vendor/libonnxruntime.so in onnxruntime-0.3.3 vs vendor/libonnxruntime.so in onnxruntime-0.4.0
- old
+ new
@@ -1,781 +1,909 @@
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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 _ZNSt11logic_errorC1ERKSs _ZNSt11logic_errorD1Ev _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 _ZNSdD2Ev _ZNSt18basic_stringstreamIcSt11char_traitsIcESaIcEED1Ev _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 __dynamic_cast __cxa_bad_cast _ZNSs7reserveEm __cxa_bad_typeid _ZNSs6insertEmPKcm localtime_r strftime strcmp _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 malloc free 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 _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 dlopen remove munmap __fxstat 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.1.3.0 $ORIGIN VERS_1.3.0 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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H=j L =j L =j L *=j L <j L =j L <j L =j L <j L *=j M H=j M V=j M e=j M <j (M =j 0M =j @M <j HM =j PM <j XM =j `M =j hM <j pM =j xM <j M 8=j M H=j M V=j M =j M <j M =j M <j M =j M <j M e=j M 8=j M H=j M =j M <j N =j N <j N 8=j N H=j N 8=j (N H=j 0N V=j 8N <j @N <j HN =j PN *=j `N 8=j hN H=j pN V=j N 8=j N H=j N V=j N H=j N V=j N 8=j N H=j N V=j N 8=j N H=j N V=j N 8=j O H=j O V=j O 8=j O H=j O 8=j (O H=j 0O V=j @O =j HO *=j PO 8=j XO H=j `O V=j pO 8=j xO H=j O V=j O 8=j O H=j O V=j O 8=j O H=j O V=j O 8=j O H=j O V=j O 8=j O H=j P V=j P 8=j P H=j P V=j 0P 8=j 8P H=j @P V=j PP 8=j XP H=j `P V=j pP 8=j xP H=j P V=j P 8=j P H=j P V=j P 8=j P H=j P V=j P =j P *=j P 8=j P H=j P V=j Q 8=j Q H=j Q V=j Q 8=j (Q H=j 0Q V=j @Q 8=j HQ H=j PQ V=j `Q 8=j hQ H=j pQ V=j xQ H=j Q H=j Q 8=j Q H=j Q 8=j Q H=j Q 8=j Q =j Q =j Q H=j Q 8=j Q =j Q <j Q =j Q <j Q =j R H=j R 8=j R <j R =j R H=j (R H=j 0R H=j 8R H=j @R H=j HR H=j `R H=j hR V=j pR =j xR =j R *=j R =j R H=j R V=j R p R p R p R PcP R fP R @fP R *=j R t=j R *=j S t=j S t=j S *=j S t=j S *=j (S *=j @S H=j HS V=j PS =j XS =j `S *=j pS =j xS *=j S 8=j S H=j S V=j S 8=j S H=j S V=j S 8=j S H=j S V=j S 8=j S H=j S V=j T 8=j T H=j T V=j T 8=j (T H=j 0T V=j 8T =j @T =j HT =j PT *=j XT <j `T <j hT <j pT <j xT t=j T 8=j T H=j T V=j T =j T =j T =j T *=j T <j T <j T <j T <j T t=j T 8=j T H=j T V=j T =j U =j U =j U *=j U <j U <j (U <j 0U <j 8U t=j @U *=j PU 8=j XU H=j `U V=j U t=j U t=j U <j U <j U <j U <j U t=j U t=j U t=j U <j U <j U <j V <j V =j V =j V =j V *=j (V 8=j 0V H=j 8V V=j @V t=j HV t=j PV t=j XV t=j `V t=j hV t=j pV t=j xV t=j V t=j V 8=j V H=j V V=j V t=j V 8=j V H=j V V=j V t=j V t=j V =j V *=j V t=j V t=j V =j W *=j W t=j W 8=j W H=j W V=j (W t=j 0W 8=j 8W H=j @W V=j HW t=j PW t=j XW t=j `W t=j hW t=j pW t=j W =j W *=j W 8=j W H=j W V=j W =j W *=j W 8=j W H=j W V=j W 8=j W H=j W V=j X 8=j X H=j X V=j X =j (X *=j @X <j HX <j PX =j XX *=j `X H=j hX V=j pX =j X =j X <j X =j X <j X =j X <j X =j X <j X =j X <j X 8=j X H=j X V=j X 8=j X H=j Y V=j Y 8=j Y H=j Y V=j 0Y 8=j 8Y H=j @Y V=j PY 8=j XY H=j `Y V=j pY 8=j xY H=j Y V=j Y 8=j Y H=j Y V=j Y 8=j Y H=j Y V=j Y 8=j Y H=j Y V=j Y 8=j Y H=j Z V=j Z 8=j Z H=j Z V=j (Z *=j @Z 8=j HZ H=j PZ V=j XZ <j `Z <j hZ =j pZ *=j Z 8=j Z H=j Z V=j Z <j Z <j Z =j Z *=j Z 8=j Z H=j Z V=j Z 8=j Z H=j Z V=j [ 8=j [ H=j [ V=j [ 8=j ([ H=j 0[ V=j @[ 8=j H[ H=j P[ V=j `[ 8=j h[ H=j p[ V=j [ 8=j [ H=j [ V=j [ <j [ <j [ =j [ *=j [ <j [ <j [ <j [ <j [ =j [ =j [ =j [ *=j \ 8=j \ H=j \ V=j \ =j (\ *=j 0\ 8=j 8\ H=j @\ V=j P\ 8=j X\ H=j `\ V=j p\ 8=j x\ H=j \ V=j \ 8=j \ H=j \ V=j \ H=j \ 8=j \ H=j \ V=j \ 8=j \ H=j \ V=j \ 8=j \ H=j ] V=j ] 8=j ] H=j ] V=j 0] 8=j 8] H=j @] V=j P] 8=j X] H=j `] V=j p] 8=j x] H=j ] V=j ] 8=j ] H=j ] V=j ] 8=j ] H=j ] V=j ] H=j ] =j ] =j ] =j ^ *=j ^ 8=j ^ V=j ^ 8=j (^ H=j 0^ V=j @^ 8=j H^ H=j P^ V=j `^ 8=j h^ H=j p^ V=j ^ 8=j ^ H=j ^ V=j ^ *=j ^ 8=j ^ H=j ^ V=j ^ *=j ^ 8=j ^ H=j ^ V=j ^ 8=j ^ H=j ^ V=j _ 8=j _ H=j _ V=j _ 8=j (_ H=j 0_ V=j @_ 8=j H_ H=j 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-s H5w؞ HfH=f؞ HH=ɞ Hr H5H؞ HrfH=7؞ HPH=qɞ Hr H5؞ HBfH=؞ H H=Aɞ Hzr H5מ HfH=מ HH=ɞ HJr H5מ HfH=מ HH=Ȟ Hr H5מ HfH={מ HH=Ȟ Hq H5]מ HfH=Lמ H`H=Ȟ Hq H5.מ HRfH= מ H0H=QȞ Hq H5֞ H"fH=֞ H H=!Ȟ HZq H5֞ HfH=֞ HH=Ǟ H*q H5֞ HfH=֞ HH=Ǟ Hp H5r֞ HfAVH=֞ AUATUSH hH=Ǟ Hp H5{֞ H\$Ll$
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H5)e HIH}HT$H5!e 4 HԞ HԞ HԞ )IHԞ H@LHԞ Ht HH HD$ HHH9uHD$H JԞ H\$HHxQHD$HHx@H=h HJn H5 Ԟ H []A\Hl$HH=Ӟ HtDHD$HHxHD$HHxHHl$H L9tI$HIHxHu AUH=Ӟ ATUSH8:H=[Ğ Hm H5Ӟ H\$+HT$ H5e HHT$
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-k H54ў HfH=#ў HH= Hj H5ў HrfH=О HPH=q Hj H5О HBfH=О H H=A Hzj H5О HfH=О HH= HJj H5xО HfH=gО HH= Hj H5IО HfH=aО HH= Hi H5CО HfH=2О H`H= Hi H5О HRfH=О H0H=Q Hi H5Ϟ H"fH=Ϟ H H=! HZi H5Ϟ HfH=Ϟ HH= H*i H5Ϟ HfH=wϞ HH= Hh H5YϞ HfH=HϞ HpH= Hh H5*Ϟ HbfH=Ϟ H@H=a Hh H5Ξ H2fH=Ξ HH=1 Hjh H5Ξ HfH=Ξ HH= H:h H5Ξ HfH=Ξ HH=Ѿ H
-h H5nΞ HfH=]Ξ HH= Hg H5?Ξ HrfH=.Ξ HPH=q Hg H5Ξ HBfH=͞ H H=A Hzg H5͞ HfH=͞ HH= HJg H5͞ HfH=͞ HH= Hg H5͞ HfH=r͞ HH= Hf H5T͞ HfH=C͞ H`H= Hf H5%͞ HRfH=͞ H0H=Q Hf H5̞ H"fH=̞ H H=! HZf H5̞ HfH=̞ HH= H*f H5̞ HfH=̞ HH= He H5i̞ HfH=X̞ HpH= He H5:̞ HbfH=)̞ H@H=a He H5 ̞ H2fH=˞ HH=1 Hje H5˞ HfH=˞ HH= H:e H5˞ HfH=˞ HH=ѻ H
-e H5~˞ HfH=˞ HH= Hd H5˞ HrfH=˞ HPH=q Hd H5{˞ HBfH=j˞ H H=A Hzd H5L˞ HfH=;˞ HH= HJd H5 ˞ HfH= ˞ HH=ẞ Hd H5ʞ HfH=ʞ HH= Hc H5ʞ HfH=ʞ H`H= Hc H5ʞ HRfH=ʞ H0H=Q Hc H5aʞ H"fH=Pʞ H H=! HZc H52ʞ HfH=!ʞ HH= H*c H5ʞ HfH=ɞ HH= Hb H5ɞ HfH=ɞ HpH= Hb H5ɞ HbfH=ɞ H@H=a Hb H5vɞ H2fH=eɞ HH=1 Hjb H5Gɞ HfH=6ɞ HH= H:b H5ɞ HfH=ɞ HH=Ѹ H
-b H5Ȟ HfH=Ȟ HH= Ha H5Ȟ HrfH=Ȟ HPH=q Ha H5Ȟ HBfH=zȞ H H=A Hza H5\Ȟ HfUH=hȞ SHH= HHa H5IȞ H\$H5Cg H=)Ȟ HiH-" Ha H5 Ȟ HH5Eg H=Ǟ HfH=W H` H5Ǟ |H5g H=Ǟ HH` H5Ǟ HPH[]f H=Ǟ H H=A Hz` H5|Ǟ HfH=kǞ HH= HJ` H5MǞ HfH=<Ǟ HH=ᶞ H` H5 Ǟ HfH=
Ǟ HH= H_ H5ƞ HfH=ƞ H`H= H_ H5ƞ HRfH=ƞ H0H=Q H_ H5ƞ H"fH=ƞ H H=! HZ_ H5ƞ HfAWH=Ǟ AVAUATUSH( H=䵞 H _ H5ƞ Hl$pH\$oLd$`L|$lLt$@H5&g HH.H}H5#k HD$xD$yH} H5g HƄ$ Ƅ$ H}0H5g HƄ$ Ƅ$ H}@H5 e HƄ$ Ƅ$ H}PH5g HƄ$ Ƅ$ H}`H5g HƄ$ Ƅ$ bH}pH5pg HƄ$ Ƅ$ ?H H5.k HƄ$ Ƅ$ H H50g HƄ$ Ƅ$ H H5g HƄ$ Ƅ$ HL$mHD$nH$ H=Ş L|$Lt$HL$0HL$HL$PMIHHL$(H $1Ƅ$ Ƅ$ HD$8z& L f D IE HIHxM9uH=@ & H\ H5Ğ H5g HHH}H5)g HD$x
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-Y H5 HfH= HH= HX H5 HrfH= HPH=q HX H5 HBfH= H H=A HzX H5z HfH=i HH= HJX H5K HfH=: HH=ᮞ HX H5 HfH= HH= HW H5 HfH=ܿ H`H= HW H5 HRfH= H0H=Q HW H5 H"fH=~ H H=! HZW H5` HfH=O HH= H*W H51 HfH= HH= HV H5 HfH= HpH= HV H5Ӿ HbfH=¾ H@H=a HV H5 H2fH= HH=1 HjV H5u HfH=d HH= H:V H5F HfH=5 HH=Ѭ H
-V H5 HfH= HH= HU H5轞 HrfH= HPH=q HU H5 HBfH= H H=A HzU H5 HfH=y HH= HJU H5[ HfH=J HH= HU H5, HfH= HH= HT H5 HfH=켞 H`H= HT H5μ HRfH= H0H=Q HT H5 H"fH= H H=! HZT H5p HfH=_ HH= H*T H5A HfH=0 HH= HS H5 HfH= HpH= HS H5㻞 HbfH=һ H@H=a HS H5 H2fH= HH=1 HjS H5 HfH=t HH= H:S H5V HfH=E HH=ѩ H
-S H5' HfH= HH= HR H5 HrfH=纞 HPH=q HR H5ɺ HBfH= H H=A HzR H5 HfH= HH= HJR H5k HfH=Z HH=᨞ HR H5< HfH=+ HH= HQ H5
HfH= H`H= HQ H5 HRfH= H0H=Q HQ H5 H"fH= H H=! HZQ H5 HfH=o HH= H*Q H5Q HfH=@ HH= HP H5" HfH= HpH= HP H5 HbfH=⸞ H@H=a HP H5ĸ H2fH= HH=1 HjP H5 HfH= HH= H:P H5f HfH=U HH=Ѧ H
-P H57 HfH=& HH= HO H5 HrfH= HPH=q HO H5ٷ HBfH=ȷ H H=A HzO H5 HfH= HH= HJO H5{ HfH=j HH=ᥞ HO H5L HfH=; HH= HN H5 HfH= H`H= HN H5 HRfH=ݶ H0H=Q HN H5 H"fH= H H=! HZN H5 HfH= HH= H*N H5a HfH=P HH= HM H52 HfH=! HpH= HM H5 HbfH= H@H=a HM H5Ե H2fH=õ HH=1 HjM H5 HfH= HH= H:M H5v HfH=e HH=ѣ H
-M H5G HfH=6 HH= HL H5 HrfH= HPH=q HL H5鴞 HBfH=ش H H=A HzL H5 HfH= HH= HJL H5 HfH=z HH=ᢞ HL H5\ HfH=K HH= HK H5- HfH= H`H= HK H5 HRfH= H0H=Q HK H5ϳ H"fH= H H=! HZK H5 HfH= HH= H*K H5q HfH=` HH= HJ H5B HfH=1 HpH= HJ H5 HbfH= H@H=a HJ H5䲞 H2fH=Ӳ HH=1 HjJ H5 HfH= HH= H:J H5 HfH= HH=Ѡ H
-J H5ӻ HfH=» HH= HI H5 HrfH= HPH=q HI H5u HBfH=d H H=A HzI H5F HfH=5 HH= HJI H5 HfH= HH= HI H5躞 H鲿fH= HH= HH H5 H邿fH= H`H= HH H5 HRfH=y H0H=Q HH H5[ H"fH=J H H=! HZH H5, HfH= HH= H*H H5 H¾fH=칞 HH= HG H5ι H钾fH= HpH= HG H5 HbfH= H@H=a HG H5p H2fH=_ HH=1 HjG H5A HfH=0 HH= H:G H5 HҽfH= HH=ѝ H
-G H5㸞 H颽fH=Ҹ HH= HF H5 HrfHwd H Hf. H= H0H=Q HF H5 H"fH=z H H=! HZF H5\ HfH=K HH= H*F H5- H¼fH= HH= HE H5 H钼fH= HpH= HE H5Ϸ HbfH= H@H=a HE H5 H2fH= HH=1 HjE H5q HfH=` HH= H:E H5B HһfH=1 HH=ћ H
-E H5 H颻fH=! HH= HD H5 HrfH= HPH=q HD H5Զ HBfH=ö H H=A HzD H5 HfH= HH= HJD H5v HfH=e HH= HD H5G H鲺fH=6 H萿H= HC H5 H邺fH= H`H= HC H5鵞 HRfH= H0H=Q HC H5ӹ H"fH=Q H H=! HZC H53 HfH=" HоH= H*C H5 H¹fH= H蠾H= HB H5 H钹fH= HpH= HB H5Ĺ HbfH= H@H=a HB H5 H2fH= HH=1 HjB H5f HfH=U HH= H:B H57 HҸfH=& H谽H=ј H
-B H5 H颸fH= H耽H= HA H5ٸ HrfH=A HPH=q HA H5# HBfH= tK @ H= HH=1 HjA H5 HfH= HH= H:A H5 HҷfH= H谼H=ї H
-A H5 H颷fH=ѻ H耼H= H@ H5 HrfH= HPH=q H@ H5ӻ HBfH=Ở H H=A Hz@ H5û HfH= HH= HJ@ H5뻞 HfH=Y HH=ᖞ H@ H5; H鲶fH= H萻H= H? H5뽞 H邶fH=y H`H= H? H5[ HRfSH= 3H=T H? H5 ) Hٿ Hֿ Hӿ 辶H
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+ t0H= HWt PJHЅ
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+Ht$葖ID$HxL9t0H=Ӝ HWt PJHЅ
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+Ht$L.H[]A\A]A^ÐSHHHtIHGHxH;= t0H=2 HWt PJHЅ
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+Ht$ID$HxL9t0H=; HWt PJHЅ
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+Ht$+ID$HxL9t0H=m HWt PJHЅ
+Ht$I$HxL9t0H=0 HWt PJHЅ
+Ht$谎LȏH[]A\A]A^ÐSHHHtIHGHxH;=6 t0H=̔ HWt PJHЅ
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+Ht$ID$HxL9t0H=C HWt PJHЅ
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+Ht$eID$HxL9t0H= HWt PJHЅ
+Ht$'I$HxL9t0H=j HWt PJHЅ
+Ht$LH[]A\A]A^ÐAVAUATIUSHH H H ِ I$ I$ Đ I|$hHt襉I|$PHt薉I\$0L-" Lt$HtYH{H+HtoHCHxL9t.H= HWt PJHЅLHH)ID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H= HWt PJHЅ
+Ht$藇ID$HxL9t0H=ٍ HWt PJHЅ
+Ht$YI$HxL9t0H= HWt PJHЅ
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+Ht$ɅID$HxL9t0H= HWt PJHЅ
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+Ht$NLfH[]A\A]A^ÐAVAUATIUSHH H H = I$ I$ ( I|$hHt I|$PHtI\$0L- Lt$HtYH{H+HtӅHCHxL9t.H= HWt PJHЅLxHH荅ID$(I|$ H 1ID$8 ID$0 I|$ YID$HxL9t0H={ HWt PJHЅ
+Ht$ID$HxL9t0H== HWt PJHЅ
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+Ht$考L蘄H[]A\A]A^ÐSHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$ H4H[AVAUATIUSHH H H I$ I$ I|$hHt߃I|$PHtЃI\$0L-\ Lt$HtYH{H+Ht詃HCHxL9t.H=̈ HWt PJHЅLNHHcID$(I|$ H 1ID$8 ID$0 I|$ /ID$HxL9t0H=Q HWt PJHЅ
+Ht$сID$HxL9t0H= HWt PJHЅ
+Ht$蓁I$HxL9t0H=և HWt PJHЅ
+Ht$VLnH[]A\A]A^ÐSHHHtIHGHxH;=܅ t0H=r HWt PJHЅ
+Ht$H
+H[AVAUATIUSHH H H I$ I$ Ԉ I|$hHt赁I|$PHt要I\$0L-2 Lt$HtYH{H+HtHCHxL9t.H= HWt PJHЅL$HH9ID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H=' HWt PJHЅ
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+Ht$}}ID$HxL9t0H= HWt PJHЅ
+Ht$?}I$HxL9t0H= HWt PJHЅ
+Ht$}L~H[]A\A]A^ÐAVAUATIUSHH H H I$ I$ ܄ I|$hHt}I|$PHt}I\$0L-: Lt$HtYH{H+Ht}HCHxL9t.H= HWt PJHЅL,|HHA}ID$(I|$ H 1ID$8 ID$0 I|$
}ID$HxL9t0H=/ HWt PJHЅ
+Ht${ID$HxL9t0H= HWt PJHЅ
+Ht$q{I$HxL9t0H= HWt PJHЅ
+Ht$4{LL|H[]A\A]A^ÐSHHHtIHGHxH;= t0H=P HWt PJHЅ
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+Ht$xHzH[AVAUATIUSHH H H I$ I$ I|$hHtyI|$PHtyI\$0L->} Lt$HtYH{H+HtyHCHxL9t.H=~ HWt PJHЅL0xHHEyID$(I|$ H 1ID$8 ID$0 I|$ yID$HxL9t0H=3~ HWt PJHЅ
+Ht$wID$HxL9t0H=} HWt PJHЅ
+Ht$uwI$HxL9t0H=} HWt PJHЅ
+Ht$8wLPxH[]A\A]A^ÐSHHHtIHGHxH;={ t0H=T} HWt PJHЅ
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+Ht$sID$HxL9t0H=y HWt PJHЅ
+Ht$sI$HxL9t0H=y HWt PJHЅ
+Ht$BsLZtH[]A\A]A^H56>j PmZSHHHtIHGHxH;=w t0H=Hy HWt PJHЅ
+Ht$rHsH[SHHHtIHGHxH;=Vw t0H=x HWt PJHЅ
+Ht$lrHsH[AUATIUSHH# H H ez I$ I$ Pz I|$hHt1sI|$PHt"sI\$0Ll$Ht]H{H+HtsHSHzH;=v t.H=!x HOt JqrʅLqHHrID$(I|$ H\$H 1ID$8 ID$0 I|$ rID$HHx.oID$HHx oI$HHx
oLErH[]A\A]SHHHtIHGHxH;=u t0H=Lw HWt PJHЅ
+Ht$pHqH[AVAUATIUSHH H H x I$ I$ x I|$hHtqI|$PHtqI\$0L- u Lt$HtYH{H+HtYqHCHxL9t.H=|v HWt PJHЅLoHHqID$(I|$ H 1ID$8 ID$0 I|$ pID$HxL9t0H=v HWt PJHЅ
+Ht$oID$HxL9t0H=u HWt PJHЅ
+Ht$CoI$HxL9t0H=u HWt PJHЅ
+Ht$oL pH[]A\A]A^ÐAVAUATIUSHH H H v I$ I$ v I|$hHtoI|$PHtoI\$0L->s Lt$HtYH{H+HtoHCHxL9t.H=t HWt PJHЅL0nHHEoID$(I|$ H 1ID$8 ID$0 I|$ oID$HxL9t0H=3t HWt PJHЅ
+Ht$mID$HxL9t0H=s HWt PJHЅ
+Ht$umI$HxL9t0H=s HWt PJHЅ
+Ht$8mLPnH[]A\A]A^ÐAUATIUSHH# H H )u I$ I$ u I|$hHtmI|$PHtmI\$0Ll$Ht]H{H+HtmHSHzH;=Oq t.H=r HOt JqrʅLglHH|mID$(I|$ H\$H 1ID$8 ID$0 I|$ CmID$HHxiID$HHxiI$HHxiL mH[]A\A]AUATIUSHH# H H s I$ I$ s I|$hHtlI|$PHtlI\$0Ll$Ht]H{H+HtlHSHzH;= p t.H=q HOt JqrʅL#kHH8lID$(I|$ H\$H 1ID$8 ID$0 I|$ kID$HHxhID$HHxhI$HHxhLkH[]A\A]AVAUATIUSHH H H r I$ I$ r I|$hHtkkI|$PHt\kI\$0L-n Lt$HtYH{H+Ht5kHCHxL9t.H=Xp HWt PJHЅLiHHjID$(I|$ H 1ID$8 ID$0 I|$ jID$HxL9t0H=o HWt PJHЅ
+Ht$]iID$HxL9t0H=o HWt PJHЅ
+Ht$ iI$HxL9t0H=bo HWt PJHЅ
+Ht$hLiH[]A\A]A^ÐAUATIUSHH# H H p I$ I$ p I|$hHtiI|$PHtiI\$0Ll$Ht]H{H+HtpiHSHzH;=l t.H=n HOt JqrʅLhHH&iID$(I|$ H\$H 1ID$8 ID$0 I|$ hID$HHxeID$HHxeI$HHx{eLhH[]A\A]AVAUATIUSHH H H o I$ I$ xo I|$hHtYhI|$PHtJhI\$0L-k Lt$HtYH{H+Ht#hHCHxL9t.H=Fm HWt PJHЅLfHHgID$(I|$ H 1ID$8 ID$0 I|$ gID$HxL9t0H=l HWt PJHЅ
+Ht$KfID$HxL9t0H=l HWt PJHЅ
+Ht$
fI$HxL9t0H=Pl HWt PJHЅ
+Ht$eLfH[]A\A]A^ÐAVAUATIUSHH H H m I$ I$ m I|$hHtfI|$PHt|fI\$0L-j Lt$HtYH{H+HtUfHCHxL9t.H=xk HWt PJHЅLdHHfID$(I|$ H 1ID$8 ID$0 I|$ eID$HxL9t0H=j HWt PJHЅ
+Ht$}dID$HxL9t0H=j HWt PJHЅ
+Ht$?dI$HxL9t0H=j HWt PJHЅ
+Ht$dLeH[]A\A]A^ÐAVAUATIUSHH H H k I$ I$ k I|$hHtdI|$PHtdI\$0L-:h Lt$HtYH{H+HtdHCHxL9t.H=i HWt PJHЅL,cHHAdID$(I|$ H 1ID$8 ID$0 I|$
dID$HxL9t0H=/i HWt PJHЅ
+Ht$bID$HxL9t0H=h HWt PJHЅ
+Ht$qbI$HxL9t0H=h HWt PJHЅ
+Ht$4bLLcH[]A\A]A^ÐAVAUATIUSHH H H #j I$ I$ j I|$hHtbI|$PHtbI\$0L-lf Lt$HtYH{H+HtbHCHxL9t.H=g HWt PJHЅL^aHHsbID$(I|$ H 1ID$8 ID$0 I|$ ?bID$HxL9t0H=ag HWt PJHЅ
+Ht$`ID$HxL9t0H=#g HWt PJHЅ
+Ht$`I$HxL9t0H=f HWt PJHЅ
+Ht$f`L~aH[]A\A]A^ÐAVAUATIUSHH H H Uh I$ I$ @h I|$hHt!aI|$PHtaI\$0L-d Lt$HtYH{H+Ht`HCHxL9t.H=f HWt PJHЅL_HH`ID$(I|$ H 1ID$8 ID$0 I|$ q`ID$HxL9t0H=e HWt PJHЅ
+Ht$_ID$HxL9t0H=Ue HWt PJHЅ
+Ht$^I$HxL9t0H=e HWt PJHЅ
+Ht$^L_H[]A\A]A^ÐAUATIUSHH# H H f I$ I$ tf I|$hHtU_I|$PHtF_I\$0Ll$Ht]H{H+Ht&_HSHzH;=b t.H=Ed HOt JqrʅL]HH^ID$(I|$ H\$H 1ID$8 ID$0 I|$ ^ID$HHxR[ID$HHxA[I$HHx1[Li^H[]A\A]AVAUATIUSHH H H Ce I$ I$ .e I|$hHt^I|$PHt ^I\$0L-a Lt$HtYH{H+Ht]HCHxL9t.H=b HWt PJHЅL~\HH]ID$(I|$ H 1ID$8 ID$0 I|$ _]ID$HxL9t0H=b HWt PJHЅ
+Ht$\ID$HxL9t0H=Cb HWt PJHЅ
+Ht$[I$HxL9t0H=b HWt PJHЅ
+Ht$[L\H[]A\A]A^ÐAUATIUSHH# H H wc I$ I$ bc I|$hHtC\I|$PHt4\I\$0Ll$Ht]H{H+Ht\HSHzH;=_ t.H=3a HOt JqrʅLZHH[ID$(I|$ H\$H 1ID$8 ID$0 I|$ [ID$HHx@XID$HHx/XI$HHx XLW[H[]A\A]AUATIUSHH# H H 3b I$ I$ b I|$hHtZI|$PHtZI\$0Ll$Ht]H{H+HtZHSHzH;=Y^ t.H=_ HOt JqrʅLqYHHZID$(I|$ H\$H 1ID$8 ID$0 I|$ MZID$HHxVID$HHxVI$HHxVLZH[]A\A]AUATIUSHH# H H ` I$ I$ ` I|$hHtYI|$PHtYI\$0Ll$Ht]H{H+HtYHSHzH;=] t.H=^ HOt JqrʅL-XHHBYID$(I|$ H\$H 1ID$8 ID$0 I|$ YID$HHxUID$HHxUI$HHxULXH[]A\A]ATIUSH? t,HGHH_(HhH9vH;HX[]I<$A\X[]A\SHHHtIHGHxH;=\ t0H=] HWt PJHЅ
+Ht$WH0XH[AVAUATIUSHH H H _ I$ I$ ^ I|$hHtWI|$PHtWI\$0L-X[ Lt$HtYH{H+HtWHCHxL9t.H=\ HWt PJHЅLJVHH_WID$(I|$ H 1ID$8 ID$0 I|$ +WID$HxL9t0H=M\ HWt PJHЅ
+Ht$UID$HxL9t0H=\ HWt PJHЅ
+Ht$UI$HxL9t0H=[ HWt PJHЅ
+Ht$RULjVH[]A\A]A^ÐSHHHtIHGHxH;=Y t0H=n[ HWt PJHЅ
+Ht$THVH[AUATIUSHH# H H \ I$ I$ \ I|$hHtUI|$PHtUI\$0Ll$Ht]H{H+HtUHSHzH;=
Y t.H=Z HOt JqrʅL%THH:UID$(I|$ H\$H 1ID$8 ID$0 I|$ UID$HHxQID$HHxQI$HHxQLTH[]A\A]SHHHtIHGHxH;=8X t0H=Y HWt PJHЅ
+Ht$NSHfTH[P OH^ H5 HHHH& AZSHHHtIHGHxH;=W t0H=BY HWt PJHЅ
+Ht$RHSH[AUATIUSHH# H H Z I$ I$ Z I|$hHtSI|$PHtxSI\$0Ll$Ht]H{H+HtXSHSHzH;=V t.H=wX HOt JqrʅLQHHSID$(I|$ H\$H 1ID$8 ID$0 I|$ RID$HHxOID$HHxsOI$HHxcOLRH[]A\A]P NH H57 HHHH& qXAUATIUSHH# H H GY I$ I$ 2Y I|$hHtRI|$PHtRI\$0Ll$Ht]H{H+HtQHSHzH;=mU t.H=W HOt JqrʅLPHHQID$(I|$ H\$H 1ID$8 ID$0 I|$ aQID$HHxNID$HHxMI$HHxML'QH[]A\A]AUATIUSHH# H H X I$ I$ W I|$hHtPI|$PHtPI\$0Ll$Ht]H{H+HtPHSHzH;=)T t.H=U HOt JqrʅLAOHHVPID$(I|$ H\$H 1ID$8 ID$0 I|$ PID$HHxLID$HHxLI$HHxLLOH[]A\A]AVAUATIUSHH H H V I$ I$ V I|$hHtOI|$PHtzOI\$0L-S Lt$HtYH{H+HtSOHCHxL9t.H=vT HWt PJHЅLMHH
OID$(I|$ H 1ID$8 ID$0 I|$ NID$HxL9t0H=S HWt PJHЅ
+Ht${MID$HxL9t0H=S HWt PJHЅ
+Ht$=MI$HxL9t0H=S HWt PJHЅ
+Ht$ MLNH[]A\A]A^ÐAVAUATIUSHH H H T I$ I$ T I|$hHtMI|$PHtMI\$0L-8Q Lt$HtYH{H+HtMHCHxL9t.H=R HWt PJHЅL*LHH?MID$(I|$ H 1ID$8 ID$0 I|$ MID$HxL9t0H=-R HWt PJHЅ
+Ht$KID$HxL9t0H=Q HWt PJHЅ
+Ht$oKI$HxL9t0H=Q HWt PJHЅ
+Ht$2KLJLH[]A\A]A^ÐSHHHtIHGHxH;=O t0H=NQ HWt PJHЅ
+Ht$JHKH[AVAUATIUSHH H H R I$ I$ R I|$hHtKI|$PHtKI\$0L-O Lt$HtYH{H+Ht[KHCHxL9t.H=~P HWt PJHЅL JHHKID$(I|$ H 1ID$8 ID$0 I|$ JID$HxL9t0H=P HWt PJHЅ
+Ht$IID$HxL9t0H=O HWt PJHЅ
+Ht$EII$HxL9t0H=O HWt PJHЅ
+Ht$IL JH[]A\A]A^ÐAVAUATIUSHH H H P I$ I$ P I|$hHtII|$PHtII\$0L-@M Lt$HtYH{H+HtIHCHxL9t.H=N HWt PJHЅL2HHHGIID$(I|$ H 1ID$8 ID$0 I|$ IID$HxL9t0H=5N HWt PJHЅ
+Ht$GID$HxL9t0H=M HWt PJHЅ
+Ht$wGI$HxL9t0H=M HWt PJHЅ
+Ht$:GLRHH[]A\A]A^ÐAVAUATIUSHH H H )O I$ I$ O I|$hHtGI|$PHtGI\$0L-rK Lt$HtYH{H+HtGHCHxL9t.H=L HWt PJHЅLdFHHyGID$(I|$ H 1ID$8 ID$0 I|$ EGID$HxL9t0H=gL HWt PJHЅ
+Ht$EID$HxL9t0H=)L HWt PJHЅ
+Ht$EI$HxL9t0H=K HWt PJHЅ
+Ht$lELFH[]A\A]A^ÐAVAUATIUSHH H H [M I$ I$ FM I|$hHt'FI|$PHtFI\$0L-I Lt$HtYH{H+HtEHCHxL9t.H=K HWt PJHЅLDHHEID$(I|$ H 1ID$8 ID$0 I|$ wEID$HxL9t0H=J HWt PJHЅ
+Ht$DID$HxL9t0H=[J HWt PJHЅ
+Ht$CI$HxL9t0H= J HWt PJHЅ
+Ht$CLDH[]A\A]A^ÐAVAUATIUSHH H H K I$ I$ xK I|$hHtYDI|$PHtJDI\$0L-G Lt$HtYH{H+Ht#DHCHxL9t.H=FI HWt PJHЅLBHHCID$(I|$ H 1ID$8 ID$0 I|$ CID$HxL9t0H=H HWt PJHЅ
+Ht$KBID$HxL9t0H=H HWt PJHЅ
+Ht$
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+Ht$ALBH[]A\A]A^ÐAVAUATIUSHH H H I I$ I$ I I|$hHtBI|$PHt|BI\$0L-F Lt$HtYH{H+HtUBHCHxL9t.H=xG HWt PJHЅL@HHBID$(I|$ H 1ID$8 ID$0 I|$ AID$HxL9t0H=F HWt PJHЅ
+Ht$}@ID$HxL9t0H=F HWt PJHЅ
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+Ht$>ID$HxL9t0H=D HWt PJHЅ
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+Ht$<I$HxL9t0H=B HWt PJHЅ
+Ht$f<L~=H[]A\A]A^ÐAVAUATIUSHH H H UD I$ I$ @D I|$hHt!=I|$PHt=I\$0L-@ Lt$HtYH{H+Ht<HCHxL9t.H=B HWt PJHЅL;HH<ID$(I|$ H 1ID$8 ID$0 I|$ q<ID$HxL9t0H=A HWt PJHЅ
+Ht$;ID$HxL9t0H=UA HWt PJHЅ
+Ht$:I$HxL9t0H=A HWt PJHЅ
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+Ht$ID$HxL9t0H= HWt PJHЅ
+Ht$AI$HxL9t0H= HWt PJHЅ
+Ht$L H[]A\A]A^ÐAVAUATIUSHH H H I$ I$ I|$hHtI|$PHtI\$0L-< Lt$HtYH{H+HtHCHxL9t.H= HWt PJHЅL.HHCID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H=1 HWt PJHЅ
+Ht$ID$HxL9t0H= HWt PJHЅ
+Ht$sI$HxL9t0H= HWt PJHЅ
+Ht$6LNH[]A\A]A^ÐAVAUATIUSHH H H % I$ I$ I|$hHtI|$PHtI\$0L-n Lt$HtYH{H+HtHCHxL9t.H= HWt PJHЅL`HHuID$(I|$ H 1ID$8 ID$0 I|$ AID$HxL9t0H=c HWt PJHЅ
+Ht$ID$HxL9t0H=% HWt PJHЅ
+Ht$I$HxL9t0H= HWt PJHЅ
+Ht$hLH[]A\A]A^ÐAVAUATIUSHH H H W I$ I$ B I|$hHt#I|$PHtI\$0L- Lt$HtYH{H+HtHCHxL9t.H= HWt PJHЅLHHID$(I|$ H 1ID$8 ID$0 I|$ sID$HxL9t0H= HWt PJHЅ
+Ht$ID$HxL9t0H=W HWt PJHЅ
+Ht$I$HxL9t0H= HWt PJHЅ
+Ht$LH[]A\A]A^ÐAVAUATIUSHH H H I$ I$ t I|$hHtUI|$PHtFI\$0L- Lt$HtYH{H+Ht HCHxL9t.H=B HWt PJHЅLHHID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H= HWt PJHЅ
+Ht$GID$HxL9t0H= HWt PJHЅ
+Ht$ I$HxL9t0H=L HWt PJHЅ
+Ht$LH[]A\A]A^ÐSHHHtIHGHxH;=R t0H= HWt PJHЅ
+Ht$hHH[AVAUATIUSHH H H _ I$ I$ J I|$hHt+I|$PHt I\$0L- Lt$HtYH{H+HtHCHxL9t.H= HWt PJHЅLHHID$(I|$ H 1ID$8 ID$0 I|$ {ID$HxL9t0H= HWt PJHЅ
+Ht$ ID$HxL9t0H=_ HWt PJHЅ
+Ht$I$HxL9t0H=" HWt PJHЅ
+Ht$LH[]A\A]A^ÐAVAUATIUSHH H H I$ I$ | I|$hHt]I|$PHtNI\$0L- Lt$HtYH{H+Ht'HCHxL9t.H=J HWt PJHЅLHHID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H= HWt PJHЅ
+Ht$OID$HxL9t0H= HWt PJHЅ
+Ht$I$HxL9t0H=T HWt PJHЅ
+Ht$
LH[]A\A]A^ÐAUATIUSHH# H H I$ I$ I|$hHtI|$PHtI\$0Ll$Ht]H{H+HtbHSHzH;= t.H= HOt JqrʅL
HHID$(I|$ H\$H 1ID$8 ID$0 I|$
ID$HHx
+ID$HHx}
+I$HHxm
+L
H[]A\A]SHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$, HD
H[AVAUATIUSHH H H # I$ I$ I|$hHt I|$PHt I\$0L-l Lt$HtYH{H+Ht HCHxL9t.H= HWt PJHЅL^ HHs ID$(I|$ H 1ID$8 ID$0 I|$ ? ID$HxL9t0H=a HWt PJHЅ
+Ht$
+ID$HxL9t0H=# HWt PJHЅ
+Ht$
+I$HxL9t0H= HWt PJHЅ
+Ht$f
+L~ H[]A\A]A^ÐAVAUATIUSHH| H H U I$ I$ @ I|$hHt! I|$PHt I\$0L- Lt$HtYH{H+Ht
+HCHxL9t.H= HWt PJHЅL HH
+ID$(I|$ H\$H 1ID$8 ID$0 I|$ l
+ID$HHxID$HxL9t.H=} HWt PJHЅHI$HxL9t.H=B HWt PJHЅHL H[]A\A]A^ÐAVAUATIUSHH H H I$ I$ I|$hHt I|$PHtp I\$0L- Lt$HtYH{H+HtI HCHxL9t.H=l HWt PJHЅLHH ID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H=
HWt PJHЅ
+Ht$qID$HxL9t0H=
HWt PJHЅ
+Ht$3I$HxL9t0H=v
HWt PJHЅ
+Ht$LH[]A\A]A^ÐAVAUATIUSHH H H I$ I$ I|$hHtI|$PHtI\$0L-. Lt$HtYH{H+Ht{HCHxL9t.H= HWt PJHЅL HH5ID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H=# HWt PJHЅ
+Ht$ID$HxL9t0H= HWt PJHЅ
+Ht$eI$HxL9t0H= HWt PJHЅ
+Ht$(L@H[]A\A]A^ÐAVAUATIUSHH H H
I$ I$
I|$hHtI|$PHtI\$0L-` Lt$HtYH{H+HtHCHxL9t.H=
+ HWt PJHЅLRHHgID$(I|$ H 1ID$8 ID$0 I|$ 3ID$HxL9t0H=U
+ HWt PJHЅ
+Ht$ID$HxL9t0H=
+ HWt PJHЅ
+Ht$I$HxL9t0H= HWt PJHЅ
+Ht$ZLrH[]A\A]A^ÐAVAUATIUSHH H H I I$ I$ 4 I|$hHtI|$PHtI\$0L- Lt$HtYH{H+HtHCHxL9t.H= HWt PJHЅLHHID$(I|$ H 1ID$8 ID$0 I|$ eID$HxL9t0H= HWt PJHЅ
+Ht$ID$HxL9t0H=I HWt PJHЅ
+Ht$I$HxL9t0H= HWt PJHЅ
+Ht$LH[]A\A]A^ÐSHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$(H@H[AUATIUSHH# H H ! I$ I$ I|$hHtI|$PHtI\$0Ll$Ht]H{H+HtHSHzH;=G t.H= HOt JqrʅL_ HHtID$(I|$ H\$H 1ID$8 ID$0 I|$ ;ID$HHxID$HHxI$HHxLH[]A\A]AVAUATIUSHH H H I$ I$ I|$hHt I|$PHt I\$0L-$ Lt$HtYH{H+Htq HCHxL9t.H= HWt PJHЅLHH+ ID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H= HWt PJHЅ
+Ht$ID$HxL9t0H= HWt PJHЅ
+Ht$[I$HxL9t0H= HWt PJHЅ
+Ht$ L6H[]A\A]A^ÐAVAUATIUSHH H H
I$ I$ I|$hHtI|$PHtI\$0L-V Lt$HtYH{H+HtHCHxL9t.H= HWt PJHЅLHHH]ID$(I|$ H 1ID$8 ID$0 I|$ )ID$HxL9t0H=K HWt PJHЅ
+Ht$ID$HxL9t0H=
HWt PJHЅ
+Ht$I$HxL9t0H= HWt PJHЅ
+Ht$PLhH[]A\A]A^ÐAVAUATIUSHH H H ? I$ I$ * I|$hHt I|$PHtI\$0L- Lt$HtYH{H+HtHCHxL9t.H= HWt PJHЅLzHHID$(I|$ H 1ID$8 ID$0 I|$ [ID$HxL9t0H=} HWt PJHЅ
+Ht$ID$HxL9t0H=? HWt PJHЅ
+Ht$I$HxL9t0H= HWt PJHЅ
+Ht$LH[]A\A]A^ÐHSHt^c H[u[ÐAUATIUSHH# H H Y I$ I$ D I|$hHt%I|$PHtI\$0Ll$Ht]H{H+HtHSHzH;= t.H= HOt JqrʅLHHID$(I|$ H\$H 1ID$8 ID$0 I|$ sID$HHx"ID$HHxI$HHxL9H[]A\A]AVAUATIUSHH H H I$ I$ I|$hHtI|$PHtI\$0L-\ Lt$HtYH{H+HtHCHxL9t.H= HWt PJHЅLNHHcID$(I|$ H 1ID$8 ID$0 I|$ /ID$HxL9t0H=Q HWt PJHЅ
+Ht$ID$HxL9t0H= HWt PJHЅ
+Ht$I$HxL9t0H= HWt PJHЅ
+Ht$VLnH[]A\A]A^ÐAVAUATIUSHH H H E I$ I$ 0 I|$hHtI|$PHtI\$0L- Lt$HtYH{H+HtHCHxL9t.H= HWt PJHЅLHHID$(I|$ H 1ID$8 ID$0 I|$ aID$HxL9t0H= HWt PJHЅ
+Ht$ID$HxL9t0H=E HWt PJHЅ
+Ht$I$HxL9t0H= HWt PJHЅ
+Ht$LH[]A\A]A^ÐSHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$$H<H[AVAUATIUSHH H H I$ I$ I|$hHtI|$PHtI\$0L-d Lt$HtYH{H+HtHCHxL9t.H= HWt PJHЅLVHHkID$(I|$ H 1ID$8 ID$0 I|$ 7ID$HxL9t0H=Y HWt PJHЅ
+Ht$ID$HxL9t0H= HWt PJHЅ
+Ht$I$HxL9t0H= HWt PJHЅ
+Ht$^LvH[]A\A]A^ÐSHHHtIHGHxH;= t0H=z HWt PJHЅ
+Ht$HH[AVAUATIUSHH H H I$ I$ I|$hHtI|$PHtI\$0L-: Lt$HtYH{H+HtHCHxL9t.H= HWt PJHЅL,HHAID$(I|$ H 1ID$8 ID$0 I|$
ID$HxL9t0H=/ HWt PJHЅ
+Ht$ID$HxL9t0H= HWt PJHЅ
+Ht$qI$HxL9t0H= HWt PJHЅ
+Ht$4LLH[]A\A]A^P H=- H5, HHHHY& AUATIUSHH# H H I$ I$ I|$hHtI|$PHtI\$0Ll$Ht]H{H+HtHSHzH;= t.H= HOt JqrʅL5HHJID$(I|$ H\$H 1ID$8 ID$0 I|$ ID$HHxID$HHxI$HHxLH[]A\A]SHHHtIHGHxH;=H t0H= HWt PJHЅ
+Ht$^HvH[AVAUATIUSHH H H U I$ I$ @ I|$hHt!I|$PHtI\$0L- Lt$HtYH{H+HtHCHxL9t.H= HWt PJHЅLHHID$(I|$ H 1ID$8 ID$0 I|$ qID$HxL9t0H= HWt PJHЅ
+Ht$ID$HxL9t0H=U HWt PJHЅ
+Ht$I$HxL9t0H= HWt PJHЅ
+Ht$LH[]A\A]A^ÐSHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$4HLH[AVAUATIUSHH H H + I$ I$ I|$hHtI|$PHtI\$0L-t Lt$HtYH{H+HtHCHxL9t.H= HWt PJHЅLfHH{ID$(I|$ H 1ID$8 ID$0 I|$ GID$HxL9t0H=i HWt PJHЅ
+Ht$ID$HxL9t0H=+ HWt PJHЅ
+Ht$I$HxL9t0H= HWt PJHЅ
+Ht$nLH[]A\A]A^ÐAVAUATIUSHH H H ] I$ I$ H I|$hHt)I|$PHtI\$0L- Lt$HtYH{H+HtHCHxL9t.H= HWt PJHЅLHHID$(I|$ H 1ID$8 ID$0 I|$ yID$HxL9t0H= HWt PJHЅ
+Ht$ID$HxL9t0H=] HWt PJHЅ
+Ht$I$HxL9t0H= HWt PJHЅ
+Ht$LH[]A\A]A^ÐAVAUATIUSHH H H I$ I$ z I|$hHt[I|$PHtLI\$0L- Lt$HtYH{H+Ht%HCHxL9t.H=H HWt PJHЅLHHID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H= HWt PJHЅ
+Ht$MID$HxL9t0H= HWt PJHЅ
+Ht$I$HxL9t0H=R HWt PJHЅ
+Ht$LH[]A\A]A^ÐAVAUATIUSHH H H I$ I$ I|$hHtI|$PHt~I\$0L-
+ Lt$HtYH{H+HtWHCHxL9t.H=z HWt PJHЅLHHID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H= HWt PJHЅ
+Ht$ID$HxL9t0H= HWt PJHЅ
+Ht$AI$HxL9t0H= HWt PJHЅ
+Ht$L H[]A\A]A^ÐSHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$HH[AVAUATIUSHH H H I$ I$ I|$hHtcI|$PHtTI\$0L- Lt$HtYH{H+Ht-HCHxL9t.H=P HWt PJHЅLHHID$(I|$ H 1ID$8 ID$0 I|$ ID$HxL9t0H= HWt PJHЅ
+Ht$UID$HxL9t0H= HWt PJHЅ
+Ht$I$HxL9t0H=Z HWt PJHЅ
+Ht$LH[]A\A]A^ÐSHHHtIHGHxH;=` t0H= HWt PJHЅ
+Ht$vHH[HSHt蚻D H[q[ÐAWAVAUIATUSHHN LgM# ID$xH- HxH9t0H=b HWt PJHЅ
+Ht$I\$XL|$HtTH{L37$_ HCHxH9t.H=
+ HWt PJHЅLHLID$PI|$HL|$H 1ID$` ID$X I|$HhMt$8I\$0I9t@HHxH9t.H=} HWt PJHЅLHI|$0Ht
+Mt$ I\$L|$I9t@HHxH9t.H= HWt PJHЅLHI|$HtID$HxH9t0H= HWt PJHЅ
+Ht$II$HxH9t0H= HWt PJHЅ
+Ht$ L$I} IE HtLH[]A\A]A^A_ÐSHHHtIHGHxH;=r t0H= HWt PJHЅ
+Ht$HH[AWAVAUIATUSHHN LgM# ID$xH- HxH9t0H= HWt PJHЅ
+Ht$I\$XL|$HtTH{L3c!_ HCHxH9t.H=6 HWt PJHЅLHLID$PI|$HL|$H 1ID$` ID$X I|$HMt$8I\$0I9t@HHxH9t.H= HWt PJHЅL+HI|$0Ht6Mt$ I\$L|$I9t@HHxH9t.H=F HWt PJHЅLHI|$HtID$HxH9t0H= HWt PJHЅ
+Ht$uI$HxH9t0H= HWt PJHЅ
+Ht$8LPI} IE Ht:L2H[]A\A]A^A_ÐAVAUATIUSHH H H I$ I$ I|$hHtI|$PHtI\$0L-P Lt$HtYH{H+HtHCHxL9t.H= HWt PJHЅLBHHWID$(I|$ H 1ID$8 ID$0 I|$ #ID$HxL9t0H=E HWt PJHЅ
+Ht$ID$HxL9t0H= HWt PJHЅ
+Ht$I$HxL9t0H= HWt PJHЅ
+Ht$JLbH[]A\A]A^ÐSHHHtIHGHxH;= t0H=f HWt PJHЅ
+Ht$HH[SHHHtIHGHxH;=t t0H=
+ HWt PJHЅ
+Ht$HH[SHHHtIHGHxH;= t0H= HWt PJHЅ
+Ht$.HFH[SHHHtIHGHxH;=ߡ t0H=R HWt PJHЅ
+Ht$HH[SHHHtIHGHxH;=`ߡ t0H= HWt PJHЅ
+Ht$vHH[AUATUHSHHHHFH6Hx 訖K HIHl t
+HE HPLHD$ Hl$0M:I LILd$ M:I HT$ H
l H5l L
&zg HMH$HqF H H*M HD$0LHxHHH[]A\A]HHD$0LHxyH!AT USH Hl$H]m H58Gm HHI HHLd$!H: LHHHݡ HHCHD$HxH B H5 H=HHD$LHxHHHtSHHHtIHGHxH;=ݡ t0H=ߡ HWt PJHЅ
+Ht$HH[SHHHtIHGHxH;=$ݡ t0H=ޡ HWt PJHЅ
+Ht$:HRH[SHHHtIHGHxH;=ܡ t0H=^ޡ HWt PJHЅ
+Ht$HH[SHGHHt HcW9}
JHDO4;O uC HߍpJa HCH; _ HcSHKrsHD[AUATUHSRHHt1Hu,D Lh1A9~I| HtHPHH} SHE X[]A\A]ÐAUATUHSRHHt1Hu,D Lh1A9~I| HtHPHH} HE X[]A\A]ÐAUATUHSRHHt1Hu,D Lh1A9~I| HtHPHH} HE X[]A\A]ÐAUATUHSRHHt1Hu,D Lh1A9~I| HtHPHH} WHE X[]A\A]ÐAUATUHSRHHt1Hu,D Lh1A9~I| HtHPHH} HE X[]A\A]ÐAUATUHSRHHt1Hu,D Lh1A9~I| HtHPHH} HE X[]A\A]f. D H= HH=9ܡ H H5 HfH= HH= ܡ Hꀠ H5 HfH= HH=ۡ H H5t HfH=c HPH=ۡ H H5E HRfH= H H=yۡ HZ H5c &H? 8 u H, HHf. fH=q HH=ۡ H H5S HfH=B HH=ڡ H H5$ HfH= H`H=ڡ H H5 HbfH= H0H=ڡ Hj H5 H2fH= H H=Yڡ H: H5 HfH= HH=)ڡ H
+ H5h HfH=W HH=١ H~ H59 HfH=( HpH=١ H~ H5
+ HrfH= H@H=١ Hz~ H5 HBfAUH= ATUSH8
+H=c١ HD~ H5 H\$ HT$ H5jg HHT$
H{H5jg rH{HT$H5jg ] H] HZ HW bIH8 H@LcLH6 f D Ht HHPHHL9uHD$ H\$H- HHxHD$HHxuHD$HHxdH=r H>} H5
+H8[]A\A]H\$HH= HtfHD$ HHxHD$HHxHD$HHxHH\$H.I9tIE HIHxHvzH=" H@H=ס Hz| H5 HBfH= HH=iס HJ| H5 HfH= HH=9ס H| H5 HfH= HH= ס H{ H5w HfH=f HH=֡ H{ H5H HfH=7 HPH=֡ H{ H5 HRfATH= USH H=u֡ HV{ H5 Hl$ HT$
H5gg HH}HT$H5gg H H H IH H@LH Ht HHHD$ HHH9uHD$H J H\$HHxHD$HHxH=o Hzz H5 FH []A\Hl$HH= HtHD$HHxSHD$HHxBHHl$H}L9tI$HIHx=H H= HH=ԡ Hy H5t HfH=c H`H=ԡ Hy H5E HbfH=4 H0H=ԡ Hjy H5 H2fH= H H=Yԡ H:y H5 HfH= HH=)ԡ H
+y H5 HfH= HH=ӡ Hx H5 HfH=x HpH=ӡ Hx H5Z HrfH=I H@H=ӡ Hzx H5+ HBfH= HH=iӡ HJx H5 HfH= HH=9ӡ Hx H5 HfH= HH= ӡ Hw H5 HfH= HH=ҡ Hw H5o HfH=^ HPH=ҡ Hw H5@ HRfH=/ H H=yҡ HZw H5 H"fH= HH=Iҡ H*w H5 HfH= HH=ҡ Hv H5 HfAVH= AUATUSH H=ѡ Hv H5 H\$Ll$
Lt$H5`cg LHH{H5Zcg LHL$ H=^ H 4
+ HD$Hl$HHxkHD$HHxZL%k H4v H5 LH5bg LH{H=ߡ LH 44
+ HD$HHxHu H5ߡ LH []A\A]A^HHD$H\$HHxHD$HHxH^HHD$HHxHBfH=rߡ H H=yС HZu H5Tߡ H"fATH=_ߡ USH H=EС H&u H5?ߡ Hl$HT$
H5ag HiH}HT$H5ag T Hޡ Hޡ Hޡ YIHޡ H@LHޡ Ht HHPHD$ HHH9uHD$H ޡ H\$HHxHD$HHxpH=i HJt H5Kޡ H []A\Hl$HH=.ޡ HttHD$HHx#HD$HHxHHl$HML9tI$HIHx
H AUH=ݡ ATUSH8ZH=Ρ Hs H5ݡ H\$[HT$ H57`g HHT$
H{H5/`g H{HT$H5(`g H]ݡ HZݡ HWݡ IH8ݡ H@LcLH6ݡ f D Ht HHHHL9uHD$ H\$H-ܡ HHxHD$HHxHD$HHxH=g Hr H5ܡ ZH8[]A\A]H\$HH=ܡ HtHD$ HHxeHD$HHxTHD$HHxCHH\$H~I9tIE HIHx>HzH="ܡ HH=̡ Hq H5ܡ HfH=ۡ H`H=̡ Hq H5ۡ HbfH=ۡ H0H=̡ Hjq H5ۡ H2fH=ۡ H H=Y̡ H:q H5ۡ HfH=ۡ HH=)̡ H
+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
+_ H5Eˡ HҳfH=4ˡ H蠸H= H^ H5ˡ H颳fH=ˡ HpH=ɹ H^ H5ʡ HrfH=ʡ H@H= Hz^ H5ʡ HBfH=ʡ HH=i HJ^ H5ʡ HfH=xʡ HH=9 H^ H5Zʡ HfH=Iʡ H谷H= H] H5+ʡ H鲲fH=ʡ H耷H=ٸ H] H5ɡ H邲fH=ɡ HPH= H] H5ɡ HRfH=ɡ H H=y HZ] H5ɡ H"fH=ɡ HH=I H*] H5oɡ HfH=^ɡ HH= H\ H5@ɡ H±fH=/ɡ H萶H=鷡 H\ H5ɡ H钱fH= ɡ H`H= H\ H5ȡ HbfH=ȡ H0H= Hj\ H5ȡ H2fH=ȡ H H=Y H:\ H5ȡ HfH=sȡ HеH=) H
+\ H5Uȡ HҰfH=Dȡ H蠵H= H[ H5&ȡ H颰fH=ȡ HpH=ɶ H[ H5ǡ HrfH=ǡ H@H= Hz[ H5ǡ HBfH=ǡ HH=i HJ[ H5ǡ HfH=ǡ HH=9 H[ H5jǡ HfH=Yǡ H谴H= HZ H5;ǡ H鲯fH=*ǡ H耴H=ٵ HZ H5 ǡ H邯fH=ơ HPH= HZ H5ơ HRfH=ơ H H=y HZZ H5ơ H"fH=ơ HH=I H*Z H5ơ HfH=nơ HH= HY H5Pơ H®fH=?ơ H萳H=鴡 HY H5!ơ H钮fH=ơ H`H= HY H5š HbfH=š H0H= HjY H5š H2fH=š H H=Y H:Y H5š HfH=š HвH=) H
+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
+V H5u¡ HҪfH=d¡ H蠯H= HU H5F¡ H颪fH=5¡ HpH=ɰ HU H5¡ HrfH=¡ H@H= HzU H5 HBfH= HH=i HJU H5 HfH= HH=9 HU H5 HfH=y H谮H= HT H5[ H鲩fH=J H耮H=ٯ HT H5, H邩fH= HPH= HT H5 HRfH= H H=y HZT H5 H"fH= HH=I H*T H5 HfH= HH= HS H5p H¨fH=_ H萭H=鮡 HS H5A H钨fH=0 H`H= HS H5 HbfH= H0H= HjS H5㿡 H2fH=ҿ H H=Y H:S H5 HfH= HЬH=) H
+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=
á H蠦H= HL H5¡ H颡fH=¡ HpH=ɧ HL H5¡ HrfH=¡ H@H= HzL H5¡ HBfH=¡ HH=i HJL H5b¡ HfH=Q¡ HH=9 HL H53¡ HfH="¡ H谥H= HK H5¡ H鲠fH= H耥H=٦ HK H5 H邠fHWf H Hf. H= H0H= HjK H5 H2fH= H H=Y H:K H5| HfH=k HФH=) H
+K H5M HҟfH=< H蠤H= HJ H5 H颟fH=
HpH=ɥ HJ H5 HrfH= H@H= HzJ H5 HBfH= HH=i HJJ H5 HfH= HH=9 HJ H5b HfH=Q H谣H= HI H53 H鲞fH=" H耣H=٤ HI H5 H邞fH= HPH= HI H5տ HRfH=Ŀ H H=y HZI H5 H"fH= HH=I H*I H5w HfH=f HH= HH H5H HfH=7 H萢H=飡 HH H5 H钝fH=Qá H`H= HH H53á HbfH=á H0H= HjH H5á H2fH=á H H=Y H:H H5dá HfH=qá HСH=) H
+H H5Sá HҜfH=Bá H蠡H= HG H5$á H颜fH=á HpH=ɢ HG H5¡ HrfH=¡ H@H= HzG H5¡ HBfH=¡ HH=i HJG H5¡ HfH=¡ HH=9 HG H5h¡ HfH=W¡ H谠H= HF H59¡ H鲛fH=¡ H耠H=١ HF H5¡ H邛fH= á TM @ H=1ġ H@H= HzF H5ġ HBfH=!ġ HH=i HJF H5ġ HfH=ġ HH=9 HF H5á HfH=Aš H谟H= HE H5#š H鲚fH=aš H耟H=٠ HE H5Cš H邚fH=Qš HPH= HE H53š HRfH=yš H H=y HZE H5[š H"fH=š HH=I H*E H5š HfH=yǡ HH= HD H5[ǡ HfH=ȡ H萞H=韡 HD H5ȡ H钙fSH=ɡ cH= HD H5vɡ i HIɡ HFɡ HCɡ H
Ln HPH ɡ H=կV H5ɡ Hɡ HH
uLn HHH
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+C H5Sɡ HҗfH=ɡ H蠜H= HB H5ɡ H颗fH=ɡ HpH=ɝ HB H5ɡ HrfH=ɡ H@H= HzB H5ɡ HBfH=Qʡ HH=i HJB H53ʡ HfH=iʡ HH=9 HB H5Kʡ HfH=iˡ H谛H= HA H5Kˡ H鲖fH=̡ H耛H=ٜ HA H5ˡ H邖fH=ˡ HPH= HA H5ˡ HRfH=ˡ H H=y HZA H5ˡ H"fH=̡ HH=I H*A H5ˡ HfH=ˡ HH= H@ H5ˡ HfH=ˡ H萚H=雡 H@ H5ˡ H钕fH=ˡ H`H= H@ H5{ˡ HbfH=mˡ H0H= Hj@ H5Oˡ H2fH=١ H H=Y H:@ H5١ Ho8o Hס ס Hס ס Hס H<8o Hס ס Hס Hס H8o ס
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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
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+ Lt$ LHL$ L詾 @ AA@ AA@f D HD$0HHxw ) @ Hx H HsPLL HH=XH[ 諵Lt$ HI<$LH蓴H諿H3 H=!H[ tH=伕 HW IHt$ i:H$A@ I AD H$A@ I AD HHD$ Ht$ HxRH{I7HEHl$0LL|0H H{XHH$ \HdH띋PJHBf. AVAUATIUSHH H H 赽I$ I$ 蠽I|$hHt聶I|$PHtrI\$0Lt$HtMH= L-칕 u
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+ HH H{@C C C@ ?HHHCHC
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+ HH H{@C C C@ ?HHHCHC^
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- AWIAVAUATUSHHH H$ HHT$PH$ lkLHD$@/kLH$ /kLH$ /kLH$ OkLHD$8jIH$ LIHHD$ L1Hט3 H$ H$ HtHCHx蠡3 HhHH+h@HCHt$8HDŽ$ HDŽ$ HDŽ$ HDŽ$ HD$xH$ H$ HDŽ$ HHDŽ$ HD$phHt$8H|$31H|$8 HDŽ$ H$ *F H|$pf|H$ HH9D$8H$ ~ HL$@HHH< tH|$/|H$ H+$ HH HD$PHxo Hf$ tH$ HH$ H H0H LL$xHL$pH$ L$ o(, H$ H$ HH$ t蝑 H$ HtBH$ Ht0 H$ HD$0 Ƅ$ H$ $ $ H$ HD$hH$0 H$ H$ H$ L|$8H$ HDŽ$ HDŽ$ HDŽ$ Lg1MH \ @ HH;\$8tMHD$@HL$0H H9 uH$ H;$ HtHHHH;\$8H$ u D Ht$0$ 9$ DH$ $ L(HvM| H$ H HHH$ ' H$ H LhH$ L4 LH4 H9 H$ Hx4 LH4 H9: IEIOIU HD$IH)T$H|$H)HH9L$ H$ H$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HL$H)t$H|$H|$ HDŽ$ HDŽ$ HDŽ$0 HDŽ$8 HDŽ$@ HL$ H$p HD$` HD$X HD$( HDŽ$ E1HD$H HD$ 1H$ G D tyHt$H$ H$ bHl$XHl$X HH;\$tuIU IH,L$HIL9t$vH$ J; t Ht$H$ H$ Ld$`HH;\$Ld$`u H$ H$ H+$ H+$ H$ H+$ HHHH$8 H+$0 HHHH$P HDŽ$P H4HDŽ$X HDŽ$` HbH$ H$ HH$X H$ H$ HH$X H$ H$ HH$X H$8 H$0 HH$X rHD$HH Hx H+xHHPi
HD$PHxo Hf$ tzHD$HH HPHLD$H$P H), H|$HL$P HDŽ$P Ht`H$P Ht`H$ HtH$ H$ H$p H+$ H+$ HHDŽ$p HDŽ$x HDŽ$ HHH4H$ H+$ HHH$8 H+$0 HH`H$ H$ HH$x H$ H$ HH$x H$8 H$0 HH$x H$ H$ HH$x H$ H Hx H+xHHg HD$PHxo Hf$ tLxH$ H HPHLD$H$@ H (, H$ L$@ HDŽ$@ Ht_H$@ Ht_H$ Ht5H$ H$ H$ H+$ H+$ HHDŽ$ HDŽ$ HDŽ$ HHH4H$ H+$ HHH$8 H+$0 HH_H$ H$ 1H)HHt=f HIE HH4\sH$ H$ HH)HH9rH$ H$ 1H)HHt:f D HIE HH4sH$ H$ HH)HH9rH$ H$ 1HH)HHu:N f D HtH HH$ HHH)HH9 H$ H;$ HDŽ$ uHt$HWH$ H$ @ L9
Ht$H|$hH$ 8Hl$(Hl$( IT , HL$PHAoHf$ tHPH= 9 H$ HDŽ$ HDŽ$ HDŽ$ Ht$pHh HH$ H$ LL$xLD$H$ H$ H$ LH$ , H$ H$p HDŽ$p Ht[H$p Ht[H$ HtH$ HtH$ @ L9
- Hl$ Hl$ w hHL$PHAoHf$ tHPH=a H$ HDŽ$ HDŽ$ HDŽ$ QHt$pHh HH$ H$ [LL$xLD$H$ H$ H$ LH$ , Ld$HH$ HDŽ$ Mt-LÓ4 LL$ MtL覓4 LnH$ Ht\H$ Ht芅Hl$H0H$0 H$8 H)HH 1f D HIHH45oH$0 H$8 HH)HH9rH$ H+$ H$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ + H$ H$ H$ H+$ H+$ HDŽ$ HHHHH4HT$[H$ H$ H)HH L$ H1f D H IHH9uH$ H$ H)HH HL$ HHH<HHJH4H9IuH$ H$ H)HHM HL$ HHH< D HHHH4H9IuH$0 H$8 H)HHt4HL$ HHH4 f HHBH H9IuHD$PHxo Hf$ tpH\$(HD$ L$P HL$`Ht$ LH$ H$ H$ WHt$XL$ MH$ HD$ H$0 H$ Ht$pHMEH$ WH$ HD$xMH$ L$ MLHIEH$>, H$0 HtH$P HtH$ Ht$H$ HHaH$ HQaH$ Ht$ H$ H+$ H$ HHm^ HD$PHxo Hf$ t5oLD$H$ H$0 HH , H$ H$0 HDŽ$0 HH$ tVH$0 HtUH$ Ht# H$ H$ HDŽ$ HtH$ HtH$ HtH$p HtH$P HtrH$0 Ht`H$ HtNH$ Ht<MtL/UMtL"UH$ tL$ L74 LH$ HtHD$0D$09$ H$ f L$ LPWH$ HH-LWLL$PH$ H$ H$ IH轩H$ H|WrfH$ H$ H$ H+$ H+$ HHHHHH4H$ H$ HH$ NH$ H$ HH$ .H$ H$ HH$ ~H$8 H$0 HH$ L$ Lt$HH+$ A HH LSIH$ H$ MH)HH ItI HtHLH$ IHtKD L$ L$ H$ jH$ H H H$ HxII+?HI}I+} H2H$ HL$0H HH$ LH$ FLH$ L$ L99 I 1e1HHHH$ H$ H HtH+4 HH$ HH []A\A]A^A_H$ H HHH$ ]H VH$ LHH$ H$ rTH$ HH LL$PL$ HH$ Ht$蜦H$ Ht:H$ H$ HH tH$ HtH$ 11HD$` HD$X HD$( HDŽ$ HD$H HD$ {H$ H H0X L$ H$ H5W LIH$ HY= H$ H
zW H5kW Iغ HDHW LHLOHOHNNH$ Ht$HxH_H5:X LX L$` `H$ H5W LIH$p H= H$P H
W H5W Iغ HPCHW LHL OHcNHMH$` Ht$HxH^H5W L_HH$0 Ht*H$ HtH$ HtH$ HtNH|$HHtNH$ HtH$ HtH9IHLMHHLH$ Ht$HxJLRAHH D HH$ HHtyH$P H>HH$p Ht$Lt$HH$ HHtHyHH$ HtL$ HH$ HtH$ HtHH$ HtxHH$P HtH$0 HHt{HH$ HtdMHDŽ$ oH$ HHt>H$ HHtgxH$ HHuI9H$ HH0&HHDŽ$ H$ HtH$ Hǃ@^H$ HHtH$ HCw9 D H@fH$ HHuH$ HHOnwEX L$ H$ H5W LH H$ H= L|$pH
W H5W Iغe L?HW LLHEKLJHIH$ Ht$HxOH[H5S HX L$ H$
H5W LImH$ H-= H$ H
NW H5?W Ir H>HYW LHLJHIH"IH$ Ht$HxHUZH5S LH|$pIIHLHH$ Ht$HxXH`f D H X L$ H$ H5W LH_H$0 H = L|$H
CW H54W Iغd L=HW LLHILHHHH$ LHxHLYH5R HH$ HH:<H$ HHHHÐHIIHLHHHGH$ Ht$HxLHHH D HIHLGHH0GH$` Ht$Hx[HHhH|$IGHLFH$ Ht$pHxh IH @ AWIAVAUATUSHHH H$ HHT$PH$ <KLHD$@JLH$ JLH$ JLH$ KLHD$8JIH$ LIHHD$L1Hx3 H$ H$ HtHCHxp3 HhHH+h@HCHt$8HDŽ$ HDŽ$ HDŽ$ HDŽ$ HD$xH$ H$ HDŽ$ HHDŽ$ HD$pHHt$8H|$1H|$8 HDŽ$ H$ *F H|$p6\H$ HH9D$8H$ ~ Ht$@HHH< tH|$[H$ H+$ HHe HD$PHxo Hf$ tp_H$ HH$ H H0H LL$xHL$pH$ L$
-, H$ H$ HH$ tmq H$ HtH$ Ht $ H$ HD$0 Ƅ$ H$ $ $ H$ HD$hH$0 H$ H$ H$ L|$8H$ HDŽ$ HDŽ$ HDŽ$ LF1MH \ @ HH;\$8tMHD$@Ht$0H H94uH$ H;$ HtHHHH;\$8H$ u D Ht$0$ 9$ DH$ @$ L(HvM| H$ H HHH$ B H$ H LhH$ L4 LH4 H9 H$ Hxv4 LHk4 H9 IEIOIU HD$IH)T$H|$H)HH9L$- H$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ Ht$H$ H)t$HDŽ$ H|$H|$ HDŽ$0 HDŽ$8 HDŽ$@ HL$ H$p HD$` HD$X HD$ HDŽ$ E1HD$H HD$( 1H$ F @ tyHt$H$ H$ 2}Hl$XHl$X HH;\$tuIU IH,L$HIL9t$vH$ J; t Ht$H$ H$ |Ld$`HH;\$Ld$`u H$ H$ H+$ H+$ H$ H+$ HHHH$8 H+$0 HHHH$P HDŽ$P H4HDŽ$X HDŽ$` HBH$ H$ HH$X 袨H$ H$ HH$X 肨H$ H$ HH$X bH$8 H$0 HH$X BHD$HH Hx H+xHH I HD$PHxo Hf$ tYHD$HH HPHLD$H$P H , H|$HL$P HDŽ$P Ht@H$P Ht@H$ HtkH$ H$ H$p H+$ H+$ HHDŽ$p HDŽ$x HDŽ$ HHH4H$ H+$ HHH$8 H+$0 HH@H$ H$ HH$x ٦H$ H$ HH$x 蹩H$8 H$0 HH$x 虦H$ H$ HH$x yH$ H Hx H+xHHTG HD$PHxo Hf$ t XH$ H HPHLD$H$@ H, H$ H$@ HDŽ$@ HHD$t>H$@ Ht>H$ Ht jH$ H$ H+$ H+$ HDŽ$ HDŽ$ HDŽ$ HHH4H$ H+$ HHH$8 H+$0 HHH$ HHD$?H$ H$ HH)HH H$ L$ 1;f D HHM tHHHH$ HHH)HH9 HH4IM L9H,uH+$ HH L$ L9H HD$HH=H$ H$ IHE LH)HH HtHE HtHL蟽H$ HHtHD$HL$ H$ H$ H$ MHL$ L91 Ht$H|$hH$ wHl$ Hl$ IT , Ht$PHFoHf$ tHPH= I H$ HDŽ$ HDŽ$ HDŽ$ uHt$pHh HH$ H$ LL$xLD$H$ H$ H$ LH$ , H$ H$p HDŽ$p Ht;H$p Ht;H$ Ht蓶H$ HtfH$ @ L9 Hl$(Hl$(w hHt$PHFoHf$ tHPH=1 H$ HDŽ$ HDŽ$ HDŽ$ !Ht$pHh HH$ H$ +LL$xLD$H$ H$ H$ LH$ + Ld$HH$ HDŽ$ Mt-Ls4 L[L$ MtLvs4 L>H$ Ht,H$ HtZeHl$H0L$ H$ LH)HH- H$ H$ 1>f HHM tHHHH$ LHH)HH9 HH4IM H9H,uH+$ HH L$ L9H HD$HH9H$ H$ IHE LH)HH HtHE HtHLH$ HHtHD$HL$ H$ H$ L$ I<HH$ H$ H$ 1HH)HHu2u D HtH HH$ HHH)HH9sEH$ H;$ HDŽ$ uHt$H|$2H$ H$ f D H$8 H$0 HH)HH H$ H$ 16HHM tHHHH$ HHH)HH9 HLIH9J,uH+$ A HH L7H$ H$ IHE LH)HH HtHE HtHL趷H$ HHt0K| L$ H$ H$0 H$8 HH$ & H$ H+$ H$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ H$ H$ H$ H+$ H+$ HDŽ$ HHHHH4HT$nL$ H$ L)HHD L$ H1
- HHHI4H9IuH$ H$ H)HH HL$ HHH< D HHJH4H9IuH$ H$ H)HH HL$ HHH< D HHHH4H9IuH$0 H$8 H)HHt,HL$ HHH4HHBH H9IuHD$PHxo Hf$ tML|$ Ll$(H$P HD$`Ht$ HL$ L$ H$ 4HD$H$ L$0 Ht$p L$ LL$ HHEHD$XH$ 4H$ HD$xMH$ L$ IHLIEH$ , H$0 HtH$P HtH$ Ht4_H$ Ht$HoH$ H_>H$ Ht轮$ H$ H+$ H$ HH{; HD$PHxo Hf$ tCLLD$HT$H$0 H$ H!+ H$ H$0 HDŽ$0 HH$ t3H$0 Ht3H$ Ht /^ H$ H$ HDŽ$ HtH$ Ht谭H$ Ht螭H$p Ht茭H$P HtzH$0 HthH$ HtVH$ HtDH|$Ht52MtL(2H$ tL$ L=k4 LH$ HtHD$0D$09$ H$ ? A Ld$HL_ D A Ld$HL' D L$ L9 I @ H$ H$ H$ H+$ H+$ HHHHHH4H$ H$ HH$ vH$ H$ HH$ VH$ H$ HH$ 覣H$8 H$0 HH$ L$ L@3H$ HH Lu3LL$PH$ H$ H$ IH荋H$ HdGZH$ HD$pLt$HH+$ A HH L30IH$ H$ MH)HH ItI HtHL6H$ IHt谪KD L$ L$ H$ NH$ H H H$ HxII+?HI}I+} HH$ Ht$0H HH$ LH$ *LH$ L$ L9 I 1HH61H$ H$ H HtHg4 H觩H$ HH []A\A]A^A_11HD$` HD$X HD$ HDŽ$ HD$H HD$( >H$ H H0HH$ H H$ LHH$ H$ 0H$ HH LL$PL$ HH$ Ht$H$ Ht謨H$ H$ HH t苨H$ HtyH$ H$ H H0X L$ ǣH$ H5W LIMH$ H
= H$ H
W H5 W Iغ H HQW LHLr,H+H+H$ Ht$Hx|H5<H54 LƭX L$` H$ H5=W LI蚨H$p HZ= H$P H
[W H5lW Iغ H HW LHL+H+HO*H$` Ht$HxɣH;H5;4 LHH$0 HtަH$ HțH$ Ht躦H$ Ht+H|$HHt+H$ Ht臦H$ HtHIHLO*HH)H$ Ht$HxLAHHHH$ HHtMVH$P H HH$p HtݥLt$HH$ HHtVHH$ Ht誥HD$H$ I9H I9H
HH$ HtVH$ HtDHH$ HtmUHH$P HtH$0 HHtHH$ Ht)HDŽ$ oH$ HHt¤H$ HHtTH I9 H$ HHuI9vf D `@KH$ HHtMH$ HLwTBfH@fH$ HHuH$ HHt4THDŽ$ H$ Ht֣H$ H99HH$ HH2袣(ff. HX L$ H$ H5W LHxH$0 H8= L|$H
<W H5MW Iغd LHުW LLH'L&H0&H$ LHx謟He7H5 0 HH|$I&HL%H$ Ht$pHxhHpIH HiX L$ H$ H5W LHxH$ H8= L|$pH
<W H5MW Iغe LHW LLH&L%H0%H$ Ht$Hx語Hc6H5 / HX L$ BH$
H5W LIȢH$ H舰= H$ H
W H5W Ir H2HW LHL%HE%H}$H$ Ht$HxH5H5i. LAH$ HH:.}IHL$H#$H$ Ht$Hx蝝L襞7HHHH$ HH訠HIHL}$HH#H$` Ht$Hx,)HH1H|$pI;$HLp#H$ Ht$Hx}IHf D AWIAVAUATUSHHH H$ HHT$PH$ 'LHD$@'LH$ 'LH$ 'LH$ 'LHD$82'IH$ LIHHD$L1H'U3 H$ H$ HtMHCHx]3 HhHH+h@HCHt$8HDŽ$ HDŽ$ HDŽ$ HDŽ$ HD$xH$ H$ HDŽ$ HHDŽ$ HD$pB%Ht$8H|$胅1H|$8 HDŽ$ H$ *F H|$p8H$ HH9D$8H$ ~ Ht$@HHH< tH|$8H$ H+$ HHe HD$PHxo Hf$ t;H$ HH$ H H0H LL$xHL$pH$ L$ _+ H$ H$ HH$ tM H$ Ht蒝H$ Ht耝$ H$ HD$0 Ƅ$ H$ $ $ H$ HD$hH$0 H$ H$ H$ L|$8H$ HDŽ$ HDŽ$ HDŽ$ LW#1MH \ @ HH;\$8tMHD$@Ht$0H H94uH$ H;$ HtHHHH;\$8H$ u D Ht$0$ 9$ DH$ @$ L(HvM| H$ H HHH$ B H$ H LhH$ L4 LH4 H9 H$ Hx4 LH4 H9 IEIOIU HD$IH)T$H|$H)HH9L$- H$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ Ht$H$ H)t$HDŽ$ H|$H|$ HDŽ$0 HDŽ$8 HDŽ$@ HL$ H$p HD$` HD$X HD$ HDŽ$ E1HD$H HD$( 1H$ F @ tyHt$H$ H$ YHl$XHl$X HH;\$tuIU IH,L$HIL9t$vH$ J; t Ht$H$ H$ 9YLd$`HH;\$Ld$`u H$ H$ H+$ H+$ H$ H+$ HHHH$8 H+$0 HHHH$P HDŽ$P H4HDŽ$X HDŽ$` H H$ H$ HH$X "H$ H$ HH$X H$ H$ HH$X H$8 H$0 HH$X HD$HH Hx H+xHH% HD$PHxo Hf$ th6HD$HH HPHLD$H$P H>+ H|$HL$P HDŽ$P Ht; H$P Ht) H$ HtWHH$ H$ H$p H+$ H+$ HHDŽ$p HDŽ$x HDŽ$ HHH4H$ H+$ HHH$8 H+$0 HHI H$ H$ HH$x YH$ H$ HH$x 9H$8 H$0 HH$x H$ H$ HH$x H$ H Hx H+xHH# HD$PHxo Hf$ t4H$ H HPHLD$H$@ Ho+ H$ H$@ HDŽ$@ HHD$tdH$@ HtRH$ HtFH$ H$ H+$ H+$ HDŽ$ HDŽ$ HDŽ$ HHH4H$ H+$ HHH$8 H+$0 HHH$ HHD$- H$ H$ HH)HH H$ L$ 1;f D HHM tHHHH$ HHH)HH9 HH4IM L9H,uH+$ HH L$ L9H HD$HH!H$ H$ IHE LH)HH HtHE HtHL H$ HHt虔HD$HL$ H$ H$ H$ MHL$ L91 Ht$H|$hH$ SHl$ Hl$ IT , Ht$PHFoHf$ tHPH=s I H$ HDŽ$ HDŽ$ HDŽ$ Ht$pHh HH$ H$ LL$xLD$H$ H$ H$ LH$ + H$ H$p HDŽ$p Ht7H$p Ht%H$ HtH$ HtACH$ @ L9 Hl$(Hl$(w hHt$PHFoHf$ tHPH=q H$ HDŽ$ HDŽ$ HDŽ$ 衒Ht$pHh HH$ H$ 諗LL$xLD$H$ H$ H$ LH$ + Ld$HH$ HDŽ$ Mt-LP4 LۑL$ MtLO4 L辑H$ Ht謑H$ HtAHl$H0L$ H$ LH)HH- H$ H$ 1>f HHM tHHHH$ LHH)HH9 HH4IM H9H,uH+$ HH L$ L9H HD$HHH$ H$ IHE LH)HH HtHE HtHLH$ HHtyHD$HL$ H$ H$ L$ I<HH$ H$ H$ 1HH)HHu2u D HtH HH$ HHH)HH9sEH$ H;$ HDŽ$ uHt$H|$PH$ H$ f D H$8 H$0 HH)HH H$ H$ 16HHM tHHHH$ HHH)HH9 HLIH9J,uH+$ A HH L8H$ H$ IHE LH)HH HtHE HtHL6H$ HHt谎K| L$ H$ H$0 H$8 HH$ & H$ H+$ H$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ H$ H$ H$ H+$ H+$ HDŽ$ HHHHH4HT$^L$ H$ L)HHD L$ H1
- HHHI4H9IuH$ H$ H)HH HL$ HHH< D HHJH4H9IuH$ H$ H)HH HL$ HHH< D HHHH4H9IuH$0 H$8 H)HHt,HL$ HHH4HHBH H9IuHD$PHxo Hf$ tL*L|$ Ll$(H$P HD$`Ht$ HL$ L$ H$ {HD$H$ L$0 Ht$p L$ LL$ HHEHD$XH$ 0H$ HD$xMH$ L$ IHLIEH$:+ H$0 Ht蘋H$P Ht膋H$ Ht;H$ Ht$HH$ HH$ Ht=$ H$ H+$ H$ HH HD$PHxo Hf$ t(LD$HT$H$0 H$ H+ H$ H$0 HDŽ$0 HH$ tH$0 HtH$ Ht : H$ H$ HDŽ$ HtBH$ Ht0H$ Ht H$p Ht H$P HtH$0 HtH$ Ht։H$ HtĉH|$HtMtLH$ tL$ LG4 L腉H$ HtsHD$0D$09$ H$ ? A Ld$HL_ D A Ld$HL' D L$ L9 I @ H$ H$ H$ H+$ H+$ HHHHHH4oH$ H$ HH$ {H$ H$ HH$ {H$ H$ HH$ &H$8 H$0 HH$ {L$ LH$ HH蝥LLL$PH$ H$ H$ IHmH$ HdLJZH$ HD$pLt$HH+$ A HH L IH$ H$ MH)HH ItI HtHL趌H$ IHt0KD L$ L$ H$ NH$ H H H$ HxII+?HI}I+} HH$ Ht$0H HH$ LH$ *LH$ L$ L9 I 1HH61H$ H$ H HtH_D4 H'H$ HH []A\A]A^A_11HD$` HD$X HD$ HDŽ$ HD$H HD$( >H$ H H0HH$ H H$ LHH$ H$ d
H$ HH LL$PL$ HH$ Ht$NkH$ Ht,H$ H$ HH t H$ HtH$ H$ H H0X L$ GH$ H5W LIͅH$ H荓= H$ H
nW H5W Iغ H7HуW LHLHJHH$ Ht$HxHH5n LFX L$` H$ H5W LIH$p Hڒ= H$P H
W H5W Iغ HH W LHL?HHH$` Ht$HxIHH5 L蓉HH$0 Ht^H$ HtLH$ Ht:H$ Ht(H|$HHtH$ HtH$ HtGHmIHLHHH$ Ht$Hx~L膀AHHHH$ HHt2H$P H wHH$p Ht]Lt$HH$ HHt2HH$ Ht*HD$H$ I9H I9H
HH$ HtցH$ HtāHH$ Ht1HH$P Ht薁H$0 HHtHH$ HthHDŽ$ oH$ HHtBH$ HHtk1H I9 H$ HHuI9vf D `@KH$ HHt̀H$ HL0BfH@fH$ HHuH$ HHt0HDŽ$ H$ HtVH$ H99HH$ HH2"(ff. HX L$ r{H$ H5cW LHH$0 H踎= L|$H
W H5W Iغd LeH^W LLH LxHH$ LHx,|HH5 HvH|$I9HLnH$ Ht$pHx{H|IH HiX L$ rzH$ H5cW LHH$ H踍= L|$pH
W H5~W Iغe LeHW LLH LxHH$ Ht$Hx*{HH5 HtX L$ yH$
H5cW LIHH$ H= H$ H
W H5~W Ir HH4}W LHLmHH H$ Ht$HxwzH0H5
- LH$ HH:.}IHLkH H$ Ht$Hx zL%{7HHHH$ HH(}HIHL HH2 H$` Ht$Hxy)HH1H|$pI HLH$ Ht$Hxjy}IHf D HHzHH9 HHHH)HLFI HpH9HrAH9@A M11IN o Hfoffs fpffpfb
+1HfGt H8[]A\A]A^A_ H H9 H HIH HI$Q A$P @AHfDG@HI$ AƄ$ @1HfO@NI|$Hl$/HՎID$HHxI<$HHtH HHl$/HHl$/HHl$/L9tIHIHx3L9uYHI|$HtRxf. AWH
+AVAUATUSHH9: H HGHHH)HHH9wH[]A\A]A^A_ @ LMI)ILH L4vILIL9LtXfHtDHB HB H HHH HKHBHJHCHKHBHJHCHHI9uL}H] I9Lt$f. H;Ht3HI9uH} Ht KdLm ID MLmHEH[]A\A]A^A_ D E1E1+H=MX f AWAVIAUATIUSH H\$0H$H{XH Ƅ$h HHDŽ$` Ƅ$i 1HDŽ$p HDŽ$x HHDŽ$ HDŽ$ H$ H HhLxH}Hl$0L?L- H{@HD$@ HD$H HD$P HD$X HD$` IEHD$h HD$0IE@H$ H HHD$8H HsH{XD$x HHD$8H) HH$ PM LLHHH$HH0bH HxHD$`I<$H
HT$PLt$XH9* L9 M@ L)HT$ 1HH$H<$IHHp H$HLLD$LD$HL;] W Lt$ LHL$ LHD$ HxH;=2 IEHD$0IE@H$ Hh HHD$8H$ HxH;= H_ H{@HHD$8HEHl$0H{XL|0H HH$ sHĘ L[]A\A]A^A_f D L9 M L)1HH$HT$ HmH<$IHHtWH$HLLD$LD$HL;4 H Lt$ LHL$ L @ AA@ AA@f D HD$0HHxw 1 @ HqH HsPL\ H!H=hyX Lt$ HI<$LHHHC H=1yX H= HW MHt$ y>H= HWtmHt$ MH$A@ I AD H$A@ I AD HHD$ Ht$ Hx&PJH덋PJHQH{I^hHEHl$0LL|0HV H{XHH$ !H)H @ AWH AVAUATUSHH9 H HGIH)HH9wH[]A\A]A^A_f. LwMI)IH H HHD$tHL9Ht(f Ht
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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
HHH;Ht$H LH<$Ht H D ATHiUSHH0HD$HBHD$(HFHD$ 謚0 HIqH5FKX LH>2 H5XW H002 H5ӻ\ H02 HH528V HB 12 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht#HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHjHKIHt
HHH;Ht贒H LH<$Ht蜒H D ATH'KUSHH0HD$HҝHD$(H֝HD$ <0 HIH5IX LH/=2 H5VZ H.2 H5c\ Hq/2 HH56V HB 02 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht賑HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtDH LH<$Ht,H D ATHcUSHH0HD$HbHD$(HfHD$ ̓0 HI葁H5fHX LH;2 H5W HP-2 H5\ H.2 HH5R5V HB .2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtCHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtԏH LH<$Ht輏H D ATHVUSHH0HD$HHD$(HHD$ \0 HI!H5FX LHO:2 H5`V H+2 H5\ H,2 HH53V HB 8-2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtӎHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtdH LH<$HtLH D ATHFUSHH0HD$HHD$(HHD$ 0 HI~H5EX LH82 H5Z Hp*2 H5\ H!+2 HH5r2V HB +2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtcHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtH LH<$Ht܌H D ATHGMUSHH0HD$HHD$(HHD$ |0 HIA}H5DX LHo72 H5FW H )2 H5\ H)2 HH51V HB X*2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HH:HKIHt
HHH;Ht脋H LH<$HtlH D ATHWbUSHH0HD$HHD$(HHD$ 0 HI{H5BX LH52 H5W H'2 H53\ HA(2 HH5/V HB (2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht胊HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;HtH LH<$HtH D ATHGaUSHH0HD$H2HD$(H6HD$ 蜋0 HIazH56AX LH42 H5HW H &2 H5ñ\ H&2 HH5".V HB x'2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHZHKIHt
HHH;Ht褈H LH<$Ht茈H D ATHVUSHH0HD$HHD$(HƓHD$ ,0 HIxH5?X LH 32 H5^>\ H$2 H5S\ Ha%2 HH5,V HB B &2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht蜇HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;Ht-H LH<$HtHf. @ ATH7ZUSHH0HD$HBHD$(HFHD$ 謉0 HIqwH5F>X LH12 H5{W H0#2 H5Ӯ\ H#2 HH52+V HB B $2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHcHKIHt
HHH;Ht譅H LH<$Ht蕅Hf. @ ATHKUSHH0HD$HHD$(HƐHD$ ,0 HIuH5<X LH 02 H50V H!2 H5S\ Ha"2 HH5)V HB B #2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht蜄HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;Ht-H LH<$HtHf. @ ATH7KUSHH0HD$HBHD$(HFHD$ 謆0 HIqtH5F;X LH.2 H5{] H0 2 H5ӫ\ H 2 HH52(V HB B !2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHcHKIHt
HHH;Ht譂H LH<$Ht蕂Hf. @ ATHJUSHH0HD$HHD$(HƍHD$ ,0 HIrH59X LH -2 H5] H 2 H5S\ Ha 2 HH5&V HB B 2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht蜁HD$ HtH|$ HH0H[]A\HHD$ HtH|$ HHHKIHt
HHH;Ht-H LH<$HtHf. @ AUHIATUSHH8HD$H@HD$(HDHD$ 誂0 I袁0 HIgqH5<8X LH+2 H5W LH+2 H5|uW H 2 H5\ H 2 HH5%V HB l 2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH8H[]A\A]HHD$ HtH|$ HHLHKIHt
HHH;HtH LH<$Ht~H AUHKATUSHH8HD$HHD$(HHD$ 0 I0 HIoH56X LH*2 H5tW LH)2 H5\ H2 H5'\ H5 2 HH5#V HB 2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Htw~HD$ HtH|$ HH8H[]A\A]HHD$ HtH|$ HHHKIHt
HHH;Ht~H LH<$Ht}H AUHKATUSHH8HD$H HD$(H$HD$ 0 I肀0 HIGnH5 5X LHu(2 H5W LHc(2 H5N\ H2 H5\ H2 HH5!V HB L2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht|HD$ HtH|$ HH8H[]A\A]HHD$ HtH|$ HH,HKIHt
HHH;Htv|H LH<$Ht^|H AUHg3ATUSHH8HD$HHD$(HHD$ }0 I}0 HIlH53X LH&2 H5T
W LH&2 H5pW Hd2 H5\ H2 HH5f V HB 2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtW{HD$ HtH|$ HH8H[]A\A]HHD$ HtH|$ HHHKIHt
HHH;HtzH LH<$HtzH AUHw1ATUSHH8HD$H HD$(HHD$ j|0 Ib|0 HI'kH51X LHU%2 H5 W LHC%2 H5DoW H2 H5w\ H2 HH5 V HB ,2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtyHD$ HtH|$ HH8H[]A\A]HHD$ HtH|$ HH HKIHt
HHH;HtVyH LH<$Ht>yH AUH/ATUSHH8HD$HpHD$(HtHD$ z0 Iz0 HIiH5l0X LH#2 H54
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HHH;HtwH LH<$HtwH AUH/ATUSHH8HD$HHD$(HHD$ Jy0 IBy0 HIhH5.X LH5"2 H5W LH#"2 H5,lW H2 H5W\ He2 HH5V HB 2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtvHD$ HtH|$ HH8H[]A\A]HHD$ HtH|$ HHHKIHt
HHH;Ht6vH LH<$Ht vH AUH'?ATUSHH8HD$HPHD$(HTHD$ w0 I}0 HIwfH5L-X LH 2 H5W LH 2 H5jW H$2 H5ǝ\ H2 HH5&V HB |2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtuHD$ HtH|$ HH8H[]A\A]HHD$ HtH|$ HH\HKIHt
HHH;HttH LH<$HttH AUH>ATUSHH8HD$HHD$(HHD$ *v0 I"u0 HIdH5+X LH 2 H5W LH 2 H5iW H2 H57\ HE2 HH5V HB 2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtsHD$ HtH|$ HH8H[]A\A]HHD$ HtH|$ HH̾HKIHt
HHH;HtsH LH<$HtrH AUH=ATUSHH8HD$H0~HD$(H4~HD$ t0 Iz0 HIWcH5,*X LH 2 H5W LHs 2 H5gW H2 H5\ H2 HH5V HB \2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtqHD$ HtH|$ HH8H[]A\A]HHD$ HtH|$ HH<HKIHt
HHH;HtqH LH<$HtnqH AUH7>ATUSHH8HD$H|HD$(H|HD$
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HHH;HtoH LH<$HtoH AUH';ATUSHH8HD$H{HD$(H{HD$ zq0 Irq0 HI7`H5 'X LHe2 H5 W LHS2 H5>\ H 2 H5\ H 2 HH5V HB <
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HHH;HtfnH LH<$HtNnH AUH9ATUSHH8HD$HyHD$(HyHD$ o0 In0 HI^H5|%X LH2 H5DV LH2 H5\ HT
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HHH;HtlH LH<$HtlHff. @ AUH7ATUSHH8HD$HwHD$(HwHD$ Jn0 IBn0 HI]H5#X LH52 H5V LH#2 H5\ H2 H5W\ He 2 HH5V HB B
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HHH;Ht/kH LH<$HtkHff. @ AUHw4ATUSHH8HD$H@vHD$(HDvHD$ l0 Im0 HIg[H5<"X LH2 H5V LH2 H5_W H2 H5\ H2 HH5V HB B e2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht jHD$ HtH|$ HH8H[]A\A]HHD$ HtH|$ HHEHKIHt
HHH;HtiH LH<$HtwiHff. @ AUHW1ATUSHH8HD$HtHD$(HtHD$
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HHH;HtgH LH<$HtgHff. @ AUH1ATUSHH8HD$H sHD$(HsHD$ ji0 Ibs0 HI'XH5 X LHU2 H5V LHC2 H5P\W H2 H5w\ H2 HH5 V HB B %2 HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtfHD$ HtH|$ HH8H[]A\A]HHD$ HtH|$ HHHKIHt
HHH;HtOfH LH<$Ht7fHff. @ AUHw.ATUSHH8HD$H`qHD$(HdqHD$ g0 Iq0 HIVH5\ X LH2 H5$V LH2 H5ZW H42 H5\ H2 HH56
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HHH;HtdH LH<$HtdHff. @ AUH0ATUSHH8HD$HoHD$(HoHD$ *f0 I"l0 HITH5X LH2 H5V LH2 H5\ H 2 H57\ HE2 HH5V HB B
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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{
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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$$H<HI$H{
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LLHD$`HtH|$P HH{EH []A\A]A^ L f.V f.V *$ HDŽ$ HT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ HA7HHt$PH|$XH$ HH$ HD$`HT$hH$ Hf D < HDŽ$ 4HT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ H!6HLL$PLT$XH$ H%H$ HD$`HT$hH$ HHD$`H$ HHtH$ HHD$`HtH|$P HH{CH腛H$ HHtH$ HHH$ HHt LLH$ HHx LLfHL$`HHnH|$P HZH|$HHIj?Hf f. AUATUSHHHHHH LA0A@% ~Ct uYHaHHH[]A\A] HXHHH[]A\A]f. uH#HHH[]A\A] D L/ Hl$ HT$H5vDV HD$HKLd$0H LP: H+HD$0HD$0 HHt?HELl$LHxHJHl$0HtHELHxH(HD$ LHxHHH[]A\A]f HhHHH[]A\A]HHD$ Ht$Hx蔏H;Ht=H H(H AWAVAUIATIUSHHHG(H8 HGhLHD$HFH9 @ I H|$I H?M I LH {?IT$AM4$H,H9II$t8Z$$$* 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$hH
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LLHD$`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
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LLHD$`HtH|$P HH{
+H []A\A]A^ L f.V f.V *$ HDŽ$ [HT$XH$ H$ HD$PHL$`H$ H$ HT$hH$ HAHHt$PH|$XH$ HeH$ HD$`HT$hH$ Hf D < HDŽ$ 4[HT$XH$ L$ HD$PHL$`L$ H$ HT$hH$ H!HLL$PLT$XH$ HH$ HD$`HT$hH$ HHD$`H$ HHtH$ HHD$`HtH|$P HH{H`H$ HHtH$ HHH$ HHt LLH$ HHx LLfHL$`HHnH|$P HZH|$HHIjY?Hf f. AUATUSHHHHHH LA0A@% ~Ct uYHaHHH[]A\A] HXHHH[]A\A]f. uH#HHH[]A\A] D L. Hl$ HT$H5v V HD$HLd$0H LP: H+HD$0HD$0 HHt?HELl$LHxUHJXHl$0HtHELHxTH(XHD$ LHxTHHH[]A\A]f HxHHH[]A\A]HHD$ Ht$HxTH;HtH H(^H AUATUHSHHHhXHIH I|$0w tz 9 H|$0HLHH+HD$0HD$0 HHt5HELd$PLHxSHWHl$0H f. HhH[]A\A]fH|$HLHH+HD$HD$ HHtHELd$PLHxoSHVHl$H ucH|$ HLHH+HD$ HD$ HH[HELd$PLHxSH>VHl$ H + 3. Hl$@HT$0H5V HD$0HLd$PH L軥: H+HD$PHD$P HHt?HELl$ LHx}RHUHl$PHtHELHx[RHUHD$@LHxBRHhH[]A\A] @ HHLH菣H+H$H$ HHDHELd$PLHxQH'UH,$HHELHxQHUHhH[]A\A]HHD$@Ht$ HxQH;Ht H H2[Hf. AUIATUSHHHAL&oHf$tHPH=Y X THE1LLHH92 H+Hl$Ht L%xY HEMtVЃtHH[]A\A]f HE HPMHE t0ЃuHE HP뺋EPU몃@XE PU HHSH|$Ht HZHf. AWAVAUATUSHHH|$H2L`1HHL>Z1 LHw2 HkH{Iw2 HII HLHL9IOH)HH?HHHE1H~$f CD TUCII9uH9J ID fD(=U fD(5U fD(- U fD(%#U f(%+U fD( 2U fD(9U fD(
@U fD(GU f(=OU f(5wU fHfA(fA(f]f_fA(fYfAXfffTf\f(f(fAYfAYf\f\fA(f(fYfYfAXfYfXfYf(fYfXǮU fYfXˮU fYfXf\f^ffYfˮU frfpcfXfYf_fA)HH9"I9~ D AD SAHL9uHD$H H[]A\A]A^A_fL12f D AUIATIHUSHؿHh1LHHW1 HIu2 HI}Huu2 LHI HHHH9HOH)HH?HHH1H~QfAHH9uH9H4}" fHfQfA)HH9H9~ QfAHH9uI$ HL[]A\A]fH1lf D AUIATUHHSH踾L`1LHLV1 LIit2 HI}HUt2 LIzI HLHL9IOH)HH?HHHH H I@H9HCI9 Hf(
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F*U fTAH9}*H f(
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8)U fTAHL9uf D
)U 1f D fTAHH9u AVIHAUATUSHڹL`1HHLQ1 LHo2 H|H{Iwo2 HH1HI~ fAD DAHH9u[]A\A]LI A^fAUIATIHUSHHHh1LHHTQ1 HIn2 HI}Hn2 LHZI HHHHڃH9HOH)HpHHIHHH H I@H9HCI9 IfI11J< o HfofA HI9wH9t-HOH9A~HH9A~ AH9H f}%f D oHfoffAHH9H9 H II)H LHAH9HB@H9@ I MIN Mt/f11oHfof$HI9wLM9t-HVH9A~HH9A~ AI$ HL[]A\A] D AHH9u @ H1@f D 1f D D AE HH9uf. D AUIATUHHSHȶL`1LHLN1 LIyl2 H聂I}Hel2 LII HLHHރL9IOH)HPHHIHH1H~# @ DDA E1E)EHH9uH9H }(o Hfofr fffA HH9I9~ 4 1)A HL9uHE HH[]A\A]L1`f D AUIATIHUSH蘵Hh1LHHM1 HIIk2 HquI}H5k2 LH
+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 H*I H|$\ I $I6LL MV $@N,M.] HLHL9IOH)HH?HHHH H
+ 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
- MILHtRf(11f Hf(fLf(ffTfUfVfAfADHL9rHL9t A] M9nRHGhHD$HGHD$HFH93 D I H|$I H|$I H,M H$ I>M^MJ @HL$I7 HLHL9IOH)HH?HHHH IPLWH9AM9A HUH9L9AD A H I1E1IO,D IA fDfALf]ffDHM9wL9tBD C]BH9LL$}?L 1Nl
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$HB0 H|$ u
H[]A\A]A^ÿX Lt$ HI#: LHh
Ld$0L ; Hl$PH
/lU H5*U M= H蘉HyT LHLSH諔LHD$ HHxb
HH5ԝ LHH|$Ht
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H[]A\A]A^ÿX Lt$ HI : LHX Ld$0L; Hl$PH
eU H5&U M= H舅HiT LHLCH蛐LӏHD$ HHxR H H5ę LHH|$HtH{Ht蜼HIHL6LnHD$ Ht$HxL 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 0 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 I H|$I M HD$oI6I~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$HB0 H|$ u
H[]A\A]A^ÿX Lt$ HI: LHhLd$0L; Hl$PH
OVU H5U M= HzHyT LHLSH諅LHD$ HHxbHH5Ԏ LHH|$Ht
-H{Ht謱HIHLFL~HD$ Ht$HxLHHHHf. AWAVIAUATUHSHHG(H8 n HFH9LohLg f H[]A\A]A^A_ÐH T$ I1fnl$ IE1J< fp fof%U o IfofofnU fffffHM9rH9t79FHGH9~"9ADFHH9D~
-9FM9N:H LH H LL H zIvIAL HM~HCH9HA@H9@1 9F<<HH9urf D H H8 g HFH9LghH_ D H Dl$ I1fnt$ IE1J< fp fof%U o IfofofU fffffHM9wH9t=D9,DFHGH9~&D9,EDFHH9D~
D9,DF,D,M9NH LL H HL El IvIIL HM~HBH9HA@H9@1 D9,DF<<HH9uo @ HFH9LghH_ f D H' I1E1IJ< o IAoT fofofU f U fffffHM9wH9tBAD 9FHGH9~*EL D9 DF HH9D ~AD 9FM9FcH LL H HL Ml IvIILHM~IELIH9@M9 HBH9L9AD @1 D A| 9<F<<HH9uV AVHAUATUSHHĀH HG Hl$Hb0 H|$ u
H[]A\A]A^ÿX Lt$ HI3: LHLd$0L ; Hl$PH
/MU H5U M= HtHT LHLsHLHD$ HHxH;H5 LHH|$Ht*H{Ht̫HIHLfL~HD$ Ht$HxL#HHHHf. AWAVIAUIATUSHHG(H8 HFH9LghHotx D I LI I HM H hMEIu IJMIE ~$1 D H9HHN H HI9uIE I9EuH[]A\A]A^A_H H8 HFH9tLghHo @ I LI I 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. AWAVAUATUSH8 HFXH|$HHT$0D E A H$ Ht$0HHD$@AAD$L$@ 11D$<AD$M}D$8H$ HD$ l D H|$09RHPIt$LL$@ H$H H$ H$ H$@ H H9HNH$
H$H H$
H$( E9D$8 H|$ I h. HL$@HT$ H|$PHt$`HD$`諁H$ LD$PHtLD$(LD$(MH$ H$ I@0xL M M`PIxL$ 2 IH$ D$ D$ $ LH$ 'HtHz1 HBL;l$<t$LHIH|$0+I{ H$ HD$HHH t2MtLEHD$HH8 []A\A]A^A_L|$0LџHxH2 H
LH貟HP1LT0 HxI2 LH}f1H~f D HL H HH9uHD$HH hX LD$ LD$ IHD$pH
S HS H5S HHD$ I0{L$ L; H$ H
_U H5S Mຂ HnHL$ HS HLNzHyLxHD$pHHx]HH5ς LI}HD$ HD$ H$ HtHD$ 蝥HD$ MHtLHtHH L$ I LH|$0L1ʪ0 H$ IHtHD$(LD$(E1,H$ HHtHE1<HHD$pH$ HxhLpHHI}HHE1HHD$ ]xHD$ HLwHf D 끿X L$ L$ H5 U LLI=H$ H; H$@ H
EU H5
U I} HlHb U LHLbxHwHvH$ HHxnH'H5 LH$ HHIHLawHvH$ LHxL HHHHHf AVHAUATUSHHĀH HG Hl$H0 H|$ u
H[]A\A]A^ÿX Lt$ NHI: LHLd$0L; Hl$PH
?U H5` U M= HHkH)T LHLwH[vLuHD$ HHxHˆH5 L\HH|$HtH{Ht\HIHLuL.uHD$ 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 PIuI} IHHIE ~$1 D H9 HHF H HH9uIE I9EuH[]A\A]A^A_HFH9HohH_4f I HM I HM M$IuI} I HHIE ~%1 D IH9HFHHH9uIE I9EuH[]A\A]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 BL(TUVBBDIM9wH9t;(L@_L9~%(HH9B_B~
(_H9}0(H H(_)HH9M H9 ILIJT I)HBI9I@AH9A I LHL, MtK(1E1 (I( L(TUVA 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$I H|$ \ I $I6LD MN $(d$@N$M&K HLHHڃL9IOH)HyHHHHHH Hd IxH9H~AI9@AE (II1E1 N (I(A 8AL8(TUV>D>HM9wL9t>(I{C_H9B~&(IL9A_~(C_BH9}0(I< H(_)HH9M&I9 HH<MHT I)HBH9HGAH9A I LHL, MtI(1E1 (I( L(TUVDHI9rLM9t9(HAA_I9~"(HI9A_~ A_ M9fHGhWHD$ HGHD$(HFH9f I H|$ )$M TI H|$(I M HD$3II~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, @ AWIAVIAUATUSH(HG(H8 HGhHD$HGH$HFH9 I H|$I I H<$II L$jI6MN\ N @M HLHL9IOH)HH?HHHH H
+ MILHtRf(11f Hf(fLf(ffTfUfVfAfADHL9rHL9t A] M9nRHGhHD$HGHD$HFH93 D I H|$I H|$I H,M H$pI>M^MJ @HL$I7 HLHL9IOH)HH?HHHH IPLWH9AM9A HUH9L9AD A H I1E1IO,D IA fDfALf]ffDHM9wL9tBD C]BH9LL$}?L 1Nl
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
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+ IDŽ$ ADŽ$ ?IDŽ$ HD$贮L H I$ H9 H, H9UH1HIHl$PPH$` H5 O M$ HHH$UH$ H$ HHo) H$ H H}H$ HHD$H5QHmTHt$ I$ HD$PHl$`HxQH$@ H5PN IDŽ$ HHDŽ$ HDŽ$ HHDŽ$ HD$0 UH$ H$ HHHDŽ$ HDŽ$( HHDŽ$0 HD$("r) 1E1H$ R H, L$ ID- 1HD$Mt IFHxH;=W L`SH$ HtNSHD$`H4$HxOHtH/SH$ Hl$pH5%N HDŽ$ HDŽ$ HHHDŽ$ HD$8SLt$0H$ HHHDŽ$@ HDŽ$H HDŽ$P Lq) L$ H\$HDŽ$ HDŽ$( HDŽ$0 MIDLsHLH)HH 1E1LI9L$ L$( H$0 HL Ht HHpNHHI9uH$( H$ HtHCH4$HxNHQH$H H$@ H9` H=V L5EU HHxL9 HH9uH$@ HtuQHD$pH4$Hx#NH|$YL;l$y H$ H9$( X H$ LHT$OH5O HH RL$ L菧3 L<$H
O H5MO M8 L H~O HLH(LLH$ Ht$HxbMH+ H5 HV @ L|$M)IMM$ E E1 K\ I|$X1HHID$PHHHt/HHqf D H9 H HtHq1HHH9t DPHtKT H H@ HPH|$ HHHHHL1IM9`I$ H H|$(MtLOH []A\A]A^A_f D HH H9N HHOILsHH$( H+$ HH H H9. H< OOH$ H$( IH)HHHKHLH:T?H$ H$( H)HHI$ 1f H$ H|$H4HHH9uH$ H$ H|$HHLIIHH$ Ht/NHD$`H4$HxJH|$I$ NH|$ I|$PMHH= HWHI$HTHHD$PH$ HxJHHH|$0HD$pH4$HxYJH|$MiL~M\HHH$ Ht$HxJH#KH|$(IHfGH- G7H$ X HHH9
+Lt$8L茣3 L|$H
O H5IO MD LHO HLH$L|LH$ LHx`IH)H5 HRH/H|$H5H|$8kH$ H4$HxIHHJHHHH<$HL#HFH GI9tIH4$IHxHL9uOHLH%P.H4$J HHxL9u2HH9uHQH$ Ht|KH$ 돋PJ҉HH4$<JH=P HWt# H4$JH$ PJHHH$ )H$ HHE @ UH SH
KHHHHH[]HHJHQfAWAVAUATIHUSHH HN HHG]) Hc I|$pID$X ID$` ID$h
+ AD$p ?ID$x HI$ID$PHD$ 謣L H ID$XH9q H, H4J1HHIEI$ I$ Ml$PIDŽ$ IDŽ$
+ 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
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? H+ H5D H, HH53> HB B E- HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( HtHD$ HtH|$ HH0H[]A\A]A^IHCHHx~HCHHxnHHHx_HLHD$ HtH|$ HHHHMIHt
HHH} HtHE LH<$HtH AVHWAUATUHSH0HD$H>HD$(HBHD$ 討 IHù 1H߾
+ HHWx H{@C C C@ ?HHH HKHK",> H HC(H9 L, LL1HI[H H5 ? Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $Y7 HRx H5 ? H: H59
+? H( H5wD H) HH50> HB B %* HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( HtHD$ HtH|$ HH0H[]A\A]A^aIHCHHx^HCHHxNHHHx?HLtHD$ HtH|$ HHHHMIHt
HHH} HtHE LH<$HtH AVHAUATUHSH0HD$H HD$(H"HD$ 舌 IHù 1H߾
+ HH7x H{@C C C@ ?HHH HKHK)> H HC(H9 L, LL1HI;H H5s? Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $94 H2x H5? H7 H5? H% H5WD He& HH5-> HB B ' HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( Ht_HD$ HtH|$ HH0H[]A\A]A^AIHCHHx>HCHHx.HHHx HLTHD$ HtH|$ HHHHMIHt
HHH} HtHE LH<$HtH AVHAUATUHSH0HD$HHD$(HHD$ h IHù 1H߾
+ HHx H{@C C C@ ?HHH HKHK%> H HC(H9 L, LkL1HIH H5S? Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $1 Hx H5? Hc4 H5? H" H57D HE# HH5*> HB B # HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( Ht?HD$ HtH|$ HH0H[]A\A]A^!IHCHHx HCHHxHHHxHL4HD$ HtH|$ HHHHMIHt
HHH} HtHE LH<$HtnH AVHAUATUHSH0HD$HޏHD$(HHD$ H IHù 1H߾
+ HHx H{@C C C@ ?HHH HKHK"> H HC(H9 L, LKL1HIH H53? Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $- Hx H5d? HC1 H5 ? Ht H5D H% HH5v'> HB B HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( Ht HD$ HtH|$ HH0H[]A\A]A^IHCHHxHCHHxHHHxHLHD$ HtH|$ HHcHHMIHt
HHH} HtgHE LH<$HtNH AVHAUATUHSH0HD$HHD$(HHD$ ( IHù 1H߾
+ HHx H{@C C C@ ?HHH HKHK > H HC(H9 L, L+L1HIH H5? Ls HCP HCX HH H H HC` HCh LHCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H $* Hx H5D? H#. H5> HT H5D H HH5V$> HB B HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( HtHD$ HtH|$ HH0H[]A\A]A^IHCHHxHCHHxHHHxHLHD$ HtH|$ HHCHHMIHt
HHH} HtGHE LH<$Ht.H AVHAUATUHSH0HD$HHD$(HHD$ I{Hù 1H߾
+ HHx H{@C C C@ ?HHH HKHK > H HC(H9 L, L L1HI軾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+ H5> H4 H5פD H HH56!> HB B HH HD$ HE HU Ht HT$(H]HEHt$HHU H<$HE( HtHD$ HtH|$ HH0H[]A\A]A^IHCHHx辽HCHHx讽HHHx蟽HLHD$ HtH|$ HH#HHMIHt
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
+ IHH H^P LH1H7> - f f ~ f SHH9tT4 1Gwǁ8v
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+ LI $I\$P 1HA <rf 8|$ L$ \L$ t$ f T HH9t8Kց
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> 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
U> HQ> H5M> LI蠡H\$0H % HD$PH
|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
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H9~%t
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@ H5 x@ Iغ7 LEHw@ HLL LXHHD$ LHxSHH5u LY\HL^ H\LH HH@HD$ Ht$ HxRLSHm\HHHf D Hl$ X IQHI.H\$0Hd$ H
ʮ@ H5+w@ Iغ; LSHv@ HLL Hl$ X PHIH\$0HBd$ Ld$PH
f@ H5v@ Iغ9 LHWv@ HLL ff. ff. ff. ff. ff. ff. f. AWAVAUIATUSH H9 Ht$u IHIb He L$ 1Hƿ I] L HH$ HPHHD$ I} HP HϿ HL$LE1ILH LHIG0x
-n LI I_Pa 1H@ ?} D 8|$ t$ \t$ t$ f T HH9t@Kց
tt 8 D p Lha LHiH|$HH H(H9HH9 H HWHHK1Hfo f> 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
+*HJ H9~St *HJ H9~<t *HJ
H9~%t
*HJH9~T*Ht$LL/I} LHPLZ Hĸ []A\A]A^A_ 1f D L *HH9uHl$ X pHIeH\$0Hh$ Ld$PH
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$HPL| H []A\A]A^A_f. W1Ҿ f D .EΈ HH9uHl$@X yKHI^H\$`H^$ L$ H
R@ H5Sq@ Iغ7 L{Hp@ HLL6LHHD$@LHxELHH5u LUX Lt$0JMG0H
AB= H=B= H59B= LI#Hl$@H ^$ H\$`H
C@ H5E= I躖 HH F= LHLHHHD$0HHxKHOH5u LTHL H-UIHLHHD$0Ht$/HxDKLLLHHfHf Hl$@X IHIH\$`H)]$ H
@ H5o@ Iغ; LHKo@ HLL[Hl$@X rIHIWH\$`H\$ L$ H
K@ H5Lo@ Iغ9 LtHn@ HLL/LHHHHD$@Ht$0Hx1JL9KHSHHHff. f. f. fAWAVAUIATUSH H9 * HHIH![ Hg Ld$P1Hƿ Mu L
ILHt$PPHI I} HP HD$W LD$HHLE1L HLHpHZ LHbLH蒷H% HhH9HH95 H+ HWH11H (H(SLSDSLS[fo[fafifofafifaHH9rH9 4,fHHQH9~{,,f4PHQH9~e$,f4PHQH9~O ,f4PHQH9~9,f4PHQH9~# H,H9f4P~
<,fHI} LHPL? Hĸ []A\A]A^A_ D 1f D ,f PHH9uHl$ X IFHI.H\$0HY$ Ld$PH
@ H5&l@ Iغ7 LNHk@ HLL LaHHD$ LHxGHH5u LbPHLg HPLHHHIHD$ Ht$ HxFLGHvPHHH Hl$ X QEHI6H\$0HX$ H
@ H53k@ Iغ; L[Hj@ HLLHl$ X DHIH\$0HJX$ Ld$PH
N@ H5j@ Iغ9 LH_j@ HLL ff.
