vendor/libonnxruntime.so in onnxruntime-0.4.0 vs vendor/libonnxruntime.so in onnxruntime-0.5.0
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+ new
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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 _ZNSs6assignERKSs _ZSt20__throw_length_errorPKc _ZNKSs7compareEPKc 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 _ZNSt13runtime_errorC1ERKSs _ZNSt13runtime_errorD1Ev _ZTISt13runtime_error _ZNSs9_M_mutateEmmm _ZSt4cout _ZNSt11logic_errorD2Ev _ZNSs6insertEmPKc _ZNSs6appendEPKc _ZNSt16invalid_argumentC1ERKSs _ZNSt16invalid_argumentD1Ev _ZTISt16invalid_argument _ZNSt11logic_errorC2ERKSs _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 _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 _ZSt4endlIcSt11char_traitsIcEERSt13basic_ostreamIT_T0_ES6_ _ZSt4cerr _ZSt15__once_callable __tls_get_addr _ZSt11__once_call __once_proxy _ZTVN10__cxxabiv120__function_type_infoE _ZTVN10__cxxabiv119__pointer_type_infoE pthread_once _ZNSt11logic_errorC1ERKSs _ZNSt11logic_errorD1Ev _ZNSs7reserveEm __cxa_bad_typeid _ZNSs6insertEmPKcm localtime_r strftime _ZNSo9_M_insertIlEERSoT_ ceilf ceil _ZTTSt14basic_ofstreamIcSt11char_traitsIcEE _ZTVSt14basic_ofstreamIcSt11char_traitsIcEE _ZNSt13basic_filebufIcSt11char_traitsIcEEC1Ev _ZNSt13basic_filebufIcSt11char_traitsIcEED1Ev _ZTVSt13runtime_error _ZNSs9push_backEc _ZSt28_Rb_tree_rebalance_for_erasePSt18_Rb_tree_node_baseRS_ __errno_location strtod strtoull strtoll localeconv _ZTISt12out_of_range _ZNSsC1EPKcmRKSaIcE sqrt sqrtf _ZNSs6assignEPKc _ZdaPv tolower _Znam 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 _ZNSbIwSt11char_traitsIwESaIwEE12_M_leak_hardEv _ZSt9use_facetISt5ctypeIwEERKT_RKSt6locale _ZNSsD1Ev _ZNSbIwSt11char_traitsIwESaIwEEC1EPKwRKS1_ _ZNSbIwSt11char_traitsIwESaIwEED1Ev _ZNKSt9exception4whatEv _ZNSo3putEc _ZNSo5flushEv _ZNSo9_M_insertIdEERSoT_ _ZNKSt5ctypeIcE13_M_widen_initEv _ZSt16__throw_bad_castv _ZNSo9_M_insertIxEERSoT_ strtol strtoul _ZNSolsEs _ZNSo9_M_insertIbEERSoT_ fesetround nearbyintf __udivti3 _ZNKSs7compareEmmPKc tanh strcmp _ZNSo9_M_insertIPKvEERSoT_ _ZNKSs4findEPKcm _ZTIf _ZTId _ZTIh _ZTIa _ZTIj _ZTIt _ZTIm _ZTIs _ZTIl _ZTIi _ZTIb _ZNSs6appendEmc _ZNSsC1EmcRKSaIcE posix_memalign _ZSt18_Rb_tree_decrementPKSt18_Rb_tree_node_base gmtime_r mktime difftime syscall _ZNSt9basic_iosIcSt11char_traitsIcEE5imbueERKSt6locale _ZNSt6localeC1ERKS_ _ZSt9use_facetISt8numpunctIcEERKT_RKSt6locale _ZNSs7replaceEmmPKcm _ZNSt13basic_filebufIcSt11char_traitsIcEE5closeEv _ZNSt13basic_filebufIcSt11char_traitsIcEE4openEPKcSt13_Ios_Openmode _ZTVSt13basic_filebufIcSt11char_traitsIcEE _ZNSt12__basic_fileIcED1Ev pthread_self strdup dirname getenv getpid __xstat nanosleep pthread_join dlerror dlsym dlclose strerror_r realpath _ZNKSs13find_first_ofEPKcmm mkdir __fxstat dlopen remove munmap pthread_attr_init pthread_create pthread_setaffinity_np pthread_attr_setstacksize nftw mmap sysconf lseek read gettimeofday ldexpf _ZTTSt14basic_ifstreamIcSt11char_traitsIcEE _ZTVSt14basic_ifstreamIcSt11char_traitsIcEE _ZTTSt13basic_fstreamIcSt11char_traitsIcEE _ZTVSt13basic_fstreamIcSt11char_traitsIcEE _ZNSt13basic_fstreamIcSt11char_traitsIcEED1Ev isspace _ZNSt15basic_stringbufIcSt11char_traitsIcESaIcEE7_M_syncEPcmm _ZSt7getlineIcSt11char_traitsIcESaIcEERSt13basic_istreamIT_T0_ES7_RSbIS4_S5_T1_ES4_ _ZNSs6resizeEmc _ZNSi4readEPcl _ZNSo5writeEPKcl strerror _ZTIc stderr fprintf fflush _ZNSs14_M_replace_auxEmmmc strpbrk _ZNSs6appendERKSsmm sprintf strchr _ZNKSs17find_first_not_ofEPKcmm fwrite pthread_rwlock_init pthread_rwlock_unlock pthread_rwlock_wrlock memchr pthread_rwlock_destroy pthread_rwlock_rdlock memrchr isalpha sched_yield pthread_getspecific pthread_setspecific libdl.so.2 librt.so.1 libstdc++.so.6 get_nprocs usleep _ZNSt8__detail12__prime_listE _ZTISt14error_category _ZNKSt14error_category23default_error_conditionEi _ZNKSt14error_category10equivalentEiRKSt15error_condition _ZNKSt14error_category10equivalentERKSt10error_codei _ZTVSt14error_category libm.so.6 libgomp.so.1 libgcc_s.so.1 libpthread.so.0 libc.so.6 ld-linux-x86-64.so.2 libonnxruntime.so.1.4.0 $ORIGIN VERS_1.4.0 GLIBC_2.3 GLIBC_2.2.5 GCC_3.0 GOMP_1.0 GLIBC_2.3.3 GLIBC_2.3.4 GLIBCXX_3.4.10 GLIBCXX_3.4.9 GLIBCXX_3.4.11 CXXABI_1.3 GLIBCXX_3.4
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-q H5tۡ HfH=cۡ HH=ˡ Hp H5Eۡ HfH=4ۡ HpH=ˡ Hp H5ۡ HrfH=ۡ H@H=ˡ Hzp H5ڡ HBfH=ڡ HH=iˡ HJp H5ڡ HfH=ڡ HH=9ˡ Hp H5ڡ HfH=ڡ HH= ˡ Ho H5ڡ HfH=rڡ HH=ʡ Ho H5Tڡ HfH=Cڡ HPH=ʡ Ho H5%ڡ HRfH=ڡ H H=yʡ HZo H5١ H"fH=١ HH=Iʡ H*o H5١ HfH=١ HH=ʡ Hn H5١ HfH=١ HH=ɡ Hn H5i١ HfH=Y١ H`H=ɡ Hn H5;١ HbfH=*١ H0H=ɡ Hjn H5 ١ H2fH=ء H H=Yɡ H:n H5ء HfH=ء HH=)ɡ H
-n H5ء HfH=ء HH=ȡ Hm H5ء HfH=nء HpH=ȡ Hm H5Pء HrfH=?ء H@H=ȡ Hzm H5!ء HBfH=ء HH=iȡ HJm H5ס HfH=ס HH=9ȡ Hm H5ס HfH=ס HH= ȡ Hl H5ס HfH=ס HH=ǡ Hl H5eס HfH=Tס HPH=ǡ Hl H56ס HRfH=%ס H H=yǡ HZl H5ס H"fH=֡ HH=Iǡ H*l H5֡ HfH=֡ HH=ǡ Hk H5֡ HfH=֡ HH=ơ Hk H5z֡ HfH=i֡ H`H=ơ Hk H5K֡ HbfH=:֡ H0H=ơ Hjk H5 ֡ H2fH= ֡ H H=Yơ H:k H5ա HfH=ա HH=)ơ H
-k H5ա HҿfH=ա HH=š Hj H5ա H颿fH=~ա HpH=š Hj H5`ա HrfH=ա H@H=š Hzj H5cա HBfH=yա HH=iš HJj H5[ա HfH=Jա HH=9š Hj H5,ա HfH=ա HH= š Hi H5ԡ H鲾fH=ԡ HH=ġ Hi H5ԡ H邾fH=ԡ HPH=ġ Hi H5ԡ HRfH=ԡ H H=yġ HZi H5pԡ H"fH=_ԡ HH=Iġ H*i H5Aԡ HfH=0ԡ HH=ġ Hh H5ԡ H½fH=ԡ HH=á Hh H5ӡ H钽fH=ӡ H`H=á Hh H5ӡ HbfH=ӡ H0H=á Hjh H5ӡ H2fH=tӡ H H=Yá H:h H5Vӡ HfH=Eӡ HH=)á H
-h H5'ӡ HҼfH=ӡ HH=¡ Hg H5ҡ H颼fH=ҡ HpH=¡ Hg H5ҡ HrfH=ҡ H@H=¡ Hzg H5ҡ HBfH=ҡ HH=i¡ HJg H5kҡ HfH=Zҡ HH=9¡ Hg H5<ҡ HfUH=Hҡ SHH=¡ Hf H5)ҡ H\$读H5j H= ҡ H)H- Hf H5ѡ H|H5j H=ѡ H6H=O Hf H5ѡ LH5j H=ѡ HƼHWf H5ѡ H H[]f H=zѡ HH=9 Hf H5\ѡ HfH=Kѡ H谿H= He H5-ѡ H鲺fH= ѡ H耿H= He H5С H邺fH=С HPH= He H5С HRfH=С H H=y HZe H5С H"fH=С HH=I H*e H5qС HfH=С HH= Hd H5С H¹fAWH=С AVAUATUSH( 胾H=ܿ Hd H5С Hl$pH\$oLd$`L|$lLt$@pH5&^j HHH}H5m HD$xD$yѺH} H5]j HƄ$ Ƅ$ 论H}0H5]j HƄ$ Ƅ$ 苺H}@H5hh HƄ$ Ƅ$ hH}PH5]j HƄ$ Ƅ$ EH}`H5]j HƄ$ Ƅ$ "H}pH5p]j HƄ$ Ƅ$ H H5m HƄ$ Ƅ$ ٹH H50]j HƄ$ Ƅ$ 賹H H5]j HƄ$ Ƅ$ 荹HL$mHD$nH$ H=Ρ L|$Lt$HL$0HL$HL$PMIHHL$(H $1Ƅ$ Ƅ$ HD$8( L f D IE HIHx輴M9uH=' Hb H5rΡ ]H5\j HH۸H}H5)\j HD$x
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-͡ H蠺H= H` H5̡ H颵fH=̡ HpH=ɻ H` H5̡ HrfSH=̡ Hp?H= Hy` H5̡ ErA H$wsA HD$m|A HD$cwA HD$YpA HD$ OuA HD$(E{A HD$0;tA HD$81zA HD$@'qA HD$H xA HD$PyA HD$X vA h HD$`H̡ H̡ Hˡ YHXhHh HHˡ H ˡ WH=Ъ( H_ H5ˡ H ˡ VHp[H=ˡ HHt輴H$ @ H=ˡ H H=Y H:_ H5tˡ HfH=cˡ HиH=) H
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-Y H5eš HҭfH=Tš H蠲H= HX H56š H颭fH=%š HpH=ɳ HX H5š HrfH=ġ H@H= HzX H5ġ HBfH=ġ HH=i HJX H5ġ HfH=ġ HH=9 HX H5zġ HfH=iġ H谱H= HW H5Kġ H鲬fH=:ġ H耱H=ٲ HW H5 ġ H邬fH= ġ HPH= HW H5á HRfH=á H H=y HZW H5á H"fH=á HH=I H*W H5á HfH=~á HH= HV H5`á H«fH=Oá H萰H=鱡 HV H51á H钫fH= á H`H= HV H5á HbfH=¡ H0H= HjV H5¡ H2fH=¡ H H=Y H:V H5¡ HfH=¡ HЯH=) H
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-S H5 HҧfH=t H蠬H= HR H5V H颧fH=E HpH=ɭ HR H5' HrfH= H@H= HzR H5 HBfH=羡 HH=i HJR H5ɾ HfH= HH=9 HR H5 HfH= H谫H= HQ H5k H鲦fH=Z H耫H=٬ HQ H5< H邦fH=+ HPH= HQ H5
HRfH= H H=y HZQ H5 H"fH=ͽ HH=I H*Q H5 HfH= HH= HP H5 H¥fH=o H萪H=髡 HP H5Q H钥fH=@ H`H= HP H5" HbfH= H0H= HjP H5 H2fH=⼡ H H=Y H:P H5ļ HfH= HЩH=) H
-P H5 HҤfH= H蠩H= HO H5f H颤fH=U HpH=ɪ HO H57 HrfH=& H@H= HzO H5 HBfH= HH=i HJO H5ٻ HfH=Aš HH=9 HO H5#š HfH=š H谨H= HN H5ġ H鲣fH=ġ H耨H=٩ HN H5ġ H那fH=ġ HPH= HN H5ġ HRfH=ġ H H=y HZN H5gġ H"fH=Vġ HH=I H*N H58ġ HfH='ġ HH= HM H5 ġ H¢fH=á H萧H=騡 HM H5á H钢fH=á H`H= HM H5á HbfH=á H0H= HjM H5|á H2fH=ká H H=Y H:M H5Má HfH=<á HЦH=) H
-M H5 á HҡfH=
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+Ht$>HHxH9t0H=R' HWt PJHЅ
+Ht$H*H[]ÐUSHHH H H H H H{hHtH{PHtHs0H{ ! HCH-% HxH9t0H=& HWt PJHЅ
+Ht$MHCHxH9t0H=`& HWt PJHЅ
+Ht$HHxH9t0H=$& HWt PJHЅ
+Ht$ԾHH[]ÐUSHHH H H H H H{hHt访H{PHt蠿Hs0H{ HCH-# HxH9t0H=o% HWt PJHЅ
+Ht$ HCHxH9t0H=2% HWt PJHЅ
+Ht$HHxH9t0H=$ HWt PJHЅ
+Ht$覽HξH[]ÐSHHHtIHGHxH;=
+# t0H=$ HWt PJHЅ
+Ht$HHpH[USHHH H H E H H 2 H{hHt$H{PHtHs0H{ i HCH-^" HxH9t0H=# HWt PJHЅ
+Ht$蕼HCHxH9t0H=# HWt PJHЅ
+Ht$XHHxH9t0H=l# HWt PJHЅ
+Ht$ HDH[]ÐHSHtmH H[%[ÐSHHHtIHGHxH;=f! t0H=" HWt PJHЅ
+Ht$褻H̼H[HSHtHmH H[鯼[ÐSHHHtIHGHxH;= t0H=~" HWt PJHЅ
+Ht$.HVH[USHHH H H + H H H{hHt
+H{PHtHs0H{ O HCH-D HxH9t0H=! HWt PJHЅ
+Ht${HCHxH9t0H=! HWt PJHЅ
+Ht$>HHxH9t0H=R! HWt PJHЅ
+Ht$H*H[]ÐUSHHH H H H H H{hHtܺH{PHtκHs0H{ ! HCH- HxH9t0H= HWt PJHЅ
+Ht$MHCHxH9t0H=` HWt PJHЅ
+Ht$HHxH9t0H=$ HWt PJHЅ
+Ht$ԸHH[]ÐSHHHtIHGHxH;=8 t0H= HWt PJHЅ
+Ht$vH螹H[USHHH H H s H H ` H{hHtRH{PHtDHs0H{ HCH- HxH9t0H= HWt PJHЅ
+Ht$÷HCHxH9t0H= HWt PJHЅ
+Ht$膷HHxH9t0H= HWt PJHЅ
+Ht$JHrH[]ÐUSHHH H H E H H 2 H{hHt$H{PHtHs0H{ i HCH-^ HxH9t0H= HWt PJHЅ
+Ht$蕶HCHxH9t0H= HWt PJHЅ
+Ht$XHHxH9t0H=l HWt PJHЅ
+Ht$ HDH[]ÐSHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$辵HH[USHHH H H H H H{hHt蚶H{PHt茶Hs0H{ HCH- HxH9t0H=[ HWt PJHЅ
+Ht$ HCHxH9t0H= HWt PJHЅ
+Ht$δHHxH9t0H= HWt PJHЅ
+Ht$蒴H躵H[]ÐUSHHH H H H H z H{hHtlH{PHt^Hs0H{ HCH- HxH9t0H=- HWt PJHЅ
+Ht$ݳHCHxH9t0H= HWt PJHЅ
+Ht$蠳HHxH9t0H= HWt PJHЅ
+Ht$dH茴H[]ÐUSHHH H H _ H H L H{hHt>H{PHt0Hs0H{ HCH-x HxH9t0H= HWt PJHЅ
+Ht$诲HCHxH9t0H= HWt PJHЅ
+Ht$rHHxH9t0H= HWt PJHЅ
+Ht$6H^H[]ÐSHHHtIHGHxH;= t0H=( HWt PJHЅ
+Ht$رH H[USHHH H H H H H{hHt贲H{PHt覲Hs0H{ HCH- HxH9t0H=u HWt PJHЅ
+Ht$%HCHxH9t0H=8 HWt PJHЅ
+Ht$HHxH9t0H= HWt PJHЅ
+Ht$謰HԱH[]ÐUSHHH H H H H H{hHt膱H{PHtxHs0H{ HCH- HxH9t0H=G HWt PJHЅ
+Ht$HCHxH9t0H=
+ HWt PJHЅ
+Ht$躯HHxH9t0H= HWt PJHЅ
+Ht$~H覰H[]ÐUSHHH H H y H H f H{hHtXH{PHtJHs0H{ HCH- HxH9t0H= HWt PJHЅ
+Ht$ɮHCHxH9t0H= HWt PJHЅ
+Ht$茮HHxH9t0H= HWt PJHЅ
+Ht$PHxH[]ÐUSHHH H H K H H 8 H{hHt*H{PHt Hs0H{ o HCH-d HxH9t0H= HWt PJHЅ
+Ht$蛭HCHxH9t0H= HWt PJHЅ
+Ht$^HHxH9t0H=r HWt PJHЅ
+Ht$"HJH[]ÐUSHHH H H H H
+ H{hHtH{PHtHs0H{ A HCH-6 HxH9t0H= HWt PJHЅ
+Ht$mHCHxH9t0H= HWt PJHЅ
+Ht$0HHxH9t0H=D HWt PJHЅ
+Ht$H H[]ÐUSHHH H H H H H{hHtάH{PHtHs0H{ HCH- HxH9t0H= HWt PJHЅ
+Ht$?HCHxH9t0H=R HWt PJHЅ
+Ht$HHxH9t0H= HWt PJHЅ
+Ht$ƪHH[]ÐUSHHH H H H H H{hHt蠫H{PHt蒫Hs0H{ HCH- HxH9t0H=a HWt PJHЅ
+Ht$HCHxH9t0H=$ HWt PJHЅ
+Ht$ԩHHxH9t0H= HWt PJHЅ
+Ht$蘩HH[]ÐUSHHH H H H H H{hHtrH{PHtdHs0H{ HCH- HxH9t0H=3 HWt PJHЅ
+Ht$HCHxH9t0H= HWt PJHЅ
+Ht$覨HHxH9t0H= HWt PJHЅ
+Ht$jH蒩H[]ÐSHHHtIHGHxH;=
t0H=\ HWt PJHЅ
+Ht$ H4H[USHHH H H H H H{hHtH{PHtڨHs0H{ - HCH-"
HxH9t0H= HWt PJHЅ
+Ht$YHCHxH9t0H=l HWt PJHЅ
+Ht$ HHxH9t0H=0 HWt PJHЅ
+Ht$HH[]ÐSHHHtIHGHxH;=D t0H=
HWt PJHЅ
+Ht$肦H誧H[USHHH H H H H l H{hHt^H{PHtPHs0H{ HCH- HxH9t0H=
HWt PJHЅ
+Ht$ϥHCHxH9t0H= HWt PJHЅ
+Ht$蒥HHxH9t0H= HWt PJHЅ
+Ht$VH~H[]ÐSHHHtIHGHxH;=
+ t0H=H HWt PJHЅ
+Ht$H H[USHHH H H H H H{hHtԥH{PHtƥHs0H{ HCH-
+ HxH9t0H= HWt PJHЅ
+Ht$EHCHxH9t0H=X HWt PJHЅ
+Ht$HHxH9t0H= HWt PJHЅ
+Ht$̣HH[]ÐUSHHH H H H H H{hHt覤H{PHt蘤Hs0H{ HCH- HxH9t0H=g
+ HWt PJHЅ
+Ht$HCHxH9t0H=*
+ HWt PJHЅ
+Ht$ڢHHxH9t0H= HWt PJHЅ
+Ht$螢HƣH[]ÐSHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$@HhH[USHHH H H = H H * H{hHt H{PHtHs0H{ a HCH-V HxH9t0H= HWt PJHЅ
+Ht$荡HCHxH9t0H= HWt PJHЅ
+Ht$PHHxH9t0H=d HWt PJHЅ
+Ht$H<H[]ÐSHHHtIHGHxH;=x t0H= HWt PJHЅ
+Ht$趠HޡH[USHHH H H H H H{hHt蒡H{PHt脡Hs0H{ HCH- HxH9t0H=S HWt PJHЅ
+Ht$HCHxH9t0H= HWt PJHЅ
+Ht$ƟHHxH9t0H= HWt PJHЅ
+Ht$芟H負H[]ÐSHHHtIHGHxH;= t0H=| HWt PJHЅ
+Ht$,HTH[Q1Ʌy߱1HyHްII t te HHH u8t wމ2
+w҉2XUSHHH H H H H H{hHt貟H{PHt褟Hs0H{ HCH- HxH9t0H=s HWt PJHЅ
+Ht$#HCHxH9t0H=6 HWt PJHЅ
+Ht$HHxH9t0H= HWt PJHЅ
+Ht$誝HҞH[]ÐUSHHH H H H H H{hHt脞H{PHtvHs0H{ HCH- HxH9t0H=E HWt PJHЅ
+Ht$HCHxH9t0H= HWt PJHЅ
+Ht$踜HHxH9t0H= HWt PJHЅ
+Ht$|H褝H[]H56s P谖ZSHHHtIHGHxH;= t0H=X HWt PJHЅ
+Ht$H0H[USHHH H H H H H{hHtH{PHt֜Hs0H{ Hl$$ HCHHxdHCHHxTHHHxEH荜H[]SHHHtIHGHxH;= t0H=X HWt PJHЅ
+Ht$H0H[USHHH H H H H H{hHtH{PHt֛Hs0H{ ) HCH- HxH9t0H= HWt PJHЅ
+Ht$UHCHxH9t0H=h HWt PJHЅ
+Ht$HHxH9t0H=, HWt PJHЅ
+Ht$ܙHH[]ÐSHHHtIHGHxH;=@ t0H= HWt PJHЅ
+Ht$~H覚H[USHHH H H { H H h H{hHtZH{PHtLHs0H{ HCH- HxH9t0H= HWt PJHЅ
+Ht$˘HCHxH9t0H= HWt PJHЅ
+Ht$莘HHxH9t0H= HWt PJHЅ
+Ht$RHzH[]ÐUSHHH H H M H H : H{hHt,H{PHt Hs0H{ q HCH-f HxH9t0H= HWt PJHЅ
+Ht$蝗HCHxH9t0H= HWt PJHЅ
+Ht$`HHxH9t0H=t HWt PJHЅ
+Ht$$HLH[]ÐUSHHH H H H H H{hHtH{PHtHs0H{ C HCH-8 HxH9t0H= HWt PJHЅ
+Ht$oHCHxH9t0H= HWt PJHЅ
+Ht$2HHxH9t0H=F HWt PJHЅ
+Ht$H H[]ÐUSHHH H H H H H{hHtЖH{PHtHs0H{ HCH-
+ HxH9t0H= HWt PJHЅ
+Ht$AHCHxH9t0H=T HWt PJHЅ
+Ht$HHxH9t0H= HWt PJHЅ
+Ht$ȔHH[]ÐUSHHH H H H H H{hHt袕H{PHt蔕Hs0H{ HCH- HxH9t0H=c HWt PJHЅ
+Ht$HCHxH9t0H=& HWt PJHЅ
+Ht$֓HHxH9t0H= HWt PJHЅ
+Ht$蚓HH[]ÐUSHHH H H H H H{hHttH{PHtfHs0H{ 蹺 HCH- HxH9t0H=5 HWt PJHЅ
+Ht$HCHxH9t0H= HWt PJHЅ
+Ht$訒HHxH9t0H= HWt PJHЅ
+Ht$lH蔓H[]ÐUSHHH H H g H H T H{hHtFH{PHt8Hs0H{ 苹 HCH- HxH9t0H= HWt PJHЅ
+Ht$跑HCHxH9t0H= HWt PJHЅ
+Ht$zHHxH9t0H= HWt PJHЅ
+Ht$>HfH[]ÐUSHHH H H 9 H H & H{hHtH{PHt
+Hs0H{ ] HCH-R HxH9t0H= HWt PJHЅ
+Ht$艐HCHxH9t0H= HWt PJHЅ
+Ht$LHHxH9t0H=` HWt PJHЅ
+Ht$H8H[]ÐUSHHH H H H H H{hHtH{PHtܐHs0H{ / HCH-$ HxH9t0H= HWt PJHЅ
+Ht$[HCHxH9t0H=n HWt PJHЅ
+Ht$ HHxH9t0H=2 HWt PJHЅ
+Ht$H
+H[]ÐUSHHH H H ݻ H H ʻ H{hHt輏H{PHt讏Hs0H{ HCH- HxH9t0H=} HWt PJHЅ
+Ht$-HCHxH9t0H=@ HWt PJHЅ
+Ht$HHxH9t0H= HWt PJHЅ
+Ht$贍HH[]ÐUSHHH H H 诺 H H 蜺 H{hHt莎H{PHt耎Hs0H{ Ӵ HCH- HxH9t0H=O HWt PJHЅ
+Ht$HCHxH9t0H= HWt PJHЅ
+Ht$HHxH9t0H= HWt PJHЅ
+Ht$膌H讍H[]ÐUSHHH H H 聹 H H n H{hHt`H{PHtRHs0H{ 襳 HCH- HxH9t0H=! HWt PJHЅ
+Ht$ыHCHxH9t0H= HWt PJHЅ
+Ht$蔋HHxH9t0H= HWt PJHЅ
+Ht$XH而H[]ÐUSHHH H H S H H @ H{hHt2H{PHt$Hs0H{ w HCH-l HxH9t0H= HWt PJHЅ
+Ht$裊HCHxH9t0H= HWt PJHЅ
+Ht$fHHxH9t0H=z HWt PJHЅ
+Ht$*HRH[]ÐUSHHH H H % H H H{hHtH{PHtHs0H{ I HCH-> HxH9t0H= HWt PJHЅ
+Ht$uHCHxH9t0H= HWt PJHЅ
+Ht$8HHxH9t0H=L HWt PJHЅ
+Ht$H$H[]ÐUSHHH H H H H H{hHt։H{PHtȉHs0H{ HCH- HxH9t0H= HWt PJHЅ
+Ht$GHCHxH9t0H=Z HWt PJHЅ
+Ht$
+HHxH9t0H= HWt PJHЅ
+Ht$·HH[]ÐUSHHH H H ɴ H H 趴 H{hHt計H{PHt蚈Hs0H{ HCH- HxH9t0H=i HWt PJHЅ
+Ht$HCHxH9t0H=, HWt PJHЅ
+Ht$܆HHxH9t0H= HWt PJHЅ
+Ht$蠆HȇH[]ÐUSHHH H H 蛳 H H 舳 H{hHtzH{PHtlHs0H{ 迭 HCH- HxH9t0H=; HWt PJHЅ
+Ht$HCHxH9t0H= HWt PJHЅ
+Ht$讅HHxH9t0H= HWt PJHЅ
+Ht$rH蚆H[]ÐSHHHtIHGHxH;= t0H=d HWt PJHЅ
+Ht$H<H[USHHH H H H H H{hHtH{PHtHs0H{ 5 HCH-* HxH9t0H= HWt PJHЅ
+Ht$aHCHxH9t0H=t HWt PJHЅ
+Ht$$HHxH9t0H=8 HWt PJHЅ
+Ht$HH[]ÐSHHHtIHGHxH;=L t0H= HWt PJHЅ
+Ht$芃H貄H[SHHHtIHGHxH;= t0H=~ HWt PJHЅ
+Ht$.HVH[USHHH H H + H H H{hHt
+H{PHtHs0H{ O HCH-D HxH9t0H= HWt PJHЅ
+Ht${HCHxH9t0H= HWt PJHЅ
+Ht$>HHxH9t0H=R HWt PJHЅ
+Ht$H*H[]ÐUSHHH H H H H H{hHt܂H{PHtHs0H{ ! HCH- HxH9t0H= HWt PJHЅ
+Ht$MHCHxH9t0H=` HWt PJHЅ
+Ht$HHxH9t0H=$ HWt PJHЅ
+Ht$ԀHH[]ÐUSHHH H H ϭ H H 輭 H{hHt讁H{PHt蠁Hs0H{ HCH- HxH9t0H=o HWt PJHЅ
+Ht$ HCHxH9t0H=2 HWt PJHЅ
+Ht$HHxH9t0H= HWt PJHЅ
+Ht$HH[]ÐUSHHH H H 衬 H H 莬 H{hHt耀H{PHtrHs0H{ Ŧ HCH- HxH9t0H=A HWt PJHЅ
+Ht$~HCHxH9t0H= HWt PJHЅ
+Ht$~HHxH9t0H= HWt PJHЅ
+Ht$x~HH[]ÐUSHHH H H s H H ` H{hHtRH{PHtDHs0H{ 藥 HCH- HxH9t0H= HWt PJHЅ
+Ht$}HCHxH9t0H= HWt PJHЅ
+Ht$}HHxH9t0H= HWt PJHЅ
+Ht$J}Hr~H[]ÐUSHHH H H E H H 2 H{hHt$~H{PHt~Hs0H{ i HCH-^ HxH9t0H= HWt PJHЅ
+Ht$|HCHxH9t0H= HWt PJHЅ
+Ht$X|HHxH9t0H=l HWt PJHЅ
+Ht$ |HD}H[]ÐUSHHH H H H H H{hHt|H{PHt|Hs0H{ ; HCH-0 HxH9t0H= HWt PJHЅ
+Ht$g{HCHxH9t0H=z HWt PJHЅ
+Ht$*{HHxH9t0H=> HWt PJHЅ
+Ht$zH|H[]ÐUSHHH H H H H ֧ H{hHt{H{PHt{Hs0H{
HCH- HxH9t0H= HWt PJHЅ
+Ht$9zHCHxH9t0H=L HWt PJHЅ
+Ht$yHHxH9t0H= HWt PJHЅ
+Ht$yHzH[]ÐUSHHH H H 軦 H H 訦 H{hHtzH{PHtzHs0H{ ߠ HCH-ޭ HxH9t0H=[ HWt PJHЅ
+Ht$ yHCHxH9t0H= HWt PJHЅ
+Ht$xHHxH9t0H=߭ HWt PJHЅ
+Ht$xHyH[]ÐUSHHH H H 荥 H H z H{hHtlyH{PHt^yHs0H{ 豟 HCH-ݭ HxH9t0H=-߭ HWt PJHЅ
+Ht$wHCHxH9t0H=ޭ HWt PJHЅ
+Ht$wHHxH9t0H=ޭ HWt PJHЅ
+Ht$dwHxH[]ÐUSHHH H H _ H H L H{hHt>xH{PHt0xHs0H{ 胞 HCH-xܭ HxH9t0H=ݭ HWt PJHЅ
+Ht$vHCHxH9t0H=ݭ HWt PJHЅ
+Ht$rvHHxH9t0H=ݭ HWt PJHЅ
+Ht$6vH^wH[]ÐUSHHH H H 1 H H H{hHtwH{PHtwHs0H{ U HCH-Jۭ HxH9t0H=ܭ HWt PJHЅ
+Ht$uHCHxH9t0H=ܭ HWt PJHЅ
+Ht$DuHHxH9t0H=Xܭ HWt PJHЅ
+Ht$uH0vH[]ÐUSHHH H H H H H{hHtuH{PHtuHs0H{ ' HCH- ڭ HxH9t0H=ۭ HWt PJHЅ
+Ht$StHCHxH9t0H=fۭ HWt PJHЅ
+Ht$tHHxH9t0H=*ۭ HWt PJHЅ
+Ht$sHuH[]ÐUSHHH H H ՠ H H H{hHttH{PHttHs0H{ HCH-ح HxH9t0H=uڭ HWt PJHЅ
+Ht$%sHCHxH9t0H=8ڭ HWt PJHЅ
+Ht$rHHxH9t0H=٭ HWt PJHЅ
+Ht$rHsH[]ÐUSHHH H H 觟 H H 蔟 H{hHtsH{PHtxsHs0H{ ˙ HCH- HxH9t0H=G٭ HWt PJHЅ
+Ht$qHCHxH9t0H=
+٭ HWt PJHЅ
+Ht$qHHxH9t0H=ح HWt PJHЅ
+Ht$~qHrH[]ÐUSHHH H H y H H f H{hHtXrH{PHtJrHs0H{ 蝘 HCH-֭ HxH9t0H=ح HWt PJHЅ
+Ht$pHCHxH9t0H= HWt PJHЅ
+Ht$pHHxH9t0H= HWt PJHЅ
+Ht$PpHxqH[]ÐUSHHH H H K H H 8 H{hHt*qH{PHt qHs0H{ o HCH-dխ HxH9t0H=֭ HWt PJHЅ
+Ht$oHCHxH9t0H=֭ HWt PJHЅ
+Ht$^oHHxH9t0H=r֭ HWt PJHЅ
+Ht$"oHJpH[]ÐUSHHH H H H H
+ H{hHtoH{PHtoHs0H{ A HCH-6ԭ HxH9t0H=խ HWt PJHЅ
+Ht$mnHCHxH9t0H=խ HWt PJHЅ
+Ht$0nHHxH9t0H=Dխ HWt PJHЅ
+Ht$mH oH[]ÐUSHHH H H H H ܚ H{hHtnH{PHtnHs0H{ HCH-ӭ HxH9t0H=ԭ HWt PJHЅ
+Ht$?mHCHxH9t0H=Rԭ HWt PJHЅ
+Ht$mHHxH9t0H=ԭ HWt PJHЅ
+Ht$lHmH[]ÐUSHHH H H H H 讙 H{hHtmH{PHtmHs0H{ HCH-ѭ HxH9t0H=aӭ HWt PJHЅ
+Ht$lHCHxH9t0H=$ӭ HWt PJHЅ
+Ht$kHHxH9t0H=ҭ HWt PJHЅ
+Ht$kHlH[]ÐSHHHtIHGHxH;=Э t0H=ҭ HWt PJHЅ
+Ht$:kHblH[USHHH H H 7 H H $ H{hHtlH{PHtlHs0H{ [ HCH-PЭ HxH9t0H=ѭ HWt PJHЅ
+Ht$jHCHxH9t0H=ѭ HWt PJHЅ
+Ht$JjHHxH9t0H=^ѭ HWt PJHЅ
+Ht$jH6kH[]ÐUSHHH H H H H H{hHtjH{PHtjHs0H{ - HCH-"ϭ HxH9t0H=Э HWt PJHЅ
+Ht$YiHCHxH9t0H=lЭ HWt PJHЅ
+Ht$ iHHxH9t0H=0Э HWt PJHЅ
+Ht$hHjH[]ÐUSHHH H H ە H H ȕ H{hHtiH{PHtiHs0H{ HCH-ͭ HxH9t0H={ϭ HWt PJHЅ
+Ht$+hHCHxH9t0H=>ϭ HWt PJHЅ
+Ht$gHHxH9t0H=ϭ HWt PJHЅ
+Ht$gHhH[]ÐUSHHH H H 譔 H H 蚔 H{hHthH{PHt~hHs0H{ ю HCH-̭ HxH9t0H=Mέ HWt PJHЅ
+Ht$fHCHxH9t0H=έ HWt PJHЅ
+Ht$fHHxH9t0H=ͭ HWt PJHЅ
+Ht$fHgH[]ÐUSHHH H H H H l H{hHt^gH{PHtPgHs0H{ 裍 HCH-˭ HxH9t0H= ͭ HWt PJHЅ
+Ht$eHCHxH9t0H=̭ HWt PJHЅ
+Ht$eHHxH9t0H=̭ HWt PJHЅ
+Ht$VeH~fH[]ÐUSHHH H H Q H H > H{hHt0fH{PHt"fHs0H{ u HCH-jʭ HxH9t0H=˭ HWt PJHЅ
+Ht$dHCHxH9t0H=˭ HWt PJHЅ
+Ht$ddHHxH9t0H=x˭ HWt PJHЅ
+Ht$(dHPeH[]ÐUSHHH H H # H H H{hHteH{PHtdHs0H{ G HCH-<ɭ HxH9t0H=ʭ HWt PJHЅ
+Ht$scHCHxH9t0H=ʭ HWt PJHЅ
+Ht$6cHHxH9t0H=Jʭ HWt PJHЅ
+Ht$bH"dH[]ÐUSHHH H H H H H{hHtcH{PHtcHs0H{ HCH-ȭ HxH9t0H=ɭ HWt PJHЅ
+Ht$EbHCHxH9t0H=Xɭ HWt PJHЅ
+Ht$bHHxH9t0H= ɭ HWt PJHЅ
+Ht$aHbH[]ÐUSHHH H H ǎ H H 贎 H{hHtbH{PHtbHs0H{ HCH-ƭ HxH9t0H=gȭ HWt PJHЅ
+Ht$aHCHxH9t0H=*ȭ HWt PJHЅ
+Ht$`HHxH9t0H=ǭ HWt PJHЅ
+Ht$`HaH[]ÐSHHHtIHGHxH;=ƭ t0H=ǭ HWt PJHЅ
+Ht$@`HhaH[USHHH H H = H H * H{hHt aH{PHtaHs0H{ a HCH-Vŭ HxH9t0H=ƭ HWt PJHЅ
+Ht$_HCHxH9t0H=ƭ HWt PJHЅ
+Ht$P_HHxH9t0H=dƭ HWt PJHЅ
+Ht$_H<`H[]ÐUSHHH H H H H H{hHt_H{PHt_Hs0H{ 3 HCH-(ĭ HxH9t0H=ŭ HWt PJHЅ
+Ht$_^HCHxH9t0H=rŭ HWt PJHЅ
+Ht$"^HHxH9t0H=6ŭ HWt PJHЅ
+Ht$]H_H[]ÐUSHHH H H H H Ί H{hHt^H{PHt^Hs0H{ HCH- HxH9t0H=ĭ HWt PJHЅ
+Ht$1]HCHxH9t0H=Dĭ HWt PJHЅ
+Ht$\HHxH9t0H=ĭ HWt PJHЅ
+Ht$\H]H[]ÐUSHHH H H 賉 H H 蠉 H{hHt]H{PHt]Hs0H{ ׃ HCH- HxH9t0H=Sí HWt PJHЅ
+Ht$\HCHxH9t0H=í HWt PJHЅ
+Ht$[HHxH9t0H= HWt PJHЅ
+Ht$[H\H[]ÐUSHHH H H 腈 H H r H{hHtd\H{PHtV\Hs0H{ 詂 HCH- HxH9t0H=% HWt PJHЅ
+Ht$ZHCHxH9t0H= HWt PJHЅ
+Ht$ZHHxH9t0H= HWt PJHЅ
+Ht$\ZH[H[]ÐUSHHH H H W H H D H{hHt6[H{PHt([Hs0H{ { HCH-p HxH9t0H= HWt PJHЅ
+Ht$YHCHxH9t0H= HWt PJHЅ
+Ht$jYHHxH9t0H=~ HWt PJHЅ
+Ht$.YHVZH[]ÐSHHHtHGHt$HxVH'ZH[ÐSHHHtIHGHxH;=d t0H= HWt PJHЅ
+Ht$XHYH[SHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$FXHnYH[USHHH H H C H H 0 H{hHt"YH{PHtYHs0H{ g HCH-\ HxH9t0H=㾭 HWt PJHЅ
+Ht$WHCHxH9t0H= HWt PJHЅ
+Ht$VWHHxH9t0H=j HWt PJHЅ
+Ht$WHBXH[]ÐSHHHtIHGHxH;=~ t0H= HWt PJHЅ
+Ht$VHWH[USHHH H H 蹃 H H 覃 H{hHtWH{PHtWHs0H{ } HCH-һ HxH9t0H=Y HWt PJHЅ
+Ht$ VHCHxH9t0H= HWt PJHЅ
+Ht$UHHxH9t0H=༭ HWt PJHЅ
+Ht$UHVH[]ÐUSHHH H H 苂 H H x H{hHtjVH{PHt\VHs0H{ | HCH- HxH9t0H=+ HWt PJHЅ
+Ht$THCHxH9t0H= HWt PJHЅ
+Ht$THHxH9t0H= HWt PJHЅ
+Ht$bTHUH[]ÐUSHHH H H ] H H J H{hHt<UH{PHt.UHs0H{ { HCH-v HxH9t0H= HWt PJHЅ
+Ht$SHCHxH9t0H= HWt PJHЅ
+Ht$pSHHxH9t0H= HWt PJHЅ
+Ht$4SH\TH[]ÐUSHHH H H / H H H{hHtTH{PHt THs0H{ Sz HCH-H HxH9t0H=Ϲ HWt PJHЅ
+Ht$RHCHxH9t0H= HWt PJHЅ
+Ht$BRHHxH9t0H=V HWt PJHЅ
+Ht$RH.SH[]ÐUSHHH H H H H ~ H{hHtRH{PHtRHs0H{ %y HCH- HxH9t0H= HWt PJHЅ
+Ht$QQHCHxH9t0H=d HWt PJHЅ
+Ht$QHHxH9t0H=( HWt PJHЅ
+Ht$PH RH[]ÐSHHHtIHGHxH;=< t0H=ʷ HWt PJHЅ
+Ht$zPHQH[USHHH H H w} H H d} H{hHtVQH{PHtHQHs0H{ w HCH- HxH9t0H= HWt PJHЅ
+Ht$OHCHxH9t0H=ڶ HWt PJHЅ
+Ht$OHHxH9t0H= HWt PJHЅ
+Ht$NOHvPH[]ÐUSHHH H H I| H H 6| H{hHt(PH{PHtPHs0H{ mv HCH-b HxH9t0H=鵭 HWt PJHЅ
+Ht$NHCHxH9t0H= HWt PJHЅ
+Ht$\NHHxH9t0H=p HWt PJHЅ
+Ht$ NHHOH[]ÐUSHHH H H { H H { H{hHtNH{PHtNHs0H{ ?u HCH-4 HxH9t0H= HWt PJHЅ
+Ht$kMHCHxH9t0H=~ HWt PJHЅ
+Ht$.MHHxH9t0H=B HWt PJHЅ
+Ht$LHNH[]ÐSHHHtIHGHxH;=V t0H=䳭 HWt PJHЅ
+Ht$LHMH[USHHH H H y H H ~y H{hHtpMH{PHtbMHs0H{ s HCH- HxH9t0H=1 HWt PJHЅ
+Ht$KHCHxH9t0H= HWt PJHЅ
+Ht$KHHxH9t0H= HWt PJHЅ
+Ht$hKHLH[]ÐUSHHH H H cx H H Px H{hHtBLH{PHt4LHs0H{ r HCH-| HxH9t0H= HWt PJHЅ
+Ht$JHCHxH9t0H=Ʊ HWt PJHЅ
+Ht$vJHHxH9t0H= HWt PJHЅ
+Ht$:JHbKH[]ÐUSHHH H H 5w H H "w H{hHtKH{PHtKHs0H{ Yq HCH-N HxH9t0H=հ HWt PJHЅ
+Ht$IHCHxH9t0H= HWt PJHЅ
+Ht$HIHHxH9t0H=\ HWt PJHЅ
+Ht$ IH4JH[]ÐUSHHH H H v H H u H{hHtIH{PHtIHs0H{ +p HCH- HxH9t0H= HWt PJHЅ
+Ht$WHHCHxH9t0H=j HWt PJHЅ
+Ht$HHHxH9t0H=. HWt PJHЅ
+Ht$GHIH[]ÐUSHHH H H t H H t H{hHtHH{PHtHHs0H{ n HCH- HxH9t0H=y HWt PJHЅ
+Ht$)GHCHxH9t0H=< HWt PJHЅ
+Ht$FHHxH9t0H= HWt PJHЅ
+Ht$FHGH[]ÐUSHHH H H s H H s H{hHtGH{PHt|GHs0H{ Hl$m HCHHx
+DHCHHxCHHHxCH3GH[]USHHH H H s H H r H{hHtFH{PHtFHs0H{ +m HCH- HxH9t0H= HWt PJHЅ
+Ht$WEHCHxH9t0H=j HWt PJHЅ
+Ht$EHHxH9t0H=. HWt PJHЅ
+Ht$DHFH[]ÐUSHHH H H q H H q H{hHtEH{PHtEHs0H{ k HCH- HxH9t0H=y HWt PJHЅ
+Ht$)DHCHxH9t0H=< HWt PJHЅ
+Ht$CHHxH9t0H= HWt PJHЅ
+Ht$CHDH[]ÐSHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$RCHzDH[USHHH H H Op H H <p H{hHt.DH{PHt DHs0H{ sj HCH-h HxH9t0H=舘 HWt PJHЅ
+Ht$BHCHxH9t0H= HWt PJHЅ
+Ht$bBHHxH9t0H=v HWt PJHЅ
+Ht$&BHNCH[]ÐUSHHH H H !o H H o H{hHt CH{PHtBHs0H{ Ei HCH-: HxH9t0H= HWt PJHЅ
+Ht$qAHCHxH9t0H= HWt PJHЅ
+Ht$4AHHxH9t0H=H HWt PJHЅ
+Ht$@H BH[]ÐUSHHH H H m H H m H{hHtAH{PHtAHs0H{ h HCH- HxH9t0H= HWt PJHЅ
+Ht$C@HCHxH9t0H=V HWt PJHЅ
+Ht$@HHxH9t0H= HWt PJHЅ
+Ht$?H@H[]ÐUSHHH H H l H H l H{hHt@H{PHt@Hs0H{ f HCH-ޤ HxH9t0H=e HWt PJHЅ
+Ht$?HCHxH9t0H=( HWt PJHЅ
+Ht$>HHxH9t0H=쥭 HWt PJHЅ
+Ht$>H?H[]ÐHSHt莩k H[?[ÐUSHHH H H }k H H jk H{hHt\?H{PHtN?Hs0H{ e HCH- HxH9t0H= HWt PJHЅ
+Ht$=HCHxH9t0H=भ HWt PJHЅ
+Ht$=HHxH9t0H= HWt PJHЅ
+Ht$T=H|>H[]ÐSHHHtIHGHxH;= t0H=F HWt PJHЅ
+Ht$<H >H[USHHH H H i H H i H{hHt=H{PHt=Hs0H{ d HCH- HxH9t0H= HWt PJHЅ
+Ht$C<HCHxH9t0H=V HWt PJHЅ
+Ht$<HHxH9t0H= HWt PJHЅ
+Ht$;H<H[]ÐUSHHH H H h H H h H{hHt<H{PHt<Hs0H{ b HCH-ޠ HxH9t0H=e HWt PJHЅ
+Ht$;HCHxH9t0H=( HWt PJHЅ
+Ht$:HHxH9t0H=졭 HWt PJHЅ
+Ht$:H;H[]ÐUSHHH H H g H H g H{hHtv;H{PHth;Hs0H{ a HCH- HxH9t0H=7 HWt PJHЅ
+Ht$9HCHxH9t0H= HWt PJHЅ
+Ht$9HHxH9t0H= HWt PJHЅ
+Ht$n9H:H[]ÐSHHHtIHGHxH;=Ҟ t0H=` HWt PJHЅ
+Ht$9H8:H[USHHH H H
f H H e H{hHt9H{PHt9Hs0H{ 1` HCH-& HxH9t0H= HWt PJHЅ
+Ht$]8HCHxH9t0H=p HWt PJHЅ
+Ht$ 8HHxH9t0H=4 HWt PJHЅ
+Ht$7H 9H[]ÐUSHHH H H d H H d H{hHt8H{PHt8Hs0H{ _ HCH- HxH9t0H= HWt PJHЅ
+Ht$/7HCHxH9t0H=B HWt PJHЅ
+Ht$6HHxH9t0H= HWt PJHЅ
+Ht$6H7H[]ÐUSHHH H H c H H c H{hHt7H{PHt7Hs0H{ ] HCH-ʛ HxH9t0H=Q HWt PJHЅ
+Ht$6HCHxH9t0H= HWt PJHЅ
+Ht$5HHxH9t0H= HWt PJHЅ
+Ht$5H6H[]ÐUSHHH H H b H H pb H{hHtb6H{PHtT6Hs0H{ Hl$\ HCHHx2HCHHx2HHHx2H 6H[]USHHH H H a H H a H{hHt5H{PHt5Hs0H{ Hl$[ HCHHx>2HCHHx.2HHHx 2Hg5H[]SHHHtIHGHxH;= t0H=2 HWt PJHЅ
+Ht$3H
+5H[USHHH H H ` H H ` H{hHt4H{PHt4Hs0H{ [ HCH- HxH9t0H= HWt PJHЅ
+Ht$/3HCHxH9t0H=B HWt PJHЅ
+Ht$2HHxH9t0H= HWt PJHЅ
+Ht$2H3H[]ÐUSHHH H H _ H H _ H{hHt3H{PHt3Hs0H{ Y HCH-ʗ HxH9t0H=Q HWt PJHЅ
+Ht$2HCHxH9t0H= HWt PJHЅ
+Ht$1HHxH9t0H=ؘ HWt PJHЅ
+Ht$1H2H[]ÐSHHHtIHGHxH;=얭 t0H=z HWt PJHЅ
+Ht$*1HR2H[USHHH H H '^ H H ^ H{hHt2H{PHt1Hs0H{ KX HCH-@ HxH9t0H=Ǘ HWt PJHЅ
+Ht$w0HCHxH9t0H= HWt PJHЅ
+Ht$:0HHxH9t0H=N HWt PJHЅ
+Ht$/H&1H[]ÐSHHHtIHGHxH;=b t0H= HWt PJHЅ
+Ht$/H0H[USHHH H H \ H H \ H{hHt|0H{PHtn0Hs0H{ V HCH- HxH9t0H== HWt PJHЅ
+Ht$.HCHxH9t0H= HWt PJHЅ
+Ht$.HHxH9t0H=ĕ HWt PJHЅ
+Ht$t.H/H[]ÐUSHHH H H o[ H H \[ H{hHtN/H{PHt@/Hs0H{ U HCH- HxH9t0H= HWt PJHЅ
+Ht$-HCHxH9t0H=Ҕ HWt PJHЅ
+Ht$-HHxH9t0H= HWt PJHЅ
+Ht$F-Hn.H[]ÐUSHHH H H AZ H H .Z H{hHt .H{PHt.Hs0H{ eT HCH-Z HxH9t0H=ᓭ HWt PJHЅ
+Ht$,HCHxH9t0H= HWt PJHЅ
+Ht$T,HHxH9t0H=h HWt PJHЅ
+Ht$,H@-H[]ÐUSHHH H H Y H H Y H{hHt,H{PHt,Hs0H{ 7S HCH-, HxH9t0H= HWt PJHЅ
+Ht$c+HCHxH9t0H=v HWt PJHЅ
+Ht$&+HHxH9t0H=: HWt PJHЅ
+Ht$*H,H[]ÐSHHHtIHGHxH;=N t0H=ܑ HWt PJHЅ
+Ht$*H+H[USHHH H H W H H vW H{hHth+H{PHtZ+Hs0H{ Q HCH- HxH9t0H=) HWt PJHЅ
+Ht$)HCHxH9t0H=쐭 HWt PJHЅ
+Ht$)HHxH9t0H= HWt PJHЅ
+Ht$`)H*H[]ÐAVAUATUHSH t x H} H\$0IHPH5u HHIh+H|$H5u HD$ L+H HD$ HD$ HD$( L0t H{ LHD$0D$8 Lt$@D$H 1> H{P .H{PL)HP L HE H []A\A]A^HH$HHx(&H00H{ HHњ HHDP HHL贚 H/USH(H?H\$Hl$HPHT$HHC*H5[u 1H$HHD$HHx%Hu!-HHD$HHx%H/H([]ÐSHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$B'Hj(H[SHHHtIHGHxH;= t0H=6 HWt PJHЅ
+Ht$&H(H[HSHtG H['[ÐAWAVAUIATUSHHN LgM# ID$xH- HxH9t0H= HWt PJHЅ
+Ht$R&I\$XL|$HtTH{L3f HCHxH9t.H=J HWt PJHЅL%HL!'ID$PI|$HL|$H 1ID$` ID$X I|$H&Mt$8I\$0I9t@HHxH9t.H= HWt PJHЅLo%HI|$0Ht&Mt$ I\$L|$I9t@HHxH9t.H=Z HWt PJHЅL %HI|$Ht'&ID$HxH9t0H= HWt PJHЅ
+Ht$$I$HxH9t0H=̋ HWt PJHЅ
+Ht$|$L%I} IE Ht%L%H[]A\A]A^A_ÐSHHHtIHGHxH;= t0H=H HWt PJHЅ
+Ht$#H %H[AWAVAUIATUSHHN LgM# ID$xH-G HxH9t0H=Ί HWt PJHЅ
+Ht$~#I\$XL|$HtTH{L3f HCHxH9t.H=v HWt PJHЅL(#HLM$ID$PI|$HL|$H 1ID$` ID$X I|$H$Mt$8I\$0I9t@HHxH9t.H=鉭 HWt PJHЅL"HI|$0Ht#Mt$ I\$L|$I9t@HHxH9t.H= HWt PJHЅL8"HI|$HtS#ID$HxH9t0H=5 HWt PJHЅ
+Ht$!I$HxH9t0H= HWt PJHЅ
+Ht$!L"I} IE Ht"L"H[]A\A]A^A_ÐUSHHH H H }N H H jN H{hHt\"H{PHtN"Hs0H{ H HCH- HxH9t0H= HWt PJHЅ
+Ht$ HCHxH9t0H= HWt PJHЅ
+Ht$ HHxH9t0H= HWt PJHЅ
+Ht$T H|!H[]ÐSHHHtIHGHxH;= t0H=F HWt PJHЅ
+Ht$ H !H[SHHHtIHGHxH;=\ t0H=ꆭ HWt PJHЅ
+Ht$ H H[SHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$> Hf H[SHHHtIHGHxH;= t0H=2 HWt PJHЅ
+Ht$ H
+ H[ATUHSHH@HFH6HxPvO HIHv t
+HE HPHD$ Hl$0HD$ IT$MD$H
v H5v L
Дo HH$Ld$ I H HvQ HD$0LHx H@H[]A\HHD$0LHxH &AT USH Hl$Hzw H5w HHCL HHLd$ $H LHHHH HHCHD$HxH; H5 H %HHD$LHxXHHk HS%SHHHtIHGHxH;=ւ t0H=d HWt PJHЅ
+Ht$ H< H[SHHHtIHGHxH;=z t0H= HWt PJHЅ
+Ht$ H H[SHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$\ H H[SHGHHt HcW9}
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- Lt$ LHL$ L @ AA@ AA@f D HD$0HHxw ) @ Hx H HsPLL HH=X|[ Lt$ HI<$LHHH3 H=!|[ tH= HW IHt$ i:H$A@ I AD H$A@ I AD HHD$ Ht$ HxRH{IkHEHl$0LL|0H H{XHH$ \HdH띋PJHBf. AWAVAUATUHSHHLsPH Hz L$ Ll$HHG 7 HĔ H5\b HCX C` HCh LHCx Hǃ LHHǃ HKHLLH8 L4$Mt IFH$P HxLHD$L- HxL9 M Lt$@H55Va LL{TLH|$0LLH8 Lt$0Mt IFH$P HxULHD$@HxL9 M Lt$pH5G<\ LDŽ$ L`H|$`H$ LH8 Lt$`Mt IFH$P HxLHD$pHxL9[ M QB[ HH HHL$ LH)HHH HH H)LH)HEHsXH5;\ H$ H$ LHp8 L$ Mt IFH$P Hx LVH$ HxL9 M H$ {pR @ Cp5 L$ L$ H5]\ HDŽ$ HDŽ$( LLHDŽ$0 H$ LLH)8 H$ Ht HEH$P HxWHH$ HxL9X H H$ H$( H)HH! E11E11IMtHHHH{xHKxHH L HtH$ HtHĈ []A\A]A^A_f. X H$ NHSpH59\ HIL$P L8=B H
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6\ H5/\ Mq L0rH!0\ HLL;}L|L{H$ LHxwH@H5Ɋ LHD$pLHxO$ H H,Ⱦ HHHH)HHH HH H)HH)HEHsX D H$ LHxH$ {ptR D Cp9 L$ L$ H5I\ HDŽ$ HDŽ$( LLHDŽ$0 H$ LLH7 H$ Ht HELHxDHH$ tH$ LHx X HI{L$p L0B H
4\ H5-\ Mw L]pH.\ HLLh{LzLyH$ LHxHmH5 Lf D X H$ 6HSpH5,\ HIL$P L 0B H
4\ H5-\ Mt LoH-\ HLLzLzLMyH$ LHxHH5K L3 H$ LHxH$ H$( H)HHuE1E111I @ H H9 H, HH$ L$( HI)IN$ HMtHLHH{xHKxLH H HtbH$ HtPHĈ []A\A]A^A_HH$ Ht)H{xHtH䋔 HHHuHH=0ݕ HW RH$P @Lt$ X >LIH$ H3.B L$ H
$2\ H5 +\ Ib LmH~,\ LLLxL xHXwHD$ H$P HxHˆH5T L<HLPJH ILMwHLvHD$ H$P HxIHLHwLvHD$PLHxfxHHHD$@H$P HxASHH$ LHx%<HLUvLHwHHD$pLHxHH$ LHxHRHH$ LHxHLfHH$ LHxHLuLHjvHLm]HH$ LHxAHLtuLH'vHHD$H$P Hx UH SHmHHHHH[]HH
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b H5Tb I躌 L|Hb LLLLHHD$PH$p HxPHJH5#D LYH
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HHHİ H[]A\HHD$`HtH|$P HH>BH$ HHtH<$HtH|$Ht;H|$0Ht;HH H f. @ ATH7USHH HD$PHHD$hHHD$`0 HD$p1 HD$x0 H$ 0 H$ 0 H$ 0 H$ 1 8 H$ HD$0 HD$8 HD$@ :Hh8Ht$p8 HHD$0Hl$@?Hl$8J0 H$ =1 H$ 00 H$ #0 H$ 0 H$ 0 H$ 0 8 H$ HD$ HD$ HD$ O:L$ Hh8HǺ8 HD$LHl$ I?HHl$lHT$H5<X H2 HT$0H5W H2 H5`^ H2 H5 ] HƑ2 HH5V HB m2 HHl$PH LHH$ HH$ Ht&H<$HtH|$Ht)9H|$0Ht9HD$`Ht
HHH H[]A\HHD$`HtH|$P HH@?H$ HHtH<$HtH|$Ht8H|$0Ht8HH D H f. @ ATHwUSHH HD$PHHD$hHHD$`0 HD$p0 HD$x0 H$ 0 H$ 0 H$ 0 H$ 0 8 H$ HD$0 HD$8 HD$@ 7Hh8Ht$p8 HHD$0Hl$@<Hl$8J0 H$ =0 H$ 00 H$ #0 H$ 0 H$ 0 H$ 0 8 H$ HD$ HD$ HD$ O7L$ Hh8HǺ8 HD$LHl$ I<HHl$lHT$H5<X H2 HT$0H5W H2 H5O] H2 H5] HƎ2 HH5V HB m2 HHl$PH LHH$ HH$ Ht&H<$HtH|$Ht)6H|$0Ht6HD$`Ht
HHH H[]A\HHD$`HtH|$P HH@<H$ HHtH<$HtH|$Ht5H|$0Ht5HH D H f. @ ATHUSHH HD$pHH$ HH$ 0 HD$HD$P HD$X HD$` 95HhHt$ HHD$PHl$`9:Hl$X0 HD$ HD$0 HD$8 HD$@ 4Ld$ HhHǺ HD$0LHl$@9HHl$8HT$0H5զX H葝2 HT$PH5W H}2 H5] H讋2 H5Q] H_2 HH5V HB B 2 HHl$pH LHHT$ HmH|$ HtH<$HtH|$0Ht3H|$PHt3H$ Ht
HHHĐ H[]A\HH$ HtH|$p HH9H|$ HHt0H<$Ht"H|$0Ht33H|$PHt$3HHH f. fUH8SHH HD$@H!HD$XH%HD$P0 HD$`0 HD$hw0 HD$pm0 HD$xc0 H$ V0 H$ I0 H$ <0 H$ /0 H$ "0 H$ 0 X H$ HD$ HD$( HD$0 h2HhXHt$`HǺX HD$ Hl$0h7HHl$(HT$ H5[X H2 H5HW HH2 H5] H2 HH5JV HB
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HHH H[]HHD$PHtH|$@ HH7H|$HHtH<$HtH|$ Ht0H @ H ATHUSHH0HD$HHD$(HHD$ \0 HI!H5X LHO2 H5iW H2 H5] H葈2 HH5V HB 82 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH6HKIHt
HHH;HtdH LH<$HtLH D ATHWUSHH0HD$HHD$(HHD$ 0 HIH5X LHߔ2 H5U] Hp2 H5] H!2 HH5rV HB ȇ2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtcHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH4HKIHt
HHH;HtH LH<$HtH D ATHUSHH0HD$HHD$(HHD$ |0 HIAH5X LHo2 H5W H 2 H5] H豅2 HH5V HB X2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH:3HKIHt
HHH;HtH LH<$HtlH D ATHUSHH0HD$HHD$(HHD$ 0 HIH5X LH2 H5dW H萃2 H53] HA2 HH5V HB 2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH1HKIHt
HHH;HtH LH<$HtH D ATHUSHH0HD$H2HD$(H6HD$ 0 HIaH56X LH菐2 H5HcW H 2 H5
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HHH;HtH LH<$HtH D ATHUSHH0HD$HHD$(HHD$ ,0 HIH5ƛX LH 2 H5aW H谀2 H5S ] Ha2 HH5V HB 2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH.HKIHt
HHH;Ht4H LH<$Ht H D ATHgUSHH0HD$HRHD$(HVHD$ 0 HIH5VX LH词2 H5h`W H@2 H5
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HHH;HtH LH<$HtH D ATHWUSHH0HD$HHD$(HHD$ L0 HIH5X LH?2 H5^W H}2 H5s ] H~2 HH5҅V HB (2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH
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HHH;HtTH LH<$Ht<H D ATHUSHH0HD$HrHD$(HvHD$ 0 HIH5vX LHϊ2 H5hW H`|2 H5] H}2 HH5bV HB }2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtSHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH*HKIHt
HHH;HtH LH<$HtH D ATHUSHH0HD$HHD$(HHD$ l0 HI1H5X LH_2 H5\W Hz2 H5] H{2 HH5V HB H|2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH*)HKIHt
HHH;HttH LH<$Ht\H D ATHUSHH0HD$HHD$(HHD$ 0 HIH5X LH2 H5ZW Hy2 H5#] H1z2 HH5V HB z2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtsHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH'HKIHt
HHH;HtH LH<$HtH D ATHWUSHH0HD$H"HD$(H&HD$ 0 HIQH5&X LH2 H58YW Hx2 H5] Hx2 HH5V HB hy2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHJ&HKIHt
HHH;HtH LH<$Ht|H D ATHǫUSHH0HD$HHD$(HHD$ 0 HIH5X LH2 H5W Hv2 H5C] HQw2 HH5~V HB w2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH$HKIHt
HHH;Ht$H LH<$Ht H D ATHUSHH0HD$HBHD$(HFHD$ 0 HIqH5FX LH蟃2 H5CW H0u2 H5 ] Hu2 HH52}V HB v2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht#HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHj#HKIHt
HHH;HtH LH<$HtH D ATHUSHH0HD$HHD$(HHD$ <0 HIH5֎X LH/2 H5W Hs2 H5c\ Hqt2 HH5{V HB u2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH!HKIHt
HHH;HtDH LH<$Ht,H D ATHUSHH0HD$HbHD$(HfHD$ 0 HIH5fX LH迀2 H5nW HPr2 H5\ Hs2 HH5RzV HB s2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtCHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH HKIHt
HHH;HtH LH<$HtH D ATHGUSHH0HD$HHD$(HHD$ \0 HI!H5X LHO2 H5W Hp2 H5\ Hq2 HH5xV HB 8r2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH HKIHt
HHH;HtdH LH<$HtLH D ATHwUSHH0HD$HHD$(HHD$ 0 HIH5X LH}2 H5W Hpo2 H5\ H!p2 HH5rwV HB p2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtcHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH HKIHt
HHH;HtH LH<$HtH D ATHNJUSHH0HD$HHD$(HHD$ |0 HIAH5X LHo|2 H5Z H n2 H5\ Hn2 HH5vV HB Xo2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH: HKIHt
HHH;HtH LH<$HtlH D ATHUSHH0HD$HHD$(HHD$ 0 HIH5X LHz2 H5;] Hl2 H53\ HAm2 HH5tV HB m2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtH LH<$HtH D ATH'USHH0HD$H2HD$(H6HD$ 0 HIaH56X LHy2 H5Z H k2 H5\ Hk2 HH5"sV HB xl2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHZHKIHt
HHH;HtH LH<$HtH D ATH7USHH0HD$HHD$(HHD$ ,0 HIH5ƄX LH x2 H58] Hi2 H5S\ Haj2 HH5qV HB k2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;Ht4H LH<$Ht H D ATH'} USHH0HD$HRHD$(HVHD$ 0 HI聼H5VX LHv2 H5mW H@h2 H5\ Hh2 HH5BpV HB i2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht3HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHzHKIHt
HHH;HtH LH<$HtH D ATHUSHH0HD$HHD$(HHD$ L0 HIH5X LH?u2 H55] Hf2 H5s\ Hg2 HH5nV HB (h2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH
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HHH;HtTH LH<$Ht<H D ATHv USHH0HD$HrHD$(HvHD$ 0 HI衹H5vX LHs2 H5W H`e2 H5\ Hf2 HH5bmV HB f2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtSHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtH LH<$HtH D ATHUSHH0HD$HHD$(HHD$ l0 HI1H5X LH_r2 H5&W Hc2 H5\ Hd2 HH5kV HB He2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH*HKIHt
HHH;HttH LH<$Ht\H D ATHUSHH0HD$HHD$(HHD$ 0 HIH5}X LHp2 H5W Hb2 H5#\ H1c2 HH5jV HB c2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtsHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtH LH<$HtH D ATHwUSHH0HD$H"HD$(H&HD$ 0 HIQH5&|X LHo2 H5V Ha2 H5\ Ha2 HH5iV HB hb2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHJHKIHt
HHH;HtH LH<$Ht|H D ATHgUSHH0HD$HHD$(HHD$ 0 HIH5zX LHn2 H5Ny\ H_2 H5C\ HQ`2 HH5gV HB `2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH
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HHH;Ht$H LH<$Ht H D ATHׂUSHH0HD$HBHD$(HFHD$ 0 HIqH5FyX LHl2 H5vW H0^2 H5\ H^2 HH52fV HB _2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht#HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHj HKIHt
HHH;HtH LH<$HtH D ATHǁUSHH0HD$HHD$(HHD$ <0 HIH5wX LH/k2 H5W H\2 H5c\ Hq]2 HH5dV HB ^2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht賿HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH
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HHH;HtDH LH<$Ht,H D ATHUSHH0HD$HbHD$(HfHD$ 0 HI葯H5fvX LHi2 H5W HP[2 H5\ H\2 HH5RcV HB \2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtCHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH HKIHt
HHH;HtԽH LH<$Ht輽H D ATHg USHH0HD$HHD$(HHD$ \0 HI!H5tX LHOh2 H5
W HY2 H5\ HZ2 HH5aV HB 8[2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtӼHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtdH LH<$HtLH D ATH~USHH0HD$HHD$(HHD$ 0 HI豬H5sX LHf2 H5W HpX2 H5\ H!Y2 HH5r`V HB Y2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtcHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtH LH<$HtܺH D ATH}USHH0HD$HHD$(HHD$ |0 HIAH5rX LHoe2 H5JW H W2 H5\ HW2 HH5_V HB XX2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH:HKIHt
HHH;Ht脹H LH<$HtlH D ATHw|USHH0HD$HHD$(HHD$ 0 HIѩH5pX LHc2 H5ڭW HU2 H53\ HAV2 HH5]V HB V2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht胸HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtH LH<$HtH D ATHGqUSHH0HD$H2HD$(H6HD$ 蜺0 HIaH56oX LHb2 H5'#] H T2 H5\ HT2 HH5"\V HB xU2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHZHKIHt
HHH;Ht褶H LH<$Ht茶H D ATHWqUSHH0HD$HHD$(HHD$ ,0 HIH5mX LH a2 H5Ml\ HR2 H5S\ HaS2 HH5ZV HB T2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht裵HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH HKIHt
HHH;Ht4H LH<$Ht H D ATHUSHH0HD$HRHD$(HVHD$ 輷0 HI聥H5VlX LH_2 H5W H@Q2 H5\ HQ2 HH5BYV HB R2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht3HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHzHKIHt
HHH;HtijH LH<$Ht謳H D ATH7oUSHH0HD$HHD$(HHD$ L0 HIH5jX LH?^2 H5mi\ HO2 H5s\ HP2 HH5WV HB (Q2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtòHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH
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HHH;HtTH LH<$Ht<H D ATHUSHH0HD$HrHD$(HvHD$ ܴ0 HI衢H5viX LH\2 H5W H`N2 H5\ HO2 HH5bVV HB O2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtSHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtH LH<$Ht̰H D ATHmUSHH0HD$HHD$(HHD$ l0 HI1H5hX LH_[2 H5 ] HL2 H5\ HM2 HH5TV HB HN2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH*HKIHt
HHH;HttH LH<$Ht\H D ATHlUSHH0HD$HHD$(HHD$ 0 HIH5fX LHY2 H5] HK2 H5#\ H1L2 HH5SV HB L2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtsHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtH LH<$HtH D ATHjUSHH0HD$H"HD$(H&HD$ 茮0 HIQH5&eX LHX2 H5(] HJ2 H5\ HJ2 HH5RV HB hK2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHJHKIHt
HHH;Ht蔬H LH<$Ht|H D ATHzUSHH0HD$HHD$(HHD$ 0 HIH5cX LHW2 H5 2W HH2 H5C\ HQI2 HH5PV HB I2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht蓫HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;Ht$H LH<$Ht H D ATHhUSHH0HD$HBHD$(HFHD$ 謭0 HIqH5FbX LHU2 H5vW H0G2 H5\ HG2 HH52OV HB H2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht#HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHjHKIHt
HHH;Ht贩H LH<$Ht蜩H D ATHgUSHH0HD$HҴHD$(HִHD$ <0 HIH5`X LH/T2 H5W HE2 H5c\ HqF2 HH5MV HB G2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht賨HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtDH LH<$Ht,H D ATHfUSHH0HD$HbHD$(HfHD$ ̫0 HI葘H5f_X LHR2 H5W HPD2 H5\ HE2 HH5RLV HB E2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtCHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtԦH LH<$Ht輦H D ATHuUSHH0HD$HHD$(HHD$ \0 HI!H5]X LHOQ2 H52W HB2 H5\ HC2 HH5JV HB 8D2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtӥHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtdH LH<$HtLH D ATHdUSHH0HD$HHD$(HHD$ 0 HI豕H5\X LHO2 H5W HpA2 H5\ H!B2 HH5rIV HB B2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtcHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtH LH<$HtܣH D ATHcUSHH0HD$HHD$(HHD$ |0 HIAH5[X LHoN2 H5FW H @2 H5\ H@2 HH5HV HB XA2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH:HKIHt
HHH;Ht脢H LH<$HtlH D ATHwbUSHH0HD$HHD$(HHD$ 0 HIђH5YX LHL2 H5֖W H>2 H53\ HA?2 HH5FV HB ?2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht胡HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtH LH<$HtH D ATH[USHH0HD$H2HD$(H6HD$ 蜡0 HIaH56XX LHK2 H5' ] H =2 H5\ H=2 HH5"EV HB x>2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHZHKIHt
HHH;Ht褟H LH<$Ht茟H D ATHwaUSHH0HD$HªHD$(HƪHD$ ,0 HIH5VX LH J2 H5W H;2 H5S\ Ha<2 HH5CV HB =2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht裞HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;Ht4H LH<$Ht H D ATHaUSHH0HD$HRHD$(HVHD$ 輠0 HI聎H5VUX LHH2 H5W H@:2 H5\ H:2 HH5BBV HB ;2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht3HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHzHKIHt
HHH;HtĜH LH<$Ht謜H D ATHaUSHH0HD$HHD$(HHD$ L0 HIH5SX LH?G2 H5vV H82 H5s\ H92 HH5@V HB (:2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtÛHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH
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HHH;HtTH LH<$Ht<H D ATH'ZUSHH0HD$HrHD$(HvHD$ ܤ0 HI衋H5vRX LHE2 H5W H`72 H5\ H82 HH5b?V HB 82 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtSHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtH LH<$Ht̙H D ATHTUSHH0HD$HHD$(HHD$ l0 HI1H5QX LH_D2 H5O\ H52 H5\ H62 HH5=V HB H72 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH*HKIHt
HHH;HttH LH<$Ht\H D ATHGTUSHH0HD$HHD$(HHD$ 0 HIH5OX LHB2 H5 N\ H42 H5#\ H152 HH5<V HB 52 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtsHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtH LH<$HtH D ATHWTUSHH0HD$H"HD$(H&HD$ 茞0 HIQH5&NX LHA2 H5(] H32 H5\ H32 HH5;V HB h42 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHJHKIHt
HHH;Ht蔕H LH<$Ht|H D ATH'OUSHH0HD$HHD$(HHD$ 0 HIH5LX LH@2 H5 ] H12 H5C\ HQ22 HH59V HB 22 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht蓔HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
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-] LHLZH>ZLvYHD$0HHx%HhH5wh L_HH{hH諿H|$@HHt) IHLYLYHD$0H$ Hx貵L躶HHHf AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I HeM I LH eIT$A H,M4$H9II$t4*$ @ H$Iڷ,AVH9uI$I9T$nHH[]A\A]A^A_H H8 HFH9tHGhLHHD$HD$0HD$HD$ HD$I I H|$dD I M L$HdI$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|$I HcM4$H IL$H,I H9I $t/ @ A*HII.,ANH9uI $I9L$rHH[]A\A]A^A_^U f. @ AVIAULjATIUSH H$ H$ H$ HLH{H$ H$h H$ H H9HNH$ `H$ H$ Hl$H H$ H H1Lb1 H|$IHtpHl$0H$ LHv HD$` 腵HD$PH LHD$hHIHD$`2 H HD$`HHDŽ$ Ld$pt)HT$hLd$pH$ Ht$PLH$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{?cH []A\A]A^ L f.,V f.&V *$ HDŽ$ dHT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ HAQHHt$PH|$XH$ HEH$ HD$`HT$hH$ Hf D < HDŽ$ 贳HT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ H!PHLL$PLT$XH$ HH$ HD$`HT$hH$ HHD$`H$ HHtH$ HHD$`HtH|$P HH{]aHH$ HHtH$ HHH$ HHt LLH$ HHx LLfHL$`HHnH|$P HZH|$HHI?Hf f. AUATUSHHHHHH LA0A@% ~Ct uYHaHHH[]A\A] HHHH[]A\A]f. uHHHH[]A\A] D LH-/ Hl$ HT$H5aV HD$HiLd$0H L ; H+HD$0HD$0 HHt?HELl$LHx蒭HʰHl$0HtHELHxpH訰HD$ LHxWHHH[]A\A]f HXHHH[]A\A]HHD$ Ht$HxH;Htw[H H訶H AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I H]M I LH \IT$I H,M4$H9II$t5H*$ $* HI:H,IVH9uI$I9T$mHH[]A\A]A^A_H H8 HFH9tHGHHD$HGhH$HD$0HD$HD$ HD$ @ I H<$I 1\M M H|$l L\I$I4ID$J<HI$H{
Ht$0H|$8HT$ HD$(HLKHT$Ht$AID$I9$mHH[]A\A]A^A_HGHD$HGhH$HFH9 I H<$I i[I L<H|$I HJ[M4$H IL$H,I H9I $t1 @ H*A*虭H,HIIINH9uI $I9L$pHH[]A\A]A^A_U f. fAVIAULjATIUSH H$ H$ H$ HLH{oH$ H$h H$ H H9HNH$ H$ H$ Hl$H H$ HH1L³1 H|$IHtЬHl$0H$ LH(q HD$` HD$PHLHD$hHAHD$`2 H HD$`HHDŽ$ Ld$pt)HT$hLd$pH$ Ht$PLH$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{ZH []A\A]A^ L` 6 HDŽ$ ֫HT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ HIHHt$PH|$XH$ HH$ HD$`HT$hH$ H HDŽ$ *HT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ HwHHLL$PLT$XH$ HKH$ HD$`HT$hH$ HHD$`HHD$`HtH|$P HH{XH蚰HH$ HHt LLH$ HHt LLH$ HHtH$ HlH|$HHt誩j D qHL$`HHOH|$P H;H$ HHH$ Hf. AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I H%VM I LH VIT$I H,M4$H9II$t6H*$ $H* HIIH,IVH9uI$I9T$lHH[]A\A]A^A_H H8 HFH9tHGHHD$HGhH$HD$0HD$HD$ HD$ I H<$I AUM M H|$Hl L!UI$I4ID$J<HI$H{ Ht$0H|$8HHT$ HD$(HLKHT$Ht$AID$I9$kHH[]A\A]A^A_HGHD$HGhH$HFH9f D I H<$I yTI L<H|$I HZTM4$H IL$H,I H9I $t1 @ H*I*試H,HIIINH9uI $I9L$pHH[]A\A]A^A_T f. fAVIAULjATIUSH H$ H$ H$ HLH{|H$ H$h H$ H H9HNH$ H$ H$ Hl$H H$ HH1LҬ1 H|$IHtHl$0H$ LH8j HD$` HD$PHLHD$hH;HD$` 2 H HD$`HHDŽ$ Ld$pt)HT$hLd$pH$ Ht$PLH$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{SH []A\A]A^ LH H H3 HDŽ$ HT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ HCHHt$PH|$XH$ HH$ HD$`HT$hH$ H D HDŽ$ :HT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ HgBHLL$PLT$XH$ HKH$ HD$`HT$hH$ HHD$`HHD$`HtH|$P HH{RH誩HH$ HHt LLH$ HHt LLH$ HHtH$ HlH|$HHt躢j D qHL$`HHOH|$P H;H$ HHH$ Hf. AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I H5OM I LH OIT$I H,M4$H9II$t8H*$ $HIZWH,IVH9uI$I9T$jHH[]A\A]A^A_H H8 HFH9tHGhLHHD$HD$0HD$HD$ HD$ D I I H|$PND I M L$H,NI$I4ID$$H<HI$H{
Ht$0H|$8HHT$ HD$(LCHT$Ht$AID$I9$dHH[]A\A]A^A_HGHD$HGhH$HFH9 I H<$I MI L<H|$I HbMM4$H IL$H,I H9I $t4 @ AH*HIIZ袟H,INH9uI $I9L$mHH[]A\A]A^A_T AVIAULjATIUSH H$ H$ H$ HLH{uH$ H$h H$ H H9HNH$ H$ H$ Hl$H H$ H蟿H1L1 H|$IHtHl$0H$ LHHc HD$` HD$PH LHD$hH4HD$`02 H HD$`HHDŽ$ Ld$pt)HT$hLd$pH$ Ht$PLH$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{LH []A\A]A^ L .V . U ,& HDŽ$ HT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ Hc=HHt$PH|$XH$ HH$ HD$`HT$hH$ H ; HDŽ$ 4HT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ HA<HLL$PLT$XH$ H5H$ HD$`HT$hH$ HHD$`H$ HHtH$ HHD$`HtH|$P HH{JH腢H$ HHtH$ HHH$ HHt LLH$ HHx LLfHL$`HHnH|$P HZH|$HHIj?Hf f. AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I H5HM I LH HIT$I H,M4$H9II$t5H*$ H$IZH,IVH9uI$I9T$mHH[]A\A]A^A_H H8 HFH9tHGhLHHD$HD$0HD$HD$ HD$I I H|$XGD I M L$H4GI$I4ID$$H<HI$H{
- Ht$0H|$8HHT$ HD$(LCHT$Ht$AID$I9$dHH[]A\A]A^A_HGHD$HGhH$HFH9 I H<$I FI L<H|$I HjFM4$H IL$H,I H9I $t1 @ AH*HII識H,INH9uI $I9L$pHH[]A\A]A^A_T f. fAVIAULjATIUSH H$ H$ H$ HLH{nH$ H$h H$ H H9HNH$ H$ H$ KHl$H H$ H蟸H1L1 H|$IHtHl$0H$ LHH\ HD$` HD$PHLHD$hH
-.HD$`02 H HD$`HHDŽ$ Ld$pt)HT$hLd$pH$ Ht$PLH$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{EH []A\A]A^ L f.V f.V *$ HDŽ$ HT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ HA7HHt$PH|$XH$ HH$ HD$`HT$hH$ Hf D < HDŽ$ 4HT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ H!6HLL$PLT$XH$ H%H$ HD$`HT$hH$ HHD$`H$ HHtH$ HHD$`HtH|$P HH{CH腛H$ HHtH$ HHH$ HHt LLH$ HHx LLfHL$`HHnH|$P HZH|$HHIj?Hf f. AUATUSHHHHHH LA0A@% ~Ct uYHaHHH[]A\A] HXHHH[]A\A]f. uH#HHH[]A\A] D L/ Hl$ HT$H5vDV HD$HKLd$0H LP: H+HD$0HD$0 HHt?HELl$LHxHJHl$0HtHELHxH(HD$ LHxHHH[]A\A]f HhHHH[]A\A]HHD$ Ht$Hx蔏H;Ht=H H(H AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I H?M I LH {?IT$AM4$H,H9II$t8Z$$$* HI躑ffZAVH9uI$I9T$hHH[]A\A]A^A_H H8 HFH9tHGHHD$HGhH$HD$0HD$HD$ HD$ I H<$I >M M H|$l L>I$I4ID$J<HI$H{ Ht$0H|$8HT$ HD$(HLKHT$Ht$AID$I9$mHH[]A\A]A^A_HGHD$HGhH$HFH9I H<$I =I L<H|$I H=M4$H IL$H,I H9I $t8 @ A*HIIZ ffZA^H9uI $I9L$iHH[]A\A]A^A_5T D AVIAULjATIUSH H$ H$ H$ HLH{eH$ H$h H$ H H9HNH$ 萿H$ H$ +Hl$H H$ HH1LB1 H|$IHtPHl$0H$ LHV HD$` eHD$PHLHD$hH%HD$`萳2 H HD$`HHDŽ$ Ld$pt)HT$hLd$pH$ Ht$PLH$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{ =H []A\A]A^ L 6 HDŽ$ VHT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ H/HHt$PH|$XH$ HGH$ HD$`HT$hH$ H HDŽ$ 認HT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ Hw.HLL$PLT$XH$ HH$ HD$`HT$hH$ HHD$`HHD$`HtH|$P HH{r;HHH$ HHt LLH$ HHt LLH$ HHtH$ HlH|$HHt*j D qHL$`HHOH|$P H;H$ HHH$ Hf. AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I H8M I LH 8IT$AM4$H,H9II$t9Z$$$H* HIɊffZAVH9uI$I9T$gHH[]A\A]A^A_H H8 HFH9tHGHHD$HGhH$HD$0HD$HD$ HD$f D I H<$I 7M M H|$Hl L7I$I4ID$J<HI$H{ Ht$0H|$8HHT$ HD$(HLKHT$Ht$AID$I9$kHH[]A\A]A^A_HGHD$HGhH$HFH9f D I H<$I 6I L<H|$I H6M4$H IL$H,I H9I $t8 @ I*HIIZffZA^H9uI $I9L$iHH[]A\A]A^A_=T f. AVIAULjATIUSH H$ H$ H$ HLH{^H$ H$h H$ H H9HNH$ 萸H$ H$ KHl$H H$ HH1LB1 H|$IHtPHl$0H$ LHO HD$` eHD$PHLHD$hHJ HD$`萬2 H HD$`HHDŽ$ Ld$pt)HT$hLd$pH$ Ht$PLH$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{ 6H []A\A]A^ L H H H3 HDŽ$ SHT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ Hp)HHt$PH|$XH$ HH$ HD$`HT$hH$ H D HDŽ$ 誆HT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ HW(HLL$PLT$XH$ HH$ HD$`HT$hH$ HHD$`HHD$`HtH|$P HH{r4HHH$ HHt LLH$ HHt LLH$ HHtH$ HlH|$HHt*j D qHL$`HHOH|$P H;H$ HHH$ Hf. AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I H1M I LH 1IT$AM4$H,$H9II$t+f D H$I誈AFH9uI$I9T$pHH[]A\A]A^A_H H8 HFH9tHGhLHHD$HD$0HD$HD$ HD$ I I H|$0D I M L$H0I$I4ID$$H<HI$H{ Ht$0H|$8HHT$ HD$(LCHT$Ht$AID$I9$dHH[]A\A]A^A_HGHD$HGhH$HFH9 I H<$I /I L<H|$I H/M4$H IL$H,I H9I $t. @ AHCIIAFH9uI $I9L$sHH[]A\A]A^A_OT f. D AVIAULjATIUSH H$ H$ H$ HLH{WH$ H$h H$ H H9HNH$ 蠱H$ H$ 英Hl$H H$ HH1LR1 H|$IHt`Hl$0H$ LHH HD$` uHD$PH*LHD$hHHD$`蠥2 H HD$`HHDŽ$ Ld$pt)HT$hLd$pH$ Ht$PLH$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{//H []A\A]A^ L@ .mV .U ,& HDŽ$ VHT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ HS#HHt$PH|$XH$ HH$ HD$`HT$hH$ H ; HDŽ$ HT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ H1"HLL$PLT$XH$ HH$ HD$`HT$hH$ HHD$`H$ HHtH$ HHD$`HtH|$P HH{M-HH$ HHtH$ HHH$ HHt LLH$ HHx LLfHL$`HHnH|$P HZH|$HHI}?Hf f. AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I H*M I LH *IT$AM4$H,H9II$t8Z$$ H$I|ffZAVH9uI$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 @ AHIIZ{ffZA^H9uI $I9L$iHH[]A\A]A^A_=T f. AVIAULjATIUSH H$ H$ H$ HLH{PH$ H$h H$ H H9HNH$ 萪H$ H$ 諫Hl$H H$ HH1LB1 H|$IHtPzHl$0H$ LHA HD$` ezHD$PH"LHD$hH*HD$`萞2 H HD$`HHDŽ$ Ld$pt)HT$hLd$pH$ Ht$PLH$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{ (H []A\A]A^ L` f. V f.V *$ HDŽ$ DyHT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ H! HHt$PH|$XH$ HH$ HD$`HT$hH$ Hf D < HDŽ$ xHT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ H HLL$PLT$XH$ H5H$ HD$`HT$hH$ HHD$`H$ HHtH$ HHD$`HtH|$P HH{=&H}H$ HHtH$ HHH$ HHt LLH$ HHx LLfHL$`HHnH|$P HZH|$HHIv?Hf f. AUATUSHHHHHH LA0A@% ~Ct uYHaHHH[]A\A] HHHHH[]A\A]f. uHHHH[]A\A] D L(. Hl$ HT$H5&V HD$H-Ld$0H L: H+HD$0HD$0 HHt?HELl$LHxrrHuHl$0HtHELHxPrHuHD$ LHx7rHHH[]A\A]f HHHHH[]A\A]HHD$ Ht$HxqH;HtW H H{H AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I H!M I LH !IT$AM4$H,$H9II$t+f D $* HItAFH9uI$I9T$pHH[]A\A]A^A_H H8 HFH9tHGHHD$HGhH$HD$0HD$HD$ HD$ I H<$I !M M H|$l L I$I4ID$J<HI$H{ Ht$0H|$8HT$ HD$(HLKHT$Ht$AID$I9$mHH[]A\A]A^A_HGHD$HGhH$HFH9 I H<$I I I L<H|$I H* M4$H IL$H,I H9I $t- @ HA*IInrAFH9uI $I9L$tHH[]A\A]A^A_T AVIAULjATIUSH H$ H$ H$ HLH{_HH$ H$h H$ H H9HNH$ 0H$ H$ 蛬Hl$H H$ HoH1Lx1 H|$IHtqHl$0H$ LH; HD$` qHD$PHLHD$hH
+HA H9~CAD D HA H9~0AD D HA
H9~ AD
D
HAH9~
+ADD[] D AHH9u D HHHكH9HOH)L@HIHE1HHH~BII9uH9fo _ ~ D fHH9H9yHIII)LHكL9IGIIFHIt#H f HH9uM9*M)LHHHHt-fo_ ME1IIfABL9wHL9HHH9HHDH9HHDH9HHDH9HHDH9HHDH9|HHDH9jHHDH9XHH DH9FHH
+D H94HH D
+H9"HH D H9HH
D H9HHD
H9D[] @ UH)SHLHXH@I<28 0 HHL3HH9HOH)LIHIHE1HHH~+G F II9uH9~ D Ao fHH9H9 HHIHH)IrH9HsAI9@A} Hs IILHHt*E11Bo HC
+II9wHH9. AHAH9 ADDHAH9 ADDHAH9 ADDHAH9 ADDHAH9 ADDHAH9 ADDHAH9 ADDHAH9~|ADDHA H9~iAD D HA
+H9~VAD
+D
+HA H9~CAD D HA H9~0AD D HA
H9~ AD
D
HAH9~
+ADD[] D AHH9u D HHHكH9HOH)L@HIHE1HHH~BII9uH9fo7_ ~ D fHH9H9yHIII)LHكL9IGIIFHIt#H f HH9uM9*M)LHHHHt-fo_ ME1IIfABL9wHL9HHH9HHDH9HHDH9HHDH9HHDH9HHDH9|HHDH9jHHDH9XHH DH9FHH
+D H94HH D
+H9"HH D H9HH
D H9HHD
H9D[] @ HH)LLXH@I<2M38 t21H9 A HH9uf. HHUHSH9HOH)LIHIHE1HHH~-C B II9uf. Ao fHH9H9 HHIIH)IsI9IrAI9@A} Hs HHHHHt*E11Co HC
+IH9wHH9. AHAH9 ADDHAH9 ADDHAH9 ADDHAH9 ADDHAH9 ADDHAH9 ADDHAH9 ADDHAH9~|ADDHA H9~iAD D HA
+H9~VAD
+D
+HA H9~CAD D HA H9~0AD D HA
H9~ AD
D
HAH9~
+ADD[] @ AHH9u D HH)LLXH@I<2M38 t21H9 A HH9uf. HHUHSH9HOH)LIHIHE1HHH~-C B II9uf. Ao fHH9H9 HHIIH)IsI9IrAI9@A} Hs HHHHHt*E11Co HC
+IH9wHH9. AHAH9 ADDHAH9 ADDHAH9 ADDHAH9 ADDHAH9 ADDHAH9 ADDHAH9 ADDHAH9~|ADDHA H9~iAD D HA
+H9~VAD
+D
+HA H9~CAD D HA H9~0AD D HA
H9~ AD
D
HAH9~
+ADD[] @ AHH9u D AVAUIATUHH)SHL0L@H@I<68 T HHHۃH9HOH)L`HLIIIH~
+H1MiI9~1HI| 1HLLHHP(i HI9L9 [H1]L)MK<&A\A]A^hfofHH9H9 IHMMH)IvI9IpAI9@A H IILHHt+E1E1CoICIM9wHH9
+HAH9 D
+DHAH9 D
+DHAH9 D
+DHAH9 D
+DHAH9 D
+DHAH9 D
+DHAH9 D
+DHAH9~uD
+DHA H9~cD
+ D HA
+H9~QD
-HD$` 2 H HD$`HHDŽ$ Ld$pt)HT$hLd$pH$ Ht$PLH$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{ H []A\A]A^ LP 6 HDŽ$ pHT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ HHHt$PH|$XH$ HgH$ HD$`HT$hH$ H HDŽ$ pHT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ HgHLL$PLT$XH$ HH$ HD$`HT$hH$ HHD$`HHD$`HtH|$P HH{ HuHH$ HHt LLH$ HHt LLH$ HHtH$ HlH|$HHtnj D qHL$`HHOH|$P H;H$ HHH$ Hf. AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I HM I LH IT$AM4$H,$H9II$t,f D $H* HI9mAFH9uI$I9T$oHH[]A\A]A^A_H H8 HFH9tHGHHD$HGhH$HD$0HD$HD$ HD$f D I H<$I 1M 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 iI L<H|$I HJM4$H IL$H,I H9I $t- @ HI*IIkAFH9uI $I9L$tHH[]A\A]A^A_T AVIAULjATIUSH H$ H$ H$ HLH{AH$ H$h H$ H H9HNH$ PH$ H$ ۙHl$H H$ H菋H1Lq1 H|$IHtjHl$0H$ LH4 HD$` jHD$PHILHD$hHHD$` 2 H HD$`HHDŽ$ Ld$pt)HT$hLd$pH$ Ht$PLH$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{H []A\A]A^ L萘H H H3 HDŽ$ iHT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ HHHt$PH|$XH$ H$H$ HD$`HT$hH$ H D HDŽ$ :iHT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ HgHLL$PLT$XH$ HH$ HD$`HT$hH$ HHD$`HHD$`HtH|$P HH{HnHH$ HHt LLH$ HHt LLH$ HHtH$ HlH|$HHtgj D qHL$`HHOH|$P H;H$ HHH$ Hf. AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I H5M I LH IT$AM4$H,$H9II$t.f D $HIZWfAFH9uI$I9T$mHH[]A\A]A^A_H H8 HFH9tHGhLHHD$HD$0HD$HD$ HD$I I H|$XD I M L$H4I$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|$I HjM4$H IL$H,I H9I $t0 @ AHIIZdAFH9uI $I9L$qHH[]A\A]A^A_ݽT f. AVIAULjATIUSH H$ H$ H$ HLH{:H$ H$h H$ H H9HNH$ `H$ H$ Hl$H H$ H蟄H1Lj1 H|$IHtcHl$0H$ LH- HD$` dHD$PH LHD$hHHD$`02 H HD$`HHDŽ$ Ld$pt)HT$hLd$pH$ Ht$PLH$ D$ $ LHHH$ Ht
LLHD$`HtH|$P HH{H []A\A]A^ LВ .V . U ,& HDŽ$ bHT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ Hc HHt$PH|$XH$ HǸH$ HD$`HT$hH$ H ; HDŽ$ 4bHT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ HAHLL$PLT$XH$ HUH$ HD$`HT$hH$ HHD$`H$ HHtH$ HHD$`HtH|$P HH{HgH$ HHtH$ HHH$ HHt LLH$ HHx LLfHL$`HHnH|$P HZH|$HHIj`?Hf f. AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I H5
M I LH
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-H~bHH@H9HCH9tbHv\HfH11H< oHfofHH9wH9t HHHI$ HL[]A\A] D 1f D H HH HH9uf. AUIATIHUSH蘴Hh1LHHL1 HIIj2 HqtI}H5j2 LH
-1H~" H4H?HH3 H)H HH9uI$ HL[]A\A]f. AWIAVIAUATUSH8HG(H8 HGhWHD$ HGHD$HFH9k @ I H|$ )$$I ,II H|$H\ M I6I~A ($$L @M : HHHHڃH9HOH)HHHHIHHH H HCH9HFAH9A (IIE1E1 J (I(B (BLTUVBBDIM9wH9t;(L@]L9~%(HH9B]B~
(]H9}0(H H(])HH9M H9 ILIJT I)HBI9I@AH9A I LHL, MtK(1E1 (I( (LTUVA AD HI9rLM9t6(HA]H9~ (HH9]~
-] M9^H8[]A\A]A^A_f(]HH9u 1Hf D 1f D (]HH9uH H8 HFH9tHGWH_hHD$ I I H)d$5I H|$ \ I $I6LD MN $(d$@N$M&K HLHHڃL9IOH)HyHHHHHH Hd IxH9H~AI9@AE (II1E1 N (I(A 8(AL8TUV>D>HM9wL9t>(I{C]H9B~&(IL9A]~(C]BH9}0(I< H(])HH9M&I9 HH<MHT I)HBH9HGAH9A I LHL, MtI(1E1 (I( (LTUVDHI9rLM9t9(HAA]I9~"(HI9A]~ A] M9fHGhWHD$ HGHD$(HFH9f I H|$ )$M I H|$(I M HD$sII~It ($H,I. IHIIAI9LOL)LBHIHHLM LFLYL9AL9AE LCL9AL9AE Ef I\ ME1E1IL $I(I(BB BDBL]BBDIL9$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(I(AA 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$H;1 H|$ u
H[]A\A]A^ÿX Lt$ 7HI資: LH7Ld$0L; Hl$PH
/:V H5U MV HHyU LHLHL#HD$ HHx7HH5$ L AHH|$HtH{Ht,HDAIHLLHD$ Ht$Hxk7Ls8HHHHf. AWIAVIAUATUSH(HG(H8 HGhHD$HGH$HFH9 I H|$I hI H<$II L$JI6MN\ N @M HLHL9IOH)HH?HHHH H
- ID$H9HF@I9@ f(HHE11fL C Hf(fCLf(ffTfUfVfBfBDIH9wI9tf(C]BH9}2f(If D fHf(f]f)HH9M I9 ILMJ<I)HGI9IBH9 I
- MILHtRf(11f Hf(fLf(ffTfUfVfAfADHL9rHI9t A] M9^H([]A\A]A^A_ f(A]HI9uf D 1L7f D 1f D f(A]HH9uH H8 6 HFH9{HGH_hHD$ I I HI H|$\ I $I6LL MV $@N,M.] HLHL9IOH)HH?HHHH H
-B IyH9H~AI9@A# f(IIE11fO$C Hf(fCLf(ffTfUfVfBfBDII9wL9tf(C]BH9}3f(I<ff D fHf(f]f)HH9M.I9 HL MH| I)HGI9ICH9 I
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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$H11 H|$ u
H[]A\A]A^ÿX Lt$ -HIC~: LHh.Ld$0L苈; Hl$PH
,V H5@U MV HH ~U LHL#H{LHD$ HHxb.H+H5 L7HH|$HtH{HtH7IHLLNHD$ Ht$Hx-L/HHHHf. AWAVAUIATIUSH HG(H8 HGhH\$ L|$H$HGHD$HFH9 I$ H<$M$ I$ H|$M4FI$ IuI} LE 1L wHMM fL$PfL$:fL$rfL$H~2f AFfL$ f9fT$LHBfGHH9uM9M]HĈ []A\A]A^A_H H8 HFH9tHGhL|$ Lt$HD$HGH$f D I$ H|$I$ I$ H<$H CI$ IuI} LDE 1L wHMM fL$PfL$:fL$rfL$H~/f D A@fL$ f9fT$LIBfGHH9uM9M] HGhH\$PL|$0H$HGHD$HFH9I$ H<$M$ 'I$ H|$M4FI$ IuI} LLE 1LwHMU ~3 D AAA FfT$0f9LHBfL$PfGHH9uM9UpNf. AVHAUATUSHHĀH HG Hl$H-1 H|$ u
H[]A\A]A^ÿX Lt$ )HI3z: LHX*Ld$0L{; Hl$PH
$V H50U MV HHyU LHLHkLHD$ HHxR*HH5܍ L3HH|$HtH{HtH3IHLL>HD$ Ht$Hx)L*HHHHf. AWIAVIAUATUSH(HG(H8 HGhHD$HGHD$HFH9 I H|$I I H|$IM H\ I6MFA| N,@M.S HLHHڃL9IOH)HHHHIHHH H$ HCH9HFAH9A <$IE1fn<$IE1J fp Bo IfofofffffBIM9rH9t@9<ADN D LHL9~(B9<AFNHH9F~9<ADN D H9}J<$Hfn4$fp fo @ oHfofffoffffHH9M.I9 IL MJT I)HBI9IAAH9A I LHL$ MtJ<$1E1fn<$fp o IfofofffffAHI9rLM9t49<NHAI9~ 9<NHI9ȉ~
-9<N<<M9nH([]A\A]A^A_Ð9<NHI9uf 1Lf D 1f D 9<ADN D HH9umH H8 HFH9tHGhHD$HGHD$ I H|$I I H|$I I6MFH| N$@M&h HLHHڃL9IOH)LIHIHHHH H LOL9LNAL9AEb $IE1fn<$IE1N fp Bo IfofofffffBIM9wL9tBB9 AFN F MKL9~)B9 AFNIL9F~B9 AFN F H9}M $L fn4$fp fof D Ao IfofffoffffHH9M&I9 HL MHT I)HBI9IAAH9A I LHL, MtJ $1E1fn4$fp o IfofofffffAHI9rLM9t49 NHAI9~ 9 NHI9ȉ~
-9 N M9fHGhHD$HGHD$HFH9 I H|$M 0I H|$I M HD$I6I~IL H,@I. IHIIAI9LOL)LBHIHHLM LAL^L9AL9AE LCL9AL9AE Eu Ik ME1E1IL $IBo IBofofffffBIL9$wM9tEFF9FNFMCM9~+FF9FNIM9F~FF9FNFI9 N E1J,N If Ao(ICo fofffoffffCIL9M L $H9 LD$HHMT H)IO LIAM`I9AM9A IBI9M9AD A H IIN, Mt@1E1Ao IAofofffffA HM9wLL9t=9NHBH9~'DD9DNHH9D~
9NH$I9F5 9NHH9u 9 NHI9uf D 1Lf D I1Nf D E1 D FF9FNFIM9uI9H,$Z @ E1 D B9 AFNFII9ufAVHAUATUSHHĀH HG Hl$H"1 H|$ u
H[]A\A]A^ÿX Lt$ HICo: LHh Ld$0Ly; Hl$PH
V H5@U MV HH oU LHL#H{LHD$ HHxb H+H5э L(HH|$HtH{HtH(IHLLNHD$ Ht$Hx L HHHHf. AWAVIAUATUHSHHG(H8 n HFH9LohLg f H[]A\A]A^A_ÐH T$ I1fnl$ IE1J< fp fof%UV o IfofofUV fffffHM9rH9t79FHGH9~"9ADFHH9D~
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D9,DF,D,M9NH LL H HL El IvIIL HM~HBH9HA@H9@1 D9,DF<<HH9uo @ HFH9LghH_ f D H' I1E1IJ< o IAoT fofofSV f SV fffffHM9wH9tBAD 9FHGH9~*EL D9 DF HH9D ~AD 9FM9FcH LL ]H HL Ml BIvIILHM~IELIH9@M9 HBH9L9AD @1 D A| 9<F<<HH9uV AVHAUATUSHHĀH HG Hl$H 1 H|$ u
H[]A\A]A^ÿX Lt$ HIci: LHLd$0Ls; Hl$PH
V H5`U MV H8H)iU LHLCH蛽LӼHD$ HHxHKH5ˍ L"HH|$HtH{HtH"IHL6LnHD$ Ht$HxL#HHHHf. AWAVIAUIATUSHHG(H8 HFH9LghHotx D I LI "I HM H MEIu IJMIE ~$1 D H9HHN H HI9uIE I9EuH[]A\A]A^A_H H8 HFH9tLghHo @ I LI zI HM H `IuI} IHHIE ~$1 D H9 HHN H HH9uIE I9EuH[]A\A]A^A_HFH9HohH_4f I HM I HM M$IuI} I HHIE ~%1 D IH9HNHHH9uIE I9EuH[]A\A]A^A_f. AWAVAUATUSH HFXH|$HHT$0D E7 A HD$pHt$0HHD$@DAD$L$ 11D$8AD$M}D$<H$ HD$ 8 H|$0AvHPIt$LL$ H$ H$ H$x H$ H H9HNH$ HH$ H$ GH$ E9D$< H|$ I 9ќ. HL$@HT$ H|$PHt$`HD$`3H$ HT$PHtHT$(HT$(IL$ H$ LD$ D$ L$ L^HtH1 HyL1;l$8t+LHIH|$0If. H|$xHD$HHH tuMtLHD$HH []A\A]A^A_L|$0L贆HxHx<2 HFLH蕆HP1L 1 HxIK<2 LH 1H~f HL H HH9uHD$HH eL$ I L7H|$0L17 1 H$ HHtHD$(=HT$(E1qH$ HHtHE1I}HD$ HD$ H|$xHtHD$ >HD$ MHtL٩HtH̩H4 f릿X L$ L$ $H5U LLHL$ Lm; H$ H
V H5U Mຖ H虬HfU LHH褷HL4H$ HHxHH52ō H E1 IHL贶LH$ LHxHH8 HHHH$ HHI}HOH{ D qH f. AVHAUATUSHHĀH HG Hl$H1 H|$ u
H[]A\A]A^ÿX Lt$ HI3a: LHXLd$0L{k; Hl$PH
U H50U MV HH`U LHLHkL裴HD$ HHxRHH5Í LHH|$Ht蚿H{HtHIHLL>HD$ Ht$HxLHHHHf. AWAVIAUIATUSHHG(H8 HFH9LghHotx D I LI I HM H MEIu IJMIE ~$1 D H9HHF H HI9uIE I9EuH[]A\A]A^A_H H8 HFH9tLghHo @ I LI JI HM H 0IuI} IHHIE ~$1 D H9 HHF H HH9uIE I9EuH[]A\A]A^A_HFH9HohH_4f I HM 調I HM M$萿IuI} I HHIE ~%1 D IH9HFHHH9uIE I9EuH[]A\A]A^A_f. AWIAVIAUATUSH8HG(H8 HGhWHD$ HGHD$HFH9k @ I H|$ )$$I ̾II H|$H\ M 謾I6I~A ($$L @M : HHHHڃH9HOH)HHHHIHHH H HCH9HFAH9A (IIE1E1 J (I(B BL(TUVBBDIM9wH9t;(L@_L9~%(HH9B_B~
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- ID$H9HF@I9@ f(HHE11fL C Hf(fCLf(ffTfUfVfBfBDIH9wI9tf(C_BH9}2f(If D fHf(f_f)HH9M I9 ILMJ<I)HGI9IBH9 I
- MILHtRf(11f Hf(fLf(ffTfUfVfAfADHL9rHI9t A_ M9^H([]A\A]A^A_ f(A_HI9uf D 1L7f D 1f D f(A_HH9uH H8 6 HFH9{HGH_hHD$ I I HʲI H|$\ I $觲I6LL MV $@N,M.] HLHL9IOH)HH?HHHH H
-B IyH9H~AI9@A# f(IIE11fO$C Hf(fCLf(ffTfUfVfBfBDII9wL9tf(C_BH9}3f(I<ff D fHf(f_f)HH9M.I9 HL MH| I)HGI9ICH9 I
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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 AWAVAUIATIUSH HG(H8 HGhH\$ L|$H$HGHD$HFH9 I$ H<$M$ GI$ H|$M4FI$ )IuI} LE 1L wHMM fL$PfL$:fL$rfL$H~2f AFfL$ f9fT$LHBfGHH9uM9M]HĈ []A\A]A^A_H H8 HFH9tHGhL|$ Lt$HD$HGH$f D I$ H|$I$ VI$ H<$H CI$ 9IuI} LDE 1L wHMM fL$PfL$:fL$rfL$H~/f D A@fL$ f9fT$LIBfGHH9uM9M] HGhH\$PL|$0H$HGHD$HFH9I$ H<$M$ 臫I$ H|$M4FI$ iIuI} LLE 1LwHMU ~3 D AFA AfT$Pf9LHBfL$0fGHH9uM9UpNf. AWIAVIAUATUSH(HG(H8 HGhHD$HGHD$HFH9{ I H|$I 蠪I H|$IM H\ 耪I6MFA| N,@M.S HLHHڃL9IOH)HHHHIHHH H$ HCH9HFAH9A <$IE1fn<$IE1J fp Bo IfofffffBIM9rH9t@9<ADM D LHL9~(B9<AFMHH9F~9<ADM D H9}N<$Hfn4$fp fo oHfofffoffffHH9M.I9 IL MJT I)HBI9IAAH9A I LHL$ MtF<$1E1fn<$fp o IfofffffAHI9rLM9t49<MHAI9~ 9<MHI9ȉ~
-9<M<<M9nH([]A\A]A^A_ D 9<MHI9uf 1Lf D 1f D 9<ADM D HH9uiH H8 HFH9yHGhHD$HGHD$ I H|$I 萧I H|$I uI6MFH| N$@M&h HLHHڃL9IOH)LIHIHHHH H LOL9LNAL9AEb $IE1fn<$IE1N fp Bo IfofffffBIM9wL9tBB9 AFM F MKL9~)B9 AFMIL9F~B9 AFM F H9}Q $L fn4$fp fof. Ao IfofffoffffHH9M&I9 HL MHT I)HBI9IAAH9A I LHL, MtF $1E1fn4$fp o IfofffffAHI9rLM9t49 MHAI9~ 9 MHI9ȉ~
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HHHĐ H[]A\HH$ HtH|$p HHA'H|$ HHtH<$HtH|$0HtR H|$PHtC HHHff. fATHUSHH HD$PHo`HD$hHs`HD$`Y20 HD$pO90 HD$xE40 H$ 8=0 H$ HD$0 HD$8 HD$@ Hh Ht$p HHD$0Hl$@%Hl$810 H$ 80 H$ 30 H$ <0 H$ HD$ HD$ HD$ ] L$ Hh HǺ HD$LHl$ $HHl$芼HT$H5^ H61 HT$0H5@\\ H"1 H5~[ H裠1 H5Dc HT1 HH5O[ HB 1 H8H@1 HE HE Hl$PLHHH$ IH$ HtH<$HtyH|$Ht* H|$0Ht HD$`Ht
HHHİ H[]A\HHD$`HtH|$P HH$H$ HHtH<$HtH|$Ht H|$0Ht HH @ H ATHUSHH HD$PH]HD$hH]HD$`10 HD$p:0 HD$x70 H$ /0 H$ {30 H$ n60 H$ a90 8 H$ HD$0 HD$8 HD$@ Hh8Ht$p8 HHD$0Hl$@D"Hl$8
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HHH H[]A\HHD$`HtH|$P HH!H$ HHtH<$HtH|$HtH|$0HtHHHÐf. fATHUSHH HD$PHZHD$hHZHD$`.0 HD$p70 HD$x40 H$ ,0 H$ {00 H$ n30 H$ a60 8 H$ HD$0 HD$8 HD$@ Hh8Ht$p8 HHD$0Hl$@D Hl$8
+.0 H$ 60 H$ 30 H$ +0 H$ /0 H$ 20 H$ 50 8 H$ HD$ HD$ HD$ _L$ Hh8HǺ8 HD$LHl$ HHl$茶HT$H5^ H81 HT$0H5BV\ H$1 H5c H襚1 H5Fc HV1 HH5I[ HB 1 H8HB1 HE HE Hl$PLHHH$ KH$ HtH<$Ht{H|$Ht,H|$0Ht HD$`Ht
HHH H[]A\HHD$`HtH|$P HH H$ HHtH<$HtH|$HtH|$0HtHHHÐf. fATHUSHH HD$pHWH$ HWH$ +0 HD$HD$P HD$X HD$` IHhHt$ HHD$PHl$` Hl$XO+0 HD$ HD$0 HD$8 HD$@ Ld$ HhHǺ HD$0LHl$@2 HHl$8%HT$0H5U^ HѮ1 HT$PH5S\ H轮1 H5hc H>1 H5߈c H1 HH5FG[ HB B 菙1 H8Hԕ1 HE HE Hl$pLHHHD$ H|$ Ht!H<$HtH|$0HtH|$PHtH$ Ht
HHHĐ H[]A\HH$ HtH|$p HH% H|$ HHtH<$HtH|$0Ht6H|$PHt'HfHH @ f. fUH8SHH HD$@HQUHD$XHUUHD$P;)0 HD$`120 HD$h'.0 HD$p 10 HD$x'0 H$ +0 H$ /0 H$ %0 H$ )0 H$ ,0 H$ .0 X H$ HD$ HD$( HD$0 hHhXHt$`HǺX HD$ Hl$0HHl$(蛱HT$ H5^ HG1 H5T\ Hȕ1 H5ic Hy1 HH5D[ HB
+ 1 H8He1 HE HE Hl$@Ht$HHHD$oH|$HtH<$HtH|$ HtSHD$PHt
HHH H[]HHD$PHtH|$@ HHH|$HHt9H<$Ht+H|$ HtHHff. fATH USHH0HD$HSHD$(HSHD$ &0 HI!H5V^ LHO1 H5\ HP1 H5c H1 HH5XC[ HB 訕1 H8H1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+HD$ HtH|$ HH0H[]A\f D 1HH<$HtHD$ HtH|$ HH2HHKIHt
HHH;HtH Lf. ATHgUSHH0HD$HQHD$(HQHD$ l%0 HI葮H5^ LH迥1 H5:b H1 H5ac Hq1 HH5A[ HB 1 H8H]1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+HD$ HtH|$ HH0H[]A\f D 1HH<$HtCHD$ HtH|$ HHHHKIHt
HHH;HtH Lf. ATH USHH0HD$HOHD$(HOHD$ %0 HIH56^ LH/1 H5Ů\ H01 H5сc H1 HH58@[ HB 舒1 H8H͎1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+HD$ HtH|$ HH0H[]A\f D 1HH<$HtHD$ HtH|$ HHHHKIHt
HHH;HtgH Lf. ATH USHH0HD$HbNHD$(HfNHD$ L&0 HIqH5^ LH蟢1 H55\ H蠏1 H5Ac HQ1 HH5>[ HB 1 H8H=1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+cHD$ HtH|$ HH0H[]A\f D 1HH<$Ht#HD$ HtH|$ HHHHKIHt
HHH;HtH Lf. ATHUSHH0HD$HLHD$(HLHD$ 0 HIH5^ LH1 H5\ H1 H5~c H1 HH5=[ HB h1 H8H譋1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+ӿHD$ HtH|$ HH0H[]A\f D 1HH<$Ht蓿HD$ HtH|$ HHHHKIHt
HHH;HtGH Lf. ATH USHH0HD$HBKHD$(HFKHD$ ,'0 HIQH5^ LH1 H5\ H而1 H5!}c H11 HH5;[ HB ؍1 H8H 1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+CHD$ HtH|$ HH0H[]A\f D 1HH<$HtHD$ HtH|$ HHbHHKIHt
HHH;Ht跽H Lf. ATHUSHH0HD$HIHD$(HIHD$ 0 HIH5^ LH1 H5+;\ H1 H5{c H衋1 HH59[ HB H1 H8H荈1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+購HD$ HtH|$ HH0H[]A\f D 1HH<$HtsHD$ HtH|$ HHHHKIHt
HHH;Ht'H Lf. ATHUSHH0HD$H"HHD$(H&HHD$ 0 HI1H5f^ LH_1 H5\ H`1 H5zc H1 HH5h8[ HB 踊1 H8H1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+#HD$ HtH|$ HH0H[]A\f D 1HH<$HtHD$ HtH|$ HHB
HHKIHt
HHH;Ht藺H Lf. ATHUSHH0HD$HFHD$(HFHD$ |0 HI衣H5^ LHϚ1 H5v\ HЇ1 H5qxc H聈1 HH56[ HB (1 H8Hm1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+蓹HD$ HtH|$ HH0H[]A\f D 1HH<$HtSHD$ HtH|$ HH HHKIHt
HHH;HtH Lf. ATHUSHH0HD$HEHD$(HEHD$ 0 HIH5F^ LH?1 H5\ H@1 H5vc H1 HH5H5[ HB 蘇1 H8H݃1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+HD$ HtH|$ HH0H[]A\f D 1HH<$Ht÷HD$ HtH|$ HH"
+HHKIHt
HHH;HtwH Lf. ATHUSHH0HD$HrCHD$(HvCHD$ \0 HI聠H5^ LH诗1 H5`\ H谄1 H5Quc Ha1 HH53[ HB 1 H8HM1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+sHD$ HtH|$ HH0H[]A\f D 1HH<$Ht3HD$ HtH|$ HHHHKIHt
HHH;HtH Lf. ATHGUSHH0HD$HAHD$(HAHD$ 0 HIH5&^ LH 1 H5ՠ\ H 1 H5sc Hу1 HH5(2[ HB x1 H8H轀1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+HD$ HtH|$ HH0H[]A\f D 1HH<$Ht裴HD$ HtH|$ HHHHKIHt
HHH;HtWH Lf. ATH
USHH0HD$HR@HD$(HV@HD$ <0 HIaH5^ LH菔1 H5K\ H萁1 H51rc HA1 HH50[ HB 1 H8H-1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+SHD$ HtH|$ HH0H[]A\f D 1HH<$HtHD$ HtH|$ HHrHHKIHt
HHH;HtDzH Lf. ATH
USHH0HD$H>HD$(H>HD$ 0 HIћH5^ LH1 H5\ H 1 H5pc H豀1 HH5/[ HB X1 H8H}1 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. ATH7 USHH0HD$H2=HD$(H6=HD$ 0 HIAH5v^ LHo1 H56\ Hp~1 H5oc H!1 HH5x-[ HB 1 H8H
|1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+3HD$ HtH|$ HH0H[]A\f D 1HH<$HtHD$ HtH|$ HHRHHKIHt
HHH;Ht觯H Lf. ATH USHH0HD$H;HD$(H;HD$ 0 HI豘H5^ LHߏ1 H5e[ H|1 H5mc H}1 HH5+[ HB
8~1 H8H}z1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+裮HD$ HtH|$ HH0H[]A\f D 1HH<$HtcHD$ HtH|$ HH HHKIHt
HHH;HtH Lf. ATH
+USHH0HD$H:HD$(H:HD$ 0 HI!H5V^ LHO1 H5[ HP{1 H5kc H|1 HH5X*[ HB
|1 H8Hx1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+HD$ HtH|$ HH0H[]A\f D 1HH<$HtӬHD$ HtH|$ HH2HHKIHt
HHH;Ht臬H Lf. ATH USHH0HD$H8HD$(H8HD$ l
0 HI葕H5^ LH迌1 H5E[ Hy1 H5ajc Hqz1 HH5([ HB
{1 H8H]w1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+胫HD$ HtH|$ HH0H[]A\f D 1HH<$HtCHD$ HtH|$ HHHHKIHt
HHH;HtH Lf. ATH USHH0HD$H6HD$(H6HD$ 0 HIH56^ LH/1 H5[ H0x1 H5hc Hx1 HH58'[ HB
y1 H8Hu1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+HD$ HtH|$ HH0H[]A\f D 1HH<$Ht賩HD$ HtH|$ HHHHKIHt
HHH;HtgH Lf. ATHUSHH0HD$Hb5HD$(Hf5HD$ L0 HIqH5^ LH蟉1 H5 a Hv1 H5Agc HQw1 HH5%[ HB w1 H8H=t1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+cHD$ HtH|$ HH0H[]A\f D 1HH<$Ht#HD$ HtH|$ HHHHKIHt
HHH;HtקH Lf. ATHwUSHH0HD$H3HD$(H3HD$ 0 HIH5^ LH1 H5ڮc Hu1 H5ec Hu1 HH5$[ HB hv1 H8Hr1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+ӦHD$ HtH|$ HH0H[]A\f D 1HH<$Ht蓦HD$ HtH|$ HHHHKIHt
HHH;HtGH Lf. ATHgUSHH0HD$HB2HD$(HF2HD$ ,0 HIQH5^ LH1 H5a Hs1 H5!dc H1t1 HH5"[ HB t1 H8H q1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+CHD$ HtH|$ HH0H[]A\f D 1HH<$HtHD$ HtH|$ HHbHHKIHt
HHH;Ht跤H Lf. ATHWUSHH0HD$H0HD$(H0HD$
0 HIH5^ LH1 H5˫c Hq1 H5bc Hr1 HH5 [ HB Hs1 H8Ho1 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. ATHW USHH0HD$H"/HD$(H&/HD$ 0 HI1H5f^ LH_1 H5,\ H`p1 H5ac Hq1 HH5h [ HB q1 H8Hm1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+#HD$ HtH|$ HH0H[]A\f D 1HH<$HtHD$ HtH|$ HHBHHKIHt
HHH;Ht藡H Lf. ATHg USHH0HD$H-HD$(H-HD$ |0 HI衊H5^ LHρ1 H5\ Hn1 H5q_c Ho1 HH5 [ HB (p1 H8Hml1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+蓠HD$ HtH|$ HH0H[]A\f D 1HH<$HtSHD$ HtH|$ HHHHKIHt
HHH;HtH Lf. ATH USHH0HD$H,HD$(H,HD$ 0 HIH5F^ LH?1 H5 \ H@m1 H5]c Hm1 HH5H [ HB n1 H8Hj1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+HD$ HtH|$ HH0H[]A\f D 1HH<$HtÞHD$ HtH|$ HH"HHKIHt
HHH;HtwH Lf. ATH USHH0HD$Hr*HD$(Hv*HD$ \/ HI聇H5^ LH~1 H5\ Hk1 H5Q\c Hal1 HH5[ HB m1 H8HMi1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+sHD$ HtH|$ HH0H[]A\f D 1HH<$Ht3HD$ HtH|$ HHHHKIHt
HHH;HtH Lf. ATHgUSHH0HD$H(HD$(H(HD$ / HIH5&^ LH }1 H51` H j1 H5Zc Hj1 HH5([ HB xk1 H8Hg1 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. ATH7USHH0HD$HR'HD$(HV'HD$ < 0 HIaH5^ LH{1 H5` Hh1 H51Yc HAi1 HH5[ HB i1 H8H-f1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+SHD$ HtH|$ HH0H[]A\f D 1HH<$HtHD$ HtH|$ HHrHHKIHt
HHH;HtǙH Lf. ATHUSHH0HD$H%HD$(H%HD$ / HIтH5^ LHy1 H5ʠc H g1 H5Wc Hg1 HH5[ HB Xh1 H8Hd1 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. ATH USHH0HD$H2$HD$(H6$HD$ / HIAH5v^ LHox1 H5F[ Hpe1 H5Vc H!f1 HH5x[ HB f1 H8H
c1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+3HD$ HtH|$ HH0H[]A\f D 1HH<$HtHD$ HtH|$ HHRHHKIHt
HHH;Ht觖H Lf. ATHUSHH0HD$H"HD$(H"HD$ / HIH5^ LHv1 H5c Hc1 H5Tc Hd1 HH5[ HB 8e1 H8H}a1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+裕HD$ HtH|$ HH0H[]A\f D 1HH<$HtcHD$ HtH|$ HHHHKIHt
HHH;HtH Lf. ATHwUSHH0HD$H!HD$(H!HD$ / HI!~H5V^ LHOu1 H5c HPb1 H5Rc Hc1 HH5X[ HB c1 H8H_1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+HD$ HtH|$ HH0H[]A\f D 1HH<$HtӓHD$ HtH|$ HH2HHKIHt
HHH;Ht臓H Lf. ATHGUSHH0HD$H HD$(H HD$ l/ HI|H5ƽ^ LHs1 H5` H`1 H5aQc Hqa1 HH5[ HB b1 H8H]^1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;HtH Lf. ATH USHH0HD$H HD$(H HD$ / HI{H56^ LH/r1 H5[ H0_1 H5Oc H_1 HH58[ HB `1 H8H\1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+HD$ HtH|$ HH0H[]A\f D 1HH<$Ht賐HD$ HtH|$ HHHHKIHt
HHH;HtgH Lf. ATHUSHH0HD$Hb HD$(Hf HD$ L/ HIqyH5^ LHp1 H5` H]1 H5ANc HQ^1 HH5 [ HB ^1 H8H=[1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+cHD$ HtH|$ HH0H[]A\f D 1HH<$Ht#HD$ HtH|$ HHHHKIHt
HHH;HtH Lf. ATHUSHH0HD$HHD$(HHD$ / HIwH5^ LHo1 H5` H\1 H5Lc H\1 HH5 [ HB h]1 H8HY1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;HtGH Lf. ATHUSHH0HD$HBHD$(HFHD$ ,/ HIQvH5^ LHm1 H5[c HZ1 H5!Kc H1[1 HH5 [ HB [1 H8H X1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;Ht跋H Lf. ATHg USHH0HD$HHD$(HHD$ / HItH5^ LHk1 H5c HX1 H5Ic HY1 HH5[ HB HZ1 H8HV1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;Ht'H Lf. ATH'USHH0HD$H"HD$(H&HD$ / HI1sH5f^ LH_j1 H5;c H`W1 H5Hc HX1 HH5h[ HB X1 H8HT1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;Ht藈H Lf. ATHUSHH0HD$HHD$(HHD$ |/ HIqH5ֲ^ LHh1 H5c HU1 H5qFc HV1 HH5[ HB (W1 H8HmS1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;HtH Lf. ATHG USHH0HD$HHD$(HHD$ / HIpH5F^ LH?g1 H5 r\ H@T1 H5Dc HT1 HH5H[ HB U1 H8HQ1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+HD$ HtH|$ HH0H[]A\f D 1HH<$HtÅHD$ HtH|$ HH"HHKIHt
HHH;HtwH Lf. ATHg USHH0HD$HrHD$(HvHD$ \/ HInH5^ LHe1 H5c HR1 H5QCc HaS1 HH5[ HB T1 H8HMP1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;HtH Lf. ATH USHH0HD$HHD$(HHD$ / HIlH5&^ LH d1 H5n\ H Q1 H5Ac HQ1 HH5( [ HB xR1 H8HN1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;HtWH Lf. ATHg USHH0HD$HRHD$(HVHD$ </ HIakH5^ LHb1 H5\m\ HO1 H51@c HAP1 HH5Z HB P1 H8H-M1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;HtǀH Lf. ATH USHH0HD$H HD$(H HD$ / HIiH5^ LH`1 H5k\ H N1 H5>c HN1 HH5Z HB XO1 H8HK1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;Ht7H Lf. ATH USHH0HD$H2 HD$(H6 HD$ / HIAhH5v^ LHo_1 H5Qc HpL1 H5=c H!M1 HH5xZ HB M1 H8H
J1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;Ht}H Lf. ATHg USHH0HD$H HD$(H HD$ / HIfH5^ LH]1 H5h\ HJ1 H5;c HK1 HH5Z HB 8L1 H8H}H1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;Ht|H Lf. ATHW USHH0HD$HHD$(HHD$ / HI!eH5V^ LHO\1 H5 g\ HPI1 H59c HJ1 HH5XZ HB J1 H8HF1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;HtzH Lf. ATH USHH0HD$HHD$(HHD$ l/ HIcH5Ƥ^ LHZ1 H5e\ HG1 H5a8c HqH1 HH5Z HB I1 H8H]E1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
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HHH;HtxH Lf. ATHUSHH0HD$HHD$(HHD$ / HIbH56^ LH/Y1 H5_ H0F1 H56c HF1 HH58Z HB G1 H8HC1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
+wHD$ HtH|$ HH0H[]A\f D 1HH<$HtwHD$ HtH|$ HHHHKIHt
HHH;HtgwH Lf. ATH USHH0HD$HbHD$(HfHD$ L/ HIq`H5^ LHW1 H5{b\ HD1 H5A5c HQE1 HH5Z HB E1 H8H=B1 HE HE HC HHD$ HtdHkHt$ HfoD$ HD$ CH<$HC( Ht
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OI HfB*fB f]fA)HH9MI9 H$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$HP0 H|$ u
H[]A\A]A^ÿX Lt$ "HICz; LH"Ld$0L;< Hl$PH
O[ H5 Z M HHHcZ LHLSH諗LHD$ HHx"HH5 Ll,HH|$Ht>H{HtH,IHLFL~HD$ Ht$Hxk"L#HHHHf. AWAVAUIATIUSH HG(H8 HGhH\$ L|$H$HGHD$HFH9 I$ H<$M$ GI$ H|$M4FI$ )IuI} LE 1L wHMM fL$PfL$:fL$rfL$H~2f AFfL$ f9fT$LHBfGHH9uM9M]HĈ []A\A]A^A_H H8 HFH9tHGhL|$ Lt$HD$HGH$f D I$ H|$I$ VI$ H<$H CI$ 9IuI} LDE 1L wHMM fL$PfL$:fL$rfL$H~/f D A@fL$ f9fT$LIBfGHH9uM9M] HGhH\$PL|$0H$HGHD$HFH9I$ H<$M$ I$ H|$M4FI$ iIuI} LLE 1LwHMU ~3 D AAA FfT$0f9LHBfL$PfGHH9uM9UpNf. AVHAUATUSHHĀH HG Hl$HL0 H|$ u
H[]A\A]A^ÿX Lt$ HI3v; LH Ld$0L+< Hl$PH
[ H5Z M H8H_Z LHLCH蛓LӒHD$ HHx H H5 L\(HH|$Ht:H{HtH(IHL6LnHD$ Ht$Hx[ Ls HHHHf. AWIAVIAUATUSH(HG(H8 HGhHD$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$ HICk; LHLd$0L;|< Hl$PH
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D9,DF,D,M9NH LL H HL El IvIIL HM~HBH9HA@H9@1 D9,DF<<HH9uo @ HFH9LghH_ f D H' I1E1IJ< o IAoT fofofj\ f b\ fffffHM9wH9tBAD 9FHGH9~*EL D9 DF HH9D ~AD 9FM9FcH LL 轰H HL Ml 袰IvIILHM~IELIH9@M9 HBH9L9AD @1 D A| 9<F<<HH9uV AVHAUATUSHHĀH HG Hl$H;0 H|$ u
H[]A\A]A^ÿX Lt$ .
HIce; LH
Ld$0L[v< Hl$PH
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H;H5 LHH|$Ht)H{HtHIHLfL螁HD$ Ht$Hx
LHHHHf. AWAVIAUIATUSHHG(H8 HFH9LghHotx D I LI 肮I HM H hMEIu IJMIE ~$1 D H9HHN H HI9uIE I9EuH[]A\A]A^A_H H8 HFH9tLghHo @ I LI ڭI HM H IuI} IHHIE ~$1 D H9 HHN H HH9uIE I9EuH[]A\A]A^A_HFH9HohH_4f I HM :I HM M$ IuI} I HHIE ~%1 D IH9HNHHH9uIE I9EuH[]A\A]A^A_f. AWAVAUATUSH HFXH|$HHT$0D E7 A HD$pHt$0HHD$@DAD$L$ 11D$8AD$M}D$<H$ HD$ 8 H|$0!HPIt$LL$ H$ H$ H$x H$ H H9HNH$ Q2H$ H$ <2H$ E9D$< H|$ I V- HL$@HT$ H|$PHt$`HD$`SH$ HT$PHtHT$(G
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H[]A\A]A^ÿX Lt$ HI3]; LHLd$0L+n< Hl$PH
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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 AWAVAUIATIUSH HG(H8 HGhH\$ L|$H$HGHD$HFH9 I$ H<$M$ 角I$ H|$M4FI$ 艒IuI} LE 1L wHMM fL$PfL$:fL$rfL$H~2f AFfL$ f9fT$LHBfGHH9uM9M]HĈ []A\A]A^A_H H8 HFH9tHGhL|$ Lt$HD$HGH$f D I$ H|$I$ 趑I$ H<$H CI$ 虑IuI} LDE 1L wHMM fL$PfL$:fL$rfL$H~/f D A@fL$ f9fT$LIBfGHH9uM9M] HGhH\$PL|$0H$HGHD$HFH9I$ H<$M$ I$ H|$M4FI$ ɐIuI} LLE 1LwHMU ~3 D AFA AfT$Pf9LHBfL$0fGHH9uM9UpNf. AWIAVIAUATUSH(HG(H8 HGhHD$HGHD$HFH9{ I H|$I I H|$IM H\ I6MFA| N,@M.S HLHHڃL9IOH)HHHHIHHH H$ HCH9HFAH9A <$IE1fn<$IE1J fp Bo IfofffffBIM9rH9t@9<ADM D LHL9~(B9<AFMHH9F~9<ADM D H9}N<$Hfn4$fp fo oHfofffoffffHH9M.I9 IL MJT I)HBI9IAAH9A I LHL$ MtF<$1E1fn<$fp o IfofffffAHI9rLM9t49<MHAI9~ 9<MHI9ȉ~
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V[ H5Z MK HIFHZ LHHTPHOLNH$ HHxH`H5Ε HjE1 IHLdOLNH$ LHxHHHHHH$ HHI}HOH{ D qH f. AWAVIAUIATUSHHG(H8 HFH9LghHotx D I LI 2{I HM H {MEIu IJMIE ~$1 D H9HHC H HI9uIE I9EuH[]A\A]A^A_H H8 HFH9tLghHo @ I LI zI HM H pzIuI} IHHIE ~$1 D H9 HHC H HH9uIE I9EuH[]A\A]A^A_HFH9HohH_4f I HM yI HM M$yIuI} I HHIE ~%1 D IH9HCHHH9uIE I9EuH[]A\A]A^A_f. AVAUATUHoSHH HWH7HHHHS@H L#H H9HNH ID$0xuCI$ID$PLcH ID$0x I$ID$PH H []A\A]A^ÿX MD$0H
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gCZ H5XFY MK H H EY LHHHL;H$ HHx'Hp%H5I HE1IHLLH$ LHxߟHHߩHHHfH$ HHI}HIH{v jH f. AWAVAUATUSH HFXH|$HHT$0D EP A HD$pHt$0HHD$@rAD$L$ 11D$8AD$M}D$<H$ HD$ 8 H|$01pHPIt$LL$ H$ 谎H$ H$x H$ H H9HNH$ H$ H$ H$ E9D$< H|$ I f$~, HL$@HT$ H|$PHt$`HD$`c0H$ HT$PHtHT$(WHT$(IL$ H$ L=D$ D$ L$ L HtHQ0 H LG;l$8t+LHIH|$0(If. H|$xHD$HHH tuWMtL踹HD$HH []A\A]A^A_L|$0Ld(HxH(0 HH]LH$ H$ 5(HP1L. HxH0 HHH$ H$ H$ H$ H$ HD$HH JL$ I L"H|$0L1,. H$ HHtHD$(貟HT$(E1VH$ HHt萟HE1I}HD$ <FHD$ H|$xHtHD$ #VHD$ MHtL^HtHQH먿X L$ L$ 苚H5l@Y LLH6L$ L; H$ H
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8S H}8S H5y8S LIL$ L,/: H$ H
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MT H5ΈT Mຖ HlHsT HHLwH wLAvH$ HHxHH5? L'HLHtHHHvLuH$ LHxHHf. @ AWAVAUATUSHx HFXH|$HHT$0D E A H$ Ht$0HHD$8AD$L$ 11D$@AD$M}D$DH$ HD$ i D H|$01HPIt$LL$ H$ (H$ H$ L$ H H9HNH$X ID$0x
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V H51V I2 LyH
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H3LKMD $(\$N4@L3 HLHHڃL9IOH)HyHHHHHH H IxH9H~AI9@A (I1IE1 N (IA8AD8X>D>HM9wL9tACI{XH9B~(AIXL9~CXBH9}%(I< HX)HH9L3I9 HH<MIT I)HBH9HGAH9A} Is MIN< Mt9(1E1 (IDXDHM9wLM9t;AHAXI9~#AHXI9~ AXL9s
H9HGhHH$HGHD$HD$OHD$(HD$pHD$8HD$PHD$0L H<$L L覰L H|$Ml L L膰H;HsIHT$(Lt$xHD$pL$ L H|$PHt$XH|$0Ht$8LL$ HCH9o;f. AXHI9u 1L9f D 1f D AXHH9uf. AWIAVAUIATUHSH H L$ Lt$@oHsI|$f$ LMLH HuHUH$ H HLH$ H H$ `L1H]HH$ HuPIl$HHI|$hHLLD$ D$ $ PHxHĨ []A\A]A^A_ÿX Lt$/L|$0 H5V LLIHl$`H0u8 H$ H
qW H5V Iz H:xHݰV LHLEH蝁HՀHD$0HHx H
H5 L^HI|$豶HIHLKH胀HD$0LHxr L
HHHf H? AWIAVIAUIATIULSLH ID$M H$ HD$` {HXL8H\$PLpHD$PHT$0HJ
X \$HLLL$0HD$hH!L$8\$@HD$` 7 HD$`Ht
HHHĈ []A\A]A^A_fI9= HD$` HXL8H\$PLpHD$PHT$0H%;X t$HHLd$0HD$hHڜd$8t$@HD$`d7 HD$`Hb HHP @ H9$ . HD$` /HXL8H\$PLpHD$PHT$0HH$ H=X Ld$HD$hH|$0|$8d$@HD$`躅7 HD$`H HHLd$ X LIPHl$0Hr8 H\$PH
V H5ȯV I HuH,V LHLHs~H}HD$ HHx HH5 L4Ld$ X 5LI躡Hl$0Hmq8 H\$PH
V H52V I HztH{V LHL~eHL$`HHt
HHHHL$`HHtH|$P HHIHL}H|HD$ Ht$ HxL HHHHL$`HHtH|$P HHL$`HHtH|$P HwHL$`HHB HH0HL$`HH HH
4bf Yf. Nf. AWAVAUATIUHSH f.X D$0% HHHǒHSH$ H$ HpH$ H{nH$0 H$ H$ H H9HNH$ O0H$ H$ :0L$ H H$ L+L1H;, L$ H$ HLsH$ Ht!
