKS0_.constprop.253 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_27DequantizeLinear_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25QuantizeLinear_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25QuantizeLinear_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_27DequantizeLinear_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_27DequantizeLinear_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25QuantizeLinear_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx18ReduceDocGeneratorEPKcbEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx18ReduceDocGeneratorEPKcbEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx18ReduceDocGeneratorEPKcbENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx18ReduceDocGeneratorEPKcbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_21ArgReduceDocGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_21ArgReduceDocGeneratorEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_18ReduceDocGeneratorEPKcbEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_18ReduceDocGeneratorEPKcbENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZZN4onnx18ReduceDocGeneratorEPKcbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx18ReduceDocGeneratorEPKcbEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx15RNNDocGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_15RNNDocGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx18RNNDocGeneratorOldEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx16RNNDocGenerator1EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_18RNNDocGeneratorOldEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_16RNNDocGenerator1EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_ _ZTSZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_ _ZNSt17_Function_handlerIFbiEZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_E9_M_invokeERKSt9_Any_datai _ZNKSt10_HashtableIPKSsS1_SaIS1_ENSt8__detail9_IdentityESt8equal_toIS1_ESt4hashIS1_ENS3_18_Mod_range_hashingENS3_20_Default_ranged_hashENS3_20_Prime_rehash_policyENS3_17_Hashtable_traitsILb0ELb1ELb1EEEE19_M_find_before_nodeEmRKS1_m.isra.362 _ZNSt17_Function_handlerIFbiEZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_E9_M_invokeERKSt9_Any_datai _ZGVZN4onnx16OpSchemaRegistry8InstanceEvE8instance _ZZN4onnx16OpSchemaRegistry8InstanceEvE8instance _ZGVZN4onnx16OpSchemaRegistry33GetMapWithoutEnsuringRegistrationEvE3map _ZZN4onnx16OpSchemaRegistry33GetMapWithoutEnsuringRegistrationEvE3map _ZNSt14_Function_base13_Base_managerIZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_E10_M_managerERSt9_Any_dataRKSA_St18_Manager_operation _ZTIZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_ _ZNSt14_Function_base13_Base_managerIZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_E10_M_managerERSt9_Any_dataRKSA_St18_Manager_operation _ZTIZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_ _ZGVZN4onnx16OpSchemaRegistry20DomainToVersionRange8InstanceEvE23domain_to_version_range _ZZN4onnx16OpSchemaRegistry20DomainToVersionRange8InstanceEvE23domain_to_version_range _GLOBAL__sub_I_schema.cc _ZTSZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_ _ZTSZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_24SequenceEmpty_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28SequenceConstruct_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25SequenceInsert_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_21SequenceAt_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_24SequenceErase_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25SequenceLength_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26SplitToSequence_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_29ConcatFromSequence_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_24SequenceEmpty_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_21SequenceAt_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_24SequenceErase_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_29ConcatFromSequence_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_26SplitToSequence_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28SequenceConstruct_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25SequenceInsert_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25SequenceLength_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19Upsample_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Resize_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Cast_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Cast_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Reshape_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Reshape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Shape_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Shape_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Size_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Concat_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Split_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Slice_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_20Transpose_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_20Transpose_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Scatter_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_20ScatterND_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26ScatterElements_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Gather_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25GatherElements_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Squeeze_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_20Unsqueeze_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_23SpaceToDepth_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_23SpaceToDepth_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_23DepthToSpace_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Tile_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Tile_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Upsample_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Resize_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver13EEENS_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_16IsNaN_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16IsNaN_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16IsInf_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Where_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18NonZero_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18NonZero_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26ReverseSequence_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _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_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Pad_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZN6google8protobuf8internal20RepeatedPtrFieldBase3AddINS0_16RepeatedPtrFieldIN4onnx26TensorShapeProto_DimensionEE11TypeHandlerEEEPNT_4TypeESB_.constprop.563 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15Size_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt18basic_stringstreamIcSt11char_traitsIcESaIcEEC1ESt13_Ios_Openmode.constprop.562 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16Shape_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18NonZero_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16IsNaN_Onnx_ver13EEENS0_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_15Cast_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver13EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_ENKUlPKNS_11TensorProtoEE_clES8_.isra.521 