-ff. ff. ff. ff. ff. f. AWAVAUATUHHSHH V HHD$~ H HHE0x @IHD$PL{I1һ H$H%f D HE0MIHsxupHHH4$HE HHUPL"AE
- LMKL;d$Pt#A} "t(H;\$AuHĈ []A\A]A^A_H=h@ WDH=h@ {KX Ld$ CLE0H
}:= Hy:= H5u:= LIH\$0H[V$ HD$PH
r@ H50>= Iغ HIH$HR>= LLLLHLHD$ LHxCHH5=u LMLd$ X fBLHKH\$0HU$ HD$PH
bq@ H5Ch@ Iغh HIH$dHg@ LLH LwHHD$ LHx.CHH5u HxLH<$H<HHqHD$ Ht$ HxBLCHLHHHÐH<$IHL'HD$ Ht$ HxBHCHTLIHH D AWAVAUIATUSH H9 Ht$L IHIS H5 L$ 1Hƿ I] L訴 HH$ HPHHD$ I} HP H HL$LE1ILH L.HIG0x
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tt 8 D p LR LHZH|$HH HPH9HSH9 H HWH11H4 (HD[HH9rH9t6,҉HVH9~! H,H9 ~ ,ӉI} Ht$HPL H []A\A]A^A_ D 1f D ,ȉ HH9uHl$@X >HIH\$`HR$ L$ H
x@ H5d@ Iغ7 LĻHd@ HLLLHHD$@LHx?HGH5 u LHX Lt$0)>MG0H
5= H5= H55= LIHl$@HhQ$ H\$`H
z@ H5=9= I躖 HHf9= LHLH(H`HD$0HHx>HH5Qu L)HHL. HvHIHLHHD$0Ht$/Hx>L?HHH @ Hl$@X !=HIH\$`HyP$ H
bw@ H5c@ Iغ; L+Hb@ HLLbHl$@X <HIH\$`HP$ L$ H
v@ H5b@ Iغ9 LĹH,b@ HLLLHHHHD$@Ht$0Hx=L>H1GHHHff. f. f. fAWAVAUIATUSH H9 HHIHqN H7 Ld$P1Hƿ Mu L] ILHt$PPHI I} HP HD$觫 LD$HHLE1Ln
- HLHHM LH=VLH袯1H~ D H,H HH9uI} LHPL Hĸ []A\A]A^A_Hl$ X :HIH\$0H"N$ Ld$PH
s@ H5`@ Iغ7 LϷH"`@ HLLLHHD$ LHx;HRH5 u LDHL H0ELHHHHD$ Ht$ HxG;LO<HDHHH Hl$ X 9HIH\$0H)M$ H
r@ H5_@ Iغ; L۶HK_@ HLLHl$ X r9HIWH\$0HL$ Ld$PH
r@ H5O_@ Iغ9 LwH^@ HLL2 ff. ff. ff. ff. ff. ff. f. AWAVAUIATUSH H9 HHIH!K H Ld$P1Hƿ Mu L
E ILHt$PPHI+ I} HP HD$W LD$HHLE1L HLHpHJ LHRLHbH 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
+~Z.Hr @EH9@| ~A.Hr
@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
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H9~'L ,ɈL
HJH9~D| A,ψLI} LHPL Hĸ []A\A]A^A_f D 1f D D ,Ȉ HH9uHl$ X 4HIH\$0HAH$ Ld$PH
l@ H5Z@ Iغ7 LHAZ@ HLL詽LH9HD$ LHx5HqH5*u L?HL HO?LH贼HHHD$ Ht$ Hxf5Ln6H?HHH Hl$ X 3HIH\$0HIG$ H
k@ H5Y@ Iغ; LHkY@ HLL趼Hl$ X 3HIwH\$0HF$ Ld$PH
nk@ H5oY@ Iغ9 L藰HX@ HLLR ff.
-ff. ff. ff. ff. ff. f. AWAVAUIATUSH H9 * HHIHAE Hg Ld$P1Hƿ Mu L- ILHt$PPHI I} HP HD$w LD$HHLE1L> HLHHD LH
MLHH% HhH9HH95 H+ HWH11H (H(SLSDSLS[fo[fafifofafifaHH9rH9 4,fHHQH9~{,,f4PHQH9~e$,f4PHQH9~O ,f4PHQH9~9,f4PHQH9~# H,H9f4P~
<,fHI} LHPL_ Hĸ []A\A]A^A_ D 1f D ,f PHH9uHl$ X i0HINH\$0HC$ Ld$PH
g@ H5FV@ Iغ7 LnHU@ HLL)L聸H蹷HD$ LHx81HH5u L:HL H:LH4HHiHD$ Ht$ Hx0L1H:HHH Hl$ X q/HIVH\$0HB$ H
f@ H5SU@ Iغ; L{HT@ HLL6Hl$ X /HIH\$0HjB$ Ld$PH
e@ H5T@ Iغ9 LHT@ HLLҷ ff.
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- L=,L;d$Pt#A} "t(H;\$AuHĈ []A\A]A^A_H= S@ w.H=S@ 5X Ld$ <-LE0H
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+ HEt Ld$ HT$H5q> HCPLHHLl$0HL$LHL Hl$0HtAHEHt$HxgHHD$ LHxNH@H[]A\A]A^f D HD$ LHx'HD$HCPH@H[]A\A]A^HHD$ Ht$HxHIVHHH#HH @ AVAUATUHX SH@7L5s HHH{IFHj HEt Ld$ HT$H5A> HCPLHHLl$0HL$LHL Hl$0HtAHEHt$Hx7HoHD$ LHx H@H[]A\A]A^f D HD$ LHxHD$HCPH@H[]A\A]A^HHD$ Ht$HxHIVHHHH[H @ AVAUATUHX SH@L5s HHH{IFH: HEt Ld$ HT$H5> HCPLHHLl$0HL$LHLS Hl$0HtAHEHt$HxH?HD$ LHxH@H[]A\A]A^f D HD$ LHxHD$HCPH@H[]A\A]A^HHD$ Ht$HxHIVHHHH+H @ AVAUATUHX SH@L5`s HHH{IFH
+ HCt Ld$ HT$H5~> HCPLHH[Ll$0HL$LHL# Hl$0HtAHEHt$HxHHD$ LHxH@H[]A\A]A^f D HD$ LHxHD$HCPH@H[]A\A]A^HHD$ Ht$HxhHIVHHHHH @ AVAUATUHX SH@L50s HHH{IFH HBt Ld$ HT$H5}> HCPLHH+Ll$0HL$LHL
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+ Hl$0HtAHEHt$HxHOHD$ LHxH@H[]A\A]A^f D HD$ LHxHD$HCPH@H[]A\A]A^HHD$ Ht$HxHIVHHHH;H @ AVAUATUHX SH@L5ps HHH{IFH H<t Ld$ HT$H5x> HCPLHHkLl$0HL$LHL3 Hl$0HtAHEHt$HxH HD$ LHxH@H[]A\A]A^f D HD$ LHxHD$HCPH@H[]A\A]A^HHD$ Ht$HxxHIVHHHH H @ SHHHtHGHxH;= t uHjH[H=t HWtHt$!͋PJHfAVIAUIATIUSH0H-Tu } t.H 'u HIHCIE HCI$H0[]A\A]A^ @ HH u tHT$ Ht$HHHCHC4Hc$ HHHOHcT$H HHOHcT$ HC HHOHC_Sf. :HNF VXJ>XB XR@ H@ PHJH9NHCNHHf. SHFHHN H)HHt8Ht"H
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HHH;HtH LH<$HtHf AVHAUATUSHH0HD$H>HD$(HBHD$ 訔 I蠔 I蘒 HI]H5 #; LH< H5"; LHy< H5\ < LHg< H5= H- H5@ H. HH55: HB B
+ I/ HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\A]A^HHD$ HtH|$ HH'HKIHt
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+ - HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\A]A^HHD$ HtH|$ HHgHKIHt
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+ + HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht4HD$ HtH|$ HH0H[]A\A]A^HHD$ HtH|$ HHHKIHt
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+ * HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HttHD$ HtH|$ HH0H[]A\A]A^HHD$ HtH|$ HHHKIHt
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+ I( HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\A]A^HHD$ HtH|$ HH'HKIHt
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+ & HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\A]A^HHD$ HtH|$ HHgHKIHt
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+ $ HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( Ht4HD$ HtH|$ HH0H[]A\A]A^HHD$ HtH|$ HHHKIHt
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+ # HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HttHD$ HtH|$ HH0H[]A\A]A^HHD$ HtH|$ HHHKIHt
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+ HH HD$ HC HHt HT$(HkHCHt$HHS H<$HC( HtHD$ HtH|$ HH0H[]A\A]A^HHD$ HtH|$ HHgHKIHt
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+HH9tT. zuL A L AfL AfL Ap KD0HD$PL9~mHCxH|$(H*L$HHD$@HD$PHI| HShL @ u L A HH9t . {L A HH9uHD$HD$PH$ HH;D$H$ IH+t$HDŽ$ M9Hl$HL$ |}H$H+t$ M9HD$@$ H|$0Ht$hHL$pLoLo($ HwGHHOHHw0HO8҃HT$xHW HW@H []A\A]A^A_H|$81HW@H2HD@H|$8 HW f D AWAVAUIATUSHx HHH$ HPH$ HPH$ H@H$ HDŽ$ HDŽ$ HDŽ$ H$ HGH$ HDŽ$ DŽ$ DŽ$ HP HDŽ$ DŽ$ DŽ$ HDŽ$ DŽ$( H$0 HP(DŽ$, H$8 HP0HHH$@ HP8H$H HP@H$P HPHH$X HPPH$ H$` HPH$ HpH$ H$ HDŽ$ HH$( H$ H$ H$ HDŽ$ H$ Ld$pHLt$HL{HD$p L= HDŽ$ HDŽ$ H$ DŽ$ H$ HDŽ$ HEHDŽ$ H$ HHDŽ$( HDŽ$0 čH$ LHHH$ H$ H$ H$ H$ VL$h Ll$@Ll$@D$H HD$P HD$X HD$` M H$ Ll$@L$ 1HD$ @ H$ HL$P HHH$ HDŽ$ HDŽ$ HHDŽ$ HDŽ$ HDŽ$ DŽ$ HIH)H$ IH)HL9LNHHLD$xHHHHH$ H$` H)H$X HH)H9HOH$x L$p H$ IHH$ H$ HH)H9HOH$ H$ H$ IHLL$pt{H$ J4IL$ ItHHL9 H t 1H9H$ HDŽ$ L$ H$ HDŽ$ $ A LLLH$ tH|$HLHD$H L9LHx []A\A]A^A_ D Zf D H$ HHH$ H$ H$ H$ H$ H$ HH$ ;HH$ H$ H$ H$ HL^H6}f D AWI1AVAUATUHHSHH8 LL$ HHD$2 IH"LHLIqH$ tFH
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X= H= A L18 H$ HE H8 H[]A\A]A^A_ Lx L,M IOPHt$L|$pLL$PLD$@LL<$LHD$p HD$x HDŽ$ 葦H$ tFH
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= H= A L1 H$ HE HH H[]A\A]A^A_ L LcD$ M IOPHt$L$ LL$`LD$PLL<$LHDŽ$ HDŽ$ HDŽ$ XH$ tMH
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- V} HH HD$0IE IU Ht HT$8I]IEHt$ HIU Ld$IE( M I$ I$ qI$ I$ qI|$hHt}I|$PHt}I\$0L|$HtKH=\q u f. HH{H+HtL}HCHxL9 H3}HuID$(I|$ 1H yI|$ ID$8 ID$0 |ID$LHxyID$LHxyI$LHxyL|HD$0HtH|$ HHHL[]A\A]A^A_úPGLZ{: D wIHCHl$HHxyHCHHxyHHHxxHL)|HD$0HtH|$ HH舂IMHHt
HHI} Ht,IE HH|$Ht,HfHH{H+Ht{HCHxL9uH{H\H̋PJ҉HLPzf. @ AWH5 AVAUIATUSHHHD$ H>HD$8H>HD$06" HY{Hù 1HL5Xq
- HH{@C C IFC@ ?HHCHCg7 H HC(H9 L$ LzL1HIvH H|$H5M9 L{ HCP HH H H HCX HC` HCh HCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H\$D H5; Hew H58= Hx HH5Wl7 HB
+ r HH HD$0IE IU Ht HT$8I]IEHt$ HIU Ld$IE( M I$ I$ K I$ I$ 6 I|$hHtI|$PHtI\$0L|$HtKH=) s u f. HH{H+HtHCHxL9 HHuID$(I|$ 1H I|$ ID$8 ID$0 yID$LHx(ID$LHxI$LHxL?HD$0HtH|$ HHHL[]A\A]A^A_úPGL: D IHCHl$HHxHCHHxHHHxtHLHD$0HtH|$ HH IMHHt
HHI} HtSIE HH|$Ht9HfHH{H+Ht(HCHxL9uHH\H̋PJ҉HLf. @ AWH5 AVAUIATUSHHHD$ HHD$8H HD$0f HHù 1HL5 s
+ HH{@C C IFC@ ?HHCHCo8 H HC(H9 L$ LiL1HIH H|$H5r: L{ HCP HH H H HCX HC` HCh HCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H\${ H5< Hl H5H> HVm HH5t8 HB
B
- x HH HD$0IE IU Ht HT$8I]IEHt$ HIU Ld$IE( M I$ I$ {lI$ I$ flI|$hHtxI|$PHtxI\$0L|$HtKH=Wq u f. HH{H+HtxHCHxL9 HxHuID$(I|$ 1H utI|$ ID$8 ID$0 YxID$LHxuID$LHxtI$LHxtL xHD$0HtH|$ HHHL[]A\A]A^A_úPGLv: D {rIHCHl$HHxstHCHHxctHHHxTtHLwHD$0HtH|$ HH}IMHHt
HHI} HtC(IE HH|$Ht)(HfHH{H+HtwHCHxL9uHvH\H̋PJ҉HLuf. @ AWHG4 AVAUIATUSHHHD$ H<:HD$8H@:HD$0 HvHù 1HL5Sq
- HH{@C C IFC@ ?HHCHCb7 H HC(H9 L$ LIvL1HIqH H|$H5BI9 L{ HCP HH H H HCX HC` HCh HCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H\$褆 H5z; Hr H5= Hvs HH5g7 HB
+ m HH HD$0IE IU Ht HT$8I]IEHt$ HIU Ld$IE( M I$ I$ I$ I$ I|$hHtwI|$PHthI\$0L|$HtKH=s u f. HH{H+Ht,HCHxL9 HHuID$(I|$ 1H I|$ ID$8 ID$0 ID$LHxID$LHxwI$LHxgLHD$0HtH|$ HHHL[]A\A]A^A_úPGL:: D
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+ HH{@C C IFC@ ?HHCHC@k8 H HC(H9 L$ LL1HIy
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B
- t HH HD$0IE IU Ht HT$8I]IEHt$ HIU Ld$IE( M I$ I$ gI$ I$ gI|$hHtWtI|$PHtHtI\$0L|$HtKH=ASq u f. HH{H+Ht tHCHxL9 HsHuID$(I|$ 1H oI|$ ID$8 ID$0 sID$LHxhpID$LHxWpI$LHxGpLsHD$0HtH|$ HHHL[]A\A]A^A_úPGLr: D mIHCHl$HHxoHCHHxoHHHxoHLrHD$0HtH|$ HHHyIMHHt
HHI} Ht#IE HH|$Ht#HfHH{H+HthrHCHxL9uHSrH\H̋PJ҉HLqf. @ AWH'- AVAUIATUSHHHD$ H5HD$8H5HD$0 HrHù 1HL5MOq
- HH{@C C IFC@ ?HHCHCP^7 H HC(H9 L$ LqL1HIYmH H|$H5;8 L{ HCP HH H H HCX HC` HCh HCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H\$ H5Mv; H%n H5= Hn HH5c7 HB }o HH HD$0IE IU Ht HT$8I]IEHt$ HIU Ld$IE( M I$ I$ BcI$ I$ -cI|$hHtoI|$PHtoI\$0L|$HtBH=Nq u HH{H+Ht|oHCHxL9 HcoHuID$(I|$ 1H EkI|$ ID$8 ID$0 )oID$LHxkID$LHxkI$LHxkLnHD$0HtH|$ HHHL[]A\A]A^A_úPGLm: D KiIHCHl$HHxCkHCHHx3kHHHx$kHLYnHD$0HtH|$ HHtIMHHt
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- HH{@C C IFC@ ?HHCHCY7 H HC(H9 L$ LmL1HIhH H|$H5l78 L{ HCP HH H H HCX HC` HCh HCp HCx ǃ Hǃ Hǃ ǃ Hǃ Hǃ H H ǃ ǃ ǃ H\$t} H5q; Hi H5h= HFj HH5^7 HB j HH HD$0IE IU Ht HT$8I]IEHt$ HIU Ld$IE( M I$ I$ ^I$ I$ ^I|$hHt.kI|$PHt kI\$0L|$HtBH=Jq u HH{H+HtjHCHxL9 HjHuID$(I|$ 1H fI|$ ID$8 ID$0 jID$LHxHgID$LHx7gI$LHx'gL_jHD$0HtH|$ HHHL[]A\A]A^A_úPGLh: D dIHCHl$HHxfHCHHxfHHHxfHLiHD$0HtH|$ HH(pIMHHt
HHI} HtIE HH|$HtiHfHH{H+HtHiHCHxL9uH3iH\H̋PJ҉HLgf. @ AWAVAUATUSHHHt/LgHGHL)HH9w+H1LHLdH]H[]A\A]A^A_ @ L?H?HM)IL)H9 I9HIICLsLhL} HUIL)HIIHtLLLmL} MH1HLL dMtLhLm MH]LmH[]A\A]A^A_ D H9xHuLE1E1H=:[7 cL4 Of. @ AWAVAUATIUSHHH HG HG@ G\ HG` L$ HGh HGp L$ HGx HLJ HLJ Ƈ Ƈ Ƈ Ƈ LJ LJ LJ H% H H-vDq D H5W; LLA HE HD$`gHD$`H$ LLHHD$a. H$ Ht#HPHzH9o HHD$vfHD$H$ HzH9
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L$ HD$L~SHD$H=3q HW LKSL>SMH=a3q HO6 L$ LHD$SHD$X L$ OHT$H5: LIWH5: LZHH$ H57 H$ HUHZHH$ H5: H$ HUHZHH$ H5EY; H$ HUHoZHH$ H5R: H$ HUHIZHH$ H5: H$ HUH#ZHH5> LH$ HUHZHL$ L$ LLH$ HUH5OL$ La H$P H
V: H5: M H_1LHL HwLH$ HHx+OH$ HHxOH$ LHxOH$ LHxNH$ LHxNH$ LHxNH$ LHxNH$ LHxNH$ LHxNHDH5o LWH=0q HW LlPX L$` LHT$?H5: LI~RH$@ H>` H
: H5x: I" LH: LLLLH;H$` LHxMHpH5)o LWH=/q HO
- L$ 8LHD$ OHD$ !H=/q HO
- LHD$ SOHD$ X LD$L$p KLD$H
2C7 H.C7 H5*C7 LHI0$L$` L _ H
: H5F7 M LH G7 LLHvLLH$p LHxLH;H5o HUHLIH$ H$ HHxBLH$ HLHx+LH$ LHxLH$ LHxLH$ LHxKH$ LHxKH$ LHxKH$ LHxKH$ LHxKLL]LILH$p LHxkKHLpLII IIHLH$` LHx'KH$p LHxKLLHIIH$ HHH$ HHH$ HtwJqrHH$ H$ HxJIHH$ LHxgJHH$ HH H$ HH HH$ H$ Hx JH$ HHH$ HHH$@ LHxIH$ HHH$ Hk^HH$0 LHxvIJHH$ Ht$>HxXIH$ LHxDIH$ HHH$ HH$ HHH$ HH$ HHH$ HX L$ oGHT$H5:: LIxOH5D: LRHLH$ HUHH$ HHD$GH$ HZ L$ H
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m: H5^: I LHL$1LLLH$H$ LHxDH$ LHxDH$ LHxxDH1H5o LMJqrJqrIHLH$ LHx DH$ LHx DH$ LHxCHHH @ ILMHL2H$` LHxCIHŐILMHLH$ H$ HxgCLoD%IHfHH$ HHH$ HPJHJqrPJHGLHLK[HJqrJqr)H$ HHvH$ H_PJHPJH
-Jqrf D AUATI USH ELHH Ld$PHL LkPLLH"o H{HHHo Hhyp HHHĨ H[]A\A]HHEH}KLHAUATI USH ELHH Ld$PHL LkPLLHo H{HHH Hxp HHHĨ H[]A\A]HHeDHJLHbAUATI USH vDLHHh Ld$PHLX LkPLLYHo H{HHH/ Hxp HHHĨ H[]A\A]HHCH=JLH¾AUATI USH CLHH Ld$PHL LkPLLHBo H{HHH Hxp HHHĨ H[]A\A]HH%CHILH"AUATI USH 6CLHH( Ld$PHL LkPLLHo H{HHH Hhvp HHHĨ H[]A\A]HHBHHLH肽AUATI USH BLHH Ld$PHLx LkPLLyHo H{HHHO Hup HHHĨ H[]A\A]HHAH]HLHLIH HJH M)ILI Hf. fHM)JD Hf. D HIo HHÐH9o HHMAf. AWAVIAUIATIUSH H\$0HT$H{X;H| q Ƅ$h HHDŽ$` Ƅ$i 1HDŽ$p HDŽ$x HHDŽ$ HDŽ$ H$ HH q HhLxH}Hl$0L?oCH q H{@HD$@ HD$H HD$P HD$X HD$` HHD$h HD$0H q H@H$ H# q HHD$8}HH q HsH{XD$x HHD$8H~ q HH$ BML L;LHH@HD$HH0?M= L:LHH@H q HxHD$`I<$HJ Lt$PLl$XL97 L9 M} L)HT$ 1HI GIIHHw HLLLD$LELD$HL; q Ll$ LHL$ L2EHD$ HxH;=j q @ H5 q HHD$0H% q H@H$ Hz q HHD$8H$ HxH;= q H q H{@HHD$8AHEHl$0H{XL|0Hq HH$ 8HĘ L[]A\A]A^A_ M9 M M)HT$ 1LEIIHHt_HLLLD$ DLD$HL;eq } Ll$ LHL$ LD D AE A@f. AE A@ D HD$0HHxw yC @ HD$0HHxw YC @ HA Ha HsPLt?C MH=x`7 :Ll$ HI<$LH9HDH[C H=A`7 :H=q HW Ht$ ; H=q HWteHt$ U;A@ M0CD0 SA@ M0CD0 mHHD$ Ht$ Hx8.PJH땋PJHYH{IHEHl$0LL|0HVq H{XHH$ 16HIBH @ AWAVIAUIATIUSH H\$0HT$H{X5Hq Ƅ$h HHDŽ$` Ƅ$i 1HDŽ$p HDŽ$x HHDŽ$ HDŽ$ H$ Hq HhLxH}Hl$0L?=Hhq H{@HD$@ HD$H HD$P HD$X HD$` HHD$h HD$0H q H@H$ Hq HHD$8BH^q HsH{XD$x HHD$8Hq HH$ E=ML L5LHHv;HD$HH0F:M= Le5LHHG;Hq HxHD$`I<$HJ Lt$PLl$XL97 L9 M} L)HT$ 1HIAIIHHw HLLLD$?LD$HL;q Ll$ LHL$ L?HD$ HxH;=q @ Hq HHD$0Hq H@H$ Hq HHD$8H$ HxH;=q Hq H{@HHD$8g<HEHl$0H{XL|0HNq HH$ -3HĘ L[]A\A]A^A_ M9 M M)HT$ 1LL@IIHHt_HLLLD$>LD$HL;q } Ll$ LHL$ L> D AE A@f. AE A@ D HD$0HHxw = @ HD$0HHxw = @ HA Ha HsPL9C MH=Z7 C5Ll$ HI<$LH+4Hc?H= H=Z7 5H=Tq HW Ht$ 6 H=$q HWteHt$ 5A@ M0CD0 SA@ M0CD0 mHHD$ Ht$ Hxn3.PJH땋PJHYH{InHEHl$0LL|0Hq H{XHH$ 0H<H @ AWAVIAUATIUSH H\$0H$H{X0Hq Ƅ$h HHDŽ$` Ƅ$i 1HDŽ$p HDŽ$x HHDŽ$ HDŽ$ H$ HLq HhLxH}Hl$0L?s8L-q H{@HD$@ HD$H HD$P HD$X HD$` IEHD$h HD$0IE@H$ H.q HHD$8=Hq HsH{XD$x HHD$8Hq HH$ 7M L 0LHH6H$HH04HCq HxHD$`I<$H
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H$ H9$ u H$H L$ LHD$HH$ HD$@H$ HD$`H$ HD$(IH$ H+$ HD$pH$ L$ 11Lt$HAM f$ HT$@HPH f$ H$ H$ HPHD$P H$ L$ MHD$0HLMMIf D AH|$0LLf$ AU f$ HT$(JAVHHHf$ AUf$ L AVHHHf$ AUf$ LAVHHHf$ AUf$ LɼAVHHHf$ AUf$ L螼AV
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f D p$ Kf D A p$ D A p$ D pAf p$ D pt @ p$ f D p$ 7f D T$(t$(X51 t$(D$( D$(l$(X-1 l$(D$( D$(d$(X%1 d$(D$(j L$(\$(X j1 \$(L$([DŽ$ DŽ$ 8E1DŽ$ DŽ$ E1DŽ$ 8DŽ$ 1D$p DŽ$ 81DŽ$ D$` 1DŽ$ 8DŽ$ A 8D$X DŽ$ 81DŽ$ D$P 1DŽ$ D$H 1DŽ$ 8DŽ$ D$( f. AWLAVAUATUSH H$P H$ J HE$P H$X H$ J4 H$ LL$ L$ H$P LHE$X H$X H$@ HH$@ HI$@ H?LH$ LH$ HH$ H)1H HHOHHH$ H$ H$P H$` H$ H$ HDŽ$ H$X HH$h H$ H$ HH$h H$ H$X HH$ HH$ H<WH$ H$P H$ HH$ HHHHHHH$ HHH$ HHHH$ H$@ HH$X H$ H$ H$ H$ HD$hIH$ HH$ HD$`H$ H$ H$ H$ HDŽ$` H9H$ HNH$ H$ H$ H$X H$ H9$ p H$` H$P H$ H$ H|$xH$` HpIH$ H$p H$x HpHH$h H$X H$ H$p H$ H$ H$ H$ H$0 H$ H$ H$ H$ H$@ H$ H$ H$ H$ H$ HD$pH$ f D H$ 11E1E1E11E1E1f$ H$ f$ H$` IfD$P fD$0 HVHH$ fD$@ f$` fD$p HfD$ HH HIH$ H$x AK HHH HIH$( H$p AI HH$h HH HIH$0 1HAJ HP1H$ H H$8 H$X f$ f$ #
+f$ LsAV HHHf$ AU f$ LHAVHHHH\$8Ld$(Hl$0f$ AUf$ LHD$hIIL$ LT$8LH+$ H9$ eLMMIH$ 1HD$0H$ H9$ }_H\$8Ld$XLMLt$pLl$0f D AlLLLf$ kf$ HT$(dHL9uH\$8Ld$X1Lt$PHT$`f$ $ LH|$(f$ Af$ HD$@f$ fAH$0 HD$HHD$@H;$ H$ H$8 H$ H$ H;$` H( []A\A]A^A_Ð8 AA
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f D p$ Kf D A p$ D A p$ D pAf p$ D pt @ p$ f D p$ 7f D T$(t$(X5r2 t$(D$( D$(l$(X-J2 l$(D$( D$(d$(X%"2 d$(D$(j L$(\$(X 2 \$(L$([DŽ$ DŽ$ 8E1DŽ$ DŽ$ E1DŽ$ 8DŽ$ 1D$p DŽ$ 81DŽ$ D$` 1DŽ$ 8DŽ$ A 8D$X DŽ$ 81DŽ$ D$P 1DŽ$ D$H 1DŽ$ 8DŽ$ D$( f. AWLAVAUATUSH H$P H$ J HE$P H$X H$ J4 H$ LL$ L$ H$P LHE$X H$X H$@ HH$@ HI$@ H?LH$ LH$ HH$ H)1H HHOHHH$ H$ H$P H$` H$ H$ HDŽ$ H$X HH$h H$ H$ HH$h H$ H$X HH$ HH$ H<WH$ H$P H$ HH$ HHHHHHH$ HHH$ HHHH$ H$@ HH$X H$ H$ H$ H$ HD$hIH$ HH$ HD$`H$ H$ H$ H$ HDŽ$` H9H$ HNH$ H$ H$ H$X H$ H9$ p H$` H$P H$ H$ H|$xH$` HpIH$ H$p H$x HpHH$h H$X H$ H$p H$ H$ H$ H$ H$0 H$ H$ H$ H$ H$@ H$ H$ H$ H$ H$ HD$pH$ f D H$ 11E1E1E11E1E1f$ H$ f$ H$` IfD$P fD$0 HVHH$ fD$@ f$` fD$p HfD$ HH HIH$ H$x AK HHH HIH$( H$p AI HH$h HH HIH$0 1HAJ HP1H$ H H$8 H$X f$ f$ #
L$ H$ IE1H D L$ 1AM`f$ Ht$hHLH$ f$ Pf$ AU f$ AUf$ AUf$ AUf$ H$ &HT$xHt$hHHHD$hH$ HLLPLLT$x]LT$xH$ HHLBHD$hH$ HLLPLLT$x~LT$xH$ HHLcHD$hH$ HLLHLLL$hLL$hH$ HHLH$ H$ H$ LLHPHD$hf$ Pf$ AUf$ AU
f$ AU f$ AUf$ HT$pذH$P LLH°Ld$`H$ LHLH$` LLd$`LHMfH$0 LLHBH$p LLHMf(H$@ LLHH$ LLH̩HD$hH$ H$ LLHPf$ P
f$ AUf$ AUf$ AUf$ AUf$ HT$p踯H$P LLH袯Ld$`H$ LHLH$` LLd$`LHMfإH$0 LLH"H$p LLHMfH$@ LLH¨H$ LLH謨HD$hH$ H$ LLHP f$ Pf$ AUf$ AUf$ AU f$ AU f$ HT$p蘮H$P LLH肮Ld$`H$ LHLפH$` LLd$`LHMf踤H$0 LLHH$p LLHMfH$@ LLH袧H$ LLH茧A HD$hH$ H$ LLHPf$ Pf$ AU f$ AU"f$ AU$f$ AU&f$ HT$ppH$P LLHZLd$`H$ LHL诣H$` LLd$`LHMf萣H$0 LLHکH$p LLHMfH$@ LLHzH$ LLHdHD$hH$ H$ LLHPf$ Pf$ AU(f$ AU*f$ AU,f$ AU.f$ HT$pPH$P LLH:Ld$`H$ LHL菢H$` LLd$`LHMfpH$0 LLH躨H$p LLHMf蠨H$@ LLHZH$ LLHDHD$hH$ H$ LLHPf$ Pf$ AU0f$ AU2f$ AU4f$ AU6f$ HT$p0H$P LLHLd$`H$ LHLoH$` LLd$`LHMfPH$0 LLH蚧H$p LLHMf耧H$@ LLH:H$ LLH$HD$hL$ LLH$ Lt$hLLP H$ f$ P HD$pf$ AU8H$ f$ AU:f$ AU<f$ AU>f$ HH$P LLHHHD$xשH$ Ht$`LLHH$ $H$` Ht$`LHHH$ H$0 MVLLHLH$ L$ 4L$ H$p LHHH$ L H$@ MVLLHLH$ L$ 订L$ H$ LHHH$ L腢H$ I@H L$ IL9$ L$ L$@ H$ H$ H9$ HL$hIL$ H$ L$H L$P MMIHyHqH$ HyH$ H$ E1fD$ AHt$hHLf$ AWf$ AU f$ AUf$ AUf$ AUf$ H$ 迧HT$xHt$hHH誧H$ H$ HLH$ H$ HHH$ H$ HL)H$ H$ HHH$ H$ HLàH$ H$ HH訠IIIL;$ L$H L$P 1E11f$ $H E1fD$ L$ f$ fD$ L$( Hl$hf$ AE H$ H$ Hf$ AEf$ A$f$ AD$f$ 0H$ H|$xHf$ H$ H$ Hf$ H$ HHf$ f$ $ HH$ H$ fAE $ fAE$ L$0 fA$$ fAD$AE L$8 f$ AEf$ A$f$ AD$f$ >H$ H$ Hf$ H$ H$ Hf$ H$ HHf$ f$ $ H$ fAE $ fAE$ fA$$ fAD$H$ H$ H$ H9$ H$` H$ H$X H$` H9$ KH$ H$ H$ H$ H$ H9$@ H$ H$ L$ Lt$xH$ H9$ H$ H$ H$ H$ H$ H$ H$ H$ H$ H$ H$ H$ H$ H\$pH H$ H$ 1L$ E1E1E1HNHAf$ LfD$ H$ H$ H<JO H$ fD$ fD$ ML$ IH$ E1LH$ 1f$ AHL$hLH$ f$ AVf$ AU f$ H$ 苢H$ H$ LHpAVHL$xHLf$ AVf$ AUf$ HT$p4HHHL#AVHL$xHLf$ AV
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f$ HT$p HHHLAVHL$xHLf$ AVf$ AU f$ HT$p賠HHHL袠AV HD$xHH|$pH$ HHD$hf$ AV H$ f$ AUf$ HLKHHHLH$ 2II H$ HH9$ 0H$ H$@ LL$ L$ H$ H9$ H$ H$ MIIf D 1f$ AHL$hHHf$ AVf$ CT} f$ H$ oH$ HHLYIIL;$ uH$ $H L$ 11f$ L|$hf$ H$ H$ f$ ALf$ AFf$ H$ H$ Lf$ f$ $ H$ fA$ fAFH$ H$ H$ H9$ H$ H$ H$ H$ H;$@ 9Lt$xH$ H$ H$ H9$ ~'H$ L$ H$ HD$hH$ D$H L$ H$ H$ H$ HD$PH$ H|$hHD$8 D$( HD$HH$@ HD$@H$ HD$0H$h HD$ H$` HD$H$X HD$H$P HD$H$ H$HH []A\A]A^A_f D UHAWAVAUATSH H]HHHHLHC IHHCH9HNHHLHLH H;HH8K HHDž( H= M
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+ H S HHHDž0 =Z2 HHHhHHHHEH@HEHHHHHH HXHHHHHHHHHEHHHHHHhH0H]HHPHHH0H9HNIH)IHHHHHHXHHH@HCHHHp~OHH$ E1IxHH}H}IHHHBHUHE xHX HXL1L< H@N48HEHxHEHpHH;X LXHLHxHpHH$ `LmMHEȸ I)ILOMiLX`H H HfT$LXH}HpH8MHEHExHD$( HD$ HD$HEHEHD$H $HxHH H`HH0HH H9 HH0HhHH@HHHHH0H9wH v HSH t H(>HHe[A\A]A^A_]H iHH@LeHDž HH`HHHJ`HHL)HH8HXHHH H(HHH9HNHX IHX HDž HHNH8H`H(HHHHH0HHEHxHEHH` P HHHH ]A5 HDžp H(H(HHEHL<WHHL GHHEHHGHPHpH`H9p Hp LPE152 f MGob EfA 1A EA
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L|$`HT$.H5 LN'Lt$PHT$-H5Ռ$ L5'HD$@HT$,H51# HH'H$ $ LHHԼH5Q H HB L H
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6% H#! D$ $ E11H,N L H
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3% H ! D$ $ E11H'K L| H
3% Hs% D$ $ E1ɾ HJ LF H
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$ H`% D$ $ E11HNG L H
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$ H% D$ $ E11HYK L$ H[TW H50 H8 H=Y H8H=Y HH$ HHxPH$ HHx<ͽ H=Y 4 HD$@Ll$pH5~$ HDŽ$ HDŽ$ HLHL|$`HT$0H5 $ L/H$ % LLHƼH5B H HX L- H
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n$ HLO D$ $ E11H;. L H
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l% H'( D$ $ E11H L! H
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% H,' D$ $ E11H L H
% H% D$ $ E1ɾ H L H
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' H# D$ $ E11H L% HX H5&! H L% HwX H5~ H H=SZ HL
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% H<' D$ $ E11HA L2:# H
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L2! H
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u% H# D$ $ E11H= LF% HX H5z H L% H]X H5z H LD$pH́% H5 H L$ H% H59' H H$ HHӄ H=OZ HH=OZ *HCHD$PHHx*HD$`HHxn*H$ H HH H=OZ <4{HͰHDŽ$ Ht&H$ HH$ HH$ L|$@L$ H5
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H H LD$`H % H5% H L! H
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w% HJ! D$ $ E11H L9 H
Bt% H% D$ $ E1ɾ H L! H
^t% H% D$ $ A H Lο H
gH% HB% D$ $ E1ɾ Hi L! H
H% H% D$ $ A H2 LW! H
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H=9IZ - HDŽ$ &'H$ H Lt$PL$ H5K% H$ H4hLLH$ 'L|$@Ll$pH5U LL'H$ LLHH5 H{ H} L! H
% H x& D$ $ E11H] L! H
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GZ !H:HD$pHHx!H$ HHxk!H$ H HHu H=FZ 4+L HDŽ$ $H$ H Lt$PL$ H5I% H$ H4eLLH$ !%L|$@Ll$pH5 % LL%H$ LLHJH5 H| H~z L#! H
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#o% Hj% D$ $ A Hb L H
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HC% H % D$ $ A H L ! H
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% Hs*' D$ $ E11H Lj H
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% H۴# D$ $ E11H LC% HxX H58! H/ H$ HHr H=<Z HmH=>Z H1HD$PHHx8HD$`HHx'H$ H HH @ H==Z ! HDŽ$ VH$ HGlLt$PL$ H5?% H$ H[LLH$ L|$@Ll$pH5,J LLH$ LLH
+H5̲ Hs H>q Lf H
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]% H% D$ $ A H胿 L^! H
^% H D$ $ A HL L^! H
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j% Hh& D$ $ E11H= L?c H
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% H?f& D$ $ E11H} L` H
% H f& D$ $ E1ɾ HI LY! H
V% HҬ# D$ $ E11H舾 LV% H
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D% H! D$ $ E11H= Lb! H
'% Hƪ# D$ $ E11H| LN% HoX H5#! H HN% H5$ A H HRO% H5$ A H HO% H5$ E1 H HP% H5$ E1 H HR% H5K$ A H H$ HHg H= 2Z HH=3Z
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H$ LLHRH5 Hh Hf H/F% H57# E1 H8 L[ H
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J% HE% D$ $ E1ɾ HO LQ[ H
K% H4& D$ $ E11H莹 LK% H@lX H5[ H, LK% HlX H5Z H
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D% H<' D$ $ E11HG LFY H
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E% H|% D$ $ E1ɾ H߲ LX H
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:% HX& D$ $ E11H Ly! H
(;% HX& D$ $ E1ɾ H躬 Lx! H
\;% HiX& D$ $ E1ɾ H膬 Lx! H
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% H! D$ $ E11H豨 Lt! H
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I% H T& D$ $ A HF Lkt! H
% HϚ# D$ $ E11H腬 L5% H_X H5,t! H# H$ HHX H="Z HaٺH=$Z H}HD$PHHx,HD$`HHxHD$pHHx
+H$ HV HHD H=Y$Z H Ll$pHT$0H5[ HDŽ$ LH$ HgH$ HgH$ HD$`HT$/H5%% HILt$PHT$.H5g$ LH$ ~ LLHǺH5 H{Y HW Ha% H5{ E1 H HC% H5- " E1 H袯 H%% H55 E1 H脯 H% H5 E1 Hf H% H5 M H船 L$ H% H5%' H腲 Lq! H
% H! D$ $ E11H褥 Lq! H
p% H% D$ $ E1ɾ Hp L H
<% Hf$ D$ $ A H9 L^q! H
% HP& D$ $ A H L'q! H
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m% H(O& D$ $ E11Hf Lo! H
l% HO& D$ $ E1ɾ H2 LWo! H
Fk% HN& D$ $ E11Hq Lz)% HZX H5o! H H= Z H`ԺH= Z H|H$ HHx(H$ HHxR H= Z *HD$`H$ H5!% HH%Lt$PL$ H5k% LLH$ l HLHKúH5
HT HR L$n! H
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4K& Hf! D$ $ E11H$ H% H5oi% A HS L$ H% H5Ri% H萬 L$ H% H5:i% Hm Lk! H
g% HF# D$ $ E11H L&% HWX H5k! H蚑 H=\Z HкH= Z HHD$`HHxHD$pHHxɲH=Y Z L|$@Ll$pH5 % HDŽ$ HDŽ$ LLHD$`HT$0H5h% HIH$ V LLHѿH5 HrQ HO Lj! H
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+ H2F H:F La! H
% H& D$ $ E11Hy Lj" H
% H D$ $ E1ɾ HE L6" H
% H D$ $ E1ɾ H L" H
% H#$ D$ $ A Hڔ L`! H
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+ HC HC H^ % H54# A Hl H % H5-Y% E1 HN L#_! H
% H D$ $ E11H͒ L^! H
W % H9=' D$ $ E11H Lm% H.KX H5^! H誄 H$ HHC H=_
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+ HA HA H% H5q E1 Hh LD$`H<Y% H5$W% H L ]! H
% H D$ $ E11Hǐ L\! H
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Z HHD$pHHxH$ HHxH$ HV HHDf D H=
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+ H? H? H W% H5 W% E1 He HW% H5W% E1 HG L [! H
V% H! D$ $ E11HƎ LZ! H
V% HO# D$ $ E11H Lf% HgGX H5Z! H裀 H$ HH ? H=Z Z HῺH= Z HHD$pHHxH$ HHxH$ HH HH6 D H= Z dHjHDŽ$ Ht&H$ H%NH$ H&NH$ L$ HT$0H52 % D$` D$P ?LBLl$pHT$/H5" L)H$ r LLHn
+ H= H= LD$PHU% H5U% H LD$`HT% H5 U% H LY! H
T% H! D$ $ E11H踌 LX! H
T% HA# D$ $ E11H LX% HyEX H5X! H~ H$ HH< H=MZ HӽH= Z HHD$pHHxH$ HHxH$ H- HH H= Z THhHDŽ$ Ht&H$ HLH$ HLH$ Lt$PL$ H5"
+% LL?L|$@Ll$pH5' LL#H$ a LLHh H; HjT% H5S% E1 H] HLT% H5 S% E1 H? LW! H
5S% H D$ $ E11H辊 LV! H
% H*5' D$ $ E11H L^ % HCX H5V! H| H$ HH: H=TZ HٻH=Z HHD$pHHxH$ HHxH$ H& HH D H=Z \HfHDŽ$ Ht&H$ H JH$ H JH$ HD$`HT$0H5-% HDŽ$ HD$p HI5Lt$PHT$/H5t# L H$ T LLHa H9 LD$pH% H5# H贕 L$ H% H5ף# H葕 LU! H
% H D$ $ E11H谈 LT! H
% H 3' D$ $ E11H LP
+% HAX H5T! Hz H$ HH8 H=GZ H˹H=Z HHD$PHHxHD$`HHxH$ H HHfH=Z TٚHD$`H$ H5`% HH}Lt$PL$ H5P% LL^H$ 4 HLH裨
+ H7 H7 H% H5eP% E1 H萐 LeS! H
% HLP% D$ $ E11H L4S! H
% H'P% D$ $ E1ɾ Hۆ L S! H
% HO% D$ $ E1ɾ H视 LR! H
% H" D$ $ E1ɾ Hs LR! H
% HO% D$ $ E11H貊 L% H?X H5YR! HPx H= Z H衷H=Z ܶHH$ HHxiܶH$ HHxUܶݘH=Z DHbHDŽ$ Ht&H$ HFH$ HFH$ L|$@L$ H5% D$` ?LL'Ll$pHT$0H5# LH$ ' LLHS
+ H5 H5 L3Q! H
M% H D$ $ E11H݄ LQ! H
M% HI/' D$ $ E11H L}% H >X H5P! Hv LD$`HL% H5J% Hz H$ HH4 H=VY HصH=AZ ڶHHD$pHHxڶH$ HHxڶH$ H HH @ H=Z \ǖ HDŽ$ ݶH$ H\ Lt$PL$ H5C% H$ HLLH$ IL|$@Ll$pH5K% LL-H$ LLHr
+ H3 H3 LRO! H
K% H7 D$ $ A 1H L O! H
K% H
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K% E1 H~ H$ HH2 H=Y H蜳H= Z ضHHD$pHHxgضH$ HHxSضH$ H HH飔H=Y $HD$`H$ H50 % HHMܶLt$PL$ H5vJ% LL.ܶH$ HLHs H1 HW % H5=J% E1 Hh L=M! H
% H$J% D$ $ E11H L M! H
% HI% D$ $ E1ɾ H賀 LL! H
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HHHޭH=M HW PHt$4AH=WM HW'
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HHHحH=wM HW PHt$$ѭAH=GM HW'
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HHHҭH=gM HW PHt$˭AH=7M HW'
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HHH̭H=WM HW PHt$ŭAH='M HW'
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IL9uJ<9HtɿIE HxH9 I(L9,$zH$ Ht蘿H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?MuHE H}1H ậHE KD5 H}ǾI$HxH9 IPHPL9$$TH$ Ht蒾H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?MuHE H}1H ṭHE KD5 H}ǽI$HxH9 IPHPL9$$TH$ Ht蒽H|$ H$ H 蜾H$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(Hĸ []A\A]A^A_HHCHf?H7pHt$8HL$HT$荻HL$HT$HH$ LHxGH$ LHx3H$ LHx HD$PHt$LHx HD$`LHxH$ HtH|$0 HHHHH=M HW Ht$8H=M HW Ht$8莺H=M HW mHt$8HL$WHL$TIHxH9u^IL9u|H=[M HW |LoPJH!PJHCpDFD@Ht$8HL$HT$HL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H WH$0 JLt$ I 飰I~p I~XH$ I~ 6H$ LHx¶bH$ HHtHD$ H Hj}H{H=͘M HWtNiHt$8|ZH=M HWt-&Ht$8NPJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=M HW sHt$8谷dH=ӗM HWu L耷H=M HW GHt$8N8H=qM HW ,Ht$8 H=?M HW Ht$8궭H=
M HW ,Ht$8踶 H=ۖM HW Ht$8膶H|$ H买TH=M HWt0HHt$8F9PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5 AVAUATUSH HD$pH|$(HHD$0AHD$`HT$NH5 HHD$8賷Ll$MLt$PH5 LL藷HD$PH M Ld$OHT$`H5 H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ 8LI
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IL9uJ<9Ht飭IE HxH9 I(L9,$zH$ Ht踣H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL? MuHE H}1H HE KD5 H}碭I$HxH9 IPHPL9$$TH$ Ht貢H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL? MuHE H}1H HE KD5 H}硭I$HxH9 IPHPL9$$TH$ Ht財H|$ H$ H 輢H$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(Hĸ []A\A]A^A_HHCHf?H7pHt$8HL$HT$譟HL$HT$HH$ LHxgH$ LHxSH$ LHx?HD$PHt$LHx,HD$`LHxH$ HtH|$0 HH跦HHH=5M HW Ht$8H=M HW Ht$8讞H=~M HW mHt$8HL$wHL$TIHxH9u^IL9u|H={~M HW |L(oPJH!PJHCpDFD@Ht$8HL$HT$HL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H wH$0 jLt$ I I~p I~XH$ I~ VH$ LHx⚭bH$ HHtHD$ H Hj蝘H{H=|M HWtNiHt$8蜜ZH=|M HWt-&Ht$8nPJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=%|M HW sHt$8ЛdH={M HWu L蠛H={M HW GHt$8n8H={M HW ,Ht$8< H=_{M HW Ht$8
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HHHlUSHHHG UfHHHCHHCHH[]HKHHt
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HHHgH=ofN HW PHt$_AH=?fN HW'
+L_H$ IHtH HH$` HtH HH FfH$ `H HHÐPJH5IHPvLIH$ HtH0 HH$ HVH H@H$` IHJH H4PJHf D AWH5?~ AVAUATIUSH Hl$@HHT$ H|$0H5^ `HD$ HL$ H5H HHH$HL$m`HD$ HT$0H\$`Lt$ H5\B H{HD$`HbN HT$hLHHD$ HD$0HD$p"`HC(H
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HHHaH=_`N HW PHt$YAH=/`N HW'
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HHH[H=OZN HW PHt$SAH= ZN HW'
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HHHĈ L[]A\A]A^A_|JIH MH{p4KH{X+KH$ H{ LgdHD$xLHxFLHD$pLHx5LHD$hLHx$LHD$`LHxLHD$ Ht$ Hx LHD$0H4$HxKHD$PHt
HHHUH=?TN HW PHt$MAH=TN HW'
+LMH$ IHtH HH$` HtH HH TH$ YNH HHÐPJH5IH dLIH$ HtH0 HH$ HVH H@H$` IHJH H4PJHf D AWH5l AVAUATUSH HD$pH|$(HHD$0HD$`HT$NH5' HHD$8SNLl$MLt$PH5 LL7NHD$PH PN Ld$OHT$`H5$0 H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ MLI
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IL9uJ<9HtHIE HxH9 I(L9,$zH$ HtXHH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?GMuHE H}1H CHE KD5 H}GI$HxH9 IPHPL9$$TH$ HtRGH$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?FMuHE H}1H BHE KD5 H}FI$HxH9 IPHPL9$$TH$ HtRFH|$ H$ H [H$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(Hĸ []A\A]A^A_HHCHf?H7pHt$8HL$HT$MDHL$HT$HH$ LHxBH$ LHxAH$ LHxAHD$PHt$LHxAHD$`LHxAH$ HtH|$0 HHGKHHH=IN HW Ht$8CH=IN HW Ht$8NCH=IN HW mHt$8HL$CHL$TIHxH9u^IL9u|H=CIN HW |LBoPJH!PJHCpDFD@Ht$8HL$HT$BHL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H HH$0
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+ H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X n H H$8 H9! H, H=HH1I9 L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ s=H$ H, HDŽ$ H$ H.H$ 7=H$ Hx,HDŽ$ H$ H-/H$ HD$ HP HD$PHDŽ$ HxH9 HD$`HxH9a Ht$0H|$ L_ HL H5̮ H L[ H}L H5 H H5 H H55 H3 Hƒ H5^ Hr H|$(H5\H$ HtHt$ H HLt$ IP A H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H @H$0 /;H$ H$ H9H$U LeHE IMIĐLH)J!HxH9p JT9N49L9 H=@N f IHxH9
IL9uJ<9Ht:IE HxH9 I(L9,$zH$ Hth:H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?9MuHE H}1H 5HE KD5 H}9I$HxH9 IPHPL9$$TH$ Htb9H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?8MuHE H}1H 4HE KD5 H}8I$HxH9 IPHPL9$$TH$ Htb8H|$ H$ H MH$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(Hĸ []A\A]A^A_HHCHf?H7pHt$8HL$HT$]6HL$HT$HH$ LHx4H$ LHx4H$ LHx3HD$PHt$LHx3HD$`LHx3H$ HtH|$0 HHW=HHH=
<N HW Ht$85H=;N HW Ht$8^5H=;N HW mHt$8HL$'5HL$TIHxH9u^IL9u|H=S;N HW |L4oPJH!PJHCpDFD@Ht$8HL$HT$4HL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H :H$0 5Lt$ I i2I~p0I~Xw0H$ I~ IH$ LHx1bH$ HHtHD$ H HjM/H{H=9N HWtNiHt$8L3ZH=9N HWt-&Ht$8 3PJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=8N HW sHt$82dH=8N HWu LP2H=8N HW GHt$8 28H=i8N HW ,Ht$81 H=78N HW Ht$81H=8N HW ,Ht$81 H=7N HW Ht$8V1H|$ HdHTH=7N HWt0HHt$819PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5ZP AVAUATUSH HD$pH|$(HHD$0HD$`HT$NH5G HHD$8s2Ll$MLt$PH5) LLW2HD$PH 4N Ld$OHT$`H5D H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ 1LI
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+ H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X { H H$8 H9! H, H"HH1I L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ !H$ H HDŽ$ H$ HH$ W!H$ HHDŽ$ H$ HMH$ HD$ HP 4v HD$PHDŽ$ HxH9 HD$`HxH9a Ht$0H|$ %} LC HL H5 H L? HuL H5l H H5 H"x H5U HSy Hv H5B Hx H|$(HU@H$ HtHt$ H HLt$ IP aw H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H %H$0 O H$ H$ H9H$U LeHE IMIĐLH)J!HxH9p JT9N49L9 H='$N f IHxH9
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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, L0HL1I༨ L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` H$P H1 HDŽ$ H$h HH$` tH$p HHP HDŽ$ H$ H㞫H$ v HD$ HDŽ$p HxH;=bH HD$0HxH;=LH K HH H5 HD H5' Hu H H5 H贠 HL ̫HHD$PHt
HHHĈ L[]A\A]A^A_IH ѨH{p覫H{XߦH$ H{ LHD$xLHx誻HD$pLHx虻HD$hLHx舻HD$`LHxwHD$ Ht$ HxdHD$0H4$HxRHD$PHt
HHHĨH={H HW Ht$(H=KH HW& NLAH$ IHtH HH$` HtH HH ʯH$ 载H HHPJH6IHտLIH$ HtH0 HH$ HWH HAH$` IHKH H5PJHff. AWH5њ 1AVAUATIUSH Hl$@HHT$ H|$0H5 нHD$ HL$ H5 HHH$HL$諽HD$ HT$0H\$`Lt$ H5 H{HD$`HH HT$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$( H H$ H9 L, L^HL1I L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` H$P H_ HDŽ$ H$h HߘH$` 袺H$p H3HP HDŽ$ H$ HH$ 褢 HD$ HDŽ$p HxH;=H HD$0HxH;=zH K HH1 H5.d Hr H5U H裛 H6 H57 } H HL7ƫHHD$PHt
HHHĈ L[]A\A]A^A_IH H{pH{X
H$ H{ LIHD$xLHxصHD$pLHxǵHD$hLHx趵HD$`LHx襵HD$ Ht$ Hx蒵HD$0H4$Hx耵HD$PHt
HHH!H=H HW Ht$VH=yH HW& NL(AH$ IHtH HH$` HtH HH H$ 뷨H HHPJH6IHLIH$ HtH0 HH$ HWH HAH$` IHKH H5PJHf AWH5 1AVAUATIUSH Hl$@HFHT$ H|$0H5L HD$ HL$ H56 HHH$HL$۷HD$ HT$0H\$`Lt$ H5* H{HD$`HH HT$hLHHD$ HD$0HD$p萷HC(H
- DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( 9 H H$ H9 L, L莵HL1I> L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` H$P H HDŽ$ H$h HH$` ҴH$p HcHP HDŽ$ H$ HAH$ Ԝ HD$ HDŽ$p HxH;=H HD$0HxH;=H K HHa H5 H袔 H5 Hӕ Hf H5g H HLgHHD$PHt
HHHĈ L[]A\A]A^A_>IH /H{pFH{X=H$ H{ LyHD$xLHxHD$pLHxHD$hLHx毨HD$`LHxկHD$ Ht$ Hx¯HD$0H4$Hx谯HD$PHt
HHHQH=ّH HW Ht$膱H=H HW& NLXAH$ IHtH HH$` HtH HH (H$ H HHPJH6IH3LIH$ HtH0 HH$ HWH HAH$` IHKH H5PJHf AWH5 1AVAUATIUSH Hl$@HvHT$ H|$0H5| 0HD$ HL$ H5f HHH$HL$ HD$ HT$0H\$`Lt$ H5Z H{HD$`HHH HT$hLHHD$ HD$0HD$pHC(H
- DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( i H H$ H9 L, L辯HL1In L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` >H$P H HDŽ$ H$h H?H$` H$p HHP HDŽ$ H$ HqH$ HD$ HDŽ$p HxH;=H HD$0HxH;=ڋH K HH葓 H5ψ HҎ H5 H H薍 H5 HB HL藺H?HD$PHt
HHHĈ L[]A\A]A^A_nIH _H{pvH{XmH$ H{ L詮HD$xLHx8HD$pLHx'HD$hLHxHD$`LHxHD$ Ht$ HxHD$0H4$Hx੨HD$PHt
HHH聳H= H HW Ht$趫H=ًH HW& NL舫AH$ IHtH HH$` HtH HH XH$ KH HHPJH6IHcLIH$ HtH0 HH$ HWH HAH$` IHKH H5PJHf AWH5P 1AVAUATIUSH Hl$@HHT$ H|$0H5 `HD$ HL$ H5 HHH$HL$;HD$ HT$0H\$`Lt$ H5 H{HD$`HxH HT$hLHHD$ HD$0HD$pHC(H
- DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( 虖 H H$ H9 L, LHL1I螥 L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` nH$P H HDŽ$ H$h HoH$` 2H$p HÄHP HDŽ$ H$ HH$ 4 HD$ HDŽ$p HxH;= H HD$0HxH;=
-H K HH H5D H H5 H3 HƇ H5Ǿ x Hr HLǴHoHD$PHt
HHHĈ L[]A\A]A^A_螢IH 菑H{p規H{X蝏H$ H{ L٨HD$xLHxhHD$pLHxWHD$hLHxFHD$`LHx5HD$ Ht$ Hx"HD$0H4$HxHD$PHt
HHH豭H=9H HW Ht$楨H= H HW& NL踥AH$ IHtH HH$` HtH HH 舘H$ {H HHPJH6IH蓨LIH$ HtH0 HH$ HWH HAH$` IHKH H5PJHf AWH5 1AVAUATIUSH Hl$@HHT$ H|$0H5 萦HD$ HL$ H5 HHH$HL$kHD$ HT$0H\$`Lt$ H5 H{HD$`HH HT$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$( ɐ H H$ H9 L, L HL1IΟ L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` 螣H$P H HDŽ$ H$h HH$` bH$p H~HP HDŽ$ H$ HсH$ d HD$ HDŽ$p HxH;=PH HD$0HxH;=:H K HH H57 H2 H5 Hc H H5 s H袃 HLH蟤HD$PHt
HHHĈ L[]A\A]A^A_ΜIH 迋H{p։H{X͉H$ H{ L HD$xLHx蘞HD$pLHx臞HD$hLHxvHD$`LHxeHD$ Ht$ HxRHD$0H4$Hx@HD$PHt
HHH᧨H=iH HW Ht$H=9H HW& NL蟨AH$ IHtH HH$` HtH HH 踒H$ 諠H HHPJH6IHâLIH$ HtH0 HH$ HWH HAH$` IHKH H5PJHf AWH5 AVAUATIUSH Hl$@HHT$ H|$0H5 轠HD$ HL$ H5 HHH$HL$蘠HD$ HT$0H\$`Lt$ H5 H{HD$`H|H HT$hLHHD$ HD$0HD$pMHC(H
- DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( H H$ H9 L, LKHL1I L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` ˝H$P HLy HDŽ$ H$h H{H$` 菝H$p H yHP HDŽ$ H$ H{H$ 葅 HD$ HDŽ$p HxH;=}zH HD$0HxH;=gzH K HH H5w H_} H5B H~ H#| H5$ i H} HL$H̞HD$PHt
HHHĈ L[]A\A]A^A_IH 셫H{pH{XH$ H{ L6HD$xLHxŘHD$pLHx贘HD$hLHx裘HD$`LHx蒘HD$ Ht$ HxHD$0H4$HxmHD$PHt
HHHH=zH HW Ht$CH=fzH HW& NLAH$ IHtH HH$` HtH HH 匫H$ ؚH HHPJH6IHLIH$ HtH0 HH$ HWH HAH$` IHKH H5PJHf D AWH5 1AVAUATIUSH Hl$@H6HT$ H|$0H5< HD$ HL$ H5& HHH$HL$˚HD$ HT$0H\$`Lt$ H5 H{HD$`HwH HT$hLHHD$ HD$0HD$p耚HC(H
- DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( ) H H$ H9 L, L~HL1I. L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` H$P Hs HDŽ$ H$h HuH$` H$p HSsHP HDŽ$ H$ H1vH$ HD$ HDŽ$p HxH;=tH HD$0HxH;=tH K HHQ| H5q Hw H5u Hx HVv H5W Hx HLWHHD$PHt
HHHĈ L[]A\A]A^A_.IH H{p6~H{X-~H$ H{ LiHD$xLHxHD$pLHx璨HD$hLHx֒HD$`LHxŒHD$ Ht$ Hx貒HD$0H4$Hx蠒HD$PHt
HHHAH=tH HW Ht$vH=tH HW& NLHAH$ IHtH HH$` HtH HH H$ H HHPJH6IH#LIH$ HtH0 HH$ HWH HAH$` IHKH H5PJHf AWH5, 1AVAUATIUSH Hl$@HfHT$ H|$0H5l HD$ HL$ H5V HHH$HL$HD$ HT$0H\$`Lt$ H5J H{HD$`H8qH HT$hLHHD$ HD$0HD$p谔HC(H
- DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( Y H H$ H9 L, L讒HL1I^ L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` .H$P Hm HDŽ$ H$h H/pH$` H$p HmHP HDŽ$ H$ HapH$ y HD$ HDŽ$p HxH;=nH HD$0HxH;=nH K HHv H5k Hq H5 Hr Hp H5 H2r HL臝H/HD$PHt
HHHĈ L[]A\A]A^A_^IH OzH{pfxH{X]xH$ H{ L虑HD$xLHx(HD$pLHxHD$hLHxHD$`LHxHD$ Ht$ Hx⌨HD$0H4$HxЌHD$PHt
HHHqH=nH HW Ht$覎H=nH HW& NLxAH$ IHtH HH$` HtH HH HH$ ;H HHPJH6IHSLIH$ HtH0 HH$ HWH HAH$` IHKH H5PJHf AWH5d 1AVAUATIUSH Hl$@H薪HT$ H|$0H5 PHD$ HL$ H5 HHH$HL$+HD$ HT$0H\$`Lt$ H5z H{HD$`HhkH HT$hLHHD$ HD$0HD$pHC(H
- DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ DŽ$ ?HDŽ$( y H H$ H9 L, LތHL1I莈 L$ DŽ$0 Ƅ$4 DŽ$8 DŽ$< DŽ$@ DŽ$D DŽ$H Ƅ$L HDŽ$` ^H$P Hg HDŽ$ H$h H_jH$` "H$p HgHP HDŽ$ H$ HjH$ $t HD$ HDŽ$p HxH;=iH HD$0HxH;=hH K HHp H5e Hk H5բ H#m Hj H5 Hbl HL跗H_HD$PHt
HHHĈ L[]A\A]A^A_莅IH tH{prH{XrH$ H{ LɋHD$xLHxXHD$pLHxGHD$hLHx6HD$`LHx%HD$ Ht$ HxHD$0H4$Hx HD$PHt
HHH衐H=)iH HW Ht$ֈH=hH HW& NL計AH$ IHtH HH$` HtH HH x{H$ kH HHPJH6IH胋LIH$ HtH0 HH$ HWH HAH$` IHKH H5PJHf AWH5
- AVAUATUSH HD$pH|$(HHD$0輤HD$`HT$NH5³ HHD$8nLl$MLt$PH5 LLRHD$PH eH Ld$OHT$`H5 H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ LI
- H(DŽ$ HDŽ$ HDŽ$ H$ H$ Ƅ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HDŽ$8 HDŽ$@ HDŽ$H DŽ$P ?HDŽ$X s H H$8 H9 H, HHH1I螂 L$0 DŽ$` Ƅ$d DŽ$h DŽ$l DŽ$p DŽ$t DŽ$x Ƅ$| HDŽ$ nH$ Ha HDŽ$ H$ HodH$ 2H$ HaHDŽ$ H$ HdH$ HD$ HP /n HD$PHDŽ$ HxH9 HD$`HxH9 Ht$0H|$ j H5.` Hf H5 H2g Hd H5ƛ n Hqf H|$(HđH$ HtHt$ H HLt$ IP o H$ HtI0 HH$ HtHt$ H HH$ HtHt$ H HHD$ H {vH$0 nH$ H$ H9H$T LeHE IMILH)J!HxH9p JT9N49L9 H= cH f IHxH9
IL9uJ<9HtكIE HxH9 I(L9,$zH$ Ht訃H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$f D ML+l$HD$ID HxH9$ HD$ID HxH9 K|5 Hu f D LL?
MuHE H}1H ~HE KD5 H}ׂI$HxH9 IPHPL9$$TH$ Ht袂H$ HH$H$ H9HD$ Hp@HhLpIHt$Hp8Ht$ML+l$HD$ID HxH9 HD$ID HxH9 K|5 Hu f D LL?
MuHE H}1H }HE KD5 H}ׁI$HxH9 IPHPL9$$TH$ Ht袁H|$ H$ H 謂H$ HxH9j H$ HxH9 H$ HxH9x H$ HxH9 H$ HtH|$0 HHD$(Hĸ []A\A]A^A_HHCHf?H7pHt$8HL$HT$HL$HT$HH$ LHxW}H$ LHxC}H$ LHx/}HD$PHt$LHx }HD$`LHx }H$ HtH|$0 HH视HHH=%_H HW Ht$8~H=^H HW Ht$8~H=^H HW mHt$8HL$g~HL$TIHxH9u^IL9u|H=k^H HW L~PJH!PJHCpDFD@Ht$8HL$HT$}HL$HT$pPrp3PJHyH$ HHtHD$ H HH$ HtHt$ H HHD$ H gpH$0 Z~Lt$ I gI~pfI~XfH$ I~ FH$ LHxzbH$ HHtHD$ H HjxH{H=\H HWtNiHt$8|ZH=\H HWt-&Ht$8^|PJH뮋PJHHH$ HtHt$ H0 HH$ HHD$ H HrH=\H HW sHt$8{dH=[H HWu 'L{H=[H HW GHt$8^{8H=[H HW ,Ht$8,{ H=O[H HW Ht$8zH= [H HW ,Ht$8z H=ZH HW Ht$8zH|$ H}TH=ZH HWt0HHt$8Vz9PJHPJH̋PJHPJH:PJH2PJHPJHtPJH, @ AWH5 AVAUATUSH HD$pH|$(HHD$0聗HD$`HT$NH5 HHD$8{Ll$MLt$PH5 LL{HD$PH XH Ld$OHT$`H5 H$ HCH$ LHD$PHD$`H$ H$ HxIHD$ H{LI
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LLHĨ L[]A\A]A^A_ H H9 H, ḤIvL놹HfH.ˣYHHD$pHtH|$` HHңHIHD$ Hl$ HHxȣHLkHD$0HHxȣIH\$@IH\$@Hl$ HL$pHHtH|$` HHHl$ HH$ ƣL9tIE HIHx<ȣL9ubϣfƣHHxL9uHI9ujPJ҉HH ʣHУH|$@Hx ˣnf. AWAVI AUATUSH HD$p )ˣHD$`H L|$0HT$ H5: HD$xH LHD$pˣ HPH+1E1HHD$@ HD$H HD$P HH LLl$HLl$@Hl$PLcH+LI9t#f D Ht HHƣHHI9uLd$ HT$ H5 g H\$HL%ˣH$ HqLf H
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[H$ LHx蹷H$ HtH$ HHBH$ HHtH$ HHLd$@jHLd$@nHLd$@rHHxL9uHI9u6HlPJ҉HH0HH$ NI9tIE HIHx嶣I9u HHxL9uHI9uJPJ҉HH̸HbH$ H&̹ HLd$@eHLd$@H$ MHLd$@H$ Iff. AWAVAUATUSHHH H1Px(7 HF H@H4 x( HF @ H߉D$0HPx( HF @ ;D$0D$@ H1HP(x(Is H AD$( 1{ HID$ LeMMW A|$(& LN AD$( 1 HID$ D$0MH߉E HPHu D Hĸ []A\A]A^A_f D H1HPHtH1HPP(t-uH@ @tH@HuHF P(uf D H@ @tHHPH{H HPHdH HPP( AH@ @3H@HuH;F f Il$ f D Hh LeMM1 IHE H@ + H@ H@ H@HHF H@ @H1HPx( HvF H@H x( HxF HpH H}MH H HPx(taH:F L`M EL}EM AD$ A;G D$t\Lȝ eH@ Pf D H@ f. H@ O HF + @ H5F 9tHD$/1L$ Lt$PHD$6 $ vhL- L% ;\$#ID$(HcLHtF IG(LHt5 D$xu$ P w @ IG(HlE(tguHE H;/F t(Ht#HHt$HD$Hy±HD$HE( Af $ t| HE E( Ht$z Ht$HHEL%F D HD$pH9$ 519 IHE D H$ H8HD$pH0HWH;V肯} L$ 蓯HL$0LL$@L H˸ H5K LH) LH莸HjE Ht$PHHH\F HHCH$ HxkHH5iE H襹HL HHH$ Ht$PHx)H1HɹH @ UHSHH1P(x(HtFH E( 1P HHE CH{C HCt H[]酚 D HX f. HCHH[]Y f AWHAVAUATUSHH HPHI HD$P HD$X 1HD$` L$ Ll$P9 H!F P HD$XH;D$`$ t;HtHHL9HD$Xt:HHHPx(uH@ f. LLUOHL9u @ HL$XHT$PH9u r9pM HHBH9uH1HP(x(H HX E( 1J IHE Il$AL$H }( H E( 1 HD$(HE HHD$PMc IMA 1HHPH9w1f H|$PHtH []A\A]A^A_f D HHHPHtHHHPP(t+uH@ @tH@HuHdF P(u @ H@ @zHI9VH1HPx(v H1F HhHk HD$(HxHHa H薬 IHD$ HD$pHD$H$ HD$HD$@HD$8HD$L9t$HHt$HPx(/ HF LhM, HD$(@tHL$(LyAMZ AE A;G D$$ L% HD$(`wH9 Hl$@画H H5WG HH& HHHfE H$ HHHƲF HHCHD$@HxثHH5ZeE HfL` Il$AL$Hi11 HID$}(ZHE HD$(Hkf D H@ LhML-F 1C} D $ vpH|$ H|$ ;l$$?IE(LcH|$Jt IG(H|$Jt $ u$ P wfIG(NdAD$(tetID$ H;F t(Ht#HHt$8HD$0Hy{HD$0H训AD$( 0$ t\ ID$ AD$( H$ H9$ Z1 IHD$(Lx D H$ H8H$ H0HWH;V_H@ H-F HHD$(HxHH|$PHtŬH- H$ +Hı H5D HH2
+ HH7HPcE Ht$pHHHF HHCH$ HxH-H5bE HNH|$H XHHD$@H$ HxӨHHة/HHH$ Ht$pHx襨H譩HEHAWAVAUATUSH HF 8 t#L%F HĨ L[]A\A]A^A_ HHtLt$ HT$H5} L{HT$I~H5x fHT$I~H5w QHT$I~H5v <HT$I~ H5u 'HT$I~(H5r HT$I~0H5p HT$I~8H5n 諣L|$I~@H5l LЫHT$I~HH5i 軫HT$I~PH5g 覫HT$I~XH5f 葫HT$ I~`H5e |HT$ I~hH5b gHT$ I~pH5d RL%+F x I$ ID$ ID$ WHH$I$MnxHxLID$ @ Ht HHHHHL9uH=F I\$ʦH=IHdTE L4H=F tKIpH 7F Hl$ IM9IHxH9tPHAH F Ll$ IDHxH9u=HuHl$ H8H9 $t6H$HH HxHD$ʥH$ۋPJ҉HLΧHl$ II<$HtѨ IDHHx{HuIH=F 蔥L IfAWIAVIAUATUSH H\$PH|$H{hVH߫F H-F HƄ$ HDŽ$ 1Ƅ$ HDŽ$ HLeHDŽ$ H$ HEHDŽ$ HDŽ$ Ld$PHHD$ID$HLPHD$X I|$虪Lm 1IELl$`H|HE(HHD$tHEHU0H{PHD$ H@HT$(HTPH F HD$p HD$x HDŽ$ HDŽ$ HHDŽ$ HDŽ$ HD$PH̪F HhH$ HF H@HD$`HF HHD$h;HF HsH{hDŽ$ HHD$hHiF HH$ 萩Mw LH{HLM LϡH{HL蠧HF HxHD$H8H$ H L$ H$ L9s H9J H H)HT$?1HI輭IIHH HLH HL;=F Hl$@H|$HL$@HHD$@Ht$>Hx胢H\F HHD$PHLF HhH$ H9F H@HD$`HyF HHD$hH$ HxH;=F HpF H{PHHD$h讨HD$ HL$(H{hH@HLPIEHL$Ll$`HL`ID$HL$Ld$PHLPHF HD$X HH$ OHD$H []A\A]A^A_ I9 H I)HT$?1LLIIHHtOHLH豪HL;= F M Hl$@H|$HL$@H蚪 D E AGY E AGf D HD$`H@H|w Mf D HD$`H@H|w 謹zf. H HW H|$Hs` D MH=4 cHl$@IHD$HH8HGHϧL穣 H=3 $H=F HW Ht$@AG M7CD7 AG M7CD7 IHD$@Ht$>Hx貟RI^IT$HL$HLd$PHLPHD$X H>F H{hHH$ HH{I#HuHL褛H븋PJH"f. AWIAVIAUATUSH H\$PH|$H{h&HF H-PF HƄ$ HDŽ$ 1Ƅ$ HDŽ$ HLeHDŽ$ H$ HEHDŽ$ HDŽ$ Ld$PHHD$ID$HLPHD$X I|$iLm 1IELl$`H|HE(HHD$DHEHU0H{PHD$ H@HT$(HTPHF HD$p HD$x HDŽ$ HDŽ$ HHDŽ$ HDŽ$ HD$PHF HhH$ HF H@HD$`HF HHD$h HF HsH{hDŽ$ HHD$hH9F HH$ `Mw L进H{HL萡M L蟛H{HLpHF HxHD$H8H$ H L$ H$ L9s H9J H H)HT$?1HI茧IIHH HLHHL;=[F Hl$@H|$HL$@H֥HD$@Ht$>HxSH,F HHD$PH F HhH$ H F H@HD$`HIF HHD$hH$ HxH;=բF H@F H{PHHD$h~HD$ 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聤HL;=F M Hl$@H|$HL$@Hj D E AGY E AGf D HD$`H@H|w 磣Mf D HD$`H@H|w 迣zf. H HW H|$Hs`ҟ D MH=- 3Hl$@IHD$HH8HH蟡L跣 H=- H=dF HW Ht$@難AG M7CD7 AG M7CD7 IHD$@Ht$>Hx肙RI^IT$HL$HLd$PHLPHD$X HF H{hHH$ ٖHᢣH{I HuHLtH븋PJH"f. AWIAVIAUATUSH H\$PH|$H{hHF H- F HƄ$ HDŽ$ 1Ƅ$ HDŽ$ HLeHDŽ$ H$ HEHDŽ$ HDŽ$ Ld$PHHD$ID$HLPHD$X I|$9Lm 1IELl$`H|HE(HHD$HEHU0H{PHD$ H@HT$(HTPHF HD$p HD$x HDŽ$ HDŽ$ HHDŽ$ HDŽ$ HD$PHlF HhH$ HYF H@HD$`HF HHD$hۢHF HsH{hDŽ$ HHD$hH F HH$ 0Mw L菕H{HL`M LoH{HL@HF HxHD$H8H$ H L$ H$ L9s H9J H H)HT$?1HI\IIHH HLH轟HL;=+F Hl$@H|$HL$@H覟HD$@HxH;=F HF HHD$PHF HhH$ H֜F H@HD$`HF HHD$hH$ HxH;=F H
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HLH[H2]A\V;X f. wfff. ATU1SDgHAt
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- x
ҍDIHlH7ҍTIAHltHH Hhfffff. H=ZB uH B P~ HB Zfff. HB H҂B HPÐfff. HGÐf. wfff. H=ZB uHB P~ H~B Zfff. HyB H2B HPÐfff. HGÐf. wfff. H=ZB uHB P~ HށB Zfff. HIB HB HPÐfff. HGÐf. ATHIUSoH@t!H;2 HcS H PHt^I;$ HHE PHǃ HuȀEvxHȀFwHLHS []A\ @ HɀHHHHvJHHȀAHwHAPf UHu UHu PHH HL H
HLH[H2]A\VT f. wfff. ATU1SDgHAt
AuGAu!HCu]H[H]A\f D S
- x
ҍDIHlHҍTIAHltHH Hhfffff. H=WB uHLB P{ H>B Zfff. fffff. wfff. GS1ۨt't HWHHJʃҍTIH\uHGu3H[ HG H HPH\DIHHH HXf D H=XB uHL~B Pz H>~B Zfff. fffff. HG Ðf. HG Ðf. HG Ðf. HG Ðf. HG Ðf. AUHATUHSHHW( C S( ufH;E Y IHA$BPHǃ It$ȀAD$ HȀFwHHHS H[]A\A]f D H;u IHLIĿ
- R A|$HR HHL0C!HSH HQ S(
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It$ȀAD$ HȀFwHH
H;u IHLIĿ* R A|$HQ HHL D H;u IHLIĿ" Q A|$HQ HHL AT$It$Qf AT$It$KAT$It$;AT$It$H INH IH INHH IHH IrHHHH2VX[]A\A]O fffff. AUHATIUSHHo@ @t`I;$ IHAE PHǃ IuȀAEv|HȀFwHLzu5HSu|H[]A\A]ÐHWHL LO g HS H L,O f. AUIuf D AUIuHLH I#HHLH2VX[]A\A]N @ AVHAUATUHSDo HE E1D D HpɀHvTHȀFwHHA E9t9H9 HU(IcH|
+ x
ҍDIMd D H MdDIItfHH L`fffff. H=I C uHDC Pe HDC Zfff. HyDC HDC HP(HP0Ð HG(Ðf. HG0Ðf. AVHAUATIUHS_t>H;2/ IHLIſ
+ S_ A}HG_ LHLt>I;$ IHLHÿ _ {H_ LHHU~q1L, ;HHpʀHPvbHHȀFHwHFHL9t'I9$v@HM H HwHHHL9uHUuF[]A\A]A^ @ HHHL HL IH I[]HLHA\H2A]A^V] fwfff. ATUSHH@C SDcAH,t AuQAu#HCukH[H]A\f H{0wHlDIHf D H{(OHlDIHAtHH Hh눐fH=C uHAC P赃 HAC Zfff. fffff. wfff. S1Gt HGH HPЃDIH\G(tHtHGuUH[ HG H HPH\DIH @ HG HHDIH\HH HX랐H= C uH@C Pł H@C Zfff. USHH(tC( H[] Ho H;-U
C tHtHE HxH;= C uH%C( H[]H=G
C HWtHt$ŋPJH @ fffff. wfff. AUIATUS1HHG(Hco HPHHEL$L9t, D H;HDHTIHHI9uIEuLHH[]A\A]HH Hhfff. H=C uH ?C PU H>C Zfff. H>C H>C HPÐfff. HGÐf. ATHIUSoH@t!H;2 HcS H PHt^I;$ HHE PHǃ HuȀEvxHȀFwHLfHS []A\ @ HɀHHHHvJHHȀAHwHAPf UHu UHu PHH HL H
HLH[H2]A\V;X f. wfff. ATU1SDgHAt
AuGAu!HCu]H[H]A\f D S
+ x
ҍDIHlH7ҍTIAHltHH Hhfffff. H=C uHl<C P~ H^<C Zfff. H9;C H"<C HPÐfff. HGÐf. wfff. H=C uH;C Pu~ H;C Zfff. H:C H;C HPÐfff. HGÐf. wfff. H=IC uH<;C P~ H.;C Zfff. H;C H:C HPÐfff. HGÐf. ATHIUSoH@t!H;2 HcS H PHt^I;$ HHE PHǃ HuȀEvxHȀFwHLHS []A\ @ HɀHHHHvJHHȀAHwHAPf UHu UHu PHH HL| H
HLH[H2]A\VT f. wfff. ATU1SDgHAt
AuGAu!HCu]H[H]A\f D S
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ҍDIHlHҍTIAHltHH Hhfffff. H=C uH8C P{ H8C Zfff. fffff. wfff. GS1ۨt't HWHHJʃҍTIH\uHGu3H[ HG H HPH\DIHHH HXf D H=C uH7C Pz H7C Zfff. fffff. HG Ðf. HG Ðf. HG Ðf. HG Ðf. HG Ðf. AUHATUHSHHW( C S( ufH;E Y IHA$BPHǃ It$ȀAD$ HȀFwHHHS H[]A\A]f D H;u IHLIĿ
+ R A|$HR HHL0C!HSH HQ S(
H;E ~ IHA$:PHǃ
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H;u IHLIĿ* R A|$HQ HHL D H;u IHLIĿ" Q A|$HQ HHL AT$It$Qf AT$It$KAT$It$;AT$It$H INH IH INHH IHHs IrHHHH2VX[]A\A]O fffff. AUHATIUSHHo@ @t`I;$ IHAE PHǃ IuȀAEv|HȀFwHLzu5HSu|H[]A\A]ÐHWHL LO g HS H L,O f. AUIuf D AUIuHL8 I#HHLH2VX[]A\A]N @ AVHAUATUHSDo HE E1D D HpɀHvTHȀFwHHA E9t9H9 HU(IcH|
WwPHpƈPHp DmA De8E E1?HpɀHv\HȀFwHHA6E9tAH9 HU@IcH| *WwPHp PHp A& DmPE E1C @ HpɀHv\HȀFwHHA6E9tAH9 HUXIcH| ZWwPHp PHp DmhE E1Ef D HpɀHvTHȀFwHHAE9t9H9l HUpIcH| bWwPHpƈPHp D E E1B HpɀHv\HȀFwHHAE9tAH9 H IcH| jWwPHp D PHp D E E1B HpɀHv\HȀFwHHAE9tAH9t H IcH| rWwPHp D PHp D E E1B HpɀHv\HȀFwHHA E9tAH; H IcH| zWwPHp D PHp HU []A\A]A^fH H HiJ B @ H H
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HHHH0VH[]A\A]A^A_? fffff. AWAVAUIATIUSHHGD$ D$ tcL;+L I$ 5@ H HHHʀH HHȀAHwLi@) D D$ t2L;+L/ Ic$ ? HHn Lh@(Mt$Lo A~~1E HL)HI9}FEEAE "MMH0LL$Lσ^L$D9N, t5L`o HLBI~LH¾ H߃- D9IuMt$0Lo A~|1C D HL)HI9}FEEAE *MMH0LL$Lσ'^L$D9N, t5Ln HLBI~LH¾ H߃ D9IuMt$HLn A~|1C D HL)HI9}FEEAE 2MMH0LL$Lσ]L$D9N, t5L@n HLBI~LH¾ H߃
D9IuAl$h E1E HqʀQv\HʀVwHHAfD9It>L9+ LID$pIcH|:GwAHq뿐AHq D$ E$ E 1AHqʀQvdHʀVwHHڃA9ItGL9+" LI$ HcH|JGwAHq AHq ID$ HL[]A\A]A^A_f I$ L H; IH HH߾ ; I@(Lhd @ HɀHHHv8HHȀAHwLicf. @hLhPLh@LHl HBLHY HHHLH0VH[]A\A]A^A_: H& HH HGfffff. AVHAUIATUSoH@t?H;2 ILLIĿ
- B; A|$H5; HHLt=H; ILNLHſ : }H: HHHEEe E 1F @ HpɀH HȀFwHHڃûA9t7H9 IU(HcH| WwHpPH茻A9uEe8E 1Hf D HpɀH HȀFwHHڃ3A9t3H9vrIU@HcH| "WwHpPH A9uIUuH[]A\A]A^f PHp @ PHpHH HH HHH[]A\H2A]A^V]8 HH I4H If. AUHATUHSHHDgA tcH; L 8 I DIʀIHP% DIHȀBIwHBD* A A\ A2 A@tdH;H- L ( o8 I DIʀIHP DIHȀBIwHBD* D A At?H; IHLIĿ: 7 A|$H7 HHL9Dm E E1F HpɀHv\HȀFwHHAFE9tAH9 HU(IcH| BWwPHp PHp Dm8E E1Ef D HpɀHvTHȀFwHHA E9t9H;= HU@IcH| rWwPHpƈPHp DmPE E1Ef D HpɀHv\HȀFwHHAE9tAH9 HUXIcH| @WwPHp @ PHp DmhE E1Ef D HpɀHv\HȀFwHHAFE9tAH9E HUpIcH| @WwPHp @ PHp HUC H[]A\A] H H Hy4 @ H H HY4 @ HUxH H<4 a H H H4 @ D(H @ D(H @ DhH DhHHH HH 3HH HH H HIH HHH I$HHHH2VH[]A\A]3 ATHIUSGHtYH;2} HHE
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HLH[H2]A\V0 f. wfff. U1SHHGt HGH HPЃDIHl{(w:S(H HcH H{ HlDIHHCuSHnHH[] @ H{ g D H{ D H{ D H{ HH Hh ATU1SDgHAt
AuGAu!HCu]H[H]A\f D S
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ҍDIHlHҍTIAHltHH Hhfffff. U1SHHGu HCu6H
HH[] HGҍTIHlHH Hh @ U1SHHGtMt HWHHJʃҍTIHlt"HS HHJHl
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B HH[Ðfff. SHH[=fffff. USHHH B HG G G HHH=&B uHC H[]J H{H$ HM
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- - e H "KB HZH=C H [e @ USHHHt:Hp uRH H߾( W5 H1Ht HHH[] ( 9HHHH[]H5A ( h7 HHk9H?fHA SHHH;=B HtHHtHPH{ H& B HH[Ðfff. SHH[8fffff. USHHH@A HG G G HHH=#B uHVB HCHC H[] F H{H HB HHH> SH - - c H 2IB HZH=#B H [ @ USHHHt:Hp uRH H߾( 3 H1Ht HHH[] ( 8HHHH[]H5A ( 5 HH7H#>fUSHHHA HG G G HHH= B uH_UB C( HCH[]D H{H H3B HHH= @ UHH
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+$fD9,$~ K44K<7AIEL D9l$HChHD$D$9$~d$PD$Lc)IH K4ME1HL$HD$ D O$7H|$:FLHIK HD$N,0IL;t$uHCxD,$DkpD;(DkpD( HE`H{PLx [ HS`IċD$CX)D$DD$E~1<$E1E1"D9,$~ K44K<7AI%D9l$HCPHD$D$9$~d$PD$Lc)IH K4ME1HL$HD$ D O$7H|$Z7LHIHD$N,0IL;t$uHC`D,$DkXD;(DkXD(H50"B H5!B ?H5X#B H4$DH4$HH H4$6H4$HH H4$2H4$HH HHH?E f. f. UHSHHH9t HHH[]f H[]Ð AWAVAUATIUSHH(HFS It$H{n It$0H{0n AD$PD$ Al$@ @t$KH{`It$`H9tH;=5A Z@t$KH{hIt$hH9tH;= A 0@t$KH{pIt$pH9tH;=A ] t9KH{xIt$xH9t'H;=A ^ H([]A\A]A^A_ H([]A\A]A^A_f ID$XH{HLx X HSXHŋD$CP)D$T$~4D$E1E1 $D9l$~ Jt5 K<7AICH D9l$HCHHD$D$9D$~iD$PD$Lc)IH JD5 ME1HL$HD$ K,7H|$r&HHIG HD$N,0IL;t$uHCXDl$DkPD;(DkP#D(H{p5qH{h'qbH{`q*H(H{x[]A\A]A^A_pHHH]B kf. UHSHHH9t HHH[]9f H[]Ð AWAVAUIHvATUSHHHHA HG HHFG GHGHHD$(c HC0Iu0HHD$0c HCHIuHHHD$8c Ch AEhHC` Cl HCp D$ ǃ A HCx ǃ Hǃ D$ IE AEH-(B H uXH t I H H9t LcioI H A HH[]A\A]A^A_ I H9tH Lc oAEf D I H{xLxT IH D$ )D$T$~;D$ 1E1,f D9d$ ~ I4.I</D AHD9d$HCxHD$D$9D$ ~bD$ 1)H HD$ HcHI M<HL$ @ HD$H|$L4(HLIWD M$/HH;l$ uH D$ ; WIEULcHHLX? = IEpH{`LxS IHCp D$Ch)D$t$~4L$ 1E1 %fD9d$ ~ I4.I</AHD9d$HC`HD$D$9D$ ~bD$ 1)H HD$ HcHIM<HT$ @ HD$H|$L4(u&HLIgM$/HH;l$ uHSpD$ Ch;ChLcHH{`HspDZH|$8Mb H|$0Cb H|$(9b L16 HA HHH;HLcHLcH{xH HHLcHLcfAWAVAUATUHSHH(HF6 HuH{g Hu0H{0g HuHH{Hg EhD$ D$ DeA ACt%CH H9 tH /CAt.H CH H9tH;=A r CAtH H Au A DcH([]A\A]A^A_ fH H{xLx
+Q H IċD$ )D$T$~;D$E1E1+ D9l$~ K44K<7AI@ D9l$HCxHD$D$9D$~jD$PD$Lc)IH K4ME1HL$HD$f. O$7H|$BLHI@ HD$N,0IL;t$uH Dl$D D;(D 5D(- D HEpH{`LxO HSpIċD$Ch)D$t$~4L$E1E1
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+$fD9,$~ K44K<7AI< D9l$HCHHD$D$9$~d$PD$Lc)IH K4ME1HL$HD$ D O$7H|$"LHI; HD$N,0IL;t$uHCXD,$DkPD;(DkPD( HE(H{LxJ HS(IċD$C )D$DD$E~1<$E1E1"D9,$~ K44K<7AI: D9l$HCHD$D$9$~d$PD$Lc)IH K4ME1HL$HD$ D O$7H|$:LHI|: HD$N,0IL;t$uHC(D,$Dk D;(Dk D( H59B H4$4H4$HH HHHX5 cf;f. UHSHHH9t jHHH[] f H[]Ð AWAVAUIATUSHH(HվA HG HHFG G HG G$ GF HG( D$ C8 AE8HC0 C< HC@ D$ CP AEPHCH CT HCX D$h Ch AEhHC` Cl HCp D$ ǃ A HCx ǃ Hǃ D$H ǃ A Hǃ ǃ Hǃ D$ ǃ A Hǃ ǃ Hǃ D$ IE AEH-7B H u<H t I H H9t LcH([]A\A]A^A_f D I H9tH LcAEf D IE(HLx5G IHC( D$C )D$D$~<D$1E1-f. D9d$~ I4.I</\AHD9d$HCHD$D$9D$~bD$1)H HD$HcHIM<HT$ @ HD$H|$L4(HLIM$/HH;l$uHS(D$C ;C F? IEXH{HLx5F IHCX D$CP)D$l$~<D\$1E1E+ D9d$~ I4.I</AHD9d$HCHHD$D$9D$~bD$1)H HD$HcHIM<HT$ @ HD$H|$L4(%HLIwM$/HH;l$uHSXD$CP;CP IE@H{0Lx5E IHC@ D$C8)D$D$~<D$1E1-f. D9d$~ I4.I</<AHD9d$HC0HD$D$9D$~bD$1)H HD$HcHI M<HL$ @ HD$H|$L4(e!HLIM$/HH;l$uHS@D$C8;C8tm I H Lx/D IH D$ )D$T$~8D$1E1)f D D9d$~ I4.I</4 AHD9d$H HD$D$9D$~_D$1)H HD$HcHIM<HT$HD$H|$L4(HLI3 M$/HH;l$uH D$ ; sIEqLcHHL. Y I H LxB IH D$ )D$t$~8L$1E1)f D D9d$~ I4.I</3 AHD9d$H HD$D$9D$~_D$1)H HD$HcHI M<HL$HD$H|$L4(HLI2 M$/HH;l$uH D$ ; f. I H{xLxA IH D$ )D$DD$E~9|$1E1* D9d$~ I4.I</AHD9d$HCxHD$D$9D$~bD$1)H HD$HcHIM<HT$ @ HD$H|$L4(HLI'M$/HH;l$uH D$ ; fIEpH{`Lx@ IHCp D$Ch)D$DT$E~:DL$1E1E)f D D9d$~ I4.I</AHD9d$HC`HD$D$9D$~bD$1)H HD$HcHI M<HL$ @ HD$H|$L4(HLI M$/HH;l$uHSpD$Ch;ChxqH H H蝝H H H{xH :H{`Hsp-H{HHsX H{0Hs@H{Hs(薞L # HA HHH(՞HLcHLcHLcHLcdHLckHLcoHLcpfff. AWAVAUIATUSHH(HA HG HHFG G HG G$ GF HG( D$upC8 AE8HC0 C< HC@ D$: IE2 AEz HCH 2 HCP H([]A\A]A^A_ IE(HLx-> IHC( D$C )D$t$~4L$1E1 %fD9d$~ I4.I</, AHD9d$HCHD$D$9D$~bD$1)H HD$HcHIM<HT$ @ HD$H|$L4(uHLIw, M$/HH;l$uHS(D$C ;C IE@H{0Lx5= IHC@ D$C8)D$T$~<D$1E1-f. D9d$~ I4.I</+ AHD9d$HC0HD$D$9D$~bD$1)H HD$HcHI M<HL$ @ HD$H|$L4(uHLIw+ M$/HH;l$uHS@D$C8;C8IEHkHHH' @ Hk˞IuPHHHkPH([]A\A]A^A_ Hkr˞IuHHHcHkHAEdHkIHW HA LHHaўHkIH{Hs(轙IH{0Hs@諙HIHkʞf D AWAVAUIATUSHH(HA HG HHFG G HG G$ GF HG( D$ Iu0H{0HC0 C8 C< HC@ ) CP AEPHCH CT HCX D$[ Ch AEhHC` Cl HCp D$
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B Hkx H t I H9tH Lc軦AEH t I H9tH Lc萦AEH t I H9tH LceAE Hǃ I H I H H([]A\A]A^A_ IuxH91H{xLcAE D IEpH{`LxU9 IHCp D$Ch)D$T$~<D$1E1-f. D9d$~ I4.I</) AHD9d$HC`HD$D$9D$~bD$1)H HD$HcHIM<HT$ @ HD$H|$L4( HLI') M$/HH;l$uHSpD$Ch;ChIELcHHL# @ IEXH{HLx58 IHCX D$CP)D$t$~<L$1E1-f. D9d$~ I4.I</L( AHD9d$HCHHD$D$9D$~bD$1)H HD$HcHI M<HL$ @ HD$H|$L4(HLI' M$/HH;l$uHSXD$CP;CP IE(HLx57 IHC( D$C )D$DD$E~:|$1E1+ D9d$~ I4.I</,' AHD9d$HCHD$D$9D$~bD$1)H HD$HcHIM<HT$ @ HD$H|$L4(uHLI& M$/HH;l$uHS(D$C ;C VO LcŞI HHH ^HH{`HspH{HHsXaH{0Hs@"H{Hs(腔L} H6A HHH˞HLcHLcHLcIHĞLLc|HLcfff. AWAVAUIHvATUSHH8HA HG HHFG GHGHHD$ B HC0Iu0HHD$(B CP AEPHCH CT HCX D$ IE AEH-ZB Hk`urHkhtIuhH9tH{hLcNAEHkptIupH9tH{pLcNAEHkxtIuxH{xH9t LcNH8[]A\A]A^A_ÐIu`H9H{`LcfNAEk D IEXH{HLx%4 IHCX D$CP)D$T$~<D$1E1-f. D9d$~ I4.I</\$ AHD9d$HCHHD$D$9D$~bD$1)H HD$HcHIM<HT$ @ HD$H|$L4(HLI# M$/HH;l$uHSXD$CP;CPQIEOLcHHL 7H{HHsXHH|$(B H|$ B L HIA HHHȞHLcHLcHLcǐfffff. AWAVAUATIUSHHHHA HG HHFG vG G GHGHG HD$( C( At$(HC(C, HC0 HD$0+ HC8It$8HHD$8? CX AD$XHCP C\ HC` D$ Cp AD$pHCh Ct HCx D$ ǃ A$ Hǃ ǃ Hǃ D$ I ID$R AD$H-B H H t"I$ H9tH Lk豝AD$H t"I$ H9tH Lk脝AD$H t"I$ H9tH LkWAD$ Hǃ ? Hǃ @ Hǃ I$ H I$ H HH[]A\A]A^A_ @ I$ H9H LkǜAD$ D I$ H Lx0 IH D$ )D$T$~7D$ 1E1( D D9l$ ~ I4.I</ AHD9l$H HD$D$9D$ ~_D$ 1)H HD$ HcHI M<HL$HD$H|$L4(HLI M,/HH;l$ uH D$ ; ID$LkHHL f. ID$xH{hLx. IHCx D$Cp)D$t$~3L$ 1E1
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+HLIwM,/HH;l$ uHS`D$ CX;CX$ HP4 AD$(C(IcT$(H{0It$0HA @ H 7 AD$CIcT$H{ It$ HM @ 8 LkI$ HHH Lk껞I$ HHH AD$f D Lk費I$ HH蟮H AD$OHH H 訉H{hHsxH{PHs` H|$8d; H|$02 H|$(5 LH HA HHHRHLkHLkHLkHLkHLkIHLk覺LSHLk wf. UHSHHHv@ubCt$HsHt+H}MHt.H[] H[]f H5A Ht$fHt$HHEHGHH@t H轺HHt$n Ht$H @ Kf. UHSHHH9t HHH[]f H[]Ð Kf. UHSHHH9t zHHH[]f H[]Ð AUATLgUHSHHH%A HG HHFG GHF EHA HCtHuH9t H{CC( E(wPHLY Hc Hf w H-A H8C( 1jHC HHH[]A\A]Ã H-`A HC( 1HC HH+H[]A\A]Ã H-A HC( 1*HC HH{H[]A\A]Ã H-pA HxC( 1jHC HH+H[]A\A]ÃtKH-A H<C( 1HC HHH[]A\A] @ Hm Hm f. Hm g Hm Hm HLI LHͷ LH= HA HHHGf D AULoATUHSHHH%A HG HHFG GHF EL%A LctHuL9t H{9AELc tHu L9t H{ AEuHC( H[]A\A]f0 fHu(HILc(H[]A\A]HLI LH辶\HL.
+ HA HHH8LH轵Րf. AUATLgUHSHHH%A HG HHFG GHFuIEuHC H[]A\A] 0 膵HuHILkH[]A\A]HLI LHHLQ H
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LH7HL HcA HHH贷LH9Րf D AUATLgUHSHHHA HG HHFG GHFu@EuHC E C H[]A\A]f0 HuHILkHLI2 LHgHL HA HHH䶞LHi 1f. HH@( 1f. HH HH HH HH HH HH HH HH HH HH HH HH HH HH HH HH HH HH HH HH HH Gf. fGf. fGf. fGf. fGf. fGf. fGf. fGf. fGf. fGf. fGf. fGf. fGf. fGf. fGf. fGf. fGf. fGf. fGf. fGf. fGf. fHHxH;=A uH=A HWt HHt$8HËPJHf D SLHcI)I9|HHFH[[骧 f. SHHXHJHA H)LH v&=? Av = Av= MIf D M)L9|[b fw(HLAH2HL讲H[ @1wAH HH@qv D vCȀHFv HʀPw@8H @ @~HF @>HFH?11HHPͮf. 1f H USHHHuH H[]HHx HuHHzH;=A uHlH H[]H=A HOtHt$ŋJqrf. f1if HX 1f H 1f H 1f H 1f H 1)f H 1f Hx 1)f H 1f H 1If H8 1yf Hh 1yf Hh 1f H 1f H 1f Hx 1f H 1f H 1f H 1If H8 1f Hx SHy5Oуɍ4xHHG[f D H[f D HHG[ HGHҍTIHAUIHrATUSHH
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+wtLCHk CXE1L)D҉S N)ՍPHcIȅ҉SXLx_HLHtOSPCXuDk k AOHcHkH+H[]A\A] Hf HHƉt H1[]A\A] AUIHrATUSHH
+wtLCHk CXE1L)D҉S N)ՍPHcIȅ҉SXLx_HL HtOSPCXuDk k AOHcHkH+H[]A\A] H HHƉt H1[]A\A] AUIHrATUSHH
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+wtLCHk CXE1L)D҉S N)ՍPHcIȅ҉SXLx_HLT)HtOSPCXuDk k AOHcHkH+H[]A\A] HF HHƉt H1[]A\A] AUIHrATUSHH
+wtLCHk CXE1L)D҉S N)ՍPHcIȅ҉SXLx_HLT0HtOSPCXuDk k AOHcHkH+H[]A\A] H HHƉt H1[]A\A] AUIHrATUSHH
+wtLCHk CXE1L)D҉S N)ՍPHcIȅ҉SXLx_HL:HtOSPCXuDk k AOHcHkH+H[]A\A] H HHƉt H1[]A\A] AUIHrATUSHH
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+ I|$|$` H= HHD$hH= H@H$ H)= HHD$pH$ HxH;== H = I|$HHHD$p]IFLt$hH<$L|hHd= HH$ 3E
+: D C4 o I|$H5 H+= HhH$ Hl$@H L$ L$ L9 L9 M L)HT$?1HIIHHH1 AEH;-= Ht$(H|$ HL$PI-HD$PHxH;== H|$@H= HWHHD$@D$`HxH;=M= HX= LpLx8 DS,EDA;@ :A;@0HT$IcA D M9 M M)HT$?1LIHHHuFAEH;-= l Ht$(H|$ HL$PI,HLL HHLLHH부{4 Hs f D HH|$ It$X
MEH= ? H= 3 Lt$PH}L"- H== HWt:"Ht$(
+HD$PHt$>HxHl$@뢋PJHI|$HH^Hl$@qHHZ= I|$`HH$ $H,HL蟟HE L} BD= ~H== HWtfHt$(H HD$@Ht$?HxH={= HWt-EHt$> 6PJH떋PJHE L} BD= I|$HFIFLt$hL|hfAVIAUAATIUHSB( H^ B0HcHHu-Ff D LLhHcU HE HHH9t;U(D}LL> @ []A\A]A^Ðfffff. AWAVAUIATIUSH B(H^ 6H|$L$4B0DD$<HcLL$HH Hl$pH߃AD$8HE`H$HD$`HD$(HD$PHD$ 3 D HD$8 A|$4 f HcHHH9 HcA;E(}HL$L IQPLAAAAO Hmu ~ Ax AHEHD$4tA |$8AG9tD9kH|$L$<LHAu I} HcHH9OH []A\A]A^A_f D H<$D$p H# = Ƅ$ 1HDŽ$ Ƅ$ HDŽ$ HDŽ$ HHDŽ$ HDŽ$ H$ H
+= LpIFLt$xH|H
+= H@HHD$ H = H}HHDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHDŽ$ HD$xH5 = H@H$ H
+= HH$ Hz = H<$HuDŽ$ HH$ H = HH$ # H}H5H
+ . ^H}H54 IH} [ H5 H'H}H5
+ H}D& |$p L|$ HO
+= HHD$xH?