+HD$ D$ $ LH{述I$ H L[]A\A]A^A_fHH$ 谑 IHINH$ IH@H{L$ HHL$HHHD$@<H$0 H$ H$ H H9HNH$ /H$ H$ /Ll$`H H$ LL1H:, H|$`HD$(Ht
+ HD$(HxL$ . HHD$8 MHm L9 1LIH H|$@f . LIk.H|$HHD$@L . LIQ.H|$(I{LL$@HT$8MD$0D$ D$ D$ LL,$HHH{Cf HDŽ$ jHL$(HMLhHHH$ HhH$ H)[H$ I*YX MZ$ $ I*HD$81H$ YD$0L$ ILHH$ H7 H$ H LL LLHH H*XU LLHH H*XcH$ HHt
LLH{赭H
H$ HHtH$ HLt$PX *LI诛H$ H_k8 L$ H
V H5!V I2 LinHV LLLtxLwHwHD$PLHxH<H5 L H'ILMwHLvHD$PH$ HxLIHHf D H$ HHH|$`HHuf. @ SHHH RV D$ D$ $ :H H[ÐAWAVAUATUHh SH HH HH{HH%D, HH HHCX HC` Hl$pHHHxH HHCP[D, H8 H HH$ H|$pH<$ uHĨ H[]A\A]A^A_H
W HV AT H1Hj8 L|$X HLI ^Ld$ L i8 Lt$@H
W H5SV MT L+l1LLL;vLuLtHD$HHx HH5 LT
+HH<$Ht HB HHCPHM HHHHv
+ILMXtHD$HLHxD L\HHfIH @ AWAVAUATUHh SH HH HH{HH5B, HG HHCX HC` Hl$pHHHHG HHCPkB, H8 H,HH$ ,H|$pH<$ uHĨ H[]A\A]A^A_H
W HV AT H1Xh8 L|$X HLI\Ld$ L.g8 Lt$@H
W H5cV MT L;j1LLLKtLsLrHD$HHxHH5 LdHH<$HtHE HHCPH] HHHHILMhrHD$HLHxTLlHHfIH @ AWAVAUATUHh SH HH HH{HHE@, H~F HHCX HC` Hl$pHHHF HHCP{@, H8 H<HH$ <H|$p H<$ uHĨ H[]A\A]A^A_H
/W HV AT H1hf8 L|$X HLI+ZLd$ L>e8 Lt$@H
RW H5sV MT LKh1LLL[rLqLpHD$HHxH#H5 LtHH<$HtHD HHCPHm HHHHILMxpHD$HLHxdL|HHfIH @ AWAVAUATUHh SH HH HH{HHU>, HNE HHCX HC` Hl$pHHHD HHCP>, H8 HLHH$ LH|$pH<$ uHĨ H[]A\A]A^A_H
W HСV AT H1xd8 L|$X HLI;XLd$ LNc8 Lt$@H
W H5V MT L[f1LLLkpLoLnHD$HHxH3H5 LHH<$HtHC HHCPH} HHHHILMnHD$HLHxtLHHfIH @ AWAVAUATUHh SH HH HH{HHe<, H D HHCX HC` Hl$pHHHC HHCP<, H8 H\HH$ \H|$pH<$ uHĨ H[]A\A]A^A_H
ϹW HV AT H1b8 L|$X )HLIKVLd$ L^a8 Lt$@H
W H5V MT Lkd1LLL{nLmL mHD$HHxHC~H5 LHH<$HtH\B HHCPH HHHHILMlHD$HLHxLHHfIH @ AWAVAUATUHh SH HH HH{HHu:, HK HHCX HC` Hl$pHHHHK HHCP:, H8 HlHH$ lH|$pH<$ uHĨ H[]A\A]A^A_H
W HV AT H1`8 L|$X 9HLI[TLd$ Ln_8 Lt$@H
BW H5V MT L{b1LLLlLkLkHD$HHx
+HS|H5, L HH<$HtH,> HHCPH HHHH ILMjHD$HLHxLHHfIH @ AWAVAUATUHh SH HH HH{HH8, H>J HHCX HC` Hl$pHHHI HHCP8, H8 H|HH$ |H|$p"H<$ uHĨ H[]A\A]A^A_H
oW H V AT H1^8 L|$X IHLIkRLd$ L~]8 Lt$@H
W H5V MT L`1LLLjLiL+iHD$HHxHczH5< LHH<$Ht#H<7 HHCPH HHH HILMhHD$HLHxLHHfIH @ AWAVAUATUHh SH HH( HH{HH6, H? HHCX HC` Hl$pHHH> HHCP6, H8 H茖HH$ 茕H|$p2H<$ uHĨ H[]A\A]A^A_H
?W HV AT H1\8 L|$X YHLI{PLd$ L[8 Lt$@H
bW H5ÙV MT L^1LLLhLhL;gHD$HHx*HsxH5L LHH<$Ht3HL= HHCPH HHH.HILMfHD$HLHxLHHfIH @ AWAVAUATUHh SH HH8 HH{HH4, H= HHCX HC` Hl$pHHHx= HHCP4, H8 H蜔HH$ 蜓H|$pBH<$ uHĨ H[]A\A]A^A_H
W H V AT H1Z8 L|$X iHLINLd$ LY8 Lt$@H
W H5ӗV MT L\1LLLfLfLKeHD$HHx:HvH5\ LHH<$HtC
H < HHCPH HHH>HILMdHD$HLHxLHHfIH @ AWAVAUATUHh SH HHH HH{HH2, HnC HHCX HC` Hl$pHHH2 HHCP2, H8 H謒HH$ 謑H|$pRH<$ uHĨ H[]A\A]A^A_H
W H0V AT H1X8 L|$X yHLILLd$ LW8 Lt$@H
BW H5V MT LZ1LLLdL#dL[cHD$HHxJHtH5l LHH<$HtS Hl1 HHCPH HHHNHILMbHD$HLHxLHHfIH @ AWAVAUATUHh SH "HHX HH{HH0, H>> HHCX HC` Hl$pHHH= HHCP0, H8 H輐HH$ 輏H|$pbH<$ uHĨ H[]A\A]A^A_H
oW H@V AT H1V8 L|$X HLIJLd$ LU8 Lt$@H
W H5V MT LX1LLLbL3bLkaHD$HHxZHrH5| LHH<$Htc H6 HHCPH HHH^HILM`HD$HLHxLHHfIH @ AWAVAUATUHh SH 2HHh HH{HH., H< HHCX HC` Hl$pHHHh< HHCP /, H8 H̎HH$ ̍H|$prH<$ uHĨ H[]A\A]A^A_H
W HPV AT H1T8 L|$X HLIHLd$ LS8 Lt$@H
W H5V MT LV1LLL`LC`L{_HD$HHxjHpH5ߑ LHH<$HtsH5 HHCPH HHHnH&ILM_HD$HLHxL HHfIH @ AWAVAUATUHh SH BHHx HH{HH,, H^8 HHCX HC` Hl$pHHH7 HHCP-, H8 H܌HH$ ܋H|$pH<$ uHĨ H[]A\A]A^A_H
W H`V AT H1S8 L|$X HLIFLd$ LQ8 Lt$@H
W H5V MT LT1LLL^LS^L]HD$HHxzHnH5ݑ LHH<$HtH6 HHCPH
HHH~H6ILM]HD$HLHxL HHfIH @ AWAVAUATUHh SH RHH HH{HH*, H6 HHCX HC` Hl$pHHH6 HHCP++, H8 HHH$ H|$pH<$ uHĨ H[]A\A]A^A_H
ߣW HpV AT H1Q8 L|$X HLIDLd$ LO8 Lt$@H
W H5#V MT LR1LLL ]Lc\L[HD$HHxHlH5ۑ L$HH<$HtH) HHCPH HHHHFILM([HD$HLHxL,HHfIH @ AWAVAUATUHh SH bHHߑ HH{HH), H~8 HHCX HC` Hl$pHHH8 HHCP;), H8 HHH$ H|$pH<$ uHĨ H[]A\A]A^A_H
W HV AT H1(O8 L|$X HLIBLd$ LM8 Lt$@H
қW H53V MT L Q1LLL[LsZLYHD$HHxHjH5ّ L4HH<$HtH' HHCPH-ޑ HHHHVILM8YHD$HLHx$L<HHfIH @ AWAVAUATUHh SH rHHݑ HH{HH', H7 HHCX HC` Hl$pHHH6 HHCPK', H8 H HH$ H|$pH<$ uHĨ H[]A\A]A^A_H
W HV AT H18M8 L|$X HLI@Ld$ LL8 Lt$@H
"W H5CV MT LO1LLL+YLXLWHD$HHxHhH5ב LDHH<$HtH% HHCPH=ܑ HHHHfILMHWHD$HLHx4LLHHfIH @ AWAVAUATUHh SH HHۑ HH{HH%%, H / HHCX HC` Hl$pHHH. HHCP[%, H8 H HH$ H|$pH<$ uHĨ H[]A\A]A^A_H
_W HV AT H1HK8 L|$X HLI ?Ld$ L J8 Lt$@H
rW H5SV MT L+M1LLL;WLVLUHD$HHxHgH5Ց LTHH<$HtH\- HHCPHMڑ HHHHvILMXUHD$HLHxDL\HHfIH @ AWAVAUATUHh SH HHّ HH{HH5#, H- HHCX HC` Hl$pHHH- HHCPk#, H8 H,HH$ ,H|$pH<$ uHĨ H[]A\A]A^A_H
W HV AT H1XI8 L|$X HLI=Ld$ L.H8 Lt$@H
W H5cV MT L;K1LLLKULTLSHD$HHxHeH5ӑ LdHH<$HtH,, HHCPH]ؑ HHHHILMhSHD$HLHxTLlHHfIH @ AWAVAUATUHh SH HHב HH{HHE!, H/ HHCX HC` Hl$pHHHX/ HHCP{!, H8 H<HH$ <H|$pH<$ uHĨ H[]A\A]A^A_H
oW HV AT H1hG8 L|$X HLI+;Ld$ L>F8 Lt$@H
W H5sV MT LKI1LLL[SLRLQHD$HHxH#cH5ё LtHH<$HtH HHCPHm֑ HHHHILMxQHD$HLHxdL|HHfIH @ AWAVAUATUHh SH HHՑ HH{HHU , HN. HHCX HC` Hl$pHHH- HHCP , H8 HLHH$ L~H|$pH<$ uHĨ H[]A\A]A^A_H
W HЂV AT H1xE8 L|$X HLI;9Ld$ LND8 Lt$@H
W H5V MT L[G1LLLkQLPLOHD$HHxH3aH5 Б LHH<$HtH HHCPH}ԑ HHHHILMOHD$HLHxtLHHfIH @ AWAVAUATUHh SH HHӑ HH{HHe , H) HHCX HC` Hl$pHHHx) HHCP , H8 H\}HH$ \|H|$pH<$ uHĨ H[]A\A]A^A_H
W HV AT H1C8 L|$X )HLIK7Ld$ L^B8 Lt$@H
W H5V MT LkE1LLL{OLNL NHD$HHxHC_H5 Α LHH<$HtH ! HHCPHґ HHHHILMMHD$HLHxLHHfIH @ AWAVAUATUHh SH HHґ HH{HHu, Hn( HHCX HC` Hl$pHHH( HHCP, H8 Hl{HH$ lzH|$pH<$ uHĨ H[]A\A]A^A_H
ߒW H~V AT H1A8 L|$X 9HLI[5Ld$ Ln@8 Lt$@H
W H5~V MT L{C1LLLMLLLLHD$HHx
+HS]H5,̑ LHH<$HtH HHCPHБ HHHHILMKHD$HLHxLHHfIH @ AWAVAUATUHh SH HHБ HH{HH, H) HHCX HC` Hl$pHHH) HHCP, H8 H|yHH$ |xH|$p"H<$ uHĨ H[]A\A]A^A_H
W H }V AT H1?8 L|$X IHLIk3Ld$ L~>8 Lt$@H
ҊW H5|V MT LA1LLLKLJL+JHD$HHxHc[H5<ʑ LHH<$Ht#H< HHCPHΑ HHH HILMIHD$HLHxLHHfIH @ H 8 SHHHH`HtHP H(Α HC` HH[ HH8HH fH7 SHHHH`HtHP H͑ HC` HHH[@USHHtb~@tuH? H1[] D H. CHU HHH[1]ÅuH6 HH1[]f HHH1[]ÐAWAVAUATIUSHH( HHFHG H)HHpH Hl$@X HIklL$ L<8 L$0 H
LX H5}X M
L%?H}X HLL0ILHLGHD$@H$ HxHXH5Ǒ LD @ I4$I|$H)HHI M H HH EHC H. HkH9 L=4; L$ H
;MV I 1LLEHt|X 1LH-: HL$ LCLLHUHL$H
LV H 1EH'|X 1LHHUH$ LHHL$- L$ I|$XH: Ƅ$ LHDŽ$ Ƅ$ 1HDŽ$ HDŽ$ HHDŽ$ HDŽ$ H$ H9 HHH@HyH$ HL$HD$ H?Hp: I|$@HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHDŽ$ H$ H: H@H$ H9 HH$ 4HU: It$I|$XDŽ$ HH$ HEH$ {H5zX LH$ LHVHD$LHPHoHD$LHPH[H$ H}H|$pH L$ L$ L9 L9o M L)H$` 1HIIIHH HLLLD$(LD$(HI9 L$ Lt$pH$ LLH$ HxH9 H8 HH$ H8 H@H$ H8 HH$ H$ HxH9 H7 I|$@HH$ HD$HT$ I|$XH$ H@H H7 HH$ H|$`L )7 LsHD$`HD$` MHCt<IFHxH9> LcLt$`MtIFHxH9 L@HD$pHxH9r H$ HxH9/ HD$HxH9 H$ HxH9t HD$HxH92 H$ HxH9 HkIEI+E L#H H$ H
xX *7 LcH$ HDŽ$ MHCtHID$H-5 HxH9 LRL$ MtID$HxH9` L+H; H{ H{ H( []A\A]A^A_f Hn1T D H H H! Lh- HtIU IEH)HH HHI9C HH9j?f. M9 M @ Z M)H$` 1L%IIHHt`HLLLD$(LD$(HI9 L$ Lt$pH$ LLTv AA@9 AA@f D HHHAf HAHHH9*WfHt HHH{HtHC HH=75 HW H$ @ Lt$pIt$PLnf MH=x2U L$ HHD$pHt$?HxmLH$ LHxQH|$LH@H$ LHx,H|$LHH$ LHxfH=1U @ HJHH9L$ X LIcH$ H28 L$ H
wX H5itX I, L5HtX LLL?L$?H\>H$ H$ HxCHOH5e LLt$PX LIcbH$ H28 L$` H
DwX H5sX I L 5H.sX LLL(?L>H=HD$PH$ HxHNH5Ľ L<H=2 HW~ LH=2 HG` ЅLSH=n2 HWC kL%^H=@2 HGF Ѕ&LH=2 HW L$ LH=1 HW LH=1 HW mLc`H=~1 HW3 H$0 0H=K1 HW LH= 1 HW LA@ M8CD8 A@ M8CD8 JH=0 HWt\H$ yqLH <LA;HD$@H$ Hx+LC HPJHHL$ PJHyHL$APQPJHHHD$pLL$ HxCHL$APQHH$ H$0 HxyPJHILM;HLK:HD$PH$ Hx5LMIHHPJHRPJHxPJHPJHHHHL$ hHHG- I|$XL$ HH$ 'HL$ PHL$ ,LI!:HLV9H$ H$ Hx=
IH PJH;I|$H HD$HL$ H$ H@H 3OPJH @ AWAVAUATIUSHHH H~ L$ Hl$ HHG, H ' H5=nX HC` LHHHH|$HHLI, L|$M IGH-+ HxH9+ LxHD$ HxH9 M H<$L|$PH5mX LLpCPiH|$@LLLf, Lt$@MtIFHxH9 LHD$PHxH9i M H<$L$ H5f\ LL{XLpCTH|$pLLL , Lt$pMtIFHxH9m LH$ HxH9B M L$ H5\ LL{\L{H$ LLLU , L$ MtID$HxH9Z LH$ HxH9 Mu H []A\A]A^A_fH-9* \L$ X 2LIZL$0 Lg*8 H$ H
pX H5lX M' Hq-HzmX LHL|7H6L 6H$ HHxHAGH5 LHH{`HtHP H HC` HHHH=* HW) H$ L$ X -LIYH$ Hb)8 H
oX H5kX I& Lt,HMlX LLL6L5H5H$ H$ HxH?FH5 LH= * HW7 H$ Lt$`X ^LIXH$ H(8 L$ H
oX H5jX I# L+HFkX LLL5L 5H84HD$`H$ Hx"HkEH5D LLt$0X 轾LIBXH$ H'8 L$P H
cnX H5LjX I L*HujX LLL5L_4H3HD$0H$ Hx聿HDH5 LH=( HW 4H$ ]"H=x( HW vH$ *dH=E( HW H$ H=( HW rH$ `H=' HWb H$ H=' HWtlH$ bwILM2HL$2HD$0H$ HxL&"HHIËPJHILM2HL1H$ H$ Hx賽L˾IHHPJHPJH%PJHPJH8IHL 2LA1H$ LHx-LEAHHHILM1HL0HD$`H$ HxIH D HH$ H$ Hx譼HHD$ H$ Hx菼HHD$PH$ HxqHH$ H$ HxPlPJHOPJHrPJHf. UHh SH荿HHH HH[]HH*HfUHh SHMHHHHH[]HHHfUHh SH
HHHHH[]HH誾HbfATHUHSH@HD$ HHD$8HHD$0) H蟾Hƹ 1HL%" HHHF(F F IL$H|$HF8HF@H HHNH H H HNHt$H H H5X R+ H5uU H?+ H5]0] Hm@+ HH5T HB A+ H8HY=+ HH HE HE HD$0H H]Ht$ HfoD$0HD$0EH\$HE( H H H )H H H{hHtH{PHtHs0H{ MHCHxL9 HCHxL9uoHHxL9u:H軼HD$0HtH|$ HH@H[]A\f D 17H=y" HW Ht$2H=P" HW pHt$aH= " HWtB7Ht$ٺ(HHD$0HtH|$ HHPJHHMIHt
HHH} Ht\HE LH|$HtBHPJH PJH$f. ATHUHSH@HD$ HHD$8HHD$0) H_Hƹ 1HL%k HHHF(F F IL$H|$HF8HF@H HHNH H H HNHt$H H H5|X {O+ H55U H|<+ H5 -] H-=+ HH5T HB B =+ H8H:+ HH HE HE HD$0H H]Ht$ HfoD$0HD$0EH\$HE( H H H H H H{hHtH{PHt賹Hs0H{ HCHxL9 HCHxL9upHHxL9u;HtHD$0HtH|$ HH@H[]A\ 16H=1 HW Ht$H= HW oHt$轷`H= HWtB6Ht$葷'HHD$0HtH|$ HHXPJHHMIHt
HHH} HtHE LH|$HtH D PJHPJH f. @ ATHUHSH@HD$ HbHD$8HfHD$0L) HHƹ 1HL% HHHF(F F IL$H|$HF8HF@H HHNH H H HNHt$H H H5,X +L+ H5U H,9+ H5)] H9+ HH54T HB B
+ }:+ H8H6+ HH HE HE HD$0H H]Ht$ HfoD$0HD$0EH\$HE( H H H H H H{hHtqH{PHtcHs0H{ HCHxL9 HCHxL9upHHxL9u;H$HD$0HtH|$ HH@H[]A\ 16H= HW Ht$蚴H= HW oHt$m`H= HWtB6Ht$A'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$[]A\A]A^A_ AWAVAUI1ATIHUSHH HBHD$(+ HHG
HxH2 HP0$ HE HD$ HE1S+ HHt HxH HP0 Lu H + HHS HxHN HP0@ L} M+ IwI HDŽ$P HDŽ$X HDŽ$` H)HH E1111HMH$P H$X H$` tHH۷HHL|$ H$X E1A}ToHD$L$P INH$ HpMA1A}Po-H$P Ht۱H$ Hj HËE HuH{ECHCHxH;= * H舱H$ H/ 譱HËE HuH{EC袭H$ I $Ht HEHxH;= H$HĈ L[]A\A]A^A_ X nH}I肅) H$ H$ H5T H$ H;5L$ L{8 L$P H
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- IDŽ$ ADŽ$ ?IDŽ$ HD$L H I$ H9 H, HIH1HIHl$PDEH$` H5_N M$ HHH$.JH$ H$ HHc) H$ H H}H$ HHD$HEHHHt$ I$ HD$PHl$`HxhEH$@ H5N IDŽ$ HHDŽ$ HDŽ$ HHDŽ$ HD$0iIH$ H$ HHHDŽ$ HDŽ$( HHDŽ$0 HD$(f) 1E1H$ R H, L$ ID- 1HD$Mt IFHxH;=fK LGH$ HtGHD$`H4$Hx\DHtHGH$ Hl$pH5N HDŽ$ HDŽ$ HHHDŽ$ HD$8WHLt$0H$ HHHDŽ$@ HDŽ$H HDŽ$P LXf) L$ H\$HDŽ$ HDŽ$( HDŽ$0 MIDLsHLH)HH 1E1LI9L$ L$( H$0 HL Ht HHBHHI9uH$( H$ HtHCH4$HxBH5FH$H H$@ H9` H=LK L5I HHxL9 HH9uH$@ HtEHD$pH4$HxBH|$L;l$y H$ H9$( X H$ AHT$OH5ZN HHFL$ L3 L<$H
N H5N M8 L}HN HLHLLH$ Ht$HxAHH5 HJ @ L|$M)IMM$ E E1 K\ I|$X1HHID$PHHHt/HHqf D H9 H HtHq1HHH9t DHtKT H H@ HPH|$ HHH$HHL1IM9`I$ H H|$(MtLCH []A\A]A^A_f D HH H9N HHCILsHH$( H+$ HH H H9. H< CH$ H$( IH)HHHKHLHH?H$ H$( H)HHI$ 1f H$ H|$H4ZHHH9uH$ H$ H|$HHLIIHH$ HtBHD$`H4$Hx=?H|$I$ fBH|$ I|$PRBHH= HWHI$HHHD$PH$ Hx>HHH|$0 HD$pH4$Hx>H|$MiLA\HHH$ Ht$Hx{>H?H|$(HfK<H- ;<7H$ X <HHLt$8L3 L|$H
N H5N MD LyHbN HLHLLH$ LHx=HH5 HFH/H|$HH|$8H$ H4$Hxv=HH{>HHHH<$HKLH$;H|;I9tIH4$IHx=L9u9DHLH%P.H4$> HHxL9u2HH9uHnEH$ Ht?H$ 돋PJ҉HH4$>H=D HWt# H4$v>H$ PJHHH$ )H$ HH9 @ UH SHm?HHHHH[]HH
-?HrEfAWAVAUATIHUSHH H HHGaQ) HZX I|$pID$X ID$` ID$h
- AD$p ?ID$x HI$ID$PHD$ L H ID$XH9q H, H>1HHID:I$ I$ Ml$PIDŽ$ IDŽ$
- IDŽ$ ADŽ$ ?IDŽ$ HD$tL H I$ H9 H, H=H1HIHl$P9H$` H5N M$ HHH$>H$ H$ HHSX) H$ H H}H$ HHD$H9H-=Ht$ I$ HD$PHl$`Hx9H$@ H5N IDŽ$ HHDŽ$ HDŽ$ HHDŽ$ HD$0=H$ H$ HHHDŽ$ HDŽ$( HHDŽ$0 HD$(Z) 1E1H$ R H, L$ ID- 1HD$Mt IFHxH;=? L <H$ Ht<HD$`H4$Hx8HtH;H$ Hl$pH5N HDŽ$ HDŽ$ HHHDŽ$ HD$8<Lt$0H$ HHHDŽ$@ HDŽ$H HDŽ$P LZ) L$ H\$HDŽ$ HDŽ$( HDŽ$0 MIDLsHLH)HH 1E1LI9L$ L$( H$0 HL Ht HH07HHI9uH$( H$ HtHCH4$Hx]7H:H$H H$@ H9` H=? L5> HHxL9 HH9uH$@ Ht5:HD$pH4$Hx6H|$L;l$y H$ H9$( X H$ f5HT$OH5N HH:L$ LO3 L<$H
N H5
N M8 LH>N HLHL@LxH$ Ht$Hx"6HH5t H\? @ L|$M)IMM$ E E1 K\ I|$X1HHID$PHHHt/HHqf D H9 H HtHq1HHH9t 9HtKT H H@ HPH|$ HHHHHL1IM9`I$ H H|$(eMtLX8H []A\A]A^A_f D HH H9N HH[8ILsHH$( H+$ HH H H9. H< 8H$ H$( IH)HHHKHLH<?H$ H$( H)HHI$ 1f H$ H|$H4HHH9uH$ H$ H|$HHLIIHH$ Ht6HD$`H4$Hx3H|$cI$ 6H|$ LI|$P6HH=x HWHI$=HHD$PH$ Hx?3HHH|$0kHD$pH4$Hx3H|$OMiL>6\HHH$ Ht$Hx2H3H|$( Hf0H- 07H$ X T1HHLt$8LL3 L|$H
N H5 N MD LHN HLHL<LtH$ LHx 2HH5r HZ;H/H|$HH|$8+H$ H4$Hx1HH2HHHH<$HLH/H/I9tIH4$IHxs1L9u8HLH%WP.H4$U3 HHxL9u2HH9uH9H$ Ht<4H$ 돋PJ҉HH4$2H=J9 HWt# H4$2H$ PJHHH$ )H$ HHuY. @ UH SH3HHHHH[]HHj3H9fAWAVAUATIHUSHH H HHGE) HL I|$pID$X ID$` ID$h
- AD$p ?ID$x HI$ID$PHD$ lL H ID$XH9q H, H21HHI.I$ I$ Ml$PIDŽ$ IDŽ$
- IDŽ$ ADŽ$ ?IDŽ$ HD$ԋL H I$ H9 H, HY2H1HIHl$P.H$` H5 N M$ HHH$2H$ H$ HHL) H$ H H}H$ HHD$HU.H1Ht$ I$ HD$PHl$`Hx(.H$@ H5pN IDŽ$ HHDŽ$ HDŽ$ HHDŽ$ HD$0)2H$ H$ HHHDŽ$ HDŽ$( HHDŽ$0 HD$(BO) 1E1H$ R H, L$ ID- 1HD$Mt IFHxH;=&4 L0H$ Htn0HD$`H4$Hx -HtHO0H$ Hl$pH5EN HDŽ$ HDŽ$ HHHDŽ$ HD$81Lt$0H$ HHHDŽ$@ HDŽ$H HDŽ$P LO) L$ H\$HDŽ$ HDŽ$( HDŽ$0 MIDLsHLH)HH 1E1LI9L$ L$( H$0 HL Ht HH+HHI9uH$( H$ HtHCH4$Hx+H.H$H H$@ H9` H= 4 L5e2 HHxL9 HH9uH$@ Ht.HD$pH4$HxC+H|$yL;l$y H$ H9$( X H$ )HT$OH5N HH?/L$ L评3 L<$H
N H5mN M8 L=HN HLHHLLH$ Ht$Hx*HKH5܅ H3 @ L|$M)IMM$ E E1 K\ I|$X1HHID$PHHHt/HHqf D H9 H HtHq1HHH9t d-HtKT H H@ HPH|$ HHHHHL1IM9`I$ H H|$(MtL,H []A\A]A^A_f D HH H9N HH,ILsHH$( H+$ HH H H9. H< o,H$ H$( IH)HHHKHLHZ1?H$ H$( H)HHI$ 1f H$ H|$H4HHH9uH$ H$ H|$HHLIIHH$ HtO+HD$`H4$Hx'H|$I$ &+H|$ I|$P+HH=ޅ HWHI$h1HHD$PH$ Hx'HHH|$0HD$pH4$Hxy'H|$MiL*\HHH$ Ht$Hx;'HC(H|$(iHf %H- $7H$ X %HHYLt$8L謀3 L|$H
N H5iN MD L9H"N HLHDLLH$ LHx&HIH5 H/H/H|$HUH|$8H$ H4$Hx6&HH;'HHHH<$H LCH#H<$I9tIH4$IHx%L9u,HLH%跬P.H4$' HHxL9u2HH9uH..H$ Ht(H$ 돋PJ҉HH4$\'H=- HWt# H4$6'H$ PJHHH$ )H$ HHի" @ UH SH-(HHHHH[]HH'H2.fAWAVAUIHATUHSH1H LIH@HD$ IT$HD$( HD$ID$ HD$0 H)HHD E1HE1E11J<9MHL$ HL$(H|$0tHL,H|$0HJ1H9HD$([ HH tHLpLt$(L|$ HD$@ HD$H HD$P M)IMq L1E1E11IHHL$@HL$HLt$PtHL+HLL|$@1HHL$HL\-) H|$@IHtj&LI~HJ* H~H 1H?"ID$0x H|$M4$Mt$PJ* HI~rLcPE1f D CH{XL,1LHIIHt+HHqf D I9tcH HtHq1HHI9tH IM9uIE H|$ Ht%Hĸ L[]A\A]A^A_ LH HAD ?H I9 ILx%Ht$ H|$(HH)HL< \H H9g L< L4%It$MD$ HI)IN4 H
N LZv2 H H|$ PLt$(H|$ HtHD$$HD$H*X Lt$ MD$0H
L HL H5L LIeHl$`Hz3 H$ H
N H5L I躖 HRH+L LHL]HHHD$HHx HeH5҅ L)'H|$@HHt#HH|$
H f D ff. L IHL
HEHD$LHx L HH|$ HHHff. @ AWAVAUIHATUHSH1H IH@HD$ IT$HD$( HD$ID$ HD$0 H)HHD E1HE1E11J<9MHL$ HL$(H|$0tHL'H|$0HJ1H9HD$([ HH tHLpLt$(L|$ HD$@ HD$H HD$P M)IMq L1E1E11IHHL$@HL$HLt$PtHL9'HLL|$@1HHL$HL() H|$@IHt!L2I~H&F* H~H 1H ID$0x H|$M4$Mt$PE* HI~rLcPE1f D CH{X1L,LHIIHt+HHqf D I9tcH HtHq1HHI9tH IM9uIE H|$ Ht Hĸ L[]A\A]A^A_ LH HAD ?H I9 IL Ht$ H|$(HH)HL< \H H9g L< Lt It$MD$ HI)IN4 H
LN Lq2 H H|$ LLt$(H|$ HtHD$ HD$H)&X Lt$*MD$0H
ZL HVL H5RL LIHl$`Hv3 H$ H
9N H5BL I躖 H蒵HkL LHLHH-HD$HHxHH5.΅ L%'H|$@HHt HH|$
f D ff. [L IHLMH腾HD$LHx4L< HH|$ HHHff. @ AWAVAUIHATUHSH1H zIH@HD$ IT$HD$( HD$ID$ HD$0 H)HH< E1HE1E11J<9MHL$ HL$(H|$0tHL
-#H|$0HJ1H9HD$(S HH tHLpLt$(L|$ HD$@ HD$H HD$P M)IMi L1E1E11IHHL$@HL$HLt$PtHLy"HLL|$@1HHL$HL#) H|$@IHt LrI~HfA* H~H 1HID$0x H|$M4$Mt$P-A* HI~jLcPE1f D OH{X1LHIIHt*HHq D I9tcH HtHq1HHI9tH IM9uIE H|$ Ht Hĸ L[]A\A]A^A_ LH HAD ?H I9 IL Ht$ H|$(HH)HL< dH H9g L< LIt$MD$ HI)IN4 H
N Ll2 H H|$ LGLt$(H|$ HtHD$HD$Hq!X Lt$rMD$0H
zL HzL H5zL LI Hl$`HPq3 H$ H
N H5~L I躖 HڰH~L LHLH=HuHD$HHx$HH5vɅ L^ 'H|$@HHt7HH|$
f D ff. L IHL蝺HչHD$LHxLHH|$ HHHff. @ H4 SHHHHhHtpH{PHtbH+ͅ HH[fHY4 SHHHHhHt0H{PHt"H̅ HH[fH3 SHHHHhHtH{PHtH̅ HH[fHY3 SHHHHhHtH{PHtHk̅ HH[fHY3 SHHHHhHtpH{PHtbH+̅ HHH[Kf. H2 SHHHHhHt H{PHtH˅ HHH[f. H93 SHHHHhHtH{PHtH˅ HHH[f. H2 SHHHHhHtH{PHtrH;˅ HHH[[f. SHIHHHHHHHrH;pvH;rv)LHHH HHH[ H1LАAWIAVAUATUSHHH Hʅ L$ Ll$ HHGD)) H=1 H5R LLHDŽ$` HDŽ$h HDŽ$p HHH$ H$ LLHDŽ$ HDŽ$ HHDŽ$ 3) H$ HCP HCX 1HC` 1E1E11IHHKPHKXLk`tHL<HH$ LHKXH HPL5N HzL9 HH$ HtHD$ HxL9 H$` HtkLl$0H5N HHDŽ$` HDŽ$h LHDŽ$p @H$ LLLHDŽ$ HDŽ$ HDŽ$ 2) H$ HCh HCp 1HCx 1E1E11IHHKhHKpLkxtHLHLL$ HKpMtIGHxL9y LtH$ HtbHD$0HxL9 H$` Ht>HSXHCPH9 H)HCpH+ChHHH9 HĘ []A\A]A^A_ L5 K @ H$ L$ HCh HCp HCx I)IMLH?I9 ILH$ H$ HH)HL< H$ L$ HCP HCX HC` I)IML"H?I9 ILFH$ H$ HH)HL4 H= HWd AL$0 L,H= HW 8L$0 LX#X L$
LCpL+ChH
L H5C H$ L1ILCXL+CPLl$pH5 H
IL L1IְHN 1LEHL$0 H5zN LH$0 IVHHHt$`HLH$ IVHHLIQH5T LHHt$PH$ IVHHHHHT$
L$ Lg3 L$` H
N H5N ME LrHL$H<$H
-N LzLұL
-HD$PLHx
H$ LHx
HD$`LHx
H$ LHx
H$0 LHxl
HD$pLHx[
H$ LHxG
HH5 H<$HH{hHt_H{PHtQHH5ą HHVHX Ld$@ HT$ H5|N LH"L$ Lf3 L$0 H
cN H5N MC L HNN LLH'LL路HD$@H$` Hxa H*H5 HH= HO L$0 H$L6H$ H$ HHtǔH$ Ht!HD$ H$0 Hx HH=3 HW fL$0 L
Qx H$ HHtGH$ HtHD$0H$0 HxK 'Jqr#PJHwL$ IH$ LMHx
-HD$`HHx
-H$ HLHx
-H$0 LHx
-HD$pLHx
-H$ LHx
-H<$ cIIHILM賭HD$PL$ LHxZ
-?ILHD$CHD$I뻋PJHPJHHHHHL$0 .HKHL$0 'LIέLHD$@H$` Hx HL
-sIIIUH SH HHHHH[]HH HfAWIAVAUATUSHHH H> L$ Ll$ HHG ) H ' H5xR LLHDŽ$` HDŽ$h HDŽ$p HH
H$ H$ LLHDŽ$ HDŽ$ HHDŽ$ ^)) H$ HCP HCX 1HC` 1E1E11IHHKPHKXLk`tHLHH$ LHKXH HPL5 HzL9 HA H$ Ht/ HD$ HxL9 H$` Ht Ll$0H5WN HHDŽ$` HDŽ$h LHDŽ$p H$ LLLHDŽ$ HDŽ$ HDŽ$ &() H$ HCh HCp 1HCx 1E1E11IHHKhHKpLkxtHLHLL$ HKpMtIGHxL9y L
-H$ Ht
-HD$0HxL9 H$` Ht HSXHCPH9 H)HCpH+ChHHH9 HĘ []A\A]A^A_ L5)
K @ H$ L$ HCh HCp HCx I)IMLH?I9 ILn H$ H$ HH)HL< H$ L$ HCP HCX HC` I)IML"H?I9 ILH$ H$ HH)HL4 H=
HWd AL$0 L.,H=y
HW 8L$0 L#X L$ LCpL+ChH
"L H5 H$ L1I褦LCXL+CPLl$pH5 H
L L1IvH(N 1L
HL$0 H5N LH$0 IVH\HHt$`HLH$ IVHHLIH5FT L"HHt$PH$ IVHHHHHT$hL$ L]3 L$` H
N H5N ME LHL$H<$HN LLrL誦HD$PLHxYH$ LHxEHD$`LHx4H$ LHx H$0 LHx HD$pLHxH$ LHxHH59 H<$ HH{hHtH{PHtHH5 HHVHH X Ld$@IHT$ H5 N LHL$ L2\3 L$0 H
N H54N MC L輛HN LLHǦL LWHD$@H$` HxHʴH5S H; H=[
- HO L$0 H$LH$H$ HHtgH$ HtHD$ H$0 HxkHH= HW fL$0 LRQH$ HHtH$ HtAHD$0H$0 Hx 'Jqr#PJHwL$ IH$ LMHx HD$`HHx H$ HLHxw H$0 LHxc HD$pLHxR H$ LHx> H<$EcIIHILMSHD$PL$ LHx?ILHD$HD$I뻋PJHPJHHHHHL$0 .HKHL$0 'LInL覢HD$@H$` HxPHLU sIIIUH SHHHHHH[]HH:HfAWIAVAUATUSHHH H L$ Ll$ HHG) H H5EnR LLHDŽ$` HDŽ$h HDŽ$p HHH$ H$ LLHDŽ$ HDŽ$ HHDŽ$ ) H$ HCP HCX 1HC` 1E1E11IHHKPHKXLk`tHL|HH$ LHKXH HPL5 HzL9 H H$ Ht HD$ HxL9 H$` Ht Ll$0H5N HHDŽ$` HDŽ$h LHDŽ$p H$ LLLHDŽ$ HDŽ$ HDŽ$ ) H$ HCh HCp 1HCx 1E1E11IHHKhHKpLkxtHLDHLL$ HKpMtIGHxL9y LH$ HtHD$0HxL9 H$` Ht~HSXHCPH9 H)HCpH+ChHHH9 HĘ []A\A]A^A_ L5 K @ H$ L$ HCh HCp HCx I)IMLH?I9 ILH$ H$ HH)HL< H$ L$ HCP HCX HC` I)IML"H?I9 ILH$ H$ HH)HL4 H=O HWd AL$0 L,H= HW 8L$0 L#X L$ !LCpL+ChH
¸L H5 H$ L1IDLCXL+CPLl$pH5X H
L L1IHȶN 1LHL$0 H5N LH$0 IVHHHt$`HLH$ IVHHLIH5T LHHt$PH$ IVHHHHHT$L$ L(S3 L$` H
9N H5*N ME L貒HL$H<$HJN L躝LLJHD$PLHxH$ LHxHD$`LHxH$ LHxH$0 LHxHD$pLHxH$ LHxHPH5٪ H<$HH{hHtH{PHtHH5W HHVHX Ld$@HT$ H5N LHbL$ LQ3 L$0 H
N H5ԴN MC L\HN LLHgL进LHD$@H$` HxHjH5 H H= HO L$0 H$LvH$8H$ HHtH$ HtaHD$ H$0 Hx HH=s HW fL$0 LQH$ HHtH$ HtHD$0H$0 Hx'Jqr#PJHwL$ IH$ LMHx?HD$`HHx.H$ HLHxH$0 LHxHD$pLHxH$ LHxH<$cIIHILMHD$PL$ LHx?ILHD$胙HD$I뻋PJHPJHHHHHL$0 .HKHL$0 'LILFHD$@H$` HxHLsIIIUH SH=HHHHH[]HHHBfAWIAVAUATUSHHH H~ L$ Ll$ HHG$
-) H H5cR LLHDŽ$` HDŽ$h HDŽ$p HH`H$ H$ LLHDŽ$ HDŽ$ HHDŽ$ ) H$ HCP HCX 1HC` 1E1E11IHHKPHKXLk`tHL HH$ LHKXH HPL5. HzL9 HH$ HtoHD$ HxL9 H$` HtKLl$0H5N HHDŽ$` HDŽ$h LHDŽ$p H$ LLLHDŽ$ HDŽ$ HDŽ$ f) H$ HCh HCp 1HCx 1E1E11IHHKhHKpLkxtHLHLL$ HKpMtIGHxL9y LTH$ HtBHD$0HxL9 H$` Ht HSXHCPH9 H)HCpH+ChHHH9 HĘ []A\A]A^A_ L5i K @ H$ L$ HCh HCp HCx I)IMLH?I9 ILH$ H$ HH)HL< H$ L$ HCP HCX HC` I)IML"H?I9 IL&H$ H$ HH)HL4 H= HWd AL$0 Ln,H= HW 8L$0 L8#X L$ LCpL+ChH
bL H5# H$ L1ILCXL+CPLl$pH5 H
)L L1I趑HhN 1L%HL$0 H5ZN LH$0 IVHHHt$`HLH$ IVHHLI1H5T LbHHt$PH$ IVHHHHHT$L$ LH3 L$` H
yN H5ʫN ME LRHL$H<$HN LZL貒LHD$PLHxH$ LHxHD$`LHxtH$ LHx`H$0 LHxLHD$pLHx;H$ LHx'HH5y H<$`HH{hHt?H{PHt1HH5 HHVHX Ld$@HT$ H5\N LHL$ LrG3 L$0 H
#N H5tN MC LH.N LLHL_L藐HD$@H$` HxAH
-H5 H{H= HO L$0 H$LH$H$ HHtuH$ HtHD$ H$0 HxHH= HW fL$0 LQXH$ HHt'uH$ HtHD$0H$0 Hx+'Jqr#PJHwL$ IH$ LMHxHD$`HHxH$ HLHxH$0 LHxHD$pLHxH$ LHx~H<$cIIHILM蓎HD$PL$ LHx:?ILHD$#HD$I뻋PJHPJHHHHHL$0 .HKHL$0 'LI讎LHD$@H$` HxHLsIIIUH SHHHHHH[]HHzHfAWHAVAUIATUSHHHD$ HHD$8HHD$0& HiHù 1HL5
- HH{@C C IFC@ ?HHCHCpFL H HC(H9 L$ LL1HIH H|$H5_N L{ HCP HH H H HCX HC` HCh HCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H\$Q( 11HL( H5P H)C( H5TN HC( HH5+KL HB D( HH HD$0IE IU Ht HT$8I]IEHt$ HIU Ld$IE( M I$ I$ 6I$ I$ !I|$hHtI|$PHtI\$0L|$HtFH= u
D HH{H+HtHCHxL9 HHuID$(I|$ 1H I|$ ID$8 ID$0 iID$LHxID$LHxI$LHxL/HD$0HtH|$ HHHL[]A\A]A^A_úPGL: D IHCHl$HHxHCHHxsHHHxdHLHD$0HtH|$ HHIMHHt
HHI} HtAoIE HH|$Ht'oHfHH{H+HtHCHxL9uHH\H̋PJ҉HLf. @ AWHAVAUIATUSHHHD$ HHD$8HHD$0V& HHù 1HL5
- HH{@C C IFC@ ?HHCHCAL H HC(H9 L$ LYL1HI H H|$H5bZN L{ HCP HH H H HCX HC` HCh HCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H\$M( 11HXH( H5[P H>( H5ON H:?( HH5FL HB ?( HH HD$0IE IU Ht HT$8I]IEHt$ HIU Ld$IE( M I$ I$ I$ I$ I|$hHtbI|$PHtSI\$0L|$HtFH=t u
D HH{H+Ht HCHxL9 HHuID$(I|$ 1H I|$ ID$8 ID$0 ID$LHxxID$LHxgI$LHxWLHD$0HtH|$ HHHL[]A\A]A^A_úPGL*: D IHCHl$HHxHCHHxHHHxHLHD$0HtH|$ HHHIMHHt
HHI} HtjIE HH|$HtjHfHH{H+HtxHCHxL9uHcH\H̋PJ҉HL f. @ AWHAVAUIATUSHHHD$ HLHD$8HPHD$0趦& H)Hù 1HL5m
- HH{@C C IFC@ ?HHCHC0=L H HC(H9 L$ LL1HIiH H|$H5UN L{ HCP HH H H HCX HC` HCh HCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H\$dH( 11HC( H5P H9( H5XKN H:( HH5AL HB A;( HH HD$0IE IU Ht HT$8I]IEHt$ HIU Ld$IE( M I$ I$ I$ I$ I|$hHtI|$PHtI\$0L|$HtFH= u
D HH{H+Ht|HCHxL9 HcHuID$(I|$ 1H EI|$ ID$8 ID$0 )ID$LHxID$LHxI$LHxLHD$0HtH|$ HHHL[]A\A]A^A_úPGL: D KIHCHl$HHxCHCHHx3HHHx$HLYHD$0HtH|$ HHIMHHt
HHI} HtfIE HH|$HteHfHH{H+HtHCHxL9uHH\H̋PJ҉HLf. @ AWHAVAUIATUSHHHD$ HHD$8HHD$0& HHù 1HL5
- HH{@C C IFC@ ?HHCHC8L H HC(H9 L$ LL1HIH H|$H5"QN L{ HCP HH H H HCX HC` HCh HCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H\$C( 11H?( H5P HI5( H5FN H5( HH5K=L HB 6( HH HD$0IE IU Ht HT$8I]IEHt$ HIU Ld$IE( M I$ I$ VI$ I$ AI|$hHt"I|$PHtI\$0L|$HtFH=4 u
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+ IDŽ$ ADŽ$ ?IDŽ$ HD$ԷH I$ H9@ H, H9H1HIHl$PԵH$` H5S M$ HHHD$H$ H$ HH ' H$ H HEHxH;= 5 H$ H$HhH4$ I$ HD$PHl$`HxH$@ H5S IDŽ$ HHDŽ$ HDŽ$ HHDŽ$ HD$0%H$ H$ HHHDŽ$ HDŽ$( HHDŽ$0 HD$(n' 1E1H$ N H, L$ ID- 1HD$Mt IFHxH;= 4 L\H$ HtJHD$`Ht$HxHtH*H$ Hl$pH5S HDŽ$ HDŽ$ HHHDŽ$ HD$8L|$0H$ HHHDŽ$@ HDŽ$H HDŽ$P LC' H$ LHDŽ$ HE $HDŽ$( HDŽ$0 LsHLH)HH 1E1LI9L$ L$( H$0 HL D Ht HH`HHI9uH$( H$ HtHCHt$Hx茳HԶL$H H$@ I9N H= H- X f D HHxH9 HI9uH$@ HtuHD$pHxH9 H<$
+'L;l$H$ r H9$( X H$ 藱HT$OH5S HH@L$ L3 L|$H
S H5S M8 L HS HLH'L0'Lh&H$ H4$HxSH7H5u H D L|$M)IMM$ E1 K\ I|$X1HHID$PHHHt/HHqf D H9 H HtHq1HHH9t DHtKT H H@ HPH|$ HHH4HHL1IM9`I$ H
+ H|$(U%MtL蘴H []A\A]A^A_f D HH H9R HH蛴ILsHH$( H+$ HH H H9 H< OH$ H$( IH)HHHOHLHzCH$( H)HHI$ 1 H$ H|$H4zHHH9uH$ H$ H<$HLH- LSIHHxH9U HI9uH= HWt$
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S H5aS MD LIHS HLHT#L"L!H$ LHxЭH3H5 HjHH|$(!0H|$H["L!H$ H4$Hx~H薮HÐH覫I9tIHt$IHxLL9u´H<$H!H|$8,!H$ Ht$HxHH+OHHHH=7 HW Ht$萪H:H P^Ht$贮OH觵H$ HtůH$ H H$ HH( H @ 빋PJHBf. @ UH SH药HHHHH[]HH*HfAWAVAUATIHUSHH H~ HHG& H*! I|$pID$X ID$` ID$h
+ AD$p ?ID$x HI$ID$PHD$ LH ID$XH9q H, H贮1HHITI$ I$ Ml$PIDŽ$ IDŽ$
+ IDŽ$ ADŽ$ ?IDŽ$ HD$贫H I$ H9 H, HH1HIHl$P贩H$` H5rS M$ HHH$ήH$ H$ HH& H$ H H}H$ HHD$HHMHt$ I$ HD$PHl$`HxةH$@ H5S IDŽ$ HHDŽ$ HDŽ$ HHDŽ$ HD$( H$ H$ HHHDŽ$ HDŽ$( HHDŽ$0 HD$0R& 1E1H$ R H, L$ ID- 1HD$Mt IFHxH;= L@H$ Ht.HD$`H4$Hx̨HtHH$ Hl$pH5uS HDŽ$ HDŽ$ HHHDŽ$ HD$8L|$(H$ HHHDŽ$@ HDŽ$H HDŽ$P L(& H$ LHDŽ$ HE\$HDŽ$( HDŽ$0 LsHLH)HH 1E1LI9L$ L$( H$0 HLf Ht HH@HHI9uH$( H$ HtHCH4$HxmH赪H$H H$@ H9` H= L5 HHxL9 HH9uH$@ HtUHD$pH4$HxH|$L;l$y H$ H9$( X H$ vHT$OH5rS HH L$ L3 L<$H
S H5S M8 LHS HLHLLHH$ Ht$Hx2H{+H5T H̯ @ L|$M)IMM$ E E1 K\ I|$X1HHID$PHHHt/HHqf D H9 H HtHq1HHH9t $HtKT H H@ HPH|$ HHHHHL1IM9`I$ H H|$05MtLxH []A\A]A^A_f D HH H9N HH{ILsHH$( H+$ HH H H9 H< /H$ H$( IH)HHHKHLHZ?H$ H$( H)HHI$ 1f H$ H|$H4JHHH9uH$ H$ H|$HHLIIHH$ HtHD$`H4$Hx譣H|$I$ H|$ I|$PҦHH=H HWHI$xHHD$PH$ HxOHHH|$(;HD$pH4$Hx)H|$ MiL^\HHH$ Ht$HxHH|$0Hf蛠H- 苠7H$ X dHH:Lt$8L
+3 L|$H
S H5S MD L
HuS HLHL LDH$ LHx0Hy'H5R HʫH/H|$HH|$8H$ H4$HxHHHHHH<$H{LHtHܟI9tIH4$IHx胡L9uPGH4$~9HHxL9uHH9u(PJ҉HH4$IH?H$ Ht]H$ H H=2
+ HWt# H4$H$ PJHHH$ HH( [f. UH SHHHHHH[]HH蚣HRfAWAVAUATIHUSHH H HHGa& H I|$pID$X ID$` ID$h
+ AD$p ?ID$x HI$ID$PHD$ 輠H ID$XH9q H, H$1HHIĞI$ I$ Ml$PIDŽ$ IDŽ$
+ IDŽ$ ADŽ$ ?IDŽ$ HD$$H I$ H9 H, H艢H1HIHl$P$H$` H5S M$ HHH$>H$ H$ HH3& H$ H H}H$ HHD$HuH轡Ht$ I$ HD$PHl$`HxHH$@ H5S IDŽ$ HHDŽ$ HDŽ$ HHDŽ$ HD$(yH$ H$ HHHDŽ$ HDŽ$( HHDŽ$0 HD$0& 1E1H$ R H, L$ ID- 1HD$Mt IFHxH;= L谠H$ Ht螠HD$`H4$Hx<HtHH$ Hl$pH5S HDŽ$ HDŽ$ HHHDŽ$ HD$8gL|$(H$ HHHDŽ$@ HDŽ$H HDŽ$P L& H$ LHDŽ$ HE\$HDŽ$( HDŽ$0 LsHLH)HH 1E1LI9L$ L$( H$0 HLf Ht HH谛HHI9uH$( H$ HtHCH4$HxݛH%H$H H$@ H9` H= L5] HHxL9 HH9uH$@ HtŞHD$pH4$HxcH|$YL;l$y H$ H9$( X H$ HT$OH5S HH菟L$ L3 L<$H
S H55S M8 L HfS HLH(LLH$ Ht$Hx袚H H5Ď H< @ L|$M)IMM$ E E1 K\ I|$X1HHID$PHHHt/HHqf D H9 H HtHq1HHH9t 蔝HtKT H H@ HPH|$ HHHHHL1IM9`I$ H H|$0
MtLH []A\A]A^A_f D HH H9N HHILsHH$( H+$ HH H H9 H< 蟜H$ H$( IH)HHHKHLHʡ?H$ H$( H)HHI$ 1f H$ H|$H4HHH9uH$ H$ H|$HHLIIHH$ HtHD$`H4$Hx H|$胼I$ VH|$ LI|$PBHH= HWHI$HHD$PH$ Hx迗HHH|$( HD$pH4$Hx虗H|$ MiLΚ\HHH$ Ht$Hx[HsH|$0I Hf H- 7H$ X ԕHHY/Lt$8L 2 L|$H
S H51S MD LHS HLH$ L| L
+H$ LHx蠖HH5 H:H/H|$H5 H|$8k
+H$ H4$HxVHHkHHHH<$H
+L#
+HHLI9tIH4$IHxL9uiPGH4$9HHxL9uHH9u(PJ҉HH4$蹗H诞H$ Ht͘H$ HH= HWt# H4$^H$ PJHHH$ HH˒f. UH SHmHHHHH[]HH
+HfH SHHHHhHtH{PHtҗHK HH[fH9 SHHHHhHt蠗H{PHt蒗H HH[fH
+ SHHHHhHt`H{PHtRHˌ HH[fH9
+ SHHHHhHt H{PHtH HH[fH
+ SHHHHhHtH{PHtҖHK HHH[黖f. H SHHHHhHt萖H{PHt肖H HHH[kf. HY SHHHHhHt@H{PHt2H HHH[f. H SHHHHhHtH{PHtH[ HHH[˕f. SHIHHHHHHHrH;pvH;rv)LaHHH HHH[ H1LАAWIAVAUATUSHHH H L$ Ll$ HHG$& H H5MrW LLHDŽ$` HDŽ$h HDŽ$p HHH$ H$ LLHDŽ$ HDŽ$ HHDŽ$ ~& H$ HCP HCX 1HC` 1E1E11IHHKPHKXLk`tHLHH$ LHKXH HPL5 HzL9 HH$ HtHD$ HxL9 H$` HtۓLl$0H5S HHDŽ$` HDŽ$h LHDŽ$p ДH$ LLLHDŽ$ HDŽ$ HDŽ$ F& H$ HCh HCp 1HCx 1E1E11IHHKhHKpLkxtHL贘HLL$ HKpMtIGHxL9y LH$ HtҒHD$0HxL9 H$` Ht讒HSXHCPH9 H)HCpH+ChHHH9 HĘ []A\A]A^A_ L5 K @ H$ L$ HCh HCp HCx I)IMLH?I9 IL>H$ H$ HH)HL< H$ L$ HCP HCX HC` I)IML"H?I9 IL趑H$ H$ HH)HL4 H=? HWd AL$0 L,H= HW 8L$0 L踏#X L$ ALCpL+ChH
'P H5s H$ L1I$ LCXL+CPLl$pH5H H
I'P L1IHS 1LHL$0 H5S LH$0 IVHlHHt$`HLH$ IVHHLIQH5'Z L2HHt$PH$ IVHHHHHT$(L$ L2 L$` H
ٹS H5
+S ME LHL$H<$H*S LL L* HD$PLHxH$ LHxHD$`LHxH$ LHxH$0 LHx̋HD$pLHx軋H$ LHx觋HH5 H<$@HH{hHtώH{PHtHH57 HHVHhX Ld$@ HT$ H5S LH貏L$ L22 L$0 H
S H5S MC L<HnS LLHG LLHD$@H$` HxH
+H5~ H[H= HO L$0 H$L薌H$8H$ HHtoH$ Ht葍HD$ H$0 Hx+HH=c HW fL$0 LQ踇H$ HHtH$ HtHD$0H$0 Hx諉'Jqr#PJHwL$ IH$ LMHx_HD$`HHxNH$ HLHx7H$0 LHx#HD$pLHxH$ LHxH<$cIIHILMHD$PL$ LHx躈?ILHD$cHD$I뻋PJHPJHHHHHL$0 .HKHL$0 'LIL&HD$@H$` HxHL%sIIIUH SHmHHHHH[]HH
+HfAWIAVAUATUSHHH H^ L$ Ll$ HHG& H H5gW LLHDŽ$` HDŽ$h HDŽ$p HH谋H$ H$ LLHDŽ$ HDŽ$ HHDŽ$ & H$ HCP HCX 1HC` 1E1E11IHHKPHKXLk`tHL茏HH$ LHKXH HPL5& HzL9 H豉H$ Ht蟉HD$ HxL9 H$` Ht{Ll$0H5wS HHDŽ$` HDŽ$h LHDŽ$p pH$ LLLHDŽ$ HDŽ$ HDŽ$ & H$ HCh HCp 1HCx 1E1E11IHHKhHKpLkxtHLTHLL$ HKpMtIGHxL9y L脈H$ HtrHD$0HxL9 H$` HtNHSXHCPH9 H)HCpH+ChHHH9 HĘ []A\A]A^A_ L5a K @ H$ L$ HCh HCp HCx I)IMLH?I9 ILއH$ H$ HH)HL< H$ L$ HCP HCX HC` I)IML"H?I9 ILVH$ H$ HH)HL4 H= HWd AL$0 L莅,H= HW 8L$0 LX#X L$ LCpL+ChH
" P H5 H$ L1ILCXL+CPLl$pH5 H
P L1IHHS 1L襌HL$0 H5:S LH$0 IVH HHt$`HLH$ IVHHLIH5 Z LҌHHt$PH$ IVHHHHHT$ȁL$ L(2 L$` H
S H5S ME L2HL$H<$HʫS L:LLHD$PLHx蹁H$ LHx襁HD$`LHx蔁H$ LHx老H$0 LHxlHD$pLHx[H$ LHxGHH5iu H<$HH{hHtoH{PHtaHH5y HHVHX Ld$@HT$ H5<S LHRL$ L2 L$0 H
íS H5TS MC LHS LLHL?LwHD$@H$` HxaHH5t HH= HO L$0 H$L6H$}H$ HHtH$ Ht1HD$ H$0 HxHH= HW fL$0 L貁QX}H$ HHtH$ Ht豂HD$0H$0 HxK'Jqr#PJHwL$ IH$ LMHx~HD$`HHx~H$ HLHx~H$0 LHx~HD$pLHx~H$ LHx~H<$cIIHILMsHD$PL$ LHxZ~?ILHD$HD$I뻋PJHPJHHHHHL$0 .HKHL$0 'LILHD$@H$` Hx}HL~sIIIUH SH
HHHHH[]HH誀HbfAWIAVAUATUSHHH Hu L$ Ll$ HHGd& H H5]W LLHDŽ$` HDŽ$h HDŽ$p HHPH$ H$ LLHDŽ$ HDŽ$ HHDŽ$ & H$ HCP HCX 1HC` 1E1E11IHHKPHKXLk`tHL,HH$ LHKXH HPL5 HzL9 HQH$ Ht?HD$ HxL9 H$` HtLl$0H5S HHDŽ$` HDŽ$h LHDŽ$p H$ LLLHDŽ$ HDŽ$ HDŽ$ & H$ HCh HCp 1HCx 1E1E11IHHKhHKpLkxtHLHLL$ HKpMtIGHxL9y L$~H$ Ht~HD$0HxL9 H$` Ht}HSXHCPH9 H)HCpH+ChHHH9 HĘ []A\A]A^A_ L5 K @ H$ L$ HCh HCp HCx I)IMLH?I9 IL~}H$ H$ HH)HL< H$ L$ HCP HCX HC` I)IML"H?I9 IL|H$ H$ HH)HL4 H= HWd AL$0 L.{,H=I HW 8L$0 Lz#X L$ wLCpL+ChH
P H5 H$ L1IdLCXL+CPLl$pH5 H
P L1I6HS 1LEHL$0 H5ڡS LH$0 IVH謂HHt$`HLH$ IVHHLIH5FZ LrHHt$PH$ IVHHHHHT$hwL$ L2 L$` H
YS H5JS ME LHL$H<$HjS LL2LjHD$PLHxYwH$ LHxEwHD$`LHx4wH$ LHx wH$0 LHx wHD$pLHxvH$ LHxvH0H5 k H<$耀HH{hHtzH{PHtzHH5wo HHVH言X Ld$@IuHT$ H5ܟS LHzL$ Lr2 L$0 H
S H5S MC L|HS LLHLLHD$@H$` HxvHJH5#j HH=+ߍ HO L$0 H$LwH$xsH$ HHtH$ HtxHD$ H$0 HxkuHH=ލ HW fL$0 LRwQrH$ HHt/H$ HtQxHD$0H$0 Hxt'Jqr#PJHwL$ IH$ LMHxtHD$`HHxtH$ HLHxwtH$0 LHxctHD$pLHxRtH$ LHx>tH<$UucIIHILMHD$PL$ LHxs?ILHD$HD$I뻋PJHPJHHHHHL$0 .HKHL$0 'LI.LfHD$@H$` HxPsHLetsIIIUH SHvHHHHH[]HHJvH}fAWIAVAUATUSHHH Hk L$ Ll$ HHG& H H5-SW LLHDŽ$` HDŽ$h HDŽ$p HHvH$ H$ LLHDŽ$ HDŽ$ HHDŽ$ ^& H$ HCP HCX 1HC` 1E1E11IHHKPHKXLk`tHLzHH$ LHKXH HPL5fٍ HzL9 HtH$ HttHD$ HxL9 H$` HttLl$0H5S HHDŽ$` HDŽ$h LHDŽ$p uH$ LLLHDŽ$ HDŽ$ HDŽ$ && H$ HCh HCp 1HCx 1E1E11IHHKhHKpLkxtHLyHLL$ HKpMtIGHxL9y LsH$ HtsHD$0HxL9 H$` HtsHSXHCPH9 H)HCpH+ChHHH9 HĘ []A\A]A^A_ L5 K @ H$ L$ HCh HCp HCx I)IMLH?I9 IL sH$ H$ HH)HL< H$ L$ HCP HCX HC` I)IML"H?I9 ILrH$ H$ HH)HL4 H= ؍ HWd AL$0 Lp,H= HW 8L$0 Lp#X L$ !mLCpL+ChH
bP H5S֍ H$ L1ILCXL+CPLl$pH5(֍ H
)P L1IHS 1LwHL$0 H5zS LH$0 IVHLxHHt$`HLH$ IVHHLI1H5Z LxHHt$PH$ IVHHHHHT$mL$ Lh2 L$` H
S H5S ME LrHL$H<$H
+S LzLL
+HD$PLHxlH$ LHxlHD$`LHxlH$ LHxlH$0 LHxlHD$pLHxlH$ LHxlHH5` H<$ vHH{hHtoH{PHtoHH5e HHVHHvX Ld$@jHT$ H5|S LHpL$ L2 L$0 H
CS H5S MC L HNS LLH'LLHD$@H$` HxkHH5_ H;uH=ԍ HO L$0 H$LvmH$iH$ HHtOH$ HtqnHD$ H$0 Hx kHH=Cԍ HW fL$0 LlQhH$ HHtH$ HtmHD$0H$0 Hxj'Jqr#PJHwL$ IH$ LMHx?jHD$`HHx.jH$ HLHxjH$0 LHxjHD$pLHxiH$ LHxiH<$jcIIHILMHD$PL$ LHxi?ILHD$CHD$I뻋PJHPJHHHHHL$0 .HKHL$0 'LILHD$@H$` HxhHLjsIIIUH SHMlHHHHH[]HHkHrfATHWUHSH@HD$ H2HD$8H6HD$0 $ HkHƹ 1HL%ύ HHHF(F F IL$H|$HF8HF@H HHNH H H HNHt$H H H5IS % 11H% H5cU H% H5O@S H% HH5O HB H% H8H% HH HE HE HD$0H H]Ht$ HfoD$0HD$0EH\$HE( H H H ]H H JH{hHt<jH{PHt.jHs0H{ 聐HCHxL9 HCHxL9usHHxL9u>HiHD$0HtH|$ HH@H[]A\f. 13H=ύ HW Ht$bhH=ύ HW lHt$5h]H=Pύ HWtB3Ht$ h$HHD$0HtH|$ HHoPJHHMIHt
HHH} HtHE LH|$HtHPJH PJH$f. ATHUHSH@HD$ HHD$8HHD$0̅$ HhHƹ 1HL%̍ HHHF(F F IL$H|$HF8HF@H HHNH H H HNHt$H H H5FS % 11HO% H5`U H% H5<S HQ% HH5O HB % H8H=% HH HE HE HD$0H H]Ht$ HfoD$0HD$0EH\$HE( H H H
H H H{hHtfH{PHtfHs0H{ 1HCHxL9 HCHxL9usHHxL9u>HfHD$0HtH|$ HH@H[]A\f. 13H=Y̍ HW Ht$eH=0̍ HW lHt$d]H= ̍ HWtB3Ht$d$HHD$0HtH|$ HHlPJHHMIHt
HHH} HtHE LH|$HtHPJH PJH$f. ATHUHSH@HD$ HHD$8HHD$0|w$ H?eHƹ 1HL%Kɍ HHHF(F F IL$H|$HF8HF@H HHNH H H HNHt$H H H5\CS [% 11H% H52]U HP% H59S H% HH5XO HB % H8H% HH HE HE HD$0H H]Ht$ HfoD$0HD$0EH\$HE( H H H 轏H H 誏H{hHtcH{PHtcHs0H{ HCHxL9 HCHxL9usHHxL9u>HOcHD$0HtH|$ HH@H[]A\f. 13H= ɍ HW Ht$aH=ȍ HW lHt$a]H=ȍ HWtB3Ht$ia$HHD$0HtH|$ HH0iPJHHMIHt
HHH} HtdHE LH|$HtJHPJH PJH$f. ATHUHSH@HD$ HBHD$8HFHD$0,v$ HaHƹ 1HL%ō HHHF(F F IL$H|$HF8HF@H HHNH H H HNHt$H H H5 @S % 11H% H5YU H % H5_6S H% HH5O HB X% H8H% HH HE HE HD$0H H]Ht$ HfoD$0HD$0EH\$HE( H H H mH H ZH{hHtL`H{PHt>`Hs0H{ 葆HCHxL9 HCHxL9usHHxL9u>H_HD$0HtH|$ HH@H[]A\f. 13H=ō HW Ht$r^H=ō HW lHt$E^]H=`ō HWtB3Ht$^$HHD$0HtH|$ HHePJHHMIHt
HHH} HtHE LH|$HtHPJH PJH$f. AWAVI1AUIATUHHSH( 輋& HH HxH HP0
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ZHٺ HH1 H$ Ht$pHxXIGH$ IG@H$ H{ HH$ H$ HxH;= * Hg I|$@HH$ 1_IEL$ I|$XL H HH$ U X VH{I/$ H\$@H$ H5VO H$ H~Hl$pH2 L$ H
RS H5KO IZ LH V HLLL.HfHD$@LHxUWHH5wK L` MFPIVXL)HH9Ht41f H1I*H\AYAD HH9uHE H( H[]A\A]A^A_f. H4 IFPIVXH)HH5Ht0I*\Y AD HH9uH
qS HDz1 sHH TX H\$ TI}IH& H@@H8, H5ږP HHCLl$0HLUHl$PH2 H$ H
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+TMG0H
O HO H5O HI?H$ H&2 L$ H
7S H5HO Iغ L0HqO HLL;LHHD$pLHxTHH5H LT^X Hl$pUSLC0H
7O H3O H5/O HIA?H$ Hq2 L$ H
BS H5O Iغ L{HO HLLFHHL3UHHLHHHHD$pHt$PHxSLTH]HHHfH\H]HHHHD$0H\$ HHxSHD$ HHxySLTHH I|$XHH$ PHW]I|$H:IEL$ L H=d HWtPH UILHMHLHD$@Ht$PHxRPJHHIHHHL ^H\HH$ Ht$pHx}RHHHH\$ Q @ f. vf. AWAVI1AUIATUHHSH( 蜂& HH HxH HP0
HHK1LHHL$|& IHC0HL$xZ IG0L+LkPx M'HCMgPH9C 4 HU( HSHHC H)HH HZI~hIFpH)HH94 H L$ I|$X@NH Ƅ$ LHDŽ$ Ƅ$ 1HDŽ$ HDŽ$ HHDŽ$ HDŽ$ H$ H LhLpI}L$ L7 WL=: I|$@HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ IGHDŽ$ H$ IG@H$ HV HH$ \H& It$I|$XDŽ$ HH$ H HH$ EVH5zS 5 LaTHL&SH5]yS HBTH$ It$HPHٺ H(1 H$ Ht$pHxrOIGH$ IG@H$ H[ HH$ H$ HxH;=䶍 P HG I|$@HH$ VIEL$ I|$XL H HH$ `L X MH{I&$ H\$@H$ H56O H$ H^Hl$pH衶2 L$ H
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zwS H5P In H賶1LHLHHSHD$0HHxBKHD$ HHx1KHzH5S? LT 1/X Hl$pIMG0H
O HO H5O HI5H$ H2 L$ H
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HHH} HtgHE LH<$HtNH AVHAUATUHSH0HD$HHD$(HHD$ ( IHù 1H߾
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HHH} HtGHE LH<$Ht.H AVHAUATUHSH0HD$HHD$(HHD$ I{Hù 1H߾
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HHH} Ht'HE LH<$HtH AVHAUATUHSH0HD$H~HD$(HHD$ I[Hù 1H߾
- HHx H{@C C C@ ?HHH HKHKb> H HC(H9 L, LL1HI蛻H H5 ? Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $$ Hx H5 ? H' H5y> H H5D H HH5 > HB B e HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( HtHD$ HtH|$ HH0H[]A\A]A^衸IHCHHx螺HCHHx莺HHHxHL贽HD$ HtH|$ HHHHMIHt
HHH} HtHE LH<$HtH AVHAUATUHSH0HD$H^HD$(HbHD$ Ȁ I;Hù 1H߾
- HHwx H{@C C C@ ?HHH HKHKB> H HC(H9 L, L˼L1HI{H H5? Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $y! Hrx H5? H$ H5Y> H H5D H HH5> HB B E HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( HtHD$ HtH|$ HH0H[]A\A]A^聵IHCHHx~HCHHxnHHHx_HL蔺HD$ HtH|$ HHHHMIHt
HHH} HtHE LH<$HtH AUATUSH HF0x
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A H5 > I躂 H OH > LHL(ZHYHXHD$ HHxgH0hH5gw L衾HLVHIHL0YHhXHD$ Ht$ HxHHf AVAUIATIHUSHĀ5HID$0x
- LI $I\$P 1HA <rf 8|$ L$ \L$ t$ f T HH9t8Kց
tt 8 D pH[]A\A]A^ÿX Lt$ MD$0H
> H> H5 > LI_Hl$0H
% H\$PH
vA H5> I躖 HOMH(> LHLZXHWHVHD$ HHx虳HbfH5ew LӼHL舴H IHLbWHVHD$ Ht$ HxGHHf. AWAVAUATUHHSHH HHD$~ HK=HHE0x 蕮IHD$PL{I1һ H$H%f D HE0MIHsxupHHH4$HE HHUPL"AE
- L
L;d$Pt#A} "t(H;\$AuHĈ []A\A]A^A_H=uA 'H=uA ;X Ld$ LE0H
> H> H5> LIhH\$0H % HD$PH
A H5> Iغ HIH$QKH*> LLL\VLUHTHD$ LHx蛱HddH5cw LպLd$ X 6LHqH\$0H. % HD$PH
b~A H5CuA Iغh HIH$JHtA LLHULUHOTHD$ LHxHcH5Pcw H8H<$HTHHTHD$ Ht$ Hx辰LƱH^HHHÐH<$ITHLSHD$ Ht$ HxtH|HIHH D AWAVAUATUHHSHH HIi H=9IHE0x 跫IHD$PI1HD$fHE0HxuoH HU Ht$HUPL:A$
- L%L;|$Pt#A<$"t(L9fA^uHĈ []A\A]A^A_H=sA _H=sA sX Ll$ $LE0H
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|A H5@> Iغ HIHD$HHa> LLLSLRH#RHD$ LHxҮHaH5$aw L Ll$ X mLHoH\$0He% HD$PH
yA H5zrA Iغh HIHD$GHqA LLHRLMRHQHD$ LHx4H`H5`w HnH|$HRHHFQHD$ Ht$ HxLH蓷HHHH|$IMQHLPHD$ Ht$ Hx覭H议HFIHH AWAVAUATUHHSHH 1 HHD$~ H8HHE0x IHD$PL{I1һ H$H%f D HE0MIHsxupHHH4$HE HHUPL"AE
- L=L;d$Pt#A} "t(H;\$AuHĈ []A\A]A^A_H=3pA wH='pA 苳X Ld$ <LE0H
m> Hi> H5e> LI踞H\$0H% HD$PH
yA H5X> Iغ HIH$EHz> LLLPLPH<OHD$ LHxH^H5=^w L%Ld$ X 膪LH+lH\$0H~% HD$PH
2vA H5oA Iغh HIH$EHoA LLHPLgOHNHD$ LHxNH^H5]w H舴H<$H,OHHaNHD$ Ht$ HxLH讴HHHÐH<$INHLNHD$ Ht$ HxĪH̫HdIHH D AWAVAUATUHSHHH Q HI H/IHC0x HIEE1A HD$HD$PH$ HC0Ixu}J HH4$HSPL*
- E L舰L;l$PHt=} "t+J H9w L;|$C>uHĈ []A\A]A^A_H=SmA 豰H=GmA 腩X Ld$ VLC0H
> H > H5 > LIқHl$0H5% HD$PH
wA H5r> I躖 HHH$BH> LHLMH MHVLHD$ HHxH[H5W[w L?Ld$ X 蠧LIEiHl$0H% HD$PH
pA H5lA Ih HHH$ BH!lA LHL)M^H<$ILuLHKHD$ Ht$ HxZLbHHHH f. fAWAVAUIATUSH H9 Ht$> HHI H} Ld$P1Hƿ I] L) HHt$PHPHI IE HX|) LE1ILLHU LM HHLH7(H? HH9HT H9a HW HfHfHHH1HIfoIHH@BoT fofhf`fefofofifofa[YYfo[ifeifafi[aa[QQH9rH9t L *HJH9Y t*HJH9> t*HJH9# t*HJH9 t*HJH9 t*HJH9 t*HJH9 t*HJH9 t*HJ H9 t *HJ
-H9~jt
-*HJ H9~St *HJ H9~<t *HJ
H9~%t
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A H5}hA Iغ7 L=HhA HLL ILXHHGHD$ LHx?HWH5Vw LyHL~~ HƭLH HHH@GHD$ Ht$ HxLH荭HHHf D Hl$ X yHI dH\$0Hq$ H
*A H5gA Iغ; L=H3gA HLLH Hl$ X HIcH\$0H$ Ld$PH
ƞA H5'gA Iغ9 L<HfA HLLG ff. ff. ff. ff. ff. ff. f. AWAVAUIATUSH H9 Ht$e IHI/ HU L$ 1Hƿ I] L# HH$ HPHHD$ IE HX# HL$LE1ILH軈 L"HIG0x
-c LI I_P 1HA <rf 8|$ t$ \t$ t$ f T HH9t8Kց
tt 8 D pL LH]H|$H H H(H9HH9 H HWHHK1Hfo ? H(I(HIIH@QIQfffofafifofafi(afa(aQQH9fffofafifofafifafof`fhfof`fhfof`fhf`B 6H9 W .@E@4HrH9x .Hr@EH9@|[ .Hr@EH9@|> .Hr@EH9@|! .Hr@EH9@| .Hr@EH9@| .Hr@EH9@| .Hr@DH9L .Hr @EH9@| .Hr
-@EH9@| ~s.Hr @EH9@|
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@EH9@| ~(.Hr@EH9@|
~.@DψLI} Ht$HPLx H []A\A]A^A_f. W1Ҿ f D .EΈ HH9uHl$@X 蹜HI^^H\$`H$ L$ H
xA H5aA Iغ7 L;7HVaA HLLFBLAH@HD$@LHx腝HNPH5Ow L迦X Lt$0 MG0H
Q > HM > H5I > LI蜏Hl$@H$ H\$`H
wA H5<> I躖 H6He> LHLAH@H'@HD$0HHx֜HOH5(Ow LHLw H]IHL@H?HD$0Ht$/Hx脜L茝HHfHf Hl$@X HI\H\$`H $ H
"wA H5#`A Iغ; L5H_A HLL@[Hl$@X 貚HIW\H\$`H$ L$ H
vA H5_A Iغ9 L45H7_A HLL?@LH?HH>HD$@Ht$0HxqLyHHHHff. f. f. fAWAVAUIATUSH H9 * HHIH Hg Ld$P1Hƿ Mu L ILHt$PPHI IE L@LD$y LD$HHLE1LP HLHrH: LHLHD"H% HhH9HH97 H- HWH11H (H(SLSDSLS[fo[fafifofafifaHH9rH9 4,fHHQH9~{,,f4PHQH9~e$,f4PHQH9~O ,f4PHQH9~9,f4PHQH9~# H,H9f4P~
<,fHI} LHPLqs Hĸ []A\A]A^A_ 1f D ,f PHH9uHl$ X 艗HI.YH\$0H$ Ld$PH
rA H5\A Iغ7 L2H)\A HLL=Lq<H;HD$ LHxXH!KH5Jw L蒡HLr HߡLH$<HHY;HD$ Ht$ HxLH覡HHH Hl$ X 葖HI6XH\$0H$ H
"qA H5[A Iغ; L1HK[A HLL&<Hl$ X 2HIWH\$0H*$ Ld$PH
pA H5?[A Iغ9 L0HZA HLL; ff.
-ff. ff. ff. ff. ff. f. AWAVAUIATUSH H9 HHIHA H Ld$P1Hƿ Mu L U ILHt$PPHI; IE L@LD$ LD$HHLE1L| HLHH躽 LHLH$ H HPH9HSH9 H HWH11H4 (HD[HH9wH9t6,҉HVH9~! H,H9 ~ ,ӉI} LHPL}o Hĸ []A\A]A^A_ 1f D ,ȉ HH9uHl$ X 蚓HI?UH\$0H$ Ld$PH
mA H5XA Iغ7 L .H:XA HLL*9L8H7HD$ LHxiH2GH5Fw L裝HLn HLH58HHj7HD$ Ht$ HxL H距HHH Hl$ X 衒HIFTH\$0H$ H
lA H5WA Iغ; L+-H[WA HLL68Hl$ X BHISH\$0H:$ Ld$PH
kA H5OWA Iغ9 L,HVA HLL7 ff. ff. ff. ff. ff. ff. f. AWAVAUIATUSH H9 HHIHQ H7 Ld$P1Hƿ Mu L ILHt$PPHI IE L@LD$ LD$HHLE1Lx HLHHʹ LH LH 1H~ D D H,H HH9uI} LHPL l Hĸ []A\A]A^A_Hl$ X JHIQH\$0HB$ Ld$PH
hA H5WUA Iغ7 L*HTA HLL5L25Hj4HD$ LHxHCH5kCw LSHLXk H蠚LH4HH4HD$ Ht$ HxǐLϑHgHHH Hl$ X QHIPH\$0HI$ H
gA H5cTA Iغ; L)H TA HLL4Hl$ X HIPH\$0H$ Ld$PH
>gA H5SA Iغ9 Lw)HzSA HLL4 ff. ff. ff. ff. ff. ff. f. AWAVAUIATUSH H9 HHIH H Ld$P1Hƿ Mu L] = ILHt$PPHI# IE L@LD$ LD$HHLE1Lu HLHHz LHLHTH HH9HT H9 H HWHHM1HH(I(HAIH@IAI[fo[fafifofafi(Yfa(YI[IH9[fofafifofafifafof`fhfof`fhfof`fhf`B FH9 Dt A,Έ HJH9g Dl A,͈LHJH9J Dd A,̈LHJH9- D\ A,ˈLHJH9 DT A,ʈLHJH9 DL A,ɈLHJH9 DD A,ȈLHJH9 | ,ψLHJH9 t ,ΈLHJ H9 l ,͈L HJ
-H9~ld ,̈L
-HJ H9~U\ ,ˈL HJ H9~>T ,ʈL HJ
H9~'L ,ɈL
HJH9~D| A,ψLI} LHPLRf Hĸ []A\A]A^A_1f D D ,Ȉ HH9uHl$ X qHILH\$0Hi$ Ld$PH
}aA H5~OA Iغ7 L$HOA HLL0LY/H.HD$ LHx@H >H5=w LzHLe HǔLH /HHA.HD$ Ht$ HxLH莔HHHÐf D Hl$ X yHI KH\$0Hq$ H
`A H5NA Iغ; L$H3NA HLL/Hl$ X HIJH\$0H$ Ld$PH
&`A H5'NA Iغ9 L#HMA HLL. ff. ff. ff. ff. ff. ff. f. AWAVAUIATUSH H9 * HHIH1 Hg Ld$P1Hƿ Mu L
- ILHt$PPHI IE L@LD$
- LD$HHLE1Lo HLHH誰 LHLHH% HhH9HH97 H- HWH11H (H(SLSDSLS[fo[fafifofafifaHH9rH9 4,fHHQH9~{,,f4PHQH9~e$,f4PHQH9~O ,f4PHQH9~9,f4PHQH9~# H,H9f4P~
<,fHI} LHPLa Hĸ []A\A]A^A_ 1f D ,f PHH9uHl$ X HIGH\$0H$ Ld$PH
[A H5KA Iغ7 L~ HJA HLL+L*H*HD$ LHxȆH9H59w LHLa HOLH*HH)HD$ Ht$ HxvL~HHHH Hl$ X HIFH\$0H$ H
ZA H5JA Iغ; L HIA HLL*Hl$ X 袄HIGFH\$0H$ Ld$PH
nZA H5IA Iغ9 L' H*IA HLL2* ff.
-ff. ff. ff. ff. ff. f. AWAVAUIATUSH H9 Ht$] IHI诬 HE L$ 1Hƿ I] L HH$ HPHHD$ IE HXd HL$LE1ILH;k L3HIG0x
-[ LI I_P 1HA <rf 8|$ l$ \l$ t$ f T HH9t8Kց
tt 8 D pL舫 LHݶH|$H H HPH9HSH9 H HWH(A H4 11(H(D(fr T\[fHH9rH9t9H,҉HVH9~# HH,H9 ~
H,ӉI} Ht$HPL$] H []A\A]A^A_f1f D H,ȉ HH9uHl$@X AHIBH\$`H9$ L$ H
UA H5KFA Iغ7 LHEA HLL&L&&H^%HD$@LHx
H4H5_4w LGX Lt$0言MG0H
= H= H5= LI$tHl$@H$ H\$`H
;\A H5= I躖 HH= LHL &Hw%H$HD$0HHx^H'4H53w L蘊HL[ HIHL'%H_$HD$0Ht$/Hx LHHH Hl$@X HIFAH\$`H$ H
RTA H5DA Iغ; L+H[DA HLL6%cHl$@X BHI@H\$`H:$ L$ H
SA H5LDA Iغ9 LHCA HLL$LH $HHT#HD$@Ht$0HxL H衉HHHff. f. f. fAWAVAUIATUSH H9 4 HHIHQ Ho Ld$P1Hƿ Mu L ILHt$PPHI IE L@LD$ LD$HHLE1Le HLHHʦ LH LH 1HH
-=> Df H,H HH9t)D .=> r\H,H H14HH9uI} LHPLX Hĸ []A\A]A^A_Hl$ X }HI>H\$0H$ Ld$PH
{PA H5 BA Iغ7 LHAA HLL"L!H/!HD$ LHx}H0H500w LHL X HeLH!HH HD$ Ht$ Hx}L~H,HHH D Hl$ X |HI=H\$0H$ H
OA H5+AA Iغ; LH@A HLL!
-Hl$ X {HI_=H\$0H$ Ld$PH
&OA H5@A Iغ9 L?HB@A HLLJ! ff. ff. ff. ff. ff. ff. f. AWAVAUIATUSH H9 Ht$^ IHIϣ HE L$ 1Hƿ I] L( HH$ HPHHD$ IE HX HL$LE1ILH[b LSHIG0x
-\ LI I_P3 1HzA <rf 8|$ \$ \\$ t$ f T HH9t8Kց
tt 8 D pL訢 LHH|$HH HH9HT H9 H HWH11H4 (HD DD$ ZZD$ f PfLPfDPfDPHH9wH9t<| HVZH9<~%t HH9Z4~l Z,I} Ht$HPLDT H []A\A]A^A_f1f D d Z$HH9uHl$PX `xHI:H\$pHX$ L$ H
IJA H5j=A Iغ7 LH<A HLL LE H} HD$PLHx,yH+H5~+w LfX Lt$@wMG0H
= H= H5= LICkHl$PH$ H\$pH
ZSA H5= I躖 H3H = LHL> H HHD$@HHx}xHF+H5*w L跁HLR HIHLF H~HD$@Ht$?Hx+xL3yHHHfHl$PX vHIf8H\$pH$ H
HA H5;A Iغ; LKH{;A HLLV dHl$PX bvHI8H\$pHZ$ L$ H
KHA H5l;A Iغ9 LH:A HLLLH?HHtHD$PHt$@Hx!wL)xHHHHff. f. f. fAWAVAUIATUSH H9 HHIHq H Ld$P1Hƿ Mu L = ILHt$PPHI# IE L@LD$) LD$HHLE1L ] HLH"H LH?LHH HH9HT H9 H HWHHM1HH(I(HAIH@IAI[fo[fafifofafi(Yfa(YI[IH9[fofafifofafifafof`fhfof`fhfof`fhf`B FH9 Dt A,Έ HJH9g Dl A,͈LHJH9J Dd A,̈LHJH9- D\ A,ˈLHJH9 DT A,ʈLHJH9 DL A,ɈLHJH9 DD A,ȈLHJH9 | ,ψLHJH9 t ,ΈLHJ H9 l ,͈L HJ
-H9~ld ,̈L
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H9~'L ,ɈL
HJH9~D| A,ψLI} LHPLM Hĸ []A\A]A^A_1f D D ,Ȉ HH9uHl$ X qHI3H\$0H$ Ld$PH
BA H56A Iغ7 Lf H6A HLLqLHHD$ LHxrHy%H5%w L{HLL H7|LH|HHHD$ Ht$ Hx^rLfsH{HHHÐf D Hl$ X pHI2H\$0H$ H
AA H55A Iغ; Ls H5A HLL~Hl$ X pHI/2H\$0H$ Ld$PH
VAA H55A Iغ9 L H5A HLL ff. ff. ff. ff. ff. ff. f. AWAVI1AUATIUHHSHX HH@HsH{ HD$HDŽ$ HDŽ$( HDŽ$0 H)HH E1E1E11IMH$ H$( L$0 tHLxHLL$ 1LH$( L[z H$ IHtfsA~P
HE S AVPH,5A HcH H H9: L< LUsHsLC HI)IN, -f D L$ LLHDŽ$ HDŽ$ Lr H$ { H
DA H]3A A L1$ H$ H$ H| f. L$ LLHDŽ$ HDŽ$ Lq H$ H
\BA H2A A L1$ H$ H$ HtK=H} H] H ei X L|$0QmHT$ H53A LIrH$0 H:$ L$ H
kJA H5L2A Iغl LLLLViL.HfHD$0LHxnH H5g w LOw HT$LH HX H[]A\A]A^A_ L$ LLHDŽ$ HDŽ$ L*p H$ 3 H
DA Hu1A A L1-$ H$ H$ H L$ LLHDŽ$ HDŽ$ Lo H$ H
=A H0A A L1$ H$ H$ H L$ LLHDŽ$ HDŽ$ L*o H$ H
TBA Hu0A A L1-$ H$ H$ H L$ LLHDŽ$ HDŽ$ Ln H$ H
FA H/A A L1$ H$ H$ H L$ LLHDŽ$ HDŽ$ L*n H$ H
DA Hu/A A L1-$ H$ H$ H L$ LLHDŽ$ HDŽ$ Lm H$ H
DCA H.A A L1$ H$ H$ H X L|$ yiHT$ H5.A LInH$ Hb$ L$ H
FA H5t.A Iغj LLLL~eLVH
HD$ LHx=jH H5 w Lws Lt$pLLHD$p HD$x Ll H$ H
EA H-A A L1$ H$ H|$xH Ls0L#LcPM;w0Lt$P I I_PI9 H|$L IILPHHLIrH} HE Hd @ L$ LLHDŽ$ HDŽ$ Lk H$ + H
:A H,A A L1$ H$ H$ H X L|$@qgIVPH5^-A LIH$P H[$ L$ H
DA H5m,A Iغn LLLLwcLO H HD$@LHx6hHH5w LpqHH} HtbHE Hq HT$LLH1f D HT$LLH1f D HT$LLH19f D HT$LLH1f D HT$LLHE1yf D HT$LLH1f D HT$LLHU19f D HT$LLHu18f D HT$LLH51f D HT$LLH1f D HE dIHL HD$@LHxfLg]HH$ HI4?H$ HHAi7H$ HHt`H$ HuHIHLD HD$0LHxeLfHHH$ HgH$ HHts`H$ H<HH$ H#gH$ HHt/`H|$xH?HH$ H&H$ HHt_H$ HH]H$ HHt_H$ HHn_dHH$ HaX Lt$`scHT$PMG0H
= H5Z(A LI}H$p HQ$ H
j6A H5= Iغ LH0(A LLLLFH~HD$`LHx-dHH5w LgmH$ HH%^ILMHL HD$`H$ Hxc>IHH,H<IHLHD$ LHxcHH$ HHt(^H$ H5H$ HH]HÐH3HÐHH$ HH*] AWAVAUATUHSHHH 衊 HHD$ HC0x HH{L|$@HD$HCPH\$pHD$a H|$PHLsX1>Hgix LhH@HD$HD$0HD$ HD$?HD$(f D Lx_Hix Ƅ$ HI}HDŽ$ 1Ƅ$ HDŽ$ HHDŽ$ HDŽ$ H$ HD$HDŽ$ Ll$pHgL%ix H{@HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ ID$HDŽ$ HD$pID$@H$ Hhx HHD$xlHxix HsLDŽ$ HHD$xHgx HH$ %gHD$Ht$ H(D$0VeHsL*bHD$PLH<IjHD$@Ht$(HHx`ID$LHD$pID$@H$ Hhx HHD$xH$ Hx`Hgx H{@HHD$xgIEHL$LLl$pHLpHgx HH$ ]H;l$H []A\A]A^A_Ld$@X ^LIy Hl$PH̹$ H\$pH
ZA H5#A IJ HYH\#A LHLdHHHD$@HHx_HlH5w LhHHD$@Ht$?Hxw_HjHiIHLYHHD$@Ht$?Hx>_LF`HhHHHÐHH{HIEHT$Ll$pHTpHex H{XHH$ ~\HhHX Ld$@]LC0H
= H= H5= LIPHl$PHa$ H\$pH
XA H5= I躞 HH= LHL+f.
ff. f. AWAVAUI1ATIUHHSH HpHx HLxHD$P HD$X HD$` H)HH E1E111HMHL$PHL$XHT$`tHL6fHLLt$P1LHL$XLg H|$PHD$Ht`A}P
AUPH"A HcHf D H H9 H HHT$`HsLC HHT$I)IN4 < @ L踄 HI-H|$HPMHT JH9J#H9 I~ LfHfHHA1HIHH@IBofofhf`fofofofifafafipXxPH9rI9 HBI9 t4HBI9 t4HBI9u t4HBI9` t4HBI9K t4HBI96 t4HBI9! t4HBI9 t4HB I9
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@C HC A L1ȴ! H$ HE H8 H[]A\A]A^A_ Lt L,M IOPHt$L|$pLL$PLD$@LL<$LHD$p HD$x HDŽ$ AH$ tFH
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+= L4 Of. @ AWAVAUIATUSHHH HG HG@ G\ HG` L$ HGh HGp L$ HGx HLJ HLJ Ƈ Ƈ Ƈ Ƈ LJ LJ LJ H H-;{ Dx(H5B LLA HE HD$`lHD$`H$ LLHHD$| H$ Ht#HPHzH9; HHD$HD$H$ HzH9U H
H|$H5j= 1҅ I} HD$ L$ H5eB LLH$ HL$@LL H$ H
+ HHL$ HD$LHyHD$H-WH$ CXHxH9 A
+N HD$pL$ H5= LHHD$H|$H5c= C HDŽ$ H
L>H$ H$ H$ H$ H$ HDŽ$ H$ H$( H$ HSH{H$ HCH$ HS HsHK H$ HCH$( LCHH$ t, LLH$ HtH$ HЃ{ u
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- H$ H5> LHHD$ H|$H5C<
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- C( HDŽ$ H
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- H$P H5B< LHHD$HT$H$P H$@ L H$P H HPHzH9 H1H$P CTHzH9 t ; A
- 1H H9 C\ǃ ǃ ǃ H$ L ~
H$ HxH9
- HD$pHxH9l
- HD$`HxH9W HH []A\A]A^A_ H$ HT$9H5@< HHD$L$ HT$8H58 LHEH$ LLH$ H$ HHD$ d H$ Ht$ HD$pHEt$HHD$H$ L$ Ht!HPHzH9 L$ HH$ HxH9 H$ LHxH$ LHx{f D |$C\ ǃ K D H|$H5?< C( HDŽ$ H$ H
L H|$H5?< W C HDŽ$ :H$ H
{L5 H|$H5~< 7 H|$H5?>< LL jL$ I~ HI~FHCxH H)HI99 B ILH L *H{xLH> ƃ fL$ HC`H58 LLHD$HL$H$ LLe H$ H HPHzH9 L$ HH$ H$ LHxGX HI袶L$ LO L$ H
#C< H5T>< M< L|H?< HLL臚LߙLH$ LHxHH5q L D D H$0 HT$;H5=< HHD$ H$ HT$:H5s8 HHD$HEHT$H$ LH$ H$ HHD$(m H$ Ht$(H$ HEt$ HHD$H$ HtHPHzH9 HH$ HxH9
- H$ LHxH$0 LHx! @ H$ L$ LHxlHt$Hƃ H$ Hx HLL$ HC`HD$I@0xn IxM8MxP HHD$2
- H{`H9{h HT$LH:Ht$H ƃ H$@ ; D$P D {(\ @ H|$H5:< 7 C HDŽ$ H$ H
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L$ LH= r HO L$ HD$LHD$H=r HG8
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L$ HD$LHD$H=Dr HW LLMH= r HO6 L$ LHD$HD$X L$ HT$H53< LI H53< LQHH$ H5 8 H$ HUH+HH$ H52< H$ HUHHH$ H5< H$ HUHHH$ H52< H$ HUHHH$ H5H2< H$ HUHHH5.? LH$ HUHrHL$ L$ LLH$ HUHL$ LD H$P H
7< H573< M H_1LHLoHǎLH$ HHxH$ HHxH$ LHxH$ LHxoH$ LHx[H$ LHxGH$ LHx3H$ LHx H$ LHx HԜH5]q LEH=er HW LX L$` uHT$?H5 4< LIH$@ H^C H
W4< H51< I" LHg1< LLLLSH苌H$` LHx7H H5q LqH=r HO
- L$ 8LHD$ HD$ !H=Qr HO
- LHD$ HD$ X LD$L$p RLD$H
K8 H~K8 H5zK8 LHI0L$` L)B H
2< H5kO8 M L軁HO8 LLHƌL LVH$p LHxH˚H5Tq H<HLI H$ H$ HHxH$ HLHxH$ LHxH$ LHxH$ LHxoH$ LHx[H$ LHxGH$ LHx3H$ LHx L']LIL?H$p LHxHLII IIHLH$` LHxH$p LHxLHIIH$ HHH$ HHH$ HtwJqrHH$ H$ HxIHH$ LHxHH$ HH H$ HH HH$ H$ HxH$ HHH$ HHH$@ LHxDH$ HHH$ Hk^HH$0 LHxJHH$ Ht$>HxH$ LHxH$ HHH$ HH$ HHH$ HH$ HHH$ HX L$ HT$H5+< LIhH5+< L9HLH$ HUHH$ HHD$H$ H= L$ H
p/< H5,< I躶 L)}HL$1LL7L菇HdžH$ LHxsH$ LHx_H$ LHxKHH5q LPJHBHJPJHLPJH+IHL)H$ LHxH$ LHxH$ LHxLHHHHH$ HHIHH$ H$ HxRH$ HHyH$ HbHH$P LHx FHH$ Ht$=HxH$ H$ HxH$ H$ HxH$ HHtwH HL$ HL$ HL$ HL$ HL$ HL$ HL$ 7H$ HHEH$ H.X L$ HT$H5(< LIH5(< LHLH$ HUHH$ HHD$,H$ HL: L$ H
+< H5(< I LyHL$1LLL<HtH$ LHx H$ LHx H$ LHxHH5Jq L2JqrJqrIHLH$ LHxH$ LHxH$ LHxxHHH @ ILMMHL肂H$` LHx.IHŐILM HL@H$ H$ HxL%IHfHH$ HHH$ HPJHJqrPJHGLHcL蛁[HJqrJqr)H$ HHvH$ H_PJHPJH
-Jqrf D AUATI USH 6LHHx Ld$PHLh LkPLLHq H{HHH? Hx6r HHHĨ H[]A\A]HHHLHAUATI USH LHH Ld$PHL LkPLLHq H{HHH H5r HHHĨ H[]A\A]HHHMLHbAUATI USH LHH8 Ld$PHL( LkPLLYHRq H{HHH H5r HHHĨ H[]A\A]HHEHLH¾AUATI USH VLHH Ld$PHL LkPLLHq H{HHH_ H5r HHHĨ H[]A\A]HHH
LH"AUATI USH LHH Ld$PHL LkPLLHq H{HHH Hx3r HHHĨ H[]A\A]HHHmLH肽AUATI USH LHHX Ld$PHLH LkPLLyHrq H{HHH H2r HHHĨ H[]A\A]HHeHLHLIH HJH M)ILI Hf. fHM)JD Hf. D Hq HHÐHq HHf. AWAVIAUIATIUSH H\$0HT$H{XH r Ƅ$h HHDŽ$` Ƅ$i 1HDŽ$p HDŽ$x HHDŽ$ HDŽ$ H$ Hr HhLxH}Hl$0L?Hr H{@HD$@ HD$H HD$P HD$X HD$` HHD$h HD$0H>r H@H$ Hr HHD$8Hr HsH{XD$x HHD$8Hr HH$ 5ML LLHHfHD$HH0FM= LeLHH7Hr HxHD$`I<$HJ Lt$PLl$XL97 L9 M} L)HT$ 1HI`IIHHw HLLLD$LD$HL;%r Ll$ LHL$ LHD$ HxH;=r @ Hr HHD$0Hr H@H$ H"r HHD$8H$ HxH;=r Hr H{@HHD$8WHEHl$0H{XL|0H^r HH$ -HĘ L[]A\A]A^A_ M9 M M)HT$ 1L,IIHHt_HLLLD$LD$HL;r } Ll$ LHL$ Lr D AE A@f. AE A@ D HD$0HHxw @ HD$0HHxw @ HA Ha HsPLC MH=h8 CLl$ HI<$LH+HCH H=h8 H=|r HW Ht$ H=Lr HWteHt$ A@ M0CD0 SA@ M0CD0 mHHD$ Ht$ Hxn.PJH땋PJHYH{IWHEHl$0LL|0Hr H{XHH$ HH @ AWAVIAUIATIUSH H\$0HT$H{XHr Ƅ$h HHDŽ$` Ƅ$i 1HDŽ$p HDŽ$x HHDŽ$ HDŽ$ H$ HXr HhLxH}Hl$0L?_Hr H{@HD$@ HD$H HD$P HD$X HD$` HHD$h HD$0Hr H@H$ H;r HHD$8]Hr HsH{XD$x HHD$8Hr HH$ ML LLHHHD$HH0M= LLHHH(r HxHD$`I<$HJ Lt$PLl$XL97 L9 M} L)HT$ 1HIIIHHw HLLLD$<LD$HL;r Ll$ LHL$ L"HD$ HxH;=zr @ HUr HHD$0HEr H@H$ Hr HHD$8H$ HxH;=.r Hr H{@HHD$8HEHl$0H{XL|0Hr HH$ HĘ L[]A\A]A^A_ M9 M M)HT$ 1LIIHHt_HLLLD$ LD$HL;ur } Ll$ LHL$ L D AE A@f. AE A@ D HD$0HHxw i @ HD$0HHxw I @ HA Ha HsPLdC MH=pc8 Ll$ HI<$LHHHK H=9c8 H=r HW Ht$ H=r HWteHt$ UA@ M0CD0 SA@ M0CD0 mHHD$ Ht$ Hx.PJH땋PJHYH{InRHEHl$0LL|0Hfr H{XHH$ 1H9H @ AWAVIAUATIUSH H\$0H$H{XHr Ƅ$h HHDŽ$` Ƅ$i 1HDŽ$p HDŽ$x HHDŽ$ HDŽ$ H$ Hr HhLxH}Hl$0L?L-r H{@HD$@ HD$H HD$P HD$X HD$` IEHD$h HD$0IE@H$ Hr HHD$8Hr HsH{XD$x HHD$8Hr HH$ @M LLHHqH$HH0RHr HxHD$`I<$H
HT$PLt$XH9* L9 M@ L)HT$ 1HH$H<$IHHp H$HLLD$LD$HL;Mr W Lt$ LHL$ LHD$ HxH;="r IEHD$0IE@H$ HXr HHD$8H$ HxH;=r HOr H{@HHD$8HEHl$0H{XL|0Hr HH$ cHĘ L[]A\A]A^A_f D L9 M L)1HH$HT$ H]H<$IHHtWH$HLLD$LD$HL;$r H Lt$ LHL$ L @ AA@ AA@f D HD$0HHxw ! @ HqH HsPLL H!H=X^8 Lt$ HI<$LHHH3 H=!^8 tH=r HW MHt$ i>H=r HWtmHt$ =H$A@ I AD H$A@ I AD HHD$ Ht$ Hx&PJH덋PJHQH{INMHEHl$0LL|0HFr H{XHH$ HH @ AWH' AVAUIATUSHHHD$ HHD$8HHD$0薗 HHù 1HL5r
- HH{@C C IFC@ ?HHCHC%8 H HC(H9 L$ L)L1HIH H|$H52>: L{ HCP HH H H HCX HC` HCh HCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H\$44 H5| < He" H5> H# HH5g*8 HB
- # HH HD$0IE IU Ht HT$8I]IEHt$ HIU Ld$IE( M I$ I$ rI$ I$ ]I|$hHt>I|$PHt/I\$0L|$HtBH=Pr u HH{H+HtHCHxL9 HHuID$(I|$ 1H I|$ ID$8 ID$0 ID$LHxXID$LHxGI$LHx7LoHD$0HtH|$ HHHL[]A\A]A^A_úPGL
-: D IHCHl$HHxHCHHxHHHxHLHD$0HtH|$ HH(IMHHt
HHI} HtIE HH|$HtߕHfHH{H+HtXHCHxL9uHCH\H̋PJ҉HL f. @ AWAVAUATUHSHHHH H{q L$ Ld$0HHGd H} r H5U< LLHHHL$H|$ LHHL$ Lt$ M IFL%r HxL9 LlHD$0HxL9g M Lt$`H5H< LLJL$P H|$PLHL Hl$PHtHEHxL9w HHD$`HxL9# Hy HT$H H$P H H9t'HCPHH []A\A]A^A_fL%1r (X L$ H53< LLIL$0 L H$ H
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yq HHQHBX L$ @H
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< H5< LI2H$ Hr L$ H
C!< H5< I LZHw< LLLfL_eHdH$ H$ Hx>HtH5sq LxLt$pX ٿLI~H$ H L$ H
< H5 < I LXZH< LLLceLdHcHD$pH$ HxHfsH5rq LH=r HWW H$ yLl$@X LI誀H$ H L$P H
< H5L< I LYH< LLLdLcH cHD$@H$ HxɿHrH5rq LH=#r HWH xH$ fH=r HW% +H$ rH=r HWt1lH$ CZPJHPJHILMbHL(bHD$pH$ HxҾLڿHIHHLIbHLaHD$@H$ Hx臾L菿HIHHLI]bLaH$ H$ Hx<HDLIIIHH$ LHxzHHD$0H$ HxH6HHD$`H$ HxŽHLIaHL`H$ H$ Hx莽IHŐ IHLkaL`DHPJHPJHf UHX SHHHH?HH[]HH:HfAWAVAUATUHSH HG0HT$hL $$ x` HIHOPHIMHD$0HHL$8HD$c IE0x IMPIE I}HD$@HHL$HHD$(2 H[HD$` H\$`HH1HDHD$PHaHH7HDE1HD$XH$HMHD$ f D HD$hH H H H\$Ld$ D ILH$LH|$HD$PL$ HD$LL$ILH$LH|$J HD$ L<HD$XI)I! H|$( HIHHzf
+ H$ H5?GE LHHD$H|$H5cB C( HDŽ$ H
LSH$ H$ H$ H$ H$ HDŽ$ H$ H$( H$ HS8H{8H$ HC0H$ HSHHs0HKHH$ HC@H$( LC@HH$ t, LLH$ HtH$ HH$@ H5bB LHHD$HT$H$0 HL$PLu H$0 D$PHt!HPHzH9 H)mB H$@ CPHxH9
+ H$P H5VbB LHHD$HT$H$P H$@ L H$P H HPHzH9-
H1H$P CTHzH9}
+ t ; A
+ 1H H9 C\ǃ ǃ ǃ H$ L ~
~ H$ HxH9
+ HD$pHxH9/
+ HD$`HxH9
+ HH []A\A]A^A_ H$ HT$9H5:`B HHD$L$ HT$8H5= LHEH$ LLH$ H$ HHD$ H$ Ht$ HD$pHEt$HHD$vH$ L$ Ht!HPHzH9 L$ HH$ HxH9 H$ LHxwH$ LHxcf D |$C\ ǃ P D H|$H5_B C( HDŽ$ H$ H
L H|$H5^B 7_ C HDŽ$ 2H$ H
cLA H|$H5B H|$H5]B LL] oL$ I~ HI~FHCxH H)HI9A J ILH LH{xLHƃ f. L$ HC`H5= LLHD$
HL$H$ LL H$ H HPHzH9 L$ HH$ H$ LHx'X HIbcL$ L3 L$ H
bB H5]B M< L 6Hm_B HLL'@L?L>H$ LHxHOH5žy L= D D H$0 HT$;H5\B HHD$ H$ HT$:H5;= HHD$HEHT$H$ LH$ H$ HHD$( H$ Ht$(H$ HEt$ HHD$tH$ HtHPHzH9 HH$ HxH9
+ H$ LHxH$0 LHxq% @ H$ L$ LHxLHt$H}ƃ H$ Hx HIL$ HC`HD$I@0xf IxM8MxP HHD$*
+ H{`H9{h HT$LH
+Ht$Hƃ H$@ 8 D$@ D {(Y @ H|$H5EZB 7 C HDŽ$ H$ H
L H|$H5aZB g7 C( HDŽ$ bH$ H
L, LH{xH)IHHD$`Ht$<HxH{xHtH{`HtHC@HtH{0 HHCHtH{ HH@Ht$H|$H{`M H|$H5
+YB o C HDŽ$ jH$ H
Ly H|$H5$YB C( HDŽ$ H$ H
cLLlH|$H5oXB C HDŽ$ H$ H
rLH|$H5;E uM C( HDŽ$ pH$ H
ALZH=/{ HW L$ LL$ X =LI]H$ Hr- L$ H
#]B H5TXB I7 L|0HYB LLL:L9H9H$ H$ HxHGJH5 y LH=(.{ HW LH=-{ HG Ѕ CTWH=-{ HW LH=-{ HW LWH=r-{ HO L$ HD$L HD$kH=2-{ HW TLGH|$H5UB #
+ C HDŽ$ H$ H
?L-H=,{ HO L$ HD$L]HD$X H$p HT$H5WB HIJ$L$` HLH$ H* L$ H
ZB H5UB IU L.1LLL8Li7H6H$` LHxH$p LHxyHGH5y LHH$ H$ HxGHD$pH$P Hx1H=l+{ HO
LHD$ HD$H=4+{ HWG HL;H=+{ HO LHD$HD$LH=*{ HO9 L$
LHD$kHD$X L$ HT$H5RB LIX"H5RB LHH$ H5= H$ HUHcHH$ H5RB H$ HUH=HH$ H5=B H$ HUHHH$ H5JRB H$ HUHHH$ H5QB H$ HUHHH5DoF LH$ HUHHL$ L$ LLH$ HUH蝿L$ L' H$P H
NWB H5RB M H+1LHL5Ho4L3H$ HHx蓿H$ HHxH$ LHxkH$ LHxWH$ LHxCH$ LHx/H$ LHxH$ LHxH$ LHxH<DH5y LH= ({ HW #LX L$` ]HT$?H5SB LIH$@ H& H
SB H5pQB I" L)HQB LLL3L2H32H$` LHx HhCH5Ay LH=I'{ HO
+ L$ 8LHD$ HD$ !H= '{ HO
+ LHD$ 軿HD$ X LD$L$p :LD$H
< H< H5< LHI0!L$` LQ% H
RB H5{< M Lc(H< LLHn2L1L0H$p LHxH3BH5 y HHH$ LHx轼LսHJqrAPJHHH$ H$ HxoHLI\0H$ H$ HHx@H$ HLHx)H$ LHxH$ LHxH$ LHxH$ LHxٻH$ LHxŻH$ LHx豻H$ LHx蝻I4IH$ @ILM20Lj/H$p LHxVHLkIIIHH$` LHx H$p LHx L$HHH$ HHH$ HjHH$ H$ Hx论HH$ Ht$=Hx萺H$ H$ HxwH$ H$ Hx^H$ HHtPfH HH$P LHxH$ HHH$ HJH$ HHH$ HpHhHH$0 LHx蘹LHH$ Ht$>HxzH$ LHxfH$ HH H$ HH$ HHH$ HH$ HHH$ HX L$ 葷HT$H5JB LIH5JB L+HLH$ HUHH$ HHD$!H$ H L$ H
NB H5cKB I躶 L#HL$1LL-L,H),H$ LHxH$ LHxH$ LHxH6=H5y LPJHBHJJqrH$ HH5H$ H IHLp+H$ LHx\H$ LHxHH$ LHx4LLHHHHH$ HHoMeHH$ H$ Hxٶ?H$ HH`H$ HIHH$@ LHx蒶- H$ HH H$ H HL$ HL$ HL$ HL$ #HL$ 'HL$ +HL$ /+H$ HHgH$ HPX L$ ^HT$H5HB LIH5 HB LHLH$ HUHH$ HHD$H$ HN L$ H
?KB H50HB I LX HL$1LLf*L)H(H$ LHxH$ LHxδH$ LHx躴H:H5ܨy LTIH(L]JqrnIHLs(H$ LHx_H$ LHxKH$ LHx7&HHH !ILM(HL(H$` LHxIHŐILM(HL'H$ H$ Hx觳IHPJHPJH,PJHJqr:LH(L@'jHJqrJqr&H$ HHH$ HPJHPJHJqr f. AUATI USH &LHH Ld$PHL LkPLLH2y H{HHH Hx`z HHHĨ H[]A\A]HHuH-LHAUATI USH 膵LHH8 Ld$PHL( LkPLLHy H{HHH H_z HHHĨ H[]A\A]HHմH荻LHBAUATI USH LHH Ld$PHL LkPLLYHy H{HHH_ H_z HHHĨ H[]A\A]HH5HLHAUATI USH FLHH Ld$PHL LkPLLHRy H{HHH H_z HHHĨ H[]A\A]HH蕳HMLHAUATI USH 観LHHX Ld$PHLH LkPLLHy H{HHH H^z HHHĨ H[]A\A]HHH譹LHbAUATI USH LHH Ld$PHL LkPLLyHy H{HHH HX^z HHHĨ H[]A\A]HHUH
LHLIH HJH M)ILI Hf. fHM)JD Hf. D HYy HHÐHIy HH齱f. AWAVIAUIATIUSH H\$0HT$H{XCH{ Ƅ$h HHDŽ$` Ƅ$i 1HDŽ$p HDŽ$x HHDŽ$ HDŽ$ H$ H{ HhLxH}Hl$0L?H@{ H{@HD$@ HD$H HD$P HD$X HD$` HHD$h HD$0H{ H@H$ Hk{ HHD$8 H>{ HsH{XD$x HHD$8H{ HH$ eML LdLHHvHD$HH06M= L5LHHGH`{ HxHD$`I<$HJ Lt$PLl$XL97 L9 M} L)HT$ 1HI蠷IIHHw HLLLD$茮LD$HL;{ Ll$ LHL$ LҵHD$ HxH;={ @ H{ HHD$0H}{ H@H$ H{ HHD$8H$ HxH;=f{ H{ H{@HHD$8藲HEHl$0H{XL|0H{ HH$ HĘ L[]A\A]A^A_ M9 M M)HT$ 1LlIIHHt_HLLLD$\LD$HL;{ } Ll$ LHL$ L袴 D AE A@f. AE A@ D HD$0HHxw ) @ HD$0HHxw @ HA Ha HsPLC MH== 3Ll$ HI<$LH H胵H H== H=,{ HW Ht$ H={ HWteHt$ 赫A@ M0CD0 SA@ M0CD0 mHHD$ Ht$ HxN.PJH땋PJHYH{I HEHl$0LL|0H{ H{XHH$ qHH @ AWAVIAUIATIUSH H\$0HT$H{XåHD{ Ƅ$h HHDŽ$` Ƅ$i 1HDŽ$p HDŽ$x HHDŽ$ HDŽ$ H$ H{ HhLxH}Hl$0L?菮H{ H{@HD$@ HD$H HD$P HD$X HD$` HHD$h HD$0Hv{ H@H$ H{ HHD$8蝳H{ HsH{XD$x HHD$8HF{ HH$ ML LLHHHD$HH0趪M= L赥LHHǫH{ HxHD$`I<$HJ Lt$PLl$XL97 L9 M} L)HT$ 1HI IIHHw HLLLD$ LD$HL;]{ Ll$ LHL$ LRHD$ HxH;=2{ @ H
{ HHD$0H{ H@H$ HZ{ HHD$8H$ HxH;=
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{ } Ll$ LHL$ L" D AE A@f. AE A@ D HD$0HHxw 詮 @ HD$0HHxw 艮 @ HA Ha HsPL脪C MH= = 賥Ll$ HI<$LH苤HH苮 H=Y = |H=
{ HW Ht$ a H=|
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+{ Ƅ$h HHDŽ$` Ƅ$i 1HDŽ$p HDŽ$x HHDŽ$ HDŽ$ H$ H
+{ HhLxH}Hl$0L?L-D { H{@HD$@ HD$H HD$P HD$X HD$` IEHD$h HD$0IE@H$ Hv
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BAIAH9 BACIAH9 BACIAH9 BACIAH9 BACIAH9 BACIAH9 BACIAH9vBACIA H9vnB ACIA
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j A> LeIIOLMw HPHIH@I~ IV IVINIFHHE蕔IF HIVIFIV(IW IF0H: > Et HhH`H`HhHH9 III IF H)HD$7H Ht<IE1 D Mt IAA IA IA II L9uHhH=LHEHXHUH`L}DmHU5XLHTI HXfH]He[A\A]A^A_] D Lp^H7rfaf D LLebMH%j HpHuLHXH H@H@tHcp HXLHHOHcMH HHOHcMHP HHOHPWfHp4 HhH=LL}HE HEH`DmHEVLqR) Lr(H I9IHXL&]HHXtHMt AWHcHAVLcAUATIULSHcH H$ H$ H$0 I LD$L$ L$ HL$ L$ HH$ L$ H$( H H$ LHSN< HMLt$(IHT$ KLLIIH L)HDŽ$ H$ K6HD$LH< H|$HLHH|$PH< H|$`HHHHD$XH|$hH< K47H|$pLHH|$xH< H$ HIVHMHH$ H$ f D LT$(M} HyHD$ HT$H<H?H9HFH HFH)HHD$0LHHLd- HD$8HDHD$@HHH H L\$HqK4HH HqJ4*HHI L\$HHqJ4HH LL$PHq J4
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N H$ L%^N Hx L9 H$ HxL9| H$ HxL9@ H$ HxL9 H$ HxL9 H$ HxL9 H$ HxL9P H$ HxL9 H$ HxL9 H$ HxL9 H$ HxL9OP=H$ Hԫ(f. H H9 H, HիILcH)LL[PH4ԫHHD$@LHxѫH۫PH$ HӫPH$ HӫPH$ HӫPbH$ H|ӫMP&H$ HUӫPH$ H.ӫPH$ HӫPrH$ Hҫ]PH$ HҫLd$PHHD$0H$ HHxsЫLsZQΫLd$PHH$ Ht HHH=N HWtBH6ҫHLd$PHHxL9uHI9ufPJH뺋PJ҉HHѫκPDH$ Hѫ/L9 H$ HxL9 H$ HxL9l H$ HxL91 H$ HxL9 H$ HxL9 H$ HxL9 H$ HxL9E H$ HxL9
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- DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( ! H H$ H9 L, LǫHL1I7ë L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` ǫH$P H8 HDŽ$ H$h HH$` ƫH$p H HP HDŽ$ H$ HH$ HD$0HDŽ$p HxH;=N HD$ HxH;=N HH" L H+M H5a8 HX^ L9 H+M H5 H6^ H5u H H5ʨ H H[ H5 X H HLHtۮHD$PHt
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HHHʫH=N HW NHt$9ë?H=N HW+ L ëH$ IHtH HH$` HtH HH ɮH$ ëHLd$ HHPJH1IHٮLH$ IHtH0 HH$ HRH H<H$` IHFH H0PJH AWH5r AVAUATIUSH Hl$@HHT$ H|$ H5Z ëHD$0HT$ H5Z HHD$ëHD$0HT$ H\$`Lt$ H5 H{HD$`HN HT$hLHHD$0HD$ HD$piëHC(H
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HHHgH=ofN HW PHt$_AH=?fN HW'
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HHH[H=OZN HW PHt$SAH= ZN HW'
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HHHUH=?TN HW PHt$MAH=TN HW'
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HHHTH=S HW PHt$LAH=ϳS HW'
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+ DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( HH H$ H9 L, L KHL1IF L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` JH$P HaX HDŽ$ H$h HZH$` DJH$p H5XHP HDŽ$ H$ HZH$ 6 HD$ HDŽ$p HxH;=
+S HD$0HxH;=S HHX L4 HEQ H5J( Hљ L: HQ H5 H诙 H5 HS H5 HU HR H5 H@T HLuH͈HD$PHt
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HHHNH=S HW PHt$FAH=S HW'
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+ DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( BH H$ H9 L, LDHL1I@ L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` pDH$P HQR HDŽ$ H$h HTH$` 4DH$p H%RHP HDŽ$ H$ HTH$ & HD$ HDŽ$p HxH;=S HD$0HxH;=S HHR L$ HQ H5:" H L* HQ H5 H蟓 H5z HM H5 HN HtL H5u H0N HLeH轂HD$PHt
HHHĈ L[]A\A]A^A_,=IH M_H{pd]H{X[]H$ H{ L'HD$xLHx?HD$pLHx?HD$hLHx>HD$`LHx>HD$ Ht$ Hx>HD$0H4$Hx>HD$PHt
HHHHH=ߧS HW PHt$@AH=S HW'
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+ DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( {<H H$ H9 L, L>HL1I: L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` `>H$P HAL HDŽ$ H$h HNH$` $>H$p HLHP HDŽ$ H$ HNH$ HD$ HDŽ$p HxH;=S HD$0HxH;=ԡS HHL L HQ H5* H豍 L HQ H5z H菍 H5u HG H5 HH HdF H5e H H HLUH|HD$PHt
HHHĈ L[]A\A]A^A_ 7IH =YH{pTWH{XKWH$ H{ L{HD$xLHx9HD$pLHx8HD$hLHx8HD$`LHx8HD$ Ht$ Hx8HD$0H4$Hx8HD$PHt
HHHBH=ϡS HW PHt$:AH=S HW'
+LV:H$ IHtH HH$` HtH HH 6`H$ );H HHÐPJH5IHzLIH$ HtH0 HH$ HVH H@H$` IHJH H4PJHf D AWH5j AVAUATIUSH Hl$@HHT$ H|$0H5ѧ R;HD$ HL$ H5 HHH$HL$-;HD$ HT$0H\$`Lt$ H5z H{HD$`H2S 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$( k6H H$ H9 L, L8HL1Ip4 L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` P8H$P H1F HDŽ$ H$h HHH$` 8H$p HFHP HDŽ$ H$ HHH$ HD$ HDŽ$p HxH;=ڛS HD$0HxH;=ěS HHF L HQ H5 H衇 L
+ HQ H5t H H5` HA H5 HB HT@ H5U HB HLEHvHD$PHt
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HHH<H=S HW PHt$t4AH=S HW'
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+ DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( [0H H$ H9 L, L2HL1I`. L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` @2H$P H!@ HDŽ$ H$h HqBH$` 2H$p H?HP HDŽ$ H$ HBH$
HD$ HDŽ$p HxH;=ʕS HD$0HxH;=S HH@ L HQ H5
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HHH6H=S HW PHt$d.AH=S HW'
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F H{PHHD$hKHD$ HL$(H{hHt$H@HLPIEHL$Ll$`HL`ID$Ld$PHtPH&F HD$X HH$ 쒣HD$H []A\A]A^A_ D I9 H I)HT$?1L쟣IIHHtOHLHQHL;=F y Hl$@H|$HL$@H: D E AGY E AGf D HD$`H@H|w 距Mf D HD$`H@H|w 菝zf. H HW H|$Hs`袙 D MH=' Hl$@IHD$HH8H瓣HoL臝 H=q' ĔH=4F HWtzHt$@轕H=F HW WHt$>荕HAG M7CD7 qAG M7CD7 PJHIHD$@Ht$>HxI%IT$HL$HLd$PHLPHD$X HF H{hHH$ pHxH{IlHuHL
Hf D lPJH f. AWAVIAUMATUSH H\$`H<$Ht$HL$LL$ H{hxHF H-F HƄ$ HDŽ$ 1Ƅ$ HDŽ$ HLeHDŽ$ H$ HEHDŽ$ HDŽ$ Ld$`HHD$ID$HL`HD$h I|$軗L} 1IGL|$pH|HE(HHD$(薗HEHU0H{PHD$0H@HT$8HT`H@F HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHDŽ$ HDŽ$ HD$`HF HhH$ HF H@HD$pH5F HHD$xWH F HsH{hDŽ$ HHD$xHF HH$ 謖H|$ Hl$HH{HHԔM L㎣H{HL贔HD$H{0ęM L賎H{HL脔HD$ H{0蔙HF HxH$H8H$ H L$ H$ L9p H9_ H H)HT$O1HI葚IIHH HLHHL;5`F Hl$PH<$HL$PHܘHD$PHt$NHxYH2F HHD$`H"F HhH$ HF H@HD$pHOF HHD$xH$ HxH;=ەF HFF H{PHHD$x脕HD$0HL$8H{hH@HL`IGHL$(L|$pHLpID$HL$Ld$`HL`H_F HD$h HH$ %H$H []A\A]A^A_ I9 H I)HT$O1L$IIHHtOHLH艗HL;5F e Hl$PH<$HL$PHsf D E AF\ E AFf D HD$pH@H|w MKf D HD$pH@H|w ǖ>fHD$pH@H|w 觖NfH H? H<$Hs`Òf D MH= #Hl$PIH$HH8HH萔L訖 H= 䍣H=TF HW Ht$PَAF M.CD. AF M.CD. IHD$PHt$NHxrRH{IHuHL貈H F H{hHH$ ։HޕI IT$HL$HLd$`HL`HD$h HrPJH f. AWAVIAUMATUSH H\$`H<$Ht$HL$LL$ H{h؈HaF H-F HƄ$ HDŽ$ 1Ƅ$ HDŽ$ HLeHDŽ$ H$ HEHDŽ$ HDŽ$ Ld$`HHD$ID$HL`HD$h I|$L} 1IGL|$pH|HE(HHD$(HEHU0H{PHD$0H@HT$8HT`HF HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHDŽ$ HDŽ$ HD$`HHF HhH$ H5F H@HD$pHF HHD$x跕H`F HsH{hDŽ$ HHD$xHF HH$ H|$ Hl$HcH{HH4M LCH{HLHD$H{0$M LH{HL䍣HD$ H{0HEF HxH$H8H$ H L$ H$ L9p H9_ H H)HT$O1HIIIHH HLHRHL;5F Hl$PH<$HL$PH<HD$PHt$NHx蹈HF HHD$`HF HhH$ HoF H@HD$pHF HHD$xH$ HxH;=;F HF H{PHHD$x䎣HD$0HL$8H{hH@HL`IGHL$(L|$pHLpID$HL$Ld$`HL`HF HD$h HH$ 腅H$H []A\A]A^A_ I9 H I)HT$O1L脒IIHHtOHLH鐣HL;5WF e Hl$PH<$HL$PHӐf D E AF\ E AFf D HD$pH@H|w OMKf D HD$pH@H|w '>fHD$pH@H|w NfH H? H<$Hs`#f D MH=0 胇Hl$PIH$HH8HhHL H= DH=F HW Ht$P9AF M.CD. AF M.CD. IHD$PHt$NHx҅RH{Iq HuHLHkF H{hHH$ 6H>I IT$HL$HLd$`HL`HD$h HrPJH f. AWIAVIAUATUSH H\$PH|$H{hFHϋF H-pF HƄ$ HDŽ$ 1Ƅ$ HDŽ$ HLeHDŽ$ H$ HEHDŽ$ HDŽ$ Ld$PHHD$ID$HLPHD$X I|$艊Lm 1IELl$`H|HE(HHD$dHEHU0H{PHD$ H@HT$(HTPHF HD$p HD$x HDŽ$ HDŽ$ HHDŽ$ HDŽ$ HD$PHF HhH$ HF H@HD$`H F HHD$h+HԋF HsH{hDŽ$ HHD$hHYF HH$ 耉Mw L߁H{HL谇M L迁H{HL萇HF HxHD$H8H$ H L$ H$ L9s H9J H H)HT$?1HI謍IIHH HLH
HL;={F Hl$@H|$HL$@HHD$@Ht$>HxsHLF HHD$PH<F HhH$ H)F H@HD$`HiF HHD$hH$ HxH;=F H`F H{PHHD$h螈HD$ HL$(H{hH@HLPIEHL$Ll$`HL`ID$HL$Ld$PHLPHyF HD$X HH$ ?HD$H []A\A]A^A_ I9 H I)HT$?1L<IIHHtOHLH衊HL;=F M Hl$@H|$HL$@H芊 D E AGY E AGf D HD$`H@H|w Mf D HD$`H@H|w ߉zf. H HW H|$Hs` D MH= SHl$@IHD$HH8H7H过L H= H=F HW Ht$@ AG M7CD7 AG M7CD7 IHD$@Ht$>HxRI^IT$HL$HLd$PHLPHD$X H.F H{hHH$ |HH{IHuHL{H븋PJH"f. AWIAVIAUATUSH H\$PH|$H{h|HF H-@F HƄ$ HDŽ$ 1Ƅ$ HDŽ$ HLeHDŽ$ H$ HEHDŽ$ HDŽ$ Ld$PHHD$ID$HLPHD$X I|$YLm 1IELl$`H|HE(HHD$4HEHU0H{PHD$ H@HT$(HTPHބF HD$p HD$x HDŽ$ HDŽ$ HHDŽ$ HDŽ$ HD$PHF HhH$ HyF H@HD$`HلF HHD$hHF HsH{hDŽ$ HHD$hH)F HH$ PMw L{H{HL老M L{H{HL`HуF HxHD$H8H$ H L$ H$ L9s H9J H H)HT$?1HI|IIHH HLH݅HL;=KF Hl$@H|$HL$@HƅHD$@Ht$>HxC|H F HHD$PH F HhH$ HF H@HD$`H9F HHD$hH$ HxH;=łF H0F H{PHHD$hnHD$ HL$(H{hH@HLPIEHL$Ll$`HL`ID$HL$Ld$PHLPHIF HD$X HH$ yHD$H []A\A]A^A_ I9 H I)HT$?1L IIHHtOHLHqHL;=߁F M Hl$@H|$HL$@HZ D E AGY E AGf D HD$`H@H|w ׃Mf D HD$`H@H|w 诃zf. H HW H|$Hs` D MH=
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@@ -5954,11 +6716,11 @@
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ELc C u|$#
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ELcC uHD$ H Hc@f D @
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DHc:AuD$"LGAG@1HD>
-HxtуuH$ H{H{H5}
- H1AG@oHH|$0H|$(H/HH1HHH$ HE{H{H5B
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- I h HS HcH ;CZ L LH{LcD; sIB;
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HD$ D$" E1I9 HD$@L$HD$8 HD$(HD$pHD$0 f|$! ELD$ Aw0LHH)5ɑHHLD$B +HH|$HcHI H@ HF A
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ELc C u|$#
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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>
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* D DDMcAIB
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L$ L$ L9 L9 M L)HT$o1HI囙IHHH AEH;-< Ht$PH|$HH$ I6H$ HxH;=< H|$pH< HWH蠛HD$pƄ$ HxH;=P< H[< LpLxf D M9 M& M)HT$o1LIHHHu_AEH;-< U Ht$PH|$HH$ Ii. @ H []A\A]A^A_HLL0HHLLH땿 L$\DT$4DL$(LD$H IcE LD$H< L9HHGAU HEHLD$LcL$(DT$4L$\H-HXHD$H|$HHpX襔sMH=" H=" L$ H}L뎙I̐H$ Ht$nHxŎHl$pE L} BD= Hl$pH=< HWt%Ht$n蘐 듋PJHE L} BD= kH|$Hl&H䗙H|$HHCH˗HHD$HxIFL$ L H< H|$HH`H$ uH}HH=8< HWt87Ht$P迏(HD$pHt$oHx臍PJHH=< HWt%1Ht$Pl"PJHא @ AWAVAUATUSH H^ Ht$Hc6L$4HT$(LD$8LL$ HHH* LL$ IHHHT$@IW`HT$HT$pHT$H' HL$Hc1HA HHHHH9 HkHtAt$@t(A$ t I$ HUHH9 D M $HcIyPLAAAA A A H|$Ƅ$ ?HȒ< Ƅ$ 1HDŽ$ Ƅ$ HDŽ$ HDŽ$ HHDŽ$ HDŽ$ H$ H< LpIFL$ I|H{< H@HHD$芑H3< IHHDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHDŽ$ H$ HԒ< H@H$ HQ< HH$ pH< H|$IwDŽ$ HH$ H< HH$ IH5~
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PJHH= @ HWtLHt$P肵HH@ H|$HH`H$ 蹰HAPJHH|$HH蓾HHHD$HxIFL$ L H=$ @ HWt%pHt$P۴aPJHAG M7CD7 LH|$H&H艼 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$HK@ IHHDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHDŽ$ H$ H@ H@H$ Ha@ HH$ H1@ H|$IwDŽ$ HH$ H@ HH$ QIH5NQ . lIH52
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ḤHH[]fH51 诣HH[]H;Ht$HHHif H٥: HHÐHɥ: HHÐH2 Hi: HHݨf. SHH[鎡 HH 1.4.0 out of index internal error not enough space: expected , got RegisterCustomOps was false. Stacktrace:
- Integer overflow opaque( ml_type != nullptr non_tensor_base != nullptr IsTensorSequence() elem_type_ != nullptr input name cannot be empty CPUExecutionProvider output name cannot be empty != T Tensor type mismatch. input array is too short shape is invalid space is not enough ++index < c.size() vector::reserve vector::_M_default_append DmlExecutionProvider onnxruntime_profile_ i < tensors_.size() vector::_M_range_check Encountered nullptr. value_type != nullptr in[idx]->IsTensor() Unsupported input type 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 Trying to get a TensorSeq, but got: is not supported in this function Tensor sequence must contain only primitive types /onnxruntime_src/onnxruntime/core/framework/TensorSeq.h /onnxruntime_src/include/onnxruntime/core/framework/tensor.h utils::IsPrimitiveDataType<T>(dtype_) Sequence is missing type entry for its element /onnxruntime_src/include/onnxruntime/core/framework/data_types_internal.h Map is missing type entry for its value Mem pattern should be disabled when using DML execution provider. Sequential execution should be enabled when using DML execution provider. onnxruntime::DataTypeImpl::GetType<T>() == type_ Input is not of one of the supported sequence types. Input is not of type sequence or map. Check for duplicate types in MLTypeCallDispatcherRet Unsupported tensor element type in the input: Invalid index requested for map type. Input is not of one of the supported map types. Tensor must always contain primitive types. Found: len >= 0 && static_cast<uint64_t>(len) < std::numeric_limits<size_t>::max() Value type is not supported yet: Number of values should be at least 1. For map type num_values MUST be 2 Key and value tensors have unequal number of elements. Key type is not supported yet. Each element of the sequence should be either tensor or map. At least one element in the sequence is of a type different from others. Expecting all elements to be tensors. Got: Sequences must have tensors of the same data type. There was at least one tensor in the input that was different. Either the key tensor or the value tensor has NumDimensions > 1 W WW W0W@WPW`WpWWWWWWWWV|ihhhhhh i i,i<iLi\ili|i|iTh 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 St16_Sp_counted_baseILN9__gnu_cxx12_Lock_policyE2EE N11onnxruntime23NotImplementedExceptionE N11onnxruntime20OnnxRuntimeExceptionE N11onnxruntime10IAllocatorE N11onnxruntime16AllocatorWrapperE St19_Sp_counted_deleterIPvPFvS0_ESaIiELN9__gnu_cxx12_Lock_policyE2EE St19_Sp_make_shared_tag St23_Sp_counted_ptr_inplaceIN11onnxruntime16AllocatorWrapperESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE /\ basic_string::substr env_ptr == p_instance_ basic_string::_S_construct null not valid /onnxruntime_src/onnxruntime/core/session/ort_env.cc static void OrtEnv::Release(OrtEnv*) N11onnxruntime7logging5ISinkE 14LoggingWrapper N11onnxruntime7logging8CLogSinkE execution_mode is not valid graph_optimization_level is not valid 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.
- Result buffer is not large enough /onnxruntime_src/onnxruntime/core/session/custom_ops.cc %u Unsupported version ' ' in custom op ' is not implemented Description CreateCustomRegistry ComputeAsync *ZN11onnxruntime20CreateCustomRegistryERKSt6vectorIP17OrtCustomOpDomainSaIS2_EERSt10shared_ptrINS_14CustomRegistryEEEUlRKNS_12OpKernelInfoEE_ N4onnx15ISchemaRegistryE N11onnxruntime30IOnnxRuntimeOpSchemaCollectionE N11onnxruntime8OpKernelE N11onnxruntime14CustomOpKernelE N4onnx8OpSchema19num_inputs_allowed_MUliE_E N4onnx8OpSchema20num_outputs_allowed_MUliE_E St23_Sp_counted_ptr_inplaceIN11onnxruntime14KernelRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime27OnnxRuntimeOpSchemaRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime14CustomRegistryESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE 12OrtAllocator N3Ort9ExceptionE 16OrtAllocatorImpl 19OrtDefaultAllocator tensor(uint8) tensor(uint16) tensor(uint32) tensor(uint64) tensor(int8) tensor(int16) tensor(int32) tensor(int64) tensor(float16) tensor(float) tensor(double) tensor(string) tensor(bool) tensor(complex64) tensor(complex128) unknown Element type of input [TypeInferenceError] expected to have tensor type Input Output com.microsoft com.microsoft.nchwc com.microsoft.mlfeaturizers (domain: ) from file , but it its domain is not known by the checker. , but it its version is not in the inclusive range [ ] (usually, this means you in onnx/defs/schema.h). Schema error: intra-op inter-op MemcpyFromHost MemcpyToHost Exception caught: Trying to register schema with name , but it is already registered from file bumped the operator version but forgot to update the version range in DomainToVersionRange Constrain to any tensor type. If the dtype attribute is not provided this must be a valid output type. /onnxruntime_src/onnxruntime/core/session/environment.cc FvRN4onnx16InferenceContextEE FvON4onnx8OpSchemaEE N4onnx14InferenceErrorE N4onnx11SchemaErrorE PFvRN4onnx16InferenceContextEE PFvON4onnx8OpSchemaEE Cpu Model was not loaded Model was not loaded. Profiler is disabled. CUDAExecutionProvider subgraph_session_state p_op_kernel ) , expected: ( Output vector pointer is NULL p_fetches->size(): Invalid Output Name: other_error [json.exception. Session was not initialized Session not initialized. %Y-%m-%d_%H-%M-%S .json Compute_ Create_State_ Release_State_ Invalid rank for input: Expected: for the following indices
- Provider has already been registered. elements, but feeds has elements. Invalid Feed Input Name: Input with name: IsSparseTensor() Running with tag: model_run Exception during loading: Unknown exception in Load() model_loading_uri Load model from model_loading_proto model_loading_istream model_loading_array Node placements ]. Provider: [ MemcpyTransformer Initializing session. session_initialization ORT_LOAD_CONFIG_FROM_MODEL are '0' and '1'. is_model_proto_parsed CastFloat16Transformer st.IsOK() Error during EndProfiling(): Attempt to use DefaultLogger but none has been registered. /onnxruntime_src/onnxruntime/core/session/inference_session.cc Received nullptr for graph transformer Graph transformers must be registered before the session is initialized. Could not write a profile because no model was loaded. Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast<int>(logging::Severity::kFATAL) Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast<int>(logging::Severity::kFATAL) /onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.h CreateSubgraphSessionState should have created an entry earlier. Unexpected input data type. Actual: ( At least one output should be requested. Output vector incorrectly sized: output_names.size(): Exceeded max transformer level. Current level is set to graph_optimization_level <= TransformerLevel::MaxLevel timeout_in_ms >0 is not supported session->NewIOBinding(&binding_).IsOK() Main Graph instance should have populated all subgraphs when being resolved. Received nullptr for custom registry Please fix either the inputs or the model. Got invalid dimensions for input: /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. Memory pattern must be disabled before registering DMLExecutionProvider Sequential execution must be enabled before registering DMLExecutionProvider Size mismatch: feed_names has is not expected to be of type tensor. is not expected to be of type sparse tensor. Trying to get a SparseTensor, but got: is not expected to be of type tensor sequence. Encountered unknown exception in Run() This session already contains a loaded model. Encountered unknown exception in Load() ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load(). ModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction. model_loading_from_saved_proto Could not find an implementation for the node All nodes have been placed on [ Session has already been initialized. Adding default CPU execution provider. Parallel execution mode doesn't support CUDA Execution Provider currently. Serializing Optimized ONNX model with Graph Optimization level greater than ORT_ENABLE_EXTENDED. The generated model may contain hardware and execution provider specific optimizations, and should only be used in the same environment the model was optimized for. Session successfully initialized. Exception during initialization: Encountered unknown exception in Initialize() The only supported values for the environment variable The environment variable contained the value: Reading the provided model for the ORT config 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. 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 GetModelOutputs GetOverridableInitializers GetModelInputs GetModelMetadata onnxruntime::FeedsFetchesInfo::FeedsFetchesInfo(const std::vector<std::basic_string<char> >&, const std::vector<std::basic_string<char> >&, const onnxruntime::OrtValueNameIdxMap&) FeedsFetchesInfo Run const T& OrtValue::Get() const [with T = onnxruntime::TensorSeq] const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor] ValidateInputs Initialize InitializeSubgraphSessions onnxruntime::common::Status onnxruntime::InferenceSession::InitializeSubgraphSessions(onnxruntime::Graph&, onnxruntime::SessionState&) CreateSubgraphSessionState onnxruntime::common::Status onnxruntime::InferenceSession::CreateSubgraphSessionState(onnxruntime::Graph&, onnxruntime::SessionState&) TransformGraph Load AddCustomOpDomains Add RegisterExecutionProvider ~InferenceSession onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, const void*, int) onnxruntime::InferenceSession::InferenceSession(const onnxruntime::SessionOptions&, const onnxruntime::Environment&, const string&) 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&) *ZN11onnxruntime16InferenceSession4LoadEvEUlRSt10shared_ptrINS_5ModelEEE_ N11onnxruntime16IDeviceAllocatorE N11onnxruntime16GraphTransformerE N11onnxruntime16InferenceSessionE N8nlohmann6detail9exceptionE N8nlohmann6detail11other_errorE ZN11onnxruntime20CPUExecutionProviderC1ERKNS_24CPUExecutionProviderInfoEEUliE_ 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: IvIjII IIII}HTJtJJJdJTJTJTJP<ODP4P$POOOOOPtPdPTPQPOQPPPPPPPPQQ(Q8QHQXQhQxQQUlSSDTTS<XVWlWW,V[Z4\$\\D\D\D\\`H0 苜X@( 荜ЍpX@(`H0 芜ЊXXpXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXhXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX ȋȋȋȋȋȋȋȋȋȋȋȋȋȋȋȋȋȋȋȋȋȋȋȋȋȋȋȋȋȋxxxx ;MMMMMMMMMMMM)MMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMMኜMMMϊMp@P`00ĘԘޘO `ؽpм0````pϜDММϜ|ќММ$Мڜ4ߜ\ߜܜٜۜڜڜڜڜd SetEnableProfiling SetGraphOptimizationLevel SetExecutionMode SetInterOpNumThreads SetIntraOpNumThreads ParseSessionOptionsFromModelProto ParseOrtConfigJsonInModelProto N8nlohmann6detail11parse_errorE N8nlohmann6detail16invalid_iteratorE N8nlohmann6detail10type_errorE N8nlohmann6detail12out_of_rangeE N8nlohmann6detail22input_adapter_protocolE N8nlohmann6detail20input_buffer_adapterE St23_Sp_counted_ptr_inplaceIN8nlohmann6detail20input_buffer_adapterESaIS2_ELN9__gnu_cxx12_Lock_policyE2EE /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer.cc Apply /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_mgr.cc This transformer is already registered ApplyTransformers Level _RuleBasedTransformer GeluApproximation NchwcTransformer EliminateIdentity EliminateSlice UnsqueezeElimination EliminateDropout ExpandElimination CastElimination FuseReluClip ShapeToInitializer ConvAddFusion ConvMulFusion ConvBNFusion Unsupported level ConstantFolding RandomUniform RandomNormal RandomUniformLike RandomNormalLike Multinomial MatMulAddFusion ReshapeFusion GemmActivationFusion DynamicQuantizeMatMulFusion ACLExecutionProvider ConvActivationFusion AttentionFusion EmbedLayerNormFusion BiasGeluFusion SkipLayerNormFusion FastGeluFusion Unsupported level /onnxruntime_src/onnxruntime/core/optimizer/graph_transformer_utils.cc std::vector<std::unique_ptr<onnxruntime::GraphTransformer> > onnxruntime::optimizer_utils::GenerateTransformers(onnxruntime::TransformerLevel, gsl::span<const onnxruntime::FreeDimensionOverride>, const std::vector<std::basic_string<char> >&) std::vector<std::unique_ptr<onnxruntime::RewriteRule> > onnxruntime::optimizer_utils::GenerateRewriteRules(onnxruntime::TransformerLevel, const std::vector<std::basic_string<char> >&) N11onnxruntime11RewriteRuleE N11onnxruntime17EliminateIdentityE Max: node_index < nodes_.size() CastDef_%d Validating no unexpected access using an invalid node_index. Got: /onnxruntime_src/include/onnxruntime/core/graph/graph.h /onnxruntime_src/include/onnxruntime/core/optimizer/graph_transformer.h /onnxruntime_src/onnxruntime/core/optimizer/insert_cast_transformer.cc cast node to cast from float16 to float32 on cpu RemoveDuplicateCastTransformer ApplyImpl Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime21InsertCastTransformerE N11onnxruntime30RemoveDuplicateCastTransformerE _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 reXK>Initializer 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 T\l|L| DDDDD ,DD< tDttttL\ttl<t|l44444 44, T , !, $#$$$$%l%$$ &$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 bn_scale bn_B _bn_nchwc spatial FusedConv GlobalMaxPool GlobalAveragePool Concat BatchNormalization coordinate_transformation_mode /onnxruntime_src/onnxruntime/core/optimizer/nchwc_transformer.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
-
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H=? HH 1.5.1 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 @_p^^^^^^^^^ __ _0_@_@_ ^̂ ,<L\l|̂̂ 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: ( 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
+ 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 CastFloat16Transformer st.IsOK() Error during EndProfiling(): 1.4.0 session.load_model_format ORT ORTM 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: ( timeout_in_ms >0 is not supported session->NewIOBinding(&binding_).IsOK() Received nullptr for custom registry Please fix either the inputs or the model. Got invalid dimensions for input: Exceeded max transformer level. Current level is set to graph_optimization_level <= TransformerLevel::MaxLevel At least one output should be requested. Output vector incorrectly sized: output_names.size(): Size mismatch: feed_names has is not expected to be of type tensor. is not expected to be of type sparse tensor. Trying to get a SparseTensor, but got: is not expected to be of type tensor sequence. Encountered unknown exception in Run() This session already contains a loaded model. Encountered unknown exception in Load() ModelProto corresponding to the model to be loaded has already been parsed. Invoke Load(). ModelProto corresponding to the model to be loaded has not been parsed yet. This API should be called in conjunction with a ctor that takes a model abstraction. model_loading_from_saved_proto /onnxruntime_src/onnxruntime/core/framework/execution_providers.h Received nullptr for exec provider Execution providers must be registered before the session is initialized. Execution providers must be registered before the session is initialized. Having memory pattern enabled is not supported while using the DML Execution Provider. So disabling it for this session since it uses the DML Execution Provider. Parallel execution mode does not support the DML Execution Provider. So making the execution mode sequential for this session since it uses the DML Execution Provider. Parallel execution mode does not support the CUDA Execution Provider. So making the execution mode sequential for this session since it uses the CUDA Execution Provider. Could not find an implementation for the node All nodes have been placed on [ The only supported values for the environment variable The environment variable contained the value: Reading the provided model for the ORT config ModelProto needs to be parsed to check for ORT config within it Could not finalize session options while constructing the inference session. Error Message: graph_transformation_mgr_.SetSteps(session_options_.max_num_graph_transformation_steps).IsOK() Creating and using per session threadpools since use_per_session_threads_ is true Failed to create the inter-op thread pool for the parallel executor, setting ExecutionMode to SEQUENTIAL Using global/env threadpools since use_per_session_threads_ is false When the session is not configured to use per session threadpools, the env must be created with the the CreateEnvWithGlobalThreadPools API. session_env.EnvCreatedWithGlobalThreadPools() Given model could not be parsed while creating inference session. Error message: Could not parse model successfully while constructing the inference session Unknown error during EndProfiling() Failed to load model because protobuf parsing failed. This session has already been initialized. 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: |$TtdddTDL48HXhhhxL ttD$$$08. ..---////)x/`/H/0// /.....,,,,,x,`,H,0,,++ 0+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))))-------------------------------0-0-0-0-0-0-0-0-0-0-0-0--0-0-x.P.P.P./,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,q,,,,_,,M,'1001111111::7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<t;7<7<7<7<7<7<7<7<7<j;Z;7<7<Z;Z;Z;Z;Z;Z;Z;Z;Z;Z;P;7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<7<;7<;7<7<7<7<7<7<7<7<;7<7<7<7<7<7<7<;7<7<7<7<7<;7<7<7<7<7<7<F;7<<;0\a`` `_`_(^P][0\0\0\0\Yt$uut\vttu$tL̅̀~T} 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 Y9YYYYYWWYY&XXYYYY&X 蕖ە 蕖Initializer 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 ? 0 000000(0000(L LLLL$4LLDLLLLDh8hhhh@Phh`0hhhh`T\l|L|pxh x @P 4,d
+ P h
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 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
+
- 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 ঞ 0 pƞŞƞƞƞƞNŞĞƞƞ
ĞkÞǞǞǞǞǞǞǞǞǞǞǞǞ=Ȟ0Ȟ=Ȟ=Ȟ=Ȟ=ȞȞ Ȟ=Ȟ=ȞǞǞ 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 R|_|_|_|_|E|8|_|_|+| |ANNNN4'NN
~~~~~~~~~ ||%%%% %%:-a}n}n}n}n}T}G}n}n}:}-}~~~~~}}~~}}
-
- N11onnxruntime14EliminateSliceE /onnxruntime_src/onnxruntime/core/optimizer/transformer_memcpy.cc dup_replacements.find(&arg) == dup_replacements.end() TensorrtExecutionProvider VitisAIExecutionProvider NGRAPHExecutionProvider ' doesn't support memcpy Execution type ' Memcpy Copy from/to host memory DnnlExecutionProvider NupharExecutionProvider OpenVINOExecutionProvider NnapiExecutionProvider ArmNNExecutionProvider 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 ߟߟߟߟߟߟߟߟߟߟߟߟߟߟߟߟvector::_M_range_insert WEWWWW0 WW xk^Q '7w+2 Start ValidateGemmInitializer Pass ValidateGemmInitializer Expected value: Start CheckNodesInPathV Pass CheckNodesInPathV 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 Where Failed to find path for mask Pass MatchInputMaskSubgraph 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 Faild to find path v k root is not layer norm MaskIndex_Int32 Count number of words ReduceSum MaskIndex keepdims Failed to create mask index 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: 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 q_transpose perm attribute not matched Output edge count not expected for Softmax Output edge count not expected for mask nodes Softmax attribute axis is expected to be 3 mask_unsqueeze_1 axes not matched. Expect: 1 mask_unsqueeze_2 axes not matched. Expect: 2 mask_sub const input not matched mask_mul const input not matched 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. 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 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 /onnxruntime_src/onnxruntime/core/optimizer/attention_fusion.cc shape of layer norm bias tensor not expected Total fused Attention node count: ProcessMask FuseSubGraph Recurse ApplyImpl FuseGptAttention CheckNodesInPathK CheckNodesInPathQ CheckNodesInPathV MatchPastSubgraph 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 data type is not supported. min FuseReluClip_ _min_zero_constant data type is not supported Unexpected data type for Clip 'min' input of /onnxruntime_src/onnxruntime/core/optimizer/relu_clip_fusion.cc `````p``Ў ````諗J _ =0==== ==ᮗ====nannnnTGnn:-nnnn:{sfYf 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 Rf_f_f_f_fEf8f_f_f+f f_f_f_f_f+f-j:j:j:j:j jj:j:jji:j:j:j:jjhhhhhhuhhheehhhheiiiiiiiiiiiiiiiig g g g ggf g gff g g g gfgggggggggyglgggggyg
+
+
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 Șǘ Ș Ș Ș ȘǘȘ Ș ȘȘǘ Ș Ș Ș ȘȘ<Ș Ș<Ș<Ș<Ș<ȘȘ$Ș<Ș<Ș4ȘȘ<Ș<Ș<Ș<Ș4Șvector::_M_range_insert RandomUniform RandomNormal RandomUniformLike RandomNormalLike Multinomial ֘֘֘֘֘֘֘֘֘֘֘w֘֘֘֘֘֘ט טטטטט֘֘טט֘֘טטטט֘SטFטSטSטSטSט9ט,טSטSט טטSטSטSטSט ט '7w+29(9(9(9(99 9(9(9 99(9(9(9(9 9
+9$9$9$9$99 9$9$999$9$9$9$99Start 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&)
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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 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 bbbbbbbbbbbb1P$P1P1P1P1PPO1P1POOd dddddccddccaaaaaaaaaaaa 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
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-hFfehoxnhohohohop(phohoqxorrrrrrrqrrqqBr9rBrBrBrBr-r!rBrBrrrjrarjrjrjrjrUrIrjrjr@r7rrrrrrr}rqrrrhr_rrrrrrrrrrrrrToProto 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 Ђ88888 880 h8hhhh@Phh`0hp`ȃȃȃȃȃȃȃȃЃ((((( (( X(XXXX0@XXP X`pPbn_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 ؔؔؔؔؔ'ؔؔәN`{Qyzڡj 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 ȲȲȲȲȲȲȲȲвಡ((((( (( X(XXXX0@XXP X`pPmul_B_tensor_proto ConvMulFusion_W_ ConvMulFusion_Mul_B_ /onnxruntime_src/onnxruntime/core/optimizer/conv_mul_fusion.cc HơġHơHơHơHơáHơHơXΡ̡XΡXΡXΡXΡ)ˡɡXΡXΡȡǡӡӡӡӡӡӡӡӡӡӡӡӡ
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-ԡӡӡJԡ=ԡJԡJԡJԡJԡ0ԡ#ԡJԡJԡԡ ԡԡ}ԡԡԡԡԡpԡcԡԡԡVԡIԡԡԡԡԡԡԡԡԡԡԡԡԡ)ۡ_ա)ۡ)ۡ)ۡ)ۡJա5ա)ۡ)ۡ ա աrءաrءrءrءrءաաrءrءvաiաfءաfءfءfءfءաաfءfءաա 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 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
- 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. 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 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. Input_ids and mask should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). fused EmbedLayerNorm subgraphs Expand NonZero ConstantOfShape Failed to match Shape node. _Cast position_embeddings Input id is not valid. Segment id is not valid. Mask is not valid. EmbedLayerNormalization '''''''''':'-'X'K'X'X'X'X'>'1'X'X'$'' onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const CheckInput MatchInputToConcatSubgraph MatchPositionEmbeddingSubgraphsFromGather 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
- N11onnxruntime20GemmActivationFusionE Not satisfied: tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_UNDEFINED && tensor_proto.data_type() != ONNX_NAMESPACE::TensorProto_DataType_STRING
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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 >\ >> 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. 666@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 윚 禚ڦͦէŧE0ErerrrrP@rr+rrrr+ 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 /VбV$l|$:1::::%::::::NENNNN9-NN$NNNN$bYbbbbMAbb8/bbbb8vmvvvvaUvvLCvvvvL:1::::%::::::NENNNN9-NN$NNNN$ToProto onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const virtual onnxruntime::common::Status onnxruntime::ConvAddFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const
N11onnxruntime13ConvAddFusionE ΚΚΚΚΚΚΚΚΚΚΚΚΚΚΚΚΚΚΚΚΚΚΚΚΚΚΚΚΚΚΚΚΚΚϚΚϚϚϚϚΚΚϚϚϚΚϚϚϚϚϚ,ϚΚ,Ϛ,Ϛ,Ϛ,ϚϚϚ,Ϛ,Ϛ$ϚΚ,Ϛ,Ϛ,Ϛ,Ϛ$ϚHϚϚHϚHϚHϚHϚ Ϛ0ϚHϚHϚ@ϚϚHϚHϚHϚHϚ@ϚdϚ4ϚdϚdϚdϚdϚ<ϚLϚdϚdϚ\Ϛ,ϚdϚdϚdϚdϚ\ϚϚPϚϚϚϚϚXϚhϚϚϚxϚHϚϚϚϚϚxϚϚlϚϚϚϚϚtϚϚϚϚϚdϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚϚbn_scale_tensor_proto bn_B_tensor_proto bn_mean_tensor_proto bn_var_tensor_proto ConvBnFusion_W_ ConvBnFusion_BN_B_ /onnxruntime_src/onnxruntime/core/optimizer/conv_bn_fusion.cc ߚHߚޚޚuݚo onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const virtual onnxruntime::common::Status onnxruntime::ConvBNFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const N11onnxruntime12ConvBNFusionE xHxxxxP`xxp@xxxxpdl|\x < <<<<$<<4<<<<4X(XXXX0@XXP XXXXPtDttttL\ttl<ttttlmul_B_tensor_proto ConvMulFusion_W_ ConvMulFusion_Mul_B_ lO llll llLllll/onnxruntime_src/onnxruntime/core/optimizer/conv_mul_fusion.cc =L L L L L QL L L L L L 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
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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
+
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. ңţңңңңңңVIңңңңV`S````F9``, ````,FuseSubGraph CheckInput MatchInputToConcatSubgraph MatchPositionEmbeddingSubgraphsFromGather FuseSubGraphDistilBert Recurse ApplyImpl ̼+
N11onnxruntime20EmbedLayerNormFusionE N11onnxruntime17ExpandEliminationE fast_gelu_output fused GPT2Gelu subgraphs GPT2Gelu /onnxruntime_src/onnxruntime/core/optimizer/fast_gelu_fusion.cc Recurse ApplyImpl onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const
N11onnxruntime14FastGeluFusionE '7=*BL? @@ ?FreeDimensionOverrideTransformer Invalid free dimension override. /onnxruntime_src/onnxruntime/core/optimizer/free_dim_override_transformer.cc which does not equal the specified override of Conflicting free dimension overrides. The model has input ' ' with a fixed dimension size ApplyImpl onnxruntime::FreeDimensionOverrideTransformer::FreeDimensionOverrideTransformer(gsl::span<const onnxruntime::FreeDimensionOverride>) N11onnxruntime32FreeDimensionOverrideTransformerE Gelu approximation /onnxruntime_src/onnxruntime/core/optimizer/gelu_approximation.cc Total Gelu Approximation (FastGelu) node count: Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl
N11onnxruntime17GeluApproximationE Erf fused Gelu subgraphs /onnxruntime_src/onnxruntime/core/optimizer/gelu_fusion.cc onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl
N11onnxruntime10GeluFusionE ??Elu HardSigmoid Selu Softsign ScaledTanh ParametricSoftplus ThresholdedRelu fused Gemm FusedGemm activation_ /onnxruntime_src/onnxruntime/core/optimizer/gemm_activation_fusion.cc onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const Recurse ApplyImpl
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N11onnxruntime20GemmActivationFusionE 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 Tried to allocate without valid type information, ort_value index= Provided CPU execution provider is a nullptr /onnxruntime_src/onnxruntime/core/optimizer/optimizer_execution_frame.cc Failed to get allocator for optimizer cpu_execution_provider allocator_ptr_ void onnxruntime::TensorSeq::SetType(onnxruntime::MLDataType) ForEachWithIndex operator() Info onnxruntime::OptimizerExecutionFrame::Info::Info(const std::vector<const onnxruntime::Node*>&, const InitializedTensorSet&, std::unique_ptr<onnxruntime::CPUExecutionProvider>) *ZN11onnxruntime23OptimizerExecutionFrame4InfoC1ERKSt6vectorIPKNS_4NodeESaIS5_EERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSE_EEESt10unique_ptrINS_20CPUExecutionProviderESt14default_deleteISR_EEEUlRKNS_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 T ē|4쒣T ̑L virtual onnxruntime::common::Status onnxruntime::ConstantOfShape::Compute(onnxruntime::OpKernelContext*) const Compute const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::ConstantOfShapeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::Tensor**) const onnxruntime::ConstantOfShapeBase::ConstantOfShapeBase(const onnxruntime::OpKernelInfo&) void onnxruntime::ConstantOfShapeBase::SetValue(size_t, void*) void onnxruntime::ConstantOfShapeBase::SetValueFromTensorProto(const onnx::TensorProto&) FPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEE N11onnxruntime19ConstantOfShapeBaseE N11onnxruntime15ConstantOfShapeE PFPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEE seed dtype Invalid dtype of high sample_size FLOAT16 is not supported Invalid data type of input count mismatch batch_size is < 1 num_classes is < 1 num_samples is < 1 /onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.h info.GetAttr<float>("mean", &mean_).IsOK() info.GetAttr<float>("scale", &scale_).IsOK() info.GetAttr<int64_t>("dtype", &dtype).IsOK() ONNX_NAMESPACE::TensorProto::DataType_IsValid(dtype_) && dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINED info.GetAttrs<int64_t>("shape", shape).IsOK() info.GetAttr<float>("high", &high_).IsOK() info.GetAttr<float>("low", &low_).IsOK() info.GetAttr<int64_t>("sample_size", &num_samples_).IsOK() ONNX_NAMESPACE::TensorProto::DataType_IsValid(output_dtype_) && output_dtype_ != ONNX_NAMESPACE::TensorProto::UNDEFINED /onnxruntime_src/onnxruntime/core/providers/cpu/generator/random.cc Could not infer data type from input tensor with data type Empty dimensions for input tensor T* onnxruntime::Tensor::MutableData() [with T = long int] MultinomialCompute T* onnxruntime::Tensor::MutableData() [with T = int] MultinomialCompute const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::RandomUniformCompute(float, float, std::default_random_engine&, onnx::TensorProto::DataType, onnxruntime::Tensor&) onnxruntime::common::Status onnxruntime::RandomNormalCompute(float, float, std::default_random_engine&, onnx::TensorProto::DataType, onnxruntime::Tensor&) onnxruntime::Multinomial::Multinomial(const onnxruntime::OpKernelInfo&) onnxruntime::RandomUniformLike::RandomUniformLike(const onnxruntime::OpKernelInfo&) onnxruntime::RandomNormalLike::RandomNormalLike(const onnxruntime::OpKernelInfo&) onnxruntime::RandomUniform::RandomUniform(const onnxruntime::OpKernelInfo&) onnxruntime::RandomNormal::RandomNormal(const onnxruntime::OpKernelInfo&) N11onnxruntime12RandomNormalE N11onnxruntime16RandomNormalLikeE N11onnxruntime13RandomUniformE N11onnxruntime17RandomUniformLikeE N11onnxruntime11MultinomialE 0 A C ? "~+@"~+@W6}+W6}++eG?+eG? ? ? ? ? @.? @.?ʫy>ʫy>K͉ ?K͉ ?~, ?~, ?_6.>_6.>9d?9d?t ?t ? @ @ Unsupported data type: delta in Range operator can not be zero! start in Range operator should be scalar like tensor, yet got shape: limit in Range operator should be scalar like tensor, yet got shape: delta in Range operator should be scalar like tensor, yet got shape: Ret onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<Ret>::operator()(int32_t) const [with Ret = onnxruntime::common::Status; int32_t = int] T* onnxruntime::Tensor::MutableData() [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime5RangeE min_ <= 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 RDRtRDRSDRtRDRDRDRDRtRDRDRDRDRDSDRDRDRDRDRDRDRdSTSDRSDRDRDRRDRDRRDRDRDRRDRDRDRDRDRDRDRDRRtSRR4S4RDRSRR4RRRRRSSSRSSSDRDRDRDRDRDRDRDRDRDRDRDRDRDRDRDRDRtRDRDRDRDRDRRtRRDRDRDRDRDRDRDRDRDRDRDRDRDRDRDRRRR4RRRR4R4RRR4RRR4RRSRRDRDR$SDet 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@ is not supported yet Einsum equation Missing 'equation' attribute -> /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum.cc Einsum op: An implementation for the input type /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum.h info.GetAttr<std::string>("equation", &equation_).IsOK() Einsum op: There must be atleast one input There was a problem acquiring temporary memory allocator in Einsum op DeviceCompute const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::Einsum::Einsum(const onnxruntime::OpKernelInfo&) FN11onnxruntime6common6StatusERKNS_6TensorERS2_E 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 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: The only subscript labels allowed are lowercase letters (a-z) 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 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) Mod Tan Atanh Sinh Cosh Asinh Atan Acos Asin BitShift Ceil Reciprocal Abs Neg Cos Sin Floor Acosh PRelu Less Or And Not Xor Greater axis == 1 || axis == largest direction LEFT RIGHT Invalid direction value of ' ) for tensor of length: Invalid start/ending offset [ Broadcast Output range [ Can broadcast 0 by 0 or 1. largest <= 1 failed to get first output! Unsupported Y type: Unsupported X type: Must have 1 or more inputs input_count >= 1 inputCount >= 1 data_n.Shape() == shape data_1.Shape() == shape p_ml_value vs fmod_ cur1 == end1 cur_out == end_out fmod must have value either 0 or 1 /onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.cc Attempting to broadcast an axis by a dimension other than 1. /onnxruntime_src/onnxruntime/core/providers/cpu/math/element_wise_ops.h '. Valid values are 'LEFT' or 'RIGHT'. 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 Shape must be 1 dimensional as it's tensor data is a shape tensor_shape.Shape().GetDims().size() == 1 All inputs must have the same shape Please fetch output tensor with specified shape. X and Y input types do not match: fmod attribute must be true for float, float16 and double types onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda135 onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda135 onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda135 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&) 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]::__lambda130; Input1Scalar = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda131; General = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda132] onnxruntime::common::Status onnxruntime::Mean_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::TBroadcastOutput<T>::TBroadcastOutput(size_t, onnxruntime::Tensor&, int64_t, int64_t) [with T = bool; size_t = long unsigned int; int64_t = long int] T* onnxruntime::Tensor::MutableData() [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = bool] 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]::__lambda109; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda110; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda111] 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]::__lambda109; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = long int]::__lambda110; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = long int]::__lambda111] 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]::__lambda109; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda110; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda111] 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]::__lambda109; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = int]::__lambda110; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = int]::__lambda111] 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]::__lambda109; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16]::__lambda110; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16]::__lambda111] 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]::__lambda109; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = double]::__lambda110; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = double]::__lambda111] 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]::__lambda109; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = float]::__lambda110; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = float]::__lambda111] 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]::__lambda106; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda107; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda108] 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]::__lambda106; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = long int]::__lambda107; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = long int]::__lambda108] 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]::__lambda106; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda107; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda108] 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]::__lambda106; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = int]::__lambda107; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = int]::__lambda108] 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]::__lambda106; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16]::__lambda107; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16]::__lambda108] 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]::__lambda106; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = double]::__lambda107; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = double]::__lambda108] 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]::__lambda106; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = float]::__lambda107; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = float]::__lambda108] 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]::__lambda103; Input1Scalar = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda104; General = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda105] onnxruntime::common::Status onnxruntime::Sum_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = double] 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] ( ( `( `(``JJ J J } M}7M}p7M}6p7M6p76pf6OfOfOfO N11onnxruntime3PowE N11onnxruntime5Min_8E N11onnxruntime5Max_8E N11onnxruntime3NotE N11onnxruntime3AndE N11onnxruntime2OrE N11onnxruntime3XorE N11onnxruntime3AddIfEE N11onnxruntime3AddIdEE N11onnxruntime3AddIiEE N11onnxruntime3AddIlEE N11onnxruntime3SubIfEE N11onnxruntime3SubIdEE N11onnxruntime3SubIiEE N11onnxruntime3SubIlEE N11onnxruntime3MulIfEE N11onnxruntime3MulIdEE N11onnxruntime3MulIiEE N11onnxruntime3MulIlEE N11onnxruntime3DivIfEE N11onnxruntime3DivIdEE N11onnxruntime3DivIiEE N11onnxruntime3DivIlEE N11onnxruntime3AbsIfEE N11onnxruntime3AbsIdEE N11onnxruntime3AbsIaEE N11onnxruntime3AbsIsEE N11onnxruntime3AbsIiEE N11onnxruntime3AbsIlEE N11onnxruntime3AbsIhEE N11onnxruntime3AbsItEE N11onnxruntime3AbsIjEE N11onnxruntime3AbsImEE N11onnxruntime3NegIfEE N11onnxruntime3NegIdEE N11onnxruntime3NegIaEE N11onnxruntime3NegIiEE N11onnxruntime3NegIlEE N11onnxruntime5FloorIfEE N11onnxruntime4CeilIfEE N11onnxruntime10ReciprocalIfEE N11onnxruntime4SqrtIfEE N11onnxruntime4SqrtIdEE N11onnxruntime3ExpIfEE N11onnxruntime3ExpIdEE N11onnxruntime3LogIfEE N11onnxruntime5Sum_6IfEE N11onnxruntime5Sum_8IfEE N11onnxruntime5Max_6IfEE N11onnxruntime5Min_6IfEE N11onnxruntime4LessIfEE N11onnxruntime4LessIdEE N11onnxruntime4LessIiEE N11onnxruntime4LessIlEE N11onnxruntime7GreaterIfEE N11onnxruntime7GreaterIdEE N11onnxruntime7GreaterIiEE N11onnxruntime7GreaterIlEE N11onnxruntime5EqualIbEE N11onnxruntime5EqualIiEE N11onnxruntime5EqualIlEE N11onnxruntime5EqualIfEE N11onnxruntime6Mean_6IfEE N11onnxruntime6Mean_8IfEE N11onnxruntime8BitShiftIhEE N11onnxruntime8BitShiftIjEE N11onnxruntime8BitShiftImEE N11onnxruntime3ErfIfEE N11onnxruntime3SinIfEE N11onnxruntime3SinIdEE N11onnxruntime3CosIfEE N11onnxruntime3TanIfEE N11onnxruntime4AsinIfEE N11onnxruntime4AcosIfEE N11onnxruntime4AtanIfEE N11onnxruntime4SinhIfEE N11onnxruntime4CoshIfEE N11onnxruntime5AsinhIfEE N11onnxruntime5AcoshIfEE N11onnxruntime5AtanhIfEE N11onnxruntime5PReluIfEE N11onnxruntime8Expand_8IfEE N11onnxruntime8Expand_8IdEE N11onnxruntime8Expand_8IaEE N11onnxruntime8Expand_8IsEE N11onnxruntime8Expand_8IiEE N11onnxruntime8Expand_8IlEE N11onnxruntime8Expand_8IhEE N11onnxruntime8Expand_8ItEE N11onnxruntime8Expand_8IjEE N11onnxruntime8Expand_8ImEE N11onnxruntime8Expand_8IbEE N11onnxruntime8Expand_8INS_9MLFloat16EEE N11onnxruntime3ModE ZN11onnxruntime12pow_internal7PowImplIiiEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEEiE0_ ZN11onnxruntime12pow_internal7PowImplIilEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEElE0_ ZN11onnxruntime12pow_internal7PowImplIifEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEEfE0_ ZN11onnxruntime12pow_internal7PowImplIidEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEEdE0_ ZN11onnxruntime12pow_internal7PowImplIliEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEEiE0_ ZN11onnxruntime12pow_internal7PowImplIllEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEElE0_ ZN11onnxruntime12pow_internal7PowImplIlfEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEEfE0_ ZN11onnxruntime12pow_internal7PowImplIldEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEEdE0_ ZN11onnxruntime12pow_internal7PowImplIfiEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIfEENS8_IKfEEiE0_ ZN11onnxruntime12pow_internal7PowImplIflEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIfEENS8_IKfEElE0_ ZN11onnxruntime12pow_internal7PowImplIffEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIfEENS8_IKfEEfE0_ ZN11onnxruntime12pow_internal7PowImplIfdEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIfEENS8_IKfEEdE0_ ZN11onnxruntime12pow_internal7PowImplIdiEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIdEENS8_IKdEEiE0_ ZN11onnxruntime12pow_internal7PowImplIdlEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIdEENS8_IKdEElE0_ ZN11onnxruntime12pow_internal7PowImplIdfEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIdEENS8_IKdEEfE0_ ZN11onnxruntime12pow_internal7PowImplIddEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIdEENS8_IKdEEdE0_ ZN11onnxruntime12pow_internal7PowImplIiiEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEEiE1_ ZN11onnxruntime12pow_internal7PowImplIiiEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEEiE2_ ZN11onnxruntime12pow_internal7PowImplIilEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEElE1_ ZN11onnxruntime12pow_internal7PowImplIilEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEElE2_ ZN11onnxruntime12pow_internal7PowImplIifEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEEfE1_ ZN11onnxruntime12pow_internal7PowImplIifEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEEfE2_ ZN11onnxruntime12pow_internal7PowImplIidEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEEdE1_ ZN11onnxruntime12pow_internal7PowImplIidEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEEdE2_ ZN11onnxruntime12pow_internal7PowImplIliEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEEiE1_ ZN11onnxruntime12pow_internal7PowImplIliEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEEiE2_ ZN11onnxruntime12pow_internal7PowImplIllEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEElE1_ ZN11onnxruntime12pow_internal7PowImplIllEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEElE2_ ZN11onnxruntime12pow_internal7PowImplIlfEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEEfE1_ 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_ ZN11onnxruntime12pow_internal7PowImplIdfEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIdEENS8_IKdEEfE2_ ZN11onnxruntime12pow_internal7PowImplIddEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIdEENS8_IKdEEdE1_ 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B B B B ? ? ? ?5?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 \< \ virtual onnxruntime::common::Status onnxruntime::ConstantOfShape::Compute(onnxruntime::OpKernelContext*) const Compute const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::ConstantOfShapeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::Tensor**) const onnxruntime::ConstantOfShapeBase::ConstantOfShapeBase(const onnxruntime::OpKernelInfo&) void onnxruntime::ConstantOfShapeBase::SetValue(size_t, void*) void onnxruntime::ConstantOfShapeBase::SetValueFromTensorProto(const onnx::TensorProto&) N11onnxruntime19ConstantOfShapeBaseE FPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEE N11onnxruntime15ConstantOfShapeE PFPN11onnxruntime8OpKernelERKNS_12OpKernelInfoEE seed 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 444Þ$ÞÞtD4ÞDTÞDTdtDÞTÞ|ÞÞÞÞ44dDTdTDTddtDet 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&) 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 FN11onnxruntime6TensorERKS0_RKSt6vectorIlSaIlEEbSt10shared_ptrINS_10IAllocatorEEPKNS_11TensorShapeEPNS_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 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&) . The shape is: in the same dimension vector::_M_fill_insert dim_iter == rank Inputs have ellipses in them but the provided output subscript does not contain an ellipsis Number of subscripts in the input equation does not match number of input tensors Found a '.' not part of an ellipsis in input: Einsum subscripts string contains too many subscript labels when compared to the rank of the input Ellipsis must indicate a fixed number of dimensions across all inputs Found '.' not part of an ellipsis in input: Einsum operands could not be broadcast together. Please check input shapes/equation provided.Input shape of operand is incompatible in the dimension Another operand has a dim value of Einsum subscripts string contains too many subscript labels when compared to the rank of the input Einsum subscripts does not contain enough subscript labels and there is no ellipsis for input The only subscript labels allowed are lower-cased letters (a-z) and upper-cased letters (A-Z) Found a '.' not part of an ellipsis in the output subscript provided Found '.' not part of an ellipsis in the output subscript provided 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 /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_compute_preprocessor.cc num_broadcasted_indices < num_of_ellipsis_dims_ The broadcasted dimensions of the inputs are incompatible void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&) onnxruntime::common::Status onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDims() Run Left shape override: Left shape: Right shape override: Right shape: left_rank == right_rank left_dim == right_dim Einsum op: The candidate output cannot be reshaped into the op's output /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils/einsum_typed_compute_processor.cc candidate_output.Shape().Size() == output_shape.Size() Not all dimensions to be reduced have been reduced in the candidate output. Candidate output dims: candidate_output_dims[iter] == 1 Einsum op: Could not copy the intermediate output's buffer into the op's output buffer. Error: The override dims are not compatible with given tensor's shape. left.Shape().Size() == left_shape_override.Size() right.Shape().Size() == right_shape_override.Size() Ranks of pair-wise operands must be equal. Einsum op: Input dimensions must be equal along an axis to be reduced across all inputs Einsum op: Input shapes do not align void onnxruntime::EinsumTypedComputeProcessor<T>::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&) [with T = long int] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumTypedComputeProcessor<T>::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, bool) [with T = long int] void onnxruntime::EinsumTypedComputeProcessor<T>::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&) [with T = double] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumTypedComputeProcessor<T>::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, bool) [with T = double] void onnxruntime::EinsumTypedComputeProcessor<T>::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&) [with T = int] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumTypedComputeProcessor<T>::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, bool) [with T = int] void onnxruntime::EinsumTypedComputeProcessor<T>::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&) [with T = float] void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&) std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumTypedComputeProcessor<T>::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, bool) [with T = float] fmod (fmod == 0) || (fmod == 1) Less Greater Not And Or Xor Mod Acos BitShift Tan Cos Asin Atan Cosh Acosh Sinh Atanh Asinh Abs Reciprocal Neg Floor Ceil Sin 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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*ZN11onnxruntime16BroadcastOneSpanIbbZNKS_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_ *ZN11onnxruntime16BroadcastOneSpanIbbZNKS_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_ *ZN11onnxruntime16BroadcastOneSpanIbbZNKS_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_ *ZN11onnxruntime16BroadcastOneSpanIbbZNKS_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_ *ZN11onnxruntime16BroadcastOneSpanIbbZNKS_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_ *ZN11onnxruntime16BroadcastOneSpanIbbZNKS_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_ *ZN11onnxruntime16BroadcastOneSpanIffZNKS_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_ *ZN11onnxruntime16BroadcastOneSpanIffZNKS_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_ *ZN11onnxruntime16BroadcastOneSpanIffZNKS_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_ 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_8IfEE N11onnxruntime8Expand_8IdEE N11onnxruntime8Expand_8IaEE N11onnxruntime8Expand_8IsEE N11onnxruntime8Expand_8IiEE N11onnxruntime8Expand_8IlEE N11onnxruntime8Expand_8IhEE N11onnxruntime8Expand_8ItEE N11onnxruntime8Expand_8IjEE N11onnxruntime8Expand_8ImEE N11onnxruntime8Expand_8IbEE N11onnxruntime8Expand_8INS_9MLFloat16EEE N11onnxruntime3ModE ZN11onnxruntime12pow_internal7PowImplIiiEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEEiE0_ ZN11onnxruntime12pow_internal7PowImplIilEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEElE0_ ZN11onnxruntime12pow_internal7PowImplIifEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEEfE0_ ZN11onnxruntime12pow_internal7PowImplIidEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEEdE0_ ZN11onnxruntime12pow_internal7PowImplIliEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEEiE0_ ZN11onnxruntime12pow_internal7PowImplIllEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEElE0_ ZN11onnxruntime12pow_internal7PowImplIlfEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEEfE0_ ZN11onnxruntime12pow_internal7PowImplIldEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEEdE0_ ZN11onnxruntime12pow_internal7PowImplIfiEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIfEENS8_IKfEEiE0_ ZN11onnxruntime12pow_internal7PowImplIflEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIfEENS8_IKfEElE0_ ZN11onnxruntime12pow_internal7PowImplIffEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIfEENS8_IKfEEfE0_ ZN11onnxruntime12pow_internal7PowImplIfdEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIfEENS8_IKfEEdE0_ ZN11onnxruntime12pow_internal7PowImplIdiEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIdEENS8_IKdEEiE0_ ZN11onnxruntime12pow_internal7PowImplIdlEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIdEENS8_IKdEElE0_ ZN11onnxruntime12pow_internal7PowImplIdfEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIdEENS8_IKdEEfE0_ ZN11onnxruntime12pow_internal7PowImplIddEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIdEENS8_IKdEEdE0_ ZN11onnxruntime12pow_internal7PowImplIiiEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEEiE1_ ZN11onnxruntime12pow_internal7PowImplIiiEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEEiE2_ ZN11onnxruntime12pow_internal7PowImplIilEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEElE1_ ZN11onnxruntime12pow_internal7PowImplIilEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEElE2_ ZN11onnxruntime12pow_internal7PowImplIifEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEEfE1_ ZN11onnxruntime12pow_internal7PowImplIifEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEEfE2_ ZN11onnxruntime12pow_internal7PowImplIidEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEEdE1_ ZN11onnxruntime12pow_internal7PowImplIidEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIiEENS8_IKiEEdE2_ ZN11onnxruntime12pow_internal7PowImplIliEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEEiE1_ ZN11onnxruntime12pow_internal7PowImplIliEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEEiE2_ ZN11onnxruntime12pow_internal7PowImplIllEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEElE1_ ZN11onnxruntime12pow_internal7PowImplIllEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEElE2_ ZN11onnxruntime12pow_internal7PowImplIlfEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIlEENS8_IKlEEfE1_ 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_ ZN11onnxruntime12pow_internal7PowImplIdfEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIdEENS8_IKdEEfE2_ ZN11onnxruntime12pow_internal7PowImplIddEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIdEENS8_IKdEEdE1_ ZN11onnxruntime12pow_internal7PowImplIddEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIdEENS8_IKdEEdE2_ pA @ ,F F @ A ? ? ? ? ? ? ? ? ? ? ? ? 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
Not satisfied: K_ == right_shape[right_num_dims - 2] || transb && K_ == right_shape[right_num_dims - 1]
left operand cannot broadcast on dim Not satisfied: left_padded_dims_[idx_dim] == 1
right operand cannot broadcast on dim Not satisfied: right_padded_dims_[idx_dim] == 1
Not satisfied: K_ == right_shape[transb ? right_num_dims - 1 : right_num_dims - 2]
Not satisfied: num_dims_with_pad == num_output_dims
Not satisfied: M_ == 1 && N_ == 1
Not satisfied: num_dims_with_pad - 1 == num_output_dims
Not satisfied: num_dims_with_pad - 2 == num_output_dims
Not satisfied: K_ == right_shape[0]
- /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul.cc const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] Compute const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] Compute const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = int] Compute const T* onnxruntime::Tensor::Data() const [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] Compute const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] Compute const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] Compute Compute N11onnxruntime6MatMulIfEE N11onnxruntime6MatMulIdEE N11onnxruntime6MatMulIiEE N11onnxruntime6MatMulIjEE N11onnxruntime6MatMulIlEE N11onnxruntime6MatMulImEE T3 /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] 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&) const T* onnxruntime::Tensor::Data() const [with T = long int] onnxruntime::TopK<OpSet, T>::TopK(const onnxruntime::OpKernelInfo&) [with int OpSet = 10; T = float] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) 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 /onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.h info.GetAttr<std::string>("cast_to", &attr).IsOK() Expected TO_FLOAT, TO_STRING or TO_INT64 /onnxruntime_src/onnxruntime/core/providers/cpu/ml/ml_common.h info.GetAttr<std::string>("map_form", &attr).IsOK() info.GetAttr<int64_t>("max_map", &max_map_).IsOK() max_map must be > 0 if map_form is SPARSE map_form_ != PACK_MAP::SPARSE || max_map_ > 0 /onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.cc cur_input == end_input || cur_input->first >= 0 Negative index values are not permitted. First entry in map has index value of Expected std::map<int64_t, float> or std::map<int64_t, std::string> CastMap cast_to TO_FLOAT TO_STRING TO_INT64 Invalid CAST_TO value of map_form DENSE Expected DENSE or SPARSE Invalid PACK_MAP value of max_map %f stof stoll Invalid input type of value: Unexpected CAST_TO value of onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = std::basic_string<char>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = long int] const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, std::basic_string<char> >] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = std::map<long int, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, float>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = float] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] onnxruntime::ml::PACK_MAP onnxruntime::ml::MakePack(const string&) onnxruntime::ml::CAST_TO onnxruntime::ml::MakeCast(const string&) onnxruntime::ml::CastMap::CastMap(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml7CastMapE CategoryMapper cats_strings cats_int64s default_string default_int64 Input of string must have output of int64 Input of int64 must have output of string /onnxruntime_src/onnxruntime/core/providers/cpu/ml/category_mapper.h info.GetAttrs<std::string>("cats_strings", string_categories).IsOK() info.GetAttrs<int64_t>("cats_int64s", int_categories).IsOK() info.GetAttr<std::string>("default_string", &default_string_).IsOK() info.GetAttr<int64_t>("default_int64", &default_int_).IsOK() num_entries == int_categories.size() T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::CategoryMapper::CategoryMapper(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml14CategoryMapperE DictVectorizer string_vocabulary int64_vocabulary /onnxruntime_src/onnxruntime/core/providers/cpu/ml/dictvectorizer.h info.GetAttrs(std::is_same<AttrType, std::string>::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK() T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, long int>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, long int>] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, float>] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, double>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, double>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, std::basic_string<char> >] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = std::map<long int, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, float>] T* onnxruntime::Tensor::MutableData() [with T = double] const T& OrtValue::Get() const [with T = std::map<long int, double>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, double>] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = double] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = float] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = std::basic_string<char>] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = double] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = float] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = long int] N11onnxruntime2ml16DictVectorizerOpISslEE N11onnxruntime2ml16DictVectorizerOpISsfEE N11onnxruntime2ml16DictVectorizerOpISsdEE N11onnxruntime2ml16DictVectorizerOpIlSsEE N11onnxruntime2ml16DictVectorizerOpIlfEE N11onnxruntime2ml16DictVectorizerOpIldEE inputdimensions FeatureVectorizer input_tensor_ptr != nullptr Invalid input type: Number of inputs ( /onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.h status.IsOK() && !input_dimensions_.empty() inputdimensions attribute must be provided /onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.cc input_count >= 0 && static_cast<size_t>(input_count) == input_dimensions_.size() ) does not match number of inputdimensions values ( const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = float] virtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml17FeatureVectorizerE imputed_value_floats imputed_value_int64s replaced_value_float replaced_value_int64 Imputer Empty input dimensions. Invalid type Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specified /onnxruntime_src/onnxruntime/core/providers/cpu/ml/imputer.cc Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specified Must provide imputed_values_float_ or imputed_values_int64_ but not both. imputed_values_float_.empty() ^ imputed_values_int64_.empty() Empty value of imputed values. T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml9ImputerOpE keys_floats values_strings _Unused keys_strings values_floats default_float keys_int64s values_int64s LabelEncoder attribtues in LabelEncoder The values is and the number of ) must have the same length. num_keys == num_values classes_strings /onnxruntime_src/onnxruntime/core/providers/cpu/ml/label_encoder.h info.GetAttrs<TKey>(_key_field_name, keys).IsOK() info.GetAttrs<TValue>(_value_field_name, values).IsOK() However, the number of key is Input of tensor(string) must have output of tensor(int64) Input of tensor(int64) must have output of tensor(string) info.GetAttrs<std::string>("classes_strings", string_classes).IsOK() gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = std::basic_string<char>] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = std::basic_string<char>] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string<char>; TValue = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = std::basic_string<char>] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = long int] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = long int; TValue = float] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = std::basic_string<char>; TValue = float] onnxruntime::ml::LabelEncoder_2<TKey, TValue>::LabelEncoder_2(const onnxruntime::OpKernelInfo&) [with TKey = float; TValue = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = long int] onnxruntime::ml::LabelEncoder::LabelEncoder(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml12LabelEncoderE N11onnxruntime2ml14LabelEncoder_2IfSsEE N11onnxruntime2ml14LabelEncoder_2ISsfEE N11onnxruntime2ml14LabelEncoder_2IlfEE N11onnxruntime2ml14LabelEncoder_2IflEE N11onnxruntime2ml14LabelEncoder_2IlSsEE N11onnxruntime2ml14LabelEncoder_2ISslEE N11onnxruntime2ml14LabelEncoder_2IllEE multi_class NONE post_transform LOGISTIC SOFTMAX SOFTMAX_ZERO intercepts classlabels_strings classlabels_ints coefficients !coefficients_.empty() LinearClassifier shape_size == out.length() /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearclassifier.cc scores.size() == static_cast<size_t>(expected_num_scores) Unexpected value for 'add_second_class' of Scores output is incorrect size. Expected: scores_output_data.length() >= scores_output_size Input shape needs to be at least a single dimension. Unsupported input element type of const T* onnxruntime::Tensor::Data() const [with T = double] void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span<float>&) [with SrcType = double] const T* onnxruntime::Tensor::Data() const [with T = long int] void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span<float>&) [with SrcType = long int] const T* onnxruntime::Tensor::Data() const [with T = int] void onnxruntime::ml::CastInputToFloat(const onnxruntime::Tensor&, gsl::span<float>&) [with SrcType = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] void onnxruntime::ml::batched_update_scores_inplace(gsl::span<T>, int64_t, int64_t, onnxruntime::ml::POST_EVAL_TRANSFORM, int, bool, onnxruntime::concurrency::ThreadPool*) [with T = float; int64_t = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] void onnxruntime::ml::LinearClassifier::ComputeImpl(gsl::span<const float>, int64_t, int64_t, int64_t, const std::vector<float>&, const std::vector<float>&, onnxruntime::Tensor&, onnxruntime::Tensor&, onnxruntime::ml::POST_EVAL_TRANSFORM, bool, onnxruntime::concurrency::ThreadPool*) const onnxruntime::ml::LinearClassifier::LinearClassifier(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml16LinearClassifierE ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_EEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE_ ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_EEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE0_ ZN11onnxruntime2ml29batched_update_scores_inplaceIfEEvN3gsl4spanIT_EEllNS0_19POST_EVAL_TRANSFORMEibPNS_11concurrency10ThreadPoolEEUlfPfE1_ @@3ֳLinearRegressor Unsupported data type of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearregressor.cc info.GetAttr<int64_t>("targets", &num_targets_).IsOK() info.GetAttrs<float>("coefficients", coefficients_).IsOK() Input shape had more than 2 dimension. Dims= T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::LinearRegressor::LinearRegressor(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml15LinearRegressorE /onnxruntime_src/onnxruntime/core/providers/cpu/ml/normalizer.h info.GetAttr<std::string>("norm", &norm).IsOK() Rank of input to Normalized must be less than 2. Got Unexpected NORMALIZE value of Invalid normalize value of const T* onnxruntime::Tensor::Data() const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::NORMALIZE onnxruntime::ml::MakeNormalize(const string&) onnxruntime::ml::Normalizer::Normalizer(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml10NormalizerE 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 2R32[2131111L11=>l= =<j><t<d<L< <<ZGGFwFF.HEEEDEEEPMQP+POQO|OlO OLOO 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] 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] 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 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 /onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.h info.GetAttr<std::string>("cast_to", &attr).IsOK() Expected TO_FLOAT, TO_STRING or TO_INT64 /onnxruntime_src/onnxruntime/core/providers/cpu/ml/ml_common.h info.GetAttr<std::string>("map_form", &attr).IsOK() info.GetAttr<int64_t>("max_map", &max_map_).IsOK() max_map must be > 0 if map_form is SPARSE map_form_ != PACK_MAP::SPARSE || max_map_ > 0 /onnxruntime_src/onnxruntime/core/providers/cpu/ml/cast_map.cc cur_input == end_input || cur_input->first >= 0 Negative index values are not permitted. First entry in map has index value of Expected std::map<int64_t, float> or std::map<int64_t, std::string> CastMap cast_to TO_FLOAT TO_STRING TO_INT64 Invalid CAST_TO value of map_form DENSE Expected DENSE or SPARSE Invalid PACK_MAP value of max_map %f stof stoll Invalid input type of value: Unexpected CAST_TO value of onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = std::basic_string<char>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = long int] const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, std::basic_string<char> >] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = std::basic_string<char>; TTo = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = std::map<long int, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, float>] onnxruntime::common::Status onnxruntime::ml::CastMap::ComputeImpl(onnxruntime::OpKernelContext&, TTo) const [with TFrom = float; TTo = float] static bool onnxruntime::utils::ContainerChecker::IsContainerOfType<std::map<K, V> >::check(const Cont&, size_t) [with K = long int; V = float; onnxruntime::utils::ContainerChecker::Cont = std::vector<onnxruntime::utils::data_types_internal::TypeNode>; size_t = long unsigned int] onnxruntime::ml::PACK_MAP onnxruntime::ml::MakePack(const string&) onnxruntime::ml::CAST_TO onnxruntime::ml::MakeCast(const string&) onnxruntime::ml::CastMap::CastMap(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml7CastMapE CategoryMapper cats_strings cats_int64s default_string default_int64 Input of string must have output of int64 Input of int64 must have output of string /onnxruntime_src/onnxruntime/core/providers/cpu/ml/category_mapper.h info.GetAttrs<std::string>("cats_strings", string_categories).IsOK() info.GetAttrs<int64_t>("cats_int64s", int_categories).IsOK() info.GetAttr<std::string>("default_string", &default_string_).IsOK() info.GetAttr<int64_t>("default_int64", &default_int_).IsOK() num_entries == int_categories.size() T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::CategoryMapper::CategoryMapper(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml14CategoryMapperE /onnxruntime_src/onnxruntime/core/providers/cpu/ml/dictvectorizer.h info.GetAttrs(std::is_same<AttrType, std::string>::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK() DictVectorizer string_vocabulary int64_vocabulary T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, long int>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, long int>] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, float>] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, double>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, double>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, std::basic_string<char> >] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = std::map<long int, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, float>] T* onnxruntime::Tensor::MutableData() [with T = double] const T& OrtValue::Get() const [with T = std::map<long int, double>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, double>] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = double] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = float] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = std::basic_string<char>] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = double] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = float] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = long int] N11onnxruntime2ml16DictVectorizerOpISslEE N11onnxruntime2ml16DictVectorizerOpISsfEE N11onnxruntime2ml16DictVectorizerOpISsdEE N11onnxruntime2ml16DictVectorizerOpIlSsEE N11onnxruntime2ml16DictVectorizerOpIlfEE N11onnxruntime2ml16DictVectorizerOpIldEE inputdimensions FeatureVectorizer input_tensor_ptr != nullptr Invalid input type: Number of inputs ( /onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.h status.IsOK() && !input_dimensions_.empty() inputdimensions attribute must be provided /onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.cc input_count >= 0 && static_cast<size_t>(input_count) == input_dimensions_.size() ) does not match number of inputdimensions values ( const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = float] virtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml17FeatureVectorizerE imputed_value_floats imputed_value_int64s replaced_value_float replaced_value_int64 Imputer Empty input dimensions. Invalid type Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specified /onnxruntime_src/onnxruntime/core/providers/cpu/ml/imputer.cc Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specified Must provide imputed_values_float_ or imputed_values_int64_ but not both. imputed_values_float_.empty() ^ imputed_values_int64_.empty() Empty value of imputed values. T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml9ImputerOpE keys_floats values_strings _Unused keys_strings values_floats default_float keys_int64s values_int64s LabelEncoder attribtues in LabelEncoder 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 scores.size() == static_cast<size_t>(expected_num_scores) Unexpected value for 'add_second_class' of /onnxruntime_src/onnxruntime/core/providers/cpu/ml/linearclassifier.cc 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 LinearClassifier multi_class NONE post_transform LOGISTIC SOFTMAX SOFTMAX_ZERO intercepts classlabels_strings classlabels_ints coefficients !coefficients_.empty() shape_size == out.length() 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 OneHotEncoder zeros num_categories_ > 0 Unknown Category and zeros = 0. /onnxruntime_src/onnxruntime/core/providers/cpu/ml/onehotencoder.cc tmp_cats_int64s.empty() || tmp_cats_strings.empty() One and only one of the 'cats_*' attributes must be defined const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = float] const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::ml::OneHotEncoderOp<T>::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = std::basic_string<char>] onnxruntime::ml::OneHotEncoderOp<T>::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::OneHotEncoderOp<T>::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = float] onnxruntime::ml::OneHotEncoderOp<T>::OneHotEncoderOp(const onnxruntime::OpKernelInfo&) [with T = long int] N11onnxruntime2ml15OneHotEncoderOpIlEE N11onnxruntime2ml15OneHotEncoderOpIfEE N11onnxruntime2ml15OneHotEncoderOpIdEE N11onnxruntime2ml15OneHotEncoderOpISsEE offset Empty scale in attributes !scale_.empty() Scale size: ( ) != ( ) or 1 /onnxruntime_src/onnxruntime/core/providers/cpu/ml/scaler.cc scale_.size() == offset_.size() Invalid argument: input has empty dimensions. Either both scale and offset can be of feature size ( const T* onnxruntime::Tensor::Data() const [with T = float] const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = int] onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = long int] onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::ml::ScalerOp<T>::ScalerOp(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime2ml8ScalerOpIfEE N11onnxruntime2ml8ScalerOpIdEE N11onnxruntime2ml8ScalerOpIlEE N11onnxruntime2ml8ScalerOpIiEE SVMClassifier LINEAR kernel_type POLY RBF kernel_params vectors_per_class prob_a prob_b support_vectors rho coefficients_.size() > 0 /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>] 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 <<J<;;H=d;T;D;,;:t;\GG GF\F
+H$FFFEE4FPfQPPOQlO\OLON,O|OZZ)ZY`YM[,Y Y YXX<Y 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<S>&, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<S>&, 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<S>&, const string&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<S>&) [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<S>&, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<S>&, 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<S>&, const string&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<S>&) [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<S>&, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<S>&, 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<S>&, const string&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<S>&) [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<S>&, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<S>&, 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<S>&, const string&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<S>&) [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
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. Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_attributes.h /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv.cc /onnxruntime_src/onnxruntime/core/util/math.h X: C: kernel channels: group: M: kernel_shape: Unknown AutoPadType String dilation == 1 pad type not supported. Invalid input shape: Out of bound access to array d_iter[d_i] < d_max onnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&) void onnxruntime::math::Im2colNd<T, 2>::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, bool, T) [with T = float; int64_t = long int] onnxruntime::common::Status onnxruntime::ComputePadAndOutputShape(int64_t, int64_t, int64_t, int64_t, onnxruntime::AutoPadType, int64_t*, int64_t*, int64_t*) [with bool ForceSymmetricAutoPadding = false; int64_t = long int] InferOutputShape Compute N11onnxruntime4ConvIfEE /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_integer.cc IsScalarOr1ElementVector(X_Zero_Point) IsScalarOr1ElementVector(W_Zero_Point) Must be a scalar or 1D tensor or size 1. Non per-tensor quantization is not supported now. ConvInteger void onnxruntime::math::Im2colNd<T, 2>::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, bool, T) [with T = unsigned char; int64_t = long int] Compute virtual onnxruntime::common::Status onnxruntime::ConvInteger::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime11ConvIntegerE ConvTranspose output_padding output_shape group count is <= 0 filter_number: num_input_channels: *out_size >= 0 pad type not supported dim_size > 0 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 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 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
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 /onnxruntime_src/onnxruntime/core/providers/cpu/nn/qlinearconv.cc IsScalarOr1ElementVector(X_zero_point) IsScalarOr1ElementVector(W_zero_point) IsScalarOr1ElementVector(Y_zero_point) IsScalarOr1ElementVector(X_scale) IsScalarOr1ElementVector(W_scale) IsScalarOr1ElementVector(Y_scale) QLinearConv : input zero point must be a scalar or 1D tensor of size 1 QLinearConv : filter zero point must be a scalar or 1D tensor of size 1 QLinearConv : result zero point must be a scalar or 1D tensor of size 1 QLinearConv : input scale must be a scalar or 1D tensor of size 1 QLinearConv : filter scale must be a scalar or 1D tensor of size 1 QLinearConv : result scale must be a scalar or 1D tensor of size 1 T4 QLinearConv void onnxruntime::math::Im2colNd<T, 2>::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, bool, T) [with T = unsigned char; int64_t = long int] Compute virtual onnxruntime::common::Status onnxruntime::QLinearConv::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime11QLinearConvE pooled_shape pooled_shape.size() == 2 pooled_height_ > 0 pooled_width_ > 0 spatial_scale spatial_scale_ > 0 MaxRoiPool R->Shape()[1] == 5 roi_batch_id >= 0 roi_batch_id < batch_size /onnxruntime_src/onnxruntime/core/providers/cpu/nn/roi_pool.h info.GetAttrs<int64_t>("pooled_shape", pooled_shape).IsOK() info.GetAttr<float>("spatial_scale", &spatial_scale_).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/nn/roi_pool.cc T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::RoiPool<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::RoiPool<T>::RoiPool(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7RoiPoolIfEE lambd Shrink /onnxruntime_src/onnxruntime/core/providers/cpu/nn/shrink.h op_kernel_info.GetAttr<float>("bias", &bias_temp).IsOK() op_kernel_info.GetAttr<float>("lambd", &lambd_temp).IsOK() Ret onnxruntime::utils::mltype_dispatcher_internal::CallableDispatchableRetHelper<Ret, UnsupportedPolicy>::Get() [with Ret = onnxruntime::common::Status; UnsupportedPolicy = onnxruntime::utils::mltype_dispatcher_internal::UnsupportedTypeDefaultPolicy<onnxruntime::common::Status>] T* onnxruntime::Tensor::MutableData() [with T = long unsigned int] const T* onnxruntime::Tensor::Data() const [with T = long unsigned int] T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = unsigned int] const T* onnxruntime::Tensor::Data() const [with T = unsigned int] T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] const T* onnxruntime::Tensor::Data() const [with T = short unsigned int] T* onnxruntime::Tensor::MutableData() [with T = short int] const T* onnxruntime::Tensor::Data() const [with T = short int] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::BFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::BFloat16] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] onnxruntime::Shrink::Shrink(const onnxruntime::OpKernelInfo&) N11onnxruntime6ShrinkE WCHAR_T StringNormalizer is_case_sensitive case_change_action locale stopwords Empty stopwords not allowed !sw.empty() p.second wstr != wconv_error Conversion Error en_US.UTF-8 :Please, install necessary language-pack-XX and configure locales Failed to construct locale with name: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/string_normalizer.cc attribute is_case_sensitive is not set attribute case_change_action is not set attribute case_change_action has invalid value Duplicate stopwords not allowed Stopword contains invalid utf8 chars Input contains invalid utf8 chars at: Single dimension value must be greater than 0 Input dimensions are either[C > 0] or [1][C > 0] allowed C o n v e r s i o n E r r o r const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::string_normalizer::Locale::Locale(const string&) onnxruntime::StringNormalizer::StringNormalizer(const onnxruntime::OpKernelInfo&) N11onnxruntime16StringNormalizerE TfIdfVectorizer 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 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 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&)::__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()
output != nullptr iou_threshold must be in range [0, 1]. /onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.cc Not satisfied: (iou_threshold >= 0 && iou_threshold <= 1.f)
center_point_box only support 0 or 1 /onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/non_max_suppression.h 0 == center_point_box_ || 1 == center_point_box_ Not satisfied: boxes_shape.NumDimensions() == 3
Not satisfied: scores_shape.NumDimensions() == 3
boxes and scores should have same num_batches. Not satisfied: boxes_dims[0] == scores_dims[0]
boxes and scores should have same spatial_dimension. Not satisfied: boxes_dims[1] == scores_dims[2]
The most inner dimension in boxes must have 4 data. Not satisfied: boxes_dims[2] == 4
- virtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const Compute GetThresholdsFromInputs const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = long int] PrepareCompute static onnxruntime::common::Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::PrepareContext&) onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase(const onnxruntime::OpKernelInfo&) N11onnxruntime21NonMaxSuppressionBaseE N11onnxruntime17NonMaxSuppressionE Null input X ptr Null rois_ptr Null batch_indices_ptr RoiAlign avg output_height output_width sampling_ratio sampling_ratio_ >= 0 Invalid mode of value Number of dimensions for batch indices should be exactly 1 Number of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't match /onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/roialign.h Sampling ratio should be >=0, but it was specified. It should be either avg or max const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::RoiAlignBase<T>::RoiAlignBase(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::RoiAlignBase<T>::RoiAlignBase(const onnxruntime::OpKernelInfo&) [with T = float] *ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIdEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUlllE_ *ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIfEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUlllE_ N11onnxruntime12RoiAlignBaseIfEE N11onnxruntime12RoiAlignBaseIdEE N11onnxruntime8RoiAlignIfEE N11onnxruntime8RoiAlignIdEE ReduceL1 ReduceL2 ReduceLogSum ReduceLogSumExp ReduceMax ReduceMin ReduceProd ReduceSumSquare ArgMax ArgMin has_value_ select_last_index Input to be reduced is null in_dim != 0 /onnxruntime_src/onnxruntime/core/providers/cpu/containers.h /onnxruntime_src/include/onnxruntime/core/common/optional.h /onnxruntime_src/onnxruntime/core/providers/cpu/reduction/reduction_ops.h info.GetAttr("keepdims", &keepdims).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/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 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&) N3gsl15narrowing_errorE N11onnxruntime12DeepCpuGruOpE ZN11onnxruntime10IAllocator13MakeUniquePtrIiEESt10unique_ptrIT_St8functionIFvPS3_EEESt10shared_ptrIS0_EmEUlPiE_ }. Actual: Input P must have shape { input_forget rows[ Input initial_c must have shape { /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.cc /onnxruntime_src/onnxruntime/core/providers/cpu/rnn/deep_cpu_lstm.h activation_func_names.size() == static_cast<size_t>(num_directions_) * 3 LSTM operator does not support double yet Invalid data type for LSTM operator of ValidateInputs const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span<T>, size_t, size_t) [with T = float; size_t = long unsigned int] const T* onnxruntime::rnn::detail::SafeRawConstPointer(gsl::span<T>, size_t, size_t) [with T = const float; size_t = long unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] ComputeImpl virtual onnxruntime::common::Status onnxruntime::DeepCpuLstmOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::rnn::detail::Direction onnxruntime::rnn::detail::MakeDirection(const string&) onnxruntime::DeepCpuLstmOp::DeepCpuLstmOp(const onnxruntime::OpKernelInfo&) *ZN11onnxruntimeL23ExecuteLambdaInParallelIZNS_6detail18UniDirectionalLstmIfE7ComputeERKN3gsl4spanIKfEERKNS5_IKiEEiS9_S9_RNS5_IfEESF_SF_EUliE_EEvT_iidPNS_11concurrency10ThreadPoolEEUlllE_ N11onnxruntime13DeepCpuLstmOpE 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: 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: SequenceEmpty SequenceLength SequenceConstruct SequenceErase SequenceInsert SequenceAt SplitToSequence I != nullptr Invalid sequence index ( split_size_sum ( ) != split_dim_size ( S != nullptr Got nullptr for input tensor. ), input tensor data type ( num_inputs >= 1 Split should be > 0 split_scalar > 0 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\L<, 콽ܽ 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. len > 0 stol stoul stoi allocator != nullptr buffer Tensor type mismatch. type == dtype_ -INF stoull stod /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/cast_op.cc Casting from 'float16' to 'string' is not supported yet. Cast op must have 'to' argument of type DataType Unexpected 'to' argument value: Input is missing. The operator Cast expects one and only one input <̾̾;;ξξϾϾоо,ѾѾ,˾˾D $\Tݾ߾ ++-<0'|()*2 3 6$6|%&M\NOQGH$IDJKSK,LDEd,e,fLh^ `La bbljbtc\] ^Txxdy4{,u|v<ww}}}l~tt$|LD<\씿D 䲿䴿̶DLDtLʿʿ˿Ϳÿ,ſ\ƿǿȿϿȿdɿ\¿ ÿݿۿTܿݿݿ,tٿtڿ4$,t<t$ ,<l|Ld4\
void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = signed char] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = double] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = float] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = long unsigned int] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = unsigned int] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = short unsigned int] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = unsigned char] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = long int] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = int] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] void onnxruntime::CastFromStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with DstType = short int] onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = std::basic_string<char>] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = signed char; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = double; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void* onnxruntime::Tensor::MutableDataRaw(onnxruntime::MLDataType) void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = long unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = short unsigned int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = unsigned char; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = long int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = short int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data const T* onnxruntime::Tensor::Data() const [with T = onnxruntime::MLFloat16] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = onnxruntime::MLFloat16; DstType = bool; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = onnxruntime::MLFloat16] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = double] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = double; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = double] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = float] onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = bool] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = bool] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = bool; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = bool] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = long int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = long int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = long int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = short int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = short int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = short int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = signed char] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = signed char; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = signed char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = long unsigned int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = long unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = long unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = unsigned int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = short unsigned int] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = short unsigned int; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = short unsigned int] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = short unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned char] void onnxruntime::CastToStringData(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&) [with SrcType = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = onnxruntime::MLFloat16] void onnxruntime::CastFloat16Data(const onnxruntime::Tensor*, onnxruntime::Tensor*, const onnxruntime::TensorShape&, const AllocatorPtr&) [with SrcType = unsigned char; DstType = onnxruntime::MLFloat16; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] CastFloat16Data onnxruntime::common::Status onnxruntime::Cast<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] onnxruntime::Cast<T>::Cast(const onnxruntime::OpKernelInfo&) [with T = unsigned char] N11onnxruntime4CastIhEE N11onnxruntime4CastItEE N11onnxruntime4CastIjEE N11onnxruntime4CastImEE N11onnxruntime4CastIaEE N11onnxruntime4CastIsEE N11onnxruntime4CastIiEE N11onnxruntime4CastIlEE N11onnxruntime4CastIbEE N11onnxruntime4CastIfEE N11onnxruntime4CastIdEE N11onnxruntime4CastINS_9MLFloat16EEE N11onnxruntime4CastISsEE 8 O O O O A A A ACompress /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/compress.cc axes_right_stride >= 0 && static_cast<uint64_t>(axes_right_stride) < std::numeric_limits<size_t>::max() const T* onnxruntime::Tensor::Data() const [with T = bool] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) virtual onnxruntime::common::Status onnxruntime::Compress::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime8CompressE Cannot concatenate scalars got: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.h /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/concat.cc Not satisfied: input_count >= 1
+ T* onnxruntime::Tensor::MutableData() [with T = long int] virtual onnxruntime::common::Status onnxruntime::NonMaxSuppression::Compute(onnxruntime::OpKernelContext*) const Compute GetThresholdsFromInputs const T* onnxruntime::Tensor::Data() const [with T = float] const T* onnxruntime::Tensor::Data() const [with T = long int] PrepareCompute static onnxruntime::common::Status onnxruntime::NonMaxSuppressionBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::PrepareContext&) onnxruntime::NonMaxSuppressionBase::NonMaxSuppressionBase(const onnxruntime::OpKernelInfo&) N11onnxruntime21NonMaxSuppressionBaseE N11onnxruntime17NonMaxSuppressionE Null input X ptr Null rois_ptr Null batch_indices_ptr RoiAlign avg Invalid mode of value output_height output_width sampling_ratio sampling_ratio_ >= 0 Number of dimensions for batch indices should be exactly 1 Number of dimensions for rois should be exactly Second dimension for rois should be exactly First dimension (num_rois) of batch_indices and rois don't match specified. It should be either avg or max /onnxruntime_src/onnxruntime/core/providers/cpu/object_detection/roialign.h Sampling ratio should be >=0, but it was const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = double] T* onnxruntime::Tensor::MutableData() [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::RoiAlignBase::RoiAlignBase(const onnxruntime::OpKernelInfo&) *ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIdEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUlllE_ *ZN11onnxruntime12_GLOBAL__N_115RoiAlignForwardIfEEvRKNS_11TensorShapeEPKT_flllS7_lPS5_NS_12RoiAlignModeEPKlPNS_11concurrency10ThreadPoolEEUlllE_ N11onnxruntime12RoiAlignBaseE N11onnxruntime8RoiAlignIfEE N11onnxruntime8RoiAlignIdEE ReduceL1 ReduceL2 ReduceLogSum ReduceLogSumExp 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] N11onnxruntime16ReduceKernelBaseILb0EEE N11onnxruntime16ReduceKernelBaseILb1EEE 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 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: 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^\^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>] 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
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 4d$d4d4d4d4ddd4d4d4dc4dc 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_ /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 y_scale.Shape().NumDimensions() == 1 && y_scale.Shape().Size() == broadcast_dim y_zero_point must be 1D tensor with size y_zero_point != nullptr && y_zero_point->Shape().NumDimensions() == 1 && y_zero_point->Shape().Size() == broadcast_dim IsScalarOr1ElementVector(&y_scale) y_zero_point == nullptr || IsScalarOr1ElementVector(y_zero_point) y_scale must be 1D tensor with size y_scale must be a scalar or 1D tensor or size 1. y_zero_point must be a scalar or 1D tensor or size 1. x_scale.Shape().NumDimensions() == 1 && x_scale.Shape().Size() == broadcast_dim x_zero_point must be 1D tensor with size x_zero_point != nullptr && x_zero_point->Shape().NumDimensions() == 1 && x_zero_point->Shape().Size() == broadcast_dim IsScalarOr1ElementVector(&x_scale) x_zero_point == nullptr || IsScalarOr1ElementVector(x_zero_point) x_scale must be 1D tensor with size x_scale must be a scalar or 1D tensor or size 1. x_zero_point must be a scalar or 1D tensor or size 1. onnxruntime::common::Status onnxruntime::DequantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::common::Status onnxruntime::DequantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] onnxruntime::common::Status onnxruntime::QuantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] onnxruntime::common::Status onnxruntime::QuantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char] N11onnxruntime16DequantizeLinearIhEE N11onnxruntime16DequantizeLinearIaEE N11onnxruntime14QuantizeLinearIhEE N11onnxruntime14QuantizeLinearIaEE 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. /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 and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) info.GetAttr<std::string>("mode", &mode).IsOK() info.GetAttrs<float>("scales", scales_).IsOK() 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_ @ ?/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: ReverseSequence batch_axis . Must be 0 or 1 Invalid batch_axis of batch_axis < 2 Invalid time_axis of time_axis < 2 batch_axis != time_axis Unknown tensor type of . batch_size= # )
y =##m gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::BFloat16] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::BFloat16] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = onnxruntime::MLFloat16] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = onnxruntime::MLFloat16] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = long unsigned int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = long int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = unsigned int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = short unsigned int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short unsigned int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = short int] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = short int] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = std::basic_string<char>] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = std::basic_string<char>] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = double] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = double] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = bool] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = bool] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] 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= yT/
-vQ,yyy~Y4{V1 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&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = unsigned char]::__lambda10 onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = short unsigned int]::__lambda10 onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = long unsigned int]::__lambda10 onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = unsigned int]::__lambda10 void* onnxruntime::SliceIteratorBase::CopyInnermostAxisNonSolitaryInnerStep(void*) onnxruntime::SliceImpl(onnxruntime::OpKernelContext*, const onnxruntime::Tensor&, std::vector<long int>&, std::vector<long int>*, const std::vector<long int>&, const std::vector<long int>&) [with T = std::basic_string<char>]::__lambda10 Compute onnxruntime::common::Status onnxruntime::SliceBase::Compute(onnxruntime::OpKernelContext*) const 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>&) const 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 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.cc Split operator does not support T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] ComputeImpl T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] ComputeImpl T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] ComputeImpl T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] ComputeImpl const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Split::Compute(onnxruntime::OpKernelContext*) const int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) onnxruntime::SplitBase::SplitBase(const onnxruntime::OpKernelInfo&) N11onnxruntime9SplitBaseE N11onnxruntime5SplitE . shape= must be 1 instead of Dimension of input input_shape[i] == 1 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/squeeze.h int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) static std::vector<long int> onnxruntime::SqueezeBase::ComputeOutputShape(const onnxruntime::TensorShape&, const onnxruntime::TensorShape&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime11SqueezeBaseE N11onnxruntime7SqueezeE Tile Input count of Tile OP mismatch, the first one is empty Input count of Tile OP mismatch, the second one is empty the tensor to be tiled using Tile OP must be atleast 1 dimensional 'repeat' input tensor must be 1 dimensional 'repeat' input tensor must have the same length as the 'input' tensor Tile doesn't have an implementation yet for the type: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/tile.cc const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Tile::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime4TileE Mismatched data types between input and output Tensors. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.h v >= 0 && static_cast<uint64_t>(v) <= std::numeric_limits<size_t>::max() Attribute perm of Transpose has an invalid value. Value /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.cc does not align with rank of input data: is outside range. is repeated. [ perm: onnxruntime::TransposeBase::TransposeBase(const onnxruntime::OpKernelInfo&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext*) const T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] N11onnxruntime13TransposeBaseE N11onnxruntime9TransposeE Unique Unsupported tensor type of gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char] N11onnxruntime6UniqueE 'axes' has a duplicate axis Missing/Invalid 'axes' attribute value /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.h info.GetAttrs("axes", axes_).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.cc 'axes' has an out of range axis Compute int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::UnsqueezeBase::Prepare&) const onnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo&) N11onnxruntime13UnsqueezeBaseE N11onnxruntime9UnsqueezeE 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. 'Linear' mode only support 2-D inputs ('Bilinear') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the Only one of scales or sizes must be provided as input. sizes != nullptr && sizes->Shape().Size() != 0 Resize: input tensor's rank does not match the output tensor's rank. X->Shape().GetDims().size() == output_dims.size() Either scales or sizes MUST be provided as input. BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = float] onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = int] onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = unsigned char] 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 @ A @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] 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] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = int] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = long int] onnxruntime::TBroadcastOutput<T>::TBroadcastOutput(size_t, onnxruntime::Tensor&, int64_t, int64_t) [with T = long int; size_t = long unsigned int; int64_t = long int] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::TBroadcastOutput<T>::TBroadcastOutput(size_t, onnxruntime::Tensor&, int64_t, int64_t) [with T = float; size_t = long unsigned int; int64_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 Scale /onnxruntime_src/onnxruntime/contrib_ops/cpu/element_wise_ops.h info.GetAttr("scale", &scale_).IsOK() info.GetAttr("alpha", &alpha_).IsOK() info.GetAttr("beta", &beta_).IsOK() onnxruntime::contrib::Scale<T>::Scale(const onnxruntime::OpKernelInfo&) [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::Affine<T>::Affine(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib6AffineIfEE N11onnxruntime7contrib5ScaleIfEE ExpandDims Axis must be within range [ Axis is /onnxruntime_src/onnxruntime/contrib_ops/cpu/expand_dims.h axis_tensor->Shape().IsScalar() axis <= X_NumDims && axis >= -X_NumDims An axis tensor must be a scalar tensor. const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] const T* onnxruntime::Tensor::Data() const [with T = int] virtual onnxruntime::common::Status onnxruntime::contrib::ExpandDims::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime7contrib10ExpandDimsE /onnxruntime_src/onnxruntime/contrib_ops/cpu/fused_conv.cc GetFusedActivationAttr(info, activation_).IsOK() onnxruntime::contrib::FusedConvFloat::FusedConvFloat(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib14FusedConvFloatE /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 const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime7contrib15MaxpoolWithMaskE across_channels normalize_variance /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/mean_variance_normalization.h info.GetAttr<int64_t>("across_channels", &across_channels_).IsOK() info.GetAttr<int64_t>("normalize_variance", &normalize_variance_).IsOK() onnxruntime::MeanVarianceNormalization_0<T>::MeanVarianceNormalization_0(const onnxruntime::OpKernelInfo&, bool) [with T = float] MurmurHash3 keys 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 QLinearAdd offset % span_size_ == 0 /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 TBroadcaster can only start at span boundary! 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::QLinearAdd<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]::__lambda11; Input1Scalar = onnxruntime::contrib::QLinearAdd<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]::__lambda12; General = onnxruntime::contrib::QLinearAdd<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char]::__lambda13] 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::QLinearAdd<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda11; Input1Scalar = onnxruntime::contrib::QLinearAdd<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda12; General = onnxruntime::contrib::QLinearAdd<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char]::__lambda13] 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_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 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& 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 = unsigned char] void onnxruntime::contrib::BuildQLinearLeakyReluLookupTable(uint8_t*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, float) [with T = unsigned char; uint8_t = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::contrib::BuildQLinearLeakyReluLookupTable(uint8_t*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, const onnxruntime::Tensor*, float) [with T = signed char; uint8_t = unsigned char] N11onnxruntime7contrib16QLinearLeakyReluIaEE N11onnxruntime7contrib16QLinearLeakyReluIhEE
-#<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)
+ 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 VVVVVVVVVVVVVlV 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 indices element out of data bounds, idx= must be within the inclusive range [ Missing/Invalid 'axis' attribute value /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather.h /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather.cc Type for Tind not supported yet in Gather. Tind 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 and 'Cubic' mode only support 2-D inputs ('Bilinear', 'Bicubic') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the scales.size() == 2 || (scales.size() == 4 && scales[0] == 1 && scales[1] == 1) info.GetAttr<std::string>("mode", &mode).IsOK() info.GetAttrs<float>("scales", scales_).IsOK() 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_ @ ?/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: ReverseSequence batch_axis . Must be 0 or 1 Invalid batch_axis of batch_axis < 2 Invalid time_axis of time_axis < 2 batch_axis != time_axis Unknown tensor type of . batch_size=
+UUGe?