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Reshape_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Concat_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16Split_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16Slice_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_20Transpose_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Scatter_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_20ScatterND_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_26ScatterElements_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Gather_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25GatherElements_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Squeeze_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_20Unsqueeze_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_23SpaceToDepth_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_23DepthToSpace_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15Tile_Onnx_ver13EEENS0_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_ 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_ZTSZN4onnx11GetOpSchemaINS_20Transpose_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_23SpaceToDepth_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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_ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver1_10_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver11_11_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver11_11_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceMax_kOnnxDomain_ver11_11_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ 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_ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_61kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver11_12_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_61kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver11_12_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceProd_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver1_10_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver1_10_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_58kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_12_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_60kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_12_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_59kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver11_12_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver13_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ReduceSum_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_63kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_65kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_64kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver1_10_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_64kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_66kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver11_12_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_65kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver11_12_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_61kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_63kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_62kCpuExecutionProvider_ReduceSumSquare_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ArgMax_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ArgMax_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ArgMax_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ArgMax_kOnnxDomain_ver11_12_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ArgMax_kOnnxDomain_ver11_12_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_ArgMax_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ArgMax_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_53kCpuExecutionProvider_ArgMax_kOnnxDomain_ver13_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ArgMin_kOnnxDomain_ver1_10_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_ArgMin_kOnnxDomain_ver1_10_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_55kCpuExecutionProvider_ArgMin_kOnnxDomain_ver11_12_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_57kCpuExecutionProvider_ArgMin_kOnnxDomain_ver11_12_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_ArgMin_kOnnxDomain_ver13_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_ArgMin_kOnnxDomain_ver13_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime16ReduceKernelBaseILb1EEC1ERKNS_12OpKernelInfoEN6nonstd13optional_lite8optionalIlEEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIlNS_19ReduceAggregatorSumIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIdNS_19ReduceAggregatorSumIddEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime16ReduceKernelBaseILb0EEC1ERKNS_12OpKernelInfoEN6nonstd13optional_lite8optionalIlEEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime14SetupForReduceEPKNS_6TensorERKSt6vectorIlSaIlEERS5_RNS_11TensorShapeES8_RbPKS9_E19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIlNS_19ReduceAggregatorSumIllEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime9ReduceSumIlE4ImplERKNS_6TensorERKSt6vectorIlSaIlEESt10shared_ptrINS_10IAllocatorEEPNS_11concurrency10ThreadPoolEbPKNS_11TensorShapeEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIdNS_19ReduceAggregatorSumIddEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime9ReduceSumIdE4ImplERKNS_6TensorERKSt6vectorIlSaIlEESt10shared_ptrINS_10IAllocatorEEPNS_11concurrency10ThreadPoolEbPKNS_11TensorShapeEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIfNS_19ReduceAggregatorSumIffEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIfNS_19ReduceAggregatorSumIffEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime9ReduceSumIfE4ImplERKNS_6TensorERKSt6vectorIlSaIlEESt10shared_ptrINS_10IAllocatorEEPNS_11concurrency10ThreadPoolEbPKNS_11TensorShapeEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIiNS_19ReduceAggregatorSumIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIiNS_19ReduceAggregatorSumIiiEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime9ReduceSumIiE4ImplERKNS_6TensorERKSt6vectorIlSaIlEESt10shared_ptrINS_10IAllocatorEEPNS_11concurrency10ThreadPoolEbPKNS_11TensorShapeEE19__PRETTY_FUNCTION__ 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_ZZN11onnxruntime17NoTransposeReduceIlNS_20ReduceAggregatorProdIllEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIlNS_20ReduceAggregatorProdIllEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ _ZZN11onnxruntime17NoTransposeReduceIiNS_20ReduceAggregatorProdIiiEEEEvPNS_6TensorERKNS_11TensorShapeERKS3_RKSt6vectorIlSaIlEEPNS_11concurrency10ThreadPoolERNS_34ResultsNoTransposePrepareForReduceEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime12CommonReduceIiNS_20ReduceAggregatorProdIiiEEEEvPNS_15OpKernelContextESt6vectorIlSaIlEElRNS_34ResultsNoTransposePrepareForReduceEbE19__PRETTY_FUNCTION__ 