+= H@H$ H
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+ . I|$H5 I|$ H5 HI|$H5
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+ . H{H5 H{ H5 HH{H5
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+ . H{H5x H{ H5^ HkH{H5
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ϙH|$ IHHd HD$ A@L;${ HD$PH|$@HL$PHHD$X͙HD$PHxH;<$ H|$@H< HWHΙHD$@D$`HxH;<$ ID$IT$H$ H9HD$tQH|$ b H+T$1HHT$ HT$?H7ΙH|$ IHH HD$ A@L;$ HD$PH|$@HL$PHHD$̙%HT$HLLD$ X̙LD$ HHT$HLLD$ 6̙LD$ HvHuXH|$@;șHT$ Ht$HLD$(˙LD$(HHT$ Ht$HLD$(˙LD$(H6HCH=V YÙHD$PHD$HD$@Ht$Hx<G H=U $ÙHuXH|$@Ǚ<HH=U f. H=U H=T< HOt>{Ht$PH$ÙH$dHD$PHt$>HxIJqrHD$ A@ I AD jH}HEIEHL$Ll$hHLhIFH}`H$ ھHʙHHHRYHʙH}H.̙HʙH}HDHD$HL$ HD$hH@HLhH=N< HWt6Ht$PHD$@Ht$?Hx^PJHH=< HWt$oHt$>`PJH *HD$ A@ I AD H=< Hw Ht$PHD$H$HD$H$^H=W< HWt^Ht$>H=+< HWtQHt$Pf. PJH뜋qDFDALPJH멐fffff. AWAVAUATUSHH H$ H t'H$ 1HĈ []A\A]A^A_ H$ Hl$IHwhAEHEHT$ 裬HT$I4$I|$D|$@L|$ HD$H D$PHHRHt$ H|$(LHHl$XD$` HD$0H$ HT$8HD$h Dl$ADt$BHD$ptNHT$HH HtaLH覭|$` t7H$ 1fHD$TD$ H$ HT$hH$ Hf D E8I$tIT$H$ H D 1HHHIǙ AWAVAUMATUHSHHHHHrL$ HHD$0Ht$8A$ { SA @t Hu H9t$0t1HH[]A\A]A^A_ @ @ t2 AA : A^ V E1I AMD$ DD @ DHuH$ DKHT$0L$$EHHHL$HL$ AHL$ED7A<$ ?7|$ t { HE H;D$ @ M C HU HD$ H)IU IE lj H|$0Hu H|$8HuH9At:E1A D E1H$ D$ A A A E1D$ A HE HUHD$0HT$8f HE HU HUHL$ H)IM IUfAWAVE1AUIATUSH HT$L$tH D[]A\A]A^A_ D HD$@H|$`I
+ HD$H HD$P HD$X D$` ?HD$h HD$ h H HD$HH9 H, H1HHH蠹H\$@H\$0IuhHH\$%Ht$pLHD$p HD$x HDŽ$ HDŽ$ Ƅ$ Ƅ$ HDŽ$ DŽ$ H$ Ƅ$ HDŽ$ HDŽ$ Ƅ$ 'A L$ I I` I11LHP譿IEHHD$(LX DD$EV D$ H|$H1LHHD$@HHHt2HHqf L9 H HtHq1HHH9t tHtH L`@ H|$ HLH HH H5 1L耽H|$H5 1m D H|$ަH|$PHufHH 赻HuHD$HH|$@1H 藷H|$@HD$X HD$P {<f D E1 H9 H|$H1LHHD$@HHHt*HHq D L9t[H HtHq1HHH9t HHtH L`@ H|$ HLH HH H LLYHtSHt Hth@
ub1(f D H v p t6LL HHuE1H|$(ĿtW H|$PHu @ HH %HuHD$HH|$@1H H|$11HD$X HD$P L$ HHPȼL$ E1H|$H1LHHD$@HHHt-HHq @ L9R H HtHq1HHH9t 费HtH L`@ H|$ HLH*
HH. H|$H1LHHD$@HHHt0HHq L9 H HtHq1HHH9t $HtH L`@ H|$ HLH HH @ Htov@f/LL HHu Ӿ LD$ D$9D$
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+ HDŽ$ HDŽ$ HDŽ$ Hx DŽ$ ?HDŽ$ HD$ H H$ H9_ H, H$HH1IԱ@ L$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ 芵H$ H$ HHH,`HE H$ Hs(HU H$ H|$H H$ H$ HE H$ $ H H$ H$ H$ H$ D$`HD$`HHD$ K
H$ H$ HWH9 HH$( t HH$ HH$ I6FXHcH< HD$ 1HD$0 LD$0H~x1f D Hc=
:T A= ~H|$0HcFE1H 4 HD$PHD$(H$ HD$HH$ H9$ H8H|$H$ HWH9 HH$ HD$H9D$0M Ld$0 Mc,$Ht$LD HHHD$`l H L$ 1IH$ LIHM I:LHwH9t6f. HH| Hq1HII9g H9HHuH8 R A I2 BD(xMM,t-I
-Hq @ H9 H HtHq1HIH9t HtH HT$`@ HPH|$HHH% HHAE IL9d$1HD$8Hx t.HD$H9 P HD$8۾ IDHP M$ tL H|$0Ht H$ Ht8H$ H$ L`I9vH} Ha I9wH$ O H$ HuHH/5 HuH$ H$ 1H H$ HDŽ$ HDŽ$ 1H|$@ H?H9 H, H H<(1HHD$0H|$HI6 Hw A I BD(xAS D Hc@ H$ HT$(Ht$ H|$$ D$P H$ H$ HWH9 HH\$`tHH$ H\$`HL$ H$ 1HIH$ HL# D Hc@E H$ H$ HPH$ H@H H$ H$ H$ H$ H$ HH+$ HH)H \ HCH$ H\$`HtHH\$`H$ HPH$ H@H H$ H$ HL= uHD$0 HH:QH|$H1Ҿ H$ TH?H9 HH 1HIJMHtL5H|$0Ht&H$ Ht7H$ H$ L`I9vH;HI9wH$ HH|$ H$ H|$@H6!aH HH' HH$ HDŽ$ HDŽ$ e HH oH$ H$ HH+$ HH)Hvb \HCH$ H$( HtHH$ HPH$ H@H H$ H$ H$ H$ 1Ҿ H$ HxHaASH7HH[;f. HuHif UHSHHHu @ HH -HuHEH} 1H HE HE H[]f. AWIAVAUIATI̹ UHH SHHWHwLw H HH96 L< L1HLI~Hu1HE LUHuU D IHIH2Ht?HHF1HHMMMuHEHHuMH> HI4HuH} 1LLu HH]IIK>HHt8HI$H L HEHL[]A\A]A^A_Lu IK>HHuHUI$LeI$HtHB1HuLM$HE HUHf HHkLu(RH HRL:5H H D ATIUH SHIHH H LLPA$GLM1LIHE HHHt*HLC D M9t;HHtLC1LIH9tHLH[] A\f. {H1[]A\ÐL% { AWIAVAUIATI̹ UHH SHHWHwLw< H HH9. L< L>1HLIHu1HE LUHuU D IHIH2Ht?HHF1HHMMMuHEHHuMH> HI4HuH} wL1H]HLu IIMl$K>HHt/HI$H L HEHL[]A\A]A^A_ Lu fHUI$LeI$HtHB1HuLM$HE HUH D H HLu(HRHLH5H f. AWAVAUATUHSHHHOHHw(HH)HL`HGM<,K<?H9vUL)H1HHEH,H+H9 HH)HH H HI{ f D H9HHCLlH I9 J< T$ H T$ LIL)1Hs(HHEH,HCHLHPH)HHt HHH;~L3LkIHk(HU JD%HCHHSH HS H HC8H HC@H[]A\A]A^A_ HIH)HHtH J|% H)w
HBSIL HH?HHHH9|
+Hq @ H9 H HtHq1HIH9t 贲HtH HT$`@ HPH|$HHH% HHAE IL9d$1HD$8Hx t.HD$H9 P HD$8۾ IDHP M$ tLӱH|$0HtıH$ Ht8H$ H$ L`I9vH} H葱I9wH$ H$ HuHH/eHuH$ H$ 1H AH$ HDŽ$ HDŽ$ 1H|$@ H?H9 H, H&H<(1HHD$0H|$HˬI6 Hw A I BD(xAS D Hc@ H$ HT$(Ht$ H|$$ D$P H$ H$ HWH9 HH\$`tHH$ H\$`HL$ H$ 1HIH$ HL# D Hc@E H$ 辯H$ HPH$ H@H H$ H$ H$ H$ H$ HH+$ HH)H 茯HCH$ H\$`HtHH\$`H$ HPH$ H@H H$ H$ HL= uHD$0 HH:QH|$H1Ҿ H$ TH?H9 HϮH 1HIzMHtLeH|$0HtVH$ Ht7H$ H$ L`I9vH;H$I9wH$ HH|$ H$ H|$@HV葨H騙ıHHGHϨH$ 貭HDŽ$ HDŽ$ 腱@HHoH$ H$ HH+$ HH)Hvb 茭HCH$ H$( HtHH$ HPH$ H@H H$ H$ H$ H$ 1Ҿ H$ HxHaqSH7HH[;f. H跪uH虨f UHSHHHu @ HH ]HuHEH} 1H AHE HE H[]f. AWIAVAUIATI̹ UHH SHHWHwLw H HH96 L< L1HLI讧Hu1HE LUHuU D IHIH2Ht?HHF1HHMMMuHEHHuMH> HI4HuH} 71LLu HH]IIK>HHt8HI$H L HEHL[]A\A]A^A_Lu IK>HHuHUI$LeI$HtHB1HuLM$HE HUHf HCH蛥Lu(rHH肥LjUHݯHŰ D ATIUH SHyHH H LLPA$GLM1LIHE HHHt*HLC D M9t;HHtLC1LIH9tHLH[] A\f. 諩H1[]A\ÐL% { AWIAVAUIATI̹ UHH SHHWHwLw< H HH9. L< Ln1HLI Hu1HE LUHuU D IHIH2Ht?HHF1HHMMMuHEHHuMH> HI4HuH} 觨L1H]HLu IIMl$K>HHt/HI$H L HEHL[]A\A]A^A_ Lu fHUI$LeI$HtHB1HuLM$HE HUH D H 転HLu(HrHL⧙ͫHUH=f. AWAVAUATUHSHHHOHHw(HH)HL`HGM<,K<?H9vUL)H1HHEH,H+H9 HH)HH H HI蛬 f D H9HHCLlH I9 J< T$ H9T$ LIL)1Hs(HHEH,HCHLHPH)HHt HHH;讦L3LkIHk(HU JD%HCHHSH HS H HC8H HC@H[]A\A]A^A_ HIH)HHtH J|% H)藫࠙HBSIL HH?HHHH9|
6 D IMZKIN EF;D } HL EH9F|uHII?LHH9t\HPII?IIH9~;FD90 DD9~ LIDI@HH?HHL9|N A [f D HD THAL f. AUHH)ATIUSHHCr HI HoHAL$II~9 9 9 E $A<$DNED$A $HIf. A9L} HDA9|HA9~ HDA9H9sD LSD DHDCA $LH$HL)HC MtJH49|%9UA $A$
-DNED$A $NE$A $ED$DN8HHHhIHHA LHLHuHA$ H1LL)HHHH[]A\A]f D AWDIAVAUEATUSH DD$$LF(H6H|$T$!L$#HI_ItIHHD$H H$HxA@
HD$ D$" E1I9 HD$@L$HD$8 HD$(HD$pHD$0 f|$! ELD$ Aw0LHH)HHLD$B +HH|$HcHI H@ HF A
t3HCLsELDIGPHt Ht$~tm|$# D$"I9\ IL;$GEzkHtAw0L)LHNHLD$} HXf. qxH9HcL qLX MdJ|f. HWC;Ps)L@LcODMcE9sC;t
ELc C u|$#
MwH $ H|$LƉLD$HHLD$=; t,HD$8Ht"HH)HD$H HHH9 H$Ht$H|$(LD$nLD$Ht$H|$0LWIw8H|$H|$(HH$> H|$0H+ H|$H@HHo H|$0HL$蕌H|$(苌HL$H\$8H H MgH H []A\A]A^A_ IGPHtH|$ ID$" I7|$$ tdIwIGIGH9 .H|$HcHDxHHHa H H[MwHD$"VI7LLI9w nf. I_H %AxEMHcL A*Lp M OLf D IAQ;Ps)LPLcODIc*D9sA;t
ELcC uHD$ H Hc@f D @
t+IWPD$"HtH|$ I_HD$"JLI7LLAG@H|$0腊H|$({1H|$HΉH $HH $Ht$Ll$pHL蟉Iw8H|$ALIH H|$H衾H> LH$H$ H/H0HcH< LR MtqHt>f. HFt6;Bs)HzLcJ<DLcE9sC;t
DHc:AuD$"LGAG@1HD>
-HxtуuH$ H{H{H5].
- H1AG@oHH|$0H|$(HHH1HHH$ HE{H{H5".
- u Hm1AG@L萈1,HL~HHH;1Hf D ATIUSH Ht8 I$ Hu*f HH{H+Ht HHuI$ HtI|$pIDŽ$ HtI|$`ID$p HtzI|$HID$` HtbI|$8ID$H HtJ[]ID$8 A\fff. AWAVAUATUSH ҉L$0LD$@LL$8H$@ AHH D$XH$ ID$ HD$H$ Ht$HF HD$ H$ HD$PHD$pHD$H l$HcD$HHLbM Ht1V҉ L$0L=-
+DNED$A $NE$A $ED$DN8HHHhIHHA LHLHuHA$ H1LL)HHHH[]A\A]f D AWDIAVAUEATUSH DD$$LF(H6H|$T$!L$#HI_ItIHHD$H H$HxA@
HD$ D$" E1I9 HD$@L$HD$8 HD$(HD$pHD$0 f|$! ELD$ Aw0LHH)գHHLD$B +HH|$HcHI H@ HF A
t3HCLsELDIGPHt Ht$~tm|$# D$"I9\ IL;$GEzkHtAw0L)LHnHLD$} HXf. qxH9HcL qLX MdJ|f. HWC;Ps)L@LcODMcE9sC;t
ELc C u|$#
MwH $ H|$LƉLD$HHLD$=Գ< t,HD$8Ht"HH)HD$H HHH9 H$Ht$H|$(LD$nLD$Ht$H|$0LWIw8H|$H|$(HH$> H|$0H+ H|$H@HHo H|$0HL$蕌H|$(苌HL$H\$8H H MgH H []A\A]A^A_ IGPHtH|$ ID$" I7|$$ tdIwIGIGH9 .H|$HcHDxHHHa H H[MwHD$"VI7LLI9w nf. I_H %AxEMHcL A*Lp M OLf D IAQ;Ps)LPLcODIc*D9sA;t
ELcC uHD$ H Hc@f D @
t+IWPD$"HtH|$ I_HD$"JLI7LLAG@H|$0腊H|$({1H|$HΉH $HH $Ht$Ll$pHL蟉Iw8H|$ALIH H|$H衾H> LH$H$ H/H0HcH< LR MtqHt>f. HFt6;Bs)HzLcJ<DLcE9sC;t
DHc:AuD$"LGAG@1HD>
+HxtуuH$ H{H{H5}
+ H1AG@oHH|$0H|$(H/HH1HHH$ HE{H{H5B
+ 襜Hm1AG@L萈1,HL~HƢHH;1Hf D ATIUSH HtXI$ Hu*f HH{H+Ht,HHuI$ HtݛI|$pIDŽ$ HtI|$`ID$p Ht誛I|$HID$` Ht蒛I|$8ID$H Htz[]ID$8 A\fff. AWAVAUATUSH ҉L$0LD$@LL$8H$@ AHH D$XH$ ID$ HD$H$ Ht$HF HD$ H$ HD$PHD$pHD$H l$HcD$HHLbM Ht1V҉ L$0L=
I h HS HcH ;CZ L LH{LcD; sIB;
* D DDMcAIB
-DHc?HHD: LH{LK HcI} HPHLL4A6Ic<Lf D H|$ Ƅ$ H; Ƅ$ 1HL$HDŽ$ Ƅ$ HDŽ$ HHDŽ$ HDŽ$ H$ H; HDŽ$ LpIFL$ H|H; LxL?HZ; HL$HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHyHHDŽ$ H$ H; HDŽ$ H@H$ H{; HH$
H3; HL$H|$ DŽ$ HHqH$ H; HH$ HD$H5Z+
- . HxDHD$H5 Hx*HD$ Hx7 H5A HHD$H5{*
+DHc?HHD: LH{LK HcI} HPHLL4A6Ic<Lf D H|$ Ƅ$ 趓H?< Ƅ$ 1HL$HDŽ$ Ƅ$ HDŽ$ HHDŽ$ HDŽ$ H$ H< HDŽ$ LpIFL$ H|H< LxL?H< HL$HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHyHHDŽ$ H$ HR< HDŽ$ H@H$ HÜ< HH$ ⠙H< HL$H|$ DŽ$ HHqH$ H< HH$ .HD$H5z
+ . HxdHD$H59e HxJHD$ HxWH5 H#HD$H5
- HxHD$Hx
-H5a*
- H$ H; HH$ H; H@H$ H"; HH$ H$ HxH;=; H&; HH$ HD$HxHIFL$ H|$ L Hd; HH$ CD$ LHt$*f. A~ ENEV|$0t|$XFA9D9f A$LbA6u$ u&t$HcHHT$HB D$Ht$8H|$@AnL$4LD$( Ѕ<ALD$(L$4 @u)|$HcHHT$HB A6D$| u&t$HcHHT$HB D$McNAU A9 HcD$DPHHD$L`I H HE I HuE I|$~0LZ1IfHHHDHDHL9uHuHD$8DT$IJA D A$Lb H{IH< I HI ?f. HSE1H<HD$H5
HxH; HhH$ Hl$pH
L$ L$ L9 L9 M L)HT$o1HIIHHH AEH;-~; Ht$PH|$HH$ IH$ HxH;=K; H|$pHa; HWHHD$pƄ$ HxH;=; H; LpLxf D M9 M& M)HT$o1LIHHHu_AEH;-; U Ht$PH|$HH$ II. @ H []A\A]A^A_HLLHHLLH땿 L$\DT$4DL$(LD$H IcE LD$H< L9HHGAU HEHLD$LcL$(DT$4L$\H-HXHD$H|$HHpXsMH=" H=" L$ H}LI̐H$ Ht$nHxHl$pE L} BD= Hl$pH=; HWt%Ht$nh 듋PJHE L} BD= kH|$Hl&HH|$HH3HHHD$Hx
-aIFL$ L Ho; H|$HH`H$ EH]HH=; HWt87Ht$P(HD$pHt$oHxWPJHH=; HWt%1Ht$P<"PJHא @ AWAVAUATUSH H^ Ht$Hc6L$4HT$(LD$8LL$ HHH* LL$ IHHHT$@IW`HT$HT$pHT$H' HL$Hc1HA HHHHH9 HkHtAt$@t(A$ t I$ HUHH9 D M $HcIyPLAAAA A A H|$Ƅ$ H; Ƅ$ 1HDŽ$ Ƅ$ HDŽ$ HDŽ$ HHDŽ$ HDŽ$ H$ HW; LpIFL$ I|H;; H@HHD$jH; IHHDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHDŽ$ H$ H; H@H$ H ; HH$ `H; H|$IwDŽ$ HH$ HZ; HH$ IH5
- . IH5 I] H587 HIH5:
+ Hx HD$HxH5
+ H嘙$ H0< HH$ H < H@H$ Hz< HH$ H$ HxH;=< Hn< HH$ HD$HxH褚IFL$ H|$ L H< HH$ sD$ LHt$*f. A~ ENEV|$0t|$XFA9D9f A$LbA6u$ u&t$HcHHT$HB D$Ht$8H|$@AnL$4LD$( Ѕ<ALD$(L$4 @u)|$HcHHT$HB A6D$| u&t$HcHHT$HB D$McNAU A9 HcD$DPHHD$L`I H HE I HuE I|$~0LZ1IfHHHDHDHL9uHuHD$8DT$IJA D A$Lb H{IH< I HI ?f. HSE1H<HD$H5w HxƕH7< HhH$ Hl$pH
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~
+ . IH5Z 玙I] H5 HŎIH5Z}
- ID H5(
- Hp$ Ll$ H; HH$ Hs; H@H$ H; HH$ H$ HxH;=i; H; IHHH$ /IFL$ H|$L H; HH$ E E I$ Ht$HE I$ HF Hc6 H9uHcҋЃt @tAD$ HuI$ ~.HP1HL L LLLLHH9uM $AƄ$ E E HD$HHt$HP H
+ 谎IDēH5H}
+ H萎$ Ll$ H֑< HH$ HÑ< H@H$ H < HH$ H$ HxH;=< H< IHHH$ OIFL$ H|$L HO< HH$ E E I$ Ht$HE I$ HF Hc6 H9uHcҋЃt @tAD$ HuI$ ~.HP1HL L LLLLHH9uM $AƄ$ E E HD$HHt$HP H
H9u m D HV H
-HH9S HCHt҉I$ HI$ f. H|$ A|$ tHD$@I9D$ @ @ A$ HMI$ t HH9r|HD$@H;Bm A|$ ~21HHH4H4HtHtHH9uI$ HD$@HBAƄ$ f D LL$ LD$8L$4Ht$(LH,$Nf IH5 H; LhH$ Ll$`H H$ L$ H9vuL9 M L)HT$_1HHD$H|$IHHE HT$HL HL;-W; y Ht$HH|$`HL$pIzL9) M @ B L)1HHT$HT$_H@H|$IHH HT$HLHL;-; Ht$HH|$`HL$pIvHD$pHxH;=; H|$`H; HWHHD$`Ƅ$ HxH;=s; g H~; LpLhHD$ 1H []A\A]A^A_AAEJAAEЋǃTTL)L IwXH|$`8HH= H= AD$ HuI$ ~5L@1I H<H<H|H|HL9uI$ HD$ HHBAƄ$ E E HD$HHt$HP H
+HH9S HCHt҉I$ HI$ f. H|$ A|$ tHD$@I9D$ @ @ A$ HMI$ t HH9r|HD$@H;Bm A|$ ~21HHH4H4HtHtHH9uI$ HD$@HBAƄ$ f D LL$ LD$8L$4Ht$(LH,$Nf IH5m 請H < LhH$ Ll$`H H$ L$ H9vuL9 M L)HT$_1HHD$̑H|$IHHE HT$HL)HL;-< y Ht$HH|$`HL$pIzL9) M @ B L)1HHT$HT$_HPH|$IHH HT$HL譏HL;-< Ht$HH|$`HL$pI薏HD$pHxH;=< H|$`H < HWHHD$`Ƅ$ HxH;=< g H< LpLhHD$ 1H []A\A]A^A_AAEJAAEЋǃTTL)L IwXH|$`͊8HH= 3H= 'AD$ HuI$ ~5L@1I H<H<H|H|HL9uI$ HD$ HHBAƄ$ E E HD$HHt$HP H
H9u f D HV H
-HH9HCHt҉I$ HI$ Lt$pI}L f. HD$IQP A Hcȋ ʉtOʃ=A@4I$ HE I$ I$ HE I$ H=; HWt,Ht$H|Ll$`+PJHH=; HWt<zHt$H5kHD$AE IE AD kPJHHD$pHt$^HxLl$`nHLHrIHH^IHUIFHL$L$ H H"; I`HH$ HHHD$AE IE AD 'HH=w; HWt80Ht$^&!HD$`Ht$_HxPJHfff. AUIATIU1SHHH|; HHF 0d @ ID$H5
- LH)H)1THA9l$ ~6HHt>HKHuI+T$H5
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fHt$HcuHcD$HD$ "HL$8HT$@Ht$HH|$: SHD$ offfff. AWAVAUATUSHh H|$Pt$0T$dHL$X H$ H$ HD$( HD$@ D$` HE`HD$HCHD$hf HD$(9D$0D$4j HL$PHHD$HHD$@Ht | D|$`Dt$4E9D D9 HVE9w |$(t$`HL$PWHcL<)HHDHD$H$ HD$ H$ HD$8 H|$ L: A$H|$Ƅ$ D$OIH,; Ƅ$0 1HDŽ$( Ƅ$1 HDŽ$8 HDŽ$@ HHDŽ$H HDŽ$P H$P H,; LhIEL$ H|H{,; LpL7QH(-; H}HHDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HHDŽ$0 H$ H,; H@H$P HN,; HH$ VH-; H|$HuDŽ$@ HH$ H+; HH$H PH}H5u 0 !OH}H5P OH} TH5} HNH}H5]u Nt$H}SH5 HHD$NH$ H|$HVN$ + H+; HH$ H+; H@H$P H+; HH$ H$H HxH;=*; | H*; H}HHH$ ]PIEL$ H|$L H=*; HH$P GH$ HxH;=1*; L[IL;|$& M'A$Mt$IcFMnHHt D AUAu H@IcVIFIHI9uf H}H5c KMH); LhH$( L$ H H$ L$ H9 L9 M L)HT$1HHD$uSH|$IHH& AL;- ); AE1 Ht$8H$ H$ IQH$ HxH;=(; X H$ H); HWH'SH$ Ƅ$ HxH;=(; H(; LhLpfAt$AL$H߉Cf D L9 M @ L)1HHT$HT$HpRH|$IHH] AL;-(; AEh Ht$8H$ H$ IPHNt$dHrt$`L$4H|$XH$ )HcHt$P$ H4HWH;WH$ Y HtHHrJBHD$XHHPH$ H|$h D$4t$0HL$H9HNL$@ND$`HL$@D$`HD$(D$(9D$0VHh []A\A]A^A_HD$H t̓nHT$HLOL;-&; HHD$AE IE AD HT$HL`OL;-&; HHD$AE IE AD | @ LkLhHuXH$ /KHH=:l FH=.l yFL$ H$ HL2 L$ I}LEEPBL$ H$ Ht$~Hx EL$ H=Z'; HWt8 Ht$8 GH$ Ht$HxD뇋PJHH='; HWt&Ht$~FzWPJH;H=&; HWtPHrFH=&; HW aHt$8@FRPJHH}I2IEL$ L H$; H}`LHH$P oAH$ H$ HxCH$ H{ H]MIHHD$H IHnLH}ILN땋PJH" @ AWAVAUATUSH H$ t$tT$pHt$xHT$tH|$xHHDŽ$ HDŽ$ HDŽ$ HD$h- H$0 L$ Ht$HH$ IEMeHt$`H$ Ht$H$ Ht$L9 L)IcUHi9 H$ EMEu1E1HD$X D9 IULcI|$J H9z IcDI)H N D H , H IUI I|$D)HHH9rA% A A I$At$D{HcHI}IL9RD9IAIIcLcI)H )ILT
+ HkD$uI~ M HA$ HLID$IT$x1Mo14M9tHLLILLL$8DD$@Hl$HLl$PH\$XLd$0HD$ QLIH)HL;HkDHSHk: H\$HH@LL$0DD$8Hl$@HHHD0HD$(H;HHt!lHtH0H;\$(u HLL$0DD$8Hl$@H\$HZ rHHH@HLH-fff. AWAVAUATUSH H|$t$HL$ HD$p1D$ 1E1D$ HD$8H$ HD$@H$ HD$HHD$`HD$ @ 9l$AD$P D$` HD$HT$Ht$PH<|$`;|$I" Dl$ E9 DD9 T$LE9H$ I t$ UHL$Hl$0Hc)HL,HD$(HDLIŐH} HL9uHl$0HL$(HD$H|$ D+T$ H HGH;GT$pH$ HH$ tH0HHP@HL$ HHAD9|$~D$`\$PMD|$ D$H9l$HĨ []A\A]A^A_ D t$P A911 9tALHA; u9u|$f D |$xHcD$ HD$(HHL$8HT$@Ht$HH|$ = -E1Cfff. AWAVAUAATU1S1H HL$D$ j HGIHD$0HD$PHD$8HD$pHD$@HD$`HD$Hf A9A I<HHD$t!E PQ <
+ f Dt$E9 DD9 H蛸E9HD$`# t$H\$(HcHM4HD$ CL)HHL$0HIf D H;HHCL9uH\$(HD$ Ht$I D+T$HL$pHFH;FT$P HHt$`tH0HHP@HL$HHAE9DNd$HOl$Dd$HA9HĈ []A\A]A^A_ËE9E f}HEvH tt t
fHt$HcuHcD$HD$ "HL$8HT$@Ht$HH|$: SHD$ offfff. AWAVAUATUSHh H|$Pt$0T$dHL$X H$ H$ HD$( HD$@ D$` HE`HD$HCHD$hf HD$(9D$0D$4j HL$PHHD$HHD$@Ht | D|$`Dt$4E9D D9 HVE9w |$(t$`HL$PWHcL<)HHDHD$H$ HD$ H$ HD$8 H|$ L: A$H|$Ƅ$ D$ܘH; Ƅ$0 1HDŽ$( Ƅ$1 HDŽ$8 HDŽ$@ HHDŽ$H HDŽ$P H$P H; LhIEL$ H|H; LpL7Hx; H}HHDŽ$ HDŽ$ HDŽ$ HDŽ$ HDŽ$( HHDŽ$0 H$ H; H@H$P H; HH$ H^; H|$HuDŽ$@ HH$ H; HH$H H}H5 0 AH}H5 ,H} >H5 H
+H}H5} t$H}H5U HHD$H$ H|$HV$ + H; HH$ H; H@H$P HN; HH$ H$H HxH;=; | HB; H}HHH$ }IEL$ H|$L H}; HH$P LژH$ HxH;=q; L[IL;|$& M'A$Mt$IcFMnHHt D AUAu H@IcVIFIHI9uf H}H5n kH; LhH$( L$ H H$ L$ H9 L9 M L)HT$1HHD$H|$IHH& AL;-_; AE1 Ht$8H$ H$ IH$ HxH;=%; X H$ HP; HWH7H$ Ƅ$ HxH;=; H; LhLpfAt$AL$H߉Cf D L9 M @ L)1HHT$HT$HH|$IHH] AL;-Z; AEh Ht$8H$ H$ IHNt$dHrt$`L$4H|$XH$ )HcHt$P$ H4HWH;WH$ Y HtHHrJBHD$XHHPH$ H|$h D$4t$0HL$H9HNL$@ND$`HL$@D$`HD$(D$(9D$0VHh []A\A]A^A_HD$H t̓nHT$HLL;--; HHD$AE IE AD HT$HLL;-; HHD$AE IE AD | @ LkLhHuXH$ OޘHH=bl ٘H=Vl ٘L$ H$ HL2 L$ I}LuؘL$ H$ Ht$~HxNؘL$ H=; HWt8 Ht$89ژH$ Ht$Hxט뇋PJHH=_; HWt&Ht$~٘zWPJH;H=; HWtPH٘H=; HW aHt$8p٘RPJHH}IZIEL$ L H; H}`LHH$P ԘH$ H$ Hx֘H$ H{ H}IHHD$H IHnLH}IL땋PJH" @ AWAVAUATUSH H$ t$tT$pHt$xHT$tH|$xHHDŽ$ HDŽ$ HDŽ$ HD$h- H$0 L$ Ht$HH$ IEMeHt$`H$ Ht$H$ Ht$L9 L)IcUHi9 H$ EMEu1E1HD$X D9 IULcI|$J H9z IcDI)H N D H , H IUI I|$D)HHH9rA% A A I$At$D{HcHI}IL9RD9IAIIcLcI)H )ILT
I D IUH J H HL9uEIA)DD9IEAE}AEuIMIE A
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DL$ AEMeHcЃHRA\AEIEL9AEIMDpAEuLAuI}ЋT$pIEIUH95 H)HHHAEL$ IEMe @ Dt$pI$At$DH$0 bH$8 H|$HD{IEHcD H At$HEMEufH|$Ƅ$ <Hg ; Ƅ$ HT$HDŽ$ Ƅ$ HDŽ$ HD$ HHDŽ$ H$ HF ; HDŽ$ HDŽ$ LxHHD$PIGL$ H|HF1HHD$@>EH ; HT$HDŽ$ HDŽ$ HDŽ$ HDŽ$ HHD$0HH$ HHzHH@HDŽ$ HDŽ$ H$ H ; HD$(HH$ &JH ; HT$H|$DŽ$ HD$8HHrH$ H ; HD$HH$ XDHD$H5Di 0 HxBHD$H5cD HxtBHD$ HxGH5} HMBHD$H5h Hx3BHD$DHxBG$ Lt$@ Ht$0HHH$ HH@H$ HD$8HH$ H$ HxH;|$ HD$(HH$ HD$HxHCIGL$ AH|$L HD$ HH$ :EMEu܉) @ HD$H5W HxFAHD$LpH$ L$ H H$ L$ H9 L9 M~ L)H$ 1HIqGIHL@ AH;L$A Ht$HH|$XL$0 IEH$0 HxH;|$ H$ H ; HWH-GH$ Ƅ$ HxH;|$h
HD$PLxLp-DL9 M L)1IH$ LFIHL@usAH;L$A Ht$HH|$XL$0 ID&LLLHL$@DHL$@H;L$IA L1BD1 LLLHL$@uDHL$@H;L$IyA L1BD1 d D MWMzHD$H|$XHpXE@L9$ HRI- HP@I0AE IE E1IE IE AEIU1L$ AuAuI}ЋT$pIEIUH9A}AE L$ IEMeZHLH=` :L$0 I~L96 Hl$`Ƅ$0 H6Hd; Ƅ$p L|$HHDŽ$h Ƅ$q HDŽ$x HD$ HHDŽ$ H$ HC; HDŽ$ HDŽ$ L`HHD$PH^1ID$L$8 I|H ??H; IHHDŽ$H HDŽ$P HDŽ$X HDŽ$` HDŽ$h HHD$0HH$8 HHDŽ$p H@H$ H; HD$(HH$@ ,DH; IwHDŽ$ HD$8HH$@ H#; HD$HH$ e>L|$HH5Qc 0 I<IH5q> <I AH5w Hd<IH5b O<HD$HDHx^A$0 Ht$0HHH$8 HH@H$ HD$8HH$@ H$ HxH;|$ HD$(HH$@ HD$HHxH>ID$L$8 H|$`H8 HD$ HH$ 4AuI}ЋT$pH=] 7
-f. HD$HH5Q HxF;HD$H$h L`HL$ H$X H$` H9 H9tAHa H)H$ 1HxAHIL`# AFL;t$ Ht$H$ L$ H?H$ HxH;|$ H$ H; HWH1AH$ Ƅ$0 HxH;|$ HD$PL`HXH9t<H H)H$ 1HH@HIL`ujAFL;t$uIHt$H$ L$ H>5H|$hHPHpk L$ IEMeAF I.AD. HHL>L$ H$ I|$H5%H6H=[ 5$0 H) Ht$0HHH$8 HH@H$ HD$8HH$@ H$ HxH;|$ Lt$HHD$(I~HHH$@ !;ID$L$8 I~`H8 HD$ HHH$ 1L$ H$ I9t.LuILH)I<HtO7I0M9uH$ LHt/7H=H=/; HW 4H$ 5"HD$HH5oN Hx8HD$L$h HXMH$ H$X L$` I9* M9tPM M)H$ 1L:>IIHX A$AFL;t$tAF M.CD. H$ H$ H$ Hh<H$ HxH;|$ H$ H; HWH=H$ Ƅ$0 HxH;|$^ HD$PL`HXLLH;MH=Y a3H$ H$ HxHE2dHH$ L9tOMttL)H$ 1HI=IIHX A$AFL;t$tAF M.CD. H$ H$ H$ H?;THftH=WX 2H$ H$ Hx1H$ +7LLH:mHD$HH$ HpX6xPJHmH=|; HWtAH$ (3qH$ H$ Hx0 PJHH= ; HWt(H$ 2 PJHL$ HAH$0 H$ Hxg0L$ rAF I.AD. HHL9 H=V 1H=h; HW Ht$2 HD$HH$ HpXh5H=; HWt%?Ht$10}PJHH=; HWtISH$ 1AH$ H$ HxI/L$ )PJH'
- D ff. 묋PJHH9IEet,LkHL)I| Ht2H0H9uH$ HHt1HĨ D[]A\A]A^A_H=; HWtR1Ht$H0"H=; HW uHt$Hi0fPJH몀$ HHl$@ Ht$0HHH$ HH@H$ HD$8HH$ H$ HxH;|$uhLt$HD$(I~HHH$ k4IGL$ I~`H HD$ HH$ -+IH|$HH83H=; HW uH$0 b/cHD$H5G Hx1HD$L$ LhML$ q H$ H$ I9 I9tOH I)H$ 1L7IILh E AFL;t$tAF M&CD& L$0 H$ L$0 L5H$0 HxH;|$ H$ H>; HWHm7H$ Ƅ$ HxH;|$u1HD$PLxHhH=R -LHLf5EH=9; HWt!H$0 -PJHH=
; HWtU9H$ -'H$0 H$ Hxk+L$ I}LW+vPJHH9tJHtH)H$ 1HI#6IILhuvE AFL;t$tAF M&CD& L$0 H$ L$0 LV4]L$ L$0 YHxH=\Q + LHL4HD$H$ HpX
-0 뛋PJHHHD$Hx{IGHt$@L$ H HD$ H|$HH`H$ 'H @ fHHD$HHxID$L$8 H8 HHD$ H|$HHH`H$ S'oHH|$HHH4TsH= ; HWt3MH$ }+;PJHPJHɐf. AWAVIAUAATIUSH89 A Á ID$ DD$ H 0LvDD$ E<$( D@EDE.,D
- HHmDk k It$ fCLHFHCHHCzt CI\$ Auq /f. (IL$I1IV HAHQH8[]A\A]A^A_f. ID$I6I~ HpHx @ Hl$H# L-L; It$ HD$$@B IEHD$$ tpH ; HHHD$Ll$M_I} Ht,IEHIm(L`I9vH} H*I9wI} *L}* IL$IHIVHAH6 ; HQHHD$>Ld$MtFI<$Ht5ID$HI\$(HhH9v H;H*H9wI<$)L)1HIEHHD$HK0HH)H80 AWAVAUIATIUSH HHHD$`H$ D$` HD$h HD$p LDHHD$x LRIE IU@ HD$@HT$H^ HD$H HL$0L|$PH-R E1E1HL$ @ H H|$@Q܀X HcT Hf D $ Ll$@HWLt$HHHHT$@Ll$PHL$HLt$XO Ht
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+; HWHИH$ Ƅ$0 HxH;|$^ HD$PL`HXLLHϘMH=>Y ƘH$ H$ HxHuŘdHH$ L9tOMttL)H$ 1HI!ИIIHX A$AFL;t$tAF M.CD. H$ H$ H$ H_ΘTHftH=X ŘH$ H$ HxĘH$ +7LLH͘mHD$HH$ HpXɘxPJHmH=; HWtAH$ XƘqH$ H$ HxĘ PJHH=x; HWt(H$ Ř PJHL$ HAH$0 H$ HxØL$ rAF I.AD. HHL̘ H=V PĘH=; HW Ht$CŘ HD$HH$ HpXȘH=s; HWt%?Ht$Ę0}PJHH=3; HWtISH$ ĘAH$ H$ HxyL$ )PJH'
+ D ff. 묋PJHH9IEet,LkHL)I| HtIŘH0H9uH$ HHt)ŘHĨ D[]A\A]A^A_H=D; HWtR1Ht$HØ"H=; HW uHt$HØfPJH몀$ HHl$@ Ht$0HHH$ HH@H$ HD$8HH$ H$ HxH;|$uhLt$HD$(I~HHH$ ǘIGL$ I~`H HD$ HH$ ]IH|$HH˘3H=; HW uH$0 cHD$H5 HxĘHD$L$ LhML$ q H$ H$ I9 I9tOH I)H$ 1LʘIILh E AFL;t$tAF M&CD& L$0 H$ L$0 LɘH$0 HxH;|$ H$ H; HWH}ʘH$ Ƅ$ HxH;|$u1HD$PLxHhH=R 4LHLȘEH=; HWt!H$0 PJHH=U; HWtU9H$ 'H$0 H$ Hx蛾L$ I}L臾vPJHH9tJHtH)H$ 1HI3ɘIILhuvE AFL;t$tAF M&CD& L$0 H$ L$0 Lvǘ]L$ L$0 YHxH=Q LHL"ǘHD$H$ HpX*Ø 뛋PJHHHD$Hx+AIGHt$@L$ H HD$ H|$HH`H$ 㺘H @ fHHD$HHx@ID$L$8 H8 HHD$ H|$HHH`H$ 胺oHH|$HHHǘTsH=); HWt3MH$ 譾;PJHPJHɐf. AWAVIAUAATIUSH89 A Á ID$ DD$ H 0LvDD$ E<$( D@EDE^D
+ HHmDk k It$ fCLHFHCHHCzt CI\$ Auq /f. (IL$I1IV HAHQH8[]A\A]A^A_f. ID$I6I~ HpHx @ Hl$H# L-; It$ HD$$@B IEHD$$ tpH; HHHD$Ll$M_I} Ht,IEHIm(L`I9vH} HýI9wI} 赽L譽 IL$IHIVHAHv; HQHHD$>Ld$MtFI<$Ht5ID$HI\$(HhH9v H;H4H9wI<$&L 1HIEHHD$HkØHHHXØ AWAVAUIATIUSH HHHD$`H$ D$` HD$h HD$p LDHHD$x LRIE IU@ HD$@HT$H^ HD$H HL$0L|$PH- E1E1HL$ @ H H|$@Q܀X HcT Hf D $ Ll$@HWLt$HHHHT$@Ll$PHL$HLt$XO Ht
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HHH[]fH5 HH[]H;Ht$HH HIf H9 HHÐH9 HHÐH H)9 HHf. SHH[^ HH 1.3.0 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 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 ȩ(8HXhxȩȩܹ乛$4DTdt| 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 Attempt to use DefaultLogger but none has been registered. 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 SetIntraOpNumThreads 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: ( 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
- Provider has already been registered. 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 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 Error during EndProfiling(): Received nullptr for graph transformer /onnxruntime_src/onnxruntime/core/session/inference_session.cc Could not write a profile because no model was loaded. Invalid run log severity level. Not a valid onnxruntime::logging::Severity value: run_options.run_log_severity_level >= 0 && run_options.run_log_severity_level <= static_cast<int>(logging::Severity::kFATAL) Invalid session log severity level. Not a valid onnxruntime::logging::Severity value: session_options_.session_log_severity_level >= 0 && session_options_.session_log_severity_level <= static_cast<int>(logging::Severity::kFATAL) CreateSubgraphSessionState should have created an entry earlier. /onnxruntime_src/onnxruntime/core/framework/feeds_fetches_manager.h Unexpected input data type. Actual: ( Exceeded max transformer level. Current level is set to graph_optimization_level <= TransformerLevel::MaxLevel timeout_in_ms >0 is not supported 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: allocator already registered. /onnxruntime_src/onnxruntime/core/framework/execution_providers.h Received nullptr for exec provider Memory pattern must be disabled before registering DMLExecutionProvider Sequential execution must be enabled before registering DMLExecutionProvider 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 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 ~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&) St14default_deleteIN11onnxruntime16IDeviceAllocatorEE *ZN11onnxruntime16InferenceSession4LoadEvEUlRSt10shared_ptrINS_5ModelEEE_ N6google8protobuf2io19ZeroCopyInputStreamE N11onnxruntime16IDeviceAllocatorE N11onnxruntime16GraphTransformerE N11onnxruntime16InferenceSessionE N11onnxruntime11controlflow18IControlFlowKernelE N8nlohmann6detail9exceptionE N8nlohmann6detail11other_errorE ZN11onnxruntime20CPUExecutionProviderC1ERKNS_24CPUExecutionProviderInfoEEUliE_ St19_Sp_counted_deleterIPN11onnxruntime16IDeviceAllocatorESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceISt13unordered_mapISsN11onnxruntime11FuncManager8FuncInfoESt4hashISsESt8equal_toISsESaISt4pairIKSsS3_EEESaISC_ELN9__gnu_cxx12_Lock_policyE2EE _ _ C%lu at line , column parse_error parse error invalid_iterator type_error out_of_range object boolean discarded number cannot use at() with key ' ' not found map::at <U+%.4X> <uninitialized> true literal false literal null literal string literal number literal '[' '{' ']' '}' ':' ',' unknown token end of input '[', '{', or a literal <parse error> syntax error while parsing - ; last read: ' unexpected ; expected cannot get value type must be number, but is Sequential mode Parallel mode false true session_options intra_op_num_threads inter_op_num_threads execution_mode Setting execution_mode to graph_optimization_level enable_profiling Setting enable_profiling to vector::_M_emplace_back_aux vector<bool>::_M_insert_aux iterator out of range cannot use erase() with invalid literal object key object separator number overflow parsing ' ort_config Parsing RunOptions from ModelProto is not supported yet 961c151d2e87f2686a955a9be24d316f1362bf21 3.7.1 The Model Proto hasn't been checked for the ORT config json. Did not find session options in the model file to be used while running the model cannot compare iterators of different containers intra_op_num_threads option in the model file must be an integer Unsupported value for intra_op_num_threads: Setting intra_op_num_threads to /onnxruntime_src/onnxruntime/core/session/inference_session_utils.cc inter_op_num_threads option in the model file must be an integer Unsupported value for inter_op_num_threads: Setting inter_op_num_threads to execution_mode option in the model file must be an integer Unsupported execution_mode value in ORT config: graph_optimization_level option in the model file must be an integer Setting graph_optimization_level to ORT_DISABLE_ALL Setting graph_optimization_level to ORT_ENABLE_BASIC Setting graph_optimization_level to ORT_ENABLE_EXTENDED Setting graph_optimization_level to ORT_ENABLE_ALL Unsupported graph_optimization_level value in ORT config: enable_profiling option in the model file must be an integer Unsupported value for enable_profiling option: Ignoring unsupported session option in ORT config: invalid string: ill-formed UTF-8 byte invalid string: control character U+000A (LF) must be escaped to \u000A or \n invalid string: missing closing quote invalid string: '\u' must be followed by 4 hex digits invalid string: surrogate U+DC00..U+DFFF must be followed by U+DC00..U+DFFF invalid string: surrogate U+DC00..U+DFFF must follow U+D800..U+DBFF invalid string: forbidden character after backslash invalid string: control character U+0000 (NUL) must be escaped to \u0000 invalid string: control character U+0001 (SOH) must be escaped to \u0001 invalid string: control character U+0002 (STX) must be escaped to \u0002 invalid string: control character U+0003 (ETX) must be escaped to \u0003 invalid string: control character U+0004 (EOT) must be escaped to \u0004 invalid string: control character U+0005 (ENQ) must be escaped to \u0005 invalid string: control character U+0006 (ACK) must be escaped to \u0006 invalid string: control character U+0007 (BEL) must be escaped to \u0007 invalid string: control character U+0008 (BS) must be escaped to \u0008 or \b invalid string: control character U+0009 (HT) must be escaped to \u0009 or \t invalid string: control character U+000B (VT) must be escaped to \u000B invalid string: control character U+000C (FF) must be escaped to \u000C or \f invalid string: control character U+000D (CR) must be escaped to \u000D or \r invalid string: control character U+000E (SO) must be escaped to \u000E invalid string: control character U+000F (SI) must be escaped to \u000F invalid string: control character U+0010 (DLE) must be escaped to \u0010 invalid string: control character U+0011 (DC1) must be escaped to \u0011 invalid string: control character U+0012 (DC2) must be escaped to \u0012 invalid string: control character U+0013 (DC3) must be escaped to \u0013 invalid string: control character U+0014 (DC4) must be escaped to \u0014 invalid string: control character U+0015 (NAK) must be escaped to \u0015 invalid string: control character U+0016 (SYN) must be escaped to \u0016 invalid string: control character U+0017 (ETB) must be escaped to \u0017 invalid string: control character U+0018 (CAN) must be escaped to \u0018 invalid string: control character U+0019 (EM) must be escaped to \u0019 invalid string: control character U+001A (SUB) must be escaped to \u001A invalid string: control character U+001B (ESC) must be escaped to \u001B invalid string: control character U+001C (FS) must be escaped to \u001C invalid string: control character U+001D (GS) must be escaped to \u001D invalid string: control character U+001E (RS) must be escaped to \u001E invalid string: control character U+001F (US) must be escaped to \u001F iterator does not fit current value invalid number; expected digit after '-' invalid number; expected digit after '.' invalid number; expected '+', '-', or digit after exponent invalid number; expected digit after exponent sign invalid BOM; must be 0xEF 0xBB 0xBF if given The Model Proto has already been checked for the ORT config json. Found session/run/environment configuration in the model file to be used while running the model ORT config json from the model: Json stored in the `ort_config` key cannot be parsed. Error message: ꝞԞ4d䞞ԞԞԞĤtttdT䤞Ԥ\D(HФXhxxxȥإ襞짞\Ĩ,쫞Tt$İİİ`@(8 ݞhP8 @(ߞߞߞߞߞߞhߞ8ߞ8ߞP8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞHߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ8ߞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞ ݞXXXX------------ --------------------------------------------ߞ-----ߞ---ߞ-------ߞ---ߞ-ߞڞP` 0ppp@gggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggggg/gggggggggggvgl
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&d&&%'$&$&D&066T56|5621/0000. 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 ACLExecutionProvider ConvActivationFusion AttentionFusion EmbedLayerNormFusion BiasGelu 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 size is different size_ == size unsupported data type: ai.onnx Sqrt Pow fused LayerNorm subgraphs epsilon /onnxruntime_src/onnxruntime/core/optimizer/initializer.h ReadExternalRawData() failed: /onnxruntime_src/onnxruntime/core/optimizer/layer_norm_fusion.cc >y>>>>T
>>f???????????v?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 4^^4^4^4^4^ ^ ^4^4^,^]d^4^d^d^d^d^<^L^d^d^\^,^^d^^^^^l^|^^^^\^^^^^^^^^^^^^^^^^^^^^^^^^$_^$_$_$_$_^ _$_$_ _^T_$_T_T_T_T_,_<_T_T_L_ __T_____\_l___|_L____________|_____________tgftgtgtgtgh<htgtgiglkllll$n\mll ol$r<q$r$r$r$r<sr$r$rs<rpads 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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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
+
- 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 PPPPP`PP ~=m`mmmmK;mm& onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ToProto onnxruntime::Initializer::Initializer(onnx::TensorProto_DataType, const string&, const std::vector<long int>&) virtual onnxruntime::common::Status onnxruntime::FuseReluClip::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const N11onnxruntime12FuseReluClipE Unsqueeze Gather Reshape Fused reshape node: Cannot replace concat node with initializer: /onnxruntime_src/onnxruntime/core/optimizer/reshape_fusion.cc Total fused reshape node count: Fuse_Subgraph1 Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime13ReshapeFusionE /onnxruntime_src/onnxruntime/core/optimizer/rule_based_graph_transformer.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl ApplyRulesOnNode N11onnxruntime25RuleBasedGraphTransformerE N11onnxruntime18ShapeToInitializerE SkipLayerNormalization /onnxruntime_src/onnxruntime/core/optimizer/skip_layer_norm_fusion.cc fused SkipLayerNorm subgraphs Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl ̼+ N11onnxruntime19SkipLayerNormFusionE starts ends "////// ꨡݨzm줡ߤ訡ۨΨ
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+ 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
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- N11onnxruntime14EliminateSliceE /onnxruntime_src/onnxruntime/core/optimizer/transformer_memcpy.cc dup_replacements.find(&arg) == dup_replacements.end() TensorrtExecutionProvider NGRAPHExecutionProvider ' doesn't support memcpy Execution type ' Memcpy Copy from/to host memory DnnlExecutionProvider NupharExecutionProvider OpenVINOExecutionProvider onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&)::__lambda9 bool onnxruntime::TransformerMemcpyImpl::ProcessInitializers(const onnxruntime::KernelRegistryManager&, const InitializedTensorSet&) ForEachWithIndex void onnxruntime::TransformerMemcpyImpl::ProcessDefs(onnxruntime::Node&, const onnxruntime::KernelRegistryManager&, onnxruntime::InitializedTensorSet&) Recurse ApplyImpl *ZN11onnxruntime21TransformerMemcpyImpl11ProcessDefsERNS_4NodeERKNS_21KernelRegistryManagerERSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsSA_EEEEUlRKNS_7NodeArgEmE_ *ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE_ *ZN11onnxruntime21TransformerMemcpyImpl19ProcessInitializersERKNS_21KernelRegistryManagerERKSt13unordered_mapISsPKN4onnx11TensorProtoESt4hashISsESt8equal_toISsESaISt4pairIKSsS8_EEEEUlRKNS_7NodeArgEmE0_ N11onnxruntime17MemcpyTransformerE UnsqueezeElimination_ UnsqueezeElimination cannot remove node /onnxruntime_src/onnxruntime/core/optimizer/unsqueeze_elimination.cc Apply N11onnxruntime20UnsqueezeEliminationE vector::_M_range_insert dZddddE5dd k^ygyyyyRByy-syoyyyybUyyH;yl '7w+2D^^^^NV^^?ITnnnn^fnnOYqkv_weights qkv_bias Faild to find path v Failed to find path for mask Failed to find path for q q_reshape const not matched qk_div const not matched. Failed to find path for k k root is not layer norm k_reshape const not matched Mask_Int32 Cast mask from int64 to int32 MaskCast MaskIndex_Int32 Count number of words ReduceSum MaskIndex keepdims Failed to create mask index Fused Attention subgraphs num_heads Fused an attention node. Output edge count not expected for nodes in path v Failed in match Transpose attribute perm. Expected: 0, 2, 1, 3 Failed in match v_transpose attribute perm. Expected: 0, 2, 1, 3 v_reshape initializer value is not expected reshape initializer value is not expected Failed in match v_matmul and v_add input shape Output edge count not expected for mask nodes Softmax attribute axis is expected to be 3 mask_unsqueeze_1 axes not matched. Expect: 1 mask_unsqueeze_2 axes not matched. Expect: 2 mask_sub const input not matched mask_mul const input not matched q root should be layer normalization q_transpose perm attribute not matched q_matmul and q_add shape not matched k_transpose perm attribute not matched k_matmul and k_add shape not matched Failed to load Q, K and V weights, or data type is not float or float16. Failed to load Q, K and V bias tensors, or data type is not float or float16. Mask shape is unknown or not 2D, or data type unknown Mask data type is not int32 or int64 or float32 /onnxruntime_src/onnxruntime/core/optimizer/attention_fusion.cc shape of layer norm bias tensor not expected Attention subgraph expects 1 Add and 3 MatMul as children of LayerNormalization. Total fused Attention node count: ProcessMask FuseSubGraph Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime15AttentionFusionE @ ?FastGelu fused Add and Gelu /onnxruntime_src/onnxruntime/core/optimizer/bias_gelu_fusion.cc Recurse onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const ApplyImpl N11onnxruntime8BiasGeluE 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 LeakyRelu fused Conv with activation fused activation_params /onnxruntime_src/onnxruntime/core/optimizer/conv_activation_fusion.cc Unexpected data type for Clip input of reo_J5ylyyyy_RyyE8H;HHHH&HH 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 z zzzz zz0 0 ui`W*!**** ** 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 xHxxxxP`xxp@xp 88888 880 h8hhhh@Phh`0hp`((((( (( bn_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 H&P%H&H&H&H&$$H&H&$^#.C+....),..('.3....4J3..2 2 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 8DD8D8D8D8DD D8D8D0D DhD8DhDhDhDhD@DPDhDhD`D0DDhDDDDDpDDDDD`DDDDDDDDDDDDDDDDDDDDDDDDD(ED(E(E(E(E EE(E(E EDmul_B_tensor_proto ConvMulFusion_W_ ConvMulFusion_Mul_B_ /onnxruntime_src/onnxruntime/core/optimizer/conv_mul_fusion.cc W;VWWWWTmSWW~R[Q_S^____\}[__ZjYle_eleleleleReEelele8e+eeeeeeeeeeeveieeeeeeeeeeeee*f f*f*f*f*fff*f*feefffffflfWfffBf-f#lg#l#l#l#lff#l#lffmMgmmmm@g3gmm&ggWmgWmWmWmWmvgigWmWm\gOgonnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const virtual onnxruntime::common::Status onnxruntime::ConvMulFusion::Apply(onnxruntime::Graph&, onnxruntime::Node&, onnxruntime::RewriteRule::RewriteRuleEffect&, const onnxruntime::logging::Logger&) const N11onnxruntime13ConvMulFusionE
- N11onnxruntime16EliminateDropoutE Input shape is unknown or not 2D, or data type unknown Input data type is not int32 or int64 /onnxruntime_src/onnxruntime/core/optimizer/embed_layer_norm_fusion.cc Failed to find path 1 of position shape. Output edge count not expected for nodes in path 1 of position shape. Second input of Gather in path 1 of position shape should be a constant with value 0. Failed to find path 2 of position shape. Output edge count not expected for nodes in path 2 of position shape. Gather node in path 2 is not linked to another subgraph. Second input of Gather in path 2 of position shape should be a constant with value 1. The parent of two shape nodes are expected to be input_ids. Cast Input from int64 to int32 Word embedding shape not expected. Input is expected to have dim value in all dimensions. Failed to get initializer tensor. Position embedding shape not matched. Position embedding data type shall be float or float16. Output edge count not expected for nodes in path1. Second input of Gather should be a constant with value 1. The parent of shape nodes are expected to be input_ids. Failed to match position subgraph. Output edge count not expected for nodes in path 1. The first input of Range should be a constant with value 0. The third input of Range should be a constant with value 1. Failed to match position embedding subgraph. Failed to get position embedding weights. Position embedding shape is not expected. Input_ids and segment id should have the same shape. Input_ids and mask should have the same shape. Gamma should be of shape (hidden_size). Beta should be of shape (hidden_size). fused EmbedLayerNorm subgraphs _Cast position_embeddings Expand Squeeze NonZero ConstantOfShape Failed to match Shape node. Failed to find path 1. Input id is not valid. Segment id is not valid. Mask is not valid. EmbedLayerNormalization ~) G:GGGG- GG onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const CheckInput MatchPositionEmbeddingSubgraph2 MatchPositionSubgraph MatchPositionEmbeddingSubgraph1 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
+ 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&)
+
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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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+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
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 _Map_base::at 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 , <d , Dl 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 ѦЦѦЦѦЦѦЦЦЦЦѦЦЦЦЦѦЦЦЦЦЦЦЦҦѦЦѦЦЦЦTѦЦЦtѦЦЦЦtѦЦЦЦЦЦЦЦЦ$ѦҦ$Ѧ4ѦѦЦЦҦ$Ѧ4ѦЦDѦѦTѦdѦ$Ҧ4Ҧ\ҦdѦlҦ|ҦҦЦЦЦЦЦЦЦЦЦЦЦЦЦЦЦЦЦѦЦЦЦЦЦѦѦtѦЦЦЦЦЦЦЦЦЦЦЦЦЦЦЦDѦ$Ѧ4ѦЦDѦѦ4ѦЦЦ$Ѧ4ѦЦDѦѦЦDѦѦTѦdѦЦЦѦDet 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@Einsum equation Missing 'equation' attribute -> is not supported yet /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 /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum.cc Einsum op: An implementation for the input type const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::Einsum::Einsum(const onnxruntime::OpKernelInfo&) N11onnxruntime6EinsumE %ld Tensor size ( ) != new size ( dim_iter == rank . The shape is: in the same dimension vector::_M_fill_insert left_rank == right_rank left_dim == right_dim Inputs have ellipses in them but the provided output subscript does not contain an ellipsis The rank of the input must match permutation size for Transpose /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_utils.cc input_rank == permutation.size() Einsum op: The candidate output does not match the actual output's shape output.SizeInBytes() == candidate.SizeInBytes() shape_.Size() == new_shape.Size() 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 Einsum op: The candidate output cannot be reshaped into the op's output candidate_output.Shape().Size() == output_shape.Size() Not all dimensions to be reduced have been reduced in the candidate output candidate_output_dims[iter] == 1 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 std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, const AllocatorPtr&, const onnxruntime::EinsumComputePreprocessor&, bool, onnxruntime::Tensor&) [with T = long int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] void onnxruntime::EinsumOp::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&, const std::vector<long int>&, onnxruntime::Tensor&, const onnxruntime::TensorShape&, const AllocatorPtr&) [with T = long int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, const AllocatorPtr&, const onnxruntime::EinsumComputePreprocessor&, bool, onnxruntime::Tensor&) [with T = double; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] void onnxruntime::EinsumOp::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&, const std::vector<long int>&, onnxruntime::Tensor&, const onnxruntime::TensorShape&, const AllocatorPtr&) [with T = double; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, const AllocatorPtr&, const onnxruntime::EinsumComputePreprocessor&, bool, onnxruntime::Tensor&) [with T = int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] void onnxruntime::EinsumOp::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&, const std::vector<long int>&, onnxruntime::Tensor&, const onnxruntime::TensorShape&, const AllocatorPtr&) [with T = int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::PairwiseOperandProcess(const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const onnxruntime::Tensor&, const onnxruntime::TensorShape&, const std::vector<long int>&, onnxruntime::concurrency::ThreadPool*, const AllocatorPtr&, const onnxruntime::EinsumComputePreprocessor&, bool, onnxruntime::Tensor&) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] void onnxruntime::EinsumOp::CopyOutputCandidateIntoOpOutout(onnxruntime::Tensor&, const onnxruntime::Tensor&) void onnxruntime::EinsumOp::FinalizeOutput(const onnxruntime::Tensor&, const std::vector<long int>&, const std::vector<long int>&, onnxruntime::Tensor&, const onnxruntime::TensorShape&, const AllocatorPtr&) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&) bool onnxruntime::EinsumOp::IsTransposeRequired(size_t, const std::vector<long unsigned int>&) onnxruntime::common::Status onnxruntime::EinsumComputePreprocessor::PostProcessBroadcastedDims() Run fmod (fmod == 0) || (fmod == 1) Abs Neg Ceil Reciprocal Less Greater BitShift Not And Or Xor Tan Asin Acos Sinh Asinh Atanh PRelu Mod Atan Acosh Sin Cos Cosh Floor axis == 1 || axis == largest direction LEFT RIGHT Invalid direction value of ' 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'. 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]::__lambda134 onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda134 onnxruntime::BitShift<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda134 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] const T* onnxruntime::Tensor::Data() const [with T = short int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short unsigned int] T* onnxruntime::Tensor::MutableData() [with T = short unsigned int] const T* onnxruntime::Tensor::Data() const [with T = short unsigned int] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = short unsigned int] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = signed char] T* onnxruntime::Tensor::MutableData() [with T = signed char] const T* onnxruntime::Tensor::Data() const [with T = signed char] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = signed char] void onnxruntime::mod_internal::BroadCastMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] void onnxruntime::mod_internal::BroadCastFMod(const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*) [with T = unsigned char] void onnxruntime::utils::MLTypeCallDispatcher<Fn, Types>::Invoke(Args&& ...) const [with Args = {const bool&, const onnxruntime::Tensor&, const onnxruntime::Tensor&, onnxruntime::OpKernelContext*&}; Fn = onnxruntime::mod_internal::CallModImpl; Types = {unsigned char, signed char, short unsigned int, short int, unsigned int, int, long unsigned int, long int}] 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]::__lambda129; Input1Scalar = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda130; General = onnxruntime::Mean_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda131] onnxruntime::common::Status onnxruntime::Mean_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = bool] onnxruntime::common::Status onnxruntime::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]::__lambda108; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda109; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda110] 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]::__lambda108; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = long int]::__lambda109; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = long int]::__lambda110] 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]::__lambda108; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda109; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda110] 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]::__lambda108; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = int]::__lambda109; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = int]::__lambda110] 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]::__lambda108; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16]::__lambda109; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16]::__lambda110] 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]::__lambda108; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = double]::__lambda109; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = double]::__lambda110] 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]::__lambda108; Input1Scalar = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = float]::__lambda109; General = onnxruntime::Max_8::ComputeImpl<T>::operator()(const onnxruntime::Max_8*, onnxruntime::OpKernelContext*) const [with T = float]::__lambda110] 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::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]::__lambda105; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda106; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = long unsigned int]::__lambda107] 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]::__lambda105; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = long int]::__lambda106; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = long int]::__lambda107] 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]::__lambda105; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda106; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = unsigned int]::__lambda107] 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]::__lambda105; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = int]::__lambda106; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = int]::__lambda107] 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]::__lambda105; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16]::__lambda106; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = onnxruntime::MLFloat16]::__lambda107] 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]::__lambda105; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = double]::__lambda106; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = double]::__lambda107] 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]::__lambda105; Input1Scalar = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = float]::__lambda106; General = onnxruntime::Min_8::ComputeImpl<T>::operator()(const onnxruntime::Min_8*, onnxruntime::OpKernelContext*) const [with T = float]::__lambda107] 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]::__lambda102; Input1Scalar = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda103; General = onnxruntime::Sum_8<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float]::__lambda104] onnxruntime::common::Status onnxruntime::Sum_6<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = double] T* onnxruntime::Tensor::MutableData() [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = 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 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_ ZN11onnxruntime12pow_internal7PowImplIddEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIdEENS8_IKdEEdE2_ ,F F @ ? ? ? ? B B B B ? ? ? ?5?5?5?5?!=!=!=!=뽸뽸뽸O]O]O]O]>>>>L>L>L>L>%=%=%=%=P*P*P*P*>>>>^^^^ 1? 1? 1? 1? ? ? ? ? "?"?"?"? @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 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_ ZN11onnxruntime12pow_internal7PowImplIddEEvPNS_15OpKernelContextERKNS_6TensorES6_EUlN3gsl4spanIdEENS8_IKdEEdE2_ ,F F @ ? ? ? ? B B B B ? ? ? ?5?5?5?5?!=!=!=!=뽸뽸뽸O]O]O]O]>>>>L>L>L>L>%=%=%=%=P*P*P*P*>>>>^^^^ 1? 1? 1? 1? ? ? ? ? "?"?"?"? @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 MatMulInteger a != nullptr && b != nullptr /onnxruntime_src/onnxruntime/core/providers/cpu/math/matmul_integer.cc t->Shape().NumDimensions() <= 1 && t->Shape().Size() == 1 t->IsDataType<int8_t>() || t->IsDataType<uint8_t>() Currently only scalar zero_point is supported. TODO: add per channel zero point support. IsScalarOr1ElementVector(a_zero_point) IsScalarOr1ElementVector(b_zero_point) MatmulInteger : input1 zero point must be a scalar or 1D tensor of size 1 MatmulInteger : input2 zero point must be a scalar or 1D tensor of size 1 MatMulInteger: Unsupported input types with zero point const T* onnxruntime::Tensor::Data() const [with T = signed char] onnxruntime::MatMulInteger<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = signed char]::__lambda9 Compute onnxruntime::common::Status onnxruntime::MatMulInteger<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = signed char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = int] Compute onnxruntime::common::Status onnxruntime::MatMulInteger<T1, T2>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = unsigned char] N11onnxruntime13MatMulIntegerIhhEE N11onnxruntime13MatMulIntegerIhaEE QLinearMatMul /onnxruntime_src/onnxruntime/core/providers/cpu/math/quantize_linear_matmul.cc IsScalarOr1ElementVector(a_offset) IsScalarOr1ElementVector(b_offset) IsScalarOr1ElementVector(y_offset) IsScalarOr1ElementVector(a_scale) IsScalarOr1ElementVector(b_scale) IsScalarOr1ElementVector(y_scale) QLinearMatmul : input zero point must be a scalar or 1D tensor of size 1 QLinearMatmul : weight zero point must be a scalar or 1D tensor of size 1 QLinearMatmul : result zero point must be a scalar or 1D tensor of size 1 QLinearMatmul : input scale must be a scalar or 1D tensor of size 1 QLinearMatmul : weight scale must be a scalar or 1D tensor of size 1 QLinearMatmul : result scale must be a scalar or 1D tensor of size 1 T* onnxruntime::Tensor::MutableData() [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] Compute onnxruntime::common::Status onnxruntime::QLinearMatMul<T1, T2, T3>::Compute(onnxruntime::OpKernelContext*) const [with T1 = unsigned char; T2 = unsigned char; T3 = unsigned char] N11onnxruntime13QLinearMatMulIhhhEE 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 string_vocabulary int64_vocabulary DictVectorizer /onnxruntime_src/onnxruntime/core/providers/cpu/ml/dictvectorizer.h info.GetAttrs(std::is_same<AttrType, std::string>::value ? "string_vocabulary" : "int64_vocabulary", vocabulary_).IsOK() T* onnxruntime::Tensor::MutableData() [with T = long int] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, long int>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, long int>] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, float>] const T& OrtValue::Get() const [with T = std::map<std::basic_string<char>, double>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<std::basic_string<char>, double>] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T& OrtValue::Get() const [with T = std::map<long int, std::basic_string<char> >] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, std::basic_string<char> >] T* onnxruntime::Tensor::MutableData() [with T = float] const T& OrtValue::Get() const [with T = std::map<long int, float>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, float>] T* onnxruntime::Tensor::MutableData() [with T = double] const T& OrtValue::Get() const [with T = std::map<long int, double>] const T* onnxruntime::OpKernelContext::Input(int) const [with T = std::map<long int, double>] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = double] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = float] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = long int; TargetType = std::basic_string<char>] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = double] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = float] onnxruntime::ml::DictVectorizerOp<AttrType, TargetType>::DictVectorizerOp(const onnxruntime::OpKernelInfo&) [with AttrType = std::basic_string<char>; TargetType = long int] N11onnxruntime2ml16DictVectorizerOpISslEE N11onnxruntime2ml16DictVectorizerOpISsfEE N11onnxruntime2ml16DictVectorizerOpISsdEE N11onnxruntime2ml16DictVectorizerOpIlSsEE N11onnxruntime2ml16DictVectorizerOpIlfEE N11onnxruntime2ml16DictVectorizerOpIldEE inputdimensions FeatureVectorizer input_tensor_ptr != nullptr Invalid input type: Number of inputs ( /onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.h status.IsOK() && !input_dimensions_.empty() inputdimensions attribute must be provided /onnxruntime_src/onnxruntime/core/providers/cpu/ml/feature_vectorizer.cc input_count >= 0 && static_cast<size_t>(input_count) == input_dimensions_.size() ) does not match number of inputdimensions values ( const T* onnxruntime::Tensor::Data() const [with T = double] const T* onnxruntime::Tensor::Data() const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] T* onnxruntime::Tensor::MutableData() [with T = float] virtual onnxruntime::common::Status onnxruntime::ml::FeatureVectorizer::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ml::FeatureVectorizer::FeatureVectorizer(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml17FeatureVectorizerE imputed_value_floats imputed_value_int64s replaced_value_float replaced_value_int64 Imputer Empty input dimensions. Invalid type Expected 'replaced_value_float' attribute since 'imputed_value_floats' is specified /onnxruntime_src/onnxruntime/core/providers/cpu/ml/imputer.cc Expected 'replace_value_int64' attribute since 'imputed_values_int64' is specified Must provide imputed_values_float_ or imputed_values_int64_ but not both. imputed_values_float_.empty() ^ imputed_values_int64_.empty() Empty value of imputed values. T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::ml::ImputerOp::Compute(onnxruntime::OpKernelContext*) const onnxruntime::ml::ImputerOp::ImputerOp(const onnxruntime::OpKernelInfo&) N11onnxruntime2ml9ImputerOpE keys_floats values_strings _Unused keys_strings values_floats default_float keys_int64s values_int64s LabelEncoder attribtues in LabelEncoder and The values is and the number of ) must have the same length. num_keys == num_values classes_strings /onnxruntime_src/onnxruntime/core/providers/cpu/ml/label_encoder.h info.GetAttrs<TKey>(_key_field_name, keys).IsOK() info.GetAttrs<TValue>(_value_field_name, values).IsOK() However, the number of key is Input of tensor(string) must have output of tensor(int64) Input of tensor(int64) must have output of tensor(string) info.GetAttrs<std::string>("classes_strings", string_classes).IsOK() gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = float] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = float] gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = std::basic_string<char>] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = 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 J++**L*+**)))$*<6655<56544445
-@v@?'?>@|>l>\>=<>>I-JiI IHJlH\HLHG,H|H 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] 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 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
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 st.IsOK() Invalid input scale: dimension != Invalid input B: Invalid input mean: Invalid input var: /onnxruntime_src/onnxruntime/core/providers/cpu/nn/batch_norm.h Invalid input X: The rank of input X must be atleast 2. Got rank: Invalid input scale: NumDimensions() != Invalid input scale: 0th dimension != Invalid input B: NumDimensions() != Invalid input B: 0th dimension != Invalid input mean: NumDimensions() != Invalid input mean: 0th dimension != Invalid input var: NumDimensions() != Invalid input var: 0th dimension != T* onnxruntime::Tensor::MutableData() [with T = float] Compute T* onnxruntime::Tensor::MutableData() [with T = double] Compute onnxruntime::BatchNorm<T>::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = double] onnxruntime::BatchNorm<T>::BatchNorm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime9BatchNormIfEE N11onnxruntime9BatchNormIdEE X num_dims does not match W num_dims. Input channels C is not equal to kernel channels * group. Output channels M is not divisible by group. kernel_shape num_dims is not compatible with W num_dims. kernel_shape is not compatible with W shape. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/autopad_type.h Dilation not supported for AutoPadType::SAME_UPPER or AutoPadType::SAME_LOWER. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_attributes.h /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv.cc /onnxruntime_src/onnxruntime/core/util/math.h X: C: kernel channels: group: M: kernel_shape: Unknown AutoPadType String dilation == 1 pad type not supported. Invalid input shape: Out of bound access to array d_iter[d_i] < d_max onnxruntime::AutoPadType onnxruntime::StringToAutoPadType(const string&) void onnxruntime::math::Im2colNd<T, 2>::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, bool, T) [with T = float; int64_t = long int] onnxruntime::common::Status onnxruntime::ComputePadAndOutputShape(int64_t, int64_t, int64_t, int64_t, onnxruntime::AutoPadType, int64_t*, int64_t*, int64_t*) [with bool ForceSymmetricAutoPadding = false; int64_t = long int] InferOutputShape Compute N11onnxruntime4ConvIfEE /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_integer.cc IsScalarOr1ElementVector(X_Zero_Point) IsScalarOr1ElementVector(W_Zero_Point) Must be a scalar or 1D tensor or size 1. Non per-tensor quantization is not supported now. ConvInteger void onnxruntime::math::Im2colNd<T, 2>::operator()(const T*, const int64_t*, const int64_t*, int64_t, int64_t, const int64_t*, const int64_t*, const int64_t*, const int64_t*, int64_t, T*, bool, T) [with T = unsigned char; int64_t = long int] Compute virtual onnxruntime::common::Status onnxruntime::ConvInteger::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime11ConvIntegerE ConvTranspose output_padding output_shape group count is <= 0 filter_number: num_input_channels: *out_size >= 0 pad type not supported dim_size > 0 Only 1D and 2D ConvTranspose is supported. filter number not equal to input channel number. Input channels is not divisible by group. /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose_attributes.h /onnxruntime_src/onnxruntime/core/providers/cpu/nn/conv_transpose.cc void onnxruntime::ConvTransposeAttributes::ComputeTransposePadAndOutputShape(int64_t, int64_t, int64_t, int64_t, int64_t, onnxruntime::AutoPadType, int64_t*, int64_t*, int64_t*) const void onnxruntime::ConvTransposeAttributes::ComputePadsAndOutputShape(onnxruntime::TensorShape, int64_t, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, const std::vector<long int>&, int64_t, std::vector<long int>*, std::vector<long int>*) const const T* onnxruntime::Tensor::Data() const [with T = long int] PrepareForCompute DoConvTranspose N11onnxruntime13ConvTransposeIfEE 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 p_ == 1 || p_ == 2 LpNormalization /onnxruntime_src/onnxruntime/core/providers/cpu/nn/lp_norm.h op_kernel_info.GetAttr<int64_t>("axis", &axis_).IsOK() op_kernel_info.GetAttr<int64_t>("p", &p_).IsOK() const T* onnxruntime::Tensor::Data() const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::LpNorm<T>::LpNorm(const onnxruntime::OpKernelInfo&) [with T = float] N11onnxruntime6LpNormIfEE size_ > 0 size_ % 2 == 1 alpha_ > 0.0f beta_ > 0.0f LRN /onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.h info.GetAttr<int64_t>("size", &size).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/nn/lrn.cc X->Shape().NumDimensions() == 4 onnxruntime::LRN<T>::LRN(const onnxruntime::OpKernelInfo&) [with T = float] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::common::Status onnxruntime::LRN<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] N11onnxruntime3LRNIfEE 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. 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
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()
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) 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 SequenceConstruct SequenceLength SequenceInsert SequenceErase SequenceAt SplitToSequence I != nullptr Invalid sequence index ( split_size_sum ( ) != split_dim_size ( S != nullptr Got nullptr for input tensor. ), input tensor data type ( num_inputs >= 1 Split should be > 0 split_scalar > 0 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<, 췾̷ܷ 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. allocator != nullptr len > 0 buffer stol stoul stoi 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 ,ſſƿƿǿǿȿȿɿɿ ʿʿ ĿĿ4 ۿܿLDֿؿ $$t&,) l!"#++./l | FLGHJ?AB4CtDtLD Et=>|] ^ _<aW Y<ZZ[\c[d\LV WDqqTr$t nlo,ppvvv\wmmtl<4,L܍4 |̯DԩLDtL,\d\ T,tt<l\\4$ 4D Lt l
-< T $ | 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
+ 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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':
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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
Not satisfied: inputs_0_rank > 0
Ranks of input data are different, cannot concatenate them. expected rank: inputs_n_rank == inputs_0_rank Non concat axis dimensions must match: Axis has mismatched dimensions of Not satisfied: dim_value == inputs_0_dims[axis_index]
Not satisfied: data_n.DataType() == p.output_tensor->DataType()
- const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) onnxruntime::common::Status onnxruntime::ConcatBase::PrepareForCompute(onnxruntime::OpKernelContext*, const std::vector<const onnxruntime::Tensor*>&, onnxruntime::Prepare&) const PrepareForCompute onnxruntime::ConcatBase::ConcatBase(const onnxruntime::OpKernelInfo&, bool) N11onnxruntime6ConcatE DynamicQuantizeLinear x_ptr != nullptr /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/dynamicquantizelinear.cc T* onnxruntime::Tensor::MutableData() [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::DynamicQuantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] N11onnxruntime21DynamicQuantizeLinearIhEE CEyeLike T1 != nullptr EyeLike : Input tensor dimension is not 2 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/eye_like.cc 4b$b4b4b4b4bbb4b4b4ba4ba 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 = GatherNDBase PrepareForCompute: Input count mismatch /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/gather_nd.cc input_tensor != nullptr && indices_tensor != nullptr indices tensor must has rank larger than 0 last dimension of indices must not be larger than rank of input tensor const T* onnxruntime::Tensor::Data() const [with T = long int] onnxruntime::common::Status onnxruntime::GatherNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::GatherNDBase::Prepare&, onnxruntime::concurrency::ThreadPool*) const [with Tind = long int] const T* onnxruntime::Tensor::Data() const [with T = int] onnxruntime::common::Status onnxruntime::GatherNDBase::PrepareForCompute(onnxruntime::OpKernelContext*, onnxruntime::GatherNDBase::Prepare&, onnxruntime::concurrency::ThreadPool*) const [with Tind = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute N11onnxruntime12GatherNDBaseE *ZNK11onnxruntime8GatherND12GatherNumberERKNS_12GatherNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ *ZNK11onnxruntime8GatherND12GatherStringERKNS_12GatherNDBase7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ N11onnxruntime8GatherNDE ZNK11onnxruntime12GatherNDBase17PrepareForComputeIiEENS_6common6StatusEPNS_15OpKernelContextERNS0_7PrepareEPNS_11concurrency10ThreadPoolEEUlllE0_ ZNK11onnxruntime12GatherNDBase17PrepareForComputeIlEENS_6common6StatusEPNS_15OpKernelContextERNS0_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 X input is required! /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/nonzero_op.cc const T* onnxruntime::Tensor::Data() const [with T = bool] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = bool] const T* onnxruntime::Tensor::Data() const [with T = unsigned char] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = int] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] const T* onnxruntime::Tensor::Data() const [with T = long int] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = float] onnxruntime::common::Status onnxruntime::NonZero<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] N11onnxruntime7NonZeroIbEE N11onnxruntime7NonZeroIhEE N11onnxruntime7NonZeroIiEE N11onnxruntime7NonZeroIlEE N11onnxruntime7NonZeroIfEE OneHot Depth is negative. Invalid argument for values; either it's rank is more than 1 or it has more than 2 elements Invalid argument for depth; it's not a scalar. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/onehot.cc 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->IsDataType<T>() && value_tensor->Shape().Size() == 1 Pads tensor should be a 1D tensor of shape [2 * input_rank] or a 2D tensor of shape [1, 2 * input_rank] Value tensor should be a 1D tensor of size 1 with the same type as that of the input tensor PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = double] const T* onnxruntime::Tensor::Data() const [with T = double] onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = double] PadInputWithDimValueOfZero onnxruntime::common::Status onnxruntime::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = 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::PadCpuImpl(onnxruntime::OpKernelContext*, const std::vector<long int>&, const std::vector<long int>&, const onnxruntime::Mode&, T) [with T = long int] onnxruntime::common::Status onnxruntime::Pad<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] onnxruntime::PadBase::PadBase(const onnxruntime::OpKernelInfo&) N11onnxruntime7PadBaseE N11onnxruntime3PadIfEE N11onnxruntime3PadIdEE N11onnxruntime3PadIiEE N11onnxruntime3PadIlEE DequantizeLinear /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/quantize_linear.cc y_scale.Shape().NumDimensions() == 1 && y_scale.Shape().Size() == broadcast_dim x_zero_point must be 1D tensor with size y_zero_point.Shape().NumDimensions() == 1 && y_zero_point.Shape().Size() == broadcast_dim IsScalarOr1ElementVector(&y_scale) IsScalarOr1ElementVector(&y_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. x_scale.Shape().NumDimensions() == 1 && x_scale.Shape().Size() == broadcast_dim x_zero_point.Shape().NumDimensions() == 1 && x_zero_point.Shape().Size() == broadcast_dim IsScalarOr1ElementVector(&x_scale) IsScalarOr1ElementVector(&x_zero_point) onnxruntime::common::Status onnxruntime::DequantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::common::Status onnxruntime::DequantizeLinear<T>::Compute(onnxruntime::OpKernelContext*) const [with T = signed char] 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_ @ ?batch_axis . Must be 0 or 1 Invalid batch_axis of batch_axis < 2 Invalid time_axis of time_axis < 2 batch_axis != time_axis ReverseSequence Unknown tensor type of . batch_size= /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.h info.GetAttr<int64_t>("batch_axis", &batch_axis).IsOK() info.GetAttr<int64_t>("time_axis", &time_axis).IsOK() time_axis and batch_axis must have different values but both are /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/reverse_sequence.cc sequence_lens shape must be {batch_size}. Got: \[W;TETPLJbG!B?=W;; \ \Y 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= [ 6 }X6 c>`;cc] 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 Unsupported input data type of 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= Split Invalid value in 'split' attribute. All values must be > 0 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.h std::all_of(split_sizes_.cbegin(), split_sizes_.cend(), [](int64_t value) { return value >= 0; }) Input cannot be split evenly on selected axis. Input shape= Cannot split using values in 'split' attribute. Axis= Num entries in 'split' (must equal number of outputs) was Sum of sizes in 'split' (must equal size of selected axis) was /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/split.cc Split operator does not support T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] ComputeImpl T* onnxruntime::Tensor::MutableData() [with T = long int] const T* onnxruntime::Tensor::Data() const [with T = long int] ComputeImpl T* onnxruntime::Tensor::MutableData() [with T = int] const T* onnxruntime::Tensor::Data() const [with T = int] ComputeImpl T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = float] ComputeImpl const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Split::Compute(onnxruntime::OpKernelContext*) const int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) onnxruntime::SplitBase::SplitBase(const onnxruntime::OpKernelInfo&) N11onnxruntime9SplitBaseE N11onnxruntime5SplitE . shape= must be 1 instead of Dimension of input input_shape[i] == 1 /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/squeeze.h int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) static std::vector<long int> onnxruntime::SqueezeBase::ComputeOutputShape(const onnxruntime::TensorShape&, const onnxruntime::TensorShape&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] N11onnxruntime11SqueezeBaseE N11onnxruntime7SqueezeE Tile Input count of Tile OP mismatch, the first one is empty Input count of Tile OP mismatch, the second one is empty the tensor to be tiled using Tile OP must be atleast 1 dimensional 'repeat' input tensor must be 1 dimensional 'repeat' input tensor must have the same length as the 'input' tensor Tile doesn't have an implementation yet for the type: /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/tile.cc const T* onnxruntime::Tensor::Data() const [with T = long int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Tile::Compute(onnxruntime::OpKernelContext*) const N11onnxruntime4TileE Mismatched data types between input and output Tensors. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.h v >= 0 && static_cast<uint64_t>(v) <= std::numeric_limits<size_t>::max() Attribute perm of Transpose has an invalid value. Value /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/transpose.cc does not align with rank of input data: is outside range. is repeated. [ perm: onnxruntime::TransposeBase::TransposeBase(const onnxruntime::OpKernelInfo&) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] virtual onnxruntime::common::Status onnxruntime::Transpose::Compute(onnxruntime::OpKernelContext*) const T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] const T* onnxruntime::Tensor::Data() const [with T = std::basic_string<char>] N11onnxruntime13TransposeBaseE N11onnxruntime9TransposeE Unique Unsupported tensor type of gsl::span<T> onnxruntime::Tensor::MutableDataAsSpan() [with T = signed char] gsl::span<const T> onnxruntime::Tensor::DataAsSpan() const [with T = signed char] N11onnxruntime6UniqueE 'axes' has a duplicate axis Missing/Invalid 'axes' attribute value /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.h info.GetAttrs("axes", axes_).IsOK() /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/unsqueeze.cc 'axes' has an out of range axis Compute int64_t onnxruntime::HandleNegativeAxis(int64_t, int64_t) const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::UnsqueezeBase::PrepareCompute(onnxruntime::OpKernelContext*, onnxruntime::UnsqueezeBase::Prepare&) const onnxruntime::UnsqueezeBase::UnsqueezeBase(const onnxruntime::OpKernelInfo&) N11onnxruntime13UnsqueezeBaseE N11onnxruntime9UnsqueezeE Resize: unexpected mode Upsample: unexpected mode Invalid roi input index. roi_input_idx_ > 0 scales != nullptr sizes == nullptr Resize: input/output value is nullptr Upsample: input/output value is nullptr Resize: input/output value's dimension mismatch Upsample: input/output value's dimension mismatch Resize: input shape needs to be at least a single dimension Upsample: input shape needs to be at least a single dimension. Resize: input tensor's dimension does not match the scales. Upsample: input tensor's dimension does not match the scales. /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/upsample.cc Rank of input and output tensor should be same. output_dims.size() == dims.size() Resize: size of roi array should be 2 * N where N is the rank of input tensor X. 'Linear' mode only support 2-D inputs ('Bilinear') or 4-D inputs with the corresponding outermost 2 scale values being 1 in the Only one of scales or sizes must be provided as input. sizes != nullptr && sizes->Shape().Size() != 0 Resize: input tensor's rank does not match the output tensor's rank. X->Shape().GetDims().size() == output_dims.size() Either scales or sizes MUST be provided as input. BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = float] onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = int] onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] BaseCompute onnxruntime::common::Status onnxruntime::Upsample<T>::BaseCompute(onnxruntime::OpKernelContext*, const std::vector<float>&, const std::vector<float>&, const std::vector<long int>&) const [with T = unsigned char] onnxruntime::common::Status onnxruntime::Upsample<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] const T* onnxruntime::Tensor::Data() const [with T = float] void onnxruntime::UpsampleBase::ParseScalesData(const onnxruntime::Tensor*, std::vector<float>&) const @ A @Where condition && X && Y condition, X, and Y inputs are required! /onnxruntime_src/onnxruntime/core/providers/cpu/tensor/where_op.cc T* onnxruntime::Tensor::MutableData() [with T = unsigned char] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = unsigned char] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = unsigned char] T* onnxruntime::Tensor::MutableData() [with T = int] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = int] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = int] T* onnxruntime::Tensor::MutableData() [with T = long int] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = long int] T* onnxruntime::Tensor::MutableData() [with T = float] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = float] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = float] T* onnxruntime::Tensor::MutableData() [with T = std::basic_string<char>] onnxruntime::TensorAllocator<T>::TensorAllocator(onnxruntime::OpKernelContext&) [with T = std::basic_string<char>] onnxruntime::common::Status onnxruntime::Where<T>::Compute(onnxruntime::OpKernelContext*) const [with T = std::basic_string<char>] N11onnxruntime5WhereIhEE N11onnxruntime5WhereIiEE N11onnxruntime5WhereIlEE N11onnxruntime5WhereIfEE N11onnxruntime5WhereISsEE /onnxruntime_src/onnxruntime/contrib_ops/cpu/cpu_contrib_kernels.cc RegisterCpuContribKernels 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 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 Input string contains invalid utf8 chars: Match contains invalid utf8 chars: /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 tensor(string) expected as input Input dimensions are either [C] or [N][C] allowed 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: 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] 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@OOOO99997777V If 'If' node has outputs. then_branch info == nullptr did not. else_branch ' attribute. Failed to create output tensor for If output outputs which doesn't match the subgraph's /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/if.cc num_subgraph_outputs == static_cast<size_t>(num_outputs) SetupSubgraphExecutionInfo should only be called once for each subgraph. Subgraph must have the shape set for all outputs but Failed to create output tensor for then_feeds_fetches_manager_ && else_feeds_fetches_manager_ Subgraph SessionState was not found for ' CreateFeedsFetchesManager must be called prior to execution of graph. info.GetAttr<ONNX_NAMESPACE::GraphProto>("then_branch", &proto).IsOK() info.GetAttr<ONNX_NAMESPACE::GraphProto>("else_branch", &proto).IsOK() Execute Initialize const T* onnxruntime::Tensor::Data() const [with T = bool] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute virtual onnxruntime::common::Status onnxruntime::If::Compute(onnxruntime::OpKernelContext*) const onnxruntime::If::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&) SetupSubgraphExecutionInfo virtual onnxruntime::common::Status onnxruntime::If::SetupSubgraphExecutionInfo(const onnxruntime::SessionState&, const string&, const onnxruntime::SessionState&) onnxruntime::If::If(const onnxruntime::OpKernelInfo&) *ZN11onnxruntime6IfImpl7ExecuteERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK13OrtMemoryInfoR8OrtValueRbE_ N11onnxruntime2IfE Expected: Loop inputs. Found: 'Loop' node has but has info_ == nullptr body feeds_fetches_manager_ Inconsistent shape in loop output for output. Graph in 'body' attribute of Loop should have /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/loop.cc static_cast<size_t>(num_subgraph_inputs) == subgraph_inputs.size() outputs so the subgraph requires num_subgraph_outputs - 1 == num_outputs 'Loop' input 'M' should be a scalar tensor. Got shape of 'Loop' input 'cond' should be a scalar tensor. Got shape of Loop had zero iterations and the shape of subgraph output was not found. Defaulting to a rank 1 shape of {0}. Subgraph SessionState was not found for 'body' attribute. info.GetAttr<ONNX_NAMESPACE::GraphProto>("body", &proto).IsOK() Execute ConcatenateLoopOutput 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_ Misuse of LoopStateVariable. Attempt to move beyond end of sequence /onnxruntime_src/onnxruntime/core/providers/cpu/controlflow/scan_utils.cc iteration_num_ < sequence_len_ Expected AllocateFinalOutput to have been called to before we increment the iterator num_entries < 0 || gsl::narrow_cast<int64_t>(directions.size()) == num_entries '. 0 == forward. 1 == reverse. cur_iteration_ < num_iterations_ Expected AllocateFinalOutput to have been called to before we read the OrtValue from the iterator. Mismatch between expected shape and shape from first output The subgraph in 'body' requires inputs but Scan was only given num_variadic_inputs == num_subgraph_inputs Failed to create output tensor for output # If shape was concrete we shouldn't be using a custom allocator onnxruntime::scan::detail::OutputIterator& onnxruntime::scan::detail::OutputIterator::operator++() typename std::enable_if<(! std::is_const<T&>::value), T&>::type onnxruntime::OrtValueTensorSlicer<T>::Iterator::operator*() [with T = OrtValue; typename std::enable_if<(! std::is_const<T&>::value), T&>::type = OrtValue&] OrtValue& onnxruntime::scan::detail::OutputIterator::operator*() AllocateFinalOutput onnxruntime::common::Status onnxruntime::scan::detail::OutputIterator::AllocateFinalOutput(const onnxruntime::TensorShape&) AllocateFinalBuffer const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Initialize void onnxruntime::scan::detail::LoopStateVariable::Next() const T& OrtValue::Get() const [with T = onnxruntime::Tensor] operator() typename std::enable_if<(! std::is_const<T&>::value), T&>::type onnxruntime::OrtValueTensorSlicer<T>::Iterator::operator*() [with T = const OrtValue; typename std::enable_if<(! std::is_const<T&>::value), T&>::type = const OrtValue&] IterateSequence CreateFeedsFetchesManager void onnxruntime::scan::detail::ReadDirections(const onnxruntime::OpKernelInfo&, const string&, std::vector<long int>&, int64_t) onnxruntime::scan::detail::Info::Info(const onnxruntime::Node&, const onnxruntime::GraphViewer&, int, bool) *ZN11onnxruntime4scan6detail15IterateSequenceERNS_23OpKernelContextInternalERKNS_12SessionStateERSt6vectorINS1_17LoopStateVariableESaIS8_EERS7_INS_20OrtValueTensorSlicerIK8OrtValueE8IteratorESaISG_EEliiiRKS7_IPSE_SaISK_EERS7_ISt10unique_ptrINS1_14OutputIteratorESt14default_deleteISQ_EESaIST_EERKNS_19FeedsFetchesManagerEEUlRKNS_11TensorShapeERK13OrtMemoryInfoRSD_RbE_ N11onnxruntime20OrtValueTensorSlicerI8OrtValueE8IteratorE for input shape Length of permutation must match the rank of the input to be permutated /onnxruntime_src/onnxruntime/core/providers/cpu/math/einsum_auxiliary_ops.cc 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] Only 1 batch dimension is allowed for MatMul 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] Cannot parse the diagonal elements along dims 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*) [with T = long int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] 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*) [with T = double; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] 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*) [with T = int; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] 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*) [with T = float; onnxruntime::AllocatorPtr = std::shared_ptr<onnxruntime::IAllocator>] void onnxruntime::Tensor::Reshape(const onnxruntime::TensorShape&) std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::DiagonalInnermostDims(const onnxruntime::Tensor&, bool, onnxruntime::AllocatorPtr) std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::Diagonal(const onnxruntime::Tensor&, int64_t, int64_t, onnxruntime::AllocatorPtr) std::unique_ptr<onnxruntime::Tensor> onnxruntime::EinsumOp::Transpose(const onnxruntime::Tensor&, const std::vector<long int>&, const std::vector<long unsigned int>&, onnxruntime::AllocatorPtr) 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= unidirectional /onnxruntime_src/onnxruntime/contrib_ops/cpu/bert/attention.cc info.GetAttr("num_heads", &num_heads).IsOK() && num_heads > 0 Input 0 is expected to have 3 dimensions, got Input 0 dimension 2 should be divisiable by value of the num_heads attribute. Input 1 is expected to have 2 dimensions, got Input 1 dimension 0 should have same length as dimension 2 of input 0 Input 1 dimension 1 should be 3 times of dimension 0 Input 2 is expected to have 1 dimension, got Input 2 dimension 0 should have same length as dimension 1 of input 1 Input 3 (mask_index) is not allowed for unidirectional Input 3 is expected to have 1 dimension, got Inputs 3 and 0 shall have same length at dimension 0 T* onnxruntime::Tensor::MutableData() [with T = float] const T* onnxruntime::Tensor::Data() const [with T = int] const T& OrtValue::Get() const [with T = onnxruntime::Tensor] const T* onnxruntime::OpKernelContext::Input(int) const [with T = onnxruntime::Tensor] Compute onnxruntime::contrib::AttentionBase::AttentionBase(const onnxruntime::OpKernelInfo&) N11onnxruntime7contrib13AttentionBaseE ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUlllE_ ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUlllE0_ ZNK11onnxruntime7contrib9AttentionIfE7ComputeEPNS_15OpKernelContextEEUlllE2_ 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] sqeuclidean CDist /onnxruntime_src/onnxruntime/contrib_ops/cpu/cdist.h info.GetAttr<std::string>("metric", &metric).IsOK() The second input of CDist kernel has wrong shape: Input shape dimensions mismatch: The first input of CDist kernel has wrong shape: 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] OrtMemoryInfo: [ id: mem_type: alloc_type: Cuda CudaPinned Specified device is not supported. /onnxruntime_src/onnxruntime/core/framework/allocator.cc CalcMemSizeForArrayWithAlignment N11onnxruntime12CPUAllocatorE St14default_deleteIN11onnxruntime8BFCArenaEE St19_Sp_counted_deleterIPN11onnxruntime8BFCArenaESt14default_deleteIS1_ESaIvELN9__gnu_cxx12_Lock_policyE2EE /onnxruntime_src/onnxruntime/core/framework/bfc_arena.cc /onnxruntime_src/onnxruntime/core/framework/bfc_arena.h !c->in_use() && (c->bin_num != kInvalidBinNum) !c->in_use() && (c->bin_num == kInvalidBinNum) p_int < base_int + memory_size_ !c1->in_use() && !c2->in_use() bin->free_chunks.count(h) == 1 BinForSize(bin_size) == BinFromIndex(b) BinForSize(bin_size + 255) == BinFromIndex(b) BinForSize(bin_size * 2 - 1) == BinFromIndex(b) BinForSize(bin_size * 2) != BinFromIndex(b) 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: BFC Arena ran out of memory trying . Current allocation summary follows. reserved_chunks_.find(ptr) == reserved_chunks_.end() Limit: InUse: TotalAllocated: MaxInUse: NumAllocs: MaxAllocSize: Could not find Region for h < chunks_.size() Size: | Requested Size: | in_use: p_int >= base_int c2->prev == h1 h != kInvalidChunkHandle c->bin_num == bin_num Creating BFCArena for Creating bins of max chunk size !chunk->in_use() cudaMalloc Extended allocation by bytes. Total allocated bytes: Allocated memory at Could not find chunk in bin Chunk Free Allocator: : Chunks . Bytes Requested Bin for bytes has max bytes of , Chunk State: , prev: , next: Overall chunks summary: chunks of size . Total Sum Total of in-use chunks: Stats:
- tried to allocate 0 bytes Extending BFCArena for . bin_num: rounded_bytes: to allocate void onnxruntime::BFCArena::DumpMemoryLog(size_t) DumpMemoryLog std::array<onnxruntime::BFCArena::BinDebugInfo, 21ul> onnxruntime::BFCArena::get_bin_debug_info() void onnxruntime::BFCArena::FreeAndMaybeCoalesce(onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::RemoveFreeChunkFromBin(onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::RemoveFreeChunkIterFromBin(onnxruntime::BFCArena::Bin::FreeChunkSet*, const iterator&) void onnxruntime::BFCArena::InsertFreeChunkIntoBin(onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::Merge(onnxruntime::BFCArena::ChunkHandle, onnxruntime::BFCArena::ChunkHandle) void onnxruntime::BFCArena::DeallocateRawInternal(void*) void onnxruntime::BFCArena::SplitChunk(onnxruntime::BFCArena::ChunkHandle, size_t) void* onnxruntime::BFCArena::FindChunkPtr(onnxruntime::BFCArena::BinNum, size_t, size_t) AllocateRawInternal virtual void* onnxruntime::BFCArena::Reserve(size_t) int onnxruntime::BFCArena::AllocationRegion::IndexFor(const void*) const RegionFor onnxruntime::BFCArena::AllocationRegion::AllocationRegion(void*, size_t) onnxruntime::BFCArena::Extend(size_t)::__lambda2 Extend bool onnxruntime::BFCArena::Extend(size_t) onnxruntime::BFCArena::Chunk* onnxruntime::BFCArena::ChunkFromHandle(onnxruntime::BFCArena::ChunkHandle) onnxruntime::BFCArena::BFCArena(std::unique_ptr<onnxruntime::IDeviceAllocator>, size_t, 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 Device: [ memory_type: device_id: 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: D4$ԦĦtdT/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 ܵܵܵLܵtعععPعȸ 4Ld|lonnxruntime::utils::ContainerChecker::ContainerChecker(onnxruntime::MLDataType) void onnxruntime::data_types_internal::DataTypeRegistry::RegisterDataType(onnxruntime::MLDataType) static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::map<long int, float>] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::map<std::basic_string<char>, float>] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = onnxruntime::BFloat16] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = onnxruntime::MLFloat16] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = std::basic_string<char>] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = bool] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = long unsigned int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = long int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = unsigned int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = short unsigned int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = short int] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = unsigned char] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = signed char] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = double] static void onnxruntime::data_types_internal::SetSequenceType<T>::Set(onnx::TypeProto&) [with T = float] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = double] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = float] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = long int] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = long int; V = std::basic_string<char>] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = double] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = float] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = long int] static void onnxruntime::data_types_internal::SetMapTypes<K, V>::Set(onnx::TypeProto&) [with K = std::basic_string<char>; V = std::basic_string<char>] virtual void onnxruntime::NonTensorTypeBase::ToDataContainer(const OrtValue&, size_t, void*) const virtual void onnxruntime::NonTensorTypeBase::FromDataContainer(const void*, size_t, OrtValue&) const bool onnxruntime::NonTensorTypeBase::IsSequenceCompatible(const onnx::TypeProto&) const bool onnxruntime::NonTensorTypeBase::IsMapCompatible(const onnx::TypeProto&) const virtual bool onnxruntime::SequenceTensorTypeBase::IsCompatible(const onnx::TypeProto&) const virtual bool onnxruntime::SparseTensorTypeBase::IsCompatible(const onnx::TypeProto&) const virtual bool onnxruntime::TensorTypeBase::IsCompatible(const onnx::TypeProto&) const bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Sequence&, const onnx::TypeProto_Sequence&) bool onnxruntime::data_types_internal::IsCompatible(const onnx::TypeProto_Map&, const onnx::TypeProto_Map&) GetElementType GetElementType GetElementType N11onnxruntime12DataTypeImplE N11onnxruntime14TensorTypeBaseE N11onnxruntime20SparseTensorTypeBaseE N11onnxruntime17NonTensorTypeBaseE N11onnxruntime22SequenceTensorTypeBaseE N11onnxruntime21PrimitiveDataTypeBaseE N11onnxruntime10TensorTypeIiEE N11onnxruntime10TensorTypeIfEE N11onnxruntime10TensorTypeIbEE N11onnxruntime10TensorTypeISsEE N11onnxruntime10TensorTypeIaEE N11onnxruntime10TensorTypeIhEE N11onnxruntime10TensorTypeItEE N11onnxruntime10TensorTypeIsEE N11onnxruntime10TensorTypeIlEE N11onnxruntime10TensorTypeIdEE N11onnxruntime10TensorTypeIjEE N11onnxruntime10TensorTypeImEE N11onnxruntime10TensorTypeINS_9MLFloat16EEE N11onnxruntime10TensorTypeINS_8BFloat16EEE N11onnxruntime16SparseTensorTypeIiEE N11onnxruntime16SparseTensorTypeIfEE N11onnxruntime16SparseTensorTypeIbEE N11onnxruntime16SparseTensorTypeIaEE N11onnxruntime16SparseTensorTypeIhEE N11onnxruntime16SparseTensorTypeItEE N11onnxruntime16SparseTensorTypeIsEE N11onnxruntime16SparseTensorTypeIlEE N11onnxruntime16SparseTensorTypeIdEE N11onnxruntime16SparseTensorTypeIjEE N11onnxruntime16SparseTensorTypeImEE N11onnxruntime16SparseTensorTypeINS_9MLFloat16EEE N11onnxruntime16SparseTensorTypeINS_8BFloat16EEE N11onnxruntime13NonTensorTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEE N11onnxruntime7MapTypeISt3mapISsSsSt4lessISsESaISt4pairIKSsSsEEEEE N11onnxruntime13NonTensorTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEE N11onnxruntime7MapTypeISt3mapISslSt4lessISsESaISt4pairIKSslEEEEE N11onnxruntime13NonTensorTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEE N11onnxruntime7MapTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEEE N11onnxruntime13NonTensorTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEE N11onnxruntime7MapTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEEE N11onnxruntime13NonTensorTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEE N11onnxruntime7MapTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEEE N11onnxruntime13NonTensorTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEE N11onnxruntime7MapTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEEE N11onnxruntime13NonTensorTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEE N11onnxruntime7MapTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEEE N11onnxruntime13NonTensorTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEE N11onnxruntime7MapTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEEE N11onnxruntime18SequenceTensorTypeIfEE N11onnxruntime18SequenceTensorTypeIdEE N11onnxruntime18SequenceTensorTypeIaEE N11onnxruntime18SequenceTensorTypeIhEE N11onnxruntime18SequenceTensorTypeIsEE N11onnxruntime18SequenceTensorTypeItEE N11onnxruntime18SequenceTensorTypeIiEE N11onnxruntime18SequenceTensorTypeIjEE N11onnxruntime18SequenceTensorTypeIlEE N11onnxruntime18SequenceTensorTypeImEE N11onnxruntime18SequenceTensorTypeIbEE N11onnxruntime18SequenceTensorTypeISsEE N11onnxruntime18SequenceTensorTypeINS_9MLFloat16EEE N11onnxruntime18SequenceTensorTypeINS_8BFloat16EEE N11onnxruntime13NonTensorTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEE N11onnxruntime12SequenceTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaIS9_EEEE N11onnxruntime13NonTensorTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEE N11onnxruntime12SequenceTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaIS9_EEEE N11onnxruntime17PrimitiveDataTypeIiEE N11onnxruntime17PrimitiveDataTypeIfEE N11onnxruntime17PrimitiveDataTypeIbEE N11onnxruntime17PrimitiveDataTypeISsEE N11onnxruntime17PrimitiveDataTypeIaEE N11onnxruntime17PrimitiveDataTypeIhEE N11onnxruntime17PrimitiveDataTypeItEE N11onnxruntime17PrimitiveDataTypeIsEE N11onnxruntime17PrimitiveDataTypeIlEE N11onnxruntime17PrimitiveDataTypeIdEE N11onnxruntime17PrimitiveDataTypeIjEE N11onnxruntime17PrimitiveDataTypeImEE N11onnxruntime17PrimitiveDataTypeINS_9MLFloat16EEE N11onnxruntime17PrimitiveDataTypeINS_8BFloat16EEE ZN11onnxruntime19data_types_internal16DataTypeRegistryC1EvEUlPKNS_12DataTypeImplEE_ Ss N11onnxruntime9MLFloat16E N11onnxruntime8BFloat16E St3mapISsfSt4lessISsESaISt4pairIKSsfEEE St3mapIlfSt4lessIlESaISt4pairIKlfEEE Caught exception while destructing CustomOpsLoader with message: /onnxruntime_src/onnxruntime/core/framework/ex_lib_loader.cc Caught exception while loading custom ops with message: Unloading DSO Failed to unload DSO: A dso with name has already been loaded. LoadExternalLib ~ExLibLoader N11onnxruntime11ExLibLoaderE Memory pattern planner is not enabled on this execution framework. /onnxruntime_src/onnxruntime/core/framework/execution_frame.h ort_value_index >= 0 && static_cast<size_t>(ort_value_index) < all_values_size_ /onnxruntime_src/onnxruntime/core/framework/node_index_info.h offset >= 0 && static_cast<size_t>(offset) < node_values_size_ /onnxruntime_src/onnxruntime/core/framework/execution_frame.cc ort_value_idx >= 0 && static_cast<size_t>(ort_value_idx) < alloc_plan.size() TraceAllocation for ort_value_idx= OrtValue shape verification failed. Current shape: shape && tensor.Shape() == *shape 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 , block not found in target location. fall back to default allocation behavior ort_value_index >= 0 && static_cast<size_t>(ort_value_index) < alloc_plan.size() We don't expect custom allocators for non-tensor types, so a shape is mandatory here. Allocation of tensor types requires a shape. buffers_.find(mem_patterns_->locations[i]) == buffers_.end() size= TraceFree for ort_value_idx= Requested shape: invalid index ort_value.Fence() == nullptr For ort_value with index: but the actually size is: Invalid allocation kind: mlvalue.Fence() == nullptr TraceFree void onnxruntime::ExecutionFrame::TraceFree(int) TraceAllocate const onnxruntime::AllocPlanPerValue& onnxruntime::ExecutionFrame::GetAllocationPlan(int) ReleaseMLValueImpl onnxruntime::common::Status onnxruntime::AllocateSparseTensor(OrtValue&, const onnxruntime::DataTypeImpl&, onnxruntime::AllocatorPtr, const onnxruntime::TensorShape&, size_t, bool, const onnxruntime::SessionState&) AllocateAsPerAllocationPlan onnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateAsPerAllocationPlan(OrtValue&, int, const onnxruntime::TensorShape*, size_t) AllocateMLValueTensorPreAllocateBuffer AllocateMLValueTensorSelfOwnBufferHelper onnxruntime::common::Status onnxruntime::ExecutionFrame::AllocateMLValueTensorSelfOwnBufferHelper(OrtValue&, int, onnxruntime::MLDataType, const OrtMemoryInfo&, const onnxruntime::TensorShape&, bool) onnxruntime::ExecutionFrame::ExecutionFrame(const std::vector<int>&, const std::vector<OrtValue>&, const std::vector<int>&, const std::vector<OrtValue>&, const std::unordered_map<long unsigned int, std::function<onnxruntime::common::Status(const onnxruntime::TensorShape&, const OrtMemoryInfo&, OrtValue&, bool&)> >&, const onnxruntime::SessionState&) 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 const T& OrtValue::Get() const [with T = onnxruntime::Tensor] onnxruntime::common::Status onnxruntime::IExecutionFrame::GetOrCreateNodeOutputMLValue(int, const onnxruntime::TensorShape*, OrtValue*&, size_t) onnxruntime::IExecutionFrame::IExecutionFrame(const 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 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!