+
+ 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= I$ګkF!תqIII⮾sN)߭pK&⮾⮾ܬ 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 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 DepthToSpace SpaceToDepth 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 /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. 'Linear' mode only support 2-D inputs ('Bilinear') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the Only one of scales or sizes must be provided as input. sizes != nullptr && sizes->Shape().Size() != 0 Resize: input tensor's rank does not match the output tensor's rank. X->Shape().GetDims().size() == output_dims.size() Either scales or sizes MUST be provided as input. BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = float] onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = int] onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = unsigned char] 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_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 QLinearMul QLinearAdd /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 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_
+#< 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] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] 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 TransposeMatMul /onnxruntime_src/onnxruntime/contrib_ops/cpu/transpose_matmul.cc info.GetAttr("transA", &trans_a_attr_).IsOK() info.GetAttr("transB", &trans_b_attr_).IsOK() 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&) N11onnxruntime7contrib15TransposeMatMulE Input tensor to Unique op should be 1D T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime7contrib6UniqueIfEE conv_window_size char_embedding_size embedding_size attribute: conv filter size: conv_window_size attribute: conv kernal size 1: Char embedding size: Conv kernal size 2 : WordConvEmbedding Conv filter size does not match embedding_size attribute. Conv kernal size 1 does not match conv_window_size attribute . Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size does not match conv kernal size 2. /onnxruntime_src/onnxruntime/contrib_ops/cpu/word_conv_embedding.cc const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime7contrib17WordConvEmbeddingE hӥhӥhӥh'is defined. No attribute with name:' unknown kernel type Attibute name and type don't match /onnxruntime_src/onnxruntime/core/providers/cpu/activation/activations.h /onnxruntime_src/onnxruntime/contrib_ops/cpu/activations.h /onnxruntime_src/onnxruntime/core/providers/cpu/activation/element_wise_ranged_transform.h input_size < std::numeric_limits<std::ptrdiff_t>::max() onnxruntime::common::Status onnxruntime::ElementWiseKernel<F>::Compute(onnxruntime::OpKernelContext*) const [with F = onnxruntime::functors::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 N11onnxruntime8functors26ElementWiseRangedTransformIfEE 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 pA A AO@O9 BBBB¥¥¥;?;?;?;?giP9giP9giP9giP9C:C:C:C:<<<<*=*=*=*=*>*>*>*> B B B BO@O@O@O@OOOO99997777If 'If' node has outputs. then_branch info == nullptr else_branch ' attribute. Failed to create output tensor for If output outputs which doesn't match the subgraph's /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/if.cc num_subgraph_outputs == static_cast<size_t>(num_outputs) SetupSubgraphExecutionInfo should only be called once for each subgraph. 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&) 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 position_embedding shall have same dimension 1 word_embedding and segment_embedding shall have same dimension 1 beta is expected to have 1 dimensions, got beta is expected to have size of gamma is expected to have 1 dimensions, got gamma is expected to have size of Input 0 and 1 shall have same shape Input 0 and 7 (mask) shall have same shape const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] 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* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = double] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::contrib::CDist<T>::CDist(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::contrib::CDist<T>::CDist(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime7contrib5CDistIfEE N11onnxruntime7contrib5CDistIdEE ConvTransposeWithDynamicPads N11onnxruntime7contrib28ConvTransposeWithDynamicPadsIfEE 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] Device:[ DeviceType: MemoryType: DeviceId: OrtMemoryInfo:[ id: OrtMemType: OrtAllocatorType: Cuda CudaPinned Specified device is not supported. /onnxruntime_src/onnxruntime/core/framework/allocator.cc CalcMemSizeForArrayWithAlignment N11onnxruntime12CPUAllocatorE St14default_deleteIN11onnxruntime16IDeviceAllocatorEE St14default_deleteIN11onnxruntime8BFCArenaEE St19_Sp_counted_deleterIPN11onnxruntime8BFCArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE St19_Sp_counted_deleterIPN11onnxruntime16IDeviceAllocatorESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE /onnxruntime_src/onnxruntime/core/framework/bfc_arena.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 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() Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: MaxAllocSize: Could not find Region for h < chunks_.size() Size: | Requested Size: | in_use: p_int >= base_int c2->prev == h1 h != kInvalidChunkHandle c->bin_num == bin_num Creating BFCArena for Creating bins of max chunk size !chunk->in_use() Available memory of cudaMalloc Extended allocation by bytes. Total allocated bytes: Allocated memory at Could not find chunk in bin Chunk Free Allocator: : Chunks . Bytes Requested Bin for bytes has max bytes of , Chunk State: , prev: , next: Overall chunks summary: chunks of size . Total Sum Total of in-use chunks: Stats:
- tried to allocate 0 bytes Extending BFCArena for . bin_num: rounded_bytes: 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)::__lambda2 Extend onnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::ChunkFromHandle(onnxruntime::BFCArena::ChunkHandle) onnxruntime::BFCArena::BFCArena(std::unique_ptr<onnxruntime::IDeviceAllocator>, size_t, onnxruntime::ArenaExtendStrategy) 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) tensor(bfloat16) Not implemented sparse tensor type elem_proto != nullptr value_proto != nullptr MLDataType for: |l\L<, /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 4
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CBBBBB C C,CDC\CtCCCCCB onnxruntime::utils::ContainerChecker::ContainerChecker(onnxruntime::MLDataType) void onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(onnxruntime::MLDataType) static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::map<long int, float>] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::map<std::basic_string<char>, float>] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = onnxruntime::BFloat16] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = onnxruntime::MLFloat16] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::basic_string<char>] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = bool] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = long unsigned int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = long int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = unsigned int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = short unsigned int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = short int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = unsigned char] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = signed char] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = double] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = float] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = double] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = float] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = long int] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = std::basic_string<char>] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = double] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = float] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = long int] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = std::basic_string<char>] virtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue&, size_t, void*) const virtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void*, size_t, OrtValue&) const bool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto&) const bool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto&) const virtual bool onnxruntime::SequenceTensorTypeBase::IsCompatible(const onnx::TypeProto&) const virtual bool onnxruntime::SparseTensorTypeBase::IsCompatible(const onnx::TypeProto&) const virtual bool onnxruntime::TensorTypeBase::IsCompatible(const onnx::TypeProto&) const bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Sequence&, const onnx::TypeProto_Sequence&) bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Map&, const onnx::TypeProto_Map&) GetElementType GetElementType GetElementType N11onnxruntime12DataTypeImplE N11onnxruntime14TensorTypeBaseE N11onnxruntime20SparseTensorTypeBaseE N11onnxruntime17NonTensorTypeBaseE N11onnxruntime22SequenceTensorTypeBaseE N11onnxruntime21PrimitiveDataTypeBaseE N11onnxruntime10TensorTypeIiEE N11onnxruntime10TensorTypeIfEE N11onnxruntime10TensorTypeIbEE N11onnxruntime10TensorTypeISsEE N11onnxruntime10TensorTypeIaEE N11onnxruntime10TensorTypeIhEE N11onnxruntime10TensorTypeItEE N11onnxruntime10TensorTypeIsEE N11onnxruntime10TensorTypeIlEE N11onnxruntime10TensorTypeIdEE N11onnxruntime10TensorTypeIjEE N11onnxruntime10TensorTypeImEE N11onnxruntime10TensorTypeINS_9MLFloat16EEE N11onnxruntime10TensorTypeINS_8BFloat16EEE N11onnxruntime16SparseTensorTypeIiEE N11onnxruntime16SparseTensorTypeIfEE N11onnxruntime16SparseTensorTypeIbEE N11onnxruntime16SparseTensorTypeIaEE N11onnxruntime16SparseTensorTypeIhEE N11onnxruntime16SparseTensorTypeItEE N11onnxruntime16SparseTensorTypeIsEE N11onnxruntime16SparseTensorTypeIlEE N11onnxruntime16SparseTensorTypeIdEE N11onnxruntime16SparseTensorTypeIjEE N11onnxruntime16SparseTensorTypeImEE N11onnxruntime16SparseTensorTypeINS_9MLFloat16EEE N11onnxruntime16SparseTensorTypeINS_8BFloat16EEE N11onnxruntime13NonTensorTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEE N11onnxruntime7MapTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEE N11onnxruntime13NonTensorTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEE N11onnxruntime7MapTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEE N11onnxruntime13NonTensorTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEE N11onnxruntime7MapTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEE N11onnxruntime13NonTensorTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEE N11onnxruntime7MapTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEE N11onnxruntime13NonTensorTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEE N11onnxruntime7MapTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEE N11onnxruntime13NonTensorTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEE N11onnxruntime7MapTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEE N11onnxruntime13NonTensorTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEE N11onnxruntime7MapTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEE N11onnxruntime13NonTensorTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEE N11onnxruntime7MapTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEE N11onnxruntime18SequenceTensorTypeIfEE N11onnxruntime18SequenceTensorTypeIdEE N11onnxruntime18SequenceTensorTypeIaEE N11onnxruntime18SequenceTensorTypeIhEE N11onnxruntime18SequenceTensorTypeIsEE N11onnxruntime18SequenceTensorTypeItEE N11onnxruntime18SequenceTensorTypeIiEE N11onnxruntime18SequenceTensorTypeIjEE N11onnxruntime18SequenceTensorTypeIlEE N11onnxruntime18SequenceTensorTypeImEE N11onnxruntime18SequenceTensorTypeIbEE N11onnxruntime18SequenceTensorTypeISsEE N11onnxruntime18SequenceTensorTypeINS_9MLFloat16EEE N11onnxruntime18SequenceTensorTypeINS_8BFloat16EEE N11onnxruntime13NonTensorTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEE N11onnxruntime12SequenceTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEE N11onnxruntime13NonTensorTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEE N11onnxruntime12SequenceTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEE N11onnxruntime17PrimitiveDataTypeIiEE N11onnxruntime17PrimitiveDataTypeIfEE N11onnxruntime17PrimitiveDataTypeIbEE N11onnxruntime17PrimitiveDataTypeISsEE N11onnxruntime17PrimitiveDataTypeIaEE N11onnxruntime17PrimitiveDataTypeIhEE N11onnxruntime17PrimitiveDataTypeItEE N11onnxruntime17PrimitiveDataTypeIsEE N11onnxruntime17PrimitiveDataTypeIlEE N11onnxruntime17PrimitiveDataTypeIdEE N11onnxruntime17PrimitiveDataTypeIjEE N11onnxruntime17PrimitiveDataTypeImEE N11onnxruntime17PrimitiveDataTypeINS_9MLFloat16EEE N11onnxruntime17PrimitiveDataTypeINS_8BFloat16EEE ZN11onnxruntime19data_types_internal16DataTypeRegistryC1EvEUlPKNS_12DataTypeImplEE_ Ss N11onnxruntime9MLFloat16E N11onnxruntime8BFloat16E St3mapISsfSt4lessISsESaISt4pairIKSsfEEE St3mapIlfSt4lessIlESaISt4pairIKlfEEE Caught exception while destructing CustomOpsLoader with message: /onnxruntime_src/onnxruntime/core/framework/ex_lib_loader.cc Caught exception while loading custom ops with message: Unloading DSO Failed to unload DSO: A dso with name has already been loaded. LoadExternalLib ~ExLibLoader N11onnxruntime11ExLibLoaderE 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 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() 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) 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 Saving initialized tensors. , Got Failed to copy tensor to Deserialize tensor Graph input with name is not used by any node. /onnxruntime_src/onnxruntime/core/framework/finalize_session_state.cc OrtValue indexes should have been populated. ort_value_name_idx_map.MaxIdx() > -1 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::SaveInitializedTensors(const onnxruntime::Env&, const std::basic_string<char>&, const onnxruntime::GraphViewer&, const OrtMemoryInfo&, const onnxruntime::OrtValueNameIdxMap&, onnxruntime::ITensorAllocator&, const T&, const onnxruntime::logging::Logger&, const onnxruntime::DataTransferManager&) [with T = onnxruntime::FinalizeSessionState(onnxruntime::SessionState&, const std::basic_string<char>&, onnxruntime::KernelRegistryManager&, const onnxruntime::Node*, ExecutionMode)::__lambda8; OrtMemoryInfo = OrtMemoryInfo] SaveInitializedTensors FinalizeSessionState *ZN11onnxruntimeL33SaveInputOutputNamesToNodeMappingERKNS_11GraphViewerERKNS_21KernelRegistryManagerERNS_12SessionStateERKSt6vectorIPKNS_7NodeArgESaISB_EEEUlRSA_mE_ *ZN11onnxruntimeL33SaveInputOutputNamesToNodeMappingERKNS_11GraphViewerERKNS_21KernelRegistryManagerERNS_12SessionStateERKSt6vectorIPKNS_7NodeArgESaISB_EEEUlRSA_mE0_ N11onnxruntime25ISequentialPlannerContextE N11onnxruntime24SequentialPlannerContextE No provider specified. 1 == capability->nodes.size() /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() 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 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, therefore, can't find a suitable kernel for 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&) /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!
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type_proto is not of type sequence! :tensor type l< OrtValue is TensorSequence type but has no element Tensor DataType. Tensor types should have been handled already 8 ($t$$$\$\$$$t$ 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 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. static onnxruntime::OrtValueTensorSlicer<T> onnxruntime::OrtValueTensorSlicer<T>::Create(T&, int64_t, int64_t) [with T = const OrtValue; int64_t = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] static onnxruntime::OrtValueTensorSlicer<T> onnxruntime::OrtValueTensorSlicer<T>::Create(T&, int64_t, int64_t) [with T = OrtValue; int64_t = long int] /onnxruntime_src/onnxruntime/core/framework/session_state.cc SetGraphAndCreateKernels must be called prior to GetExecutionInfo. Could not create kernel for node: as there's no execution provider allocated. Kernel creation failed for node: Allocator already registered for . Do you have duplicated calls to SessionState::AddInitializedTensor function? Failed to find output name in the mapping: Failed to find input name in the mapping: Done saving OrtValue mappings. Using an input in multiple nodes on different devices is not supported currently. Input: Only one node should produce an output. Existing entry for output_names_to_nodeinfo.empty() existing_entries.find(attribute_name) == existing_entries.cend() Could not find OrtValue with idx ' node_index_info_ with error: . Ignoring allocator from duplicated ort_value index: SaveMLValueNameIndexMapping is used by node ) and node Entry exists in node for attribute onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const 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&) CreateGraphInfo SetupAllocators *ZN11onnxruntime12SessionState23UpdateToBeExecutedNodesERKSt6vectorIiSaIiEEEUlPKNS_4NodeEE_ *ZN11onnxruntime12SessionState15SetupAllocatorsEvEUli10OrtMemTypeE_ 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
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const T& OrtValue::Get() const [with T = onnxruntime::SparseTensor] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] OrtStatus* OrtApis::GetTensorTypeAndShape(const OrtValue*, OrtTensorTypeAndShapeInfo**) Invalid TensorProto ) in proto in 'Constant' node ' data overflow UnpackTensor: the pre-allocate size does not match the size in proto source and destination buffer size mismatch /onnxruntime_src/onnxruntime/core/framework/endian_utils.h Not satisfied: source_bytes.size_bytes() == destination.size_bytes()
- UnpackTensor: the pre-allocated size does not match the raw data size, expected /onnxruntime_src/onnxruntime/core/framework/tensorprotoutils.cc ) does not match the data size( corrupted protobuf data: tensor shape size( 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: ZVZrZrZ:Z:ZVZZZrZ:ZZVZZZZ:Z[x[[[Y[Y[x[5[5[[Y[5[x[5[[[Y[ŦhLT4D$Ll+qUA
-
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 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] 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:
+ 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 /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 epsilon_ >= 0 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 embedding_size attribute: conv filter size: conv_window_size attribute: conv kernal size 1: Char embedding size: Conv kernal size 2 : WordConvEmbedding Conv filter size does not match embedding_size attribute. Conv kernal size 1 does not match conv_window_size attribute . Char embedding size does not match char_embedding_size attribute. char_embedding_size attribute: Char embedding size does not match conv kernal size 2. /onnxruntime_src/onnxruntime/contrib_ops/cpu/word_conv_embedding.cc const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime7contrib17WordConvEmbeddingE 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 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: 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: |l\L<, /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\4 000h0 988 9 9,9<9L9\9t99999998 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&) T/6=%ELU\e l/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 < OrtValue is TensorSequence type but has no element Tensor DataType. Tensor types should have been handled already XxXXXxD,,dD 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
+ 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
+ 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: &&&lLlllllll=T4l23<32T43t33T4T41NIDJJKtKKLLNLMMMdNNNlI[ZX[sYZW2[YxZ\XYWHW[[V
+
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:
Execution Plan:
- ) , use fence when async Index out-of-range! nullptr != node Free ml-values: INVALID INDEX vector<bool>::_M_fill_insert /onnxruntime_src/onnxruntime/core/framework/allocation_planner.cc id >= 0 && static_cast<size_t>(id) < ort_value_info_.size() n >= 0 && static_cast<size_t>(n) < ort_value_info_.size() n >= 0 && static_cast<size_t>(n) < plan_.allocation_plan.size() kernel_create_info != nullptr && kernel_create_info->kernel_def != nullptr (ort_value_idx) output_name : <allocation plan>
- No suitable kernel definition found for op Can not find the execution provider onnxruntime::OrtValueIndex& onnxruntime::PlannerImpl::Buffer(onnxruntime::OrtValueIndex) void onnxruntime::PlannerImpl::Reuse(onnxruntime::OrtValueIndex, onnxruntime::OrtValueIndex, onnxruntime::AllocKind) static size_t onnxruntime::PlannerImpl::GetElementSize(const string* const&) OrtMemoryInfo onnxruntime::PlannerImpl::GetLocationForNodeInput(size_t, const onnxruntime::Node&) GeneratePlanForWeights ComputeReusePlan onnxruntime::AllocPlanPerValue& onnxruntime::PlannerImpl::AllocPlan(onnxruntime::OrtValueIndex) int& onnxruntime::PlannerImpl::UseCount(onnxruntime::OrtValueIndex) void onnxruntime::PlannerImpl::ProcessDef(onnxruntime::OrtValueIndex, const onnxruntime::NodeArg*) onnxruntime::OrtValueIndex onnxruntime::PlannerImpl::Index(const OrtValueName&) ForEachWithIndex ComputeUseCounts CreatePlan N11onnxruntime17ExecutionPlanBaseE N11onnxruntime23SequentialExecutionPlanE ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEmE_ ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEbE0_ ZN11onnxruntime11PlannerImpl22GeneratePlanForWeightsEvEUlRKNS_7NodeArgEmE_ Divide by zero static void SafeIntExceptionHandler<onnxruntime::OnnxRuntimeException>::SafeIntOnDivZero() func info for node: already exist. Can't use func with null ptr /onnxruntime_src/onnxruntime/core/framework/fuse_nodes_funcs.cc GetFuncs *ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlS6_S9_SB_E_ *ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlSG_SH_E0_ *ZNK11onnxruntime11FuncManager8GetFuncsERKSsPSt8functionIFNS_6common6StatusEPvPK6OrtApiP16OrtKernelContextEEPS3_IFiPNS_14ComputeContextEPS6_EEPS3_IFvS6_EEEUlS6_E1_ Begin execution does not. entry != nullptr op_name _fence_before node. Name:' ' Status Message: _kernel_time _fence_after Multiple errors were found. ParallelExecutor::Execute Unknown exception was caught by catch-all handler. Exception running nodes starting at Exiting due to terminate flag being set to true. /onnxruntime_src/onnxruntime/core/framework/parallel_executor.cc Got nullptr from GetKernel for node: All implicit inputs should have OrtValue instances by now. /onnxruntime_src/onnxruntime/core/framework/op_kernel_context_internal.h Non-zero status code returned while running onnxruntime::OpKernelContextInternal::OpKernelContextInternal(const onnxruntime::SessionState&, onnxruntime::IExecutionFrame&, const onnxruntime::OpKernel&, const onnxruntime::logging::Logger&, const bool&) onnxruntime::common::Status onnxruntime::ParallelExecutor::RunNodeAsync(size_t, const onnxruntime::SessionState&, const onnxruntime::logging::Logger&) RunNodeAsync const T& OrtValue::Get() const [with T = onnxruntime::Tensor] Execute *ZN11onnxruntime16ParallelExecutor11EnqueueNodeEmRKNS_12SessionStateERKNS_7logging6LoggerEEUlvE_ N11onnxruntime16ParallelExecutorE N11onnxruntime23OpKernelContextInternalE graph_index exec_plan_index activation_size parameter_size output_size SequentialExecutor::Execute /onnxruntime_src/onnxruntime/core/framework/sequential_executor.cc 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 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
Performs element-wise binary {name} on 8 bit data types (with Numpy-style broadcasting support).
{additionalDocumentation}
- Input A's scale. It's a scalar, which means a per-tensor/layer quantization. Input A zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Input B's scale. It's a scalar, which means a per-tensor/layer quantization. Input B zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Output scale. It's a scalar, which means a per-tensor/layer quantization. Output zero point. Default value is 0 if it's not specified. It's a scalar, which means a per-tensor/layer quantization. Result, has same element type as two inputs Constrain input and output types to 8 bit signed and unsigned tensors. first input tensor has wrong dimension rois input tensor has wrong dimension batch_indices shape input tensor has wrong dimension crop_size shape input tensor has wrong dimension 'Border' attribute must be present and must contain exactly 4 values - (left_border, top_border, right_border, bottom_border) 'Scale' must contain exactly 2 values - (height, width) ) needs to be greater than or equal to the top_border ( ) needs to be greater than or equal to the left_border ( First input does not have rank 2 Second input does not have rank 2 inputs are expected to have tensor type and output type should not be null. both data and indices tensor need to have rank larger than zero. last dimension of indices must not be larger and rank of data tensor 'pads' input must be a 1D (shape: [input_rank]) or 2D tensor (shape: [1, input_rank]) of type int64 /onnxruntime_src/onnxruntime/core/graph/contrib_ops/contrib_defs.cc 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 (http://pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap09.html#tag_09_03). If set, tokenizer may produce tokens matching the specified pattern. Note that one and only of 'tokenexp' and 'separators' should be set. an optional list of strings attribute that contains a list of separators - regular expressions to match separators Two consecutive segments in X connected by a separator would be divided into two tokens. For example, if the input is "Hello World!" and this attribute contains only one space character, the corresponding output would be ["Hello", "World!"]. To achieve character-level tokenization, one should set the 'separators' to [""], which contains an empty string. Minimum number of characters allowed in the output. For example, if mincharnum is 2, tokens such as "A" and "B" would be ignored Matrix multiply results from A * B Constrain input A data types as 16-bit integer tensor Constrain input B data types as 16-bit integer tensor Constrain output Y data types as 32-bit integer tensor.T3 must be tensor(uint32) when both T1 and T2 are tensor(uint16),or must be tensor(int32) when either T1 or T2 is tensor(int16). 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. 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. Input data type does not match the expected data type Input data type does not match the expected data type. Current data type is Scale and Zero-point must be a scalar Scale and Zero-point must be of rank 1 Scale and Zero-point must be of rank 1 and the number of elements should be equal to the number of rows of the corresponding input. axis must be in [-rank, rank-1]. input rank was inputs are expected to have tensor type. Inputs 0 shall be 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. 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 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 [ShapeInferenceError] Input's shape must be 4-D ) + bottom_border ( ) + right_border ( ) + scale[0] ( ) + scale[1] ( Number of attention heads mask_index 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 QLinearMul 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. 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. [ShapeInferenceError] Attribute should specify a shape Incompatible dimensions 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: ̼+̼+ *ZZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_ENKUlRN4onnx8OpSchemaEE_clES5_EUlRNS3_16InferenceContextEE_ *ZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_EUlRN4onnx8OpSchemaEE_ *ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE1_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE10_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE11_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE12_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE13_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE14_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE15_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE16_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE17_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE18_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE19_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE20_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE21_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE22_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE23_ 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 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_ Inputs:
Nodes:
) -> Outputs:
- index < data_.size() _token_ Not satisfied: dims_eq()
- Input to set must exist. input->Exists() node_arg Constant !found Removing initializer ' 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 ai.onnx.training ai.onnx.preview.training 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. This is an invalid model. The sum of input arg count is not equal to size of input defs in node ( Invalid source node arg slot specified when removing edge. Invalid destination node arg slot specified when removing edge. Argument mismatch when removing edge. Invalid node indexes specified when removing edge. No Graph instance was found for attribute Error: Duplicate definition-site for ( Failed to find existing initializer with name Not satisfied: name_to_initializer_it != name_to_initial_tensor_.end()
+ 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
+ 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
+ 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()
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. 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 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() x|L$tT 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) 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 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] onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const BuildConnections void onnxruntime::Graph::RemoveEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int) void onnxruntime::Graph::AddEdge(onnxruntime::NodeIndex, onnxruntime::NodeIndex, int, int) SetOuterScopeNodeArgs void onnxruntime::Graph::InitializeStateFromModelFileGraphProto() 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&, const std::unordered_map<std::basic_string<char>, const onnx::FunctionProto*>&) 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 itr != node.InputDefs().end() ExplicitInputs: . Index: Invalid input index for node ImplicitInputs: Implicit input name cannot be safely updated to in one of the subgraphs. Attempting to get index for an input which does not exist. /onnxruntime_src/onnxruntime/core/graph/graph_utils.cc index >= 0 && static_cast<size_t>(index) < inputs.size() Attempting to get an input that does not exist. index >= 0 && static_cast<size_t>(index) < outputs.size() Attempting to get an output that does not exist. Failed since multiple edges matched: Can only add a new input at the end of the current ones. num_explicit_inputs == static_cast<size_t>(target_input_idx) std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0 Node must only have one used output std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; }) Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here. Initializer with same name exists. Name: !graph.GetInitializedTensor(new_initializer.name(), existing) FindPath void onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&) void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&) onnxruntime::NodeArg& onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph&, const onnx::TensorProto&) void onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph(onnxruntime::Node&, const string&, const string&) bool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(onnxruntime::Graph&, onnxruntime::Node&) bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph&, onnxruntime::Node&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const CanUpdateImplicitInputNameInSubgraphs const string& onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node&, int) const string& onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node&, int) int onnxruntime::graph_utils::GetNodeInputIndexFromInputName(const onnxruntime::Node&, const string&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const *ZN11onnxruntime11GraphViewerC1ERKNS_5GraphEEUlPKNS_4NodeEE_ N11onnxruntime11NodeCompareE Protobuf parsing failed. <p_fd> is less than 0. Invalid istream object. Null model_proto ptr. <p_fd> less than 0. failed. File doesn't exist Load model system error number ModelProto does not have a graph. Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported. ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators. Unknown model file format version. /onnxruntime_src/onnxruntime/core/graph/model.cc No graph was found in the protobuf. Failed to load model with error: Protobuf serialization failed. Save SaveModel Load Model N6google8protobuf2io19ZeroCopyInputStreamE St15_Sp_counted_ptrIPN11onnxruntime5ModelELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime21SchemaRegistryManagerESaIS1_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 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::FunctionImpl::FunctionImpl(const onnxruntime::Graph&, std::unique_ptr<onnxruntime::IndexedSubGraph>, const onnxruntime::logging::Logger&) *ZN11onnxruntime12FunctionImplC1ERKNS_5GraphERKmRKN4onnx13FunctionProtoERKNS_7logging6LoggerEEUlRNS6_16InferenceContextEE_ N11onnxruntime8FunctionE N11onnxruntime12FunctionImplE Default logger already set. ISink must be provided. System onnxruntime default_logger_id must be provided if instance_type is InstanceType::Default Only one instance of LoggingManager created with InstanceType::Default can exist at any point in time. N@VIWEF is not a valid year is not a valid day is not a valid date N11onnxruntime7logging11OStreamSinkE Failed to parse path root: src root: , dst root: /onnxruntime_src/onnxruntime/core/common/path.cc Not satisfied: has_trailing_separator
- 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. [
+ 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
+ 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
+ Node is missing. Invalid ORT format model. Satisfied, but should not be: nullptr == fbs_node
+ 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() U`UUUUxUUU@Uoperator() 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
+ 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
+ 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. Attempting to get index for an input which does not exist. /onnxruntime_src/onnxruntime/core/graph/graph_utils.cc index >= 0 && static_cast<size_t>(index) < inputs.size() Attempting to get an input that does not exist. index >= 0 && static_cast<size_t>(index) < outputs.size() Attempting to get an output that does not exist. Failed since multiple edges matched: Can only add a new input at the end of the current ones. num_explicit_inputs == static_cast<size_t>(target_input_idx) std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0 Node must only have one used output std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; }) Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here. Initializer with same name exists. Name: !graph.GetInitializedTensor(new_initializer.name(), existing) FindPath void onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&) void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&) onnxruntime::NodeArg& onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph&, const onnx::TensorProto&) void onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph(onnxruntime::Node&, const string&, const string&) bool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(onnxruntime::Graph&, onnxruntime::Node&) bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph&, onnxruntime::Node&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const CanUpdateImplicitInputNameInSubgraphs const string& onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node&, int) const string& onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node&, int) int onnxruntime::graph_utils::GetNodeInputIndexFromInputName(const onnxruntime::Node&, const string&) 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
+ 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] ~~o^M<+ ~ /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 degree_of_parallelism >= 1 /onnxruntime_src/onnxruntime/core/common/threadpool.cc 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_ Failed to get symbol lib .so Got null library handle file " " failed: realpath close open \/ Invalid fd was supplied: fstat Failed to load library remove() failed. Error code: , path: munmap failed. error code: - error code: pthread_attr_init failed pthread_create failed pthread_setaffinity_np failed 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] Uqc /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
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 Failed to close file descriptor pthread_attr_setstacksize failed 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 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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+ 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
+ bad dimensions ? @ @@ @ @ @ @x AQ5@.N3;A8X} <2?~>++{16?:>=4?~? ? AQ5@.N3;A8X} <2?~>++{16?:>=4?~? A A A AQ5@.Q5@.Q5@.Q5@.N3N3N3N3;A8;A8;A8;A8X} <X} <X} <X} <2?~>2?~>2?~>2?~>++++++++{1{1{1{16666?:?:?:?:>=>=>=>=4?~?4?~?4?~?4?~? A%~3a*7A \3JCy7
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=ppppu>u>u>u> l? l? l? l?7777fȹfȹfȹfȹ~;~;~;~;ƼƼƼƼX=X=X=X=P"?P"?P"?P"?>>>> r1 r1 r1 r1mL:mL:mL:mL:e9 <e9 <e9 <e9 <*=*=*=*=*>*>*>*>>>>> rB¥B @K;? r1 :n/ <*=(*>> ? ?µµµrBrBrBrB : : : :n/ <n/ <n/ <n/ <*=*=*=*=(*>(*>(*>(*> *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:
Bad node spec:
-==> Context: attr Attribute (name: since_version function ( Unable to open model file: Unrecognized type value case (value_info name: setting data_type field (tensor name: ) is stored externally and should not have data field. Data of TensorProto ( tensor name: ) is stored externally but doesn't have a location. , but it doesn't exist or is not accessible. ) is 0-element but contains data! ) should contain one and only one value field. ) should not be stored in raw_data field Unrecognized data_type (tensor name: ) first dimension size does not equal NNZ. ) second dimension size does not match rank of tensor. ] not in lexicographic sorted order. ) dimensions are not positive. ) has zero input and zero output. is deprecated in domain_version of experimental ops. In the future, we may directly reject this operator. Please update your model as soon ' has been used as graph input names multiple times. Graph must be in single static assignment (SSA) form, however ' in initializer but not in graph input Nodes in a graph must be topologically sorted, however input '
- is not output of any previous nodes. ' has been used as output names multiple times. ) should not contain more than one value field. type field and data field mismatch in attribute ) should refer to attribute in parent node. The model does not have an ir_version set properly. Your model ir_version is higher than the checker's. Your model has duplicate keys in metadata_props. model with IR version >= 3 must specify opset_import for ONNX model with IR version < 3 cannot have opset_import specified ' has been used multiple times. ) should not have duplicate outputs specified. ) should not have duplicate attributes specified. Nodes in a function must be topologically sorted, however input '
- is neither output of any previous nodes nor input of the function. Function must be in single static assignment (SSA) form, however ' . Please check if it is a valid file. . Please check if it is a valid protobuf file of model. Unable to parse model from file: dl\,x88888X888 N4onnx7checker15ValidationErrorE Input type was null Input element type of axis value All Tensor types Condition for the if Only bool v_initial v_final_and_scan_outputs initial_state_and_scan_inputs final_state_and_scan_outputs Int64 tensor Scan input was not a tensor. outputs. Expected was not If node has but subgraphs produce Mismatched type for output then= else= Input was expected to have tensor type. Got Element type of input was unknown does not match existing output type of Output was expected to have tensor type. Got is invalid for a tensor of rank Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same data type. The `then_branch` and `else_branch` may produce tensors with the same element type and different shapes. If corresponding outputs from the then-branch and the else-branch have static shapes S1 and S2, then the shape of the corresponding output variable of the if-node (if present) must be compatible with both S1 and S2 as it represents the union of both possible shapes.For example, if in a model file, the the first output of `then_branch` is typed float tensor with shape [2] and the first output of `else_branch` is another float tensor with shape [3], If's first output should have (a) no shape set, or (b) a shape of rank 1 with neither `dim_value` nor `dim_param` set, or (c) a shape of rank 1 with a unique `dim_param`. In contrast, the first output cannot have the shape [2] since [2] and [3] are not compatible. Graph to run if condition is true. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the else_branch. Graph to run if condition is false. Has N outputs: values you wish to be live-out to the enclosing scope. The number of outputs must match the number of outputs in the then_branch. /onnxruntime_src/cmake/external/onnx/onnx/defs/controlflow/defs.cc A maximum trip-count for the loop specified at runtime. Optional. Pass empty string to skip. A boolean termination condition. Optional. Pass empty string to skip. The initial values of any loop-carried dependencies (values that change across loop iterations) Final N loop carried dependency values then K scan_outputs The graph run each iteration. It has 2+N inputs: (iteration_num, condition, loop carried dependencies...). It has 1+N+K outputs: (condition, loop carried dependencies..., scan_outputs...). Each scan_output is created by concatenating the value of the specified output value at the end of each iteration of the loop. It is an error if the dimensions or data type of these scan_outputs change across loop iterations. tensor of int64, which should be a scalar. tensor of bool, which should be a scalar. Initial values of the loop's N state variables followed by M scan_inputs Final values of the loop's N state variables followed by K scan_outputs The graph run each iteration. It has N+M inputs: (loop state variables..., scan_input_elts...). It has N+K outputs: (loop state variables..., scan_output_elts...). Each scan_output is created by concatenating the value of the specified scan_output_elt value at the end of each iteration of the loop. It is an error if the dimensions of these values change across loop iterations. An attribute specifying the number of scan_inputs M. An optional list of M flags. The i-th element of the list specifies the direction to be scanned for the i-th scan_input tensor: 0 indicates forward direction and 1 indicates reverse direction. If omitted, all scan_input tensors will be scanned in the forward direction. An optional list of K flags, one for each scan_output. The i-th element of the list specifies whether the i-th scan_output should be constructed by appending or prepending a new value in each iteration: 0 indicates appending and 1 indicates prepending. If omitted, all scan_output tensors will be produced by appending a value in each iteration. An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Negative value for an axis means counting dimensions from the back. Accepted range is [-r, r-1]. Number of scan input axes specified ( ) is not equal to number of scan inputs ( Number of scan output axes specified ( ) is not equal to number of scan outputs ( Graph attribute inferencing returned type information for Scan 'body' subgraph outputs should all be tensors but output then_branch and else_branch produce different number of outputs. Mismatched tensor element type for output Loop 'body' subgraph outputs should all be tensors but output Optional tensor specifying lengths of the sequences in a batch. If this input is not specified, all sequences are assumed to be of the maximum sequence length (the dimension of the sequence axis of the scan_input tensors). /onnxruntime_src/cmake/external/onnx/onnx/defs/controlflow/old.cc An optional list of M flags. The i-th element of the list specifies the axis to be scanned (the sequence axis) for the i-th scan_input. If omitted, 0 will be used as the scan axis for every scan_input. An optional list of K flags. The i-th element of the list specifies the axis for the i-th scan_output. The scan outputs are accumulated along the specified axis. If omitted, 0 will be used as the scan axis for every scan_output. Values that are live-out to the enclosing scope. The return values in the `then_branch` and `else_branch` must be of the same shape and same data type. bfloat16 double uint8 uint16 uint32 uint64 complex64 complex128 seq( map( sparse_tensor( seq opaque sparse_tensor \\\<\, 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 tensor must be 2-dimensional Input to 'Range' op should be scalars (Tensor with only one element and shape empty) All inputs to 'Range' op must be of the same type Shape input must be a one-dimensional tensor. The value for the elements of the output tensor. The value for the elements of the output tensor in sparse format. 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. 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_ver12EEENS_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_18Constant_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Constant_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Constant_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Result tensor. greater less xor Z O1 O2 less_equal LessOrEqual greater_equal GreaterOrEqual First input operand for the logical operator. Second input operand for the logical operator. Constrains input types to all numeric tensors. Constrains output to boolean tensor. /onnxruntime_src/cmake/external/onnx/onnx/defs/logical/defs.cc Constrains input to boolean tensor. 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. /onnxruntime_src/cmake/external/onnx/onnx/defs/logical/old.cc Constrains input to integral tensors. Constrains input to float tensors. *ZN4onnx30BinaryLogicDocGenerator_opset1EPKcEUlRNS_8OpSchemaEE_ *ZZN4onnx30BinaryLogicDocGenerator_opset7EPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx30BinaryLogicDocGenerator_opset7EPKcEUlRNS_8OpSchemaEE_ List of tensors for data_0 Output tensor. 'axis' must be in [ ]. Its actual value is: 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 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. (Optional) A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back. Output tensor of the same type as 'x' with cumulative sums of the x's elements axis tensor can be int32 or int64 only Constrain input and output types to floating-point tensors. 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_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Pow_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZZN4onnx30ElementwiseMultiOpDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx30ElementwiseMultiOpDocGeneratorEPKcEUlRNS_8OpSchemaEE_ *ZN4onnx11GetOpSchemaINS_15Gemm_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16MatMul_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15TopK_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Expand_Onnx_ver8EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24QLinearMatMul_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24MatMulInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Det_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Einsum_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_34SoftmaxCrossEntropyLoss_Onnx_ver12EEENS_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 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 legacy optimization attribute. /onnxruntime_src/cmake/external/onnx/onnx/defs/math/old.cc
+==> 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 '
+ 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: \>Xxpp>x 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 4d_4d4d|ba4dlc_ 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
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
@@ -6682,26 +7453,26 @@
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.
- *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. Ratio of Dropout must be a scalar. training_mode of Dropout must be a scalar. The output of each pooling window is divided by the number of elements (exclude pad when attribute count_include_pad is zero). /onnxruntime_src/cmake/external/onnx/onnx/defs/nn/defs.cc 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.
+ *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.
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 weight and zero_point pair is expected to have same type. MaxUnpool op must have either two or three inputs. Input tensor X must have atleast 2 dimensions. Attribute pads has incorrect size. Attribute strides has incorrect size. Attribute kernel_shape has incorrect size. Attribute kernel_shape must be specified. 'output_shape' must be rank 1 tensor. 'output_shape' must have same number of elements as the shape of input tensor X. Input tensor must have at least 2 dimensions RoIs tensor must have 2 dimensions Attribute pooled_shape has incorrect length Attribute pooled_shape must be specified ngram_indexes must be non-empty with no negative values Input tensor must have rank 1 or 2 Input shape must have either [C] or [1,C] dimensions where C > 0 A list of integers, along which to reduce. The default is to caculate along axes [0,2,3] for calculating mean and variance along each channel. Two variables with the same C-coordinate are associated with the same mean and variance. 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_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Flatten_Onnx_ver11EEENS_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. The output of each pooling window is divided by the number of elements exclude pad. /onnxruntime_src/cmake/external/onnx/onnx/defs/nn/old.cc 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 *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_ Dimension mismatch in unification between 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. /onnxruntime_src/cmake/external/onnx/onnx/defs/object_detection/defs.cc 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]. 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's a scalar, which means a per-tensor/layer quantization. Zero point for input 'x'. It's a scalar, which means a per-tensor/layer quantization. It's optional. 0 is the default value when it's not specified. Constrain 'x_zero_point' and 'x' to 8-bit/32-bit integer tensor. /onnxruntime_src/cmake/external/onnx/onnx/defs/quantization/defs.cc Scale for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. Zero point for doing quantization to get 'y'. It's a scalar, which means a per-tensor/layer quantization. Default value is uint8 typed 0 if it's not specified. Constrain 'x' to float or int32 tensor. Constrain 'y_zero_point' and 'y' to 8-bit integer tensor. Output zero point. It's a scalar, which means a per-tensor/layer quantization. Constrain 'x' to float tensor. Constrain 'y_zero_point' and 'y' to 8-bit unsigned integer tensor. Q_Min Q_Max X_Min X_Min_Adjusted X_Max_Adjusted X_Range Min_Scaled Initial_ZeroPoint_FP Clipped_ZeroPoint_FP Rounded_ZeroPoint_FP Zeropoint Quantized output tensor *ZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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 L1 norm L2 norm sum square log sum exponent product log sum *ZZN4onnx18ReduceDocGeneratorEPKcbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx18ReduceDocGeneratorEPKcbEUlRNS_8OpSchemaEE_ *ZZN4onnx21ArgReduceDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ *ZN4onnx21ArgReduceDocGeneratorEPKcEUlRNS_8OpSchemaEE_
+ 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.
Computes the indices of the {name} elements of the input tensor's element along the
provided axis. The resulting tensor has the same rank as the input if keepdims equal 1.
If keepdims equal 0, then the resulting tensor have the reduced dimension pruned.
-The type of the output tensor is integer. The axis in which to compute the arg indices. /onnxruntime_src/cmake/external/onnx/onnx/defs/reduction/old.cc *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. 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 /onnxruntime_src/cmake/external/onnx/onnx/defs/rnn/defs.cc Couple the input and forget gates if 1. 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 ' is missing. Required attribute ' )'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. @ ` *ZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_ *ZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_ N4onnx16OpSchemaRegistryE seq(tensor(uint8)) seq(tensor(uint16)) seq(tensor(uint32)) seq(tensor(uint64)) seq(tensor(int8)) seq(tensor(int16)) seq(tensor(int32)) seq(tensor(int64)) seq(tensor(float16)) seq(tensor(float)) seq(tensor(double)) seq(tensor(string)) seq(tensor(bool)) seq(tensor(complex64)) seq(tensor(complex128)) ], Value= sum of split values= Empty sequence. Tensors. Constrain to any tensor type. Input sequence. input_sequence position The tensor to split Concatenated tensor concat_result Tensor= Attribute dtype should be of integer type and specify a type. Required attribute axis is missing new_axis must be either 0 or 1 Invalid value of attribute 'axis'. Accepted range=[ Invalid value of attribute 'axis'. Rank= Only supports `int32_t` or `int64_t` inputs for split Input 'split' can not be empty. Sum of split values not equal to 'input' dim size on 'axis'. 'axis' dim size= Constrain output types to any tensor type. (Optional) The data type of the tensors in the output sequence. The default type is 'float'. /onnxruntime_src/cmake/external/onnx/onnx/defs/sequence/defs.cc Constrain input types to any tensor type. Sequence enclosing the input tensors. Input tensor to be inserted into the input sequence. Position in the sequence where the new tensor is inserted. It is optional and default is to insert to the back of the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape). Output sequence that contains the inserted tensor at given position. Constrain position to integral tensor. It must be a scalar(tensor of empty shape). Position of the tensor in the sequence. Negative value means counting positions from the back. Accepted range in `[-n, n - 1]`, where `n` is the number of tensors in 'input_sequence'. It is an error if any of the index values are out of bounds. It must be a scalar(tensor of empty shape). Output tensor at the specified position in the input sequence. Output sequence that has the tensor at the specified position removed. Length of input sequence. It must be a scalar(tensor of empty shape). Constrain output to integral tensor. It must be a scalar(tensor of empty shape). Constrain output types to all tensor types. Constrain input types to all tensor types. Length of each output. It can be either a scalar(tensor of empty shape), or a 1-D tensor. All values must be >= 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 the same elem type. Sequence= SequenceConstruct is expected to have at least 1 input. Element type of inputs are expected to be the same. *ZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ axis must be in [-r, r-1] 'input' must have rank >= 2 Invalid attribute perm { }, input shape = { Invalid position of 0 Invalid dimension value: Input tensor to be cast. Specified shape for output. Reshaped data. reshaped Shape of the input tensor 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 ) and outputs ( Input axes has invalid data 'step' cannot be 0 Blocksize must be positive 'Repeats' input must be 1D tensor of type int64 'Repeats' input has incorrect number of values. The number of values in 'repeats' must be equal to the number of input dimensions. axis must be in [-rank, rank-1]. All inputs to Concat must have same rank data tensor must have rank >= 1 Indices tensor must have rank >= 1 OneHot node must have three inputs. Input 'depth' must be a scalar or rank 1 tensor. Input 'depth' must have exactly one element. Input 'values' must be rank 1 tensor. Input 'values' must have exactly two elements. 'pads' input must be a 1D (shape: [2 * input_rank]) tensor of type int64 'sequence_lens' must have rank of 1 Both `data` and `indices` input tensors in GatherND op need to have rank larger than 0. Last dimension of `indices` input tensor in GatherND op must not be larger than the rank of `data` tensor Target shape may not have multiple -1 dimensions Invalid Target shape product of 0 Dimension could not be inferred: incompatible shapes values in 'axes' are beyond the bounds of the computed output shape 'axes' attribute must not contain any duplicates Only supports `int32_t` or `int64_t` inputs for starts/ends/axes/steps The data type to which the elements of the input tensor are cast. Strictly must be one of the types from DataType enum in TensorProto Output tensor with the same shape as input with type specified by the 'to' argument Constrain input types. Casting from complex is not supported. Constrain output types. Casting to complex is not supported. /onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/defs.cc Input tensor can be of arbitrary type. Constrain output to int64 tensor. Total number of elements of the input tensor Constrain output to int64 tensor, which should be a scalar though. Which axis to concat on. A negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(inputs).. List of tensors for concatenation One or more outputs forming list of tensors after splitting Which axis to split on. A negative value means counting dimensions from the back. Accepted range is [-rank, rank-1] where r = rank(input). 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_ 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. @@8 *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.
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/>
@@ -6719,35 +7490,31 @@
if coordinate_transformation_mode is "tf_half_pixel_for_nn", <br/>
x_original = (x_resized + 0.5) / scale, <br/>
if coordinate_transformation_mode is "tf_crop_and_resize", <br/>
-x_original = length_resized > 1 ? start_x * (length_original - 1) + x_resized * (end_x - start_x) * (length_original - 1) / (length_resized - 1) : 0.5 * (start_x + end_x) * (length_original - 1). Four modes: round_prefer_floor (default, as known as round half down), round_prefer_ceil (as known as round half up), floor, ceil. Only used by nearest interpolation. It indicates how to get "nearest" pixel in input tensor from x_original, so this attribute is valid only if "mode" is "nearest". When coordinate_transformation_mode is "tf_crop_and_resize" and x_original is outside the range [0, length_original - 1], this value is used as the corresponding output value. Default is 0.0f. 1-D tensor given as [start1, ..., startN, end1, ..., endN], where N is the rank of X. The RoIs' coordinates are normalized in the coordinate system of the input image. It only takes effect when coordinate_transformation_mode is "tf_crop_and_resize" The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified. If 'size' is needed, the user can use an empty string as the name of 'scales' in this operator's input list. The size of the output tensor. The number of elements of 'sizes' should be the same as the rank of input 'X'. Only one of 'scales' and 'sizes' can be specified. Constrain roi type to float or double. (Optional) Axis along which to take slices. If not specified, input is flattened before elements being selected. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). Rank 1 tensor of booleans to indicate which slices or data elements to be selected. Its length can be less than the input length along the axis or the flattened input size if axis is not specified. In such cases data slices or elements exceeding the condition length are discarded. Tensor of rank r if axis is specified. Otherwise output is a Tensor of rank 1. Constrains to boolean tensors. (Optional) Axis along which one-hot representation in added. Default: axis=-1. axis=-1 means that the additional dimension will be inserted as the innermost/last dimension in the output tensor. Negative value means counting dimensions from the back. Accepted range is [-r-1, r] where r = rank(indices). Input tensor containing indices. Any entries in the 'indices' input tensor with values outside the range [-depth, depth-1] will result in one-hot representation with all 'off_value' values in the output tensor.In case 'indices' is of non-integer type, the values will be casted to int64 before use. Scalar specifying the number of classes in one-hot tensor. This is also the size of the one-hot dimension (specified by 'axis' attribute) added on in the output tensor. The values in the 'indices' input tensor are expected to be in the range [-depth, depth-1]. In case 'depth' is of non-integer type, it will be casted to int64 before use. Rank 1 tensor containing exactly two elements, in the format [off_value, on_value], where 'on_value' is the value used for filling locations specified in 'indices' input tensor, and 'off_value' is the value used for filling locations other than those specified in 'indices' input tensor. Tensor of rank one greater than input tensor 'indices', i.e. rank(output) = rank(indices) + 1. The data type for the elements of the output tensor is the same as the type of input 'values' is used. Constrain output types to boolean tensors. (Optional) Whether map positive infinity to true. Default to 1 so that positive infinity induces true. Set this attribute to 0 if positive infinity should be mapped to false. (Optional) Whether map negative infinity to true. Default to 1 so that negative infinity induces true. Set this attribute to 0 if negative infinity should be mapped to false. When True (nonzero), yield X, otherwise yield Y values selected at indices where condition is True values selected at indices where condition is False Tensor of shape equal to the broadcasted shape of condition, X, and Y. Constrain to all tensor types. (Optional) Specify which axis is time axis. Must be one of 0 (default), or 1. (Optional) Specify which axis is batch axis. Must be one of 1 (default), or 0. Tensor specifying lengths of the sequences in a batch. It has shape `[batch_size]`. Tensor with same shape of input. (Optional) Whether to sort the unique elements in ascending order before returning as output. Must be one of 0, or 1 (default). (Optional) The dimension to apply unique. If not specified, the unique elements of the flattened input are returned. Negative value means counting dimensions from the back. Accepted range is [-r, r-1] where r = rank(input). A N-D input tensor that is to be processed. A tensor of the same type as 'X' containing all the unique values or subtensors sliced along a provided 'axis' in 'X', either sorted or maintained in the same order they occur in input 'X' A 1-D INT64 tensor containing indices of 'Y' elements' first occurance in 'X'. When 'axis' is provided, it contains indices to subtensors in input 'X' on the 'axis'. When 'axis' is not provided, it contains indices to values in the flattened input tensor. A 1-D INT64 tensor containing, for elements of 'X', its corresponding indices in 'Y'. When 'axis' is provided, it contains indices to subtensors in output 'Y' on the 'axis'. When 'axis' is not provided, it contains indices to values in output 'Y'. A 1-D INT64 tensor containing the count of each element of 'Y' in input 'X' 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). 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_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ *ZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ Ranks inferred ( 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. 'axis' must be in [-rank(indices)-1, rank(indices)] rank must be greater than axis ) is not equal to the existing rank value ( Number of elements of attribute 'scales' must be same as rank of input 'X' Attribute 'scales' must have floats type. Attribute 'scales' is required. Attribute value for pads is required Attribute pads has incorrect length Incorrect or missing attribute value for starts and ends Attribute axes has incorrect length Invalid value of attribute 'axis' Constrain input types. Casting from strings and complex are not supported. Constrain output types. Casting to strings and complex are not supported. /onnxruntime_src/cmake/external/onnx/onnx/defs/tensor/old.cc Which axis to concat on. Default value is 1. Optional list of output lengths (see also arg 'split') List of integers indicate the padding element count at the beginning and end of each axis, for 2D it is the number of pixel. `paddings` rank should be double of the input's rank. `paddings` format should be as follow [x1_begin, x2_begin...x1_end, x2_end,...], where xi_begin the number of pixels added at the beginning of axis `i` and xi_end, the number of pixels added at the end of axis `i`. Three modes: constant(default), reflect, edge One float, indicates the value to be filled, default is 0 Number of repeated copies to make of the input tensor. Output tensor of same shape and type as input. Constrain tiles and axis's type to int64 tensors. The scale along width dimension. It takes value greater than or equal to 1. The scale along height dimension. It takes value greater than or equal to 1. Two interpolation modes: nearest(default), bilinear 4-D tensor after resizing, [N,C,H,W] Constrain output types to bool, int32, int64, float16, float, double tensors. The scale array along each dimension. It takes value greater than 0. If it's less than 1, it's sampling down, otherwise, it's upsampling. The number of elements of 'scales' should be the same as the rank of input 'X'. Axes that `starts` and `ends` apply to. It's optional. If not present, will be treated as [0, 1, ..., len(`starts`) - 1]. Starting indices of corresponding axis in `axes` Ending indices (exclusive) of corresponding axis in axes` 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. *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_ is out of bounds sequence_type map_type opaque_type sparse_tensor_type NOT_SET ) vs ( inferred= type case mismatch. existing= type mismatch. existing= does not contain a graph. no name (op_type: , name: inputs but Graph has were provided Graph input # ̃ll ,lLInferred elem type differs from existing elem type: ( Inferred shape and existing shape differ in rank: ( Inferred shape and existing shape differ in dimension type case unsupported. existing= GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. is tensor type, but provided type is N4onnx15shape_inference19GraphInferencerImplE N4onnx15shape_inference20InferenceContextImplE Func_ Input for function node Output for function node onnx.AttributeProto onnx.ValueInfoProto onnx.NodeProto onnx.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.FunctionProto onnx.TensorShapeProto.Dimension /build/Release/onnx/onnx-ml.pb.cc @P`0k\kk\\k\\P 8d$|L T\L8 XXH8888888h H(x@hxhh P
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` p H,`_,`,`T__,`^ _HȧȦ EXPERIMENTALSTABLE 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). 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 ӝ杆 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 NNNNNNNpNNPPPPPPPPPФФФx@İ$< ܮTԭd \ Hh(hXHHHHHHHx0ȷX8Pxxhpp8 Xppppppppppd dddDX YXX`YYXXX IR_VERSIONIR_VERSION_2017_10_10IR_VERSION_2017_10_30IR_VERSION_2017_11_3IR_VERSION_2019_1_22IR_VERSION_2019_3_18IR_VERSION_2019_9_19_START_VERSION DEFAULTEXTERNAL
BFLOAT16BOOLCOMPLEX128COMPLEX64DOUBLEFLOATFLOAT16INT16INT32INT64INT8STRINGUINT16UINT32UINT64UINT8UNDEFINED
FLOATFLOATSGRAPHGRAPHSINTINTSSPARSE_TENSORSPARSE_TENSORSSTRINGSTRINGSTENSORTENSORSUNDEFINED
- N4onnx14AttributeProtoE N4onnx14ValueInfoProtoE N4onnx9NodeProtoE N4onnx17TrainingInfoProtoE N4onnx10ModelProtoE N4onnx22StringStringEntryProtoE N4onnx16TensorAnnotationE N4onnx10GraphProtoE N4onnx19TensorProto_SegmentE N4onnx11TensorProtoE N4onnx17SparseTensorProtoE N4onnx26TensorShapeProto_DimensionE N4onnx16TensorShapeProtoE N4onnx16TypeProto_TensorE N4onnx18TypeProto_SequenceE N4onnx13TypeProto_MapE N4onnx22TypeProto_SparseTensorE N4onnx16TypeProto_OpaqueE N4onnx9TypeProtoE N4onnx18OperatorSetIdProtoE N4onnx13FunctionProtoE N6google8protobuf8internal29InternalMetadataWithArenaBaseISsNS1_29InternalMetadataWithArenaLiteEE9ContainerE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/arena.cc CHECK failed: (min_bytes) <= (std::numeric_limits<size_t>::max() - kBlockHeaderSize): /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/generated_message_util.cc CHECK failed: (scc->visit_status.load(std::memory_order_relaxed)) == (SCCInfoBase::kRunning): Not implemented field number with type L,d L,d<< Dt,\ L| <l<<Dl\,LL l |<<ltLTl<,<<4lL4 D|4<<ld'&&& &%$%$$$$##$#"4"! ''T t L $ D T L''l4D4|D''4
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-d
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-
-
- lt''4d4T$DDt''T /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/coded_stream.cc A protocol message was rejected because it was too big (more than bytes). To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in third_party/protobuf/src/google/protobuf/io/coded_stream.h. CHECK failed: (buffer_size) >= (0): /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl.cc Can't BackUp() after failed Next(). CHECK failed: !is_closed_: close() failed: N6google8protobuf2io19CopyingOutputStreamE N6google8protobuf2io15FileInputStream22CopyingFileInputStreamE N6google8protobuf2io15FileInputStreamE N6google8protobuf2io16FileOutputStream23CopyingFileOutputStreamE N6google8protobuf2io16FileOutputStreamE N6google8protobuf2io18IstreamInputStream25CopyingIstreamInputStreamE N6google8protobuf2io18IstreamInputStreamE N6google8protobuf2io19OstreamOutputStream26CopyingOstreamOutputStreamE N6google8protobuf2io19OstreamOutputStreamE N6google8protobuf2io24ConcatenatingInputStreamE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl_lite.cc CHECK failed: (last_returned_size_) > (0): BackUp() can only be called after a successful Next(). CHECK failed: (count) <= (last_returned_size_): CHECK failed: (count) >= (0): CHECK failed: target_ != NULL: CHECK failed: (buffer_used_) == (buffer_size_): BackUp() can only be called after Next(). CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next(). CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: Parameter to BackUp() can't be negative. CHECK failed: (count) <= (target_->size()): Cannot allocate buffer larger than kint32max for CHECK failed: (backup_bytes_) == (0): StringOutputStream. N6google8protobuf2io16ArrayInputStreamE N6google8protobuf2io17ArrayOutputStreamE N6google8protobuf2io18StringOutputStreamE N6google8protobuf2io18CopyingInputStreamE N6google8protobuf2io25CopyingInputStreamAdaptorE N6google8protobuf2io26CopyingOutputStreamAdaptorE N6google8protobuf2io19LimitingInputStreamE (cannot determine missing fields for lite message) " because it is missing required fields: /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/message_lite.cc exceeded maximum protobuf size of 2GB: CHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): was modified concurrently during serialization. CHECK failed: (bytes_produced_by_serialization) == (byte_size_before_serialization): Byte size calculation and serialization were inconsistent. This may indicate a bug in protocol buffers or it may be caused by concurrent modification of This shouldn't be called if all the sizes are equal. MessageLite at 0x Can't parse message of type " N6google8protobuf11MessageLiteE N6google8protobuf24ZeroCopyCodedInputStreamE ' tPparsing Can't happen /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/parse_context.h ХЦhȢ[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 8Xx8XxTT 3I_um 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'): x00000000 p`P0 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 P8Ȝ@PȥP Ȥh( EXPERIMENTALSTABLE N4onnx13FunctionProtoE N4onnx13OperatorProtoE N4onnx16OperatorSetProtoE onnx.SequenceProto onnx.MapProto /build/Release/external/onnx/onnx/onnx-data.pb.cc P ` ,lt4 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 4lL4lL$%$%,\Dt 4 d $
+T
+$%$%
+, T D ! 4#4!T t# " $d$%$%T
+\ 4
l<Tl$$%$% T4l ,d $%$%TLVUUtUT|T TSSlSRRR RQ QlPOVV<ON\N4N NM,MLLlL<LKKtKKJ4JIHVVlHGTGFE EDC|CB,BA A@??d?>,>VV= =<;L;:,:9L949 998|87T76\65VV5 54|4L4 43<3 322,2111,100\0VV/</ /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 ξ$ parsing Can't happen /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/parse_context.h hx[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 ( H h D D
# 9 O e { ] 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'): h p ` P @ 0123456789abcdef ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_ ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/ >456789:;<=
? !"#$%&'()*+,-./0123>?456789:;<=
-
!"#$%&'()*+,-./012300010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 ? 888X8778;;<:p:<
+
!"#$%&'()*+,-./012300010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 ? fffpf`fgij(ki i`k
- /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. lllllllllm mqnn!p[ppnnjqtqtqtqtqqonn(qp nln\nnnnln\n]onjo onfon\nln=o{o#qXn@nst[tXn@nq r_rrrOss@nXnq`qdu4u4u4u4u4u4u4u4uDuTuvvPwxwvv w(wvwuҌ̌̌̌̌̌̌̌̌܌؎ 0Hx@ɢ*͘oWԙ ~[mwdHEjğΦL+ee u#ݯ
@0J d6D8 *R#z_D,l <L |l<\<pF3Yz**** \ 4LDd0 XXp 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 , |llԽԽԽܼ llTTT$ 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 <L4Compiler::Copy called! should never happen No ranges in char class Missing case in Compiler: hello, world /onnxruntime_src/cmake/external/re2/re2/compile.cc Ttd|dL4$T4 N3re26Regexp6WalkerINS_4FragEEE N3re28CompilerE /onnxruntime_src/cmake/external/re2/re2/dfa.cc StateSaver failed to restore state. Unexpected special state in RunStateOnByte RunStateOnByteUnlocked failed after ResetCache RunStateOnByteUnlocked failed after Reset Failed to analyze start state. %s%d (%p) flag=%#x || unhandled opcode: DeadState in RunStateOnByte NULL state in RunStateOnByte context does not contain text @ aX X unhandled in AddToThreadq Unhandled in step (?,?) (%d,?) (%d,%d) Bad args: nsubmatch= in ComputeFirstByte in Prog::Fanout() /onnxruntime_src/cmake/external/re2/re2/nfa.cc Unexpected opcode in short circuit: u`zxyxxHymx`0008`` `/onnxruntime_src/cmake/external/re2/re2/onepass.cc Cannot use SearchOnePass for unanchored matches. 2iS 0 0 0 0 Bad hex digit Concat of Any RE2: unexpected op: unknown round: /onnxruntime_src/cmake/external/re2/re2/parse.cc RepetitionWalker::ShortVisit called AddFoldedRange recurses too much. Bad call to ParseState::ParsePerlFlags ^YYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYGY5YYQRRR<Q QP\PRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRdR$RRRRRRRRRRRRRRRRRRRRRRRRRRRRRRRQQ YZZZZZZZYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYYZYYYYZYYYYYYY~ZYYYvZYnZYXZYY 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. >LLLLLLLLDX+XRlXqllLL͝ МiМ8ߡ М@ģģģģģģģģԣ@h0`b\\\\\\\\l|(hؽ(@t2E2Y]d0( yN{*BT^uuL5m$y 0 8`0 4
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o*f7k`kx O !!L" | ,!\ \ #"c $O#*K"V"Q 0t1111111202/H0h3P/4455H0 6x2 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 < || ,
+ | | d d d 4 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 4ܶD4Compiler::Copy called! should never happen No ranges in char class Missing case in Compiler: hello, world /onnxruntime_src/cmake/external/re2/re2/compile.cc Ttd|dL4$T4 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 @-a,X,-,X,-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: x0`X=0 `E@X /onnxruntime_src/cmake/external/re2/re2/onepass.cc Cannot use SearchOnePass for unanchored matches. A$$$ 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 . 0 0 0 0 0 0 00000000000000000000000000000000000000000000000/0000000/0000000/0.,}<~<~<~|||l|{<~<~,~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~ ~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~}}<~}<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~<~L}<}yЅ< 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
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+ /onnxruntime_src/cmake/external/re2/re2/prog.cc Unexpected opcode in IsMatch: ؝8Xxxxxpxxx 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 <<<<HHH<<<<<<<<<XXXXXXXXXP
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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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( hp 8 X! ! X#P $ % ( X- 1 = > X?8 H@x A BX C C C C I Ox W hX 8eX Xi i 8sp s | H~p 88 HP Xh h x` x x` x ؐ( (p 8 x x0 (h 8 ز ؼ h 8 Hh (( 8@ X Hp h x 8( X@ hX p h 80 H p x ( 8 (X x ( 8 H0 ` H h 8 P Hh h 8@ ` x ( X x P 0 Xp ( HH ` x 8 H X( hx zR x $ C FJ w ?;*3$" D \ t
+ 艄
+ zPLR xź1 $ $ + m
+ A]
+B J ȉ
+ 4 ȉ L d ȉ | Љ ؉ ؉ Љ ȉ l
gl
+ < T l ȉ Љ ؉ 艄: Ax $ L k
+ AJ L
+AA t p} {k
+ G I
+A $ Њ sk
+ AJ L
+AA 8 ck
+ D `
+A [k
+ D `
+A $ 8 Sk
+ AF b
+AA $ Ck
+ < /k
+ , u BDI bAB , < E BGA uAD $ j
+ AG U
+AA x j
+ p- L AAG ]
+CAF [
+ACA R
+CAJ JCA L AAG ]
+CAF [
+ACA R
+CAJ JCA L d 荄 AAG ]
+CAF c
+CAI R
+CAJ JCA 4 (g i
+ BAD D
+ABA `- < x BDA c
ABK A
-ABD < | BDA c
+ABD < D X BDA c
ABK A
-ABD < , B | BBD I(DPN
-(A ABBA $ v AG z
-AA $ $ `^ AG b
-AA , c| ADDpn
-AAA , x O| AFDpt
-AAA , X ;| ADDpt
-AAA 4 L 8> '| BDG DPO
- AABD < @u | BED G(D`K
-(A ABBI < u { BED G(D`K
-(A ABBI < u { BED G(D`K
-(A ABBI < D { AGDPO
+ABD < 8B h
+ BBD I(DPN
+(A ABBA $ Hv AG z
+AA $ ^ AG b
+AA $ ؑ^ AG b
+AA , 3h
+ ADDpn
+AAA , h
+ AFDpt
+AAA , Г h
+ ADDpt
+AAA < g
+ AADPZ
+AAF D
+CAA 4 T p> g
+ BDG DPO
+ AABD < xu g
+ BED G(D`K
+(A ABBI < u g
+ BED G(D`K
+(A ABBI < u g
+ BED G(D`K
+(A ABBI < L 8 _g
+ AGDPO
AAC X
-CAA < Т { AGDPO
+CAA < ?g
+ AGDPO
AAC X
-CAA < o{ AGDPO
+CAA < ؛ g
+ AGDPO
AAC X
-CAA 4 p> O{ BDG DPO
- AABD 4 < x> 7{ BDG DPO
- AABD $ ^ AG b
-AA , z AGJP[
-AAA , z ADKP[
-AAA , X z ADKP[
-AAA , , ( z AGJP[
-AAA , \ z ADKP[
-AAA $ Ȫ~ AG B
-AA $ D k AG o
-AA L h[ Wz BBB E(D0D8GP
-8A0A(B BBBI T , x 3z BEB E(D0A8G
-8D0A(B BBBH D
- BEB A(A0D@
-0A(A BBBA \
- Ȱ . y BBB E(A0D8D
+CAA 4 > f
+ BDG DPO
+ AABD 4 D > f
+ BDG DPO
+ AABD $ 4
+ k AG o
+AA , 2 f
+ ADKPZ
+AAA , 4 f
+ AGJPZ
+AAA ,
+ 4 f
+ AGJPZ
+AAA , 4
+ 02 wf
+ ADKPZ
+AAA , d
+ @2 gf
+ ADKPZ
+AAA T
+ P Wf
+ BED A(Dp|
+(A ABBF D
+(C ABBA T
+ #f
+ BED A(Dp{
+(A ABBG D
+(C ABBA T D e
+ BED A(Dp{
+(A ABBG D
+(C ABBA L [ e
+ BBB E(D0D8GP
+8A0A(B BBBI T e
+ BEB E(D0A8G
+8D0A(B BBBH \ . _e
+ BBB E(A0D8D
8A0A(B BBBD U
8A0A(B BBBI U
-8A0A(B BBBA ,
- h0 AI
FK
- C D , h BBE A(A0D@P
-0A(A BBBA , t AAK0i
-AAA T Ph x BBE E(A0A8G
-8A0A(B BBBI 4 l h x BGB A(A0D L k x BEB B(A0A8GP
-8A0A(B BBBB T @ kx BEE E(K0A8G
-8A0A(B BBBA L L Ƚ wx BEE E(K0A8G
-8A0A(B BBBA $ ,
m AAG0aAA$ T
@u AAG0iAA< 3x BDD g
+8A0A(B BBBA ,
@0 AI
FK
+ C D
@ BBE A(A0D@P
+0A(A BBBA , AAK0i
+AAA T
(0 cd
+ BBB B(D0D8G7
+8A0A(B BBBH 4
kd
+ BGB A(A0D L k Kd
+ BEB B(A0A8GP
+8A0A(B BBBB T l ط d
+ BEE E(K0A8G
+8A0A(B BBBA L ` #d
+ BEE E(K0A8G
+8A0A(B BBBA , AAG0Z
+AAA $ `u AAG0iAA< l c
+ BDD g
ABD k
-ABF T ,
H x BBE E(A0A8G
-8D0A(B BBBI <
Ď6 w BBA A(J
-(A ABBA <
Ŏ6 w BBA A(J
-(A ABBA D Ǝ w BBB A(A0J
-0A(A BBBA < XȎN BED D(D@\
-(D ABBA T hȎ ?w BBE E(A0A8G
-8D0A(B BBBI < ʎV w BBA A(J
-(C ABBA T $ ̎" v BBB E(A0A8G
-8D0A(B BBBA 4 ͎J BDA l
+ABF T h c
+ BBE E(A0A8G
+8D0A(B BBBI < 6 vc
+ BBA A(J
+(A ABBA < D Ä6 Uc
+ BBA A(J
+(A ABBA D Ą 7c
+ BBB A(A0J
+0A(A BBBA < xƄN BED D(D@\
+(D ABBA T Ƅ b
+ BBB B(D0A8G
+8A0A(B BBBE T d ˄ b
+ BBE E(A0A8G
+8D0A(B BBBI < ̈́V b
+ BBA A(J
+(C ABBA T ΄" mb
+ BBB E(A0A8G
+8D0A(B BBBA d Є BLB B(A0A8D`c
+8A0A(B BBBK
+8A0A(B BBBD 4 t фJ BDA l
ABJ A
-ABA D Ύ= A{ L Ύ) Ov BBB B(A0A8J
-8A0A(B BBBJ 4 ҎZ BDA y
+ABA ф= A{ L ҄) sa
+ BBB B(A0A8J
+8A0A(B BBBJ D Մ BBE A(A0D@P
+0A(A BBBA 4 hք a
+ BHA K0p
+ AABA 4 Z BDA y
ABM A
-ABA < (Ҏ BDD k
+ABA < 8 BDD k
ABH k
-ABA d , Ҏ BBB B(A0A8D`
+ABA d BBB B(A0A8D`
8A0A(B BBBE d
-8A0A(B BBBA T Ԏ Ou BBE E(A0A8G
-8D0A(B BBBI D \ ֎B u BBB I(A0DD
-0D(A BBBA 4 4 z AEG `
+8A0A(B BBBA d | 0ڄ BBE B(A0A8D`
+8D0A(B BBBG
+8A0A(B BBBA T , X܄ _
+ BBB B(A0G8D
+8A0A(B BBBE $ G g_
+ As
+L A
+A T C_
+ BBE E(A0D8G
+8D0A(B BBBK $ G _
+ As
+L A
+A T , 5 ^
+ BBB E(D0D8D
+8D0A(B BBBJ T ^
+ BBE E(A0A8G
+8D0A(B BBBI D 8B ^
+ BBB I(A0DD
+0D(A BBBA 4 @z AEG `
AAA p
-AAB L t BBE E(D0C8Io
-8D0A(B BBBF T , ڎ t BBE E(D0C8G
-8C0A(B BBBG D ݎ t BBB A(A0I
-0A(A BBBF T HZ st BBB B(A0A8J
-8D0A(B BBBA , $ P t AID0y
-DAA D / BBE A(A0D@
-0A(A BBBA < Ct BBD A(G@,
-(A ABBA < ?t BBA G(D`O
-(A ABBK L ?t BBB B(A0A8G
-8A0A(B BBBH $ xs AAG0gAAL , s BBB B(A0D8D`
-8A0A(B BBBI < t BBG A(G@
-(A ABBG $ p s D i
-C < BBG A(G@
-(A ABBG 7s D u
-G T /s BEJ E(D0D8G
-8D0A(B BBBK < " r BBA A(GU
-(A ABBA \ < r BEB D(A0D@[
+AAB L \ 7^
+ BBE E(D0C8Io
+8D0A(B BBBF T H '^
+ BBE E(D0C8G
+8C0A(B BBBG D ^
+ BBB A(A0I
+0A(A BBBF T L ]
+ BBB B(A0A8J
+8D0A(B BBBA L \ @ BEB A(D0i
+(I BBBJ q
+(A BBBA , ]
+ AID0y
+DAA D @/ BBE A(A0D@
+0A(A BBBA < l ( w]
+ BBD A(G@,
+(A ABBA L s]
+ BBB I(H0A8DP
+8A0A(B BBBC < 8 []
+ BBA G(D`O
+(A ABBK D BLB A(A0D@
+0A(A BBBA L ]
+ BBB B(A0A8G
+8A0A(B BBBH $ p s AAG0gAAT \
+ BBB E(D0A8G9
+8A0A(B BBBF L T 4 \
+ BBB B(A0D8D`
+8A0A(B BBBD < \ BBG A(G@
+(A ABBG \
+ D i
+C < P BBG A(G@
+(A ABBG D 7\
+ D u
+G T d & /\
+ BEE E(D0A8GC
+8D0A(B BBBC < " [
+ BBA A(GU
+(A ABBA \ [
+ BEE A(A0D@[
0A(A BBBK D
-0M(A BBBE < " r BBA A(GU
-(A ABBA \ x& cr BBB A(D0G
-0A(A BBBC j
-0C(A BBBA d < H> 7r BBE E(D0A8GP
-8A0A(B BBBE D
-8M0A(B BBBE T k q BBB B(A0A8J
-8A0A(B BBBA \ 8 q BBB A(A0Dh
+0M(A BBBE < \ 8" [
+ BBA A(GU
+(A ABBA d ( H k[
+ BBE E(A0D8GP
+8A0A(B BBBG K
+8F0A(B BBBF \ D [
+ BBB A(A0Jf
+0A(A BBBH
+0A(A BBBA d d [
+ BBE B(A0D8D\
+8C0A(B BBBD U
+8A0A(B BBBA \ 8!6 Z
+ BBB A(A0J
+0A(A BBBF X
+0C(A BBBA \ , $j Z
+ BBB A(A0Jo
+0A(A BBBG
+0A(A BBBH \ ('9 Z
+ BBB A(D0G
+0A(A BBBA k
+0C(A BBBA \ * Z
+ BBB A(A0Dh
0A(A BBBD D
-0C(A BBBA \ \ q BBB A(A0Dh
+0C(A BBBA \ L + Z
+ BBB A(A0Dh
0A(A BBBD D
-0C(A BBBA d L BLB B(A0A8D`c
-8A0A(B BBBK
-8A0A(B BBBD L BOB A(A0]
+0C(A BBBA L d! h- BOB A(A0]
(A BBBG Y
-(A BBBH 4 t `R p ]GB A(A0D L < BOB A(A0]
+(A BBBH 4 -R Z
+ ]GB A(A0D L ! / BOB A(A0]
(A BBBG Y
-(A BBBH 4 R hp ]GB A(A0D L BOB A(A0]
+(A BBBH 4 ! p/R Y
+ ]GB A(A0D L t" 0 BOB A(A0]
(A BBBG Y
-(A BBBH 4 R p ]GB A(A0D 4 R o ]GB A(A0D 4
-R o ]GB A(A0D 4 , R o ]GB A(A0D d 0
BBB E(A0A8DP{
+(A BBBH 4 " 1R IY
+ ]GB A(A0D 4 D" 02R 2Y
+ ]GB A(A0D 4 |" X3R Y
+ ]GB A(A0D 4 " 4R Y
+ ]GB A(A0D d # 5 BBB E(A0A8DP{
8A0A(B BBBJ
-8A0A(B BBBB T =o BBE E(A0A8G
-8D0A(B BBBI , ` ADD0e
-AAA L ] BBB H(A0D8DP
-8A0A(B BBBG T B n BBE B(A0D8G
-8D0A(B BBBG T On BBB B(D0A8G
-8A0A(B BBBF T T @ b [n BBB B(D0A8G
-8A0A(B BBBH T X# cn BBE E(A0A8G
-8D0A(B BBBE < %' &n BBA A(JU
-(A ABBA < D &' n BBA A(JU
-(A ABBA L ' m BBA D(GpS
+8A0A(B BBBB T T# P7 X
+ BBE E(A0A8G
+8D0A(B BBBI , d$ 9 ADD0e
+AAA L $ H:] BBB H(A0D8DP
+8A0A(B BBBG T ,$ X;& W
+ BBB E(A0A8D
+8D0A(B BBBB 4 $ 0=Q W
+ BGA P@B
+ AABH 4 $ X>Q W
+ BDA P@E
+ AABH T $ ? gW
+ BBE E(A0A8G
+8D0A(B BBBE < L% A' *W
+ BBA A(JU
+(A ABBA < % B'
W
+ BBA A(JU
+(A ABBA L % C V
+ BBA D(GpS
(A ABBG g
-(A ABBC < (' m BBA A(JU
-(A ABBA < )' m BBA A(JU
-(A ABBA < * BBG A(G@
-(A ABBG < $ p+ BBG A(G@
-(A ABBD < d , BBG A(G@
-(A ABBD < ," l BBA A(GU
-(A ABBA < T -" l BBA A(GU
-(A ABBA < ." l BBA A(GU
-(A ABBA < /" nl BBA A(GU
-(A ABBA < ! p0" Ml BBA A(GU
-(A ABBA < T! `1" ,l BBA A(GU
-(A ABBA < ! P2" l BBA A(GU
-(A ABBA < ! @3" k BBA A(GU
-(A ABBA < " 04" k BBA A(GU
-(A ABBA < T" 5" k BBA A(GU
-(A ABBA < " 6" k BBA A(GU
-(A ABBA < " 7" fk BBA A(GU
-(A ABBA < # 7" Ek BBA A(GU
-(A ABBA T T# 8r $k BBB B(A0A8J
-8A0A(B BBBD D <$ A BBB A(A0G@R
-0A(A BBBA $ ` DR
-E A d $ pA BLB B(A0A8D@q
+(A ABBC < & E' V
+ BBA A(JU
+(A ABBA < \& F' V
+ BBA A(JU
+(A ABBA < T' F BBG A(G@
+(A ABBG < ' G BBG A(G@
+(A ABBD < ' 8H BBG A(G@
+(A ABBD < \' H" U
+ BBA A(GU
+(A ABBA < ' I" U
+ BBA A(GU
+(A ABBA < ' J" U
+ BBA A(GU
+(A ABBA < ( K" rU
+ BBA A(GU
+(A ABBA < \( L" QU
+ BBA A(GU
+(A ABBA < ( M" 0U
+ BBA A(GU
+(A ABBA < ( xN" U
+ BBA A(GU
+(A ABBA < ) hO" T
+ BBA A(GU
+(A ABBA < \) XP" T
+ BBA A(GU
+(A ABBA < ) HQ" T
+ BBA A(GU
+(A ABBA < ) 8R" T
+ BBA A(GU
+(A ABBA < * (S" jT
+ BBA A(GU
+(A ABBA < \* T" IT
+ BBA A(GU
+(A ABBA T * Ur (T
+ BBB B(A0A8J
+8A0A(B BBBD D + 0] BBB A(A0G@R
+0A(A BBBA + ) DR
+E A d , ] BLB B(A0A8D@q
8A0A(B BBBE @
-8A0A(B BBBF D |$ B gj BBJ A(D0
-(A BBEA L $ 0D cj BLE B(A0D8D@
-8A0A(B BBBG L % pF BBB B(A0A8Gp8A0A(B BBB % DR
-E A d & G BLB B(A0A8D@q
+8A0A(B BBBF D + ^ kS
+ BBJ A(D0
+(A BBEA L , X` gS
+ BLE B(A0D8D@
+8A0A(B BBBG L - b BBB B(A0A8Gp8A0A(B BBB d- ' DR
+E A d - c BLB B(A0A8D@q
8A0A(B BBBE @
-8A0A(B BBBF D % (I [i BBJ A(D0
-(A BBEA L 4& J 3i BLE B(A0D8D@
-8A0A(B BBBG L & @L' i BBB E(D0D8JV
-8A0A(B BBBC < & s" Ok BBA A(GU
-(A ABBA < ' t" .k BBA A(GU
-(A ABBA < T' pu"
k BBA A(GU
-(A ABBA < ' `v" j BBA A(GU
-(A ABBA < ' Pw" j BBA A(GU
-(A ABBA < ( @x" j BBA A(GU
-(A ABBA < T( 0y" j BBA A(GU
-(A ABBA < ( z" hj BBA A(GU
-(A ABBA < ( {" Gj BBA A(GU
-(A ABBA < ) |" &j BBA A(GU
-(A ABBA < T) |" j BBA A(GU
-(A ABBA < ) }" i BBA A(GU
-(A ABBA < ) ~" i BBA A(GU
-(A ABBA T * b i BBB B(D0D8D
-8D0A(B BBBG L l* ؑ3 gi BBH E(D0D8DPj
-8D0A(B BBBD L L+ Ȓ9 BBB B(A0D8D 8A0A(B BBB L + BBB B(A0D8Dm8A0A(B BBB L + BBB B(A0D8Dm8A0A(B BBB L + Oh BBB B(A0D8J8A0A(B BBB L , 虏 BBB B(A0D8Dm8A0A(B BBB L , ( BBB B(A0A8Gp8A0A(B BBB L ,- BBB B(A0A8Gp8A0A(B BBB L , (. h BBB B(A0A8J q
-8A0A(B BBBA - 8. K^ - - 輏 . ༏ ,. ؼ D. м \. ؼ t. ༏ 4 . { ADD g
-AAE AAA T 4. . ki BBE B(D0A8Gc
-8D0A(B BBBI T . | ci BFG B(D0A8G
-8A0A(B BBBE t/ ŏ / ŏ L / ŏ| i BBB B(A0A8G
-8D0A(B BBBE D d/ ˏ
i BBB A(A0G
-0A(A BBBA <0 . K^ L T0 `͏ BEB B(A0A8DP$
-8A0A(B BBBA L 0 Ϗ( h BBB I(H0A8DP
-8A0A(B BBBH < d0 ӏ h BJA A(DPI
-(A ABBA 41 `Տ" DN
-N A 4 0 pՏ Ch BID D0]
- AABA 1 Տ% , 1 ֏, h AGG XCA , D1 ֏0 g ADG _CA t1 ֏$ g D_ $2 ֏ <2 ֏ T2 ֏ l2 ֏ , 1 ֏, ?g AGG XCA 2 ֏ 2 Տ 2 Տr l2 X֏# f D^ 3 h֏ 43 `֏
- L L3 X֏ BRB E(A0A8D`
-8A0A(B BBBE 4 3 ؏ /f BAD GPo
- CABE 4 D3 ؏ e BAD GPo
- CABE 4 X. K^ $4 `ُ, D0g <4 xُ 3 pُ- e Hd t4 ُ A\ 4 ُ 4 xُ 4 pُ 4 hُ 4 `ُ 5 Xُ $5 Pُ <5 Hُ 4 Pُ4 d Hg t5 pُ 5 xُ 5 ُ 5 ُ 5 ُ 5 ُ 6 ُ 6 ُ 46 ُ HS L T6 ُ AAG ]
+8A0A(B BBBF D 4- Pe _R
+ BBJ A(D0
+(A BBEA L |- (f 7R
+ BLE B(A0D8D@
+8A0A(B BBBG L - hh& R
+ BBB E(D0D8JV
+8A0A(B BBBC < . 莅" ST
+ BBA A(GU
+(A ABBA < \. ؏" 2T
+ BBA A(GU
+(A ABBA < . Ȑ" T
+ BBA A(GU
+(A ABBA < . " S
+ BBA A(GU
+(A ABBA < / " S
+ BBA A(GU
+(A ABBA < \/ " S
+ BBA A(GU
+(A ABBA < / " S
+ BBA A(GU
+(A ABBA < / x" lS
+ BBA A(GU
+(A ABBA < 0 h" KS
+ BBA A(GU
+(A ABBA < \0 X" *S
+ BBA A(GU
+(A ABBA < 0 H" S
+ BBA A(GU
+(A ABBA < 0 8" R
+ BBA A(GU
+(A ABBA < 1 (" R
+ BBA A(GU
+(A ABBA T \1 b R
+ BBB B(D0D8D
+8D0A(B BBBG L 1 03 kR
+ BBH E(D0D8DPj
+8D0A(B BBBD L 2 9 BBB B(A0D8D 8A0A(B BBB L 3 BBB B(A0D8Dm8A0A(B BBB L \3 P BBB B(A0D8Dm8A0A(B BBB L 2 ! SQ
+ BBB B(A0D8J8A0A(B BBB L 3 @ BBB B(A0D8Dm8A0A(B BBB L L4 BBB B(A0A8Gp8A0A(B BBB L 4 BBB B(A0A8Gp8A0A(B BBB L 44 . Q
+ BBB B(A0A8J q
+8A0A(B BBBA <5 T KH T5 H l5 @ 5 8 5 0 5 ( 5 0 5 8 L 5 B2 BEE A(A0
+(A BMBE A(A BBB T 5 ׅ. WR
+ BBE B(D0A8Gc
+8D0A(B BBBI T 5 ݅| OR
+ BFG B(D0A8G
+8A0A(B BBBE 6 7 L t6
+ Q
+ BBE E(D0A8Gx
+8D0A(B BBBA 4 6 p@ Q
+ BAA G0d
+ DABJ T 6 x Q
+ BBB B(D0A8G
+8A0A(B BBBA 8 PT KH $8 21\ AG RA D8 | < \8 0 BDA \
+ABJ i
+ABL L 8 g BEB B(A0A8Dp\
+8A0A(B BBBI L 8 BEB B(A0A8Dpw
+8A0A(B BBBF <9 p T9 h l9 ` 9 X 9 P L 9 H BBB B(A0A8D
+8A0A(B BBBA | : BBE B(G0L8DPu
+8A0A(B BBBG S
+8E0A(B BBBG W
+8E0A(B BBBK L : X~ BOB B(A0A8Dp~
+8A0A(B BBBE T : ! N
+ BEB E(A0D8G;
+8D0A(B BBBF < ,; ` BBG A(G@
+(A ABBB L l; j BBE B(D0D8G`,
+8D0A(B BBBE ; @ AO 4 $; @ [ N
+ BDA GP
+ DABD < . K^ ,< P % , ; h , M
+ AGG XCA , ; h 0 zM
+ ADG _CA ; h $ NM
+ D_ < x < p < h = ` , l< X , L
+ AGG XCA T= X l= P = H r < # ^L
+ D^ = =
+ $ 4= L
+ AM@F
+AA < \= (
K
+ BBI A(DP
+(A ABBA L = ` K
+ BBB I(H0A8DP
+8A0A(B BBBH 4 = K
+ BID D0]
+ AABA L > @ BBB E(A0A8DP<
+8A0A(B BBBA ,? " DN
+N A L L? BRB E(A0A8D`
+8A0A(B BBBE 4 > p J
+ BAD GPo
+ CABE 4 ? ( J
+ BAD GPo
+ CABE @ . K^ $@ <@ / D j T@ ? - 7J
+ Hd @ A\ @ @ @ @ A $A <A TA @ 4 ;I
+ Hg A A A A A B B 4B LB HS L lB AAG ]
CAF [
ACA R
-CAJ JCA , 6 ُb D\
+CAJ JCA , B @b D\
H P
H Q
-G I , 6 ڏb D\
+G I , B b D\
H P
H Q
-G I 7 `ڏ- 7 xڏ- 47 ڏ- < L7 ڏ BAA a
+G I C - 4C - LC - < dC BAA a
ABH i
-ABL < 6 xۏ Ob BED A(Dpz
-(A ABBH < <7 H b BED A(Dpz
-(A ABBH 8 ݏ' Ae ,8 (ݏ' Ae 7 8ݏ a AN < 7 (ݏP {a BEJ D(DPn(C ABB L 8 8ݏ ?a BEE E(A0A8D
-8A0A(B BBBG D l8 ޏ2 ` BBB D(A0Dn
-0D(A BBBA L D9 BBB B(A0A8D
-8A0A(B BBBA L 9 ` BBB B(A0A8D
-8A0A(B BBBA T T9 \ F` BBE E(D0A8G
-8D0A(B BBBF $ 9 ,` AFD@ $ d: V ADD JAAL : W BBB E(A0A8DP
-8A0A(B BBBA L : BBB B(A0A8Gp8A0A(B BBB L : H K_ BEB E(I0H8DP
-8D0A(B BBBD 4 |; @ BAD D0
- AABA D ; B BBE A(A0D@m
-0A(A BBBA < l; ^ ^ BBA D(G0
-(A ABBA 4 ; ^ BFD D0I
- DABA D ; {^ BBB D(A0G@:
-0A(A BBBA L ,< 8] s^ BBB B(D0A8D`
-8A0A(B BBBA = H
$= p . K^ D <= (" BBB A(A0G@m
-0A(A BBBA T < ] BBE B(A0A8D
-8A0A(B BBBA D L= X [] BBJ A(D0G@q
-0D(A BBBA T = > {] BEB B(A0A8G
-8A0A(B BBBC T = ] BBB B(A0A8G"
-8A0A(B BBBA L > @ BBE B(A0A8D`
-8A0A(B BBBA T > [] BLB B(A0A8JX
-8A0A(B BBBA T > x![ 3] BBB B(D0A8G
-8A0A(B BBBE ? A ? xA @ pA @ xA 4@ A+ Hb T@ A2 He t@ A @ A $ @ Ag AG k
-AA $ @ Ao AG s
-AA 4 d@ 0B \ BAA G@I
- AABA ,A B DA B \A B , @ B" G\ AAG0i
-AAD A T KH A C A CF A C B CF B - AG cA $ <B Cg AG k
-AA $ dB Dg AG k
-AA $ B XDo AG s
-AA $ B Do AG s
-AA L LB D> Z BBB B(A0A8GZ
-8D0A(B BBBH ,C G N D DC G BBE A(A0D@l
-0A(A BBBA < B xH oZ BBD A(G0T
-(A ABBA , <C (Jt wZ AID {
-DAA 4 lC xJ cZ BFD D0
- DABA , 4D @K ADD Z
-AAA \ dD K BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 D L BDA w
-AEA {
-AFI T lD L] Y BEB B(A0A8G
-8D0A(B BBBD T D Q xY BBE B(A0A8G
-8D0A(B BBBD T E V] eY BEB B(A0A8G
-8D0A(B BBBD L tE [% RY BCA G`w
- AABH C
- AABI TF . K^ lF _ F _ 4 F _5 Y ADG
+ABL < B G
+ BED A(Dpz
+(A ABBH < ,C F
+ BED A(Dpz
+(A ABBH $D x ' Ae DD ' Ae C OF
+ AN < C P 3F
+ BEJ D(DPn(C ABB L D E
+ BEE E(A0A8D
+8A0A(B BBBG L \D 8 E
+ BBB B(D0A8Gq
+8D0A(B BBBA L dE ! BBB B(A0A8D
+8A0A(B BBBA L E X# BBB B(A0A8D
+8A0A(B BBBA T LE %\ E
+ BBE E(D0A8G
+8D0A(B BBBF $ E * D
+ AFD@ $ F x*V ADD JAAL F *W BBB E(A0A8DP
+8A0A(B BBBA L F + BBB B(A0A8D
+8A0A(B BBBA L LG 1 BBB B(A0A8Gp8A0A(B BBB , G 26 AAG0
+AAA 4 G 3 C
+ BAD D0
+ AABA L LG X5 SC
+ BEB E(I0H8DP
+8D0A(B BBBD L G 6 +C
+ BEB E(I0H8DP
+8D0A(B BBBD \ H x8 BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 I 8@ BAD D0
+ AABA D <I 9B BBE A(A0D@m
+0A(A BBBA < H :^ #B
+ BBA D(G0
+(A ABBA 4 I ; B
+ BFD D0I
+ DABA D DI < A
+ BBB D(A0G@:
+0A(A BBBA L I (> A
+ BBB B(A0A8GP0
+8A0A(B BBBA J h@
J p. K^ D J H@" BBB A(A0G@m
+0A(A BBBA T TJ 0A /A
+ BBE B(A0A8D
+8A0A(B BBBA L dK G BBB B(A0A8Gp8A0A(B BBB L K I BBE B(A0A8D`
+8A0A(B BBBA D LK (K ;@
+ BBJ A(D0G@q
+0D(A BBBA T K M5 [@
+ BEB B(A0A8G
+
+8A0A(B BBBH L K xX- @
+ BLB B(D0A8DP
+8A0A(B BBBF T <L XZT @
+ BBB B(D0A8G
+8A0A(B BBBD LM `p dM Xp |M Pp M Xp M `p+ Hb M pp2 He M p N p $ N pg AG k
+AA $ DN po AG s
+AA 4 M q W@
+ BAA G@I
+ AABA N q N q N xq , 4N q" ?
+ AAG0i
+AAD O 0T KH 4O hr
LO `r
dO Xr L |O Pr AAG ]
+CAF [
+ACA R
+CAJ JCA , O rb D\
+H P
+H Q
+G I $ O r] AID IAD $P rU AS DP 8s \P 0s tP (s < O s c>
+ BEA A(MP^
+(A ABBF T P tw ?>
+ BBB B(A0D8GH
+8A0A(B BBBJ 4 $Q y BDA r
+ABL y
+ABA \Q PT KH tQ (z Q zF Q Xz Q PzF Q - AG cA $ Q hzg AG k
+AA $ R zg AG k
+AA $ DR zo AG s
+AA $ lR @{o AG s
+AA R {9 dT L Q {T {<
+ BBB B(A0A8GZ
+8D0A(B BBBH T DR ~ S<
+ BBB B(A0A8G
+8A0A(B BBBF T R <
+ BBB B(A0A8G
+8A0A(B BBBF T R P ;
+ BBB B(A0A8G
+8A0A(B BBBF < LS ;
+ AAGPQ
+AAD O
+AAG T S ؋ ;
+ BBB B(A0A8G
+8A0A(B BBBF T N D T BBE A(A0D@l
+0A(A BBBA < DT @ ;
+ BBD A(G0T
+(A ABBA d <U BBB B(A0D8D`~
+8A0A(B BBBG t
+8A0A(B BBBB L U h BBE E(A0A8DP
+8D0A(B BBBH , <U 蘆t g:
+ AID {
+DAA T lU 8v S:
+ BIE E(D0A8D
+8A0A(B BBBA 4 U ` +:
+ BFD D0
+ DABA , V ( ADD Z
+AAA V (T KH V W 4 tV 5 9
+ ADG
-AAA , DF n \ ADGv
-AAA , tF PoG \ ADGe
-AAA , F poG \ ADGe
-AAA , F oG \ ADGe
-AAA 4 G ou \ ADGO
-AAA 4 <G s ] ADG
-AAA , tG w ^ ADG
-AAA 4 G `x5 ^ ADG4
-AAA 4 G h| _ ADG&
-AAA 4 H ~ q` ADG
-AAA 4 LH Hf b ADG
-AAA 4 H f sc ADG
-AAA , H G d ADGe
-AAA T H ،" c BIB B(A0A8D;
-8A0A(B BBBF < I BEE A(D@M
-(A ABBA L I c BEB E(I0H8D@
-8D0A(B BBBD L I : c BJE B(A0A8D@
-8A0A(B BBBA L J BBE B(A0A8D`
-8A0A(B BBBA L tJ P Wc BEB E(I0H8D@
-8D0A(B BBBD L J Й /c BJE B(A0A8D`8A0A(B BBB D K `n b BBB A(C0K@
-0A(A BBBA L \K b BBH E(D0D8D`
-8D0A(B BBBF l K أN b BBB E(D0I8DD
-8D0A(B BBBC
-8D0A(B BBBF T L b BEB B(A0A8J
-8A0A(B BBBA T tL 0n b BBE B(D0D8J
-8D0A(B BBBB L L HT 7c BBE E(D0D8L`
-8D0A(B BBBA M X M P M X M `> A| N DN
-E A L 4N ` AAG ]
+AAA , V =
+ ADGv
+AAA , V G ~=
+ ADGe
+AAA , W 8G ^=
+ ADGe
+AAA , <W XG >=
+ ADGe
+AAA 4 lW xu =
+ ADGO
+AAA 4 W <>
+ ADG
+AAA , W H .?
+ ADG
+AAA 4 X (5 K?
+ ADG4
+AAA 4 DX 0 ^@
+ ADG&
+AAA 4 |X A
+ ADG
+AAA 4 X ņf vC
+ ADG
+AAA 4 X Hdž D
+ ADGz
+AAA 4 $Y ӆf >G
+ ADG
+AAA , \Y ՆG G
+ ADGe
+AAA T Y ֆ G
+ BIB B(A0A8D
+8A0A(B BBBE < Z ߆ BEE A(D@M
+(A ABBA L $Z ߆ G
+ BEB E(I0H8D@
+8D0A(B BBBD L tZ `: G
+ BJE B(A0A8D@
+8A0A(B BBBA L |[ P BBE B(A0A8D`
+8A0A(B BBBA L [ [G
+ BEB E(I0H8D@
+8D0A(B BBBD L d[ 3G
+ BJE B(A0A8D`8A0A(B BBB D [ 0n F
+ BBB A(C0K@
+0A(A BBBA l [ X F
+ BBE B(D0I8D&
+8D0A(B BBBA }
+8D0A(B BBBA L l\ F
+ BBH E(D0D8D`
+8D0A(B BBBF T \ kF
+ BEB B(A0A8Jq
+8A0A(B BBBA T ]
F
+ BBE B(D0D8J
+8D0A(B BBBJ T l] Hg #G
+ BBB E(D0I8D
+8D0A(B BBBA T ] `
+ F
+ BBB B(D0A8J
+8A0A(B BBBA < ^ F
+ ACG`I
+AAJ
+AAG _ H ,_ @ D_ 8 \_ @ t_ H _ P _ X0 An _ h> A| L _ AAG ]
+CAF k
+ACA R
+CAJ JCA L 4` AAG ]
+CAF k
+ACA R
+CAJ JCA ` \ DN
+E A ` - AG cA ` - AG cA L ` AAG ]
CAF [
ACA R
-CAJ JCA , N b D\
+CAJ JCA L 4a 8 AAG ]
+CAF c
+CAI R
+CAJ JCA , a xb D\
H P
H Q
-G I < $N a BBA A(D@(A ABB N pÐU AS O Ð- L N Ðr ~a BBB B(E0A8J@
-8A0A(B BBBA $ |O Đ^ AG b
-AA $ O 0Ő^ AG b
-AA < <O hŐp ` BED A(G@|
-(A ABBA D |O Ő ` BAA G0c
+G I L a AAG ]
+CAF [
+ACA R
+CAJ JCA L La r C
+ BBB B(E0A8J@
+8A0A(B BBBA < a C
+ BBA A(D@(A ABB b - b U AS b - L ,b ( 7C
+ BEE E(A0D8G
+8D0A(B BBBC < |b ?C
+ AAG0M
+DAE K
+DAA < b p C
+ BED A(G@|
+(A ABBA D b H B
+ BAA G0c
DABK k
- DABA $ O Ɛ ` RFD@ D |P ƐS BBB D(A0D@
-0A(A BBBA 4 4P ǐ .` BFA K0
- AABA D P xȐ BBE A(A0D@
-0A(A BBBA $ DQ ɐ^ AG b
-AA L P ɐ _ BFB E(A0A8GP
-8A0A(B BBBH Q Hː2 Al 4 Q hː BBD A(D@(A ABB$ R ̐_ AAG LDAD Q 8̐ ^ BEE A(C0G@n
-0A(A BBBC L Q ͐+ ^ BBI A(D0MZ
-0D(A BBBD L DR ΐ+ ^ BBI A(D0MZ
-0D(A BBBD L R А3 ^ BBI A(D0MZ
-0D(A BBBD L R Ґ3 ^ BBI A(D0MZ
-0D(A BBBD < 4S Ԑ ^ BBA D(Jz
-(D ABBJ \ tS @א9 ^ BBB A(A0J
-0A(A BBBC {
-0A(A BBBE D S ڐ q^ BBB A(A0J
-0A(A BBBD < T xܐ% U^ BEA D(G
-(D ABBA L \T hݐ /^ BEE E(A0C8J
-8A0A(B BBBC T T ߐ +^ BEB E(A0A8G
-
-8A0A(B BBBE d U :^ BEB B(A0A8D@
+ DABA L Dc f B
+ BBB B(A0D8GP^
+8A0A(B BBBD L c !2 nB
+ BBB B(A0A8GP
+8A0A(B BBBB L c " 1B
+ BBB B(A0A8GPd
+8A0A(B BBBA 4 d $ BEA A(D@(A ABB4 ld $ A
+ BFA K0
+ AABA \e % te % AS e %- AG cA L d % ;A
+ BFB E(A0A8GP
+8A0A(B BBBH f &2 Al $ $f '_ AAG LDAD e P' @
+ BEE A(C0G@n
+0A(A BBBC L e ( @
+ BBB H(D0M2
+0D(A BBBD L ,f ) @
+ BBB H(D0M2
+0D(A BBBD L |f +
+ @
+ BBB H(D0M2
+0D(A BBBD L f X-
+ p@
+ BBB H(D0M2
+0D(A BBBD 4 g / [@
+ SGB A(A0D \ Tg /9 ;@
+ BBB A(A0J
+0A(A BBBE s
+0A(A BBBE D g 2 @
+ BBB A(A0J
+0A(A BBBA < g 5 ?
+ BBA D(Jz
+(D ABBJ d h 7 BEB B(A0A8D@{
8A0A(B BBBJ D
-8A0A(B BBBA < lU HZ ] BDA z
+8A0A(B BBBA L h P8 ?
+ BEB B(A0A8GP
+8A0A(B HBBH D i ;; BDA G0
+ AABF N
+ AABF < <i <% ,?
+ BEA D(G
+(D ABBA L |i < ?
+ BEE E(A0C8J
+8A0A(B BBBB < i h?Z >
+ BDA z
ABL A
-ABA < U h ] BBD D(J@F
-(D ABBA \ U G f] BBB D(D0Du
+ABA < j ? >
+ BBD D(J@F
+(D ABBA \ Lj ?G >
+ BBB D(D0Du
0D(A BBBF K
-0D(A BBBA T LV A] BBB B(D0A8J
-8D0A(B BBBA T V +
] BEL B(A0D8G
-8D0A(B BBBD L V . \ BBB B(D0A8D`
-8D0A(B BBBE 4 W Z BDA y
-ABM A
-ABA d X BEB B(A0A8D@{
+0D(A BBBA T j A+ j>
+ BEL B(A0D8G
+8D0A(B BBBD L k D. S>
+ BBB B(D0A8D`
+8D0A(B BBBE d l G BEB B(A0A8D@{
8A0A(B BBBJ D
-8A0A(B BBBA L W h
O\ BEE A(A0G
-0A(A BBBA X ( < X BEB D(A0M
-(A BBBF 4 $Y Z BDA y
+8A0A(B BBBA 4 tl HZ BDA y
ABM A
-ABA T X P [ BBB B(A0A8Gw
-8A0A(B BBBA $ Y i AAG0]AA4 Y BFD A(D@(A ABBd Z ; BLB B(A0A8D`q
+ABA , l @HY BDA U
+AEF L $l pH
{=
+ BEE A(A0G
+0A(A BBBA ,m 0J < Dm (J BEB D(A0M
+(A BBBF 4 m KZ BDA y
+ABM A
+ABA 4 m Ku BEA A(D@a(A ABB$ m Lf AAK0VAAD dm `L `<
+ BGB A(A0K@
+0A(A BBBA < dn MO BBD A(D@;(A ABB $ n Oi AAG0]AA4 n PO BFD A(D@(A ABBT Ln O ;
+ BEB A(A0[
+(A BBBK A
+(A BBBA d \o P; BLB B(A0A8D`q
8A0A(B BBBE S
-8A0A(B BBBK L |Z xM BBB B(A0D8Dp18A0A(B BBB L Z x# BEB B(A0A8DP
-8A0A(B BBBA L Z X
Z BEJ A(A0D
-0D(A BBBG L l[ + BEB B(A0A8DP
-8A0A(B BBBA D [ BBE A(A0D@l
-0A(A BBBA \ A AK sA $\ P I AK {A , [ T Y BGA FAB D t\ BBE A(A0D@l
-0A(A BBBA < \ H BEA A(D0
-(A ABBA $ \ V ADD JAAL $] P) BEB B(A0A8D`
-8A0A(B BBBA 4 t] 0 BEA A(D@o(A ABB< ] BEE A(D0(A BBB 4 ] BEA D(D0(A ABBD ] 8W BBB A(D0D@
-0A(A BBBA l^ (" DN
-N A D ^ 8 BBB B(A0A8D`8A0A(B BBBL D^ Z
- V BBE E(A0C8G`M
-8A0A(B BBBH T ^ " V BEB B(A0D8G1
-8A0A(B BBBA , ^ e V AID l
-DAA D _ ( V BBB D(A0&
-(A BBBA D d_ p V BJB A(A0Gp
-0A(A BBBA < <` H BEB A(A0{
-(A BBRA D |` BBE A(A0D@
-0A(A BBBA < ` p BEA A(D0o
-(A ABBA 4 a BEA A(D@o(A ABBL <a hO BEB B(A0A8D`
-8A0A(B BBBE T ` h (U BBD D(D@%
+8A0A(B BBBK L o Q :
+ BBE B(H0D8M
+8D0A(B BBBH L p XTM BBB B(A0D8Dp18A0A(B BBB L dp XV BBB B(A0A8Gp8A0A(B BBB L p WP BBL B(A0A8DP
+8A0A(B BBBA L q XX BBB L(A0A8DP
+8A0A(B BBBA T p Y {9
+ BBE J(A0A8Gi
+8D0A(B BBBF L q `] BBB B(A0A8Gp8A0A(B BBB , Dq ^z 9
+ AAG c
+AAB 4 ,r 0_u BEA A(D@a(A ABB dr x_ AL r x_A AK sA r _I AK {A $ r _V ADD JAA, 4r `T "8
+ BGA FAB , s @`v BDA kAB $ Ls `V ADD JAAL ts `) BEB B(A0A8D`
+8A0A(B BBBA < s a BHB D(A0(A BBB 4 t (b BEA A(D@o(A ABB< <t b BEE A(D0(A BBB 4 |t c BEA D(D0(A ABBD s xc ^6
+ BBB A(D0D@
+0A(A BBBA t @f" DN
+N A D u Pf BBB B(A0A8D`8A0A(B BBBL t g 5
+ BBE E(A0I8Dp>
+8D0A(B BBBI , t hre 6
+ AID l
+DAA D ,u r 6
+ BBB D(A0&
+(A BBBA D tu s 5
+ BJB A(A0Gp
+0A(A BBBA < tv t BEB A(A0
+(A BBBA 4 v 8u BEA A(D@|(A ABB< v u BEA A(D0o
+(A ABBA 4 ,w 0v BEA A(D@o(A ABBL dw v BBB B(A0D8D`O
+8A0A(B BBBF T v x 4
+ BBD D(D@%
(D ABBB ]
-(D ABBJ < Ta z T BBG A(G`k
-(D ABBI , $b P v BAA i
-ABA 4 Tb ~ BEA A(D@j(A ABB< b BBG A(G@
-(A ABBG L <b &T BEE E(D0A8G
-8A0A(B BBBF L c $ BLB B(A0A8D`
-8A0A(B BBBJ L b & S BEB E(I0H8DP
-8D0A(B BBBD < c ( BBG A(G@
-(A ABBG \ c X) BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBT c ) R BGB D(A0z
+(D ABBJ < Tw yz K4
+ BBG A(G`k
+(D ABBI , Lx zv BAA i
+ABA 4 |x @{~ BEA A(D@j(A ABBL x { BEB A(D0
+(A BBBB O
+(A BBBJ , y H| ADD ]
+DAE < 4y | BBD I(D0m
+(D ABBE < ty } BBG A(G@
+(A ABBG L x } 2
+ BEE E(D0A8G
+8A0A(B BBBF L Ly 8 2
+ BEB E(I0H8DP
+8D0A(B BBBD 4 y Ȃ {2
+ BFD D0
+ DABA < z BBG A(G@
+(A ABBG \ z P BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 tz Y 1
+ AIG t
+AAI DCA T z Ȅ 1
+ BBB B(A0A8G
+8A0A(B BBBA T { @ 1
+ BGB D(A0z
(A BBDE H
-(A BBGA T $d 0* R BBE F(D0D8J
-8D0A(B BBBG T |d - R BBE B(A0D8G<
-8D0A(B BBBH \ de 0 BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB\ e @1 BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBL e 1 Q BBL B(D0A8G
-8A0A(B BBBA L e > R BEB E(D0D8J
-
-8D0A(B BBBC D f I BBB B(A0G8G`c8D0A(B BBB< |f I R BLD D(G0
-(A ABBE 4 f J= R BFD D0
- DABF < f K R BED I(K@`
-(D ABBH T 4g @L` _R BBB B(A0D8G
-8A0A(B BBBG < g HO 'R BLD D(G0
-(A ABBE < g P* R BED I(KPc
-(D ABBE T h P Q BBB B(A0D8G
-8A0A(B BBBF h PU AL T h @U Q BBB E(A0D8J ~
-8D0A(B BBBK T h \w Q BBD A(GpO
+(A BBGA L \{ Ȉa O1
+ BBB B(A0D8Kp
+8A0A(B BBBA \ d| 艇 BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBT | 8K 0
+ BBE B(D0A8G
+8D0A(B BBBH \ } 0 BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBL | 0
+ BBL B(D0A8G
+8A0A(B BBBA L } s 0
+ BBB E(D0D8J
+
+8D0A(B BBBI D ~ @ BBB B(A0G8G`c8D0A(B BBB< } 0
+ BLD D(G0
+(A ABBE 4 } H= 0
+ BFD D0
+ DABF < $~ P 0
+ BED I(K@`
+(D ABBH T d~ 0
+ BBB B(A0D8J
+8A0A(B BBBG < ~ ( O0
+ BLD D(G0
+(A ABBE < ~ 誇 30
+ BED I(KPc
+(D ABBE L < 0
+ BGD A(G0z
+(C ABBI `(F ABB L D 談 BBB E(A0A8G
+8A0A(B BBBF x AL T h= [/
+ BEB E(A0D8J 6
+8D0A(B BBBH T T P /
+ BHE E(A0A8G
+8D0A(B BBBB T w /
+ BBD A(GpO
(D ABBH
-(D ABBA D i ] BBE A(A0D@e
-0A(A BBBA L |i ^ {Q BEB E(I0H8D@
-8D0A(B BBBD L i `* SQ BJE B(A0A8D@
-8A0A(B BBBA L j ` +Q BEB B(I0D8Ge
-8D0A(B BBBG L lj Hl Q BBB B(A0D8J(
-8D0A(B BBBD 4 Lk ȁh BEA A(G@Q(A ABB\ k BBD A(D@V
+(D ABBA й1 AK cA L $ W/
+ BEB E(I0H8D@
+8D0A(B BBBD L t p //
+ BJE B(A0A8D`8A0A(B BBB T ā > .
+ BFE E(D0D8G
+8D0A(B BBBJ T 过 .
+ BBB B(D0D8GT
+8D0A(B BBBH , `Ň( AK ZA L pŇ .
+ BEB E(I0H8D@
+8D0A(B BBBD L Ƈ* .
+ BJE B(A0A8D@
+8A0A(B BBBA L 4 LJW .
+ BEB B(A0D8Oe
+8D0A(B BBBG 4 < Ӈh BEA A(G@Q(A ABB\ t ԇ BBD A(D@V
(A ABBG Y
(A ABBA a
-(A ABBI T Tk ` R BBB B(A0A8J
-8A0A(B BBBD <l ݍ. K^ Tl ll % HX l - H` l % HX l ȕ- H` T \l ؕ
R BEB J(A0D8N
-8A0A(B BBBA T l @ ?S BBE J(A0A8N
-8A0A(B BBBA T m x S BBB B(A0A8J
-8A0A(B BBBJ m AL n ,n Dn \n
tn n n n n n ȴ% HX o ش- H` ,o 贑% HX Lo - H` lo % HX o - H` o (% HX o 8- H` o H- , p `0 AF
FK
- D $ 4p `^ AG b
-AA L o P BVB D(A0D3
-0D(A BBBG T p X P BGB B(A0D8G
-8D0A(B BBBH L tp ` P BLB E(A0A8Gp
-8D0A(B BBBA L p ` P BLB E(A0A8Gp
-8D0A(B BBBA L q о` |P BLB E(A0A8Gp
-8D0A(B BBBA , q ࿑ ADD0e
-AAA , $r P AAG0M
-AAH 4 q O BAA G0c
- AABF T q X O BBE E(A0A8G
-8D0A(B BBBA L Tr ÑE O BBB B(A0A8G
-8E0A(B BBBF , r ƑY wO ADG i
-AAA T r Ƒ SO AAG@r
+(A ABBI T xԇ .
+ BBE B(A0D8J
+8A0A(B BBBG , @. K^ D ( \ % HX | 0- H` @% HX P- H` T $ ` .
+ BBB B(I0D8K
+8A0A(B BBBA T | .
+ BJB B(D0D8N
+8A0A(B BBBA T ԅ p
+ L/
+ BBB B(A0D8N
+8A0A(B BBBA T , /
+ BBB B(A0D8G
+8A0A(B BBBD < @ AL < @ K/
+ BEA D(G
+(D ABBD y AG oAL P
/
+ BBE B(H0G8J
+8D0A(B BBBG 4 T 0
+ BDD L@]
+ DABA L /
+ BBE L(D0A8D
+8D0A(B BBBI 4 ܇ /
+ BDA LPz
+ DABA T /
+ BEB L(D0A8Gi
+8D0A(B BBBI $ "z AD sAT X#/
+ [/
+ BEB B(D0A8G
+8D0A(B BBBE L 0- O/
+ BBB B(A0D8J%
+8D0A(B BBBD B B B 4 B
L B d B | B B B Ċ B% HX B- H` B% HX $ B- H` D B% HX d C- H` C% HX (C- H` ċ 8C- , ܋ PC0 AF
FK
+ D $ PD^ AG b
+AA L | D c.
+ BVB D(A0D3
+0D(A BBBG T ̋ HF =.
+ BGB B(A0D8G
+8D0A(B BBBH L $ J` F.
+ BLB E(A0A8Gp
+8D0A(B BBBA L t K` .
+ BLB E(A0A8Gp
+8D0A(B BBBA L Č L` -
+ BLB E(A0A8Gp
+8D0A(B BBBA , ̍ M ADD0e
+AAA , @N AAG0M
+AAH 4 t N _-
+ BAA G0c
+ AABF T HO +-
+ BBE E(A0A8G
+8D0A(B BBBA L QE ,
+ BBB B(A0A8G
+8E0A(B BBBF , T T\ ,
+ ADG l
+AAA T T ,
+ AAG@r
AAC G
AAG y
AAE ]
-AAA L ,s ȑ= O BBB B(D0A8DP
-8D0A(B BBBA T |s ȑ4 N BBE E(K0A8G
-8D0A(B BBBG < s Α5 N NAG JH
-CAA < t ё5 N KAG MH
-CAA D Tt ԑ N AAD@e
-AAC H
-AAF h
-AAE ,u Ց L t Ց N BBB B(A0A8Gi
-8A0A(B BBBD L u `ؑC N BBB B(D0A8GA{
-8A0A(B BBBG < u `7 BBA A(G0\
-(A ABBA d $v ` BEB E(A0D8Gp
+AAA L U= ,
+ BBB B(D0A8DP
+8D0A(B BBBA T , V4 I,
+ BBE E(K0A8G
+8D0A(B BBBG < \5 \,
+ NAG JH
+CAA < ď _5 X,
+ KAG MH
+CAA t b T,
+ BBB A(A0De
+0A(A BBBG H
+0A(A BBBH (
+0A(A BBFA 4 d L d ,
+ BBB B(A0A8Gi
+8A0A(B BBBD L g +
+ BBB B(D0A8GA{
+8A0A(B BBBG < @7 BBA A(G0\
+(A ABBA d , @ BBB E(D0D8Gp
8A0A(B BBBD f
-8A0A(B BBBA v p \ Sd v T BIB I(D0H8G
+8A0A(B BBBA y \ \d T BIB I(D0H8G
8A0A(B BBBJ Q
-8A0A(B BBGH d w BBE F(A0H8GP
+8A0A(B BBGH d 荈 BBE F(A0H8GP
8C0A(B BBBD
-8F0A(B BBBE d v ; N BIB B(A0E8DP
+8F0A(B BBBE d Ē @ A,
+ BIB B(A0E8DP
8A0A(B BBBJ \
-8F0A(B BBBE < w BBG A(G@
-(A ABBG x X
- $ w P
-b 2N AY
-F A L w
-% N BBB B(A0A8Dp
-8D0A(B BBBF < x x BBA A(G@d
-(A ABBI \x H M BFB A(E0G@
+8F0A(B BBBE < x BBG A(G@
+(A ABBG $ 8 $ 0b +
+ AY
+F A L xE l+
+ BBB B(A0A8D
+8D0A(B BBBF < x BBA A(G@d
+(A ABBI < H ,+
+ BFB A(E0G@
0F(A BBBH y
0A(A BBBG
0F(A BBBD
-0F(A BBBD d |y BBB A(A0
+0F(A BBBD d آ BBB A(A0
(A BBBA
(A BBBE N
-(A BBBE , Ty C L AID \
-DAA L y L BBB B(A0D8DP
-8A0A(B BBBF 4 dz AAG0^
-AAG H
-AAF < z h BBG A(G@
-(A ABBG L z (= BBB H(A0D8DP
-8A0A(B BBBE L ,{ = BBB H(A0D8DP
-8A0A(B BBBE , |{ AG ]
+(A BBBE , 4 C =*
+ AID \
+DAA L d *
+ BBB B(A0D8DP
+8A0A(B BBBI < l BBG A(G@
+(A ABBG L @= BBB H(A0D8DP
+8A0A(B BBBE L 0= BBB H(A0D8DP
+8A0A(B BBBE , L AG ]
AJ D
-AK L { @ BBB H(A0D8D@
-8A0A(B BBBJ L { = BBB H(A0D8DP
-8A0A(B BBBE L { J BBE B(A0D8D@
-8A0A(B BBBF L | X = BBB H(A0D8DP
-8A0A(B BBBE L | H M BBB H(A0D8DP
-8A0A(B BBBE <} H!5 D | p! I BAC GPF
- EABE
- AABD < } # I AAGPF
-EAK
-AAD D L} & WI BAA GPA
- ECBJ X
- CABB D } p( I BAC GPF
- EABE
- AABI d l~ +E BBB B(A0A8DP
+AK L | @ BBB H(A0D8D@
+8A0A(B BBBJ L ̗ = BBB H(A0D8DP
+8A0A(B BBBE L d ?(
+ BBE B(A0D8D@
+8A0A(B BBBI L l p= BBB H(A0D8DP
+8A0A(B BBBE L `M BBB H(A0D8DP
+8A0A(B BBBE `5 D l _'
+ BAC GPF
+ EABE
+ AABJ < +'
+ AAGPF
+EAK
+AAB D P &
+ BAA GPI
+ ECBB X
+ CABB D < &
+ BAC GPF
+ EABE
+ AABG d < PE BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l D~ += H BBB E(I0D8D#
-8D0A(B BBBD
-8D0A(B BBBA L D .' BFB E(A0A8Dp
-8F0A(B BBBH T / G BBB B(A0D8J
-8C0A(B BBBH , \ F H AID0H
-DAA D Fl H BBE A(D0K@
-0D(A BBBA G H BBB B(D0A8D@Q
+8F0A(B BBBA l 8 ;&
+ BBB J(D0D8D0
+8D0A(B BBBG w
+8A0A(B BBBG L ' BFB E(A0A8Dp
+8F0A(B BBBH T 5 %
+ BBB B(A0D8J
+8C0A(B BBBH , ؈ s&
+ AID0H
+DAA D 4 ؈l g&
+ BBE A(D0K@
+0D(A BBBA | ڈ [&
+ BBB B(D0A8D@Q
8A0A(B BBBD L
8A0A(B BBBJ N
8A0A(B BBBH *
8A0A(B BBBD K
-8A0A(B BBBC 4 I> AAG0}
+8A0A(B BBBC 4 ۈ> AAG0}
FAC
-FAA T Ā J
H BBB B(A0A8O
-8F0A(B BBBE 4 HM BAA G`{
- FABA T T N G BBB B(A0D8J
-8A0A(B BBBD T i H BBE E(A0A8J
-8A0A(B BBBK T qq H BBB B(A0D8J
5
-8D0A(B BBBG ƍ. K^ < y BDA \
-ABJ i
-ABL D z \ xz t pz hz `z L Xz BBB B(A0A8D
-8A0A(B BBBA | ~ BBE B(G0L8DPu
-8A0A(B BBBG S
-8E0A(B BBBG W
-8E0A(B BBBK (ō. K^ F\ AG RAT 4 0F ?G BKD D(Dp
+FAA T l ܈ %
+ BBB B(A0A8O
+8F0A(B BBBE 4 | ߈ BAA G`{
+ FABA T B%
+ BBB B(A0D8J
+8A0A(B BBBD T T h&
+ BBE E(A0A8J
+8A0A(B BBBK T H w&
+ BBB B(A0D8J
2
+8D0A(B BBBJ . K^ Ԟ &˂\ AG RAT <
F &
+ BKD D(Dp
(D ABBD Q
-(D ABBF č. K^ 4
l F BBB K(A0A8Dg
+(D ABBF L . K^ d h
l Ğ ` =&
+ BBB K(A0A8Dg
8A0A(B BBBH
-8D0A(B BBBE L , x F BEB E(I0H8DP
-8D0A(B BBBD L | WF BJE B(A0A8D@
-8A0A(B BBBA L ̅ /F BEB E(I0H8D@
-8D0A(B BBBD L ] BBB H(A0D8DP
-8A0A(B BBBG T l c E BBE B(D0D8D8
-8D0A(B BBBD T . K^ l Y Y } N $ 苒g AG k
-AA , ć 00 AC
FK
- E 4 d 0 D BRA GP
- DABA 4 , 荒Z BDA y
+8D0A(B BBBE L 4 %
+ BEB E(I0H8DP
+8D0A(B BBBD L ` %
+ BJE B(A0A8D@
+8A0A(B BBBA L ԟ 0 %
+ BEB E(I0H8D@
+8D0A(B BBBD L ܠ ] BBB H(A0D8DP
+8A0A(B BBBG T t c %
+ BBE B(D0D8D8
+8D0A(B BBBD x. K^ Y , 0 AC
FK
+ E Y } N $ @g AG k
+AA 4 l E$
+ BRA GP
+ DABA 4 \ @Z BDA y
ABM A
-ABA L d BBB B(A0A8GPl
-8A0A(B BBBI L ` BBB B(A0A8GPl
-8A0A(B BBBI L BBB B(A0A8GPl
-8A0A(B BBBI L T BBB B(A0A8GPl
-8A0A(B BBBI L @ BBB B(A0A8GPl
-8A0A(B BBBI L BBB B(A0A8GPl
-8A0A(B BBBI L D BBB B(A0A8GPl
-8A0A(B BBBI L BBB B(A0A8GPl
-8A0A(B BBBI L p BBB B(A0A8GPl
-8A0A(B BBBI L 4 BBB B(A0A8GPl
-8A0A(B BBBI L BBB B(A0A8GPl
-8A0A(B BBBI L ԋ ` BBB B(A0A8GPl
-8A0A(B BBBI L $ BBB B(A0A8GPl
-8A0A(B BBBI L t BBB B(A0A8GPl
-8A0A(B BBBI L Č P BBB B(A0A8GPl
-8A0A(B BBBI L BBB B(A0A8GPl
-8A0A(B BBBI L d BBB B(A0A8GPl
-8A0A(B BBBI < $ @ 8? BEA I(DPo
-(A DBBA < d ऒV ? BED I(D@
-(A ABBA D : > BBB I(A0D
-0A(A EBBA L | ] BBB H(A0D8DP
-8A0A(B BBBG ̎ = T T 02
- @> BBB E(A0A8G
-8A0A(B BBBA T > BBB B(D0A8G1
-8A0A(B BBBA L k> BHI E(A0A8G`@
-8A0A(B BBBE T T 0j C> BJB E(A0A8G)
-8D0A(B BBBA $ < H= ADG nAA T ԏ ` = BBB B(A0A8Gr
-8A0A(B BBBA . K^ $ Ԑ ͒0 AGJ XDA ͒ ͒Y } N $ , (Βg AG k
-AA 4 Đ pΒ > BHA G@h
- DABA 踍. K^ \ AG RAD đ Β7 BBE A(A0D@
-0A(A BBBA D ϒ7 BBE A(A0D@
-0A(A BBBA D T ВC BBE A(A0D@
-0A(A BBBA D ђC BBE A(A0D@
-0A(A BBBA , ҒU AGG m
-AAB l Ӓ < BBB B(A0A8D1
-8A0A(B BBBA Z
-8A0A(B BBBA T ֒ < BEE E(D0A8G
-8D0A(B BBBE L ܓ ے BBB B(A0A8Gp8A0A(B BBB $ , (ݒV ADD JAAL T `ݒ BBB B(A0A8Gp8A0A(B BBB < ޒ BBG A(G@
-(A ABBF l ߒy BBF D(A0G@I
+ABA L ܡ h #
+ BBE J(A0D8Dp
+8D0A(B BBBA < , V #
+ BED I(D@
+(A ABBA D l 8 : #
+ BBB I(A0D
+0A(A EBBA L 0 R#
+ BJB D(A0D
+0D(A BBBA L ] BBB H(A0D8DP
+8A0A(B BBBG = T l 2
+ "
+ BBB E(A0A8G
+8A0A(B BBBA T ģ * +#
+ BBB B(D0A8G1
+8A0A(B BBBA L , "
+ BHI E(A0A8G`@
+8A0A(B BBBE $ $ -= ADG nAA < L -a BBA A(G@l
+(A ABBA < /a BBA A(G@l
+(A ABBA < ̥ 00a BBA A(G@l
+(A ABBA < `1a BBA A(G@l
+(A ABBA < L 2a BBA A(G@l
+(A ABBA < 3a BBA A(G@l
+(A ABBA < ̦ 4a BBA A(G@l
+(A ABBA < 6a BBA A(G@l
+(A ABBA < L P7a BBA A(G@l
+(A ABBA < 8a BBA A(G@l
+(A ABBA < ̧ 9a BBA A(G@l
+(A ABBA < :a BBA A(G@l
+(A ABBA < L <a BBA A(G@l
+(A ABBA < @=a BBA A(G@l
+(A ABBA < ̨ p>a BBA A(G@l
+(A ABBA < ?a BBA A(G@l
+(A ABBA < L @a BBA A(G@l
+(A ABBA < Ba BBA A(G@l
+(A ABBA < ̩ 0Ca BBA A(G@l
+(A ABBA T T `D
+ BBB B(A0A8G
+8A0A(B BBBA d ȍ. K^ $ | [0 AGJ XDA [ [Y } N $ Ԫ H[g AG k
+AA 4 D [
+ BHA G@h
+ DABA 4 (. K^ L
+\ AG RAD l \7 BBE A(A0D@
+0A(A BBBA D \7 BBE A(A0D@
+0A(A BBBA D ]C BBE A(A0D@
+0A(A BBBA D D ^C BBE A(A0D@
+0A(A BBBA 4 `V AGG k
+CAB SAA T (`
+ BBB B(A0A8D
+8A0A(B BBBA T d c
+ BEE E(D0A8G
+8D0A(B BBBE < hR
+ BBA A(Jj
+(A ABBA L i BBB B(A0A8Gp8A0A(B BBB $ hkV ADD JAAL , k BBB B(A0A8Gp8A0A(B BBB L ĭ m
+ BJB A(A0Z
+(A BBDE ^(A BBB< ̮ pm BBG A(G@
+(A ABBF l ny BBF D(A0G@I
0A(A BBBI w
0A(A BBBA D
-0F(A BBBG l T y BBF D(A0G@I
+0F(A BBBG l | 0py BBF D(A0G@I
0A(A BBBI w
0A(A BBBA D
-0F(A BBBG L 4 : BEB E(I0H8D@
-8D0A(B BBBA D 0 c: BKI D(A0G@L
-0A(A BBBA T ̕
C: BBB B(D0D8J
-8A0A(B BBBF . K^ L < + w: BEB E(I0H8DP
-8D0A(B BBBD d O: BBB B(I0A8G`
+0F(A BBBG L 4 @r [
+ BEB E(I0H8D@
+8D0A(B BBBA D s 3
+ BKI D(A0G@L
+0A(A BBBA T ̯ Hu
+ BBB B(D0D8J
+8A0A(B BBBF ܰ . K^ , Ȁt AAG0v
+AAA L l
+ BEB E(I0H8D@
+8D0A(B BBBD d
+ BBB B(I0A8G`
8F0A(B BBBF g
-8F0A(B BBBA T 9 BEB B(A0D8G
-8D0A(B BBBA L L (m 9 BEB B(A0A8G
-8A0A(B BBBA , v\ AG RAD L (7 BBE A(A0D@
-0A(A BBBA D C BBE A(A0D@
-0A(A BBBA $ ܘ ( AG }
-AA + \N T , z9 BBE B(D0A8G
-8D0A(B BBBK < B `9 BBA A(Jd
-(A ABBA < BBG A(G@
-(A ABBH | ` BBE B(G0L8DPu
+8F0A(B BBBA T $
+ BEB B(K0C8G
+8D0A(B BBBA L |
+ BEB B(A0A8G
+8A0A(B BBBA .\ AG RAD 7 BBE A(A0D@
+0A(A BBBA D C BBE A(A0D@
+0A(A BBBA $ 4 AG }
+AA \ P+ \N T h, /
+ BBE B(D0A8G
+8D0A(B BBBK < ̳ @ BBG A(G@
+(A ABBH | BBE B(G0L8DPu
8A0A(B BBBG S
8E0A(B BBBG W
-8E0A(B BBBK , t V BAA D0I AABL P8 BJB A(A0Z
-(A BBDE ^(A BBB< d 8 BJB A(A0(A BBB < 4 P BBG A(G@
-(A ABBG < t BBG A(G@x
-(A ABBG < p BBG A(G@
-(A ABBD < BBG A(G@
-(A ABBD < 4 BBG A(G@
-(A ABBH T P 6 BEB B(D0A8Jm
-8A0A(B BBBA ̜ 3% T T 3/ 7 BEE B(A0G8G
-8A0A(B BBBA < {9 BIA A(D`)
-(A ABBA | ؞\ AG RAD b7 BBE A(A0D@
-0A(A BBBA D cC BBE A(A0D@
-0A(A BBBA < , d BBG A(G@
-(A ABBG l Xe5 . K^ 4 heJ BDA m
+8E0A(B BBBK , V BAA D0I AABL %
+ BJB A(A0Z
+(A BBDE ^(A BBB< T p
+ BJB A(A0(A BBB < L Щ BBG A(G@
+(A ABBG < p BBG A(G@x
+(A ABBG < ̵ BBG A(G@
+(A ABBD < BBG A(G@
+(A ABBD < L 0 BBG A(G@
+(A ABBH T Ե ЬZ a
+ BEB B(D0A8Jm
+8A0A(B BBBA ؿ% T D l/
+ BEE B(A0G8G
+8A0A(B BBBA < h O
+ BIA A(D`)
+(A ABBA г\ AG RAD 7 BBE A(A0D@
+0A(A BBBA D C BBE A(A0D@
+0A(A BBBA < D BBG A(G@
+(A ABBG h5 . K^ 4 xJ BDA m
ABI A
-ABA T D e 8 BBE B(D0G8G5
-8A0A(B BBBA , xL Ad
-K W L (y[ d py[ | y[ z[ Hz[ ğ z[ ܟ z[ {[ h{[ $ {[ D < { BBE A(A0D@
-0A(A BBBA ,\ AG RAD |7 BBE A(A0D@
-0A(A BBBA $ }Q AG L
-DH oA, } 6 AAG@G
-AAF t D ~- BBA D(D@g
-(D ABBC F
-(D ABBA
-(D ABBD D
-(D ABBC L HP BBB B(A0D8DP
-8D0A(B BBBG L H BBB B(A0D8DP
-8D0A(B BBBG L \ @ BBB B(A0D8DP
-8D0A(B BBBG t BBD A(D@]
-(D ABBE x
-(D ABBG y
-(D ABBF J
-(D ABBE L 4 BEB B(A0K8D
-8A0A(B BBBD T P 4 BBE B(A0A8G>
-8A0A(B BBBD 4 ̣ 3 BAD
-ABE AAB * A[
-L A , M 44 ADG0_
-DAA D ģ Л 4 BGB D(M0B
-(A BBBK < 8 BDA k
+ABA T 4
+ BBE B(D0G8Gg
+8A0A(B BBBA < D (F BBA A(G@q
+(A ABBD D 8 BBE A(A0D@
+0A(A BBBA ̹ \ AG RAD BBE A(A0D@
+0A(A BBBA 4
+* A[
+L A T , R
+ BBE B(D0A8G
+8D0A(B BBBK , 8
+ LGA A(D , $ %
+ LGA A(D , v BAA i
+ABA L
+ BJB A(A0W
+(A BBDH ^(A BBBL 4 BBB E(A0A8D`
+8A0A(B BBBA ܻ 4 < .