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_ZZN11onnxruntime21BuildKernelCreateInfoINS_48kCpuExecutionProvider_MaxPool_kOnnxDomain_ver1_7EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_GlobalMaxPool_kOnnxDomain_ver1EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_GlobalLpPool_kOnnxDomain_ver2EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_48kCpuExecutionProvider_LpPool_kOnnxDomain_ver2_10EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_46kCpuExecutionProvider_LpPool_kOnnxDomain_ver11EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_AveragePool_kOnnxDomain_ver10_10EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_56kCpuExecutionProvider_GlobalAveragePool_kOnnxDomain_ver1EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_AveragePool_kOnnxDomain_ver11EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_AveragePool_kOnnxDomain_ver7_9EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZNKSt14default_deleteIN11onnxruntime9KernelDefEEclEPS1_.isra.214 _ZZNK11onnxruntime8PoolBase7ComputeEPNS_15OpKernelContextE17MLAS_POOLING_KINDE12__FUNCTION__ _ZZNK11onnxruntime9MaxPoolV811ComputeImplIfEENS_6common6StatusEPNS_15OpKernelContextEE12__FUNCTION__ _ZZNK11onnxruntime9MaxPoolV811ComputeImplIdEENS_6common6StatusEPNS_15OpKernelContextEE12__FUNCTION__ _ZZNK11onnxruntime9MaxPoolV811ComputeImplIaEENS_6common6StatusEPNS_15OpKernelContextEE12__FUNCTION__ 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_ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE10_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE10_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE11_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _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_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE17_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE17_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE18_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE18_ _ZN6google8protobuf8internal20RepeatedPtrFieldBase3AddINS0_16RepeatedPtrFieldIN4onnx26TensorShapeProto_DimensionEE11TypeHandlerEEEPNT_4TypeESB_.constprop.476 _ZNSt18basic_stringstreamIcSt11char_traitsIcESaIcEEC1ESt13_Ios_Openmode.constprop.478 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E10_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E7_E9_M_invokeERKSt9_Any_dataS2_ _GLOBAL__sub_I_contrib_defs.cc _ZGVZN11onnxruntime7contrib19RegisterBertSchemasEvE33op_schema_register_onceAttention0 _ZGVZN11onnxruntime7contrib19RegisterBertSchemasEvE34op_schema_register_onceQAttention1 _ZGVZN11onnxruntime7contrib19RegisterBertSchemasEvE43op_schema_register_onceLongformerAttention2 _ZGVZN11onnxruntime7contrib19RegisterBertSchemasEvE47op_schema_register_onceEmbedLayerNormalization3 _ZGVZN11onnxruntime7contrib19RegisterBertSchemasEvE32op_schema_register_onceFastGelu4 _ZGVZN11onnxruntime7contrib19RegisterBertSchemasEvE46op_schema_register_onceSkipLayerNormalization5 _ZZN11onnxruntime7contrib19RegisterBertSchemasEvE46op_schema_register_onceSkipLayerNormalization5 _ZZN11onnxruntime7contrib19RegisterBertSchemasEvE32op_schema_register_onceFastGelu4 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib19RegisterBertSchemasEvEUlS2_E1_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib19RegisterBertSchemasEvE47op_schema_register_onceEmbedLayerNormalization3 _ZZN11onnxruntime7contrib19RegisterBertSchemasEvE43op_schema_register_onceLongformerAttention2 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib19RegisterBertSchemasEvEUlS2_E0_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib19RegisterBertSchemasEvE34op_schema_register_onceQAttention1 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib19RegisterBertSchemasEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib19RegisterBertSchemasEvE33op_schema_register_onceAttention0 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceAffine6 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceParametricSoftplus7 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceImageScaler8 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE28op_schema_register_onceCrop9 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceThresholdedRelu10 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE37op_schema_register_onceDynamicSlice11 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceGivenTensorFill12 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceScale13 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE32op_schema_register_onceGRUUnit14 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceGivenTensorFill15 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceScale16 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE32op_schema_register_onceGRUUnit17 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE50op_schema_register_onceMeanVarianceNormalization18 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceScaledTanh19 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE31op_schema_register_onceAffine20 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE43op_schema_register_onceParametricSoftplus21 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceImageScaler22 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE29op_schema_register_onceCrop23 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE37op_schema_register_onceDynamicSlice24 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceScaledTanh25 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceSampleOp26 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceMaxpoolWithMask27 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE29op_schema_register_onceRfft28 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceIrfft29 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceComplexMul30 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE39op_schema_register_onceComplexMulConj31 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE53op_schema_register_onceConvTransposeWithDynamicPads32 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE34op_schema_register_onceFusedConv33 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE34op_schema_register_onceFusedGemm34 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceExpandDims35 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceAttnLSTM36 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceRange37 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE34op_schema_register_onceTokenizer38 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceMatMulInteger1639 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceTransposeMatMul40 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceFusedMatMul41 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceMurmurHash342 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceGatherND43 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceWordConvEmbedding44 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE28op_schema_register_oncePad45 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE31op_schema_register_onceUnique46 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceCDist47 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE38op_schema_register_onceCropAndResize48 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE43op_schema_register_onceLayerNormalization49 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE53op_schema_register_onceSimplifiedLayerNormalization50 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE29op_schema_register_onceGelu51 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceBiasGelu52 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE32op_schema_register_onceInverse53 