-
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 hhhhT<<tT 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] node_index_info_ with error: duplicated ort_value index: SaveMLValueNameIndexMapping is used by node ) and node Entry exists in node for attribute SetGraphAndCreateKernels must be called prior to GetExecutionInfo. /onnxruntime_src/onnxruntime/core/framework/session_state.cc Could not create kernel for node: as there's no execution provider allocated. Kernel creation failed for node: . Do you have duplicated calls to SessionState::AddInitializedTensor function? 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 ' 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&) SetGraph *ZN11onnxruntime12SessionState23UpdateToBeExecutedNodesERKSt6vectorIiSaIiEEEUlPKNS_4NodeEE_ /onnxruntime_src/onnxruntime/core/framework/session_state_initializer.cc OrtValue indexes should have been populated. ort_value_name_idx_map.MaxIdx() > -1 Invalid allocation info. Provider name = string tensor is not supported for copying between allocators Internal error. The preallocated buffer is too small. Requires Failed to copy tensor to execution provider: Done saving initialized tensors Execution plan was not found in SessionState. CreatePlan must be called first. Saving initialized tensors. , Got Deserialize tensor Subgraph Graph exec_plan_ptr input with name is not used by any node. ForEachWithIndex operator() operator() SaveInputOutputNamesToNodeMapping DeserializeTensorProto onnxruntime::common::Status onnxruntime::SaveInitializedTensors(const onnxruntime::Env&, const std::basic_string<char>&, const onnxruntime::Graph&, const onnxruntime::ExecutionProviders&, const onnxruntime::OrtValueNameIdxMap&, onnxruntime::ITensorAllocator*, const T&, const onnxruntime::logging::Logger&, const onnxruntime::DataTransferManager&) [with T = onnxruntime::SessionStateInitializer::CreatePlan(const onnxruntime::Node*, const onnxruntime::ConstPointerContainer<std::vector<onnxruntime::NodeArg*> >*, ExecutionMode)::__lambda8] SaveInitializedTensors onnxruntime::common::Status onnxruntime::SessionStateInitializer::CreatePlan(const onnxruntime::Node*, const onnxruntime::ConstPointerContainer<std::vector<onnxruntime::NodeArg*> >*, ExecutionMode) CreatePlan *ZN11onnxruntimeL33SaveInputOutputNamesToNodeMappingERKNS_5GraphERKNS_21KernelRegistryManagerERNS_12SessionStateEPKNS_21ConstPointerContainerISt6vectorIPNS_7NodeArgESaISB_EEEEEUlRKSA_mE_ *ZN11onnxruntimeL33SaveInputOutputNamesToNodeMappingERKNS_5GraphERKNS_21KernelRegistryManagerERNS_12SessionStateEPKNS_21ConstPointerContainerISt6vectorIPNS_7NodeArgESaISB_EEEEEUlRKSA_mE0_ N11onnxruntime25ISequentialPlannerContextE N11onnxruntime24SequentialPlannerContextE tensor size overflow shape.Size() must >=0 dtype_ != nullptr p_type != nullptr /onnxruntime_src/onnxruntime/core/framework/tensor.cc Tensor is expected to contain one of the primitive data types. Got: tensor failed memory size calculation void onnxruntime::Tensor::Init(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, onnxruntime::AllocatorPtr, ptrdiff_t) size_t onnxruntime::Tensor::SizeInBytes() const onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, std::shared_ptr<onnxruntime::IAllocator>, ptrdiff_t) onnxruntime::Tensor::Tensor(onnxruntime::MLDataType, const onnxruntime::TensorShape&, void*, const OrtMemoryInfo&, ptrdiff_t) Internal error. Mem pattern for initializer ' is not found ' failed duplicated location /onnxruntime_src/onnxruntime/core/framework/tensor_allocator_with_mem_pattern.h Weight buffer for initializer ' Get preallocated buffer for initializer ' Failed to get allocator for location: Trace FinalizePlan N11onnxruntime16ITensorAllocatorE N11onnxruntime29TensorAllocatorWithMemPatternE 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::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)
+ 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
+ 4
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+ \ 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!
+
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
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: fJJf))J)f)JiiEEiEEiNMNNNNxM!MNNNL\LJigdhhDhiTih4iii\g$
|އ;نe Q
-
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_ NnapiExecutionProvider RknpuExecutionProvider invalid allocator copy_info.size() == num_feeds /onnxruntime_src/onnxruntime/core/framework/utils.cc feed_locations.size() == copy_info.size() fetch_alloc_info.size() == copy_info.size() Only one thread was configured for parallel execution. Hence will use sequential execution. CopyOutputsAcrossDevices CopyInputsAcrossDevices onnxruntime::common::Status onnxruntime::utils::CopyInputsAcrossDevices(const std::vector<OrtValue>&, std::vector<OrtValue>&, const std::vector<onnxruntime::MLValueCopyInfo>&, const onnxruntime::DataTransferManager&) ExecuteGraphImpl ExecuteGraph T* OrtValue::GetMutable() [with T = onnxruntime::Tensor] BatchOrCopyMLValue const T& OrtValue::Get() const [with T = onnxruntime::Tensor] bool onnxruntime::utils::FinalizeCopyInfoForFetches(const onnxruntime::SessionState&, const std::vector<const OrtMemoryInfo*>&, std::vector<onnxruntime::MLValueCopyInfo>&) bool onnxruntime::utils::FinalizeCopyInfoForFeeds(const std::vector<OrtDevice>&, std::vector<onnxruntime::MLValueCopyInfo>&) CalculateStaticCopyInfoForFeed CalculateStaticCopyInfoForFeeds InitializeFeedFetchCopyInfo const OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::OrtValueNameIdxMap&, const onnxruntime::SequentialExecutionPlan&, const string&) FindMemoryInfoForValue const OrtMemoryInfo& onnxruntime::utils::FindMemoryInfoForValue(const onnxruntime::SessionState&, const string&) N11onnxruntime9IExecutorE /onnxruntime_src/onnxruntime/core/framework/allocation_planner.cc id >= 0 && static_cast<size_t>(id) < ort_value_info_.size() n >= 0 && static_cast<size_t>(n) < ort_value_info_.size() n >= 0 && static_cast<size_t>(n) < plan_.allocation_plan.size() kernel_create_info != nullptr && kernel_create_info->kernel_def != nullptr (ort_value_idx) output_name : <allocation plan>
- No suitable kernel definition found for op Can not find the execution provider Allocate AllocateStatically PreExisting Reuse AllocateOutput Share nullptr != tensor_type_base reused != reused_for Can not find the node p_provider Allocation Plan:
+ 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:
Execution Plan:
- ) , use fence when async Index out-of-range! nullptr != node Free ml-values: INVALID INDEX vector<bool>::_M_fill_insert onnxruntime::OrtValueIndex& onnxruntime::PlannerImpl::Buffer(onnxruntime::OrtValueIndex) void onnxruntime::PlannerImpl::Reuse(onnxruntime::OrtValueIndex, onnxruntime::OrtValueIndex, onnxruntime::AllocKind) static size_t onnxruntime::PlannerImpl::GetElementSize(const string* const&) OrtMemoryInfo onnxruntime::PlannerImpl::GetLocationForNodeInput(size_t, const onnxruntime::Node&) GeneratePlanForWeights ComputeReusePlan onnxruntime::AllocPlanPerValue& onnxruntime::PlannerImpl::AllocPlan(onnxruntime::OrtValueIndex) int& onnxruntime::PlannerImpl::UseCount(onnxruntime::OrtValueIndex) void onnxruntime::PlannerImpl::ProcessDef(onnxruntime::OrtValueIndex, const onnxruntime::NodeArg*) onnxruntime::OrtValueIndex onnxruntime::PlannerImpl::Index(const OrtValueName&) ForEachWithIndex ComputeUseCounts CreatePlan N11onnxruntime17ExecutionPlanBaseE N11onnxruntime23SequentialExecutionPlanE ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEmE_ ZN11onnxruntime11PlannerImpl16ComputeUseCountsEvEUlRKNS_7NodeArgEbE0_ ZN11onnxruntime11PlannerImpl22GeneratePlanForWeightsEvEUlRKNS_7NodeArgEmE_ 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 /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
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 index with shape (batch_size). 3D output tensor with shape (batch_size, sequence_length, hidden_size) Constrain input and output types to float tensors. Constrain mask index to integer types 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. 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). 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. 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 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 QAttention 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 Matrix multiply results ReduceSumInteger An input tensor. Reduced output tensor. reduced addition QLinearAdd multiplication QLinearMul QLinearReduceMean data_scale data_zero_point reduced_scale reduced_zero_point QLinearAveragePool An input tensor to hash. 32-bit hash value. Tensor of rank r >= 1. Tensor of rank q >= 1. Specify weights of conv Specify bias of conv Constrain to tensor(int32). Constrain to tensor(float). Input tensor. Tensor after padding. idx 2D matrix with shape (M,N) 2D matrix with shape (K,N) rois batch_indices crop_size Scale tensor. Bias tensor. Output data tensor. The input data as Tensor. The output. The normal input data. Attribute should specify a shape Input axis is invalid: Incompatible dimensions Input rank must be >= 2. != mat_h: ̼+̼+ *ZZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_ENKUlRN4onnx8OpSchemaEE_clES5_EUlRNS3_16InferenceContextEE_ *ZN11onnxruntime7contrib23QLinearMathDocGeneratorEPKcS2_EUlRN4onnx8OpSchemaEE_ *ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib19RegisterBertSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE0_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE1_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE2_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE3_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE4_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE5_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE6_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE7_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE8_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE9_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE10_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE11_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE12_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE13_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE14_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE15_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE16_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE17_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE18_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE19_ *ZN11onnxruntime7contrib22RegisterContribSchemasEvEUlRN4onnx16InferenceContextEE20_ 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:
+ 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. Attribute should specify a shape Input axis is invalid: Incompatible dimensions 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_ 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: 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()
+ 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()
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. 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 appears in graph inputs and will not be treated as constant value/weight. This may fail 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() q@qqq`qqq8qqq qqqqqqqp 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 ExplicitInputs: . Index: Invalid input index for node ImplicitInputs: Implicit input name cannot be safely updated to in one of the subgraphs. /onnxruntime_src/onnxruntime/core/graph/graph_utils.cc index >= 0 && static_cast<size_t>(index) < inputs.size() Attempting to get an input that does not exist. index >= 0 && static_cast<size_t>(index) < outputs.size() Attempting to get an output that does not exist. Failed since multiple edges matched: Can only add a new input at the end of the current ones. num_explicit_inputs == static_cast<size_t>(target_input_idx) std::count_if(subgraph_node.InputEdgesBegin(), subgraph_node.InputEdgesEnd(), [input_slot_index](const Node::EdgeEnd& entry) { return entry.GetDstArgIndex() == input_slot_index; }) == 0 Node must only have one used output std::all_of(output_edges.cbegin(), output_edges.cend(), [&src_idx](const GraphEdge& edge) { return edge.src_arg_index == src_idx; }) Should be unreachable if CanRemoveNodeAndMergeEdges is in sync with the logic here. Initializer with same name exists. Name: !graph.GetInitializedTensor(new_initializer.name(), existing) FindPath void onnxruntime::graph_utils::AddNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&) void onnxruntime::graph_utils::ReplaceNodeInput(onnxruntime::Node&, int, onnxruntime::NodeArg&) onnxruntime::NodeArg& onnxruntime::graph_utils::AddInitializer(onnxruntime::Graph&, const onnx::TensorProto&) void onnxruntime::graph_utils::UpdateImplicitInputNameInSubgraph(onnxruntime::Node&, const string&, const string&) bool onnxruntime::graph_utils::RemoveNodeWithSingleNodeInSingleUsedOutput(onnxruntime::Graph&, onnxruntime::Node&) bool onnxruntime::graph_utils::RemoveNode(onnxruntime::Graph&, onnxruntime::Node&) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const CanUpdateImplicitInputNameInSubgraphs const string& onnxruntime::graph_utils::GetNodeOutputName(const onnxruntime::Node&, int) const string& onnxruntime::graph_utils::GetNodeInputName(const onnxruntime::Node&, int) onnxruntime::Node* onnxruntime::Graph::NodeAtIndexImpl(onnxruntime::NodeIndex) const *ZN11onnxruntime11GraphViewerC1ERKNS_5GraphEEUlPKNS_4NodeEE_ N11onnxruntime11NodeCompareE Protobuf parsing failed. <p_fd> less than 0. failed. File doesn't exist Load model system error number <p_fd> is less than 0. Invalid istream object. Null model_proto ptr. ModelProto does not have a graph. Missing opset in the model. All ModelProtos MUST have at least one entry that specifies which version of the ONNX OperatorSet is being imported. ONNX Runtime only *guarantees* support for models stamped with opset version 7 or above for opset domain 'ai.onnx'. Please upgrade your model to opset 7 or higher. For now, this opset model may run depending upon legacy support of some older opset version operators. Unknown model file format version. /onnxruntime_src/onnxruntime/core/graph/model.cc No graph was found in the protobuf. Failed to load model with error: Protobuf serialization failed. Save SaveModel Load Model St15_Sp_counted_ptrIPN11onnxruntime5ModelELN9__gnu_cxx12_Lock_policyE2EE St23_Sp_counted_ptr_inplaceIN11onnxruntime21SchemaRegistryManagerESaIS1_ELN9__gnu_cxx12_Lock_policyE2EE Domain already set in registry /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
+ 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. [
]
{"cat" : " "pid" : "tid" : "dur" : "ts" : "ph" : "X", "name" :" "args" : { }
},
- enabled_ session_logger != nullptr custom_logger != nullptr Session Node EndProfiling EndTimeAndRecordEvent void onnxruntime::profiling::Profiler::Initialize(const onnxruntime::logging::Logger*) onnxruntime::TimePoint onnxruntime::profiling::Profiler::StartTime() const SUCCESS INVALID_ARGUMENT NO_SUCHFILE NO_MODEL ENGINE_ERROR RUNTIME_EXCEPTION INVALID_PROTOBUF MODEL_LOADED NOT_IMPLEMENTED INVALID_GRAPH EP_FAIL GENERAL ERROR OK SystemError [ONNXRuntimeError] b@/
/onnxruntime_src/onnxruntime/core/common/status.cc code != static_cast<int>(common::OK) onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const char*) onnxruntime::common::Status::Status(onnxruntime::common::StatusCategory, int, const string&) fn != nullptr initial_count >= 0 n >= 0 num_threads >= 1 /onnxruntime_src/onnxruntime/core/common/threadpool.cc underlying_threadpool_ != nullptr void onnxruntime::concurrency::ThreadPool::Schedule(std::function<void()>) onnxruntime::concurrency::ThreadPool::ThreadPool(onnxruntime::Env*, const onnxruntime::ThreadOptions&, const NAME_CHAR_TYPE*, int, bool) N5Eigen19ThreadPoolInterfaceE N11onnxruntime15ThreadPoolTemplINS_3EnvEEE Failed to get symbol lib .so Got null library handle file " " failed: realpath close open \/ Failed to load library remove() failed. Error code: , path: munmap failed. error code: - error code: fstat 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] ~~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
Not satisfied: file_path
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- 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 >
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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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=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. op_type NodeProto (name: , type: ATen ConstantFill No opset import for domain ' No Op registered for with domain_version of Warning: was a removed as possible. ' of node:
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==> 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 '
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8 0 0 8 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 jLfjjhgji|e 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 leakage. 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 b_scale b_zero_point 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'. 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'. Constrain input A data type to 8-bit integer tensor. Constrain input B data type to 8-bit integer tensor. Constrain output Y data type as 32-bit integer tensor. If set to 1 will return exclusive sum in which the top element is not included. In other terms, if set to 1, the j-th output element would be the sum of the first (j-1) elements. Otherwise, it would be the sum of the first j elements. If set to 1 will perform the sums in reverse direction. An input tensor that is to be processed. (Optional) A 0-D tensor. Must be in the range [-rank(x), rank(x)-1]. Negative value means counting dimensions from the back. Output tensor of the same type as 'x' with cumulative sums of the x's elements axis tensor can be int32 or int64 only Constrain input and output types to floating-point tensors. 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
+ 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
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
@@ -6454,11 +6697,11 @@
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_
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. 0Pp *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. 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.
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/>
@@ -6476,27 +6719,35 @@
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 # <\|Inferred elem type differs from existing elem type: ( Inferred shape and existing shape differ in rank: ( Inferred shape and existing shape differ in dimension type case unsupported. existing= GraphProto attribute inferencing is not enabled in this InferenceContextImpl instance. is tensor type, but provided type is N4onnx15shape_inference19GraphInferencerImplE N4onnx15shape_inference20InferenceContextImplE Func_ Input for function node Output for function node onnx.AttributeProto onnx.ValueInfoProto onnx.NodeProto onnx.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 `p @{l{{ll{ll ?`> ? ?=0=<H<;JtJ4JI\I,IHHdH$HGGlGF\FHVVUUhU(UThTSSRXRHVRHVHVHVHVHVHVxQ0QQXP8[ZZPZZXXxXbbabb_bbbb_(ax`^x]\k`k kjxj(jiihhrrpr0rqqq0qprrrrrorrrrrnrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrXm<<<d<Ի
X
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 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
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 1<1l1112L2222,3l3334L444\S\S,5d5555 6L6|666 7<7l7777,8\88\S\S8dHHKN|J PLMlQlI<OLQ,KPMREG\S\SF99 ::l; << =t=====4>>\??L@\S\SA ATAAAA4BlBBB CTCC,DdDDD ETE\S\SE0,D܁$ĀDT~}t}}|l|D|{d{zzztzzyy<yxlxwwvvutsTsr4rqqdpoTon4nmmmdlkTkj4jihdhgglgTg<g$gffeedccTcbbbTbataDaa`d`4``_d_^^^$^t] /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/coded_stream.cc A protocol message was rejected because it was too big (more than bytes). To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in third_party/protobuf/src/google/protobuf/io/coded_stream.h. CHECK failed: (buffer_size) >= (0): /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl.cc Can't BackUp() after failed Next(). CHECK failed: !is_closed_: close() failed: N6google8protobuf2io19CopyingOutputStreamE N6google8protobuf2io15FileInputStream22CopyingFileInputStreamE N6google8protobuf2io15FileInputStreamE N6google8protobuf2io16FileOutputStream23CopyingFileOutputStreamE N6google8protobuf2io16FileOutputStreamE N6google8protobuf2io18IstreamInputStream25CopyingIstreamInputStreamE N6google8protobuf2io18IstreamInputStreamE N6google8protobuf2io19OstreamOutputStream26CopyingOstreamOutputStreamE N6google8protobuf2io19OstreamOutputStreamE N6google8protobuf2io24ConcatenatingInputStreamE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream_impl_lite.cc CHECK failed: (last_returned_size_) > (0): BackUp() can only be called after a successful Next(). CHECK failed: (count) <= (last_returned_size_): CHECK failed: (count) >= (0): CHECK failed: target_ != NULL: CHECK failed: (buffer_used_) == (buffer_size_): BackUp() can only be called after Next(). CHECK failed: (count) <= (buffer_used_): Can't back up over more bytes than were returned by the last call to Next(). CHECK failed: backup_bytes_ == 0 && buffer_.get() != NULL: Parameter to BackUp() can't be negative. CHECK failed: (count) <= (target_->size()): Cannot allocate buffer larger than kint32max for CHECK failed: (backup_bytes_) == (0): StringOutputStream. N6google8protobuf2io16ArrayInputStreamE N6google8protobuf2io17ArrayOutputStreamE N6google8protobuf2io18StringOutputStreamE N6google8protobuf2io18CopyingInputStreamE N6google8protobuf2io25CopyingInputStreamAdaptorE N6google8protobuf2io26CopyingOutputStreamAdaptorE N6google8protobuf2io19LimitingInputStreamE (cannot determine missing fields for lite message) " because it is missing required fields: /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/message_lite.cc exceeded maximum protobuf size of 2GB: CHECK failed: (byte_size_before_serialization) == (byte_size_after_serialization): was modified concurrently during serialization. CHECK failed: (bytes_produced_by_serialization) == (byte_size_before_serialization): Byte size calculation and serialization were inconsistent. This may indicate a bug in protocol buffers or it may be caused by concurrent modification of This shouldn't be called if all the sizes are equal. MessageLite at 0x Can't parse message of type " N6google8protobuf11MessageLiteE N6google8protobuf24ZeroCopyCodedInputStreamE G>
pparsing Can't happen /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/parse_context.h [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 (KL(KXLxLLLLLM8MXMxMMK8LKtMMtMMMN'N=NSNiNNNNNMMM 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'): &MNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNPNPNPNPNPNPNPNPNNNNNNNN@NNNNNNNNNNNNNNNNNNNNNNNNNMNNNMNNNNMMNNNMNNNNNNNMNNNMNpMNPMNM 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 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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+
+
+ 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:;<=
? !"#$%&'()*+,-./0123>?456789:;<=
-
!"#$%&'()*+,-./012300010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 ? 0Xx 8
+
!"#$%&'()*+,-./012300010203040506070809101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899 ? 888X8778;;<:p:<
- /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. $<8A{88H,||} @ |]Cx`;{x`,o`xTTTTTTTTdt(p H 8Ph
-`
-J w@ { 3 %7h e1lK?####
C
- ../// 0@0`0. /P/;j><>>===V=(=<<<<<d<6<<;;X;=BJ@rB@CA?BAA? CA@:d?.?>L>J<O\POPlNN<JON NM\ML|L$LK\KJJRfQSHFGyRQGJJJJJJJJ?GIQP:IH|^__ `$`<`T`l`ad_`P^^a]xbbxcc^d` N6google8protobuf8internal20RepeatedPtrFieldBaseE N6google8protobuf8internal15ExtensionFinderE N6google8protobuf8internal24GeneratedExtensionFinderE N6google8protobuf8internal12ExtensionSet8KeyValueE N6google8protobuf13RepeatedFieldIiEE N6google8protobuf13RepeatedFieldIlEE N6google8protobuf13RepeatedFieldIjEE N6google8protobuf13RepeatedFieldImEE N6google8protobuf13RepeatedFieldIfEE N6google8protobuf13RepeatedFieldIdEE N6google8protobuf13RepeatedFieldIbEE N6google8protobuf16RepeatedPtrFieldISsEE N6google8protobuf16RepeatedPtrFieldINS0_11MessageLiteEEE St3mapIiN6google8protobuf8internal12ExtensionSet9ExtensionESt4lessIiESaISt4pairIKiS4_EEE N6google8protobuf8internal19ImplicitWeakMessageE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/io/zero_copy_stream.cc This ZeroCopyOutputStream doesn't support aliasing. Reaching here usually means a ZeroCopyOutputStream implementation bug. N6google8protobuf2io20ZeroCopyOutputStreamE /onnxruntime_src/cmake/external/protobuf/src/google/protobuf/stubs/stringprintf.cc CHECK failed: (v.size()) <= (kStringPrintfVectorMaxArgs): StringPrintfVector currently only supports up to Feel free to add support for more if you need it. arguments. Error reverse compiling ' Unknown encoding Error parsing ' ': Error compiling ' \x00 Invalid RE2: startpos: endpos: text size: DFA out of memory: size bytemap range list count SearchDFA inconsistency SearchOnePass inconsistency SearchBitState inconsistency SearchNFA inconsistency requested group in regexp invalid rewrite pattern: /onnxruntime_src/cmake/external/re2/re2/re2.cc pattern too large - reverse compile failed pattern too large - compile failed RE2: invalid startpos, endpos pair. [ Rewrite schema error: '\' not allowed at end. Rewrite schema error: '\' must be followed by a digit or '\'. Rewrite schema requests %d matches, but the regexp only has %d parenthesized subexpressions.
-
Regexp not destroyed. Stack not empty. Bad reference count Walk NULL no error unexpected error invalid escape sequence invalid character class invalid character class range missing ] missing ) trailing \ invalid repetition size bad repetition operator invalid perl operator invalid UTF-8 invalid named capture group /onnxruntime_src/cmake/external/re2/re2/regexp.cc Unexpected op in Regexp::Equal: NumCapturesWalker::ShortVisit called NamedCapturesWalker::ShortVisit called CaptureNamesWalker::ShortVisit called /onnxruntime_src/cmake/external/re2/re2/walker-inl.h no argument for repetition operator L,,, ,,,,,,,< 9668888t8t8t8D8$86666666 876 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 \l$TCompiler::Copy called! should never happen No ranges in char class Missing case in Compiler: hello, world /onnxruntime_src/cmake/external/re2/re2/compile.cc $&t## $#%$$$&$&%%%l%T%D%t$T$4$ % N3re26Regexp6WalkerINS_4FragEEE N3re28CompilerE /onnxruntime_src/cmake/external/re2/re2/dfa.cc StateSaver failed to restore state. Unexpected special state in RunStateOnByte RunStateOnByteUnlocked failed after ResetCache RunStateOnByteUnlocked failed after Reset Failed to analyze start state. %s%d (%p) flag=%#x || unhandled opcode: DeadState in RunStateOnByte NULL state in RunStateOnByte context does not contain text `]\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: hPPPX
/onnxruntime_src/cmake/external/re2/re2/onepass.cc Cannot use SearchOnePass for unanchored matches. %)R*))))s( 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 ~_```````yayayayayayayayayayayayayayayayayayayayayayayayayayayayayayayayayayayayayayayayaya`yayayaya`yayayayayayaya`yayayagayaUaya`ya_ܮ\, |ܯ̯D4)x 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. 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
0 9 [:alnum:] [:^alnum:] [:alpha:] [:^alpha:] [:ascii:] [:^ascii:] [:blank:] [:^blank:] [:cntrl:] [:^cntrl:] [:digit:] [:^digit:] [:graph:] [:^graph:] [:lower:] [:^lower:] [:print:] [:^print:] [:punct:] [:^punct:] [:space:] [:^space:] [:upper:] [:^upper:] [:word:] [:^word:] [:xdigit:] [:^xdigit:] \d \D \s \S \w \W /i alt -> %d | %d altmatch -> %d | %d byte%s [%02x-%02x] -> %d capture %d -> %d emptywidth %#x -> %d match! %d nop -> %d fail %d. %s
%d+ %s
[%02x-%02x] -> %d
- /onnxruntime_src/cmake/external/re2/re2/prog.cc Unexpected opcode in IsMatch: Hh(( 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 0HH0 H$%%! ! $% %%%%%% =;;;;;;<<< =@=;;;;;;;;;; N3re26Regexp6WalkerIPS0_EE N3re214CoalesceWalkerE N3re214SimplifyWalkerE []^-\ \r \t \n \f \x{%x} (){}[]*+?|.^$\ [^\x00-\x{10ffff}] (?:) Bad final char: ? + {%d,} {%d} {%d,%d} \C ^ $ (?-m:^) (?-m:$) \z \b \B (?HaveMatch:%d) (?: kRegexpCapture cap() == 0 ?P< [truncated] /onnxruntime_src/cmake/external/re2/re2/tostring.cc \gjjj|gggll|l l$mmDmlkkkkkDkkjrrrssss$s N3re214ToStringWalkerE A Z . @2 6 @9 G @J v @x x y } @ @ O @ @ @ @ @ @ ~" 2 @: : +* ; ; = = ]> > (* A A C C =D D E E E G F N @E E t p r @v v t & % @ ? @ ~ P / ` @ @ @ . @1 V 0 ` ` ` É @ @ @ A @ ( / 8 ? H M Y Y [ [ ] ] _ _ h o &! &! *! *! A+! +! 2! 2! `! o! ! ! $ $ , ., 0 `, `, b, b, c, c, d, d, g, k, @m, m, n, n, o, o, p, p, r, r, u, u, ~, , , , @, , @, , @ l @ @" . @2 n @y { @} } u~ @ Z @ @ Z Z Z Z Z Z Z Z @p 0h! : ' ( ( @ @n _n ! " ] A Z a j k k l r s s t z F y / 2 7 9 H J w x x y ~ O a 8 ~" 3 : : +* ; < = = ]> > (* ? @ ?* A B C C =D D E E E G F O P P * Q Q * R R * S S .T T 2V W 3Y Y 6[ [ 5\ \ O ` ` 3a a K c c 1e e ( f f D h h /i i -j j D k k ) l l A o o -q q ) r r +u u *} } ) & & * & ' % E E T p s v w { } t & % @ ? @ 6 0 ] # ~ P / 0 1 2 2 N 3 3 4 4 M 5 = > > D ? @ A B B C I J J < K O P _ ` b c c $ d / 1 V 0 a ` ` ` З @ @y y } } ` a a : b A ' ( / 0 7 8 ? @ E H M Q Q S S U U W W Y Y [ [ ] ] _ _ ` g h o p q J r u V v w d x y z { p | } ~ &! &! *! *! !+! +! 2! 2! N! N! `! o! p! ! ! ! $ $ $ $ , ., 0 0, ^, `, a, b, b, c, c, d, d, e, e, f, f, g, l, m, m, n, n, o, o, p, p, r, s, u, v, ~, , , , , , , , - %- '- '- -- -- @ J K K =vL m " / 2 o y | } } u~ Z Z Z Z Z Z Z Z Z S S `p 0h! : A Z ' ( ( O ( @ @n _n `n n ! " " C 68<>>@ACDFO 5 9 ? P ` h 8j ` ~ ! " $ $ ' ' ) 2 4 7 9 9 ; ; B B G G I I K K M O Q R T T W W Y Y [ [ ] ] _ _ a b d d g j l r t w y | ~ ~
?AJVoqPP=Pptv 4 6 F ,,,, 3 7 ? & ) f j z V ` x T V
+ /onnxruntime_src/cmake/external/re2/re2/prog.cc Unexpected opcode in IsMatch: (sHshssssstxxؘؖؖŚ`hhh 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 llll((xxxlllllllll(ppppppp ppppppppp N3re26Regexp6WalkerIPS0_EE N3re214CoalesceWalkerE N3re214SimplifyWalkerE []^-\ \r \t \n \f \x{%x} (){}[]*+?|.^$\ [^\x00-\x{10ffff}] (?:) Bad final char: ? + {%d,} {%d} {%d,%d} \C ^ $ (?-m:^) (?-m:$) \z \b \B (?HaveMatch:%d) (?: kRegexpCapture cap() == 0 ?P< [truncated] /onnxruntime_src/cmake/external/re2/re2/tostring.cc <
+
l
\
+
+
+\$Ll$
N3re214ToStringWalkerE A Z . @2 6 @9 G @J v @x x y } @ @ O @ @ @ @ @ @ ~" 2 @: : +* ; ; = = ]> > (* A A C C =D D E E E G F N @E E t p r @v v t & % @ ? @ ~ P / ` @ @ @ . @1 V 0 ` ` ` É @ @ @ A @ ( / 8 ? H M Y Y [ [ ] ] _ _ h o &! &! *! *! A+! +! 2! 2! `! o! ! ! $ $ , ., 0 `, `, b, b, c, c, d, d, g, k, @m, m, n, n, o, o, p, p, r, r, u, u, ~, , , , @, , @, , @ l @ @" . @2 n @y { @} } u~ @ Z @ @ Z Z Z Z Z Z Z Z @p 0h! : ' ( ( @ @n _n ! " ] A Z a j k k l r s s t z F y / 2 7 9 H J w x x y ~ O a 8 ~" 3 : : +* ; < = = ]> > (* ? @ ?* A B C C =D D E E E G F O P P * Q Q * R R * S S .T T 2V W 3Y Y 6[ [ 5\ \ O ` ` 3a a K c c 1e e ( f f D h h /i i -j j D k k ) l l A o o -q q ) r r +u u *} } ) & & * & ' % E E T p s v w { } t & % @ ? @ 6 0 ] # ~ P / 0 1 2 2 N 3 3 4 4 M 5 = > > D ? @ A B B C I J J < K O P _ ` b c c $ d / 1 V 0 a ` ` ` З @ @y y } } ` a a : b A ' ( / 0 7 8 ? @ E H M Q Q S S U U W W Y Y [ [ ] ] _ _ ` g h o p q J r u V v w d x y z { p | } ~ &! &! *! *! !+! +! 2! 2! N! N! `! o! p! ! ! ! $ $ $ $ , ., 0 0, ^, `, a, b, b, c, c, d, d, e, e, f, f, g, l, m, m, n, n, o, o, p, p, r, s, u, v, ~, , , , , , , , - %- '- '- -- -- @ J K K =vL m " / 2 o y | } } u~ Z Z Z Z Z Z Z Z Z S S `p 0h! : A Z ' ( ( O ( @ @n _n `n n ! " " C 68<>>@ACDFO 5 9 ? P ` h 8j ` ~ ! " $ $ ' ' ) 2 4 7 9 9 ; ; B B G G I I K K M O Q R T T W W Y Y [ [ ] ] _ _ a b d d g j l r t w y | ~ ~
?AJVoqPP=Pptv 4 6 F ,,,, 3 7 ? & ) f j z V ` x T V
9 ; > @ D F F J P R q + 0 k p ; @ H P Q ` e s G P Y ` > @ p s v z z | ` m @ [ ` { tt~~ @@d e ??56 d f p t ~ !%!'!)!,!1!3!M!O!_!!!!&$@$J$`$' )s+v++++++ .N.// 00000 00070<0?00000001111 2_222X33MM !09..ϩϩ[[>?0RTfhk ;@[epp @j ^j `j ij nj oj ` @SФ -0> 0 c o o 0 Y 0 .4 9 ? ? 7 ; I M O H ; 6 @ U ` g
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@@ -6890,5693 +7141,6100 @@
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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+ zR x $ FJ w ?;*3$" zR x 8 4 0 L 8 d 0
+ | (
+ zPLR x+0 $ $ + K A]
+B J
+ $ < T l < 蓎
7 蓎 , D \ t : Ax $ 8 [~ AJ L
+AA D } K~ G I
+A $ d C~ AJ L
+AA h 3~ D `
+A 蕎 +~ D `
+A , \ hu BDI bAB , E BGA uAD , ؖ } Ж- 4 \ 薎g } BAD D
+ABA $ - < < 8 BDA c
ABK A
-ABD < 4 K BDA c
+ABD < | BDA c
ABK A
-ABD < KB {L BBD I(DPN
-(A ABBA $ Mv AG z
-AA $ `M^ AG b
-AA $ M^ AG b
-AA , M K ADDpn
-AAA , N K AFDpt
-AAA , O K ADDpt
-AAA 4 pP> K BDG DPO
- AABD < L xQu K BED G(D`K
-(A ABBI < Ru oK BED G(D`K
-(A ABBI < Su OK BED G(D`K
-(A ABBI < 8U /K AGDPO
+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
AAC X
-CAA < L V K AGDPO
+CAA < Т { AGDPO
AAC X
-CAA < V J AGDPO
+CAA < o{ AGDPO
AAC X
-CAA 4 W> J BDG DPO
- AABD 4 X> J BDG DPO
- AABD , < Y J ADKP[
-AAA , l Z J AGJP[
-AAA , X[ J AGJP[
-AAA , (\ oJ ADKP[
-AAA , \ _J ADKP[
-AAA $ ]~ AG B
-AA $ ^k AG o
-AA L | h^[ I BBB E(D0D8GP
-8A0A(B BBBI T x_ I BEE E(D0A8G|
-8D0A(B BBBJ D c BEB A(A0D@
-0A(A BBBA
- xd pd. [I BBB E(A0D8D
+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
8A0A(B BBBD U
8A0A(B BBBI U
-8A0A(B BBBA ,
- f0 AI
FK
- C D
- g BBE A(A0D@P
-0A(A BBBA , , g AAK0i
-AAA T
- h0 _H BBB B(D0D8G7
-8A0A(B BBBH 4 m gH BGB A(A0D L D nk GH BEB B(A0A8GP
-8A0A(B BBBB T o H BEE E(K0A8G
-8A0A(B BBBA L 8s H BEE E(K0A8G
-8A0A(B BBBA $ hvm AAG0aAA$
vu AAG0iAAT w" G BBB E(A0A8G
-8D0A(B BBBA < x G BDD g
+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
ABD k
-ABF T $
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-8D0A(B BBBI < |
|6 >G BBA A(J
-(A ABBA <
}6 G BBA A(J
-(A ABBA D
~ F BBB A(A0J
-0A(A BBBA < N BED D(D@\
-(D ABBA T F BBE E(A0A8G
-8D0A(B BBBI < 8V nF BBA A(J
-(C ABBA 4 XJ BDA l
+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
ABJ A
-ABA p= A{ L t ) E BBB B(A0A8J
-8A0A(B BBBJ 4 l pZ BDA y
+ABA D Ύ= A{ L Ύ) Ov BBB B(A0A8J
+8A0A(B BBBJ 4 ҎZ BDA y
ABM A
-ABA < BDD k
+ABA < (Ҏ BDD k
ABH k
-ABA d BBB B(A0A8D`
+ABA d , Ҏ BBB B(A0A8D`
8A0A(B BBBE d
-8A0A(B BBBA T D BBE E(A0A8G
-8D0A(B BBBI D B D BBB I(A0DD
-0D(A BBBA 4 z AEG `
+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 `
AAA p
-AAB L | h ZD BBE E(D0C8Io
-8D0A(B BBBF T ؏ 0D BBE E(D0D8Ft
-8C0A(B BBBD D $ C BBB A(A0I
-0A(A BBBF T l 蕑 C BBB B(A0A8J
-8D0A(B BBBA , D AID0y
-DAA D / BBE A(A0D@
-0A(A BBBA < < h C BBD A(G@,
-(A ABBA < | 褑 C BBA G(D`O
-(A ABBK L C BBB B(A0A8G
-8A0A(B BBBH $ Hs AAG0gAAL 4 , cC BBB B(A0D8D`
-8A0A(B BBBI < , BBG A(G@
-(A ABBG @ B D i
-C < BBG A(G@
-(A ABBG $ B D u
-G T D P& B BEE E(D0A8GC
-8D0A(B BBBC < (" fB BBA A(GU
-(A ABBA \ GB BEB D(A0D@[
+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@[
0A(A BBBK D
-0M(A BBBE < < ȱ" A BBA A(GU
-(A ABBA \ | & A BBB A(D0G
+0M(A BBBE < " r BBA A(GU
+(A ABBA \ x& cr BBB A(D0G
0A(A BBBC j
-0C(A BBBA d > A BBE E(D0A8GP
+0C(A BBBA d < H> 7r BBE E(D0A8GP
8A0A(B BBBE D
-8M0A(B BBBE T D `k cA BBB B(A0A8J
-8A0A(B BBBA \ x GA BBB A(A0Dh
+8M0A(B BBBE T k q BBB B(A0A8J
+8A0A(B BBBA \ 8 q BBB A(A0Dh
0A(A BBBD D
-0C(A BBBA \ ( %A BBB A(A0Dh
+0C(A BBBA \ \ q BBB A(A0Dh
0A(A BBBD D
-0C(A BBBA d غ BLB B(A0A8D`c
+0C(A BBBA d L BLB B(A0A8D`c
8A0A(B BBBK
-8A0A(B BBBD L l 0 BOB A(A0]
+8A0A(B BBBD L BOB A(A0]
(A BBBG Y
-(A BBBH 4 R K@ ]GB A(A0D L Ƚ BOB A(A0]
+(A BBBH 4 t `R p ]GB A(A0D L < BOB A(A0]
(A BBBG Y
-(A BBBH 4 8R ? ]GB A(A0D L | ` BOB A(A0]
+(A BBBH 4 R hp ]GB A(A0D L BOB A(A0]
(A BBBG Y
-(A BBBH 4 $ пR }? ]GB A(A0D 4 \ R f? ]GB A(A0D 4 R O? ]GB A(A0D 4 HÑR 8? ]GB A(A0D d pđ BBB E(A0A8DP{
+(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{
8A0A(B BBBJ
-8A0A(B BBBB T l Ƒ > BBE E(A0A8G
-8D0A(B BBBI , l ȑ ADD0e
-AAA L ɑ] BBB H(A0D8DP
-8A0A(B BBBG T D ʑB = BBE B(A0D8G
-8D0A(B BBBG T Α = BBB B(D0A8G
-8A0A(B BBBF T pԑR = BBB B(D0A8G
-8A0A(B BBBH T L xڑ = BBE E(A0A8G
-8D0A(B BBBE < ܑ' = BBA A(JU
-(A ABBA < ݑ' = BBA A(JU
-(A ABBA L $ ޑ k= BBA D(GpS
+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
(A ABBG g
-(A ABBC < t ' G= BBA A(JU
-(A ABBA < ' *= BBA A(JU
-(A ABBA < BBG A(G@
-(A ABBG < BBG A(G@
-(A ABBD < 0 BBG A(G@
-(A ABBD < " M< BBA A(GU
-(A ABBA < " ,< BBA A(GU
-(A ABBA < 4 " < BBA A(GU
-(A ABBA < t " ; BBA A(GU
-(A ABBA < " ; BBA A(GU
-(A ABBA < " ; BBA A(GU
-(A ABBA < 4! p" ; BBA A(GU
-(A ABBA < t! `" f; BBA A(GU
-(A ABBA < ! P" E; BBA A(GU
-(A ABBA < ! @" $; BBA A(GU
-(A ABBA < 4" 0" ; BBA A(GU
-(A ABBA < t" " : BBA A(GU
-(A ABBA < " " : BBA A(GU
-(A ABBA T " r : BBB B(A0A8J
-8A0A(B BBBD D # ( BBB A(A0G@R
-0A(A BBBA <$ ӏ DR
-E A d \$ BLB B(A0A8D@q
+(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
8A0A(B BBBE @
-8A0A(B BBBF D $ 9 BBJ A(D0
-(A BBEA L d$ P 9 BLE B(A0D8D@
-8A0A(B BBBG L \% BBB B(A0A8Gp8A0A(B BBB % я DR
-E A d % BLB B(A0A8D@q
+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 BBBE @
-8A0A(B BBBF D % H 8 BBJ A(D0
-(A BBEA L % 8 BLE B(A0D8D@
-8A0A(B BBBG L $& `' 8 BBB E(D0D8JV
-8A0A(B BBBC < t& *" : BBA A(GU
-(A ABBA < & +" : BBA A(GU
-(A ABBA < & ," : BBA A(GU
-(A ABBA < 4' p-" h: BBA A(GU
-(A ABBA < t' `." G: BBA A(GU
-(A ABBA < ' P/" &: BBA A(GU
-(A ABBA < ' @0" : BBA A(GU
-(A ABBA < 4( 01" 9 BBA A(GU
-(A ABBA < t( 2" 9 BBA A(GU
-(A ABBA < ( 3" 9 BBA A(GU
-(A ABBA < ( 4" 9 BBA A(GU
-(A ABBA < 4) 4" `9 BBA A(GU
-(A ABBA < t) 5" ?9 BBA A(GU
-(A ABBA T ) 6b 9 BBB B(D0D8D
-8D0A(B BBBG L * H3 8 BBH E(D0D8DPj
-8D0A(B BBBD L + I9 BBB B(A0D8D 8A0A(B BBB L T+ L BBB B(A0D8Dm8A0A(B BBB L + O BBB B(A0D8Dm8A0A(B BBB L L+ ˏ 7 BBB B(A0D8J8A0A(B BBB L D, P BBB B(A0D8Dm8A0A(B BBB L , 8S BBB B(A0A8Gp8A0A(B BBB L , T BBB B(A0A8Gp8A0A(B BBB L , 8V. 8 BBB B(A0A8J q
-8A0A(B BBBA - ϐ. K^ - t - s - s - s - s . s ,. s 4 D. J{ ADD g
-AAE AAA T - s. 8 BBE B(D0A8Gc
-8D0A(B BBBI T ,. y| 8 BFG B(D0A8G
-8A0A(B BBBE ,/ | D/ | L . || 8 BBB B(A0A8G
-8D0A(B BBBE D / 8 BBB A(A0G
-0A(A BBBA / ̐. K^ L 0 p BEB B(A0A8DP$
-8A0A(B BBBA L / І 8 BBB I(H0A8DPz
-8A0A(B BBBH < 0 7 BJA A(DPB
-(A ABBA 0 P" DN
-N A 4 d0 ` 7 BID D0]
- AABA D1 茒% , 0 , 7 AGG XCA , 0 0 c7 ADG _CA 1 $ 77 D_ 1 1 2 $2 , 1 , 6 AGG XCA l2 2 茒 2 r L 2 H G6 BBB A(A0GD
-0C(A BBBC 3 3
- L 43 BRB E(A0A8D`
-8A0A(B BBBE 4 2 5 BAD GPo
- CABE 4 3 ` 5 BAD GPo
- CABE 3 ɐ. K^ 4 , D0g $4 3 - 5 Hd \4 A\ |4 4 4 4 4 4 5 $5 蕒 4 4 4 Hg \5 t5 5 5 ( 5 0 5 8 5 @ 6 H 6 P HS <6 P- L T6 h AAG ]
+8A0A(B BBBF D % (I [i BBJ A(D0
+(A BBEA L 4& J 3i BLE B(A0D8D@
+8A0A(B BBBG L & @L' i BBB E(D0D8JV
+8A0A(B BBBC < & s" Ok BBA A(GU
+(A ABBA < ' t" .k BBA A(GU
+(A ABBA < T' pu"
k BBA A(GU
+(A ABBA < ' `v" j BBA A(GU
+(A ABBA < ' Pw" j BBA A(GU
+(A ABBA < ( @x" j BBA A(GU
+(A ABBA < T( 0y" j BBA A(GU
+(A ABBA < ( z" hj BBA A(GU
+(A ABBA < ( {" Gj BBA A(GU
+(A ABBA < ) |" &j BBA A(GU
+(A ABBA < T) |" j BBA A(GU
+(A ABBA < ) }" i BBA A(GU
+(A ABBA < ) ~" i BBA A(GU
+(A ABBA T * b i BBB B(D0D8D
+8D0A(B BBBG L l* ؑ3 gi BBH E(D0D8DPj
+8D0A(B BBBD L L+ Ȓ9 BBB B(A0D8D 8A0A(B BBB L + BBB B(A0D8Dm8A0A(B BBB L + BBB B(A0D8Dm8A0A(B BBB L + Oh BBB B(A0D8J8A0A(B BBB L , 虏 BBB B(A0D8Dm8A0A(B BBB L , ( BBB B(A0A8Gp8A0A(B BBB L ,- BBB B(A0A8Gp8A0A(B BBB L , (. h BBB B(A0A8J q
+8A0A(B BBBA - 8. K^ - - 輏 . ༏ ,. ؼ D. м \. ؼ t. ༏ 4 . { ADD g
+AAE AAA T 4. . ki BBE B(D0A8Gc
+8D0A(B BBBI T . | ci BFG B(D0A8G
+8A0A(B BBBE t/ ŏ / ŏ L / ŏ| i BBB B(A0A8G
+8D0A(B BBBE D d/ ˏ
i BBB A(A0G
+0A(A BBBA <0 . K^ L T0 `͏ BEB B(A0A8DP$
+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 ]
CAF [
ACA R
-CAJ JCA , 6 b D\
+CAJ JCA , 6 ُb D\
H P
H Q
-G I , 6 ؖb D\
+G I , 6 ڏb D\
H P
H Q
-G I 7 - 7 0- < 47 H BAA a
+G I 7 `ڏ- 7 xڏ- 47 ڏ- < L7 ڏ BAA a
ABH i
-ABL < 6 1 BED A(Dpz
-(A ABBH < 7 蘒 1 BED A(Dpz
-(A ABBH 7 ' Ae 8 ș' Ae 7 ؙ +1 AN < 7 șP 1 BEJ D(DPn(C ABB L 7 ؙ 0 BEE E(A0A8D
-8A0A(B BBBG D <8 x2 0 BBB D(A0Dn
-0D(A BBBA L ,9 p BBB B(A0A8D
-8A0A(B BBBA L |9 BBB B(A0A8D
-8A0A(B BBBA T $9 \ / BBE E(D0A8G
-8D0A(B BBBF $ |9 / AFD@ $ L: V ADD JAAL t: XW BBB E(A0A8DP
-8A0A(B BBBA L : h BBB B(A0A8Gp8A0A(B BBB L l: 訒 . BEB E(I0H8DP
-8D0A(B BBBD 4 d; x@ BAD D0
- AABA D ; B BBE A(A0D@m
-0A(A BBBA < <; ^ 7. BBA D(G0
-(A ABBA 4 |; . BFD D0I
- DABA D ; 0 . BBB D(A0G@:
-0A(A BBBA L ; د] . BBB B(D0A8D`
-8A0A(B BBBA < 貒
= . K^ D $= Ȳ" BBB A(A0G@m
-0A(A BBBA T < C- BBE B(A0A8D
-8A0A(B BBBA D = , BBJ A(D0G@q
-0D(A BBBA T d= `> - BEB B(A0A8G
-8A0A(B BBBC L = HȒ- - BLB B(D0A8DP
-8A0A(B BBBF T > (ʒ s- BBB B(A0A8G
-8A0A(B BBBA L ? Ӓ BBE B(A0A8D`
-8A0A(B BBBA T > 0Ւ( , BBB B(D0A8G
-8A0A(B BBBA ? h ? ` ? X ? ` @ h+ Hb 4@ x2 He T@ l@ $ @ g AG k
-AA $ @ o AG s
-AA 4 ,@ W, BAA G@I
- AABA A $A <A , @ " + AAG0i
-AAD A ػT KH A p A hF A A F A ɏ- AG cA $ B g AG k
-AA $ DB g AG k
-AA $ lB @ o AG s
-AA $ B o AG s
-AA L B > * BBB B(A0A8GZ
-8D0A(B BBBH T dB f s* BBB B(A0A8G
-8A0A(B BBBK T B f =* BBB B(A0A8G
-8A0A(B BBBK T C f * BBB B(A0A8G
-8A0A(B BBBK L lC % ) BCA G`w
+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 dD N D |D BBE A(A0D@l
-0A(A BBBA < D {) BBD A(G0T
-(A ABBA , \D 8t ) AID {
-DAA 4 D o) BFD D0
- DABA , lE P ADD Z
-AAA \ E BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB E . K^ F ,F 4 E 5 ( ADG
+ AABI TF . K^ lF _ F _ 4 F _5 Y ADG
-AAA , E ' e, ADGv
-AAA , F 'G b, ADGe
-AAA , 4F 'G B, ADGe
-AAA , dF 'G ", ADGe
-AAA 4 F 'u , ADGO
-AAA 4 F 8, - ADG
-AAA , G / . ADG
-AAA 4 4G 05 /. ADG4
-AAA 4 lG 4 B/ ADG&
-AAA 4 G 7 / ADG
-AAA 4 G @f Z2 ADG
-AAA 4 H Bf 2 ADG
-AAA , LH DG x3 ADGe
-AAA T |H E* X3 BIB B(A0A8DA
-8A0A(B BBBH < |I L BEE A(D@M
-(A ABBA L I PM k3 BEB E(I0H8D@
-8D0A(B BBBD L dI N: C3 BJE B(A0A8D@
-8A0A(B BBBA L \J O BBE B(A0A8D`
-8A0A(B BBBA L J P 2 BEB E(I0H8D@
-8D0A(B BBBD L TJ R 2 BJE B(A0A8D`8A0A(B BBB D J Rn \2 BBB A(C0K@
-0A(A BBBA L J U C2 BBH E(D0D8D`
-8D0A(B BBBF l <K \N 2 BBB E(D0I8DD
+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 K a 1 BEB B(A0A8Jq
-8A0A(B BBBA T L j
/2 BBE B(D0D8J
-8D0A(B BBBB L \L xT 2 BBE E(D0D8L`
-8D0A(B BBBA TM (y lM y $ L y >2 AJ@W
-DA M y M xy M py M xy N y $N y <N y TN y lN y> A| N y- N DN
-E A N y- AG cA N y- AG cA L O y AAG ]
+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 ]
CAF [
ACA R
-CAJ JCA , TO zb D\
+CAJ JCA , N b D\
H P
H Q
-G I L N Hzr K0 BBB B(E0A8J@
-8A0A(B BBBA < ,O x{ 0 BBA A(D@(A ABB , lO | / AID o
-DAA DP |- < O |p / BED A(G@|
-(A ABBA D O | n/ BAA G0c
+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
DABK k
- DABA $ <P x} 8/ RFD@ D Q }S BBB D(A0D@
-0A(A BBBA 4 P . BFA K0
- AABA D Q BBE A(A0D@
-0A(A BBBA $ Q ^ AG b
-AA Q P2 Al L tQ p . BFB E(A0A8GP
-8A0A(B BBBH $ lR _ AAG LDAD Q - BEE A(C0G@n
-0A(A BBBC L 4R + - BBI A(D0MZ
-0D(A BBBD L R + - BBI A(D0MZ
-0D(A BBBD L R
- - BBB H(D0M2
-0D(A BBBD L $S @3 t- BBI A(D0MZ
-0D(A BBBD < tS 0 _- BBA D(Jz
-(D ABBJ \ S Ѝ9 z- BBB A(A0J
+ 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 T Q- BBB A(A0J
-0A(A BBBD T \T 5- BEB E(A0A8G
-8A0A(B BBBI < T `% I- BEA D(G
-(D ABBA L T P #- BEE E(A0C8J
-8A0A(B BBBB , DU Y - ADG i
-AAA < tU Z , BDA z
+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@
+8A0A(B BBBJ D
+8A0A(B BBBA < lU HZ ] BDA z
ABL A
-ABA < U , BBD D(J@F
-(D ABBA \ U `G , BBB D(D0Du
+ABA < U h ] BBD D(J@F
+(D ABBA \ U G f] BBB D(D0Du
0D(A BBBF K
-0D(A BBBA T TV P+ f, BEL B(A0D8G
-8D0A(B BBBD L V (. O, BBB B(D0A8D`
-8D0A(B BBBE 4 W Z BDA y
+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 W 0 BEB B(A0A8D@{
+ABA d X BEB B(A0A8D@{
8A0A(B BBBJ D
-8A0A(B BBBA L W Ȥ
+ BEE A(A0G
-0A(A BBBA X < X BEB D(A0M
-(A BBBF 4 X Z BDA y
+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
ABM A
-ABA T |X (P * BBB B(A0A8Gw
-8A0A(B BBBA 4 |Y BFD A(D@(A ABBT Y * BEB A(A0[
-(A BBBK A
-(A BBBA d Z ; BLB B(A0A8D`q
+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
8A0A(B BBBE S
-8A0A(B BBBK L tZ ȭM BBB B(A0D8Dp18A0A(B BBB Z ȯI AG A L <Z L i) BLB A(A0J
-0A(A BBBB 4[ Q AG GAD T[ 8 BBE A(A0D@l
-0A(A BBBA [ дA AK sA [ I AK {A L [ 0 BFB B(A0A8D@f
-8D0A(B BBBK d ,\ BFB E(D0A8D`x
-8F0A(B BBBA +
-8D0A(B BBGA D \ X BBE A(A0D@l
-0A(A BBBA , \ v BDA YAT $ ] @V ADD JAAL 4] x) BEB B(A0A8D`
-8A0A(B BBBA 4 ] X BEA A(D@o(A ABB< ] BEE A(D0(A BBB 4 ] 0 BEA D(D0(A ABBL ] f& BBB B(A0D8DP
-8A0A(B BBBA ^ x" DN
-N A D ^ BBB B(A0A8D`8A0A(B BBBL D^ P % BEB B(I0A8D`
-8A0A(B BBBJ T ^ Ǔ & BBE B(A0A8J
-8A0A(B BBBA , ^ ̓e % AID l
-DAA D _ H̓ % BBB D(A0&
-(A BBBA D d_ ͓ % BJB A(A0Gp
-0A(A BBBA < T` hΓ BEB A(A0{
-(A BBRA 4 ` Γ BEA A(D@|(A ABB< ` 0ϓ BEA A(D0o
-(A ABBA L a ϓ BBB B(A0A8Gp8A0A(B BBB 4 \a Pѓ BBH A(D@(A ABB4 a ѓ BEA A(D@o(A ABBL a Pғ BBB B(A0D8D`O
-8A0A(B BBBF T ta ӓ # BBD D(D@%
+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@%
(D ABBB ]
-(D ABBJ < a xՓz # BBG A(G`k
-(D ABBI , b ֓v BAA i
-ABA 4 b ד~ BEA A(D@j(A ABB< c Pד BBG A(G@
-(A ABBG L b ד " BEE E(D0A8G
-8A0A(B BBBF L c pۓ BLB B(A0A8D`
-8A0A(B BBBJ L Tc @ޓ _" BEB E(I0H8DP
-8D0A(B BBBD < Ld BBG A(G@
-(A ABBG \ d BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBT Dd ! BGB D(A0z
+(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
(A BBDE H
-(A BBGA T d c! BFE B(D0D8JX
-8D0A(B BBBJ d e! BBB A(D0G`
-0D(A BBBF ]
-0D(A BBBH m
-0D(A BBBH ]
-0D(A BBBA \ $f 8 BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBT e S BBE B(D0A8G
-8D0A(B BBBK \ f BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBL f BBL B(D0A8G
-8A0A(B BBBA T f p BEE B(D0D8J
-
-8D0A(B BBBI D g 8t BBB E(A0G8GPL8D0A(B BBB< g p BLD D(G0
-(A ABBE 4 g 0= BFD D0
- DABF < g 8 BED I(K@`
-(D ABBH T <h ` BBB B(A0D8G
-8A0A(B BBBG < h G BLD D(G0
-(A ABBE < h * + BED I(KPc
-(D ABBE T i BBB B(A0D8G
-8A0A(B BBBF j
AL T i
BBB E(A0D8J ~
-8D0A(B BBBK T i @w BBD A(GpO
+(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
(D ABBH
-(D ABBA D j h BBE A(A0D@e
-0A(A BBBA L j 0 BEB E(I0H8D@
-8D0A(B BBBD L j * s BJE B(A0A8D@
-8A0A(B BBBA L $k K BEB B(I0D8Ge
-8D0A(B BBBG L tk $ BBB B(A0D8J(
-8D0A(B BBBD 4 ll p9h BEA A(G@Q(A ABB\ l 9 BBD A(D@V
+(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
(A ABBG Y
(A ABBA a
-(A ABBI T \l : BEB B(A0D8J
-8A0A(B BBBJ \m . K^ tm M m M% HX m M- H` m M% HX m M- H` T dm M
BEB J(A0D8N
-8A0A(B BBBA T m X[ 4! BBE J(A0A8N
-8A0A(B BBBA T n gq ! BBB B(A0D8G
-8A0A(B BBBD o k AL 4o k Lo k do k |o k
o k o k o k o k o k p k% HX ,p l- H` Lp l% HX lp l- H` p 0l% HX p @l- H` p Pl% HX p `l- H` q pl- , $q l0 AF
FK
- D $ Tq m^ AG b
-AA L p m BVB D(A0D3
-0D(A BBBG T $q o BGB B(A0D8G
-8D0A(B BBBH L |q s` BLB E(A0A8Gp
-8D0A(B BBBA L q t` BLB E(A0A8Gp
-8D0A(B BBBA L r u` p BLB E(A0A8Gp
-8D0A(B BBBA , s w ADD0e
-AAA , Ds xw AAG0M
-AAH 4 r w BAA G0c
- AABF T s x BBE E(A0A8G
-8D0A(B BBBA L \s zE s BBB B(A0A8G
-8E0A(B BBBF , s }Y k ADG i
-AAA T s } G AAG@r
+(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
AAC G
AAG y
AAE ]
-AAA L 4t 0= BBB B(D0A8DP
-8D0A(B BBBA T t 4 BBE E(K0A8G
-8D0A(B BBBG < t 5 NAG JH
-CAA < u 5 KAG MH
-CAA D \u AAD@e
+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 Lv L u ، BBB B(A0A8Gi
-8A0A(B BBBD L v BBB B(D0A8GA{
-8A0A(B BBBG < w 诔7 BBA A(G0\
-(A ABBA d Dw 谔 BEB E(A0D8Gp
+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
8A0A(B BBBD f
-8A0A(B BBBA w @p \ Sd w T BIB I(D0H8G
+8A0A(B BBBA v p \ Sd v T BIB I(D0H8G
8A0A(B BBBJ Q
-8A0A(B BBGH d ,x BBE F(A0H8GP
+8A0A(B BBGH d w BBE F(A0H8GP
8C0A(B BBBD
-8F0A(B BBBE d w H@ BIB B(A0E8DP
+8F0A(B BBBE d v ; N BIB B(A0E8DP
8A0A(B BBBJ \
-8F0A(B BBBE < x BBG A(G@
-(A ABBG <y $ x b , AY
-F A L x % BBB B(A0A8Dp
-8D0A(B BBBF < y Ɣ BBA A(G@d
-(A ABBI dy Ɣ BFB A(E0G@
+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@
0F(A BBBH y
0A(A BBBG
0F(A BBBD
-0F(A BBBD d z `ʔ BBB A(A0
+0F(A BBBD d |y BBB A(A0
(A BBBA
(A BBBE N
-(A BBBE L \z ͔ BBB B(A0D8DP
-8A0A(B BBBF 4 T{ Δ AAG0^
+(A BBBE , Ty C L AID \
+DAA L y L BBB B(A0D8DP
+8A0A(B BBBF 4 dz AAG0^
AAG H
-AAF < { ϔ BBG A(G@
-(A ABBG L { Д= BBB H(A0D8DP
-8A0A(B BBBE L | є= BBB H(A0D8DP
-8A0A(B BBBE , l| pҔ AG ]
+AAF < z h BBG A(G@
+(A ABBG L z (= BBB H(A0D8DP
+8A0A(B BBBE L ,{ = BBB H(A0D8DP
+8A0A(B BBBE , |{ AG ]
AJ D
-AK L | `Ӕ@ BBB H(A0D8D@
-8A0A(B BBBJ L | PԔ= BBB H(A0D8DP
-8A0A(B BBBE L | @Ք BBE B(A0D8D@
-8A0A(B BBBF L } ֔= BBB H(A0D8DP
-8A0A(B BBBE L } הM BBB H(A0D8DP
-8A0A(B BBBE ,~ ؔ5 D } ؔ BAC GPF
+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 < } 0۔ AAGPF
+ AABD < } # I AAGPF
EAK
-AAD D $~ ݔ s BAA GPA
+AAD D L} & WI BAA GPA
ECBJ X
- CABB D l~ ߔ ; BAC GPF
+ CABB D } p( I BAC GPF
EABE
- AABI d \ pE BBB B(A0A8DP
+ AABI d l~ +E BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l X= BBB E(I0D8D#
+8F0A(B BBBA l D~ += H BBB E(I0D8D#
8D0A(B BBBD
-8D0A(B BBBA L 4 (' BFB E(A0A8Dp
-8F0A(B BBBH T W BBB B(A0D8J
-8C0A(B BBBH , 4 AID0H
-DAA D d l BBE A(D0K@
-0D(A BBBA BBB B(D0A8D@Q
+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
8A0A(B BBBD L
8A0A(B BBBJ N
8A0A(B BBBH *
8A0A(B BBBD K
-8A0A(B BBBC 4 > AAG0}
+8A0A(B BBBC 4 I> AAG0}
FAC
-FAA T
7 BBB B(A0A8O
-8F0A(B BBBE 4 @ BAA G`{
- FABA T , BBB B(A0D8J
-8A0A(B BBBD T " BBE E(A0A8J
-8A0A(B BBBK T ܂ *q BBB B(A0D8J
5
-8D0A(B BBBG ܃ @z. K^ < 2 BDA \
+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 4 x3 L p3 d h3 | `3 L X3 BBB B(A0A8D
-8A0A(B BBBA | 7 BBE B(G0L8DPu
+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 d x. K^ | օ\ AG RAT 09F BKD D(Dp
+8E0A(B BBBK (ō. K^ F\ AG RAT 4 0F ?G BKD D(Dp
(D ABBD Q
-(D ABBF x. K^ ;
l | ; % BBB K(A0A8Dg
+(D ABBF č. K^ 4
l F BBB K(A0A8Dg
8A0A(B BBBH
-8D0A(B BBBE L x< BEB E(I0H8DP
-8D0A(B BBBD L < > BJE B(A0A8D@
-8A0A(B BBBA L > { BEB E(I0H8D@
-8D0A(B BBBD L @] BBB H(A0D8DP
-8A0A(B BBBG T , Ac BBE B(D0D8D8
-8D0A(B BBBD , v. K^ D D Y \ DY } N $ t Dg AG k
-AA , 0E0 AC
FK
- E 4 $ 0FM - BRD DP
- DABA 4 HGZ BDA y
+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
ABM A
-ABA L < pG BBB B(A0A8GPl
-8A0A(B BBBI L H BBB B(A0A8GPl
-8A0A(B BBBI L ܉ J BBB B(A0A8GPl
-8A0A(B BBBI L , @K BBB B(A0A8GPl
-8A0A(B BBBI L | L BBB B(A0A8GPl
-8A0A(B BBBI L ̊ M BBB B(A0A8GPl
-8A0A(B BBBI L O BBB B(A0A8GPl
-8A0A(B BBBI L l PP BBB B(A0A8GPl
-8A0A(B BBBI L Q BBB B(A0A8GPl
-8A0A(B BBBI L R BBB B(A0A8GPl
-8A0A(B BBBI L \ @T BBB B(A0A8GPl
-8A0A(B BBBI L U BBB B(A0A8GPl
-8A0A(B BBBI L V BBB B(A0A8GPl
-8A0A(B BBBI L L X BBB B(A0A8GPl
-8A0A(B BBBI L pY BBB B(A0A8GPl
-8A0A(B BBBI L Z BBB B(A0A8GPl
-8A0A(B BBBI < \
BEA I(DPo
-(A DBBA < ԍ \V
BED I(D@
-(A ABBA D ]: ~
BBB I(A0D
-0A(A EBBA L ^] BBB H(A0D8DP
-8A0A(B BBBG T _= T Ď _2
- BBB E(A0A8G
-8A0A(B BBBA T i B
BBB B(D0A8G1
-8A0A(B BBBA L t Pk
BHI E(A0A8G`@
-8A0A(B BBBE T ď lj BJB E(A0A8G)
-8D0A(B BBBA $ Đ p= ADG nAA T D p y BBB B(A0A8G
-8A0A(B BBBA D m. K^ $ \ P0 AGJ XDA X PY } N $ g AG k
-AA 4 4
BHA G@h
- DABA l. K^ , y\ AG RAD L P7 BBE A(A0D@
-0A(A BBBA D H7 BBE A(A0D@
-0A(A BBBA D ܒ @C BBE A(A0D@
-0A(A BBBA D $ HC BBE A(A0D@
-0A(A BBBA , l PU AGG m
-AAB l g BBB B(A0A8D1
+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 d BEE E(D0A8G
-8D0A(B BBBE L d BBB B(A0A8Gp8A0A(B BBB $ V ADD JAAL ܔ Д BBB B(A0A8Gp8A0A(B BBB < , P BBG A(G@
-(A ABBF l l y BBF D(A0G@I
+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
0A(A BBBI w
0A(A BBBA D
-0F(A BBBG l ܕ y BBF D(A0G@I
+0F(A BBBG l T y BBF D(A0G@I
0A(A BBBI w
0A(A BBBA D
-0F(A BBBG L BEB E(I0H8D@
-8D0A(B BBBA D BKI D(A0G@L
-0A(A BBBA T < (
BBB B(D0D8J
-8A0A(B BBBF < h. K^ L H+ BEB E(I0H8DP
-8D0A(B BBBD d ( BBB B(I0A8G`
+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`
8F0A(B BBBF g
-8F0A(B BBBA T d P BEB B(A0D8G
-8D0A(B BBBA L m BEB B(A0A8G
-8A0A(B BBBA Vs\ AG RAD Ԙ ɕ7 BBE A(A0D@
-0A(A BBBA D ʕC BBE A(A0D@
-0A(A BBBA $ d ˕ AG }
-AA 0̕+ \N T H̕, BBE B(D0A8G
-8D0A(B BBBK < T ѕB BBA A(Jd
-(A ABBA | < 0ҕ BBE B(G0L8DPu
+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
8A0A(B BBBG S
8E0A(B BBBG W
-8E0A(B BBBK , qV BAA D0I AAB< pӕ BBG A(G@
-(A ABBG < , ԕ BBG A(G@x
-(A ABBG < l ԕ BBG A(G@
-(A ABBD < 0Օ BBG A(G@
-(A ABBD < Օ BBG A(G@
-(A ABBH T p֕ BEB B(D0A8Jm
-8A0A(B BBBA % T * m BEE B(A0G8G
Y
-8A0A(B BBBA < L xb { BIA A(D`)
-(A ABBA 4 o\ AG RAD T 87 BBE A(A0D@
-0A(A BBBA D 0C BBE A(A0D@
-0A(A BBBA < 8 BBG A(G@
-(A ABBH < $ BBG A(G@
-(A ABBG d 5 | b. K^ 4 J BDA m
+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
ABI A
-ABA T $ BBE B(D0G8G5
-8A0A(B BBBA $ 8*L Ad
-K W D h*[ \ *[ t *[ @+[ +[ +[ ԟ ,[ `,[ ,[ ,[ D 4 8-
- BBE A(A0D@
-0A(A BBBA D | . BBE A(A0D@
-0A(A BBBA Ġ Tl\ AG RA$ .Q AG L
-DH oA, d . f AAG@G
-AAF t < /- BBA D(D@g
+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 X2P BBB B(A0D8DP
-8D0A(B BBBG L X7 BBB B(A0D8DP
-8D0A(B BBBG L T <@ BBB B(A0D8DP
-8D0A(B BBBG t @ BBD A(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 t A h BEB B(A0K8D
-8A0A(B BBBD T Ģ `F U BBE B(A0A8G>
-8A0A(B BBBD 4 ģ J3 BAD
-ABE AAB L* A[
-L A , t LM ADG0_
-DAA D L BGB D(M0B
-(A BBBK < HM BDA k
+(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
ABK @
-ABA L Ԥ M BOB A(A0]
+ABA L ܤ BOB A(A0]
(A BBBG Y
-(A BBBH < $ N4 BBB A(A0d
-(A BBBE L d OK BBB B(A0A8G@d
-8A0A(B BBBA T Pd { BBB B(D0A8G
-8D0A(B BBBA RT $ hRT L R BEB E(I0H8D@
-8D0A(B BBBA d PTj BBB E(D0A8GP
+(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
8A0A(B BBBK D
-8A0A(B BBBA L L XU BEB E(I0H8D@
-8D0A(B BBBA < D V BDI ~
+8A0A(B BBBA L T 1 BEB E(I0H8D@
+8D0A(B BBBA < 4 BDI ~
AGK K
-ABB < XW BBG A(G@
-(A ABBG L ħ X BBB B(A0D8GP>
-8A0A(B BBBD , XY BAD b
-ABD L Y _ BDB B(A0D8JT
-8A0A(B BBBI H^1 Dl X. K^ ̨ P^" DR
-J A , `^t AAG0v
-AAA d t ^ BBB E(D0A8Dp
+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
8A0A(B BBBE k
-8A0A(B BBBA T ܨ _ BDB B(D0D8G
-8A0A(B BBBA T 4 eg BBE E(D0A8G
-8A0A(B BBBA l 8h t BBB L(A0D8DM
+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
-8A0A(B BBBA Xj ! BDB B(D0D8DpL
+8A0A(B BBBA y/ BDB B(D0D8DpL
8A0A(B BBBD P
8A0A(B BBBF
-8A0A(B BBBA L plj BDB B(A0D8JT
-8A0A(B BBBI L Ԫ t BBB B(A0A8JU
-8A0A(B BBBE L $ v BBB E(A0A8J
-8A0A(B BBBB L t p{
- BBB B(A0D8Jd
-8A0A(B BBBK L ī c
- / BBB B(A0A8JU
-8A0A(B BBBE L BBB B(A0A8Q
-8A0A(B BBBK T d P s BEE E(A0D8G}
-8A0A(B BBBA d ȥY } N $ | g AG k
-AA 4 X ? BHA G@h
- DABA L 4 @ # BBB B(D0A8D
-8A0A(B BBBG , > BDI X
-ABA L BJB A(A0W
-(A BBDH ^(A BBBT P0 BAA G0@
+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@
AABA
AABG e
- AABG T \ (u ? BHB B(D0D8Jo
-8A0A(B BBBG \ @R. K^ T ̮ 8Ö
- BBB B(A0A8G
-8A0A(B BBBH d ̯ ݖS BBB E(D0E8D@_
-8F0A(B BBBA
-8D0A(B BBGA 4 @]\ AG RAL T ޖ BBB B(A0A8Gp8A0A(B BBB D H7 BBE A(A0D@
-0A(A BBBA D @C BBE A(A0D@
-0A(A BBBA , 4 H AAG0s
-AAA L d n BBB B(A0A8Dp
-8A0A(B BBBA L BBB A(A0E
+ 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
+AK D
+CA L ̰ 2n BBB B(A0A8Dp
+8A0A(B BBBA T (3 / BBB B(A0A8G
+8A0A(B BBBF L t ; BBB A(A0E
(A BBGG I
-(A BBDA O. K^ T t @ + BBB B(A0A8G '
-8A0A(B BBBA T ̱ v BEE E(A0G8G
+(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 BBB D(A0D@
-0A(A BBBA D 4 BBB D(A0D@
-0A(A BBBA d Բ BBB E(D0D8D`
+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`
8D0A(B BBBD O
-8A0A(B BBBA T < N BBE E(G0A8Gb
-8A0A(B BBBG < < BBG A(G@
-(A ABBF L Գ P W BEB E(I0H8DP
-8D0A(B BBBD T $ 3 / BBE E(A0A8D
-8A0A(B BBBE $ L. K^ < !Y } N $ T 8"g AG k
-AA l Դ " BBB B(D0K8G
+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
8C0A(B BBBE |
-8A0A(B BBBA 4 D $ S BHA G@h
- DABA $ L. K^ T $\ BEE B(A0A8G>
-8D0A(B BBBA W\ AG RAD '7 BBE A(A0D@
-0A(A BBBA D (C BBE A(A0D@
-0A(A BBBA D D )? BHE A(A0G@[
-0A(A BBBE < *i BEA A(G@[
-(A ABBG L $ + BJB A(A0Z
-(A BBDE ^(A BBB4 t @J { BHA D@
- AABA D T + BBE A(A0D@u
-0A(A BBBA < p,. # BBD A(G@l
-(A ABBA T 4 `.% 7 BHE B(A0A8G
-8A0A(B BBBA $ 4 S0 AGJ XDA $ \ SD XYOC 4 HSc AAG \
-CAG dFA SY } N $ Թ Sg AG k
-AA 4 T T BHA G@h
- DABA L TT BBB E(A0A8J
-8C0A(B BBBC hI. K^ L g AAG e
+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
CAF s
ACA R
-CAJ JCA L h AAG ]
+CAJ JCA L L h۔ AAG ]
CAF c
CAI R
-CAJ JCA L < @h AAG ]
+CAJ JCA L ۔ AAG ]
CAF c
CAI R
-CAJ JCA R\ AG RA4 `h BDD G@g
- AABD D h BBE A(A0D@
-0A(A BBBA D , j BBE A(A0D@
+CAJ JCA 4\ AG RA4 ۔ BDD G@g
+ AABD D D ܔ BBE A(A0D@
+0A(A BBBA D ݔ BBE A(A0D@
-0A(A BBBA T ̻ Xl| BBE E(D0A8G
-8D0A(B BBBE L ̼ q BBB B(A0A8Gp8A0A(B BBB L s BBB B(A0A8Gp8A0A(B BBB L l t BBB B(A0A8Gp8A0A(B BBB L v; BBB B(A0A8Gp 8A0A(B BBB $ w AG
-AA L 4 x BFE E(A0A8DP
-8D0A(B BBBJ d hyr BFB E(D0A8DP
+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 z2 BFE E(A0A8DP
+8D0A(B BBGA d L 2 BFE E(A0A8DP
8F0A(B BBBD
-8D0A(B BBGD T X| d l @}5 BBE B(D0I8DP
+8D0A(B BBGD d ̿ 5 BBE B(D0I8DP
8D0A(B BBBC q
-8D0A(B BBBB T , BBB B(A0A8G
-8A0A(B BBBB L , P BBB B(D0A8D@d
-8D0A(B BBBF L Կ # BEB E(I0H8DP
-8D0A(B BBBD d I BBE E(A0A8D@
+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@
8F0A(B BBBC S
-8F0A(B BBBF L 4 h BBB E(A0D8D@x
-8A0A(B BBBJ T ( C BBH B(D0A8DY
-8D0A(B BBBC L BBB E(A0D8D@x
-8A0A(B BBBJ , , ADD ]
-AAA D BBJ A(D0
-(A BBEA 4 Ȏ BDA w
-AEA {
-AFI , 4 P 7 GA A(D L d G ( BBE H(A0A8J~
-8A0A(B BBBA \ @. K^ t " DR
-J A T Q BIE E(G0A8J
-8A0A(B BBBI T D ` Y BBB B(A0A8J
-8C0A(B BBBF T * BBB B(A0I8Gl
-8D0A(B BBBA T r Y BBE E(D0A8J
-8A0A(B BBBA ! A[ ȷY } N $ , g AG k
-AA 4 X BHA G@h
- DABA >. K^ T 踗 BEE E(A0A8Gs
-8A0A(B BBBI ×& ×$ A[
-D C $ 4 ×d AW
+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
H y
-G C \ \ ė BBB E(A0A8DP{
-8D0A(B BBBG D8F0A(B BBBL pėp BBA D(D@E
-(C ABBF D(F ABB < d ė BDG J_
- CABD < L @ɗ+ BED G(JB
-(C ABBJ | 0̗ BBB B(D0A8DP
+G C \ P8 BBB E(A0A8DP{
+8D0A(B BBBG D8F0A(B BBBl d 8[ $ 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 BBBB U
8G0A(B BBBK
-8A0A(B BBBI L d ΗE BBB B(D0D8Jt
-8C0A(B BBBF \ <. K^ l t ї BBB A(A0G`]
+8A0A(B BBBI L $ C Z# BBB B(D0D8Jt
+8C0A(B BBBF . K^ l F BBB A(A0G`]
0A(A BBBD c
-0C(A BBBK D0F(A BBB , FZ BDD D@G AAB, *F_ BDD D@L AAB D ZF; \ ~F; < t F BEE D(D0(A BBB 4 Gg O BAD D
-ABA DG\ AG RAD З BBE A(A0D@
-0A(A BBBA D T ї7 BBE A(A0D@
-0A(A BBBA , җ s AAG@G
-AAF Pӗ% AQ
-F L `ӗ. Aa
-F E D d pӗ BGB D(M0B
-(A BBBK L ӗX BIN B(A0C8Gpg
-8A0A(B BBBI D BGB D(M0B
-(A BBBK P֗m A~
-A l d ֗= BIB B(A0A8D`
+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
+8A0A(B BBBB U
+8G0A(B BBBK
+8A0A(B BBBI L 0 BBF B(A0A8D@
+8D0A(B BBBB d = BIB B(A0A8D`
8F0A(B BBBI
-8D0A(B BBBA L t xؗ BBB B(A0A8D
-8A0A(B BBBA L ݗ8 BBB B(A0D8D@
-8A0A(B BBBJ < ޗ BBG A(G@
-(A ABBG \ T ޗ KFF E(D0D8G@8A0A(B BBBG@ < xߗ BBG A(G@
-(A ABBG % 6. K^ T | hCL R BEE E(D0D8J
-8A0A(B BBBA T ߗL C BBB B(A0A8G
-8A0A(B BBBE T , X, BBE B(A0D8M
-8A0A(B BBBA , I\ AG RAD L p47 BBE A(A0D@
-0A(A BBBA D h5C BBE A(A0D@
-0A(A BBBA T 4 p6n BEE B(A0G8G%
-8A0A(B BBBA 4 4. K^ $ L pG0 AGJ XDA t xGY } N $ Gg AG k
-AA 4 H r BDA MPO
- AABI 4 D H O BHA G@h
- DABA $ 3. K^ < I T xI $ l I^ AG b
-AA D Iu BBE A(A0D@z
-0A(A BBBA D Mm BBE A(A0D@r
-0A(A BBBA D | R 3 BBB A(D0D@
-0A(A BBBH L PX BJB A(A0W
-(A BBDH ^(A BBBD X BBE A(A0D@l
-0A(A BBBA 82. K^ T t Y C BHB B(A0A8G
-8A0A(B BBBA t E\ AG RAD y7 BBE A(A0D@
-0A(A BBBA D zC BBE A(A0D@
-0A(A BBBA T | {6 # BEE B(A0G8G
-8A0A(B BBBA | 0. K^ Y } N $ @g AG k
-AA 4 , BHA G@h
- DABA T d 0^
- BEB B(A0D8G<
-8A0A(B BBBC \ d 8l BBB A(D0D@
+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@
+(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 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 HØ Q BBB E(K0A8D
-8A0A(B BBBA /. K^ L pȘ
- # BBB E(A0D8J
-8D0A(B BBBD | Ә[ XӘ[ Ә[ Ә[ 0Ԙ[ xԘ[ Ԙ[ $ [ < P[ T [ l Y } N $ (֘g AG k
-AA 4 p֘ s BHA G@h
- DABA L < ט* W BBB E(K0A8D
-8C0A(B BBBF L 4 ܘ BEB A(D0l
+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
(A BBBG
-(A BBBA H-. K^ T ݘ' BEB B(A0A8GU
-8A0A(B BBBE [ @[ $ [ < [ T [ l `[ Y } N $ g AG k
-AA 4 8 BHA G@h
- DABA L T BBB E(K0A8D
-8A0A(B BBBA L +. K^ L
W$ BEE B(A0A8G
-8A0A(B BBBB $ 10 AGJ XDA 1Y } N $ h1g AG k
-AA d t 1 BEL B(D0A8D
+(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
8C0A(B BBBE c
-8D0A(B BBBA 4 2 BHA G@h
- DABA p*. K^ 4 , x3g g BAD D
-ABA 4 d 3g ; BAD D
-ABA D L=\ AG RAD d 3C BBE A(A0D@
-0A(A BBBA D 47 BBE A(A0D@
-0A(A BBBA 4 L <W _ BAD JA AAB , 5E lX D 5% 4 \ = BED C(D@(A ABBL >
- BBB E(D0K8G
-8D0A(B BBB x(. K^ L T H F BBB H(D0A8G8A0A(B BBB T 4: i BEE B(A0G8GtI
-8A0A(B BBBA $ 8o0 AGJ XDA @oY } N $ og AG k
-AA 4 d o 7 BHA G@h
- DABA D H'. K^ L `p BBB H(A0J8DQ
-8C0A(B BBBF L\ AG RAL $ ss BEE L(D0A8Gt
-8A0A(B BBBF D 0t7 BBE A(A0D@
-0A(A BBBA D d (uC BBE A(A0D@
-0A(A BBBA D 0v BEE E(J0J@X
-0A(A BBBK L L v
- BLB B(A0D8J
-8D0A(B BBBI T H7 BLB B(D0D8J9
-8D0A(B BBBF t 0 KHE A(GP(A ABBEP
+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
+ 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
(A ABBM 4
-(A ABBF D l $n BIB A(A0DP
-0A(A BBBA T BEE B(A0G8GJ
-8A0A(B BBBA D i BBB H(A0D@
-0A(A BBBA D 0q BBB H(A0D@
-0A(A BBBA L D h BBF B(A0A8D@
-8D0A(B BBBB d E BBB B(A0A8DP
+(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
8A0A(B BBBE \
-8F0A(B BBBA T T ঙy _ BBE B(A0A8O
-8A0A(B BBBG T #. K^ T ' BBB B(A0A8G
-8A0A(B BBBJ 0H\ AG RAD ͙7 BBE A(A0D@
-0A(A BBBA D , ΙC BBE A(A0D@
-0A(A BBBA T ϙn BBE B(A0D8Mr
-8A0A(B BBBG 4 $ " ' BHA D@
- AABA LG\ AG RAD $ 7 BBE A(A0D@
-0A(A BBBA D l C BBE A(A0D@
-0A(A BBBA < g BGD D(K0l
-(A ABBB , FV BAA D0I AABT | 8 BEE B(A0G8G
-C
-8A0A(B BBBA < p" W BIA A(D`)
-(A ABBA L FF^ BED D(E0o
-(G ABBE A(A ABD TF\ AG RAD , 7 BBE A(A0D@
-0A(A BBBA D t C BBE A(A0D@
-0A(A BBBA #. K^ T , 0 ' BEE B(A0G8G 7
-8A0A(B BBBA , E\ AG RAD L 02 BBE A(A0D@e
-0A(A BBBA T 2< O BBE B(D0A8G
-8D0A(B BBBB T D 7F3 7 BBB B(A0A8J
-8A0A(B BBBG D !. K^ \ j AZ | j j HS j AT L j AAG ]
+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 $ $ k^ AG b
-AA 4 L @k BAA G0y
- AABH T l BEB B(A0H8G
-8A0A(B BBBC T 4 p BBE B(A0D8G
-8D0A(B BBBG L Xv s BEB E(I0H8DP
-8D0A(B BBBA L x K BEB E(I0H8D@
-8D0A(B BBBA L , y # BEB E(I0H8D@
-8D0A(B BBBA , $ ({ BCD D0 AABL { BEB E(I0H8D@
-8D0A(B BBBA L h} BEB E(I0H8D@
-8D0A(B BBBA T L ~w { BHE B(D0A8L
-8D0A(B BBBA T 3 BBE B(A0A8J
-8A0A(B BBBD L BEB E(I0H8DP
-8D0A(B BBBD d L , w BGE E(A0A8D@
+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 BBBJ g
-8F0A(B BBBA T { 7 BBE B(A0A8G
-8A0A(B BBBF h . K^ $ Ab >\ AG RA, < p AAG0W
-AAF 4 0 k BDA D0
- AABA 4 إ 7 BDA D0
- AABA L 4 BBE B(A0A8DPn
-8A0A(B BBBA T @ BBB I(A0A8G
-8A0A(B BBBA T w BBB I(A0A8G
-8A0A(B BBBA L 4 J 7 BBB A(D0DF
-0D(A BBBH , . K^ D س \ г t ȳ ȳ г س- 4 BGI _
-ABD NAB , HE BDC rAH T h[ BBB J(A0A8Gg
-8D0A(B BBBC L p/ BEB B(A0A8D`u
-8A0A(B BBBH , P AAG0
-CAH \ . K^ t F ( :\ AG RAL L X BBB B(A0A8Ji
-8A0A(B BBBI T ǚ BBE B(D0A8G
-8D0A(B BBBG T ˚, BBE B(D0A8G
-8D0A(B BBBK T L К BBB B(D0D8G
-8A0A(B BBBH D L 9 BBB D(A0D@0A(A BBB T BIB E(A0A8G5
-8D0A(B BBBA L D @ BDE E(G0A8G
-8D0A(B BBBH L 3 BBB I(A0A8JY
-8D0A(B BBBG . K^ L BBE B(D0A8G
-8A0A(B BBBA , L g AID @
-DAA $ < T l 0 Hg (7 Hj H0 Hg X7 Hj $ 4 x^ AG b
-AA T BBE B(D0A8G
-8D0A(B BBBC T H j BBB B(A0D8R
-8A0A(B BBBK , d > AID W
-DAA T )
- BBB B(A0D8R
-8A0A(B BBBA , > m AID W
-DAA T / M BBB B(A0D8Rh
-8A0A(B BBBG , t "> AID W
-DAA T "/ r BBB B(A0D8Rh
-8A0A(B BBBG , )> AID W
-DAA T , )g BBB B(A0I8G
-8D0A(B BBBI T . BBE B(D0A8G
-8D0A(B BBBB T 3 BBE B(D0A8G
-8D0A(B BBBC D 8 BBB D(A0D@
-0A(A BBBA D | x9| : BIA D(D
-(D ABBA D <| # BIA D(D
-(D ABBA L ? BIB A(D0D
-0D(A BBBA L \ 8C BIB A(D0D
-0D(A BBBA L F BIB A(D0D
-0D(A BBBA L I BBB B(G0D8G
-8A0A(B BBBA < L L AAG L
+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
AAI p
-AAB L Lq s BBB A(A0G
-0D(A BBBF < Sr H BBA K(M0x
-(D ABBA T S BBB G(D0G8G
-8D0A(B BBBH D t PW 3 BBA A(G
-(D ABBE < Zr BBA K(M0x
-(D ABBA T 8[ BBB G(D0G8G
-8D0A(B BBBH < ^g BBA A(DV(A ABB T b BBB E(A0A8L
-8A0A(B BBBA H . K^ r r , 4 r[ AID t
-DAA , d Pr[ AID t
-DAA < < b/D BBD A(D@0(A ABB T @r& D BIB E(A0A8G
-8D0A(B BBBA T , w& BIB E(A0A8G
-8D0A(B BBBA 4 { BGB A(C0G T | BBE B(D0A8G
-8D0A(B BBBB L U BBB B(F0G8G
-8D0A(B BBBK T d \& # BDB B(G0D8G
>
-8D0A(B BBBI d . K^ | , 4 [ AID t
-DAA , d Ю[ o AID t
-DAA T O BBB B(A0I8G(
-8D0A(B BBBB D N- BBB D(A0D@0A(A BBB T 4 BIB E(A0A8DN
-8D0A(B BBBA T 8 BIB E(A0A8DN
-8D0A(B BBBA T 3 BIB E(A0A8G
-8D0A(B BBBA T < h l BBE B(D0A8G
-8D0A(B BBBH \ 0ěu R BEE A(A0De
-0A(A BBBA j
-0A(A BBBF \ P̛E BEE A(A0D=
+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=
0A(A BBBI j
-0A(A BBBF D T @ԛ BEE A(A0D
-0A(A BBBD D ٛ J BEE A(A0D
-0A(A BBBD L pߛ5 BBB A(D0G
-0A(A BBBB T 4 ` BBB G(D0G8G
-8D0A(B BBBB L 3 BBB A(D0G[
-0A(A BBBK T
- BDB B(A0G8J
-8D0A(B BBBJ `. K^ H
- @ $
- 8 <
- 0 T
- ( l
- 0
- 8
- @ $
- H^ AG b
-AA ,
- BGD l
-ABD L q BBA D(G@n
+0A(A BBBF \ u ?! 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
(A ABBD
-(A ABBH T
- 09 BEE E(A0A8G
-8A0A(B BBBA T BEE E(A0A8G
-8A0A(B BBBA T d ` f BBB E(A0A8KP
-8A0A(B BBBF , > 1 AID W
-DAA T BBB E(A0A8KP
-8A0A(B BBBF , D
-> AID W
-DAA T t
- BBB E(A0A8KP
-8A0A(B BBBF , x > AID W
-DAA T g BBB E(A0A8KP
-8A0A(B BBBF , T
0> 2 AID W
-DAA D , @ BBB D(A0D@
-0A(A BBBA L
BIB A(D0D
-0D(A BBBA L BIB A(D0D
-0D(A BBBA L l 8 BIB A(D0D
-0D(A BBBA L m BIB A(D0D
-0D(A BBBA D N BBB A(A0J`
-0D(A BBBC * A[
-L A 0 * A[
-L A < @ * A[
-L A \ P * A[
-L A | ` * A[
-L A T p BEJ E(D0D8G(
-8D0A(B BBBF T L ! g BEJ E(D0D8G(
-8D0A(B BBBF T # " BEJ E(D0D8G(
-8D0A(B BBBF T %B BBB B(A0A8J
-8A0A(B BBBA T T +N BEB E(A0D8G
-8A0A(B BBBF T , BEB E(A0A8G
-8A0A(B BBBF T 0 BEE B(A0A8JN
-8A0A(B BBBF L \ 7
+(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
- O BBB B(A0D8G
-8A0A(B BBBG T @B BBB B(A0A8J
-8A0A(B BBBA T FF BEB E(A0D8G
-8A0A(B BBBF T \ H BEB E(A0A8G
-8A0A(B BBBF T pL W BEE B(A0A8JN
-8A0A(B BBBF L R
+ 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 \ x\B q BBB B(A0A8J
-8A0A(B BBBA T pbF BEB E(A0D8G
-8A0A(B BBBF T hd e BEB E(A0A8G
-8A0A(B BBBF T d h ! BEE B(A0A8JW
-8A0A(B BBBE L hn
+ 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 (xB ; BBB B(A0A8J
-8A0A(B BBBA T d ~F t BEB E(A0D8G
-8A0A(B BBBF T / BEB E(A0A8G
-8A0A(B BBBF T Ѓ BEE B(A0A8JN
-8A0A(B BBBF L l
+ 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
- BBB B(A0D8G
-8A0A(B BBBG d . K^ | , [ AID t
-DAA D y BBB B(A0A8[
-0A(B BBBA D $ ( BEB B(A0A8
-0A(B BBBA l 2 I Ț AN
-A < Ț AHx
+ 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
AF
AH
-EH < D BBD A(D@0(A ABB T | U BIB E(A0A8D7
-8D0A(B BBBA < | ` BBD D(G0b
-(A ABBE , У ADT
+EH < " \D BBD A(D@0(A ABB T d" Ț BIB E(A0A8D6
+8D0A(B BBBA < L# ͚ BBD D(G0b
+(A ABBE , # ͚ ADT
AF
-AA D H BBB B(A0A8
-0A(B BBBE L 4 ] BBB B(D0A8G
-8A0A(B BBBA d c BBB E(D0D8D
+AA D # HΚH BBB B(A0A8
+0A(B BBBE L $ Pњ] BBB B(D0A8G
+8A0A(B BBBA d T$ `՚c BBB E(D0D8D
8A0A(B BBBD
-8A0A(B BBBK L BBB B(A0D8D@
-8A0A(B BBBA L < p BEE E(D0A8D
-8A0A(B BBBA 4 @ɜ AF
P
- H D Μ BBE B(A0A8
-0A(B BBBA 4 d pМ AC
PG
- A \ D (ڜ BBE B(A0A8
-0A(B BBBA 0A(B BBB L Xݜ% BBE B(A0A8D
-8A0A(B BBBA , 8$ AM
DL
- E d $ 8c BBB E(D0D8D
+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 BBBD
-8A0A(B BBBK d @H BEE B(E0A8DX
+8A0A(B BBBK d \' H BEE B(E0A8DX
8A0A(B BBBA I
-8A0A(B BBBA < L (D
- AC
FM
+8A0A(B BBBA < 4' D
+ m AC
FM
A
- D T 8 BBA A(G`
+ D T t' ' A BBA A(G`
(A ABBD {
-(A ABBG 4 8 AC
BGM
+(A ABBG 4 \( h)8 AC
BGM
C
- F T BEB B(D0A8Jd
-8A0A(B BBBA L p BBB B(A0A8G
-8A0A(B BBBB D BBB D(A0G@
-0A(A BBBD T X x BBA A(DU
+ 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
(A ABBK
-(A ABEC L d
- ; BDB E(A0A8G
-8A0A(B BBBI \! @. K^ t! \ AG RAL ! ("N BBE B(A0A8DP
-8A0A(B BBBF L ! ($\ BBB E(A0A8DP
-8A0A(B BBBF D 4" 8&u BBB D(A0D@
-0A(A BBBI |" p)X T ! *| { BBE E(D0A8G
-8D0A(B BBBI D " d BBB D(A0D@0A(A BBB T " /L BIE B(A0A8D9
-8D0A(B BBBA T " 4 BBB B(A0A8O6
-8A0A(B BBBA < # ? BBG A(G@
-(A ABBG L |# @ BEE B(A0D8Gu
-8D0A(B BBBD t$ X. K^ $ @O" DN
-N A $ r DN
-E A $ 0O Y 4 <$ 8Og BAD D
-ABA L % pO BLA A(D0]
-(A ABBI X
-(A ABBB L l% O BLA A(D0]
-(A ABBI X
-(A ABBB $ % PP^ AG b
-AA % P % P & xP ,& pP D& hP \& `P t& XP L % PP G BBE E(A0A8G_
-8D0A(B BBBE 4 4& TH . SGB A(A0D D l& UB BBB A(D0Gx
-0A(A BBBA D & W BBB D(E0Ml
-0A(A BBBH L & [+ BBE B(D0A8D`
-8A0A(B BBBB d ' _g BLB B(A0A8D@q
+(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
+AAF p
+AAB < , PW s AAG
+AAF p
+AAB < , X 7 AAG
+AAF p
+AAB < - X 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
8A0A(B BBBE
-8A0A(B BBBF < \( ` BBA A(G@a
-(A ABBD d ( `a< BLB B(A0A8DPc
+8A0A(B BBBF T 9 P BBE B(D0A8G
+8D0A(B BBBG d : < BLB B(A0A8DPc
8A0A(B BBBK
-8A0A(B BBBF d ) 8bg BLB B(A0A8D@q
+8A0A(B BBBF d 4; g BLB B(A0A8D@q
8A0A(B BBBE
-8A0A(B BBBF l) @c L ( Hc W BOB B(A0D8D@
-8A0A(B BBBI T ,) e' / BBB E(A0A8G
-8A0A(B BBBA L ,* pl BLB H(A0D8DPV
-8A0A(B BBBG L ) m BEB B(A0A8J|
-8A0A(B BBBC d * } BBB B(D0A8DP
+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
-8A0A(B BBBH T * 肝& BBB B(A0A8J>
-8A0A(B BBBD L + m BBB H(A0D8DP
-8A0A(B BBBA T 4+ q BFB B(A0D8G
-8A0A(B BBBD 4 + Xb n BDA G0x
- DABC L l, ; BBB B(A0D8DPd
-8A0A(B BBBA T , " BEB E(D0H8G
-8A0A(B BBBH T l, 1 BEB E(D0H8G
-8A0A(B BBBH T , @ BEB E(D0H8G
-8A0A(B BBBH T - O BEB E(D0H8G
-8A0A(B BBBH | . BBB B(D0A8DP
-8A0A(B BBBB U
-8G0A(B BBBK
-8A0A(B BBBI d . g BBB B(A0A8GP_
+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]
+(A ABBI X
+(A ABBB L |? 4 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_
8A0A(B BBBF <
-8A0A(B BBBJ d / (g BBB B(A0A8GP_
+8A0A(B BBBJ L dC `S; BBB B(A0D8DPd
+8A0A(B BBBA d C PVg BBB B(A0A8GP_
8A0A(B BBBF <
-8A0A(B BBBJ | l/ 0 BBB B(D0A8DP
+8A0A(B BBBJ | D XZ BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI T D/ h! BEB B(A0A8J
-8A0A(B BBBA T / ڝ, BEB B(A0A8J
-8A0A(B BBBA T / z# BEB B(A0A8J
-8A0A(B BBBA T L0 z# BEB B(A0A8J
-8A0A(B BBBA L1 . K^ d1 @ |1 A2 1 B 1 C2 1 E 1 F2 1 8G 2 0H2 $2 XI <2 J T2 K l2 K 2 L 2 M 2 N 2 PO 2 XP 2 PQ" 3 hR ,3 `S" D3 xT \3 pU" t3 V 3 W" 3 X 3 @Y 3 Z 3 Z 4 x[ 4 \ 44 \ L4 ] d4 X^ |4 P^ 4 H^ 4 @^ 4 8^ 4 0^ 4 (^ 5 ^ $5 ^ <5 ^ T5 ^ l5 ^ 5 ] 5 ] 5 ] 5 ] 5 ] 5 ] 6 ] ,6 ] D6 ] \6 ] t6 ] 6 ] 6 ] 6 ] 6 ] 6 ] 7 x] 7 p] 47 h] L7 `] d7 X] |7 P] 7 H] 7 @] 7 8] 7 0] 7 (] 8 ] $8 ] <8 ] T8 ] l8 ] 8 \ 8 \ 8 \ 8 \ 8 \ 8 \ 9 \ ,9 \ D9 \ \9 \ t9 \ 9 \ 9 \ 9 \ 9 \ 9 \ : x\ : p\ 4: h\ L: `\ d: X\ |: P\ : H\ : @\ : 8\ : 0\ : (\ ; \ $; \ <; \ T; \ l; \ ; [ ; [ ; [ ; [ ; [ ; [ < [ ,< [ D< [ \< [ t< [ < [ < [ < [ < [ < [ = x[ = p[ 4= x[ L= [ d= [ |= [ = [ = [ = [ = [ = [ > [ $> [ <> [ T> [ l> [ > [ > [ > [ > \ > \ > \ ? \ ,? \ D? (\ \? 0\ t? 8\ ? @\ ? H\ ? P\ ? X\ ? `\ @ h\ @ p\ 4@ x\ L@ \ d@ \ |@ \ @ \ @ \ @ \ @ \ @ \ A \ $A \ <A \ TA \ lA \ A \ A \ A \ A ] A ] A ] B ] ,B ] DB (] \B 0] tB 8] B @] B H] B P] B X] B `] C h] C p] 4C x] LC ] dC ] |C ] C ] C ] C ] C ] C ] D ] $D ] <D ] TD ] lD ] D ] D ] D ] D ^ D ^ D ^ E ^ ,E ^ DE (^ \E 0^ tE 8^ E @^ E H^ E P^ , E X^T BAA D0G AAB, F ^T BAA D0G AAB, 4F ^T BAA D0G AAB, dF ^L BAA D0 AAB , F _W BAA D0J AAB, F 8_W BAA D0J AAB, F h_W BAA D0J AAB, $G _O BAA D0B AAB, TG _] BAA D0P AAB, G _] BAA D0P AAB, G `R BAA D0E AAB, G H`R BAA D0E AAB, lG x`[ $ AID t
-DAA , G `[ AID t
-DAA , G `[ AID t
-DAA , G a[ AID t
-DAA , ,H 8a[ AID t
-DAA , \H ha[ AID t
-DAA , H a[ d AID t
-DAA , H a[ D AID t
-DAA , H a[ $ AID t
-DAA , I (b[ AID t
-DAA , LI Xb[ AID t
-DAA , |I b[ AID t
-DAA , I b[ AID t
-DAA , I b[ AID t
-DAA , J c[ d AID t
-DAA , <J Hc[ D AID t
-DAA , lJ xc[ $ AID t
-DAA , J c[ AID t
-DAA , J c[ AID t
-DAA , J d[ AID t
-DAA , ,K 8d[ AID t
-DAA , \K hd[ AID t
-DAA , K d[ d AID t
-DAA , K d[ D AID t
-DAA , K d[ $ AID t
-DAA , L (e[ AID t
-DAA , LL Xe[ AID t
-DAA , |L e[ AID t
-DAA , L e[ AID t
-DAA , L e[ AID t
-DAA , M f[ d AID t
-DAA , <M Hf[ D AID t
-DAA , lM xf[ $ AID t
-DAA , M f[ AID t
-DAA , M f[ AID t
-DAA , M g[ AID t
-DAA , ,N 8g[ AID t
-DAA , \N hg[ AID t
-DAA , N g[ d AID t
-DAA , N g[ D AID t
-DAA , N g[ $ AID t
-DAA , O (h[ AID t
-DAA , LO Xh[ AID t
-DAA , |O h[ Ŀ AID t
-DAA , O h[ AID t
-DAA , O h[ AID t
-DAA , P i[ d AID t
-DAA , <P Hi[ D AID t
-DAA , lP xi[ $ AID t
-DAA , P i[ AID t
-DAA , P i[ AID t
-DAA , P j[ ľ AID t
-DAA , ,Q 8j[ AID t
-DAA , \Q hj[ AID t
-DAA , Q j[ d AID t
-DAA , Q j[ D AID t
-DAA , Q j[ $ AID t
-DAA , R (k[ AID t
-DAA , LR Xk[ AID t
-DAA , |R k[ Ľ AID t
-DAA , R k[ AID t
-DAA , R k[ AID t
-DAA , S l[ d AID t
-DAA , <S Hl[ D AID t
-DAA , lS xl[ $ AID t
-DAA , S l[ AID t
-DAA , S l[ AID t
-DAA , S m[ ļ AID t
-DAA , ,T 8m[ AID t
-DAA , \T hm[ AID t
-DAA , T m[ d AID t
-DAA , T m[ D AID t
-DAA , T m[ $ AID t
-DAA , U (n[ AID t
-DAA , LU Xn[ AID t
-DAA , |U n[ Ļ AID t
-DAA , U n[ AID t
-DAA , U n[ AID t
-DAA , V o[ d AID t
-DAA , <V Ho[ D AID t
-DAA , lV xo[ $ AID t
-DAA , V o[ AID t
-DAA , V o[ AID t
-DAA , V p[ ĺ AID t
-DAA , ,W 8p[ AID t
-DAA , \W hp[ AID t
-DAA , W p[ d AID t
-DAA , W p[ D AID t
-DAA , W p[ $ AID t
-DAA , X (q[ AID t
-DAA , LX Xq[ AID t
-DAA , |X q[ Ĺ AID t
-DAA , X q[ AID t
-DAA , X q[ AID t
-DAA , Y r[ d AID t
-DAA Y Hr" DN
-N A Z Xr Y , Z `rb D\
+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
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+DAA , $` @-[ ^ AID t
+DAA , T` p-[ > AID t
+DAA , ` -[ AID t
+DAA , ` -[ AID t
+DAA , ` .[ AID t
+DAA , a 0.[ AID t
+DAA , Da `.[ AID t
+DAA , ta .[ ~ AID t
+DAA , a .[ ^ AID t
+DAA , a .[ > AID t
+DAA , b /[ AID t
+DAA , 4b P/[ AID t
+DAA , db /[ AID t
+DAA , b /[ AID t
+DAA , b /[ AID t
+DAA , b 0[ ~ AID t
+DAA , $c @0[ ^ AID t
+DAA , Tc p0[ > AID t
+DAA , c 0[ AID t
+DAA , c 0[ AID t
+DAA , c 1[ AID t
+DAA , d 01[ AID t
+DAA , Dd `1[ AID t
+DAA , td 1[ ~ AID t
+DAA , d 1[ ^ AID t
+DAA , d 1[ > AID t
+DAA , e 2[ AID t
+DAA , 4e P2[ AID t
+DAA , de 2[ AID t
+DAA , e 2[ AID t
+DAA , e 2[ AID t
+DAA , e 3[ ~ AID t
+DAA , $f @3[ ^ AID t
+DAA , Tf p3[ > AID t
+DAA , f 3[ AID t
+DAA , f 3[ AID t
+DAA , f 4[ AID t
+DAA , g 04[ AID t
+DAA , Dg `4[ AID t
+DAA , tg 4[ ~ AID t
+DAA , g 4[ ^ AID t
+DAA , g 4[ > AID t
+DAA , h 5[ AID t
+DAA , 4h P5[ AID t
+DAA , dh 5[ AID t
+DAA , h 5[ AID t
+DAA , h 5[ AID t
+DAA , h 6[ ~ AID t
+DAA , $i @6[ ^ AID t
+DAA , Ti p6[ > AID t
+DAA , i 6[ AID t
+DAA , i 6[ AID t
+DAA , i 7[ AID t
+DAA , j 07[ AID t
+DAA , Dj `7[ AID t
+DAA , tj 7[ ~ AID t
+DAA , j 7[ ^ AID t
+DAA , j 7[ > AID t
+DAA , k 8[ AID t
+DAA , 4k P8[ AID t
+DAA , dk 8[ AID t
+DAA , k 8[ AID t
+DAA , k 8[ AID t
+DAA , k 9[ ~ AID t
+DAA , $l @9[ ^ AID t
+DAA , Tl p9[ > AID t
+DAA , l 9[ AID t
+DAA , l 9[ AID t
+DAA , l :[ AID t
+DAA , m 0:[ AID t
+DAA , Dm `:[ AID t
+DAA , tm :[ ~ AID t
+DAA , m :[ ^ AID t
+DAA , m :[ > AID t
+DAA , n ;[ AID t
+DAA , 4n P;[ AID t
+DAA , dn ;[ AID t
+DAA , n ;[ AID t
+DAA , n ;[ AID t
+DAA , n <[ ~ AID t
+DAA , $o @<[ ^ AID t
+DAA , To p<[ > AID t
+DAA , o <[ AID t
+DAA , o <[ AID t
+DAA , o =[ AID t
+DAA , p 0=[ AID t
+DAA , Dp `=[ AID t
+DAA , tp =[ ~ AID t
+DAA , p =[ ^ AID t
+DAA , p =[ > AID t
+DAA , q >[ AID t
+DAA , 4q P>[ AID t
+DAA , dq >[ AID t
+DAA , q >[ AID t
+DAA , q >[ AID t
+DAA r ?" DN
+N A r ? Y , r (?b D\
H P
H Q
-G I , LZ rb D\
+G I , r h?b D\
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-G I , |Z rb D\
+G I , s ?b D\
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-G I , Z sb D\
+G I , Ls ?b D\
H P
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-G I , Z `sb D\
+G I , |s (@b D\
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-G I , [ sb D\
+G I , s h@b D\
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+G I , s @b D\
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-G I , l[ tb D\
+G I , t @b D\
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+G I , <t (Ab D\
H P
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+G I , lt hAb D\
H P
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+G I , t Ab D\
H P
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+G I , t Ab D\
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+G I , t (Bb D\
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+G I , ,u hBb D\
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+G I , u Bb D\
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+G I , u (Cb D\
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+G I , u hCb D\
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+G I , v Cb D\
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-G I , ] wb D\
+G I , Lv Cb D\
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+G I , |v (Db D\
H P
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-G I , ^ wb D\
+G I , v hDb D\
H P
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-G I , <^ wb D\
+G I , v Db D\
H P
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-G I , l^ xb D\
+G I , w Db D\
H P
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+G I , <w (Eb D\
H P
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+G I , lw hEb D\
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-G I , ^ xb D\
+G I , w Eb D\
H P
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-G I , ,_ yb D\
+G I , w Eb D\
H P
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+G I , w (Fb D\
H P
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-G I , _ yb D\
+G I , ,x hFb D\
H P
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+G I , \x Fb D\
H P
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-G I , _ zb D\
+G I , x Fb D\
H P
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-G I , ` `zb D\
+G I , x (Gb D\
H P
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-G I , L` zb D\
+G I , x hGb D\
H P
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-G I , |` zb D\
+G I , y Gb D\
H P
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-G I , ` {b D\
+G I , Ly Gb D\
H P
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-G I , ` `{b D\
+G I , |y (Hb D\
H P
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-G I , a {b D\
+G I , y hHb D\
H P
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-G I , <a {b D\
+G I , y Hb D\
H P
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-G I , la |b D\
+G I , z Hb D\
H P
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-G I , a `|b D\
+G I , <z (Ib D\
H P
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-G I , a |b D\
+G I , lz hIb D\
H P
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-G I , a |b D\
+G I , z Ib D\
H P
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-G I , ,b }b D\
+G I , z Ib D\
H P
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-G I , \b `}b D\
+G I , z (Jb D\
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-G I , b }b D\
+G I , ,{ hJb D\
H P
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+G I , \{ Jb D\
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+G I , { Jb D\
H P
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-G I , c `~J BAA D0} AAB , Lc ~R BAA D0E AAB, |c ~J BAA D0} AAB , c ~U BAA D0H AABL 4c r L BBB B(E0A8J@
-8A0A(B BBBA $ ,d 0^ AG b
-AA < c h BBI A(D@I
-(A ABBA d d p2 Ah
-G A d r Af
-I AL Dd " d BBB J(A0A8G
-8D0A(B BBBE D d Є O BGB D(D0J
-(A BBBD T d 8 BBE E(D0A8G/
-8D0A(B BBBJ T 4e ٭ BBE E(D0A8G
-8D0A(B BBBE T e h BBE E(A0A8G
-8D0A(B BBBI D f BBB D(A0D@
-0A(A BBBA 4 ,f H BDA H
-ABA 4 df 7 BHA G`
- DABA 4 f 7 ͬ BHA G`
- DABA 4 f 7 BHA G`
- DABA 4 g 7 BHA G`
- DABA 4 Dg 7 p BHA G`
- DABA 4 |g ȓ7 Q BHA G`
- DABA 4 g Д7 2 BHA G`
- DABA 4 g ؕ7 BHA G`
- DABA 4 $h 7 BHA G`
- DABA 4 \h 藞7 ի BHA G`
- DABA 4 h 7 BHA G`
- DABA 4 h 7 BHA G`
- DABA 4 i 7 x BHA G`
- DABA 4 <i 7 Y BHA G`
- DABA 4 ti 7 : BHA G`
- DABA 4 i 7 BHA G`
- DABA 4 i 7 BHA G`
- DABA 4 j (7 ݪ BHA G`
- DABA 4 Tj 07 BHA G`
- DABA 4 j 87 BHA G`
- DABA 4 j @7 BHA G`
- DABA 4 j H7 a BHA G`
- DABA 4 4k P7 B BHA G`
- DABA 4 lk X7 # BHA G`
- DABA 4 k `7 BHA G`
- DABA 4 k h7 BHA G`
- DABA 4 l p7 Ʃ BHA G`
- DABA 4 Ll x7 BHA G`
- DABA 4 l 7 BHA G`
- DABA 4 l 7 i BHA G`
- DABA 4 l 7 J BHA G`
- DABA 4 ,m B + BHA G`
- DABA 4 dm 7 BHA G`
- DABA 4 m B BHA G`
- DABA 4 m бB Ψ BHA G`
- DABA < n 貞\ BIA A(Gp
-(D ABBA < Ln \ BIA A(Gp
-(D ABBA < n (U a BIA A(Gp
-(D ABBA < n HU : BIA A(Gp
-(D ABBA < o h\ BIA A(Gp
-(D ABBA < Lo U BIA A(Gp
-(D ABBA < o U ŧ BIA A(Gp
-(D ABBA < o Ⱥ\ BIA A(Gp
-(D ABBA < p 軞\ w BIA A(Gp
-(D ABBA < Lp \ P BIA A(Gp
-(D ABBA < p (U ) BIA A(Gp
-(D ABBA < p HU BIA A(Gp
-(D ABBA < q hU ۦ BIA A(Gp
-(D ABBA < Lq U BIA A(Gp
-(D ABBA 4 q B BHA G`
- DABA 4 q Þ7 n BHA G`
- DABA 4 q Ğ7 O BHA G`
- DABA < 4r ŞU 0 BIA A(Gp
-(D ABBA < tr ƞU BIA A(Gp
-(D ABBA < r ȞU BIA A(Gp
-(D ABBA < r 0ɞU BIA A(Gp
-(D ABBA 4 4s Pʞ7 BHA G`
- DABA 4 ls X˞7 u BHA G`
- DABA 4 s `̞7 V BHA G`
- DABA 4 s h͞7 7 BHA G`
- DABA 4 t pΞ7 BHA G`
- DABA 4 Lt xϞ7 BHA G`
- DABA 4 t ОB ڤ BHA G`
- DABA 4 t ў7 BHA G`
- DABA 4 t Ҟ7 BHA G`
- DABA 4 ,u Ӟ7 } BHA G`
- DABA 4 du Ԟ7 ^ BHA G`
- DABA 4 u ՞7 ? BHA G`
- DABA 4 u ֞7 BHA G`
- DABA 4 v מ7 BHA G`
- DABA 4 Dv ؞7 BHA G`
- DABA 4 |v ٞ7 ã BHA G`
- DABA < v ڞi BHA J
- DABA < v ݞi BHA J
- DABA < 4w @ߞ BHA J
- DABA < tw x BHA JO
- DABA < w p j BHA JO
- DABA < w 0T \ BHA J
- DABA 4 4x P) M AHJ
-DAA 4 lx Hk ; BHA GP
- DABA 4 x k BHA GP
- DABA 4 x k BHA GP
- DABA 4 y k ޢ BHA GP
- DABA 4 Ly (k BHA GP
- DABA 4 y `k BHA GP
- DABA 4 y k BHA GP
- DABA 4 y k b BHA GP
- DABA 4 ,z k C BHA GP
- DABA 4 dz @k $ BHA GP
- DABA 4 z xk BHA GP
- DABA 4 z k BHA GP
- DABA 4 { k ǡ BHA GP
- DABA 4 D{ k BHA GP
- DABA 4 |{ Xk BHA GP
- DABA 4 { k j BHA GP
- DABA 4 { k K BHA GP
- DABA 4 $| k , BHA GP
- DABA 4 \| 8k
BHA GP
- DABA 4 | pk BHA GP
- DABA < t} BBA A(G@a
-(A ABBD D } hY BBB A(A0Gj
-0A(A BBBG < T} m BBA A(JE
-(A ABBE d <~ 0B BBB B(A0A8G@x
+G I , { (KJ BAA D0} AAB , { HKU BAA D0H AAB, | xKR BAA D0E AAB, L| KJ BAA D0} AAB L { Kr BBB B(E0A8J@
+8A0A(B BBBA $ | L^ AG b
+AA < d| 0M # BBI A(D@I
+(A ABBA , 4} N> EK
+AF |A d} O2 Ah
+G A } Or Af
+I AL } 0P" BBB J(A0A8G
+8D0A(B BBBE D d} R q BGB D(D0J
+(A BBBD T } xR 8 BBE E(D0A8G/
+8D0A(B BBBJ T ~ U BBE E(D0A8G
+8D0A(B BBBE T \~ W BBE E(A0A8G
+8D0A(B BBBI D D 0Z BBB D(A0D@
+0A(A BBBA 4 ~ [ 9 BDA H
+ABA < 4 [i BHA J
+ DABA < t ^i BHA J
+ DABA < @` BHA J
+ DABA < b BHA JO
+ DABA < 4 pe BHA JO
+ DABA < t 0hT BHA J
+ DABA 4 Pj) AHJ
+DAA 4 Hlk BHA GP
+ DABA 4 $ mk BHA GP
+ DABA 4 \ nk g BHA GP
+ DABA 4 ok H BHA GP
+ DABA 4 ́ (qk ) BHA GP
+ DABA 4 `rk
+ BHA GP
+ DABA 4 < sk BHA GP
+ DABA 4 t tk BHA GP
+ DABA 4 vk BHA GP
+ DABA 4 @wk BHA GP
+ DABA 4 xxk o BHA GP
+ DABA 4 T yk P BHA GP
+ DABA 4 zk 1 BHA GP
+ DABA 4 ă |k BHA GP
+ DABA 4 X}k BHA GP
+ DABA 4 4 ~k BHA GP
+ DABA 4 l k BHA GP
+ DABA 4 k BHA GP
+ DABA 4 ܄ 8k w BHA GP
+ DABA 4 pk X BHA GP
+ DABA 4 L k 9 BHA GP
+ DABA 4 k BHA GP
+ DABA 4 k BHA GP
+ DABA 4 Pk BHA GP
+ DABA 4 , k BHA GP
+ DABA 4 d k BHA GP
+ DABA 4 k BHA GP
+ DABA 4 Ԇ 0k ` BHA GP
+ DABA 4 hk A BHA GP
+ DABA 4 D k " BHA GP
+ DABA 4 | ؐk BHA GP
+ DABA 4 k BHA GP
+ DABA 4 Hk BHA GP
+ DABA 4 $ k BHA GP
+ DABA 4 \ k BHA GP
+ DABA 4 k h BHA GP
+ DABA 4 ̈ (k I BHA GP
+ DABA 4 `k * BHA GP
+ DABA 4 < k BHA GP
+ DABA 4 t Лk BHA GP
+ DABA 4 k BHA GP
+ DABA 4 @k BHA GP
+ DABA 4 xk BHA GP
+ DABA 4 T k p BHA GP
+ DABA 4 衝k Q BHA GP
+ DABA 4 Ċ k 2 BHA GP
+ DABA 4 Xk BHA GP
+ DABA 4 4 k BHA GP
+ DABA 4 l Ȧk BHA GP
+ DABA 4 k BHA GP
+ DABA 4 ܋ 8k BHA GP
+ DABA 4 pk x BHA GP
+ DABA 4 L k Y BHA GP
+ DABA 4 ଝk : BHA GP
+ DABA 4 k BHA GP
+ DABA 4 Pk BHA GP
+ DABA 4 , k BHA GP
+ DABA 4 d k BHA GP
+ DABA 4 k BHA GP
+ DABA 4 ԍ 0k BHA GP
+ DABA 4 hk a BHA GP
+ DABA 4 D k B BHA GP
+ DABA 4 | طk # BHA GP
+ DABA 4 k BHA GP
+ DABA 4 Hk BHA GP
+ DABA 4 $ k BHA GP
+ DABA 4 \ k BHA GP
+ DABA 4 k BHA GP
+ DABA 4 ̏ (k i BHA GP
+ DABA 4 `r J BHA GP
+ DABA 4 < r + BHA GP
+ DABA 4 t r BHA GP
+ DABA 4 8ĝr BHA GP
+ DABA 4 ŝr BHA GP
+ DABA < Ɲ BIA A(G`
+(D ABBA < \ ȝ BIA A(G`
+(D ABBA < hɝ a BIA A(G`
+(D ABBA < ܑ ʝ : BIA A(G`
+(D ABBA < ̝ BIA A(G`
+(D ABBA < \ X͝ BIA A(G`
+(D ABBA < Ν BIA A(G`
+(D ABBA < ܒ ϝ BIA A(G`
+(D ABBA < Hѝ w BIA A(G`
+(D ABBA < \ ҝ P BIA A(G`
+(D ABBA < ӝ ) BIA A(G`
+(D ABBA < ܓ 8՝ BIA A(G`
+(D ABBA < ֝ BIA A(G`
+(D ABBA < \ ם BIA A(G`
+(D ABBA < Hٝ BIA A(G`
+(D ABBA < ܔ ڝ f BIA A(G`
+(D ABBA < ܝ ? BIA A(G`
+(D ABBA < \ hݝ BIA A(G`
+(D ABBA < ޝ BIA A(G`
+(D ABBA < l ( BBA A(G@a
+(A ABBD D Y BBB A(A0Gj
+0A(A BBBG < d h BBA A(JE
+(A ABBE d 4 B BBB B(A0A8G@x
8A0A(B BBBE
-8A0A(B BBBF T } BBE E(D0A8G
-8D0A(B BBBI L T~ M BBB B(A0D8N
-8A0A(B BBBH , ~ > AID W
-DAA L ~ M BBB B(A0D8N
-8A0A(B BBBH , $ > AID W
-DAA L T M u BBB B(A0D8N
-8A0A(B BBBH , > y AID W
-DAA L t [ BGB B(A0D8G
-8A0A(B BBBA L $ s [ BDB B(A0D8G
-8A0A(B BBBA < @l BBE A(A0Y(A BBB < \ pl BBE A(A0Y(A BBB < BEG A(D0M
-(D ABBG L ܁ @ J BBB B(A0A8GP
-8A0A(B BBBK < , @ BHB A(A0n(A BBL L l z BBB B(A0A8GPJ
-8A0A(B BBBK < BHB A(A0~(A BBL < BHB A(A0~(A BBL < < BHB A(A0~(A BBL < | j BEG A(D0A
-(D ABBC < " BEG A(D0J
-(D ABBJ < $ BEG A(D0J
-(D ABBJ < < P' BHB A(A0n(A BBL < | ' BHB A(A0n(A BBL < ' BHB A(A0n(A BBL < @( BHB A(A0n(A BBL < < ( BHB A(A0n(A BBL < | ( BHB A(A0n(A BBL L 0) BBB B(A0A8G
-8A0A(B BBBD L . BBB B(A0A8G
-8A0A(B BBBD L 4 BBE A(G0D
-0D(A BBBE D 7 BBE A(G0D
-0D(A BBBA L L H:b BBE G(A0D4
-0D(A BBBD L h=K BBE A(G0D
-0D(A BBBG L hA] BBE G(A0D
-0D(A BBBB D < xE BBE G(A0D
-0D(A BBBF D @H ݙ BBE A(G0D
-0D(A BBBA D ̇ JS ؙ BEB A(A0Gh
-0A(A BBBA < KG BBA D(Go
-(A ABBA | L BEB B(D0A8Dp
+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 < Ԉ `OH BBA D(Gp
-(A ABBA | pP BEB B(D0A8Dp
+8A0A(B BBBA | 4 `L BEB B(D0A8Dp
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA T S BEB E(A0A8J
-8A0A(B BBBE , \y Ƙ AEG V
-AAA T \] BBB B(A0A8J
-8A0A(B BBBI e AO D e E BBB A(E0J{
-0A(A BBBA T ܊ Xg F BBB B(A0A8J
-8A0A(B BBBH ܋ o AO T T o BBB B(A0A8J
-8A0A(B BBBG T hz AO T ̋ hz BBB B(A0A8J
-8A0A(B BBBE ̌ AO T D b # BBB B(A0H8G
-8A0A(B BBBA L 8G BBB H(A0G
-0A(A BBBA < 8H BBA D(Gp
-(A ABBA | ԍ H BBB E(D0A8D
+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
+8A0A(B BBBA D $ ~' { BBB H(A0G
+0A(A BBBA | BBB E(D0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA T h d BBB B(D0G8GY
-8D0A(B BBBH T T BBB B(A0D8G8
-8A0A(B BBBJ T \ ȝb = BBB B(A0D8G
-8A0A(B BBBA T ࠟ & BBB B(D0G8G
-8D0A(B BBBJ | 8 BBB E(D0A8D
+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
8A0A(B BBBA
-8A0A(B BBBA | 4 X BBB E(D0A8D
+8A0A(B BBBA L l (E BEF D(A0G
+0A(A BBBH | L ( BBB E(D0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA T x BBB E(G0A8G
-8D0A(B BBBG | @ BBB E(D0A8D
+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
8A0A(B BBBA
-8A0A(B BBBA | ` BBB E(D0A8D
+8A0A(B BBBA L (U BEF D(A0G
+0A(A BBBH 8 BBA A(Mpp
+(D ABBD L
+(D ABBK Q
+(D ABBF
+(D ABBJ L
+(D ABBA | P BBB E(D0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA | BBB E(D0A8D
+8A0A(B BBBA L pE BEF D(A0G
+0A(A BBBH | pŞ BBB E(D0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA | BBB E(D0A8D
+8A0A(B BBBA L ԯ ǞE BEF D(A0G
+0A(A BBBH | ˞ BBB E(D0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA | BBB E(D0A8D
+8A0A(B BBBA L ͞U BEF D(A0G
+0A(A BBBH | ў BBB E(D0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA | ༟ BBB E(D0A8D
+8A0A(B BBBA L t ӞU BEF D(A0G
+0A(A BBBH ı מ } BBA A(Mpp
+(D ABBD L
+(D ABBK Q
+(D ABBF
+(D ABBJ L
+(D ABBA | ܲ ٞ BBB E(D0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA L d U BEB E(D0G
-0A(A BBBF | \ ß BBB E(D0A8D
+8A0A(B BBBA L ̲ (۞E BEF D(A0G
+0A(A BBBH | (ߞ BBB E(D0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA L 4 @ş[ v BEB E(D0G
-0A(A BBBF | , Pɟ BBB E(D0A8D
+8A0A(B BBBA L XE
BEF D(A0G
+0A(A BBBH | | X BBB E(D0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA L p˟_ BEB E(D0G
-0A(A BBBF | ϟ BBB E(D0A8D
+8A0A(B BBBA L l xU BEF D(A0G
+0A(A BBBH | L BBB E(D0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA L ԕ џk r BEB E(D0G
-0A(A BBBF $ ՟ p BBA A(Mpp
+8A0A(B BBBA L < U BEF D(A0G
+0A(A BBBH BBA A(Mpp
(D ABBD L
(D ABBK Q
(D ABBF
(D ABBJ L
-(D ABBA | T ֟ BBB E(D0A8D
+(D ABBA | BBB E(D0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA | ԗ ؟ BBB E(D0A8D
+8A0A(B BBBA L E BEF D(A0G
+0A(A BBBH | t BBB E(D0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA | T ۟ BBB E(D0A8D
+8A0A(B BBBA L d E BEF D(A0G
+0A(A BBBH | D BBB E(D0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA | Ԙ (ݟ BBB E(D0A8D
+8A0A(B BBBA L 4 U BEF D(A0G
+0A(A BBBH | 0 BBB E(D0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA < Hߟ BEA A(G@^
-(D ABBA L ߟZ BBB B(A0A8GP2
-8A0A(B BBBC < BEG A(D0
-(D ABBC < $ m BEA G(D0
-(D ABBB < d m BEA G(D0
-(D ABBB < BHB A(A0n(A BBL L < XG BBB H(A0G
-0A(A BBBA L Xk v BEB E(D0G
-0A(A BBBF L ܚ x[ t BEB E(D0G
-0A(A BBBF L , U r BEB E(D0G
-0A(A BBBF L | _ p BEB E(D0G
-0A(A BBBF ̛ n BBA A(Mpp
+8A0A(B BBBA L PU BEF D(A0G
+0A(A BBBH T ` BBA A(Mpp
(D ABBD L
(D ABBK Q
(D ABBF
(D ABBJ L
-(D ABBA < q BEG A(D0
-(D ABBF < < * BEA G(D0
-(D ABBA L Ԝ U BEB E(D0G
-0A(A BBBF L $ [ ~ BEB E(D0G
-0A(A BBBF L t _ | BEB E(D0G
-0A(A BBBF L ĝ
-k z BEB E(D0G
-0A(A BBBF @ x BBA A(Mpp
-(D ABBD L
-(D ABBK Q
-(D ABBF
-(D ABBJ L
-(D ABBA < D X BEG A(D0
-(D ABBF 4 BEG A(D0x(D ABBL [ BEB E(D0G
-0A(A BBBF L d U BEB E(D0G
-0A(A BBBF L k BEB E(D0G
-0A(A BBBF L _ BEB E(D0G
-0A(A BBBF T BBA A(Mpp
-(D ABBD L
-(D ABBK Q
-(D ABBF
-(D ABBJ L
-(D ABBA \ ܠ ![ BAD J
- DABE
- DABB \
- DABA T < # ۉ BBB B(A0D8G
-8A0A(B BBBA L < @(l BEE B(A0A8Dp
-8A0A(B BBBC D `1S { BEB A(A0Gh
-0A(A BBBA L Ԣ x2 BEE B(A0A8D`6
-8A0A(B BBBD D | 8:S
BEB A(A0Gh
-0A(A BBBA L l P; BBB E(D0A8G
-8A0A(B BBBA D =S BEB A(A0Gh
-0A(A BBBA L > BEE B(A0A8D`
-8A0A(B BBBB D HS 1 BEB A(A0Gh
-0A(A BBBA L I} BBE B(A0D8DPp
-8A0A(B BBBB D D PMS Ç BEB A(A0Gh
-0A(A BBBA | 4 hN BBE E(A0A8D@
+(D ABBA d ܹ x # BBA D(J
+(D ABBC r
+(D ABBE r
+(D ABBA < D ڽ BEA A(G@}
+(D ABBJ L Z BBB B(A0A8GP2
+8A0A(B BBBC < d BEG A(D0
+(D ABBC < m BEA G(D0
+(D ABBB < m BEA G(D0
+(D ABBB < $ @ BHB A(A0n(A BBL < d q BEG A(D0
+(D ABBF < * BEA G(D0
+(D ABBA < BEG A(D0
+(D ABBF 4 $ BEG A(D0x(D ABBL \ l BEE B(A0A8Dp
+8A0A(B BBBC D %S BEB A(A0Gh
+0A(A BBBA L & BEE B(A0A8D`6
+8A0A(B BBBD D -S BEB A(A0Gh
+0A(A BBBA L . BBB E(D0A8G
+8A0A(B BBBA D L X1S 9 BEB A(A0Gh
+0A(A BBBA L $ p2 BEE B(A0A8D`
+8A0A(B BBBB D ;S ˹ BEB A(A0Gh
+0A(A BBBA L <} BBE B(A0D8DPp
+8A0A(B BBBB D | @S ] BEB A(A0Gh
+0A(A BBBA | T B BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB T O % BBB B(A0A8G6
-8A0A(B BBBA D d US R BEB A(A0Gh
-0A(A BBBA | T V BBE E(A0A8D@
+8A0A(B BBBA 8A0A(B BBB T D C BBB B(A0A8G
+8A0A(B BBBA D HS BEB A(A0Gh
+0A(A BBBA | t I BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB L Ԧ HXl BEE B(A0A8Dp
-8A0A(B BBBC L $ ha BEE B(A0A8D`6
-8A0A(B BBBD L t (i BBB E(D0A8G
-8A0A(B BBBA L ħ k BEE B(A0A8D`
-8A0A(B BBBF L t} BBE B(A0D8DPp
-8A0A(B BBBB | d x BBE E(A0A8D@
+8A0A(B BBBA 8A0A(B BBB L Jl BEE B(A0A8Dp
+8A0A(B BBBC L D S BEE B(A0A8D`6
+8A0A(B BBBD L [ BBB E(D0A8G
+8A0A(B BBBA L ] BEE B(A0A8D`
+8A0A(B BBBF L 4 0g} BBE B(A0D8DPp
+8A0A(B BBBB | `k BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB T < z BBB B(A0A8G6
-8A0A(B BBBA | < 0 BBE E(A0A8D@
+8A0A(B BBBA 8A0A(B BBB T t l BBB B(A0A8G
+8A0A(B BBBA | \ Xq BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB D Q BBB A(E0J{
-0A(A BBBA L & BEE B(A0A8D
-8A0A(B BBBA L BBB H(A0G<
-0A(A BBBA L (& BEE B(A0A8D
-8A0A(B BBBA L L BBB H(A0G<
-0A(A BBBA L D 8z BBE E(A0A8DP
-8A0A(B BBBA L h BBB H(A0G<
-0A(A BBBA | Y BEE B(A0A8D
+8A0A(B BBBA 8A0A(B BBB < L r BBH D(J
+(A ABBA L u& BEE B(A0A8D
+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
8A0A(B BBBA
-8A0A(B BBBA L x BBB H(A0G=
-0A(A BBBA | BBE E(A0A8DP
+8A0A(B BBBA L l BBB A(H0G
+0A(A BBBA | L h BBE E(A0A8DP
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB L H BBB H(A0G=
-0A(A BBBA | xY BEE B(A0A8D
+8A0A(B BBBA 8A0A(B BBB L < BBB A(H0G
+0A(A BBBA < ؚ ش BBH D(J
+(A ABBA | \ Y BEE B(A0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA L \ X J BBB H(A0G=
-0A(A BBBA | T BBE E(A0A8DP
+8A0A(B BBBA L L r BBB A(H0G
+0A(A BBBA | , X BBE E(A0A8DP
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB L , BBB H(A0G=
-0A(A BBBA | $ 蹠Y BEE B(A0A8D
+8A0A(B BBBA 8A0A(B BBB L BBB A(H0G
+0A(A BBBA | XY BEE B(A0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA L ȼ ڂ BBB H(A0G=
-0A(A BBBA | BBE E(A0A8DP
+8A0A(B BBBA L 8 ij BBB A(H0G
+0A(A BBBA | BBE E(A0A8DP
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB L ̯ à BBB H(A0G=
-0A(A BBBA | İ Ƞ BEE B(A0A8Dp
+8A0A(B BBBA 8A0A(B BBB L m BBB A(H0G
+0A(A BBBA | x BEE B(A0A8Dp
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA L hˠT j BBB H(A0G
-0A(A BBBA | xΠ BBE E(A0A8DP
+8A0A(B BBBA D ' BBB H(A0G
+0A(A BBBA | d BBE E(A0A8DP
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB L l РT BBB H(A0G
-0A(A BBBA d Ӡ BEE B(A0A8D_
+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 D 0٠ BBB H(A0G=
-0A(A BBBA | < `ޠ. BBE E(A0A8DP
+8A0A(B BBBA L $ ğ BBB A(H0G
+0A(A BBBA | `ȟ. BBE E(A0A8DP
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB L J BBB H(A0G=
-0A(A BBBA | @R BEE B(A0A8D
+8A0A(B BBBA 8A0A(B BBB L ʟ BBB A(H0G
+0A(A BBBA | ͟R BEE B(A0A8D
8A0A(B BBBA
8A0A(B BBBA
-8A0A(B BBBA L T BBB H(A0G
-0A(A BBBA | ܴ 0 BBE E(A0A8DP
+8A0A(B BBBA D П' T BBB H(A0G
+0A(A BBBA | ҟ BBE E(A0A8DP
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB L pT BBB H(A0G
-0A(A BBBA $ ) KA|A T , BBB B(D0A8Gm
-8A0A(B BBBA T 0 v BBB B(A0A8G#
-8A0A(B BBBA T ܵ BBB B(D0A8Gm
-8A0A(B BBBA T 4 p p BBB B(A0A8G#
-8A0A(B BBBA 4 4 BBA A(^
- ABBG T Ķ F BBB B(D0A8G.