+ BGB A(C0G D t
+ BED D(G
+(D ABBI \ c
+ BBB A(G0Je
+0C(A BBBI C
+0A(A BFFA T 4 U
+ BBB E(A0A8Qg
+8A0A(B BBBA L t $9 c
+ BEB B(A0A8G`
+8D0A(B BBBD d | %E BBB B(A0A8DP
+8A0A(B BBBE \
+8F0A(B BBBA | &R BIE B(A0A8GP
+8A0A(B BBBC
+8D0A(B BBBD o
+8A0A(B BBBG d X|. K^ $ | (Y AK Y
+AA L ( '
+ BEB E(I0H8D@
+8D0A(B BBBA L < x*?
+ BJE B(A0A8DP
+8A0A(B BBBA D D h+ BBE A(A0D@u
+0A(A BBBA < Ծ ,.
+ BBD A(G@l
+(A ABBA T .(#
+ BBE B(A0G8GA
+8A0A(B BBBA $ PL Ad
+K W D Q[ \ PQ[ t Q[ Q[ (R[ pR[ R[ S[ HS[ S[ D 4 S BBE A(A0D@
+0A(A BBBA | \ AG RAD T7 BBE A(A0D@
+0A(A BBBA $ UQ AG L
+DH oA, T U
+ AAG@G
+AAF d < pV BBA D(DP
+(D ABBK
+(D ABBH S
+(D ABBD L Y BBB B(A0D8D`
+8D0A(B BBBG L h_` BBB B(A0D8D`
+8D0A(B BBBG L D xe BBB B(A0D8D`
+8D0A(B BBBG t j BBD A(DP
+(D ABBK y
+(D ABBF @
+(D ABBG N
+(D ABBA L T k
+ BEB B(A0K8D
+8A0A(B BBBD T Pp
+ BBE B(A0A8G>
+8A0A(B BBBD 4 t3 BAD
+ABE AAB , 4 vM y
+ ADG0_
+DAA D d v Y
+ BGB D(M0B
+(A BBBK < d (w BDA k
ABK @
-ABA L ܤ BOB A(A0]
+ABA L xw BOB A(A0]
(A BBBG Y
-(A BBBH < , 4 BBB A(A0d
-(A BBBE L l K BBB B(A0A8G@d
-8A0A(B BBBA T , d 2 BBB B(D0A8G
-8D0A(B BBBA T L X [2 BEB E(I0H8D@
-8D0A(B BBBA d j 32 BBB E(D0A8GP
+(A BBBH < w4 BBB A(A0d
+(A BBBE L 4 xK BBB B(A0A8G@d
+8A0A(B BBBA T yd
+ BBB B(D0A8G
+8D0A(B BBBA |T L < H|
+ BEB E(I0H8D@
+8D0A(B BBBA d }j w
+ BBB E(D0A8GP
8A0A(B BBBK D
-8A0A(B BBBA L T 1 BEB E(I0H8D@
-8D0A(B BBBA < 4 BDI ~
+8A0A(B BBBA L ~ #
+ BEB E(I0H8D@
+8D0A(B BBBA < p BDI ~
AGK K
-ABB < t BBG A(G@
-(A ABBG L BBB B(A0D8GP>
-8A0A(B BBBD , BAD b
-ABD L 0 BDB B(A0D8JT
-8A0A(B BBBI 1 Dl 0. K^ " DR
-J A , ܨ t AAG0v
-AAA d | X >0 BBB E(D0A8Dp
+ABB < < BBG A(G@
+(A ABBG L | BBB B(A0D8GP>
+8A0A(B BBBD , BAD b
+ABD L D p
+ BDB B(A0D8JT
+8A0A(B BBBI L Ї1 Dl l q. K^ ؇" DR
+J A , 臊t AAG0v
+AAA d 8
+ BBB E(D0A8Dp
8A0A(B BBBE k
-8A0A(B BBBA T / BDB B(D0D8G
-8A0A(B BBBA T < ȳg / BBE E(D0A8G
-8A0A(B BBBA l ඓ / BBB L(A0D8DM
+8A0A(B BBBA T ` 9
+ BDB B(D0D8G
+8A0A(B BBBA T g 2
+ BBE E(D0A8G
+8A0A(B BBBA l 4
+ BBB L(A0D8DM
8A0A(B BBBA
-8A0A(B BBBA y/ BDB B(D0D8DpL
-8A0A(B BBBD P
-8A0A(B BBBF
-8A0A(B BBBA L j #/ BDB B(A0D8JT
-8A0A(B BBBI L ܪ 8Ó V/ BBB B(A0A8JU
-8A0A(B BBBE L , xœ 7/ BBB E(A0A8J
-8A0A(B BBBB L | ʓ= &/ BBB B(A0D8Jd
-8A0A(B BBBK L ̫ Փc
- / BBB B(A0A8JU
-8A0A(B BBBE L (ߓ / BBB B(A0A8Q
-8A0A(B BBBF T l 0 BEE E(A0D8G}
-8A0A(B BBBA T Y } N $ l hg AG k
-AA 4 0 BHA G@h
- DABA L < X@ 0 BBB B(D0A8D
-8A0A(B BBBG , H> ~0 BDI X
-ABA L X ^0 BJB A(A0W
-(A BBDH ^(A BBBT 0 BAA G0@
+8A0A(B BBBA r
+ BDB B(D0D8DpL
+8A0A(B BBBD I
+8A0A(B BBBE
+8A0A(B BBBA L , ؕ2 g
+ BDB B(A0D8JT
+8A0A(B BBBI L | ȝ
+ BBB B(A0A8JU
+8A0A(B BBBE L ( v
+ BBB E(A0A8J
+8A0A(B BBBB L - e
+ BBB B(A0D8Jd
+8A0A(B BBBK L l c
+
+ BBB B(A0A8JU
+8A0A(B BBBE T % #
+ BBB B(A0A8Q\
+8A0A(B BBBG T Ɋ
+ BEE E(A0D8G
+8A0A(B BBBA $ ъY } N $ < `ъg AG k
+AA 4 ъ
+ BHA G@h
+ DABA L PҊ6
+ BBB B(D0A8D
+8A0A(B BBBG , 4 @Պ>
+ BDI X
+ABA L d PՊ
+ BJB A(A0W
+(A BBDH ^(A BBBT l Պ0 BAA G0@
AABA
AABG e
- AABG T d u / BHB B(D0D8Jo
-8A0A(B BBBG L . K^ T Ԯ
- 0 BBB B(A0A8G
-8A0A(B BBBH P\ AG RAD ܯ (/7 BBE A(A0D@
-0A(A BBBA D $ 0C BBE A(A0D@
-0A(A BBBA , l (1 AAG0s
-AAA , 1 0 AG0L
+ AABG T x܊
+ BHE B(D0D8J4
+8A0A(B BBBG k. K^ T | (6
+ BBB E(A0A8G
+8A0A(B BBBF 웂\ AG RAD 7 BBE A(A0D@
+0A(A BBBA D C BBE A(A0D@
+0A(A BBBA , <
+ AAG0s
+AAA , p
+ AG0L
AK D
-CA L ̰ 2n BBB B(A0A8Dp
-8A0A(B BBBA T (3 / BBB B(A0A8G
-8A0A(B BBBF L t ; BBB A(A0E
+CA L R
+ BIN B(A0C8Gpg
+8A0A(B BBBI L
n BBB B(A0A8Dp
+8A0A(B BBBA T s
+ BBB B(A0A8G
+8A0A(B BBBF L BBB A(A0E
(A BBGG I
-(A BBDA T 4 < k/ BBB B(A0A8G
-8A0A(B BBBI L E / BBB B(A0A8Jw
-8A0A(B BBBK l Ț. K^ L Z '0 BBB B(I0A8G
-8D0A(B BBBA T D iv 0 BEE E(A0G8G
-
-8A0A(B BBBA , <\ AG RAD L z BBB D(A0D@
-0A(A BBBA D p} BBB D(A0D@
-0A(A BBBA d L 0 BBB E(D0D8D`
+(A BBDA T , 7
+ BBB B(A0A8G0
+8A0A(B BBBE L &
+ BBB B(A0A8Jw
+8A0A(B BBBK g. K^ L (;. /
+ BBB B(I0A8G
+8D0A(B BBBA T < K
+ BEE E(A0G8G
+
+8A0A(B BBBA L \ AG RAD l 0\ BBB D(A0D@
+0A(A BBBA D ^ BBB D(A0D@
+0A(A BBBA d D a
+ BBB E(D0D8D`
8D0A(B BBBD O
-8A0A(B BBBA T N *0 BBE E(G0A8Gb
-8A0A(B BBBG < BBG A(G@
-(A ABBF L L 8 / BEB E(I0H8DP
-8D0A(B BBBD T 3 / BBE E(A0A8D
-8A0A(B BBBE . K^ ؊Y } N $ g AG k
-AA l L h ;/ BBB B(D0K8G
+8A0A(B BBBA T a> 2
+ BBE E(G0A8GR
+8A0A(B BBBG < f BBG A(G@
+(A ABBF L D `g
+ BEB E(I0H8DP
+8D0A(B BBBD T 0i3
+ BBE E(A0A8D
+8A0A(B BBBE d. K^ lY } N $ Hlg AG k
+AA l D l C
+ BBB B(D0K8G
8C0A(B BBBE |
-8A0A(B BBBA 4 . BHA G@h
- DABA . K^ T \ . BEE B(A0A8G>
-8D0A(B BBBA І\ AG RAD p7 BBE A(A0D@
-0A(A BBBA D \ hC BBE A(A0D@
-0A(A BBBA D p? BHE A(A0G@[
-0A(A BBBE < hi BEA A(G@[
-(A ABBG L V- BJB A(A0Z
-(A BBDE ^(A BBB4 H - BHA D@
- AABA D BBE A(A0D@u
-0A(A BBBA < l X. , BBD A(G@l
-(A ABBA T H/ , BHE B(A0A8G
-8A0A(B BBBF $ Ɣ0 AGJ XDA $ ƔD XYOC 4 Ɣc AAG \
-CAG dFA ƔY } N $ 4 0ǔg AG k
-AA 4 ̹ xǔ W- BHA G@h
- DABA L ȔT ;- BBB E(A0A8J
-8C0A(B BBBC p. K^ L ۔ AAG e
+8A0A(B BBBA 4 n
+ BHA G@h
+ DABA d. K^ T n\
+ BEE B(A0A8G>
+8D0A(B BBBA \ AG RAD 4 q7 BBE A(A0D@
+0A(A BBBA D | rC BBE A(A0D@
+0A(A BBBA D s/ BEE A(A0D@Z
+0A(A BBBD < ti BEA A(D@Z
+(A ABBC < b _
+ BIA A(D`)
+(A ABBA T pu/ G
+ BHE B(A0A8G
+8A0A(B BBBC $ 0 AGJ XDA $ D XYOC 4 4 8c AAG \
+CAG dFA l pY } N $ g AG k
+AA 4
+ BHA G@h
+ DABA L , T
+ BBB E(A0A8J
+8C0A(B BBBC 4 xb. K^ L L AAG e
CAF s
ACA R
-CAJ JCA L L h۔ AAG ]
+CAJ JCA L AAG ]
CAF c
CAI R
-CAJ JCA L ۔ AAG ]
+CAJ JCA L 0 AAG ]
CAF c
CAI R
-CAJ JCA 4\ AG RA4 ۔ BDD G@g
- AABD D D ܔ BBE A(A0D@
-0A(A BBBA D ݔ BBE A(A0D@
+CAJ JCA < P\ AG RA4 \ P BDD G@g
+ AABD D BBE A(A0D@
-0A(A BBBA T D ߔ| + BBE E(D0A8G
-8D0A(B BBBE L , BBB B(A0A8Gp8A0A(B BBB L | h BBB B(A0A8Gp8A0A(B BBB L ̽ BBB B(A0A8Gp8A0A(B BBB L h; BBB B(A0A8Gp 8A0A(B BBB $ l X AG
-AA L BFE E(A0A8DP
-8D0A(B BBBJ d r BFB E(D0A8DP
-8F0A(B BBBG
-8D0A(B BBGA d L 2 BFE E(A0A8DP
+0A(A BBBA D BBE A(A0D@
+0A(A BBBA T l H|
+
+
+ BBE E(D0A8G
+8D0A(B BBBE , pË
+ GA A(D L ċ BBB B(A0A8Gp8A0A(B BBB L `Ƌ BBB B(A0A8Gp8A0A(B BBB L L Nj BBB B(A0A8Gp8A0A(B BBB L `ɋ; BBB B(A0A8Gp 8A0A(B BBB $ Pˋ AG
+AA L ˋ BFE E(A0A8DP
+8D0A(B BBBJ d d ̋H BBB F(D0D8D@r
+8F0A(B BBBG z
+8D0A(B BBGD d ͋2 BFE E(A0A8DP
8F0A(B BBBD
-8D0A(B BBGD d ̿ 5 BBE B(D0I8DP
+8D0A(B BBGD 4 ϋ d L pЋ5 BBE B(D0I8DP
8D0A(B BBBC q
-8D0A(B BBBB T d( BBB B(A0A8G
-8A0A(B BBBB L BBB B(D0A8D@d
-8D0A(B BBBF L L ( ' BEB E(I0H8DP
-8D0A(B BBBD d , I BBE E(A0A8D@
+8D0A(B BBBB T HӋn
+ BBB B(A0A8G
+8A0A(B BBBB L `؋ BBB B(D0A8D@d
+8D0A(B BBBF L ً G
+ BEB E(I0H8DP
+8D0A(B BBBD d ۋI BBE E(A0A8D@
8F0A(B BBBC S
-8F0A(B BBBF L BBB E(A0D8D@x
-8A0A(B BBBJ T T & BBH B(D0A8DY
-8D0A(B BBBC L < ( BBB E(A0D8D@x
-8A0A(B BBBJ , ADD ]
-AAA D , X G& BBJ A(D0
-(A BBEA , t 0 & GA A(D L d & BBE H(A0A8J~
-8A0A(B BBBA . K^ T V Y& BLB E(G0A8J&
-8A0A(B BBBI T d a& BBB B(A0A8J
-8C0A(B BBBF T (!* & BBB B(A0I8Gl
-8D0A(B BBBA T & a& BBE E(D0A8J/
-8A0A(B BBBA h+! A[ x+Y } N $ 4 +g AG k
-AA 4 , % BHA G@h
- DABA . K^ T , % BEE E(A0A8Gs
-8A0A(B BBBI P7[ 7[ 4 7& L 7$ A[
-D C $ l 8d AW
+8F0A(B BBBF L x܋ BBB E(A0D8D@x
+8A0A(B BBBJ T 8 g
+ BBH B(D0A8DY
+8D0A(B BBBC L ߋ BBB E(A0D8D@x
+8A0A(B BBBJ L T
+ BBB E(A0D8Qv
+8A0A(B BBBA D O
+ BBJ A(D0
+(A BBEA 8Z. K^ " DR
+J A T $
+ BIE E(G0A8J
+8A0A(B BBBI T | Xw
+ BBB B(A0D8GZ
+8C0A(B BBBF T * :
+ BBB B(A0I8Gl
+8D0A(B BBBA T , X
+ BBE E(D0A8J/
+8A0A(B BBBA < ! A[ \ Y } N $ t g AG k
+AA 4 ` k
+ BHA G@h
+ DABA 8X. K^ T 4
+ 7
+ BEE B(A0D8Gs
+8A0A(B BBBI D [ \ [ L t 8 BBB E(A0A8D`
+8A0A(B BBBA & $ A[
+D C ^ $ d AW
H y
-G C \ P8 BBB E(A0A8DP{
-8D0A(B BBBG D8F0A(B BBBl d 8[ $ BBB E(A0A8D
+G C \ < ` BBB E(A0A8DP{
+8D0A(B BBBG D8F0A(B BBBD ' BBE A(A0D@
+0A(A BBBA l , [
+ BBB E(A0A8D
8A0A(B BBBA v
-8A0A(B BBBA L d :p BBA D(D@E
-(C ABBF D(F ABB < $ : F$ BDG J_
- CABD < p? BED G(JB
-(C ABBJ | 4 PA BBB B(D0A8DP
+8A0A(B BBBA L T p BBA D(D@E
+(C ABBF D(F ABB L BBB E(A0A8D`
+8A0A(B BBBA d <
+ BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB d v
+ BJB B(D0A8D@Y
+8C0A(B BBBB p8A0A(B BBB < i
+ BDG Jg
+ CABD < " BED G(JB
+(C ABBJ | D # BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI L $ C Z# BBB B(D0D8Jt
-8C0A(B BBBF . K^ l F BBB A(A0G`]
+8A0A(B BBBI L x& &
+ BBB B(D0D8Jt
+8C0A(B BBBF L \ ( BEB E(I0H8D@
+8D0A(B BBBD L X*w BHI E(A0A8Gp
+8A0A(B BBBF D R> BIA A(G
+(A ABBA l @, BBB A(A0G`]
0A(A BBBD c
-0C(A BBBK D0F(A BBB , xFZ BDD D@G AAB, F_ BDD D@L AAB t; t; < t BEE D(D0(A BBB 4 hFg ! BAD D
-ABA 4 Fg ! BAD D
-ABA 4 < Fg n! BAD D
-ABA D G BBE A(A0D@
-0A(A BBBA L Zt\ AG RAD l G7 BBE A(A0D@
-0A(A BBBA , $ H AAG@G
-AAF pI. Aa
-F E L t I
W BBB F(A0D8G`
-8A0A(B BBBH L @Lf ? BEB E(A0A8GX
-8C0A(B BBBE D `P p BGB D(M0B
-(A BBBK T \ P 7 BEB E(A0D8G
-8D0A(B BBBI L UX ? BIN B(A0C8Gpg
-8A0A(B BBBI L Wd # BEE E(A0A8GM
-8D0A(B BBBD L T 0` BBE E(D0A8G
-8D0A(B BBBG L eI BEE E(A0A8J
-8D0A(B BBBC T j BBE E(A0D8G/o
-8A0A(B BBBE D L H~ ! BGB D(M0B
-(A BBBK | $ ~ BBB B(D0A8DP
+0C(A BBBK D0F(A BBB , l ,Z BDD D@G AAB, ,_ BDD D@L AAB ; @; < c BEE D(D0(A BBB 4 ,g Q BAD D
+ABA 4 ,g % BAD D
+ABA f\ AG RAD , BBE A(A0D@
+0A(A BBBA D -7 BBE A(A0D@
+0A(A BBBA L .
K BBB F(A0D8G`
+8A0A(B BBBH L p1B 3 BEB E(A0A8GX
+8C0A(B BBBE D D p5 ^ BGB D(M0B
+(A BBBK T 5t % BEB E(A0D8G
+8D0A(B BBBI D : , BGB D(M0B
+(A BBBK T , h: BBB B(D0A8G,
+8C0A(B BBBF L P\ BEE B(I0A8Gw
+8C0A(B BBBF L U BBE E(D0A8G
+8D0A(B BBBG L $ `[I ) BEE E(A0A8J
+8D0A(B BBBC T t `` F BBB B(D0D8J/g
+8A0A(B BBBE L t
+ O BEE E(A0A8G M
+8D0A(B BBBD h~H DPCD 4 ~ BGB D(M0B
+(A BBBK | 4 BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI L 0 BBF B(A0A8D@
-8D0A(B BBBB d = BIB B(A0A8D`
+8A0A(B BBBI < BGD D(K0l
+(A ABBB L BBF B(A0A8D@
+8D0A(B BBBB d D = BIB B(A0A8D`
8F0A(B BBBI
-8D0A(B BBBA L \ BBB B(A0A8D
-8A0A(B BBBA < 8 BBG A(G@
-(A ABBG ؈% T t BBB B(D0C8GE
-8C0A(B BBBE D \ BDD G0t
- AABG
- AABD < 0 BBG A(G@
-(A ABBG L 8 BBB B(A0D8D@
-8A0A(B BBBJ < 4 ક BBG A(G@
-(A ABBG \ t KFF E(D0D8G@8A0A(B BBBG@ \ Pm BBD D(J@
+8D0A(B BBBA L BBB B(A0A8D
+8A0A(B BBBA < BBG A(G@
+(A ABBG < % T ؉] p BBB B(D0C8GE
+8C0A(B BBBE $ ৌ AG0
+AF D BDD G0r
+ AABI
+ AABA < 0 BBG A(G@
+(A ABBG L \ 8 BBB B(A0D8D@
+8A0A(B BBBJ < ଌ BBG A(G@
+(A ABBG \ KFF E(D0D8G@8A0A(B BBBG@ < L P BBG A(G@
+(A ABBF t KHE A(GP(A ABBEP
+(A ABBM 4
+(A ABBF \ m BBD D(J@
(A ABBD T
(A ABBF _
-(A ABBK T `J BBB B(A0A8JF-
-8A0A(B BBBE T X̕ Q! BBB B(A0A8G7@
-8A0A(B BBBE 0|. K^ " DR
-J A T e" BEB B(A0D8G
-8A0A(B BBBK T " BEG D(GPa
+(A ABBK T } BBB B(A0A8JF%
+8A0A(B BBBE T Ќ
BBB E(A0A8G6
+8D0A(B BBBE hH. K^ , 0" DR
+J A T @ " BEB B(A0D8G
+8A0A(B BBBK T c BEG D(GPa
(A ABBH P
-(D ABBA T < g# `" BBE B(D0A8GA
-8D0A(B BBBI T jL I$ BEE E(D0D8J
-8A0A(B BBBA T - :$ BBB B(D0A8GY<
-8C0A(B BBBD T D ?R & BBB B(D0A8M
-P
-8A0A(B BBBD , "q\ AG RAD L F7 BBE A(A0D@
-0A(A BBBA D GC BBE A(A0D@
-0A(A BBBA T L H 2& BEE B(A0G8G
-8A0A(B BBBD 4 y. K^ $ L Z0 AGJ XDA t ZY } N $ XZg AG k
-AA 4 $ Z #& BDA MPO
- AABI 4 \ [ % BHA G@h
- DABA $ Px. K^ < \ T \ $ l \^ AG b
-AA D P\u BBE A(A0D@z
-0A(A BBBA D `m BBE A(A0D@r
-0A(A BBBA D d $ BBB A(D0D@
-0A(A BBBH L j $ BJB A(A0W
-(A BBDH ^(A BBBL , 8k S$ BEB I(A0D
-0D(A BBBC D n BBE A(A0D@l
-0A(A BBBA T Pv. K^ T (o # BHB B(A0A8G}
-8A0A(B BBBA l\ AG RAD 7 BBE A(A0D@
-0A(A BBBA D , C BBE A(A0D@
-0A(A BBBA T 6 $ BEE B(A0G8G
-8A0A(B BBBA u. K^ ǖY } N $ 8Ȗg AG k
-AA 4 Ȗ G& BHA G@h
- DABA T (ɖ^
- +& BEB B(A0D8G<
-8A0A(B BBBC \ 0Ӗl BBB A(D0D@
-0D(A BBBA x
-0D(A BBBE L @ٖ % BBB E(K0A8D
-8A0A(B BBBA d s. K^ L hޖ
- % BBB E(A0D8J
-8D0A(B BBBD [ P[ [ [ , ([ D p[ \ [ t [ H[ [ Y } N $ g AG k
-AA 4 l h $ BHA G@h
- DABA L * $ BBB E(K0A8D
-8C0A(B BBBF L BEB A(D0l
+(D ABBA T D p BEB B(D0A8GA
+8D0A(B BBBI T vL
+ BEE E(D0D8J
+8A0A(B BBBA L
+ BBB B(A0D8I:S
+8C0A(B BBBK T D + 1
+ BBB E(A0A8G*X
+8D0A(B BBBG T F c
+ BBB B(D0A8M
+
+8A0A(B BBBA |\ AG RAD M7 BBE A(A0D@
+0A(A BBBA D NC BBE A(A0D@
+0A(A BBBA T Oq
+ BEE B(A0G8G%
+8A0A(B BBBD D. K^ $ a0 AGJ XDA aY } N $ Pag AG k
+AA 4 | a
+ BDA MPO
+ AABI 4 b
+ BHA G@h
+ DABA 8D. K^ 4 cJ BDA q
+ABE A
+ABA < (c BBG A(G@
+(A ABBG L | c
+ BEB E(I0H8D@
+8D0A(B BBBD L he
+ BEB E(I0H8D@
+8D0A(B BBBD By\ AG RAL fv BBE B(A0A8Dp
+8A0A(B BBBA , D .y BDA D0 AABL h
+ BBB B(D0A8Dp
+8A0A(B BBBI D k7 BBE A(A0D@
+0A(A BBBA D lC BBE A(A0D@
+0A(A BBBA d T m' BEB B(A0D8D
+8C0A(B BBBH M
+8D0A(B BBBA < Pr BLD D(G0
+(A ABBE D s BKM D(A0D@
+0A(A BBBA T s0- BEB B(A0A8G
+8A0A(B BBBA p@. K^ $ ^ AG b
+AA D BBE A(A0D@
+0A(A BBBA D T h BBE A(A0D@
+0A(A BBBA D e BBB A(D0D@
+0A(A BBBG D , 8 o BEE K(A0GPl
+0A(A BBBA D , Ь BBE A(A0D@l
+0A(A BBBA t >. K^ T P=# BBH B(A0A8G
+8A0A(B BBBA du\ AG RAD Ѝ7 BBE A(A0D@
+0A(A BBBA D L эC BBE A(A0D@
+0A(A BBBA T ҍH6 BEE B(A0G8G
+8A0A(B BBBA =. K^ Y } N $ @g AG k
+AA 4 ;
+ BHA G@h
+ DABA T 0
+
+ BEB B(A0D8G<
+8A0A(B BBBC t Y BBB A(D0DP
+0D(A BBBA q
+0D(A BBBD
+0D(A BBBE L v
+ BBB E(K0A8D
+8A0A(B BBBA <. K^ L
+
+ BBB E(A0D8J
+8D0A(B BBBC )[ )[ 4 (*[ L p*[ d *[ | +[ H+[ +[ +[ ,[ h,Y } N $ ,g AG k
+AA 4 | , BHA G@h
+ DABA L - BBB E(K0A8D
+8C0A(B BBBF L p3 BEB A(D0l
(A BBBG
-(A BBBA `q. K^ T \ ' f$ BEB B(A0A8GU
-8A0A(B BBBE D [ \ 8[ t [ [ [ X Y } N $ g AG k
-AA 4 l $ BHA G@h
- DABA L $ BBB E(K0A8D
-8A0A(B BBBA o. K^ L ".$ Y$ BEE B(A0A8G
-8A0A(B BBBB $ F0 AGJ XDA FY } N $ , Fg AG k
-AA d 0G $ BEL B(D0A8D
+(A BBBA 9. K^ T l (4E% : BEB B(A0A8GV
+8A0A(B BBBD | Y[ hY[ Y[ Y[ @Z[ Z[ Z[ $ [[ < `[[ T [[ l [Y } N $ 8\g AG k
+AA 4 \ BHA G@h
+ DABA L (]l BEE B(A0A8Dp
+8A0A(B BBBB L | Hfv BBB E(K0A8D
+8A0A(B BBBA 4
+ xk BDA w
+AEA {
+AFI
+ @7. K^ L
+ k ) BEB B(D0D8G
+
+8D0A(B BBBE $ $ h0 AGJ XDA L pY } N $ d g AG k
+AA d
+ BEL B(D0A8D
8C0A(B BBBE c
-8D0A(B BBBA 4 , hH ?$ BHA G@h
- DABA n. K^ d\ AG RAD , H7 BBE A(A0D@
-0A(A BBBA D t IC BBE A(A0D@
-0A(A BBBA n. K^ T D J% C# BEB B(A0A8G$
-8A0A(B BBBA 4 Hpg K$ BAD D
-ABA 4 pg $ BAD D
-ABA D p
+8D0A(B BBBA 4 < 8 K BHA G@h
+ DABA , 6. K^ D `l\ AG RAD d 7 BBE A(A0D@
+0A(A BBBA D C BBE A(A0D@
+0A(A BBBA h5. K^ T T % O BEB B(A0A8G?
+8A0A(B BBBA 4 (g W BAD D
+ABA 4 `g + BAD D
+ABA D
BBE A(A0D@
-0A(A BBBA D q BBE A(A0D@
-0A(A BBBA , 6c\ AG RA L 8r% AQ
-F L 4 RcW ## BAD JA AAB rE lX Hrm A~
-A l r% 4 !d BED C(D@(A ABBL d< " BEE E(A0K8G!
- 8D0A(B BBB L o+ " BEB B(A0A8J48D0A(B BBB (k. K^ L T # BBB H(D0A8G8A0A(B BBB T pqA' # BEE B(A0G8GG
-8A0A(B BBBD $ h0 AGJ XDA pY } N $ g AG k
-AA 4 d % BHA G@h
- DABA , i. K^ L {% BBB H(A0J8DQ
-8C0A(B BBBF v\ AG RAD 07 BBE A(A0D@
-0A(A BBBA D (C BBE A(A0D@
-0A(A BBBA D 0 $ BEE E(J0J@X
-0A(A BBBK L s$ BEE E(D0K8Ge
-8A0A(B BBBJ T L 85 =$ BLB B(D0D8J;
-8D0A(B BBBD L
- $ BLB B(A0D8J
-8D0A(B BBBH t KHE A(GP(A ABBEP
+0A(A BBBA j\ AG RAD < @ BBE A(A0D@
+0A(A BBBA % AQ
+F L (. Aa
+F E 4 8 BAD Jd
+ AABG T D b BBB E(A0A8G%
+8C0A(B BBBI T 8Ŏ< BBB B(A0A8G6
+8D0A(B BBBF m A~
+A l , p s AID0|
+DAA T D d g BBB B(A0A8Q7
+8C0A(B BBBB T 82. K^ L hv 0 BBB H(D0A8GQ8A0A(B BBB T n S BBB E(D0D8G
+8A0A(B BBBA T \ (
+ u BBE B(K0A8G C
+8A0A(B BBBE T % BBB B(D0A8G9M
+8A0A(B BBBE T xU BEE B(A0G8GS
+8A0A(B BBBF $ 0 AGJ XDA D Y } N $ \ g AG k
+AA 4 BHA G@h
+ DABA 0. K^ L BBB H(A0J8DQ
+8C0A(B BBBF $ fq\ AG RAD D H7 BBE A(A0D@
+0A(A BBBA D @C BBE A(A0D@
+0A(A BBBA D H BEE E(J0J@X
+0A(A BBBK L d J BEB L(D0D8G\
+8A0A(B BBBF T H BLB E(A0D8J8
+8D0A(B BBBG L H'
+ BLB B(A0D8J
+8D0A(B BBBH t 1 KHE A(GP(A ABBEP
(A ABBM 4
-(A ABBF D l Xgn $ BIB A(A0DP
-0A(A BBBA T кJ T$ BEE B(A0G8G
-8A0A(B BBBA D Зi BBB H(A0D@
-0A(A BBBA D їq BBB H(A0D@
-0A(A BBBA L , (ӗ BBF B(A0A8D@
-8D0A(B BBBB d | ӗE BBB B(A0A8DP
+(A ABBF D `- BIB A(A0D`*
+0A(A BBBA T H4 c BEE B(A0G8G
+
+8A0A(B BBBA D , Ji BBB H(A0D@
+0A(A BBBA D t Kq BBB H(A0D@
+0A(A BBBA L M BBF B(A0A8D@
+8D0A(B BBBB d ME BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA T T ԗy # BBE B(A0A8O
-8A0A(B BBBG < f. K^ T $ BBB B(A0A8G
-8A0A(B BBBJ \ AG RAD 7 BBE A(A0D@
-0A(A BBBA D C BBE A(A0D@
-0A(A BBBA T n $ BBE B(A0D8Mr
-8A0A(B BBBG 4 $ @e $ BHA D@
- AABA ֈ\ AG RAD 7 BBE A(A0D@
-0A(A BBBA D T C BBE A(A0D@
-0A(A BBBA < # BGD D(K0l
-(A ABBB , BV BAA D0I AABT | # BEE B(A0G8G
-
-8A0A(B BBBA < e $ BIA A(D`)
-(A ABBA L Ї^ BED D(E0o
-(G ABBE A(A ABD އ\ AG RAD -7 BBE A(A0D@
-0A(A BBBA D \ .C BBE A(A0D@
-0A(A BBBA e. K^ T , /0 # BEE B(A0G8G 7
-8A0A(B BBBA \ AG RAD 4 ` BBE A(A0D@e
-0A(A BBBA T `< $ BBE B(D0A8G
-8D0A(B BBBB T D eF3 $ BBB B(A0A8J
-8A0A(B BBBG , Xd. K^ D AZ d | HS AT L AAG ]
-CAF c
-CAI R
-CAJ JCA ~\ AG RAT ؘ % BEB B(A0H8G
-8A0A(B BBBC T g% BBE B(A0D8G
-8D0A(B BBBG L L [% BEB E(I0H8DP
-8D0A(B BBBA L H 3% BEB E(I0H8D@
-8D0A(B BBBA L ȥ % BEB E(I0H8D@
-8D0A(B BBBA , X BCD D0 AABL l $ BEB E(I0H8D@
-8D0A(B BBBA L $ BEB E(I0H8D@
-8D0A(B BBBA T r c$ BHE B(D0A8L
-8D0A(B BBBA T d @ $ BBE B(A0A8J
-8A0A(B BBBD L Ę % BEB E(I0H8DP
-8D0A(B BBBD d hƘ, _% BGE E(A0A8D@
+8F0A(B BBBA T xN BBE B(A0A8O
+8A0A(B BBBG ,. K^ T , h BBB B(A0A8G
+8A0A(B BBBJ < l\ AG RAD \ t7 BBE A(A0D@
+0A(A BBBA D uC BBE A(A0D@
+0A(A BBBA T 4 v BBE B(A0D8Ms
+8A0A(B BBBF < + BIA A(D`)
+(A ABBA k\ AG RAD 7 BBE A(A0D@
+0A(A BBBA D C BBE A(A0D@
+0A(A BBBA < | BGD D(K0l
+(A ABBB , t *kV BAA D0I AABT h BEE B(A0G8G
+
+8A0A(B BBBA < D + BIA A(D`)
+(A ABBA 4 < jL BDA v
+ABE AAD t j\ AG RAD 87 BBE A(A0D@
+0A(A BBBA D 0C BBE A(A0D@
+0A(A BBBA $ H,. K^ T T1 BEE B(A0G8G D
+8A0A(B BBBA j\ AG RAD ڏ BBE A(A0D@e
+0A(A BBBA T D ڏ< BBE B(D0A8G
+8D0A(B BBBB T ߏF3 BBB B(A0A8J
+8A0A(B BBBG *. K^ A^ HS AT L < AAG ]
+CAF k
+ACA R
+CAJ JCA $ ^ AG b
+AA T BEB B(A0H8G
+8A0A(B BBBC T T P g BBE B(A0D8G
+8D0A(B BBBH L [ BEB E(I0H8DP
+8D0A(B BBBA L 3 BEB E(I0H8D@
+8D0A(B BBBA L L BEB E(I0H8D@
+8D0A(B BBBA , T ! BCD D0 AABL H" BEB E(I0H8D@
+8D0A(B BBBA L # BEB E(I0H8D@
+8D0A(B BBBA d l h%, c BGE E(A0A8D@
8F0A(B BBBJ g
-8F0A(B BBBA T t 0ǘ{ % BBE B(A0A8G
-8A0A(B BBBF \ X_. K^ t @И 8И 0И$ Ab "\ AG RA, И AAG0W
-AAF L И S$ BBB B(D0A8DP
-8A0A(B BBBA L PҘ $ BBB B(D0A8DP
-8A0A(B BBBA L $ Ә # BBE B(A0A8DPn
-8A0A(B BBBA T t p֘ o# BBB I(A0A8G
-8A0A(B BBBA T ٘ /# BBB I(A0A8G
-8A0A(B BBBA L $ ݘJ " BBB A(D0DF
-0D(A BBBH \. K^ 4 L d | - 4 BGI _
-ABD NAB , xE ! BDC rAH L ! BBB I(A0D
-0D(A BBBA L / BEB B(A0A8D`u
-8A0A(B BBBH , l 9! AAG0
-CAH , Z. K^ D \ F t |\ AG RAL 4 w BBB B(A0A8Ji
-8A0A(B BBBI T BBE B(D0A8G
-8D0A(B BBBG T , } BBE B(D0A8G
-8D0A(B BBBK T 4 c BBB B(D0D8G
-8A0A(B BBBH D { BBB D(A0D@0A(A BBB T [" BIB E(A0A8G5
-8D0A(B BBBA L , 8 7" BDE E(G0A8G
-8D0A(B BBBH L | 3 " BBB I(A0A8JY
-8D0A(B BBBG \ W. K^ L ( w" BBE B(D0A8G
-8A0A(B BBBA , 4 +g " AID @
-DAA , , $ , < + T , l , ,0 Hg ,7 Hj @,0 Hg P,7 Hj $ p,^ AG b
-AA T , (! BBE B(D0A8G
-8D0A(B BBBC T @1 ! BBB B(A0D8R
-8A0A(B BBBK , L <> ! AID W
-DAA T | <)
- ! BBB B(A0D8R
-8A0A(B BBBA , E> " AID W
-DAA T F/ ! BBB B(A0D8Rh
-8A0A(B BBBG , \ L> 6" AID W
-DAA T L/ " BBB B(A0D8Rh
-8A0A(B BBBG , S> [" AID W
-DAA T Sg ;" BBB B(A0I8G
-8D0A(B BBBI T l X Z" BBE B(D0A8G
-8D0A(B BBBB T ] @" BBE B(D0A8G
-8D0A(B BBBC D b BBB D(A0D@
-0A(A BBBA D d pc| ! BIA D(D
-(D ABBA D f| ! BIA D(D
-(D ABBA L i ! BIB A(D0D
-0D(A BBBA L D 0m ! BIB A(D0D
-0D(A BBBA L p r! BIB A(D0D
-0D(A BBBA L s S! BBB B(G0D8G
-8A0A(B BBBA < 4 v S! AAG L
+8F0A(B BBBA T 0&
+ # BBE B(A0A8G
+8A0A(B BBBF T ,! / BHE B(D0A8L
+8D0A(B BBBA T ! 8 { BBE B(A0A8J
+8A0A(B BBBD " 8&. K^ " 0H " (H " H " (H # 0H$ Ab ,# c\ AG RA, L# H AAG0W
+AAF L " H' 5 BBB B(D0A8D`B
+8A0A(B BBBA L # L/ BBB B(D0A8D`J
+8A0A(B BBBA T d# P BBB I(A0A8K]
+8A0A(B BBBE T # 8U _ BBB I(A0A8K]
+8A0A(B BBBE L $ YJ ! BBB A(D0DF
+0D(A BBBH % #. K^ 4% ] L% ] d% ] |% ] % ] % ] % ] % ] % ]- 4 & ] BGI _
+ABD NAB , % ^E BDC rAH T % 8^ BBB B(I0A8G
+8D0A(B BBBD L & b/ BEB B(A0A8D`u
+8A0A(B BBBH , d& d o AAG0
+CAH L' !. K^ d' he |' `eF ' e ' e ' _\ AG RAL ,' xeX BBB B(A0A8Ji
+8A0A(B BBBI T |' n BBE B(D0A8G
+8D0A(B BBBG T ' Ps, BBE B(D0A8G
+8D0A(B BBBK T ,( (x BBB B(D0D8G'
+8A0A(B BBBH , <) t^- AAG0!AA < ( 9 ] BHD G
+ DABA L ( G BDE E(G0A8G
+8D0A(B BBBH L D) @3 BBB I(A0A8JY
+8D0A(B BBBG L* . K^ L ) BBE B(D0A8G
+8A0A(B BBBA , ) hg AID @
+DAA * * + ,+ D+ \+ t+ 0 Hg + 7 Hj + ؤ0 Hg + 褐7 Hj $ + ^ AG b
+AA T d+ @ @ BBE B(D0A8G
+8D0A(B BBBC T + ة
+ & BBB B(A0D8R
+8A0A(B BBBA , , p> AID W
+DAA T D, BBB B(A0D8R
+8A0A(B BBBK , , H> 4 AID W
+DAA T , X/ BBB B(A0D8Rh
+8A0A(B BBBG , $- 0Ɛ> Y AID W
+DAA T T- @Ɛ/ 9 BBB B(A0D8Rh
+8A0A(B BBBG , - ͐> ~ AID W
+DAA T - (͐g ^ BBB B(A0I8G
+8D0A(B BBBI T 4. @Ґ } BBE B(D0A8G
+8D0A(B BBBB T . c BBE B(D0A8G
+8D0A(B BBBC , / BY- AAG0!AA < / @ې BHD DW
+ DABA < T/ ސ BHD DW
+ DABA < / BHD Dq
+ DABA < / BHD Dq
+ DABA < 0 BHD Dq
+ DABA L T0 ` BBB B(G0D8G
+8A0A(B BBBA < 0 AAG L
AAI p
-AAB L t vq ! BBB A(A0G
-0D(A BBBF < }r BBA K(M0x
-(D ABBA T } BBB G(D0G8G
-8D0A(B BBBH D \ H BBA A(G
-(D ABBE < r BBA K(M0x
-(D ABBA T 0 BBB G(D0G8G
-8D0A(B BBBH < g BBA A(DV(A ABB T | _ BBB E(A0A8L
-8A0A(B BBBA d
- N. K^ |
-
- ,
- [ h AID t
-DAA , L
- H[ H AID t
-DAA < `qD BBD A(D@0(A ABB T
- 8& BIB E(A0A8G
-8D0A(B BBBA T & BIB E(A0A8G
-8D0A(B BBBA 4 l 襙 BGB A(C0G T Ц BBE B(D0A8G
-8D0A(B BBBB L U h BBB B(F0G8G
-8D0A(B BBBK T L \& BDB B(G0D8G
>
-8D0A(B BBBI 4
pK. K^ L
ؙ d
ؙ |
ؙ
ؙ ,
ؙ[ 3" AID t
-DAA , L
ؙ[ " AID t
-DAA D o BBB D(A0D@0A(A BBB T
ؙ ! BIB E(A0A8DN
-8D0A(B BBBA T ܙ y! BIB E(A0A8DN
-8D0A(B BBBA T t @3 G! BIB E(A0A8G
-8D0A(B BBBA T ( !! BBE B(D0A8G
-8D0A(B BBBH T $ ! BBB B(A0I8G(
-8D0A(B BBBB \ | (E BEE A(A0D=
+AAB L 0 Pq [ BBB A(A0G
+0D(A BBBF < 41 r 0 BBA K(M0x
+(D ABBA T t1 BBB G(D0G8G
+8D0A(B BBBH D 1 x BBA A(G
+(D ABBE < 2 r BBA K(M0x
+(D ABBA T T2 ` BBB G(D0G8G
+8D0A(B BBBH < d3 g BBA A(DV(A ABB T 2 HS BBB E(A0A8L
+8A0A(B BBBA 3 . K^ 4 8 ,4 0 , 3 8[ AID t
+DAA , 3 h[ AID t
+DAA , 4 hU- AAG0!AA < 4 h 4 BHD Gr
+ DABA < \4 BHD Gr
+ DABA T 4 BBE B(D0A8G
+8D0A(B BBBB T 4 P [* BDB B(G0D8G
+8D0A(B BBBE 6 . K^ 6 @H 46 8H L6 0H d6 8H , 5 @H[ AID t
+DAA , 5 pH[ AID t
+DAA , 6 ^T- AAG0!AA 4 T6 pHE = BHD D`
+ DABD 4 6 KE BHD D`
+ DABD D 6 N BIA D(G
+(D ABBA T 7 8R BBE B(D0A8G
+8D0A(B BBBH T d7 W BBB B(A0I8G(
+8D0A(B BBBB \ 7 8ZE BEE A(A0D=
0A(A BBBI j
-0A(A BBBF \ u ?! BEE A(A0De
+0A(A BBBF \ 8 (bu BEE A(A0De
0A(A BBBA j
-0A(A BBBF D < 8 ! BEE A(A0D
-0A(A BBBD D ! BEE A(A0D
-0A(A BBBD L h 5 T" BBB A(D0G
-0A(A BBBB T X " BBB G(D0G8G
-8D0A(B BBBB L t " BBB A(D0G[
-0A(A BBBK T !
- 3# BDB B(A0G8J
-8D0A(B BBBJ (F. K^ @+ 8+ 0+ (+ $ + < (+ T 0+ l 8+ $ @+^ AG b
-AA , x+ BGD l
-ABD L +q BBA D(G@n
+0A(A BBBF D |8 Hj F BEE A(A0D
+0A(A BBBD D 8 o BEE A(A0D
+0A(A BBBD L 9 xu5 BBB A(D0G
+0A(A BBBB T \9 h} k BDB B(A0G8J
+8D0A(B BBBD T 9 { BGB B(D0G8G
+8D0A(B BBBB : (. K^ : 0 : ( ; $; <; T; l; ; ( $ ; 0^ AG b
+AA , ; h BGD l
+ABD L ; 蓑q BBA D(G@n
(A ABBD
-(A ABBH T (-9 _" BEE E(A0A8G
-8A0A(B BBBA T / 1" BEE E(A0A8G
-8A0A(B BBBA T L X1
-" BBB E(A0A8KP
-8A0A(B BBBF , 3> ! AID W
-DAA T 3 ! BBB E(A0A8KP
-8A0A(B BBBF , , 4> ! AID W
-DAA T \ 4 `! BBB E(A0A8KP
-8A0A(B BBBF , p6> +! AID W
-DAA T 6 ! BBB E(A0A8KP
-8A0A(B BBBF , < (8> AID W
-DAA D 88 BBB D(A0D@
-0A(A BBBA L 9 n BIB A(D0D
-0D(A BBBA L < O BIB A(D0D
-0D(A BBBA L T 0@ 0 BIB A(D0D
-0D(A BBBA L C BIB A(D0D
-0D(A BBBA D F BBB A(A0J`
-0D(A BBBC J* A[
-L A (J* A[
-L A 8J* A[
-L A , HJ* A[
-L A L XJ* A[
-L A T hJ P BEJ E(D0D8G(
-8D0A(B BBBF T 4 K BEJ E(D0D8G(
-8D0A(B BBBF T xM BEJ E(D0D8G(
-8D0A(B BBBF T OB BBB B(A0A8J
-8A0A(B BBBA T < TN BEB E(A0D8G
-8A0A(B BBBF T V u BEB E(A0A8G
-8A0A(B BBBF T Z 1 BEE B(A0A8JN
-8A0A(B BBBF L D a
-
- BBB B(A0D8G
-8A0A(B BBBG T jB K BBB B(A0A8J
-8A0A(B BBBA T pF BEB E(A0D8G
-8A0A(B BBBF T D r ? BBB B(D0D8Gw
-8A0A(B BBBH T Xv BEE B(A0A8JN
-8A0A(B BBBF L |
-
- BBB B(A0D8G
-8A0A(B BBBG T D `B BBB B(A0A8J
-8A0A(B BBBA T XF N BEB E(A0D8G
-8A0A(B BBBF T P BEB E(A0A8G
-8A0A(B BBBF T L BEE B(A0A8JN
-8A0A(B BBBF L P
-
- BBB B(A0D8G
-8A0A(B BBBG T B BBB B(A0A8J
-8A0A(B BBBA T L F BEB E(A0D8G
-8A0A(B BBBF T BEB E(A0A8G
-8A0A(B BBBF T BEE B(A0A8JN
-8A0A(B BBBF L T
-
- Q BBB B(A0D8G
-8A0A(B BBBG 4! 7. K^ L! d! , [ a AID t
-DAA D ! ؽy BBB B(A0A8[
-0A(B BBBA D ! BEB B(A0A8
-0A(B BBBA <" Ě2 I T" Ě AN
-A < t" Ě AHx
+(A ABBH T ; 9 BEE E(A0A8G
+8A0A(B BBBA T ; BEE E(A0A8G
+8A0A(B BBBA T << H BBB E(A0A8KP
+8A0A(B BBBF , < > \ AID W
+DAA T < < BBB E(A0A8KP
+8A0A(B BBBF , = > AID W
+DAA T L= BBB E(A0A8KP
+8A0A(B BBBF , = `> AID W
+DAA T = p BBB E(A0A8KP
+8A0A(B BBBF , ,> > ] AID W
+DAA $ ? ( AAG0AA< > BHD Dq
+ DABA < > BHD Dq
+ DABA < ? p BHA G
+ DABA < D? BHD Dq
+ DABA D ? BBB A(A0J`
+0D(A BBBC @ 讑* A[
+L A @ * A[
+L A @ * A[
+L A @ * A[
+L A A (* A[
+L A T l@ 8 BEJ E(D0D8G(
+8D0A(B BBBF T @ BEJ E(D0D8G(
+8D0A(B BBBF T A H } BEJ E(D0D8G(
+8D0A(B BBBF T tA гB 8 BBB B(A0A8J
+8A0A(B BBBA T A ȹN q BEB E(A0D8G
+8A0A(B BBBF T $B , BEB E(A0A8G
+8A0A(B BBBF T |B BEE B(A0A8JN
+8A0A(B BBBF L B ő
+ BBB B(A0D8G
+8A0A(B BBBG T $C ϑB BBB B(A0A8J
+8A0A(B BBBA T |C ՑN ; BEB E(A0D8G
+8A0A(B BBBF T C ב BEB E(A0A8G
+8A0A(B BBBF T ,D Xۑ BEE B(A0A8JN
+8A0A(B BBBF L D
+ t BBB B(A0D8G
+8A0A(B BBBG T D pB BBB B(A0A8J
+8A0A(B BBBA T ,E hN BEB E(A0D8G
+8A0A(B BBBF T E ` BEB E(A0A8G
+8A0A(B BBBF T E ( | BEE B(A0A8JN
+8A0A(B BBBF L 4F p
+ > BBB B(A0D8G
+8A0A(B BBBG T F @B BBB B(A0A8J
+8A0A(B BBBA T F 8
N BEB E(A0D8G
+8A0A(B BBBF T 4G 0 BEB E(A0A8G
+8A0A(B BBBF T G F BEE B(A0A8JN
+8A0A(B BBBF L G @
+ BBB B(A0D8G
+8A0A(B BBBG H 0. K^ I " I " , |H "[ AID t
+DAA D dI (# BBB B(A0A8s
+0A(B BBBA L I $ BBB B(A0A8J@
+8A0A(B BBBA I 0*2 I J X* AN
+A < 4J X* AM
AF
-AH
-EH < " \D BBD A(D@0(A ABB T d" Ț BIB E(A0A8D6
-8D0A(B BBBA < L# ͚ BBD D(G0b
-(A ABBE , # ͚ ADT
+AH
+EH , tJ A- AAG0!AA 4 I . BHD D`
+ DABF < J 0 BBD D(G0b
+(A ABBE , K P1 ADT
AF
-AA D # HΚH BBB B(A0A8
-0A(B BBBE L $ Pњ] BBB B(D0A8G
-8A0A(B BBBA d T$ `՚c BBB E(D0D8D
+AA D LK 2H BBB B(A0A8
+0A(B BBBE L K 5] BBB B(D0A8G
+8A0A(B BBBA d K (9c BBB E(D0D8D
8A0A(B BBBD
-8A0A(B BBBK L $ hٚ BBB B(A0D8D@
-8A0A(B BBBA L % (ߚ BEE E(D0A8D
-8A0A(B BBBA 4 $ AF
P
- H D % ` BBE B(A0A8
-0A(B BBBA 4 L% ( F AC
PG
- A \ & BBE B(A0A8
-0A(B BBBA 0A(B BBB L t& % BBE B(A0A8D
-8A0A(B BBBA , & $ AM
DL
- E d & c BBB E(D0D8D
+8A0A(B BBBK L LL 0=Y BEB B(A0A8DX/
+8A0A(B BBBA L L @C BEE E(D0A8D
+8A0A(B BBBA 4 4L W AF
P
+ H D $M x\ BBE B(A0A8
+0A(B BBBA 4 L @^ AC
PG
+ A \ M g BBE B(A0A8
+0A(B BBBA 0A(B BBB L N (k% BBE B(A0A8D
+8A0A(B BBBA , TN w$ AM
DL
+ E d N xc BBB E(D0D8D
8A0A(B BBBD
-8A0A(B BBBK d \' H BEE B(E0A8DX
+8A0A(B BBBK d N |H BEE B(E0A8DX
8A0A(B BBBA I
-8A0A(B BBBA < 4' D
- m AC
FM
+8A0A(B BBBA < N D
+ @ AC
FM
A
- D T t' ' A BBA A(G`
+ D T N BBA A(G`
(A ABBD {
-(A ABBG 4 \( h)8 AC
BGM
+(A ABBG 4 O 8 AC
BGM
C
- F T ( p- BEB B(D0A8Jd
-8A0A(B BBBA L ( (6 BBB B(A0A8G
-8A0A(B BBBB L ( ; ' BBB B(D0A8GP
-8A0A(B BBBB T ( ? BBA A(DU
+ F T lO BEB B(D0A8Jd
+8A0A(B BBBA L |P @ BBB B(A0A8G
+8A0A(B BBBB D P BBB D(A0
+(A BBBH T \P BBA A(DU
(A ABBK
-(A ABEC L T) pA
- BDB E(A0A8G
-8A0A(B BBBI 4* /. K^ L* KF d* 0L A] * 0LF * hLu ASP\D * LF * MF * 8M AR + 8MF $+ pMu ASP\D D+ MF \+ Nu ASP\D |+ hNF + Nu ASP\D + OF + T\ AG RAL + ON BBE B(A0A8DP
-8A0A(B BBBF L <, Q\ BBB E(A0A8DP
-8A0A(B BBBF D + (S BBB D(A0D@
-0A(A BBBI < D, V AAG
+(A ABEC L P %
+ BGB B(A0A8G
+8A0A(B BBBH Q . K^ Q HF Q A] R F $R u ASP\D DR F \R PF tR AR R F R u ASP\D R F R Xu ASP\D S F S u ASP\D <S PF TS B:\ AG RAL tS hN BBE B(A0A8DP
+8A0A(B BBBF L S h\ BBB E(A0A8DP
+8A0A(B BBBF D \S x BBB D(A0D@
+0A(A BBBI < S { AAG
AAF p
-AAB < , PW s AAG
+AAB < S ? AAG
AAF p
-AAB < , X 7 AAG
+AAB < $T P AAG
AAF p
-AAB < - X AAG
+AAB < dT AAG
AAF p
-AAB - `YX 4 \- Z BAA P0[
- DABJ 4 - P[ } BAA M@z
- DABF T - \l S BEE E(A0A8G
-
-8D0A(B BBBF D . jR BBB D(A0D@0A(A BBB T l. n BIB E(A0A8G
-8D0A(B BBBA T . s|
- BBB B(A0A8O6
-8A0A(B BBBA < / } BBG A(G@
-(A ABBG L \/ ~ BEE B(A0D8Gu
-8D0A(B BBBD <0 (). K^ T0 AL t0 xR DN
-E A 0 rR\ AG RAT $0 BEE E(D0A8G'
-8D0A(B BBBG T |0 XT BEB E(D0A8G
-8D0A(B BBBJ D d1 ` BBB B(A0A8Dp8A0A(B BBBD 1 BBB B(A0A8Dp8A0A(B BBBD 1 BBB B(A0A8Dp8A0A(B BBBD <2 ( BBB B(A0A8Dp8A0A(B BBB4 1 _ BHA G@n
- DABI 4 ,2 5 BHA G@n
- DABI 4 d2 P BHA G@n
- DABI 4 2 BHA G@n
- DABI D 2 BBE A(D0G`
-0D(A BBBB T 3 BEE E(A0D8Jg
-8D0A(B BBBD T t3 M BEE E(A0D8Jg
-8D0A(B BBBD T 3 ( BEE E(A0D8Jg
-8D0A(B BBBD T $4 p BEE E(A0D8Jg
-8D0A(B BBBD T |4 T BEB E(D0A8G
-8D0A(B BBBJ D 4 R BBB D(G0GA
-0D(A BBBA 4 5 إH h SGB A(A0D T T5 Q BEB E(A0D8G
-8D0A(B BBBD T 5 X P BEB B(D0A8G
-8D0A(B BBBA T 6 M BFB B(D0A8G
-8D0A(B BBBE T \6 Xe
- BBB E(A0A8J<
-8D0A(B BBBH T 6 p˛e
- \ BBB E(A0A8J<
-8D0A(B BBBH T 7 ՛e
- BBB E(A0A8J<
-8D0A(B BBBH T d7 ߛe
- BBB E(A0A8J<
-8D0A(B BBBH L8 H!. K^ $ d8 ^ AG b
-AA 8 8 8 8 8 9 9 4 8 c BAD G@7
- AABG L 8 g ? BBE E(A0A8G_
-8D0A(B BBBE L ,9 8S O BBE B(D0A8Dp
-8A0A(B BBBD d : Hg BLB B(A0A8D@q
+AAB \U X 4 T s BAA P0[
+ DABJ 4 T I BAA M@z
+ DABF T ,U h BEE E(A0A8G
+8D0A(B BBBH , <V 7- AAG0!AA < U pђ BHD Db
+ DABA < V Ԓ BBG A(G@
+(A ABBG L 4V Ւ BEE B(A0D8Gu
+8D0A(B BBBD T V `ܒ|
+ BBB B(A0A8O6
+8A0A(B BBBA W . K^ W p AL W T7 DN
+E A W N7\ AG RAD TW R BBB D(G0GA
+0D(A BBBA 4 W 8H } SGB A(A0D T W P f BEE E(D0A8G'
+8D0A(B BBBG T ,X T L BEB E(D0A8G
+8D0A(B BBBJ D <Y BBB B(A0A8Dp8A0A(B BBBD Y BBB B(A0A8Dp8A0A(B BBBD Y BBB B(A0A8Dp8A0A(B BBBD Z BBB B(A0A8Dp8A0A(B BBB4 Y P BHA G@n
+ DABI 4 Y BHA G@n
+ DABI 4 Z BHA G@n
+ DABI 4 LZ BHA G@n
+ DABI D Z p j BBE A(D0G`
+0D(A BBBB T Z ( 8 BEE E(A0D8Jg
+8D0A(B BBBD T $[ p BEE E(A0D8Jg
+8D0A(B BBBD T |[ BEE E(A0D8Jg
+8D0A(B BBBD T [ BEE E(A0D8Jg
+8D0A(B BBBD T ,\ H X BEB E(A0D8G
+8D0A(B BBBD T \ T W BEB E(D0A8G
+8D0A(B BBBJ T \ i = BEB B(D0A8G
+8D0A(B BBBA T 4] @ BFB B(D0A8G
+8D0A(B BBBE T ] h$e
+ BBB E(A0A8J<
+8D0A(B BBBH T ] .e
+ O BBB E(A0A8J<
+8D0A(B BBBH T <^ 8e
+ BBB E(A0A8J<
+8D0A(B BBBH T ^ Be
+ BBB E(A0A8J<
+8D0A(B BBBH _ . K^ $ _ L^ AG b
+AA _ L _ L ` L ,` L D` L \` L t` L < _ L V BBD D(GPC
+(A ABBD L ` 0N * BBE E(A0A8G_
+8D0A(B BBBE L d` US ; BBE B(D0A8Dp
+8A0A(B BBBD d la Yg BLB B(A0A8D@q
8A0A(B BBBE
-8A0A(B BBBF T 9 P BBE B(D0A8G
-8D0A(B BBBG d : < BLB B(A0A8DPc
+8A0A(B BBBF T a Z BBE B(D0A8G
+8D0A(B BBBG d ,b p_< BLB B(A0A8DPc
8A0A(B BBBK
-8A0A(B BBBF d 4; g BLB B(A0A8D@q
+8A0A(B BBBF d b H`g BLB B(A0A8D@q
8A0A(B BBBE
-8A0A(B BBBF L ; BOB B(A0D8D@
-8A0A(B BBBI T \; H ' BBB E(A0A8G
-8A0A(B BBBA L D< BLB H(A0D8DPV
-8A0A(B BBBG L < A t BEB B(A0A8J
-8A0A(B BBBF d < BBB B(D0A8DP
+8A0A(B BBBF L Db Pa BLE B(A0D8D@
+8A0A(B BBBI L Lc c BLB H(A0D8DPV
+8A0A(B BBBG L b e O BEB B(A0A8J
+8A0A(B BBBF d c t BBB B(D0A8DP
8A0A(B BBBF
-8A0A(B BBBH T < M BBB E(A0A8J(
-8D0A(B BBBD L = @ m BBB H(A0D8DP
-8A0A(B BBBA T d= `!
BBB B(A0H8G
-8A0A(B BBBG 4 = (3b m BDA G0x
- DABC > @. K^ > D DN
-E A > (4" DN
-N A > 84 Y 4 d> @4g BAD D
-ABA L ,? x4 BLA A(D0]
+8A0A(B BBBH T c z 0 BBB E(A0A8J(
+8D0A(B BBBD L d P~m BBB H(A0D8DP
+8A0A(B BBBA T Dd p BBB B(A0H8G
+8A0A(B BBBG T d 萓' 4 BBB E(A0A8G
+8A0A(B BBBA 4 d b = BDA G0x
+ DABC e . K^ e ) DN
+E A f " DN
+N A <f И Y 4 e ؘg BAD D
+ABA L f BLA A(D0]
(A ABBI X
-(A ABBB L |? 4 BLA A(D0]
+(A ABBB L f BLA A(D0]
(A ABBI X
-(A ABBB < ? X5 BBA A(G@b
-(A ABBK $ @ 6^ AG b
-AA D ? P6 BBB D(E0Ml
-0A(A BBBH D ? ; BBE D(D0GP-
-0A(A BBBC D 4@ = BBE D(D0GP-
-0A(A BBBC D |@ x@ BBE D(D0GP-
-0A(A BBBC D @ 0C b BBE D(D0GP-
-0A(A BBBC A E < A E BBA A(G@a
-(A ABBD T dA FZ BEE E(D0A8Gi
-8D0A(B BBBE T A I BBE E(D0A8G,
-8D0A(B BBBE T B PLZ h BEE E(D0A8Gf
-8D0A(B BBBH d B XOg BBB B(A0A8GP_
+(A ABBB < ,g BBA A(G@b
+(A ABBK $ lg ^ AG b
+AA D f 蚓 BBB D(E0Ml
+0A(A BBBH L $g # BEB E(D0D8G`B
+8A0A(B BBBG L tg # z BEB E(D0D8G`B
+8A0A(B BBBG L g `# J BEB E(D0D8G`B
+8A0A(B BBBG L h @# BEB E(D0D8G`B
+8A0A(B BBBG i < 4i ( BBA A(G@a
+(A ABBD T h 諓Z BEE E(D0A8Gi
+8D0A(B BBBE T i U BBE E(D0A8G,
+8D0A(B BBBE T li Z BEE E(D0A8Gf
+8D0A(B BBBH d |j g BBB B(A0A8GP_
8A0A(B BBBF <
-8A0A(B BBBJ L dC `S; BBB B(A0D8DPd
-8A0A(B BBBA d C PVg BBB B(A0A8GP_
+8A0A(B BBBJ L j ; BBB B(A0D8DPd
+8A0A(B BBBA d 4k g BBB B(A0A8GP_
8A0A(B BBBF <
-8A0A(B BBBJ | D XZ BBB B(D0A8DP
+8A0A(B BBBJ | k BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI T D \ BBE E(A0A8G
-8D0A(B BBBC D _ FP
-A T D _ . BBE E(A0A8G
-8D0A(B BBBD T D Ha? BBB B(D0D8J
-8A0A(B BBBA T 4E 0j? O BBB B(D0D8J
-8A0A(B BBBA T E s? BBB B(D0D8J
-8A0A(B BBBA T E |N
- BBB B(D0D8J
-8A0A(B BBBF T <F i
- BEB E(A0A8K
-8A0A(B BBBI T F p# N BBB B(A0A8J
-8A0A(B BBBA T F H . BEB E(A0A8K
-8A0A(B BBBI T DG Ĝ# BBB B(A0A8J
-8A0A(B BBBA T G hʜ BEB E(A0A8K
-8A0A(B BBBI T G # BBB B(A0A8J
-8A0A(B BBBA T LH E BEB E(A0A8G
-8A0A(B BBBI T H #
BBB B(A0A8J
-8A0A(B BBBA I h. K^ I p
I h2 I I 2 J J 2 4J LJ 2 dJ |J J J h J p J ( J 0 K $K <K " TK lK " K ! K "" K # K $" K 0% K % L & ,L H' DL ( \L ( tL ) L (* L * L * L * L * M * M * 4M * LM * dM * |M * M * M * M * M * M * N x* $N p* <N h* TN `* lN X* N P* N H* N @* N 8* N 0* N (* O * ,O * DO * \O * tO * O ) O ) O ) O ) O ) P ) P ) 4P ) LP ) dP ) |P ) P ) P ) P ) P ) P ) Q x) $Q p) <Q h) TQ `) lQ X) Q P) Q H) Q @) Q 8) Q 0) Q () R ) ,R ) DR ) \R ) tR ) R ( R ( R ( R ( R ( S ( S ( 4S ( LS ( dS ( |S ( S ( S ( S ( S ( S ( T x( $T p( <T h( TT `( lT X( T P( T H( T @( T 8( T 0( T (( U ( ,U ( DU ( \U ( tU ( U ( U ( U ( U ( U (( V 0( V 8( 4V @( LV H( dV P( |V X( V `( V h( V p( V x( V ( W ( $W ( <W ( TW ( lW ( W ( W ( W ( W ( W ( W ( X ( ,X ( DX ( \X ( tX ) X ) X ) X ) X ) X () Y 0) Y 8) 4Y @) LY H) dY P) |Y X) Y `) Y h) Y p) Y x) Y ) Z ) $Z ) <Z ) TZ ) lZ ) Z ) Z ) Z ) Z ) Z ) Z ) [ ) ,[ ) D[ ) \[ ) t[ * [ * [ * [ * [ * [ (* \ 0* \ 8* 4\ @* L\ H* d\ P* |\ X* \ `* \ h* \ p* \ x* \ * ] * $] * <] * T] * l] * ] * ] * ] * ] * ] * ] * ^ * ,^ * , D^ *T BAA D0G AAB, t^ +T BAA D0G AAB, ^ P+T BAA D0G AAB, ^ +L BAA D0 AAB , _ +W BAA D0J AAB, 4_ +W BAA D0J AAB, d_ ,W BAA D0J AAB, _ 0,O BAA D0B AAB, _ P,] BAA D0P AAB, _ ,] BAA D0P AAB, $` ,R BAA D0E AAB, T` ,R BAA D0E AAB, _ -[ ~ AID t
-DAA , $` @-[ ^ AID t
-DAA , T` p-[ > AID t
-DAA , ` -[ AID t
-DAA , ` -[ AID t
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+ $ 8 < 0 T ( l 3 H @ 菔 ̀ 萔 , D \ t x p ԁ h
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+D c
+E c 8! " "" # x$ % 4 %j L ( d ( L | * BJH E(A0A8D@
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+(A BBBJ t 4@ 04= L X4: BJH E(A0A8D@
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+(A BBBJ < H` BMB A(A0
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+ <
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+(F LBBG
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+ DABG 4 | k 8[ BHA GP
+ DABG 4 @m [ BHA GP
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+ DABG 4 $ o Z BHA GP
+ DABG 4 \ Hq Z BHA GP
+ DABG 4 r Z BHA GP
+ DABG 4 s ~Z BHA GP
+ DABG 4
Pu _Z BHA GP
+ DABG 4 <
v @Z BHA GP
+ DABG 4 t
x !Z BHA GP
+ DABG 4
Xy Z BHA GP
+ DABG 4
z Y BHA GP
+ DABG 4 | Y BHA GP
+ DABG 4 T `} Y BHA GP
+ DABG 4 ~ Y BHA GP
+ DABG 4 gY BHA GP
+ DABG 4 h HY BHA GP
+ DABG 4 4 )Y BHA GP
+ DABG 4 l
+Y BHA GP
+ DABG 4 p X BHA GP
+ DABG 4 Ȇ X BHA GP
+ DABG 4 X BHA GP
+ DABG 4 L x X BHA GP
+ DABG 4 Њ oX BHA GP
+ DABG 4 ( PX BHA GP
+ DABG 4 1X BHA GP
+ DABG 4 , ؎ X BHA GP
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+ DABG 4 W BHA GP
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+ DABH 4 D wW BHA GP
+ DABH 4 | 薖 XW BHA GP
+ DABH 4 @ 9W BHA GP
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+ DABH 4 V BHA GP
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8A0A(B BBBE
-8A0A(B BBBF T BBE E(D0A8G
-8D0A(B BBBI L d 0M BBB B(A0D8N
-8A0A(B BBBH , 0> AID W
-DAA L @M BBB B(A0D8N
-8A0A(B BBBH , 4 @> AID W
-DAA L d PM p BBB B(A0D8N
-8A0A(B BBBH , P> t AID W
-DAA L `t W BGB B(A0D8G
-8A0A(B BBBA L 4 s W BDB B(A0D8G
-8A0A(B BBBA < l BBE A(A0Y(A BBB < T l BBE A(A0Y(A BBB < BEG A(D0M
-(D ABBG L Ԛ J BBB B(A0A8GP
-8A0A(B BBBK < $ BHB A(A0n(A BBL L d z BBB B(A0A8GPJ
-8A0A(B BBBK < @ BHB A(A0~(A BBL < BHB A(A0~(A BBL < 4 BHB A(A0~(A BBL < t `j BEG A(D0A
-(D ABBC < BEG A(D0J
-(D ABBJ < 0 BEG A(D0J
-(D ABBJ < 4 BHB A(A0n(A BBL < t BHB A(A0n(A BBL < p BHB A(A0n(A BBL < BHB A(A0n(A BBL < 4 BHB A(A0n(A BBL < t ` BHB A(A0n(A BBL L BBB B(A0A8G
-8A0A(B BBBD L 0 BBB B(A0A8G
-8A0A(B BBBD L Ğ BBE A(G0D
-0D(A BBBE D p
- BBE A(G0D
-0D(A BBBA L \ b BBE G(A0D4
-0D(A BBBD L K BBE A(G0D
-0D(A BBBG L ] BBE G(A0D
-0D(A BBBB D L ! BBE G(A0D
-0D(A BBBF D $ BBE A(G0D
-0D(A BBBA L ܠ '- BBE A(G0D
-0D(A BBBE D , ,S BEB A(A0Gh
-0A(A BBBA T t .j BEE E(D0A8G
-8D0A(B BBBG T ̡ (1j l BEE E(D0A8G
-8D0A(B BBBG < $ @4 / BBH D(J
-(A ABBA < d 7 I BBA H(J
-(A ABBA < 9 c BBA H(J
-(A ABBA < < } BBA H(J
-(A ABBA < $ P? BBA H(J
-(A ABBA < d Bb BBA D(N
-(A ABBA < @D BBA H(J
-(A ABBA < G BBA H(J
-(A ABBA | I BEB B(D0A8Dp
-8A0A(B BBBA
-8A0A(B BBBA
-8A0A(B BBBA | 4 `L BEB B(D0A8Dp
-8A0A(B BBBA
-8A0A(B BBBA
-8A0A(B BBBA T $ O BEB E(A0A8J
-8A0A(B BBBE , | Xy + AEG V
-AAA T X BBB B(A0A8Jf
-8A0A(B BBBD ` AO D $ ` BBB A(E0J{
-0A(A BBBA L l bF BBB B(D0A8G
-8A0A(B BBBE L h AO T ܦ hW R BBB B(A0A8J
-8A0A(B BBBJ ħ r AO T T r% BBB B(G0D8G
-8D0A(B BBBF < y AO T ̧ yb BBB B(A0H8G
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-0A(A BBBA | BBB E(D0A8D
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+8D0A(B BBBA T D ǖG R BBB B(A0G8G
+8D0A(B BBBA T ɖG R BBB B(A0G8G
+8D0A(B BBBA T ˖G R BBB B(A0G8G
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+8D0A(B BBBA T \ v O BBB B(A0G8G
+8D0A(B BBBA T v O BBB B(A0G8G
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+8A0A(B BBBA 8A0A(B BBB T % N BEB E(A0A8J
+8A0A(B BBBE , ,& y 'O AEG V
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+8D0A(B BBBK ( + AO < t' + *O BBH D(J
+(A ABBA | l( p. BBB E(D0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA L E BEF D(A0G
-0A(A BBBH T < BBB B(D0G8GY
-8D0A(B BBBH T BBB E(G0A8G
-8D0A(B BBBF L (R BBB D(G0G
-0D(A BBBA T < 8 BBB B(D0G8G
-8D0A(B BBBJ T BBB E(G0A8G
-8D0A(B BBBA | | BBB E(D0A8D
+8A0A(B BBBA L 4( 05 N BEF D(A0G
+0A(A BBBF T ( 4[ N BGB B(D0G8G
+8D0A(B BBBG T ( ; N BGB E(G0A8G&
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+0D(A BBBA T ) F N BGB B(D0G8G
+8D0A(B BBBB T ) xM nN BGB E(G0A8G{
+8D0A(B BBBA | * Q BBB E(D0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA L l (E BEF D(A0G
-0A(A BBBH | L ( BBB E(D0A8D
+8A0A(B BBBA L * T8 M BEF D(A0G
+0A(A BBBF | + X BBB E(D0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA L < HU BEF D(A0G
-0A(A BBBH T X BBB E(G0A8G
-8D0A(B BBBG D ' { BBB H(A0G
-0A(A BBBA | BBB E(D0A8D
+8A0A(B BBBA L + ZE \M BEF D(A0G
+0A(A BBBF | , ^ BBB E(D0A8D
8A0A(B BBBA
8A0A(B BBBA
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(D ABBD L
(D ABBK Q
(D ABBF
(D ABBJ L
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8A0A(B BBBA
8A0A(B BBBA
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8A0A(B BBBA
8A0A(B BBBA
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-0A(A BBBH | ˞ BBB E(D0A8D
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8A0A(B BBBA
8A0A(B BBBA
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8A0A(B BBBA
8A0A(B BBBA
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(D ABBD L
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(D ABBF
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8A0A(B BBBA
8A0A(B BBBA
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8A0A(B BBBA
8A0A(B BBBA
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BEF D(A0G
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8A0A(B BBBA
8A0A(B BBBA
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8A0A(B BBBA
8A0A(B BBBA
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(D ABBF
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8A0A(B BBBA
8A0A(B BBBA
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8A0A(B BBBA
8A0A(B BBBA
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8A0A(B BBBA
8A0A(B BBBA
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8A0A(B BBBA
8A0A(B BBBA
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(D ABBD L
(D ABBK Q
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-0A(A BBBA | T B BBE E(A0A8D@
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8A0A(B BBBA
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-0A(A BBBA | t I BBE E(A0A8D@
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8A0A(B BBBA
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-8A0A(B BBBD L [ BBB E(D0A8G
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8A0A(B BBBA
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8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB < L r BBH D(J
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-8A0A(B BBBA L x w BBB A(H0G
-0A(A BBBA < , h| BBH D(J
-(A ABBA L 8& BEE B(A0A8D
-8A0A(B BBBA L j BBB A(H0G
-0A(A BBBA L 腟z BBE E(A0A8DP
-8A0A(B BBBA L \ C BBB A(H0G
-0A(A BBBA < 苟 l BBH D(J
-(A ABBA | | Y BEE B(A0A8D
-8A0A(B BBBA
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8A0A(B BBBA
-8A0A(B BBBA L l BBB A(H0G
-0A(A BBBA | L h BBE E(A0A8DP
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8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB L < BBB A(H0G
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-8A0A(B BBBA
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8A0A(B BBBA
-8A0A(B BBBA L L r BBB A(H0G
-0A(A BBBA | , X BBE E(A0A8DP
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8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB L BBB A(H0G
-0A(A BBBA | XY BEE B(A0A8D
-8A0A(B BBBA
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8A0A(B BBBA
-8A0A(B BBBA L 8 ij BBB A(H0G
-0A(A BBBA | BBE E(A0A8DP
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8A0A(B BBBA
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-8A0A(B BBBA
-8A0A(B BBBA
-8A0A(B BBBA D ' BBB H(A0G
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8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB D T ' BBB H(A0G
-0A(A BBBA , BEE B(A0A8D_
-8A0A(B BBBC
-8A0A(B BBBA
-8A0A(B BBBA L $ ğ BBB A(H0G
-0A(A BBBA | `ȟ. BBE E(A0A8DP
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8A0A(B BBBA
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-0A(A BBBA | ͟R BEE B(A0A8D
-8A0A(B BBBA
-8A0A(B BBBA
-8A0A(B BBBA D П' T BBB H(A0G
-0A(A BBBA | ҟ BBE E(A0A8DP
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8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB D ԟ' ΰ BBB H(A0G
-0A(A BBBA $ d ֟) KA|A T ؟ BBB B(D0A8Gm
-8A0A(B BBBA T T ܟ BBB B(A0A8G
-8A0A(B BBBA T BBB B(D0A8Gm
-8A0A(B BBBA T BBB B(A0A8G
-8A0A(B BBBA 4 8 BBA A(^
- ABBG T \ BBB B(D0A8G.
-8A0A(B BBBD T ` BBB B(A0A8G
-8A0A(B BBBA $ - ADK WDA T l @ BBB B(A0A8G
-8A0A(B BBBA < H BBE D(Mi
-(D ABBD T \ BBB B(D0A8G
-8A0A(B BBBA T \ X ` BBB B(D0G8G
-8D0A(B BBBK T n P BBB B(A0A8G6
-8A0A(B BBBA T "n BBB B(A0A8G6
-8A0A(B BBBA T d ) ڰ BBB E(G0A8G
-8D0A(B BBBA T H. ʰ BBB B(A0A8G6
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-8A0A(B BBBA T l ;= T BBB E(G0A8G
-8D0A(B BBBA T @ D BBB B(H0A8G
-8A0A(B BBBA L hD . BBE E(D0A8Gc
-8A0A(B BBBI L l G4 " BEB A(G0GE
-0D(A BBBH T K= = BBB E(G0A8G
-8D0A(B BBBA T P - BBB B(D0G8G
-8D0A(B BBBF T l HW BBB B(D0G8G
-8D0A(B BBBF T ] T X^ BBB B(A0A8G
-8A0A(B BBBA T 4 b BBB B(A0A8G
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-8A0A(B BBBA T Hn > BBB B(A0A8G6
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-8D0A(B BBBA T , ( ^ BBB B(A0A8G
-8A0A(B BBBA T l BEB E(G0A8G
-8D0A(B BBBA L x a BBE B(A0A8J
-8A0A(B BBBI T , O BEE B(G0D8G(
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8A0A(B BBBF {
8A0A(B BBBC
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8A0A(B BBBA
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8A0A(B BBBI
8A0A(B BBBE
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8A0A(B BBBF K
8A0A(B BBBK s
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+ BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB < L (ǠB k BBD G(G
-(D ABBA 8Ƞ' AJ0WD | < HȠ BBE E(A0A8DP
+8A0A(B BBBA 8A0A(B BBB L o 2 BEB E(A0D8G
+8A0A(B BBBA p 0
L o (
u 1 BEE E(D0D8J
+8A0A(B BBBK T Lp X 1 BBB B(D0D8G
+8D0A(B BBBK \q / AJ0_D | |q BBE E(A0A8DP
8A0A(B BBBJ
8A0A(B BBBE
-8A0A(B BBBK < , ϠB BBD G(G
-(D ABBA Р' AJ0WD | Рd
+8A0A(B BBBK L Dq ` \1 BEB E(A0D8G
+8A0A(B BBBA Lr L q u %1 BEE E(D0D8J
+8A0A(B BBBK T q 8$ ;1 BBB B(D0D8G
+8D0A(B BBBK s )/ AJ0_D | ,s )d
BBE E(A0A8D`G
8A0A(B BBBK
8A0A(B BBBK
-8A0A(B BBBH < ڠB BBD G(G
-(D ABBA ۠' AJ0WD | ܠ BBE E(A0A8DP
+8A0A(B BBBH L r 3 0 BEB E(A0D8G
+8A0A(B BBBA s P5 L \s H5u s0 BEE E(D0D8J
+8A0A(B BBBK T s x9 0 BBB B(D0D8G
+8D0A(B BBBK t >/ AJ0_D | t > BBE E(A0A8DP
8A0A(B BBBH A
8A0A(B BBBE
-8A0A(B BBBI < HB % BBD G(G
-(D ABBA X' AJ0WD | h BBE E(A0A8D@
+8A0A(B BBBI L t 0G / BEB E(A0D8G
+8A0A(B BBBA u H L u Hu / BEE E(D0D8J
+8A0A(B BBBK T \u M / BBB B(D0D8G
+8D0A(B BBBK lv pR/ AJ0_D | v R BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB < B c BBD G(G
-(D ABBA ' AJ0WD | BBE E(A0A8DP
-8A0A(B BBBI
-8A0A(B BBBF
-8A0A(B BBBK < xB BBD G(G
-(D ABBA | ' AJ0WD | \
+8A0A(B BBBA 8A0A(B BBB L Tv S F/ BEB E(A0D8G
+8A0A(B BBBA \w U L v Uu / BEE E(D0D8J
+8A0A(B BBBK T w Y %/ BBB B(D0D8G
+8D0A(B BBBK x _/ AJ0_D | <x 0_ BBE E(A0A8DP
+8A0A(B BBBI
+8A0A(B BBBE
+8A0A(B BBBK L x f . BEB E(A0D8G
+8A0A(B BBBA y ph L lx hhu ]. BEE E(D0D8J
+8A0A(B BBBK T x l s. BBB B(D0D8G
+8D0A(B BBBK y r/ AJ0_D | y r\
BBE E(A0A8D`
8A0A(B BBBG w
8A0A(B BBBG
-8A0A(B BBBH < xB ߨ BBD G(G
-(D ABBA \ ' AJ0WD | | BBE E(A0A8DP
+8A0A(B BBBH L y { - BEB E(A0D8G
+8A0A(B BBBA z } L z }u - BEE E(D0D8J
+8A0A(B BBBK T lz ȁ - BBB B(D0D8G
+8D0A(B BBBK |{ 0/ AJ0_D | { @ BBE E(A0A8DP
8A0A(B BBBG
8A0A(B BBBF
-8A0A(B BBBI < l B BBD G(G
-(D ABBA < ' AJ0WD | \ BBE E(A0A8D@
+8A0A(B BBBI L d{ p 0- BEB E(A0D8G
+8A0A(B BBBA l| L { u , BEE E(D0D8J
+8A0A(B BBBK T | H - BBB B(D0D8G
+8D0A(B BBBK ,} / AJ0_D | L} BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB < L h9 [ BBD G(G
-(D ABBA h' AJ0WD | < x BBE E(A0A8D@
+8A0A(B BBBA 8A0A(B BBB L } @ ~, BEB E(A0D8G
+8A0A(B BBBA ~ L |} 蝚U G, BEE E(D0D8J
+8A0A(B BBBC T } ], BBB B(D0D8G
+8D0A(B BBBK ~ P/ AJ0_D | ~ ` BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB < , 9 BBD G(G
-(D ABBA ' AJ0WD L BEE B(A0A8D`
-8A0A(B BBBE < H9 BBD G(G
-(D ABBA H' AJ0WD L X- BEE B(A0A8Dp
-8A0A(B BBBH < 89 u BBD G(G
-(D ABBA \ 8 ' AJ0WD | | H BBB B(A0D8GP
+8A0A(B BBBA 8A0A(B BBB L ~ + BEB E(A0D8G
+8A0A(B BBBA p L , hU + BEE E(D0D8J
+8A0A(B BBBC T | x + BBB B(D0D8G
+8D0A(B BBBK г/ AJ0_D L BEE B(A0A8D`
+8A0A(B BBBE L D @ J+ BEB E(A0D8G
+8A0A(B BBBA L L 輚U + BEE E(D0D8J
+8A0A(B BBBC T )+ BBB B(D0D8G)
+8D0A(B BBBC @ƚ/ AJ0_D L , Pƚ- BEE B(A0A8Dp
+8A0A(B BBBH L ā 0Ϛ * BEB E(A0D8G
+8A0A(B BBBA ̂ К L , КU * BEE E(D0D8J
+8A0A(B BBBC T | Ԛ * BBB B(D0D8G)
+8D0A(B BBBC 0ښ/ AJ0_D | @ښ BBB B(A0D8GP
8A0A(B BBBB ;
8A0A(B BBBK
-8A0A(B BBBJ < l 9 BBD G(G
-(D ABBA < ' AJ0WD L \ BEE B(A0A8D`
-8A0A(B BBBE < 8*9 ! BBD G(G
-(D ABBA 8+' AJ0WD L H+ BEE B(A0A8D`
-8A0A(B BBBE < 29 BBD G(G
-(D ABBA 3' AJ0WD L 3- BEE B(A0A8Dp~
-8A0A(B BBBD < | <9 BBD G(G
-(D ABBA L =' AJ0WD | l =w BBB B(A0D8GP
+8A0A(B BBBJ L t ݚ * BEB E(A0D8G
+8A0A(B BBBA | ߚ L ܃ xߚU ) BEE E(D0D8J
+8A0A(B BBBC T , ) BBB B(D0D8G
+8D0A(B BBBK < / AJ0_D L \ BEE B(A0A8D`
+8A0A(B BBBE L @ ) BEB E(A0D8G
+8A0A(B BBBA L \ U ]) BEE E(D0D8J
+8A0A(B BBBC T s) BBB B(D0D8G)
+8D0A(B BBBC @/ AJ0_D L ܆ P BEE B(A0A8D`
+8A0A(B BBBE L t ) BEB E(A0D8G
+8A0A(B BBBA | ` L ܆ XU ( BEE E(D0D8J
+8A0A(B BBBC T , h
+ ( BBB B(D0D8G)
+8D0A(B BBBC < / AJ0_D L \ - BEE B(A0A8Dp~
+8A0A(B BBBD L ( BEB E(A0D8G
+8A0A(B BBBA P L \ HU Y( BEE E(D0D8J
+8A0A(B BBBC T X o( BBB B(D0D8G)
+8D0A(B BBBC #/ AJ0_D | ܉ #w BBB B(A0D8GP
8A0A(B BBBF
8A0A(B BBBH
-8A0A(B BBBJ < \ @9 ; BBD G(G
-(D ABBA , A' AJ0WD L L Aq BEE B(A0A8D`d
-8A0A(B BBBF < I9 BBD G(G
-(D ABBA J' AJ0WD L J BEE B(A0A8D`
-8A0A(B BBBE < XR9 BBD G(G
-(D ABBA XS' AJ0WD L hS- BEE B(A0A8Dp
-8A0A(B BBBH < l H\9 BBD G(G
-(D ABBA < H]' AJ0WD | \ X]w BBB B(A0D8GP
+8A0A(B BBBJ L & ' BEB E(A0D8G
+8A0A(B BBBA `( L X(U ' BEE E(D0D8J
+8A0A(B BBBC T \ h, ' BBB B(D0D8G
+8D0A(B BBBK l 1/ AJ0_D L 1q BEE B(A0A8D`d
+8A0A(B BBBF L $ : \' BEB E(A0D8G
+8A0A(B BBBA , ; L ;U %' BEE E(D0D8J
+8A0A(B BBBC T ܋ ? ;' BBB B(D0D8G)
+8D0A(B BBBC E/ AJ0_D L E BEE B(A0A8D`
+8A0A(B BBBE L pL & BEB E(A0D8G
+8A0A(B BBBA N L NU & BEE E(D0D8J
+8A0A(B BBBC T \ (R & BBB B(D0D8G)
+8D0A(B BBBC l pW/ AJ0_D L W- BEE B(A0A8Dp
+8A0A(B BBBH L $ `` X& BEB E(A0D8G
+8A0A(B BBBA , b L bU !& BEE E(D0D8J
+8A0A(B BBBC T f 7& BBB B(D0D8G)
+8D0A(B BBBC `k/ AJ0_D | pkw BBB B(A0D8GP
8A0A(B BBBJ
8A0A(B BBBK
-8A0A(B BBBJ < L X`9 ß BBD G(G
-(D ABBA Xa' AJ0WD L < haa BEE B(A0A8D`\
-8A0A(B BBBF < i9 1 BBD G(G
-(D ABBA j' AJ0WD L j BEE B(A0A8D`
-8A0A(B BBBE < q9 BBD G(G
-(D ABBA | r' AJ0WD L s BBB B(D0A8J
-8A0A(B BBBF < \ y9
BBD G(G
-(D ABBA , z' AJ0WD L d. K^ d z9 Hp z z@ Hs D , z [ BBE A(A0
-(A BBBA L { AAG ]
+8A0A(B BBBJ L ԏ pn % BEB E(A0D8G
+8A0A(B BBBA ܐ p L < pU o% BEE E(D0D8J
+8A0A(B BBBC T (t % BBB B(D0D8G
+8D0A(B BBBK y/ AJ0_D L ya BEE B(A0A8D`\
+8A0A(B BBBF L T $% BEB E(A0D8G
+8A0A(B BBBA \ ` L XU $ BEE E(D0D8J
+8A0A(B BBBC T h % BBB B(D0D8G)
+8D0A(B BBBC / AJ0_D L < BEE B(A0A8D`
+8A0A(B BBBE L Ԓ $ BEB E(A0D8G
+8A0A(B BBBA ܓ Е L < ȕU k$ BEE E(D0D8J
+8A0A(B BBBC T ؙ $ BBB B(D0D8G)
+8D0A(B BBBC / AJ0_D L 0 BBB B(D0A8J
+8A0A(B BBBF L T $ BEB E(A0D8G
+8A0A(B BBBA \ ` L XU # BEE E(D0D8J
+8A0A(B BBBC T h # BBB B(D0D8G)
+8D0A(B BBBC / AJ0_D < . K^ L # BBB B(A0I8G
+8D0A(B BBBA L H # BBB B(A0I8G
+8D0A(B BBBA L < 賛 # BBB B(A0I8G
+8D0A(B BBBA L # BBB B(A0I8G
+8D0A(B BBBA L ܖ ( j# BBB B(A0I8G
+8D0A(B BBBA L , ȸ O# BBB B(A0I8G
+8D0A(B BBBA L | h 4# BBB B(A0I8G
+8D0A(B BBBA L ̗ # BBB B(A0I8G
+8D0A(B BBBA L " BBB B(A0I8G
+8D0A(B BBBA L l H " BBB B(A0I8G
+8D0A(B BBBA L " BBB B(A0I8G
+8D0A(B BBBA L " BBB B(A0I8G
+8D0A(B BBBA L \ (ě " BBB B(A0I8G
+8D0A(B BBBA L ś w" BBB B(A0I8G
+8D0A(B BBBA L hǛ \" BBB B(A0I8G
+8D0A(B BBBA L L ɛ A" BBB B(A0I8G
+8D0A(B BBBA L ʛ &" BBB B(A0I8G
+8D0A(B BBBA L H̛ " BBB B(A0I8G
+8D0A(B BBBA L < ͛ ! BBB B(A0I8G
+8D0A(B BBBA L ϛ ! BBB B(A0I8G
+8D0A(B BBBA L ܛ (ћ ! BBB B(A0I8G
+8D0A(B BBBA L , қ ! BBB B(A0I8G
+8D0A(B BBBA L | hԛ ! BBB B(A0I8G
+8D0A(B BBBA ֛9 Hp (֛ ֛@ Hs L ܝ @֛ AAG ]
CAF [
ACA R
-CAJ JCA T (\ AG RAT 0{ BBB B(D0A8J
-8A0A(B BBBJ T < x BBB B(D0A8N
-8A0A(B BBBC , > ^ AID W
-DAA , > > AID W
-DAA , > AID W
-DAA D $ BBB D(A0D@0A(A BBB T l Ȓ BIB E(A0A8DF
-8D0A(B BBBA T BIB E(A0A8D6
-8D0A(B BBBA T H R BIB E(A0A8DF
-8D0A(B BBBA D 8 BBB B(A0A8$0A(B BBB T ۛ BBB G(G0A8JD
-8D0A(B BBBH `. K^ T \ r BBA I(Dp
+CAJ JCA , \ AG RAT P֛ BBB B(D0A8J
+8A0A(B BBBJ T BBB B(D0A8N
+8A0A(B BBBC , D > X! AID W
+DAA , t > 8! AID W
+DAA , > ! AID W
+DAA , - AAG0!AA 4 5 BHD D`
+ DABG 4 < B BHD D`
+ DABH 4 t B BHD D`
+ DABH D d 88 BBB B(A0A8$0A(B BBB T 07 ' BBB G(G0A8JD
+8D0A(B BBBH h. K^ 4 T r ! BBA I(Dp
(D ABBH L
-(D ABBA T @r BBA I(Dp
+(D ABBA T (r BBA I(Dp
(D ABBH L
-(D ABBA T h1 G BEE E(D0A8GY
-8D0A(B BBBE D BBB D(A0D@0A(A BBB T ¡R BIE B(A0A8DA
-8D0A(B BBBA T ǡL BIE B(A0A8D9
-8D0A(B BBBA T \ ̡ BBB H(A0A8L
-8D0A(B BBBA D @^. K^ \ ١ t ١ ١ ١ ١ ١ ١ ١ ١ 4 ١ L ١ d ١ , ١[ AID t
-DAA , ١[ ך AID t
-DAA , L ڡ[ AID t
-DAA , | Hڡ[ AID t
-DAA , xڡ[ w AID t
-DAA , ڡ[ W AID t
-DAA , ڡ[ 7 AID t
-DAA , < ۡ[ AID t
-DAA L l 8ۡr BBB B(E0A8J@
-8A0A(B BBBA L (\ AG RAL l Hܡ>
BEB B(A0A8G
8A0A(B BBB < 8 BEB D(A0M
-(A BBBF T l BBE E(A0A8G
-8D0A(B BBBF T @ Ϙ BBE E(A0A8G
-8D0A(B BBBF T BBE E(A0A8G
-8D0A(B BBBF T t P U BBE E(A0A8G
-8D0A(B BBBF D \ BBB D(A0D@
-0A(A BBBA D 0 З BIB A(D0D`+
-0D(A BBBA D \ BIB A(D0D`+
-0D(A BBBA D BIB A(D0D`+
-0D(A BBBA D X j BIB A(D0D`+
-0D(A BBBA D 4 H BIB A(D0D`+
-0D(A BBBA D | & BIB A(D0D`+
-0D(A BBBA D BIB A(D0D`2
-0D(A BBBA D H BIB A(D0D`2
-0D(A BBBA d ?
+(D ABBA T D P1 BEE E(D0A8GY
+8D0A(B BBBE , T - AAG0!AA 4 ̡ 5 Q BHD D`
+ DABG 4 B 3 BHD D`
+ DABH T < ( _ BBB H(A0A8L
+8D0A(B BBBA L P. K^ d . | . . . ģ . ܣ . , < .[ AID t
+DAA , l H.[ AID t
+DAA , x.[ AID t
+DAA , ̣ .[ AID t
+DAA , . AID d
+DAA , , 8/ x AID d
+DAA 4 \ / Y BHE l
+ABB 4 00 % BHE o
+ABO L ̤ 0r BBB B(E0A8J@
+8A0A(B BBBA ԥ \ AG RAL 1>
BEB B(A0A8G
8A0A(B BBB D > A
+J A< d h? BEB D(A0M
+(A BBBF T @ BBE E(A0A8G
+8D0A(B BBBF T D pC BBE E(A0A8G
+8D0A(B BBBF T E l BBE B(D0A8G
+8D0A(B BBBE , - AAG0!AA 4 $ J " BHD D`
+ DABF 4 \ L BHD D`
+ DABF 4 0O BHD D`
+ DABG 4 ̧ Q BHD D`
+ DABG < T BHD DW
+ DABA < D V BHD DW
+ DABA L Yk t BBB B(A0D8GT
+8D0A(B BBBK d [?
BBB B(A0A8GP
8G0A(B BBBP ^
-8A0A(B BBBH T X BEB B(A0A8G
-8A0A(B BBBI 3 A
-J AT 4 4| BDB E(A0A8J
-8A0A(B BBBB T H:| BDB E(A0A8J
-8A0A(B BBBB T p@| Ɨ BDB E(A0A8J
-8A0A(B BBBB T < F| BDB E(A0A8J
-8A0A(B BBBB T L|
- BDB E(A0A8J
-8A0A(B BBBB T R| , BDB E(A0A8J
-8A0A(B BBBB S. K^ X X , X[ AID t
-DAA D L BBB D(A0D@0A(A BBB T X= BIB E(A0A8G
-8D0A(B BBBA T \ ] w BGB E(A0A8G
-8A0A(B BBBJ D R. K^ \ Hg t @g , Hg[ AID t
-DAA \ AG RA< D BBD A(D@0(A ABB T g BIB B(A0D8Df
-8D0A(B BBBA T k ܖ BDB B(A0D8J
-8A0A(B BBBA HQ. K^
- ,
- D
- \
- , [ AID t
-DAA ,
- [ j AID t
-DAA , D
- [ J AID t
-DAA D : BBB D(A0D@0A(A BBB T
- L BIE B(A0A8D9
-8D0A(B BBBA T L BIE B(A0A8D9
-8D0A(B BBBA T l L ~ BIE B(A0A8D9
-8D0A(B BBBA T O BDH B(A0D8Gl
-8D0A(B BBBA T 8 BDH B(A0D8Gd
-8D0A(B BBBA T t BDH B(A0D8Gd
-8D0A(B BBBA \
M. K^ t
,
[ AID t
-DAA D
8 BBB D(A0D@0A(A BBB T
; / BIB E(A0A8G(
-8D0A(B BBBA D
- BBE A(H0D
-0A(A BBBA < , 褢b BBA D(K
-(A ABBA L l BBE E(A0D8Gx
-8A0A(B BBBD T xǢ, BDB B(D0A8M/
-8D0A(B BBBH K. K^ 8ʢ 0ʢ (ʢ 0ʢ ~\ AG RAD ʢ G BBB A(I0Dp
-0D(A BBBG D ˢ ! BBB A(I0Dp
-0D(A BBBG D < ͢ BBB A(I0Dp
-0D(A BBBG D Ֆ BBB A(I0Dp
-0D(A BBBG D Ϣ BBB A(I0Dp
-0D(A BBBG D pѢ BBB A(I0Dp
-0D(A BBBG D \ Ң c BBB A(I0Dp
-0D(A BBBG D `Ԣ = BBB A(I0Dp
-0D(A BBBG D | բJ BBB D(A0D@
-0A(A BBBA D 4 ע ϕ BIB A(D0D`2
-0D(A BBBA T | ڢ BIB E(A0A8DN
-8D0A(B BBBA D ޢ { BIB A(D0D`7
-0D(A BBBA D Y BIB A(D0D`>
-0D(A BBBA D d 7 BIB A(D0D`7
-0D(A BBBA T XL BIE B(A0A8D9
-8D0A(B BBBA T P BIB E(A0A8DF
-8D0A(B BBBA T \ L BIE B(A0A8D9
-8D0A(B BBBA T BBB G(D0A8M
-8D0A(B BBBH T ǔ BBB G(D0A8M
-8D0A(B BBBH E. K^ - Ak T ה BBE B(A0H8G
-8A0A(B BBBD PE. K^ D H 8 BBB B(A0A8
-0A(B BBBA D @
8 BBB B(A0A8
-0A(B BBBA D , 8B BBB B(A0A8
-0A(B BBBA D t @B BBB B(A0A8
-0A(B BBBA H @ 8 0 ( 4 0 L 8 d @ L | H AAG e
+8A0A(B BBBH T < xeE BEB B(A0A8G
+8A0A(B BBBI T p % BEH B(A0F8I
+8D0A(B BBBH T 艜 BGB B(A0A8J
+8A0A(B BBBG T D p BGB B(A0A8J
+8A0A(B BBBG T BGB B(A0A8J
+8A0A(B BBBG . K^ ī h ܫ ` , < h[ AID t
+DAA T l < BBE B(D0A8G
+8D0A(B BBBB , | v- AAG0!AA D PY BIA D(D
+(D ABBA T < h BDB B(A0G8J
+8D0A(B BBBB L . K^ d | , ܬ [ AID t
+DAA ĭ \\ AG RA, - AAG0!AA < \ ة BIA D(Dp-
+(D ABBF T h ] BDB B(A0D8J
+8A0A(B BBBA . K^ Į ܮ x p $ x < , [
AID t
+DAA , ̮ [ AID t
+DAA , [ AID t
+DAA , - AAG0!AA 4 \ 5 } BHD D`
+ DABG 4 Ĝ5 _ BHD D`
+ DABG 4 ̯ ǜ5 A BHD D`
+ DABG T ˜ # BDH B(A0D8Gl
+8D0A(B BBBA T \ Ϝ _ BDH B(A0D8Gd
+8D0A(B BBBA T Ԝ BDH B(A0D8Gd
+8D0A(B BBBA ı . K^ ܱ x , T [ AID t
+DAA , [ o AID t
+DAA , l l- AAG0!AA < " BHD G
+ DABA < $ ܜ) BHD G
+ DABA D d BBE A(H0D
+0A(A BBBA < b BBA D(K
+(A ABBA L ( BBE E(A0D8Gx
+8A0A(B BBBD T < BDB B(D0A8M5
+8D0A(B BBBB L @. K^ d | Ĵ B\ AG RAD , / BBB A(I0Dp
+0D(A BBBG D t p BBB A(I0Dp
+0D(A BBBG D BBB A(I0Dp
+0D(A BBBG D `
+ BBB A(I0Dp
+0D(A BBBG D L BBB A(I0Dp
+0D(A BBBG D P
q BBB A(I0Dp
+0D(A BBBG D ܵ K BBB A(I0Dp
+0D(A BBBG D $ @ % BBB A(I0Dp
+0D(A BBBG , $ >- AAG0!AA 4 5 BHD D`
+ DABG 4 Զ 5 BHD D`
+ DABG 4 B BHD D`
+ DABH 4 D B u BHD D`
+ DABH 4 | E W BHD D`
+ DABK 4 E 9 BHD D`
+ DABK 4 #E BHD D`
+ DABD 4 $ 'E BHD D`
+ DABD T \ (*G BBB G(D0A8M
+8D0A(B BBBH T 0G / BBB G(D0A8M
+8D0A(B BBBH Ĺ . K^ ܹ 6- Ak T D 6 G BBE B(A0H8G
+8A0A(B BBBD T . K^ D l A8 BBB B(A0A8
+0A(B BBBA D B8 BBB B(A0A8
+0A(B BBBA D CB BBB B(A0A8
+0A(B BBBA D D DB BBB B(A0A8
+0A(B BBBA E E E Ի E E E E 4 E L L E AAG e
CAF S
ACA R
-CAJ JCA L ADD e
+CAJ JCA L 0F ADD e
CAF [
ACI R
-CAJ JCA L 8 ADD e
+CAJ JCA L F ADD e
CAF [
ACI R
-CAJ JCA L l AAG e
+CAJ JCA L < 0G AAG e
CAF S
ACA R
-CAJ JCA L ( ADD e
+CAJ JCA L G ADD e
CAF [
ACI R
-CAJ JCA L ADD e
+CAJ JCA L ܽ H ADD e
CAF [
ACI R
-CAJ JCA L \ ( AAG e
+CAJ JCA L , H AAG e
CAF S
ACA R
-CAJ JCA L ADD e
+CAJ JCA L | I ADD e
CAF [
ACI R
-CAJ JCA L ADD e
+CAJ JCA L ̾ I ADD e
CAF [
ACI R
-CAJ JCA L L AAG e
+CAJ JCA L J AAG e
CAF S
ACA R
-CAJ JCA L ADD e
+CAJ JCA L l J ADD e
CAF [
ACI R
-CAJ JCA L ADD e
+CAJ JCA L K ADD e
CAF [
ACI R
-CAJ JCA 4 < BEA A(X
- ABBJ 4 t BEA A(X
- ABBJ , BDA
-ABD , BDA z
-ABD < | hq ` BBA A(D0[
-(A ABBA T n $ BBB B(A0D8N+
-8A0A(B BBBH , > I AID W
-DAA L D Y ) BBB E(A0A8N
-8A0A(B BBBK , > AID W
-DAA T BLB E(A0A8G
-8A0A(B BBBA , 8%Z BHI s
-ABA , L h%> AID W
-DAA , | x%Z э BHI s
-ABA , %> AID W
-DAA D l % BBB D(A0D@
-0A(A BBBA L $ ' I BIB B(A0D8DpE
-8D0A(B BBBA L t ) BIB B(A0D8DpE
-8D0A(B BBBA L ," BIB B(A0D8DpL
-8D0A(B BBBA L /" ˌ BIB B(A0D8DpL
-8D0A(B BBBA T d p2 BEE E(D0A8Gn
-8D0A(B BBBH T 5| d BEB E(A0D8JR
-8D0A(B BBBD D ! D BFD JPs
+CAJ JCA 4 K BEA A(X
+ ABBJ 4 D XL BEA A(X
+ ABBJ , | 0M BDA
+ABD , N BDA z
+ABD < $ Nq BBA A(D0[
+(A ABBA O N T | (O | BBB B(A0D8N/
+8A0A(B BBBA , S> AID W
+DAA L SY BBB E(A0A8N
+8A0A(B BBBK , T U> f AID W
+DAA T Uj F BBB E(A0A8G"
+8A0A(B BBBA , YZ d BHI s
+ABA , (Z> D AID W
+DAA , < 8ZZ $ BHI s
+ABA , l hZ> AID W
+DAA , T <׀- AAG0!AA 4 HZ BHD D`
+ DABD 4 \ BHD D`
+ DABD 4 < 8_ x BHD D`
+ DABE 4 t a Z BHD D`
+ DABE T (dZ < BEE E(D0A8Gk
+8D0A(B BBBK T 0g< BEB E(A0D8JR
+8D0A(B BBBD D \ v 2 BFD JPs
DABH F
- DABA \ \! hE n BBG A(D0J
-0D(A BBBJ
-0D(A BBBF L ! G O BBB B(A0D8M
-8D0A(B BBBE T " I @ BEB B(D0G8G
-8A0A(B BBBK T d" `P 6 BEB B(D0G8G
-8A0A(B BBBK L " V , BBB B(A0D8M
-8D0A(B BBBG < # h\ BDA k
+ DABA T v BGA D(J`
+(D ABBF x
+(D ABBG L w BBB B(A0D8Jp
+8D0A(B BBBF T L y BBE B(D0G8G
+8A0A(B BBBB T `~ z BBE B(D0G8G
+8A0A(B BBBB L N BBB B(A0D8M
+8D0A(B BBBG < H BDA k
ABK @
-ABA L # \Q BBB B(A0A8D
-8A0A(B BBBH L ,$ `Q BBB B(A0A8D
-8A0A(B BBBH L |$ df BBB B(A0A8GM
-8A0A(B BBBH L $ hf BBB B(A0A8GM
-8A0A(B BBBH D % m BHB B(A0A8DP8A0A(B BBBD d% m BHB B(A0A8DP8A0A(B BBBD % n BEE B(A0A8DP8A0A(B BBBD % `o BEE B(A0A8DP8A0A(B BBB, <& (p EG
+ABA L D Q BBB B(A0A8D
+8A0A(B BBBH L Q BBB B(A0A8D
+8A0A(B BBBH L f BBB B(A0A8GM
+8A0A(B BBBH L 4 ؕf BBB B(A0A8GM
+8A0A(B BBBH D BHB B(A0A8DP8A0A(B BBB h D BHB B(A0A8DP8A0A(B BBB, D ؞R EKH
+ABA D t BEE B(A0A8DP8A0A(B BBB, РR EKH
+ABA D BEE B(A0A8DP8A0A(B BBB, 4 Ȣ EG
AE o
-AH D l& q BHE E(D0A8DP8A0A(B BBBL & q= BHE E(A0A8D@8A0A(B BBB L ' s BBB B(A0A8G
-8A0A(B BBBH , T' y EG
+AH D d BHE E(D0A8DP8A0A(B BBBL 0= BHE E(A0A8D@8A0A(B BBB L BBB B(A0A8GO
+8A0A(B BBBA , L ` EG
AE o
-AH D ' z BHE E(D0A8DP8A0A(B BBBL ' X{M BHE E(A0A8D@(8A0A(B BBB L ( X} BBB B(A0A8G
-8A0A(B BBBH < l( FDK
+AH D | P BHE E(D0A8DP8A0A(B BBBL ȬM BHE E(A0A8D@(8A0A(B BBB L Ȯ BBB B(A0A8GO
+8A0A(B BBBA < d FDK
ABB w
-ABF L ( BHE E(D0A8DP
-8A0A(B BBBK L ( 8@ BHE E(A0A8D@
-8A0A(B BBBA L L) ( BBB B(A0A8G
-8A0A(B BBBA < ) FDK
+ABF L 贝 BHE E(D0A8DP
+8A0A(B BBBK L h@ BHE E(A0A8D@
+8A0A(B BBBA L D X BBB B(A0A8Gl
+8A0A(B BBBA < FDK
ABB w
-ABF L ) BHE E(D0A8DP
-8A0A(B BBBK L ,* @ BHE E(A0A8D@
-8A0A(B BBBA L |* BBB B(A0A8G
-8A0A(B BBBA * 81. K^ * * x + p ,+ h D+ ` \+ X t+ ` + h + p + x , D+ [ AID t
-DAA , t+ [ AID t
-DAA , + [ σ AID t
-DAA , + [ AID t
-DAA , , @[ AID t
-DAA D , rb BBB D(A0D@0A(A BBB T |, ( ' BIB E(A0A8D6
-8D0A(B BBBA T , ` BIB E(A0A8D6
-8D0A(B BBBA T ,- Â BIB E(A0A8D6
-8D0A(B BBBA T - Ф BIB E(A0A8D6
-8D0A(B BBBA T - _ BIB E(A0A8D6
-8D0A(B BBBA T 4. @ - BEE E(D0A8G*
-8D0A(B BBBD T . ز BDB B(A0D8G&
-8A0A(B BBBA T . @U BDE H(A0A8G$
-8D0A(B BBBA T </ H BDE H(A0A8G$
-8D0A(B BBBA T / ȣU BDE H(A0A8G$
-8D0A(B BBBA T / ѣ # BDE H(A0A8G,
-8D0A(B BBBA 0 `+. K^ 0 ף 1 ף D 0 ףb BBB A(D0Kp
-0A(A BBBJ , 0 أ> Ԃ AID W
-DAA D 1 p_ BBB D(A0D@0A(A BBB T L1 أL l BIE B(A0A8D9
-8D0A(B BBBA L 1 ݣl ; BBE B(F0G8G
-8D0A(B BBBI 2 ). K^ 2 2 , 2 0 AF
FK
- D T l2 | BBE E(D0A8G
-8D0A(B BBBE T 2 | BBE E(D0A8G
-8D0A(B BBBE D 3 &_ BBB D(A0D@0A(A BBB T d3 BIB E(A0A8GJ
-8D0A(B BBBA T 3 8 BBE B(D0A8G
-8D0A(B BBBG T 4 E
- g BBB E(A0A8N
-8A0A(B BBBA , l4 > AID W
-DAA T 4 . ρ BBB B(A0F8J
-8A0A(B BBBB T 4 ? BBB B(A0F8J
-8A0A(B BBBF T L5 X BBB H(F0A8J
-8D0A(B BBBD T 5 2 BBE G(A0G8J
-8D0A(B BBBD T 5 ; BBB B(A0D8L
-8A0A(B BBBG T T6 p% BGB B(A0D8J
-8A0A(B BBBA T 6 + Ã BBB G(A0D8M
-8D0A(B BBBB 7 %. K^ D 7 X3 BBE A(A0D@
-0A(A BBBA D 7 6 BBE A(A0D@
-0A(A BBBA D <8 d\ BBB D(A0D@0A(A BBB T 7 `8u BIB E(A0A8G=
-8D0A(B BBBA T L8 >t ۂ BBE B(A0G8L-
-8D0A(B BBBG < 8 G [ BLD D(G0
-(A ABBE < 8 pH* ? BED I(KPc
-(D ABBE < $9 `I BLD D(G0
-(A ABBE < d9 J* BED I(KP_
-(D ABBI T 9 K- BBM B(A0D8G
-8A0A(B BBBD , 9 ]> AID W
-DAA : ". K^ : ]0 Hg : ]7 Hj ; ^0 Hg 4; ^7 Hj T; @^0 Hg t; P^7 Hj D ; p^ BBE A(A0D@g
-0A(A BBBA D ; ^ BBE A(A0D@g
-0A(A BBBA D $< _ BBE A(A0D@g
-0A(A BBBA D l< ` BBE A(A0D@o
-0A(A BBBA D < ` BBE A(A0D@o
-0A(A BBBA D < 8a BBE A(A0D@o
-0A(A BBBA D D= *Y BBB D(A0D@0A(A BBB T < a BIB E(A0A8DV
-8D0A(B BBBA T T= e } BIB E(A0A8DV
-8D0A(B BBBA T = 8j K BIB E(A0A8DV
-8D0A(B BBBA T > n BIB E(A0A8DV
-8D0A(B BBBA T \> r BIB E(A0A8DV
-8D0A(B BBBA T > @w BIB E(A0A8DV
-8D0A(B BBBA L ? { BBJ B(A0A8G
-8D0A(B BBBD L \? } n BBJ B(A0A8G
-8D0A(B BBBD L ? X Y BBJ B(A0A8G
-8D0A(B BBBD L ? D BBB B(A0I8G
-8D0A(B BBBD L L@ / BBB B(A0I8G
-8D0A(B BBBD L @ X BBB B(A0I8G
-8D0A(B BBBD T @ BBB E(F0A8My
-8D0A(B BBBA T DA @ + BBB E(F0A8Mx
-8D0A(B BBBA T A Ȓ O BBG B(A0A8J
-8A0A(B BBBA T A BBB B(D0F8G2
-8A0A(B BBBK T LB h BBB B(D0F8G1
-8A0A(B BBBD T B BBB B(D0A8L6
-8A0A(B BBBG C h. K^ C 0 Hg C 7 Hj T TC @ BBB J(A0A8G?
-8D0A(B BBBK T C v BEE E(D0A8G
-8D0A(B BBBH T D 0 \ BBE B(D0A8G
-8D0A(B BBBG D D PS BBB D(A0D@0A(A BBB T D ~ BIB E(A0A8G
-8D0A(B BBBA T D (5 ~ BBB B(F0A8G
-8A0A(B BBBE E @. K^ E ֤> Hu F פE Hx <F S\ AG RAT E (פ
- BBB B(D0A8N
-8A0A(B BBBD , $F >
- AID W
-DAA D F S BBB D(A0D@0A(A BBB T F BIB E(A0A8D
-8D0A(B BBBA T F u { BBE E(A0A8GV
-8D0A(B BBBF T LG (U BBE B(D0A8GH
-8D0A(B BBBD T G 0 BBB G(D0A8M
-8D0A(B BBBE H . K^ $ H ADD0AA$ H ( ADD0AA$ H ADD0AA$ I 8 ADD0AA$ DI ADD0AA$ lI H ADD0AAD I 1 BBE A(A0D@
-0A(A BBBA D I > BBB D(A0D@
-0A(A BBBA D $J F BBB D(A0D@
-0A(A BBBA D lJ N BBB D(A0D@
-0A(A BBBA $ J ^ AG b
-AA L LJ 2 } BGB B(A0K8U
-8A0A(B BBBA < J } BQA A(N`
-(A ABBA < J Q} BQA A(N`
-(A ABBA < K 8 #} BQA A(N`
-(A ABBA L \K 2 | BGB B(A0K8U
-8A0A(B BBBA < K | BQA A(N`
-(A ABBA < K h | BQA A(N`
-(A ABBA D L BBB D(A0D@
-0A(A BBBA < tL ` | BHA J
- DABA < L
-| BHA J
- DABA < L { BHA J
- DABA < 4M { BHA J
- DABA < tM
{ BHA J
- DABA < M @ { BHA J
- DABA < M { BHA J
- DABA < 4N { BHA J
- DABA D O BBE A(A0D@l
-0A(A BBBA $ LO i AAG0]AA$ tO `q AAG0eAAD O BBE A(A0D@l
-0A(A BBBA $ O P| AAG0pAA P i AG _A ,P q AG gA$ LP X AAG0xAAT O 1z BEJ E(D0D8G
-8D0A(B BBBG T <P R y BBB I(A0A8JN
-8A0A(B BBBA , P %> z AID W
-DAA \ TQ % BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBL $Q % y BBB E(A0G8L
-8D0A(B BBBA < tQ * y BGB D(A0~
-(A BBBK T Q `+
- y BBB M(A0A8J
-8A0A(B BBBA , R 5> y AID W
-DAA T <R 5 y BBB E(A0H8K
-8A0A(B BBBA , R p<> y AID W
-DAA T R < y BBB E(I0A8K}
-8D0A(B BBBF T S > y BBB B(A0H8JH
-8A0A(B BBBA , tS G> y AID W
-DAA L S G y BEB E(I0H8DP
-8D0A(B BBBD L S I _y BEB E(I0H8D@
-8D0A(B BBBA T DT K( 7y BBB I(A0A8J
-8A0A(B BBBA , T Q> ^y AID W
-DAA T T Q ?y BBB E(I0A8K}
-8D0A(B BBBF T $U 0TF y BBB B(A0D8N
-8A0A(B BBBA , |U (\> >y AID W
-DAA L U 8\ y BEB E(I0H8D@
-8D0A(B BBBA T U ]L x BBI B(A0A8J.
-8A0A(B BBBA , TV d> y AID W
-DAA \ W d BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBT V e x BBB M(A0A8J
-8A0A(B BBBA , <W l> x AID W
-DAA < lW lH x BBA A(Gp
-(D ABBA < W mH x BBA A(Gp
-(D ABBA L W nl x BEB B(G0A8Ip
-8D0A(B BBBH < <X oH Qx BBA A(Gp
-(D ABBA l |X p 5x BBB E(A0F8DQ
-8A0A(B BBBG j
-8A0A(B BBBA < X rH w BBA A(Gp
-(D ABBA L ,Y se w BEB B(G0A8Ip
-8D0A(B BBBC < |Y tH w BBA A(Gp
-(D ABBA l Y u@ gw BEB B(A0A8I
-8A0A(B BBBA @
-8A0A(B BBBA T ,Z w^
w BBB E(A0F8G
-8A0A(B BBBA < Z yH v BBA A(Gp
-(D ABBA L Z zg v BEB B(G0A8Ip
-8D0A(B BBBE T [ { qv BBH E(A0F8DG
-8D0A(B BBBH [ . K^ \ p} ,\ x} , D\ }b D\
+ABF L BHE E(D0A8DP
+8A0A(B BBBK L $ @ BHE E(A0A8D@
+8A0A(B BBBA L t BBB B(A0A8Gl
+8A0A(B BBBA X~. K^ Pŝ Hŝ @ŝ $ 8ŝ < 0ŝ T (ŝ l 0ŝ 8ŝ @ŝ Hŝ , Pŝ[ AID t
+DAA , D ŝ[ AID t
+DAA , t ŝ[ a AID t
+DAA , ŝ[ A AID t
+DAA , Ɲ[ ! AID t
+DAA , ˀ- AAG0!AA 4 4 Ɲ5 BHD D`
+ DABG 4 l ɝ5 BHD D`
+ DABG 4 ̝5 BHD D`
+ DABG 4 (ϝ5 w BHD D`
+ DABG 4 0ҝ5 Y BHD D`
+ DABG T L 8՝ ; BEE E(D0A8G*
+8D0A(B BBBD T ڝ ! BDB B(A0D8G&
+8A0A(B BBBA T 8 - BDE H(A0A8G,
+8D0A(B BBBA T T W BDE H(A0A8G$
+8D0A(B BBBA T H BDE H(A0A8G$
+8D0A(B BBBA T G BDE H(A0A8G$
+8D0A(B BBBA 8y. K^ , 0 D ( D 0 b ) BBB A(D0Kp
+0A(A BBBJ , X> AID W
+DAA , Ȁ- AAG0!AA 4 L 85 BHD D`
+ DABG L @ BBE B(F0G8G
+8D0A(B BBBI w. K^ , 0 AF
FK
+ D T L | o BBE E(D0A8G
+8D0A(B BBBE T | U BBE E(D0A8G
+8D0A(B BBBE , fǀ- AAG0!AA < , p BHD G
+ DABA T l BBE B(D0A8G
+8D0A(B BBBG T E
+ BBB E(A0A8N
+8A0A(B BBBA , )> h AID W
+DAA T L ) K BBB B(A0F8J
+8A0A(B BBBJ T p1u BBB B(A0F8Jw
+8A0A(B BBBF T 8 BBH E(F0A8G?
+8D0A(B BBBE T T @> C BBB E(F0A8J>
+8A0A(B BBBD T C BBB B(F0D8Gv
+8A0A(B BBBG T I BBB B(D0F8Jy
+8A0A(B BBBA T \ HP BBB G(A0D8M
+8D0A(B BBBB l @s. K^ D W BBE A(A0D@
+0A(A BBBA D PZ BBE A(A0D@
+0A(A BBBA , 4Ā- AAG0!AA < \` @ BHD G
+ DABA T ` 3 BBB E(A0G8L@
+8D0A(B BBBA < $ i BLD D(G0
+(A ABBE < d `j* BED I(KPc
+(D ABBE < Pk g BLD D(G0
+(A ABBE < l* K BED I(KP_
+(D ABBI T $ m- + BBM B(A0D8G
+8A0A(B BBBD , | > AID W
+DAA d xp. K^ | 0 Hg 7 Hj 0 Hg 7 Hj 00 Hg @7 Hj D < ` BBE A(A0D@g
+0A(A BBBA D 耞 BBE A(A0D@g
+0A(A BBBA D p BBE A(A0D@g
+0A(A BBBA D BBE A(A0D@o
+0A(A BBBA D \ BBE A(A0D@o
+0A(A BBBA D ( BBE A(A0D@o
+0A(A BBBA , - AAG0!AA < d U BIA D(Dp
+
+(D ABBI < U BIA D(Dp
+
+(D ABBI < ЉU BIA D(Dp
+
+(D ABBI < $ U BIA D(Dp
+
+(D ABBI < d U w BIA D(Dp
+
+(D ABBI < 0U Q BIA D(Dp
+
+(D ABBI L P + BBJ B(A0A8G
+8D0A(B BBBD L 4 BBJ B(A0A8G
+8D0A(B BBBD L BBJ B(A0A8G
+8D0A(B BBBD L p BBB B(A0I8G
+8D0A(B BBBD L $ BBB B(A0I8G
+8D0A(B BBBD L t BBB B(A0I8G
+8D0A(B BBBD T ` BBE B(A0F8M9
+8D0A(B BBBA T ب BBE B(A0F8M0
+8D0A(B BBBA T t @q BBG B(A0A8J
+8A0A(B BBBA T hv ' BBB E(A0A8L
+8A0A(B BBBK T $ m K BBB E(A0A8L
+8A0A(B BBBD T | } o BBB B(D0A8L
+8A0A(B BBBG h. K^ 0 Hg 7 Hj T , ; BBB J(A0A8G?
+8D0A(B BBBK T PŞ BEE E(D0A8G
+8D0A(B BBBH T ʞ BBE B(D0A8G
+8D0A(B BBBG , - AAG0!AA < d pϞ BHD Db
+ DABA T Ҟc BBB B(F0A8G
+8A0A(B BBBE f. K^ > Hu E Hx ƺ\ AG RAT t
+ BBB B(D0A8N
+8A0A(B BBBD , > AID W
+DAA , z- AAG0!AA < , BHD DN
+ DABA T l o BBE E(A0A8GV
+8D0A(B BBBF T p BBE B(D0A8GH
+8D0A(B BBBD T BBB G(D0A8M
+8D0A(B BBBE , @d. K^ $ D h
ADD0AA$ l
ADD0AA$ x ADD0AA$ ADD0AA$ ADD0AA$ ADD0AAD 4 1 BBE A(A0D@
+0A(A BBBA D | > BBB D(A0D@
+0A(A BBBA D F BBB D(A0D@
+0A(A BBBA D N BBB D(A0D@
+0A(A BBBA $ T ^ AG b
+AA L 2 BGB B(A0K8U
+8A0A(B BBBA < BQA A(N`
+(A ABBA < T ` Q BQA A(N`
+(A ABBA < # BQA A(N`
+(A ABBA L 2 BGB B(A0K8U
+8A0A(B BBBA < $ BQA A(N`
+(A ABBA < d 0 BQA A(N`
+(A ABBA $ \ AAG0AA< X 9 BHA JI
+ DABA < ( * BHA JI
+ DABA < L BHA JI
+ DABA < " BHA JI
+ DABA < % BHA JI
+ DABA < h( BHA JI
+ DABA < L 8+ BHA JI
+ DABA < .4 BHA Ju
+ DABA D 1 BBE A(A0D@l
+0A(A BBBA $ 1i AAG0]AA$ 1q AAG0eAAD @2 BBE A(A0D@l
+0A(A BBBA $ d 2| AAG0pAA 03i AG _A 3q AG gA$ 3 AAG0xAAT < H4 Q BEJ E(D0D8G
+8D0A(B BBBG T 8M BBB I(A0A8JN
+8A0A(B BBBA , ?> 3 AID W
+DAA \ ? BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBL | h? BBB E(A0G8I[
+8D0A(B BBBA < 8C BGB D(A0~
+(A BBBK T C
+ BBB M(A0A8J
+8A0A(B BBBA , d `N> AID W
+DAA T pN BBB E(A0H8K
+8A0A(B BBBA , T> AID W
+DAA T T BBB E(I0A8K}
+8D0A(B BBBF T t `W BBB B(A0H8JH
+8A0A(B BBBA , (`> AID W
+DAA L 8` BEB E(I0H8DP
+8D0A(B BBBD L L a w BEB E(I0H8D@
+8D0A(B BBBA T xc' O BBB I(A0A8J
+8A0A(B BBBA , Pj> v AID W
+DAA T $ `j W BBB E(I0A8K}
+8D0A(B BBBF T | l= / BBB B(A0D8N
+8A0A(B BBBA , t> V AID W
+DAA L t 7 BEB E(I0H8D@
+8D0A(B BBBA T T vH BBI B(A0A8J.
+8A0A(B BBBA , }> 6 AID W
+DAA \ } BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBT < h} BBB M(A0A8J
+8A0A(B BBBA , > AID W
+DAA < H BBA A(Gp
+(D ABBA < H BBA A(Gp
+(D ABBA T 8 BEB F(G0DP
+0D(A BBBA l0D(A BBB < H O BBA A(Gp
+(D ABBA d / 3 BBB E(A0F8Dp
+8A0A(B BBBF d8A0A(B BBB < D H BBA A(Gp
+(D ABBA T < 9 BEB G(A0IP
+0D(A BBBE m0D(A BBB < ؋H d BBA A(Gp
+(D ABBA d 茟 H BGB B(A0D8D
+8A0A(B BBBA @8A0A(B BBB d # BBB B(D0F8D
+8A0A(B BBBA E8A0A(B BBB < XH BBA A(Gp
+(D ABBA T h; BEB F(G0DP
+0D(A BBBE o0D(A BBB L P BBH E(A0F8D`
+8D0A(B BBBH Q. K^ , b D\
H P
H Q
-G I , t\ }b D\
+G I , Xb D\
H P
H Q
-G I , \ ~b D\
+G I , 4 b D\
H P
H Q
-G I , \ @~ AJ@
-AG MA ] 0?\ AG RAD $] ~ BBE A(A0D@
-0A(A BBBA D l] BBE A(A0D@
-0A(A BBBA T $] ` {t BEB B(H0A8G?
-8A0A(B BBBD , |] ؐ> t AID W
-DAA T ] 营 t BEE E(D0A8G
-8D0A(B BBBE D ^ = BBB D(A0D@0A(A BBB T L^ ȕ et BIB E(A0A8GZ
-8D0A(B BBBA $ ^ ` Jt AL0p
-DA $ ^ 蛥 0t AL0p
-DA $ ^ p t AL0p
-DA d _ s KGB B(A0A8R8A0A(B BBBG T _ G s BBB B(A0A8G
-8A0A(B BBBK T _ Wt BBH B(F0A8J
-8D0A(B BBBG ` . K^ ` ̥> Hu ` ̥E Hx a B=\ AG RAT ` ̥
- t BBB B(D0A8N
-8A0A(B BBBJ , a `ץ> %u AID W
-DAA D a < BBB D(A0D@0A(A BBB T |a (ץ t BIB E(A0A8D6
-8D0A(B BBBA T a `ۥL t BBB E(F0A8Gj
-8A0A(B BBBC b (. K^ b @ b 8 T tb @, t BBE B(D0A8G
-8D0A(B BBBK D \c ,= BBB D(A0D@0A(A BBB T c & t BIB E(A0A8G
-8D0A(B BBBA L lc kt BBB J(A0A8G
-8D0A(B BBBH T c x tt BBE B(D0A8G
-8D0A(B BBBG T d @ Zt BBE B(D0A8G
-8D0A(B BBBH \ d ^ KLY H(D0I8J8A0A(B BBBG \ \e KEJ E(J0I8J8A0A(B BBBG \ e KLQ I(G0I8M8A0A(B BBBI L e H s BHB E(A0F8G
-8D0A(B BBBD L e f $s BEB G(A0G8G
-8D0A(B BBBD L ,f ' -s BEB G(A0A8J
-8D0A(B BBBG L |f x, 6s BEB G(A0A8M
-8D0A(B BBBD T f 4e ?s BBB G(D0G8G
-8D0A(B BBBG g `. K^ g :\ AG RAD g 8} BBB D(A0D@
-0A(A BBBA D 4h 9} BBB D(A0D@
-0A(A BBBA D |h (;} BBB D(A0D@
-0A(A BBBA D h `<} BBB D(A0D@
-0A(A BBBA D i = BBB D(A0D@
-0A(A BBBA D Ti > BBB D(A0D@
-0A(A BBBA D i (@ BBB D(A0D@
-0A(A BBBA D i pA BBB D(A0D@
-0A(A BBBA D ,j 8v BBB D(A0D@^0A(A BBB T i pB p BIB E(A0A8D6
-8D0A(B BBBA T <j F ]p BIB E(A0A8D6
-8D0A(B BBBA T j J +p BIB E(A0A8D6
-8D0A(B BBBA T j O o BIB E(A0A8D6
-8D0A(B BBBA D k PS BBE A(A0D@l
-0A(A BBBA L k S o BJE B(A0A8D@
-8A0A(B BBBA $ ll TV ADD JAAL l T /o BEB E(I0H8D@
-8D0A(B BBBA T Tl pV o BBB B(A0D8L7
-8A0A(B BBBF T l [\ o BBB B(H0A8J
-8A0A(B BBBG , m f> wo AID W
-DAA T 4m 0f\ Wo BBB B(H0A8J
-8A0A(B BBBG , m 8q> o AID W
-DAA T m Hq\ o BBB B(H0A8J
-8A0A(B BBBG , n P|> o AID W
-DAA T Dn `|\ o BBB B(H0A8J
-8A0A(B BBBG , n h> 7p AID W
-DAA T n x p BBB H(A0D8L
-8D0A(B BBBH T $o $p BBB H(A0D8L
-8D0A(B BBBH T |o H 1p BBB H(A0D8L
-8D0A(B BBBH dp . K^ |p > Hu p > Hu p Ȕ> Hu p 蔦> Hu p E Hx q 8E Hx <q hE Hx \q E Hx |q 2\ AG RA q _ AF
-I T ,q 蕦
- n BEB B(A0A8N
-8A0A(B BBBH , q > +o AID W
-DAA T q
- o BEB B(A0A8N
-8A0A(B BBBH , r > Po AID W
-DAA T <r
- 0o BEB B(A0A8N
-8A0A(B BBBH , r `> uo AID W
-DAA T r p
- Uo BEB B(A0A8N
-8A0A(B BBBH , s 8> o AID W
-DAA D s 0 BBB D(A0D@0A(A BBB T s 2o BIB E(A0A8DF
-8D0A(B BBBA T s H o BIB E(A0A8DF
-8D0A(B BBBA T Dt Ŧ n BIB E(A0A8DF
-8D0A(B BBBA T t ɦ n BIB E(A0A8DF
-8D0A(B BBBA T t Φ kn BBG E(A0G8GY
-8D0A(B BBBK T Lu ֦ n BBG E(A0G8GY
-8D0A(B BBBK T u o BBG E(A0G8GY
-8D0A(B BBBK T u
- [o BBG E(A0G8Go
-8D0A(B BBBE v . K^ v w >/\ AG RAD 4w v BBB A(D0D@
-0A(A BBBA D |w ~ BBB A(D0D@
-0A(A BBBA L w BBB B(A0D8DPd
-8A0A(B BBBA L x K BBB B(A0A8G@d
-8A0A(B BBBA D dx J.v BBB D(A0D@^0A(A BBB T x H m BIB E(A0A8GZ
-8D0A(B BBBA T y O BAA G05
+G I , d ؓ AJ@
+AG MA ȥ\ AG RAD X BBE A(A0D@
+0A(A BBBA D BBE A(A0D@
+0A(A BBBA T % BEE E(D0A8G
+8D0A(B BBBE , - AAG0!AA < { BHD G
+ DABA $ T 0 AL0p
+DA $ | AL0p
+DA $ @ AL0p
+DA d Ƞ ~ KGB B(A0A8R8A0A(B BBBG T 4 ` BEB B(H0A8G;
+8A0A(B BBBH , Ƚ> AID W
+DAA T ؽQ BBB B(A0A8G
+8A0A(B BBBK T ͟ ' BBH B(F0A8J
+8D0A(B BBBG $ L. K^ < > Hu \ E Hx | j\ AG RAT
+ BBB B(D0A8N
+8A0A(B BBBJ , < > AID W
+DAA , $ - AAG0!AA 4 x5 BHD D`
+ DABG T 4 s BBB B(D0F8G
+
+8A0A(B BBBC K. K^ P H T t P, BBE B(D0A8G
+8D0A(B BBBK L (
BBB J(A0A8G
+8D0A(B BBBH , - AAG0!AA < L BHD Dn
+ DABA T H BBE B(D0A8G
+8D0A(B BBBG T e BBE B(D0A8G
+8D0A(B BBBH \ ^ KLY H(D0I8J8A0A(B BBBG \ T KEJ E(J0I8J8A0A(B BBBG \ x" KLQ I(G0I8M8A0A(B BBBI L \ % + BHB E(A0F8G
+8D0A(B BBBD L 0 / BEB G(A0G8G
+8D0A(B BBBD L : 3 BEB G(A0A8M
+8D0A(B BBBD L L xB < BEB G(A0A8J
+8D0A(B BBBG T 8G C BBB G(D0G8G
+8D0A(B BBBG F. K^ ڟ\ AG RAD XK} BBB D(A0D@
+0A(A BBBA D , L} BBB D(A0D@
+0A(A BBBA D t M} BBB D(A0D@
+0A(A BBBA D O} BBB D(A0D@
+0A(A BBBA D
+ 8P BBB D(A0D@
+0A(A BBBA D L
+ Q BBB D(A0D@
+0A(A BBBA D
+ R BBB D(A0D@
+0A(A BBBA D
+ T BBB D(A0D@
+0A(A BBBA , $ ֝- AAG0!AA 4
+ (U5 BHD D`
+ DABG 4
+ 0X5 BHD D`
+ DABG 4 8[5 o BHD D`
+ DABG 4 D @^5 Q BHD D`
+ DABG D 4 Ha BBE A(A0D@l
+0A(A BBBA T a BBB B(A0D8L7
+8A0A(B BBBF T (fW BBB H(A0D8L
+8D0A(B BBBH T t 0jb BBB H(A0D8L
+8D0A(B BBBH T Hof BBB H(A0D8L$
+8D0A(B BBBH L $
`t BJE B(A0A8D@
+8A0A(B BBBA $ , 0uV ADD JAAL
hu BEB E(I0H8D@
+8D0A(B BBBA T
vE BBB B(H0A8J
+8A0A(B BBBG , D > AID W
+DAA T t BBB B(H0A8J
+8A0A(B BBBG , x> # AID W
+DAA T E BBB B(H0A8J
+8A0A(B BBBG , T > c AID W
+DAA T E C BBB B(H0A8J
+8A0A(B BBBG , > AID W
+DAA >. K^ > Hu > Hu > Hu < ࣠> Hu \ E Hx | 0E Hx `E Hx E Hx L\ AG RA _ AF
+I T d ठ
+ + BEB B(A0A8N
+8A0A(B BBBH , > p AID W
+DAA T
+ P BEB B(A0A8N
+8A0A(B BBBH , D > AID W
+DAA T t
+ u BEB B(A0A8N
+8A0A(B BBBH , X > AID W
+DAA T h
+ BEB B(A0A8N
+8A0A(B BBBH , T 0̠> AID W
+DAA , < H- AAG0!AA 4 ̠E BHD D`
+ DABK 4 (ϠE q BHD D`
+ DABK 4 $ @ҠE S BHD D`
+ DABK 4 \ XՠE 5 BHD D`
+ DABK T pؠ BBG E(A0G8GY
+8D0A(B BBBK T 8E
+ k BBG E(A0G8GY
+8D0A(B BBBK T D 0
+ BBG E(A0G8Go
+8D0A(B BBBE T 5
+ BBG E(A0G8GY
+8D0A(B BBBK 8. K^ ֔\ AG RAD v BBB A(D0D@
+0A(A BBBA D D ~ BBB A(D0D@
+0A(A BBBA V ATL BBB B(A0D8DPd
+8A0A(B BBBA T < Hr
BEB B(D0A8Je
+8A0A(B BBBA L L pK BBB B(A0A8G@d
+8A0A(B BBBA , ȓ- AAG0!AA < @{ y BHD G
+ DABA T O BAA G05
AABD
AABD e
- AABO \y .V ATT x r
Xm BEB B(D0A8Je
-8A0A(B BBBA T <y 2m BOB B(A0A8G]
-8A0A(B BBBA < y g Bm BBA A(D0V(A ABB T y P
m BBE E(A0A8J
-8A0A(B BBBA T ,z - cm BBB B(A0D8M
-8D0A(B BBBA T z 6! m BBB B(A0D8N E
-8A0A(B BBBF , z HX> 9o AID W
-DAA { . K^ { @X { 8XR HI { xXY HL $ | X^ AG b
-AA T 4| X- BDA ]
+ AABO T x ! BBB B(D0A8N E
+8A0A(B BBBF , => AID W
+DAA T 4 = BOB B(A0A8G]
+8A0A(B BBBA < (Lg BBA A(D0V(A ABB T XL T BBE E(A0A8J#
+8A0A(B BBBA T $ X
+ BBB B(A0D8M
+8D0A(B BBBA 4 4. K^ L b d xbR HI bY HL $ b^ AG b
+AA T 0c- BDA ]
ADG w
ADD Q
ABD I
-ABD T { Y )n BBB B(D0A8N
-8A0A(B BBBD , T| 0n> n AID W
-DAA D } f+ BBB D(A0D@0A(A BBB T | m n BIB E(A0A8D6
-8D0A(B BBBA L $} 0rr Wn BBB B(A0A8G~
-8A0A(B BBBG T t} `{ /n BBE B(F0A8G
-8A0A(B BBBD \~ x. K^ t~ ~ ~ ~ ~ d D L
-H G ~ P G t
-E - < ~ q 7n BBA A(D0[
-(A ABBA l H2 AF iA hi AX
-G * HN L AAG e
+ABD T l d BBB B(D0A8N
+8A0A(B BBBD , px> AID W
+DAA , 搀- AAG0!AA 4 $ Px5 g BHD D`
+ DABG $ \ X{G I As
+L A
+A L {r % BBB B(A0A8G~
+8A0A(B BBBG T u BBG B(A0D8G
+8A0A(B BBBH 2. K^ , D \ d D L
+H G | G t
+E - < Ȟq BBA A(D0[
+(A ABBA 2 AF iA (i AX
+G 4 HN L L ` AAG e
CAF C
ACA R
-CAJ JCA L AAG e
+CAJ JCA L AAG e
CAF S
ACA R
-CAJ JCA L d p AAG e
+CAJ JCA L 0 AAG e
CAF C
ACA R
-CAJ JCA L Д AAG e
+CAJ JCA L < AAG e
CAF S
ACA R
-CAJ JCA L @ AAG e
+CAJ JCA L AAG e
CAF C
ACA R
-CAJ JCA L T AAG e
+CAJ JCA L ` AAG e
CAF S
ACA R
-CAJ JCA L AAG e
+CAJ JCA L ,! С AAG e
CAF C
ACA R
-CAJ JCA L p AAG e
+CAJ JCA L |! 0 AAG e
CAF S
ACA R
-CAJ JCA D (\ AG RA< d BLF A(A0
-(H BFEA < ` BLF A(A0
-(H BFEA < BLF A(A0
-(H BFEA < $ BLF A(A0
-(H BFEA < d @ BII A(A0
-(A BJBE < BII A(A0
-(A BJBE < BII A(A0
-(A BJBE < $ BII A(A0
-(A BJBE D d BBB D(A0D@
-0A(A BBBA T h BIB E(A0A8D
-8D0A(B BBBA L t ࡧ h BIB A(D0D
-0D(A BBBA T Ą 0 uh BIB E(A0A8D
-8D0A(B BBBA L Nh BIB A(D0D
-0D(A BBBA D l H /h BEB A(A0G`l
-0D(A BBBA 4 Z g BDA G`
- DABJ 4 HZ g BDA G`
- DABJ L $ p g BBB E(A0A8G
-8D0A(B BBBB L бf BEB A(A0|
+CAJ JCA !