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceTrilu54 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceBiasSoftmax55 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceBiasSoftmax55 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E18_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceTrilu54 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE29op_schema_register_onceGelu51 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceBiasGelu52 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E17_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE32op_schema_register_onceInverse53 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E15_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE43op_schema_register_onceLayerNormalization49 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E14_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE38op_schema_register_onceCropAndResize48 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceCDist47 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E13_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE31op_schema_register_onceUnique46 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E12_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE28op_schema_register_oncePad45 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceWordConvEmbedding44 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E11_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceGatherND43 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceMurmurHash342 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E9_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceFusedMatMul41 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E8_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceTransposeMatMul40 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceMatMulInteger1639 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E6_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE34op_schema_register_onceTokenizer38 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceRange37 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceAttnLSTM36 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E5_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceExpandDims35 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E4_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE34op_schema_register_onceFusedGemm34 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_ZZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceSampleOp26 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceScaledTanh25 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE37op_schema_register_onceDynamicSlice24 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E1_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE29op_schema_register_onceCrop23 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceImageScaler22 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE43op_schema_register_onceParametricSoftplus21 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE31op_schema_register_onceAffine20 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceScaledTanh19 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE50op_schema_register_onceMeanVarianceNormalization18 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE32op_schema_register_onceGRUUnit17 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceScale16 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E0_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceGivenTensorFill15 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE32op_schema_register_onceGRUUnit14 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceScale13 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceGivenTensorFill12 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE37op_schema_register_onceDynamicSlice11 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceThresholdedRelu10 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE28op_schema_register_onceCrop9 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceImageScaler8 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceParametricSoftplus7 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceAffine6 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E16_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE53op_schema_register_onceSimplifiedLayerNormalization50 _ZN4onnx34propagateElemTypeFromInputToOutputERNS_16InferenceContextEmm.constprop.477 _ZTSZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE_ _ZTSZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE0_ _ZTSZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE1_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE0_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_ 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_ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_23LpPoolOpSchemaGeneratorEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx23LpPoolOpSchemaGeneratorEPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_24RoiPoolOpSchemaGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_24RoiPoolOpSchemaGeneratorEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx24RoiPoolOpSchemaGeneratorEPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_21ConvOpSchemaGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ 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_ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_30GlobalPoolingOpSchemaGeneratorEPKcS5_ENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS3_ENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_32GlobalLpPoolingOpSchemaGeneratorEPKcS5_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_32GlobalLpPoolingOpSchemaGeneratorEPKcS5_ENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS3_ENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt14_Function_base13_Base_managerIZN4onnx21PoolOpSchemaGeneratorEPKcS3_S3_bbEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx21PoolOpSchemaGeneratorEPKcS3_S3_bbENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_20MaxUnpool_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_20MaxUnpool_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx23LpPoolOpSchemaGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation 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_ZTSZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25TfIdfVectorizer_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_27StringNormalizer_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_24PoolOpSchemaGenerator_10EPKcS5_S5_biEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_24PoolOpSchemaGenerator_10EPKcS5_S5_biENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx24PoolOpSchemaGenerator_10EPKcS3_S3_biENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _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_26LpPoolOpSchemaGenerator_10EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_26LpPoolOpSchemaGenerator_10EPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx26LpPoolOpSchemaGenerator_10EPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_24ConvOpSchemaGenerator_10EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_24ConvOpSchemaGenerator_10EPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx24ConvOpSchemaGenerator_10EPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_33ConvTransposeOpSchemaGenerator_10EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Dropout_Onnx_ver12EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver12EEENS_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_managerIZN4onnx23PoolOpSchemaGenerator_9EPKcS3_S3_EUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_ _ZTIZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx24PoolOpSchemaGenerator_10EPKcS3_S3_biEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biEUlRNS_8OpSchemaEE_ _ZTIZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19MaxUnpool_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx26LpPoolOpSchemaGenerator_10EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx26LpPoolOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTIZZN4onnx26LpPoolOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx24ConvOpSchemaGenerator_10EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx24ConvOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTIZZN4onnx24ConvOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTIZZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Dropout_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28BatchNormalization_Onnx_ver6EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Flatten_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Flatten_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28BatchNormalization_Onnx_ver7EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZN6google8protobuf8internal20RepeatedPtrFieldBase3AddINS0_16RepeatedPtrFieldIN4onnx26TensorShapeProto_DimensionEE11TypeHandlerEEEPNT_4TypeESB_.constprop.448 _ZNSt6vectorISsSaISsEEC2ESt16initializer_listISsERKS0_.constprop.446 _ZNSt6vectorISsSaISsEEC2ESt16initializer_listISsERKS0_.constprop.447 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Dropout_Onnx_ver12EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Flatten_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19MaxUnpool_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Dropout_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28BatchNormalization_Onnx_ver6EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Flatten_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Flatten_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28BatchNormalization_Onnx_ver7EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_ _ZTSZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_biEUlRNS_8OpSchemaEE_ _ZTSZN4onnx11GetOpSchemaINS_19MaxUnpool_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZZN4onnx26LpPoolOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx26LpPoolOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx24ConvOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx24ConvOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcEUlRNS_8OpSchemaEE_ _ZTSZN4onnx11GetOpSchemaINS_18Dropout_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Flatten_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19RoiAlign_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28NonMaxSuppression_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19RoiAlign_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28NonMaxSuppression_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.219 _ZTSZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28NonMaxSuppression_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28NonMaxSuppression_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25QuantizeLinear_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_27DequantizeLinear_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt6vectorISsSaISsEEC2ESt16initializer_listISsERKS0_.constprop.253 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_27DequantizeLinear_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25QuantizeLinear_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25QuantizeLinear_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_27DequantizeLinear_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_27DequantizeLinear_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25QuantizeLinear_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx18ReduceDocGeneratorEPKcbbEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx18ReduceDocGeneratorEPKcbbEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx18ReduceDocGeneratorEPKcbbENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx18ReduceDocGeneratorEPKcbbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_21ArgReduceDocGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_21ArgReduceDocGeneratorEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_18ReduceDocGeneratorEPKcbbEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_18ReduceDocGeneratorEPKcbbENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZZN4onnx18ReduceDocGeneratorEPKcbbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx18ReduceDocGeneratorEPKcbbEUlRNS_8OpSchemaEE_ _ZTSZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx15RNNDocGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_15RNNDocGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx15RNNDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx18RNNDocGeneratorOldEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx18RNNDocGeneratorOldEPKcEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx16RNNDocGenerator1EPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx16RNNDocGenerator1EPKcEUlRNS_8OpSchemaEE_ 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_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_ 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_ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26SplitToSequence_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_29ConcatFromSequence_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_24SequenceEmpty_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_21SequenceAt_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_24SequenceErase_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_29ConcatFromSequence_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_26SplitToSequence_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28SequenceConstruct_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25SequenceInsert_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25SequenceLength_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ 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_ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_20Transpose_Onnx_ver13EEENS0_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_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_26ScatterElements_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Gather_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25GatherElements_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ 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_ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17OneHot_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15Where_Onnx_ver9EEENS0_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_17Unique_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19GatherND_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Pad_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ 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