-8A0A(B BBBD T X J BBB B(A0A8G
-8A0A(B BBBA $ - ADK WDA T * BBB B(A0A8G
-8A0A(B BBBA < " 2 BBE D(Mi
-(D ABBD T 4 ( F BBB B(D0A8G
-8A0A(B BBBA T 80_ J BBB B(A0A8G6
-8A0A(B BBBA T @6_ w BBB B(A0A8G6
-8A0A(B BBBA T < H< BBB B(A0D8G
-8A0A(B BBBI T B BBB B(A0A8G6
-8A0A(B BBBA T G BBB B(A0A8G6
-8A0A(B BBBA T D `M BBB B(A0D8G
-8A0A(B BBBA T Q ހ BBB B(A0J8G1
-8A0A(B BBBA L U BBE E(D0A8Gc
-8A0A(B BBBI L D X4 BEB A(G0GE
-0D(A BBBH T \ π BBB B(A0D8G@
-8A0A(B BBBB D b) BBG D(D
-(D ABBF T 4 g BBB B(A0D8G@
-8A0A(B BBBB T m BBB B(A0D8G}
-8A0A(B BBBE T `u | BBB B(A0D8G}
-8A0A(B BBBE | T T `} T BBB B(A0A8G#
-8A0A(B BBBA T b BBB B(A0A8G#
-8A0A(B BBBA , T g X BBB B(A0A8Gf
-8A0A(B BBBA T t g BBB B(A0A8Gf
-8A0A(B BBBA $ t ؖ -AaA T BBB B(A0A8G6
-8A0A(B BBBA T L h # BEB E(G0A8G
-8D0A(B BBBA T 0 BBB B(A0A8G6
-8A0A(B BBBA T E BEB E(G0A8G
-8D0A(B BBBA L T : BBE B(A0A8J
-8A0A(B BBBI T ` ( BEE B(G0D8GJ
-8D0A(B BBBA DX L " ) BBE B(A0A8J
-8A0A(B BBBD T l BEE B(G0D8GJ
-8D0A(B BBBA l п DX L п BBE B(A0A8J
-8A0A(B BBBJ T 4 0ɡ BEE B(G0D8GH
-8D0A(B BBBA 4 x͡ DX | T x͡ BBE E(A0A8D`t
+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
+8A0A(B BBBA T 5 BBB B(A0A8G6
+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
+8A0A(B BBBA hg, T g BBB B(A0A8G6
+8A0A(B BBBA T Hn > BBB B(A0A8G6
+8A0A(B BBBA $ u -AaA T | v [ BBB B(A0A8G
+8A0A(B BBBA T `{ i BEB E(G0A8G
+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(
+8D0A(B BBBA P DX L P" 2 BBE B(A0A8J
+8A0A(B BBBD T 0 BEE B(G0D8G(
+8D0A(B BBBA h DX L l h BBE B(A0A8J
+8A0A(B BBBJ T Ȣ BEE B(G0D8G-
+8D0A(B BBBA DX | BBE E(A0A8D`t
8A0A(B BBBF {
8A0A(B BBBC
-8A0A(B BBBA T , ֡i BBB B(G0D8G
-8D0A(B BBBA , ء' AJ0WD | L ١ BBE E(A0A8D@
+8A0A(B BBBA < `B T BBD G(G
+(D ABBA p' AJ0WD | BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB T $ `ڡi BBB B(G0D8G
-8D0A(B BBBA $ xܡ' AJ0WD | D ܡ BBE E(A0A8DP
+8A0A(B BBBA 8A0A(B BBB < ౠB BBD G(G
+(D ABBA d ' AJ0WD | BBE E(A0A8DP
8A0A(B BBBI
8A0A(B BBBE
-8A0A(B BBBK T i BBB B(G0D8G
-8D0A(B BBBA ' AJ0WD | < o BBE E(A0A8D@,
+8A0A(B BBBK < t pB Ю BBD G(G
+(D ABBA D ' AJ0WD | d o BBE E(A0A8D@,
8A0A(B BBBF K
8A0A(B BBBK s
-8A0A(B BBBC L S~ BBB G(D0GF
-0D(A BBBA `' AJ0WD | , p BBE E(A0A8D@
+8A0A(B BBBC T T BBB B(G0D8G-
+8D0A(B BBBA < Š' AJ0WD | \ Š BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB T i } BBB B(G0D8G
-8D0A(B BBBA ' AJ0WD | $ BBE E(A0A8DP
+8A0A(B BBBA 8A0A(B BBB < L (ǠB k BBD G(G
+(D ABBA 8Ƞ' AJ0WD | < HȠ BBE E(A0A8DP
8A0A(B BBBJ
8A0A(B BBBE
-8A0A(B BBBK T i } BBB B(G0D8G
-8D0A(B BBBA ' AJ0WD | BBE E(A0A8DP
+8A0A(B BBBK < , ϠB BBD G(G
+(D ABBA Р' AJ0WD | Рd
+ 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 A
8A0A(B BBBE
-8A0A(B BBBI T i ^| BBB B(G0D8G
-8D0A(B BBBA ' AJ0WD | BBE E(A0A8D@
+8A0A(B BBBI < HB % BBD G(G
+(D ABBA X' AJ0WD | h BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB T x
-i { BBB B(G0D8G
-8D0A(B BBBA ' AJ0WD | BBE E(A0A8DP
+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 T 0i z BBB B(G0D8G
-8D0A(B BBBA H' AJ0WD | X\
+8A0A(B BBBK < xB BBD G(G
+(D ABBA | ' AJ0WD | \
BBE E(A0A8D`
8A0A(B BBBG w
8A0A(B BBBG
-8A0A(B BBBH T 8 i *z BBB B(G0D8G
-8D0A(B BBBA P"' AJ0WD | `" BBE E(A0A8DP
+8A0A(B BBBH < xB ߨ BBD G(G
+(D ABBA \ ' AJ0WD | | BBE E(A0A8DP
8A0A(B BBBG
8A0A(B BBBF
-8A0A(B BBBI T *i ny BBB B(G0D8G
-8D0A(B BBBA ,' AJ0WD | , BBE E(A0A8D@
+8A0A(B BBBI < l B BBD G(G
+(D ABBA < ' AJ0WD | \ BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB T 8.r x BBB B(G0D8G!
-8D0A(B BBBA `0' AJ0WD | p0 BBE E(A0A8D@
+8A0A(B BBBA 8A0A(B BBB < L h9 [ BBD G(G
+(D ABBA h' AJ0WD | < x BBE E(A0A8D@
8A0A(B BBBA
-8A0A(B BBBA 8A0A(B BBB T 1r w BBB B(G0D8G!
-8D0A(B BBBA 3' AJ0WD L 4 BEE B(A0A8D`
-8A0A(B BBBE D h;U jw BBD G(G
-(D ABBA | <' AJ0WD L <- BEE B(A0A8Dp
-8A0A(B BBBH D D pEU v BBD G(G
-(D ABBA 4 F' AJ0WD | T F BBB B(A0D8GP
+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 BBBB ;
8A0A(B BBBK
-8A0A(B BBBJ T , (Jr v BBB B(G0D8G!
-8D0A(B BBBA , PL' AJ0WD L L `L BEE B(A0A8D`
-8A0A(B BBBE T Ur zu BBB B(G0D8G!
-8D0A(B BBBA W' AJ0WD L W BEE B(A0A8D`
-8A0A(B BBBE D H_U t BBD G(G
-(D ABBA ``' AJ0WD L p`- BEE B(A0A8Dp~
-8A0A(B BBBD D t PiU Tt BBD G(G
-(D ABBA d hj' AJ0WD | xjw BBB B(A0D8GP
+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 BBBF
8A0A(B BBBH
-8A0A(B BBBJ T \ xmr s BBB B(G0D8G!
-8D0A(B BBBA \ o' AJ0WD L | oq BEE B(A0A8D`d
-8A0A(B BBBF T $ wr r BBB B(G0D8G!
-8D0A(B BBBA $ z' AJ0WD L D z BEE B(A0A8D`
-8A0A(B BBBE D xU rr BBD G(G
-(D ABBA ' AJ0WD L - BEE B(A0A8Dp
-8A0A(B BBBH D U q BBD G(G
-(D ABBA ' AJ0WD | w BBB B(A0D8GP
+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
8A0A(B BBBK
-8A0A(B BBBJ T r q BBB B(G0D8G!
-8D0A(B BBBA Б' AJ0WD L a BEE B(A0A8D`\
-8A0A(B BBBF T T r p BBB B(G0D8G!
-8D0A(B BBBA T (' AJ0WD L t 8 BEE B(A0A8D`
-8A0A(B BBBE D U o BBD G(G
-(D ABBA ' AJ0WD L , BBB B(D0A8J
-8A0A(B BBBF D @U \o BBD G(G
-(D ABBA X' AJ0WD H3. K^ P9 Hp p 4 h@ Hs D n BBE A(A0
-(A BBBA L AAG ]
+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 ]
CAF [
ACA R
-CAJ JCA L\\ AG RAT d m BBB B(D0A8J
-8A0A(B BBBJ T H In BBB B(D0A8N
-8A0A(B BBBC , Ģ> n AID W
-DAA , D Ģ> n AID W
-DAA , t Ģ> en AID W
-DAA D L H[ BBB D(A0D@0A(A BBB T Ģ m BIB E(A0A8DF
-8D0A(B BBBA T D Ȣ m BIB E(A0A8D6
-8D0A(B BBBA T ͢ m BIB E(A0A8DF
-8D0A(B BBBA D `Ѣ8 BBB B(A0A8$0A(B BBB T < XҢ m BBB G(G0A8JD
-8D0A(B BBBH < /. K^ T l T r n BBA I(Dp
+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
(D ABBH L
-(D ABBA T 4 r m BBA I(Dp
+(D ABBA T @r BBA I(Dp
(D ABBH L
-(D ABBA T 81 m BEE E(D0A8GY
-8D0A(B BBBE D Y BBB D(A0D@0A(A BBB T , R )m BIE B(A0A8DA
-8D0A(B BBBA T L l BIE B(A0A8D9
-8D0A(B BBBA T l BBB H(A0A8L
-8D0A(B BBBA ,. K^ $ x < p T h l ` X ` h p x , l [ ;l AID t
-DAA , [ l AID t
-DAA , [ k AID t
-DAA , [ k AID t
-DAA , , H [ k AID t
-DAA , \ x [ k AID t
-DAA , [ {k AID t
-DAA , [ [k AID t
-DAA L
r ;k BBB B(E0A8J@
-8A0A(B BBBA LW\ AG RAL >
BEB B(A0A8G
8A0A(B BBB < T BEB D(A0M
-(A BBBF T Pj BBE E(A0A8G
-8D0A(B BBBF T D j BBE E(A0A8G
-8D0A(B BBBF T ! i BBE E(A0A8G
-8D0A(B BBBF T $ i BBE E(A0A8G
-8D0A(B BBBF D & BBB D(A0D@
-0A(A BBBA D ( i BIB A(D0D`+
-0D(A BBBA D * h BIB A(D0D`+
-0D(A BBBA D $ p- h BIB A(D0D`+
-0D(A BBBA D l (0 h BIB A(D0D`+
-0D(A BBBA D 2 h BIB A(D0D`+
-0D(A BBBA D 5 jh BIB A(D0D`+
-0D(A BBBA D D P8 Hh BIB A(D0D`2
-0D(A BBBA D ; &h BIB A(D0D`2
-0D(A BBBA \ | =m BBD D(J@
-(A ABBD T
-(A ABBF _
-(A ABBK d >?
+(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 ?
BBB B(A0A8GP
8G0A(B BBBP ^
-8A0A(B BBBH T H <g BEB B(A0A8G
-8A0A(B BBBI f A
-J AT f| kh BDB E(A0A8J
-8A0A(B BBBB T l l| h BDB E(A0A8J
-8A0A(B BBBB T r| h BDB E(A0A8J
-8A0A(B BBBB T y| h BDB E(A0A8J
-8A0A(B BBBB T t @| h BDB E(A0A8J
-8A0A(B BBBB T h| i BDB E(A0A8J
-8A0A(B BBBB !. K^ x p h , p 4 x h BAI a
-ABA 4 h BAI a
-ABA N\ AG RA\ , ( Yh BBB A(A0Dr
-0C(A BBBH V
-0C(A BBBH < 4 ND BBD A(D@0(A ABB T h g BIB B(A0D8Df
-8D0A(B BBBA T $ Г g BIB B(A0D8Df
-8D0A(B BBBA T | 8
- g BGB B(A0A8J
-8A0A(B BBBF T h BHB B(A0A8J
-8D0A(B BBBE . K^ p h , t p[ h AID t
-DAA L M\ AG RA< l ND BBD A(D@0(A ABB T @ g BIB B(A0D8Df
-8D0A(B BBBA T \ dg BDB B(A0A8J
-8A0A(B BBBF \ . K^ t ţ ţ ţ ţ ţ ţ , \ ţ[ 0h AID t
-DAA , ƣ[ h AID t
-DAA , Hƣ[ g AID t
-DAA D 8M BBB D(A0D@0A(A BBB T 4 0ƣL g BIE B(A0A8D9
-8D0A(B BBBA T (ˣL Vg BIE B(A0A8D9
-8D0A(B BBBA T УL $g BIE B(A0A8D9
-8D0A(B BBBA T < գ f BDH B(A0D8Gl
-8D0A(B BBBA T `٣ #g BDH B(A0D8Gd
-8D0A(B BBBA T ݣ Sg BDH B(A0D8Gd
-8D0A(B BBBA `. K^ , [ ;g AID t
-DAA D d 6L BBB D(A0D@0A(A BBB T ; f BIB E(A0A8G(
-8D0A(B BBBA D \ f BBE A(H0D
-0A(A BBBA < b f BBA D(K
-(A ABBA L @ f BBE E(A0D8Gx
-8A0A(B BBBD T 4 , h BDB B(D0A8M/
-8D0A(B BBBH 4 H. K^ L ` d X | P X K\ AG RAD $ @ g BBB A(I0Dp
-0D(A BBBG D l g BBB A(I0Dp
-0D(A BBBG D 0 g BBB A(I0Dp
-0D(A BBBG D yg BBB A(I0Dp
-0D(A BBBG D D Sg BBB A(I0Dp
-0D(A BBBG D -g BBB A(I0Dp
-0D(A BBBG D g BBB A(I0Dp
-0D(A BBBG D f BBB A(I0Dp
-0D(A BBBG D J BBB D(A0D@
-0A(A BBBA D sf BIB A(D0D`2
-0D(A BBBA T Qf BIB E(A0A8DN
-8D0A(B BBBA D L $ f BIB A(D0D`7
-0D(A BBBA D & e BIB A(D0D`>
-0D(A BBBA D ) e BIB A(D0D`7
-0D(A BBBA T $ ,L e BIE B(A0A8D9
-8D0A(B BBBA T | x1 e BIB E(A0A8DF
-8D0A(B BBBA T 5L Ue BIE B(A0A8D9
-8D0A(B BBBA T , : #e BBB G(D0A8M
-8D0A(B BBBH T @@ ke BBB G(D0A8M
-8D0A(B BBBH (. K^ E- Ak T E {e BBE B(A0H8G
-8A0A(B BBBD . K^ D , pQ8 BBB B(A0A8
-0A(B BBBA D t hR8 BBB B(A0A8
-0A(B BBBA D `SB BBB B(A0A8
-0A(B BBBA D hTB BBB B(A0A8
-0A(B BBBA L pU d hU | `U XU PU XU `U hU L pU AAG e
+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
CAF S
ACA R
-CAJ JCA L \ U ADD e
+CAJ JCA L ADD e
CAF [
ACI R
-CAJ JCA L `V ADD e
+CAJ JCA L 8 ADD e
CAF [
ACI R
-CAJ JCA L V AAG e
+CAJ JCA L l AAG e
CAF S
ACA R
-CAJ JCA L L PW ADD e
+CAJ JCA L ( ADD e
CAF [
ACI R
-CAJ JCA L W ADD e
+CAJ JCA L ADD e
CAF [
ACI R
-CAJ JCA L PX AAG e
+CAJ JCA L \ ( AAG e
CAF S
ACA R
-CAJ JCA L < X ADD e
+CAJ JCA L ADD e
CAF [
ACI R
-CAJ JCA L @Y ADD e
+CAJ JCA L ADD e
CAF [
ACI R
-CAJ JCA L Y AAG e
+CAJ JCA L L AAG e
CAF S
ACA R
-CAJ JCA L , 0Z ADD e
+CAJ JCA L ADD e
CAF [
ACI R
-CAJ JCA L | Z ADD e
+CAJ JCA L ADD e
CAF [
ACI R
-CAJ JCA 4 0[ BEA A(X
- ABBJ 4 \ BEA A(X
- ABBJ , < \ BDA
-ABD , l ] BDA z
-ABD < ^q _ BBA A(D0[
-(A ABBA T 4 ^n ^ BBB B(A0D8N+
-8A0A(B BBBH , c> ^ AID W
-DAA L cY ^ BBB E(A0A8N
-8A0A(B BBBK , f> ^ AID W
-DAA T < f ^ BLB E(A0A8G
-8A0A(B BBBA , `jZ ^ BHI s
-ABA , j> ^ AID W
-DAA , jZ u^ BHI s
-ABA , $ j> U^ AID W
-DAA D j BBB D(A0D@
-0A(A BBBA L 8l ] BIB B(A0D8DpE
-8D0A(B BBBA L o ] BIB B(A0D8DpE
-8D0A(B BBBA L < q" ] BIB B(A0D8DpL
-8D0A(B BBBA L t" o] BIB B(A0D8DpL
-8D0A(B BBBA T w E] BEE E(D0A8Gn
-8D0A(B BBBH T 4 z| ] BEB E(A0D8JR
-8D0A(B BBBD D G] BFD JPs
+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
DABH F
- DABA \ ] BBG A(D0J
+ DABA \ \! hE n BBG A(D0J
0D(A BBBJ
-0D(A BBBF L 4 Ќ \ BBB B(A0D8M
-8D0A(B BBBE T \ BEB B(D0G8G
-8A0A(B BBBK T \ BEB B(D0G8G
-8A0A(B BBBK L 4
- \ BBB B(A0D8M
-8D0A(B BBBG < , BDA k
+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
ABK @
-ABA L l ࡤQ BBB B(A0A8D
-8A0A(B BBBH L Q BBB B(A0A8D
-8A0A(B BBBH L f BBB B(A0A8GM
-8A0A(B BBBH L \ f BBB B(A0A8GM
-8A0A(B BBBH D @ BHB B(A0A8DP8A0A(B BBBD BHB B(A0A8DP8A0A(B BBBD <
BEE B(A0A8DP8A0A(B BBBD
BEE B(A0A8DP8A0A(B BBB,
P EG
+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
AE o
-AH D
@ BHE E(D0A8DP8A0A(B BBBL D = BHE E(A0A8D@8A0A(B BBB L BBB B(A0A8G
-8A0A(B BBBH , EG
+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
AE o
-AH D BHE E(D0A8DP8A0A(B BBBL \ M BHE E(A0A8D@(8A0A(B BBB L ¤ BBB B(A0A8G
-8A0A(B BBBH < ɤ FDK
+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
ABB w
-ABF L < ɤ BHE E(D0A8DP
-8A0A(B BBBK L `ʤ@ BHE E(A0A8D@
-8A0A(B BBBA L P̤ BBB B(A0A8G
-8A0A(B BBBA < , Ҥ FDK
+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
ABB w
-ABF L l Ӥ BHE E(D0A8DP
-8A0A(B BBBK L @Ԥ@ BHE E(A0A8D@
-8A0A(B BBBA L 0֤ BBB B(A0A8G
-8A0A(B BBBA \ . K^ t ܤ ܤ ܤ ܤ ܤ ܤ ܤ ܤ 4 ܤ L ܤ , ܤ[ T AID t
-DAA , ܤ[ T AID t
-DAA , ݤ[ sT AID t
-DAA , L 8ݤ[ ST AID t
-DAA , | hݤ[ 3T AID t
-DAA D T p/ BBB D(A0D@0A(A BBB T Pݤ S BIB E(A0A8D6
-8D0A(B BBBA T L S BIB E(A0A8D6
-8D0A(B BBBA T gS BIB E(A0A8D6
-8D0A(B BBBA T 5S BIB E(A0A8D6
-8D0A(B BBBA T T 0 S BIB E(A0A8D6
-8D0A(B BBBA T h R BEE E(D0A8G*
-8D0A(B BBBD T R BDB B(A0D8G&
-8A0A(B BBBA T \ hU R BDE H(A0A8G$
-8D0A(B BBBA T p /S BDE H(A0A8G$
-8D0A(B BBBA T (
U [S BDE H(A0A8G$
-8D0A(B BBBA T d 0 S BDE H(A0A8G,
-8D0A(B BBBA d . K^ | D b S BBB A(D0Kp
-0A(A BBBJ , L > xS AID W
-DAA D $ n, BBB D(A0D@0A(A BBB T L S BIE B(A0A8D9
-8D0A(B BBBA L "l R BBE B(F0G8G
-8D0A(B BBBI X. K^ , ) D ) , \ )0 AF
FK
- D T *| R BBE E(D0A8G
-8D0A(B BBBE T < /| R BBE E(D0A8G
-8D0A(B BBBE D < $, BBB D(A0D@0A(A BBB T 4 ?R BIB E(A0A8GJ
-8D0A(B BBBA T 4 `: %R BBE B(D0A8G
-8D0A(B BBBG T (?E
- R BBB E(A0A8N
-8A0A(B BBBA , I> R AID W
-DAA T 0I. sR BBB B(A0F8J
-8A0A(B BBBB T l Q R BBB B(A0F8J
-8A0A(B BBBF T X SS BBB H(F0A8J
-8D0A(B BBBD T (^2 S BBE G(A0G8J
-8D0A(B BBBD T t d S BBB B(A0D8L
-8A0A(B BBBG T j #T BGB B(A0D8J
-8A0A(B BBBA T $ q gT BBB G(A0D8M
-8D0A(B BBBB $ x. K^ D < x BBE A(A0D@
-0A(A BBBA D ({ BBE A(A0D@
-0A(A BBBA D b) BBB D(A0D@0A(A BBB T l }u S BIB E(A0A8G=
-8D0A(B BBBA T t S BBE B(A0G8L-
-8D0A(B BBBG < ، S BLD D(G0
-(A ABBE < \ * S BED I(KPc
-(D ABBE < S BLD D(G0
-(A ABBE < H* S BED I(KP_
-(D ABBI T ! 8- S BBM B(A0D8G
-8A0A(B BBBD , t! > CT AID W
-DAA L" . K^ d" 0 Hg " 7 Hj " 80 Hg " H7 Hj " h0 Hg # x7 Hj D $# BBE A(A0D@g
-0A(A BBBA D l# BBE A(A0D@g
-0A(A BBBA D # BBE A(A0D@g
-0A(A BBBA D # 0 BBE A(A0D@o
-0A(A BBBA D D$ ȥ BBE A(A0D@o
-0A(A BBBA D $ ` BBE A(A0D@o
-0A(A BBBA L ,$ Q BBJ B(A0A8G
-8D0A(B BBBD L |$ X Q BBJ B(A0A8G
-8D0A(B BBBD L $ qQ BBJ B(A0A8G
-8D0A(B BBBD L % \Q BBB B(A0I8G
-8D0A(B BBBD L l% h GQ BBB B(A0I8G
-8D0A(B BBBD L % 2Q BBB B(A0I8G
-8D0A(B BBBD D & H$ BBB D(A0D@0A(A BBB T T& P BIB E(A0A8DV
-8D0A(B BBBA T & P BIB E(A0A8DV
-8D0A(B BBBA T ' p qP BIB E(A0A8DV
-8D0A(B BBBA T \' ?P BIB E(A0A8DV
-8D0A(B BBBA T ' ƥ
P BIB E(A0A8DV
-8D0A(B BBBA T ( xʥ O BIB E(A0A8DV
-8D0A(B BBBA T d( Υ O BBB E(F0A8My
-8D0A(B BBBA T ( hӥ O BBB E(F0A8Mx
-8D0A(B BBBA T ) ץ O BBG B(A0A8J
-8A0A(B BBBA T l) 8ݥ #P BBB B(D0F8G2
-8A0A(B BBBK T ) GP BBB B(D0F8G1
-8A0A(B BBBD T * kP BBB B(D0A8L6
-8A0A(B BBBG + . K^ 4+ 80 Hg T+ H7 Hj T * h 7P BBB J(A0A8G?
-8D0A(B BBBK T $+ P BEE E(D0A8G
-8D0A(B BBBH T |+ X P BBE B(D0A8G
-8D0A(B BBBG D |, N BBB D(A0D@0A(A BBB T , O BIB E(A0A8G
-8D0A(B BBBA T t, P 5 {O BBB B(F0A8G
-8A0A(B BBBE t- . K^ - > Hu - @ E Hx - \ AG RAT D- P
- cP BBB B(D0A8N
-8A0A(B BBBD , - &> P AID W
-DAA D t. BBB D(A0D@0A(A BBB T . & FP BIB E(A0A8D
-8D0A(B BBBA T l. (,u P BBE E(A0A8GV
-8D0A(B BBBF T . P2U [P BBE B(D0A8GH
-8D0A(B BBBD T / X8 P BBB G(D0A8M
-8D0A(B BBBE 0 @. K^ $ 40 ; ADD0AA$ \0 P< ADD0AA$ 0 < ADD0AA$ 0 `= ADD0AA$ 0 = ADD0AA$ 0 p> ADD0AAD $1 >1 BBE A(A0D@
-0A(A BBBA D l1 ?> BBB D(A0D@
-0A(A BBBA D 1 @F BBB D(A0D@
-0A(A BBBA D 1 AN BBB D(A0D@
-0A(A BBBA $ D2 B^ AG b
-AA L 1 0C2 [N BGB B(A0K8U
-8A0A(B BBBA < 2 D #N BQA A(N`
-(A ABBA < T2 D M BQA A(N`
-(A ABBA < 2 `E M BQA A(N`
-(A ABBA L 2 F2 M BGB B(A0K8U
-8A0A(B BBBA < $3 F aM BQA A(N`
-(A ABBA < d3 G 3M BQA A(N`
-(A ABBA D L4 0H BBB D(A0D@
-0A(A BBBA < 3 I L BHA J
- DABA < ,4 K L BHA J
- DABA < l4 HN L BHA J
- DABA < 4 P L BHA J
- DABA < 4 S L BHA J
- DABA < ,5 hU rL BHA J
- DABA < l5 W cL BHA J
- DABA < 5 (Z TL BHA J
- DABA D 6 \ BBE A(A0D@l
-0A(A BBBA $ 6 @]i AAG0]AA$ 7 ]q AAG0eAAD ,7 ] BBE A(A0D@l
-0A(A BBBA $ t7 x^| AAG0pAA 7 ^i AG _A 7 _q AG gA$ 7 _ AAG0xAAT \7 _ J BEJ E(D0D8G
-8D0A(B BBBG T 7 cR J BBB I(A0A8JN
-8A0A(B BBBA , 8 j> J AID W
-DAA \ 8 j BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBL 8 k 7J BBB E(A0G8L
-8D0A(B BBBA < 8 o ;J BGB D(A0~
-(A BBBK T ,9 pM
- #J BBB M(A0D8Gg
-8A0A(B BBBA , 9 z> cJ AID W
-DAA T 9 z CJ BBB E(A0H8K
-8A0A(B BBBA , : > iJ AID W
-DAA T <: KJ BBB E(I0A8K}
-8D0A(B BBBF T : #J BBB B(A0H8JH
-8A0A(B BBBA , : X> JJ AID W
-DAA L ; h +J BEB E(I0H8DP
-8D0A(B BBBD L l; ( J BEB E(I0H8D@
-8D0A(B BBBA T ; ( I BBB I(A0A8J
-8A0A(B BBBA , < > J AID W
-DAA T D< I BBB E(I0A8K}
-8D0A(B BBBF T < ȘF I BBB B(A0D8N
-8A0A(B BBBA , < > I AID W
-DAA L $= Р I BEB E(I0H8D@
-8D0A(B BBBA T t= PL I BBI B(A0A8J.
-8A0A(B BBBA , = H> I AID W
-DAA \ > X BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBT \> CI BBB M(A0A8J
-8A0A(B BBBA , > 0> I AID W
-DAA < > @H kI BBA A(Gp
-(D ABBA < $? PH OI BBA A(Gp
-(D ABBA L d? `l 3I BEB B(G0A8Ip
-8D0A(B BBBH < ? H H BBA A(Gp
-(D ABBA l ? H BBB E(A0F8DQ
+ABF L ) BHE E(D0A8DP
+8A0A(B BBBK L ,* @ BHE E(A0A8D@
+8A0A(B BBBA L |* BBB B(A0A8G
+8A0A(B BBBA * 81. K^ * * x + p ,+ h D+ ` \+ X t+ ` + h + p + x , D+ [ AID t
+DAA , t+ [ AID t
+DAA , + [ σ AID t
+DAA , + [ AID t
+DAA , , @[ AID t
+DAA D , rb BBB D(A0D@0A(A BBB T |, ( ' BIB E(A0A8D6
+8D0A(B BBBA T , ` BIB E(A0A8D6
+8D0A(B BBBA T ,- Â BIB E(A0A8D6
+8D0A(B BBBA T - Ф BIB E(A0A8D6
+8D0A(B BBBA T - _ BIB E(A0A8D6
+8D0A(B BBBA T 4. @ - BEE E(D0A8G*
+8D0A(B BBBD T . ز BDB B(A0D8G&
+8A0A(B BBBA T . @U BDE H(A0A8G$
+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 < d@ 0H H BBA A(Gp
-(D ABBA L @ @e eH BEB B(G0A8Ip
-8D0A(B BBBC < @ `H 'H BBA A(Gp
-(D ABBA l 4A p@ H BEB B(A0A8I
+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 A @^ G BBB E(A0F8G
-8A0A(B BBBA < A HH oG BBA A(Gp
-(D ABBA L <B Xg SG BEB B(G0A8Ip
-8D0A(B BBBE T B x G BBH E(A0F8DG
-8D0A(B BBBH C Ύ. K^ C ¦ C ¦ , C ¦b D\
+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\
H P
H Q
-G I , D X¦b D\
+G I , t\ }b D\
H P
H Q
-G I , 4D ¦b D\
+G I , \ ~b D\
H P
H Q
-G I , dD ¦ AJ@
-AG MA D . \ AG RAD D Xæ BBE A(A0D@
-0A(A BBBA D D Ħ BBE A(A0D@
-0A(A BBBA T D Ħ E BEB B(H0A8G?
-8A0A(B BBBD , D pզ> E AID W
-DAA T $E զ kE BEE E(D0A8G
-8D0A(B BBBE D $F
- BBB D(A0D@0A(A BBB T E `ڦ E BIB E(A0A8GZ
-8D0A(B BBBA $ F ߦ D AL0p
-DA $ DF D AL0p
-DA $ lF D AL0p
-DA d F
D KGB B(A0A8R8A0A(B BBBG T F 8G D BBB B(A0A8G
-8A0A(B BBBK T TG 0 D BBH B(F0A8J
-8D0A(B BBBG TH hɎ. K^ lH `> Hu H E Hx H @
-\ AG RAT $H
- {E BBB B(D0A8N
-8A0A(B BBBJ , |H > E AID W
-DAA D TI BBB D(A0D@0A(A BBB T H YE BIB E(A0A8D6
-8D0A(B BBBA T LI L 'E BBB E(F0A8Gj
-8A0A(B BBBC LJ ǎ. K^ dJ 9 |J 9 T I 9, E BBE B(D0A8G
-8D0A(B BBBK D J *
- BBB D(A0D@0A(A BBB T J x>& -E BIB E(A0A8G
-8D0A(B BBBA L J PC E BBB J(A0A8G
-8D0A(B BBBH T 4K G E BBE B(D0A8G
-8D0A(B BBBG T K K D BBE B(D0A8G
-8D0A(B BBBH \ L P^ KLY H(D0I8J8A0A(B BBBG \ L S KEJ E(J0I8J8A0A(B BBBG \ LM PU KLQ I(G0I8M8A0A(B BBBI L M W C BHB E(A0F8G
-8D0A(B BBBD L TM cf C BEB G(A0G8G
-8D0A(B BBBD L M l C BEB G(A0A8J
-8D0A(B BBBG L M q C BEB G(A0A8M
-8D0A(B BBBD T DN ye C BBB G(D0G8G
-8D0A(B BBBG DO . K^ \O \ AG RAD |O `}} BBB D(A0D@
-0A(A BBBA D O ~} BBB D(A0D@
-0A(A BBBA D P } BBB D(A0D@
-0A(A BBBA D TP } BBB D(A0D@
-0A(A BBBA D P @ BBB D(A0D@
-0A(A BBBA D P BBB D(A0D@
-0A(A BBBA D ,Q Є BBB D(A0D@
-0A(A BBBA D tQ BBB D(A0D@
-0A(A BBBA < Q D BBD A(D@0(A ABB T TQ 3A BIB E(A0A8D6
-8D0A(B BBBA T Q X A BIB E(A0A8D6
-8D0A(B BBBA T R @ BIB E(A0A8D6
-8D0A(B BBBA T \R ȓ @ BIB E(A0A8D6
-8D0A(B BBBA D \S BBE A(A0D@l
-0A(A BBBA L R #@ BJE B(A0A8D@
-8A0A(B BBBA $ S hV ADD JAAL tS ? BEB E(I0H8D@
-8D0A(B BBBA T S ? BBB B(A0D8L7
-8A0A(B BBBF T T ȟ\ ? BBB B(H0A8J
-8A0A(B BBBG , tT Ъ> @ AID W
-DAA T T ધ\ ? BBB B(H0A8J
-8A0A(B BBBG , T 赧> [@ AID W
-DAA T ,U ;@ BBB B(H0A8J
-8A0A(B BBBG , U > @ AID W
-DAA T U \ {@ BBB B(H0A8J
-8A0A(B BBBG , V ̧> @ AID W
-DAA T <V ̧ @ BBB H(A0D8L
-8D0A(B BBBH T V ѧ @ BBB H(A0D8L
-8D0A(B BBBH T V xէ @ BBB H(A0D8L
-8D0A(B BBBH W `. K^ X ٧> Hu $X ٧> Hu DX ٧> Hu dX ڧ> Hu X 8ڧE Hx X hڧE Hx X ڧE Hx X ڧE Hx Y \ AG RA $Y ڧ_ AF
-I T X ۧ
- ? BEB B(A0A8N
-8A0A(B BBBH , X > ? AID W
-DAA T $Y
- ? BEB B(A0A8N
-8A0A(B BBBH , |Y > ? AID W
-DAA T Y
- ? BEB B(A0A8N
-8A0A(B BBBH , Z > @ AID W
-DAA T 4Z
- ? BEB B(A0A8N
-8A0A(B BBBH , Z h> >@ AID W
-DAA D d[ | BBB D(A0D@0A(A BBB T [ 0 ? BIB E(A0A8DF
-8D0A(B BBBA T \[ x ? BIB E(A0A8DF
-8D0A(B BBBA T [
- r? BIB E(A0A8DF
-8D0A(B BBBA T \ @? BIB E(A0A8DF
-8D0A(B BBBA T d\ P ? BBG E(A0G8GY
-8D0A(B BBBK T \ _? BBG E(A0G8GY
-8D0A(B BBBK T ] P% ? BBG E(A0G8GY
-8D0A(B BBBK T l] .
- ? BBG E(A0G8Go
-8D0A(B BBBE l^ . K^ ^ 8 ^ \ AG RAD ^ 8v BBB A(D0D@
-0A(A BBBA D _ :~ BBB A(D0D@
-0A(A BBBA L L_ P; BBB B(A0D8DPd
-8A0A(B BBBA L _ >K BBB B(A0A8G@d
-8A0A(B BBBA D _ v BBB D(A0D@^0A(A BBB T _ x? > BIB E(A0A8GZ
-8D0A(B BBBA T ` EO BAA G05
+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
AABD
AABD e
- AABO ` V ATT T` Ir
= BEB B(D0A8Je
-8A0A(B BBBA T ` W = BOB B(A0A8G]
-8A0A(B BBBA < a Peg = BBA A(D0V(A ABB T Da e
= BBE E(A0A8J
-8A0A(B BBBA T a r > BBB B(A0D8M
-8D0A(B BBBA T a |! O> BBB B(A0D8N E
-8A0A(B BBBF , Lb x> ? AID W
-DAA $c . K^ <c p Tc hR HI tc Y HL $ c 蝨^ AG b
-AA T c - BDA ]
+ 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 ]
ADG w
ADD Q
ABD I
-ABD T lc > BBB B(D0A8N
-8A0A(B BBBD , c `> ? AID W
-DAA D d : BBB D(A0D@0A(A BBB T <d ( -? BIB E(A0A8D6
-8D0A(B BBBA $ d `G > As
-L A
-A L d r > BBB B(A0A8G~
-8A0A(B BBBG T e u > BBE B(F0A8G
-8A0A(B BBBD f Ь. K^ $f ֨ <f ֨ Tf ֨ lf ֨ f ֨d D L
-H G f ר G t
-E f ר- < 4f רq > BBA A(D0[
-(A ABBA g ب2 AF iA <g 0بi AX
-G \g G HN L tg hب AAG e
+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
CAF C
ACA R
-CAJ JCA L g ب AAG e
+CAJ JCA L AAG e
CAF S
ACA R
-CAJ JCA L h 8٨ AAG e
+CAJ JCA L d p AAG e
CAF C
ACA R
-CAJ JCA L dh ٨ AAG e
+CAJ JCA L Д AAG e
CAF S
ACA R
-CAJ JCA L h ڨ AAG e
+CAJ JCA L @ AAG e
CAF C
ACA R
-CAJ JCA L i hڨ AAG e
+CAJ JCA L T AAG e
CAF S
ACA R
-CAJ JCA L Ti ڨ AAG e
+CAJ JCA L AAG e
CAF C
ACA R
-CAJ JCA L i 8ۨ AAG e
+CAJ JCA L p AAG e
CAF S
ACA R
-CAJ JCA i \ AG RA< j ۨ BLF A(A0
-(H BFEA < Tj (ܨ BLF A(A0
-(H BFEA < j ܨ BLF A(A0
-(H BFEA < j hݨ BLF A(A0
-(H BFEA < k ި BII A(A0
-(A BJBE < Tk ި BII A(A0
-(A BJBE < k Hߨ BII A(A0
-(A BJBE < k ߨ BII A(A0
-(A BJBE D l BBB D(A0D@
-0A(A BBBA L k ;9 BIB A(D0D
-0D(A BBBA L l 0 9 BIB A(D0D
-0D(A BBBA L Tl 8 BIB A(D0D
-0D(A BBBA L l 8 BIB A(D0D
-0D(A BBBA D l 8 BEB A(A0G`l
-0D(A BBBA 4 <m Z 8 BDA G`
- DABJ 4 tm Z f8 BDA G`
- DABJ L m H C8 BBB E(A0A8G
-8D0A(B BBBB L n f BEB A(A0|
+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|
(A BBBJ A
-(A BBBA L Ln 7 BBB B(D0A8D`
-8D0A(B BBBI L Do HK BBB B(A0A8G@d
-8A0A(B BBBA < o H BEB D(A0M
-(A BBBF T ,o R 77 BEE E(D0A8Gj
-8D0A(B BBBD T o R 6 BEE E(D0A8Gj
-8D0A(B BBBD T o R 6 BEE E(D0A8Gj
-8D0A(B BBBD T 4p R 6 BEE E(D0A8Gj
-8D0A(B BBBD L 4q BBB B(A0A8Gp8A0A(B BBB d q h0 BBE B(D0E8D@P
+(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
8F0A(B BBBA
-8D0A(B BBGA \ q 0 KLB B(D0A8DPX8A0A(B BBBJP \ Lr BLE A(A0]
+8D0A(B BBGA \ L Xŧ KLB B(D0A8DPX8A0A(B BBBJP \ ǧ BLE A(A0]
(A BBBG ~
-(A FBBG A(A BBB L r
- BBB B(A0A8Gp8A0A(B BBB r { d s z BBB E(D0E8D@S
+(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 < |s
BEB A(F0z
-(A BBBG 4 s
ADD S
+8D0A(B BBGA < ܋ (˧ BEB A(F0z
+(A BBBG 4 ˧ ADD S
AAB I
-FAH < s ( BBG A(G@
-(A ABBG \ 4t 8 BFD D(G0~
+FAH < T P̧ BBG A(G@
+(A ABBG \ ͧ8 BFD D(G0~
(A ABBE i
-(A ABBA \(F ABB T s 2 BBB B(A0D8J
-8A0A(B BBBD T Dt `) 4 BBB B(A0D8N
-8A0A(B BBBJ , t ?> 95 AID W
-DAA T t ? 5 BBB B(A0D8J
-8A0A(B BBBD T $u Y 6 BBB B(A0D8N
-8A0A(B BBBJ , |u o> o7 AID W
-DAA T u p O7 BBB B(A0D8J
-8A0A(B BBBD T v 8 BBB B(A0D8N
-8A0A(B BBBJ , \v > 9 AID W
-DAA T v ( 9 BBB B(A0D8J
-8A0A(B BBBD T v 0; BBB B(A0D8N
-8A0A(B BBBJ , <w 8Щ> ; AID W
-DAA x HЩ D ,x @ө BBB B(A0A8D@8A0A(B BBBT w ө [; BBB B(D0A8Gj
-8A0A(B BBBA L x @ש BBD A(G0o
+(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 ABBK X
-(A ABBA y ש) d < x ש6 : BBA A(DG
-(A ABBA , x ة : AAG d
-AAA < y 8٩ BDD k
+(A ABBA | ) d < 6 `j BBA A(DG
+(A ABBA , D ?j AAG d
+AAA < 0 BDD k
ABH M
-ABA y ٩ D y ܩ BBB B(A0A8D@8A0A(B BBBT y ܩ : BBB B(D0A8Gj
-8A0A(B BBBA < y 6 : BBA A(DG
-(A ABBA , 4z 9 AAG d
-AAA { D ${ BBB B(A0A8D@8A0A(B BBBT z e9 BBB B(D0A8Gj
-8A0A(B BBBA < { 6 b9 BBA A(DG
-(A ABBA , \{ A9 AAG d
-AAA 4| Q D L| P BBB B(A0A8D@8A0A(B BBBT { 8 BBB B(D0A8Gj
-8A0A(B BBBA < D| P6 8 BBA A(DG
-(A ABBA , | P 8 AAG d
-AAA < \} d BBB A(A0d
-(A BBBE } % t } BBE A(D0D@"
+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@"
0A(A BBBD
0A(A BBBE 7
-0A(A BBBI < } @ 7 BED D(LP
-(A ABBH L } C z7 BBB B(A0A8D*8A0A(B BBB < ~ .7 BED D(LP
-(A ABBH L T~ C 7 BBB B(A0A8D*8A0A(B BBB < ~ 6 BED D(LP
-(A ABBH L ~ `C 6 BBB B(A0A8D*8A0A(B BBB < 4 ` <6 BED D(LP
-(A ABBH L t C 6 BBB B(A0A8D*8A0A(B BBB < l BBG A(G@
-(A ABBD 5 Ā BBE A(A0G@
+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 BBBC
0A(A BBBE
0A(A BBBE R
-0A(A BBBF D r 4 BBA A(G
-(A ABBD T X 4 BBB B(A0D8G
-8A0A(B BBBF T D 4 BEE E(A0A8J
-8A0A(B BBBA L 4 BEE E(A0A8J
-8A0A(B BBBH T x$b 4 BGE H(A0D8Fz
-8D0A(B BBBH D D &r }4 BBA A(G
-(A ABBD T + y4 BBB B(A0D8G
-8A0A(B BBBF T `. R4 BEE E(A0A8J
-8A0A(B BBBA L < 7 K4 BEE E(A0A8J
-8A0A(B BBBH T ;b 74 BGE H(A0D8Fz
-8D0A(B BBBH D >r 4 BBA A(G
-(A ABBD T , 8C 4 BBB B(A0D8G
-8A0A(B BBBF T E 3 BEE E(A0A8J
-8A0A(B BBBA L ܄ N 3 BEE E(A0A8J
-8A0A(B BBBH T , XSb 3 BGE H(A0D8Fz
-8D0A(B BBBH D pUr 3 BBA A(G
-(A ABBD T ̅ Z 3 BBB B(A0D8G
-8A0A(B BBBF T $ @] 3 BEE E(A0A8J
-8A0A(B BBBA L | f {3 BEE E(A0A8J
-8A0A(B BBBH T ̆ jb g3 BGE H(A0D8Fz
-8D0A(B BBBH ̇ @. K^ l G t
-E md D L
-H G < | mq 2 BBA A(D0[
-(A ABBA d n2 AF iA L 8n AAG e
+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
CAF C
ACA R
-CAJ JCA L Ԉ n AAG e
+CAJ JCA L 4 + AAG e
CAF S
ACA R
-CAJ JCA L $ o AAG e
+CAJ JCA L + AAG e
CAF C
ACA R
-CAJ JCA L t ho AAG e
+CAJ JCA L ԡ P, AAG e
CAF S
ACA R
-CAJ JCA L ĉ o AAG e
+CAJ JCA L $ , AAG e
CAF C
ACA R
-CAJ JCA L 8p AAG e
+CAJ JCA L t - AAG e
CAF S
ACA R
-CAJ JCA L d p AAG e
+CAJ JCA L Ģ - AAG e
CAF C
ACA R
-CAJ JCA L q AAG e
+CAJ JCA L - AAG e
CAF S
ACA R
-CAJ JCA L xq AAG e
+CAJ JCA L d `. AAG e
CAF C
ACA R
-CAJ JCA L T q AAG e
+CAJ JCA L . AAG e
CAF S
ACA R
-CAJ JCA L Hr AAG e
+CAJ JCA L 0/ AAG e
CAF C
ACA R
-CAJ JCA L r AAG e
+CAJ JCA L T / AAG e
CAF S
ACA R
-CAJ JCA L D s AAG e
+CAJ JCA L 0 AAG e
CAF C
ACA R
-CAJ JCA L xs AAG e
+CAJ JCA L `0 AAG e
CAF S
ACA R
-CAJ JCA L s AAG e
+CAJ JCA L D 0 AAG e
CAF C
ACA R
-CAJ JCA L 4 Ht AAG e
+CAJ JCA L 01 AAG e
CAF S
ACA R
-CAJ JCA < t BLF A(A0p
-(H BFEA < č (u BLF A(A0p
-(H BFEA < u BLF A(A0p
-(K BFEE < D v BLF A(A0p
-(K BFEE xv g0
-A xw g0
-A Ď RЍ\ AG RA< ЍD BBD A(D@0(A ABB T | x + BIB E(A0A8DF
-8D0A(B BBBA T Ԏ `| + BIB E(A0A8DF
-8D0A(B BBBA T , + BBE B(A0A8J
-8A0A(B BBBB , 0> C, AID W
-DAA T @ #, BBE B(A0A8J
-8A0A(B BBBB , ȫ> , AID W
-DAA D ث BBB B(A0A8D@8A0A(B BBBL , 0 BBB B(A0A8D`
-8A0A(B BBBF D | BBB B(A0A8D@8A0A(B BBBL đ H BBB B(A0A8D`
-8A0A(B BBBG 4 诪 BBA A(D0(A ABBL L `F BBB B(A0A8D`(
-8A0A(B BBBH 4 ` BBA A(D0(A ABBL Ԓ زF BBB B(A0A8D`(
-8A0A(B BBBH T | ش w* BBB B(D0A8Gj
-8A0A(B BBBA T Ԓ t* BBB B(D0A8Gj
-8A0A(B BBBA T , ( q* BBB E(A0A8Is
-8A0A(B BBBA T K* BBB E(A0A8Is
-8A0A(B BBBA , ܓ %* AAG d
-AAA , (ª * AAG d
-AAA , < ª ) AAG d
-AAA D ê BBB B(A0A8D@8A0A(B BBBL \ `ê BBB B(A0A8D`
-8A0A(B BBBF D Ū BBB B(A0A8D@8A0A(B BBBL xŪ BBB B(A0A8D`
-8A0A(B BBBG 4 D Ǫ BBA A(D0(A ABBL | ǪF BBB B(A0A8D`(
-8A0A(B BBBH 4 ̖ ɪ BBA A(D0(A ABBL ʪF BBB B(A0A8D`(
-8A0A(B BBBH T ̪ |' BBB B(D0A8Gj
-8A0A(B BBBA T Ϫ y' BBB B(D0A8Gj
-8A0A(B BBBA T \ XӪ v' BBB E(A0A8Is
-8A0A(B BBBA T ֪ P' BBB E(A0A8Is
-8A0A(B BBBA , ت *' AAG d
-AAA , < X٪ ' AAG d
-AAA , l ٪ & AAG d
-AAA D 8ڪ BBE A(A0G@
+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@
0A(A BBBC
0A(A BBBE
0A(A BBBE R
-0A(A BBBF < $ 9& BEA A(Dh
-(A ABBE T d
- & BEB E(A0A8J
-8A0A(B BBBA T & BEB E(A0A8Jo
-8A0A(B BBBA T 0 % BBB B(D0A8Dd
-8A0A(B BBBA T l % BBB B(D0A8Dd
-8A0A(B BBBA < Ě f% BEA A(Dh
-(A ABBE T ` K% BEB E(A0A8Jo
-8A0A(B BBBA T \ 4% BEB E(A0A8Jo
-8A0A(B BBBA T P % BBB B(D0A8Dd
-8A0A(B BBBA T
$ BBB B(D0A8Dd
-8A0A(B BBBA T d @ $ BBB B(A0D8G
-8A0A(B BBBA T @ l$ BBB B(A0D8G
-8A0A(B BBBA T w
- E$ BBE E(A0A8J_
-8A0A(B BBBA T l w
- B$ BBE E(A0A8J_
-8A0A(B BBBA T ĝ @) ?$ BBB B(A0D8G;
-8A0A(B BBBG T - 6$ BBB B(A0D8G;
-8A0A(B BBBG T t p2w -$ BBE B(A0D8Ja
-8A0A(B BBBA ̞ @" c$ BBB A(A0D
+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 \ 8C # BBB G(D0A8M
-8D0A(B BBBG T Fw $ BBE B(A0D8Ja
-8A0A(B BBBA T" 9$ BBB A(A0D
+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 xW # BBB G(D0A8M
-8D0A(B BBBG q. K^ \ AG RAD ԡ Z BBE A(A0D@u
-0A(A BBBA D P[ BBE A(A0D@}
-0A(A BBBA T [W # BBI B(A0A8J
-8A0A(B BBBA , c> '# AID W
-DAA D & BBB D(A0D@0A(A BBB T c " BIB E(A0A8D
-8D0A(B BBBA \ i KLE B(A0A8DPi8A0A(B BBBIP \ Pk KLE B(A0A8DPi8A0A(B BBBIP l L my BBF D(A0G@I
+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 BBBI w
0A(A BBBA D
-0F(A BBBG d oE BBB B(A0A8DP
+0F(A BBBG d )E BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l | xpB ! BBB J(D0D8D4
+8F0A(B BBBA l p*B ;P BBB J(D0D8D4
8D0A(B BBBC i
-8A0A(B BBBE L Xt BBB B(A0A8J
-8A0A(B BBBA m. K^ O HF V HI T < +! BBE B(D0A8G
-8D0A(B BBBB T < ! BBE B(D0A8G
-8D0A(B BBBB D v BBB D(A0D@^0A(A BBB T BIB E(A0A8D^
-8D0A(B BBBA T } BIB E(A0A8DV
-8D0A(B BBBA T < XG K BGE B(A0A8Gl
-8A0A(B BBBA T P ! BBF B(A0D8N{
-8A0A(B BBBA , ȼ> ! AID W
-DAA , ؼ> ! AID W
-DAA j. K^ м $ ȼ < T ȼ L Ĩ мr F! BBB B(E0A8J@
-8A0A(B BBBA $ ^ AG b
-AA 4 8l ADG b
+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
AAH l
-AAA 4 pl ADG b
+AAA 4 | 8zl ADG b
AAH l
-AAA T s BBB B(A0I8G
-8D0A(B BBBE T b BBB B(A0I8G
-8D0A(B BBBE T \ ī Q BEE E(D0A8G
-8D0A(B BBBA T ʫ 7 BBE B(D0A8G
-8D0A(B BBBH T Ϋ BBE B(D0A8G
-8D0A(B BBBG T d ӫ BEE E(D0A8G
-8D0A(B BBBF T ث BBB B(D0A8G
-8D0A(B BBBD < D BBD A(D@0(A ABB T T . BIB E(A0A8DF
-8D0A(B BBBA T X BIB E(A0A8DF
-8D0A(B BBBA L & BGB E(A0A8G
-8A0A(B BBBA L T 2 BGB E(A0A8G
-8A0A(B BBBA L e. K^ d X DV $ | `^ AG b
-AA $ AG u
-AA $ ̮ 0 AG }
-AA T L 4
" BBB B(D0A8J
-8D0A(B BBBH D ! A BGB D(M0B
-(A BBBK T "e
- BBB B(D0D8J
-8D0A(B BBBK L 0,; BBB B(A0D8DPd
-8A0A(B BBBA $ < /r DS
+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
I H
-E < d 6D BBD A(D@0(A ABB T 8/ h BIB E(A0A8D6
-8D0A(B BBBA T T p3 6 BIB E(A0A8DF
-8D0A(B BBBA T 7| BBB L(A0A8J
-8A0A(B BBBG 4 Jx # BAI H
-ABA 4 < (Kx BAI H
-ABA T t pK BBB B(A0A8G
-8A0A(B BBBK L ̱ P BIE B(A0A8J
-8D0A(B BBBC IJ 8a. K^ $ ܲ Pf^ AG b
-AA $ f AG u
-AA $ , g AG }
-AA T gT D l r BBB D(A0D@0A(A BBB T i BIB B(A0D8Df
-8D0A(B BBBA 4 d nq _ BAI A
-ABA L hnf ; BEB B(A0A8J
+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
-8A0A(B BBBA _. K^ p DV L xr BBB B(E0A8J@
-8A0A(B BBBA $ ^ AG b
-AA $ < AG
-AA $ d AG
-AA , P @ AAG@A
-AAA $ AG M
-AA T < R BBE B(D0A8G
-8D0A(B BBBF < < pD BBD A(D@0(A ABB T Ե Ў BIB E(A0A8D6
-8D0A(B BBBA T , ` BIB E(A0A8DF
-8D0A(B BBBA T Pe . BBB B(A0I8G
-8D0A(B BBBF T ܶ hw BBB B(A0I8G
-8D0A(B BBBD \ ܷ m BBD D(J@
+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@
(A ABBD T
(A ABBF _
-(A ABBK T 6 BBE E(A0C8L
-8A0A(B BBBH Z AK
-L AT Ƚ w BBB E(A0C8J
-8A0A(B BBBK ̬ AN , 0[. K^ D ˬ8 Ho d ˬ? Hr ̬8 Ho 8̬? Hr Ĺ X̬8 Ho x̬? Hr ̬8 Ho $ ̬? Hr D ̬ H BBB A(I0Dp
-0D(A BBBF D `ά # BBB A(I0Dp
-0D(A BBBF D , Ϭ BBB A(I0Dp
-0D(A BBBF D t pѬ BBB A(I0Dp
-0D(A BBBF D d BBB D(A0D@0A(A BBB T Ҭ l BIB B(A0D8Df
-8D0A(B BBBA T \ : BIB B(A0D8Df
-8D0A(B BBBA T ۬ BIB B(A0D8Df
-8D0A(B BBBA T ߬ BIB B(A0D8Df
-8D0A(B BBBA \ d P BBB A(A0GP
+(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
0A(A BBBI L
-0A(A BBBA \ ļ BBB A(A0GL
+0A(A BBBA \ I BBB A(A0GL
0A(A BBBE L
-0A(A BBBA T $ p7 BBE B(A0A8G
-8A0A(B BBBG T | X7 BBE B(A0A8G
-8A0A(B BBBG T Խ @L
- 0 BBE B(A0A8G
-8A0A(B BBBG T , 8L
- BBE B(A0A8G
-8A0A(B BBBG , `U. K^ D \ L ̾ q BBB B(A0I8G
-8D0A(B BBBD L Hq BBB B(A0I8G
-8D0A(B BBBD L l xq | BBB B(A0I8G
-8D0A(B BBBD T g BBH B(D0F8Gl
-8D0A(B BBBH D BBB D(A0D@0A(A BBB T \ k BIE B(A0A8DH
-8D0A(B BBBA T p 9 BIB E(A0A8DO
-8D0A(B BBBA T $ BIB E(A0A8DN
-8D0A(B BBBA R. K^ $ ) < ) T ) BBE E(A0A8G
-8D0A(B BBBE L @q j BBB B(A0I8G
-8D0A(B BBBD D BBB D(A0D@0A(A BBB T BL BIE B(A0A8D9
-8D0A(B BBBA T G BBB B(A0A8J"
-8A0A(B BBBH P. K^ Z $ Z , < Z MPAC , l Z JXAD T [j BBB B(A0D8R
-8A0A(B BBBD , L `> @ AID W
-DAA D $ 6 BBB D(A0D@0A(A BBB T p`L BIE B(A0A8D9
-8D0A(B BBBA L he BBE G(A0G8G
-8D0A(B BBBG O. K^ , m D m < \ mt BBB I(M0F@I0A(A BBBD Pm BBE A(A0
-(A BBBA L m AAG e
+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
CAF s
ACA R
-CAJ JCA T nx BBB B(D0A8N9
-8A0A(B BBBB , 0y> AID W
-DAA D l BBB D(A0D@0A(A BBB T \ xL ] BIE B(A0A8D9
-8D0A(B BBBA T }L + BBB B(A0A8G
-8A0A(B BBBA L. K^ L Ѝ ADD e
+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
CAF
ACA V
-CAF JCA L ADD e
+CAF JCA L G ADD e
CAF
ACA V
-CAF JCA L l 0 ADD e
+CAF JCA L l HH ADD e
CAF
ACA V
-CAF JCA L ADD e
+CAF JCA L H ADD e
CAF
ACA V
-CAF JCA L AAG ]
+CAF JCA L I AAG ]
CAF b
CAJ R
-CAJ JCA D А BBE A(A0
-(A BBBA L 8o BBB B(A0D8D`G
-8A0A(B BBBA L X BBB B(A0D8Dt
-8A0A(B BBBA L D Ȕ BBB B(A0D8G
-8A0A(B BBBF L hr BBB B(A0D8D`I
-8A0A(B BBBA L BBB B(A0D8D}
-8A0A(B BBBH L 4 BBB B(A0D8G
-8A0A(B BBBF < ؟E BBB D(A0#
-(A BBBA L 蠭u BBB B(A0D8DB
-8A0A(B BBBC L BBB B(A0D8G8A0A(B BBB < d xB BBB D(A0#
-(A BBBA L r BBB B(A0D8DB
-8A0A(B BBBC L BBB B(A0D8G8A0A(B BBB L D } BBB B(A0D8D`a8A0A(B BBB L HU BBB B(A0D8D%
-8A0A(B BBBH L X BBB B(A0D8D8A0A(B BBB 4 : AD0sA T (: AD0sA t H: AD0sA h: AD0sA : AD0sA : AD0sA ȳ: AD0sA 賭: AD0sA 4 : AD0sA T (: AD0sA t H: AD0sA h: AD0sA : AD0sA : AD0sA ȴ: AD0sA $ 购M ADG@~AA L < ADD e
+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
CAF k
ACI V
-CAF JCA L AAG ]
+CAF JCA L n AAG ]
CAF b
CAJ R
-CAJ JCA L ൭ ADD e
+CAJ JCA L n ADD e
CAF k
ACI V
-CAF JCA L , p AAG ]
+CAF JCA L , o AAG ]
CAF b
CAJ R
-CAJ JCA L | ADD e
+CAJ JCA L | o ADD e
CAF k
ACI V
-CAF JCA L @ AAG ]
+CAF JCA L Xp AAG ]
CAF b
CAJ R
-CAJ JCA L ADD e
+CAJ JCA L p ADD e
CAF k
ACI V
-CAF JCA L l AAG ]
+CAF JCA L l (q AAG ]
CAF b
CAJ R
-CAJ JCA L P ADD e
+CAJ JCA L hq ADD e
CAF k
ACI V
-CAF JCA L อ ADD e
+CAF JCA L q ADD e
CAF
ACA V
-CAF JCA $ \ ^ AG b
-AA $ AG r
-AA $ @ AG r
-AA $ Ⱥ AG r
-AA $ P AG r
-AA $ $ ػ AG z
-AA $ L p AG z
-AA $ t AG z
-AA $ AG z
-AA $ 8 AG L
-AA D D w BEB A(A0Nj
-0A(A BBBA L Y BEB L(D0A8M
-8A0A(B BBBD < ҭ
BBA I(D@p
-(D ABBA < xӭ ]
BBA I(D@p
-(D ABBA \ \ ԭ, 9
BBB A(I0D@
+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 \ ԭ, BBB A(I0D@
+0D(A BBBA \ , o< BBB A(I0D@
0D(A BBBD W
-0D(A BBBA \ խ, BBB A(I0D@
+0D(A BBBA \ 4 p, 1< BBB A(I0D@
0D(A BBBD W
-0D(A BBBA \ | x֭, BBB A(I0D@
+0D(A BBBA \ @, ; BBB A(I0D@
0D(A BBBD W
-0D(A BBBA \ H, A BBB A(I0D@
+0D(A BBBA \ , ; BBB A(I0D@
0D(A BBBD W
-0D(A BBBA \ < ح, BBB A(I0D@
+0D(A BBBA \ T , w; BBB A(I0D@
0D(A BBBD W
-0D(A BBBA \ ح, BBB A(I0D@
+0D(A BBBA \ , 9; BBB A(I0D@
0D(A BBBD W
-0D(A BBBA \ ٭, BBB A(I0D@
+0D(A BBBA \ , : BBB A(I0D@
0D(A BBBD W
-0D(A BBBA \ \ ڭ, I BBB A(I0D@
+0D(A BBBA \ t P, : BBB A(I0D@
0D(A BBBD W
-0D(A BBBA D d Xۭ BBB D(A0D@
-0A(A BBBA D ܭ
- BIB A(D0D`+
-0D(A BBBA D L h߭
- BIB A(D0D`+
-0D(A BBBA D
- BIB A(D0D`+
-0D(A BBBA D ]
- BIB A(D0D`+
-0D(A BBBA D $ ;
- BIB A(D0D`+
-0D(A BBBA D l H
- BIB A(D0D`2
-0D(A BBBA D BIB A(D0D`2
-0D(A BBBA D BIB A(D0D`2
-0D(A BBBA D D BIB A(D0D`2
-0D(A BBBA L h BIB A(D0D
-0D(A BBBA < r BHA G}
- DABA T U BAA G0
+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
AABA
AABK f
- AABF T t BEE B(G0D8G
-8D0A(B BBBD T h % BBE G(A0G8G.
-8D0A(B BBBF AR AY T d BBE E(A0A8G
-8D0A(B BBBE T
- BEB B(D0C8Jd
-8D0A(B BBBC T
BBB B(D0F8GB
-8A0A(B BBBK T l )
g BBB B(D0F8GB
-8A0A(B BBBK T 7J
BBB B(D0F8G?
-8A0A(B BBBF T Dr B
- BBF G(GP
+ 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
(D ABBI L
-(D ABBK T t @E{
-
- BBB E(G0A8L3
-8D0A(B BBBA t 4. K^ $ PO^ AG b
-AA $ O AG u
-AA $ P AG }
-AA < D BBD A(D@0(A ABB T xP l BIB B(A0D8D
-8D0A(B BBBA 4 Uq : BAI A
-ABA $ HU DL
+(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
A V
-I T T U3 BBE B(A0A8G
-8A0A(B BBBD T X2. K^ l s s T s BBB B(A0D8N
-8A0A(B BBBC , L {> V
- AID W
-DAA D $ BBB D(A0D@0A(A BBB T {L BIE B(A0A8D9
-8D0A(B BBBA T " BBB B(A0A8J
-8A0A(B BBBA 0. K^ 4 L T y
- BBB B(A0I8G>
-8A0A(B BBBG D L BBB D(A0D@0A(A BBB T \
- BIB E(A0A8DF
-8D0A(B BBBA D @d i
- BEI A(D0D
-0D(A BBBA < hr d
- BBH D(J
-(D ABBA D < f
- BBE H(D0D
-0D(A BBBF D P a
- BBE H(D0D
-0D(A BBBF D \
- BBE D(H0D
-0D(A BBBA D W
- BBE D(H0D
-0D(A BBBA D \ h R
- BBE D(H0D
-0D(A BBBA D M
- BBE D(H0D
-0D(A BBBA D H
- BBE D(H0D
-0D(A BBBA D 4 `® C
- BBE D(H0D
-0D(A BBBA D | Ů >
- BBE D(H0D
-0D(A BBBA L ǮV 9
- BBE D(H0D
-0D(A BBBI T ʮ /
- BBB E(G0A8L
+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
-8D0A(B BBBJ +. K^ $ , ^ AG b
-AA D T H BBB D(A0D@
-0A(A BBBA D ٮ BBB D(A0D@%
-0A(A BBBA D < ܮ BBB D(A0Gn
-0D(A BBBE D ޮ BBB D(A0Gn
-0D(A BBBE T x߮ s BEE E(D0A8G2
-8D0A(B BBBD d @ BLB B(A0A8D@c
+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
8A0A(B BBBK s
-8A0A(B BBBC < 4 D BBD A(D@0(A ABB T BIB E(A0A8D6
-8D0A(B BBBA d $ BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d 8 BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB < BBG A(G@
-(A ABBF L ` BHB E(A0A8D`
-8A0A(B BBBE d ' BJE B(A0A8D@
+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 BEB E(I0H8DP
-8D0A(B BBBD < < X; BBA A(JO
-(A ABBA T | X BBE B(A0A8M =
-8A0A(B BBBA , > AID W
-DAA T BBE B(D0A8GR
-8A0A(B BBBE T \ h BBE B(D0A8GS
-8A0A(B BBBD T 0B BBB B(D0A8Jc
-8A0A(B BBBD L (!I
/ BBB B(A0A8G
-8A0A(B BBBA $ 8% AHD0AAL , . AAG e
+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
CAF s
ACA R
-CAJ JCA < P.q / BBA A(D0[
-(A ABBA T .| BBE E(D0A8G
-8D0A(B BBBE T l 3, BBE B(D0A8G
-8D0A(B BBBK T 8 BEE E(D0A8G
-8D0A(B BBBE T =, BBE B(D0A8G
-8D0A(B BBBK T t B BEE E(D0A8G
-8D0A(B BBBE T G q BEE E(D0A8G
-8D0A(B BBBG T $ @M| W BBE E(D0A8G
-8D0A(B BBBE < $ D BBD A(D@0(A ABB T (R& BIB E(A0A8G
-8D0A(B BBBA L W BBB B(A0A8D`8A0A(B BBB L `X BBB B(A0A8D`8A0A(B BBB L \ Y` BBB E(A0A8DP
-8A0A(B BBBA Z AL T $ Z BEE E(D0A8G
-8D0A(B BBBE L | _ BEB B(A0D8O8A0A(B BBB t c T c L BEE B(A0A8G@
-8A0A(B BBBG d Xl BBB B(A0D8DPJ
+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
8A0A(B BBBK
-8A0A(B BBBD T rb BBB G(D0A8M
-8D0A(B BBBC L w BEB E(I0H8D@
-8D0A(B BBBA T L XyJ BBE B(A0A8G
-8A0A(B BBBA L P| BEB E(I0H8D@
-8D0A(B BBBD T } BBB B(A0D8GW
-8A0A(B BBBA T L X>, y BBB I(A0A8J
-8A0A(B BBBA , @> AID W
-DAA | . K^ 8 0 4 > BDA f
-AFE AAB , T AAG q
-AAA L , BBB B(A0D8G@r
-8A0A(B BBBH | \ AG RA< BHH R
+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 , BFL H(A0A8DxK
-8A0A(B BBBI T { BBB B(D0A8Gm
-8D0A(B BBBB L h x BBB B(I0A8G
-8D0A(B BBBE L , b BBB B(I0A8G
-8D0A(B BBBE T | < L BBE B(D0A8G
-8D0A(B BBBB T ů< 2 BBE B(D0A8G
-8D0A(B BBBB T , ʯ< BBE B(D0A8G
-8D0A(B BBBB D , } BBB D(A0D@0A(A BBB T xϯc BIB J(A0A8D
-8D0A(B BBBA T $ ӯr BIB J(A0A8D!
-8D0A(B BBBA T | ׯ[ S BBB B(F0D8Gm
-8A0A(B BBBH < | 1 BBG A(GP
-(A ABBF T BIE B(A0G8G.
-8A0A(B BBBA (. K^ , D \ t L BGB B(A0A8G
-8A0A(B BBBC L ~ BFB B(A0A8G
-8A0A(B BBBD L , ADD e
+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
CAF {
ACI V
-CAF JCA L | p ADD e
+CAF JCA L t pծ ADD e
CAF {
ACI V
-CAF JCA D $ ] BBE A(A0
-(A BBBA {\ AG RAD X BAA GP
+CAF JCA D 4 ֮ 3 BBE A(A0
+(A BBBA ,\ AG RAD X֮ y3 BAA GP
DABK
- DABG D | { BBB D(A0D@0A(A BBB T BIB E(A0A8DV
-8D0A(B BBBA T t % b BIB E(A0A8DV
-8D0A(B BBBA T x)| 0 BBE E(D0A8G
-8D0A(B BBBE T $ . BBE B(D0A8G
-8D0A(B BBBG T | h3 BBB L(A0D8G
-8A0A(B BBBB 4 <q $ BAI A
-ABA T = BBB L(A0D8G
-8A0A(B BBBB 4 d `Fq ( BAI A
-ABA T F BGB B(A0G8J
-8D0A(B BBBE T `X BGB B(A0G8J
-8D0A(B BBBE x. K^ j0 Hg , j7 Hj L 0j0 Hg l @j7 Hj `j0 Hg pj7 Hj j0 Hg j7 Hj j0 Hg , j7 Hj L j0 Hg l k7 Hj k0 Hg 0k7 Hj Pk0 Hg `k7 Hj k0 Hg , k7 Hj L k0 Hg l k7 Hj k0 Hg k7 Hj l0 Hg l7 Hj @l0 Hg , Pl7 Hj L pl0 Hg l l7 Hj l0 Hg l7 Hj l0 Hg l7 Hj m0 Hg , m7 Hj L 0m0 Hg l @m7 Hj `m0 Hg pm7 Hj m0 Hg m7 Hj
- m0 Hg ,
- m7 Hj L
- m0 Hg l
- n7 Hj
- n0 Hg
- 0n7 Hj
- Pn0 Hg
- `n7 Hj n0 Hg , n7 Hj L n0 Hg l n7 Hj n0 Hg n7 Hj o0 Hg o7 Hj @o0 Hg , Po7 Hj L po0 Hg l o7 Hj o0 Hg o7 Hj o0 Hg o7 Hj
p0 Hg ,
p7 Hj L
0p0 Hg l
@p7 Hj
`p0 Hg
pp7 Hj
p0 Hg
p7 Hj < p BBA A(D@_
-(A ABBA zPR xf, L $ Pr BBB B(A0A8D@V
-8A0A(B BBBB L t s BBB B(A0A8D@6
-8A0A(B BBBB L u BBB B(A0A8DP
-8A0A(B BBBB L y BBB B(A0A8D@n
-8A0A(B BBBJ L d { BBB B(A0A8DP
-8A0A(B BBBB n\ AG RAD t D BHB A(D0J^
-0A(A BBBA , d (C AAG n
-DAA D H BBB D(A0D@
-0A(A BBBA 4 4 B m BHA G`
- DABA 4 l B N BHA G`
- DABA 4 Ѕ7 / BHA G`
- DABA 4 ؆7 BHA G`
- DABA 4 B BHA G`
- DABA 4 L B BHA G`
- DABA 4 7 BHA G`
- DABA 4 7 BHA G`
- DABA 4 B u BHA G`
- DABA 4 , 8B V BHA G`
- DABA 4 d P7 7 BHA G`
- DABA 4 X7 BHA G`
- DABA 4 `B BHA G`
- DABA 4 xB BHA G`
- DABA 4 D 7 BHA G`
- DABA 4 | 7 BHA G`
- DABA 4 B } BHA G`
- DABA 4 B ^ BHA G`
- DABA 4 $ Ж7 ? BHA G`
- DABA 4 \ ؗ7 BHA G`
- DABA 4 B BHA G`
- DABA 4 7 BHA G`
- DABA 4 7 BHA G`
- DABA 4 < 7 BHA G`
- DABA 4 t 7 BHA G`
- DABA 4 7 f BHA G`
- DABA 4 7 G BHA G`
- DABA 4 (B ( BHA G`
- DABA 4 T @B BHA G`
- DABA 4 X7 BHA G`
- DABA 4 `7 BHA G`
- DABA 4 hB BHA G`
- DABA 4 4 B BHA G`
- DABA 4 l 7 n BHA G`
- DABA 4 7 O BHA G`
- DABA 4 B 0 BHA G`
- DABA 4 7 BHA G`
- DABA 4 L Ȫ7 BHA G`
- DABA 4 Ы7 BHA G`
- DABA 4 ج7 BHA G`
- DABA 4 ୰7 BHA G`
- DABA 4 , 记7 v BHA G`
- DABA 4 d B W BHA G`
- DABA 4 B 8 BHA G`
- DABA 4 7 BHA G`
- DABA 4 (7 BHA G`
- DABA 4 D 0B BHA G`
- DABA 4 | H7 BHA G`
- DABA 4 PB BHA G`
- DABA 4 hB ~ BHA G`
- DABA 4 $ 7 _ BHA G`
- DABA 4 \ 7 @ BHA G`
- DABA 4 B ! BHA G`
- DABA 4 7 BHA G`
- DABA 4 B BHA G`
- DABA 4 < Ƚ7 BHA G`
- DABA 4 t оB BHA G`
- DABA 4 述B BHA G`
- DABA 4 7 g BHA G`
- DABA 4 °7 H BHA G`
- DABA 4 T ðB ) BHA G`
- DABA 4 (İ7
- BHA G`
- DABA 4 0ŰB BHA G`
- DABA 4 HưB BHA G`
- DABA 4 4 `ǰB BHA G`
- DABA 4 l xȰB BHA G`
- DABA 4 ɰ7 o BHA G`
- DABA 4 ʰ7 P BHA G`
- DABA 4 ˰B 1 BHA G`
- DABA 4 L ̰B BHA G`
- DABA 4 ͰB BHA G`
- DABA 4 ΰB BHA G`
- DABA 4 а7 BHA G`
- DABA 4 , Ѱ7 BHA G`
- DABA < d Ұ w BBA A(J
-(D ABBA < Ӱ c BBA A(J
-(D ABBA < pհ O BBA A(J
-(D ABBA < $! װ ; BBA A(J
-(D ABBA $ d! ذ* ' A_
-H A D ! ذ BBF K(H0G@Q
-0A(A BBBC D ! @ڰ BGB D(D0J
-(A BBBD < " ڰ/ BEI D(G
-(D ABBG $ \" ۰* b A_
-H A $ " ۰* > A_
-H A $ " ۰* A_
-H A , " ۰ LGA A(D T # ܰ BBL B(A0A8JE
-8A0A(B BBBC 4 \# u BAI E
-ABA 4 # u BAI E
-ABA 4 # Xu BAI E
-ABA 4 $ u o BAI E
-ABA 4 <$ u K BAI E
-ABA 4 t$ 0u ' BAI E
-ABA 4 $ xu BAI E
-ABA 4 $ u BAI E
-ABA 4 % u BAI E
-ABA 4 T% Pu BAI E
-ABA 4 % u s BAI E
-ABA 4 % u O BAI E
-ABA 4 % (u + BAI E
-ABA 4 4& pu BAI E
-ABA 4 l& u BAI E
-ABA 4 & u BAI E
-ABA 4 & Hu BAI E
-ABA 4 ' u w BAI E
-ABA 4 L' u S BAI E
-ABA 4 ' u / BAI E
-ABA 4 ' hu BAI E
-ABA 4 ' u BAI E
-ABA 4 ,( u BAI E
-ABA 4 d( @u BAI E
-ABA 4 ( u { BAI E
-ABA 4 ( u W BAI E
-ABA 4 ) u 3 BAI E
-ABA 4 D) `u BAI E
-ABA 4 |) u BAI E
-ABA 4 ) u BAI E
-ABA 4 ) 8u BAI E
-ABA 4 $* u BAI E
-ABA 4 \* u [ BAI E
-ABA 4 * u 7 BAI E
-ABA 4 * Xu BAI E
-ABA 4 + u BAI E
-ABA 4 <+ u BAI E
-ABA 4 t+ 0u BAI E
-ABA 4 + xu BAI E
-ABA 4 + u _ BAI E
-ABA 4 , u ; BAI E
-ABA 4 T, Pu BAI E
-ABA 4 , u BAI E
-ABA 4 , u BAI E
-ABA 4 , (u BAI E
-ABA 4 4- pu BAI E
-ABA 4 l- u c BAI E
-ABA 4 - u ? BAI E
-ABA 4 - Hu BAI E
-ABA 4 . u BAI E
-ABA 4 L. u BAI E
-ABA 4 . u BAI E
-ABA 4 . hu BAI E
-ABA 4 . u g BAI E
-ABA 4 ,/ u C BAI E
-ABA 4 d/ @u BAI E
-ABA 4 / u BAI E
-ABA 4 / u BAI E
-ABA 4 0 u BAI E
-ABA 4 D0 `u BAI E
-ABA 4 |0 u k BAI E
-ABA 4 0 u G BAI E
-ABA T 0 8 # BBB B(A0I8Gz
-8D0A(B BBBH T D1 BBB B(A0I8Gz
-8D0A(B BBBH T 1 BBB B(A0I8Gz
-8D0A(B BBBH T 1 @ BBB B(A0I8Gz
-8D0A(B BBBH T L2 BBB B(A0I8Gz
-8D0A(B BBBH T 2
- BBB B(A0I8Gz
-8D0A(B BBBH T 2 H BBB B(A0I8Gz
-8D0A(B BBBH T T3 BBB B(A0I8Gz
-8D0A(B BBBH T 3 BBB B(A0I8Gz
-8D0A(B BBBH T 4 P BBB B(A0I8Gz
-8D0A(B BBBH T \4 BBB B(A0I8Gz
-8D0A(B BBBH T 4 BBB B(A0I8Gz
-8D0A(B BBBH 5 X KBB A(D0G@y0A(A BBBL@f
+ 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
0A(A BBBJ ,
-0A(A BBBL < D6 BED A(D0Y
-(A ABBA 6 8 G 6 p KBB A(D0G@y0A(A BBBL@a
+0A(A BBBL < 4N ֯ BED A(D0Y
+(A ABBA tN @ׯG N xد KBB A(D0G@y0A(A BBBL@a
0A(A BBBO (
-0A(A BBBH ,7 $: D7 ( KBB A(D0G@a0A(A BBBL@1
+0A(A BBBH O ܯ: 4O KBB A(D0G@a0A(A BBBL@1
0A(A BBBG ,
-0A(A BBBL 7 - KBB A(D0G@a0A(A BBBL@-
+0A(A BBBL O KBB A(D0G@a0A(A BBBL@-
0A(A BBBK *
-0A(A BBBN d8 (1 E
-K < 8 2 BKD A(G0(A ABB < 8 3 BDD
+0A(A BBBN TP 0 E
+K < tP BKD A(G0(A ABB < P BDD
ABA L
-ABA L \8 4 BBB B(A0A8G
-8C0A(B BBBJ T 8 D BHB E(A0D8G-
-8D0A(B BBBA T 9 G BEB B(G0A8J.