+\ AG RA< ! BLF A(A0
+(H BFEA < ," BLF A(A0
+(H BFEA < l" BLF A(A0
+(H BFEA < " ` BLF A(A0
+(H BFEA < " BII A(A0
+(A BJBE < ,# BII A(A0
+(A BJBE < l# @ BII A(A0
+(A BJBE < # ড BII A(A0
+(A BJBE $ # F AAG0AA< \# X BHD Dq
+ DABA < # 8 BHD Dq
+ DABA < # i BHD Dq
+ DABA < $ N BHD Dq
+ DABA D \$ ز 3 BEB A(A0G`l
+0D(A BBBA 4 $ Z BDA G`
+ DABJ 4 $ شZ BDA G`
+ DABJ L % BBB E(A0A8G
+8D0A(B BBBB L & Pf BEB A(A0|
(A BBBJ A
-(A BBBA L Ć Cg BBB B(D0A8D`
-8D0A(B BBBI L pK BBB B(A0A8G@d
-8A0A(B BBBA < p BEB D(A0M
-(A BBBF T R f BEE E(D0A8Gj
-8D0A(B BBBD T R jf BEE E(D0A8Gj
-8D0A(B BBBD T T R -f BEE E(D0A8Gj
-8D0A(B BBBD T R e BEE E(D0A8Gj
-8D0A(B BBBD L ç BBB B(A0A8Gp8A0A(B BBB d ħ0 BBE B(D0E8D@P
+(A BBBA L % p ? BBB B(D0A8D`
+8D0A(B BBBI L & K BBB B(A0A8G@d
+8A0A(B BBBA < ' BEB D(A0M
+(A BBBF T & pR BEE E(D0A8Gj
+8D0A(B BBBD T & xR f BEE E(D0A8Gj
+8D0A(B BBBD T D' ¡R ) BEE E(D0A8Gj
+8D0A(B BBBD T ' šR BEE E(D0A8Gj
+8D0A(B BBBD L ( ȡ BBB B(A0A8Gp8A0A(B BBB d ( ʡ0 BBE B(D0E8D@P
8F0A(B BBBA
-8D0A(B BBGA \ L Xŧ KLB B(D0A8DPX8A0A(B BBBJP \ ǧ BLE A(A0]
-(A BBBG ~
-(A FBBG A(A BBB L (ȧ BBB B(A0A8Gp8A0A(B BBB \ ɧ{ d t ʧz BBB E(D0E8D@S
-8F0A(B BBBF
-8D0A(B BBGA < ܋ (˧ BEB A(F0z
-(A BBBG 4 ˧ ADD S
+8D0A(B BBGA \ d) ʡ KLB B(D0A8DPX8A0A(B BBBJP \ ) h͡ BLE A(A0]
+(A BBBG B
+(A BBBG A(A BBB L $* ͡ BBB B(A0A8Gp8A0A(B BBB t* (ϡ{ < * ϡ BEB A(F0z
+(A BBBG 4 * С ADD S
AAB I
-FAH < T P̧ BBG A(G@
-(A ABBG \ ͧ8 BFD D(G0~
+FAH T + С BBB D(D0D@
+0D(A BBGK D
+0C(A BBBB < \+ `ѡ BBG A(G@
+(A ABBG \ + ҡ8 BFD D(G0~
(A ABBE i
-(A ABBA \(F ABB T d ͧ Sb BBB B(A0D8J
-8A0A(B BBBD T c BBB B(A0D8N
-8A0A(B BBBJ , > d AID W
-DAA T D d BBB B(A0D8J
-8A0A(B BBBD T 4f BBB B(A0D8N
-8A0A(B BBBJ , .> f AID W
-DAA T $ 0. f BBB B(A0D8J
-8A0A(B BBBD T | G jh BBB B(A0D8N
-8A0A(B BBBJ , Ԏ @^> i AID W
-DAA T P^ h BBB B(A0D8J
-8A0A(B BBBD T \ w j BBB B(A0D8N
-8A0A(B BBBJ , `> Kk AID W
-DAA t p D h BBB B(A0A8D@8A0A(B BBBT D j BBE B(A0A8GU
-8A0A(B BBBA L , 8 BBD A(G0o
+(A ABBA \(F ABB T D+ ӡ _ BBB B(A0D8J
+8A0A(B BBBD T +
+ BBB B(A0D8N
+8A0A(B BBBJ , + > AID W
+DAA T $, BBB B(A0D8J
+8A0A(B BBBD T |, @ BBB B(A0D8N
+8A0A(B BBBJ , , 03> AID W
+DAA T - @3 BBB B(A0D8J
+8A0A(B BBBD T \- L v BBB B(A0D8N
+8A0A(B BBBJ , - Pc> ! AID W
+DAA T - `c BBB B(A0D8J
+8A0A(B BBBD T <. | BBB B(A0D8N
+8A0A(B BBBJ , . p> W AID W
+DAA |/ D / x BBB B(A0A8D@8A0A(B BBBT $/ Ж BBE B(A0A8GU
+8A0A(B BBBA L 40 H BBD A(G0o
(A ABBK X
-(A ABBA | ) d < 6 `j BBA A(DG
-(A ABBA , D ?j AAG d
-AAA < 0 BDD k
+(A ABBA 0 ) d < / К6 l BBA A(DG
+(A ABBA , $0 Л K AAG d
+AAA < 1 @ BDD k
ABH M
-ABA D D \ BBB B(A0A8D@8A0A(B BBBT |i BBE B(A0A8GU
-8A0A(B BBBA < l X6 yi BBA A(DG
-(A ABBA , X Xi AAG d
-AAA l ȟ D ` BBB B(A0A8D@8A0A(B BBBT < h BBE B(A0A8GU
-8A0A(B BBBA < 06 h BBA A(DG
-(A ABBA , ԓ 0 h AAG d
-AAA Q D 諨 BBB B(A0A8D@8A0A(B BBBT d @ .h BBE B(A0A8GU
-8A0A(B BBBA < 6 +h BBA A(DG
-(A ABBA ,
-h AAG d
-AAA < (d BBB A(A0d
-(A BBBE X% t p BBE A(D0D@"
+ABA L1 D d1 BBB B(A0A8D@8A0A(B BBBT 0 BBE B(A0A8GU
+8A0A(B BBBA < L1 h6 BBA A(DG
+(A ABBA , 1 h d AAG d
+AAA t2 ؤ D 2 p BBB B(A0A8D@8A0A(B BBBT 2 Ȩ BBE B(A0A8GU
+8A0A(B BBBA < t2 @6 BBA A(DG
+(A ABBA , 2 @ AAG d
+AAA 3 Q D 3 BBB B(A0A8D@8A0A(B BBBT D3 P : BBE B(A0A8GU
+8A0A(B BBBA < 3 ȴ6 7 BBA A(DG
+(A ABBA , 3 ȵ AAG d
+AAA < 4 8d BBB A(A0d
+(A BBBE 5 h% t 5 BBE A(D0D@"
0A(A BBBD
0A(A BBBE 7
-0A(A BBBI < g BED D(LP
-(A ABBH L < C f BBB B(A0A8D*8A0A(B BBB < f BED D(LP
-(A ABBH L ̖ hC qf BBB B(A0A8D*8A0A(B BBB < h %f BED D(LP
-(A ABBH L \ C e BBB B(A0A8D*8A0A(B BBB < ¨ e BED D(LP
-(A ABBH L (ĨC e BBB B(A0A8D*8A0A(B BBB < ̘ (Ũ BBG A(G@
-(A ABBD Ũ5 $ ƨ BBE A(A0G@
+0A(A BBBI < 4 # BED D(LP
+(A ABBH L 5 ¢C BBB B(A0A8D*8A0A(B BBB < l5 â BED D(LP
+(A ABBH L 5 xĢC } BBB B(A0A8D*8A0A(B BBB < 5 xŢ 1 BED D(LP
+(A ABBH L <6 ƢC BBB B(A0A8D*8A0A(B BBB < 6 Ǣ BED D(LP
+(A ABBH L 6 8ɢC BBB B(A0A8D*8A0A(B BBB < 7 8ʢ BBG A(G@
+(A ABBD 8 ʢ5 ,8 ˢ BBE A(A0G@
0A(A BBBC
0A(A BBBE
0A(A BBBE R
-0A(A BBBF D ̨r Sd BBA A(G
-(A ABBD T d Ѩ Od BBB B(A0D8G
-8A0A(B BBBF T XԨ: (d BEB E(A0A8J
-8A0A(B BBBA L @ݨ. !d BEE E(A0A8J
-8A0A(B BBBH T d b c BGE H(A0D8Fz
-8D0A(B BBBH D 8r c BBA A(G
-(A ABBD T p c BBB B(A0D8G
-8A0A(B BBBF T \ : c BEB E(A0A8J
-8A0A(B BBBA L x c BEE E(A0A8J
-8A0A(B BBBH T b c BGE H(A0D8Fz
-8D0A(B BBBH D \ 8r ic BBA A(G
-(A ABBD T p ec BBB B(A0D8G
-8A0A(B BBBF T : >c BEB E(A0A8J
-8A0A(B BBBA L T x 7c BEE E(A0A8J
-8A0A(B BBBH T b %c BGE H(A0D8Fz
-8D0A(B BBBH D 8r c BBA A(G
-(A ABBD T D p b BBB B(A0D8G
-8A0A(B BBBF T : b BEB E(A0A8J
-8A0A(B BBBA L #. b BEE E(A0A8J
-8A0A(B BBBH T D 'b b BGE H(A0D8Fz
-8D0A(B BBBH , ؾ. K^ D ) G t
-E d p*d D L
-H G < *q 'b BBA A(D0[
-(A ABBA Ġ +2 AF iA L + AAG e
+0A(A BBBF D 7 Ѣr _ BBA A(G
+(A ABBD T D8 ֢ [ BBB B(A0D8G
+8A0A(B BBBF T 8 h٢g 4 BEE E(A0A8J
+8A0A(B BBBA L 8 + - BEE E(A0A8J
+8A0A(B BBBH L D9 ` BBB G(A0G8J
+8D0A(B BBBH D 9 r BBA A(G
+(A ABBD T 9 BBB B(A0D8G
+8A0A(B BBBF T 4: : BEB E(A0A8J
+8A0A(B BBBA L : + BEE E(A0A8J
+8A0A(B BBBH L : h BBB G(A0G8J
+8D0A(B BBBH D ,; r BBA A(G
+(A ABBD T t; BBB B(A0D8G
+8A0A(B BBBF T ; : BEB E(A0A8J
+8A0A(B BBBA L $< + } BEE E(A0A8J
+8A0A(B BBBH L t< p O BBB G(A0G8J
+8D0A(B BBBH D < r W BBA A(G
+(A ABBD T = S BBB B(A0D8G
+8A0A(B BBBF T d= !: , BEB E(A0A8J
+8A0A(B BBBA L = *+ % BEE E(A0A8J
+8A0A(B BBBH L > x. BBB G(A0G8J
+8D0A(B BBBH ? . K^ ,? 1 G t
+E L? p2d D L
+H G < > 2q BBA A(D0[
+(A ABBA ? 32 AF iA L ? 3 AAG e
CAF C
ACA R
-CAJ JCA L 4 + AAG e
+CAJ JCA L @ 3 AAG e
CAF S
ACA R
-CAJ JCA L + AAG e
+CAJ JCA L l@ 3 AAG e
CAF C
ACA R
-CAJ JCA L ԡ P, AAG e
+CAJ JCA L @ P4 AAG e
CAF S
ACA R
-CAJ JCA L $ , AAG e
+CAJ JCA L A 4 AAG e
CAF C
ACA R
-CAJ JCA L t - AAG e
+CAJ JCA L \A 5 AAG e
CAF S
ACA R
-CAJ JCA L Ģ - AAG e
+CAJ JCA L A 5 AAG e
CAF C
ACA R
-CAJ JCA L - AAG e
+CAJ JCA L A 5 AAG e
CAF S
ACA R
-CAJ JCA L d `. AAG e
+CAJ JCA L LB `6 AAG e
CAF C
ACA R
-CAJ JCA L . AAG e
+CAJ JCA L B 6 AAG e
CAF S
ACA R
-CAJ JCA L 0/ AAG e
+CAJ JCA L B 07 AAG e
CAF C
ACA R
-CAJ JCA L T / AAG e
+CAJ JCA L <C 7 AAG e
CAF S
ACA R
-CAJ JCA L 0 AAG e
+CAJ JCA L C 8 AAG e
CAF C
ACA R
-CAJ JCA L `0 AAG e
+CAJ JCA L C `8 AAG e
CAF S
ACA R
-CAJ JCA L D 0 AAG e
+CAJ JCA L ,D 8 AAG e
CAF C
ACA R
-CAJ JCA L 01 AAG e
+CAJ JCA L |D 09 AAG e
CAF S
ACA R
-CAJ JCA < 1 BLF A(A0p
-(H BFEA < $ 2 BLF A(A0p
-(H BFEA < d 2 BLF A(A0p
-(K BFEE < 2 BLF A(A0p
-(K BFEE `3 g0
-A `4 g0
-A $ \ AG RA< D D BBD A(D@0(A ABB T 5 +[ BIB E(A0A8DF
-8D0A(B BBBA T L H9 Z BIB E(A0A8DF
-8D0A(B BBBA T = Z BBE B(A0A8J
-8A0A(B BBBB , S> {[ AID W
-DAA T , (S [[ BBE B(A0A8J
-8A0A(B BBBB , h> \ AID W
-DAA D D h BBB B(A0A8D@8A0A(B BBBL i BBB B(A0A8D`
-8A0A(B BBBF D ܩ j BBB B(A0A8D@8A0A(B BBBL $ 0k BBB B(A0A8D`
-8A0A(B BBBG 4 t l BBA A(D0(A ABBL HmF BBB B(A0A8D`(
-8A0A(B BBBH 4 Ho BBA A(D0(A ABBL 4 oF BBB B(A0A8D`(
-8A0A(B BBBH T q Y BBE B(A0A8GU
-8A0A(B BBBA T L 8u Y BBE B(A0A8GU
-8A0A(B BBBA T x Y BBB E(A0A8I^
-8A0A(B BBBA T 8{ Y BBB E(A0A8I^
-8A0A(B BBBA , T } ]Y AAG d
-AAA , 0~ :Y AAG d
-AAA , ~ Y AAG d
-AAA D t BBB B(A0A8D@8A0A(B BBBL h BBB B(A0A8D`
-8A0A(B BBBF D ( BBB B(A0A8D@8A0A(B BBBL T BBB B(A0A8D`
-8A0A(B BBBG 4 BBA A(D0(A ABBL ܮ F BBB B(A0A8D`(
-8A0A(B BBBH 4 , BBA A(D0(A ABBL d F BBB B(A0A8D`(
-8A0A(B BBBH T $ V BBE B(A0A8GU
-8A0A(B BBBA T | V BBE B(A0A8GU
-8A0A(B BBBA T ԯ V BBB E(A0A8I^
-8A0A(B BBBA T , V BBB E(A0A8I^
-8A0A(B BBBA , bV AAG d
-AAA , ?V AAG d
-AAA , V AAG d
-AAA ` BBE A(A0G@
+CAJ JCA < D 9 BLF A(A0p
+(H BFEA < E : BLF A(A0p
+(H BFEA < LE : BLF A(A0p
+(K BFEE < E : BLF A(A0p
+(K BFEE E `; g0
+A E `< g0
+A F i\ AG RA, ,F j- AAG0!AA 4 E = BHD D`
+ DABB 4 E x? BHD D`
+ DABB T F A BBE B(A0A8J
+8A0A(B BBBB , lF 8W> 3 AID W
+DAA T F HW BBE B(A0A8J
+8A0A(B BBBB , F l> AID W
+DAA D G l BBB B(A0A8D@8A0A(B BBBL $H m BBB B(A0A8D`
+8A0A(B BBBF D tH n BBB B(A0A8D@8A0A(B BBBL H o BBB B(A0A8D`
+8A0A(B BBBG 4 I p BBA A(D0(A ABBL DI 8qF BBB B(A0A8D`(
+8A0A(B BBBH 4 I 8s BBA A(D0(A ABBL I sF BBB B(A0A8D`(
+8A0A(B BBBH T dI u g BBE B(A0A8GU
+8A0A(B BBBA T I (y d BBE B(A0A8GU
+8A0A(B BBBA T J | a BBB E(A0A8I^
+8A0A(B BBBA T lJ ( ; BBB E(A0A8I^
+8A0A(B BBBA , J AAG d
+AAA , J AAG d
+AAA , $K AAG d
+AAA D L BBB B(A0A8D@8A0A(B BBBL TL X BBB B(A0A8D`
+8A0A(B BBBF D L BBB B(A0A8D@8A0A(B BBBL L p BBB B(A0A8D`
+8A0A(B BBBG 4 <M BBA A(D0(A ABBL tM F BBB B(A0A8D`(
+8A0A(B BBBH 4 M BBA A(D0(A ABBL M F BBB B(A0A8D`(
+8A0A(B BBBH T M l BBE B(A0A8GU
+8A0A(B BBBA T M x i BBE B(A0A8GU
+8A0A(B BBBA T DN f BBB E(A0A8I^
+8A0A(B BBBA T N x @ BBB E(A0A8I^
+8A0A(B BBBA , N AAG d
+AAA , $O p AAG d
+AAA , TO AAG d
+AAA <P P BBE A(A0G@
0A(A BBBC
0A(A BBBE
0A(A BBBE R
-0A(A BBBF < ؛ qU BEA A(Dh
-(A ABBE T ܱ 8 VU BEB E(A0A8Jo
-8A0A(B BBBA T 4 ?U BEB E(A0A8Jo
-8A0A(B BBBA T 诩 (U BBB B(D0A8Dd
-8A0A(B BBBA T P T BBB B(D0A8Dd
-8A0A(B BBBA < < T BEA A(Dh
-(A ABBE T | T BEB E(A0A8Jo
-8A0A(B BBBA T Գ p lT BEB E(A0A8Jo
-8A0A(B BBBA T , Ʃ UT BBB B(D0A8Dd
-8A0A(B BBBA T 0ȩ T BBB B(D0A8Dd
-8A0A(B BBBA T ܴ ɩ@ S BBB B(A0D8G
-8A0A(B BBBA T 4 ̩@ S BBB B(A0D8G
-8A0A(B BBBA T hϩ`
- }S BBE E(A0A8Ja
-8A0A(B BBBA T p٩`
- zS BBE E(A0A8Ja
-8A0A(B BBBA T < x wS BBB B(A0D8G;
-8A0A(B BBBG T nS BBB B(A0D8G;
-8A0A(B BBBG T T eS BBE B(A0D8Ja
-8A0A(B BBBA D " S BBB A(A0D
-0A(A BBBI c
-0A(A BBBE g
-0A(A BBBA '
-0A(A BBBA T Է P /S BBB G(D0A8M
-8D0A(B BBBG T , T ;S BBE B(A0D8Ja
-8A0A(B BBBA " qS BBB A(A0D
-0A(A BBBI c
-0A(A BBBE g
-0A(A BBBA '
-0A(A BBBA T p S BBB G(D0A8M
-8D0A(B BBBG 8. K^ F\ AG RAD 4 BBE A(A0D@u
-0A(A BBBA D | H BBE A(A0D@}
-0A(A BBBA T 4 W GR BBI B(A0A8J
-8A0A(B BBBA , > _R AID W
-DAA D L j BBB D(A0D@0A(A BBB T Q BIB E(A0A8D
-8D0A(B BBBA \ # KLE B(A0A8DPi8A0A(B BBBIP \ L H% KLE B(A0A8DPi8A0A(B BBBIP l x'y BBF D(A0G@I
+0A(A BBBF < P ȟ ) BEA A(Dh
+(A ABBE T LP ( BEB E(A0A8Jo
+8A0A(B BBBA T P BEB E(A0A8J
+8A0A(B BBBA T P BBB B(D0A8Dd
+8A0A(B BBBA T TQ p BBB B(D0A8Dd
+8A0A(B BBBA < Q ض V BEA A(Dh
+(A ABBE T Q 8 ; BEB E(A0A8Jo
+8A0A(B BBBA T DR $ BEB E(A0A8Jo
+8A0A(B BBBA T R ʣ
BBB B(D0A8Dd
+8A0A(B BBBA T R P̣ BBB B(D0A8Dd
+8A0A(B BBBA T LS ͣH BBB B(A0D8G
+8A0A(B BBBH T S УH \ BBB B(A0D8G
+8A0A(B BBBH T S ӣ`
+ 5 BBE E(A0A8Ja
+8A0A(B BBBA T TT ݣ`
+ 2 BBE E(A0A8Ja
+8A0A(B BBBA T T / BBB B(A0D8G;
+8A0A(B BBBG T U P & BBB B(A0D8G;
+8A0A(B BBBG T \U T BBE B(A0D8Ja
+8A0A(B BBBA T U S BBB E(G0A8L
+8D0A(B BBBD T V hT s BBE B(A0D8Ja
+8A0A(B BBBA T dV p BBB E(G0A8L
+8D0A(B BBBD tW . K^ W Y\ AG RAD W BBE A(A0D@u
+0A(A BBBA D W H BBE A(A0D@}
+0A(A BBBA T W J BBI B(A0A8J
+8A0A(B BBBA , W "> AID W
+DAA , X X- AAG0!AA < <X " BHD DN
+ DABA \ 4Y & KLE B(A0A8DPi8A0A(B BBBIP \ Y H( KLE B(A0A8DPi8A0A(B BBBIP l Y x*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 dZ ,E BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l p*B ;P BBB J(D0D8D4
+8F0A(B BBBA l Z p-B BBB J(D0D8D4
8D0A(B BBBC i
-8A0A(B BBBE L d P. O BBB B(A0A8J
-8A0A(B BBBA D . K^ \ AO HF | AV HI T B< cP BBE B(D0A8G
-8D0A(B BBBB T d G< IP BBE B(D0A8G
-8D0A(B BBBB D L 8 BBB D(A0D@0A(A BBB T K O BIB E(A0A8DV
-8D0A(B BBBA T \ O O BIB E(A0A8D^
-8D0A(B BBBA T PTE O BGE B(A0A8G
-8A0A(B BBBH T Ho P BBF B(D0A8Nk
-8A0A(B BBBA , d x> :Q AID W
-DAA , x> Q AID W
-DAA T @. K^ l x x x x L < xr P BBB B(E0A8J@
-8A0A(B BBBA $ y^ AG b
-AA 4 D zl ADG b
+8A0A(B BBBE L Z P1# BBB B(A0A8J
+8A0A(B BBBA [ . K^ [ EO HF [ HEV HI T ,[ E< K BBE B(D0A8G
+8D0A(B BBBB T [ pJ< 1 BBE B(D0A8G
+8D0A(B BBBB , \ VV- AAG0!AA < \ (OU BIA D(Dp
+
+(D ABBI < L\ HRU BIA D(Dp
+(D ABBB T \ hU4 BGE B(A0A8G
+8A0A(B BBBI T \ Pp BBF B(D0A8Nk
+8A0A(B BBBA , <] y> Z AID W
+DAA , l] y> : AID W
+DAA T^ X. K^ l^ y ^ y ^ y ^ y L ^ yr BBB B(E0A8J@
+8A0A(B BBBA $ _ z^ AG b
+AA 4 D_ {l ADG b
AAH l
-AAA 4 | 8zl ADG b
+AAA 4 |_ @{l ADG b
AAH l
-AAA T $ pz O BBB B(A0I8G
-8D0A(B BBBE T | } O BBB B(A0I8G
-8D0A(B BBBE T O BEE E(D0A8G
-8D0A(B BBBA T , 腪 sO BBE B(D0A8G
-8D0A(B BBBH T YO BBE B(D0A8G
-8D0A(B BBBG T x ?O BEE E(D0A8G
-8D0A(B BBBF T 4 e %O BBB B(D0A8G
-8D0A(B BBBD < 6D BBD A(D@0(A ABB T x jO BIB E(A0A8DF
-8D0A(B BBBA T $ 8O BIB E(A0A8DF
-8D0A(B BBBA L | & O BGB E(A0A8G
-8A0A(B BBBA L 輪2 O BGB E(A0A8G
-8A0A(B BBBA . K^ Ϊ DV $ Ϊ^ AG b
-AA $ Ϫ AG u
-AA $ , Ϫ AG }
-AA T 0Ъ4
^N BBB B(D0A8J
-8D0A(B BBBH T ݪe
- }N BBB B(D0D8J
-8D0A(B BBBK L 0; BBB B(A0D8DPd
-8A0A(B BBBA $ T r DS
+AAA T ^ x{ BBB B(A0I8G
+8D0A(B BBBE T T_ ~ BBB B(A0I8G
+8D0A(B BBBE T _ ȁ BEE E(D0A8G
+8D0A(B BBBF T ` BBE B(D0A8G
+8D0A(B BBBH T \` y BBE B(D0A8G
+8D0A(B BBBG T ` _ BEE E(D0A8G
+8D0A(B BBBF T a e E BBB B(D0A8G
+8D0A(B BBBD , b Q- AAG0!AA 4 a BHD D`
+ DABG 4 a | BHD D`
+ DABG L b `< ^ BGB E(A0A8G
+8A0A(B BBBA L Tb PG d BGB E(A0A8G
+8A0A(B BBBA \c . K^ tc 8̤ DV L b @̤r : BBB B(E0A8J@
+8A0A(B BBBA $ c pͤ^ AG b
+AA $ d ͤ AG u
+AA $ ,d @Τ AG }
+AA T c Τ
+ BBB B(A0D8J$
+8D0A(B BBBH T c ؤ BBB B(D0D8J
+8D0A(B BBBC L e 8; BBB B(A0D8DPd
+8A0A(B BBBA $ Te (r DS
I H
-E < | D BBD A(D@0(A ABB T , 8 M BIB E(A0A8D6
-8D0A(B BBBA T p M BIB E(A0A8DG
-8D0A(B BBBA T | yM BBB L(A0A8J
-8A0A(B BBBG 4 4 x M BAI H
-ABA 4 l Xx tM BAI H
-ABA T PM BBB B(A0A8G
-8A0A(B BBBK L FM BIE B(A0A8J
-8D0A(B BBBC . K^ $ !^ AG b
-AA $ ! AG u
-AA $ D " AG }
-AA l (#T D V BBB D(A0D@0A(A BBB T < (% M BIB B(A0D8Df
-8D0A(B BBBA 4 )q L BAI A
-ABA L )v L BEB B(A0A8J
+E , |e O- AAG0!AA 4 d P BHD D`
+ DABF 4 ,e BHD D`
+ DABG T de BBB E(A0A8G
+8A0A(B BBBG L e BIE B(A0A8J
+8D0A(B BBBC T f ( x BBB L(A0A8J"
+8A0A(B BBBF 4 df x BAI H
+ABA 4 f x x BAI H
+ABA g . K^ L f Hr < BBB B(E0A8J@
+8A0A(B BBBA $ g x^ AG b
+AA $ h AG u
+AA $ Dh H AG }
+AA lh T , h M- AAG0!AA < g A BIA D(Dp-
+(D ABBF L <h
BEB B(A0A8J
-8A0A(B BBBA x. K^ L 4 Ar M BBB B(E0A8J@
-8A0A(B BBBA $ C^ AG b
-AA $ < XC AG
-AA $ d D AG
-AA , D L AAG@A
-AAA $ E AG M
-AA T T E yL BBE B(D0A8G
-8D0A(B BBBF < < lD BBD A(D@0(A ABB T I L BIB E(A0A8DF
-8D0A(B BBBA T D N K BIB E(A0A8D6
-8D0A(B BBBA T XRe K BBB B(A0I8G
-8D0A(B BBBF T pWw K BBB B(A0I8G
-8D0A(B BBBD \ \m BBD D(J@
+8A0A(B BBBA 4 h H9q BAI A
+ABA |i . K^ L h x9r BBB B(E0A8J@
+8A0A(B BBBA $ i :^ AG b
+AA $ j : AG
+AA $ 4j ; AG
+AA $ \j P< AG M
+AA T i < BBE B(D0A8G
+8D0A(B BBBF , j L- AAG0!AA 4 Tj A BHD D`
+ DABF 4 j xC } BHD D`
+ DABG T j Ew _ BBB B(A0I8G
+8D0A(B BBBD T k Ke I BBB B(A0I8G
+8D0A(B BBBF \ ,l Pm BBD D(J@
(A ABBD T
(A ABBF _
-(A ABBK T ] /K BBE E(A0C8L
-8A0A(B BBBE @vZ AK
-L AT $ v K BBB E(A0C8J
-8A0A(B BBBK AN , (. K^ D 8 Ho d ? Hr Є8 Ho ? Hr 8 Ho 0? Hr P8 Ho $ p? Hr D J BBB A(I0Dp
-0D(A BBBF D J BBB A(I0Dp
-0D(A BBBF D D rJ BBB A(I0Dp
-0D(A BBBF D ( MJ BBB A(I0Dp
-0D(A BBBF D d BBB D(A0D@0A(A BBB T h I BIB B(A0D8Df
-8D0A(B BBBA T t Џ I BIB B(A0D8Df
-8D0A(B BBBA T 8 |I BIB B(A0D8Df
-8D0A(B BBBA T $ JI BIB B(A0D8Df
-8D0A(B BBBA \ | I BBB A(A0GP
+(A ABBK T k 0QT BBE E(A0C8L
+8A0A(B BBBE l 8jZ AK
+L AT Ll xj BBB E(A0C8J
+8A0A(B BBBK \m x AN |m . K^ m x8 Ho m x? Hr m x8 Ho m x? Hr n y8 Ho 4n (y? Hr Tn Hy8 Ho tn hy? Hr D m y f BBB A(I0Dp
+0D(A BBBF D $n { A BBB A(I0Dp
+0D(A BBBF D ln | BBB A(I0Dp
+0D(A BBBF D n ~ BBB A(I0Dp
+0D(A BBBF , o I- AAG0!AA D ,o xu BIB A(D0Dp$
+0D(A BBBK D to u t BIB A(D0Dp$
+0D(A BBBK D o 腥u F BIB A(D0Dp$
+0D(A BBBK D p u BIB A(D0Dp$
+0D(A BBBK \ Lp X BBB A(A0GP
0A(A BBBI L
-0A(A BBBA \ I BBB A(A0GL
+0A(A BBBA \ p 莥 BBB A(A0GL
0A(A BBBE L
-0A(A BBBA T < (7 I BBE B(A0A8G
-8A0A(B BBBG T 7 ]I BBE B(A0A8G
-8A0A(B BBBG T L
- I BBE B(A0A8G
-8A0A(B BBBG T D L
- J BBE B(A0A8G
-8A0A(B BBBG , X. K^ D ǫ \ ǫ L ǫq J BBB B(A0I8G
-8D0A(B BBBD L 4 ʫq J BBB B(A0I8G
-8D0A(B BBBD L 0̫q I BBB B(A0I8G
-8D0A(B BBBD T `Ϋ I BBH B(D0F8Gl
-8D0A(B BBBH D ފ BBB D(A0D@0A(A BBB T t ԫ I BIB E(A0A8DN
-8D0A(B BBBA T ث\ I BIE B(A0A8DI
-8D0A(B BBBA T $ ݫ {I BIB E(A0A8DO
-8D0A(B BBBA . K^ $ 0 < ( T 0 I BBE E(A0A8G
-8D0A(B BBBE D ފ BBB D(A0D@0A(A BBB T d L I BIE B(A0A8D9
-8D0A(B BBBA L xq dI BBB B(A0I8G
-8D0A(B BBBD T OI BBB B(A0A8J"
-8A0A(B BBBH . K^ 8 $ 0 , < 8 MPAC , l JXAD D ފ BBB D(A0D@0A(A BBB T T `L GI BIE B(A0A8D9
-8D0A(B BBBA T Xj I BBB B(A0D8R
-8A0A(B BBBD , p > :I AID W
-DAA L 4 I BBE G(A0G8G
-8D0A(B BBBG . K^ , (& D & < \ (&t BBB I(M0F@I0A(A BBBD h& I BBE A(A0
-(A BBBA L & AAG e
+0A(A BBBA T q x7 BBE B(A0A8G
+8A0A(B BBBG T dq `7 / BBE B(A0A8G
+8A0A(B BBBG T q H7 v BBE B(A0A8G
+8A0A(B BBBG T r 0L
+ BBE B(A0A8G
+8A0A(B BBBG $s xހ. K^ <s Ts L r q BBB B(A0I8G
+8D0A(B BBBD L s @q BBB B(A0I8G
+8D0A(B BBBD L Ts pq BBB B(A0I8G
+8D0A(B BBBD T s BBH B(D0F8G
+8D0A(B BBBA , t JE- AAG0!AA 4 ,t ¥E BHD D`
+ DABK 4 dt ťE BHD D`
+ DABD 4 t ȥE k BHD D`
+ DABD u @܀. K^ u ˥ u ˥ T u ˥ BBE E(A0A8G
+8D0A(B BBBE , ,v E- AAG0!AA 4 u P5 BHD D`
+ DABG L u Xq BBB B(A0I8G
+8D0A(B BBBD T ,v BBB B(A0A8J"
+8A0A(B BBBH <w ڀ. K^ Tw lw , w MRAC , w JXAD , w vD- AAG0!AA 4 \w 85 BHD D`
+ DABG T w @ j BBB B(A0D8R
+8A0A(B BBBD , w X> AID W
+DAA L x h5 BBE G(A0G8G
+8D0A(B BBBD $y ـ. K^ <y @
Ty 8
< ly @
t BBB I(M0F@I0A(A BBBL y
AAG e
CAF s
ACA R
-CAJ JCA T 'x H BBB B(D0A8N9
-8A0A(B BBBB , H2> 9I AID W
-DAA D h݊ BBB D(A0D@0A(A BBB T t 2L H BIE B(A0A8D9
-8D0A(B BBBA T 7L H BBB B(A0A8G
-8A0A(B BBBA . K^ L F ADD e
+CAJ JCA T Dy
x ? BBB B(D0A8N9
+8A0A(B BBBB , y > AID W
+DAA , z C- AAG0!AA 4 y 5 BHD D`
+ DABG T 4z BBB B(A0A8G
+8A0A(B BBBA D{ ׀. K^ L \{ , ADD e
CAF
ACA V
-CAF JCA L G ADD e
+CAF JCA L { , ADD e
CAF
ACA V
-CAF JCA L l HH ADD e
+CAF JCA L { - ADD e
CAF
ACA V
-CAF JCA L H ADD e
+CAF JCA L L| 0. ADD e
CAF
ACA V
-CAF JCA L I AAG ]
+CAF JCA L | . AAG ]
CAF b
CAJ R
-CAJ JCA D I wG BBE A(A0
-(A BBBA L PJo BBB B(A0D8D`G
-8A0A(B BBBA L pK BBB B(A0D8Dt
-8A0A(B BBBA L D M BBB B(A0D8G
-8A0A(B BBBF L Qr BBB B(A0D8D`I
-8A0A(B BBBA L R BBB B(A0D8D}
-8A0A(B BBBH L 4 0U BBB B(A0D8G
-8A0A(B BBBF < XE BBB D(A0#
-(A BBBA L Zu BBB B(A0D8DB
-8A0A(B BBBC L 0\ BBB B(A0D8G8A0A(B BBB < d _B BBB D(A0#
-(A BBBA L `r BBB B(A0D8DB
-8A0A(B BBBC L b BBB B(A0D8G8A0A(B BBB L D 0f} BBB B(A0D8D`a8A0A(B BBB L `gU BBB B(A0D8D%
-8A0A(B BBBH L pi BBB B(A0D8D8A0A(B BBB 4 l: AD0sA T @l: AD0sA t `l: AD0sA l: AD0sA l: AD0sA l: AD0sA l: AD0sA m: AD0sA 4 m: AD0sA T @m: AD0sA t `m: AD0sA m: AD0sA m: AD0sA m: AD0sA m: AD0sA $ nM ADG@~AA L < (n ADD e
+CAJ JCA L | / ADD e
CAF k
ACI V
-CAF JCA L n AAG ]
+CAF JCA L <} /o BBB B(A0D8D`G
+8A0A(B BBBA L } 0 BBB B(A0D8Dt
+8A0A(B BBBA L } @3 BBB B(A0D8G
+8A0A(B BBBF L ,~ 6r BBB B(A0D8D`I
+8A0A(B BBBA L |~ 8 BBB B(A0D8D}
+8A0A(B BBBH L ~ : BBB B(A0D8G
+8A0A(B BBBF < P>E BBB D(A0#
+(A BBBA L \ `?u BBB B(A0D8DB
+8A0A(B BBBC L A BBB B(A0D8G8A0A(B BBB < DB BBB D(A0#
+(A BBBA L < Fr BBB B(A0D8DB
+8A0A(B BBBC L 0H BBB B(A0D8G8A0A(B BBB L ܀ K} BBB B(A0D8D`a8A0A(B BBB L , LU BBB B(A0D8D%
+8A0A(B BBBH L | N BBB B(A0D8D8A0A(B BBB ́ Q: AD0sA Q: AD0sA Q: AD0sA , Q: AD0sA L R: AD0sA l R: AD0sA @R: AD0sA `R: AD0sA ̂ R: AD0sA R: AD0sA R: AD0sA , R: AD0sA L S: AD0sA l S: AD0sA @S: AD0sA L `S AAG ]
CAF b
CAJ R
-CAJ JCA L n ADD e
+CAJ JCA L S ADD e
CAF k
ACI V
-CAF JCA L , o AAG ]
+CAF JCA L L 0T AAG ]
CAF b
CAJ R
-CAJ JCA L | o ADD e
+CAJ JCA L pT ADD e
CAF k
ACI V
-CAF JCA L Xp AAG ]
+CAF JCA L U AAG ]
CAF b
CAJ R
-CAJ JCA L p ADD e
+CAJ JCA L < @U ADD e
CAF k
ACI V
-CAF JCA L l (q AAG ]
+CAF JCA L U AAG ]
CAF b
CAJ R
-CAJ JCA L hq ADD e
+CAJ JCA L ܅ V ADD e
CAF k
ACI V
-CAF JCA L q ADD e
+CAF JCA L , V ADD e
CAF
ACA V
-CAF JCA $ \ r^ AG b
-AA $ r AG r
-AA $ Xs AG r
-AA $ s AG r
-AA $ ht AG r
-AA $ $ t AG z
-AA $ L u AG z
-AA $ t v AG z
-AA $ v AG z
-AA $ Pw AG L
-AA D \ w ; BEB A(A0Nj
-0A(A BBBA L 0y ; BEB B(K0D8M
-8A0A(B BBBD < < BBA I(D@p
-(D ABBA < 4 @ < BBA I(D@p
-(D ABBA \ t Ќ, < BBB A(I0D@
-0D(A BBBD W
-0D(A BBBA \ , o< BBB A(I0D@
-0D(A BBBD W
-0D(A BBBA \ 4 p, 1< BBB A(I0D@
-0D(A BBBD W
-0D(A BBBA \ @, ; BBB A(I0D@
-0D(A BBBD W
-0D(A BBBA \ , ; BBB A(I0D@
-0D(A BBBD W
-0D(A BBBA \ T , w; BBB A(I0D@
-0D(A BBBD W
-0D(A BBBA \ , 9; BBB A(I0D@
-0D(A BBBD W
-0D(A BBBA \ , : BBB A(I0D@
-0D(A BBBD W
-0D(A BBBA \ t P, : BBB A(I0D@
-0D(A BBBD W
-0D(A BBBA D d BBB D(A0D@
-0A(A BBBA D x 7: BIB A(D0D`+
-0D(A BBBA D d 0 : BIB A(D0D`+
-0D(A BBBA D 蚬 9 BIB A(D0D`+
-0D(A BBBA D 9 BIB A(D0D`+
-0D(A BBBA D < X 9 BIB A(D0D`+
-0D(A BBBA D 9 BIB A(D0D`2
-0D(A BBBA D إ k9 BIB A(D0D`2
-0D(A BBBA D I9 BIB A(D0D`2
-0D(A BBBA D \ h '9 BIB A(D0D`2
-0D(A BBBA L 0 9 BIB A(D0D
-0D(A BBBA < 8 BHA G}
- DABA T `U BAA G0
+CAF JCA $ | @W^ AG b
+AA $ xW AG r
+AA $ ̆ X AG r
+AA $ X AG r
+AA $ Y AG r
+AA $ D Y AG z
+AA $ l 0Z AG z
+AA $ Z AG z
+AA $ `[ AG z
+AA $ [ AG L
+AA D T `\ K BEB A(A0Nj
+0A(A BBBA L ]r - BEB L(D0A8M
+8A0A(B BBBD < p U BBA I(D@e
+(D ABBA < , p 1 BBA I(D@e
+(D ABBA \ l q$
BBB A(I0D@
+0D(A BBBG W
+0D(A BBBA \ ̈ q$ BBB A(I0D@
+0D(A BBBG W
+0D(A BBBA \ , r$ BBB A(I0D@
+0D(A BBBG W
+0D(A BBBA \ xs$ P BBB A(I0D@
+0D(A BBBG W
+0D(A BBBA \ Ht$ BBB A(I0D@
+0D(A BBBG W
+0D(A BBBA \ L u$ BBB A(I0D@
+0D(A BBBG W
+0D(A BBBA \ u$ BBB A(I0D@
+0D(A BBBG W
+0D(A BBBA \ v$ T BBB A(I0D@
+0D(A BBBG W
+0D(A BBBA \ l w$ BBB A(I0D@
+0D(A BBBG W
+0D(A BBBA $ Xx AAG0AA< x BHA G
+ DABA < 4 0{ BHA G
+ DABA 4 t } x BHD D`
+ DABF 4 Z BHA GPh
+ DABG 4 < BHD D`
+ DABG 4 BHA GPo
+ DABH 4 T BHA GPo
+ DABH 4 BHA GPo
+ DABH 4 č p BHD D`
+ DABF 4 ȋ BHD D`
+ DABF 4 4 BHD D`
+ DABF T $ xU BAA G0
AABA
AABK f
- AABF T h s8 BEE B(G0D8G
-8D0A(B BBBD T 0¬ 8 BBE G(A0G8G.
-8D0A(B BBBF hɬ AR hɬ AY T | hɬ 8 BBE E(A0A8G
-8D0A(B BBBE T ˬ
- F8 BEB B(D0C8Jd
-8D0A(B BBBC T , xլJ
s8 BBB B(D0F8G?
-8A0A(B BBBF T pJ
8 BBB B(D0F8G?
-8A0A(B BBBF T h
Y9 BBB B(D0F8GB
-8A0A(B BBBK T 4 r 9 BBF G(GP
+ AABF T Ď BEE B(G0D8G
+8D0A(B BBBD T HE 8 BBE G(A0G8G.
+8D0A(B BBBF , @ AR L @ AY T @ # BBE E(A0A8G
+8D0A(B BBBE T BBB E(D0C8J
+8D0A(B BBBC T d ദ
BBB B(D0G8L2
+8D0A(B BBBB T ¦
[ BBB B(D0G8L2
+8D0A(B BBBB T PϦ
BBB B(D0G8L2
+8D0A(B BBBB T l ܦr BBF G(GP
(D ABBI L
-(D ABBK T {
- 9 BBB E(G0A8L3
-8D0A(B BBBA t h. K^ $ ^ AG b
-AA $ AG u
-AA $ AG }
-AA < ĊD BBD A(D@0(A ABB T 8 BIB B(A0D8D
-8D0A(B BBBA 4
q 8 BAI A
-ABA $
DL
+(D ABBK T đ ݦ BBB E(G0A8L3
+8D0A(B BBBA Ԓ . K^ L 4 `r BBB B(E0A8J@
+8A0A(B BBBA $ < ^ AG b
+AA $ d AG u
+AA $ ` AG }
+AA Q tU
+A , ԓ *- AAG0!AA D L BIB A(D0DpG
+0D(A BBBH 4 q BAI A
+ABA $ DL
A V
-I T l X m8 BBE B(A0A8G
-8A0A(B BBBD T Pf. K^ l + + T + T9 BBB B(A0D8N
-8A0A(B BBBC , d 4> 9 AID W
-DAA D $ Ċ BBB D(A0D@0A(A BBB T 3L m9 BIE B(A0A8D9
-8D0A(B BBBA T 4 8" ;9 BBB B(A0A8J
-8A0A(B BBBA d. K^ 4 0[ L ([ T 0[y S: BBB B(A0I8G>
-8A0A(B BBBG < HĊD BBD A(D@0(A ABB T l _ : BIB E(A0A8DF
-8D0A(B BBBA D `cd 9 BEI A(D0D
-0D(A BBBA < er 9 BBH D(J
-(D ABBA D L g 9 BBE H(D0D
-0D(A BBBF D pj 9 BBE H(D0D
-0D(A BBBF D 8m 9 BBE D(H0D
-0D(A BBBA D $ o 9 BBE D(H0D
-0D(A BBBA D l r 9 BBE D(H0D
-0D(A BBBA D 0u 9 BBE D(H0D
-0D(A BBBA D w 9 BBE D(H0D
-0D(A BBBA D D z 9 BBE D(H0D
-0D(A BBBA D (} 9 BBE D(H0D
-0D(A BBBA L V 9 BBE D(H0D
-0D(A BBBI T $ 9 BBB E(G0A8L
+I T i ^ BBE B(A0A8G
+8A0A(B BBBK . K^ 4 x T @ BBB B(A0D8N
+8A0A(B BBBC , > AID W
+DAA , ԕ *- AAG0!AA 4 L 5 q BHD D`
+ DABG T d S BBB B(A0A8J
+8A0A(B BBBA X. K^ 6 Ė 6 T $ 6y O BBB B(A0I8G>
+8A0A(B BBBG , 4 )- AAG0!AA 4 :E + BHD D`
+ DABK D =d
BEI A(D0D
+0D(A BBBA < , ?r BBH D(J
+(D ABBA D l 8B
+ BBE H(D0D
+0D(A BBBF D D BBE H(D0D
+0D(A BBBF D G BBE D(H0D
+0D(A BBBA D D PJ BBE D(H0D
+0D(A BBBA D L BBE D(H0D
+0D(A BBBA D Ԙ O BBE D(H0D
+0D(A BBBA D HR BBE D(H0D
+0D(A BBBA D d T BBE D(H0D
+0D(A BBBA D W BBE D(H0D
+0D(A BBBA L @ZV BBE D(H0D
+0D(A BBBI T D P]
BBB E(G0A8L
-8D0A(B BBBJ _. K^ $ $ 0^ AG b
-AA D L h BBB D(A0D@
-0A(A BBBA D BBB D(A0D@%
-0A(A BBBA D L S9 BBB D(A0Gn
-0D(A BBBE D %9 BBB D(A0Gn
-0D(A BBBE T 8 BEE E(D0A8G2
-8D0A(B BBBD d ` BLB B(A0A8D@c
+8D0A(B BBBJ T ȷ. K^ $ l i^ AG b
+AA D (j BBB D(A0D@
+0A(A BBBA D ܛ l BBB D(A0D@%
+0A(A BBBA D l ho w BBB D(A0Gn
+0D(A BBBE D p I BBB D(A0Gn
+0D(A BBBE d Xr BLB B(A0A8D@c
8A0A(B BBBK s
-8A0A(B BBBC < , D BBD A(D@0(A ABB T 58 BIB E(A0A8D6
-8D0A(B BBBA d 4 8 BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d X 7 BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB < Т BBG A(G@
-(A ABBF L BHB E(A0A8D`
-8A0A(B BBBE d 6 BJE B(A0A8D@
-8F0A(B BBBA b
-8F0A(B BBBA L k6 BEB E(I0H8DP
-8D0A(B BBBD < L x; C6 BBA A(JO
-(A ABBA T x 36 BBE B(A0A8M =
-8A0A(B BBBA , 0> k7 AID W
-DAA T @ K7 BBE B(D0A8GR
-8A0A(B BBBE T l ǭ u7 BBE B(D0A8GS
-8A0A(B BBBD T PЭB 7 BBB B(D0A8Jc
-8A0A(B BBBD L H٭I
7 BBB B(A0A8G
-8A0A(B BBBA $ 8Y AHD0AAL $ AAG e
+8A0A(B BBBC , $%- AAG0!AA 4 r5 BHD D`
+ DABG d ̜ u e BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB < 0v BBG A(G@
+(A ABBF L , v BHB E(A0A8D`
+8A0A(B BBBE L ĝ `x s BEB E(I0H8DP
+8D0A(B BBBD 4 z^ K BBB A(A0O T L H{5 3 BBE B(A0A8M M
+8A0A(B BBBA , 0> { AID W
+DAA T Ԟ @ [ BBE B(D0A8GO
+8A0A(B BBBH T , ș m BBE B(D0A8GO
+8A0A(B BBBH T P^
+ BBE B(D0A8GI
+8A0A(B BBBF L ܟ X
BBB B(A0A8G
+8A0A(B BBBA $ h AHD0AAL й AAG e
CAF s
ACA R
-CAJ JCA < pq 7 BBA A(D0[
-(A ABBA T $ | w7 BBE E(D0A8G
-8D0A(B BBBE T | , ]7 BBE B(D0A8G
-8D0A(B BBBK T C7 BEE E(D0A8G
-8D0A(B BBBE T ,
- , )7 BBE B(D0A8G
-8D0A(B BBBK T
- 7 BEE E(D0A8G
-8D0A(B BBBE T
- 6 BEE E(D0A8G
-8D0A(B BBBG T 4 `| 6 BBE E(D0A8G
-8D0A(B BBBE < pD BBD A(D@0(A ABB T H
-& 6 BIB E(A0A8G
-8D0A(B BBBA L BBB B(A0A8D`8A0A(B BBB L
BBB B(A0A8D`8A0A(B BBB L T
` BBB E(A0A8DP
-8A0A(B BBBA
AL T 4
J5 BEE E(D0A8G
-8D0A(B BBBE L
05 BEB B(A0D8O8A0A(B BBB l T
4 BEE B(A0A8G@
-8A0A(B BBBG d x$ BBB B(A0D8DPJ
+CAJ JCA < q BBA A(D0[
+(A ABBA T `| BBE E(D0A8G
+8D0A(B BBBE T < , i BBE B(D0A8G
+8D0A(B BBBK T `ħ O BEE E(D0A8G
+8D0A(B BBBE T ɧ, 5 BBE B(D0A8G
+8D0A(B BBBK T D `Χ BEE E(D0A8G
+8D0A(B BBBE T ӧ BEE E(D0A8G
+8D0A(B BBBJ T ا| BBE E(D0A8G
+8D0A(B BBBE , j - AAG0!AA < | ݧ BHD D{
+ DABA L t BBB B(A0A8D`8A0A(B BBB 4 Ĥ BEA A(D0|(A ABBL `` BBB E(A0A8DP
+8A0A(B BBBA L p AL T p BEE E(D0A8G
+8D0A(B BBBE L p BEB B(A0D8O8A0A(B BBB h T t p BEE B(A0A8G<
+8A0A(B BBBC d BBB B(A0D8DPJ
8A0A(B BBBK
-8A0A(B BBBD T *b [4 BBB G(D0A8M
-8D0A(B BBBC L / w4 BEB E(I0H8D@
-8D0A(B BBBA T \ x1J O4 BBE B(A0A8G
-8A0A(B BBBA L p4 ;4 BEB E(I0H8D@
-8D0A(B BBBD T 6 4 BBB B(A0D8GW
-8A0A(B BBBA T \ x9x, 3 BBB I(A0A8J
-8A0A(B BBBA , e> 6 AID W
-DAA t Q. K^ e e 4 > BDA f
-AFE AAB , d `e 6 AAG q
-AAA L $ f BBB B(A0D8G@r
-8A0A(B BBBH t \ AG RA< g BHH R
-ABI >
-ABG L i, BFL H(A0A8DxK
-8A0A(B BBBI T l 4 BBB B(D0A8Gm
-8D0A(B BBBB L t 4 BBB B(I0A8G
-8D0A(B BBBE L < w 4 BBB B(I0A8G
-8D0A(B BBBE T hy< 4 BBE B(D0A8G
-8D0A(B BBBB T P~< 4 BBE B(D0A8G
-8D0A(B BBBB T < 8< 4 BBE B(D0A8G
-8D0A(B BBBB D $ F BBB D(A0D@0A(A BBB T ؇c :4 BIB J(A0A8D
-8D0A(B BBBA T 4 r 4 BIB J(A0A8D!
-8D0A(B BBBA T 3 BBB B(F0D8G]
-8A0A(B BBBH < t 1 BBG A(GP
-(A ABBF T $ O5 BIE B(A0G8G.
-8A0A(B BBBA (L. K^ $ Į < Į T Į l Į L Į BGB B(A0A8G
-8A0A(B BBBC L ̮~ BFB B(A0A8G
-8A0A(B BBBD L $ Ԯ ADD e
+8A0A(B BBBD T 4 BBB G(D0A8M
+8D0A(B BBBC L H BEB E(I0H8D@
+8D0A(B BBBA T ܦ J BBE B(A0A8G
+8A0A(B BBBA L 4 o BEB E(I0H8D@
+8D0A(B BBBD T G BBB B(A0D8GW
+8A0A(B BBBA T ܧ >, 1 BBB I(A0A8J
+8A0A(B BBBA , 4 5> AID W
+DAA . K^ 4 5 L 5 $ d 5 EDf
+AH < 7 BBA A(GP
+(A ABBE T : BBB E(A0D8GJ
+8D0A(B BBBB T l H@ BBB B(D0A8G]
+8D0A(B BBBB L ĩ H BBB B(I0A8G
+8D0A(B BBBE L K BBB B(I0A8G
+8D0A(B BBBE T d `M< BBE B(D0A8G
+8D0A(B BBBB T HR< BBE B(D0A8G
+8D0A(B BBBB , ̫ - AAG0!AA 4 D W m BHI D`
+ DABA 4 | Y% O BHI D`
+ DABJ T \ 1 BBG E(A0A8G
+8A0A(B BBBD < Ĭ v1 BBG A(GP
+(A ABBF T L wV K BIB B(A0G8G
+8A0A(B BBBA \ . K^ t L ԭ BGB B(A0A8G
+8A0A(B BBBC L $ (~ BFB B(A0A8G
+8A0A(B BBBD L t X ADD e
CAF {
ACI V
-CAF JCA L t pծ ADD e
+CAF JCA L Į ADD e
CAF {
ACI V
-CAF JCA D 4 ֮ 3 BBE A(A0
-(A BBBA ,\ AG RAD X֮ y3 BAA GP
+CAF JCA \ AG RAD | x BAA GP
DABK
- DABG < t D BBD A(D@0(A ABB T $ خ 3 BIB E(A0A8DV
-8D0A(B BBBA T | (ݮ 2 BIB E(A0A8DV
-8D0A(B BBBA T | 2 BBE E(D0A8G
-8D0A(B BBBE T , 2 BBE B(D0A8G
-8D0A(B BBBG T p 2 BBB L(A0D8G
-8A0A(B BBBB 4 q 2 BAI A
-ABA T 2 BBB L(A0D8G
-8A0A(B BBBB 4 l hq 2 BAI A
-ABA T 2 BGB B(A0G8J
-8D0A(B BBBE T h 3 BGB B(A0G8J
-8D0A(B BBBE F. K^ "0 Hg "7 Hj < 8"0 Hg \ H"7 Hj | h"0 Hg x"7 Hj "0 Hg "7 Hj "0 Hg "7 Hj < "0 Hg \ #7 Hj | (#0 Hg 8#7 Hj X#0 Hg h#7 Hj #0 Hg #7 Hj < #0 Hg \ #7 Hj | #0 Hg #7 Hj $0 Hg ($7 Hj H$0 Hg X$7 Hj < x$0 Hg \ $7 Hj | $0 Hg $7 Hj $0 Hg $7 Hj %0 Hg ! %7 Hj <! 8%0 Hg \! H%7 Hj |! h%0 Hg ! x%7 Hj ! %0 Hg ! %7 Hj ! %0 Hg " %7 Hj <" %0 Hg \" &7 Hj |" (&0 Hg " 8&7 Hj " X&0 Hg " h&7 Hj " &0 Hg # &7 Hj <# &0 Hg \# &7 Hj |# &0 Hg # &7 Hj # '0 Hg # ('7 Hj # H'0 Hg $ X'7 Hj <$ x'0 Hg \$ '7 Hj |$ '0 Hg $ '7 Hj $ '0 Hg $ '7 Hj $ (0 Hg % (7 Hj <% 8(0 Hg \% H(7 Hj |% h(0 Hg % x(7 Hj % (0 Hg % (7 Hj < % ( BBA A(D@_
-(A ABBA zPR xN- L $ X* BBB B(A0A8D@V
-8A0A(B BBBB L t + BBB B(A0A8D@6
-8A0A(B BBBB L - BBB B(A0A8DP
-8A0A(B BBBB L 1 BBB B(A0A8D@n
-8A0A(B BBBJ L d 3 BBB B(A0A8DP
-8A0A(B BBBB ' 졊\ AG RAD |' (8D ) BHB A(D0J^
-0A(A BBBA , T( 0:C AAG n
-DAA D ( P: BBB D(A0D@
-0A(A BBBA 4 <( ;B ( BHA G`
- DABA 4 t( <B ( BHA G`
- DABA 4 ( =7 ( BHA G`
- DABA 4 ( >7 ( BHA G`
- DABA 4 ) ?B y( BHA G`
- DABA 4 T) AB Z( BHA G`
- DABA 4 ) B7 ;( BHA G`
- DABA 4 ) C7 ( BHA G`
- DABA 4 ) (DB ' BHA G`
- DABA 4 4* @EB ' BHA G`
- DABA 4 l* XF7 ' BHA G`
- DABA 4 * `G7 ' BHA G`
- DABA 4 * hHB ' BHA G`
- DABA 4 + IB b' BHA G`
- DABA 4 L+ J7 C' BHA G`
- DABA 4 + K7 $' BHA G`
- DABA 4 + LB ' BHA G`
- DABA 4 + MB & BHA G`
- DABA 4 ,, N7 & BHA G`
- DABA 4 d, O7 & BHA G`
- DABA 4 , PB & BHA G`
- DABA 4 , R7 j& BHA G`
- DABA 4 - S7 K& BHA G`
- DABA 4 D- T7 ,& BHA G`
- DABA 4 |- U7
& BHA G`
- DABA 4 - V7 % BHA G`
- DABA 4 - (W7 % BHA G`
- DABA 4 $. 0XB % BHA G`
- DABA 4 \. HYB % BHA G`
- DABA 4 . `Z7 r% BHA G`
- DABA 4 . h[7 S% BHA G`
- DABA 4 / p\B 4% BHA G`
- DABA 4 </ ]B % BHA G`
- DABA 4 t/ ^7 $ BHA G`
- DABA 4 / _7 $ BHA G`
- DABA 4 / `B $ BHA G`
- DABA 4 0 a7 $ BHA G`
- DABA 4 T0 b7 z$ BHA G`
- DABA 4 0 c7 [$ BHA G`
- DABA 4 0 d7 <$ BHA G`
- DABA 4 0 e7 $ BHA G`
- DABA 4 41 f7 # BHA G`
- DABA 4 l1 gB # BHA G`
- DABA 4 1 iB # BHA G`
- DABA 4 1 (j7 # BHA G`
- DABA 4 2 0k7 # BHA G`
- DABA 4 L2 8lB c# BHA G`
- DABA 4 2 Pm7 D# BHA G`
- DABA 4 2 XnB %# BHA G`
- DABA 4 2 poB # BHA G`
- DABA 4 ,3 p7 " BHA G`
- DABA 4 d3 q7 " BHA G`
- DABA 4 3 rB " BHA G`
- DABA 4 3 s7 " BHA G`
- DABA 4 4 tB k" BHA G`
- DABA 4 D4 u7 L" BHA G`
- DABA 4 |4 vB -" BHA G`
- DABA 4 4 wB " BHA G`
- DABA 4 4 y7 ! BHA G`
- DABA 4 $5 z7 ! BHA G`
- DABA 4 \5 {B ! BHA G`
- DABA 4 5 0|7 ! BHA G`
- DABA 4 5 8}B s! BHA G`
- DABA 4 6 P~B T! BHA G`
- DABA 4 <6 hB 5! BHA G`
- DABA 4 t6 B ! BHA G`
- DABA 4 6 7 BHA G`
- DABA 4 6 7 BHA G`
- DABA 4 7 B BHA G`
- DABA 4 T7 B BHA G`
- DABA 4 7 B { BHA G`
- DABA 4 7 B \ BHA G`
- DABA 4 7 7 = BHA G`
- DABA 4 48 7 BHA G`
- DABA < l8 BBA A(J
-(D ABBA < 8 ȋ BBA A(J
-(D ABBA < 8 x BBA A(J
-(D ABBA < ,9 ( BBA A(J
-(D ABBA $ l9 ؐ* A_
-H A D 9 BBF K(H0G@Q
-0A(A BBBC D 9 H Q BGB D(D0J
-(A BBBD < $: / BEI D(G
-(D ABBG $ d: * A_
-H A $ : * A_
-H A $ : * A_
-H A , : ~ LGA A(D T ; k BBL B(A0A8JE
-8A0A(B BBBC 4 d; Иu c BAI E
-ABA 4 ; u ? BAI E
-ABA 4 ; `u BAI E
-ABA 4 < u BAI E
-ABA 4 D< u BAI E
-ABA 4 |< 8u BAI E
-ABA 4 < u BAI E
-ABA 4 < Țu g BAI E
-ABA 4 $= u C BAI E
-ABA 4 \= Xu BAI E
-ABA 4 = u BAI E
-ABA 4 = 蛯u BAI E
-ABA 4 > 0u BAI E
-ABA 4 <> xu BAI E
-ABA 4 t> u k BAI E
-ABA 4 > u G BAI E
-ABA 4 > Pu # BAI E
-ABA 4 ? u BAI E
-ABA 4 T? u BAI E
-ABA 4 ? (u BAI E
-ABA 4 ? pu BAI E
-ABA 4 ? u o BAI E
-ABA 4 4@ u K BAI E
-ABA 4 l@ Hu ' BAI E
-ABA 4 @ u BAI E
-ABA 4 @ ؟u BAI E
-ABA 4 A u BAI E
-ABA 4 LA hu BAI E
-ABA 4 A u s BAI E
-ABA 4 A u O BAI E
-ABA 4 A @u + BAI E
-ABA 4 ,B u BAI E
-ABA 4 dB Сu BAI E
-ABA 4 B u BAI E
-ABA 4 B `u BAI E
-ABA 4 C u w BAI E
-ABA 4 DC u S BAI E
-ABA 4 |C 8u / BAI E
-ABA 4 C u BAI E
-ABA 4 C ȣu BAI E
-ABA 4 $D u BAI E
-ABA 4 \D Xu BAI E
-ABA 4 D u { BAI E
-ABA 4 D 褯u W BAI E
-ABA 4 E 0u 3 BAI E
-ABA 4 <E xu BAI E
-ABA 4 tE u BAI E
-ABA 4 E u BAI E
-ABA 4 E Pu BAI E
-ABA 4 F u BAI E
-ABA 4 TF যu [ BAI E
-ABA 4 F (u 7 BAI E
-ABA 4 F pu BAI E
-ABA 4 F u BAI E
-ABA 4 4G u BAI E
-ABA 4 lG Hu BAI E
-ABA 4 G u BAI E
-ABA 4 G بu _ BAI E
-ABA 4 H u ; BAI E
-ABA 4 LH hu BAI E
-ABA 4 H u BAI E
-ABA 4 H u BAI E
-ABA T H @ BBB B(A0I8Gz
-8D0A(B BBBH T LI BBB B(A0I8Gz
-8D0A(B BBBH T I BBB B(A0I8Gz
-8D0A(B BBBH T I H BBB B(A0I8Gz
-8D0A(B BBBH T TJ BBB B(A0I8Gz
-8D0A(B BBBH T J BBB B(A0I8Gz
-8D0A(B BBBH T K P BBB B(A0I8Gz
-8D0A(B BBBH T \K BBB B(A0I8Gz
-8D0A(B BBBH T K ů BBB B(A0I8Gz
-8D0A(B BBBH T L Xȯ ~ BBB B(A0I8Gz
-8D0A(B BBBH T dL ˯ y BBB B(A0I8Gz
-8D0A(B BBBH T L ϯ t BBB B(A0I8Gz
-8D0A(B BBBH M `ү KBB A(D0G@y0A(A BBBL@f
+ DABG T Į 0| BBE E(D0A8G
+8D0A(B BBBE T X BBE B(D0A8G
+8D0A(B BBBG , , - AAG0!AA < U \ BIA D(Dp
+
+(D ABBI < U 6 BIA D(Dp
+
+(D ABBI T $ 0 BBB L(A0D8G
+8A0A(B BBBB 4 | q 8 BAI A
+ABA 4 ඨq BAI A
+ABA L (: BGB B(A0G8J
+8D0A(B BBBJ L < Ǩ: BGB B(A0G8J
+8D0A(B BBBJ D 8. K^ \ ֨0 Hg | ר7 Hj ר0 Hg 0ר7 Hj ܲ Pר0 Hg `ר7 Hj ר0 Hg < ר7 Hj \ ר0 Hg | ר7 Hj ר0 Hg ר7 Hj ܳ ب0 Hg ب7 Hj @ب0 Hg < Pب7 Hj \ pب0 Hg | ب7 Hj ب0 Hg ب7 Hj ܴ ب0 Hg ب7 Hj ٨0 Hg < ٨7 Hj \ 0٨0 Hg | @٨7 Hj `٨0 Hg p٨7 Hj ܵ ٨0 Hg ٨7 Hj ٨0 Hg < ٨7 Hj \ ٨0 Hg | ڨ7 Hj ڨ0 Hg 0ڨ7 Hj ܶ Pڨ0 Hg `ڨ7 Hj ڨ0 Hg < ڨ7 Hj \ ڨ0 Hg | ڨ7 Hj ڨ0 Hg ڨ7 Hj ܷ ۨ0 Hg ۨ7 Hj @ۨ0 Hg < Pۨ7 Hj \ pۨ0 Hg | ۨ7 Hj ۨ0 Hg ۨ7 Hj ܸ ۨ0 Hg ۨ7 Hj ܨ0 Hg < ܨ7 Hj \ 0ܨ0 Hg | @ܨ7 Hj `ܨ0 Hg pܨ7 Hj ܹ ܨ0 Hg ܨ7 Hj ܨ0 Hg < ܨ7 Hj \ ܨ0 Hg | ݨ7 Hj ݨ0 Hg 0ݨ7 Hj ܺ Pݨ0 Hg `ݨ7 Hj ݨ0 Hg < ݨ7 Hj \ ݨ0 Hg | ݨ7 Hj < ݨ BBA A(D@_
+(A ABBA ܻ ߨ^ zPR x- L $ ߨ BBB B(A0A8D@V
+8A0A(B BBBB D d H 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 @D BHB A(D0J^
+0A(A BBBA d Hb BBB B(A0A8D
+8A0A(B BBBG E
+8A0A(B BBBA $ Pi AJ@V
+AF A$ D i AJ@V
+AF A, l C AAG n
+DAA $ AAG0AA4 K BHA G`
+ DABA 4 D K BHA G`
+ DABA 4 | D BHA G`
+ DABA 4 D d BHA G`
+ DABA 4 K E BHA G`
+ DABA 4 $ K & BHA G`
+ DABA 4 \ D BHA G`
+ DABA 4 0D BHA G`
+ DABA 4 ̿ HK ɴ BHA G`
+ DABA 4 `K BHA G`
+ DABA 4 < xD BHA G`
+ DABA 4 t D l BHA G`
+ DABA 4 K M BHA G`
+ DABA 4 K . BHA G`
+ DABA 4 D BHA G`
+ DABA 4 T D BHA G`
+ DABA 4 K ѳ BHA G`
+ DABA 4 K BHA G`
+ DABA 4 8K BHA G`
+ DABA 4 4 P K t BHA G`
+ DABA 4 l hK U BHA G`
+ DABA 4 K 6 BHA G`
+ DABA 4 D BHA G`
+ DABA 4 D BHA G`
+ DABA 4 L D ٲ BHA G`
+ DABA 4 D BHA G`
+ DABA 4 D BHA G`
+ DABA 4 K | BHA G`
+ DABA 4 , (
+K ] BHA G`
+ DABA 4 d @ D > BHA G`
+ DABA 4 X D BHA G`
+ DABA 4 p
K BHA G`
+ DABA 4 K BHA G`
+ DABA 4 D K ± BHA G`
+ DABA 4 | K BHA G`
+ DABA 4 K BHA G`
+ DABA 4 K e BHA G`
+ DABA 4 $ D F BHA G`
+ DABA 4 \ D ' BHA G`
+ DABA 4 0D BHA G`
+ DABA 4 HD BHA G`
+ DABA 4 `D ʰ BHA G`
+ DABA 4 < xK BHA G`
+ DABA 4 t K BHA G`
+ DABA 4 D m BHA G`
+ DABA 4 D N BHA G`
+ DABA 4 K / BHA G`
+ DABA 4 T D BHA G`
+ DABA 4 K BHA G`
+ DABA 4 !K ү BHA G`
+ DABA 4 8"D BHA G`
+ DABA 4 4 P#D BHA G`
+ DABA 4 l h$K u BHA G`
+ DABA 4 %D V BHA G`
+ DABA 4 &K 7 BHA G`
+ DABA 4 'D BHA G`
+ DABA 4 L (K BHA G`
+ DABA 4 )K ڮ BHA G`
+ DABA 4 *D BHA G`
+ DABA 4 ,D BHA G`
+ DABA 4 , (-K } BHA G`
+ DABA 4 d @.D ^ BHA G`
+ DABA 4 X/K ? BHA G`
+ DABA 4 p0K BHA G`
+ DABA 4 1K BHA G`
+ DABA 4 D 2K BHA G`
+ DABA 4 | 3K í BHA G`
+ DABA 4 4D BHA G`
+ DABA 4 5D BHA G`
+ DABA 4 $ 7D f BHA G`
+ DABA 4 \ 8K G BHA G`
+ DABA 4 09K ( BHA G`
+ DABA 4 H:K BHA G`
+ DABA 4 `;K BHA G`
+ DABA 4 < x<D ˬ BHA G`
+ DABA 4 t =D BHA G`
+ DABA < > BBA A(J
+(D ABBA < X@ y BBA A(J
+(D ABBA < , B e BBA A(J
+(D ABBA < l C Q BBA A(J
+(D ABBA $ hE* = A_
+H A D pE BBF K(H0G@Q
+0A(A BBBC D F ߫ BGB D(D0J
+(A BBBD < d @G/ BEI D(G
+(D ABBG $ 0H* x A_
+H A $ 8H* T A_
+H A $ @H* 0 A_
+H A , HH LGA A(D T L Ip BBB B(K0A8J
+8A0A(B BBBC 4 0Nu BAI E
+ABA 4 xNu ߪ BAI E
+ABA 4 Nu BAI E
+ABA 4 L Ou BAI E
+ABA 4 POu s BAI E
+ABA 4 Ou O BAI E
+ABA 4 Ou + BAI E
+ABA 4 , (Pu BAI E
+ABA 4 d pPu BAI E
+ABA 4 Pu BAI E
+ABA 4 Qu BAI E
+ABA 4 HQu w BAI E
+ABA 4 D Qu S BAI E
+ABA 4 | Qu / BAI E
+ABA 4 Ru BAI E
+ABA 4 hRu BAI E
+ABA 4 $ Ru è BAI E
+ABA 4 \ Ru BAI E
+ABA 4 @Su { BAI E
+ABA 4 Su W BAI E
+ABA 4 Su 3 BAI E
+ABA 4 < Tu BAI E
+ABA 4 t `Tu BAI E
+ABA 4 Tu ǧ BAI E
+ABA 4 Tu BAI E
+ABA 4 8Uu BAI E
+ABA 4 T Uu [ BAI E
+ABA 4 Uu 7 BAI E
+ABA 4 Vu BAI E
+ABA 4 XVu BAI E
+ABA 4 4 Vu ˦ BAI E
+ABA 4 l Vu BAI E
+ABA 4 0Wu BAI E
+ABA 4 xWu _ BAI E
+ABA 4 Wu ; BAI E
+ABA 4 L Xu BAI E
+ABA 4 PXu BAI E
+ABA 4 Xu ϥ BAI E
+ABA 4 Xu BAI E
+ABA 4 , (Yu BAI E
+ABA 4 d pYu c BAI E
+ABA 4 Yu ? BAI E
+ABA 4 Zu BAI E
+ABA 4 HZu BAI E
+ABA 4 D Zu Ӥ BAI E
+ABA 4 | Zu BAI E
+ABA 4 [u BAI E
+ABA 4 h[u g BAI E
+ABA 4 $ [u C BAI E
+ABA 4 \ [u BAI E
+ABA 4 @\u BAI E
+ABA 4 \u ף BAI E
+ABA 4 \u BAI E
+ABA 4 < ]u BAI E
+ABA 4 t `]u k BAI E
+ABA 4 ]u G BAI E
+ABA 4 ]u # BAI E
+ABA 4 8^u BAI E
+ABA 4 T ^u ۢ BAI E
+ABA 4 ^u BAI E
+ABA 4 _u BAI E
+ABA 4 X_u o BAI E
+ABA T 4 _j K BBB L(A0A8J
+8A0A(B BBBH 4 cu I BAI E
+ABA 4 du % BAI E
+ABA 4 Hdu BAI E
+ABA 4 4 du ݡ BAI E
+ABA 4 l du BAI E
+ABA 4 eu BAI E
+ABA T he q BBB B(I0A8G)
+8D0A(B BBBA T 4 i y BBB B(I0A8G)
+8D0A(B BBBA T n BBB B(I0A8G)
+8D0A(B BBBA T pr BBB B(I0A8G)
+8D0A(B BBBA T < v BBB B(I0A8G)
+8D0A(B BBBA T { BBB B(I0A8G)
+8D0A(B BBBA T x BBB B(I0A8G)
+8D0A(B BBBA T D Ѓ BBB B(I0A8G)
+8D0A(B BBBA T (G l ` KBB A(D0G@y0A(A BBBL@f
0A(A BBBJ ,
-0A(A BBBL < 4N ֯ BED A(D0Y
-(A ABBA tN @ׯG N xد KBB A(D0G@y0A(A BBBL@a
+0A(A BBBL < Ѝ BED A(D0Y
+(A ABBA < @ KBB A(D0G@y0A(A BBBL@a
0A(A BBBO (
-0A(A BBBH O ܯ: 4O KBB A(D0G@a0A(A BBBL@1
+0A(A BBBH KBB A(D0G@a0A(A BBBL@1
0A(A BBBG ,
-0A(A BBBL O KBB A(D0G@a0A(A BBBL@-
+0A(A BBBL \ KBB A(D0G@a0A(A BBBL@-
0A(A BBBK *
-0A(A BBBN TP 0 E
-K < tP BKD A(G0(A ABB < P BDD
+0A(A BBBN К L ȚR BBD A(DP
+(A ABBF *
+(A ABBH L T ؞ BBB B(A0A8D8A0A(B BBB L h BBB B(A0A8D8A0A(B BBB d z BBB B(A0A8D
+8A0A(B BBBK E
+8A0A(B BBBA \ E
+K < | BKD A(G0(A ABB < p BDD
ABA L
-ABA L dP BBB B(A0A8G
-8C0A(B BBBJ T P { BHB E(A0D8G-
-8D0A(B BBBA T Q ( B BEB B(G0A8J.
-8D0A(B BBBH T dQ b BEI B(D0A8J
-8A0A(B BBBE T Q BBH B(A0A8J(
-8A0A(B BBBD T R BBH E(A0D8G
-8D0A(B BBBA T lR a BBH E(A0D8G
-8D0A(B BBBA T R ) BBH E(A0A8G
-8A0A(B BBBH T S c BBH B(D0A8G]
-8A0A(B BBBG L tS BBB B(A0A8G
-8C0A(B BBBJ T S + BEB B(G0A8JB
-8D0A(B BBBD T T /s BEI B(D0A8J
-8A0A(B BBBI T tT 3 BHB B(A0D8G2
-8A0A(B BBBA T T 5 j BBH B(A0A8J
-8A0A(B BBBJ T $U @: 1 BBH E(A0D8G
-8D0A(B BBBA T |U = BBH E(A0D8G
-8D0A(B BBBA T U @m BHE B(D0A8G
-8D0A(B BBBI T ,V C BBH B(D0A8G
-8A0A(B BBBG T V L
- O BBH B(D0A8G`
-8A0A(B BBBD L V W BBB B(A0A8G
-8C0A(B BBBJ T ,W g r BBE B(G0A8Jh
-8D0A(B BBBF T W k 9 BIB E(D0A8J
-8D0A(B BBBE L W o BBB B(A0A8G
-8C0A(B BBBJ T ,X Ȁ{ ^ BHB E(A0A8J
-8D0A(B BBBC T X
- % BBI B(D0A8J*
-8D0A(B BBBK T X R BHB B(D0D8G
-8D0A(B BBBG T 4Y f BHB E(A0D8G
-8D0A(B BBBA T Y ȋf | BHB E(A0D8G
-8D0A(B BBBA < Y C BBA A(J
-(D ABBA < $Z / BBA A(J
-(D ABBA $ dZ @* A_
-H A $ Z H*
A_
-H A d D[ Pq BBB B(D0A8DP
+ABA L D BBB B(A0A8G
+8C0A(B BBBI T _ e BBB E(G0D8G
+8D0A(B BBBA T BHB E(A0D8G
+8D0A(B BBBI T D ܜ BEE B(D0D8JO
+8D0A(B BBBD T U BBH B(A0A8J
+8A0A(B BBBD T a BBE H(D0A8G
+8D0A(B BBBA T L é BBE H(D0A8G
+8D0A(B BBBA T ǩ ٛ BBH B(D0A8G~
+8A0A(B BBBF T ʩ BBE H(A0A8G-
+8A0A(B BBBG L T Pϩ O BBB B(A0A8G
+8C0A(B BBBI T BHB E(A0D8G
+8D0A(B BBBI T g BEE B(D0D8JO
+8D0A(B BBBD T T f 1 BBB B(G0D8G
+8A0A(B BBBA T (A BBH B(A0A8J
+8A0A(B BBBJ T [ BBH E(D0A8G
+8D0A(B BBBA T \ ([ c BBH E(D0A8G
+8D0A(B BBBA T 09 BHB E(D0A8Gp
+8D0A(B BBBF T e
+ ڙ BBB H(A0A8J
+8A0A(B BBBD T d 0X
+ BBB H(A0A8J
+8A0A(B BBBA T 8x
+ P BBB H(A0A8J*
+8A0A(B BBBA T `C BBB H(A0A8J
+8A0A(B BBBE L l X Ƙ BBB B(A0A8G
+8C0A(B BBBI T ( " BHB E(A0D8G
+8D0A(B BBBI T ) ޘ BEE B(D0D8JO
+8D0A(B BBBD T l - BBB E(G0D8G
+8D0A(B BBBA L 0 d BBB B(A0A8G
+8C0A(B BBBI T @A BHB E(A0D8G
+8D0A(B BBBI T l C | BEE B(D0D8JO
+8D0A(B BBBD T @F F BEH B(A0D8G
+8D0A(B BBBH T I BEH B(A0D8Gc
+8D0A(B BBBA T t K BEH B(A0D8Gc
+8D0A(B BBBA < xM w BBA A(J
+(D ABBA < (O c BBA A(J
+(D ABBA $ L P* O A_
+H A $ t P* + A_
+H A d T Pq BBB B(D0A8DP
8A0A(B BBBB _
-8H0A(B BBBH L [ h k
BBB B(A0A8G
-8C0A(B BBBJ T l[ 裰5
BBH B(A0D8G
-8A0A(B BBBH T [ Ъ5
BBH B(A0D8G
-8A0A(B BBBH d \ q BBB B(D0A8DP
+8H0A(B BBBH L S BBB B(A0A8G
+8C0A(B BBBI T T c BBE H(A0A8G
+8A0A(B BBBH T j BBE H(A0A8G
+8A0A(B BBBH d pq BBB B(D0A8DP
8A0A(B BBBB _
-8H0A(B BBBH L \ г BBB B(A0A8G
-8C0A(B BBBJ T \ Pİ I
BBH B(A0D8G`
-8A0A(B BBBD T ,] ˰
BBH B(A0D8Gx
-8A0A(B BBBD D ^ Ӱ BBB B(A0A8
-0A(B BBBF T ] հ BHB B(D0D8G
-8D0A(B BBBH L ^ ٰ BBB B(A0A8DF
-8A0A(B BBBB T t^ ~ BBB H(A0A8Jo
-8A0A(B BBBE D \_ BBB B(A0A8
-0A(B BBBF T _ ` BHB E(A0D8G
-8D0A(B BBBE D _ BBB B(A0A8
-0A(B BBBA T _ + BBH B(A0A8J
-8A0A(B BBBG D ` s BBB B(A0A8I
-0A(B BBBA T T` 0
- BHB E(A0D8GI
-8D0A(B BBBA D <a BBB B(A0A8
-0A(B BBBA T ` @K
- BBH B(A0A8J
-8A0A(B BBBG D a 8 s BBB B(A0A8I
-0A(B BBBA T a p BHB E(A0D8GI
-8D0A(B BBBA D |b BBB B(A0A8
-0A(B BBBF T 4b BHB E(A0D8G|
-8D0A(B BBBJ D c (1 BBB B(A0A8
-0A(B BBBF T b BHB E(A0D8G
-8D0A(B BBBE D c BBB B(A0A8
-0A(B BBBF T tc BHB E(A0D8G
-8D0A(B BBBE \d 8. K^ td ! d ! d ! HS d pe/ A D d ! BBE A(A0D@g
-0A(A BBBA D $e 0" BBE A(A0D@o
-0A(A BBBA le d\ AG RAT e "- BDA ]
+8H0A(B BBBH L l r BBB B(A0A8G
+8C0A(B BBBI T H e BBE H(A0A8G0
+8A0A(B BBBD T BBE H(A0A8GH
+8A0A(B BBBD $ 葪 T Õ BBH B(A0D8Gh
+8A0A(B BBBA T ~ BBB B(G0A8Jb
+8A0A(B BBBJ D @ BBB B(A0A8
+0A(B BBBF T | BHB B(A0D8J
+8D0A(B BBBI D BBB B(A0A8
+0A(B BBBF T hh d BHB B(D0A8J
+8D0A(B BBBF D , s BBB B(A0A8I
+0A(B BBBA T ד BHB B(D0A8J(
+8D0A(B BBBA D s BBB B(A0A8I
+0A(B BBBA T \ X J BHB B(D0A8J(
+8D0A(B BBBA D l ª BBB B(A0A8
+0A(B BBBF T Ūh BHB B(D0A8J[
+8D0A(B BBBK D Ȫ BBB B(A0A8
+0A(B BBBF T H˪x 0 BHB B(D0A8J
+8D0A(B BBBF Y. K^ XΪ D PΪ BBE A(A0D@g
+0A(A BBBA D $ Ϊ BBE A(A0D@o
+0A(A BBBA l D\ AG RAT PϪ- BDA ]
ADG w
ADD Q
ABD I
-ABD T Te #C = BBE E(D0A8G
-8D0A(B BBBH D <f x( BBE A(A0D@P
-0A(A BBBA $ e ( A
+ABD T , (Ъ| BBE E(D0A8G
+8D0A(B BBBE D < Pժ BBE A(A0D@P
+0A(A BBBA $ ժ Q A
G A
-A L f x, BBE D(A0G
-0D(A BBBA < f cD BBD A(D@0(A ABB T f x. < BIB E(A0A8D
-8D0A(B BBBA L g @3) BBB B(A0D8D`
-8A0A(B BBBA L Tg 5) BBB B(A0D8D`
-8A0A(B BBBA T g 7 BEB I(A0A8G
+A , `- AAG0!AA < $ ٪ BHD Dq
+ DABA L d ܪ4 BBB B(A0D8D`
+8A0A(B BBBD L ݪ4 BBB B(A0D8D`
+8A0A(B BBBD T ߪ{ BEB I(A0A8G
-8A0A(B BBBE , g HL> AID W
-DAA $ ,h XLG As
+8A0A(B BBBE , \ > w AID W
+DAA T W BBE E(A0D8G
+8D0A(B BBBD $ G As
L A
-A T Th L s BBE E(A0D8G
-8D0A(B BBBD $ h PG 0 As
-L A
-A T h @P BFA A(GP
-(A ABBD W
-(A ABBA l ,i U BBB B(A0A8G
+A T BFA A(GP
+(A ABBF k
+(A ABBA l d BBB B(A0A8G
8A0A(B BBBD
-8A0A(B BBBF T i a BBE E(A0D8G
-8D0A(B BBBF D j d BEB B(A0A80A(B BBB < <j hB BBA A(G
-(A ABBA , |j k E PGA A(D , j k 2 ]GA A(D , j l PGA A(D < k mB BBA A(G
-(A ABBA T Lk p ? BEB B(A0D8J
-8A0A(B BBBA T k BHB B(A0F8J
-8D0A(B BBBJ T k BBH B(F0D8G
-8D0A(B BBBA l . K^ D ll P BBE A(A0
-(A BBBA Dm ^/ A L \m AAG ]
+8A0A(B BBBF T BBE E(A0D8G
+8D0A(B BBBF D x
BEB B(A0A80A(B BBB < t B BBA A(G
+(A ABBA , ) PGA A(D , ]GA A(D , PGA A(D < D pB BBA A(G
+(A ABBA T # BBB B(D0D8J
+8A0A(B BBBA T +/ ێ BHB B(A0F8J
+8D0A(B BBBJ T 4 = f BBB H(F0D8G
+8D0A(B BBBA D Q. K^ L \ G AAG ]
CAF c
CAI R
-CAJ JCA D m H BBE A(A0D@g
-0A(A BBBA D m М BBE A(A0D@o
-0A(A BBBA $ <n h^ AG b
-AA , dn 0 AC
FK
- E < n BBP J(G
-(D ABBD L Dn p BBE D(A0G
-0D(A BBBA < $o ]D BBD A(D@0(A ABB T n p C BIB E(A0A8D
-8D0A(B BBBA T ,o 84 BEB B(H0A8G X
-8A0A(B BBBC , o > AID W
-DAA $ o 0w Ad
+CAJ JCA D @G BBE A(A0D@g
+0A(A BBBA D G BBE A(A0D@o
+0A(A BBBA $ < `H^ AG b
+AA , d H0 AC
FK
+ E < I BBP J(G
+(D ABBD $ hO A
K A
-A D o ű BEA G@Z
- AABC
- AABA l $p `˱l
BBB B(A0A8G@'
+A , >- AAG0!AA < t T BHD Dq
+ DABA D VR BEA G@
+ AABI
+ AABA l \l
BBB B(A0A8G@'
8A0A(B BBBF x
-8A0A(B BBBF , p `ر o ZGA A(D , p @ٱ \ ZGA A(D T p ڱ I BBB B(A0A8G;
-8A0A(B BBBJ L q k BBB B(A0A8G2
-8A0A(B BBBA T q BEB B(A0A8J
-8A0A(B BBBA T q BHB B(F0A8J
-8D0A(B BBBE T Lr (F BBB H(F0D8G
-8D0A(B BBBA 4s . K^ Ls ds |s s s s s s F t 8) G] $t PJ W c <t D k
-Q z \t H G
-A |t x H0W
-I $ t ^ AG b
-AA D 4t P ` BBE A(A0D@
-0A(A BBBA $ |t (g AG k
-AA , t pY ADG i
-AAA < du XD BBD A(D@0(A ABB T u ` BIB E(A0A8DV
-8D0A(B BBBA D u | BBB A(D0D@
-0A(A BBBA D Dv BBB A(D0D@
-0A(A BBBA T u 8 , BBE B(D0A8G
-8D0A(B BBBK T Tv $e BEB B(A0A8GL
-8A0A(B BBBA T v (& BBB B(A0A8G
-8A0A(B BBBG L w `) BBF B(A0A8D@
-8D0A(B BBBB D Tw ) " BBE A(A0D@
-0A(A BBBA L w * BEB E(I0H8D@
-8D0A(B BBBD L w 8,w BHI E(A0A8Gp
-8A0A(B BBBF L <x h. BJE B(A0A8D@
-8A0A(B BBBA L x 8/V BBB B(A0D8J
-8D0A(B BBBE T x H5 U BBB B(A0D8J
-8A0A(B BBBA d y ME BBB B(A0A8DP
+8A0A(B BBBF , l i N ZGA A(D , j ; ZGA A(D T k ( BBB B(A0A8G;
+8A0A(B BBBJ L w BBB B(A0A8Go8A0A(B BBB T t } BEB B(A0A8J
+8A0A(B BBBA T hI BHB B(F0A8J
+8D0A(B BBBE T $ `F BBB H(F0D8G
+8D0A(B BBBA T | X ό BEB B(H0A8G w
+8A0A(B BBBD , > AID W
+DAA PL. K^ 4 L d | F ؽ) G] J W c ( D k
+Q z H G
+A
+ H0W
+I $ $
+ ^ AG b
+AA D BBE A(A0D@
+0A(A BBBA $ g Ë AG k
+AA ,
+ \ ADG l
+AAA ,
+ |- AAG0!AA < d
+ U K BIA D(Dp
+
+(D ABBI D \ 0ī| BBB A(D0D@
+0A(A BBBA D hū BBB A(D0D@
+0A(A BBBA T 4 ƫ, BBE B(D0A8G
+8D0A(B BBBK T ˫e { BEB B(A0A8GL
+8A0A(B BBBA T ͫ I BBB B(A0A8G
+8A0A(B BBBG L Ы BBF B(A0A8D@
+8D0A(B BBBB D hѫ BBE A(A0D@
+0A(A BBBA L 0ҫ BEB E(I0H8D@
+8D0A(B BBBD L $
ӫw { BHI E(A0A8Gp
+8A0A(B BBBF L t
ի k BJE B(A0A8D@
+8A0A(B BBBA L
֫^ C BBB B(A0D8J
+8D0A(B BBBE T ܫ BBB B(A0D8J
+8A0A(B BBBA d $ E BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA L y N _ BJB B(D0A8GPx
-8A0A(B BBBB T y XPt 3 BBB B(A0A8N
-
-8A0A(B BBBA , Dz b> AID W
-DAA { . K^ { xb 4{ Pgr L{ kJ d{ le |{ Hs { x7 { yZ { P} { (B { ` | H $| 0J L { hr BBB B(E0A8J@
-8A0A(B BBBA D | BBE A(A0D@u
-0A(A BBBA D | 0 BBE A(A0D@u
-0A(A BBBA } ȑJ 4} J L L} 8 BBB B(A0A8D`
-8A0A(B BBBA , } | AAD@W
-AAA , <} X AAD@W
-AAA , l} h 4 AAD@W
-AAA , } ȓ AAD@W
-AAA , } ( AAD@W
-AAA , } AAD@W
-AAA , ,~ 蔲 AAD@W
-AAA , \~ H AAD@W
-AAA $ RMIp L D BBB B(A0A8D`
-8A0A(B BBBA , AAD@W
-AAA , 4 AAD@W
-AAA , d P AAD@W
-AAA , x AAD@W
-AAA , T AAD@W
-AAA , p 0 AAD@W
-AAA , $ Й AAD@W
-AAA , T 0 AAD@W
-AAA L BBB B(A0A8Dp
-8A0A(B BBBA , Ԁ P t AAD@W
-AAA , P AAD@W
-AAA , 4 , AAD@W
-AAA , d p AAD@W
-AAA , М AAD@W
-AAA , ā 0 AAD@W
-AAA , AAD@W
-AAA , $ x AAD@W
-AAA < P BBA A(D@i
-(A ABBA , AAD@W
-AAA , Ă AAD@W
-AAA , P AAD@W
-AAA , $ AAD@W
-AAA , T AAD@W
-AAA , p ` AAD@W
-AAA , Р < AAD@W
-AAA , 0 AAD@W
-AAA T BBB B(D0D8G
-8D0A(B BBBJ T l إ BEE E(D0A8G
-8D0A(B BBBD T Ą 0, BBE B(D0A8G
-8D0A(B BBBK T J BIA A(GT
+8F0A(B BBBA T BBB B(A0A8N
+8A0A(B BBBA , , > AID W
+DAA (E. K^ , D Xr \ J t e P 7 Z X$ 0)B h* P/ 4 81J L p6r BBB B(E0A8J@
+8A0A(B BBBA D 7 BBE A(A0D@u
+0A(A BBBA D 88 BBE A(A0D@u
+0A(A BBBA , 8J D 9J L \ @9 BBB B(A0A8D`
+8A0A(B BBBA , 9 ` AAD@W
+AAA , $ : < AAD@W
+AAA , T p: AAD@W
+AAA , : AAD@W
+AAA , 0; І AAD@W
+AAA , ; AAD@W
+AAA , ; AAD@W
+AAA , D P< d AAD@W
+AAA $ , < RMIp L T (> BBB B(A0A8D`
+8A0A(B BBBA , > ȅ AAD@W
+AAA , > AAD@W
+AAA , L X? AAD@W
+AAA , | ? \ AAD@W
+AAA , @ 8 AAD@W
+AAA , x@ AAD@W
+AAA , @ AAD@W
+AAA , < 8A ̄ AAD@W
+AAA L $ A BBB B(A0A8Dp
+8A0A(B BBBA , XB X AAD@W
+AAA , B 4 AAD@W
+AAA , C AAD@W
+AAA , L xC AAD@W
+AAA , | C ȃ AAD@W
+AAA , 8D AAD@W
+AAA , D AAD@W
+AAA , D \ AAD@W
+AAA < XE BBA A(D@i
+(A ABBA , | E AAD@W
+AAA , E Ԃ AAD@W
+AAA , XF AAD@W
+AAA , F AAD@W
+AAA , < G h AAD@W
+AAA , l xG D AAD@W
+AAA , G AAD@W
+AAA , 8H AAD@W
+AAA T H BBB B(D0D8G
+8D0A(B BBBJ T T L BEE E(D0A8G
+8D0A(B BBBD T 8S, BBE B(D0A8G
+8D0A(B BBBK T XJ w BIA A(GT
(A ABBB ^
-(A ABBD T t , [ BBE B(D0A8G
-8D0A(B BBBK T ̅ طJ A BIA A(GT
+(A ABBD T \ Z, ? BBE B(D0A8G
+8D0A(B BBBK T ^J % BIA A(GT
(A ABBB ^
-(A ABBD T $ й, BBE B(D0A8G
-8D0A(B BBBK T | J BIA A(GT
+(A ABBD T `, BBE B(D0A8G
+8D0A(B BBBK T d eJ Ӏ BIA A(GT
(A ABBB ^
-(A ABBD T Ԇ , BBE B(D0A8G
-8D0A(B BBBK T , xŲJ BIA A(GT
+(A ABBD T g, BBE B(D0A8G
+8D0A(B BBBK T lJ BIA A(GT
(A ABBB ^
-(A ABBD T pDz2 e BBB E(A0D8G/
-8D0A(B BBBE L ܇ X۲ BBB B(D0A8G`8
-8A0A(B BBBJ T , ݲ BBB B(D0A8G
-8A0A(B BBBA L BEB E(I0H8DP
-8D0A(B BBBD L Ԉ [ BHI E(A0A8Gps
-8A0A(B BBBB L $ G BEB E(I0H8DP
-8D0A(B BBBD L t P BHI E(A0A8Gpr
-8A0A(B BBBC T ĉ ڊ BIB B(A0A8GS
-8A0A(B BBBA Ċ T L BBB J(A0A8G
-8D0A(B BBBD D 4 C BBB D(A0D@0A(A BBB T L H BIE B(A0A8D9
-8D0A(B BBBA T D BEB B(G0A8G
-8D0A(B BBBJ , ܊. K^ D 0 \ ( t Ԍ 4 L d | ( , 0[ AID t
-DAA , 4 `[ AID t
-DAA , d [ AID t
-DAA , [ AID t
-DAA , č [ c AID t
-DAA 2 I 4 H BAA s
-AEK AAB L D r BBB B(E0A8J@
-8A0A(B BBBA $ $ ^ AG b
-AA L d Ԏ x BBB B(A0D8Op
+(A ABBD T l xn2 I BBB E(A0D8G/
+8D0A(B BBBE L ` BBB B(D0A8G`8
+8A0A(B BBBJ T BBB B(D0A8G
+8A0A(B BBBA L l ( g BEB E(I0H8DP
+8D0A(B BBBD L 般 ? BHI E(A0A8Gps
+8A0A(B BBBB L + BEB E(I0H8DP
+8D0A(B BBBD L \ X BHI E(A0A8Gpr
+8A0A(B BBBC T 6 BIB B(A0A8GS
+8A0A(B BBBA T 4 BBB J(A0A8G
+8D0A(B BBBD , D R- AAG0!AA 4 ؒ5 D BHD D`
+ DABG T ' BEB B(G0A8G
+8D0A(B BBBJ ! X8. K^ ! 4! x L! p d! h |! ` ! X ! P ! H ! P ! X " ` $" h <" p T" x l" DV , ! [ } AID t
+DAA , ! [ } AID t
+DAA , ," 蜬[ } AID t
+DAA , \" [ } AID t
+DAA , " H[ g} AID t
+DAA 4 t# x BAA s
+AEK AAB L " Нr } BBB B(E0A8J@
+8A0A(B BBBA $ # ^ AG b
+AA $$ 8 d # @ | BBB B(A0D8Op
8A0A(B BBBH U
-8A0A(B BBBA , < > ' AID W
-DAA T l BBB B(D0A8K#
-8A0A(B BBBK , ď @> AID W
-DAA D P BBB A(A0JR
-0A(A BBBA L < (t BBB D(D0J
-0D(A BBBG , X qGA A(D T H~ BBA A(Ik
+8A0A(B BBBA , # > C| AID W
+DAA T $ #| BBB B(D0A8K#
+8A0A(B BBBK , t$ p> { AID W
+DAA D $ { BBB A(A0JR
+0A(A BBBA L $ X{ { BBB D(D0J
+0D(A BBBG , <% { qGA A(D T l% x~ { BBA A(Ik
(C ABBF
-(A ABBA T p S BEE E(D0D8G(
-8D0A(B BBBK d = BLB B(A0A8DPq
+(A ABBA T % o{ BEE E(D0D8G(
+8D0A(B BBBK d & (= BLB B(A0A8DPq
8A0A(B BBBE
-8A0A(B BBBE < ԑ g BED D(G
-(D ABBD D BBB D(A0D@
-0A(A BBBA D \ X 0 BIB A(D0D`+
-0D(A BBBA L BIB B(A0D8DpE
-8D0A(B BBBA L
BIB B(A0D8DpE
-8D0A(B BBBA < D BHA Gi
- DABA < BHA Gi
- DABA D ē P$ BIA A(G
-(D ABBA < 8 a BHA J
- DABA L L S BBB B(G0D8G
-8A0A(B BBBA D 4 S BDB A(G0G
-0A(D BBBA \ " p BBB A(A0G
+8A0A(B BBBE < & g z BED D(G
+(D ABBD $ |' 0 AAG0AA4 &
lz BHA GP
+ DABE 4 $'
Nz BHA GP
+ DABE < \' h 0z BHA G
+ DABA < ' z BHA G
+ DABA < ' y BHA G
+ DABA < ( hT y BHA J
+ DABA 4 \( y BHD D`
+ DABF L ( ༬ y BBB B(G0D8G
+8A0A(B BBBA D ( ` y BDB D(A0J~
+0D(A BBBA \ ,) (¬" y BBB A(A0G
0A(A BBBI O
-0A(A BBBA L D $ / BDH A(D0Go
-0D(A BBBD < $0 V BBA A(Jc
-(A ABBG T ԕ %a 4 BEE B(D0A8G
-8D0A(B BBBI L -B BLH B(D0A8DP
-8A0A(B BBBB .9 Af
-I D L . BHB E(A0A8Gn
-8D0A(B BBBK T x1 BDB E(A0A8G
-8A0A(B BBBJ T D 094 BDB B(D0A8G
-8A0A(B BBBH T > BBB B(A0D8G
-8A0A(B BBBA < `A BBH A(Jx
-(A ABBK T 4 Cy BBB B(A0E8G
-8A0A(B BBBA T HOy 6 BBB B(A0E8G
-8A0A(B BBBA T p[y BBB B(A0E8G
-8A0A(B BBBA T < g BBB B(A0A8K
-8A0A(B BBBA L 0t a BBB G(A0D8M
-8D0A(B BBBG t Ί. K^ xw pw hw Ԛ `w Xw Pw Hw 4 @w L 8w d 0w | (w w w ě w ܛ w w (w $ 0w < 8w T @w l Hw Pw Xw `w ̜ hw pw xw L wr BBB B(E0A8J@
-8A0A(B BBBA $ d x^ AG b
-AA T x ? BBE E(A0A8G4
-8A0A(B BBBK L T P} G BBB J(A0A8G
-8D0A(B BBBA L ~ 1 BBB J(A0A8G
-8D0A(B BBBA L BBB J(A0A8G
-8D0A(B BBBA L D 0 BBB J(A0A8G
-8D0A(B BBBA L Ѓ BBB J(A0A8G
-8D0A(B BBBA L p BBB J(A0A8G
-8D0A(B BBBA L 4 BBB J(A0A8G
-8D0A(B BBBA L BBB J(A0A8G
-8D0A(B BBBA L ԟ P* BBB J(A0A8G
-8D0A(B BBBA L $ 0 BBB J(A0A8G
-8D0A(B BBBA L t Ѝ k BBB J(A0A8G
-8D0A(B BBBA L Ġ p U BBB J(A0A8G
-8D0A(B BBBA L ? BBB J(A0A8G
-8D0A(B BBBA T d ) BBE E(D0A8G/
-8D0A(B BBBJ T X BBE E(A0A8G
-8D0A(B BBBA D BBB D(A0D@
-0A(A BBBA D \ 蘳 g BIB A(D0D`A
-0D(A BBBA D E BIB A(D0D`H
-0D(A BBBA D # BIB A(D0D`H
-0D(A BBBA D 4 p BIB A(D0D`H
-0D(A BBBA D | H BIB A(D0D`H
-0D(A BBBA D ģ BIB A(D0D`H
-0D(A BBBA D BIB A(D0D`H
-0D(A BBBA D T Ь y BIB A(D0D`H
-0D(A BBBA D W BIB A(D0D`H
-0D(A BBBA D 5 BIB A(D0D`H
-0D(A BBBA D , X BIB A(D0D`H
-0D(A BBBA D t 0 BIB A(D0D`H
-0D(A BBBA D BIB A(D0D`H
-0D(A BBBA < ཱི BBA A(G
-(A ABBA D D BBE G(A0D
-0A(A BBBA L o BBB B(A0G8J
-8A0A(B BBBA L ܦ ó c BBB B(A0G8J
-8A0A(B BBBA L , Ƴ W BBB B(A0G8J
-8A0A(B BBBA L | ȳt K BBB B(A0D8M
-8A0A(B BBBA T ̧ ʳ > BBB E(A0A8G
-8A0A(B BBBI T $ гd B BBB E(A0A8G7
-8A0A(B BBBK T | (׳u o BBB E(A0A8G
-8A0A(B BBBH T Ԩ P۳ s BBB E(A0A8Gv
-8A0A(B BBBD L , E w BBB E(A0A8G
-8A0A(B BBBA T | BBB E(A0A8Ga
-8A0A(B BBBA T ԩ `u BBB E(A0A8G
-8A0A(B BBBH T , T BBB E(A0A8G?
-8A0A(B BBBC T u BBB E(A0A8G3
-8A0A(B BBBA T ܪ T BBB E(A0A8G?
-8A0A(B BBBC T 4 BBB E(A0A8Ga
-8A0A(B BBBA T 8 BBG B(D0G8G
-8D0A(B BBBD T BBB B(A0D8M
-8A0A(B BBBA T < hd BBB G(D0G8G
-8D0A(B BBBD T , BBB B(D0A8M
-8A0A(B BBBA T 0 BBB B(A0D8M
-8A0A(B BBBA T D `5 BBB B(A0D8M
-8A0A(B BBBA T 9 BBB B(A0D8M
-8A0A(B BBBA T @> BBB B(D0A8M
-8A0A(B BBBA T L B BBB B(A0D8M
-8A0A(B BBBA T G BBB B(A0D8M
-8A0A(B BBBA T K BBB B(A0D8M
-8A0A(B BBBA L T Oe BBB B(A0D8M
-8A0A(B BBBH T UX BBB G(D0G8G
-8D0A(B BBBD L jU BBB B(A0D8M
-8A0A(B BBBC T L (oe BBB E(A0A8G
-8A0A(B BBBE T @s BBB G(D0G8G0
-8D0A(B BBBD T 致 BBB E(A0A8G
-8A0A(B BBBE T T m BBB G(D0G8G
-8D0A(B BBBD T u BBB E(A0A8G(
-8A0A(B BBBA T ࡴ BBB G(D0G8G
-8D0A(B BBBD T \ BBB E(A0A8G%
-8A0A(B BBBE T ഴt BBB G(D0G8G
-8D0A(B BBBD T Ҵe BBB E(A0A8G
-8A0A(B BBBE T d ִb BBB G(D0G8G
-8D0A(B BBBD T 8 BBB E(A0A8Gg
-8A0A(B BBBK T = BBB G(D0G8G
-8D0A(B BBBD L l 5 BBB E(A0A8G
-8A0A(B BBBA T
BBB G(D0G8G
-8D0A(B BBBD L 5 BBB E(A0A8G
-8A0A(B BBBA T d V
- BBB G(D0G8G`
-8D0A(B BBBD T BBB E(A0A8G
-8A0A(B BBBE T # BBB G(D0G8GH
-8D0A(B BBBD T l H8 BBG E(G0A8G
-8D0A(B BBBD , Ķ P< AK
-AA F F L $ Fm BBB B(A0I8Dp
-8D0A(B BBBF L t Gm BBB B(A0I8Dp
-8D0A(B BBBF D T 6 BBB D(A0D@0A(A BBB T Hl BIE B(A0A8DY
-8D0A(B BBBA T d Mr BIE B(A0A8Da
-8D0A(B BBBA T R BBE B(A0A8G
-8A0A(B BBBA @. K^ d Թ d l\ AG RAL xd BLB B(A0A8D
-8A0A(B BBBE L ̹ e BBB B(I0A8G
-8D0A(B BBBF L Hh BBB B(I0A8G
-8D0A(B BBBF T l j BEE E(D0A8G
-8D0A(B BBBE L T o BOB A(A0k
+0A(A BBBA D ) ìD y BDH D(A0G
+0D(A BBBD < ) Ȭ0 y BBA A(Jc
+(A ABBG T * Ȭa y BEE B(D0A8G
+8D0A(B BBBI L $+ ѬB BLH B(D0A8DP
+8A0A(B BBBB t+ Ҭ9 Af
+I D L * (Ҭ y BHB E(A0A8Gn
+8D0A(B BBBK T ,+ Ԭ% x BDB E(A0A8G*
+8A0A(B BBBF T + ܬl :y BDB B(D0A8G6
+8A0A(B BBBJ T + Yy BBB B(A0D8G
+8A0A(B BBBA < 4, Oy BBH A(Jx
+(A ABBK T t, E
/y BBB B(A0E8G
+8A0A(B BBBA T , E
y BBB B(A0E8G
+8A0A(B BBBA T $- E
y BBB B(A0E8G
+8A0A(B BBBA T |-
2
Uz BBB B(A0A8K
+8A0A(B BBBA L - z BBB G(A0D8M
+8D0A(B BBBG . *. K^ . / $/ L . r 9z BBB B(E0A8J@
+8A0A(B BBBA $ / ^ AG b
+AA L . 8 * y BBB J(A0A8G
+8D0A(B BBBA L L/ !* y BBB J(A0A8G
+8D0A(B BBBA T / "X y BBA D(G`
+(A ABBK T
+(D ABBA , 0 - AAG0!AA 4 $0 # 6y BHI D`
+ DABH 4 \0 H& y BHI D`
+ DABI D 0 ( x BBE G(A0D
+0A(A BBBA < 0 X*x x BBD A(Jg
+(A ABBA L 1 + x BBB B(A0D8M
+8A0A(B BBBH T l1 (0 x BBE E(A0A8G
+8D0A(B BBBF T 1 2%7 sx BBB H(A0D8J
+8A0A(B BBBG L 2 i z BBB E(D0D8G
+8A0A(B BBBI L l2 }u z BBB E(D0D8G
+8A0A(B BBBI L 2 8 z BBE E(D0A8G
+8A0A(B BBBI L 3 ء z BBE E(D0A8G
+8A0A(B BBBI L \3 z BBE E(D0A8G
+8A0A(B BBBI L 3 ĭ wz BBE E(D0A8G
+8A0A(B BBBI L 3 (խ kz BBB E(D0D8G
+8A0A(B BBBI L L4
_z BBB E(D0D8G
+8A0A(B BBBI L 4 Sz BBB E(D0D8G
+8A0A(B BBBI L 4 g6 Gz BEE E(D0A8G
+8A0A(B BBBI T <5 . z BGB B(D0A8M
+8D0A(B BBBE , 5 p#F { AK
+AA |6 A 6 A L 5 Am { BBB B(A0I8Dp
+8D0A(B BBBF L D6 Bm W{ BBB B(A0I8Dp
+8D0A(B BBBF , L7 - AAG0!AA < 6 CU z BIA D(Dp
+
+(D ABBI < 7 FU z BIA D(Dp
+(D ABBB T D7 I z BBB E(A0A8G
+8A0A(B BBBA T8 ". K^ l8 \ 8 [ L 8 \ BLB B(A0A8D
+8A0A(B BBBE L 48 ] z BBB B(I0A8G
+8D0A(B BBBF L 8 _ z BBB B(I0A8G
+8D0A(B BBBF T 8 b z BEE E(D0A8G
+8D0A(B BBBE L 9 Hg BOB A(A0k
(A BBBI b
-(A BBBG T Pp< BBE B(D0A8G
-8D0A(B BBBB D BBB D(A0D@0A(A BBB T t BIB E(A0A8DF
-8D0A(B BBBA T 8y BIB E(A0A8D6
-8D0A(B BBBA L p} BBB E(D0D8G@L
-8D0A(B BBBE < D ~ BLH D(A0
-(A BBBF T BBB B(A0A8G 0
-8A0A(B BBBE T L 虵 BEB I(D0A8GM
-8D0A(B BBBH 4 . K^ L d , [ s AID t
-DAA D BBB D(A0D@0A(A BBB T d L BIE B(A0A8D9
-8D0A(B BBBA T 裵w BDB H(A0A8J
-8D0A(B BBBE . K^ Կ T \ BBB B(I0A8D'
-8D0A(B BBBF D D BBB D(A0D@0A(A BBB T BIB E(A0A8G
-8D0A(B BBBA L T BBB B(A0D8Jo
-8D0A(B BBBE L m BBB B(A0D8Jo
-8D0A(B BBBE L л M BBB B(A0D8Jo
-8D0A(B BBBE L D - BBB B(A0D8Jo
-8D0A(B BBBE L @µ
BBB B(A0D8Jo
-8D0A(B BBBE T ŵ
BBB G(D0G8GN
-8D0A(B BBBF . K^ ҵ ҵ < ҵ BBD A(D0
-(A ABBF < T ҵ BBD A(D0
-(A ABBD D ӵ BBE A(A0
-(A BBBA L Ե AAG ]
+(A BBBG T |9 g< =z BBE B(D0A8G
+8D0A(B BBBB , : - AAG0!AA 4 : l5 y BHD D`
+ DABG 4 <: oB y BHD D`
+ DABH L ,; r BBB E(D0D8G@L
+8D0A(B BBBE < |; t BLH D(A0
+(A BBBF T ; t 'y BBB B(A0A8G 0
+8A0A(B BBBE T \; x y BBB I(D0A8GU
+8D0A(B BBBC l< . K^ < < , ; [ y AID t
+DAA , < j- AAG0!AA 4 \< 5 y BHD D`
+ DABG T < t gy BDB H(A0A8J
+8D0A(B BBBJ = . K^ = 0 = ( T 4= 0 y BBB B(I0A8D'
+8D0A(B BBBF , D> 8- AAG0!AA < = h [y BHD G6
+ DABA L = Ȩ6 My BBB B(A0D8Jo
+8D0A(B BBBE L L> g "y BBB B(A0D8Jo
+8D0A(B BBBE L > خs x BBB B(A0D8Go
+8D0A(B BBBH L > V x BBB B(A0D8Jo
+8D0A(B BBBE L <? V x BBB B(A0D8Jo
+8D0A(B BBBE T ? ( wx BBB G(D0G8GN
+8D0A(B BBBF @ . K^ @ î @ î < @ î BBD A(D0
+(A ABBF < $A Į BBD A(D0
+(A ABBD L dA HŮ AAG ]
CAF [
ACA R
-CAJ JCA L , 8Ե AAG ]
+CAJ JCA L A xŮ AAG ]
CAF [
ACA R
-CAJ JCA L hԵ BBB B(I0A8G
-8D0A(B BBBF L < ֵ BBB B(I0A8G
-8D0A(B BBBF D BBB D(A0D@0A(A BBB T ص W BIB E(A0A8D
-8D0A(B BBBA T , hݵ 0 BIB E(A0A8D
-8D0A(B BBBA T 0D BBH E(A0A8L
-8D0A(B BBBE T (~ BBB B(A0A8G
-8D0A(B BBBD T 4 P~ BBB B(A0A8G
-8D0A(B BBBF L x BFB B(D0A8JS
-8D0A(B BBBE l 8. K^ < BBB D(A0&
-(A BBBH L d p BBB B(A0I8G
-8D0A(B BBBD T BEE E(D0A8G*
-8D0A(B BBBD T H BBE B(D0A8G
-8D0A(B BBBG D d BAA G@u
+CAJ JCA , B - AAG0!AA < |A xŮ kw BHD Dh
+ DABA < A Ȯ Pw BHD Do
+ DABA T A ̮P 5w BBB B(A0A8G
+8D0A(B BBBE T TB ծP Kw BBB B(A0A8G
+8D0A(B BBBG L B xޮ aw BBB B(I0A8G
+8D0A(B BBBF L B Kw BBB B(I0A8G
+8D0A(B BBBF T LC 7w BBH E(A0A8L
+8D0A(B BBBE L C w BFB B(D0A8JS
+8D0A(B BBBE D . K^ D ( D < D ( BBB D(A0&
+(A BBBH L |D x v BBB B(A0I8G
+8D0A(B BBBD T D v BEE E(D0A8G*
+8D0A(B BBBD T $E P v BBE B(D0A8G
+8D0A(B BBBG D |E v BAA G@u
DABI
- DABF D < BBB D(A0D@0A(A BBB T F BIB E(A0A8D
-8D0A(B BBBA T L Xj BEE B(D0A8N[
-8D0A(B BBBG L p BHG B(A0D8G
-8D0A(B BBBJ P. K^ T /Z MGB B(A0H8)0A(B BBBB8T 0 MGB B(A0A8`0A(B BBBB8 L X1 d P1 4 | X1s BBA A(D0b(A ABBD 1 BBB B(A0A8DP8A0A(B BBBD l 2 BBE A(A0
-(A BBBA L D 2 AAG ]
+ DABF , |F \- AAG0!AA < E \v BHD Dh
+ DABA T 4F 0C Av BEE B(D0A8N[
+8D0A(B BBBG L F ( /v BHG B(A0D8G
+8D0A(B BBBJ G h. K^ T G Z MGB B(A0H8)0A(B BBBB8T H MGB B(A0A8`0A(B BBBB8 \H tH 4 H s BBA A(D0b(A ABBD H BBB B(A0A8DP8A0A(B BBBL I AAG ]
CAF [
ACA R
-CAJ JCA L 2 AAG ]
+CAJ JCA L \I AAG ]
CAF [
ACA R
-CAJ JCA L 2 AAG ]
+CAJ JCA L I AAG ]
CAF [
ACA R
-CAJ JCA L 4 3 AAG ]
+CAJ JCA L I AAG ]
CAF [
ACA R
-CAJ JCA L @3x BBB B(A0I8Dp
-8D0A(B BBBJ L D p4x BBB B(A0I8Dp
-8D0A(B BBBJ L 5x BBB B(A0I8Dp
-8D0A(B BBBJ < 6 g BED I(D
-(D ABBA < $ 7 : BED I(D
-(D ABBA D BBB D(A0D@0A(A BBB T (8l BIE B(A0A8DY
-8D0A(B BBBA T @=l BIE B(A0A8DY
-8D0A(B BBBA T \ XB a BIE B(A0A8D
-8D0A(B BBBA T G : BBE B(D0A8G
-8D0A(B BBBG T LU BAA G0
+CAJ JCA L I @ x Wt BBB B(A0I8Dp
+8D0A(B BBBJ L I p!x #t BBB B(A0I8Dp
+8D0A(B BBBJ L 4J "x s BBB B(A0I8Dp
+8D0A(B BBBJ 4 J # s BDI Gp
+ DABG 4 J $ s BDI Gp
+ DABG , K Z- AAG0!AA < $K p%U Ms BIA D(Dp
+
+(D ABBI < dK (U 's BIA D(Dp
+(D ABBB < K + s BHD Dh
+ DABA T K 0/ r BBE B(D0A8G
+8D0A(B BBBG T L 3U BAA G0
AABA
AABK f
- AABF L d P> BBE B(D0A8G|
-8A0A(B BBBC L V> BBE B(D0A8G|
-8A0A(B BBBC | \ BBB B(D0A8DP
+ AABF L L 8" tr BBE B(D0A8G|
+8A0A(B BBBC L L =" |r BBE B(D0A8G|
+8A0A(B BBBC | M C BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI T _% [ BBB H(D0A8G
-8D0A(B BBBH l . K^ i i i i , T i[ o AID t
-DAA , i[ O AID t
-DAA , j[ / AID t
-DAA D t
- BBB D(A0D@0A(A BBB T , j\ BIE B(A0A8DI
-8D0A(B BBBA T o BIB E(A0A8G
-8D0A(B BBBA T t n BIB E(A0A8G
-8D0A(B BBBA L 4 0zo G BDE H(A0A8G
-8D0A(B BBBK L P BDB H(D0A8G
-8D0A(B BBBB d Е. K^ | T { BBB B(A0D8R:
-8A0A(B BBBA , t Ј> AID W
-DAA T b BBE B(D0A8G
-8D0A(B BBBG D BBB D(A0D@0A(A BBB T D ` BIB E(A0A8D
-8D0A(B BBBA ؒE BEE E(G0G
+8A0A(B BBBI T M @F r BBB H(D0A8G
+8D0A(B BBBH N h
. K^ N Q N Q O Q $O Q , N Q[ 'r AID t
+DAA , N Q[ r AID t
+DAA , N R[ q AID t
+DAA T O PR q BEE E(D0A8G
+8D0A(B BBBE , $P - AAG0!AA 4 O HWE }q BHD D`
+ DABK D O `Z _q BIA D(G|
+(D ABBA D P ] <q BIA D(G|
+(D ABBA L dP a q BDE H(A0A8G
+8D0A(B BBBK L P fl _q BDB H(D0A8G
+8D0A(B BBBB Q
+. K^ Q k Q k T LQ k [q BBB B(A0D8R:
+8A0A(B BBBA , Q p> bq AID W
+DAA T Q p Bq BBE B(D0A8G
+8D0A(B BBBG , R ~- AAG0!AA D \R hu p BIA D(Dh
+(D ABBA R x
p BEE E(G0GE
+0A(A BBBD Q
+0A(A BBBG
+0A(A BBBE K
+0A(A BBBE 4S @E p BEE E(G0G
0A(A BBBK Q
0A(A BBBG ,
0A(A BBBD
-0A(A BBBG ,
BEE E(G0G>
-0A(A BBBK Q
-0A(A BBBG
-0A(A BBBE K
-0A(A BBBE T BBB G(D0A8M5
-8D0A(B BBBG . K^ , [ g AID t
-DAA , د[ G AID t
-DAA D | BBB D(A0D@0A(A BBB T 4 l BIE B(A0A8DY
-8D0A(B BBBA T شl BIE B(A0A8DY
-8D0A(B BBBA T { BDB E(A0G8G
-8D0A(B BBBF T < XJ BDB B(D0G8G
-8D0A(B BBBI $ p. K^ < 8ȶ T 0ȶ l (ȶ 0ȶ D V BBB D(A0D@0A(A BBB T T ǶL # BIE B(A0A8D9
-8D0A(B BBBA \ ̶j BBB A(I0DW
+0A(A BBBG T S n p BBB G(D0A8M5
+8D0A(B BBBG T . K^ T U U 4U , T [ kp AID t
+DAA , T 0[ Kp AID t
+DAA , U - AAG0!AA < $U 0U o BIA D(Dp
+
+(D ABBI < dU PU o BIA D(Dp
+
+(D ABBI T U p
+ o BDB E(A0G8G
+8D0A(B BBBF T U ( o BDB B(D0G8G
+8D0A(B BBBI W . K^ $W X <W P TW H lW P , W :- AAG0!AA 4 V (5 o BHD D`
+ DABG \ 4W 0j io BBB A(I0DW
0D(A BBBJ n
-0D(A BBBG L ж BBE B(A0A8G
-8A0A(B BBBG ؎. K^ p h 4 ` L X d P | H P X ` h , d p[ # AID t
-DAA , [ AID t
-DAA , [ AID t
-DAA , [ AID t
-DAA , $ 0[ AID t
-DAA D BBB D(A0D@0A(A BBB T ; BIB E(A0A8D6
-8D0A(B BBBA T P BIB E(A0A8D6
-8D0A(B BBBA T L BIB E(A0A8D6
-8D0A(B BBBA T BIB E(A0A8D6
-8D0A(B BBBA T s BIB E(A0A8D6
-8D0A(B BBBA T T 0B A BDB B(A0A8G*
-8A0A(B BBBA T (C BDB B(A0A8G"
-8A0A(B BBBA T 2 BDB B(A0A8G
-8A0A(B BBBA T \ 2 BDB B(A0A8G
-8A0A(B BBBA T (} h BDB B(A0A8G*
-8A0A(B BBBA X. K^ 2 1 1 1 1 , 1 D 1 \ 1 t 1 1 1 1 1 1 1 1 4 1 L 1 d 1 | 1 1 1 2 Y \ L 2, n BBB A(I0Dp
+0D(A BBBG L W @ /o BBB B(D0A8G
+8A0A(B BBBF X P. K^ X Я X Я X Я X Я Y Я ,Y Я DY Я \Y Я tY Я Y Я , X Я[ n AID t
+DAA , Y Я[ {n AID t
+DAA , LY ѯ[ [n AID t
+DAA , |Y 8ѯ[ ;n AID t
+DAA , Y hѯ[ n AID t
+DAA , Z X- AAG0!AA 4 Z hѯ5 m BHD D`
+ DABG 4 DZ pԯ5 m BHD D`
+ DABG 4 |Z xׯ5 m BHD D`
+ DABG 4 Z گ5 qm BHD D`
+ DABG 4 Z ݯ5 Sm BHD D`
+ DABG T $[ 5m BDB B(A0A8G
+8A0A(B BBBA T |[ nm BDB B(A0A8G
+8A0A(B BBBA T [ m BDB B(A0A8G
+8A0A(B BBBA T ,\ ( m BDB B(A0A8G
+8A0A(B BBBA T \
+n BDB B(A0A8G
+8A0A(B BBBA ] . K^ ]
+ ]
+ ]
+ ]
+ ^
+ $^
+ <^
+ T^
+ l^
+ ^ x
+ ^ p
+ ^ h
+ ^ p
+ ^ x
+ ^
+ _
+ ,_
+ D_
+ \_
+ t_
+ _
+ _
+ _
+ Y \ _
+, k BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ 2, * BBB A(I0Dp
+0D(A BBBA \ |_ , k BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ 3, BBB A(I0Dp
+0D(A BBBA \ _ h , vk BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ l x4, BBB A(I0Dp
+0D(A BBBA \ <` 8
, 2k BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ H5, ^ BBB A(I0Dp
+0D(A BBBA \ ` , j BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ , 6, BBB A(I0Dp
+0D(A BBBA \ ` , j BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ 6, BBB A(I0Dp
+0D(A BBBA \ \a , fj BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ 7, BBB A(I0Dp
+0D(A BBBA \ a x, "j BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ L 8, N BBB A(I0Dp
+0D(A BBBA \ b H, i BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ X9,
- BBB A(I0Dp
+0D(A BBBA \ |b , i BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ (:, BBB A(I0Dp
+0D(A BBBA \ b , Vi BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ l :, BBB A(I0Dp
+0D(A BBBA \ <c , i BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ ;, > BBB A(I0Dp
+0D(A BBBA \ c , h BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ , <, BBB A(I0Dp
+0D(A BBBA \ c X, h BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ h=, BBB A(I0Dp
+0D(A BBBA \ \d (, Fh BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ 8>, r BBB A(I0Dp
+0D(A BBBA \ d , h BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ L ?, . BBB A(I0Dp
+0D(A BBBA \ e , g BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ ?, BBB A(I0Dp
+0D(A BBBA \ |e , zg BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ @, BBB A(I0Dp
+0D(A BBBA \ e h, 6g BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ l xA, b BBB A(I0Dp
+0D(A BBBA \ <f 8, f BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ HB, BBB A(I0Dp
+0D(A BBBA \ f , f BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ , C, BBB A(I0Dp
+0D(A BBBA \ f , jf BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA $ C^ AG b
-AA D D D BEE D(A0D`n
-0A(A BBBE DV $ E Az
+0D(A BBBA $ h ^ AG b
+AA D <h BEE D(A0D`n
+0A(A BBBE h x V $ h Az
E
-A D xE BBB D(A0D@
-0A(A BBBA D F BIB A(A0G`
-0D(A BBBA D 8H o BIB A(A0G`
-0D(A BBBA D I @ BIB A(A0G`
-0D(A BBBA D \ K BIB A(A0G`
-0D(A BBBA D pL BIB A(A0G`
-0D(A BBBA D M BIB A(A0G`
-0D(A BBBA D 4 @O BIB A(A0G`
-0D(A BBBA D | P U BIB A(A0G`
-0D(A BBBA D R & BIB A(A0G`
-0D(A BBBA D xS BIB A(A0G`
-0D(A BBBA D T T BIB A(A0G`
-0D(A BBBA D HV BIB A(A0G`#
-0D(A BBBA D W j BIB A(A0G`#
-0D(A BBBA D , 8Y ; BIB A(A0G`#
-0D(A BBBA D t Z BIB A(A0G`#
-0D(A BBBA D (\ BIB A(A0G`#
-0D(A BBBA D ] BIB A(A0G`#
-0D(A BBBA D L _ BIB A(A0G`#
-0D(A BBBA D ` P BIB A(A0G`#
-0D(A BBBA D b ! BIB A(A0G`#
-0D(A BBBA D $ c BIB A(A0G`#
-0D(A BBBA D l d BIB A(A0G`#
-0D(A BBBA L D pf BBB B(D0D8G@H
-8A0A(B BBBG L g BBB B(D0D8G@H
-8A0A(B BBBG T T iV BBE E(D0E8J$
-8D0A(B BBBF L < n BOB A(A0]
+A $ h 8 AAG0AAD 4h Ne BIB A(A0G`,
+0D(A BBBC D |h 8 e BIB A(A0G`,
+0D(A BBBC D h ! d BIB A(A0G`,
+0D(A BBBC D i (# d BIB A(A0G`,
+0D(A BBBC D Ti $ d BIB A(A0G`,
+0D(A BBBC D i & cd BIB A(A0G`,
+0D(A BBBC D i ' 4d BIB A(A0G`,
+0D(A BBBC D ,j ) d BIB A(A0G`,
+0D(A BBBC D tj * c BIB A(A0G`,
+0D(A BBBC D j + c BIB A(A0G`,
+0D(A BBBC D k p- xc BIB A(A0G`,
+0D(A BBBC D Lk . Ic BIB A(A0G`3
+0D(A BBBD D k p0 c BIB A(A0G`3
+0D(A BBBD D k 1 b BIB A(A0G`3
+0D(A BBBD D $l 3 b BIB A(A0G`3
+0D(A BBBD D ll 5 b BIB A(A0G`3
+0D(A BBBD D l 6 ^b BIB A(A0G`3
+0D(A BBBD D l 8 /b BIB A(A0G`3
+0D(A BBBD D Dm 9 b BIB A(A0G`3
+0D(A BBBD D m (; a BIB A(A0G`3
+0D(A BBBD D m < a BIB A(A0G`3
+0D(A BBBD D n 8> sa BIB A(A0G`3
+0D(A BBBD L o ? BBB B(D0D8G@H
+8A0A(B BBBG L lo A BBB B(D0D8G@H
+8A0A(B BBBG T o `BV ` BBE E(D0E8J$
+8D0A(B BBBF L p hG BOB A(A0]
(A BBBG Y
-(A BBBH < n GDPE
-AAE
-AAA 4 Xqg ADD H
-AAM DAA , q BTA _DG T q BBB B(A0A8G
-8A0A(B BBBE L 8u* BGB B(A0G8J|
-8D0A(B BBBH L L z BBH B(F0A8J{
-8D0A(B BBBI L , x BBB B(A0A8Dxi
-8A0A(B BBBG | ^ DPYL P BBB B(A0A8DxQ
-8A0A(B BBBG < BEB A(F0z
-(A BBBG L $ P BBB B(D0A8GP
-8A0A(B BBBH D t 8 ADG m
+(A BBBH < dp G GDPE
+AAE
+AAG 4 p Jg ADD H
+AAM DAA , p J BTA gAB T Tp PK _ BBB B(A0A8G
+8A0A(B BBBE L p N* =_ BGB B(A0G8J|
+8D0A(B BBBH L p xS 5_ BBH B(F0A8J{
+8D0A(B BBBI L r Z BBB B(A0A8Dxi
+8A0A(B BBBG L Tr h^ BBB B(A0A8DxN
+8A0A(B BBBJ < r a BEB A(F0z
+(A BBBG L r hb BBB B(D0A8GP
+8A0A(B BBBH D 4s c8 ADG m
AAE V
-DAE UAA L O BEB B(A0A8Q
-8A0A(B BBBA T | BBB E(A0A8G
-8A0A(B BBBF L * ` BGB B(A0G8J|
-8D0A(B BBBH L $ Й* X BGB B(A0G8J|
-8D0A(B BBBH L t * P BGB B(A0G8J|
-8D0A(B BBBH L T : BEB B(A0A8Q
-8A0A(B BBBA T BBB E(A0A8G
-8A0A(B BBBF L l جQ BGB B(A0G8J|
-8D0A(B BBBH L L 谷b BEB B(A0A8Q
-8A0A(B BBBA T W BBB E(A0A8G
-8A0A(B BBBF L d `/ BGB B(A0G8J|
-8D0A(B BBBH L @B BGB B(A0G8J|
-8D0A(B BBBH L @· BBB B(A0H8J
-8A0A(B BBEA T T Ʒ2 BBB B(D0A8Gn
-8A0A(B BBBD L x̷9 e BGB B(A0G8J|
-8D0A(B BBBH D hѷ8 ADG m
+DAE UAA L |s dO BEB B(A0A8Q
+8A0A(B BBBA T s h2 q] BBB B(D0A8Gn
+8A0A(B BBBD L ls n* (] BGB B(A0G8J|
+8D0A(B BBBH L s xs* ] BGB B(A0G8J|
+8D0A(B BBBH L t Xx* ] BGB B(A0G8J|
+8D0A(B BBBH L u 8}: BEB B(A0A8Q
+8A0A(B BBBA T t (2 \ BBB B(D0A8Gn
+8A0A(B BBBD L u Q w\ BGB B(A0G8J|
+8D0A(B BBBH L v b BEB B(A0A8Q
+8A0A(B BBBA T u @2 \ BBB B(D0A8Gn
+8A0A(B BBBD L u (/ [ BGB B(A0G8J|
+8D0A(B BBBH L Lv B [ BGB B(A0G8J|
+8D0A(B BBBH L Tw BBB B(A0H8J
+8A0A(B BBEA T v X2 v[ BBB B(D0A8Gn
+8A0A(B BBBD L Dw @* -[ BGB B(A0G8J|
+8D0A(B BBBH D Lx 8 ADG m
AAE V
-DAE UAA L `ҷ BEB B(A0A8Q5
-8A0A(B BBBA T շX BBB B(A0D8G
-8A0A(B BBBA L ݷQ | BGB B(A0G8J|
-8D0A(B BBBH L BEB B(A0A8QN
-8A0A(B BBBA T X $ BBB B(A0D8G
-8A0A(B BBBA L * BGB B(A0G8J|
-8D0A(B BBBH `l. K^ > Hu E Hx D ( BEB E(D0D8PP~8A0A(B BBB$ d ^ AG b
-AA d q BEB B(A0A8G@
+DAE UAA L x BEB B(A0A8Q5
+8A0A(B BBBA T ,x XX Z BBB B(A0D8G
+8A0A(B BBBA L x `Q DZ BGB B(A0G8J|
+8D0A(B BBBH L y p BEB B(A0A8QN
+8A0A(B BBBA T $y X Y BBB B(A0D8G
+8A0A(B BBBA L |y ǰ* Y BGB B(A0G8J|
+8D0A(B BBBH z . K^ z ̰> Hu z ̰E Hx D z ̰ BEB E(D0D8PP~8A0A(B BBB$ ${ HͰ^ AG b
+AA d L{ Ͱq BEB B(A0A8G@
8A0A(B BBBC
-8A0A(B BBBF T d u BEE B(A0D8J
-8A0A(B BBBA T BBE E(A0A8G
-8D0A(B BBBC T @ m BBE E(A0A8G
-8D0A(B BBBA L l xk 0 AAGPa
+8A0A(B BBBF T z ΰu kX BEE B(A0D8J
+8A0A(B BBBA T T{ Ӱ rX BBE E(A0A8G
+8D0A(B BBBD T { (ְ 5X BBE E(A0A8G
+8D0A(B BBBB L | ذk W AAGPa
DAA A
DAJ }
-DAA T BBE E(A0A8G
-8D0A(B BBBH D BBB D(A0D@
-0A(A BBBA D \ ( x BIB A(D0D`+
-0D(A BBBA D V BIA D(D
-(D ABBA D ( ? BIA D(G
-(D ABBA < [ BBE H(A0
-(A BBBF T t BBB B(K0A8Jo
-8A0A(B BBBI 4 Hq BAI A
-ABA , ADG0
-AAB T 4 C BBB B(I0A8G
-8D0A(B BBBC T C BBB B(I0A8G
-8D0A(B BBBC T " BBB B(A0A8J~
-8A0A(B BBBA T < A> BEE B(A0A8G
+DAA T T| ٰ W BBE E(A0A8G
+8D0A(B BBBA , d} ^- AAG0!AA 4 | ۰ XW BHD D`
+ DABF < } @ް :W BHD D^
+ DABA < T} B W BHD G
+ DABA < L~ [ BBE D(A0
+(A BBBF T } 0 V BBB B(K0A8Jo
+8A0A(B BBBI 4 ,~ q V BAI A
+ABA , d~ V ADG0
+AAB T ~ pC V BBB B(I0A8G
+8D0A(B BBBC T ~ hC V BBB B(I0A8G
+8D0A(B BBBC T D `" V BBB B(A0A8J~
+8A0A(B BBBA T > V BEE B(A0A8G
-8A0A(B BBBA $ 0f. K^ < T l L l u BBB B(A0I8Dp
-8D0A(B BBBF L 8u BBB B(A0I8Dp
-8D0A(B BBBF L hu BBB B(A0I8Dp
-8D0A(B BBBF L \ u ~ BBB B(A0I8Dp
-8D0A(B BBBF D < Rމ BBB D(A0D@0A(A BBB T BIB E(A0A8D6
-8D0A(B BBBA T L BIB E(A0A8D6
-8D0A(B BBBA T BIB E(A0A8DV
-8D0A(B BBBA T H l BIB E(A0A8DV
-8D0A(B BBBA T T 5 : BGE B(A0A8Gl
-8A0A(B BBBA T 5 y BGE B(A0A8Gl
-8A0A(B BBBA T
- p BBB B(A0D8G
-8A0A(B BBBA T \
- 8
- " BBB B(A0D8G
-8A0A(B BBBA D @a. K^ \ ȶ t ȶ0 Hg ض7 Hj , < [ AID t
-DAA L l ( BBB J(A0A8G
-8D0A(B BBBA T x| BBE E(D0A8G
-8D0A(B BBBF L BBE A(D0G
-0A(A BBBA D hۉ BBB D(A0D@0A(A BBB T ¸ BIB E(A0A8DN
-8D0A(B BBBA T
0Ǹ BIB E(A0A8Df
-8D0A(B BBBA T \
˸ S BBE B(A0D8J
-8D0A(B BBBF T
@и o BGH B(A0D8G
-8D0A(B BBBH ^. K^ `ٸ Xٸ$ pٸ
hٸ1 ٸi , ٸ D ٸ AL d ٸ DI | ٸ DI ٸ DI ٸ AL ٸ AL ٸ# TI ٸA I[
-L K , $ ڸb D\
+8A0A(B BBBA . K^ Ā XX ܀ PX HX @X $ 8X < @X T HX l PX L ̀ XXx W BBB B(A0I8Dp
+8D0A(B BBBE L Yx W BBB B(A0I8Dp
+8D0A(B BBBE L l Zx W BBB B(A0I8Dp
+8D0A(B BBBE L [x ZW BBB B(A0I8Dp
+8D0A(B BBBE L ]x &W BBB B(A0I8Dp
+8D0A(B BBBE L \ H^x V BBB B(A0I8Dp
+8D0A(B BBBE L x_x V BBB B(A0I8Dp
+8D0A(B BBBE L `x V BBB B(A0I8Dp
+8D0A(B BBBE T L a VV BBE B(D0A8G
+8D0A(B BBBG T f <V BBE B(D0A8G
+8D0A(B BBBG , X- AAG0!AA 4 , 8k5 U BHD D`
+ DABG 4 d @n5 U BHD D`
+ DABG 4 HqB U BHD D`
+ DABH 4 Ԅ `tB U BHD D`
+ DABH < xwU zU BIA D(Dp
+
+(D ABBI < L zU TU BIA D(Dp
+
+(D ABBI < }U .U BIA D(Dp
+(D ABBB < ̅ U U BIA D(Dp
+(D ABBB T T BEE B(D0D8Jw
+8A0A(B BBBG T d y U BBG B(A0A8J(
+8A0A(B BBBE T y T BBG B(A0A8J(
+8A0A(B BBBE T Ќv T BBB B(A0A8JY
+8A0A(B BBBI T l f T BBB B(A0A8JI
+8A0A(B BBBI | 0. K^ Ĉ 0 Hg 7 Hj , L ([ ,T AID t
+DAA L | X T BBB J(A0A8G
+8D0A(B BBBA T ̈ | S BBE E(D0A8G
+8D0A(B BBBF L $ Т S BBE A(D0G
+0A(A BBBA , , JT- AAG0!AA 4 0E S BHD D`
+ DABD < ܉ Hb S BIA D(Dp
+(D ABBE T xL S BBE B(A0D8J
+8D0A(B BBBF T t p+
+ S BGH B(A0D8G
+8D0A(B BBBH X. K^ 0 ($ ̋ @
81 `i , AL L DI d DI | DI Ƚ AL AL Ԍ # TI A I[
+L K , b D\
H P
H Q
-G I , T `ڸb D\
+G I , < 0b D\
H P
H Q
-G I , ڸb D\
+G I , l pb D\
H P
H Q
-G I , ڸb D\
+G I , b D\
H P
H Q
-G I , ۸b D\
+G I , ̍ b D\
H P
H Q
-G I , `۸b D\
+G I , 0b D\
H P
H Q
-G I , D ۸b D\
+G I , , pb D\
H P
H Q
-G I , t ۸b 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 , 0b D\
H P
H Q
-G I , ܸb D\
+G I , pb D\
H P
H Q
-G I $ ܸr o H\
-L A $ 8ݸr K H\
-L A $ ݸr ' H\
-L A $ ݸr H\
-L A $ D @r ߽ H\
-L A $ l r H\
-L A q Hd P߸q { Hd ߸q _ Hd q C Hd pq ' Hd 4 q Hd F։\ AG RAT t t ϼ BBE B(D0A8G
-8D0A(B BBBH D 8 BBB A(A0G\
-0A(A BBBE $ R ȼ AI
-F A D Չv BBB D(A0D@^0A(A BBB T p \ BIB E(A0A8D6
-8D0A(B BBBA T * BIB E(A0A8DF
-8D0A(B BBBA T 4 BIB E(A0A8DF
-8D0A(B BBBA T 8 ƻ BIB E(A0A8DF
-8D0A(B BBBA T BIB E(A0A8D6
-8D0A(B BBBA T < b BIB E(A0A8D6
-8D0A(B BBBA d $ B BBB B(A0A8G@x
+G I $ d r P H\
+L A $ r P H\
+L A $ `r P H\
+L A $ r {P H\
+L A $ ±r WP H\
+L A $ , h±r 3P H\
+L A T ±q P Hd t ñq O Hd ñq O Hd ñq O Hd ԏ @ıq O Hd ıq O Hd ̐ N\ AG RAT 4 ıt GO BBE B(D0A8G
+8D0A(B BBBH D ʱ -O BBB A(A0G\
+0A(A BBBE $ Ԑ PͱR @O AI
+F A , LN- AAG0!AA 4 , Xͱ5 N BHD D`
+ DABG 4 d `б5 N BHD D`
+ DABG 4 hӱ5 N BHD D`
+ DABG 4 ԑ pֱB N BHD D`
+ DABH 4 ٱB tN BHD D`
+ DABH 4 D ܱB VN BHD D`
+ DABH d 4 ߱B BBB B(A0A8G@x
8A0A(B BBBE
-8A0A(B BBBF T " Ⱥ BBB B(D0A8J F
-8A0A(B BBBI < T % Ƽ BBI A(Ga
-(D ABBA < & BBI A(Ga
-(D ABBA < `& t BBI A(Ga
-(D ABBA < & K BBI A(Ga
-(D ABBA < T ' " BBI A(Ga
-(D ABBA < ' BBI A(Ga
-(D ABBA d S. K^ | '
'
' ' ' ' T | '| ( BBE E(D0A8G
-8D0A(B BBBI T ,| BBE E(D0A8G
-8D0A(B BBBI T , 2 BBE B(D0A8G
-8D0A(B BBBG D Љ BBB D(A0D@0A(A BBB T 6 BIB E(A0A8D6
-8D0A(B BBBA T $ : ` BBB B(A0D8NH
-8A0A(B BBBC , | B> º AID W
-DAA T (B BBB B(A0D8Gs
-8A0A(B BBBA T pH BBB B(A0D8G
-8A0A(B BBBA T \ 8N; z BBB B(A0D8G(
-8A0A(B BBBA T R56 f BBB B(F0A8J
-8A0A(B BBBJ xO. K^ 臹 L T r BBB B(E0A8J@
-8A0A(B BBBA T BEB E(A0A8G;
-8D0A(B BBBA T n BEB E(A0A8G;
-8D0A(B BBBA L T ) 1 BBB B(A0I8G
-8D0A(B BBBA L Џ) BBB B(A0I8G
-8D0A(B BBBA T BBE E(A0A8G
-8D0A(B BBBA D BBB D(A0D@
-0A(A BBBA L p BIB A(D0D
-0D(A BBBA L И a BIB A(D0D
-0D(A BBBA < 0Q BEH H(A0n(A BBE T t P BEB B(A0D8J
-8A0A(B BBBA T չ BEB B(A0D8J
-8A0A(B BBBA T $! ह BEB B(A0D8J
-8A0A(B BBBA T |! ( { BEB B(A0D8J
-8A0A(B BBBA T ! p N BEB B(A0D8J
-8A0A(B BBBA T ," ر ! BEB B(A0D8J
-8A0A(B BBBA T " BEB B(A0D8J
-8A0A(B BBBA T " h Ǹ BEB B(A0D8J
-8A0A(B BBBA T 4# BEB B(A0D8J
-8A0A(B BBBA T # ¹ m BEB B(A0D8J
-8A0A(B BBBA T # ǹ @ BEB B(A0D8J
-8A0A(B BBBA T <$ h˹ BEB B(A0D8J
-8A0A(B BBBA T $ Ϲ BEB B(A0D8J
-8A0A(B BBBA T $ ӹ BEB B(A0D8J
-8A0A(B BBBA T D% pع BEB B(A0D8J
-8A0A(B BBBA T % ܹ e BEB B(A0D8J
-8A0A(B BBBA T % 8 BEB B(A0D8J
-8A0A(B BBBA T L& H BEB B(A0D8J
-8A0A(B BBBA T & BEB B(A0D8J
-8A0A(B BBBA T & BEB B(A0D8J
-8A0A(B BBBA T T' BEB B(A0D8J
-8A0A(B BBBA T ' h W BEB B(A0D8J
-8A0A(B BBBA T ( * BEB B(A0D8J
-8A0A(B BBBA T \( BEB B(A0D8J
-8A0A(B BBBA T ( @ е BEB B(A0D8J
-8A0A(B BBBA T ) BEB B(A0D8J
-8A0A(B BBBA T d) v BEB B(A0D8J
-8A0A(B BBBA T ) I BEB B(A0D8J
-8A0A(B BBBA L * p " BBH B(D0F8G
-8D0A(B BBBD * PB. K^ + " $+ " 4 <+ "v BBA A(D0e(A ABBD t+ " BBB B(A0A8DP8A0A(B BBB, ,+ X#[ ɴ AID t
-DAA D \+ # BBE A(A0
-(A BBBA L 4, # AAG ]
+8A0A(B BBBF T " M BBB E(A0A8J C
+8A0A(B BBBD < < h O BBI A(Ga
+(D ABBA < | O BBI A(Ga
+(D ABBA < ( |O BBI A(Ga
+(D ABBA < SO BBI A(Ga
+(D ABBA < < *O BBI A(Ga
+(D ABBA < | H O BBI A(Ga
+(D ABBA t . K^
ԕ T d | 0N BBE E(D0A8G
+8D0A(B BBBI T | N BBE E(D0A8G
+8D0A(B BBBI T M BBE B(D0A8G
+8D0A(B BBBG , $
+J- AAG0!AA 4 x5 M BHD D`
+ DABG T Ԗ M BBB B(A0D8NH
+8A0A(B BBBC , , > M AID W
+DAA T \ M BBB B(A0D8Gs
+8A0A(B BBBA T $ M BBB B(A0D8Gs
+8A0A(B BBBA T h* M BBB B(A0D8G
+8A0A(B BBBA T d 00; M BBB B(A0D8G(
+8A0A(B BBBA T 47 M BBB B(F0A8J
+8A0A(B BBBJ ̙ p. K^ xk pk L \ xkr O BBB B(E0A8J@
+8A0A(B BBBA T l N BEB E(A0A8G;
+8D0A(B BBBA T o N BEB E(A0A8G;
+8D0A(B BBBA L \ xq) LN BBB B(A0I8G
+8D0A(B BBBA L Xs) 6N BBB B(A0I8G
+8D0A(B BBBA T 8u N BBE E(A0A8G
+8D0A(B BBBA , PF- AAG0!AA < pw M BHD D}
+ DABA < ě Pz" M BHD D
+ DABA < @}Q BEH H(A0n(A BBE T D `} =M BEB B(A0D8J
+8A0A(B BBBA T M BEB B(A0D8J
+8A0A(B BBBA T L BEB B(A0D8J
+8A0A(B BBBA T L 8 L BEB B(A0D8J
+8A0A(B BBBA T L BEB B(A0D8J
+8A0A(B BBBA T Ȓ \L BEB B(A0D8J
+8A0A(B BBBA T T /L BEB B(A0D8J
+8A0A(B BBBA T X L BEB B(A0D8J
+8A0A(B BBBA T K BEB B(A0D8J
+8A0A(B BBBA T \ 裲 K BEB B(A0D8J
+8A0A(B BBBA T {K BEB B(A0D8J
+8A0A(B BBBA T X NK BEB B(A0D8J
+8A0A(B BBBA T d !K BEB B(A0D8J
+8A0A(B BBBA T 贲 J BEB B(A0D8J
+8A0A(B BBBA T ` J BEB B(A0D8J
+8A0A(B BBBA T l J BEB B(A0D8J
+8A0A(B BBBA T ġ sJ BEB B(A0D8J
+8A0A(B BBBA T 8Ʋ FJ BEB B(A0D8J
+8A0A(B BBBA T t ʲ J BEB B(A0D8J
+8A0A(B BBBA T ̢ β I BEB B(A0D8J
+8A0A(B BBBA T $ 0Ӳ I BEB B(A0D8J
+8A0A(B BBBA T | xײ I BEB B(A0D8J
+8A0A(B BBBA T ԣ ۲ eI BEB B(A0D8J
+8A0A(B BBBA T , 8I BEB B(A0D8J
+8A0A(B BBBA T @ I BEB B(A0D8J
+8A0A(B BBBA T ܤ x H BEB B(A0D8J
+8A0A(B BBBA T 4 H BEB B(A0D8J
+8A0A(B BBBA T H BEB B(A0D8J
+8A0A(B BBBA L ]H BBH B(D0F8G
+8D0A(B BBBD . K^ 4 4 v BBA A(D0e(A ABBD l BBB B(A0A8DP8A0A(B BBB, h[ H AID t
+DAA L AAG ]
CAF [
ACA R
-CAJ JCA L , $ AAG ]
+CAJ JCA L 4 AAG ]
CAF [
ACA R
-CAJ JCA < D, P$ ų BED I(D
-(D ABBA < , % BED I(D
-(D ABBA D T- v BBB D(A0D@0A(A BBB T - %l # BIE B(A0A8DY
-8D0A(B BBBA T d- *# BDE B(A0D8G +
-8A0A(B BBBB D - N{ ô BBB D(A0G
-0D(A BBBG . >. K^ . 8O . 0O , L. 8O[ N AID t
-DAA D / BBB D(A0D@0A(A BBB T . Ol BIE B(A0A8DY
-8D0A(B BBBA T / 8T BDE H(A0A8G<
-8D0A(B BBBK 0 =. K^ 0 X 40 X , / X[ AID t
-DAA D |0 BBB D(A0D@0A(A BBB T 40 XK + BIB E(A0A8G8
-8D0A(B BBBA L 0 ^H BDB B(D0A8M
-8D0A(B BBBA l1 h<. K^ 1 bO HF 1 bO HF 1 bV HI 1 0cV HI $ 2 pc^ AG b
-AA L 1 c o BBB A(A0M
-0D(A BBBF , 1 Hj F AID
-AAA , 2 j ( AID
-AAA T L2 hkX
- BBB B(A0I8G
-8D0A(B BBBH T 2 pp $ BBB B(A0D8J
-8A0A(B BBBG T 2 s BBB B(A0D8J
-8A0A(B BBBG T T3 u BBB B(A0D8J
-8A0A(B BBBG T 3 8x BBB B(A0D8J
-8A0A(B BBBG T 4 z \ BBB B(A0D8J
-8A0A(B BBBG d 4 h} BEB B(A0D8DPi
+CAJ JCA 4 ̧ DG BDI Gp
+ DABG 4 %G BDI Gp
+ DABG , :- AAG0!AA < l U F BIA D(Dp
+
+(D ABBI T 6# F BDB B(D0A8G
+8A0A(B BBBI D ,{ H BBB D(A0G
+0D(A BBBG . K^ - 4 - , -[ &H AID t
+DAA , | <:- AAG0!AA < -U G BIA D(Dp
+
+(D ABBI T 4 0 G BDB E(G0A8Gw
+8D0A(B BBBA D . K^ \ 5 t 4 , Ԫ 5[ G AID t
+DAA , 05[ cG AID t
+DAA , 9- AAG0!AA D d 05< G BIA D(G
+(D ABBA D (9D F BIA D(G
+(D ABBA L 0= F BDB B(D0A8M
+8D0A(B BBBA . K^ xAO HF 4 AO HF T AV HI t BV HI $ XB^ AG b
+AA BB Aw
+H A L $ BT F BBB B(A0A8JU
+8A0A(B BBBE 4 t Fs 8F BAI C
+ABA 4 Gv F BAI F
+ABA 4 `Gv E BAI F
+ABA L G E BBB A(A0M
+0D(A BBBF T l HN E BBB B(A0D8J
+8A0A(B BBBG T Į P qE BBB B(A0D8J
+8A0A(B BBBG T xS ?E BBB B(A0D8J
+8A0A(B BBBG T t V
E BBB B(A0D8J
+8A0A(B BBBG T ̯ X D BBB B(A0D8J
+8A0A(B BBBG d ܰ @[ BEB B(A0D8DPi
8A0A(B BBBI
-8G0A(B BBBJ D T5 BBB D(A0D@
-0A(A BBBA D 5 z BIB A(D0D`2
-0D(A BBBA L T5 Ѓ X BIB A(D0D
-0D(A BBBA L 5 9 BIB A(D0D
-0D(A BBBA $ 6 pV ADD JAAT 6 U BBB B(A0D8G
-8A0A(B BBBE L t6 ? BEB E(I0H8D@
-8D0A(B BBBA < T7 0 BFA D0}
- FABE D FAB T 7 e ׯ BHB B(A0A8G
-8A0A(B BBBI T \7 ɯ BEE E(A0A8GK
-8A0A(B BBBA T 7 $ BBB H(D0F8G
-8D0A(B BBBA 8 Hʺ AP 8 Hʺ AP 8 (5. K^ 8 0ʺ 9 (ʺ $9 ʺ <9 ʺ T9 ʺ l9 (ʺ D 9 BBB D(A0D@0A(A BBB T <9 ɺ ˯ BIB E(A0A8D6
-8D0A(B BBBA T 9 κ BIB E(A0A8DF
-8D0A(B BBBA T 9 hҺ g BIB E(A0A8D6
-8D0A(B BBBA L D: ֺ 5 BBB B(A0I8G
-8D0A(B BBBF T : غg BBB B(I0A8Gc
-8D0A(B BBBG T : g + BBB B(I0A8Gc
-8D0A(B BBBG L ; BBB B(A0A8G&
-8A0A(B BBBA L $< y BEB E(D0A8G@
-8A0A(B BBBD T ; BJB B(A0A8G
-
-8A0A(B BBBE T << ( O BEB B(F0A8G
-8A0A(B BBBD T < p Ǯ BBG B(A0G8G
-8D0A(B BBBD |= 0. K^ = &0 Hg = &7 Hj = @&2 I $ = h&^ AG b
-AA T = &< BBB B(I0A8GD
-8D0A(B BBBF T = ,< BBB B(I0A8GD
-8D0A(B BBBF T 4> p2
BBB B(D0D8G
-8D0A(B BBBF T > ?| ή BBE E(D0A8G
-8D0A(B BBBI D t? BBB D(A0D@0A(A BBB T ,? D l BIB E(A0A8G
-8D0A(B BBBA T ? `I F BIB E(A0A8G
-8D0A(B BBBA T ? (N BEB B(D0D8G
-8A0A(B BBBA T 4@ T = BEB B(D0D8G
-8A0A(B BBBA T @ [ Z BEB B(D0D8G
-8A0A(B BBBA T @ a w BBE E(E0D8G
-8A0A(B BBBA L <A 8h BBB G(D0A8M
-8D0A(B BBBG B H,. K^ 4B n0 Hg TB n7 Hj tB \\ AG RAT B n G BEE E(D0A8G'
-8D0A(B BBBG D B @ BBB D(A0D@0A(A BBB T B t BIB E(A0A8DF
-8D0A(B BBBA T B hx BIB E(A0A8DN
-8D0A(B BBBA L TC | BBB B(I0A8D
-8D0A(B BBBD T C pU [ BBB B(A0F8G
-8A0A(B BBBG L C x BBB B(I0A8D
-8D0A(B BBBD D ). K^ D E , D [ x AID t
-DAA L TE P BEB E(A0A8GN
-8A0A(B BBBA T E BBE B(D0A8G
-8D0A(B BBBG D E BBB D(A0D@0A(A BBB T E C BIB E(A0A8G0
-8D0A(B BBBA L F x
BDB B(A0D8M
-8D0A(B BBBA F '. K^ G 0 Hg $G 7 Hj DG Щ BEB E(D0E8D
+8G0A(B BBBJ , D 5- AAG0!AA 4 X] D BHD D`
+ DABG < _ C BHD Dq
+ DABA < 4 b C BHD Dx
+ DABA $ , eV ADD JAAT e C BBB B(A0D8G
+8A0A(B BBBB L Pr C BEB E(I0H8D@
+8D0A(B BBBA < s BFA D0}
+ FABE D FAB T @t C BBE B(D0A8G
+8A0A(B BBBA T ܲ } {C BEH B(A0D8G
+8A0A(B BBBA T 4 ! DC BGH E(A0A8G
+8D0A(B BBBE D X AP d X AP . K^ @ 8 ̴ 0 ( 0 8 , , 3- AAG0!AA 4 5 C BHD D`
+ DABG 4 ܴ 5 lC BHD D`
+ DABG 4 B NC BHD D`
+ DABH L L 8 0C BBB B(A0I8G
+8D0A(B BBBF T g C BBB B(I0A8Gc
+8D0A(B BBBG T g &C BBB B(I0A8Gc
+8D0A(B BBBG L BBB B(A0A8G&
+8A0A(B BBBA L T hɳ BEB E(D0A8Gp
+8A0A(B BBBD T س B BJB B(A0A8G
+
+8A0A(B BBBE T D @E KB BBB E(F0G8GO
+8D0A(B BBBE T 8 B BGB B(D0G8G;
+8D0A(B BBBI . K^ ĸ 0 Hg 7 Hj $ ^ AG b
+AA T t 0< B BBB B(I0A8GD
+8D0A(B BBBF T ̸ < B BBB B(I0A8GD
+8D0A(B BBBF T $
+
B BBB B(D0D8G
+8D0A(B BBBF T | H| B BBE E(D0A8G
+8D0A(B BBBI , .- AAG0!AA < @ B BHD Db
+ DABA < D oB BHD Di
+ DABA T @# TB BEB B(D0D8G
+8A0A(B BBBA T ܺ ) kB BEB B(D0D8G
+8A0A(B BBBA T 4 00 B BEB B(D0D8G
+8A0A(B BBBA T 6 B BBE E(E0D8G
+8A0A(B BBBA L =+ B BBB G(D0A8M
+8D0A(B BBBG . K^ C0 Hg $ C7 Hj D Z-\ AG RAT C [B BEE E(D0A8G'
+8D0A(B BBBG , >-- AAG0!AA 4 4 @IE B BHD D`
+ DABK 4 l XLE A BHD D`
+ DABD L pO A BBB B(I0A8D
+8D0A(B BBBD T 0Sd A BBB B(A0F8G
+8A0A(B BBBK L L H_ 'B BBB B(I0A8D
+8D0A(B BBBD T h. K^ l b b , b[ A AID t
+DAA L ̿ c BEB E(A0A8GN
+8A0A(B BBBA T d d HA BBE B(D0A8G
+8D0A(B BBBG , t +- AAG0!AA D Xi4 @ BIA D(G
+(D ABBA L 4 PmD @ BDB B(A0D8M
+8D0A(B BBBA < . K^ T 8{0 Hg t H{7 Hj h{ BEB E(D0E8D
8A0A(B BBBA |
8A0A(B BBBA p
8A0A(B BBBA
-8A0A(B BBBA q8A0A(B BBB G BEE F(D0D8D
+8A0A(B BBBA q8A0A(B BBB D X~ BEE F(D0D8D
8A0A(B BBBA t
8A0A(B BBBA p
8A0A(B BBBA
-8A0A(B BBBA q8A0A(B BBB , H A|
+8A0A(B BBBA q8A0A(B BBB , 8 A|
C K
E R
-A $ H p^ AG b
-AA T lH | / BBE E(D0A8G
-8D0A(B BBBI T H е| BBE E(D0A8G
-8D0A(B BBBI D I BBB D(A0D@0A(A BBB T dI BIB E(A0A8D6
-8D0A(B BBBA d LJ 辻B BBB B(A0A8G@x
+A $ $ ^ AG b
+AA T @| ? BBE E(D0A8G
+8D0A(B BBBI T h| }? BBE E(D0A8G
+8D0A(B BBBI L D c? BBB B(K0A8J
+8A0A(B BBBF 4 q ? BAI A
+ABA , (- AAG0!AA 4 5 -? BHD D`
+ DABG d B BBB B(A0A8G@x
8A0A(B BBBE
-8A0A(B BBBF T $J п BBE F(A0A8G
-8A0A(B BBBA T |J (ջ : BBB B(D0D8J}
-8D0A(B BBBD L J `ݻ 3 BBB B(K0A8J
-8A0A(B BBBF 4 $K q Q BAI A
-ABA T \K > / BBB B(A0A8L
-
-8A0A(B BBBC DL ". K^ \L tL 4 K g _ BAD D
-ABA T 4L 3 BBB B(I0A8D%
-8D0A(B BBBH D M BBB D(A0D@
-0A(A BBBA T L BIB E(A0A8D6
-8D0A(B BBBA 4 M 8J BDA q
+8A0A(B BBBF T > BBE F(A0A8G
+8A0A(B BBBA T ` > BBB B(D0D8J}
+8D0A(B BBBD T L 2 > BHB B(A0A8L
+8D0A(B BBBE \ . K^ t hŴ `Ŵ 4 hŴg > BAD D
+ABA T $ Ŵ > BBB B(A0I8D
+8D0A(B BBBE , 4 P'- AAG0!AA 4 8Ǵ h> BHD D`
+ DABF 4 ɴJ BDA q
ABE A
-ABA < dM PF R BBA A(Gq
-(A ABBA L 4N ` BBB B(A0A8Gp8A0A(B BBB L N M BBB B(A0D8Dp18A0A(B BBB L N BBB B(A0A8Gp8A0A(B BBB L $O `M BBB B(A0D8Dp18A0A(B BBB L tO ` BBB B(A0A8Gp8A0A(B BBB < 4O F BBA A(Gq
-(A ABBA L P M BBB B(A0D8Dp18A0A(B BBB L TP
- BBB B(A0A8D
-8A0A(B BBBA L P BBB E(D0A8D`
-8A0A(B BBBA P @m , Q ADD X
-AAA d <Q BHD G0W
+ABA < ɴF > BBA A(Gq
+(A ABBA L ʴ BBB B(A0A8Gp8A0A(B BBB L d 8̴M BBB B(A0D8Dp18A0A(B BBB L 8δ BBB B(A0A8Gp8A0A(B BBB L ϴM BBB B(A0D8Dp18A0A(B BBB L T Ѵ BBB B(A0A8Gp8A0A(B BBB < 8ӴF f< BBA A(Gq
+(A ABBA L HԴM BBB B(A0D8Dp18A0A(B BBB D 4 Hִ BBB A(A0G@R
+0A(A BBBA L | ִ BBB E(D0A8D`
+8A0A(B BBBA ״m , ״ ADD X
+AAA d Hش BHD G0W
AABH F
AABF g
AABE D
- FABK , Q ADD ]
-AAA d Q 0 BBB E(I0D8DP
+ FABK , | ٴ ADD ]
+AAA d pٴ BBB E(I0D8DP
8D0A(B BBBC |
-8D0A(B BBBA , <R h ADD [
-AAA t lR BIA D(G0C
+8D0A(B BBBA , ڴ ADD [
+AAA t D ۴ BIA D(G0C
(A ABBH o
(A ABBC g
(A ABBC D
-(F ABBA d R PE BBB B(A0A8DP
+(F ABBA d ۴E BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l R 8 C BBB E(I0D8D
+8F0A(B BBBA l l xܴ 9 BBB E(I0D8D
8A0A(B BBBG
-8D0A(B BBBE S u L S P BEB B(A0A8DP
-8A0A(B BBBH l S ' BBB E(D0I8D`M
+8D0A(B BBBE (ߴu L ߴ BEB B(A0A8DP
+8A0A(B BBBH l D ' S8 BBB E(D0I8D`M
8D0A(B BBBJ
-8D0A(B BBBB T T U ? BBB B(A0A8J
-8A0A(B BBBA T 2^ L U 2 BEB B(A0A8DP
-8A0A(B BBBH l T 3' K BBB E(D0I8D`M
+8D0A(B BBBB T M 8 BBB B(A0A8J
+8A0A(B BBBA ^ L BEB B(A0A8DP
+8A0A(B BBBH l t ' 7 BBB E(D0I8D`M
8D0A(B BBBJ
-8D0A(B BBBB T 4U @6
BBB B(A0A8JX
-8A0A(B BBBA V LV L 4V @M BEB B(A0A8DP
-8A0A(B BBBH l U M BBB E(D0I8D`:
+8D0A(B BBBB T p s7 BBB B(A0A8J
+8A0A(B BBBA V L ` BEB B(A0A8DP
+8A0A(B BBBH l 77 BBB E(D0I8D`:
8D0A(B BBBE
-8D0A(B BBBJ T dV PPZ ˢ BBB B(A0A8J
-8A0A(B BBBA d LW Xi! BHF K(H0A8D`}
-8A0A(B BBBA d8C0A(B BBB L W j BEB B(A0A8DP
-8A0A(B BBBH | X jf BIB B(A0D8GPz
+8D0A(B BBBJ T p 6 BBB B(A0A8J
+8A0A(B BBBA d $ -! BHF K(H0A8D`}
+8A0A(B BBBA d8C0A(B BBB L - BEB B(A0A8DP
+8A0A(B BBBH | p.f BIB B(A0D8GPz
8A0A(B BBBA v
8A0A(B BBBH D
-8F0A(B BBBE d W k BBJ B(A0A8G`
+8F0A(B BBBE d `/ 5 BBJ B(A0A8G`
8D0A(B BBBE N
-8A0A(B BBBH T \X Xm4 BBB B(A0A8J]
-8A0A(B BBBA D X @ o BGA D(M
-(D ABBF Y . K^ Y Љ0 Hg Y 7 Hj L TY BBB B(A0I8G
-8D0A(B BBBD L Y ` ߡ BBB B(A0I8G
-8D0A(B BBBD T Y ˡ BEG E(A0G8G-
-8D0A(B BBBD \ LZ 蕼 BFE A(A0D`W
+8A0A(B BBBH T 1 5 BBB B(A0A8J4
+8A0A(B BBBA D d G 5 BGA D(M
+(D ABBF d . K^ | pL0 Hg L7 Hj L L ^5 BBB B(A0I8G
+8D0A(B BBBD L T O I5 BBB B(A0I8G
+8D0A(B BBBD L `Q 45 BBB B(A0I8G
+8D0A(B BBBD T S 5 BEG E(A0G8G-
+8D0A(B BBBD \ L X\ 5 BFE A(A0D`W
0D(A BBBK f
-0D(A BBBG D <[ Z BBB D(A0D@0A(A BBB T Z Ж\ BIE B(A0A8DI
-8D0A(B BBBA T L[ ؛b X BIE B(A0A8DQ
-8D0A(B BBBA 4\ . K^ L\ ؠ $ d\ Р^ AG b
-AA \ $ L \ BBB B(A0A8G
-8A0A(B BBBA < \ pD BBD A(D@0(A ABB T \ ` BIB E(A0A8DF
-8D0A(B BBBA T \ O BIB E(A0A8DF
-8D0A(B BBBA T T] BIB E(A0A8DF
-8D0A(B BBBA L ] 86 BBB B(A0A8Gb
-8A0A(B BBBC < ] ( ݟ BEA A(Gc
-(D ABBA < <^ BEA A(Gc
-(D ABBA < |^ 踼 BEA A(Gc
-(D ABBA , ^ HY _ ADG i
-AAA , ^ xY ; ADG i
-AAA _ N _ R < _ BBG A(G@
-(A ABBD ` X% $ 4` pV ADD JAA4 \` BEA A(D0(A ABBd ` 01 / BHB B(D0A8Dpv
+0D(A BBBG , d N- AAG0!AA 4 X]E 5 BHD D`
+ DABK 4 p`E 5 BHD D`
+ DABD 4 L cE 4 BHD D`
+ DABD < @. K^ $ T f^ AG b
+AA | f$ gH Gj
+G F
+E L hA L4 BBB B(A0A8G
+8A0A(B BBBA , - AAG0!AA 4 | k 3 BHD D`
+ DABG 4 @n 3 BHD D`
+ DABG 4 p 3 BHD D`
+ DABG L $ s6 3 BBB B(A0A8Gb
+8A0A(B BBBC < t v 3 BEA A(Gc
+(D ABBA < `v l3 BEA A(Gc
+(D ABBA < v B3 BEA A(Gc
+(D ABBA , 4 w\ 3 ADG l
+AAA , d Pw\ 2 ADG l
+AAA L L w BBB E(A0A8G`
+8A0A(B BBBA L x BBB E(A0A8G`
+8A0A(B BBBA L @z BBB E(A0A8G`
+8A0A(B BBBA < { N < T { BDD k
+ABH [
+ABA L |! 1 BEB B(A0D8Gm
+8A0A(B BBBA L , " 1 BEB B(A0A8Jm
+8A0A(B BBBA L | ؚ 1 BBB B(A0A8Jp
+8A0A(B BBBA < h BBG A(G@
+(A ABBD % $ V ADD JAA4 X BEA A(D0(A ABBd 1 f1 BHB B(D0A8Dpv
8C0A(B BBBG {
-8D0A(B BBBA T l` D ˝ BBB B(A0A8M
-8A0A(B BBBA T ` żX BBB B(A0A8M
-8A0A(B BBBA T a μ w BBB B(A0E8J?