-8D0A(B BBBH T \9 Jb BEI B(D0A8J
-8A0A(B BBBE T 9 N J BBH B(A0A8J(
-8A0A(B BBBD T : S BBH E(A0D8G
-8D0A(B BBBA T d: V BBH E(A0D8G
-8D0A(B BBBA T : Z BBH E(A0A8G
-8A0A(B BBBH T ; ]c h BBH B(D0A8G]
-8A0A(B BBBG L l; b / BBB B(A0A8G
-8C0A(B BBBJ T ; s BEB B(G0A8JB
-8D0A(B BBBD T < ws R BEI B(D0A8J
-8A0A(B BBBI T l< { BHB B(A0D8G2
-8A0A(B BBBA T < } BBH B(A0A8J
-8A0A(B BBBJ T = 8 BBH E(A0D8G
-8D0A(B BBBA T t= q BBH E(A0D8G
-8D0A(B BBBA T = 舱m 9 BHE B(D0A8G
-8D0A(B BBBI T $> BBH B(D0A8G
-8A0A(B BBBG T |>
- BBH B(D0A8G`
-8A0A(B BBBD L > BBB B(A0A8G
-8C0A(B BBBJ T $? ௱ BBE B(G0A8Jh
-8D0A(B BBBF T |? BIB E(D0A8J
-8D0A(B BBBE L ? z BBB B(A0A8G
-8C0A(B BBBJ T $@ ȱ{ BHB E(A0A8J
-8D0A(B BBBC T |@ ʱ
- BBI B(D0A8J*
-8D0A(B BBBK T @ ͱR f BHB B(D0D8G
-8D0A(B BBBG T ,A ѱf - BHB E(A0D8G
-8D0A(B BBBA T A ӱf BHB E(A0D8G
-8D0A(B BBBA < A ձ BBA A(J
-(D ABBA < B ױ BBA A(J
-(D ABBA $ \B 8ٱ* A_
-H A $ B @ٱ* o A_
-H A d TC Hٱq BBB B(D0A8DP
+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
8A0A(B BBBB _
-8H0A(B BBBH L C `۱ BBB B(A0A8G
-8C0A(B BBBJ T dC 5 ? BBH B(A0D8G
-8A0A(B BBBH T C 5 BBH B(A0D8G
-8A0A(B BBBH d D q BBB B(D0A8DP
+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
8A0A(B BBBB _
-8H0A(B BBBH L |D e BBB B(A0A8G
-8C0A(B BBBJ T D H BBH B(A0D8G`
-8A0A(B BBBD T $E BBH B(A0D8Gx
-8A0A(B BBBD D $F BBB B(A0A8
-0A(B BBBF T E BHB B(D0D8G
-8D0A(B BBBH L F x! BBB B(A0A8DF
-8A0A(B BBBB T lF )~ ~ BBB H(A0A8Jo
-8A0A(B BBBE D lG 0 BBB B(A0A8
-0A(B BBBF T G X3 BHB E(A0D8G
-8D0A(B BBBE D H 6 BBB B(A0A8
-0A(B BBBA T G 9+ | BBH B(A0A8J
-8A0A(B BBBG D H @s BBB B(A0A8I
-0A(B BBBA T LH (C BHB E(A0D8GI
-8D0A(B BBBA D LI F BBB B(A0A8
-0A(B BBBA T H 8IK z BBH B(A0A8J
-8A0A(B BBBG D I 0Qs BBB B(A0A8I
-0A(B BBBA T I hS BHB E(A0D8GI
-8D0A(B BBBA D J W BBB B(A0A8
-0A(B BBBF T ,J Y x BHB E(A0D8G|
-8D0A(B BBBJ D ,K ]1 BBB B(A0A8
-0A(B BBBF T J ` BHB E(A0D8G
-8D0A(B BBBE D K c BBB B(A0A8
-0A(B BBBF T lK xf v BHB E(A0D8G
-8D0A(B BBBE lL 0ˍ. K^ L i L i L i HS L L2/ A D L i BBE A(A0D@g
-0A(A BBBA D 4M (j BBE A(A0D@o
-0A(A BBBA |M 1\ AG RAT M j- BDA ]
+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 ]
ADG w
ADD Q
ABD I
-ABD T LM xkC BBE E(D0A8G
-8D0A(B BBBH D LN pp BBE A(A0D@P
-0A(A BBBA $ M p S A
+ABD T Te #C = BBE E(D0A8G
+8D0A(B BBBH D <f x( BBE A(A0D@P
+0A(A BBBA $ e ( A
G A
-A L N pt / BBE D(A0G
-0D(A BBBA < O 0D BBD A(D@0(A ABB T N pv BIB E(A0A8D
-8D0A(B BBBA L N 8{) BBB B(A0D8D`
-8A0A(B BBBA L LO }) _ BBB B(A0D8D`
-8A0A(B BBBA T O ~ / BEB I(A0A8G
+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
-8A0A(B BBBE , O @> / AID W
-DAA T $P P BBE E(A0D8G
-8D0A(B BBBD $ |P 藲G As
+8A0A(B BBBE , g HL> AID W
+DAA $ ,h XLG As
L A
-A T P BFA A(GP
+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 P BBB B(A0A8G
+(A ABBA l ,i U BBB B(A0A8G
8A0A(B BBBD
-8A0A(B BBBF T lQ 訲 9 BBE E(A0D8G
-8D0A(B BBBF D lR p BEB B(A0A80A(B BBB < R دB BBA A(G
-(A ABBA , LR 貲 PGA A(D , |R ]GA A(D , R PGA A(D < R hB BBA A(G
-(A ABBA T S x BEB B(A0D8J
-8A0A(B BBBA T tS ɲ' BHB B(A0F8J
-8D0A(B BBBJ T S ܲ BBH B(F0D8G
-8D0A(B BBBA T Í. K^ D <T BBE A(A0
-(A BBBA ,U +/ A L DU AAG ]
+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 ]
CAF c
CAI R
-CAJ JCA D U H BBE A(A0D@g
-0A(A BBBA D U BBE A(A0D@o
-0A(A BBBA $ $V h^ AG b
-AA , LV 0 AC
FK
- E < U X BBP J(G
-(D ABBD L V p Z BBE D(A0G
-0D(A BBBA < W *D BBD A(D@0(A ABB T V p BIB E(A0A8D
-8D0A(B BBBA T V 84 BEB B(H0A8G X
-8A0A(B BBBC , TW > AID W
-DAA $ W 0 w Ad
+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
K A
-A D W
c BEA G@Z
+A D o ű BEA G@Z
AABC
- AABA l W `l
k BBB B(A0A8G@'
+ AABA l $p `˱l
BBB B(A0A8G@'
8A0A(B BBBF x
-8A0A(B BBBF , dX ` ZGA A(D , X @! ZGA A(D T X " BBB B(A0A8G;
-8A0A(B BBBJ L Y -k BBB B(A0A8G2
-8A0A(B BBBA T lY 4 w BEB B(A0A8J
-8A0A(B BBBA T Y B @ BHB B(F0A8J
-8D0A(B BBBE T Z XF BBB H(F0D8G
-8D0A(B BBBA [ །. K^ 4[ \ L[ ] d[ \ |[ \ [ \ [ \ [ \ [ \F [ (]) G] \ @]J W c $\ x] D k
-Q z D\ ] H G
-A d\ h^ H0W
-I $ \ _^ AG b
-AA D \ @_ BBE A(A0D@
-0A(A BBBA $ L\ `g AG k
-AA , t\ ``Y ADG i
-AAA < L] %D BBD A(D@0(A ABB T \ P` + BIB E(A0A8DV
-8D0A(B BBBA D ] d| BBB A(D0D@
-0A(A BBBA D ,^ e BBB A(D0D@
-0A(A BBBA T ] (ge i BEB B(A0A8GL
-8A0A(B BBBA T $^ @i 7 BBB B(A0A8G
-8A0A(B BBBG L $_ xl BBF B(A0A8D@
-8D0A(B BBBB D ^ m BBE A(A0D@
-0A(A BBBA L _ m BEB E(I0H8D@
-8D0A(B BBBD L d_ Pow g BHI E(A0A8Gp
-8A0A(B BBBF L _ q W BJE B(A0A8D@
-8A0A(B BBBA L ` PrV / BBB B(A0D8J
-8D0A(B BBBE T T` `x BBB B(A0D8J
-8A0A(B BBBA d Ta E BBB B(A0A8DP
+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 BBBE \
-8F0A(B BBBA T a BBB B(A0A8N
-8A0A(B BBBA , la > AID W
-DAA Db 赍. K^ \b tb ةr b @J b xe b е b h7 b Z c ؿ c ijB 4c ų Lc ʳ dc ̳J L b ѳr BBB B(E0A8J@
-8A0A(B BBBA D c ӳ BBE A(A0D@u
-0A(A BBBA D d ӳ BBE A(A0D@u
-0A(A BBBA \d PԳJ td ԳJ L d Գ BBB B(A0A8D`
-8A0A(B BBBA , 4d 0ճ \ AAD@W
-AAA , dd ճ 8 AAD@W
-AAA , d ճ AAD@W
-AAA , d Pֳ AAD@W
-AAA , d ֳ AAD@W
-AAA , $e ׳ AAD@W
-AAA , Te p׳ AAD@W
-AAA , e ׳ ` AAD@W
-AAA $ \f 0س RMIp L f ٳ BBB B(A0A8D`
-8A0A(B BBBA , ,f ڳ AAD@W
-AAA , \f xڳ AAD@W
-AAA , f ڳ | AAD@W
-AAA , f 8۳ X AAD@W
-AAA , f ۳ 4 AAD@W
-AAA , g ۳ AAD@W
-AAA , Lg Xܳ AAD@W
-AAA , |g ܳ AAD@W
-AAA L Th ݳ BBB B(A0A8Dp
-8A0A(B BBBA , g ݳ T AAD@W
-AAA , ,h 8 0 AAD@W
-AAA , \h AAD@W
-AAA , h AAD@W
-AAA , h X߳ AAD@W
-AAA , h ߳ AAD@W
-AAA , i | AAD@W
-AAA , Li x X AAD@W
-AAA < $j BBA A(D@i
-(A ABBA , i AAD@W
-AAA , i x AAD@W
-AAA , j AAD@W
-AAA , Lj 8 AAD@W
-AAA , |j d AAD@W
-AAA , j @ AAD@W
-AAA , j X AAD@W
-AAA , k AAD@W
-AAA T <k BBB B(D0D8G
-8D0A(B BBBJ T k ` BEE E(D0A8G
-8D0A(B BBBD T k , BBE B(D0A8G
-8D0A(B BBBK T Dl J s BIA A(GT
+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
(A ABBB ^
-(A ABBD T l , ; BBE B(D0A8G
-8D0A(B BBBK T l `J ! BIA A(GT
+(A ABBD T t , [ BBE B(D0A8G
+8D0A(B BBBK T ̅ طJ A BIA A(GT
(A ABBB ^
-(A ABBD T Lm X, BBE B(D0A8G
-8D0A(B BBBK T m 0J BIA A(GT
+(A ABBD T $ й, BBE B(D0A8G
+8D0A(B BBBK T | J BIA A(GT
(A ABBB ^
-(A ABBD T m (, BBE B(D0A8G
-8D0A(B BBBK T Tn J } BIA A(GT
+(A ABBD T Ԇ , BBE B(D0A8G
+8D0A(B BBBK T , xŲJ BIA A(GT
(A ABBB ^
-(A ABBD T n 2 E BBB E(A0D8G/
-8D0A(B BBBE L o BBB B(D0A8G`8
-8A0A(B BBBJ T To @ BBB B(D0A8G
-8A0A(B BBBA L o " c BEB E(I0H8DP
-8D0A(B BBBD L o h$ ; BHI E(A0A8Gps
-8A0A(B BBBB L Lp ' ' BEB E(I0H8DP
-8D0A(B BBBD L p ( BHI E(A0A8Gpr
-8A0A(B BBBC T p Ȧ BIB B(A0A8GS
-8A0A(B BBBA q 0+ r (+ T tq 0+ BBB J(A0A8G
-8D0A(B BBBD D tr 4 BBB D(A0D@0A(A BBB T r @.L ( BIE B(A0A8D9
-8D0A(B BBBA T lr 83 BEB B(G0A8G
-8D0A(B BBBJ ls . K^ s 9 s 9 s 9 s 9 s 9 s 9 t 9 ,t 9 Dt 9 \t 9 tt 9 t 9 t 9 t 9 , ,t 9[ AID t
-DAA , \t 9[ AID t
-DAA , t :[ AID t
-DAA , t H:[ c AID t
-DAA , t x:[ C AID t
-DAA u :2 I 4 u : BAA s
-AEK AAB L lu (;r BBB B(E0A8J@
-8A0A(B BBBA $ dv X<^ AG b
-AA v < d u < X BBB B(A0D8Op
+(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
8A0A(B BBBH U
-8A0A(B BBBA , dv P=> AID W
-DAA T v `= BBB B(D0A8K#
-8A0A(B BBBK , v >> AID W
-DAA D w > BBB A(A0JR
-0A(A BBBA , dw ? n qGA A(D L w @{ \ BBB D(D0J
-0D(A BBBG T w D~ f BBA A(Ik
+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
(C ABBF
-(A ABBA T <x E 3 BEE E(D0D8G(
-8D0A(B BBBK d <y G= BLB B(A0A8DPq
+(A ABBA T p S BEE E(D0D8G(
+8D0A(B BBBK d = BLB B(A0A8DPq
8A0A(B BBBE
-8A0A(B BBBE < x XHg BED D(G
-(D ABBD D y I BBB D(A0D@
-0A(A BBBA D y J BIB A(D0D`+
-0D(A BBBA L y M BIB B(A0D8DpE
-8D0A(B BBBA L z hP ľ BIB B(A0D8DpE
-8D0A(B BBBA < lz 8S BHA Gi
- DABA < z U BHA Gi
- DABA D z V$ d BIA A(G
-(D ABBA < 4{ X A BHA J
- DABA L t{ `[ 3 BBB B(G0D8G
-8A0A(B BBBA D { ]4 3 BDB A(G0G
-0A(D BBBA \ | `" P BBB A(A0G
+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
0A(A BBBI O
-0A(A BBBA L l| hb$ BDH A(D0Go
-0D(A BBBD < | Hg0 6 BBA A(Jc
-(A ABBG T | 8ha BEE B(D0A8G
-8D0A(B BBBI L } PpB BLH B(D0A8DP
-8A0A(B BBBB L~ Pq9 Af
-I D L } pq BHB E(A0A8Gn
-8D0A(B BBBK T ~ t l BDB E(A0A8G
-8A0A(B BBBJ T l~ {l BDB B(D0A8G6
-8A0A(B BBBJ T ~ Ѐ ߽ BBB B(A0D8G
-8A0A(B BBBA < ս BBH A(Jx
-(A ABBK T \ 5
BBB B(A0E8G
-8A0A(B BBBA T 5
BBB B(A0E8G
-8A0A(B BBBA T y BBB B(A0E8G
-8A0A(B BBBA T d Ы BBB B(A0A8K
-8A0A(B BBBA L h O BBB G(A0D8M
-8D0A(B BBBG Ț. K^ ́ , D \ t x p h ` Ԃ X P H P 4 X L ` d h | p x ă ܃ $ < L r BBB B(E0A8J@
-8A0A(B BBBA $ 輴^ AG b
-AA T $ / BBE E(A0A8G4
-8A0A(B BBBK L | * 7 BBB J(A0A8G
-8D0A(B BBBA L ̄ hô* ! BBB J(A0A8G
-8D0A(B BBBA L HŴ BBB J(A0A8G
-8D0A(B BBBA L l ƴ BBB J(A0A8G
-8D0A(B BBBA L ȴ BBB J(A0A8G
-8D0A(B BBBA L (ʴ* ɻ BBB J(A0A8G
-8D0A(B BBBA L \ ̴ BBB J(A0A8G
-8D0A(B BBBA L ʹ BBB J(A0A8G
-8D0A(B BBBA L Hϴ BBB J(A0A8G
-8D0A(B BBBA L L д q BBB J(A0A8G
-8D0A(B BBBA L Ҵ [ BBB J(A0A8G
-8D0A(B BBBA L (Դ* E BBB J(A0A8G
-8D0A(B BBBA L < ִ* / BBB J(A0A8G
-8D0A(B BBBA T ״ BBE E(D0A8G/
-8D0A(B BBBJ T ڴ ܺ BBE E(A0A8G
-8D0A(B BBBA D ܴ BBB D(A0D@
-0A(A BBBA D W BIB A(D0D`A
-0D(A BBBA D ̉ 5 BIB A(D0D`H
-0D(A BBBA D BIB A(D0D`H
-0D(A BBBA D \ BIB A(D0D`H
-0D(A BBBA D Ϲ BIB A(D0D`H
-0D(A BBBA D X BIB A(D0D`H
-0D(A BBBA D 4 0 BIB A(D0D`H
-0D(A BBBA D | i BIB A(D0D`H
-0D(A BBBA D ċ G BIB A(D0D`H
-0D(A BBBA D % BIB A(D0D`H
-0D(A BBBA D T BIB A(D0D`H
-0D(A BBBA D h BIB A(D0D`H
-0D(A BBBA D @ BIB A(D0D`H
-0D(A BBBA < , BBA A(G
-(A ABBA D l BBE G(A0D
-0A(A BBBA T @ _ BBB E(A0A8G
-8A0A(B BBBD T x t c BBB E(A0A8GG
-8A0A(B BBBK T d u BBB E(A0A8G
-8A0A(B BBBF L BBB B(A0G8J
-8A0A(B BBBA T XD BBB E(A0A8G,
-8A0A(B BBBF L d P E BBB E(A0A8G
-8A0A(B BBBA T P! BBB E(A0A8Gc
-8A0A(B BBBG T &u ĸ BBB E(A0A8G
-8A0A(B BBBF L d + ȸ BBB B(A0G8J
-8A0A(B BBBA T -d BBB E(A0A8GO
-8A0A(B BBBC T 2u BBB E(A0A8G3
-8A0A(B BBBA T d 5 BBB E(A0A8G
-8A0A(B BBBG L 9t BBB B(A0D8M
-8A0A(B BBBA T ; BBB E(A0A8Gc
-8A0A(B BBBG T d @A BBG B(D0G8G
-8D0A(B BBBD T N ι BBB B(A0D8M
-8A0A(B BBBA T pSd ̹ BBB G(D0G8G
-8D0A(B BBBD T l o ι BBB B(D0A8M
-8A0A(B BBBA T ē t ̹ BBB B(A0D8M
-8A0A(B BBBA T hx ʹ BBB B(A0D8M
-8A0A(B BBBA T t | ȹ BBB B(A0D8M
-8A0A(B BBBA T ̔ H ƹ BBB B(D0A8M
-8A0A(B BBBA T $ Ĺ BBB B(A0D8M
-8A0A(B BBBA T | ( ¹ BBB B(A0D8M
-8A0A(B BBBA T ԕ BBB B(A0D8M
-8A0A(B BBBA L , e BBB B(A0D8M
-8A0A(B BBBH T | X ʹ BBB G(D0G8G
-8D0A(B BBBD L Ԗ U BBB B(A0D8M
-8A0A(B BBBC T $ 0e BBB E(A0A8G
-8A0A(B BBBH T | H Ź BBB G(D0G8G0
-8D0A(B BBBD T ԗ ʵ ǹ BBB E(A0A8G
-8A0A(B BBBH T , εm ˹ BBB G(D0G8G
-8D0A(B BBBD T u BBB E(A0A8G(
-8A0A(B BBBA T ܘ ѹ BBB G(D0G8G
-8D0A(B BBBD T 4 ӹ BBB E(A0A8G*
-8A0A(B BBBH T t BBB G(D0G8G
-8D0A(B BBBD T u ٹ BBB E(A0A8G
-8A0A(B BBBH T < 8b ݹ BBB G(D0G8G
-8D0A(B BBBD T P. ߹ BBB E(A0A8Gl
-8A0A(B BBBF T 1= BBB G(D0G8G
-8D0A(B BBBD L D E5 BBB E(A0A8G
-8A0A(B BBBA T H BBB G(D0G8G
-8D0A(B BBBD L VE BBB E(A0A8G
-8A0A(B BBBA T < Y BBB G(D0G8G`
-8D0A(B BBBD T @c BBB E(A0A8G
-8A0A(B BBBH T f BBB G(D0G8GH
-8D0A(B BBBD T D {U BBG E(G0A8G0
-8D0A(B BBBD , h~< F AK
-AA t X P L Xm BBB B(A0I8Dp
-8D0A(B BBBF L L xm ƺ BBB B(A0I8Dp
-8D0A(B BBBF D D 2 BBB D(A0D@0A(A BBB T Pl J BIE B(A0A8DY
-8D0A(B BBBA T < hr BIE B(A0A8Da
-8D0A(B BBBA T BBE B(A0A8G
-8A0A(B BBBA X}. K^ 0 Ġ ( ܠ h\ AG RAL BLB B(A0A8D
-8A0A(B BBBE L BBB B(I0A8G
-8D0A(B BBBF L ୶ BBB B(I0A8G
-8D0A(B BBBF T D 0 BEE E(D0A8G
-8D0A(B BBBE L D X BOB A(A0k
+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
(A BBBI b
-(A BBBG T 赶< M BBE B(D0A8G
-8D0A(B BBBB D BBB D(A0D@0A(A BBB T BIB E(A0A8D6
-8D0A(B BBBA T BIB E(A0A8DF
-8D0A(B BBBA L ö BBB E(D0D8G@L
-8D0A(B BBBE < 4 hĶ BLH D(A0
-(A BBBF T ̣ (Ŷ BBB B(A0A8G 0
-8A0A(B BBBE T $ ߶ ۸ BEB I(D0A8GM
-8D0A(B BBBH $ x. K^ < T , Ĥ [ AID t
-DAA D BBB D(A0D@0A(A BBB T < L ? BIE B(A0A8D9
-8D0A(B BBBA T
- BDB H(D0A8G
-8D0A(B BBBD w. K^ Ħ T 4 W BBB B(I0A8D'
-8D0A(B BBBF D 4 BBB D(A0D@0A(A BBB T Ԧ 0 ߷ BIB E(A0A8G
-8D0A(B BBBA L , ŷ BBB B(A0D8Jo
-8D0A(B BBBE L | ( BBB B(A0D8Jo
-8D0A(B BBBE L ̧ BBB B(A0D8Jo
-8D0A(B BBBE L e BBB B(A0D8Jo
-8D0A(B BBBE L l X
- E BBB B(A0D8Jo
-8D0A(B BBBE T
' BBB G(D0G8GN
-8D0A(B BBBF t. K^ ԩ 8 0 < 8 BBD A(D0
-(A ABBF < D BBD A(D0
-(A ABBD D ܩ BBE A(A0
-(A BBBA L ̪ AAG ]
+(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 ]
CAF [
ACA R
-CAJ JCA L P AAG ]
+CAJ JCA L , 8Ե AAG ]
CAF [
ACA R
-CAJ JCA L Ī BBB B(I0A8G
-8D0A(B BBBF L BBB B(I0A8G
-8D0A(B BBBF D BBB D(A0D@0A(A BBB T BIB E(A0A8D
-8D0A(B BBBA T % h BIB E(A0A8D
-8D0A(B BBBA T \ H*D C BBH E(A0A8L
-8D0A(B BBBE T @3~ BBB B(A0A8G
-8D0A(B BBBD T h<~ ҵ BBB B(A0A8G
-8D0A(B BBBF L d E BFB B(D0A8JS
-8D0A(B BBBE \ Pp. K^ t 8H 0H < 8H BBB D(A0&
-(A BBBH L < I 0 BBB B(A0I8G
-8D0A(B BBBD T K BEE E(D0A8G*
-8D0A(B BBBD T `Q BBE B(D0A8G
-8D0A(B BBBG D < (V BAA G@u
+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
DABI
- DABF D , ތ BBB D(A0D@0A(A BBB T ̯ W ~ BIB E(A0A8D
-8D0A(B BBBA T $ p\j W BEE B(D0A8N[
-8D0A(B BBBG L | ` W BHG B(A0D8G
-8D0A(B BBBJ t hm. K^ T 0wZ MGB B(A0H8)0A(B BBBB8T 8x MGB B(A0A8`0A(B BBBB8 < py T hy 4 l pys BBA A(D0b(A ABBD y BBB B(A0A8DP8A0A(B BBBD D 0z BBE A(A0
-(A BBBA L 4 z AAG ]
+ 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 ]
CAF [
ACA R
-CAJ JCA L z AAG ]
+CAJ JCA L 2 AAG ]
CAF [
ACA R
-CAJ JCA L Գ z AAG ]
+CAJ JCA L 2 AAG ]
CAF [
ACA R
-CAJ JCA L $ ({ AAG ]
+CAJ JCA L 4 3 AAG ]
CAF [
ACA R
-CAJ JCA L ̳ X{x ; BBB B(A0I8Dp
-8D0A(B BBBJ L |x BBB B(A0I8Dp
-8D0A(B BBBJ L l }x ӱ BBB B(A0I8Dp
-8D0A(B BBBJ < ~ BED I(D
-(D ABBA < r BED I(D
-(D ABBA D ڌ BBB D(A0D@0A(A BBB T @ BIB E(A0A8DV
-8D0A(B BBBA T ܵ ˰ BIB E(A0A8DV
-8D0A(B BBBA T 4 BIB E(A0A8D
-8D0A(B BBBA T r BBE B(D0A8G
-8D0A(B BBBG T U BAA G0
+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
AABA
AABK f
- AABF L < u BBB B(F0D8G
-8A0A(B BBBH L ǰ BBB B(F0D8G
-8A0A(B BBBH T ܷ 8p BBJ B(A0G8J1
-8D0A(B BBBH ܸ 0f. K^ 8 0 $ ( < 0 , 8[ AID t
-DAA , ܸ h[ AID t
-DAA , [ ߰ AID t
-DAA D ، BBB D(A0D@0A(A BBB T \ w BIE B(A0A8DI
-8D0A(B BBBA T ܹ E BIB E(A0A8G
-8D0A(B BBBA T 4 ķ BIB E(A0A8G
-8D0A(B BBBA L ɷo BDE H(A0A8G
-8D0A(B BBBK L ܺ η 7 BDB H(D0A8G
-8D0A(B BBBB Ի hc. K^ ӷ xӷ T t ӷ + BBB B(A0D8R:
-8A0A(B BBBA , ̻ Hط> 2 AID W
-DAA T Xط BBE B(D0A8G
-8D0A(B BBBG D F BBB D(A0D@0A(A BBB T ܷ BIB E(A0A8D
-8D0A(B BBBA PE BEE E(G0G
+ AABF L d P> BBE B(D0A8G|
+8A0A(B BBBC L V> BBE B(D0A8G|
+8A0A(B BBBC | \ 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
0A(A BBBK Q
0A(A BBBG ,
0A(A BBBD
-0A(A BBBG
BEE E(G0G>
+0A(A BBBG ,
BEE E(G0G>
0A(A BBBK Q
0A(A BBBG
0A(A BBBE K
-0A(A BBBE T p BBB G(D0A8M5
-8D0A(B BBBG X`. K^ , D \ t , [ AID t
-DAA , P[ AID t
-DAA D $ BBB D(A0D@0A(A BBB T 8l BIE B(A0A8DY
-8D0A(B BBBA T Pl ] BIE B(A0A8DY
-8D0A(B BBBA T < h + BDB E(A0G8G
-8D0A(B BBBF T J _ BDB B(D0G8G
-8D0A(B BBBI ^. K^ \ d j BBB A(I0DW
+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
0D(A BBBJ n
-0D(A BBBG D l rՌ BBB D(A0D@0A(A BBB T xL BIE B(A0A8D9
-8D0A(B BBBA L d p g BBE B(A0A8G
-8A0A(B BBBG \ p\. K^ t > > > > > > > > 4 > L > , >[ Ӭ AID t
-DAA , ?[ AID t
-DAA , H?[ AID t
-DAA , L x?[ s AID t
-DAA , | ?[ S AID t
-DAA D T XԌ BBB D(A0D@0A(A BBB T ? BIB E(A0A8D6
-8D0A(B BBBA T L C BIB E(A0A8D6
-8D0A(B BBBA T H BIB E(A0A8D6
-8D0A(B BBBA T 8L U BIB E(A0A8D6
-8D0A(B BBBA T T pP # BIB E(A0A8D6
-8D0A(B BBBA T TA
- BDB B(A0A8G
-8A0A(B BBBA T ^)
- B BDB B(A0A8G
-8A0A(B BBBA T \ xh
- BDB B(A0A8G
-8A0A(B BBBA T 0r
- BDB B(A0A8G
-8A0A(B BBBA T {e
- A BDB B(A0A8G
-8A0A(B BBBA V. K^ $ 腸 < T l Ѕ ȅ , D \ t ȅ Ѕ 4 腸 Y \ , Q BBB A(I0Dp
+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 ^
-0D(A BBBA \ ,
BBB A(I0Dp
+0D(A BBBA \ 2, * BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ d , ɩ BBB A(I0Dp
+0D(A BBBA \ 3, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ `, BBB A(I0Dp
+0D(A BBBA \ l x4, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ $ 0, A BBB A(I0Dp
+0D(A BBBA \ H5, ^ BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ , BBB A(I0Dp
+0D(A BBBA \ , 6, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ Њ, BBB A(I0Dp
+0D(A BBBA \ 6, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ D , u BBB A(I0Dp
+0D(A BBBA \ 7, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ p, 1 BBB A(I0Dp
+0D(A BBBA \ L 8, N BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ @, BBB A(I0Dp
+0D(A BBBA \ X9,
+ BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ d , BBB A(I0Dp
+0D(A BBBA \ (:, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ , e BBB A(I0Dp
+0D(A BBBA \ l :, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ $ , ! BBB A(I0Dp
+0D(A BBBA \ ;, > BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ , ݦ BBB A(I0Dp
+0D(A BBBA \ , <, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ P, BBB A(I0Dp
+0D(A BBBA \ h=, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ D , U BBB A(I0Dp
+0D(A BBBA \ 8>, r BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ , BBB A(I0Dp
+0D(A BBBA \ L ?, . BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ , ͥ BBB A(I0Dp
+0D(A BBBA \ ?, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ d , BBB A(I0Dp
+0D(A BBBA \ @, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ `, E BBB A(I0Dp
+0D(A BBBA \ l xA, b BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ $ 0, BBB A(I0Dp
+0D(A BBBA \ HB, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA \ , BBB A(I0Dp
+0D(A BBBA \ , C, BBB A(I0Dp
0D(A BBBG ^
-0D(A BBBA $ З^ AG b
-AA D BEE D(A0D`n
-0A(A BBBE V $ 蘸 Az
+0D(A BBBA $ C^ AG b
+AA D D D BEE D(A0D`n
+0A(A BBBE DV $ E Az
E
-A D < ` BBB D(A0D@
-0A(A BBBA D BIB A(A0G`
-0D(A BBBA D $ R BIB A(A0G`
-0D(A BBBA D l # BIB A(A0G`
-0D(A BBBA D BIB A(A0G`
-0D(A BBBA D X Ţ BIB A(A0G`
-0D(A BBBA D D BIB A(A0G`
-0D(A BBBA D ( g BIB A(A0G`
-0D(A BBBA D 8 BIB A(A0G`
-0D(A BBBA D BIB A(A0G`
-0D(A BBBA D d ` ڡ BIB A(A0G`
-0D(A BBBA D Ȩ BIB A(A0G`
-0D(A BBBA D 0 | BIB A(A0G`#
-0D(A BBBA D < M BIB A(A0G`#
-0D(A BBBA D BIB A(A0G`#
-0D(A BBBA D BIB A(A0G`#
-0D(A BBBA D BIB A(A0G`#
-0D(A BBBA D \ BIB A(A0G`#
-0D(A BBBA D b BIB A(A0G`#
-0D(A BBBA D x 3 BIB A(A0G`#
-0D(A BBBA D 4 BIB A(A0G`#
-0D(A BBBA D | h ՟ BIB A(A0G`#
-0D(A BBBA D ุ BIB A(A0G`#
-0D(A BBBA L X BBB B(D0D8G@H
-8A0A(B BBBG L BBB B(D0D8G@H
-8A0A(B BBBG T V מ BBE E(D0E8J$
-8D0A(B BBBF L ¸ BOB A(A0]
+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 BBBG Y
-(A BBBH < p¸ GDPE
+(A BBBH < n GDPE
AAE
-AAA 4 < @Ÿg ADD H
-AAM DAA , t xŸ BTA _DG T Ÿ BBB B(A0A8G
-8A0A(B BBBE L T ɸ* p BGB B(A0G8J|
-8D0A(B BBBH L θ h BBH B(F0A8J{
-8D0A(B BBBI L `ո BBB B(A0A8Dxi
-8A0A(B BBBG ظ^ DPYL 8ٸ BBB B(A0A8DxQ
-8A0A(B BBBG < T ܸ BEB A(F0z
-(A BBBG L 8ݸ BBB B(D0A8GP
-8A0A(B BBBH D 8 ADG m
+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
AAE V
-DAE UAA L , ߸O BEB B(A0A8Q
-8A0A(B BBBA T BBB E(A0A8G
-8A0A(B BBBF L , * C BGB B(A0G8J|
-8D0A(B BBBH L | * ; BGB B(A0G8J|
-8D0A(B BBBH L * 3 BGB B(A0G8J|
-8D0A(B BBBH L x: BEB B(A0A8Q
-8A0A(B BBBA T l h ۚ BBB E(A0A8G
-8A0A(B BBBF L Q BGB B(A0G8J|
-8D0A(B BBBH L b BEB B(A0A8Q
-8A0A(B BBBA T d : BBB E(A0A8G
-8A0A(B BBBF L H/ BGB B(A0G8J|
-8D0A(B BBBH L (B BGB B(A0G8J|
-8D0A(B BBBH L ( BBB B(A0H8J
-8A0A(B BBEA T x2 BBB B(D0A8Gn
-8A0A(B BBBD L ` * H BGB B(A0G8J|
-8D0A(B BBBH D @%8 ADG m
+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
AAE V
-DAE UAA L D 8& BEB B(A0A8Q5
-8A0A(B BBBA T x)X BBB B(A0D8G
-8A0A(B BBBA L D 1Q _ BGB B(A0G8J|
-8D0A(B BBBH L < 5 BEB B(A0A8QN
-8A0A(B BBBA T 8X BBB B(A0D8G
-8A0A(B BBBA L < @* BGB B(A0G8J|
-8D0A(B BBBH 4 9. K^ D L E BEB E(D0D8PP~8A0A(B BBB F> Hu 8F> Hu XF> Hu xF> Hu FE Hx 4 FE Hx T FE Hx t (GE Hx 泌\ AG RAd 8Gq BEB B(A0A8G@
+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@
8A0A(B BBBC
-8A0A(B BBBF T t PHu Ε BEE B(A0D8J
-8A0A(B BBBA T xM Օ BBE E(A0A8G
-8D0A(B BBBC T $ O BBE E(A0A8G
-8D0A(B BBBA L | Qk [ AAGPa
+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
DAA A
DAJ }
-DAA D t S BBB D(A0D@
-0A(A BBBA D `T BIB A(D0D`+
-0D(A BBBA D \ W BIB A(D0D`+
-0D(A BBBA D Y BIB A(D0D`+
-0D(A BBBA D \ z BIB A(D0D`+
-0D(A BBBA D 4 @_ X BIB A(D0D`+
-0D(A BBBA D | a 6 BIB A(D0D`2
-0D(A BBBA < l d[ BBE H(A0
-(A BBBF T e ԓ BBB B(K0A8Jo
-8A0A(B BBBI 4 \ Xkq ړ BAI A
-ABA 4 kq BAI A
-ABA 4 kq BAI A
-ABA 4 0lq n BAI A
-ABA 4 < xlq J BAI A
-ABA 4 t lq & BAI A
-ABA , m ADG0
-AAB T n BBB B(D0A8Gm
-8A0A(B BBBA T 4 E 5 BEE E(A0A8G>
-8D0A(B BBBA T " BBB B(A0A8J~
-8A0A(B BBBA T e ԓ BEE E(A0A8G>
-8D0A(B BBBA T < ȶ < BBB B(A0A8G^
-8A0A(B BBBA T pйu BEE E(A0A8G<
-8D0A(B BBBA T # BBB B(A0A8Gv
-8A0A(B BBBA T D Pu M BEE E(A0A8G9
-8D0A(B BBBA D 0. K^ \ ` t X P H @ H P X L t `u ݔ BBB B(A0I8Dp
-8D0A(B BBBF L u BBB B(A0I8Dp
-8D0A(B BBBF L u u BBB B(A0I8Dp
-8D0A(B BBBF L d u A BBB B(A0I8Dp
-8D0A(B BBBF D \ z BBB D(A0D@0A(A BBB T œ BIB E(A0A8D6
-8D0A(B BBBA T T
- BIB E(A0A8D6
-8D0A(B BBBA T H a BIB E(A0A8DV
-8D0A(B BBBA T / BIB E(A0A8DV
-8D0A(B BBBA T \ e BGB B(A0A8J
-8A0A(B BBBA T e Y BGB B(A0A8J
-8A0A(B BBBA T (#
- BBB B(F0D8Gn
-8A0A(B BBBA T d - BBB B(F0D8G
-8A0A(B BBBA d (+. K^ | `8 X8 `80 Hg p87 Hj , D 8[ AID t
-DAA L t 8 BBB J(A0A8G
-8D0A(B BBBA T ;| ̓ BBE E(D0A8G
-8D0A(B BBBF L 8@ BBE A(D0G
-0A(A BBBA D BBB D(A0D@0A(A BBB T D BIB E(A0A8DN
-8D0A(B BBBA T H BIB E(A0A8Df
-8D0A(B BBBA T d 0M O BBE B(A0D8J
-8D0A(B BBBF T Q k BGH B(A0D8G
-8D0A(B BBBH (. K^ Z Z$ [
[1 4 ([i L [ d [ AL x[ DI [ DI [ DI [ AL [ AL p[# TI $ [A I[
-L K , D [b D\
+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
+
+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\
H P
H Q
-G I , t [b D\
+G I , T `ڸb D\
H P
H Q
-G I , 8\b D\
+G I , ڸb D\
H P
H Q
-G I , x\b D\
+G I , ڸb D\
H P
H Q
-G I , \b D\
+G I , ۸b D\
H P
H Q
-G I , 4 \b D\
+G I , `۸b D\
H P
H Q
-G I , d 8]b D\
+G I , D ۸b D\
H P
H Q
-G I , x]b D\
+G I , t ۸b 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 , $ 8^b D\
+G I , ܸb D\
H P
H Q
-G I $ x^r k H\
-L A $ ^r G H\
-L A $ (_r # H\
-L A $ $ _r H\
-L A $ L _r ۏ H\
-L A $ t 0`r H\
-L A `q Hd `q w Hd Haq [ Hd aq ? Hd bq # Hd < hbq Hd n\ AG RAT | bt ˎ BBE B(D0A8G
-8D0A(B BBBH D g BBB A(A0G\
-0A(A BBBE $ kR Ď AI
-F A D ⠌v BBB D(A0D@^0A(A BBB T k X BIB E(A0A8D6
-8D0A(B BBBA T @o & BIB E(A0A8DF
-8D0A(B BBBA T < s BIB E(A0A8DF
-8D0A(B BBBA T w BIB E(A0A8DF
-8D0A(B BBBA T | BIB E(A0A8D6
-8D0A(B BBBA T D P ^ BIB E(A0A8D6
-8D0A(B BBBA d D B BBB B(A0A8G@x
+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
8A0A(B BBBE
-8A0A(B BBBF T p" Č BBB B(D0A8J F
-8A0A(B BBBI < \ 8 BBI A(Ga
-(D ABBA < BBI A(Ga
-(D ABBA < p BBI A(Ga
-(D ABBA < X G BBI A(Ga
-(D ABBA < \ BBI A(Ga
-(D ABBA < BBI A(Ga
-(D ABBA h . K^ `
X
P X ` X T `| $ BBE E(D0A8G
-8D0A(B BBBI T |
- BBE E(D0A8G
-8D0A(B BBBI T 4 BBE B(D0A8G
-8D0A(B BBBG T x BBB B(A0D8NH
-8A0A(B BBBC , > 8 AID W
-DAA < D BBD A(D@0(A ABB T T ، BIB E(A0A8D6
-8D0A(B BBBA T ú^ BBB B(A0D8G3
-8A0A(B BBBA T ɺ BBB B(A0D8Gs
-8A0A(B BBBA T \ к ~ BBB B(A0D8G
-8A0A(B BBBA T պ3 j BBB B(F0A8J
-8A0A(B BBBG h. K^ P H L T P r BBB B(E0A8J@
-8A0A(B BBBA T
- BEB E(A0A8G;
-8D0A(B BBBA T r BEB E(A0A8G;
-8D0A(B BBBA L T P) 5 BBB B(A0I8G
-8D0A(B BBBA L 0) BBB B(A0I8G
-8D0A(B BBBA T BBE E(A0A8G
-8D0A(B BBBA D x BBB D(A0D@
-0A(A BBBA L BIB A(D0D
-0D(A BBBA L 0 e BIB A(D0D
-0D(A BBBA <
- Q BEH H(A0n(A BBE T t
- BEB B(A0D8J
-8A0A(B BBBA T
- ! ً BEB B(A0D8J
-8A0A(B BBBA T $ @& BEB B(A0D8J
-8A0A(B BBBA T | * BEB B(A0D8J
-8A0A(B BBBA T . R BEB B(A0D8J
-8A0A(B BBBA T , 3 % BEB B(A0D8J
-8A0A(B BBBA T `7 BEB B(A0D8J
-8A0A(B BBBA T ; ˊ BEB B(A0D8J
-8A0A(B BBBA T 4
? BEB B(A0D8J
-8A0A(B BBBA T
(D q BEB B(A0D8J
-8A0A(B BBBA T
`H D BEB B(A0D8J
-8A0A(B BBBA T < L BEB B(A0D8J
-8A0A(B BBBA T P BEB B(A0D8J
-8A0A(B BBBA T 8U BEB B(A0D8J
-8A0A(B BBBA T D Y BEB B(A0D8J
-8A0A(B BBBA T ] i BEB B(A0D8J
-8A0A(B BBBA T @b < BEB B(A0D8J
-8A0A(B BBBA T L f BEB B(A0D8J
-8A0A(B BBBA T j BEB B(A0D8J
-8A0A(B BBBA T o BEB B(A0D8J
-8A0A(B BBBA T T `s BEB B(A0D8J
-8A0A(B BBBA T w [ BEB B(A0D8J
-8A0A(B BBBA T { . BEB B(A0D8J
-8A0A(B BBBA T \ 8 BEB B(A0D8J
-8A0A(B BBBA T ԇ BEB B(A0D8J
-8A0A(B BBBA T Ȉ BEB B(A0D8J
-8A0A(B BBBA T d z BEB B(A0D8J
-8A0A(B BBBA T 8 M BEB B(A0D8J
-8A0A(B BBBA L & BBH B(D0F8G
-8D0A(B BBBD @ . K^ $ 8 < 0 4 T 8v BBA A(D0e(A ABBD BBB B(A0A8DP8A0A(B BBB, , [ ͆ AID t
-DAA D \ ( BBE A(A0
-(A BBBA L L AAG ]
+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 ]
CAF [
ACA R
-CAJ JCA L AAG ]
+CAJ JCA L , $ AAG ]
CAF [
ACA R
-CAJ JCA < D Ʌ BED I(D
-(D ABBA < BED I(D
-(D ABBA D l BBB D(A0D@0A(A BBB T Hl ' BIE B(A0A8DY
-8D0A(B BBBA T d `U# BDE B(A0D8G +
-8A0A(B BBBB D hϻ{ dž BBB D(A0G
-0D(A BBBG . K^ л л , L л[ R AID t
-DAA D $ 2 BBB D(A0D@0A(A BBB T pлl BIE B(A0A8DY
-8D0A(B BBBA T ջ BDE H(A0A8G<
-8D0A(B BBBK . K^ 4 ڻ L ڻ , ڻ[ AID t
-DAA D BBB D(A0D@0A(A BBB T 4 ڻK / BIB E(A0A8G8
-8D0A(B BBBA L H BDB B(D0A8M
-8D0A(B BBBA X. K^ O HF O HF @V HI V HI $ ^ AG b
-AA L s BBB A(A0M
-0D(A BBBF , J AID
-AAA , ( , AID
-AAA T L BBB B(I0A8G
-8D0A(B BBBH T 0 ( BBB B(A0D8J
-8A0A(B BBBG T BBB B(A0D8J
-8A0A(B BBBG T T ` ă BBB B(A0D8J
-8A0A(B BBBG T BBB B(A0D8J
-8A0A(B BBBG T ` BBB B(A0D8J
-8A0A(B BBBG d ( BEB B(A0D8DPi
+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
8A0A(B BBBI
-8G0A(B BBBJ D l p BBB D(A0D@
-0A(A BBBA D ~ BIB A(D0D`2
-0D(A BBBA L T \ BIB A(D0D
-0D(A BBBA L = BIB A(D0D
-0D(A BBBA $ 0 V ADD JAAT h BBB B(A0D8G
-8A0A(B BBBE L t K BEB E(I0H8D@
-8D0A(B BBBA < l! BFA D0}
- FABE D FAB T ! e BHB B(A0A8G
-8A0A(B BBBI T \! " Ձ BEE E(A0A8GK
-8A0A(B BBBA T ! (# BBB H(D0F8G
-8D0A(B BBBA " hL AP " hL AP " . K^ # PL $# HL <# @L T# 8L l# @L # HL D # V BBB D(A0D@0A(A BBB T <# L ݁ BIB E(A0A8D6
-8D0A(B BBBA T # @P BIB E(A0A8DF
-8D0A(B BBBA T # T y BIB E(A0A8D6
-8D0A(B BBBA L D$ X G BBB B(A0I8G
-8D0A(B BBBF T $ [g 1 BBB B(I0A8Gc
-8D0A(B BBBG T $ (`g = BBB B(I0A8Gc
-8D0A(B BBBG L % @e BBB B(A0A8G&
-8A0A(B BBBA L <& sy BEB E(D0A8G@
-8A0A(B BBBD T % BJB B(A0A8G
+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 <& H c BEB B(F0A8G
-8A0A(B BBBD T & ۀ BBG B(A0G8G
-8D0A(B BBBD ' . K^ ' 00 Hg ' @7 Hj ' `2 I $ ( ^ AG b
-AA T ' < Ӏ BBB B(I0A8GD
-8D0A(B BBBF T ' < BBB B(I0A8GD
-8D0A(B BBBF T 4(
BBB B(D0D8G
-8D0A(B BBBF T ( | BBE E(D0A8G
-8D0A(B BBBI D ) 4 BBB D(A0D@0A(A BBB T ,) Ƽ BIB E(A0A8G
-8D0A(B BBBA T ) ˼ Z BIB E(A0A8G
-8D0A(B BBBA T ) Hм 4 BEB B(D0D8G
-8A0A(B BBBA T 4* ּ Q BEB B(D0D8G
-8A0A(B BBBA T * 8ݼ n BEB B(D0D8G
-8A0A(B BBBA T * BBE E(E0D8G
-8A0A(B BBBA L <+ X BBB G(D0A8M
-8D0A(B BBBG 4, 8. K^ L, 0 Hg l, 7 Hj , \ AG RAT , [ BEE E(D0A8G'
-8D0A(B BBBG D - ~ BBB D(A0D@0A(A BBB T , @ BIB E(A0A8DF
-8D0A(B BBBA T , BIB E(A0A8DN
-8D0A(B BBBA L T- BBB B(I0A8D
-8D0A(B BBBD T - U o BBB B(A0F8G
-8A0A(B BBBG L - BBB B(I0A8D
-8D0A(B BBBD . . K^ / @ $/ 8 , . @[ AID t
-DAA L l/ p BEB E(A0A8GN
-8A0A(B BBBA T / BBE B(D0A8G
-8D0A(B BBBG D 0 } BBB D(A0D@0A(A BBB T / C ~ BIB E(A0A8G0
-8D0A(B BBBA L 0
~ BDB B(A0D8M
-8D0A(B BBBA 1 . K^ 1 +0 Hg <1 +7 Hj \1 + BEB E(D0E8D
+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
8A0A(B BBBA |
8A0A(B BBBA p
8A0A(B BBBA
-8A0A(B BBBA q8A0A(B BBB 2 . BEE F(D0D8D
+8A0A(B BBBA q8A0A(B BBB G BEE F(D0D8D
8A0A(B BBBA t
8A0A(B BBBA p
8A0A(B BBBA
-8A0A(B BBBA q8A0A(B BBB , 2 1 A|
+8A0A(B BBBA q8A0A(B BBB , H A|
C K
E R
-A $ 2 2^ AG b
-AA T l2 2| C} BBE E(D0A8G
-8D0A(B BBBI T 2 7| )} BBE E(D0A8G
-8D0A(B BBBI D 3 { BBB D(A0D@0A(A BBB T d3 < | BIB E(A0A8D6
-8D0A(B BBBA d d4 AB BBB B(A0A8G@x
+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
8A0A(B BBBE
-8A0A(B BBBF T $4 A -| BBE F(A0A8G
-8A0A(B BBBA T |4 HW N| BBB B(D0D8J}
-8D0A(B BBBD L 4 _ G| BBB B(K0A8J
-8A0A(B BBBF 4 $5 fq e| BAI A
-ABA T \5 8g C| BBB B(A0A8L
+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 \6 . K^ t6 x 6 x 4 5 xg s| BAD D
-ABA T 46 x G| BBB B(I0A8D%
-8D0A(B BBBH D 47 xz BBB D(A0D@
-0A(A BBBA T 6 { { BIB E(A0A8D6
-8D0A(B BBBA 4 7 J BDA q
+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
ABE A
-ABA < d7 F f{ BBA A(Gq
-(A ABBA L L8 0 BBB B(A0A8Gp8A0A(B BBB L 8 M BBB B(A0D8Dp18A0A(B BBB L 8 BBB B(A0A8Gp8A0A(B BBB L <9 0M BBB B(A0D8Dp18A0A(B BBB L 9 0 BBB B(A0A8Gp8A0A(B BBB < 49 F y BBA A(Gq
-(A ABBA L : M BBB B(A0D8Dp18A0A(B BBB L l: BBB B(A0A8D
-8A0A(B BBBA L : ` BBB E(D0A8D`
-8A0A(B BBBA ; m , $; h ADD X
-AAA d T; ؒ BHD G0W
+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
AABH F
AABF g
AABE D
- FABK , ; ADD ]
-AAA d ; BBB E(I0D8DP
+ FABK , Q ADD ]
+AAA d Q 0 BBB E(I0D8DP
8D0A(B BBBC |
-8D0A(B BBBA , T< 8 ADD [
-AAA t < BIA D(G0C
+8D0A(B BBBA , <R h ADD [
+AAA t lR BIA D(G0C
(A ABBH o
(A ABBC g
(A ABBC D
-(F ABBA d < E BBB B(A0A8DP
+(F ABBA d R PE BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l < Wv BBB E(I0D8D
+8F0A(B BBBA l R 8 C BBB E(I0D8D
8A0A(B BBBG
-8D0A(B BBBE = u L = BEB B(A0A8DP
-8A0A(B BBBH l = ' u BBB E(D0I8D`M
+8D0A(B BBBE S u L S P BEB B(A0A8DP
+8A0A(B BBBH l S ' BBB E(D0I8D`M
8D0A(B BBBJ
-8D0A(B BBBB T > pU Su BBB B(A0A8J
-8A0A(B BBBA ? x^ L ? BEB B(A0A8DP
-8A0A(B BBBH l > P' _u BBB E(D0I8D`M
+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 BBBJ
-8D0A(B BBBB T 4?
u BBB B(A0A8JX
-8A0A(B BBBA 4@ νV L L@ Ͻ BEB B(A0A8DP
-8A0A(B BBBH l ? Ͻ /u BBB E(D0I8D`:
+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 BBBE
-8D0A(B BBBJ T d@ ҽZ t BBB B(A0A8J
-8A0A(B BBBA d dA (! BHF K(H0A8D`}
-8A0A(B BBBA d8C0A(B BBB L A BEB B(A0A8DP
-8A0A(B BBBH | B f BIB B(A0D8GPz
+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
8A0A(B BBBA v
8A0A(B BBBH D
-8F0A(B BBBE d A p /t BBJ B(A0A8G`
+8F0A(B BBBE d W k BBJ B(A0A8G`
8D0A(B BBBE N
-8A0A(B BBBH T \B (4 s BBB B(A0A8J]
-8A0A(B BBBA D B t BGA D(M
-(D ABBF C ߌ. K^ C 0 Hg C 7 Hj L TC t BBB B(A0I8G
-8D0A(B BBBD L C 0 s BBB B(A0I8G
-8D0A(B BBBD T C s BEG E(A0G8G-
-8D0A(B BBBD \ LD 3t BFE A(A0D`W
+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
0D(A BBBK f
-0D(A BBBG D TE l BBB D(A0D@0A(A BBB T D b s BIE B(A0A8DQ
-8D0A(B BBBA T LE \ ls BIE B(A0A8DI
-8D0A(B BBBA LF . K^ dF " $ |F "^ AG b
-AA F "$ < F flD BBD A(D@0(A ABB T TF # r BIB E(A0A8DF
-8D0A(B BBBA T F ' Xr BIB E(A0A8DF
-8D0A(B BBBA T G @, &r BIB E(A0A8DF
-8D0A(B BBBA L \G 06 q BBB B(A0A8Gb
-8A0A(B BBBC < G x3 q BEA A(Gc
-(D ABBA < G 3 q BEA A(Gc
-(D ABBA < ,H 84 q BEA A(Gc
-(D ABBA , lH 4Y hq ADG i
-AAA , H 4Y Dq ADG i
-AAA tI 4 N I 5 R < I 5 BBG A(G@
-(A ABBD I 5% T TI 5 p BBB B(A0A8G
-8A0A(B BBBA T I 8?Z
- np BBB B(A0A8Js
-8A0A(B BBBA $ J @IV ADD JAA4 J xI BEA A(D0(A ABBd dJ J1 o BHB B(D0A8Dpv
+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
8C0A(B BBBG {
-8D0A(B BBBA T J J o BBB B(A0A8G
-8A0A(B BBBA T $K T Wo BBB E(I0A8K}
-8D0A(B BBBF L |K HV /o BEB B(D0A8Gpu
-8A0A(B BBBA L K W n BEB B(D0A8Gpu
-8A0A(B BBBA T L X n BBB E(I0A8K}
-8D0A(B BBBF L tL p[* {n BEB B(D0A8Gp
-8A0A(B BBBA $ lM P]V ADD JAA, M ] JP
+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 , M ` JP
+A , c JP
I ^
-B f
-J k
-A L LM b m BBB B(A0A8G}
-8A0A(B BBBH < M Hx ?n BLD D(G0
-(A ABBE D N y BHI H(D0D@
-0A(A BBBA T $N yX m BBB B(A0A8GC
-8A0A(B BBBA T |N ؉
- m BGE E(G0D8G<
-8D0A(B BBBB T N 0
m BBB H(D0A8L"
-8D0A(B BBBB , O JP
-I ^
B j
F k
-A , P H JP
+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 L O ئ n BBB B(A0A8G~
-8A0A(B BBBG T O x
- n BGE E(G0D8G<
-8D0A(B BBBB T 4P Ǿ
,o BBB H(D0A8L"
-8D0A(B BBBB , 4Q Xվ JP
+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 , dQ JP
+A , g [ JP
I ^
B j
F k
-A L P xھ o BBB B(A0A8G~
-8A0A(B BBBG T <Q
- "p BGE E(G0D8G<
-8D0A(B BBBB T Q `
ap BBB H(D0A8L"
-8D0A(B BBBB R Ќ. K^ R R R R S $S <S TS lS S , R [ ;p AID t
-DAA , $S [ p AID t
-DAA , TS 0 [ o AID t
-DAA , S ` [ o AID t
-DAA , S [ o AID t
-DAA T _- AG cA T " DN
-N A < T BBD A(D@(A ABB 4 dT
-k o BHA GP
- DABA 4 T k n BHA GP
- DABA 4 T
k n BHA GP
- DABA 4 U 8k n BHA GP
- DABA 4 DU pk n BHA GP
- DABA D |U S n BEB A(A0Gh
-0A(A BBBA T U l
- bn BBL B(A0A8JL
-8A0A(B BBBD T V W Ln BDB B(A0A8J
-8A0A(B BBBJ T tV + n BEB B(A0G8J
-8A0A(B BBBD T V x3X vn BDB B(A0A8JV
-8A0A(B BBBJ T $W FX n BEB B(A0G8J
-8A0A(B BBBD T |W N n BDB B(A0A8J
-8A0A(B BBBK T W @b7 o BEB I(A0D8G
-8A0A(B BBBA T ,X (i'
- $o BDB B(A0A8J
-8A0A(B BBBA T X s o BEB B(A0G8JM
-8A0A(B BBBD T X w-
- xo BDB B(A0A8J
-8A0A(B BBBD Y Ɍ. K^ Y Z\ AG RAT lY ( yo BBB I(A0A8G
-8A0A(B BBBA T Y :o BBB I(A0A8GL
-8A0A(B BBBA Z (Ɍ. K^ Z 02 Hb
-F A Z P7 Hj [ Y\ AG RAT Z Pf n BBB B(I0A8G
-8D0A(B BBBF T Z h qn BBE B(D0A8G
-8D0A(B BBBG D [ `Y BBB D(A0D@0A(A BBB T [ n BIB E(A0A8D6
-8D0A(B BBBA L [
- Sn BBB G(D0A8G
-8A0A(B BBBA \ @nj. K^ T L\ n BBB B(A0A8G
-8A0A(B BBBA $ L] гm AG q
-AA $ t] u AG y
-AA ] ~Y\ AG RAD ] Y BBB D(A0D@0A(A BBB T \] m BIB E(A0A8DV
-8D0A(B BBBA T ] `| Um BBE E(D0A8G
-8D0A(B BBBE T ^ T ;m BBB B(I0A8G
-8D0A(B BBBH T d^ ¿ 7m BBB B(A0G8J
-8D0A(B BBBE d_ Č. K^ , ^ n AID
-AAA D _ Y BBB D(A0D@0A(A BBB T L_ h %n BIE B(A0A8D
-8D0A(B BBBA L` 0Č. K^ d` ܿ |` ܿ ` ܿ ` ܿ L ` ܿq m BBB B(A0I8G
-8D0A(B BBBD T l` pm BBB B(A0I8G
-8D0A(B BBBD D la Y BBB D(A0D@0A(A BBB T a L 4m BIE B(A0A8D9
-8D0A(B BBBA T da L m BIE B(A0A8D9
-8D0A(B BBBA T a Z l BBB G(D0G8G
-8D0A(B BBBD T b
- l BBB G(D0G8G
-8D0A(B BBBE c . K^ ,c Dc x , b [ l AID t
-DAA T b A l BEE E(D0A8GY
-8D0A(B BBBE D c X BBB D(A0D@0A(A BBB T c ` | Yl BIE B(A0A8Di
-8D0A(B BBBA T c h 'l BGH B(A0D8G
-8D0A(B BBBA d . K^ $ d AG u
-AA $ e AG }
-AA D De jY BBB D(A0D@0A(A BBB T d pL 3l BIE B(A0A8D9
-8D0A(B BBBA T <e h8 l BBB J(A0A8G>
-8D0A(B BBBA <f о. K^ Tf 89 Hp tf X@ Hs f Y\ AG RAD f $Z BBB D(A0D@0A(A BBB T Tf #k BIB E(A0A8D6
-8D0A(B BBBA Tg 轌. K^ $ lg 0 Y AK Y
-AA L f h j BEB E(I0H8D@
-8D0A(B BBBA T <g j BBE B(A0A8J
-8A0A(B BBBA , g &> j AID W
-DAA lh &0 Hg h &7 Hj T h ' Sj BBB B(A0I8G6
-8D0A(B BBBD D i Y BBB D(A0D@0A(A BBB T h +L j BIE B(A0A8D9
-8D0A(B BBBA T h /' i BBB E(A0A8Ga
-8A0A(B BBBA i p. K^ j E ,j E L Dj E AAG e
+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
+
+8A0A(B BBBD ā . K^ ܁ ɾ ɾ L ɾ AAG e
CAF {
ACI R
-CAJ JCA , i F[ j AID t
-DAA < j HFq i BBA A(D0[
-(A ABBA D k F BEB B(A0A8
-0A(B BBBA D Lk L BEB B(A0A8
-0A(B BBBA L k Q
BIE E(D0A8J
-8A0A(B BBBA k x_% k b% l e% ,l h% L Dl k BBB B(D0D8G@o
-8A0A(B BBBH l u l w T l x g BDB B(D0G8GA
-8D0A(B BBBF < m XWD BBD A(D@0(A ABB T l | g BIB E(A0A8D
-8D0A(B BBBA m AY m AY m AY < n y BDD H
+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
ABK A
-ABL < Tn BBD D(G0b
-(A ABBE n Xe aC , n AET
+ABL < BBD D(G0b
+(A ABBE \ Xe aC , | AET
AE
-AA D n x BBB B(A0A80A(B BBB L ,o BIB B(A0D8G
-8D0A(B BBBD d |o `S BBB E(D0D8D
+AA D x BBB B(A0A80A(B BBB L
+ BIB B(A0D8G
+8D0A(B BBBD d D `S BBB E(D0D8D
8A0A(B BBBH
-8A0A(B BBBG D o X BBB B(A0A8
-0A(B BBBA D ,p c BEB B(A0A8H
-0A(B BBBA D tp 8' BBE B(A0A8
-0A(B BBBA p L p (3 BBB B(A0A8G3
-8A0A(B BBBB L $q BEB B(A0A8Gk8A0A(B BBB 4 p XD c AC
P\
- A L q p BBI B(A0A8G
-8A0A(B BBBA , q AC
BEH
- D d ,r `S BBB E(D0D8D
+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 BBBI
-8A0A(B BBBG L r X BBB B(D0A8J:
-8A0A(B BBBA D r ( BIB B(A0A8
-0A(B BBBF L ,s u BBB B(D0A8G
-8A0A(B BBBA d |s 3 BBB E(D0D8D
+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 BBBB
-8A0A(B BBBK D s BBB B(A0A8
-0A(B BBBA D ,t Pt BEB B(A0A8Y
-0A(B BBBA D tt BBB B(A0A8
-0A(B BBBA < t P BBB A(A0
-(A BBBA D Tt c` AC
BEO2
+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
A f
A
- K T t /` BBA A(Gp
+ K T | ؗ BBA A(Gp
(A ABBG
-(A ABBK 4 u `~ AC
BGM
+(A ABBK 4 d `~ AC
BGM
I
- F t ,u _ BBB A(A0JW
+ F t s BBB A(A0JW
0A(A BBBG
0A(A BBBD
-0A(A BBBD T u ;_ BEB B(A0A8Jp
-8A0A(B BBBA D v ( BBB A(A0G`
-0A(A BBBD D Dv @0 ^ BBB D(A0G@D
-0A(A BBBB 4 v 2 ^ AC
PC
- I D lw D BKB B(G0A8t
-0A(B BBBA L w K% BEE E(D0A8G
-8A0A(B BBBK 4 \w [P ] AC
P\
- A \ <x d BFB B(A0A8
-0A(B BBBA 0A(B BBB L x gP
+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 , x q$ AM
DL
- E d y rS BBB E(D0D8D
+8A0A(B BBBA , $ AM
DL
+ E d S BBB E(D0D8D
8A0A(B BBBB
-8A0A(B BBBC d y v BEE B(E0A8DX7
+8A0A(B BBBC d L BEE B(E0A8DX7
8A0A(B BBBA S
-8A0A(B BBBA 4 Dy }
- \ AC
P
- A D $z Hh BBB B(A0A8J
-0A(B BBBA D y pN [ AC
BEOR
+8A0A(B BBBA 4 $
+ Ԉ AC
P
+ A D H h BBB B(A0A8J
+0A(B BBBA D p
N c AC
BEOR
A f
A
- G T z x j[ BBA A(G`
+ G T x : BBA A(G`
(A ABBD {
-(A ABBG 4 { H AC
BGM
+(A ABBG 4 Ԓ H AC
BGM
E
- F T z 5 Z BEB B(D0A8J
-8A0A(B BBBA T z Z BBB E(A0A8J`
-8A0A(B BBBG 4 L{ x
- Z AC
P^
- A T { ж nZ BBB B(A0A8J
-8A0A(B BBBA | (3 D0n | . K^ | 8 | 0 | ( | 0 L } 8 BBB B(A0A8D`/
-8A0A(B BBBI L d} BBB B(A0A8D`s
-8A0A(B BBBE } H\ AG RAD } @H BBB D(A0D@0A(A BBB T t} Y BIB E(A0A8D6
-8D0A(B BBBA T } H Y BIB E(A0A8D6
-8D0A(B BBBA T $~ i Y BBB B(A0D8R
-8A0A(B BBBF , |~ > Y AID W
-DAA T ~ i fY BBB B(A0D8R
-8A0A(B BBBF , > mY AID W
-DAA < BBG A(G@
-(A ABBG T t p
Y BHB E(A0A8G
-8D0A(B BBBA T Y BHB E(A0A8G
-8D0A(B BBBA ̀ . K^ H
- @
- , l H
-[ eZ AID t
-DAA < D x
- BBB A(A0
-(A BBBA D ( = BEB B(A0A8
-0A(B BBBA L ́ K BBB B(A0A8G@d
-8A0A(B BBBA D BBB D(A0D@
-0A(A BBBA T xB %Y BIB B(A0D8Dg
-8D0A(B BBBA < p BBD D(G0b
-(A ABBE L K BBB B(A0A8D@28A0A(B BBB d L S BBB E(D0D8D
+ 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
8A0A(B BBBD
-8A0A(B BBBG L ! BBB B(A0A8Dxv
-8A0A(B BBBA L 8'
+8A0A(B BBBG L | BBB B(A0A8Dxv
+8A0A(B BBBA L ̛
BIB B(A0A8G
-8A0A(B BBB < 1 [W AC
FM-
+8A0A(B BBB < h + AC
FM-
A X
- C T : /W BBA A(Gp
+ C T ̛ H BBA A(Gp
(A ABBG u
-(A ABBE 4 D < V AF
P
- G 4 $ @8 AC
BGM
+(A ABBE 4 $ AF
P
+ G 4 8 AC
BGM
H
- F l D zV BBB B(A0D8Gc
+ F l J BBB B(A0D8Gc
8A0A(B BBBG
-8A0A(B BBBG T $ T 2V BEB B(D0D8Je
-8A0A(B BBBD T | \v V BDH B(A0A8J
-8D0A(B BBBC | . K^ L d BBB B(A0D8D
-8A0A(B BBBA L e] BBB B(A0D8G
-8A0A(B BBBC L 4 h BBB B(A0D8G
-8A0A(B BBBE $ m AG r
-AA $ m AG z
-AA < , 8n T BBA I(D@p
-(D ABBA T l n T BBE B(D0A8G
-8D0A(B BBBB D l v? BBB D(A0D@0A(A BBB T Hs bT BIB E(A0A8D6
-8D0A(B BBBA T d w! 3T BBE B(A0A8L
-8A0A(B BBBD d Ȝ. K^ T Ԉ U BBB B(A0I8G
-8D0A(B BBBD D ԉ ? BBB D(A0D@0A(A BBB T t R WU BIE B(A0A8DA
-8D0A(B BBBA t 蛌. K^ T T BBB B(I0A8D
-8D0A(B BBBE , " < D BGE D(A0j
-(A BFBH D ? BBB D(A0D@0A(A BBB T $ ` S BIB E(A0A8G
-8D0A(B BBBA L | Ȫ S BGB B(A0D8G
-8A0A(B BBBC t . K^ p h ` Ԍ h p0 Hg 7 Hj , [ S AID t
-DAA \ б} AG sA | 0} AG sA AG {A AG {A ܍ p AG vA г AG ~AL t @r R BBB B(E0A8J@
-8A0A(B BBBA $ l p^ AG b
-AA T C TR BBB B(A0D8Ra
-8A0A(B BBBA , D > sR AID W
-DAA T t SR BBB B(A0D8R9
-8A0A(B BBBA , ̎ X> mR AID W
-DAA L h MR BBB B(I0A8D
-8D0A(B BBBA L L 7R BBB B(I0A8D
-8D0A(B BBBA L !R BBB B(I0A8D
-8D0A(B BBBA L H R BBB B(I0A8D
-8D0A(B BBBA D BBB D(A0D@
-0A(A BBBA D @ Q BIB A(D0D`+
-0D(A BBBA D ̐ Q BIB A(D0D`+
-0D(A BBBA 4 k iQ BHA GP
- DABA D L JQ BIB A(D0D`+
-0D(A BBBA 4 k (Q BHA GP
- DABA D ̑ Q BIB A(D0D`+
-0D(A BBBA D P BIB A(D0D`+
-0D(A BBBA 4 \ Hz P BHA GP
- DABA T P BBB E(D0A8G
-8A0A(B BBBC D P BDB D(G0Dy
-0D(A BBBA T 4 P BBE H(A0F8DE
-8D0A(B BBBB L xP BBG D(G0D
-0D(A BBBA T ܓ vP BBB J(A0A8G&
-8D0A(B BBBA T 4 ` XP BBB B(A0D8G
-8A0A(B BBBI T E Q BBB H(D0F8G
-8D0A(B BBBG AR AY ̕ . K^ L < u P BBB B(A0I8Dp
-8D0A(B BBBF L u P BBB B(A0I8Dp
-8D0A(B BBBF L ܕ ( u P BBB B(A0I8Dp
-8D0A(B BBBF L , Xu RP BBB B(A0I8Dp
-8D0A(B BBBF D $ 5 BBB D(A0D@0A(A BBB T Ė @l O BIE B(A0A8DY
-8D0A(B BBBA T Xl O BIE B(A0A8DY
-8D0A(B BBBA T t p l rO BIE B(A0A8DY
-8D0A(B BBBA T ̗ l @O BIE B(A0A8DY
-8D0A(B BBBA ̘ . K^ , l [ N AID t
-DAA D D 5 BBB D(A0D@0A(A BBB T p\ ^N BIE B(A0A8DI
-8D0A(B BBBA T < x /N BDB E(G0A8G3
-8D0A(B BBBD < . K^ T l < BBA A(D@
-(A ABBK < h# BBA A(DPC
-(A ABBE L 8&h gM BBB B(D0A8R
-8A0A(B BBBK , ܚ X)> jM AID W
-DAA L h)h JM BBB B(D0A8R
-8A0A(B BBBK , \ ,> MM AID W
-DAA D 4 4 BBB D(A0D@0A(A BBB T ԛ P, L BIB E(A0A8D6
-8D0A(B BBBA T , 0 L BIB E(A0A8D6
-8D0A(B BBBA T 4 L BBE B(D0A8G
-8D0A(B BBBG T ܜ 9 gL BBE B(D0A8G
-8D0A(B BBBG T 4 P> ML BBE B(D0A8G
-8D0A(B BBBG T Cu 3L BBG B(G0D8G
-8D0A(B BBBG T @U gM BBG B(G0D8G
-8D0A(B BBBC h. K^ 3 DN
-E A $ @i^ AG b
-AA < D xi BEA A(D@
-(A ABBA < (j BEA A(D@
-(A ABBA ğ j L ܟ lp BEA A(D0H
+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
+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 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 t 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 s
+ACA V
+CAF JCA L , ADD e
+CAF
+ACA V
+CAF JCA L | 8 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
(A ABBE D
-(A ABBA T m M BEE E(D0A8G
-8D0A(B BBBE T ܟ 8r, uM BBE B(D0A8G
-8D0A(B BBBK 4 ܠ wJ BDA q
+(A ABBA T t ;s BEE E(D0A8G
+8D0A(B BBBE T , !s BBE B(D0A8G
+8D0A(B BBBK 4 J BDA q
ABE A
-ABA d (wg BLB B(A0A8D@q
+ABA d g BLB B(A0A8D@q
8A0A(B BBBE
-8A0A(B BBBF D | (1 BBB D(A0D@0A(A BBB T w sL 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 L < BLB H(A0D8DPV
-8A0A(B BBBG L pd J BBB E(D0D8J
-8A0A(B BBBA T 4
- J BEE B(A0A8N
-8A0A(B BBBB T 8 J BBE B(A0H8J
-8A0A(B BBBB T p J BBB B(H0A8J
+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 BBBA , < > L AID W
-DAA T l ( K BBB G(D0A8M
-8D0A(B BBBC l . K^ T i K BBB B(A0D8R
-8A0A(B BBBF , d > K AID W
-DAA < <
-.D BBD A(D@0(A ABB T ԥ vK BIB E(A0A8D6
-8D0A(B BBBA T , Ȼ DK BBB G(A0A8J
-8A0A(B BBBH , . K^ D x \ p , ̦ x[ K AID t
-DAA D - BBB D(A0D@0A(A BBB T D `L J BIE B(A0A8D9
-8D0A(B BBBA $ D XV ADD JAAd ħ 1 \J BHB B(D0A8Dpv
+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
8C0A(B BBBG {
-8D0A(B BBBA T , h I BBB E(I0A8K}
-8D0A(B BBBF d I BEB B(D0A8Gp
+8D0A(B BBBA T 7 +p BBB E(I0A8K}
+8D0A(B BBBF d t : p BEB B(D0A8Gp
8E0A(B BBBC
-8E0A(B BBBA T l wI BDB B(A0A8Gp
-8A0A(B BBBA }. K^ 4 $-\ AG RAT I BBB B(A0I8G
-8D0A(B BBBE D @ BBB B(A0A8D`8A0A(B BBB o T d GI BBB E(D0A8G
-8D0A(B BBBE T HX }I BBA D(G`
+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 P 9I BBB B(D0A8G
-8A0A(B BBBA D + BBB D(A0D@0A(A BBB T H BIB E(A0A8DV
-8D0A(B BBBA T H BBB B(D0A8M>
-8D0A(B BBBK {. K^ $ < T l x ̭ p h ` X , P D H \ P t X ` h p Ԯ x 4 L d | x p į h ܯ ` X P $ H < @ T 8 l @" X `" x ̰ " " , " D \ " t " " Ա 8 @" X `" 4 x L d B A@ B A@ 8 Av IJ . Al . Al (. Al $ 8. Al D HB A@ d x. Al 8 Av B A@ ij 8 Av , D B BBE A(A0
-(A BBBA L ` AAG k
+(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
CAH |
CAH R
-CAB JCA L ܴ AAG k
+CAB JCA L 0 AAG k
CAH r
CAJ R
-CAJ JCA L , AAG k
+CAJ JCA L AAG k
CAH r
CAJ R
-CAJ JCA L | AAG k
+CAJ JCA L T AAG k
CAH r
CAJ R
-CAJ JCA L ̵ AAG k
+CAJ JCA L P AAG k
CAH r
CAJ R
-CAJ JCA L @ AAG k
+CAJ JCA L AAG k
CAH F
CAF R
-CAJ JCA L l AAG k
+CAJ JCA L D AAG k
CAH r
CAJ R
-CAJ JCA L AAG k
+CAJ JCA L AAG k
CAH |
CAH R
-CAB JCA L p AAG k
+CAB JCA L AAG k
CAH F
CAF R
-CAJ JCA L \ AAG k
+CAJ JCA L 4 P AAG k
CAH |
CAH R
-CAB JCA @R ķ ܷ " 4 t BJD G(D@R(A ABB , 4 D BLD G AAB | , g> AID c
-DAA , H> AID c
-DAA , L h )> AID c
-DAA , |
-> AID c
-DAA , ( = AID c
-DAA 4 | BLD I\ AAB L BHB J(D0A8DPe
-8A0A(B BBBB " < $ " BLA "
+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 "
ABD q
-ABD < d x$ BLA
+ABD < < BLA
ABH N
-ABG ' '2 Ժ ) )5 (-" < @. BLA "
+ABG | X P2 x p5 " < BLA "
ABD q
-ABD $ \ 0^ AG b
-AA T ܺ 0 ; BBE E(D0A8G
-8D0A(B BBBI L 4 p3Y ; BBB E(D0A8Lp
-8D0A(B BBBG D , 4 BBB D(A0D@
-0A(A BBBA 4 ̻ 5w ; BHA GP
- DABA 4 7w : BHA GP
- DABA 4 < h8w : BHA GP
- DABA 4 t 9w : BHA GP
- DABA 4 :w : BHA GP
- DABA 4 @<w p: BHA GP
- DABA 4 =w Q: BHA GP
- DABA 4 T >w 2: BHA GP
- DABA 4 @w : BHA GP
- DABA 4 Ľ `Aw 9 BHA GP
- DABA L Bl 9 BBL E(A0A8Gy
-8D0A(B BBBD L L Cl 9 BBL E(A0A8Gy
-8D0A(B BBBD L Dl 9 BBL E(A0A8Gy
-8D0A(B BBBD L Fl `9 BBL E(A0A8Gy
-8D0A(B BBBD D < (G 99 BBE D(K0J`
-0D(A BBBC L L5 9 BBB B(A0I8G
-8D0A(B BBBD L Կ O5 !9 BBB B(A0I8G
-8D0A(B BBBD L $ R+ %9 BBB B(A0I8G
-8D0A(B BBBA L t `T+ 9 BBB B(A0I8G
-8D0A(B BBBA L @V+ 8 BBB B(A0I8G
-8D0A(B BBBA T Xw 8 BBG B(A0G8J0
-8D0A(B BBBA T l HZW 8 BDB B(A0G8G
-8D0A(B BBBA T P\W 8 BDB B(A0G8G
-8D0A(B BBBA T X^W 8 BDB B(A0G8G
-8D0A(B BBBA T t ``W a8 BDB B(A0G8G
-8D0A(B BBBA T hb ?8 BBB G(A0G8JG
-8D0A(B BBBA T $ dW 8 BDB B(A0G8G
-8D0A(B BBBA T | fw 7 BBG B(A0G8J0
-8D0A(B BBBA T h 7 BBB G(A0G8JG
-8D0A(B BBBA T , kw 7 BBG B(A0G8J0
-8D0A(B BBBA , c. K^ L D m AAG ]
+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 ]
CAF k
ACA R
-CAJ JCA $ Xm^ AG b
-AA D m BBE A(A0D@n
-0A(A BBBA D (n BBB D(A0D@
-0A(A BBBA L L oh BBB B(D0A8DP9
-8A0A(B BBBA t AL t, d t BBB B(A0A8D`
+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`
8A0A(B BBBE d
-8A0A(B BBBA T Pv 5 BBE B(D0A8G
-8D0A(B BBBC L zi k5 BBE E(D0A8Dp2
-8D0A(B BBBJ D BBB D(A0D@
-0A(A BBBA T ` 4 BIB E(A0A8DV
-8D0A(B BBBA L $ 4 BIB B(A0D8DpO
-8D0A(B BBBA T , 4 BBB B(D0A8J
-8A0A(B BBBA , Ќq r4 BDA TAT L \ BOB B(A0A8Dp
-8A0A(B BBBJ < BBG A(G@
-(A ABBG T D p 3 BEB B(A0A8JB
-8A0A(B BBBE D L \ BOB B(A0A8Dp
-8A0A(B BBBJ T 3 BBB B(A0A8J
-8A0A(B BBBH D \ & y3 BDA 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@
DABE L
- DABA L O3 BEB E(I0H8D@
-8D0A(B BBBA T '3 BBB B(A0D8G\
-8A0A(B BBBA T L +3 BBB B(A0D8G
-8A0A(B BBBI L \. K^ T H 3 BBB E(A0A8NH
-8A0A(B BBBC , > 3 AID W
-DAA , D > 3 AID W
-DAA AU < F $ T X^ AG b
-AA D | BBB D(A0D@
-0A(A BBBA D Z 2 BBE A(A0G@
-0A(A BBBC d d 2 BJE B(A0A8D@Y
-8C0A(B BBBB i8A0A(B BBB L x} O2 BBB B(A0D8DP
-8A0A(B BBBA AL L < 1 BEE B(A0D8G
-8A0A(B BBBA d 4 (" BBB B(A0A8DpO
+ 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
8A0A(B BBBI j
-8A0A(B BBBA d r BBB B(A0A8Dp
+8A0A(B BBBA d @6r BBB B(A0A8Dp
8A0A(B BBBI j
-8A0A(B BBBA D BBB D(A0D@
-0A(A BBBA T `B 0 BIB B(A0D8Dg
-8D0A(B BBBA T XI c0 BIB B(A0D8Dn
-8D0A(B BBBA T T P 10 BBB B(A0A8J
-8A0A(B BBBA D 8 H0 BFB A(D0J
-0A(A BBBB < K0 BEA A(G@}
-(D ABBJ L 4 0 BBE D(C0Ju
-0D(A BBBF L 0 BBE D(C0Ju
-0D(A BBBF d | i BBB B(A0A8Dm
+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
-8A0A(B BBBG L < ( / BEB B(A0A8L
-8A0A(B BBBD L 4 BEE B(A0A8D`
-8A0A(B BBBB , ( BDA n
-ABH 5 T $ / BBB B(A0A8J
-8A0A(B BBBG $ ( T I/ BBE E(D0A8D
-8D0A(B BBBI T -/ BBE B(A0A8G
-8A0A(B BBBA T D 0 / BBB B(K0D8G
-8A0A(B BBBH D XS. K^ T p / BBE B(A0A8NE
-8A0A(B BBBA , > / AID W
-DAA , < (> / AID W
-DAA 8 AU 4 8F L p AU l pF A[ $ . BFA D@ , b D\
+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\
H P
H Q
-G I , P b D\
+G I , \ b D\
H P
H Q
-G I < , ( BHE m
+G I < ( BHE m
ABI A
-ABD , l ( BHE u
-ABI L r - BBB B(E0A8J@
-8A0A(B BBBA $ ^ AG b
-AA < l ^ - BDA n
+ABD , ( BHE u
+ABI L l r zT BBB B(E0A8J@
+8A0A(B BBBA $ L 8^ AG b
+AA < p^ T BDA n
EBD C
-EBA $ b V- A]
-B A , P 2- BHI kAB ! AL L $ ! , BBB B(A0A8J
-8D0A(B BBBA `# 4 4 X#J BDA o
+EBA $ $ b S A]
+B A L L ؉ S BBB B(A0A8J
+8D0A(B BBBA , z S BHE gAB \ ، AL 4 | ،J BDA o
ABG A
-ABA d l p# BLB B(A0A8Dq
+ABA d BLB B(A0A8Dq
8A0A(B BBBE
-8A0A(B BBBJ D (, BBB D(A0D@
-0A(A BBBA L t -" + BIB B(A0D8DpL
-8D0A(B BBBA L `0g + BBE B(D0D8Gp
-8A0A(B BBBD \ 6m BBD D(J@
+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@
(A ABBD T
(A ABBF _
-(A ABBK D t 7 s+ BBB D(A0J
+(A ABBK D R BBB D(A0J
-0D(A BBBJ L X?P + BEB E(A0D8DP
-8D0A(B BBBA d XA9 w+ BLB B(D0A8D@q
+0D(A BBBJ L بP WR BEB E(A0D8DP
+8D0A(B BBBA d l ت9 #R BLB B(D0A8D@q
8A0A(B BBBJ
-8A0A(B BBBA L t 0B
- 7+ BLB B(D0D8D@
-8A0A(B BBBG L l D BBD D(D0O
+8A0A(B BBBA L
+ Q BLB B(D0D8D@
+8A0A(B BBBG L p BBD D(D0O
(A ABBK \
-(A ABBE < F BAD
+(A ABBE < BAD
ABE
-ABJ T T K * BBB B(A0A8D
-8A0A(B BBBJ d T T BBB B(A0A8D7
+ABJ T +Q BBB B(A0A8D
+8A0A(B BBBJ d BBB B(A0A8D7
8A0A(B BBBI l
-8A0A(B BBBA 4 YJ BDA l
+8A0A(B BBBA 4
+ pJ BDA l
ABJ A
-ABA \ Z% GLE B(A0A8DPV8A0A(B BBBHP T [P G) BBB B(A0D8G
-8A0A(B BBBA T c| k) BBB E(A0A8G+
-8A0A(B BBBA I. K^ T t j. Y) BBB B(A0D8N
-8A0A(B BBBA , r> t) AID W
-DAA d ry BEB B(A0A8D@
-8A0A(B BBBI @
-8A0A(B BBBA T d s ( BFB B(A0D8JT
-8A0A(B BBBA d d v BEB B(A0A8D@
-8A0A(B BBBJ @
-8A0A(B BBBA L w BBE B(A0A8D`
-8A0A(B BBBC L t z /( BBE B(A0A8J
-8A0A(B BBBC < P /( BEA D(DP
-(D ABBC T D ( BBB B(A0A8J
-8D0A(B BBBA AX $ A[ , D b D\
+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\
H P
H Q
-G I , t b D\
+G I , Hb D\
H P
H Q
-G I L 8r ' BBB B(E0A8J@
-8A0A(B BBBA $ h^ AG b
-AA , t '' BHI kAB L AL L & BBB B(A0A8J
-8D0A(B BBBA T , M & BBB E(A0A8GI
-8A0A(B BBBI L r & BBE E(D0D8I{
-8D0A(B BBBI D | В BBB D(A0D@
-0A(A BBBA L ( w& BIB B(A0D8DpE
-8D0A(B BBBA L l " M& BIB B(A0D8DpL
-8D0A(B BBBA T ؙ #& BBB B(D0A8JW
-8D0A(B BBBE T % BBB B(A0A8J
-8A0A(B BBBA أ L , BLB B(A0A8D
-8A0A(B BBBA L % BEB B(D0D8G
-8A0A(B BBBE T $ % BBB B(A0D8G
-8A0A(B BBBF $ A. K^ T % BBB B(A0D8N
-8A0A(B BBBG , 8> & AID W
-DAA , H> % AID W
-DAA T L X % BBF A(A0
+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
(A BBBG W
-(A BBBJ d L BBB B(A0A8DPB
+(A BBBJ d | X- BBB B(A0A8DPB
8A0A(B BBBE L
-8A0A(B BBBA L h % BBE B(A0A8J
-8A0A(B BBBC D \ $ % BBE A(D0D`
-0D(A BBBD T r % BBB B(A0A8J
-8A0A(B BBBK L AAG ]
+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 ]
CAF k
ACA R
-CAJ JCA $ (^ AG b
-AA < `( BHE m
+CAJ JCA $ $ ;^ AG b
+AA < L ;( BHE m
ABI A
-ABD , \ P( BHE u
-ABI D P BBB D(A0D@
-0A(A BBBA 4 BDA D0
- AABE `5 $ = 4 # SGB A(A0D 4 X t# cGB A(A0D L ]# BHB B(G0G8Ga
-8A0A(B BBBB \ T P c# BBB A(A0Do
+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
0A(A BBBE i
-0E(A BBBA L \ BOB A(A0]
+0E(A BBBA L K BOB A(A0]
(A BBBG Y
-(A BBBH T
# BBE E(D0A8G)
-8D0A(B BBBH < \ " ACG@s
+(A BBBH T L hL I BBE E(D0A8G)
+8D0A(B BBBH < O I ACG@s
DAE J
-DAA 4 D HJ BDA o
+DAA 4 t OJ BDA o
ABG A
-ABA | ` FP
-A < ` O" BEA D(G@)
-(D ABBK \ 4 C" BBB D(D0J
+ABA O FP
+A < < O I BEA D(G@)
+(D ABBK \ | (R H BBB D(D0J
0A(A BBBF
-0A(A BBBG T 0/ " BBB B(A0A8J
-8A0A(B BBBA \ KOB B(A0A8Dpk8A0A(B BBBGp d x3 BOB B(A0A8D`E
+0A(A BBBG T V/ H BBB B(A0A8J
+8A0A(B BBBA \ [ KOB B(A0A8Dpk8A0A(B BBBGp d $ _3 BOB B(A0A8D`E
8A0A(B BBBF |
-8D0A(B BBBE L \ P BLB E(A0A8DpC
-8A0A(B BBBH T b BEB F(D0D8J
-8D0A(B BBBC T \ % BBB B(A0A8Ga
-8A0A(B BBBD L \ F BEE B(D0D8D`
-8A0A(B BBBF L BEE B(G0D8G`C
-8A0A(B BBBC L T BLB B(A0D8D`
-8A0A(B BBBD L L BBD D(D0O
+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
(A ABBK \
-(A ABBE T pR BBB B(A0A8J
-8A0A(B BBBH D L x!w O BBB A(D0J
-0D(A BBBD D $ k BBB D(A0D
-0D(A BBBD L ( k BEE E(A0D8D`
-8D0A(B BBBK X6. K^ | D *} & BFE E(D0D8Gp
+(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
8A0A(B BBBF T
8A0A(B BBBB |
-8A0A(B BBBA T ,
BBE E(G0A8GJ
-8D0A(B BBBD D h5 GBA A(G@H(A ABBH@ L 6 BEE E(D0A8G@p
-8D0A(B BBBF \ 5. K^ t DN
-E A T 6 $ BEE E(D0A8G'
-8D0A(B BBBG D D <
- BBE A(D0G`
-0D(A BBBB T 8>L BEE E(D0D8J3
-8D0A(B BBBE T 0@L BEE E(D0D8J3
-8D0A(B BBBE T < (BL T BEE E(D0D8J3
-8D0A(B BBBE T DL BEE E(D0D8J3
-8D0A(B BBBE T F BBB E(A0D8G
-8D0A(B BBBB T D I BBB B(D0A8J
-8A0A(B BBBA T (R D BBB B(D0A8J
-8A0A(B BBBA T Z BBB B(D0A8J
-8A0A(B BBBA T L Xc J BBB B(D0A8J
-8A0A(B BBBA T k2
- BEB B(D0A8G
-8D0A(B BBBA T u BFB B(A0D8G
-8D0A(B BBBF 0. K^ , D \ t " 0" D H BBE A(A0
-(A BBBA L D U f BBB B(A0A8D`
-8A0A(B BBBJ , [ AID t
-DAA L l AAG k
+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
CAH F
CAF R
-CAJ JCA L ` AAG k
+CAJ JCA L L" AAG k
CAH F
CAF R
-CAJ JCA Њ $ xދ\ AG RA D 5 D \ ދ BBB D(A0D@0A(A BBB T BIB E(A0A8D6
-8D0A(B BBBA T T BIB E(A0A8DN
-8D0A(B BBBA T ^ BIB E(A0A8DF
-8D0A(B BBBA T P , BIB E(A0A8DF
-8D0A(B BBBA T \ BBB B(A0I8G
-8D0A(B BBBG T BBB B(A0I8G
-8D0A(B BBBG T
- BBB B(A0A8O
-8D0A(B BBBJ T d BBB G(A0G8J
-8D0A(B BBBG T 8 O BDB B(D0G8G
-8D0A(B BBBA T BBB G(A0G8J
-8D0A(B BBBG *. K^ , ܋/ A D D BBE A(A0D@g
-0A(A BBBA D BBE A(A0D@o
-0A(A BBBA L , r BBB B(E0A8J@
-8A0A(B BBBA $ $ H^ AG b
-AA $ G A4
+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
K A
-A $ G A<
-E D BBB D(A0D@
-0A(A BBBA L < ( BIB A(D0D
-0D(A BBBA T x BBB B(D0A8N
-
-8A0A(B BBBE , > AID W
-DAA $ g AR
-M A
-A < < H i BBA A(Gp
-(D ABBA T | M BEB B(A0D8G+t
-8D0A(B BBBH T N a BBH B(F0A8J)
-8D0A(B BBBK T , x 8 BBB G(A0D8M
-8D0A(B BBBA , &. K^ , D 0 AC
FK
- E $ t ~ Al
+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
C V
-J < ADG v
+J < ,+ 5 ADG v
GAF
-AAF `S L + ULB A(A0@(A BBBD0 L BBE D(A0G,
-0D(A BBBD L X! BBE D(A0G
-0D(A BBBK D < $U BFB A(A0GP
-0A(A BBBD d 029 BBB B(A0A8G@s
+AAF l+ 8S L + `8+ ULB A(A0@(A BBBD0 L D+ @= C BBE D(A0G,
+0D(A BBBD L + @ B BBE D(A0G
+0D(A BBBK D + BU B BFB A(A0GP
+0A(A BBBD d ,, P9 B BBB B(A0A8G@s
8A0A(B BBBB _
-8A0A(B BBBG T 9 D BBB B(A0A8J
-8A0A(B BBBK T D PH 7 BBB B(A0D8G~
-8A0A(B BBBA D #. K^ \ Y t xY L Y BBB B(A0D8Dc8A0A(B BBB 4 Z BDD D@ AAB D l [ 8 BBE A(A0
-(A BBBA L \ \ AAG e
+8A0A(B BBBG T , W IB BBB B(A0A8J
+8A0A(B BBBK T , g <B BBB B(A0D8G~
+8A0A(B BBBA - H. K^ - 8x . 0x L . 8x BBB B(A0D8Dc8A0A(B BBB D - hy uA BBE A(A0
+(A BBBA L . y AAG e
CAF S
ACA R
-CAJ JCA L p\ AAG e
-CAF S
-ACA R
-CAJ JCA L \ AAG e
-CAF S
-ACA R
-CAJ JCA L Ӌ\ AG RAL 0]^ BEL B(A0A8G
-8A0A(B BBBH 4 @`q BAI A
-ABA T L ` BBE B(D0A8G
-8D0A(B BBBG T Pe BBE B(D0A8G
-8D0A(B BBBG L j r BBE E(D0D8G
-8D0A(B BBBE D \ҋ BBB D(A0D@0A(A BBB T r ~ BIB E(A0A8D6
-8D0A(B BBBA T Hve$ O BBH G(A0A8JK
-8D0A(B BBBI T D
- `R ' BBB B(D0A8G6
-8A0A(B BBBA D . K^ \ P t H @ H , P[ } AID t
-DAA L D ] BBB B(A0A8D`^
-8A0A(B BBBJ < ы\ AG RA\ \ BEB D(A0C
+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
(A BBBH n
-(A BBBK Q(A BBB< p BBA A(D0(A ABB \ ТX BBD A(G0
+(A BBBK Q(A BBB< 3 @ @ BBA A(D0(A ABB \ d4 X BBD A(G0
(A ABBB q
-(A ABBI d(G ABB < Х BBA A(D0(A ABB D
Ћ BBB D(A0D@0A(A BBB T <
L q BIE B(A0A8D9
-8D0A(B BBBA L
? BBH B(D0A8JK
-8D0A(B BBBK L
`
BBH B(D0A8JK
-8D0A(B BBBK . K^ T L BBG B(A0G8Gv
-8D0A(B BBBA L `. K^ d H | @ D H1 BBB A(A0DP
-0A(A BBBE L 4 @h BBB B(D0A8R
-8A0A(B BBBK , `> AID W
-DAA D \ ϋ BBB D(A0D@0A(A BBB T (L BIE B(A0A8D9
-8D0A(B BBBA L T ' W BBB B(D0D8G T
-8D0A(B BBBA L . K^ T BBE B(D0A8G
-8D0A(B BBBG T `
q BEE E(D0A8G
-8D0A(B BBBE T l W BEE E(D0A8G
-8D0A(B BBBE T - ? BBB B(A0A8L Y
-8A0A(B BBBG H. K^ , 4 E[ AID t
-DAA D
-ϋ BBB D(A0D@0A(A BBB T DL BIE B(A0A8D9
-8D0A(B BBBA . K^ I I I I L | I BBB J(A0A8G
-8D0A(B BBBA L M BBB J(A0A8G
-8D0A(B BBBA D ϋ BBB D(A0D@0A(A BBB T d P BIB E(A0A8D6
-8D0A(B BBBA T XT u BIB E(A0A8D6
-8D0A(B BBBA T X C BBE B(D0A8G
-8D0A(B BBBB T l X] ) BBE B(D0A8G
-8D0A(B BBBB T b| BBE E(D0A8G
-8D0A(B BBBF Hg" `hr i O_ L l j BBH B(A0D8G
-8D0A(B BBBE $ d z TAA L { + BBH B(A0D8G
-8D0A(B BBBE . K^ AQ ͋\ AG RA$ 4 x AG
-AA $ \ 0 AG
-AA D ͋ BBB D(A0D@0A(A BBB T $ L BIE B(A0A8D9
-8D0A(B BBBA T | BBB B(A0I8G
-8D0A(B BBBK | . K^ $ (^ AG b
-AA L ` ^ BBE B(A0D8G
-8D0A(B BBBF . K^ , $ j ADG`
-AAH T ț l ЛY l BBB B(D0A8G
+(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
8A0A(B BBBD Z
-8A0A(B BBBD . K^ $ < T ( l L g BEB B(A0A8J
-8A0A(B BBBA 4 D @ BHE n
-ABH 4 | ^ BHE q
-ABM T + BEB B(A0D8J
-8A0A(B BBBA H. T $ ` ; BBB B(A0A8J
-8A0A(B BBBB $ . K^ D <
BBB B(A0A8
-0A(B BBBA D X
BBB B(A0A8
-0A(B BBBA < BBB A(A0
-(A BBBA < BBB A(A0
-(A BBBA L L @ BIB B(A0A8OA
-8A0A(B BBBA < + AF
Pf
+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 pR D@M R D@M W D@R $ HX D@S < , $ T AA L | 0 BEE B(D0A8D@
-8A0A(B BBBF $ < VAA 4 BHA I(
- ABBG , , p{ BLI
-ABI $ \ HN
+ 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
J W
-Y L BRI B(A0D8G!
-8A0A(B BBBJ ' ! Xw T t +
- ; BEE B(A0D8J
-8A0A(B BBBA L t! BIB B(A0A8J
-8A0A(B BBBF T ! ( BBE B(D0A8G
-8D0A(B BBBH L " BIB B(A0A8Gd
-8A0A(B BBBA L l" P BIB B(A0A8Gd
-8A0A(B BBBA d " $S BBB E(D0D8D
+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
8A0A(B BBBD
-8A0A(B BBBG L $# ( BBB B(A0A8Gd
-8A0A(B BBBA d t# /S BBB E(D0D8D
+8A0A(B BBBG L 4J 9 BBB B(A0A8Gd
+8A0A(B BBBA d J P@S BBB E(D0D8D
8A0A(B BBBD
-8A0A(B BBBG L # 3 BBB B(A0A8Gd
-8A0A(B BBBA d ,$ `9S BBB E(D0D8D
+8A0A(B BBBG L J HD BBB B(A0A8Gd
+8A0A(B BBBA d <K JS BBB E(D0D8D
8A0A(B BBBD
-8A0A(B BBBG < # X= a AC
BEO#
+8A0A(B BBBG < K N 9 AC
BEO#
A +
- G T ,$ (F 0 BBA A(Gp
+ G T TK pW b9 BBA A(Gp
(A ABBE w
-(A ABBK 4 ,% G8 AC
BGM
+(A ABBK 4 <L X8 AC
BGM
H
- F L d% K- BBE B(A0A8GK
-8A0A(B BBBG , % `( AC
BN
- E T <% a $ BBA A(Gp
+ F L tL ]- BBE B(A0A8GK
+8A0A(B BBBG , L q( AC
BN
+ E T dL r V8 BBA A(Gp
(A ABBG u
-(A ABBE 4 <& cX AC
BGM
+(A ABBE 4 LM XtX AC
BGM
J
- F T % 8g BKE E(D0A8J
-8A0A(B BBBA L & v- BBE B(A0A8GK
-8A0A(B BBBG , ' `( AC
BN
- E D L' ` BBB B(A0A8
-0A(B BBBA < ' BBB A(A0
-(A BBBA L ' Ȑ BEB B(A0A8Gy
-8A0A(B BBBA < |' 8 AC
BEO+
+ 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 ' BBA A(Gp
+ G T N P 5 BBA A(Gp
(A ABBG u
-(A ABBE 4 ( X AC
BGM
+(A ABBE 4 O ȾX AC
BGM
J
- F T L( # ; BEE E(D0D8M
-8A0A(B BBBG L L) Z BEB B(A0A8G3
-8A0A(B BBBG , ) * AC
BN
- D L ) BEB B(A0A8G
-8A0A(B BBBA < t) 0+
- # AC
BEO
+ 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
A h
- J T )
BBA A(Gp
+ J T P h $4 BBA A(Gp
(A ABBE w
-(A ABBK < *
AF
P
+(A ABBK < 4Q 3 AF
P
F R
- A 4 * 88 AC
BGM
+ A 4 R 8 AC
BGM
H
- F T * @ 5
BHE B(D0D8J,
-8A0A(B BBBG L + J BEB B(A0A8G#
-8A0A(B BBBG , + * AC
BN
- D T \+ m BHE B(D0D8J,
-8A0A(B BBBG , \, AF
K
- E T + ` BBB E(G0G8J9
-8A0A(B BBBJ , x. K^ , "4 - "4 ,- `. K^ , #+ Ai d- # < |- # BEB D(A0M
-(A BBBF L - $ BBB B(A0A8JS
-8D0A(B BBBD . & $. &8 Ho D. &? Hr d. ' |. ' T - ' a
- BBE B(D0A8G`
-8D0A(B BBBD $ D. , T
- AG@q
-AA D l. -5 =
- BIC Q0v
+ 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 \/ . t/ - / - / - / - / -T l g < D/ .6 k BBA A(G
-(A ABBG ,0 . K^ D0 0 \0 0 t0 0 0 0 0 0- < 0 00 BBA D(D`
-(D ABBD 0 3 1 x3 ,1 p3 D1 h3 AT d1 h3 DV < 0 p3w BLD A(G@|
-(A ABBA L 1 3
BBB F(A0D8G`
-8A0A(B BBBH T d1 p6S BBE B(D0A8G
-8D0A(B BBBD L 1 x<l BBB B(A0A8JC
-8A0A(B BBBG 2 B
- , $2 B# }GA A(D $ 2 C* ADG [AA T |2 C[ BBE B(D0A8G
-8D0A(B BBBF < 2 H= ~ BBD D(D
-(A ABBA l 3 I ` BBB E(A0A8G
+ 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
8A0A(B BBBK
-8A0A(B BBBB < 3 L BBD D(DU
-(A ABBA T 3 M BBE B(D0A8G
-8A0A(B BBBA T 4 HO BIB B(A0A8G
-8A0A(B BBBA 4 5 WR BDA r
+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
ABL A
-ABA T 4 8W BBE B(A0A8J a
-8A0A(B BBBF T 5 i BBE B(D0A8G
-8D0A(B BBBH T \5 m BBE B(D0A8G
-8D0A(B BBBG L \6 r BBB B(A0A8Gp8A0A(B BBB 4 6 t g BHH k
-ABA 6 hu AL \ 7 huh KLA A(G@(A ABBI@a
-(A ABBI 4 d7 xw AAG d
+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
DAF K
-DAH T 6 w { BEB B(D0A8J
-8A0A(B BBBI l L7 ~ KEB B(V0Q8L
-8A0A(B BBBE {CL d8 BBB B(A0A8Gp8A0A(B BBB d 8 X BBB E(A0A8DP
+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
8A0A(B BBBE !
-8A0A(B BBBF L t8 Ѕ BDB B(A0D8J^
-8A0A(B BBBG L l9 p BBF E(A0D8DPV
-8D0A(B BBBE T 9 BBB B(A0A8J[
-8A0A(B BBBA D l9 BBA D(G
-(A ABBH D 9 BBB A(D0J
-0A(A BBBI T 9 X & BEA A(D0
+8A0A(B BBBF T <` x + BBB E(A0D8G
+8A0A(B BBBE L $a BBF E(A0D8DPV
+8D0A(B BBBE T ` P - BBB B(A0A8J[
+8A0A(B BBBA D <a , BBA D(G
+(A ABBH D a 0( , BBB A(D0J
+0A(A BBBI T a - BEA A(D0
(D ABBK D
-(A ABBF l : y BBF D(A0G@I
+(A ABBF l b py BBF D(A0G@I
0A(A BBBI w
0A(A BBBA D
-0F(A BBBG T : ( s BBB B(A0A8G! !