-8A0A(B BBBA T ta O BBB E(I0A8K}
-8D0A(B BBBF L a ټ ' BEB B(D0A8Gpu
-8A0A(B BBBA L b 8ۼ BEB B(D0A8Gpu
-8A0A(B BBBA T lb ܼ BBB E(I0A8K}
-8D0A(B BBBF L b * s BEB B(D0A8Gp
-8A0A(B BBBA $ c V ADD JAA, c ( JP
-I ^
-B j
-F k
-A , c JP
-I ^
-B j
-F k
-A L c H Ǜ BBB B(A0A8G}
-8A0A(B BBBH < c 7 BLD D(G0
-(A ABBE D d BHI H(D0D@
-0A(A BBBA T td X ӛ BBB B(A0A8GC
-8A0A(B BBBA T d
- BBE E(D0G8GQ
-8D0A(B BBBB T $e
BBB H(D0A8L"
-8D0A(B BBBB , f 8% JP
-I ^
-B j
-F k
-A , <f ' JP
-I ^
-B f
-J k
-A L e 8* w BBB B(A0A8G~
-8A0A(B BBBG T ,f @
- BBE E(D0G8GQ
-8D0A(B BBBB T f K
$ BBB H(D0A8L"
-8D0A(B BBBB , lg xX JP
-I ^
-B j
-F k
-A , g [ JP
-I ^
-B j
-F k
-A L <g ] BBB B(A0A8G~
-8A0A(B BBBG T g 8t
- BBE E(D0G8GQ
-8D0A(B BBBB T g p~
Y BBB H(D0A8L"
-8D0A(B BBBB h . K^ h h i ,i Di \i ti i i i , Di [ 3 AID t
-DAA , ti [ AID t
-DAA , i [ AID t
-DAA , i P[ ӝ AID t
-DAA , j [ AID t
-DAA j 䔉- AG cA j " DN
-N A D k BBB D(A0D@
-0A(A BBBA 4 j k BHA GP
- DABA D j 0 BIB A(D0D`+
-0D(A BBBA 4 <k 葽k ʜ BHA GP
- DABA D tk BIB A(D0D`+
-0D(A BBBA D k ؕ BIB A(D0D`+
-0D(A BBBA D l S g BEB A(A0Gh
-0A(A BBBA T Ll b I BBE B(K0A8G
-8A0A(B BBBI T l BDB B(A0A8J
-8A0A(B BBBF < l 8 / BBH D(J
-(A ABBA T <m I BEB B(A0D8M
-8A0A(B BBBD T m BDB B(A0D8G,
-8A0A(B BBBD < m Ƚ Z BBH D(J
-(A ABBA T ,n ʽ( t BEB B(A0D8M
-8A0A(B BBBD T n ѽy F BDB B(A0D8Gs
-8A0A(B BBBE < n BBH D(J
-(A ABBA T o BEI B(A0A8J
-8A0A(B BBBA T to ' BDB B(A0A8J
-8A0A(B BBBA < o X ۜ BBH D(J
-(A ABBA T p ( BEB B(D0A8M
-8A0A(B BBBA T dp p BDB B(D0A8G
-8A0A(B BBBA Lq h. K^ dq r\ AG RAT p BBB I(A0A8G
-8A0A(B BBBA T Lq d BBB I(A0A8G
-8A0A(B BBBA T q p
- $ BBB I(A0A8GL
-8A0A(B BBBA r X. K^ r 2 Hb
-F A r 7 Hj r N\ AG RAT tr f x BBB B(I0A8G
-8D0A(B BBBF T r \ BBE B(A0A8G
-8D0A(B BBBE D s ڌ BBB D(A0D@0A(A BBB T ls # _ BIB E(A0A8D6
-8D0A(B BBBA L s P' / BBB E(F0A8G
-8A0A(B BBBA t p. K^ T ,t 0 g BBB B(A0A8G
-8A0A(B BBBA $ u 8m AG q
-AA $ <u 8u AG y
-AA du \ AG RAD u 4 BBB D(A0D@0A(A BBB T <u 8l [ BIE B(A0A8DY
-8D0A(B BBBA T u >| ) BBE E(D0A8G
-8D0A(B BBBE T u (CT BBB B(I0A8G
-8D0A(B BBBH T Dv 0H BBB B(A0G8J
-8D0A(B BBBE ,w . K^ , v \ _ AID
-AAA D tw BBB D(A0D@0A(A BBB T ,w ] BIE B(A0A8D
-8D0A(B BBBA x `. K^ ,x hb Dx `b \x Xb tx `b D x ȍ BBB D(A0D@0A(A BBB T Dx bL BIE B(A0A8D9
-8D0A(B BBBA T x gL BIE B(A0A8D9
-8D0A(B BBBA L x lq BBB B(A0I8G
-8D0A(B BBBD T Dy @n BBB B(A0I8G
-8D0A(B BBBD T y xrZ BBB G(D0G8G
-8D0A(B BBBD T y y
- ϙ BBB G(D0G8G
-8D0A(B BBBE z . K^ z { , z [ AID t
-DAA T z PA BEE E(D0A8GY
-8D0A(B BBBE D { v BBB D(A0D@0A(A BBB T d{ | - BIE B(A0A8Di
-8D0A(B BBBA T { (h BGH B(A0D8G
-8D0A(B BBBA | 0. K^ $ | ( AG u
-AA $ | AG }
-AA D } 䌉 BBB D(A0D@0A(A BBB T | L BIE B(A0A8D9
-8D0A(B BBBA T } 8 BBB J(A0A8G>
-8D0A(B BBBA ~ . K^ ~ ؝9 Hp <~ @ Hs \~ b\ AG RAD |~ BBB D(A0D@0A(A BBB T 4~ BIB E(A0A8D6
-8D0A(B BBBA . K^ $ 4 СY AK Y
-AA L ~ BEB E(I0H8D@
-8D0A(B BBBA T _ BBE B(A0A8J
-8A0A(B BBBA , t `> AID W
-DAA 4 p0 Hg T 7 Hj D t t BBB D(A0D@0A(A BBB T , XL ߖ BIE B(A0A8D9
-8D0A(B BBBA T P BBB B(A0I8G6
-8D0A(B BBBD T ܀ BBB E(A0A8G
+8D0A(B BBBA T 1 BBB B(A0A8M
+8A0A(B BBBA T D 0 BBB B(A0A8M
+
+8A0A(B BBBA T hG
+ 0 BBB B(A0E8J
+8A0A(B BBBA T `ɵ 0 BBB E(I0A8K}
+8D0A(B BBBF L L ˵ _0 BEB B(D0A8Gpu
+8A0A(B BBBA L ̵ 0 BEB B(D0A8Gpu
+8A0A(B BBBA T 8ε / BBB E(I0A8K}
+8D0A(B BBBF L D е* / BEB B(D0A8Gp
+8A0A(B BBBA $ L ҵV ADD JAA< ҵ _/ BLD D(G0
+(A ABBE D ӵ BHI H(D0D@
+0A(A BBBA , `Ե BFA D0 AABT t Ե` . BEB B(A0A8G9
+8A0A(B BBBA L . BEB E(D0G8G
+8D0A(B BBBE T h*
. BBB H(D0A8L"
+8D0A(B BBBB L t @ / BEB E(D0G8G
+8D0A(B BBBE T *
/ BBB H(D0A8L"
+8D0A(B BBBB L 0 BEB E(D0G8G
+8D0A(B BBBE T l *
0 BBB H(D0A8L"
+8D0A(B BBBB | 0~. K^ & & & & & & $ & < & T & l & , &[ 0 AID t
+DAA , '[ q0 AID t
+DAA , , 8'[ Q0 AID t
+DAA , \ h'[ 10 AID t
+DAA , '[ 0 AID t
+DAA t
- AG cA '" DN
+N A $ ' AAG0AA4 $ @( / BHA GPh
+ DABG 4 \ * k/ BHA GPh
+ DABG 4 + M/ BHA GPh
+ DABG 4 - // BHA GPh
+ DABG 4 `/ / BHA GPh
+ DABG D < (1S . BEB A(A0Gh
+0A(A BBBA T @2b . BBE B(K0A8G
+8A0A(B BBBI T X9 . BDB B(A0A8J
+8A0A(B BBBF < 4 E . BBH D(J
+(A ABBA T t H . BEI B(A0A8J
+8A0A(B BBBA T hN' . BDB B(A0A8J
+8A0A(B BBBA T $ @W( / BEB B(A0D8M
+8A0A(B BBBD T | ^y . BDB B(A0D8Gs
+8A0A(B BBBE T @p "/ BEB B(A0D8M
+8A0A(B BBBE T , v . BDB B(A0D8G,
+8A0A(B BBBD T P 3/ BEB B(D0A8M
+8A0A(B BBBA T . BDB B(D0A8G
+8A0A(B BBBA v. K^ \ AG RAT . BBB I(A0A8KL
+8A0A(B BBBF T h . BBB I(A0A8KL
+8A0A(B BBBF T 4 I. BBB I(A0A8K
+8A0A(B BBBG D u. K^ \ 02 Hb
+F A | P7 Hj \ AG RAT Pf - BBB B(I0A8G
+8D0A(B BBBF T \ h - BBE B(A0A8G
+8D0A(B BBBE , l - AAG0!AA 4 5 - BHD D`
+ DABG L \
+ - BBB E(F0A8G
+8A0A(B BBBA $ t. K^ T Ŷ - BBB B(A0A8G
+8A0A(B BBBA $ ̶m AG q
+AA $ Ͷu AG y
+AA >\ AG RA, z- AAG0!AA < | ͶU , BIA D(Dp
+
+(D ABBI T 8ж| , BBE E(D0A8G
+8D0A(B BBBE T `նT , BBB B(I0A8G
+8D0A(B BBBH T l hڶ , BBB B(A0G8J
+8D0A(B BBBE | q. K^ , - AID
+AAA , - AAG0!AA < < X - BHD Dh
+ DABA 4 hq. K^ L d | , .- AAG0!AA 4 $ 5 , BHD D`
+ DABG 4 \ 5 , BHD D`
+ DABG L q , BBB B(A0I8G
+8D0A(B BBBD T , BBB B(A0I8G
+8D0A(B BBBD T < , BBB G(D0G8G
+8D0A(B BBBD T `J , BBB G(D0G8G
+8D0A(B BBBE (o. K^ @ 8 , 4 @[ , AID t
+DAA T d pA , BEE E(D0A8GY
+8D0A(B BBBE , t - AAG0!AA < 8b M, BIA D(Dp
+(D ABBE T , h ', BGH B(A0D8G
+8D0A(B BBBI < m. K^ $ T # AG u
+AA $ | # AG }
+AA , - AAG0!AA 4 $5 S, BHD D`
+ DABG T T '8 5, BBB J(A0A8G>
+8D0A(B BBBA d l. K^ | (9 Hp )@ Hs \ AG RA, - AAG0!AA 4 T (5 o+ BHD D`
+ DABG D l. K^ T + ;+ BBE B(A0A8J
+8A0A(B BBBA , 85> c+ AID W
+DAA H50 Hg X57 Hj , $ - AAG0!AA 4 H55 * BHD D`
+ DABG T P8 * BBB B(A0I8G6
+8D0A(B BBBD T , < * BBB E(A0A8G
-8A0A(B BBBD ā . K^ ܁ ɾ ɾ L ɾ AAG e
+8A0A(B BBBD < Pj. K^ T P l P L P AAG e
CAF {
ACI R
-CAJ JCA , ́ ʾ[ AID t
-DAA < Hʾq ˖ BBA A(D0[
-(A ABBA D ̂ ʾ BEB B(A0A8
-0A(B BBBA D о BEB B(A0A8
-0A(B BBBA L \ վ
BIE E(D0A8J
-8A0A(B BBBA x% ă % ܃ % % L BBB B(D0D8G@o
-8A0A(B BBBH \ t T ϔ BDB B(D0G8GA
-8D0A(B BBBF < ҊD BBD A(D@0(A ABB T BIB E(A0A8D
-8D0A(B BBBA | AY AY AY < ܅ y BDD H
+CAJ JCA , (Q[ * AID t
+DAA < L XQq * BBA A(D0[
+(A ABBA L D Q BBB B(A0A8J@
+8A0A(B BBBA D HW BEB B(A0A8
+0A(B BBBA L ]
BIE E(D0A8J
+8A0A(B BBBA , j% D m% \ p% t t% L w BBB B(D0D8G@o
+8A0A(B BBBH Ѐ H T T Ѓf ( BDB B(D0G8GA
+8D0A(B BBBF , d - AAG0!AA < ( BHD Db
+ DABA AY AY AY < 4 y BDD H
ABK A
-ABL < BBD D(G0b
-(A ABBE \ Xe aC , | AET
+ABL < t Ȋ BBD D(G0b
+(A ABBE (e aC , x AET
AE
-AA D x BBB B(A0A80A(B BBB L
- BIB B(A0D8G
-8D0A(B BBBD d D `S BBB E(D0D8D
+AA D H BBB B(A0A80A(B BBB L L BIB B(A0D8G
+8D0A(B BBBD d 0S BBB E(D0D8D
8A0A(B BBBH
-8A0A(B BBBG D X BBB B(A0A8
-0A(B BBBA D c BEB B(A0A8H
-0A(B BBBA D < 8' BBE B(A0A8
-0A(B BBBA L ( 3 BBB B(A0A8G3
-8A0A(B BBBB L 0 BEB B(A0A8Gk8A0A(B BBB 4 XJD AC
P\
- A L t pV BBI B(A0A8G
-8A0A(B BBBA , ĉ d AC
BEH
- D d `gS 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 h BEB B(A0A8G
+8A0A(B BBBA 4 ͷD $ AC
P\
+ A L 0ٷ BBI B(A0A8G
+8A0A(B BBBA , ` AC
BEH
+ D d L S BBB E(D0D8D
8A0A(B BBBI
-8A0A(B BBBG L \ Xk BBB B(D0A8J:
-8A0A(B BBBA D (~ BIB B(A0A8
-0A(B BBBF L u BBB B(D0A8G
-8A0A(B BBBA d D 3 BBB E(D0D8D
+8A0A(B BBBG L BBB B(D0A8J:
+8A0A(B BBBA D BEB B(A0A8
+0A(B BBBF L L @u BBB B(D0A8G
+8A0A(B BBBA d p3 BBB E(D0D8D
8A0A(B BBBB
-8A0A(B BBBK D BBB B(A0A8
-0A(B BBBA D Pt BEB B(A0A8Y
-0A(B BBBA D < BBB B(A0A8
-0A(B BBBA < P BBB A(A0
-(A BBBA D 4 3 AC
BEO2
+8A0A(B BBBK D H BBB B(A0A8
+0A(B BBBA D L w BEB B(A0A8Y
+0A(B BBBA D H
BBB B(A0A8
+0A(B BBBA < BBB A(A0
+(A BBBA D d W! AC
BEO"
A f
A
- K T | ؗ BBA A(Gp
+ K T #! BBA A(Gp
(A ABBG
-(A ABBK 4 d `~ AC
BGM
+(A ABBK 4 0 ~ AC
BGM
I
- F t s BBB A(A0JW
-0A(A BBBG
-0A(A BBBD
-0A(A BBBD T BEB B(A0A8Jp
-8A0A(B BBBA D l BBB A(A0G`
-0A(A BBBD D $ @ BBB D(A0G@D
-0A(A BBBB 4 l n AC
PC
- I D 4 ȿ BKB B(G0A8t
-0A(B BBBA L | Ͽ% BEE E(D0A8G
-8A0A(B BBBK 4 < ߿P AC
P\
- A \ BFB B(A0A8
-0A(B BBBA 0A(B BBB L d P
- BBB B(A0A8D
-8A0A(B BBBA , $ AM
DL
- E d S BBB E(D0D8D
+ F t < x BBB A(A0JW
+0A(A BBBG
+0A(A BBBB @
+0A(A BBBH T % / BEB B(A0A8Jp
+8A0A(B BBBA D . BBB A(A0G`
+0A(A BBBD L T P6 BBB B(D0A8GP:
+8A0A(B BBBH 4 8 AC
PC
+ I D (J BKB B(G0A8t
+0A(B BBBA L Q% BEE E(D0A8G
+8A0A(B BBBJ 4 t aP AC
P\
+ A \ d j BBB B(A0A8
+0A(B BBBA 0A(B BBB L mT
+ BBB B(A0A8D
+8A0A(B BBBA , x$ AM
DL
+ E d D yS BBB E(D0D8D
8A0A(B BBBB
-8A0A(B BBBC d L BEE B(E0A8DX7
+8A0A(B BBBC d } BEE B(E0A8DX7
8A0A(B BBBA S
-8A0A(B BBBA 4 $
- Ԉ AC
P
- A D H h BBB B(A0A8J
-0A(B BBBA D p
N c AC
BEOR
+8A0A(B BBBA 4 \
+ AC
P
+ A D L ` BBB B(A0A8j
+0A(B BBBA D > z AC
BEOB
A f
A
- G T x : BBA A(G`
+ G T $ Q BBA A(G`
(A ABBD {
-(A ABBG 4 Ԓ H AC
BGM
+(A ABBG 4 4 H AC
BGM
E
- F T | 5 BEB B(D0A8J
-8A0A(B BBBA T Ԓ & p BBB E(A0A8J`
-8A0A(B BBBG 4 , h0
- g AC
P^
- A T d : > BBB B(A0A8J
-8A0A(B BBBA L R3 D0n d 0݉. K^ | (R R R Ĕ R L ܔ (R BBB B(A0A8D`/
-8A0A(B BBBI L , T BBB B(A0A8D`s
-8A0A(B BBBE | ~{\ AG RAD { BBB D(A0D@0A(A BBB T T W BIB E(A0A8D6
-8D0A(B BBBA T 8[ BIB E(A0A8D6
-8D0A(B BBBA T p_i O BBB B(A0D8R
-8A0A(B BBBF , \ c> V AID W
-DAA T ci 6 BBB B(A0D8R
-8A0A(B BBBF , g> = AID W
-DAA < g BBG A(G@
-(A ABBG T T `h ݅ BHB E(A0A8G
-8D0A(B BBBA T { BHB E(A0A8G
-8D0A(B BBBA 0ى. K^ Ę , L [ 5 AID t
-DAA < ȍ BBB A(A0
-(A BBBA D L x= BEB B(A0A8
-0A(B BBBA L pK BBB B(A0A8G@d
-8A0A(B BBBA D p BBB D(A0D@
-0A(A BBBA T ȕ BIB B(A0D8Df
-8D0A(B BBBA < 0 BBD D(G0b
-(A ABBE L Ě K BBB B(A0A8D@28A0A(B BBB d S BBB E(D0D8D
+ F T 05 BEB B(D0A8J
+8A0A(B BBBA T BBB E(A0A8G
+8A0A(B BBBB 4 d p
+ n AC
P^
+ A T Ȼ E BBB B(A0A8J
+8A0A(B BBBA T u 1 BBB B(A0A8J"
+8A0A(B BBBA Ҹ3 D0n W. K^ 4 Ҹ L Ҹ d Ҹ | Ҹ L Ҹ BBB B(A0A8D`/
+8A0A(B BBBI L Hո BBB B(A0A8D`s
+8A0A(B BBBE 4 ~\ AG RA, T R~- AAG0!AA 4 5 BHD D`
+ DABG 4 ڸ5 i BHD D`
+ DABG T < ݸi K BBB B(A0D8R
+8A0A(B BBBF , > R AID W
+DAA T i 2 BBB B(A0D8R
+8A0A(B BBBF ,
> 9 AID W
+DAA < BBG A(G@
+(A ABBG T
BHB E(A0A8G
+8D0A(B BBBA T
` BHB E(A0A8G
+8D0A(B BBBA S. K^ $ , [ 1 AID t
+DAA < l BBB A(A0
+(A BBBA L BBB B(A0A8JHv
+8A0A(B BBBA , ~- AAG0!AA < t Q BIA D(Dp-
+(D ABBF < l BBD D(G0b
+(A ABBE L K BBB B(A0A8D@28A0A(B BBB d S BBB E(D0D8D
8A0A(B BBBD
-8A0A(B BBBG L | BBB B(A0A8Dxv
-8A0A(B BBBA L ̛
+8A0A(B BBBG L d ! BBB B(A0A8Dxv
+8A0A(B BBBA L x&
BIB B(A0A8G
-8A0A(B BBB < h + AC
FM-
+8A0A(B BBB < L 0 AC
FM-
A X
- C T ̛ H BBA A(Gp
+ C T 9 g BBA A(Gp
(A ABBG u
-(A ABBE 4 $ AF
P
- G 4 8 AC
BGM
+(A ABBE 4 @; AF
P
+ G 4 (@8 AC
BGM
H
- F l J BBB B(A0D8Gc
-8A0A(B BBBG
-8A0A(B BBBG T ` BEB B(D0D8Je
-8A0A(B BBBD T \ v Ђ BDH B(A0A8J
-8D0A(B BBBC D Ӊ. K^ L \ BBB B(A0D8D
-8A0A(B BBBA L X] BBB B(A0D8G
-8A0A(B BBBC L h BBB B(A0D8G
-8A0A(B BBBE $ L AG r
-AA $ t ` AG z
-AA < BBA I(D@p
-(D ABBA T L BBE B(D0A8G
-8D0A(B BBBB D 4 r BBB D(A0D@0A(A BBB T 2 BIB E(A0A8D6
-8D0A(B BBBA T D @! BBE B(A0A8L
-8A0A(B BBBD , Љ. K^ D D s BBB D(A0D@0A(A BBB T 8 R BIE B(A0A8DA
-8D0A(B BBBA T T @" BBB B(A0I8G
-8D0A(B BBBD < Љ. K^ T `& l X& T `& BBB B(I0A8D
-8D0A(B BBBE ܢ ( (" < ( BGE D(A0j
-(A BFBH D L Hs BBB D(A0D@0A(A BBB T ) ƀ BIB E(A0A8G
-8D0A(B BBBA L \ / BGB B(A0D8G
-8A0A(B BBBC < HΉ. K^ T 06 l (6 6 (6 060 Hg Ԥ @67 Hj , d `6[ g AID t
-DAA $ 6} AG sA D 6} AG sA d P7 AG {A 7 AG {A 08 AG vA ĥ 8 AG ~AL T 9r BBB B(E0A8J@
-8A0A(B BBBA $ 4 0:^ AG b
-AA T ̥ h:C $ BBB B(A0D8Ra
-8A0A(B BBBA , $ `>> C AID W
-DAA T T p> # BBB B(A0D8R9
-8A0A(B BBBA , B> = AID W
-DAA L ܦ (B BBB B(I0A8D
-8D0A(B BBBA L , C BBB B(I0A8D
-8D0A(B BBBA L | hE ~ BBB B(I0A8D
-8D0A(B BBBA L ̧ G ~ BBB B(I0A8D
-8D0A(B BBBA D H BBB D(A0D@
-0A(A BBBA D d J }~ BIB A(D0D`+
-0D(A BBBA D L [~ BIB A(D0D`+
-0D(A BBBA D pO 9~ BIB A(D0D`+
-0D(A BBBA 4 < (Rk ~ BHA GP
- DABA D t `S } BIB A(D0D`+
-0D(A BBBA D V } BIB A(D0D`+
-0D(A BBBA 4 Xk } BHA GP
- DABA 4 < Zz } BHA GP
- DABA T t P[ v} BBB E(D0A8G
-8A0A(B BBBC D ̪ c W} BDB D(G0Dy
-0D(A BBBA T e Q} BBE H(A0F8DE
-8D0A(B BBBB L l Hh H} BBG D(G0D
-0D(A BBBA T j F} BBB J(A0A8G&
-8D0A(B BBBA T l (} BBB B(A0D8G
-8A0A(B BBBI T l {E } BBB H(D0F8G
-8D0A(B BBBG T AR t AY ʼn. K^ ĭ ܭ x T D O $ ЂT D O < W D R T `T D O l T D O W D RD 8 | BBE A(A0
-(A BBBA , T [ | AID t
-DAA , Є[ j| AID t
-DAA L D AAG e
+ F l T 0DO BBB B(D0A8Gc
+8A0A(B BBBG P
+8A0A(B BBBN T T j BEB B(D0D8Ju
+8A0A(B BBBD T [ 8 BDH B(A0A8J
+8D0A(B BBBC , N. K^ L D c BBB B(A0D8D
+8A0A(B BBBA L 8e] BBB B(A0D8G
+8A0A(B BBBC L Hh BBB B(A0D8G
+8A0A(B BBBE $ 4 l AG r
+AA $ \ @m AG z
+AA < m BBA I(D@e
+(D ABBA T Xn BBE B(D0A8G
+8D0A(B BBBB , ~- AAG0!AA 4 r5 BHD D`
+ DABG T u BBE B(A0A8L 6
+8A0A(B BBBG pL. K^ , <~- AAG0!AA 4 l 8B ? BHD D`
+ DABH T P ! BBB B(A0I8G
+8D0A(B BBBD K. K^ p h T D p BBB B(I0A8D
+8D0A(B BBBE T ؛ l Л" L 蛹 BEE B(D0A8G@i
+8A0A(B BBBC , ~- AAG0!AA < L 8 BHD G/
+ DABA L U BGB B(A0D8G
+8A0A(B BBBC J. K^ 0 Hg , 7 Hj , [ AID t
+DAA | - AG cA } AG sA `} AG sA AG {A 0 AG {A AG vA < AG ~AL pr BBB B(E0A8J@
+8A0A(B BBBA T C | BBB B(A0D8Ra
+8A0A(B BBBA , L > AID W
+DAA T | { BBB B(A0D8R9
+8A0A(B BBBA , P> AID W
+DAA L ` u BBB B(I0A8D}
+8D0A(B BBBA L T _ BBB B(I0A8D}
+8D0A(B BBBA L I BBB B(I0A8D}
+8D0A(B BBBA L 3 BBB B(I0A8D}
+8D0A(B BBBA $ AAG0AA4 l ( BHA GPh
+ DABG 4 BHA GPh
+ DABG 4 BHA GPh
+ DABG 4 BHA GPh
+ DABG 4 L H } BHA GPh
+ DABG 4 ù _ BHA GPh
+ DABG 4 Ĺ A BHA GPh
+ DABG 4 ƹ # BHA GPw
+ DABH T , xȹ BBB E(D0A8G
+8A0A(B BBBC D ѹ BDB D(G0Dy
+0D(A BBBA D ҹ BEG A(I
+(D ABBA L 0չ BBG D(G0D
+0D(A BBBA T d BBB J(A0A8G&
+8D0A(B BBBA T ٹb BBB B(A0D8G
+8A0A(B BBBG T ! Z BBB E(G0F8G
+8D0A(B BBBJ $" AR D" AY d" xA. K^ |" " " " " " # $# <# T D O T# T D O l# PW D R # T D O # T D O # (W D R # pT D O # T D O # W D R $ HT D O ,$ T D O D$ W D R, # [ M AID t
+DAA , # P[ - AID t
+DAA , $ [
AID t
+DAA , 4$ [ AID t
+DAA L % AAG e
CAF C
ACA R
-CAJ JCA L ` AAG e
+CAJ JCA L l% @ AAG e
CAF C
ACA R
-CAJ JCA L ADD e
+CAJ JCA L % AAG e
+CAF C
+ACA R
+CAJ JCA L & AAG e
+CAF C
+ACA R
+CAJ JCA L \& ` ADD e
CAF x
ACD V
-CAF JCA L 4 ` ADD e
+CAF JCA L & ADD e
CAF x
ACD V
-CAF JCA L ADD e
+CAF JCA L & ADD e
CAF x
ACD V
-CAF JCA L ADD e
+CAF JCA L L' @ ADD e
CAF x
ACD V
-CAF JCA L $ @ ADD e
+CAF JCA L ' ADD e
CAF x
ACD V
-CAF JCA L t ADD e
+CAF JCA L ' ADD e
CAF x
ACD V
-CAF JCA D ı BBB D(A0D@
-0A(A BBBA T | ؊ y BIB E(A0A8D6
-8D0A(B BBBA D Ա Py BIB A(D0D`+
-0D(A BBBA T ȑ .y BEB B(A0A8Jq
-8A0A(B BBBF T t 0 y BEB B(A0A8Jq
-8A0A(B BBBF T ̲ u x BBB E(A0A8R
-8D0A(B BBBA / AJ0_D T D Х: .y BEE E(D0D8G
-8A0A(B BBBB , T u >y BBB E(A0A8R
-8D0A(B BBBA ػ/ AJ0_D T , : y BEE E(D0D8G
-8A0A(B BBBB , . K^ D \ t D b BBB D(A0D@0A(A BBB T \ hL x BIE B(A0A8D9
-8D0A(B BBBA T `L x BIE B(A0A8D9
-8D0A(B BBBA L X x BBB A(A0J E
-0A(A BBBA l \ y BBB B(A0H8Gb
-8A0A(B BBBD s
-8A0A(B BBBK , ̶ h> x AID W
-DAA L x x BBB A(A0J O
-0A(A BBBA l L y BBB B(A0H8Gb
-8A0A(B BBBD s
-8A0A(B BBBK , > x AID W
-DAA T x BBB H(D0F8GH
-8D0A(B BBBD T D P y BBB H(D0F8GH
-8D0A(B BBBD , 蹉. K^ L D BBB B(A0D8D8A0A(B BBB D z BBE A(A0
-(A BBBA L ܹ ADD e
+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 ,) 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 $ * p~ AAG0AA4 )
+ BHD D`
+ DABF 4 )
+ BHD D`
+ DABF 4 ) h i
+ BHD D`
+ DABF 4 4* K
+ BHD D`
+ DABF T l* % -
+ BEB B(A0A8JO
+8A0A(B BBBH D * 2
+ BGB A(D0Ge
+0A(A BBBA T + % BEB B(A0A8JO
+8A0A(B BBBH D d+ 2 BGB A(D0Ge
+0A(A BBBA T + % BEB B(A0A8JO
+8A0A(B BBBH T , % i BEB B(A0A8JO
+8A0A(B BBBH T \, h C BBB B(A0D8R
+8D0A(B BBBG l- / AJ0_D T , BBE B(D0A8Jw
+8A0A(B BBBE - " T D- "
+ R BBB E(A0A8RB
+8D0A(B BBBB T. (-/ AJ0_D T - 8- BBE B(D0A8Jw
+8A0A(B BBBE . 0 T ,. / [ BBB B(A0D8R
+8D0A(B BBBG </ P;/ AJ0_D T . `; BBE B(D0A8Jw
+8A0A(B BBBE / (> T / > j BBB B(A0D8R
+8D0A(B BBBG $0 xI/ AJ0_D T / I BBE B(D0A8Jw
+8A0A(B BBBE 0 PL 0 X3. K^ 0 0L 0 8L 0 @L , 1 HL BGA
+ABA D1 L \1 L0 Hg |1 M7 Hj 1 M0 Hg 1 0M7 Hj 1 PM0 Hg 1 `M7 Hj 2 M0 Hg <2 M7 Hj \2 M0 Hg |2 M7 Hj 2 M0 Hg 2 M7 Hj L 2 N BBB B(D0A8D@b
+8A0A(B BBBA ,3 PN D3 HN \3 @N t3 8N L 3 0N AAG ]
+CAF [
+ACA R
+CAJ JCA L 3 `N AAG ]
+CAF [
+ACA R
+CAJ JCA L ,4 N AAG ]
+CAF [
+ACA R
+CAJ JCA L |4 N AAG ]
+CAF [
+ACA R
+CAJ JCA , 4 Nb D\
+H P
+H Q
+G I , 4 0Ob D\
+H P
+H Q
+G I , ,5 pOb D\
+H P
+H Q
+G I L \5 O AAG e
+CAF s
+ACA R
+CAJ JCA , 5 Pb D\
+H P
+H Q
+G I L 5 @P AAG e
+CAF s
+ACA R
+CAJ JCA L ,6 P AAG ]
+CAF [
+ACA R
+CAJ JCA L |6 P AAG e
+CAF s
+ACA R
+CAJ JCA L 6 Q AAG ]
+CAF [
+ACA R
+CAJ JCA L 7 @Q AAG e
+CAF s
+ACA R
+CAJ JCA L l7 Q BBB B(D0A8D@b
+8A0A(B BBBA , 7 Q BGA
+ABA , 7 pR BGA
+ABA , 8 S BGA
+ABA , L8 ~- AAG0!AA 4 7 S5 BHD D`
+ DABG 4 7 V5 BHD D`
+ DABG 4 48 Y5 u BHD D`
+ DABG 4 l8 \5 W BHD D`
+ DABG L 8 _g 9 BBE D(D0JU
+0A(A BBBH \ 8 h BBB G(D0O
+0A(A BBBB
+0A(A BBBC 4 T9 k BAI D0\
+ DABA L 9 xk[ g BBE B(G0D8R
+8A0A(B BBBG L 9 n ; BBB B(A0D8K
+8A0A(B BBBG , ,: o> AID W
+DAA L \: o7 BBE D(D0J2
+0A(A BBBA \ : w u BBB G(D0O
+0A(A BBBB
+0A(A BBBC 4 ; (z 8 BAI D0\
+ DABA L D; z[ BBE B(G0D8R
+8A0A(B BBBG L ; } BBB B(A0D8K
+8A0A(B BBBG , ; > AID W
+DAA T < d BBH G(A0A8J
+8D0A(B BBBI $= 8 AG MD T < 8d BBH G(A0A8J
+8D0A(B BBBI = P AG MD T = PL o BBH G(D0A8J
+8D0A(B BBBJ > H AO T |= HL g BBH G(D0A8J
+8D0A(B BBBJ > @ AO > %. K^ L > ( BBB B(A0D8D8A0A(B BBB L ? ؒ ADD e
CAF s
ACA V
-CAF JCA L , ADD e
+CAF JCA L d? h ADD e
CAF
ACA V
-CAF JCA L | 8 ADD e
+CAF JCA L ? ADD e
CAF
ACA V
-CAF JCA L ̺ BBB B(A0D8Dp8A0A(B BBB $ 8^ AG b
-AA 4 p x BHE ^
-ABH 4 x BHE a
-ABM \ DX
-D L Ի BBE B(A0A8D`
-8A0A(B BBBA D $ ^ BBB D(A0D@0A(A BBB T ܻ M w BIB B(A0D8G
-8D0A(B BBBA L 4 w BBJ B(A0A8D
-8D0A(B BBBE T < mw BBE B(D0A8G
-8D0A(B BBBB L l BBB E(A0A8G
-8A0A(B BBBH L , h w BBB B(D0D8J
-8D0A(B BBBF T | 3# w BBG H(A0A8J
-8D0A(B BBBD d ഉ. K^ 4 H x BHE ^
-ABH 4 $ `I [x BHE ^
-ABH 4 \ I 'x BHE ^
-ABH 4 pJ w BHE a
-ABM 4 ̾ K w BHE a
-ABM 4 K w BHE a
-ABM ̿ 2\\ AG RAL \ L 7w BBB B(A0D8Ol
-8A0A(B BBBF 4 hM w BAI f
-ABA 4 M v BAI f
-ABA < [D BBD A(D@0(A ABB T \ M }v BIB E(A0A8D
-8D0A(B BBBA T R= Vv BIB E(A0A8G
-8D0A(B BBBA T W /v BBH K(A0A8J
-8A0A(B BBBJ T d `]2 v BBB B(G0A8J
-8D0A(B BBBF T He Uv BBG B(G0A8J]
-8D0A(B BBBG а. K^ L , ku v BBB B(A0I8Dp
-8D0A(B BBBF L | (mu v BBB B(A0I8Dp
-8D0A(B BBBF L Xnu _v BBB B(A0I8Dp
-8D0A(B BBBF L ou +v BBB B(A0I8Dp
-8D0A(B BBBF D Y BBB D(A0D@0A(A BBB T ppl u BIE B(A0A8DY
-8D0A(B BBBA T ul }u BIE B(A0A8DY
-8D0A(B BBBA T d zl Ku BIE B(A0A8DY
-8D0A(B BBBA T l u BIE B(A0A8DY
-8D0A(B BBBA . K^ , \ [ t AID t
-DAA D PY BBB D(A0D@0A(A BBB T \ 7t BIE B(A0A8DI
-8D0A(B BBBA T , t BDB E(G0A8G3
-8D0A(B BBBD . K^ , D \ t < BBA A(D@
-(A ABBK < BBA A(DPC
-(A ABBE D .Y BBB D(A0D@0A(A BBB T r BIB E(A0A8D6
-8D0A(B BBBA T X r BIB E(A0A8D6
-8D0A(B BBBA L t h r BBB B(D0A8R
-8A0A(B BBBK , > r AID W
-DAA L h vr BBB B(D0A8R
-8A0A(B BBBK , D > yr AID W
-DAA T t Yr BBE B(D0A8G
-8D0A(B BBBG T ?r BBE B(D0A8G
-8D0A(B BBBG T $ %r BBE B(D0A8G
-8D0A(B BBBG T | H5 r BBG B(A0G8J
-8D0A(B BBBG T 05 Gs BBG B(A0G8J
-8D0A(B BBBG H. K^ 4W DN
-E A $ ^ AG b
-AA < BEA A(D@
-(A ABBA < \ BEA A(D@
-(A ABBA L p BEA A(D0H
+CAF JCA 4 L? BAI w
+ABA L <@ BBB B(A0D8Dp8A0A(B BBB $ @ ^ AG b
+AA 4 ? n BHE l
+ABB 4 4@ P : BHE o
+ABO $A \ DX
+D L DA 8 BBE B(A0A8D`
+8A0A(B BBBA , A ~- AAG0!AA L A h f BIB A(D0D
+0D(A BBBA L \A ; BBB B(D0A8GT
+8D0A(B BBBK T A X< BBE B(D0A8G
+8D0A(B BBBB L B @ BBB E(A0A8G
+8A0A(B BBBH L TB BBB B(D0D8J
+8D0A(B BBBF T B ĺ: Y BHG B(D0A8J
+8D0A(B BBBF C . K^ , C pٺ AID g
+DAA , DC ٺ AID g
+DAA 4 tC 0ں BHE r
+ABD 4 C ں x BHE r
+ABD 4 C `ۺ D BHE r
+ABD 4 D ۺ BHE u
+ABI 4 TD ܺ BHE u
+ABI 4 D Hݺ BHE u
+ABI |E ~\ AG RAL D ݺ T BBB B(A0D8GT
+8D0A(B BBBK , E ~- AAG0!AA < dE ߺ BHD Dq
+ DABA < E ' BHD D
+ DABA T E 7 BEB E(G0A8J
+8A0A(B BBBD T <F BGB B(G0A8J
+8D0A(B BBBJ T F 5 BBE G(G0A8J
+8D0A(B BBBG G . K^ L G x V BBB B(A0I8Dp
+8D0A(B BBBE L TG x " BBB B(A0I8Dp
+8D0A(B BBBE L G 0x BBB B(A0I8Dp
+8D0A(B BBBE L G `x BBB B(A0I8Dp
+8D0A(B BBBE , H ~- AAG0!AA < tH ` U V BIA D(Dp
+
+(D ABBI < H U 0 BIA D(Dp
+
+(D ABBI < H U
+ BIA D(Dp
+
+(D ABBI < 4I U BIA D(Dp
+
+(D ABBI ,J . K^ DJ \J , I [ v AID t
+DAA , J ~- AAG0!AA 4 J E & BHD D`
+ DABK T TJ B BDB E(G0A8G3
+8D0A(B BBBD dK . K^ |K K K K < K BBA A(D@
+(A ABBK < L @ BBA A(DPC
+(A ABBE , \L ~- AAG0!AA 4 K 5 BHD D`
+ DABG 4 L 5 BHD D`
+ DABG L DL h BBB B(D0A8R
+8A0A(B BBBK , L $> AID W
+DAA L L $h BBB B(D0A8R
+8A0A(B BBBK , M @'> AID W
+DAA T DM P' BBE B(D0A8G
+8D0A(B BBBG T M , BBE B(D0A8G
+8D0A(B BBBG T M 0 q BBE B(D0A8G
+8D0A(B BBBG T LN 55 W BBG B(A0G8J
+8D0A(B BBBG T N Gm BGB B(A0G8Jr
+8D0A(B BBBB O x. K^ O Լ~ DN
+E A $ O pV^ AG b
+AA < P V BEA A(D@
+(A ABBA < TP XW BEA A(D@
+(A ABBA P X L P Zp BEA A(D0H
(A ABBE D
-(A ABBA T t ;s BEE E(D0A8G
-8D0A(B BBBE T , !s BBE B(D0A8G
-8D0A(B BBBK 4 J BDA q
+(A ABBA T DP @Z BEE E(D0A8G
+8D0A(B BBBE T P h_, e BBE B(D0A8G
+8D0A(B BBBK 4 Q @dJ BDA q
ABE A
-ABA d g BLB B(A0A8D@q
+ABA d Q Xdg BLB B(A0A8D@q
8A0A(B BBBE
-8A0A(B BBBF < T TD BBD A(D@0(A ABB T 'r BIB E(A0A8D6
-8D0A(B BBBA < BBG A(G@
-(A ABBF L , ~ BLB H(A0D8D@
-8A0A(B BBBD < | BBG A(G@
-(A ABBF L m BBB H(A0D8DP
-8A0A(B BBBA T | p BBB B(H0A8J
+8A0A(B BBBF , LR n~- AAG0!AA 4 Q 0e5 { BHD D`
+ DABG < R 8h BBG A(G@
+(A ABBF L R i~ BLB H(A0D8D@
+8A0A(B BBBG < DS 8j BBG A(G@
+(A ABBF L S jm BBB H(A0D8DP
+8A0A(B BBBA T S l = BBB B(H0A8J
"
-8A0A(B BBBA , P> r AID W
-DAA L `d q BBB E(D0D8J
-8A0A(B BBBA T T q BBE B(A0H8J
-8A0A(B BBBB L < BLB H(A0D8DPV
-8A0A(B BBBG T (
- q BEE B(A0A8N
-8A0A(B BBBB T T q BBB G(D0A8M
-8D0A(B BBBC < . K^ T P l H T P i q BBB B(A0D8R
-8A0A(B BBBF , L h!> q AID W
-DAA D ZQ BBB D(A0D@0A(A BBB T 0! "q BIB E(A0A8D6
-8D0A(B BBBA T h% p BBB G(A0A8J<
-8A0A(B BBBA `. K^ 1 4 1 , 1[ Oq AID t
-DAA D | Q BBB D(A0D@0A(A BBB T 4 1L p BIE B(A0A8D9
-8D0A(B BBBA $ 6V ADD JAAd 71 p BHB B(D0A8Dpv
+8A0A(B BBBA , tS > s AID W
+DAA L S Ёd S BBB E(D0D8J
+8A0A(B BBBA T S X BBE B(A0H8J
+8A0A(B BBBB L U ( BLB H(A0D8DPV
+8A0A(B BBBG T T
+ BEE B(A0A8N
+8A0A(B BBBB T T @" BBB G(D0A8M
+8D0A(B BBBC V X. K^ V 4V LV ~\ AG RAT U BBB B(A0D8R3
+8A0A(B BBBA , V (> AID W
+DAA , V P~- AAG0!AA 4 lV 5 BHD D`
+ DABG T V r
BBG B(A0A8J<
+8A0A(B BBBA W
. K^ W W L DW BBB J(A0A8G
+8A0A(B BBBG , LX &~- AAG0!AA D W 0 BIA D(De
+(D ABBA T X H BBB G(A0A8J
+8A0A(B BBBA Y . K^ 4Y Ի LY Ի , X Ի[ 7 AID t
+DAA , Y ~- AAG0!AA 4 Y Ի5 BHD D`
+ DABG $ Y V ADD JAAd lY 1 BHB B(D0A8Dpv
8C0A(B BBBG {
-8D0A(B BBBA T 7 +p BBB E(I0A8K}
-8D0A(B BBBF d t : p BEB B(D0A8Gp
+8D0A(B BBBA T Y ػ ? BBB E(I0A8K}
+8D0A(B BBBF d ,Z ڻ BEB B(D0A8Gp
8E0A(B BBBC
-8E0A(B BBBA T 8;l o BDB B(A0A8Gp
-8A0A(B BBBA Н. K^ 8I 0I P\ AG RAT I o BBB B(A0I8G
-8D0A(B BBBE D L BBB B(A0A8D`8A0A(B BBB (Mo T T M wo BBB E(D0A8G
-8D0A(B BBBE T [X o BBA D(G`
-(A ABBK T
-(D ABBA T \ io BBB B(D0A8G
-8A0A(B BBBA D rO BBB D(A0D@0A(A BBB T h n BIB E(A0A8DV
-8D0A(B BBBA T xl n BBB B(D0A8M>
-8D0A(B BBBK . K^ , D \ t Ё ȁ 4 L ȁ d Ё | $ < T l Ё ȁ , D " \ ȁ t Ё" " " ( 0" H 4 P" L h d p" | " " Ȃ Ђ" $ < B A@ \ (B A@ | X8 Av x. Al . Al . Al . Al B A@ < . Al \ 8 Av | B A@ H8 Av h p h p D h h BBE A(A0
-(A BBBA L d Є AAG k
+8E0A(B BBBA T Z ܻ BDB B(A0A8Gp
+8A0A(B BBBA [ H
+. K^ [ P [ H [ ~\ AG RAT T[ 0 # BBB B(A0I8G
+8D0A(B BBBE D d\ BBB B(A0A8D`8A0A(B BBB \ @o T \ E BBE B(D0A8G
+8D0A(B BBBE T d\ BBB B(D0A8G
+8A0A(B BBBA , t] ~- AAG0!AA < \
+U BIA D(Dp
+
+(D ABBI T ,] (
g BBB B(A0G8J?
+8D0A(B BBBJ <^ . K^ T^ $ l^ $ ^ $ ^ $ ^ $ ^ $ ^ x$ ^ p$ _ h$ ,_ `$ D_ X$ \_ P$ t_ H$ _ P$ _ X$ _ `$ _ h$ _ p$ ` x$ ` $ 4` $ L` $ d` $ |` $ ` $ ` $ ` x$ ` p$ ` h$ a `$ $a X$ <a P$ Ta H$ la @$ a 8$ a @$" a X$ a `$" a x$ a $" b $ ,b $" Db $ \b $" tb $ b $" b $ b %" b % b %" c 8% c @%" 4c X% Lc `%" dc x% |c % c %B A@ c %B A@ c %8 Av c &. Al d &. Al 4d (&. Al Td 8&. Al td H&B A@ d x&. Al d &8 Av d &B A@ d &8 Av e & ,e ' De & \e ' L te & AAG k
CAH |
CAH R
-CAB JCA L 0 AAG k
+CAB JCA L e X' AAG k
CAH r
CAJ R
-CAJ JCA L AAG k
+CAJ JCA L f ' AAG k
CAH r
CAJ R
-CAJ JCA L T AAG k
+CAJ JCA L df ( AAG k
CAH r
CAJ R
-CAJ JCA L P AAG k
+CAJ JCA L f x( AAG k
CAH r
CAJ R
-CAJ JCA L AAG k
+CAJ JCA L g ( AAG k
CAH F
CAF R
-CAJ JCA L D AAG k
+CAJ JCA L Tg H) AAG k
CAH r
CAJ R
-CAJ JCA L AAG k
+CAJ JCA L g ) AAG k
CAH |
CAH R
-CAB JCA L AAG k
+CAB JCA L g * AAG k
CAH F
CAF R
-CAJ JCA L 4 P AAG k
+CAJ JCA L Dh x* AAG k
CAH |
CAH R
-CAB JCA R " 4 t BJD G(D@R(A ABB P 4 H BLD G AAB T , d AID c
-DAA , x xd AID c
-DAA , < ؋ Yd AID c
-DAA , l 8 :d AID c
-DAA , d AID c
-DAA 4 \ | BLD I\ AAB L @ BHB J(D0A8DPe
-8A0A(B BBBB < BLA "
+CAB JCA 4 h *t BJD G(D@R(A ABB h + h +R h `+ i X+" 4 ,i p, BLD G AAB di H- , h @- AID [
+DAA , h - AID [
+DAA , $i . AID [
+DAA , Ti `. _ AID [
+DAA , i . ? AID [
+DAA , i / AID [
+DAA 4 j /| BLD I\ AAB L j 0 BHB J(D0A8DPe
+8A0A(B BBBB $k 3 < <k 3 BLA "
ABD q
-ABD < < BLA
-ABH N
-ABG | X P2 x p5 " < BLA "
+ABD < |k p5 BLA
+ABG N
+ABG k 8 k 82 k : l 9" < l ; BLA "
ABD q
-ABD $ 4 ^ AG b
-AA T H a BBE E(D0A8G
-8D0A(B BBBI L $ Y a BBB E(D0A8Lp
-8D0A(B BBBG D BBB D(A0D@
-0A(A BBBA 4 Hw ;a BHA GP
- DABA 4 w a BHA GP
- DABA 4 , ئw ` BHA GP
- DABA 4 d w ` BHA GP
- DABA 4 hw ` BHA GP
- DABA 4 w ` BHA GP
- DABA 4 w ` BHA GP
- DABA 4 D @w b` BHA GP
- DABA 4 | w C` BHA GP
- DABA 4 Яw $` BHA GP
- DABA L l ` BBL E(A0A8Gy
-8D0A(B BBBD L < 8l _ BBL E(A0A8Gy
-8D0A(B BBBD L Xl _ BBL E(A0A8Gy
-8D0A(B BBBD L xl _ BBL E(A0A8Gy
-8D0A(B BBBD D , i_ BBE D(K0J`
-0D(A BBBC L t 5 M_ BBB B(A0I8G
-8D0A(B BBBD L 5 Q_ BBB B(A0I8G
-8D0A(B BBBD L + U_ BBB B(A0I8G
-8D0A(B BBBA L d + A_ BBB B(A0I8G
-8D0A(B BBBA L + -_ BBB B(A0I8G
-8D0A(B BBBA T w _ BBG B(A0G8J0
-8D0A(B BBBA T \ W ^ BDB B(A0G8G
-8D0A(B BBBA T W ^ BDB B(A0G8G
-8D0A(B BBBA T W ^ BDB B(A0G8G
-8D0A(B BBBA T d W ^ BDB B(A0G8G
-8D0A(B BBBA T o^ BBB G(A0G8JG
-8D0A(B BBBA T W M^ BDB B(A0G8G
-8D0A(B BBBA T l w +^ BBG B(A0G8J0
-8D0A(B BBBA T @ ^ BBB G(A0G8JG
-8D0A(B BBBA T xw ] BBG B(A0G8J0
-8D0A(B BBBA . K^ L AAG ]
+ABD $ \l p=^ AG b
+AA l = D l >E BBJ G(D0DP
+0A(A BBBD D ,l Aw BEJ D(D0G
+0D(A BBBE D tl Bw BEJ D(D0G
+0D(A BBBE L l C~ M BBB E(D0A8Lp
+8D0A(B BBBH $ m (E AAG0AA4 4m E BHA GPt
+ DABC 4 lm xG BHA GPt
+ DABC 4 m @I BHA GPt
+ DABC 4 m K BHA GPt
+ DABC 4 n L | BHA GPt
+ DABC 4 Ln N ^ BHA GPt
+ DABC 4 n `P @ BHA GPt
+ DABC 4 n (R " BHA GPt
+ DABC 4 n S BHA GPt
+ DABC 4 ,o U BHA GPt
+ DABC 4 do W BHA GP{
+ DABD L o XY8 BBB B(A0I8G
+8D0A(B BBBH L o H\8 BBB B(A0I8G
+8D0A(B BBBH L <p 8_' BBB B(A0I8G
+8D0A(B BBBA L p a' BBB B(A0I8G
+8D0A(B BBBA L p b' BBB B(A0I8G
+8D0A(B BBBA L ,q dl v BBL E(A0A8Gy
+8D0A(B BBBD L |q el O BBL E(A0A8Gy
+8D0A(B BBBD L q gl ( BBL E(A0A8Gy
+8D0A(B BBBD L r 8hl BBL E(A0A8Gy
+8D0A(B BBBD D lr Xi BBE D(K0J`
+0D(A BBBC T r ng BBG B(A0G8J"
+8D0A(B BBBA T s pG BDB B(A0G8G
+8D0A(B BBBA T ds rG z BDB B(A0G8G
+8D0A(B BBBA T s tG X BDB B(A0G8G
+8D0A(B BBBA T t vG 6 BDB B(A0G8G
+8D0A(B BBBA T lt xy BBB G(A0G8J1
+8D0A(B BBBA T t zG BDB B(A0G8G
+8D0A(B BBBA T u |g BBG B(A0G8J"
+8D0A(B BBBA T tu BBB G(A0G8J9
+8D0A(B BBBA T u 8g BBG B(A0G8J"
+8D0A(B BBBA v p~. K^ L v 8 AAG ]
CAF k
ACA R
-CAJ JCA $ l ^ AG b
-AA D BBE A(A0D@n
-0A(A BBBA D BBB D(A0D@
-0A(A BBBA $ , L < ( BBB B(D0A8DP
-8A0A(B BBBA AL d BBB B(A0A8D`
+CAJ JCA $ Dw x^ AG b
+AA D lw BBE A(A0D@n
+0A(A BBBA D w H BBB D(A0D@
+0A(A BBBA L w h BBB B(D0A8DP9
+8A0A(B BBBA Lx AL lx , T w BBE E(D0A8G
+8D0A(B BBBE d x 0 BBB B(A0A8D`
8A0A(B BBBE d
-8A0A(B BBBA T ` [ BBE B(D0A8G
-8D0A(B BBBC L i [ BBE E(D0A8Dp2
-8D0A(B BBBJ D BBB D(A0D@
-0A(A BBBA T t p [ BIB E(A0A8DV
-8D0A(B BBBA T Z BIB E(A0A8D^
-8D0A(B BBBA T $ Z BBB B(D0A8J
-8A0A(B BBBA , | Xq Z BDA TAT < < BBG A(G@
-(A ABBG T h .Z BBB E(A0A8J>
-8A0A(B BBBA L BOB B(A0A8Dp
-8A0A(B BBBJ T P Z BBB B(A0D8Gz
-8A0A(B BBBA D & Y BDA D@
+8A0A(B BBBA T x @ BBE B(D0A8G
+8D0A(B BBBH L x pi & BBE E(D0A8Dp2
+8D0A(B BBBJ , y l~- AAG0!AA 4 dy ` BHD D`
+ DABD 4 y BHD D`
+ DABE T y P ~ BBB B(D0A8J
+8A0A(B BBBA , ,z q e BDA fAB < { ؤ BBG A(G@
+(A ABBG T z @ BBB E(A0A8J;
+8A0A(B BBBD L { BOB B(A0A8Dp
+8A0A(B BBBJ T D{ BBB B(A0D8G
+8A0A(B BBBA D { h& BDA D@
DABE L
- DABA L 4 Y BEB E(I0H8D@
-8D0A(B BBBA T Y BBB B(A0D8G\
-8A0A(B BBBA T ` Y BBB B(A0D8G
-8A0A(B BBBA `|. K^ T L ) +Z BBB E(A0A8N@
-8A0A(B BBBK , P.> 9Z AID W
-DAA , `.> Z AID W
-DAA p. AU p.F $ .^ AG b
-AA D . BBB D(A0D@
-0A(A BBBA D X0Z SY BBE A(A0G@
-0A(A BBBC d p1 7Y BJE B(A0A8D@Y
-8C0A(B BBBB i8A0A(B BBB L \ 1t X BBB B(A0D8DP
-8A0A(B BBBA < 2 AL L 2 gX BEE B(A0D8G
-8A0A(B BBBA d x4" BBB B(A0A8DpO
+ DABA L { P BEB E(I0H8D@
+8D0A(B BBBA T 4| ༼ [ BBB B(A0D8G\
+8A0A(B BBBA T | hüe _ BBB B(A0D8G
+8A0A(B BBBH } ~. K^ T | hμ BBB E(A0A8N@
+8A0A(B BBBK , T} Ӽ> AID W
+DAA , } 0Ӽ> AID W
+DAA l~ @Ӽ AU ~ @ӼF $ ~ xӼ^ AG b
+AA D ~ Ӽ BBB D(A0D@
+0A(A BBBA D \~ (ռZ BBE A(A0G@
+0A(A BBBC d ~ @ּ BJE B(A0A8D@Y
+8C0A(B BBBB i8A0A(B BBB L ּ( s BBB B(D0A8DPf
+8A0A(B BBBA xؼ AL T | xؼ BEB B(D0A8G
+8A0A(B BBBA d ۼ" BBB B(A0A8DpO
8A0A(B BBBI j
-8A0A(B BBBA d @6r BBB B(A0A8Dp
+8A0A(B BBBA d ܼr BBB B(A0A8Dp
8A0A(B BBBI j
-8A0A(B BBBA D | X8 BBB D(A0D@
-0A(A BBBA T 4 9B W BIB B(A0D8Dg
-8D0A(B BBBA T <I V BIB B(A0D8Dn
-8D0A(B BBBA T ?2 V BBB B(A0A8J
-8A0A(B BBBA D < E8 V BFB A(D0J
-0A(A BBBB < H V BEA A(G@}
-(D ABBJ L `I V BBE D(C0Ju
-0D(A BBBF L L V BBE D(C0Ju
-0D(A BBBF d Oi BBB B(A0A8Dm
+8A0A(B BBBA , \ *~- AAG0!AA < Ԁ BIA D(Dp-
+(D ABBF < P BIA D(Dp4
+(D ABBG T T BBB B(A0A8J
+8A0A(B BBBA D X8 BFB A(D0J
+0A(A BBBB < P BEA A(G@}
+(D ABBJ L 4 0 BBE D(C0Ju
+0D(A BBBF L p BBE D(C0Ju
+0D(A BBBF d i BBB B(A0A8Dm
8A0A(B BBBA
-8A0A(B BBBG L S 7V BEB B(A0A8L
-8A0A(B BBBD L Z BEE B(A0A8D`
-8A0A(B BBBB , ] BDA n
-ABH , Ha5 T pa U BBB B(A0A8J
-8A0A(B BBBE T i U BBE E(D0A8D
-8D0A(B BBBI T d `l U BBE B(A0A8G
-8A0A(B BBBA T { TV BBB B(K0D8G
-8A0A(B BBBH s. K^ T , SV BBE B(A0A8N7
-8A0A(B BBBA , > 6V AID W
-DAA , > V AID W
-DAA t AU F AU F 0 A[ $ t 0 fU BFA D@ , , b D\
+8A0A(B BBBG L < # BEB B(A0A8L
+8A0A(B BBBD L D h BEE B(A0A8D`
+8A0A(B BBBB , BDA n
+ABH T BBB B(A0D8G
+8D0A(B BBBH T d BBE E(D0A8D
+8D0A(B BBBE T 8 BBE B(A0A8G
+8A0A(B BBBA T p! BBB B(K0D8G
+8A0A(B BBBH $ ~. K^ T &
+ BBE B(A0A8N7
+8A0A(B BBBA , ܅ H*> AID W
+DAA , X*> AID W
+DAA h* AU h*F , * AU L *F d * A[ $ ̆ * BFA D@ , @+b D\
H P
H Q
-G I , \ b D\
+G I , ܇ +b D\
H P
H Q
-G I < ( BHE m
+G I < +( BHE m
ABI A
-ABD , ( BHE u
-ABI L l r zT BBB B(E0A8J@
-8A0A(B BBBA $ L 8^ AG b
-AA < p^ T BDA n
+ABD , L ,( BHE u
+ABI L ć -r 1 BBB B(E0A8J@
+8A0A(B BBBA $ ̈ .^ AG b
+AA < < /^ BDA n
EBD C
-EBA $ $ b S A]
-B A L L ؉ S BBB B(A0A8J
-8D0A(B BBBA , z S BHE gAB \ ، AL 4 | ،J BDA o
+EBA $ | 8/b A]
+B A L / z BBB B(A0A8J
+8D0A(B BBBA , 01z Y BHE gAB ܉ 2 AL 4 2J BDA o
ABG A
-ABA d BLB B(A0A8Dq
+ABA T | 2 BBE E(A0A8G
+8D0A(B BBBB d 5 BLB B(A0A8Dq
8A0A(B BBBE
-8A0A(B BBBJ D BBB D(A0D@
-0A(A BBBA L " nR BIB B(A0D8DpL
-8D0A(B BBBA L $ g GR BBE B(D0D8Gp
-8A0A(B BBBD \ m BBD D(J@
+8A0A(B BBBJ $ ~ AAG0AA4 d = BHD D`
+ DABE L @ BBE B(D0D8Gp
+8A0A(B BBBD \ XFm BBD D(J@
(A ABBD T
(A ABBF _
-(A ABBK D R BBB D(A0J
+(A ABBK D L hG BBB D(A0J
-0D(A BBBJ L بP WR BEB E(A0D8DP
-8D0A(B BBBA d l ت9 #R BLB B(D0A8D@q
+0D(A BBBJ L 0OP BEB E(A0D8DP
+8D0A(B BBBA d 0Q9 BLB B(D0A8D@q
8A0A(B BBBJ
-8A0A(B BBBA L
- Q BLB B(D0D8D@
-8A0A(B BBBG L p BBD D(D0O
+8A0A(B BBBA L L R
+ BLB B(D0D8D@
+8A0A(B BBBG L T T BBD D(D0O
(A ABBK \
-(A ABBE < BAD
+(A ABBE < xV BAD
ABE
-ABJ T +Q BBB B(A0A8D
-8A0A(B BBBJ d BBB B(A0A8D7
+ABJ T , Z BBB B(A0A8D
+8A0A(B BBBJ d < `d BBB B(A0A8D7
8A0A(B BBBI l
-8A0A(B BBBA 4
- pJ BDA l
+8A0A(B BBBA 4 iJ BDA l
ABJ A
-ABA \ <
- % GLE B(A0A8DPV8A0A(B BBBHP T
- X O BBB B(A0D8G
-8A0A(B BBBA T d
- | P BBB E(A0A8G+
-8A0A(B BBBA L 8j. K^ T
- P BBB B(A0D8N
-8A0A(B BBBA , , (> P AID W
-DAA d 8 BEB B(A0A8D@
-8A0A(B BBBE I
-8A0A(B BBBA T O BFB B(A0D8JT
-8A0A(B BBBA d BEB B(A0A8D@
-8A0A(B BBBE I
-8A0A(B BBBA L
0 BBE B(A0A8D`
-8A0A(B BBBA L N BBE B(A0A8J
-8A0A(B BBBC < $
N BEA D(DP
-(D ABBC T d
D N BBB B(A0A8J
-8D0A(B BBBA L AX l A[ , b D\
+ABA \ i% GLE B(A0A8DPV8A0A(B BBBHP T k BBB B(A0D8G
+8A0A(B BBBA T xs| BBB E(A0A8G+
+8A0A(B BBBA ~. K^ T L z BBB B(A0D8N
+8A0A(B BBBA , > AID W
+DAA d 0 BEB B(A0A8D@
+8A0A(B BBBE
+8A0A(B BBBA T < ȄR ? BFB B(A0D8JT
+8A0A(B BBBA d L Ї BEB B(A0A8D@
+8A0A(B BBBF
+8A0A(B BBBA L x BBE B(A0A8D`
+8A0A(B BBBC L L ȋ BBE B(A0A8J
+8A0A(B BBBC < x BEA D(DP
+(D ABBC T ܑ 蒽 q BBB B(A0A8J
+8D0A(B BBBG ` AX ` A[ , , `b D\
H P
H Q
-G I , Hb D\
+G I , \ b D\
H P
H Q
-G I L \ r 1N BBB B(E0A8J@
-8A0A(B BBBA $ < ^ AG b
-AA L M BBB B(A0A8J
-8D0A(B BBBA , $ z M BHE gAB AL T t M aM BBB E(A0A8GI
-8A0A(B BBBI L r M BBE E(D0D8I{
-8D0A(B BBBI D BBB D(A0D@
-0A(A BBBA L d ` 6M BIB B(A0D8DpE
-8D0A(B BBBA L 0" M BIB B(A0D8DpL
-8D0A(B BBBA T L BBB B(D0A8JW
-8D0A(B BBBE T \ H L BBB B(A0A8J
-8A0A(B BBBA D L \ BLB B(A0A8D
-8A0A(B BBBA L X qL BEB B(D0D8G
-8A0A(B BBBE T l @ kL BBB B(A0D8G
-8A0A(B BBBI T `b. K^ T ?L BBB B(A0D8N
-8A0A(B BBBG , 4 ,> L AID W
-DAA , d ,> L AID W
-DAA T , L BBF A(A0
+G I L Ԓ r BBB B(E0A8J@
+8A0A(B BBBA $ ܓ ^ AG b
+AA L L H v BBB B(A0A8J
+8D0A(B BBBA , 蚽z U BHE gAB 8 AL T 8 BBB E(A0A8GQ
+8A0A(B BBBA L D r ? BBE E(D0D8I{
+8D0A(B BBBI $ L ~ AAG0AA4 BHD D`
+ DABD 4 BHD D`
+ DABE T , BBB B(D0A8JW
+8D0A(B BBBE T 0 BBB B(A0A8J
+8A0A(B BBBA L д BLB B(A0A8D
+8A0A(B BBBA L D @ : BEB B(D0D8G
+8A0A(B BBBE T 4 BBB B(A0D8GL
+8A0A(B BBBF h~. K^ T ƽ BBB B(A0D8N
+8A0A(B BBBG , \ Hս> AID W
+DAA , Xս> v AID W
+DAA T hս V BBF A(A0
(A BBBG W
-(A BBBJ d | X- BBB B(A0A8DPB
+(A BBBJ d ̘ ֽ BBB B(A0A8DPB
8A0A(B BBBE L
-8A0A(B BBBA L T .h K BBE B(A0A8J
-8A0A(B BBBC D 3$ K BBE A(D0D`
-0D(A BBBD T 5% K BBB B(A0A8J
-8A0A(B BBBK L `; AAG ]
+8A0A(B BBBA L | h BBE B(A0A8J
+8A0A(B BBBC D ̘ ܽ$ BBE A(D0D`
+0D(A BBBD T % BBB B(A0A8J
+8A0A(B BBBK L $ AAG ]
CAF k
ACA R
-CAJ JCA $ $ ;^ AG b
-AA < L ;( BHE m
+CAJ JCA $ t ^ AG b
+AA < ( BHE m
ABI A
-ABD , <( BHE u
-ABI D = BBB D(A0D@
-0A(A BBBA 4 @? BDA D0
- AABE < @5 T A= 4 (A IJ SGB A(A0D 4 BX /J cGB A(A0D L L (C J BHB B(G0G8Ga
-8A0A(B BBBB \ I J BBB A(A0Do
+ABD , ܚ ( BHE u
+ABI D BBB D(A0D@
+0A(A BBBA 4 T BDA D0
+ AABE 5 H= 4 p SGB A(A0D 4 < HX cGB A(A0D L t p BBB E(D0A8Sf
+8A0A(B BBBE \ ě @ BBB A(A0Do
0A(A BBBE i
-0E(A BBBA L K BOB A(A0]
+0E(A BBBA L ܜ p BOB A(A0]
(A BBBG Y
-(A BBBH T L hL I BBE E(D0A8G)
-8D0A(B BBBH < O I ACG@s
+(A BBBH T t # BBE E(D0A8G,
+8D0A(B BBBE < ̜ U ACG@s
DAE J
-DAA 4 t OJ BDA o
+DAA 4 ĝ xJ BDA o
ABG A
-ABA O FP
-A < < O I BEA D(G@)
-(D ABBK \ | (R H BBB D(D0J
+ABA AU
+A < d BEA D(G@)
+(D ABBK \ BBB D(D0J
0A(A BBBF
-0A(A BBBG T V/ H BBB B(A0A8J
-8A0A(B BBBA \ [ KOB B(A0A8Dpk8A0A(B BBBGp d $ _3 BOB B(A0A8D`E
+0A(A BBBG T `l BBB B(A0A8J
+8A0A(B BBBA \ x KOB B(A0A8Dpk8A0A(B BBBGp d t 3 BOB B(A0A8D`E
8A0A(B BBBF |
-8D0A(B BBBE L a BLB E(A0A8DpC
-8A0A(B BBBH T L xeR G BEB F(D0D8J
-8D0A(B BBBC T j% G BBB B(A0A8Ga
-8A0A(B BBBD L XvF BEE B(D0D8D`
-8A0A(B BBBF L Xw BEE B(G0D8G`C
-8A0A(B BBBC L x ;G BLB B(A0D8D`
-8A0A(B BBBD L | (~ BBD D(D0O
+8D0A(B BBBE L ܟ
BLB E(A0A8DpC
+8A0A(B BBBH T t p7 _ BEB F(D0D8J
+8D0A(B BBBC T ̟ X% M BBB B(A0A8Ga
+8A0A(B BBBD L ܠ 0"F BEE B(D0D8D`
+8A0A(B BBBF L , 0# BEE B(G0D8G`C
+8A0A(B BBBC L Ġ $ BLB B(A0D8D`
+8A0A(B BBBD L ̡ * BBD D(D0O
(A ABBK \
-(A ABBE T < R F BBB B(A0A8J
-8A0A(B BBBH D w G BBB A(D0J
-0D(A BBBD D #G BBB D(A0D
-0D(A BBBI L $ "G BEE E(A0D8D`
-8D0A(B BBBK V. K^ | 8} F BFE E(D0D8Gp
+(A ABBE T d + BBB B(A0D8G
+8A0A(B BBBF D 4 BBB A(D0J
+0D(A BBBD D p8 BBB D(A0D
+0D(A BBBD L L ; BEE E(A0D8G
+8D0A(B BBBA T ~. K^ | p?} BFE E(D0D8Gp
8A0A(B BBBF T
8A0A(B BBBB |
-8A0A(B BBBA T 8 |F BBE E(G0A8GJ
-8D0A(B BBBD D GBA A(G@H(A ABBH@ L < ؚ BEE E(D0A8G@p
-8D0A(B BBBF U. K^ 0 ( 0 8 ! @" ! X" D p kE BBE A(A0
-(A BBBA L ؛U 'E BBB B(A0A8D`
-8A0A(B BBBJ , <! [ D AID t
-DAA L ! AAG k
+8A0A(B BBBA T 4 pA s BBE E(G0A8GJ
+8D0A(B BBBD D D I GBA A(G@H(A ABBH@ L K BEE E(D0A8G@p
+8D0A(B BBBF ܤ ~. K^ hK `K $ hK < pK T xK" l K" , ̤ K[ b AID t
+DAA L KU B BBB B(A0A8D`
+8A0A(B BBBJ L N AAG k
CAH F
CAF R
-CAJ JCA L L" AAG k
+CAJ JCA L T XO AAG k
CAH F
CAF R
-CAJ JCA " " x\ AG RA " П5 D " BBB D(A0D@0A(A BBB T " C BIB E(A0A8D6
-8D0A(B BBBA T " QC BIB E(A0A8DN
-8D0A(B BBBA T T# 0 C BIB E(A0A8DF
-8D0A(B BBBA T # x B BIB E(A0A8DF
-8D0A(B BBBA T $ B BBB B(A0I8G
-8D0A(B BBBG T \$ 8 B BBB B(A0I8G
-8D0A(B BBBG T $ B BBB B(A0A8O
-8D0A(B BBBJ T % ( B BBB G(A0G8J
-8D0A(B BBBG T d% ` C BDB B(D0G8G
-8D0A(B BBBA T % ?C BBB G(A0G8J
-8D0A(B BBBG & O. K^ & / A D & BBE A(A0D@g
-0A(A BBBA D ' BBE A(A0D@o
-0A(A BBBA L & @r B BBB B(E0A8J@
-8A0A(B BBBA $ ' p^ AG b
-AA $ L' G pB A4
+CAJ JCA O O5 Ԧ (p~\ AG RA, dp~- AAG0!AA 4 l S5 BHD D`
+ DABG 4 VE BHD D`
+ DABK 4 ܦ YE BHD D`
+ DABK 4 \E | BHD D`
+ DABD T L _ ^ BBB B(A0I8G6
+8D0A(B BBBD T Pe l BBB B(A0I8G6
+8D0A(B BBBD T j^ z BBB B(A0A8O
+8D0A(B BBBG T T n s BBB G(A0G8J
+8D0A(B BBBE T uU BDB B(D0G8G
+8D0A(B BBBA T z BBB G(A0G8J
+8D0A(B BBBE ~. K^ D , ` BBE A(A0D@g
+0A(A BBBA D t 耾 BBE A(A0D@o
+0A(A BBBA L r BBB B(E0A8J@
+8A0A(B BBBA $ ^ AG b
+AA $ | 肾G d A4
K A
-A $ t' G LB A<
-E < ,( XD BBD A(D@0(A ABB T ' A BIB E(A0A8D
-8D0A(B BBBA T 4( A BEB B(H0A8G X
-8A0A(B BBBC , ( > B AID W
-DAA $ ( 7 B A#
-L A
-A < ( H gB BBA A(Gp
-(D ABBA T $) G KB BEB B(A0D8G+y
-8D0A(B BBBC T |) lC BBH B(F0A8J)
-8D0A(B BBBE T ) `1~ =D BBB G(A0D8M
-8D0A(B BBBA * K. K^ , * p40 AC
FK
- E $ + p5~ Al
+A $ G @ A<
+E , m~- AAG0!AA < BHD Dq
+ DABA T < 臾Z BEB B(H0A8G H
+8A0A(B BBBC , > AID W
+DAA $ ī A
+
+E A
+A < H o BBA A(Gp
+(D ABBA T , S BEB B(A0D8G+y
+8D0A(B BBBC T a BBG B(A0A8J)
+8A0A(B BBBB T ܬ hܾ~ BBB G(A0D8M
+8D0A(B BBBA ~. K^ , x߾0 AC
FK
+ E $ 4 x~ Al
C V
-J < ,+ 5 ADG v
+J < \ ADG v
GAF
-AAF l+ 8S L + `8+ ULB A(A0@(A BBBD0 L D+ @= C BBE D(A0G,
-0D(A BBBD L + @ B BBE D(A0G
-0D(A BBBK D + BU B BFB A(A0GP
-0A(A BBBD d ,, P9 B BBB B(A0A8G@s
+AAF S L h+ ULB A(A0@(A BBBD0 L L H BBE D(A0G,
+0D(A BBBD D u BFB A(A0GP
+0A(A BBBD d P9 BBB B(A0A8G@s
8A0A(B BBBB _
-8A0A(B BBBG T , W IB BBB B(A0A8J
-8A0A(B BBBK T , g <B BBB B(A0D8G~
-8A0A(B BBBA - H. K^ - 8x . 0x L . 8x BBB B(A0D8Dc8A0A(B BBB D - hy uA BBE A(A0
-(A BBBA L . y AAG e
+8A0A(B BBBG T L ( \ BBB B(A0A8J
+8A0A(B BBBK T p O BBB B(A0D8G~
+8A0A(B BBBA H~. K^ L ̰ BBE B(A0A8D
+8A0A(B BBBD L `" AAG e
CAF S
ACA R
-CAJ JCA / \ AG RAL . z @ BKB B(D0D8G
-8D0A(B BBBA L . {^ @ BEL B(A0A8G
-8A0A(B BBBH 4 4/ q @ BAI A
-ABA T l/ H d@ BBE B(D0A8G
-8D0A(B BBBG T / J@ BBE B(D0A8G
-8D0A(B BBBH T 0 ؈ 0@ BBE B(D0A8G
-8D0A(B BBBH T t0 @ BBE B(D0A8G
-8D0A(B BBBG D \1 BBB D(A0D@0A(A BBB T 1 ? BIB E(A0A8D6
-8D0A(B BBBA T l1 XE ? BBB E(A0A8G
-8D0A(B BBBF T 1 P8 /@ BBB G(A0G8J
-8D0A(B BBBC 2 (D. K^ 2 2 2 3 , 2 [ @ AID t
-DAA L 2 P @ BBB B(A0A8D`^
-8A0A(B BBBJ 3 N\ AG RA\ 3 BEB D(A0C
+CAJ JCA l Hh~\ AG RA4 " H BHE l
+ABB 4 H# BHE o
+ABO L D # BKB B(D0D8G
+8D0A(B BBBA L %^ BEL B(A0A8G
+8A0A(B BBBH 4 ( BAI w
+ABA T H) BBE B(D0A8G
+8D0A(B BBBG T t . l BBE B(D0A8G
+8D0A(B BBBH T ̲ 2 R BBE B(D0A8G
+8D0A(B BBBH T $ 7 8 BBE B(D0A8G
+8D0A(B BBBG T | h<b
BBB E(A0A8G
+8D0A(B BBBD , e~- AAG0!AA 4 PI5 BHD D`
+ DABG L < XL e BBB B(D0A8GT
+8D0A(B BBBK T Pn 3 BBB B(G0D8L
+8D0A(B BBBH ~. K^ b ̵ b b b , \ b[ AID t
+DAA L b BBB B(A0A8D`^
+8A0A(B BBBJ d~\ AG RA\ (f BEB D(A0C
(A BBBH n
-(A BBBK Q(A BBB< 3 @ @ BBA A(D0(A ABB \ d4 X BBD A(G0
+(A BBBK Q(A BBB< \ h BBA A(D0(A ABB \ T 8iX BBD A(G0
(A ABBB q
-(A ABBI d(G ABB < 44 g? BBA A(D0(A ABB D 5 J BBB D(A0D@0A(A BBB T 4 L > BIE B(A0A8D9
-8D0A(B BBBA L 5 > BBH B(D0A8JK
-8D0A(B BBBK L d5 0
k? BBH B(D0A8JK
-8D0A(B BBBK D6 @. K^ T 5 @ BBB G(A0G8G
-8D0A(B BBBD 6 @. K^ 6 8 6 0 D 6 81 BBB A(A0DP
-0A(A BBBE D D7 BBB D(A0D@0A(A BBB T 6 L @ BIE B(A0A8D9
-8D0A(B BBBA L T7 h ? BBB B(D0A8R
-8A0A(B BBBK , 7 > ? AID W
-DAA L 7 $ ? BBB B(D0D8G T
-8D0A(B BBBA 8 >. K^ T <8 h B BEE E(D0A8G
-8D0A(B BBBE T 8 % B BEE E(D0A8G
-8D0A(B BBBE T 8 *+ B BBB B(A0D8OC
-8D0A(B BBBD 9 =. K^ , \9 HV[ ;E AID t
-DAA D : BBB D(A0D@0A(A BBB T 9 0VL D BIE B(A0A8D9
-8D0A(B BBBA : =. K^ : [ : [ ; [ ; [ D 4; BBB D(A0D@0A(A BBB T : Z C BIB E(A0A8D6
-8D0A(B BBBA T D; _ C BIB E(A0A8D6
-8D0A(B BBBA L ; 8c }C BBB J(A0A8G
-8D0A(B BBBA L ; f zC BBB J(A0A8G
-8D0A(B BBBA T << i wC BBE B(D0A8G
-8D0A(B BBBB T < n ]C BBE B(D0A8G
-8D0A(B BBBB T < hs| CC BBE E(D0A8G
-8D0A(B BBBF = x" = yr > { O_ L = { B BBH B(A0D8G
-8D0A(B BBBE $ t> P TAA L > H _C BBH B(A0D8G
-8D0A(B BBBE > 9. K^ ? AQ $? b\ AG RA$ D? AG
-AA $ l? x AG
-AA D ? N BBB D(A0D@0A(A BBB T L? L C BIE B(A0A8D9
-8D0A(B BBBA T ? B BBB B(A0I8G
-8D0A(B BBBK @ 7. K^ $ @ p^ AG b
-AA L <@ B BBE B(A0D8G
-8D0A(B BBBF A 87. K^ , 4A Ыj ADG`
-AAH dA |A Y l A ` B BBB B(D0A8G
+(A ABBI d(G ABB < 8l W BBA A(D0(A ABB , c~- AAG0!AA 4 l hl5 BHD D`
+ DABG L poe
BBH B(D0A8JK
+8D0A(B BBBK L |
BBH B(D0A8JK
+8D0A(B BBBK `~. K^ T \ u O BBB G(A0G8G
+8D0A(B BBBD l ~. K^ Ț D Ț BBB A(A0D@
+0A(A BBBB , b~- AAG0!AA 4 t 5 W BHD D`
+ DABG L h 9 BBB B(D0A8R
+8A0A(B BBBK , > < AID W
+DAA L , (& BBB B(A0D8J T
+8D0A(B BBBA 4 ~. K^ T ʿ S BEE E(D0A8G
+8D0A(B BBBE T Ͽ 9 BEE E(D0A8G
+8D0A(B BBBE T D տ1 BBB B(D0A8OJ
+8D0A(B BBBE T ~. K^ , [ + AID t
+DAA , Za~- AAG0!AA 4 5 BHD D`
+ DABG ~. K^ 4 L d , | a~- AAG0!AA 4 `5 BHD D`
+ DABG 4 , h 5 BHD D`
+ DABG L d p BBB J(A0A8G
+8D0A(B BBBA L BBB J(A0A8G
+8D0A(B BBBA T BBE B(D0A8G
+8D0A(B BBBB T \ BBE B(D0A8G
+8D0A(B BBBB T | BBE E(D0A8G
+8D0A(B BBBF Ŀ #" L $ $% o BBH B(A0D8G
+8D0A(B BBBE , 3r $ D (5 TAA L 6 BBH B(A0D8G
+8D0A(B BBBE ~. K^ HE AQ ^_~\ AG RA$ (E AG
+AA $ < E AG
+AA , d J_~- AAG0!AA 4 hF5 BHD D`
+ DABG T pI q BBB B(A0I8G
+8D0A(B BBBK $ X~. K^ $ < O^ AG b
+AA L 8O BBE B(A0D8G
+8D0A(B BBBF ~. K^ , `Sj ADG`
+AAH T TY l t T BBB B(D0A8G
8A0A(B BBBD Z
-8A0A(B BBBD B 6. K^ B h 4B ` LB h dB p |B h B ` L B hg BEB B(A0A8J
-8A0A(B BBBA 4 lB @ BHE n
-ABH 4 B 0 @ BHE q
-ABM T B ؼ _@ BEB B(A0D8J
-8A0A(B BBBA C . T LC o@ BBB B(A0A8J
-8A0A(B BBBB 4D 4. K^ D LD
BBB B(A0A8
-0A(B BBBA D D
BBB B(A0A8
-0A(B BBBA < D h BBB A(A0
-(A BBBA < E BBB A(A0
-(A BBBA L \E BIB B(A0A8OA
-8A0A(B BBBA < E + > AF
Pf
- C
- A E R D@M F R D@M F HW D@R 4F X D@S LF , $ dF AA L F x BEE B(D0A8D@
-8A0A(B BBBF $ F 8< VAA 4 G P BHA I(
- ABBG , <G { BLI
-ABI $ lG HN
+8A0A(B BBBD @~. K^ Z Z Z [ Z , Z L D Zg BEB B(A0A8J
+8A0A(B BBBA 4 a N BHE n
+ABH 4 b BHE q
+ABM T L hd BEB B(A0D8J
+8A0A(B BBBA \ o. T o BBB B(A0A8J
+8A0A(B BBBB @~. K^ D w
BBB B(A0A8
+0A(B BBBA < , w BBB A(A0
+(A BBBA l pxR D@M xR D@M yW D@R HyX D@S y, $ y AA L 0| BEE B(D0A8D@
+8A0A(B BBBF $ \ }< VAA 4 BHA I(
+ ABBG , p{ BLI
+ABI $ HN
J W
-Y L G BRI B(A0D8G!
-8A0A(B BBBJ G ' G ( H w L ,H BIB B(A0A8J
-8A0A(B BBBF T G < BBE B(D0A8G
-8D0A(B BBBH T DH `+
- < BEE B(A0D8J
-8A0A(B BBBA L ,I 8 BIB B(A0A8Gd
-8A0A(B BBBA L |I # BIB B(A0A8Gd
-8A0A(B BBBA d I 5S BBB E(D0D8D
+Y L BRI B(A0D8G!
+8A0A(B BBBJ d Ȍ' | Xw L BIB B(A0A8J
+8A0A(B BBBF T D P BBE B(D0A8G
+8D0A(B BBBH T +
+ BEE B(A0D8J
+8A0A(B BBBA L BIB B(A0A8Gd
+8A0A(B BBBA L P BBB B(A0A8Gd
+8A0A(B BBBA d L S BBB E(D0D8D
8A0A(B BBBD
-8A0A(B BBBG L 4J 9 BBB B(A0A8Gd
-8A0A(B BBBA d J P@S BBB E(D0D8D
-8A0A(B BBBD
-8A0A(B BBBG L J HD BBB B(A0A8Gd
-8A0A(B BBBA d <K JS BBB E(D0D8D
-8A0A(B BBBD
-8A0A(B BBBG < K N 9 AC
BEO#
- A +
- G T TK pW b9 BBA A(Gp
-(A ABBE w
-(A ABBK 4 <L X8 AC
BGM
- H
- F L tL ]- BBE B(A0A8GK
-8A0A(B BBBG , L q( AC
BN
- E T dL r V8 BBA A(Gp
+8A0A(B BBBG L - BBE B(A0A8GK
+8A0A(B BBBG , ( AC
BN
+ E T | BBA A(Gp
(A ABBG u
-(A ABBE 4 LM XtX AC
BGM
+(A ABBE 4 X AC
BGM
J
- F T L x 7 BKE E(D0A8J
-8A0A(B BBBA L M ȇ- BBE B(A0A8GK
-8A0A(B BBBG , ,N ( AC
BN
- E D \N BBB B(A0A8
-0A(B BBBA < N ` BBB A(A0
-(A BBBA L N BEB B(A0A8Gy
-8A0A(B BBBA < N *6 AC
BEO+
- A
- G T N P 5 BBA A(Gp
-(A ABBG u
-(A ABBE 4 O ȾX AC
BGM
- J
- F T tO # m5 BEE E(D0D8M
-8A0A(B BBBG L \P Z BEB B(A0A8G3
-8A0A(B BBBG , P * AC
BN
- D L P BEB B(A0A8G
-8A0A(B BBBA < P x+
- U4 AC
BEO
+ F T ( BKE E(D0A8J
+8A0A(B BBBA L `Z BEB B(A0A8G3
+8A0A(B BBBG , l p* AC
BN
+ D L p BEB B(A0A8G
+8A0A(B BBBA < 4 +
+ o AC
BEO
A h
- J T P h $4 BBA A(Gp
+ J T t > BBA A(Gp
(A ABBE w
-(A ABBK < 4Q 3 AF
P
+(A ABBK < AF
P
F R
- A 4 R 8 AC
BGM
+ A 4 8 AC
BGM
H
- F T Q g3 BHE B(D0D8J,
-8A0A(B BBBG L R 0
J BEB B(A0A8G#
-8A0A(B BBBG , R 0"* AC
BN
- D T R 0# 2 BHE B(D0D8J,
-8A0A(B BBBG , lS * AF
K
- E T S + '2 BBB E(G0G8J9
-8A0A(B BBBJ S $. K^ T 4? $T @4? <T $. K^ S P4+ 1 Ai tT `4 < T X4 BEB D(A0M
-(A BBBF L <T 5 G1 BBB B(A0A8JS
-8D0A(B BBBD U (8 4U 8 LU (8 dU 8 T T 8s 0 BBB B(D0A8G
-8A0A(B BBBD $ DU @D 0 AG@q
-AA D lU D5 0 BIC Q0v
- MABK |
- CABF DV E \V E tV E V E V E V ET l g < DV E6 0 BBA A(G
-(A ABBG W 0". K^ ,W G DW G \W G tW G W G W G W G W G W G- X G- < W H~ . BBA D(D`
-(D ABBC \X HK tX @K X 8K AT X 8K DV < 4X @Kw X. BLD A(G@|
-(A ABBA $ Y K^ AG b
-AA L X K
. BBB F(A0D8G`
-8A0A(B BBBH T X xNS - BBE B(D0A8G
-8D0A(B BBBD L DY T - BBB B(A0A8JC
-8A0A(B BBBG $Z X
- , Y X# - }GA A(D $ lZ Y* ADG [AA T Z Y[ - BBE B(D0A8G
-8D0A(B BBBF < \Z ^ z- BBA A(D\
-(D AEFA < Z 8`= u- BBD D(D
-(A ABBA l Z 8a W- BBB E(A0A8G
+ F T D $ BHE B(D0D8J,
+8A0A(B BBBG , T + AF
K
+ E T , BBB E(G0G8J9
+8A0A(B BBBJ `~. K^ 5? 05? $ H~. K^ @5+ Ai \ P5 t H5 P5 X5? < 5 BEB D(A0M
+(A BBBF L D 7 BBB B(A0A8JS
+8D0A(B BBBD L P9 d H9 | P9 H9 T @9s U BBB B(D0A8G
+8A0A(B BBBD $ L hE AG@q
+AA D t E= t BLF K0
+ MABJ |
+ CABF t F F F F F FT l g D L O~D BBB A(D0G
+0A(A BBBA $ Fh D0
+A W
+I t (~. K^ G G G G G G G 4 G L H- d H- T 0H c BBB B(A0D8G<
+8D0A(B BBBC W W W AT , l N~{ AAD@rAA T XW DV < `Ww s BLD A(G@|
+(A ABBA $ W^ AG b
+AA L W
BBB F(A0D8G`
+8A0A(B BBBH T l ZS BBE B(D0A8G
+8D0A(B BBBD | `
+ L ` ۾ BBB B(A0A8JC
+8A0A(B BBBG , , d# ߾ }GA A(D $ e* ADG [AA T f BBE E(A0A8G
+8D0A(B BBBH < l BBA A(D\
+(D AEFA < m= BBD D(D
+(A ABBA l \ n BBB E(A0A8G
8A0A(B BBBK
-8A0A(B BBBB < L[ c - BBD D(DU
-(A ABBA T [ He - BBE B(D0A8G
-8A0A(B BBBA T [ f , BIB B(A0A8G
-8A0A(B BBBA 4 \ nR BDA r
+8A0A(B BBBB < xq 5 BBD D(DU
+(A ABBA T s 7 BBE B(D0A8G
+8A0A(B BBBA T d t BIB B(A0A8G
+8A0A(B BBBA 4 t h|R BDA r
ABL A
-ABA T t\ n% - BBE B(A0A8J
-8A0A(B BBBF T \ ~ - BBE B(D0A8G
-8D0A(B BBBH T $] p - BBE B(D0A8G
-8D0A(B BBBG L ^ 8 BBB B(A0A8Gp8A0A(B BBB 4 ] W- BHH k
-ABA ^ AL \ ^ h KLA A(G@(A ABBI@a
-(A ABBI 4 _ AAG d
+ABA T | C BBE B(D0D8J #
+8A0A(B BBBF T L BBE B(D0A8G
+8D0A(B BBBH T BBE B(D0A8G
+8D0A(B BBBG L BBB B(A0A8Gp8A0A(B BBB 4 L BHH k
+ABA < ` AL \ \ `h KLA A(G@(A ABBI@a
+(A ABBI 4 p AAG d
DAF K
-DAH T ^ k, BEB B(D0A8J
-8A0A(B BBBI l _ ` , KEB B(V0Q8L
-8A0A(B BBBE {CL ` BBB B(A0A8Gp8A0A(B BBB d d` BBB E(A0A8DP
+DAH T < BEB B(D0A8J
+8A0A(B BBBI l KEB B(V0Q8L
+8A0A(B BBBJ {CL Ф BBB B(A0A8Gp8A0A(B BBB d P BBB E(A0A8DP
8A0A(B BBBE !
-8A0A(B BBBF T <` x + BBB E(A0D8G
-8A0A(B BBBE L $a BBF E(A0D8DPV
-8D0A(B BBBE T ` P - BBB B(A0A8J[
-8A0A(B BBBA D <a , BBA D(G
-(A ABBH D a 0( , BBB A(D0J
-0A(A BBBI T a - BEA A(D0
+8A0A(B BBBF T ȩ BBB E(A0D8G
+8A0A(B BBBE L BBF E(A0D8DPV
+8D0A(B BBBE T d ' BBB B(A0A8J[
+8A0A(B BBBA D BBA D(G
+(A ABBH D ( BBB A(D0J
+0A(A BBBI T L + BEA A(D0
(D ABBK D
-(A ABBF l b py BBF D(A0G@I
+(A ABBF l \ y BBF D(A0G@I
0A(A BBBI w
0A(A BBBA D
-0F(A BBBG T b E' c, BBB B(A0A8G X
-8A0A(B BBBA L b x$ . BBB B(A0A8J l
-8A0A(B BBBF c X L Tc P / BEB E(I0H8D@
-8D0A(B BBBA \ c . BBB D(A0Da
+0F(A BBBG T $ w BBB B(A0A8G k
+8A0A(B BBBA L l ~ W BBB B(A0A8J l
+8A0A(B BBBF t L BEB E(I0H8D@
+8D0A(B BBBA \ $ p BBB D(A0Da
0D(A BBBE
-0C(A BBBB d AO d AP d d e p. K^ e DM <e Te le e e d $e % - BBB B(A0A8Dp
+0C(A BBBB < AO \ AP | ~. K^ DM x , p D x d % p BBB B(A0A8Dp
8D0A(B BBBE P
-8A0A(B BBBA L e R ?- BBE E(D0A8DM
-8D0A(B BBBA lf 8. K^ f ,ӈ\ AG RAT f { , BBE B(D0A8G
-8D0A(B BBBH T lf , BEE E(D0A8G
-8D0A(B BBBE L f h , BEB I(A0A8G}
-8D0A(B BBBK g AO T 4g 4 ^, BBB B(A0A8GK
-8A0A(B BBBJ L h ] BBB H(A0D8DP
-8A0A(B BBBG lh D ~
+8A0A(B BBBA L H R BBE E(D0A8DM
+8D0A(B BBBA ~. K^ , pA~\ AG RAT
+{ BBE B(D0A8G
+8D0A(B BBBH T H BEE E(D0A8G
+8D0A(B BBBE L D ph BEB I(A0A8G}
+8D0A(B BBBK L AO T 4 : BBB B(A0A8GK
+8A0A(B BBBJ L x(] BBB H(A0D8DP
+8A0A(B BBBG ) D ~
F b
-F h H. K^ h @ h 8 h 0 h ( i i 4i Li di |i i i i i i j $j <j Tj lj j j j j j j x k p ,k h Dk ` \k X tk P k H k @ k 8 k 0 k ( l l 4l Ll dl |l l l l l l m $m <m Tm lm m m m m m m x n p ,n h Dn ` \n X tn P n H n @ n 8 n 0 n ( o o 4o Lo do |o o o o o o p $p <p Tp lp p p p p p p x q p ,q h Dq ` \q X tq P q H q @ q 8 q 0 q ( r r 4r Lr ( dr 0 |r 8 r @ r H r P r X r ` s h $s p <s x Ts ls s s s s x s p s h t ` ,t X Dt P \t H tt @ t 8 t 0 t ( , AG bA t 8 * De t P * De u h " DN
-N A L ,u x AAG ]
+F 4 ~. K^ L ) d ) | ) ) ) ) ) ) ) $ ) < ) T ) l ) x) p) h) `) X) P) H) , @) D 8) \ 0) t () ) ) ) ) ) ( ( 4 ( L ( d ( | ( ( ( ( ( ( ( $ ( < ( T ( l ( x( p( h( `( X( P( H( , @( D 8( \ 0( t (( ( ( ( ( ( ' ' 4 ' L ' d ' | ' ' ' ' ' ' ' $ ' < ' T ' l ' x' p' h' `' X' P' H' , @' D 8' \ 0' t (' ' ' ' ' ' & & 4 & L & d & | & & & & & & & $ & < & T & l & & ' ' ' ' ' (' , 0' D (' \ ' t ' ' ' ' & & & & 4 & L & d &, AG bA &* De &* De '" DN
+N A L ' AAG ]
CAF [
ACA R
-CAJ JCA |u 6 Dq u 6 Dq u 6 Dq u 6 Dq u H 6 Dq u p 6 Dq v 6 Dq $v 6 Dq <v : Dk
-I A 4 \v Z BGA w
+CAJ JCA $ H'6 Dq < p'6 Dq T '6 Dq l '6 Dq '6 Dq (6 Dq 8(6 Dq `(6 Dq (: Dk
+I A 4 (Z BGA w
AEI A
-ABA $ v 0 ^ ADD NAA4 v h y BDD q
-AEO aAB 4 v y BDD q
-AEO aAB ,w
- Dw v fN \w X
- , v P g \ BHH j
-ABA w $ ,w $ YX
+ABA $ < (^ ADD NAA4 d )y BDD q
+AEO aAB 4 P)y BDD q
+AEO aAB )
+ )v fN )
+ , d )g 8 BHH j
+ABA L 0* $ (* YX
G z
-A w , lw g BHH j
-ABA ,x ( $ w YX
+A * , *g ͭ BHH j
+ABA * $ 4 * YX
G z
-A lx , w g BHH j
-ABA x $ <x N YX
+A (+ , t +g b BHH j
+ABA \ `+ $ X+ * YX
G z
-A x ! , |x !g BHH j
-ABA <y X! t Ty P!I DN
+A + , +g BHH j
+ABA + t +I DN
F H
H H
H H
H H
H H
@@ -13245,4317 +15649,5338 @@
H H
H H
H H
H H
H H
-A F y ("z cV y " y " z " ,z x" Dz p"z cV \z " tz " z " z " z "z cV z # z # { # { # 4{ #z cV L{ h# d{ `# |{ X# { P#z cV { # { # { # { # | #z cV $| $ <| # T| # l| # | #z cV | H$ | @$ | 8$ | 0$ | ($z cV } $ ,} $ D} $ \} x$ t} p$z cV } $ } $ } $ } $ } $z cV ~ % ~ % 4~ % L~ % d~ %z cV |~ h% ~ `% ~ X% ~ P% ~ H%z cV ~ % % $ % < % T %z cV l % % % % %z cV @& 8& 0& , (& , D &{ AAG r
-DAH 4 p& BGB A(A0D 4 8' BGB A(A0D < T (_ BJD D(G
-(D ABBC $ ) _ AFD@ $ ) C AFD@ $ ) ' AFD@ X*D BDA A(DL
+A F t ,z cV 0- (- - - -z cV x- p- 4 h- L `- d X-z cV | - - - - -z cV . . $ - < -z cV T X. l P. H. @. 8.z cV . . . . , .z cV D . \ . t . . .z cV 0/ (/ / / /z cV 4 x/ L p/ d h/ | `/ X/z cV / / / / /z cV $ 0 < 0 T / l / /z cV P0 H0 @0 80 00z cV 0 , 0 D 0 \ 0 t x0z cV 0 0 0 0 , 0{ AAG r
+DAH 4 d 1 BGB A(A0D 4 1 BGB A(A0D < 2_ i BJD D(G
+(D ABBC $ 3 ; AFD@ $ < (4 AFD@ $ d 4 AFD@ 4D BDA A(DL
(A ABBB l
(C ABBD m
(A ABBE }
(A ABBE }
(A ABBE }
(A ABBE }
-(A ABBE t - rEB B(A0A8Dt
-8A0A(B BBBA Pp T 4 P3 3 BBA A(Gl
+(A ABBE \ < 8 _ rEA A(Dx
+(A ABBE p T 8< ' BBA A(Gl
(A ABBA D
-(F ABBA T 4 BBA A(GZ
+(F ABBA T = BBA A(GZ
(A ABBC d
-(F ABBA T 6 BBA A(GZ
+(F ABBA T L ? BBA A(GZ
(A ABBC t
-(F ABBA L < 7 ZBA A(Db(A ABBL x9 4 p9 L h9 d `9 | X9 P9 H9 Ą @9 L L 89% ZBA A(Dj(A ABBN , ; D ; \ ; N D ; BEJ D(D0G
-0D(A BBBA D , H<w BEJ D(D0G
-0D(A BBBE $ =V ADD JAA< , = BBG A(G@
-(A ABBH l x>- < > BBG A(G@
-(A ABBH l 4 P? BBB B(A0D8D
+(F ABBA L @ ZBA A(Db(A ABBL `B XB PB HB @B $ 8B < 0B T (B L B% ZBA A(Dj(A ABBN D C C N D L C BEJ D(D0G
+0D(A BBBA D 0Ew BEJ D(D0G
+0D(A BBBE $ hFV ADD JAA< F BBG A(G@
+(A ABBH `G- < xG BBG A(G@
+(A ABBH l 8H BBB B(A0D8D
8A0A(B BBBH K
-8A0A(B BBBK < Bo \ BJD D(G
-(D ABBG L 0C / BEB E(I0H8D@
-8D0A(B BBBA D 4 DJ BBB D(G0G
-0A(A BBBE G $ G* De < G* De T G6 Dq l H6 Dq 0H6 Dq XH6 Dq H6 Dq ̈ H6 Dq H6 Dq H6 Dq I" DR
-J A 4 0I" DR
-J A 4 T @IZ BGA z
+8A0A(B BBBK < Jo P BJD D(G
+(D ABBG L L L # BEB E(I0H8D@
+8D0A(B BBBA D MJ BBB D(G0G
+0A(A BBBE P P* De P* De P6 Dq P6 Dq Q6 Dq , @Q6 Dq D hQ6 Dq \ Q6 Dq t Q6 Dq Q6 Dq R" DR
+J A R" DR
+J A 4 (RZ BGA z
AEF A
-ABA hI" DR
-J A xI" DR
-J A ̉ I" DR
-J A I" DR
-J A 4 IZ BGA z
+ABA PR" DR
+J A < `R" DR
+J A \ pR" DR
+J A | R" DR
+J A 4 RZ BGA z
AEF A
-ABA D I" DR
-J A d I" DR
-J A . K^ $ IE AAG yAA Ċ J AL J J HS J 4 J HS T J l J HS J J HS ċ J ܋ (J HS (J 0J HS 4 0J L 8J HS l 8J @J HS @J HJ HS ܌ HJ PJ HS PJ , XJ HS L XJ d `J HS `J hJ HS hJ ԍ pJ HS , d pJ
- YX
+ABA R" DR
+J A R" DR
+J A He~. K^ $ , RE AAG yAA T R AL t R R HS R R HS R S HS S 4 S HS T S l S HS S S HS S S HS S (S HS 4 (S L 0S HS l 0S 8S HS 8S @S HS @S HS HS HS , PS HS L PS d XS HS , XS YX
G
-B $ 0K , (K YX
+B T , T ߚ YX
G
-B l K , K YX
+B T , \ T YX
G
-B L , < L YX
+B D
+ U , U u YX
G
-B XM , PM L YX
+B
+ @V , 8V @ YX
G
-B D N , ̎ N YX
+B
+ V , 4
+ V YX
G
-B N , N YX
+B W , |
+ W ֙ YX
G
-B ԏ O , \ xO YX
+B d hX ,
+ `X YX
G
-B 8P , 0P x YX
+B Y , Y l YX
G
-B d P , P C YX
+B Y , T Y 7 YX
G
-B Q , 4 Q YX
+B < Z , Z YX
G
-B `R , | XR YX
+B H[ , @[ ͘ YX
G
-B < S , Đ S YX
+B \ , , [ YX
G
-B S , S o YX
+B
\ , t \ c YX
G
-B ̑ T , T T : AADP~
-AAJ 4 0Vu YKM
+B \
p] , h] . AADP~
+AAJ 4 _u YKM
HG
-HA 4 xW YKM
+HA 4 $
`` YKM
HG
-HA 4 Xk YKpM
+HA 4 \
ak ˗ YKpM
HG
-HA $ YE AAG yAA Z AL Z (Z HS < (Z T 0Z HS t 0Z 8Z HS 8Z ē @Z HS @Z HZ HS HZ 4 PZ HS T PZ l XZ HS XZ `Z HS Ĕ `Z ܔ hZ HS hZ pZ HS 4 pZ L xZ HS l xZ Z HS Z Z HS , L Z YX
+HA $ L bE AAG yAA t c AL c c HS c c HS c c HS < c T (c HS t (c 0c HS 0c 8c HS 8c @c HS @c 4 Hc HS T Hc l Pc HS Pc Xc HS Xc `c HS `c hc HS 4 hc L pc HS , pc YX
G
-B H[ , @[ ^ YX
+B 0d , (d R YX
G
-B T \ , ܕ [ ) YX
+B d , D d YX
G
-B \ , $ \ YX
+B , e , e YX
G
-B p] , l h] YX
+B t Xf , Pf YX
G
-B , (^ , ^ YX
+B g , g ~ YX
G
-B t ^ , ^ U YX
+B g , d g I YX
G
-B _ , D _ YX
+B L h , xh YX
G
-B P` , H` YX
+B 8i , 0i ߒ YX
G
-B L a , ԗ a YX
+B i , < i YX
G
-B a , a YX
+B $ j , j u YX
G
-B ܘ xb , d pb L YX
+B l `k , Xk @ YX
G
-B $ 0c , (c YX
+B l , l YX
G
-B l c $ cE AAG yAA d AL ̙ d d HS d d HS < d T d HS t d (d HS (d Ě 0d HS 0d 8d HS 8d 4 @d HS T @d l Hd HS Hd Pd HS ě Pd ܛ Xd HS Xd `d HS 4 `d L hd HS l hd pd HS pd xd HS D L xd ZIA A(GP
-(H ABBG $ f D f [ ZIA A(GP
-(H ABBG g D g , ZIA A(GP
-(H ABBG `i D l Xi ZIA A(GP
-(H ABBG D k D ̝ j ZIA A(GP
-(H ABBG l D , l ZIA A(GP
-(H ABBG 0n D (n p ZIA A(GP
-(H ABBG d o D o A ZIA A(GP
-(H ABBG ğ `q D L Xq ZIA A(GP
-(H ABBG $ r D r ZIA A(GP
-(H ABBG t D t ZIA A(GP
-(H ABBG 0v D l (v ZIA A(GP
-(H ABBG D w D ̠ w V ZIA A(GP
-(H ABBG py D , hy ' ZIA A(GP
-(H ABBG { 4 { YKM
+B l $ lE AAG yAA < l AL \ l t l HS l m HS m m HS m m HS < m T m HS t m m HS m (m HS (m 0m HS 0m 4 8m HS T 8m l @m HS @m Hm HS Hm Pm HS Pm Xm HS 4 Xm L `m HS D `m ~ ZIA A(GP
+(H ABBG o D o O ZIA A(GP
+(H ABBG p D t p ZIA A(GP
+(H ABBG t Hr D @r ZIA A(GP
+(H ABBG s D 4 s ZIA A(GP
+(H ABBG 4 xu D pu ZIA A(GP
+(H ABBG w D w d ZIA A(GP
+(H ABBG x D T x 5 ZIA A(GP
+(H ABBG T Hz D @z ZIA A(GP
+(H ABBG { D { ZIA A(GP
+(H ABBG x} D t p} ZIA A(GP
+(H ABBG t D y ZIA A(GP
+(H ABBG D 4 J ZIA A(GP
+(H ABBG 4 X D P ZIA A(GP
+(H ABBG 4 YKM
HG
-HA $ T P|E AAG yAA | x| AL x| | HS Ԣ | | HS | $ | HS D | \ | HS | | | HS | ̣ | HS | | HS $ | < | HS \ | t | HS | | HS ̤ | | HS | | HS < | T | HS t | | HS | ĥ | HS | | HS | 4 } HS T } l } HS } } HS Ħ } ܦ } HS D l }( - ZIA A(GP
-(H ABBG D D ̦ ~7 ZIA A(GP
-(H ABBG D , 7 ZIA A(GP
-(H ABBG D 7 ZIA A(GP
-(H ABBG d Є D Ȅ7 q ZIA A(GP
-(H ABBG Ĩ D L B ZIA A(GP
-(H ABBG $ ` D X7 ZIA A(GP
-(H ABBG P D H( ZIA A(GP
-(H ABBG 0 D l (7 ZIA A(GP
-(H ABBG D D ̩ ZIA A(GP
-(H ABBG 5 A/ \ L ؒ 7 BBB A(D0DPk
+HA $ 8E AAG yAA ` AL , ` D h HS d h | p HS p x HS x HS $ HS D \ HS | HS HS HS $ < HS \ t HS HS ȅ HS ! ȅ ! Ѕ HS <! Ѕ T! HS t! ! HS ! ! HS ! ! HS " 4" HS T" l" HS D ! ( ! ZIA A(GP
+(H ABBG " D 4" 7 ZIA A(GP
+(H ABBG 4# ؉ D " Љ7 Æ ZIA A(GP
+(H ABBG # ȋ D " 7 ZIA A(GP
+(H ABBG # D T# 7 e ZIA A(GP
+(H ABBG T$ D # 6 ZIA A(GP
+(H ABBG $ H D $ @7 ZIA A(GP
+(H ABBG % 8 D t$ 0( ZIA A(GP
+(H ABBG t% D $ 7 ZIA A(GP
+(H ABBG % D 4% z ZIA A(GP
+(H ABBG 4& L& 5 A/ \ % + BBB A(D0DPk
0C(A BBBD H
-0A(A BBBH D BBB A(A0G`
-0A(A BBBG Ж Ȗ < < i BHD A(D0A
-(A ABBF , q BDA X
-ABF < 8 L T 0 BBB B(A0A8D
-8A0A(B BBBA T Л) BBB B(A0A8G
-8A0A(B BBBA AL L BBF B(A0A8D@
-8D0A(B BBBB d l 8E BBB B(A0A8DP
+0A(A BBBH D & p BBB A(A0G`
+0A(A BBBG ' ,' D' < & y BED A(D0I
+(A ABBA , ' q BDA X
+ABF ' 0 ' }\ AG RAL ( BBB B(A0A8D
+8A0A(B BBBA T ' 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 B BBB J(D0D8D4
+8F0A(B BBBA l ( B BBB J(D0D8D4
8D0A(B BBBC i
-8A0A(B BBBE L W BFE B(H0D8M
-8D0A(B BBBD AW AP Ԯ 8 DN
-E A L d r BBB B(E0A8J@
-8A0A(B BBBA $ D ^ AG b
-AA d l BBB B(A0A8D`<
+8A0A(B BBBE L <) x~ ǃ BFB F(G0D8M
+8D0A(B BBBC D* \* AW |* AP * } DN
+E A L * r G BBB B(E0A8J@
+8A0A(B BBBA $ + ^ AG b
+AA d 4+ BBB B(A0A8D`<
8A0A(B BBBL d
-8A0A(B BBBA ԯ AL 7 dO DJ , DJ L d 9 AX
-G X < t BBA A(DU
-(E ABFA < 4 p
- R BBA A(D[
-(A ABBA @$ AX
-G C $ P T HC BBE E(D0F8J(
-8D0A(B BBBI < @; BBA A(G
-(E AFEA T D @ BBB B(A0A8Jc
-8A0A(B BBBG T BBB B(A0D8G
-8A0A(B BBBG L BEB E(D0D8G
-8A0A(B BBBB T D BBE E(D0A8G/
-8D0A(B BBBJ L { BEE B(A0D8GP
-8A0D(B BBBA T XV _ BBE B(A0A8G
-8A0A(B BBBC D D ` BBD D(G
+8A0A(B BBBA + AL + 7 dO + DJ + DJ , ,, 9 AX
+G X < + ́ BBA A(DU
+(E ABFA < + p
+ BBA A(D[
+(A ABBA , @$ AX
+G C , P T L, HC P BBE E(D0F8J(
+8D0A(B BBBI 4 \- @ ADG _
+CAA PFC < , ; BBA A(G
+(E AFEA T - BBB B(A0A8Jc
+8A0A(B BBBG T t- ߀ BBB B(A0D8G
+8A0A(B BBBG L - h BEB E(D0D8G
+8A0A(B BBBB L . 8 ր BEE E(D0D8G
+8D0A(B BBBD L l. x BEE B(A0D8GP
+8A0D(B BBBA T . HV BBE B(A0A8G
+8A0A(B BBBC D / P BBD D(G
-(D ABBJ L l BED A(G@x
+(D ABBJ L \/ BED A(G@x
(D ABBD V
-(D ABBA T ܳ H / BBB B(A0A8Ge
-8A0A(B BBBH T 4 { BEB E(A0A8G
-8A0A(B BBBA D S BEE A(A0
-(A BBBA < d 0 BFB A(A0S
-(A EBBG T 7 BBB B(A0A8Ii
-8A0A(B BBBJ ( AG [D T e
BEB E(D0D8G
-8D0A(B BBBA t AL L BEB E(I0H8D@
-8D0A(B BBBA < ` BBG A(G@
-(A ABBF D $ BBE B(A0D8D@8A0A(B BBB l h" DR
-J A , x BRE AE ( AL L ܷ ( BBE E(A0A8D@i
-8A0A(B BBBI l z / BBB E(I0D8DP
+(D ABBA T / 8 O BBB B(A0A8Ge
+8A0A(B BBBH T 0 BEB E(A0A8G
+8A0A(B BBBA , \0 xq s BDA fAB , 0 k G BGA ]AB < t1 BFB A(A0S
+(A EBBG L 0 BJE B(A0A8D@8A0A(B BBB T L1 X} BBB B(A0A8G
+8A0A(B BBBD \2 ( AG [D T 1 e
BEB E(D0D8G
+8D0A(B BBBA 2 AL L <2 k BEB E(I0H8D@
+8D0A(B BBBA < D3 ( BBG A(G@
+(A ABBF D 3 BBE B(A0D8D@8A0A(B BBB 3 0 " DR
+J A , 3 @ ' BRE AE 4 @
AL L <4 @
BBE E(A0A8D@i
+8A0A(B BBBI l 3 z ~ BBB E(I0D8DP
8D0A(B BBBA
-8D0A(B BBBA T
BEE B(A0D8J
-_
-8A0A(B BBBJ @. K^ 8 $ 0 < ( T l AV AV ܹ L O BEB B(A0A8D`
-8A0A(B BBBE l D x BBF D(A0G@F
+8D0A(B BBBA T D4 } ~ BEE B(A0D8J9
+8A0A(B BBBH T5 86~. K^ l5 0$ 5 ($ 5 $ 5 $ 5 $ 5 $ 5 $ AV 6 $ AV <6 $ < T6 $ BFB A(A0S
+(A EBBG L 6 (%O BEB B(A0A8D`
+8A0A(B BBBE l 6 ('x BBF D(A0G@F
0A(A BBBD v
0A(A BBBJ D
-0F(A BBBG L $ BBB A(D0OC
-0A(A BBBK `. K^ L
-w BBB E(A0A8DP[8A0A(B BBB L l BBB B(A0A8DT
-8A0A(B BBBA L BBB B(A0A8DI
-8A0A(B BBBG T | ! ^ BBE B(A0A8D
-8A0A(B BBBA d :\ AG RAT ,|
BBE E(D0A8G
-8D0A(B BBBE T L 1% BBA A(D^
+0F(A BBBG L T7 8) BBB E(H0A8DPI
+8A0A(B BBBE L 7 x+ BBE E(A0A8D@i
+8A0A(B BBBI t 7 H,< BFE D(D0Y
+(A BBBK }
+(A BBJD l
+(A BBBE F
+(A BBBK T 7 . ;| BBE B(A0H8G
+8A0A(B BBBA 8 2~. K^ L 8 }w BBB E(A0A8DP[8A0A(B BBB L ,9 p2 BBB B(A0A8DT
+8A0A(B BBBA L |9 6 BBB B(A0A8DI
+8A0A(B BBBG T 9 P<k { BBE B(A0A8D
+8A0A(B BBBA $: }\ AG RAT 9 HG| z BBE E(D0A8G
+8D0A(B BBBE T 9 pL% z BBA A(D^
(A ABBB P
-(A ABBA T 24 BBB B(D0D8G
-8A0A(B BBBK D 5 BFE D(A0G
-0D(A BBBH D Խ 9 GBA A(G@X(A ABBH@ H; ; BIB E(D0A8F`w
+(A ABBA T <: HM4 bz BBB B(D0D8G
+8A0A(B BBBK D : 0P <z BFE D(A0G
+0D(A BBBH D ; xT GBA A(G@X(A ABBH@ $; U y BIB E(D0A8F`w
8A0A(B BBBB
8A0A(B BBBA V
-8A0A(B BBBA T ? BEG E(A0A8J
-8D0A(B BBBA . K^ 0G , (G L D G AAG e
-CAF K
-ACI R
-CAJ JCA L G AAG e
-CAF C
-ACA R
-CAJ JCA $ G^ AG b
-AA L (H BBE A(D0
-(E BBED V
-(A BBEH L ̿ I BBE B(D0D8FP
-8D0A(B BBBA L XJ5 BBB E(D0A8Gp
-8D0A(B BBBG L l HK) R BBB B(D0G8Dc
-8D0A(B BBBI L (M BBB B(D0G8Dc
-8D0A(B BBBI L O BOB A(A0]
-(A BBBG Y
-(A BBBH < (P BBG A(G@
-(A ABBG L P n BJB A(A0W
-(A BBDH ^(A BBBL 8Q # BEB E(I0H8D@
-8D0A(B BBBA T < R BBB B(A0A8J
-
-8A0A(B BBBA T 0l 3 BBB B(A0D8J
-8A0A(B BBBJ | H. K^ , D BDD n
-DBA $ Ё^ AG b
-AA 4 \ BHE
-ABH 4 Ђ K BHE
-ABE 4 \ d BBD G(D0G(D ABBd Ѓ BBB B(A0A8DP
+8A0A(B BBBA T ; XZ y BEG E(A0A8J
+8D0A(B BBBA < 0/~. K^ , < aD BDD n
+DBA $ = a^ AG b
+AA 4 t< b cy BHE
+ABH 4 < b /y BHE
+ABE 4 = cT BBD D(D0z(D ABB d = c x BBB B(A0A8DP
8A0A(B BBBH D
-8A0A(B BBBA T l ( { BJB B(A0A8G@
-8D0A(B BBBJ D T J BBB D(A0D@
-0A(A BBBA L 6 BJB A(A0W
-(A BBDH ^(A BBB< 8 BBG A(G@
-(A ABBG < , BBG A(G@
-(A ABBG < l BBG A(G@
-(A ABBG H. K^ L 6vw BBB E(A0A8DP[8A0A(B BBB T BEB B(A0A8G
-8A0A(B BBBH l x AO , x AAG@s
-DAA AS , L AAG@w
-DAA X AS , X D AAG@w
-DAA \ ȯ L
BBE B(A0A8D`c
-8A0A(B BBBA < BBG A(G@
-(A ABBF ] D r
-J $ ] D r
-J L @ C BEB E(I0H8DP
-8D0A(B BBBD L BBJ B(D0A8DpS
-8D0A(B BBBA 4 T BDD DPv
- AABA L BBE J(A0A8DP9
-8A0A(B BBBD 4 X BDD DPv
- AABA . K^ D BBE A(A0D@
-0A(A BBBA D nt BBB D(A0D@0A(A BBB d L ( BFB B(A0A8DP
+8A0A(B BBBA , <> 0d6 AAG0
+AAA L = @e /x BJB A(A0W
+(A BBDH ^(A BBB< > e BBG A(G@
+(A ABBG < > Pf BBG A(G@
+(A ABBG < <? g BBG A(G@
+(A ABBG T > g #w BJB B(A0A8GK
+8D0A(B BBBG ? H,~. K^ L ? V}w BBB E(A0A8DP[8A0A(B BBB T ? 0m v BBB B(A0A8J
+8A0A(B BBBH T ? w BHB B(A0A8GF
+8A0A(B BBBA @ @ AO , T@ @ sw AAG@s
+DAA <A AS , @ 4w AAG@w
+DAA A AS , @ v AAG@w
+DAA A L A f BBB B(A0A8Dh
+8A0A(B BBBA < DB BBG A(G@
+(A ABBF B h] D r
+J B ] D r
+J d B E BBB B(A0A8DP
+8A0A(B BBBE \
+8F0A(B BBBA l tB Зs u BBB J(D0D8D
+8D0A(B BBBI |
+8A0A(B BBBJ T B 7u BEB B(A0A8G'
+8A0A(B BBBK 4 <C H t BDD DPv
+ AABA L ,D BBB B(D0A8D%
+8A0A(B BBBH 4 C @ {t BDD DPv
+ AABA D '~. K^ D DV D D BBE A(A0D@
+0A(A BBBA , ,E }- AAG0!AA d \E 0 BFB B(A0A8DP
8A0A(B BBEK
-8D0A(B BBBH L $ w BBB E(I0D8DP^
-8D0A(B BBBA T t G BBE B(D0A8D
-8D0A(B BBBK T XV * BBE B(D0A8G
-8D0A(B BBBE d ` BFB B(A0A8DP
+8D0A(B BBBH d E BFB B(A0A8DP
8A0A(B BBEK
-8D0A(B BBBH L BEB E(I0H8DP
-8D0A(B BBBD d x{ _ BBB J(A0D8G`
+8D0A(B BBBH L tE r BBB E(I0D8DP^
+8D0A(B BBBA T E P r BBE B(D0A8D
+8D0A(B BBBK L F 9 r BBE D(D0G
+0D(A BBBA L lF cr BEB E(I0H8DP
+8D0A(B BBBD d F h{ ;r BBB J(A0D8G`
8F0A(B BBBF B
-8F0A(B BBBA T D w BBE B(A0A8G<
-8D0A(B BBBA T i BEE B(A0A8Jj
-8A0A(B BBBA L @U { BEE I(D0A8JG
-8D0A(B BBBH . K^ Tr\ AG RA4 BAD D0~
- AABC 4 D } AAG R
+8F0A(B BBBA T $G D q BBE B(A0A8G
+8D0A(B BBBA T |G x Sr BLE B(A0A8NE
+8A0A(B BBBD H @ AR L G @] Wr BEE I(D0A8JP
+8D0A(B BBBG H #~. K^ L I 8 BEB H(A0D8G]
+8D0A(B BBBA 4 dI x BAD D0~
+ AABC 4 I } AAG R
GAM D
-AAA T BBB B(D0A8GY
-8A0A(B BBBA T D 0 BEE B(A0A8G9
-8A0A(B BBBA T h BFE E(E0D8G
-8C0A(B BBBC 4 BAD D0~
- AABC L , h
BEB E(I0H8D@
-8D0A(B BBBD d | g BBB B(I0A8G`
+AAA T I X Aq BEB B(A0A8GI
+8A0A(B BBBI ,J AQ T I uq BFE E(E0D8G
+8C0A(B BBBC 4 J BAD D0~
+ AABC L $J q BEB E(I0H8D@
+8D0A(B BBBD d tJ p BBB B(I0A8G`
8F0A(B BBBF r
-8F0A(B BBBG T BBB B(A0A8G
-8A0A(B BBBG . K^ < T 0 BBA A(DV
-(A ABBA $ . K^ < T L l AAG ]
+8F0A(B BBBG T J p BBB B(A0A8G
+8A0A(B BBBG K ~. K^ < LK cp BBA A(DV
+(A ABBA DL ~. K^ \L Fq
+A |L p L h L L ` AAG ]
CAF [
ACA R
-CAJ JCA L AAG ]
+CAJ JCA L L AAG ]
CAF k
ACA R
-CAJ JCA L H AAG ]
+CAJ JCA L LM AAG ]
CAF [
ACA R
-CAJ JCA L \ x AAG ]
+CAJ JCA L M AAG ]
CAF k
ACA R
-CAJ JCA m\ AG RAL < - BBB B(A0A8Ji
-8A0A(B BBBI L BBB B(A0A8Ji
-8A0A(B BBBI L 8 o BBB B(A0A8D
-8A0A(B BBBA < , X' BBD D(G0g
-(A ABBA L l 'o BBB B(A0A8D
-8A0A(B BBBA , / ADG0Q
-AAA | h. K^ 00 < 80\ BBD D(D@`
-(D ABBA X0 t P0~ D M
-A $ 02 D]
-O A L ȥ. K^ T 0 BBA A(Di
+CAJ JCA M }\ AG RAL TM }n BBB B(A0A8Ji
+8A0A(B BBBI L M p dn BBB B(A0A8Ji
+8A0A(B BBBI L M " Kn BBB B(A0A8D
+8A0A(B BBBA < DN :n BBD D(G0g
+(A ABBA L N " n BBB B(A0A8D
+8A0A(B BBBA , N
m ADG0Q
+AAA O P ~. K^ O 8 < O @\ BBD D(D@`
+(D ABBA ,P ` O X~ Om D M
+A $ O 2 Gm D]
+O A P ~. K^ T O m BBA A(Di
(A ABBG e
-(A ABBE X1 4 P1R BAD D0`
- DABA | x1~ / D M
-A $ 1* ' D\
-H A T . K^ D 1 BAA D`R
+(A ABBE P ` 4 Q XR BAD D0`
+ DABA P ~ wl D M
+A $ P * ol D\
+H A Q ~. K^ D P 3l BAA D`R
AABB U
- AABG P2* h2& 2& 2& $ 2v AG z
-AA < 3 T 3 l 3 3 3 3" DN
-N A \ 03 BBA A(G@P
+ AABG Q X* R p& $R & <R & $ TR v AG z
+AA |R R R R R R (" DN
+N A \ S 8 BBA A(G@P
(C ABBK \
(D ABBK
-(A ABBE `4\ 5 BBB E(A0A8DJ
+(A ABBE R h\ }j BBB E(A0A8DJ
8D0A(B BBBH e
8A0A(B BBBA q
8D0A(B BBBB n
-8C0A(B BBBF T D 9t BBE B(D0A8G
-8D0A(B BBBH T H>f BBD D(G
+8C0A(B BBBF T \S (t j BBE B(D0A8G
+8D0A(B BBBH T S P f i BBD D(G
(A ABBK
-(C ABBF . K^ \g\ AG RA< (C BBD D(I@
-(D ABBF D D$ A]
-B C 4 4 DY ACG
-AAD DCA l D 4 Da ACG E
-AAF DCA E 4 EY ACG
-AAD DCA (E DQ 4 $ 0ES AAF h
-CAD RCA 4 \ XE[ AAF n
-CAF RCA 4 ES AAF h
-CAD RCA T < E BBA A(Ir
+(C ABBF T ~. K^ T h}\ AG RA< T 0! BBD D(I@
+(D ABBF <U " TU "$ A]
+B C 4 tU "Y ACG
+AAD DCA U " 4 U "a ACG E
+AAF DCA U # 4 V #Y ACG
+AAD DCA LV 0# DT 4 dV 8#S AAF h
+CAD RCA 4 V `#[ AAF n
+CAF RCA 4 V #S AAF h
+CAD RCA T TV # g BBA A(Ir
(C ABBG E
-(A ABBA < FW U BBA A(J
-(A ABBA < HR ? BDA A(G
-(A ABBH D 0I" " BBB A(D0G
-0A(A BBBA T \ L 5 BEE E(D0A8G
-8D0A(B BBBE < @QS BBA A(Gx
-(A ABBA . K^ HR2 pR6 G n R AQ R R < RD BEE D(D0h(A BBB D R& $ BLB A(A0GQ
-0A(A BBBF T Tk BBB B(A0A8G
-8A0A(B BBBA . K^ W , xW L pW BEB A(A0g
-(A BBDE _(A BBBD W B BJD G@o
+(A ABBA < V $W g BBA A(J
+(A ABBA < V &R g BDA A(G
+(A ABBH < ,W 8' jg BED A(G`
+(A ABBC L lW ( 9g BDB E(D0C8Dp
+8A0A(B BBBD D W ( f BBB A(D0G
+0A(A BBBA T X + g BEE E(D0A8G
+8D0A(B BBBE < \X 0S f BBA A(Gx
+(A ABBA TY x~. K^ lY 12 Y 16 G n Y 2 AQ Y 2 Y 1 Y 1& Ad D TY 2& f BLB A(A0GQ
+0A(A BBBF T Y 3k e BBB B(A0A8G
+8A0A(B BBBA Z P~. K^ Z 6 Z 6 L <Z 6 e BJB A(A0b
+(A BBDE _(A BBBD Z 87 =e BJD G@o
DABC ]
- DABD D L X
BJD G@l
+ DABD D Z 8 e BJD G@l
DABF ]
- DABD D Y{ BJD G@s
+ DABD D [ 8{ d BJD G@s
DABG @
- DABA T l Z BHE A(D0D@|
-0D(A BBBA m0D(A BBB T @[ BHE A(D0D@y
-0D(A BBBD m0D(A BBB L [ BHE B(A0D8D`
-8D0A(B BBBA l . K^ D `]` BJD JT
+ DABA T \ : BHE A(D0D@|
+0D(A BBBA m0D(A BBB T t\ : BHE A(D0D@y
+0D(A BBBD m0D(A BBB L \ 0; c BHE B(A0D8D`
+8D0A(B BBBA ] ~. K^ D |\ <` c BJD JT
DABK `
- DABI D < x^` BJD JT
+ DABI D \ =` zc BJD JT
DABK `
- DABI L _ BBB B(A0A8Gp8A0A(B BBB $ d aV ADD JAA< Ha! BLG A(G@
-(A ABBF d 8b BBB J(A0D8GP
+ DABI $ ] >V ADD JAA< ] 0?! BLG A(G@
+(A ABBF d ,^ @ BBB J(A0D8GP
8F0A(B BBBG
-8D0A(B BBGG T pd / BFB E(A0A8D
-8A0A(B BBBA < e BFB A(A0S
-(A EBBG \ < Xf BBB D(C0GP
-0D(A BBBD n0D(A BBB \ fy ] BEB D(D0Gp
+8D0A(B BBGG T ] XB zb BFB E(A0A8D
+8A0A(B BBBA < ^ C BFB A(A0S
+(A EBBG \ t^ @D a BBB D(C0GP
+0D(A BBBD n0D(A BBB \ ^ Dy a BEB D(D0Gp
0D(A BBBH f
-0D(A BBBG < i1 BLG A(G@
-(A ABBH L < j BEB E(I0H8D@
-8D0A(B BBBA L k BLB B(A0D8Dp#
-8A0A(B BBBA l o k BBE B(A0A8DJ
+0D(A BBBG < _ G1 BLG A(G@
+(A ABBH L t_ H 'a BEB E(I0H8D@
+8D0A(B BBBA L _ I ` BLB B(D0A8Dp8
+8A0A(B BBBA l ` M ` BBE B(A0A8DJ
8A0A(B BBBA T
-8A0A(B BBBA X. K^ T d r BBB B(D0D8J
-8A0A(B BBBG T Xw & BBB B(D0D8J
-8A0A(B BBBG T |( ? BBB A(A0U
+8A0A(B BBBA <a
~. K^ T ` P) Z` BBB B(D0D8J
+8A0A(B BBBC T ` V) ` BBB B(D0D8J
+8A0A(B BBBC T La X\( ` BBB A(A0U
(A BBBD h
-(H BBBJ T l ~( BBB A(A0U
+(H BBBJ T a 0_( ` BBB A(A0U
(A BBBD h
-(H BBBJ T BBE B(D0A8G
-8D0A(B BBBG T BBE E(D0A8G
-8D0A(B BBBK T t BEE E(D0A8G
-8D0A(B BBBE D BBB A(D0J
-0A(A BBBD D P BBB A(D0J
-0A(A BBBD x. K^ $ t p YX
+(H BBBJ T a b g` BBE B(D0A8G
+8D0A(B BBBG T Tb f M` BBE E(D0A8G
+8D0A(B BBBK T b l 3` BEE E(D0A8G
+8D0A(B BBBE D c 0q ` BBB A(D0J
+0A(A BBBD D Lc u ^` BBB A(D0J
+0A(A BBBD Ld @
+~. K^ $ c yp ` YX
G `
-A , D P DP $ ș AL r
-AA P H , $ @1 AAG cCA P H @
- , 8 D 0 \ ( AV L | ( AAG ]
+A d 0z d
+~ DP $ d z ?` AL r
+AA d z d z , \d z1 _ AAG cCA De z \e z te z
+ e z e xz L e pz [_ BBB E(D0A8GPx
+8D0A(B BBBD L Te z _ BEB E(I0H8D@
+8D0A(B BBBD L e p| ^ BJE B(A0A8D@8A0A(B BBB T e }* ^ BEE E(A0G8G
+8D0A(B BBBG g g AV <g A Dz Tg ( lg - AG cA L g 0 AAG ]
CAF [
ACA R
-CAJ JCA L X AAG ]
+CAJ JCA L g ` AAG ]
CAF [
ACA R
-CAJ JCA < w + BLD A(G@|
-(A ABBA L Țr BBB B(E0A8J@
-8A0A(B BBBA $ ^ AG b
-AA 02 X P H 4 @ 4 8 BAD F0q
- DABK 4 S BDA J
-ABD nAK ؝ Н 4 \ ȝ PGB A(A0D T @ o BEE E(D0D8G
-8D0A(B BBBF < | BEB D(A0
-(A EBBD , BGD l
-ABD L \ BHI E(D0D8HPo
-8D0A(B BBBD L 8 n BJB A(A0W
-(A BBDH ^(A BBBL BFB B(A0A8D@f
-8D0A(B BBBK 4 L BHD G0y
- DABA < ` BBG A(G@
-(A ABBF $ T @V ADD JAA | x* A^
-I A < { BBA D(G0$
-(A ABBA , ȧs AAG0u
-AAA L 6 BBE B(A0A8DP
-8A0A(B BBBA < \ BGB A(A0
-(A BBBA < ة BBG A(G@
-(A ABBG d BBB B(D0A8DP
+CAJ JCA L ,h AAG ]
+CAF [
+ACA R
+CAJ JCA L |h AAG ]
+CAF [
+ACA R
+CAJ JCA h S L ,h 8r ] BBB B(E0A8J@
+8A0A(B BBBA < |h hw \ BLD A(G@|
+(A ABBA ti 2 De
+G A , i ȎD AKD i
+DAA , i ADD \
+AAI L i H BBB B(A0C8Gp
+8A0A(B BBBF < Dj ȑ BEA H(G0
+(A ABBE j j j j 4 j S BDA J
+ABD wAB k 4k < j Z BVA A(G`
+(D ABBA 4 j P Z PGB A(A0D < k (D Z BGA A(D
+(A ABBA T Lk 8 lZ BEE E(D0D8G
+8D0A(B BBBD < \l ` BEB D(A0
+(A EBBD , l BGD l
+ABD L l p BOB A(A0]
+(A BBBG Y
+(A BBBH L dl gY BHI E(D0D8HPo
+8D0A(B BBBD L lm $ BDB A(D0
+(A BBBB A
+(A BBDA L m ` BFB B(A0A8D@f
+8D0A(B BBBK 4 Tm Л {X BHD G0y
+ DABA d m 8 GX BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB < n BBG A(G@
+(A ABBF $ n V ADD JAA o ȝ* A^
+I A 4 |n ؝0 [W BAD
+ABA < lo О BBG A(G@
+(A ABBG < o BBG A(G@
+(A ABBG d o P BBB B(D0A8DP
8A0A(B BBBC
-8A0A(B BBBH T y BBB B(D0A8G_
-8A0A(B BBBA L ( BEB E(I0H8D@
-8D0A(B BBBA < BED A(D0U
-(A ABBE L BEB E(I0H8D@
-8D0A(B BBBA , | BAA AB \ BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBL X BHB B(A0A8D`q
-8A0A(B BBBA \ ( BFB B(D0D8GPv
+8A0A(B BBBH D Tp BCD D0
+ FABE D
+ FABA T o 9 V BEE E(A0D8I
+8D0A(B BBBG p x AV 4 \p x U BFD y
+ABH , Lq @h BDA ]AB |q " DN
+N A $ q B AAG0vAA L q P 3U BEB E(I0H8DP
+8D0A(B BBBA < r BED A(D0U
+(A ABBE < Tr ( BBG A(G@
+(A ABBH < r BBG A(G@
+(A ABBH \ r BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 |r خY S AIG t
+AAI DCA ls ) CF
+G X T r S BBB B(A0A8O
+8A0A(B BBBA L ,s S BBB B(D0A8Dp
+8A0A(B BBBA L |s [S BEB E(I0H8D@
+8D0A(B BBBA , t BCA AB D s d S BGB E(D0G`w
+0A(A BBBF T Dt R BEB B(A0A8Gc
+8A0A(B BBBA L Tu Ⱦ BHB B(A0A8D`q
+8A0A(B BBBA u BFB B(D0D8GPv
8A0A(B BBBE S
8A0A(B BBBC w
8A0A(B BBBG D
-8F0A(B BBBE T d 3 w BED I(D@
+8F0A(B BBBE T u 3 Q BED I(D@
(D ABBK c
-(D ABBD T ' BBE B(A0A8G>
-8A0A(B BBBD D BCD D0
- FABE D
- FABA T \ I BEE E(A0D8I
-8D0A(B BBBG , D ADD ]
-AAA t t BIA D(G0B
+(D ABBD T u c Q BBE B(A0A8G>
+8A0A(B BBBD L 4v Q BEB E(I0H8D@
+8D0A(B BBBA T v WQ BBE B(A0A8G
+8A0A(B BBBA T v X Q BBB B(A0A8G
+8A0A(B BBBF , w ADD ]
+AAA l dw `D Q BBB E(I0D8DP
+8D0A(B BBBG
+8D0A(B BBBA T w @d Q BDB E(A0A8G
+8D0A(B BBBF , x X ADD ]
+AAA t y BIA D(G0B
(A ABBA o
(A ABBK g
(A ABBC D
-(F ABBA d x BBB E(D0A8L`x
+(F ABBA d y @ BBB E(D0A8L`x
8D0A(B BBBG
-8D0A(B BBBF T 6 BBB B(D0D8G
-8D0A(B BBBA \ BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBT | BED D(GPT
+8D0A(B BBBF T <y 6 WP BBB B(D0D8G
+8D0A(B BBBA \ Lz BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBT y O BED D(GPT
(D ABBE C
-(D ABBA \ d BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 4 U AID L
-CAB AA h D `d BGB E(D0G`w
-0A(A BBBF T BEB B(A0A8G{
-8A0A(B BBBA $ P^ AG b
-AA L L BEB E(I0H8D@
-8D0A(B BBBD < o BGB D(A0~
-(A BBBK T
W BBB E(N0D8DU
-8D0A(B BBBE D 4 p, BIB A(D0JF
-0A(A BBBF , Xs AAG0u
-AAA L BEB E(I0H8D@
-8D0A(B BBBD \ ( BBB I(D0D@
-0F(A BBBD U0F(A BBB T \ B [ BEG B(D0G8G
-8A0A(B BBBA D E
-D < d BKD A(G0(A ABB ^ < H BDB D(C0E
-(A EBBA L l BBH E(D0D8DPU
-8D0A(B BBBA d L BBE B(A0A8DP
+(D ABBA \ { h BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 z Y O AIG t
+AAI DCA { z \Y
+A { @ T { 8
N BBB B(D0A8J
+8A0A(B BBBF $ ,| ^ AG b
+AA L { 8 O BEB E(I0H8D@
+8D0A(B BBBD < { N BGB D(A0~
+(A BBBK T ,| h N BBB E(D0N8GU
+8D0A(B BBBJ D | 0, N BIB A(D0JF
+0A(A BBBF $ } B AAG0vAA L | @ SN BEB E(I0H8D@
+8D0A(B BBBD \ D} +N BBB I(D0D@
+0F(A BBBD U0F(A BBB T } p M BBE B(F0D8Mc
+8A0A(B BBBA T } 8 M BBB B(D0A8G1
+
+8A0A(B BBBA L T~ B N BBE B(D0D8GpS
+8D0A(B BBBF $ \ AAD AAD BBB E(A0A8HP8A0A(B BBBL g BBE B(A0A8Dp+
+8A0A(B BBBJ E
+D < < BKD A(G0(A ABB | ^ < H BDB D(C0E
+(A EBBA L M BBH E(D0D8DPU
+8D0A(B BBBA d $ BBE B(A0A8DP
8F0A(B BBBC [
-8F0A(B BBBF T $ 0 BBB B(D0D8G
-
-8A0A(B BBBI . K^ T M BBE B(D0D8G]
-8A0A(B BBBG | h. K^ 4 0 BAD `
-ABF D BBB A(D0DPa
-0A(A BBBH < AAG @
+8F0A(B BBBF T Ԁ 0 ( {L BBB B(D0A8J
+8A0A(B BBBH L ( BLB I(D0A8D@
+8A0A(B BBBD L 4 * BHB E(D0D8Gpl8A0A(B BBB L + BHB B(A0A8D
+8A0A(B BBBA T 1
K BLE B(A0A8G
+
+8A0A(B BBBA , }. K^ L D > AAG e
+CAF K
+ACI R
+CAJ JCA L 8? AAG e
+CAF C
+ACA R
+CAJ JCA $ ?^ AG b
+AA L ? BBE A(D0
+(E BBED V
+(A BBEH L `A J BBE B(D0D8FP
+8D0A(B BBBA L B5 PJ BBB E(D0A8Gp
+8D0A(B BBBG L D B& J BBB B(D0G8Dc
+8D0A(B BBBI L D I BBB B(D0G8Dc
+8D0A(B BBBI L `G I BJB A(A0W
+(A BBDH ^(A BBBT 4 G tI BBB B(D0A8J
+8A0A(B BBBA L (Q I BEB E(I0H8D@
+8D0A(B BBBA T ܅ R I BBE B(A0A8G
+8A0A(B BBBA }. K^ T L n J BBE B(D0D8G]
+8A0A(B BBBG \ }. K^ 4 r nJ BAD `
+ABF D Pt BBB A(D0DPa
+0A(A BBBH < < t I AAG @
AAE p
-AAB D 0 L BBB A(A0Gz
-0A(A BBBA D " BEB A(D0?
-(D BBBI T T t BBE B(D0A8G
-8D0A(B BBBH L Ŀ BEE B(D0D8M
-8A0A(B BBBE D BBB A(A0G
-0A(A BBBC T D ` BBE B(D0D8JI
-8A0A(B BBBH , |. K^ D \ t @\ AG RAT 6 BBA D(GPR
+AAB D | Xu0 I BBB A(A0Gz
+0A(A BBBA D ć @v I BEB A(D0?