-8A0A(B BBBA L ; BBB B(A0A8Jo
-8A0A(B BBBC < h L ; ` BEB E(I0H8D@
-8D0A(B BBBA \ ; BBB D(A0Da
+0F(A BBBG T b E' c, BBB B(A0A8G X
+8A0A(B BBBA L b x$ . BBB B(A0A8J l
+8A0A(B BBBF c X L Tc P / BEB E(I0H8D@
+8D0A(B BBBA \ c . BBB D(A0Da
0D(A BBBE
-0C(A BBBB < AO < AP = 4= L= . K^ d= DM = = = = = d T= % BBB B(A0A8Dp
+0C(A BBBB d AO d AP d d e p. K^ e DM <e Te le e e d $e % - BBB B(A0A8Dp
8D0A(B BBBE P
-8A0A(B BBBA L = R 3 BBE E(D0A8DM
-8D0A(B BBBA > h. K^ > \ AG RAT D> { BBE B(D0A8G
-8D0A(B BBBH T > BEE E(D0A8G
-8D0A(B BBBE L > h BEB I(A0A8G}
-8D0A(B BBBK ? AO T d? 4 R BBB B(A0A8GK
-8A0A(B BBBJ L d@ ] BBB H(A0D8DP
-8A0A(B BBBG @ D ~
+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 ~
F b
-F @ x. K^ @ P A H A @ 4A 8 LA 0 dA ( |A A A A A A B $B <B TB lB B B B B B B C ,C DC \C tC x C p C h C ` C X C P D H D @ 4D 8 LD 0 dD ( |D D D D D D E $E <E TE lE E E E E E E F ,F DF \F tF x F p F h F ` F X F P G H G @ 4G 8 LG 0 dG ( |G G G G G G H $H <H TH lH H H H H H H I ,I DI \I tI x I p I h I ` I X I P J H J @ 4J 8 LJ 0 dJ ( |J 0 J 8 J @ J H J P J X K ` $K h <K p TK x lK K K K K K K L ,L DL x \L p tL h L ` L X L P L H L @ M 8, AG bA $M H* De <M `* De TM x" DN
-N A L tM AAG ]
+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 ]
CAF [
ACA R
-CAJ JCA M 6 Dq M 6 Dq M 6 Dq N 06 Dq $N X6 Dq <N 6 Dq TN 6 Dq lN 6 Dq N : Dk
-I A 4 N Z BGA w
+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
AEI A
-ABA $ N @^ ADD NAA4 O xy BDD q
-AEO aAB 4 <O y BDD q
-AEO aAB tO
- O v fN O h
- , O `g P BHH j
-ABA O $ \O YX
+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
G z
-A ,P , O g BHH j
-ABA tP 8 $ O 0 YX
+A w , lw g BHH j
+ABA ,x ( $ w YX
G z
-A P , $P g z BHH j
-ABA P $ lP B YX
+A lx , w g BHH j
+ABA x $ <x N YX
G z
-A <Q 0 , P (g BHH j
-ABA Q h t Q `I DN
+A x ! , |x !g BHH j
+ABA <y X! t Ty P!I DN
F H
H H
H H
H H
H H
@@ -12587,4286 +13245,4317 @@
H H
H H
H H
H H
H H
-A F R 8z cV ,R DR \R tR R z cV R R R R S z cV S 0 4S ( LS dS |S z cV S x S p S h S `z cV S T $T <T TT z cV lT T T T T z cV T X T P U H ,U @ DU 8 z cV \U tU U U U z cV U U V V 4V z cV LV 0
- dV (
- |V
- V
- V
-z cV V x
- V p
- V h
- W `
- $W X
-z cV <W
- TW
- lW
- W
- W
-z cV W W W
- W
- X
-z cV ,X P DX H \X @ tX 8 , X 0 { AAG r
-DAH 4 X BGB A(A0D 4 LX H BGB A(A0D < X
_ BJD D(G
-(D ABBC $ X 0 S AFD@ $ X 7 AFD@ $ Y AFD@ <Y hD BDA A(DL
+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 ABBB l
(C ABBD m
(A ABBE }
(A ABBE }
(A ABBE }
(A ABBE }
-(A ABBE \ Y w rEA A(Dx
-(A ABBE p T LZ ? BBA A(Gl
+(A ABBE t - rEB B(A0A8Dt
+8A0A(B BBBA Pp T 4 P3 3 BBA A(Gl
(A ABBA D
-(F ABBA T Z ' BBA A(GZ
+(F ABBA T 4 BBA A(GZ
(A ABBC d
-(F ABBA T Z x BBA A(GZ
+(F ABBA T 6 BBA A(GZ
(A ABBC t
-(F ABBA L T[ ZBA A(Db(A ABBL L\ d\ |\ \ \ \ \ \ L d\ % + ZBA A(Dj(A ABBN \] p t] h ] ` N D \ h BEJ D(D0G
-0D(A BBBA D D] w BEJ D(D0G
-0D(A BBBE $ 4^ V ADD JAA< \^ ! BBG A(G@
-(A ABBH ^ !- < ^ ! BBG A(G@
-(A ABBH l L^ " BBB B(A0D8D
+(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
8A0A(B BBBH K
-8A0A(B BBBK < ^ X%o h BJD D(G
-(D ABBG L ^ & ; BEB E(I0H8D@
-8D0A(B BBBA D L_ (J BBB D(G0G
-0A(A BBBE <` + T` +* De l` +* De ` 8+6 Dq ` `+6 Dq ` +6 Dq ` +6 Dq ` +6 Dq ` ,6 Dq a (,6 Dq ,a P,6 Dq Da x," DR
-J A da ," DR
-J A 4 a ,Z BGA z
+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
AEF A
-ABA a ," DR
-J A a ," DR
-J A a ," DR
-J A b ," DR
-J A 4 <b -Z BGA z
+ABA hI" DR
+J A xI" DR
+J A ̉ I" DR
+J A I" DR
+J A 4 IZ BGA z
AEF A
-ABA tb (-" DR
-J A b 8-" DR
-J A b ʋ. K^ $ b 0-E AAG yAA b X- AL c X- ,c `- HS Lc `- dc h- HS c h- c p- HS c p- c x- HS c x- d - HS ,d - Dd - HS dd - |d - HS d - d - HS d - d - HS e - $e - HS De - \e - HS |e - e - HS e - e - HS e - f - HS , |e - , YX
+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
G
-B Tf . , e . YX
+B $ 0K , (K YX
G
-B f @/ , f 8/ YX
+B l K , K YX
G
-B f / , Tf / YX
+B L , < L YX
G
-B ,g 0 , f 0 X YX
+B XM , PM L YX
G
-B tg h1 , f `1 # YX
+B D N , ̎ N YX
G
-B g 2 , ,g 2 YX
+B N , N YX
G
-B h 2 , tg 2 YX
+B ԏ O , \ xO YX
G
-B Lh 3 , g 3 YX
+B 8P , 0P x YX
G
-B h H4 , h @4 O YX
+B d P , P C YX
G
-B h 5 , Lh 4 YX
+B Q , 4 Q YX
G
-B $i 5 , h 5 YX
+B `R , | XR YX
G
-B li p6 , h h6 YX
+B < S , Đ S YX
G
-B i (7 , $i 7 { YX
+B S , S o YX
G
-B i 7 , li 7 F AADP~
-AAJ 4 i 9u * YKM
+B ̑ T , T T : AADP~
+AAJ 4 0Vu YKM
HG
-HA 4 i : YKM
+HA 4 xW YKM
HG
-HA 4 j <k YKpM
+HA 4 Xk YKpM
HG
-HA $ j P=E AAG yAA k x= AL 4k x= Lk = HS lk = k = HS k = k = HS k = k = HS l = ,l = HS Ll = dl = HS l = l = HS l = l = HS l = m = HS ,m = Dm = HS dm = |m = HS m = m = HS m = m = HS , dm = YX
+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
G
-B <n > , m > j YX
+B H[ , @[ ^ YX
G
-B n X? , m P? 5 YX
+B T \ , ܕ [ ) YX
G
-B n @ , <n @ YX
+B \ , $ \ YX
G
-B o @ , n @ YX
+B p] , l h] YX
G
-B \o A , n xA YX
+B , (^ , ^ YX
G
-B o 8B , o 0B a YX
+B t ^ , ^ U YX
G
-B o B , \o B , YX
+B _ , D _ YX
G
-B 4p C , o C YX
+B P` , H` YX
G
-B |p `D , o XD YX
+B L a , ԗ a YX
G
-B p E , 4p E YX
+B a , a YX
G
-B q E , |p E X YX
+B ܘ xb , d pb L YX
G
-B Tq F , p F # YX
+B $ 0c , (c YX
G
-B q @G $ q 8GE AAG yAA q `G AL q `G r hG HS 4r hG Lr pG HS lr pG r xG HS r xG r G HS r G r G HS s G ,s G HS Ls G ds G HS s G s G HS s G s G HS s G t G HS ,t G Dt G HS dt G |t G HS t G t G HS t G t G HS D dt G ZIA A(GP
-(H ABBG Tu xI D t pI g ZIA A(GP
-(H ABBG u K D $u K 8 ZIA A(GP
-(H ABBG v L D u L ZIA A(GP
-(H ABBG tv XN D u PN ZIA A(GP
-(H ABBG v O D Dv O ZIA A(GP
-(H ABBG 4w Q D v Q | ZIA A(GP
-(H ABBG w S D w S M ZIA A(GP
-(H ABBG w T D dw T ZIA A(GP
-(H ABBG Tx HV D w @V ZIA A(GP
-(H ABBG x W D $x W ZIA A(GP
-(H ABBG y Y D x Y ZIA A(GP
-(H ABBG ty ([ D x [ b ZIA A(GP
-(H ABBG y \ D Dy \ 3 ZIA A(GP
-(H ABBG 4z h^ 4 y `^ YKM
+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
HG
-HA $ z _E AAG yAA z _ AL z _ z _ HS { _ { _ HS <{ _ T{ _ HS t{ _ { _ HS { _ { _ HS { _ { ` HS | ` 4| ` HS T| ` l| ` HS | ` | ` HS | ` | ` HS | ` } (` HS 4} (` L} 0` HS l} 0` } 8` HS } 8` } @` HS } @` } H` HS ~ H` ,~ P` HS L~ P` d~ X` HS ~ X` ~ `` HS ~ `` ~ h` HS ~ h` p` HS D ~ p`( 9 ZIA A(GP
-(H ABBG t Xb D ~ Pb7
- ZIA A(GP
-(H ABBG Hd D D @d7 ZIA A(GP
-(H ABBG 4 8f D 0f7 ZIA A(GP
-(H ABBG (h D h7 } ZIA A(GP
-(H ABBG j D d j N ZIA A(GP
-(H ABBG T k D Ā k7 ZIA A(GP
-(H ABBG m D $ m( ZIA A(GP
-(H ABBG o D o7 ZIA A(GP
-(H ABBG t xq D pq ZIA A(GP
-(H ABBG Ԃ s s5 A/ \ d 0v C BBB A(D0DPk
+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
0C(A BBBD H
-0A(A BBBH D Ă w BBB A(A0G`
-0A(A BBBG (z ̃ z z < T zi BHD A(D0A
-(A ABBF , < @zq BDA X
-ABF l z L z BBB B(A0A8D
-8A0A(B BBBA T , () BBB B(A0A8G
-8A0A(B BBBA , AL L L BBF B(A0A8D@
-8D0A(B BBBB d E BBB B(A0A8DP
+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
8A0A(B BBBE \
-8F0A(B BBBA l \ xB BBB J(D0D8D4
+8F0A(B BBBA l D B BBB J(D0D8D4
8D0A(B BBBC i
-8A0A(B BBBE L ̅ X c BFE B(H0D8M
-8D0A(B BBBD Ć AW AP 4a DN
-E A $ $ ^ AG b
-AA d L BBB B(A0A8D`<
+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 BBBL d
-8A0A(B BBBA AL ԇ 7 dO DJ О DJ , D 9 AX
-G X < ȟ BBA A(DU
-(E ABFA <
- BBA A(D[
-(A ABBA h$ AX
-G C x < t p; ? BBA A(G
-(E AFEA T p # BBB B(A0A8Jz
-8A0A(B BBBH L V BBB A(A0J
-0A(A BBBI T \ BBE E(D0F8J
-8D0A(B BBBI L Ь BEB E(D0D8G
-8A0A(B BBBB T + BBE E(D0A8G2
-8D0A(B BBBG L \ x BEE B(A0D8GP
-8A0D(B BBBA T HP BBE B(A0A8G
-8A0A(B BBBG T @) BBB B(D0D8G
-8D0A(B BBBI L \ BED A(G@x
+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
+
+(D ABBJ L l BED A(G@x
(D ABBD V
-(D ABBA T h [ BBB B(A0A8G
-8A0A(B BBBH T BEB E(A0A8G
-8D0A(B BBBD D \ ( BEE A(A0
-(A BBBA < L BFB A(A0S
-(A EBBG T p BBB B(D0C8G b
-8D0A(B BBBK ( AG [D T \ 3 BEE B(D0D8GH
-8D0A(B BBBA \ AL L ԍ BEB E(I0H8D@
-8D0A(B BBBA < ̎ BBG A(G@
-(A ABBF D BBE B(A0D8D@8A0A(B BBB T (" DR
-J A , ̎ 8 BRE AE AL L ď BBE E(A0A8D@i
-8A0A(B BBBI l l z BBB E(I0D8DP
+(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
8D0A(B BBBA
-8D0A(B BBBA T BEE B(A0D8JY
-8A0A(B BBBH ܐ М. K^ h ` $ X < P T H l @ H AV H AV đ H < ܑ BBG A(G@
-(A ABBF l px BBF D(A0G@F
+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
0A(A BBBD v
0A(A BBBJ D
-0F(A BBBG T BBB B(D0A8K*
-8A0A(B BBBD . K^ L VUw BBB E(A0A8DP[8A0A(B BBB L L P BBB B(A0A8DT
-8A0A(B BBBA L BBB B(A0A8DI
-8A0A(B BBBG T D 0w { BBE B(A0A8D
-8A0A(B BBBA D V\ AG RAT 8
-% * BBA A(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^
(A ABBB P
-(A ABBA T | BBE E(D0A8G
-8D0A(B BBBE T l 84 BBB B(D0D8G
-8A0A(B BBBK D Ĕ BFE D(A0G
-0D(A BBBH \ h KBA A(G@(A ABBE@r
-(A ABBH l ? BIB E(D0A8F`w
+(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
8A0A(B BBBB
8A0A(B BBBA V
-8A0A(B BBBA T 0 BEG E(A0A8J
-8D0A(B BBBA . K^ , $D BDD n
-DBA $ < $^ AG b
-AA 4 $ BHE
-ABH 4 % BHE
-ABE 4 ԗ &d BBD G(D0G(D ABBd d & BBB B(A0A8DP
+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 BBBH D
-8A0A(B BBBA D t 'J BBB D(A0D@
-0A(A BBBA L ) s BJB A(A0W
-(A BBDH ^(A BBB< p) BBG A(G@
-(A ABBG < L 0* BBG A(G@
-(A ABBG < * BBG A(G@
-(A ABBG T $ + g BJB B(A0A8G@
-8D0A(B BBBJ $ . K^ L < Pw BBB E(A0A8DP[8A0A(B BBB T 1 BBB E(A0A8G
-8A0A(B BBBH xQ AO , \ xQ AAG@s
-DAA 4 Q AS , Q AAG@w
-DAA XR AS , XR AAG@w
-DAA D ԛ R BBE A(A0D@z
-0A(A BBBA 4 t S- BFD D0
- DABB L xTm BBB B(D0A8GP
-8A0A(B BBBA V L WY BBB B(A0D8G^
-8A0A(B BBBA < ] BBG A(G@
-(A ABBF L `^] D r
-J l ^] D r
-J L ^ BEB E(I0H8DP
-8D0A(B BBBD L 4 ` BBJ B(D0A8DpS
-8D0A(B BBBA 4 b ӿ BDD DPv
- AABA L b BBE J(A0A8DP9
-8A0A(B BBBD 4 d BDD DPv
- AABA . K^ D Xe BBE A(A0D@
-0A(A BBBA D L RN BBB D(A0D@0A(A BBB d e BFB B(A0A8DP
+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 BBEK
-8D0A(B BBBH L T g S BBB E(I0D8DP^
-8D0A(B BBBA T h # BBE B(D0A8D
-8D0A(B BBBK T kV BBE B(D0A8G
-8D0A(B BBBE d o BFB B(A0A8DP
+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
8A0A(B BBEK
-8D0A(B BBBH L Xp c BEB E(I0H8DP
-8D0A(B BBBD d r{ ; BBB J(A0D8G`
+8D0A(B BBBH L BEB E(I0H8DP
+8D0A(B BBBD d x{ _ BBB J(A0D8G`
8F0A(B BBBF B
-8F0A(B BBBA T t 0sw BBE B(A0A8G<
-8D0A(B BBBA T ̡ X E BEE B(A0A8Jj
-8A0A(B BBBA L $ U W BEE I(D0A8JG
-8D0A(B BBBH . K^ 4 8L\ AG RA4 T BAD D0~
- AABC 4 P} AAG R
+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
GAM D
-AAA T q BBB B(D0A8GY
-8A0A(B BBBA T t 00 ˼ BEE B(A0A8G9
-8A0A(B BBBA T ̣ ּ BFE E(E0D8G
-8C0A(B BBBC 4 ̤ p BAD D0~
- AABC L \ k BEB E(I0H8D@
-8D0A(B BBBD d C BBB B(I0A8G`
+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`
8F0A(B BBBF r
-8F0A(B BBBG T BBB B(A0A8G
-8A0A(B BBBG . K^ < ϻ BBA A(DV
-(A ABBA l . K^ L x AAG ]
+8F0A(B BBBG T BBB B(A0A8G
+8A0A(B BBBG . K^ < T 0 BBA A(DV
+(A ABBA $ . K^ < T L l AAG ]
CAF [
ACA R
-CAJ JCA L AAG ]
+CAJ JCA L AAG ]
CAF k
ACA R
-CAJ JCA L T AAG ]
+CAJ JCA L H AAG ]
CAF [
ACA R
-CAJ JCA L AAG ]
+CAJ JCA L \ x AAG ]
CAF k
ACA R
-CAJ JCA G\ AG RAL l 8 BBB B(A0A8Ji
-8A0A(B BBBI L BBB B(A0A8Ji
-8A0A(B BBBI L o BBB B(A0A8D
-8A0A(B BBBA < \ ˹ BBD D(G0g
-(A ABBA L ho BBB B(A0A8D
-8A0A(B BBBA , ADG0Q
-AAA ĩ h. K^ ܩ < \ BBD D(D@`
-(D ABBA 4 ~ D M
-A $ ĩ P2 ۸ D]
-O A Ȅ. K^ T P BBA A(Di
+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
(A ABBG e
-(A ABBE 4 R BAD D0`
- DABA ~ D M
-A $ ̪ x* D\
-H A . K^ D h Ƿ BAA D`R
+(A ABBE X1 4 P1R BAD D0`
+ DABA | x1~ / D M
+A $ 1* ' D\
+H A T . K^ D 1 BAA D`R
AABB U
- AABG * & , & D 8& $ \ Pv AG z
-AA ̬ " DN
-N A \ BBA A(G@P
+ 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
(C ABBK \
(D ABBK
-(A ABBE Ԭ \ BBB E(A0A8DJ
+(A ABBE `4\ 5 BBB E(A0A8DJ
8D0A(B BBBH e
8A0A(B BBBA q
8D0A(B BBBB n
-8C0A(B BBBF T t t BBE B(D0A8G
-8D0A(B BBBH T ̭ f BBD D(G
+8C0A(B BBBF T D 9t BBE B(D0A8G
+8D0A(B BBBH T H>f BBD D(G
(A ABBK
-(C ABBF ̮ . K^ @A\ AG RA< BBD D(I@
-(D ABBF D H \ @$ A]
-B C 4 | PY ACG
-AAD DCA x 4 ̯ pa ACG E
-AAF DCA 4 Y ACG
-AAD DCA T DQ 4 l S AAF h
-CAD RCA 4 [ AAF n
-CAF RCA 4 ܰ S AAF h
-CAD RCA T l H ^ BBA A(Ir
+(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 ABBG E
-(A ABBA < İ W 1 BBA A(J
-(A ABBA D " BBB A(D0G
-0A(A BBBA < L R . BDA A(G
-(A ABBH T BEE E(D0A8G
-8D0A(B BBBE < S BBA A(Gx
-(A ABBA ̲ }. K^ 2 6 G n 8 AQ 4 8 L 0 < d (D BEE D(D0h(A BBB D 8$ BLB A(A0GS
-0A(A BBBD T D k BBB B(A0A8G
-8A0A(B BBBA D {. K^ \ t L o BEB A(A0g
-(A BBDE _(A BBBD 4 p # BJD G@o
+(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
DABC ]
- DABD D | H BJD G@l
+ DABD D L X
BJD G@l
DABF ]
- DABD D Ĵ { BJD G@s
+ DABD D Y{ BJD G@s
DABG @
- DABA T X BHE A(D0D@|
-0D(A BBBA m0D(A BBB T BHE A(D0D@y
-0D(A BBBD m0D(A BBB L h ޯ BHE B(A0D8D`
-8D0A(B BBBA y. K^ D $ ` BJD JT
+ 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
DABK `
- DABI D l ` ` BJD JT
+ DABI D < x^` BJD JT
DABK `
- DABI L \ 0 BBB B(A0A8Gp8A0A(B BBB $ V ADD JAA< Է ! BLG A(G@
-(A ABBF d BBB J(A0D8GP
+ DABI L _ BBB B(A0A8Gp8A0A(B BBB $ d aV ADD JAA< Ha! BLG A(G@
+(A ABBF d 8b BBB J(A0D8GP
8F0A(B BBBG
-8D0A(B BBGG T Է BFB E(A0A8D
-8A0A(B BBBA < Ը X
BFB A(A0S
-(A EBBG \ l
BBB D(C0GP
-0D(A BBBD n0D(A BBB \ ̸ y > BEB D(D0Gp
+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
0D(A BBBH f
-0D(A BBBG < Թ 1 BLG A(G@
-(A ABBH L l BEB E(I0H8D@
-8D0A(B BBBA L 8 BLB B(A0D8Dp#
-8A0A(B BBBA l O BBE B(A0A8DJ
+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
8A0A(B BBBA T
-8A0A(B BBBA $ Xu. K^ T @ BBB B(D0D8J
-8A0A(B BBBG T
- BBB B(D0D8J
-8A0A(B BBBG T D #( # BBB A(A0U
+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
(A BBBD h
-(H BBBJ T &( BBB A(A0U
+(H BBBJ T l ~( BBB A(A0U
(A BBBD h
-(H BBBJ T `) ۫ BBE B(D0A8G
-8D0A(B BBBG T L (. BBE E(D0A8G
-8D0A(B BBBK T `3 BEE E(D0A8G
-8D0A(B BBBE D 8 BBB A(D0J
-0A(A BBBD D D < ҫ BBB A(D0J
-0A(A BBBD 4 xr. K^ $ @Ap YX
+(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
G `
-A t A Pr DP $ hA AL v
-AA ̾ B A , T A1 s AAG cCA , B D A \ A
- t A A L A AAG ]
+A , D P DP $ ș AL r
+AA P H , $ @1 AAG cCA P H @
+ , 8 D 0 \ ( AV L | ( AAG ]
CAF [
ACA R
-CAJ JCA L L Br BBB B(E0A8J@
-8A0A(B BBBA $ D 8C^ AG b
-AA l pC hC2 C C C xC $ T pC `FD@ $ C < C 4 C E BAD F0q
- DABK 4 `ET BDA J
-ABD oAK E E 4 L xE PGB A(A0D T PF@ BEE E(D0D8G
-8D0A(B BBBF < 8I BEB D(A0
-(A EBBD , J BGD l
-ABD L L HK ߧ BJB A(A0W
-(A BBDH ^(A BBB< D K BBG A(G@
-(A ABBF $ xLV ADD JAA L* A^
-I A < $ L{ BBA D(G0$
-(A ABBA , Ns AAG0u
-AAA L < PN6 BBE B(A0A8DP
-8A0A(B BBBA < @O BGB A(A0
-(A BBBA < P BBG A(G@
-(A ABBG d P BBB B(D0A8DP
+CAJ JCA L X 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
8A0A(B BBBC
-8A0A(B BBBH T 8Vy BBB B(D0A8G_
-8A0A(B BBBA L $ `[ s BEB E(I0H8D@
-8D0A(B BBBA < \ BED A(D0U
-(A ABBE L P] BEB E(I0H8D@
-8D0A(B BBBA , ^ BAA AB \ @_ BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBD < _ BCD D0
+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 BBBE S
+8A0A(B BBBC w
+8A0A(B BBBG D
+8F0A(B BBBE T d 3 w BED I(D@
+(D ABBK c
+(D ABBD T ' BBE B(A0A8G>
+8A0A(B BBBD D BCD D0
FABE D
- FABA T h` BEE E(A0D8I
-8D0A(B BBBG , c ADD ]
-AAA t `d BIA D(G0B
+ FABA T \ I BEE E(A0D8I
+8D0A(B BBBG , D ADD ]
+AAA t t BIA D(G0B
(A ABBA o
(A ABBK g
(A ABBC D
-(F ABBA d d BBB E(D0A8L`x
+(F ABBA d x BBB E(D0A8L`x
8D0A(B BBBG
-8D0A(B BBBF T D 0f6 ʢ BBB B(D0D8G
-8D0A(B BBBA \ D i BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBBT hi & BED D(GPT
+8D0A(B BBBF T 6 BBB B(D0D8G
+8D0A(B BBBA \ BBE B(A0D8D@g
+8D0A(B BBBH D8C0A(B BBBT | BED D(GPT
(D ABBE C
-(D ABBA \ j BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 Pj AID L
-CAB AA j D jd 5 BGB E(D0G`w
-0A(A BBBF T L kg BEB B(A0A8J|
-8A0A(B BBBA $ L p^ AG b
-AA L 8p Ǡ BEB E(I0H8D@
-8D0A(B BBBD < q BGB D(A0~
-(A BBBK T \ hr
BBB E(N0D8DU
-8D0A(B BBBE D t, M BIB A(D0JF
-0A(A BBBF , vs AAG0u
-AAA L , Xv BEB E(I0H8D@
-8D0A(B BBBD \ | w ۟ BBB I(D0D@
-0F(A BBBD U0F(A BBB T xB BEG B(D0G8G
-8A0A(B BBBA | E
-D < P} BKD A(G0(A ABB < ~^ < T ~ BDB D(C0E
-(A EBBA L מ BBH E(D0D8DPU
-8D0A(B BBBA d H BBE B(A0A8DP
+(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
8F0A(B BBBC [
-8F0A(B BBBF T C BBB E(D0A8G
+8F0A(B BBBF T $ 0 BBB B(D0D8G
-8A0A(B BBBI Xa. K^ L AAG e
-CAF K
-ACI R
-CAJ JCA L < AAG e
-CAF C
-ACA R
-CAJ JCA DV $ ^ AG b
-AA \ BEG O(G0
-(A ABBA Q
-(E ABBE Q
-(E ABBE L BBE B(D0D8FP
-8D0A(B BBBA $ | @: ACG lAA L X5 BBB E(D0A8Gp
-8D0A(B BBBG L L H/ r BBB B(D0G8Dc
-8D0A(B BBBI L (2 = BBB B(D0G8Dc
-8D0A(B BBBI L BFB B(A0A8D@f
-8D0A(B BBBK < BBG A(G@
-(A ABBG L | H BJB A(A0W
-(A BBDH ^(A BBBL C BEB E(I0H8D@
-8D0A(B BBBA T " BBB B(A0A8J
-8A0A(B BBBA T t w BBB B(A0A8G
-8A0A(B BBBE t \. K^ T p ' BBE B(D0D8G]
-8A0A(B BBBG x\. K^ 4 T ޜ BAD `
-ABF D 4 BBB A(D0DPa
-0A(A BBBH < b AAG @
+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 @
AAE p
-AAB D 0 & BBB A(A0Gz
-0A(A BBBA D \ BEB A(D0?
-(D BBBI T t BBE B(D0A8G
-8D0A(B BBBH L BEE B(D0D8M
-8A0A(B BBBE D L x BBB A(A0G
-0A(A BBBC T @ n BBE E(D0D8JK
-8A0A(B BBBK Y. K^ DV \ AG RAT l BBA D(GPR
+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
(D ABBE P
-(D ABBG L ɚ BBB B(A0A8JW
-8D0A(B BBBH d 0 BBB B(A0A8DP
+(D ABBG L \ BEB B(A0A8G[
+8D0A(B BBBD d < BBB B(A0A8DP
8A0A(B BBBE D
-8A0A(B BBBA L $ BEB B(A0A8DP
-8D0A(B BBBG L x b BEE B(D0D8D@
-8D0A(B BBBJ d
BEE F(A0A8D@`
+8A0A(B BBBA 0 AP L 4 0 ־ BEE B(D0D8D@
+8D0A(B BBBJ d
BEE F(A0A8D@`
8F0A(B BBBI
-8D0A(B BBGG L , BBB B(A0A8Gp8A0A(B BBB L | @ BLB B(A0A8DP
-8A0A(B BBBA L BLB B(A0A8DP
-8A0A(B BBBE L t ޘ BLB E(A0A8D`
-8A0A(B BBBF T % BBB B(A0D8G
-8A0A(B BBBF 4 BEH G0x
- DABK U. K^ T l H | P BBE E(D0A8G
-8D0A(B BBBE l U. K^ T XJ BBE B(D0A8G
-8A0A(B BBBJ , P0 AC
FK
- E d P BBB E(A0A8G@p
+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
8A0A(B BBBB i
-8A0A(B BBBA T BBB A(D0o
+8A0A(B BBBA T 6 4 BBB A(D0o
(A BBBG E
-(A BBBD L $ 0 K BGB E(A0K8Dp
-8D0A(B BBBA $ t ! AG@q
-AA D ( 2
- BBB A(A0D
-0A(A BBBA HP < ) ]SN
-AAD DHAO T \ h BEE E(D0A8G
-8D0A(B BBBG < #< N BEA A(Gh
-(A ABBA < $7 2 BBA A(Gf
-(A ABBA @R. K^ $ L % AL b
-AA D & BBE A(A0D@n
-0A(A BBBA d & $ & AG@c
-AA H' @' H'0 Dk `'H \ ' AR $ ' < '' DR
-J F $ ' ˔ AG0S
-AD 8( K IN T ( BEB B(A0A8D
-8A0A(B BBBD L T . BBB E(D0D8FS
-8A0A(B BBBJ T /; k BBD A(J`
+(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 ABBD U
-(A ABBA L x0N ' BBB A(D0G
-0A(A BBBE \ L x5 c BBB D(D0G`
+(A ABBA L $ MN Ǹ BBB A(D0G
+0A(A BBBE \ t R BBB D(D0G`
0A(A BBBG U
-0A(A BBBK D 6 BEF A(A0G`
-0A(A BBBI N. K^ h7 p7 x7 L 7 AAG ]
+0A(A BBBK D S BEF A(A0G`
+0A(A BBBI (r. K^ T T T L
T AAG ]
CAF k
ACA R
-CAJ JCA L L 7 AAG ]
+CAJ JCA L \
T AAG ]
CAF k
ACA R
-CAJ JCA L 8 AAG ]
+CAJ JCA L
U AAG ]
CAF c
CAI R
-CAJ JCA L D @8r BBB B(E0A8J@
-8A0A(B BBBA $ < p9^ AG b
-AA , d 9 AAG0q
-AAA T (:1 BBE B(A0D8G
-8D0A(B BBBA , =k AAF Q
-CAC \ P= BBA D(D@h
+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
(D ABBB B
(D ABBE O
-(D ABBH < | > BBE D(D0_
-(A BBBI >? T > GBB A(D0D@G0A(A BBBB@ , ? AV L ? d ? , | ?N BHA p
-AHE T 0?Z BEE E(D0A8Gk
-8D0A(B BBBK L 8BQ BEE E(A0A8DPz
-8D0A(B BBBB L T HC] BEE E(A0A8DPz
-8D0A(B BBBB T XD . BBE E(D0A8G/
-8D0A(B BBBJ T T G BBE E(A0A8G
-8D0A(B BBBD T hI BBE E(A0A8G
-8D0A(B BBBF T K w BBB E(A0A8D~
-8D0A(B BBBD t \ 8Ne J BBA D(Gm
+(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 ABBJ
(D ABBF i
(D ABBF
-(D ABBB T 0Q BBB E(A0A8D~
-8D0A(B BBBD t , xS ύ BBA D(Gm
+(D ABBB T Hn BBB E(A0A8D~
+8D0A(B BBBD t T p o BBA D(Gm
(D ABBJ
(D ABBF l
(D ABBC r
-(D ABBE T U BBB E(A0A8Dv
-8D0A(B BBBD t W T BBA D(Gm
+(D ABBE T r ! BBB E(A0A8Dv
+8D0A(B BBBD t $ u BBA D(Gm
(D ABBJ
(D ABBF i
(D ABBF {
-(D ABBD T t 0Z BBB E(A0A8Dv
-8D0A(B BBBD t x\ ٌ BBA D(Gm
+(D ABBD T Hw BBB E(A0A8Dv
+8D0A(B BBBD t y y BBA D(Gm
(D ABBJ
(D ABBF i
(D ABBF {
-(D ABBD T D ^ BBB E(A0A8D~
-8D0A(B BBBD | `U ^ BBA D(Gm
+(D ABBD T l { + BBB E(A0A8D~
+8D0A(B BBBD | ~U BBA D(Gm
(D ABBJ
(D ABBF i
(D ABBF
-(D ABBC T d BBB E(A0A8D~
-8D0A(B BBBD | t gU ۋ BBA D(Gm
+(D ABBC T D BBB E(A0A8D~
+8D0A(B BBBD | 8U { BBA D(Gm
(D ABBJ
(D ABBF i
(D ABBF
-(D ABBC T k BBB E(A0A8D~
-8D0A(B BBBD t L Hm X BBA D(Gm
+(D ABBC T % BBB E(A0A8D~
+8D0A(B BBBD t t ` BBA D(Gm
(D ABBJ
(D ABBF i
(D ABBF z
-(D ABBE T o
- BBB E(A0A8D~
-8D0A(B BBBD t q ݊ BBA D(Gm
+(D ABBE T BBB E(A0A8D~
+8D0A(B BBBD t D } BBA D(Gm
(D ABBJ
(D ABBF i
(D ABBF o
-(D ABBH T t BBB E(A0A8D~
-8D0A(B BBBD t Hv b BBA D(Gm
+(D ABBH T / BBB E(A0A8D~
+8D0A(B BBBD t ` BBA D(Gm
(D ABBJ
(D ABBF l
(D ABBC o
-(D ABBH T d x BBB E(A0A8D~
-8D0A(B BBBD t z BBA D(Gm
+(D ABBH T BBB E(A0A8D~
+8D0A(B BBBD t BBA D(Gm
(D ABBJ
(D ABBF l
(D ABBC @
-(D ABBG T 4 }" BEB B(A0A8G Z
-8A0A(B BBBA L x s BBB E(A0A8G
-8D0A(B BBBD T U BBB E(A0A8Dv
-8D0A(B BBBD T 4 ` ( BAD G
+(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
DABG
DABE n
- DABC T ȫ BBB E(A0A8D~
-8D0A(B BBBD T Ê BAD G
+ DABC T BBB E(A0A8D~
+8D0A(B BBBD T ( c BAD G
DABG [
DABF n
- DABC T < H BBB E(A0A8D~
-8D0A(B BBBD T ] BAD G
+ DABC T d ` * BBB E(A0A8D~
+8D0A(B BBBD T BAD G
DABG [
DABF n
- DABC T ȴ5 ' BBE E(G0D8J4
-8D0A(B BBBD =. K^ L \ G BBE B(A0D8G`
-8D0A(B BBBA T l $ ^ AG b
-AA , @0 AC
FK
- E T 4 @ BBB B(A0I8D
-8A0A(B BBBA T h i BBB B(I0A8D
-8A0A(B BBBA , f AG d
+ 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
AC D
-CA P L H BEB B(A0A8DP
-8D0A(B BBBG $ AY
-F t, # ACG0
-AAI L e BBE B(D0A8Ds
-8D0A(B BBBD T V . BBE B(A0A8G
-8A0A(B BBBG T t BEB B(A0A8J
-8A0A(B BBBA T 3 BEE B(D0A8G
-8D0A(B BBBC d 0 BLB B(A0A8D@q
+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
8A0A(B BBBE
-8A0A(B BBBA D , BBB D(D0IPb
-0D(A BBBD < | BLG A(G@
-(A ABBH T D BEE E(D0D8JX
-8D0A(B BBBH L ~ BOB B(A0A8Dp~
-8A0A(B BBBE T G BBE B(A0A8J
-8A0A(B BBBK P9 AG0lD L 4 pV BBE E(D0A8Gw
-8A0A(B BBBE , 7. K^ D h \ p$ t Z HQ L AAG e
+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
CAF {
ACI R
-CAJ JCA L ( AAG ]
+CAJ JCA L $ ' AAG ]
CAF [
ACA R
-CAJ JCA L d X AAG e
+CAJ JCA L $ (( AAG e
CAF s
ACA R
-CAJ JCA @
-a HT $
-^ AG b
-AA $
-; F^
-D K 4 <
-M BDF o
-ABJ AAB L
- B BJB A(A0G
-0A(A BBBA <
$ * BBA A(D
-(A ABBA < \ BBA A(D
-(E ABGA < x3 BBA A(D
-(E AFEA < x Ņ BBA A(D
-(E ABGA L X$ BBB H(D0D8J
-8A0A(B BBBJ l l 8Y BEB E(A0F8DW
+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 < (M 9 BBA A(D
-(A ABBA D 8 BBA A(D
-(A ABBF T d W BEB B(A0A8G
-8A0A(B BBBE d d X BLB B(A0A8D@c
+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
8A0A(B BBBK |
-8A0A(B BBBJ T $ BBE E(A0A8G
-8D0A(B BBBA l | X BBB B(A0D8D;
+8A0A(B BBBJ T $) = թ BBE E(A0A8G
+8D0A(B BBBA l |) @ BBB B(A0D8D;
8A0A(B BBBJ W
-8A0A(B BBBA T " % BBE E(A0A8G
-8D0A(B BBBE 4 @%J BDA q
+8A0A(B BBBA 4 |* BJ BDA q
ABE A
-ABA L | X% BBB B(D0A8D
-8D0A(B BBBF T & y BBE B(A0A8G
-8A0A(B BBBA l $ , S BBB B(A0A8G
+ABA T $* B BBE B(A0A8G
+8A0A(B BBBA l |* XH ި BBB B(A0A8G
8D0A(B BBBA E
-8A0A(B BBBA T 03/ BBE E(G0A8GF
-8D0A(B BBBH d 8 BEE F(A0A8D@`
+8A0A(B BBBA T * N
+ BBE E(G0A8GF
+8D0A(B BBBH d + S BEE F(A0A8D@`
8F0A(B BBBI
-8D0A(B BBGG L T 9 BBE F(A0A8F`
-8D0A(B BBBH D L : GBA A(G@Q(A ABBG@ \ X<m KLA A(G@(A ABBI@n
-(A ABBD h> BBD G(R@
+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@
(D ABBD Q
(D ABBF Q
(E ABBE Q
(E ABBE Q
-(E ABBA L ? 5 BBB H(A0A8Dp
-8D0A(B BBBD d $ A BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB < 4 A1 BLG A(G@
-(A ABBH D Bg [ BBE A(A0J`g
-0D(A BBBI < D BBG A(G@
-(A ABBF L D
+(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 N BBB B(A0I8G
-8A0A(B BBBF L XZ BBB E(D0A8Dp
-8D0A(B BBBH L L ]4 BEE E(D0D8L
-8D0A(B BBBA D *. K^ \ ^ t ^ 4 ^ 7 BGB A(A0D $ X_ Dw
+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 c
-M *. K^ _ A[ $ _ < _ L T _ AAG ]
+M ,1 hN. K^ D1 | A[ d1 | |1 | L 1 | AAG ]
CAF [
ACA R
-CAJ JCA L ` AAG ]
+CAJ JCA L 1 | AAG ]
CAF [
ACA R
-CAJ JCA L H` AAG ]
+CAJ JCA L 42 | AAG ]
CAF [
ACA R
-CAJ JCA D
- \ AG RA$ X` w} AG Y
-AA T `k S} BBE E(D0A8G
-8A0A(B BBBA , <
- k } AID J
-DAA , l
- Hl } AAG0i
-AAA L
- l [} BEB E(I0H8D@
-8D0A(B BBBD L
- xn 3} BJE B(A0A8D`8A0A(B BBB T < o9 | BBE E(A0A8J
-8D0A(B BBBA T s | BEE E(A0D8J
-8D0A(B BBBA &. K^ z0 Hg 0z7 Hj L Pz AAG e
+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
CAF s
ACA R
-CAJ JCA d <
z BBB B(A0A8DP
+CAJ JCA d |5 H BBB B(A0A8DP
8A0A(B BBBE D
-8A0A(B BBBA $
({^ AG b
-AA ,
`{ ADD0e
-AAA \ T
{ 8{ BBB A(D0GPq
+8A0A(B BBBA $ 5 З^ AG b
+AA , 6 ADD0e
+AAA \ 5 x BBB A(D0GPq
0A(A BBBE w
-0A(A BBBA L \ | BOB A(A0]
+0A(A BBBA L 6 8 BOB A(A0]
(A BBBG Y
-(A BBBH T }| z BBE E(D0A8G
-8D0A(B BBBE D ( BBE A(A0D@d
-0A(A BBBA T Ђ1 Cz BEE E(D0A8GI
-8D0A(B BBBE < )z BBA D(Gp
-(D ABBA L < x y BBB B(A0D8D`b
-8A0A(B BBBC < 4 BBG A(G@
-(A ABBG T y BBE B(A0A8G
-8A0A(B BBBA T $ " z BBB B(A0A8D:
-8A0A(B BBBF $ H". K^ < z BBD A(D0P
-(A ABBE D | 4 BBE A(A0D@
-0A(A BBBA T z BEE B(D0A8J
-8A0A(B BBBF D й@ BBE A(A0D@
-0A(A BBBA d Ⱥ | d Ⱥ BBB B(A0A8DP
+(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
8A0A(B BBBE D
-8A0A(B BBBA $ P^ AG b
-AA , $ 0 AC
FK
- E T < y BBE B(D0A8G
-8D0A(B BBBF . K^ T X% Sy BBB B(A0D8J
-8A0A(B BBBF 4 DV L L , BEB B(A0A8DP8A0A(B BBB L 3 BBE B(A0A8DP8A0A(B BBB < BEH D(A0H@0A(A EBBT 0J z BHG B(A0D8J
-8D0A(B BBBF H . K^ D z BBA A(N
-(A ABBA L AAG ]
+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 ]
CAF c
CAI R
-CAJ JCA , 4 b D\
+CAJ JCA , t> Pb D\
H P
H Q
-G I , d Xb 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 , 4? Pb D\
H P
H Q
-G I , $ Xb D\
+G I , d? 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 , ? Pb D\
H P
H Q
-G I , Xb D\
+G I , $@ b D\
H P
H Q
-G I , b D\
+G I , T@ b D\
H P
H Q
-G I , D b D\
+G I , @ b D\
H P
H Q
-G I , t b D\
+G I , @ P b D\
H P
H Q
-G I , Xb D\
+G I , @ b D\
H P
H Q
-G I , b D\
+G I , A b D\
H P
H Q
-G I , b D\
+G I , DA b D\
H P
H Q
-G I , 4 b D\
+G I , tA P b D\
H P
H Q
-G I , d Xb D\
+G I , A b D\
H P
H Q
-G I , b D\
+G I , A b D\
H P
H Q
-G I , b D\
+G I , B b D\
H P
H Q
-G I , b D\
+G I , 4B P b D\
H P
H Q
-G I , $ Xb D\
+G I , dB b D\
H P
H Q
-G I , T b D\
+G I , B b D\
H P
H Q
-G I , b D\
+G I , B b D\
H P
H Q
-G I , b D\
+G I , B P b D\
H P
H Q
-G I X m A~
-A l L \ Au BBB F(A0A8J@X
-8A0A(B BBBA 4 T ( ADG X
-CAH [AA L t BBB B(A0A8DW
-8D0A(B BBBF , 4 h t BDD y
-ABA < d t BEA G(GPE
-(A ABBG < dt AAGPQ
+G I , $C b D\
+H P
+H Q
+G I , TC b D\
+H P
+H Q
+G I , C 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 ( A[ ( A[ < $ u t BBA C(JU
-(A ABBC Ah
-G b\ ` t BDB A(A0LP~
+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 < B s AADPU
+0A(A BBBD < <F X;B c AADPU
AAC
-AAA 4 ACO a
+AAA 4 G h< ACO a
AAJ
-GAO 4 h ) AAJ P
+GAO 4 DG 0=) AAJ P
AAB
-GAO D ` 6s BAA G@
+GAO D F (> ̗ BAA G@
AABH
- AABH L ! s BCA LP
- AABH )
- AABK D , h& r BED D(G$
-(D ABBE D t ' r BED D(G$
-(D ABBE L d 8) BBB A(A0E
+ 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
(A BBGG I
-(A BBDA L )Z "r BDB B(I0A8G
-8A0A(B BBBG D \ > r BED D(G$
-(D ABBE D @ }r BED D(G$
-(D ABBE T A Hr BBB B(A0A8G
-8A0A(B BBBG L D Du r BDB B(A0C8OZ
-8A0A(B BBBG T J r BBB B(A0A8G
-8A0A(B BBBG D hM q BED D(G$
-(D ABBE < 4! Na q BBA A(JP
-(A ABBI T t! P q BBB B(A0A8G
-8A0A(B BBBG T ! XS Mq BDB B(A0A8L
-8A0A(B BBBJ < " W BBG A(G@
-(A ABBG T d" Xo p BDB B(A0A8O+
-8A0A(B BBBH d# ] |# . K^ L " ] p BAA GD
- AABE :
- AABJ T <# o p BBB B(A0A8G
-8A0A(B BBBG T # P3 y BBE E(D0C8J
-
-8A0A(B BBBG L # 8# y BDB B(I0A8G
-8A0A(B BBBA , <$ y ACL@F
-AAH L l$ y BDB M(A0A8Gi
-8A0A(B BBBG T $ $ y BEB B(A0A8J
-8A0A(B BBBA L % ' y BDB B(A0A8O
-8A0A(B BBBF L d% +S y BDB B(A0A8O
-8A0A(B BBBD L % /C y BDJ B(A0A8GZ
-8A0A(B BBBA l & 3H qy BDB J(A0A8D\
+(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\
8A0A(B BBBB
-8A0A(B BBBD L t& 6 (y BFB B(A0C8J
+8A0A(B BBBD L TP 0Y ɝ BFB B(A0C8J
]
-8A0A(B BBBG T & H y BDB B(A0A8O
-8A0A(B BBBA L ' N y BDB B(A0A8O\
-8A0A(B BBBG T l' S y BBB B(D0A8Je
-8A0A(B BBBA T ' pW y BBB B(A0A8MZ
-8A0A(B BBBA L ( ] y BFB B(A0A8J
-8A0A(B BBBJ ) l ,) l D) kF , ( 0lD y AHGP
-AAA ) Pm , ) Hmb D\
+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\
H P
H Q
-G I , ) mb D\
+G I , S b D\
H P
H Q
-G I , * mb D\
+G I , S ؕb D\
H P
H Q
-G I , 4* nb D\
+G I , S b D\
H P
H Q
-G I , d* Hnb D\
+G I , ,T Xb D\
H P
H Q
-G I , ) n} ~x AHG`
-AAA 4 * p& rx BAA J`w
- AABG 4 T* r& sx BAA J`z
- AABD T * te tx BEE B(A0A8G
-8A0A(B BBBF T * ze lx BEE B(A0A8G
-8A0A(B BBBF T <+ e dx BEE B(A0A8G
-8A0A(B BBBF <, . K^ L + 4 Dx BBB B(A0A8Gk
-8A0A(B BBBJ T + y BBB B(A0G8G
-8A0A(B BBBG T T, P9 z BBB B(A0G8G
-8A0A(B BBBG , T- 8b D\
+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\
H P
H Q
-G I , , x 2z AALPd
-DAA T - e z BEE B(A0A8G
-8A0A(B BBBF $ d- z JFD@ $ - y GFD@ $ - y GFD@ $ - Ê y BFA D@ $ . ` y JFD@ T ,. 8u y BEE B(A0A8G
-8A0A(B BBBE T . `e y BEE B(A0A8G
-8A0A(B BBBF / . K^ L . ` |y BBB A(A0M
-0A(A BBBH / 0 0 40 L0 d0 |0
0 0 A] 0 F 0 0 1 ,1 , 0 \ x AAG o
-AAA , 0 b x ADG r
-AAA $ 1 HA ACH pCA $ 1 pA ACH pCA , 1 b D\
+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\
H P
H Q
-G I , $2 b D\
+G I , [ b D\
H P
H Q
-G I L T2 AAG ]
+G I L \ ( AAG ]
CAF [
ACA R
-CAJ JCA 2 H Y < 2 Pw Mw BLD A(G@|
-(A ABBA , 2 { BFA i
-ABA 4 2 v BKE
-AJA 4 2 x v BKE
-AJE L 2 v BJB A(A0W
-(A BBDH _(A BBBL D3 `r 9v BBB B(E0A8J@
-8A0A(B BBBA $ <4 ^ AG b
-AA d4 B Av
-I A 4 4 4 Af
-I C 4 4 % 4 G DB4 5 XM AAG0o
+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
AAF D
-AAJ $ <5 pV AD s
-AG d5 |5
- 5 5 5 5 5 x
- 6 p
- $6 h AO D6 h \6 ` t6 X < 6 P BDD R
+AAJ $ $_ V AD s
+AG L_ d_
+ |_ _ _ _ _
+ _
+ ` AO ,` D` \` < t` BDD R
ABA h
-CEH 6 6 6 7 ,7 D7 \7 t7 DR 7 7 7 x 7 7 L d7 .r BJB A(A0_
-(A BBDH _(A BBBd \8 BBE B(D0A8G@
+CEH ` ` ` ` a ,a Da DR da |a a a a L La BJB A(A0_
+(A BBDH _(A BBBd ,b BBE B(D0A8G@
8A0A(B BBBJ
-8A0A(B BBBK $ 8 AIK0AA 8 e , \8 x :q ADD@n
-AAG L 8 H2 (q BBB B(A0D8D
-8D0A(B BBBH 9 8 9 0 9 ( 9 9 2 T T9 @9 p BBA A(Dq
+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
(C ABBE L
-(A ABBA < T: ( BEB D(A0M
-(A BBBF D 9 o p BEE D(D0G
-0D(A BBBB 4 : Z BDA y
+(A ABBA < $d `$ BEB D(A0M
+(A BBBF D c %o ͔ BEE D(D0G
+0D(A BBBB 4 d 'Z BDA y
ABM A
-ABA ; Q , : @ Y o ADG i
-AAA T : p qo BBB B(A0A8G
-8A0A(B BBBD T ; Z ;o BEB B(A0A8G
+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
-8D0A(B BBBE 4 < J BDA l
+8D0A(B BBBE 4 Lf h0J BDA l
ABJ A
-ABA , ; Y n ADG i
-AAA T ; 8F n BBB B(A0A8G>
-8A0A(B BBBG T $< 0
- n BEE E(D0D8G
-8D0A(B BBBD T |< h >n BEE E(D0D8G9
-8D0A(B BBBJ L |= BOB A(A0]
+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 = `
' Ae < D= p
m BBA D(K@C
-(A ABBA L ,>
BBB B(A0A8Gp8A0A(B BBB |> p+ Ae 4 > I ADG q
-AAA DCA D > BBE A(A0D@l
-0A(A BBBA $ ? 0 ADD AA4 > Jl BFA b
-ABA $ |? V ADD JAAL > ,l BJB A(A0W
-(A BBDH ^(A BBB< L? k BBD A(G0L
-(A ABBA $ 4@ c AG g
-AA , \@ v BAA i
-ABA < @ P BEB A(A0D@0A(A BBBD @ BBE A(A0D@l
-0A(A BBBA < l@ x k BBD A(G0T
-(A ABBA D TA ( BBE A(A0D@l
-0A(A BBBA A 5 L A BBB B(A0D8D@
-8A0A(B BBBB L \A cj BJB A(A0W
-(A BBDH ^(A BBBd TB BBE F(D0A8D@
+(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 B p i BEB B(A0D8J
+8D0A(B BBGH T l G Í BEB B(A0D8J
-8A0A(B BBBA L lB ( i BJB A(A0W
-(A BBDH ^(A BBB< dC x BBG A(G@
-(A ABBF < C 8! BBG A(G@
-(A ABBF 4 C !U BDA w
-ABO AAB D "T d C h" h BBB E(A0A8DPD
+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
8A0A(B BBBA x
-8A0A(B BBBF D D $ BBE A(A0D@l
-0A(A BBBA L <D (% 'h BJB A(A0W
-(A BBDH ^(A BBBL 4E x%] BBB H(A0D8DP
-8A0A(B BBBG , D &m g AID t
-DAA < E & wg BBD A(G0$
-(A ABBA E ( L F )] BBB H(A0D8DP
-8A0A(B BBBG D \F * BBE A(A0D@l
-0A(A BBBA < F * BBG A(G@
-(A ABBG L <F h+ {f BEB E(I0H8DP
-8D0A(B BBBD L F (- Sf BJE B(A0A8D@8A0A(B BBB 4 F - f BFD D0
- DABA , G . e AID0|
-DAA L G /] BBB H(A0D8DP
-8A0A(B BBBG L G 0 e BBB B(D0D8Dp
-8D0A(B BBBD L H 39 BEF B(D0A8D@z
-8D0A(B BBBA L H 5 BBB B(D0A8D@m
-8A0A(B BBBH T H 6 d BBB B(D0A8G
-8A0A(B BBBK < I X: BBG A(G@
-(A ABBG L I ;c d BBB B(A0A8Dp#
-8A0A(B BBBA l lI 8< Od BNB B(A0D8G
+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 BBBA n
-8A0A(B BBBA D I =, d BJB K(A0j
-(_ aBBA D $J > c BBJ A(D0
-(A BBEA $ K x? AHG tAAL <K ?] BBB H(A0D8DP
-8A0A(B BBBG D J @ Sc BBB A(D0GP
-0D(A BBBK < K HB BBG A(G@
-(A ABBG L lK C b BEB E(I0H8DP
-8D0A(B BBBD L K D b BJE B(A0A8D@
-8A0A(B BBBA < L E BBG A(G@
-(A ABBG L L XF BBB B(A0D8DP
-8A0A(B BBBA < DM G BED A(D0Y
-(A ABBA < M G BBG A(G@
-(A ABBG L M HH a BEB E(I0H8DP
-8D0A(B BBBD L lM J ca BJE B(A0A8D@
-8A0A(B BBBA L M J ;a BEB E(I0H8DP
-8D0A(B BBBD < N L BFA D0}
- FABE D FAB 4 LN MY ` AIG t
-AAI DCA T N 0Mz ` BEE I(A0A8D
-8D0A(B BBBA < O XN BED D(GPf
-(A ABBF T O N ` BEH B(A0A8G
-8A0A(B BBBA T tO P _ BEB B(A0A8M
-8A0A(B BBBA T O R _ BHB B(A0A8J
-8A0A(B BBBE \ P V BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB\ ,Q V BBE B(A0D8D@r
-8D0A(B BBBE D8C0A(B BBB4 P Wi ^ ADD N
-AAG DCAL Q XW ^ BEB E(I0H8DP
-8D0A(B BBBD < lQ Y ^ BLD D(G0
-(A ABBE \ TR Y BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 R (ZY ^ AIG t
-AAI DCA < DR PZe ] BID ~
-AFD AEF < R Z ] BGB D(A0~
-(A BBBK T R [R {] BBB B(A0K8J#
-8A0A(B BBBA L S (] O] BEB E(I0H8DP
-8D0A(B BBBD d lS ^, '] BGE E(A0A8D@
+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@
8F0A(B BBBI g
-8F0A(B BBBA T S _x \ BBB B(A0A8G
-8A0A(B BBBA L ,T k 7] BEB E(I0H8DP
-8D0A(B BBBD T |T m* ] BLB B(A0A8Gq
-8A0A(B BBBA L T ps ] BEB E(I0H8D@
-8D0A(B BBBA < U t BFA D0}
- FABE D FAB V `u N L $V hu BBB B(A0D8G@j
-8A0A(B BBBH $ tV vq ADD0eAAL U Pw '\ BEB E(I0H8D@
-8D0A(B BBBD L DV x [ BJE B(A0A8D`8A0A(B BBB < <W `y BBG A(G@
-(A ABBF < V z x[ BLA D(G@
-(D ABBC W z T ,W z 5[ BBE B(A0A8G
-8A0A(B BBBH ,X p AO L W p 7[ BLE I(D0D8D
-8A0A(B BBBC T W 2 [ BBB E(A0D8G
-8A0A(B BBBA T LX M Z BHB B(D0D8G
-8A0A(B BBBA T X Z BEB B(A0A8G8
-8A0A(B BBBA Y T Y Z BBB B(A0E8M
-8A0A(B BBBH Z x E
-K < 4Z X BKD A(G0(A ABB L Y ؟
-
HZ BEB B(A0A8Ga
-8A0A(B BBBA L Z [Z BBL E(H0A8D`
-8D0A(B BBBA [ 8 AO 4[ 8 T Z 0
- Y BBB B(A0A8J )
-8A0A(B BBBA , [ غE BAG wAB [ 1 Dl [ ؊. K^ D d[ Y BGB D(A0G@~
-0A(A BBBC T\ 1 Ak < [ Ȼ SY BED D(G
-(A ABBH < \ h %Y BED D(G
-(A ABBI D L\ X BEE A(D0G
-0A(A BBBI L \ , X BEE B(A0D8G
-8A0A(B BBBA L \ , X BEE B(A0D8G
-8A0A(B BBBA L ] BBB B(A0A8D`
-8A0A(B BBBA L ,^ BEB B(A0A8D
-8A0A(B BBBA L |^ ` BDB E(A0A8DP
-8A0A(B BBBK T $^ y XW BBE E(A0A8GZ
-8A0A(B BBBA T |^
1W BBB B(A0A8G+
-8A0A(B BBBA T ^ AW BBB B(A0D8D
-8D0A(B BBBF < ,_ H1 6W BGA D(G@
-(A ABBB d l_ H V BBB F(D0D8J`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
8A0A(B BBBA K
-8A0A(B BBBK |` " DR
-J A L ` & BBE B(A0A8DP
-8A0A(B BBBA T D` 5V BEE B(D0A8G/
-8D0A(B BBBA L Da H BBB L(A0A8D`
-8A0A(B BBBA a AL , a AAG f
-AAG T <a ( iU RHB B(A0A8G{
-8A0A(B BBBD L a 'U BEB B(A0A8G\
-8D0A(B BBBC L a `F U BEE B(A0D8JS
-8D0A(B BBBC 4 4b ` U BAA G@
- DABJ $ c Y AK Y
-AA L b P [U BEB E(I0H8D@
-8D0A(B BBBD L b 2 3U BJE B(A0A8DP
-8A0A(B BBBA L 4c U BEB B(A0A8G
-8A0A(B BBBD D c @ U BGF D(A0JI
-0A(A BBBA D td BBE A(A0D@u
-0A(A BBBA T d 0 T BEE E(D0D8G _
-8A0A(B BBBA , ld , U AADP
-AAH T d X- U BBE E(D0A8L'
-8A0A(B BBBH l d . [U BGB B(A0D8GF
+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 BBBG (
-8A0A(B BBBF T de 1 7U BEE E(A0H8G;
-8A0A(B BBBJ T e 9U U BBB B(A0A8J}
-8D0A(B BBBA T f ?7 *U BEB B(D0A8T7
-8D0A(B BBBH T lf B T BBB B(A0A8G
-8A0A(B BBBA T f @JP T BBE B(A0A8G
-8A0A(B BBBA D g 8dK 7V BBB A(D0DE
-0D(A BBBA D h @e BBI A(A0D@
-0A(A BBBA D Th f BBI A(A0D@
-0A(A BBBA D g fs U BBB A(D0D@
-0A(A BBBA 4 h (hD BEA A(D0p(A ABB T th @h U BBB B(D0A8J
-8A0A(B BBBA ti xo# HZ L h o% T BBB E(D0A8KP
-8A0A(B BBBA T <i hrh T BBE B(A0A8Gd
-8A0A(B BBBA < i [U BED A(G@x
-(D ABBD T i 7U BBB B(D0D8J?
-8A0A(B BBBE L ,j h =U BBB B(D0D8JX
-8D0A(B BBBA T |j %
~U BBB B(D0A8G
-8A0A(B BBBI |k П, A^
-A K D k A
+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 \ k L ADD
+A S \ $ L ADD
AAD N
AAH N
AAH N
AAA `
-AAA 4 Dl ADD H
-AAE HAA L |l ~ BED D(G0Z
-(C ABBH m(G ABB L $l P eT BEB A(A0g
-(A BBDE _(A BBB4 m W BDH q
-AGI AAD Tm آ d l У S BEL B(A0A8D`
-8C0A(B BBBF L8A0A(B BBB d ,m H nS BEL B(A0A8D`
-8C0A(B BBBF L8A0A(B BBB L <n p BEH A(D0y
-(D ABBJ D(C DBB n ' DQ
-K F D n ) R BBB A(A0D
-0A(A BBBA D Ln إ) yR BBB A(A0D
-0A(A BBBA L <o BEB B(A0A8D`\
-8A0A(B BBBA T n P, Q BBE B(D0A8G
-8D0A(B BBBK T <o (A Q BEE E(D0A8Go
-8D0A(B BBBG T o Q BBA A(JK
+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
(A ABBG K
-(A ABBA L p x BOB A(A0]
+(A ABBA L BOB A(A0]
(A BBBG Y
-(A BBBH T <p ) >Q BEE B(A0D8Dn
-8C0A(B BBBA T p P RBE B(G0A8Dv
-8A0A(B BBBA T p X P BBB B(A0A8G#
-8C0A(B BBBA q 0 AP
-G W \ r ? BEA C(GPf
+(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 lr DR r AW < r { BDD G0V
- AABE D CAB < r BBG A(G@
-(A ABBG T |r a O BBB E(A0G8J
-8A0A(B BBBA L |s c BKB B(A0A8Dpa
-8A0A(B BBBF T $s gO BKB B(D0A8Dz
-8D0A(B BBBA L |s xw $O BBB B(D0A8Dg
-8A0A(B BBBA L s N BBB B(D0A8Dr
-8A0A(B BBBA L t V N BBB B(D0A8D
-8D0A(B BBBA L lt } ~N BBB B(D0H8Kp
-8A0A(B BBBA , du (7 BDD eAB T t 8 N BBB B(A0A8G
-8A0A(B BBBA D Du c M BBB A(D0GB
-0A(A BBBD u " M BBB E(A0D8D
+(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
8A0A(B BBDA
8F0A(B BBBE p
-8C0A(B BBBA v . K^ T ,v b CM BBB B(A0A8G_
-8A0A(B BBBF T v K .M BBB B(D0D8J
-8F0A(B BBBA T v CM BBB I(A0A8G
-8A0A(B BBBJ L 4w P IM BBB B(D0A8Jr
-8A0A(B BBBE T w dM BEB B(A0A8G
-8D0A(B BBBA $ x ) AGD ZAA L x AAG ]
+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 ]
CAF [
ACA R
-CAJ JCA , x b D\
+CAJ JCA , < 1b D\
H P
H Q
-G I ,y
- Dy
- \y
- ty
- y y y y L Dy K BJB A(A0_
-(A BBDH _(A BBB< y @B K BBA A(Gg
-(A ABBA $ |z PN AD p
-AJ T y x ^K BBB B(A0D8G
-8A0A(B BBBA z . K^ { ,{ D{ \{ t{ { " { { { (- $ { @^ AG b
-AA | x ,| D| x \| p t| h | p | h | ` | X , | Pc ADG0s
-DAD $ } ^ C\
-A Z
-F ] L | H BJE B(A0A8D@
-8A0A(B BBBA L | H BJE B(A0A8D@
-8A0A(B BBBA T <} h| H BBE E(D0A8G
-8D0A(B BBBE T } | H BBE E(D0A8G
-8D0A(B BBBE T } kH BBE B(D0A8G
-8D0A(B BBBH D D~ SH BED A(J
-(D ABBA L ~ H _H BEB E(I0H8D@
-8D0A(B BBBD L ~ " 7H BJE B(A0A8D@
-8A0A(B BBBA H. K^ T D ' G BBB E(D0D8J
-8A0A(B BBBA T h# [H BBE E(D0A8J
-8A0A(B BBBD D 07L BBE A(A0D@
-0A(A BBBA T < 88 H BBB B(D0A8G
-8D0A(B BBBJ D < :h BBB A(A0D@
-0A(A BBBA D ܀ <N H BBE A(A0Jo
-0D(A BBBA T $ =| _H BBD D(G
+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 l | H@ CH BEE E(D0H8D`i
+(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
8A0A(B BBBD
-8A0A(B BBBB 0p\ AG RAd hB G BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d t B SG BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB L ܂ XC F BJB A(A0W
-(A BBDH ^(A BBBL ԃ C BBB B(A0D8G@
-8A0A(B BBBB $ xE_ L E ;F BEB E(I0H8DP
-8D0A(B BBBD L G F BJE B(A0A8D@
-8A0A(B BBBA L 4 PH9 E BEB F(G0D8D`
-8D0A(B BBBA L @I E BBB I(I0A8DPv
-8D0A(B BBBH d Ԅ Js E BBB J(A0A8G`
+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`
8F0A(B BBBF B
-8F0A(B BBBA d < L] GE BBB B(I0A8G`
+8F0A(B BBBA d ȇ] Kg BBB B(I0A8G`
8F0A(B BBBF A
-8F0A(B BBBA T M D BBB B(A0I8N
-8D0A(B BBBF | xQ BBB B(D0A8DP
+8F0A(B BBBA T t f BBB B(A0I8N
+8D0A(B BBBF | \ 8 BBB B(D0A8DP
8A0A(B BBBB U
8G0A(B BBBK
-8A0A(B BBBI L $ S BEB B(A0A8DP4
-8A0A(B BBBA , t HW BAH D` AAB, W BAH D` AAB ԇ x. K^ l D 0XY C BBB J(D0A8DR
+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 \ g BBE B(A0A8D`
-8A0A(B BBBA L i BBE B(A0A8D`
-8A0A(B BBBA T T l$ B BBB B(D0D8G
-8A0A(B BBBA T AL \ ̈ C BBE D(A0P@|
+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@|
0D(A BBBK d
-0D(A BBBA T , (~ lC BBE E(D0C8J>
-8A0A(B BBBI 4 Py 5C BKA Dp
- AABA d . K^ | x p Ċ h , 4 pb B ADG r
-AAA L $ AAG ]
+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 ]
CAF [
ACA R
-CAJ JCA $ t ^ AG b
-AA , 0 AF
FK
- D ̋ 4 = BDA Y
-JBL AAB 4 J BDA o
+CAJ JCA $ , ^ AG b
+AA , T 0 AF
FK
+ D 4 = BDA Y
+JBL AAB 4 Թ J BDA o
ABG A
-ABA < T ( BDD k
+ABA < BDD k
ABH V
-ABA L 6 BEB B(A0A8D`h
-8A0A(B BBBA < x4 BBB A(A0d
-(A BBBE T | x \@ BBE E(A0A8G
-8D0A(B BBBD D | BBE A(A0D@l
-0A(A BBBA d H ? BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d s? BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB L 8 ? BEB E(I0H8DP
-8D0A(B BBBD L < > BJE B(A0A8D@
-8A0A(B BBBA 4 Ȧ5 T j > BBB E(A0A8IL
-8A0A(B BBBA 4 Y {> AIG t
-AAI DCA L 4 0 G> BHF E(D0D8GP`
-8A0A(B BBBB < 3> BGD A(G0~
-(A ABBG l @. K^ , X AAK0i
-AAA L Ȭ BEB B(A0A8DP#
-8A0A(B BBBA h" DN
-N A L $ x
BEE A(A0]
+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]
(A BBBF
-(E BBBK T ̐ 8 < BBB B(A0A8J3
-8A0A(B BBBA T $ о# = BBB B(A0I8D
-8D0A(B BBBA $ " DR
-J A d | \= BGB E(D0A8Dp
+(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
8A0A(B BBBH k
-8A0A(B BBBA T T = BIE B(D0D8Go
+8A0A(B BBBA T Կ T _ BIE B(D0D8Go
-8A0A(B BBBA D \ = BBB D(I0GP
-0D(A BBBA D L BBB A(K0D@
-0A(A BBBA AL T u = BEB B(D0D8J t
-8A0A(B BBBA D d # > BBB A(D0D@
-0A(A BBBA 4 T ZHA G HAB 4 P O> BAA G0_
- AABB L > BEH B(D0D8Dp`
-8D0A(B BBBA ML , = AAD@l
-AAA 4 ( = BAA GP
- AABB D BBB D(D0G@T
-0A(A BBBG DP DP < += BBA A(G@z
-(A ABBC T ĕ P! < BBB B(A0D8G
-8A0A(B BBBA Ė ܖ ,\\ AG RA< l ] = AAG0O
+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
DAC [
-DAA D r= BEB A(A0G@y
-0D(A BBBA @ H D $ @ = BEE A(C0G@
-0D(A BBBA L l < BBB B(A0D8D`m
-8A0A(B BBBH T (# < BBB B(A0A8G
-8A0A(B BBBB D >t T= BEB A(A0GC
-0D(A BBBA \ ?@ ;= BBB E(A0A8D
+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
8A0A(B BBBI
8A0A(B BBBG
-8A0A(B BBBI L Df BBB B(D0G8D`u
-8D0A(B BBBG T 4 E
- < BEB B(A0A8G
-8A0A(B BBBA < P < BBA A(DO
-(A ABBA , ̙ P < OGA A(D 4 Q < BJD K@g
- AABJ $ ܚ Rc AK c
-AA L \ R J< BBB B(A0A8Gp@
-8A0A(B BBBA T Ts ; BLB B(D0A8G
-8A0A(B BBBA T W ; BHF E(D0A8G
-8A0A(B BBBD T \ Z ; BEL B(D0D8J
-8A0A(B BBBE \ . K^ t W\ AG RA c c Ĝ cp cL ܜ c D L cq c; BBB A(D0Gpt
-0D(A BBBG D 0g B; BBB D(D0D`
-0A(A BBBA D ܜ h +; BBB D(D0De
-0A(A BBBA ̝ @j 8j 0j (j , j( Af L 0j 4 8j |: BAD D@Z
- AABA , j V: AAD@s
-AAA D $ ke 3: BBB D(A0D@
-0A(A BBBH T l Hl` 9 BBE E(A0D8G
-8A0A(B BBBB T Ğ Pt 9 BBB E(A0A8D
-8A0A(B BBBA T w y9 BBB E(A0A8D
-8A0A(B BBBA {2 Ad
-K A < {) DS
-I D < { 8 BBA A(D~
-(A ABBA }' 8 A[
-A d } 8 BBB B(A0A8DP
-8A0A(B BBBE D8A0A(B BBB 4 $ }] BDA A
-ABM AAB T }S 8 BBE E(G0D8J_
-8A0A(B BBBD T ȃ3 7 BFE E(D0A8D
-8C0A(B BBBA T d G 7 BEE B(A0K8G
-8A0A(B BBBA d FP < ԡ v 7 BEA A(G0D
-(D ABBA
Ԣ
М 4 Ȝ L d Ȝ" AN ND " $ ̣ G AG a
-DC RD ( , d b 6 AAG V
-CAA < `8 Gp4 T BAD D0
- AABA $ 4 5 AN cA $ $ < w5 AN kA H L P BBA D(D@x
+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 ]
+CAF k
+ACA R
+CAJ JCA L AAG ]
+CAF [
+ACA R
+CAJ JCA L AAG ]
+CAF c
+CAI R
+CAJ JCA 4 p[ BAD D@Z
+ AABA 4 J[ BAA
+ABD $ `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
+(A ABBA U
+(A ABBA
+(C ABBA , x2 Ad
+K A L ) DS
+I D T Y 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
(D ABBB
-(D ABBG \ Q \ ̤ h 4 BEB D(D0Gp
+(D ABBG Q \ (h sU BEB D(D0Gp
0D(A BBBK L
-0D(A BBBA D , Ȣ 4 BBE D(D0D
-0D(A BBBD D t 4 BAD GPl
+0D(A BBBA D 8 3U BBE D(D0D
+0D(A BBBD D 4 "U BAD GPl
DABG
- DABA T a R4 BEE I(A0A8G ^
-8A0A(B BBBJ L J4 BGD D(D0z
+ DABA T | ha T BEE I(A0A8G ^
+8A0A(B BBBJ L T BGD D(D0z
(D ABBH V
-(D ABBA , d ` 4 AAI@T
-DAD , < T BKI _
-ABH , l D BHD ^
-ABA L @ 3 BBB E(D0A8F
-8A0A(B BBBH D D C W3 BBB D(D0Gpn
-0D(A BBBB L 1 33 BBE B(A0A8GS
-8D0A(B BBBD L ܧ c g3 BBB B(D0A8G_
-8A0A(B BBBC L , Kh k3 BBB B(D0A8GI8A0A(B BBB L | ؿ 3 BFH B(A0D8Jl
-8D0A(B BBBF , ̨ C4 YX
+(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
G Z
-A L ( !4 BLE E(A0D8J
-8A0A(B BBBE < L d ?4 BJD D(D@o
-(D ABBA T < 4 BBE B(D0A8G
-8D0A(B BBBB T < 3 BBE B(D0A8G
-8D0A(B BBBB L < 3 BVB B(D0A8G^
-8D0A(B BBBE T "( 34 BEB B(A0A8G
-8A0A(B BBBG T v7 5 BBB B(A0A8G
-8A0A(B BBBA Q Q Q , Q D Q \ Q t Q Q Q Q Ԭ Q Q Q < QJ BEE D(D0\(Q BBB \ Q HT | Q# DT Q Q ĭ Q ܭ Q Q# D ^ , d Q 5 YX
+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
G H
-A < @R $ T 8R HM
-K | 0U x Uv D Į xUX BIB B(A0A85
-0A(B BBBA 4 V BFA A(
- ABBA D D Y, BIE B(A0A8
-0A(B BBBA , ] AC
TS
- A ae G@]L ԯ a BEE E(D0D8J
-8A0A(B BBBI d $ d BJB B(D0D8G
-8A0A(B BBBD B8A0A(B BBB pe D eh BFB B(A0A8H
-0A(B BBBA 4 g BFA A(
- ABBA L $ k BEB B(A0A8G
-8A0A(B BBBA 4 t xs% AC
T
+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
- H pyd G@\L ı y BEE E(D0D8J
-8A0A(B BBBI d | BHB B(D0D8G
-8A0A(B BBBF B8A0A(B BBB 4 | P}B AC
Tv
- B , h AC
TV
- B , AGDP
-AAH L BEB B(A0A8Dj
-8A0A(B BBBA L d 8 BBB B(A0A8J
-8A0A(B BBBH L BEB A(A0z
-(A BBGG [
-(A BBBF L ( BBB B(A0A8D
-8A0A(B BBBA L T BBB B(D0A8G
-8A0A(B BBBH L BEB A(A0*
-(A BBGG [
-(A BBBF < h< BBE A(A0
-(A BBBH 4 h D T ؒ D L t H BDB B(A0D8G
-8A0A(B BBBB L ĵ Ȕ BME B(A0A8DX
-8A0A(B BBBG L BNE B(A0G8DQ
-8A0A(B BBBB L d X BEE B(A0A8G:
-8A0A(B BBBA M G EL ̶ X BBE B(A0A8J
-8A0A(B BBBH d BBB B(A0A8GPl
+ H d G@\L X BEE E(D0D8J
+8A0A(B BBBI d ( BHB B(D0D8G
+8A0A(B BBBF B8A0A(B BBB , d ) 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
+0A(B BBBB V
+0A(B BBBA , AA
+AA A , @ AC
T , $ AC
T < T < BBE A(A0
+(A BBBI , AKJAA , AKJAA , AKJAA $ $ @ 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
8A0A(B BBBI
-8A0A(B BBBI D X BEB E(A0A80A(B BBB D ̷ BEB B(D0A8
-0A(B BBBD L x BEB B(A0A8D
-8A0A(B BBBB D d BBB K(A0A8
-0A(B BBBD L 2 BBB B(A0D8DD
-8A0A(B BBBD L вp BEB B(A0A8D
-8A0A(B BBBE L d ȶ L | BBB E(A0A8GBb
-8A0A(B BBBA L ̹ @ BBB B(A0D8GA
-8A0A(B BBBE L BBE B(A0A8GA
-8A0A(B BBBA L l p BBB B(A0A8G@6
-8A0A(B BBBG < ) BBA A(D0(A ABB L P BBB B(A0A8G
-8A0A(B BBBB L L d BBB B(A0A8G=
-8A0A(B BBBA L u BBB B(A0A8JG
-8A0A(B BBBA L @, BBB B(A0A8G
-8A0A(B BBBG L < BBB B(A0D8J8A0A(B BBB L BBB B(A0D8G8A0A(B BBB ܼ p L h{ BBE B(A0C8G3
-8A0A(B BBBE L D ` BBB B(D0A8J
-8A0A(B BBBH L
+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
BBB B(A0C8JO
-8A0A(B BBBA L h BBB B(A0C8J
-8A0A(B BBBG L 4 BBB B(A0C8J
-8A0A(B BBBA , ` BDA
-RID < t BBA A(G
-(A ABBH , { AAGoAA L $ hT BBB B(A0D8G58A0A(B BBB t x KIA D(G@(A ABBF@
+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@
(A ABBO
-(G EBBH D(G EBB x ` 4 X L 0 4 d ( BDD G0 AAB L ` BDB B(A0A8Dp
-8A0A(B BBBD O @! 4 8!; L `# d X$ | 0% , (&q BDD GP[ AAB, x&] FLQ )
-ABA ' `( $ xO < Oq ! BBA A(D0[
-(A ABBA L | O AAG ]
+(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 ]
CAF c
CAI R
-CAJ JCA < $ P
! AAG`
+CAJ JCA < H \? AAG`
AAJ M
-AAA P $ X6 AG lA D x> AG tA , d XP{ BFA i
-ABA L P [ BBB F(A0A8J@X
-8A0A(B BBBA L < (R & BBE A(A0J
-0A(A BBBA , ȶp + AHK s
-AAA L G BEE D(D0G
-0D(A BBBA T q BEE E(D0D8G0
-8D0A(B BBBA T d 0q BEE E(D0D8G0
-8D0A(B BBBA T Xq A BEE E(D0D8G0
-8D0A(B BBBA T q BEE E(D0D8G0
-8D0A(B BBBA < l U% BBA A(J
-(A ABBC T hq BEE E(D0D8G0
-8D0A(B BBBA L G BEE D(D0G
-0D(A BBBA T T Z BEE E(D0D8G
-8D0A(B BBBA L G N BEE D(D0G
-0D(A BBBA L G BEE D(D0G
-0D(A BBBA L L G BEE D(D0G
-0D(A BBBA L G BEE D(D0G
-0D(A BBBA L V BEE E(D0D8G
-8D0A(B BBBA L < ( BEE E(D0D8G
-8D0A(B BBBA T Z BEE E(D0D8G
-8D0A(B BBBA T Z BBB B(A0A8G
-8A0A(B BBBB L < hG ; BEE D(D0G
-0D(A BBBA L hG BEE D(D0G
-0D(A BBBA T i_ BBE B(A0D8G
-8A0A(B BBBA T 4 q BEE E(D0D8G0
-8D0A(B BBBA L 8G { BEE D(D0G
-0D(A BBBA L 8G = BEE D(D0G
-0D(A BBBA L , 8G BEE D(D0G
-0D(A BBBA L | 8G BEE D(D0G
-0D(A BBBA d (l BBA A(J
+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
(A ABBA U
(A ABBA U
-(A ABBA T 4 q BEE E(D0D8G0
-8D0A(B BBBA L G L BEE D(D0G
-0D(A BBBA L G BEE D(D0G
-0D(A BBBA L , G BEE D(D0G
-0D(A BBBA T | q BEE E(D0D8G0
-8D0A(B BBBA T q X BEE E(D0D8G0
-8D0A(B BBBA L , HG BEE D(D0G
-0D(A BBBA L | HG BEE D(D0G
-0D(A BBBA L HG BEE D(D0G
-0D(A BBBA L HG X BEE D(D0G
-0D(A BBBA L l HG BEE D(D0G
-0D(A BBBA L HG BEE D(D0G
-0D(A BBBA L HG BEE D(D0G
-0D(A BBBA L \ HG ` BEE D(D0G
-0D(A BBBA , H\ k BEH m
-ABA x 4 i ADG S
+(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
CAE ]
-FCB T D 0d BEB E(A0C8J
-8A0A(B BBBA | D H BIE B(A0A8GP
+FCB T xPd 6 BEB E(A0C8J
+8A0A(B BBBA | U BIE B(A0A8GP
8A0A(B BBBD B
8D0A(B BBBA
-8A0A(B BBBG T je BBE B(A0A8GV
-8A0A(B BBBD T t u _ BBE B(A0A8G
-8A0A(B BBBA L E
- BBB D(A0GP
-0A(A BBBF T | BBE B(A0A8G^
-8A0A(B BBBD < t " BJA A(D
-(A ABBA T BBB E(A0A8G
-8A0A(B BBBA ha. K^ T $ P1 BBB B(A0A8G
-8A0A(B BBBF $ m A~
-A l L X$ BBB B(A0A8JW
-8A0A(B BBBK T 4 W BEJ E(D0D8G
-8D0A(B BBBD < D 7| AAD`
+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`
AAH p
-AAF T P8! BEJ E(D0D8Gn
-8D0A(B BBBH L (: BBB B(D0A8Gi
-8A0A(B BBBI T , <1 BEJ E(D0D8G
-8D0A(B BBBH 4 ? BCA GPc
- AABA d h>M BLB B(A0A8DPq
+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
8A0A(B BBBE
-8A0A(B BBBG T $ P?! BEJ E(D0D8Go
-8D0A(B BBBG < $ (A BBG A(G@
-(A ABBG L A G BLB B(D0A8DP
-8A0A(B BBBG L 8C^ BLH B(D0A8G@
-8A0A(B BBBB HD5 < pD BBG A(G@
-(A ABBG \ \. K^ D E 3 BIB A(A0G`o
-0A(A BBBA T R # BIE B(A0A8G
-8D0A(B BBBD T l BIE B(A0A8G<
-8D0A(B BBBD T p BIE B(A0A8G\
-8D0A(B BBBD T F BBE B(A0A8G
-8A0A(B BBBA T t BIB E(A0A8G1
-8A0A(B BBBJ T 8
1 BEB B(A0A8G
-8A0A(B BBBB Fm A~
-A l L D G [ BBB B(A0A8Gv
-8A0A(B BBBG T I
a BIE B(A0A8G
-8A0A(B BBBD T 8 BEJ E(D0D8GP
-8D0A(B BBBF T D BEJ E(D0D8GP
-8D0A(B BBBF 4 xV ` BCA GPc
- AABA < pQ BDD v
-ABE AAB X. K^ T , V H BIB B(D0A8G
-8D0A(B BBBA T Z BIB B(D0A8G
-8D0A(B BBBA T x] BIB B(D0A8G
-8D0A(B BBBA T 4 `0 BIB B(D0A8G8
-8D0A(B BBBA 4 4 cD BEA A(D0p(A ABB L D BBB E(A0C8GP#
-8A0A(B BBBE T pc BBB E(A0A8G:
-8A0A(B BBBH T l o b BBE B(A0A8G
+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 @ . BEB B(A0A8G
-8A0A(B BBBB D BBE A(A0D@I
-0A(A BBBA < d YHD M
+8A0A(B BBBA T z: BEB B(A0A8G
+8A0A(B BBBB D `: BBE A(A0D@I
+0A(A BBBA < < 8 c: YHD M
HAE
-HAA L Д L Ȕ BBB B(A0A8G@
-8D0A(B BBBA x , p D x \ p t x& PT / SZ L e BEA A(D0s
-(C ABBH D(G ABB Е' AR
-E N , L D m BEA A(D0~
-(C ABBE D(G ABB ' AR
-E N $ ! AAG SCA ( $ 01 ADJ [AA $ 4 H AAG`AA \ ЖB t AZ L L P BJB A(A0W
-(A BBDH ^(A BBBT BGB I(A0A8G
-8A0A(B BBBD $ C YX
+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
G z
-A $ v ACG eDAT D '
- BBB E(A0D8D
-8D0A(B BBBG L K BEB A(A0d
-(A BBDH [(A BBGL / BBE B(A0A8DP
-8A0A(B BBBA O. K^ D P BBE A(A0D@~
-0A(A BBBA T t BBB B(A0G8G
-8A0A(B BBBF T BIB B(A0D8DH
-8D0A(B BBBC , L U ADD`v
-AAA , $ b D\
+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\
H P
H Q
-G I , T Xb D\
+G I , 8b D\
H P
H Q
-G I , b D\
+G I , D 8b D\
H P
H Q
-G I , b D\
+G I , t 9b D\
H P
H Q
-G I , b D\
+G I , `9b D\
H P
H Q
-G I , Xb D\
+G I , 9b D\
H P
H Q
-G I , D b D\
+G I , 9b D\
H P
H Q
-G I , t b D\
+G I , 4 :b D\
H P
H Q
-G I , b D\
+G I , d `:b D\
H P
H Q
-G I 4 , X BAD D
-ABA , d & AAG@B
-AAC , p& AAG@B
-AAC , l & ADG [
-AAG 4 P'y BDD G0y
- AABA < , ' ACDP
+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
AAA O
-AAA < l x( AADP
+AAA < D 5 AADP
AAA O
-AAA D X) BJD D(G$
-(D ABBH D * BJD D(G$
-(D ABBH < < (, r AADP
+AAA D (5 BJD D(G$
+(D ABBH D 4 BJD D(G$
+(D ABBH < p 4 AADP
AAI O
-AAA T | . h BEJ E(D0D8GT
-8D0A(B BBBJ T / # BEJ E(D0D8GT
-8D0A(B BBBJ T , 1 BEJ E(D0D8GT
-8D0A(B BBBJ < `3 BED C(GP{
-(A ABBJ \ Pn BBB A(A0GP
+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 \ $ `n K BBB A(A0GP
+0A(A BBBA \ 8n 3 BBB A(A0GP
0A(A BBBD j
-0A(A BBBA D 4 J BJD D(G$
-(D ABBH D (W BBB A(A0GP
-0A(A BBBH 4 05J BDA l
+0A(A BBBA D \ X 3 BJD D(G$
+(D ABBH D p9W a3 BBB A(A0GP
+0A(A BBBH 4 | xJ BDA l
ABJ A
-ABA H5m A~
-A l 5m A~
-A l D 5 BJD D(G$
-(D ABBH 4 P7 N AAD
-DAA 4 8 : AAD$
-DAA 4 D : & AAD
-DAA L | BDB A(A0L
-0A(A BBBE T h BBB B(A0A8JB
-8A0A(B BBBA F. K^ 4 < :g BDD G~
- AABA 4 t P BAD G
-ABA 4 T :J BDA o
+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
ABG A
-ABA L p d BBB I(A0G^
-0D(A BBBA < 4 @ ? BAI G|
- DABA < t BAI G
- DABA < 0 BAI G
- DABA < 0 BAI G
- DABA T 4 4 BBB B(I0A8Gg
-8D0A(B BBBA T 4 v BBB B(I0A8Gg
-8D0A(B BBBA < ; I BAI G
- DABA 4 `8v BDA D0a
- AABA L 8 BBB B(A0A8Dp
-8A0A(B BBBA L 89z BBB E(D0D8GP
-8A0A(B BBBC T { BIB B(A0A8J9
-8A0A(B BBBB ` < l X BBK A(DPn
-(A ABBA < c BBK A(DPn
-(A ABBA T x > BBB B(A0A8J.
-8A0A(B BBBD T D K BBB B(A0A8G N
-8A0A(B BBBG D Ht BBI A(G
-(D ABBA , 8b D\
+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\
H P
H Q
-G I , 8b D\
+G I , |( b D\
H P
H Q
-G I , 9b D\
+G I , ( Hb D\
H P
H Q
-G I T t @9 BBB B(A0A8G
-8A0A(B BBBJ T e BEE B(A0A8G
-8A0A(B BBBF T $ p<% BBB B(A0A8Gk
-8A0A(B BBBJ T | he BEE B(A0A8G
-8A0A(B BBBF T e BEE B(A0A8G
-8A0A(B BBBF T , ?u BBB B(A0A8G
-8A0A(B BBBB , =. K^ T Fb BBB B(A0A8G
-8A0A(B BBBA T [B BBB B(A0A8G
-8A0A(B BBBA T L pd BBB B(A0A8G(
-8A0A(B BBBA , L b D\
+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\
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 < 4 { BBD A(G@a
-(A ABBA 4 t c BAD D
-ABA , ؓS G AYL`
-AAA L h 3 BBB B(A0A8DW
-8D0A(B BBBF , , _ O AAG o
-DAA D \
6 BCA GPg
+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 L :. K^ T g BIE B(A0A8G&
-8D0A(B BBBB T o BIE B(A0A8G&
-8D0A(B BBBB T l X w BIB B(D0A8G
-8D0A(B BBBA T a BIB B(D0A8G
-8D0A(B BBBA T ȶ K BIB B(D0A8G
-8D0A(B BBBA T t 5 BIB B(D0A8G
-8D0A(B BBBA T 8j BBB B(A0D8G
-8D0A(B BBBA T $ P BBB B(A0I8GU
-8D0A(B BBBA T | BIB B(A0A8G
-8A0A(B BBBF T BIB B(A0A8G
-8A0A(B BBBK T , BBB B(A0A8M
-8A0A(B BBBA L BBB B(A0A8Mi
-8A0A(B BBBA , | P b D\
+ 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\
H P
H Q
-G I , b D\
+G I , l2 b D\
H P
H Q
-G I , b D\
+G I , 2 b D\
H P
H Q
-G I , d
S # AYL`
-AAA , @z
AMQP"
-AAA , _ AAG o
-DAA , _ AAG o
-DAA D $ v6 BCA GPg
+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 X5. K^ T
- BIB B(D0A8G
-8D0A(B BBBA T H
- BIB B(D0A8G
-8D0A(B BBBA T 4
- BIB B(D0A8G
-8D0A(B BBBA T
- BIB B(D0A8G
-8D0A(B BBBA T p&
BIB B(A0A8G
-8A0A(B BBBA T < 4
BIB B(A0A8G
-8A0A(B BBBA T A
k BIB B(A0A8G
-8A0A(B BBBA T 8O
U BIB B(A0A8G
-8A0A(B BBBA T D \
? BIB B(A0A8G
-8A0A(B BBBA L hj ) BBB B(A0A8Mi
-8A0A(B BBBA F T Hu BBB B(K0A8G
-8A0A(B BBBA , t wu AMQ`
-AAA , L @yb D\
+ 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\
H P
H Q
-G I , | yb D\
+G I , <8 b D\
H P
H Q
-G I , yb D\
+G I , l8 b D\
H P
H Q
-G I , zb D\
+G I , 8 Hb D\
H P
H Q
-G I , @zb D\
+G I , 8 b D\
H P
H Q
-G I , < zb D\
+G I , 8 b D\
H P
H Q
-G I L l z AAG ]
+G I L ,9 AAG ]
CAF [
ACA R
-CAJ JCA , zb D\
+CAJ JCA , |9 8b D\
H P
H Q
-G I , 0{b D\
+G I , 9 xb D\
H P
H Q
-G I , p{b D\
+G I , 9 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 , <: 8b D\
H P
H Q
-G I , 0|b D\
+G I , l: xb D\
H P
H Q
-G I , p|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 , : 8b D\
H P
H Q
-G I , l 0}b D\
+G I , ,; xb D\
H P
H Q
-G I , p}b D\
+G I , \; b D\
H P
H Q
-G I 4 $ }g R BAD D
-ABA
- }m A~
-A l 4 | 8~ BAD D
-ABA < ~ BBD A(G@a
-(A ABBA T 0 BZB J(A0A8G
-8A0A(B BBBD , L
- _ G AAG o
-DAA , |
- _ # AAG o
-DAA ,
- e ADG r
-DAA T
- 8 BBB B(A0A8Gw
-8A0A(B BBBF d 4 i BDB B(A0A8L\
+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\
8A0A(B BBBB
-8A0A(B BBBI L BDB J(A0A8Ib
-8A0A(B BBBG T BBB B(A0A8G
-8A0A(B BBBG T D BBB B(A0A8G
-8A0A(B BBBG T x n BBB B(A0A8G
-8A0A(B BBBG T p BBB B(A0D8G s
-8A0A(B BBBG T L
BDA I(Dp
+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
(A ABBA U
-(A ABBE L P). K^ ,
6 O ADG T
-AAA 4
/ BAD G
-ABA L $ _ BLH B(D0D8D@
-8A0A(B BBBG 4 t BKA G
- DABA 4 h _ BKA G
- DABA T ? BBB J(A0A8G
-8D0A(B BBBG 4 < + BKA G
- DABA 4 t p
- BKA G
- DABA < ( BAD GC
- DABA T xM BBE B(A0A8G#
-8D0A(B BBBD < D p BAD GC
- DABA < { BAD GC
- DABA < O BAD GC
- DABA < ` # BAD GC
- DABA < D BAD GC
- DABA < BAD Gk
- DABA < BAD Gk
- DABA < s BAD G
- DABA < D G BAD Gk
- DABA D BBA D(G
-(D ABBA < BAD GC
- DABA < H BAD GC
- DABA < L BAD GC
- DABA < d BAI G
- DABA < = BAD Gw
- DABA < 8 BAD GC
- DABA < L BAD G
- DABA T ( BIE B(A0A8G
-8D0A(B BBBG T BIE B(A0A8G
-8D0A(B BBBG 4 < BKA G
- DABA 4 t r BKA G
- DABA T S BBB E(A0A8G
-8D0A(B BBBD 4 @ ; BRA G
- DABA 4 < BRA G
- DABA 4 t BRA G
- DABA < h BAD GC
- DABA < BAD GC
- DABA < , BAI GI
- DABA < l Z BAI Gp
- DABA T ( 3 BBB J(A0A8GH
-8D0A(B BBBB < # BAD GC
- DABA < D BAD GC
- DABA < BAD GC
- DABA < p BAD GC
- DABA < s BAD GC
- DABA < D G BAD GC
- DABA T ` BBB J(A0A8G
-8D0A(B BBBE < # BAD GC
- DABA < ( BAD GC
- DABA < \ x BAD GC
- DABA < BAD GC
- DABA < s BAD GC
- DABA T hM G BBE B(A0A8G#
-8D0A(B BBBD T t `M / BBE B(A0A8G#
-8D0A(B BBBD < XY BAI G
- DABA < x BAI G"
- DABA < L 5 BAD G
- DABA < BAD GC
- DABA < q BAI G<
- DABA D J BBA I(Gz
-(D ABBA T T ` # BBJ B(A0A8GG
-8D0A(B BBBC D Y BBA I(G
-(D ABBA L BLB B(A0A8D
-8A0A(B BBBA P T \ X BBB B(A0A8J G
-8F0A(B BBBA T # BDB E(A0C8GR
-8A0A(B BBBA D X BDB A(I0F`K
-0A(A BBBE T T V BDA C(L`^
+(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 ABBF
-(A ABBE T "V BDA C(L`^
+(A ABBE T Q V / BDA C(L`^
(A ABBF
-(A ABBE T $V p BDA C(L`^
+(A ABBE T Q V / BDA C(L`^
(A ABBF
-(A ABBE L \ & 7 BDB B(A0A8O\
-8A0A(B BBBG L x. 7 BDB B(A0A8M`
-8A0A(B BBBE , > AALP
-DAJ T ,! ?
n BBB B(A0D8J
-8F0A(B BBBA , ! M AALP
-DAD , ! M AALP
-DAK T ! Nd BBE B(A0A8Gu#
-8F0A(B BBBA L <" Ȳb
BDB B(A0A8O|
-8A0A(B BBBG L " BDJ B(A0A8Gl
-8A0A(B BBBG L " BDB B(A0A8L
-8A0A(B BBBF L ,# BBB B(D0G8JP
-8A0A(B BBBI $$ 8 AZ 4 # 8T ; BAA J@
- AABB < # `a < BBA A(JP
-(A ABBI , $ Pb D\
+(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\
H P
H Q
-G I , $ b D\
+G I , V ob D\
H P
H Q
-G I , % b D\
+G I , V pb D\
H P
H Q
-G I , L% b D\
+G I , W Xpb D\
H P
H Q
-G I , |% Pb D\
+G I , <W pb D\
H P
H Q
-G I , % b D\
+G I , lW pb D\
H P
H Q
-G I , % b D\
+G I , W qb D\
H P
H Q
-G I , & b D\
+G I , W Xqb D\
H P
H Q
-G I , <& Pb D\
+G I , W qb D\
H P
H Q
-G I , l& b D\
+G I , ,X qb D\
H P
H Q
-G I L & AAG ]
+G I L \X r AAG ]
CAF [
ACA R
-CAJ JCA , & b D\
+CAJ JCA , X Hrb D\
H P
H Q
-G I ' @m A~
-A l T & BBB B(K0A8G
-8A0A(B BBBA T & X } { BNO B(A0A8G
-8A0A(B BBBH , D' J AMQP
-AAA , t' J d AMQ@
-AAA , ' _ C AAG o
-DAA , ' _ AAG o
-DAA , ( _ AAG o
-DAA , 4( P e ADG r
-DAA d( R BDB B(A0C8Lpo
+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
8A0A(B BBBE 4
8A0A(B BBBJ /
-8A0A(B BBBG T ( s BBB B(A0A8G
-8A0A(B BBBG L D)
( = BDB B(A0C8OZ
-8A0A(B BBBG L ) ( ; BDB B(A0C8OZ
-8A0A(B BBBG * @
. K^ 4 ) x BRA G
- DABA 4 4* 0 BRA G
- DABA 4 l* BRA G
- DABA 4 * BKA G
- DABA 4 * X BKA G
- DABA 4 + w BKA G
- DABA 4 L+ V BKA G
- DABA 4 + 5 BKA G
- DABA 4 + 8 BKA G
- DABA 4 + BKA G
- DABA 4 ,, BKA G
- DABA D d, ` BBA D(G
-(D ABBA L , BBB A(I0G
-0D(A BBBJ 4 , X"- j ADG
-DAA 4 4- P#- E ADG
-DAA 4 l- H$- ADG
-DAA 4 - @%- ADG
-DAA 4 - 8&- ADG
-DAA 4 . 0'- ADG
-DAA 4 L. ((- ADG
-DAA 4 . )U g ADG0
-DAA 4 . H*} B ADGX
-DAA 4 . +U ADG0
-DAA 4 ,/ ,- ADG
-DAA 4 d/ -- ADG
-DAA 4 / .- ADG
-DAA 4 / /a ADG<
-DAA < 0 0 d BAD Gw
- DABA < L0 h2 8 BAD Gw
- DABA 4 0 3- ADG
-DAA 4 0 4} ADGX
-DAA 4 0 860 ADG
-DAA 4 41 070 ADG
-DAA 4 l1 (80 x ADG
-DAA 4 1 90 S ADG
-DAA 4 1 :i . ADGD
-DAA 4 2 P;B ADG
-DAA < L2 h= BAI Gr
- DABA < 2 @ BAI GF
- DABA < 2 B BAI GF
- DABA < 3 E r BAD GF
- DABA < L3 XF F BAD GF
- DABA < 3 G BAD GF
- DABA < 3 H BAD GF
- DABA < 4 HJ BAI Gp
- DABA < L4 KI BAI G
- DABA < 4 M\ u BAI G
- DABA < 4 P O BAD G
- DABA < 5 Q # BAD G
- DABA 4 L5 xS BKA G
- DABA 4 5 0T BKA G
- DABA D 5 T BDB A(I0F`K
-0A(A BBBE T 6 PVV BDA C(L`^
+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`^
(A ABBF
-(A ABBE L \6 XX BDB B(A0A8O|
-8A0A(B BBBG L 6 (` BDB B(A0A8O|
-8A0A(B BBBG T 6 p BFB B(A0A8J
-8A0A(B BBBD 7 8 U 4 7 G BSA Qp
- AABA , 7 Ȅ ASQ`M
-AAA 4 7 h BYA LpT
- AABA 4 $8 1 BYA Lp
- AABA , \8 (P AYL`
-AAA , 8 Hu AMQ`
-AAA L d9 AAG ]
+(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 ]
CAF [
ACA R
-CAJ JCA L 9 Ȏ AAG ]
+CAJ JCA L tk AAG ]
CAF Z
ACB R
-CAJ JCA , : b D\
+CAJ JCA , k @b D\
H P
H Q
-G I , 4: 8b D\
+G I , k b D\
H P
H Q
-G I , d: xb D\
+G I , $l b D\
H P
H Q
-G I , : b D\
+G I , Tl b D\
H P
H Q
-G I , : b D\
+G I , l @b D\
H P
H Q
-G I , : 8b D\
+G I , l b D\
H P
H Q
-G I , $; xb D\
+G I , l b D\
H P
H Q
-G I , T; b D\
+G I , m b D\
H P
H Q
-G I , ; b D\
+G I , Dm @b D\
H P
H Q
-G I , ; 8b D\
+G I , tm b D\
H P
H Q
-G I , ; xb D\
+G I , m b D\
H P
H Q
-G I , < b D\
+G I , m b D\
H P
H Q
-G I , D< b D\
+G I , n @b D\
H P
H Q
-G I , t< 8b D\
+G I , 4n b D\
H P
H Q
-G I , < xb D\
+G I , dn b D\
H P
H Q
-G I , < b D\
+G I , n b D\
H P
H Q
-G I , = b D\
+G I , n @b D\
H P
H Q
-G I , 4= 8b D\
+G I , n b D\
H P
H Q
-G I , d= xb D\
+G I , $o b D\
H P
H Q
-G I , = b D\
+G I , To b D\
H P
H Q
-G I , = b D\
+G I , o @ b D\
H P
H Q
-G I , = 8b D\
+G I , o b D\
H P
H Q
-G I $> xm A~
-A l < = Ȕ BBD A(G@a
-(A ABBA 4 = h BAD D
-ABA L > BBB F(A0A8J@X
-8A0A(B BBBA T d> @ [ BBB E(A0A8D
-8D0A(B BBBA T > ; G BTB B(A0A8Q;
-8A0A(B BBBA , ? h ( BDD y
-ABA , D? 0_ AAG o
-DAA , t? `_ AAG o
-DAA , ? _ AAG o
-DAA , ? _ AAG o
-DAA , @ _ t AAG o
-DAA , 4@ _ P AAG o
-DAA A P A`
-G b ,A A`
-G b LA 0 A`
-G bD @ pL BED D(G$
-(D ABBE D A M BED D(G$
-(D ABBE D TA @O BED D(G$
-(D ABBE < A Ȟ ` ACLPj
+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 A hP BED D(G$
-(D ABBE L B Q BBB B(A0A8Gp8A0A(B BBB , C PS ADD Z
-AAA $ B A
-E D B S BPR M(D0c
-(A BBBA 4 C B\D G
- DABA < LC B_G G5
- DABA < C h BAI G,
- DABA 4 C ȥ X BKA G
- DABA 4 D 7 BKA G
- DABA 4 <D 8 BKA G
- DABA D tD BBA I(G4
-(D ABBA D D x BBA I(G
-(D ABBA 4 E Я BKA G
- DABA 4 <E BRA G
- DABA 4 tE @ a BRA G
- DABA 4 E @ BRA G
- DABA T E BBB B(A0A8G
-8A0A(B BBBG < <F 9
BAI G
- DABA < |F
BAI G
- DABA < F ػ
BAI GC
- DABA T F H
BBE B(A0A8G<
-8D0A(B BBBC T TG p o
BBJ B(A0A8G
-8D0A(B BBBE < G & c
BAD G
- DABA < G 8
BAI G~
- DABA T ,H x
BBB B(A0I8G
-8D0A(B BBBA T H O #
BBE E(D0D8L
-8A0A(B BBBK , I BCA x
-ABO I 8( A[ I H( A[ 4 LI Xd
BAA J@
- AABJ < I a
BBA A(JP
-(A ABBA T I ]
BBB B(A0D8G
-8A0A(B BBBE J L 4J %
BFB B(A0C8J]
-8A0A(B BBBG ,K d J {
BFB B(A0A8Gp_
+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 K K \ 4K U r
BBB A(A0MR
+8A0A(B BBBA l} Hz } @z \ } 8zU + BBB A(A0MR
0A(A BBBA
-0A(A BBBF L K t
BAA JV
+0A(A BBBF L l} 8 + BAA JV
AABH
- AABD L K o
BFB B(A0C8J]
-8A0A(B BBBG L T LL 8K
BBE E(A0C8G^
-8D0A(B BBBD L L
BDB B(A0A8O\
-8A0A(B BBBG T L L
BBB E(A0G8G
-8D0A(B BBBJ T LM #
BIB B(A0A8G
-8A0A(B BBBD L M Pr BTB A(A0N
-0A(A BBBA 4 M S BSA Qp
- AABA 4 ,N TG BSA Qp
- AABA 4 dN W BYA LpT
- AABA 4 N Y1 BYA Lp
- AABA , |O \b D\
+ 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\
H P
H Q
-G I , O ]b D\
+G I , l Xb D\
H P
H Q
-G I L O P] AAG ]
+G I L AAG ]
CAF [
ACA R
-CAJ JCA L ,P ] AAG ]
+CAJ JCA L AAG ]
CAF Z
ACB R
-CAJ JCA , |P ]b D\
+CAJ JCA , < b D\
H P
H Q
-G I , P ]b D\
+G I , l 8b D\
H P
H Q
-G I , P 0^b D\
+G I , xb D\
H P
H Q
-G I , Q p^b D\
+G I , ̂ b D\
H P
H Q
-G I , <Q ^b D\
+G I , b D\
H P
H Q
-G I , lQ ^b D\
+G I , , 8b D\
H P
H Q
-G I , Q 0_b D\
+G I , \ xb D\
H P
H Q
-G I , Q p_b D\
+G I , b D\
H P
H Q
-G I , Q _b D\
+G I , b D\
H P
H Q
-G I , ,R _b D\
+G I , 8b D\
H P
H Q
-G I , \R 0`b D\
+G I , xb D\
H P
H Q
-G I , R p`b D\
+G I , L b D\
H P
H Q
-G I R `m A~
-A l , 4R a_ w AAG o
-DAA , dR 0ah S BDD y
-ABA , R pa_ / AAG o
-DAA , R a_ AAG o
-DAA , R a_
- AAG o
-DAA S b A`
-G b S pb A`
-G b< dS b ACLPo
+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
AAG
-AAE T S ) BEJ E(D0D8GP
-8D0A(B BBBF T (. K^ 4 T @d BYA G
- DABA 4 LT d BYA G
- DABA 4 T e! B_D G
- DABA 4 T f u BYA G
- DABA < T gt T BYA G
- DABA < 4U h , B_G G5
- DABA < tU 0j B_G G5
- DABA < U k BAI G,
- DABA L U m BIB A(D0G
-0D(A BBBA 4 DV o } BKA G
- DABA 4 |V hp \ BKA G
- DABA 4 V q ; BKA G
- DABA 4 V q7 ADG
-DAA T $W r
- BDB I(A0A8Np
-8A0A(B BBBE 4 |W hw
- ADG
-DAA 4 W x
- ADG
-DAA < W xz o
- BAD G
- DABA < ,X X| C
- BAD G
- DABA < lX ~*
- BAI G
- DABA T X V BBB B(A0A8G
-8A0A(B BBBD < Y BAI Ge
- DABA T DY X BBJ B(A0A8G
-8D0A(B BBBI T Y & BBB B(A0A8G
-8A0A(B BBBD T Y S P BEE B(D0D8L
-8A0A(B BBBK , Z ACL d
-JAQ , $[ BCA x
-ABO T[ ( A[ t[ ( A[ T Z " BBB B(A0D8G
-8A0A(B BBBE [ L \[ x 0
- BFB B(A0C8J]
-8A0A(B BBBG T\ 8 L [ 0M
- BDB B(A0A8O\
-8A0A(B BBBG L \ 0M
- BDB B(A0A8O\
-8A0A(B BBBG , ] @b D\
+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\
H P
H Q
-G I , <] b D\
+G I , Mb D\
H P
H Q
-G I T \ E I
- BBE E(A0A8G6
-8A0A(B BBBI T ] B
- BBE E(A0A8G
-8A0A(B BBBK , t] P :
- AADPR
-AAA T ] E
- BBE E(A0A8G6
-8A0A(B BBBI ^ Xډ. K^ T ^ pg BBB B(A0A8G(
-8A0A(B BBBA T l^ BBB B(A0A8G
-8A0A(B BBBA T ^ BDB B(A0A8J
-8A0A(B BBBH _ x < 4_ p
- BBA A(J`x
-(A ABBB ` p%Q Az
-E P , <` %b D\
+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\
H P
H Q
-G I l` ؉. K^ T _ %g
- BBB B(A0A8G(
-8A0A(B BBBA , ` 4b D\
+G I , . K^ T g ^( BBB B(A0A8G(
+8A0A(B BBBA , 8b D\
H P
H Q
-G I , a 05b D\
+G I , ̒ xb D\
H P
H Q
-G I 4 ` p5 BAD D
-ABA D ` 5
- BAA Jp
+G I 4 l ' BAD D
+ABA D ) BAA Jp
AABH
- AABC D a 9l
- BAA JPV
+ AABC D خl ) BAA JPV
AABH
- AABC b X. K^ T ta ; BBB B(A0A8G
-8A0A(B BBBA T a (J p BBB B(A0A8G
-8A0A(B BBBA T $b X>A _
BIB B(A0A8G+"
-8A0A(B BBBI L $c AAG ]
+ 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 ]
CAF Z
ACB R
-CAJ JCA , tc b D\
+CAJ JCA , 4 0b D\
H P
H Q
-G I , c (b D\
+G I , d pb D\
H P
H Q
-G I , c hb D\
+G I , b D\
H P
H Q
-G I T \c i BNO B(A0A8G
-8A0A(B BBBH , c d ACG r
-DAA , c _ AAG o
-DAA T d u { BEE B(A0A8G
-8A0A(B BBBD T ld "e s BEE B(A0A8G
-8A0A(B BBBF L d k BBB B(A0G8Dh
-8A0A(B BBBJ T e Ы
_ BBB B(A0A8J
-8D0A(B BBBK T le BQB B(K0A8G
-8A0A(B BBBA T e 0p X BBB B(G0A8Gx
-8A0A(B BBBA f ҉. K^ T 4f 0 3 BIB B(D0A8Gb
-8D0A(B BBBA T f BKB B(D0A8Gb
-8D0A(B BBBA T f BKB B(D0A8Gb
-8D0A(B BBBA T <g BKB B(D0A8Gb
-8D0A(B BBBA T g BKB B(D0A8Gb
-8D0A(B BBBA T g BKB B(D0A8Gb
-8D0A(B BBBA T Dh BNB B(D0A8Gb
-8D0A(B BBBA T h x BKB B(D0A8Gb
-8D0A(B BBBA T h { BKB B(D0A8Gb
-8D0A(B BBBA T Li h d BKB B(D0A8Gb
-8D0A(B BBBA T i
M BNB B(A0A8Gs
-8A0A(B BBBA T i 8
6 BIB B(A0A8Gx
-8A0A(B BBBA L j # AAG ]
+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 ]
CAF Y
ACC R
-CAJ JCA , Lk #b D\
+CAJ JCA , b D\
H P
H Q
-G I , |k #b D\
+G I , < 8b D\
H P
H Q
-G I , k 0$b D\
+G I , l xb D\
H P
H Q
-G I L k p$ AAG ]
+G I L AAG ]
CAF [
ACA R
-CAJ JCA , ,l $b D\
+CAJ JCA , b D\
H P
H Q
-G I \l 䍉Z AX T k $ BBB L(A0A8G
-8A0A(B BBBI T ,l H1B
- BNL B(A0A8G
-8A0A(B BBBD T l @; BNO B(A0A8G
-8A0A(B BBBH , l Gc ACG q
-DAA , m HG_ AAG o
-DAA , <m xGe s ADG r
-DAA L lm G ? BBB B(A0G8Dh
-8A0A(B BBBJ L m XL: p BBB B(A0G8Dh
-8A0A(B BBBJ T n HQP b BBB B(G0A8Gx
-8A0A(B BBBA o ʉ. K^ T |n (Y + BNB B(D0A8Gb
-8D0A(B BBBA T n ^ BIB B(D0A8Gb
-8D0A(B BBBA T ,o d BIB B(D0A8Gb
-8D0A(B BBBA T o i BIB B(D0A8Gb
-8D0A(B BBBA T o o BNB B(D0A8Gb
-8D0A(B BBBA T 4p t BNB B(D0A8Gb
-8D0A(B BBBA T p y BNB B(D0A8Gb
-8D0A(B BBBA T p p BNB B(D0A8Gb
-8D0A(B BBBA T <q s BIB B(D0A8Gb
-8D0A(B BBBA T q ` \ BNB B(D0A8Gb
-8D0A(B BBBA T q ؏ E BNB B(D0A8Gb
-8D0A(B BBBA T Dr P . BNB B(D0A8Gb
-8D0A(B BBBA T r Ț BNB B(D0A8Gb
-8D0A(B BBBA T r @ BNB B(D0A8Gb
-8D0A(B BBBA T Ls BNB B(D0A8Gb
-8D0A(B BBBA T s 0 BNB B(D0A8Gb
-8D0A(B BBBA , t b D\
+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\
H P
H Q
-G I , ,t AAN`V
-AAA , \t _ } AAG o
-DAA 4u ĉ. K^ T t [ BIB B(A0A8Gq
-8A0A(B BBBA T t W E BIB B(A0A8G!
-8A0A(B BBBA T Tu ( / BIB B(A0A8G
-8A0A(B BBBA T u 5 G BBB B(D0A8G
-8A0A(B BBBB , v b D\
+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\
H P
H Q
-G I , v b D\
+G I , hnb D\
H P
H Q
-G I , dv ` AANP4
-AAA , v AAN`}
-AAA , v _ AAG o
-DAA , v _ AAG o
-DAA w P. K^ T <w v ? BIB B(A0A8GA
-8A0A(B BBBA T w
( + BIB B(A0A8G
-8A0A(B BBBA T w W C BIB B(A0A8G!
-8A0A(B BBBA T Dx . - BIB B(A0A8Gq
-8A0A(B BBBA T x X< BBB B(A0A8J N
-8A0A(B BBBD y RA y y y RS y SA z XS ,z PS Dz HS \z @S tz 8S z 0S z (S z S z S^ cz z `S
- { XS , tz PS AAG@
-DAF L{ T d{ S AL , z Sh u ADDP~
-AAA $ { (T$ ACK ODA 4 4{ 0Tr - BDD DPA
- AABA | xT AP , { xTh ADDP~
-AAA d| T , { T AAG@
-DAF | `U+ 4 | xU BIA A(G0(D ABB< | V BEI A(A0(A BBB <} V T} V AZ L t} V@ BBB B(D0D8D@
-8A0A(B BBBK T } X s BBB E(D0A8D|
-8D0A(B BBBC < t} p_ YHD M
+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
HAE
-HAA \~ P` D t~ H` BBE E(G0D8DP~8D0A(B BBB, ~ BAA
-ABA T D~ BEE J(D0D8G>
-8D0A(B BBBA L ~ P BJE D(D0G
-0D(A BBBA L ~ P C BJE D(D0G
-0D(A BBBA T < _ BJE E(D0D8G
-8D0A(B BBBA T { BJE E(D0D8G5
-8D0A(B BBBA T { y BJE E(D0D8G5
-8D0A(B BBBA T D 3 BJE E(D0D8GZ
-8D0A(B BBBA T X{ BJE E(D0D8G5
-8D0A(B BBBA T BJE E(D0D8GZ
-8D0A(B BBBA T L { a BJE E(D0D8G5
-8D0A(B BBBA L BJE D(D0G!
-0D(A BBBD T 0c BJE E(D0D8G
-8D0A(B BBBA L L H BJE D(D0G!
-0D(A BBBD L L [ BJE D(D0G
-0D(A BBBA L L BJE D(D0G
-0D(A BBBA L < BJE D(D0G!
-0D(A BBBD L L BJE D(D0G
-0D(A BBBA 0 AG fA $ F AAG0zAA ̄ AL 7 Dh
-D F D | s BBJ A(D0
-(A BBEA T Ą Z K BDD u
+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
ADD
ABE U
ABH I
-ABA T Z BDD u
+ABA T R, BDD u
ADD
ABE U
ABH I
-ABA < t [
BDA G
- DABG < ]
BDA G
- DABG L @ g
BHE B(D0D8DP
-8D0A(B BBBB L BLB B(A0A8Dz
-8A0A(B BBBA < ' BGA A(Kp
-(A ABBA $ Ԇ p_ YX
+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
G z
-A T _ +
BBB B(D0A8G
-8A0A(B BBBA \ BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 Y * AIG t
-AAI DCA \ BBE B(A0D8D@g
-8D0A(B BBBH D8C0A(B BBB4 L XY
AIG t
-AAI DCA L c
BEB E(I0H8D@
-8D0A(B BBBA T Ԉ yD ;
BBB B(A0A8G
-8A0A(B BBBA D , {F BLB A(A0GpG
-0A(A BBBA $ `F AAG0zAA L BEB E(I0H8D@
-8D0A(B BBBD \ ] BBB I(D0D@
+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@
0F(A BBBK U
-0F(A BBBA T L / BKB B(A0A8G
-8A0A(B BBBA T BLB B(A0A8G
-8A0A(B BBBE T (i BEB B(A0A8O
-8A0A(B BBBH L T U BBB E(E0E8G`
-8A0A(B BBBA l = BEB B(A0A8D
+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 l - O BBF H(A0D8D
-8A0A(B BBBA T 9 BBB B(A0F8G
-8D0A(B BBBB L - BBF H(A0D8D
-8A0A(B BBBA @. K^ $ $ xM AAK0}AA D L BEB B(A0A8DP8A0A(B BBBL h
BEE A(A0]
+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]
(A BBBF
-(E BBBJ D Q T T z [ BBB B(A0A8J
-8A0A(B BBBG < BBD A(G
-(D ABBA L BHF E(D0A8Dm
-8A0A(B BBBA T < u BBB B(A0A8GM
-8D0A(B BBBE L @ BHE E(D0A8Dm
-8A0A(B BBBA T Кu S BBB B(A0A8GM
-8D0A(B BBBE L < % BHE E(D0A8Dm
-8A0A(B BBBA T
- BBB B(A0A8G]
-8D0A(B BBBE L П
- BHE E(D0A8Dm
-8A0A(B BBBA T 4 `
- BBB B(A0A8GR
-8D0A(B BBBH T W
- BBB B(A0A8GR
-8D0A(B BBBH T )
- BBB B(A0A8G
-8A0A(B BBBA T < h
- BBB B(A0A8G
-8A0A(B BBBA T o BBB B(A0A8GJ
-8A0A(B BBBA T BBB B(A0A8G
-8A0A(B BBBA T D BBB B(A0A8G
-8A0A(B BBBA $ D 3 AAJ ZGA , l f AAJ m
-GAO , b D\
+(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\
H P
H Q
-G I , ̔ `b D\
+G I , Xb D\
H P
H Q
-G I , b D\
+G I , Xb D\
H P
H Q
-G I , , b D\
+G I , (Yb D\
H P
H Q
-G I , \ b D\
+G I , hYb D\
H P
H Q
-G I , `b D\
+G I , L Yb D\
H P
H Q
-G I , b D\
+G I , | Yb D\
H P
H Q
-G I , b D\
+G I , (Zb D\
H P
H Q
-G I L t 5> BBB B(A0A8GZ
-8D0A(B BBBH T ĕ 8# _ BEE B(A0A8G
-8A0A(B BBBI 4 x" W BAA GP
- AABG T T 0># N BEE B(A0A8G
-8A0A(B BBBI T De F BEE B(A0A8G
-8A0A(B BBBF T J > BBE E(A0A8G
-8A0A(B BBBH T \ hP 6 BBE E(A0A8G
-8A0A(B BBBH T V# . BEE B(A0A8G
-8A0A(B BBBI T \# & BEE B(A0A8G
-8A0A(B BBBI T d `b BBE E(A0A8G
-8A0A(B BBBF T hu f BEE B(A0A8G
-8A0A(B BBBF T n ^ BBE E(A0A8G2
-8A0A(B BBBE L l XU W BBB B(A0A8J
-8A0A(B BBBB | d (u BBB B(D0A8DP
-8A0A(B BBBB U
-8G0A(B BBBK
-8A0A(B BBBI | w BBB B(A0D8DP
+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
8A0A(B BBBB
8A0A(B BBBE "
-8A0A(B BBBD L h4 U BBB B(A0A8M
-8A0A(B BBBH Ȟ. K^ T $ @ BBJ B(A0A8G_
-8D0A(B BBBK T | % BBB B(I0A8G
-8D0A(B BBBE T ԛ ` BBJ B(A0A8G4
-8D0A(B BBBF T , o BBJ B(A0A8G
-8D0A(B BBBF T
-v BBJ B(A0A8G
-8D0A(B BBBD T ܜ 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 % $ BEB B(A0A8J
-8A0A(B BBBG t x x
, xb D\
+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\
H P
H Q
-G I , ԟ xb D\
+G I , b D\
H P
H Q
-G I , (yb D\
+G I , b D\
H P
H Q
-G I , 4 hyb D\
+G I , b D\
H P
H Q
-G I , d yb D\
+G I , L Hb D\
H P
H Q
-G I , yb D\
+G I , | b D\
H P
H Q
-G I , Ġ (zb D\
+G I , b D\
H P
H Q
-G I , hzb D\
+G I , b D\
H P
H Q
-G I , $ zb D\
+G I , Hb D\
H P
H Q
-G I , T zb D\
+G I , < b D\
H P
H Q
-G I , ({b D\
+G I , l b D\
H P
H Q
-G I , h{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 , D (|b D\
+G I , , b D\
H P
H Q
-G I , t h|b D\
+G I , \ b D\
H P
H Q
-G I , |b D\
+G I , Hb D\
H P
H Q
-G I , Ԣ |b D\
+G I , b D\
H P
H Q
-G I , (}b D\
+G I , b D\
H P
H Q
-G I , 4 h}b D\
+G I , b D\
H P
H Q
-G I , d }b D\
+G I , L Hb D\
H P
H Q
-G I , }b D\
+G I , | b D\
H P
H Q
-G I , ģ (~b D\
+G I , b D\
H P
H Q
-G I , h~b D\
+G I , b D\
H P
H Q
-G I , $ ~b D\
+G I , Hb D\
H P
H Q
-G I , T ~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 , b D\
+G I , Hb D\
H P
H Q
-G I , b D\
+G I , b D\
H P
H Q
-G I D (m A~
-A l 4 d x 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 ! BBB F(A0A8J@X
+8A0A(B BBBA < ] BAA m
ABD
-ABD T BDB B(A0I8G
-8A0A(B BBBH A`
-G b x A`
-G b Ħ A`
-G b X A`
-G m, \ ؋! ADDP
-AAE < ، AAJPh
+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 < ̦ h AAJPh
+AAH < AAJPh
AAJ
-AAH 4 BAA N
- AABD D D 0r + BED D(G$
-(D ABBE D s BED D(G$
-(D ABBE L ԧ BDB B(A0A8L`\
-8A0A(B BBBB L $ P BDB J(A0A8GL
-8A0A(B BBBA L t BDB B(A0I8G
-8A0A(B BBBA D Ĩ t BED D(G$
-(D ABBE D xu BED D(G$
-(D ABBE T T p BDB B(A0I8D(
-8A0A(B BBBF D v O BED D(G$
-(D ABBE < - BDA A(LP`
-(A ABBF T 4 p BEB B(A0A8Jn
-8A0A(B BBBA T 8 p BDB B(A0A8O
-8A0A(B BBBA < U AEL@Z
+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
AAB
-AAE D $ v @ BED D(G$
-(D ABBE D l (x BED D(G$
-(D ABBE T @ BDB B(A0A8L
-8A0A(B BBBF T BFB B(A0D8Gd
-8A0A(B BBBA T | BDB B(A0I8G
-8A0A(B BBBF L Ԭ P
- BDB J(A0A8G
-8A0A(B BBBA ̭ x1 Dl L D BBB B(A0A8Je
-8D0A(B BBBJ < p. K^ < ( BAI G
- DABA L BBB I(A0G
-0D(A BBBA L < ( BBB I(A0G
-0D(A BBBA L h g BBB I(A0G
-0D(A BBBA L ܮ B BBB I(A0G
-0D(A BBBA L , BBB I(A0G
-0D(A BBBA L | XW BBB I(A0G
-0D(A BBBA L ̯ h} BBB I(A0G
-0D(A BBBA T 5 BBB B(I0A8Gn
-8D0A(B BBBA L t BBB I(A0G
-0D(A BBBA T İ ( \ BBB B(I0A8Ga
-8D0A(B BBBA T / BBB B(I0A8G-
-8D0A(B BBBA T t BBB B(I0A8G-
-8D0A(B BBBA L ̱ (} BBB I(A0G
-0D(A BBBA L X BBB I(A0G
-0D(A BBBA L l BBB I(A0G
-0D(A BBBA T f BBB B(I0A8G
-8D0A(B BBBA L ` ? BBB I(A0G
-0D(A BBBA T d
BBB B(I0A8G/
-8D0A(B BBBA T E BBJ B(A0A8G
-8D0A(B BBBA L C BBB D(A0G
-0D(A BBBA L d BBB I(A0G
-0D(A BBBA T V BBJ B(A0A8G
-8D0A(B BBBA < ( BAI G^
- DABA < L Y BAI G
- DABA L " 3 BBB I(A0G)
-0D(A BBBA L ܵ 8%_ BBB I(A0G
-0D(A BBBA T , H' BBB B(I0A8G?
-8D0A(B BBBA T *a BBB B(I0A8G
-8D0A(B BBBA T ܶ - BBB B(I0A8G
-8D0A(B BBBA T 4 /C b BBB B(I0A8G^
-8D0A(B BBBA T 2 ; BBB B(A0C8J
-8A0A(B BBBG L ? i BBB B(A0A8J
-8A0A(B BBBA ܸ pR < L hR ' AEOP_
+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 8U BFB B(A0D8G
-8A0A(B BBBH Z T Z BFB B(A0D8G
-8A0A(B BBBH ` l , l , D lb D\
+AAE T BFB B(A0D8G
+8A0A(B BBBH t P T H BFB B(A0D8G
+8A0A(B BBBH , , b D\
H P
H Q
-G I , t Hlb D\
+G I , \ b D\
H P
H Q
-G I , lb D\
+G I , (b D\
H P
H Q
-G I , Ժ lb D\
+G I , hb D\
H P
H Q
-G I , mb D\
+G I , b D\
H P
H Q
-G I , 4 Hmb D\
+G I , b D\
H P
H Q
-G I , d mb D\
+G I , L (b D\
H P
H Q
-G I , mb D\
+G I , | hb D\
H P
H Q
-G I , Ļ nb D\
+G I , b D\
H P
H Q
-G I , Hnb D\
+G I , b D\
H P
H Q
-G I , $ nb D\
+G I , (b D\
H P
H Q
-G I , T nb D\
+G I , < hb D\
H P
H Q
-G I , ob D\
+G I , l b D\
H P
H Q
-G I , Hob D\
+G I , b D\
H P
H Q
-G I , ob D\
+G I , (b D\
H P
H Q
-G I , ob D\
+G I , hb D\
H P
H Q
-G I , D pb D\
+G I , , b D\
H P
H Q
-G I t Hpm A~
-A l L p s BBB F(A0A8J@X
-8A0A(B BBBA r r A`
-G b4 t r [ BAA N
- AABD T u BEB B(A0A8Jn
-8A0A(B BBBA L = BDB B(I0A8GL
-8A0A(B BBBA T T 0 BBB J(A0A8G
-8A0A(B BBBJ L ؔ% g BDB B(A0A8O
-8A0A(B BBBD L
f BDJ A(A0Ge
-0A(A BBBJ T L x s BDB B(A0I8D(
-8A0A(B BBBF L У x BDB B(A0A8L
-8A0A(B BBBJ T S BDB B(A0A8O
-8A0A(B BBBD L L x d BBB B(A0C8JA
-8A0A(B BBBG T ȶ T BBB B(A0A8J=
-8D0A(B BBBB L ' BDB J(A0A8D
-8A0A(B BBBA x. K^ 4 \ xR $ ADG-
-DAA < BAI G
- DABA 4 P0 ADG
-DAA L H BBB I(A0G
-0D(A BBBA 4 \ ADG`
-DAA < i BHD Gt
- DABA 4 `K A ADG&
-DAA < x3 BAI G
- DABA < L x BHD Gz
- DABA T BBB B(I0A8G
-8D0A(B BBBA T BBB B(I0A8G'
-8D0A(B BBBA T < h BBB B(I0A8G'
-8D0A(B BBBA L X BBB I(A0G
-0D(A BBBA L W 3 BBB I(A0G
-0D(A BBBA T 4 ( BBB B(I0A8Ga
-8D0A(B BBBA L x BBB I(A0G
-0D(A BBBA T BBB B(I0A8G-
-8D0A(B BBBA L 4 P} BBB I(A0G
-0D(A BBBA L j BBB I(A0G
-0D(A BBBA T V E BBJ B(A0A8G
-8D0A(B BBBA L , : < BIB D(A0G
-0D(A BBBA L | BBB I(A0G
-0D(A BBBA < R BAI G
- DABA L 8 BBB I(A0G
-0D(A BBBA L \ B BBB B(A0A8J
-8A0A(B BBBA T
T
3 D BFB B(A0D8G0
-8A0A(B BBBF h D `{ BBE E(A0A8D@\8A0A(B BBBT | h'5 BBE E(A0A8G$
-8A0A(B BBBK L @ BEB E(A0A8F
-8A0A(B BBBB L $ BEB E(A0A8F
-8A0A(B BBBB T t -% BBE E(A0A8G"
-8A0A(B BBBE T 4e BEE B(A0A8G
-8A0A(B BBBF T $ :e BEE B(A0A8G
-8A0A(B BBBF L | BBB E(A0A8JX
-8A0A(B BBBG \ m BDB A(A0Js
+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 8n. K^ L D ?V v BBB B(A0A8GpB
-8D0A(B BBBH L CV G BBB B(A0A8GpB
-8D0A(B BBBH L FV BBB B(A0A8GpB
-8D0A(B BBBH L 4 IV BBB B(A0A8GpH
-8D0A(B BBBJ , m. K^ , L} ADG0@
-DAG , hLm ADG q
-DAF , Lm b ADG p
-DAG , , Lm B ADG r
-DAE , \ (M{ " ADG ~
-DAA , xM ADG0L
-DAC , MP ADG k
-DAA < M BBA H(G@x
-(D ABBA L , hN BBB E(A0A8KP
-8D0A(B BBBK D | O X BBB A(D0q
-(A BBBA D `O " BBB A(H0j
-(D BBBA D O BBB A(K0
-(D BBBA < T P BBA K(G@
-(D ABBA < P} BEE A(G0L
-(D ABBA , | Pb D\
+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\
H P
H Q
-G I , Qb D\
+G I , b D\
H P
H Q
-G I , `Qb D\
+G I , b D\
H P
H Q
-G I , Qb D\
+G I , @b D\
H P
H Q
-G I , < Qb D\
+G I , $ b D\
H P
H Q
-G I , l Rb D\
+G I , T b D\
H P
H Q
-G I , `Rb D\
+G I , b D\
H P
H Q
-G I , Rb D\
+G I , @b D\
H P
H Q
-G I D R AAJ X
+G I D AAJ X
AAJ O
AAG X
-AAF 4 D XS AAJ o
+AAF 4 , AAJ o
AAC O
-AAG D Sg [ BAA G@
+AAG D g BAA G@
AABD b
- AABB T U ' BBB L(A0A8D
-8A0A(B BBBJ T t X BBB L(A0A8D
-8A0A(B BBBJ T X\9 BBB L(A0A8D
-8A0A(B BBBA T $ e BEE B(A0A8G
-8A0A(B BBBF T | a BBB B(A0D8G7
-8A0A(B BBBK | `u e. K^ 4 @uC ADG
-DAA 4 < Xv7 ADG
-DAA D t `w& BBA I(G
-(D ABBA < Hz { BAI G
- DABA < | Z BAI G
- DABA 4 < ~0 3 ADG
-DAA 4 t ADGw
-DAA < d BAI G
- DABA D 8k BBI A(G
-(D ABBA < 4 ` BHD G
- DABA < t 0 w BHD G,
- DABA 4 t O ADGO
-DAA 4 K * 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 c BAI Gx
- DABA T u ? BIB B(D0A8G
-8A0A(B BBBD ȫ D 0 AAJ X
+ 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
AAJ O
AAG X
-AAF , b D\
+AAF , H b D\
H P
H Q
-G I `. K^ T в + BBB B(A0A8G
-8A0A(B BBBA ! L < BJB A(A0W
-(A BBDH _(A BBBD 4 h BBB A(D0D@
-0A(A BBBA | AL $ i aFA Dp $ hc [FA Dp $ D ~ vFA Dp T AG }
+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 p1 BBB A(D0JH
-0A(A BBBK T `e BEE B(A0A8G
-8A0A(B BBBF T l x BBE E(A0A8G
-8A0A(B BBBI T BBE E(A0A8G
-8A0A(B BBBC T x BBE E(A0A8G
-8A0A(B BBBE \ t BBB A(D0G
+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 \ BBE E(A0A8G
-8A0A(B BBBC D , T BBE A(A0G
-0A(A BBBD D * BDA
+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@l
-0A(A BBBA D % BBE A(A0D@
-0A(A BBBA < L & BBG A(G@
-(A ABBG D ' BBE A(A0D@l
-0A(A BBBA < , ( S BBD A(G0T
-(A ABBA d l ) [ BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d @* BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB L < * BJE B(A0A8D@
-8A0A(B BBBA L + BEB E(I0H8DP
-8D0A(B BBBD L H- k BJE B(A0A8D@
-8A0A(B BBBA l , . C BEE B(D0A8G
+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
8A0A(B BBBA d
-8A0A(B BBBA L 5 C BEB E(I0H8DP
-8D0A(B BBBD L X7 BJE B(A0A8D@
-8A0A(B BBBA $ (8g AG k
-AA L d p8 C BEB E(I0H8D@
-8D0A(B BBBD d 9 BBB J(A0D8G`
+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`
8F0A(B BBBF `
-8F0A(B BBBA T 8; BBB B(D0A8D
-8D0A(B BBBH , t =h BGD c
-ABA L U. K^ T BBE B(A0A8G
-8A0A(B BBBA T BEB E(A0D8G
-8A0A(B BBBF T l W BBE F(F0D8D
-8A0A(B BBBA T 9 BBB B(A0A8Gi
-8A0A(B BBBD T BBE B(D0A8G
-8D0A(B BBBA S. K^ 4 (< BDD H
-ABK 4 < BDD H
-ABK T < BED A(G0Q
+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 T @= > BDD Q
-ABB 4 = BDD Q
-ABB < > BBD D(G0
-(D ABBA < > BBD D(G0
-(D ABBA < D 0? BEA D(G0m
-(D ABBA < ? j BBD D(G0
-(D ABBA < @ = BBD D(G0
-(D ABBA D `A BEE D(A0Q
-(A BBBG A AQ D l A BEE A(D0DPt
-0D(A BBBA L B BLB B(A0A8D`
-8A0A(B BBBA d @] { BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB d l ] BJB B(A0A8G@
-8D0A(B BBBG D8C0A(B BBB | O. K^ T hD g BBB B(A0A8G
-8A0A(B BBBG 4 x] L p] d h] | `] X] P] H] @] 8] 0] $ (] < ] T ] l ] ] ] \ \ \ \ \ , D \ t x p h ` X P H @ 4 8 L 0 d ( | $ Z" AN ND D Z" AN ND d Z" AN ND [" AN ND [" AN ND ([" AN ND 8[" AN ND H[" AN ND $ X[" AN ND D h[" AN ND d x[" AN ND [" AN ND [" AN ND [" AN ND [" AN ND [" AN ND $ [" AN ND D [" AN ND d [" AN ND \" AN ND \" AN ND H @ 8 0 $ ( < T l , D \ t < 0Zg BBA D(D0Y
-(A ABBD 4 S BBA D(A0B(A ABB< 4 (Zg BBA D(D0Y
-(A ABBD 4 t zS BBA D(A0B(A ABB< Zg BBA D(D0Y
-(A ABBD < PZg BBA D(D0Y
-(A ABBD 4 , S BBA D(A0B(A ABB4 d 2S BBA D(A0B(A ABB< Zg BBA D(D0Y
-(A ABBD < @Zg BBA D(D0Y
-(A ABBD 4 S BBA D(A0B(A ABB4 T S BBA D(A0B(A ABB, ZC k AID _
-AAA J n N D Z BHF E(E0G@>
-0A(A BBBA D `[ BHF E(E0G@>
-0A(A BBBA , d \ AAG0p
-AAA (] AL & A^
-A A L ] BED A(G0_
-(A ABBH (A ABB 4 $ ]H BDA
-AEA jAB L \ ^ BED A(G0_
-(A ABBH (A ABB L _8 BED A(G0v
-(A ABBA (A ABB $ AX
+(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
G g
-I 4 $ `i BDD h
-ABK DB L \ a BHA D(D0
-(A ABBK j(A ABB L `b BHA D(D0
-(A ABBK M(A ABB P l , c D c $ c HD
+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
D E
-A c2 AL cA c $ c q HD
+A $- 2 AL cA D- $ , HD
D E
-A (d2 AL cA Hd $ t @d # HD
+A - 02 AL cA - P $ ,- H HD
D E
-A D d2 AL cA d d $ d HD
+A - 2 AL cA . $ - A HD
D E
-A he2 AL cA e $ 4 e HD
+A D. p2 AL cA d. $ - HD
D E
-A f2 AL cA $ (f2 < Pf T Hf l @f 8f 0f$ VM Hf Pf Hf @f$ VM Xf , Pf D Hf$ VM \ `f t hf `f Xf Pf$ VM hf pf hf `f$ VM 4 xf L pf d hfy D@
-D f$ VM f f < f BEA A(F0
-(D ABBA @g$ VM $ Xg < Pg T Hg$ VM l `g Xg5 g; Au g gg Dx
-D g$ VM h $ h d < h* BEB B(D0A8GP{
+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{
8D0A(B BBBA
-8A0A(B BBBE o < o" BBB A(H0
-(A EBBA r$ VM r , r D r L \ r~ BEB D(D0
-(A BBBE z(A LEB s , s BAA |
-DBJ ht$ VM t $ xt < pt AG
-H \ t$ VM 4 t u AAG0Q
+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
AAD v
-AAA `u Xu < Pu BEA A(F0^
-(D ABBA u$ VM 4 u L u 4 d uU BGA
-ABE DB v , v BAC m
-DBG Hw$ VM `w hw , `w D Xw$ VM \ pw t xw pw hw$ VM w w 4 wU BGA
-ABE DB $ x , < x BAC m
-DBG l y$ VM (y y yy D@
-D xy$ VM y y y 4 xy L py d hy L | `y BEA D(G0
-(A ABBG (A ABB L |, BED A(G0
-(A ABBB (A ABB L | BEB A(D08
-(A BBBC (A BEB d l $ BEB B(A0D8GP)
+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)
8D0A(B BBBC
-8A0A(B BBBE d BBF H(A0D8GP
-8D0A(B BBBC 8A0A(B BBB d <
- BBB E(D0A8GP
+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
- F BEE A(A0
+8A0A(B BBBE T D; F BEE A(A0
(A BBBJ A
-(A BEBH L
- BEA D(G0
-(A ABBI D(A ABB 4 L P BGA l
-ABG tDB 4 U BGA
-ABE DB , ACG {
-DAE , BAC m
-DBG , 4 (\ ACG \
-DAD , d X ACG j
-DAF ؙ$ VM 8 Av $ ( ADG YAA $ ( ADG YAA $
( ADG YAA $ D
(( ADG YAA $ l
0( ADG YAA
8
0
(o D@
-D
x$ VM
, $ VM <
1 AAG y
+(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
AAD Y
-DAA 4 AAG0R
+DAA $? 3 <? 3 4 T? 3 AAG0R
AAA q
-AAA , D AAG L
-AAA ; Au < U AAG y
+AAA , > AAG L
+AAA ? ; Au < L? AAG y
AAD Y
-DAA | P H , y AAG D
-AAA p; Au < T AAG y
+DAA @ X3 4@ P3 , ? (y a AAG D
+AAA |@ x; Au < @ AAG y
AAD Y
-DAA < T , Q AAG [
-AAA ; Au < @ AAG y
+DAA @ 2 @ 2 , |@ AAG [
+AAA <A (; Au < @ H z AAG y
AAD Y
-DAA , `[ HR L AL , | n AAG G
-AAA ; Au < * AAG y
+DAA A 2 A 2 A h[ HR A AL , |A | AAG G
+AAA <B ; Au < A AAG y
AAD Y
-DAA 0 ( , 0y AAG D
-AAA \ ; Au < AAG y
+DAA B 81 B 01 , <B 8 y 6 AAG D
+AAA B ; Au < B AAG y
AAD Y
-DAA C Hz AL , r AAG }
-AAA < @ AAG y
+DAA \C 0 tC 0 C C Hz C AL , <C r R AAG }
+AAA < lC H
+ . AAG y
AAD Y
-DAA `C Hz AL , d y " AAG D
-AAA < AAG y
+DAA <D / TD / lD h
+C Hz D
+ AL , D
+y AAG D
+AAA < LD
+ j AAG y
AAD Y
-DAA | C Hz 0 AL , D 0y ^ AAG D
-AAA ; Au < AAG y
+DAA E / 4E / LE C Hz lE 8 AL , D 8 y AAG D
+AAA E ; Au < LE AAG y
AAD Y
-DAA | 0 ( C Hz AL , D y z AAG D
-AAA @; Au < ` 6 AAG y
+DAA F 8. 4F 0. LF C Hz lF AL , E y AAG D
+AAA F H ; Au < LF h AAG y
AAD Y
-DAA | P H , | AAG G
-AAA $ Р ARD hAA< \ 8 AAG y
+DAA G X- 4G P- , F | B AAG G
+AAA $ |G ARD hAA< G @
AAG y
AAD Y
-DAA D \ , X} * AAG H
-AAA ; Au < ȡ AAG y
+DAA G , G , , G `
} AAG H
+AAA DH
; Au < G
R AAG y
AAD Y
-DAA 4 4 BAA G0
- AABA l Т AL 4 Т BAA G0~
- AABA AL , < AAG0c
-AAA AL 4 4 BAA G0~
- AABA l AL , 2 AAG0c
-AAA ` AL , ` AAG0T
-AAA Х AL 4 Х BHI
-ABA < H r AAG 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
AAD Y
-DAA < h BBA A(G@|
-(A ABBA AL < BBA E(G0~
-(A ABBA < AAG y
+DAA DK ) \K ) < tK p BBA A(G@|
+(A ABBA K AL < DK BBA E(G0~
+(A ABBA < K AAG y
AAD Y
-DAA < F BBA A(G@
-(A ABBE $ Ȭ AL , Ȭ AAG
-AAA t h; Au < AAG y
+DAA TL ( lL ( < L F BBA A(G@
+(A ABBE L AL , TL 7 AAG
+AAA M p; Au < L AAG y
AAD Y
-DAA l Hc $ AL , AAG ^
-AAA t h; Au < AAG y
+DAA tM ' M ' M l Hc M AL , TM V AAG ^
+AAA N p; Au < M AAG y
AAD Y
-DAA , AAG0a
-AAA 4 AL 4 BHI $
-ABA ; Au < а AAG y
+DAA tN & N & , N AAG0a
+AAA N AL 4 dN c BHI $
+ABA ,O ; Au < N AAG y
AAD Y
-DAA 4 BAA G0-
- AABA T AL , W AAG
-AAA س; Au < AAG y
+DAA O % O % 4 O BAA G0-
+ AABA O AL , O W g AAG
+AAA DP ; Au < O & AAG y
AAD Y
-DAA ȸ < 4 BBA A(G@R
-(A ABBK t AL D R BIF A(E0
-(A BBBA з; Au < T AAG 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
AAD Y
-DAA <! T! 4 l! F BAA G0
- AABA ! ( AL , ! ( AAG =
-AAA $ ! غ ARD hAA< t! @ AAG 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
AAD Y
-DAA \" t" < " ` BBA A(G@
-(A ABBG " AL , D" AAG S
-AAA # `; Au < " AAG 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 |# # < # BBA D(D0D
-(A ABBI # AL $ $ @I De
+DAA T ! 4T ! < LT ) BBA D(D0D
+(A ABBI T * AL $ T H!I De
G I
-G H D 4$ ؽ_ BIB D(C0G@~
-0A(A BBBG L |$ BHF F(A0C8G`
-8A0A(B BBBG L $ 0+ BEF F(A0D8G`
-8A0A(B BBBA L % u BBB E(H0D8GPx
-8D0A(B BBBE l% 0 DK < % (^ BBB A(L0[
-(A EBBH < % H BBF A(G0
-(A EBBF < & BBA D(F0s
-(D ABBE < D& xx BBF A(G0%
-(A EBBG < & BBA D(F0
-(D ABBG < & BBA D(F0*
-(D ABBF L ' BIA A(G0r
-(A ABBD d(C ABB L T' BEF F(E0D8GP
-8A0A(B BBBA L ' ز BIA A(G0r
-(A ABBD d(C ABB L ' H BIA A(G0r
-(A ABBD d(C ABB L D( BEF B(D0D8GP
-8D0A(B BBBK L ( ( BEF B(D0D8GP
-8D0A(B BBBK L ( BIB E(H0D8G`
-8D0A(B BBBD L 4) Ȳ BIA A(G0r
-(A ABBD d(C ABB L ) BEF F(E0D8G@
-8D0A(B BBBA L ) BIA A(G0r
-(A ABBD d(C ABB < $* (A BFD D(G0
-(D ABBA L d* 8 BEF B(D0D8GP
-8D0A(B BBBK , * Ȳ ADD c
-ADG * H2 W V T* p0 D]
-E L + 0 BEE B(A0A8G
-8A0A(B BEBG L l+ 0 BIA A(G0r
-(A ABBD d(C ABB L + Pp BFF B(D0D8G`
-8D0A(B BBBA L , P BIA A(G0r
-(A ABBD d(C ABB L \, BIE B(A0A8GpG
-8A0A(B BBEA L , `I BEE B(A0A8K`
-8D0A(B BBBD L , BIA A(G0r
-(A ABBD d(C ABB L L- BEB B(H0A8G`
-8D0A(B BBBD L - @ BIA A(G0r
-(A ABBD d(C ABB L - + BFF F(D0D8G@
-8D0A(B BBBA L <. X BIE B(E0A8Gpz
-8A0A(B BBEJ L . BIE E(A0A8D
-8A0A(B BBED , . @ ADG0y
-AAI , / ADG0u
-AAE , </ ` ADG0E
-AAE , l/ ADG0E
-AAE , / `
ADG0E
-AAE , /
ADG0A
-AAA , / P ADG0E
-AAE , ,0 ADG0A
-AAA , \0 @ ADG0U
-AAE , 0 ADG0A
-AAA , 0 @ ADG0Q
-AAA , 0 ADG0M
-AAE , 1 @ ADG0Q
-AAA , L1 ADG0]
-AAE , |1 P ADG0m
-AAE , 1 ADG0
-AAE , 1 D ADG0
-AAE , 2 ADG0
-AAE < 1 ı BBA D(G0d
-(A ABBA , |2 ADG0
-AAE , 2 ~ ADG0h
-AAJ 2 , 2 L ADG0
-AAI 4 $3 ( BDD D0U
- AABI \3 , t3 ADG0Y
-AAI T 3 H BDA G0e
+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
AABI D
AABH T
- FABE 3 T 4 BDA G0e
+ FABE d T d BDA G0e
AABI D
AABH T
- FABE l4 p < 3 h BBE D(G0Z
-(A ABBA D 4 & ADG0p
+ FABE e x < d p BBE D(G0Z
+(A ABBA D de & ADG0p
DAO a
AAE Q
-DAJ 5 < |4 BFA D(G0p
-(A ABBA < 4 X! Ů BFA D(G0p
-(A ABBA 4 !L 4 5 "7 ADG N
-GAN DAA $ L5 ("U G Ac
-D d 4 6 `"7 ADG N
-GAN DAA T 5 h" BFA D(G0
+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
(A ABBI [
-(A ABBA 4 6 0# AAG i
-AAD KAA4 6 #7 ADG N
-GAN DAA 4 t6 # 6 AAG i
-AAD KAA4 T7 #7 ADG N
-GAN DAA L 6 # ʬ BEA A(D0F
-(A ABBG g(E ABB T 47 @$b BBA H(D0
+(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 ABBB \
-(E ABBJ 4 7 X%z 8 AAG p
-AAE sAA $ 7 %U Ac
-D d 4 7 % AAG o
-AAF [AAL 8 P& BBA D(D0
+(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
(A ABBJ d
-(E ABBJ L 9 ( BBA H(D0
+(E ABBJ L i BBA H(D0
(A ABBA \
-(E ABBJ T 8 ), BIA A(D0x
+(E ABBJ T |i , BIA A(D0x
(A ABBA l
-(E ABBJ L 9 *# BBA H(D0
+(E ABBJ L dj # BBA H(D0
(A ABBE f
-(E ABBH L : h+ BEA A(D0
+(E ABBH L j p BEA A(D0
(A ABBK d
-(E ABBJ L d: , BBA D(D0
+(E ABBJ L k BBA D(D0
(A ABBG g
-(E ABBO $ : -E ݩ A[
-D \ $ 4: -M Ab
-E \ $ \: -U Ac
-D d 4 ,; .7 ADG N
-GAN DAA L d; .k BEA A(D0
-(A ABBF D(E ABB $ ; 8.M Ab
-E \ $ 4; `.M ը Ab
-E \ < \ < BBB B(A0A8D`P
-8A0A(B BBBH 8A0A(B BBB| |< . BBB B(D0A8D`H
+(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
8A0A(B BBBM D
8A0A(B BBBB
-8F0A(B BBBE < 0/ 4 = (/7 ADG N
-GAN DAA L < 0/! m BBB E(D0A8D`
-8A0A(B BBBK L = 1} BBB B(A0D8G`
-8A0A(B BBBH = @5 4 > 857 ADG N
-GAN DAA D <> @5R BAD G0
+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
AABK D
- AABH > X6 4 > P67 ADG N
-GAN DAA > T D> @6 BBB B(A0D8G,
-8A0A(B BBBF T > ; BIF E(D0`
+ 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`
(A BBBG
-(A BBBA ? @ ? 8 d ? <f BBB B(A0A8G`^
+(A BBBA <p H Tp @ d lp f BBB B(A0A8G`^
8A0A(B BBBG
-8A0A(B BBBA 4@ = 4 L@ =7 ADG N
-GAN DAA 4 @ = BDD D0G
- AABG @ > 4 @ >7 ADG N
-GAN DAA 4 A > BDD D0G
- AABG DA (? 4 \A ?7 ADG N
-GAN DAA d A (? ADG0
+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
DAH {
DAH {
DAH {
DAH {
DAH D
-AAJ 4 A A BDD D0G
- AABG 4B (B 4 LB B7 ADG N
-GAN DAA D B (B/ BAD G0
+AAJ 4 r BDD D0G
+ AABG r 0 4 r (7 ADG N
+GAN DAA D $s 0/ BAD G0
AABL D
- AABH B C 4 B C7 ADG N
-GAN DAA d C C BBB B(A0D8G`
+ AABH ls 4 s 7 ADG N
+GAN DAA d s BBB B(A0D8G`
8A0A(B BBBO D
-8A0A(B BBBB C hK 4 C `K7 ADG N
-GAN DAA d C hK# BBB E(A0A8G`
+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 <D 0N 4 TD (N7 ADG N
-GAN DAA D p D h L D N6 BBB B(A0D8G`I
-8A0A(B BBBI E S 4 $E S7 ADG N
-GAN DAA \E | tE S BBB B(D0A8G`
+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 BBBL D
8A0A(B BBBJ 0
-8E0A(B BBBE E U 4 F U7 ADG N
-GAN DAA T E U. BBB I(A0A8G.
-8A0A(B BBBH L F Yk BBB B(A0D8G`
-8A0A(B BBBI F \ 4 G \7 ADG N
-GAN DAA <G L TG \ BBB B(A0D8G`P
-8A0A(B BBBB G 8a 4 G 0a7 ADG N
-GAN DAA L LG 8aS
- BBB E(A0A8G`
-8A0A(B BBBG l G Hk BBB E(A0A8G`
+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`
8A0A(B BBBD =
-8A0A(B BBBI L H n N BBB E(A0A8G`
-8A0A(B BBBD L \H t ; BBB I(A0A8Gp
-8A0A(B BBBB T H vu BBB B(D0A8G5
-8A0A(B BBBE 4 I } ADG0j
+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
AAH D
-AAJ I 8~ 4 I 0~7 ADG N
-GAN DAA 4J 8~ 4 LJ 0~7 ADG N
-GAN DAA I 8~Y B BBE D(G0
+AAJ z @ 4 z 87 ADG N
+GAN DAA z @ 4 z 87 ADG N
+GAN DAA z @Y BBE D(G0
(A ABBA y
(A ABBA y
(A ABBA y
(A ABBA u
-(A ABBE T dJ ɛ BFA D(G0
+(A ABBE T { 5 BFA D(G0
(A ABBC a
-(A ABBA L J BBE D(G0}
+(A ABBA L t{ BBE D(G0}
(A ABBI a
-(A ABBA < K h T BBE D(G0C
-(A ABBC l LK - BBB B(D0A8D`d
+(A ABBA < { p BBE D(G0C
+(A ABBC l | BBB B(D0A8D`d
8A0A(B BBBA "
-8A0A(B BBBA < K ٚ BBE D(G0C
-(A ABBC < K BBE D(G0C
-(A ABBC L x K M H D tL @ S BEB D(A0GpY
-0A(A BBBJ $ dM [ ADG LAA4 M 0 BDA G0x
- AABF M ȗ M 6 $ M . ACD `DA < N L BBA A(D0t
-(D ABBA , \N < BDD kAE , N b BAA ZAB N P N X# Aa LN h AL O hD , ,O AFG |
-AAD $ \O + AAG \DA 4 O BDD D0I
- AABE O Q Ad
-A c O Й. Ac
-A D 4 O v BDD D0|
- AABA L 4P ( BED D(D@R
+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
(A ABBA T
-(G ABEE 4 P B ADG b
-AAA JAA D P x BDD z
+(G ABEE 4 $ B ADG b
+AAA JAA D \ x BDD z
ABA Y
-ABE AGB < Q r ADG x
+ABE AGB < r ADG x
AAA S
-GAE ADA 4 DQ (O AAG n
-DDI DAA |Q @" Q . K^ 4 Q @L AAD r
-AAF DFA L Q X BIF H(H0A8D`
-8C0A(B BBBD 4R z Em
-A FL TR x KGF B(D0D8I`y8F0A(B BBB R . K^ R 4 R BAD H
-ABF rAB 4 dR BPH DPW
- AABA 4 R h _ BPH DPW
- AABA |S J n N S S S H S S T P $ $T DE
+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
G W
-I V LT `t dT Ȣ , |T p ADF0w
-AAD , T 0/ ACG W
-AAA , T 01 ADG X
-AAA D U @ ACG a
+I V Xt , h ADF0w
+AAD , L (s/ ACG W
+AAA , | (s1 ADG X
+AAA D 8s ACG a
AAB v
AAH P
-AAA D TU ADG b
+AAA D s ADG b
AAH z
AAD P
-AAA U $ U t QQ
+AAA < $ ą t l QQ
F b
-S U ` D U ADD k
+S | X D s ADD k
AAJ u
AAI O
-AAA D <V ADD q
+AAA D ܆ s ADD q
AAD y
AAE O
-AAA < V H BDA _
+AAA < $ @t BDA _
ABG M
-ABH V DR < <V ܐ BLD D(D`
-(A ABBA , |V l ATDP}
-AAA TW 8 lW 0 W ( W W W W IT , X b ACG D
-AAA , 4X H4 AAG a
-AAD 4 W Xq ~ BAD L
-ABF < W E V ADD`r
+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
AAC ~
-AAA X 4 X L AAG d
-DAF NDA D ,Y ] BDD G0z
+AAA | r 4 L AAG d
+DAF NDA D ̉ r] BDD G0z
AIBI |
- DABM L tY h, BBB B(A0A8D`
-8A0A(B BBBG Y Y `( \ Y BED A(G@y
+ DABM L `, BBB B(A0A8D`
+8A0A(B BBBG d < | Xs( \ < BED A(G@y
(A ABBF
(A ABBD `
-(A ABBA , TZ AJ T
+(A ABBA , s AJ T
HI l
-DP T Y [. BBB B(A0A8G
-8A0A(B BBBK Z AT < Z ! ADG
+DP T =[. BBB B(A0A8G
+8A0A(B BBBK | k AT < k! ADG
AAH }
-AAA ^AA <[ p. K^ T[ (C AA Z X [ ` [ 5 [ EN $ [ AAG QAC \ AL $\ & <\ M T\ (& , [ @| ARDPO
-AAA 4 \ BAA S
+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
CBD |
-CBG < ,\ f BBA A(Gn
-(A ABBG $ ] H
-P AG ]
-AJ < <] p
-l BEE A(D0S(A BBB , |]
-N AAG0f
-AAG , ]
-N AAG0h
-AAE L ]
- BED C(G0T
+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 $ ,^ 0 D D
+(A ABBA $ ̎ (w D D
H I
-G T^ AO $ t^ D b
+G z AO $ z D b
J
E K
-E $ ^ T Ag
+E $ < {T Ag
H F
J K
-E ^ " D] ^ 0 , ^ x Ac
+E d |" D] | (| , x Ac
D O
I o
-A G $_ 8N D ^ p BEB D(A0
-(A BBBA < _ (s FAD I
-ABI AAB _ h FS $ _ h/ AAD cDA | ` p BFE E(E0A8Dp
+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
8A0A(B BBBA ]
8A0A(B EBBA K
-8A0A(B IBBA $ ` > AM
+8A0A(B IBBA $ , x> AM
B U
-K $ ` J EJ
+K $ T J EJ
A h
-H L ` BBB B(A0A8F`d
-8C0A(B BBBH d ,a p BEB A(D0T
+H L | BBB B(A0A8F`d
+8C0A(B BBBH d ̑ h BEB A(D0T
(A BBBG n
(A BBBK S
-(D BBBC < a BHD B
+(D BBBC < 4 BHD B
ABE [
-ABJ L a XV BFE B(E0A8Dp
-8A0A(B BBBA < $b h BBE D(C0c(E BBB \ db BED D(G0F
+ABJ L t PV BFE B(E0A8Dp
+8A0A(B BBBA < Ē ` BBE D(C0c(E BBB \ BED D(G0F
(A ABBF W
-(E ABBA A(A ABB < b ACG0
+(E ABBA A(A ABB < d ACG0
EAF T
-GAE uGA4 c ADF0|
+GAE uGA4 ADF0|
EAC h
-GAE L <c P BEE D(GP}
+GAE L ܓ H BEE D(GP}
(A ABBF |
-(A ABBF b Մ EM c p AP
-G c ߈. K^ c ) c ) d ,d 4 Dd o BAA Z
-ABG AAD 4 |d BAA o
-ABJ AAD d ) d ) d AL e AL $e ( L d ) BED A(Gpq
+(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
(C ABBD G
-(C ABBA D d BAC G`^
+(C ABBA D x c BAC G`^
AABA b
- CABA d ,e ˂ BEE B(D0A8Dj
+ CABA d 7 BEE B(D0A8Dj
8A0A(B BBBE I
-8C0A(B BBBA <f : Ax \f & AT |f ' QU < e " AADPX
+8C0A(B BBBA ܖ : Ax Ȋ& AT ؊' QU < AADPX
AAH D
-AAA f @ % < Lf X ځ ADD`y
+AAA | 8% < P F ADD`y
AAD O
-CAE 4g 4 f BAA GPf
- AABK , f X ^ l BKI ^
-ABA g AL g g ! < \g ADD`y
+CAE ԗ 4 \ ؋ BAA GPf
+ AABK , P^ ؠ BKI ^
+ABA T AL t x! < h ADD`y
AAD O
-CAE $ Dh (!- AAK [AC 4 g 0! BAA GPf
- AABK , g !^ ~ BKI ^
-ABA h ! AL h ! , dh !^ & BKI ^
-ABA <i " , h "^ BKI ^
-ABA i H" i pو. K^ i 8" i 0" i (" i " j " ,j " Dj " \j " , i !I ~ AEG yAA j " AL , j "I ~ AEG yAA j 8" AL k 8" ,k 0" Dk (" \k " tk " k " k x"5 Hl k "< Ho k "5 Hl l "< Ho $l 6 <l ": Tl # ll #: l (# D l #b BBB A(C0G B
-0C(A BBBA l H# l @# $ ll 8#L F| Ai
-F [ 4 <m `#E ADG ]
-AAE RAA 4 tm x#Y ADG f
-CAJ VAA m 4 m 4 m 4 m 4 n @# AL ,n p4 Dn x4 \n 4 4 m "b z BAC G`a
- AABF 4 n 0$ z BAC G`k
- AABD 4 <n $b z BAC G`a
- AABF , tn % z AAG`T
-AAA D n @& z BAC G`q
+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
AABF ]
- AABG D n ' z BAC G`b
+ AABG D . BAC G`b
AABE
- AABA 4 4o (d z BAD G`k
- AABC l lo * z BBE E(A0D8Du
+ AABA 4 ؔd 3 BAD G`k
+ AABC l $ ; BBE E(A0D8Du
8F0A(B BBBE U
-8C0A(B BBBG p 8+M HD p h+ AL 4 p h+Q AAG w
-AAF FAA p +- AY
-F L D tp + y BAC G`a
+8C0A(B BBBG $ 0M HD D ` AL 4 d `Q AAG w
+AAF FAA - AY
+F L D , X BAC G`a
AABF e
- AABG dq (,, |q @,, q X, q `,F q ,, AK
-D W , <q , Cy AAG`h
-AAE L r - BBA A(G0O
+ AABG , 8, 4 P L XF d , AK
+D W , AAG`h
+AAE L BBA A(G0O
(C ABBD z
-(A ABBH dr -? |r -, AK
-D W r -" , r -f AI V
+(A ABBH ? , AK
+D W < ș" , T f AI V
AG e
-AB <r (.R Yx HI s h. AL $s h. 4 <s `.V BDD v
-ABE JAB r .? w H`
-A s ψ. K^ s @I s 8I s 0I
s (I t . $t I <t I Tt I lt ." AN ND D t H\ BDD R
+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
ABA c
-ABJ HAB t I3 $ t P.G AFGsDAL lt x.H 9v BNE A(A0D
-0A(A BBBA T du x0h BAD G
+ABJ HAB t 3 $ HG AFGsDAL $ pH BNE A(A0D
+0A(A BBBA T ph BAD G
FABA D
CABF H
- FABG u 18 AQ
-F _ $ u 1# ADG JGA $ v 1# ADG JGA $ ,v 1^ ADG`OAA\ u 15 $u BBB A(D0G
+ FABG \ 8 AQ
+F _ $ | # ADG JGA $ # ADG JGA $ ̦ ^ ADG`OAA\ d 5 BBB A(D0G
0C(A BBBG
-0F(A BBDC v 5 T $v 5 "u BBD A(G
+0F(A BBDC T С T ܦ ȡ BBD A(G
(C ABBJ T
-(A ABEA $w 7 T v 7 t BBA D(G
+(A ABEA ħ T L 5 BBA D(G
(C ABBG o
-(F ABBA w X8 , w P80 qt AFJ RGA , 4w P80 It AFJ RGA d dw P8 !t BED A(G\
+(F ABBA 4 P , H0 ݓ AFJ RGA , H0 AFJ RGA d H BED A(G\
(A ABBK M
(F ABBH
-(A ABBA tx x9 , w p9_ s AAM0n
-DAA , x 9_ s AAM0n
-DAA x 9 y 9 y 9 4 x 9l +s RAA
-AIH L ly D5 BED D(D
-(A ABBG T(C ABB $ y :) ADG ZAA , y :& BDD XAB 4 z :] ADG `
-CAH ^CC 4 Lz :S BDD ^
-ADC [CD z ;@ A[
-D _ 4 z D AAJ
-AAE DCA $ z :# ADG JGA 4 { :U ADG [
-CAE ^CC 4 <{ D BDD D
- AABF t{ : FQ , { :$ BDC WAB { :@ CY
-D _ 4 { ;S BDC ]
-ADE [CD $ | 8;, AAG ]DA < <| E BBG A(G@
-(A ABBF D { ;Z o BEE A(A0D@
-0A(A BBBF | < | ƈ. K^ , L| ;_ o AIDpp
-CAF , || (<_ oo AIDpp
-CAF D | X< Oo BID Dy
+(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
AABH T
- AABA , | <P o AIDl
-AAA , $} < n AIDK
-CAA , T} = n AIDK
-CAA 4 } = n BID DpS
- CABA 4 } = n BID DpS
- CABA ~ C ~ C ~ C
- 4 ~ C AJ0_
+ 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_
AE e
-AJ kD L D BBB B(I0D8G@'
-8A0A(B BBBA L l E BBJ B(D0A8GP<
-8D0A(E BBBE G~ hG @HQ Hm L H BIA A(G0t
-(A ABBB d(C ABB l PI D HI BBB A(A0G@v
-0D(A BBBH L ̀ K BHD A(G0c
+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
(A ABBA X
-(A ABBA 4 Lm AFG F
-CAH DDD d 8L k BBE B(G0D8I@
-8C0A(B BBBH U8E0A(B BBB \ L BBE B(D0G8G@
-8C0A(B BBBH U8E0A(B BBB `M A[ $ `M j AJ@Z
-AJ L d N BHD A(D0f
-(G ABEH n(A ABB d XNL BEB B(A0G8D@
+(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@
8A0A(B BBBH Z
-8A0A(B BBBA d @OL BEB B(A0G8D@
+8A0A(B BBBA d 8L BEB B(A0G8D@
8A0A(B BBBH Z
-8A0A(B BBBA d (PL BEB B(A0G8D@
+8A0A(B BBBA d $ L BEB B(A0G8D@
8A0A(B BBBH Z
-8A0A(B BBBA d QL BEB B(A0G8D@
+8A0A(B BBBA d L BEB B(A0G8D@
8A0A(B BBBH Z
-8A0A(B BBBA d T QL BEB B(A0G8D@
+8A0A(B BBBA d L BEB B(A0G8D@
8A0A(B BBBH Z
-8A0A(B BBBA d RL BEB B(A0G8D@
+8A0A(B BBBA d \ ؾL BEB B(A0G8D@
8A0A(B BBBH Z
-8A0A(B BBBA L | X
- g BBB B(A0A8K
-8D0A(B BBBG < t c AGG h
+8A0A(B BBBA L 4
+ i BBB B(A0A8K
+8D0A(B BBBG < AGG h
CDB N
-DAE iDD 8S D P ̅ @S D R d HS BBB L(D0A8GP
+DAE iDD T 0 D P l 8 D R d @ BBB L(D0A8GP
8C0A(B BBBA
-8D0A(B BBBA d L T BBH F(D0A8G`
+8D0A(B BBBA d BBH F(D0A8G`
8C0A(B BBBH
-8D0A(B BBBA $ U@ AF R
-CE L ܆ bG BBH A(D0
-(A BBDD (A BBB , U GO D U GO L \ PcG BBH A(D0
-(A BBDD (A BBB pU GO L ć 8dF BHB A(D0
-(A BBDC (A BBB (U GO L , eF BHB A(D0
-(A BBDC (A BBB | T GO L fZ BHB D(A0
-(A BBDJ (A BBB T GO L gV BHB D(A0
-(A BBDE (A BBB L PT GO L d gB BHB A(D0
-(A BBDA (A BBB T GO ̉ й. K^ k kJ n N L h BBB A(A0
+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
(A BBGG Q
-(A FBGG d hi | piB 4 iM ADG T
+(A FBGG ` hB 4 4 M ADG T
AAF
-AAG ̊ k% L 8k BBB A(A0
+AAG l % L 0 BBB A(A0
(A BBBB Q
-(A EBBE 4 k L k% L d k BBB A(A0
+(A EBBE Ի % L BBB A(A0
(A BBBB Q
-(A EBBE , l= ADG [
-AAG l l% L l BBB A(A0
+(A EBBE , T = ADG [
+AAG % L BBB A(A0
(A BBBB Q
-(A EBBE d Xm | `m% L xm BBB A(A0
+(A EBBE P X% L 4 p BBB A(A0
(A BBBJ Q
-(A EBBE , n= ADG ]
-AAE (n , 0n% L D Hn BBB A(A0
+(A EBBE , = ADG ]
+AAE ̽ (% L @ BBB A(A0
(A BBBJ Q
-(A EBBE , n= ADG ]
-AAE č n ܍ o% L o BBB A(A0
+(A EBBE , 4 = ADG ]
+AAE d | % L BBB A(A0
(A BBBB Q
-(A EBBE , D o= ADG _
-AAC t o# o% L o BBB A(A0
+(A EBBE , = ADG _
+AAC # , % L D BBB A(A0
(A BBBJ Q
-(A EBBE , p= ADG _
-AAC $ p# < p T p d l p BBB B(A0A8G`j
+(A EBBE , = ADG _
+AAC Ŀ # ܿ d BBB B(A0A8G`j
8A0A(B BBBK )
-8A0A(B BBBA L ԏ xr BBB B(D0A8DP^
-8A0A(B BBBG L | (sF ] BBB E(A0A8G`U
-8D0A(B BBBJ t (u u u
u
4 Ԑ u AAG }
+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 }
ADE o
-AAG < u BDA
+AAG < BDA
ABA l
-ABI v$ [ < vQ [ BBD A(D0}(A ABB d v BBB B(A0A8D`P
+ABI \ x$ ^{ < t Q J{ BBD A(D0}(A ABB d D BBB B(A0A8D`P
8A0A(B BBBH
-8A0A(B BBBA x $ x# < 0x T 8x l `. K^ (x x x ̒ x xB Es Hx AL $ Hx AL 4 D (7 AAG Z
-AAK DAA $ | xg AG k
-AA Xx AL , ē Xx PAQqDA x AP $ x- ADJ XDA , x
-Y AAG@C
-DAG , ē `y X AAG@o
-DAA T y X BBE B(A0A8G
-8D0A(B BBBH , 8 AVGaDA , $ ATGaDA , T ATGaDA , X AUGaDA , AUGaDA , AUGaDA , x AUGaDA , D AUGaDA , t 8 AUGaDA K o N , , V AAG Z
-AAK X L t Pg V BEB B(D0A8FY
-8A0A(B BBBG l p) x ̗ p$ . K^ t T U BEE D(D0G`w
+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
0A(A BBBF
0A(A BBBF B
-0A(A BBBF t _ AG0UA 8` AG0VAT x VU BBE B(A0D8G
-8A0A(B BBBF . K^ $ K o N < 4 ȓ 7U BAA G@Y
- AABH p x AY ę x5 fN < 4 T BBA A(G`p
-(D ABBJ $ t ` T AG0q
-DA , D BOA AB \ t BIB E(A0A8I
+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(B BBBA
-0A(B IBBK d Ԛ BGB B(A0C8D`S
+0A(B IBBK d t BGB B(A0C8D`S
8A0A(B BBBA
-8A0A(B BBBG < 0> QR YA \ P8 T c D t x BBE E(A0A8D`8D0A(B BBB `6 Kj D ԛ Ȝ BEB E(D0A8DP]8A0A(B BBB| t R BBB B(A0D8D@
+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@
8F0A(B BBBL D
8A0A(B BBBB
-8F0A(B BBBG d P :R BDE E(D0A8D@s
-8D0A(B BBBE ~8G0A(B BBB , \ R Q AAT0Z
-DAA L 4 Q BBE E(A0A8D
-8A0A(B BBBA Y P < H _Q BED D(DpH
-(C ABBE 4 d s +Q BDD D0E
- DABA d D (% BHE B(E0D8DP
+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 BBBB D
-8C0A(B BBBH L BBD D(G0!
+8C0A(B BBBH L L BBD D(G0!
(A ABBF d
-(G AEBE , T `L KP AAJ0^
-DAA < 'P BEA D(D@Y
-(D ABBA < Ğ Ц O BEA D(D@Y
-(D ABBA d BEB D(D0K
+(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
(A BBBE F
(A BBBK d
-(A BBBE \ BEB D(D0C
+(A BBBE \ BEB D(D0C
(A BBBE D
-(A BBDC L(A BBB t 4 x N BAD F`x
- AABG ܠ F X
- P $ H < 0 4 T 8 AAI
-AAK DAA (- AI@^D < ġ 8 BBA F(D`V
-(D ABBB . AJ@^D < $ BBA G(D`V
-(D ABBA d (. AJ@^D < 8 BBA G(D`V
-(D ABBA 4 Ģ AFG N
-AAB GA T T 0 L BBD D(Gpa
+(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 ABBF f
-(F ABBG , T ȱN BDA G0{ AAB D ܢ p 7L BBB A(A0J`
-0A(A BBBF D $ L BBB A(G0D`
-0A(A BBBF D l K BBB A(A0Jh
-0A(A BBBF D K BBB A(G0D`
-0A(A BBBF 4 DJ0
+(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
DF Z
DJ N
-DF ܤ + AG@^D I 4 l Ⱦ ;K BDD G0^
- DABA < 0 K BED D(G@y
-(D ABBA D J BEE D(D0G@
-0D(A BBBA L , H J BEE E(D0D8GP
-8D0A(B BBBA L | bJ BEE E(D0D8GP
-8D0A(B BBBA L ̥ E J BEE E(D0D8GP
-8D0A(B BBBA L n I BEE E(D0D8GP$
-8D0A(B BBBA L l I BEE E(D0D8GPM
-8D0A(B BBBA d 8 < Ԧ 0s BI BBD D(G0U(A ABB < p
-I BBE D(D0`(L BFB L T H BEB E(D0D8G@8A0A(B BBB l 0 H BEE E(A0A8D
+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
8A0A(B BBBF D
-8C0A(B BBBA E Ԩ
- \ D H BBB A(A0D`
+8C0A(B BBBA \ 3E t 4
+ \ 4 wg BBB A(A0D`
0F(A BBBG
-0D(A BBBE L p T h G BED I(G0
+0D(A BBBE h: T t `: g BED I(G0
(F ABBE X
-(C ABBH T `G BED I(G0
+(C ABBH T : f BED I(G0
(F ABBE X
-(C ABBH l l h G JEA K(D`
+(C ABBH l $ ;h f JEA K(D`
(A ABBH D
-(C ABBD XH` L F BEE B(E0H8G@\
-8F0A(B BBBK 4
- L d
- < | BDA A(G@X
-(C ABBA L ԫ BED A(D0
+(C ABBD XH` $ = < = L = e BEE B(E0H8G@\
+8F0A(B BBBK = = =
+ = =
+ < = BDA A(G@X
+(C ABBA \ > L t > BED A(D0
(A ABBH }
-(G ABBG D | )E BDA G@G
+(G ABBG D 4 @ d BDA G@G
AABG [
- AABA L ī D BBB B(A0D8Gb
-8A0A(B BBBH 4 P AK0{
+ AABA L | @ ^d BBB B(A0D8Gb
+8A0A(B BBBH 4 \ HC AK0{
DE J
DB v
-DF + AG@^D , e ADG0|
-AAF L X _D BBB B(D0D8Gp
-8A0A(B BBBB L ( 4D BBE E(D0A8Fp;
-8A0A(B BBBA x. K^
, P D
\
T t < BDC D0
+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
AABH _
AABE _
- AABA ̮ k Ae
-A 4 P AFD0{
+ AABA l Ik Ae
+A 4 HJ AFD0{
AAH I
-FAH $
- < < T BDA `
+FAH K
+ K < K BDA `
ABF D
-ALA < HX RB BIA A(F`K
-(C ABBJ ԯ h p d h BBE E(D0D8GPj
+ALA < @LX a BIA A(F`K
+(C ABBJ t `R hR d `R BBE E(D0D8GPj
8C0A(B BBBE X
-8F0A(B BBBA , l L AAN l
-AAA , L AAN l
-AAA , ̰ L AAN l
-AAA , L AAN l
-AAA , , L AAN l
-AAA , \ 0L AAN l
-AAA , PG ADG i
-AAA D p. AHG0
+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
AAF ^
AAH _
-AAA D X. AHG0
+AAA D PV. AHG0
AAF ^
AAH _
-AAA D L @& AHF0
+AAA D 8W& AHF0
AAD ^
AAH _
-AAA D (. AHG0
+AAA D 4 X. AHG0
AAF ^
AAH _
-AAA D ܲ . ACK0
+AAA D | Y. ACK0
AAD ^
AAH _
-AAA D $ > ADK0
+AAA D Y> ADK0
AAF ^
AAH _
-AAA , l ACK0Z
-AAE , ADK0[
-AAC , ̳ ACK0Z
-AAE , ADK0[
-AAC 4 , 0 BAH G0i
- AABA 4 d BAH G0j
- AABH , @ ACK0[
-AAD D ̴ . AHG0
+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
AAF ^
AAH _
-AAA < l < BBD H(Gp
-(A ABBD T h < BBD H(Gp
+AAA < $ _ ^\ BBD H(Gp
+(A ABBD T d `a 9\ BBD H(Gp
(A ABBF `
-(A ABBJ < 0 < BBD H(Gp
-(A ABBD T D p< BBD H(Gp
+(A ABBJ < (c \ BBD H(Gp
+(A ABBD T d [ BBD H(Gp
(A ABBF `
-(A ABBJ \ D W BEA H(N0
+(A ABBJ \ fW BEA H(N0
(A ABBI _
(A ABBK `
-(A ABBA , = BID
-ABF Զ AU \ g BJH D(G0
+(A ABBA , D g= BID
+ABF t h AU \ hg BJH D(G0
(A ABBK _
(A ABBK `
-(A ABBA , T ) BDH
-AEN 4 Z ADG b
+(A ABBA , i) BDH
+AEN 4 $ jZ ADG b
CAF K
-FDE 4 AAG0n
+FDE 4 \ j AAG0n
CAE K
-FDK T L XU : BLB E(A0A8D+
-8A0A(B BBBA $ L `H ADF0zAA t B 0 : B Ը 83 `3 A p. K^ 4 L 0 d | HS AL Թ ص е 4 ȵ L d | " DN
-N A 4 w ADD [
-DAG DAA R DG
-E A< t BBA D(D0]
-(A ABBH l W7 BBB E(A0D8D
+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
8A0A(B BBBH
-8A0A(B BBBA $ @ UL
-G p L < #7 BBE E(D0D8Fc
-8A0A(B BBBG L 7 BEE E(D0D8F
-8A0A(B BBBA L ܻ 6 BHE E(D0D8F
-8A0A(B BBBA Լ X ` h , p s AAD y
-CAA < .6 BED H(G`o
-(A ABBA P
- d X
- 5 BBA A(G0q
+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
(A ABBD M
(A ABBE w
-(A ABBK , m AAD x
-AAA 4 P ^5 BAD DP
- DABE , t { BAA |
-DBA , 8 5 AADPs
-AAE \ Ծ BBA A(D@j
+(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 ABBF j
(A ABBH h
-(A ABBJ < 4 ADD Z
+(A ABBJ < ADD Z
AAK Z
-AAD HAA4 t h ADD
+AAD HAA4 ` ADD
AAF D
-FAA N Ŀ ܿ ' DR
-J F ' DQ
-K F , t 3 AADP[
-AAE L @ AZ 4 Ŀ @ t3 BAD D`[
- AABF A] 4 =3 BAD D`\
- AABE 0 AZ 4 t 0 3 BAD D`[
- AABF T A] 4 2 BAD D`\
- AABE ) D d 4 8 2 BAD D`]
- AABD ) D d 4 l q2 BAD D`]
- AABD L @ A[ 4 @ :2 BAD D`\
- AABE AQ
-F D AZ 4 < 1 BAD D`[
- AABF 0 A] 4 0 1 BAC D`[
- AABG $ t 5 AW
+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
H G
-I D 4 m1 BAD D`]
- AABD 8 N p % DQ
-K D < | 1 BBD D(Dp\
-(A ABBF < h 0 BBD D(Dp\
-(A ABBF < 0 BBD D(Dp\
-(A ABBF < < H 0 BBD D(Dp\
-(A ABBF 4 | 0 BAD D`i
- AABH 4 @ t0 BAD D`i
- AABH < ^0 BBD D(Dp\
-(A ABBF , , 8! ?0 AAD`T
-AAD < \ ! -0 BBD D(Dp\
-(A ABBF 4 " 0 BAC D`[
- AABG 4 " / BAD D`]
- AABD , # / AAD`D
-AAD , < # / AAD`C
-AAE T l x$ / BBD D(DpL
+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
(A ABBF Q
-(A ABBA d l %2 BBB B(A0D8D
+(A ABBA d ȑ2 BBB B(A0D8D
8A0A(B BBBJ >
-8A0A(B BBBH , , * ./ YX
+8A0A(B BBBH , N YX
G
-A < \ +
/ YHD T
+A < yN YHD T
AAE B
-AAA T ,i+ . BEE B(A0C8G
-8D0A(B BBBG < W< BEG A(D@
-(D ABBA \ 4 X c. BEE D(A0G`[
+AAA T T i+ QN BEE B(A0C8G
+8D0A(B BBBG < < < BEG A(D@
+(D ABBA \ M BEE D(A0G`[
0A(A BBBE
-0E(A BBBE 4 < Y BDA D0F
- DABA , t Ze XGH _ID , XZ{ BAA |
-DBA xR DG
-E A " DQ
-K A L ȧ BBB B(A0A8Gp8A0A(B BBB D Z , BAD G
+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
ABG J
-ABE KAB , = BDA rAB d Q BBB E(D0E8D@O
+ABE KAB , L = BDA rAB d | Q BBB E(D0E8D@O
8F0A(B BBBA
-8D0A(B BBGA D D Y\ BDC D`
+8D0A(B BBGA D \ BDC D`
AABA U
- AABA $ [$ ADG QCC $ [2 AFF0bAA D 0[ BHE D(D0D@
-0A(A BBBD $ $ [4 AFG0cAA D L \ BHE D(D0D@
-0A(A BBBD $ \2 AFF0bAA D \ BHE D(D0D@
-0A(A BBBD $ ]4 AFG0cAA D , ] BHE D(D0D@
-0A(A BBBD t X^> AI0rA < x^ BHD D(DP
-(A ABBF 8_> AI0rA < X_ BHD D(DP
-(A ABBF $ 4 `2 AFF0bAA D \ 0` BHE D(D0D@
-0A(A BBBD < ` ( BEG D(D@
-(A ABBJ $ Hd2 AFF0bAA D `d BHE D(D0D@
-0A(A BBBD 4 T e BGA G0f
- AABE 4 e ' BGD D0f
- AABE \ (f BBG D(G@e
+ 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
(A ABBG r
-(A ABBH P(A ABB d $ hf0 BBB B(D0C8GP}
+(A ABBH P(A ABB d `0 BBB B(D0C8GP}
8A0A(B BBBC A
-8A0A(B BBBE D 0g BAD G0s
+8A0A(B BBBE D , ( BAD G0s
AABK y
- AABC L xl BBE F(A0A8D@
-8A0A(B BBBJ , $ Hg AG r
+ AABC L t pl BBE F(A0A8D@
+8A0A(B BBBJ , @ AG r
AA G
-AA < T g BED D(D0D
-(A ABBC < Xht BED D(D0h
-(A ABBG 4
ADG H
+AA < BED D(D0D
+(A ABBC < 4 Pt BED D(D0h
+(A ABBG 4 t
ADG H
AAJ k
-AAC L `h5 BBG D(GP
+AAC L X5 BBG D(GP
(A ABBB T
-(A ABBF L Pi !% BEE E(D0A8J
-8A0A(B BBBF T ? $ BEE E(A0C8J0
-8D0A(B BBBD Hu L t @u( $ BBE E(D0D8GO
-8A0A(B BBBJ T l } BHE D(A0Gpv
-0A(A BBBG T0A(A BBB 4 H} $$ BAA DPT
- AABA 4 T # BAA DPY
- AABA L r # BBB E(A0C8G
-8A0A(B BBBG < BED D(G0K
-(A ABBI L x W# BEH E(A0A8G
-8A0A(B BBBA D l (
- # BBB D(D0G`
-0A(A BBBD L \ BBB B(D0D8D@
-8A0A(B BBBK D " BAD G@P
+(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
AABF t
- AABA L L m" BBB B(D0D8Dp
-8A0A(B BBBE D xo. K^ \ Ш t Ȩ P- DZ " AN ND 8 Ds | ب P! <
T ب7 Al
-A A $ t u AG y
-AA , d AEG L
-DAA $ DG
+ 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
E p
-A $ m AG q
-AA s D d
-A < m. K^ T l , xb ARDPs
-CAA hm. K^ L Щ BBE B(A0D8JW
-8A0A(B BBBE , t l AAJ
-DAA , K AAJDA | G D 2 BEA D(J?
-(D ABBA hl. K^ $ F An
+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 L
-D $ $ (F An
+D $
+ F An
A L
-D L P D< ȯ# t AAJ@j
+D
+ H D< t
+ # = AAJ@j
DAE Y
-DAB L 8: G BBF B(D0E8GPr
-8A0A(B BBBH L L ; BBB B(E0A8Gp
-8A0A(B BBBK L BLB B(A0A8Jm
-8A0A(B BBBK ض# DZ I L ( BBB B(A0A8J
-8A0A(B BBBD T l BBB E(D0A8J
-8A0A(B BBBE 4 P , BAF GP
- AABH 4 BFA GP
- AABA 4 4 BFA G@
- AABA 4 l x BAA G0x
- AABA $ L n AG Z
-AA t X `- KQJ $ pn AG Z
-AA $ n AG Z
-AA L T BBB B(D0D8DP
-8D0A(B BBBE l , BBB E(A0A8D_
+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_
8A0A(B BBBF D
-8C0A(B BBBA L PZ* O BBB B(A0D8G[
-8D0A(B BBBD L d `^ BEB B(A0A8GZ
-8A0A(B BBBH \ p
- t x
-
-6 AU ^A
-3 AR ^A ,
- Dl
+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
H _
A Z
-A Z ` b L l % BEB B(D0A8G
-8A0A(B BBBG T x BBB E(G0D8G
-8C0A(B BBBC T = BBB E(A0A8J#
-8A0A(B BBBD T l BBE E(G0D8G
-8A0A(B BBBF l ! !. LW ! !& !& "& L "R BBB B(A0A8GK
-8A0A(B BBBJ L L 0#d BBB B(A0A8GK
-8A0A(B BBBJ $ P$> AG _
-AH LA$ h$> AG ^
-AI LA$ $= AG ^
-AI KA$ $= 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 l p)
- h)
- `)
- X) P)
- H)
- @)
- 8) , 0)
- D ()
- \ )
- t ) )
- )
- )
- ( (
- (
- (
- 4 ( L (
- d (
- | (
- ( (
- (
- (
- ( (
- $ (
- < (
- T x( L l P3 BBB B(A0A8D
-8A0A(B BBBA L 7 BBB B(A0A8D
-8A0A(B BBBA 4 d ' BAA G0
- AABG L D X< BBB B(A0A8Gp8A0A(B BBB l =x BBF D(A0G@F
+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
0A(A BBBD v
0A(A BBBJ D
-0F(A BBBG T \ ( BLB E(A0A8D
-8C0A(B BBBA \ + $ t (+7 HTTF е ص е ȵ >^ AW
-A L t 0?; BDB B(A0A8GQ
-8A0A(B BBBB T P BEB B(A0A8G:
-8D0A(B BBBE T BEB B(A0A8G:
-8D0A(B BBBE T t BEB B(A0A8G:
-8D0A(B BBBE t H DM
-A D; D E BDA
+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
ABA O
-ABF ]AB , L ED \ BDH _
-ABA | E < BBB E(A0A8F
+ABF ]AB , D 2 BDH _
+ABA 4 2 BBB E(A0A8F
8A0A(B BBBD
8A0A(B BBBF
8D0A(B BBBH W
-8A0A(B BBBG H H H < d H` BEI C(D0l
-(D ABBA < Ic Y BEJ D(C0p
-(A BBBA , @ID ) BDH _
-ABA d `I BED A(D0O
+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
(D ABBH F
(C ABBJ a
-(D ABBA , | IF BDI `
-ABA T In AL@_A t 8J4 AS [D \ XJ# ] BBB A(D0G`u
+(D ABBA , 4 F )1 BDI `
+ABA n AL@_A 04 AS [D \ P# 0 BBB A(D0G`u
0D(A BBBF K
-0D(A BBBA (K 0K8 $ XKA < K L Kw BBB B(A0A8G`
-8D0A(B BBBA L2 Gj L L A
-A xMC < Ms BBE E(A0X
-(A BBBA L L BBB B(A0A8Gp8A0A(B BBB L M BBB B(A0A8Je
-8A0A(B BBBE T! A_ < BBG A(G@
-(A ABBG L L ` BEB A(D0l
+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
(A BBBG
-(A BBBA L S
- BBB E(D0A8Gd
-8A0A(B BBBK T D BBB B(A0A8J
-8A0A(B BBBG < xu BHE L
-AEO AAB < u u BHE L
-AEO AAB < | 9 BEA A(D0a(D ABB < \ 8|
BEA A(D0a(D ABB < x
BHE |
-AEO AAB <
BBD A(D0(D ABB < X I
BHE |
-AEO AAB < \
BBD A(D0(D ABB d D 82 BBB E(D0E8D@P
+(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
8F0A(B BBBA
-8D0A(B BBGA d [ BBE B(A0A8FP[
+8D0A(B BBGA d L% BBE B(A0A8FP[
8C0A(B BBBA W
-8F0A(B BBBA , 8]; BDE lAB d D x2 BBB E(D0E8D@P
+8F0A(B BBBA , % 0; BDE lAB d % p92 BBB E(D0E8D@P
8F0A(B BBBA
-8D0A(B BBGA L \? BBB B(A0C8DP
-8A0A(B BBBA l y BBF D(A0G@I
+8D0A(B BBGA L L& ? BBB B(A0C8DP
+8A0A(B BBBA l & 9y BBF D(A0G@I
0A(A BBBI w
0A(A BBBA D
-0F(A BBBG L `]W
- BBB B(A0A8GPQ
-8C0A(B BBBB L p^ h
- BBB B(A0A8G`U
-8D0A(B BBBE d d ` -
- BEB B(F0D8Je
+0F(A BBBG L |& XW * BBB B(A0A8GPQ
+8C0A(B BBBB L & h ) BBB B(A0A8G`U
+8D0A(B BBBE d ' ) BEB B(F0D8Je
8A0A(B BBBB S
-8C0A(B BBBA L t BBB B(A0A8Gp8A0A(B BBB d c
BBB B(A0A8GPZ
+8C0A(B BBBA L ( ; BBB B(A0A8Gp8A0A(B BBB d '
( BBB B(A0A8GPZ
8A0A(B BBBK D
-8A0A(B BBBJ T pg 7 BBB B(A0A8JS
-8A0A(B BBBA T n ; BBD C(G0J
+8A0A(B BBBJ T <( h ( BBB B(A0A8JS
+8A0A(B BBBA T ( ( BBD C(G0J
(A ABBF d
-(D ABBA `o Xo Po l $ (x BBF D(A0G@F
+(D ABBA |) X ) P ) H l ) ;x BBF D(A0G@F
0A(A BBBD v
0A(A BBBJ D
-0F(A BBBG L 8 BBB B(A0C8D`I
-8C0A(B BBBK < BBG A(G@
-(A ABBF T | Hn BLB E(A0A8D
-8A0A(B BBBG D 0/ BJB K(A0j
-(c `BBA D M o BFA Dp
+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
AABH O
- AABA d E BBB B(A0A8DP
+ AABA d + BE BBB B(A0A8DP
8A0A(B BBBE \
-8F0A(B BBBA l B BBB J(D0D8D4
+8F0A(B BBBA l + CB G& BBB J(D0D8D4
8D0A(B BBBC i
-8A0A(B BBBE L N BBB B(A0A8DV
-8A0A(B BBBB L 4 2 BBB B(A0D8G@@
-8A0A(B BBBB T n
BBB B(A0D8Jc
-8A0A(B BBBD < 4 } 2 BBD A(DP
-(D ABBA T t ~ BBB B(D0A8J
-8A0A(B BBBD < P BBA D(DP
-(D ABBA T H " BBB B(A0D8J+
-8A0A(B BBBD < d t BBA D(DP
-(D ABBA L + Ai l < ( ZBA A(%
- DBFA ) 4 bGHX
-AG X , AS L d | ` D0W hB , K BAC G0w CAB L | BAC G0L
+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
AABC D
- CABF D DAB L , BEE B(A0A8G
-8A0A(B BBBF L | BBB E(A0A8GP
-8D0A(B BBBA , p AAG j
-AAA , T AAJ
-DAA , p u AAJAA \ G | ps , _ AAG N
-AAA L BBB E(A0D8GG
-8A0A(B BBBH T l BDB B(A0D8G
+ 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
-8A0A(B BBBH T l p BBB A(D0F
+8A0A(B BBBH T 3 hr BBB A(D0F
(A BBBH :
-(E BBBH L BEE B(D0D8G
-8C0A(B BBBF
- , B D B \ B L { BBE B(A0A8D`s
-8D0A(B BBBG L x J BBE B(A0A8D`s
-8D0A(B BBBG (
,
D (
A < \ `
h BGD A(D0T
-(A ABBD <
BBA D(D0
-(A ABBG 0 ( DM T d 0
I BEB B(A0A8GG
-8A0A(B BBBK \ d BIH D(G0Q
+(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
(A ABBK s
-(C ABEB I(C AEB L h BEA D(G0O
+(C ABEB I(C AEB L d6 ` BEA D(G0O
(A ABBH E
-(G AFEH L BEB I(D0^
-(A BFEF a(A BBB d ( A\ L (q BEE I(D0v
-(A BFEC I(A BBB 4 X_ AID i
-JDK LAA L BBA D(G0^
+(G AFEH L 6 BEB I(D0^
+(A BFEF a(A BBB 7 A\ L $7 q BEE I(D0v
+(A BFEC I(A BBB 4 t7 P_ AID i
+JDK LAA L 7 x BBA D(G0^
(A ABBD a
-(A ABBA 4 \ E BID a
-ABE IAB 4 ] BID n
-EEI IAB \ BBB B(A0D8G@p
-8G0A(B BFEE L8A0A(B BBB$ , l ADD `AA4 T b BNA o
-ABE TCB x5 T BBB E(D0D8J
-8A0A(B BBBC | , BBB B(D0C8GP
+(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
8A0A(B BBBH
8C0A(B BBBD A
-8D0A(B BBBJ D BAA L
+8D0A(B BBBJ D 4: BAA L
ABE G
ABF K
-ABJ B X^ 4
- BPD A(K`Z(A ABB| L
- P BBB A(A0GPU
+ABJ |: ȋ : B X^ 4 : BPD A(K`Z(A ABB| : H BBB A(A0GPU
0C(A BBBB R
0A(A BBBF o
-0D(A BBBF N0A(A BBB
- n 4
- ! AAG
-AAB tCA !$ Ab T
- ; BBB B(A0A8J+
-8A0A(B BBBG <
- Au BHE L
-AEO AAB < , B| g BEA A(D0a(D ABB < l + BHH
-AEG AAB T ! AL L A BEB E(I0H8D@
-8D0A(B BBBA L x!4 BIF A(R0
-(A ABBA K(D ABB d
h"W BDB B(A0A8Dpq
+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
8A0A(B BBBE
-8A0A(B BBBA |
`% T h%x BBB B(A0A8J
-8A0A(B BBBG D D
@BU OMB L(E0Y
-(g ~BBA D
XCM BFA Dp
+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
AABH O
- AABA 4
3 BAA
-ABA L (E BBB B(A0D8G@
-8A0A(B BBBD T \ F
- b BBB E(D0H8J
-8A0A(B BBBG L 3W k BBE D(C0G
-0A(A BBBG 4
- 4
L 4 4c BBE D(D0G
-0D(A BBBK , 5 L hP BBB F(A0E8GPg
-8A0A(B BBBA DM
-A l Q ~ BFB B(A0D8G`"
+ 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`"
8A0A(B BBBD V
-8A0A(B BBBH , | Ve _ AAD I
-AAA , HV[ ? AAD
-AAA < xV AAD R
+8A0A(B BBBH , 4A e AAD I
+AAA , dA @[ AAD
+AAA < A p AAD R
AAF d
-AAA 4 V BKD D0
- DABA < T `Wb BEA D(DP
-(D ABBE $ < X ADD AA, Y+ w ADD
-AAA Z% DW
-A $ Z5 AJ
+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
E ]
-A 0Z DP
-A 0ZK AI 4 `Z ADG Z
-AAP bAA T Z l Z< , Z( AG o
+A |C ( DP
+A C (K AI 4 C X ADG Z
+AAP bAA C D < , $D ( AG o
AH V
-AI [" DQ
-K A \ T D \M BBB H(A0A8Jr
-8A0A(B BBBJ < D c BEE D(D0e(A BBB T Hdf BBB E(D0A8GP
-8A0A(B BBBG 4 `l BED D(F0(A ABB$ hV ADD JAAL K BEB E(I0H8D@
-8D0A(B BBBA < BDI ~
+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 ~
AGK K
-ABB L $ BEB E(I0H8D@
-8D0A(B BBBD d k? BBB B(H0G8Fp0
+ABB L E . O BEB E(I0H8D@
+8D0A(B BBBD d F ? BBB B(H0G8Fp0
8D0A(B BBBF W
-8A0A(B BBBG L nM S BBB B(A0D8GQ
-8A0A(B BBBA L h BBB B(A0D8GP>
-8A0A(B BBBD $ E
-G < D x BHD A(D0(A ABB d t BBB B(K0D8Fpb
+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 j
-8A0A(B BBBA T D y 5 BBB B(D0A8Jo
-8A0A(B BBBH < hf BED D(H0m
-(D ABBA T Z ~ BHB E(A0A8GP
-8A0A(B BBBD @ T L H P BBB E(A0A8G
-8A0A(B BBBF L d | x T p BBB E(A0A8G
-8A0A(B BBBF Ȗ 4 L L Z BBB B(A0D8I@L
-8C0A(B BBBB T xA
BBB B(A0A8J
-8D0A(B BBBH L d p H BBB B(A0A8Ji
-8A0A(B BBBI L \ Хm BBB E(A0D8GR
-8A0A(B BBBE L BBE E(A0A8GV
-8D0A(B BBBF , `, BDD WAB L `
- o BBE B(A0A8IR
-8C0A(B BBBA | AO , BDA AK T $
- ; BDB B(A0A8G$
-8A0A(B BBBA T | h$ > BBB B(A0A8Gs
-8A0A(B BBBA < @ < BED C(G@
-(D ABBC T s
- BBB B(A0A8J
-8A0A(B BBBE 2 Ap < n BBB A(C0Z
-(A BBBA < @ BBA A(G0d
-(A ABBA L ( BEH E(D0D8Gr
-8A0A(B BBBA 2 Ap T | BLE B(A0A8G
-8C0A(B BBBC T P
- BLB E(A0A8G
-8A0A(B BBBA H j T \ z BBB B(A0D8G
-8A0A(B BBBG d \ '' BBB B(A0A8GPe
+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
8D0A(B BBBM
-8A0A(B BBBB L X " BBB B(A0A8Jk
-8C0A(B BBBE ! )A , ,! @+ BDD E
-ABF \! +% L t! + BBD A(D@|
+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@|
(A ABBA D
-(F ABBA T ! (, BBB B(A0A8G3
-8C0A(B BBBH L t! 1 BEB B(A0A8I_
-8A0A(B BBBA l" `7: < ! 7 BBA A(D@z
-(A ABBA " X8u T 4" 8 - BEB B(A0A8G<
-8A0A(B BBBF 4# H?h L# ?; H^
-J F l# ? # ?S L " @ BDA }
+(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 $T S L S # BDA }
ABI \
ABA G
-ABF # @y L \# A ` BBB B(A0D8I
-8A0A(B BBBG d T$ H@ BBB B(D0A8D@l
+ABF T y L T
BBB B(A0D8I
+8A0A(B BBBG d T @ BBB B(D0A8D@l
8A0A(B BBBI
-8C0A(B BBBA < $ I, BDA D(L0u
-(A ABGI , T$ JG BDI ]
-ABE ,% J D% J 4 \% JB AAG L
-FAL WAA < $ J V BAA x
+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
ABI A
-ABJ < ,% HK 0 BBD A(L@e
-(A ABBH , l% KQ
- AID c
-DDE T % Kb BBD D(D0m
+ABJ < U @ BBD A(L@e
+(A ABBH , $V Q v AID c
+DDE T TV b V BBD D(D0m
(A ABBE
-(D ABBG D & M BND `
+(D ABBG D <W BND `
GBE o
-IBL _AB L & xM BBB B(A0A8DPj
-8D0A(B BBBK L 4' N BEB B(A0A8Gp
-8C0A(B BBBH \ ' R BEB E(D0D8G@i
-8A0A(B BBBH T8C0A(B BBBT <' (S } BEB B(A0D8G
-8A0A(B BBBG D ' ]| v BDE A(G0DP\0A(A BBB 4 ( (^l AAD Y
+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
DAD v
-DAE d ( `^1 BBA D(G0
+DAE d X X1 j
+ BBA D(G0
(A ABGC `
(A ABBJ S
-(F ABBB l |( 8_; BEB B(A0A8D`
+(F ABBB l 4Y 0;
+ BEB B(A0A8D`
8A0A(B BBBO C
-8A0A(B BBBA , ( ak M AAG @
-IAE $ ) Ha Aw
+8A0A(B BBBA , Y k AAG @
+IAE $ dZ @ Aw
H h
-H ) a( Ab 4 * a ADD0|
+H Z ( Ab 4 Z ADD0|
AAI }
-GAK D* xbN Ad
-K ] ) b BBE D(A0Gp
+GAK Z pN Ad
+K ] tZ BBE D(A0Gp
0A(A BBBH o
0A(A BBBI
0A(A BBBG d
-0A(A BBBA d * d BGB B(A0A8FI
+0A(A BBBA d [ BGB B(A0A8FI
8A0A(B BBBH Y
-8F0A(B BBBH L * 8h BBB B(D0A8JG
-8A0A(B BBBH < * u BHE L
-AEO AAB < <+ 8| BEA A(D0a(D ABB L $, x, BLB B(D0A8D`
-8A0A(B BBBJ < t, X1 BBG A(GP
-(A ABBF L , ls BBB B(A0A8G
-8A0A(B BBBF L - n1 BBB E(A0C8I
-8A0A(B BBBA T , p,
- % BBB B(A0A8G
-8A0A(B BBBA T - z A BBB B(A0A8G
-8D0A(B BBBA T \- 8 < BEE B(A0C8J
-8A0A(B BBBG L - @ BBE E(D0C8Dp
-8C0A(B BBBK T . ps BBB E(D0C8G
-8D0A(B BBBD / H$ Ab D $/ X' BKK
+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
ABH B
-ABC sAB < l/ @ BHD A(D0(A ABB L / R BGB B(A0A8GJ
-8A0A(B BBBF / 0 X ,0 P D0 X \0 ` t0 h 0 ` 0 X D 0 P3 Aw
+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
H [
E [
E u
K [
E [
E U
-K R L \0 H BBB E(D0A8F
-8D0A(B BBBA L 0 8 P BEE B(D0A8Dp}
-8D0A(B BBBG 1 1 ? A} 4 41 0 BFD D0u
- DABA $ 2 m AG Y
-AA <2
T 1 7 w BBA D(G`
+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`
(D ABBE L
-(D ABBC T 2 7 6 BBA D(G`
+(D ABBC T b 07 BBA D(G`
(D ABBE L
-(D ABBC L \2 e BLB B(A0D8Dp
-8A0A(B BBBA T3 (2 Ap T 2 u BEE B(A0D8J
-8A0A(B BBBJ d 3 B BBB B(A0A8G@x
+(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
8A0A(B BBBE
-8A0A(B BBBF 44 x] D X
+8A0A(B BBBF d p@] D X
D `
-H 4 T4 BAA D@
- AABD L 4 ? BBB B(A0D8D@
-8A0A(B BBBA < 4 t BEE D(A0S(A GBE T t4 P M BGE B(A0A8Q
-8A0A(B BBBA < t5 BBA A(G@_
-(A ABBF L 5 H~ BLB H(A0D8D@
-8A0A(B BBBD < 6 x BBG A(G@
-(A ABBH L D6 z BHE B(G0A8J
-8A0A(B BBBC < 6 BBG A(G0
-(A ABBB L 6 - BEE B(D0A8Gp
-8A0A(B BBBC L |6 { [ BBB B(A0A8JM
-8A0A(B BBBE t7 x5 7 p5 7 h5 7 7 7 T \7 BBB B(A0A8G@
-8A0A(B BBBE T 7 @ BBB B(A0A8G@
-8A0A(B BBBE L 8 . BBB B(A0A8Gh
-8A0A(B BBBE 4 9 J AAG P
+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
AAE
-FAE T 8 BEE B(A0A8G
-8A0A(B BBBH 8 X BBB B(A0A8G@
+FAE T Li r BEE B(A0A8G
+8A0A(B BBBH i P e BBB B(A0A8G@
8A0A(B BBBH t
8D0A(B BBBO
-8D0A(B BBBG < t9 o BEI D(D0z
-(D ABBA L 9 o l BEE B(D0D8G
-8A0A(B BBBJ D : @ AAD V
+8D0A(B BBBG < ,j o BEI D(D0z
+(D ABBA L lj o BEE B(D0D8G
+8A0A(B BBBJ D j 8 AAD V
AAJ d
DAG d
-DAA L L: e BBB B(A0A8JB
-8D0A(B BBBE T : 1 BBB B(A0A8J+
-8A0A(B BBBG < : 8u o BHE L
-AEO AAB < 4; 8u 3 BHE L
-AEO AAB < t; 9u BHE L
-AEO AAB < ; P9| BEA A(D0a(D ABB < ; 9| BEA A(D0a(D ABB < 4< 9| C BEA A(D0a(D ABB D t< :U OMB L(E0Y
-(g ~BBA D < (;M BFA Dp
+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
+AEO AAB < ll H| ' BEA A(D0a(D ABB < l | BEA A(D0a(D ABB < l ȥ| BEA A(D0a(D ABB D ,m U s OMB L(E0Y
+(g ~BBA D tm M S BFA Dp
AABH O
- AABA L = 0= BBB B(A0D8G@
-8A0A(B BBBD T T= >H
- h BBE B(A0D8J]
-8A0A(B BBBG T = % q BBB B(A0A8GE
-8D0A(B BBBE L > P' 1 BBH B(A0D8Jn
-8A0A(B BBBK > e T ? (H AGF n
+ AABA L Ln ( BBB B(A0D8G@
+8A0A(B BBBD T n H
+ BBE B(A0D8J]
+8A0A(B BBBG T dn BBB B(A0A8GE
+8D0A(B BBBE L n H BBH B(A0D8Jn
+8A0A(B BBBK o T o AGF n
FAM b
DAI D
KAP D
-KAP mFA 4 l? H BGD o
-IBI VAB L > HI BBB B(D0C8JS
-8C0A(B BBBH < L? du BHE L
-AEO AAB < ? e| BEA A(D0a(D ABB L ? XT BBB B(A0A8JC
-8D0A(B BBBD T @ Y BBB E(A0A8J
-8A0A(B BBBG < t@ pb BED A(D`
-(D ABBA \A `d , tA XdG BHI pAB A xd9 < A dx BLI D(D0m
-(A ABBF L A d BBB B(A0A8G
-8A0A(B BBBG LB g dB g L |B g BEE E(A0D8D
-8A0A(B BBBG d B k BEB B(A0A8DpJ
+KAP mFA 4 p ȴ BGD o
+IBI VAB L o @ BBB B(D0C8JS
+8C0A(B BBBH < p u c BHE L
+AEO AAB < Dp | + BEA A(D0a(D ABB L p P 0 BBB B(A0A8JC
+8D0A(B BBBD T p + BBB E(A0A8J
+8A0A(B BBBG < ,q h ) BED A(D`
+(D ABBA q X , r PG BHI pAB Dr p9 < \r x BLI D(D0m
+(A ABBF L r BBB B(A0A8G
+8A0A(B BBBG r s L s BEE E(A0D8D
+8A0A(B BBBG d ls BEB B(A0A8DpJ
8A0A(B BBBK C
-8A0A(B BBBC \ 4C l* BBB B(A0A8G`J
-8A0A(B BBBK 8A0A(B BBB C hm& D a C m C m C m C xm: D m $D m) DS
-I H DD m DT dD m |D m D m D m D m L D m BEE F(D0A8G
-8A0A(B BBBA ,E oA $ DE Xos AK@V
-AA lE o> $ E op AK@V
-AA 4 E po AAG h
-AAE hIA < E Xp BBA A(G@e
-(D ABBE L $F pT BBB B(A0D8IU
-8A0A(B BBBK tF t| D0[
-A F (u D0[
-A F u F u F u! A_ L G u BBB B(D0A8G
-8D0A(B BBBF TG xw , lG pws AAG@_
-AAF D G w BBE A(D0G`f
-0A(A BBBE G xT D0O G x H x= ,H y DH x DQ \H y tH y H y$ < H (y BBA D(G0~
-(D ABBI H y` AMG I x. K^ I yZ D0~
-F Q < <I zi BEB G(C0G@C0D(A BBB |I @zl Aj I z- I zI I z0 , I z BAA
-ABE J x|n An
-A } 4J | < LJ |K BBB D(A0
-(A BBBH < J } BBA E(J0
-(D ABBI 4 J ~ BDE y
-ABI fAB L K BBB B(D0A8D@Y
-8G0A(B BBBF TK DI < lK BBD A(K0z
-(D ABBI K 0/ Dg L K HK BBE B(D0I8DP
-8D0A(B BBBA L L H" BFB B(A0G8DP
-8D0A(B BBBM dL (2 Ai
-F A L HL D@D L L L x BBB B(D0D8G
-8A0A(B BBBC M | D s
-A $ $M AG z
-AA LM L dM x BEB B(A0A8G
-8A0A(B BBBA M H@ D c
-I K M h| D0[
-A M ȋ N p $N x <N p DP TN xB An
-Q A tN 2 I N Ќ* D e 4 N K ADD0
+8A0A(B BBBC \ s * BBB B(A0A8G`J
+8A0A(B BBBK 8A0A(B BBB 4t `& D a Lt x dt |t x t p: t t ) DS
+I H t DT u u 4u Lu du L |u BEE F(D0A8G
+8A0A(B BBBA u A $ u Ps AK@V
+AA v > $ $v p AK@V
+AA 4 Lv o AAG h
+AAE hIA < v P BBA A(G@e
+(D ABBE L v T BBB B(A0D8IU
+8A0A(B BBBK w | D0[
+A 4w D0[
+A Tw lw w ! A_ L w BBB B(D0A8G
+8D0A(B BBBF w p , x hs AAG@_
+AAF D <x BBE A(D0G`f
+0A(A BBBE x T D0O x x = x x DQ x y ,y $ < Dy BBA D(G0~
+(D ABBI y ` AMG y `. K^ y Z D0~
+F Q < y i BEB G(C0G@C0D(A BBB z 8l Aj <z - Tz I lz 0 , z BAA
+ABE z pn An
+A } z < z K BBB D(A0
+(A BBBH < ,{ BBA E(J0
+(D ABBI 4 l{ BDE y
+ABI fAB L { BBB B(D0A8D@Y
+8G0A(B BBBF { DI < | BBD A(K0z
+(D ABBI L| (/ Dg L d| @K BBE B(D0I8DP
+8D0A(B BBBA L | @" BFB B(A0G8DP
+8D0A(B BBBM } 2 Ai
+F A $} @L D@D <} x L T} p BBB B(D0D8G
+8A0A(B BBBC } | D s
+A $ } AG z
+AA } x L ~ p BEB B(A0A8G
+8A0A(B BBBA T~ @@ D c
+I K t~ `| D0[
+A ~ ~ h ~ p ~ h DP ~ pB An
+Q A 2 I , * D e 4 D K ADD0
AAA
-DAI , N p AJ0
+DAI , | p AJ0
IF t
-EG O @ $O 8 <O 0 HO \O 0 c_\ N x AAG0x
+EG 8 0 ( HO ( c_\ AAG0x
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_ZZNK11onnxruntime6Tensor4DataISsEEPKT_vE19__PRETTY_FUNCTION__ _ZZN11onnxruntime5utils16ContainerChecker17IsContainerOfTypeISt6vectorISt3mapISsfSt4lessISsESaISt4pairIKSsfEEESaISB_EEE5checkERKS3_INS0_19data_types_internal8TypeNodeESaISG_EEmE19__PRETTY_FUNCTION__ _ZZN11onnxruntime5utils16ContainerChecker17IsContainerOfTypeISt3mapISsfSt4lessISsESaISt4pairIKSsfEEEE5checkERKSt6vectorINS0_19data_types_internal8TypeNodeESaISE_EEmE19__PRETTY_FUNCTION__ _ZZN11onnxruntime5utils16ContainerChecker17IsContainerOfTypeISt6vectorISt3mapIlfSt4lessIlESaISt4pairIKlfEEESaISB_EEE5checkERKS3_INS0_19data_types_internal8TypeNodeESaISG_EEmE19__PRETTY_FUNCTION__ _ZZN11onnxruntime5utils16ContainerChecker17IsContainerOfTypeISt3mapIlfSt4lessIlESaISt4pairIKlfEEEE5checkERKSt6vectorINS0_19data_types_internal8TypeNodeESaISE_EEmE19__PRETTY_FUNCTION__ _ZZN11onnxruntime5utils16ContainerChecker17IsContainerOfTypeISt3mapISsdSt4lessISsESaISt4pairIKSsdEEEE5checkERKSt6vectorINS0_19data_types_internal8TypeNodeESaISE_EEmE19__PRETTY_FUNCTION__ _ZZN11onnxruntime5utils16ContainerChecker17IsContainerOfTypeISt3mapIlSsSt4lessIlESaISt4pairIKlSsEEEE5checkERKSt6vectorINS0_19data_types_internal8TypeNodeESaISE_EEmE19__PRETTY_FUNCTION__ _ZZN11onnxruntime5utils16ContainerChecker17IsContainerOfTypeISt3mapIllSt4lessIlESaISt4pairIKllEEEE5checkERKSt6vectorINS0_19data_types_internal8TypeNodeESaISE_EEmE19__PRETTY_FUNCTION__ _ZZN11onnxruntime5utils16ContainerChecker17IsContainerOfTypeISt3mapIldSt4lessIlESaISt4pairIKldEEEE5checkERKSt6vectorINS0_19data_types_internal8TypeNodeESaISE_EEmE19__PRETTY_FUNCTION__ _ZN12_GLOBAL__N_124LoadAndInitializeSessionEPK6OrtEnvPK17OrtSessionOptionsRSt10unique_ptrIN11onnxruntime16InferenceSessionESt14default_deleteIS8_EEPP10OrtSession.isra.610 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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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_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_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_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Squeeze_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_20Unsqueeze_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_22SpaceToDepth_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_23DepthToSpace_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Tile_Onnx_ver6EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Upsample_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Resize_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Compress_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17OneHot_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15IsNaN_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16IsInf_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Where_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17NonZero_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26ReverseSequence_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Unique_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19GatherND_Onnx_ver12EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Pad_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZN6google8protobuf8internal20RepeatedPtrFieldBase3AddINS0_16RepeatedPtrFieldIN4onnx26TensorShapeProto_DimensionEE11TypeHandlerEEEPNT_4TypeESB_.constprop.559 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Size_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt18basic_stringstreamIcSt11char_traitsIcESaIcEEC1ESt13_Ios_Openmode.constprop.558 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15Shape_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Tile_Onnx_ver6EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Scatter_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_20ScatterND_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_26ScatterElements_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25GatherElements_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17NonZero_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16IsInf_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15IsNaN_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Cast_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Unique_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Concat_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Squeeze_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Gather_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19Compress_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17OneHot_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Pad_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_26ReverseSequence_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19GatherND_Onnx_ver12EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.533 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19Transpose_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Reshape_Onnx_ver5EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_20Unsqueeze_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_ENKUlPKNS_11TensorProtoEE_clES8_.isra.536 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16Split_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16Slice_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_22SpaceToDepth_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_23DepthToSpace_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15Where_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.547 _ZZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.548 _ZZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.549 _ZTSZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _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_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Cast_Onnx_ver6EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Concat_Onnx_ver4EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Tile_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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Q ȓ 2 N s ͔ 5 S v ɕ ݕ S g { Ɩ # 7 K i } ԗ & @ Y m " ɘ ۘ , d Ι ' ` Ě 5 H ћ & N h } ǜ , " F ҝ " + > מ A ` ԟ % 8 Y k Š ؠ 4 e Ρ / ] Ӣ * D V ӣ - f y ͤ ' = ^ | /usr/lib/../lib64/crti.o call_gmon_start onnxruntime_c_api.cc _ZL14ort_api_1_to_4 _ZL6StrDupRKSsP12OrtAllocator _ZNSt6vectorIlSaIlEEC2ESt16initializer_listIlERKS0_.constprop.719 _ZNKSt14default_deleteI8OrtValueEclEPS0_.isra.71 _ZNKSt14default_deleteIN11onnxruntime6common6Status5StateEEclEPS3_.isra.83 _ZL12ort_api_base _ZZN23SafeIntExceptionHandlerIN11onnxruntime20OnnxRuntimeExceptionEE17SafeIntOnOverflowEvE19__PRETTY_FUNCTION__ _ZZN7OrtApis17CreateOpaqueValueEPKcS1_PKvmPP8OrtValueE19__PRETTY_FUNCTION__ _ZZN7OrtApis14GetOpaqueValueEPKcS1_PK8OrtValuePvmE19__PRETTY_FUNCTION__ _ZZN8OrtValue10GetMutableIN11onnxruntime6TensorEEEPT_vE19__PRETTY_FUNCTION__ _ZZNK8OrtValue3GetIN11onnxruntime6TensorEEERKT_vE19__PRETTY_FUNCTION__ _ZZNK8OrtValue3GetIN11onnxruntime9TensorSeqEEERKT_vE19__PRETTY_FUNCTION__ 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_ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceImageScaler21 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE29op_schema_register_onceCrop22 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE37op_schema_register_onceDynamicSlice23 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceScaledTanh24 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceSampleOp25 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceMaxpoolWithMask26 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE29op_schema_register_onceRfft27 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceIrfft28 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceComplexMul29 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE39op_schema_register_onceComplexMulConj30 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE53op_schema_register_onceConvTransposeWithDynamicPads31 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE34op_schema_register_onceFusedConv32 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE34op_schema_register_onceFusedGemm33 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceExpandDims34 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceAttnLSTM35 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceRange36 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE39op_schema_register_onceQuantizeLinear37 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE41op_schema_register_onceDequantizeLinear38 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE34op_schema_register_onceTokenizer39 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceMatMulInteger1640 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE46op_schema_register_onceDynamicQuantizeMatMul41 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE45op_schema_register_onceMatMulIntegerToFloat42 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE40op_schema_register_onceTransposeMatMul43 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE41op_schema_register_onceReduceSumInteger44 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceQLinearAdd45 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceQLinearMul46 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceQLinearReduceMean47 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE35op_schema_register_onceMulInteger48 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE43op_schema_register_onceQLinearAveragePool49 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE41op_schema_register_onceQLinearLeakyRelu50 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE36op_schema_register_onceMurmurHash351 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceGatherND52 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE42op_schema_register_onceWordConvEmbedding53 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE28op_schema_register_oncePad54 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE31op_schema_register_onceUnique55 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE30op_schema_register_onceCDist56 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE38op_schema_register_onceCropAndResize57 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE43op_schema_register_onceLayerNormalization58 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE29op_schema_register_onceGelu59 _ZGVZN11onnxruntime7contrib22RegisterContribSchemasEvE33op_schema_register_onceBiasGelu60 _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_ 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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 _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib26NchwcPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS3_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSA_St18_Manager_operation _ZTIZN11onnxruntime7contrib26NchwcPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ _ZTIZN11onnxruntime7contrib32NchwcGlobalPoolOpSchemaGeneratorERN4onnx8OpSchemaEEUlRNS1_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE0_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE0_ _ZNSt14_Function_base13_Base_managerIZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE1_E10_M_managerERSt9_Any_dataRKS8_St18_Manager_operation _ZTIZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlRN4onnx16InferenceContextEE1_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib26NchwcPoolOpSchemaGeneratorERNS0_8OpSchemaEEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZN11onnxruntime7contrib20RegisterNchwcSchemasEvEUlS2_E0_E9_M_invokeERKSt9_Any_dataS2_ _GLOBAL__sub_I_nchwc_schema_defs.cc _ZGVZN11onnxruntime7contrib20RegisterNchwcSchemasEvE36op_schema_register_onceReorderInput0 _ZGVZN11onnxruntime7contrib20RegisterNchwcSchemasEvE37op_schema_register_onceReorderOutput1 _ZGVZN11onnxruntime7contrib20RegisterNchwcSchemasEvE28op_schema_register_onceConv2 _ZGVZN11onnxruntime7contrib20RegisterNchwcSchemasEvE31op_schema_register_onceMaxPool3 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_ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_23PoolOpSchemaGenerator_9EPKcS5_S5_ENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx23PoolOpSchemaGenerator_9EPKcS3_S3_ENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_26LpPoolOpSchemaGenerator_10EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_26LpPoolOpSchemaGenerator_10EPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx26LpPoolOpSchemaGenerator_10EPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_24ConvOpSchemaGenerator_10EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_24ConvOpSchemaGenerator_10EPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx24ConvOpSchemaGenerator_10EPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt17_Function_handlerIFvRN4onnx8OpSchemaEEZNS0_33ConvTransposeOpSchemaGenerator_10EPKcEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZZNS0_33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS0_8OpSchemaEE_clES7_EUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZZN4onnx33ConvTransposeOpSchemaGenerator_10EPKcENKUlRNS1_8OpSchemaEE_clES5_EUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKSB_St18_Manager_operation _ZNSt14_Function_base13_Base_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.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_ 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_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_ 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_ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_21SequenceAt_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_24SequenceEmpty_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28SequenceConstruct_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25SequenceInsert_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_21SequenceAt_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_24SequenceErase_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25SequenceLength_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26SplitToSequence_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_29ConcatFromSequence_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_24SequenceEmpty_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_29ConcatFromSequence_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_26SplitToSequence_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_28SequenceConstruct_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25SequenceInsert_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25SequenceLength_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZTSZN4onnx11GetOpSchemaINS_24SequenceEmpty_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_28SequenceConstruct_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25SequenceInsert_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_21SequenceAt_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_24SequenceErase_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25SequenceLength_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_26SplitToSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_29ConcatFromSequence_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19Upsample_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Resize_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Cast_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Reshape_Onnx_ver5EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Shape_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Size_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Concat_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Split_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Slice_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Transpose_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Scatter_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_20ScatterND_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26ScatterElements_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Gather_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25GatherElements_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Squeeze_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_20Unsqueeze_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_22SpaceToDepth_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_23DepthToSpace_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Tile_Onnx_ver6EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Upsample_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Resize_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Compress_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17OneHot_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15IsNaN_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16IsInf_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Where_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17NonZero_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26ReverseSequence_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Unique_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19GatherND_Onnx_ver12EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Pad_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZN6google8protobuf8internal20RepeatedPtrFieldBase3AddINS0_16RepeatedPtrFieldIN4onnx26TensorShapeProto_DimensionEE11TypeHandlerEEEPNT_4TypeESB_.constprop.559 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Size_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt18basic_stringstreamIcSt11char_traitsIcESaIcEEC1ESt13_Ios_Openmode.constprop.558 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15Shape_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Tile_Onnx_ver6EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Scatter_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_20ScatterND_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_26ScatterElements_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_25GatherElements_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17NonZero_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16IsInf_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15IsNaN_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Cast_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Unique_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Concat_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Squeeze_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Gather_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19Compress_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17OneHot_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Pad_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_26ReverseSequence_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19GatherND_Onnx_ver12EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.533 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19Transpose_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Reshape_Onnx_ver5EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_20Unsqueeze_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_ENKUlPKNS_11TensorProtoEE_clES8_.isra.536 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16Split_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16Slice_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_22SpaceToDepth_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_23DepthToSpace_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15Where_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.547 _ZZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.548 _ZZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.549 _ZTSZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Reshape_Onnx_ver5EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_15Shape_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_14Size_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Concat_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_16Split_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_19Transpose_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_18Scatter_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_20ScatterND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_26ScatterElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Gather_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_25GatherElements_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_18Squeeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_20Unsqueeze_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_22SpaceToDepth_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_23DepthToSpace_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_19Upsample_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_19Compress_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17OneHot_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_15IsNaN_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_16IsInf_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_15Where_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17NonZero_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_26ReverseSequence_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Unique_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver12EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_14Pad_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _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_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Cast_Onnx_ver6EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Concat_Onnx_ver4EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_14Tile_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Upsample_Onnx_ver7EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18Upsample_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Resize_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Slice_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Slice_Onnx_ver10EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Scatter_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_22DepthToSpace_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16Gather_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_17Squeeze_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19Unsqueeze_Onnx_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_16OneHot_Onnx_ver9EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_15Split_Onnx_ver2EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_13Pad_Onnx_ver2EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19GatherND_Onnx_ver11EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZN6google8protobuf8internal20RepeatedPtrFieldBase3AddINS0_16RepeatedPtrFieldIN4onnx26TensorShapeProto_DimensionEE11TypeHandlerEEEPNT_4TypeESB_.constprop.442 _ZNSt18basic_stringstreamIcSt11char_traitsIcESaIcEEC1ESt13_Ios_Openmode.constprop.443 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Tile_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Scatter_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_14Cast_Onnx_ver6EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16OneHot_Onnx_ver9EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16Concat_Onnx_ver4EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_18Upsample_Onnx_ver7EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_17Squeeze_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_13Pad_Onnx_ver2EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16Gather_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19GatherND_Onnx_ver11EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15Slice_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_15Split_Onnx_ver2EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_ENKUlPKNS_11TensorProtoEE_clES8_.isra.423 _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_19Unsqueeze_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_16Slice_Onnx_ver10EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZNSt17_Function_handlerIFvRN4onnx16InferenceContextEEZNS0_11GetOpSchemaINS0_22DepthToSpace_Onnx_ver1EEENS0_8OpSchemaEvEUlS2_E_E9_M_invokeERKSt9_Any_dataS2_ _ZZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.433 _ZZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEvENKUlRNS_16InferenceContextEE_clES4_.isra.434 _ZTSZN4onnx11GetOpSchemaINS_14Cast_Onnx_ver6EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_16Concat_Onnx_ver4EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_14Tile_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver7EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_18Upsample_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Resize_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_15Slice_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_16Slice_Onnx_ver10EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Scatter_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_22DepthToSpace_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_16Gather_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_17Squeeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_19Unsqueeze_Onnx_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_16OneHot_Onnx_ver9EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_15Split_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_13Pad_Onnx_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZTSZN4onnx11GetOpSchemaINS_19GatherND_Onnx_ver11EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ tensor_proto_util.cc _GLOBAL__sub_I_tensor_proto_util.cc _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_19CastMap_OnnxML_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_19CastMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26CategoryMapper_OnnxML_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26CategoryMapper_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_26DictVectorizer_OnnxML_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_26DictVectorizer_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_24LabelEncoder_OnnxML_ver2EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_24LabelEncoder_OnnxML_ver2EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_28LinearClassifier_OnnxML_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_28LinearClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_25SVMClassifier_OnnxML_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_25SVMClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_34TreeEnsembleClassifier_OnnxML_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_34TreeEnsembleClassifier_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ _ZNSt14_Function_base13_Base_managerIZN4onnx11GetOpSchemaINS1_18ZipMap_OnnxML_ver1EEENS1_8OpSchemaEvEUlRNS1_16InferenceContextEE_E10_M_managerERSt9_Any_dataRKS9_St18_Manager_operation _ZTIZN4onnx11GetOpSchemaINS_18ZipMap_OnnxML_ver1EEENS_8OpSchemaEvEUlRNS_16InferenceContextEE_ 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