+(D BBBI T xt HI BBE B(D0A8G
+8D0A(B BBBH L d @} .I BEE B(D0D8M
+8A0A(B BBBE D I BBB A(A0G
+0A(A BBBC T H BBE B(D0D8JI
+8A0A(B BBBH `}. K^ $ 8 < 0 T L}\ AG RAT H BBA D(GPR
(D ABBE P
-(D ABBG L \ BEB B(A0A8G[
-8D0A(B BBBD d < BBB B(A0A8DP
+(D ABBG L P pH BEB B(A0A8G[
+8D0A(B BBBD d BBB B(A0A8DP
8A0A(B BBBE D
-8A0A(B BBBA 0 AP L 4 0 ־ BEE B(D0D8D@
-8D0A(B BBBJ d
BEE F(A0A8D@`
+8A0A(B BBBA h AP L h dH BEE B(D0D8D@
+8D0A(B BBBJ d
BEE F(A0A8D@`
8F0A(B BBBI
-8D0A(B BBGG L | x BBB B(A0A8Gp8A0A(B BBB L BLB B(A0A8DP
-8A0A(B BBBA L BLB B(A0A8DP
-8A0A(B BBBE L h R BLB E(A0A8D`
-8A0A(B BBBF L , "
- BIB B(A0H8G]
-8A0A(B BBBJ 8y. K^ T `)| BBE E(D0A8G
-8D0A(B BBBE | x. K^ T p.J BBE B(D0A8G
-8A0A(B BBBJ , h50 AC
FK
- E d h6 BBB E(A0A8G@p
+8D0A(B BBGG L \ BBB B(A0A8Gp8A0A(B BBB L 0 BLB B(A0A8DP
+8A0A(B BBBA L BLB B(A0A8DP
+8A0A(B BBBE L F BLB E(A0A8D`
+8A0A(B BBBF L F BBE B(A0D8Gj
+8A0A(B BBBE }. K^ T L h| F BBE E(D0A8G
+8D0A(B BBBE \ p}. K^ T xJ QF BBE B(D0A8G
+8A0A(B BBBJ , ̎ p0 AC
FK
+ E d p BBB E(A0A8G@p
8A0A(B BBBB i
-8A0A(B BBBA T 6 4 BBB A(D0o
+8A0A(B BBBA T ط E BBB A(D0o
(A BBBG E
-(A BBBD L L H7 BGB E(A0K8Dp
-8D0A(B BBBA $ 8 AG@q
-AA D @92 BBB A(A0D
-0A(A BBBA 8: HP < 8:) ]SN
-AAD DHAO (; T 0; BEE E(D0A8G
-8D0A(B BBBG < @< BEA A(Gh
-(A ABBA < A7 Ѻ BBA A(Gf
-(A ABBA u. K^ $ t B AL b
-AA D , C BBE A(A0D@n
-0A(A BBBA t C $ C / AG@c
-AA `D XD `D0 Dk xDH D AR 4
- D L
- D' DR
-J F $ D k AG0S
-AD
- PE [ K IN T $
- E S BEB B(A0A8D
-8A0A(B BBBD L |
- K G BBB E(D0D8FS
-8A0A(B BBBJ T
- L; BBD A(J`
+(A BBBD L P ~E BGB E(A0K8Dp
+8D0A(B BBBA $ T й TE AG@q
+AA D | H2 =E BBB A(A0D
+0A(A BBBA | @ AW < @) ]SN
+AAD DHAO ܐ 0 T < 8 D BEE E(D0A8G
+8D0A(B BBBG < < D BEA A(Gh
+(A ABBA < Ԑ 7 eD BBA A(Gf
+(A ABBA ̑ 0}. K^ $ , 3D AL b
+AA D BBE A(A0D@n
+0A(A BBBA T $ C AG@c
+AA h ` Ē h0 Dk ܒ H < SC AR , ' DR
+J F $ B AG0S
+AD X B K IN T ܒ B BEB B(A0A8D
+8A0A(B BBBD L 4 B BBB E(D0D8FS
+8A0A(B BBBJ T ; B BBD A(J`
(A ABBD U
-(A ABBA L $ MN Ǹ BBB A(D0G
-0A(A BBBE \ t R BBB D(D0G`
+(A ABBA L ܓ 3 [B BBB A(D0G
+0A(A BBBE \ , B BBB D(D0G`
0A(A BBBG U
-0A(A BBBK D S BEF A(A0G`
-0A(A BBBI (r. K^ T T T L
T AAG ]
+0A(A BBBK D KB BEF A(A0G`
+0A(A BBBI }. K^ x ԕ L AAG ]
CAF k
ACA R
-CAJ JCA L \
T AAG ]
+CAJ JCA L < AAG ]
CAF k
ACA R
-CAJ JCA L
U AAG ]
+CAJ JCA L AAG ]
CAF c
CAI R
-CAJ JCA L l
XUr . BBB B(E0A8J@
-8A0A(B BBBA $ L V^ AG b
-AA , t V AAG0q
-AAA T @W1 BBE B(A0D8G
-8D0A(B BBBA , (Zk AAF Q
-CAC \ , hZ BBA D(D@h
+CAJ JCA L $ Pr @ BBB B(E0A8J@
+8A0A(B BBBA $ , ^ AG b
+AA , T AAG0q
+AAA T ̖ 81 /@ BBE B(A0D8G
+8D0A(B BBBA , ܗ k AAF Q
+CAC \ ` BBA D(D@h
(D ABBB B
(D ABBE O
-(D ABBH < [ BBE D(D0_
-(A BBBI [? T [ GBB A(D0D@G0A(A BBBB@ < (\ AV \ (\ t 0\ , (\N BHA p
-AHE T , H\Z BEE E(D0A8Gk
-8D0A(B BBBK L P_Q BEE E(A0A8DPz
-8D0A(B BBBB L d ``] BEE E(A0A8DPz
-8D0A(B BBBB T $ pa γ BBE E(D0A8G/
-8D0A(B BBBJ T | d BBE E(A0A8G
-8D0A(B BBBD T f T BBE E(A0A8G
-8D0A(B BBBF T , i BBB E(A0A8D~
-8D0A(B BBBD t Pke BBA D(Gm
+(D ABBH < l BBE D(D0_
+(A BBBI ? T Ę GBB A(D0D@G0A(A BBBB@ AV < T ( , l N BHA p
+AHE T @Z ?> BEE E(D0A8Gk
+8D0A(B BBBK L HQ BEE E(A0A8DPz
+8D0A(B BBBB L D X] BEE E(A0A8DPz
+8D0A(B BBBB T ܙ h b= BBE E(A0A8G
+8D0A(B BBBB T 4 + %= BBE E(D0A8G2
+8D0A(B BBBG T < BBE E(A0A8G
+8D0A(B BBBD T < BBE E(A0A8G
+8D0A(B BBBF T < n< BBB E(A0A8D~
+8D0A(B BBBD t e A< BBA D(Gm
(D ABBJ
(D ABBF i
(D ABBF
-(D ABBB T Hn BBB E(A0A8D~
-8D0A(B BBBD t T p o BBA D(Gm
+(D ABBB T ; BBB E(A0A8D~
+8D0A(B BBBD t d ; BBA D(Gm
(D ABBJ
(D ABBF l
(D ABBC r
-(D ABBE T r ! BBB E(A0A8Dv
-8D0A(B BBBD t $ u BBA D(Gm
+(D ABBE T ܜ X x; BBB E(A0A8Dv
+8D0A(B BBBD t 4 K; BBA D(Gm
(D ABBJ
(D ABBF i
(D ABBF {
-(D ABBD T Hw BBB E(A0A8Dv
-8D0A(B BBBD t y y BBA D(Gm
+(D ABBD T : BBB E(A0A8Dv
+8D0A(B BBBD t : BBA D(Gm
(D ABBJ
(D ABBF i
(D ABBF {
-(D ABBD T l { + BBB E(A0A8D~
-8D0A(B BBBD | ~U BBA D(Gm
+(D ABBD T | X : BBB E(A0A8D~
+8D0A(B BBBD | Ԟ U U: BBA D(Gm
(D ABBJ
(D ABBF i
(D ABBF
-(D ABBC T D BBB E(A0A8D~
-8D0A(B BBBD | 8U { BBA D(Gm
+(D ABBC T T 9 BBB E(A0A8D~
+8D0A(B BBBD | U 9 BBA D(Gm
(D ABBJ
(D ABBF i
(D ABBF
-(D ABBC T % BBB E(A0A8D~
-8D0A(B BBBD t t ` BBA D(Gm
+(D ABBC T , |9 BBB E(A0A8D~
+8D0A(B BBBD t
O9 BBA D(Gm
(D ABBJ
(D ABBF i
(D ABBF z
-(D ABBE T BBB E(A0A8D~
-8D0A(B BBBD t D } BBA D(Gm
+(D ABBE T ( 9 BBB E(A0A8D~
+8D0A(B BBBD t T p 8 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 ̡ 8 BBB E(A0A8D~
+8D0A(B BBBD t $ Y8 BBA D(Gm
(D ABBJ
(D ABBF l
(D ABBC o
-(D ABBH T BBB E(A0A8D~
-8D0A(B BBBD t BBA D(Gm
+(D ABBH T ( 8 BBB E(A0A8D~
+8D0A(B BBBD t p 7 BBA D(Gm
(D ABBJ
(D ABBF l
(D ABBC @
-(D ABBG T \ (" 9 BEB B(A0A8G Z
-8A0A(B BBBA L BBB E(A0A8G
-8D0A(B BBBD T 0 BBB E(A0A8Dv
-8D0A(B BBBD T \ x ȯ BAD G
+(D ABBG T l " 7 BEB B(A0A8G d
+8A0A(B BBBA L ģ p? q9 BBB E(A0A8G
+8D0A(B BBBD T E S9 BBB E(A0A8Dv
+8D0A(B BBBD T l XG &9 BAD G
DABG
DABE n
- DABC T BBB E(A0A8D~
-8D0A(B BBBD T ( c BAD G
+ DABC T Ĥ K 8 BBB E(A0A8D~
+8D0A(B BBBD T N 8 BAD G
DABG [
DABF n
- DABC T d ` * BBB E(A0A8D~
-8D0A(B BBBD T BAD G
+ DABC T t @P 8 BBB E(A0A8D~
+8D0A(B BBBD T ̥ R [8 BAD G
DABG [
DABF n
- DABC T 5 Ǯ BBE E(G0D8J4
-8D0A(B BBBD a. K^ L BBE B(A0D8G`
-8D0A(B BBBA d | $ ^ AG b
-AA , X0 AC
FK
- E 4 \ X 4 BDA GPg
- DABD T BBB B(A0I8D
-8A0A(B BBBA T BBB B(I0A8D
-8A0A(B BBBA , f AG d
+ DABC < $ T "8 BBA D(G`
+(D ABBC T d ]5 7 BBE E(G0D8J4
+8D0A(B BBBD t }. K^ L Ԧ o 9 BBE B(A0D8G`
+8D0A(B BBBA ܧ `q Xq $ `q^ AG b
+AA , 4 q0 AC
FK
+ E 4 r d8 BDA GPg
+ DABD T 0s ?8 BBB B(A0I8Dr
+8A0A(B BBBA T < u 8 BBB B(I0A8D
+8A0A(B BBBA , L @wf AG d
AC D
-CA AY
-F d V Q KBA A(DX(A ABBGN
-(A ABBD T - BEB B(A0A8J
-8A0A(B BBBA T l 8 C BEE B(D0A8G
-8D0A(B BBBC T , c BBE B(D0A8G
-8D0A(B BBBK d ! BLB B(A0A8D@q
+CA | w xw AY
+F d xV 7 KBA A(DX(A ABBGN
+(A ABBD T d { ]7 BEB B(A0A8J
+8A0A(B BBBA T s7 BEE B(D0A8G
+8D0A(B BBBC T @, 7 BBE B(D0A8G
+8D0A(B BBBK d $ BLB B(A0A8D@q
8A0A(B BBBE
-8A0A(B BBBA < " 0 BLG A(G@
-(A ABBH T ! m BEE E(D0D8J^
-8D0A(B BBBB L " ( ~ BOB B(A0A8Dp~
-8A0A(B BBBE T l" X BBE B(A0A8J
-8A0A(B BBBE T# 9 AG0lD L " @ V BBE E(D0A8Gw
-8A0A(B BBBE # p[. K^ # 8' # @'$ $ X' $$ P' <$ X'Z HQ L \$ ' AAG e
+8A0A(B BBBA < BLG A(G@
+(A ABBH T m 6 BEE E(D0D8J^
+8D0A(B BBBB L l W 7 BBB B(D0D8J
+8D0A(B BBBA L t ț~ BOB B(A0A8Dp~
+8A0A(B BBBE T 6 BBE B(A0A8J
+8A0A(B BBBK L d pf 27 BBE E(D0A8Gw
+8A0A(B BBBE l 9 AG0lD }. K^ $ ԭ Z HQ L $ AAG e
CAF {
ACI R
-CAJ JCA L $ ' AAG ]
+CAJ JCA L t X AAG ]
CAF [
ACA R
-CAJ JCA L $ (( AAG e
+CAJ JCA L Į AAG e
CAF s
ACA R
-CAJ JCA L% x( d% )a HT $ % `)^ AG b
-AA L % ) BBB B(D0A8D
-8D0A(B BBBK $ % +; F^
-D K 4 $& 0+M BDF o
-ABJ AAB L % H+g Y BJB A(A0G%
-0A(A BBBA < & h-$ A BBA A(D
-(A ABBA < \& X. BBA A(D
-(E ABGA < & 8/3 BBA A(D
-(E AFEA < & 80 ܪ BBA A(D
-(E ABGA L ' 1$ BBB H(D0D8J
-8A0A(B BBBJ l l' 3Y BEB E(A0F8DW
-8A0A(B BBBA
-8A0A(B BBBA < ' 5M P BBA A(D
-(A ABBA D ( 6 3 BBA A(D
-(A ABBF T d( `: n BEB B(A0A8G
-8A0A(B BBBE d L) = BLB B(A0A8D@c
+CAJ JCA ط , pa HT $ L ^ AG b
+AA L 5 BBB B(D0A8D
+8D0A(B BBBK $ į x; F^
+D K 4 4 4 BAA GP
+ AABE 4 $ M BDF o
+ABJ AAB L g X4 BJB A(A0G%
+0A(A BBBA < $ @4 BBA A(D
+(A ABBA < 4 4 BBA A(D
+(E ABGA < t 3 3 BBA A(D
+(E AFEA < 3 BBA A(D
+(E ABGA L $ 3 BBB H(D0D8J
+8A0A(B BBBJ < D M 3 BBA A(D
+(A ABBA D 3 BBA A(D
+(A ABBF L ̱ ( 3 BEB B(A0A8Gi
+8A0A(B BBBI T 3 BBE E(A0A8G
+8D0A(B BBBA < t PW c3 BGA A(G
+(A ABBA L l p BBE H(D0A8D`
+8A0A(B BBBH d BLB B(A0A8D@c
8A0A(B BBBK |
-8A0A(B BBBJ T $) = թ BBE E(A0A8G
-8D0A(B BBBA l |) @ BBB B(A0D8D;
+8A0A(B BBBJ T l 2 BBE E(A0A8G
+8D0A(B BBBA l ij N2 BBB B(A0D8D;
8A0A(B BBBJ W
-8A0A(B BBBA 4 |* BJ BDA q
+8A0A(B BBBA 4 pJ BDA q
ABE A
-ABA T $* B BBE B(A0A8G
-8A0A(B BBBA l |* XH ި BBB B(A0A8G
-8D0A(B BBBA E
-8A0A(B BBBA T * N
- BBE E(G0A8GF
-8D0A(B BBBH d + S BEE F(A0A8D@`
+ABA T l ) 1 BBE B(A0A8G
+8A0A(B BBBJ l Ĵ ` 1 BBB B(A0A8G
+8D0A(B BBBA ]
+8A0A(B BBBA d BEE F(A0A8D@`
8F0A(B BBBI
-8D0A(B BBGG L + HU , BBE F(A0A8F`
-8D0A(B BBBH D , V GBA A(G@Q(A ABBG@ \ , Wm KLA A(G@(A ABBI@n
-(A ABBD 4- Z BBD G(R@
+8D0A(B BBGG L 0 BBE F(A0A8F`
+8D0A(B BBBH D GBA A(G@Q(A ABBG@ \ `m KLA A(G@(A ABBI@n
+(A ABBD L p BBD G(R@
(D ABBD Q
(D ABBF Q
(E ABBE Q
(E ABBE Q
-(E ABBA L ,- X[ BBB H(A0A8Dp
-8D0A(B BBBF d |- ] BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB < t. ]1 BLG A(G@
-(A ABBH D $. ^g BBE A(A0J`g
-0D(A BBBI < . _ BBG A(G@
-(A ABBF L </ x`
- BBB E(D0D8G
-8A0A(B BBBE T . Hj
3 BBB B(A0I8G
-8A0A(B BBBF L T/ w BBB E(D0A8Dp
-8D0A(B BBBH L / pz4 b BEE E(D0D8L
-8D0A(B BBBA 0 N. K^ 0 H{ 0 P{ 4 <0 H{ ݤ BGB A(A0D $ 1 | Dw
+(E ABBA L q/ BBB H(A0A8Dp
+8D0A(B BBBF d l :/ BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB < 1 BLG A(G@
+(A ABBH L V . BBB B(G0D8G[
+8D0A(B BBBF < BBG A(G@
+(A ABBF L \ %
+ BBB B(D0D8Go
+8A0A(B BBBH T , - BBB B(A0I8Gf
+8A0A(B BBBG L L P. BBB E(D0A8Dp
+8D0A(B BBBH L 4 +. BEE E(D0D8L
+8D0A(B BBBA }. K^ Ժ 4 4 - BGB A(A0D $ $
Dw
E c
-M ,1 hN. K^ D1 | A[ d1 | |1 | L 1 | AAG ]
+M L }. K^ d A[ L AAG ]
CAF [
ACA R
-CAJ JCA L 1 | AAG ]
+CAJ JCA L H AAG ]
CAF [
ACA R
-CAJ JCA L 42 | AAG ]
+CAJ JCA L T x AAG ]
CAF [
ACA R
-CAJ JCA 2 ,\ AG RA$ 2 } AG Y
-AA T <2 x}k BBE E(D0A8G
-8A0A(B BBBA , 2 C AID J
-DAA , 2 / AAG0i
-AAA L 2 BEB E(I0H8D@
-8D0A(B BBBD L D3 ۢ BJE B(A0A8D`8A0A(B BBB T 3 9 BBE E(A0A8J
-8D0A(B BBBA T 3 b BEE E(A0D8J
-8D0A(B BBBA 4 J. K^ 4 Ȗ0 Hg 5 ؖ7 Hj L ,5 AAG e
+CAJ JCA Xo}\ AG RA$ + AG Y
+AA T 4 k + BBE E(D0A8G
+8A0A(B BBBG , , AID J
+DAA , x + AAG0i
+AAA L ( + BEB E(I0H8D@
+8D0A(B BBBD L < + BJE B(A0A8D`8A0A(B BBB T 8 9 \+ BBE E(A0A8J
+8D0A(B BBBA T " *+ BEE E(A0D8J
+8D0A(B BBBA (}. K^ `(0 Hg , p(7 Hj L L ( AAG e
CAF s
ACA R
-CAJ JCA d |5 H BBB B(A0A8DP
+CAJ JCA d ( BBB B(A0A8DP
8A0A(B BBBE D
-8A0A(B BBBA $ 5 З^ AG b
-AA , 6 ADD0e
-AAA \ 5 x BBB A(D0GPq
+8A0A(B BBBA $ h)^ AG b
+AA , , ) ADD0e
+AAA \ * ) BBB A(D0GPq
0A(A BBBE w
-0A(A BBBA L 6 8 BOB A(A0]
+0A(A BBBA L * BOB A(A0]
(A BBBG Y
-(A BBBH T \6 | M BBE E(D0A8G
-8D0A(B BBBE D D7 О BBE A(A0D@d
-0A(A BBBA T 6 x1 BEE E(D0A8GI
-8D0A(B BBBE < T7 ( џ BBA D(Gp
-(D ABBA L 7 BBB B(A0D8D`b
-8A0A(B BBBC < t8 BBG A(G@
-(A ABBG T $8 ` ? BBE B(A0A8G
{
-8A0A(B BBBA T |8 " W BBB B(A0A8D:
-8A0A(B BBBF d9 F. K^ < 8 h / BBD A(D0H
-(A ABBE D 9 4 BBE A(A0D@
-0A(A BBBA T t9 BEE B(D0A8J
-8A0A(B BBBF D \: H@ BBE A(A0D@
-0A(A BBBA : @ : 8 d : @ BBB B(A0A8DP
+(A BBBH T T @+| ) BBE E(D0A8G
+8D0A(B BBBE D d h0 BBE A(A0D@d
+0A(A BBBA T 11 ( BEE E(D0A8GI
+8D0A(B BBBE 4 L Tj} ( BAD G`
+ DABA L 6 w( BBB B(A0D8D`b
+8A0A(B BBBC < @9 BBG A(G@
+(A ABBG T : ( BEB B(A0A8G
u
+8A0A(B BBBA T l HQ" #) BBB B(A0A8D:
+8A0A(B BBBF | Э}. K^ , T ( AAG
+AAI D U4 BBE A(A0D@
+0A(A BBBA T T V ( BEE B(D0A8J
+8A0A(B BBBF D d h@ BBE A(A0D@
+0A(A BBBA i i d i BBB B(A0A8DP
8A0A(B BBBE D
-8A0A(B BBBA $ <; ^ AG b
-AA , d; 0 AC
FK
- E T ; < / BBE B(D0A8G
-8D0A(B BBBF ; 8D. K^ T t; % BBB B(A0D8J
-8A0A(B BBBF \< t< DV L < , BEB B(A0A8DP8A0A(B BBB L < 3 BBE B(A0A8DP8A0A(B BBB < ,= BEH D(A0H@0A(A EBBT < x> BKB E(A0D8K
-8D0A(B BBBE = B. K^ D L= H BBA A(N
-(A ABBA L $> AAG ]
+8A0A(B BBBA $ D i^ AG b
+AA , l i0 AC
FK
+ E T j< ( BBE B(D0A8G
+8D0A(B BBBF }. K^ T T o- ' BBB B(A0D8J
+8A0A(B BBBF d DV L | , BEB B(A0A8DP8A0A(B BBB L Н3 BBE B(A0A8DP8A0A(B BBB L , BGB B(D0D8D
+8D0A(B BBBA < l BEH D(A0H@0A(A EBBT 0 ( BKB E(A0D8K
+8D0A(B BBBE }. K^ D d pW ) BBA A(N
+(A ABBA L d AAG ]
CAF c
CAI R
-CAJ JCA , t> Pb D\
+CAJ JCA , ȵb D\
H P
H Q
-G I , > b D\
+G I , b D\
H P
H Q
-G I , > b D\
+G I , Hb D\
H P
H Q
-G I , ? b D\
+G I , D b D\
H P
H Q
-G I , 4? Pb D\
+G I , t ȶb D\
H P
H Q
-G I , d? b D\
+G I , b D\
H P
H Q
-G I , ? b D\
+G I , Hb D\
H P
H Q
-G I , ? b D\
+G I , b D\
H P
H Q
-G I , ? Pb D\
+G I , 4 ȷb D\
H P
H Q
-G I , $@ b D\
+G I , d b D\
H P
H Q
-G I , T@ b D\
+G I , Hb D\
H P
H Q
-G I , @ b D\
+G I , b D\
H P
H Q
-G I , @ P b D\
+G I , ȸb D\
H P
H Q
-G I , @ b D\
+G I , $ b D\
H P
H Q
-G I , A b D\
+G I , T Hb D\
H P
H Q
-G I , DA b D\
+G I , b D\
H P
H Q
-G I , tA P b D\
+G I , ȹb D\
H P
H Q
-G I , A b D\
+G I , b D\
H P
H Q
-G I , A b D\
+G I , Hb D\
H P
H Q
-G I , B b D\
+G I , D b D\
H P
H Q
-G I , 4B P b D\
+G I , t Ⱥb D\
H P
H Q
-G I , dB b D\
+G I , b D\
H P
H Q
-G I , B b D\
+G I , Hb D\
H P
H Q
-G I , B b D\
+G I , b D\
H P
H Q
-G I , B P b D\
+G I , 4 Ȼb D\
H P
H Q
-G I , $C b D\
+G I , d b D\
H P
H Q
-G I , TC b D\
+G I , Hb D\
H P
H Q
-G I , C b D\
+G I , b D\
H P
H Q
-G I C P m A~
-A l C t AR
-E UL dC ! BBB F(A0A8J@X
-8A0A(B BBBA 4 DD " ADG X
-CAH [AA L C " BBB B(A0A8DW
-8D0A(B BBBF , <D (&h k BDD y
-ABA < lD h& G BEA G(GPE
-(A ABBG < D ( AAGPQ
-AAD O
-AAG |E )( A[ E )( A[ E ) AZ E )# A] L lE *
BBB B(A0C8JY
-8A0A(B BBBG LF 7 Ah
-G b\ E (8 BDB A(A0LP~
-0A(A BBBD
-0A(A BBBD < <F X;B c AADPU
+G I , ȼb D\
+H P
+H Q
+G I $ m A~
+A l D Xt AR
+E UL " BBB F(A0A8J@X
+8A0A(B BBBA 4 8 ADG X
+CAH [AA L 4 " BBB B(A0A8DW
+8D0A(B BBBF L > [" BBB B(A0A8GZ
+8D0A(B BBBH , h 3" BDD y
+ABA 4 Y " BAG GP}
+ AABA < < 8B ! AADPU
AAC
-AAA 4 G h< ACO a
-AAJ
-GAO 4 DG 0=) AAJ P
+AAA 4 4 H) AAJ P
AAB
-GAO D F (> ̗ BAA G@
+GAO D @ ! BAA G@
AABH
- AABH L 4G ?N BCA LP
- AABH
- AABK D G C r BED D(G$
-(D ABBE D G E = BED D(G$
-(D ABBE L H F BBB A(A0E
+ AABH D e! BED D(G$
+(D ABBE D D 0 0! BED D(G$
+(D ABBE L D BBB A(A0E
(A BBGG I
-(A BBDA L dH FM BDB J(A0A8G
-8A0A(B BBBG D H Y ; BED D(G$
-(D ABBE D H X[ BED D(G$
-(D ABBE D DI \ і BED D(G$
-(D ABBE D I (^ BED D(G$
-(D ABBE L I _u g BDB B(A0C8OZ
-8A0A(B BBBG D $J d k BED D(G$
-(D ABBE T lJ (f 6 BBB B(A0A8G
-8A0A(B BBBG < J ia BBA A(JP
-(A ABBI D K j ؕ BED D(G$
-(D ABBE L LK l BDB B(A0C8LZ
-8A0A(B BBBB < ,L p BBG A(G@
-(A ABBG L K q > BDB B(A0A8O\
-8A0A(B BBBG L (w L 3. K^ L \L w< BAA GD
- AABE 5
- AABG T L ̢ R BBB B(A0A8G
-8A0A(B BBBH T M p,E
- BBE E(D0D8J
-8A0A(B BBBG L \M h6# BDB B(I0A8G
-8A0A(B BBBA , M H> ACL@F
-AAH L M ? BDB B(A0A8L|
-8A0A(B BBBB l ,N HC BDB M(A0A8Di
-8A0A(B BBBB
-8A0A(B BBBA T N F O BEB B(A0A8J
-8A0A(B BBBA L N J 2 BDB B(A0A8O
-8A0A(B BBBF L DO @N 4 BDB B(A0A8O
-8A0A(B BBBF L O R 6 BDJ B(A0A8G\
-8A0A(B BBBG l O PVH BDB J(A0A8D\
+(A BBDA D BED D(G$
+(D ABBE D $ p v BED D(G$
+(D ABBE D l A BED D(G$
+(D ABBE D @ BED D(G$
+(D ABBE D BED D(G$
+(D ABBE D D BED D(G$
+(D ABBE < xa m BBA A(JP
+(A ABBI D E BED D(G$
+(D ABBE D BED D(G$
+(D ABBE < x BBG A(G@
+(A ABBG T 8 l p}. K^ L k BAA GD
+ AABE x
+ AABD T H BBB B(A0A8G7
+8A0A(B BBBF T t E
+ e( BBE E(D0D8J
+8A0A(B BBBG T x ( BEB B(A0A8J
+8A0A(B BBBA T $ 0 n( BBB B(D0A8Je
+8A0A(B BBBA T | P( BBB B(A0A8MZ
+8A0A(B BBBA < @" N( AFGPe
+AAK T
+AAJ L 0 '( BDB B(A0C8J
+_
+8A0A(B BBBG L d ( BBB B(A0A8Lv
+8A0A(B BBBJ T P7 ( BBB B(A0A8O
+8A0A(B BBBA L 8 ( BBB H(A0A8GX
+8A0A(B BBBG L \ ( BBB B(A0A8O
+8A0A(B BBBH L X ( BBB B(A0A8O
+8A0A(B BBBH L M ( BBB J(A0A8G
+8A0A(B BBBG L L u ) BBB B(A0C8O\
+8A0A(B BBBG L H ) BBJ B(A0A8G^
+8A0A(B BBBG l H ( BBB J(A0A8D^
8A0A(B BBBB
-8A0A(B BBBD L TP 0Y ɝ BFB B(A0C8J
-]
-8A0A(B BBBG T P pm7 g BDB B(A0A8O
-8A0A(B BBBA L P Xt c BDB B(A0I8G\
-8A0A(B BBBG T LQ { h BBB B(D0A8Je
-8A0A(B BBBA T Q @ J BBB B(A0A8MZ
-8A0A(B BBBA L Q ȅ H BFB B(A0A8J
-8A0A(B BBBD R R S F , R @D V AHGP
-AAA TS ` , lS Xb D\
+8A0A(B BBBD L \ x# ( BBB B(I0A8G
+8A0A(B BBBA , X ( AAL@H
+AAH L ( BBB B(A0C8L\
+8A0A(B BBBB L , p( BBB B(A0A8O^
+8A0A(B BBBG L | W( BAA LP
+ AABH
+ AABK < u 3( BBA C(JU
+(A ABBC L } N( BBB B(A0A8O^
+8A0A(B BBBG \ \ A( BBB A(A0LP@
+0A(A BBBD
+0A(A BBBD , H#) ' AAJPy
+AAI < H$ ' AAJPH
+AAJ j
+AAD < , % ' AAJPH
+AAJ
+AAJ , l x'! ' AAJPv
+AAD , x() |' AAJP~
+AAD , x)) e' AAJP~
+AAD < x*2 N' AAJPH
+AAJ
+AAI L < x, 9' BDB B(A0A8J
+8A0A(B BBBF D : \ : t :F , :T G' AHG`
+AAA < , <b D\
H P
H Q
-G I , S b D\
+G I , X<b D\
H P
H Q
-G I , S ؕb D\
+G I , 4 <b D\
H P
H Q
-G I , S b D\
+G I , d <b D\
H P
H Q
-G I , ,T Xb D\
+G I , =b D\
H P
H Q
-G I , S } 8 AHG`
-AAA 4 S & , BAA J`w
- AABG 4 4T & - BAA J`z
- AABD T lT e . BEE B(A0A8G
-8A0A(B BBBF T T e & BEE B(A0A8G
-8A0A(B BBBF T U e BEE B(A0A8G
-8A0A(B BBBF V *. K^ L U 4 BBB B(A0A8Gk
-8A0A(B BBBJ T U BBB B(A0G8G
-8A0A(B BBBG T 4V `9 Ҟ BBB B(A0G8G
-8A0A(B BBBG , W Hb D\
+G I , X= )& AHGp
+AAA T < ? & BBE E(A0A8G
+8A0A(B BBBE T F & BBE E(A0A8G
+8A0A(B BBBE T Le
& BEE B(A0A8G
+8A0A(B BBBF T D Re & BEE B(A0A8G
+8A0A(B BBBF T }. K^ T X % BBA A(Gk
+(A ABBB
+(A ABBC D (mG & BBB A(A0JE
+0A(A BBBA T T 0s7 & BBA D(Gi
+(A ABBA |
+(A ABBA D yW (' BBB A(A0JH
+0A(A BBBF L 0 y' BDB B(A0I8G\
+8A0A(B BBBG L D z' BDB B(A0I8G\
+8A0A(B BBBG , L 0b D\
H P
H Q
-G I , V AALPd
-DAA T V e О BEE B(A0A8G
-8A0A(B BBBF $ DW 0 Ȟ JFD@ $ lW GFD@ $ W GFD@ $ W BFA D@ $ W p s JFD@ T X Hu ^ BEE B(A0A8G
-8A0A(B BBBE T dX pe V BEE B(A0A8G
-8A0A(B BBBF LY '. K^ L X p
- 6 BBB A(A0M
-0A(A BBBH Y Y Y Y Z ,Z DZ
\Z tZ A] Z F Z Z Z Z , |Z \ g AAG o
-AAA , Z b C ADG r
-AAA $ l[ XA ACH pCA $ [ A ACH pCA , [ b D\
+G I , p i' AAL`
+DAA T e M' BEE B(A0A8G
+8A0A(B BBBF $ L 8 E' JFD@ $ t 0' GFD@ $ ؔ ' GFD@ $ H} ' BFA D@ $ x & JFD@ T Pu & BEE B(A0A8G
+8A0A(B BBBE T l xe & BEE B(A0A8G
+8A0A(B BBBF | }. K^ L x & BBB A(A0M
+0A(A BBBB 8 0 ( , D \ t (
A] F P H @ $ H < P T X l ` h , p\ % AAG o
+AAA , b % ADG r
+AAA $ A ACH pCA $ $ A ACH pCA L 0- AG cA , l @b D\
H P
H Q
-G I , [ b D\
+G I , b D\
H P
H Q
-G I L \ ( AAG ]
+G I L AAG ]
CAF [
ACA R
-CAJ JCA l\ X Y < [ `w BLD A(G@|
-(A ABBA , \ { BFA i
-ABA 4 d\ BKE
-AJA 4 \ s BKE
-AJE L \ ? BJB A(A0W
-(A BBDH _(A BBBL $] pr BBB B(E0A8J@
-8A0A(B BBBA $ ^ ^ AG b
-AA ,^ B Av
-I A L^ d^ 4 Af
-I C ^ ^ % ^ 0G DB ^ h2 D]
-G I 4 ^ M AAG0o
+CAJ JCA Y 4 S L @m A~
+A l < w /$ BLD A(G@|
+(A ABBA д- , { BFA i
+ABA , 8{ BFA i
+ABA 4 l # BKE
+AJA 4 S# BKE
+AJE L # BBB B(A0A8GPd
+8A0A(B BBBA L , " BJB A(A0W
+(A BBDH _(A BBBL | H " BBB B(A0A8GP
+8A0A(B BBBA L r b" BBB B(E0A8J@
+8A0A(B BBBA T H '" BBB B(D0A8J]
+8D0A(B BBBA , < D BED A(G0
+(A ABBA (B Av
+I A X P, A^
+A K `, A^
+A K p h% , G DB D 2 D]
+G I 4 d ؿM AAG0o
AAF D
-AAJ $ $_ V AD s
-AG L_ d_
- |_ _ _ _ _
- _
- ` AO ,` D` \` < t` BDD R
-ABA h
-CEH ` ` ` ` a ,a Da DR da |a a a a L La BJB A(A0_
-(A BBDH _(A BBBd ,b BBE B(D0A8G@
+AAJ $ V AD s
+AG (
+
+ $
+ < AO \ t D[ $ AIK0AAd 4 BBE B(D0A8G@
8A0A(B BBBJ
-8A0A(B BBBK $ b AIK0AA b X e , Db ADD@n
-AAG L tb !2 ڕ BBB B(A0D8D
-8D0A(B BBBH Tc p# lc h# c `# c X# c P#2 T <c x#9 F BBA A(Dq
+8A0A(B BBBK 0 4 e BDA D@u
+ AABD L 4 2 F BBB B(A0D8D
+8D0A(B BBBH < T l : d BBB B(D0A8D`H
+8A0A(B BBBE
+8A0A(B BBBK d L w BBB B(D0A8D`H
+8A0A(B BBBE
+8A0A(B BBBK T p9 $ BBA A(Dq
(C ABBE L
-(A ABBA < $d `$ BEB D(A0M
-(A BBBF D c %o ͔ BEE D(D0G
-0D(A BBBB 4 d 'Z BDA y
+(A ABBA T X BEE E(D0D8G
+8D0A(B BBBI L BOB A(A0]
+(A BBBG Y
+(A BBBH D @? V BEE D(D0G
+0D(A BBBA < 8 BEB D(A0M
+(A BBBF D < o BEE D(D0G
+0D(A BBBB 4 < Z BDA y
ABM A
-ABA T Td 0' _ BBE E(A0A8G
-8D0A(B BBBD <e h)Q Te * N , d *Y ADG i
-AAA T e * Γ BBB B(A0A8G
-8A0A(B BBBD T de `.Z BEB B(A0A8G
+ABA t Q P N , X\ B ADG l
+AAA T BBB B(A0A8G
+8A0A(B BBBD T t Z BEB B(A0A8G
-8D0A(B BBBE 4 Lf h0J BDA l
+8D0A(B BBBE 4 J BDA l
ABJ A
-ABA , e 0Y : ADG i
-AAA T $f 0F BBB B(A0A8G>
-8A0A(B BBBG T |f 4 BEE E(D0D8G
-8D0A(B BBBD T f 5 BEE E(D0D8G9
-8D0A(B BBBJ L g h7 BOB A(A0]
-(A BBBG Y
-(A BBBH h 7' Ae D ,h 7 BBE A(A0D@l
-0A(A BBBA < g 8 BBA D(K@C
-(A ABBA L h 9 BBB B(A0A8Gp8A0A(B BBB i :+ Ae 4 $i :I ADG q
-AAA DCA D \i : BBE A(A0D@l
-0A(A BBBA $ i @; ADD AA4 <i ; _ BFA b
-ABA $ j >V ADD JAAL i > A BJB A(A0W
-(A BBDH ^(A BBB< i (? BBD A(G0L
-(A ABBA $ j @c AG g
-AA , j Av BAA i
-ABA < k `A BEB A(A0D@0A(A BBBD Tk A BBE A(A0D@l
-0A(A BBBA < k B BBD A(G0T
-(A ABBA D k 8D BBE A(A0D@l
-0A(A BBBA $l D5 L <l D BBB B(A0D8D@
-8A0A(B BBBB L k E w BJB A(A0W
-(A BBDH ^(A BBBd l E BBE F(D0A8D@
-8F0A(B BBBC
-8D0A(B BBGH T l G Í BEB B(A0D8J
-
-8A0A(B BBBA L m 8K ƍ BJB A(A0W
-(A BBDH ^(A BBBd \m K z BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB < Tn L BBG A(G@
-(A ABBF < n L BBG A(G@
-(A ABBF 4 n MU BDA w
-ABO AAB o MT d n M G BBB E(A0A8DPD
+ABA , \ ADG l
+AAA T 4 PF f BBB B(A0A8G>
+8A0A(B BBBG T H 0 BEE E(D0D8G
+8D0A(B BBBD T / BEE E(D0D8GZ
+8D0A(B BBBI T < X BEE E(D0D8G9
+8D0A(B BBBJ T a BEE E(D0D8G
+8D0A(B BBBI X' Ae D h BBE A(A0D@l
+0A(A BBBA < T BBA D(K@J
+(A ABBA < BBD A(G0L
+(A ABBA D BBI A(A0D@
+0A(A BBBA 2 AG hA d i BEB B(A0A8Dp
+8F0A(B BBBA /
+8D0A(B BBBA d \ T BEB B(D0D8DS
+8A0A(B BBBD W
+8A0A(B BBBG L _ BBE B(A0A8DP
+8A0A(B BBBA L \ X BBB B(A0A8Gp
+8A0A(B BBBA , d (= BGA oAB 8+ Ae D H BBE A(A0D@l
+0A(A BBBA $ ADD AA4 l : BFA b
+ABA $ \ @V ADD JAA< x BBG A(G@
+(A ABBH < 8 BEB A(A0D@0A(A BBBD BBE A(A0D@l
+0A(A BBBA L `5 L d BBB B(A0D8D@
+8A0A(B BBBB L ( BJB A(A0W
+(A BBDH ^(A BBBL L x BJB A(A0W
+(A BBDH ^(A BBB< T BBG A(G@
+(A ABBF < BBG A(G@
+(A ABBF 4 HU BDA w
+ABO AAB pT d l BBB E(A0A8DPD
8A0A(B BBBA x
-8A0A(B BBBF D o P BBE A(A0D@l
-0A(A BBBA L Do P BJB A(A0W
-(A BBDH ^(A BBBL $p Q] BBB H(A0D8DP
-8A0A(B BBBG , o Rm ; AID t
-DAA < p PR ' BBD A(G0$
-(A ABBA p S L p T] BBB H(A0D8DP
-8A0A(B BBBG D Lq U BBE A(A0D@l
-0A(A BBBA < q 0V BBG A(G@
-(A ABBG L Dq V + BEB E(I0H8DP
-8D0A(B BBBD L q X BJE B(A0A8D@8A0A(B BBB 4 q pY BFD D0
- DABA , r 8Z AID0|
-DAA L r Z] BBB H(A0D8DP
-8A0A(B BBBG L r [ O BBB B(D0D8Dp
-8D0A(B BBBD L |s (_9 BEF B(D0A8D@z
-8D0A(B BBBA L s a BBB B(D0A8D@m
-8A0A(B BBBH T s aR t BEB E(A0A8G
-8A0A(B BBBF < tt e BBG A(G@
-(A ABBG L $t `fc 5 BBB B(A0A8Dp#
-8A0A(B BBBA l tt g BNB B(A0D8G
+8A0A(B BBBF D BBE A(A0D@l
+0A(A BBBA L x BJB A(A0W
+(A BBDH ^(A BBBL $ ] BBB H(A0D8DP
+8A0A(B BBBG < t BBG A(G@
+(A ABBH < BBG A(G@
+(A ABBH < X BBG A(G@
+(A ABBH , | m AID t
+DAA < X BBD A(G0$
+(A ABBA L ] BBB H(A0D8DP
+8A0A(B BBBG D BBE A(A0D@l
+0A(A BBBA < T 8 BBG A(G@
+(A ABBG L BEB E(I0H8DP
+8D0A(B BBBD L ,
+ BJE B(A0A8D@8A0A(B BBB 4 | x ; BFD D0
+ DABA < l @ BLG A(G@
+(A ABBH T
B BLB E(A0A8DK
+8C0A(B BBBF L ] BBB H(A0D8DP
+8A0A(B BBBG | T U BBB E(A0A8DP
+8A0A(B BBBA |
+8A0A(B BBBJ "
+8A0A(B BBBD , ADD v
+AAG T BEB A(D0D@
+0D(A BBGE D
+0C(A BBBJ T v BEB B(A0D8J
+8A0A(B BBBA T X y BBB B(A0A8J
+8A0A(B BBBC T T $h g BEB E(D0A8G
+8D0A(B BBBH L ( h BBB B(D0A8Gp
+8D0A(B BBBG ,V L `- BBB B(D0A8D@m
+8A0A(B BBBH T d -b
BEB E(A0A8G
+8A0A(B BBBF < t 1 BBG A(G@
+(A ABBG L 2
BBK I(K0G8Mp
+8D0A(B BBBA L L 4m Z
BBB B(A0A8Dp#
+8A0A(B BBBA l 5 $
BNB B(A0D8G
8A0A(B BBBA n
-8A0A(B BBBA D t i, Ç BJB K(A0j
-(_ aBBA D ,u i BBJ A(D0
-(A BBEA $ v j AHG tAAL ,v (k] BBB H(A0D8DP
-8A0A(B BBBG D u 8l BBB A(D0GP
-0D(A BBBK < v m BBG A(G@
-(A ABBG L tv Pn BEB E(I0H8DP
-8D0A(B BBBD L v p s BJE B(A0A8D@
-8A0A(B BBBA < w p BBG A(G@
-(A ABBG L w q BBB B(A0D8DP
-8A0A(B BBBA < 4x `r BED A(D0Y
-(A ABBA L w r { BEB E(I0H8DP
-8D0A(B BBBD L 4x t S BJE B(A0A8D@
-8A0A(B BBBA L x `u + BEB E(I0H8DP
-8D0A(B BBBD < dy w BFA D0}
- FABE D FAB 4 y wY Ä AIG t
-AAI DCA T Ly wz BEE I(A0A8D
-8D0A(B BBBA < 4z x BED D(GPf
-(A ABBF T y @y BEH B(A0A8G
-8A0A(B BBBA T <z { ܃ BEB B(A0A8M
-8A0A(B BBBA T z @} BHB B(A0A8J
-8A0A(B BBBE \ |{ BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB\ { X BBE B(A0D8D@r
-8D0A(B BBBE D8C0A(B BBB4 { i Ȃ ADD N
-AAG DCAL { BEB E(I0H8DP
-8D0A(B BBBD < 4| o BLD D(G0
-(A ABBE \ } ` BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 | Y AIG t
-AAI DCA < } e BID ~
-AFD AEF < L} BGB D(A0~
-(A BBBK T } R k BBB B(A0K8J#
-8A0A(B BBBA L } ? BEB E(I0H8DP
-8D0A(B BBBD d 4~ p, BGE E(A0A8D@
+8A0A(B BBBA D (7, BJB K(A0
+(A BBBA D T 8 BBJ A(D0
+(A BBEA $ T 8 AHG tAAL | P9] BBB H(A0D8DP
+8A0A(B BBBG < `: BBG A(G@
+(A ABBG L T ; BEB E(I0H8DP
+8D0A(B BBBD L < BJE B(A0A8D@
+8A0A(B BBBA < = BBG A(G@
+(A ABBG L p> BBB B(A0D8DP
+8A0A(B BBBA < < 0? BBG A(G@
+(A ABBH < | ? BED A(D0Y
+(A ABBA L `@
+ BEB E(I0H8DP
+8D0A(B BBBD L T B _
+ BJE B(A0A8D@
+8A0A(B BBBA < \
+ B BBG A(G@
+(A ABBG < C BGD A(G0~
+(A ABBG L $
+ 0D BGB A(A0N
+0A(A BBBA \ , H BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 HI ADG q
+AAA DCA T Ik U BBB B(D0A8G
+8A0A(B BBBC L d O z BBB B(A0A8J
+8A0A(B BBBC < l pQ BFA D0}
+ FABE D FAB 4 QY AIG t
+AAI DCA T , R BEE I(A0A8D
+8D0A(B BBBA < <
@S BED D(GPf
+(A ABBF T S c BEH B(A0A8G
+8A0A(B BBBA T
hU / BBB B(G0A8G
+8A0A(B BBBA T t
W BHB B(A0A8J
+8A0A(B BBBE \ h[ BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 , [a { AIG
+AAF DCA D d [ G BBB A(D0GP
+0D(A BBBK T H];
+ BBB B(D0D8G
+8A0A(B BBBE T 0g2
+ . BLB B(A0A8J
+8A0A(B BBBE L \ q C BEB E(I0H8DP
+8D0A(B BBBD \ r BEH D(I0D@
+0F(A BBBF U
+0F(A BBBA \ s BBE B(A0D8D@r
+8D0A(B BBBE D8C0A(B BBB4 l si AID G
+AAI DCA< t K BLD D(G0
+(A ABBE \ t BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 D uY AIG t
+AAI DCA < | Hum BID D
+AFF AEF \ t xu? BEA C(GPf
+(A ABBB {
+(A ABBG
+(A ABBF Xw4 Af
+I C T < xwb BBB B(A0K8J+
+8A0A(B BBBA < y BLD D(G0
+(A ABBE < Pz BGB D(A0~
+(A BBBK L z BEB E(I0H8DP
+8D0A(B BBBD d d |, W BGE E(A0A8D@
8F0A(B BBBI g
-8F0A(B BBBA T ~ 8r ׀ BBB B(A0A8G
-8A0A(B BBBA L ~ ` ' BEB E(I0H8DP
-8D0A(B BBBD T D * BLB B(A0A8Gq
-8A0A(B BBBA L BEB E(I0H8D@
-8D0A(B BBBA < | x BFA D0}
- FABE D FAB L BBB B(A0D8G@j
-8A0A(B BBBH $ xq ADD0eAAL С / BEB E(I0H8D@
-8D0A(B BBBD L P BJE B(A0A8D`8A0A(B BBB < ԁ BBG A(G@
-(A ABBF < BLA D(G@
-(D ABBC T ` T ܁ X = BBE B(A0A8G
-8A0A(B BBBH Ă AO L T ? BLE I(D0D8D
-8A0A(B BBBC T 2 BBB E(A0D8G
-8A0A(B BBBA T hM ~ BHB B(D0D8G
-8A0A(B BBBA T T ` ~ BBB B(A0D8Gw
-8A0A(B BBBA , Z
AAD@o
-AAA l ( T ~ BBB B(A0E8M
-8A0A(B BBBH ܄ E
-K < h BKD A(G0(A ABB L
-
D~ BEB B(A0A8Ga
-8A0A(B BBBA L W~ BBL E(H0A8D`
-8D0A(B BBBA ܅ H AO H T @
- } BBB B(A0A8J )
-8A0A(B BBBA , l E BAG wAB 1 Dl X. K^ D D } BGB D(A0G@~
-0A(A BBBC 1 Ak < O} BED D(G
-(A ABBH < x !} BED D(G
-(A ABBI D , | BEE A(D0G
-0A(A BBBI L t , | BEE B(A0D8G
-8A0A(B BBBA L ć , | BEE B(A0D8G
-8A0A(B BBBA L BBB B(A0A8D`
-8A0A(B BBBA L BEB B(A0A8D
-8A0A(B BBBA L D p BDB E(A0A8DP
-8A0A(B BBBK T y T{ BBE E(A0A8GZ
-8A0A(B BBBA T \ -{ BBB B(A0A8G
-8A0A(B BBBA T I{ BBB B(A0D8D
-8D0A(B BBBF < X 1 >{ BGA D(G@
-(A ABBB d L X
{ BBB F(D0D8J`g
+8F0A(B BBBA T x}%
BBB B(A0A8G
+8A0A(B BBBA L $ P k BEB E(I0H8DP
+8D0A(B BBBD T t C BLB B(A0A8Ga
+8A0A(B BBBA L ؑ S BEB E(I0H8D@
+8D0A(B BBBA < X BFA D0}
+ FABE D FAB L ȓ BBB B(A0D8G@j
+8A0A(B BBBH < X BLD D(G0
+(A ABBE $ q ADD0eAAL p W BEB E(I0H8D@
+8D0A(B BBBD L d / BJE B(A0A8D`8A0A(B BBB < l BBG A(G@
+(A ABBF < 0 BLA D(G@
+(D ABBC T L l _ BBB B(A0A8G
+8A0A(B BBBA \ AO L ` BLE I(D0D8D
+8A0A(B BBBC T 2 / BBB E(A0D8G
+8A0A(B BBBA L l BHB B(A0D8J
+8A0A(B BBBI T X BBB B(D0D8Gc
+8A0A(B BBBA , Z AAD@o
+AAA T \ BBB B(A0H8M
+8A0A(B BBBH l E
+K < BKD A(G0(A ABB L O BEB B(A0A8Ga
+8A0A(B BBBA L d P \ BBL E(H0A8D`
+8D0A(B BBBA l AO T
+ BBB B(A0A8J _
+8A0A(B BBBA , PE BAG wAB , p1 Dl L V}. K^ D x BGB D(A0G@~
+0A(A BBBC 1 Ak T @ S BHB B(D0A8Jy
+8A0A(B BBBH < l BED D(G
+(A ABBH < X t BED D(G
+(A ABBI D F BEE A(D0G
+0A(A BBBI L 4 , BEE B(A0D8G
+8A0A(B BBBA L , BEE B(A0D8G
+8A0A(B BBBA < p BBA D(D@
+(A ABBA L & BBB B(A0A8D`
+8A0A(B BBBA L : BEB B(A0A8Dp
+8A0A(B BBBA L l BDB E(A0A8DP
+8A0A(B BBBK \ P- g BBB D(D0G
+0A(A BBBA G
+0C(A BBBA T d W
8 BBB B(A0A8GN
+8A0A(B BBBA \ t ( GEB B(A0A8Dp|8A0A(B BBBDp L BBB A(D0D
+0D(A BBBA L l
+} BEE B(D0D8Dp
+8D0A(B BBBA < 81 BGA D(G@
+(A ABBB d 8 BBB F(D0D8J`g
8A0A(B BBBA K
-8A0A(B BBBK D " DR
-J A L d & BBE B(A0A8DP
-8A0A(B BBBA L H BBB L(A0A8D`
-8A0A(B BBBA AL T y BEE B(D0A8G/
-8D0A(B BBBA , | ( AAG f
-AAG T ( qy RHB B(A0A8G{
-8A0A(B BBBD L t /y BEB B(A0A8G\
-8D0A(B BBBC L Č p)F y BEE B(A0D8JS
-8D0A(B BBBC 4 p0 y BAA G@
- DABJ $ ܍ (1Y AK Y
-AA L t `1 cy BEB E(I0H8D@
-8D0A(B BBBD L č 22 ;y BJE B(A0A8DP
-8A0A(B BBBA L 3 y BEB B(A0A8G
-8A0A(B BBBD D d P: &y BGF D(A0JI
-0A(A BBBA D < = BBE A(A0D@u
-0A(A BBBA T @> x BEE E(D0D8G
-
-8A0A(B BBBA , L Y y AADP
-AAH T | Z y BEE B(D0A8L
-8A0A(B BBBI l ԏ ] y BGB B(A0D8GF
+8A0A(B BBBK < d! / BDE C
+ABG H
+ABE \" " DR
+J A < |" 0 BEB A(A0D@0A(A BBBT " BEB B(A0A8J
+8A0A(B BBBK T \" # 0 BEE B(D0A8G/
+8D0A(B BBBA , l# P% AAG f
+AAG T " & RHB B(A0A8G{
+8A0A(B BBBD L <# ' BEB B(A0A8G \
+8D0A(B BBBC L # 6R BEE B(A0D8JS
+8D0A(B BBBC 4 # ; BAA G@
+ DABJ $ $ P<Y AK Y
+AA L <$ < BEB E(I0H8D@
+8D0A(B BBBD L $ >2 BJE B(A0A8DP
+8A0A(B BBBA L $ > o BEB B(A0A8G
+8A0A(B BBBD D ,% E t BGI D(A0JL
+0A(A BBBK 4 ,& H~ BEA A(D@j(A ABBT % (I + BEE E(D0A8J
+8A0A(B BBBA , & d F AADP~
+AAJ , 4& Pe $ AGDP
+AAG l d& 0f BGB B(A0D8GF
8A0A(B BBBG (
-8A0A(B BBBF T D 0_ _y BEE E(A0H8G;
-8A0A(B BBBJ T gU Gy BBB B(A0A8J}
-8D0A(B BBBA T m7 Ry BEB B(D0A8T7
-8D0A(B BBBH T L p y BBB B(A0A8G
-8A0A(B BBBA T `xP y BBE B(A0A8G
-8A0A(B BBBA D XK _z BBB A(D0DE
-0D(A BBBA D Ԓ ` BBI A(A0D@
-0A(A BBBA D 8 BBI A(A0D@
-0A(A BBBA D Ԓ s y BBB A(D0D@
-0A(A BBBA 4 HD BEA A(D0p(A ABB T T ` 0y BBB B(D0A8J
-8A0A(B BBBA < # HZ L ̓ % y BBB E(D0A8KP
-8A0A(B BBBA T x BBB B(A0A8GY
-8A0A(B BBBD * AM
-B Y T
x BJB B(A0A8G
-8A0A(B BBBA < ر 'y BED A(G@x
-(D ABBD T , X y BBB B(D0D8J?
-8A0A(B BBBE L y BBB B(D0D8JX
-8D0A(B BBBA T ԕ P%
Jy BBB B(D0A8G
-8A0A(B BBBI (, A^
-A K D ܖ 8 A
-A O
-A O
-E G
-I K
-A ]
-A G
-A S \ $ L ADD
-AAD N
-AAH N
-AAH N
-AAA `
-AAA 4 ADD H
-AAE HAA L x~ BED D(G0Z
-(C ABBH m(G ABB L | 1x BEB A(A0g
-(A BBDE _(A BBB4 \ W BDH q
-AGI AAD 0 d ( w BEL B(A0A8D`
-8C0A(B BBBF L8A0A(B BBB d :w BEL B(A0A8D`
-8C0A(B BBBF L8A0A(B BBB L | p BEH A(D0y
-(D ABBJ D(C DBB ̙ 8' DQ
-K F D \ Hr ov BBE A(D0DU
-0A(A BBBA D ) Hv BBB A(A0D
-0A(A BBBA D h) v BBB A(A0D
-0A(A BBBA L Ě P BEB B(A0A8D`\
-8A0A(B BBBA T , u BBE B(D0A8G
-8D0A(B BBBK T ܚ A u BEE E(D0A8Go
-8D0A(B BBBG T 4 pu BBA A(JK
+8A0A(B BBBF T & `h BEE E(A0H8G;
+8A0A(B BBBJ T ,' q BBB B(A0A8J~
+8D0A(B BBBA T ' v7 BEB B(D0A8T7
+8D0A(B BBBH T ' y BBB B(A0A8G
+8A0A(B BBBA L ( M BBL B(A0A8D`
+8A0A(B BBBA <) AL L ( U BEE F(A0A8G`
+8A0A(B BBBJ L ( O BEE B(D0D8LPT
+8D0A(B BBBE T D) ` BFE B(A0A8G
+8A0A(B BBBA D ) 5 W BBB A(A0Dr
+0D(A BBBA D * BBI A(A0D@
+0A(A BBBA D * x BBI A(A0D@
+0A(A BBBA < t* P BBB A(K0(A BBB 4 l+ D BEA A(D0p(A ABB T * ةv . BBB E(A0A8J
+8A0A(B BBBA + # Aa L d+ BBB E(D0A8KPq
+8A0A(B BBBA T + BBB B(A0A8G`
+8A0A(B BBBE , (* AM
+B Y T ,, 8T BBB B(A0A8Gw
+8A0A(B BBBA < , @ BED A(G@x
+(D ABBD T , I BBB B(D0D8J?
+8A0A(B BBBE L - BBB B(D0D8JX
+8D0A(B BBBA L l- Hh ! BEE E(A0D8L
+8D0A(B BBBD T - h BBB B(A0A8J
+8A0A(B BBBK L . 0r BBB B(E0A8J@
+8A0A(B BBBA $ / `^ AG b
+AA L . BBB B(A0A8GPd
+8A0A(B BBBA d . BBB B(A0A8G`I
+8A0A(B BBBD j
+8A0A(B BBBL L D/ 0 - BBB E(A0C8G`x
+8A0A(B BBBH L / BBE E(A0C8GP
+8A0A(B BBBA d / BBB E(D0A8G`
+8A0A(B BBBD N
+8A0A(B BBBH T L0 t BBB B(D0A8G
+8A0A(B BBBH d 0 0 k BBB B(D0A8D`H
+8A0A(B BBBE
+8A0A(B BBBK D 1 o BEE D(D0G
+0D(A BBBB D T1 ? BEE D(D0G
+0D(A BBBA D 1 ? BEE D(D0G
+0D(A BBBA < 2 BBG A(G@
+(A ABBH T $2 ` 6 BBB B(A0D8J_
+8A0A(B BBBA T |2 E BBB B(A0D8Gk
+8A0A(B BBBG T 2 `
+: BEB B(D0D8M
+8D0A(B BBBD 3 >}. K^ T D3 0 BHB E(A0D8J
+8D0A(B BBBE T 3 &% BEB B(D0D8J
+8A0A(B BBBG T 3 9 BBB E(G0D8Jd
+8D0A(B BBBG T L4 =u BEE B(G0D8J\
+8D0A(B BBBD L 4 O BBB B(D0A8Dp
+8C0A(B BBBA 5 S 5 S 4 5 T AEG @
+AAA HAA 4 6 HU_ BAD U
+ADG oAB L 5 pU BJB A(A0b
+(A BBDE _(A BBB4 6 UT AAK q
+CAF DFA 6 U 6 V 7 V DR 7 V AW d 6 V + BEL B(A0A8D`
+8C0A(B BBBF L8A0A(B BBB d 6 hW BEL B(A0A8D`
+8C0A(B BBBF L8A0A(B BBB 4 8 WS AAK q
+CAF DEA D8 X$ DQ
+K D 7 Xr BBE A(D0DU
+0A(A BBBA D 7 PY) BBB A(A0D
+0A(A BBBA D <8 8Z) BBB A(A0D
+0A(A BBBA L <9 [ BEB B(A0A8D`\
+8A0A(B BBBA T 8 [, R BBE B(D0A8G
+8D0A(B BBBK T ,9 `A 8 BEE E(D0A8Go
+8D0A(B BBBG T 9 f BBA A(JK
(A ABBG K
-(A ABBA L BOB A(A0]
+(A ABBA L : g BOB A(A0]
(A BBBG Y
-(A BBBH l x T ` t BBB B(D0A8D
-8C0A(B BBBD T L t RBE B(G0A8Dv
-8A0A(B BBBA T Ct BBB B(A0A8G
-8C0A(B BBBA X0 AP
-G W \ h? BEA C(GPf
-(A ABBB {
-(A ABBG
-(A ABBF H DR $ P AW < D P{ BDD G0V
- AABE D CAB < BBG A(G@
-(A ABBG T 4 Pa 7s BBB E(A0G8J
-8A0A(B BBBA 4 hU BDA w
-ABO AAB L T c BKB B(A0A8Dpa
-8A0A(B BBBF T r BKB B(D0A8Dz
-8D0A(B BBBA L l hw lr BBB B(D0A8Dg
-8A0A(B BBBA T 6r BBB B(D0A8D
-8A0A(B BBBA L V q BBB B(D0A8D
-8D0A(B BBBA L d } q BBB B(D0H8Kp
-8A0A(B BBBA , D 7 BDD eAB T 0 Pq BBB B(A0A8G
-8A0A(B BBBA D < c q BBB A(D0GB
-0A(A BBBD D , p BJB K(A0j
-(_ aBBA ̡ " p BBB E(A0D8D
+(A BBBH D ,: 8h BEB D(D0D@
+0D(A BBBA T t: i X RBE B(G0A8Dv
+8A0A(B BBBA T : xk BBB B(A0A8G+
+8C0A(B BBBA ; r. [R < ; r{ BDD G0V
+ AABE D CAB < 4< s BBG A(G@
+(A ABBG T ; sO BBB B(D0G8J#
+8A0A(B BBBA L < zc BKB B(A0A8Dpa
+8A0A(B BBBF T d< | c BBB I(D0D8D)
+8D0A(B BBBA L < X~d BBB B(D0A8Dg
+8A0A(B BBBA T = x BBB B(D0A8D
+8A0A(B BBBA L d= V BBB B(D0A8D
+8D0A(B BBBA L = Ѕm r BBB B(D0H8Kp
+8A0A(B BBBA , > 0 BDD ^AB T 4> BBE E(A0A8G
+8A0A(B BBBA D > Hc BBB A(D0GB
+0A(A BBBD L ? p BBB B(A0D8G@j
+8A0A(B BBBH $? " W BBB E(A0D8D
8A0A(B BBDA
8F0A(B BBBE p
-8C0A(B BBBA L T {p BBB B(D0M8JL
-8D0A(B BBBD L 4 p BBB B(A0D8GP>
-8A0A(B BBBD T p BBB B(A0D8LE
-8C0A(B BBBA ܣ h݇. K^ " DR
-J A T U o BBB B(A0A8G_
-8A0A(B BBBF T ܣ K o BBB B(D0D8J
-8F0A(B BBBA T 4 # o BBB I(A0A8G
-8A0A(B BBBJ L ) o BBB B(D0A8Jr
-8A0A(B BBBE T ܤ - o BEB B(A0A8G
-8D0A(B BBBA $ ĥ 1) AGD ZAA L 1 AAG ]
+8C0A(B BBBA T ? BBB E(A0F8G*
+8C0A(B BBBA L @ @< BBB B(D0M8JL
+8D0A(B BBBD A 1}. K^ $A " DR
+J A T @ (2 y BBB E(A0A8G5
+8A0A(B BBBE T @ w c BBB B(D0D8J
+8F0A(B BBBA T <A 8` x BIB B(A0A8G
+8A0A(B BBBA L A @ x BBB B(A0D8Jr
+8A0A(B BBBE T A BEB B(A0A8G
+8D0A(B BBBA L B x AAG ]
CAF [
ACA R
-CAJ JCA , < 1b D\
+CAJ JCA , DC b D\
H P
H Q
-G I l 2
- 2
- 2
- 2
- ̦ 1 1 1 1 L 1 Jn BJB A(A0_
-(A BBDH _(A BBB | 82 02 < (2B m BBA A(Gg
-(A ABBA $ 83N AD p
-AJ T `3 m BBB B(A0D8G
-8A0A(B BBBA l ه. K^ 8 7 7 ̨ 7 7 7 7 , 7" D 8 \ 8 t 8- $ (8^ AG b
-AA `8 ̩ h8 `8 X8 P8 , X8 D P8 \ H8 t @8 , 88c ADG0s
-DAD L , x8 k BJE B(A0A8D@
-8A0A(B BBBA L | H9 j BJE B(A0A8D@
-8A0A(B BBBA T ̪ :| j BBE E(D0A8G
-8D0A(B BBBE T $ @?| j BBE E(D0A8G
-8D0A(B BBBE T | hD j BBE B(D0A8G
-8D0A(B BBBH L ԫ 0I sj BEB E(I0H8D@
-8D0A(B BBBD L $ J" Kj BJE B(A0A8D@
-8A0A(B BBBA ԇ. K^ T xK[ j BBB E(D0D8J
-
-8A0A(B BBBA T YJ sj BBE E(D0A8J}
-8A0A(B BBBD D ̭ xlL BBE A(A0D@
-0A(A BBBA T m j BBB B(D0A8G
-8D0A(B BBBJ D l 8ph BBB A(A0D@
-0A(A BBBA D $ `qN j BBE A(A0Jo
-0D(A BBBA T l hr| j BBD D(G
-(D ABBG
-(D ABBA < Į u cj BBD D(G
-(D ABBK D @w ;j BED A(J
-(D ABBA l L | Gj BEE E(D0H8D`i
+G I tC
+ C C س
+ C г
+ C ȳ
+ C
+ D D 4D LD
+ L C BJB A(A0_
+(A BBDH _(A BBB D D DU < ,D R BBA A(Gm
+(A ABBA $E (> D k
+A T D H ^ BBB B(A0D8G
+8A0A(B BBBA E `-}. K^ E E E ع E й F ȹ ,F й DF ع" \F tF < E w D BLD A(G@|
+(A ABBA F @- $ F X^ AG b
+AA L TF BBB B(A0A8GPd
+8A0A(B BBBA L F л BBB B(A0A8GPl
+8A0A(B BBBI G G ( G G H $H <H TH lH , H c ADG0s
+DAD L G 8 T BBB A(A0G9
+0C(A BBBA d LH ȿ 4 BBB B(D0D8G`
+8A0A(B BBBH N
+8A0A(B BBBH D H w BEJ D(D0G
+0D(A BBBE D H (w BEJ D(D0G
+0D(A BBBE T DI ` w BBE E(A0A8G
+8D0A(B BBBA < TJ BBG A(G@
+(A ABBH T I X
+ BBE B(A0A8J
+8A0A(B BBBA T 4J D BBE E(I0D8J
+8A0A(B BBBK < J BGB D(A0~
+(A BBBK L J (" BJE B(A0A8D@
+8A0A(B BBBA < K BAD '
+ABG O
+ABA L '}. K^ T tK ^
+ BEE B(A0D8JU
+8A0A(B BBBD T K ^ BBE E(D0A8J
+8A0A(B BBBI D L BBE A(A0D@
+0A(A BBBA T lL x] C BBB B(D0A8G
+8D0A(B BBBI 4 L K BAA
+ABA T L Hw
& BBB B(A0A8J
+8A0A(B BBBE D N p BBB A(A0D@
+0A(A BBBA D TN H4 BBB A(A0D@
+0A(A BBBA D M @N BBE A(A0Jo
+0D(A BBBA D ,N H% BDD Gp
+ DABF ~
+ DABC < tN 0 BBD D(G
+(D ABBJ < N
+ BED A(J
+(D ABBB L N p BEE E(A0D8Gg
+8D0A(B BBBG T DO BEE E(A0D8J%
+8D0A(B BBBF l O X BEE E(D0H8D`i
8A0A(B BBBD
-8A0A(B BBBB L D\ AG RAd ܯ (~ i BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d D ~ Wi BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB L h BJB A(A0W
-(A BBDH ^(A BBBL h BBB B(A0D8G@
-8A0A(B BBBB ܱ 8_ L d ?h BEB E(I0H8DP
-8D0A(B BBBD L @ h BJE B(A0A8D@
-8A0A(B BBBA L 9 g BEB F(G0D8D`
-8D0A(B BBBA L T g BBB I(I0A8DPv
-8D0A(B BBBH d s g BBB J(A0A8G`
+8A0A(B BBBB P |\ AG RAd ,P x BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB d P BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB L P h ? BJB A(A0W
+(A BBDH ^(A BBBL R BBB B(A0D8G@
+8A0A(B BBBB TR _ L Q BBB I(I0A8DPv
+8D0A(B BBBH L R g BEB E(I0H8DP
+8D0A(B BBBD L TR @ ? BJE B(A0A8D@
+8A0A(B BBBA L R 9 BEB F(G0D8D`
+8D0A(B BBBA d R s BBB J(A0A8G`
8F0A(B BBBF B
-8F0A(B BBBA d ȇ] Kg BBB B(I0A8G`
+8F0A(B BBBA T \S BBB B(A0A8J
+8A0A(B BBBC d S `"] R BBB B(I0A8G`
8F0A(B BBBF A
-8F0A(B BBBA T t f BBB B(A0I8N
-8D0A(B BBBF | \ 8 BBB B(D0A8DP
+8F0A(B BBBA T T X#t BBJ B(A0H8Fa
+8D0A(B BBBC T tT %t BBJ B(A0H8Fa
+8D0A(B BBBC | U ' BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI L ܴ BEB B(A0A8DP4
-8A0A(B BBBA , , BAH D` AAB, \ BAH D` AAB Ḣ. K^ l Y e BBB J(D0A8DR
-
-8F0A(B BBBA b
-8D0A(B BBGA L BBE B(A0A8D`
-8A0A(B BBBA L d ` BBE B(A0A8D`
-8A0A(B BBBA T $ Ч$ d BBB B(D0D8G
-8A0A(B BBBA X AL \ H e BBE D(A0P@|
+8A0A(B BBBI L V (* BEB B(A0A8DP4
+8A0A(B BBBA , TV x- BAH D` AAB, V - BAH D` AAB V }. K^ \ V `. K BBB I(D0D@
+0F(A BBBA `
+0D(A BBGA L ,W 5 BBE B(A0A8D`
+8A0A(B BBBA L |W 7 BBE B(A0A8D`
+8A0A(B BBBA T W : s BHB B(D0A8J
+8D0A(B BBBB $X XR AL \ W HR K BBE D(A0P@|
0D(A BBBK d
-0D(A BBBA T ~ pe BBE E(D0C8J>
-8A0A(B BBBI 4 T y 9e BKA Dp
- AABA ɇ. K^ 4 @ L 8 d 0 | ( , 0b d ADG r
-AAA ĸ p L ܸ h AAG ]
+0D(A BBBA T W R~ BBE E(D0C8J>
+8A0A(B BBBI 4 DX Y BKA Dp
+ AABA 4Y X}. K^ LY ^ dY ^ |Y ^ Y ^ , X ^b 4 ADG r
+AAA Y _ L Y ^ AAG ]
CAF [
ACA R
-CAJ JCA $ , ^ AG b
-AA , T 0 AF
FK
- D 4 = BDA Y
-JBL AAB 4 Թ J BDA o
+CAJ JCA $ DZ (_^ AG b
+AA , lZ `_0 AF
FK
+ D Z `` 4 Z X`= BDA Y
+JBL AAB 4 Z ``J BDA o
ABG A
-ABA < BDD k
+ABA < $[ x` BDD k
ABH V
-ABA L L H6 BEB B(A0A8D`h
-8A0A(B BBBA < 84 BBB A(A0d
-(A BBBE T L 8 `b BBE E(A0A8G
-8D0A(B BBBD D 4 p BBE A(A0D@l
-0A(A BBBA d a BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d T wa BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB L a BEB E(I0H8DP
-8D0A(B BBBD L ` BJE B(A0A8D@
-8A0A(B BBBA 5 T t j ` BBB E(A0A8IL
-8A0A(B BBBA 4 ̼ Y ` AIG t
-AAI DCA L K` BHF E(D0D8GP`
-8A0A(B BBBB < T 7` BGD A(G0~
-(A ABBG $ ć. K^ , < AAK0i
-AAA L l BEB B(A0A8DP#
-8A0A(B BBBA (" DN
-N A L ܾ 8
BEE A(A0]
+ABA L d[ `6 BEB B(A0A8D`h
+8A0A(B BBBA < [ a4 BBB A(A0d
+(A BBBE T <[ b BBE E(A0A8G
+8D0A(B BBBD D L\ e BBE A(A0D@l
+0A(A BBBA d [ e s BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB d D\ f BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB L \ f BEB E(I0H8DP
+8D0A(B BBBD L \ Hh BJE B(A0A8D@
+8A0A(B BBBA ^ i5 4 d] @iY C AIG t
+AAI DCA 4 ] hiY AIG t
+AAI DCA T ] i BBB B(A0D8I|
+8A0A(B BBBA ^ }. K^ , ^ l AAK0i
+AAA ,_ 0m" DN
+N A L L_ @m
BEE A(A0]
(A BBBF
-(E BBBK T ^ BBB B(A0A8J3
-8A0A(B BBBA T # _ BBB B(A0I8D
-8D0A(B BBBA ܿ h" DR
-J A d l x| `_ BGB E(D0A8Dp
+(E BBBK T ^ n BBB B(A0A8Jg
+8A0A(B BBBA T <_ }# BBB B(A0I8D
+8D0A(B BBBA L L` BBB E(A0A8DP
+8A0A(B BBBA ` " DR
+J A d ` | BGB E(D0A8Dp
8A0A(B BBBH k
-8A0A(B BBBA T Կ T _ BIE B(D0D8Go
-
-8A0A(B BBBA D , _ BBB D(I0GP
-0D(A BBBA D p BBB A(K0D@
-0A(A BBBA L H AL T H _ BBE B(D0D8J
-8A0A(B BBBA D 4 0# ` BBB A(D0D@
-0A(A BBBA 4 2 ZHA G HAB 4 3 K` BAA G0_
- AABB L 84 ` BEH B(D0D8Dp`
-8D0A(B BBBA 4 ML , \ 4 _ AAD@l
-AAA 4 h5 _ BAA GP
- AABB D T 7 BBB D(D0G@T
-0A(A BBBG 8 8 DP 8 DP < T 8 '_ BBA A(G@z
-(A ABBC T 8! ^ BBB B(A0D8G
-8A0A(B BBBA | HY Z @\ AG RA< < Z] _ AAG0O
+8A0A(B BBBA T l` BIB B(G0D8G
+8A0A(B BBBA D ` q BBB D(I0GP
+0D(A BBBA D a h BBB A(K0D@
+0A(A BBBA b @ AL T ta @ BBE E(H0D8G
+
+8A0A(B BBBA D a ȶ# BBB A(D0D@
+0A(A BBBA 4 b ZHA G HAB 4 Lb h BAA G0_
+ AABB L b BEH B(D0D8Dp`
+8D0A(B BBBA c AX D c BBB D(D0G@T
+0A(A BBBG c d DP $d DP T c M BBB B(D0D8G
+8A0A(B BBBB D c W BBB A(A0Dl
+0A(A BBBA D $d , BBB A(A0J
+0A(A BBBA < ld X @ BBA A(G@z
+(A ABBC T d ! BBB B(A0D8G
+8A0A(B BBBA e e X e :|\ AG RA< Te ] AAG0O
DAC [
-DAA D | Z n_ BEB A(A0G@y
-0D(A BBBA T [ l [ D [ _ BEE A(C0G@
-0D(A BBBA L < \ ^ BBB B(A0D8D`m
-8A0A(B BBBH T h_ ^ BBB B(A0A8G
-8A0A(B BBBB D zt P_ BEB A(A0GC
-0D(A BBBA , |@ 7_ BBB E(A0A8D
+DAA D e 0 BEB A(A0G@y
+0D(A BBBA f f D f # BEE A(C0G@
+0D(A BBBA L Tf BBB B(A0D8D`m
+8A0A(B BBBH T f BBB B(A0A8G
+8A0A(B BBBB D f t V BEB A(A0GC
+0D(A BBBA Dg @ ; BBB E(A0A8D
8A0A(B BBBI
8A0A(B BBBG
-8A0A(B BBBI L D Ѐf BBB B(D0G8D`u
-8D0A(B BBBG T
- ^ BEB B(A0A8G
-8A0A(B BBBA < \ X ^ BBA A(DO
-(A ABBA , ( ^ OGA A(D 4 ^ BJD K@g
- AABJ $ c AK c
-AA L , F^ BBB B(A0A8Gp@
-8A0A(B BBBA T | ؐs ] BLB B(D0A8G
-8A0A(B BBBA T ] BHF E(D0A8G
-8A0A(B BBBD T , Ȗ ] BEL B(D0D8J
-8A0A(B BBBE P. K^ , {\ AG RA L ȟ d П | ؟p cL 0 D 8q _] BBB A(D0Gpt
-0D(A BBBG D d p >] BBB D(D0D`
-0A(A BBBA D '] BBB D(D0De
-0A(A BBBA x p h `( Af p x L 4 p AAG ]
+8A0A(B BBBI L h f BBB B(D0G8D`u
+8D0A(B BBBG T h
+ BEB B(A0A8G
+8A0A(B BBBA < th ` BBA A(DO
+(A ABBA , h 0! OGA A(D 4 h " BJD K@g
+ AABJ $ i "c AK c
+AA L Di # J BBB B(A0A8Gp@
+8A0A(B BBBA T i $s BLB B(D0A8G
+8A0A(B BBBA T i ( BHF E(D0A8G
+8A0A(B BBBD T Dj * BEL B(D0D8J
+8A0A(B BBBE Tk }. K^ lk |\ AG RA k 3 k 3 k 3p cL k 84 D 4k @4q c BBB A(D0Gpt
+0D(A BBBG D |k x7 B BBB D(D0D`
+0A(A BBBA D k 9 + BBB D(D0De
+0A(A BBBA l : l : l x: m p: $m h:( Af Dm x: \m : L tm x: AAG ]
CAF k
ACA R
-CAJ JCA L AAG ]
+CAJ JCA L m : AAG ]
CAF [
ACA R
-CAJ JCA L AAG ]
+CAJ JCA L n : AAG ]
CAF c
CAI R
-CAJ JCA 4 p[ BAD D@Z
- AABA 4 J[ BAA
-ABD $ `b $[ Ag
+CAJ JCA D dn (; BEB A(D0O@d0D(A BBB L m p< , BOB B(A0A8Dp
+8A0A(B BBBA < Dn < BBD D(DPT
+(A ABBA 4 n @= BAA
+ABD $ n >b Ag
H ^
-E T , [ BBB B(A0A8J
-8A0A(B BBBC T 0 Z BBE B(A0A8D*
-8A0A(B BBBA T Z BBB E(A0A8D)
-8A0A(B BBBA d 4 @ 5Z BBA A(D
+E T n P> p BBB B(A0A8J
+8A0A(B BBBC T <o E E BBE B(A0A8D2
+8A0A(B BBBA T o I BBB E(A0A8D!
+8A0A(B BBBA d o M BBA A(D
(A ABBA U
(A ABBA
-(C ABBA , x2 Ad
-K A L ) DS
-I D T Y BBA A(D
+(C ABBA q PQ2 Ad
+K A ,q pQ) DS
+I D T p Q BBA A(D
(A ABBE J
-(A ABBA 4 ' sY A[
-A d T [Y BBB B(A0A8DP
-8A0A(B BBBE D8A0A(B BBB 4 L ] BDA A
-ABM AAB T нr X BBE E(D0G8J
-8A0A(B BBBA T L X BLB E(A0A8I
-8A0A(B BBBA 4 ` FP < L h BBG A(G@
-(A ABBF < BBG A(G@
-(A ABBH T < X BBB B(A0A8I)
-8A0A(B BBBA < v 8X BEA A(G0D
-(D ABBA d `
| X
P H @ 8 0 8" AN ND , H D P \ X" $ t pG AG a
-DC RD , $ b V AAG V
-CAA 8 Gp4 BAD D0
- AABA 4 ` $ X4 ;V AN cA $ p< V AN kA H L BBA D(D@x
+(A ABBA t p R BBA A(GM
+(A ABBH ]
+(A ABBE ^
+(A ABBD ]
+(A ABBA dq `U' A[
+A d q pU BBB B(A0A8DP
+8A0A(B BBBE D8A0A(B BBB 4 r U] BDA A
+ABM AAB T $r Vr BBE E(D0G8J
+8A0A(B BBBA T |r H\ BLB E(A0A8I
+8A0A(B BBBA s l FP < s l BBG A(G@
+(A ABBF T ,s m v BBB B(A0A8I5
+8A0A(B BBBA < s }v BEA A(G0D
+(D ABBA |t P}
t H}
t @} t 8} t 0} t (} u } $u (}" AN ND Du 8} \u @} tu H}" $ u `}G AG a
+DC RD u } , u }b AAG V
+CAA u }8 Gp4 v } BAD D0
+ AABA Lv P~ $ u H~4 AN cA $ u `~< s AN kA v x~H L v ~ BBA D(D@x
(D ABBB
-(D ABBG Q \ (h sU BEB D(D0Gp
+(D ABBG w Q \ |v h BEB D(D0Gp
0D(A BBBK L
-0D(A BBBA D 8 3U BBE D(D0D
-0D(A BBBD D 4 "U BAD GPl
+0D(A BBBA D v ( BBE D(D0D
+0D(A BBBD D $w ~ BAD GPl
DABG
- DABA T | ha T BEE I(A0A8G ^
-8A0A(B BBBJ L T BGD D(D0z
+ DABA T lw Xa N BEE I(A0A8G ^
+8A0A(B BBBJ L w p F BGD D(D0z
(D ABBH V
-(D ABBA , $ T AAI@T
-DAD , `T BKI _
-ABH , D BHD ^
-ABA L )T BBB E(D0A8F
-8A0A(B BBBH L S BBB B(A0C8J
F
-8D0A(B BBBG D T @C DT BBB D(D0Gpn
-0D(A BBBB L H 1 T BBE B(A0A8GS
-8D0A(B BBBD L 8c ST BBB B(D0A8G_
-8A0A(B BBBC L < X WT BBB B(D0A8GQ
-8A0A(B BBBA < d T BGF A(J0i
-(D ABBA , ( OT YX
+(D ABBA , x AAI@T
+DAD , x PT BKI _
+ABH , ,y D BHD ^
+ABA L x BBB E(D0A8F
+8A0A(B BBBH D x pC S BBB D(D0Gpn
+0D(A BBBB L <y x , BBB B(A0C8J
F
+8D0A(B BBBG , y 8 y YX
G Z
-A L -T BLE E(A0D8J
-8A0A(B BBBE < L "d KT BJD D(D@o
-(D ABBA T 8"< T BBE B(D0A8G
-8D0A(B BBBB T '< T BBE B(D0A8G
-8D0A(B BBBB L < , S BVB B(D0A8G^
-8D0A(B BBBE T X:"( ?T BEB B(A0A8G
-8A0A(B BBBG T 0bv7 U BBB B(A0A8G
-8A0A(B BBBA X ` X P , H D @ \ 8 t 0 ( < J BEE D(D0\(Q BBB D HT d # DT | 8 0 ( 0 8# D ^ , d P U YX
+A L y W BLE E(A0D8J
+8A0A(B BBBE < z d u BJD D(D@o
+(D ABBA L Lz H G BBE B(A0A8GS
+8D0A(B BBBD L z r BBB B(D0A8G_
+8A0A(B BBBC L z ( BBB B(D0A8GQ
+8A0A(B BBBA < <{ d BGF A(J0i
+(D ABBA T |{ ȼ< BBE B(D0A8G
+8D0A(B BBBB T { < BBE B(D0A8G
+8D0A(B BBBB L ,| o BVB B(D0A8G^
+8D0A(B BBBE T || ( BEE B(A0A8G
+8D0A(B BBBE T | p
+6 W BBB B(A0A8G
+8A0A(B BBBA } (3 } 3 ~ (3 ,~ 3 D~ 3 \~ 3 t~ 3 ~ 3 ~ 2 ~ 2 ~ 2 ~ 2 2 2 4 2 < L 2J BEE D(D0\(Q BBB 2 HT 2# DT 2 2 2 2 $ 2# D ^ , 3 ' YX
G H
-A $ $ < 0 HD
-D d | v D X BIB B(A0A85
-0A(B BBBA 4 BFA A(
- ABBA D , , BIE B(A0A8
-0A(B BBBA , t AC
TS
- A e G@]L x BEE E(D0D8J
-8A0A(B BBBI d H BJB B(D0D8G
-8A0A(B BBBD B8A0A(B BBB t D xh BFB B(A0A8H
-0A(B BBBA 4 BFA A(
- ABBA L X BEB B(A0A8G
-8A0A(B BBBA 4 \ % AC
T
+A l 3 $ x3T HD
+D 7 Ā X| ܀ 7v D 7X BIB B(A0A85
+0A(B BBBA 4 < 9 BFA A(
+ ABBA D t ;, BIE B(A0A8
+0A(B BBBA , ? AC
TS
+ A De G@]L hD BEE E(D0D8J
+8A0A(B BBBI d T 8G BJB B(D0D8G
+8A0A(B BBBD B8A0A(B BBB G D Ԃ hHh BFB B(A0A8H
+0A(B BBBA 4 I BFA A(
+ ABBA L T HN BEB B(A0A8G
+8A0A(B BBBA 4 UM AC
T
A
- H d G@\L X BEE E(D0D8J
-8A0A(B BBBI d ( BHB B(D0D8G
-8A0A(B BBBF B8A0A(B BBB , d ) G
+ H L ܃ \ BEB B(A0A8Gb
+8A0A(B BBBA L , _ BHB B(A0A8Dp
+8A0A(B BBBD < | `< BBE A(A0
+(A BBBI , a AKFAA , Pb AKFjAA b- Ae L < b BBF F(D0A8D`}
+8A0A(B BBBH , `c) G
A j
F `
-H P L BBB B(A0A8Dh
-8A0A(B BBBA B L @ BBB B(A0A8J
-8A0A(B BBBJ \ d Q BEB B(A0A8
+H P `d L ԅ He BBB B(A0A8Dh
+8A0A(B BBBA $ fB L < f BBB B(A0A8J
+8A0A(B BBBJ \ jQ BEB B(A0A8
0A(B BBBB V
-0A(B BBBA , AA
-AA A , @ AC
T , $ AC
T < T < BBE A(A0
-(A BBBI , AKJAA , AKJAA , AKJAA $ $ @ Cb
+0A(B BBBA , l AA
+AA A , m AC
T , L `s AC
T < | y< BBE A(A0
+(A BBBI , z AKJAA , z AKJAA , { AKJAA $ L { Cb
K E
-K 4 Y yN AC
BEHS
- J , AF
T
- J 4 AF
T~
- G N , H= AF
T
- G L X BDB B(A0D8G
-8A0A(B BBBB L l BME B(A0A8DX
-8A0A(B BBBG L ( BNE B(A0G8DQ
-8A0A(B BBBB L h BEE B(A0A8G:
-8A0A(B BBBA \ M G EL t X BBE B(A0A8J
-8A0A(B BBBH d BBB B(A0A8GPl
+K 4 (|Y AC
BEHS
+ J , P AF
T
+ J 4 ܈ AF
T~
+ G N , ȉ= AF
T
+ G L D ؎ BDB B(A0D8G
+8A0A(B BBBB L X BME B(A0A8DX
+8A0A(B BBBG L BNE B(A0G8DQ
+8A0A(B BBBB L 4 BEE B(A0A8G:
+8A0A(B BBBA 8M G EL pX BBE B(A0A8J
+8A0A(B BBBH d BBB B(A0A8GPl
8A0A(B BBBI
-8A0A(B BBBI D , h BEB E(A0A80A(B BBB D t BEB B(D0A8
-0A(B BBBD L
- BEB B(A0A8D
-8A0A(B BBBB D (
BBB K(A0A8
-0A(B BBBD L T
2 BBB B(A0D8DD
-8A0A(B BBBD L p BEB B(A0A8D
-8A0A(B BBBE L $ BBB E(A0A8GBb
-8A0A(B BBBA L t @ BBB B(A0D8GA
-8A0A(B BBBE L BBE B(A0A8GA
-8A0A(B BBBA L # BBB B(A0A8G@6
-8A0A(B BBBG < ( |H BBA A(D0(A ABB L `( BBB B(A0A8G
-8A0A(B BBBB L -d BBB B(A0A8G=
-8A0A(B BBBA L D /u BBB B(A0A8JG
-8A0A(B BBBA L P2, BBB B(A0A8G
-8A0A(B BBBG L 04 BBB B(A0D8J8A0A(B BBB L 4 6 BBB B(A0D8G8A0A(B BBB 8 L x8{ BBE B(A0C8G3
-8A0A(B BBBE L ;` BBB B(D0A8J
-8A0A(B BBBH L < A
+8A0A(B BBBI D T BEB E(A0A80A(B BBB D p BEB B(D0A8
+0A(B BBBD L BEB B(A0A8D
+8A0A(B BBBB D 4 BBB K(A0A8
+0A(B BBBD L | p2 BBB B(A0D8DD
+8A0A(B BBBD L ̌ `p BEB B(A0A8D
+8A0A(B BBBE 4 X L L BBB E(A0A8GBb
+8A0A(B BBBA L P@ BBB B(A0D8GA
+8A0A(B BBBE L @ BBE B(A0A8GA
+8A0A(B BBBA L < BBB B(A0A8G@6
+8A0A(B BBBG < ԍ BBA A(D0(A ABB T BBB B(A0A8G
+8A0A(B BBBJ L $ d BBB B(A0A8G=
+8A0A(B BBBA L t 8u BBB B(A0A8JG
+8A0A(B BBBA L ď h, BBB B(A0A8G
+8A0A(B BBBG L H BBB B(A0D8J8A0A(B BBB L d BBB B(A0D8G8A0A(B BBB L ̐ { BBE B(A0C8G3
+8A0A(B BBBE L ` BBB B(D0A8J
+8A0A(B BBBH L l
BBB B(A0C8JO
-8A0A(B BBBA L xK BBB B(A0C8J
-8A0A(B BBBG L \ BBB B(A0C8J
-8A0A(B BBBA , , f` BDA
-RID < \ it BBA A(G
-(A ABBH , (k{ AAGoAA L xkT BBB B(A0D8G58A0A(B BBB m KIA D(G@(A ABBF@
+8A0A(B BBBA L BBB B(A0C8J
+8A0A(B BBBG L BBB B(A0C8J
+8A0A(B BBBA , \ ` BDA
+ABD < t BBA A(G
+(A ABBH , ̒ @
+{ AAGoAA L
+T BBB B(A0D8G58A0A(B BBB L KIA D(G@(A ABBF@
(A ABBO
-(G EBBH D(G EBB t pv hv @x 4 8x BDD G0 AAB D | L \ |` BDB B(A0A8Dp
-8A0A(B BBBD }O P~ H~; p hf $ < , T q BDD GP[ AAB, ] FLQ )
-ABA x , D $ \ AC
- I , AG
FEO
- H , P AG
FEO
- H < l q ? BBA A(D0[
-(A ABBA L < AAG ]
+(G EBBH D(G EBB ܓ $ X 4 < P BDD G0 AAB t ( L ` BDB B(A0A8Dp
+8A0A(B BBBD ܔ 0 O h ` ; $ < f T ! l " , #q BDD GP[ AAB, #] FLQ )
+ABA (% % % , & D & \ & t * D . BKB A(A0D`Q
+0A(A BBBB D Ԗ (7 BKB A(A0D`a
+0A(A BBBB $ AC
+ I , D g AG
FEO
+ H , t Hn AG
FEO
+ H < q BBA A(D0[
+(A ABBA L 0 AAG ]
CAF c
CAI R
-CAJ JCA < H \? AAG`
+CAJ JCA < p ] AAG`
AAJ M
-AAA , ,6 AG lA ,> AG tA , $ { BFA i
-ABA L > BBB F(A0A8J@X
-8A0A(B BBBA L p r> BBE A(A0J
-0A(A BBBA , d ,p w> AHK s
-AAA L P,G W> BEE D(D0G
-0D(A BBBA T P-q > BEE E(D0D8G0
-8D0A(B BBBA T < x.q = BEE E(D0D8G0
-8D0A(B BBBA T /q = BEE E(D0D8G0
-8D0A(B BBBA T 0q G= BEE E(D0D8G0
-8D0A(B BBBA < D 0% = BBA A(J
-(A ABBC T 1q d= BEE E(D0D8G0
-8D0A(B BBBA L 2G = BEE D(D0G
-0D(A BBBA T , 3Z < BEE E(D0D8G
-8D0A(B BBBA L 4G < BEE D(D0G
-0D(A BBBA L 5G \< BEE D(D0G
-0D(A BBBA L $ 6G < BEE D(D0G
-0D(A BBBA L t 7G ; BEE D(D0G
-0D(A BBBA L 8 ; BEE E(D0D8G
-8D0A(B BBBA L p9 d; BEE E(D0D8G
-8D0A(B BBBA T d :Z &; BEE E(D0D8G
-8D0A(B BBBA T : BBB B(A0A8G
-8A0A(B BBBB L :G ; BEE D(D0G
-0D(A BBBA L d ;G I; BEE D(D0G
-0D(A BBBA T _ ; BBE B(A0D8G
-8A0A(B BBBA T X<q
; BEE E(D0D8G0
-8D0A(B BBBA L d =G : BEE D(D0G
-0D(A BBBA L >G : BEE D(D0G
-0D(A BBBA L ?G K: BEE D(D0G
-0D(A BBBA L T @G
: BEE D(D0G
-0D(A BBBA d p 9 BBA A(J
+AAA 0r 8r6 AG lA Ę Xr> AG tA , { BFA i
+ABA L \ BBB F(A0A8J@X
+8A0A(B BBBA L v BBE A(A0J
+0A(A BBBA , qp { AHK s
+AAA \ BBB B(A0A8D@
+8C0A(B BBEA D8C0A(B BBBL qG BEE D(D0G
+0D(A BBBA T ܙ rq BEE E(D0D8G0
+8D0A(B BBBA T 4 sq w BEE E(D0D8G0
+8D0A(B BBBA T tq 1 BEE E(D0D8G0
+8D0A(B BBBA T vq BEE E(D0D8G0
+8D0A(B BBBA T < (wq BEE E(D0D8G0
+8D0A(B BBBA T Pxq _ BEE E(D0D8G0
+8D0A(B BBBA <
BBA A(J
+(A ABBC T , 8yq p BEE E(D0D8G0
+8D0A(B BBBA L `zG * BEE D(D0G
+0D(A BBBA T Ԝ `{Z BEE E(D0D8G
+8D0A(B BBBA L , h|G BEE D(D0G
+0D(A BBBA L | h}G h BEE D(D0G
+0D(A BBBA L ̝ h~G * BEE D(D0G
+0D(A BBBA L hG BEE D(D0G
+0D(A BBBA L l h BEE E(D0D8G
+8D0A(B BBBA L p BEE E(D0D8G
+8D0A(B BBBA T Z 2 BEE E(D0D8G
+8D0A(B BBBA T d 0 BBB B(A0A8G
+8A0A(B BBBB T 8q BEE E(D0D8G0
+8D0A(B BBBA L ` M BEE E(D0D8Gp
+8D0A(B BBBA L d ЃG BEE D(D0G
+0D(A BBBA L ЄG ѹ BEE D(D0G
+0D(A BBBA L ЅG BEE D(D0G
+0D(A BBBA L T ІG U BEE D(D0G
+0D(A BBBA L ЇG BEE D(D0G
+0D(A BBBA T _ ٸ BBE B(A0D8G
+8A0A(B BBBA L L xG ۸ BEE D(D0G
+0D(A BBBA L xG BEE D(D0G
+0D(A BBBA L xG _ BEE D(D0G
+0D(A BBBA L < xG ! BEE D(D0G
+0D(A BBBA d x BBA A(J
(A ABBA U
(A ABBA U
-(A ABBA T Aq 9 BEE E(D0D8G0
-8D0A(B BBBA L d @BG 9 BEE D(D0G
-0D(A BBBA L @CG Z9 BEE D(D0G
-0D(A BBBA L @DG 9 BEE D(D0G
-0D(A BBBA T T @Eq 8 BEE E(D0D8G0
-8D0A(B BBBA T hFq 8 BEE E(D0D8G0
-8D0A(B BBBA L GG ^8 BEE D(D0G
-0D(A BBBA L T HG 8 BEE D(D0G
-0D(A BBBA L IG 7 BEE D(D0G
-0D(A BBBA L JG 7 BEE D(D0G
-0D(A BBBA L D KG f7 BEE D(D0G
-0D(A BBBA L LG (7 BEE D(D0G
-0D(A BBBA L MG 6 BEE D(D0G
-0D(A BBBA L 4 NG 6 BEE D(D0G
-0D(A BBBA , O\ 6 BEH m
-ABA D O \ HP 4 t @Pi ADG S
+(A ABBA T % BDA DPm
+ AABD x
+ AABD
+ AABA D L 8' Ʒ BBA A(J
+(A ABBA L pG BEE D(D0G
+0D(A BBBA L pG BEE D(D0G
+0D(A BBBA L 4 pG s BEE D(D0G
+0D(A BBBA T pq A BEE E(D0D8G0
+8D0A(B BBBA L ܥ G BEE D(D0G
+0D(A BBBA L , G BEE D(D0G
+0D(A BBBA T | q BEE E(D0D8G0
+8D0A(B BBBA L Ԧ G 9 BEE D(D0G
+0D(A 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 A BEE D(D0G
+0D(A BBBA L d G BEE D(D0G
+0D(A BBBA , \ BEH m
+ABA x 4 ̩ pi ADG S
CAE ]
-FCB T xPd 6 BEB E(A0C8J
-8A0A(B BBBA | U BIE B(A0A8GP
+FCB T L & ' BBE B(A0A8G
+8A0A(B BBBA L 0
+ BBB D(A0GP
+0A(A BBBF T : O BBB B(A0D8G1
+8A0A(B BBBA T L XBi BBB E(A0A8G
+8A0A(B BBBA T pM| BBE B(A0A8G^
+8A0A(B BBBD < U" Q BJA A(D
+(A ABBA T < f + BEB E(A0C8J
+8A0A(B BBBA | L М BIE B(A0A8GP
8A0A(B BBBD B
8D0A(B BBBA
-8A0A(B BBBG T e /6 BBE B(A0A8GV
-8A0A(B BBBD T L 8 6 BBE B(A0A8G
-8A0A(B BBBA L E
- 7 BBB D(A0GP
-0A(A BBBF T | 7 BBE B(A0A8G^
-8A0A(B BBBD < L " 8 BJA A(D
-(A ABBA T 7 BBB E(A0A8G
-8A0A(B BBBA t 0|. K^ T 1 <8 BBB B(A0A8G
-8A0A(B BBBF `Tm A~
-A l L t K8 BBB B(A0A8JW
-8A0A(B BBBK T 8 BEJ E(D0D8G
-8D0A(B BBBD < X| ^8 AAD`
+8A0A(B BBBG T `| BIB B(A0A8G?
+8A0A(B BBBA T l XU1 [ BBB B(A0A8G
+8A0A(B BBBE | |. K^ 4 ܬ 8 r BDD H
+ABK 4 J BDD H
+ABK T L " BED A(G0Q
+(D ABBC k
+(D ABBA 4 P ܵ BDD Q
+ABB 4 ܭ BDD Q
+ABB < BBD D(G0
+(D ABBA < T c BBD D(G0
+(D ABBA < @ 6 BEA D(G0m
+(D ABBA < Ԯ BBD D(G0
+(D ABBA < ۴ BBD D(G0
+(D ABBA D T p BEE D(A0Q
+(A BBBG T AQ t m A~
+A l L ܯ 8 ; BBB F(A0A8J@X
+8A0A(B BBBA L , ( ? BBB B(A0A8GZ
+8D0A(B BBBH D | : BED D(G$
+(D ABBE D İ BED D(G$
+(D ABBE < 5 г BBA A(JW
+(A ABBC L L BEE D(D0G0
+0D(A BBBJ T = BEE E(D0D8GU
+8D0A(B BBBA < @| AAD`
AAH p
-AAF T \ ! 78 BEJ E(D0D8Gn
-8D0A(B BBBH L p 7 BBB B(D0A8Gi
-8A0A(B BBBI T 1 7 BEJ E(D0D8G
-8D0A(B BBBH 4 \ R 7 BCA GPc
- AABA d M f7 BLB B(A0A8DPq
+AAF T 4 г BEE E(D0D8GE
+8D0A(B BBBF L BBB B(D0A8Gi
+8A0A(B BBBI D ܲ H f BED D(G$
+(D ABBE T $ 1 BEE E(D0D8GH
+8D0A(B BBBA 4 | h BCA GPc
+ AABA d M Ʋ BLB B(A0A8DPq
8A0A(B BBBE
-8A0A(B BBBG T ! 7 BEJ E(D0D8Go
-8D0A(B BBBG < p BBG A(G@
-(A ABBG L 0 6 BLB B(D0A8DP
-8A0A(B BBBG L ^ c6 BLH B(D0A8G@
-8A0A(B BBBB
- 5 <
- BBG A(G@
-(A ABBG w. K^ D
- ` 7 BIB A(A0G`o
-0A(A BBBA T
- 8QR o7 BIE B(A0A8G
-8D0A(B BBBD T D @U _7 BIE B(A0A8G<
-8D0A(B BBBD T Y O7 BIE B(A0A8G\
-8D0A(B BBBD T P^F ?7 BBE B(A0A8G
-8A0A(B BBBA T L Hw .8 BIB E(A0A8G1
-8A0A(B BBBJ T
}8 BEB B(A0A8G
-8A0A(B BBBB
m A~
-A l L
X 8 BBB B(A0A8Gv
-8A0A(B BBBG T l
8 BIE B(A0A8G
-8A0A(B BBBD T
9 BEJ E(D0D8GP
-8D0A(B BBBF T 8 8 BEJ E(D0D8GP
-8D0A(B BBBF 4 t 8 BCA GPc
- AABA <
-Q 8 BDD v
-ABE AAB | s. K^ T 0 : BIB B(D0A8G
-8D0A(B BBBA T \ h a: BIB B(D0A8G
-8D0A(B BBBA T .: BIB B(D0A8G
-8D0A(B BBBA T 0 9 BIB B(D0A8G8
-8D0A(B BBBA 4 D BEA A(D0p(A ABB L ( 9 BBB E(A0C8GP#
-8A0A(B BBBE T S9 BBB E(A0A8G:
-8A0A(B BBBH T D P 9 BBE B(A0A8G
-
-8A0A(B BBBA T z: BEB B(A0A8G
-8A0A(B BBBB D `: BBE A(A0D@I
-0A(A BBBA < < 8 c: YHD M
+8A0A(B BBBG T l BEE E(D0D8GX
+8D0A(B BBBK < , 0 BBG A(G@
+(A ABBG L BLB B(D0A8DP
+8A0A(B BBBJ L `^ BLH B(D0A8G@
+8A0A(B BBBB p5 < $ BBG A(G@
+(A ABBG X C BBB E(A0A8D
+8A0A(B BBBI n
+8A0A(B BBBH
+8A0A(B BBBI @|. K^ D L BIB A(A0G`o
+0A(A BBBA T P% t BIB B(D0A8GW
+8D0A(B BBBA T ( M BIB B(D0A8G
+8D0A(B BBBA L D 3 & BIB D(A0G
+0D(A BBBA < @ l BBA A(J
+(A ABBJ 4 Է x BAA GP_
+ AABB T h BEB B(A0A8G
+8A0A(B BBBA L d K BBB B(A0A8J]
+8A0A(B BBBE T [ BIB B(D0A8G
+8A0A(B BBBA T H
d BBB B(D0A8G
+8A0A(B BBBJ Z 4 m A~
+A l L
+ s BBB F(A0A8J@X
+8A0A(B BBBA L ! BBB B(A0A8Gv
+8A0A(B BBBG T < `V 5 BEE E(D0D8G
+8D0A(B BBBA T aV BEE E(D0D8G
+8D0A(B BBBA 4 h
ʹ BCA GPc
+ AABA T $ b BEE E(D0D8GB
+8D0A(B BBBA T | c b BEE E(D0D8GB
+8D0A(B BBBA T Ի dr K BEE E(D0D8G1
+8D0A(B BBBA T , fr BEE E(D0D8G1
+8D0A(B BBBA T @gr BEE E(D0D8G1
+8D0A(B BBBA T ܼ hh y BEE E(D0D8GU
+8D0A(B BBBA T 4 i 3 BEE E(D0D8GU
+8D0A(B BBBA T BIB B(A0A8J$
+8A0A(B BBBG < jQ ? BDD v
+ABE AAB ܾ |. K^ L < ( Ĵ BIB D(A0Gq
+0D(A BBBA L : BIB D(A0G
+0D(A BBBA L ܾ : n BIB D(A0G
+0D(A BBBA L , 3 C BIB D(A0G
+0D(A BBBA L | BIB D(A0G
+0D(A BBBA L ̿ 8" BIB D(A0G
+0D(A BBBA 4 $D BEA A(D0p(A ABB L T hj c BBB E(A0C8DP
+8A0A(B BBBA T p$ & BBB B(A0A8J
+8A0A(B BBBA T /$ BEB B(A0A8G
+
+8A0A(B BBBA T T ?% 9 BEB B(A0A8G
+8A0A(B BBBA T G% D BEB B(A0A8G
+8A0A(B BBBA T `O O BIB B(A0D8G$
+8A0A(B BBBG D BBE A(A0D@~
+0A(A BBBA D \ @ BBE A(A0D@I
+0A(A BBBA < hg YHD M
HAE
-HAA
- L
- : BBB B(A0A8G@
-8D0A(B BBBA t
-
-
-
-
-
-
-
- 4
-& PT L
-/ SZ d
- L |
-e BEA A(D0s
-(C ABBH D(G ABB ' AR
-E N ( L 0 m BEA A(D0~
-(C ABBE D(G ABB T P ' AR
-E N $ t ` ! AAG SCA h p $ x 1 ADJ [AA $ AAG`AA B 4 P L H AZ l H @ 8 L $ : 6 BJB A(A0W
-(A BBDH ^(A BBBT t 6 BGB I(A0A8G
-8A0A(B BBBD $ h 7 YX
+HAA Hh L D @h BBB B(A0A8G@
+8D0A(B BBBG L i d i | i xi pi hi pi hi pi& PT $ i/ SZ < i L T ie BEA A(D0s
+(C ABBH D(G ABB i' AR
+E N i L im BEA A(D0~
+(C ABBE D(G ABB , j' AR
+E N $ L j! AAG SCA t j j $ (j1 ADJ [AA $ @j AAG`AA jB k $ j AZ D j \ j t j L ( BJB A(A0W
+(A BBDH ^(A BBBT $ j BGB I(A0A8G
+8A0A(B BBBD $ | z s YX
G z
-A $ v ACG eDAT ( '
- L7 BBB E(A0D8D
-8D0A(B BBBG L t & 7 BEB A(A0d
-(A BBDH [(A BBGL `&/ O7 BBE B(A0A8DP
-8A0A(B BBBA j. K^ D 8 BBE A(A0D@~
-0A(A BBBA T t &t #7 BBB B(A0G8G
-8A0A(B BBBF T 0 "7 BIB B(A0D8DH
-8D0A(B BBBC , $ 2U 7 ADD`v
-AAA , `8b D\
+A L \ z BBD D(D@v
+(D ABBA X(D ABB T z'
+ BBB E(A0D8D
+8D0A(B BBBG L L Ȅ S BEB A(A0d
+(A BBDH [(A BBGL (/ BBE B(A0A8DP
+8A0A(B BBBA |. K^ T t ϱ BBB B(A0G8G
+8A0A(B BBBF T \ α BIB B(A0D8DH
+8D0A(B BBBC , U ADD`v
+AAA , pb D\
H P
H Q
-G I , 8b D\
+G I , b D\
H P
H Q
-G I , D 8b D\
+G I , b D\
H P
H Q
-G I , t 9b D\
+G I , , 0b D\
H P
H Q
-G I , `9b D\
+G I , \ pb D\
H P
H Q
-G I , 9b D\
+G I , b D\
H P
H Q
-G I , 9b D\
+G I , b D\
H P
H Q
-G I , 4 :b D\
+G I , 0b D\
H P
H Q
-G I , d `:b D\
+G I , pb D\
H P
H Q
-G I 4 : 75 BAD D
-ABA , < H 5 AAG@B
-AAC , l 4 AAG@B
-AAC , , ( ADG [
-AAG 4 y 5 BDD G0y
- AABA < (5 ACDP
+G I 4 ~ BAD D
+ABA L ǯ BBB F(A0A8J@X
+8A0A(B BBBA , AAG@B
+AAC , L ( s AAG@B
+AAC , 4 ADG [
+AAG 4 y BDD G0y
+ AABA < P ACDP
AAA O
-AAA < D 5 AADP
+AAA < $ 0 AADP
AAA O
-AAA D (5 BJD D(G$
-(D ABBH D 4 BJD D(G$
-(D ABBH < p 4 AADP
+AAA D d BED D(G$
+(D ABBE D x BED D(G$
+(D ABBE < Q AADP
AAI O
-AAA T T P 4 BEJ E(D0D8GT
-8D0A(B BBBJ T o4 BEJ E(D0D8GT
-8D0A(B BBBJ T *4 BEJ E(D0D8GT
-8D0A(B BBBJ < \ 3 BED C(GP{
-(A ABBJ \ 7n 3 BBB A(A0GP
-0A(A BBBD j
-0A(A BBBA \ 8n 3 BBB A(A0GP
-0A(A BBBD j
-0A(A BBBA D \ X 3 BJD D(G$
-(D ABBH D p9W a3 BBB A(A0GP
-0A(A BBBH 4 | xJ BDA l
+AAA T 4 G BEE E(D0D8GT
+8D0A(B BBBG T x BEE E(D0D8GT
+8D0A(B BBBG T 0 BEE E(D0D8GT
+8D0A(B BBBG < <
+ x BED C(GP{
+(A ABBJ D | X BED D(G$
+(D ABBE 4 |
J BDA l
ABJ A
-ABA m A~
-A l m A~
-A l D d 0 2 BJD D(G$
-(D ABBH 4 2 AAD
-DAA 4 2 AAD$
-DAA 4 ! H r2 AAD
-DAA L T! : ^2 BDB A(A0L
-0A(A BBBE T ! = F2 BBB B(A0A8JB
-8A0A(B BBBA " `. K^ 4 " Яg 33 BDD G~
- AABA 4 L" D 3 BAD G
-ABA 4 # ЯJ BDA o
+ABA
m A~
+A l (m A~
+A l D < x BED D(G$
+(D ABBE 4 Э AAD
+DAA 4 8 AAD$
+DAA 4 AAD
+DAA T , h] BBB B(A0A8JB
+8A0A(B BBBA < |. K^ 4 hg BDD G~
+ AABA 4 [ BAD G
+ABA 4 hJ BDA o
ABG A
-ABA L " D 3 BBB I(A0G^
-0D(A BBBA < # G 3 BAI G|
- DABA < L# 8I d3 BAI G
- DABA < # K0 =3 BAI G
- DABA < # M0 3 BAI G
- DABA T $ N4 2 BBB B(I0A8Gg
-8D0A(B BBBA T d$ Q4 2 BBB B(I0A8Gg
-8D0A(B BBBA < $ T; 2 BAI G
- DABA 4 % v BDA D0a
- AABA L % BBB B(A0A8Dp
-8A0A(B BBBA L % z 1 BBB E(D0D8GP
-8A0A(B BBBC T % U 1 BIB B(A0A8J9
-8A0A(B BBBB & i < D& i 2 BBK A(DPn
-(A ABBA < & 0j 2 BBK A(DPn
-(A ABBA T & j 2 BBB B(A0A8J.
-8A0A(B BBBD T ' q 2 BBB B(A0A8G N
-8A0A(B BBBG D t' t #4 BBI A(G
-(D ABBA , L( ȭb D\
+ABA T D @ BBB B(A0I8G
+8D0A(B BBBF < BAI G
+ DABA < h" BAI G
+ DABA < X8 BAI G
+ DABA < \ X8 l BAI G
+ DABA T XD E BBB B(I0A8Gw
+8D0A(B BBBA T PD BBB B(I0A8Gw
+8D0A(B BBBA < L HK BAI G
+ DABA 4 D 8v BDA D0a
+ AABA L | BBB B(A0A8Dp
+8A0A(B BBBA L z ? BBB E(D0D8GP
+8A0A(B BBBC T d BIB B(A0A8J9
+8A0A(B BBBB t < , BBK A(DPq
+(A ABBA < BBK A(DPq
+(A ABBA D T W S BBB A(A0GP
+0A(A BBBH \ 8n 9 BBB A(A0GP
+0A(A BBBD j
+0A(A BBBA \ Hn BBB A(A0GP
+0A(A BBBD j
+0A(A BBBA L \ X BDB A(A0L
+0A(A BBBE T BBB B(A0A8J.
+8A0A(B BBBD T BBB B(A0A8G
+8A0A(B BBBI D \ 3 BBI A(G
+(D ABBA , \ b D\
H P
H Q
-G I , |( b D\
+G I , @b D\
H P
H Q
-G I , ( Hb D\
+G I , b D\
H P
H Q
-G I T L( k3 BBB B(A0A8G
-8A0A(B BBBJ T ( e I3 BEE B(A0A8G
-8A0A(B BBBF T ( % A3 BBB B(A0A8Gk
-8A0A(B BBBJ T T) e +3 BEE B(A0A8G
-8A0A(B BBBF T ) e #3 BEE B(A0A8G
-8A0A(B BBBF T * u 3 BBB B(A0A8G
-8A0A(B BBBB * X. K^ T t* b 5 BBB B(A0A8G
-8A0A(B BBBA T * B 75 BBB B(A0A8G
-8A0A(B BBBA T $+ d k5 BBB B(A0A8G(
-8A0A(B BBBA , , hb D\
+G I , b D\
H P
H Q
-G I , <, b D\
+G I T d
K BBB B(A0A8G
+8A0A(B BBBJ T Hpe ) BEE B(A0A8G
+8A0A(B BBBF T `ve BEE B(A0A8G
+8A0A(B BBBF T l x|e BEE B(A0A8G
+8A0A(B BBBF T e BEE B(A0A8G
+8A0A(B BBBF T e BEE B(A0A8G
+8A0A(B BBBF T t e BEE B(A0A8G
+8A0A(B BBBF T V BBB B(A0A8Gk
+8A0A(B BBBJ T $ e BEE B(A0A8G
+8A0A(B BBBF T | X BBB B(A0A8G
+8A0A(B BBBB `|. K^ T x g BBB B(A0I8G
+8D0A(B BBBA T D #
S BBJ B(A0A8G
+8D0A(B BBBD T (1
BBJ B(A0A8G
+8D0A(B BBBD T >e BBB B(A0A8G
+
+8A0A(B BBBA T L T
+ BIB B(A0A8J
+8A0A(B BBBJ \ Pk , t Xb D\
H P
H Q
-G I , l, b D\
+G I , b D\
H P
H Q
-G I < , ( 4 BBD A(G@a
-(A ABBA 4 L, 4 BAD D
-ABA , , S 4 AYL`
-AAA L , ~ 4 BBB B(A0A8DW
-8D0A(B BBBF , -
-_ 4 AAG o
-DAA D 4- Ђ6 g5 BCA GPg
- AABH C
- AABI . U. K^ T - 7 BIE B(A0A8G&
-8D0A(B BBBB T - 8 7 BIE B(A0A8G&
-8D0A(B BBBB T D. 7 BIB B(D0A8G
-8D0A(B BBBA T . X& 7 BIB B(D0A8G
-8D0A(B BBBA T . , 7 BIB B(D0A8G
-8D0A(B BBBA T L/ 1 7 BIB B(D0A8G
-8D0A(B BBBA T / 7j k7 BBB B(A0D8G
-8D0A(B BBBA T / = Q7 BBB B(A0I8GU
-8D0A(B BBBA T T0 D ?7 BIB B(A0A8G
-8A0A(B BBBF T 0 XS 7 BIB B(A0A8G
-8A0A(B BBBK T 1 i 8 BBB B(A0A8M
-8A0A(B BBBA L \1 q 8 BBB B(A0A8Mi
-8A0A(B BBBA , <2 b D\
+G I , ؘb D\
H P
H Q
-G I , l2 b D\
+G I < L BBD A(G@a
+(A ABBA 4 ș~ o BAD D
+ABA , k S AYLp
+AAA L p BBB B(A0A8DW
+8D0A(B BBBF , D _ [ AAG o
+DAA < t ACG`i
+AAJ
+AAA l |. K^ 4 ؚ& BKA G
+ DABA 4 Л& BKA G
+ DABA T < Ȝ s BIE B(A0A8G&
+8D0A(B BBBB T ` { BIE B(A0A8G&
+8D0A(B BBBB 4 & BKA G
+ DABA < $ c BAD GS
+ DABA < d P 8 BAI G
+ DABA T 0 BIB B(D0A8G
+8D0A(B BBBA T BIB B(A0A8G p
+
+8A0A(B BBBF T T { ߵ BIB B(A0A8G x
+8A0A(B BBBF L { BBB B(A0A8Mi
+8A0A(B BBBA L BBB B(A0A8MF
+8A0A(B BBBA , b D\
H P
H Q
-G I , 2 b D\
+G I , 4 b D\
H P
H Q
-G I , <2 XS o8 AYL`
-AAA , l2 z Y8 AMQP"
-AAA , 2 _ >8 AAG o
-DAA , 2 _ 8 AAG o
-DAA D 2 H6 F8 BCA GPg
- AABH C
- AABI 3 P. K^ T \3
- ;: BIB B(D0A8G
-8D0A(B BBBA T 3
- %: BIB B(D0A8G
-8D0A(B BBBA T 4 H
- : BIB B(D0A8G
-8D0A(B BBBA T d4
- 9 BIB B(D0A8G
-8D0A(B BBBA T 4
9 BIB B(A0A8G
-8A0A(B BBBA T 5 P
9 BIB B(A0A8G
-8A0A(B BBBA T l5
9 BIB B(A0A8G
-8A0A(B BBBA T 5
9 BIB B(A0A8G
-8A0A(B BBBA T 6
9 BIB B(A0A8G
-8A0A(B BBBA L t6 u9 BBB B(A0A8Mi
-8A0A(B BBBA T7 p l7 pF T 6 O9 BBB B(K0A8G
-8A0A(B BBBA , L7 8u 39 AMQ`
-AAA , 8 b D\
+G I , d (b D\
H P
H Q
-G I , <8 b D\
+G I , hb D\
H P
H Q
-G I , l8 b D\
+G I , b D\
H P
H Q
-G I , 8 Hb D\
+G I , < k AYLp
+AAA , l (k ѵ AYLp
+AAA , h AMQ`:
+AAA , _ AAG o
+DAA , _ | AAG o
+DAA , , 8_ X AAG o
+DAA < \ hi ACG`i
+AAJ
+AAA T |. K^ T
+ BIB B(D0A8G
+8D0A(B BBBA T
+ m BIB B(D0A8G
+8D0A(B BBBA T d
+ W BIB B(D0A8G
+8D0A(B BBBA T 8
+ A BIB B(D0A8G
+8D0A(B BBBA T
+ + BIB B(D0A8G
+8D0A(B BBBA T l
+ BIB B(D0A8G
+8D0A(B BBBA T `
+ BIB B(D0A8G
+8D0A(B BBBA T "
+ BIB B(D0A8G
+8D0A(B BBBA T t '
+ Ӷ BIB B(D0A8G
+8D0A(B BBBA T -
+ BIB B(D0A8G
+8D0A(B BBBA T $ @3 BIE B(A0A8G"
+8D0A(B BBBF T | C ۶ BIE B(A0A8G"
+8D0A(B BBBF L T BBB B(A0A8Mi
+8A0A(B BBBA L $ \ BBB B(A0A8Mi
+8A0A(B BBBA , D F T g BBB B(K0A8G
+8A0A(B BBBA , i Ӷ AMQp
+AAA , 8kb D\
H P
H Q
-G I , 8 b D\
+G I , xkb D\
H P
H Q
-G I , 8 b D\
+G I , D kb D\
H P
H Q
-G I L ,9 AAG ]
+G I , t kb D\
+H P
+H Q
+G I , 8lb D\
+H P
+H Q
+G I , xlb D\
+H P
+H Q
+G I L l AAG ]
CAF [
ACA R
-CAJ JCA , |9 8b D\
+CAJ JCA , T lb D\
H P
H Q
-G I , 9 xb D\
+G I , (mb D\
H P
H Q
-G I , 9 b D\
+G I , hmb D\
H P
H Q
-G I , : b D\
+G I , mb D\
H P
H Q
-G I , <: 8b D\
+G I , mb D\
H P
H Q
-G I , l: xb D\
+G I , D (nb D\
H P
H Q
-G I , : b D\
+G I , t hnb D\
H P
H Q
-G I , : b D\
+G I , nb D\
H P
H Q
-G I , : 8b D\
+G I , nb D\
H P
H Q
-G I , ,; xb D\
+G I , (ob D\
H P
H Q
-G I , \; b D\
+G I , 4 hob D\
H P
H Q
-G I 4 : g 5 BAD D
-ABA ; 0m A~
-A l 4 T; S5 BAD D
-ABA < ; 75 BBD A(G@a
-(A ABBA T ; x 5 BZB J(A0A8G
-8A0A(B BBBD , $< _ 5 AAG o
-DAA , T< 0_ o5 AAG o
-DAA , < `e K5 ADG r
-DAA T < j ?6 BBB B(A0A8Gw
-8A0A(B BBBF d = Hi 6 BDB B(A0A8L\
+G I 4 og > BAD D
+ABA om A~
+A l < 0p BBD A(G@a
+(A ABBA 4 D p~ ۲ BAD D
+ABA T | (q BZB J(A0A8G)
+8A0A(B BBBD L Dz BBB B(A0A8DW
+8D0A(B BBBF , $ pv_ AAG o
+DAA , T v_ { AAG o
+DAA , ve W ADG r
+DAA T C BBB B(A0A8Gw
+8A0A(B BBBF T h ; BBB B(A0A8G
+8A0A(B BBBG T d ' BBB B(A0A8G
+8A0A(B BBBG T BBB B(A0A8G
+8A0A(B BBBG T ui ! BBB B(A0D8G K
+8A0A(B BBBG $ X|. K^ , 6 ADG T
+AAA 4 BAD G
+ABA L BLH B(D0D8D@
+8A0A(B BBBG 4 < BKA G
+ DABA 4 t @ BKA G
+ DABA T ״ BBB J(A0A8G
+8D0A(B BBBD 4 ô BKA G
+ DABA 4 < x BKA G
+ DABA < t 0 BAD GS
+ DABA T h W BBE B(A0A8G>
+8D0A(B BBBA < ? BAD GS
+ DABA < L BAD GS
+ DABA < h BAD GS
+ DABA < ȕ BAD GS
+ DABA < ( BAD GS
+ DABA < L h BAD G{
+ DABA < < BAD G{
+ DABA < BAD G
+ DABA < X BAD G{
+ DABA D L ' BBA D(G
+(D ABBA < Р BAD GS
+ DABA < 0 Z BAD GS
+ DABA < / BAD GS
+ DABA < T BAI G
+ DABA < Ц ݱ BAD G
+ DABA < p BAD GS
+ DABA < Щ BAD G
+ DABA T T [ BIE B(A0A8G
+8D0A(B BBBG T G BIE B(A0A8G
+8D0A(B BBBG 4 p 3 BKA G
+ DABA 4 < H BKA G
+ DABA T t BBB E(A0A8G
+8D0A(B BBBI 4 ȵ ۰ BRA G
+ DABA 4 BRA G
+ DABA 4 < 8 BRA G
+ DABA < t x BAD GS
+ DABA < P M BAD GS
+ DABA < " BAI Gl
+ DABA < 4 P BAI G
+ DABA T t R ׯ BBB J(A0A8G
+8D0A(B BBBB < ϯ BAD GS
+ DABA < x BAD GS
+ DABA < L y BAD GS
+ DABA < 8 N BAD GS
+ DABA < # BAD GS
+ DABA < BAD GS
+ DABA T L X Ϯ BBJ B(A0A8G
+8D0A(B BBBJ < î BAD GS
+ DABA < ` BAD GS
+ DABA < $ m BAD GS
+ DABA < d B BAD GS
+ DABA < BAD GS
+ DABA T h BBE B(A0A8G>
+8D0A(B BBBA T < h BBE B(A0A8G>
+8D0A(B BBBA < BAI G4
+ DABA < BAI GJ
+ DABA < M q BAD G
+ DABA < T E BAD GS
+ DABA < p BAI GL
+ DABA D B BBA I(G
+(D ABBA T R ˬ BBB J(A0A8G
+8D0A(B BBBB D t BBA I(G
+(D ABBA L t BLB B(A0A8D
+8A0A(B BBBA H T $ 5 / BBB B(A0A8J v
+8F0A(B BBBA d | i BDB B(A0A8L\
8A0A(B BBBB
-8A0A(B BBBI L t= P 5 BDB J(A0A8Ib
-8A0A(B BBBG T = n 6 BBB B(A0A8G
-8A0A(B BBBG T > hq 5 BBB B(A0A8G
-8A0A(B BBBG T t> t 5 BBB B(A0A8G
-8A0A(B BBBG T > 5 BBB B(A0D8G s
-8A0A(B BBBG T $? R6 BDA I(Dp
+8A0A(B BBBI L ` BDB J(A0A8Ib
+8A0A(B BBBG T 4 @ p BDA I(Dp
(A ABBA U
-(A ABBE @ D. K^ , ? Pw6 6 ADG T
-AAA 4 ? {6 BAD G
-ABA L ? (w 6 BLH B(D0D8D@
-8A0A(B BBBG 4 L@ 7 BKA G
- DABA 4 @ 7 BKA G
- DABA T @ h 7 BBB J(A0A8G
-8D0A(B BBBG 4 A w7 BKA G
- DABA 4 LA V7 BKA G
- DABA < A p 57 BAD GC
- DABA T A M 7 BBE B(A0A8G#
-8D0A(B BBBD < B 6 BAD GC
- DABA < \B ! 6 BAD GC
- DABA < B X" 6 BAD GC
- DABA < B # o6 BAD GC
- DABA < C $ C6 BAD GC
- DABA < \C H& 6 BAD Gk
- DABA < C ' 5 BAD Gk
- DABA < C H) 5 BAD G
- DABA < D * 5 BAD Gk
- DABA D \D h, g5 BBA D(G
-(D ABBA < D @. 45 BAD GC
- DABA < D / 5 BAD GC
- DABA < $E 0 4 BAD GC
- DABA < dE 02 4 BAI G
- DABA < E 3 4 BAD Gw
- DABA < E 5 ]4 BAD GC
- DABA < $F 6 14 BAD G
- DABA T dF p8 4 BIE B(A0A8G
-8D0A(B BBBG T F : 3 BIE B(A0A8G
-8D0A(B BBBG 4 G `= 3 BKA G
- DABA 4 LG 8> 3 BKA G
- DABA T G ? 3 BBB E(A0A8G
-8D0A(B BBBD 4 G B 3 BRA G
- DABA 4 H @C f3 BRA G
- DABA 4 LH C E3 BRA G
- DABA < H D $3 BAD GC
- DABA < H F 2 BAD GC
- DABA < I PG 2 BAI GI
- DABA < DI I 2 BAI Gp
- DABA T I pK 2 BBB J(A0A8GH
-8D0A(B BBBB < I O o2 BAD GC
- DABA < J Q C2 BAD GC
- DABA < \J hR 2 BAD GC
- DABA < J S 1 BAD GC
- DABA < J U 1 BAD GC
- DABA < K XV 1 BAD GC
- DABA T \K W g1 BBB J(A0A8G
-8D0A(B BBBE < K ^ o1 BAD GC
- DABA < K p_ C1 BAD GC
- DABA < 4L ` 1 BAD GC
- DABA < tL b 0 BAD GC
- DABA < L `c 0 BAD GC
- DABA T L dM 0 BBE B(A0A8G#
-8D0A(B BBBD T LM gM {0 BBE B(A0A8G#
-8D0A(B BBBD < M jY c0 BAI G
- DABA < M m <0 BAI G"
- DABA < $N p5 0 BAD G
- DABA < dN r / BAD GC
- DABA < N `s / BAI G<
- DABA D N t / BBA I(Gz
-(D ABBA T ,O w o/ BBJ B(A0A8GG
-8D0A(B BBBC D O zY [/ BBA I(G
-(D ABBA L \P h BLB B(A0A8D
-8A0A(B BBBA P l T 4P } . BBB B(A0A8J G
-8F0A(B BBBA T P Hm o0 BDB E(A0C8GR
-8A0A(B BBBA D P b0 BDB A(I0F`K
-0A(A BBBE T ,Q V .0 BDA C(L`^
+(A ABBE T A BDB E(A0C8GR
+8A0A(B BBBA D 0 4 BDB A(I0F`K
+0A(A BBBE T , V BDA C(L`^
(A ABBF
-(A ABBE T Q V / BDA C(L`^
+(A ABBE T V ǭ BDA C(L`^
(A ABBF
-(A ABBE T Q V / BDA C(L`^
+(A ABBE T V BDA C(L`^
(A ABBF
-(A ABBE L 4R / BDB B(A0A8O\
-8A0A(B BBBG L R / BDB B(A0A8M`
-8A0A(B BBBE , R / AALP
-DAJ T S д
/ BBB B(A0D8J
-8F0A(B BBBA , \S h `0 AALP
-DAD , S I0 AALP
-DAK T S d 20 BBE B(A0A8Gu#
-8F0A(B BBBA L T (b
'; BDB B(A0A8O|
-8A0A(B BBBG L dT 05 h; BDJ B(A0A8Gl
-8A0A(B BBBG L T < ]; BDB B(A0A8L
-8A0A(B BBBF L U @? U; BBB B(D0G8JP
-8A0A(B BBBI U M AZ 4 tU MT ; BAA J@
- AABB < U Oa ; BBA A(JP
-(A ABBI , |V ob D\
+(A ABBE L 4 U BDB B(A0A8O\
+8A0A(B BBBG L P&i U BLB B(A0A8IZ
+8A0A(B BBBG , p6 AALP
+DAJ T 07
BBB B(A0D8Ju
+8F0A(B BBBA , \ C 1 AALP
+DAD , D AALP
+DAK T hEd BBE B(A0A8Gx#
+8F0A(B BBBA L b
BDB B(A0A8O|
+8A0A(B BBBG L d 7 2 BDJ B(A0A8Ir
+8A0A(B BBBG < G ' BDA A(L
+(A ABBF L BBB B(D0G8JP
+8A0A(B BBBI
+ @ AZ 4 d
+ @T I BAA J@
+ AABB <
+ ha J BBA A(JP
+(A ABBI , b D\
H P
H Q
-G I , V ob D\
+G I , b D\
H P
H Q
-G I , V pb D\
+G I , 8b D\
H P
H Q
-G I , W Xpb D\
+G I , $ xb D\
H P
H Q
-G I , <W pb D\
+G I , T b D\
H P
H Q
-G I , lW pb D\
+G I , b D\
H P
H Q
-G I , W qb D\
+G I L 8 AAG ]
+CAF [
+ACA R
+CAJ JCA ,
hb D\
H P
H Q
-G I , W Xqb D\
+G I , 4
b D\
H P
H Q
-G I , W qb D\
+G I , d
b D\
H P
H Q
-G I , ,X qb D\
+G I ,
(b D\
H P
H Q
-G I L \X r AAG ]
+G I ,
hb D\
+H P
+H Q
+G I ,
b D\
+H P
+H Q
+G I , $ b D\
+H P
+H Q
+G I , T (b D\
+H P
+H Q
+G I , hb D\
+H P
+H Q
+G I , b D\
+H P
+H Q
+G I , b D\
+H P
+H Q
+G I , (b D\
+H P
+H Q
+G I , D hb D\
+H P
+H Q
+G I , t b D\
+H P
+H Q
+G I , b D\
+H P
+H Q
+G I L ( AAG ]
CAF [
ACA R
-CAJ JCA , X Hrb D\
+CAJ JCA , $ Xb D\
H P
H Q
-G I X rm A~
-A l T lX r 8 BBB B(K0A8G
-8A0A(B BBBA T X u} 8 BNO B(A0A8G
-8A0A(B BBBH , Y {J 8 AMQP
-AAA , LY |J 8 AMQ@
-AAA , |Y ~_ 8 AAG o
-DAA , Y 8~_ k8 AAG o
-DAA , Y h~_ G8 AAG o
-DAA , Z ~e #8 ADG r
-DAA <Z ~ 8 BDB B(A0C8Lpo
+G I T m A~
+A l T C BBB B(K0A8G
+8A0A(B BBBA T > ' BBB B(K0A8G
+8A0A(B BBBA 4 l ~ BAD D
+ABA , Z AMQ`
+AAA , v Գ AMQp
+AAA , `b AMQP&
+AAA , 4 _ dz AAG o
+DAA , d _ AAG o
+DAA , e ADG r
+DAA , @_ [ AAG o
+DAA , p_ 7 AAG o
+DAA , $ _ AAG o
+DAA , T e ADG r
+DAA T BZB B(I0A8G
+8A0A(B BBBJ T ׳ BNB O(A0A8GT
+8A0A(B BBBH T 4 BBB B(A0A8G
+8A0A(B BBBG D hd|. K^ 4 ` , BKA G
+ DABA 4 BKA G
+ DABA 4 BKA G
+ DABA 4 L ɵ BKA G
+ DABA 4 @ BRA G
+ DABA 4 BRA G
+ DABA 4 f BRA G
+ DABA T , h E BBB B(I0A8Gm
+8D0A(B BBBA < BAD GS
+ DABA T BBB B(D0A8G
+8D0A(B BBBA < BAD GS
+ DABA < \ BAD GS
+ DABA < h e BAD GS
+ DABA < : BAD GS
+ DABA < ( BAD GS
+ DABA < \ ! BAD GS
+ DABA < " BAD GS
+ DABA < H$ BAD GS
+ DABA < % c BAI G
+ DABA < \ ' < BAD GS
+ DABA T (U BIB E(A0A8Ga
+8D0A(B BBBA T *U BIB E(A0A8Ga
+8D0A(B BBBA 4 L , BKA G
+ DABA 4 - BKA G
+ DABA T .t m BBB B(D0A8G
+8D0A(B BBBA < 1 ; BAI Gl
+ DABA < T p4 BAI G
+ DABA T 06 BBJ B(A0A8G
+8D0A(B BBBJ T ; BBB B(D0A8G
+8D0A(B BBBA T D p> BBB B(D0A8G
+8D0A(B BBBA D A BBA I(G
+(D ABBA 4 `D W BRA G
+ DABA 4 E 6 BRA G
+ DABA 4 T E BRA G
+ DABA 4 F BKA G
+ DABA 4 @G Ӱ BKA G
+ DABA 4 G BKA G
+ DABA 4 4 H BKA G
+ DABA 4 l hI p BKA G
+ DABA 4 J O BKA G
+ DABA 4 J . BKA G
+ DABA 4 K
BKA G
+ DABA D L HL BBA D(G6
+(D ABBA L N į BBB A(I0G,
+0D(A BBBB 4 pQ= ADG
+DAA 4 xR= ADG
+DAA 4 T S= [ ADG
+DAA 4 T= 6 ADG
+DAA 4 U= ADG
+DAA 4 V= ADG
+DAA 4 4 W= Ǯ ADG
+DAA 4 l Xe ADG@
+DAA 4 Y } ADGh
+DAA 4 8[e X ADG@
+DAA 4 ! p\= 3 ADG
+DAA 4 L! x]= ADG
+DAA 4 ! ^= ADG
+DAA 4 ! _y ĭ ADGT
+DAA < ! ` BAD G
+ DABA < 4" pb t BAD G
+ DABA 4 t" d= I ADG
+DAA 4 " e $ ADGh
+DAA 4 " pf@ ADG
+DAA 4 # xg@ ڬ ADG
+DAA 4 T# h@ ADG
+DAA 4 # i@ ADG
+DAA 4 # jy k ADGT
+DAA 4 # kb F ADG=
+DAA < 4$ n ! BAI G
+ DABA < t$ p BAI Gi
+ DABA < $ s ի BAI Gi
+ DABA < $ 0v BAD GV
+ DABA < 4% w BAD GV
+ DABA < t% x Y BAD GV
+ DABA < % Pz . BAD GV
+ DABA < % { BAI G
+ DABA < 4& p}a ܪ BAI G
+ DABA < t& | BAI G
+ DABA < & BAD G
+ DABA < & d BAD G
+ DABA 4 4' p 9 BKA G
+ DABA 4 l' ( BKA G
+ DABA T ' R BBB B(A0I8J ;
+8F0A(B BBBA L ' x ǫ BBB B(F0A8Jp
+8A0A(B BBBE d L( i G BBB B(A0A8L^
+8A0A(B BBBB
+8A0A(B BBBI L ( ( BBB B(A0C8O\
+8A0A(B BBBG ) BBB B(A0C8Lpq
8A0A(B BBBE 4
8A0A(B BBBJ /
-8A0A(B BBBG T Z 8 BBB B(A0A8G
-8A0A(B BBBG L [ ( 8 BDB B(A0C8OZ
-8A0A(B BBBG L l[ ( 8 BDB B(A0C8OZ
-8A0A(B BBBG L\ (. K^ 4 [ h: BRA G
- DABA 4 \ x G: BRA G
- DABA 4 D\ 0 &: BRA G
- DABA 4 |\ : BKA G
- DABA 4 \ 9 BKA G
- DABA 4 \ X 9 BKA G
- DABA 4 $] 9 BKA G
- DABA 4 \] ȏ 9 BKA G
- DABA 4 ] `9 BKA G
- DABA 4 ] 8 ?9 BKA G
- DABA 4 ^ 9 BKA G
- DABA D <^ 8 BBA D(G
-(D ABBA L ^ 8 BBB A(I0G
-0D(A BBBJ 4 ^ - 8 ADG
-DAA 4 _ - 8 ADG
-DAA 4 D_ - l8 ADG
-DAA 4 |_ - G8 ADG
-DAA 4 _ - "8 ADG
-DAA 4 _ x- 7 ADG
-DAA 4 $` p- 7 ADG
-DAA 4 \` hU 7 ADG0
-DAA 4 ` } 7 ADGX
-DAA 4 ` ؠU i7 ADG0
-DAA 4 a - D7 ADG
-DAA 4 <a - 7 ADG
-DAA 4 ta - 6 ADG
-DAA 4 a a 6 ADG<
-DAA < a 6 BAD Gw
- DABA < $b 6 BAD Gw
- DABA 4 db @- X6 ADG
-DAA 4 b 8} 36 ADGX
-DAA 4 b 0 6 ADG
-DAA 4 c x0 5 ADG
-DAA 4 Dc p0 5 ADG
-DAA 4 |c h0 5 ADG
-DAA 4 c `i z5 ADGD
-DAA 4 c B U5 ADG
-DAA < $d 05 BAI Gr
- DABA < dd P
-5 BAI GF
- DABA < d з 4 BAI GF
- DABA < d P 4 BAD GF
- DABA < $e 4 BAD GF
- DABA < de f4 BAD GF
- DABA < e @ :4 BAD GF
- DABA < e 4 BAI Gp
- DABA < $f 0I 3 BAI G
- DABA < df @\ 3 BAI G
- DABA < f ` 3 BAD G
- DABA < f o3 BAD G
- DABA 4 $g C3 BKA G
- DABA 4 \g x "3 BKA G
- DABA D g 0 D3 BDB A(I0F`K
-0A(A BBBE T g V 3 BDA C(L`^
+8A0A(B BBBG L ) ( BBB B(A0C8O\
+8A0A(B BBBG L ) x( BBB B(A0C8O\
+8A0A(B BBBG D ,* X BBB A(I0F`E
+0A(A BBBE D t* ګ BBB A(I0F`E
+0A(A BBBE T * V BBA C(L``
(A ABBF
-(A ABBE L 4h 2 BDB B(A0A8O|
-8A0A(B BBBG L h p 2 BDB B(A0A8O|
-8A0A(B BBBG T h E3 BFB B(A0A8J
-8A0A(B BBBD i h i 4 \i G _3 BSA Qp
- AABA , i N3 ASQ`M
-AAA 4 i 93 BYA LpT
- AABA 4 i h1 (3 BYA Lp
- AABA , 4j pP 3 AYL`
-AAA , dj u 3 AMQ`
-AAA L $k AAG ]
+(A ABBE T + V m BBA C(L``
+(A ABBF
+(A ABBE T l+ V 4 BBA C(L``
+(A ABBF
+(A ABBE T + V BBA C(L``
+(A ABBF
+(A ABBE L , (K ª BBJ B(A0A8Il
+8A0A(B BBBG < l, (G BBA A(L
+(A ABBF L , 8
+ BBB B(A0A8Ov
+8A0A(B BBBG L , BBB B(A0A8Ov
+8A0A(B BBBG T L- x
+ BDB B(D0A8G,
+8A0A(B BBBD \. L - c
+ # BBB B(D0D8MP
+8A0A(B BBBI . AX . ( < D. `W BSA Q
+ AABA , . ASQpe
+AAA < . @ BYA Lt
+ AABA < . Q ت BYA L
+ AABA , 4/ 0` AYLp
+AAA , d/ ` AMQp
+AAA L L0 AAG ]
CAF [
ACA R
-CAJ JCA L tk AAG ]
+CAJ JCA L 0 AAG ]
CAF Z
ACB R
-CAJ JCA , k @b D\
+CAJ JCA , 0 b D\
H P
H Q
-G I , k b D\
+G I , 1 `b D\
H P
H Q
-G I , $l b D\
+G I , L1 b D\
H P
H Q
-G I , Tl b D\
+G I , |1 b D\
H P
H Q
-G I , l @b D\
+G I , 1 b D\
H P
H Q
-G I , l b D\
+G I , 1 `b D\
H P
H Q
-G I , l b D\
+G I , 2 b D\
H P
H Q
-G I , m b D\
+G I , <2 b D\
H P
H Q
-G I , Dm @b D\
+G I , l2 b D\
H P
H Q
-G I , tm b D\
+G I , 2 `b D\
H P
H Q
-G I , m b D\
+G I , 2 b D\
H P
H Q
-G I , m b D\
+G I , 2 b D\
H P
H Q
-G I , n @b D\
+G I , ,3 b D\
H P
H Q
-G I , 4n b D\
+G I , \3 `b D\
H P
H Q
-G I , dn b D\
+G I , 3 b D\
H P
H Q
-G I , n b D\
+G I , 3 b D\
H P
H Q
-G I , n @b D\
+G I , 3 b D\
H P
H Q
-G I , n b D\
+G I , 4 `b D\
H P
H Q
-G I , $o b D\
+G I , L4 b D\
H P
H Q
-G I , To b D\
+G I , |4 b D\
H P
H Q
-G I , o @ b D\
+G I , 4 b D\
H P
H Q
-G I , o b D\
+G I , 4 `b D\
H P
H Q
-G I o m A~
-A l < to
- . BBD A(G@a
-(A ABBA 4 o
- - BAD D
-ABA L o - BBB F(A0A8J@X
-8A0A(B BBBA T <p - BBB E(A0A8D
-8D0A(B BBBA T p P; - BTB B(A0A8Q;
-8A0A(B BBBA , p 8h t- BDD y
-ABA , q x_ P- AAG o
-DAA , Lq _ ,- AAG o
-DAA , |q _ - AAG o
-DAA , q _ , AAG o
-DAA , q 8_ , AAG o
-DAA , r h_ , AAG o
-DAA r A`
-G b r A`
-G b s x A`
-G bD r - BED D(G$
-(D ABBE D r , BED D(G$
-(D ABBE D ,s , BED D(G$
-(D ABBE < ts , ACLPj
-AAD I
-AAE D s , BED D(G$
-(D ABBE L t BBB B(A0A8Gp8A0A(B BBB , t ADD Z
-AAA $ |t x - A
-E D t , BPR M(D0c
-(A BBBA 4 t h . B\D G
- DABA < $u P - B_G G5
- DABA < du - BAI G,
- DABA 4 u - BKA G
- DABA 4 u - BKA G
- DABA 4 v b- BKA G
- DABA D Lv 8 A- BBA I(G4
-(D ABBA D v ! - BBA I(G
-(D ABBA 4 v % , BKA G
- DABA 4 w % , BRA G
- DABA 4 Lw & , BRA G
- DABA 4 w @' , BRA G
- DABA T w ' k, BBB B(A0A8G
-8A0A(B BBBG < x `-9 ?, BAI G
- DABA < Tx `/ , BAI G
- DABA < x 1 + BAI GC
- DABA T x 3 + BBE B(A0A8G<
-8D0A(B BBBC T ,y 8 + BBJ B(A0A8G
-8D0A(B BBBE < y >& + BAD G
- DABA < y @ + BAI G~
- DABA T z B ]+ BBB B(A0I8G
-8D0A(B BBBA T \z FO o+ BBE E(D0D8L
-8A0A(B BBBK , D{ \ BCA x
-ABO t{ \( A[ { \( A[ 4 ${ \d + BAA J@
- AABJ < \{ ^a + BBA A(JP
-(A ABBA T { ` + BBB B(A0D8G
-8A0A(B BBBE | `q L | Xq% 4, BFB B(A0C8J]
-8A0A(B BBBG | 8x d t| 0x{ A, BFB B(A0A8Gp_
-8A0A(B BBBB
-8A0A(B BBBA l} Hz } @z \ } 8zU + BBB A(A0MR
-0A(A BBBA
-0A(A BBBF L l} 8 + BAA JV
+G I 5 m A~
+A l 4 t4 ~ BAD D
+ABA < 4 8 BBD A(G@a
+(A ABBA L 4 BBB F(A0A8J@X
+8A0A(B BBBA T <5 h O BBB E(A0A8D
+8D0A(B BBBA T 5 0 K ; BTB B(A0A8QK
+8A0A(B BBBA , 5 ("h BDD y
+ABA , 6 h"_ AAG o
+DAA , L6 "_ Ԥ AAG o
+DAA , |6 "_ AAG o
+DAA , 6 "_ AAG o
+DAA , 6 (#_ h AAG o
+DAA , 7 X#_ D AAG o
+DAA D <7 8 ݤ BED D(G$
+(D ABBE D 7 BED D(G$
+(D ABBE D 7 BED D(G$
+(D ABBE L 8 p BBB B(A0A8Gp8A0A(B BBB , 9 ADD Z
+AAA $ 8 ?| ' A
+E D 8 8 BPR M(D0c
+(A BBBA 4 9 ! 4 B\D G
+ DABA < <9 " B_G G=
+ DABA < |9 $ BAI GL
+ DABA 4 9 & ĥ BKA G
+ DABA 4 9 P' BKA G
+ DABA 4 ,: ( BKA G
+ DABA D d: ( a BBA I(G
+(D ABBA D : - 8 BBA I(G9
+(D ABBA 4 : 1 BKA G
+ DABA 4 ,; 1 BRA G
+ DABA 4 d; 2 ͤ BRA G
+ DABA 4 ; H3 BRA G
+ DABA T ; 4
+ BBB B(A0A8G>
+8A0A(B BBBG < ,< 9Y _ BAI G
+ DABA < l< ; 8 BAI G
+ DABA < < = BAI Gk
+ DABA T < H@ BBE B(A0A8GO
+8D0A(B BBBH T D= E ۣ BBJ B(A0A8G
+8D0A(B BBBJ < = (K6 ϣ BAD G
+ DABA < = (M BAI G
+ DABA T > O } BBB B(A0I8G
+8D0A(B BBBA T t> @S BEE B(D0D8L
+8A0A(B BBBK < > ia ` BBA A(JP
+(A ABBA T ? 8j1 8 BBA A(MT
+(A ABBC \
+(A ABBF L d? p 0 BAA JV
AABH
- AABD L } + BFB B(A0C8J]
-8A0A(B BBBG ~ Ȕ T $~ $, BBE E(A0C8G^
-8D0A(B BBBD L |~ h + BDB B(A0A8O\
-8A0A(B BBBG T ~ + BBB E(A0G8G
-8D0A(B BBBJ T $ P + BIB B(A0A8G
-8A0A(B BBBD L | (r g- BTB A(A0N
-0A(A BBBA 4 X >- BSA Qp
- AABA 4 @G -- BSA Qp
- AABA 4 < X - BYA LpT
- AABA 4 t 1 - BYA Lp
- AABA , < b D\
+ AABD , l@ s BCA x
+ABO T ? pt BBB B(A0D8G
+8A0A(B BBBE @ ȅ A ( A[ L t@ Ѕ% BFB B(A0C8J]
+8A0A(B BBBG |A A ( A[ 4 @ d BAA J@
+ AABJ L 4A BFB B(A0A8Gp
+8A0A(B BBBJ <B TB < A ACLPj
+AAD I
+AAE B @ A`
+G b B A`
+G b B A`
+G bL TB / BFB B(A0C8J]
+8A0A(B BBBG \C T B X BBE E(A0C8G^
+8D0A(B BBBD L C d BDB B(A0A8O\
+8A0A(B BBBG T dC K BBB E(A0G8G
+8D0A(B BBBJ T C h& k BIB B(A0A8G
+8A0A(B BBBF L D ӥ BTB A(A0N
+0A(A BBBA < dD 00 BSA Q
+ AABA < D W BSA Q
+ AABA < D @ x BYA Lt
+ AABA < $E Q _ BYA L
+ AABA , F 0b D\
H P
H Q
-G I , l Xb D\
+G I , LF pb D\
H P
H Q
-G I L AAG ]
+G I , |F b D\
+H P
+H Q
+G I , F b D\
+H P
+H Q
+G I L F 0 AAG ]
CAF [
ACA R
-CAJ JCA L AAG ]
+CAJ JCA L ,G ` AAG ]
CAF Z
ACB R
-CAJ JCA , < b D\
+CAJ JCA , |G b D\
H P
H Q
-G I , l 8b D\
+G I , G b D\
H P
H Q
-G I , xb D\
+G I , G b D\
H P
H Q
-G I , ̂ b D\
+G I , H Pb D\
H P
H Q
-G I , b D\
+G I , <H b D\
H P
H Q
-G I , , 8b D\
+G I , lH b D\
H P
H Q
-G I , \ xb D\
+G I , H b D\
H P
H Q
-G I , b D\
+G I , H Pb D\
H P
H Q
-G I , b D\
+G I , H b D\
H P
H Q
-G I , 8b D\
+G I , ,I b D\
H P
H Q
-G I , xb D\
+G I , \I b D\
H P
H Q
-G I , L b D\
+G I , I Pb D\
H P
H Q
-G I | m A~
-A l , H_ ) AAG o
-DAA , < xh ) BDD y
-ABA , l _ {) AAG o
-DAA , _ W) AAG o
-DAA , ̄ _ 3) AAG o
-DAA H A`
-G b A`
-G b< < ( Z) ACLPo
+G I I m A~
+A l < $I BBD A(G@a
+(A ABBA 4 dI ~ o BAD D
+ABA , I _ { AAG o
+DAA , I h W BDD y
+ABA , I H_ 3 AAG o
+DAA , ,J x_ AAG o
+DAA , \J _ AAG o
+DAA T J ؐ BEJ E(D0D8GP
+8D0A(B BBBF $ J .| Ӣ A
+E < K X BAI Gk
+ DABA L LK BBB I(A0G
+0D(A BBBA < K h6 BAD G
+ DABA 4 K h BYA G
+ DABA 4 L p BYA G
+ DABA 4 LL ! O B_D G
+ DABA 4 L . BYA G
+ DABA < L |
BYA G
+ DABA < L B_G G=
+ DABA < <M h B_G G=
+ DABA < |M BAI GL
+ DABA L M X n BIB A(D0G
+0D(A BBBA 4 N ( 6 BKA G
+ DABA 4 DN BKA G
+ DABA 4 |N BKA G
+ DABA 4 N PG ӡ ADG"
+DAA T N h BEE B(A0H8N
+8D0A(B BBBF 4 DO r ADG
+DAA 4 |O H M ADG
+DAA < O - ( BAD G
+ DABA < O BAD G
+ DABA < 4P B Р BAI G
+ DABA T tP BJB B(A0A8GE
+8D0A(B BBBE < P h r BAI Gu
+ DABA L Q L BBB I(A0G
+0D(A BBBA T \Q ' BJB B(A0A8G
+8D0A(B BBBE < Q ACL@j
+AAD
+AAG R A`
+G b R 0 A`
+G b< 4R ; ACLPo
AAG
-AAE T | 8T u) BEJ E(D0D8GP
-8D0A(B BBBF d . K^ 4 $+ BYA G
- DABA 4 $ @ + BYA G
- DABA 4 \ (! * B_D G
- DABA 4 * BYA G
- DABA < ̆ t * BYA G
- DABA < x* B_G G5
- DABA < L x P* B_G G5
- DABA < (* BAI G,
- DABA L ̇ 8 * BIB A(D0G
-0D(A BBBA 4 ) BKA G
- DABA 4 T ) BKA G
- DABA 4 h ) BKA G
- DABA 4 Ĉ 7 f) ADG
-DAA T ( A) BDB I(A0A8Np
-8A0A(B BBBE 4 T ) ADG
-DAA 4 8 ( ADG
-DAA < ĉ ( BAD G
- DABA < ( BAD G
- DABA < D P* c( BAI G
- DABA T @V <( BBB B(A0A8G
-8A0A(B BBBD < ܊ H ( BAI Ge
- DABA T X ' BBJ B(A0A8G
-8D0A(B BBBI T t & ' BBB B(A0A8G
-8A0A(B BBBD T ̋ S ' BEE B(D0D8L
-8A0A(B BBBK , ACL d
-JAQ , P BCA x
-ABO ( A[ 4 ( A[ T Č " ' BBB B(A0D8G
-8A0A(B BBBE 3 L 4 3 |( BFB B(A0C8J]
-8A0A(B BBBG C L xCM ) BDB B(A0A8O\
-8A0A(B BBBG L xGM ) BDB B(A0A8O\
-8A0A(B BBBG , ̎ Mb D\
+AAE L tR p
? BDB B(A0A8OZ
+8A0A(B BBBA L R 0$ 1 BDB B(A0A8OZ
+8A0A(B BBBA L S ' # BDB B(A0A8OZ
+8A0A(B BBBA T dS +H s BIB B(A0A8G
+8A0A(B BBBD T S F ס BEE B(D0D8L
+8A0A(B BBBK , T ^ ACL d
+JAQ , T @_ BCA x
+ABO ,U _( A[ LU _( A[ T T _ BBB B(A0D8G
+8A0A(B BBBE U u L $U u BFB B(A0C8J]
+8A0A(B BBBG ,V , DV b D\
H P
H Q
-G I , Mb D\
+G I , tV (b D\
H P
H Q
-G I T E ( BBE E(A0A8G6
-8A0A(B BBBI T ( BBE E(A0A8G
-8A0A(B BBBK , L ( AADPR
-AAA T | 8E i( BBE E(A0A8G6
-8A0A(B BBBI d . K^ T Lg J( BBB B(A0A8G(
-8A0A(B BBBA T D [ 6( BBB B(A0A8G
-8A0A(B BBBA T l (( BDB B(A0A8J
-8A0A(B BBBH < ( BBA A(J`x
-(A ABBB ܑ Q Az
-E P , b D\
+G I T U HE BBE E(A0A8G6
+8A0A(B BBBI T DV @ BBE E(A0A8G
+8A0A(B BBBK T V E BBE E(A0A8G6
+8A0A(B BBBI W "|. K^ T W H BBB B(A0A8G
+
+8A0A(B BBBA T dW ~ BBB B(A0A8GX
+8A0A(B BBBA T W y j BDB B(A0A8J
+8A0A(B BBBH X < ,X BBA A(JPx
+(A ABBB $Y HQ Az
+E P , DY b D\
H P
H Q
-G I , . K^ T g ^( BBB B(A0A8G(
-8A0A(B BBBA , 8b D\
+G I tY 8!|. K^ T X ' BBB B(A0A8GX
+8A0A(B BBBA , Y b D\
H P
H Q
-G I , ̒ xb D\
+G I , Z 8b D\
H P
H Q
-G I 4 l ' BAD D
-ABA D ) BAA Jp
+G I 4 Y x~ BAD D
+ABA D Y l ˣ BAA JPV
AABH
- AABC D خl ) BAA JPV
+ AABC Z |. K^ T $Z D BBB B(A0I8G
+8D0A(B BBBA T |Z f o BBB B(A0I8G
+8D0A(B BBBA \ Z BBB A(A0MJ
+0A(A BBBI
+0A(A BBBD T 4[ p= BIB B(A0A8G+ "
+8A0A(B BBBA , D\ 8@b D\
+H P
+H Q
+G I , t\ x@b D\
+H P
+H Q
+G I D [ @l o BAA JPV
AABH
- AABC ē . K^ T L * BBB B(A0A8G
-8A0A(B BBBA T p * BBB B(A0A8G
-8A0A(B BBBA T >A + BIB B(A0A8G+"
-8A0A(B BBBI L AAG ]
+ AABC \ |. K^ T L\ B BBB B(A0A8G
+8A0A(B BBBA T \ Q' ߬ BBB B(A0A8G
+8A0A(B BBBA \ \ X` ˬ BBB A(A0MJ
+0A(A BBBI
+0A(A BBBD L ^ g AAG ]
CAF Z
ACB R
-CAJ JCA , 4 0b D\
+CAJ JCA , d^ hb D\
H P
H Q
-G I , d pb D\
+G I , ^ Xhb D\
H P
H Q
-G I , b D\
+G I , ^ hb D\
H P
H Q
-G I T 4 i . BNO B(A0A8G
-8A0A(B BBBH , d . ACG r
-DAA , H _ - AAG o
-DAA T u . BEE B(A0A8G
-8A0A(B BBBD T D @e . BEE B(A0A8G
-8A0A(B BBBF L . BBB B(A0G8Dh
-8A0A(B BBBJ T !
. BBB B(A0A8J
-8D0A(B BBBK T D @. . BQB B(K0A8G
-8A0A(B BBBA T x1p . BBB B(G0A8Gx
-8A0A(B BBBA . K^ T x: 0 BIB B(D0A8Gb
-8D0A(B BBBA T d ? h0 BKB B(D0A8Gb
-8D0A(B BBBA T hE Q0 BKB B(D0A8Gb
-8D0A(B BBBA T J :0 BKB B(D0A8Gb
-8D0A(B BBBA T l XP #0 BKB B(D0A8Gb
-8D0A(B BBBA T ę U 0 BKB B(D0A8Gb
-8D0A(B BBBA T H[ / BNB B(D0A8Gb
-8D0A(B BBBA T t ` / BKB B(D0A8Gb
-8D0A(B BBBA T ̚ 8f / BKB B(D0A8Gb
-8D0A(B BBBA T $ k / BKB B(D0A8Gb
-8D0A(B BBBA T | (q
/ BNB B(A0A8Gs
-8A0A(B BBBA T ԛ ~
/ BIB B(A0A8Gx
-8A0A(B BBBA L Ș AAG ]
+G I T <^ h
+ / BNO B(A0A8G
+8A0A(B BBBH , ^ @sd 7 ACG r
+DAA , ^ s_ AAG o
+DAA T ^ u BEE B(A0A8G
+8A0A(B BBBD L L_ Xs P BBB B(A0G8Dh
+8A0A(B BBBJ T _ xo C BBB B(A0A8J
+8D0A(B BBBK T _ k BQB B(K0A8G
+8A0A(B BBBA a 8|. K^ T d` BIB B(D0A8Gb
+8D0A(B BBBA T ` x BIB B(D0A8Gb
+8D0A(B BBBA T a կ BKB B(D0A8Gb
+8D0A(B BBBA T la h BKB B(D0A8Gb
+8D0A(B BBBA T a BKB B(D0A8Gb
+8D0A(B BBBA T b X BKB B(D0A8Gb
+8D0A(B BBBA T tb Ъ y BNB B(D0A8Gb
+8D0A(B BBBA T b H b BNB B(D0A8Gb
+8D0A(B BBBA T $c K BKB B(D0A8Gb
+8D0A(B BBBA T |c 8 4 BKB B(D0A8Gb
+8D0A(B BBBA T c BKB B(D0A8Gb
+8D0A(B BBBA T ,d ( BKB B(D0A8Gb
+8D0A(B BBBA L d J ; BBB B(G0A8GH
+8A0A(B BBBA L e AAG ]
+CAF Z
+ACB R
+CAJ JCA , e Pb D\
+H P
+H Q
+G I , f b D\
+H P
+H Q
+G I , <f b D\
+H P
+H Q
+G I L lf AAG ]
CAF Y
ACC R
-CAJ JCA , b D\
+CAJ JCA , f @b D\
H P
H Q
-G I , < 8b D\
+G I , f b D\
H P
H Q
-G I , l xb D\
+G I , g b D\
H P
H Q
-G I L AAG ]
+G I L Lg AAG ]
CAF [
ACA R
-CAJ JCA , b D\
+CAJ JCA , g 0b D\
H P
H Q
-G I Z AX T - BBB L(A0A8G
-8A0A(B BBBI T B
- - BNL B(A0A8G
-8A0A(B BBBD T \ - BNO B(A0A8G
-8A0A(B BBBH , Pc . ACG q
-DAA , _ - AAG o
-DAA , e - ADG r
-DAA L D . BBB B(A0G8Dh
-8A0A(B BBBJ L : . BBB B(A0G8Dh
-8A0A(B BBBJ T P . BBB B(G0A8Gx
-8A0A(B BBBA ̠ x. K^ T T p w0 BNB B(D0A8Gb
-8D0A(B BBBA T `0 BIB B(D0A8Gb
-8D0A(B BBBA T ` I0 BIB B(D0A8Gb
-8D0A(B BBBA T \ 20 BIB B(D0A8Gb
-8D0A(B BBBA T P 0 BNB B(D0A8Gb
-8D0A(B BBBA T 0 BNB B(D0A8Gb
-8D0A(B BBBA T d @ / BNB B(D0A8Gb
-8D0A(B BBBA T / BNB B(D0A8Gb
-8D0A(B BBBA T 0 / BIB B(D0A8Gb
-8D0A(B BBBA T l / BNB B(D0A8Gb
-8D0A(B BBBA T ģ / BNB B(D0A8Gb
-8D0A(B BBBA T
- z/ BNB B(D0A8Gb
-8D0A(B BBBA T t c/ BNB B(D0A8Gb
-8D0A(B BBBA T ̤ L/ BNB B(D0A8Gb
-8D0A(B BBBA T $ 5/ BNB B(D0A8Gb
-8D0A(B BBBA T | x / BNB B(D0A8Gb
-8D0A(B BBBA , d %b D\
+G I g {Z AX T 4g P BNB L(A0A8G
+8A0A(B BBBH T g BNB O(A0A8G
+8A0A(B BBBG T g 0 BNB O(A0A8Ge
+8A0A(B BBBG T <h xD BLB B(A0A8G
+8A0A(B BBBB , h pd ߬ ACG r
+DAA , h _ AAG o
+DAA , h c ACG q
+DAA , $i _ s AAG o
+DAA , Ti Pe O ADG r
+DAA L i BBB B(A0G8Dh
+8A0A(B BBBJ L i BBB B(A0G8Dh
+8A0A(B BBBJ L $j BBB B(A0G8Dh
+8A0A(B BBBJ \ tj p BBE A(A0Da
+0D(A BBBE
+0D(A BBBH T j g BQB B(K0A8G
+8A0A(B BBBA k |. K^ 4 Dk 4 BPA G
+ DABA 4 |k BPA G
+ DABA 4 k P BMA G
+ DABA 4 k ѯ BMA G
+ DABA 4 $l BMA G
+ DABA 4 \l x BMA G
+ DABA 4 l 0 n BMA G
+ DABA 4 l M BMA G
+ DABA 4 m , BMA G
+ DABA 4 <m X
+ BMA G
+ DABA 4 tm BKA G
+ DABA 4 m ɮ BKA G
+ DABA 4 m BPA G
+ DABA 4 n 8
BPA G
+ DABA 4 Tn
f BPA G
+ DABA 4 n E BPA G
+ DABA 4 n ` $ BPA G
+ DABA 4 n BPA G
+ DABA 4 4o BPA G
+ DABA 4 lo BPA G
+ DABA 4 o @ BPA G
+ DABA 4 o BPA G
+ DABA 4 p ^ BPA G
+ DABA 4 Lp h = BPA G
+ DABA 4 p BKA G
+ DABA 4 p BKA G
+ DABA 4 p ڬ BKA G
+ DABA 4 ,q H BKA G
+ DABA L dq 1 BDB B(I0A8G\
+8A0A(B BBBA l q BDB B(I0A8D\
+8A0A(B BBBA
+8A0A(B BBBA , r P$b D\
H P
H Q
-G I , 0& . AAN`V
-AAA , 4 (_ . AAG o
-DAA ߆. K^ T | ) 1 BIB B(A0A8Gq
-8A0A(B BBBA T Ԧ `7W 1 BIB B(A0A8G!
-8A0A(B BBBA T , hF( {1 BIB B(A0A8G
-8A0A(B BBBA T @X5 1 BBB B(D0A8G
-8A0A(B BBBB , l (nb D\
+G I , Tr $
+ AANp~
+AAA , r p'_ AAG o
+DAA ls 0|. K^ T r ' o BIB B(D0A8G-
+8D0A(B BBBA T $s . Y BIB B(A0A8G
+8A0A(B BBBA T |s X=C C BIB B(A0A8G
+8A0A(B BBBA T s POW$ [ BBB B(A0A8J
+8A0A(B BBBK , t Xsb D\
H P
H Q
-G I , hnb D\
+G I , u sb D\
H P
H Q
-G I , < n O2 AANP4
-AAA , l q 12 AAN`}
-AAA , s_ #2 AAG o
-DAA , ̨ (t_ 1 AAG o
-DAA ݆. K^ T @tv 4 BIB B(A0A8GA
-8A0A(B BBBA T l h( w4 BIB B(A0A8G
-8A0A(B BBBA T ĩ @W 4 BIB B(A0A8G!
-8A0A(B BBBA T H y4 BIB B(A0A8Gq
-8A0A(B BBBA T t c4 BBB B(A0A8J N
-8A0A(B BBBD \ A t A A S hA ԫ 4 L x d p | h `^ cz
- Ĭ , L 4 AAG@
-DAF H $ @ AL , 0h 3 ADDP~
-AAA $ t p$ ACK ODA 4 xr y3 BDD DPA
- AABA ԭ AP , d h 13 ADDP~
-AAA $ , 2 AAG@
-DAF l + 4 BIA A(G0(D ABB< X BEI A(A0(A BBB AZ L 4 @ BBB B(D0D8D@
-8A0A(B BBBK T 1 BBB E(D0A8D|
-8D0A(B BBBC < L 1 YHD M
+G I , t s ? AAN`\
+AAA , t xv1 ! AANp
+AAA , t y_ AAG o
+DAA , u y_ AAG o
+DAA v |. K^ T du yC k BIB B(A0A8G
+8A0A(B BBBA T u ȋ BIB B(A0A8Ga
+8A0A(B BBBA T v m BIB B(A0A8GQ
+8A0A(B BBBA T lv H W BIB B(A0A8G
+8A0A(B BBBA T v $ A BBB B(A0A8J n
+8A0A(B BBBD w A w 0\ x (\ x S 4x XA Lx dx |x x x x p x h x ` x X y P $y H^ cz <y
+ Ty 4 x BAD G@
+ DABK y 8 y 0 AL , $y h ADDP~
+AAA $ z `$ ACK ODA 4 |y hr E BDD DPA
+ AABA lz AP , y h ADDP~
+AAA z 4 z Ŵ BAD G@
+ DABK { + 4 ${ BIA A(G0(D ABB< \{ P BEI A(A0(A BBB { { AZ L { @ BBB B(D0D8D@
+8A0A(B BBBK T l{ " BBB E(D0A8D
+8D0A(B BBBK < { YHD M
HAE
-HAA D 4 BBE E(G0D8DP~8D0A(B BBB, | 8< BAA
-ABA T < 1 BEE J(D0D8G>
-8D0A(B BBBA L t >P 0 BJE D(D0G
-0D(A BBBA L İ ?P 0 BJE D(D0G
-0D(A BBBA T @_ Q0 BJE E(D0D8G
-8D0A(B BBBA T l A{ 0 BJE E(D0D8G5
-8D0A(B BBBA T ı 0B{ / BJE E(D0D8G5
-8D0A(B BBBA T XC / BJE E(D0D8GZ
-8D0A(B BBBA T t D{ 9/ BJE E(D0D8G5
-8D0A(B BBBA T ̲ E . BJE E(D0D8GZ
-8D0A(B BBBA T $ G{ . BJE E(D0D8G5
-8D0A(B BBBA L | 8H g. BJE D(D0G!
-0D(A BBBD T ̳ xIc *. BJE E(D0D8G
-8D0A(B BBBA L $ J - BJE D(D0G!
-0D(A BBBD L t KL - BJE D(D0G
-0D(A BBBA L Ĵ LL i- BJE D(D0G
-0D(A BBBA L M +- BJE D(D0G!
-0D(A BBBD L d OL , BJE D(D0G
-0D(A BBBA D P0 AG fA $ d PF AAG0zAA HP AL HP7 Dh
-D F ̶ hP D T P , BBJ A(D0
-(A BBEA T H , BDD u
+HAA | D | BBE E(G0D8DP~8D0A(B BBB, } 0W BAA
+ABA T | W ز BEE J(D0D8G>
+8D0A(B BBBA L | X BJE D(D0G%
+0D(A BBBH T <} 8Z{ U BJE E(D0D8G5
+8D0A(B BBBA T } `[{ BJE E(D0D8G5
+8D0A(B BBBA T } \ ɱ BJE E(D0D8GZ
+8D0A(B BBBA T D~ ]{ BJE E(D0D8G5
+8D0A(B BBBA T ~ ^ = BJE E(D0D8GZ
+8D0A(B BBBA T ~ @`{ BJE E(D0D8G5
+8D0A(B BBBA L L ha BEE I(D0G
+0D(A BBBE L b t BEE I(D0G
+0D(A BBBE L c 7 BEE I(D0G
+0D(A BBBE L < (e BEE I(D0G
+0D(A BBBE D hf0 AG fA $ d xfF AAG0zAA f7 Dh
+D F f D Hg BBJ A(D0
+(A BBEA T T BDD u
ADD
ABE U
ABH I
-ABA T R, BDD u
+ABA T BDD u
ADD
ABE U
ABH I
-ABA < L
, BDA G
- DABG < 8 + BDA G
- DABG L ̷ P + BHE B(D0D8DP
-8D0A(B BBBB L Q BLB B(A0A8Dz
-8A0A(B BBBA < l hT' C+ BGA A(Kp
-(A ABBA $ @+ YX
+ABA < i BDA G
+ DABG < D < BDA G
+ DABG L f BHE B(D0D8DP
+8D0A(B BBBB L 0h BLB B(A0A8Dz
+8A0A(B BBBA $ $ YX
G z
-A T Ը w+ BBB B(D0A8G
-8A0A(B BBBA \ U BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 (VY v, AIG t
-AAI DCA \ T PV BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 $ VY + AIG t
-AAI DCA L \ V + BEB E(I0H8D@
-8D0A(B BBBA T D + BBB B(A0A8G
-8A0A(B BBBA D F H+ BLB A(A0GpG
-0A(A BBBA $ ܻ WF AAG0zAA L t W K+ BEB E(I0H8D@
-8D0A(B BBBD \ Ļ PY] #+ BBB I(D0D@
+A T L 8 S BBB B(D0A8Gb
+8A0A(B BBBA \ \ @j BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 jY j AIG t
+AAI DCA \ j BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBB4 kY ֯ AIG t
+AAI DCA L Ԅ 0k BEB E(I0H8D@
+8D0A(B BBBA T $
+D { BBB B(A0A8G
+8A0A(B BBBA D | F < BLB A(A0GpG
+0A(A BBBA $ | lF AAG0zAA L 8l ? BEB E(I0H8D@
+8D0A(B BBBD \ < m] BBB I(D0D@
0F(A BBBK U
-0F(A BBBA T $ / * BKB B(A0A8G
-8A0A(B BBBA T | * BLB B(A0A8G
-8A0A(B BBBE T Լ pi #+ BEB B(A0A8O
-8A0A(B BBBH L , HYU '+ BBB E(E0E8G`
-8A0A(B BBBA l | XZ= * BEB B(A0A8D
-8A0A(B BBBG U
-8A0A(B BBBA T * BBB B(A0F8G
-8D0A(B BBBI L D - * BBF H(A0D8D
-8A0A(B BBBA T 9 c* BBB B(A0F8G
-8D0A(B BBBB L - +* BBF H(A0D8D
-8A0A(B BBBA ̿ dž. K^ $ [M AAK0}AA D [ BEB B(A0A8DP8A0A(B BBBL T \
BEE A(A0]
+0F(A BBBA T H! ߮ BLH B(A0A8G
+8D0A(B BBBA T ˮ BLB B(A0A8G
+8A0A(B BBBE T L BEB B(A0A8O
+8A0A(B BBBI L mU # BBB E(E0E8G`
+8A0A(B BBBA l nM BEB B(A0A8D
+8A0A(B BBBF Y
+8A0A(B BBBE T d ` Ϯ BBB E(A0F8G-
+8D0A(B BBBE L "= BBB F(A0D8D
+8A0A(B BBBA T #_ _ BBB E(A0F8G
+8D0A(B BBBF L d &= ' BBB F(A0D8D
+8A0A(B BBBA l {. K^ $ 8pM AAK0}AA D `p BEB B(A0A8DP8A0A(B BBBL (q
BEE A(A0]
(A BBBF
-(E BBBJ D Q T , z * BBB B(A0A8J
-8A0A(B BBBG < l* BBD A(G
-(D ABBA L ?* BHF E(D0A8Dm
-8A0A(B BBBA T `
u * BBB B(A0A8GM
-8D0A(B BBBE L l ) BHE E(D0A8Dm
-8A0A(B BBBA T u ) BBB B(A0A8GM
-8D0A(B BBBE L @ q) BHE E(D0A8Dm
-8A0A(B BBBA T d 8) BBB B(A0A8G]
-8D0A(B BBBE L
-) BHE E(D0A8Dm
-8A0A(B BBBA T ( BBB B(A0A8GR
-8D0A(B BBBH T d ( BBB B(A0A8GR
-8D0A(B BBBH T 8 u( BBB B(A0A8G
-8A0A(B BBBA T L( BBB B(A0A8G
-8A0A(B BBBA T l 8 o #( BBB B(A0A8GJ
-8A0A(B BBBA T P! ' BBB B(A0A8G
-8A0A(B BBBA T # ' BBB B(A0A8G
-8A0A(B BBBA $ X3 AAJ ZGA , , (Xf AAJ m
-GAO , \ hXb D\
+(E BBBJ D & D Q T &z BBB B(A0A8J
+8A0A(B BBBG < ( h BBD A(G
+(D ABBA L < ) ; BHF E(D0A8Dm
+8A0A(B BBBA T X*u BBB B(A0A8GM
+8D0A(B BBBE L , ԭ BHE E(D0A8Dm
+8A0A(B BBBA T 4 -u BBB B(A0A8GM
+8D0A(B BBBE L 8/ m BHE E(D0A8Dm
+8A0A(B BBBA T ܌ / 4 BBB B(A0A8G]
+8D0A(B BBBE L 4 2 BHE E(D0A8Dm
+8A0A(B BBBA T 2 ͬ BBB B(A0A8GR
+8D0A(B BBBH T ܍ 4 BBB B(A0A8GR
+8D0A(B BBBH T 4 07 q BBB B(A0A8G
+8A0A(B BBBA T 9 H BBB B(A0A8G
+8A0A(B BBBA T 0<o BBB B(A0A8GJ
+8A0A(B BBBA T < H> BBB B(A0A8G
+8A0A(B BBBA T @ Ϋ BBB B(A0A8G
+8A0A(B BBBA , lb D\
H P
H Q
-G I , Xb D\
+G I , Ԑ lb D\
H P
H Q
-G I , Xb D\
+G I , mb D\
H P
H Q
-G I , (Yb D\
+G I , 4 Hmb D\
H P
H Q
-G I , hYb D\
+G I , d mb D\
H P
H Q
-G I , L Yb D\
+G I , mb D\
H P
H Q
-G I , | Yb D\
+G I , đ nb D\
H P
H Q
-G I , (Zb D\
+G I , Hnb D\
H P
H Q
-G I L L > % BBB B(A0A8GZ
-8D0A(B BBBH T ح# % BEE B(A0A8G
-8A0A(B BBBI 4 Y" % BAA GP
- AABG T , x# % BEE B(A0A8G
-8A0A(B BBBI T Pe % BEE B(A0A8G
-8A0A(B BBBF T h % BBE E(A0A8G
-8A0A(B BBBH T 4 % BBE E(A0A8G
-8A0A(B BBBH T # z% BEE B(A0A8G
-8A0A(B BBBI T # r% BEE B(A0A8G
-8A0A(B BBBI T < j% BBE E(A0A8G
-8A0A(B BBBF T & BBE E(A0A8G2
-8A0A(B BBBE L XU % BBB B(A0A8J
-8A0A(B BBBB | H BBB B(A0D8DP
+G I T l # & BEE B(A0A8G
+8A0A(B BBBI 4 đ 0n" BAA GP
+ AABG T BBB B(A0A8G
+8A0A(B BBBD T T @# BEE B(A0A8G
+8A0A(B BBBI T # ة BEE B(A0A8G
+8A0A(B BBBI T v Щ BBB B(A0A8G7
+8A0A(B BBBF T \ # BEE B(A0A8G
+8A0A(B BBBI , pn
+ AAJ@}
+AAE , Po { AAJ@j
+AAA T # b BEE B(A0A8G
+8A0A(B BBBI L l ou
+ BBB B(A0A8M
+8A0A(B BBBG | t BBB B(A0D8DP
8A0A(B BBBB
8A0A(B BBBE "
-8A0A(B BBBD L ^4 y% BBB B(A0A8M
-8A0A(B BBBH h. K^ T $ `j ' BBJ B(A0A8G_
-8D0A(B BBBK T | n% ' BBB B(I0A8G
-8D0A(B BBBE T t ' BBJ B(A0A8G4
-8D0A(B BBBF T , zo ' BBJ B(A0A8G
-8D0A(B BBBF T v ' BBJ B(A0A8G
-8D0A(B BBBD T 6 ' BBJ B(A0A8G
-8D0A(B BBBD T 4 ' BBJ B(A0A8GT
-8D0A(B BBBF T xu ' BBJ B(A0A8G
-8D0A(B BBBC T , ' BBB B(A0A8M
-8A0A(B BBBG 4 x ADD K
-AAJ ]DA T t Л H' BEB B(A0A8J
-8A0A(B BBBG \ X t P
, Hb D\
+8A0A(B BBBD T < 8 BEB B(A0A8G=
+8D0A(B BBBB L y BBB B(A0A8M
+8A0A(B BBBG {. K^ T h BBJ B(A0A8Gi
+8D0A(B BBBA T T 0 ۫ BBB B(A0I8G
+8D0A(B BBBA T { BBB B(A0I8G
+8D0A(B BBBA T ; BBB B(A0I8Ge
+8D0A(B BBBA T \ BBJ B(A0A8G
+8D0A(B BBBI T `V BBJ B(A0A8G
+8D0A(B BBBA T h s BBB B(A0I8G
+8D0A(B BBBA T d A Z BBB B(A0I8Gh
+8D0A(B BBBA T ? BBB B(A0A8M
+8A0A(B BBBG 4 ̙ ` ADD K
+AAJ ]DA T L k BEB B(A0A8J
+8A0A(B BBBG \ 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 , b D\
+G I , Pb D\
H P
H Q
-G I , L Hb D\
+G I , L 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 , b D\
+G I , ܛ Pb D\
H P
H Q
-G I , Hb D\
+G I , b D\
H P
H Q
-G I , < b D\
+G I , < b D\
H P
H Q
-G I , l b D\
+G I , l b D\
H P
H Q
-G I , b D\
+G I , Pb D\
H P
H Q
-G I , Hb 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 , , b D\
H P
H Q
-G I , \ b D\
+G I , \ Pb D\
H P
H Q
-G I , Hb 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 , b D\
H P
H Q
-G I , b D\
+G I , Pb D\
H P
H Q
-G I , L Hb D\
+G I , L 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 , b D\
+G I , ܞ Pb D\
H P
H Q
-G I , Hb D\
+G I , b D\
H P
H Q
-G I , < b D\
+G I , < b D\
H P
H Q
-G I , l b D\
+G I , l b D\
H P
H Q
-G I , b D\
+G I , Pb D\
H P
H Q
-G I , 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 L AAJ v
-AAD fAA L ! BBB F(A0A8J@X
-8A0A(B BBBA < ] BAA m
+G I , m A~
+A l 4 L ` AAJ v
+AAD fAA L ̟ Ϥ BBB F(A0A8J@X
+8A0A(B BBBA < Ԡ h] BAA m
ABD
-ABD T @ ! BDB B(A0I8G
-8A0A(B BBBH l A`
-G b A`
-G b A`
-G b A`
-G m, \ x! (! ADDP
-AAE < x ! AAJPh
-AAJ
-AAH < AAJPh
-AAJ
-AAH 4 BAA N
- AABD D D O! BED D(G$
-(D ABBE D 8 -! BED D(G$
-(D ABBE L ! BDB B(A0A8L`\
-8A0A(B BBBB L $ BDB J(A0A8GL
-8A0A(B BBBA L t ! BDB B(A0I8G
-8A0A(B BBBA D BED D(G$
-(D ABBE D BED D(G$
-(D ABBE T T BDB B(A0I8D(
-8A0A(B BBBF D ( s BED D(G$
-(D ABBE < Q BDA A(LP`
-(A ABBF T 4 B BEB B(A0A8Jn
-8A0A(B BBBA T " BDB B(A0A8O
-8A0A(B BBBA < ' y AEL@Z
+ABD , \ ! ADDP
+AAE , D AAJPl
+AAF , D x AAJPl
+AAF 4 BAA N
+ AABD D $ @ BED D(G$
+(D ABBE D l BED D(G$
+(D ABBE D BED D(G$
+(D ABBE D x BED D(G$
+(D ABBE D D { BED D(G$
+(D ABBE D H l BED D(G$
+(D ABBE D Ԣ 7 BED D(G$
+(D ABBE ԣ 1 Dl L < ( ϥ BBB B(A0A8Je
+8D0A(B BBBJ D {. K^ < BAI G
+ DABA L `
+ Ŧ BBB I(A0G
+0D(A BBBA L 4 BBB I(A0G
+0D(A BBBA L 0 { BBB I(A0G
+0D(A BBBA L Ԥ V BBB I(A0G
+0D(A BBBA L $ 1 BBB I(A0G
+0D(A BBBA L t BBB I(A0G
+0D(A BBBA L ĥ BBB I(A0G
+0D(A BBBA T a ¥ BBB B(I0A8G
+8D0A(B BBBA L l 8 BBB I(A0G3
+0D(A BBBA T !T p BBB B(I0A8G
+8D0A(B BBBA T $ C BBB B(I0A8GK
+8D0A(B BBBA T l ' BBB B(I0A8GK
+8D0A(B BBBA L ħ * BBB I(A0G
+0D(A BBBA L , Ĥ BBB I(A0G
+0D(A BBBA L d 0/ BBB I(A0G
+0D(A BBBA T 1 z BBB B(I0A8G*
+8D0A(B BBBA L X4 S BBB I(A0G
+0D(A BBBA T \ 68 . BBB B(I0A8GM
+8D0A(B BBBA T 9m BBJ B(A0A8G(
+8D0A(B BBBA L >b BBB D(A0G
+0D(A BBBA L \ A £ BBB I(A0G7
+0D(A BBBA T C BBJ B(A0A8G
+8D0A(B BBBA < H BAI Gn
+ DABA < D J- m BAI G
+ DABA L pL G BBB I(A0GU
+0D(A BBBA L ԫ @O " BBB I(A0G
+0D(A BBBA T $ Q$ BBB B(I0A8GW
+8D0A(B BBBA T | XT Т BBB B(I0A8G
+8D0A(B BBBA T Ԭ W BBB B(I0A8G)
+8D0A(B BBBA T , HZo v BBB B(I0A8G
+8D0A(B BBBA T `] BEB B(A0A8JP
+8A0A(B BBBG < ܭ i" AFGPe
+AAK T
+AAJ T j Т BBB B(A0I8G
+8A0A(B BBBF L t v BBB J(A0A8GF
+8A0A(B BBBA T Į @~ BDB B(A0D8G^
+8A0A(B BBBA ԯ Ѕ A`
+G b @ A`
+G b , A`
+G bT BBB B(A0I8D*
+8A0A(B BBBF x A`
+G mL > t BBJ B(A0A8G
+8A0A(B BBBA L \
+ Z BBB J(A0A8G
+8A0A(B BBBA T ^ BBB B(A0I8G
+8A0A(B BBBH < P BBA A(LPW
+(A ABBA < D @ ACL@\
AAB
-AAE D $ ` d BED D(G$
-(D ABBE D l / BED D(G$
-(D ABBE T ( BDB B(A0A8L
-8A0A(B BBBF T - BFB B(A0D8Gd
-8A0A(B BBBA P5 T | H5 BDB B(A0I8G
-8A0A(B BBBF L @
- BDB J(A0A8G
-8A0A(B BBBA 1 Dl L D PK BBB B(A0A8Je
-8D0A(B BBBJ $ . K^ < N ! BAI G
- DABA L xP ! BBB I(A0G
-0D(A BBBA L < R ! BBB I(A0G
-0D(A BBBA L U ! BBB I(A0G
-0D(A BBBA L HW f! BBB I(A0G
-0D(A BBBA L , Y A! BBB I(A0G
-0D(A BBBA L | [W ! BBB I(A0G
-0D(A BBBA L _} BBB I(A0G
-0D(A BBBA T 8a5 BBB B(I0A8Gn
-8D0A(B BBBA L t d BBB I(A0G
-0D(A BBBA T f( BBB B(I0A8Ga
-8D0A(B BBBA T xi S BBB B(I0A8G-
-8D0A(B BBBA T t l & BBB B(I0A8G-
-8D0A(B BBBA L n} BBB I(A0G
-0D(A BBBA L p BBB I(A0G
-0D(A BBBA L l (s BBB I(A0G
-0D(A BBBA T Xu BBB B(I0A8G
-8D0A(B BBBA L x c BBB I(A0G
-0D(A BBBA T d pz > BBB B(I0A8G/
-8D0A(B BBBA T 8}E BBJ B(A0A8G
-8D0A(B BBBA L 0C BBB D(A0G
-0D(A BBBA L d 0 BBB I(A0G
-0D(A BBBA T V BBJ B(A0A8G
-8D0A(B BBBA < ȋ BAI G^
- DABA < L X } BAI G
- DABA L 8 W BBB I(A0G)
-0D(A BBBA L ؑ_ 2 BBB I(A0G
-0D(A BBBA T ,
BBB B(I0A8G?
-8D0A(B BBBA T a BBB B(I0A8G
-8D0A(B BBBA T BBB B(I0A8G
-8D0A(B BBBA T 4 @C BBB B(I0A8G^
-8D0A(B BBBA T 8 _ BBB B(A0C8J
-8A0A(B BBBG L BBB B(A0A8J
-8A0A(B BBBA < L K AEOP_
-AAJ
-AAE T BFB B(A0D8G
-8A0A(B BBBH t P T H BFB B(A0D8G
-8A0A(B BBBH , , b D\
+AAE L + BBB B(A0A8L`N
+8A0A(B BBBB T Ա @ BBB B(A0A8L
+8A0A(B BBBH T , BBB B(A0A8O
+8A0A(B BBBA T в š BBB B(A0C8J
+8A0A(B BBBG L ܲ H BBB B(A0A8J
+8A0A(B BBBA , D AGD`
+AAH T t @ BDB B(A0D8G
+8A0A(B BBBH T BDB B(A0D8G
+8A0A(B BBBH $ < T
, l 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 , hb 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 , \ @b D\
H P
H Q
-G I , L (b D\
+G I , b D\
H P
H Q
-G I , | hb 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 , @b D\
H P
H Q
-G I , (b D\
+G I , L b D\
H P
H Q
-G I , < hb D\
+G I , | 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 , ܷ @b D\
H P
H Q
-G I , (b D\
+G I , b D\
H P
H Q
-G I , hb D\
+G I , < b D\
H P
H Q
-G I , , b D\
+G I , l b D\
H P
H Q
-G I \ m A~
-A l L 8 BBB F(A0A8J@X
-8A0A(B BBBA A`
-G b4 t BAA N
- AABD T BEB B(A0A8Jn
-8A0A(B BBBA L @= + BDB B(I0A8GL
-8A0A(B BBBA T T 0 T BBB J(A0A8G
-8A0A(B BBBJ L x% BDB B(A0A8O
-8A0A(B BBBD L X
BDJ A(A0Ge
-0A(A BBBJ T L BDB B(A0I8D(
-8A0A(B BBBF L p BDB B(A0A8L
-8A0A(B BBBJ T w BDB B(A0A8O
-8A0A(B BBBD L L BBB B(A0C8JA
-8A0A(B BBBG T h# x BBB B(A0A8J=
-8D0A(B BBBB L ( K BDB J(A0A8D
-8A0A(B BBBA . K^ 4 \ .R H ADG-
-DAA < @/ # BAI G
- DABA 4 00 ADG
-DAA L 1 BBB I(A0G
-0D(A BBBA 4 \ (4 ADG`
-DAA < 5 BHD Gt
- DABA 4 7K e ADG&
-DAA < 83 @ BAI G
- DABA < L : BHD Gz
- DABA T ; BBB B(I0A8G
-8D0A(B BBBA T P> BBB B(I0A8G'
-8D0A(B BBBA T < A BBB B(I0A8G'
-8D0A(B BBBA L C | BBB I(A0G
-0D(A BBBA L 0FW W BBB I(A0G
-0D(A BBBA T 4 @I( 2 BBB B(I0A8Ga
-8D0A(B BBBA L L BBB I(A0G
-0D(A BBBA T HN BBB B(I0A8G-
-8D0A(B BBBA L 4 P} BBB I(A0G
-0D(A BBBA L S BBB I(A0G
-0D(A BBBA T PUV i BBJ B(A0A8G
-8D0A(B BBBA L , XZ: ` BIB D(A0G
-0D(A BBBA L | H\ 2 BBB I(A0G
-0D(A BBBA < ^R
BAI G
- DABA L ` BBB I(A0G
-0D(A BBBA L \ (cB BBB B(A0A8J
-8A0A(B BBBA < (z T z3 h BFB B(A0D8G0
-8A0A(B BBBF D { BBE E(A0A8D@\8A0A(B BBBT | 5 " BBE E(A0A8G$
-8A0A(B BBBK L BEB E(A0A8F
-8A0A(B BBBB L $ 0 BEB E(A0A8F
-8A0A(B BBBB T t P% BBE E(A0A8G"
-8A0A(B BBBE T (e BEE B(A0A8G
-8A0A(B BBBF T $ @e BEE B(A0A8G
-8A0A(B BBBF L | x 8 BBB E(A0A8JX
-8A0A(B BBBG \ BDB A(A0Js
-0A(A BBBA
-0A(A BBBE ؉. K^ L D V BBB B(A0A8GpB
-8D0A(B BBBH L V k BBB B(A0A8GpB
-8D0A(B BBBH L V < BBB B(A0A8GpB
-8D0A(B BBBH L 4 V
BBB B(A0A8GpH
-8D0A(B BBBJ . K^ , } ADG0@
-DAG , m ADG q
-DAF , Hm ADG p
-DAG , , m f ADG r
-DAE , \ ȹ{ F ADG ~
-DAA , & ADG0L
-DAC , xP ADG k
-DAA < BBA H(G@x
-(D ABBA L , BBB E(A0A8KP
-8D0A(B BBBK D | | BBB A(D0q
-(A BBBA D F BBB A(H0j
-(D BBBA D X BBB A(K0
-(D BBBA < T BBA K(G@
-(D ABBA < @} BEE A(G0L
-(D ABBA , d 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 , 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 , T 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 , b D\
H P
H Q
-G I D AAJ X
+G I , @b D\
+H P
+H Q
+G I , L b D\
+H P
+H Q
+G I , | b D\
+H P
+H Q
+G I , b D\
+H P
+H Q
+G I , ܺ @b D\
+H P
+H Q
+G I , b D\
+H P
+H Q
+G I , < b D\
+H P
+H Q
+G I , l b D\
+H P
+H Q
+G I , @b D\
+H P
+H Q
+G I , ̻ b D\
+H P
+H Q
+G I , b D\
+H P
+H Q
+G I , , b D\
+H P
+H Q
+G I , \ @b D\
+H P
+H Q
+G I , b D\
+H P
+H Q
+G I , b D\
+H P
+H Q
+G I , b D\
+H P
+H Q
+G I @m A~
+A l 4 < AAJ v
+AAD fAA L ? BBB F(A0A8J@X
+8A0A(B BBBA < Ľ BAA M
+ABD
+ABD , L ! ADDP
+AAE < | ך AAJPh
+AAJ
+AAH 4 BAA N
+ AABD 4 $ BAA N
+ AABD D , (e BED D(G$
+(D ABBE D t f ț BED D(G$
+(D ABBE D g BED D(G$
+(D ABBE T `i! BEE E(D0D8Go
+8D0A(B BBBD T \ h BBB B(A0A8G:
+8D0A(B BBBH T ~ BBB B(A0A8G:
+8D0A(B BBBH Ȼ{. K^ < $ h BAI G
+ DABA L d
A BBB I(A0G
+0D(A BBBA L BBB I(A0G
+0D(A BBBA L P BBB I(A0G
+0D(A BBBA L T Ҝ BBB I(A0G
+0D(A BBBA L BBB I(A0G
+0D(A BBBA L BBB I(A0G
+0D(A BBBA L D c BBB I(A0G
+0D(A BBBA T @ a > BBB B(I0A8G
+8D0A(B BBBA L X" BBB I(A0G3
+0D(A BBBA T < %T BBB B(I0A8G
+8D0A(B BBBA T ( BBB B(I0A8GK
+8D0A(B BBBA T * BBB B(I0A8GK
+8D0A(B BBBA L D - e BBB I(A0G
+0D(A BBBA L / @ BBB I(A0G
+0D(A BBBA T P2: BBB B(I0A8G%
+8D0A(B BBBA T < 85 BBB B(I0A8G*
+8D0A(B BBBA L 8 ǚ BBB I(A0G
+0D(A BBBA T :8 BBB B(I0A8GM
+8D0A(B BBBA T < x=m { BBJ B(A0A8G(
+8D0A(B BBBA T B ` BBB B(D0A8G
+
+8D0A(B BBBA < 8E . BAI Gn
+ DABA T , F BBB B(I0A8G
+8D0A(B BBBA T I ڙ BBB B(I0A8G)
+8D0A(B BBBA T HLo BBB B(I0A8G
+8D0A(B BBBA 4 4 `Ob ADG=
+DAA < l P a BAI G
+ DABA 4 XR@ : ADG
+DAA T `SB BBB B(I0A8G-
+8D0A(B BBBA 4 < XV ADGx
+DAA < t W Ø BHD G
+ DABA 4 PY[ ADG6
+DAA < xZS v BAI G
+ DABA < , \ O BHD G
+ DABA T l 8^ ' BBB B(I0A8G*
+8D0A(B BBBA T a0 BBB B(I0A8GE
+8D0A(B BBBA T c0 ٗ BBB B(I0A8GE
+8D0A(B BBBA L t f BBB I(A0G
+0D(A BBBA L Pi BBB I(A0G
+0D(A BBBA T lT h BBB B(I0A8G
+8D0A(B BBBA T l o: ; BBB B(I0A8G%
+8D0A(B BBBA T r BBB B(I0A8GK
+8D0A(B BBBA L Xu BBB I(A0G
+0D(A BBBA T l w: BBB B(I0A8G%
+8D0A(B BBBA T z BBJ B(A0A8G
+8D0A(B BBBA L BIE A(A0G
+0D(A BBBA L l X X BBB I(A0G
+0D(A BBBA < b 3 BAI G
+ DABA L BBB I(A0G
+0D(A BBBA T L
E BEB B(A0A8JP
+8A0A(B BBBG < " AFGPe
+AAK T
+AAJ T t BBB B(A0I8G
+8A0A(B BBBF L < BBB B(A0I8GF
+8A0A(B BBBA T BDB B(A0D8G^
+8A0A(B BBBA A`
+G b A`
+G b A`
+G bT \ M BBB B(A0I8D*
+8A0A(B BBBF l P A`
+G mL ж BBB B(A0A8O
+8A0A(B BBBA L $ @
+ BBB J(A0A8G
+8A0A(B BBBA T t BBB B(A0I8G
+8A0A(B BBBH T x BBB B(A0A8L
+8A0A(B BBBH T $ @ BBB B(A0A8O
+8A0A(B BBBA L | BBB B(A0I8GF
+8A0A(B BBBA T BBB J(A0A8G
+8A0A(B BBBD T $ 8 BBB B(A0A8Oq
+8A0A(B BBBD 4 A`
+G bT x ͕ BBB B(A0I8D*
+8A0A(B BBBF L BBB B(A0A8L
+8A0A(B BBBH L D BBB J(A0A8D
+8A0A(B BBBA L J r BBB B(A0C8JA
+8A0A(B BBBG L
a BBJ A(A0Gg
+0A(A BBBJ L 4 p [ BBB B(A0A8L
+8A0A(B BBBD <
+! 6 AAJPg
+AAK S
+AAK T BBB B(A0C8J
+8A0A(B BBBG L g = BBB B(A0A8J
+8A0A(B BBBA $ 8) L 0) BBB B(A0A8J
+8A0A(B BBBA 9 T 9 @ BDB B(A0D8G
+8A0A(B BBBH `? T \ X? < BDB B(A0D8G
+8A0A(B BBBH l D T D 8 BDB B(A0D8G
+8A0A(B BBBH J D XQ{ BBE E(A0A8D@\8A0A(B BBBT l5 BBE E(A0A8G$
+8A0A(B BBBK L 8Q BEB E(A0A8F
+8A0A(B BBBB L , R BEB E(A0A8F
+8A0A(B BBBB T | (s% y BBE E(A0A8G"
+8A0A(B BBBE T ze r BEE B(A0A8G
+8A0A(B BBBF ؠ{. K^ L D S B BBB G(A0A8JF
+8A0A(B BBBG \ _' BDB A(A0JC
+0A(A BBBA
+0A(A BBBB L PV x BBB B(A0A8GpB
+8D0A(B BBBH L D `V I BBB B(A0A8GpB
+8D0A(B BBBH L pV BBB B(A0A8GpB
+8D0A(B BBBH L V BBB B(A0A8GpH
+8D0A(B BBBJ {. K^ , L x} ADG0@
+DAG , | ȋm ADG q
+DAF , m d ADG p
+DAG , Hm D ADG r
+DAE , { $ ADG ~
+DAA , < ، ADG0L
+DAC , l 8P ADG k
+DAA < X ŕ BBA H(G@x
+(D ABBA L ȍ BBB E(A0A8KP
+8D0A(B BBBK D , h Z BBB A(D0q
+(A BBBA D t $ BBB A(H0j
+(D BBBA D BBB A(K0
+(D BBBA < BBA K(G@
+(D ABBA < D } BEE A(G0L
+(D ABBA , < @b D\
+H P
+H Q
+G I , l b D\
+H P
+H Q
+G I , b D\
+H P
+H Q
+G I , b D\
+H P
+H Q
+G I , @b D\
+H P
+H Q
+G I , , b D\
+H P
+H Q
+G I , \ b D\
+H P
+H Q
+G I , b D\
+H P
+H Q
+G I D @ AAJ X
AAJ O
AAG X
-AAF 4 , AAJ o
+AAF 4 AAJ o
AAC O
-AAG D g BAA G@
+AAG D @g [ BAA G@
AABD b
- AABB T K BBB L(A0A8D
-8A0A(B BBBJ T t P + BBB L(A0A8D
-8A0A(B BBBJ T 9 BBB L(A0A8D
-8A0A(B BBBA T $ 0 e ? BEE B(A0A8G
-8A0A(B BBBF T | BBB B(A0D8G7
-8A0A(B BBBK d | x. K^ 4 C ADG
-DAA 4 < 7 ADG
-DAA D t & BBA I(G
-(D ABBA < BAI G
- DABA < x ~ BAI G
- DABA 4 < H0 W ADG
-DAA 4 t @ 2 ADGw
-DAA < d
BAI G
- DABA D k BBI A(G
-(D ABBA < 4 BHD G
- DABA < t BHD G,
- DABA 4 @t s ADGO
-DAA 4 K N ADG&
-DAA D $ ? ) BBA I(G1
-(D ABBA L l ? BIB D(A0Gi
-0D(A BBBA < s BAI G
- DABA D BIA D(G6
-(D ABBA < D
- BAI Gx
- DABA T
- @
-u c BIB B(D0A8G
-8A0A(B BBBD l h D AAJ X
+ AABB T h ' BBB L(A0A8D
+8A0A(B BBBJ T $ BBB L(A0A8D
+8A0A(B BBBJ T | 9 BBB L(A0A8D
+8A0A(B BBBA T e BEE B(A0A8G
+8A0A(B BBBF T , H\ BBB B(A0D8G
+8A0A(B BBBC < P T ȗ{. K^ 4 0[ ADG6
+DAA 4 XG Ö ADG"
+DAA D $ p9 BBA I(G+
+(D ABBA < l h { BAI G
+ DABA < Z BAI G
+ DABA 4 @ 3 ADG
+DAA 4 $ ADG
+DAA < \ hw BAI G
+ DABA D ȕ BBI A(G
+(D ABBA < BHD G
+ DABA < $ w BHD G<
+ DABA 4 d P O ADG_
+DAA 4 [ * ADG6
+DAA D W BBA I(GI
+(D ABBA L O BIB D(A0Gy
+0D(A BBBA < l BAI G
+ DABA D 8 BIA D(GF
+(D ABBA < d BAI G
+ DABA T 4
? BIB B(A0D8G
+8A0A(B BBBD D D \ p AAJ X
AAJ O
AAG X
-AAF , H b D\
+AAF , b D\
H P
H Q
-G I (|. K^ T p O BBB B(A0A8G
-8A0A(B BBBA l d d! d d L < d BJB A(A0W
-(A BBDH _(A BBBD
e BBB A(D0D@
-0A(A BBBA d
h AL $ hi - aFA Dp $
jc $ [FA Dp $ D
Pk~ vFA Dp T
H- AG }
-DG U
-DG U
-DG U
-DG U
-DG U
-DG aD L
.1 BBB A(D0JH
-0A(A BBBK T le BEE B(A0A8G
-8A0A(B BBBF T l r BBE E(A0A8G
-8A0A(B BBBI T x BBE E(A0A8G
-8A0A(B BBBC T BBE E(A0A8G
-8A0A(B BBBE \ t 1 BBB A(D0G
-0A(A BBBE Q
-0A(A BBBG T 0 BBE E(A0A8G
-8A0A(B BBBC D , x3 x BBE A(A0G
-0A(A BBBD D P6* BDA
-ABM A
-ABD EAG T (% BBE E(A0A8G"
-8A0A(B BBBE D BBE A(A0D@
-0A(A BBBA < Ȓ BBG A(G@
-(A ABBG D , BBE A(A0D@l
-0A(A BBBA < BBD A(G0T
-(A ABBA d $ Е BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d H c BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB L ' BJE B(A0A8D@
-8A0A(B BBBA L D BEB E(I0H8DP
-8D0A(B BBBD L P BJE B(A0A8D@
-8A0A(B BBBA l BEE B(D0A8G
+G I x{. K^ T 4 + BBB B(A0A8G=
+8A0A(B BBBA D h= \ p=! t = = C{ AS L p= BJB A(A0W
+(A BBDH _(A BBB, T =? Y AEK X
+AAA < < BEA A(G@
+(A ABBF T M BBE B(D0D8DZ
+8D0A(B BBBB $ 6 AAD mAA $ D =i aFA Dp $ l X>c [FA Dp $ ?~ vFA Dp t A{ AG DL x! j BBB A(D0JH
+0A(A BBBK T , @e BEE B(A0A8G
+8A0A(B BBBF T F w BBE E(A0A8G
+8A0A(B BBBI T M o BBE E(A0A8G
+8A0A(B BBBC T 4 S g BBE E(A0A8G
+8A0A(B BBBE \ _ BBB A(D0GZ
+0A(A BBBD
+0A(A BBBA D 8
+ BAD
+ABS A
+ABD EEB , pY BGD l
+ABD T d Y% BBE E(A0A8G"
+8A0A(B BBBE D t ` BBB D(A0D@
+0A(A BBBA D 0c BBB D(A0D@
+0A(A BBBA D e BBE A(A0D@
+0A(A BBBA < L `f BBG A(G@
+(A ABBG D g BBE A(A0D@l
+0A(A BBBA < g ې BBD A(G0T
+(A ABBA L \ hi BJB A(A0W
+(A BBDH ^(A BBB< d i BBG A(G@
+(A ABBG d xj W BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB d T j BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB L hk BJE B(A0A8D@
+8A0A(B BBBA L 8l BEB E(I0H8DP
+8D0A(B BBBD L \ m BJE B(A0A8D@
+8A0A(B BBBA l n g BEE B(D0A8G
8A0A(B BBBA d
-8A0A(B BBBA L T BEB E(I0H8DP
-8D0A(B BBBD L ` BJE B(A0A8D@
-8A0A(B BBBA $ 0g AG k
-AA L x BEB E(I0H8D@
-8D0A(B BBBD d l BBB J(A0D8G`
+8A0A(B BBBA L Hv g BEB E(I0H8DP
+8D0A(B BBBD L l x ? BJE B(A0A8D@
+8A0A(B BBBA t x5 $ yg AG k
+AA L Hy ' BEB E(I0H8D@
+8D0A(B BBBD d L z BBB J(A0D8G`
8F0A(B BBBF `
-8F0A(B BBBA T @ C BBB B(D0A8D
-8D0A(B BBBH , , ةh BGD c
-ABA hq. K^ T t 1 BBE B(A0A8G
-8A0A(B BBBA T @ k BEB E(A0D8G
-8A0A(B BBBF T $ pM BBE F(F0D8D
-8A0A(B BBBA T | N9 BBB B(A0A8Gi
-8A0A(B BBBD T Q g BBE B(D0A8G
-8D0A(B BBBA o. K^ 4 D 0 @ BDD H
-ABK 4 | BDD H
-ABK T BED A(G0Q
-(D ABBC k
-(D ABBA 4 H BDD Q
-ABB 4 D BDD Q
-ABB < | ^ BBD D(G0
-(D ABBA < 1 BBD D(G0
-(D ABBA < 8 BEA D(G0m
-(D ABBA < < BBD D(G0
-(D ABBA < | BBD D(G0
-(D ABBA D h | BEE D(A0Q
-(A BBBG AQ D $ ' BEE A(D0DPt
-0D(A BBBA L l BLB B(A0A8D`
-8A0A(B BBBA d H BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d $ BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB k. K^ T p BBB B(A0A8G
-8A0A(B BBBG P P P x p h 4 ` L X d P | H @ 8 0 ( $ < T l N N N N , xN D pN \ hN t `N XN PN HN @N 8N ! 0N ! (N 4! N L! N d! N |! N ! N ! M ! " AN ND ! " AN ND " " AN ND $" " AN ND D" " AN ND d" 0" AN ND " @" AN ND " P" AN ND " `" AN ND " p" AN ND # " AN ND $# " AN ND D# " AN ND d# " AN ND # " AN ND # " AN ND # " AN ND # " AN ND $ " AN ND $$ " AN ND D$ " AN ND d$ PK |$ HK $ @K $ 8K $ 0K $ (K $ K % K $% K <% K T% K l% J % J % J % J % J % J % J & J ,& J D& J < \& 8g BBA D(D0Y
-(A ABBD 4 & b S BBA D(A0B(A ABB< & 0g BBA D(D0Y
-(A ABBD 4 ' > S BBA D(A0B(A ABB< L' (g BBA D(D0Y
-(A ABBD < ' Xg BBA D(D0Y
-(A ABBD 4 ' S BBA D(A0B(A ABB4 ( S BBA D(A0B(A ABB< <( g BBA D(D0Y
-(A ABBD < |( Hg BBA D(D0Y
-(A ABBD 4 ( S BBA D(A0B(A ABB4 ( S BBA D(A0B(A ABB, ( C AID _
-AAA \) GJ n N D t) BHF E(E0G@>
-0A(A BBBA D ) h BHF E(E0G@>
-0A(A BBBA , * AAG0p
-AAA 4* 0 AL T* G& A^
-A A L t* BED A(G0_
-(A ABBH (A ABB 4 * H BDA
-AEA jAB L * BED A(G0_
-(A ABBH (A ABB L L+ 8 BED A(G0v
-(A ABBA (A ABB $ + E AX
+8F0A(B BBBA T | BBB B(D0A8D
+8D0A(B BBBH , ~h BGD c
+ABA {. K^ T T g BEB B(A0A8G
+8A0A(B BBBA T (r ێ BBE E(A0D8G
+P
+8A0A(B BBBA T P) BBE F(F0D8D
+8A0A(B BBBA T \ *A BBB B(A0A8Gs
+8A0A(B BBBB T - BEE B(A0D8Dm
+8D0A(B BBBG D } c BEE A(D0DPt
+0D(A BBBA L T } / BLB B(A0A8D`
+8A0A(B BBBA d @ # BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB d BJB B(A0A8G@
+8D0A(B BBBG D8C0A(B BBB , {. K^ T BEB B(A0A8G
+8A0A(B BBBG x p h , ` D X \ P t H @ 8 0 ( 4 L d | $ < T x l p h ` X P H @ 8 , 0 D ( \ t " AN ND " AN ND " AN ND (" AN ND $ 8" AN ND D H" AN ND d X" AN ND h" AN ND x" AN ND " AN ND " AN ND " AN ND $ " AN ND D Ȗ" AN ND d ؖ" AN ND " AN ND " AN ND " AN ND " AN ND (" AN ND $ < T x l p h ` X P H @ 8 , 0 D ( \ t < Xg BBA D(D0Y
+(A ABBD 4 D ,{S BBA D(A0B(A ABB< | Pg BBA D(D0Y
+(A ABBD 4 ,{S BBA D(A0B(A ABB< Hg BBA D(D0Y
+(A ABBD 4 4 ,{S BBA D(A0B(A ABB4 l ,{S BBA D(A0B(A ABB4 ,{S BBA D(A0B(A ABB< Дg BBA D(D0Y
+(A ABBD 4 ,{S BBA D(A0B(A ABB4 T ,{S BBA D(A0B(A ABB, C AID _
+AAA 0J n N D BHF E(E0G@>
+0A(A BBBA D BHF E(E0G@>
+0A(A BBBA , d H AAG0p
+AAA AL & A^
+A A L BED A(G0_
+(A ABBH (A ABB 4 $ hH BDA
+AEA jAB L \ BED A(G0_
+(A ABBH (A ABB $ AX
G g
-I 4 + i BDD h
-ABK DB L + BHA D(D0
-(A ABBK j(A ABB L L, h BHA D(D0
-(A ABBK M(A ABB , EP $, E _ , , $ l, + HD
+I 4 (i BDD h
+ABK DB L ` BHA D(D0
+(A ABBK j(A ABB 4 \ BGA h
+ABK WAB L BHA D(D0
+(A ABBK M(A ABB XP D W X , ` $ X # HD
D E
-A $- 2 AL cA D- $ , HD
+A l 2 AL cA $ Ձ HD
D E
-A - 02 AL cA - P $ ,- H HD
+A 2 AL cA $ L HD
D E
-A - 2 AL cA . $ - A HD
+A ,
+ 2 AL cA L
+ @ $ 8 9 HD
D E
-A D. p2 AL cA d. $ - HD
-D E
-A . 2 AL cA . 02 . X . P / H $/ @ </ 8$ VM T/ P l/ X / P / H$ VM / ` / X / P$ VM / h 0 p ,0 h D0 ` \0 X$ VM t0 p 0 x 0 p 0 h$ VM 0 0 x 1 py D@
-D $1 $ VM <1 T1 < l1 BEA A(F0
-(D ABBA 1 H$ VM 1 ` 1 X 1 P$ VM 2 h $2 `5 <2 ; Au \2 t2 g Dx
-D 2 $ VM 2 2 d 2 * BEB B(D0A8GP{
+A
+ 2 AL cA
+ 2
+
+
+ $ $ VM < T l $ VM $ VM , D $ VM \ t ( $ VM 0 ( y AC
+D
$ VM $
<
< T
BEA A(F0
+(D ABBA
$ VM
$ VM
5 $ 8; Au D X \ Pg A{
+D | $ VM d * BEB B(D0A8GP{
8D0A(B BBBA
-8A0A(B BBBE D3 < \3 " BBB A(H0
-(A EBBA 3 $ VM 3 3 3 L 3 ~ BEB D(D0
-(A BBBE z(A LEB L4 , d4 BAA |
-DBJ 4 p$ VM 4 4 4 x AG
-H 4 $ VM 4 5 AAG0Q
+8A0A(B BBBE , < D x" BBB A(H0
+(A EBBA h$ VM L x~ BEB D(D0
+(A BBBE z(A LEB 4 , L BAA |
+DBJ | $ VM 8 0 ( AG
+H $ VM 4 AAG0Q
AAD v
-AAA L5 h d5 ` < |5 X BEA A(F0^
-(D ABBA 5 $ VM 5 5 4 6 U BGA
-ABE DB <6 , T6 BAC m
-DBG 6 P$ VM 6 h 6 p 6 h 6 `$ VM 6 x 7 ,7 x D7 p$ VM \7 t7 4 7 U BGA
-ABE DB 7 , 7 BAC m
-DBG 8 $ VM $8 0 <8 ( T8 y D@
-D t8 $ VM 8 8 8 8 8 x 9 p L 9 h BEA D(G0
-(A ABBG (A ABB L l9 , BED A(G0
-(A ABBB (A ABB L 9 BEB A(D08
-(A BBBC (A BEB d : $ BEB B(A0D8GP)
+AAA 4 L < d BEA A(F0^
+(D ABBA X$ VM p x 4 pU BGA
+ABE DB $ , < BAC m
+DBG l $ VM $ VM ( 0 ( , $ VM D 8 \ @ 4 t 8U BGA
+ABE DB ` , X BAC m
+DBG Ȳ$ VM $ ز < вy AC
+D \ 0$ VM t H @ 8 0 ( L BEA D(G0
+(A ABBG (A ABB L T ȵ, BED A(G0
+(A ABBB (A ABB L BEB A(D08
+(A BBBC (A BEB d x$ BEB B(A0D8GP)
8D0A(B BBBC
-8A0A(B BBBE d t: BBF H(A0D8GP
-8D0A(B BBBC 8A0A(B BBB d : BBB E(D0A8GP
-8D0A(B BBBJ
-8A0A(B BBBE T D; F BEE A(A0
+8A0A(B BBBE d \ @ BBF H(A0D8GP
+8D0A(B BBBC 8A0A(B BBB T F BEE A(A0
(A BBBJ A
-(A BEBH L ; BEA D(G0
-(A ABBI D(A ABB 4 ; X BGA l
-ABG tDB 4 $< U BGA
-ABE DB \< , t< ACG {
-DAE , < BAC m
-DBG , < 0\ ACG \
-DAD , = ` ACG j
-DAF 4= $ VM L= 8 Av $ l= ( ADG YAA $ = ( ADG YAA $ = (( ADG YAA $ = 0( ADG YAA $ > 8( ADG YAA 4> @ L> 8 d> 0o D@
-D > $ VM > > > $ VM < T> AAG y
+(A BEBH L BED A(G0
+(A ABBJ D
+(A ABBE 4 l BGA l
+ABG tDB 4 U BGA
+ABE DB , ACG {
+DAE , $ X BAC m
+DBG , T \ ACG \
+DAD , ACG j
+DAF x$ VM 8 Av $ ( ADG YAA $ ( ADG YAA $ < ( ADG YAA $ d ( ADG YAA $ ( ADG YAA o AC
+D $ VM < d 0 q AAG y
AAD Y
-DAA $? 3 <? 3 4 T? 3 AAG0R
+DAA \ P t H 4 @ AAG0R
AAA q
-AAA , > AAG L
-AAA ? ; Au < L? AAG y
+AAA , p AAG L
+AAA x; Au < \ p AAG y
AAD Y
-DAA @ X3 4@ P3 , ? (y a AAG D
-AAA |@ x; Au < @ AAG y
+DAA T l , y Wp AAG D
+AAA ; Au < ( p AAG y
AAD Y
-DAA @ 2 @ 2 , |@ AAG [
-AAA <A (; Au < @ H z AAG y
+DAA h , ` , H o AAG [
+AAA t ; Au < po AAG y
AAD Y
-DAA A 2 A 2 A h[ HR A AL , |A | AAG G
-AAA <B ; Au < A AAG y
+DAA [ HR $ 8 AL , 8| n AAG G
+AAA t ; Au < n AAG y
AAD Y
-DAA B 81 B 01 , <B 8 y 6 AAG D
-AAA B ; Au < B AAG y
+DAA , L y ,n AAG D
+AAA 4 ; Au < 8 m AAG y
AAD Y
-DAA \C 0 tC 0 C C Hz C AL , <C r R AAG }
-AAA < lC H
- . AAG y
+DAA H @ XC Hz AL , L r Hm AAG }
+AAA < | $m AAG y
AAD Y
-DAA <D / TD / lD h
-C Hz D
- AL , D
-y AAG D
-AAA < LD
- j AAG y
+DAA t C Hz ( AL , , (y l AAG D
+AAA < \ x `l AAG y
AAD Y
-DAA E / 4E / LE C Hz lE 8 AL , D 8 y AAG D
-AAA E ; Au < LE AAG y
+DAA T l C Hz AL , y k AAG D
+AAA ; Au < \ 8 |k AAG y
AAD Y
-DAA F 8. 4F 0. LF C Hz lF AL , E y AAG D
-AAA F H ; Au < LF h AAG y
+DAA T! l! ! XC Hz ! AL , ! y j AAG D
+AAA ! ; Au < \! j AAG y
AAD Y
-DAA G X- 4G P- , F | B AAG G
-AAA $ |G ARD hAA< G @
AAG y
+DAA T" l" , ! | 8j AAG G
+AAA $ " h ARD hAA< $" i AAG y
AAD Y
-DAA G , G , , G `
} AAG H
-AAA DH
; Au < G
R AAG y
+DAA # ` 4# X , " } i AAG H
+AAA |# @; Au < " ` Hi AAG y
AAD Y
-DAA H , H , 4 H
BAA G0
- AABA I AL 4 ,I BAA G0~
- AABA dI AL , H B AAG0c
-AAA I AL 4 I BAA G0~
- AABA J AL , I AAG0c
-AAA \J h AL , |J h AAG0T
-AAA J AL 4 <J BHI
-ABA < tJ P AAG y
+DAA # # 4 $ BAA G0
+ AABA D$ h AL 4 d$ h BAA G0~
+ AABA $ 0 AL , $ 0 8h AAG0c
+AAA $ AL 4 % BAA G0~
+ AABA D% x AL , $ x g AAG0c
+AAA % AL , % AAG0T
+AAA % h AL 4 L% h f BHI
+ABA < % f AAG y
AAD Y
-DAA DK ) \K ) < tK p BBA A(G@|
-(A ABBA K AL < DK BBA E(G0~
-(A ABBA < K AAG y
+DAA |& @ & 8 < & BBA A(G@|
+(A ABBA & AL < T& f BBA E(G0~
+(A ABBA < & 0 e AAG y
AAD Y
-DAA TL ( lL ( < L F BBA A(G@
-(A ABBE L AL , TL 7 AAG
-AAA M p; Au < L AAG y
+DAA ' P ' H < ' PF BBA A(G@
+(A ABBE ' ` AL , d' ` -e AAG
+AAA L( ; Au < ' d AAG y
AAD Y
-DAA tM ' M ' M l Hc M AL , TM V AAG ^
-AAA N p; Au < M AAG y
+DAA ( P ( H ( @l Hc ( AL , d( Ld AAG ^
+AAA L) ; Au < ( d AAG y
AAD Y
-DAA tN & N & , N AAG0a
-AAA N AL 4 dN c BHI $
-ABA ,O ; Au < N AAG y
+DAA ) p ) h , ) @ AAG0a
+AAA * AL 4 t) Yc BHI $
+ABA d* H; Au < ) h c AAG y
AAD Y
-DAA O % O % 4 O BAA G0-
- AABA O AL , O W g AAG
-AAA DP ; Au < O & AAG y
+DAA * x * p 4 * BAA G0-
+ AABA ,+ @ AL , * @# ]b AAG
+AAA |+ @; Au < * ` b AAG y
AAD Y
-DAA P $ P $ < P BBA A(G@R
-(A ABBK Q " AL D P "R f BIF A(E0
-(A BBBA |Q #; Au < Q # AAG y
+DAA + + x < , BBA A(G@8
+(A ABBE L, AL , + \a AAG =
+AAA $ , ARD hAA< , a AAG y
AAD Y
-DAA Q # Q # 4 R $F BAA G0
- AABA DR 0% AL , Q 0% d AAG =
-AAA $ R & ARD hAA< ,R H' AAG y
+DAA - x - p < 4- 8 BBA A(G@
+(A ABBF t- AL , , S` AAG S
+AAA - ; Au < ,- ` AAG y
AAD Y
-DAA R " S " < ,S h' BBA A(G@
-(A ABBG lS ( AL , R ( [ AAG S
-AAA S h); Au < LS ) AAG y
-AAD Y
-DAA T ! 4T ! < LT ) BBA D(D0D
-(A ABBI T * AL $ T H!I De
+DAA $. x <. p < T. BBA D(D0D
+(A ABBI . ( AL $ . I De
G I
-G H D T )_ BIB D(C0G@~
-0A(A BBBG L U * BHF F(A0C8G`
-8A0A(B BBBG L lU 8,+ BEF F(A0D8G`
-8A0A(B BBBA L U .u BBB E(H0D8GPx
-8D0A(B BBBE V 8 DK < $V 00^ BBB A(L0[
-(A EBBH < dV P3 BBF A(G0
-(A EBBF < V 4 BBA D(F0s
-(D ABBE < V 7x BBF A(G0%
-(A EBBG < $W 9 BBA D(F0
-(D ABBG < dW : BBA D(F0*
-(D ABBF L W BIA A(G0r
-(A ABBD d(C ABB L W < BEF F(E0D8GP
-8A0A(B BBBA L DX BIA A(G0r
-(A ABBD d(C ABB L X P BIA A(G0r
-(A ABBD d(C ABB L X = BEF B(D0D8GP
-8D0A(B BBBK L 4Y 0? BEF B(D0D8GP
-8D0A(B BBBK L Y @ BIB E(H0D8G`
-8D0A(B BBBD L Y BIA A(G0r
-(A ABBD d(C ABB L $Z E BEF F(E0D8G@
-8D0A(B BBBA L tZ BIA A(G0r
-(A ABBD d(C ABB < Z 0GA BFD D(G0
-(D ABBA L [ @H BEF B(D0D8GP
-8D0A(B BBBK , T[ ADD c
-ADG [ P 2 W V [ x 0 h D]
-E L [ 8I BEE B(A0A8G
-8A0A(B BEBG L \ 8 BIA A(G0r
-(A ABBD d(C ABB L \\ XPp BFF B(D0D8G`
-8D0A(B BBBA L \ X BIA A(G0r
-(A ABBD d(C ABB L \ (R BIE B(A0A8GpG
-8A0A(B BBEA L L] hZI BEE B(A0A8K`
-8D0A(B BBBD L ] ( BIA A(G0r
-(A ABBD d(C ABB L ] ^ BEB B(H0A8G`
-8D0A(B BBBD L <^ H BIA A(G0r
-(A ABBD d(C ABB L ^ e+ BFF F(D0D8G@
-8D0A(B BBBA L ^ hX BIE B(E0A8Gpz
-8A0A(B BBEJ L ,_ m BIE E(A0A8D
-8A0A(B BBED , |_ Hu ADG0y
-AAI , _ w ADG0u
-AAE , _ hx ADG0E
-AAE , ` x ADG0E
-AAE , <` hy ADG0E
-AAE , l` y ADG0A
-AAA , ` Xz ADG0E
-AAE , ` z ADG0A
-AAA , ` H{ ADG0U
-AAE , ,a { ADG0A
-AAA , \a H| ADG0Q
-AAA , a | ADG0M
-AAE , a H} ADG0Q
-AAA , a } ADG0]
-AAE , b X~ ADG0m
-AAE , Lb ~ ADG0
-AAE , |b D ADG0
-AAE , b ADG0
-AAE < Lb 0 BBA D(G0d
-(A ABBA , c ADG0
-AAE , Lc Ȅ~ ADG0h
-AAJ |c , c L ADG0
-AAI 4 c 0 BDD D0U
- AABI c ȇ , d ADG0Y
-AAI T Dd P BDA G0e
+G H D . _ BIB D(C0G@~
+0A(A BBBG L $/ BHF F(A0C8G`
+8A0A(B BBBG L t/ X+ BEF F(A0D8G`
+8A0A(B BBBA L / 8u BBB E(H0D8GPx
+8D0A(B BBBE 0 DK < ,0 P^ BBB A(L0[
+(A EBBH < l0 p BBF A(G0
+(A EBBF < 0 BBA D(F0s
+(D ABBE < 0 BBA D(F0
+(D ABBG < ,1 5 BBA D(F0
+(D ABBG L l1 BIA A(G0r
+(A ABBD d(C ABB L 1 0 BEF F(E0D8GP
+8A0A(B BBBA L 2 BIA A(G0r
+(A ABBD d(C ABB L \2 BEF B(D0D8GP
+8D0A(B BBBK L 2 BEF B(D0D8GP
+8D0A(B BBBK L 2 p BIB E(H0D8G`
+8D0A(B BBBD L L3 ` BIA A(G0r
+(A ABBD d(C ABB L 3 BEF F(E0D8G@
+8D0A(B BBBA L 3 BIA A(G0r
+(A ABBD d(C ABB < <4 A BFD D(G0
+(D ABBA L |4 BEF B(D0D8GP
+8D0A(B BBBK , 4 ` ADD c
+ADG 4 2 W V \4 0 X D]
+E L 45
BEE B(A0A8G
+8A0A(B BEBG L 5 BIA A(G0r
+(A ABBD d(C ABB L 5 p BFF B(D0D8G`
+8D0A(B BBBA L $6 BIA A(G0r
+(A ABBD d(C ABB L t6 X BIE B(A0A8GpG
+8A0A(B BBEA L 6 I BEE B(A0A8K`
+8D0A(B BBBD L 7 " BEB E(D0A8Hp
+8D0A(B BBBD L d7 h BIA A(G0r
+(A ABBD d(C ABB L 7 *+ BFF F(D0D8G@
+8D0A(B BBBA L 8 - BIE E(A0A8D
+8A0A(B BEBD , T8 X4 ADG0y
+AAI , 8 7 ADG0E
+AAE , 8 7 ADG0E
+AAE , 8 8 ADG0u
+AAE , 9 x8 ADG0E
+AAE , D9 8 ADG0A
+AAA , t9 h9 ADG0E
+AAE , 9 9 ADG0U
+AAE , 9 x: ADG0A
+AAA , : : ADG0A
+AAA , 4: X; ADG0M
+AAE , d: ; ADG0Q
+AAA , : X< ADG0Q
+AAA , : < ADG0]
+AAE , : h= ADG0m
+AAE , $; > ADG0
+AAE , T; >D ADG0
+AAE , ; ? ADG09
+AAI < : hA XR BBA D(G0d
+(A ABBA , ; A~ ADG0h
+AAJ $< B , << BL ADG0
+AAI 4 l< 0D BDD D0U
+ AABI < D , < D ADG0Y
+AAI T < PG BDA G0e
AABI D
AABH T
- FABE d T d BDA G0e
+ FABE D= G T \= G BDA G0e
AABI D
AABH T
- FABE e x < d p BBE D(G0Z
-(A ABBA D de & ADG0p
+ FABE = xH < = pH MP BBE D(G0Z
+(A ABBA D > H& ADG0p
DAO a
AAE Q
-DAJ e ، < 4e Ќ c BFA D(G0p
-(A ABBA < te ` 1 BFA D(G0p
-(A ABBA e L 4 \f (7 ADG N
-GAN DAA $ f 0U Ac
-D d 4 f h7 ADG N
-GAN DAA T df p W BFA D(G0
+DAJ T> I < = I O BFA D(G0p
+(A ABBA < = `J O BFA D(G0p
+(A ABBA 4> JL WO 4 ? (K7 ADG N
+GAN DAA $ > 0KU O Ac
+D d 4 d? hK7 ADG N
+GAN DAA T > pK N BFA D(G0
(A ABBI [
-(A ABBA 4 f 8 AAG i
-AAD KAA4 g 7 ADG N
-GAN DAA 4 ,g AAG i
-AAD KAA4 g 7 ADG N
-GAN DAA L g 6 BEA A(D0F
-(A ABBG g(E ABB T g Hb BBA H(D0
+(A ABBA 4 <? 8L fN AAG i
+AAD KAA4 ,@ L7 ADG N
+GAN DAA 4 ? L M AAG i
+AAD KAA4 @ L7 ADG N
+GAN DAA L @ L M BEA A(D0F
+(A ABBG g(E ABB T l@ HMb KM BBA H(D0
(A ABBB \
-(E ABBJ 4 Dh `z AAG p
-AAE sAA $ |h U t Ac
-D d 4 h T AAG o
-AAF [AAL li X BBA D(D0
+(E ABBJ 4 @ `Nz L AAG p
+AAE sAA $ @ NU L Ac
+D d 4 $A N L AAG o
+AAF [AAL B XO BBA D(D0
(A ABBJ d
-(E ABBJ L i BBA H(D0
+(E ABBJ L dB Q BBA H(D0
(A ABBA \
-(E ABBJ T |i , BIA A(D0x
+(E ABBJ T A R, K BIA A(D0x
(A ABBA l
-(E ABBJ L dj # BBA H(D0
-(A ABBE f
-(E ABBH L j p BEA A(D0
+(E ABBJ L C S BEA A(D0
(A ABBK d
-(E ABBJ L k BBA D(D0
+(E ABBJ L \C 0T BBA D(D0
(A ABBG g
-(E ABBO $ j E I A[
-D \ $ j M ) Ab
-E \ $ k U Ac
-D d 4 k 7 ADG N
-GAN DAA L l k BEA A(D0
-(A ABBF D(E ABB $ k @M a Ab
-E \ $ k hM A Ab
-E \ l \ l BBB B(A0A8D`P
-8A0A(B BBBH 8A0A(B BBB| m BBB B(D0A8D`H
+(E ABBO $ B pUE J A[
+D \ $ C UM J Ab
+E \ $ DC UU J Ac
+D d 4 $D U7 ADG N
+GAN DAA L \D Vk BEA A(D0
+(A ABBF D(E ABB $ C VM J Ab
+E \ $ D HVM I Ab
+E \ D \ E BBB B(A0A8D`P
+8A0A(B BBBH 8A0A(B BBB| tE U BEB B(D0A8D`E
8A0A(B BBBM D
8A0A(B BBBB
-8F0A(B BBBE m 8 4 m 07 ADG N
-GAN DAA L \m 8! BBB E(D0A8D`
-8A0A(B BBBK L <n } BBB B(A0D8G`
-8A0A(B BBBH n H 4 n @7 ADG N
-GAN DAA D n HR BAD G0
+8F0A(B BBBE E HW 4 F @W7 ADG N
+GAN DAA L E HW! H BBB E(D0A8D`
+8A0A(B BBBK L F (Y} BBB B(A0D8G`
+8A0A(B BBBH F X] 4 F P]7 ADG N
+GAN DAA D 4G X]R BAD G0
AABK D
- AABH $o ` 4 <o X7 ADG N
-GAN DAA to T n H [ BBB B(A0D8G,
-8A0A(B BBBF T To V BIF E(D0`
+ AABH |G p^ 4 G h^7 ADG N
+GAN DAA G T ,G X^ G BBB B(A0D8G,
+8A0A(B BBBF T G d F BIF E(D0`
(A BBBG
-(A BBBA <p H Tp @ d lp f BBB B(A0A8G`^
+(A BBBA H 8 H 0 d H ef BBB B(A0A8G`^
8A0A(B BBBG
-8A0A(B BBBA p 4 p 7 ADG N
-GAN DAA 4 $q BDD D0G
- AABG \q 4 tq 7 ADG N
-GAN DAA 4 q BDD D0G
- AABG q 0 4 q (7 ADG N
-GAN DAA d 4r 0 ADG0
+8A0A(B BBBA ,I f 4 DI f7 ADG N
+GAN DAA 4 |I f BDD D0G
+ AABG I f 4 I f7 ADG N
+GAN DAA 4 J f BDD D0G
+ AABG <J @g 4 TJ 8g7 ADG N
+GAN DAA d J @g ADG0
DAH {
DAH {
DAH {
DAH {
DAH D
-AAJ 4 r BDD D0G
- AABG r 0 4 r (7 ADG N
-GAN DAA D $s 0/ BAD G0
+AAJ 4 J i BDD D0G
+ AABG ,K @j 4 DK 8j7 ADG N
+GAN DAA D |K @j/ BAD G0
AABL D
- AABH ls 4 s 7 ADG N
-GAN DAA d s BBB B(A0D8G`
+ AABH K (k 4 K k7 ADG N
+GAN DAA d L (k BBB B(A0D8G`
8A0A(B BBBO D
-8A0A(B BBBB $t p 4 <t h7 ADG N
-GAN DAA d tt p# BBB E(A0A8G`
-8A0A(B BBBN D
-8A0A(B BBBB t 8 4 t 07 ADG N
-GAN DAA ,u x
- Du p
- L \u 6 BBB B(A0D8G`I
-8A0A(B BBBI u 4 u 7 ADG N
-GAN DAA u | v BBB B(D0A8G`
+8A0A(B BBBB |L s 4 L xs7 ADG N
+GAN DAA d L sC BBB E(A0A8G`
8A0A(B BBBL D
+8A0A(B BBBB 4M hv 4 LM `v7 ADG N
+GAN DAA M h L M Pv BBB B(A0D8G`(
+8A0A(B BBBJ M z 4 N z7 ADG N
+GAN DAA <N L TN z6 BBB B(A0D8G`I
+8A0A(B BBBI N x 4 N p7 ADG N
+GAN DAA N | O ` BBB B(D0A8G`
+8A0A(B BBBL D
8A0A(B BBBJ 0
-8E0A(B BBBE v 4 v 7 ADG N
-GAN DAA T Tv . BBB I(A0A8G.
-8A0A(B BBBH L <w k BBB B(A0D8G`
-8A0A(B BBBI w 4 w 7 ADG N
-GAN DAA w L w BBB B(A0D8G`P
-8A0A(B BBBB Dx @ 4 \x 87 ADG N
-GAN DAA L x @S
- BBB E(A0A8G`
-8A0A(B BBBG l Tx P BBB E(A0A8G`
+8E0A(B BBBE O p 4 O h7 ADG N
+GAN DAA L $O pS
+ ? BBB E(A0A8G`
+8A0A(B BBBG l tO ? BBB E(A0A8G`
8A0A(B BBBD =
-8A0A(B BBBI L x BBB E(A0A8G`
-8A0A(B BBBD L y BBB I(A0A8Gp
-8A0A(B BBBB T dy u } BBB B(D0A8G5
-8A0A(B BBBE 4 Lz ADG0j
+8A0A(B BBBI L O k @? BBB E(A0A8G`e
+8A0A(B BBBE L 4P &? BBB I(A0A8Gp
+8A0A(B BBBB T P `u > BBB B(D0A8G5
+8A0A(B BBBE 4 Q ADG0j
AAH D
-AAJ z @ 4 z 87 ADG N
-GAN DAA z @ 4 z 87 ADG N
-GAN DAA z @Y BBE D(G0
+AAJ Q 4 Q 7 ADG N
+GAN DAA R 4 4R 7 ADG N
+GAN DAA Q Y -> BBE D(G0
(A ABBA y
(A ABBA y
(A ABBA y
(A ABBA u
-(A ABBE T { 5 BFA D(G0
+(A ABBE T <R ؟ = BFA D(G0
(A ABBC a
-(A ABBA L t{ BBE D(G0}
+(A ABBA L R v= BBE D(G0}
(A ABBI a
-(A ABBA < { p BBE D(G0C
-(A ABBC l | BBB B(D0A8D`d
+(A ABBA < R 0 ?= BBE D(G0C
+(A ABBC l $S = BBB B(D0A8D`d
8A0A(B BBBA "
-8A0A(B BBBA < t| E BBE D(G0C
-(A ABBC < | BBE D(G0C
-(A ABBC } ` K } @ D ,} 8 BEB D(A0GpY
-0A(A BBBJ $ ~ [ ADG LAA4 ,~ ( BDA G0x
- AABF d~ |~ 6 $ ~ . ACD `DA < ~ L BBA A(D0t
-(D ABBA , ~ < BDD kAE , , b BAA ZAB \ H t P# Aa ` AL `D , AFG |
-AAD $ + AAG \DA 4 $ BDD D0I
- AABE \ Q Ad
-A c | . Ac
-A D 4 v BDD D0|
- AABA L Ԁ BED D(D@R
+8A0A(B BBBA < S P < BBE D(G0C
+(A ABBC < S < BBE D(G0C
+(A ABBC T ){ K T 0 U ( U 4U LU dU |U г" AN ND U " AN ND U " AN ND U 0 U ( V 4 $V 6zS BBA D(A0B(A ABB4 \V RzS BBA D(A0B(A ABB4 V nzS BBA D(A0B(A ABB4 V zS BBA D(A0B(A ABB4 W ز] BAA G0
+ AABA <W AL < \W ] BBA A(G@
+(A ABBE W AL < W BBA A(G@F
+(A ABBG W AL L X BED D(D0
+(A ABBE M(A ABB lX P $ W H 8 HD
+D E
+A X л2 AL cA X X X $ VM Y ,Y DY A
+J dY $ VM |Y Y Y $ VM , Y 7 AAG S
+AAA Y ; Au < \Y ( M7 AAG y
+AAD Y
+DAA TZ lZ D Y HR 6 BIF A(E0
+(A BBBA Z `; Au < 4Z 6 AAG y
+AAD Y
+DAA ,[ 0 D[ ( , Z C6 AAG
+AAA [ `; Au < Z 6 AAG y
+AAD Y
+DAA [ \ L \ BFE F(D0D8GP
+8D0A(B BBBD , l\ ADG0
+AAE , \ ADG0a
+AAA , \ pL ADG0
+AAE 4 \ BDD D0
+ AABC 4] h 4 \ ` r4 AAG o
+AAF cAA4 ] 7 ADG N
+GAN DAA < ] 4 BFA D(G0~
+(A ABBA L ] BBB B(A0D8G`
+8A0A(B BBBH L^ T ] > l3 BBB I(A0A8GP
+8A0A(B BBBF ^ ^ L ^ BBB B(A0D8G`@
+8A0A(B BBBB <_ @ L ^ 8 2 BBB E(A0A8G`O
+8A0A(B BBBC _ #{u Ki4 _ D BEA A(D0p(A ABB T <_ Z >2 BBA H(D0
+(A ABBE z
+(A ABBH 4 L` 7 ADG N
+GAN DAA L _ 1 BBD A(D0
+(A ABBE (A ABB 4 ` X7 ADG N
+GAN DAA 4 a `O BEA A(D0{(A ABB L Da x BEE B(A0A8Kp
+8A0A(B BBEE L a 8K BEE B(D0A8Hp
+8A0A(B BBED d a 8w BBB E(D0A8GP
+8D0A(B BBBJ r
+8A0A(B BBBE L Lb P BHA A(G0
+(A ABBH
+(A ABBE < b x BBF A(G0%
+(A EBBG < b BBA D(F0
+(D ABBH c 4c Lc % dc % |c `" AN ND c p" AN ND c p% c h% 4 c zS BBA D(A0B(A ABB4 $d zS BBA D(A0B(A ABB4 \d zS BBA D(A0B(A ABB4 d zS BBA D(A0B(A ABB, d p AAG0
+AAA d ` AL < e ` BBA A(G@
+(A ABBB \e AL |e $ d , HD
+D E
+A e 02 AL cA e P e H f @$ VM $f X <f ` Tf X lf P$ VM 4 e h + BHI b
+ABA < f + AAG y
+AAD Y
+DAA f P" g H" , tf + K+ AAG
+AAA $ \g g ARD IAA< f X + AAG y
+AAD Y
+DAA g ! g ! , g xT ADG0
+AAE , $h ADG0
+AAI Th 8! L lh p BBB B(A0D8G`
+8A0A(B BBBE h 0 4 h (O BAD G0
+ AABG i @ $i x L h Z) BBB E(A0A8G`
+8A0A(B BBBG L h A) BBB B(E0D8G@
+8A0A(B BBBA i 0{ K4 i D BEA A(D0p(A ABB L ti 1 ( BBA D(D0
+(A ABBE n(A ABB 4 |j 7 ADG N
+GAN DAA 4 i N( AAK |
+AAE CAA4 j p7 ADG N
+GAN DAA 4 $k xD BEA A(D0p(A ABB L \k 1 BEB E(D0A8Hp
+8A0A(B BBEC L k BIA A(G0r
+(A ABBD d(C ABB L k 0 BIE B(E0E8G`
+8A0A(B BBEB L Ll BEA D(G0
+(A ABBI
+(A ABBE d l A BEB B(D0D8DP
+8D0A(B BBBB
+8A0A(B BBBE < m BBA D(F0
+(D ABBH < Dm , BIB E(A0
+(D BBBC m D l n% BEB D(A0GpY
+0A(A BBBJ $ m [ ADG LAA4 n BDA G0x
+ AABF Dn x \n p 6 $ tn . ACD `DA < n L BBA A(D0t
+(D ABBA , n < BDD kAE , o b BAA ZAB <o To # Aa n # AL o D , o P AFG |
+AAD $ o + AAG \DA 4 p BDD D0I
+ AABE <p 0 Q Ad
+A c \p p . Ac
+A D 4 |p v BDD D0|
+ AABA L p BED D(D@R
(A ABBA T
-(G ABEE 4 $ B ADG b
-AAA JAA D \ x BDD z
+(G ABEE 4 q 8!B ADG b
+AAA JAA D <q P!x BDD z
ABA Y
-ABE AGB < r ADG x
+ABE AGB < q !r ADG x
AAA S
-GAE ADA 4 O AAG n
-DDI DAA 8" 4 . K^ 4 L 8L AAD r
-AAF DFA L P BIF H(H0A8D`
-8C0A(B BBBD Ԃ z Em
-A FL p KGF B(D0D8I`y8F0A(B BBB D . K^ \ t 4 t BAD H
-ABF rAB 4 BPH DPW
- AABA 4 T ` ˳ BPH DPW
- AABA J n N 4 L
- d @ | H
$ Ą
DE
+GAE ADA 4 q !O AAG n
+DDI DAA q !" r {. K^ 4 ,r !L AAD r
+AAF DFA L dr " BIF H(H0A8D`
+8C0A(B BBBD r "z Em
+A FL r (# KGF B(D0D8I`y8F0A(B BBB $s {. K^ <s 4 Ts h# BAD H
+ABF rAB 4 r # BPH DPW
+ AABA 4 s $ z BPH DPW
+ AABA s p$J n N t $ ,t P% Dt % \t & tt H' t ( $ t ( DE
G W
-I V Xt , h ADF0w
-AAD , L (s/ ACG W
-AAA , | (s1 ADG X
-AAA D 8s ACG a
+I V t )t t x) , t * ADF0w
+AAD , ,u / ACG W
+AAA , \u 1 ADG X
+AAA D u ACG a
AAB v
AAH P
-AAA D s ADG b
+AAA D u 8 ADG b
AAH z
AAD P
-AAA < $ ą t l QQ
+AAA v ) $ |u )t QQ
F b
-S | X D s ADD k
+S \v * D tv 8 ADD k
AAJ u
AAI O
-AAA D ܆ s ADD q
+AAA D v ADD q
AAD y
AAE O
-AAA < $ @t BDA _
+AAA < w BDA _
ABG M
-ABH d DR < H BLD D(D`
-(A ABBA , 4 l ATDP}
-AAA 0 ( $ < T l IT , b ACG D
-AAA , Ԉ @4 AAG a
-AAD 4 t Pq BAD L
-ABF < E ® ADD`r
+ABH Dw H) DR < v H) BLD D(D`
+(A ABBA , v )l ATDP}
+AAA w ) w ) x ) x ) 4x ) Lx ) dx ) A\ , x )b ACG D
+AAA , x )4 AAG a
+AAD 4 ,x *q BAD L
+ABF < dx P*E q ADD`r
AAC ~
-AAA | r 4 L AAG d
-DAF NDA D ̉ r] BDD G0z
+AAA \y 4 ty H+L AAG d
+DAF NDA D y `] BDD G0z
AIBI |
- DABM L `, BBB B(A0A8D`
-8A0A(B BBBG d < | Xs( \ < BED A(G@y
+ DABM L y +, BBB B(A0A8D`
+8A0A(B BBBG Dz XW \z ( \ tz 8W BED A(G@y
(A ABBF
(A ABBD `
-(A ABBA , s AJ T
+(A ABBA , z ȍ AJ T
HI l
-DP T =[. BBB B(A0A8G
-8A0A(B BBBK | k AT < k! ADG
+DP T Lz 8X[. BBB B(A0A8G
+8A0A(B BBBK \{ @ AT < |{ @! ADG
AAH }
-AAA ^AA ܋ X. K^ sC AA Ps , Xs D s5 \ s EN $ | s AAG QAC s AL Č s& ܌ sM t& , | 8t| * ARDPO
-AAA 4 < t BAA S
+AAA ^AA { {. K^ { ؍C AA <{ | $| x5 <| AR $ \| AAG QAC | AL | & | M | ؎& , 4| | ARDPO
+AAA 4 } @ BAA S
CBD |
-CBG < t Ҫ BBA A(Gn
-(A ABBG $ @vP AG ]
-AJ < ܍ hvl BEE A(D0S(A BBB , vN AAG0f
-AAG , L vN AAG0h
-AAE L | v BED C(G0T
-(C ABBG L
-(A ABBA $ ̎ (w D D
+CBG < | BBA A(Gn
+(A ABBG $ } P AG ]
+AJ < } l BEE A(D0S(A BBB , } PN AAG0f
+AAG , ,~ pN AAG0h
+AAE < \~ BED C(G0Y
+(A ABBA $ ~ Б D D
H I
-G z AO $ z D b
+G ~ AM $ ~ x D b
J
E K
-E $ < {T Ag
+E $ pT Ag
H F
J K
-E d |" D] | (| , x Ac
+E 4 " D] L , d xx Ac
D O
I o
-A G ď 0N D L h BEB D(A0
-(A BBBA < $ s FAD I
-ABI AAB d ` FS $ `/ AAD cDA | h BFE E(E0A8Dp
+A G ȚN D ~ H BEB D(A0
+(A BBBA < s FAD I
+ABI AAB 4 FS $ T / AAD cDA | | BFE E(E0A8Dp
8A0A(B BBBA ]
8A0A(B EBBA K
-8A0A(B IBBA $ , x> AM
+8A0A(B IBBA $ > AM
B U
-K $ T J EJ
+K $ $ (J AN
A h
-H L | BBB B(A0A8F`d
-8C0A(B BBBH d ̑ h BEB A(D0T
-(A BBBG n
-(A BBBK S
-(D BBBC < 4 BHD B
+H T L P BBB A(A0FPb
+0A(A BBBH
+0C(A BBBB \ BEA A(G0T
+(A ABBF g
+(F ABBF X
+(A ABBJ < x BDD F
ABE [
-ABJ L t PV BFE B(E0A8Dp
-8A0A(B BBBA < Ē ` BBE D(C0c(E BBB \ BED D(G0F
+ABJ L D V BFE B(E0A8Dp
+8A0A(B BBBA < BBE D(C0c(E BBB \ Ԃ 8 BED D(G0F
(A ABBF W
-(E ABBA A(A ABB < d ACG0
+(E ABBA A(A ABB < 4 x ACG0
EAF T
-GAE uGA4 ADF0|
+GAE uGA4 t 8 ADF0|
EAC h
-GAE L ܓ H BEE D(GP}
+GAE L BEE D(GP}
(A ABBF |
-(A ABBF A EM L p AP
-G l . K^ ̔ 4 o BAA Z
-ABG AAD 4 BAA o
-ABJ AAD T l AL AL ĕ Д L L BED A(Gpq
+(A ABBF D P AQ Pp AP
+G < @{. K^ T X l P X P 4 Ho BAA Z
+ABG AAD 4 BAA o
+ABJ AAD $ < T AL t AL h L T BED A(Gpq
(C ABBD G
-(C ABBA D x c BAC G`^
+(C ABBA D D " BAC G`^
AABA b
- CABA d 7 BEE B(D0A8Dj
+ CABA d
BEE B(D0A8Dj
8A0A(B BBBE I
-8C0A(B BBBA ܖ : Ax Ȋ& AT ؊' QU < AADPX
+8C0A(B BBBA @: Ax ̆ `& AT p' QU < T M
AADPX
AAH D
-AAA | 8% < P F ADD`y
+AAA L Х% <
ADD`y
AAD O
-CAE ԗ 4 \ ؋ BAA GPf
- AABK , P^ ؠ BKI ^
-ABA T AL t x! < h ADD`y
+CAE x 4 p BAA GPf
+ AABK , < ^ BKI ^
+ABA $ AL D \ ! < ( ' ADD`y
AAD O
-CAE $ - AAK [AC 4 | ( BAA GPf
- AABK , ^ BKI ^
-ABA t Ѝ AL Ѝ , ؍^ BKI ^
-ABA ܙ , d ^ Z BKI ^
-ABA $ @ < X. K^ T 0 l ( ̚ , I J AEG yAA D AL , Ԛ I AEG yAA 0 AL 0 ̛ ( , D p5 Hl d < Ho 5 Hl Ў< Ho Ĝ ܜ ؎: : $ D < b BBB A(C0G B
-0C(A BBBA @ 8 $ $ 0L Ai
-F [ 4 ܝ XE ADG ]
-AAE RAA 4 pY ADG f
-CAJ VAA L d | 8 AL ̞ h p x 4 b V BAC G`a
- AABF 4 ( ` BAC G`k
- AABD 4 b @ BAC G`a
- AABF , , ؑ J AAG`T
-AAA D \ 8 / BAC G`q
+CAE $ - AAK [AC 4 $ BAA GPf
+ AABK , \ 8^ BKI ^
+ABA D h AL d h , Ĉ p^ Q BKI ^
+ABA , ^ BKI ^
+ABA ب z. K^ $ Ȩ < T l ̊ , , I
+ AEG yAA AL , | I AEG yAA d Ȩ AL Ȩ ̋ 5 Hl 4 (< Ho T H5 Hl t h< Ho p: Č ܌ : D b BBB A(C0G B
+0C(A BBBA T ة l Щ $ ̌ ȩL q Ai
+F [ 4 E ADG ]
+AAE RAA 4 Y ADG f
+CAJ VAA 0 4 ( L d | Щ AL ̎ 4 , b BAC G`a
+ AABF 4 d BAC G`k
+ AABD 4 8b BAC G`a
+ AABF , Ԏ p AAG`T
+AAA D Ь BAC G`q
AABF ]
- AABG D . BAC G`b
+ AABG D L BAC G`b
AABE
- AABA 4 ؔd 3 BAD G`k
- AABC l $ ; BBE E(A0D8Du
+ AABA 4 pd BAD G`k
+ AABC l ̏ BBE E(A0D8Du
8F0A(B BBBE U
-8C0A(B BBBG $ 0M HD D ` AL 4 d `Q AAG w
-AAF FAA - AY
-F L D , X BAC G`a
+8C0A(B BBBG ȱM HD AL 4 4 Q AAG w
+AAF FAA l - AY
+F L D Ԑ 0 BAC G`a
AABF e
- AABG , 8, 4 P L XF d , AK
-D W , AAG`h
-AAE L BBA A(G0O
+ AABG ԑ , в, F 4 (, AK
+D W , 8 n AAG`h
+AAE L BBA A(G0O
(C ABBD z
-(A ABBH ? , AK
-D W < ș" , T f AI V
+(A ABBH Ԓ (? P, AK
+D W `" , $ xf AI V
AG e
-AB R ŗ HI ` AL ģ ` 4 ܣ XV BDD v
-ABE JAB ? 9 H`
-A 4 . K^ L 8 d 0 | (
Ĥ ܤ " AN ND D , \ BDD R
+AB R HI t AL 4 V BDD v
+ABE JAB , ? H`
+A z. K^ 4 L
d | Ĕ ܔ 0" AN ND D \ BDD R
ABA c
-ABJ HAB t 3 $ HG AFGsDAL $ pH BNE A(A0D
-0A(A BBBA T ph BAD G
+ABJ HAB D 3 $ \ G AFGsDAL ̔ H d BNE A(A0D
+0A(A BBBA T ԕ h BAD G
FABA D
CABF H
- FABG \ 8 AQ
-F _ $ | # ADG JGA $ # ADG JGA $ ̦ ^ ADG`OAA\ d 5 BBB A(D0G
+ FABG , 8 AQ
+F _ $ L @# ADG JGA $ t H# ADG JGA $ P^ ADG`OAA\ 5 O BBB A(D0G
0C(A BBBG
-0F(A BBDC T С T ܦ ȡ BBD A(G
+0F(A BBDC $ h T ` M BBD A(G
(C ABBJ T
-(A ABEA ħ T L 5 BBA D(G
+(A ABEA T BBA D(G
(C ABBG o
-(F ABBA 4 P , H0 ݓ AFJ RGA , H0 AFJ RGA d H BED A(G\
+(F ABBA , d 0 AFJ RGA , 0 t AFJ RGA d ė L BED A(G\
(A ABBK M
(F ABBH
-(A ABBA p , h_ ' AAM0n
-DAA , ̨ _ AAM0n
-DAA ȥ ȥ 4 D l RAA
-AIH L 5 BED D(D
-(A ABBG T(C ABB $ \ ) ADG ZAA , & BDD XAB 4 ] ADG `
-CAH ^CC 4 ئS BDD ^
-ADC [CD $ @ A[
-D _ 4 D AAJ
-AAE DCA $ | # ADG JGA 4 U ADG [
-CAE ^CC 4 ܫ ذ BDD D
- AABF FQ , , $ BDC WAB \ @ CY
-D _ 4 | S BDC ]
-ADE [CD $ 0, AAG ]DA < ܬ BBG A(G@
-(A ABBF D Z ^ BEE A(A0D@
-0A(A BBBF d | x. K^ , _ AIDpp
-CAF , 4 _ ێ AIDpp
-CAF D d P BID Dy
+(A ABBA , D _ AAM0n
+DAA , t 0_ AAM0n
+DAA \ ` t X ` 4 Xl V RAA
+AIH L ܙ @5 BED D(D
+(A ABBG T(C ABB $ , @) ADG ZAA , T H& BDD XAB 4 H] ADG `
+CAH ^CC 4 pS BDD ^
+ADC [CD @ A[
+D _ 4 H AAJ
+AAE DCA $ L # ADG JGA 4 t U ADG [
+CAE ^CC 4 p BDD D
+ AABF x FR , % BDC XAB , @ CZ
+C _ 4 L S BDC ^
+ADD [CD $ , AAG ]DA < BBG A(G@
+(A ABBF D 4 Z BEE A(A0D@
+0A(A BBBF 4 L z. K^ , _ AIDpp
+CAF , ܜ _ AIDpp
+CAF D z BID Dy
AABH T
- AABA , P AIDl
-AAA , ܭ c AIDK
-CAA , D AIDK
-CAA 4 < x % BID DpS
- CABA 4 t BID DpS
- CABA < T l
- 4 AJ0_
+ AABA , T 0P B AIDl
+AAA , P " AIDK
+CAA , AIDK
+CAA 4 BID DpS
+ CABA 4 x BID DpS
+ CABA 0 $ ( <
+ 4 T AJ0_
AE e
-AJ kD L BBB B(I0D8G@'
-8A0A(B BBBA L x BBJ B(D0A8GP<
-8D0A(E BBBE \ ~ t ` 8Q m L ش BIA A(G0t
-(A ABBB d(C ABB H D $ @ BBB A(A0G@v
-0D(A BBBH L l BHD A(G0c
+AJ kD L BBB B(I0D8G@'
+8A0A(B BBBA L ܟ BBJ B(D0A8GP<
+8D0A(E BBBE , ~ D \ Q t m L p BIA A(G0t
+(A ABBB d(C ABB ܠ D BBB A(A0G@v
+0D(A BBBH L < 0 BHD A(G0c
(A ABBA X
-(A ABBA 4 m AFG F
-CAH DDD d d 0 BBE B(G0D8I@
-8C0A(B BBBH U8E0A(B BBB \ \ ȸ BBE B(D0G8G@
-8C0A(B BBBH U8E0A(B BBB X A[ $ L X @ AJ@Z
-AJ L BHD A(D0f
-(G ABEH n(A ABB d T PL BEB B(A0G8D@
+(A ABBA 4 m AFG F
+CAH DDD d BBE B(G0D8I@
+8C0A(B BBBH U8E0A(B BBB \ , ` BBE B(D0G8G@
+8C0A(B BBBH U8E0A(B BBB A[ $ AJ@Z
+AJ L Ԣ BHD A(D0f
+(G ABEH n(A ABB d $ L BEB B(A0G8D@
8A0A(B BBBH Z
-8A0A(B BBBA d 8L BEB B(A0G8D@
+8A0A(B BBBA d L BEB B(A0G8D@
8A0A(B BBBH Z
-8A0A(B BBBA d $ L BEB B(A0G8D@
+8A0A(B BBBA d L BEB B(A0G8D@
8A0A(B BBBH Z
-8A0A(B BBBA d L BEB B(A0G8D@
+8A0A(B BBBA d \ L BEB B(A0G8D@
8A0A(B BBBH Z
-8A0A(B BBBA d L BEB B(A0G8D@
+8A0A(B BBBA d Ĥ L BEB B(A0G8D@
8A0A(B BBBH Z
-8A0A(B BBBA d \ ؾL BEB B(A0G8D@
+8A0A(B BBBA d , pL BEB B(A0G8D@
8A0A(B BBBH Z
-8A0A(B BBBA L 4
- i BBB B(A0A8K
-8D0A(B BBBG < AGG h
+8A0A(B BBBA L ܤ H
+ ( BBB B(A0A8K
+8D0A(B BBBG < AGG h
CDB N
-DAE iDD T 0 D P l 8 D R d @ BBB L(D0A8GP
+DAE iDD $ D P < D R d T BBB L(D0A8GP
8C0A(B BBBA
-8D0A(B BBBA d BBH F(D0A8G`
+8D0A(B BBBA d BBH F(D0A8G`
8C0A(B BBBH
-8D0A(B BBBA $ T @ AF R
-CE L | xG BBH A(D0
-(A BBDD (A BBB ̷ GO GO L HG BBH A(D0
-(A BBDD (A BBB L h GO L d 0F BHB A(D0
-(A BBDC (A BBB GO L ̸ F BHB A(D0
-(A BBDC (A BBB GO L 4 Z BHB D(A0
-(A BBDJ (A BBB GO L V BHB D(A0
-(A BBDE (A BBB H GO L B BHB A(D0
-(A BBDA (A BBB T GO l Յ. K^ J n N L BBB A(A0
+8D0A(B BBBA $ $ x@ AF R
+CE L L G BBH A(D0
+(A BBDD (A BBB @ GO H GO L ̧ G BBH A(D0
+(A BBDD (A BBB GO L 4 F BHB A(D0
+(A BBDC (A BBB GO L F BHB A(D0
+(A BBDC (A BBB p GO L Z BHB D(A0
+(A BBDJ (A BBB T ( GO L l V BHB D(A0
+(A BBDE (A BBB GO L ԩ B BHB A(D0
+(A BBDA (A BBB $ GO < z. K^ T 8 l 0J n N L ( BBB A(A0
(A BBGG Q
-(A FBGG ` hB 4 4 M ADG T
+(A FBGG Ԫ B 4 8M ADG T
AAF
-AAG l % L 0 BBB A(A0
+AAG < % L T BBB A(A0
(A BBBB Q
-(A EBBE Ի % L BBB A(A0
+(A EBBE X `% L ԫ x BBB A(A0
(A BBBB Q
-(A EBBE , T = ADG [
-AAG % L BBB A(A0
+(A EBBE , $ = ADG [
+AAG T ( l 0% L H BBB A(A0
(A BBBB Q
-(A EBBE P X% L 4 p BBB A(A0
+(A EBBE Ԭ % L BBB A(A0
(A BBBJ Q
-(A EBBE , = ADG ]
-AAE ̽ (% L @ BBB A(A0
+(A EBBE , T = ADG ]
+AAE % L BBB A(A0
(A BBBJ Q
-(A EBBE , 4 = ADG ]
-AAE d | % L BBB A(A0
+(A EBBE , x= ADG ]
+AAE 4 L % L d BBB A(A0
(A BBBB Q
-(A EBBE , = ADG _
-AAC # , % L D BBB A(A0
+(A EBBE , H= ADG _
+AAC X# p% L BBB A(A0
(A BBBJ Q
-(A EBBE , = ADG _
-AAC Ŀ # ܿ d BBB B(A0A8G`j
+(A EBBE , d (= ADG _
+AAC 8# P į X d ܯ ` BBB B(A0A8G`j
8A0A(B BBBK )
-8A0A(B BBBA L t p BBB B(D0A8DP^
-8A0A(B BBBG L 4 F || BBB E(A0A8G`U
-8D0A(B BBBJ , D
\
4 t AAG }
+8A0A(B BBBA L D BBB B(D0A8DP^
+8A0A(B BBBG L ܯ F ; BBB E(A0A8G`U
+8D0A(B BBBJ
,
4 D AAG }
ADE o
-AAG < BDA
+AAG < | ` BDA
ABA l
-ABI \ x$ ^{ < t Q J{ BBD A(D0}(A ABB d D BBB B(A0A8D`P
+ABI $ < (Q BBD A(D0}(A ABB d H BBB B(A0A8D`P
8A0A(B BBBH
-8A0A(B BBBA # ( 0 Hͅ. K^ $ < T l B Es @ AL @ AL 4 7 AAG Z
-AAK DAA $ g AG k
-AA D P AL , d P PAQqDA AP $ - ADJ XDA , L vx AAG@C
-DAG , | X Xx AAG@o
-DAA T 8x BBE B(A0A8G
-8D0A(B BBBH , 0 AVGaDA , ATGaDA , ATGaDA , $ P AUGaDA , T AUGaDA , AUGaDA , p AUGaDA , AUGaDA , 0 AUGaDA D \ K o N , Jv AAG Z
-AAK P L , Hg v BEB B(D0A8FY
-8A0A(B BBBG h) $ < x T p l h$ ȅ. K^ t hu BEE D(D0G`w
+8A0A(B BBBA | # IJ ܲ `z. K^ $ < T l B Es AL ij AL 4 7 AAG Z
+AAK DAA $ g AG k
+AA D AL , d PAQqDA h AP $ h- ADJ XDA , $ p AAG@C
+DAG , T AAG@o
+DAA T ` BBE B(A0A8G
+8D0A(B BBBH , AVGaDA , ĵ 8 ATGaDA , ATGaDA , $ AUGaDA , T X AUGaDA , AUGaDA , AUGaDA , x AUGaDA , AUGaDA D 8 \ @ K o N , x AAG Z
+AAK L g BEB B(D0A8FY
+8A0A(B BBBG
) $ (
<
T
l
$ z. K^ t P
BEE D(D0G`w
0A(A BBBF
0A(A BBBF B
-0A(A BBBF _ AG0UA 4 0` AG0VAT p t BBE B(A0D8G
-8A0A(B BBBF Dž. K^ K o N 4 d t BAA G@Y
- AABH , h D p AY d p 5 fN < .t BBA A(G`p
-(D ABBJ $ , X t AG0q
-DA , ƅ BOA AB \ BIB E(A0A8I
+0A(A BBBF _ AG0UA 4 ` AG0VAT Q BBE B(A0D8G
+8A0A(B BBBF z. K^ Ĺ !K o N ܹ ` 4 < h 2 BAA G@Y
+ AABH , D AY d 5 fN < Ĺ @ BBA A(G`p
+(D ABBJ $ AG0q
+DA , z BOA AB \ BIB E(A0A8I
0A(B BBBA
-0A(B IBBK d t BGB B(A0C8D`S
+0A(B IBBK d t (" BGB B(A0C8D`S
8A0A(B BBBA
-8A0A(B BBBG (> QR YA H8 T c D p BBE E(A0A8D`8D0A(B BBB \ HŅ6 Kj D t BEB E(D0A8DP]8A0A(B BBB| , r BBB B(A0D8D@
+8A0A(B BBBG ܻ "> QR YA "8 T c D # BBE E(A0A8D`8D0A(B BBB \ `z6 Kj D t h# BEB E(D0A8DP]8A0A(B BBB| # BBB B(A0D8D@
8F0A(B BBBL D
8A0A(B BBBB
-8F0A(B BBBG d H
- q BDE E(D0A8D@s
-8D0A(B BBBE ~8G0A(B BBB ,
-R Pq AAT0Z
-DAA L D 4 ,q BBE E(A0A8D
-8A0A(B BBBA $ Y < H < @ p BED D(DpH
-(C ABBE 4 s p BDD D0E
- DABA d % BHE B(E0D8DP
+8F0A(B BBBG d $ 5 BDE E(D0A8D@s
+8D0A(B BBBE ~8G0A(B BBB , %R AAT0Z
+DAA L %4 BBE E(A0A8D
+8A0A(B BBBA $ &Y < ( < ( Z BED D(DpH
+(C ABBE ) 4 )s & BDD D0E
+ DABA d )% BHE B(E0D8DP
8F0A(B BBBB D
-8C0A(B BBBH L L BBD D(G0!
+8C0A(B BBBH L L + BBD D(G0!
(A ABBF d
-(G AEBE , XL o AAJ0^
-DAA < < x o BEA D(D@Y
-(D ABBA < | do BEA D(D@Y
-(D ABBA d L BEB D(D0K
+(G AEBE , -L F AAJ0^
+DAA < - " BEA D(D@Y
+(D ABBA < T p- BEA D(D@Y
+(D ABBA d L - BEB D(D0K
(A BBBE F
(A BBBK d
-(A BBBE \ BEB D(D0C
+(A BBBE \ (. BEB D(D0C
(A BBBE D
-(A BBDC L(A BBB , 4 p =n BAD F`x
- AABG | F P
- H @ ( 4 0 AAI
-AAK DAA , D - AI@^D < d 0 BBA F(D`V
-(D ABBB . AJ@^D < BBA G(D`V
-(D ABBA . AJ@^D < $ 0 BBA G(D`V
-(D ABBA 4 d AFG N
-AAB GA T (
-l BBD D(Gpa
+(A BBDC L(A BBB X. , P/ 4 0 BAD F`x
+ AABG | 0F 0
+ 0 0 2 4 2 AAI
+AAK DAA , 3 D 3- AI@^D < d 3 BBA F(D`V
+(D ABBB H4. AJ@^D < X4 BBA G(D`V
+(D ABBA 4. AJ@^D < $ 4 BBA G(D`V
+(D ABBA 4 d H5 AFG N
+AAB GA T 5 BBD D(Gpa
(A ABBF f
-(F ABBG , N BDA G0{ AAB D p k BBB A(A0J`
-0A(A BBBF D ! k BBB A(G0D`
-0A(A BBBF D $ # ]k BBB A(A0Jh
-0A(A BBBF D l ' :k BBB A(G0D`
-0A(A BBBF 4 D ) DJ0
+(F ABBG , h8N BDA G0{ AAB D l 8T 2 BBB A(A0J`
+0A(A BBBF D ;
+ BBB A(G0D`
+0A(A BBBF D > BBB A(A0J`
+0A(A BBBF D D pA BBB A(G0D`
+0A(A BBBF 4 D C DJ0
DF Z
DJ N
-DF | x*+ AG@^D *I 4 $ * j BDD G0^
- DABA < \ (+ |j BED D(G@y
-(D ABBA D + Jj BEE D(D0G@
-0D(A BBBA L @, j BEE E(D0D8GP
-8D0A(B BBBA L 4 , i BEE E(D0D8GP
-8D0A(B BBBA L -E i BEE E(D0D8GP
-8D0A(B BBBA L .n Ji BEE E(D0D8GP$
-8D0A(B BBBA L $ / i BEE E(D0D8GPM
-8D0A(B BBBA 01 < (1s h BBD D(G0U(A ABB < h1 vh BBE D(D0`(L BFB L 1 >h BEB E(D0D8G@8A0A(B BBB l \ (2 g BEE E(A0A8D
+DF | D+ AG@^D DI 4 E , BDD G0^
+ DABA < 4 pE BED D(G@y
+(D ABBA D t E BEE D(D0G@
+0D(A BBBA L F BEE E(D0D8GP
+8D0A(B BBBA L 8G S BEE E(D0D8GP
+8D0A(B BBBA L \ HE BEE E(D0D8GP
+8D0A(B BBBA L In BEE E(D0D8GP$
+8D0A(B BBBA L (J BEE E(D0D8GPM
+8D0A(B BBBA xK < d pKs 3 BBD D(G0U(A ABB < K BBE D(D0`(L BFB L L BEB E(D0D8G@8A0A(B BBB l 4 pL { BEE E(A0A8D
8A0A(B BBBF D
-8C0A(B BBBA \ 3E t 4
- \ 4 wg BBB A(A0D`
+8C0A(B BBBA \ NE t XN
+ \ PN BBB A(A0D`
0F(A BBBG
-0D(A BBBE h: T t `: g BED I(G0
+0D(A BBBE T T L T BED I(G0
(F ABBE X
-(C ABBH T : f BED I(G0
+(C ABBH T @U Q BED I(G0
(F ABBE X
-(C ABBH l $ ;h f JEA K(D`
+(C ABBH l Uh
+ JEA K(D`
(A ABBH D
-(C ABBD XH` $ = < = L = e BEE B(E0H8G@\
-8F0A(B BBBK = = =
- = =
- < = BDA A(G@X
-(C ABBA \ > L t > BED A(D0
+(C ABBD XH` $ W < W L W ~ BEE B(E0H8G@\
+8F0A(B BBBK 8X 0X (X
+ X X
+ < X BDA A(G@X
+(C ABBA \ X L t X BED A(D0
(A ABBH }
-(G ABBG D 4 @ d BDA G@G
+(G ABBG D XZ BDA G@G
AABG [
- AABA L | @ ^d BBB B(A0D8Gb
-8A0A(B BBBH 4 \ HC AK0{
+ AABA L T Z BBB B(A0D8Gb
+8A0A(B BBBH 4 \ ] AK0{
DE J
DB v
-DF D+ AG@^D , De ADG0|
-AAF L T PD c BBB B(D0D8Gp
-8A0A(B BBBB L G( c BBE E(D0A8Fp;
-8A0A(B BBBA `. K^ y y HI y y T I< BDC D0
+DF H^+ AG@^D , X^e ADG0|
+AAF L , ^ P BBB B(D0D8Gp
+8A0A(B BBBB L | a( % BBE E(D0A8Fp;
+8A0A(B BBBA xz. K^ c T Xc< BDC D0
AABH _
AABE _
- AABA l Ik Ae
-A 4 HJ AFD0{
+ AABA l @dk Ae
+A 4 d AFD0{
AAH I
-FAH K
- K < K BDA `
-ABF D
-ALA < @LX a BIA A(F`K
-(C ABBJ t `R hR d `R BBE E(D0D8GPj
+FAH e
+ e < e BDA `
+ABF N
+ABA < | fX C BIA A(F`K
+(C ABBJ t l l d l BBE E(D0D8GPj
8C0A(B BBBE X
-8F0A(B BBBA , TL AAN l
-AAA , < TL AAN l
-AAA , l TL AAN l
-AAA , TL AAN l
-AAA , UL AAN l
-AAA , (UL AAN l
-AAA , , HUG ADG i
-AAA D \ hU. AHG0
+8F0A(B BBBA , nL AAN l
+AAA , < nL AAN l
+AAA , l oL AAN l
+AAA , 0oL AAN l
+AAA , PoL AAN l
+AAA , poL AAN l
+AAA , , oG ADG i
+AAA D \ o. AHG0
AAF ^
AAH _
-AAA D PV. AHG0
+AAA D p. AHG0
AAF ^
AAH _
-AAA D 8W& AHF0
+AAA D q& AHF0
AAD ^
AAH _
-AAA D 4 X. AHG0
+AAA D 4 hr. AHG0
AAF ^
AAH _
-AAA D | Y. ACK0
+AAA D | Ps. ACK0
AAD ^
AAH _
-AAA D Y> ADK0
+AAA D 8t> ADK0
AAF ^
AAH _
-AAA , Z ACK0Z
-AAE , < x[ ADK0[
-AAC , l \ ACK0Z
-AAE , \ ADK0[
-AAC 4 (] BAH G0i
- AABA 4 ] BAH G0j
- AABH , < 8^ ACK0[
-AAD D l ^. AHG0
+AAA , 0u ACK0Z
+AAE , < u ADK0[
+AAC , l Pv ACK0Z
+AAE , v ADK0[
+AAC 4 pw BAH G0i
+ AABA 4 w BAH G0j
+ AABH , < x ACK0[
+AAD D l y. AHG0
AAF ^
AAH _
-AAA < $ _ ^\ BBD H(Gp
-(A ABBD T d `a 9\ BBD H(Gp
+AAA < y BBD H(Gp
+(A ABBD T < { BBD H(Gp
(A ABBF `
-(A ABBJ < (c \ BBD H(Gp
-(A ABBD T d [ BBD H(Gp
+(A ABBJ < p} BBD H(Gp
+(A ABBD T a BBD H(Gp
(A ABBF `
-(A ABBJ \ fW BEA H(N0
+(A ABBJ \ W BEA H(N0
(A ABBI _
(A ABBK `
-(A ABBA , D g= BID
-ABF t h AU \ hg BJH D(G0
+(A ABBA , D = BID
+ABF t AU \ g BJH D(G0
(A ABBK _
(A ABBK `
-(A ABBA , i) BDH
-AEN 4 $ jZ ADG b
+(A ABBA , ) BDH
+AEN 4 $ Z ADG b
CAF K
-FDE 4 \ j AAG0n
+FDE 4 \ 0 AAG0n
CAE K
-FDK T PkU Y BLB E(A0A8D+
-8A0A(B BBBA $ XmH ADF0zAA mB , m0 D m: \ mB t 0n3 Xn3 nA X. K^ o (" n n 4 " HS T n AL t ! ! ! ! ! ! ! ! 4 !" DN
-N A 4 T mw ADD [
-DAG DAA x!R DG
-E A< !t BBA D(D0]
-(A ABBH l \ m V BBB E(A0D8D
+FDK T U y BLB E(A0A8D+
+8A0A(B BBBA $ H ADF0zAA ȇB , 0 D : \ @B t x3 3 ȈA pz. K^ H < ( 4 h< HS T AL t H< @< 8< 0< (< < < < 4 <" DN
+N A 4 T w ADD [
+DAG DAA ;R DG
+E A< <t BBA D(D0]
+(A ABBH l 4 G BBB E(A0D8D
8A0A(B BBBH
-8A0A(B BBBA $ \ 8r UL
-G p L r V BBE E(D0D8Fc
-8A0A(B BBBG L D t V BEE E(D0D8F
-8A0A(B BBBA L t JV BHE E(D0D8F
-8A0A(B BBBA t Pu Xu `u , hus AAD y
-CAA < \ u U BED H(G`o
-(A ABBA , Hv d Pv UU BBA A(G0q
+8A0A(B BBBA $ \ UL
+G p L BBE E(D0D8Fc
+8A0A(B BBBG L H BEE E(D0D8F
+8A0A(B BBBA L l BHE E(D0D8F
+8A0A(B BBBA t , s AAD y
+CAA < 4 BED H(G`o
+(A ABBA , d BBA A(G0q
(A ABBD M
(A ABBE w
-(A ABBK , wm AAD x
-AAA 4 L Hw T BAD DP
- DABE , ~{ BAA |
-DBA , 0 xT AADPs
-AAE \ t BBA A(D@j
+(A ABBK , Pm AAD x
+AAA 4 $ N BAD DP
+ DABE , ({ BAA |
+DBA , x AADPs
+AAE \ t BBA A(D@j
(A ABBF j
(A ABBH h
-(A ABBJ < ADD Z
+(A ABBJ < H ADD Z
AAK Z
-AAD HAA4 ` ADD
+AAD HAA4 ADD
AAF D
-FAA L ȂN d | x' DR
-J F ' DQ
-K F , , S AADP[
-AAE 8 AZ 4 | 8 R BAD D`[
- AABF D A] 4 R BAD D`\
- AABE ( AZ 4 , ( rR BAD D`[
- AABF A] 4 ;R BAD D`\
- AABE L ) D d 4 0 R BAD D`]
- AABD ) D d 4 $ Q BAD D`]
- AABD 8 A[ 4 | 8 Q BAD D`\
- AABE D AQ
-F D d AZ 4 OQ BAD D`[
- AABF ( A] 4 L ( Q BAC D`[
- AABG $ 5 AW
+FAA L N d H | ' DR
+J F Н' DQ
+K F , AADP[
+AAE AZ 4 T d BAD D`[
+ AABF D A] 4 - BAD D`\
+ AABE p AZ 4 p BAD D`[
+ AABF A] 4 \ BAD D`\
+ AABE L `) D d 4 x BAD D`]
+ AABD ) D d 4 a BAD D`]
+ AABD A[ 4 T * BAD D`\
+ AABE D AQ
+F D d AZ 4 Ӽ BAD D`[
+ AABF p A] 4 $ p BAC D`[
+ AABG $ 5 AW
H G
-I D 4 P BAD D`]
- AABD t 0N h % DQ
-K D < 4 rP BBD D(Dp\
-(A ABBF < t ` SP BBD D(Dp\
-(A ABBF < Њ 4P BBD D(Dp\
-(A ABBF < @ P BBD D(Dp\
-(A ABBF 4 4 O BAD D`i
- AABH 4 l 8 O BAD D`i
- AABH < O BBD D(Dp\
-(A ABBF , 0 O AAD`T
-AAD < O BBD D(Dp\
-(A ABBF 4 T zO BAC D`[
- AABG 4 cO BAD D`]
- AABD , LO AAD`D
-AAD , Џ AO AAD`C
-AAE T $ p 4O BBD D(DpL
+I D 4 ] BAD D`]
+ AABD t xN (% DQ
+K D < 8 BBD D(Dp\
+(A ABBF < L BBD D(Dp\
+(A ABBF < BBD D(Dp\
+(A ABBF < BBD D(Dp\
+(A ABBF 4 z BAD D`i
+ AABH 4 D d BAD D`i
+ AABH < | N BBD D(Dp\
+(A ABBF , x / AAD`T
+AAD < BBD D(Dp\
+(A ABBF 4 , X BAC D`[
+ AABG 4 d Ш BAD D`]
+ AABD , H к AAD`D
+AAD , ź AAD`C
+AAE T BBD D(DpL
(A ABBF Q
-(A ABBA d ȑ2 BBB B(A0D8D
+(A ABBA d 2 BBB B(A0D8D
8A0A(B BBBJ >
-8A0A(B BBBH , N YX
+8A0A(B BBBH , YX
G
-A < yN YHD T
+A < ر YHD T
AAE B
-AAA T T i+ QN BEE B(A0C8G
-8D0A(B BBBG < < < BEG A(D@
-(D ABBA \ M BEE D(A0G`[
+AAA T , Hi+ չ BEE B(A0C8G
+8D0A(B BBBG < < `< BEG A(D@
+(D ABBA \ ` S BEE D(A0G`[
0A(A BBBE
-0E(A BBBE 4 BDA D0F
- DABA , e XGH _ID , D P{ BAA |
-DBA t pR DG
-E A " DQ
-K A L BBB B(A0A8Gp8A0A(B BBB D t YL BAD G
+0E(A BBBE 4 BDA D0F
+ DABA , Xe XGH _ID , D { BAA |
+DBA t -R DG
+E A ." DQ
+K A L (. BBB B(A0A8Gp8A0A(B BBB D L X ݷ BAD G
ABG J
-ABE KAB , L = BDA rAB d | Q BBB E(D0E8D@O
+ABE KAB , L `/= BDA rAB d | p/Q BBB E(D0E8D@O
8F0A(B BBBA
-8D0A(B BBGA D \ BDC D`
- AABA U
- AABA $ , $ ADG QCC $ T 2 AFF0bAA D | ( BHE D(D0D@
-0A(A BBBD $ 4 AFG0cAA D BHE D(D0D@
-0A(A BBBD $ 4 2 AFF0bAA D \ BHE D(D0D@
-0A(A BBBD $ 4 AFG0cAA D BHE D(D0D@
-0A(A BBBD P> AI0rA < 4 p BHD D(DP
-(A ABBF t 0> AI0rA < P BHD D(DP
-(A ABBF $ 2 AFF0bAA D ( BHE D(D0D@
-0A(A BBBD < H BEG D(D@
-(A ABBJ $ @2 AFF0bAA D X BHE D(D0D@
-0A(A BBBD 4 BGA G0f
- AABE 4 JG BGD D0f
- AABE \ d BBG D(G@e
+8D0A(B BBGA D 8w BDC D`
+ AABA W
+ AABA $ , p" ADG QAC $ T x2 AFF0bAA D | BHE D(D0D@
+0A(A BBBD $ H4 AFG0cAA D ` BHE D(D0D@
+0A(A BBBD $ 4 2 AFF0bAA D \ 0 BHE D(D0D@
+0A(A BBBD $ 4 AFG0cAA D BHE D(D0D@
+0A(A BBBD > AI0rA < 4 BHD D(DP
+(A ABBF t > AI0rA < BHD D(DP
+(A ABBF $ x2 AFF0bAA D BHE D(D0D@
+0A(A BBBD < H BEG D(D@
+(A ABBJ $ 2 AFF0bAA D BHE D(D0D@
+0A(A BBBD 4 x BGA G0f
+ AABE 4 t β BGD D0f
+ AABE \ d BBG D(G@e
(A ABBG r
-(A ABBH P(A ABB d `0 BBB B(D0C8GP}
+(A ABBH P(A ABB d 0 BBB B(D0C8GP}
8A0A(B BBBC A
-8A0A(B BBBE D , ( BAD G0s
+8A0A(B BBBE D , BAD G0s
AABK y
- AABC L t pl BBE F(A0A8D@
-8A0A(B BBBJ , @ AG r
+ AABC L t *l BBE F(A0A8D@
+8A0A(B BBBJ , AG r
AA G
-AA < BED D(D0D
-(A ABBC < 4 Pt BED D(D0h
-(A ABBG 4 t
ADG H
+AA < X BED D(D0D
+(A ABBC < 4 t BED D(D0h
+(A ABBG 4 t H+
ADG H
AAJ k
-AAC L X5 BBG D(GP
+AAC L 5 BBG D(GP
(A ABBB T
-(A ABBF L l H D BEE E(D0A8J
-8A0A(B BBBF T ? _D BEE E(A0C8J0
-8D0A(B BBBD @ L , 8( #D BBE E(D0D8GO
-8A0A(B BBBJ T } BHE D(A0Gpv
-0A(A BBBG T0A(A BBB 4 @} C BAA DPT
- AABA 4 dC BAA DPY
- AABA L D r 8C BBB E(A0C8G
-8A0A(B BBBG < $ BED D(G0K
-(A ABBI L p B BEH E(A0A8G
-8A0A(B BBBA D $
- B BBB D(D0G`
-0A(A BBBD L x BBB B(D0D8D@
-8A0A(B BBBK D B BAD G@P
+(A ABBF L D BEE E(D0A8J
+8A0A(B BBBF T +? BEE E(A0C8J0
+8D0A(B BBBD L ( BBE E(D0D8GO
+8A0A(B BBBJ T } BHE D(A0Gpv
+0A(A BBBG T0A(A BBB 4 } BAA DPT
+ AABA 4 BAA DPY
+ AABA L H
r BBB E(A0C8G
+8A0A(B BBBG < $ x BED D(G0K
+(A ABBI L G BEH E(A0A8G
+8A0A(B BBBA D
+ BBB D(D0G`
+0A(A BBBD L BBB B(D0D8D@
+8A0A(B BBBK D BAD G@P
AABF t
- AABA L A BBB B(D0D8Dp
-8A0A(B BBBE `. K^ , D \ H- t DZ " AN ND 8 Ds 4 @
7 Al
-A A $ u AG y
-AA , < d AEG L
-DAA $ l DG
+ AABA L ] BBB B(D0D8Dp
+8A0A(B BBBE xz. K^ 0/ (/ , / D / \ -- t . DZ /" AN ND /8 Ds 8/ @ @/
8/7 Al
+A A $ X/u AG y
+AA , < -d AEG L
+DAA $ l / DG
E p
-A $ m AG q
-AA s D d
-A . K^ , pb `? ARDPs
-CAA T P. K^ L l BBE B(A0D8JW
-8A0A(B BBBE , , x > AAJ
-DAA , \ > AAJDA
- G D 2 p> BEA D(J?
-(D ABBA
- P. K^ $
- F An
+A $ /m AG q
+AA ,s D d
+A z. K^ x0 p0 , l /b ARDPs
+CAA T hz. K^ L l 00 BBE B(A0D8JW
+8A0A(B BBBE , 0 \ AAJ
+DAA , 4 1 ; AAJDA 2 G D 26 BEA D(JF
+(D ABBA hz. K^ $ `5F An
A L
-D $
- F An
+D $ 5F An
A L
-D
- H D< t
- # = AAJ@j
+D 5 D< L (6# d AAJ@j
DAE Y
-DAB L
- 0 = BBF B(D0E8GPr
-8A0A(B BBBH L ~= BBB B(E0A8Gp
-8A0A(B BBBK L T V= BLB B(A0A8Jm
-8A0A(B BBBK 4 "# DZ L "I L # *= BBB B(A0A8J
-8A0A(B BBBD T $ ) = BBB E(D0A8J
-8A0A(B BBBE 4 | H; = BAF GP
- AABH 4 < o= BFA GP
- AABA 4 < F= BFA G@
- AABA 4 $
p= = BAA G0x
- AABA $
>n AG Z
-AA P> , X>- KQJ $ L h>n AG Z
-AA $ t >n AG Z
-AA L > C< BBB B(D0D8DP
-8D0A(B BBBE l \ @, < BBB E(A0A8D_
+DAB L 7 BBF B(D0E8GPr
+8A0A(B BBBH L BBB B(E0A8Gp
+8A0A(B BBBK L , x6 ڨ BLB B(A0A8Jm
+8A0A(B BBBK 4 8=# DZ L P=I L = BBB B(A0A8J
+8A0A(B BBBD T C BBB E(D0A8J
+8A0A(B BBBE 4 T U BAF GP
+ AABH 4 xV BFA GP
+ AABA 4 0W ʨ BFA G@
+ AABA 4 W BAA G0x
+ AABA $ pXn AG Z
+AA X , X- KQJ $ L Xn AG Z
+AA $ t Yn AG Z
+AA L `Y ǧ BBB B(D0D8DP
+8D0A(B BBBE l 4 Z, BBB E(A0A8D_
8A0A(B BBBF D
-8C0A(B BBBA L HCZ* ; BBB B(A0D8G[
-8D0A(B BBBD L Xm^ I= BEB B(A0A8GZ
-8A0A(B BBBH hv pv , xv6 AU ^A L v3 AR ^A , l v Dl
+8C0A(B BBBA L ]Z* ? BBB B(A0D8G[
+8D0A(B BBBD L ^ ͨ BEB B(A0A8GZ
+8A0A(B BBBH А
+ ؐ ,
+ 6 AU ^A L
+ 3 AR ^A , l
+ Dl
H _
A Z
-A Z Xwb L $ w < BEB B(D0A8G
-8A0A(B BBBG T t p < BBB E(G0D8G
-8C0A(B BBBC T ȇ < BBB E(A0A8J#
-8A0A(B BBBD T $ f< BBE E(G0D8G
-8A0A(B BBBF $ . LW < ȍ T Ѝ& l & & L R BBB B(A0A8GK
-8A0A(B BBBJ L (d BBB B(A0A8GK
-8A0A(B BBBJ $ < H> AG _
-AH LA$ d `> AG ^
-AI LA$ x= AG ^
-AI KA$ = AG ]
-AJ KAL R BBB B(A0A8GK
-8A0A(B BBBJ L , d BBB B(A0A8GK
-8A0A(B BBBJ D | ؒ BBB D(A0GN
-0A(A BBBH D BBB D(A0GN
-0A(A BBBH h
- $ `
- < X
- T P l H
- @
- 8
- 0 (
-
-
- ,
- D
- \
- t
-
- ؔ
- Д Ȕ
-
-
- 4 L
- d
- |
-
-
- x
- p L H BBB B(A0A8D
-8A0A(B BBBA L \ BBB B(A0A8D
-8A0A(B BBBA 4 ȓ |5 BAA G0
- AABG L P BBB B(A0A8Gp8A0A(B BBB l 4 Щx BBF D(A0G@F
+A Z
+ b L d
BEB B(D0A8G
+8A0A(B BBBG T L
+ 8 p BBB E(G0D8G
+8C0A(B BBBC T
+ , BBB E(A0A8J#
+8A0A(B BBBD T
+ ȥ BBE E(G0D8G
+8A0A(B BBBF
+ $
+ x. LW <
+ T
+ & l
+ &
+ ȧ& L
+ R BBB B(A0A8GK
+8A0A(B BBBJ L
+ d BBB B(A0A8GK
+8A0A(B BBBJ $ <
+ > AG _
+AH LA$ d
+ (> AG ^
+AI LA$
+ @= AG ^
+AI KA$
+ X= AG ]
+AJ KAL
+ pR BBB B(A0A8GK
+8A0A(B BBBJ L ,
+ d BBB B(A0A8GK
+8A0A(B BBBJ D |
+ BBB D(A0GN
+0A(A BBBH D
+ BBB D(A0GN
+0A(A BBBH
+ 0
+ $
+ (
+ <
+
+ T
+ l
+
+
+
+
+
+
+
+
+
+
+
+
+
+ خ ,
+ Ю
+ D
+ Ȯ
+ \
+
+ t
+
+
+
+
+
+
+
+
+
+
+
+
+
+ 4
+ x L
+ p
+ d
+ h
+ |
+ `
+
+ X
+ P
+
+ H
+
+ @
+
+ 8 L
+ BBB B(A0A8D
+8A0A(B BBBA L \
+ BBB B(A0A8D
+8A0A(B BBBA 4
+ BAA G0
+ AABG L
+ BBB B(A0A8Gp8A0A(B BBB l 4
+ x BBF D(A0G@F
0A(A BBBD v
0A(A BBBJ D
-0F(A BBBG T 4 BLB E(A0A8D
-8C0A(B BBBA $ 7 HTTF < ! T ! l ! ! ^ AW
-A L , (; 3 BDB B(A0A8GQ
-8A0A(B BBBB T | H! 3 BEB B(A0A8G:
-8D0A(B BBBE T & n3 BEB B(A0A8G:
-8D0A(B BBBE T , , [3 BEB B(A0A8G:
-8D0A(B BBBE @2 DM
-A 4 ; D L BDA
+0F(A BBBG T
+ BLB E(A0A8D
+8C0A(B BBBA
+ $
+
+ 7 HTTF <
+
+ : T
+
+ : l
+
+ :
+
+ x:
+
+ ^ AW
+A L
+
+ ; BDB B(A0A8GQ
+8A0A(B BBBB T T
+
+ : BEB B(A0A8G:
+8D0A(B BBBE T
+
+ ? BEB B(A0A8G:
+8D0A(B BBBE T
+ PE ۞ BEB B(A0A8G:
+8D0A(B BBBE
+ J DM
+A 4
+ ; D L
+ BDA
ABA O
-ABF ]AB , D 2 BDH _
-ABA 4 2 BBB E(A0A8F
+ABF ]AB ,
+ D H BDH _
+ABA
+ ( BBB E(A0A8F
8A0A(B BBBD
8A0A(B BBBF
8D0A(B BBBH W
-8A0A(B BBBG d д | ش < ` 1 BEI C(D0l
-(D ABBA < \ c 1 BEJ D(C0p
-(A BBBA , 8D 1 BDH _
-ABA d X u1 BED A(D0O
+8A0A(B BBBG d
+ |
+
+ <
+ ` u BEI C(D0l
+(D ABBA < 4
+ c E BEJ D(C0p
+(A BBBA , t
+ D BDH _
+ABA d
+ BED A(D0O
(D ABBH F
(C ABBJ a
-(D ABBA , 4 F )1 BDI `
-ABA n AL@_A 04 AS [D \ P# 0 BBB A(D0G`u
+(D ABBA ,
+ F BDI `
+ABA
+ n AL@_A
+ 4 AS [D \ |
+ # I BBB A(D0G`u
0D(A BBBF K
-0D(A BBBA (8 PA L d w !0 BBB B(A0A8G`
-8D0A(B BBBA D 2 Gj \ ظ t A
-A pC < s BBE E(A0X
-(A BBBA L - BBB B(A0A8Gp8A0A(B BBB L . BBB B(A0A8Je
-8A0A(B BBBE ! ! A_ < ! . BBG A(G@
-(A ABBG L ! X/ BEB A(D0l
+0D(A BBBA
+
+ 8
+ A
+ P L <
+ Hw BBB B(A0A8G`
+8D0A(B BBBA D
+ x2 Gj \
+ t
+ A
+A
+ 8C <
+ ps BBE E(A0X
+(A BBBA L
+ @F BBB B(A0A8Gp8A0A(B BBB L
+ ` k BBB B(A0A8Je
+8A0A(B BBBE
+ ! A_ <
+ PG BBG A(G@
+(A ABBG L
+ H BEB A(D0l
(A BBBG
-(A BBBA L ! x
- %. BBB E(D0A8Gd
-8A0A(B BBBK T ! / 0. BBB B(A0A8J
-8A0A(B BBBG < T" p6u . BHE L
-AEO AAB < " 6u - BHE L
-AEO AAB < " 6| - BEA A(D0a(D ABB < # 07| i- BEA A(D0a(D ABB < T# p7 -- BHE |
-AEO AAB < # 7 , BBD A(D0(D ABB < # P8 , BHE |
-AEO AAB < $ 8 y, BBD A(D0(D ABB d $ 092 BBB E(D0E8D@P
+(A BBBA L
+ @
+ BBB E(D0A8Gd
+8A0A(B BBBK T
+ H BBB B(A0A8J
+8A0A(B BBBG < ,
+ (Ou BHE L
+AEO AAB < l
+ hOu a BHE L
+AEO AAB <
+ O| % BEA A(D0a(D ABB <
+ O| BEA A(D0a(D ABB < ,
+ (P BHE |
+AEO AAB < l
+ P q BBD A(D0(D ABB <
+ Q 5 BHE |
+AEO AAB <
+ xQ BBD A(D0(D ABB d
+ Q2 BBB E(D0E8D@P
8F0A(B BBBA
-8D0A(B BBGA d L% BBE B(A0A8FP[
+8D0A(B BBGA d L
+ BBE B(A0A8FP[
8C0A(B BBBA W
-8F0A(B BBBA , % 0; BDE lAB d % p92 BBB E(D0E8D@P
+8F0A(B BBBA ,
+ ; BDE lAB d
+ (R2 BBB E(D0E8D@P
8F0A(B BBBA
-8D0A(B BBGA L L& ? BBB B(A0C8DP
-8A0A(B BBBA l & 9y BBF D(A0G@I
+8D0A(B BBGA L L
+ ? BBB B(A0C8DP
+8A0A(B BBBA l
+ Ry BBF D(A0G@I
0A(A BBBI w
0A(A BBBA D
-0F(A BBBG L |& XW * BBB B(A0A8GPQ
-8C0A(B BBBB L & h ) BBB B(A0A8G`U
-8D0A(B BBBE d ' ) BEB B(F0D8Je
+0F(A BBBG L T
+ W BBB B(A0A8GPQ
+8C0A(B BBBB L
+ 0 T BBB B(A0A8G`U
+8D0A(B BBBE d
+ BEB B(F0D8Je
8A0A(B BBBB S
-8C0A(B BBBA L ( ; BBB B(A0A8Gp8A0A(B BBB d '
( BBB B(A0A8GPZ
+8C0A(B BBBA L
+ S BBB B(A0A8Gp8A0A(B BBB d
+
w BBB B(A0A8GPZ
8A0A(B BBBK D
-8A0A(B BBBJ T <( h ( BBB B(A0A8JS
-8A0A(B BBBA T ( ( BBD C(G0J
+8A0A(B BBBJ T
+ 0m # BBB B(A0A8JS
+8A0A(B BBBA T l
+ H BBD C(G0J
(A ABBF d
-(D ABBA |) X ) P ) H l ) ;x BBF D(A0G@F
+(D ABBA |
+
+
+ l
+ Sx BBF D(A0G@F
0A(A BBBD v
0A(A BBBJ D
-0F(A BBBG L 4* 0= BBB B(A0C8D`I
-8C0A(B BBBK < * > BBG A(G@
-(A ABBF T 4* @ $' BLB E(A0A8D
-8A0A(B BBBG D * (?/ & BJB K(A0j
-(c `BBA D * @M & BFA Dp
+0F(A BBBG L 4
+ U BBB B(A0C8D`I
+8C0A(B BBBK <
+ W BBG A(G@
+(A ABBF T
+ BLB E(A0A8D
+8A0A(B BBBG D d
+ W/ S BJB K(A0
+(A BBBA D
+ XM 3 BFA Dp
AABH O
- AABA d + BE BBB B(A0A8DP
+ AABA d
+ ZE BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l + CB G& BBB J(D0D8D4
+8F0A(B BBBA l \
+ [B BBB J(D0D8D4
8D0A(B BBBC i
-8A0A(B BBBE L , FN BBB B(A0A8DV
-8A0A(B BBBB L , H2 BBB B(A0D8G@@
-8A0A(B BBBB T ,
W% BBB B(A0D8Jc
-8A0A(B BBBD < , x % BBD A(DP
-(D ABBA T ,- p% BBB B(D0A8J
-8A0A(B BBBD < - % BBA D(DP
-(D ABBA T - @ % BBB B(A0D8J+
-8A0A(B BBBD < . x % BBA D(DP
-(D ABBA . I+ Ai / I < $/ I ZBA A(%
- DBFA d/ K) 4 |/ L bGHX
-AG / OX / O AS / O 0 P 0 XQ D0W 40 `QB , L0 QK BAC G0w CAB L |0 Q| BAC G0L
+8A0A(B BBBE L
+ _N BBB B(A0A8DV
+8A0A(B BBBB L
+ a2 BBB B(A0D8G@@
+8A0A(B BBBB T l
+ 8 BBB B(A0D8Jc
+8A0A(B BBBD <
+ ߐ BBD A(DP
+(D ABBA T
+ `x BBB B(D0A8J
+8A0A(B BBBD < \
+ BBA D(DP
+(D ABBA T
+ (Q BBB B(A0D8J+
+8A0A(B BBBD <
+ 0% BBA D(DP
+(D ABBA
+ a+ Ai
+ a < $
+ a ZBA A(%
+ DBFA d
+ hd) 4 |
+ e bGHX
+AG
+ HhX
+ h AS
+ h
+ Xi
+ j D0W 4
+ jB , L
+ PjK BAC G0w CAB L |
+ pj| BAC G0L
AABC D
- CABF D DAB L 0 Q BEE B(A0A8G
-8A0A(B BBBF L 1 R BBB E(A0A8GP
-8D0A(B BBBA , l1 hT AAG j
-AAA , 1 T # AAJ
-DAA , <1 hU " AAJAA 1 U G 2 hVs , 42 V_ AAG N
-AAA L 1 W R" BBB E(A0D8GG
-8A0A(B BBBH T $2 ] L" BDB B(A0D8G
+ CABF D DAB L
+ j BEE B(A0A8G
+8A0A(B BBBF L !
+ pk BBB E(A0A8GP
+8D0A(B BBBA , l!
+ m AAG j
+AAA ,
+ m AAJ
+DAA , !
+ n AAJAA !
+ n G "
+ os , 4"
+ o_ AAG N
+AAA L !
+ o e BBB E(A0D8GG
+8A0A(B BBBH T !
+ Xv _ BDB B(A0D8Gx
-8A0A(B BBBH T 3 hr BBB A(D0F
+8A0A(B BBBH T #
+ BBB A(D0F
(A BBBH :
-(E BBBH L 2 t " BEE B(D0D8G
-8C0A(B BBBF 3 v 3 3 3 خ L 3 u ! BBE B(A0A8D`s
-8D0A(B BBBG L 3 pw ! BBE B(A0A8D`s
-8D0A(B BBBG 4 y 4 y 4 yA < 4 Xyh BGD A(D0T
-(A ABBD < <5 y BBA D(D0
-(A ABBG |5 (z 5 z DM T 5 (z
BEB B(A0A8GG
-8A0A(B BBBK \ 6 BIH D(G0Q
+(E BBBH L "
+ BEE B(D0D8G
+8C0A(B BBBF #
+ Ȏ #
+ #
+ #
+ L \#
+ x BBE B(A0A8D`s
+8D0A(B BBBG L #
+ ( ʌ BBE B(A0A8D`s
+8D0A(B BBBG $
+ ؑ $
+ Б $
+ ؑA < $
+ h BGD A(D0T
+(A ABBD < <%
+ @ BBA D(D0
+(A ABBG |%
+ %
+ ؒ DM T $
+
ɋ BEB B(A0A8GG
+8A0A(B BBBK \ &
+ BIH D(G0Q
(A ABBK s
-(C ABEB I(C AEB L d6 ` BEA D(G0O
+(C ABEB I(C AEB L d&
+ BEA D(G0O
(A ABBH E
-(G AFEH L 6 BEB I(D0^
-(A BFEF a(A BBB 7 A\ L $7 q BEE I(D0v
-(A BFEC I(A BBB 4 t7 P_ AID i
-JDK LAA L 7 x BBA D(G0^
+(G AFEH L &
+ h BEB I(D0^
+(A BFEF a(A BBB '
+ ؙ A\ L $'
+ ؙq BEE I(D0v
+(A BFEC I(A BBB 4 t'
+ _ AID i
+JDK LAA L '
+ 0 BBA D(G0^
(A ABBD a
-(A ABBA 4 7 E BID a
-ABE IAB 4 48 ] BID n
-EEI IAB \ l8 BBB B(A0D8G@p
-8G0A(B BFEE L8A0A(B BBB$ 8 xl ADD `AA4 8 b BNA o
-ABE TCB ,9 D9 p5 T 8 O BBB E(D0D8J
-8A0A(B BBBC | 9 Љ, BBB B(D0C8GP
+(A ABBA 4 '
+ E BID a
+ABE IAB 4 4(
+ ] BID n
+EEI IAB \ l(
+ К BBB B(A0D8G@p
+8G0A(B BFEE L8A0A(B BBB$ (
+ 0l ADD `AA4 (
+ xb BNA o
+ABE TCB ,)
+ D)
+ (5 T (
+ P c BBB E(D0D8J
+8A0A(B BBBC | )
+ , BBB B(D0C8GP
8A0A(B BBBH
8C0A(B BBBD A
-8D0A(B BBBJ D 4: BAA L
+8D0A(B BBBJ D 4*
+ 8 BAA L
ABE G
ABF K
-ABJ |: ȋ : B X^ 4 : BPD A(K`Z(A ABB| : H BBB A(A0GPU
+ABJ |*
+ *
+ xB X^ 4 *
+ BPD A(K`Z(A ABB| *
+ BBB A(A0GPU
0C(A BBBB R
0A(A BBBF o
-0D(A BBBF N0A(A BBB l; n 4 ; AAG
-AAB tCA ; $ Ab T L; " BBB B(A0A8J+
-8A0A(B BBBG < ; u BHE L
-AEO AAB < ; | BEA A(D0a(D ABB < $< BHH
-AEG AAB < AL L < ; BEB E(I0H8D@
-8D0A(B BBBA L d= p4 BIF A(R0
-(A ABBA K(D ABB d = `W BDB B(A0A8Dpq
+0D(A BBBF N0A(A BBB l+
+ pn 4 +
+ ȥ AAG
+AAB tCA +
+ p$ Ab T $+
+ 6 BBB B(A0A8J+
+8A0A(B BBBG < |+
+ xu # BHE L
+AEO AAB < +
+ | BEA A(D0a(D ABB < +
+ BHH
+AEG AAB ,
+ x AL L \,
+ O BEB E(I0H8D@
+8D0A(B BBBA L d-
+ (4 BIF A(R0
+(A ABBA K(D ABB d -
+ W BDB B(A0A8Dpq
8A0A(B BBBE
-8A0A(B BBBA > X T = `x C BBB B(A0A8J
-8A0A(B BBBG D = 8U { OMB L(E0Y
-(g ~BBA D D> PM [ BFA Dp
+8A0A(B BBBA .
+ T |-
+ x W BBB B(A0A8J
+8A0A(B BBBG D -
+ U OMB L(E0
+(A BBBA D .
+ M o BFA Dp
AABH O
- AABA 4 > , BAA
-ABA L T? BBB B(A0D8G@
-8A0A(B BBBD T ?
- BBB E(D0H8J
-8A0A(B BBBG L l? W BBE D(C0G
-0A(A BBBG L@ Ƞ
- d@
L ? c j BBE D(D0G
-0D(A BBBK @ ء L T@ ` BBB F(A0E8GPg
-8A0A(B BBBA 4A 0 DM
-A l @ н BFB B(A0D8G`"
+ AABA 4 d.
+ @ BAA
+ABA L T/
+ BBB B(A0D8G@
+8A0A(B BBBD T .
+
+ BBB E(D0H8J
+8A0A(B BBBG L D/
+ pW ߃ BBE D(C0G
+0A(A BBBG L0
+
+ d0
+ x
L /
+ pc r BBE D(D0G
+0D(A BBBK 0
+ L ,0
+ BBB F(A0E8GPg
+8A0A(B BBBA 41
+ HI DM
+A l 0
+ BFB B(A0D8G`"
8A0A(B BBBD V
-8A0A(B BBBH , 4A e AAD I
-AAA , dA @[ AAD
-AAA < A p AAD R
+8A0A(B BBBH , 1
+ e ӂ AAD I
+AAA , <1
+ [ AAD
+AAA < l1
+ ( AAD R
AAF d
-AAA 4 A f BKD D0
- DABA < B Xb < BEA D(DP
-(D ABBE $ B ADD AA, tB + ADD
-AAA 4C % DW
-A $ TC 5 AJ
+AAA 4 1
+ n BKD D0
+ DABA < 1
+ b D BEA D(DP
+(D ABBE $ 2
+ @ ADD AA, L2
+ + ADD
+AAA 43
+ % DW
+A $ T3
+ 5 AJ
E ]
-A |C ( DP
-A C (K AI 4 C X ADG Z
-AAP bAA C D < , $D ( AG o
+A |3
+ DP
+A 3
+ K AI 4 3
+ ADG Z
+AAP bAA 3
+ x 4
+ < , $4
+ ( AG o
AH V
-AI TD " DQ
-K A tD T C M _ BBB H(A0A8Jr
-8A0A(B BBBJ < D BEE D(D0e(A BBB T D @f BBB E(D0A8GP
-8A0A(B BBBG 4 |E X BED D(F0(A ABB$ E `,V ADD JAAL LE , BEB E(I0H8D@
-8D0A(B BBBA < ,F . BDI ~
+AI T4
+ " DQ
+K A t4
+ T 3
+ @ g BBB H(A0A8Jr
+8A0A(B BBBJ < 4
+ BEE D(D0e(A BBB T l4
+ a BBB E(D0A8GP
+8A0A(B BBBG 4 |5
+ BED D(F0(A ABB$ 5
+ DV ADD JAAL $5
+ E BEB E(I0H8D@
+8D0A(B BBBA < ,6
+ F BDI ~
AGK K
-ABB L E . O BEB E(I0H8D@
-8D0A(B BBBD d F ? BBB B(H0G8Fp0
+ABB L 5
+ G C BEB E(I0H8D@
+8D0A(B BBBD d 6
+ ? BBB B(H0G8Fp0
8D0A(B BBBF W
-8A0A(B BBBG L F M BBB B(A0D8GQ
-8A0A(B BBBA L tG `/ BBB B(A0D8GP>
-8A0A(B BBBD G 0 E
-G < G p1 BHD A(D0(A ABB d $H BBB B(K0D8Fpb
+8A0A(B BBBG L l6
+ XM ~ BBB B(A0D8GQ
+8A0A(B BBBA L t7
+ G BBB B(A0D8GP>
+8A0A(B BBBD 7
+ (I E
+G < 7
+ I BHD A(D0(A ABB d $8
+ BBB B(K0D8Fpb
8A0A(B BBBG j
-8A0A(B BBBA T G BBB B(D0A8Jo
-8A0A(B BBBH < TH `f BED D(H0m
-(D ABBA T H 1Z BHB E(A0A8GP
-8A0A(B BBBD |I 8 T I @ BBB E(A0A8G
-8A0A(B BBBF I J J x 4J p T I h A BBB E(A0A8G
-8A0A(B BBBF J J J J L tJ BBB B(A0D8I@L
-8C0A(B BBBB T J pA
BBB B(A0A8J
-8D0A(B BBBH L K h BBB B(A0A8Ji
-8A0A(B BBBI L K m BBB E(A0D8GR
-8A0A(B BBBE L K ' BBE E(A0A8GV
-8D0A(B BBBF , L X, BDD WAB L <L X
- BBE B(A0A8IR
-8C0A(B BBBA M $ AO , <M 5 BDA AK T L 5
- BDB B(A0A8G$
-8A0A(B BBBA T 4M `@$ BBB B(A0A8Gs
-8A0A(B BBBA < M 8K BED C(G@
-(D ABBC T M Ks v BBB B(A0A8J
-8A0A(B BBBE N x2 Ap < DN x BBB A(C0Z
-(A BBBA < N ` BBA A(G0d
-(A ABBA L N b BEH E(D0D8Gr
-8A0A(B BBBA O x2 Ap T 4O b ) BLE B(A0A8G
-8C0A(B BBBC T O Hl
- BLB E(A0A8G
-8A0A(B BBBA tP @xj P x T P xz BBB B(A0D8G
-8A0A(B BBBG d P ' BBB B(A0A8GPe
+8A0A(B BBBA T 7
+ P } BBB B(D0A8Jo
+8A0A(B BBBH < ,8
+ f ~ BED D(H0m
+(D ABBA T l8
+ XJZ } BHB E(A0A8GP
+8A0A(B BBBD |9
+ T 8
+ } BBB E(A0A8G
+8A0A(B BBBF 9
+ 0 :
+ ( :
+ 4:
+ T 9
+ 5} BBB E(A0A8G
+8A0A(B BBBF :
+ h :
+ ` :
+ X :
+ P L L:
+ H | BBB B(A0D8I@L
+8C0A(B BBBB T :
+ A
x| BBB B(A0A8J
+8D0A(B BBBH L :
+ ) | BBB B(A0D8Gk
+8C0A(B BBBE L ;
+ P*m BBB E(A0D8GR
+8A0A(B BBBE L ;
+ p, | BBE E(A0A8GV
+8D0A(B BBBF , <
+ 2, BDD WAB L <
+ 2
+ { BBE B(A0A8IR
+8C0A(B BBBA =
+ @= AO , <=
+ M BDA AB T <
+ PN
+ { BDB B(A0A8G$
+8A0A(B BBBA T =
+ X { BBB B(A0A8G{
+8A0A(B BBBA < d=
+ c { BED C(G@
+(D ABBC T =
+ ds S{ BBB B(A0A8J
+8A0A(B BBBE >
+ ؐ2 Ap < >
+ { BBB A(C0Z
+(A BBBA < \>
+ x { BBA A(G0d
+(A ABBA L >
+ xz d{ BEH E(D0D8Gr
+8A0A(B BBBA ?
+ 2 Ap T ?
+ { { BLE B(A0A8G
+8C0A(B BBBC T d?
+
+ z BLB E(A0A8G
+8A0A(B BBBA t@
+ j @
+ T ?
+ ؐ[ z BBB B(A0D8G
+8A0A(B BBBG d @
+ ' BBB B(A0A8GPe
8D0A(B BBBM
-8A0A(B BBBB L P P BBB B(A0A8Jk
-8C0A(B BBBE Q A , Q 8 BDD E
-ABF Q % L R BBD A(D@|
+8A0A(B BBBB L @
+ x Sz BBB B(A0A8Jk
+8C0A(B BBBE A
+ A , A
+ @ BDD E
+ABF A
+ % L B
+ BBD A(D@|
(A ABBA D
-(F ABBA T Q # BBB B(A0A8G3
-8C0A(B BBBH L ,R BEB B(A0A8I_
-8A0A(B BBBA S X: < R BBA A(D@z
-(A ABBA dS Pu T R BEB B(A0A8G<
-8A0A(B BBBF S @h S ; H^
-J F T $T S L S # BDA }
+(F ABBA T A
+ ( y BBB B(A0A8G3
+8C0A(B BBBH L B
+ y BEB B(A0A8I_
+8A0A(B BBBA C
+ `: < lB
+ y BBA A(D@z
+(A ABBA dC
+ Xu T B
+ Qy BEB B(A0A8G<
+8A0A(B BBBF C
+ Hh C
+ ; H^
+J F D
+ $D
+ S L C
+ x BDA }
ABI \
ABA G
-ABF T y L T
BBB B(A0D8I
-8A0A(B BBBG d T @ BBB B(D0A8D@l
+ABF D
+ y L C
+ x BBB B(A0D8I
+8A0A(B BBBG d D
+ @ BBB B(D0A8D@l
8A0A(B BBBI
-8C0A(B BBBA < T , k
BDA D(L0u
-(A ABGI , U xG J
BDI ]
-ABE U U 4 U B AAG L
-FAL WAA < U BAA x
+8C0A(B BBBA < D
+ , x BDA D(L0u
+(A ABGI , D
+ G w BDI ]
+ABE E
+ E
+ 4 E
+ B AAG L
+FAL WAA < |E
+ nw BAA x
ABI A
-ABJ < U @ BBD A(L@e
-(A ABBH , $V Q v AID c
-DDE T TV b V BBD D(D0m
+ABJ < E
+ H Hw BBD A(L@e
+(A ABBH , E
+ Q "w AID c
+DDE T ,F
+ b w BBD D(D0m
(A ABBE
-(D ABBG D <W BND `
+(D ABBG D <G
+ BND `
GBE o
-IBL _AB L W p BBB B(A0A8DPj
-8D0A(B BBBK L W BEB B(A0A8Gp
-8C0A(B BBBH \ $X BEB E(D0D8G@i
-8A0A(B BBBH T8C0A(B BBBT W
- BEB B(A0D8G
-8A0A(B BBBG D LX |
- BDE A(G0DP\0A(A BBB 4 $Y l AAD Y
+IBL _AB L G
+ x BBB B(A0A8DPj
+8D0A(B BBBK L G
+ BEB B(A0A8Gp
+8C0A(B BBBH \ $H
+ BEB E(D0D8G@i
+8A0A(B BBBH T8C0A(B BBBT G
+ 8 u BEB B(A0D8G
+8A0A(B BBBG D $H
+ | u BDE A(G0DP\0A(A BBB 4 $I
+ 8l AAD Y
DAD v
-DAE d X X1 j
- BBA D(G0
+DAE d H
+ p1 u BBA D(G0
(A ABGC `
(A ABBJ S
-(F ABBB l 4Y 0;
- BEB B(A0A8D`
+(F ABBB l I
+ H; t BEB B(A0A8D`
8A0A(B BBBO C
-8A0A(B BBBA , Y k AAG @
-IAE $ dZ @ Aw
+8A0A(B BBBA , |I
+ k et AAG @
+IAE $ dJ
+ X Aw
H h
-H Z ( Ab 4 Z ADD0|
+H J
+ ( Ab 4 J
+ ADD0|
AAI }
-GAK Z pN Ad
-K ] tZ BBE D(A0Gp
+GAK J
+ N Ad
+K ] LJ
+ s BBE D(A0Gp
0A(A BBBH o
0A(A BBBI
0A(A BBBG d
-0A(A BBBA d [ BGB B(A0A8FI
+0A(A BBBA d K
+ BGB B(A0A8FI
8A0A(B BBBH Y
-8F0A(B BBBH L d[ 0 BBB B(D0A8JG
-8A0A(B BBBH < [ u 5 BHE L
-AEO AAB < [ 0| BEA A(D0a(D ABB L \ p, BLB B(D0A8D`
-8A0A(B BBBJ < ] P1 BBG A(GP
-(A ABBF L T] s BBB B(A0A8G
-8A0A(B BBBF L ] 1 BBB E(A0C8I
-8A0A(B BBBA T d] ,
- BBB B(A0A8G
-8A0A(B BBBA T ] BBB B(A0A8G
-8D0A(B BBBA T ^ 8 BEE B(A0C8J
-8A0A(B BBBG L l^ BBE E(D0C8Dp
-8C0A(B BBBK T ^ hs { BBB E(D0C8G
-8D0A(B BBBD _ @ $ Ab D _ P ' BKK
+8F0A(B BBBH L <K
+ H r BBB B(D0A8JG
+8A0A(B BBBH < K
+ H,u r BHE L
+AEO AAB < K
+ ,| r BEA A(D0a(D ABB L L
+ ,$ BLB B(D0A8D`
+8A0A(B BBBE < M
+ .1 BBG A(GP
+(A ABBF L TM
+ s BBB B(A0A8G
+8A0A(B BBBF L M
+ 1 BBB E(A0C8I
+8A0A(B BBBA T <M
+ $
+ =q BBB B(A0A8G
+8A0A(B BBBA T M
+ + Mq BBB B(A0A8G
+8D0A(B BBBA T M
+ X.* (r BEE B(A0C8J
+8A0A(B BBBG L DN
+ r BBE E(D0C8Dp
+8C0A(B BBBK T N
+ q BBB E(D0C8G
+8D0A(B BBBD O
+ 6$ Ab D O
+ 6' BKK
ABH B
-ABC sAB < ` 8! BHD A(D0(A ABB L _ "R BGB B(A0A8GJ
-8A0A(B BBBF ` ( ` P) ` H) ` P) ` X) a `) ,a X) Da P) D \a H)3 Aw
+ABC sAB < P
+ 7 BHD A(D0(A ABB L O
+ 8= hq BGB B(A0A8GJ
+8A0A(B BBBF P
+ > P
+ x? P
+ p? P
+ x? P
+ ? Q
+ ? ,Q
+ ? DQ
+ x? D \Q
+ p?3 Aw
H [
E [
E u
K [
E [
E U
-K R L a @* BBB E(D0A8F
-8D0A(B BBBA L da +8 BEE B(D0A8Dp}
-8D0A(B BBBG Db - a .? i A} 4 a (. M BFD D0u
- DABA $ b .m AG Y
-AA b .
T db /7 BBA D(G`
+K R L P
+ h@ Vp BBB E(D0A8F
+8D0A(B BBBA L <Q
+ A8 %p BEE B(D0A8Dp}
+8D0A(B BBBG DR
+ C Q
+ 0D? o A} 4 Q
+ PD o BFD D0u
+ DABA $ R
+ Dm AG Y
+AA R
+ E
T <R
+ F7 Lo BBA D(G`
(D ABBE L
-(D ABBC T b 07 BBA D(G`
+(D ABBC T R
+ F7 o BBA D(G`
(D ABBE L
-(D ABBC L c 1e a BLB B(A0D8Dp
-8A0A(B BBBA c ^2 Ap T c 5u
- BEE B(A0D8J
-8A0A(B BBBJ d ld ]B BBB B(A0A8G@x
+(D ABBC L R
+ Ge n BLB B(A0D8Dp
+8A0A(B BBBA S
+ s2 Ap T \S
+ K7 sn BEE B(A0D8J
+8A0A(B BBBJ d lT
+ psB BBB B(A0A8G@x
8A0A(B BBBE
-8A0A(B BBBF d p@] D X
+8A0A(B BBBF T
+ XV] D X
D `
-H 4 d @ BAA D@
- AABD L ,e B? BBB B(A0D8D@
-8A0A(B BBBA < |e Ct BEE D(A0S(A GBE T ,e HC BGE B(A0A8Q
-8A0A(B BBBA < f ] BBA A(G@_
-(A ABBF L Tf @^~ BLB H(A0D8D@
-8A0A(B BBBD < f p_ BBG A(G@
-(A ABBH L f Fz BHE B(G0A8J
-8A0A(B BBBC < 4g _ BBG A(G0
-(A ABBB L tg I- BEE B(D0A8Gp
-8A0A(B BBBC L 4g M{ BBB B(A0A8JM
-8A0A(B BBBE h p ,h h Dh ` \h _ th _ h _ T h _ w BBB B(A0A8G@
-8A0A(B BBBE T lh 8e i BBB B(A0A8G@
-8A0A(B BBBE L h j. [ BBB B(A0A8Gh
-8A0A(B BBBE 4 i qJ AAG P
+H 4 T
+ V BAA D@
+ AABD L ,U
+ X? BBB B(A0D8D@
+8A0A(B BBBA < |U
+ Xt BEE D(A0S(A GBE T U
+ 0Y m BGE B(A0A8Q
+8A0A(B BBBA < V
+ s BBA A(G@_
+(A ABBF L TV
+ s~ BLB H(A0D8D@
+8A0A(B BBBG < V
+ t BBG A(G@
+(A ABBH L V
+ [V BHB E(G0A8G
+8A0A(B BBBA < 4W
+ Hu BBG A(G0
+(A ABBB L tW
+ _- BEE B(D0A8Gp
+8A0A(B BBBC L W
+ c> $k BBB B(A0A8JM
+8A0A(B BBBE X
+ ,X
+ DX
+ \X
+ u tX
+ u X
+ u T W
+ u j BBB B(A0A8G@
+8A0A(B BBBE T DX
+ z j BBB B(A0A8G@
+8A0A(B BBBE L X
+ h. j BBB B(A0A8Gh
+8A0A(B BBBE 4 Y
+ HJ AAG P
AAE
-FAE T Li r BEE B(A0A8G
-8A0A(B BBBH i P e BBB B(A0A8G@
+FAE T $Y
+ ` dj BEE B(A0A8G
+8A0A(B BBBH |Y
+ ؔ j BBB B(A0A8G@
8A0A(B BBBH t
8D0A(B BBBO
-8D0A(B BBBG < ,j o BEI D(D0z
-(D ABBA L lj o BEE B(D0D8G
-8A0A(B BBBJ D j 8 AAD V
+8D0A(B BBBG < Z
+ po Tj BEI D(D0z
+(D ABBA L DZ
+ o $j BEE B(D0D8G
+8A0A(B BBBJ D Z
+ Jj AAD V
AAJ d
DAG d
-DAA L k BBB B(A0A8JB
-8D0A(B BBBE T Tk Н BBB B(A0A8J+
-8A0A(B BBBG < k u BHE L
-AEO AAB < k Ȥu BHE L
-AEO AAB < ,l u c BHE L
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-(g ~BBA D tm M S BFA Dp
+DAA L Z
+ j BBB B(A0A8JB
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+ X :j BBB B(A0A8J+
+8A0A(B BBBG < [
+ u 'j BHE L
+AEO AAB < [
+ Pu i BHE L
+AEO AAB < \
+ u i BHE L
+AEO AAB < D\
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-8A0A(B BBBD T n H
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-8A0A(B BBBK o T o AGF n
+ AABA L L^
+ BBB B(A0D8G@
+8A0A(B BBBD T ]
+ p:
+ h BBE B(A0D8J]
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-8A0A(B BBBG d ls BEB B(A0A8DpJ
+KAP mFA 4 `
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+IBI VAB L _
+ 7g BBB B(D0C8JS
+8C0A(B BBBH < _
+ (u g BHE L
+AEO AAB < `
+ h| kg BEA A(D0a(D ABB L \`
+ pg BBB B(A0A8JC
+8D0A(B BBBD T `
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+ x BLI D(D0m
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+ BEE B(D0D8D
+8A0A(B BBBH d lc
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-8D0A(B BBBA L | @" BFB B(A0G8DP
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+ x DI < l
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+ / Dg L dl
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+ " BFB B(A0G8DP
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+ 2 Ai
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_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_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_41kCpuExecutionProvider_Or_kOnnxDomain_ver7EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_42kCpuExecutionProvider_Xor_kOnnxDomain_ver7EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_48kCpuExecutionProvider_Add_kOnnxDomain_ver7_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ 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_ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Sub_kOnnxDomain_ver7_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_48kCpuExecutionProvider_Mul_kOnnxDomain_ver7_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_49kCpuExecutionProvider_Mul_kOnnxDomain_ver7_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Mul_kOnnxDomain_ver7_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Mul_kOnnxDomain_ver7_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_48kCpuExecutionProvider_Div_kOnnxDomain_ver7_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_49kCpuExecutionProvider_Div_kOnnxDomain_ver7_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Div_kOnnxDomain_ver7_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Div_kOnnxDomain_ver7_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_48kCpuExecutionProvider_Abs_kOnnxDomain_ver6_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_49kCpuExecutionProvider_Abs_kOnnxDomain_ver6_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_49kCpuExecutionProvider_Abs_kOnnxDomain_ver6_int8_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Abs_kOnnxDomain_ver6_int16_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Abs_kOnnxDomain_ver6_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Abs_kOnnxDomain_ver6_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Abs_kOnnxDomain_ver6_uint8_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Abs_kOnnxDomain_ver6_uint16_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Abs_kOnnxDomain_ver6_uint32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ 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_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 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_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 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_ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE32op_schema_register_onceInverse61 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E22_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE32op_schema_register_onceInverse61 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE38op_schema_register_onceCropAndResize57 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceBiasGelu60 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE29op_schema_register_onceGelu59 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceCDist56 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE31op_schema_register_onceUnique55 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE28op_schema_register_oncePad54 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceWordConvEmbedding53 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceGatherND52 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceMurmurHash351 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE41op_schema_register_onceQLinearLeakyRelu50 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E15_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE43op_schema_register_onceQLinearAveragePool49 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E14_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceMulInteger48 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E13_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceQLinearReduceMean47 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceQLinearMul46 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceQLinearAdd45 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE41op_schema_register_onceReduceSumInteger44 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E12_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceTransposeMatMul43 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE45op_schema_register_onceMatMulIntegerToFloat42 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE46op_schema_register_onceDynamicQuantizeMatMul41 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceMatMulInteger1640 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E8_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE34op_schema_register_onceTokenizer39 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE41op_schema_register_onceDequantizeLinear38 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE39op_schema_register_onceQuantizeLinear37 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceRange36 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceAttnLSTM35 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E5_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceExpandDims34 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE34op_schema_register_onceFusedGemm33 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE34op_schema_register_onceFusedConv32 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE53op_schema_register_onceConvTransposeWithDynamicPads31 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE39op_schema_register_onceComplexMulConj30 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceComplexMul29 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceIrfft28 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE29op_schema_register_onceRfft27 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceMaxpoolWithMask26 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceSampleOp25 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceScaledTanh24 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE37op_schema_register_onceDynamicSlice23 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE29op_schema_register_onceCrop22 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceImageScaler21 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE43op_schema_register_onceParametricSoftplus20 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_ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceGivenTensorFill11 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE37op_schema_register_onceDynamicSlice10 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE39op_schema_register_onceThresholdedRelu9 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE28op_schema_register_onceCrop8 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceImageScaler7 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceParametricSoftplus6 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceAffine5 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE43op_schema_register_onceLayerNormalization58 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvENKUlRN4onnx16InferenceContextEE12_clES3_.isra.510 _ZTSZZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_ENKUlRN4onnx8OpSchemaEE_clES5_EUlRNS3_16InferenceContextEE_ _ZTSZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_EUlRN4onnx8OpSchemaEE_ _ZTSZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE_ _ZTSZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE0_ _ZTSZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE1_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE0_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE10_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE11_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE12_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE13_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE14_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE15_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE16_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE17_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE18_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE19_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE20_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE21_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE22_ nchwc_schema_defs.cc _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib32NchwcGlobalPoolOpSchemaGeneratorERNS0_8OpSchemaEEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib32NchwcGlobalPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS3_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSA_St18_Manager_operation 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_ZNSt14_Function_base13_Base_managerIZZN4onnx16MathDocGeneratorEPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx16MathDocGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx25SoftmaxFamilyDocGeneratorEPKcS3_EUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_EUlRNS_8OpSchemaEE_ _ZTIZZN4onnx25SoftmaxFamilyDocGeneratorEPKcS1_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_managerIZN4onnx30ElementwiseMultiOpDocGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx30ElementwiseMultiOpDocGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZTIZZN4onnx30ElementwiseMultiOpDocGeneratorEPKcENKUlRNS_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_ 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_ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_36NegativeLogLikelihoodLoss_Onnx_ver12EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_36NegativeLogLikelihoodLoss_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Einsum_Onnx_ver12EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Einsum_Onnx_ver12EEENS_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_ _ZNSt6vectorIlSaIlEEC2ESt16initializer_listIlERKS0_.constprop.475 _ZN6google8protobuf8internal20RepeatedPtrFieldBase3AddINS0_16RepeatedPtrFieldIN4onnx26TensorShapeProto_DimensionEE11TypeHandlerEEEPNT_4TypeESB_.constprop.485 _ZNSt6vectorISsSaISsEEC2ESt16initializer_listISsERKS0_.constprop.484 _ZNSt6vectorISsSaISsEEC2ESt16initializer_listISsERKS0_.constprop.483 _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_16MathDocGeneratorEPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_16MathDocGeneratorEPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15Gemm_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Einsum_Onnx_ver12EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt6vectorIN4onnx18FunctionBodyHelper21AttributeProtoWrapperESaIS2_EEC2ESt16initializer_listIS2_ERKS3_.constprop.476 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_ZNSt14_Function_base13_Base_managerIZZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS3_ENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_32GlobalLpPoolingOpSchemaGeneratorEPKcS5_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_32GlobalLpPoolingOpSchemaGeneratorEPKcS5_ENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS3_ENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt14_Function_base13_Base_managerIZN4onnx21PoolOpSchemaGeneratorEPKcS3_S3_bbEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx21PoolOpSchemaGeneratorEPKcS3_S3_bbENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_20MaxUnpool_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_20MaxUnpool_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx23LpPoolOpSchemaGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx23LpPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZTIZZN4onnx23LpPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ 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_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.391 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28BatchNormalization_Onnx_ver7EEENS0_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_18Dropout_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19MaxUnpool_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Flatten_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Flatten_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZTSZN4onnx23PoolOpSchemaGenerator_9EPKcS1_S1_EUlRNS_8OpSchemaEE_ _ZTSZZN4onnx24PoolOpSchemaGenerator_10EPKcS1_S1_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_28NonMaxSuppression_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19RoiAlign_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.217 _ZTSZN4onnx11GetOpSchemaINS_19RoiAlign_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28NonMaxSuppression_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28NonMaxSuppression_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28NonMaxSuppression_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25QuantizeLinear_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_27DequantizeLinear_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt6vectorISsSaISsEEC2ESt16initializer_listISsERKS0_.constprop.251 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25QuantizeLinear_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_27DequantizeLinear_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx11GetOpSchemaINS_25QuantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_27DequantizeLinear_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_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_ schema.cc _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.344 _ZNSt17_Function_handlerIFbiEZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_E9_M_invokeERKSt9_Any_datai _ZGVZN4onnx16OpSchemaRegistry8InstanceEvE8instance _ZZN4onnx16OpSchemaRegistry8InstanceEvE8instance _ZGVZN4onnx16OpSchemaRegistry33GetMapWithoutEnsuringRegistrationEvE3map _ZZN4onnx16OpSchemaRegistry33GetMapWithoutEnsuringRegistrationEvE3map _ZNSt14_Function_base13_Base_managerIZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_E10_M_managerERSt9_Any_dataRKSA_St18_Manager_operation _ZTIZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_ _ZNSt14_Function_base13_Base_managerIZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_E10_M_managerERSt9_Any_dataRKSA_St18_Manager_operation _ZTIZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_ _ZGVZN4onnx16OpSchemaRegistry20DomainToVersionRange8InstanceEvE23domain_to_version_range _ZZN4onnx16OpSchemaRegistry20DomainToVersionRange8InstanceEvE23domain_to_version_range _GLOBAL__sub_I_schema.cc _ZTSZN4onnx8OpSchema9NumInputsESt3setIiSt4lessIiESaIiEEEUliE_ _ZTSZN4onnx8OpSchema10NumOutputsESt3setIiSt4lessIiESaIiEEEUliE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_24SequenceErase_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_21SequenceAt_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_24SequenceEmpty_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28SequenceConstruct_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25SequenceInsert_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_21SequenceAt_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_24SequenceErase_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25SequenceLength_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26SplitToSequence_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_29ConcatFromSequence_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_24SequenceEmpty_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_29ConcatFromSequence_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_26SplitToSequence_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28SequenceConstruct_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25SequenceInsert_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25SequenceLength_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19Upsample_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Resize_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Cast_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Reshape_Onnx_ver5EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Shape_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Size_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Concat_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Split_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Slice_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Transpose_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Scatter_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_20ScatterND_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26ScatterElements_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Gather_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25GatherElements_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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_ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_23DepthToSpace_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Tile_Onnx_ver6EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Upsample_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Resize_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Compress_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17OneHot_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15IsNaN_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16IsInf_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Where_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17NonZero_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26ReverseSequence_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Unique_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19GatherND_Onnx_ver12EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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_ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Mul_kOnnxDomain_ver7_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Mul_kOnnxDomain_ver7_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_48kCpuExecutionProvider_Div_kOnnxDomain_ver7_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_49kCpuExecutionProvider_Div_kOnnxDomain_ver7_doubleEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Div_kOnnxDomain_ver7_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Div_kOnnxDomain_ver7_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Sum_kOnnxDomain_ver6_7_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_48kCpuExecutionProvider_Sum_kOnnxDomain_ver8_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Max_kOnnxDomain_ver6_7_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_50kCpuExecutionProvider_Min_kOnnxDomain_ver6_7_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_Less_kOnnxDomain_ver7_8_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_49kCpuExecutionProvider_Less_kOnnxDomain_ver9_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ 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_ZTSZN11onnxruntime12BroadcastTwoIbbZNKS_2Or7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIbLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEEbNS5_IKS7_Li0ES9_EEE_ZNKS1_7ComputeES3_EUlSA_SC_bE0_ZNKS1_7ComputeES3_EUlSA_SC_SC_E1_EENS_6common6StatusERS2_T1_T2_T3_dEUlllE_ _ZTSZN11onnxruntime12BroadcastTwoIbbZNKS_3Xor7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS4_6MatrixIbLin1ELi1ELi0ELin1ELi1EEELi0ENS4_6StrideILi0ELi0EEEEEbNS5_IKS7_Li0ES9_EEE_ZNKS1_7ComputeES3_EUlSA_SC_bE0_ZNKS1_7ComputeES3_EUlSA_SC_SC_E1_EENS_6common6StatusERS2_T1_T2_T3_dEUlllE_ _ZTSZN11onnxruntime12BroadcastTwoIffZNKS_5PReluIfE7ComputeEPNS_15OpKernelContextEEUlN5Eigen3MapINS5_6MatrixIfLin1ELi1ELi0ELin1ELi1EEELi0ENS5_6StrideILi0ELi0EEEEEfNS6_IKS8_Li0ESA_EEE_ZNKS2_7ComputeES4_EUlSB_SD_fE0_ZNKS2_7ComputeES4_EUlSB_SD_SD_E1_EENS_6common6StatusERS3_T1_T2_T3_dEUlllE_ _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 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_ZZN11onnxruntime2ml21BuildKernelCreateInfoINS0_62kCpuExecutionProvider_LabelEncoder_kMLDomain_ver2_string_int64EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES6_ _ZZN11onnxruntime2ml21BuildKernelCreateInfoINS0_61kCpuExecutionProvider_LabelEncoder_kMLDomain_ver2_int64_int64EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES6_ _ZZN11onnxruntime2ml21BuildKernelCreateInfoINS0_51kCpuExecutionProvider_LabelEncoder_kMLDomain_ver1_1EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES6_ _ZZN11onnxruntime2ml14LabelEncoder_2IlSsEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml12LabelEncoderC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml14LabelEncoder_2ISslEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml14LabelEncoder_2IfSsEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml14LabelEncoder_2IlfEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml14LabelEncoder_2IflEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml14LabelEncoder_2IllEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime2ml14LabelEncoder_2ISsfEC1ERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _GLOBAL__sub_I_label_encoder.cc _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.77 _ZZN11onnxruntime18PoolProcessContext4initERKNS_12OpKernelInfoEE19__PRETTY_FUNCTION__ _ZZN11onnxruntime14PoolAttributesC1ERKNS_17OpNodeProtoHelperINS_22ProtoHelperNodeContextEEERKSsiE19__PRETTY_FUNCTION__ _ZZN11onnxruntime21BuildKernelCreateInfoINS_49kCpuExecutionProvider_MaxPool_kOnnxDomain_ver8_11EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_47kCpuExecutionProvider_MaxPool_kOnnxDomain_ver12EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_48kCpuExecutionProvider_MaxPool_kOnnxDomain_ver1_7EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_GlobalMaxPool_kOnnxDomain_ver1EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_46kCpuExecutionProvider_LpPool_kOnnxDomain_ver11EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_51kCpuExecutionProvider_GlobalLpPool_kOnnxDomain_ver2EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_48kCpuExecutionProvider_LpPool_kOnnxDomain_ver2_10EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_54kCpuExecutionProvider_AveragePool_kOnnxDomain_ver10_10EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_52kCpuExecutionProvider_AveragePool_kOnnxDomain_ver7_9EEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ 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_ZZN11onnxruntime21BuildKernelCreateInfoINS_70kCpuExecutionProvider_OneHot_kOnnxDomain_ver11_int64_t_int64_t_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_68kCpuExecutionProvider_OneHot_kOnnxDomain_ver11_float_int64_t_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_68kCpuExecutionProvider_OneHot_kOnnxDomain_ver11_int64_t_int32_t_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_67kCpuExecutionProvider_OneHot_kOnnxDomain_ver11_float_string_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_64kCpuExecutionProvider_OneHot_kOnnxDomain_ver11_float_float_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_68kCpuExecutionProvider_OneHot_kOnnxDomain_ver11_int32_t_float_int32_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_71kCpuExecutionProvider_OneHot_kOnnxDomain_ver9_10_int64_t_string_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_66kCpuExecutionProvider_OneHot_kOnnxDomain_ver11_int64_t_float_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_68kCpuExecutionProvider_OneHot_kOnnxDomain_ver9_10_int32_t_float_floatEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ _ZZN11onnxruntime21BuildKernelCreateInfoINS_72kCpuExecutionProvider_OneHot_kOnnxDomain_ver9_10_int64_t_int64_t_int64_tEEENS_16KernelCreateInfoEvENUlRKNS_12OpKernelInfoEE_4_FUNES5_ 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_ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceQLinearAdd45 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceQLinearMul46 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceQLinearReduceMean47 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceMulInteger48 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE43op_schema_register_onceQLinearAveragePool49 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE41op_schema_register_onceQLinearLeakyRelu50 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE39op_schema_register_onceQLinearSigmoid51 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceMurmurHash352 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceGatherND53 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceWordConvEmbedding54 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE28op_schema_register_oncePad55 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE31op_schema_register_onceUnique56 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceCDist57 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE38op_schema_register_onceCropAndResize58 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE43op_schema_register_onceLayerNormalization59 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE29op_schema_register_onceGelu60 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceBiasGelu61 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE32op_schema_register_onceInverse62 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceTrilu63 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E23_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceTrilu63 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE29op_schema_register_onceGelu60 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceBiasGelu61 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E22_E9_M_invokeERKSt9_Any_dataS2_ _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 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E17_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceGatherND53 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceMurmurHash352 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE39op_schema_register_onceQLinearSigmoid51 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE41op_schema_register_onceQLinearLeakyRelu50 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E15_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE43op_schema_register_onceQLinearAveragePool49 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E14_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceMulInteger48 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E13_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceQLinearReduceMean47 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceQLinearMul46 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceQLinearAdd45 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE41op_schema_register_onceReduceSumInteger44 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_ZZN11onnxruntime7contrib22RegisterContribSchemasEvE50op_schema_register_onceMeanVarianceNormalization17 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE32op_schema_register_onceGRUUnit16 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceScale15 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E0_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceGivenTensorFill14 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE32op_schema_register_onceGRUUnit13 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceScale12 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceGivenTensorFill11 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE37op_schema_register_onceDynamicSlice10 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE39op_schema_register_onceThresholdedRelu9 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE28op_schema_register_onceCrop8 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceImageScaler7 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceParametricSoftplus6 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceAffine5 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib22RegisterContribSchemasEvEUlS2_E21_E9_M_invokeERKSt9_Any_dataS2_ _ZZN11onnxruntime7contrib22RegisterContribSchemasEvE43op_schema_register_onceLayerNormalization59 _ZN4onnx34propagateElemTypeFromInputToOutputERNS_16InferenceContextEmm.constprop.529 _ZZN11onnxruntime7contrib22RegisterContribSchemasEvENKUlRN4onnx16InferenceContextEE12_clES3_.isra.520 _ZTSZZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_ENKUlRN4onnx8OpSchemaEE_clES5_EUlRNS3_16InferenceContextEE_ _ZTSZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_EUlRN4onnx8OpSchemaEE_ _ZTSZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE_ _ZTSZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE0_ _ZTSZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE1_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE0_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE10_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE11_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE12_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE13_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE14_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE15_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE16_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE17_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE18_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE19_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE20_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE21_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE22_ _ZTSZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE23_ ._161 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib32NchwcGlobalPoolOpSchemaGeneratorERNS0_8OpSchemaEEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ 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_ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Expand_Onnx_ver13EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_24QLinearMatMul_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_24MatMulInteger_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Det_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_36NegativeLogLikelihoodLoss_Onnx_ver12EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Einsum_Onnx_ver12EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ 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_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_ _ZNSt14_Function_base13_Base_managerIZZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS3_ENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt14_Function_base13_Base_managerIZN4onnx21PoolOpSchemaGeneratorEPKcS3_S3_bbEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbEUlRNS_8OpSchemaEE_ _ZNSt14_Function_base13_Base_managerIZZN4onnx21PoolOpSchemaGeneratorEPKcS3_S3_bbENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZTIZZN4onnx21PoolOpSchemaGeneratorEPKcS1_S1_bbENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_20MaxUnpool_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_20MaxUnpool_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx23LpPoolOpSchemaGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx23LpPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZTIZZN4onnx23LpPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx24RoiPoolOpSchemaGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx24RoiPoolOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZTIZZN4onnx24RoiPoolOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx21ConvOpSchemaGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx21ConvOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZTIZZN4onnx21ConvOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_22QLinearConv_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_22QLinearConv_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_22ConvInteger_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_22ConvInteger_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx30ConvTransposeOpSchemaGeneratorEPKcEUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx30ConvTransposeOpSchemaGeneratorEPKcEUlRNS_8OpSchemaEE_ _ZTIZZN4onnx30ConvTransposeOpSchemaGeneratorEPKcENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS3_EUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_ _ZTIZZN4onnx30GlobalPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS3_EUlRNS1_8OpSchemaEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_EUlRNS_8OpSchemaEE_ _ZTIZZN4onnx32GlobalLpPoolingOpSchemaGeneratorEPKcS1_ENKUlRNS_8OpSchemaEE_clES3_EUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28BatchNormalization_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28BatchNormalization_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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_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_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 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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_ _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_ 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_ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Size_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Size_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Concat_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Split_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Split_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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_ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17OneHot_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16IsNaN_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16IsNaN_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16IsInf_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Where_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18NonZero_Onnx_ver13EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18NonZero_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26ReverseSequence_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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_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_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver13EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19Upsample_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Upsample_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Resize_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Resize_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ 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